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economic
re p ort
of the
president

transmitted to the congress
march 2013
together with

the annual report
of the

council of economic advisers
united states government printing office
washington : 2013
For sale by the Superintendent of Documents, U.S. Government Printing Office
Internet: bookstore.gpo.gov Phone: toll free (866) 512-1800; DC area (202) 512-1800
Fax: (202) 512-2104 Mail: Stop IDCC, Washington, DC 20402-0001
ISBN

C O N T E N T S
ECONOMIC REPORT OF THE PRESIDENT................................................ 1
ANNUAL REPORT OF THE COUNCIL OF ECONOMIC ADVISERS*... 7
CHAPTER 1.

RECOVERING FROM THE PAST AND
PREPARING FOR THE FUTURE...................................21

CHAPTER 2.

 HE YEAR IN REVIEW AND THE
T
YEARS AHEAD.................................................................41

CHAPTER 3.

FISCAL POLICY................................................................91

CHAPTER 4.

JOBS, WORKERS AND SKILLS................................... 119

CHAPTER 5.

REDUCING COSTS AND IMPROVING THE
QUALITY OF HEALTH CARE.................................... 161

CHAPTER 6.

 LIMATE CHANGE AND THE PATH TOWARD
C
SUSTAINABLE ENERGY SOURCES........................... 185

CHAPTER 7.

INTERNATIONAL TRADE AND
COMPETITIVENESS..................................................... 209

CHAPTER 8.

 HALLENGES AND OPPORTUNITIES IN U.S.
C
AGRICULTURE............................................................... 237

REFERENCES

............................................................................................265

APPENDIX A

REPORT TO THE PRESIDENT ON THE
ACTIVITIES OF THE COUNCIL OF ECONOMIC
ADVISERS DURING 2012............................................... 299

APPENDIX B.

STATISTICAL TABLES RELATING TO INCOME,
EMPLOYMENT, AND PRODUCTION....................... 313

____________

*For a detailed table of contents of the Council’s Report, see page 11.

iii

economic report
of the

president

economic report of the president

To the Congress of the United States:
This year’s Economic Report of the President describes the progress
we have made recovering from the worst economic crisis since the Great
Depression. After years of grueling recession, our businesses have created
over six million new jobs. As a nation, we now buy more American cars
than we have in 5 years, and less foreign oil than we have in 20 years. Our
housing market is healing, and consumers, patients, and homeowners
enjoy stronger protections than ever before.
But there are still millions of Americans whose hard work and
dedication have not yet been rewarded. Our economy is adding jobs,
but too many of our fellow citizens still can’t find full-time employment.
Corporate profits have reached all-time highs, but for more than a decade,
wages and incomes for working Americans have barely budged.
Our top priority must be to do everything we can to grow our
economy and create good, middle-class jobs. That has to be our North
Star. That has to drive every decision we make in Washington. Every day,
we should ask ourselves three questions: How do we attract more jobs to
our shores? How do we equip our people with the skills needed to do those
jobs? And how do we make sure that hard work leads to a decent living?
We can begin by making America a magnet for new jobs and manufacturing. After shedding jobs for more than a decade, our manufacturers
have added about half a million new jobs over the past 3 years. We need to
accelerate that trend, by launching more manufacturing hubs that transform hard-hit regions of the country into global centers of high-tech jobs
and manufacturing. We need to make our tax code more competitive, by
ending tax breaks for companies that ship jobs overseas, and rewarding
companies that create jobs here at home. And we need to invest in the
research and technology that will allow us to harness more of our own
energy and put more people back to work repairing our crumbling roads
and bridges.
Economic Report of the President

| 3

These steps will help entrepreneurs and small business owners
expand and create new jobs. But we also need to provide every American
with the skills and training they need to fill those jobs. We should start
in the earliest years by offering high-quality preschool to every child in
America, because we know kids in programs like these do better throughout their academic lives. We should redesign America’s high schools to
better prepare our students with skills that employers are looking for right
now. And because taxpayers can’t continue subsidizing the soaring cost
of higher education, we should take affordability and value into account
when determining which colleges receive certain types of Federal aid.
We also need to reward hard work and declare that no one who
works full-time should have to live in poverty by raising the minimum
wage so that it’s a wage you can live on. And it’s time to harness the talents
and ingenuity of hardworking immigrants by finally passing commonsense immigration reform—continuing to strengthen border security,
holding employers accountable, establishing a responsible path to earned
citizenship, reuniting families, and attracting the highly-skilled entrepreneurs, engineers, and scientists that will help create jobs.
As we continue to grow our economy, we must also take further
action to shrink our deficits. We don’t have to choose between these two
important priorities—we just have to make smart choices.
Over the last few years, both parties have worked together to reduce
the deficit by more than $2.5 trillion, which puts us more than halfway
towards the goal of $4 trillion in deficit reduction that economists say
we need to stabilize our finances. Now we need to finish the job. But we
shouldn’t do it by making harsh and arbitrary cuts that jeopardize our
military readiness, devastate priorities like education and energy, and
cost jobs. That’s not how you grow the economy. We shouldn’t ask senior
citizens and working families to pay down the rest of our deficit while the
wealthiest are asked for nothing more. That doesn’t grow our middle class.
Most Americans—Democrats, Republicans, and Independents—
understand that we can’t just cut our way to prosperity. That’s why I have
put forward a balanced approach to deficit reduction that makes responsible reforms to bring down the cost of health care for an aging generation—the single biggest driver of our long-term debt—and saves hundreds
of billions of dollars by getting rid of tax loopholes and deductions for
the well-off and well-connected. And we should finally pursue bipartisan,
comprehensive tax reform that encourages job creation and helps bring
down the deficit.

4 |

Economic Report of the President

The American people don’t expect their government to solve every
problem. They don’t expect those of us in Washington to agree on every
issue. But they do expect us to put the Nation’s interests before party interests. They do expect us to forge reasonable compromise where we can. Our
work will not be easy. But America only moves forward when we do so
together—when we accept certain obligations to one another and to future
generations. That’s the American story. And that’s how we will write the
next great chapter—together.

the white house
march 2013

Economic Report of the President

| 5

the annual report
of the

council of economic advisers

letter of transmittal
Council of Economic Advisers
Washington, D.C., March 15, 2013

Mr. President:
The Council of Economic Advisers herewith submits its 2013
Annual Report in accordance of the Employment Act of 1946 as amended
by the Full Employment and Balanced Growth Act of 1978.
Sincerely yours,

Alan B. Krueger
Chairman

Katharine G. Abraham
Member

James H. Stock
Member

9

C O N T E N T S

CHAPTER 1. RECOVERING FROM THE PAST AND
PREPARING FOR THE FUTURE.............................................................. 21
TRACKING THE PROGRESS OF THE RECOVERY................................... 22

Placing the Recovery in Historical Context ..................................... 25
Making Progress Toward a Sustainable Fiscal Path ...................... 27

BUILDING A STRONGER, FAIRER, MORE RESILIENT ECONOMY...... 31

Strengthening the Foundations of Growth........................................ 32
Ensuring Fairness for the Middle Class............................................. 34
Making the Economy More Resilient to Future Challenges........... 37

CONCLUSION............................................................................................... 38

CHAPTER 2. THE YEAR IN REVIEW AND THE
YEARS AHEAD................................................................................................ 41
AN ECONOMY IN RECOVERY: KEY EVENTS OF 2012........................... 42

European Crisis and the Slowdown in Global Growth................... 46
Hurricane Sandy and the Drought.................................................... 46
Monetary Policy.................................................................................... 47
Fiscal Policy............................................................................................ 51

DEVELOPMENTS IN 2012 AND THE NEAR-TERM OUTLOOK............. 52

Labor Market Trends........................................................................... 52
Consumption and Saving.................................................................... 55
Business Fixed Investment................................................................... 59
Business Inventories.............................................................................. 61
Government Outlays, Consumption, and Investment.................... 61
State and Local Governments............................................................. 63
Real Exports and Imports.................................................................... 65
Housing Markets................................................................................... 67
Financial Markets................................................................................. 70
Wage and Price Inflation..................................................................... 70

11

THE RECOVERY IN HISTORICAL PERSPECTIVE................................... 72

Demographics, Productivity, and Long-Term
Economic Growth ................................................................................ 75
Reasons for the Slower Cyclical Component .................................... 84

OUTLOOK FOR 2013 AND BEYOND......................................................... 87
CONCLUSION............................................................................................... 88

CHAPTER 3. FISCAL POLICY................................................................... 91
THE FEDERAL BUDGET OUTLOOK.......................................................... 93
FEDERAL INCOME TAX REFORM............................................................. 96

Tax Expenditures.................................................................................. 97
Vertical Equity..................................................................................... 100
Efficiency and Simplification............................................................ 103
Reforming the International Corporate Tax ................................. 106

THE STATE AND LOCAL BUDGET OUTLOOK.....................................109

The Cyclicality of State and Local Government
Expenditures........................................................................................ 111
Federal Grants to States Through the Recovery Act...................... 114
State and Local Pensions................................................................... 115

CHAPTER 4. JOBS, WORKERS AND SKILLS....................................119
DEMOGRAPHIC AND LABOR FORCE TRENDS....................................121

A Slowdown in Women’s Participation Rates................................ 124
Work Schedules and Workplace Flexibility .................................. 126

GOVERNMENT AS A PARTNER IN HUMAN CAPITAL AND SKILL
FORMATION...............................................................................................132

Expanded Pell Grants......................................................................... 136
Expanded American Opportunity Tax Credit .............................. 136
Aggregate Student Loan Debt........................................................... 137
Income-Based Repayment ................................................................ 140
Federal Loan Consolidation.............................................................. 140
The Growth of For-Profit Colleges................................................... 141
Gainful Employment.......................................................................... 142
What Is Driving Up Tuition Costs? ................................................ 143
Government as a Partner in Training............................................. 147

IMMIGRATION...........................................................................................148

A Brief History of U.S. Immigration Policy.................................... 149

12 |

Annual Report of the Council of Economic Advisers

The Economic Benefits of Immigration........................................... 154
A Magnet for High-Skilled Immigration......................................... 156
Boosting Innovation and Entrepreneurship.................................... 157
Conclusion............................................................................................ 158

CHAPTER 5. REDUCING COSTS AND IMPROVING THE
QUALITY OF HEALTH CARE.................................................................161
HEALTH CARE SPENDING.......................................................................161

Long-Term Spending Growth............................................................ 163
Medical Productivity ......................................................................... 165
Sources of Inefficiency in Health Care Spending........................... 167

EARLY IMPLEMENTATION OF THE AFFORDABLE CARE ACT.........171

Economic Benefits of Insurance ....................................................... 171
Expanding Affordable Health Insurance Coverage ...................... 172
Consumer Protection ......................................................................... 174
Health Care Spending and Quality of Care ................................... 174
Medicare Payment Reform ............................................................... 176
Is the Cost Curve Bending? ............................................................... 179

CHAPTER 6. CLIMATE CHANGE AND THE PATH
TOWARD SUSTAINABLE ENERGY SOURCES................................185
CONSEQUENCES AND COSTS OF CLIMATE CHANGE.......................186

The Changing Climate....................................................................... 186
Estimating the Economic Cost of Climate Change:
The Social Cost of Carbon................................................................. 188
Policy Implications of Scientific and Economic Uncertainty....... 191

CARBON EMISSIONS: PROGRESS AND PROJECTIONS.......................194
POLICY RESPONSES TO THE CHALLENGE OF CLIMATE
CHANGE......................................................................................................196

Market-Based Solutions..................................................................... 197
Direct Regulation of Carbon Emissions and the Vehicle
Greenhouse Gas / Corporate Average Fuel Economy (CAFE)
Standards............................................................................................. 197
Energy Efficiency................................................................................. 198

ENERGY PRODUCTION IN TRANSITION..............................................202

Oil and Natural Gas........................................................................... 202
Renewable Energy............................................................................... 204
Advanced Technologies and R&D.................................................... 206
Contents

| 13

PREPARING FOR CLIMATE CHANGE ...................................................206
CONCLUSION.............................................................................................207

CHAPTER 7. INTERNATIONAL TRADE AND
COMPETITIVENESS....................................................................................209
THE WORLD ECONOMY AND U.S. TRADE ..........................................209

Growth in World Economies............................................................. 210
The Euro Crisis.................................................................................... 210
Global Imbalances.............................................................................. 213

TRADE AND THE MANUFACTURING SECTOR...................................214

Trade and Productivity...................................................................... 216

GROWTH OF TRADED SERVICES...........................................................217
TRADE POLICY ..........................................................................................220
BUILDING U.S. COMPETITIVENESS.......................................................222

Manufacturing.................................................................................... 222
Spillovers Between Manufacturing Production
and Innovation.................................................................................... 223
Rise of Global Supply Chains............................................................ 226
Prospects for U.S. Manufacturing.................................................... 227
Productivity in Services...................................................................... 231

CREATING AN ECONOMY BUILT TO LAST..........................................232

Strengthening Competitiveness: The Manufacturing
Example................................................................................................ 233

CONCLUSION.............................................................................................235

CHAPTER 8. CHALLENGES AND OPPORTUNITIES IN
U.S. AGRICULTURE....................................................................................237
THE AGRICULTURAL SECTOR IN 2012..................................................239

Barriers to Entry and Succession Planning in
U.S. Agriculture................................................................................... 242
A Mature Domestic Food Market.................................................... 245
New Markets in Agriculture ............................................................. 246
Today’s Farm Structure..................................................................... 248
Investing in Agricultural Productivity............................................. 249
Research and Development Drives Productivity Growth............. 250
Conservation Practices and the Environment ............................... 252
Natural Capital, Conservation, and the Outdoor Economy ....... 253

14 |

Annual Report of the Council of Economic Advisers

GROWING GLOBAL DEMAND FOR FOOD AND AGRICULTURAL
COMMODITIES...........................................................................................254

Population Growth and Urbanization............................................ 254
Pressure on Agricultural Land and the Environment................... 256

GLOBAL COMMODITY MARKETS AND PRICE VOLATILITY............257
MEETING THE CHALLENGES AND HARNESSING THE
OPPORTUNITIES OF GLOBAL DEMAND GROWTH............................258

Open Trade and Access to Global Food Markets........................... 258
Hired Farm Labor Costs in a Global Economy.............................. 259
Improving Risk Management........................................................... 261
The Dodd-Frank Wall Street Reform and Consumer
Protection Act...................................................................................... 263

CONCLUSION.............................................................................................264

REFERENCES..................................................................................................265
A.		
B.		

1-1.
1-2.
1-3.
1-4.
1-5.
1-6.
1-7.
2-1.
2-2.
2-3.
2-4.
2-5.
2-6.

APPENDIXES
Report to the President on the Activities of the Council of
Economic Advisers During 2012........................................................ 299
Statistical Tables Relating to Income, Employment, and
Production.............................................................................................. 313
FIGURES
Monthly Change in Private Nonfarm Payrolls, 2007–2013.............. 24
Real Gross Domestic Product and Trends, 1960–2012..................... 25
Cumulative Over- and Under-Building of Residential and
Manufactured Homes, 1996–2012........................................................ 26
Real GDP, 2007–2012............................................................................. 28
Average Annual Difference Between Growth in Real GDP
Per Capita and Growth in Real Health Expenditures Per Capita,
1965–2012................................................................................................. 30
Population Growth by Age Group, 1950–2040.................................. 33
Average Tax Rates for Selected Income Groups Under a
Fixed Income Distribution, 1960–2013................................................ 35
Real GDP Growth, 2007–2012.............................................................. 43
Nonfarm Payroll Employment, 2007–2013......................................... 52
Private Nonfarm Employment During Recent Recoveries............... 53
Unemployment Rate, 1979–2013.......................................................... 54
Initial Unemployment Insurance Claims, 2004–2013....................... 54
Consumption and Wealth Relative to Disposable Personal
Income (DPI), 1952–2012...................................................................... 57

Contents

| 15

2-7.
2-8.
2-9.
2-10.
2-11.
2-12.
2-13.
2-14.
2-15.
2-16.
2-17.
2-18.
2-19.
3-1.
3-2.
3-3.
3-4.
3-5.
3-6.
3-7.
3-8.
3-9.
3-10.
3-11.
3-12.
3-13.
4-1.
4-2.

16 |

Business Fixed Investment and Cash Flow, 1990–2012.................... 62
Real State and Local Government Purchases During
Recoveries................................................................................................. 63
Real Exports During Recoveries............................................................ 66
Housing Starts, 1960–2012..................................................................... 67
Home Prices and Owners’ Equivalent Rent, 1975–2012................... 68
Cumulative Over- and Under-Building of Residential and
Manufactured Homes, 1996–2012........................................................ 69
10-Year Treasury Yields, 2004–2013.................................................... 71
Consumer Price Inflation, 2004–2012.................................................. 71
Real GDP During Recoveries................................................................. 73
Productivity Growth and Estimated Trend, 1960–2012.................... 78
Employment Percent Growth and Estimated Trend,
1960–2012................................................................................................. 79
Quarterly Change in Employment and Estimated Trend,
1960–2012................................................................................................. 79
Real Gross Domestic Product and Trends, 1947–2012..................... 81
Average Tax Rates for Selected Income Groups Under a
Fixed Income Distribution, 1960–2013................................................ 92
Real State and Local Government Gross Investment During
Recoveries................................................................................................. 93
Federal Receipts and Outlays, 1970–2023........................................... 94
Federal Budget Deficit, 1970–2023....................................................... 95
Federal Debt Held by the Public, 1970–2023...................................... 95
Distribution of Benefits of Selected Tax Expenditures, 2013......... 100
Effective Marginal Tax Rates on Wage Income for Selected
Income Groups Under a Fixed Income Distribution,
1960–2013............................................................................................... 102
Top Marginal Tax Rates, 1960–2013.................................................. 103
Composition of Federal Receipts, 1960–2011................................... 105
Individual Income Tax Compliance Costs by Reporting
Activity, 2010......................................................................................... 107
Real Annual Changes in State General Fund Spending,
1981–2012............................................................................................... 110
Year-to-Year Change in City General Fund Tax Receipts,
2005–2012............................................................................................... 111
Federal Grants to State and Local Governments by Type,
1960–2012............................................................................................... 114
Labor Force Participation Rate by Population Group,
1970–2012............................................................................................... 123
Age-Specific Labor Force Participation Rate by Birth Cohort
for Women, 1926–1992........................................................................ 125

Annual Report of the Council of Economic Advisers

4-3.

Labor Force Participation Rate of Women Aged 25–54,
1991–2011............................................................................................... 128
4-4. Percent of Women Ages 25 Years and Older Working
Full-Time, 1991–2009........................................................................... 128
4-5. Median Weekly Earnings by Education Level, 2012........................ 133
4-6. Tuition and Fees for Full-Time Undergraduate Students,
1990–2012
		Private institutions................................................................................ 135
		Public institutions.................................................................................. 135
4-7. Compositions of Household Debt Balance, 2003–2012.................. 139
4-8. Total Postsecondary Enrollment by Type of Institution,
1990–2010............................................................................................... 139
4-9. Average Expenditures per Full-Time-Equivalent Student by
Component, 2000–2010
		Private institutions................................................................................ 146
		Public institutions.................................................................................. 146
4-10. Legal Immigration by Decade, 1820s to 2000s.................................. 151
5-1. GDP and Health Spending, 1980–2011............................................. 162
5-2. Contribution of Population Growth and Aging to Health Care
Spending, 1996–2010............................................................................ 163
5-3. Cancer Spending per New Cancer Case, 1983–1999....................... 166
5-4. Life Expectancy after Cancer Diagnosis, 1983–1999........................ 166
5-5. Acute Care Hospital Readmission Rates, 2007–2012...................... 176
5-6. Real Annual Growth Rates of National Health Expenditures
Per Capita and Medicare Spending Per Enrollee, 1990–2012........ 179
5-7. Relationship Between Change in State Unemployment Rate
and Change in Real Per-Capita Personal Health Spending,
2007–2009............................................................................................... 181
5-8. Projected Medicare Spending as a Share of GDP, 2013-2085........ 182
6-1. Illustrative Average Temperature Distribution................................ 188
6-2. U.S. Energy-Related Carbon Dioxide Emissions, 1973-2040......... 195
6-3. Decomposition of CO2 Emission Reductions, 2005-2012.............. 196
6-4. Energy Use per Dollar of GDP, Selected Countries,
1988−2009............................................................................................... 200
6-5. U.S. Energy Intensity, 1950-2010........................................................ 200
6-6. Total U.S. Primary Energy Production, 2011.................................... 203
6-7. U.S. Natural Gas Consumption and Production, 2000-2025......... 203
6-8. Annual and Cumulative Growth in U.S. Wind Power
Capacity, 1998-2011.............................................................................. 205
7.1. Real GDP Growth by Country, 2007-2012........................................ 211
7.2. 10-Year Government Bond Yields, 2011-2013................................. 212
7.3. Current Account Balance by Country, 2000-2011........................... 215

Contents

| 17

7.4.

U.S. Current Account Balance and its Components,
2000-2012................................................................................................ 215
7.5. Monthly Change in Manufacturing Employment, 1990-2012....... 228
7.6. Employment in Export Intensive and Export Nonintensive
Manufacturing Industries, 2011-2012................................................ 229
7.7.		 Change in Manufacturing Unit Labor Costs, 2003-2011................ 231
8-1. Median Income for Farm Households by Farm Type and
Income Type, 2010−2012..................................................................... 241
8-2. Distribution of Farms by Age of Principal Operator, 2010............ 242
8-3. U.S. Real Per Capita Food Expenditures, 1985−2011...................... 246
8-4. Farm and Nonfarm Productivity, 1948-2009 ................................... 250
8-5. Public and Private U.S. Agricultural R&D Spending,
1971-2009................................................................................................ 251
8-6. Population by Region, 1950−2050...................................................... 254
8-7. Percentage of Population Residing in Urban Areas, 1950–2050.... 255
8-8. Middle-Class Population by Region, 2009−2030.............................. 256
8-9. Corn Yields and Price, 1866−2012...................................................... 258
8-10. Government Commodity Payments by Farm Type......................... 262
2.1.		
2.2.		
3.1.		
4-1.
4-2.
4-3.
4-4.
4-5.
7.1.		
8-1.
8-2.
8-3.

TABLES
Labor Force Participation Rates, 1980–1985 and 2007–2012........... 56
Real GDP Growth During Three Years Following Business
Cycle Trough............................................................................................ 85
Cyclical Behavior of State and Local Government Expenditures,
1977–2008............................................................................................... 113
Labor Force Participation Rate of Women Aged 25–54,
1969–2007............................................................................................... 125
Education Tax Incentives: The American Opportunity
Tax Credit, 2010.................................................................................... 138
Foreign-Born Persons in Selected Countries.................................... 150
Distribution of Education, Age, and Employment for
Natives and Foreign Born Individuals, 2010–2012.......................... 153
Percentage of Foreign-Born College Graduates by Degree and
Occupation, 2010................................................................................... 157
Euro Area Selected Economic Indicators.......................................... 213
90 Years of Structural Change in U.S. Agriculture.......................... 240
Farm Types............................................................................................. 240
Farm Income and Farm Operator Household Income
by USDA Farm Size Classification, 2010........................................... 241

BOXES
Box 2-1: Effectiveness of Iran Sanctions.......................................................... 44
Box 2-2: Why Is the Labor Share Declining?.................................................. 60
18 |

Annual Report of the Council of Economic Advisers

Box 2-3: Economic Impacts of the American Recovery and
Reinvestment Act.................................................................................... 76
Box 2-4: Implications of Demographic Trends for Household
Consumption........................................................................................... 82
Box 3-1: Estimates of Tax Expenditures in the President’s Budget.............. 98
Box 4-1: Minimum Wages and Employment................................................ 120
Box 6-1: The Cost of Hurricanes..................................................................... 189
Box 6-2: Handling Uncertainty About Equilibrium Climate
Sensitivity................................................................................................ 192
Box 7-1: Small Businesses and the NEI.......................................................... 221

DATA WATCH
Data Watch 2-1: Seasonal Adjustment in Light of the Great Recession..... 48
Data Watch 2-2: The Effect of Statistical Sampling on Laspeyres
Indexes...................................................................................................... 74
Data Watch 3-1: Federal Tax Information and Synchronization of
Interagency Business Data................................................................... 108
Data Watch 4-1: New Evidence on Access to Paid Leave............................ 130
Data Watch 5-1: Toward Disease-Based Health Care Accounting............ 168
Data Watch 6-1: Tracking Sources of Emissions: The Greenhouse Gas
Reporting Program................................................................................ 190
Data Watch 7-1: Implications of Global Value Chains for the
Measurement of Trade Flows.............................................................. 218
Data Watch 7-2: Measuring Supply Chains................................................... 227
ECONOMICS APPLICATION
Economics Application Box 3-1: Marginal Tax Rates and Average
Tax Rates on Individual Income......................................................... 101
Economics Application Box 4-1: Baumol’s Cost Disease (or Bowen’s
Curse) and the Price of Education...................................................... 144
Economics Application Box 5-1: Matching in Health Care........................ 170
Economics Application Box 5-2: Economics of Adverse Selection
and the Benefits of Broad Enrollment................................................ 173
Economics Application Box 7-1: Agglomeration Economies and
Spillovers Across Regions..................................................................... 224
Economics Application Box 8-1: The 2012 Drought................................... 238
Economics Application Box 8-2: The Federal Estate Tax and Farm
Business Succession Planning.............................................................. 244

Contents

| 19

C H A P T E R

1

RECOVERING FROM THE
PAST AND PREPARING
FOR THE FUTURE

A

lthough economics has long been called “the dismal science,” it is
more appropriately viewed as a “hopeful science.” The right mix of
economic policies and leadership can help a country to recover from a deep
recession and point to the investments and reforms that will build a stronger, more stable, and more prosperous economy that works for the middle
class. Conversely, government dysfunction or misguided fiscal policy can
cause self-inflicted wounds to the economy. This year’s Economic Report of
the President highlights the progress that has been made in recovering from
the deepest recession since the Great Depression, together with the policies
that the Obama Administration is advancing to address the fundamental
imbalances and threats that have built up for decades and that have created
severe stress on the middle class and those striving to get into the middle
class.
As President Obama embarks on a second term, the U.S. economy
unquestionably stands on firmer ground than when he first took office, but
more work remains to be done. Our Nation’s economic recovery continued
to make progress in 2012: payroll employment rose by more than 2 million,
the unemployment rate fell to its lowest level in four years, new cars sold
at the fastest rate since 2007, and the housing sector showed clear signs of
turning a corner for the first time in more than five years. In the near term,
sustaining and building upon this progress must be a priority. At the same
time, the Obama Administration also remains focused on addressing a
number of underlying, structural problems, many of which developed over
the course of decades. Some of these problems—like stagnant middle-class
incomes and excessive risk-taking in the financial sector—played a role in
bringing our economy to the brink of collapse in late 2008 and early 2009.
Other challenges—like the dangers of climate change and rising health care
21

costs—could jeopardize our prosperity and security in the years ahead.
Another theme that runs throughout this Report is that demographic
changes associated with an aging population are having a profound effect on
economic performance in a number of domains, from labor force participation to household consumption, as well as placing increasing pressure on
the Federal budget. The Obama Administration is committed to addressing
these issues, while also supporting the ongoing recovery, and in turn building an economy that is stronger, fairer and more resilient.
This Report reviews the progress of the ongoing economic recovery
during 2012 and highlights the main goals of the President’s economic
agenda. These goals include strengthening the foundations of economic
growth by investing in education, research, and infrastructure, and by fixing
a broken immigration system through commonsense immigration reform;
ensuring fairness for the middle class by reforming the tax code and health
insurance system; and bolstering the economy’s resilience to future challenges by addressing the dangers of climate change, moving toward energy
independence, pursuing a balanced approach to deficit reduction, adding
safeguards to the financial system, opening up new markets for U.S. exports,
and equipping American workers to compete in the global economy.

Tracking the Progress of the Recovery
When President Obama first entered office on January 20, 2009,
the U.S. economy was in the midst of the worst downturn since the Great
Depression. Real gross domestic product (GDP), the total amount of goods
and services produced in the country adjusted for inflation, had just contracted at the sharpest rate in any quarter in more than 50 years, shrinking
by 8.9 percent at an annual rate. This severe decline in economic output was
accompanied by devastating job losses. In the year before President Obama’s
first inauguration, the U.S. economy lost 4.6 million private sector jobs,
including 821,000 in January 2009. As bad as things were at the time, a dark
cloud of uncertainty hovered over the economy, and the risk of even further
deterioration was still very real. At the end of 2008, the financial system
teetered on the brink of collapse and credit for businesses and households
had seized up. Home prices were steadily declining, with no bottom in sight,
and the fate of the American auto industry hung in the balance, as auto sales
in early 2009 plunged to their lowest level in 27 years. A total of $16 trillion
in wealth was erased by the financial and housing crisis, causing families to
pull back on spending plans, reduce personal debt and increase savings, in
turn leading companies to cut back hiring, lay off valued employees, and halt
investment plans. In short, the economy was caught in a downward spiral,

22 |

Chapter 1

where consumers were pulling back because they had less income and feared
job loss, businesses pulled back and reduced employment even further, and
around this vicious cycle went.
Against this backdrop, the Obama Administration acted quickly and
decisively to raise aggregate demand, stem the job losses, restore the flow
of credit, and put the economy in a position to begin growing once again.
The American Recovery and Reinvestment Act of 2009 (the Recovery Act)
was the boldest measure of countercyclical fiscal stimulus in U.S. history.
The Recovery Act’s mix of tax cuts for individuals and businesses, aid to
State and local governments, and infrastructure investment was designed to
provide the economy with an immediate boost. In addition to the Recovery
Act, the Obama Administration worked to stabilize the financial sector
through a series of measures including stress tests for banks and rigorous
requirements for banks to raise private capital and repay the government
for assistance from the Troubled Asset Relief Program. The Making Home
Affordable program put in place a number of initiatives that have helped
millions of Americans modify or refinance their mortgages and stay in their
homes. The Administration also rescued and reformed the auto industry by
guiding the successful restructuring of two of America’s largest automakers
and preserving the critical supply network.
Soon after these steps were taken, the economy reversed course. The
contraction in economic output eased in 2009 and GDP began to grow again
in the third quarter of that year. The economy has now expanded for 14
consecutive quarters. Similarly, the pace of job losses slowed over the course
of 2009, and the monthly change in private employment turned positive in
March 2010. In recent recoveries following the end of recessions, job growth
has lagged economic growth, as employers either managed to implement
ways to raise labor productivity to meet demand or delayed hiring out
of caution that demand would not recover. During the current recovery,
sustained job growth started 9 months after GDP growth resumed, which is
sooner than in the 1991 and 2001 recoveries. As shown in Figure 1-1, private
employers have now increased payrolls for 35 consecutive months. The 6.1
million jobs added over this time constitute the best 35-month period of
job creation since 1998–2001, more than a decade ago. In addition, some
$13.5 trillion of the $16 trillion in lost wealth has been restored due to the
rebounding of the equity markets and firming of house prices, although the
gains in wealth have not been uniformly shared.
In 2012, the recovery continued to make progress, and the economy
and American people showed their resilience in the face of several headwinds. Total nonfarm payroll employment grew by 2.2 million during the
year, or roughly 181,000 jobs per month, a bit above the forecast of 167,000
Recovering from the Past and Preparing for the Future

| 23

Figure 1-1
Monthly Change in Private Nonfarm Payrolls, 2007–2013

Thousands, seasonally adjusted
400
300
200
100

0

Jan-2013

-100
-200
-300
-400
-500
-600
-700
-800

-900
Jan-2007
Jan-2008
Jan-2009
Jan-2010
Jan-2011
Jan-2012
Note: Shading denotes recession.
Source: Bureau of Labor Statistics, Current Employment Statistics.

Jan-2013

jobs per month that appeared in last year’s Economic Report of the President.
The unemployment rate fell 0.7 percentage point over the course of the year
and reached its lowest level since January 2009. Almost the entire drop in the
unemployment rate resulted from increased employment rather than labor
force withdrawal. GDP expanded by 1.6 percent during the four quarters of
2012.
Although 2012 was a year of progress, it was not without challenges.
A severe drought in the Midwest subtracted from GDP growth in the second
and third quarters. Hurricane Sandy struck in late October, and based on
the latest estimates of private property damage, it was the second costliest
natural disaster in the United States during the last 40 years, behind only
Hurricane Katrina. In addition, the euro area slipped back into recession,
reflecting continued uncertainty in financial markets, further deleveraging
by households and companies, and sizable fiscal austerity measures undertaken by many European governments. The slowdown among our trading
partners in Europe and also in Asia reduced overseas demand for U.S. goods
and services. And here in the United States, the threat of scheduled tax
increases and automatic spending cuts known as the “fiscal cliff” lingered for
most of the year. The economy’s performance in 2012 is reviewed in greater
detail in Chapter 2. Despite the economy’s resilience during the past year,
the unemployment rate remains elevated, and more work remains to be
24 |

Chapter 1

Figure 1-2
Real Gross Domestic Product and Trends, 1960–2012

Trillions of chained 2005 dollars, log scale
18
14
10

6
4

1960:Q1

1970:Q1

1980:Q1

1990:Q1

2000:Q1

2010:Q1

Note: Shading denotes recession. Trend lines represent the average growth rate between successive
business-cycle peaks.
Source: Bureau of Economic Analysis, National Income and Product Accounts; National Bureau of
Economic Research; CEA calculations.

done to boost growth and job creation. In 2013, the Administration remains
focused on the need to keep moving forward, while once again avoiding the
threat of self-inflicted wounds.

Placing the Recovery in Historical Context
Chapter 2 also places the recovery in broader historical context. The
pattern in recoveries over the last 50 years has been that more recent recoveries tend to be marked by slower growth than the recoveries that preceded
them. This tendency is documented in Figure 1-2, which shows real GDP
along with trend lines based on the average growth rate between successive
business-cycle peaks. The current recovery, so far, is no exception to this
pattern. The single largest cause of slower trend GDP growth in recent years
is changing demographics, as the rate of overall population growth moderates, the baby boomers move into retirement, and the share of the population that is of prime working age begins to decline. Productivity growth also
appears to have slowed down after the 1990s, although it is unclear if the
slowdown will continue.
At the same time, however, several of the factors that have restrained
growth in recent years are temporary constraints that are unique to the
current situation and will likely subside in the years ahead. For instance,

Recovering from the Past and Preparing for the Future

| 25

Figure 1-3
Cumulative Over- and Under-Building of Residential
and Manufactured Homes, 1996–2012

Millions of units
3.0
Relative to Projected
2.5
Annual Average Demand
for New Units Based
2.0
on Demographic Trends
1.5

Apr-2007

1.0
0.5
0.0

"Boom Years"
1996-2006

-0.5
-1.0

"Correction
Years"
2007-2012

-1.5
-2.0
1996

Dec-2012
1998

2000

2002

2004

2006

2008

2010

2012

Note: The 1998 Economic Report of the President projected that 1.6 million new housing units per year would
be needed from 1996–2006 to keep pace with demographics. Cumulative over- and under-building is measured
relative to this projection.
Source: Census Bureau, New Residential Construction (completions) and Manufactured Homes Survey
(placements); CEA (1998); CEA calculations.

a growing body of research has shown that recoveries following financial
crises tend to be slower, because of delays in the reemergence of credit
and reductions in consumer spending as households pay down debt or
rebuild their savings. The Administration expects growth to quicken after
households repair their balance sheets and consumers have more money
to spend on goods and services. In addition, the housing sector is just now
emerging from several depressed years, and much of the overbuilding that
took place during the boom years has been offset by underbuilding since
2007. As Figure 1-3 shows, by the Council of Economic Advisers’ (CEA)
calculations, the U.S. housing market has likely worked off the nationwide
cumulative total of excess building that took place in the housing boom
years. Consequently, activity in the housing sector is likely to return to more
normal levels in the years ahead, although some regions are further ahead in
this process than others.
Furthermore, despite the Administration’s efforts to support State
and local governments through the Recovery Act and other measures,
employment in this sector has undergone an unprecedented decline. The
Obama Administration will continue to look for ways to boost the hiring of
teachers, police officers and firefighters, and these efforts should be helped
by a broadly improving economy that eases the strain on State and local

26 |

Chapter 1

government finances. Thus, while some of the slower growth experienced in
recent years is likely the unavoidable consequence of changing demography,
there are still strong reasons to believe that the pace of economic growth will
nonetheless pick up.

Making Progress Toward a Sustainable Fiscal Path
During 2012, the Obama Administration continued to pursue a balanced approach to fiscal policy that supports the recovery in the near term
while looking to reduce the deficit and stabilize the debt over the medium
and long term. The Recovery Act provided substantial support for growth in
2009 and 2010, and the economy benefited in 2011 and 2012 from extended
unemployment insurance benefits and a 2 percentage point reduction in
the employee contribution to the payroll tax, among other measures. At the
same time, the Administration agreed to and Congress enacted $1.4 trillion in discretionary spending cuts, spread over the next decade to ease the
impact on an economy that is still healing. Together with the additional revenue from the American Taxpayer Relief Act (ATRA) and interest savings,
the deficit has been reduced by more than $2.5 trillion over the next 10 years.
Thanks to these actions and steady economic growth, the Federal budget
deficit has declined from 10.1 percent of GDP in 2009 to 7.0 percent of GDP
in 2012, the largest three-year drop since 1949. The Congressional Budget
Office (CBO 2013) projects that the deficit will fall to 5.3 percent of GDP in
2013. The Obama Administration has repeatedly proposed policies to bring
the deficit down to below 3 percent of GDP and stabilize the national debt
relative to the size of the economy in the 10-year budget window. A further
discussion of the Federal budget outlook can be found in Chapter 3.
A comparison of recent economic performance in the United States
with that of countries undertaking more abrupt fiscal consolidation underscores the importance of a balanced and responsible approach to return over
time to a sustainable Federal budget. Figure 1-4 shows that while GDP in
the United States has expanded for 14 consecutive quarters and surpassed
its pre-recession peak, the recovery has faltered in places where austerity has
been implemented more rapidly. President Obama has put it succinctly: “We
cannot just cut our way to prosperity.”
The American Taxpayer Relief Act, enacted January 2, 2013, represents an important component of the Obama Administration’s approach
to reducing the deficit and returning more fairness to the tax code. Before
the enactment of ATRA, the Congressional Budget Office (CBO 2012a,
2012b) estimated that if the massive tax hikes and spending cuts originally
scheduled to take place in 2013 had been allowed to occur, the full force of

Recovering from the Past and Preparing for the Future

| 27

Index, 2007:Q4 = 100
104

Figure 1-4
Real GDP, 2007–2012

United States

102

2012:Q4

100
Euro Area

98
96

United
Kingdom

94
92
2007:Q1

2008:Q1

2009:Q1

2010:Q1

2011:Q1

2012:Q1

Source: U.S. Bureau of Economic Analysis, National Income and Product Accounts; U.K. Office for
National Statistics; Statistical Office of the European Communities.

these austerity measures, equivalent in dollar terms to roughly 4 percent of
GDP, would have caused the unemployment rate to rise by more than one
percentage point and likely driven the economy into another recession. The
Council of Economic Advisers (CEA 2012) projected that if tax rates rose for
middle-class families earning less than $250,000 a year as was planned under
then-current law, U.S. consumers would have reined in their spending by
nearly $200 billion in 2013. To put this in perspective, this reduction of $200
billion is approximately four times larger than the total amount that 226
million shoppers spent on the post-Thanksgiving “Black Friday” weekend
in 2011, or roughly the same amount Americans spent on all the new cars
and trucks sold in the United States that year. This would have been a deeply
damaging self-inflicted wound to the economy.
ATRA avoided this massive fiscal retrenchment, securing permanent
income tax relief for 98 percent of Americans and 97 percent of small businesses, while also asking wealthier Americans to contribute a bit more to
deficit reduction. ATRA reduces the deficit by more than $700 billion over
the next 10 years, largely by restoring the top marginal tax rate on upperincome households to the levels that prevailed in the 1990s and taxing these
households’ capital income at a 20 percent rate instead of 15 percent. At the
same time, it locks in lower tax rates for the middle class permanently and
28 |

Chapter 1

extends President Obama’s expansions of key tax credits that help working
families pay the bills and send their children to college. Other tax credits for
business investment and R&D were also extended, as was unemployment
insurance for 2 million Americans who are still searching for a job. By avoiding the bulk of the tax increases that would have jeopardized the recovery
while also making substantial progress on reducing the deficit, ATRA was
a positive step that is representative of the balanced approach that the
Administration will continue to pursue.
As this Report goes to press, the U.S. economy is once again confronted with the risk of a self-inflicted wound, in the form of automatic,
across-the-board spending cuts known as the sequester. When originally
put into place with the Budget Control Act of 2011 (BCA), these cuts were
never intended to be policy, but rather to force Congress to reach agreement
on a broad, long-term deficit reduction package. In the absence of such an
agreement, the cuts went into effect on March 1, 2013, and threaten to slow
the economy and cause hundreds of thousands of job losses if not replaced.
Private economists suggest the cuts could reduce GDP growth in 2013 by
around half a percentage point. This potential reduction in output is sizable, considering that most analysts expect the economy to grow around
2 to 3 percent during the year. Moreover, in the weeks and months ahead,
sequestration will begin to disrupt basic functions of government on which
Americans depend, from education to emergency first-response to airport
security. Already, the Navy has been forced to delay the deployment of an
aircraft carrier to the Persian Gulf because of the threat of the cuts. The
Administration will continue to call on Congress to replace the across-theboard, indiscriminate BCA sequester with a balanced alternative that closes
unfair tax loopholes, reforms entitlements, and cuts unnecessary spending.
This type of approach is the best way to support the recovery in the short
run, while also making progress toward returning to a sustainable budget in
the long run.
While the immediate budgetary concern in 2013 is the need to replace
the sequester, it is also important to remain focused on the main driver
of our long-term budget challenge: the cost of health care for an aging
population. One positive development, with significant implications for
the economy and Federal budget if it persists, is the recent slowdown in the
growth of health care spending. The rate of growth in nationwide real per
capita health care expenditures has been on a downward trend since 2002,
with a particularly marked slowdown over the past three years. Since 2010,
health care expenditures per capita grew at essentially the same rate as GDP
per capita. As shown in Figure 1-5, this development is unusual, because
growth in health spending has tended to outpace overall economic growth
Recovering from the Past and Preparing for the Future

| 29

Figure 1-5
Average Annual Difference Between Growth in Real GDP Per Capita
and Growth in Real Health Expenditures Per Capita, 1965–2012

Percentage points
4.5
4.0
3.5
3.0

3.6
3.1

3.1

2.5

2.8

3.2

3.1

2.2

2.1

2.0
1.5
1.0
0.5
0.0

0.0

0.0

'65-'69 '70-'74 '75-'79 '80-'84 '85-'89 '90-'94 '95-'99 '00-'04 '05-'09 '10-'12

Note: Health expenditures per capita are deflated by the GDP price index.
Source: Bureau of Economic Analysis, National Income and Product Accounts; Centers for Medicare
and Medicaid Services, National Health Expenditure Accounts; CEA calculations.

for most of the last five decades. Although some of the narrowing of this gap
can be attributed to the effects of the recession, Chapter 5 presents evidence
that structural shifts in the health care sector are underway, spurred on in
part by the 2010 Patient Protection and Affordable Care Act (Affordable
Care Act). If the recent trends can be sustained, the resulting lower health
care costs will have a tremendously positive impact on employers, middleclass families, and importantly, the Federal budget. Indeed, if the growth rate
of Medicare spending per beneficiary over the last five years persists into the
future, then after 75 years Medicare spending would account for only 3.8
percent of GDP, little changed from its share today, and substantially less
than what the Medicare Trustees estimate. This should not be interpreted as
a forecast but rather an indication of how sensitive long-term projections are
to the assumed rate of growth of Medicare spending per beneficiary.
In sum, the U.S. economy has come a long way over the last four
years, though more work remains. A staggering total of 8.8 million private
sector jobs were destroyed as a result of the Great Recession, but 6.1 million
jobs have been gained back. Similarly, $16 trillion in household wealth was
lost when the housing bubble burst and the economy went into recession,
but now more than $13 trillion—over 80 percent—has been regained. And
of the estimated $4 trillion in deficit reduction that many budget experts

30 |

Chapter 1

agree is needed over the next 10 years to place the economy on a sustainable fiscal path, more than $2.5 trillion has been achieved. House prices and
residential construction are on the rise, the domestic manufacturing sector
is showing signs of resurgence after a decade of shedding jobs, and the U.S.
auto industry is back on track, selling new cars at an increasing rate. More
work remains to be done, but our Nation has come too far now to turn back.

Building a Stronger, Fairer,
More Resilient Economy
While continuing to build on the progress in recovering from
the recession and increasing job creation in the near term, the Obama
Administration has also kept a careful focus on preparing the U.S. economy
for a stronger, fairer, more resilient future. Many of the problems that
caused the financial crisis and recession built up over decades, and our
Nation will not have a durable economy that works for the middle class until
these underlying, fundamental issues are addressed. For instance, middleclass incomes stagnated in the 2000s, and many economists have argued
that households turned to credit to make up for this weak income growth.
Lightly regulated—or unregulated—financial companies were all too willing
to provide easy credit at nontransparent terms to meet this demand. The
borrowing was unsustainable, as evidenced by the bursting of the housing
bubble and the fact that outstanding household debt burdens have restrained
consumer spending during the course of the recovery.
Part of the weak income growth for middle-class families can be
traced to rising health care costs. By one estimate, if health care costs during the 2000s had risen at the same rate as general consumer price inflation—rather than exceeding it—the median family of four would have had
an additional $5,400 in 2009 to spend on other expenses (Auerbach and
Kellermann 2011). Slowing the rise in health care costs is therefore a critical
part of ensuring that middle-class families can see their take-home pay start
to grow consistently again.
This mix of underlying problems—stagnant middle-class incomes,
excessive reliance on borrowing, and rising health care costs—motivated two
of the Administration’s key initiatives during the first term: the Affordable
Care Act and the Dodd-Frank Wall Street Reform and Consumer Protection
Act. The Affordable Care Act expands insurance coverage and puts in place
meaningful reforms that will reduce the cost of medical care, ensuring that
families will not be forced into bankruptcy because of an unexpected illness. The Dodd-Frank law puts an end to taxpayer-financed bailouts for
big banks, restricts many of the riskiest financial practices that developed in
Recovering from the Past and Preparing for the Future

| 31

the run-up to the crisis, and creates a new consumer watchdog to increase
transparency and fairness for American families.

Strengthening the Foundations of Growth
The economy’s long-run growth potential fundamentally depends
on the number of workers and the productivity of those workers, which, of
course, depends on the productivity of American businesses and the creativity and risk-taking of American entrepreneurs. During the second half of
the 20th century, the U.S. economy benefited substantially from favorable
demographics. The baby boomers were in their prime working years, and
women entered the labor force in record numbers. As the size of the labor
force grew more quickly during these years, so too did the economy’s potential output. However, as discussed previously, population growth is expected
to slow in the years ahead, and the United States is expected to undergo a
dramatic demographic transition. Figure 1-6 displays the latest projections
from the Census Bureau, showing that overall population growth is estimated to decline from an average of 1.2 percent per year since 1950, to just
0.7 percent per year over the next three decades. Notably, as the baby boomers move into retirement, the only major age group that will grow faster
over the next 30 years than it did during the last 60 is persons aged 65 and
up. As a result, the share of the population that is of prime working age will
fall steadily, and the number of retirees per worker will rise. Consequently,
one of the major challenges facing the U.S. economy in the decades ahead is
the slowdown in potential output growth that will result from a more slowly
growing population and labor force.1
Although the recession caused a decline in the labor force participation rate, it is important to recall that even well before the recession, the
labor force participation rate showed signs that it had reached its peak in the
late 1990s. This fact largely reflected the aging of the population discussed
above and the plateauing of female labor force participation following four
decades during which American society was transformed by an increasing
number of women in the workforce. So while some discouraged workers are
likely to reenter the labor force in the near term as the economy continues
to heal, the long-term trend for the labor force participation rate is still likely
1 Although the changing demographics of the United States are likely to have a large effect on
the economy and the Federal budget in the years ahead, the challenges are even greater in other
advanced countries. According to United Nations projections (UN 2011), in 2040, the ratio
of persons aged 65 and older to persons aged 20–64 will be even higher in Canada, France,
Germany, Italy, Japan, Korea, and the United Kingdom than it will be in the United States.
The Organisation for Economic Co-operation and Development (OECD 2012) has said that
the aging of populations across developed countries will be the main contributor to slower
potential output growth in OECD countries in the decades ahead.

32 |

Chapter 1

Figure 1-6
Population Growth by Age Group, 1950–2040

Average annual percent change
2.5

2.29

1950-2011
2.0

1.5

1.0

0.5

2.03

2012-2040

1.25

1.20

0.69

0.81
0.36

0.37

0.0

Total
Total
Ages 0-19
Ages 20-64
Ages 65+
Source: Census Bureau, Annual Estimates of the Resident Population and 2012 National
Population Projections; CEA calculations.

to be downward. This likelihood was acknowledged in the 2004 Economic
Report of the President, which noted, “the long-term trend of rising participation appears to have come to an end. . . . The decline [in the labor force
participation rate] may be greater, however, after 2008, which is the year
that the first baby boomers (those born in 1946) reach the early-retirement
age of 62.”
In the face of the demographic challenges of an aging population and a
more slowly growing workforce, the Administration believes it is imperative
to boost the productivity of American workers by investing in education,
innovation, research, and infrastructure. One way in particular to enhance
the productivity of the workforce is to have a more educated workforce. As
discussed in Chapter 4, the value of a college degree—as measured by the
premium paid to college-educated workers—is significant. Shortly after taking office in 2009, President Obama set the goal that America would once
again have the highest proportion of college-educated young people in the
world by 2020. Chapter 4 details the steps the Obama Administration has
taken to meet that goal, including expanding Pell Grants, establishing the
American Opportunity Tax Credit, and reforming the student loan system
to help make repayment more manageable for 1.6 million responsible borrowers. More recently, President Obama has called for a new Federal-State

Recovering from the Past and Preparing for the Future

| 33

partnership that would provide all low- and moderate-income four-yearolds with high-quality preschool.
Commonsense immigration reform is another key aspect of the
Administration’s efforts to enhance the productivity of the American workforce, create more jobs for workers and more customers for businesses, and
ease the looming demographic challenges. With a more slowly growing
population and more retirees to support, the time is ripe for America to
once again renew its long tradition of welcoming immigrants to our shores.
Chapter 4 summarizes the economic case for reforming our immigration
system to make the American economy more dynamic. Indeed, immigrants
founded more than one in four new businesses in the United States in 2011
(Fairlie 2012). Moreover, commonsense immigration reform that gives
undocumented immigrants a pathway to earned citizenship is needed to
bring these workers out of the shadows and ensure that employers who
hire only legally authorized workers and pay a decent wage are not put at a
disadvantage. This type of commonsense reform strengthens the economy
as a whole by maintaining competition on a level playing field. Immigrants
own more than 2 million American businesses of all sizes and were critical
to the creation of many of our largest companies like Yahoo! and Google.
To make sure that America has a dynamic, competitive workforce and is the
home of the next major innovation, it is essential to move toward an immigration system that is geared to help us grow our economy and strengthen
the middle class.

Ensuring Fairness for the Middle Class
As discussed above, the American Taxpayer Relief Act was significant
not just because it averted the massive tax increases and automatic spending cuts that were slated to occur at the beginning of 2013, but also because
it reversed a decades-long trend of declining tax rates for the wealthiest
American households. Figure 1-7 shows the average Federal (individual
income plus payroll) tax rate for the top 0.1 percent of earners, as well as
for the top 1 percent and the middle 20 percent. Since the mid-1990s, the
average tax rate on income earned by the wealthiest Americans has trended
down and was close to its historical low for most of the 2000s. Beginning in
2013, however, top earners will contribute a bit more to deficit reduction,
reducing pressure to cut key investments in education, research, and infrastructure. Even with the tax changes beginning this year, the average tax rate
on these high earners is still well within the lower end of its historical range.
The move toward greater fairness in the tax code is motivated by
President Obama’s belief that the best way to grow an economy is from the
middle out, not from the top down. Over the last 30 years, the wealthiest
34 |

Chapter 1

Figure 1-7
Average Tax Rates for Selected Income Groups
Under a Fixed Income Distribution, 1960–2013

Average tax rate, percent
60

Top 0.1 percent

50
40

Top 1 percent

30

2013

Middle 20 percent

20
10
0
1960

1965

1970

1975

1980

1985

1990

1995

2000

2005

2010

Note: Average Federal (individual income plus payroll) tax rates for a 2005 sample of taxpayers
after adjusting for growth in the National Average Wage Index.
Source: Internal Revenue Service, Statistics of Income Public Use File; National Bureau of
Economic Research, TAXSIM (preliminary for 2012 and 2013); CEA calculations.

Americans have seen their share of the nation’s income increase substantially. America celebrates success, but Americans also recognize that when
the middle class is squeezed and working families struggle to afford the
goods and services that businesses are selling, the prosperity of the nation as
a whole is jeopardized. ATRA rolls back some of the inequality that has built
up since the 1980s and marks the beginning of the return to a tax code that
reflects basic principles of fairness and the critical importance of the middle
class to the nation’s overall economic health. The Administration has proposed to raise additional revenue by closing loopholes for investment fund
managers and cutting tax preferences that benefit only high-income households, as well as by making changes to the corporate tax code that would
eliminate special breaks for oil and gas companies and corporate jet owners.
Chapter 3 provides further detail on how the President’s tax and budget
policies are informed by the goal of ensuring fairness for the middle class.
In his 2013 State of the Union Address, President Obama emphasized
that “our economy is stronger when we reward an honest day’s work with
honest wages. But today, a full-time worker making the minimum wage
earns $14,500 a year. Even with the tax relief we’ve put in place, a family with
two kids that earns the minimum wage still lives below the poverty line.” For
these reasons, the President proposed raising the Federal minimum wage to
$9.00 an hour and indexing it to inflation thereafter. While economists have

Recovering from the Past and Preparing for the Future

| 35

long debated the effects of the minimum wage on employment, the available evidence suggests that modest increases in the minimum wage raise
the incomes of low-wage workers as a group and have little, if any, effect
on employment. Doucouliagos and Stanley’s (2009) careful meta-analysis
of the literature concludes, “with 64 studies containing approximately 1,500
estimates, we have reason to believe that if there is some adverse employment effect from minimum-wage raises, it must be of a small and policyirrelevant magnitude.” Similarly, another literature review by Schmitt (2013)
considered the most recent research published since 2000 and found, “The
weight of that evidence points to little or no employment response to modest
increases in the minimum wage.”
In addition to being paid a wage they can live on, working families
should also have some protection from the tremendous hardship that could
arise in the event of an unforeseen illness or medical condition. There is a
fundamental economic rationale for providing this sort of protection. As
President Obama said in his second inaugural address, “The commitments
we make to each other through Medicare and Medicaid and Social Security,
these things do not sap our initiative, they strengthen us. They do not make
us a nation of takers; they free us to take the risks that make this country
great.” The insurance coverage expansion and cost reduction measures
contained in the Affordable Care Act are the next major steps toward ensuring that American workers have a fair shot at realizing their full potential.
Already, the number of uninsured young people is falling, due to the law’s
requirement that health insurance plans offer dependent children coverage
until age 26. In addition, millions of Americans are now receiving rebates
from their health insurers as a result of the law’s requirement that insurers
use no more than 20 percent of premiums for profits, administrative costs,
and marketing. Chapter 5 details these and other important steps that are
being taken to improve our Nation’s health care system, as well as the major
benefits that will result for middle-class workers and families.
The President’s top priority remains to make America a magnet for
jobs and manufacturing in order to strengthen the middle class and promote
economic growth. As discussed in Chapter 7, manufacturing has historically provided Americans with a path to the middle class, especially for less
educated Americans. But as foreign competition from companies in China
and elsewhere began to emerge, manufacturing work increasingly moved
overseas, and millions of American jobs were lost. Manufacturing employment in the United States had been fairly stable at around 18 million jobs
from 1965 to 2000, but from 2000 to 2007—before the Great Recession—
manufacturing employment dropped precipitously, falling by 3.5 million
jobs. Another 2.3 million manufacturing jobs were lost in the recession and
36 |

Chapter 1

its aftermath. Chapter 7 details the Administration’s efforts to reverse this
trend and bring manufacturing jobs back to the United States. These efforts
include supporting new skills training programs for workers, investing in
advanced manufacturing R&D to replenish the technology pipeline and
strengthen engineering capabilities, providing tax credits for manufacturers
that hire more employees in the United States, and encouraging fair trade
by expanding America’s global market access and leveling the playing field
across nations. Many of these initiatives began during President Obama’s
first term and contributed to the nearly 500,000 manufacturing jobs that
have been added over the last 3 years, the best period of job creation in manufacturing since the 1990s. This turnaround in manufacturing would have
been inconceivable even just a few years ago, and sustaining this momentum
is a key part of the Obama Administration’s second-term agenda for the
middle class.

Making the Economy More Resilient to Future Challenges
While the Administration works to repair the damage of the Great
Recession and build an economy that works for middle-class families, it
is critical that we also take steps to ensure that the economy is resilient in
the face of gathering challenges. For example, although much progress has
been made in moving America toward a clean energy future that does not
depend on foreign oil, more work remains to be done. Chapter 6 details the
scientific consensus around climate change and the dangerous consequences
that could result if greenhouse gas emissions are not reduced. In addition,
Chapter 6 discusses the preparatory steps being taken to avoid these harmful
outcomes and ensure the economy’s resiliency in the face of these risks. The
Administration has increased fuel efficiency standards, launched an array of
programs to encourage more efficient household energy use, and provided
tax credits to companies developing renewable energy sources—all actions
that will reduce greenhouse gas emissions. In 2012, net imports of petroleum
products were at a 20-year low, domestic natural gas production was at an
all-time high, and the use of renewable sources like wind and solar had more
than doubled from 2008. These are positive steps in the right direction, and
the Administration aims to continue this progress in the second term.
Chapter 8 presents the challenges and opportunities in the U.S.
agricultural sector, as well as the lessons learned from the rapid productivity advances in agriculture that can be built on to raise job creation and
output in other areas of the economy. In 2012, America’s farmers faced the
most severe drought since the 1950s but showed their resilience as net farm
income for the year as a whole is estimated to have fallen just 4 percent from
the record high level reached in 2011. In the years ahead, America’s farmers
Recovering from the Past and Preparing for the Future

| 37

have an especially important role to play in helping to feed a growing global
population. From 2010 to 2050, the world’s population is projected to rise by
more than 2 billion people, and most of this increase is expected to occur in
developing countries. A growing, increasingly urbanized world population
will present unique challenges to the agricultural sector, as urban areas rely
heavily on a stable and efficient worldwide food chain to provide nutrientdense and diverse foods. At the same time, trade in agricultural commodities will continue to be a global endeavor in which prices respond to supply
and demand conditions around the world. Chapter 8 outlines the steps the
Administration is taking to build on our Nation’s trade surplus in agricultural products and help farmers manage the risk of volatile prices.

Conclusion
As President Obama begins his second term, the U.S. economy is
undoubtedly in a far stronger position and headed in a much better direction
than it was when he first took office in January 2009, but more work remains
to be done. 2012 was a year of progress, with private employers adding more
than 2 million jobs and the unemployment rate falling to its lowest level in
four years. While the worst of the recession is now behind us, many of its
aftereffects still linger, as do a number of underlying, structural issues that
built up for decades and could threaten our economy’s prosperity in the
years ahead. As such, the Administration’s efforts in the second term will
proceed along two critically important and related tracks: recovering from
the past and preparing for the future.
The goals of the President’s economic agenda described above—
strengthening the foundations of growth, ensuring fairness for the middle
class, and making the economy more resilient to future challenges—are all
mutually reinforcing. America built the most prosperous economy on Earth
because we recognized that investments in our individual success were inextricably linked to our success as a nation. Today, investments in research
and innovation can lead to new technologies that allow for more effective,
less expensive health care or cleaner sources of energy. To facilitate these
new technological innovations, it is critical to have a vibrant manufacturing
sector with advanced engineering capabilities. A growing manufacturing
sector can also provide a path to the middle class for many American workers. And when middle-class families see their incomes rise, their increased
spending on goods and services supports broad-based, sustainable economic
growth—in other words, an economy that is built to last. This is just one
set of examples of the synergies across the various aspects of the President’s
economic agenda—many more can be found in the chapters of this Report.

38 |

Chapter 1

These synergies underlie the economic recovery that began during President
Obama’s first term and will drive the Administration’s work during his second term to continue moving our economy forward.

Recovering from the Past and Preparing for the Future

| 39

C H A P T E R

2

THE YEAR IN REVIEW AND
THE YEARS AHEAD

F

ollowing the recession that began in December 2007, the most severe
since the Great Depression, the economy is healing and moving in the
right direction. By the fourth quarter of 2012, real output was 2.5 percent
above the level at its previous business-cycle peak in the fourth quarter
of 2007. The economy has added 6.1 million private sector jobs, and 5.5
million jobs overall, since the level of employment hit bottom in February
2010. During the four quarters of 2012, real gross domestic product (GDP)
increased at a moderate 1.6 percent rate. Over the 12 months of the year, 2.2
million jobs were added, and the unemployment rate, while still elevated,
dropped 0.7 percentage point to 7.8 percent.
The near-term outlook is for further expansion. Consumer spending
is rising moderately, as the gradual healing in the labor market lifts income
and as households continue to pay off debt and rebuild wealth. A wide array
of indicators suggests the housing sector is finally recovering, and the long
contraction in the State and local sector appears to be coming to an end.
Financial conditions continue to become more supportive; for example,
senior loan officers report that banks have become more willing to lend to
both small and large businesses.
Although many of the headwinds that have buffeted growth are receding, some remain. Long-term fiscal sustainability requires a path of declining
government spending and rising revenue that will exert fiscal drag on the
economy. In addition, ongoing congressional deliberations over the appropriate means through which long-term fiscal sustainability will be achieved
foster uncertainty that could weigh on consumer and business confidence.
Moreover, tepid growth across the global economy—particularly in Europe
and Asia—may reduce growth in U.S. exports and slow the rebound in
domestic manufacturing activity.
This chapter provides an overview of the economic recovery so far,
beginning with a review of notable macroeconomic events of 2012. The
41

chapter then turns to a broader discussion of the recovery in historical context. Although the recovery has been slow by historical standards, much—
perhaps two-thirds, according to a recent study by the Congressional Budget
Office (CBO 2012d)—of the slower growth relative to previous postwar
recoveries reflects the long-term demographic shifts discussed in Chapter
4 as well as other long-term structural factors. The remaining one-third
reflects unique cyclical factors largely related to the financial crisis, including limitations on the ability of households and small businesses to borrow,
which led to associated reductions in consumption and investment; the slow
recovery of the housing sector as it works off excess inventories of foreclosed
and distressed properties; the contraction of State and local government
budgets arising, in part, from the drop in assessed house values and property
taxes; softening export demand resulting from slower growth in Asia and
Europe; and limitations on conventional monetary policy due to the Federal
Reserve’s lowering of its main policy rate to zero percent (the “zero lower
bound”).
As severe as the recent recession was, the drop in real GDP in the
United States as a result of the financial crisis of 2007–08 was smaller than
both the average decline in other global financial crises over the past 40 years
and the contraction in the aftermath of the 1929 stock market crash here
in the United States. Furthermore, the recovery since June 2009 has been
stronger than in most other developed economies. Active government policies helped the economy avoid an even deeper recession and have played an
important role in supporting the recovery. These active policies include the
American Recovery and Reinvestment Act (the Recovery Act), the temporary payroll tax cut, the extension of unemployment insurance benefits, and
both standard and nonstandard monetary policy conducted by the Federal
Reserve.

An Economy in Recovery: Key Events of 2012
The past year was another challenging one for an economy in the
midst of a recovery from a global financial crisis. Concern over European
sovereign debt and the ongoing fiscal consolidation in Europe contributed
to a contraction in the European economy during the year, and growth
among several of our Asian trading partners also slowed. Natural disasters
such as the severe drought in the Midwest and Hurricane Sandy in the
Northeast impaired economic output over much of the year. Although the
economic sanctions against Iran do not appear responsible (Box 2-1), retail
gasoline prices fluctuated widely over the course of 2012, which may have
intermittently dampened economic activity. The possibility of tax increases

42 |

Chapter 2

and mandatory spending cuts that had been scheduled to take place at the
beginning of 2013 loomed large as the year closed and may have hampered
consumer and business sentiment.
Real GDP rose 1.6 percent over the four quarters of 2012, a bit below
the pace in 2011 (quarterly figures are shown in Figure 2-1). Growth was
uneven (but no more than usual) throughout the course of the year, reflecting, in part, the impact of the drought and Hurricane Sandy, as well as outsized swings in Federal defense outlays and inventory investment. Outside
of these factors, business fixed investment and exports slowed notably from
2011. In contrast, personal consumption spending continued to post moderate gains, rising 1.9 percent over the four quarters of 2012, matching the rate
of growth recorded in 2011. The fiscal contraction among State and local
governments appears to be easing somewhat, and the residential construction sector, which turned a corner in 2011, strengthened further in 2012,
growing for seven consecutive quarters for the first time since 2004–05.
The recovery in payroll employment, like that in real output, was
uneven. Payrolls expanded briskly at the beginning of the year, but job
growth slowed in the spring and early summer before picking up again in the
late summer and fall. The fact that the worst months of the crisis occurred
during the winter raises the question of whether normal seasonal adjustment
procedures contributed volatility to higher frequency indicators, but that
Figure 2-1
Real GDP Growth, 2007–2012

Percent change (annual rate)
6
3.6

4
2

1.7

1.4

1.3

0.5

-4

2.3 2.2 2.6 2.4

3.1

2.5
1.3

2.0

1.3

0.1

0
-2

4.1

4.0

3.0

-0.3

0.1

2012:Q4

-1.8
-3.7

-6

-5.3

-8
-10

-8.9

2007:Q1
2008:Q1
2009:Q1
2010:Q1
2011:Q1
2012:Q1
Note: Shading denotes recession.
Source: Bureau of Economic Analysis, National Income and Product Accounts.

The Year In Review And The Years Ahead

| 43

Box 2-1: Effectiveness of Iran Sanctions
In cooperation with an international coalition, the United States
has established strict economic sanctions against the Islamic Republic
of Iran, sanctions described by this Administration and others as
“comprehensive and biting.” The goal of these sanctions is to persuade
the Iranian government to abandon its nuclear weapons program.
Since President Obama took office, he has steadily increased unilateral
and multilateral pressure on Iran because of its inability to meet its
international obligations. As a part of that effort, Congress passed and
the President signed the Comprehensive Iran Sanctions, Accountability,
and Divestment Act of 2010, the National Defense Authorization Act for
Fiscal Year 2012, and the Iran Threat Reduction and Syria Human Rights
Act of 2012. These laws increased our ability to target the Iranian Central
Bank, private banks supporting the Iranian regime, and—importantly—
the Iranian petroleum sector. In addition to these efforts with Congress,
the President has signed Executive Orders imposing additional sanctions
against the Iranian energy and petrochemical sectors. These actions
received support from members of the international community, including the European Union and our allies in the Middle East. The United
States has also worked to establish multilateral sanctions. For example,
the United States collaborated with other members of the United Nations
Security Council to adopt Resolution 1929, which called on Iran to end
its nuclear program and imposed the broadest multilateral sanctions ever
faced by the regime.
For Iran, the consequences of the sanctions have been severe.
Iranian President Mahmoud Ahmadinejad called these sanctions “the
most severe and strictest sanctions ever imposed on a country.” The
value of Iran’s currency, the rial, has dropped substantially in 2012.
Governments and private firms from around the world have ended business with, and divested from, Iran, as these actions now carry a heavy
price. And perhaps most importantly, oil production in Iran has nosedived (see the figure below). According to the U.S. Energy Information
Administration (EIA), Iran’s crude oil production, which averaged 3.7
million barrels a day in 2011, dropped to approximately 2.7 million
barrels a day by the end of 2012, a decline of about 30 percent. That
amounts to billions of dollars in lost revenues for the regime.
The effect of these sanctions on the U.S. economy has been minimal. The sanctions do not appear to have increased the price of oil. As
shown in the figure above, while Iranian oil production has dropped,
world supply has not. The effects of the sanctions are reviewed regularly;
for example, Federal agencies, such as the EIA, watch closely for developments in international energy markets. The President and Congress have

44 |

Chapter 2

structured the implementation of the sanctions to minimize any impact
on global energy markets and, by extension, the U.S. economy, and the
authorities granted to the executive branch allow us to continue to monitor those effects going forward.
Sanctions do not always prevent or replace war. Indeed, sanctions
have sometimes led to war, as shown by Lektzian and Sprecher (2007).
Moreover, the fact that Iran’s currency has depreciated, its oil production
and exports have plunged, and its economy has slowed does not, by itself,
fully answer the question: “Are the sanctions working?” The sanctions
will have succeeded if and when Iran ends its nuclear program.
Evidence on the effectiveness of sanctions in other settings is mixed.
In a widely-cited study, Hufbauer, Shott, and Elliott (1990) find that the
rate of success of economic sanctions is low—about 35 percent. Some
argue that even 35 percent is an overestimate (Pape 1997). However,
Morgan, Bapat, and Krustev (2009) find that adjusting the sample of
sanctions to include threats of sanctions in addition to sanctions actually imposed, and limiting the focus to more recent events, increases
the success rate from 35 percent to 45 percent. The success rate is even
higher when costs borne by the target are severe or when sanctions are
multilateral, both of which are the case with Iran. Moreover, Marinov
(2005) finds economic sanctions do tend to destabilize the governments
they target, that is, they increase the probability of leadership or regime
change.
Iran Oil Production and World Supply, 2009–2013

Million barrels a day (bbl/d)

Million barrels a day (bbl/d)

92.0
90.0
88.0
86.0

4.00

Iran crude oil
production (right
axis)

3.75
Jan-2013

World crude oil and
liquid fuels supply
(left axis)

3.50
3.25

84.0

3.00

82.0

2.75

80.0
Jan-09
Jan-10
Jan-11
Source: Energy Information Administration.

Jan-12

Jan-13

2.50

The Year In Review And The Years Ahead

| 45

does not seem to be the case, as discussed in Data Watch 2-1. The unemployment rate, which fell 0.8 percentage point during 2011, fell another 0.7
percentage point during 2012, reaching 7.8 percent by the end of the year.
The drop in the jobless rate during 2012 was concentrated in the first and
third quarters of the year, with most—roughly 90 percent—of this decline
accounted for by employment growth rather than withdrawal from the labor
force.

European Crisis and the Slowdown in Global Growth
In 2012, the consequences of the European debt crisis continued to
affect the world economy. In many advanced economies, fiscal consolidation, vulnerable financial systems, and market uncertainty have suppressed
demand, and world economic growth has suffered as a consequence. While
these adverse shocks are, for the most part, external to the United States,
the globalized nature of world trade and financial markets means that the
United States cannot escape their impact. Likewise, the turmoil in European
financial markets led U.S. branches of foreign banks to tighten credit standards for commercial and industrial loans.

Hurricane Sandy and the Drought
Natural disasters cause human suffering and physical destruction.
From the perspective of economic activity, their widespread disruptions also
lead to lost work and output. Historical experience suggests, however, that
over time much of this lost production is recouped. After storms, some of
the missed work is made up and sizable additional expenditures are required
for cleanup, repairs, and rebuilding. Thus, while hurricanes can have a major
impact on regional economies, national trends in economic activity typically
have not been affected by calamities such as hurricanes and droughts.
Hurricane Sandy is now estimated to have resulted in $35.8 billion
in damages to private fixed assets according to the Commerce Department,
which would rank it as the second costliest natural disaster in recent U.S. history after adjusting for inflation, though still well behind Hurricane Katrina
in 2005. In addition, power outages that affected 8.2 million customers on
October 30, and left 930,000 without power a week later, rendered many
workers unable to perform their jobs. The storm also disrupted transportation centers such as seaports, airports, and rail lines, as well as refineries and
factories, many of which were restored only gradually.
All told, analysts currently estimate that Hurricane Sandy lowered
real GDP growth in the fourth quarter by around 0.2 to 0.5 percentage
point at an annual rate. Although indicators such as industrial production,
vehicle sales, and jobless claims were adversely affected in October or early
46 |

Chapter 2

November, they subsequently improved and rebuilding activity is likely to
provide some support to economic growth going forward. The region hit by
Sandy has ample spare capacity available to be mobilized for storm recovery
efforts: in October 2012, just before the storm hit, the unemployment rate
was 0.6 percentage point higher in the five states most directly affected by
Hurricane Sandy than in the rest of the country. Construction employment,
in particular, had declined in the first 10 months of 2012 across these five
states while seeming to have stabilized or expanded elsewhere. Supplemental
Federal relief for reconstruction after Sandy, which was enacted in January
2013, should provide needed repairs and reconstruction and thereby support short-term economic growth in the region.
As a result of the severe drought in the Midwest that damaged corn
and soybean harvests, farm inventory investment subtracted an average of
one-fourth of a percentage point from real GDP growth in the second and
third quarters of 2012 (for additional discussion, see Chapter 8). In 2013, the
initial estimates of quarterly farm output will be based on the Agriculture
Department’s initial projection of annual farm output, which in turn will
be based on an assumption of normal growing conditions. As a result, farm
production, as measured in the National Income and Product Accounts,
will probably jump up beginning in first quarter of 2013, bringing with it an
associated bump up in estimated GDP growth.

Monetary Policy
In 2012, the Federal Open Market Committee (FOMC) continued
to provide substantial policy accommodation and announced several new
steps, including for the first time linking its forward guidance for the main
policy interest rate to a specific level of the unemployment rate.
Between September 2011 and June 2012, the FOMC conducted
the first installment of its Maturity Extension Program, widely known as
Operation Twist. As first announced, the Fed said it would purchase “by the
end of June 2012, $400 billion of Treasury securities with remaining maturities of 6 years to 30 years and…sell an equal amount of Treasury securities
with remaining maturities of 3 years or less.” According to the FOMC,
the objective of this program was to “put downward pressure on longerterm interest rates” and thus provide an additional stimulus for the overall
economy. In June 2012, the Committee decided to continue this program
at a pace of approximately $45 billion a month, which corresponded to an
additional “face value of about $267 billion by the end of December 2012,”
according to the minutes of the June meeting. Then, in September 2012,
the FOMC announced it would further “increase policy accommodation by

The Year In Review And The Years Ahead

| 47

Data Watch 2-1: Seasonal Adjustment in Light of the Great Recession
For the purposes of economic analysis, researchers are primarily
interested in the longer-term direction of a time series and any deviations
from that trend. Seasonal fluctuations in the data arising from summer
holidays, seasonal shopping, and so forth can obscure these trends and
deviations. As a result, most public sources of economic data endeavor
to remove normal seasonal patterns from their high-frequency indicators. Unfortunately, this process of seasonally adjusting economic data
is fraught with complexity. Seasonal factors cannot be directly observed
and must be estimated using various statistical techniques. Moreover, the
seasonal patterns for a particular series may not be constant over time.
Thus, the accurate estimation of seasonal patterns is a challenge of great
importance to the economics community and policymakers.
A number of analysts have argued that the severity of the Great
Recession may have distorted several high-frequency economic indicators. The Great Recession, which lasted from December 2007 through
June 2009, was particularly acute during the fall of 2008 and the winter of
2009. Real GDP fell more than 7 percent at an annual rate over the fourth
quarter of 2008 and the first quarter of 2009, and total nonfarm payroll
employment plunged by more than 4 million jobs from September 2008
to March 2009. Given the severity of the downturn during this period,
some commentators have hypothesized that the outsized decline in economic activity may have been inadvertently incorporated into the seasonal factors for several key economic indicators. And as a consequence
of this statistical bias in the seasonal adjustment process, these observers
have raised concerns that the pace of the current recovery has exhibited
an abnormal seasonal pattern in which economic activity has appeared
not only substantially stronger than it really is during the fall and winter
but also correspondingly weaker during the spring and summer.
A few providers of economic data have acknowledged this concern
and noted that unusually sharp swings in certain indicators may not be
properly accounted for by standard seasonal adjustment techniques.
The Federal Reserve reported that the application of default seasonal
adjustment procedures to its monthly industrial production data would
have artificially raised output in many industries during the first halves
of the years 2008 through 2010, if these distortions not been identified in
advance and corrected (Federal Reserve Board of Governors 2011). And
the Institute for Supply Management concluded that its typical seasonal
adjustment procedures did not adequately identify outlier observations
during the recent recession. As a result, it introduced more precise
criteria for the detection of outliers as part of the seasonal adjustment of
its purchasing manager survey data (Institute for Supply Management

48 |

Chapter 2

2012). Nevertheless, it is important to emphasize that these particular
issues pertain to the use of default seasonal adjustment techniques. In
general, statistical agencies approach the seasonal adjustment of economic data idiosyncratically based upon the unique characteristics of
each individual time series.
Indeed, detailed studies of a wide range of principal economic indicators suggest that the seasonal adjustment techniques that had already
been employed by the Bureau of Labor Statistics (BLS) adequately
accounted for the effects of the Great Recession. BLS analysts calculated
alternative seasonal factors for total nonfarm payroll employment after
manually excluding the sharp declines that were recorded during the
downturn (Kropf and Hudson 2012). This counterfactual experiment
failed to generate meaningful revisions to the actual published estimates
of total nonfarm payroll employment since January 2010. In fact, the
BLS analysts concluded that the implementation of these counterfactual
seasonal factors would have revised total nonfarm payroll employment
upward by a mere 24,000 jobs over the second and third quarters of 2011
(in other words, an average of 4,000 jobs a month) and downward by just
19,000 jobs over the fourth quarter of 2011 and the first quarter of 2012
(or an average of roughly 3,000 jobs a month). BLS analysts also thoroughly investigated the seasonal adjustment of the Current Population
Survey data over the course of the recovery (Evans and Tiller 2012).
This inquiry showed that alternative assumptions regarding seasonal
adjustment did not meaningfully affect estimates of the unemployment
rate since 2007.
Macroeconomic Advisers (2012) tested the stability of seasonally
adjusted nominal GDP by comparing the official estimates to a proxy
series that had been constructed using the source data for the national
accounts. Contrary to the hypothesis that inaccuracies in the seasonal
adjustment process have been artificially suppressing economic activity
during the spring and summer months of the current recovery, this
analysis found that seasonal factors had not been subtracting as much
from GDP growth during the second and third quarters of each calendar
year as they had before the downturn. All told, these analyses provide
little evidence to support serious concerns over the soundness of seasonally adjusted high-frequency economic variables.

purchasing additional agency mortgage-backed securities at a pace of $40
billion per month.”
The September and June actions together, the Committee said, were
intended to increase the Federal Reserve’s “holdings of longer-term securities
by about $85 billion each month through the end of the year.” In December
The Year In Review And The Years Ahead

| 49

2012, the Committee announced that it would replace the expiring Maturity
Extension Program with a program of purchases of longer-dated Treasuries
at a pace of $45 billion a month, thereby further expanding its balance sheet,
rather than funding these purchases with the sale of shorter-dated securities, as was the practice under Operation Twist. These purchases, combined
with its September 2012 decision to purchase $40 billion a month in agency
mortgage-backed securities, kept total purchases of longer-term securities at
$85 billion a month.
The nature of the Fed’s forward guidance also evolved over the year.
The FOMC announced in September 2012 that it would explicitly condition future policy decisions on progress in the labor market and issued
additional forward guidance that the Fed’s main policy interest rate would
likely remain low through mid-2015, an extension from late 2014 as previously announced. In December 2012, the Committee went a step further
and announced that it would maintain the “exceptionally low range for
the federal funds rate…at least as long as the unemployment rate remains
above 6½ percent, inflation between one and two years ahead is projected
to be no more than a half percentage point above the Committee’s 2 percent
longer-run goal, and longer-term inflation expectations continue to be well
anchored.” The explicit link to numerical values of economic variables
replaced the previous reference to a “mid-2015” reference date that had been
introduced in September.
In August 2012, during a speech at the annual Federal Reserve
Bank of Kansas City Economic Symposium, Federal Reserve Chairman
Ben Bernanke assessed the effectiveness of the balance sheet and forward
guidance policies that had been implemented in response to the recession.
Bernanke (2012a) surveyed research finding that large-scale asset purchases
(LSAPs) had significantly lowered yields on long-term Treasury notes,
corporate bonds, and mortgage-backed securities; reduced retail mortgage
rates; and also boosted stock prices (see for example, Krishnamurthy and
Vissing-Jorgenson 2011). One study by Chung and others (2012) used the
Federal Reserve Board’s FRB/US model of the economy and found that the
early phase of the Fed’s LSAPs may have raised the level of real GDP by
almost 3 percent and increased private payroll employment by more than
2 million jobs, relative to what otherwise would have occurred. Although
Chairman Bernanke cautioned against putting too much weight on the
estimates of any particular study, he concluded that “a balanced reading of
the evidence supports the conclusion that central bank securities purchases
have provided meaningful support to the economic recovery while mitigating deflationary risks.”

50 |

Chapter 2

Fiscal Policy
After months of negotiations, in February 2012 Congress extended
both the 2 percentage point cut in the payroll tax and the Emergency
Unemployment Compensation program through the end of the year.
These temporary measures, which were among the Administration’s key
economic priorities for 2012, had originally been put in place with the passage of the 2010 Tax Relief, Unemployment Insurance Reauthorization, and
Job Creation Act. The extension through December 2012 provided critical
support to American families trying to weather the various headwinds that
threatened the recovery over the course of the year.
The economy faced great uncertainty as the end of calendar year 2012
approached. As a result of the confluence of various policies that had been
passed in previous years, the economy faced a “fiscal cliff” of across-theboard tax hikes as the Bush-era tax cuts expired, a sharp reduction of the
Alternative Minimum Tax (AMT) exemption amounts to the levels that had
been in effect in 2001, the imposition of substantial spending cuts through
budget sequestration, and the expiration of a number of other tax provisions. In addition, temporary measures to support the economy, including
the extension of unemployment insurance benefits and the payroll tax
reduction, were also set to expire. As the end-of-year deadline approached,
uncertainty in financial markets ticked up, although not as much as during
the August 2011 debt ceiling debate. This uncertainty was partly resolved by
the passage of the American Taxpayer Relief Act by the House on January 1,
2013, averting what could have been sharply contractionary policies.1
Looking ahead, the American Taxpayer Relief Act—which permanently extends the middle-class tax cuts, indexes the AMT to inflation, and
raises rates on the highest-income taxpayers in order to reduce the deficit
relative to the previous policy baseline (see Chapter 3)—has removed much
of the uncertainty about taxes facing the economy.

1 Several studies suggested that going over the full fiscal cliff would likely result in a recession
and substantial job losses; see for example CBO (2012a). These studies, including the CBO
report, focused on cash flow effects of the fiscal cliff (revenues and spending). A growing
body of literature suggests that the uncertainty created by going over the cliff would have
further hurt economic activity and employment, although those channels are more difficult to
quantify; see for example Bloom (2009).

The Year In Review And The Years Ahead

| 51

Developments in 2012 and the Near-Term Outlook
Labor Market Trends
The labor market continued to heal in 2012. The private sector added
2.2 million jobs, although State and local government employment fell by
32,000, after falling by 286,000 in 2011. Private sector payroll employment
has grown in each month since February 2010. Focusing on 12-month
changes to abstract from monthly and seasonal volatility, the 12-month
change in total nonfarm payroll employment excluding Census hiring has
been smooth, hovering around 2 million jobs since the fall of 2011, as shown
in Figure 2-2.
Private-sector job growth during the current recovery has been
roughly comparable with that in the 1991 recovery and noticeably faster
than in the 2001 recovery, as illustrated in Figure 2-3. As is typical, the
recovery in hiring since 2009 lagged the recovery in output. Private nonfarm
payrolls in the current recovery began growing 9 months after the businesscycle trough. By comparison, payrolls first began expanding consistently 12
months into the 1990–91 recovery, and sustained private-sector job growth
in the 2001 recovery did not begin until 21 months after the official end date
of the recession. Thus, although the 2007–09 recession lasted longer and led
Figure 2-2
Nonfarm Payroll Employment, 2007–2013

12-month change, millions, not seasonally adjusted
4

Private

2

Jan-2013

Total

0
-2
-4
-6
-8
Jan-07
Jan-08
Jan-09
Jan-10
Jan-11
Jan-12
Jan-13
Note: Shading denotes recession. Total excludes temporary decennial Census workers.
Source: Bureau of Labor Statistics, Current Employment Statistics.

52 |

Chapter 2

Figure 2-3
Private Nonfarm Employment During Recent Recoveries

Indexed to 100 at NBER-defined trough
110
Current (June
2009 trough)

108

1991

106
104
102
100

2001

98
96
94
92

-48

-36

-24

-12

Trough
12
Months from trough

24

36

48

Source: Bureau of Labor Statistics, Current Employment Statistics; National Bureau of
Economic Research; CEA calculations.

to deeper job losses than did the recessions of 1990–91 and 2001, recovery
in the labor market began somewhat sooner.
Despite continuing improvements in hiring, the unemployment rate
remains elevated, reflecting both the deep losses during the recession and
the steady but moderate pace of hiring during the recovery. The unemployment rate has receded from its peak of 10.0 percent in October 2009 to 7.8
percent in December 2012, with 0.7 percentage point of that decline during
the 12 months of 2012 (Figure 2-4). Layoffs—as measured by the four-week
average of initial claims for unemployment insurance—fell in 2012 (Figure
2-5), and other indicators of labor market adjustment such as the workweek
continued to show improvement. By December 2012, the workweek had
increased to 34.4 hours, recovering most of the 0.8 hour lost during the
recession.2
Almost all of the decline in the unemployment rate in 2012 reflects
growth in employment rather than labor force withdrawal.3 Nevertheless,
the recession coincided with a sharp drop in the labor force participation
2 A lengthening of the workweek by 0.1 hour is roughly equivalent, in terms of labor input, to
an increase in employment of more than 300,000 jobs.
3 This calculation reflects an adjustment for updated Census Bureau population estimates that
were incorporated into the January 2012 Current Population Survey by the Bureau of Labor
Statistics (BLS). In accordance with usual practice, the BLS does not revise the official Current
Population Survey estimates for earlier months to reflect the updated population values.

The Year In Review And The Years Ahead

| 53

Figure 2-4
Unemployment Rate, 1979–2013

Percent
11
10
9
8

Jan-2013

7
6
5
4
3
1979
1983
1987
1991
1995
1999
2003
Note: Shading denotes recession.
Source: Bureau of Labor Statistics, Current Population Survey.

2007

2011

Figure 2-5
Initial Unemployment Insurance Claims, 2004–2013

Thousands, seasonally adjusted
700

600

500

400
Week ended
2/23/2013

300

200
2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013
Note: Shading denotes recession. Four-week moving average.
Source: Department of Labor, Employment and Training Administration.

54 |

Chapter 2

rate, which fell from 66.0 percent in December 2007 to 64.9 percent in
February 2010—a period when the economy shed jobs at an average rate
of 320,000 a month. Since then, labor force participation has continued to
decline, reaching 63.6 percent by December 2012.
To what extent can this sharp drop in the labor force participation
rate be attributed to the prolonged slack in the labor market? Answering
this question requires distinguishing between cyclical movements arising
from the prolonged downturn and the demographic trends of an aging, and
thus retiring, workforce. To this end, Table 2-1 provides a decomposition
of the labor force participation rate into a trend component and a cyclical
component over the current business cycle. The trend, or demographic,
component from 2007–12 is estimated by extrapolating a linear trend in the
labor force participation rate from the 10 years preceding 2007,4 and the
cyclical component is computed as the difference between the actual labor
force participation rate and this trend.
As can be seen in the bottom half of Table 2-1, the labor force participation rate fell by 2.2 percentage points from 2007–12. Of that drop, 1.2
percentage points are attributed to a declining trend caused primarily by the
aging of the workforce, while 1.0 percentage point is cyclical. An analogous
calculation for 1980–85—the only other postwar period that includes a
double-digit unemployment rate—shows that the labor force participation
rate rose by 1.0 percentage point over the twin recessions of the early 1980s.
But at that time, trend labor force participation was rising by 2.0 percentage
points—a consequence primarily of the rising participation of women during that period—so the cyclical component during the early 1980s declined
by 0.9 percentage point. Thus, the cyclical component of the change in
the labor force participation rate during 2007–12 is close to its value over
1980–85, and so, by this measure, the recession-induced rate of labor force
decline differs little from the early 1980s.

Consumption and Saving
Consumer spending, which accounts for approximately 70 percent
of GDP, rose moderately in 2012, as credit conditions continued to ease,
household liabilities fell relative to income, and the labor market improved.
Real household consumption grew 1.9 percent during the four quarters of
the year and was supported by an extension of the payroll tax cut, which first
went into effect in January 2011 as part of the Tax Relief, Unemployment
Insurance Reauthorization, and Job Creation Act.
4 Specifically, for each gender and age group, labor force participation rates are projected using
the previous 10-year trend, and the trend in the overall participation rate over the subsequent
period is computed using actual population weights for each group.

The Year In Review And The Years Ahead

| 55

Table 2-1
Labor Force Participation Rates, 1980–1985 and 2007–2012
Labor Force Participation Rate, Percent
Years

Year of cycle peak (actual)

Projection for five years
ahead

After five years (actual)

1980–1985

63.8

65.7

64.8

2007–2012

65.9

64.6

63.7

Decomposition of Five-Year Change, Percentage Points
Total

Trend

Cycle

1980–1985

1.0

2.0

-0.9

2007–2012

-2.2

-1.2

-1.0

Note: Numbers may not sum due to rounding. Based on annual averages and historically adjusted by the CEA
for population controls. The projections for five years ahead are estimated by extrapolating a linear trend in
age/gender-specific labor force participation rates from the 10 years preceding 1980 and 2007, respectively.
Source: Bureau of Labor Statistics, Current Population Survey; CEA calculations.

Several key developments in 2012 shaped the contours of consumer
spending.
Household Income in 2012. Nominal personal income grew 5.0 percent during the four quarters of 2012, a somewhat faster pace of growth than
in 2011. Growth in nominal personal income over the course of the year was
largely attributable to gains in employee wages, salaries, and benefits. Real
disposable personal income, which is personal income less personal taxes
and adjusted for price inflation, rose 3.2 percent over the four quarters of
2012, a substantial improvement over the 2011 increase of 0.3 percent. The
pattern partly reflects a moderation in inflation mostly due to a drop in
energy price inflation. The expiration of the temporary payroll tax cut will
subtract about $120 billion from disposable income in 2013.
Household Wealth and Saving in 2012. Households continued to
rebuild their balance sheets in the aftermath of the worst economic downturn since the Great Depression. On balance, the wealth-to-income ratio,
depicted in Figure 2-6, rose over the first three quarters of 2012 and has
improved considerably since the beginning of 2009. Consumption as a share
of disposable income tends to fluctuate with the wealth-to-income ratio.
As a rule of thumb, a one dollar drop in wealth reduces annual consumer
spending by two to five cents. The decline in the wealth-to-income ratio
from the first quarter of 2007 to its low point in the first quarter of 2009
was equivalent to roughly 1.7 years of disposable income. Through the third
quarter of 2012, this measure regained the equivalent of nearly 0.7 year of
disposable income. This simple framework suggests that the household
wealth lost during the recession has not yet been recovered and that this
loss of wealth has left the level of consumption roughly 2 to 6 percent below

56 |

Chapter 2

Figure 2-6
Consumption and Wealth Relative to
Disposable Personal Income (DPI), 1952–2012

Consumption/DPI ratio
1.10
1.05
1.00
0.95

Years of disposable income
7
2012:Q3

Consumption-to-DPI ratio (left axis)

0.90
0.85
0.80

6

Total-wealth-to-DPI
ratio (right axis)

Net housing
wealth-to-DPI ratio
(right axis)

5

Stock market
wealth-to-DPI
ratio (right axis)

0.75
1952
1960
1968
1976
1984
1992
2000
2008
Note: Shading denotes recession. Consumption-to-DPI line includes 2012:Q4.
Source: Bureau of Economic Analysis, National Income and Product Accounts; Federal
Reserve Board, Z.1; CEA calculations.

4
3
2
1
0

what it would have been otherwise. Much of that loss of wealth resulted from
the bursting of the housing bubble, and the wealth-to-income ratio now is
where it was in the mid-1990s (before the information technology stock
price bubble) and early 2000s (before the housing bubble).
The personal saving rate—expressed in the National Income and
Product Accounts as personal saving as a share of disposable personal
income—averaged 3.9 percent in 2012, a bit lower than the rate observed in
2011. The rate of personal saving jumped during the recession as households
sharply curtailed spending in response to the crisis, but overall, the saving
rate fell modestly over the course of the recovery and is now at the level it
was in the early 2000s.
Household Credit and Deleveraging in 2012. Lending standards for
consumers, as reported in the Federal Reserve’s Senior Loan Officer Opinion
Survey, eased for the third consecutive year. Moreover, driven by a surge in
nonrevolving lending categories (such as auto and student loans), consumer
credit expanded 5.7 percent at an annual rate over the four quarters of 2012.
However, because mortgage credit continued to decline, the overall level of
household debt decreased 0.6 percent at an annual rate over the first three
quarters of 2012. Household debt has declined every year since 2007, as
households continue to deleverage.

The Year In Review And The Years Ahead

| 57

Although household debt increased in the period before the financial
crisis, the extent to which household leverage has restrained consumer
spending during the recovery remains unsettled. Traditional models of
consumption imply that, absent borrowing constraints, households consume a fraction of their expected lifetime wealth, which implies that the
consumption-wealth ratio fluctuates around its mean (Campbell 1987;
Lettau and Ludvigson 2003). This theory and its extensions imply that consumption and saving will adjust to maintain appropriate lifetime savings,
so for example a loss in housing wealth will cause consumers to increase
saving, as they did during and shortly after the recession, to pay down debts
and rebuild retirement savings. But consumers, of course, face borrowing
constraints and can be locked into mortgage or debt payment streams that
might impose additional, direct limitations on consumption. Dynan (2012)
and Mian, Rao, and Sufi (2012) provide evidence that these additional effects
of the so-called debt overhang from the collapse in housing have further
suppressed consumption during the recovery.
Whether one looks at wealth or leverage, household finances have
improved substantially in recent years. From the third quarter of 2007 to
the first quarter of 2009, household net worth fell by an estimated $16.1
trillion. By the third quarter of 2012, however, households had added $13.5
trillion, recovering more than 80 percent of wealth lost. Households have
also made progress in reducing debt burdens. Total household debt stood
at 81.4 percent of GDP in the third quarter of 2012, the lowest since 2003
and down from a peak of nearly 98 percent in 2009. Moreover, payments
on mortgage and consumer debt took up about 10.6 percent of household
disposable income in the third quarter of 2012, the lowest household debt
service ratio since 1993.
Effect of Rising Inequality on Consumption. Some of the recent patterns in aggregate consumption behavior—including the sluggish growth in
consumer spending relative to previous recoveries—may reflect the sharp
rise in income inequality over the past 30 years. According to CBO (2012c),
after-tax incomes of the top 1 percent of households rose by more than 155
percent from 1979 to 2009, while those of median households increased by
less than 33 percent. About one-fifth of this increase in inequality is due to
the declining share of income that goes to labor (Box 2-2). As discussed in
the 2012 Economic Report of the President, some research suggests that this
rise in inequality may have reduced aggregate demand, because the highest
income earners typically spend a lower share of their income—at least over
intermediate time horizons—than do other income groups.

58 |

Chapter 2

Business Fixed Investment
Real business fixed investment grew 4.6 percent during the four
quarters of 2012, after rising 10.2 percent in the four quarters of 2011. Both
of its principal components—equipment and software investment and
nonresidential structures investment—contributed to this slower growth.
Investment in equipment and software slowed to 4.6 percent over the
four quarters of 2012, down from robust growth of 11.4 percent in 2011.
Investment in nonresidential structures increased 4.7 percent, following a
6.9 percent increase in 2011.
Within equipment and software investment, major components
such as industrial equipment, transportation equipment, and informationprocessing equipment all posted notably slower growth in 2012 than in 2011.
The relatively stable pace of GDP growth during 2011 and 2012 provided
little overall stimulus to equipment investment. The slowing pattern of
equipment investment growth may also partially reflect the reduced pace of
bonus depreciation, which had been available at a 100 percent rate during
2011 but fell to 50 percent in 2012. (Bonus depreciation encourages investment by allowing firms to write-off equipment purchases immediately,
rather than over an extended period). The American Taxpayer Relief Act
(ATRA) extended the 50 percent rate through 2013.
Real investment in nonresidential structures grew 4.7 percent during
the four quarters of 2012, down from 6.9 percent during 2011. Solid growth
in office buildings and electric power plants was partially offset by a decline
in petroleum and natural gas drilling, which followed strong growth during
the preceding two years.
Despite the slower growth of business investment in 2012, the sector is poised to grow rapidly if demand accelerates because corporations
have ample internal funds (Figure 2-7). Corporate profits continued to rise
through the first three quarters of 2012, exceeding their pre-recession level,
even as a percent of GDP, while corporate dividends remained at roughly
pre-recession levels through the first three quarters of the year before
spiking in the fourth quarter, before ATRA was passed. As a consequence,
corporate cash flow, the sum of undistributed profits and depreciation that
represents the internal funds that corporations have available for investment, has remained elevated during the recovery. Cash flow now exceeds
investment, an unusual situation insofar as corporations usually have to
borrow funds to finance their capital spending plans. A large portion of
these investable funds has been channeled to financial investments rather
than to new physical capital, as can be seen by the rising level of liquid assets
held by nonfinancial corporations. Indeed, as of the third quarter of 2012,
nonfinancial corporations held $1.7 trillion of liquid financial assets.
The Year In Review And The Years Ahead

| 59

Box 2-2: Why Is the Labor Share Declining?
The “labor share” is the fraction of income that is paid to workers
in wages, bonuses, and other compensation. Income of self-employed
workers is also included in some definitions of labor income, as it is in
the figure below. The labor share in the United States was remarkably
stable in the post-war period until the early 2000s. Since then, it has
dropped 5 percentage points. Because capital income is distributed more
unequally than labor income, the decline in the labor share accounts for
some, but not all, of the rise in inequality. CBO (2011) has estimated that
21 percent of the increase in inequality from 1979 to 2007 was accounted
for by shifts between labor and other sources of income, with the remaining 79 percent accounted for by rising inequality within capital, business,
or labor income. Nevertheless, the decline in the labor share has adverse
implications for government revenues because wages and salaries are
taxed at a higher rate than other major income sources.
The decline in the labor share is widespread across industries
and across countries. An examination of the United States shows that
the labor share has declined since 2000 in every major private industry
except construction, although about half of the decline is attributable to
manufacturing. Moreover, for 22 other developed economies (weighted
by their GDP converted to dollars at current exchange rates), the labor
share fell from 72 percent in 1980 to 60 percent in 2005.
Proposed explanations for the declining labor share in the United
States and abroad include changes in technology, increasing globalization, changes in market structure, and the declining negotiating power
of labor. Changes in technology can affect the share of income going to
labor by changing the nature of the labor needed for production. More
specifically, much of the investment made by firms over the past two
decades has been in information technology, and some economists have
suggested that information technology reduces the need for traditional
types of skilled labor (Bound and Johnson 1992; Autor, Katz, and
Krueger 1998). According to this argument, the labor share has fallen
because traditional middle-skill work is being supplanted by computers,
and the marginal product of labor has declined.
Increasing globalization also puts pressure on wages, especially
wages in the production of tradable goods that can be produced in
emerging market countries and some less-developed countries. These
pressures on wages can lead to reductions in the labor share. Changes in
market structure and in the negotiating power of labor could also lead
to a declining labor share. One such change is the decline in unions and
collective bargaining agreements in the United States.

60 |

Chapter 2

These explanations are neither exhaustive nor mutually exclusive
(OECD 2012). Overall, these changes have moved the distribution of
income towards a winner-take-all society.
Percent
80

Labor Share of Nonfarm Business Income, 1947–2012

75
Other developed countries
(through 2012)

70
65

United States
(through 2012:Q4)

60
55
50
45
0
1947

1957

1967

1977

1987

1997

2007

Note: "Other Developed Countries" refers to the OECD member states. The U.S. labor share includes
imputed proprietor's income. The OECD labor share excludes the farm, mining, fuel, and real estate
sectors, and is aggregated by the CEA on an annual basis for 22 countries using GDP weights at
current exchange rates.
Source: Bureau of Labor Statistics, Productivity and Costs; OECD, Annual Indicators.

Business Inventories
Inventory investment—measured as the change in inventories from
one quarter to the next—is typically an important contributor to the changes
in real GDP during recessions and the early stages of recoveries. During the
recession, inventories fell but by less than sales, so the ratio of inventories
to sales rose; through the first two years of the recovery, inventories rose
less rapidly than sales, and by the end of 2011, the inventory-sales ratio had
returned to its level of the mid-2000s. With this inventory cycle behind us,
real private nonfarm inventory accumulation in 2012 made only a small,
slightly positive contribution to real GDP growth. Looking ahead, inventory
investment is expected to make only a minor contribution to growth during
2013.

Government Outlays, Consumption, and Investment
The Federal budget deficit during fiscal year (FY) 2012—which ended
on September 30, 2012—was $1.1 trillion, about $200 billion less than the

The Year In Review And The Years Ahead

| 61

Figure 2-7
Business Fixed Investment and Cash Flow, 1990–2012

Percent of potential GDP
14
13
12

Nonresidential fixed
investment

11

2012:Q3

Cash flow

10
9
8

Liquid assets held by
nonfinancial corporations

7
6

5
1990:Q1 1993:Q1 1996:Q1 1999:Q1 2002:Q1 2005:Q1 2008:Q1 2011:Q1

Note: Shading denotes recession. Potential GDP is a CBO estimate. Cash flow, from the National Income and
Product Accounts, and liquid assets held by nonfinancial corporations are plotted using three-quarter moving
averages. Nonresidential fixed investment line includes 2012:Q4.
Source: Bureau of Economic Analysis, National Income and Product Accounts; Federal Reserve Board, Z.1;
Congressional Budget Office.

preceding year. As a share of GDP, the deficit fell to 7.0 percent in FY 2012,
down from 8.7 percent in FY 2011.
As measured in the Federal unified budget, Federal receipts rose 6.4
percent in FY 2012 compared with the previous year, reflecting a 3.7 percent
increase in individual income tax receipts, a 33.8 percent increase in corporate tax receipts, and a 3.2 percent increase in receipts for social insurance.
The $61 billion increase in corporate tax receipts accounted for 42 percent of
the rise in overall revenues. Current dollar values of individual income taxes
and social insurance and retirement receipts have each risen to 97 percent
of their FY 2007 levels, while corporate tax receipts were just 65 percent of
their previous high.
Federal outlays declined 1.7 percent in nominal dollars in FY 2012
from FY 2011, falling from 24.1 percent of GDP to 22.8 percent of GDP. The
decline in spending during the fiscal year reflected several factors, including reduced outlays on unemployment insurance, Medicaid, and defense.
Specifically, fewer individuals received unemployment benefits, a temporary
increase in Federal aid to states for Medicaid expired, and the number of
U.S. Army personnel stationed in Afghanistan and Iraq was reduced.
During the four quarters of calendar year 2012, the National Income
and Product Accounts measure of real Federal expenditures on consumption and gross investment (which does not include Federal transfers to

62 |

Chapter 2

States and individuals) declined 2.8 percent, as a 4.9 percent decline in real
defense spending more than offset a 1.5 percent increase in real nondefense
spending.
The Federal deficit as a share of GDP fell for the third consecutive
fiscal year in 2012. The change in this ratio is one measure of the drag on
the economy imposed by fiscal consolidation, and in FY 2012, this drag was
1.7 percentage points (the difference between the deficit-GDP ratio of 8.7
percent in FY 2011 and 7.0 percent in FY 2012). Moreover, the drop in the
deficit-to-GDP ratio from 10.1 percent in 2009 to 7.0 percent in 2012 is the
largest 3-year decrease since 1949. Looking further ahead, policy changes
to be recommended in the FY 2014 Budget will put debt as a share of the
economy on a stable path and place the budget in a fiscally sustainable position in the 10-year budget window.

State and Local Governments
Although State and local governments continued to experience fiscal
pressure in 2012, the long contraction in the sector finally appears to be
coming to an end. State and local consumption and investment (purchases)
have shown unprecedented weakness compared with previous recoveries
(Figure 2-8). From the end of the recession in mid-2009 to the fourth quarter
of 2012, real State and local purchases declined 6.8 percent. By contrast, during the comparable period of each of the six previous recoveries, real State
Figure 2-8
Real State and Local Government Purchases During Recoveries

Indexed to 100 at NBER-defined trough
120
115

Average,
1960–2007

110

1991
2001

105
100
95

Current
(2009:Q2 trough)

90
85

80

-16

-12

-8

-4

Trough
4
Quarters from trough

8

12

16

Note: The 1960-2007 average excludes the 1980 recession due to overlap with the
1981-82 recession.
Source: Bureau of Economic Analysis, National Income and Product Accounts; National
Bureau of Economic Research; CEA calculations.

The Year In Review And The Years Ahead

| 63

and local purchases posted positive growth, averaging an increase of 10.3
percent over the first three and a half years of the recovery. Nominal State
and local government tax receipts increased during the first three quarters of
2012. Federal support from the Recovery Act—which helped support State
and local governments during 2009 and 2010—phased out during 2011 and
2012. And while the pace of State and local government job losses eased in
2012, employment in this sector remained 724,000 jobs below its previous
peak as of the end of the year, with more than 40 percent of the loss in educational services jobs.
On the revenue side, State and local tax receipts rose at an annual rate
of 2.6 percent during the first three quarters of 2012, a bit below the pace
during 2011. The slow recovery in State and local tax revenue reflects in part
the effect of lower house prices on property tax collections. Historically,
property taxes have accounted for about 30 percent of State and local government tax receipts and are critical to local governments, but property tax
receipts have edged up slowly in the years after the housing bubble burst.
Nationwide, property tax receipts have grown just 11.4 percent over the past
five years, only slightly faster than inflation, compared with 36.0 percent
growth during the preceding five year period from 2002–07. Moreover, State
and local governments are still feeling the effect of the drop in house prices:
because property value assessments lag behind market valuations, the effect
of house prices on property tax receipts operates with a delay of about three
years (Lutz 2008). Although policymakers in some states have increased the
tax rate on assessed property values to partially offset declines in those values
(Lutz, Molloy, and Shan 2011), local governments have still needed to adjust
spending to make up for the lost revenue. Despite these difficulties, the
recent upturn in house prices suggests that improvement in State and local
government finances is on the horizon. In addition, revenues from sales and
income taxes—which make up about 50 to 60 percent of State and local tax
receipts—have also continued to recover, with income tax collections up 7.6
percent during the four quarters of 2012, and sales taxes growing 2.2 percent.
Another factor weighing on State and local government revenues has
been the phase-out of the Recovery Act. After rising notably in 2009 and
2010, Federal grants-in-aid to State and local governments plunged $82.1
billion in 2011 before stabilizing during 2012. Both the earlier increase and
the recent return to a lower level were largely attributable to the Recovery
Act, which was designed to offer temporary support to State and local governments. The portion of Federal grants-in-aid to the States from Recovery
Act programs stood at just $17.9 billion in 2012, down from a peak of more
than $100 billion in 2010.

64 |

Chapter 2

Current State and local government expenditures—which include
transfers to individuals as well as government consumption—rose 2.8 percent over the four quarters of 2012, following a 0.2 percent increase in the
previous year. A recent CBO report (CBO 2012b) noted that the weakness in
State and local government spending relative to previous recoveries could be
attributed roughly equally to three different areas: hiring of employees, purchases of goods and services, and construction spending. Despite continued
spending restraint across these major components, the operating position
of State and local governments deteriorated to an aggregate deficit of $140
billion by the third quarter of 2012, on pace for a fifth consecutive year of
operating deficits for the sector.
State and local government employment fell 32,000 during the 12
months of 2012, a much shallower decline than the 286,000 jobs lost in
2011. Nevertheless, employment in the sector remains well below its peak
in 2008. To date, the Administration has taken important steps to help
State and local governments maintain critical services in public safety and
education. In addition to the grants-in-aid components of the Recovery
Act, the Administration established a new fund to support teaching jobs
and extended the enhanced Federal matching formula for certain social
services and medical insurance expenditures. In 2011, the President proposed additional resources for the teacher job fund as part of the American
Jobs Act, which also would have supported the modernization of more than
35,000 schools. Although Congress did not enact this proposal, the President
remains committed to supporting educators and first responders in his
second term.

Real Exports and Imports
Compared with previous recessions, real exports experienced a
sharper-than-usual contraction and rebound during 2007–10. This sharp
cyclical decline was partly attributable to the synchronized nature of the
2007–09 contraction and recovery across nearly all countries, a collapse and
rebound in commodity prices, and foreign consumers’ postponement of
purchases of U.S. durable goods, which account for a large share of tradable
goods (Baldwin 2009). Now, with the recent slowing of world growth, real
exports appear to be reverting to their historical trend (Figure 2-9), growing
1.8 percent during the four quarters of 2012, after rising 4.3 percent in 2011
and 8.8 percent in 2010. As discussed in Chapter 7, the recent slowing in
export growth appears to have restrained the pace of U.S. manufacturing
activity. Continued export growth will depend, in part, on healthy growth
of the world economy and on exchange rates. The value of the dollar has
been generally increasing since July 2011, in part reflecting increased
The Year In Review And The Years Ahead

| 65

Figure 2-9
Real Exports During Recoveries

Indexed to 100 at NBER-defined trough
140

130

1991

Current
(2009:Q2 trough)

120

2001

110
100
Average,
1960–2007

90

80
70
60

-16

-12

-8

-4

Trough
4
Quarters from trough

8

12

16

Note: The 1960-2007 average excludes the 1980 recession due to overlap with the
1981-82 recession.
Source: Bureau of Economic Analysis, National Income and Product Accounts; National
Bureau of Economic Research; CEA calculations.

international demand for U.S. Treasury bonds in a time of global financial
turmoil and rapidly deteriorating global growth. Changes in the terms of
trade have contributed to the weakening demand for U.S. goods abroad.
Real imports grew 0.1 percent during the four quarters of 2012,
down from 10.9 percent and 3.5 percent in 2010 and 2011, respectively. A
decline in imports of petroleum products offset a moderate rise in imports
of nonpetroleum goods. Consistent with Houthakker and Magee (1969), the
pattern in real imports parallels, but is sharper than, the general shape of
the contraction and rebound in overall U.S. personal consumption spending. Because imports tend to be concentrated more in goods than is overall
consumer spending, real imports move more closely with goods consumption—which is cyclically sensitive—than with total consumption. In addition, because business equipment investment includes imported capital
goods, real imports track this cyclical series as well.
Shrinking exports subtracted from real GDP growth in each quarter of
the worst period of the recession from the third quarter of 2008 to the first
quarter of 2009, but real exports have added to real GDP in every quarter
since, except for in the fourth quarter of 2012.

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Chapter 2

Housing Markets
Housing activity firmed markedly in 2012 and, although the level of
activity remains low by historical standards, the recovery in the sector finally
appears to be gaining momentum. On the production side, new housing
starts increased to an annual rate of 900,000 units by the fourth quarter of
2012, up from an annual low of 550,000 units in 2009, and 610,000 units
in 2011 (Figure 2-10). Demand for housing has also increased, with new
and existing home sales reaching their highest levels of the recovery period
during 2012. Similarly, inventories of unsold new homes have fallen to their
lowest ever recorded level.
Following large declines from 2007 through 2011, housing prices
bottomed out in early 2012, and rose 8.3 percent over the 12 months of the
year, according to the CoreLogic home price index. Private sector housing
experts expect house prices to appreciate at a 3.0 to 3.5 percent annual pace
for the next several years. Because households have a choice between renting
and owning a home, the price of new homes should increase in tandem with
rental costs, at least over long periods of time. As seen in Figure 2-11, house
prices increased to a level above parity with rents during the mid-2000s but
descended to a level consistent with rents by the end of 2011.

Millions (annual rate)
3.0

Figure 2-10
Housing Starts, 1960–2012

2.5
Total
2.0
1.5
1.0
0.5

One-unit
structures

Multifamily structures
0.0
1960:Q1
1970:Q1
1980:Q1
1990:Q1
Note: Shading denotes recession.
Source: Census Bureau, New Residential Construction.

2000:Q1

2012:Q4

2010:Q1

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Figure 2-11
Home Prices and Owners' Equivalent Rent, 1975–2012

Index, 1988-95=100, log scale
250
200

2012:Q4

House prices

150
100

Owners' equivalent
rent

50

25
1975

1980

1985

1990

1995

2000

2005

2010

Note: Shading denotes recession. House prices are measured by the Federal Housing Finance Agency's price index
(total index before 1991, purchase-only index after 1991). Owners' equivalent rent is measured by the Personal
Consumption Expenditures price index for imputed rent of owner-occupied nonfarm housing (before 1983) and the
Consumer Price Index for owners' equivalent rent of residence (1983-present).
Source: Federal Housing Finance Agency, House Price Index; Bureau of Economic Analysis, National Income and
Product Accounts; Bureau of Labor Statistics, Consumer Price Index; CEA calculations.

In 1998, the Council of Economic Advisers estimated that the pace of
construction of new housing units and mobile homes that would be consistent with projected rates of population and household formation would be
1.64 million units a year over the 10 years from 1996 to 2006. Relative to this
1996 estimate, the subsequent 10 years through 2006 saw a period of tremendous overbuilding that led to an excess supply of 2.6 million housing units
by 2007 (Figure 2-12). Since then, the very low levels of new construction
effectively allowed the underlying demographics of household formation to
catch up to the supply of constructed and manufactured homes nationwide
by 2011, with some possible overshooting in 2012.
Although construction, sales, and prices are finally rising, progress
has been impaired by the substantial stock of vacant homes and homes
still in the foreclosure process; therefore, a recovery in housing starts to the
annual pace of roughly 1.76 million units suggested by the demographics of
household formation will likely still take several years to achieve (Masnick,
McCue, and Belsky 2010). Nevertheless, sustained increases in homebuilding should provide a major impetus to economic growth over the medium
term.
Several other factors also appear to be restraining the housing recovery. First, although mortgage rates are at historically low levels, approximately 22 percent of current mortgage holders were underwater (that is, the
68 |

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Figure 2-12
Cumulative Over- and Under-Building of Residential
and Manufactured Homes, 1996–2012

Millions of units
3.0
Relative to projected
2.5
annual average demand
for new units based
2.0
on demographic trends
1.5

Apr-2007

1.0
0.5
0.0

"Boom years"
1996–2006

-0.5
-1.0

"Correction
years"
2007–2012

-1.5
-2.0
1996

Dec-2012
1998

2000

2002

2004

2006

2008

2010

2012

Source: Census Bureau, New Residential Construction (completions) and Manufactured
Homes Survey (placements); CEA (1998); CEA calculations.

amount owed on their mortgage exceeded the market value of their home)
through the third quarter of 2012, impeding their ability to refinance or sell.
Second, although some tightening of lending standards was inevitable
in the aftermath of the financial crisis, these standards have not eased by
as much as expected this far into the recovery. According to the Federal
Reserve Senior Loan Officer Opinion Survey, the net percentage of responding banks that have eased their standards for approving prime residential
mortgage loans has been flat since the beginning of 2011, even though
demand for prime residential mortgages has increased sharply. According
to the April 2012 survey, which included special questions on real estate
lending, more than half the lenders reported they were less likely to originate
a mortgage to a borrower with a credit score of 680 today than in 2006. All
told, the origination of first-lien mortgages to homebuyers now stands at its
lowest level since 1995.
As the President emphasized in the State of the Union, moving forward with programs to help homeowners with strong payment histories refinance their homes will provide them with additional liquidity and will spur
consumption. In addition, streamlining regulations associated with issuing
new mortgages will provide creditworthy potential borrowers the opportunity to purchase homes and will further the recovery of the housing sector.

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Financial Markets
Financial market conditions in the United States continued to
improve, on net, in 2012, reflecting the ongoing economic recovery and
the highly accommodative monetary policies undertaken by the Federal
Reserve. The broad, overall improvement in financial conditions is consistent with the performance of the Standard and Poor’s (S&P) 500 Composite
Index, a measure of U.S. equity prices, which rose 14.4 percent over the 12
months of 2012. Measures of market volatility, such as the Chicago Board
Options Exchange Market Volatility Index (also known as the VIX), were
also more subdued in 2012 than they were in 2011.
Yields on 10-year Treasury notes averaged 1.7 percent in December
2012, down slightly from 2.0 percent in December 2011. For the year as a
whole, the 10-year yield averaged 1.8 percent, the lowest since at least 1953
when the Federal Reserve’s constant-maturity series began. Long-term
interest rates in the United States were driven even lower than in 2011 by
the relative safety of U.S. issues in the presence of concern over sovereign
debt issues abroad and by the Federal Reserve System’s program to lengthen
the maturity of its holdings of U.S. government securities. With these nominal yields falling to historic lows, long-term real interest rates (that is, the
nominal yield less expected inflation) also fell. Yields on Treasury InflationProtected Securities, an indicator of real rates, averaged negative 0.5 percent
in 2012 (Figure 2-13).
Credit standards for commercial and industrial loans, as measured
by the Federal Reserve Board’s Senior Loan Officer Opinion Survey, have
eased since the financial crisis for firms of all sizes, including small firms.
Data from the Federal Deposit Insurance Corporation also suggest that
the number of loans to small businesses increased in 2012, after having
remained depressed through 2011. Nevertheless, the value of small-business
commercial and industrial loans remains below its pre-recession level.

Wage and Price Inflation
Core consumer price inflation (the consumer price index excluding
the volatile components of food and energy) was stable from 2011 to 2012,
rising 1.9 percent in 2012, and down slightly from a 2.2 percent year-earlier
increase (Figure 2-14). Twelve-month increases in core consumer prices
have fluctuated in the fairly narrow range of 0.6 to 2.3 percent during the
past three years. This relative stability is striking, given that standard Phillips
curve models of inflation would predict sustained disinflationary pressure
over this period because of the considerable slack in labor and product
markets.

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Figure 2-13
10-Year Treasury Yields, 2004–2013

Percent
6

Nominal

5
4
3

Feb. 26

2

Real

2.04%

1

-0.55%

0
-1
2004

2005

2006

2007

2008

2009

2010

Note: Real yield based on 10-year inflation-indexed securities.
Source: Federal Reserve Board, H.15.

2011

2012

2013

Figure 2-14
Consumer Price Inflation, 2004–2012

12-month percent change
6
5

Headline

4
3
2
1

Core

Jan-2013

0
-1
-2
-3

2004
2005
2006
2007
2008
2009
2010
Note: Shading denotes recession.
Source: Bureau of Labor Statistics, Consumer Price Index.

2011

2012

2013

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As is usually the case, the overall, or headline, consumer price index,
including food and energy prices, fluctuated more in 2012 than did core
inflation. Inflation as measured by the overall consumer price index fell
from 3.0 percent during the 12 months of 2011 to 1.7 percent in 2012, with
the decline stemming from lower rates of food and energy inflation. Energy
prices edged up only 0.5 percent during 2012, more than 6 percentage points
below their 2011 pace, and food price inflation dropped 2.9 percentage
points. Data Watch 2-2 discusses one of the challenges faced by statistical
agencies when constructing price indexes based on statistical samples.

The Recovery in Historical Perspective
Following the worst recession since the Great Depression, the recovery that began in the third quarter of 2009 has been a long and difficult one
for many Americans. During the recession, 7.5 million jobs were lost, and
real GDP fell by 4.7 percent. To date during the subsequent recovery, 4.2
million jobs have been added since June 2009, and real GDP has grown by
7.5 percent. Since the trough in employment in February 2010, the private
sector has grown for 35 straight months and added over 6.1 million jobs.
Real GDP growth in the United States has exceeded the cumulative growth
in the euro area and the United Kingdom (Figure 1-4) as well as in Japan
since the fourth quarter of 2007. Nevertheless, U.S. real GDP growth since
the end of the recession has been less than the average increase in previous
postwar recoveries.
From 1960 to 2007, the U.S. economy had seven recessions, and the
average annual rate of growth of real GDP during the 12 quarters following those recessions was 4.2 percent. In contrast, during the 12 quarters
following the trough in the second quarter of 2009, the average annual rate
of growth of real GDP was 2.2 percent. After three years of recovery, the
cumulative growth of real GDP was 6.3 percentage points lower than its
average value for the earlier post-1960 recessions. This shortfall is depicted
in Figure 2-15, which shows the paths of real GDP for the three most recent
business cycles (with cyclical troughs in the first quarter of 1991, the fourth
quarter of 2001, and the second quarter of 2009), along with the average path
for U.S. business-cycle recoveries from 1960 through 2007. For each of the
three most recent cycles, the recovery in real GDP has been slower than the
1960–2007 average. It is worth noting that the most recent recovery has been
stronger than the post-2001 recovery if only private demand is considered
(that is, excluding government purchases). Still, the fact remains that these
three recoveries have been slower than the pre-2007 average.

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Figure 2-15
Real GDP During Recoveries

Indexed to 100 at NBER-defined trough
125

120

Average,
1960-2007

115
110

1991
2001

105

Current
(2009:Q2 trough)

100
95

90
85

-16

-12

-8

-4

Trough
4
Quarters from trough

8

12

16

Note: The 1960-2007 average excludes the 1980 recession due to overlap with the
1981-82 recession.
Source: Bureau of Economic Analysis, National Income and Product Accounts; National
Bureau of Economic Research; CEA calculations.

The reasons underlying the relatively slow pace of the current recovery have been the subject of considerable research. This research, discussed
in more detail below, reaches three main conclusions. First, most—perhaps
two-thirds, using a central estimate across studies—of the gap between the
12-quarter growth of GDP after the second quarter of 2009 and the average
12-quarter growth following previous troughs is accounted for primarily by
changes in the long-term dynamics of the U.S. labor force and economy,
mainly long-term demographic shifts. These demographic changes also
help explain why the 1991 and 2001 recoveries were slower than the post1960 average. Second, much of the remaining one-third of the gap can be
attributed to the financial crisis dynamics discussed by Reinhart and Rogoff
(2009), Reinhart and Reinhart (2010), Hall (2010), Woodford (2010), and
others. This research finds that recoveries following financial crises tend
to be slow because of delays in the reemergence of credit and reductions in
consumer spending as households pay down debt or rebuild their savings,
a process referred to as “deleveraging.” Third, some unique factors proved
to be particularly important impediments to this recovery, as discussed
previously: the limited effectiveness of standard monetary policy caused by
the zero lower bound on nominal interest rates; the presence of millions
of underwater and foreclosed properties, which has impaired the recovery
of the housing market; and the contraction in State and local government

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Data Watch 2-2: The Effect of Statistical
Sampling on Laspeyres Indexes
The purpose of a price index is to provide a single measure of the
overall rate of change in prices for some set of goods and services, for
example, all purchases made by consumers. If data on all prices were
readily available, the true rate of price increase could be calculated by
weighting the relative increases in the prices for every item in the bundle
using weights that reflect spending on the items, then combining those
weighted price increases to form a price index. Because it is not possible
to collect all prices, however, statistical agencies collect a sample of prices
and use the sample to construct the price index.
The consequences of using a sample of prices, instead of all prices,
can be significant. To be concrete, consider a Laspeyres price index, in
which inflation is measured as an arithmetic weighted average of price
increases for individual categories of items and the weights are spending
shares measured at the beginning of the interval. In practice, each item
(for example, apples or a haircut) is sold in an area (such as the Seattle
metropolitan region), so the price increase of interest is an item-area
price (the increase in the price of apples in Seattle from one month to
the next). In reality, there are many item-area prices (one can purchase
apples or haircuts at many shops in Seattle), so a sample of item-area
prices is taken, and the sampled price increases (the increase in the price
of apples at a given store, relative to last month’s price at that store) are
averaged. Since 1999, the Bureau of Labor Statistics (BLS) has computed
this average of the sample of price increases within an item-area using
the geometric mean.1
If the number of sampled prices for an item-area is large, the geometric mean of sample price changes will be close to the true item-area
price. But collecting many item-area prices is expensive, so in many cases
only a small number of item-area prices are collected. When computed
using a small sample, the sample geometric mean tends to overstate the
true geometric mean. The extent of this overstatement—the statistical
bias arising from using a small sample—decreases as the number of
prices sampled for an item-area increases.
How large is this finite sample bias? As an example, consider a
1 The geometric mean of two numbers is the square root of their product. Suppose apple
prices are sampled at two stores, one of which held prices constant and the other increased
apple prices by 20 percent. Then the arithmetic mean relative price is (1 + 1.2)/2 = 1.10
(an increase of 10 percent), and the geometric mean is (1×1.2)1/2 = 1.095 (an increase of 9.5
percent). The BLS adopted the geometric mean in part because its slightly lower increase
captures the effect of shoppers migrating to the store at which apple prices remain constant,
so that from the shopper’s perspective the overall price increase is in fact less than 10
percent.

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Chapter 2

Laspeyres price index constructed using equal weights (that is, an index
for which all item-areas have the same consumption shares), with many
item-areas and with 10 prices randomly sampled per item-area. Suppose
that the true item-area price increase is zero and the standard deviation
of the price changes (a measure of the dispersion of the price changes)
for sampled goods within each item-area is 10 percentage points.
Then the bias is small: The geometric mean index for each item-area
overstates the price change by only 0.05 percentage point per period,
and under the assumptions made here, this translates into an upward
bias of 0.05 percentage point in the overall Laspeyres index. But if only
5 items are sampled per item-area, and the standard deviation of the
price changes across stores is a bit larger, say, 15 percentage points, then
the bias is larger, and the price change is overstated by 0.23 percentage
point per period. If this bias can be calculated (as has been done in the
simple example laid out here), a technical correction can be made to
the Laspeyres index to eliminate the bias. At a technical level, this bias
arises because the Laspeyres index is an arithmetic weighted average of
the item-area geometric means. Interestingly, if the geometric means
for each item-area are aggregated to a national index using a weighted
geometric mean, as with a Törnqvist price index, rather than a weighted
arithmetic mean, as with the Laspeyres, the small-sample bias is eliminated, and there is no need for a technical bias correction. For further
reading on small-sample bias in index numbers, see McClelland and
Reinsdorf (1999) and Bradley (2005).

hiring due to sharply eroded property and sales tax bases. Given the deep
and prolonged effects of financial crises, the cyclical component of the current recovery would have lagged even further behind the postwar average
were it not for Federal fiscal stimulus—notably through the Recovery Act
(Box 2-3), the temporary payroll tax cut, and extended unemployment
insurance benefits—and for the nonstandard monetary stimulus provided
by the Federal Reserve.

Demographics, Productivity, and Long-Term Economic Growth
A useful starting point for analyzing long-term trends in output is to
note that GDP is the product of two terms: real GDP per worker times the
number of workers. In turn, GDP per worker is the product of real GDP per
hour of labor input—that is, labor productivity—times average hours per
worker. Although average hours per worker have been declining, the rate of
this decline since the mid-1980s has been relatively small. Thus, variation in
the long-run growth rate of GDP is, to a first approximation, determined by
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Box 2-3: Economic Impacts of the American
Recovery and Reinvestment Act
To counter the contraction of aggregate demand in the Great
Recession, Congress passed and President Obama signed into law
the American Recovery and Reinvestment Act (the Recovery Act) in
February 2009. The Recovery Act was a major part of the Federal government’s efforts to reinvigorate the economy through direct fiscal stimulus.
The Recovery Act authorized an estimated $787 billion for purchases
of goods and services by the Federal government, transfers to State and
local governments, payments to individuals, and temporary tax reductions for individuals and businesses (based on actual outcomes, the final
total exceeded $800 billion).
Numerous studies have examined the success of the Recovery
Act in raising employment and stimulating growth. As is the case with
policy evaluation generally, the methodological challenge is to compare
outcomes from an event that actually happened (implementation of the
Recovery Act) to outcomes from a counterfactual event that did not (no
Recovery Act). One approach is to use a large macroeconometric model
or other statistical techniques to estimate a baseline, non-stimulus forecast that excludes Recovery Act provisions and a stimulus forecast that
includes them, and then either compare the two forecasts or compare
the actual data to the non-stimulus forecast. Of the studies employing
this method, most estimate that the Recovery Act stimulated growth.
A Congressional Budget Office study (CBO 2012b) estimated that the
Recovery Act boosted the level of GDP by 0.4–1.8 percent in 2009,
0.7–4.1 percent in 2010, 0.4–2.3 percent in 2011, and 0.1–0.8 percent
in 2012, with more than 90 percent of the Recovery Act’s budgetary
impact realized by the end of September 2012. The most recent review
by the Council of Economic Advisers (CEA 2013) estimated that the
Recovery Act raised the level of GDP as of the third quarter of 2010 by
2.7 percent, which is roughly in the same range estimated by CBO. A
report by Blinder and Zandi (2010) estimated that the stimulus raised
GDP in 2010 by 3.4 percent. Additional reports by IHS Global Insight
and Macroeconomic Advisers provide estimates consistent with these
ranges (as reported in CEA 2013). Estimates based on macroeconometric
models typically do not include the additional benefits of avoiding very
high levels of unemployment, which could be particularly persistent
and exhibit so-called hysteresis; see DeLong and Summers (2012) for
additional discussion.
A different approach to evaluating the Recovery Act is to use crossstate variation in Recovery Act spending levels to estimate the effects of
the spending, and then to extrapolate these effects to the full economy.

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Wilson (2012) studied state-level variation in Recovery Act spending
to determine its employment effect; he estimated that Recovery Act
spending created 2 million jobs in its first year and 3.4 million by March
2011, with substantial gains in the construction, manufacturing, education, and health industries. Conley and Dupor (2012) estimated that the
spending components of the Act created between 82,000 and 1.5 million
jobs. Other papers that use state-level variation to estimate Recovery
Act effects on employment include Chodorow-Reich and others (2012),
who investigated the employment effects of the Recovery Act’s aid to
states through increased Federal Medicaid matching funds, and Feyrer
and Sacerdote (2011), who considered both total spending and type of
spending; both papers found positive employment effects.
The range of estimates of the effect of the Recovery Act is large, and
research on this topic is ongoing. Surveying the literature, however, the
evidence suggests that the Recovery Act substantially lessened the impact
of the Great Recession by increasing employment and output in the years
immediately following the crisis.

the long-run growth rate of both productivity and the number of workers.5
The discussion here focuses on the growth of productivity for nonfarm businesses and the growth of overall payroll employment.
Figure 2-16 shows quarterly growth of nonfarm business productivity
and its cyclically adjusted long-term mean at an annual rate.6 According to
this mean, annual trend productivity growth fell from 2.6 percent in 1965
to 1.5 percent in 1985, recovered to 2.3 percent in 2005, and then fell to 2.0
percent as of 2010. Despite the considerable uncertainty and difficulty in
distinguishing the trend from cyclical components given the severity of the
recent recession, this pattern is in line with others in the academic literature.
Gordon (2010) found that trend productivity growth declined from 2.75
percent in 1962 to 1.25 percent in 1979, then rebounded to 2.45 percent by
2002. Fernald (2012) divided the period since 1973 into three regimes of
average labor productivity growth: 1.5 percent from 1973 to 1997, 3.6 percent from 1997 to 2003, and 1.6 percent from 2003 to 2012. The very strong

5 Because labor productivity is conventionally measured for the nonfarm business sector, there
are additional terms that account for the difference between the growth of GDP per hour and
nonfarm business output per hour and between nonfarm business hours and total hours.
6 The cyclically adjusted long-term mean, or trend, is estimated using regression methods with
a cyclical component, specifically two leads and lags of the CBO’s unemployment gap, and a
flexible trend component. The flexible trend component is estimated by a smooth weighted
average using a two-sided 15-year moving window, which is truncated at the ends of the
sample.

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Figure 2-16
Productivity Growth and Estimated Trend, 1960–2012

Percent change (annual rate)
14
12
10
8
6
4
2
0
-2
-4

2012:Q4

-6
-8
1960:Q1

1970:Q1

1980:Q1

1990:Q1

2000:Q1

2010:Q1

Note: Shading denotes recession. Trend productivity growth was estimated by a smoothed weighted
average over a 15-year moving window.
Source: Bureau of Labor Statistics, Productivity and Costs; CEA calculations.

productivity growth of the late 1990s and early 2000s evident in Figure 2-16
appears, in part, to have been transitory.
Figure 2-17 plots the quarterly growth of total payroll employment
and its cyclically adjusted long-term mean at an annual rate, and Figure 2-18
plots the quarterly change in employment, measured by the number of jobs;
the method for computing the trends in both figures is the same as that used
to calculate the trend shown in Figure 2-16. The smoothed mean growth of
employment rose from 2.2 percent annually in 1965 to 2.4 percent in 1975
but then declined steadily to 2.0 percent in 1985 and just 0.8 percent in 2005.
The trend in the number of jobs added remained high through the 1990s,
and in fact more jobs were added in the 1990s than in the 1980s.
The high growth rate of employment in the 1970s reflected the
historic surge of women into the U.S. labor force. The trend decline in
employment growth since the late 1990s has been largely associated with
demographics, in particular the plateauing of female labor force participation during the late-1990s, the steady multi-decade trend decline in male
labor force participation, the downward trend in youth labor force participation, and, starting in the 2000s, the entry of the baby-boom generation into
retirement. Demographic trends are discussed in more detail in Chapter 4.
Indeed, the implications of demographic trends extend beyond the labor

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Figure 2-17
Employment Percent Growth and Estimated Trend, 1960–2012

Percent change (annual rate)
8
6
4

2012:Q4

2
0
-2
-4
-6
-8
1960:Q1

1970:Q1

1980:Q1

1990:Q1

2000:Q1

2010:Q1

Note: Shading denotes recession. Trend employment growth was estimated by a smoothed weighted
average over a 15-year moving window.
Source: Bureau of Labor Statistics, Current Employment Statistics; CEA calculations.

Figure 2-18
Quarterly Change in Employment and Estimated Trend, 1960–2012

Quarterly change, thousands
2,000
1,500
1,000

2012:Q4

500
0
-500
-1,000
-1,500
-2,000
-2,500
1960:Q1

1970:Q1

1980:Q1

1990:Q1

2000:Q1

2010:Q1

Note: Shading denotes recession. Trend employment growth was estimated by a smoothed weighted
average over a 15-year moving window.
Source: Bureau of Labor Statistics, Current Employment Statistics; CEA calculations.

The Year In Review And The Years Ahead

| 79

force to include, for example, changes in the patterns of consumption as the
population ages (Box 2-4).
The net effect of the declines in the long-term trends for productivity and employment has been a fairly steady decline in the long-run mean
growth rate of GDP over the past 50 years. Indeed, the cyclically adjusted
long-term mean growth rate of real GDP fell from 3.7 percent in 1965
to 2.9 percent in 1985 and 2.4 percent in 2005. This steady slowdown is
evident in Figure 2-19, in which real GDP is plotted along with trend lines
estimated using the quarterly data spanning a full business cycle as dated
by the National Bureau of Economic Research (NBER), measured from one
business-cycle peak to the next.7 The slopes of these trend lines are less steep
over time; in other words, the trend growth of real GDP has been slowing
over this period. Indeed, trend growth has slowed enough that, after every
post-1960 recession, real GDP has never attained the previous trend growth
line that is implied using data from the preceding business cycle. From
this perspective, the slower pace of the current recovery is not unusual or
unexpected.
In a November 2012 study of the current recovery, CBO decomposed
the growth of real GDP in the 12 quarters following a NBER-dated trough
into trend growth plus a cyclical component. It attributed about two-thirds
of the difference between the growth in real GDP in the current recovery
and the average for other recoveries to slow growth in potential GDP. The
CBO study estimated potential real GDP growth—that is, the maximum
sustainable rate of growth of real GDP—using a presumed economy-wide
production function in which potential GDP varied with the capital stock.
For comparison purposes, the long-term mean growth rate of GDP is
computed here using the methodology of Figures 2-16 and 2-17. The results
from this analysis are summarized in Table 2-2. As reported earlier, during
the first 12 quarters of recoveries from 1960 through 2007, real GDP grew,
on average, at an annual rate of 4.2 percent, whereas during the 12 quarters
following the trough in the second quarter of 2009, the annual rate of GDP
growth was 2.2 percent, or 2.1 percentage points below the 1960–2007 average. The estimated trend growth rate of real GDP since the second quarter of
2009, however, was 2.1 percent, or 1.1 percentage points below the average
trend growth during the 1960-2007 recoveries (3.2 percent). Thus, of the
2.1 percentage points of slower-than-average growth in this recovery, fully

7 The cycle starting with the peak in the first quarter of 1980 lasted only six quarters. Because
it is not meaningful to estimate trends using only six quarterly observations, the cycles for the
first quarter of 1980 and the third quarter of 1981 are merged for the trend estimates in Figure
2-19.

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Figure 2-19
Real Gross Domestic Product and Trends, 1947–2012

Trillions of chained 2005 dollars, log scale
18
14
10
6
4

2
1947:Q1

1957:Q1

1967:Q1

1977:Q1

1987:Q1

1997:Q1

2007:Q1

Note: Shading denotes recession. Trend lines represent the average growth rate between successive
business-cycle peaks.
Source: Bureau of Economic Analysis, National Income and Product Accounts; National Bureau of
Economic Research; CEA calculations.

1.1 percentage points, or 53 percent, can be attributed to the overall trend
slowdown in real GDP growth over the past 50 years.8
The 1991 and 2001 recoveries also exhibited slower than average
growth in real GDP (Kliesen 2003; Berger 2011; Bachmann 2011). As can
be seen in Table 2-2, the slowdown in trend growth accounted for less than
one-fifth of the relatively slower growth in real GDP following the 1991
recession (-0.2 percentage point of the gap of -1.1 percentage points). In
contrast, slightly more than one-third of the relatively slower growth following the 2001 recession was attributable to the slowing of long-term real GDP
growth (-0.5 percentage point of the gap of -1.3 percentage points).
Stock and Watson (2012) also examined reasons why the current
expansion has been slower than previous postwar recoveries. They focused
on the first eight quarters of the recovery and estimated that 80 percent of
the slower growth in real GDP, relative to the post-1960 average for recoveries, reflected a slowdown in the long-term trend growth rate rather than
cyclical factors.
8 This calculation includes the 12 quarters after all troughs, so that the 1980 and 1982
recoveries overlap. Alternatively, if the 12 quarters following the trough in the fourth quarter of
1982 are dropped, 63 percent of the slower than average growth in real GDP is attributable to a
slowdown in trend growth. If instead the 12 quarters following the trough in the third quarter
of 1980 are dropped, 47 percent of the slower growth in real GDP is attributable to a slowdown
in trend growth.

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Box 2-4: Implications of Demographic Trends
for Household Consumption
The aging of the U.S. population has two implications for patterns
of consumption. First, people purchase different things at different ages;
for example, younger households spend more on child care services and
clothing, while older households spend relatively more on health care.
Second, empirical research suggests that families’ total amount of spending changes over time as priorities evolve. Because the age distribution
of the population will change over the coming decade as the baby boom
generation moves into retirement, these changes in household-level consumption will lead to aggregate changes in the types of goods consumed
and, potentially, to changes in the fraction of income spent.
One way to forecast how demographic changes will affect consumption is to use data on a sample of households today to estimate
average household consumption within spending categories (clothing,
health care, and so on), for each subset of the population defined by age,
race, sex, and ethnicity of the household head. Then, one can aggregate
these averages using the projected future population for each subset to
produce an overall estimate for all households. The Council of Economic
Advisers undertook this exercise using consumption data from the
Consumer Expenditure Survey and demographic projections from the
Census Bureau. As the figure below indicates, demographic changes suggest that a greater share of household income will be spent on health care
and housing, and a reduced share on education. In percentage terms,
however, these changes are likely to be small.
Households’ total consumption also varies over their lifetime.
In Milton Friedman’s (1957) permanent income hypothesis model of
consumption, individuals smooth consumption to match their lifetime
income, but doing so requires the ability to borrow against future
income, as well as considerable planning and discipline. As an empirical matter, on average, household consumption rises as children grow
up and then declines as parents enter into retirement (Attanasio et al
1999; Fernandez-Villaverde and Krueger 2007; Bullard and Feigenbaum
2007).1 Consistent with this research, CEA projects that the aging
population will lead average household consumption to decline over the
next decade, with an implied reduction in the growth rate of consumer
spending of perhaps 0.1 percentage point a year, relative to a benchmark
in which demographics are held constant.
1 One reason for the decline in consumption upon retirement, at least for some households,
is reduced work-related spending such as commuting costs and uniforms, which are
counted as consumption expenditures, but such declining work-related expenses do not
fully account for this drop.

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Many factors other than demographics will also influence future
consumer spending. These factors include technological improvements,
changes in income and wealth, and changes in the composition of
households within demographic groups. In addition, changes in relative
prices will affect the composition of spending. For example, if the price of
health care increases relative to other areas, and if the demand for health
care is insensitive to its price, then the share of spending on health care
might be larger than these projections suggest.

Projected Effect of Demographic Change on Share of Household
Expenditures, 2011–2023

Change in share of household expenditures, percentage points
0.4
0.3
0.2
0.1
0.0
-0.1
-0.2
-0.3

Food
Housing
Health care
Education
Transport
Note: Percentage point changes over 12 years, not annualized.
Source: Bureau of Labor Statistics, Consumer Expenditure Survey; Department of
Commerce, Census Bureau; CEA calculations.

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In summary, these estimates of the share of the relatively slower
growth in real GDP during this recovery which is attributable to a slowdown
in long-term trends range from 53 percent, shown in Table 2-2, to 80 percent
according to Stock and Watson (2012). This fairly wide range of estimates
reflects both inherent difficulties in calculating trend growth rates and
conceptual differences among these approaches.9 Taken together, however,
these studies suggest that most of the relatively slower growth in real GDP
during the current recovery—two-thirds, using the CBO (2012d) estimate,
which is also the midpoint of these estimates—has been attributable to the
slowdown in long-term trend growth, which, in turn, has been driven largely
by demographic changes in the U.S. workforce.

Reasons for the Slower Cyclical Component
If two-thirds of the slower growth in real GDP during the current
recovery relative to growth in previous postwar recessions is attributable to
the slowdown in underlying long-term trends, then the remaining one-third
can be attributed to cyclical factors that are specific to this recovery. This
section summarizes four complementary attempts to quantify those cyclical
factors: the 2012 CBO study discussed above, an analysis undertaken here
of the sources of forecast errors during the recovery, work done on this
question by the Federal Reserve as reported by Bernanke (2012b) and Yellen
(2013), and the study by Stock and Watson (2012).
The CBO (2012d) study approaches the question of why the cyclical
part of this recovery has been relatively slow by identifying those components of GDP that have exhibited unusually slow growth relative to their
cyclical pattern. In decreasing order of importance, CBO found that the
cyclical contributions to GDP of State and local government purchases,
Federal government purchases (primarily defense spending), residential
investment, and consumer spending were all weaker than their respective
historical averages during the first 12 quarters of this recovery. In turn,
CBO attributed the weakness in these components to several underlying
factors. For instance, the CBO study highlighted the extraordinary weakness
in housing markets during the current recovery. CBO associated the sharp
9 In CBO’s framework, the increase in long-term unemployment associated with the recession
could result in skill deterioration and thereby a decline in potential GDP growth; this general
point is also made by Federal Reserve Chairman Ben Bernanke (Bernanke 2012b). Because
such declines in potential GDP are an indirect result of the recession, they may be better
understood as cyclical rather than long-term trends. The trend estimates in Table 2-2 and in
Stock and Watson (2012) are instead based on long-term weighted moving averages; because
the resulting estimates are comparable with CBO’s, one can infer that this further distinction
of a cyclical change in the growth rate of potential GDP is secondary to the long-term
demographic and technological trends that drive the growth slowdown.

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Table 2-2
Real GDP Growth During Three Years Following Business Cycle Trough
Business Cycle Trough

(percent change at an annual rate)
Total

Trend

Cycle

1991:Q1

3.2

3.0

0.2

2001:Q4

2.9

2.7

0.2

2009:Q2

2.2

2.1

0.1

Average of 7 recoveries, 1960-2007

4.2

3.2

1.1

Total

Trend

Cycle

1991:Q1

Difference from Average

-1.1

-0.2

-0.9

2001:Q4

-1.3

-0.5

-0.8

2009:Q2

-2.1

-1.1

-1.0

Note: Trend growth is based on the 15-year moving average smoothed cyclically adjusted growth rate of real
GDP.
Source: Bureau of Economic Analysis, National Income and Product Accounts; National Bureau of Economic
Research; CEA calculations.

fall in house prices with reductions in State and local property tax revenues
and the persistent glut of vacant and foreclosed homes with the weakness in
residential construction. Similarly, CBO noted that, in contrast to previous
postwar recoveries, the ability of monetary policy to spur economic activity
has been constrained by the zero lower bound on the Federal Reserve’s main
policy interest rate during this expansion. The CBO analysis also pointed to
low consumer confidence and heightened uncertainty as additional factors
that have restrained aggregate demand since the second quarter of 2009.
A second approach to the question of why the cyclical component of
this recovery has been slower than that of the postwar average is to examine
whether the expansion has been hindered by unexpected events and forces.
Specifically, this approach contrasts the actual, realized values for each component of GDP from the corresponding estimates that were forecast at the
start of the recovery. Whereas CBO’s approach identifies which components
of GDP grew more slowly than their historical average, the approach used
here is to identify the components that grew either more slowly or more
rapidly than was forecast, thereby identifying the unexpected, or unforecast,
sources of the slow growth.
Implementing this method of forecast error analysis requires a
quantitative model of the U.S. economy. The one used here is developed
and maintained by Macroeconomic Advisers (MA). This model is used to
decompose the Administration’s economic forecast for the FY 2011 Budget,
which was made in November 2009. The MA model uses quarterly data to
forecast hundreds of macroeconomic variables. By partitioning the variables
into groups, it is possible to see how the forecast errors for each group
contributed to the forecast errors for GDP. The variables were divided into
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five categories: international (foreign GDP, exchange rates, oil prices), fiscal
(both Federal and State and local), financial and monetary (financial prices,
house prices, monetary indicators, credit flows), housing activity, and other.
That Administration forecast overpredicted output growth by a small
amount in 2010 and by larger amounts in 2011 and the first half of 2012;
in this sense, the recovery was slower than expected. The forecast error
decomposition sheds light on the sources of this unexpectedly slow recovery.
During the first part of the recovery, the housing sector was weaker than
anticipated, and this unexpected weakness more than accounts for the total
GDP forecast error in 2010. Early in the recovery, financial and monetary
factors buoyed economic activity relative to the forecast, presumably because
the forecast did not fully capture the stimulative effect of nonstandard monetary policy, which was unprecedented and thus difficult to incorporate
quantitatively into the forecast. Moving farther out in the forecast, however,
the outlook for consumption turned overly optimistic, possibly reflecting an
underestimation of the degree of deleveraging as households reduced the
amount of new debt they took on and paid down existing debt. This shift in
the consumption outlook explains a substantial part of the overall forecast
error for both 2011 as well as the first half of 2012. Finally, deteriorating
international conditions, largely owing to events unfolding in Europe, added
further unanticipated drag in 2011 and especially in the first half of 2012.
These results complement Chairman Bernanke’s (2012b) and Vice
Chair Yellen’s (2013) analyses of the relatively slow growth in the cyclical
component of GDP during this recovery. In particular, Chairman Bernanke
pointed to unexpected headwinds from the prolonged recovery of the housing sector, the lingering effects of the financial crisis, and the fiscal and
financial problems in Europe. Yellen also noted the restraint on consumer
spending from the large loss of wealth during the recession. Both emphasized the unexpectedly large declines in the State and local government sector. Indeed, Yellen estimates that, once the drag from the State and local government sector is included, the net fiscal stimulus to the economy was less
in the current recovery than it was on average for prior postwar recoveries.
Stock and Watson (2012) also addressed the question of why the cyclical component of the recovery has been slower than the postwar average. In
contrast to the two approaches discussed above, Stock and Watson focused
on the forecasts of eight-quarter GDP growth from the vantage point of the
trough. They found that these forecasts predicted slower-than-average cyclical growth during this expansion. These slow growth forecasts stem from
the shocks that produced the recession, which they identify as primarily
financial factors (such as borrowing constraints) and uncertainty. Thus, the
Stock and Watson analysis is consistent with the Reinhart and Rogoff (2009)
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Chapter 2

view that recoveries following financial recessions typically exhibit slower
growth than those following other kinds of recessions. In contrast to Stock
and Watson’s approach, Hall (2012) used a stylized macroeconomic model
to distinguish between the deleveraging effect of cutting back on consumption to rebuild wealth and the liquidity effect of higher borrowing costs,
which would arise from tightened lending standards. He concluded that
both effects were important during the recession, but that the deleveraging
effect was short-lived, whereas the liquidity effect has been more persistent
and continues to restrain investment and to contribute to the slow cyclical
component of GDP.
Although the CBO analysis, the forecast error decomposition, the
analyses by Bernanke and by Yellen, the study by Stock and Watson, and
the study by Hall produced different numerical estimates of the causes of the
relatively slow recovery, these analyses point to a common understanding
of why the cyclical component of the current expansion was slow relative
to previous recessions: a financial crisis that led to reductions in the ability
of households and small businesses to borrow, spend, and invest; a weak
recovery of the housing sector as a result of the excess inventory of vacant,
foreclosed, and distressed properties; a decline in State and local spending
and employment; monetary policy restrained by the zero lower bound on
the Federal Reserve’s main policy interest rate; and in more recent stages of
the recovery, the detrimental effects of a global slowdown on U.S. economic
activity. Against all of these headwinds, the stimulus from Federal fiscal
policy actions and aggressive unconventional monetary policy contributed
positively to the cyclical component of the recovery.

Outlook for 2013 and Beyond
The Administration’s economic forecast was finalized in midNovember 2012, a schedule that is dictated by its role in supporting the
Administration’s outlook for the FY 2014 Budget, and will be released later
this year in conjunction with the Budget.
Consensus-based forecasts—that is, forecasts that combine multiple,
survey-based individual forecasts (e.g., the mean or median)—typically
outperform the constituent individual private forecasters’ forecasts of macroeconomic variables such as GDP and the unemployment rate (Clemen
1989; Aiolfi, Capistrán, and Timmerman 2011). Consensus forecasts are
thus worth following. In February 2013 the Blue Chip consensus of professional forecasters projected that real GDP would increase 2.4 percent over
the four quarters of 2013, faster than the 1.6 percent gain recorded in 2012.
The Philadelphia Federal Reserve Bank’s Survey of Professional Forecasters

The Year In Review And The Years Ahead

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(SPF) also projected a 2.4 percent increase in 2013. For 2014, the Blue Chip
consensus and the SPF consensus forecast that the economy will continue
to strengthen and that year-over-year real GDP growth will increase to a 2.8
percent pace.
Looking further ahead, the Survey of Professional Forecasters expects
year-over-year growth will pick up to a 2.9 percent pace in 2015 and a 3.0
percent pace in 2016. With these rates of growth, the unemployment rate,
which was 7.8 percent during the fourth quarter of 2012, is projected to edge
down slowly to 6.3 percent in 2016.
Importantly, most private sector forecasts reflected in the consensus
forecast have not incorporated an effect for the across-the-board budget
cuts, known as sequestration, which took effect on March 1.10 These cuts
will severely reduce both Federal defense and nondefense discretionary
spending, with ripple effects throughout the economy. The Congressional
Budget Office (2013) and Macroeconomic Advisers (2013) have estimated
that, if sequestration were to remain in effect for the rest of the calendar
year, it would reduce real GDP growth by 0.6 percentage point during the
four quarters of 2013, relative to its path without the sequester. Moody’s
Analytics (2013) has estimated a reduction in real GDP growth by 0.5 percentage point.
Additionally, CBO (2013) has estimated that sequestration would
lead to the loss of 750,000 lost jobs due to the sequester by the end of 2013
compared with a path without sequestration.11 From this perspective, by the
end of this year sequestration would set back the recovery by four to five
months at a time when the unemployment rate remains unacceptably high.
As President Obama has stated, “The longer these cuts remain in place, the
greater the damage to our economy—a slow grind that will intensify with
every passing day.”

Conclusion
While much work remains, the economy is healing and moving in the
right direction. The permanent extension of middle-class tax cuts and the
increase in rates on the highest-income taxpayers through the enactment
of the American Taxpayer Relief Act resolved the uncertainty about future
tax rates that overshadowed the economy in 2012 and helped move the U.S.
budget toward a more sustainable course. Some of the other headwinds
that have restrained the economy during the recovery are also easing, most
10 In February, 77 percent of Blue Chip panelists reported that their forecasts did not reflect the
effects of full sequestration.
11 The Bipartisan Policy Center (2012) estimates that over two years the effect would be 1
million jobs lost compared with the no-sequestration alternative.

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Chapter 2

notably in the housing sector. While risks remain, these indicators suggest a
continued strengthening of the recovery, which in turn provides an increasingly resilient framework for continued progress toward fiscal sustainability
and a more durable economy that works for the broad middle class.

The Year In Review And The Years Ahead

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C H A P T E R

3

FISCAL POLICY

T

he American Taxpayer Relief Act of 2012 (ATRA), which was enacted
on January 2, 2013, permanently extended the 2001 and 2003 Federal
income tax cuts for 98 percent of taxpayers. The tax relief act reflects the
approach supported by the President to reduce the Federal budget deficit—
an approach that balances responsible reductions in government spending
with new revenues and increased progressivity of the tax code. The new
law extended the expansions of several tax credits enacted in the American
Recovery and Reinvestment Act of 2009 (the Recovery Act) that have provided economic opportunities through tax relief and college expense assistance to 25 million low- and middle-income students and working families
each year. In addition, the new law prevented a substantial cut in Medicare
physician payment rates, extended emergency unemployment insurance
benefits to protect 2 million workers from losing their benefits in January
2013, and permanently indexed to inflation the exemption amounts for the
Alternative Minimum Tax (AMT) to provide tax certainty to tens of millions
of middle-class families. The permanent fix to the AMT will protect middleclass families from being subject to a tax designed to ensure that wealthy
taxpayers pay their fair share in taxes.
Together with the additional Medicare and investment income taxes
for high-income taxpayers in the Affordable Care Act (ACA), ATRA has
made the Federal tax system more progressive. Figure 3-1 shows the trends
in average Federal individual income and employment tax rates by income
class. These average tax rates, defined as the share of taxpayer income paid in
taxes, are measured by holding the distribution of taxpayer income constant
over time (using the 2005 distribution with incomes adjusted for growth in
the National Average Wage Index) to isolate the effects of tax law changes.
The tax law changes in 2013 increased the average tax rate for taxpayers in
the top 1 percent and the top 0.1 percent of the income distribution by 4.9
and 6.5 percentage points, respectively, while leaving individual income tax
rates unchanged for 98 percent of Americans.

91

Figure 3-1
Average Tax Rates for Selected Income Groups
Under a Fixed Income Distribution, 1960–2013

Average tax rate, percent
60

Top 0.1 percent

50
40

Top 1 percent

30

2013

Middle 20 percent

20
10
0
1960

1965

1970

1975

1980

1985

1990

1995

2000

2005

2010

Note: Average Federal (individual income plus payroll) tax rates for a 2005 sample of taxpayers
after adjusting for growth in the National Average Wage Index.
Source: Internal Revenue Service, Statistics of Income Public Use File; National Bureau of
Economic Research, TAXSIM (preliminary for 2012 and 2013); CEA calculations.

Another recent development in government finance is that the fiscal
outlook for State and local governments has improved, although expenditures remain below pre-recession levels and State and local investment
spending remains notably low. As shown in Figure 3-2, the continued
decline in State and local investment is atypical. In other recoveries, State
and local governments’ gross real investment was typically flat for several
quarters following a business-cycle trough and then increased, but, in this
recovery, gross investment has failed to rebound.
This chapter highlights the declining Federal budget deficit since 2009
and the additional work needed to achieve medium- and long-term fiscal
health. It then outlines the principles for Federal income tax reform set forth
by President Obama in September 2011 and describes specific plans proposed by the Administration to meet these goals. The enactment of ATRA
is a step toward achieving these goals, but substantial work remains to make
the tax code more equitable and efficient. The chapter also reviews the State
and local budget outlook and the Federal Government’s role in mitigating
the recent recession’s effect on government finances at these levels. Finally,
the chapter discusses the long-term financial challenge facing State and local
governments from the underfunding of pension plans.

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Chapter 3

Figure 3-2
Real State and Local Government Gross Investment
During Recoveries

Indexed to 100 at NBER-defined trough
140

1982

130

Average,
1950–2007

120
110
100

1991

90

Current
(2009:Q2 Trough)

80
70

2001

-16

-12

-8

-4

Trough
4
Quarters from trough

8

12

16

Source: Bureau of Economic Analysis, National Income and Product Accounts; National
Bureau of Economic Research; CEA calculations.

The Federal Budget Outlook
The Obama Administration has taken significant steps to restore the
country’s fiscal health without disrupting the continuing economic recovery.
In fiscal year (FY) 2009, the Federal budget deficit was 10.1 percent of gross
domestic product (GDP). This ratio fell 3.1 percentage points to 7.0 percent
in 2012, the largest three-year reduction in the deficit since 1949. Under
current law, the deficit is projected to fall to 5.3 percent in 2013 (CBO 2013).
This decline in the deficit largely reflects the wind-down of Recovery Act
spending, the reductions in spending set forth in the Budget Control Act of
2011, new revenues as a result of ATRA, and the improved performance of
the economy.
The Congressional Budget Office (CBO) projects that Federal receipts
will grow by 11 percent to $2.7 trillion, or 16.9 percent of GDP, in 2013
(Figure 3-3). This is the highest receipts-to-GDP ratio since 2008, but still
below the average of 18.3 percent of GDP recorded between 1970 and 2000.
As a percent of GDP, outlays are projected to fall from 22.2 percent in 2013
to 21.5 percent in 2017 due in large part to the spending caps put in place by
the Budget Control Act as well as reductions in certain mandatory spending
as the economy continues to improve. After 2017, outlays will rise, relative to
GDP, as interest payments on the national debt increase and as mandatory

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Figure 3-3
Federal Receipts and Outlays, 1970–2023

Percent of GDP
28

Actual Projected

26
24
Outlays

22

2023

Average outlays
1970–2000

20
18

Average receipts
1970–2000

Receipts

16
14
12
10
1970

1975

1980

1985

1990

Source: OMB (2012b); CBO (2013).

1995 2000
Fiscal year

2005

2010

2015

2020

health and retirement spending grows in accordance with the cost of health
care and an aging population. Over the long term, these factors—rising
health costs and changing demographics—are the primary drivers of fiscal
imbalance (CBO 2012).
The Administration’s goal of stabilizing the debt-to-GDP ratio
requires reducing the deficit to 3 percent of GDP or lower. Increases in
revenues and decreases in outlays in recent years have brought the Federal
budget deficit—the gap between outlays and receipts—closer to that target
(Figure 3-4). CBO projects that, under current law, deficits will continue to
shrink over the next few years, falling below 3 percent of GDP by 2015, but
will then increase steadily to 3.8 percent of GDP by 2022. Under current law,
publicly held Federal debt is projected to reach 77 percent of GDP in 2023
(Figure 3-5).
Although enacted legislation and overall economic improvements
will help reduce the budget deficit, other structural changes will be needed
to achieve fiscal sustainability. The President has put forward a balanced
deficit-reduction plan to achieve approximately $1.8 trillion in savings
through a combination of reductions in discretionary spending, savings in
entitlement programs, and new revenue raised by reforming tax expenditures and closing tax loopholes. When added to the more than $2.5 trillion
in deficit reduction the President already signed into law, the total deficit

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Figure 3-4
Federal Budget Deficit, 1970–2023

Percent of GDP
12

Actual Projected

10
8
6
2023

4
2
0
-2
-4
1970

1975

1980

1985

1990

Source: OMB (2012b); CBO (2013).

Percent of GDP

1995 2000
Fiscal year

2005

2010

2015

2020

Figure 3-5
Federal Debt Held by the Public, 1970–2023
Actual Projected

90

2023

80
70
60
50
40
30
20
10
0
1970

1975

1980

1985

1990

Source: OMB (2012b); CBO (2013).

1995 2000
Fiscal year

2005

2010

2015

2020

Fiscal Policy

| 95

reduction would amount to more than $4 trillion over ten years, a goal set
by the President to stabilize the debt-to-GDP ratio and to put the country on
a sustainable fiscal path over the next decade.

Federal Income Tax Reform
A fair, simple, and efficient tax code lays the foundation for job creation, economic growth, and an equitable society. Recognizing the crucial
role tax reform can play in deficit reduction and economic growth, President
Obama set forth a list of principles in September 2011 for comprehensive
tax reform. These principles include lowering tax rates, cutting inefficient
and unfair tax breaks, observing the “Buffett Rule” to enhance tax fairness,
reducing the deficit, and increasing job creation and growth in the United
States (OMB 2011).
Because revenue must be raised to finance essential services provided
by the government, sound tax policy attempts to raise revenue fairly and efficiently. A number of notions of fairness can help guide tax policy: “horizontal equity” demands equal treatment of equals; the ability-to-pay principle
prescribes that a taxpayer’s burden should be related to her ability to pay; the
benefit principle suggests that a taxpayer’s burden should be related to the
benefits she receives from government services. Such notions of fairness are
often incomplete, and sometimes they are in conflict with each other. Still,
these principles can serve as useful guides.
Fairness, however, must be balanced with efficiency. High tax rates,
combined with a complex tax system and a narrow tax base (that is, with
many deductions, exclusions, or exemptions), provide incentives for taxpayers to shift income between the individual and corporate tax bases, retime income, and alter behavior in other ways to reduce tax liability (Saez,
Slemrod, and Giertz 2012). In addition, although tax subsidies could encourage socially beneficial activity or correct market failures, when there are no
externalities or other market failures, tax provisions that favor one activity
over another can lead to an inefficient allocation of resources.
A key feature of the tax code is the schedule of statutory tax rates on
marginal income. To achieve myriad tax, economic, and social policy goals,
the tax code also contains a dizzying web of deductions, exemptions, exclusions, credits, and special treatment of certain income. The fact that taxpayers modify their behavior to reap the benefits of special tax provisions is
bittersweet. On one hand, it means that well-thought-out tax provisions that
are designed to encourage a particular activity are working. On the other
hand, a taxpayer determined to avoid liability can engage in tax avoidance

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and thereby expend socially unproductive resources navigating the jungle of
tax provisions.1

Tax Expenditures
The tax code contains numerous special tax provisions, referred to
as “tax expenditures,” which lead the tax system to deviate from taxing
economic income (Box 3-1). Economic income generally follows the HaigSimons definition of comprehensive income as consumption plus changes
in net worth. Relative to a tax structure built on a comprehensive income
measure, tax expenditures erode the tax base, causing the government to
forgo revenue, but they provide important tax benefits to individuals and
families. How such benefits are distributed over the income distribution
varies widely across tax provisions. To assess the distributional effects of a
given tax expenditure, the Treasury Department estimated the tax benefits
of each major individual income tax expenditure under 2013 income tax law
for taxpayers in different income classes.
As illustrated in Figure 3-6, the Earned Income Tax Credit (EITC) and
the Child Tax Credit (including the refundable portion) provide substantial
benefits to taxpayers in the lowest income quintile but have little impact
on the after-tax income of taxpayers in the top three income quintiles. By
contrast, the bottom two income quintiles receive almost no benefits from
tax expenditures like the charitable giving deduction and deductions for
State and local taxes. Almost all of those tax benefits accrue to taxpayers in
the top two income quintiles. Middle and upper-middle income taxpayers
benefit the most from the exclusion of employer-provided health insurance,
whereas taxpayers in the bottom quintile and those in the top percentile of
the income distribution receive relatively little benefit from the exclusion.
Because the tax value of deductions and exclusions increases with
taxpayers’ marginal tax rates, these tax expenditures provide larger benefits
to high-income taxpayers than to low- and middle-income taxpayers for
a given amount of deductions or exclusions. (For various measures of tax
rates, see Economics Application Box 3-1.) In particular, an additional dollar
of deductions or exclusions reduces taxable income by $1 and consequently
reduces the liability of taxpayers in the 39.6-percent bracket and 25-percent
bracket, respectively, by 39.6 cents and 25 cents. In an effort to improve tax
fairness, improve efficiency, and reduce the deficit, the President has proposed to reduce the tax value of selected tax expenditures to 28 percent for
high-income taxpayers, a level comparable to the tax value provided by the
tax code for middle-income taxpayers.
1 Behavior that reduces tax remittances without altering real investment, savings, or labor
decisions is called tax avoidance when it is legal and tax evasion when it is illegal.

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Box 3-1: Estimates of Tax Expenditures in the President’s Budget
Tax expenditures, commonly viewed as government spending
through the tax code, are defined in the Congressional Budget Act of
1974 as “revenue losses attributable to provisions of the Federal tax laws
which allow a special exclusion, exemption, or deduction from gross
income or which provide a special credit, a preferential rate of tax, or a
deferral of tax liability.”
Each year the Treasury Department estimates the value of tax
expenditures in terms of the Federal income tax loss and reports the
estimates in the annual Budget of the United States Government.1 Table
17-1 of the President’s fiscal year 2013 Budget lists 173 corporate and
individual income tax expenditures in the tax code. Tax expenditures
take many different forms:
• Exclusions and exemptions allow specific types or sources of
income—such as compensation received as medical insurance or interest
from municipal bonds—to be excluded or exempt from income for tax
purposes.
• Deductions permit taxpayers to deduct certain types of expenses
from income to calculate the taxable base. Examples include itemized
deductions (which include deductions for home mortgage interest,
charitable giving, State and local taxes, and medical expenses) and
“above-the-line” deductions (which include deductions for student loan
interest, self-employed retirement and health insurance contributions,
and educators’ out-of-pocket expenses).
• Tax credits reduce tax liability by the amount of the credit. When
the amount of a tax credit exceeds tax liability before the credit is applied,
the credit will erase the tax liability, and, if the credit is refundable, the
government will pay the filer the excess amount. In the Federal Budget,
the portion of a refundable credit that reduces tax liability is treated as
a revenue loss, and the portion that exceeds tax liability is treated as an
outlay.
• Special rates apply a lower tax rate to specific sources of income
than the rate applied to ordinary income. For example, long-term capital
gains and qualified dividends are taxed at lower rates than ordinary
income.
• Deferrals permit taxpayers to delay including certain income in
the taxable base. Such tax expenditures include accelerated depreciation
1 The Joint Committee on Taxation also annually publishes a list of tax expenditures. Tax
expenditure estimates do not equal the amount of revenue that would be generated if the
expenditure were eliminated for two reasons: first, eliminating a tax expenditure would
result in behavioral effects that could offset the revenue gain; second, removing multiple
tax expenditures simultaneously creates interaction effects that depend on the particular
expenditures.

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or immediate expensing of business investment as well as tax incentives
for retirement saving.
Table 17-3 of the FY 2013 Budget ranks tax expenditures by projected revenue effect. The 10 largest tax expenditures by the projected
revenue effect for 2013–2017 are:2
• Exclusion of employer contributions for medical insurance premiums and medical care ($1,012 billion)
• Deductibility of mortgage interest on owner-occupied homes
($606 billion)
• 401(k)-type plans ($429 billion)
• Accelerated depreciation of machinery and equipment ($375
billion)
• Exclusion of net imputed rental income on owner-occupied
housing ($337 billion)
• Special rates for capital gains ($321 billion)
• Defined benefit pension plans ($298 billion)
• Deductibility of State and local taxes other than on owneroccupied homes ($295 billion)
• Deductibility of charitable contributions, other than education
and health ($239 billion)
• Exclusion of interest on public purpose State and local bonds
($228 billion).
2 The estimates do not include effects on Federal outlays. Refundable tax credits, such as the
Earned Income Tax Credit and the Child Tax Credit, can carry significant outlay effects.

The preferential rate on capital gains and dividends gives rise to tax
benefits because these sources of income are taxed at a lower rate than ordinary income.2 Of the selected tax expenditures in Figure 3-6, the benefits of
the preferential tax rate on capital gains and dividends are most skewed to
the upper end of the income distribution. The underlying tax data for Figure
3-6 suggest that taxpayers in the top 0.1 percent of the income distribution
receive 41 percent of the total positive capital gains realizations and qualified
dividends. Because of this unequal distribution of capital gains realizations
and qualified dividends, the preferential rate provides substantially more
benefit to the top 0.1 percent of taxpayers than to taxpayers in any other
income class.
2 One argument for the preferential rate is that corporations already pay income taxes so
individual income taxes on capital gains and dividends result in double taxation. However,
evidence shows that not all of the long-term capital gains are attributable to corporate stocks
or mutual funds, and therefore some capital gains are never taxed at the corporate level
(Wilson and Liddell 2010; Burman 2012).

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Figure 3-6
Distribution of Benefits of Selected Tax Expenditures, 2013

Change in after-tax cash income, percent
15
Preferential rate on capital gains and dividends
Deductibility of State and local taxes
12
Deductibility of charitable contributions
Deductibility of home mortgage interest
9
Exclusion of employer-provided health insurance
EITC and Child Tax Credit
6
3
0

0–20

20–40 40–60 60–80 80–90 90–95 95–99 99–99.9 Top 0.1
Pre-tax cash income percentile adjusted for family size

Note: Estimates are the percentage reduction in after-tax cash income (2013 income levels
under current law, including ATRA) from eliminating each tax expenditure. Families with
negative incomes are excluded from the lowest income class.
Source: Department of the Treasury, Office of Tax Analysis calculations.

Vertical Equity
Vertical equity holds that individuals who have a greater ability to
pay should contribute more in taxes than those who are less able to pay
(for a discussion of tax fairness, see Economics Application Box 3-1). The
President has called one specific formulation of this idea, the Buffett Rule,
a basic principle of tax fairness. The Buffett Rule states that no household
making over $1 million should pay a smaller share of income in taxes
than middle-class families pay. Several studies have shown that the current tax system violates the Buffett Rule; many high-income families pay a
smaller share of income in Federal taxes than do middle-income families
(Hungerford 2011; CEA 2012; Cronin, DeFilippes, and Lin 2012). Thus,
implementing the Buffett Rule, or adopting the rule as a guiding principle
for tax reform, would improve tax fairness.
While the current Federal tax system is progressive, its progressivity has significantly declined since the 1960s. Figure 3-1 above shows that
average tax rates for middle-income taxpayers rose slightly in the 1960s and
the 1970s and then remained relatively stable since the 1980s. By contrast,
Federal tax burdens for the wealthiest taxpayers have dropped dramatically
since 1960 as a result of changes in tax laws. The share of income the top 0.1
percent paid in Federal individual income and employment taxes fell to 24.1
percent in 2012, about half of what this group paid in 1960.
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Economics Application Box 3-1: Marginal Tax Rates
and Average Tax Rates on Individual Income
Marginal and average tax rates are two tax rates commonly used
to describe a tax system and to measure the fraction of income people
pay in taxes. A statutory marginal tax rate for an income tax is the tax
rate specified by law and applied to one additional dollar of taxable
income. A tax system may consist of multiple statutory rates, with each
applying to a range of taxable income to form a tax bracket. A taxpayer’s
statutory marginal tax rate thus depends on the tax bracket in which her
taxable income falls. An effective marginal tax rate is the fraction of an
additional dollar of income a taxpayer actually pays to the government.
The effective marginal tax rate is determined by the statutory rate as
well as by other tax provisions, such as phase-ins or phase-outs of tax
credits. An average, or effective, tax rate is the fraction of a taxpayer’s
total income that is owed as tax liability. The share of total income paid
in taxes indicates the tax burden faced by a taxpayer.
One criterion for evaluating tax systems is fairness. Economics
provides useful tools to help evaluate a tax system’s fairness. Two
important concepts are horizontal and vertical equity. Horizontal equity
means equal treatment of equals, which is commonly interpreted as
equal treatment of those with an equal ability to pay; vertical equity holds
that those who have a greater ability to pay should contribute more in
taxes than those who are less able to pay. To evaluate vertical equity, a
tax can be classified as being proportional, regressive, or progressive.
A tax is proportional if average tax rates are equal for taxpayers at all
income levels. A tax is regressive if average tax rates fall with income,
and a tax is progressive if average tax rates increase with income. Under
a progressive tax system, high-income taxpayers face a larger tax burden
than low-income taxpayers. This notion is long ingrained in economics.
In fact, endorsing progressive taxes, Adam Smith wrote in The Wealth of
Nations that “it is not very unreasonable that the rich should contribute
to the public expense, not only in proportion to their revenue, but something more than in that proportion.”

Figure 3-7 depicts the trends in effective marginal tax rates on wage
income. As shown, effective marginal tax rates faced by middle-income taxpayers have been relatively constant during the past five decades, in contrast
with the dramatic decline in the effective marginal tax rates faced by the top
1 percent or 0.1 percent of taxpayers. In other words, taxpayers at the top
of the income distribution have always faced higher marginal tax rates on
wage income than middle-income taxpayers, but the spread between their
marginal tax rates has narrowed significantly since 1960. Before ATRA was
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Figure 3-7
Effective Marginal Tax Rates on Wage Income for Selected Income Groups
Under a Fixed Income Distribution, 1960–2013

Average effective marginal tax rate, percent
90
80

Top 0.1 percent

70
60
50

Top 1 percent

40
30

2013

Middle 20 percent

20
10
0
1960

1965

1970

1975

1980

1985

1990

1995

2000

2005

2010

Note: Average effective marginal Federal (individual income) tax rates on wage income for a 2005
sample of taxpayers after adjusting for growth in the National Average Wage Index.
Source: Internal Revenue Service, Statistics of Income Public Use File; National Bureau of Economic
Research, TAXSIM (preliminary for 2012 and 2013); CEA calculations.

enacted, the top effective marginal rate on wage income was close to its
lowest level in the past five decades; there was only a short period in the late
1980s and early 1990s when the top effective marginal tax rate was lower
than the rate in 2012.
As noted, the preferential rate on long-term capital gains is particularly regressive, and evidence suggests that capital gains realizations have
become more concentrated over time. The portion of total capital gains
realized by the 0.1 percent of taxpayers who reported the most capital gains
income increased from 25 percent in 1987 to over 40 percent in 2010 (Lurie
and Pearce 2012). Relative to the increased income concentration, the top
effective marginal tax rate on long-term capital gains declined during the
period (Figure 3-8). The rate ranged between 20 percent and 30 percent
from the 1980s to the early 2000s, fell to 16 percent in 2003, and fell further
to 15 percent in 2010 because of the scheduled elimination of the phase-out
of itemized deductions under the 2001 tax cut. The rate rose to 25 percent
in 2013.
In addition to individual income and employment taxes, the Federal
Government collects corporate income taxes and estate taxes. Piketty and
Saez (2007) examined the combined effect on vertical equity of Federal
individual, employment, corporate, and estate taxes from 1960 to 2004.
They argued that corporate and estate taxes substantially contributed to a
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Percent
100

Figure 3-8
Top Marginal Tax Rates, 1960–2013

90

Top statutory
marginal rate on
ordinary income

80
70
60
50
40

Top effective
marginal rate on longterm capital gains

2013

30
20
10
0
1960

1970

1980

1990

2000

2010

Note: The top rate on qualified dividends is equal to the top rate on ordinary income until 2003;
thereafter, it is equal to the top rate on long-term capital gains. The top marginal rates on long-term
gains calculated by Treasury include the effects of the Alternative Minimum Tax (AMT) and the
phase-out of itemized deductions.
Source: Internal Revenue Service, Statistics of Income; Department of the Treasury, Office of Tax
Analysis; CEA calculations.

more progressive tax system in 1960 than in 2004. Because the wealthiest
taxpayers own a disproportionately large share of the nation’s capital income
and wealth, they bear the largest burden of the corporate income and estate
taxes.3 The Federal Government, however, has shifted away from relying on
these two Federal taxes as revenue sources, leaving taxpayers at the top of
the income distribution with a much lower tax burden in 2004 than in 1960.
As shown in Figure 3-9, corporate tax revenues as a percent of total Federal
receipts declined from 23.2 percent in 1960 to 10.1 percent in 2004. The
share for estate and gift taxes declined modestly from 1.7 percent in 1960 to
1.3 percent in 2004 (OMB 2012b).

Efficiency and Simplification
From the current point of a complex tax code with many special provisions, simultaneously eliminating special provisions and lowering tax rates
could make the tax code both simpler and more efficient. Cutting unfair and
3 Piketty and Saez (2007) assume the burden of the corporate income tax falls on owners of
capital income. Several tax policy groups, including the Treasury Department’s Office of Tax
Analysis, the Congressional Budget Office, and the Tax Policy Center, assume in their current
tax models that the majority of the corporate tax burden—about 80 percent—is borne by
capital income, whereas the remainder is borne by labor. Cronin et al. (2013) provide details of
the different corporate tax incidence assumptions.

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inefficient tax breaks and simplifying the tax system with lower tax rates are
among the principles the President set forth for tax reform. High tax rates,
coupled with a narrow tax base, cause taxpayers to adopt economically
inefficient behavior. When examining the efficiency gains from tax reform,
it is important to identify the behavioral margins that are in response to
changes in tax policy and the resulting economic effects. In theory, lowering tax rates can lead to an increase in labor supply (or a decrease in labor
supply if the income effect dominates the substitution effect), but evidence
suggests that, when tax rates change, labor supply effects are small compared
with tax avoidance effects (Saez, Slemrod, and Giertz 2012). One such effect
occurs when investors delay realizing capital gains and hold onto assets only
to avoid capital gains tax. Despite this inefficient “lock-in” effect, negative
associations between top individual income tax rates on capital gains and
private saving, investment, or changes in real GDP are not supported by U.S.
experience (Hungerford 2012; Burman 2012).
When taxpayers make decisions in response to special provisions in
the tax code, they engage in more of the tax-preferred activity than they
would otherwise, thereby steering resources away from other more productive uses.4 One major unfair and inefficient tax break is the tax treatment of
partners’ profits interests, also known as carried interests, in an investment
partnership. Carried interests, despite being derived from performance of
labor services, receive capital gains treatment. This preferential tax treatment provided for income derived from performing a specific activity
induces a behavioral distortion and is economically inefficient. To improve
fairness and efficiency of the tax code, the Administration has proposed to
tax carried interests as ordinary income and subject that income to selfemployment taxes.
In addition, the Administration has proposed to improve the tax
code’s efficiency by closing business loopholes and broadening the business
tax base. For example, corporations currently use life insurance as a form
of tax shelter because of its favorable tax treatment. Investment returns on
life insurance products are allowed to accumulate tax free until policies are
cashed in. As a result, businesses can take interest deductions for investmentoriented life insurance policies that cover their officers and employees before
any gain is realized—and taxed—on the policies. The Administration’s
recent Budget would close this loophole and encourage businesses to make
more efficient investment decisions by limiting the interest deductions allocable to investment in certain life insurance policies.
4 If the tax-preferred activity is underconsumed or underproduced because of market failures
or externalities, then a favorable treatment could increase quantity and result in more efficient
allocations of resources.

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Percent of total
100
90

Figure 3-9
Composition of Federal Receipts, 1960–2011
Other

80

Social insurance taxes

70
60
50

Corporate income tax

40
30
20

Individual income tax

10
0
1960

1970

1980
1990
2000
Fiscal year
Note: Other includes excise taxes, estate taxes, customs duties, and other receipts.
Source: OMB (2012b).

2010

The President has also proposed making the Federal subsidy for State
and local governments’ borrowing costs more efficient by extending Build
America Bonds (BABs), in which the Federal Government makes direct
payments to State and local governments. Traditional tax-exempt bonds
provide a Federal subsidy through a Federal tax exemption to investors for
interest income received from the bonds. One study finds that as much as 20
percent of the tax revenue the Federal Government forgoes from tax-exempt
bonds accrues to investors, leaving only 80 percent of the subsidy to benefit
State and local governments (CBO/JCT 2009).
Complexity is another source of inefficiency in the tax code because
it increases the amount of time and money taxpayers spend to comply with
the law and creates opportunities for them to engage in the unproductive
activity of tax avoidance. It is estimated that complying with the Federal
income tax cost businesses at least $100 billion for tax year 2009 (Contos et
al., forthcoming) and individuals over $50 billion for tax year 2010,5 with
the total costs amounting to approximately 1 percent of GDP. Estimating
the time and monetary costs incurred by taxpayers for preparing individual
income tax returns, an analysis by the Internal Revenue Service (IRS) shows
5 The IRS estimates of the business and individual income tax compliance costs include outof-pocket costs and the monetized burden associated with the time spent on preparing the
returns.

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sources of individual income tax compliance costs by reporting activity
(Figure 3-10).6 More than half—55 percent—of compliance costs arise from
keeping track of and reporting income, and the remaining compliance costs
arise mostly from calculations for tax deductions and credits. Thus, tax
simplification—such as having fewer deductions and credits or streamlining income reporting—has the potential to reduce compliance burdens.
Tax simplification could also enhance taxpayer compliance by reducing the
opportunities for tax evasion and decreasing inadvertent taxpayer errors in
calculating tax liabilities (Kopczuk 2006).7

Reforming the International Corporate Tax
The international provisions of the corporate tax code create opportunities for U.S. companies to reduce their taxes by locating their operations
and profits abroad. The tax system is subject to gaming, as corporations
manipulate complex tax rules to minimize taxes and, in some cases, shift
profit that is attributable to activity performed in the United States or elsewhere to low-tax jurisdictions.
The current U.S. tax system subjects foreign subsidiaries of U.S.based multinationals to taxes on their overseas income while allowing a tax
credit for foreign taxes paid. However, corporations often do not need to
pay taxes to the Federal Government on that income until they repatriate
it to the United States, a rule called deferral (because it defers taxation of
the income). Many companies reinvest, rather than repatriate, a significant
portion of their income overseas and, as a result, may never face U.S. taxes
on much of that income. The U.S. tax system is often described as “worldwide” because it taxes U.S. companies on profits earned abroad. For many
companies, however, opportunities for deferral can make it effectively much
closer to a territorial system—a system in which taxes are never paid on
foreign income. By contrast, although most other developed countries have
taken a territorial approach, some countries, including Japan and the United
Kingdom, have implemented tax “triggers” that effectively apply worldwide
taxation if a multinational is operating in a low-tax country.
U.S. multinational corporations have a significant opportunity to
reduce overall taxes paid by shifting profits to low-tax jurisdictions—either
by moving their operations and jobs there or by relying on accounting
tools and transfer pricing principles to shift profits. Studies show that U.S.
6 Under current law, the IRS is authorized access to Federal tax information for tax
administration purposes. Certain Federal agencies have limited access to tax data for
governmental statistical use. See Data Watch 3-1.
7 For example, studies have shown that complexity may have affected EITC compliance and
kept eligible taxpayers from claiming the tax credit (Holtzblatt and McCubbin 2004; Kopczuk
and Pop-Eleches 2007).

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Figure 3-10
Individual Income Tax Compliance Costs by Reporting Activity, 2010
Other tax-related
reporting
Deductions
Credits

Income reporting
2%

Wages

4%
18%

14%

AMT

Self-employment
income
Other income

Other taxes

19%
25%

18%
Note: Tax year 2010. The cost of reporting the self-employment tax deduction is included in Other
taxes.
Source: Internal Revenue Service, Office of Research, Analysis, and Statistics calculations.

multinationals’ decisions about the choice of where to invest are sensitive to
effective tax rates in foreign jurisdictions (OECD 2008). Evidence also suggests that U.S. firms’ reported profits in a foreign country increase when the
country’s tax rate declines relative to the U.S. rate, after taking into account
other factors that would have influenced the level of income earned by U.S.
firms in that foreign country (Clausing 2009; Grubert 2012).
The incentive to shift profits to low-tax jurisdictions can lead to inefficient overinvestment abroad and underinvestment in the United States. It
can also erode the U.S. tax base, requiring higher tax rates on income that
remains taxable in the United States to collect the same amount of revenue.
Finally, the international tax system is very complex, which not only burdens companies with complicated accounting and tax requirements but also
benefits companies that avoid paying taxes by manipulating intricate rules.
Business tax reform should be a foundation to maximize investment,
growth, and jobs in the United States. It should properly balance the need
to reduce tax incentives for U.S. companies to locate overseas with the need
for them to be able to compete overseas; some overseas investments and
operations are necessary to serve and expand into foreign markets in ways
that benefit U.S. jobs and economic growth. The President has proposed to
protect the U.S. tax base, strengthen the international corporate tax system,
and encourage domestic investment by establishing a new minimum tax on

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Data Watch 3-1: Federal Tax Information and
Synchronization of Interagency Business Data
Each year, the Internal Revenue Service (IRS) collects tax data from
hundreds of millions of taxpayers. During fiscal year 2011, more than
200 million individual income, employment, corporate income, and
estate tax returns and 1.8 billion third-party information returns, such
as W-2 and 1099 forms, were filed with the IRS (IRS 2012). Successful
tax administration builds on taxpayers’ willingness to share personal
information with the tax authority and voluntarily comply with tax law
(Greenia and Mazur 2006). To ensure taxpayer confidence in the tax
system, the tax code contains provisions to safeguard taxpayer confidentiality by requiring each access to Federal tax information (FTI) to be
authorized by law.
Under current law, access to FTI is authorized within the IRS
for tax administration purposes; in other limited cases, disclosures of
FTI are allowed only for specified information to specific parties for
specific tasks. When considering whether to amend the law to authorize
a disclosure of FTI, Congress should evaluate several factors, including the potential benefits resulting from the data usage and the risk of
compromising taxpayer confidentiality or affecting their willingness to
voluntarily comply with tax law.
Tax law currently authorizes disclosure of business FTI for government statistical use. It authorizes disclosure of business FTI—either
for corporate or noncorporate businesses—to the Census Bureau but
permits disclosure of business FTI to the Bureau of Economic Analysis
(BEA) only for corporate businesses. Another Federal statistical agency,
the Bureau of Labor Statistics (BLS), currently does not have access to
any business FTI. The Census Bureau uses business FTI to construct its
business list, and therefore many Census data products are considered
to be “comingled” with tax information (Pilot 2011). Because of the
access limits on BEA and BLS, the Census Bureau cannot share many of
its products with these two agencies, a situation that prevents the three
Federal statistical agencies from synchronizing their business data.
Business data are the fundamental elements for measuring national
and local economic activity. National and local statistics on income,
output, productivity, payroll, and employment are all based on business
data collected by these Federal statistical agencies. Policymakers and
businesses rely on these statistics to guide their decisionmaking. Thus,
improving the accuracy, consistency, and reliability of national and
local economic statistics can yield tremendous benefits because policy
formation and business decisionmaking will be based on better quality
economic statistics.

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Greater synchronization of interagency business data could advance
the quality of economic statistics. For example, BLS and the Census
Bureau currently have different coverage and classifications in their
business data. BEA’s National Income and Product Accounts (NIPA)
produce two measures of national economic activity: gross domestic
product (GDP, which uses Census Bureau data as its primary source
data) and gross domestic income (GDI, part of which uses BLS data).
The two measures of national economic activity differ in part because
of discrepancies in the underlying business data. Allowing Federal
statistical agencies to share and coordinate business data would help to
reconcile these discrepancies and thereby result in a better measurement
of economic activity.

income earned by subsidiaries of U.S. corporations operating abroad (White
House/Treasury 2012). That requirement would stop the tax system from
rewarding companies for moving profits offshore. Thus, foreign income
in a low-tax jurisdiction would be subject to immediate U.S. taxation up to
the minimum tax rate, with a foreign tax credit allowed for income taxes
on that income paid to the host country. At the same time, this minimum
tax would be designed to keep U.S. companies on a level playing field with
competitors when engaged in activities that, by necessity, must occur in a
foreign country.

The State and Local Budget Outlook
State and local government expenditures have continued to rebound
from the challenges created by the Great Recession, although many State
and local governments have yet to return to their pre-recession spending
and investment levels. State general fund spending grew by 1.6 percent in
real terms in FY 2012, after a small 0.6 percent drop in FY 2011 (NASBO
2012a). In the two previous fiscal years, State general fund spending shrunk
dramatically, falling by 2.6 percent in FY 2009 and 8.0 percent in FY 2010
(Figure 3-11); the real gain since 1979 has averaged 1.6 percent a year.
As local economic conditions have rebounded, fiscal distress faced by
States has abated, although challenges remain. One such indicator of fiscal
distress is the need to institute midyear budget cuts in response to lowerthan-expected revenues or higher-than-expected outlays. In FY 2012, just 8
States made midyear budget cuts ($1.7 billion total), down from 23 States in
FY 2011 ($7.8 billion), 39 States in FY 2010 ($18.3 billion), and 41 States in
FY 2009 ($31.3 billion).

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Figure 3-11
Real Annual Changes in State General Fund Spending, 1981–2012

Percent
10
8
6
4
2
0
-2
-4
-6
-8
-10
1980

1984

1988

1992

1996
2000
2004
2008
2012
Fiscal year
Note: Changes are adjusted for inflation using the state and local government implicit
price deflator.
Source: NASBO (2012a).

Like State spending, local government expenditures fell sharply during the recession. Constrained by lower revenues, cities cut back on spending
more than they have in 25 years (National League of Cities 2012). General
fund expenditures dropped at least 4 percent in both FY 2010 and FY
2011, almost twice as much as they did following the recession in FY 2001.
Asked how they plan to change expenditures in FY 2012, local government
budget officers most often said they would reduce the size of the municipal
workforce, followed by delays or cancellations of capital infrastructure projects. The National League of Cities projected that expenditures will finally
increase in FY 2012, but only by 0.3 percent, because local government revenues have yet to grow since the recession (National League of Cities 2012).
On the revenue side, State general fund tax revenues are poised to
increase by $26.1 billion in FY 2013 after increasing by $16.6 billion in
FY 2012. In nominal terms, general fund revenues are set to surpass prerecession levels for the first time in FY 2013. The reason for this jump several
years after the onset of the national recovery is that State revenues follow a
cyclical pattern with macroeconomic growth but often do so with a lag.
Local government tax receipts were also decimated by the recession
and have yet to rebound. A projected decrease in city general fund revenues for FY 2012 will mark the sixth consecutive year of year-over-year
decreases in revenues, and city budget officers will continue to face lingering

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challenges. Each of the primary tax streams used by local governments—
property taxes, sales taxes, and income taxes—was affected by the economic
downturn. Sales tax revenues dropped sharply and first, as consumers cut
back on purchases. In 2011 and 2012, however, city sales tax receipts started
to rebound, with sales tax revenues increasing year-over-year in both years
(Figure 3-12). Because home values fell, cities—many of which rely heavily
on property taxes—faced another area of shrinking revenue. The decline in
property tax collections came with a lag, however, probably because of the
time needed for lower prices to translate into lower assessed values. Property
tax receipts fell in 2010 and 2011 and will continue to pose challenges for
strapped local governments. Home prices have started to recover, but slowly.
Finally, local governments also face lower income tax receipts as unemployment challenges persist.

The Cyclicality of State and Local Government Expenditures
Particular types of State and local government spending are more
sensitive to cyclical factors than others. For example, when economic
conditions deteriorate, spending on “automatic stabilizers”—programs like
Medicaid that provide means-tested benefits—increases. While automatic
stabilizers are widely recognized as being countercyclical, less attention has
Figure 3-12
Year-to-Year Change in City General Fund Tax Receipts, 2005–2012

Percent
10
8

Sales taxes

Income taxes

Property taxes

2008

2010

6
4
2
0
-2
-4
-6
-8
-10

2005

2006

2007

2009

Fiscal year
Source: National League of Cities (2012).

2011

2012
(budgeted)

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been paid to the cyclical behavior of public investment spending. One study
by the Government Accountability Office (GAO 2011) examined trends in
State and local government spending across the business cycle and found
that capital expenditures—primarily spending on land, buildings, and
equipment—are more procyclical than other types of spending (Table 3-1).
The GAO found that spending on health and public welfare is countercyclical, while current expenditures on elementary and secondary education,
current expenditures on highways, and capital outlays are the most procyclical categories of State and local government spending. The GAO noted that
trends in capital outlays and current expenditures tend to lag the business
cycle by one to two years, although there is substantial variation in the lag
for current expenditures by type.
Private economists have reached similar conclusions. Echoing the
GAO finding, Wang, Hou, and Duncombe (2007) studied the determinants
of capital spending, noting that capital expenditures tend to be more procyclical than current expenditures. The authors cited evidence that States’ and
municipalities’ financing decisions are affected by the business cycle, but
the study did not draw conclusions about the impact of the business cycle
on the level of capital spending. Similarly, McGranahan (1999) found that
capital spending is more procyclical than current expenditures. On average,
McGranahan found that each percentage point increase in the unemployment rate leads to a $6.94 fall in per capita capital outlays (average per capita
spending is $239.85); this drop is split evenly between construction spending
($3.57) and other capital outlays ($3.37). Moreover, McGranahan found that
even though State operating budgets do not include capital expenditures,
States tend to reduce budgetary pressure by reducing capital spending during downturns. Hines, Hoynes, and Krueger (2001) found that all components of State and local government spending are procyclical, with capital
spending (on highways, parks, and recreation, for example) generally more
procyclical than current spending (on health and education, for example).
Bureau of Economic Analysis (BEA) data on State and local expenditures show that the most recent recession was somewhat atypical, with gross
investment failing to rebound as in other recoveries (see Figure 3-2 above).
Ideally, State and local governments would increase investment spending
during recessions, both as a means of employing capital and labor, thereby
helping to drive the economy out of the recession, and also as a mechanism
for strengthening the economy in the future. Moreover, lower labor costs
during recessions make capital projects relatively cheap, meaning that
investment during recessions can provide taxpayers with a higher return
on investment; historically low interest rates in recent years have further
lowered the cost of capital projects. Greater investment by State and local
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Table 3-1
Cyclical Behavior of State and Local Government Expenditures, 1977–2008
Correlation with
GDP

Cyclical behavior

General expenditures

0.34

Procyclical

Capital outlays

0.50

Procyclical

Current expenditures

0.23

Procyclical

Elementary and secondary education

0.60

Procyclical

Higher education

0.29

Procyclical

Health and hospitals

-0.36

Countercyclical

Highways

0.53

Procyclical

Police and corrections

0.38

Procyclical

Public welfare

-0.31

Countercyclical

All other current expenditures

0.40

Procyclical

Expenditure function

Source: GAO (2011).

governments in the most recent recession would have both contributed to
the recovery and built a stronger economy in future years at a relatively low
cost.
Despite the downturn in investment spending relative to past recessions, the procyclical nature of State and local fiscal policy means that
Federal policies can prove particularly effective at mitigating the economic
effects of a downturn. State and local governments serve a vital role in providing services to their residents, and the Federal Government contributes
to that role by aiding State and local governments through grants, loans, and
implicit support through the tax system.
Federal grants-in-aid—which include both cash grants and grants
in-kind—have been expanding over time.8 In constant dollars (FY 2005),
Federal grants to State and local governments increased from $45.3 billion in
1960 to an estimated $504.4 billion in 2012 (Figure 3-13). The composition
of Federal grants to State and local governments has changed dramatically as
well. In 1960, 35.3 percent of Federal grants were for payments to individuals, 47.3 percent were for physical capital, and 17.4 percent were for other
uses. As projected, in 2012, the share of grants for payments to individuals
grew to 60.2 percent, while the share for physical capital fell to 15.7 percent,
and the share for other uses grew to 24.1 percent. Thus, over the past five
decades, the share of Federal grants for physical capital has plummeted while
the share devoted to individual payments has skyrocketed.
8 Federal grants generally fall into one of two broad categories: categorical grants or block
grants. In addition, these grants may have characteristics of one or more other types of grants:
formula grants, project grants, and matching grants. Categorical grants have a narrowly
defined purpose and may be awarded on a formula basis or as a project grant.

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Figure 3-13
Federal Grants to State and Local Governments by Type, 1960–2012

Billions of FY 2005 dollars
600
Other grants
500

Physical capital
Payments for individuals

400
300
200
100
0

1960 1965 1970 1975 1980 1975 1990 1995 2000 2005 2011 2012
Fiscal year
Note: Grants that are both payments for individuals and capital investment are shown
under capital investment. Figures for FY 2012 are estimates.
Source: OMB (2012a).

Federal Grants to States Through the Recovery Act
The Federal Government used the existing grants structure to provide
swift fiscal relief during the recent recession—a time when states faced severe
and unforeseen economic conditions. It did so through the Recovery Act,
which provided enhanced grant funding in the areas of education, Medicaid,
transportation, energy, water, and other programs.9 Most provisions of the
Recovery Act expired in 2010, but some were extended in August 2010 by
Public Law 111-226, an act providing education and Medicaid assistance to
the States. The temporary fiscal relief provided by the Recovery Act accounts
for most of the $141.1 billion increase in Federal outlays for grants-in-aid to
States from 2008 to 2010. In 2011, Federal grant outlays were $606.8 billion;
this was a $1.6 billion decrease from 2010, reflecting the expiration of the
temporary increase in the Federal share of State Medicaid costs and other
provisions of the Recovery Act. Grant outlays for 2012 are estimated to
increase by $5.7 billion to $612.4 billion.
However, outlays from grants funded through annual appropriations
are estimated to decrease by $24.9 billion in 2012 from the previous year
and to decrease again by $20.5 billion in 2013. These decreases reflect the
9 In addition to grant funding to States, the Recovery Act created Build America Bonds, which
provided State and local governments a lower-cost borrowing tool to finance public capital
projects. Authority to issue Build America Bonds expired at the end of 2010.

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Chapter 3

winding down of discretionary grant spending on Recovery Act programs
such as the State Fiscal Stabilization Fund as well as the enactment of caps on
discretionary spending in the Budget Control Act of 2011, which constrains
appropriations of new discretionary budget authority, including appropriations for grants.
By transferring aid to State and local governments, the Recovery Act
helped stabilize programs that would have been cut and kept States and
localities from having to institute tax increases. Had the Recovery Act not
provided grants-in-aid to State and local governments, these governments
would have been forced either to make deeper cuts in funding for important
public programs, including critical education and health programs (and the
associated jobs to support those programs), or to raise taxes to compensate
for the shortfall. Either option would have been detrimental to the economic
recovery. The billions of dollars provided to State and local governments
were one of the reasons the Recovery Act was able to dampen the recession
and put the country on a faster track to recovery.

State and Local Pensions
State and local pension plans are an important part of the nation’s
retirement security framework, promising future retirement benefits to
14.5 million workers employed by State and local governments in 2011
(Census Bureau 2012). About 19 percent of total employer contributions
to employee retirement plans were made through State and local pension
plans, and approximately 28 percent of all plan assets were accounted for
by State and local pensions (CBO 2011). Pension plan contributions make
up a significant component of the compensation provided to State and local
government workers, including police officers, firefighters, and teachers.
Most State and local plans are defined benefit plans, which provide
workers with a designated benefit based on years of service and final salary.10 For example, a worker covered by a defined benefit plan might earn
benefits equal to 2 percent of wages (often measured over the last several
years of employment) multiplied by years of work and adjusted for inflation. The structure of defined benefit plans means that employer liability
grows as workers earn wages and increase their tenure with State and local
governments; this liability can also grow with inflation because the value
of a defined benefit plan is often indexed to the cost of living. From this
10 Defined benefit plans are fundamentally different from defined contribution plans, which
allow workers to contribute to an individual retirement account and often offer some form
of an employer match. Defined contribution plans do not provide workers with a designated
retirement benefit; rather, the individual account balance grows with new contributions and
investment returns.

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perspective, defined benefit plans can be viewed as a form of deferred compensation, with workers reaching retirement age being owed compensation
earned earlier in their career.
Defined benefit programs offer workers a steady stream of income
for life, thus providing insurance against outliving assets and investment
risk. One drawback to these plans, however, is the problem of underfunding, which presents a serious long-term fiscal challenge for State and local
governments. Underfunding arises when the accumulated contributions in
State and local government pension accounts are insufficient to cover the
expected liabilities owed to public sector workers. The Pew Center on the
States estimated that the public pension programs of State and local governments were underfunded by $757 billion in FY 2010, carrying $3.07 trillion
in liabilities and $2.31 trillion in assets (Pew Center on the States 2012).
Another study showed that the ratio of State and local pension fund assets
to liabilities declined from 103 percent in 2000 to 75 percent in 2011, due
in large part to market trends and the specific accounting rules adopted by
most plans to value assets (Munnell et al. 2012a). While aggregate funding
levels have decreased over the past decade, funding adequacy varies considerably from state to state.
Alternative approaches to calculating pension funding suggest even
lower levels of funding adequacy. Unlike private pension systems, which
are governed by Federal law and regulations, no Federal rules apply to
State and local plans in determining plan liabilities and required contributions. Most States and local pension plans adhere to guidelines drafted by
the Governmental Accounting Standards Board (GASB) to report funding
adequacy, but the board does not have enforcement authority, nor can it
require States and localities to adopt specific funding policies. Until June
2012, GASB standards allowed plans to use discount rates based on the
expected rates of return—typically around 8 percent—to determine pension liabilities. Under this approach, pension underfunding was about $700
billion at the end of 2009 (CBO 2011), consistent with the Pew Center’s
estimate. In sharp contrast, CBO found that a broader measure of liabilities
that uses the fair value discount rate, an approach often applied in corporate
accounting, produces an underfunding estimate of $2 trillion to $3 trillion.
Low levels of funding threaten the welfare of both taxpayers and State
and local government employees. One concern is that underfunded pensions
will dominate State and local government budgets in upcoming decades, as
an increasingly high share of revenue may be needed to provide retired
government workers with promised benefits. If taxpayers must devote
higher revenue to paying promised benefits to retired workers, less funding
may be available for key programs like elementary education, health care,
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Chapter 3

and infrastructure development. From another perspective, underfunded
pensions may also pose a risk to government employees, who may see their
benefits challenged as a means of achieving cuts in government spending.
Increased transparency in the budget process is a key step toward
improving the adequacy of State and local pension funding. One important
strategy often proposed to increase transparency is for State and local governments to adopt discount rates for liabilities that accurately portray the
magnitude of their promised obligations. Critics of the old GASB discount
rate argued that the high discount rate of around 8 percent ignored the role
of asset risks in calculating the present value of future promised benefits.
Economists often argue that pension liabilities should be discounted by the
riskless rate of return because the payments to retired workers will be made
with certainty (Novy-Marx and Rauh 2011).11
Under the new discount method approved by GASB, plans will project
the portion of pension liabilities that are backed by underlying plan assets
(that is, the funded portion) and the portion of liabilities that need to be covered by other resources (that is, the unfunded portion). The new standards
allow States and localities to use a roughly 8 percent discount rate for funded
liabilities but require the use of a riskless discount rate for pension liabilities
that are unfunded (NASBO 2012b). With the new GASB standards, the
estimated funding ratio of State and local pension plans would have been
57 percent in 2010, markedly lower than the 76 percent estimated under
the previous method (Munnell et al. 2012b).12 Once State and local pension
underfunding is better understood through heightened reporting transparency, State and local governments might be more willing to undertake difficult financial decisions and pension reforms to shore up their pension plans.

11 In a sample of 77 municipal plans, the discount rate ranged from 7.5 percent to 10.0 percent,
with a median of 8.0 percent (Novy-Marx and Rauh 2011).
12 This rate change incorporates the effects of the new discount method and other pension
accounting reforms approved by GASB.

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C H A P T E R

4

JOBS, WORKERS AND SKILLS

T

he future of the American economy depends critically on our workers
and their skills, especially in today’s global economy. For the past three
decades, American workers have faced a challenging job market. Computers
and robots now perform routine tasks, reducing demand for workers in
many industries and occupations. In addition, advances in communication
technology and low transportation costs have enabled many production jobs
to be performed in lower-wage countries abroad. The United States needs to
invest in the skills of its workforce to engage effectively in the global competition for good jobs, especially in high-end manufacturing. The Nation also
needs to produce and attract highly skilled workers who lead innovation,
entrepreneurship, and growth.
Aside from the “skills” challenge, the United States, like many other
advanced economies, also faces a “demographic” challenge. Rising longevity
and lower birth rates have increased the average age of the population and
reduced population growth. Even though the United States is in a relatively
strong position compared to many other developed nations in this regard,
the latest Census estimates project that the prime working-age population,
defined as individuals aged 25–54, will continue to decline as a share of
the total population, falling from 40.5 percent in 2012 to 37.9 percent by
2040. By affecting the size of the labor force as well as the ratio of retirees
to the working-age population, ongoing demographic changes have a direct
impact on the long-run growth of the economy.
This chapter begins by describing the demographic and labor force
trends that pose challenges in the near future. It next turns to education and
the steps the President has taken to ensure that all Americans have access
to the education and training they need to succeed in the changing labor
market. The chapter ends with an overview of immigration and its potential
to help address both of the challenges ahead—the need for more workers
and the need for a more skilled, innovative, and entrepreneurial workforce.

119

Box 4-1: Minimum Wages and Employment
In his State of the Union address, delivered on February 12, 2013,
President Obama called on Congress to raise the Federal minimum wage
from $7.25 to $9.00 in stages by the end of 2015 and index it to inflation
thereafter. His guiding principle was that in the wealthiest nation in the
world, no one who works full-time should have to live in poverty. By way
of example, President Obama noted that a full-time worker making the
minimum wage earns $14,500 a year. Even with the tax relief for lowerincome workers that exists in current law, a family with two children
and one minimum wage income still lives below the poverty line. Raising
the minimum wage to $9.00 would raise the wages of approximately 15
million workers. In addition to making America a magnet for jobs and
equipping workers with the skills they need, ensuring that hard work
leads to a decent living is a cornerstone of the President’s vision to build
a stronger economy.
Economists have long studied how the minimum wage affects
employment and the economy. A comprehensive survey article written
in 1982 concluded that a 10 percent increase in the minimum wage lowers teen employment by 1 to 3 percent. While this reflected the opinion
of most economists at the time, the consensus view among economists
has since shifted as more evidence has accumulated. Indeed, by the
early 1990s time-series estimates of the effect of the minimum wage on
teenage employment were turning up statistically insignificant effects
(Wellington 1991). The 1999 Economic Report of the President concluded
that “modest increases in the minimum wage have had very little or no
effect on employment.”
The shift in consensus reflects two decades worth of studies that
have made some methodological advances in the field. Since the 1990s,
after the shift in the time-series evidence, economists have used differences across states in the level and timing of changes to minimum wage
laws to study the effect of the minimum wage on employment of low wage
workers (Card 1992). This approach arguably produces more robust
estimates than the previous time-series approach of relating changes in
nationwide teenage employment to movements in the federal minimum
wage because it allows researchers to do a better job of controlling for
other factors, such as underlying economy-wide trends, that might also
affect low-wage employment. A further refinement of the state-level
analysis is to focus more specifically on comparisons of adjacent states,
which has the advantage that underlying economic trends are more
likely to have had similar effects on nearby states (Card and Krueger
1994). A particularly compelling recent study takes this approach a step
further by comparing all contiguous county-pairs in the United States

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Chapter 4

that are located on the opposite side of a state border (Arindrajit Dube,
T. William Lester, and Michael Reich 2011). The authors show that
workers benefited in states that increased their minimum wage, such as
California, Rhode Island, New York, Vermont, and Washington, relative
to similar workers across the state borders. The study concluded, “For
cross-state contiguous counties, we find strong earnings effects and no
employment effects of minimum wage increases.”
A meta-analysis by Doucouliagos and Stanley (2009) of 64 studies
on the minimum wage published between 1972 and 2007, encompassing
over 1,000 estimates, finds that most estimates are concentrated around
zero, indicating no detectable effect (see figure). The authors conclude
that the available research finds “no evidence of a meaningful adverse
employment effect” of the minimum wage.

1/se
350

Estimates of the Effect of Minimum Wage on Employment
by Statistical Precision

300
250
200
150
100
50
0

-20

-15

-10
-5
Employment elasticity estimates

0

5

Note: "SE" refers to the standard error.
Source: Doucouliagos and Stanley (2009); data provided by John Schmitt.

Commonsense immigration reform can be a key contributor to future economic growth and job creation.

Demographic and Labor Force Trends
The U.S. adult civilian non-institutional population stood at 237.8
million in 2010 and is projected to reach 263.0 million by 2020, growing at a
projected annual rate of 1.0 percent, down from 1.1 percent in the 2000s and
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1.2 percent in the 1990s. Further, the share of older Americans is projected
to grow over the 2010–20 period, with the number of individuals aged 55
and older increasing 2.6 percent a year, while the number of 16–24 year
olds remains roughly constant and the size of the working-age population
grows by just 0.3 percent a year (Toossi 2012). These population projections
reflect the aging of the baby-boom generation born between 1946 and 1964.
Because older men and women are considerably less likely to participate in
the labor force than younger individuals, these demographic trends imply
that the fraction of the population in the labor force will fall. This trend has
already begun.
After increasing at a steady clip for two and half decades starting in
the mid-1960s, labor force participation exhibited slower growth during the
1990s and began to fall during the 2000s. The overall labor force participation rate (LFPR), which peaked at 67.1 percent in 2000, fell to 63.7 percent
in 2012. Approximately half of this decline can be attributed to the aging of
the population and the retirement of the oldest members of the baby-boom
generation together with long-term declines in labor force participation
among several of the groups shown in Figure 4-1 not related to cyclical factors (see Table 2-1 in Chapter 2).
As the figure illustrates, participation rates have fallen for all major
demographic groups since 2000 with the exception of men and women
aged 55 and older. The LFPR for younger men and women fell in the 2000s,
although the decline for men is a continuation of a long-term trend, whereas
the gradual decline for women in the 2001–07 recovery is a new development that reverses a long period of rising participation. The labor force
participation rate for 16–24 year olds has dropped precipitously since 2000
after trending down since 1980.
Recent studies suggest two different explanations for the declining
trend among teens and young adults. On the one hand, the increasing
monetary return to educational attainment has made it more likely that
young people enroll in school rather than become employed. One recent
study found that while about two-thirds of the decline in participation
among teens stems from an increasing share of teens enrolled in school, an
additional portion is due to declining participation among those enrolled
in school (Aaronson, Park, and Sullivan 2007). To the extent that young
people are forgoing work for education, the decline in their labor force participation is less of a concern because they are acquiring skills that will raise
their productivity when they do enter or return to work. Less optimistically,
other researchers have argued that competition for low-wage jobs has been
a major cause of the decline in the teen LFPR, with low-skilled adults now
filling jobs that teenagers used to take (Smith 2011).
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Chapter 4

Figure 4-1
Labor Force Participation Rate by Population Group, 1970–2012

Percent
100
90
80

2012

Men 25–54
Women 25–54

70
All 16–24

60
50
40
30
20

Men 55+

Women 55+

1970
1980
1990
2000
2010
Source: Bureau of Labor Statistics, Current Population Survey, Annual Social and
Economic Supplement; CEA calculations.

On the other end of the age spectrum, older workers have increased
their labor force participation. Researchers have identified rising education
levels and the growth of white-collar and service jobs as important explanations. Other plausible explanations that have not yet been investigated fully
are improved health and reductions in the value of retirement savings (Blau
and Goodstein 2010; Maestas and Zissimopoulos 2010).
The labor force participation of working-age men has declined
steadily since the 1970s. One likely factor behind this trend is that real
wages have declined for less skilled men. Since the early 1970s, the average
real wage has fallen about 25 percent for high school dropouts and about 15
percent for high school graduates with no further education (Acemoglu and
Autor 2011).
The pattern for women has been different. During the 1970s and
1980s, the economy benefited greatly as married women entered the labor
force and increased potential and actual gross domestic product (GDP). As
Figure 4-1 above illustrates, the growth in female labor force participation
abated in the early 2000s. Different forces appear to be at work for different
groups of women. Gains in employment for less educated women during the
1990s were encouraged by policy changes (for example, the Earned Income
Tax Credit and welfare reform) and by strong economic growth that was not
sustained in the early 2000s. Highly educated women, particularly mothers,

Jobs, Workers and Skills

| 123

have pulled back from the pattern of large increases in labor force participation observed in the 1970s and 1980s. Lack of hours flexibility and the
challenges inherent in balancing career and family appear to be important
factors for these women.

A Slowdown in Women’s Participation Rates
Table 4-1 reports participation rates of working-age women in selected
years that correspond to peak years of the business cycle and thus allow a
focus on long-term trends. From 1969 to 1989, the labor force participation
rate of working-age women increased 24.5 percentage points. The most dramatic changes in participation have occurred among married women, and
more starkly, among married mothers. The LFPR among married mothers
increased an astounding 31.4 percentage points from 1969 to 1999. Growth
among all working-age women was slower during the 1990s, but the LFPR
for working-age women increased another 4 percentage points to 77 percent
in 1999. As the table shows, however, since 1999, the participation rate for
these women has declined, falling to 75.6 percent by 2007.
Figure 4-2, which compares the participation rates of women born in
different periods, provides insight into the rise and subsequent stagnation
of participation among married mothers. Among women born between
1936 and 1945, labor force participation is moderately high at younger ages,
drops during the peak child-bearing years, exhibits a subsequent reprise in
mid-life, and finally declines as retirement approaches. The curve tends to
rise across successive generations of women, indicating higher participation
rates for each successive cohort, and the dip associated with child-bearing
ages has largely disappeared. The rise in participation, however, appears
to have stopped with the most recent generation. Given this pattern across
birth cohorts, it is difficult to be optimistic about future increases in the
labor supply of prime-age women. New birth cohorts work no more than the
immediately preceding cohort at the same ages, and it is therefore unlikely
they will work more at later ages. The gains during the 1970s and 1980s
achieved from the increased participation of married mothers appear to
have come to a standstill and perhaps even partially reversed.
What has brought about this change? One candidate explanation—
that labor market prospects have declined for women in the 2000s—cannot
be the whole story, since participation has fallen even among groups for
whom average wages have risen. For example, according to one recent
investigation, the average weekly wage of women aged 25–39 with a college
degree increased 2.4 percent from 1999 to 2007, after adjusting for inflation,
even as the share of this group who are employed fell 3.0 percentage points
(Moffitt 2012).
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Chapter 4

Table 4-1
Labor Force Participation Rate of Women Aged 25-54, 1969–2007
Percent
1969

1979

1989

1999

2007

48.8

62.1

73.3

77.0

75.6

All married

43.5

57.4

70.2

74.1

73.3

Widowed/divorced

69.6

73.4

78.4

81.6

79.0

Never married

80.5

80.8

81.8

82.6

79.9

Married mothers

40.8

54.4

67.8

72.2

71.6

Widowed/divorced mothers

65.5

70.9

76.1

82.5

81.2

Never-married mothers

50.4

57.6

64.0

78.4

75.4

White

47.6

61.6

73.3

76.9

75.6

Black

58.7

66.5

74.1

79.6

77.8

Other

49.1

62.3

69.5

71.4

72.1

High school dropouts

45.0

48.7

51.3

56.1

53.2

High school graduates

49.8

62.7

73.4

75.2

73.2

Some college

48.2

66.9

78.3

80.2

79.1

College graduates

58.2

74.9

83.4

84.3

81.8

Prime-Age Women
Marital Status

Marital status and presence of children

Race

Education

Source: Bureau of Labor Statistics, Current Population Survey; CEA calculations.

Figure 4-2
Age-Specific Labor Force Participation Rate
by Birth Cohort for Women, 1926–1992

Participation rate, percent
90
80
70
60
50
40

1976–1985

1956–1965
1966–1975

1986–1992
1946–1955
1936–1945

1926–1935

30

20 22 24 26 28 30 32 34 36 38 40 42 44 46 48 50 52 54 56 58 60
Age
Source: Bureau of Labor Statistics, Current Population Survey, Annual Social and
Economic Supplement; CEA calculations.

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The one subgroup of women most likely to have been affected by
declining labor market prospects is never-married mothers, a population
that tends to have lower levels of education and correspondingly lower
wages. As Table 4-1 illustrates, the labor force participation of these women
rose dramatically from 64.0 percent in 1989 to 78.4 percent in 1999. One
factor contributing to this increase was the 1996 welfare reform act, which
replaced the welfare entitlements embodied in the old Aid for Families
with Dependent Children with more temporary and conditional assistance
under the Temporary Assistance to Needy Families program (Blank 2002;
Moffitt 2003; Grogger 2003). Another important factor was the expansion
of the Earned Income Tax Credit (EITC) in 1986, 1990, and 1993, which
made work more attractive and encouraged the entry of low-wage workers into the labor force (Eissa and Liebman 1996; Meyer and Rosenbaum
2001). The impacts of these program and tax changes were amplified by the
strong labor market of the second half of the 1990s, a situation that was not
sustained as labor markets weakened in the 2000s. The further expansion of
the EITC under the Recovery Act and the American Taxpayer Relief Act,
and increasing and indexing the minimum wage as proposed by President
Obama, would be expected to encourage greater labor force participation for
this group in the future.

Work Schedules and Workplace Flexibility
Recent studies that examine the career trajectories of highly educated
women in business and law provide some perspective on the challenges
women face as they attempt to balance career and family. One study followed
a cohort of University of Chicago graduates who had earned a master’s in
business administration (Bertrand, Goldin, and Katz 2010). While male and
female graduates started their careers with similar earnings, 17 percent of the
women were not working at all 10 years later, compared with only 1 percent
of the men. In addition, only 62 percent of female graduates were working
year-round full-time 10 years after graduation, compared with more than
92 percent of the men. The lower levels of work among these career-minded
women generally were associated with motherhood, suggesting that workfamily balance issues played a role.
One way that women (and others with family responsibilities) may
achieve greater flexibility for juggling these competing demands is to work
part time rather than full time during some periods. Traditionally, however,
given that part-time jobs tended to pay lower wages, the fact that women
were more likely to be in part-time work was thought to be a major impediment to women gaining equal pay (Blank and Burtless 1990; Manning
and Petrongolo 2008; Bardasi and Gornick 2008). In some cases, however,
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Chapter 4

offering part-time work—and greater hours flexibility more generally—may
be seen by employers as a way to attract highly qualified workers, especially
highly qualified women who might otherwise choose not to work.
Other advanced economies appear to be offering a different mix of
work schedules and employment opportunities. Figure 4-3 shows a comparison of labor force participation rates for women, 25–54 years old, in
selected advanced economies. While participation rates in France, Germany,
and the United Kingdom were slightly below the U.S. rate in 1991, they were
higher than the U.S. rate by 2011. Much of the rapid rise in the European
participation rates for working-age women has come from increases in parttime work. In contrast, women in the United States are more likely either to
work full-time—defined as 35 hours or more a week—or not to work at all.
Figure 4-4 shows that, among the selected countries, U.S. women are still the
most likely to work full-time.
The labor force participation rate and average hours worked among
those who do participate can be used to calculate average hours worked per
woman across countries. In 2005–09, women worked an average of 26.8
hours a week in the United States, more than the average of 26.4 hours per
capita in France, 24.4 in the United Kingdom, 22.3 in Germany, and 20.2
in the Netherlands. The U.S. average, however, was down from 27.4 hours
a week in 1995–99, while women’s hours worked had risen in all the other
countries.
A recent study by Blau and Kahn (2013) noted that in 1990, the
United States ranked 6th among 22 developed countries in women’s labor
force participation, but by 2010 the United States had fallen to the 17th position. Blau and Kahn found that the increased prevalence of “family-friendly
policies”—parental leave as well as part-time work entitlements—in other
developed countries can account for up to 29 percent of the decline in U.S.
women’s LFPR relative to other countries. Among the countries shown in
Figure 4-3, the greatest change in labor force participation for prime-age
women occurred in the Netherlands, where the rate rose by nearly 20 percentage points between 1991 and 2011. During this period, the Netherlands
instituted laws that mandate equal pay per working hour regardless of total
weekly hours worked. These requirements were accompanied by other laws
that establish employees’ right to request changes in their weekly working
hours or request parental leave on a part-time basis (OECD 2012a). As Data
Watch 4-1 highlights, the United States lags behind in the availability of both
paid and unpaid leave.
One question is whether rising labor force participation comes at a
cost. In particular, women in other developed countries could be accepting
lower wages in exchange for being able to work part-time or having access
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| 127

Figure 4-3
Labor Force Participation Rate of Women Aged 25–54, 1991–2011

Percent
100
95
90

2011

Sweden

85

France

80
75

United States

Germany

70

Netherlands

65

United
Kingdom

60
55
50
1991

1993

1995

1997

1999

2001

2003

2005

2007

2009

2011

Note: Workers on leave are considered employed. The participation rates in the KILM data are harmonized to
account for differences in national data and scope of coverage, collection and tabulation methodologies, as
well as for other country-specific factors such as military service requirements.
Source: International Labour Organization, Key Indicators of the Labor Market (KILM).

Figure 4-4
Percent of Women Ages 25 Years and Older
Working Full-Time, 1991–2009

Percent
60

2009

United States

55
50
45

France

40

Sweden

United Kingdom

35

Germany

30
25
20
15

Netherlands

10
1991

1993

1995

1997

1999

2001

2003

2005

2007

2009

Note: Full-time is defined as 35 hours per week or more. Workers on leave are considered employed. The
participation rates in the KILM data are harmonized to account for differences in national data and scope of
coverage, collection and tabulation methodologies, as well as for other country-specific factors such as military
service requirements.
Source: International Labour Organization, Key Indiciators of the Labor Market (KILM).

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to other forms of workplace flexibility. Contrary to this notion, however,
gender wage gaps are actually smaller in other developed countries than in
the United States. For example, in 2010, the female-to-male hourly wage
ratio was 77.7 percent in Germany, 78.7 percent in the United Kingdom,
81.9 percent in the Netherlands, 84.4 percent in France, and 84.4 percent in
Sweden. In all of these countries, part-time work and other types of workplace flexibility, such as paid parental leave, are more available than in the
United States, where the female-to-male hourly wage ratio was 75.0 percent.
Part of what lies behind this phenomenon is that the wage distribution is
more compressed in these other countries (Blau and Kahn 2003). Although
women in the United States and France are at similar percentile positions of
the overall wage distribution relative to their male counterparts, for example,
wage compression translates into a much smaller gender wage gap between
the average working man and woman in France compared to the United
States. Comparisons across countries also suggest, however, that it is not
inherently the case that greater flexibility implies lower wages.
Other recent work comparing wages and hours flexibility across
occupations also challenges the notion that hours flexibility necessarily
comes at a cost. Goldin and Katz (2012) provide an illustrative case study
of the pharmacist occupation, where consolidation brought about by scale
economies led to the rise of large retail giants. The new market structure
made it possible for two part-time pharmacists to substitute for one full-time
pharmacist, creating a much more flexible work environment for women.
Notably, part-time pharmacists earn no less per hour than full-time pharmacists in contrast to other occupations employing female college graduates
where working part-time is associated with wages as much as 20 percent
lower. Among women aged 35–39 with pharmacy degrees, only 12 percent
were not in the labor force, compared with 18 percent among other college
graduates. The study also found that only 11 percent of women with active
pharmacy licenses ever had a spell out of the workforce. Given this pattern
of continuous participation, female pharmacists are likely to work more over
their lifetimes than other women who start working long hours but drop out
altogether mid-career as they face the often stark choice between work and
family.
To be sure, not all occupations can easily accommodate flexible hours.
There is some evidence, however, that even in fields such as medicine, where
part-time work is rare, jobs may be evolving to accommodate more flexible
schedules (Goldin and Katz 2011). More flexible schedules also seem to be
gaining acceptance in the business community (CEA 2010). As more businesses adopt these practices, the cost to any one firm of their adoption will
be lowered. An individual employer may be less likely to offer flexible work
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Data Watch 4-1: New Evidence on Access to Paid Leave
The traditional family today is vastly different than it was decades
ago. In contrast to 1975, when just 43 percent of women with children
were working, nearly two-thirds of women with children were at work
in 2010. The juggling of work and family is not a challenge for women
alone. Among married households with children, 60 percent had two
working parents. In addition, Americans are getting older. With an
aging population, working families will face growing challenges in providing eldercare in the years to come. Access to paid leave and scheduling flexibility can help families deal with these challenges.
Each of the President’s Budgets since FY 2011 has proposed money
for a State Paid Leave Fund at the Department of Labor that would
provide competitive grants to help cover start-up costs for states that
choose to launch their own paid leave programs. The value to families
of paid leave is illustrated by California’s experience with its Paid Family
Leave (PFL) program. Since 2004, employed individuals in California
have been able to take up to six weeks of paid leave to spend time with
a newborn or a newly adopted child or to care for a seriously ill relative.
During this time, workers receive payments through the State Disability
Insurance system for up to 55 percent of their earnings. A recent study
found that the California program more than doubled the overall use of
maternity leave, increasing it from around three to six or seven weeks
for the typical new mother, with especially large growth among less
advantaged mothers, while also raising the hours and wage incomes of
employed mothers in the affected group by 6 to 9 percent (Rossin-Slater,
Ruhm, and Waldfogel 2011).
The President’s FY 2011 Budget included funding to add a module
to the American Time Use Survey (ATUS) asking workers about the
leave policies at their place of work. The module had questions on
leave access, leave use, and unmet need for leave. Because the ATUS
is linked to the Current Population Survey, rich data are available on
the characteristics of people surveyed. The ATUS survey also provides
much-needed information on workers’ ability to adjust their schedules
or location or to work from home, as well as other dimensions of workplace flexibility that can help in balancing work and family obligations.
This new survey indicates that a large fraction of American workers still lacks access to paid leave, including paid sick leave and paid
family leave for the birth of a child. In addition, only 53 percent of
the workers reported that they had the ability to adjust their schedule
or work location. Previous studies using the National Compensation
Survey have shown large disparities in access to paid leave by level of
earnings. The new data confirm these findings and, in addition, docu-

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ment large disparities in access to paid leave and scheduling adjustments
across education groups and between Hispanics and non-Hispanics (see
table). Those in the top quartile of earnings are 1.7 times as likely to
have access to paid leave as workers in the bottom quartile (83 percent
vs. 50 percent). College-educated workers are about twice as likely to
have access to paid leave as workers without a high school degree (72
percent vs. 35 percent). Only 43 percent of Hispanics have access to paid
leave, compared with 61 percent of non-Hispanics. Although a large and
roughly similar share of workers in most groups has access to unpaid
leave, that is a poor substitute for paid leave that can be taken when the
need arises.
Access to Leave by Selected Characteristics, 2011
Percent
Access to
paid leave

Access to unpaid
leave

Access to schedule
adjustment or
location

59.0

76.6

55.9

Male

60.3

75.4

55.5

Female

57.5

77.9

56.3

White only

58.9

76.9

56.6

Black only

60.6

76.7

49.8

Asian only

62.2

72.1

59.8

Hispanic

43.0

71.2

48.2

Non-Hispanic

61.4

77.4

57.1

Less than high school

34.9

70.4

37.6

High school

61.1

75.8

48.2

Some college

66.4

78.2

55.8

Bachelor’s or higher

71.6

75.3

60.5

$0–$540

50.1

78.0

47.2

$541–$830

77.1

78.9

48.8

$831–$1,230

81.3

74.9

51.4

$1,230+

82.7

75.4

59.9

Total
Gender

Race/Ethnicity

Education

Weekly Earnings

Notes: Education breakdown is only for individuals age 25 and over. Each earnings range
represents approximately 25 percent of full-time wage and salary workers (except self-employed
incorporated workers) who held only one job.
Source: Bureau of Labor Statistics, American Time Use Survey, Leave Module; CEA calculations.

Jobs, Workers and Skills

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schedules when other firms have not adopted the same practice out of the
fear that it will attract less committed workers. This situation is similar to
health insurance, where before enactment of the Affordable Care Act, a firm
might not have offered health insurance in an environment where employerprovided health insurance was rare out of the fear that it would attract the
least healthy workers. If all firms engage in the practice, the risk to any one
firm is lowered.
Such developments may well provide a boost to the economy. Women
received a majority of both bachelor’s degrees (57 percent) and master’s
degrees (60 percent) awarded in 2010. Educational attainment commands a
high return in an increasingly knowledge-based economy. It is in society’s
collective interest to encourage women to make full use of these educational
investments by remaining in the labor market where the return to their jobrelated skills can be realized.

Government as a Partner in Human
Capital and Skill Formation
Overwhelming evidence shows that the average return to obtaining a
college education is large. In 2011, the median weekly earnings of individuals with a bachelor’s degree was $1,053, compared with $638 for individuals
with only a high school diploma—a 65 percent premium for the college
graduate. A bachelor’s degree is also the gateway to other advanced degrees
that command even higher earnings premiums (Figure 4-5). The premium
for college and beyond has been rising since 1980 and has continued to
increase, albeit at a slower rate than in the 1980s (Acemoglu and Autor
2011). Because the number of college graduates also has been increasing
over this time, the rising premium is a signal that the economy is demanding
still more college graduates.
From a national perspective, an educated workforce is vital. The
productivity of a nation’s labor force is a key input into future economic
growth, and the most direct prescription for increasing labor productivity is investment in skills. The United States has historically been a leader
among developed countries in the share of its population with postsecondary education (referred to by the Organisation for Economic Co-operation
and Development as “tertiary” education). That standing has fallen over the
past generation, with the United States now ranked 14th among a set of 34
industrialized nations in the share of 25–34 year olds with such education
(OECD 2012b). While other measures can be used to assess a nation’s ability to educate its workforce—including measures of educational quality, test
scores, and how well people with skills are matched to jobs that can make use
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Figure 4-5
Median Weekly Earnings by Education Level, 2012
Less than high
school
High school
Some college
Associate
Bachelor's
Master's
Professional
Doctoral
0

500
1,000
1,500
Median weekly earnings, dollars
Note: Data are for full-time wage and salary workers, 25 years and older.
Source: Bureau of Labor Statistics, Current Population Survey.

2,000

of them—the fall in the U.S. postsecondary education ranking is a reminder
that we have more to do to provide America’s workers with the skills to
compete in today’s economy.
Early learning and the quality of education from kindergarten through
high school (K–12) are key determinants of successfully completing a college degree. Study after study finds that early life conditions have persistent
and large effects on later life outcomes such as high school graduation rates,
employment, and earnings (Cunha and Heckman 2008; Cunha, Heckman,
and Schennach 2010; Almond and Currie 2011). In his State of the Union
address delivered to Congress on February 12, 2013, President Obama proposed to work with states to make high-quality preschool available to every
single child in America. Four years ago, the President launched the Race to
the Top competition, which has proven to be successful in convincing states
to develop smarter curricula and higher standards for grades K-12. In his
2013 State of the Union address, the President announced a new challenge to
high schools to partner with colleges and employers to better equip students
with the problem-solving and math skills that are in demand in today’s hightech economy.
President Obama wants to make the United States the leader in postsecondary attainment. In his address to Congress on February 24, 2009, he
set 2020 as the year by which the Nation would once again have the highest
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proportion in the world of young people graduating from college. The U.S.
Department of Education projects that the share of college graduates will
need to increase by 50 percent to achieve this goal. That means 8 million
more young adults will need to earn associate degrees, bachelor’s degrees,
and meaningful postsecondary certificates by 2020. To achieve this ambitious goal, the higher education system must undertake far-reaching reforms
to improve college readiness, widen access, ensure quality, promote affordability and value, and accelerate completion. Colleges and universities in
every state have a vital role and a unique opportunity to help America again
lead the world in college attainment.
Giving America’s workers the skills to compete for good jobs will
require the necessary resources to educate millions of additional students.
Unfortunately, State and local government support for higher education—traditionally the cornerstone of public higher education funding—has
been falling for at least a decade. From 2000 to 2010, State appropriations
for public four-year institutions fell from $8,029 to $6,388 per full-time
student, while appropriations for public community colleges fell from
$7,095 to $5,712 (in 2010 dollars).1 This sharp drop in State support has left
postsecondary institutions in need of alternative revenue sources, including additional tuition dollars. In fact, in 2010, for the first time ever, public
research and master’s institutions received more revenue from tuition than
from State appropriations. While State appropriations fell only 0.4 percent
in 2012, the effects of budget cuts stemming from the economic downturn
are expected to last for some time.
Sticker tuition is the price of tuition advertised by the individual colleges. Net tuition is the price students actually pay after deducting Federal,
State, and private aid, as well as various discounts offered by the institutions
themselves. Between 2000 and 2012, sticker tuition increased from $4,860
to $8,370 (in 2012 dollars) per full-time student at public institutions, an
increase of $3,510, and from $21,310 to $28,280 at private institutions,
an increase of $6,970 (Figure 4-6). Net tuition per full-time student has
increased much less than sticker tuition, going up $1,260 at public institutions and $820 at private institutions over this period. The relatively
modest increase in the net cost of attending college resulted in large part
from Federal policies aimed at reducing the price of education. President
Obama has worked to expand these Federal programs. Expanded Pell Grants
made college more affordable for 9.4 million low-income students in 2011
1 States provide substantially less appropriations to private institutions on a per-student basis.
State funding for private institutions was more stable over this period. For example, state
appropriations per full-time student rose from $513 to $523 at private research institutions
and fell from $537 to $288 at private master’s institutions. (College Board 2010). See: http://
chronicle.com/article/State-Spending-on-Higher/136745/

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Figure 4-6
Tuition and Fees for Full-Time Undergraduate Students, 1990–2012
a. Private institutions

2012 dollars

30,000

2011–12

25,000
20,000

Sticker tuition and fees

15,000

Tuition and fees net of grant
and tax benefits

10,000
5,000
0
1990–91

1994–95

1998–99
2002–03
Academic year

2006–07

2010–11

b. Public institutions
2012 dollars

9,000
8,000

2011–12

7,000
6,000
5,000

Sticker tuition and fees

4,000
Tuition and fees net of grant
and tax benefits

3,000
2,000
1,000
0
1990–91

1994–95

1998–99
2002–03
Academic year

2006–07

2010–11

Source: The College Board, Annual Survey of Colleges, Trends in Student Aid (2012).

Jobs, Workers and Skills

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(2.4 million more than in 2009), and the establishment of the American
Opportunity Tax Credit (AOTC) has lowered the cost of attending college
for millions more.

Expanded Pell Grants
Pell Grants are the foundation of the Nation’s efforts to make college affordable for students from lower- and middle-income families. Pell
Grants help more than 9 million Americans a year pay for college, but the
purchasing power of these grants has diminished over time. Recognizing the
importance of the Pell Grant program to so many people, President Obama
worked aggressively to increase the maximum award. The Health Care and
Education Reconciliation Act, signed into law in 2010, raised the maximum
grant from $5,550 for the 2012–13 academic year to $5,975 in 2017–18. The
Act invests approximately $40 billion a year in Pell Grants to ensure that all
eligible students receive an award and that these awards will be increased in
future years to keep pace with inflation. These steps, together with the funding provided in the American Recovery and Reinvestment Act of 2009 (the
Recovery Act) and President Obama’s first two Budgets, more than doubled
the total amount of funding available for Pell Grant awards.
President Obama also took steps to stabilize Pell Grant funding. In the
past, the budgeting process for Pell Grants often led to funding shortfalls,
as Pell Grant funding is subject to the annual appropriations process rather
than financed through mandatory funding. The appropriations bill that
funds Pell Grants for the upcoming academic year is passed almost a full year
before the funds become available, and thus the funding is established before
it can be clear what the program will cost. The recent shortfall was expected
to be particularly severe because of the large number of students qualifying
for the award. The Act covered the expected funding shortfall and much of
the recent growth in Pell costs, putting the program on a sounder footing
going forward. The Act increased investments in Pell Grants by reforming
existing student loan programs to deliver loans directly to students instead
of subsidizing banks through the more costly Federal Family Educational
Loan program. Direct student loans are more efficient and affordable for
taxpayers, and the reform allowed more than $60 billion to be reinvested
in Pell Grants and other programs that support and sustain college access,
while cutting billions from the national deficit (CBO 2010).

Expanded American Opportunity Tax Credit
Tax credits for higher education expenses were substantially expanded
by President Obama in the Recovery Act. Before 2009, taxpayers could claim
either the Lifetime Learning Credit or the Hope Scholarship Credit toward
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Chapter 4

higher education expenses. The Recovery Act established the American
Opportunity Tax Credit, an expanded version of the Hope Credit. The
AOTC offers a larger maximum benefit, makes more middle-income taxpayers eligible, and is partially refundable. These provisions substantially
enlarged both the pool of taxpayers eligible for education tax credits and the
amount of money available to qualifying taxpayers.2
In 2010, the AOTC was one of the most widely used education tax
incentives, with 11.9 million taxpayers (8.3 percent of all taxpayers) claiming
the credit (Table 4-2). The AOTC benefits totaled $12.3 billion, likely making
the credit more important to college affordability than all other education
deductions and credits combined. The benefits of the AOTC were spread
throughout the income distribution with low- and middle-income families
receiving substantial benefits. Seventy-nine percent of the beneficiaries had
household incomes below $100,000, and 13.1 percent of beneficiaries had
household incomes below $25,000. The refundable aspect of the AOTC was
particularly beneficial to low-income households. In 2010, AOTC benefits
claimed as refundable credits were worth a total of $6.0 billion to American
households, with those benefits flowing overwhelmingly to households with
incomes under $50,000. The majority of beneficiaries of the refundable portion of the AOTC—63.6 percent—had household incomes under $25,000.
In recent budget negotiations, the Administration achieved an agreement
with Congress to extend the AOTC for an additional five years. If the AOTC
program had been allowed to expire, 11 million college students and their
families would have seen tax increases averaging $1,100. President Obama
has called on Congress to make this tax credit permanent so that families can
plan ahead and count on this credit for all four years of college.

Aggregate Student Loan Debt
While net tuition has risen considerably less than sticker tuition, for
some low- and middle-income families, even the rise in net tuition may have
put a quality education out of reach; for other students, the rise in college
costs has led to substantially higher levels of borrowing. Aggregate student
debt has grown steadily, from $241 billion in the first quarter of 2003 to
$966 billion in the fourth quarter of 2012 (in dollars not adjusted for inflation). In contrast, after increasing earlier in the 2000s, aggregate amounts
of other types of consumer debt, including mortgages, home equity loans,
2 The AOTC is available to taxpayers with income below $90,000 ($180,000 if married),
offering a maximum credit amount of $2,500 per student for the first four years of
postsecondary education; students must be enrolled at least part-time and be pursuing a degree
to be eligible. The AOTC is 40 percent refundable, meaning that taxpayers with no tax liability
can claim up to $1,000 toward higher education expenses.

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Table 4-2
Education Tax Incentives: The American Opportunity Tax Credit, 2010
Returns

Amount
(thousands of
dollars)

$0 to $24,999

2,829,111

$25,000 to $49,999

3,628,972

$50,000 to $99,999
$100,000 to $199,999

Percent of income
class benefitting

Percent of total
benefit

1,605,855

4.8

13.1

3,579,601

10.5

29.2

3,628,533

4,500,639

11.8

36.7

1,776,318

2,582,592

12.4

21.0

$200,000 or more

4,122

3,385

0.1

0.0

All returns, total

11,867,055

12,272,073

8.3

100.0

Income Class

Source: Internal Revenue Service, Statistics of Income.

and credit card and auto debt, have fallen since the financial crisis (Figure
4-7).3 In fact, more student loan debt is now outstanding than either credit
card debt or auto loan debt; only the mortgage debt category is larger. This
rise in aggregate student loan debt, coupled with an increase in the share of
student borrowers in delinquency status, has focused growing attention on
student borrowing.
The rise in aggregate student debt—apparent even after adjusting the
figures to account for inflation—has been driven partly by increased enrollment in postsecondary education (Figure 4-8). Between 1990 and 2012, the
number of students attending college increased from 13.8 million to 21.0
million. From this perspective, the rise in aggregate student debt is partly
the result of increased investment in human capital, which can be expected
to lead to higher wages in the future and to a more prosperous standard of
living for the cohorts who have been entering the labor market. The rise in
aggregate student debt also reflects increases in the share of students who
take out student loans and increases in the amount they borrow. Total borrowing has fallen in the aftermath of the financial crisis, and some of the
increase in student debt may reflect families taking out student loans rather
than home equity lines of credit to pay for college, but concern has been
expressed about the increase in student debt.
Among students who received a bachelor’s degree from a four-year
public college between academic years 1999–2000 and 2010–11, the share
who took out student loans rose from 54 percent to 57 percent. More
importantly, the average loan amount rose by 16.1 percent, from $20,500
to $23,800 (in constant 2011 dollars). Sharply rising student loan debt not
only threatens the financial stability of recent graduates but also may serve
as a disincentive for younger students who are deciding whether to invest
3 Aggregate mortgage debt peaked in 2008:Q3, home equity debt peaked in 2009:Q1, and auto
debt, credit card debt, and other debt peaked in 2008:Q4.

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Chapter 4

Figure 4-7
Compositions of Household Debt Balance, 2003–2012

Trillions of dollars
14
12
10

Other
Student loans
Credit card
Auto loan
Home equity revolving
Mortgage

2012:Q4
Total: $11.3 Trillion

8
6
4
2
0
2003:Q1

2005:Q1

2007:Q1

2009:Q1

2011:Q1

2013:Q1

Source: Federal Reserve Bank of New York, Quarterly Report on Household Debt and
Credit.

Figure 4-8
Total Postsecondary Enrollment by Type of Institution, 1990–2010

Millions of students
16
14

2010

Public

12
10
8
6
4
2

Private nonprofit
Private for-profit

0
1990 1992 1994 1996 1998 2000 2002 2004 2006 2008 2010
Source: Department of Education, National Center for Education Statistics, Digest of
Education Statistics (2011).

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in their future and obtain a college degree. To help protect taxpayers, borrowers, and the broader economy against the threat of rising student loan
delinquencies, the Administration has advanced several polices designed
to make it easier for students to pay back their education loans and to hold
schools accountable for poor student debt outcomes after graduation.

Income-Based Repayment
Since 2009, responsible former students have been able to enroll in
an Income-Based Repayment (IBR) plan to cap student loan payments. In
October 2011, the Administration announced a new “Pay As You Earn”
option that will reduce monthly payments for about 1.6 million current college students and borrowers; eligible borrowers include those holding any
type of Federal student loan, such as Stafford, PLUS, and consolidation loans
(nonfederal loans and loans in default are not eligible). Starting in 2012,
the new IBR option has allowed eligible students to cap their annual loan
payments at 10 percent of their discretionary income. The amount that an
eligible student borrower is required to pay each month is based on adjusted
gross income (AGI) and family size. Specifically, the maximum monthly
payment equals 15 percent of the difference between AGI and 150 percent
of the poverty threshold for a given family size, divided by 12. Eligible borrowers never have to pay more than the maximum monthly threshold; if a
borrower’s monthly payments are higher than this threshold, they may apply
to have their monthly payments lowered. Ultimately, IBR helps responsible
student loan borrowers continue to make payments on their student loans
at a manageable rate. As of November 2012, the Department of Education
estimated that approximately 1.37 million borrowers are participating in the
IBR program.

Federal Loan Consolidation
The Administration also took important steps to allow student borrowers to better manage their debt by consolidating their Federal student
loans. Starting in January 2012, an estimated 6 million current students
and recent college graduates were eligible to consolidate their loans as a
Direct Loan, and by so doing, reduce their interest rates. Before this policy
change, approximately 5.8 million borrowers had both a Direct Loan and
a Federal Family Education Loan. These loans require separate payments
making borrowers more likely to default. By consolidating these loans, borrowers could achieve the convenience of a single payment to a single lender.
Borrowers who took advantage of this consolidation option also received
up to a 0.5 percentage point reduction in their interest rate on some of their

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loans, which means lower monthly payments that may save each borrower
hundreds of dollars in interest over the life of the loan.

The Growth of For-Profit Colleges
Although they still account for only a small fraction of all postsecondary education students, for-profit colleges are the fastest-growing type of
postsecondary school. They offer both an opportunity and a challenge for
America’s system of higher education. For-profit colleges have been shown
to be flexible and innovative in meeting the needs of many postsecondary students, especially those who seek a nontraditional education or who
require flexible arrangements for receiving their education, such as on-line
and evening courses. Many for-profit colleges respond quickly to the changing needs of employers, and they can play an important role in helping more
Americans earn college degrees. However, the experiences of some students
at for-profit schools have been a cause for concern.
For-profit colleges have shown mixed outcomes with respect to
completion rates relative to other types of institutions. For-profit completion rates in one- and two-year programs tend to be higher than completion
rates for similar programs at other schools, but completion rates in for-profit
bachelor programs are significantly lower. Low graduation rates not only
waste taxpayer funds devoted to subsidizing the cost of education but can
lead to prolonged financial hardship for students who borrow to finance
their education but do not gain a college diploma to add to their earning
potential.
Students at for-profit schools are about twice as likely as other students to be idle—not working or enrolled in school—six years following
matriculation. In 2009, 23.6 percent of enrollees at for-profit schools were
idle six years later, compared with just 10.6 percent of matriculating students at four-year public and nonprofit private schools, and 13.3 percent of
matriculating students at two-year public and nonprofit private schools. As
a result, the average annual earnings of for-profit graduates are about $2,000
less relative to their counterparts at other types of schools, after accounting
for differences in student characteristics (Deming, Goldin, and Katz 2012).
Yet another study that uses detailed data to take account of differences in
student characteristics found large and significant earnings benefits from
obtaining an associate degree from public and nonprofit institutions but not
from for-profit institutions (Lang and Weinstein 2012).
Given the higher tuition costs at many for-profit institutions, students
at these schools also leave with substantially higher debt than their counterparts at public and nonprofit schools. In 2007–08, 53 percent of bachelor’s
degree recipients at some for-profit four-year schools had accumulated
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more than $30,500 in debt, compared with 24 percent of graduates at private
nonprofit schools and just 12 percent of public school graduates (Baum and
Steele 2010). Default on student loans is a much more serious problem at
for-profit schools. For fiscal year 2009, the three-year “cohort default rate,”
which measures the percentage of borrowers who enter repayment with
student loans and default over a three-year period, was 22.7 percent among
for-profit students, compared with just 7.5 percent for private nonprofits
and 11 percent for public institutions (Department of Education 2012).

Gainful Employment
In 2010 and 2011, the Obama Administration issued a broad set of
rules to strengthen occupational higher education programs at for-profit,
nonprofit, and public institutions by protecting students from aggressive or
misleading recruiting practices, providing consumers with better information about the effectiveness of such education and training programs, and
ensuring that only eligible students or programs receive aid. One notable
provision in this set of regulatory reforms was the “gainful employment”
rule, which made occupational programs ineligible for Federal aid if they
failed to meet a set of tests related to students’ financial situations after
graduation. While many occupational and for-profit institutions have pioneered new ways to reach adult students, offer online education, and meet
the needs of employers, some programs have left students with large debts
and poor employment prospects. Specifically, the rule stated that programs
could become ineligible for financial aid if fewer than 35 percent of graduates were actively repaying their student loans; graduates were spending in
excess of 30 percent of their discretionary income on student loan payments;
and graduates were spending more than 12 percent of their total income on
student loan payments. The gainful employment provisions were intended
to align institutional incentives with the interests of students, by conditioning eligibility to receive Federal aid on student outcomes. In June 2012, a
Federal judge vacated the key provisions of the gainful employment rule on
the grounds that there was no factual basis for the rule’s 35 percent repayment standard and that the better-grounded debt-to-income ratio standards
were so intertwined with the repayment standard as to invalidate the whole
rule. The Department of Education has appealed a portion of the judge’s
decision, asking that schools continue to be required to report information
about their students’ loan repayment rates and debt-to-income ratio to the
Department even if this information is not used to determine eligibility
for Federal funds. The Obama Administration remains committed to the
principles of accountability and transparency in the use of taxpayer funds
in occupational higher education programs and will continue efforts to
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provide students with good information about the quality and value of such
programs.

What Is Driving Up Tuition Costs?
One often-posed explanation for the increase in tuition costs is that
colleges require skilled labor inputs—highly educated instructors—and
as education premiums rise, so do the costs of these skilled labor inputs.
This explanation—an example of the Baumol’s cost disease (Economics
Application Box 4-1)—may be a contributing factor at private colleges but
is unlikely to be the major part of the story at public institutions. Over the
period 2000–10, average full-time faculty salaries increased 2 percent at public four-year colleges and actually fell at community colleges. Instructional
spending as a share of total costs has been falling at public colleges as institutions seek to cut costs by substituting non-tenured and adjunct faculty for
full-time tenure-track faculty. Evidence is mixed on whether this compositional shift has hurt learning outcomes with some arguing that graduation
rates have suffered while others find no measurable changes. But, faculty
salaries have not driven up costs.
So, what is driving up tuition costs? A recent survey article by economist Ronald Ehrenberg suggests that no single answer fits across all institutional types. Different types of institutions—private and public universities
engaged in research, private and public institutions largely devoted to
teaching, and public community colleges specializing in two-year instructional programs—are subject to different market forces and cost pressures
(Ehrenberg 2012).
One driver of costs for many colleges is increased competition for
students. The higher education market has been transformed from a statebased model where a majority of students attend local state universities to
a more national—even international—market where students search over a
large set of options. In this competitive environment, many institutions seek
to position themselves as unique by offering an attractive mix of amenities.
Published rankings likely contribute to this spending race because expenditures per student and average faculty salaries are often inputs into the rankings. Private research institutions, including the elite private universities,
are in the best position to compete in this environment. These universities
seek to have the most appealing facilities and the most renowned research
faculty, and so at these types of institutions, the rise in tuition reflects rising
average expenditure per student. At private research institutions, average
spending per full-time equivalent (FTE) student on “education and related”
items increased by more than $10,000, from $42,449 in 2000 to $52,710
in 2010, all measured in 2010 dollars. Spending increases have been fairly
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Economics Application Box 4-1: Baumol’s Cost Disease
(or Bowen’s Curse) and the Price of Education
In the 1960s, economists William Baumol and William Bowen
developed the notion, known as “Baumol’s cost disease,” that in certain
labor-intensive industries—the example they chose was the performing
arts—there is less opportunity for productivity gains to reduce labor
costs. The number of musicians needed to perform Beethoven’s Ninth
Symphony is the same today as it was decades ago, but the number of
workers needed to produce a single car has fallen considerably. Because
markets dictate that wages remain comparable across industries for
equally skilled workers, the relative price of products and services in
sectors where productivity is stagnant will rise over time. Baumol’s cost
disease has been cited as a partial explanation for the long-term growth
in education costs. Compensation for higher-education faculty and
administrators has been rising over time, even though productivity in
education has changed very little.
Whether and to what extent Baumol’s cost disease plays a role in
the continued rise in higher education cost is a topic of much debate.
Regardless of its importance as a possible explanatory factor, improved
technology and productivity growth offers a potential solution to growth
in the cost of college, opening up potential new ways to deliver education. One such innovation is massive open online courses, or MOOCs,
that can accommodate tens of thousands of students in a single class.
Another promising innovation is courses delivered through a hybrid of
online lectures and in-person tutoring. One study that used randomized
trials found no significant difference in learning outcomes between
traditional face-to-face statistics courses and hybrid online statistics
courses, yet costs were lower in the hybrid course. Another study, also
using a randomized design, found a slight advantage for live economics
lectures over online lectures in the case where all ancillary materials such
as web-based assignments and availability of tutors were comparable.
The relatively small advantage demonstrated by live lectures, however,
suggests there is room for considerable cost saving with relatively little
reduction in learning outcomes (Bowen et al. 2012; Figlio, Rush, and Lin
2010).

evenly spread across categories such as instructional expenditures (faculty
salaries and benefits), research (grants and contracts as well as matching
funds), student services (admissions, registrar, and counseling services), and
academic support (libraries and academic computing) (Figure 4-9a). While
these increases may look like rising labor costs, spending on physical plant—
“operation and maintenance costs”—has also increased. An important

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factor for private institutions is “tuition discounting,” or the share of each
tuition dollar that is returned to students in the form of need-based or
merit grant aid. Tuition discounting at these institutions is substantial and
increased from 28.6 percent in 2000 to 33.1 percent in 2008. The ability to
offer tuition discounts essentially allows institutions to price discriminate in
order to obtain a diverse mix of students.
In contrast, at public institutions, where most students enroll, average
spending per student has not risen nearly as much, and tuition increases
largely reflect institutions’ attempts to compensate for declining State support (Figure 4-9b). At public community colleges, the average level of State
and local appropriations per FTE student to these institutions fell from
$7,095 in 2000 to $5,712 in 2010. Other public institutions lie somewhere
between these two extremes, with public research institutions looking
more like private research institutions, and public master’s- and bachelor’sdegree-granting institutions that are more oriented toward teaching looking
more like community colleges. Average expenditure per FTE student at
public research institutions increased from $24,178 in 2000 to $26,971 in
2010. Public research institutions shifted resources away from instructional
spending by substituting non-tenured and part-time faculty for full-time,
tenured faculty. Meanwhile institutional spending to support research activities increased, likely reflecting the attempt to gather new funding sources
such as Federal and private research grants as State and local appropriations
decreased. To compete with private universities for faculty who can attract
Federal and private grants, public institutions often provide “start-up”
research funds and build expensive lab facilities.
The Administration is committed to keeping college affordable for
middle-class families. The Department of Education has released a College
Scorecard to provide transparency for families as they evaluate their options
for their higher education. The Department, along with the Consumer
Financial Protection Bureau, has also designed a College Shopping Sheet
to help families and students understand exactly how much money
they will owe at each of the schools to which they have been accepted.
President Obama has proposed a Race to the Top: College Affordability and
Completion challenge to reward States that increase the number of college
graduates while containing the costs of tuition. The President has also called
on Congress to work with him to hold colleges accountable by considering
value, affordability, and student outcomes in making determinations about
which colleges and universities receive access to Federal student aid.

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Figure 4-9
Average Expenditures per Full-Time-Equivalent Student
by Component, 2000–2010
a. Private institutions

2010 dollars
60,000

Operations and maintenance

50,000

Public services, academic and
institutional support
Student services

40,000

Research

30,000

Instruction

20,000
10,000
0

2000

2010

2000

Research

2010

2000

Master's

2010

Bachelor's

b. Public institutions

2010 dollars
60,000

Operations and maintenance

50,000

Public services, academic and
institutional support
Student services

40,000

Research

30,000

Instruction

20,000
10,000
0

2000

2010

Research

2000

2010

Master's

2000

2010

Bachelor's

2000

Community College

Source: Integrated Postsecondary Education Data System, Delta Cost Project.

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2010

Government as a Partner in Training
As part of the Administration’s efforts to prepare workers for
America’s 21st century economy, meet the needs of local employers, and
achieve President Obama’s goal of ensuring that every American worker has
the opportunity to secure at least one year of postsecondary education, the
Department of Labor, along with the Department of Education, launched
the Trade Adjustment Assistance Community College and Career Training
(TAACCCT) grant program. This $2 billion initiative expands the capacity
of community colleges to provide training and credentials to local workers
needed for high-wage, high-skill employment in industries like advanced
manufacturing, biotechnology, information technology, and other emerging
fields. To date, the Department of Labor has awarded 45 grants to colleges
across the nation to develop curricula for advanced manufacturing. For
example, the Department of Labor funded the National STEM Consortium,
led by Anne Arundel Community College in Maryland. This collaboration
of 10 leading community colleges in nine states organized to develop nationally portable, certificate-level programs in science, technology, engineering,
and mathematics and is also building a national model of multi-college
cooperation in the design and delivery of high-quality, labor-market-driven
occupational programs. Spokane Community College, in partnership with
11 other community colleges, worked with aerospace employers including
Boeing to design an advanced curriculum in aerospace maintenance and
manufacturing. The consortium known as Air Washington has been recognized by the Boeing Company for this curriculum development and for
its ongoing assistance to the Boeing Academic Alignment Team. This effort
includes the development of a pre-employment program to offer training in
basic aerospace-related skills to adult learners, a web-based curriculum component on English as a second language, and assessments of prior learning,
particularly for active military or veterans, to evaluate credit and classroom
advancements based on military experiences and training. The programs
funded by TAACCCT are establishing a national repository of high-quality
technical curricula and related materials that can be made available at no
charge to community colleges around the country.
Several existing U.S. training consortia provide successful models.
Among those worth noting are Project QUEST and the Wisconsin Regional
Training Partnership. Project QUEST is a training program in San Antonio
aimed at the working poor with high school diplomas. The program works
with firms (many of which are hospitals) in the city to identify job openings and the skills required to fill them. The firms then make a good-faith
pledge to hire program graduates into jobs that meet living-wage standards
and may redesign their jobs to create advancement ladders. The training is
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provided by local community colleges and typically lasts a year and a half.
The program, which offers modest financial support and extensive counseling to the trainees, is organized and managed by a nonprofit closely linked to
a community-based organization. More than 2,000 people have participated
in QUEST. An evaluation found that those who completed the program
saw their earnings rise by an average of $5,000 a year (Kochan, Finegold,
and Osterman 2012). The Wisconsin Regional Training Partnership was
established by unions and firms in Milwaukee in the 1990s and does training for manufacturing and construction. A study with random assignment
of participants to treatment and control groups found significant increases
in employment and incomes for program participants compared with nonparticipants (Holzer 2011).
Key features of these successful programs are the involvement of
industry and worker-focused organizations, along with a commitment
to continually evaluate what works and what does not, and a willingness
to make adjustments. The involvement of employer groups ensures that
the training is relevant; the involvement of worker-focused organizations
ensures that workers share in the gains of their improved productivity.
Together, the groups can work together to upgrade jobs, rather than taking current job duties and career paths as given. In some cases, as in the
Wisconsin program, upgrading has meant calling on other agencies (in
that case, the federally funded Manufacturing Extension Program) to help
firms upgrade their management, operations, and information-technology
practices so that they offer a greater return to skill (Maguire et al. 2010). The
programs also have used a variety of tools (focus groups with employers,
unions, and workers but also randomized controlled trials) to evaluate their
programs, adjusting if necessary based on the results.

Immigration
We are a nation of immigrants and their descendants. Now, more
than ever, the economic and social benefits of immigration loom large.
Immigrants increase the size of the population and thus of the labor force
and customer base, making an important contribution to economic growth.
In 2010, there were nearly 40 million foreign-born people in the United
States, representing 13 percent of the population and 16 percent of the workforce. As the United States faces the prospect of a slow-growing population,
immigrants are likely to play an increasingly important role in the American
economy. Immigrants work in diverse industries and occupations. While
they represent 16 percent of the workforce, they account for more than
20 percent of workers in agriculture, construction, food services, and

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information technology. They are agricultural laborers, domestic workers,
and cabdrivers as well as health care workers, computer software engineers,
and medical scientists (Singer 2012). This diversity promotes economic
growth as immigrants and natives often specialize in different tasks and
occupations.
In addition, many highly skilled workers in the STEM fields are immigrants, and research has shown that these workers contribute importantly to
innovation and growth. Many immigrants start businesses and create jobs
for American workers. The United States has a distinct advantage compared
with other developed nations in that flexible labor markets and robust
returns to skills encourage the in-migration of these highly qualified workers. Our open society also allows immigrants to integrate better than in other
countries, and we benefit from their vitality and creativity. Commonsense
immigration reform can honor America’s historical legacy of welcoming
those willing to work hard for a better life, while also promoting its national
and economic interests.

A Brief History of U.S. Immigration Policy
International migration flows from developing to developed countries
are on the rise across the world. According to the latest United Nations
estimates, more than 200 million people, or 3.1 percent of the world’s
population, live in a country that is not their original country of birth. Table
4-3 shows immigrants as a share of total population in selected advanced
economies. In addition to the historical immigrant-receiving countries such
as Australia, Canada, New Zealand, and the United States, the European
Union, Scandinavian countries, and even Russia now have substantial
foreign-born populations.4
Between 2001 and 2010, 10.5 million foreign-born individuals received
legal-resident status (green cards) in the United States. While this is a large
number, Figure 4-10 illustrates that the flow of legal immigrants is only now
surpassing levels attained at the turn of the 20th century, when the population was much smaller but immigration was virtually unrestricted. The
figure also shows that immigrant inflows, as a share of the total population,
are far below the levels reached in the 19th century. In reaction to the large
inflows in the early 1900s, particularly from Eastern and Southern Europe,
Congress enacted a national quota system in 1921. The 1965 amendments to
the Immigration and Nationality Act repealed the national quota system and
made family reunification a priority. Under current law, immediate relatives
4 The list does not include countries in the Middle East, such as Israel, Jordan, Kuwait, Qatar,
and United Arab Emirates that have substantial guest-worker programs and foreign-born
populations who generally make up 40 percent or more of the total population.

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Table 4-3
Foreign-Born Persons in Selected Countries
Country

Percent of Total Population
1990

2010

New Zealand

15.5

22.4

Australia

21.0

21.9

Canada

16.2

21.3

Spain

2.1

14.1

Sweden

9.1

14.1

United States

9.1

13.5

Germany

7.5

13.1

France

10.4

10.7

United Kingdom

6.5

10.4

Russia

7.8

8.7

Japan

0.9

1.7

Source: United Nations, Department of Economic and Social Affairs, Population Division, Trends in
International Migrant Stock (2008).

of U.S. citizens—spouses, minor children, and parents—are not subject
to annual numerical limits. For other family members including siblings
and adult children of U.S. citizens and spouses and minor children of legal
permanent residents, a numerical cap of 226,000 applies. Over the 10-year
period from 2002 to 2011, an average of 469,777 immediate relatives of U.S.
citizens and an average of 207,927 other family members obtained permanent residency status annually (DHS 2011). As a result of numerical limits
and processing backlogs, applications in the “other family member” category
have long waiting times. The longest waiting periods are for applications
from countries such as China, India, Mexico, and the Philippines; under the
law, no more than 7 percent of total family-sponsored visas can be allotted
to any single country.
Foreign workers also come to the United States through employmentbased green cards. A maximum of 140,000 employment-based slots for
permanent residency are available each year, although the actual cap varies
since unused visas in the family program are carried over to the employment
system. On average over 2002–11, 157,181 employment visas were issued
annually (DHS 2011). Employment-based green cards typically require the
worker to have at least a college degree or documented evidence of special
skills; only 10,000 employment-based green cards are available to workers
without formal education or skill requirements. Individuals can obtain
employment-based green cards for making large direct investments in
job-creating enterprises, although this category is limited to approximately
10,000 visas.

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Figure 4-10
Legal Immigration by Decade, 1820s to 2000s

Percent of population
1.2
1.0

Millions of new immigrants
12

Percent of
population
(left axis)

10
Total new
immigrants
(right axis)

0.8
0.6

8
6

0.4

4

0.2

2

0.0

1820s

1850s

1880s

1910s

1940s

1970s

2000s

0

Source: Department of Homeland Security, Yearbook of Immigration Statistics (2011);
Department of Commerce, Census Bureau.

Foreign-born individuals are also allowed to reside and work in the
United States on a temporary basis through several temporary immigrant
visa programs. For example, individuals are admitted to work in the agricultural industry (H-2A visas) and other seasonal industries (H-2B visas)
for short durations on specific jobs with specific employers. These visas help
alleviate peak seasonal demands in certain sectors of the economy but cannot be used to employ less-skilled workers for longer durations. H-1B visas
permit temporary employment for skilled professionals who are sponsored
by a U.S. employer, typically in science, computer, or engineering occupations. A worker can remain in H-1B status for up to six years. Current law
permits 65,000 new H-1B issuances a year, although up to 20,000 individuals who either hold advanced degrees from U.S. universities or are going to
work for institutions of higher education or government research organizations are exempt from the cap. Applications for the H-1B visa are accepted
starting in April for the following fiscal year. The application window closes
when the annual cap is met. Demand for H-1B visas slowed during the
recent recession but has picked up again, pointing to increasing demand for
workers in the rapidly growing STEM occupations. One study published by
the Department of Commerce found that employment in STEM occupations increased 7.9 percent from 2000 to 2010 while employment in nonSTEM jobs grew just 2.6 percent over the same period. Moreover, STEM

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jobs are projected to grow by 17.0 percent from 2008 to 2018 (Langdon et
al. 2011). In 2010, 151,710 foreign graduate students were enrolled in U.S.
postsecondary institutions in STEM fields (NSF/NIH 2010). Allowing this
population—already here and educated in the United States—to stay by
increasing the number of visas available will ultimately position the Nation
well in the global competition for new ideas, new businesses, and jobs of the
future.
In part because of the limited pathways for less skilled workers to
obtain legal status, an estimated 11.5 million foreign-born individuals in
the United States are undocumented (Hoefer, Rytina, and Baker 2012).
Bipartisan support for strengthened immigration enforcement has resulted
in a well-resourced and modernized enforcement system. While effective, the fiscal burden of this system is also substantial. The Border Patrol
has doubled in size over the past seven years to 21,370 agents in FY 2012.
Spending for the two main immigration agencies—U.S. Customs and Border
Protection and U.S. Immigration and Customs Enforcement—surpassed
$17.9 billion in FY 2012, an amount that is higher than all other spending on criminal Federal law enforcement agencies (Meissner et al. 2013).
Workplace enforcement, which could alleviate some of the fiscal burdens
of border enforcement, has not kept pace. Effective workplace enforcement
would entail enabling employers to quickly and accurately verify employees’
eligibility by using an electronic employment verification system (E-Verify),
and also holding those employers accountable who deliberately break the
law by hiring unauthorized workers or violating labor laws.
The Department of Homeland Security estimates that of the 11.5
million unauthorized immigrant population residing in the United States in
2011, approximately 1.3 million were under 18 years of age (Hoefer, Rytina,
and Baker 2012). Undocumented young people who were brought to the
country as children have no clear path to future legal status that would enable
them to further their education and find gainful employment outside of the
shadow economy. Various versions of legislation to address the undocumented student population, often referred to as the DREAM Act, have
been introduced in recent congressional sessions. The latest effort in 2010
passed the House but failed to pass the Senate. In June 2012, the Secretary
of Homeland Security announced and implemented a new process, known
as “Deferred Action for Childhood Arrivals,” which provides work-status
eligibility and relief from deportation for unauthorized immigrants who
are no more than 30 years old and who arrived in the United States before
age 16. While a smaller number are currently eligible to petition, up to 1.7
million young people could potentially benefit from this program once they
reach the requisite age (Passel and Lopez 2012).
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Foreign-born workers in the United States tend to be concentrated at
both the low and the high end of the educational spectrum. Table 4-4 shows
that 29.1 percent of the foreign-born have less than a high school degree. On
the other hand, 10.9 percent have a master’s degree or higher, a share on a
par with that of the native-born. The table also shows that the foreign-born
are more likely to be of working age, with 67.2 percent of the foreign-born
aged 25–54 years old compared with 55.9 percent of the native population.
The table also shows that foreign-born men are much more likely to be
employed than native-born men.
Other countries that receive large numbers of immigrants, such
as Australia and Canada, admit a majority of their immigrants based on
employment skills. Australian work visas are most commonly granted to
highly skilled workers. Candidates are assessed against a system that grants
points for certain standards of education. In Canada, almost two-thirds
of visas are issued to economic immigrants, primarily skilled workers and
their dependents. Skilled workers are selected on factors such as education,
English or French language abilities, and work experience. In contrast, the
United States has a more “outcome”-based approach to granting visas. For
example, employment visas are awarded to persons with extraordinary
ability (EB-1), outstanding professors and researchers (EB-2), and skilled
and unskilled workers with job offers from a U.S. employer (EB-3). While
Table 4-4
Distribution of Education, Age, and Employment
For Natives and Foreign Born Individuals, 2010–2012
Native

Foreign Born

Less than high school

9.3

29.1

High school, no college

31.7

26.0

Some college or associates

28.2

16.2

Bachelor’s

19.9

17.8

Master’s or higher

10.9

10.9

16-19

0.6

0.3

19-24

6.9

5.0

25-54

55.9

67.2

55-64

17.5

13.6

65+

19.1

13.9

Education Attainment (Age 25+)

Age Group

Work Status
Employed

60.3

62.4

Men

64.7

73.8

Women

56.2

51.2

Note: Sample limited to individuals 16 and over who are not enrolled in school.
Source: Bureau of Labor Statistics, Current Population Survey, Annual Social and Economic Supplement; CEA
calculations.

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some may argue that Canada and Australia might do a better job of attracting skilled immigrants than the United States because of their point-based
systems, a recent study using detailed data compares the United States with
Australia and finds that, by and large, the two countries attract similar immigrants. Skill premiums and geographic proximity, rather than the specific
details of the admission criteria, play the predominant role in determining
the quality of employment-based immigrants (Jasso and Rosenzweig 2008).
Since enactment of the Immigration and Nationality Act of 1965, family reunification has been a cornerstone of U.S. immigration policy. Debate
continues on whether the United States should maintain this family-based
system or move more toward an occupation- and skills-based system. While
the question is often posed as a stark choice between two systems, in reality
the two visa categories—family and employment—complement each other
in important ways. In choosing a country to move to, skilled prospective
immigrants envision a better life not only for themselves but for their families. Using data arranged by year of arrival and country of origin, one study
found a positive correlation between the fraction of immigrants arriving on
sibling preference and mean education levels of the immigrants. The data
seem to support the notion that highly educated immigrants who arrive
based on employment and occupational preference categories then sponsor
their siblings who are also highly educated (Duleep and Regets 1996). As
proposals are made to increase skill-based immigration, it is important to
keep in mind that a welcoming policy toward the family is an important
factor in attracting skilled workers to live and invest in the United States.

The Economic Benefits of Immigration
Conventional theory suggests that the destination country as a whole
gains from immigration, though these gains may be uneven across groups.
Immigrants add to the labor force and increase the economy’s total output.
The gains accrue to natives whose productivity is enhanced by immigrant
workers—often referred to as complementary factors—as well as to capital
owners. A major study published by the National Research Council in 1997
estimated the size of the “immigrant surplus” to be on the order of $14 billion
in 1996 dollars, or 0.2 percent of GDP. Given the size of today’s economy,
this translates into $31.4 billion in 2012 dollars, even without accounting for
growth in the share of the population that is foreign born.
There are additional reasons to think the above calculations may
understate the full economic benefit of immigration. For one, the calculations do not take into account the fact that capital owners may boost investment in response to the increased number of workers, which may induce
further economic growth. For another, the simple approach assumes a
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negative impact on the average wages of native workers that has been difficult to establish empirically. The same National Research Council study
concluded that the body of empirical evidence pointed to a very small negative impact from immigration on wages of competing native workers—on
the order of 1–2 percent and often statistically insignificant.5 In fact, to the
extent that new immigrants crowd out existing workers, research shows that
those most adversely affected are recent immigrants (Lalonde and Topel
1991; Ottaviano and Peri 2012). A new immigrant with limited English
skills, for example, will likely compete closely with other recent immigrants
with poor English ability in jobs that do not require institutional, technical,
or advanced language skills, thereby lowering the recent immigrants’ wages.
Recent studies suggest, in fact, that the skills and talents that immigrants and natives bring to the labor market may not be substitutes for each
other. Low-skilled immigrants may enhance the productivity of high-skilled
natives. Even within skill groups, the various talents that immigrants and
native workers bring to the labor market may complement each other rather
than compete. The intuition behind the gains to both natives and immigrants in this case would follow from the principle of comparative advantage. For example, an immigrant worker may be an extraordinary computer
programmer but have limited English skills. Rather than filling the programming job with a native worker who is not as skilled in this particular task, the
employer might assign the native worker to tasks that use communication
and English language skills. Some of these ideas are pursued in recent work
by Giovanni Peri and co-authors (Peri and Sparber 2009; Ottaviano and Peri
2012). Other research also by Giovanni Peri compares states with differing
levels of immigration and finds that immigration raises productivity by
promoting efficient task specialization (Peri 2012).
Another question regards the impact of immigration on the public
finances of the host country. Immigrants contribute positively to government finances by paying taxes but add to costs by using publicly provided
goods and services such as roads, police, and schools. The 1997 National
Research Council study estimated that, over the long run, a typical immigrant and his or her descendants would contribute about $80,000 more in
taxes (in 1996 dollars) than they would receive in terms of public goods
and services. This would translate into nearly $120,000 in 2012 dollars. This
positive fiscal impact is attributable to several factors: most immigrants
arrive at young ages; their descendants are expected to have higher incomes;
immigrants help to pay for public goods such as national defense that do
not entail congestion costs; and the 1996 Personal Responsibility and Work
5 NRC (1997), chapter 5. Also see Card (1990), Friedberg and Hunt (1995), Card (2009), Cortes
(2008). See Borjas (2003) and Borjas, Grogger, and Hanson (2011) for the opposing view.

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Opportunity Reconciliation Act prohibited new immigrants from receiving
public benefits for five years after arrival.
A recent Congressional Budget Office study also found that allowing
undocumented immigrants a pathway to citizenship is likely to help the
Federal budget. The study estimates that, had a pathway been established,
Federal revenues would have increased by $48.3 billion while Federal outlays
would have increased by $22.7 billion over the 2008–12 period, leading to
a surplus of $25.6 billion. The revenue increase stems largely from greater
receipts of Social Security payroll taxes, while the increase in outlays would
be in the form of refundable income tax credits and Medicaid. This calculation does not take into account possible increases in Federal discretionary
spending. There may be also additional expenditures at the State and local
level on education and healthcare, which are harder to forecast (CBO 2007).
Another important economic benefit of providing a pathway to
earned citizenship is that, by bringing immigrant workers out of the shadows, they will be able to obtain above-ground jobs, advance in their careers,
and contribute more fully to the economy. Moreover, with a pathway to
earned citizenship, immigrant workers and their employers will invest more
in their skills, raising the benefit to the economy even further. Legalizing
this population will also benefit U.S.-born citizens as they need no longer
compete with workers who may work at below market wages due to their
unauthorized status.

A Magnet for High-Skilled Immigration
A growing area of study is how high-skilled immigrants—particularly
those in the STEM fields—contribute to innovation and growth. Based on
the 2010 National Survey of College Graduates conducted by the National
Science Foundation, immigrants represent 13.6 percent of all employed
college graduates, but they account for 50 percent of PhDs working in math
and computer science occupations, and 57.3 percent of PhDs in engineering
occupations (Table 4-5). About two-thirds of these foreign-born PhDs hold
U.S. degrees, suggesting that many of them either immigrated as children or
came to attend U.S. universities and stayed.
Interestingly, one study found that 26 percent of all U.S.-based Nobel
laureates over the past 50 years were foreign born. The same study also
found that in the EU-12 countries, immigrants made up slightly less that
5 percent of total population and accounted for about 4 percent of those
holding masters’ and PhDs, in contrast to the United States (Wasmer et al.
2007).6
6 According to the study, the data for Nobel Laureates were found at the official website of the
Nobel Foundation: http://nobelprize.org/nobel/.

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Table 4-5
Percentage of Foreign-Born College Graduates
by Degree and Occupation, 2010
All

Bachelor’s

Master’s

Professional

Doctorate

13.6

11.8

15.3

12.9

32.2

28.6

20.3

38.1

50.7

44.2

Math/computer sciences

29.2

21.8

42.4

30.5

50.0

Life and related sciences

28.8

14.5

27.3

59.4

44.2

Physical and related sciences

23.9

12.2

21.3

49.6

38.8

24.1

16.2

33.3

44.4

57.3

Total
All sciences

Engineering

Note: Occupation refers to occupation for principal job. Sample limited to employed individuals.
Source: National Science Foundation/National Center for Science and Engineering Statistics, National Survey
of College Graduates (2010).

These statistics support the view that the United States continues to
be a magnet for highly skilled immigrants. Two factors likely play a role.
First, the United States has flexible labor markets that are able to integrate
immigrants relatively quickly. Second, the skill premium is high in the
United States, and individuals with exceptional ability and willingness to
work hard can thrive. These factors have enabled the Nation to benefit from
large inflows of highly skilled workers.

Boosting Innovation and Entrepreneurship
In addition to the benefits already covered, recent studies have shown
that immigrants promote productivity and innovation, directly and also
indirectly through positive spillover effects on native researchers and scientists. Gauthier-Loiselle and Hunt (2010) found that immigrants patent at
two to three times the rate of U.S.-born citizens. The study also found that
immigrants further boost innovation in the economy by having positive
spillovers on the native rate of innovation. Another study found that raising
the number of skilled information-technology workers—as has been done
by raising the cap on H-1B visas—spurs innovative activity in states that
more heavily employ these workers (Kerr and Lincoln 2009).
Studies also have found that immigrants are not only exceptional workers and innovators but also highly entrepreneurial. One study found that 25
percent of venture capital companies between 1991 and 2006 were started
by immigrants (Anderson and Platzer 2006). Another found that immigrants started 25 percent of engineering and technology companies founded
between 1995 and 2005 (Wadhwa et al. 2007). Even outside the high-tech
sector, one study found that immigrants are more likely than natives to start
a company with more than 10 workers (Fairlie 2012). Immigrants are 30 percent more likely to form new businesses than U.S.-born citizens. A study by
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Partnership for a New American Economy found that more than 40 percent
of Fortune 500 companies were founded by immigrants or their children.
The study also found that these companies are responsible for many jobs
here and abroad—employing more than 10 million people worldwide—and
that they generate annual revenues of $4.2 trillion.
While there is clearly room for further study, these studies generally
provide little systematic evidence that increases in the supply of foreign scientists and engineers discourage natives from entering these fields or from
engaging in innovative activity. For example, Gauthier-Loiselle and Hunt
found that the inflow of high-skilled immigrant science and engineering
workers into a state did not decrease the number of patents originated by
native science and engineering workers in the state. Borjas (2007) also found
that, on the whole, rising enrollment of foreign graduate students did not
discourage native enrollment in science and engineering programs, although
there were some disparate impacts across groups.
President Obama has supported a recent initiative to graduate 1
million more college graduates with STEM degrees. At the same time, all
evidence points to the fact the United States is extraordinarily successful at
attracting highly skilled workers from other countries. Sensible immigration
policy would entail taking advantage of this unique situation and allowing
more high-skilled immigration. The lack of clear evidence of crowding out
bolsters confidence that these are not two conflicting policy goals.

Conclusion
With slowing population growth and aging of the workforce, America
needs more workers. The Nation also needs to invest in the education, skills,
and training of its citizens so they can fill the jobs of the future. Over the
past four years, President Obama has taken an aggressive stance toward
combating the rising cost of college. The expansion of the Federal Pell Grant
program and the American Opportunity Tax Credit has made college more
affordable for millions of students and families. Challenges still remain,
including the continuing rise of tuition and levels of student debt. In his
recent State of the Union address, President Obama called upon colleges
to join in the effort to keep costs down. He proposed using metrics such as
value, affordability, and student outcomes in distributing Federal campusbased aid. He also announced a new Race to the Top program for College
Affordability and Completion, which will reward states who are willing to
change their higher education policies and practices to contain tuition costs
and ease students’ progress toward a college degree.
With the potential to address both the need for workers and the need
for skills, the gains from commonsense immigration reform loom large.
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Immigration can boost the economy by adding workers and making our
labor force younger and more dynamic. Offering a path to citizenship to
more than 11 million currently undocumented residents will further expand
the economy as this group invests in education, finds gainful employment,
and pays taxes. Border enforcement has proven to be effective, but it is a
drain on our public finances. Smart enforcement that balances border security with crackdowns on worksite fraud will not only have higher returns
going forward, but it will also save taxpayers money. America has historically been a magnet for capable and hard-working immigrants who seek
opportunities and a better life. Many of these immigrants are innovators
and entrepreneurs. The smart policy ahead is to leverage America’s unique
advantage for future prosperity and growth.
Smart policy also involves making sure that all Americans benefit
from economic growth. In his 2013 State of the Union address, President
Obama reiterated his commitment that an honest day’s work is rewarded
with decent pay, enough to feel secure and support a family. A Federal
minimum wage that keeps up with the cost of living, policies that strengthen
workers’ ability to bargain for decent wages and safe working conditions,
and tax policies such as refundable credits that allow lower-income families
to invest in their children’s education, are important pieces of the foundation upon which an economy that works for the middle class is built.

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C H A P T E R

5

REDUCING COSTS AND
IMPROVING THE QUALITY
OF HEALTH CARE

I

n March 2010, the President signed into law the Affordable Care Act.
Provisions of the Act have already helped millions of young adults obtain
health insurance coverage and have made preventive services more affordable for most Americans. When fully implemented, the law will expand
coverage to an estimated 27 million previously uninsured Americans and
ensure the availability of affordable comprehensive coverage through traditional employer-sponsored insurance and new health insurance marketplaces or exchanges. There are signs that the Affordable Care Act has started
to slow the growth of costs and improve the quality of care through pay-forperformance programs, strengthened primary care and care coordination,
and pioneering Medicare payment reforms. These provisions, as well as
others in the Affordable Care Act, will help to bend the cost curve downward
while laying the foundation for moving the health care system toward higher
quality and more efficient care.

Health Care Spending
Health care spending has increased dramatically over the past half
century, both in absolute terms and as a share of gross domestic product
(GDP) (Figure 5-1). Spending in the U.S. health care sector totaled $2.7 trillion in 2011, up by a factor of 3.9 from the $698.3 billion (in 2011 dollars)
spent in 1980. Health care spending in 2011 accounted for 17.9 percent of
GDP—almost twice its share in 1980.
Some of the increase in health care spending is attributable to demographic changes. Of the real increase in spending on prescription drugs,
office-based visits, hospitalizations, and all other personal care from 1996
to 2010, for example, 11.5 percent can be accounted for by the changing

161

Percent
20
18

Figure 5-1
GDP and Health Spending, 1980–2011

Trillions of 2011 dollars
3.5
2011
3.0

Health spending as
a share of GDP
(left axis)

16
14

2.5

12

2.0

10

Real health spending
(right axis)

8
6

1.5
1.0

4

0.5

2

0.0
0
1980
1985
1990
1995
2000
2005
2010
Source: Centers for Medicare and Medicaid Services, National Health Expenditure
Accounts; Bureau of Economic Analysis, National Income and Product Accounts; CEA
calculations.

age structure of the population and 22.8 percent can be accounted for by
increases in the size of the population (Figure 5-2).1 The effects of population aging will become a more important driver of higher spending in
coming years; by 2030, one in five Americans will be over age 65, compared with only one in eight today, and per capita medical costs in a given
year are approximately three times greater for those 65 and over than for
younger individuals. The majority of the increase in health care spending,
historically, has come from increases in the amount spent per person over
and above any effects attributable purely to population aging and population
growth, reflecting increases in the use of medical services driven at least in
part by the development of new technologies and increases in unit costs that
exceed the overall rate of inflation.
1 Total annual spending on prescription drugs, office-based visits, hospitalizations and other
personal care between 1996 and 2010 was estimated using the Medical Expenditure Panel
Survey (MEPS). To estimate the effect of changes in the age distribution between 1996 and
2010 on spending, age-specific spending levels and total U.S. population were held constant
at 1996 levels, but the proportion of the population within each age group was allowed to
reflect the 2010 age distribution. To estimate the effect of population growth between 1996
and 2010 on spending, total spending increases were calculated holding age-specific spending
levels constant at 1996 levels, but allowing both the age distribution and total population to
reflect their 2010 values. Then, the estimated spending increases due to changes in the age
distribution were subtracted from this figure.

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Figure 5-2
Contribution of Population Growth and Aging
to Health Care Spending, 1996–2010

Billions of 2010 dollars
1,400
1,200
1,000

Spending due to
population growth

Spending due to aging
population

800
Medical spending due to
other factors

600
400
200

0
1996
1998
2000
2002
2004
2006
2008
2010
Source: Department of Health and Human Services, Agency for Healthcare Research and
Quality, Medical Expenditure Panel Survey; CEA calculations.

Long-Term Spending Growth
Why has health care spending risen so much, even after taking into
account changes in the size and age mix of the population? A likely piece of
the story is that long-term growth in health care wages has not been accompanied by corresponding labor-saving technological progress. The theory of
“cost disease” as developed by Baumol and Bowen (1966) notes that laborsaving technological progress has led to significant increases in labor productivity and hence wage growth in some important parts of the economy
(such as the manufacturing sector). To compete for workers, labor-intensive
sectors such as health care, education, and the performing arts also must
raise their wages. According to the theory, productivity growth has been
slower in these sectors. The result, the argument concludes, is an increase in
the relative cost of output in these labor-intensive sectors, as higher costs are
passed on to consumers in the form of higher prices.
Consistent with this theory, Nordhaus (2006) found that labor-intensive sectors generally experienced rising relative prices between 1948 and
2001. Nordhaus also found that shifts in labor from sectors that experienced
labor-saving technological progress to sectors that remained relatively laborintensive lowered overall productivity growth, as the share of labor-intensive
sectors in overall output rose over the second half of the 20th century.

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The cost-disease diagnosis assumes that, in labor-intensive sectors,
it is difficult to reduce the amount of labor required to produce a given set
of outputs. The health care sector, however, has experienced substantial
technological progress, as new pharmaceutical therapies, diagnostic and
medical devices, and surgical procedures have been introduced, allowing
many conditions to be treated more effectively than in the past.
While some of these innovations have been labor-saving (some pharmaceuticals, for example), most others are complementary to expensive
specialist labor (such as imaging and advances in surgical procedures).
Consequently, technological change in medicine has caused the cost per
treatment to rise, even as improvements in clinical effectiveness have led
to increases in medical productivity. Technological change in medicine
has contributed to long-term increases in spending. A recent study found
that a quarter to a half of the rise in health care spending since 1960 can
be explained by technological change in the health care system (Smith,
Newhouse, and Freeland 2009). And rather than satisfying a relatively fixed
demand for health care at lower cost, the development of many of these new
technologies has contributed to an increase in the demand for health care
services.
For some researchers, the importance of technological change for
health care spending points to increases in demand as an additional explanation to the cost disease theory for why health care spending has increased
disproportionately with income. If health care is a “super-normal good”—a
good associated with an elasticity of consumption with respect to income
that is greater than one—then as incomes rise by a certain percentage, consumption of health care rises by a greater percentage. Hall and Jones (2007)
argue that this can happen if, after achieving a certain level of consumption,
individuals prefer to spend additional income on life-extending health care
(which allows for consumption in the extended years of life) rather than on
extra consumption now. Consequently, as incomes rise, people choose to
spend ever more on health care over other goods.
The disproportionate effect of income on the demand for health care
may also operate through larger institutional mechanisms. Consistent with
this idea, Smith, Newhouse, and Freeland (2009) find that income growth
affects health care spending growth primarily through the actions of governments and employers on behalf of large insurance pools, suggesting a key
role for payment reform in affecting medical spending growth.
These factors are not only a U.S. phenomenon. Indeed, while the
United States has higher levels of health care spending than other members
of the Organisation for Economic Co-operation and Development (OECD),
the annual real rate of growth in health care spending per capita in the
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United States between 1960 and 2010 was not too different from elsewhere,
averaging 4.13 percent compared with 3.62 percent in the other OECD
countries, adjusted for purchasing power parity. In more recent years, health
care spending has continued to grow at similar annual real rates—3.10
percent in the United States and 3.30 percent in the other OECD countries
between 2000 and 2010, somewhat below the long-term rates of spending
growth observed since 1960.

Medical Productivity
Productivity growth in health care largely has taken the form of
improvements in the quality of care, with developments in new procedures
and care practices contributing to increased survival, decreased morbidity,
reduction in pain, and less onerous treatment administration in many cases.
A full accounting of medical productivity growth should reflect
changes not only in cost per service but also in health outcomes. However,
medical productivity is often hard to measure because health outcomes
are hard to measure. Recent studies comparing increases in life expectancy to increases in treatment costs over time suggest that productivity
growth in the health care sector has been enormous. For example, Cutler
and McClellan (2001) found that the value of increased survival rates and
decreased morbidity rates as a result of improved treatment of heart attacks,
low-birth-weight infants, and depression over the past few decades has far
exceeded the increased spending on these conditions over the period. Using
a similar methodology, Philipson et al. (2012) found that survival gains
across all cancer patients in the United States between 1983 and 1999 cost
on average only $8,670 per life-year gained. Estimates of the value of a statistical life-year, based on compensating wage differentials that measure the
implied trade-off between wages and increased risk of fatality, are typically
multiples higher (Viscusi and Aldy 2003). Therefore, even if some piece of
the apparent gain in longevity results from earlier diagnosis, the introduction of these cancer therapies represents an enormous improvement in
productivity. Faster growth in spending on cancer treatment in the United
States than in Europe over this period is sometimes mistakenly taken to
indicate the inefficiency of U.S. medical care, but it is also the case that the
improvement in life expectancy for cancer patients was greater in the United
States than in Europe. From 1983 to 1999, U.S. spending per cancer patient
rose by $16,700 (in 2010 dollars) more than European spending per cancer
patient (Figure 5-3), and U.S. cancer patient life expectancy rose by 0.4 years
more than European cancer patient life expectancy (Figure 5-4), implying a
cost per extra life year saved of approximately $42,000. Given the consensus

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2010 dollars
80,000

Figure 5-3
Cancer Spending per New Cancer Case, 1983–1999
1999

70,000
United States

60,000
50,000

$25,100

$8,400

40,000

European Union

30,000
20,000
10,000
0
1983

1985

1987

1989

1991

1993

1995

1997

1999

Source: Philipson et al. (2012), updated data provided by the authors.

Figure 5-4
Life Expectancy after Cancer Diagnosis, 1983–1999

Years from diagnosis
12

11.1

11
10

9.2

9
8
7

United
9.7States

10.2

8.5

9.3

1.4
years
7.1

1.8
years

7.6

7.9

8.4
Selected
European countries

6
5

1983–1985

1986–1988

1989–1991

1992–1994

1995–1999

Note: European countries included are Finland, France, Germany, Iceland, Norway, Slovakia, Slovenia,
Sweden, Scotland, and Wales.
Source: Philipson et al. (2012), updated data provided by the authors; Surveillance, Epidemiology and
End Results (SEER); European Cancer Registry (EUROCARE).

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in the literature that the value of additional life-years is much higher, the
additional U.S. spending has been a good value.
Murphy and Topel (2006) directly estimate the aggregate monetary value of increases in longevity, finding that, if valued in the national
accounts, increases in life expectancy since 1970 would have added $3.2
trillion a year to national wealth. While a different set of assumptions about
the statistical value of a life year, the elasticity of intertemporal substitution,
and the value individuals place on non-working hours lowers the aggregate
valuation of the observed longevity increase, the order of magnitude of the
estimated valuation nonetheless suggests an enormous return to the increase
in health care spending over this period.
In general, estimating how much the productivity of health care has
grown is a difficult task. Changes in health outcomes, morbidity rates, and
patient convenience are hard to measure, hard to attribute to the use of specific technologies, and hard to value. Furthermore, limitations in available
data mean that spending often cannot be disaggregated to the treatment of
specific diseases or patients. Given these difficulties, it is widely agreed that
aggregate measures of the output of the health care sector do a poor job of
capturing the effects of productivity growth. Developing better methods to
measure real output and productivity growth in health care is an important
area of ongoing research (Data Watch 5-1).

Sources of Inefficiency in Health Care Spending
Although growth in overall medical productivity has been large, not
all increases in medical spending are productive. Cutler and McClellan
(2001) showed that improved treatment of heart attacks produced significant increases in patient longevity between 1984 and 1998. By contrast,
Skinner, Staiger, and Fisher (2006) found little improvement in survival
rates among heart attack patients between 1996 and 2002 despite significant
growth in treatment costs. The latter study also found that the regions with
the largest increases in spending also experienced the smallest gains in survival. Geographic variation in practice patterns and health outcomes implies
that more than 20 percent of Medicare spending on heart attack treatment
produces little health value (Skinner, Fisher, and Wennberg 2005). The case
of heart attack treatment points to more general inefficiencies in the allocation of spending within the health care system.
Among the many possible sources of spending inefficiencies, several
stand out as key sources of waste. First, the fragmentation of the delivery
system contributes to a failure to provide patients with necessary care. That
in turn can lead to complications and readmissions, particularly for the
chronically ill for whom care coordination is most essential for health.
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Data Watch 5-1: Toward Disease-Based Health Care Accounting
Existing national data on health expenditures generally are organized by the type of medical care that individuals purchase (such as doctor visits or drugs). For addressing questions related to the productivity
of health care, however, data on health care spending by disease would
be far more useful.
Switching to disease-based accounting poses a challenge because
patients often suffer from more than one disease at once, making it
difficult to allocate spending to specific diseases. Three conceptual
approaches to allocating spending across disease have been suggested:
tracking each encounter with the health care system; tracking disease
“episodes”; or identifying all conditions a person has and using regression analysis to allocate spending to diseases. All three approaches have
advantages and limitations, and a consensus has not yet developed on
which one is preferable. Whichever approach is adopted, the universe of
conditions will need to be categorized into a set of disease groups, at an
appropriate level of detail, to which medical costs then can be assigned
for analysis.
The Medical Expenditure Panel Survey (MEPS) is a nationally
representative survey that provides information on most health spending, although it fails to capture spending on behalf of institutionalized
patients and active duty military. The MEPS sample is too small,
however, to represent rare conditions. Although not comprehensive in
their coverage, data on health care claims provide another valuable—and
potentially much more detailed—source of information on health care
spending. In addition to data on spending, data on health outcomes that
can be linked to the disease-based spending data also are needed.
Important progress has been made toward developing diseasebased health care data. The Bureau of Economic Analysis is working on
a health care satellite account that will provide disease-based measures
of household medical expenditures. These estimates will be based on
private insurance claims data, Federal data on Medicare and Medicaid
spending, and data from MEPS on the uninsured. Simultaneously, the
Bureau of Labor Statistics is developing disease-based price indexes that
account for shifts in treatment patterns. These indexes will be useful
to the Bureau of Economic Analysis for decomposing spending into
changes in prices versus changes in quantities.
The Affordable Care Act has significantly increased funding for
research on patient-centered outcomes, and data will be available to
qualified entities to evaluate the performance of providers and suppliers with respect to quality, efficiency, effectiveness, and resource use.
Under the President’s Open Data initiative, the Department of Health

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and Human Services has launched a Health Data Initiative to promote
the availability of Medicare and Medicaid data, where appropriate, to
researchers and entrepreneurs. Paralleling these initiatives, the Health
Care Cost Institute, a nonprofit organization, has developed a claims
database to be made available to researchers to foster a better understanding of what drives health care costs. These administrative data
on claims hold the potential for further progress on understanding the
drivers of health care spending increases and identifying high value
medical care.

Second, lack of care coordination also contributes to duplicate care
and overtreatment, a source of waste exacerbated by payment systems
that compensate physicians based on the number of services provided (see
Economic Applications Box 5-1). Overuse of expensive medical technologies
is particularly costly, and some research suggests that a significant portion
of coronary artery bypass graft surgery, angioplasty, hysterectomy, cataract
surgery, and angiography is of questionable or low medical value (Goldman
and McGlynn 2005).
Third, the failure of providers to adopt widely recognized best medical
practices also contributes to waste. These failures include lack of adherence
to established preventive care practices and patient safety systems, as well
as widespread failure to adopt best treatment practices. In cases where the
best medical practice is both clinically more effective and lower in cost—for
example, the use of beta blockers in the treatment of acute myocardial
infarction (Skinner and Staiger 2005, 2009)—failure to follow these practices
results in worse clinical outcomes and higher readmissions and contributes
to wasteful spending.
Finally, payment fraud also adds to system waste, not only through
inappropriate payments but also through the administrative burden on honest providers who must adhere to the regulatory requirements of unavoidable but burdensome fraud detection systems.
Taken together, fragmentation of care, overtreatment, failures of care
delivery, and payment fraud have been estimated to account for between
13 and 26 percent of national health expenditures in 2011 (Berwick and
Hackbarth 2012). The magnitude of this waste offers an equally large opportunity for spending reductions and improvement in quality of care—an
opportunity that underpins many of the provisions of the Affordable Care
Act.

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Economics Application Box 5-1: Matching in Health Care
Traditional economic analysis focuses on markets in which prices
and quantities adjust so that in principle, supply equals demand. In
some markets, however, prices do not exist and cannot be used to
allocate resources. Gale and Shapley (1962) made early theoretical
contributions to our understanding of how markets can be designed
to allocate resources efficiently in the absence of prices. Taking the
“marriage market” as an example, Gale and Shapley studied how, in the
absence of prices, these markets can produce stable matches—matches
where no alternative pairing would make both individuals in any
match better off. These principles were extended by Roth, who applied
them to the practical design of market institutions—for example, the
market for medical students in residency programs (Roth 1984), and
the assignment of students to public high schools in New York City and
Boston (Abdulkadiroglu, Pathak, and Roth 2005). For these pioneering
contributions, Shapley and Roth were awarded the 2012 Nobel Prize in
Economic Sciences.
The market for live kidney transplants is yet another market where
prices do not determine allocation. Paying for organs is a felony under
the 1984 National Organ Transplant Act. Patients can receive a kidney
from a compatible donor or are placed on a waiting list for a cadaveric
kidney. Currently, nearly 95,000 patients in the United States are waiting
for a kidney transplant. Dialysis for these patients costs approximately
$60,000 a year, for a total of $30 billion a year, or 6.7 percent of total
Medicare spending, the single most expensive component of Medicare.
In 2011, there were about 11,000 transplants of deceased donor kidneys
and only 5,770 transplants from living donors; in the same year, more
than 4,700 patients died while waiting for a kidney transplant.
Many patients have willing potential donors. However, immunological incompatibility greatly limits the number of transplants using
live kidneys, which are preferred to cadaverous kidneys for their tissue
quality and greater longevity. Patients receiving a live kidney transplant
are estimated to live 10-15 years longer than they would on dialysis.
Increasing exchanges between incompatible patient-donor pairs
would greatly expand the opportunity for dialysis patients to receive
a living donor kidney, and increase the quality of matches. In paired
kidney exchanges, a donated kidney from one (immunologically incompatible) patient-donor pair is transplanted in the patient of a second
patient-donor pair, and vice versa. The potential for improving the
number of live kidney transplants is greater with “chains”—exchanges
involving many donor-recipient pairs. The 2007 amendment to the
National Organ Transplant Act clarified that kidney paired donations

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(KPD) do not constitute “valuable consideration” (that is, financial compensation), thereby paving the way for the creation of KPD exchanges.
The economic principles of stable matches developed by Shapley
and Roth can be applied to KPD exchanges. Whereas the concept of
stability in the medical residency setting, for example, is based on the
mutual preferences of medical students and residency programs, stability
in a kidney exchange is primarily based on obtaining the best matches
along immunological criteria. Using these principles, transplant centers
have established KPD programs, as have nonprofit organizations such
as the New England Program for Kidney Exchange, founded by Roth
and colleagues. Congress also established a national KPD pilot program,
operated under the Organ Procurement and Transplantation Network
(OPTN) as a nonprofit under Federal contract.
In 2011, the separate pilot KPD programs, including OPTN,
resulted in 430 transplants—a promising start to paired kidney exchanges,
but nevertheless representing only a fraction of the potential number of
possible transplants.
Computer models suggest that many more transplants could be
achieved each year if there were a nationwide pool of all eligible donors
and recipients. A larger pool of eligible donor-recipient pairs also could
potentially increase the quality of matches. A living kidney transplant
(and all subsequent care) saves money over dialysis after roughly two
years. On average, Medicare would save $60,000 a year for every patient
who receives a living kidney transplant rather than continuing to receive
dialysis, all while increasing the life expectancy of a kidney recipient by
10–15 years, again relative to dialysis treatment.

Early Implementation of the Affordable Care Act
The Affordable Care Act includes a series of provisions that will
transform the Nation’s health care system. By expanding coverage, the
health reform law stabilizes insurance markets and makes health insurance
affordable. The Affordable Care Act also includes important provisions that
are aimed at reducing inefficient spending, promoting competition, and
improving the quality of medical care.

Economic Benefits of Insurance
Insurance provides important economic benefits to covered households. It covers unforeseen medical expenditures, allowing individuals to
receive necessary medical treatment without suffering potentially crippling
financial consequences.
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The 2008 Medicaid expansion in Oregon provided a unique setting in
which to study the effects of health insurance on health and financial security. Because access to the Oregon Medicaid coverage expansion was offered
through a lottery, the benefits of insurance could be estimated without the
usual statistical concerns that purchasers of insurance differ from non-purchasers in ways related to health and financial outcomes. Finkelstein et al.
(2011) found that, after one year of Medicaid coverage, previously uninsured
adults in Oregon were 10 percent less likely to report having depression and
25 percent more likely to report their health as good, very good, or excellent.
They also experienced lower financial strain because of medical expenses,
including lower out-of-pocket expenditures, lower debt on medical bills,
and lower rates of refused medical treatment because of medical debt, than
individuals who were not randomly assigned to Medicaid coverage.
The benefits of having insurance coverage are large. A recent study
(CBO 2012a) estimated that the insurance value of Medicaid to enrollees
in the lowest quintile of income earners is equivalent to 11 percent of their
before-tax income, defined by the CBO as market income plus cash transfers. As a comparison, real average before-tax incomes in the lowest quintile
rose 15 percent between 1995 and 2009, while real incomes in the highest
quintile rose 24 percent. Hence, the value of Medicaid is roughly comparable
to the additional income that would have kept average income in the lowest
quintile growing at the same rate as average income in the highest quintile.

Expanding Affordable Health Insurance Coverage
The Affordable Care Act is projected to increase the number of
insured individuals in the United States by 14 million in 2014 and by 27
million in 2022 (CBO 2012b). The requirement that health insurance plans
offer dependent coverage to children up to age 26 went into effect in 2010.
Sommers (2012) found that this provision resulted in more than 3 million
uninsured young adults gaining health insurance between September of
2010 and December of 2011.
Looking ahead to 2022, the Congressional Budget Office (CBO
2012b) projects that the Affordable Care Act will lead to an additional 12
million people being insured through Medicaid and the Children’s Health
Insurance Program (CHIP), with the remainder of the estimated 27 million newly insured individuals covered through employer-based insurance,
the Affordable Insurance exchanges, or the Small Business Health Options
Program (SHOP) exchanges (Economics Application Box 5-2). The law
likely will cause some firms that currently do not offer health benefits to
begin doing so, and some workers who are currently uninsured will take
up employer coverage that is already offered. At the same time, the new
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Economics Applications Box 5-2: Economics of Adverse
Selection and the Benefits of Broad Enrollment
In health insurance markets, adverse selection occurs when relatively unhealthy individuals are more likely than healthy individuals to
purchase health insurance coverage at a given price. Insurers understand
this tendency and attempt to set premiums to reflect average expected
expenditures in a plan. The selection of relatively unhealthy enrollees
into coverage raises average expected expenditures, resulting in higher
premiums and more adverse selection into coverage.
Adverse selection explains why offered premiums in the individual
and small group health insurance markets often are too high for most
healthy people compared with the health costs they actuarially can be
expected to incur, meaning that they either pay too much for coverage
or choose to go uninsured rather than pay the high premiums. In some
cases, insurance markets subject to extreme adverse selection may disappear completely (Cutler and Reber 1998).
Encouraging broad participation in health insurance coverage
helps tremendously to solve the market failure associated with adverse
selection. For example, adverse selection is virtually nonexistent in the
large group employer sponsored insurance (ESI) market. Take-up rates
in this market are very high, thanks both to the tax advantages associated
with ESI and to the fact that employers typically pay a portion of premiums, which makes ESI a good deal for the vast majority of employees.
While employer contributions are offset by lower wages in equilibrium
(Gruber 1994; Baicker and Chandra 2005), employees who decline
coverage rarely recoup the employer contribution on the margin. The
large enrollment in many ESI plans means that a small number of high
expenditure enrollees does not dramatically affect premiums for a large
risk pool. This prevents adverse selection from taking root and reinforces
broad enrollment through premium stabilization and affordability.
Similarly, the Affordable Care Act encourages broad enrollment
through the widespread accessibility of health insurance exchanges, the
individual responsibility requirement related to the purchase of health
insurance, and the financial assistance offered to lower-income earners
to purchase private plans on an insurance exchange. Other provisions of
the Affordable Care Act raise consumer awareness and foster consumer
choice through information campaigns, standardization, and consumer
search tools, similar to those implemented in the successful rollouts
of the Medicare Advantage and Medicare Part D prescription drug
programs. As in ESI, broad enrollment in the exchanges is expected to
foster premium stability and affordability and to reduce the incidence of
cost-shifting from uncompensated care to the insured.

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options created by the Affordable Care Act may make employer-sponsored
insurance (ESI) coverage less attractive for some employers. The net effects
on the prevalence of employer-sponsored coverage, however, are likely to
be small.
Based on microsimulations of firms’ optimizing behavior, analysts
have estimated effects of the Affordable Care Act on the number of individuals with ESI coverage ranging from a 1.8 percent decline (CBO 2012b) to a
2.9 percent increase (Eibner et al. 2011). Other estimates fall with this narrow range (Buettgens, Garrett, and Holahan 2010; Lewin Group 2010; Foster
2010) and are consistent with the small positive effects of health reform on
ESI coverage observed in Massachusetts, where similar statewide health
insurance reforms were legislated in 2006 (Long, Stockley, and Yemane
2009).

Consumer Protection
The Affordable Care Act also establishes numerous consumer protections related to the purchase of private health insurance, some of which are
already in effect. Starting in 2014, individual and group health plans will not
be allowed to deny or limit coverage on the basis of an individual’s health
status. And within certain limits, premiums will be allowed to vary by age,
geography, family size, and smoking status, but not by individual health
status, gender, or other factors.
The Affordable Care Act also requires that double-digit increases in
insurance premiums be reviewed by States or the Department of Health and
Human Services, with insurance companies needing to provide justification
for any such premium increases. Plans may be excluded from an insurance
exchange based on premium increases that are not justified. Further, since
the beginning of 2011, most insurers have been allowed to retain no more
than 20 percent of consumers’ premiums for profits, marketing, and other
administrative costs. Overhead and administrative costs in excess of this
limit are to be rebated to consumers (or in the case of employer-sponsored
insurance, to employers, who must pass a share of these rebates to their
employees as cash, improved benefits, or lower premiums, with the share
depending on the proportion of the total health plan premium paid by the
employees). As of August 2012, an estimated 12.8 million Americans had
received rebates totaling $1.1 billion from insurers as a result of this 80/20
medical loss ratio rule.

Health Care Spending and Quality of Care
The Affordable Care Act includes a series of provisions designed
to reduce spending while improving the quality of care in the health
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care system. Reducing excessive payments to Medicare Advantage plans,
strengthening antifraud efforts, and initiating reforms to Medicare provider
payment systems, among other policies, are expected to extend the life of the
Medicare Trust Fund by an additional eight years. These reforms complement numerous other provisions that improve health care quality while
lowering costs.
The Hospital Value-Based Purchasing Program went into effect in
October 2012. The program rewards more than 3,500 hospitals for providing high-quality care and reduces payments for hospitals demonstrating
poor performance. Similar pay-for-performance programs in Medicare
Advantage and the end-stage renal disease prospective payment system
encourage higher-quality care and more efficient care delivery. Additionally,
pay-for-reporting initiatives in which providers are rewarded for reporting
procedures and outcomes have been launched in virtually every Medicare
payment category, and mark the first step toward value-based purchasing.
The Partnership for Patients program is a public-private partnership
that aims to reduce hospital complications and improve care transitions
in more than 3,700 hospitals and partnering community-based clinical
organizations. By stopping millions of preventable injuries and complications in patient care, this nationwide initiative has set as its goal saving
60,000 lives and up to $35 billion in spending, including up to $10 billion in
Medicare spending, over the three years following its launch. Data provided
by the Centers for Medicare and Medicaid Services (CMS) show that since
the Partnership for Patients program was introduced in 2011, the hospital
readmission rate within Medicare has fallen to 17.8 percent, down from
an average of about 19 percent that had prevailed from 2007 through 2010
(CMS 2013) (Figure 5-5). The data also show that the declines were larger
in hospitals participating in Partnership for Patients.
The Affordable Care Act builds on the investments made in the
Recovery Act to encourage the use of health information technology. By
making it easier for physicians, hospitals, and other providers to assess
patients’ medical status and provide care, electronic medical records may
help eliminate redundant and costly procedures. More than 186,000 health
care professionals (about one-third of eligible providers) and 3,500 hospitals
(about two-thirds of eligible hospitals) have already qualified for incentive
payments for the meaningful use of electronic health records authorized by
the Recovery Act.
The Affordable Care Act also launched extensive efforts to prevent and
detect fraudulent payments under Medicare, Medicaid, and the Children’s
Health Insurance Program. An important goal of the Administration’s
efforts has been to prevent fraudulent payments before they are made rather
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Figure 5-5
Acute Care Hospital Readmission Rates, 2007–2012

Percent of cases
20

2007–2011 Average

19

18
Sep-2012

17
2007

2008

2009

2010

2011

2012

Source: Center for Medicare and Medicaid Services, Office of Enterprise Management.

than chasing them afterward, but there also are ongoing efforts to recover
fraudulent payments if they occur. Antifraud efforts have recovered a
record-high $14.9 billion over the last four years.

Medicare Payment Reform
Traditional fee-for-service Medicare reimburses physicians for each
service provided, creating incentives for overutilization. Spending inefficiencies are exacerbated by fragmentation across providers, who historically
have had few incentives to coordinate care. Likewise, the prospective payment system (PPS) for Part A hospital services, which is designed to control
costs by paying hospitals a prospective amount per diagnostic-related group
(DRG) episode, is not immune to waste. While the DRG-based PPS encourages more efficient care and reductions in length of stay compared with
cost-based reimbursement (Sloan et al. 1988; Seshamani, et al. 2006), it also
can encourage a reduction in necessary care, leading to negative short-term
health effects and readmissions (Cutler 1995; Encinosa and Bernard 2005;
Seshamani, et al. 2006). Further, the inpatient PPS also can be susceptible to
“upcoding,” whereby providers code patients as being sicker than they are
to raise the risk-adjusted prospective payments (Cutler 1995; Carter et al.
2002; Dafny 2005).

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To curb these inefficiencies, the Affordable Care Act has established
initiatives that lay a foundation for reforming care delivery and physician
payment. At their core, these initiatives are designed to foster greater coordination of care across providers, while simultaneously aligning financial
incentives to encourage provider organizations to deliver higher-quality,
more efficient medical care. Each initiative builds on a core of clinical and
patient engagement quality measures to ensure that cost savings are derived
from more efficient delivery of care and not reduced patient access or care
quality.
One such initiative is the Medicare Shared Savings Program (MSSP).
Under this program, providers deliver care through accountable care
organizations (ACOs), contractual organizations of primary care physicians, nurses, and specialists responsible for providing care to at least 5,000
beneficiaries. The Federal Government shares any savings generated for
those beneficiaries, relative to benchmarks, with ACOs that meet rigorous
quality standards, giving the ACOs incentives to invest in delivery practices,
infrastructure, and organizational changes that help deliver higher-quality
care for lower costs. Currently, more than 4 million beneficiaries receive
care from more than 250 ACOs participating in the MSSP and other CMS
projects, with ACO participation and covered beneficiaries continuing to
increase as the program expands.
The Affordable Care Act also created the Center for Medicare and
Medicaid Innovation, which is charged with identifying, testing, and
ultimately expanding new and effective systems of delivering and paying
for care. The CMS Innovation Center is authorized to invest up to $10 billion in initiatives that have the potential to reduce program expenditures
while preserving or enhancing quality of care furnished to individuals
under Medicare, Medicaid, and the Children’s Health Insurance Program.
Initiatives within the CMS Innovation Center include shared savings models, as well as bundled payments to hospitals and post-acute-care providers.
The Innovation Center’s Pioneer ACO program is a more aggressive
version of the MSSP and is open to organizations that have had success with
risk-based payment arrangements. Pioneer ACOs may keep a greater share
of Medicare savings than ACOs in the MSSP but are also at greater risk for
losses if spending benchmarks are not met. Successful Pioneer ACOs are
also eligible to move to a population-based payment arrangement whereby
they assume greater financial risks and rewards for a predetermined set of
patients. This greater risk-reward profile further encourages investments in
care coordination and best practice delivery reforms. Pioneer ACOs must
also develop similar outcomes-based payment arrangements with other

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payers, extending payment innovations to the commercial market and maximizing the impact of the program’s incentives.
Currently, roughly 860,000 beneficiaries are enrolled in 32 Pioneer
ACOs. The Pioneer program is just entering its second year, so it is too early
for any comprehensive assessment, but Pioneer ACOs do seem to be making
substantial investments in infrastructure and care processes. Infrastructure
investments include health information technology adoption and improved
data analytic capabilities, which enable providers to identify opportunities
for improvements in care processes and the quality of care. For example,
the potential savings associated with early identification and treatment of
patients with high propensity for developing a chronic disease have led some
Pioneer ACOs to make organizational changes that place greater focus on
primary care and disease management. CMS is supporting Pioneer ACOs by
providing privacy-protected patient information to promote care coordination, hosting collaborative learning networks, and offering other technical
assistance.
Care coordination is also central to the Comprehensive Primary
Care (CPC) initiative. Primary care is critical to promoting overall health
and reducing medical spending. Yet because any one insurer accounts for
only a fraction of a provider’s business, insurers underinvest in primary
care systems that would improve care coordination. Through the CPC
initiative, Medicare partners with State and commercial insurers to promote
community-wide investments in the delivery of coordinated primary care.
Simultaneously, through direct financial payments or shared Medicare savings, the CPC initiative rewards high-quality providers who reduce health
care costs through investments in care coordination. At the end of 2012,
about 500 primary care practices were participating in the CPC initiative,
representing 2,343 providers serving approximately 314,000 Medicare
beneficiaries.
The CMS Innovation Center has introduced bundled payments as
a model for hospital payment and delivery reform. A bundled payment
is a fixed payment for a comprehensive set of hospital and/or post-acute
services, including services associated with readmissions. Moving from
individual payments for different services to a bundled payment for a set
of services across providers and care settings encourages integration and
coordination of care that will raise care quality and reduce readmissions.
Variants on bundled payments are being demonstrated, differing in the
scope of services included in the bundle, and whether payment is retrospective (based on shared Medicare savings) or prospective, which intensifies the
financial risk and return to investing in changes to the efficiency and quality

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of care. Currently, 467 health care organizations across 46 states are engaged
in the bundled payment initiative.

Is the Cost Curve Bending?
The real rate of health expenditure growth has declined or remained
constant in every year between 2002 and 2011. For each of the three years
2009, 2010 and 2011, National Health Expenditure data show the real rate of
annual growth in overall health spending was between 3.0 and 3.1 percent,
the lowest rates since reporting began in 1960.
Additionally, the National Health Expenditure data show that growth
in Medicare spending fell from an average of 8.6 percent a year between 2000
and 2005 to an average of 6.7 percent a year between 2006 and 2010. Notably,
over a third—2.5 percentage points—of the 2006–2010 growth was attributable to increases in Medicare enrollment. With the exception of a spike in
2006, the year Medicare Part D was introduced, the growth rate of Medicare
spending per enrollee—a measure of health care spending intensity—has
been on a downward trend since 2001, with a particularly significant slowdown over the past three years (see Figure 5-6). Projections suggest the
growth rate of Medicare spending per beneficiary will decline even further.
While Medicare enrollment is expected to increase 3 percent a year over
the next decade (CMS 2012), the rate of growth in spending per enrollee is
Figure 5-6
Real Annual Growth Rates of National Health Expenditures Per Capita and
Medicare Spending Per Enrollee, 1990–2012

Percent
1990

1994

1998

14

8

2006

2010

Introduction of
Medicare Part D

12
10

2002

16

Growth in Medicare
spending per enrollee

11

6

6

4
2012

2
0
-2

Growth in per capita
national health
expeditures

1

-4
-4
1990
1994
1998
2002
2006
2010
Note: Estimates for 2012 are projected.
Source: Center for Medicare and Medical Services, National Health Expenditure Accounts;
CEA calculations.

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projected to be approximately the same as the rate of growth in GDP per
capita, according to the CBO and Office of the Actuary at CMS (Kronick and
Po 2013). Similarly, the rate of growth in spending per Medicaid enrollee is
projected to be near the rate of growth in GDP per capita. In the commercial
health insurance market, per enrollee spending growth also has declined in
recent years, the proximate cause being a slowdown in the growth rate of
per-enrollee use of medical services (HCCI 2012).
There are several potential causes of the recent declines in the growth
rate of spending per enrollee. One factor is the recent recession, in which job
losses have caused the loss of insurance coverage. However, the recession
explains only a small fraction of the declines in spending growth rates since
the start of the recession. The slowdown in the growth rate of per-capita
health expenditures began before the recession took hold, and has continued
through the economic recovery and into 2012.
As expected, changes in real per-capita total health care spending at
the state level are negatively correlated with changes in unemployment in
the state between 2007 and 2009 (Figure 5-7). If the relationship in Figure
5-7 holds at the national level, then the increase in the national unemployment rate between 2007 and 2011 of 4.3 percentage points was associated
with a $199 decline in spending per-capita (in 2007 dollars), or 2.6 percent
of per-capita health care spending in 2007. This accounts for only 18 percent
of the slowdown in spending growth since the start of the recession in 2007
and an even smaller proportion of the slowdown in spending growth since
2002, when the growth rate in real per-capita total health care spending
began to decline.2
Structural changes in the health care market offer another explanation for the decline in per-enrollee spending growth. One possibility is
that hospitals and provider groups have increasingly sought to improve
efficiency—through adopting more high value medical practices and performing fewer low value procedures—in response to evidence showing their
potential for cost savings and quality improvements (Fisher and Skinner,
2010). At the same time, formulary changes that encourage substitution
away from branded to generic drugs, and changes in insurance design that
increase patient cost sharing for both services and pharmaceuticals, also may
explain a portion of the declines in spending growth per enrollee over the
past decade. For example, the sharp slowdown in the growth rate of medical
2 Between 2001 and 2006, real per-capital spending grew by 21.5 percent. Between 2006 and
2011, real per-capital spending grew by 7.1 percent, where the 14.4 percentage point difference
in spending growth captures the slowdown in spending growth. The 2.6 percent decline in
total health care spending between 2007 and 2011 attributable to the recession accounts for
approximately (2.6/14.4)*100 = 18 percent of the slowdown in spending growth since the start
of the recession.

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Figure 5-7
Relationship Between Change in State Unemployment Rate and Change in
Real Per-Capita Personal Health Spending, 2007–2009

Change in per capita health spending 2007–2009, 2007 dollars
900
800
700
600

y = -46.161x + 495.3
R² = 0.2076

500
400
300
200
100
0

0

1
2
3
4
5
6
Change in unemployment rate, percentage points, 2007–2009

7

8

Source: Centers for Medicare and Medicaid Services, National Health Expenditure
Accounts; Bureau of Labor Statistics, Current Population Survey; CEA calculations.

imaging since 2006 likely was due to a confluence of reforms including prior
authorization, increased cost sharing and reduced reimbursements (Lee and
Levy 2012). Notably, Lee and Levy found that a large fraction of the declines
involved imaging identified as having unproven medical value. Similarly,
payment reforms and regulations are thought to have contributed to longrun declines in Medicare spending growth rates (White 2008).
Early responses to the Affordable Care Act may have contributed
to the decline in per enrollee spending since 2010 (Kronick and Po 2013).
Relevant provisions of the law include provisions intended to foster coordinated care, improve primary care, reduce preventable health complications
during hospitalizations, and promote the adoption of health information
technology.
The decline in the hospital readmission rate, coinciding with the
introduction of the Partnership for Patients program in 2011, also may point
to early effects of the Affordable Care Act on spending. The Act’s Medicare
hospital readmissions reduction program, introduced in October 2012,
should reinforce these effects. Likewise, infrastructure investments and care
process changes, either funded directly by the Affordable Care Act or stimulated through the Affordable Care Act’s payment reform, are other possible
sources for the recent declines in spending growth.

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Percent
8

Figure 5-8
Projected Medicare Spending as a Share of GDP, 2013–2085

7

2085

Current law
projection

6
5
4

Using average annual
growth rate, 2008–2012

3
2
1
0
2013

2025

2037

2049

2061

2073

2085

Source: Medicare Trustees (2012); Social Security Trustees (2012); CEA calculations.

In addition, spending declines may reflect early changes in medical
care delivery made in anticipation of impending Medicare payment reform.
The Affordable Care Act moves providers towards savings-based payment models in Medicare that encourage improved coordination of care.
Hospitals seeking new ways to reduce costs and increase bargaining power
with suppliers and insurers may respond by consolidating their operations.
Recent years have seen a continued consolidation and integration of physicians into provider networks.
The long-run growth rate of per-capita spending has significant
implications for the budget. Medicare spending represented 3.7 percent of
GDP in 2011 (Medicare Trustees 2012). Under current law, including cost
control measures of the Affordable Care Act and the Sustainable Growth
Rate-mandated physician payment cut, CMS projects that Medicare spending will rise to represent 6.7 percent of GDP in 75 years, with long-term
nominal per-beneficiary spending growing at a rate on average equal to 4.3
percent per year (Medicare Trustees 2012). However, nominal growth rates
of per-beneficiary Medicare spending have been declining since 2001, and
over the past five years have averaged 3.6 percent. At least some of the recent
decline in Medicare spending growth appears to be structural, implying that

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the low spending growth rates from the past few years may persist.3 If the
per-beneficiary growth rate of Medicare spending were to remain 3.6 percent per year, then after 75 years Medicare spending would account for only
3.8 percent of GDP, little changed from its share today, and substantially less
than what the Medicare Trustees estimate. (Figure 5-8). This should not be
interpreted as a forecast but rather an indication of how sensitive long-term
projections are to the assumed rate of growth of Medicare spending per
beneficiary. In this hypothetical scenario where per-beneficiary Medicare
spending grows at a rate equal to the one observed over the past five years,
Medicare spending as a share of GDP would be much lower than what current long-term projections suggest.
The causes for the recent and projected declines in the growth rate
of medical spending and utilization, and their relationship to the major
quality-improving and cost-saving provisions of the Affordable Care Act,
remain an important area for future research. Enacted provisions of the
health reform law appear to be having positive effects on care coordination,
hospital outcomes and spending. And payment reforms that better align
payment with cost and provide incentives for efficiency such as shared
savings and bundled payment programs hold potential to improve to care
quality and reduce medical spending.

3 Regression analysis shows a flat and insignificant relationship between state-level 2007-09
changes in per-beneficiary Medicare spending and changes in unemployment, suggesting that
little if any of the recent declines in per-beneficiary Medicare spending growth is related to
regional cyclical factors.

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C H A P T E R

6

CLIMATE CHANGE AND THE
PATH TOWARD SUSTAINABLE
ENERGY SOURCES

T

he Administration is committed to a comprehensive energy strategy
that supports economic and job growth, bolsters energy security, positions the United States to lead the world in clean energy, and addresses the
global challenge of climate change. Finding a responsible path that balances
the economic benefits of low-cost energy, the social and environmental costs
associated with energy production, and our duty to future generations is a
central challenge of energy and environmental policy.
The most significant long-term pollution challenge facing America
and the world is the anthropogenic emissions of greenhouse gases. The
scientific consensus, as reflected in the 2009 assessment by the U.S. Global
Change Research Program (USGCRP) on behalf of the National Science
and Technology Council, is that anthropogenic emissions of greenhouse
gases are causing changes in the climate that include rising average national
and global temperatures, warming oceans, rising average sea levels, more
extreme heat waves and storms, and extinctions of species and loss of biodiversity. A multitude of other impacts have been observed in every region of
the country and virtually all economic sectors.
As part of the United Nations Climate Change Conferences in
Copenhagen and Cancún, the United States pledged to cut its carbon dioxide (CO2) and other human-induced greenhouse gas emissions in the range
of 17 percent below 2005 levels by 2020, and to meet its long-term goal
of reducing emissions by 83 percent by 2050. Approximately 87 percent
of U.S. anthropogenic emissions of all greenhouse gases (primarily CO2
and methane) are energy-related, and fossil-fuel combustion accounts for
approximately 94 percent of U.S. CO2 emissions (EPA 2010a).
Climate change is often described in terms of changes in background
conditions that unfold over decades, but extreme events superimposed on,
185

and possibly amplified by, those background changes can cause severe damage. For example, storm surges superimposed on higher sea levels will cause
greater flooding, heat waves superimposed on already warmer temperatures
will cause greater damage to crops, and a warmer atmosphere amplifies the
potential for both droughts and floods.
From an economist’s perspective, greenhouse gas emissions impose
costs on others who are not involved in the transaction resulting in the
emissions; that is, greenhouse gas emissions generate a negative externality.
Appropriate policies to address this negative externality would internalize
the externality, so that the price of emissions reflects their true cost, or would
seek technological solutions that would similarly reduce the externality.
Such policies encourage energy efficiency and clean energy production. In
addition, prudence mandates that the Nation prepare now for the consequences of climate change.

Consequences and Costs of Climate Change
The clear scientific consensus is that anthropogenic greenhouse gas
emissions are causing our climate to change. These changes include increasing temperatures, rising sea levels, changing weather patterns, and increasingly severe heat waves, with negative consequences for human health,
property, and ecosystems.1

The Changing Climate
Projections using a wide variety of climate models paint a broadly
similar picture of how global temperatures can be expected to rise in
response to emissions—a picture that is also consistent with observed
temperature changes (Rohling et al. 2012). Likely temperature paths, from
a comparison of models by the USGCRP (2009), predict that the average global temperature under a low-emissions scenario will increase by
approximately 4°F by the end of this century; under the medium and high
emissions scenarios, end-of-century increases are 7°F and 8°F, respectively.
Some regions are projected to experience greater temperature increases
than others. The Arctic has warmed by almost twice the global average in
recent decades, in part because warming melts snow and ice, leading to less
reflected sunlight, which causes yet more warming (Arctic Monitoring and
Assessment Programme 2011).
1 The scientific consensus on the effects of greenhouse gas emissions on climate is summarized
in reports by the USGCRP (2009) and the International Panel on Climate Change (IPCC
2012). The draft Third National Climate Assessment report, prepared by the National Climate
Assessment Development Advisory Committee, was issued for public comment in January
2013.

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Warming temperatures raise sea levels because of expanding ocean
water, melting mountain glaciers and ice caps, and partial melting of the
Greenland and continental Antarctic ice sheets. Since 1880, the global sea
level has risen about 20 centimeters, more than half of which has occurred
since 1950. Projections by the National Oceanographic and Atmospheric
Administration show sea levels rising over the 21st century by 19 to 200
centimeters (NOAA 2012).
Increasingly common extreme events, such as heat waves, droughts,
floods, and storms, pose some of the most significant risks of climate
change. In its assessment of the current scientific literature, the IPCC
(2012) concluded that increases in greenhouse gases will almost certainly
increase the frequency and magnitude of hot daily temperature extremes
during the 21st century, while episodes of cold extremes will decrease. In
addition, the length, frequency, and intensity of heat waves are very likely
to increase over most land areas, and droughts may intensify (Hansen, Sato,
and Ruedy 2012; Rhines and Huybers 2013). In fact, an increase in the mean
temperature implies more very hot days and fewer very cold days, even if the
variability of daily temperatures around the mean remains unchanged. This
phenomenon—a disproportionate increase in previously extreme temperatures as the mean temperature increases—is illustrated in Figure 6-1, which
displays a shift in a hypothetical distribution of possible daily temperatures.
The implications of Figure 6-1 accord with observed changes over the past
decades and centuries as well as with climate model simulations. For example, according to the USGCRP estimates, under a high-emissions scenario,
areas of the Southeast and Southwest that currently experience an average
of 60 days a year with a high temperature above 90°F will experience 150 or
more such days by the end of the century.
Patterns of precipitation and storms are also likely to change, although
the nature of these changes currently is more uncertain than those for
temperature. Northern areas of the United States are projected to become
wetter, especially in the winter and spring; southern areas, especially the
Southwest, are projected to become drier. Moreover, heavy precipitation
events will likely be more frequent: downpours that currently occur about
once every 20 years are projected to occur every 4 to 15 years by 2100,
depending on location. The strongest cold-season storms are projected to
become stronger, more frequent, and more costly. For more on the costs of
storms, see Box 6-1.

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Figure 6 - 1
Illustrative Average Temperature Distribution

Cooler temperature
Warmer temperature

Source: CEA illustration.

90 Fahrenheit

Estimating the Economic Cost of Climate Change: The Social Cost
of Carbon
Because greenhouse gas emissions cause climate change, policies to
reduce climate change must focus on reducing anthropogenic greenhouse
gas emissions. An important step in informing a policy response is knowing
precisely where carbon emissions are coming from, and that is the purpose
of the Environmental Protection Agency (EPA) Greenhouse Gas Reporting
Program discussed in Data Watch 6-1.
Another critical step in formulating policy responses to climate
change is to estimate the economic costs induced by emitting an additional,
or marginal, ton of CO2. This cost—which covers health, property damage,
agricultural impacts, the value of ecosystem services, and other welfare
costs of climate change—is often referred to as the “social cost of carbon”
(SCC). Having a range for the SCC provides a benchmark that policymakers and the public can use to assess the net benefits of emissions reductions
stemming from a proposed policy. Although various studies, notably Stern
(2006), have estimated the cost of climate change, until recently the Federal
Government did not generate its own unique set of estimates of the SCC.
In 2010, a Federal interagency working group, led by the Council of
Economic Advisers and the Office of Management and Budget, produced
a white paper that outlined a methodology for estimating the SCC and
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Box 6-1: The Cost of Hurricanes
Hurricanes draw energy from the temperature difference between
the surface ocean and mid-level atmosphere. Although no one hurricane
or storm can be attributed to global warming, there is some expectation
that warming surface waters will increase the maximum intensity of
hurricanes, and a trend toward increasing hurricane intensity has been
observed in the North Atlantic over the past three decades (Kossin et al.
2007). As the figure shows, insured losses from storms have also been
increasing over the past 20 years, a trend that is driven by losses from
recent large hurricanes. Because many of the losses from hurricanes are
uninsured, total costs can substantially exceed insured costs.
Development near vulnerable coasts, increasing intensity of storms,
and rising sea levels point toward hurricane winds, precipitation, and
storm surges that are increasingly destructive. In fact, several studies
project substantial increases in hurricane-related costs because of climate
change.1 It is difficult to isolate the contribution of climate change to the
historical increase in hurricane costs. Nonetheless, from the perspective
of social cost, the relevant facts are that the total cost is increasing, and
that storm costs will increase with coastal development and could well
also increase in response to greater storm severity.

Total Insured Market Losses Caused by All Storm Types, 19852012
Billions of 2011 dollars
120

Katrina, Rita, and Wilma

100
80
Charley, Frances, Ivan, and Jeanne

60

Andrew

40
20

Ike

Sandy
Irene

Hugo

0
1985
1990
1995
2000
2005
Note: Years with the 12 costliest hurricanes in U.S. history are labeled.
Source: Munich Reinsurance Company (2012).

2010

1 Mendelsohn et al. (2012); Nordhaus (2010); Pielke (2007); Narita et al. (2009).

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Data Watch 6-1: Tracking Sources of Emissions:
The Greenhouse Gas Reporting Program
In October 2009, the Environmental Protection Agency (EPA)
launched its Greenhouse Gas Reporting Program, an ambitious effort
to collect and make publicly available facility-level data on greenhouse
gas emissions across the United States. Today, experts and non-experts
alike can view, explore, and download comprehensive information on
greenhouse gas emissions using the EPA’s convenient online data tool.
The program is a leap forward for greenhouse gas data collection and
the first of its kind in its scale and “bottom-up” approach. It will be an
important piece of administrative infrastructure for any future effort to
regulate or price greenhouse gas emissions.
Since 1990, the EPA has reported estimates of greenhouse gas
emissions in its annual Inventory of U.S. Greenhouse Gas Emissions
and Sinks, in compliance with the U.S. commitment under the United
Nations Framework Convention on Climate Change. These estimates,
however, are mostly “top-down,” in that the EPA estimates national
emissions using aggregate data on fuel production, imports and exports,
and inventories. In 2008, Congress instructed the agency to begin to
collect facility-level data, and the EPA developed the Greenhouse Gas
Reporting Program to augment the data collected through the National
Greenhouse Gas Inventory. The first wave of data, which covers emissions in 2010, was made publicly available in January 2012. More than
6,000 facilities—refineries, power plants, chemical plants, landfills, and
more—were required to report their emissions, which amounted to 3.2
billion tons of carbon dioxide equivalent (CO2e) that year alone.1 The
EPA will release data on 2011 emissions in early 2013.
The EPA provides its database of facility-level greenhouse gas
emissions online (http://ghgdata.epa.gov), and visitors can view data by
sector or geography or both. The site’s rich interface and powerful maps
software permits easy spatial analysis of emissions, and built-in charts
help users glean useful information from what might otherwise be an
unwieldy dataset. Although the Greenhouse Gas Reporting Program is
an important step forward for greenhouse gas data collection, there are a
few limitations: only facilities that emit more than 25,000 tons of greenhouse gases (measured in CO2e) a year are required to report (although
some sectors are “all in,” meaning even emitters below the 25,000-ton
threshold report for the first three to five years), and the program does
not cover emissions from agriculture or land use.

1 http://www.epa.gov/ghgreporting/ghgdata/reported/index.html

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provided numeric estimates (White House 2010). The SCC calculation
estimates the cost of a small, or marginal, increase in global emissions. This
process was the first Federal Government effort to consistently calculate the
social benefits of reducing CO2 emissions for use in policy assessment. To
date, the 2010 interagency SCC values have been used to evaluate at least 17
rules at various stages in the rulemaking process by the EPA, the Department
of Transportation (DOT), and the Department of Energy (DOE).
To estimate the SCC, the working group used three different peerreviewed models from the academic literature of the economic costs of
climate change and tackled some key issues in computing those costs. One
issue is the choice of the discount rate used to compute the present value of
future costs: because many of the costs occur in the distant future, the SCC is
sensitive to the weight placed on the welfare of future generations. Another
issue is how to handle some of the uncertainty surrounding climate projections. Box 6-2 explains how the working group dealt with uncertainty about
the equilibrium climate sensitivity, which serves as a proxy for the climate
system’s response to greenhouse gas emissions.
The working group report provided four values for the social cost of
emitting a ton of CO2 in 2011: $5, $22, $36, and $67, in 2007 dollars. The first
three estimates, which average the cost of carbon across various models and
scenarios, differ depending on the rate at which future costs and benefits are
discounted (5, 3, and 2.5 percent, respectively). The fourth value, $67, comes
from focusing on the worst 5 percent of modeled outcomes, discounted
at 3 percent. All four values rise over time because the marginal damages
increase as atmospheric CO2 concentrations rise.
The SCC study acknowledged that these estimates, while a substantial
step forward, need refinement, for example by a more complete treatment
of some damage categories. A detailed discussion of the methodology can be
found in Greenstone, Kopits, and Wolverton (2013). The interagency working group has committed to update its estimates of the SCC as the literature
evolves and as new scientific and economic evidence become available.

Policy Implications of Scientific and Economic Uncertainty
As a general matter, policy decisions must commonly be made in the
presence of uncertainty. A standard approach for cost estimation or policy
evaluation in the presence of uncertainty is to consider different scenarios
and to compute a weighted average (expected value) over those scenarios.
But in some cases it is difficult to quantify this uncertainty. In particular,
some of the unknowns about climate change concern extreme scenarios
that are far outside recorded human experience. Although such events are

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Box 6-2: Handling Uncertainty About Equilibrium Climate Sensitivity
The 2010 Federal study on the social cost of carbon (SCC) used
three integrated economic-geophysical models to estimate the cost of
climate change: the DICE model, the PAGE5 model, and the FUND
model.1 The costs estimated by each model are sensitive to climatic,
economic, and emissions parameters. A key input parameter for each
model is the equilibrium climate sensitivity, defined as the increase in the
long-term annual global-average surface temperature increase associated
with a doubling of atmospheric carbon dioxide (CO2) concentration
relative to pre-industrial levels.
Estimates of Uncertainty About Equilibrium Climate Sensitivity

Probability density
0.7

Andronova 01

0.6

Frame 05

Calibrated
Roe & Baker

0.5

Forest 02
Forest 06

0.4

Forster/Gregory 06

0.3

Gregory 02

0.2

Knutti 02

0.1
0.0

0

1

2

3

4

5

6

Equilibrium climate sensitivity (C)

7

8

9

10

Source: IPCC (2007); Roe and Baker (2007).

The Intergovernmental Panel on Climate Change (IPCC 2012)
suggests a range for the equilibrium climate sensitivity of 2–4.5ºC
(3.2–7.2ºF), but the scientific uncertainty extends outside this range. The
figure shows distributions of possible values of this parameter arising
from different studies; each line in the figure corresponds to a given
study, and the higher the line, the greater the chances (according to that
study) of the corresponding value of the equilibrium climate sensitivity.
1 The DICE model was developed by William Nordhaus, David Popp, Zili Yang, Joseph
Boyer, and colleagues. The PAGE model was developed by Chris Hope with John Anderson,
Paul Wenman, and Erica Plambeck. The FUND model was developed by David Anthoff
and Richard Tol.

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Although the distributions from different studies differ, each holds open
the possibility that the value of this parameter might be very large.
This range of uncertainty over the equilibrium climate sensitivity
matters for estimating the economic costs of carbon emissions: a higher
value implies a more amplified response of temperature to carbon emissions, which would be associated with greater human consequences. To
handle this uncertainty, the task force adopted a standard approach used
by economists, which is to compute a weighted average—technically,
an expected value—where the weighting reflects the uncertainty in the
scientific literature. Specifically, simulations were run for many values
of the equilibrium climate sensitivity drawn randomly from an assumed
probability distribution and the results were averaged, producing the
expected value for the SCC. The resulting SCC estimate incorporates the
uncertainty in the equilibrium climate sensitivity.

therefore difficult to quantify, the possibility of very severe outcomes can
and should inform policy.
One principle of policy design under uncertainty is that the policy
should be able to adapt as more is learned and the uncertainty is resolved;
another is that a policy should be robust to uncertainty.2 A robust policy
aims to give acceptable outcomes no matter what happens, within a given
range of possible outcomes. As applied to climate change, this idea of robust
policy in the face of uncertainty leads to policies that avoid worst-case outcomes. Such an approach has been advocated by Weitzman (2009, 2011),
who argues that, when considering the expected damages of unmitigated
global climate change, it is important to consider low probability but
potentially catastrophic impacts that could occur. By focusing on avoiding
the most costly climate outcomes, a climate change policy that is robust to
scientific uncertainty would be more aggressive than a policy that simply
focuses on quantifiable uncertainty or a consensus temperature path. If
future scientific knowledge were to determine that the worst outcomes could
be ruled out, then a robust policy could be adjusted. Thus, although uncertainty complicates the task of computing costs, it is not in itself a reason for
inaction or delay.

2 An important early paper on policymaking under uncertainty is Brainard (1967). Recent
work in economics on robust policy in the face of model uncertainty includes Hansen and
Sargent (2001, 2007), Giannoni (2002), Onatski and Stock (2002), and Funke and Paetz (2011).

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Carbon Emissions: Progress and Projections
The past five years have seen a remarkable turnaround in U.S. emissions of carbon dioxide. As can be seen in Figure 6-2, from the early 1980s
through the mid-2000s, energy-related CO2 emissions increased from
approximately 4,500 million metric tons (MMT) to a peak of just over
6,000 MMT in 2007. Since 2007, however, emissions have fallen sharply to
approximately 5,500 MMT in 2011, the most recent year for which there is
complete data. Indeed, as shown in the figure, this reduction in emissions
makes significant progress toward achieving the Copenhagen Accord target
of a 17 percent reduction in greenhouse gas emissions below 2005 levels by
2020.3
A natural question is what set of new events or initiatives led to the
sharp reduction in emissions. There are a number of candidate explanations:
reductions in the carbon content of energy, most notably the substitution of
natural gas and renewables for coal; improvements in economy-wide energy
efficiency; and unexpectedly low energy demand because of the recession.
To estimate the contribution of these factors to the decline in emissions,
one needs to posit a counterfactual path for these three variables, that is, for
the carbon content of energy (CO2 per British thermal unit, or Btu), energy
use per dollar of gross domestic product (Btu/GDP), and GDP. Given a
counterfactual, or baseline, path for these variables, one can decompose the
decline in carbon emissions to a decline in the carbon content of energy, an
accelerated improvement in energy efficiency, or a shortfall of GDP, relative
to the baseline path.4 Because the question focuses on the role of new developments, a natural approach is for the baseline to be a business-as-usual
projection from a given starting point. For the purpose of this exercise, the
starting point is taken to be the 2005 values of the carbon content of energy,
energy efficiency, and GDP; the business-as-usual projections are made
either by using historical published forecasts or by extrapolating historical
trends.
The results of this decomposition estimate that actual 2012 carbon
emissions are approximately 17 percent below the “business as usual” baseline. As shown in Figure 6-3, of this reduction, 52 percent was due to the
recession (the shortfall of GDP, relative to trend growth), 40 percent came
3 United Nations Framework Convention on Climate Change, Appendix I, http://unfccc.int/
meetings/copenhagen_dec_2009/items/5264.php.
4 Specifically, CO2 emissions are the product of (CO2/Btu)×(Btu/GDP)×GDP, where CO2
represents U.S. CO2 emissions in a given year, Btu represents energy consumption in that year,
and GDP is that year’s GDP. Taking logarithms of this expression, and then subtracting the
baseline from the actual values, gives a decomposition of the CO2 reduction into contributions
from clean energy, energy efficiency, and the recession.

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Figure 6-2
U.S. Energy-Related Carbon Dioxide Emissions, 1973–2040

Millions metric tons CO2
6,500
6,000

Actual

EIA projections

5,500

2040

5,000
U.S. target under Copenhagen
Accord

4,500
4,000
1970

1980

1990

Note: Shading denotes recession.
Source: EIA (2012b).

2000

2010

2020

2030

2040

from cleaner energy (fuel switching), and 8 percent came from accelerated
improvements in energy efficiency, relative to trend. Of the cleaner energy
improvements, most (approximately two-thirds) came from reductions in
emissions from burning coal. Reductions in emissions from petroleum
combustion also made important contributions (approximately one-third),
as these high-carbon content fuels were replaced by lower carbon-content
natural gas and clean renewable energy sources, notably wind and biofuels.
The contribution from energy efficiency stems from efficiency improvements over the 2005–12 period that were faster than projected; in particular,
the Energy Information Administration (EIA 2005) forecast a reduction in
the energy content of GDP of 1.6 percent per year, but energy efficiency
improved by more than this forecast.5
As the economy improves, GDP will rise, and the weakness of the
economy in 2007–09 will no longer restrain energy consumption. Thus if
the recent reductions in emissions are to be continued, a greater share will
need to be borne by fuel switching into natural gas and into zero-emissions
renewables, and by accelerating improvement in economy-wide energy
efficiency.
5 Houser and Mohan (forthcoming) undertake a similar decomposition. They use different
assumptions for the baseline, including somewhat stronger post-2005 GDP growth in the
“business as usual” case than is assumed here, and as a result attribute slightly more of the
post-2005 reduction in CO2 emissions to slower economic growth.

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Figure 6-3
Decomposition of CO2 Emission Reductions, 20052012
Accelerated
energy
efficiency
(8%)

Cleaner energy

fuel switching to natural
gas and renewables

Slower growth
(52%)

(40%)

Source: Bureau of Economic Analysis, National Income and Product Accounts; EIA
(2013); CEA calculations.

Policy Responses to the Challenge
of Climate Change
As a general matter, government intervention may be warranted if
an individual’s action produces a negative externality; that is, if the action
imposes costs on another person and those costs are not borne by the person
taking the action. As with many environmental problems, the impacts of
pollution are broadly shared by society, and individuals emitting pollution
do not bear the full, direct costs of their individual action (or reap the full
benefits individually of reducing pollution). In the case of anthropogenic
emissions of greenhouse gases, the costs of climate change are borne by
others, including future generations, and those costs are not reflected in
the price of greenhouse gas emissions. This market failure is also present
in reverse: an entrepreneur with a clever idea for reducing greenhouse gas
emissions, such as a novel energy conservation technology, cannot recoup
the full benefit of her innovation because there is no way she can charge
those who will benefit from the abatement of those emissions.
This diagnosis of the market failure underlying climate change
clarifies the need for government to protect future generations that will
be affected by today’s emissions. Responding to the challenge of climate
change leads to a multipronged approach to policy. Four such responses
are implementing market-based solutions; technology-based regulation of
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greenhouse gas emissions; supporting the transition of the U.S. energy sector to technologies, such as renewables and energy efficiency, that reduce
our overall carbon footprint; and taking actions now to prepare for those
impacts that are by now unavoidable.

Market-Based Solutions
In his 2013 State of the Union Address, President Obama urged
Congress to pursue a bipartisan, market-based solution to climate change.
Market-based solutions to greenhouse gas emissions provide economic
incentives so that the cost of polluting reflects the economic harm caused
to others by that pollution. In this sense, market-based solutions are said
to “internalize” the externality caused by the pollution. Under the standard
assumptions of economic theory, market-based solutions to pollution are
economically efficient because those who create the externality can choose
the least costly and disruptive way to reduce their emissions. Under marketbased solutions, the effective price of the activity producing the negative
externality is adjusted so that it reflects the cost of that externality. There are
various ways that market-based solutions can be implemented, one of which
is a cap-and-trade system like the one Senators McCain and Lieberman
worked on.6
Another example of a market-based solution is a Clean Energy
Standard that would require electric utilities to obtain an increasing share
of delivered electricity from clean sources but would allow them to meet
the standard by trading clean-energy credits. By allowing trading in credits,
electric utilities that produce renewable energy at relatively low cost can sell
credits to those for which renewable production would be high-cost. Thus
the total cost across all utilities of meeting the standard is reduced, relative
to the cost were each utility required to meet the standard without tradable
credits. In this way, a market for clean energy credits harnesses privatesector incentives to minimize the cost of generating electricity from clean
energy sources.7

Direct Regulation of Carbon Emissions and the Vehicle Greenhouse
Gas / Corporate Average Fuel Economy (CAFE) Standards
Another way to address the externality of carbon emissions is by
direct regulation. In 2007, the Supreme Court ruled in Massachusetts v. EPA
that it is incumbent upon the EPA to determine whether greenhouse gases
6 For a more detailed discussion of cap-and-trade, see the 2010 Economic Report of the
President, chapter 9.
7 For further discussion of a Clean Energy Standard, see the 2012 Economic Report of the
President, chapter 6.

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pose a risk to public health or welfare and, if so, to regulate greenhouse gas
emissions under the Clean Air Act. In 2012, the U.S. Court of Appeals for
the District of Columbia Circuit upheld the EPA’s authority to regulate
greenhouse gas emissions.
The Administration’s corporate average fuel economy (CAFE) and
greenhouse gas regulations, released in 2012 jointly by the EPA and the
DOT, require automakers to increase the fuel economy of passenger cars
and light trucks so that they are estimated to achieve 54.5 miles per gallon
by 2025, approximately doubling the previous mileage standards.8 The new
fuel economy standards are expected to save more than 2 million barrels
of oil a day by 2025—more than we import from any country other than
Canada—and to reduce consumer expenditures on gasoline. The standards
are projected to reduce annual CO2 emissions by over 6 billion metric tons
over the life of the program, roughly equivalent to the emissions from the
United States in 2010 (White House 2011a).
The new fuel economy standards help to correct the externality that
the cost of carbon emissions is not accounted for in the price of gasoline.
The standards also provide a clear signal to the thousands of firms in the
auto supply chain that investments in fuel-saving innovation will pay off.
These innovations range from large (batteries for electric cars) to small
(lighter-weight bolts), and often require suppliers to coordinate with each
other. For example, use of innovative high-strength steels can reduce the
overall weight of a vehicle, but only if firms making automotive parts and
those making tooling for the parts each invest in new production processes
(Helper, Krueger, and Wial 2012). The new standards ensure demand for
fuel-saving innovations and thus provide an incentive for such investments.

Energy Efficiency
An important way to reduce greenhouse gas emissions is to use
energy more efficiently, that is, to use less energy to provide a given service
outcome. For example, weatherizing a home improves efficiency by requiring less energy to maintain a given inside temperature. Using less energy, in
turn, reduces greenhouse gas emissions.
The Administration has made energy efficiency initiatives an important component of its energy plan.9 These initiatives include major research
8 Because the standards regulate greenhouse gas emissions, they can be met in part in ways that
do not improve fuel economy. In particular, if improvements are made by reducing leakage of
greenhouse gases in auto air conditioners, or by replacing refrigerants with non-greenhouse
gases, then the goal of reducing greenhouse gas emissions is achieved without improving fleet
fuel economy.
9 http://www.whitehouse.gov/sites/default/files/email-files/the_blueprint_for_a_secure_energy_
future_oneyear_progress_report.pdf

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investments to improve the efficiency of building designs and components
such as lighting, heating, and air conditioning, along with smart building
controls. Other important initiatives include the weatherization of more
than 1 million homes across the country, the President’s Better Buildings
Challenge with $2 billion in private-sector commitments to energy efficiency
retrofits, new standards for residential and commercial appliances, and the
Rural Energy for America Program. The Administration has also introduced
a variety of programs to help consumers learn about developments in energy
efficiency; one such example is the Home Energy Score, a new voluntary
program from the DOE to help homeowners make cost-effective decisions
about energy improvements. Additionally, as part of a broader manufacturing strategy, the Administration has partnered with manufacturing companies representing more than 1,400 plants that plan to make investments that
will improve energy efficiency by 25 percent over 10 years.
An overall measure of economy-wide energy use is the amount of
energy needed to generate a dollar’s worth of goods and services (“energy
intensity”). As is shown in Figure 6-4, the energy intensity of the U.S. economy has fallen steadily over the past quarter century, with an annual average
rate of decline of 1.7 percent from 1990 through 2011. However, U.S. energy
intensity is still one-third higher than that of Germany and Japan, in part
because Germany and Japan have automobiles and building codes that are
more energy efficient, as well as smaller homes set more densely.10
One reason for the decline in the energy intensity of the U.S.
economy is the increasing importance of services as a share of U.S. GDP.
Manufacturing is more energy-intensive than is the production of services,
and for decades the share of U.S. GDP derived from services has been
growing while the share derived from manufacturing has been declining.
This shift from manufacturing to services therefore has reduced the energy
intensity of the U.S. economy.
To control for changes in the energy-GDP ratio driven by changes in
the sectoral composition of output, the DOE developed an “Economy-wide
Energy Intensity Index.” This index estimates the amount of energy needed
to produce a basket of goods in one year, relative to the previous year. As
indicated in Figure 6-5, between 1985 and 2010, the DOE Energy Intensity
Index fell by 14 percent. In contrast, the energy-GDP ratio fell by 33 percent.
Thus, while much of the decline in energy usage per dollar of GDP has come
from improvements in energy efficiency, much of it has also come from

10 In neither Germany nor Japan is the lower energy intensity due to having less manufacturing
than the United States. In fact, manufacturing (an energy-intensive sector) is almost twice as
high as a share of GDP in Germany as it is in the United States.

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Figure 6-4
Energy Use per Dollar of GDP, Selected Countries, 1988−2009

British thermal unit per 2005 U.S. dollar
25,000

20,000

15,000
2009

China

United States
10,000

Germany

India
Japan

5,000
1988 1990 1992 1994 1996 1998 2000 2002 2004 2006 2008
Source: Energy Information Administration, International Energy Statistics.

Figure 6-5
U.S. Energy Intensity, 19502010

Index, 1985 = 1

2010

2.0
Energy/GDP

1.5

1.0
Energy
GDP

0.5

0.0
1950

1962

1974

Energy intensity index

1986

1998

2010

Note: "Energy" is the amount of energy consumed (measured in Btu) compared to 1985 levels.
"Energy/GDP" is energy consumed divided by GDP, compared to 1985 levels. The energy intensity index is
available starting in 1970.
Source: Department of Energy, Office of Energy Efficiency and Renewable Energy, Energy Intensity
Indicators: Trend Data.

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factors other than improved efficiency such as shifts in the composition of
output.
The energy intensity index measures the energy footprint of U.S.
production, not of U.S. consumption. This distinction arises because energy
intensity includes energy used to produce exported goods and services
(which are not consumed domestically) and excludes energy used to produce imports. To estimate the CO2 intensity of consumption, as opposed to
the CO2 intensity of production, one needs to adjust U.S. CO2 emissions for
the difference of foreign emissions in the production of imports less domestic emissions in the production of exports.
Technical developments that use less energy to provide a service, such
as maintaining a room at a comfortable temperature, can both reduce energy
consumption and improve consumer welfare. Because technical improvements in energy efficiency reduce the energy cost of the service, consumers
are better off, and because the price of the service declines, they might use
more of it. For example, weatherizing a home might tempt the homeowner
to bump up the thermostat a couple of degrees. This consumer response of
using more of the newly efficient service is known as the rebound effect. The
magnitude of the rebound effect depends on the particular service, more
specifically on the elasticity of demand for the service. Viewed solely through
the lens of CO2 reduction—a lens that is appropriate because CO2 emissions
are underpriced—the rebound effect suggests that government efforts on
energy efficiency should emphasize services with inelastic demand, so that
price changes do not substantially alter service consumption and actual
energy savings approach the technically feasible energy savings.
One such example is the services derived from automobiles. In the
context of the vehicle greenhouse gas–CAFE standard discussed earlier, the
EPA assumes a rebound effect of about 10 percent11, that is, consumers will
drive about 10 percent more than if the efficiency of their vehicles had not
increased (EPA 2010b). In their reviews of the rebound effect, Greening,
Greene, and Difiglio (2000) and Gillingham et al. (2013) suggest more
generally that the rebound effect tends to range between 10 percent and 30
percent. Although much has been written on the rebound effect, the base
of original research is limited, and more research is needed concerning the
rebound effect (and the associated price elasticities) empirically, both in the
short and long run.

11 The EPA rebound estimate draws on the literature, for example, Small and Van Dender
(2007).

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Energy Production in Transition
The United States is in a period of swift and profound change in the
way that energy is produced and consumed. Thanks to recent advances in
technology, more of the country’s domestic oil and gas resources are now
accessible. As a result, U.S. oil production has climbed to the highest level in
15 years and natural gas production reached an all-time high. This increase
in domestic oil production enhances energy security, and increased natural
gas production has substituted for coal, which reduces CO2 emissions per
unit of energy produced. At the same time, the Obama Administration has
taken historic steps to promote greater energy efficiency and the deployment of renewable energy across the U.S. economy. In the past five years,
the United States has more than doubled non-hydroelectric renewable electricity generation. The Administration is working to continue these trends
through a comprehensive “all of the above” approach to energy policy that
takes advantage of all domestic energy resources, while also igniting the
innovation needed to lead the world in clean energy.
The transformation of the U.S. energy sector to one with a smaller
carbon footprint is central to climate change policy. As Figure 6-6 shows,
approximately 77 percent of U.S. energy production in 2011 came from
burning fossil fuels, and the remaining 23 percent was approximately evenly
split between nuclear and renewables. In broad terms, the share of natural
gas (the fossil fuel with the lowest carbon content) and the share of renewables have been expanding, displacing the share of coal (the fossil fuel with
the highest carbon content).

Oil and Natural Gas
New developments in exploration and production techniques and
technology have made the extraction of new sources of oil and natural gas
economically viable, resulting in a U.S. production boom. Figure 6-7 shows
the changing consumption and production trends of natural gas in the
United States, along with the U.S. share of global production since 2000. As
a result of the developments in shale gas production, total U.S. natural gas
production rose 27 percent, from 18.1 trillion cubic feet in 2005 to 23.0 trillion cubic feet in 2011, and wellhead prices fell 46 percent, from $7.33 per
thousand cubic feet to $3.95 per thousand cubic feet. In 2011, for the first
time in 30 years, energy production from dry natural gas exceeded energy
production from coal.
The benefits of increased production of natural gas are observed
throughout the U.S. economy. In recent years, low energy costs have become
a competitive advantage to the U.S. industrial sector. Additionally, low
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Figure 6-6
Total U.S. Primary Energy Production, 2011

Natural Gas
34%

Hydroelectric
34%

Nuclear
11%

Geothermal
2%

Wind 13%

Renewables
12%

Solar
2%

Biofuels
22%

Petroleum
15%

Other Biomass
27%

Coal
28%

Note: Natural gas includes natural gas plant liquids.
Source: EIA (2012a).

Figure 6-7
U.S. Natural Gas Consumption and Production, 2000–2025

Trillions cubic feet
30

Projections

28
26
24

Percent
30
28

2025
Consumption (left axis)

24
Production (left axis)

22

26

22
20

20
18
16
2000
2005
Source: EIA (2012b).

18

Share of global production
(right axis)
2010

2015

2020

2025

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| 203

prices for byproducts of natural gas such as methane, ethane, and propane
spur growth in agriculture, petrochemical manufacturing, and other industries that use these byproducts.
In the power sector, burning natural gas produces nitrogen oxides,
carbon dioxide, and other pollutants, but in lower quantities than burning
coal or oil. The life-cycle emissions of greenhouse gases from a combinedcycle natural gas plant is roughly half that of a typical coal-fired power plant
per kilowatt hour (Logan et al. 2012). On the other hand, methane, a primary
component of natural gas and a greenhouse gas, can be emitted from natural
gas systems into the atmosphere through production processes, component
leaks, losses in transportation, or incomplete combustion. Measuring fugitive methane emissions from the U.S. natural gas supply chain and, more
generally, understanding the potential impacts of natural gas development
on water quality, air quality, ecosystems, and induced seismicity, are critical
to understanding the impact on the environment of the increasing use of
natural gas.

Renewable Energy
In the long run, large reductions in carbon emissions require large
increases in energy production from zero-emissions sources, especially
renewable energy. In the beginning of his Administration, President Obama
set a goal of doubling U.S. renewable energy generation capacity from
wind, solar, and geothermal sources by 2012. This ambitious goal has been
achieved, thanks both to the Administration’s historic investments in clean
energy technologies and to decades of government-funded research and
development (R&D) aimed at driving costs down to the point where renewable energy is competitive with traditional fossil-fuel energy.
Since 2008, the most significant increase in renewable energy production has been in wind energy. The dramatic increase in wind generating
capacity is shown in Figure 6-8. In 2011, wind power constituted more than
30 percent of new additions to U.S. electric generating capacity: close to 6.8
gigawatts of new wind generating capacity was installed in the United States,
representing an investment of $14 billion. Wind energy supplies 20 percent
of electricity consumption in some states, including Iowa and South Dakota.
As a nation, the United States accounts for 20 percent of total global wind
power generation and 16 percent of global installed capacity. In 2012, wind
power provided more than 3 percent of the nation’s electricity generation
(EIA 2013b).
The Administration also continues a strong commitment to the
development and promotion of solar energy. An important aim is bringing
the cost of solar photovoltaics down closer to grid parity with traditional,
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Gigawatts
50

Figure 6-8
Annual and Cumulative Growth in U.S.Wind
Power Capacity, 1998–2011
2011

45
40
35
30
25
20
15
10
5
0

1998

2000

2002

2004

2006

2008

2010

Note: Orange bars are annual additions to capacity and blue bars are total installed
capacity at the outset of the year.
Source: DOE (2012b).

fossil sources of energy, including natural gas. The Administration’s support for solar energy has included more than $13 billion since September
2009 through DOE programs for solar-related projects, including applied
R&D, demonstrations, and the DOE clean energy loan guarantee program.
In 2011, the DOE launched an ambitious new effort, the Sunshot Initiative,
aimed at reducing the installed costs of solar energy systems of all sizes
(residential, commercial, and utility) by an additional 75 percent by the end
of the decade.
Solar photovoltaic capacity is growing rapidly, with current installed
capacity estimated to be approximately 4 gigawatts.12 The Interstate
Renewable Energy Council estimates that grid-connected photovoltaic
capacity increased more than tenfold between 2007 and 2011.
President Obama has set a goal of once again doubling generation from wind, solar, and geothermal sources by 2020, and has called on
Congress to make the renewable energy Production Tax Credit permanent
and refundable, as part of comprehensive corporate tax reform, providing
incentives and certainty for investments in clean energy.13
12 The Interstate Renewable Energy Council (IREC), the Solar Energy Industries Association
(SEIA), and the National Renewable Energy Lab (NREL).
13 http://www.whitehouse.gov/sites/default/files/uploads/sotu_2013_blueprint_embargo.pdf.

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Advanced Technologies and R&D
The Federal Government also has an important role to play in R&D
involving frontier fossil-fuel technologies. Notably, the Administration has
invested nearly $6 billion in clean coal technology R&D—the largest such
investment in U.S. history—and this strategy has attracted more than $10
billion in additional private sector capital investment. Clean coal technology
involves removing CO2 from flue gases released from burning coal, then
preventing its escape into the atmosphere by injecting it underground, a
process known as carbon capture and sequestration. The recovered CO2 can
potentially be used to recover hard-to-reach oil reserves, partially offsetting the carbon capture costs. Another clean coal technology in the R&D
stage is hydrogen production from coal, in which the highly concentrated
CO2 stream is captured and sequestered. Advanced technologies also have
the potential to make natural gas burn even cleaner by capturing and storing CO2 emissions, and the government has a role to play in encouraging
research into these technologies.
Federal research efforts on zero- and reduced-emissions energy
sources extend into other domains as well, including research toward shifting cars and trucks to nonpetroleum fuels.

Preparing for Climate Change
The policies discussed so far aim to reduce emissions of greenhouse
gases and thereby to stem future costs of climate change. But the climate
has not yet fully adjusted to current levels of greenhouse gases, and ongoing
anthropogenic emissions will continue to increase greenhouse gas concentrations because CO2 remains in the atmosphere for centuries. Thus, while
it is important for all countries to sharply reduce CO2 emissions to limit the
extent of further climate change, even with the most concerted international
efforts additional climate change is inevitable. We therefore face a world
with an unavoidably changing climate for which we need to prepare.
Policies to prepare for climate change occur at many scales. At the
local level, preparing for climate change can entail changing building codes
to make structures more storm- and flood-resistant and investing in stronger
community planning and response. More substantially, destructive effects of
coastal storms can be partially dissipated by restoring natural storm barriers
such as tidal wetlands, sand dunes, and coastal barrier landforms.
National policies to prepare for climate change range from providing
information about likely changes in local climates and weather patterns,
to supporting further research on and monitoring of climate change and
its consequences, to providing proper incentives for individuals to prepare
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for climate change. For example, federal insurance programs, such as
the Agriculture Department’s crop insurance program and the Federal
Emergency Management Agency’s flood insurance program, provide insurance either with a subsidy or where there is no private market (that is, the
price a private insurer would charge would exceed what a purchaser would
be willing to pay). Revisiting federal insurance subsidies could encourage
practices that could be increasingly important in the face of accelerating
climate changes, such as farmers planting drought-resistant varietals or
homeowners building or renovating away from flood plains.
Preparing for climate change will also entail larger-scale infrastructure
investments. Some of these investments involve maintaining existing infrastructure. For example, a 2007 investigation by the American Society of Civil
Engineers reported that chronic underfunding of the New Orleans hurricane
protection system was one of the principal causes of the levee failures after
Hurricane Katrina, a storm that inflicted over $110 billion of damages.
Other investments involve enhancing or extending existing infrastructure. For example, the electric power grid can be made more resilient to
increasingly severe storms and rising sea levels by using smart grid technology, which pinpoints outage locations and helps to isolate outages, reducing
the risk of widespread power shutdowns. The Recovery Act provided the
single largest smart grid investment in U.S. history ($4.5 billion matched by
an additional $5.5 billion from the private sector), funding both the Smart
Grid Investment Grant and Smart Grid Demonstration programs, among
others, to spur the Nation’s transition to a smarter, stronger, more efficient,
and more reliable electricity system (White House 2011b).

Conclusion
The scientific consensus is that the anthropogenic emission of greenhouse gases is causing climate change. The results can be seen already in
higher temperatures and extreme weather, and these are but precursors of
what lies ahead. Although greenhouse gas emissions and climate change are
global problems, the United States is in a unique position to tackle these
challenges and to provide global leadership.
The Nation has made substantial progress toward the Administration’s
ambitious short-term Copenhagen targets for reducing emissions of carbon
dioxide, but much difficult work lies ahead. Undertaking this work, which
reflects the Administration’s commitment to future generations, entails
many policy steps that are economically justified by the negative externalities imposed by greenhouse gas emissions. Policies to reduce emissions
of greenhouse gases include market-based policies; encouraging energy

Climate Change and the Path Toward Sustainable Energy Sources

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efficiency; direct regulation; encouraging fuel switching to reduced-emissions fuels; and supporting the development and widespread adoption of
zero-emissions energy sources such as wind and solar. And, as the country
reduces emissions along this path, it also needs to prepare for the climate
change that is occurring and will continue to occur. Together these policies
pave the way toward a sustainable energy future.

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C H A P T E R

7

INTERNATIONAL TRADE
AND COMPETITIVENESS

T

he United States is more closely linked with other nations through
trade, investment, and financial flows than ever before. For example,
total trade in goods and services as a share of gross domestic product
(GDP) was approximately 31 percent in 2012, compared with 26 percent in
2000 and 11 percent in 1970. International linkages are also reaching more
deeply than ever before into the organization of industries and firms. U.S.
companies are increasingly part of global supply chains, in which firms buy
inputs from subcontractors located in many countries. These linkages bring
both challenges and opportunities for the U.S. economy and for government
policy. Macroeconomic shocks and policies halfway around the world have
direct effects on growth, employment, and national balance sheets here
at home, just as shocks and policies in the United States affect economies
across the globe.
Significant opportunities are available for U.S. firms to expand exports
and create jobs, for resources to be allocated to their most productive uses,
for innovation to flourish, and for consumers to enjoy higher incomes,
lower prices, and expanded choice. These opportunities, however, have been
accompanied by job displacement, downward wage pressures, and other
adjustment costs. Government policy plays an important role in providing
infrastructure and incentives that reduce these adjustment costs, promote
the creation of middle-class jobs, and foster innovative ecosystems in the
private sector. Administration policies in both trade and competitiveness
seek to create a fair, firm foundation for the long-term prosperity of the
United States and its trading partners.

The World Economy and U.S. Trade
Fiscal consolidation, weak financial systems, and market uncertainty
have adversely affected demand in many advanced economies, and world
209

economic growth has suffered. In 2012, there were a number of shocks
to global growth, including the impact of financial stresses in Europe that
reached a peak in mid-summer. Given the globalized nature of world trade
and finance, the United States cannot fully escape the impact of development in other nations.

Growth in World Economies
Unlike the U.S. economy, which has sustained positive economic
growth for the past three years, several of the nation’s major trading partners
have slipped into economic contraction. In 2012, the euro area fell into
recession once again, as severe austerity measures put in place to combat
the region’s debt crisis impeded growth. The International Monetary Fund
(IMF) estimates that in 2012, the euro area economy contracted 0.4 percent,
compared with growth of 2.0 percent in 2010 and 1.4 percent in 2011. While
Japan was temporarily able to recover from the harsh economic slowdown
resulting from the earthquake and tsunami that struck the country in early
2011, slower global demand and the phase-out of reconstruction spending
brought the third largest economy in the world back into recession.
With the euro area, Japan, and the United States accounting for
almost half of global GDP, slower average growth in these economies was
sufficient to lower growth at the global level. Emerging market economies
have relied on import demand from these large, high income economies to
sustain high growth for over a decade. As import demand has weakened,
particularly from Japan and Europe, economic growth in emerging markets
has decelerated as well (Figure 7-1). For example, in 2012:Q2, real GDP
in China grew approximately 5.65 percent at an annual rate, the lowest
quarterly GDP growth China has recorded since the beginning of the global
slowdown in 2008.

The Euro Crisis
After financial tensions reached a peak in mid-2012, steps were taken
by both the governments of Europe and the central bank to reassure markets
of the integrity of the euro area and to begin the process of reforms. In the
summer of 2012, the European Central Bank announced it stood ready to
stabilize the bond markets of any member state in a reform program, while
governments launched the European Stability Mechanism (ESM), a joint
fund to provide direct loans to governments that replaces the temporary
European Financial Stability Fund (EFSF). These firewalls against financial
contagion have helped restore confidence, allowing Ireland and Portugal
to begin their return to financial markets. In Greece, meanwhile, European

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Figure 7-1
Real GDP Growth by Country, 2007–2012

Four quarter percent change
10

Emerging
markets

6

United
States

2
-2

Japan

Euro area

-6
-10
2007:Q1

2008:Q1

2009:Q1

2010:Q1

2011:Q1

2012:Q1

Note: Data through 2012:Q4 for all but emerging markets, for which data is available only for
2012:Q3.
Source: Country sources; U.S. Department of Commerce, Bureau of Economic Analysis; Cabinet
Office of Japan; Statistical Office of the European Communities; CEA calculations.

governments made important concessions in a redesigned program that
reduces Greek borrowing costs and supports continued reforms.
The combined impact of these measures produced noticeable results.
Bond yields in vulnerable countries fell dramatically to more sustainable
levels; in the week of the announcement of the bond buying plan, Spanish
10-year bond yields declined from 6.9 percent to 5.6 percent, and Italian
10-year bond yields fell from 5.8 percent to 5.0 percent (Figure 7-2).
Meanwhile, European authorities have taken important measures to
ensure that their banks have access to liquidity and hold adequate capital.
The authorities have also committed to launching a banking union with a
single supervisor and a European facility to recapitalize banks in troubled
countries where the governments are already facing problems managing
their debts. Uncertainty remains about access to a capital backstop as well
as about prospects for euro area institutions for common resolution and
deposit guarantees.
Finally, while the global recovery is clearly underway, European
nations are still facing challenges. The euro area reentered recession in 2012,
and the IMF in January forecast a further contraction of 0.2 percent in 2013
with continuing declines in output in Italy and Spain. Unemployment in the
euro area is hitting record highs, with 2012 unemployment rates in Greece

International Trade and Competitiveness

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Figure 7-2
10-Year Government Bond Yields, 2011–2013

Percent
8

Mar. 1, 2013

7
6

Spain

5
ECB announces
bond buying plan

4
3

Italy
France
United
States

2

Germany

1
0
Jan-2011

Jun-2011

Source: Bloomberg.

Nov-2011

Apr-2012

Sep-2012

Feb-2013

and Spain in excess of 23 percent (Table 7-1). Sustained fiscal consolidation
and the deleveraging in the banking and business sectors in the euro area
continue to act as headwinds to growth. Even as European leaders continue
to undertake structural reforms aimed at increasing competitiveness over
the medium term, markets remain sensitive to growth and reform prospects in large economies, including countries like France, Italy and Spain.
Meanwhile, a number of countries with stronger budget positions, including
Germany and the Netherlands, are running significant balance of payments
surpluses and thus are not an important source of demand for the European
recovery. More broadly, the euro area’s combined trade surplus, after adjusting for the effect of commodity prices, is rising quite rapidly, contributing
to global imbalances. Weaker European economies are closing their trade
deficits as imports decline with fiscal consolidation and contracting domestic demand, and Germany’s current account surplus has risen back to its
pre-crisis level of 6 percent thanks to the strong performance of German
exports around the world.
While we are making progress on increasing U.S. exports, these also
depend on expansion in overseas markets. Europe is a significant destination for American exports, accounting for more than 20 percent of U.S.
goods exports and almost 40 percent of U.S. service exports. Europe is also
the leading foreign source of investment in America, accounting for more
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Table 7-1
Euro Area Selected Economic Indicators
Greece

Spain

Italy

Germany

2009

2012

2009

2012

2009

2012

2009

2012

GDP growth (percent)

-3.3

-6.0

-3.7

-1.4

-5.5

-2.1

-5.1

0.9

Unemployment rate
(percent)

9.5

23.8

18.0

25.1

7.8

10.6

7.8

5.5

Current account balance
(percent of GDP)

-11.2

-2.9

-4.8

-0.8

-2.0

-1.5

5.9

6.4

Primary budget balance
(percent of GDP)

-10.4

-1.7

-9.9

-4.5

-1.0

2.6

-0.9

1.4

General government
debt (percent of GDP)

128.9

170.7

53.9

90.7

116.0

126.3

74.7

83.0

Source: IMF (2012); European Commission Statistical Office.

than 70 percent of all foreign direct investment in the United States in 2011.
Global and U.S. economic performance will depend, in part, on continuing
progress to resolve Europe’s challenges.

Global Imbalances
“Global rebalancing” has been one of the Administration’s major
international economic policy goals for the past four years. In June 2012,
the G-20 nations reiterated their support for this goal, calling upon countries with current account deficits to boost national savings, consistent
with evolving economic conditions, and for countries with large current
account surpluses to strengthen domestic demand and move toward greater
exchange rate flexibility.
A country’s current account consists predominantly of the difference
between its exports and its imports of goods and services (other factors
include net income on overseas assets and unilateral transfers such as foreign aid and remittances). A current account deficit occurs when a country’s
absorption (the sum of domestic consumption, investment and government
spending) exceeds its production. In this case, it must either borrow from
abroad or sell foreign assets. Current account deficits in certain countries
correspond to current account surpluses in others. A current account deficit
may indicate that a country offers sound investment opportunities, or it
may be caused by investment bubbles or fiscal deficits. Large and persistent current account surpluses can occur when governments intervene in
financial markets to prevent market-driven adjustments in interest rates
and exchange rates from taking place. While large current account imbalances may not directly cause financial crises, they often indicate underlying
dynamics that are unsustainable and thus have historically been important
precursors to financial crises (Reinhart and Rogoff 2011).
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Before the 2008 crisis, the United States was running a large current
account deficit financed by surpluses from creditor nations such as China
and Japan, a situation that Federal Reserve Chairman Ben Bernanke referred
to as the “global saving glut” (Bernanke 2005). In China, for example, low
levels of social insurance and policies designed to encourage excessive saving by firms contributed to large surpluses (Obstfeld 2012). From 2000 to
2007, the U.S. deficit ballooned to more than 5 percent of GDP, while current account surpluses in China, Germany, and Japan grew to 10, 7, and 5
percent of GDP, respectively. Current account deficits in Europe’s periphery
reached alarming levels. The surplus countries came to rely on unsustainable growth in net exports to drive their economies. The deficit countries
relied on unsustainable growth in household consumption, construction of
residential real estate, and government budget deficits for economic growth.
The crisis of 2008 brought about a distinct change in global imbalances: the U.S. current account deficit shrank to 3 percent of GDP in 2009,
while current account surpluses in China and Japan dropped as well (Figure
7-3). The Administration, along with the wider international community,
continues to press for a more balanced approach to growth in the world.
Greater reliance on consumption, and less on exports and investment, will
provide those countries with large current account surpluses with a more
sustainable source of growth over the long run. The members of the G-20
have committed to moving more quickly to market-determined exchange
rate systems and exchange rates that reflect underlying fundamentals.

Trade and the Manufacturing Sector
Although the Nation’s current account balance has improved substantially since its record deficit level of $800.6 billion in 2006, much of this
improvement is due to growing surpluses of trade in services and income on
investments, while the trade deficit in goods appears to have increased since
the recovery from the recession began in the third quarter of 2009 (Figure
7-4). However, the increase in the goods deficit conceals the fact that from
2010 to 2012, exports of manufactures grew at a faster rate (22.0 percent)
than imports (19.3 percent). The goods deficit has widened only because
manufacturing imports began the period at a much higher level.
U.S. trade in manufactures, both imports and exports, has grown
rapidly in recent decades primarily as a result of reductions in trade costs,
the rapid growth of emerging markets, and the increasing international
specialization of supply chains. Technological improvements in transportation and communication have lowered trade costs, as have reductions of
tariffs and other trade barriers both at home and abroad. Emerging markets,

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Percent of GDP
12

Figure 7-3
Current Account Balance by Country, 2000–2011

10
8

Germany

6

China

4
2
0

Japan

United
Kingdom

-2

United States

-4
-6

-8
2000
2002
2004
2006
2008
2010
2012
Note: Germany and Japan current account data available through 2012, U.S., U.K., and
China data only available through 2011.
Source: Deutsche Bundesbank; Bank of Japan; United Kingdom Office for National
Statistics; U.S. Department of Commerce, Bureau of Economic Analysis; Chinese State
Administration of Foreign Exchange.

Figure 7-4
U.S. Current Account Balance and its Components, 2000–2012

Billions of dollars
100
50
0
-50
-100
-150
-200

1
Balance on services
Balance on
income

1

1
Overall current
account balance

0

2012:Q3
0
Balance on goods

-250
2000:Q1 2002:Q1 2004:Q1 2006:Q1 2008:Q1 2010:Q1 2012:Q1
Note: Shading denotes recession.
Source: U.S. Department of Commerce, Bureau of Economic Analysis.

International Trade and Competitiveness

0

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particularly China, have grown at an impressive pace in the past decade and
have moved aggressively into manufacturing. In the past 10 years, China’s
share of world manufacturing exports has grown from 5 percent to over
15 percent. Finally, improvements in information technology (IT) have led
to the emergence of global value chains, in which tasks and components
involved in production are allocated across countries to take advantage of
differences in costs, skills, technology, or proximity to the market (Data
Watch 7-1). As a result, trade in intermediate goods and services has grown
rapidly. The effects of these forces on the U.S. economy have been profound.

Trade and Productivity
Greater openness of world markets enhances the productivity of U.S.
industries and firms. Research finds that the U.S. industries experiencing the
largest declines in tariffs have exhibited some of the strongest productivity
gains. Bernard, Jensen, and Schott (2006) find that falling trade costs led
individual U.S. manufacturing plants that already export to increase their
shipments abroad, high-productivity nonexporters to become more likely
to export, and low-productivity plants to become more likely to exit the
domestic market. Together, these effects result in a reallocation of economic
activity toward high-productivity firms, thereby raising overall industry
productivity. Studies of numerous other countries show similar gains in
industry productivity through trade-induced reallocation across firms.
Evidence also shows that decreases in industry-level trade costs lead
to within-firm productivity growth. Lileeva and Trefler (2010), for example,
found that the Canada-U.S. Free Trade Agreement caused increases in labor
productivity, product innovation, and adoption rates for advanced manufacturing technologies among Canadian exporters. Pierce (2011) showed
that U.S. tariffs lower the productivity of U.S. firms, in part by slowing the
rate at which older, less-productive production lines are phased out in favor
of new product lines. Several other studies have found that trade liberalization increases research and development (R&D) and technology upgrading.
Firm productivity and exports also can be enhanced when trade liberalization lowers the cost, and expands the variety, of imported intermediate
inputs.1 Although much of the evidence for this channel comes from studies
of middle- and low-income countries, Amiti and Wei (2009) found that
1 Houseman et al. (2011) concluded that the decline in input prices associated with shifts to
lower-cost producers may not be fully captured by statistical agencies, and as a result the data
may suggest that manufacturers are producing more goods with fewer inputs, when in fact
the real value of those inputs has simply been understated. After attempting to correct for
this so-called “offshoring bias,” the authors concluded that average annual manufacturing
productivity growth would be between 6 percent and 14 percent lower, and value-added
growth would be 7 percent to 18 percent lower than official estimates between 1997 and 2007.

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imports of service inputs, such as telecommunications, insurance, finance,
computing, and other business services, have a significant positive effect on
manufacturing productivity in the United States. In a similar vein, Francois
and Woerz (2008) showed that, across advanced economies, increased
import penetration in producer services results in better export performance, particularly by skill- and technology-intensive industries.

Growth of Traded Services
The United States is currently the world’s largest services exporter.
In 2011, U.S. exports of private services exceeded $600 billion, and sales
through foreign affiliates exceeded $1 trillion. Taken together, international
sales of services by U.S. companies are on the order of $1.7 trillion a year,
an amount equal to approximately 11 percent of U.S. GDP. Services trade
accounts for approximately 30 percent of U.S. exports and 15 percent of
U.S. imports. A study by the Organisation for Economic Co-operation and
Development and the World Trade Organization (WTO), however, estimated that nearly 60 percent of the value of U.S. exports can be attributed
to the service sector. This estimate takes into account both direct services
exports, as measured in official trade statistics, and indirect services exports
embodied as intermediate inputs in goods exports. The main traded service
categories are “other private services” (which includes items such as business, professional, and technical services, insurance services, and financial
services), royalties and license fees, and private travel.
Falling costs of travel, communication, and information technology
have increased the opportunities for trade in services. Over the past 10 years,
services imports and exports both almost doubled. Much of the growth was
accounted for by increased trade in business services, especially digitally
enabled services, defined by the Bureau of Economic Analysis (BEA) as
those for which digital information and communications technologies (ICT)
significantly facilitate cross-border trade. According to the BEA, from 1998
to 2010, exports of all ICT-enabled services grew at an annual rate of 9 percent to reach 61 percent of total U.S. services exports, up from 45 percent in
1998. Imports of ICT-enabled services grew at an annual rate of 10 percent,
rising to 56 percent of U.S. services imports, from 34 percent. Increases in
business, professional, and technical services contributed most to the overall
increase in ICT-enabled services trade. The private services surplus was
$162 billion in 2010; of this, $116 billion resulted from a trade surplus in
ICT-enabled services.
Some estimates suggest that about 70 percent of employment in business services is in industries potentially subject to international competition

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Data Watch 7-1: Implications of Global Value
Chains for the Measurement of Trade Flows
While international trade and foreign direct investment have been
growing rapidly for decades, recent advances in information technology
along with improving industrial capabilities in emerging markets have
made it profitable to segment production processes and relocate them
throughout the world, creating global value chains. This shift has made
it increasingly difficult to interpret international trade statistics. In the
past, it was safe to assume that most if not all of the value of a traded
product was created in the country that exported it. Thus, a country’s
industrial capabilities could be judged by the content of exports, trade
rules could be tied to gross levels of trade in specific products, and
exports could be directly related to domestic job creation. With the rise
of global value chains, however, one can no longer be sure how much of
the value of a product or service is added in the country that declares it
as an export. For example, in 2009, between one-third to one-half of the
total value of exports of transport parts and equipment from most major
producing countries originated in a different country. Similar patterns
emerge in the electronics sector: in China and Japan, the world’s largest
exporters of electronic goods in 2009, the foreign content of electronics
exports was about 40 percent. In Mexico, the share was over 60 percent
(OECD 2013).
Official trade statistics are measured in gross terms—the amount
the importer pays the exporter for the good. That approach is appropriate for adding up a country’s balance of payments made to, and received
from, the rest of the world. To determine how much value an exporter
adds to a good or service traded internationally, however, one must
subtract the value of intermediate inputs supplied by other countries,
including the country importing it. Removing these intermediate flows
from exports gives a measure of “value-added” trade.
Measuring value-added trade reveals a number of surprising facts.
For example, according to Koopman et al. (2010), in 2004 about 8 percent of total gross U.S. imports was U.S. value added in the form of U.S.
intermediate inputs used in foreign production. About 25 percent of the
value of U.S. gross exports was made up of imported intermediate inputs;
however, about half the value of those inputs originated in the United
States, so only about 13 percent of U.S. gross exports were not U.S. value
added. By contrast, about 37 percent of China’s exports were value added
somewhere else. Johnson and Noguera (2012) estimate that, while still
large, the U.S.-China imbalance is approximately 40 percent smaller
when measured on a value-added basis, and the U.S.-Japan imbalance
is approximately 33 percent higher. They also show that domestic value

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added in gross exports for the world as a whole has fallen dramatically in
recent years, indicating the rise of global value chains.
The Organisation for Economic Co-operation and Development
and the World Trade Organization recently released a new data set
containing estimates of value-added trade for 40 countries and 18 industries for 2005, 2008, and 2009 (OECD 2013). Future releases will see
an expansion in the number of countries, industries, and time periods,
dating back to 1995. This effort represents a substantial improvement in
the availability of information about global value chains.

(Jensen 2009). There is a widespread concern that, as business services
become more tradable over time, these jobs will be lost to import competition from low-wage, labor-abundant countries. However, given the
abundance of capital and highly skilled workers in the United States, the
most successful U.S. export industries tend to be those that employ capital
and skilled labor most intensively. In the services sector, the largest export
industries—integrated record production and distribution, software publishers, web search portals, satellite telecommunications, and motion picture
and video production—also pay the highest wages (Jensen 2011). The fact
that the United States has consistently maintained a positive trade balance
in services, and high-skill business services in particular, suggests that the
world is willing to pay for the high-quality, skill-intensive services that the
United States provides.
Despite America’s apparent comparative advantage in tradable highskill, high-wage business services, export activity on the part of these
firms faces significant impediments. About 25 percent of manufacturing
plants export; in business services, only about 5 percent of businesses
export (Jensen 2009). While differences in language and culture may pose
greater barriers to trade in services than in manufactures, services also are
differentially affected by an array of government-imposed impediments,
such as restrictions on foreign ownership and partnership arrangements;
nationality, residency, or local presence requirements for service providers; licensing and accreditation requirements; and limitations on the scope
of activities. Hufbauer, Schott, and Wong (2010) have estimated that the
aggregate level of barriers to services imports in emerging markets such as
China, India, and Indonesia is equivalent to a tariff on these imports of more
than 60 percent. After decades of liberalization through trade agreements,
tariffs in that range are relatively rare for goods. Recent research also has
found that restrictions on foreign acquisitions, discrimination in licensing,
restrictions on the repatriation of earnings, and inadequate legal recourse all
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have a significant negative effect on investment inflows into services sectors
(Borchert, Gootiiz, and Mattoo 2012). The Administration has undertaken
several important initiatives to address these impediments, discussed further
below.

Trade Policy
World trade collapsed in 2009; the recovery, while substantial, is
being held back by slow global growth. In response, in his 2010 State of the
Union address, the President launched the National Export Initiative (NEI),
an Administration-wide effort to double U.S. exports in support of up to 2
million additional American jobs by the end of 2014. Under the NEI, the
Administration continues to focus on improving trade advocacy and export
promotion efforts, removing or reducing barriers to U.S. exports of goods
and services, increasing access to credit, robustly enforcing trade rules, and
pursuing policies at the global level to promote strong, sustainable, and balanced growth. In 2012, U.S. exports of goods and services amounted to $2.2
trillion, an all-time record, despite challenging global economic conditions.
Longer-term trends affecting trade include the rapid growth in emerging markets and the rise of global value chains. The growth of emerging
markets makes them the most likely source of future U.S. export growth.
The International Monetary Fund estimates that developing countries will
account for more than three-quarters of the economic growth of all U.S.
trading partners in the next five years. It is vital, therefore, that the United
States secure from these countries more open and transparent market access
for U.S. firms. In addition, because of their growing involvement in global
value chains, U.S. firms are increasingly exposed to policies and barriers
behind the borders, not just at the borders, of countries around the world.
Countries vary widely in their use of subsidies, export taxes, support for
state-owned enterprises, financial market restrictions, ownership restrictions on foreign direct investment, government procurement, and enforcement of intellectual property rights, to name a few.
To address these challenges, the United States has pursued a robust
program of enforcement of existing rules through WTO dispute settlement
and a negotiating strategy for new agreements aimed at securing deep commitments with like-minded countries on a broad array of trade-related measures. The overriding goal of these latter initiatives, whether multilateral,
plurilateral or bilateral, is to open markets and set standards for conduct
that eventually shape the standards adopted by the global trading system.
The United States continues to adhere strongly to the precept that trade
liberalization at the multilateral level holds the highest potential for securing

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Box 7-1: Small Businesses and the NEI
Small businesses, defined by the Small Business Administration
as independent businesses having 500 or fewer employees, account for
more than half of nonfarm private GDP. These 27.5 million businesses,
many of them family-owned companies, are a key part of the U.S.
economy. However, they are far less likely to export or to use inputs from
abroad than are larger firms. In a world of imperfect financial markets,
the costs of financing export operations pose an especially high barrier
for smaller firms, because they are more likely to need external financing
to undertake export transactions. Small businesses also can find it more
difficult to learn about foreign markets and to overcome foreign trade
barriers and unfair trade practices compared with larger firms.
Through the NEI, the Obama Administration is committed to
helping small businesses overcome such barriers to exporting. The NEI
calls for a national outreach campaign both to identify small businesses
that may be able to increase their exports and to raise awareness generally among the nation’s small businesses about export opportunities.
The NEI provides training and other technical assistance to help small
businesses prepare to become exporters, sets up pilot programs to
match small businesses with export intermediaries, and outlines several
measures to support small businesses once they begin to export to new
markets. Thanks in part to the efforts of the NEI, a record of nearly
287,000 U.S. small and medium-size enterprises (SME) exported in 2010
(98 percent of all exporters), a total increase of more than 16,600 SMEs
over 2009. The goal is to increase the national base of SME exporters by
50,000 by 2017.

wide-ranging market-opening outcomes. The United States will continue to
complement its multilateral approaches with discussions at the plurilateral
and bilateral levels to build consensus for, and commitments to, marketopening agreements critical to the growth of trade-supported jobs.
In 2012, market-opening trade agreements with Korea, Colombia,
and Panama entered into force. The United States is currently negotiating
with 10 partners in the Trans-Pacific Partnership to tackle 21st-century
trade issues in the Asia-Pacific region. In January 2013, the President
announced plans to negotiate toward an international services agreement
with an initial group of 20 trading partners, aimed at removing impediments to global services trade. In February, the Administration announced
its intention to launch negotiations for a comprehensive Transatlantic Trade
and Investment Partnership with the 27-member European Union, aimed
at expanding what is already the world’s largest economic relationship,

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accounting for one-third of total goods and services trade and nearly half of
global economic output.
In the WTO, the United States is advocating new approaches that
can offer opportunities for agreements on issues that have been part of the
Doha Development Agenda, such as trade facilitation, and in areas that are
outside the Doha agenda, such as expansion of the Information Technology
Agreement. The United States also welcomed Russia’s membership in the
WTO, a membership that will provide significant commercial opportunities
for U.S. exporters.
Finally, the Administration aims to address potential disruptions that
trade can cause to domestic labor markets. The Federal Government’s Trade
Adjustment Assistance (TAA) program is designed to assist workers whose
jobs have been lost to import competition or threatened by trade-related
circumstances. The program provides financial, job training, and relocation
assistance to newly unemployed workers displaced by trade, with the goal of
making it easier for these workers to develop new skills and then enter more
vibrant sectors of the economy. In fiscal year 2012, the TAA program certified 1,131 petitions that permitted more than 81,000 workers to participate
in the program.

Building U.S. Competitiveness
The Nation must construct an economy based on a solid foundation
of educating, innovating, and building better infrastructure, a foundation
that can be strengthened in both manufacturing and in services. A hallmark
of the Administration’s policies is the recognition that there are many
spillovers within and between economic sectors and regions. Thus, wellchosen policies reinforce each other both to increase competitiveness and
to provide more middle-class jobs. For example, grants that assist workers
and firms that invest in apprenticeships benefit other firms in their industry
and region that can draw on a pool of skilled labor. Because of the myriad
benefits that arise from having a broad base of innovative workers, economic
growth and fairness go hand in hand. That is, Administration policies are
built around the idea that the country does best when everyone does their
fair share and plays by the same rules.

Manufacturing
While manufacturing employment has declined as a share of the
workforce for the past 50 years, the absolute number of manufacturing jobs
was relatively constant at about 18 million from 1965 until 2000. However,
starting in 2000, manufacturing employment dropped precipitously. The
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United States lost 3.5 million manufacturing jobs in the 7 years before the
Great Recession and then lost another 2.3 million during the recession.
This job loss has serious implications for the economy. First, the
decline in manufacturing employment significantly reduced the number of
middle-class jobs, especially for less educated workers. Wages and salaries
in manufacturing are 7 percent higher than in the rest of the economy,
and total hourly compensation (which includes the value of benefits such
as health care and pensions) is 13 percent higher. After controlling for
factors such as education, age, gender, race, union status, and location, the
compensation premium for manufacturing rises above 14 percent. A 2012
Department of Commerce study comparing manufacturing workers to
those in other private industries finds similar results (ESA 2012). Workers
of all education levels and occupations in manufacturing—from assemblers
to design engineers—earn more than their peers in other industries, showing manufacturing’s value in maintaining a strong American middle class.
Second, growing evidence shows that manufacturing production has positive spillover impacts on other parts of the economy. Spillovers occur when
one company’s activities benefit other businesses even though the latter did
not pay for them (Economic Application Box 7-1). As discussed below, the
loss of manufacturing activity has reduced these benefits.

Spillovers Between Manufacturing Production and Innovation
The argument is sometimes made that loss of U.S. production jobs is
part of an efficient global division of labor in which the United States focuses
on higher-end innovative activity and cedes lower-skill production activity
to other countries. However, this argument does not always hold.
First, production need not be a low-skill activity. Some of our main
competitors in manufacturing employ more highly skilled production workers and pay significantly higher wages than do companies in the United
States. Countries such as Germany and Denmark compete through business
and government support for “high-road” production practices, in which
workers participate in innovation as well as production. The higher wages
paid to these highly-skilled workers are offset by their higher productivity
(Helper, Krueger, and Wial 2012).
Despite its private and social benefits, however, companies do not
always adopt the high-road strategy because successful implementation
requires them to adopt a whole suite of interrelated practices. For example,
a study of U.S. valve producers found that more-efficient firms adopted
advanced information technology, while simultaneously changing their
product strategy (to produce more customized valves), their operations
strategy (using their new IT capability to reduce setup times, run times,
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Economics Application Box 7-1: Agglomeration
Economies and Spillovers Across Regions
Businesses are not spread out evenly across space but tend to
clump together, or “agglomerate.” As explained in Alfred Marshall’s
Principles of Economics (1890), firms group together because proximity allows them to share workers, ideas, and other inputs more easily.
Numerous studies have found that establishments located near other
establishments, whether in related industries (a cluster) or in diverse
industries (urbanization), tend to be more productive (Rosenthal and
Strange 2003).
A cluster is a geographically concentrated ecosystem of customers,
suppliers, trade associations, and labor unions that do business with one
another. These groups have collective capabilities. Like the common
pasture in medieval English villages on which the livestock owned by
many residents grazed, this “industrial commons” allows firms, particularly small firms, to nourish their technological capability using shared
assets. These common resources help to accelerate innovation and
commercialization. For example, firms located near each other can share
equipment needed for testing, and can more easily meet face-to-face,
which improves knowledge-sharing and trust-building. Service firms
(such as those in the Los Angeles film industry)—not just manufacturers—benefit from agglomeration.
In some cases, both the grouping of firms and the higher productivity may be the result of a third factor. For example, several firms
may each decide to locate near a natural harbor; their lower transport
costs may increase their productivity, but at least initially there may be
little benefit due to the proximity of other firms. Still, research suggests
that the entry of a large factory to a community tends to increase the
productivity of surrounding firms (Greenstone, Hornbeck, and Moretti
2010). Other research indicates that the benefits of R&D investment
are primarily local, suggesting that ideas—and by extension productivity—are improved in geographically concentrated industries. Jaffe (1989)
uses data from patent citations to show that inventors disproportionately
build on the work of nearby scientists. Branstetter (2001) argues that the
benefits of R&D appear to be primarily confined to the borders of the
investing country.
Because the benefits of a shared asset spill over to help even firms
that did not contribute to paying for it, and because profit-maximizing
firms will not value this benefit to other firms in making their plans, market forces are unlikely to provide enough investment in shared assets.
A case thus can be made for government to subsidize such activity. For
example, government support for key local assets such as a university or

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apprenticeship program may help a cluster to develop through improved
access to specialized R&D and skilled workers. Other successful clusters
have emerged from a mix of firm- and government-led actions such as
the cluster of computer and technology companies in Silicon Valley.
Once lost, these ecosystems can be hard to recreate. For any single
firm, the decision to move production elsewhere may make economic
sense. But that decision affects suppliers and the local talent pool, making
it easier for the next firm to leave and harder for the next firm considering
coming there to say yes. Conversely, new industries can build on foundations left by older clusters. For example, Optimus, a Pittsburgh biofuels
startup, uses a 100-year-old union training program to reduce the
costs of training technicians to service its innovative equipment—and
to demonstrate its product. Supported by the new federal Workforce
Innovation Fund, a partnership of startups, unions, and Carnegie Mellon
University is creating apprenticeship programs that build on this model
of shared training and product demonstration assets.

and inspection times), and human resource policies (employing workers
with more problem-solving skills and using more teamwork). The success
of changes in one area depended on success in other areas. For example,
customizing products was not profitable without reductions in the time
required to change over to making a new product, something made possible
both by improved IT capabilities and the improved use of this capability
by the empowered workers. Conversely, the IT and training investments
often did not pay off in firms that did not customize their products (Bartel,
Ichniowski, and Shaw 2007).
Second, there may be spillovers from production to innovation. Thus,
while Moretti (2012) shows that the positive wage spillovers associated with
innovation jobs are greater than those associated with manufacturing jobs,
it may not be possible to keep the innovation jobs in the long run if production jobs are lost. For example, when production in consumer electronics
migrated to Asia decades ago, the United States lost the potential to compete
for follow-on innovations and subsequent production in flat-panel displays,
LED lighting, and advanced batteries (Pisano and Shih 2012). Making
products exposes engineers to the problems and the capabilities of existing
technology, generating ideas both for improving processes and for applying
a given technology to new markets. Losing this exposure makes it harder to
come up with innovative ideas.2
2 The U.S. auto industry could have ended up on this path, but as a result of the
Administration’s rescue of General Motors and Chrysler, and investments in innovation, the
industry is growing and healthy.

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Even when American firms do maintain a technological edge, their
operations may be less profitable than if they were part of a vibrant industrial
commons. E-ink, a Massachusetts firm now owned by its Taiwanese business partner, designed the electronic “ink” that represents the Kindle’s key
innovative element. Because the firm was located so far away from its Asian
suppliers, its engineers were not able to interact on a daily basis with other
firms in the supply chain that were inventing new products, making it hard
for the firm to find new markets for its inks. The situation is similar throughout the rest of the LCD flat-panel-display industry. Harvard Business School
Professor Willy Shih estimates that, because the United States has offshored
much of its production capacity in this industry, U.S. firms capture only
about 24 percent of the profits from U.S. Kindle sales (Pisano and Shih
2012).

Rise of Global Supply Chains
In recent decades, the structure of manufacturing has changed dramatically. Instead of vertically-integrated firms that obtain most of their
inputs from within national borders, lead firms now purchase many inputs
from outside suppliers around the world. Most manufacturing production
today occurs in layers of specialized, smaller firms that provide components for final assembly and sale by large lead firms or original equipment
manufacturers (OEMs). For example, CEA calculations estimate that in the
United States in 1988, there were fewer than two employees in firms making
automotive parts for every automaker employee. By 2010, parts companies
had four employees for every automaker employee (Data Watch 7-2).
Because of this vertical dis-integration, almost all large U.S. manufacturers now depend on their suppliers for well over half their value-added. In
most cases, these suppliers are shared with other firms. This arrangement
has some advantages—for example, it may create opportunities for crossfertilization. But shared supply chains also have a weakness in that firms’
incentives to invest in their suppliers are reduced. If an OEM helps its supplier develop a new technology, the supplier’s other customers—often the
OEM’s rivals—will enjoy these improvements without having contributed.
As a result, OEMs have less incentive to make such investments and may
be more inclined to shift costs and risks down the supply chain to smaller
suppliers. These practices, called “free-riding” by economists, improve the
larger firms’ financial performance in the short run but may weaken the
entire supply chain in the long run.

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Data Watch 7-2: Measuring Supply Chains
The potential collapse of General Motors and Chrysler in December
2008 underscored the importance of understanding the operation of
supply chains. Because the large auto manufacturers all relied on a common set of suppliers, a failure of any of the major players could have
threatened the viability of the entire industry.
Measuring the size of this supply chain presents a statistical challenge. U.S. government statistical agencies assign each worksite in the
United States to a single industry on the basis of its primary activity.
Two North American Industrial Classification System (NAICS) codes
are commonly used for reporting sales and employment in the auto
industry—NAICS 3363 (motor vehicle parts manufacturing) and NAICS
3362 (motor vehicle body manufacturing)—but these codes do not
capture all workplaces involved in the auto supply chain. First, many
firms that make auto parts are not classified as serving the automotive
market, but rather by the materials or the technology they use, such as
“plastics product manufacturing” or “forging and stamping.” Similarly,
the NAICS codes do not link tooling producers to their customer industry. Second, the worksites that focus on nonproduction activities such
as research or management are not categorized with the industry they
serve; rather, they are grouped together in “Professional, Scientific, and
Technical Services.” In addition, contract workers in auto parts plants
are assigned to the temporary help industry, rather than to motor vehicle
parts production.
Using survey data for late 2010, the Council of Economic Advisers
has estimated the number of jobs in the auto supply chain based on
a more inclusive definition that includes all of this activity. While the
conventional definition of auto parts showed employment of 553, 860 for
this period, the CEA estimate was more than 1 million. The high degree
of interdependence in the auto industry made the 2008 financial crisis
particularly perilous, because contagion from financial troubles at one
firm in the industry easily could have spread to others. The CEA’s larger
estimates of the size of the auto supply sector imply this risk was greater
than previously realized.

Prospects for U.S. Manufacturing
The U.S. economy gained nearly 500,000 manufacturing jobs between
January 2010 and January 2013, after losing more than 5 million manufacturing jobs in the previous decade (Figure 7-5). These job gains represent not
just a cyclical recovery but also potentially the start of a longer-term trend
toward the “in-sourcing” of manufacturing. About three-quarters of the

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Figure 7-5
Monthly Change in Manufacturing Employment, 1990–2012

3-month moving average, thousands, seasonally adjusted
100
50

100
Jan-2013

0

50
0

-50

-50

-100

-100

-150

-150

-200

-200

-250
1990 1992 1994 1996 1998 2000 2002 2004 2006 2008 2010 2012
Note: Shading denotes recession.
Source: Bureau of Labor Statistics, Current Employment Statistics; CEA calculations.

-250

increase in U.S. manufacturing shipments since the end of the recession is
due to an increase in domestic demand and inventory restocking; the other
quarter comes from an increase in exports. Because of the extensive spillover
benefits associated with a vibrant manufacturing sector, this recovery has
positive implications for long-term growth of the economy as a whole.
Since early 2012, diminished impetus from several key drivers of
growth, as described in Chapter 2, has challenged the growth of U.S.
manufacturing. First and most important, export growth has begun to slow,
reflecting the slower pace of global growth. Second, after surging during
the past few years, demand by domestic business for new capital equipment
appears to have slowed. Third, firms finally appear to have replenished their
inventories to levels more consistent with demand after heavily depleting
stockpiles during the recession.
As noted above, “export-intensive” industries have played a large role
in the recovery of manufacturing since the end of the recession. From April
2011 through February 2012, industries that export at least 20 percent of
their shipments accounted for 57 percent of manufacturing output and 51
percent of manufacturing employment. During this period, manufacturing
production and hiring rose faster in these industries than in others. Since
February 2012, however, manufacturing production and hiring has slowed,

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Figure 7-6
Employment in Export Intensive and Export Nonintensive
Manufacturing Industries, 2011–2012

Annualized percent change
2.8
2.4
2.0
1.6
1.2
0.8

Export intensive
2.4

All
manufacturing
1.7

Export
Export
nonintensive
All
Export
intensive
manufacturing
1.0
nonintensive
0.9
0.8
0.7

0.4
0.0

Apr-2011 to Feb-2012

Feb-2012 to Dec-2012

Note: Export-intensive manufacturing industries are three-digit NAICS industries in which exports as
a share of total shipments exceeded 19.9 percent, the average for the manufacturing sector as a whole
in 2011. Export-intensive industries accounted for about 57 percent of manufacturing output in 2011.
Source: Federal Reserve Board, G.17; CEA calculations.

with nearly two-thirds of the slowdown in output and 90 percent of the
slowdown in hiring occurring in export-intensive industries (Figure 7-6).
Other trends, however, suggest a brightening outlook for manufacturing. The continued recovery in the housing sector should lead to greater
demand for construction supplies, and the order backlog for commercial
aircraft is substantial. In addition, although production of nondurable goods
like food and beverage products, plastics and rubber, and chemicals has
lagged that of durable goods so far during the recovery, it should accelerate
as consumer and business demand becomes more broad-based. Indeed,
with capacity utilization now close to its historical average, and weekly work
hours elevated above it, even a moderate rise in demand could quickly translate into a pickup in production, hiring, and investment.
Prospects for In-sourcing. Several recent reports have concluded that
manufacturers increasingly view the United States as a favorable production
location.3 Factors cited for this change include trends in unit labor costs,
expansion of domestic energy resources such as wind and natural gas, and
greater recognition of the “hidden costs” of moving production abroad.
Over the past decade, U.S. unit labor costs—the cost of labor required
to produce one unit of output—have grown much more slowly than in other
3 Academic literature often refers to this phenomenon of work returning to the United States
from abroad as “on-shoring.”

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developed nations (Figure 7-7). U.S. hourly compensation in manufacturing
has grown somewhat over the past decade, but rapid productivity growth has
reduced the cost of producing a unit of manufactured output in the United
States. Meanwhile, when measured in U.S. dollars, the cost of manufacturing
a unit of output in key trading partners has risen, in some cases substantially.
Several recent studies by management consultants argue that these
trends create the potential for a “manufacturing renaissance” in the United
States and estimate that the result could be 1 million or more new manufacturing jobs by 2015 (Boston Consulting Group 2012; Inch and Dutta
2012; Simchi-Levi et al. 2011). A key assumption of most of these analyses
is that U.S. manufacturing wages continue to be stagnant. Thus, while these
trends provide favorable tailwinds for U.S. manufacturing, they will not by
themselves lead to sustainable prosperity. In contrast, the “high road” model
discussed above also yields favorably low unit labor costs—but does so by
increasing productivity, rather than by reducing wages.
Reassessing the Costs of Moving Production Abroad. Based on their
experience during the past decade, American firms now have a greater
understanding of the magnitudes of hard-to-measure costs attributable
to the risks and complexities of operating far from home. Initially, “many
manufacturers who had offshored their operations likely did so without a
complete understanding of the ‘total costs,’ and thus, the total cost of offshoring was considerably higher than initially thought,” according to a study
of 287 manufacturers conducted by Accenture (Ferreira and Heilala 2011).
Compared with operating in the United States, setting up a supply
chain in China and learning to communicate with suppliers requires many
long trips and much time of top executives—time that could be spent on
introducing new products or processes at home. There is also greater risk
from a long supply chain, because shipping prices and delivery times can
vary enormously. In addition, U.S. companies are coming to value more
highly the advantages that come from having production, innovation, and
design close together. For example, Intel manufactures its most advanced
chips in the United States, near where they are designed (Helper, Krueger,
and Wial 2012).
To take another example, Sleek Audio, a start-up manufacturer with
innovative headphone technology, initially went to China for all of its production. After years of flying several times a year to China, and an incident
in which millions of dollars of product had to be scrapped because of poor
quality, the owners moved manufacturing to the United States. They began
to work with a local manufacturer with experience in making precision
products for the military, Dynamic Innovation, located within 10 minutes
of Sleek Audio in Florida. In the course of redesigning the product for more
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Figure 7-7
Change in Manufacturing Unit Labor Costs, 2003–2011

Average annual percent change
8

Canada

7
6
Germany

5
4
3
2

Singapore

1

United
Japan Kingdom

Korea

Italy

China* France

Sweden

0
-1
-2
-3

Taiwan

United
States

Note: Average annual percent change for China represents 2003–2009 data. The BLS does not track
manufacturing unit labor costs for China, and many economists have expressed concern over the
reliability of recent Chinese economic statistics (Wan 2013).
Source: Bureau of Labor Statistics, International Comparisons of Manufacturing Productivity and Unit
Labor Costs; Ceglowski and Golub (2011).

automated U.S. production, the firms dramatically improved product quality, replacing hand-welded plastic panels with robot-welded aluminum ones
that also significantly improved sound quality (winning an award from the
Consumer Electronics Association). The price was higher in the United
States, but the improved product features and ability to customize design
more than offset this cost (Prasso 2011; Koerner 2011; Hackel 2011).
Numerous other collaborations that bring together different forms of
expertise are keeping jobs in the United States. Many of these collaborations
bring together shopfloor workers with a concrete understanding of plant
conditions and engineers with deep technical knowledge. For example, management and members of the machinists’ union at an Ashland, Kentucky
chemical plant have worked together for two decades to improve both product quality and working conditions (Davidson 2013).

Productivity in Services
The service sector encompasses widely varied activities, ranging from
house cleaning to data entry to investment advice. Despite this diversity,
some common trends can be observed—trends similar in many respects to
those seen in manufacturing.
As noted, many services are becoming increasingly globalized; as in
manufacturing, there is also less vertical integration. In the hotel industry,

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for example, it is now common for a lead firm such as Marriott to create and
advertise an overall brand, while the day-to-day oversight of the workforce
is handled by a separate hotel operating company, and staffing may be organized by a temporary-services firm (Weil 2011).
As in manufacturing, there are wide variations in performance across
firms within individual service industries. In retail trade, for example, in the
late 1980s and 1990s, Wal-Mart’s real value-added per worker was more
than 40 percent higher than that of other general merchandise retailers
(Johnson 2002). Trucks with on-board computers had 13 percent higher
capacity utilization than trucks without them (Hubbard 2003). Much of the
productivity improvement realized by high-productivity service firms has
been associated with investments in information technology (Bosworth and
Triplett 2007). Obtaining these performance improvements often involves
investing simultaneously in information technology and in complementary
organizational changes, as in the valve case described earlier. For example,
retailers who can quickly integrate data on consumers’ purchases with their
systems for replenishing inventory are more productive than those who cannot (Wailgum 2007; Zhu 2004).
Finally, although the use of IT and other innovations in services has
led to large productivity gains, the benefits of these gains have not been
evenly shared. Although IT adoption has led to increased pay and autonomy
for workers who interpret information, such as financial advisers, it has led
to reduced employment and pay for jobs that can be described in rules that
a computer can follow—jobs such as routine claims processing that require
moderate skills and that once paid middle-class wages (Levy and Murnane
2005).

Creating An Economy Built To Last
A hallmark of the Administration’s policies to reverse the middleclass jobs deficit is leveraging positive spillovers to raise labor demand and
productivity, and to create new industries and products, while equipping
American workers with the tools they need to succeed in a modern economy. The President’s blueprint for creating an economy built to last aims
to promote synergies within local areas and among companies that add to
growth in investment and good jobs.
The following discussion uses manufacturing as an example to illustrate these policies, but their usefulness is not limited to manufacturing.
For example, the U.S. Department of Agriculture has for decades helped an
industry made up largely of small producers remain internationally competitive, by providing an integrated set of services with large spillover benefits

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to farmers and rural communities: land-grant universities for research and
training; cooperative extension agents that help to diffuse practices shown
by this research to be effective; access to capital (in part through the department’s own credit agencies); and programs that help farmers set up cooperatives to achieve economies of scale in purchasing and marketing.

Strengthening Competitiveness: The Manufacturing Example
A competitive U.S. manufacturing sector is a key to the Administration’s
vision of a U.S. economy that is innovative and competitive and that provides good jobs. Rising costs abroad coupled with sustained domestic productivity gains make the United States an increasingly attractive location for
investment. But good policy is also needed to fully capture the benefits of
this underlying trend and encourage investment in middle-class jobs in the
United States. The view that a strong “industrial commons” is important for
competitiveness, but also subject to market failure, suggests that government
policy should promote the creation of, and access to, these shared resources.
Thus, the Administration’s policies work to promote the type of manufacturing that builds innovative capability and raises living standards.
The Administration’s proposals help in several ways to strengthen
these types of manufacturing. First, general policies to improve productivity and wages (such as the policies to support education, health care, and a
clean environment discussed in other chapters of this Report) are essential
to building long-term economic competitiveness.
Second, the Administration has made trade policy a priority. These
policies have particular importance in manufacturing. Some argue that
much of the steep manufacturing employment decline in the early 2000s was
caused by a sharp rise in imports from emerging nations, especially China
(Autor, Dorn, and Hanson, forthcoming; Pierce and Schott 2012). In some
cases, producers exporting from these nations have benefited from policies
that gave them an unfair advantage relative to manufacturers in the United
States. In response to these policies, the Obama Administration, in addition to pursuing the broader trade policies discussed earlier in the chapter,
launched an Interagency Trade Enforcement Center charged with protecting American companies from unfair trade competition.
Third, the Administration has championed tax credits to reduce the
costs of socially beneficial actions (such as R&D). These policies aim to
reward firms for providing lasting social benefits. In contrast, a “smoke
stack-chasing” approach tries to lure individual firms to a particular location using tax abatements and other incentives. In general, these subsidies
are awarded to firms for undertaking activity that would have occurred
anyway; the subsidy simply influences the location of the activity. Thus these
International Trade and Competitiveness

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individual incentives generally do not lead to net investment (Chirinko and
Wilson 2008). State and local governments provide more than $80 billion a
year on such incentives, including $25 billion to manufacturers (Story 2012).
Finally, the Administration has championed sector-specific policies
that use the convening power of government to promote coordination and
investment. Productive ecosystems that promote innovation and good jobs
require strong partnerships among industry stakeholders, including business, government, unions, trade associations, and universities. A sectoral
approach to encouraging the development of such ecosystems (in manufacturing and in other industries) can help to build simultaneously both
the demand for and the supply of shared assets, such as trained workers,
competent customers engaged in innovation, suppliers of components, and
standards for equipment design. The supply-chain analysis above suggests
that policy may be needed to address two key issues: free-rider problems that
lead to underinvestment and information barriers that hinder coordination
among stakeholders in a supply chain.
The Administration’s flagship manufacturing initiative is a $1 billion
National Network for Manufacturing Innovation fund that will create up
to 15 institutes to help ensure that new technology bridges the gaps from
invention to product development to manufacturing at scale. Leveraging the
assets of a particular region, each institute will bring together universities,
companies, and government to co-invest in the development of new technologies that spill over to provide general benefits to a region’s manufacturing base, rather than just a single company. Institutes will build workforce
skills and business capabilities in large and small companies. A pilot center,
the National Additive Manufacturing Innovation Institute, opened last
year in Youngstown, Ohio. The universities and firms participating in the
institute matched the initial $30 million in federal funding with $40 million
of their own.
As discussed, many firms have been slow to adopt even well-known
improved practices and thus lack the capability to participate in such
innovative endeavors. To help these firms upgrade their operations, the
Administration has proposed increased funding for the Manufacturing
Extension Partnership program, which provides a range of business services
to small manufacturers.
The Administration also has proposed initiatives to replenish the
technology pipeline, by increasing funding for advanced manufacturing
R&D. Despite tightening budgets, the Administration has emphasized the
importance of funding industrially relevant, advanced manufacturing technologies such as advanced materials, smart manufacturing, and robotics.

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Conclusion
The United States economy benefits from being closely linked with
other nations through trade, investment, and financial flows. The Nation’s
economic recovery and long-run growth prospects depend in large part on
U.S. businesses being able to compete in an open, fair and growing world
economy. The Federal government is determined to do its part to facilitate
this outcome. Sound macroeconomic policies that aim at strong, balanced,
and sustainable growth are but one element. Another is a trade policy aimed
at the maintenance of open, competitive markets, compliance with WTO
obligations, and leadership in the multilateral trading system. The United
States pursues a policy that supports jobs through trade, enforces trade rules,
bolsters international trade relationships, and partners with developing
countries to fight poverty and expand opportunities.
Creating and maintaining a competitive industry or region requires
continuous investment by firms, workers, and communities. These investments are often more productive if others are also investing. In a number of
cases (especially in manufacturing), investments in these productive ecosystems were allowed to lapse, affecting both competitiveness and job quality.
Administration policy has helped to reverse these lapses, leading to domestic
economic growth and increased exports.
Many of the policies discussed in connection with manufacturing also
benefit consumers and workers in the services sector, such as policies that
promote access to education. In addition, sector-specific policies for services
are discussed in other chapters of this Report. For example, as discussed in
Chapter 5, the administration has convened the Partnership for Patients,
which brings together hospitals and clinics in a community to work to
reduce errors in patient care.
While much remains to be done, these policies have laid a foundation
for competitiveness and prosperity for both the United States and its trading
partners.

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C H A P T E R

8

CHALLENGES AND
OPPORTUNITIES IN U.S.
AGRICULTURE

U

.S. agriculture fared better during the Great Recession than many
other sectors and remains a bright spot in the U.S. economy. Despite
an extensive and severe drought in 2012, net farm income is forecast to total
$112.8 billion, only 4.3 percent below the previous year’s record of $117.9
billion (USDA 2013a). Strong demand for agricultural products and belowaverage crop yields pushed up crops prices, and along with significant crop
insurance indemnity payments, helped to make the 2012 income figure
the second-highest since 1974 after adjusting for inflation. (See Economics
Application Box 8-1 on the 2012 drought).
The strength of the U.S. agricultural sector is due in part to the
demand for American agricultural exports. The value of agricultural exports
has steadily risen and now accounts for a projected 31 percent of gross farm
cash income. Exports reached a near record level of $135.8 billion in 2012
and are projected to reach $142 billion in 2013 (USDA 2012a).
Increasing demand from abroad created by rising incomes and a
growing middle class will present opportunities for U.S. agriculture. The
world population is expected to reach more than 9.2 billion by 2050, with
growth coming primarily in developing countries, most of which are net
importers of food products. The convergence of population growth and
rapid urbanization, especially in developing regions of the world, will likely
result in growing demand for food as well as changing dietary patterns.
Trade in agricultural commodities is a global endeavor, and the U.S.
agricultural sector is subject to significant price volatility at the commodity
level. Because of its high degree of integration with the international marketplace, U.S. agriculture is vulnerable to price volatility induced by other
countries’ agricultural policies—import and export restrictions—and growing conditions. Further, while the effects of climate change on livestock and
237

Economics Application Box 8-1: The 2012 Drought
A drought in the summer of 2012 across much of the United States
caused significant crop losses and some livestock liquidation. About 80
percent of agricultural land experienced low rainfall and high temperatures, making the 2012 drought the most extensive since 1956. A striking
aspect of the 2012 drought was the rapid increase in severity in early July.
While the drought eased somewhat during early September, conditions
during the June to August period largely determine production for most
crops. By mid-August, crops worth 50 percent of the total value of all
crops were exposed to drought.
Crop losses were most substantial for corn. In the spring of 2012,
the U.S. Department of Agriculture estimated an expected corn yield of
166.0 bushels an acre. By October 2012, those estimates had dropped to
122.3 bushels an acre—a reduction of 27 percent. Soybeans, somewhat
more drought tolerant, experienced a 14 percent yield reduction (from
43.9 to 37.8 bushels an acre). The livestock industry, still recovering
from the 2011 drought in the Southern Plains, was hit especially hard.
As of late October of 2012, 54 percent of pastures and ranges in the
United States were rated poor to very poor. Beef production in 2012
was projected to decline 2.3 percent from 2011 levels and to fall another
4.2 percent in 2013. Broiler and pork production were also expected to
experience declines in 2013, while milk production is expected to remain
stable.
The effects of the drought on food prices were reflected first in the
livestock sector, with increases in the price of meat and dairy products in
late 2012 and projected into 2013. The full effects of the increase in corn
and other commodity prices will likely take as long as a year to be fully
captured in higher retail food prices.
Despite the drought, average income for farm businesses remained
steady in 2012 at $86,200, reflecting the increased prices for corn and
soybeans as well as increases in crop insurance indemnities, which as of
February 2013 had already paid out $12.9 billion for 2012 losses (USDA
2013). Income increases on crop farms should more than offset livestock
farmers’ higher feed expenses and a decline in sales of wholesale milk.
Additionally, the longstanding environment of strong commodity
prices and low interest rates means that farm debt-to-equity ratios are
approaching historic lows, which has reduced the financial vulnerability
of farms to the production shocks.

crop production systems are expected to be mixed in the next 25 years, over
the long term, continued changes are expected to have generally detrimental
effects on most crops and livestock.

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The Agricultural Sector in 2012
In the 1920s, farm households accounted for more than 25 percent of
the U.S. workforce and generated approximately 8 percent of gross domestic product (GDP). Today they account for only 1.6 percent of the work
force and generate approximately 1 percent of GDP. Over the same period,
the rural share of the population has fallen far less, from 49 percent to 19
percent, suggesting that rural areas are less dependent on farming’s contribution to the rural economy (Table 8-1). The agricultural sector is still vital
to our country, but because of growth in other sectors of the economy and
rapid gains in agricultural productivity that have lowered the relative prices
of agricultural products, it has become a smaller share of the U.S. economy.
The structure of farming continues to move toward fewer, but larger
commercial operations producing the bulk of farm commodities, complemented by a growing number of smaller farms earning most of their income
from off-farm sources. Small family farms—those with annual sales less
than $250,000—make up 90 percent of U.S. farms. They also hold about 62
percent of all farm assets, including 49 percent of the land owned by farms.
However, commercial farms, which make up the other 10 percent of the
sector, account for 83 percent of the value of U.S. production (Table 8-2).
While most of these large farms have a positive profit margin, average
profit margins for small farms are negative because of high operating costs,
low sales, and lower productivity (Table 8-3). Farms are predominantly
organized as sole proprietorships (86.5 percent), followed by partnerships
(7.9 percent) and corporations (4.4 percent).1
Fifty years ago, average household income for the farm population
was approximately half that of the general population. Today, however,
farm households tend to be better off than other American households; in
2011, median income for farm households was about 13 percent higher than
the U.S. median household income (Figure 8-1). The difference in income
between farm households and the nonfarm households is due in part to the
broad Department of Agriculture (USDA) definition of what constitutes a
farm, which includes farms where the principal operator is retired or has
a main occupation other than farming (“residence farms”). Households
operating rural residence farms earn more than the U.S. median household income even though their net cash income from farming is negative.
Households operating intermediate farms (farms where the principal operator is not retired and reports farming as his or her main occupation) have
on average positive net cash income from their farming operations, but most
household income comes from sources other than farming. The sources of
1 Corporations include both Sub-chapter C and S corporations.

Challenges and Opportunities in U.S. Agriculture

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Table 8-1
90 Years of Structural Change in U.S. Agriculture
Year
Number of farms (thousands)

1920

1950

1980

2000

2010

6,518

5,648

2,440

2,167

2,192

Average farm size (acres)

147

213

426

436

419

Rural share of population (percent)

48.8

36.0

26.3

21.0

19.3

Farm share of workforce (percent)

25.4

12.1

3.4

1.8

1.6

Farm share of GDP (percent)

7.7

6.8

2.2

1.0

0.9

Note: 1920 data for farm share of GDP not available. Value reported is for 1930, as calculated by the Department
of Agriculture, Economic Research Service.
Source: Department of Agriculture, National Agricultural Statistics Service, Farms, Land in Farms, and
Livestock Operations; Bureau of Economic Analysis, GDP by Industry; Sobek (2006); CEA calculations.

Table 8-2
Farm Types

Small family
farms (gross
sales less than
$250,000)

Large-scale
family farms
(gross sales of
$250,000 or
more)
Nonfamily farms

Retirement farms. Small farms whose operators report they are retired.
Rural-residence
Residential/lifestyle
farms. Small farms whose operators report a major
family farms:
occupation other than farming.
Intermediate
family farms:

Farming-occupation farms.
Small family farms whose
operators report farming as
their major occupation.

Low-sales farms. Gross sales less than
$100,000.
High-sales farms. Gross sales between
$100,000 and $249,999.

Large family farms. Gross sales between $250,000 and $499,999.
Commercial
family farms:

Very large family farms. Gross sales of $500,000 or more

Any farm not classified as a family farm, that is, any farm for which the majority of the
farm business is not owned by individuals related by blood, marriage, or adoption.

Note: The National Commission on Small Farms selected $250,000 in gross sales as the cutoff between small
and large-scale farms.
Source: Department of Agriculture, Economic Research Service, Farm Household Well-being

income for farm households are increasingly diversified, which means that
many of them are less vulnerable to the fluctuations of farm income. In 2011,
households operating commercial farms had median household incomes
two and a half times the overall U.S. median household income, with most
of their income from farming.
By 2000, 93 percent of farm households had income from off-farm
sources, including off-farm wages, salaries, business income, investments,
and Social Security. Off-farm work has played a key role in raising farm
household income. In 2011, only 46 percent of principal operators of farms
reported that farming was their main occupation. While farm household
incomes have become more diversified, farm operations have become
increasingly specialized: In 1900, a farm produced an average of about five

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Table 8-3
Farm Income and Farm Operator Household Income by
USDA Farm Size Classification, 2010
Rural
residence
farms

Intermediate
farms

Commercial
farms

All farms

1,311,117

617,876

214,070

2,143,063

14,974

52,790

840,315

108,320

Crop, livestock, and other farm-related
income

91.6

94.6

97.0

96.2

Government payments

8.4

5.4

3.0

3.8

Total cash farm expenses

17,216

46,142

613,486

85,117

Net cash farm income

-2,242

6,648

226,829

23,203

83,738

51,054

185,098

84,440

Farm operator households
Average gross cash farm income (dollars)
Average gross cash farm income, by source
(%)

Average per farm operator household
(dollars)

Farm operator household income

Source: Department of Agriculture, Agricultural Resource Management Survey.

Figure 8-1
Median Income for Farm Households by Farm Type
and Income Type, 2010−2012
Residence
Intermediate
Commercial
All

Median farm
income

Residence
Intermediate
Commercial
All

Median off-farm
income

Residence
Intermediate
Commercial
All

Median total
income

-10,000

20,000
50,000
80,000
110,000
Median income, curent dollars
Note: 2012 forecasted values included for "all" farms. Values for farm-type breakouts are
2010−2011 averages.
Source: Department of Agriculture, Economic Research Service, Agricultural Resource
Management Survey.

commodities; by 2000, the average had fallen to just over one. This change
reflects not only the production and marketing efficiencies gained by concentration on fewer commodities, but also the effects of farm price and

Challenges and Opportunities in U.S. Agriculture

| 241

income policies that have reduced the risk of depending on returns from
only one crop or just a few crops.
The average age of U.S. farmers and ranchers has been increasing
over time. In 1978, 16.4 percent of principal farm operators were over age
65. By 2007, 30 percent of all farms were operated by producers over 65. In
comparison, only 8 percent of self-employed workers in nonagricultural
industries in 2007 were that old (Hoppe, McDonald, and Korb 2010). One
reason the farming sector is relatively older is that farmers are living longer
and often reside on their farms. Many established farmers never retire.
Additionally, one-third of beginning farmers are over age 55, indicating
that many farmers move into agriculture only after retiring from a different
career. More than 20 percent of farm operators report that they are retired.
Another 32 percent of all farms are operated by farmers aged 55 to 64 years.
Farmers aged 55 and older account for more than half of the total value of
production. Farmers under 35 contribute only 6 percent of the total value of
production (Figure 8-2). This demographic transition has implications for
the future of the U.S. agricultural sector.

Barriers to Entry and Succession Planning in U.S. Agriculture
Starting a farm operation can be an expensive endeavor. Startup
requires access to land and capital equipment, as well as the operator’s time.

Percent
50
45

Figure 8-2
Distribution of Farms by Age of Principal Operator, 2010
Less than $10,000

$10,000 to $249,999

$250,000 or more

40
35
30
25
20
15
10
5
0

Less than 35 years
old
Source: USDA (2010).

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Chapter 8

35–54 years old

55–64 years old

65 years old or more

In 2011, the average farm operated 415 acres and held assets worth just
under $1 million, accounted for mostly by land and structures. Even for
farm operators under age 35, asset values averaged $811,500, highlighting
the extent to which startup costs represent a hurdle for new entrants (USDA
2011).
The Federal Government recognizes the need to support and develop
new farm operators. Through the Farm Service Agency, the USDA helps
beginning farmers who are unable to obtain financing from commercial
lenders by targeting a portion of its direct and guaranteed loan funds to
farmers and ranchers who have not operated a farm or ranch for more than
10 years and do not own a farm or ranch greater than 30 percent of the
median size farm in the county, as determined by the most current Census
for Agriculture.
After spending a lifetime accumulating wealth in agricultural assets,
farmers often wish to pass the farm business to their heirs. Special provisions
in the Federal estate tax, such as a rule that allows farm assets of an estate to
be valued at their farm-use value rather than a higher market value, facilitate
the transfer of farm estates from one generation to the next. (See Economics
Application Box 8-2 on the Federal estate tax.)
As farmers begin to consider transitioning from active operation to
retirement, questions about what will happen to their land remain. In some
cases, the land is passed to an heir who continues the family business; in
other cases, it is sold at auction perhaps to another farmer, but sometimes
for other purposes such as residential or commercial development. As much
as 2 million acres of America’s farms, ranches, forests, wildlife habitat, and
other open spaces are lost to fragmentation and development each year, with
significant implications for water resources, outdoor recreation, wildlife,
rural economies, and other resources.
Making a donation of a qualified conservation easement is one way for
farmers and ranchers to maintain their current operation and conserve the
amenities and natural assets of rural America for future generations. Such
a donation allows the farmer to create a separate, special right on the designated land stipulating that it will be used only for certain purposes, such as
agricultural production. The farmer or rancher can continue to use the land
for production, knowing that in the future, it will continue to be used in the
same manner. In return for placing the land into a qualified conservation
easement, the landowner may deduct the value of the easement from his or
her income for tax purposes.
Starting in 2006, a new law encouraged additional conservation easements by significantly expanding the tax benefits landowners may receive
when they donate easements to qualified organizations, such as a land
Challenges and Opportunities in U.S. Agriculture

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Economics Application Box 8-2: The Federal Estate
Tax and Farm Business Succession Planning
An estate—in general, a collection of assets passed down from a
decedent upon his or her death—is one vehicle available to farmers to
transfer agricultural property from one generation to the next. Under
current law, only those returns that have a taxable estate above the
exempt amount after deductions for expenses, debts, and bequests to a
surviving spouse or charity are subject to the tax.
While the estate tax has been amended many times, it has never
directly affected a large percentage of taxpayers, including farmers. In
fact, in no year since 1916 has the percentage of adult deaths generating
a taxable estate surpassed 8 percent (Jacobson, Raub, Johnson 2012).
Several targeted provisions have reduced the potential impact of estate
taxes on the transfer of a farm or other small business to the next generation (Durst 2009). These provisions include:
• A special provision that allows farm real estate to be valued at
farm-use value rather than at its fair-market value, which is often higher
because it reflects the value of the land for housing or commercial development.
• An installment payment provision that allows an estate to elect
to pay the estate tax attributable to the decedent’s interest in a closely held
business in up to 10 equal, annual installments. The provision covers a
The Share of Farm Estates Required to File a Return and Pay
Federal Estate Taxes, 2001−2013
Percent
25

Billions of dollars
3.0

20

2.5
2.0

15
Returns filed (left axis)

1.5

10
Total taxes (right axis)
5

1.0
0.5

Taxable estates (left axis)
0
2001

2003

2005

2007

2009

2011

2013

Note: 2012 and 2013 are forecasts based on 2011 data.
Source: Department of Agriculture, Economic Research Service, Federal Tax Issues.

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Chapter 8

0.0

decedent whose interest in the closely held business exceeds 35 percent of
the adjusted gross estate, which describes a typical farm estate.
• A provision aimed at encouraging farmers and other landowners to donate an easement or other restriction on development that has
provided additional estate tax relief.
The box figure illustrates the relatively low and declining burden
the Federal estate tax has placed on farm estates. In 2001, 16.9 percent of
farm estates were required to file a tax return and less than 4 percent had
an estate tax liability. By 2011, as a result of the generous tax-exemption
amount and low tax rate, those figures had declined to 1.28 percent and
0.6 percent, respectively. Total tax liability in 2011 was also lower than it
had been the prior 10 years, despite record high agricultural land value,
which represents a large majority of the assets in a farm estate. The
American Taxpayer Relief Act of 2012 made permanent a maximum
estate tax rate of 40 percent; it also set the exclusion amount at $5 million
and allowed for inflation adjustment, continuing the tax relief to most
farm estates.

trust or public agency. More specifically, this enhanced incentive raises the
maximum annual deduction a donor can take for the donation of a conservation easement and extends the period to claim the deduction from 5 to 15
years, from the year of the donation. In 2007 and 2008, a survey found that
this incentive helped America’s 1,700 local land trusts increase the pace of
conservation by about 250,000 acres each year—a 36 percent increase over
previous years.
The enhanced incentive provisions expired in 2009 but were renewed
through December 31, 2013, by the American Taxpayer Relief Act of 2012.
Making permanent the expanded tax incentives beyond 2013 would further
bolster land conservation and job creation, especially on working lands,
helping to keep landowners on their property and achieve a broad range of
conservation outcomes.

A Mature Domestic Food Market
Americans benefit from a highly efficient agricultural sector and have
higher standards of living now than at any point in the past. Of concern to
producers in the U.S. food market is how much of their disposable income
American consumers will spend on food in the future as well as what food
products they will demand. Engel’s law, which postulates that rising incomes
lead to an increase in the nominal amount of income spent on food while
the proportion of income spent on food falls, still holds in the United States.
The share of American household budgets devoted to food fell from 15
Challenges and Opportunities in U.S. Agriculture

| 245

percent in 1984 to 13 percent in 2009. However, a rise in per capita income
since 1984 has counteracted the decrease in the share of household budgets
devoted to food, as real per capita spending on food has increased from
$3,592 in 1985 to $4,229 in 2011 (in 2011 dollars) (Figure 8-3).
As their real incomes rise, most Americans do not need larger quantities of food to satisfy their nutritional needs. They are, however, changing
their food choices to include higher value foods, such as better cuts of meat,
a variety of fruits and vegetables, and organic and specialty food items. A
mature U.S. food market will require the agricultural sector to focus on
innovations that produce value-added products for the domestic market in
order to satisfy rising U.S. consumer demand for specialty goods.

New Markets in Agriculture
Organic farming has been one of the fastest-growing sectors in
agriculture, and double-digit growth in sales of organic foods has provided
market incentives for the U.S. agricultural sector across a broad range of
products. The retail value of the organic industry grew to $31.4 billion a
year in 2011, up from $21.1 billion in 2008 and $3.6 billion in 1997 (Dimitri
and Oberholtzer 2009; USDA 2012a). Between 2002 and 2008, acres under
organic production grew by an average of 16.5 percent a year. Organic

2011 Dollars
4,300

Figure 8-3
U.S. Real Per Capita Food Expenditures, 1985−2011
2011

4,200
4,100
4,000
3,900
3,800
3,700
3,600
3,500

1985

1990

Source: USDA (2013c).

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Chapter 8

1995

2000

2005

2010

sales currently account for more than 3 percent of total U.S. food sales, and
provide a larger share in categories such as produce and dairy. Growth has
been particularly evident in the organic dairy sector, which accounted for 16
percent of organic sales in 2008. The number of organic milk cows on U.S.
farms increased by annual average of 26 percent between 2000 and 2008.
As demand for organic food has increased, the U.S. agricultural sector has
taken steps to meet it; the number of operations certified as organic grew by
1,109—or more than 6 percent—between 2009 and 2011.
The USDA has taken steps both to promote and to regulate the growing organic food industry by establishing the National Organic Program
(NOP), whose mission is to ensure the integrity of USDA-certified organic
products in the United States and throughout the world. The NOP accredits
nearly 50 domestic organic certifying agents who are authorized to issue
an organic certificate to operations that comply with the USDA organic
regulations. Between 2009 and 2011, the USDA has also supported its own
scientists and university researchers with more than $117 million in funding
focused on improving the productivity and success of organic agriculture.
For example, USDA research on weed management for organic vegetable
production has produced techniques and tools that can help control 70 percent of weeds at 15 percent of the previous cost for weed control. Spreading
the USDA organic research findings to people in the field is critical, and the
“eOrganic” electronic extension service funded by the USDA has become
an essential tool for compiling and disseminating knowledge about organic
production.
The increasing demand for organic foods has been accompanied by a
growing “local” movement. The markets for organic and local food regularly
overlap: organic farmers are much more likely than conventional farmers to
sell their products locally (Kremen, Greene, and Hanson 2004), with about
a quarter of all organic sales in 2004 made within an hour’s drive of the
farm (Greene et al. 2009). Similarly, 82 percent of all farmers’ markets had
at least one organic vendor. Sales of locally produced foods make up a small
but growing part of U.S. agricultural sales, particularly for small farms. The
USDA estimates that the farm-level value of local food sales totaled nearly $5
billion in 2008, or 1.6 percent of the U.S. market for agricultural products.
An estimated 107,000 farms, or 5 percent of all U.S. farms, are engaged in
local food systems, with small farms (those with less than $50,000 in gross
annual sales) accounting for 81 percent of all farms reporting local food sales
in 2008 (Low and Vogel 2011). Examples of the types of farming businesses
that are engaged in local foods are direct-to-consumer marketing, farmers’
markets, farm-to-school programs, community-supported agriculture, community gardens, school gardens, food hubs and market aggregators, kitchen
Challenges and Opportunities in U.S. Agriculture

| 247

incubators, and mobile slaughter units, among a myriad of other types of
operations.
Local goods are also good for the economy. A USDA study found
that produce growers selling into local and regional markets generated 13
full-time operator jobs for every $1 million in revenue earned, for a total
of 61,000 jobs in 2008 (Low and Vogel 2011). Farmers that did not sell into
these markets generated only three full-time operator jobs per $1 million
revenue. To foster exposure to and growth in local foods, the USDA has
created the Know Your Farmer, Know Your Food management and communications initiative, which helps stakeholders navigate USDA resources
and efforts related to local and regional food systems. Future growth of the
agricultural economy can be enhanced by growth in those sectors.

Today’s Farm Structure
The current strength of the farm economy is also built on the restructuring that has taken place over time, making the most productive farms
larger and more efficient. Agricultural innovations have been labor-saving,
greatly reducing the amount of labor needed for specific farm tasks. Laborsaving innovations also affect farm structure, because they allow a farmer
to manage more cropland or raise more livestock. In addition, innovations
have led farms to contract out for specialized services. Farmers now rely
extensively on private consultants, government extension agents, lenders,
and supplier representatives for technical advice.
Some of these managerial innovations rely on further developments
in the design of organizations and contractual relationships to effectively
manage a series of complicated commercial relationships. The share of
production under marketing or production contracts increased from 28
percent in 1991 to more than 38 percent by 2010. Corn, soybean, and wheat
producers, for example, place about half of their production under forward
contracts; many of them also invest in storage facilities to store products
when anticipating future price increases, and nearly 30 percent of them use
futures markets to hedge the risks from their cash sales (MacDonald and
Korb 2011). Similarly, farmers have realized more intensive use of capital
by leasing equipment from specialized suppliers, and they often engage
additional specialized expertise and capital equipment by contracting with
custom service providers for farm tasks such as spraying, field preparation,
or harvesting.
Livestock operations have undergone dramatic changes in the last
30 years. Farmers now use information technology to adjust feed mixes
and climate controls automatically to meet the precise needs of animals in
confined feeding operations. Integrated hog operations, for example, sharply
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reduced the amount of feed, capital, and labor needed to produce hogs as
new technologies and organizational forms swept the industry. As a result,
live hog prices were nearly a third lower than they would have been without
the productivity growth that occurred between 1992 and 2004, and retail
pork prices were 9 percent lower (Key and McBride 2007).
The market, scientific, and technological opportunities beckoning
American farmers are as great as they have ever been. Over the past three
decades, a series of revolutions in the understanding of the science of living
organisms and exponential growth in the processing power of information
technology have raised the potential for productivity growth in American
agriculture that could outstrip even the impressive record of growth it
logged over the course of the 20th century. But as America’s own history
shows, neither revolutions in science and technology nor market signals will
find practical application on America’s farms and ranches without careful,
effective, smart investment by public science institutions. Even America’s
larger farms are too small to support sophisticated basic research, and many
of the most significant improvements that farms can be expected to make
as they apply the fruits of this research are not patentable. The partnership
between public science and the private farm must continue if these possibilities are to be realized, particularly in the face of climate change. The Obama
Administration believes America’s agricultural future is worth investing in
and has committed to increases in scientific research that could benefit the
agricultural sector for decades to come.

Investing in Agricultural Productivity
In 1950, the average dairy cow produced about 5,300 pounds of milk.
Today the average cow produces about 22,000 pounds of milk, thanks to
improvements in cow genetics, feed formula, and management practices.
Over that time period, the number of dairy cows in America has fallen by
more than half, yet U.S. milk production has nearly doubled.
Persistent gains in efficiency have defined American agriculture.
Public and private investments in agricultural research and development
(R&D) have helped U.S. farmers find ways to grow more with less. While
growth in U.S. industrial output over the past 50 years has come primarily
from increases in capital and labor, agricultural output growth mainly has
come from substantial increases in total factor productivity. American farmers have continually found ways to grow more with less; new seeds are less
susceptible to disease and produce higher yields, new tractors are guided
by satellites and spread fertilizer optimally across the field, and animals’
diets are optimally calibrated to grow larger animals with less feed. These

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innovations have caused improvements in farm productivity to outpace
improvements in non-farm productivity over the past 25 years.
From 1948 to 2009, farm productivity nearly tripled, growing at a rate
of 1.6 percent a year. In the early part of that period, increased productivity,
measured as output per unit of combined inputs, combined with increased
use of equipment and chemical inputs to drive the growth in agricultural
output. Between 1980 and 2009, equipment stocks fell along with continued
declines in labor and land inputs; chemical use continued to rise, but at a
much slower rate. Despite reduced input use, agricultural output grew by
1.5 percent a year in 1980–2009, with increasing productivity accounting for
almost all of the growth (Figure 8-4).

Research and Development Drives Productivity Growth
Increasing productivity on U.S. farms stems largely from the rapid
and widespread adoption of a continuing series of biological, chemical,
mechanical, and organizational advances. Formal research programs are
carried out in universities, government labs, and private firms. Agricultural
innovations building on that research are developed by input suppliers in
the private sector or by public institutions.
Public support of agricultural R&D generates high payoffs for farmers
and the public. Fuglie and Heisey (2007) found that every dollar invested
Figure 8-4
Farm and Nonfarm Productivity, 1948–2009

Productivity index (1948 = 100)
300

Total farm output

250

2009

200
Nonfarm
productivity

150
100
50

Total farm input
Farm
productivity

0
1948
1958
1968
1978
1988
1998
2008
Source: Department of Agriculture, Economic Research Service, Agricultural
Productivity in the U.S.; Bureau of Labor Statistics, Major Sector Productivity and
Costs.

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in public agricultural research generates 10 times that amount in benefits
to society. Another recent study (Alston et al. 2009) found an even higher
return on Federal and State agricultural research expenditures, with estimated benefits of $20 for every $1 invested. Other academic studies reached
broadly similar conclusions.
Total R&D spending in agriculture reached $11 billion in 2007, or
nearly 8 percent of the value added in the sector. Annual public agricultural
R&D spending, through universities as well as government laboratories, rose
77 percent between 1970 and 2002 (after accounting for inflation). Public
expenditures have not kept up with R&D cost inflation since, however, falling by 13 percent in real terms between 2002 and 2009. Private R&D expenditures are sensitive to the business cycle but doubled in inflation-adjusted
terms between 1970 and 2007 (Figure 8-5).
Spillovers are ubiquitous in R&D in general and in agricultural R&D
in particular. Ideas that are discovered by one institution may have an
impact on the research productivity of another. Some of the important, and
overlapping, categories of spillovers in agricultural R&D are geographical,
for example, from one state or one country to another; institutional, from
the private sector to the public, or vice versa, across competing institutions
Figure 8-5
Public and Private U.S. Agricultural R&D Spending, 1971–2009

Billions of dollars
12
10

Public & private R&D

8
6

2007

Private R&D

4

Public R&D
2009

2
0
1970

1975

1980

1985

1990

1995

2000

2005

Note: All R&D spending; in 2006 dollars using ERS R&D deflator.
Source: Department of Agriculture, Economic Research Service, Agricultural
Research Funding in the Public and Private Sectors.

2010

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such as universities, or from one industry to another; and across scientific
areas, from “pretechnology” sciences to agricultural sciences, for example, or
from biomedical science to agricultural science.
Economists have studied spillovers related to agriculture R&D (see,
for example, Evenson 1988 or Griliches 1998). One of the more commonly
addressed spillover areas for agricultural research is the geographical spillover from one state to another. Pardey and Alston (2011) estimated that
roughly one-third of the benefits of state-level agricultural R&D are generated through spillovers to states other than those in which the research was
conducted.

Conservation Practices and the Environment
The overuse of nitrogen fertilizer has widely recognized detrimental effects on the environment, especially downstream of treated fields.
Particularly in the Gulf of Mexico, excess nitrogen is associated with lowoxygen environments, or “dead zones.” Corn is the most widely planted
crop in the United States and the largest user of nitrogen fertilizer. In 2010,
more than 97 percent of planted corn acres received nitrogen fertilizer (commercial and manure), an increase of 18 percent from 2001. At the same time,
farmers have improved their use of nitrogen—corn acres where nitrogen
was applied in excess of agronomically necessary rates declined from 41
percent to 31 percent (Ribaudo et al. 2012).
Adoption of other conservation management practices also has the
potential to reduce environmentally harmful impacts of agricultural production. Since 2000, corn, cotton, soybean, and wheat acreage under conservation tillage (mulch, ridge, and no till) has increased; conservation tillage may
reduce soil erosion and water pollution but increase pest management costs
(Osteen, Gottlieb, and Vasavada 2012).
The Federal Government plays an important role in encouraging
conservation adoption by offering numerous conservation programs to
assist private landowners in conserving the soil, water, wildlife, and other
natural resources found on their property. These programs give landowners incentives to consider natural resources in their agricultural practices.
Two relatively new programs, Working Lands for Wildlife and the National
Water Quality Initiative, help producers stay in operation by providing
financial and technical support, as well as regulatory certainty, if the landowner takes steps to restore and conserve wildlife habitat or water quality
on their property.
The USDA’s National Water Quality Initiative works with farmers,
ranchers, and forest landowners in priority watersheds to help improve water
quality and aquatic habitats in impaired streams. As of 2012, approximately
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$34 million had been obligated for improvements on about 161,000 acres.
Another $21 million was obligated through more than 800 contracts with
private landowners for Working Lands for Wildlife, also administered by the
Natural Resources Conservation Service and Fish and Wildlife Service. The
contracts will restore wildlife habitat on more than 310,000 acres of range,
pasture, and forest lands across the country.

Natural Capital, Conservation, and the Outdoor Economy
Agriculture, as a land use, affects a large amount of natural capital
(land, water, air, and genetic resources on farms and ranches) in the United
States. Based on 2002 data, private farms accounted for 41 percent of all U.S.
land, including 434 million acres of cropland, 395 million acres of pasture
and range, and 76 million acres of forest and woodland (Ribaudo et al. 2008).
This capital can provide a host of environmental services, including water
quality, air quality, flood control, wildlife, and carbon sequestration. These
services can be consumed directly or combined by consumers with other
goods to create final goods, such as sightseeing, fishing, wildlife viewing, or
hunting, all of which support the outdoor economy.
Multisector efforts under the President’s America’s Great Outdoors
initiative have bolstered outdoor recreation, conservation, and restoration
of America’s natural resources on public lands, as well as on working farms,
ranches, and forests. In a 2012 study of 11 western states, economists found
that national parks, monuments, and other protected Federal public lands
promote more rapid job growth and are correlated with higher levels of per
capita income in surrounding areas. Companies use the high quality of life
provided by localities with access to healthy and protected lands and waters
as a recruiting tool to attract new and talented employees who value natural
beauty and outdoor recreational opportunities.
Outdoor recreation is an often overlooked but significant economic
driver in the United States, with one industry study estimating that it
provided 6.1 million jobs, spurred $646 billion in spending, much of it on
travel and tourism, and raised $80 billion in Federal, State, and local tax
revenue in 2010 (Outdoor Industry Association 2012). National parks and
Federal lands and waters located across the entire United States, including
in many rural areas, play a significant role in supporting the travel and tourism industry. Each year, millions of international tourists visit U.S. public
lands and small towns, spending money at local businesses that provide
lodging, dining, retail shopping, and entertainment. Rural America plays a
particularly important role in the national tourism economy by attracting
and retaining tourists for longer visits (Interior 2012).

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Growing Global Demand for Food
and Agricultural Commodities
The U.N. Food and Agricultural Organization (FAO) estimates that
global agricultural production will need to increase by around 60 percent
to meet the anticipated increase in demand in 2050, given an additional
2.3 billion people and current consumption patterns. Meeting this demand
will depend largely on increases in agricultural productivity because input
scarcity, particularly of natural resources and environmental services, will
become more binding with population growth and climate change.

Population Growth and Urbanization
The world’s population grows by more than 200,000 people each day
and is expected to increase from 7 billion in 2012 to more than 9.2 billion in
2050. More than 95 percent of all population growth is expected to occur in
low-income countries (Figure 8-6).
As the worldwide population increases, most of the growth will
come from urbanization. More than half of the world’s population was
living in urban areas by 2008, compared with just 29 percent in the 1950s.
Approximately 70 percent of the world population is expected to be living in
urban areas by 2050 (Figure 8-7).
Figure 8-6
Population by Region, 1950−2050

Billions of people
9
8
7
6
5
4

United States
Latin America
Africa
Asia

3
2
1
0

1950 1960 1970 1980 1990 2000 2010 2020 2030 2040 2050
Note: 2020−2050 data are projections.
Source: UN (2011).
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Percent
100

Figure 8-7
Percentage of Population Residing in Urban Areas, 1950–2050

90
80

United States

70

Latin America

60
50
40

East Asia

World
Africa

30
20
10
0
1950 1960 1970
Source: UN (2012a).

1980

1990

2000

2010

2020

2030

2040

2050

A world population living primarily in cities and towns will present
unique challenges to the agricultural sector, because urban populations
rely heavily on a stable and efficient worldwide food chain to provide the
nutrient-dense and diverse foods they demand. The rising global population
is also expected to be accompanied by falling poverty rates and increasing
incomes for a large fraction of the world’s population, particularly in Asia.
Notably, the poverty rate in East Asia fell from nearly 80 percent in 1980 to
less than 20 percent in 2005. Along with the decline in poverty, there is an
emerging middle class in the Asia Pacific region that the OECD projects will
increase rapidly, from 525 million in 2009, to more than 1.7 billion in 2020,
and to 3.2 billion in 2030 (Figure 8-8) (Kharas 2010). The result will likely
be increased consumption of food per capita and a change in diets toward a
higher proportion of meat.
Rising global food demand and the expected change in dietary patterns
accompanying the growth in income throughout the world, particularly in
China, will lead to opportunities for growth in the U.S. agricultural sector,
most notably in meat export. World meat and dairy consumption doubled
between 1950 and 2009. Global meat consumption has been growing much
more rapidly than consumption of grains and oilseeds, and between 1985
and 1990, production of meat (beef, pork, chicken, and turkey) rose more
than 3 percent a year, well above the world’s population growth rate of 1.7
percent a year.

Challenges and Opportunities in U.S. Agriculture

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Figure 8-8
Middle-Class Population by Region, 2009−2030

Billions of people
6
Middle East and North
Africa
5
Central and South
America

4

North America
Europe

3

Asia Pacific

2
1
0

2009
Source: Kharas (2010).

2020

2030

Pressure on Agricultural Land and the Environment
Continuing increases in the demand for agricultural products, especially resource intensive foods such as meat, are expected to have a deleterious impact on agricultural land, soil, and water, and to create broader
ecosystem-level pressures (UN 2012b). According to the United Nations,
global food production currently uses nearly one-quarter of all the habitable
land on earth, accounts for more than 70 percent of fresh water consumption, and produces more than 30 percent of global greenhouse gas emissions.
In addition, global food production accounts for 80 percent of deforestation
and is the largest single cause of species and biodiversity loss.
A collaborative report on climate change prepared by the USDA and
scholars from a variety of universities and other Federal and nongovernmental agencies suggests that climate change will impact both agricultural
productivity and commodity price volatility (Walthall et al. 2012). The
increased temperature will increase the likelihood of grain and oilseed crop
failure, forest fires, insect outbreaks, and tree mortality. Further, elevated
levels of carbon dioxide are expected to reduce the productivity of livestock
and dairy animals and increase weed growth. Although some agricultural
and forest systems may experience productivity increases in the near term,
the benefits provided by these ecosystems, such as clean drinking water and
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natural waste decomposition, will diminish over the long term, requiring
a change in management regimes. Management of water resources will
become more challenging, and natural disasters such as forest fires, insect
outbreaks, severe storms, and drought will occur with increased frequency
and severity, placing heavy demands on management resources, such as
Federal disaster assistance. (For additional discussion of climate change, see
Chapter 6.)

Global Commodity Markets and Price Volatility
Trade in agricultural commodities is a global endeavor and prices
respond to supply and demand conditions around the world. As a result,
agricultural commodity markets are characterized by a high degree of
volatility. Four major market fundamentals explain why that is the case.
First, agricultural output is in large part at the mercy of nature. Shocks from
weather, pests, and other natural phenomena have unpredictable effects on
supply. With the effects of global climate change already being seen in many
parts of the globe and projected to continue, the unpredictability of these
impacts is likely to increase over time. Second, diets are somewhat inflexible
in the short run, which means demand for certain foods remains relatively
constant.2 A third source of volatility is the natural growing cycle, which
contributes to a relatively fixed short-run supply. Finally, declining stock-toconsumption ratios amplify the effects of food price shocks.
The integration of markets can also be a source of volatility. Food
and energy markets in the United States and around the world have become
increasingly interlinked through the use of agricultural feedstock in the
production of ethanol and the use of oil and natural gas in agricultural production.3 Growth in the use of biofuels, for example, not only increases the
demand for agricultural feedstocks but may also make demand less elastic
through such measures as biofuel blending requirements. As such, integration can cause shocks in one market to be transmitted to another.
Since the early 1970s, food prices have become much more volatile.
In general, high food prices bring with them higher price volatility, and
average real food prices in the past five years were 35 percent higher than
prices in the previous decade, according to the FAO’s Food Price Index. The
index tracks the monthly change in the average international prices of five
commodity groups, namely, meat, dairy, cereals, oils, and sugar. The index
peaked in February 2011 and has since fallen 10 percent. Overall food prices
2 For data on commodity and food elasticities, see USDA Economic Research Service, http://
www.ers.usda.gov/data-products/commodity-and-food-elasticities.aspx.
3 Natural gas is the primary feedstock in the production of ammonia, and ammonia is the
primary input for all nitrogen fertilizers.

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surged in the summer of 2012, driven by higher cereal prices. Food price
spikes are not uncommon, and in most cases prices eventually fall as much
as they have risen. Figure 8-9 demonstrates the increasing variability in the
nominal price of corn since 1866–67.

Meeting the Challenges and Harnessing the
Opportunities of Global Demand Growth
For U.S. agriculture to benefit fully from the growing food demand
and changing food patterns around the world, access to the global market
must be ensured. Successful efforts by the Federal Government to open
foreign markets have contributed to an agricultural export boom. In FY
2012, American agricultural exports reached $135.7 billion, just short of the
record high level of $137.4 billion set in FY 2011. Additionally, America runs
a trade surplus in agricultural goods—a surplus that reached $32.4 billion in
FY 2012 (USDA 2012b).

Open Trade and Access to Global Food Markets
The Obama Administration has made reducing trade barriers to market access overseas for U.S. farmers and ranchers a top priority, alongside

Bushels per acre
185
165

Figure 8-9
Corn Yields and Price, 1866−2012
Dollars per bushel (nominal)
7
2011/12
Yield per
harvested acre
(left axis)

145
125

6
5

105

4

85

3

65

2

45
25
5
1866/67

Weighted-average farm price
(right axis)

1906/07 1926/27 1946/47 1966/67 1986/87 2006/07
Marketing year (September to August)
Source: Department of Agriculture, Economic Research Service, Feed Grains Database.

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1
0

efforts to ensure that America’s trading partners fully honor all the commitments they have made under existing trade agreements. The President has
signed several historic trade agreements that significantly expand market
access for U.S. agricultural exporters. The recently implemented U.S.-Korea
Free Trade Agreement (KORUS) is set to deliver substantial gains for U.S.
agricultural exports in coming years. In a separate beef import protocol
concluded in 2008, Korea agreed to adjust its import restrictions on U.S.
beef. As a result, U.S. beef exports to Korea more than doubled in value from
2008 to 2011, to about $686 million. Under KORUS, Korea will gradually
bring its tariffs on imports of U.S. beef and pork down to zero, and the U.S.
meat industry will benefit from even greater gains in trade. The improved
access provided by the agreement for a wide range of other products, beginning in 2012 and continuing over the agreement’s phase-in period, will yield
new market opportunities for U.S. exporters. The USDA estimates that,
when fully implemented, KORUS will expand U.S. agricultural exports to
Korea by an estimated $1.9 billion a year—gains that will benefit agricultural producers and processors across the United States. The Korean Free
Trade Agreement, together with the free trade agreements with Panama
and Colombia passed at the same time is expected to boost U.S. agricultural
exports by $2.3 billion a year (Wainio, Gehlhar, and Dyck 2011).
The Obama Administration has worked with a number of other developing and developed countries to reopen their markets to U.S. beef products.
Partly as a consequence of these steps, U.S. beef exports in 2011 exceeded
2003’s historic levels for the first time, reaching $5.4 billion. Similarly, 57
countries, including many important emerging markets, have now lifted
bans on U.S. poultry products. Between 2007 and 2011, the value of U.S.
poultry exports increased from $4.1 billion to $5.6 billion. U.S. pork exports
to the rapidly growing Chinese market soared after H1N1-related bans were
lifted. Immediately before the ban, the United States exported on average
about $132 million a year in pork and pork products to China. In 2010, pork
exports to China totaled only $79.3 million. In 2011, pork exports to China
grew by a factor of six, exceeding $477 million and quickly demonstrating
the value of better access to this key emerging market. In the first quarter of
2012, roughly two years after the ban was lifted, the United States exported
about $122 million in pork and pork products to China.

Hired Farm Labor Costs in a Global Economy
Hired labor is a crucial component of U.S. agricultural production.
Costs associated with such labor account for 17 percent of variable production expenses for all agricultural commodities and 40 percent of expenses

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in the production of labor-intensive crops such as fruits, vegetables, and
nursery products.
For fruits and vegetables, total agricultural production expenses are
near parity between U.S. and international producers, but labor costs are
often much lower for foreign growers. In response to higher labor costs, U.S.
farms have already turned to mechanization of the harvesting and production processes. For example, mechanized production of raisins, including
harvesting and drying of grapes, increased from 1 percent of the raisin crop
to 45 percent between 2000 and 2007. Harvesting of baby leaf lettuce is currently 70–80 percent mechanized (Calvin and Martin 2010). These trends
will likely increase if wages rise and could potentially lead to consolidation
among growers. Some crops are not well suited for fully mechanical production, however. U.S. growers of such commodities may invest in technology
that increases labor productivity, such as conveyor belts now common in
Southern California strawberry fields.
Although mechanization is attractive in many cases, the costs associated with converting to mechanical processes are high, and larger farms typically stand to profit the most from mechanization. Moreover, growers may
be hesitant to adopt the technology because of concerns about loss of quality.
Given the difficulties associated with converting to mechanized production
in the short run, the affordability of hired farm labor, and immigrant labor
in particular, takes on greater importance. It is estimated that, for the past 15
years, about half of all hired laborers working in crop agriculture have lacked
the proper immigration designation to work in the United States (Zahniser
et al. 2012). Immigration policy, which influences the supply of and demand
for labor as well as food prices ultimately paid by the consumer, is an important issue in the agricultural sector.
In their research, Zahniser et al. (2012) used a simulation to illustrate
the effects different changes in immigration policy could have on the agricultural sector, including the effects of disruptions in the supply of labor
on farm wages and crop production. Expanding the number of agricultural
workers eligible for the H-2A Temporary Agricultural Program, which
allows U.S. farms to hire temporary nonimmigrant foreign workers if not
enough domestic workers are available, would increase agricultural production and exports by around 1.6 percent and 2.5 percent, respectively, in the
long run for labor-intensive sectors like produce and nursery products. On
the other hand, a 5.8 million decrease in the overall number of undocumented workers would reduce production and exports throughout all sectors of the economy, with agriculture and other labor-intensive sectors the
hardest hit. Agricultural exports would fall by about 3.7 percent.

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Improving Risk Management
Traditionally, every five years, Congress passes a bundle of legislation,
commonly called the “Farm Bill” that sets national agriculture, nutrition,
conservation, and forestry policy. The last Farm Bill, passed in 2008, was set
to expire on September 30, 2012 but was extended through fiscal year 2013.
The coming expiration of the current Farm Bill represents an opportunity
to make the most significant reforms in agricultural policy in decades. The
Senate Agricultural Reform, Food and Jobs Act of 2012 would end direct
payments—fixed annual payments to farmers based on their farms’ historical crop production, paid without regard to whether a crop is currently
grown—and streamline and consolidate farm programs, as well as reduce
the Federal deficit by as much as $23.6 billion over 10 years (CBO 2012). It
could also strengthen priorities, such as efficient risk management, that help
farmers, ranchers, and small business owners protect their investments and
ensure a stable supply of needed agricultural product, while continuing to
help the U.S. agricultural sector grow the economy.
Highly volatile agricultural commodity prices can create significant
income risk for farmers. At the same time, the current farm safety net is
inefficient and unfair, creating distortions in production and crowding
out market-based risk management options. Because program commodity
production is concentrated on larger farms, these farms receive the largest
share of taxpayer-supported program payments, even though this group of
farm households has incomes that are on average three times the average
U.S. household (Figure 8-10).
Currently, those households with an average adjusted gross nonfarm
income up to $500,000 are eligible to receive government payments, while
those with as much as $750,000 in average adjusted farm income are eligible
for direct payments. Farmers who produce fruits and vegetables do not
receive any government program payments. Adding provisions that make
lands that have not previously been used to grow crops ineligible for crop
insurance or other Federal benefits would help protect the nation’s prairies
and forests from being converted into marginal cropland.
Today’s agricultural commodity support programs are rooted in the
landmark New Deal legislation that followed the agricultural depression of
the 1920s and 1930s. These programs were designed to sustain prices and
incomes for producers of cotton, milk, wheat, rice, corn, sugar, tobacco,
peanuts, and other crops, at a time when a large portion of the U.S. population was engaged in farming. Today, less the 2 percent of the U.S. population is engaged in farming, and changing economic conditions and trends
in agriculture since these programs began suggest that many of the original
motivations for these farm programs no longer apply.
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Figure 8-10
Government Commodity Payments by Farm Type

Percent share of commodity payments
50
45
40
2001

35

2011

30
25
20
15
10
5
0

Rural residence

Source: USDA (2001, 2011).

Intermediate

Large commerical

Farm sales class

Very large
commercial

For example, the increasing reliance of farm families on income earned
from sources other than their farms and a shift toward market-oriented
farm policies have made farms and commodity markets less vulnerable to
adverse price changes than before. These changes imply that moving away
from traditional commodity support programs would have a much smaller
impact on farm household income than in previous decades. Nonetheless,
substantial government support of agriculture remains.
Risk management involves choosing among many options for reducing the financial effects of such uncertainties. In addition to participating
in government commodity programs that are available for certain commodities, farmers today have private options for managing risk that were
not available when commodity price support programs were introduced. For
instance, the growth of futures and options markets provides a market-based
method for farmers to protect themselves against short-term price declines.
Other private means to stabilize farm incomes include saving; borrowing;
diversifying among different types of crops, trees, livestock and ecosystem
services; contracting farm output with processors at assured prices; crop
insurance and total revenue insurance; utilizing a wide range of farm management practices that reduce crop loss (such as irrigation, pesticide use);
leasing out farmland; and taking advantage of expanded opportunities for
earning nonfarm income.
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The Dodd-Frank Wall Street Reform and Consumer Protection Act
In 2010, President Obama signed the Dodd-Frank Wall Street Reform
and Consumer Protection Act, with the goal of addressing the lack of
transparency, systemic risks, and interconnectedness risks in the over-thecounter (OTC) derivatives markets that, in part, precipitated the recent
financial crisis. Modern farm operations—and agribusiness in general—rely
greatly on services provided by the OTC derivatives market, including the
swaps market. Derivatives, which are financial instruments whose value
is based on the value of an underlying asset, liability, or event, perform
essential economic functions of price discovery and risk management. The
Act strengthens financial market regulation by requiring most standardized
swaps to be centrally cleared and traded on an exchange or execution facility,
with exemptions from clearing for commercial end-users; subjecting dealers
and major participants that trade these derivatives to registration, business
conduct, risk management, and collateral requirements; and subjecting all
swaps to new recordkeeping and reporting rules.
Although the OTC derivatives market serves an important risk-management role amounting to trillions of dollars in notional value, in the past,
OTC derivatives were essentially an unregulated market. The lack of market
oversight allowed substantial counterparty credit risk to build up in these
markets, with significant consequences for the financial system. In addition,
the lack of regulation created inefficiencies by reducing information available to market participants and regulators, hampering price discovery, and
facilitating opportunities for fraud. Before passage of the Act, regulators had
no authority to monitor the market and prescribe rules. The new clearing
and margin requirements will act as safeguards for the performance of the
OTC derivatives markets, eliminating counterparty credit risk between the
original traders. In addition, new real-time public reporting requirements
and execution standards will improve market transparency and lower transaction costs.
The Act further seeks to protect the market for agricultural swaps,
while ensuring that agricultural market participants are still able to access
risk-management markets. The Act provides that derivatives on agricultural
commodities may be conducted only by eligible contract participants—that
is, counterparties who hold more than $10 million in assets or have a net
worth of $1 million or more. Because many smaller farmers would not
qualify as eligible contract participants and consequently could not engage
in swap contracts that are not traded on a designated contract market (an
exchange) or swap execution facility (SEF), the U.S. Commodity Futures
Trading Commission granted them an exemption for physical commodity

Challenges and Opportunities in U.S. Agriculture

| 263

options. This exemption provides flexibility for all farmers to manage risk
using agricultural derivatives contracts.

Conclusion
Although farming has become a progressively smaller share of the
U.S. economy, the President believes that a vibrant U.S. agricultural sector is
vital for the Nation’s prosperity. U.S. agriculture has remained a bright spot
in the economy during the Great Recession and its immediate aftermath and
despite the most severe drought in more than a half-century. Much of the
sector’s success can be attributed to growth in global demand for American
agricultural exports. In 2012, agricultural exports reached a near record level
and are projected to continue to expand. The world’s population is expected
to reach more than 9.2 billion people by 2050, with most of the growth
occurring in countries that are net food importers. President Obama believes
that expanding overseas market access is crucial for the continued strength
of American agriculture.
Persistent gains in efficiency have defined American agriculture
and nearly tripled farm productivity in the second half of the twentieth
century. To continue this tradition and maintain the strength of the sector,
the Nation must continue to invest in agricultural R&D, helping farmers
find new ways to grow more with less and to continue their stewardship of
natural resources for future generations. The agricultural sector is increasingly vulnerable to price volatility because of the globalization of agricultural
commodities, volatile weather conditions as a result of climate change, and
changing consumption patterns. To cope with these challenges, U.S. agriculture must stay at the forefront of agricultural innovation.

264 |

Chapter 8

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Chapter 7
International Trade and Competitiveness
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Bartel, Ann, Casey Ichniowski, and Kathryn Shaw. 2007. “How Does
Information Technology Really Affect Productivity? Plant-Level
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Bernard, Andrew B., J. Bradford Jensen, and Peter K. Schott. 2006. “Trade
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A P P E N D I X

A

REPORT TO THE PRESIDENT
ON THE ACTIVITIES OF THE
COUNCIL OF ECONOMIC
ADVISERS DURING 2012

letter of transmittal
Council of Economic Advisers
Washington, D.C., December 31, 2012
Mr. President:
The Council of Economic Advisers submits this report on its activities
during calendar year 2012 in accordance with the requirements of the
Congress, as set forth in section 10(d) of the Employment Act of 1946 as
amended by the Full Employment and Balanced Growth Act of 1978.
Sincerely yours,

Alan B. Krueger, Chairman
Katharine G. Abraham, Member
James H. Stock, Member

Activities of the Council of Economic Advisers During 2012

| 301

Council Members and Their Dates of Service
Name

Position

Oath of office date

Separation date

Edwin G. Nourse
Leon H. Keyserling

Chairman
Vice Chairman
Acting Chairman
Chairman
Member
Vice Chairman
Member
Member
Chairman
Member
Member
Member
Chairman
Member
Member
Member
Member
Chairman
Member
Member
Member
Chairman
Member
Member
Member
Chairman
Member
Member
Member
Chairman
Member
Member
Chairman
Member
Member
Member
Member
Chairman
Member
Member
Chairman
Member
Member
Member
Member

August 9, 1946
August 9, 1946
November 2, 1949
May 10, 1950
August 9, 1946
May 10, 1950
June 29, 1950
September 8, 1952
March 19, 1953
September 15, 1953
December 2, 1953
April 4, 1955
December 3, 1956
May 2, 1955
December 3, 1956
November 1, 1958
May 7, 1959
January 29, 1961
January 29, 1961
January 29, 1961
August 3, 1962
November 16, 1964
May 17, 1963
September 2, 1964
November 16, 1964
February 15, 1968
February 2, 1966
February 15, 1968
July 1, 1968
February 4, 1969
February 4, 1969
February 4, 1969
January 1, 1972
September 9, 1971
March 13, 1972
July 23, 1973
October 31, 1973
September 4, 1974
June 13, 1975
July 22, 1975
January 22, 1977
March 18, 1977
March 18, 1977
June 6, 1979
August 20, 1980

November 1, 1949

John D. Clark
Roy Blough
Robert C. Turner
Arthur F. Burns
Neil H. Jacoby
Walter W. Stewart
Raymond J. Saulnier
Joseph S. Davis
Paul W. McCracken
Karl Brandt
Henry C. Wallich
Walter W. Heller
James Tobin
Kermit Gordon
Gardner Ackley
John P. Lewis
Otto Eckstein
Arthur M. Okun
James S. Duesenberry
Merton J. Peck
Warren L. Smith
Paul W. McCracken
Hendrik S. Houthakker
Herbert Stein
Ezra Solomon
Marina v.N. Whitman
Gary L. Seevers
William J. Fellner
Alan Greenspan
Paul W. MacAvoy
Burton G. Malkiel
Charles L. Schultze
William D. Nordhaus
Lyle E. Gramley
George C. Eads
Stephen M. Goldfeld
302 |

Appendix A

January 20, 1953
February 11, 1953
August 20, 1952
January 20, 1953
December 1, 1956
February 9, 1955
April 29, 1955
January 20, 1961
October 31, 1958
January 31, 1959
January 20, 1961
January 20, 1961
November 15, 1964
July 31, 1962
December 27, 1962
February 15, 1968
August 31, 1964
February 1, 1966
January 20, 1969
June 30, 1968
January 20, 1969
January 20, 1969
December 31, 1971
July 15, 1971
August 31, 1974
March 26, 1973
August 15, 1973
April 15, 1975
February 25, 1975
January 20, 1977
November 15, 1976
January 20, 1977
January 20, 1981
February 4, 1979
May 27, 1980
January 20, 1981
January 20, 1981

Council Members and Their Dates of Service
Name

Position

Oath of office date

Separation date

Murray L. Weidenbaum
William A. Niskanen
Jerry L. Jordan
Martin Feldstein
William Poole
Beryl W. Sprinkel
Thomas Gale Moore
Michael L. Mussa
Michael J. Boskin
John B. Taylor
Richard L. Schmalensee
David F. Bradford
Paul Wonnacott
Laura D’Andrea Tyson
Alan S. Blinder
Joseph E. Stiglitz

Chairman
Member
Member
Chairman
Member
Chairman
Member
Member
Chairman
Member
Member
Member
Member
Chair
Member
Member
Chairman
Member
Member
Chair
Member
Member
Chairman
Member
Member
Chairman
Member
Member
Chairman
Member
Member
Chairman
Chairman
Member
Member
Chairman
Member
Chair
Member
Chairman
Member
Member
Member
Chairman
Member

February 27, 1981
June 12, 1981
July 14, 1981
October 14, 1982
December 10, 1982
April 18, 1985
July 1, 1985
August 18, 1986
February 2, 1989
June 9, 1989
October 3, 1989
November 13, 1991
November 13, 1991
February 5, 1993
July 27, 1993
July 27, 1993
June 28, 1995
June 30, 1995
January 29, 1996
February 18, 1997
April 23, 1997
October 22, 1998
August 12, 1999
August 12, 1999
May 31, 2000
May 11, 2001
July 25, 2001
November 30, 2001
May 29, 2003
November 21, 2003
November 21, 2003
February 23, 2005
June 21, 2005
November 18, 2005
November 18, 2005
February 27, 2006
July 17, 2008
January 29, 2009
March 11, 2009
September 10, 2010
March 11, 2009
April 19, 2011
April 19, 2011
November 7, 2011
February 7, 2013

August 25, 1982
March 30, 1985
July 31, 1982
July 10, 1984
January 20, 1985
January 20, 1989
May 1, 1989
September 19, 1988
January 12, 1993
August 2, 1991
June 21, 1991
January 20, 1993
January 20, 1993
April 22, 1995
June 26, 1994

Martin N. Baily
Alicia H. Munnell
Janet L. Yellen
Jeffrey A. Frankel
Rebecca M. Blank
Martin N. Baily
Robert Z. Lawrence
Kathryn L. Shaw
R. Glenn Hubbard
Mark B. McClellan
Randall S. Kroszner
N. Gregory Mankiw
Kristin J. Forbes
Harvey S. Rosen
Ben S. Bernanke
Katherine Baicker
Matthew J. Slaughter
Edward P. Lazear
Donald B. Marron
Christina D. Romer
Austan D. Goolsbee
Cecilia Elena Rouse
Katharine G. Abraham
Carl Shapiro
Alan B. Krueger
James H. Stock

February 10, 1997
August 30, 1996
August 1, 1997
August 3, 1999
March 2, 1999
July 9, 1999
January 19, 2001
January 12, 2001
January 19, 2001
February 28, 2003
November 13, 2002
July 1, 2003
February 18, 2005
June 3, 2005
June 10, 2005
January 31, 2006
July 11, 2007
March 1, 2007
January 20, 2009
January 20, 2009
September 3, 2010
August 5, 2011
February 28, 2011
May 4, 2012

Activities of the Council of Economic Advisers During 2012

| 303

Report to the President
on the Activities of the
Council of Economic Advisers
During 2012
The Council of Economic Advisers was established by the Employment
Act of 1946 to provide the President with objective economic analysis and
advice on the development and implementation of a wide range of domestic
and international economic policy issues. The Council is governed by a
Chairman and two Members. The Chairman is appointed by the President
and confirmed by the United States Senate. The Members are appointed by
the President.

The Chairman of the Council
Alan B. Krueger continued to chair the Council during 2012. Dr.
Krueger is on a leave of absence from Princeton University, where he is the
Bendheim Professor of Economics and Public Affairs. He served as Assistant
Secretary for Economic Policy at the Treasury Department from 2009 to
2010.
Chairman Krueger is a member of the President’s Cabinet and is
responsible for communicating the Council’s views on economic matters
directly to the President through personal discussions and written reports.
Chairman Krueger represents the Council at Presidential economic briefings, daily White House senior staff meetings, budget meetings, Cabinet
meetings, a variety of inter-agency meetings, and other formal and informal
meetings with the President, the Vice President, and other senior government officials. He also meets with members of Congress well as with
business, academic and labor leaders to discuss economic policy issues.

The Members of the Council
Katharine G. Abraham is a Member of the Council of Economic
Advisers. She is on a leave of absence from the University of Maryland, where
she is a faculty associate in the Maryland Population Research Center and a

Activities of the Council of Economic Advisers During 2012

| 305

professor in the Joint Program in Survey Methodology. Dr. Abraham served
as the Commissioner of the Bureau of Labor Statistics from 1993 to 2001.
James H. Stock was appointed by the President on February 7, 2013.
He served as Chief Economist of the Council of Economic Advisers from
September 12, 2012 until then. Dr. Stock is on leave from Harvard University,
where he is the Harold Hitchings Burbank Professor of Political Economy.
Dr. Stock served as the Chair of the Harvard University Department of
Economics from 2006 to 2009.
Carl Shapiro resigned as Member of the Council on May 4, 2012 to
return to the University of California, where he is the Transamerica Professor
of Business Strategy at the Haas School of Business.

Areas of Activities
A central function of the Council is to advise the President on all
economic issues and developments. In the past year, as in the three previous
years, advising the President on policies to spur economic growth and job
creation, and evaluating the effects of the policies on the economy, have been
a priority.
The Council works closely with various government agencies,
including the National Economic Council, the Office of Management and
Budget, White House senior staff, and other officials and engages in discussions on numerous policy matters. In the area of international economic
policy, the Council coordinates with other units of the White House, the
Treasury Department, the State Department, the Commerce Department,
and the Federal Reserve on matters related to the global financial system.
Among the specific economic policy areas that received attention in
2012 were: housing policies, including foreclosure mitigation and prevention and refinancing; implementation of the Affordable Care Act; income
inequality; individual and corporate taxation; college affordability; small
business lending; regional development; intellectual property and innovation; infrastructure investment; regulatory measures; trade policies;
unemployment insurance; job training; and policies to promote the international competitiveness of American manufacturing companies. The Council
also worked on several issues related to the quality of the data available for
assessing economic conditions.
The Council prepares for the President, the Vice President, and the
White House senior staff a daily economic briefing memo analyzing current
economic developments, and almost-daily memos on key economic data
releases. Chairman Krueger has also presented regular monthly briefings on
the state of the economy to senior White House officials.
306 |

Appendix A

The Council, the Department of Treasury, and the Office of
Management and Budget—the Administration’s economic “troika”—
are responsible for producing the economic forecasts that underlie the
Administration’s budget proposals. The Council initiates the forecasting
process twice each year, consulting with a wide variety of outside sources,
including leading private sector forecasters and other government agencies.
The Council was an active participant in the trade policy process,
participating in the Trade Policy Staff Committee and the Trade Policy
Review Group. The Council provided analysis and opinions on a range of
trade-related issues involving the enforcement of existing trade agreements,
reviews of current U.S. trade policies, and consideration of future policies. The Council also participated on the Trade Promotion Coordinating
Committee, helping to examine the ways in which exports may support
economic growth in the years to come. In the area of investment and security, the Council participated on the Committee on Foreign Investment in
the United States (CFIUS), reviewing individual cases before the committee.
Council Members and staff regularly met with economists, policy officials, and government officials from other countries to discuss issues relating
to the global economy. The Council’s role also included policy development
and planning for the G-20 Summit in Saint Petersburg, Russia, and the G-8
Summit in Northern Ireland.
The Council is a leading participant in the Organisation for Economic
Co-operation and Development (OECD), an important forum for economic
cooperation among high-income industrial economies. The Council coordinated and oversaw the OECD’s review of the U.S. economy. Dr. Krueger
is chairman of the OECD’s Economic Policy Committee, and Council
Members and staff participate actively in working-party meetings on macroeconomic policy and coordination and contribute to the OECD’s research
agenda.
The Council issued a series of reports in 2012. In February, the
Council released two reports: Supporting Retirement for American Families
and The Economic Benefits of New Spectrum for Wireless Broadband. In
May, the Council led the preparation of a White House report on the labor
market situation of America’s veterans. In June, the Council was a primary
contributor to a White House report on job creation in rural communities.
In November, the Council led the preparation of a White House report
on the impact of tax cuts on the middle class and the subsequent effect
on consumer spending and retailers. The Council continued its efforts to
improve the public’s understanding of economic developments and of the
Administration’s economic policies through briefings with the economic and
financial press, speeches, discussions with outside economists, presentations
Activities of the Council of Economic Advisers During 2012

| 307

to outside organizations, and regular updates on major data releases on the
CEA blog. The Chairman and Members also regularly met to exchange
views on the economy with the Chairman and Members of the Board of
Governors of the Federal Reserve System.

Public Information
The Council’s annual Economic Report of the President is an important vehicle for presenting the Administration’s domestic and international
economic policies. It is available for purchase through the Government
Printing Office, and is viewable on the Internet at www.gpo.gov/erp.
The Council prepared numerous reports in 2012, and the Chairman
and Members gave numerous public speeches. The reports and texts of
speeches are available at the Council’s website, www.whitehouse.gov/cea.
Finally, the Council published the monthly Economic Indicators, which is
available on-line at www.gpo.gov/economicindicators.

The Staff of the Council of Economic Advisers
The staff of the Council consists of the senior staff, senior economists,
economists, staff economists, research economists, a research assistant, and
the administrative and support staff. The staff at the end of 2012 was:

Senior Staff
David P. Vandivier	�������������������������������Chief of Staff
Petra Smeltzer Starke	����������������������������General Counsel
Steven N. Braun	������������������������������������Director of Macroeconomic
Forecasting
Adrienne Pilot 	��������������������������������������Director of Statistical Office
Archana Snyder	������������������������������������Director of Finance and
Administration

Senior Economists
Bevin Ashenmiller	��������������������������������Environment, Energy
Benjamin H. Harris	������������������������������Tax, Budget
Susan Helper	������������������������������������������Manufacturing, Innovation, Small
Business
Chinhui Juhn 	����������������������������������������Labor
Paul Lengermann	����������������������������������Macroeconomics
Emily Y. Lin 	������������������������������������������Tax, Budget

308 |

Appendix A

Rodney D. Ludema 	������������������������������International
James M. Williamson 	��������������������������Agriculture, Transportation, Tax
Wesley Yin 	��������������������������������������������Health, Housing

Economist
David Cho	����������������������������������������������Macroeconomics

Staff Economists
Nicholas Li 	��������������������������������������������Labor, Health, Housing
Ben Meiselman	��������������������������������������Macroeconomics, Public Finance
Nicholas Tilipman	��������������������������������Labor, Health, Immigration
Lee Tucker	����������������������������������������������Labor, Immigration, Housing
Jeffery Y. Zhang	������������������������������������Energy, Environment,
Macroeconomics

Research Economists
Matthew L. Aks 	������������������������������������Macroeconomics, International
Carys Golesworthy	��������������������������������International, Trade
Dina Grossman	�������������������������������������Labor, Health, Immigration
Cordaye T. Ogletree	������������������������������Energy, Environment, International
Trade
Spencer Smith	����������������������������������������Public Finance, Energy, Environment
Rudy Telles Jr	����������������������������������������Agriculture, Tax

Research Assistant
Philip K. Lambrakos 	����������������������������Macroeconomics, International

Statistical Office
The Statistical Office gathers, administers, and produces statistical
information for the Council. Duties include preparing the statistical appendix
to the Economic Report of the President and the monthly publication Economic
Indicators. The staff also creates background materials for economic analysis
and verifies statistical content in Presidential memoranda. The Office serves
as the Council’s liaison to the statistical community.
Brian A. Amorosi	����������������������������������Statistical Analyst
Sarah Murray 	����������������������������������������Economic Statistician

Activities of the Council of Economic Advisers During 2012

| 309

Office of the Chairman
Michael P. Bourgeois	����������������������������Special Assistant to the Chairman
Emily C. Berret	��������������������������������������Special Assistant to the Members
Natasha S. Lawrence	����������������������������Staff Assistant

Administrative Office
The Administrative Office provides general support for the Council’s
activities. This includes financial management, human resource management, travel, operations of facilities, security, information technology, and
telecommunications management support.
Doris T. Searles	��������������������������������������Administrative and Information
Management Specialist
Thomas F. Hunt	������������������������������������Staff Assistant

Interns
Student interns provide invaluable help with research projects,
day- to-day operations, and fact-checking. Interns during the year were:
Norm Dannen, Laura Du, Shawn Du, Conor Foley, Scott Freitag, Rebecca
Freidman, Isaac Green, Sonya Huang, Christopher Kilgore, Zachary
Kleinbart, Amaze Lusompa, Nathan Mayo, John McDonough, Joel Moore,
Yolanda Ngo, Robert Owens, Scott Pippin, Katharine Rodihan, Charles
Rubenfeld, Rebecca Sachs, Zachary Silvis, Craig Smyser, Michael Sullivan,
David Wasser, William Weber, Derek Wu, and Barr Yaron.

Departures in 2012
Judith K. Hellerstein left her position as Chief Economist of the
Council in May, and she has returned to her position as Professor of
Economics at the University of Maryland, College Park.
The senior economists who resigned in 2012 (with the institutions to
which they returned after leaving the Council in parentheses) were: Gene
Amromin (Federal Reserve Bank of Chicago), Lee G. Branstetter (Carnegie
Mellon University, Heinz College), Thomas C. Buchmueller (University
of Michigan, Ross School of Business), Lisa D. Cook (Michigan State
University), Robert Johansson (U.S. Department of Agriculture), Craig T.
Peters (Department of Justice), Charles R. Pierret (U.S. Bureau of Labor
Statistics), and Daniel J. Vine (Federal Reserve Board).

310 |

Appendix A

The economist who departed in 2012 was Reid Stevens (UC, Berkeley).
Reid served the CEA for more than two and a half years and was the first
recipient of the Robert M. Solow Award for Distinguished Service.
The staff economists who departed in 2012 were Jeffrey Borowitz,
Colleen M. Carey, Judd N.L. Cramer, and Edward Zhong.
The research economists who departed in 2012 at the were Julia H.
Yoo and Pedro Spivakovsky-Gonzalez.
The research assistants who departed in 2012 were Sandra M. Levy,
Carter Mundell and Seth H. Werfel.
Andres Bustamante resigned from his position as Special Assistant to
the Chairman and Staff Economist to pursue other endeavors. Paige Shevlin
resigned from her position as Special Assistant to the Chairman. Sharon
Thomas resigned from her position as Administrative Support Assistant,
after serving in the Federal Government for over 25 years. Lindsay M.
Kuberka completed her detail as a statistical analyst and returned to the
Census Bureau.

Activities of the Council of Economic Advisers During 2012

| 311

A P P E N D I X

B

STATISTICAL TABLES RELATING TO
INCOME, EMPLOYMENT,
AND PRODUCTION

C O N T E N T S
NATIONAL INCOME OR EXPENDITURE

Page

B–1.

Gross domestic product, 1964–2012������������������������������������������������������������������������

322

B–2.

Real gross domestic product, 1964–2012����������������������������������������������������������������

324

B–3.

Quantity and price indexes for gross domestic product, and percent changes,
1964–2012�������������������������������������������������������������������������������������������������������������������

326

B–4.

Percent changes in real gross domestic product, 1964–2012��������������������������������

327

B–5.

Contributions to percent change in real gross domestic product, 1964–2012���

328

B–6.

Chain-type quantity indexes for gross domestic product, 1964–2012����������������

330

B–7.

Chain-type price indexes for gross domestic product, 1964–2012����������������������

332

B–8.

Gross domestic product by major type of product, 1964–2012���������������������������

334

B–9.

Real gross domestic product by major type of product, 1964–2012��������������������

335

B–10. Gross value added by sector, 1964–2012�����������������������������������������������������������������

336

B–11. Real gross value added by sector, 1964–2012����������������������������������������������������������

337

B–12. Gross domestic product (GDP) by industry, value added, in current dollars
and as a percentage of GDP, 1981–2011������������������������������������������������������������������

338

B–13. Real gross domestic product by industry, value added, and percent changes,
1981–2011�������������������������������������������������������������������������������������������������������������������

340

B–14. Gross value added of nonfinancial corporate business, 1964–2012���������������������

342

B–15. Gross value added and price, costs, and profits of nonfinancial corporate
business, 1964–2012���������������������������������������������������������������������������������������������������

343

B–16. Personal consumption expenditures, 1964–2012���������������������������������������������������

344

B–17. Real personal consumption expenditures, 1995–2012������������������������������������������

345

B–18. Private fixed investment by type, 1964–2012����������������������������������������������������������

346

B–19. Real private fixed investment by type, 1995–2012�������������������������������������������������

347

B–20. Government consumption expenditures and gross investment by type,
1964–2012�������������������������������������������������������������������������������������������������������������������

348

B–21. Real government consumption expenditures and gross investment by type,
1995–2012�������������������������������������������������������������������������������������������������������������������

349

B–22. Private inventories and domestic final sales by industry, 1964–2012������������������

350

B–23. Real private inventories and domestic final sales by industry, 1964–2012����������

351

B–24. Foreign transactions in the national income and product accounts,
1964–2012�������������������������������������������������������������������������������������������������������������������

352

  315

NATIONAL INCOME OR EXPENDITURE—Continued
B–25. Real exports and imports of goods and services, 1995–2012�������������������������������

353

B–26. Relation of gross domestic product, gross national product, net national
product, and national income, 1964–2012�������������������������������������������������������������

354

B–27. Relation of national income and personal income, 1964–2012����������������������������

355

B–28. National income by type of income, 1964–2012����������������������������������������������������

356

B–29. Sources of personal income, 1964–2012������������������������������������������������������������������

358

B–30. Disposition of personal income, 1964–2012�����������������������������������������������������������

360

B–31. Total and per capita disposable personal income and personal consumption
expenditures, and per capita gross domestic product, in current and real
dollars, 1964–2012������������������������������������������������������������������������������������������������������

361

B–32. Gross saving and investment, 1964–2012����������������������������������������������������������������

362

B–33. Median money income (in 2011 dollars) and poverty status of families and
people, by race, 2002–2011���������������������������������������������������������������������������������������

364

POPULATION, EMPLOYMENT, WAGES, AND PRODUCTIVITY
B–34. Population by age group, 1940–2012�����������������������������������������������������������������������

365

B–35. Civilian population and labor force, 1929–2012����������������������������������������������������

366

B–36. Civilian employment and unemployment by sex and age, 1966–2012����������������

368

B–37. Civilian employment by demographic characteristic, 1966–2012�����������������������

369

B–38. Unemployment by demographic characteristic, 1966–2012���������������������������������

370

B–39. Civilian labor force participation rate and employment/population ratio,
1966–2012�������������������������������������������������������������������������������������������������������������������

371

B–40. Civilian labor force participation rate by demographic characteristic,
1972–2012�������������������������������������������������������������������������������������������������������������������

372

B–41. Civilian employment/population ratio by demographic characteristic,
1972–2012�������������������������������������������������������������������������������������������������������������������

373

B–42. Civilian unemployment rate, 1966–2012�����������������������������������������������������������������

374

B–43. Civilian unemployment rate by demographic characteristic, 1972–2012�����������

375

B–44. Unemployment by duration and reason, 1966–2012���������������������������������������������

376

B–45. Unemployment insurance programs, selected data, 1980–2012��������������������������

377

B–46. Employees on nonagricultural payrolls, by major industry, 1968–2012�������������

378

B–47. Hours and earnings in private nonagricultural industries, 1966–2012 ��������������

380

B–48. Employment cost index, private industry, 1997–2012�������������������������������������������

381

B–49. Productivity and related data, business and nonfarm business sectors,
1963–2012�������������������������������������������������������������������������������������������������������������������

382

B–50. Changes in productivity and related data, business and nonfarm business
sectors, 1963–2012�����������������������������������������������������������������������������������������������������

383

316 |

Appendix B

PRODUCTION AND BUSINESS ACTIVITY
B–51. Industrial production indexes, major industry divisions, 1965–2012�����������������

384

B–52. Industrial production indexes, market groupings, 1965–2012�����������������������������

385

B–53. Industrial production indexes, selected manufacturing industries,
1972–2012�������������������������������������������������������������������������������������������������������������������

386

B–54. Capacity utilization rates, 1965–2012����������������������������������������������������������������������

387

B–55. New construction activity, 1968–2012���������������������������������������������������������������������

388

B–56. New private housing units started, authorized, and completed and houses
sold, 1967–2012����������������������������������������������������������������������������������������������������������

389

B–57. Manufacturing and trade sales and inventories, 1971–2012���������������������������������

390

B–58. Manufacturers’ shipments and inventories, 1971–2012����������������������������������������

391

B–59. Manufacturers’ new and unfilled orders, 1971–2012���������������������������������������������

392

PRICES
B–60. Consumer price indexes for major expenditure classes, 1969–2012�������������������

393

B–61. Consumer price indexes for selected expenditure classes, 1969–2012����������������

394

B–62. Consumer price indexes for commodities, services, and special groups,
1969–2012�������������������������������������������������������������������������������������������������������������������

396

B–63. Changes in special consumer price indexes, 1969–2012���������������������������������������

397

B–64. Changes in consumer price indexes for commodities and services,
1941–2012�������������������������������������������������������������������������������������������������������������������

398

B–65. Producer price indexes by stage of processing, 1966–2012�����������������������������������

399

B–66. Producer price indexes by stage of processing, special groups, 1974–2012��������

401

B–67. Producer price indexes for major commodity groups, 1966–2012����������������������

402

B–68. Changes in producer price indexes for finished goods, 1973–2012���������������������

404

MONEY STOCK, CREDIT, AND FINANCE
B–69. Money stock and debt measures, 1973–2012����������������������������������������������������������

405

B–70. Components of money stock measures, 1973–2012����������������������������������������������

406

B–71. Aggregate reserves of depository institutions and the monetary base,
1982–2012�������������������������������������������������������������������������������������������������������������������

408

B–72. Bank credit at all commercial banks, 1975–2012���������������������������������������������������

409

B–73. Bond yields and interest rates, 1941–2012��������������������������������������������������������������

410

B–74. Credit market borrowing, 2004–2012����������������������������������������������������������������������

412

B–75. Mortgage debt outstanding by type of property and of financing,
1955–2012�������������������������������������������������������������������������������������������������������������������

414

B–76. Mortgage debt outstanding by holder, 1955–2012�������������������������������������������������

415

B–77. Consumer credit outstanding, 1961–2012��������������������������������������������������������������

416

Contents

| 317

GOVERNMENT FINANCE
B–78. Federal receipts, outlays, surplus or deficit, and debt, fiscal years, 1946–2013��

417

B–79. Federal receipts, outlays, surplus or deficit, and debt, as percent of gross
domestic product, fiscal years 1940–2013���������������������������������������������������������������

418

B–80. Federal receipts and outlays, by major category, and surplus or deficit, fiscal
years 1946–2013���������������������������������������������������������������������������������������������������������

419

B–81. Federal receipts, outlays, surplus or deficit, and debt, fiscal years 2007–2012���

420

B–82. Federal and State and local government current receipts and expenditures,
national income and product accounts (NIPA), 1964–2012��������������������������������

421

B–83. Federal and State and local government current receipts and expenditures,
national income and product accounts (NIPA), by major type, 1964–2012������

422

B–84. Federal Government current receipts and expenditures, national income and
product accounts (NIPA), 1964–2012���������������������������������������������������������������������

423

B–85. State and local government current receipts and expenditures, national
income and product accounts (NIPA), 1964–2012������������������������������������������������

424

B–86. State and local government revenues and expenditures, selected fiscal years,
1948–2010�������������������������������������������������������������������������������������������������������������������

425

B–87. U.S. Treasury securities outstanding by kind of obligation, 1974–2012��������������

426

B–88. Maturity distribution and average length of marketable interest-bearing
public debt securities held by private investors, 1974–2012���������������������������������

427

B–89. Estimated ownership of U.S. Treasury securities, 1999–2012�������������������������������

428

CORPORATE PROFITS AND FINANCE
B–90. Corporate profits with inventory valuation and capital consumption
adjustments, 1964–2012��������������������������������������������������������������������������������������������

429

B–91. Corporate profits by industry, 1964–2012���������������������������������������������������������������

430

B–92. Corporate profits of manufacturing industries, 1964–2012����������������������������������

431

B–93. Sales, profits, and stockholders’ equity, all manufacturing corporations,
1971–2012�������������������������������������������������������������������������������������������������������������������

432

B–94. Relation of profits after taxes to stockholders’ equity and to sales, all
manufacturing corporations, 1963–2012����������������������������������������������������������������

433

B–95. Historical stock prices and yields, 1949–2003��������������������������������������������������������

434

B–96. Common stock prices and yields, 2000–2012���������������������������������������������������������

435

AGRICULTURE
B–97. Real farm income, 1950–2012�����������������������������������������������������������������������������������

436

B–98. Farm business balance sheet, 1960–2012����������������������������������������������������������������

437

B–99. Farm output and productivity indexes, 1950–2009�����������������������������������������������

438

B–100. Farm input use, selected inputs, 1950–2012������������������������������������������������������������

439

318 |

Appendix B

AGRICULTURE—Continued
B–101. Agricultural price indexes and farm real estate value, 1975–2012�����������������������

440

B–102. U.S. exports and imports of agricultural commodities, 1951–2012���������������������

441

INTERNATIONAL STATISTICS
B–103. U.S. international transactions, 1953–2012�������������������������������������������������������������

442

B–104. U.S. international trade in goods by principal end-use category, 1965–2012�����

444

B–105. U.S. international trade in goods by area, 2004–2012��������������������������������������������

445

B–106. U.S. international trade in goods on balance of payments (BOP) and Census
basis, and trade in services on BOP basis, 1985–2012������������������������������������������

446

B–107. International investment position of the United States at year-end,
2005–2011�������������������������������������������������������������������������������������������������������������������

447

B–108. Industrial production and consumer prices, major industrial countries,
1986–2012�������������������������������������������������������������������������������������������������������������������

448

B–109. Civilian unemployment rate, and hourly compensation, major industrial
countries, 1986–2012�������������������������������������������������������������������������������������������������

449

B–110. Foreign exchange rates, 1993–2012��������������������������������������������������������������������������

450

B–111. International reserves, selected years, 1992–2012��������������������������������������������������

451

B–112. Growth rates in real gross domestic product, 1994–2013�������������������������������������

452

Contents

| 319

General Notes
Detail in these tables may not add to totals because of rounding.
Because of the formula used for calculating real gross domestic
product (GDP), the chained (2005) dollar estimates for the detailed
components do not add to the chained-dollar value of GDP or to
any intermediate aggregate. The Department of Commerce (Bureau
of Economic Analysis) no longer publishes chained-dollar estimates
prior to 1995, except for selected series.
Unless otherwise noted, all dollar figures are in current dollars.
Symbols used:
p Preliminary.
... Not available (also, not applicable).
Data in these tables reflect revisions made by the source agencies
through January 30, 2013 with two exceptions. Current employment
statistics (CES) estimates from the Department of Labor (Bureau
of Labor Statistics) include revisions released February 1, 2013, and
national income and product account (NIPA) estimates from the
Department of Commerce (Bureau of Economic Analysis) incorporate revisions released on February 28, 2013.

General Notes

| 321

National Income or Expenditure

Table B–1. Gross domestic product, 1964–2012
[Billions of dollars, except as noted; quarterly data at seasonally adjusted annual rates]
Personal consumption expenditures

Year or quarter

1964 ����������������������
1965 ����������������������
1966 ����������������������
1967 ����������������������
1968 ����������������������
1969 ����������������������
1970 ����������������������
1971 ����������������������
1972 ����������������������
1973 ����������������������
1974 ����������������������
1975 ����������������������
1976 ����������������������
1977 ����������������������
1978 ����������������������
1979 ����������������������
1980 ����������������������
1981 ����������������������
1982 ����������������������
1983 ����������������������
1984 ����������������������
1985 ����������������������
1986 ����������������������
1987 ����������������������
1988 ����������������������
1989 ����������������������
1990 ����������������������
1991 ����������������������
1992 ����������������������
1993 ����������������������
1994 ����������������������
1995 ����������������������
1996 ����������������������
1997 ����������������������
1998 ����������������������
1999 ����������������������
2000 ����������������������
2001 ����������������������
2002 ����������������������
2003 ����������������������
2004 ����������������������
2005 ����������������������
2006 ����������������������
2007 ����������������������
2008 ����������������������
2009 ����������������������
2010 ����������������������
2011 ����������������������
2012 p ��������������������
2009: I ������������������
      II �����������������
      III ����������������
      IV ����������������
2010: I ������������������
      II �����������������
      III ����������������
      IV ����������������
2011: I ������������������
      II �����������������
      III ����������������
      IV ����������������
2012: I ������������������
      II �����������������
      III ����������������
      IV p �������������

Gross
domestic
product

663.6
719.1
787.7
832.4
909.8
984.4
1,038.3
1,126.8
1,237.9
1,382.3
1,499.5
1,637.7
1,824.6
2,030.1
2,293.8
2,562.2
2,788.1
3,126.8
3,253.2
3,534.6
3,930.9
4,217.5
4,460.1
4,736.4
5,100.4
5,482.1
5,800.5
5,992.1
6,342.3
6,667.4
7,085.2
7,414.7
7,838.5
8,332.4
8,793.5
9,353.5
9,951.5
10,286.2
10,642.3
11,142.2
11,853.3
12,623.0
13,377.2
14,028.7
14,291.5
13,973.7
14,498.9
15,075.7
15,681.5
13,923.4
13,885.4
13,952.2
14,133.6
14,270.3
14,413.5
14,576.0
14,735.9
14,814.9
15,003.6
15,163.2
15,321.0
15,478.3
15,585.6
15,811.0
15,851.2

Fixed investment
Total

411.5
443.8
480.9
507.8
558.0
605.1
648.3
701.6
770.2
852.0
932.9
1,033.8
1,151.3
1,277.8
1,427.6
1,591.2
1,755.8
1,939.5
2,075.5
2,288.6
2,501.1
2,717.6
2,896.7
3,097.0
3,350.1
3,594.5
3,835.5
3,980.1
4,236.9
4,483.6
4,750.8
4,987.3
5,273.6
5,570.6
5,918.5
6,342.8
6,830.4
7,148.8
7,439.2
7,804.1
8,270.6
8,803.5
9,301.0
9,772.3
10,035.5
9,845.9
10,215.7
10,729.0
11,120.9
9,768.4
9,763.9
9,888.8
9,962.5
10,069.1
10,148.3
10,243.6
10,401.9
10,566.3
10,684.9
10,791.2
10,873.8
11,007.2
11,067.2
11,154.4
11,254.6

See next page for continuation of table.

322 |

Appendix B

Gross private domestic investment

Goods

212.3
229.7
249.6
259.0
284.6
304.7
318.8
342.1
373.8
416.6
451.5
491.3
546.3
600.4
663.6
737.9
799.8
869.4
899.3
973.8
1,063.7
1,137.6
1,195.6
1,256.3
1,337.3
1,423.8
1,491.3
1,497.4
1,563.3
1,642.3
1,746.6
1,815.5
1,917.7
2,006.8
2,110.0
2,290.0
2,459.1
2,534.0
2,610.0
2,728.0
2,892.1
3,076.7
3,224.7
3,363.9
3,381.7
3,194.4
3,364.9
3,624.8
3,783.2
3,125.5
3,142.0
3,244.4
3,265.5
3,318.2
3,321.7
3,361.0
3,458.6
3,561.4
3,604.3
3,643.6
3,690.0
3,755.9
3,741.5
3,792.5
3,843.0

Services

199.2
214.1
231.3
248.8
273.4
300.4
329.5
359.5
396.4
435.4
481.4
542.5
604.9
677.4
764.1
853.2
956.0
1,070.1
1,176.2
1,314.8
1,437.4
1,580.0
1,701.1
1,840.7
2,012.7
2,170.7
2,344.2
2,482.6
2,673.6
2,841.2
3,004.3
3,171.7
3,355.9
3,563.9
3,808.5
4,052.8
4,371.2
4,614.8
4,829.2
5,076.1
5,378.5
5,726.8
6,076.3
6,408.3
6,653.8
6,651.5
6,850.9
7,104.2
7,337.6
6,642.9
6,621.9
6,644.4
6,697.0
6,750.9
6,826.6
6,882.6
6,943.3
7,004.9
7,080.6
7,147.6
7,183.8
7,251.3
7,325.7
7,361.9
7,411.6

Total

102.1
118.2
131.3
128.6
141.2
156.4
152.4
178.2
207.6
244.5
249.4
230.2
292.0
361.3
438.0
492.9
479.3
572.4
517.2
564.3
735.6
736.2
746.5
785.0
821.6
874.9
861.0
802.9
864.8
953.3
1,097.3
1,144.0
1,240.2
1,388.7
1,510.8
1,641.5
1,772.2
1,661.9
1,647.0
1,729.7
1,968.6
2,172.3
2,327.1
2,295.2
2,087.6
1,549.3
1,737.3
1,854.9
2,058.6
1,645.8
1,495.3
1,465.6
1,590.4
1,660.4
1,724.7
1,793.3
1,770.9
1,755.9
1,819.0
1,853.8
1,991.1
2,032.2
2,041.7
2,080.1
2,080.3

Nonresidential
Total

97.2
109.0
117.7
118.7
132.1
147.3
150.4
169.9
198.5
228.6
235.4
236.5
274.8
339.0
412.2
474.9
485.6
542.6
532.1
570.1
670.2
714.4
739.9
757.8
803.1
847.3
846.4
803.3
848.5
932.5
1,033.5
1,112.9
1,209.4
1,317.7
1,447.1
1,580.7
1,717.7
1,700.2
1,634.9
1,713.3
1,903.6
2,122.3
2,267.2
2,266.1
2,128.7
1,703.5
1,679.0
1,818.3
2,000.9
1,812.5
1,698.0
1,666.1
1,637.2
1,627.2
1,683.0
1,683.8
1,721.9
1,722.3
1,784.2
1,857.8
1,909.0
1,959.7
1,986.9
1,997.9
2,059.0

Total
63.0
74.8
85.4
86.4
93.4
104.7
109.0
114.1
128.8
153.3
169.5
173.7
192.4
228.7
280.6
333.9
362.4
420.0
426.5
417.2
489.6
526.2
519.8
524.1
563.8
607.7
622.4
598.2
612.1
666.6
731.4
810.0
875.4
968.6
1,061.1
1,154.9
1,268.7
1,227.8
1,125.4
1,135.7
1,223.0
1,347.3
1,505.3
1,637.5
1,656.3
1,349.3
1,338.4
1,479.6
1,618.0
1,442.9
1,356.0
1,312.9
1,285.4
1,285.8
1,325.2
1,353.8
1,388.8
1,390.8
1,448.0
1,519.4
1,560.1
1,595.5
1,614.1
1,610.0
1,652.5

EquipStructures ment and
software
23.7
28.3
31.3
31.5
33.6
37.7
40.3
42.7
47.2
55.0
61.2
61.4
65.9
74.6
93.6
117.7
136.2
167.3
177.6
154.3
177.4
194.5
176.5
174.2
182.8
193.7
202.9
183.6
172.6
177.2
186.8
207.3
224.6
250.3
275.1
283.9
318.1
329.7
282.8
281.9
306.7
351.8
433.7
524.9
586.3
451.1
376.3
404.8
460.5
530.5
467.1
421.0
385.6
362.7
376.6
377.1
389.0
362.4
397.0
421.8
438.2
454.7
458.9
460.1
468.2

39.2
46.5
54.0
54.9
59.9
67.0
68.7
71.5
81.7
98.3
108.2
112.4
126.4
154.1
187.0
216.2
226.2
252.7
248.9
262.9
312.2
331.7
343.3
349.9
381.0
414.0
419.5
414.6
439.6
489.4
544.6
602.8
650.8
718.3
786.0
871.0
950.5
898.1
842.7
853.8
916.4
995.6
1,071.7
1,112.6
1,070.0
898.2
962.1
1,074.7
1,157.6
912.4
888.9
891.9
899.8
923.1
948.6
976.8
999.8
1,028.4
1,051.0
1,097.6
1,122.0
1,140.8
1,155.2
1,149.9
1,184.3

Residential
34.3
34.2
32.3
32.4
38.7
42.6
41.4
55.8
69.7
75.3
66.0
62.7
82.5
110.3
131.6
141.0
123.2
122.6
105.7
152.9
180.6
188.2
220.1
233.7
239.3
239.5
224.0
205.1
236.3
266.0
302.1
302.9
334.1
349.1
385.9
425.8
449.0
472.4
509.5
577.6
680.6
775.0
761.9
628.7
472.4
354.1
340.6
338.7
382.8
369.6
342.0
353.1
351.9
341.3
357.8
330.0
333.1
331.4
336.2
338.5
348.8
364.2
372.8
387.9
406.5

Change
in
private
inventories
4.8
9.2
13.6
9.9
9.1
9.2
2.0
8.3
9.1
15.9
14.0
–6.3
17.1
22.3
25.8
18.0
–6.3
29.8
–14.9
–5.8
65.4
21.8
6.6
27.1
18.5
27.7
14.5
–.4
16.3
20.8
63.8
31.2
30.8
71.0
63.7
60.8
54.5
–38.3
12.0
16.4
64.9
50.0
60.0
29.1
–41.1
–154.2
58.4
36.6
57.7
–166.7
–202.7
–200.5
–46.8
33.2
41.7
109.5
49.0
33.7
34.8
–4.1
82.1
72.6
54.8
82.3
21.3

Table B–1. Gross domestic product, 1964–2012—Continued
[Billions of dollars, except as noted; quarterly data at seasonally adjusted annual rates]
Net exports of
goods and services

Government consumption expenditures
and gross investment

Year or quarter

Federal
Net
exports Exports Imports

1964 ����������������������
1965 ����������������������
1966 ����������������������
1967 ����������������������
1968 ����������������������
1969 ����������������������
1970 ����������������������
1971 ����������������������
1972 ����������������������
1973 ����������������������
1974 ����������������������
1975 ����������������������
1976 ����������������������
1977 ����������������������
1978 ����������������������
1979 ����������������������
1980 ����������������������
1981 ����������������������
1982 ����������������������
1983 ����������������������
1984 ����������������������
1985 ����������������������
1986 ����������������������
1987 ����������������������
1988 ����������������������
1989 ����������������������
1990 ����������������������
1991 ����������������������
1992 ����������������������
1993 ����������������������
1994 ����������������������
1995 ����������������������
1996 ����������������������
1997 ����������������������
1998 ����������������������
1999 ����������������������
2000 ����������������������
2001 ����������������������
2002 ����������������������
2003 ����������������������
2004 ����������������������
2005 ����������������������
2006 ����������������������
2007 ����������������������
2008 ����������������������
2009 ����������������������
2010 ����������������������
2011 ����������������������
2012 p ��������������������
2009: I ������������������
      II �����������������
      III ����������������
      IV ����������������
2010: I ������������������
      II �����������������
      III ����������������
      IV ����������������
2011: I ������������������
      II �����������������
      III ����������������
      IV ����������������
2012: I ������������������
      II �����������������
      III ����������������
      IV p �������������

6.9
5.6
3.9
3.6
1.4
1.4
4.0
.6
–3.4
4.1
–.8
16.0
–1.6
–23.1
–25.4
–22.5
–13.1
–12.5
–20.0
–51.7
–102.7
–115.2
–132.5
–145.0
–110.1
–87.9
–77.6
–27.0
–32.8
–64.4
–92.7
–90.7
–96.3
–101.4
–161.8
–262.1
–382.1
–371.0
–427.2
–504.1
–618.7
–722.7
–769.3
–713.1
–709.7
–388.7
–511.6
–568.1
–560.8
–385.4
–331.6
–398.6
–439.3
–490.2
–521.1
–533.1
–502.1
–555.4
–572.5
–549.5
–594.8
–615.8
–576.9
–516.8
–533.6

35.0
37.1
40.9
43.5
47.9
51.9
59.7
63.0
70.8
95.3
126.7
138.7
149.5
159.4
186.9
230.1
280.8
305.2
283.2
277.0
302.4
302.0
320.3
363.8
443.9
503.1
552.1
596.6
635.0
655.6
720.7
811.9
867.7
954.4
953.9
989.3
1,093.2
1,027.7
1,003.0
1,041.0
1,180.2
1,305.1
1,471.0
1,661.7
1,846.8
1,587.4
1,844.4
2,094.2
2,182.6
1,523.5
1,525.3
1,594.7
1,706.3
1,751.9
1,814.3
1,861.2
1,950.4
2,030.5
2,092.8
2,133.3
2,120.3
2,157.9
2,188.5
2,198.7
2,185.2

28.1
31.5
37.1
39.9
46.6
50.5
55.8
62.3
74.2
91.2
127.5
122.7
151.1
182.4
212.3
252.7
293.8
317.8
303.2
328.6
405.1
417.2
452.9
508.7
554.0
591.0
629.7
623.5
667.8
720.0
813.4
902.6
964.0
1,055.8
1,115.7
1,251.4
1,475.3
1,398.7
1,430.2
1,545.1
1,798.9
2,027.8
2,240.3
2,374.8
2,556.5
1,976.2
2,356.1
2,662.3
2,743.3
1,908.9
1,856.9
1,993.3
2,145.5
2,242.0
2,335.4
2,394.3
2,452.5
2,585.9
2,665.3
2,682.8
2,715.1
2,773.7
2,765.4
2,715.5
2,718.8

Total

143.2
151.4
171.6
192.5
209.3
221.4
233.7
246.4
263.4
281.7
317.9
357.7
383.0
414.1
453.6
500.7
566.1
627.5
680.4
733.4
796.9
878.9
949.3
999.4
1,038.9
1,100.6
1,181.7
1,236.1
1,273.5
1,294.8
1,329.8
1,374.0
1,421.0
1,474.4
1,526.1
1,631.3
1,731.0
1,846.4
1,983.3
2,112.6
2,232.8
2,369.9
2,518.4
2,674.2
2,878.1
2,967.2
3,057.5
3,059.8
3,062.9
2,894.6
2,957.8
2,996.4
3,020.0
3,030.9
3,061.7
3,072.3
3,065.2
3,048.1
3,072.2
3,067.7
3,051.0
3,054.6
3,053.7
3,093.3
3,049.9

Total
78.4
80.4
92.4
104.6
111.3
113.3
113.4
113.6
119.6
122.5
134.5
149.0
159.7
175.4
190.9
210.6
243.7
280.2
310.8
342.9
374.3
412.8
438.4
459.5
461.6
481.4
507.5
526.6
532.9
525.0
518.6
518.8
527.0
531.0
531.0
554.9
576.1
611.7
680.6
756.5
824.6
876.3
931.7
976.3
1,080.1
1,143.6
1,223.1
1,222.1
1,214.3
1,104.9
1,135.9
1,157.6
1,175.9
1,193.7
1,225.1
1,239.8
1,233.8
1,215.2
1,234.3
1,227.5
1,211.2
1,207.7
1,210.7
1,241.4
1,197.4

National Nondefense defense
60.2
60.6
71.7
83.4
89.2
89.5
87.6
84.6
86.9
88.1
95.6
103.9
111.1
120.9
130.5
145.2
168.0
196.2
225.9
250.6
281.5
311.2
330.8
350.0
354.7
362.1
373.9
383.1
376.8
363.0
353.8
348.8
354.8
349.8
346.1
361.1
371.0
393.0
437.7
497.9
550.8
589.0
624.9
662.3
737.8
776.0
817.7
820.8
809.2
748.0
772.0
788.5
795.5
799.3
815.5
831.6
824.5
804.9
827.7
837.8
812.8
806.4
807.8
834.5
787.9

18.2
19.8
20.8
21.2
22.0
23.8
25.8
29.1
32.7
34.3
39.0
45.1
48.6
54.5
60.4
65.4
75.8
83.9
84.9
92.3
92.7
101.6
107.6
109.6
106.8
119.3
133.6
143.4
156.1
162.0
164.8
170.0
172.2
181.1
184.9
193.8
205.0
218.7
242.9
258.5
273.9
287.3
306.8
314.0
342.3
367.6
405.3
401.3
405.1
356.9
364.0
369.1
380.4
394.3
409.6
408.1
409.3
410.3
406.6
389.7
398.4
401.3
402.9
406.8
409.4

State
and
local
64.8
71.0
79.2
87.9
98.0
108.2
120.3
132.8
143.8
159.2
183.4
208.7
223.3
238.7
262.7
290.2
322.4
347.3
369.7
390.5
422.6
466.1
510.9
539.9
577.3
619.2
674.2
709.5
740.6
769.8
811.2
855.3
894.0
943.5
995.0
1,076.3
1,154.9
1,234.7
1,302.7
1,356.1
1,408.2
1,493.6
1,586.7
1,697.9
1,798.0
1,823.6
1,834.4
1,837.7
1,848.6
1,789.7
1,821.9
1,838.8
1,844.1
1,837.2
1,836.6
1,832.5
1,831.4
1,832.8
1,837.9
1,840.2
1,839.7
1,846.9
1,843.0
1,851.9
1,852.5

Final
sales of
domestic
product

Gross
domestic
purchases 1

Addendum:
Gross
national
product 2

658.8
709.9
774.1
822.6
900.8
975.3
1,036.3
1,118.6
1,228.8
1,366.4
1,485.5
1,644.0
1,807.5
2,007.8
2,268.0
2,544.2
2,794.5
3,097.0
3,268.1
3,540.4
3,865.5
4,195.6
4,453.5
4,709.2
5,081.9
5,454.5
5,786.0
5,992.5
6,326.0
6,646.5
7,021.4
7,383.5
7,807.7
8,261.4
8,729.8
9,292.7
9,896.9
10,324.5
10,630.3
11,125.8
11,788.3
12,573.0
13,317.3
13,999.6
14,332.7
14,127.9
14,440.6
15,039.0
15,623.8
14,090.2
14,088.1
14,152.7
14,180.5
14,237.0
14,371.8
14,466.6
14,686.9
14,781.2
14,968.7
15,167.3
15,238.9
15,405.7
15,530.8
15,728.8
15,829.9

656.7
713.5
783.8
828.9
908.5
983.0
1,034.4
1,126.2
1,241.3
1,378.2
1,500.3
1,621.7
1,826.2
2,053.2
2,319.1
2,584.8
2,801.2
3,139.4
3,273.2
3,586.3
4,033.6
4,332.7
4,592.6
4,881.3
5,210.5
5,570.0
5,878.1
6,019.1
6,375.1
6,731.7
7,177.9
7,505.3
7,934.8
8,433.7
8,955.3
9,615.6
10,333.5
10,657.2
11,069.5
11,646.3
12,471.9
13,345.7
14,146.5
14,741.7
15,001.3
14,362.4
15,010.6
15,643.7
16,242.3
14,308.9
14,217.0
14,350.8
14,572.9
14,760.4
14,934.7
15,109.2
15,238.0
15,370.3
15,576.1
15,712.7
15,915.9
16,094.0
16,162.5
16,327.8
16,384.8

668.6
724.4
792.8
837.8
915.9
990.5
1,044.7
1,134.4
1,246.4
1,394.9
1,515.0
1,650.7
1,841.4
2,050.4
2,315.3
2,594.2
2,822.3
3,159.8
3,289.7
3,571.7
3,967.2
4,244.0
4,477.7
4,754.0
5,123.8
5,508.1
5,835.0
6,022.0
6,371.4
6,698.5
7,109.2
7,444.3
7,870.1
8,355.8
8,810.8
9,381.3
9,989.2
10,338.1
10,691.4
11,210.9
11,944.5
12,720.1
13,449.6
14,151.9
14,460.7
14,117.2
14,708.2
15,327.5
�������������
14,041.7
14,001.3
14,115.2
14,310.8
14,461.7
14,629.3
14,793.0
14,948.9
15,050.1
15,253.6
15,421.5
15,585.0
15,693.2
15,832.9
16,054.2
�������������

Percent change
from preceding
period
Gross
Gross
domes- domestic
tic
purproduct chases
1
7.4
8.4
9.5
5.7
9.3
8.2
5.5
8.5
9.9
11.7
8.5
9.2
11.4
11.3
13.0
11.7
8.8
12.1
4.0
8.7
11.2
7.3
5.8
6.2
7.7
7.5
5.8
3.3
5.8
5.1
6.3
4.7
5.7
6.3
5.5
6.4
6.4
3.4
3.5
4.7
6.4
6.5
6.0
4.9
1.9
–2.2
3.8
4.0
4.0
–4.4
–1.1
1.9
5.3
3.9
4.1
4.6
4.5
2.2
5.2
4.3
4.2
4.2
2.8
5.9
1.0

7.2
8.6
9.9
5.7
9.6
8.2
5.2
8.9
10.2
11.0
8.9
8.1
12.6
12.4
13.0
11.5
8.4
12.1
4.3
9.6
12.5
7.4
6.0
6.3
6.7
6.9
5.5
2.4
5.9
5.6
6.6
4.6
5.7
6.3
6.2
7.4
7.5
3.1
3.9
5.2
7.1
7.0
6.0
4.2
1.8
–4.3
4.5
4.2
3.8
–9.6
–2.5
3.8
6.3
5.2
4.8
4.8
3.5
3.5
5.5
3.6
5.3
4.6
1.7
4.2
1.4

1 Gross domestic product (GDP) less exports of goods and services plus imports of goods and services.
2 GDP plus net income receipts from rest of the world.

Source: Department of Commerce (Bureau of Economic Analysis).

National Income or Expenditure | 323

Table B–2. Real gross domestic product, 1964–2012
[Billions of chained (2005) dollars, except as noted; quarterly data at seasonally adjusted annual rates]
Personal consumption expenditures

Year or quarter

1964 ����������������������
1965 ����������������������
1966 ����������������������
1967 ����������������������
1968 ����������������������
1969 ����������������������
1970 ����������������������
1971 ����������������������
1972 ����������������������
1973 ����������������������
1974 ����������������������
1975 ����������������������
1976 ����������������������
1977 ����������������������
1978 ����������������������
1979 ����������������������
1980 ����������������������
1981 ����������������������
1982 ����������������������
1983 ����������������������
1984 ����������������������
1985 ����������������������
1986 ����������������������
1987 ����������������������
1988 ����������������������
1989 ����������������������
1990 ����������������������
1991 ����������������������
1992 ����������������������
1993 ����������������������
1994 ����������������������
1995 ����������������������
1996 ����������������������
1997 ����������������������
1998 ����������������������
1999 ����������������������
2000 ����������������������
2001 ����������������������
2002 ����������������������
2003 ����������������������
2004 ����������������������
2005 ����������������������
2006 ����������������������
2007 ����������������������
2008 ����������������������
2009 ����������������������
2010 ����������������������
2011 ����������������������
2012 p ��������������������
2009: I ������������������
      II �����������������
      III ����������������
      IV ����������������
2010: I ������������������
      II �����������������
      III ����������������
      IV ����������������
2011: I ������������������
      II �����������������
      III ����������������
      IV ����������������
2012: I ������������������
      II �����������������
      III ����������������
      IV p �������������

Gross
domestic
product

3,389.4
3,607.0
3,842.1
3,939.2
4,129.9
4,258.2
4,266.3
4,409.5
4,643.8
4,912.8
4,885.7
4,875.4
5,136.9
5,373.1
5,672.8
5,850.1
5,834.0
5,982.1
5,865.9
6,130.9
6,571.5
6,843.4
7,080.5
7,307.0
7,607.4
7,879.2
8,027.1
8,008.3
8,280.0
8,516.2
8,863.1
9,086.0
9,425.8
9,845.9
10,274.7
10,770.7
11,216.4
11,337.5
11,543.1
11,836.4
12,246.9
12,623.0
12,958.5
13,206.4
13,161.9
12,757.9
13,063.0
13,299.1
13,591.1
12,711.0
12,701.0
12,746.7
12,873.1
12,947.6
13,019.6
13,103.5
13,181.2
13,183.8
13,264.7
13,306.9
13,441.0
13,506.4
13,548.5
13,652.5
13,656.8

Fixed investment
Total

2,107.5
2,240.8
2,367.9
2,438.8
2,579.6
2,676.2
2,738.9
2,843.3
3,018.1
3,167.7
3,141.4
3,212.6
3,391.5
3,534.3
3,690.1
3,777.8
3,764.5
3,821.6
3,874.9
4,096.4
4,313.6
4,538.3
4,722.4
4,868.0
5,064.3
5,207.5
5,313.7
5,321.7
5,503.2
5,698.6
5,916.2
6,076.2
6,288.3
6,520.4
6,862.3
7,237.6
7,604.6
7,810.3
8,018.3
8,244.5
8,515.8
8,803.5
9,054.5
9,262.9
9,211.7
9,032.6
9,196.2
9,428.8
9,604.9
9,039.5
8,999.3
9,046.2
9,045.4
9,100.8
9,159.4
9,216.0
9,308.5
9,380.9
9,403.2
9,441.9
9,489.3
9,546.8
9,582.5
9,620.1
9,670.0

See next page for continuation of table.

324 |

Appendix B

Gross private domestic investment

Goods

�����������������
�����������������
�����������������
�����������������
�����������������
�����������������
�����������������
�����������������
�����������������
�����������������
�����������������
�����������������
�����������������
�����������������
�����������������
�����������������
�����������������
�����������������
�����������������
�����������������
�����������������
�����������������
�����������������
�����������������
�����������������
�����������������
�����������������
�����������������
�����������������
�����������������
�����������������
1,896.0
1,980.9
2,075.3
2,215.5
2,392.0
2,518.2
2,597.3
2,702.9
2,827.2
2,953.3
3,076.7
3,178.9
3,273.5
3,192.9
3,098.2
3,209.1
3,331.0
3,433.0
3,083.2
3,067.0
3,123.1
3,119.5
3,159.5
3,185.4
3,215.1
3,276.5
3,320.3
3,312.2
3,323.5
3,367.9
3,406.6
3,409.4
3,439.7
3,476.4

Services

�����������������
�����������������
�����������������
�����������������
�����������������
�����������������
�����������������
�����������������
�����������������
�����������������
�����������������
�����������������
�����������������
�����������������
�����������������
�����������������
�����������������
�����������������
�����������������
�����������������
�����������������
�����������������
�����������������
�����������������
�����������������
�����������������
�����������������
�����������������
�����������������
�����������������
�����������������
4,208.5
4,331.7
4,465.3
4,662.1
4,853.1
5,093.6
5,219.1
5,318.5
5,418.2
5,562.7
5,726.8
5,875.6
5,990.2
6,017.0
5,930.6
5,987.6
6,101.5
6,178.0
5,951.5
5,926.9
5,920.7
5,923.2
5,940.4
5,973.6
6,001.4
6,034.9
6,064.8
6,094.0
6,121.1
6,126.0
6,145.9
6,178.2
6,186.7
6,201.3

Total

382.1
435.7
474.1
452.4
478.7
506.6
473.4
527.3
589.8
658.9
610.3
502.2
603.7
694.9
778.7
803.5
715.2
779.6
670.3
732.8
948.7
939.8
933.5
962.2
984.9
1,024.4
989.9
909.4
983.1
1,070.9
1,216.4
1,254.3
1,365.3
1,535.2
1,688.9
1,837.6
1,963.1
1,825.2
1,800.4
1,870.1
2,058.2
2,172.3
2,231.8
2,159.5
1,939.8
1,458.1
1,658.0
1,744.0
1,911.0
1,516.0
1,400.7
1,394.8
1,521.1
1,591.4
1,646.4
1,710.1
1,684.3
1,661.6
1,711.3
1,735.8
1,867.3
1,895.1
1,898.4
1,928.8
1,921.7

Residential

Change
in
private
inventories

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456.1
492.5
501.8
540.4
574.2
580.0
583.3
613.8
664.3
729.5
775.0
718.2
584.2
444.4
344.8
332.2
327.6
367.1
355.3
333.7
347.2
343.0
332.7
350.5
322.2
323.3
322.2
325.5
326.6
336.0
352.1
359.3
370.9
386.1

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32.1
31.2
77.4
71.6
68.5
60.2
–41.8
12.8
17.3
66.3
50.0
59.4
27.7
–36.3
–139.0
50.9
31.0
42.7
–150.2
–185.5
–181.5
–38.8
30.5
33.2
94.9
45.0
30.3
27.5
–4.3
70.5
56.9
41.4
60.3
12.0

Nonresidential
Total

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1,231.2
1,341.6
1,465.4
1,624.4
1,775.5
1,906.8
1,870.7
1,791.5
1,854.7
1,992.5
2,122.3
2,172.7
2,130.6
1,978.6
1,602.2
1,598.7
1,704.5
1,850.1
1,677.3
1,593.7
1,581.2
1,556.8
1,553.1
1,606.5
1,602.7
1,632.3
1,627.0
1,675.4
1,736.8
1,778.7
1,820.6
1,840.6
1,844.8
1,894.4

Total
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787.9
861.5
965.5
1,081.4
1,194.3
1,311.3
1,274.8
1,173.7
1,189.6
1,263.0
1,347.3
1,455.5
1,550.0
1,537.6
1,259.8
1,268.5
1,378.2
1,484.9
1,324.3
1,262.0
1,236.7
1,216.4
1,222.7
1,258.6
1,282.1
1,310.5
1,306.3
1,351.3
1,411.3
1,443.7
1,470.0
1,482.9
1,476.1
1,510.7

EquipStructures ment and
software
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342.0
361.4
387.9
407.7
408.2
440.0
433.3
356.6
343.0
346.7
351.8
384.0
438.2
466.4
368.1
310.6
319.2
351.3
417.7
380.1
351.7
323.1
302.6
312.1
310.4
317.4
292.2
315.0
330.2
339.3
349.7
350.2
350.2
355.1

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489.4
541.4
615.9
705.2
805.0
889.2
860.6
824.2
850.0
917.3
995.6
1,071.1
1,106.8
1,059.4
885.2
963.9
1,070.0
1,143.5
892.9
873.2
880.8
893.8
925.0
951.6
978.7
1,000.4
1,027.0
1,046.5
1,091.5
1,114.8
1,129.6
1,142.8
1,135.4
1,166.3

Table B–2. Real gross domestic product, 1964–2012—Continued
[Billions of chained (2005) dollars, except as noted; quarterly data at seasonally adjusted annual rates]
Net exports of
goods and services

Government consumption expenditures
and gross investment

Year or quarter

1964 ����������������������
1965 ����������������������
1966 ����������������������
1967 ����������������������
1968 ����������������������
1969 ����������������������
1970 ����������������������
1971 ����������������������
1972 ����������������������
1973 ����������������������
1974 ����������������������
1975 ����������������������
1976 ����������������������
1977 ����������������������
1978 ����������������������
1979 ����������������������
1980 ����������������������
1981 ����������������������
1982 ����������������������
1983 ����������������������
1984 ����������������������
1985 ����������������������
1986 ����������������������
1987 ����������������������
1988 ����������������������
1989 ����������������������
1990 ����������������������
1991 ����������������������
1992 ����������������������
1993 ����������������������
1994 ����������������������
1995 ����������������������
1996 ����������������������
1997 ����������������������
1998 ����������������������
1999 ����������������������
2000 ����������������������
2001 ����������������������
2002 ����������������������
2003 ����������������������
2004 ����������������������
2005 ����������������������
2006 ����������������������
2007 ����������������������
2008 ����������������������
2009 ����������������������
2010 ����������������������
2011 ����������������������
2012 p ��������������������
2009: I ������������������
      II �����������������
      III ����������������
      IV ����������������
2010: I ������������������
      II �����������������
      III ����������������
      IV ����������������
2011: I ������������������
      II �����������������
      III ����������������
      IV ����������������
2012: I ������������������
      II �����������������
      III ����������������
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Federal
Net
exports

Exports

Imports

Total

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�������������
–98.8
–110.7
–139.8
–252.5
–356.4
–451.3
–471.8
–548.5
–603.7
–687.9
–722.7
–729.4
–648.8
–494.8
–355.2
–419.7
–408.0
–401.5
–403.5
–322.8
–346.9
–347.5
–372.7
–428.7
–458.9
–418.3
–416.6
–399.6
–397.9
–418.0
–415.5
–407.4
–395.2
–387.9

124.5
128.0
136.9
140.0
151.0
158.3
175.3
178.3
191.7
227.8
245.8
244.3
255.0
261.1
288.6
317.2
351.4
355.7
328.5
320.1
346.2
356.7
384.1
425.4
493.5
550.2
599.7
639.5
683.5
705.9
767.4
845.1
915.3
1,024.3
1,047.7
1,093.4
1,187.4
1,120.8
1,098.3
1,116.0
1,222.5
1,305.1
1,422.1
1,554.4
1,649.3
1,498.7
1,665.6
1,776.9
1,836.0
1,452.5
1,454.6
1,502.3
1,585.2
1,608.2
1,645.4
1,683.9
1,724.7
1,748.8
1,766.4
1,792.9
1,799.3
1,818.7
1,842.1
1,850.9
1,832.5

136.9
151.5
174.0
186.7
214.5
226.7
236.4
249.0
277.0
289.9
283.3
251.8
301.1
334.0
362.9
369.0
344.5
353.5
349.1
393.1
488.8
520.5
565.0
598.4
621.9
649.3
672.6
671.6
718.7
780.8
873.9
943.9
1,026.0
1,164.1
1,300.2
1,449.9
1,638.7
1,592.6
1,646.8
1,719.7
1,910.4
2,027.8
2,151.5
2,203.2
2,144.0
1,853.8
2,085.2
2,184.9
2,237.6
1,856.0
1,777.4
1,849.3
1,932.7
1,980.9
2,074.2
2,142.8
2,143.0
2,165.4
2,166.0
2,190.8
2,217.3
2,234.2
2,249.6
2,246.1
2,220.4

1,018.0
1,048.7
1,141.1
1,228.7
1,267.2
1,264.3
1,233.7
1,206.9
1,198.1
1,193.9
1,224.0
1,251.6
1,257.2
1,271.0
1,308.4
1,332.8
1,358.8
1,371.2
1,395.3
1,446.3
1,494.9
1,599.0
1,696.2
1,737.1
1,758.9
1,806.8
1,864.0
1,884.4
1,893.2
1,878.2
1,878.0
1,888.9
1,907.9
1,943.8
1,985.0
2,056.1
2,097.8
2,178.3
2,279.6
2,330.5
2,362.0
2,369.9
2,402.1
2,434.2
2,497.4
2,589.4
2,605.8
2,523.9
2,481.3
2,531.6
2,590.4
2,614.3
2,621.1
2,600.4
2,618.7
2,616.7
2,587.4
2,540.7
2,535.4
2,516.6
2,502.7
2,483.7
2,479.4
2,503.1
2,458.9

Total
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704.1
696.0
689.1
681.4
694.6
698.1
726.5
779.5
831.1
865.0
876.3
894.9
906.1
971.1
1,030.6
1,076.8
1,047.0
1,024.1
995.8
1,028.2
1,043.9
1,054.6
1,056.2
1,081.0
1,090.7
1,079.4
1,050.4
1,057.5
1,045.9
1,034.2
1,023.1
1,022.5
1,045.9
1,005.0

National Nondefense defense
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476.8
470.4
457.2
447.5
455.8
453.5
470.7
505.3
549.2
580.4
589.0
598.4
611.8
657.7
696.9
717.6
699.1
677.3
670.8
696.3
709.1
711.4
704.8
717.3
729.9
718.6
691.3
705.2
709.8
690.1
677.6
677.3
698.1
656.0

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227.5
225.7
231.9
233.7
238.7
244.4
255.5
273.9
281.7
284.6
287.3
296.6
294.2
313.3
333.7
359.2
347.9
347.0
325.0
331.8
334.7
343.2
351.5
363.7
360.8
360.8
359.3
352.3
335.9
344.1
345.6
345.3
347.8
349.3

State
and
local
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1,183.6
1,211.1
1,254.3
1,303.8
1,361.8
1,400.1
1,452.3
1,500.6
1,499.7
1,497.1
1,493.6
1,507.2
1,528.1
1,528.1
1,561.8
1,534.1
1,482.0
1,461.9
1,538.3
1,565.2
1,573.6
1,570.2
1,548.3
1,542.7
1,531.6
1,513.6
1,495.3
1,483.4
1,475.9
1,473.3
1,465.3
1,461.6
1,462.7
1,458.0

AddenFinal
Gross
dum:
sales of domestic
Gross
domespurnational
tic
1
prodproduct chases
uct 2
3,390.8
3,587.6
3,803.4
3,920.0
4,115.8
4,245.0
4,284.3
4,403.6
4,636.7
4,884.0
4,870.0
4,922.1
5,115.9
5,340.3
5,634.9
5,836.2
5,873.6
5,954.4
5,918.2
6,167.6
6,490.0
6,833.1
7,092.7
7,289.9
7,601.3
7,860.8
8,025.8
8,027.9
8,277.2
8,508.0
8,801.7
9,065.4
9,404.4
9,774.2
10,208.3
10,706.5
11,158.0
11,382.0
11,533.6
11,820.5
12,181.3
12,573.0
12,899.3
13,177.5
13,200.5
12,899.7
13,010.3
13,265.3
13,537.5
12,870.3
12,890.0
12,928.3
12,910.2
12,914.7
12,985.4
13,005.5
13,135.6
13,154.4
13,234.1
13,311.2
13,361.4
13,440.1
13,497.9
13,577.4
13,634.7

3,423.4
3,656.1
3,907.0
4,014.8
4,222.1
4,355.0
4,348.3
4,503.1
4,751.8
4,987.0
4,922.1
4,867.9
5,184.8
5,459.8
5,758.4
5,898.3
5,784.8
5,939.7
5,860.4
6,203.1
6,739.7
7,039.4
7,297.2
7,512.1
7,752.2
7,984.2
8,097.8
8,027.8
8,302.7
8,585.7
8,968.5
9,181.3
9,534.0
9,984.4
10,531.1
11,131.8
11,671.6
11,815.8
12,097.5
12,444.7
12,935.5
13,345.7
13,688.1
13,855.3
13,653.1
13,102.3
13,473.0
13,698.8
13,984.4
13,103.7
13,014.4
13,082.0
13,209.3
13,309.3
13,438.9
13,553.4
13,590.5
13,592.1
13,655.2
13,696.4
13,851.4
13,914.4
13,948.5
14,039.3
14,035.5

3,417.5
3,636.4
3,869.8
3,967.7
4,160.6
4,288.0
4,295.8
4,442.2
4,678.9
4,960.3
4,939.8
4,917.2
5,186.8
5,429.1
5,728.4
5,925.2
5,908.3
6,047.3
5,934.0
6,197.1
6,634.1
6,888.0
7,110.4
7,335.9
7,643.9
7,917.3
8,075.0
8,048.8
8,319.4
8,556.0
8,893.0
9,121.7
9,463.1
9,873.4
10,295.3
10,802.9
11,259.2
11,395.0
11,597.1
11,909.9
12,341.6
12,720.1
13,028.3
13,322.0
13,316.9
12,889.0
13,253.4
13,522.0
�������������
12,819.5
12,806.8
12,895.3
13,034.5
13,121.9
13,216.5
13,301.1
13,374.2
13,394.3
13,486.1
13,534.7
13,672.9
13,693.8
13,763.6
13,862.9
�������������

Percent change
from preceding
period
Gross
Gross
domes- domestic
tic
purproduct chases
1
5.8
6.4
6.5
2.5
4.8
3.1
.2
3.4
5.3
5.8
–.6
–.2
5.4
4.6
5.6
3.1
–.3
2.5
–1.9
4.5
7.2
4.1
3.5
3.2
4.1
3.6
1.9
–.2
3.4
2.9
4.1
2.5
3.7
4.5
4.4
4.8
4.1
1.1
1.8
2.5
3.5
3.1
2.7
1.9
–.3
–3.1
2.4
1.8
2.2
–5.3
–.3
1.4
4.0
2.3
2.2
2.6
2.4
.1
2.5
1.3
4.1
2.0
1.3
3.1
.1

5.5
6.8
6.9
2.8
5.2
3.1
–.2
3.6
5.5
5.0
–1.3
–1.1
6.5
5.3
5.5
2.4
–1.9
2.7
–1.3
5.8
8.7
4.4
3.7
2.9
3.2
3.0
1.4
–.9
3.4
3.4
4.5
2.4
3.8
4.7
5.5
5.7
4.8
1.2
2.4
2.9
3.9
3.2
2.6
1.2
–1.5
–4.0
2.8
1.7
2.1
–7.3
–2.7
2.1
4.0
3.1
3.9
3.5
1.1
.0
1.9
1.2
4.6
1.8
1.0
2.6
–.1

1 Gross domestic product (GDP) less exports of goods and services plus imports of goods and services.
2 GDP plus net income receipts from rest of the world.

Source: Department of Commerce (Bureau of Economic Analysis).

National Income or Expenditure | 325

Table B–3. Quantity and price indexes for gross domestic product, and percent changes,
1964–2012
[Quarterly data are seasonally adjusted]
Percent change from preceding period 1

Index numbers, 2005=100
Gross domestic product (GDP)
Year or quarter

1964 ����������������������
1965 ����������������������
1966 ����������������������
1967 ����������������������
1968 ����������������������
1969 ����������������������
1970 ����������������������
1971 ����������������������
1972 ����������������������
1973 ����������������������
1974 ����������������������
1975 ����������������������
1976 ����������������������
1977 ����������������������
1978 ����������������������
1979 ����������������������
1980 ����������������������
1981 ����������������������
1982 ����������������������
1983 ����������������������
1984 ����������������������
1985 ����������������������
1986 ����������������������
1987 ����������������������
1988 ����������������������
1989 ����������������������
1990 ����������������������
1991 ����������������������
1992 ����������������������
1993 ����������������������
1994 ����������������������
1995 ����������������������
1996 ����������������������
1997 ����������������������
1998 ����������������������
1999 ����������������������
2000 ����������������������
2001 ����������������������
2002 ����������������������
2003 ����������������������
2004 ����������������������
2005 ����������������������
2006 ����������������������
2007 ����������������������
2008 ����������������������
2009 ����������������������
2010 ����������������������
2011 ����������������������
2012 p ��������������������
2009: I ������������������
      II �����������������
      III ����������������
      IV ����������������
2010: I ������������������
      II �����������������
      III ����������������
      IV ����������������
2011: I ������������������
      II �����������������
      III ����������������
      IV ����������������
2012: I ������������������
      II �����������������
      III ����������������
      IV p �������������

Real GDP
GDP
(chain-type chain-type
quantity price index
index)
26.851
28.575
30.437
31.206
32.717
33.733
33.798
34.932
36.788
38.920
38.705
38.623
40.695
42.566
44.940
46.345
46.217
47.390
46.470
48.570
52.060
54.214
56.092
57.887
60.266
62.420
63.591
63.442
65.595
67.466
70.214
71.980
74.672
78.000
81.397
85.326
88.857
89.816
91.445
93.769
97.021
100.000
102.658
104.622
104.270
101.069
103.486
105.356
107.670
100.697
100.618
100.980
101.981
102.572
103.142
103.807
104.423
104.443
105.084
105.418
106.481
106.999
107.333
108.156
108.190

19.589
19.945
20.511
21.142
22.040
23.130
24.349
25.567
26.670
28.148
30.695
33.606
35.535
37.796
40.447
43.811
47.817
52.326
55.514
57.705
59.874
61.686
63.057
64.818
67.047
69.579
72.274
74.826
76.602
78.288
79.935
81.602
83.154
84.627
85.580
86.840
88.724
90.731
92.192
94.134
96.784
100.000
103.237
106.231
108.565
109.532
111.002
113.369
115.382
109.526
109.318
109.463
109.820
110.234
110.686
111.248
111.838
112.389
113.109
113.937
114.041
114.608
115.050
115.807
116.063

GDP
implicit
price
deflator
19.580
19.936
20.502
21.133
22.031
23.119
24.338
25.554
26.657
28.136
30.690
33.591
35.519
37.783
40.435
43.798
47.791
52.270
55.459
57.652
59.817
61.628
62.991
64.819
67.046
69.577
72.262
74.824
76.598
78.290
79.940
81.606
83.159
84.628
85.584
86.842
88.723
90.727
92.196
94.135
96.786
100.000
103.231
106.227
108.582
109.529
110.993
113.359
115.381
109.539
109.325
109.457
109.793
110.216
110.706
111.238
111.795
112.372
113.109
113.950
113.987
114.599
115.035
115.810
116.068

Personal consumption
expenditures (PCE)

PCE
Real GDP
GDP
PCE
food (chain-type chain-type
chain-type lessenergy
quantity price index
price index and
price index
index)
19.536
19.819
20.322
20.834
21.645
22.626
23.685
24.692
25.536
26.913
29.716
32.198
33.966
36.171
38.705
42.137
46.663
50.833
53.640
55.948
58.065
59.965
61.427
63.618
66.151
69.025
72.180
74.789
76.989
78.679
80.302
82.078
83.864
85.433
86.246
87.636
89.818
91.530
92.778
94.658
97.121
100.000
102.723
105.499
108.943
109.004
111.087
113.790
115.784
108.063
108.496
109.315
110.142
110.642
110.800
111.154
111.751
112.640
113.633
114.293
114.593
115.300
115.496
115.952
116.389

1 Quarterly percent changes are at annual rates.

Source: Department of Commerce (Bureau of Economic Analysis).

326 |

Appendix B

20.091
20.345
20.805
21.442
22.362
23.412
24.510
25.664
26.493
27.505
29.687
32.174
34.130
36.320
38.749
41.569
45.377
49.342
52.526
55.247
57.541
59.724
61.974
64.331
67.120
69.889
72.872
75.709
78.256
80.106
81.875
83.761
85.386
87.022
88.284
89.597
91.154
92.783
94.390
95.823
97.815
100.000
102.265
104.631
107.020
108.536
110.214
111.802
113.704
107.827
108.285
108.694
109.339
109.739
110.121
110.395
110.602
110.973
111.599
112.138
112.500
113.122
113.603
113.912
114.181

Personal consumption
expenditures (PCE)

Gross domestic product (GDP)

5.8
6.4
6.5
2.5
4.8
3.1
.2
3.4
5.3
5.8
–.6
–.2
5.4
4.6
5.6
3.1
–.3
2.5
–1.9
4.5
7.2
4.1
3.5
3.2
4.1
3.6
1.9
–.2
3.4
2.9
4.1
2.5
3.7
4.5
4.4
4.8
4.1
1.1
1.8
2.5
3.5
3.1
2.7
1.9
–.3
–3.1
2.4
1.8
2.2
–5.3
–.3
1.4
4.0
2.3
2.2
2.6
2.4
.1
2.5
1.3
4.1
2.0
1.3
3.1
.1

1.6
1.8
2.8
3.1
4.2
4.9
5.3
5.0
4.3
5.5
9.0
9.5
5.7
6.4
7.0
8.3
9.1
9.4
6.1
3.9
3.8
3.0
2.2
2.8
3.4
3.8
3.9
3.5
2.4
2.2
2.1
2.1
1.9
1.8
1.1
1.5
2.2
2.3
1.6
2.1
2.8
3.3
3.2
2.9
2.2
.9
1.3
2.1
1.8
1.0
–.8
.5
1.3
1.5
1.7
2.0
2.1
2.0
2.6
3.0
.4
2.0
1.6
2.7
.9

GDP
implicit
price
deflator
1.6
1.8
2.8
3.1
4.2
4.9
5.3
5.0
4.3
5.5
9.1
9.5
5.7
6.4
7.0
8.3
9.1
9.4
6.1
4.0
3.8
3.0
2.2
2.9
3.4
3.8
3.9
3.5
2.4
2.2
2.1
2.1
1.9
1.8
1.1
1.5
2.2
2.3
1.6
2.1
2.8
3.3
3.2
2.9
2.2
.9
1.3
2.1
1.8
.9
–.8
.5
1.2
1.6
1.8
1.9
2.0
2.1
2.6
3.0
.1
2.2
1.5
2.7
.9

PCE
chain-type
price index
1.5
1.4
2.5
2.5
3.9
4.5
4.7
4.3
3.4
5.4
10.4
8.4
5.5
6.5
7.0
8.9
10.7
8.9
5.5
4.3
3.8
3.3
2.4
3.6
4.0
4.3
4.6
3.6
2.9
2.2
2.1
2.2
2.2
1.9
1.0
1.6
2.5
1.9
1.4
2.0
2.6
3.0
2.7
2.7
3.3
.1
1.9
2.4
1.8
–2.1
1.6
3.1
3.1
1.8
.6
1.3
2.2
3.2
3.6
2.3
1.1
2.5
.7
1.6
1.5

PCE
less food
and energy
price index
1.5
1.3
2.3
3.1
4.3
4.7
4.7
4.7
3.2
3.8
7.9
8.4
6.1
6.4
6.7
7.3
9.2
8.7
6.5
5.2
4.2
3.8
3.8
3.8
4.3
4.1
4.3
3.9
3.4
2.4
2.2
2.3
1.9
1.9
1.5
1.5
1.7
1.8
1.7
1.5
2.1
2.2
2.3
2.3
2.3
1.4
1.5
1.4
1.7
.7
1.7
1.5
2.4
1.5
1.4
1.0
.8
1.3
2.3
1.9
1.3
2.2
1.7
1.1
.9

Table B–4. Percent changes in real gross domestic product, 1964–2012
[Percent change from preceding period; quarterly data at seasonally adjusted annual rates]
Personal consumption
expenditures
Year or quarter

1964 ���������������������
1965 ���������������������
1966 ���������������������
1967 ���������������������
1968 ���������������������
1969 ���������������������
1970 ���������������������
1971 ���������������������
1972 ���������������������
1973 ���������������������
1974 ���������������������
1975 ���������������������
1976 ���������������������
1977 ���������������������
1978 ���������������������
1979 ���������������������
1980 ���������������������
1981 ���������������������
1982 ���������������������
1983 ���������������������
1984 ���������������������
1985 ���������������������
1986 ���������������������
1987 ���������������������
1988 ���������������������
1989 ���������������������
1990 ���������������������
1991 ���������������������
1992 ���������������������
1993 ���������������������
1994 ���������������������
1995 ���������������������
1996 ���������������������
1997 ���������������������
1998 ���������������������
1999 ���������������������
2000 ���������������������
2001 ���������������������
2002 ���������������������
2003 ���������������������
2004 ���������������������
2005 ���������������������
2006 ���������������������
2007 ���������������������
2008 ���������������������
2009 ���������������������
2010 ���������������������
2011 ���������������������
2012 p �������������������
2009: I �����������������
      II ����������������
      III ���������������
      IV ���������������
2010: I �����������������
      II ����������������
      III ���������������
      IV ���������������
2011: I �����������������
      II ����������������
      III ���������������
      IV ���������������
2012: I �����������������
      II ����������������
      III ���������������
      IV p ������������

Gross
domestic
product

5.8
6.4
6.5
2.5
4.8
3.1
.2
3.4
5.3
5.8
–.6
–.2
5.4
4.6
5.6
3.1
–.3
2.5
–1.9
4.5
7.2
4.1
3.5
3.2
4.1
3.6
1.9
–.2
3.4
2.9
4.1
2.5
3.7
4.5
4.4
4.8
4.1
1.1
1.8
2.5
3.5
3.1
2.7
1.9
–.3
–3.1
2.4
1.8
2.2
–5.3
–.3
1.4
4.0
2.3
2.2
2.6
2.4
.1
2.5
1.3
4.1
2.0
1.3
3.1
.1

Gross private domestic investment

Exports and
imports of goods
and services

Government consumption
expenditures and gross
investment

Exports

Imports

Total

11.8
2.8
6.9
2.3
7.9
4.8
10.7
1.7
7.5
18.9
7.9
–.6
4.4
2.4
10.5
9.9
10.8
1.2
–7.6
–2.6
8.2
3.0
7.7
10.8
16.0
11.5
9.0
6.6
6.9
3.3
8.7
10.1
8.3
11.9
2.3
4.4
8.6
–5.6
–2.0
1.6
9.5
6.7
9.0
9.3
6.1
–9.1
11.1
6.7
3.3
–28.7
.6
13.8
24.0
5.9
9.6
9.7
10.0
5.7
4.1
6.1
1.4
4.4
5.3
1.9
–3.9

5.3
10.6
14.9
7.3
14.9
5.7
4.3
5.3
11.3
4.6
–2.3
–11.1
19.6
10.9
8.7
1.7
–6.6
2.6
–1.3
12.6
24.3
6.5
8.5
5.9
3.9
4.4
3.6
–.2
7.0
8.6
11.9
8.0
8.7
13.5
11.7
11.5
13.0
–2.8
3.4
4.4
11.1
6.1
6.1
2.4
–2.7
–13.5
12.5
4.8
2.4
–33.9
–15.9
17.2
19.3
10.4
20.2
13.9
.0
4.3
.1
4.7
4.9
3.1
2.8
–.6
–4.5

Nonresidential fixed
Total

6.0
6.3
5.7
3.0
5.8
3.7
2.3
3.8
6.1
5.0
–.8
2.3
5.6
4.2
4.4
2.4
–.4
1.5
1.4
5.7
5.3
5.2
4.1
3.1
4.0
2.8
2.0
.2
3.4
3.6
3.8
2.7
3.5
3.7
5.2
5.5
5.1
2.7
2.7
2.8
3.3
3.4
2.9
2.3
–.6
–1.9
1.8
2.5
1.9
–1.6
–1.8
2.1
.0
2.5
2.6
2.5
4.1
3.1
1.0
1.7
2.0
2.4
1.5
1.6
2.1

Goods

6.0
7.1
6.3
2.0
6.2
3.1
.8
4.2
6.5
5.2
–3.6
.7
7.0
4.3
4.1
1.6
–2.5
1.2
.7
6.4
7.2
5.3
5.6
1.8
3.7
2.5
.6
–2.0
3.2
4.2
5.3
3.0
4.5
4.8
6.8
8.0
5.3
3.1
4.1
4.6
4.5
4.2
3.3
3.0
–2.5
–3.0
3.6
3.8
3.1
.2
–2.1
7.5
–.5
5.2
3.3
3.8
7.9
5.4
–1.0
1.4
5.4
4.7
.3
3.6
4.3

Services

6.0
5.5
5.0
4.1
5.3
4.5
3.9
3.5
5.8
4.7
1.9
3.8
4.3
4.1
4.7
3.1
1.5
1.8
1.9
5.2
3.9
5.2
3.0
4.0
4.2
3.0
3.0
1.5
3.6
3.2
3.0
2.5
2.9
3.1
4.4
4.1
5.0
2.5
1.9
1.9
2.7
3.0
2.6
1.9
.4
–1.4
1.0
1.9
1.3
–2.5
–1.6
–.4
.2
1.2
2.3
1.9
2.3
2.0
1.9
1.8
.3
1.3
2.1
.6
.9

Total

11.9
17.4
12.5
–1.3
4.5
7.6
–.5
.0
9.2
14.5
.8
–9.9
4.9
11.3
15.0
10.1
–.3
5.7
–3.8
–1.3
17.6
6.6
–2.9
–.1
5.2
5.6
.5
–5.4
3.2
8.7
9.2
10.5
9.3
12.1
12.0
10.4
9.8
–2.8
–7.9
1.4
6.2
6.7
8.0
6.5
–.8
–18.1
.7
8.6
7.7
–28.9
–17.5
–7.8
–6.4
2.1
12.3
7.7
9.2
–1.3
14.5
19.0
9.5
7.5
3.6
–1.8
9.7

Structures
10.4
15.9
6.8
–2.5
1.4
5.4
.3
–1.6
3.1
8.2
–2.2
–10.5
2.4
4.1
14.4
12.7
5.9
8.0
–1.6
–10.8
13.9
7.1
–11.0
–2.9
.7
2.0
1.5
–11.1
–6.0
–.6
1.8
6.4
5.7
7.3
5.1
.1
7.8
–1.5
–17.7
–3.8
1.1
1.4
9.2
14.1
6.4
–21.1
–15.6
2.7
10.1
–30.5
–31.4
–26.7
–28.8
–23.0
13.1
–2.2
9.3
–28.2
35.2
20.7
11.5
12.9
.6
.0
5.8

Residential
fixed

Equipment
and
software
12.8
18.3
16.0
–.7
6.2
8.8
–1.0
1.0
12.9
18.3
2.6
–9.5
6.3
15.1
15.2
8.7
–3.6
4.3
–5.2
5.4
19.8
6.4
1.9
1.4
7.5
7.3
.0
–2.6
7.3
12.5
11.9
12.0
10.6
13.8
14.5
14.1
10.5
–3.2
–4.2
3.1
7.9
8.5
7.6
3.3
–4.3
–16.4
8.9
11.0
6.9
–27.9
–8.6
3.6
6.0
14.7
12.0
11.9
9.2
11.1
7.8
18.3
8.8
5.4
4.8
–2.6
11.3

5.8
–2.9
–8.9
–3.1
13.6
3.0
–6.0
27.4
17.8
–.6
–20.6
–13.0
23.5
21.5
6.3
–3.7
–21.2
–8.0
–18.2
41.4
14.8
1.6
12.3
2.0
–1.0
–3.0
–8.6
–9.6
13.8
8.2
9.7
–3.3
8.0
1.9
7.7
6.3
1.0
.6
5.2
8.2
9.8
6.2
–7.3
–18.7
–23.9
–22.4
–3.7
–1.4
12.1
–35.1
–22.2
17.2
–4.8
–11.4
23.1
–28.6
1.5
–1.4
4.1
1.4
12.1
20.5
8.5
13.5
17.5

2.2
3.0
8.8
7.7
3.1
–.2
–2.4
–2.2
–.7
–.4
2.5
2.3
.4
1.1
2.9
1.9
1.9
.9
1.8
3.7
3.4
7.0
6.1
2.4
1.3
2.7
3.2
1.1
.5
–.8
.0
.6
1.0
1.9
2.1
3.6
2.0
3.8
4.7
2.2
1.4
.3
1.4
1.3
2.6
3.7
.6
–3.1
–1.7
1.8
9.6
3.7
1.1
–3.1
2.8
–.3
–4.4
–7.0
–.8
–2.9
–2.2
–3.0
–.7
3.9
–6.9

Federal

–1.3
.0
11.1
10.0
.8
–3.4
–7.4
–7.7
–4.1
–4.2
.9
.3
.0
2.1
2.5
2.4
4.7
4.8
3.9
6.6
3.1
7.8
5.7
3.6
–1.6
1.6
2.0
–.2
–1.8
–3.9
–3.8
–2.7
–1.2
–1.0
–1.1
1.9
.5
4.1
7.3
6.6
4.1
1.3
2.1
1.2
7.2
6.1
4.5
–2.8
–2.2
–3.0
13.7
6.3
4.2
.6
9.7
3.7
–4.1
–10.3
2.8
–4.3
–4.4
–4.2
–.2
9.5
–14.8

State
and
local

6.8
6.7
6.3
5.1
5.9
3.4
2.8
3.1
2.2
2.9
3.8
3.7
.7
.4
3.3
1.5
–.1
–2.0
.0
1.2
3.6
6.2
6.4
1.4
3.7
3.7
4.1
2.1
2.2
1.5
2.6
2.7
2.3
3.6
3.9
4.5
2.8
3.7
3.3
–.1
–.2
–.2
.9
1.4
.0
2.2
–1.8
–3.4
–1.4
4.9
7.2
2.2
–.9
–5.5
–1.4
–2.9
–4.6
–4.7
–3.2
–2.0
–.7
–2.2
–1.0
.3
–1.3

Note: Percent changes based on unrounded data.
Source: Department of Commerce (Bureau of Economic Analysis).

National Income or Expenditure | 327

Table B–5. Contributions to percent change in real gross domestic product, 1964–2012
[Percentage points, except as noted; quarterly data at seasonally adjusted annual rates]
Personal consumption expenditures

Year or quarter

1964 ����������������������
1965 ����������������������
1966 ����������������������
1967 ����������������������
1968 ����������������������
1969 ����������������������
1970 ����������������������
1971 ����������������������
1972 ����������������������
1973 ����������������������
1974 ����������������������
1975 ����������������������
1976 ����������������������
1977 ����������������������
1978 ����������������������
1979 ����������������������
1980 ����������������������
1981 ����������������������
1982 ����������������������
1983 ����������������������
1984 ����������������������
1985 ����������������������
1986 ����������������������
1987 ����������������������
1988 ����������������������
1989 ����������������������
1990 ����������������������
1991 ����������������������
1992 ����������������������
1993 ����������������������
1994 ����������������������
1995 ����������������������
1996 ����������������������
1997 ����������������������
1998 ����������������������
1999 ����������������������
2000 ����������������������
2001 ����������������������
2002 ����������������������
2003 ����������������������
2004 ����������������������
2005 ����������������������
2006 ����������������������
2007 ����������������������
2008 ����������������������
2009 ����������������������
2010 ����������������������
2011 ����������������������
2012 p ��������������������
2009: I ������������������
      II �����������������
      III ����������������
      IV ����������������
2010: I ������������������
      II �����������������
      III ����������������
      IV ����������������
2011: I ������������������
      II �����������������
      III ����������������
      IV ����������������
2012: I ������������������
      II �����������������
      III ����������������
      IV p �������������

Gross
domestic
product
(percent
change)

5.8
6.4
6.5
2.5
4.8
3.1
.2
3.4
5.3
5.8
–.6
–.2
5.4
4.6
5.6
3.1
–.3
2.5
–1.9
4.5
7.2
4.1
3.5
3.2
4.1
3.6
1.9
–.2
3.4
2.9
4.1
2.5
3.7
4.5
4.4
4.8
4.1
1.1
1.8
2.5
3.5
3.1
2.7
1.9
–.3
–3.1
2.4
1.8
2.2
–5.3
–.3
1.4
4.0
2.3
2.2
2.6
2.4
.1
2.5
1.3
4.1
2.0
1.3
3.1
.1

Fixed investment
Total

3.69
3.91
3.50
1.82
3.51
2.29
1.44
2.37
3.81
3.08
–.52
1.40
3.51
2.66
2.77
1.48
–.22
.95
.86
3.65
3.43
3.32
2.62
2.01
2.64
1.86
1.34
.10
2.27
2.37
2.57
1.81
2.35
2.48
3.50
3.68
3.44
1.85
1.85
1.97
2.30
2.35
1.98
1.60
–.39
–1.36
1.28
1.79
1.33
–1.06
–1.21
1.50
–.01
1.72
1.81
1.75
2.84
2.22
.70
1.18
1.45
1.72
1.06
1.12
1.47

See next page for continuation of table.

328 |

Appendix B

Gross private domestic investment

Goods

1.91
2.26
2.02
.62
1.92
.95
.24
1.27
1.97
1.57
–1.12
.20
2.08
1.28
1.22
.47
–.74
.34
.19
1.74
1.97
1.41
1.49
.48
.98
.66
.16
–.51
.78
1.02
1.29
.73
1.09
1.16
1.61
1.90
1.29
.77
.99
1.12
1.09
1.01
.80
.71
–.59
–.69
.82
.89
.74
.06
–.46
1.68
–.10
1.18
.76
.86
1.78
1.27
–.22
.33
1.29
1.11
.08
.85
1.03

Services

1.78
1.66
1.48
1.21
1.59
1.34
1.19
1.10
1.84
1.51
.60
1.20
1.43
1.38
1.56
1.02
.52
.62
.67
1.91
1.47
1.90
1.13
1.53
1.66
1.20
1.18
.61
1.49
1.35
1.27
1.08
1.26
1.33
1.90
1.78
2.15
1.09
.86
.85
1.22
1.34
1.18
.89
.21
–.67
.46
.90
.60
–1.12
–.75
–.18
.09
.54
1.05
.88
1.06
.95
.92
.85
.16
.61
.99
.26
.44

Total

1.25
2.16
1.44
–.76
.90
.90
–1.04
1.67
1.87
1.96
–1.31
–2.98
2.84
2.43
2.16
.61
–2.12
1.55
–2.55
1.45
4.63
–.17
–.12
.51
.39
.64
–.53
–1.20
1.07
1.21
1.94
.48
1.35
1.95
1.65
1.50
1.19
–1.24
–.22
.60
1.57
.93
.47
–.56
–1.66
–3.59
1.50
.62
1.17
–7.02
–3.52
–.14
3.85
2.13
1.65
1.87
–.75
–.68
1.40
.68
3.72
.78
.09
.85
–.20

Nonresidential
Total

1.37
1.50
.87
–.28
.99
.90
–.31
1.10
1.81
1.47
–1.04
–1.71
1.42
2.18
2.04
1.02
–1.21
.39
–1.21
1.17
2.68
.89
.20
.09
.53
.47
–.32
–.94
.79
1.14
1.30
.94
1.33
1.41
1.70
1.52
1.24
–.32
–.70
.54
1.15
1.05
.40
–.33
–1.15
–2.80
–.03
.76
1.03
–4.73
–2.49
–.32
–.69
–.10
1.58
–.10
.87
–.14
1.39
1.75
1.19
1.18
.56
.12
1.36

Total
1.07
1.65
1.29
–.15
.46
.78
–.06
.00
.93
1.50
.09
–1.14
.52
1.19
1.69
1.23
–.03
.74
–.50
–.17
2.05
.82
–.36
–.01
.58
.61
.05
–.57
.31
.83
.91
1.08
1.01
1.33
1.38
1.24
1.20
–.35
–.94
.14
.63
.69
.86
.73
–.09
–2.08
.07
.80
.76
–3.54
–1.86
–.73
–.57
.20
1.07
.70
.83
–.11
1.30
1.71
.93
.74
.36
–.19
.96

EquipStructures ment and
software
0.36
.57
.27
–.10
.05
.20
.01
–.06
.12
.31
–.09
–.43
.09
.15
.54
.53
.27
.40
–.09
–.57
.60
.32
–.50
–.11
.02
.07
.05
–.39
–.18
–.02
.05
.17
.16
.21
.16
.00
.24
–.05
–.58
–.10
.03
.04
.27
.46
.24
–.85
–.50
.07
.27
–1.39
–1.31
–.98
–.98
–.70
.31
–.06
.23
–.84
.77
.51
.31
.35
.02
.00
.16

0.71
1.07
1.02
–.05
.41
.58
–.07
.07
.81
1.19
.18
–.70
.43
1.04
1.15
.71
–.30
.34
–.42
.41
1.45
.50
.15
.10
.55
.54
.00
–.18
.50
.85
.86
.91
.85
1.12
1.22
1.24
.96
–.30
–.36
.24
.60
.65
.59
.26
–.34
–1.23
.56
.72
.49
–2.16
–.54
.25
.40
.90
.76
.76
.60
.72
.53
1.20
.62
.39
.35
–.19
.79

Residential
0.30
–.15
–.43
–.13
.53
.13
–.26
1.10
.89
–.04
–1.13
–.57
.90
.99
.35
–.21
–1.17
–.35
–.71
1.33
.64
.07
.55
.10
–.05
–.14
–.37
–.37
.47
.31
.39
–.14
.33
.08
.32
.28
.05
.03
.24
.40
.52
.36
–.46
–1.05
–1.05
–.73
–.09
–.03
.27
–1.18
–.63
.40
–.12
–.30
.51
–.80
.03
–.03
.09
.03
.26
.43
.19
.31
.40

Change
in
private
inventories
–0.13
.66
.58
–.49
–.10
.00
–.73
.58
.06
.50
–.27
–1.27
1.41
.25
.12
–.41
–.91
1.16
–1.34
.29
1.95
–1.06
–.32
.42
–.14
.17
–.21
–.26
.29
.07
.63
–.46
.02
.54
–.05
–.02
–.05
–.92
.48
.06
.42
–.13
.07
–.23
–.51
–.78
1.52
–.14
.14
–2.29
–1.03
.19
4.55
2.23
.07
1.97
–1.61
–.54
.01
–1.07
2.53
–.39
–.46
.73
–1.55

Table B–5. Contributions to percent change in real gross domestic product,
1964–2012—Continued
[Percentage points, except as noted; quarterly data at seasonally adjusted annual rates]
Government consumption expenditures
and gross investment

Net exports of goods and services
Year or quarter

1964 ����������������������
1965 ����������������������
1966 ����������������������
1967 ����������������������
1968 ����������������������
1969 ����������������������
1970 ����������������������
1971 ����������������������
1972 ����������������������
1973 ����������������������
1974 ����������������������
1975 ����������������������
1976 ����������������������
1977 ����������������������
1978 ����������������������
1979 ����������������������
1980 ����������������������
1981 ����������������������
1982 ����������������������
1983 ����������������������
1984 ����������������������
1985 ����������������������
1986 ����������������������
1987 ����������������������
1988 ����������������������
1989 ����������������������
1990 ����������������������
1991 ����������������������
1992 ����������������������
1993 ����������������������
1994 ����������������������
1995 ����������������������
1996 ����������������������
1997 ����������������������
1998 ����������������������
1999 ����������������������
2000 ����������������������
2001 ����������������������
2002 ����������������������
2003 ����������������������
2004 ����������������������
2005 ����������������������
2006 ����������������������
2007 ����������������������
2008 ����������������������
2009 ����������������������
2010 ����������������������
2011 ����������������������
2012 p ��������������������
2009: I ������������������
      II �����������������
      III ����������������
      IV ����������������
2010: I ������������������
      II �����������������
      III ����������������
      IV ����������������
2011: I ������������������
      II �����������������
      III ����������������
      IV ����������������
2012: I ������������������
      II �����������������
      III ����������������
      IV p �������������

Net
exports
0.36
–.30
–.29
–.22
–.30
–.04
.34
–.19
–.21
.82
.75
.89
–1.08
–.72
.05
.66
1.68
–.15
–.60
–1.35
–1.58
–.42
–.30
.16
.82
.52
.43
.64
–.05
–.57
–.43
.11
–.15
–.32
–1.18
–.99
–.85
–.20
–.65
–.45
–.66
–.27
–.06
.62
1.21
1.14
–.52
.07
.03
2.45
2.47
–.70
–.05
–.83
–1.81
–.95
1.24
.03
.54
.02
–.64
.06
.23
.38
.24

Exports
Total
0.59
.15
.36
.12
.41
.25
.56
.10
.42
1.12
.58
–.05
.37
.20
.82
.82
.97
.12
–.73
–.22
.63
.23
.54
.77
1.24
.99
.81
.63
.68
.32
.85
1.03
.90
1.30
.26
.47
.91
–.61
–.20
.15
.90
.67
.93
1.03
.73
–1.14
1.29
.87
.46
–3.78
.10
1.48
2.55
.70
1.14
1.18
1.24
.75
.56
.83
.21
.60
.72
.27
–.55

Goods
0.52
.02
.27
.02
.30
.20
.44
–.02
.43
1.01
.46
–.16
.31
.08
.68
.77
.86
–.09
–.67
–.19
.46
.20
.26
.56
1.04
.75
.56
.46
.52
.23
.67
.85
.68
1.11
.18
.29
.82
–.48
–.25
.12
.56
.52
.68
.75
.53
–1.05
1.11
.65
.41
–3.29
–.17
1.46
2.14
.79
.97
.76
.96
.52
.35
.59
.58
.39
.67
.11
–.56

Imports
Services
0.07
.13
.09
.10
.10
.05
.12
.11
–.01
.11
.12
.10
.05
.11
.15
.06
.11
.21
–.06
–.03
.17
.02
.28
.21
.20
.24
.26
.16
.16
.10
.19
.19
.22
.19
.08
.18
.08
–.13
.05
.03
.34
.15
.25
.28
.20
–.10
.18
.22
.05
–.49
.27
.02
.42
–.09
.17
.41
.28
.23
.21
.25
–.38
.21
.05
.16
.00

Total
–0.23
–.45
–.65
–.34
–.71
–.29
–.22
–.29
–.63
–.29
.18
.94
–1.45
–.92
–.78
–.16
.71
–.27
.12
–1.13
–2.21
–.65
–.84
–.61
–.43
–.48
–.38
.02
–.72
–.90
–1.28
–.92
–1.04
–1.62
–1.43
–1.45
–1.76
.41
–.46
–.60
–1.55
–.95
–.98
–.40
.47
2.28
–1.81
–.80
–.43
6.24
2.37
–2.18
–2.60
–1.53
–2.95
–2.13
–.01
–.72
–.02
–.81
–.85
–.54
–.49
.11
.79

Goods
–0.19
–.41
–.49
–.17
–.68
–.20
–.15
–.33
–.57
–.34
.17
.87
–1.35
–.84
–.67
–.14
.67
–.18
.20
–1.01
–1.83
–.52
–.82
–.39
–.36
–.38
–.26
–.04
–.78
–.85
–1.18
–.86
–.94
–1.44
–1.21
–1.31
–1.52
.39
–.42
–.56
–1.29
–.87
–.81
–.37
.57
2.19
–1.74
–.72
–.31
5.68
2.22
–2.12
–2.55
–1.46
–2.92
–1.79
–.15
–.73
.10
–.43
–.90
–.29
–.42
.18
.60

Federal
Services
–0.04
–.04
–.16
–.16
–.03
–.09
–.07
.04
–.06
.05
.00
.07
–.10
–.07
–.11
–.02
.04
–.09
–.08
–.13
–.39
–.13
–.02
–.22
–.07
–.09
–.13
.05
.06
–.05
–.10
–.06
–.10
–.17
–.22
–.14
–.24
.02
–.04
–.04
–.26
–.07
–.18
–.04
–.10
.09
–.07
–.08
–.12
.56
.15
–.06
–.05
–.06
–.03
–.34
.15
.01
–.12
–.38
.05
–.25
–.07
–.07
.19

Total
0.49
.65
1.87
1.68
.73
–.05
–.55
–.50
–.16
–.08
.52
.48
.10
.23
.60
.37
.38
.19
.35
.76
.70
1.41
1.27
.51
.26
.55
.64
.22
.10
–.16
.00
.11
.19
.34
.38
.63
.36
.67
.84
.42
.26
.06
.26
.25
.50
.74
.14
–.67
–.34
.37
1.94
.79
.23
–.69
.59
–.06
–.94
–1.49
–.16
–.60
–.43
–.60
–.14
.75
–1.38

Total
–0.17
–.01
1.24
1.17
.10
–.42
–.86
–.85
–.42
–.41
.08
.03
.00
.19
.22
.20
.39
.42
.35
.63
.30
.74
.55
.35
–.16
.14
.18
–.02
–.16
–.33
–.30
–.20
–.08
–.07
–.07
.12
.03
.24
.44
.43
.28
.09
.15
.09
.50
.46
.37
–.23
–.18
–.23
1.04
.51
.34
.04
.78
.31
–.35
–.89
.23
–.36
–.35
–.34
–.02
.71
–1.23

National
defense

Nondefense

–0.39
–.19
1.21
1.19
.16
–.49
–.83
–.97
–.60
–.39
–.05
–.06
–.02
.07
.05
.17
.25
.38
.48
.50
.35
.60
.47
.35
–.03
–.03
.00
–.07
–.32
–.31
–.27
–.19
–.06
–.13
–.09
.07
–.02
.14
.28
.36
.26
.07
.07
.11
.36
.31
.17
–.15
–.17
–.37
.83
.42
.07
–.22
.40
.40
–.35
–.84
.45
.15
–.60
–.39
–.01
.64
–1.28

0.23
.19
.03
–.02
–.06
.06
–.03
.12
.18
–.02
.13
.09
.03
.12
.16
.03
.14
.04
–.13
.13
–.05
.14
.08
.00
–.12
.17
.18
.05
.16
–.02
–.04
–.01
–.02
.06
.02
.04
.05
.09
.15
.07
.02
.02
.07
–.02
.15
.16
.20
–.09
–.01
.14
.21
.09
.27
.26
.38
–.09
.00
–.05
–.22
–.51
.25
.05
–.01
.08
.04

State
and
local
0.65
.66
.63
.51
.63
.37
.31
.36
.26
.33
.44
.45
.09
.04
.38
.17
–.01
–.23
.01
.13
.40
.67
.71
.17
.42
.41
.46
.24
.26
.17
.30
.30
.27
.41
.45
.51
.33
.43
.40
–.01
–.02
–.03
.11
.17
.00
.28
–.23
–.43
–.16
.60
.90
.28
–.12
–.73
–.19
–.37
–.59
–.60
–.39
–.24
–.08
–.26
–.12
.04
–.15

Source: Department of Commerce (Bureau of Economic Analysis).

National Income or Expenditure | 329

Table B–6. Chain-type quantity indexes for gross domestic product, 1964–2012
[Index numbers, 2005=100; quarterly data seasonally adjusted]
Personal consumption expenditures

Year or quarter

1964 ����������������������
1965 ����������������������
1966 ����������������������
1967 ����������������������
1968 ����������������������
1969 ����������������������
1970 ����������������������
1971 ����������������������
1972 ����������������������
1973 ����������������������
1974 ����������������������
1975 ����������������������
1976 ����������������������
1977 ����������������������
1978 ����������������������
1979 ����������������������
1980 ����������������������
1981 ����������������������
1982 ����������������������
1983 ����������������������
1984 ����������������������
1985 ����������������������
1986 ����������������������
1987 ����������������������
1988 ����������������������
1989 ����������������������
1990 ����������������������
1991 ����������������������
1992 ����������������������
1993 ����������������������
1994 ����������������������
1995 ����������������������
1996 ����������������������
1997 ����������������������
1998 ����������������������
1999 ����������������������
2000 ����������������������
2001 ����������������������
2002 ����������������������
2003 ����������������������
2004 ����������������������
2005 ����������������������
2006 ����������������������
2007 ����������������������
2008 ����������������������
2009 ����������������������
2010 ����������������������
2011 ����������������������
2012 p ��������������������
2009: I ������������������
      II �����������������
      III ����������������
      IV ����������������
2010: I ������������������
      II �����������������
      III ����������������
      IV ����������������
2011: I ������������������
      II �����������������
      III ����������������
      IV ����������������
2012: I ������������������
      II �����������������
      III ����������������
      IV p �������������

Gross
domestic
product

26.851
28.575
30.437
31.206
32.717
33.733
33.798
34.932
36.788
38.920
38.705
38.623
40.695
42.566
44.940
46.345
46.217
47.390
46.470
48.570
52.060
54.214
56.092
57.887
60.266
62.420
63.591
63.442
65.595
67.466
70.214
71.980
74.672
78.000
81.397
85.326
88.857
89.816
91.445
93.769
97.021
100.000
102.658
104.622
104.270
101.069
103.486
105.356
107.670
100.697
100.618
100.980
101.981
102.572
103.142
103.807
104.423
104.443
105.084
105.418
106.481
106.999
107.333
108.156
108.190

Fixed investment
Total

23.939
25.453
26.897
27.703
29.301
30.399
31.112
32.297
34.283
35.982
35.683
36.492
38.525
40.146
41.916
42.912
42.761
43.410
44.015
46.531
48.998
51.551
53.642
55.297
57.525
59.152
60.359
60.450
62.511
64.731
67.203
69.021
71.429
74.066
77.950
82.213
86.382
88.718
91.080
93.650
96.731
100.000
102.850
105.218
104.637
102.602
104.460
107.103
109.103
102.681
102.224
102.757
102.747
103.377
104.042
104.685
105.736
106.559
106.812
107.251
107.790
108.443
108.849
109.276
109.843

See next page for continuation of table.

330 |

Appendix B

Gross private domestic investment

Goods

22.994
24.623
26.184
26.697
28.350
29.216
29.447
30.679
32.685
34.378
33.124
33.349
35.684
37.215
38.753
39.373
38.376
38.830
39.101
41.589
44.586
46.931
49.556
50.448
52.322
53.643
53.975
52.904
54.571
56.838
59.836
61.623
64.383
67.453
72.010
77.745
81.847
84.417
87.848
91.890
95.988
100.000
103.322
106.394
103.776
100.697
104.304
108.263
111.580
100.211
99.684
101.506
101.389
102.691
103.531
104.499
106.495
107.915
107.655
108.021
109.462
110.722
110.812
111.796
112.992

Services

23.885
25.204
26.453
27.541
29.009
30.303
31.487
32.574
34.458
36.091
36.783
38.164
39.802
41.447
43.375
44.700
45.389
46.203
47.103
49.568
51.508
54.173
55.784
58.007
60.469
62.301
64.151
65.110
67.431
69.589
71.666
73.488
75.640
77.973
81.409
84.744
88.944
91.134
92.870
94.611
97.134
100.000
102.599
104.599
105.067
103.558
104.554
106.543
107.879
103.924
103.494
103.385
103.429
103.729
104.310
104.795
105.380
105.903
106.412
106.886
106.970
107.318
107.882
108.031
108.286

Total

17.589
20.058
21.825
20.827
22.039
23.323
21.791
24.275
27.150
30.331
28.097
23.120
27.791
31.989
35.846
36.989
32.926
35.886
30.859
33.733
43.672
43.266
42.971
44.295
45.337
47.156
45.569
41.862
45.254
49.299
55.998
57.743
62.851
70.672
77.747
84.592
90.371
84.023
82.879
86.090
94.749
100.000
102.742
99.412
89.296
67.124
76.327
80.284
87.973
69.786
64.480
64.208
70.022
73.259
75.792
78.722
77.535
76.492
78.778
79.906
85.959
87.241
87.394
88.793
88.463

Nonresidential
Total

17.882
19.708
20.838
20.453
21.881
23.242
22.754
24.477
27.420
29.926
28.055
25.042
27.511
31.465
35.274
37.265
34.844
35.623
33.125
35.541
41.543
43.729
44.237
44.480
45.947
47.328
46.340
43.335
45.904
49.839
54.500
58.010
63.213
69.045
76.537
83.658
89.843
88.142
84.412
87.390
93.880
100.000
102.375
100.390
93.228
75.494
75.326
80.311
87.173
79.032
75.092
74.501
73.352
73.180
75.696
75.515
76.913
76.660
78.942
81.835
83.807
85.785
86.724
86.923
89.258

Total
13.701
16.088
18.100
17.856
18.654
20.070
19.963
19.964
21.797
24.968
25.177
22.689
23.800
26.486
30.450
33.517
33.429
35.333
34.003
33.563
39.486
42.103
40.901
40.870
43.008
45.409
45.633
43.186
44.565
48.456
52.915
58.478
63.940
71.658
80.264
88.640
97.327
94.614
87.112
88.290
93.740
100.000
108.027
115.039
114.125
93.507
94.148
102.288
110.214
98.291
93.667
91.786
90.285
90.749
93.411
95.162
97.269
96.954
100.297
104.746
107.156
109.108
110.065
109.557
112.124

Structures
57.399
66.553
71.109
69.313
70.299
74.096
74.300
73.082
75.359
81.520
79.755
71.355
73.073
76.079
87.058
98.098
103.837
112.161
110.325
98.404
112.125
120.095
106.935
103.859
104.539
106.616
108.187
96.150
90.354
89.768
91.405
97.235
102.744
110.280
115.911
116.049
125.101
123.191
101.377
97.514
98.571
100.000
109.180
124.578
132.595
104.659
88.308
90.733
99.875
118.743
108.062
99.980
91.848
86.033
88.731
88.245
90.222
83.055
89.561
93.866
96.449
99.421
99.560
99.558
100.962

Equipment and
software
7.303
8.641
10.024
9.958
10.578
11.513
11.399
11.512
12.997
15.381
15.774
14.272
15.164
17.449
20.106
21.861
21.075
21.971
20.829
21.950
26.303
27.974
28.504
28.895
31.074
33.351
33.361
32.504
34.873
39.226
43.904
49.158
54.383
61.861
70.837
80.857
89.320
86.438
82.789
85.377
92.138
100.000
107.590
111.168
106.411
88.911
96.822
107.473
114.862
89.688
87.704
88.474
89.777
92.913
95.582
98.309
100.486
103.161
105.120
109.637
111.972
113.460
114.790
114.049
117.148

Residential
34.011
33.017
30.063
29.117
33.086
34.063
32.026
40.808
48.061
47.752
37.895
32.975
40.740
49.486
52.602
50.672
39.949
36.747
30.075
42.524
48.836
49.608
55.696
56.807
56.231
54.524
49.819
45.032
51.263
55.450
60.840
58.850
63.550
64.751
69.732
74.092
74.834
75.258
79.204
85.712
94.130
100.000
92.667
75.379
57.345
44.489
42.862
42.268
47.368
45.843
43.058
44.799
44.257
42.934
45.223
41.570
41.720
41.577
41.994
42.139
43.361
45.433
46.364
47.855
49.819

Table B–6. Chain-type quantity indexes for gross domestic product, 1964–2012—Continued
[Index numbers, 2005=100; quarterly data seasonally adjusted]

Year or quarter

1964 ����������������������
1965 ����������������������
1966 ����������������������
1967 ����������������������
1968 ����������������������
1969 ����������������������
1970 ����������������������
1971 ����������������������
1972 ����������������������
1973 ����������������������
1974 ����������������������
1975 ����������������������
1976 ����������������������
1977 ����������������������
1978 ����������������������
1979 ����������������������
1980 ����������������������
1981 ����������������������
1982 ����������������������
1983 ����������������������
1984 ����������������������
1985 ����������������������
1986 ����������������������
1987 ����������������������
1988 ����������������������
1989 ����������������������
1990 ����������������������
1991 ����������������������
1992 ����������������������
1993 ����������������������
1994 ����������������������
1995 ����������������������
1996 ����������������������
1997 ����������������������
1998 ����������������������
1999 ����������������������
2000 ����������������������
2001 ����������������������
2002 ����������������������
2003 ����������������������
2004 ����������������������
2005 ����������������������
2006 ����������������������
2007 ����������������������
2008 ����������������������
2009 ����������������������
2010 ����������������������
2011 ����������������������
2012 p ��������������������
2009: I ������������������
      II �����������������
      III ����������������
      IV ����������������
2010: I ������������������
      II �����������������
      III ����������������
      IV ����������������
2011: I ������������������
      II �����������������
      III ����������������
      IV ����������������
2012: I ������������������
      II �����������������
      III ����������������
      IV p �������������

Exports of goods and services

Imports of goods and services

Total

Goods

Total

Goods

9.540
9.807
10.487
10.728
11.572
12.131
13.435
13.663
14.689
17.458
18.837
18.718
19.536
20.006
22.115
24.307
26.925
27.256
25.173
24.524
26.526
27.331
29.429
32.594
37.815
42.161
45.954
49.005
52.370
54.086
58.802
64.755
70.133
78.490
80.281
83.785
90.985
85.880
84.160
85.514
93.677
100.000
108.969
119.108
126.376
114.835
127.623
136.152
140.687
111.295
111.460
115.116
121.467
123.231
126.079
129.030
132.151
134.004
135.352
137.379
137.871
139.356
141.152
141.824
140.415

9.180
9.228
9.870
9.916
10.701
11.262
12.546
12.497
13.840
17.020
18.371
17.944
18.796
19.042
21.170
23.671
26.492
26.205
23.837
23.151
24.982
25.903
27.233
30.252
35.953
40.237
43.623
46.633
50.122
51.756
56.790
63.436
69.031
78.955
80.717
83.788
93.080
87.318
84.176
85.687
92.995
100.000
109.425
120.090
127.691
112.414
128.479
137.695
143.462
108.374
107.650
112.939
120.692
123.571
127.096
129.877
133.371
135.239
136.464
138.516
140.559
141.961
144.389
144.774
142.724

6.752
7.471
8.581
9.206
10.578
11.181
11.658
12.280
13.662
14.296
13.972
12.419
14.848
16.471
17.898
18.195
16.987
17.433
17.214
19.386
24.105
25.669
27.863
29.511
30.671
32.022
33.168
33.118
35.440
38.505
43.098
46.547
50.595
57.409
64.119
71.500
80.813
78.540
81.213
84.806
94.212
100.000
106.099
108.652
105.733
91.422
102.832
107.746
110.345
91.526
87.652
91.196
95.312
97.689
102.286
105.672
105.680
106.787
106.816
108.037
109.345
110.179
110.936
110.766
109.499

5.367
6.127
7.093
7.466
9.009
9.502
9.874
10.702
12.158
13.016
12.654
11.059
13.560
15.213
16.577
16.861
15.610
15.931
15.531
17.641
21.908
23.279
25.665
26.855
27.943
29.146
29.995
30.130
32.971
36.270
41.114
44.817
49.018
56.082
62.727
70.549
80.018
77.464
80.341
84.302
93.637
100.000
105.920
108.674
104.500
88.200
101.309
106.561
108.779
88.241
83.843
87.957
92.760
95.478
100.897
104.287
104.571
105.907
105.723
106.491
108.122
108.652
109.422
109.084
107.956

Government consumption expenditures and gross investment
Federal

Services
10.180
11.215
11.986
12.932
13.925
14.442
15.729
16.942
16.835
18.025
19.432
20.626
21.236
22.606
24.496
25.250
26.826
29.683
28.860
28.380
30.911
31.279
35.820
39.390
42.939
47.375
52.372
55.505
58.496
60.437
64.275
68.316
73.101
77.436
79.303
83.857
86.102
82.534
84.115
85.107
95.237
100.000
107.935
116.885
123.395
120.204
125.805
132.793
134.517
117.732
119.859
119.966
123.258
122.563
123.912
127.228
129.517
131.342
132.979
134.954
131.896
133.573
133.940
135.259
135.297

Services
15.328
15.779
17.783
19.957
20.315
21.596
22.722
22.075
23.011
22.235
22.210
21.247
22.714
23.846
25.546
25.897
25.319
26.778
28.205
30.483
38.126
41.026
41.488
46.378
47.954
50.278
53.564
52.173
50.768
52.124
54.901
56.556
59.514
64.687
71.721
76.569
84.955
84.292
85.837
87.474
97.252
100.000
107.059
108.539
112.488
108.740
111.507
114.630
119.335
109.184
107.866
108.625
109.284
109.935
110.262
113.646
112.185
112.023
113.188
116.906
116.402
118.950
119.637
120.394
118.359

Total
42.958
44.250
48.149
51.844
53.472
53.347
52.059
50.926
50.556
50.379
51.648
52.812
53.049
53.630
55.210
56.241
57.337
57.860
58.876
61.027
63.078
67.471
71.573
73.300
74.220
76.240
78.655
79.514
79.885
79.253
79.245
79.705
80.507
82.020
83.759
86.761
88.519
91.917
96.192
98.336
99.668
100.000
101.359
102.713
105.381
109.262
109.955
106.497
104.701
106.825
109.307
110.312
110.602
109.727
110.498
110.416
109.179
107.210
106.985
106.189
105.604
104.804
104.622
105.620
103.756

Total
59.725
59.697
66.303
72.903
73.491
70.969
65.738
60.677
58.197
55.748
56.243
56.426
56.453
57.647
59.092
60.519
63.390
66.420
68.989
73.561
75.829
81.771
86.407
89.477
88.010
89.379
91.185
91.000
89.351
85.842
82.555
80.353
79.423
78.641
77.758
79.270
79.661
82.901
88.953
94.839
98.710
100.000
102.127
103.399
110.819
117.613
122.883
119.480
116.871
113.639
117.333
119.129
120.352
120.535
123.355
124.468
123.172
119.864
120.681
119.351
118.024
116.751
116.685
119.359
114.688

National
defense
69.951
68.481
78.306
88.567
90.001
85.556
77.800
68.981
63.588
60.061
59.595
59.030
58.828
59.511
60.019
61.845
64.541
68.628
73.814
79.110
82.971
90.002
95.766
100.301
99.826
99.335
99.305
98.214
93.351
88.401
84.072
80.936
79.856
77.618
75.978
77.386
76.986
79.908
85.782
93.243
98.535
100.000
101.588
103.867
111.649
118.311
121.829
118.683
114.977
113.880
118.200
120.387
120.776
119.646
121.776
123.906
121.987
117.354
119.717
120.496
117.163
115.031
114.987
118.518
111.370

Nondefense
40.157
42.878
43.320
42.913
41.897
43.019
42.567
44.575
47.722
47.429
49.891
51.594
52.085
54.324
57.700
58.309
61.573
62.396
59.402
62.471
61.279
64.900
67.130
67.081
63.499
68.795
74.465
76.170
81.218
80.687
79.525
79.207
78.577
80.737
81.374
83.095
85.066
88.945
95.357
98.071
99.067
100.000
103.237
102.420
109.081
116.154
125.049
121.114
120.804
113.123
115.522
116.510
119.460
122.357
126.607
125.617
125.614
125.072
122.662
116.929
119.792
120.317
120.205
121.082
121.610

State
and
local
32.626
34.813
36.998
38.868
41.168
42.557
43.738
45.077
46.068
47.381
49.164
50.970
51.346
51.532
53.216
53.998
53.958
52.873
52.898
53.514
55.444
58.879
62.669
63.575
65.933
68.340
71.112
72.585
74.156
75.244
77.197
79.247
81.090
83.980
87.291
91.179
93.744
97.236
100.473
100.408
100.234
100.000
100.910
102.311
102.310
104.568
102.711
99.224
97.877
102.992
104.794
105.359
105.128
103.665
103.292
102.544
101.342
100.117
99.317
98.818
98.643
98.103
97.858
97.932
97.615

Source: Department of Commerce (Bureau of Economic Analysis).

National Income or Expenditure | 331

Table B–7. Chain-type price indexes for gross domestic product, 1964–2012
[Index numbers, 2005=100, except as noted; quarterly data seasonally adjusted]
Personal consumption expenditures

Year or quarter

1964 ����������������������
1965 ����������������������
1966 ����������������������
1967 ����������������������
1968 ����������������������
1969 ����������������������
1970 ����������������������
1971 ����������������������
1972 ����������������������
1973 ����������������������
1974 ����������������������
1975 ����������������������
1976 ����������������������
1977 ����������������������
1978 ����������������������
1979 ����������������������
1980 ����������������������
1981 ����������������������
1982 ����������������������
1983 ����������������������
1984 ����������������������
1985 ����������������������
1986 ����������������������
1987 ����������������������
1988 ����������������������
1989 ����������������������
1990 ����������������������
1991 ����������������������
1992 ����������������������
1993 ����������������������
1994 ����������������������
1995 ����������������������
1996 ����������������������
1997 ����������������������
1998 ����������������������
1999 ����������������������
2000 ����������������������
2001 ����������������������
2002 ����������������������
2003 ����������������������
2004 ����������������������
2005 ����������������������
2006 ����������������������
2007 ����������������������
2008 ����������������������
2009 ����������������������
2010 ����������������������
2011 ����������������������
2012 p ��������������������
2009: I ������������������
      II �����������������
      III ����������������
      IV ����������������
2010: I ������������������
      II �����������������
      III ����������������
      IV ����������������
2011: I ������������������
      II �����������������
      III ����������������
      IV ����������������
2012: I ������������������
      II �����������������
      III ����������������
      IV p �������������

Gross
domestic
product

19.589
19.945
20.511
21.142
22.040
23.130
24.349
25.567
26.670
28.148
30.695
33.606
35.535
37.796
40.447
43.811
47.817
52.326
55.514
57.705
59.874
61.686
63.057
64.818
67.047
69.579
72.274
74.826
76.602
78.288
79.935
81.602
83.154
84.627
85.580
86.840
88.724
90.731
92.192
94.134
96.784
100.000
103.237
106.231
108.565
109.532
111.002
113.369
115.382
109.526
109.318
109.463
109.820
110.234
110.686
111.248
111.838
112.389
113.109
113.937
114.041
114.608
115.050
115.807
116.063

Fixed investment
Total

19.536
19.819
20.322
20.834
21.645
22.626
23.685
24.692
25.536
26.913
29.716
32.198
33.966
36.171
38.705
42.137
46.663
50.833
53.640
55.948
58.065
59.965
61.427
63.618
66.151
69.025
72.180
74.789
76.989
78.679
80.302
82.078
83.864
85.433
86.246
87.636
89.818
91.530
92.778
94.658
97.121
100.000
102.723
105.499
108.943
109.004
111.087
113.790
115.784
108.063
108.496
109.315
110.142
110.642
110.800
111.154
111.751
112.640
113.633
114.293
114.593
115.300
115.496
115.952
116.389

See next page for continuation of table.

332 |

Appendix B

Gross private domestic investment

Goods

30.013
30.328
30.996
31.542
32.642
33.907
35.200
36.258
37.186
39.404
44.322
47.903
49.777
52.435
55.653
60.916
67.737
72.769
74.753
76.102
77.541
78.785
78.417
80.939
83.072
86.268
89.801
91.996
93.106
93.915
94.870
95.757
96.809
96.696
95.237
95.735
97.655
97.563
96.563
96.492
97.929
100.000
101.441
102.764
105.912
103.105
104.852
108.822
110.202
101.386
102.455
103.890
104.687
105.025
104.283
104.540
105.561
107.266
108.820
109.633
109.569
110.256
109.743
110.261
110.546

Services

14.572
14.845
15.276
15.785
16.467
17.324
18.285
19.284
20.102
21.077
22.866
24.834
26.556
28.558
30.777
33.350
36.802
40.555
43.709
46.429
48.846
51.049
53.375
55.409
58.123
60.840
63.808
66.581
69.236
71.294
73.200
75.365
77.473
79.812
81.689
83.509
85.818
88.422
90.801
93.686
96.688
100.000
103.414
106.981
110.584
112.157
114.418
116.435
118.770
111.614
111.724
112.224
113.065
113.647
114.282
114.687
115.057
115.503
116.193
116.772
117.270
117.989
118.576
118.997
119.519

Total

26.710
27.136
27.692
28.424
29.485
30.883
32.190
33.794
35.206
37.107
40.797
45.833
48.366
51.994
56.235
61.323
67.080
73.422
77.180
76.987
77.538
78.332
80.029
81.561
83.424
85.418
87.064
88.302
87.993
88.997
90.157
91.173
90.786
90.449
89.435
89.315
90.283
91.080
91.451
92.483
95.633
100.000
104.302
106.313
107.501
106.274
104.854
106.439
107.743
108.487
106.695
105.130
104.784
104.474
104.573
104.916
105.453
105.786
106.272
106.686
107.013
107.292
107.647
107.818
108.214

Nonresidential
Total

25.640
26.077
26.626
27.372
28.472
29.877
31.162
32.731
34.135
36.020
39.568
44.525
47.106
50.803
55.094
60.088
65.710
71.816
75.747
75.628
76.070
77.028
78.870
80.332
82.415
84.410
86.125
87.404
87.152
88.163
89.352
90.393
90.149
89.921
89.085
89.029
90.083
90.888
91.261
92.374
95.543
100.000
104.347
106.360
107.587
106.318
105.023
106.680
108.170
108.076
106.579
105.414
105.203
104.784
104.762
105.061
105.487
105.866
106.509
106.992
107.352
107.661
107.977
108.324
108.718

Total
34.142
34.532
35.047
35.939
37.203
38.740
40.571
42.479
43.914
45.605
50.008
56.893
60.048
64.157
68.453
74.013
80.541
88.316
93.181
92.350
92.127
92.850
94.427
95.275
97.392
99.435
101.339
102.906
102.048
102.100
102.592
102.811
101.612
100.326
98.125
96.704
96.750
96.317
95.889
95.471
96.837
100.000
103.425
105.645
107.717
107.102
105.514
107.359
108.990
108.975
107.494
106.224
105.714
105.188
105.304
105.589
105.973
106.483
107.174
107.687
108.092
108.562
108.878
109.104
109.417

Structures
11.801
12.143
12.580
12.973
13.621
14.518
15.473
16.664
17.863
19.247
21.910
24.534
25.741
27.973
30.675
34.238
37.421
42.567
45.927
44.757
45.147
46.219
47.106
47.863
49.895
51.848
53.522
54.491
54.502
56.103
58.089
60.601
62.141
64.516
67.480
69.559
72.298
76.087
79.292
82.174
88.441
100.000
112.922
119.780
125.706
122.527
121.158
126.850
131.221
127.259
123.208
120.038
119.605
119.968
120.670
121.442
122.552
124.097
126.118
127.882
129.302
130.167
131.198
131.540
131.978

Equipment and
software
53.952
54.001
54.144
55.344
56.831
58.411
60.560
62.360
63.112
64.184
68.917
79.100
83.754
88.730
93.412
99.335
107.819
115.524
120.030
120.284
119.234
119.090
120.976
121.637
123.155
124.695
126.310
128.112
126.605
125.322
124.604
123.163
120.199
116.639
111.454
108.195
106.893
104.364
102.240
100.450
99.900
100.000
100.049
100.525
101.000
101.477
99.806
100.445
101.232
102.166
101.799
101.266
100.678
99.799
99.690
99.797
99.939
100.134
100.430
100.562
100.656
101.001
101.094
101.282
101.554

Residential
13.003
13.372
13.857
14.339
15.100
16.144
16.666
17.632
18.703
20.359
22.460
24.547
26.124
28.759
32.281
35.902
39.789
43.036
45.340
46.380
47.713
48.944
50.994
53.079
54.913
56.680
58.011
58.771
59.486
61.890
64.069
66.403
67.828
69.557
71.412
74.151
77.415
80.994
83.002
86.953
93.297
100.000
106.081
107.612
106.296
102.713
102.520
103.406
104.272
104.065
102.494
101.716
102.576
102.573
102.064
102.421
103.020
102.861
103.300
103.650
103.812
103.439
103.754
104.593
105.302

Table B–7. Chain-type price indexes for gross domestic product, 1964–2012—Continued
[Index numbers, 2005=100, except as noted; quarterly data seasonally adjusted]
Exports and imports
of goods
and services

Government consumption expenditures
and gross investment
Federal

Year or quarter
Exports

Imports

Total
Total

1964 �����������������
1965 �����������������
1966 �����������������
1967 �����������������
1968 �����������������
1969 �����������������
1970 �����������������
1971 �����������������
1972 �����������������
1973 �����������������
1974 �����������������
1975 �����������������
1976 �����������������
1977 �����������������
1978 �����������������
1979 �����������������
1980 �����������������
1981 �����������������
1982 �����������������
1983 �����������������
1984 �����������������
1985 �����������������
1986 �����������������
1987 �����������������
1988 �����������������
1989 �����������������
1990 �����������������
1991 �����������������
1992 �����������������
1993 �����������������
1994 �����������������
1995 �����������������
1996 �����������������
1997 �����������������
1998 �����������������
1999 �����������������
2000 �����������������
2001 �����������������
2002 �����������������
2003 �����������������
2004 �����������������
2005 �����������������
2006 �����������������
2007 �����������������
2008 �����������������
2009 �����������������
2010 �����������������
2011 �����������������
2012 p ���������������
2009: I �������������
      II ������������
      III �����������
      IV �����������
2010: I �������������
      II ������������
      III �����������
      IV �����������
2011: I �������������
      II ������������
      III �����������
      IV �����������
2012: I �������������
      II ������������
      III �����������
      IV p ��������

28.128
29.023
29.900
31.045
31.723
32.796
34.053
35.310
36.956
41.816
51.517
56.781
58.645
61.033
64.752
72.545
79.903
85.810
86.204
86.544
87.347
84.674
83.406
85.516
89.945
91.443
92.063
93.283
92.904
92.879
93.914
96.070
94.799
93.174
91.042
90.477
92.069
91.696
91.322
93.282
96.539
100.000
103.440
106.900
111.975
105.924
110.738
117.860
118.874
104.936
104.898
106.187
107.674
108.972
110.303
110.562
113.117
116.123
118.485
118.992
117.839
118.652
118.802
118.792
119.249

20.526
20.812
21.297
21.379
21.704
22.270
23.587
25.035
26.789
31.446
44.989
48.734
50.201
54.624
58.482
68.483
85.301
89.886
86.855
83.601
82.879
80.157
80.154
85.008
89.074
91.021
93.630
92.848
92.922
92.210
93.075
95.625
93.958
90.691
85.809
86.311
90.027
87.824
86.846
89.851
94.164
100.000
104.131
107.785
119.237
106.598
112.989
121.851
122.616
102.932
104.547
107.855
111.058
113.200
112.595
111.726
114.434
119.417
123.057
122.466
122.463
124.156
122.942
120.907
122.458

14.070
14.444
15.044
15.671
16.520
17.517
18.945
20.421
21.989
23.594
25.977
28.586
30.469
32.583
34.670
37.575
41.669
45.768
48.775
50.717
53.319
54.974
55.977
57.541
59.074
60.924
63.405
65.606
67.276
68.949
70.819
72.753
74.488
75.854
76.879
79.337
82.513
84.764
87.003
90.650
94.531
100.000
104.842
109.863
115.245
114.592
117.334
121.233
123.435
114.342
114.186
114.620
115.220
116.555
116.916
117.406
118.461
119.964
121.168
121.898
121.903
122.979
123.157
123.574
124.031

14.995
15.379
15.914
16.386
17.287
18.226
19.699
21.383
23.471
25.080
27.315
30.158
32.302
34.742
36.888
39.727
43.900
48.165
51.434
53.218
56.358
57.635
57.938
58.642
59.884
61.504
63.548
66.070
68.101
69.830
71.725
73.717
75.763
77.047
77.931
79.886
82.524
84.201
87.318
91.024
95.335
100.000
104.107
107.753
111.225
110.959
113.583
116.721
118.564
110.956
110.481
110.897
111.504
113.016
113.339
113.668
114.309
115.696
116.714
117.365
117.111
118.038
118.403
118.679
119.135

National Nondefense defense
14.620
15.024
15.535
15.994
16.834
17.757
19.116
20.810
23.209
24.911
27.223
29.880
32.057
34.486
36.908
39.853
44.179
48.542
51.953
53.775
57.603
58.696
58.642
59.236
60.326
61.882
63.917
66.222
68.522
69.712
71.438
73.161
75.431
76.517
77.328
79.225
81.821
83.484
86.624
90.659
94.895
100.000
104.421
108.249
112.187
111.347
113.951
117.411
119.482
111.503
110.875
111.193
111.818
113.420
113.696
113.947
114.742
116.440
117.375
118.047
117.780
119.008
119.268
119.541
120.111

15.798
16.104
16.708
17.215
18.327
19.284
21.143
22.746
23.892
25.231
27.245
30.505
32.549
34.993
36.514
39.100
42.906
46.917
49.825
51.501
52.779
54.574
55.915
56.953
58.679
60.497
62.568
65.672
67.034
70.002
72.267
74.830
76.406
78.095
79.120
81.188
83.907
85.612
88.689
91.774
96.234
100.000
103.468
106.743
109.240
110.177
112.843
115.337
116.722
109.847
109.686
110.303
110.871
112.206
112.624
113.105
113.435
114.207
115.384
115.994
115.764
116.096
116.664
116.948
117.182

State
and
local
13.293
13.662
14.334
15.137
15.945
17.013
18.411
19.720
20.896
22.495
24.970
27.410
29.114
31.005
33.042
35.976
40.002
43.975
46.786
48.857
51.034
53.002
54.577
56.849
58.621
60.654
63.474
65.443
66.856
68.494
70.351
72.252
73.806
75.219
76.320
79.036
82.482
85.019
86.810
90.425
94.062
100.000
105.276
111.112
117.666
116.763
119.579
124.001
126.449
116.349
116.405
116.852
117.446
118.654
119.038
119.639
120.985
122.565
123.895
124.678
124.866
126.042
126.089
126.605
127.061

Final
sales of
domestic
product

19.440
19.798
20.363
20.996
21.898
22.988
24.203
25.415
26.516
27.992
30.519
33.418
35.350
37.614
40.266
43.614
47.598
52.074
55.280
57.464
59.624
61.466
62.856
64.607
66.865
69.397
72.102
74.655
76.436
78.123
79.775
81.449
83.024
84.522
85.516
86.795
88.698
90.709
92.168
94.123
96.774
100.000
103.240
106.238
108.576
109.521
110.993
113.371
115.412
109.476
109.294
109.472
109.841
110.242
110.680
111.238
111.814
112.371
113.111
113.948
114.056
114.628
115.065
115.849
116.104

Gross domestic
purchases 1

Total

19.191
19.524
20.071
20.654
21.526
22.582
23.798
25.021
26.134
27.647
30.484
33.328
35.238
37.617
40.286
43.833
48.448
52.909
55.906
57.865
59.904
61.605
63.000
64.978
67.215
69.765
72.601
74.980
76.788
78.404
80.029
81.743
83.220
84.468
85.034
86.377
88.537
90.198
91.498
93.584
96.415
100.000
103.354
106.402
109.858
109.620
111.421
114.208
116.149
109.188
109.235
109.706
110.350
110.920
111.110
111.488
112.165
113.099
114.067
114.709
114.958
115.674
115.888
116.298
116.734

Percent change 2

Less
food and
energy

Gross
domestic
product

��������������
��������������
��������������
��������������
��������������
��������������
��������������
��������������
��������������
��������������
��������������
��������������
��������������
��������������
��������������
��������������
��������������
��������������
55.408
57.569
59.704
61.577
63.464
65.506
67.900
70.346
73.043
75.539
77.520
79.228
80.947
82.722
84.077
85.344
86.171
87.463
89.243
90.851
92.384
94.214
96.779
100.000
103.127
105.938
108.719
109.417
110.912
112.995
114.892
109.142
109.212
109.401
109.912
110.403
110.728
111.050
111.466
112.079
112.825
113.394
113.682
114.348
114.745
115.077
115.399

1.6
1.8
2.8
3.1
4.2
4.9
5.3
5.0
4.3
5.5
9.0
9.5
5.7
6.4
7.0
8.3
9.1
9.4
6.1
3.9
3.8
3.0
2.2
2.8
3.4
3.8
3.9
3.5
2.4
2.2
2.1
2.1
1.9
1.8
1.1
1.5
2.2
2.3
1.6
2.1
2.8
3.3
3.2
2.9
2.2
.9
1.3
2.1
1.8
1.0
–.8
.5
1.3
1.5
1.7
2.0
2.1
2.0
2.6
3.0
.4
2.0
1.6
2.7
.9

Gross domestic
purchases 1
Total
1.6
1.7
2.8
2.9
4.2
4.9
5.4
5.1
4.4
5.8
10.3
9.3
5.7
6.8
7.1
8.8
10.5
9.2
5.7
3.5
3.5
2.8
2.3
3.1
3.4
3.8
4.1
3.3
2.4
2.1
2.1
2.1
1.8
1.5
.7
1.6
2.5
1.9
1.4
2.3
3.0
3.7
3.4
2.9
3.2
–.2
1.6
2.5
1.7
–2.4
.2
1.7
2.4
2.1
.7
1.4
2.5
3.4
3.5
2.3
.9
2.5
.7
1.4
1.5

Less
food and
energy
��������������
��������������
��������������
��������������
��������������
��������������
��������������
��������������
��������������
��������������
��������������
��������������
��������������
��������������
��������������
��������������
��������������
��������������
��������������
3.9
3.7
3.1
3.1
3.2
3.7
3.6
3.8
3.4
2.6
2.2
2.2
2.2
1.6
1.5
1.0
1.5
2.0
1.8
1.7
2.0
2.7
3.3
3.1
2.7
2.6
.6
1.4
1.9
1.7
–.4
.3
.7
1.9
1.8
1.2
1.2
1.5
2.2
2.7
2.0
1.0
2.4
1.4
1.2
1.1

1 Gross domestic product (GDP) less exports of goods and services plus imports of goods and services.
2 Quarterly percent changes are at annual rates.

Source: Department of Commerce (Bureau of Economic Analysis).

National Income or Expenditure | 333

Table B–8. Gross domestic product by major type of product, 1964–2012
[Billions of dollars; quarterly data at seasonally adjusted annual rates]
Goods

Year or quarter

1964 ����������������������
1965 ����������������������
1966 ����������������������
1967 ����������������������
1968 ����������������������
1969 ����������������������
1970 ����������������������
1971 ����������������������
1972 ����������������������
1973 ����������������������
1974 ����������������������
1975 ����������������������
1976 ����������������������
1977 ����������������������
1978 ����������������������
1979 ����������������������
1980 ����������������������
1981 ����������������������
1982 ����������������������
1983 ����������������������
1984 ����������������������
1985 ����������������������
1986 ����������������������
1987 ����������������������
1988 ����������������������
1989 ����������������������
1990 ����������������������
1991 ����������������������
1992 ����������������������
1993 ����������������������
1994 ����������������������
1995 ����������������������
1996 ����������������������
1997 ����������������������
1998 ����������������������
1999 ����������������������
2000 ����������������������
2001 ����������������������
2002 ����������������������
2003 ����������������������
2004 ����������������������
2005 ����������������������
2006 ����������������������
2007 ����������������������
2008 ����������������������
2009 ����������������������
2010 ����������������������
2011 ����������������������
2012 p ��������������������
2009: I ������������������
      II �����������������
      III ����������������
      IV ����������������
2010: I ������������������
      II �����������������
      III ����������������
      IV ����������������
2011: I ������������������
      II �����������������
      III ����������������
      IV ����������������
2012: I ������������������
      II �����������������
      III ����������������
      IV p �������������

Gross
domestic
product

Final
sales of
domestic
product

Change
in
private
inventories

663.6
719.1
787.7
832.4
909.8
984.4
1,038.3
1,126.8
1,237.9
1,382.3
1,499.5
1,637.7
1,824.6
2,030.1
2,293.8
2,562.2
2,788.1
3,126.8
3,253.2
3,534.6
3,930.9
4,217.5
4,460.1
4,736.4
5,100.4
5,482.1
5,800.5
5,992.1
6,342.3
6,667.4
7,085.2
7,414.7
7,838.5
8,332.4
8,793.5
9,353.5
9,951.5
10,286.2
10,642.3
11,142.2
11,853.3
12,623.0
13,377.2
14,028.7
14,291.5
13,973.7
14,498.9
15,075.7
15,681.5
13,923.4
13,885.4
13,952.2
14,133.6
14,270.3
14,413.5
14,576.0
14,735.9
14,814.9
15,003.6
15,163.2
15,321.0
15,478.3
15,585.6
15,811.0
15,851.2

658.8
709.9
774.1
822.6
900.8
975.3
1,036.3
1,118.6
1,228.8
1,366.4
1,485.5
1,644.0
1,807.5
2,007.8
2,268.0
2,544.2
2,794.5
3,097.0
3,268.1
3,540.4
3,865.5
4,195.6
4,453.5
4,709.2
5,081.9
5,454.5
5,786.0
5,992.5
6,326.0
6,646.5
7,021.4
7,383.5
7,807.7
8,261.4
8,729.8
9,292.7
9,896.9
10,324.5
10,630.3
11,125.8
11,788.3
12,573.0
13,317.3
13,999.6
14,332.7
14,127.9
14,440.6
15,039.0
15,623.8
14,090.2
14,088.1
14,152.7
14,180.5
14,237.0
14,371.8
14,466.6
14,686.9
14,781.2
14,968.7
15,167.3
15,238.9
15,405.7
15,530.8
15,728.8
15,829.9

4.8
9.2
13.6
9.9
9.1
9.2
2.0
8.3
9.1
15.9
14.0
–6.3
17.1
22.3
25.8
18.0
–6.3
29.8
–14.9
–5.8
65.4
21.8
6.6
27.1
18.5
27.7
14.5
–.4
16.3
20.8
63.8
31.2
30.8
71.0
63.7
60.8
54.5
–38.3
12.0
16.4
64.9
50.0
60.0
29.1
–41.1
–154.2
58.4
36.6
57.7
–166.7
–202.7
–200.5
–46.8
33.2
41.7
109.5
49.0
33.7
34.8
–4.1
82.1
72.6
54.8
82.3
21.3

Total

Total

Final
sales

277.8
304.3
337.1
345.4
370.8
397.6
408.7
432.6
472.0
547.1
588.0
628.6
706.6
773.5
872.6
977.2
1,035.2
1,167.3
1,148.8
1,226.9
1,402.2
1,452.8
1,491.2
1,570.7
1,703.7
1,851.9
1,923.1
1,943.5
2,031.5
2,124.2
2,290.7
2,379.5
2,516.3
2,701.2
2,819.2
2,990.1
3,124.5
3,077.6
3,101.2
3,170.7
3,333.8
3,475.7
3,663.7
3,844.1
3,758.6
3,614.5
3,921.9
4,184.7
4,458.1
3,554.8
3,563.1
3,604.5
3,735.5
3,840.1
3,841.3
3,964.4
4,041.9
4,082.9
4,131.2
4,199.2
4,325.3
4,373.5
4,399.3
4,530.0
4,529.4

273.0
295.1
323.5
335.5
361.7
388.4
406.7
424.4
462.9
531.2
574.0
634.8
689.5
751.2
846.8
959.2
1,041.5
1,137.5
1,163.7
1,232.6
1,336.8
1,431.0
1,484.7
1,543.6
1,685.2
1,824.2
1,908.5
1,943.9
2,015.1
2,103.4
2,226.9
2,348.3
2,485.5
2,630.2
2,755.5
2,929.3
3,070.0
3,115.9
3,089.1
3,154.3
3,268.9
3,425.8
3,603.7
3,815.0
3,799.7
3,768.6
3,863.6
4,148.0
4,400.3
3,721.5
3,765.7
3,804.9
3,782.3
3,806.9
3,799.6
3,854.9
3,992.9
4,049.2
4,096.4
4,203.3
4,243.2
4,301.0
4,344.5
4,447.7
4,508.1

Durable goods
Change
in
private
inventories
4.8
9.2
13.6
9.9
9.1
9.2
2.0
8.3
9.1
15.9
14.0
–6.3
17.1
22.3
25.8
18.0
–6.3
29.8
–14.9
–5.8
65.4
21.8
6.6
27.1
18.5
27.7
14.5
–.4
16.3
20.8
63.8
31.2
30.8
71.0
63.7
60.8
54.5
–38.3
12.0
16.4
64.9
50.0
60.0
29.1
–41.1
–154.2
58.4
36.6
57.7
–166.7
–202.7
–200.5
–46.8
33.2
41.7
109.5
49.0
33.7
34.8
–4.1
82.1
72.6
54.8
82.3
21.3

Final
sales
119.3
131.6
145.4
150.0
162.8
175.7
178.6
186.7
208.4
243.6
262.4
293.2
330.9
374.6
424.9
483.9
512.3
554.8
552.5
592.3
665.9
727.9
758.3
785.3
863.3
939.7
973.2
967.6
1,010.7
1,072.9
1,149.8
1,225.9
1,321.0
1,430.7
1,524.2
1,633.8
1,734.4
1,731.5
1,678.9
1,699.3
1,759.3
1,873.8
1,973.4
2,087.3
2,043.1
1,905.3
1,941.7
2,090.7
2,212.4
1,907.9
1,903.4
1,919.3
1,890.6
1,908.3
1,921.4
1,940.3
1,996.7
2,024.0
2,067.1
2,117.8
2,154.1
2,180.1
2,186.7
2,215.3
2,267.7

Change
in
private
inventories 1
3.8
6.2
10.0
4.8
4.5
6.0
–.2
2.9
6.4
13.0
10.9
–7.5
10.8
9.5
18.2
12.8
–2.3
7.3
–16.0
2.5
41.4
4.4
–1.9
22.9
22.7
20.0
7.7
–13.6
–3.0
17.1
35.7
33.6
19.1
40.0
39.3
37.4
35.6
–44.4
17.7
13.0
37.3
35.2
25.9
11.2
–23.1
–118.6
42.5
37.6
66.1
–142.6
–150.1
–136.6
–45.0
28.2
43.7
66.4
31.8
43.0
42.5
32.6
32.4
59.9
78.8
84.8
40.8

Nondurable goods
Final
sales
153.7
163.5
178.0
185.5
198.9
212.7
228.2
237.7
254.5
287.6
311.7
341.6
358.6
376.6
422.0
475.3
529.2
582.6
611.2
640.3
670.9
703.1
726.4
758.3
821.9
884.5
935.3
976.3
1,004.4
1,030.4
1,077.1
1,122.4
1,164.5
1,199.5
1,231.3
1,295.5
1,335.6
1,384.4
1,410.3
1,455.0
1,509.6
1,552.0
1,630.3
1,727.7
1,756.6
1,863.3
1,921.9
2,057.3
2,187.9
1,813.6
1,862.3
1,885.6
1,891.7
1,898.5
1,878.2
1,914.6
1,996.2
2,025.3
2,029.3
2,085.5
2,089.2
2,120.9
2,157.9
2,232.5
2,240.4

Change
in
private
inventories 1
1.0
3.0
3.6
5.0
4.5
3.2
2.2
5.3
2.7
2.9
3.1
1.2
6.3
12.8
7.6
5.2
–4.0
22.5
1.1
–8.2
24.0
17.4
8.4
4.2
–4.3
7.7
6.8
13.2
19.3
3.7
28.1
–2.4
11.7
31.0
24.4
23.4
19.0
6.2
–5.6
3.3
27.6
14.7
34.0
17.9
–18.0
–35.6
15.8
–1.0
–8.4
–24.1
–52.6
–63.9
–1.8
5.0
–2.0
43.1
17.2
–9.3
–7.6
–36.7
49.7
12.7
–24.1
–2.5
–19.5

Services 2

307.4
330.1
362.6
397.5
439.1
478.6
519.9
565.8
619.0
672.2
745.8
842.4
926.8
1,029.9
1,147.2
1,271.7
1,431.6
1,606.9
1,759.9
1,939.1
2,102.9
2,305.9
2,488.7
2,668.0
2,881.7
3,101.2
3,343.9
3,548.6
3,788.1
3,985.1
4,187.2
4,396.7
4,625.5
4,882.5
5,159.7
5,485.1
5,878.0
6,208.7
6,535.5
6,891.2
7,304.9
7,783.8
8,260.8
8,751.8
9,174.0
9,245.9
9,559.6
9,870.4
10,116.5
9,157.7
9,200.1
9,263.8
9,361.9
9,435.2
9,532.0
9,596.6
9,674.8
9,754.3
9,862.5
9,930.2
9,934.8
10,021.0
10,090.9
10,169.3
10,184.7

Structures

78.4
84.7
88.0
89.6
100.0
108.3
109.7
128.4
146.9
162.9
165.6
166.7
191.2
226.8
273.9
313.3
321.3
352.6
344.5
368.7
425.8
458.7
480.1
497.6
515.0
529.0
533.5
499.9
522.7
558.1
607.3
638.5
696.7
748.6
814.5
878.2
949.0
999.9
1,005.7
1,080.4
1,214.5
1,363.4
1,452.7
1,432.8
1,359.0
1,113.3
1,017.4
1,020.5
1,107.0
1,211.0
1,122.2
1,083.9
1,036.2
994.9
1,040.2
1,015.1
1,019.2
977.6
1,009.9
1,033.8
1,060.9
1,083.7
1,095.4
1,111.8
1,137.1

1 Estimates for durable and nondurable goods for 1996 and earlier periods are based on the Standard Industrial Classification (SIC); later estimates are based
on the North American Industry Classification System (NAICS).
2 Includes government consumption expenditures, which are for services (such as education and national defense) produced by government. In current
dollars, these services are valued at their cost of production.
Source: Department of Commerce (Bureau of Economic Analysis).

334 |

Appendix B

Table B–9. Real gross domestic product by major type of product, 1964–2012
[Billions of chained (2005) dollars; quarterly data at seasonally adjusted annual rates]
Goods

Year or quarter

1964 ����������������������
1965 ����������������������
1966 ����������������������
1967 ����������������������
1968 ����������������������
1969 ����������������������
1970 ����������������������
1971 ����������������������
1972 ����������������������
1973 ����������������������
1974 ����������������������
1975 ����������������������
1976 ����������������������
1977 ����������������������
1978 ����������������������
1979 ����������������������
1980 ����������������������
1981 ����������������������
1982 ����������������������
1983 ����������������������
1984 ����������������������
1985 ����������������������
1986 ����������������������
1987 ����������������������
1988 ����������������������
1989 ����������������������
1990 ����������������������
1991 ����������������������
1992 ����������������������
1993 ����������������������
1994 ����������������������
1995 ����������������������
1996 ����������������������
1997 ����������������������
1998 ����������������������
1999 ����������������������
2000 ����������������������
2001 ����������������������
2002 ����������������������
2003 ����������������������
2004 ����������������������
2005 ����������������������
2006 ����������������������
2007 ����������������������
2008 ����������������������
2009 ����������������������
2010 ����������������������
2011 ����������������������
2012 p ��������������������
2009: I ������������������
      II �����������������
      III ����������������
      IV ����������������
2010: I ������������������
      II �����������������
      III ����������������
      IV ����������������
2011: I ������������������
      II �����������������
      III ����������������
      IV ����������������
2012: I ������������������
      II �����������������
      III ����������������
      IV p �������������

Gross
domestic
product

Final
sales of
domestic
product

Change
in
private
inventories

3,389.4
3,607.0
3,842.1
3,939.2
4,129.9
4,258.2
4,266.3
4,409.5
4,643.8
4,912.8
4,885.7
4,875.4
5,136.9
5,373.1
5,672.8
5,850.1
5,834.0
5,982.1
5,865.9
6,130.9
6,571.5
6,843.4
7,080.5
7,307.0
7,607.4
7,879.2
8,027.1
8,008.3
8,280.0
8,516.2
8,863.1
9,086.0
9,425.8
9,845.9
10,274.7
10,770.7
11,216.4
11,337.5
11,543.1
11,836.4
12,246.9
12,623.0
12,958.5
13,206.4
13,161.9
12,757.9
13,063.0
13,299.1
13,591.1
12,711.0
12,701.0
12,746.7
12,873.1
12,947.6
13,019.6
13,103.5
13,181.2
13,183.8
13,264.7
13,306.9
13,441.0
13,506.4
13,548.5
13,652.5
13,656.8

3,390.8
3,587.6
3,803.4
3,920.0
4,115.8
4,245.0
4,284.3
4,403.6
4,636.7
4,884.0
4,870.0
4,922.1
5,115.9
5,340.3
5,634.9
5,836.2
5,873.6
5,954.4
5,918.2
6,167.6
6,490.0
6,833.1
7,092.7
7,289.9
7,601.3
7,860.8
8,025.8
8,027.9
8,277.2
8,508.0
8,801.7
9,065.4
9,404.4
9,774.2
10,208.3
10,706.5
11,158.0
11,382.0
11,533.6
11,820.5
12,181.3
12,573.0
12,899.3
13,177.5
13,200.5
12,899.7
13,010.3
13,265.3
13,537.5
12,870.3
12,890.0
12,928.3
12,910.2
12,914.7
12,985.4
13,005.5
13,135.6
13,154.4
13,234.1
13,311.2
13,361.4
13,440.1
13,497.9
13,577.4
13,634.7

17.3
32.9
47.1
33.9
30.8
30.3
5.6
25.0
25.7
39.0
29.1
–12.8
34.3
43.1
45.6
28.0
–9.3
39.0
–19.7
–7.7
78.3
25.4
8.5
33.2
21.9
30.6
16.6
–1.4
17.9
22.3
69.3
32.1
31.2
77.4
71.6
68.5
60.2
–41.8
12.8
17.3
66.3
50.0
59.4
27.7
–36.3
–139.0
50.9
31.0
42.7
–150.2
–185.5
–181.5
–38.8
30.5
33.2
94.9
45.0
30.3
27.5
–4.3
70.5
56.9
41.4
60.3
12.0

Total

Total

718.1
778.4
846.0
848.3
882.2
912.6
905.0
931.8
995.5
1,101.4
1,090.8
1,063.5
1,147.0
1,202.1
1,282.9
1,335.9
1,324.2
1,384.0
1,312.8
1,369.5
1,539.3
1,576.1
1,622.2
1,687.5
1,792.5
1,894.4
1,914.2
1,881.9
1,958.7
2,034.1
2,177.1
2,257.1
2,380.4
2,566.0
2,714.7
2,905.1
3,046.9
2,997.7
3,049.9
3,160.3
3,324.4
3,475.7
3,659.1
3,819.6
3,789.7
3,569.1
3,893.0
4,091.4
4,312.9
3,495.5
3,506.4
3,559.6
3,715.0
3,839.6
3,829.2
3,923.4
3,979.9
4,017.0
4,050.3
4,071.8
4,226.5
4,266.9
4,281.0
4,345.2
4,358.3

Final
sales
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���������������
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���������������
���������������
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���������������
���������������
���������������
���������������
���������������
���������������
2,234.2
2,356.6
2,502.1
2,654.8
2,847.0
2,993.5
3,034.2
3,038.0
3,142.4
3,259.1
3,425.8
3,599.9
3,792.1
3,834.7
3,726.1
3,837.8
4,057.2
4,257.0
3,671.8
3,714.7
3,759.9
3,758.1
3,806.0
3,794.2
3,818.3
3,932.6
3,987.7
4,019.7
4,079.7
4,141.5
4,196.8
4,228.4
4,265.4
4,337.5

Durable goods
Change
in
private
inventories
17.3
32.9
47.1
33.9
30.8
30.3
5.6
25.0
25.7
39.0
29.1
–12.8
34.3
43.1
45.6
28.0
–9.3
39.0
–19.7
–7.7
78.3
25.4
8.5
33.2
21.9
30.6
16.6
–1.4
17.9
22.3
69.3
32.1
31.2
77.4
71.6
68.5
60.2
–41.8
12.8
17.3
66.3
50.0
59.4
27.7
–36.3
–139.0
50.9
31.0
42.7
–150.2
–185.5
–181.5
–38.8
30.5
33.2
94.9
45.0
30.3
27.5
–4.3
70.5
56.9
41.4
60.3
12.0

Nondurable goods

Final
sales

Change
in
private
inventories 1

Final
sales

Change
in
private
inventories 1

���������������
���������������
���������������
���������������
���������������
���������������
���������������
���������������
���������������
���������������
���������������
���������������
���������������
���������������
���������������
���������������
���������������
���������������
���������������
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���������������
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���������������
���������������
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���������������
���������������
���������������
1,017.9
1,105.4
1,216.7
1,334.8
1,469.2
1,582.7
1,606.7
1,588.8
1,658.0
1,750.4
1,873.8
1,989.5
2,133.1
2,129.9
1,989.3
2,053.3
2,216.3
2,346.0
1,983.3
1,981.4
2,011.1
1,981.5
2,012.5
2,033.0
2,053.3
2,114.2
2,146.2
2,191.5
2,243.2
2,284.1
2,310.5
2,314.8
2,348.9
2,409.8

���������������
���������������
���������������
���������������
���������������
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���������������
���������������
���������������
���������������
���������������
���������������
���������������
���������������
���������������
���������������
���������������
���������������
���������������
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���������������
���������������
31.4
17.9
40.2
40.6
39.5
37.7
–46.4
18.1
13.5
38.1
35.2
25.2
10.8
–21.1
–110.7
38.8
33.2
57.4
–133.4
–141.1
–126.7
–41.7
26.1
40.0
60.0
29.0
38.1
37.4
28.6
28.7
52.0
68.2
73.8
35.5

���������������
���������������
���������������
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���������������
���������������
���������������
���������������
���������������
���������������
���������������
���������������
���������������
���������������
���������������
���������������
���������������
���������������
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1,259.3
1,286.0
1,309.2
1,333.6
1,384.2
1,411.0
1,427.4
1,451.0
1,485.2
1,508.8
1,552.0
1,610.6
1,660.7
1,704.8
1,729.5
1,777.4
1,839.8
1,913.4
1,683.6
1,725.8
1,741.8
1,766.5
1,783.8
1,754.6
1,759.1
1,812.3
1,835.6
1,826.4
1,837.7
1,859.7
1,887.9
1,913.2
1,918.5
1,933.8

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–3.3
12.5
36.1
29.5
27.7
21.4
7.3
–6.4
3.6
28.1
14.7
34.1
16.9
–15.5
–30.9
13.6
.6
–8.2
–20.4
–47.5
–57.1
1.5
5.5
–4.9
36.4
17.3
–4.5
–6.1
–28.2
41.3
9.5
–18.6
–5.5
–18.0

Services 2

2,189.6
2,299.2
2,441.1
2,577.0
2,712.9
2,801.0
2,858.4
2,927.0
3,034.9
3,125.7
3,194.8
3,309.3
3,400.4
3,517.3
3,651.8
3,740.4
3,811.4
3,887.6
3,957.1
4,120.4
4,234.4
4,449.0
4,635.5
4,785.6
4,961.7
5,115.1
5,269.7
5,363.4
5,522.0
5,648.3
5,781.5
5,902.9
6,045.7
6,208.7
6,422.2
6,664.0
6,919.2
7,095.8
7,276.1
7,415.9
7,598.2
7,783.8
7,961.0
8,131.5
8,216.6
8,221.8
8,310.8
8,389.3
8,429.1
8,179.7
8,216.9
8,231.6
8,259.2
8,260.3
8,301.5
8,326.5
8,355.0
8,366.2
8,396.9
8,407.3
8,386.6
8,398.7
8,423.3
8,459.2
8,435.4

Structures

631.5
663.1
663.9
654.2
694.5
703.3
673.0
735.5
790.2
807.1
723.4
657.6
719.2
787.2
862.8
887.4
823.0
811.9
742.6
796.3
903.9
951.0
965.1
969.3
967.6
961.0
941.9
869.1
902.4
930.5
978.4
988.9
1,053.1
1,097.8
1,155.1
1,202.2
1,245.3
1,254.1
1,223.2
1,263.6
1,325.6
1,363.4
1,341.1
1,267.0
1,169.9
974.5
893.8
869.8
919.1
1,035.6
980.6
962.7
919.0
879.7
917.4
890.6
887.6
846.1
864.7
876.4
891.8
907.8
911.5
920.2
936.8

1 Estimates for durable and nondurable goods for 1996 and earlier periods are based on the Standard Industrial Classification (SIC); later estimates are based
on the North American Industry Classification System (NAICS).
2 Includes government consumption expenditures, which are for services (such as education and national defense) produced by government. In current
dollars, these services are valued at their cost of production.
Source: Department of Commerce (Bureau of Economic Analysis).

National Income or Expenditure | 335

Table B–10. Gross value added by sector, 1964–2012
[Billions of dollars; quarterly data at seasonally adjusted annual rates]
Business 1
Year or quarter

1964 ����������������������
1965 ����������������������
1966 ����������������������
1967 ����������������������
1968 ����������������������
1969 ����������������������
1970 ����������������������
1971 ����������������������
1972 ����������������������
1973 ����������������������
1974 ����������������������
1975 ����������������������
1976 ����������������������
1977 ����������������������
1978 ����������������������
1979 ����������������������
1980 ����������������������
1981 ����������������������
1982 ����������������������
1983 ����������������������
1984 ����������������������
1985 ����������������������
1986 ����������������������
1987 ����������������������
1988 ����������������������
1989 ����������������������
1990 ����������������������
1991 ����������������������
1992 ����������������������
1993 ����������������������
1994 ����������������������
1995 ����������������������
1996 ����������������������
1997 ����������������������
1998 ����������������������
1999 ����������������������
2000 ����������������������
2001 ����������������������
2002 ����������������������
2003 ����������������������
2004 ����������������������
2005 ����������������������
2006 ����������������������
2007 ����������������������
2008 ����������������������
2009 ����������������������
2010 ����������������������
2011 ����������������������
2012 p ��������������������
2009: I ������������������
      II �����������������
      III ����������������
      IV ����������������
2010: I ������������������
      II �����������������
      III ����������������
      IV ����������������
2011: I ������������������
      II �����������������
      III ����������������
      IV ����������������
2012: I ������������������
      II �����������������
      III ����������������
      IV p �������������

Gross
domestic
product

663.6
719.1
787.7
832.4
909.8
984.4
1,038.3
1,126.8
1,237.9
1,382.3
1,499.5
1,637.7
1,824.6
2,030.1
2,293.8
2,562.2
2,788.1
3,126.8
3,253.2
3,534.6
3,930.9
4,217.5
4,460.1
4,736.4
5,100.4
5,482.1
5,800.5
5,992.1
6,342.3
6,667.4
7,085.2
7,414.7
7,838.5
8,332.4
8,793.5
9,353.5
9,951.5
10,286.2
10,642.3
11,142.2
11,853.3
12,623.0
13,377.2
14,028.7
14,291.5
13,973.7
14,498.9
15,075.7
15,681.5
13,923.4
13,885.4
13,952.2
14,133.6
14,270.3
14,413.5
14,576.0
14,735.9
14,814.9
15,003.6
15,163.2
15,321.0
15,478.3
15,585.6
15,811.0
15,851.2

Total

524.9
570.7
624.3
653.6
713.5
769.1
802.2
868.3
957.1
1,077.4
1,164.5
1,265.8
1,420.7
1,590.0
1,809.4
2,028.5
2,186.1
2,454.0
2,514.9
2,741.1
3,065.5
3,283.9
3,461.5
3,662.0
3,940.2
4,235.7
4,453.9
4,558.6
4,829.2
5,084.1
5,425.2
5,677.8
6,030.2
6,442.8
6,810.8
7,249.0
7,715.5
7,913.6
8,132.8
8,502.8
9,070.1
9,680.1
10,262.4
10,738.3
10,787.8
10,367.0
10,836.0
11,341.2
11,880.2
10,341.7
10,282.6
10,339.1
10,504.4
10,619.7
10,742.3
10,912.5
11,069.5
11,108.7
11,272.8
11,417.6
11,565.7
11,693.0
11,793.3
12,006.8
12,027.5

Nonfarm 1

507.5
550.7
603.5
633.5
693.0
746.3
778.5
842.9
927.5
1,030.6
1,120.3
1,220.1
1,377.7
1,546.5
1,758.7
1,968.4
2,134.7
2,389.0
2,454.5
2,696.2
3,001.3
3,220.5
3,402.1
3,600.5
3,879.4
4,162.0
4,376.6
4,488.0
4,748.9
5,012.7
5,341.3
5,608.7
5,936.9
6,354.9
6,731.6
7,177.8
7,641.9
7,837.4
8,060.5
8,410.4
8,951.9
9,578.0
10,169.4
10,623.4
10,657.4
10,253.7
10,711.2
11,202.5
11,747.0
10,236.6
10,171.1
10,224.9
10,382.3
10,498.8
10,618.7
10,787.5
10,939.8
10,969.4
11,137.1
11,277.5
11,426.0
11,555.7
11,662.7
11,876.7
11,892.9

Households and institutions

Farm

17.3
19.9
20.8
20.1
20.5
22.8
23.7
25.4
29.7
46.8
44.2
45.6
43.0
43.5
50.7
60.1
51.4
65.0
60.4
44.9
64.2
63.4
59.5
61.5
60.7
73.8
77.3
70.6
80.4
71.4
83.9
69.1
93.3
87.9
79.2
71.2
73.6
76.2
72.3
92.4
118.3
102.0
93.1
114.9
130.5
113.2
124.8
138.7
133.2
105.1
111.5
114.2
122.1
120.9
123.6
124.9
129.6
139.3
135.6
140.1
139.7
137.3
130.6
130.1
134.6

Total

57.7
61.8
66.6
71.8
77.5
85.4
92.6
102.2
111.4
121.7
133.6
147.5
160.5
175.5
196.9
220.8
253.5
287.5
319.3
348.2
380.3
410.1
442.3
482.8
529.7
574.2
624.0
665.9
711.1
752.1
800.0
852.1
897.0
949.2
1,010.1
1,082.9
1,157.2
1,232.9
1,298.0
1,347.2
1,423.8
1,506.4
1,602.9
1,685.8
1,805.7
1,844.9
1,851.2
1,892.1
1,928.5
1,836.6
1,843.4
1,846.3
1,853.1
1,852.4
1,857.3
1,849.4
1,845.9
1,874.9
1,889.5
1,896.8
1,907.1
1,923.7
1,923.7
1,926.3
1,940.3

Households

41.2
43.6
46.2
49.1
51.9
56.0
59.8
65.5
70.8
76.5
83.0
90.8
98.7
107.9
121.3
136.0
156.5
177.8
196.7
212.5
231.0
250.3
268.0
288.0
313.1
337.2
363.3
383.7
405.3
428.3
461.3
492.2
519.8
550.9
583.9
628.4
673.5
719.5
746.0
762.7
806.0
864.4
924.8
968.1
1,042.8
1,048.3
1,038.5
1,055.2
1,066.1
1,054.1
1,047.6
1,048.0
1,043.6
1,049.1
1,047.1
1,029.4
1,028.6
1,045.9
1,055.2
1,056.9
1,062.9
1,066.4
1,065.8
1,062.9
1,069.5

Nonprofit
institutions
serving
households 2
16.5
18.2
20.4
22.7
25.6
29.4
32.8
36.7
40.5
45.2
50.6
56.7
61.8
67.6
75.6
84.8
97.0
109.7
122.7
135.6
149.3
159.8
174.3
194.8
216.6
237.0
260.6
282.2
305.9
323.8
338.7
359.9
377.2
398.3
426.3
454.5
483.7
513.4
552.1
584.5
617.7
642.0
678.1
717.8
762.9
796.5
812.7
836.9
862.4
782.6
795.8
798.3
809.5
803.3
810.2
820.0
817.3
829.0
834.3
839.9
844.2
857.4
858.0
863.4
870.8

General government 3

Total

81.1
86.6
96.8
107.0
118.8
130.0
143.5
156.4
169.4
183.2
201.3
224.5
243.5
264.6
287.5
313.0
348.5
385.3
419.0
445.4
485.1
523.4
556.3
591.5
630.6
672.2
722.7
767.6
801.9
831.2
859.9
884.8
911.3
940.3
972.5
1,021.6
1,078.8
1,139.6
1,211.4
1,292.2
1,359.3
1,436.5
1,512.0
1,604.6
1,698.0
1,761.9
1,811.7
1,842.4
1,872.9
1,745.1
1,759.4
1,766.9
1,776.1
1,798.1
1,814.0
1,814.2
1,820.5
1,831.3
1,841.3
1,848.8
1,848.2
1,861.5
1,868.5
1,878.0
1,883.4

Federal

40.7
42.4
47.2
51.5
56.3
59.9
64.0
67.7
71.5
73.9
79.6
87.3
93.8
102.0
109.7
117.6
131.2
147.4
161.2
171.2
192.1
205.0
212.6
223.3
234.8
246.4
258.8
274.8
282.0
285.2
285.2
283.6
287.6
290.0
292.2
300.4
315.1
324.9
351.8
382.9
412.0
438.7
460.6
486.0
517.7
553.2
589.2
607.0
616.7
543.5
550.9
556.2
562.3
581.1
592.1
590.7
593.1
601.4
606.0
610.0
610.5
613.9
615.7
617.6
619.5

State
and
local
40.4
44.2
49.6
55.5
62.5
70.0
79.5
88.6
97.9
109.3
121.8
137.2
149.7
162.6
177.8
195.4
217.3
237.9
257.7
274.1
293.1
318.4
343.7
368.2
395.8
425.8
463.9
492.8
519.9
546.0
574.7
601.2
623.7
650.3
680.3
721.2
763.7
814.7
859.6
909.3
947.3
997.7
1,051.3
1,118.6
1,180.3
1,208.6
1,222.5
1,235.4
1,256.2
1,201.6
1,208.5
1,210.7
1,213.8
1,217.0
1,221.9
1,223.6
1,227.4
1,229.9
1,235.3
1,238.7
1,237.8
1,247.6
1,252.8
1,260.3
1,264.0

Addendum:
Gross
housing
value
added
51.6
54.9
58.2
62.1
65.9
71.3
76.7
83.9
91.1
98.3
106.8
117.2
126.6
140.5
155.5
172.9
199.8
228.8
255.7
277.7
301.3
333.1
359.7
385.5
415.3
443.4
477.8
508.1
538.6
562.9
602.6
640.7
671.3
708.6
745.3
798.3
849.9
904.4
932.5
938.2
988.7
1,054.0
1,130.8
1,200.6
1,299.7
1,322.4
1,322.0
1,352.0
1,375.4
1,323.8
1,321.1
1,323.8
1,321.1
1,331.1
1,330.8
1,312.8
1,313.4
1,337.0
1,350.9
1,355.4
1,364.6
1,371.5
1,373.1
1,373.1
1,383.8

1 Gross domestic business value added equals gross domestic product excluding gross value added of households and institutions and of general
government. Nonfarm value added equals gross domestic business value added excluding gross farm value added.
2 Equals compensation of employees of nonprofit institutions, the rental value of nonresidential fixed assets owned and used by nonprofit institutions serving
households, and rental income of persons for tenant-occupied housing owned by nonprofit institutions.
3 Equals compensation of general government employees plus general government consumption of fixed capital.
Source: Department of Commerce (Bureau of Economic Analysis).

336 |

Appendix B

Table B–11. Real gross value added by sector, 1964–2012
[Billions of chained (2005) dollars; quarterly data at seasonally adjusted annual rates]
Business 1
Year or quarter

Gross
domestic
product

Total

Nonfarm 1

1964 ����������������������
1965 ����������������������
1966 ����������������������
1967 ����������������������
1968 ����������������������
1969 ����������������������
1970 ����������������������
1971 ����������������������
1972 ����������������������
1973 ����������������������
1974 ����������������������
1975 ����������������������
1976 ����������������������
1977 ����������������������
1978 ����������������������
1979 ����������������������
1980 ����������������������
1981 ����������������������
1982 ����������������������
1983 ����������������������
1984 ����������������������
1985 ����������������������
1986 ����������������������
1987 ����������������������
1988 ����������������������
1989 ����������������������
1990 ����������������������
1991 ����������������������
1992 ����������������������
1993 ����������������������
1994 ����������������������
1995 ����������������������
1996 ����������������������
1997 ����������������������
1998 ����������������������
1999 ����������������������
2000 ����������������������
2001 ����������������������
2002 ����������������������
2003 ����������������������
2004 ����������������������
2005 ����������������������
2006 ����������������������
2007 ����������������������
2008 ����������������������
2009 ����������������������
2010 ����������������������
2011 ����������������������
2012 p ��������������������
2009: I ������������������
      II �����������������
      III ����������������
      IV ����������������
2010: I ������������������
      II �����������������
      III ����������������
      IV ����������������
2011: I ������������������
      II �����������������
      III ����������������
      IV ����������������
2012: I ������������������
      II �����������������
      III ����������������
      IV p �������������

3,389.4
3,607.0
3,842.1
3,939.2
4,129.9
4,258.2
4,266.3
4,409.5
4,643.8
4,912.8
4,885.7
4,875.4
5,136.9
5,373.1
5,672.8
5,850.1
5,834.0
5,982.1
5,865.9
6,130.9
6,571.5
6,843.4
7,080.5
7,307.0
7,607.4
7,879.2
8,027.1
8,008.3
8,280.0
8,516.2
8,863.1
9,086.0
9,425.8
9,845.9
10,274.7
10,770.7
11,216.4
11,337.5
11,543.1
11,836.4
12,246.9
12,623.0
12,958.5
13,206.4
13,161.9
12,757.9
13,063.0
13,299.1
13,591.1
12,711.0
12,701.0
12,746.7
12,873.1
12,947.6
13,019.6
13,103.5
13,181.2
13,183.8
13,264.7
13,306.9
13,441.0
13,506.4
13,548.5
13,652.5
13,656.8

2,325.4
2,489.6
2,658.0
2,708.9
2,843.7
2,930.7
2,930.0
3,042.6
3,238.5
3,465.5
3,413.7
3,381.8
3,605.2
3,805.8
4,045.6
4,179.9
4,132.8
4,247.7
4,119.1
4,341.0
4,717.9
4,937.0
5,121.2
5,289.8
5,516.6
5,720.9
5,808.8
5,757.9
5,985.1
6,178.1
6,481.0
6,663.3
6,966.8
7,327.5
7,693.8
8,123.7
8,491.4
8,559.5
8,726.8
9,001.6
9,363.0
9,680.1
9,974.0
10,172.5
10,038.4
9,604.7
9,888.9
10,123.4
10,425.4
9,608.5
9,553.4
9,570.8
9,686.1
9,759.3
9,828.9
9,942.0
10,025.4
10,014.0
10,086.5
10,129.3
10,263.6
10,332.0
10,381.9
10,489.8
10,498.0

2,297.1
2,459.8
2,635.6
2,681.0
2,821.6
2,907.6
2,904.4
3,014.8
3,215.2
3,450.9
3,400.3
3,344.8
3,579.3
3,778.7
4,027.9
4,155.0
4,110.3
4,197.8
4,062.4
4,323.6
4,679.3
4,880.9
5,070.4
5,239.3
5,478.3
5,671.7
5,753.4
5,700.5
5,914.6
6,121.3
6,407.0
6,610.4
6,901.6
7,253.2
7,624.8
8,051.5
8,408.3
8,482.3
8,646.1
8,910.5
9,265.1
9,578.0
9,874.6
10,082.1
9,934.2
9,484.7
9,774.2
10,032.3
10,340.4
9,496.8
9,436.3
9,442.3
9,563.4
9,641.2
9,707.1
9,827.5
9,921.0
9,917.9
10,000.8
10,040.5
10,169.9
10,237.4
10,290.7
10,409.9
10,423.5

Households and institutions

Farm

24.9
26.5
25.5
27.6
26.6
27.5
28.3
29.8
29.8
29.5
28.8
34.3
32.7
34.5
33.3
36.3
35.2
46.5
48.8
31.9
43.3
52.9
50.8
51.3
45.6
52.3
56.0
56.9
66.2
57.8
70.5
56.4
65.3
72.5
69.4
72.8
83.5
77.7
81.2
91.6
97.9
102.0
99.1
90.3
101.7
117.5
111.7
91.9
87.9
108.4
114.4
127.3
120.1
115.0
118.6
111.1
102.0
95.4
87.9
90.3
94.2
95.0
92.5
84.0
80.2

Total

399.9
419.7
438.9
457.1
480.1
501.2
510.2
531.7
554.8
574.6
597.7
617.9
628.2
637.5
666.4
695.3
730.9
754.1
778.9
801.0
826.8
841.2
863.4
895.8
937.2
974.8
1,009.6
1,038.5
1,071.4
1,106.9
1,140.0
1,175.5
1,199.8
1,240.5
1,280.2
1,325.5
1,376.2
1,407.0
1,417.3
1,417.8
1,457.4
1,506.4
1,539.8
1,571.9
1,628.6
1,621.5
1,634.8
1,647.7
1,646.4
1,584.2
1,614.2
1,639.9
1,647.8
1,648.3
1,644.1
1,624.7
1,622.2
1,636.8
1,648.5
1,652.1
1,653.6
1,652.4
1,648.1
1,643.5
1,641.6

Nonprofit
institutions
serving
households 2

Households

236.0
246.9
256.8
267.1
274.6
285.9
292.6
305.9
319.1
330.6
345.0
354.2
360.9
365.0
387.4
405.0
430.6
444.1
452.1
460.5
476.4
487.4
493.7
506.8
525.7
542.0
555.7
572.0
589.0
603.5
631.9
651.3
665.4
687.6
703.7
740.3
774.1
793.1
789.9
787.1
821.7
864.4
898.0
914.2
954.8
943.0
948.0
948.2
933.6
947.8
938.8
941.7
943.5
954.2
958.2
942.1
937.6
946.9
951.3
948.4
946.2
941.5
936.5
930.4
926.0

159.4
168.6
178.5
186.6
204.9
214.9
216.7
224.5
234.4
242.7
251.0
262.5
265.8
271.3
276.7
287.8
297.1
306.8
324.3
338.5
348.3
351.2
368.0
388.0
411.1
432.9
454.9
467.4
483.5
504.9
508.7
524.8
535.0
553.5
577.8
585.3
601.8
613.4
627.7
631.1
635.9
642.0
642.0
657.8
674.2
678.6
686.7
698.8
711.3
638.0
675.2
697.6
703.6
693.9
686.2
682.4
684.3
689.6
696.7
702.8
706.3
709.7
710.2
711.5
713.8

General government 3

Total

768.4
794.2
843.9
888.7
923.6
947.2
950.8
952.4
950.6
954.9
974.4
990.1
998.7
1,009.2
1,028.5
1,039.5
1,054.4
1,060.2
1,071.0
1,077.9
1,091.3
1,122.5
1,150.1
1,175.3
1,205.8
1,234.6
1,266.2
1,279.4
1,283.7
1,286.5
1,286.8
1,287.7
1,289.8
1,299.6
1,314.3
1,326.3
1,349.4
1,373.7
1,401.4
1,418.2
1,426.8
1,436.5
1,445.0
1,462.5
1,492.3
1,522.4
1,532.7
1,524.7
1,519.8
1,511.6
1,523.3
1,525.1
1,529.5
1,531.3
1,538.7
1,531.5
1,529.4
1,528.4
1,525.9
1,522.4
1,522.1
1,521.2
1,518.5
1,520.5
1,518.8

Federal

400.7
403.4
429.9
457.9
465.7
467.1
447.1
426.5
405.8
390.7
389.4
387.3
387.9
389.0
393.9
393.5
399.7
405.9
412.5
422.0
431.6
443.9
451.8
463.6
469.3
475.1
483.8
486.7
476.5
467.4
452.2
435.1
423.2
415.2
410.4
407.1
410.5
412.1
420.2
431.5
435.8
438.7
438.4
441.8
459.0
486.0
503.8
507.8
505.5
475.1
485.7
489.8
493.4
498.7
507.2
504.4
505.0
507.2
507.8
507.5
508.6
507.4
505.6
504.8
504.2

State
and
local
377.5
400.5
424.2
442.1
468.6
490.0
511.7
532.5
550.9
570.2
590.9
608.9
616.9
626.4
641.0
652.4
661.2
660.9
665.2
662.5
666.4
685.6
705.4
719.0
743.6
766.4
789.2
799.4
813.0
824.2
838.5
855.1
868.4
885.6
904.6
919.5
939.0
961.3
980.9
986.7
991.0
997.7
1,006.5
1,020.8
1,033.3
1,036.7
1,029.5
1,017.7
1,015.0
1,036.7
1,037.9
1,035.7
1,036.6
1,033.2
1,032.1
1,027.7
1,025.1
1,021.9
1,018.8
1,015.6
1,014.3
1,014.6
1,013.7
1,016.4
1,015.3

Addendum:
Gross
housing
value
added
291.6
307.1
320.9
335.6
348.3
364.6
376.6
393.6
412.5
427.8
448.5
462.2
469.3
481.2
503.2
523.0
555.0
576.7
592.3
605.4
624.6
649.1
661.1
676.8
696.4
712.2
730.2
754.6
776.7
789.1
821.7
846.9
860.4
885.6
900.9
942.3
977.8
997.8
988.5
969.3
1,008.4
1,054.0
1,098.6
1,132.4
1,183.9
1,181.8
1,196.3
1,203.5
1,192.4
1,182.7
1,176.4
1,182.0
1,186.2
1,201.3
1,206.9
1,190.5
1,186.5
1,199.7
1,207.1
1,204.6
1,202.6
1,199.0
1,194.8
1,189.4
1,186.2

1 Gross domestic business value added equals gross domestic product excluding gross value added of households and institutions and of general
government. Nonfarm value added equals gross domestic business value added excluding gross farm value added.
2 Equals compensation of employees of nonprofit institutions, the rental value of nonresidential fixed assets owned and used by nonprofit institutions serving
households, and rental income of persons for tenant-occupied housing owned by nonprofit institutions.
3 Equals compensation of general government employees plus general government consumption of fixed capital.
Source: Department of Commerce (Bureau of Economic Analysis).

National Income or Expenditure | 337

Table B–12. Gross domestic product (GDP) by industry, value added, in current dollars and
as a percentage of GDP, 1981–2011
[Billions of dollars; except as noted]
Private industries
Year

Gross
domestic
product

Total
private
industries

Agriculture,
forestry,
fishing,
and
hunting

Manufacturing
Mining

Construction

Total
manufacturing

Durable
goods

Nondurable
goods

Utilities

Wholesale
trade

Retail
trade

Value added
1981 �����������
1982 �����������
1983 �����������
1984 �����������
1985 �����������
1986 �����������
1987 �����������
1988 �����������
1989 �����������
1990 �����������
1991 �����������
1992 �����������
1993 �����������
1994 �����������
1995 �����������
1996 �����������
1997 �����������
1998 �����������
1999 �����������
2000 �����������
2001 �����������
2002 �����������
2003 �����������
2004 �����������
2005 �����������
2006 �����������
2007 �����������
2008 �����������
2009 �����������
2010 �����������
2011 �����������

3,126.8
3,253.2
3,534.6
3,930.9
4,217.5
4,460.1
4,736.4
5,100.4
5,482.1
5,800.5
5,992.1
6,342.3
6,667.4
7,085.2
7,414.7
7,838.5
8,332.4
8,793.5
9,353.5
9,951.5
10,286.2
10,642.3
11,142.2
11,853.3
12,623.0
13,377.2
14,028.7
14,291.5
13,973.7
14,498.9
15,075.7

2,701.6
2,791.4
3,041.7
3,393.0
3,634.6
3,840.4
4,077.9
4,395.3
4,729.7
4,994.3
5,133.2
5,442.0
5,735.9
6,119.9
6,420.0
6,812.6
7,271.0
7,694.4
8,199.6
8,736.1
9,010.8
9,289.3
9,706.9
10,345.6
11,037.1
11,709.4
12,268.8
12,437.1
12,056.7
12,532.3
13,081.8

75.6
71.6
57.2
77.0
76.6
73.7
78.8
78.1
91.6
95.7
88.3
99.3
90.6
105.6
91.3
114.2
108.4
100.3
92.8
95.6
98.6
94.4
115.5
142.7
127.1
122.5
144.5
159.4
142.4
157.6
173.5

121.5
118.5
102.8
107.2
106.2
70.3
73.1
74.1
78.6
88.4
79.5
73.6
74.4
75.9
76.7
90.0
94.8
81.0
82.0
108.9
119.3
109.5
134.9
159.3
192.3
229.8
254.5
319.2
221.7
251.9
289.9

Percent
1981 �����������
1982 �����������
1983 �����������
1984 �����������
1985 �����������
1986 �����������
1987 �����������
1988 �����������
1989 �����������
1990 �����������
1991 �����������
1992 �����������
1993 �����������
1994 �����������
1995 �����������
1996 �����������
1997 �����������
1998 �����������
1999 �����������
2000 �����������
2001 �����������
2002 �����������
2003 �����������
2004 �����������
2005 �����������
2006 �����������
2007 �����������
2008 �����������
2009 �����������
2010 �����������
2011 �����������

100.0
100.0
100.0
100.0
100.0
100.0
100.0
100.0
100.0
100.0
100.0
100.0
100.0
100.0
100.0
100.0
100.0
100.0
100.0
100.0
100.0
100.0
100.0
100.0
100.0
100.0
100.0
100.0
100.0
100.0
100.0

133.1
131.0
139.6
160.7
177.0
197.2
210.1
226.5
238.6
243.6
228.8
233.2
250.4
277.2
294.2
320.9
346.7
383.7
428.4
467.3
490.5
494.3
516.1
554.2
612.5
651.0
653.8
614.2
542.9
523.3
529.5

619.6
606.5
657.5
731.8
751.4
777.4
823.1
900.2
950.2
968.9
976.7
1,016.7
1,058.9
1,127.3
1,180.9
1,208.5
1,277.3
1,326.7
1,368.1
1,415.6
1,343.9
1,355.5
1,374.3
1,482.7
1,569.3
1,648.4
1,698.0
1,628.5
1,540.1
1,630.5
1,731.5

376.2
359.2
385.5
451.0
458.6
468.4
492.5
537.9
562.4
558.9
554.2
574.5
603.0
650.2
675.4
705.0
748.9
781.2
802.4
839.1
758.8
767.8
766.4
822.0
878.3
921.3
939.9
904.1
787.0
866.7
910.1

243.4
247.3
272.0
280.7
292.8
308.9
330.6
362.2
387.7
410.1
422.5
442.2
456.0
477.1
505.5
503.5
528.3
545.6
565.6
576.5
585.2
587.8
607.9
660.6
691.0
727.1
758.1
724.4
753.2
763.8
821.3

72.0
83.2
94.4
105.7
113.0
117.5
125.8
125.1
138.2
145.5
153.8
159.7
164.3
171.2
175.3
173.4
169.9
165.1
172.7
173.9
177.6
181.0
192.0
208.0
205.9
236.0
248.6
257.7
264.7
284.5
297.9

206.2
206.6
222.4
249.8
269.2
279.3
285.6
314.3
335.7
347.7
362.6
380.1
402.5
444.5
460.2
492.5
524.9
557.3
579.1
617.7
613.3
614.9
638.1
684.2
725.5
769.7
816.7
824.1
766.3
799.0
845.1

218.0
226.9
255.3
286.8
309.1
331.4
345.7
366.8
390.7
400.4
407.9
430.0
462.9
500.5
525.0
556.8
589.9
626.9
653.4
686.2
703.9
731.2
769.5
795.1
837.6
875.8
887.9
848.6
846.8
876.0
905.7

2.3
2.6
2.7
2.7
2.7
2.6
2.7
2.5
2.5
2.5
2.6
2.5
2.5
2.4
2.4
2.2
2.0
1.9
1.8
1.7
1.7
1.7
1.7
1.8
1.6
1.8
1.8
1.8
1.9
2.0
2.0

6.6
6.4
6.3
6.4
6.4
6.3
6.0
6.2
6.1
6.0
6.1
6.0
6.0
6.3
6.2
6.3
6.3
6.3
6.2
6.2
6.0
5.8
5.7
5.8
5.7
5.8
5.8
5.8
5.5
5.5
5.6

7.0
7.0
7.2
7.3
7.3
7.4
7.3
7.2
7.1
6.9
6.8
6.8
6.9
7.1
7.1
7.1
7.1
7.1
7.0
6.9
6.8
6.9
6.9
6.7
6.6
6.5
6.3
5.9
6.1
6.0
6.0

Industry value added as a percentage of GDP (percent)
86.4
85.8
86.1
86.3
86.2
86.1
86.1
86.2
86.3
86.1
85.7
85.8
86.0
86.4
86.6
86.9
87.3
87.5
87.7
87.8
87.6
87.3
87.1
87.3
87.4
87.5
87.5
87.0
86.3
86.4
86.8

2.4
2.2
1.6
2.0
1.8
1.7
1.7
1.5
1.7
1.6
1.5
1.6
1.4
1.5
1.2
1.5
1.3
1.1
1.0
1.0
1.0
.9
1.0
1.2
1.0
.9
1.0
1.1
1.0
1.1
1.2

3.9
3.6
2.9
2.7
2.5
1.6
1.5
1.5
1.4
1.5
1.3
1.2
1.1
1.1
1.0
1.1
1.1
.9
.9
1.1
1.2
1.0
1.2
1.3
1.5
1.7
1.8
2.2
1.6
1.7
1.9

4.3
4.0
3.9
4.1
4.2
4.4
4.4
4.4
4.4
4.2
3.8
3.7
3.8
3.9
4.0
4.1
4.2
4.4
4.6
4.7
4.8
4.6
4.6
4.7
4.9
4.9
4.7
4.3
3.9
3.6
3.5

19.8
18.6
18.6
18.6
17.8
17.4
17.4
17.6
17.3
16.7
16.3
16.0
15.9
15.9
15.9
15.4
15.3
15.1
14.6
14.2
13.1
12.7
12.3
12.5
12.4
12.3
12.1
11.4
11.0
11.2
11.5

12.0
11.0
10.9
11.5
10.9
10.5
10.4
10.5
10.3
9.6
9.2
9.1
9.0
9.2
9.1
9.0
9.0
8.9
8.6
8.4
7.4
7.2
6.9
6.9
7.0
6.9
6.7
6.3
5.6
6.0
6.0

7.8
7.6
7.7
7.1
6.9
6.9
7.0
7.1
7.1
7.1
7.1
7.0
6.8
6.7
6.8
6.4
6.3
6.2
6.0
5.8
5.7
5.5
5.5
5.6
5.5
5.4
5.4
5.1
5.4
5.3
5.4

1 Consists of agriculture, forestry, fishing, and hunting; mining; construction; and manufacturing.
2 Consists of utilities; wholesale trade; retail trade; transportation and warehousing; information; finance, insurance, real estate, rental, and leasing;
professional and business services; educational services, health care, and social assistance; arts, entertainment, recreation, accommodation, and food services;
and other services, except government.
Note: Data shown in Tables B–12 and B–13 are consistent with the 2012 annual revision of the industry accounts released in December 2012. For details
see Survey of Current Business, December 2012.
See next page for continuation of table.

338 |

Appendix B

Table B–12. Gross domestic product (GDP) by industry, value added, in current dollars and
as a percentage of GDP, 1981–2011—Continued
[Billions of dollars; except as noted]
Private industries—Continued

Year

Transportation
and
warehousing

Information

Finance,
insurance,
real estate,
rental,
and
leasing

123.5
135.3
152.5
160.0
176.4
185.6
197.4
205.4
222.4
235.6
244.3
260.5
279.6
299.4
311.5
338.6
349.4
386.1
438.5
417.8
451.1
499.7
506.6
558.8
586.5
590.6
635.5
636.8
604.8
612.2
646.6

502.8
544.7
611.6
677.5
739.4
804.0
850.3
915.7
981.0
1,049.2
1,109.8
1,192.1
1,259.3
1,321.6
1,405.7
1,490.3
1,610.6
1,696.8
1,834.0
1,997.7
2,154.8
2,222.3
2,316.5
2,400.4
2,598.8
2,765.3
2,857.0
2,916.6
2,941.8
3,021.8
3,058.1

Professional
and
business
services

Educational
services,
health care,
and
social
assistance

Arts,
entertainment,
recreation,
accommodation,
and food
services

Private
goodsOther
Government producing
services,
industries 1
except
government

Private
servicesproducing
industries 2

Value added
1981 �������������
1982 �������������
1983 �������������
1984 �������������
1985 �������������
1986 �������������
1987 �������������
1988 �������������
1989 �������������
1990 �������������
1991 �������������
1992 �������������
1993 �������������
1994 �������������
1995 �������������
1996 �������������
1997 �������������
1998 �������������
1999 �������������
2000 �������������
2001 �������������
2002 �������������
2003 �������������
2004 �������������
2005 �������������
2006 �������������
2007 �������������
2008 �������������
2009 �������������
2010 �������������
2011 �������������

110.1
106.3
118.0
131.4
137.1
147.0
152.6
161.4
166.3
172.8
182.3
192.0
206.4
223.7
231.7
241.3
261.8
275.6
287.1
301.4
302.6
302.4
319.8
347.0
369.5
394.0
404.9
415.0
396.6
422.6
447.9

197.3
213.2
242.4
280.9
316.3
352.4
384.5
424.3
470.4
516.5
524.0
566.6
600.9
639.7
687.3
756.5
842.1
927.0
1,010.2
1,116.8
1,170.7
1,198.3
1,260.0
1,347.5
1,460.2
1,567.2
1,697.6
1,783.2
1,693.2
1,769.6
1,883.9

152.9
169.2
189.7
207.1
225.4
245.2
277.7
301.5
337.4
376.7
413.4
452.9
476.4
500.2
523.9
545.4
571.4
601.2
638.5
678.0
729.2
789.8
847.1
906.1
953.5
1,015.3
1,076.9
1,153.9
1,225.6
1,269.2
1,311.1

92.9
100.0
111.5
120.8
132.0
144.0
152.3
168.8
184.0
199.6
205.9
219.0
230.9
242.3
255.3
272.8
300.3
321.1
355.4
381.6
391.2
411.1
427.8
458.7
485.4
512.4
549.0
537.3
525.4
558.0
591.1

76.0
78.3
86.8
96.3
105.3
115.3
121.1
133.0
144.8
153.9
155.9
166.3
178.3
190.7
200.7
211.2
223.8
245.6
259.3
277.6
264.2
285.0
288.8
300.8
313.0
331.6
343.8
342.7
344.4
356.0
369.9

425.2
461.8
492.9
537.9
582.9
619.7
658.4
705.1
752.4
806.2
858.9
900.3
931.4
965.3
994.6
1,025.9
1,061.3
1,099.1
1,153.9
1,215.4
1,275.4
1,353.0
1,435.3
1,507.7
1,585.9
1,667.8
1,759.9
1,854.4
1,917.0
1,966.6
1,993.8

949.9
927.7
957.1
1,076.7
1,111.2
1,118.6
1,185.0
1,278.8
1,358.9
1,396.5
1,373.2
1,422.8
1,474.3
1,586.1
1,643.1
1,733.6
1,827.2
1,891.7
1,971.3
2,087.4
2,052.3
2,053.7
2,140.8
2,338.9
2,501.2
2,651.6
2,750.9
2,721.2
2,447.1
2,563.4
2,724.4

1,751.7
1,863.7
2,084.6
2,316.3
2,523.4
2,721.8
2,892.9
3,116.5
3,370.8
3,597.7
3,760.0
4,019.2
4,261.6
4,533.8
4,776.9
5,079.0
5,443.8
5,802.7
6,228.3
6,648.7
6,958.5
7,235.6
7,566.1
8,006.6
8,535.8
9,057.8
9,517.9
9,715.9
9,609.6
9,968.9
10,357.4

13.6
14.2
13.9
13.7
13.8
13.9
13.9
13.8
13.7
13.9
14.3
14.2
14.0
13.6
13.4
13.1
12.7
12.5
12.3
12.2
12.4
12.7
12.9
12.7
12.6
12.5
12.5
13.0
13.7
13.6
13.2

30.4
28.5
27.1
27.4
26.3
25.1
25.0
25.1
24.8
24.1
22.9
22.4
22.1
22.4
22.2
22.1
21.9
21.5
21.1
21.0
20.0
19.3
19.2
19.7
19.8
19.8
19.6
19.0
17.5
17.7
18.1

56.0
57.3
59.0
58.9
59.8
61.0
61.1
61.1
61.5
62.0
62.8
63.4
63.9
64.0
64.4
64.8
65.3
66.0
66.6
66.8
67.6
68.0
67.9
67.5
67.6
67.7
67.8
68.0
68.8
68.8
68.7

Industry value added as a percentage of GDP (percent)
1981 �������������
1982 �������������
1983 �������������
1984 �������������
1985 �������������
1986 �������������
1987 �������������
1988 �������������
1989 �������������
1990 �������������
1991 �������������
1992 �������������
1993 �������������
1994 �������������
1995 �������������
1996 �������������
1997 �������������
1998 �������������
1999 �������������
2000 �������������
2001 �������������
2002 �������������
2003 �������������
2004 �������������
2005 �������������
2006 �������������
2007 �������������
2008 �������������
2009 �������������
2010 �������������
2011 �������������

3.5
3.3
3.3
3.3
3.3
3.3
3.2
3.2
3.0
3.0
3.0
3.0
3.1
3.2
3.1
3.1
3.1
3.1
3.1
3.0
2.9
2.8
2.9
2.9
2.9
2.9
2.9
2.9
2.8
2.9
3.0

4.0
4.2
4.3
4.1
4.2
4.2
4.2
4.0
4.1
4.1
4.1
4.1
4.2
4.2
4.2
4.3
4.2
4.4
4.7
4.2
4.4
4.7
4.5
4.7
4.6
4.4
4.5
4.5
4.3
4.2
4.3

16.1
16.7
17.3
17.2
17.5
18.0
18.0
18.0
17.9
18.1
18.5
18.8
18.9
18.7
19.0
19.0
19.3
19.3
19.6
20.1
20.9
20.9
20.8
20.3
20.6
20.7
20.4
20.4
21.1
20.8
20.3

6.3
6.6
6.9
7.1
7.5
7.9
8.1
8.3
8.6
8.9
8.7
8.9
9.0
9.0
9.3
9.7
10.1
10.5
10.8
11.2
11.4
11.3
11.3
11.4
11.6
11.7
12.1
12.5
12.1
12.2
12.5

4.9
5.2
5.4
5.3
5.3
5.5
5.9
5.9
6.2
6.5
6.9
7.1
7.1
7.1
7.1
7.0
6.9
6.8
6.8
6.8
7.1
7.4
7.6
7.6
7.6
7.6
7.7
8.1
8.8
8.8
8.7

3.0
3.1
3.2
3.1
3.1
3.2
3.2
3.3
3.4
3.4
3.4
3.5
3.5
3.4
3.4
3.5
3.6
3.7
3.8
3.8
3.8
3.9
3.8
3.9
3.8
3.8
3.9
3.8
3.8
3.8
3.9

2.4
2.4
2.5
2.4
2.5
2.6
2.6
2.6
2.6
2.7
2.6
2.6
2.7
2.7
2.7
2.7
2.7
2.8
2.8
2.8
2.6
2.7
2.6
2.5
2.5
2.5
2.5
2.4
2.5
2.5
2.5

Note (cont’d): Value added is the contribution of each private industry and of government to GDP. Value added is equal to an industry’s gross output minus
its intermediate inputs. Current-dollar value added is calculated as the sum of distributions by an industry to its labor and capital, which are derived from the
components of gross domestic income.
Value added industry data shown in Tables B–12 and B–13 are based on the 2002 North American Industry Classification System (NAICS).
Source: Department of Commerce (Bureau of Economic Analysis).

National Income or Expenditure | 339

Table B–13. Real gross domestic product by industry, value added, and percent changes,
1981–2011
Private industries
Year

Gross
domestic
product

Total
private
industries

Agriculture,
forestry,
fishing,
and
hunting

Manufacturing
Mining

Construction

Total
manufacturing

Durable
goods

Nondurable
goods

Utilities

Wholesale
trade

Retail
trade

Chain-type quantity indexes for value added (2005=100)
1981 �����������
1982 �����������
1983 �����������
1984 �����������
1985 �����������
1986 �����������
1987 �����������
1988 �����������
1989 �����������
1990 �����������
1991 �����������
1992 �����������
1993 �����������
1994 �����������
1995 �����������
1996 �����������
1997 �����������
1998 �����������
1999 �����������
2000 �����������
2001 �����������
2002 �����������
2003 �����������
2004 �����������
2005 �����������
2006 �����������
2007 �����������
2008 �����������
2009 �����������
2010 �����������
2011 �����������

47.390
46.470
48.570
52.060
54.214
56.092
57.887
60.266
62.420
63.591
63.442
65.595
67.466
70.214
71.980
74.672
78.000
81.397
85.326
88.857
89.816
91.445
93.769
97.021
100.000
102.658
104.622
104.270
101.069
103.486
105.356

45.387
44.282
46.325
49.753
51.961
53.470
55.466
58.098
60.243
61.264
61.161
63.537
65.296
68.374
70.112
73.146
76.840
80.541
84.778
88.667
89.792
91.300
93.464
96.945
100.000
102.980
104.953
103.909
99.908
102.626
104.711

48.384
51.011
36.388
47.087
55.753
54.881
56.750
50.675
56.742
60.074
60.756
67.964
58.983
70.448
59.555
66.286
71.591
69.837
73.031
81.603
78.861
82.079
90.644
96.510
100.000
100.756
93.149
101.279
114.472
111.233
96.068

114.882
109.757
104.252
114.545
121.137
116.810
122.364
136.911
132.276
130.787
133.113
129.022
131.161
142.428
143.474
133.682
138.097
148.848
137.847
121.027
136.785
138.414
120.511
119.237
100.000
108.435
111.427
107.236
134.267
121.976
122.020

1981 �����������
1982 �����������
1983 �����������
1984 �����������
1985 �����������
1986 �����������
1987 �����������
1988 �����������
1989 �����������
1990 �����������
1991 �����������
1992 �����������
1993 �����������
1994 �����������
1995 �����������
1996 �����������
1997 �����������
1998 �����������
1999 �����������
2000 �����������
2001 �����������
2002 �����������
2003 �����������
2004 �����������
2005 �����������
2006 �����������
2007 �����������
2008 �����������
2009 �����������
2010 �����������
2011 �����������

2.5
–1.9
4.5
7.2
4.1
3.5
3.2
4.1
3.6
1.9
–.2
3.4
2.9
4.1
2.5
3.7
4.5
4.4
4.8
4.1
1.1
1.8
2.5
3.5
3.1
2.7
1.9
–.3
–3.1
2.4
1.8

2.6
–2.4
4.6
7.4
4.4
2.9
3.7
4.7
3.7
1.7
–.2
3.9
2.8
4.7
2.5
4.3
5.1
4.8
5.3
4.6
1.3
1.7
2.4
3.7
3.2
3.0
1.9
–1.0
–3.8
2.7
2.0

25.8
5.4
–28.7
29.4
18.4
–1.6
3.4
–10.7
12.0
5.9
1.1
11.9
–13.2
19.4
–15.5
11.3
8.0
–2.5
4.6
11.7
–3.4
4.1
10.4
6.5
3.6
.8
–7.5
8.7
13.0
–2.8
–13.6

–0.6
–4.5
–5.0
9.9
5.8
–3.6
4.8
11.9
–3.4
–1.1
1.8
–3.1
1.7
8.6
.7
–6.8
3.3
7.8
–7.4
–12.2
13.0
1.2
–12.9
–1.1
–16.1
8.4
2.8
–3.8
25.2
–9.2
.0

68.529
60.546
62.785
70.655
75.849
77.499
79.148
82.976
85.326
84.779
78.616
80.403
82.649
87.293
88.224
92.982
95.170
98.277
103.607
106.961
104.536
100.882
101.161
101.134
100.000
96.982
91.606
85.547
74.490
73.620
73.388

45.199
41.913
45.226
49.545
51.109
51.078
54.843
58.683
59.359
58.575
57.674
59.597
61.987
66.078
68.798
70.997
75.261
79.022
83.268
88.584
84.499
86.606
89.347
96.658
100.000
104.159
107.847
101.545
92.209
98.564
101.039

34.438
31.046
33.064
38.389
39.540
39.836
42.637
46.870
47.610
46.726
45.243
46.187
48.129
51.830
55.832
59.253
64.194
70.550
75.962
84.443
79.298
82.246
85.053
93.004
100.000
106.663
110.655
108.932
91.138
103.223
110.238

66.320
64.152
70.536
70.782
73.192
72.251
77.950
80.123
80.544
80.093
80.651
84.672
87.853
92.380
91.805
91.157
93.699
92.120
94.101
93.958
91.571
92.420
95.052
101.453
100.000
101.069
104.394
93.038
92.674
93.049
91.132

58.963
57.737
60.798
66.262
70.538
74.025
82.732
82.022
90.437
95.576
96.834
97.689
96.434
99.397
102.620
101.716
97.108
95.007
104.692
108.309
93.854
97.378
100.904
104.815
100.000
100.539
104.004
108.818
98.997
109.020
111.834

30.726
30.871
32.224
34.845
36.656
40.323
39.192
41.306
43.307
42.692
44.438
48.490
49.957
53.134
52.901
57.783
64.068
74.157
78.059
83.510
87.671
88.479
93.901
98.912
100.000
102.995
108.619
107.416
93.075
96.225
99.098

35.287
35.240
38.504
42.183
44.468
47.777
46.100
50.726
52.973
53.825
53.661
56.467
59.225
63.523
66.714
72.881
79.185
84.195
86.596
89.942
92.731
95.770
97.961
97.982
100.000
102.176
102.473
96.613
94.746
101.361
101.521

7.9
–3.3
10.0
.3
3.4
–1.3
7.9
2.8
.5
–.6
.7
5.0
3.8
5.2
–.6
–.7
2.8
–1.7
2.2
–.2
–2.5
.9
2.8
6.7
–1.4
1.1
3.3
–10.9
–.4
.4
–2.1

–0.2
–2.1
5.3
9.0
6.5
4.9
11.8
–.9
10.3
5.7
1.3
.9
–1.3
3.1
3.2
–.9
–4.5
–2.2
10.2
3.5
–13.3
3.8
3.6
3.9
–4.6
.5
3.4
4.6
–9.0
10.1
2.6

6.1
.5
4.4
8.1
5.2
10.0
–2.8
5.4
4.8
–1.4
4.1
9.1
3.0
6.4
–.4
9.2
10.9
15.7
5.3
7.0
5.0
.9
6.1
5.3
1.1
3.0
5.5
–1.1
–13.4
3.4
3.0

2.9
–.1
9.3
9.6
5.4
7.4
–3.5
10.0
4.4
1.6
–.3
5.2
4.9
7.3
5.0
9.2
8.6
6.3
2.9
3.9
3.1
3.3
2.3
.0
2.1
2.2
.3
–5.7
–1.9
7.0
.2

Percent change from year earlier
–8.8
–11.6
3.7
12.5
7.4
2.2
2.1
4.8
2.8
–.6
–7.3
2.3
2.8
5.6
1.1
5.4
2.4
3.3
5.4
3.2
–2.3
–3.5
.3
.0
–1.1
–3.0
–5.5
–6.6
–12.9
–1.2
–.3

4.8
–7.3
7.9
9.5
3.2
–.1
7.4
7.0
1.2
–1.3
–1.5
3.3
4.0
6.6
4.1
3.2
6.0
5.0
5.4
6.4
–4.6
2.5
3.2
8.2
3.5
4.2
3.5
–5.8
–9.2
6.9
2.5

2.8
–9.8
6.5
16.1
3.0
.7
7.0
9.9
1.6
–1.9
–3.2
2.1
4.2
7.7
7.7
6.1
8.3
9.9
7.7
11.2
–6.1
3.7
3.4
9.3
7.5
6.7
3.7
–1.6
–16.3
13.3
6.8

1 Consists of agriculture, forestry, fishing, and hunting; mining; construction; and manufacturing.
2 Consists of utilities; wholesale trade; retail trade; transportation and warehousing; information; finance, insurance, real estate, rental, and leasing;

professional and business services; educational services, health care, and social assistance; arts, entertainment, recreation, accommodation, and food services;
and other services, except government.
See next page for continuation of table.

340 |

Appendix B

Table B–13. Real gross domestic product by industry, value added, and percent changes,
1981–2011—Continued
Private industries—Continued

Year

Transportation
and
warehousing

Information

Finance,
insurance,
real estate,
rental,
and
leasing

Educational
services,
health care,
and
social
assistance

Professional
and
business
services

Arts,
entertainment,
recreation,
accommodation,
and food
services

Private
goodsOther
Government producing
services,
industries 1
except
government

Private
servicesproducing
industries 2

Chain-type quantity indexes for value added (2005=100)
1981 �������������
1982 �������������
1983 �������������
1984 �������������
1985 �������������
1986 �������������
1987 �������������
1988 �������������
1989 �������������
1990 �������������
1991 �������������
1992 �������������
1993 �������������
1994 �������������
1995 �������������
1996 �������������
1997 �������������
1998 �������������
1999 �������������
2000 �������������
2001 �������������
2002 �������������
2003 �������������
2004 �������������
2005 �������������
2006 �������������
2007 �������������
2008 �������������
2009 �������������
2010 �������������
2011 �������������

40.790
38.832
43.831
45.938
46.619
46.696
48.989
50.432
52.397
55.147
57.664
61.325
64.042
69.180
71.236
75.138
79.006
78.063
80.801
86.201
83.090
81.948
86.133
93.911
100.000
104.049
105.231
106.182
95.382
101.721
106.590

32.049
31.956
34.198
33.874
34.821
34.983
37.356
38.579
41.288
42.649
43.057
45.429
47.837
50.285
52.034
55.321
56.402
62.107
70.528
67.832
72.885
80.958
82.501
92.679
100.000
101.530
109.310
111.156
104.993
108.313
114.722

48.938
49.393
50.583
52.452
53.847
54.648
56.560
58.607
60.088
61.497
62.438
64.388
66.268
67.851
69.615
71.251
74.419
76.667
81.686
87.064
92.351
92.155
93.538
94.519
100.000
104.035
105.125
104.357
105.607
106.040
106.391

35.550
35.428
37.922
42.010
45.365
48.917
51.538
54.138
57.635
60.141
58.046
59.787
61.282
63.418
65.656
70.179
75.051
79.327
82.819
86.923
89.035
89.688
92.228
95.440
100.000
103.229
106.140
110.288
103.846
106.089
111.203

1981 �������������
1982 �������������
1983 �������������
1984 �������������
1985 �������������
1986 �������������
1987 �������������
1988 �������������
1989 �������������
1990 �������������
1991 �������������
1992 �������������
1993 �������������
1994 �������������
1995 �������������
1996 �������������
1997 �������������
1998 �������������
1999 �������������
2000 �������������
2001 �������������
2002 �������������
2003 �������������
2004 �������������
2005 �������������
2006 �������������
2007 �������������
2008 �������������
2009 �������������
2010 �������������
2011 �������������

–2.5
–4.8
12.9
4.8
1.5
.2
4.9
2.9
3.9
5.2
4.6
6.3
4.4
8.0
3.0
5.5
5.1
–1.2
3.5
6.7
–3.6
–1.4
5.1
9.0
6.5
4.0
1.1
.9
–10.2
6.6
4.8

5.5
–.3
7.0
–.9
2.8
.5
6.8
3.3
7.0
3.3
1.0
5.5
5.3
5.1
3.5
6.3
2.0
10.1
13.6
–3.8
7.5
11.1
1.9
12.3
7.9
1.5
7.7
1.7
–5.5
3.2
5.9

1.4
.9
2.4
3.7
2.7
1.5
3.5
3.6
2.5
2.3
1.5
3.1
2.9
2.4
2.6
2.4
4.4
3.0
6.5
6.6
6.1
–.2
1.5
1.0
5.8
4.0
1.0
–.7
1.2
.4
.3

2.5
–.3
7.0
10.8
8.0
7.8
5.4
5.0
6.5
4.3
–3.5
3.0
2.5
3.5
3.5
6.9
6.9
5.7
4.4
5.0
2.4
.7
2.8
3.5
4.8
3.2
2.8
3.9
–5.8
2.2
4.8

57.200
57.034
59.229
60.919
62.423
63.597
67.638
68.238
70.866
73.463
75.173
77.453
77.728
78.052
79.293
80.204
81.559
82.657
84.776
86.688
88.822
92.487
95.460
98.332
100.000
103.265
104.978
109.833
112.056
113.472
115.397

46.189
47.380
51.042
53.218
55.848
59.483
59.082
62.454
64.701
66.671
64.814
67.092
69.166
71.235
73.630
76.742
80.225
82.504
87.572
91.104
89.691
91.313
93.634
97.751
100.000
102.563
105.614
100.271
94.050
100.114
105.492

73.651
70.878
74.147
78.074
80.627
82.446
83.865
87.958
91.973
93.971
91.234
93.331
96.564
101.126
103.010
103.940
102.674
108.399
109.304
110.957
99.325
102.420
100.428
100.685
100.000
101.704
101.659
97.388
93.221
93.916
95.105

75.162
75.297
75.976
76.794
78.818
80.650
82.216
84.340
86.397
88.511
88.991
89.513
89.512
89.780
89.719
90.120
91.101
92.284
93.395
95.142
95.941
97.802
98.749
99.445
100.000
100.437
101.209
103.008
103.940
104.589
103.820

52.361
48.901
50.241
55.880
58.708
58.664
62.184
65.702
66.909
66.431
64.989
67.163
68.816
73.841
75.400
78.077
82.210
85.786
89.880
94.368
91.430
92.368
94.040
99.161
100.000
102.528
103.194
97.973
92.363
95.059
95.631

42.951
42.869
45.236
47.804
49.789
51.881
53.341
55.673
58.155
59.704
60.060
62.511
64.309
66.769
68.566
71.717
75.282
79.023
83.304
87.019
89.318
90.987
93.288
96.307
100.000
103.112
105.471
105.673
102.135
104.860
107.386

–3.0
–3.8
4.6
5.3
3.3
2.3
1.7
4.9
4.6
2.2
–2.9
2.3
3.5
4.7
1.9
.9
–1.2
5.6
.8
1.5
–10.5
3.1
–1.9
.3
–.7
1.7
.0
–4.2
–4.3
.7
1.3

0.4
.2
.9
1.1
2.6
2.3
1.9
2.6
2.4
2.4
.5
.6
.0
.3
–.1
.4
1.1
1.3
1.2
1.9
.8
1.9
1.0
.7
.6
.4
.8
1.8
.9
.6
–.7

3.5
–6.6
2.7
11.2
5.1
–.1
6.0
5.7
1.8
–.7
–2.2
3.3
2.5
7.3
2.1
3.6
5.3
4.3
4.8
5.0
–3.1
1.0
1.8
5.4
.8
2.5
.6
–5.1
–5.7
2.9
.6

2.2
–.2
5.5
5.7
4.2
4.2
2.8
4.4
4.5
2.7
.6
4.1
2.9
3.8
2.7
4.6
5.0
5.0
5.4
4.5
2.6
1.9
2.5
3.2
3.8
3.1
2.3
.2
–3.3
2.7
2.4

Percent change from year earlier
1.9
–.3
3.8
2.9
2.5
1.9
6.4
.9
3.9
3.7
2.3
3.0
.4
.4
1.6
1.1
1.7
1.3
2.6
2.3
2.5
4.1
3.2
3.0
1.7
3.3
1.7
4.6
2.0
1.3
1.7

3.5
2.6
7.7
4.3
4.9
6.5
–.7
5.7
3.6
3.0
–2.8
3.5
3.1
3.0
3.4
4.2
4.5
2.8
6.1
4.0
–1.6
1.8
2.5
4.4
2.3
2.6
3.0
–5.1
–6.2
6.4
5.4

Note: Data are based on the 2002 North American Industry Classification System (NAICS).
See Note, Table B–12.
Source: Department of Commerce (Bureau of Economic Analysis).

National Income or Expenditure | 341

Table B–14. Gross value added of nonfinancial corporate business, 1964–2012
[Billions of dollars; quarterly data at seasonally adjusted annual rates]
Net value added

Year or
quarter

Gross
value
Conadded sumpof nonfinancial tion
of
corpofixed
rate
capital
business 1

1964 �������������
356.1
1965 �������������
391.2
1966 �������������
429.0
1967 �������������
451.2
1968 �������������
497.8
1969 �������������
540.5
1970 �������������
558.3
1971 �������������
603.0
1972 �������������
669.4
1973 �������������
750.8
1974 �������������
809.8
1975 �������������
876.7
1976 �������������
989.7
1977 ������������� 1,119.4
1978 ������������� 1,272.7
1979 ������������� 1,414.4
1980 ������������� 1,534.5
1981 ������������� 1,742.2
1982 ������������� 1,802.6
1983 ������������� 1,929.1
1984 ������������� 2,161.4
1985 ������������� 2,293.9
1986 ������������� 2,383.2
1987 ������������� 2,551.0
1988 ������������� 2,765.4
1989 ������������� 2,899.2
1990 ������������� 3,035.2
1991 ������������� 3,104.1
1992 ������������� 3,241.1
1993 ������������� 3,398.4
1994 ������������� 3,677.6
1995 ������������� 3,888.0
1996 ������������� 4,119.4
1997 ������������� 4,412.5
1998 ������������� 4,668.3
1999 ������������� 4,955.5
2000 ������������� 5,279.4
2001 ������������� 5,252.5
2002 ������������� 5,307.7
2003 ������������� 5,503.7
2004 ������������� 5,877.5
2005 ������������� 6,302.8
2006 ������������� 6,740.3
2007 ������������� 6,946.0
2008 ������������� 6,991.4
2009 ������������� 6,590.8
2010 ������������ 6,952.4
2011 ������������� 7,366.7
2012 p ����������� �������������
2009: I ��������� 6,633.6
      II �������� 6,527.7
      III ������� 6,521.4
      IV ������� 6,680.7
2010: I ��������� 6,828.1
      II �������� 6,894.9
      III ������� 7,033.7
      IV ������� 7,053.0
2011: I ��������� 7,200.6
      II �������� 7,367.0
      III ������� 7,418.6
      IV ������� 7,480.5
2012: I ��������� 7,605.5
      II �������� 7,670.8
      III ������� 7,693.7
      IV p ���� �������������

Addenda

Net operating surplus

Total

27.0
329.0
29.1
362.1
31.9
397.1
35.2
416.0
38.7
459.1
42.9
497.5
47.5
510.8
52.0
551.1
56.5
613.0
63.1
687.6
74.2
735.7
88.6
788.0
97.8
892.0
110.1 1,009.2
125.1 1,147.5
144.3 1,270.2
166.7 1,367.8
192.4 1,549.8
212.8 1,589.8
219.3 1,709.8
228.8 1,932.6
244.0 2,049.9
258.0 2,125.2
270.0 2,280.9
287.3 2,478.1
303.9 2,595.3
321.0 2,714.2
336.1 2,768.0
344.1 2,897.0
359.0 3,039.3
380.1 3,297.5
408.3 3,479.7
435.1 3,684.4
466.9 3,945.6
499.9 4,168.5
539.3 4,416.3
590.1 4,689.4
632.0 4,620.5
654.5 4,653.1
669.0 4,834.7
695.6 5,181.9
743.0 5,559.8
800.9 5,939.4
840.1 6,106.0
864.3 6,127.1
862.5 5,728.3
860.1 6,092.3
893.7 6,473.0
933.6 �������������
874.2 5,759.4
863.5 5,664.2
856.6 5,664.8
855.7 5,825.0
855.3 5,972.8
857.8 6,037.1
860.7 6,173.0
866.6 6,186.4
876.0 6,324.6
888.8 6,478.2
900.3 6,518.4
909.7 6,570.8
920.8 6,684.7
930.8 6,740.1
937.1 6,756.5
945.7 �������������

Taxes
Comon
pensa- production tion and
of
imports
employ- less
ees
subsidies

225.7
245.4
272.9
291.1
321.9
357.1
376.5
399.4
443.9
502.2
552.2
575.5
651.4
735.3
845.1
958.4
1,047.2
1,157.6
1,200.4
1,263.1
1,400.0
1,496.1
1,575.4
1,678.4
1,804.7
1,905.7
2,005.5
2,044.8
2,152.9
2,244.0
2,382.1
2,511.5
2,631.3
2,814.6
3,049.7
3,256.5
3,541.8
3,559.4
3,544.2
3,651.3
3,786.7
3,976.3
4,182.3
4,361.0
4,441.2
4,173.7
4,252.0
4,472.7
4,659.7
4,209.2
4,174.4
4,150.5
4,160.9
4,176.8
4,235.0
4,288.6
4,307.5
4,435.1
4,465.0
4,487.9
4,502.9
4,607.1
4,644.8
4,673.7
4,713.1

33.9
36.0
37.0
39.3
45.5
50.2
54.2
59.5
63.7
70.1
74.4
80.2
86.7
94.6
102.7
108.8
121.5
146.7
152.9
168.0
185.0
196.6
204.6
216.8
233.8
248.2
263.5
285.7
302.5
318.0
347.8
354.2
365.6
381.0
393.1
414.6
439.4
434.5
461.9
484.2
517.7
558.4
593.3
607.7
615.2
589.2
612.2
645.8
657.6
584.4
587.9
584.4
600.0
605.8
609.4
614.2
619.3
637.8
646.3
646.0
653.1
656.1
657.8
656.9
659.7

Total

Net
interest Business
and
current
miscel- transfer
laneous paypayments ments

69.5
5.2
80.7
5.8
87.2
7.0
85.6
8.4
91.7
9.7
90.3
12.7
80.1
16.6
92.1
17.6
105.4
18.6
115.4
21.8
109.1
27.5
132.4
28.4
153.9
26.0
179.3
28.5
199.7
33.4
203.0
41.8
199.1
54.2
245.5
67.2
236.5
77.4
278.7
77.0
347.5
86.0
357.2
91.5
345.2
98.5
385.6
95.9
439.6
107.9
441.5
133.9
445.2
143.1
437.5
139.6
441.6
114.2
477.3
99.8
567.5
98.8
614.0
112.7
687.5
112.1
750.0
124.7
725.7
146.8
745.1
164.5
708.2
192.8
626.7
197.7
647.1
163.7
699.2
147.9
877.5
134.4
1,025.1
148.2
1,163.7
164.0
1,137.4
232.3
1,070.8
257.7
965.4
227.4
1,228.2
221.7
1,354.5
255.9
������������ �������������
965.8
257.4
901.8
224.4
929.9
212.9
1,064.2
214.9
1,190.1
216.2
1,192.6
215.1
1,270.3
220.7
1,259.6
234.9
1,251.7
248.5
1,367.0
248.9
1,384.4
263.7
1,414.8
262.5
1,421.6
263.2
1,437.5
254.2
1,425.9
263.4
������������ �������������

Corporate profits with inventory valuation and capital
consumption adjustments

Total

Taxes
on
corporate
income

Profits
after
tax 2

Profits
before
tax

Inven- Capital
tory
convalua- sumption
tion
adjustadjustment
ment

2.0
62.4
23.9
38.5
55.9
–0.5
2.2
72.7
27.1
45.5
66.1
–1.2
2.7
77.5
29.5
48.0
71.4
–2.1
2.8
74.4
27.8
46.5
67.6
–1.6
3.1
78.9
33.5
45.4
74.0
–3.7
3.2
74.4
33.3
41.0
71.2
–5.9
3.3
60.2
27.3
32.9
58.5
–6.6
3.7
70.8
30.0
40.8
67.4
–4.6
4.0
82.8
33.8
49.0
79.5
–6.6
4.7
88.9
40.4
48.5
99.5
–19.6
4.1
77.5
42.8
34.6
110.2
–38.2
5.0
98.9
41.9
57.0
110.7
–10.5
7.0
121.0
53.5
67.5
138.2
–14.1
9.0
141.9
60.6
81.3
159.5
–15.7
9.5
156.8
67.6
89.2
183.7
–23.7
9.5
151.8
70.6
81.2
197.2
–40.1
10.2
134.7
68.2
66.5
184.1
–42.1
11.4
166.8
66.0 100.8
185.0
–24.6
8.8
150.2
48.8 101.5
140.0
–7.5
10.5
191.2
61.7 129.5
163.4
–7.4
11.7
249.8
75.9 173.9
197.6
–4.0
16.1
249.6
71.1 178.6
173.5
.0
27.3
219.5
76.2 143.2
149.7
7.1
29.9
259.9
94.2 165.7
213.5
–16.2
27.4
304.3
104.0 200.3
264.1
–22.2
24.0
283.5
101.2 182.3
243.1
–16.3
25.4
276.7
98.5 178.3
243.3
–12.9
26.6
271.3
88.6 182.7
226.8
4.9
31.3
296.1
94.4 201.7
258.6
–2.8
30.1
347.5
108.0 239.5
308.7
–4.0
35.3
433.5
132.4 301.1
391.9
–12.4
30.7
470.6
140.3 330.3
431.2
–18.3
38.0
537.4
152.9 384.5
471.3
3.1
39.2
586.2
161.4 424.8
506.8
14.1
35.2
543.7
158.7 385.1
460.5
15.7
47.1
533.5
171.4 362.1
468.6
–4.0
47.9
467.5
170.2 297.3
432.5
–16.8
58.9
370.1
111.2 258.8
315.1
8.0
56.3
427.2
97.1 330.1
342.3
–2.6
65.2
486.1
132.9 353.2
425.9
–11.3
65.5
677.5
187.0 490.6
662.1
–34.3
79.3
797.6
271.9 525.8
957.1
–30.7
75.8
923.9
307.6 616.2 1,117.9
–38.0
69.1
835.9
293.8 542.2 1,042.0
–47.2
58.1
755.0
227.4 527.7
831.2
–44.5
77.4
660.6
177.8 482.8
712.9
3.2
89.3
917.1
222.9 694.3
990.5
–38.7
91.5 1,007.1
246.8 760.3 1,007.0
–62.6
84.7 ������������� ������������� ������������ ������������� �������������
76.1
632.3
167.6 464.6
612.0
81.4
81.5
595.9
161.9 434.1
634.3
15.0
72.6
644.4
170.0 474.4
713.3
–17.6
79.4
769.9
211.7 558.2
892.0
–66.2
85.3
888.6
211.9 676.7
980.5
–27.2
88.2
889.4
221.1 668.3
974.1
–14.3
91.9
957.6
231.5 726.1 1,020.3
–26.0
91.8
932.9
227.0 705.9
987.0
–87.2
91.9
911.3
244.0 667.3
963.0 –121.7
91.7 1,026.4
253.9 772.5 1,037.8
–75.0
91.2 1,029.6
248.1 781.4 1,010.8
–40.6
91.3 1,061.0
241.2 819.8 1,016.3
–12.9
90.0 1,068.3
304.3 764.0 1,240.4
–23.7
87.1 1,096.1
304.6 791.6 1,229.8
16.0
80.5 1,082.0
307.9 774.1 1,256.5
–26.8
81.2 ������������� ������������� ������������ ������������� �������������

7.0
7.8
8.1
8.3
8.6
9.1
8.3
8.0
9.9
9.0
5.5
–1.2
–3.2
–1.9
–3.2
–5.3
–7.2
6.5
17.8
35.2
56.2
76.2
62.7
62.6
62.3
56.7
46.3
39.6
40.3
42.9
54.0
57.6
63.0
65.3
67.5
68.9
51.8
47.0
87.5
71.5
49.7
–128.8
–156.0
–158.8
–31.7
–55.4
–34.7
62.7
–148.2
–61.1
–53.4
–51.3
–56.0
–64.7
–70.4
–36.7
33.1
70.1
63.6
59.3
57.6
–148.4
–149.7
–147.7
–147.1

1 Estimates for nonfinancial corporate business for 2000 and earlier periods are based on the Standard Industrial Classification (SIC); later estimates are
based on the North American Industry Classification System (NAICS).
2 With inventory valuation and capital consumption adjustments.
Source: Department of Commerce (Bureau of Economic Analysis).

342 |

Appendix B

Table B–15. Gross value added and price, costs, and profits of nonfinancial corporate
business, 1964–2012
[Quarterly data at seasonally adjusted annual rates]
Price per unit of real gross value added of nonfinancial corporate business (dollars) 1, 2

Gross value added of
nonfinancial corporate
business (billions
of dollars) 1
Year or quarter
Current
dollars
1964 ����������������������
1965 ����������������������
1966 ����������������������
1967 ����������������������
1968 ����������������������
1969 ����������������������
1970 ����������������������
1971 ����������������������
1972 ����������������������
1973 ����������������������
1974 ����������������������
1975 ����������������������
1976 ����������������������
1977 ����������������������
1978 ����������������������
1979 ����������������������
1980 ����������������������
1981 ����������������������
1982 ����������������������
1983 ����������������������
1984 ����������������������
1985 ����������������������
1986 ����������������������
1987 ����������������������
1988 ����������������������
1989 ����������������������
1990 ����������������������
1991 ����������������������
1992 ����������������������
1993 ����������������������
1994 ����������������������
1995 ����������������������
1996 ����������������������
1997 ����������������������
1998 ����������������������
1999 ����������������������
2000 ����������������������
2001 ����������������������
2002 ����������������������
2003 ����������������������
2004 ����������������������
2005 ����������������������
2006 ����������������������
2007 ����������������������
2008 ����������������������
2009 ����������������������
2010 ����������������������
2011 ����������������������
2009: I ������������������
      II �����������������
      III ����������������
      IV ����������������
2010: I ������������������
      II �����������������
      III ����������������
      IV ����������������
2011: I ������������������
      II �����������������
      III ����������������
      IV ����������������
2012: I ������������������
      II �����������������
      III ����������������

356.1
391.2
429.0
451.2
497.8
540.5
558.3
603.0
669.4
750.8
809.8
876.7
989.7
1,119.4
1,272.7
1,414.4
1,534.5
1,742.2
1,802.6
1,929.1
2,161.4
2,293.9
2,383.2
2,551.0
2,765.4
2,899.2
3,035.2
3,104.1
3,241.1
3,398.4
3,677.6
3,888.0
4,119.4
4,412.5
4,668.3
4,955.5
5,279.4
5,252.5
5,307.7
5,503.7
5,877.5
6,302.8
6,740.3
6,946.0
6,991.4
6,590.8
6,952.4
7,366.7
6,633.6
6,527.7
6,521.4
6,680.7
6,828.1
6,894.9
7,033.7
7,053.0
7,200.6
7,367.0
7,418.6
7,480.5
7,605.5
7,670.8
7,693.7

Chained
(2005)
dollars
1,368.1
1,481.8
1,588.1
1,630.9
1,736.7
1,806.9
1,792.4
1,866.3
2,009.0
2,132.7
2,099.0
2,068.2
2,237.2
2,402.9
2,560.2
2,640.4
2,613.4
2,717.8
2,653.0
2,781.1
3,027.7
3,157.9
3,235.5
3,402.5
3,599.1
3,658.8
3,713.1
3,695.4
3,804.9
3,905.0
4,155.3
4,349.0
4,588.6
4,887.8
5,167.3
5,452.4
5,745.7
5,637.8
5,675.5
5,818.1
6,085.1
6,302.8
6,543.2
6,606.4
6,515.9
6,036.8
6,369.1
6,595.6
6,028.2
5,963.9
5,992.1
6,162.9
6,312.8
6,347.1
6,421.9
6,394.8
6,499.2
6,611.2
6,586.5
6,685.6
6,768.5
6,803.6
6,738.6

Total

0.260
.264
.270
.277
.287
.299
.311
.323
.333
.352
.386
.424
.442
.466
.497
.536
.587
.641
.679
.694
.714
.726
.737
.750
.768
.792
.817
.840
.852
.870
.885
.894
.898
.903
.903
.909
.919
.932
.935
.946
.966
1.000
1.030
1.051
1.073
1.092
1.092
1.117
1.100
1.095
1.088
1.084
1.082
1.086
1.095
1.103
1.108
1.114
1.126
1.119
1.124
1.127
1.142

Compensation
of
employees
(unit
labor
cost)
0.165
.166
.172
.178
.185
.198
.210
.214
.221
.235
.263
.278
.291
.306
.330
.363
.401
.426
.452
.454
.462
.474
.487
.493
.501
.521
.540
.553
.566
.575
.573
.577
.573
.576
.590
.597
.616
.631
.624
.628
.622
.631
.639
.660
.682
.691
.668
.678
.698
.700
.693
.675
.662
.667
.668
.674
.682
.675
.681
.674
.681
.683
.694

Corporate profits with inventory
valuation and capital consumption
adjustments 4

Unit nonlabor cost

Total

0.050
.050
.049
.053
.056
.061
.067
.071
.071
.075
.085
.098
.098
.101
.106
.116
.135
.154
.170
.171
.169
.173
.182
.180
.183
.194
.203
.214
.208
.207
.207
.209
.207
.208
.208
.214
.222
.235
.235
.234
.232
.243
.249
.264
.276
.291
.280
.286
.298
.295
.289
.284
.278
.279
.278
.284
.285
.284
.289
.286
.285
.283
.287

ConinterTaxes on Net
sumption production
est and
of
misceland
fixed
laneous
imports 3 payments
capital
0.020
.020
.020
.022
.022
.024
.026
.028
.028
.030
.035
.043
.044
.046
.049
.055
.064
.071
.080
.079
.076
.077
.080
.079
.080
.083
.086
.091
.090
.092
.091
.094
.095
.096
.097
.099
.103
.112
.115
.115
.114
.118
.122
.127
.133
.143
.135
.135
.145
.145
.143
.139
.135
.135
.134
.136
.135
.134
.137
.136
.136
.137
.139

0.026
.026
.025
.026
.028
.030
.032
.034
.034
.035
.037
.041
.042
.043
.044
.045
.050
.058
.061
.064
.065
.067
.072
.073
.073
.074
.078
.085
.088
.089
.092
.089
.088
.086
.083
.085
.085
.088
.091
.094
.096
.101
.102
.102
.103
.110
.110
.112
.110
.112
.110
.110
.109
.110
.110
.111
.112
.112
.112
.111
.110
.109
.109

0.004
.004
.004
.005
.006
.007
.009
.009
.009
.010
.013
.014
.012
.012
.013
.016
.021
.025
.029
.028
.028
.029
.030
.028
.030
.037
.039
.038
.030
.026
.024
.026
.024
.026
.028
.030
.034
.035
.029
.025
.022
.024
.025
.035
.040
.038
.035
.039
.043
.038
.036
.035
.034
.034
.034
.037
.038
.038
.040
.039
.039
.037
.039

Total

0.046
.049
.049
.046
.045
.041
.034
.038
.041
.042
.037
.048
.054
.059
.061
.057
.052
.061
.057
.069
.083
.079
.068
.076
.085
.077
.075
.073
.078
.089
.104
.108
.117
.120
.105
.098
.081
.066
.075
.084
.111
.127
.141
.127
.116
.109
.144
.153
.105
.100
.108
.125
.141
.140
.149
.146
.140
.155
.156
.159
.158
.161
.161

Taxes on
corporate
income
0.017
.018
.019
.017
.019
.018
.015
.016
.017
.019
.020
.020
.024
.025
.026
.027
.026
.024
.018
.022
.025
.023
.024
.028
.029
.028
.027
.024
.025
.028
.032
.032
.033
.033
.031
.031
.030
.020
.017
.023
.031
.043
.047
.044
.035
.029
.035
.037
.028
.027
.028
.034
.034
.035
.036
.035
.038
.038
.038
.036
.045
.045
.046

Profits
after
tax 5
0.028
.031
.030
.029
.026
.023
.018
.022
.024
.023
.016
.028
.030
.034
.035
.031
.025
.037
.038
.047
.057
.057
.044
.049
.056
.050
.048
.049
.053
.061
.072
.076
.084
.087
.075
.066
.052
.046
.058
.061
.081
.083
.094
.082
.081
.080
.109
.115
.077
.073
.079
.091
.107
.105
.113
.110
.103
.117
.119
.123
.113
.116
.115

1 Estimates for nonfinancial corporate business for 2000 and earlier periods are based on the Standard Industrial Classification (SIC); later estimates are
based on the North American Industry Classification System (NAICS).
2 The implicit price deflator for gross value added of nonfinancial corporate business divided by 100.
3 Less subsidies plus business current transfer payments.
4 Unit profits from current production.
5 With inventory valuation and capital consumption adjustments.
Source: Department of Commerce (Bureau of Economic Analysis).

National Income or Expenditure | 343

Table B–16. Personal consumption expenditures, 1964–2012
[Billions of dollars; quarterly data at seasonally adjusted annual rates]
Goods
Durable
Personal
consumption
expenditures

Year or
quarter

1964 ��������������
1965 ��������������
1966 ��������������
1967 ��������������
1968 ��������������
1969 ��������������
1970 ��������������
1971 ��������������
1972 ��������������
1973 ��������������
1974 ��������������
1975 ��������������
1976 ��������������
1977 ��������������
1978 ��������������
1979 ��������������
1980 ��������������
1981 ��������������
1982 ��������������
1983 ��������������
1984 ��������������
1985 ��������������
1986 ��������������
1987 ��������������
1988 ��������������
1989 ��������������
1990 ��������������
1991 ��������������
1992 ��������������
1993 ��������������
1994 ��������������
1995 ��������������
1996 ��������������
1997 ��������������
1998 ��������������
1999 ��������������
2000 ��������������
2001 ��������������
2002 ��������������
2003 ��������������
2004 ��������������
2005 ��������������
2006 ��������������
2007 ��������������
2008 ��������������
2009 ��������������
2010 ��������������
2011 ��������������
2012 p ������������
2009: I ����������
      II ���������
      III ��������
      IV ��������
2010: I ����������
      II ���������
      III ��������
      IV ��������
2011: I ����������
      II ���������
      III ��������
      IV ��������
2012: I ����������
      II ���������
      III ��������
      IV p �����

411.5
443.8
480.9
507.8
558.0
605.1
648.3
701.6
770.2
852.0
932.9
1,033.8
1,151.3
1,277.8
1,427.6
1,591.2
1,755.8
1,939.5
2,075.5
2,288.6
2,501.1
2,717.6
2,896.7
3,097.0
3,350.1
3,594.5
3,835.5
3,980.1
4,236.9
4,483.6
4,750.8
4,987.3
5,273.6
5,570.6
5,918.5
6,342.8
6,830.4
7,148.8
7,439.2
7,804.1
8,270.6
8,803.5
9,301.0
9,772.3
10,035.5
9,845.9
10,215.7
10,729.0
11,120.9
9,768.4
9,763.9
9,888.8
9,962.5
10,069.1
10,148.3
10,243.6
10,401.9
10,566.3
10,684.9
10,791.2
10,873.8
11,007.2
11,067.2
11,154.4
11,254.6

Total
Total 1

212.3
229.7
249.6
259.0
284.6
304.7
318.8
342.1
373.8
416.6
451.5
491.3
546.3
600.4
663.6
737.9
799.8
869.4
899.3
973.8
1,063.7
1,137.6
1,195.6
1,256.3
1,337.3
1,423.8
1,491.3
1,497.4
1,563.3
1,642.3
1,746.6
1,815.5
1,917.7
2,006.8
2,110.0
2,290.0
2,459.1
2,534.0
2,610.0
2,728.0
2,892.1
3,076.7
3,224.7
3,363.9
3,381.7
3,194.4
3,364.9
3,624.8
3,783.2
3,125.5
3,142.0
3,244.4
3,265.5
3,318.2
3,321.7
3,361.0
3,458.6
3,561.4
3,604.3
3,643.6
3,690.0
3,755.9
3,741.5
3,792.5
3,843.0

59.6
66.4
71.7
74.0
84.8
90.5
90.0
102.4
116.4
130.5
130.2
142.2
168.6
192.0
213.3
226.3
226.4
243.9
253.0
295.0
342.2
380.4
421.4
442.0
475.1
494.3
497.1
477.2
508.1
551.5
607.2
635.7
676.3
715.5
780.0
857.4
915.8
946.3
992.1
1,019.9
1,072.9
1,123.4
1,155.0
1,188.4
1,108.9
1,029.6
1,079.4
1,146.4
1,219.1
1,016.3
1,010.4
1,052.7
1,038.9
1,049.1
1,070.2
1,082.6
1,115.7
1,133.9
1,131.8
1,144.8
1,175.1
1,204.6
1,200.3
1,218.9
1,252.5

Services
Household consumption
expenditures

Nondurable

Motor
vehicles
and
parts

25.8
29.6
29.9
29.6
35.4
37.4
34.5
43.2
49.4
54.4
48.2
52.6
68.2
79.8
89.2
90.2
84.4
93.0
100.0
122.9
147.2
170.1
187.5
188.2
202.2
207.8
205.1
185.7
204.8
224.7
249.8
255.7
273.5
293.1
320.2
350.7
363.2
383.3
401.3
401.0
403.9
408.2
394.8
399.9
339.3
316.0
342.7
373.6
407.0
299.2
303.6
341.3
320.0
321.1
336.5
346.3
367.0
374.5
362.2
367.4
390.3
402.1
396.0
404.5
425.6

Total 1

152.7
163.3
177.9
185.0
199.8
214.2
228.8
239.7
257.4
286.1
321.4
349.2
377.7
408.4
450.2
511.6
573.4
625.4
646.3
678.8
721.5
757.2
774.2
814.3
862.3
929.5
994.2
1,020.3
1,055.2
1,090.8
1,139.4
1,179.8
1,241.4
1,291.2
1,330.0
1,432.6
1,543.4
1,587.7
1,617.9
1,708.1
1,819.3
1,953.4
2,069.8
2,175.5
2,272.8
2,164.8
2,285.5
2,478.4
2,564.2
2,109.2
2,131.6
2,191.7
2,226.7
2,269.1
2,251.5
2,278.4
2,342.9
2,427.5
2,472.4
2,498.7
2,515.0
2,551.3
2,541.2
2,573.6
2,590.5

Food and
beverages
Gasoline
purand
chased
other
for offenergy
premises goods
consumption
69.5
74.4
80.6
82.6
88.8
95.4
103.5
107.1
114.5
126.7
143.0
156.6
167.3
179.8
196.1
218.4
239.2
255.3
267.1
277.0
291.1
303.0
316.4
324.3
342.8
365.4
391.2
403.0
404.5
413.5
432.1
443.7
461.9
474.8
486.5
513.6
537.5
559.7
569.6
587.5
613.0
644.5
674.2
711.2
746.4
742.3
760.6
810.2
829.1
739.7
739.8
741.1
748.7
758.0
753.3
757.7
773.5
791.8
807.3
817.3
824.4
827.0
827.5
829.2
832.6

17.7
19.1
20.7
21.9
23.2
25.0
26.3
27.6
29.4
34.3
43.8
48.0
53.0
57.8
61.5
80.4
101.9
113.4
108.4
106.5
108.2
110.5
91.2
96.4
99.9
110.4
124.2
121.1
125.0
126.9
129.2
133.4
144.7
147.7
133.4
148.8
188.8
183.6
174.6
209.5
249.4
303.8
335.2
364.8
410.5
299.3
352.4
428.3
439.8
261.4
275.7
321.5
338.7
357.9
335.2
344.2
372.2
419.2
431.4
435.0
427.6
440.5
428.5
443.1
447.1

Total

199.2
214.1
231.3
248.8
273.4
300.4
329.5
359.5
396.4
435.4
481.4
542.5
604.9
677.4
764.1
853.2
956.0
1,070.1
1,176.2
1,314.8
1,437.4
1,580.0
1,701.1
1,840.7
2,012.7
2,170.7
2,344.2
2,482.6
2,673.6
2,841.2
3,004.3
3,171.7
3,355.9
3,563.9
3,808.5
4,052.8
4,371.2
4,614.8
4,829.2
5,076.1
5,378.5
5,726.8
6,076.3
6,408.3
6,653.8
6,651.5
6,850.9
7,104.2
7,337.6
6,642.9
6,621.9
6,644.4
6,697.0
6,750.9
6,826.6
6,882.6
6,943.3
7,004.9
7,080.6
7,147.6
7,183.8
7,251.3
7,325.7
7,361.9
7,411.6

Total 1

Housing
and
utilities

Health
care

192.5
206.9
223.5
240.4
264.0
290.4
318.4
347.2
382.8
420.7
465.0
524.4
584.9
655.6
739.6
825.4
924.1
1,033.9
1,136.1
1,271.9
1,389.8
1,529.7
1,645.8
1,782.1
1,946.0
2,099.0
2,264.5
2,398.4
2,581.3
2,746.6
2,901.9
3,064.6
3,240.2
3,451.6
3,677.5
3,907.4
4,205.9
4,428.6
4,624.2
4,864.8
5,169.1
5,515.1
5,836.3
6,154.4
6,369.3
6,372.0
6,571.2
6,812.3
7,035.2
6,360.5
6,345.5
6,366.6
6,415.4
6,472.5
6,546.3
6,603.6
6,662.4
6,722.1
6,790.5
6,848.1
6,888.5
6,956.4
7,019.4
7,060.6
7,104.5

72.1
76.6
81.2
86.3
92.7
101.0
109.4
120.0
131.2
143.5
158.6
176.5
194.7
217.8
244.3
273.4
311.8
352.0
387.0
421.2
458.3
500.7
535.7
571.8
614.5
655.6
696.4
735.5
771.2
814.5
866.5
913.8
961.2
1,009.9
1,065.2
1,125.0
1,198.6
1,287.7
1,334.8
1,393.9
1,462.4
1,582.6
1,686.2
1,756.2
1,831.0
1,871.6
1,891.9
1,929.9
1,965.9
1,870.2
1,868.8
1,870.1
1,877.2
1,882.5
1,885.6
1,896.8
1,902.8
1,909.7
1,926.0
1,945.2
1,938.9
1,935.2
1,968.3
1,983.5
1,976.5

24.2
26.0
28.7
31.9
36.6
42.1
47.7
53.7
59.8
67.2
76.1
89.0
101.8
115.7
131.2
148.8
171.7
201.9
225.2
253.1
276.5
302.2
330.2
366.0
410.1
451.2
506.2
555.8
612.8
648.8
680.5
719.9
752.1
790.9
832.0
863.6
918.4
996.6
1,082.9
1,148.2
1,228.5
1,308.9
1,373.7
1,457.7
1,532.6
1,601.6
1,663.0
1,751.6
1,817.9
1,573.5
1,595.4
1,613.1
1,624.3
1,626.8
1,648.3
1,674.7
1,702.2
1,726.7
1,749.6
1,754.2
1,775.9
1,800.4
1,803.5
1,825.9
1,841.9

Addendum:
Personal
consumption
expendiFinancial
tures
services excludand
ing
insurfood
ance
and
energy 2
17.7
19.4
21.3
22.8
25.8
28.5
31.1
34.1
38.3
41.5
45.9
54.0
59.3
67.8
80.6
87.6
95.6
102.0
116.3
145.9
156.6
180.5
196.7
207.1
219.4
235.7
253.2
282.0
311.8
341.0
349.0
364.7
393.6
431.3
469.6
514.2
570.0
562.8
576.2
602.5
651.7
698.4
732.6
790.3
807.0
741.8
796.3
807.1
828.6
746.8
733.9
735.2
751.2
779.8
804.5
800.4
800.4
800.1
800.6
815.0
812.5
827.5
830.9
825.3
831.0

1 Includes other items not shown separately.
2 Food consists of food and beverages purchased for off-premises consumption; food services, which include purchased meals and beverages, are not

classified as food.
Source: Department of Commerce (Bureau of Economic Analysis).

344 |

Appendix B

313.8
339.3
368.1
391.1
432.9
470.8
503.3
550.1
607.9
670.9
722.4
800.6
898.3
1,002.5
1,127.8
1,245.4
1,358.3
1,507.1
1,627.2
1,824.2
2,016.9
2,215.1
2,401.8
2,587.3
2,813.2
3,019.8
3,221.3
3,351.1
3,601.1
3,828.2
4,072.3
4,291.9
4,542.0
4,821.6
5,173.5
5,554.6
5,966.4
6,255.9
6,549.4
6,846.7
7,240.0
7,665.3
8,090.7
8,485.9
8,655.0
8,588.9
8,881.0
9,271.1
9,642.3
8,542.4
8,535.9
8,617.0
8,660.2
8,733.6
8,839.5
8,916.5
9,034.5
9,139.0
9,223.8
9,310.3
9,411.4
9,544.2
9,593.0
9,659.9
9,772.3

Table B–17. Real personal consumption expenditures, 1995–2012
[Billions of chained (2005) dollars; quarterly data at seasonally adjusted annual rates]
Goods
Durable
Year or
quarter

1995 ��������������
1996 ��������������
1997 ��������������
1998 ��������������
1999 ��������������
2000 ��������������
2001 ��������������
2002 ��������������
2003 ��������������
2004 ��������������
2005 ��������������
2006 ��������������
2007 ��������������
2008 ��������������
2009 ��������������
2010 ��������������
2011 ��������������
2012 p ������������
2009: I ����������
      II ���������
      III ��������
      IV ��������
2010: I ����������
      II ���������
      III ��������
      IV ��������
2011: I ����������
      II ���������
      III ��������
      IV ��������
2012: I ����������
      II ���������
      III ��������
      IV p �����

Personal
consumption
expenditures

6,076.2
6,288.3
6,520.4
6,862.3
7,237.6
7,604.6
7,810.3
8,018.3
8,244.5
8,515.8
8,803.5
9,054.5
9,262.9
9,211.7
9,032.6
9,196.2
9,428.8
9,604.9
9,039.5
8,999.3
9,046.2
9,045.4
9,100.8
9,159.4
9,216.0
9,308.5
9,380.9
9,403.2
9,441.9
9,489.3
9,546.8
9,582.5
9,620.1
9,670.0

Total
Total 1

1,896.0
1,980.9
2,075.3
2,215.5
2,392.0
2,518.2
2,597.3
2,702.9
2,827.2
2,953.3
3,076.7
3,178.9
3,273.5
3,192.9
3,098.2
3,209.1
3,331.0
3,433.0
3,083.2
3,067.0
3,123.1
3,119.5
3,159.5
3,185.4
3,215.1
3,276.5
3,320.3
3,312.2
3,323.5
3,367.9
3,406.6
3,409.4
3,439.7
3,476.4

510.5
548.6
593.3
665.6
752.0
818.0
862.4
927.9
989.1
1,060.9
1,123.4
1,174.2
1,232.4
1,171.8
1,109.1
1,178.3
1,262.6
1,361.0
1,091.4
1,085.8
1,138.6
1,120.7
1,135.9
1,164.5
1,184.9
1,227.7
1,249.4
1,242.3
1,258.6
1,300.1
1,336.1
1,335.3
1,364.0
1,408.8

Services
Household consumption
expenditures

Nondurable

Motor
vehicles
and
parts

255.6
268.0
286.1
316.0
345.1
356.1
374.3
394.0
404.8
410.4
408.2
394.4
401.4
346.8
322.6
329.5
347.4
373.3
312.8
313.7
347.7
316.3
312.4
324.2
331.0
350.3
355.0
336.6
338.1
360.1
371.2
361.8
370.5
389.9

Total 1

1,437.7
1,479.2
1,522.7
1,580.2
1,660.7
1,714.5
1,745.4
1,780.1
1,840.7
1,892.8
1,953.4
2,005.0
2,042.9
2,019.1
1,982.8
2,029.3
2,075.2
2,094.4
1,983.7
1,973.3
1,981.4
1,992.9
2,017.7
2,018.3
2,029.4
2,052.0
2,075.3
2,073.5
2,071.4
2,080.5
2,088.9
2,092.0
2,098.2
2,098.7

Food and
beverages
Gasoline
purand
chased
other
for offenergy
premises goods
consumption
548.4
553.9
558.8
565.5
587.3
600.5
607.5
608.9
616.5
623.9
644.5
663.0
673.2
666.0
654.8
668.8
685.3
685.8
646.4
652.3
657.0
663.5
669.4
663.2
666.1
676.7
682.8
686.0
685.9
686.4
686.4
685.4
685.9
685.7

264.3
268.5
273.9
283.7
292.4
287.1
289.2
294.0
301.9
305.9
303.8
296.9
294.4
280.6
282.4
281.3
271.5
268.3
289.0
282.9
280.0
277.6
285.9
282.2
281.5
275.6
280.2
269.9
267.9
268.2
266.5
272.0
270.0
264.8

Total

4,208.5
4,331.7
4,465.3
4,662.1
4,853.1
5,093.6
5,219.1
5,318.5
5,418.2
5,562.7
5,726.8
5,875.6
5,990.2
6,017.0
5,930.6
5,987.6
6,101.5
6,178.0
5,951.5
5,926.9
5,920.7
5,923.2
5,940.4
5,973.6
6,001.4
6,034.9
6,064.8
6,094.0
6,121.1
6,126.0
6,145.9
6,178.2
6,186.7
6,201.3

Total 1

Housing
and
utilities

Health
care

4,068.9
4,183.6
4,327.6
4,511.0
4,690.8
4,918.2
5,029.3
5,109.8
5,199.4
5,345.1
5,515.1
5,640.6
5,745.2
5,745.6
5,656.3
5,710.2
5,814.3
5,880.6
5,676.1
5,655.8
5,647.9
5,645.2
5,664.3
5,694.5
5,724.2
5,757.8
5,786.1
5,810.1
5,826.6
5,834.5
5,855.1
5,877.6
5,888.8
5,900.8

1,234.8
1,261.6
1,290.3
1,329.7
1,371.7
1,413.6
1,451.4
1,461.9
1,480.2
1,512.8
1,582.6
1,616.8
1,626.6
1,637.8
1,655.2
1,668.7
1,677.7
1,677.7
1,652.3
1,653.6
1,655.7
1,659.3
1,662.7
1,665.2
1,672.8
1,673.9
1,672.4
1,679.6
1,686.7
1,672.0
1,662.7
1,685.2
1,690.6
1,672.5

947.6
967.2
997.2
1,029.6
1,045.7
1,081.6
1,135.6
1,202.4
1,228.3
1,267.4
1,308.9
1,333.0
1,364.0
1,396.5
1,420.8
1,439.0
1,488.5
1,516.6
1,410.4
1,421.0
1,427.2
1,424.6
1,418.3
1,429.1
1,445.1
1,463.7
1,478.8
1,489.3
1,486.2
1,499.7
1,513.3
1,508.4
1,518.4
1,526.5

Addendum:
Personal
consumption
expendiFinancial
tures
services excludand
ing
insurfood
ance
and
energy 2
489.9
508.2
525.7
559.1
606.2
666.0
661.3
658.9
659.2
675.5
698.4
716.4
739.8
732.3
680.6
683.7
681.8
685.6
693.3
683.1
675.0
670.9
682.2
690.0
682.2
680.3
680.8
678.3
685.8
682.2
688.6
688.4
680.0
685.3

5,123.9
5,319.4
5,540.7
5,860.1
6,199.5
6,545.5
6,742.5
6,938.6
7,145.2
7,401.8
7,665.3
7,911.5
8,110.4
8,087.2
7,913.4
8,058.0
8,292.4
8,480.3
7,922.2
7,882.8
7,927.9
7,920.7
7,958.7
8,027.3
8,077.2
8,168.7
8,235.6
8,265.4
8,302.8
8,366.0
8,437.3
8,444.6
8,480.4
8,558.8

1 Includes other items not shown separately.
2 Food consists of food and beverages purchased for off-premises consumption; food services, which include purchased meals and beverages, are not

classified as food.
Note: See Table B–2 for data for total personal consumption expenditures for 1964–94.
Source: Department of Commerce (Bureau of Economic Analysis).

National Income or Expenditure | 345

Table B–18. Private fixed investment by type, 1964–2012
[Billions of dollars; quarterly data at seasonally adjusted annual rates]
Nonresidential

Residential

Equipment and software

Year or quarter

1964 ����������������������
1965 ����������������������
1966 ����������������������
1967 ����������������������
1968 ����������������������
1969 ����������������������
1970 ����������������������
1971 ����������������������
1972 ����������������������
1973 ����������������������
1974 ����������������������
1975 ����������������������
1976 ����������������������
1977 ����������������������
1978 ����������������������
1979 ����������������������
1980 ����������������������
1981 ����������������������
1982 ����������������������
1983 ����������������������
1984 ����������������������
1985 ����������������������
1986 ����������������������
1987 ����������������������
1988 ����������������������
1989 ����������������������
1990 ����������������������
1991 ����������������������
1992 ����������������������
1993 ����������������������
1994 ����������������������
1995 ����������������������
1996 ����������������������
1997 ����������������������
1998 ����������������������
1999 ����������������������
2000 ����������������������
2001 ����������������������
2002 ����������������������
2003 ����������������������
2004 ����������������������
2005 ����������������������
2006 ����������������������
2007 ����������������������
2008 ����������������������
2009 ����������������������
2010 ����������������������
2011 ����������������������
2012 p ��������������������
2009: I ������������������
      II �����������������
      III ����������������
      IV ����������������
2010: I ������������������
      II �����������������
      III ����������������
      IV ����������������
2011: I ������������������
      II �����������������
      III ����������������
      IV ����������������
2012: I ������������������
      II �����������������
      III ����������������
      IV p �������������

Private
fixed
investment

97.2
109.0
117.7
118.7
132.1
147.3
150.4
169.9
198.5
228.6
235.4
236.5
274.8
339.0
412.2
474.9
485.6
542.6
532.1
570.1
670.2
714.4
739.9
757.8
803.1
847.3
846.4
803.3
848.5
932.5
1,033.5
1,112.9
1,209.4
1,317.7
1,447.1
1,580.7
1,717.7
1,700.2
1,634.9
1,713.3
1,903.6
2,122.3
2,267.2
2,266.1
2,128.7
1,703.5
1,679.0
1,818.3
2,000.9
1,812.5
1,698.0
1,666.1
1,637.2
1,627.2
1,683.0
1,683.8
1,721.9
1,722.3
1,784.2
1,857.8
1,909.0
1,959.7
1,986.9
1,997.9
2,059.0

Total
nonresidential

63.0
74.8
85.4
86.4
93.4
104.7
109.0
114.1
128.8
153.3
169.5
173.7
192.4
228.7
280.6
333.9
362.4
420.0
426.5
417.2
489.6
526.2
519.8
524.1
563.8
607.7
622.4
598.2
612.1
666.6
731.4
810.0
875.4
968.6
1,061.1
1,154.9
1,268.7
1,227.8
1,125.4
1,135.7
1,223.0
1,347.3
1,505.3
1,637.5
1,656.3
1,349.3
1,338.4
1,479.6
1,618.0
1,442.9
1,356.0
1,312.9
1,285.4
1,285.8
1,325.2
1,353.8
1,388.8
1,390.8
1,448.0
1,519.4
1,560.1
1,595.5
1,614.1
1,610.0
1,652.5

Information processing equipment
and software
Structures

Total
Total

23.7
28.3
31.3
31.5
33.6
37.7
40.3
42.7
47.2
55.0
61.2
61.4
65.9
74.6
93.6
117.7
136.2
167.3
177.6
154.3
177.4
194.5
176.5
174.2
182.8
193.7
202.9
183.6
172.6
177.2
186.8
207.3
224.6
250.3
275.1
283.9
318.1
329.7
282.8
281.9
306.7
351.8
433.7
524.9
586.3
451.1
376.3
404.8
460.5
530.5
467.1
421.0
385.6
362.7
376.6
377.1
389.0
362.4
397.0
421.8
438.2
454.7
458.9
460.1
468.2

39.2
46.5
54.0
54.9
59.9
67.0
68.7
71.5
81.7
98.3
108.2
112.4
126.4
154.1
187.0
216.2
226.2
252.7
248.9
262.9
312.2
331.7
343.3
349.9
381.0
414.0
419.5
414.6
439.6
489.4
544.6
602.8
650.8
718.3
786.0
871.0
950.5
898.1
842.7
853.8
916.4
995.6
1,071.7
1,112.6
1,070.0
898.2
962.1
1,074.7
1,157.6
912.4
888.9
891.9
899.8
923.1
948.6
976.8
999.8
1,028.4
1,051.0
1,097.6
1,122.0
1,140.8
1,155.2
1,149.9
1,184.3

7.4
8.5
10.7
11.3
11.9
14.6
16.6
17.3
19.5
23.1
27.0
28.5
32.7
39.2
48.7
58.5
68.8
81.5
88.3
100.1
121.5
130.3
136.8
141.2
154.9
172.6
177.2
182.9
199.9
217.6
235.2
263.0
290.1
330.3
366.1
417.1
478.2
452.5
419.8
430.9
455.3
475.3
505.2
536.6
536.4
502.1
517.7
539.6
555.2
495.5
494.1
505.4
513.5
511.9
511.1
518.5
529.1
529.8
538.6
541.6
548.5
556.3
552.0
547.2
565.2

1 Includes other items not shown separately.

Source: Department of Commerce (Bureau of Economic Analysis).

346 |

Appendix B

Structures

Computers
and
peripheral
equipment
0.9
1.2
1.7
1.9
1.9
2.4
2.7
2.8
3.5
3.5
3.9
3.6
4.4
5.7
7.6
10.2
12.5
17.1
18.9
23.9
31.6
33.7
33.4
35.8
38.0
43.1
38.6
37.7
44.0
47.9
52.4
66.1
72.8
81.4
87.9
97.2
103.2
87.6
79.7
77.6
80.2
78.9
84.9
87.0
84.9
73.5
72.8
78.3
79.3
73.8
73.4
71.9
75.1
73.1
73.3
71.7
73.1
72.3
79.0
80.3
81.6
84.3
79.3
71.9
81.8

Software

0.5
.7
1.0
1.2
1.3
1.8
2.3
2.4
2.8
3.2
3.9
4.8
5.2
5.5
6.3
8.1
9.8
11.8
14.0
16.4
20.4
23.8
25.6
29.0
34.2
41.9
47.6
53.7
57.9
64.3
68.3
74.6
85.5
107.5
126.0
157.3
184.5
186.6
183.0
191.3
205.7
218.0
229.8
245.0
257.2
256.9
260.9
278.7
293.2
253.7
255.6
256.8
261.5
259.5
257.5
261.3
265.5
271.1
275.8
281.1
286.9
288.1
292.1
293.7
298.7

Other

5.9
6.7
8.0
8.2
8.7
10.4
11.6
12.2
13.2
16.3
19.2
20.2
23.1
28.0
34.8
40.2
46.4
52.5
55.3
59.8
69.6
72.9
77.7
76.4
82.8
87.6
90.9
91.5
98.1
105.4
114.6
122.3
131.9
141.4
152.2
162.5
190.6
178.4
157.0
162.0
169.4
178.4
190.6
204.6
194.3
171.7
183.9
182.6
182.7
168.0
165.2
176.7
177.0
179.4
180.4
185.5
190.4
186.5
183.8
180.3
180.0
183.9
180.5
181.6
184.7

Industrial
equipment

Transportation
equipment

Other
equipment

11.4
13.7
16.2
16.9
17.3
19.1
20.3
19.5
21.4
26.0
30.7
31.3
34.1
39.4
47.7
56.2
60.7
65.5
62.7
58.9
68.1
72.5
75.4
76.7
84.2
93.3
92.1
89.3
93.0
102.2
113.6
129.0
136.5
140.4
147.4
149.1
162.9
151.9
141.7
142.6
142.0
159.6
178.4
193.0
194.5
155.2
155.3
181.2
197.4
163.8
155.2
151.7
149.9
146.9
156.4
156.5
161.3
169.6
171.6
187.0
196.6
190.7
197.8
198.0
203.2

10.6
13.2
14.5
14.3
17.6
18.9
16.2
18.4
21.8
26.6
26.3
25.2
30.0
39.3
47.3
53.6
48.4
50.6
46.8
53.5
64.4
69.0
70.5
68.1
72.9
67.9
70.0
71.5
74.7
89.4
107.7
116.1
123.2
135.5
147.1
174.4
170.8
154.2
141.6
132.9
161.1
181.7
198.2
190.2
146.9
75.9
123.2
164.7
196.9
73.5
74.4
76.5
79.4
101.9
117.3
135.1
138.6
149.2
155.6
170.7
183.1
193.6
200.5
193.4
200.0

9.9
11.0
12.7
12.4
13.0
14.4
15.6
16.3
19.0
22.6
24.3
27.4
29.6
36.3
43.2
47.9
48.3
55.2
51.2
50.4
58.1
59.9
60.7
63.9
69.0
80.2
80.2
70.8
72.0
80.2
88.1
94.7
101.0
112.1
125.4
130.4
138.6
139.5
139.6
147.5
157.9
178.9
189.8
192.8
192.2
165.0
165.9
189.2
208.1
179.5
165.2
158.3
156.9
162.4
163.8
166.7
170.9
179.8
185.2
198.2
193.7
200.1
204.9
211.3
216.0

Total
residential 1

34.3
34.2
32.3
32.4
38.7
42.6
41.4
55.8
69.7
75.3
66.0
62.7
82.5
110.3
131.6
141.0
123.2
122.6
105.7
152.9
180.6
188.2
220.1
233.7
239.3
239.5
224.0
205.1
236.3
266.0
302.1
302.9
334.1
349.1
385.9
425.8
449.0
472.4
509.5
577.6
680.6
775.0
761.9
628.7
472.4
354.1
340.6
338.7
382.8
369.6
342.0
353.1
351.9
341.3
357.8
330.0
333.1
331.4
336.2
338.5
348.8
364.2
372.8
387.9
406.5

Total 1

33.6
33.5
31.6
31.6
37.9
41.6
40.2
54.5
68.1
73.6
64.1
60.8
80.4
107.9
128.9
137.8
119.8
118.9
102.0
148.6
175.9
183.1
214.6
227.9
233.2
233.4
218.0
199.4
230.4
259.9
295.9
296.5
327.7
342.8
379.2
418.5
441.2
464.4
501.3
569.1
671.4
765.2
751.6
618.4
462.7
345.4
331.7
329.7
373.5
360.6
333.3
344.5
343.1
332.6
348.8
321.2
324.3
322.7
327.3
329.4
339.6
354.8
363.5
378.5
397.1

Single
family

17.6
17.8
16.6
16.8
19.5
19.7
17.5
25.8
32.8
35.2
29.7
29.6
43.9
62.2
72.8
72.3
52.9
52.0
41.5
72.5
86.4
87.4
104.1
117.2
120.1
120.9
112.9
99.4
122.0
140.1
162.3
153.5
170.8
175.2
199.4
223.8
236.8
249.1
265.9
310.6
377.6
433.5
416.0
305.2
185.8
105.3
112.6
108.2
128.7
112.1
92.9
105.0
111.3
114.7
118.8
110.4
106.4
107.4
106.1
108.2
111.1
117.1
122.3
131.9
143.4

Table B–19. Real private fixed investment by type, 1995–2012
[Billions of chained (2005) dollars; quarterly data at seasonally adjusted annual rates]
Nonresidential

Residential

Equipment and software

Year or quarter

1995 ����������������������
1996 ����������������������
1997 ����������������������
1998 ����������������������
1999 ����������������������
2000 ����������������������
2001 ����������������������
2002 ����������������������
2003 ����������������������
2004 ����������������������
2005 ����������������������
2006 ����������������������
2007 ����������������������
2008 ����������������������
2009 ����������������������
2010 ����������������������
2011 ����������������������
2012 p ��������������������
2009: I ������������������
      II �����������������
      III ����������������
      IV ����������������
2010: I ������������������
      II �����������������
      III ����������������
      IV ����������������
2011: I ������������������
      II �����������������
      III ����������������
      IV ����������������
2012: I ������������������
      II �����������������
      III ����������������
      IV p �������������

Private
fixed
investment

1,231.2
1,341.6
1,465.4
1,624.4
1,775.5
1,906.8
1,870.7
1,791.5
1,854.7
1,992.5
2,122.3
2,172.7
2,130.6
1,978.6
1,602.2
1,598.7
1,704.5
1,850.1
1,677.3
1,593.7
1,581.2
1,556.8
1,553.1
1,606.5
1,602.7
1,632.3
1,627.0
1,675.4
1,736.8
1,778.7
1,820.6
1,840.6
1,844.8
1,894.4

Total
nonresidential

787.9
861.5
965.5
1,081.4
1,194.3
1,311.3
1,274.8
1,173.7
1,189.6
1,263.0
1,347.3
1,455.5
1,550.0
1,537.6
1,259.8
1,268.5
1,378.2
1,484.9
1,324.3
1,262.0
1,236.7
1,216.4
1,222.7
1,258.6
1,282.1
1,310.5
1,306.3
1,351.3
1,411.3
1,443.7
1,470.0
1,482.9
1,476.1
1,510.7

Structures

Information processing equipment
and software
Structures

342.0
361.4
387.9
407.7
408.2
440.0
433.3
356.6
343.0
346.7
351.8
384.0
438.2
466.4
368.1
310.6
319.2
351.3
417.7
380.1
351.7
323.1
302.6
312.1
310.4
317.4
292.2
315.0
330.2
339.3
349.7
350.2
350.2
355.1

Total

Computers
and
peripheral
equipment 1

147.3
176.5
217.6
267.1
327.2
386.2
384.5
373.9
403.7
443.1
475.3
516.3
558.2
569.7
546.4
571.7
600.2
622.9
533.9
537.3
551.9
562.4
563.7
564.1
573.7
585.1
585.9
598.2
603.5
613.4
622.2
618.4
614.5
636.3

������������
������������
������������
������������
������������
������������
������������
������������
������������
������������
������������
������������
������������
������������
������������
������������
������������
������������
������������
������������
������������
������������
������������
������������
������������
������������
������������
������������
������������
������������
������������
������������
������������
������������

Total

489.4
541.4
615.9
705.2
805.0
889.2
860.6
824.2
850.0
917.3
995.6
1,071.1
1,106.8
1,059.4
885.2
963.9
1,070.0
1,143.5
892.9
873.2
880.8
893.8
925.0
951.6
978.7
1,000.4
1,027.0
1,046.5
1,091.5
1,114.8
1,129.6
1,142.8
1,135.4
1,166.3

Software

66.9
78.5
101.7
122.8
151.5
172.4
173.7
173.4
185.6
204.6
218.0
227.1
240.9
250.8
252.9
259.4
277.2
292.8
248.2
251.2
254.1
258.0
257.1
255.7
260.1
264.5
269.5
274.3
279.5
285.4
286.8
291.1
293.8
299.6

Other

90.1
98.7
107.2
120.7
134.6
162.0
157.0
142.7
155.1
168.1
178.4
192.8
208.4
202.4
182.4
197.6
196.7
198.4
177.5
176.0
187.4
188.7
192.5
193.9
200.0
204.2
199.1
197.5
194.6
195.4
199.4
195.9
197.4
200.9

Industrial
equipment

Transportation
equipment

Other
equipment

145.5
150.9
154.1
160.8
161.8
175.8
162.8
151.9
151.6
147.4
159.6
172.9
179.9
172.9
136.2
134.6
152.6
163.3
143.9
136.6
133.2
131.2
128.3
135.9
135.6
138.9
144.5
144.7
156.6
164.4
158.5
163.6
163.7
167.5

131.5
136.8
148.2
162.0
190.3
186.2
169.6
154.2
140.4
162.3
181.7
196.5
185.8
142.7
69.1
119.6
156.7
183.6
66.8
65.8
68.6
75.0
99.4
114.2
131.0
133.8
143.1
147.9
162.3
173.6
181.7
188.5
180.4
183.7

110.6
114.8
125.9
138.8
142.4
150.4
149.3
148.2
155.0
164.4
178.9
185.5
184.2
177.8
145.5
149.9
168.6
179.6
157.0
144.9
140.4
139.6
146.9
148.7
149.9
154.1
162.9
165.8
175.7
169.9
174.7
177.6
181.6
184.7

Total
residential 2

456.1
492.5
501.8
540.4
574.2
580.0
583.3
613.8
664.3
729.5
775.0
718.2
584.2
444.4
344.8
332.2
327.6
367.1
355.3
333.7
347.2
343.0
332.7
350.5
322.2
323.3
322.2
325.5
326.6
336.0
352.1
359.3
370.9
386.1

Total 2

450.1
486.8
496.3
534.5
567.5
572.6
575.6
605.9
655.9
720.1
765.2
708.1
574.2
434.9
336.1
323.0
318.0
357.2
346.6
325.2
338.5
334.1
323.7
341.2
313.0
314.0
312.8
315.9
316.9
326.2
342.3
349.5
360.9
376.0

Single
family

240.2
262.4
261.6
290.1
311.5
315.0
315.4
327.7
362.6
406.1
433.5
391.1
284.0
178.4
105.5
114.5
109.3
128.9
109.6
93.2
106.9
112.1
115.8
121.8
112.8
107.8
108.8
107.4
109.3
111.7
118.5
123.4
131.5
142.1

1 Because computers exhibit rapid changes in prices relative to other prices in the economy, the chained-dollar estimates should not be used to measure
the component’s relative importance or its contribution to the growth rate of more aggregate series. The quantity index for computers can be used to accurately
measure the real growth rate of this series. For information on this component, see Survey of Current Business Table 5.3.1 (for growth rates), Table 5.3.2 (for
contributions), and Table 5.3.3 (for quantity indexes).
2 Includes other items not shown separately.
Source: Department of Commerce (Bureau of Economic Analysis).

National Income or Expenditure | 347

Table B–20. Government consumption expenditures and gross investment by type,
1964–2012
[Billions of dollars; quarterly data at seasonally adjusted annual rates]
Government consumption expenditures and gross investment
Federal

State and local

National defense
Year or quarter

1964 ����������������������
1965 ����������������������
1966 ����������������������
1967 ����������������������
1968 ����������������������
1969 ����������������������
1970 ����������������������
1971 ����������������������
1972 ����������������������
1973 ����������������������
1974 ����������������������
1975 ����������������������
1976 ����������������������
1977 ����������������������
1978 ����������������������
1979 ����������������������
1980 ����������������������
1981 ����������������������
1982 ����������������������
1983 ����������������������
1984 ����������������������
1985 ����������������������
1986 ����������������������
1987 ����������������������
1988 ����������������������
1989 ����������������������
1990 ����������������������
1991 ����������������������
1992 ����������������������
1993 ����������������������
1994 ����������������������
1995 ����������������������
1996 ����������������������
1997 ����������������������
1998 ����������������������
1999 ����������������������
2000 ����������������������
2001 ����������������������
2002 ����������������������
2003 ����������������������
2004 ����������������������
2005 ����������������������
2006 ����������������������
2007 ����������������������
2008 ����������������������
2009 ����������������������
2010 ����������������������
2011 ����������������������
2012 p ��������������������
2009: I ������������������
      II �����������������
      III ����������������
      IV ����������������
2010: I ������������������
      II �����������������
      III ����������������
      IV ����������������
2011: I ������������������
      II �����������������
      III ����������������
      IV ����������������
2012: I ������������������
      II �����������������
      III ����������������
      IV p �������������

Total

143.2
151.4
171.6
192.5
209.3
221.4
233.7
246.4
263.4
281.7
317.9
357.7
383.0
414.1
453.6
500.7
566.1
627.5
680.4
733.4
796.9
878.9
949.3
999.4
1,038.9
1,100.6
1,181.7
1,236.1
1,273.5
1,294.8
1,329.8
1,374.0
1,421.0
1,474.4
1,526.1
1,631.3
1,731.0
1,846.4
1,983.3
2,112.6
2,232.8
2,369.9
2,518.4
2,674.2
2,878.1
2,967.2
3,057.5
3,059.8
3,062.9
2,894.6
2,957.8
2,996.4
3,020.0
3,030.9
3,061.7
3,072.3
3,065.2
3,048.1
3,072.2
3,067.7
3,051.0
3,054.6
3,053.7
3,093.3
3,049.9

Total

78.4
80.4
92.4
104.6
111.3
113.3
113.4
113.6
119.6
122.5
134.5
149.0
159.7
175.4
190.9
210.6
243.7
280.2
310.8
342.9
374.3
412.8
438.4
459.5
461.6
481.4
507.5
526.6
532.9
525.0
518.6
518.8
527.0
531.0
531.0
554.9
576.1
611.7
680.6
756.5
824.6
876.3
931.7
976.3
1,080.1
1,143.6
1,223.1
1,222.1
1,214.3
1,104.9
1,135.9
1,157.6
1,175.9
1,193.7
1,225.1
1,239.8
1,233.8
1,215.2
1,234.3
1,227.5
1,211.2
1,207.7
1,210.7
1,241.4
1,197.4

Total

60.2
60.6
71.7
83.4
89.2
89.5
87.6
84.6
86.9
88.1
95.6
103.9
111.1
120.9
130.5
145.2
168.0
196.2
225.9
250.6
281.5
311.2
330.8
350.0
354.7
362.1
373.9
383.1
376.8
363.0
353.8
348.8
354.8
349.8
346.1
361.1
371.0
393.0
437.7
497.9
550.8
589.0
624.9
662.3
737.8
776.0
817.7
820.8
809.2
748.0
772.0
788.5
795.5
799.3
815.5
831.6
824.5
804.9
827.7
837.8
812.8
806.4
807.8
834.5
787.9

Consumption
expenditures
48.8
50.6
59.9
69.9
77.1
78.1
76.5
77.1
79.5
79.4
84.5
90.9
95.8
104.2
112.7
123.8
143.7
167.3
191.1
208.7
232.8
253.7
267.9
283.6
293.5
299.4
308.0
319.7
315.2
307.5
300.8
297.0
303.2
304.5
300.3
313.0
321.8
342.0
380.7
435.2
481.2
514.8
543.9
575.4
633.3
664.4
702.5
712.1
703.5
642.2
659.4
674.6
681.5
689.4
700.3
713.2
707.0
697.3
716.7
730.5
704.0
703.5
701.1
728.1
681.4

Gross investment
Structures
1.3
1.1
1.3
1.2
1.2
1.5
1.3
1.8
1.8
2.1
2.2
2.3
2.1
2.4
2.5
2.5
3.2
3.2
4.0
4.8
4.9
6.2
6.8
7.7
7.4
6.4
6.1
4.6
5.2
5.3
5.8
6.7
6.3
6.1
5.8
5.4
5.4
5.3
5.8
7.3
7.1
7.5
8.1
10.1
13.7
17.1
16.7
13.5
8.5
16.6
16.9
17.6
17.1
15.9
16.8
17.3
16.8
15.6
14.6
12.8
11.1
9.5
8.3
7.2
8.8

Source: Department of Commerce (Bureau of Economic Analysis).

348 |

Appendix B

Gross investment

Nondefense

Equipment
and
software
10.2
8.9
10.5
12.3
10.9
9.9
9.8
5.7
5.7
6.6
8.9
10.7
13.2
14.4
15.3
18.9
21.1
25.7
30.8
37.1
43.8
51.3
56.1
58.8
53.9
56.3
59.8
58.8
56.3
50.1
47.2
45.1
45.4
39.2
39.9
42.8
43.8
45.6
51.2
55.4
62.4
66.8
72.9
76.9
90.9
94.5
98.6
95.2
97.2
89.1
95.6
96.4
96.8
94.0
98.5
101.1
100.7
92.1
96.4
94.5
97.7
93.4
98.4
99.2
97.8

Total

18.2
19.8
20.8
21.2
22.0
23.8
25.8
29.1
32.7
34.3
39.0
45.1
48.6
54.5
60.4
65.4
75.8
83.9
84.9
92.3
92.7
101.6
107.6
109.6
106.8
119.3
133.6
143.4
156.1
162.0
164.8
170.0
172.2
181.1
184.9
193.8
205.0
218.7
242.9
258.5
273.9
287.3
306.8
314.0
342.3
367.6
405.3
401.3
405.1
356.9
364.0
369.1
380.4
394.3
409.6
408.1
409.3
410.3
406.6
389.7
398.4
401.3
402.9
406.8
409.4

Consumption
expenditures
14.0
15.1
15.9
17.0
18.2
20.2
22.1
24.9
28.2
29.4
33.4
38.7
41.4
46.5
50.6
55.1
63.8
71.0
72.1
77.7
77.1
84.7
90.1
90.1
88.3
99.1
111.0
118.6
128.9
133.7
139.9
143.2
143.4
153.0
154.3
160.3
174.2
188.1
209.8
225.1
240.2
251.0
267.1
273.5
298.5
322.5
353.3
349.4
355.9
312.3
320.1
324.1
333.5
344.8
356.7
355.1
356.6
356.9
354.3
338.5
348.0
352.1
353.7
358.2
359.8

Gross investment
Structures
2.5
2.8
2.8
2.2
2.1
1.9
2.1
2.5
2.7
3.1
3.4
4.1
4.6
5.0
6.1
6.3
7.1
7.7
6.8
6.7
7.0
7.3
8.0
9.0
6.8
6.9
8.0
9.2
10.3
11.2
10.2
10.8
11.3
9.9
10.8
10.7
8.3
8.1
9.9
10.3
9.1
8.3
9.5
11.1
11.4
12.1
16.6
16.1
12.9
12.1
11.3
12.1
13.0
14.8
17.7
17.2
16.8
17.6
16.6
15.6
14.5
13.4
13.1
12.3
12.8

Equipment
and
software
1.6
1.9
2.1
1.9
1.7
1.7
1.7
1.7
1.8
1.8
2.2
2.4
2.7
3.0
3.7
4.0
4.9
5.3
6.0
7.8
8.7
9.6
9.5
10.4
11.7
13.4
14.6
15.7
16.9
17.0
14.7
16.0
17.5
18.2
19.9
22.7
22.6
22.5
23.2
23.1
24.6
28.0
30.2
29.4
32.4
32.9
35.4
35.7
36.3
32.5
32.6
32.9
33.8
34.8
35.1
35.8
35.9
35.8
35.6
35.6
35.9
35.8
36.1
36.3
36.9

Total

Consumption
expenditures

64.8
71.0
79.2
87.9
98.0
108.2
120.3
132.8
143.8
159.2
183.4
208.7
223.3
238.7
262.7
290.2
322.4
347.3
369.7
390.5
422.6
466.1
510.9
539.9
577.3
619.2
674.2
709.5
740.6
769.8
811.2
855.3
894.0
943.5
995.0
1,076.3
1,154.9
1,234.7
1,302.7
1,356.1
1,408.2
1,493.6
1,586.7
1,697.9
1,798.0
1,823.6
1,834.4
1,837.7
1,848.6
1,789.7
1,821.9
1,838.8
1,844.1
1,837.2
1,836.6
1,832.5
1,831.4
1,832.8
1,837.9
1,840.2
1,839.7
1,846.9
1,843.0
1,851.9
1,852.5

45.8
50.2
56.1
62.6
70.4
79.8
91.5
102.7
113.2
126.0
143.7
165.1
179.5
195.9
213.2
233.3
258.4
282.3
304.9
324.1
347.7
381.8
418.1
441.4
471.0
504.5
547.0
577.5
606.2
634.2
668.2
701.3
730.2
764.5
808.6
870.6
930.6
994.2
1,049.4
1,096.5
1,139.1
1,212.0
1,282.3
1,368.9
1,449.2
1,473.3
1,496.2
1,518.0
1,530.9
1,436.1
1,465.8
1,487.9
1,503.5
1,505.2
1,494.2
1,488.6
1,496.9
1,511.4
1,520.3
1,522.0
1,518.4
1,531.4
1,525.5
1,532.4
1,534.4

Structures

17.2
19.0
21.0
23.0
25.2
25.6
25.8
27.0
27.1
29.1
34.7
38.1
38.1
36.9
42.8
49.0
55.1
55.4
54.2
54.2
60.5
67.6
74.2
78.8
84.8
88.7
98.5
103.2
104.2
104.5
108.7
117.3
126.8
139.5
143.6
159.7
176.0
192.3
205.8
211.8
220.2
230.8
249.9
268.4
285.0
287.7
276.0
256.3
251.7
291.1
293.6
288.8
277.4
269.0
280.3
282.3
272.3
259.4
254.3
254.2
257.5
251.3
251.6
253.6
250.3

Equipment
and
software
1.8
1.9
2.1
2.3
2.4
2.7
3.0
3.1
3.5
4.1
4.9
5.5
5.7
5.9
6.6
7.8
8.9
9.5
10.6
12.2
14.4
16.8
18.6
19.6
21.5
26.0
28.7
28.9
30.1
31.2
34.3
36.7
36.9
39.4
42.9
46.1
48.3
48.2
47.5
47.8
48.9
50.8
54.5
60.7
63.8
62.6
62.2
63.3
66.0
62.6
62.5
62.1
63.2
63.1
62.0
61.6
62.2
62.1
63.4
64.0
63.8
64.2
65.9
65.9
67.9

Table B–21. Real government consumption expenditures and gross investment by type,
1995–2012
[Billions of chained (2005) dollars; quarterly data at seasonally adjusted annual rates]
Government consumption expenditures and gross investment
Federal

State and local

National defense
Year or quarter

1995 ����������������������
1996 ����������������������
1997 ����������������������
1998 ����������������������
1999 ����������������������
2000 ����������������������
2001 ����������������������
2002 ����������������������
2003 ����������������������
2004 ����������������������
2005 ����������������������
2006 ����������������������
2007 ����������������������
2008 ����������������������
2009 ����������������������
2010 ����������������������
2011 ����������������������
2012 p ��������������������
2009: I ������������������
      II �����������������
      III ����������������
      IV ����������������
2010: I ������������������
      II �����������������
      III ����������������
      IV ����������������
2011: I ������������������
      II �����������������
      III ����������������
      IV ����������������
2012: I ������������������
      II �����������������
      III ����������������
      IV p �������������

Total

1,888.9
1,907.9
1,943.8
1,985.0
2,056.1
2,097.8
2,178.3
2,279.6
2,330.5
2,362.0
2,369.9
2,402.1
2,434.2
2,497.4
2,589.4
2,605.8
2,523.9
2,481.3
2,531.6
2,590.4
2,614.3
2,621.1
2,600.4
2,618.7
2,616.7
2,587.4
2,540.7
2,535.4
2,516.6
2,502.7
2,483.7
2,479.4
2,503.1
2,458.9

Total

704.1
696.0
689.1
681.4
694.6
698.1
726.5
779.5
831.1
865.0
876.3
894.9
906.1
971.1
1,030.6
1,076.8
1,047.0
1,024.1
995.8
1,028.2
1,043.9
1,054.6
1,056.2
1,081.0
1,090.7
1,079.4
1,050.4
1,057.5
1,045.9
1,034.2
1,023.1
1,022.5
1,045.9
1,005.0

Total

476.8
470.4
457.2
447.5
455.8
453.5
470.7
505.3
549.2
580.4
589.0
598.4
611.8
657.7
696.9
717.6
699.1
677.3
670.8
696.3
709.1
711.4
704.8
717.3
729.9
718.6
691.3
705.2
709.8
690.1
677.6
677.3
698.1
656.0

Consumption
expenditures
424.5
418.5
412.2
401.2
407.6
403.9
418.5
445.8
484.1
509.4
514.8
519.1
528.0
559.6
592.1
610.0
599.0
580.4
571.5
590.4
601.9
604.4
601.5
609.5
619.2
609.8
591.9
602.9
611.0
590.0
582.9
579.8
600.5
558.7

Gross investment
Structures
10.1
9.2
8.7
8.1
7.2
6.9
6.5
7.0
8.5
7.8
7.5
7.5
8.8
11.5
14.4
14.2
11.2
6.8
13.7
14.2
14.9
14.5
13.6
14.3
14.7
14.2
13.0
12.2
10.5
9.0
7.8
6.7
5.8
7.0

Gross investment

Nondefense

Equipment
and
software
43.7
43.8
38.9
40.1
42.4
43.6
46.3
52.7
57.0
63.3
66.8
71.9
75.1
87.0
90.7
93.7
89.1
90.7
85.7
92.0
92.6
92.7
89.8
93.7
96.3
95.0
86.2
90.2
88.2
91.7
87.2
91.5
92.5
91.4

Total

227.5
225.7
231.9
233.7
238.7
244.4
255.5
273.9
281.7
284.6
287.3
296.6
294.2
313.3
333.7
359.2
347.9
347.0
325.0
331.8
334.7
343.2
351.5
363.7
360.8
360.8
359.3
352.3
335.9
344.1
345.6
345.3
347.8
349.3

Consumption
expenditures
201.2
196.2
203.2
201.2
202.9
212.4
224.2
239.7
247.1
250.2
251.0
257.5
254.7
271.0
289.8
308.8
298.4
300.2
281.8
289.2
290.7
297.5
303.2
312.4
309.6
309.8
307.8
302.4
287.3
296.1
298.7
298.6
301.6
302.1

Gross investment
Structures
15.7
15.9
13.8
14.5
14.0
10.4
9.8
11.8
11.9
9.9
8.3
8.8
9.8
9.6
10.1
14.0
13.2
10.2
9.9
9.4
10.2
11.1
12.5
15.0
14.5
14.1
14.7
13.7
12.7
11.7
10.7
10.4
9.7
10.1

Equipment
and
software
13.7
15.5
16.6
18.7
21.7
21.5
21.6
22.7
23.0
24.6
28.0
30.3
29.7
33.0
33.7
36.2
36.3
36.8
33.3
33.3
33.7
34.5
35.6
35.9
36.6
36.8
36.6
36.2
36.0
36.4
36.3
36.6
36.8
37.5

Total

Consumption
expenditures

1,183.6
1,211.1
1,254.3
1,303.8
1,361.8
1,400.1
1,452.3
1,500.6
1,499.7
1,497.1
1,493.6
1,507.2
1,528.1
1,528.1
1,561.8
1,534.1
1,482.0
1,461.9
1,538.3
1,565.2
1,573.6
1,570.2
1,548.3
1,542.7
1,531.6
1,513.6
1,495.3
1,483.4
1,475.9
1,473.3
1,465.3
1,461.6
1,462.7
1,458.0

983.0
1,001.0
1,027.7
1,070.8
1,109.5
1,133.7
1,172.6
1,211.3
1,207.5
1,207.4
1,212.0
1,220.7
1,239.8
1,237.1
1,275.9
1,258.9
1,229.4
1,219.1
1,253.2
1,275.5
1,285.1
1,289.9
1,276.4
1,263.6
1,252.2
1,243.3
1,237.3
1,231.0
1,225.8
1,223.5
1,221.9
1,218.7
1,219.4
1,216.5

Structures

175.4
184.3
196.7
196.5
210.9
222.2
234.8
244.2
245.5
241.3
230.8
231.4
227.6
227.9
224.8
214.8
192.9
182.3
224.1
228.5
227.6
219.0
211.3
218.8
219.2
210.1
198.7
192.6
190.1
190.1
184.0
182.5
183.0
179.8

Equipment
and
software
29.1
29.9
33.1
37.7
41.8
44.3
45.3
45.8
47.2
48.6
50.8
55.2
61.6
64.4
62.6
62.7
63.6
65.5
62.4
62.4
62.3
63.5
63.4
62.4
62.0
62.8
62.6
63.7
64.1
63.8
64.0
65.5
65.3
67.4

Note: See Table B–2 for data for total government consumption expenditures and gross investment for 1964–94.
Source: Department of Commerce (Bureau of Economic Analysis).

National Income or Expenditure | 349

Table B–22. Private inventories and domestic final sales by industry, 1964–2012
[Billions of dollars, except as noted; seasonally adjusted]
Private inventories 1
Quarter
Total 2
Fourth quarter:
1964 ����������������
1965 ����������������
1966 ����������������
1967 ����������������
1968 ����������������
1969 ����������������
1970 ����������������
1971 ����������������
1972 ����������������
1973 ����������������
1974 ����������������
1975 ����������������
1976 ����������������
1977 ����������������
1978 ����������������
1979 ����������������
1980 ����������������
1981 ����������������
1982 ����������������
1983 ����������������
1984 ����������������
1985 ����������������
1986 ����������������
1987 ����������������
1988 ����������������
1989 ����������������
1990 ����������������
1991 ����������������
1992 ����������������
1993 ����������������
1994 ����������������
1995 ����������������
NAICS:
1996 ����������������
1997 ����������������
1998 ����������������
1999 ����������������
2000 ����������������
2001 ����������������
2002 ����������������
2003 ����������������
2004 ����������������
2005 ����������������
2006 ����������������
2007 ����������������
2008 ����������������
2009: I ������������������
      II �����������������
      III ����������������
      IV ����������������
2010: I ������������������
      II �����������������
      III ����������������
      IV ����������������
2011: I ������������������
      II �����������������
      III ����������������
      IV ����������������
2012: I ������������������
      II �����������������
      III ����������������
      IV p �������������

Farm

Mining,
utilities,
and
construction 2

Manufac- Wholesale
turing
trade

Retail
trade

Other
industries 2

Nonfarm 2

Final
sales
of
domestic
business 3

Ratio of private
inventories
to final sales of
domestic business
Total

Nonfarm

154.5
169.4
185.6
194.8
208.1
227.4
235.7
253.7
283.6
351.5
405.6
408.5
439.6
482.0
570.9
667.6
739.0
779.1
773.9
796.9
869.0
875.9
858.0
924.2
999.7
1,044.3
1,082.0
1,057.2
1,082.6
1,116.0
1,194.5
1,257.2

42.2
47.2
47.3
45.7
48.8
52.8
52.4
59.3
73.7
102.2
87.6
89.5
85.3
90.6
119.3
134.9
140.3
127.4
131.3
131.7
131.4
125.8
113.0
119.9
130.7
129.6
133.1
123.2
133.1
132.3
134.5
131.1

�����������������
�����������������
�����������������
�����������������
�����������������
�����������������
�����������������
�����������������
�����������������
�����������������
�����������������
�����������������
�����������������
�����������������
�����������������
�����������������
�����������������
�����������������
�����������������
�����������������
�����������������
�����������������
�����������������
�����������������
�����������������
�����������������
�����������������
�����������������
�����������������
�����������������
�����������������
�����������������

58.6
63.4
73.0
79.9
85.1
92.6
95.5
96.6
102.1
121.5
162.6
162.2
178.7
193.2
219.8
261.8
293.4
313.1
304.6
308.9
344.5
333.3
320.6
339.6
372.4
390.5
404.5
384.1
377.6
380.1
404.3
424.5

20.8
22.5
25.8
28.1
29.3
32.5
36.4
39.4
43.1
51.7
66.9
66.5
74.1
84.0
99.0
119.5
139.4
148.8
147.9
153.4
169.1
175.9
182.0
195.8
213.9
222.8
236.8
239.2
248.3
258.6
281.5
303.7

25.2
28.0
30.6
30.9
34.2
37.5
38.5
44.7
49.8
58.4
63.9
64.4
73.0
80.9
94.1
104.7
111.7
123.2
123.2
137.6
157.0
171.4
176.2
199.1
213.2
231.4
236.6
240.2
249.4
268.6
293.6
312.2

7.7
8.3
8.9
10.1
10.6
12.0
12.9
13.7
14.8
17.7
24.7
25.9
28.5
33.3
38.8
46.6
54.1
66.6
66.8
65.2
66.9
69.5
66.3
69.9
69.5
70.1
71.0
70.5
74.3
76.5
80.6
85.6

112.2
122.2
138.3
149.1
159.3
174.6
183.3
194.4
209.9
249.4
318.1
319.0
354.2
391.4
451.7
532.6
598.7
651.7
642.6
665.1
737.6
750.2
745.1
804.4
869.1
914.7
948.9
934.0
949.5
983.7
1,060.0
1,126.1

40.8
44.9
47.4
49.9
55.0
58.7
61.9
67.5
75.7
83.7
89.8
101.1
111.2
124.0
143.6
159.4
174.1
186.7
194.8
215.7
233.6
249.5
264.2
277.7
304.1
322.8
335.9
345.7
370.9
391.4
413.9
436.0

3.79
3.77
3.92
3.90
3.79
3.88
3.81
3.76
3.74
4.20
4.52
4.04
3.95
3.89
3.98
4.19
4.24
4.17
3.97
3.69
3.72
3.51
3.25
3.33
3.29
3.23
3.22
3.06
2.92
2.85
2.89
2.88

2.75
2.72
2.92
2.99
2.90
2.98
2.96
2.88
2.77
2.98
3.54
3.16
3.19
3.16
3.15
3.34
3.44
3.49
3.30
3.08
3.16
3.01
2.82
2.90
2.86
2.83
2.82
2.70
2.56
2.51
2.56
2.58

1,284.7
1,327.3
1,341.6
1,432.7
1,524.0
1,447.3
1,489.1
1,545.7
1,681.5
1,804.6
1,917.1
2,077.5
2,024.3
1,950.6
1,905.7
1,866.8
1,889.7
1,930.8
1,936.9
1,998.6
2,080.8
2,180.6
2,211.1
2,225.7
2,249.5
2,286.1
2,272.5
2,320.9
2,337.7

136.6
136.9
120.5
124.3
132.1
126.2
135.9
151.0
157.2
165.2
165.1
188.3
185.4
180.9
176.0
171.2
173.1
182.5
181.3
191.0
208.7
232.9
231.3
235.8
240.4
242.8
238.3
236.6
236.4

31.1
33.0
36.6
38.5
42.3
45.3
46.5
54.7
64.1
81.7
90.7
95.6
94.0
89.3
85.9
85.1
84.3
86.7
86.9
88.0
89.8
92.1
95.0
95.7
97.5
99.4
98.6
98.1
103.9

421.0
432.0
432.3
457.6
476.5
440.9
443.7
447.6
487.2
531.5
575.7
635.6
604.5
586.4
580.9
578.8
588.6
603.0
599.5
613.8
643.7
681.3
690.7
690.8
699.5
711.3
694.8
710.7
710.7

285.1
302.5
312.0
334.8
357.7
335.8
343.2
352.6
388.9
422.8
456.4
497.2
496.9
472.9
455.9
438.6
447.6
455.6
459.1
485.5
512.3
535.0
549.7
554.8
562.8
574.1
570.2
594.5
597.6

328.7
335.9
349.2
377.7
400.8
386.0
408.0
425.5
460.9
473.7
491.6
511.8
488.9
471.1
458.9
445.1
447.0
452.7
460.3
470.3
474.1
482.5
485.6
489.5
489.2
498.4
507.9
517.2
522.6

82.1
87.1
91.1
99.8
114.6
113.0
111.8
114.3
123.2
129.8
137.7
148.9
154.6
150.0
148.1
148.1
149.1
150.3
149.7
149.9
152.1
156.8
158.9
159.1
160.1
160.1
162.7
163.8
166.5

1,148.1
1,190.4
1,221.1
1,308.4
1,391.8
1,321.1
1,353.2
1,394.7
1,524.3
1,639.4
1,752.0
1,889.2
1,838.9
1,769.6
1,729.7
1,695.6
1,716.5
1,748.3
1,755.6
1,807.6
1,872.1
1,947.7
1,979.8
1,989.8
2,009.1
2,043.3
2,034.2
2,084.3
2,101.3

465.6
492.2
525.8
557.2
588.3
603.0
608.5
646.2
683.4
727.5
769.6
807.0
782.5
775.3
768.8
770.2
768.7
772.1
781.3
787.4
805.4
811.0
823.2
837.2
844.8
855.6
865.6
877.7
888.2

2.76
2.70
2.55
2.57
2.59
2.40
2.45
2.39
2.46
2.48
2.49
2.57
2.59
2.52
2.48
2.42
2.46
2.50
2.48
2.54
2.58
2.69
2.69
2.66
2.66
2.67
2.63
2.64
2.63

2.47
2.42
2.32
2.35
2.37
2.19
2.22
2.16
2.23
2.25
2.28
2.34
2.35
2.28
2.25
2.20
2.23
2.26
2.25
2.30
2.32
2.40
2.40
2.38
2.38
2.39
2.35
2.37
2.37

1 Inventories at end of quarter. Quarter-to-quarter change calculated from this table is not the current-dollar change in private inventories component of
gross domestic product (GDP). The former is the difference between two inventory stocks, each valued at its respective end-of-quarter prices. The latter is
the change in the physical volume of inventories valued at average prices of the quarter. In addition, changes calculated from this table are at quarterly rates,
whereas change in private inventories is stated at annual rates.
2 Inventories of construction, mining, and utilities establishments are included in other industries through 1995.
3 Quarterly totals at monthly rates. Final sales of domestic business equals final sales of domestic product less gross output of general government, gross
value added of nonprofit institutions, compensation paid to domestic workers, and imputed rental of owner-occupied nonfarm housing. Includes a small amount
of final sales by farm and by government enterprises.
Note: The industry classification of inventories is on an establishment basis. Estimates through 1995 are based on the Standard Industrial Classification
(SIC). Beginning with 1996, estimates are based on the North American Industry Classification System (NAICS).
Source: Department of Commerce (Bureau of Economic Analysis).

350 |

Appendix B

Table B–23. Real private inventories and domestic final sales by industry, 1964–2012
[Billions of chained (2005) dollars, except as noted; seasonally adjusted]
Private inventories 1
Quarter
Total 2
Fourth quarter:
1964 ����������������
1965 ����������������
1966 ����������������
1967 ����������������
1968 ����������������
1969 ����������������
1970 ����������������
1971 ����������������
1972 ����������������
1973 ����������������
1974 ����������������
1975 ����������������
1976 ����������������
1977 ����������������
1978 ����������������
1979 ����������������
1980 ����������������
1981 ����������������
1982 ����������������
1983 ����������������
1984 ����������������
1985 ����������������
1986 ����������������
1987 ����������������
1988 ����������������
1989 ����������������
1990 ����������������
1991 ����������������
1992 ����������������
1993 ����������������
1994 ����������������
1995 ����������������
NAICS:
1996 ����������������
1997 ����������������
1998 ����������������
1999 ����������������
2000 ����������������
2001 ����������������
2002 ����������������
2003 ����������������
2004 ����������������
2005 ����������������
2006 ����������������
2007 ����������������
2008 ����������������
2009: I ������������������
      II �����������������
      III ����������������
      IV ����������������
2010: I ������������������
      II �����������������
      III ����������������
      IV ����������������
2011: I ������������������
      II �����������������
      III ����������������
      IV ����������������
2012: I ������������������
      II �����������������
      III ����������������
      IV p �������������

Farm

Mining,
utilities,
and
construction 2

Manufac- Wholesale
turing
trade

Other
industries 2

Retail
trade

Nonfarm 2

Final
sales
of
domestic
business 3

Ratio of private
inventories
to final sales of
domestic business
Total

Nonfarm

557.9
590.8
637.9
671.8
702.6
732.9
738.5
763.5
789.1
828.1
857.2
844.4
878.7
921.8
967.4
995.4
986.0
1,025.0
1,005.3
997.7
1,075.9
1,101.3
1,109.8
1,143.0
1,164.9
1,195.6
1,212.1
1,210.7
1,228.6
1,250.8
1,320.1
1,352.2

135.1
137.7
136.3
138.8
142.9
142.9
140.5
144.6
145.0
146.8
142.4
148.2
146.6
153.9
155.9
160.2
153.0
163.1
170.6
153.1
159.4
166.5
164.2
155.1
142.0
142.0
148.6
146.7
153.8
146.3
160.0
147.0

�����������������
�����������������
�����������������
�����������������
�����������������
�����������������
�����������������
�����������������
�����������������
�����������������
�����������������
�����������������
�����������������
�����������������
�����������������
�����������������
�����������������
�����������������
�����������������
�����������������
�����������������
�����������������
�����������������
�����������������
�����������������
�����������������
�����������������
�����������������
�����������������
�����������������
�����������������
�����������������

198.2
212.2
240.6
259.6
271.5
284.1
284.0
280.6
288.3
309.6
333.0
324.6
340.1
349.6
365.6
379.7
380.1
385.2
367.9
367.5
399.4
392.4
388.3
397.6
416.2
431.8
441.6
434.2
429.0
432.9
446.3
461.7

82.2
87.8
99.5
107.7
111.5
119.7
128.7
135.5
141.6
145.4
158.9
152.1
162.2
175.3
189.3
198.7
204.0
209.8
207.2
206.3
222.8
229.2
237.7
245.4
254.9
258.5
267.2
271.5
280.3
286.5
302.7
316.2

81.1
89.3
96.6
96.6
104.8
112.1
112.2
127.4
137.3
148.4
146.2
138.8
149.5
158.1
168.7
168.6
163.8
172.8
168.9
182.7
205.0
220.8
224.3
246.1
253.9
268.8
267.2
267.7
272.5
288.3
309.4
321.9

44.7
46.6
47.9
53.5
55.1
57.9
58.6
60.7
63.7
67.0
71.4
73.3
74.0
79.6
84.4
84.3
82.9
92.3
89.4
88.3
89.7
94.8
98.3
100.8
99.3
94.8
91.2
94.8
97.7
101.2
106.1
108.6

407.3
437.8
487.9
519.5
545.9
576.8
585.5
606.1
632.8
673.3
712.3
690.9
728.5
764.2
809.1
832.8
832.4
860.6
833.3
844.0
916.3
934.7
945.1
986.2
1,021.6
1,052.4
1,066.4
1,066.8
1,077.7
1,107.6
1,163.4
1,207.7

176.1
191.3
195.4
200.3
211.2
215.5
218.1
229.3
248.4
257.1
247.5
259.3
272.0
286.4
307.8
315.0
314.7
312.4
311.2
334.7
353.1
369.4
383.3
393.8
414.2
426.4
427.7
427.4
450.6
466.3
484.9
502.7

3.17
3.09
3.26
3.35
3.33
3.40
3.39
3.33
3.18
3.22
3.46
3.26
3.23
3.22
3.14
3.16
3.13
3.28
3.23
2.98
3.05
2.98
2.90
2.90
2.81
2.80
2.83
2.83
2.73
2.68
2.72
2.69

2.31
2.29
2.50
2.59
2.58
2.68
2.68
2.64
2.55
2.62
2.88
2.66
2.68
2.67
2.63
2.64
2.65
2.75
2.68
2.52
2.60
2.53
2.47
2.50
2.47
2.47
2.49
2.50
2.39
2.38
2.40
2.40

1,383.4
1,460.8
1,532.4
1,600.9
1,661.1
1,619.4
1,632.1
1,649.5
1,715.8
1,765.8
1,825.2
1,852.9
1,816.6
1,779.1
1,732.7
1,687.3
1,677.6
1,685.3
1,693.6
1,717.3
1,728.5
1,736.1
1,743.0
1,741.9
1,759.6
1,773.8
1,784.2
1,799.2
1,802.2

155.3
159.0
160.6
156.9
155.2
155.3
152.2
152.4
160.3
160.4
156.7
155.9
156.9
156.8
156.6
155.4
155.5
155.2
154.2
151.6
149.3
148.0
146.6
145.8
145.5
144.8
142.8
138.1
134.3

47.6
50.1
59.1
57.1
54.3
65.1
61.0
68.2
69.6
73.4
90.3
90.3
81.8
82.1
81.6
80.1
75.5
74.4
75.9
75.9
76.9
75.8
76.1
76.2
78.1
82.0
82.8
81.6
82.1

465.7
490.0
507.6
523.8
531.9
505.7
500.5
492.0
498.0
519.0
536.0
551.4
537.3
530.2
520.6
511.9
511.7
514.5
513.4
520.1
528.8
534.1
538.2
538.8
547.7
550.7
550.2
559.3
559.2

298.0
324.9
348.6
369.7
390.4
376.8
376.7
376.3
396.8
415.0
428.3
432.8
441.7
425.3
404.9
386.8
386.1
389.6
393.2
406.3
411.8
415.5
421.6
422.3
429.8
434.6
438.0
446.0
448.5

335.3
349.5
364.7
390.5
411.1
400.5
424.2
441.5
465.2
469.8
480.6
484.8
458.3
444.5
430.5
415.4
411.5
414.5
420.8
427.7
426.6
426.8
424.4
423.2
422.3
427.6
434.9
439.7
444.6

87.6
93.2
99.0
106.6
119.3
119.1
118.0
119.6
126.0
128.3
132.9
137.2
138.8
137.9
136.4
135.7
135.0
134.7
134.0
133.6
133.2
134.0
134.1
133.7
134.2
132.7
135.3
136.3
137.0

1,230.9
1,304.4
1,373.9
1,444.7
1,505.9
1,464.4
1,480.0
1,497.2
1,555.6
1,605.4
1,668.6
1,697.3
1,659.7
1,622.0
1,575.7
1,531.5
1,521.8
1,529.7
1,539.1
1,565.7
1,579.8
1,589.0
1,597.9
1,597.7
1,616.3
1,631.8
1,645.1
1,667.2
1,675.5

528.6
550.7
585.4
615.6
638.0
644.2
644.8
676.3
696.6
718.7
744.4
766.1
730.4
723.6
717.4
716.5
713.3
715.5
721.6
723.9
736.6
739.9
745.9
751.9
758.2
765.2
770.5
775.0
783.6

2.62
2.65
2.62
2.60
2.60
2.51
2.53
2.44
2.46
2.46
2.45
2.42
2.49
2.46
2.42
2.35
2.35
2.36
2.35
2.37
2.35
2.35
2.34
2.32
2.32
2.32
2.32
2.32
2.30

2.33
2.37
2.35
2.35
2.36
2.27
2.30
2.21
2.23
2.23
2.24
2.22
2.27
2.24
2.20
2.14
2.13
2.14
2.13
2.16
2.14
2.15
2.14
2.12
2.13
2.13
2.14
2.15
2.14

1 Inventories at end of quarter. Quarter-to-quarter changes calculated from this table are at quarterly rates, whereas the change in private inventories
component of gross domestic product (GDP) is stated at annual rates.
2 Inventories of construction, mining, and utilities establishments are included in other industries through 1995.
3 Quarterly totals at monthly rates. Final sales of domestic business equals final sales of domestic product less gross output of general government, gross
value added of nonprofit institutions, compensation paid to domestic workers, and imputed rental of owner-occupied nonfarm housing. Includes a small amount
of final sales by farm and by government enterprises.
Note: The industry classification of inventories is on an establishment basis. Estimates through 1995 are based on the Standard Industrial Classification
(SIC). Beginning with 1996, estimates are based on the North American Industry Classification System (NAICS).
See Survey of Current Business, Tables 5.7.6A and 5.7.6B, for detailed information on calculation of the chained (2005) dollar inventory series.
Source: Department of Commerce (Bureau of Economic Analysis).

National Income or Expenditure | 351

Table B–24. Foreign transactions in the national income and product accounts, 1964–2012
[Billions of dollars; quarterly data at seasonally adjusted annual rates]
Current receipts from rest of the world

Current payments to rest of the world
Imports of goods
and services

Exports of goods
and services
Year or quarter

Total
Total

1964 ����������������������
1965 ����������������������
1966 ����������������������
1967 ����������������������
1968 ����������������������
1969 ����������������������
1970 ����������������������
1971 ����������������������
1972 ����������������������
1973 ����������������������
1974 ����������������������
1975 ����������������������
1976 ����������������������
1977 ����������������������
1978 ����������������������
1979 ����������������������
1980 ����������������������
1981 ����������������������
1982 ����������������������
1983 ����������������������
1984 ����������������������
1985 ����������������������
1986 ����������������������
1987 ����������������������
1988 ����������������������
1989 ����������������������
1990 ����������������������
1991 ����������������������
1992 ����������������������
1993 ����������������������
1994 ����������������������
1995 ����������������������
1996 ����������������������
1997 ����������������������
1998 ����������������������
1999 ����������������������
2000 ����������������������
2001 ����������������������
2002 ����������������������
2003 ����������������������
2004 ����������������������
2005 ����������������������
2006 ����������������������
2007 ����������������������
2008 ����������������������
2009 ����������������������
2010 ����������������������
2011 ����������������������
2012 p ��������������������
2009: I ������������������
      II �����������������
      III ����������������
      IV ����������������
2010: I ������������������
      II �����������������
      III ����������������
      IV ����������������
2011: I ������������������
      II �����������������
      III ����������������
      IV ����������������
2012: I ������������������
      II �����������������
      III ����������������
      IV p �������������

42.3
45.0
49.0
52.1
58.0
63.7
72.5
77.0
87.1
118.8
156.5
166.7
181.9
196.6
233.1
298.5
359.9
397.3
384.2
378.9
424.2
414.5
431.3
486.6
595.5
680.3
740.6
764.7
786.8
810.8
904.8
1,041.1
1,113.5
1,233.9
1,240.1
1,308.8
1,473.7
1,350.8
1,316.5
1,394.4
1,628.8
1,878.1
2,192.1
2,532.7
2,702.9
2,229.9
2,560.9
2,877.9
�����������
2,151.3
2,140.3
2,234.0
2,393.9
2,438.9
2,519.3
2,587.2
2,698.3
2,791.8
2,890.2
2,922.2
2,907.3
2,927.5
2,963.6
2,974.5
�����������

35.0
37.1
40.9
43.5
47.9
51.9
59.7
63.0
70.8
95.3
126.7
138.7
149.5
159.4
186.9
230.1
280.8
305.2
283.2
277.0
302.4
302.0
320.3
363.8
443.9
503.1
552.1
596.6
635.0
655.6
720.7
811.9
867.7
954.4
953.9
989.3
1,093.2
1,027.7
1,003.0
1,041.0
1,180.2
1,305.1
1,471.0
1,661.7
1,846.8
1,587.4
1,844.4
2,094.2
2,182.6
1,523.5
1,525.3
1,594.7
1,706.3
1,751.9
1,814.3
1,861.2
1,950.4
2,030.5
2,092.8
2,133.3
2,120.3
2,157.9
2,188.5
2,198.7
2,185.2

ServGoods 1 ices
1

26.7
27.8
30.7
32.2
35.3
38.3
44.5
45.6
51.8
73.9
101.0
109.6
117.8
123.7
145.4
184.0
225.8
239.1
215.0
207.3
225.6
222.2
226.0
257.5
325.8
369.4
396.6
423.6
448.0
459.9
510.1
583.3
618.3
687.7
680.9
697.2
784.3
731.2
700.3
726.8
817.0
906.1
1,024.4
1,162.0
1,297.5
1,064.7
1,278.5
1,474.5
1,542.3
1,012.0
1,010.6
1,073.7
1,162.5
1,206.1
1,257.3
1,288.1
1,362.6
1,425.8
1,471.8
1,498.5
1,501.9
1,525.8
1,550.5
1,555.1
1,537.8

Income
receipts

Total
Total

ServGoods 1 ices
1

8.3
7.2
34.8
28.1
19.4
9.4
7.9
38.9
31.5
22.2
10.2
8.1
45.2
37.1
26.3
11.3
8.7
48.7
39.9
27.8
12.6
10.1
56.5
46.6
33.9
13.7
11.8
62.1
50.5
36.8
15.2
12.8
68.8
55.8
40.9
17.4
14.0
76.7
62.3
46.6
19.0
16.3
91.2
74.2
56.9
21.3
23.5 109.9
91.2
71.8
25.7
29.8 150.5 127.5 104.5
29.1
28.0 146.9 122.7
99.0
31.7
32.4 174.8 151.1 124.6
35.7
37.2 207.5 182.4 152.6
41.5
46.3 245.8 212.3 177.4
46.1
68.3 299.6 252.7 212.8
55.0
79.1 351.4 293.8 248.6
66.1
92.0 393.9 317.8 267.8
68.2 101.0 387.5 303.2 250.5
69.7 101.9 413.9 328.6 272.7
76.7 121.9 514.3 405.1 336.3
79.8 112.4 528.8 417.2 343.3
94.3 111.0 574.0 452.9 370.0
106.2 122.8 640.7 508.7 414.8
118.1 151.6 711.2 554.0 452.1
133.8 177.2 772.7 591.0 484.8
155.5 188.5 815.6 629.7 508.1
173.0 168.1 756.9 623.5 500.7
187.0 151.8 832.4 667.8 544.9
195.7 155.2 889.4 720.0 592.8
210.6 184.1 1,019.5 813.4 676.8
228.6 229.3 1,146.2 902.6 757.4
249.3 245.8 1,227.6 964.0 807.4
266.7 279.5 1,363.3 1,055.8 885.7
273.0 286.2 1,444.6 1,115.7 930.8
292.1 319.5 1,600.7 1,251.4 1,047.7
308.9 380.5 1,884.1 1,475.3 1,246.5
296.5 323.0 1,742.4 1,398.7 1,171.7
302.7 313.5 1,768.1 1,430.2 1,193.9
314.2 353.3 1,910.5 1,545.1 1,289.3
363.2 448.6 2,253.4 1,798.9 1,501.7
399.0 573.0 2,618.6 2,027.8 1,708.0
446.6 721.1 2,990.5 2,240.3 1,884.9
499.7 871.0 3,248.7 2,374.8 2,000.7
549.3 856.1 3,381.9 2,556.5 2,146.3
522.7 642.4 2,612.0 1,976.2 1,587.5
565.9 716.5 3,009.8 2,356.1 1,947.0
619.7 783.7 3,343.7 2,662.3 2,229.2
640.2 ����������� ����������� 2,743.3 2,291.6
511.5 627.8 2,546.6 1,908.9 1,521.5
514.7 615.0 2,494.3 1,856.9 1,475.1
521.1 639.2 2,615.0 1,993.3 1,605.1
543.8 687.6 2,792.1 2,145.5 1,748.1
545.7 687.1 2,891.6 2,242.0 1,841.2
557.0 705.1 2,966.7 2,335.4 1,932.6
573.0 726.1 3,051.8 2,394.3 1,978.3
587.7 747.9 3,129.0 2,452.5 2,035.8
604.7 761.4 3,269.5 2,585.9 2,165.2
621.0 797.4 3,364.3 2,665.3 2,234.9
634.8 788.9 3,357.1 2,682.8 2,239.6
618.4 787.1 3,383.7 2,715.1 2,277.3
632.1 769.6 3,480.7 2,773.7 2,324.3
637.9 775.1 3,448.5 2,765.4 2,312.4
643.5 775.8 3,408.2 2,715.5 2,260.6
647.5 ����������� ����������� 2,718.8 2,268.9

Income
payments

8.7
2.3
9.3
2.6
10.7
3.0
12.2
3.3
12.6
4.0
13.7
5.7
14.9
6.4
15.8
6.4
17.3
7.7
19.3
10.9
22.9
14.3
23.7
15.0
26.5
15.5
29.8
16.9
34.8
24.7
39.9
36.4
45.3
44.9
49.9
59.1
52.6
64.5
56.0
64.8
68.8
85.6
73.9
85.9
82.9
93.4
93.9 105.2
101.9 128.3
106.2 151.2
121.7 154.1
122.8 138.2
122.9 122.7
127.2 124.0
136.6 160.0
145.1 199.6
156.5 214.2
170.1 256.1
184.9 268.9
203.7 291.7
228.8 342.8
227.0 271.1
236.3 264.4
255.9 284.6
297.3 357.4
319.8 475.9
355.4 648.6
374.0 747.7
410.1 686.9
388.7 498.9
409.1 507.2
433.0 531.8
451.8 �����������
387.4 509.6
381.8 499.2
388.2 476.2
397.4 510.5
400.8 495.6
402.8 489.3
416.0 509.1
416.7 534.9
420.7 526.1
430.4 547.4
443.2 530.6
437.8 523.1
449.3 554.7
453.0 527.8
454.9 532.7
449.9 �����������

Current taxes and
transfer payments
to rest of the world (net)

Total

4.4
4.7
5.1
5.5
5.9
5.9
6.6
7.9
9.2
7.9
8.7
9.1
8.1
8.1
8.8
10.6
12.6
17.0
19.8
20.5
23.6
25.7
27.8
26.8
29.0
30.4
31.7
–4.9
41.9
45.4
46.1
44.1
49.5
51.4
60.0
57.6
66.1
72.6
73.5
80.7
97.1
115.0
101.5
126.2
138.4
137.0
146.5
149.6
157.1
128.2
138.2
145.4
136.0
154.0
142.0
148.4
141.7
157.5
151.6
143.8
145.5
152.3
155.4
160.0
160.5

From
persons
(net)
0.7
.8
.8
1.0
1.0
1.1
1.3
1.4
1.4
1.6
1.4
1.3
1.4
1.4
1.6
1.7
2.0
5.6
6.7
7.0
7.9
8.3
9.1
10.0
10.8
11.6
12.2
14.1
14.5
17.1
18.9
20.3
22.6
25.7
29.7
32.2
34.6
38.1
40.6
41.2
43.6
48.4
51.6
59.3
66.2
66.1
73.5
73.9
76.4
63.7
65.2
65.9
69.4
73.4
73.2
74.2
73.1
73.5
73.8
73.3
75.1
75.5
76.4
76.6
77.1

From
government
(net)
3.5
3.8
4.1
4.2
4.6
4.5
4.9
6.1
7.4
5.6
6.4
7.1
5.7
5.3
5.9
6.8
8.3
8.3
9.7
10.1
12.2
14.4
15.4
13.4
13.7
14.2
14.7
–24.0
22.0
22.9
21.1
15.6
20.0
16.7
17.4
18.0
20.0
16.2
21.6
25.8
27.2
35.3
28.8
36.1
37.1
49.7
51.2
55.5
54.3
39.6
53.3
61.0
45.0
56.3
46.2
51.6
50.6
54.4
63.0
51.7
52.9
57.4
54.6
55.7
49.6

From
business
(net)

Balance
on
current
account,
NIPA 2

0.2
7.5
.2
6.2
.2
3.8
.2
3.5
.3
1.5
.3
1.6
.4
3.7
.4
.3
.5
–4.0
.7
8.9
1.0
6.0
.7
19.8
1.1
7.1
1.4
–10.9
1.4
–12.6
2.0
–1.2
2.4
8.5
3.2
3.4
3.4
–3.3
3.4
–35.1
3.5
–90.1
2.9 –114.3
3.2 –142.7
3.4 –154.1
4.5 –115.7
4.6
–92.4
4.8
–74.9
5.0
7.9
5.4
–45.6
5.4
–78.6
6.0 –114.7
8.2 –105.1
6.9 –114.1
9.1 –129.3
13.0 –204.5
7.4 –291.9
11.4 –410.4
18.3 –391.6
11.3 –451.6
13.7 –516.1
26.3 –624.6
31.3 –740.5
21.1 –798.4
30.8 –716.0
35.2 –679.0
21.2 –382.2
21.9 –448.8
20.2 –465.8
26.4 �������������
24.9 –395.4
19.7 –354.0
18.5 –381.0
21.6 –398.2
24.2 –452.7
22.6 –447.4
22.6 –464.6
18.0 –430.8
29.7 –477.7
14.8 –474.1
18.7 –434.9
17.5 –476.3
19.3 –553.2
24.4 –485.0
27.8 –433.7
33.9 �������������

1 Certain goods, primarily military equipment purchased and sold by the Federal Government, are included in services. Beginning with 1986, repairs and
alterations of equipment were reclassified from goods to services.
2 National income and product accounts (NIPA).
Source: Department of Commerce (Bureau of Economic Analysis).

352 |

Appendix B

Table B–25. Real exports and imports of goods and services, 1995–2012
[Billions of chained (2005) dollars; quarterly data at seasonally adjusted annual rates]

Year or quarter

1995 ����������������������
1996 ����������������������
1997 ����������������������
1998 ����������������������
1999 ����������������������
2000 ����������������������
2001 ����������������������
2002 ����������������������
2003 ����������������������
2004 ����������������������
2005 ����������������������
2006 ����������������������
2007 ����������������������
2008 ����������������������
2009 ����������������������
2010 ����������������������
2011 ����������������������
2012 p ��������������������
2009: I ������������������
      II �����������������
      III ����������������
      IV ����������������
2010: I ������������������
      II �����������������
      III ����������������
      IV ����������������
2011: I ������������������
      II �����������������
      III ����������������
      IV ����������������
2012: I ������������������
      II �����������������
      III ����������������
      IV p �������������

Total

845.1
915.3
1,024.3
1,047.7
1,093.4
1,187.4
1,120.8
1,098.3
1,116.0
1,222.5
1,305.1
1,422.1
1,554.4
1,649.3
1,498.7
1,665.6
1,776.9
1,836.0
1,452.5
1,454.6
1,502.3
1,585.2
1,608.2
1,645.4
1,683.9
1,724.7
1,748.8
1,766.4
1,792.9
1,799.3
1,818.7
1,842.1
1,850.9
1,832.5

Exports of goods and services

Imports of goods and services

Goods 1

Goods 1

Total
574.8
625.5
715.4
731.4
759.2
843.4
791.2
762.7
776.4
842.6
906.1
991.5
1,088.1
1,157.0
1,018.6
1,164.1
1,247.6
1,299.9
982.0
975.4
1,023.3
1,093.6
1,119.7
1,151.6
1,176.8
1,208.5
1,225.4
1,236.5
1,255.1
1,273.6
1,286.3
1,308.3
1,311.8
1,293.2

Durable
goods
363.0
404.8
478.0
493.4
517.0
583.7
535.1
504.8
513.7
570.7
624.9
692.0
756.1
795.8
660.4
770.8
841.1
881.5
644.0
625.7
660.0
711.8
730.3
768.0
783.0
801.9
817.8
837.3
852.9
856.4
883.2
884.6
886.0
872.1

Nondurable
goods
216.2
223.4
237.9
237.6
240.8
256.5
255.2
259.1
263.8
272.2
281.2
299.6
331.9
359.8
351.1
387.2
403.0
415.9
332.1
342.4
355.7
374.3
381.9
378.3
388.1
400.3
402.2
396.6
400.3
413.0
403.4
420.6
422.4
417.4

Services 1

272.6
291.7
308.9
316.4
334.6
343.5
329.3
335.6
339.6
380.0
399.0
430.6
466.3
492.3
479.6
501.9
529.8
536.7
469.7
478.2
478.6
491.8
489.0
494.4
507.6
516.7
524.0
530.5
538.4
526.2
532.9
534.4
539.6
539.8

Total

943.9
1,026.0
1,164.1
1,300.2
1,449.9
1,638.7
1,592.6
1,646.8
1,719.7
1,910.4
2,027.8
2,151.5
2,203.2
2,144.0
1,853.8
2,085.2
2,184.9
2,237.6
1,856.0
1,777.4
1,849.3
1,932.7
1,980.9
2,074.2
2,142.8
2,143.0
2,165.4
2,166.0
2,190.8
2,217.3
2,234.2
2,249.6
2,246.1
2,220.4

Total
765.5
837.2
957.9
1,071.4
1,205.0
1,366.7
1,323.1
1,372.2
1,439.9
1,599.3
1,708.0
1,809.1
1,856.1
1,784.8
1,506.4
1,730.3
1,820.0
1,857.9
1,507.1
1,432.0
1,502.3
1,584.3
1,630.8
1,723.3
1,781.2
1,786.1
1,808.9
1,805.7
1,818.8
1,846.7
1,855.8
1,868.9
1,863.1
1,843.9

Durable
goods
422.3
467.5
544.6
616.4
706.2
813.7
763.4
795.4
829.7
944.6
1,025.4
1,115.6
1,141.2
1,099.3
870.9
1,066.6
1,161.7
1,244.5
846.1
811.3
874.2
952.0
984.2
1,057.9
1,101.3
1,123.1
1,145.2
1,141.4
1,165.9
1,194.1
1,238.0
1,253.5
1,243.0
1,243.6

Nondurable
goods

Services 1

360.0
384.1
424.1
462.9
500.2
549.2
564.2
580.2
615.2
655.8
682.6
694.5
715.7
686.6
626.4
662.0
666.2
639.7
649.8
610.4
619.1
626.5
641.1
662.8
678.6
665.4
668.3
668.7
662.4
665.3
642.4
642.4
645.1
628.8

180.9
190.3
206.9
229.4
244.9
271.7
269.6
274.5
279.8
311.0
319.8
342.4
347.1
359.8
347.8
356.6
366.6
381.6
349.2
345.0
347.4
349.5
351.6
352.6
363.5
358.8
358.3
362.0
373.9
372.3
380.4
382.6
385.0
378.5

1 Certain goods, primarily military equipment purchased and sold by the Federal Government, are included in services. Beginning with 1986, repairs and
alterations of equipment were reclassified from goods to services.
Note: See Table B–2 for data for total exports of goods and services and total imports of goods and services for 1964–94.
Source: Department of Commerce (Bureau of Economic Analysis).

National Income or Expenditure | 353

Table B–26. Relation of gross domestic product, gross national product, net national
product, and national income, 1964–2012
[Billions of dollars; quarterly data at seasonally adjusted annual rates]

Year or quarter

1964 ����������������������
1965 ����������������������
1966 ����������������������
1967 ����������������������
1968 ����������������������
1969 ����������������������
1970 ����������������������
1971 ����������������������
1972 ����������������������
1973 ����������������������
1974 ����������������������
1975 ����������������������
1976 ����������������������
1977 ����������������������
1978 ����������������������
1979 ����������������������
1980 ����������������������
1981 ����������������������
1982 ����������������������
1983 ����������������������
1984 ����������������������
1985 ����������������������
1986 ����������������������
1987 ����������������������
1988 ����������������������
1989 ����������������������
1990 ����������������������
1991 ����������������������
1992 ����������������������
1993 ����������������������
1994 ����������������������
1995 ����������������������
1996 ����������������������
1997 ����������������������
1998 ����������������������
1999 ����������������������
2000 ����������������������
2001 ����������������������
2002 ����������������������
2003 ����������������������
2004 ����������������������
2005 ����������������������
2006 ����������������������
2007 ����������������������
2008 ����������������������
2009 ����������������������
2010 ����������������������
2011 ����������������������
2012 p ��������������������
2009: I ������������������
      II �����������������
      III ����������������
      IV ����������������
2010: I ������������������
      II �����������������
      III ����������������
      IV ����������������
2011: I ������������������
      II �����������������
      III ����������������
      IV ����������������
2012: I ������������������
      II �����������������
      III ����������������
      IV p �������������

Gross
domestic
product

Plus:
Income
receipts
from rest
of the
world

Less:
Income
payments
to rest
of the
world

Equals:
Gross
national
product

663.6
7.2
2.3
668.6
719.1
7.9
2.6
724.4
787.7
8.1
3.0
792.8
832.4
8.7
3.3
837.8
909.8
10.1
4.0
915.9
984.4
11.8
5.7
990.5
1,038.3
12.8
6.4
1,044.7
1,126.8
14.0
6.4
1,134.4
1,237.9
16.3
7.7
1,246.4
1,382.3
23.5
10.9
1,394.9
1,499.5
29.8
14.3
1,515.0
1,637.7
28.0
15.0
1,650.7
1,824.6
32.4
15.5
1,841.4
2,030.1
37.2
16.9
2,050.4
2,293.8
46.3
24.7
2,315.3
2,562.2
68.3
36.4
2,594.2
2,788.1
79.1
44.9
2,822.3
3,126.8
92.0
59.1
3,159.8
3,253.2
101.0
64.5
3,289.7
3,534.6
101.9
64.8
3,571.7
3,930.9
121.9
85.6
3,967.2
4,217.5
112.4
85.9
4,244.0
4,460.1
111.0
93.4
4,477.7
4,736.4
122.8
105.2
4,754.0
5,100.4
151.6
128.3
5,123.8
5,482.1
177.2
151.2
5,508.1
5,800.5
188.5
154.1
5,835.0
5,992.1
168.1
138.2
6,022.0
6,342.3
151.8
122.7
6,371.4
6,667.4
155.2
124.0
6,698.5
7,085.2
184.1
160.0
7,109.2
7,414.7
229.3
199.6
7,444.3
7,838.5
245.8
214.2
7,870.1
8,332.4
279.5
256.1
8,355.8
8,793.5
286.2
268.9
8,810.8
9,353.5
319.5
291.7
9,381.3
9,951.5
380.5
342.8
9,989.2
10,286.2
323.0
271.1
10,338.1
10,642.3
313.5
264.4
10,691.4
11,142.2
353.3
284.6
11,210.9
11,853.3
448.6
357.4
11,944.5
12,623.0
573.0
475.9
12,720.1
13,377.2
721.1
648.6
13,449.6
14,028.7
871.0
747.7
14,151.9
14,291.5
856.1
686.9
14,460.7
13,973.7
642.4
498.9
14,117.2
14,498.9
716.5
507.2
14,708.2
15,075.7
783.7
531.8
15,327.5
15,681.5 ������������������� ������������������� �������������������
13,923.4
627.8
509.6
14,041.7
13,885.4
615.0
499.2
14,001.3
13,952.2
639.2
476.2
14,115.2
14,133.6
687.6
510.5
14,310.8
14,270.3
687.1
495.6
14,461.7
14,413.5
705.1
489.3
14,629.3
14,576.0
726.1
509.1
14,793.0
14,735.9
747.9
534.9
14,948.9
14,814.9
761.4
526.1
15,050.1
15,003.6
797.4
547.4
15,253.6
15,163.2
788.9
530.6
15,421.5
15,321.0
787.1
523.1
15,585.0
15,478.3
769.6
554.7
15,693.2
15,585.6
775.1
527.8
15,832.9
15,811.0
775.8
532.7
16,054.2
15,851.2 ������������������� ������������������� �������������������

Source: Department of Commerce (Bureau of Economic Analysis).

354 |

Appendix B