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March 2016

Expect 25 years of rapid change
Editor’s note: This essay is part of a series being published
to help commemorate the Monthly Labor Review’s
centennial (July 1915–July 2015). The essays―written by
eminent authorities and distinguished experts in a broad
range of fields―cover a variety of topics pertinent to the
Review and the work of the Bureau of Labor Statistics.
Each essay is unique and comprises the words and opinion
of the author. We’ve found these essays to be enlightening
and inspirational. We hope you do as well.
It seems almost impossible to imagine, but it was only
about 25 years ago that the first web page was published
on the World Wide Web. Looking ahead from today, then, it
is clear that the pace of technological change is so rapid
that many core characteristics of the economy of 2040 are

Kevin A. Hassett

impossible to predict. Above all, this unpredictability is a key
characteristic of the modern world that challenges scholars
and workers alike.
What shifts are predictable? What will be the labor market’s

Kevin A. Hassett is the State Farm James Q.
Wilson Chair in American Politics and Culture at
the American Enterprise Institute (AEI). He is also
a resident scholar and AEI’s director of economic
policy studies.

drivers, challenges, and opportunities in 2040? Looking
ahead, we see several key factors that will revolutionize the
workplace and challenge the writers and analysts of the

Michael R. Strain
Michael R. Strain is deputy director of economic
policy studies and resident scholar at the
American Enterprise Institute for Public Policy
Research.

Monthly Labor Review.
Let’s start with an easy one: Workers will live and work
longer, perhaps much, much longer. If you subscribe to the
lump of labor fallacy, then you would think that these older
workers would take jobs away from younger workers. But

we doubt that will happen—older workers will have more income, consume more goods and services, drive up
demand, and create more jobs for younger workers in the process.
But all may not be well. Twenty-five years from now, there may be a good number of workers in their 50s waiting
for their 75-year-old boss to retire so they can advance to the next level of their career. What will this do to morale?
What will it do to the internal culture of organizations for jobs to become permanent?

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MONTHLY LABOR REVIEW

We expect the second factor that is relatively easy to predict will partially mitigate any negative effects of
“permanent” careers: the workplace itself will be less and less relevant to the organization of society. Applications
such as Uber have already made it much easier for workers to be matched to those who demand their services in
real time and on their terms.
You might set an income target for yourself: “I want to make $50,000 dollars this year.” One day you sell your labor
as a taxi driver. The next day, you wake up and decide you’d like to do some programming, so you log onto a job
board and bid on a project. You work on that project for a month, and then decide to take 2 weeks off. When you
feel like working again, you pick another task.
Task is the key word here. You perform tasks, and you receive income. But you work for yourself.
The third factor that is sure to transform the workplace is the robot. Today a robot can deposit your checks and
give you cash. Next year a robot will be able to pilot your car down the highway. Twenty-five years from now, what
might robots be able to do? Perhaps much, much more. Today, rudimentary robots, such as the VGo, allow
individuals to remotely interact with distant places. Some scientists have already attended academic conferences
remotely via robot.
Intelligent robots will likely take over many everyday tasks. Your heart surgeon will still have a beating heart—
though she will likely be assisted by a tin man. The same will likely be somewhat less true of lower paying
occupations. Today robots can’t clean office buildings after hours, but in the year 2040 they might be able to.
And in the year 2040 we will probably still be on what economists call a “transition path.” Technology will still be
destroying and creating occupations at an unusually rapid clip. The winners in this process will win big, and the
losers will face much hardship. A central challenge for public policy will be mitigating the damage and helping
individuals to build the skills needed in the new economy. But these advancements mean that machines will offer
increasingly abundant and inexpensive labor. Society will, accordingly, be much wealthier than it is even today. If
all goes according to plan, there should be plenty of resources to assure that lesser skilled workers are not left
behind.
Fifty years ago, you went to a job and remained until you retired. You left with a gold watch and a pension. Twentyfive years ago, you started your career, hopped from job to job, maybe took a break for additional schooling, had
no expectation of a defined-benefit pension, and had a relatively weak relationship with any individual employer.
Twenty-five years from now, the workplace will still exist in some industries but will be a distant memory in others.
Charting that distance will be a key challenge for economists in the quarter century to come.
SUGGESTED CITATION

Kevin A. Hassett and Michael R. Strain, "Expect 25 years of rapid change," Monthly Labor Review, U.S. Bureau of
Labor Statistics, March 2016, https://doi.org/10.21916/mlr.2016.12.
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The Monthly Labor Review turns 100, Monthly Labor Review, July 2015.

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MONTHLY LABOR REVIEW

Bureau of labor statistics

March 2016

I, robot?
Editor’s note: This essay is part of a series being published
to help commemorate the Monthly Labor Review’s
centennial (July 1915–July 2015). The essays―written by
eminent authorities and distinguished experts in a broad
range of fields―cover a variety of topics pertinent to the
Review and the work of the Bureau of Labor Statistics.
Each essay is unique and comprises the words and opinion
of the author. We’ve found these essays to be enlightening
and inspirational. We hope you do as well.
A worker retiring at age 66 in 2015 likely joined the
workforce sometime between 1968 and 1975. Over that
period, cutting-edge technology advanced from gee-whiz
hand calculators to wristband devices with as much
communications and computing power as the Space

J.D. Foster

Shuttle, while cars have become advanced computer
J.D. Foster is deputy chief economist at the U.S.
Chamber of Commerce.

networks that happen to have wheels, an engine, and
bucket seats. Assuming technology continues its
exponential growth, what technologies will today’s hyper
social-media-savvy job market entrants need to master to

remain productive 25 years from now or when they retire in 2050 or beyond, and how will they manage—and how
can the Monthly Labor Review help?
The good news is the accelerating advance of technology lowers costs, creates whole new product categories,
and generally works to increase labor productivity and income. The disquieting news is that tomorrow’s workers
will have to be perpetual students to keep up, while lesser skilled workers will find themselves increasingly
marginalized and sometimes, ultimately, replaced by technology in some form or fashion. Productive capital
requires constant maintenance and upgrades to remain productive. Increasingly, human capital will be no different.
But culturally, we’re not ready.
Perpetual student workers likely will not mean permanent night-school attendance. Keeping up with technology’s
advances generally occurs naturally in the workplace—but not always. In any event, staying on top of technology
may involve new policy paradigms; will probably require a national conversation led by business, education, and
political leaders; and above all will certainly require new personal and cultural attitudes toward career-centered
lifelong learning.

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MONTHLY LABOR REVIEW

Go to school, learn some skills, get a job, advance with experience, retire—this traditional model for American
workers is obviously obsolete, but culturally many Americans still retain much of this traditional perspective as life’s
template. The cultural change necessary to adapt to the new job realities will be as fundamental as was the shift
from a mainly agricultural workforce to an industrial one during the 19th century.
Economic data generally, and Bureau of Labor Statistics (BLS) data specifically, will likely prove ineffective causal
agents for changing cultural attitudes favoring career-centered lifelong learning, but they can help in important
ways to lay the groundwork. The professionals at BLS need to be thinking about how new data sources may be
developed to highlight the importance of career-centered lifelong learning and the relevant efficacy of different
approaches thereto.
The task ahead is even tougher and more important than it first appears. Consider those segments of society most
and least likely to be receptive to the needed change in cultural attitudes toward lifelong learning. The high skilled
and the learned are naturally receptive to further learning, while the less skilled and the lightly educated are largely
self-selected to be less receptive. Anyone concerned about income inequality, growing or otherwise, should take
this issue very seriously.
Go into a fast-food or moderately priced restaurant today, and as likely or not you will be greeted by a computer
tablet screen. A server may seat you and bring water and drinks, but your order is placed via a series of interactive
screens. When your meal is finished, your bill is not presented by a server but rather comes up on the same
screen on which you ordered and on which you then swipe a card and leave.
Highly skilled individuals designed, built, and tested these new surrogate restaurant server systems. Those
individuals also tend to be highly paid, and their efforts are eliminating other jobs at restaurants, at checkout
counters, at airports, and so on. There is nothing to lament here. This transition toward capital and technology and
higher skilled labor is inevitable and ultimately is income increasing.
While not lamentable, this evolution is a clear threat to those at risk of being left behind—those whose jobs are
especially dangerous or relatively low skilled or too highly paid compared with their worker-competitors overseas
or their robot-competitors on the shop floor. Such displacement is not a new experience but threatens to increase
rapidly. How American workers through policies and cultural evolution adapt to facilitate finding new gainful
employment will depend significantly on how successful the workers become at relentlessly climbing the learning
curve. Much will be required for these transitions to proceed as smoothly as possible. An essential element of what
will be required is information about labor skills and training—perhaps the most important direction the BLS can
explore in the years ahead.
SUGGESTED CITATION

J.D. Foster, "I, robot?," Monthly Labor Review, U.S. Bureau of Labor Statistics, March 2016, https://doi.org/
10.21916/mlr.2016.10.
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The Monthly Labor Review turns 100, Monthly Labor Review, July 2015.

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MONTHLY LABOR REVIEW

History

March 2016

Current Employment Statistics data and their
contributions as key economic indicators
To help mark the Monthly Labor Review’s centennial, the
editors invited several producers and users of BLS data to
take a look back at the last 100 years. This article highlights
the important role of the Current Employment Statistics
(CES) survey as a key source of economic information for
data users seeking to obtain a timely and broad view of the
U.S. labor market. The CES program publishes first
estimates of employment, hours, and earnings each month,
approximately 3 weeks after the week including the 12th of
the month. CES estimates, widely used on their own, also
serve as important inputs to other closely watched
economic indicators. Among the users of CES data are
government agencies, private businesses, and research
organizations.
The Current Employment Statistics (CES) survey was first
conducted in October 1915 for four manufacturing
industries—boots and shoes, cotton goods, cotton finishing,
and hosiery and underwear. The first CES data release,
published in the January 1916 issue of the Monthly Review,
indicated that it would be useful to employers in these

Richa Ajmera
richa.ajmera@fsis.usda.gov
Richa Ajmera was formerly a senior economist in
the Office of Employment and Unemployment
Statistics, U.S. Bureau of Labor Statistics.
Angie Clinton
clinton.angie@bls.gov

industries, as well as to workers, the unemployed, and
policymakers:
The amount of authoritative data concerning the ebb

Angie Clinton is a senior economist in the Office
of Employment and Unemployment Statistics,
U.S. Bureau of Labor Statistics.

and flow of industrial employment in the United States
is limited. The necessity for figures on this subject is
apparent. Every successful employer must know his
own business, and to continue successful [sic] he needs to know the condition of the industry of which his
establishment is a part, for so closely are industrial affairs related that the prosperity of any establishment may
be affected materially by the conditions of the industry as a whole. To the workingmen, the unemployed, and
those seeking to relieve unemployment such figures are also of service.1

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MONTHLY LABOR REVIEW

Over the past 100 years, the CES survey has evolved considerably. In 2015, CES published more than 900
national employment series for nonfarm and government industries. Two sets of estimates of hours and earnings
for private sector employees also were produced—one for all employees and one for production employees in
goods-producing industries and nonsupervisory employees in private service-providing industries. Further,
numerous additional series were derived from basic estimates. CES also has expanded to produce estimates, by
industry, for all 50 states, the District of Columbia, Puerto Rico, the U.S. Virgin Islands, and about 450 metropolitan
areas and divisions.
CES data serve as one of the first available monthly economic indicators for evaluating the health of the U.S.
economy. Each month, the U.S. Bureau of Labor Statistics (BLS) publishes estimates of employment, hours, and
earnings from the CES survey. The Employment Situation, a news release combining national estimates from the
CES survey and labor force data from the Current Population Survey (CPS), is published about 3 weeks after the
week that includes the 12th of the month. The reference period for CES respondents is the pay period that
includes the 12th of the month. BLS releases CES estimates for states and metropolitan areas about 2 weeks after
the release of national estimates.
Because of their breadth of industry detail, geographic scope, and timeliness, CES data are followed closely by a
wide array of economic data users—policymakers, government agencies and entities, major news media, financial
market analysts in the United States and around the world, and other business and academic analysts,
researchers, and forecasters. This article highlights the strengths of CES data and their important contributions as
economic indicators.

Why follow CES?
The breadth and quality of CES data help one understand why data users choose to follow them. The CES survey,
also known as the payroll or establishment survey, is a monthly survey of approximately 146,000 businesses and
government agencies representing about 623,000 worksites throughout the United States. Using data from this
sample, BLS produces and publishes estimates of employment, hours, and earnings for the nation, states, and
metropolitan areas, by detailed industry. The CES survey produces over 27,000 data series for the nation and
about 23,000 series for states and areas.2
CES employment series include all employees, production and nonsupervisory employees (referred to in this
article as production employees),3 and women employees. Monthly employment estimates for major industry
sectors and some detailed industries begin as early as 1939, but all employment series generally begin no later
than 1990.
BLS also produces estimates of average hourly earnings, average weekly hours, and, for manufacturing industries,
average weekly overtime hours. Hours and earnings estimates cover private sector workers in two employee sets
—all employees and production employees. Since 1964, monthly estimates of hours and earnings for production
employees have been produced for all private industry sectors. The hours and earnings estimates for all
employees start in 2006.
BLS also derives other series, such as average weekly earnings and diffusion indexes, from the basic series of
employment, hours, and earnings. Each month, CES publishes real earnings (a Principal Federal Economic

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MONTHLY LABOR REVIEW

Indicator) in conjunction with the release of the Consumer Price Index (CPI). Average hourly and average weekly
earnings are deflated by the CPI to provide information on the real buying power of consumers.
Industry classification. All data on employment, hours, and earnings for the nation, states, and metropolitan areas
are classified in accordance with the North American Industry Classification System, developed under the auspices
of the Office of Management and Budget.4 The United States, Canada, and Mexico share this classification
system, which allows a direct comparison of economic data across the three countries.5
Revisions. Because not all respondents report their payroll data by the initial release of employment, hours, and
earnings, CES estimates are considered preliminary when first published each month. BLS continues to collect
payroll data and revises estimates twice before the annual benchmark update (discussed below). For a given
month, BLS publishes second preliminary estimates 1 month after the initial release and final sample-based
estimates 2 months after the initial release. The estimates published with the second and third (final) releases
incorporate additional data from respondents and corrected data. With each new monthly observation and the
revisions to previous months’ estimates, BLS recalculates CES seasonal adjustment factors, which also can
contribute to revisions in the seasonally adjusted estimates.6
Benchmarking. On an annual basis, BLS recalculates nearly 2 years of CES data in a process known as
benchmarking. The process corrects for sampling and modeling error by reanchoring sample-based estimates for
March of each year to nearly complete employment counts based primarily on unemployment insurance tax
records.7 Historically, benchmark revisions of total nonfarm employment have been relatively small, averaging 0.3
percent (in absolute terms) from March 2005 through March 2015.8
Seasonality. CES data are available both seasonally adjusted and not seasonally adjusted. To reveal underlying
economic trends, the seasonal adjustment process removes from the data the effects of normal employment
variation, which results from recurring events within a year (such as holidays and weather changes). BLS uses a
concurrent seasonal adjustment methodology for national CES estimates that incorporate estimates up through
the current month’s data.

Uses and users of CES data
CES data as inputs to other important economic indicators. While CES estimates, on their own, serve as important
economic indicators for analyzing the health of the U.S. economy, they also serve as inputs to other economy-wide
indicators. Total nonfarm employment and aggregate weekly hours (the product of employment and average
weekly hours) are considered coincident economic indicators. In other words, employment and aggregate hours
are indicative of the current state of the economy and tend to move in sync with U.S. business cycles, reaching
peaks and troughs at about the same time as the cycle. In fact, the Business Cycle Dating Committee of the
National Bureau of Economic Research (NBER) uses CES employment data to determine turning points in the
U.S. business cycle.
Table 1 shows the relationship between turning points in the U.S. business cycle, as determined by NBER, and
turning points in CES employment. Eight out of 11 peak months in CES employment coincided within 3 months of
the NBER peak months. Eight out of the 11 trough months coincided within 3 months of the NBER trough months.
However, this coincident pattern for identifying business cycle troughs (i.e., ending points of recessions) broke
down after the last two recessions. CES employment continued to decline for 21 months after the end of the 2001

U.S. BUREAU OF LABOR STATISTICS

MONTHLY LABOR REVIEW

recession and for 8 months after the June 2009 business cycle trough. For both recessions, NBER found that
business cycle peak dates coincided with the peaks in CES employment, whereas the central indicators—real
gross domestic product and real gross domestic income—gave mixed signals about the peak dates.9
Table 1. Turning points in the U.S. business cycle and CES employment
NBER turning points
Peak month
November 1948
July 1953
August 1957
April 1960
December 1969
November 1973
January 1980
July 1981
July 1990
March 2001
December 2007

Trough month
October 1949
May 1954
April 1958
February 1961
November 1970
March 1975
July 1980
November 1982
March 1991
November 2001
June 2009

CES employment turning points
Peak month
September 1948
July 1953
April 1957
April 1960
March 1970
July 1974

Trough month
October 1949
August 1954
June 1958
February 1961
November 1970
April 1975

(1)

July 1981
June 1990
February 2001
January 2008

Months lead (lag)
Peak month

Trough month

2
0
4
0
(3)
(8)

0
(3)
(2)
0
0
(1)

(1)

December 1982
May 1991
August 2003
February 2010

0
1
1
(1)

(1)
(2)
(21)
(8)

Notes:
(1) No peak or trough month declared for CES data because period of decline did not meet criteria. For information on how peaks and troughs are identified in

CES data, see https://www.bls.gov/ces/cespeaktrough.htm.
Note: CES data are subject to annual benchmarking and reseasonal adjustment.
Sources: U.S. Bureau of Labor Statistics and National Bureau of Economic Research.

The Federal Reserve uses aggregate weekly hours in manufacturing, mining and logging, electric and gas utilities,
and publishing industries to calculate industrial production indexes (IPIs), which measure real output in those
industries.10 Investors use IPIs to analyze growth in these industries. Growth in month-over-month IPIs for a
particular industry signals that companies within that industry are performing well.11
The Bureau of Economic Analysis uses aggregate weekly earnings (the product of average hourly earnings,
average weekly hours, and employment ) for all private sector jobs to estimate wages and salaries for personal
income, a coincident economic indicator.12
The Conference Board combines various statistics to produce its composite leading and coincident economic
indexes. These indexes are designed to signal peaks and troughs in the U.S. business cycle and to summarize
and reveal common turning-point patterns in economic data by smoothing out some of the volatility of individual
economic series. The Coincident Economic Index provides information on the current state of the economy. CES
employment is a direct input into the index, whereas aggregate weekly earnings for all private industries and
aggregate weekly hours of production employees in selected industries are indirect inputs (through personal
income and industrial production, respectively).
The Conference Board’s Leading Economic Index is used to predict the direction of the economy’s movements in
months to come. The manufacturing workweek from the CES survey, also useful as a short-term predictor of
changing economic trends, is a direct input into the index.13

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MONTHLY LABOR REVIEW

Changes in employment trends of the temporary help services industry typically lead employment changes for the
overall U.S. economy. Therefore, The Conference Board uses temporary help employment from the CES survey
as an input in calculating the Employment Trend Index. This index aggregates eight separate economic indicators
and offers a short-term, forward look at potential changes in employment trends.14 (See figure 1.)
CES data used in other BLS programs. CES data are used as inputs to other BLS data, in programs such as the
Local Area Unemployment Statistics (LAUS) program, which produces statistics on the labor force by state and
metropolitan area. LAUS uses CES state and area employment estimates as primary inputs to its employment
estimates for states and metropolitan areas. LAUS data are based on place of residence, whereas CES estimates
are based on location of work. Therefore, LAUS adjusts its estimates with U.S. Census Bureau data, to “residencyadjust” the CES employment inputs.
The National Compensation Survey program, another user of CES data, produces statistics on employer cost
levels (Employer Costs of Employee Compensation (ECEC) estimates), changes in employer labor costs
(Employment Cost Index), and employee healthcare and retirement benefits. ECEC estimates are weighted by
current employment derived from CES employment estimates and from the Quarterly Census of Employment and
Wages program.
The Job Openings and Labor Turnover Survey (JOLTS) uses CES employment as a monthly benchmark. A gauge
of labor shortages and churn, data collected by JOLTS include estimates of job openings, hires, and separations.
JOLTS weighted employment is ratio adjusted to match CES employment each month, and the resulting ratio is
then applied to these estimates. JOLTS also aligns estimates of hires and separations to closely track CES overthe-month employment changes.
The Office of Productivity and Technology produces measures of labor productivity (output per hour), unit labor
costs, and multifactor productivity (output per unit of combined inputs) for major U.S. economic sectors and
industries. The labor hours measure underlying the productivity series is based primarily on CES employment and

U.S. BUREAU OF LABOR STATISTICS

MONTHLY LABOR REVIEW

average weekly hours data, supplemented with CPS hours data (adjusted to an hours-worked concept) for selfemployed and unpaid family workers. Productivity and related cost measures may be used to analyze and forecast
changes in prices, wages, production, and technology.
BLS also produces employment projections for 10 years into the future. CES estimates of employment by industry,
supplemented with data from the CPS survey, serve as the base-year employment from which projections are
made. Employment projections are widely used by policymakers and other officials to make decisions about
education and training policy, funding, and program offerings. In addition, federal agencies, researchers, and
academia use employment projections to understand potential future trends in the economy and the labor
market.15
Uses of CES data in formulating monetary and fiscal policy. Policymakers closely follow CES data for their depth
of coverage—geographic and industry detail—and for their timely release each month. The Federal Reserve uses
CES data to gauge economic conditions in the U.S. labor market when formulating monetary policy. Federal, state,
and local government officials use CES employment and aggregate earnings data to forecast tax revenues and
plan budgets. Thus, CES data are useful in determining both monetary and fiscal policy.
Other uses of CES data. Each year, CES data are used by a broad spectrum of researchers, including BLS
economists, to analyze the economy, labor markets, and industries. For example, Lawrence Mishel and colleagues
used various CES statistics in analyzing the economic experience of workers and their families in the United
States.16
CES data also are used in the private sector by firms, labor unions, universities, trade associations, and research
organizations to study economic conditions and to develop plans for the future. Firms, for example, may use CES
employment, hours, and earnings data for planning business activity. A manufacturer could choose to set up
factories in an area with a strong manufacturing sector and a skilled workforce. Likewise, a retail chain may decide
to open an upscale store or an economy store on the basis of average earnings in an area. Businesses use CES
data for forecasting and economic analysis of the labor market and the overall U.S. economy. Every month,
national and local news media report results from the CES news releases. Also, trade association journals, the
labor press, and general reference works regularly republish CES data in summary form or for specific industries
or areas.

Conclusion
The CES program produces employment, hours, and earnings estimates for approximately 900 industries for the
nation, states, and areas. National CES estimates represent some of the earliest economic indicators of the U.S.
economy. CES data, important not only on their own, serve as inputs into other key economic indicators. The data
also provide an important source of information for businesses, major news media, researchers, and students.
SUGGESTED CITATION

Richa Ajmera and Angie Clinton, "Current Employment Statistics data and their contributions as key economic
indicators," Monthly Labor Review, U.S. Bureau of Labor Statistics, March 2016, https://doi.org/10.21916/mlr.
2016.11.

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MONTHLY LABOR REVIEW

NOTES
1 “Amount of employment in certain industries in October and November, 1915,” Monthly Review, vol. II, no. 1, January 1916, pp. 11–
12.
2 See “Current Employment Statistics—CES (national)” (U.S. Bureau of Labor Statistics), https://www.bls.gov/ces/.
3 Production employees are defined differently depending on the industry. Production and nonsupervisory employees include
production employees in mining and logging and manufacturing, construction employees in construction, and nonsupervisory
employees in private service-providing industries.
4 For more information on the North American Industry Classification System, see “Introduction to NAICS” (U.S. Census Bureau),
https://www.census.gov/eos/www/naics/.
5 For example, a recent article on bilateral trade directly compares CES estimates of U.S. industry employment with similar estimates
for Mexico and Canada; see Christopher E. Wilson, “Working together: economic ties between the United States and
Mexico” (Washington, DC: Mexico Institute, Woodrow Wilson International Center for Scholars, November 2011), https://
www.wilsoncenter.org/sites/default/files/Working%20Together%20Full%20Document.pdf.
6 See “Revisions,” in Technical notes for the Current Employment Statistics survey (U.S. Bureau of Labor Statistics, February 5,
2016), https://www.bls.gov/web/empsit/cestn.pdf.
7 Unemployment insurance tax records are collected, reviewed, and edited through the BLS Quarterly Census of Employment and
Wages program, https://www.bls.gov/cew/.
8 For more information on the benchmark process, see the latest CES national benchmark article at https://www.bls.gov/web/empsit/
cesbmart.pdf.
9 “The NBER’s business cycle dating procedure: frequently asked questions” (Cambridge, MA: National Bureau of Economic
Research), http://www.nber.org/cycles/recessions_faq.html.
10 “Industrial production and capacity utilization—G.17” (Board of Governors of the Federal Reserve System), http://
www.federalreserve.gov/releases/g17/About.htm.
11 “Industrial Production Index—IPI,” Investopedia, http://www.investopedia.com/terms/i/ipi.asp.
12 The latest news release for personal income from the Bureau of Economic Analysis is available at https://www.bea.gov/
newsreleases/national/pi/pinewsrelease.htm.
13 “Global business cycle indicators” (The Conference Board), https://www.conference-board.org/data/bcicountry.cfm?cid=1.
14 “The Conference Board Employment Trend Index (ETI)” (The Conference Board), https://www.conference-board.org/data/eti.cfm.
15 See “Employment projections” (U.S. Bureau of Labor Statistics), https://www.bls.gov/emp/.
16 Lawrence Mishel, Josh Bivens, Elise Gould, and Heidi Shierholz, The state of working America, 12th ed. (Ithaca, NY: Cornell
University Press, 2012).

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March 2016

Growing up in high-poverty areas can affect your
employment
Eleni X. Karageorge
A recent working paper titled Childhood environment and gender gaps in adulthood (National Bureau of Economic
Research working paper no. 21936, January 2016) examines the relationship, by gender, between childhood
environment and economic well-being in adulthood. The researchers—Raj Chetty, Nathaniel Hendren, Frina Lin,
Jeremy Majerovitz, and Benjamin Scuderi—found that men who experienced poverty as children suffered greater
economic consequences than women who grew up in poverty.
Using tax records, the researchers examined gender gaps—that is, whether one gender was affected more than
the other—at age 30 in employment, income, and college enrollment (between ages 18 and 23) for 10 million
individuals born between 1980 and 1982. These factors were then assessed on the basis of the incomes of these
individuals’ parents and where these individuals lived when they were children. Differences in these factors
between men and women varied depending on the income and marital status of their parents.
Gender differences in employment rates varied. Among people whose parents were in the bottom fifth of income
distribution when they were young, the 30-year-old men were less likely to have a job than were the women. This
was especially true among boys who were raised by a single parent. But for all other income groups, the opposite
case was true; specifically, men were employed at higher rates.
The authors found that employment rates increased with parental incomes for both genders and that the increase
was more pronounced for men. However, at the lowest income quintile, the trend was even more evident, with men
experiencing lower rates of employment than women. The effect on employment rates was most apparent among
men raised in single-parent households. Men have higher earnings than women across the parent-income
distribution, but the difference was less pronounced in the lowest income quintile. Men were also less likely to
attend college compared with women across the parent-income distribution, with the greatest disparity observed
among men from lower income families.
The authors also explain how the employment gender gap varies geographically. They looked at these patterns
across “commuting zones,” which are clusters of counties that act like local labor markets because they are
connected by transit, and found substantial differences among them. According to the authors’ analysis, the areas
where men are less likely than women to have a job are those with a concentration of minority residents, singleparent households, and high racial segregation. The authors note that the gap is only 3 percentage points for lowincome boys from New York City, while the gap is 12 percentage points for Charlotte, North Carolina. These two
areas are representative of the large variation across local labor markets in the United States.

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Gender differences, particularly related to employment, varied significantly across geographic areas. Men who
grew up in low-income families living in high-poverty, racially segregated areas were significantly less likely to be
employed as adults than their female peers. Higher crime rates were observed in these same geographies, leading
the researchers to wonder if males growing up in these places are more likely to resort to criminal activities as a
substitute for employment.
The authors conclude that growing up in a low-income, single-parent household or in a neighborhood with high
levels of economic and racial inequality has a disproportionately negative impact on men when they reach
adulthood. The authors suggest that these findings could shed new light on the recent decline of male participation
in the U.S. labor force, which is often attributed to an aging population, structural changes in the economy, and
globalization.

March 2016

A new approach to work-hour instability
Serah Hyde
Before the 1938 enactment of the Fair Labor Standards Act (FLSA), workers often faced subsistence wages and
untenably long work hours. The FLSA was created to ensure a basic standard of living for all workers. The
approach was twofold: a minimum wage was established so workers could earn livable wages, and overtime pay
was mandated to help guard against exploitatively long work hours. In “Underwork, work-hour insecurity, and a
new approach to wage and hour regulation” (Industrial Relations, October 2015), authors Charlotte Alexander and
Anna Haley-Lock juxtapose the workplace conditions the FLSA was designed to address with the challenges faced
by hourly or low-wage workers in today’s labor force. Alexander and Haley-Lock find the FLSA only partially
addresses the needs of today’s workers, many of whom have work hours that are too few, too variable, and too
unpredictable.
Alexander and Haley-Lock focus on hourly or low-wage workers who are subject to “just-in-time scheduling,”
meaning they can be sent home before the end of a scheduled shift when there is a lag in customer traffic. This
phenomenon, which is also known as “early send home,” is a function of an employer’s goal to reduce or eliminate
labor costs. Workers in these situations cannot predict their earnings or plan ahead, and worklife balance for them
is tenuous.
Some unions, some private employers, and some states have attempted to address the problem of work-hour
insecurity. Analyzing data on New York City retail workers and data from the Current Population Survey, the
authors assess the efficacy of state reporting-pay or “show-up pay” laws, which disincentivize sending workers
home early and help to stabilize work hours and earnings. The laws require employers to pay for a minimum
number of hours of an employee’s scheduled shift, whether worked or not. Different variations of reporting laws
have been enacted in seven states and the District of Columbia.
The authors find that employer noncompliance with reporting-pay laws along with only modest financial penalties
for reporting-pay violations reduce the positive impact of these laws. Moreover, some employers react by
shortening the lengths of shifts they schedule or classifying more workers as contractors. According to Alexander
and Haley-Lock, the FLSA is still necessary for workplace rights, but it needs to be revised or new legislation
needs to be enacted to provide protection for workers facing work-hour instability. The authors call for a more
robust national reporting-pay law that “would require payment of the required minimum hours at a higher rate than
a worker’s regular rate of pay, would not be linked to a worker’s scheduled shift length, and would contain greater
penalties for violation.”

March 2016

Comparing the Consumer Price Index with the
gross domestic product price index and gross
domestic product implicit price deflator
The Consumer Price Index (CPI) and the gross domestic
product (GDP) price index and implicit price deflator are
measures of inflation in the U.S. economy. The CPI
measures price changes in goods and services purchased
out of pocket by urban consumers, whereas the GDP price
index and implicit price deflator measure price changes in
goods and services purchased by consumers, businesses,
government, and foreigners, but not importers. Thus, which
one to use in a given scenario depends on one’s purpose.
Inflation can be defined as a consistent increase in an
economy’s “price level,” or the price component of total
expenditures on a set of goods and services, over a given
period. The Consumer Price Index (CPI), a product of the

Jonathan D. Church
church.jonathan@bls.gov

Bureau of Labor Statistics (BLS), is perhaps the most
widely used measure of inflation in the United States. The
CPI measures the average change over time in the prices

Jonathan D. Church is an economist in the Office
of Prices and Living Conditions, U.S. Bureau of
Labor Statistics.

paid by urban consumers in the United States for a market
basket of goods and services.
The Personal Consumption Expenditures (PCE) price
index, produced by the U.S. Bureau of Economic Analysis (BEA), is another measure of consumer inflation and is
followed closely by the U.S. Board of Governors of the Federal Reserve System (the Federal Reserve). Despite
differences in scope, weight, and methodology, the CPI and the PCE price index both measure inflation from the
perspective of the consumer. However, one might be interested in an index that measures price change across a
broader or narrower range of goods and services—for example, an index that measures price change across a set
of goods and services that includes not just consumer goods and services, but also goods and services purchased
by businesses, government, and other entities.
One such measure is the price index associated with the nation’s gross domestic product (GDP). Each quarter,
BEA releases data on the level of, and change in, GDP. These data include a breakdown of GDP into price and
quantity indexes, as well as a GDP implicit price deflator. The GDP price index and implicit price deflator are
derived from the measurement of GDP, giving rise to three main issues that distinguish the GDP price indexes

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from other measures of inflation. The first issue is the scope of goods and services for which prices are collected
and indexes are calculated. The second is the weight attached to prices for these goods and services. The third is
the methodological details of price index calculation.
This article compares and contrasts BEA’s GDP price index and implicit price deflator with the BLS CPI.1

The CPI
The CPI is a measure of the average change over time in the prices paid by urban consumers for a constantquality market basket of goods and services—that is, a sample of goods and services that consumers purchase for
day-to-day living. Produced monthly, the CPI weights the price of each item in the market basket on the basis of
the amount of spending reported by a sample of families and individuals.
The CPI has two primary inputs: prices and expenditure weights. Data on prices are collected from the BLS
Commodities and Services (C&S) Survey and Housing Survey. The C&S survey collects price data on
approximately 80,000 goods and services per month in roughly 23,000 retail establishments in 87 urban areas
around the United States. The Housing Survey collects approximately 6,000 rent quotes per month in the same 87
urban areas. Retail establishments for which price data are collected are selected primarily via a sampling process
that uses data from the Telephone Point-of-Purchase Survey (TPOPS), administered quarterly by the U.S. Census
Bureau on behalf of BLS. Once retail establishments are selected for price collection, field staff employed by BLS
visit the establishments, select a unique item for pricing, and continue to collect the price data monthly or
bimonthly, unless the item is no longer sold or a different retail establishment is selected in the next TPOPS
rotation. Housing units are selected by means of a different survey process, one that relies on data from both the
decennial census and the U.S. Census Bureau’s American Community Survey for sampling.
The second primary input into the CPI, the expenditure weights, is based on Consumer Expenditure (CE) survey
data collected by the U.S. Census Bureau for BLS. The CE survey identifies the dollar amount households spend
on a broad range of goods and services. About 14,000 1-week diaries and 28,000 quarterly interviews are
collected from the current CE survey sample each year.
Once price and expenditure data are collected, price indexes can be calculated with the use of price index
formulas. The CPI uses a hybrid of geometric and arithmetic mean calculation, depending on whether “lower level”
or “upper level” indexes are being constructed. Currently, the CPI measures price change for 211 item categories
(e.g., breakfast cereal) in 38 geographic areas (e.g., Boston–Brockton–Nashua), forming 8,018 basic item–area
index cells (211 × 38) that serve as the building blocks from which aggregate indexes are constructed. These
building blocks are the so-called lower level indexes. Aggregate indexes constructed from them are the so-called
upper level indexes. For example, the intermediate upper level index for cereals and cereal products is constructed
from three item categories: (1) flour and prepared flour mixes; (2) breakfast cereal; and (3) rice, pasta, and
cornmeal. The index for cereals and cereal products can be computed for the Boston–Brockton–Nashua
metropolitan area, for a set of cities that make up the Northeast urban geographic area, or for all cities in which
prices are collected. The last forms an index at the level of the U.S. city average. In total, the CPI consists of
thousands of indexes that measure price change for narrow and broad categories of goods and services across
multiple geographic areas. The result is a set of CPI indexes that measure the average change over time in the
price paid specifically by urban consumers for a constant-quality market basket of goods and services.

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The CPI uses an arithmetic mean (or Laspeyres) formula for all upper level index calculation, but employs a
geometric mean for approximately 60 percent of all lower level indexes in terms of weight (a Laspeyres formula is
used for the remaining 40 percent). The geometric mean formula allows the CPI to reflect changes in consumer
spending patterns among goods and services within item–area combinations—changes that occur in response to
changes in relative price. The formula assumes that the change in quantity is equal (in percentage terms), and
inversely related, to the change in price. Thus, if the relative price of one brand of bananas in the Boston–
Brockton–Nashua metropolitan area increases, then the quantity purchased of that brand is assumed to decrease
percentagewise by the same amount. Similarly, if a pint of ice cream increases in (per-unit) price relative to a quart
of ice cream, then the quantity purchased of a pint is assumed to decrease by a percentage reflective of the
change in relative price.2
Unlike the geometric mean formula, the Laspeyres formula implemented in the CPI is an arithmetic mean of price
relatives weighted by expenditures that implicitly contain information on quantity. Because expenditure data are
updated every 2 years, the month-to-month changes in upper level CPI indexes reflect price change under the
assumption that quantity remains fixed. This assumption means that the CPI does not account for real-time
changes that may occur in expenditure shares across aggregate categories of goods and services, perhaps in
response to changes in relative price across the same aggregate categories. In short, the Laspeyres formula
introduces “consumer substitution bias” into the CPI; that is, it does not account for the possibility that consumers
will switch to different products or shop in different outlets in response to increases in the price of close substitutes.
In sum, the CPI is a measure of price change across a set of goods and services purchased by urban consumers
and is calculated with the use of a combination of geometric and arithmetic means that can capture some degree
of consumer substitution limited to goods and services within item groups.

GDP price indexes
BEA is responsible for producing the National Income and Product Accounts (NIPAs). According to BEA, “The
NIPAs are a set of economic accounts that provide information on the value and composition of output produced in
the United States during a given period and on the types and uses of the income generated by that production.”3
The NIPAs are one of the three major components of the U.S. national economic accounts. The others are the
industry (input–output) accounts prepared by BEA and the financial (flow-of-funds) accounts prepared by the
Federal Reserve.4 Together, the national economic accounts present a macrolevel diagnosis of the health of the
U.S. economy and encompass the full scope of economic activity in the nation.
The NIPAs measure, in particular, (1) domestic income and product (i.e., output), (2) private enterprise income, (3)
personal income and outlays, (4) government receipts and expenditures, (5) foreign transactions, (6) the domestic
capital account, and (7) the foreign transactions capital account. The NIPA domestic income and product account
provides expenditure and income summaries of GDP. GDP is one of the most important and closely followed NIPA
accounts and measures the market value of final goods and services produced by the U.S. economy over a given
period.5 The expenditure and income approaches reflect two of the three ways to calculate GDP: (1) as the sum of
goods and services sold to final users (expenditures approach); and (2) as the sum of income payments and other
costs incurred in the production of goods and services (income approach). The third approach calculates GDP as
the sum of “value added” by all industries in the economy (the production approach).6

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The expenditures approach “is used to identify the final goods and services purchased by persons, businesses,
governments, and foreigners” and reflects a summation of personal consumption expenditures, gross private fixed
investment, change in private inventories, net exports of goods and services, and government consumption
expenditures and gross investment.7 The expenditures approach is perhaps the most intuitive for illustrating the
differences in scope between the CPI and the GDP price index. In this approach, the four basic categories of
expenditures that contribute to GDP are expenditures made by consumers, businesses, government, and
foreigners. BEA constructs a price index for each of these categories, and the various price indexes are
aggregated into the overall GDP price index for the United States.
The GDP price index, like the CPI, measures price change for consumer goods and services, but also measures
price change for goods and services purchased by businesses, governments, and foreigners. However, unlike the
CPI, the GDP price index does not measure price change for imports.
Although the GDP price index and the CPI both measure changes in the prices of goods and services purchased
by consumers, the GDP relies on the PCE price index as its measure of change in consumer prices. Also, whereas
the CPI uses a Laspeyres formula, the PCE price index relies on a Fisher ideal price index calculation.8 In
addition, the CPI weights are derived from out-of-pocket expenditures by consumers, while the PCE weights are
derived from out-of-pocket expenditures by consumers as well as third-party expenditures on behalf of
consumers.9 Finally, the actual goods and services for which prices are collected vary. (For example, the CPI for
financial services includes only checking accounts and other bank services, as well as tax return preparation and
other accounting fees, and has less than a 0.5-percent share in the CPI, whereas the PCE is more expansive and
includes pension funds, regulated investment companies such as mutual funds, and securities commissions).10
The GDP price index is calculated with a Fisher ideal index formula, which is able to pick up changes in the
allocation of expenditures by consumers across the broad categories of consumer goods and services covered by
GDP. The GDP price index is similar in concept to the chained CPI-U, or CPI for All Urban Consumers.11

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The GDP implicit price deflator deflates the current nominal-dollar value of GDP by the chained-dollar value of
GDP.12 The chained-dollar value is derived by updating a base-period dollar value amount by the change in the
GDP quantity index, which in turn is derived with the use of a Fisher ideal index formula that aggregates from
component GDP quantity indexes. Once the component quantity indexes are calculated, the GDP quantity index
can be derived and the GDP implicit price deflator calculated by dividing nominal GDP by real GDP. The change in
the GDP implicit price deflator is roughly equal to the change in the GDP price index. As shown in figure 1, the
GDP implicit price deflator has risen at a systematically lower rate than the CPI-U over time (2 percent annually for
the GDP price index and implicit price deflator, versus 2.4 percent annually for the CPI-U), in part because the
CPI-U employs a Laspeyres aggregation while the GDP implicit price deflator employs a Fisher ideal aggregation.

Summary
The CPI and the GDP price index and implicit price deflator are alternative measures of inflation in the U.S.
economy. The choice of which one to use in a given scenario likely depends on the set of goods and services in
which one is interested as a measure of price change. The CPI measures price change from the perspective of an
urban consumer and thus pertains to goods and services purchased out of pocket by urban consumers. The GDP
price index and implicit price deflator measure price change from the perspective of domestic production of good
and services and thus pertain to goods and services purchased by consumers, businesses, government, and
foreigners, but not importers. In addition, the formulas used to calculate these two measures differ.
SUGGESTED CITATION

Jonathan D. Church, "Comparing the Consumer Price Index with the gross domestic product price index and gross
domestic product implicit price deflator," Monthly Labor Review, U.S. Bureau of Labor Statistics, March 2016,
https://doi.org/10.21916/mlr.2016.13.

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NOTES
1 An

earlier Monthly Labor Review article compares the PPI final demand indexes with other government indexes, including the GDP
price indexes. See Jonathan Weinhagen, “Comparing new final-demand producer price indexes with other government price
indexes,” Monthly Labor Review, January 2014, https://www.bls.gov/opub/mlr/2014/article/comparing-new-final-demand-producerprice-indexes-with-other-government-price-indexes.htm.
2

See Kenneth V. Dalton, John S. Greenlees, and Kenneth J. Stewart, “Incorporating a geometric mean formula into the CPI,” Monthly
Labor Review, October 1998, pp. 4–5, https://www.bls.gov/mlr/1998/10/art1full.pdf.
3

Concepts and methods of the U.S. National Income and Product Accounts (U.S. Bureau of Economic Analysis, December 2015),
chapters 1–4, p. 1-1, https://apps.bea.gov/national/pdf/chapters1-4.pdf.
4

The industry accounts “trace the flow of goods and services among industries in the production process” and “show the value added
by each industry and the detailed commodity composition of national output,” while “[t]he financial accounts record the acquisition of
nonfinancial and financial assets (and the incurrence of liabilities) throughout the U.S. economy, the sources of the funds used to
acquire those assets, and the value of assets held and of liabilities owed” (Concepts and methods of the U.S. National Income and
Product Accounts, chapters 1–4, p. 1-1).
5

Measuring the economy: a primer on GDP and the National Income and Product Accounts (U.S. Bureau of Economic Analysis,
December 2015), pp. 1-2, 8-13 https://www.bea.gov/national/pdf/nipa_primer.pdf. See also Concepts and methods of the U.S.
National Income and Product Accounts, chapters 1–4, p. 2-7.
6

Concepts and methods of the U.S. National Income and Product Accounts, chapters 1–4, pp. 2-7 to 2-11.

The income approach “is used to examine the purchasing power of households and the financial status of businesses” and reflects a
summation of employee compensation, taxes on production and imports, subsidies, net operating surplus (i.e., profits earned by
private and government enterprises), and consumption of fixed capital. The production approach “is used to analyze the industrial
composition of U.S. output.” See Concepts and methods of the U.S. National Income and Product Accounts, chapters 1–4, pp. 2-7 to
2-10.
8

The Fisher ideal formula is the geometric mean of a Laspeyres index and a Paasche index. In the Laspeyres index calculation,
price relatives are weighted by quantity in a base period (i.e., some point in time in the past). In the Paasche index calculation, price
relatives are weighted by quantity in the current period. In the case of price indexes, the Fisher ideal index allows for the
measurement of real-time changes in quantity.
9

Third parties are entities that make payments on behalf of consumers, such as health insurance companies when they pay a
medical bill to meet their insurance obligation.
10

Clinton P. McCully, Brian C. Moyer, and Kenneth J. Stewart, “A reconciliation between the Consumer Price Index and the Personal
Consumption Expenditures price index” (U.S. Bureau of Economic Analysis and U.S. Bureau of Labor Statistics, September 2007), p.
12, https://www.bea.gov/papers/pdf/cpi_pce.pdf. See also Clinton P. McCully, “Trends in consumer spending and personal saving,
1959–2009,” Survey of Current Business (U.S. Bureau of Economic Analysis, June 2011), pp. 14–23, especially pp. 17–18, https://
www.bea.gov/scb/pdf/2011/06%20June/0611_pce.pdf.
11

The chained CPI-U is a closer approximation to a cost-of-living index than the traditional CPI-U is. The chained CPI-U is designed
to account for the product and outlet substitution bias that exists in the traditional CPI-U, which fails to capture changes in
expenditures across consumer items (and the outlets where they buy them) in response to changes in price.
12 As

stated by BEA, an “implicit price deflator is the ratio of the current-dollar value of a series, such as gross domestic product
(GDP), to its corresponding chained-dollar value, multiplied by 100.” (See “What is an implicit price deflator and where can I find the
GNP IPD?” in Frequently asked questions (U.S. Bureau of Economic Analysis), https://www.bea.gov/faq/index.cfm?faq_id=513.

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