Full text of Economic Perspectives (Federal Reserve Bank of Chicago) : Third Quarter 2002, Volume XXVI, Issue 3

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Federal Reserve Bank
of Chicago
Third Quarter 2002

persp
2

ives

Unprepared for boom or bust: Understanding the current
state fiscal crisis

26

The 2001 recession and the Chicago Fed National Activity
Index: Identifying business cycle turning points

44

Why do we use so many checks?

60

Analyzing the relationship between health insurance,
health costs, and health care utilization

Economic .

perspectives

President
Michael H. Moskow
Senior Vice President and Director of Research
William C. Hunter

Research Department
Financial Studies
Douglas Evanoff, Vice President
Macroeconomic Policy
Charles Evans, Vice President

Microeconomic Policy
Daniel Sullivan, Vice President
Regional Programs
William A, Testa, Vice President
Economics Editor
David Marshall

Editor
Helen O’D. Koshy
Associate Editor
Kathryn Moran

Production
Julia Baker, Rita Molloy,
Yvonne Peeples, Nancy Wellman
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Contents
Third Quarter 2002, Volume XXVI, Issue 3

2

Unprepared for boom or bust: Understanding the current
state fiscal crisis
Leslie McGranahan
The headlines concerning state government finances have become increasingly alarming since
mid-2001. This article discusses the roots of the current state fiscal crisis by looking at the
decisions made by state government leaders during the long expansion. The author suggests
increased use of rainy day funds as a way to avoid future crises.

26

The 2001 recession and the Chicago Fed National Activity Index:
Identifying business cycle turning points
Charles L. Evans, Chin Te Liu, and Genevieve Pham-Kanter
The initial release of the Chicago Fed National Activity Index (CFNAI) in early 2001 pointed
to the very real possibility that the U.S. economy was teetering on the brink of recession. This
article quantifies the statistical ability of the CFNAI to act as an early warning indicator of
economic recessions. In simulation experiments, the CFNAI performed virtually as well as
the statistical model’s ideal measure of the business cycle.

44

Why do we use so many checks?
Sujit Chakravorti and Timothy McHugh
The authors identify underlying disincentives for payment system participants to migrate to
electronic payments. Their analysis sheds light on why check usage remains higher in the
United States relative to other industrialized countries when the real resource cost of
processing payments may decrease by using electronic payment networks.

60

Analyzing the relationship between health insurance, health costs,
and health care utilization
Eric French and Kirti Kamboj
Using data from the Health and Retirement Survey and the Assets and Health Dynamics
among the Oldest Old, this article provides an empirical analysis of the determinants
of whether an individual purchases health insurance. The authors describe the relationship
between health costs and health care utilization of individuals aged 50 and older and explore
how these factors vary with access to health insurance.

Unprepared for boom or bust: Understanding the current
state fiscal crisis

Leslie McGranahan

In October 2001, the state governors sent a letter to the
U.S. Senate concerning the Senate’s proposed stimu­
lus package. The governors sought to prevent the pas­
sage of a package that would be detrimental to already
weak state budgets and to ask for specific assistance
from the federal government for state budget items.
As a result of the connection between federal and state
revenues and spending, state leaders often comment
on federal changes. What is remarkable about this let­
ter is that only seven months after the end of the great­
est post-war economic boom, the states were already
seeking fiscal help from the federal government. In
addition to its concerns about mounting defense and
intelligence obligations and the flailing macroecono­
my, the government now faced the specter of service
shutdowns by bankrupt state governments.
The crisis facing the state governments emerged
quickly. In August 2000, commentators at the National
Conference of State Legislatures (NCSL) were boast­
ing that states were “in their best financial conditions
in decades” (NCSL, 2000). In January 2001, the NCSL
asserted that the states remained in “excellent fiscal
condition” (NCSL, 2001c). But by August 2001, the
NCSL was detailing how states were coping with bud­
getary shortfalls (NCSL, 2001a).
In this article, I ask how the states found themselves
in fiscal trouble so quickly. I begin by discussing the
excellent revenue news from the states throughout the
economic expansion. Tax revenues increased, welfare
reform kept block grants at high levels, and the tobac­
co settlement provided a generous new form of funds.
As a result, states faced the pleasant dilemma of what
to do with their windfall revenues.
I investigate the ways states decided to use these
revenues. They faced four fundamental choices: they
could spend the money on high-priority programs; they
could return the money to taxpayers in the form of
rebates and reductions; they could reduce indebtedness;

2

or they could save the money for a less brilliant fu­
ture. All states chose a combination of these four.
States increased spending. Much of the spending
increase was due to mounting expenditure pressures
in health-care related areas. States also aggressively cut
taxes, particularly personal income taxes, throughout
the expansion. While the states did not move to reduce
indebtedness, they did increase their savings. States
save money by maintaining balances in their reserve
funds. Most states have created budget stabilization
or “rainy day” funds as a way to cope with unexpected
shortfalls. (The only states without such funds as of
October 2001 were Arkansas, Montana, and Oregon).
Money transferred into these funds can be withdrawn
under specific circumstances. States also maintain re­
serve balances in their general fund accounts. During
the expansion, balance increases in these accounts
were substantial but were insufficient to offset even
a mild downturn.
When state revenues began to deteriorate in the
third quarter of 2000 as the first signs of the pending
recession surfaced, state budgets soon ran into deficit.
Because nearly all states are required to pass balanced
budgets and are limited in their ability to issue debt,
they needed to deal with the budget imbalance quick­
ly. State debt limits restrict states’ ability to borrow if
changes in economic circumstances lead to shortfalls
during the fiscal year (National Association of State
Budget Officers [NASBO], 2002).1
Now, instead of the four pleasant choices outlined
earlier, states faced four difficult options to deal with
these revenue shortfalls. Because of the restrictions

Leslie McGranahan is a consultant to the Federal Reserve
Bank of Chicago and an associatefellow in the Department
ofEconomics at the University of Warwick. The author
would like to thank Bill Testa, David Marshall, and Helen
Koshy for comments and guidance.

3Q/2002, Economic Perspectives

on debt, debt issuance was not one of the options. States
could increase taxes; they could cut spending; they
could reduce the balances available in their reserve
funds; or they could rely on the federal government to
bail them out. Few states have chosen to increase taxes.
Tax increases are both politically very unpopular and
slow and difficult to shepherd through legislatures. Most
states have used a combination of spending cuts and
reserve fund withdrawals to bring their budgets into
balance. While the states have asked the federal gov­
ernment for assistance, federal aid has not been par­
ticularly forthcoming.
The current fiscal crisis highlights the problems
inherent in the states’ balanced budget system. States
cut taxes and increase expenditure during booms only
to be faced with revenue shortfalls during recessions.
Then, the states have to cut spending just when the need
for government services becomes most pronounced
and must raise taxes when taxpayers are at their poor­
est. To prepare for future downturns, state governments
should consider some policy changes. First, states
should control spending during both expansions and
recessions in order to avoid the need for dramatic cuts
during difficult times. Second, states should restruc­
ture their rainy day funds, so that they can draw on
these more heavily to maintain services during diffi­
cult times. In order to do this, reserve fund balances
would need to grow much larger than they did in the
recent expansion.
I highlight the experience of the five states that
make up the Federal Reserve’s Seventh District—
Illinois, Indiana, Iowa, Michigan, and Wisconsin. This
allows me to paint a more precise picture than would
come from only generalizing across 50 states. These
midwestern states are interesting because they were
among the first to be harmed by the economic slow­
down. As a result, they were forced to make difficult
decisions earlier than other states. At the same time,
the behavior of the midwestern states has been fairly
typical of that of states nationwide.

The boom: Revenues
The U.S. Census Bureau segregates state funds into
four separate categories—the general fund, insurance
trust funds, utility funds, and funds for state-operated
liquor stores. In this article, I focus on the general fund
as it is the source of revenues and expenditures over
which the state has the most control and it supports
the largest state government expenditures. The other
funds are very small, with the exception of the insur­
ance trust fund. This fund supports unemployment
insurance, workers’ compensation, and programs for
state government employees.2

Federal Reserve Bank of Chicago

States generate revenues from a variety of sources,
the two most important being taxes and the federal
government. In 1999, the federal government provid­
ed just over 25 percent of general fund revenues while
taxes provided 55 percent. In 1999, most state taxes
came from general and selective sales taxes (48 per­
cent), personal income taxes (35 percent) and corpo­
rate income taxes (6 percent).
Over time, general state revenues have been in­
creasing along with the rise in national income. Be­
tween 1980 and 1992 (the first year of positive economic
growth during the recent expansion), real general rev­
enues increased by an average of 4 percent per year
in total and 3 percent per year per capita. Throughout
the recent expansion, strong national economic con­
ditions translated into continued strong state revenue
performance. Between 1992 and 1999, state revenues
grew an average of 4 percent per year and 3 percent
per capita, despite significant enacted tax reductions
(U.S. Department of Commerce, Bureau of the Cen­
sus, 1981 and 2002a).3
The reason for the continued revenue growth was
that everything was going right. Robust consumer
spending translated into high sales tax revenues. Sales
tax revenues are procyclical both because spending is
itself procyclical and because states exempt the least
cyclically sensitive products from taxes—in particu­
lar, food and drugs. Between 1992 and 2000, real to­
tal state general sales tax revenues increased by 40
percent, or by an average of 4.2 percent per year. Rev­
enues from the even more procyclical personal income
tax also increased dramatically during the expansion,
by 59 percent in real terms between 1992 and 2000,
or by 6 percent per year (U.S. Department of Com­
merce, Bureau of the Census, 2002b). One reason for
the increase in income tax revenues was the high lev­
el of employment and earnings. But likely even more
important was the dramatic increase in revenue from
taxes on capital gains and dividends.
The exact role of the growth of capital gains and
dividends in boosting the revenue performance of the
states is difficult to ascertain because data on income
tax revenues from different sources are not available
for most states.4 However, two sources point to a sig­
nificant increase in revenues derived from capital gains
taxes. First, state capital gains taxes are closely linked
to the federal capital gains tax and readily available
data on capital gains and dividends declared on fed­
eral individual income tax returns show a dramatic
hike over the 1990s, especially post 1994. The growth
in capital gains and dividends reported on federal in­
come tax returns is pictured in figure 1. Second, we
do have separate data on state personal income tax

3

withholding, estimated payments, and final settlements
paid when taxes are filed. Trends in estimated payments
give some indication of the level of capital gains and
dividends received, because these are taxes paid on
non-wage income. While estimated payments are high­
ly volatile, they did increase dramatically at times
during the expansion. For example, estimated payments
for 2000 taxes made between April 2000 and Febru­
ary 2001 were 17.1 percent higher than similar pay­
ments made the previous year (Jenny and Boyd, 2001).
Again, in parallel with the experience of the federal
government, the states’ personal income tax revenues
exceeded expectations every year during the expansion,
probably due to the high level of realized capital gains.
Two less obvious factors also contributed to the
impressive state revenue performance of the end of
the millennium. First, 46 states and four major tobacco
companies signed the Master Settlement Agreement
in November 1998. To settle state lawsuits aimed at
recovering tobacco-related Medicaid costs, the tobacco
companies promised the states $206 billion over a
25-year period. The states began receiving money in
late November 1999 following the approval of the agree­
ment by the required number of states. During 1998,
2000, and 2001, states received $2.4 billion, $6.4 bil­
lion, and $6.9 billion, respectively, from the tobacco
settlement.5 Some states received even greater revenues
than indicated by the settlement, because they used
financial intermediaries to trade the 25-year stream of
benefits for a single lump sum. Wisconsin, for example,
arranged for a single payment. These tobacco monies
are large, even in the context of multibillion-dollar

4

state budgets. The $8.3 billion due to the states in 2002
is equivalent to 1.8 percent of state general fund rev­
enues in 2002 recommended budgets. For 2000, with­
out funds from the tobacco settlement, revenue growth
would have equaled 3.7 percent; including the settle­
ment raised the growth rate to 4.5 percent (Wilson,
1999; NCSL, 1999a).
While anti-smoking groups anticipated that these
funds would be spent on state smoking cessation ini­
tiatives and other health causes, the funds entered state
coffers with no strings attached. While some of these
funds have been spent to curb smoking, most have
simply served to increase revenues and have not been
earmarked for specific causes.
The second factor, aside from taxes, contributing
to state revenues during the expansion was the change
in the welfare program. When Aid to Families with
Dependent Children (AFDC) changed to Temporary
Assistance to Needy Families (TANF) in 1996 (dis­
cussed in more detail below), welfare funding changed
from a federal matching program to a fixed federal
block grant. Under the AFDC program, the declines
in caseloads that accompanied the programmatic change
and economic expansion would have led to a decline
in spending and, therefore, a decline in the federal
match. By contrast, under the new program, block
grants stayed fixed in the face of declines in the re­
cipient population. As a result, states could both cut
their own spending down to the levels required by
the legislation and use their funds to increase benefits
and restructure programs to support a wide array of
social services for their welfare populations.

Spending
Like revenues, state government spending has
generally been increasing over time. Between 1980
and 1992, real general government expenditure in­
creased by 4.4 percent overall per year and by 3.4
percent per capita. During the expansion, between
1992 and 1999, real expenditure growth slowed to
3.5 percent per year, or 2.5 percent per capita. Cen­
sus data on state government spending are only avail­
able until 1999 (U.S. Department of Commerce, Bureau
of the Census, 2002a). More recent data from NASBO show that real total state expenditure increased
by 5 percent between 1999 and 2000 and 5 percent
between 2000 and 2001 (NASBO, 2001c).6 Even in
the presence of impressive revenue growth, by 1999
appropriations growth was expected to outpace revenue
growth. In the light of the fiscal problems emerging in
fiscal 2002, governors recommended that appropria­
tions growth slow substantially.

3Q/2002, Economic Perspectives

State government spending is less cyclical than
period or from just under 3 percent to just over 4 per­
revenue, because many of the major state services
cent per year. The table also shows spending growth
are not particularly cyclically sensitive. For example,
in current and capital spending. Current expenditures
enrollment in elementary and secondary education is
grew more quickly than capital outlays.
While funding increases during the expansion were
a function of past fertility decisions and is not very
responsive to the condition of the economy.
pretty universal, two areas deserve special attention:
However, spending in some programmatic areas is
education and Medicaid, which is classified by the
sensitive to economic conditions. The most obviously
Census Bureau as part of public welfare.
cyclically sensitive area is need-based services. De­
Trends in Medicaid spending
mand for these services declines as the economy im­
The almost universally acknowledged source
proves and employment rates increase. Then, demand
of the states’ most significant spending woes is the
grows in a downturn. States partially fund three cru­
Medicaid program. Medicaid is the health insurance
cial need-based programs: unemployment insurance,
program for low-income people. The program covered
Medicaid (the health insurance program for the lowover 40 million recipients in 1998. The states and fed­
income population), and welfare (TANF, formerly
eral government split Medicaid expenditures, with the
AFDC). Among these, unemployment insurance is
federal government picking up between 50 percent and
covered by funds not considered as general expendi­
76.8 percent of the program’s costs. The federal share
ture in the Census Bureau definitions.
decreases as state per capita income increases. Between
That said, some expenditure pressures are even
fiscal 1992 and fiscal 2001, real total Medicaid pro­
slightly procyclical. This minor procyclicality derives
gram costs are estimated to have increased from $135
from the fact that states are major employers and com­
billion to $209 billion 1999 dollars or by 56 percent
pete for their employees in the labor market. Labor
in total and 5.1 percent per year (which actually rep­
market tightness should lead to increased wage de­
resents a decline from the average annual rate of growth
mand among teachers, highway workers, police of­
ficers, and others employed by the state.
Looking at teachers, for example, we see
TABLE 1
that real salaries rose during the expan­
Changes
in
expenditure,
1992-99
sion. Between the 1991-92 and 19992000 academic years, average nominal
1992
1999
Percent
expenditure
expenditure
change
teacher salaries rose by 23 percent and
(1999 dollars, OOOs)
(OOOs)
starting teacher salaries by 28 percent
(American Federation of Teachers, 2002).
Education
240,790,734
318,601,796
32
Public welfare
177,170,480
221,166,721
25
This second number is a better indication
Highways
55,787,475
68,317,477
22
of labor market tightness because educa­
All other
224,402,866
281,389,231
25
tion systems compete against other em­
All categories,
ployers for new college graduates. Between
current operations
368,391,413
476,968,246
29
1992 and 2000, total inflation was about
All categories,
17 percent (this is the increase in prices as
capital outlays
57,117,136
68,508,917
20
measured by the gross national product
Source: U.S. Department of Commerce, Bureau of the Census, 2002a.
price index) (Executive Office of the Presi­
dent, Council of Economic Advisers, 2002).
In their role as employers, states were
between 1980 and 1992 of 9.1 percent per year). Over
faced with increased spending pressures during the
expansion, while in their role as providers of services
the same period, state Medicaid program costs are esti­
to the needy, they faced declining pressures. On bal­
mated to have grown by a similar percentage (U.S. Con­
ance, the expansion probably cut expenditure pres­
gress, House Committee on Ways and Means, 2002).
sures somewhat, but by no means dramatically.
Figure 2 depicts the growth in total nominal
To further investigate increases in expenditures,
Medicaid program costs from 1992 to 2001 (costs
I look at increases by spending category. Table 1 shows
for 1999-2001 are estimates), compared with the over­
the real dollar and percentage change in expenditure
all growth in the Consumer Price Index (CPI) and the
in four major state spending categories between 1992
growth in the Consumer Price Index for Medical Care.
and 1999. Spending increased between 22 percent and
The figure shows that medical care expenses were
32 percent in all of these categories over the entire
growing much more rapidly than the overall price

Federal Reserve Bank of Chicago

5

level as reflected in the CPI, and Medicaid expenditures
were growing dramatically more rapidly than medical
care expenses. In other words, while some of the in­
crease in Medicaid expenditures can be attributed to an
overall increase in health care costs, most of the increase
needs to be explained by other factors. Medicaid ex­
penditures have also been increasing rapidly relative
to state government expenditures more generally, as
illustrated in figure 3, which depicts the growth in to­
tal state Medicaid program costs relative to the growth
in total state expenditure from 1992 to 1999.
We can break down the increase in costs into
two component parts—first, the increase
in the number of program recipients and,
second, the increase in the cost per recipi­
ent. Figure 4 shows three comparisons
between 1992 and 1998: the growth in
number of recipients by eligibility cate­
gory (panel A), the growth in per capita
Medicaid costs by eligibility category
(panel B), and the growth in total expen­
ditures by eligibility category (panel C).7
Spending increases by eligibility category
have been fairly similar—between 1992
and 1998, the percentage of expenditures
represented by each eligibility category
has been nearly constant. But the reasons
underlying these similar growth rates in
spending have differed somewhat. For
aged recipients, an increase in costs com­
bined with a relatively flat recipient pop­
ulation led to increased total spending.
For disabled recipients, both costs and

6

the recipient population grew. For children and adults,
a dramatic increase in the recipient population and
nearly constant costs per recipient underlie the growth
in total spending. The increase in the number of re­
cipient children and adults derives from legislated
extensions of coverage to children and parents of
poor families not receiving public assistance.8
As this discussion shows, it is difficult to attribute
the increase in Medicaid spending to one single force
as both eligibility and costs have increased. That said,
much of the debate on Medicaid program costs has
naturally focused on the aged and disabled groups.
Although these groups represent less than 30 percent
of all recipients, they account for over 70 percent of
program costs. Two particular areas of spending have
received special attention: nursing facilities and pre­
scription drugs. Nursing facilities accounted for 22.4
percent of total Medicaid payments in 1998 and 62.9
percent of the costs for aged recipients. It is the single
largest programmatic spending category. In fact,
Medicaid pays 46 percent of all U.S. nursing home
expenditures (U.S. Department of Health and Human
Services, Health Care Financing Administration, 2000).
Prescription drugs represented 9.5 percent of all
Medicaid program costs in 1998 and spending for
drugs has been increasing rapidly. In 1992, drugs were
only 7.4 percent of program costs. These increases in
drug expenditures are attributed to a nationwide in­
crease in drug prices and the advent of a number of
new (hence, expensive) drugs. Medicaid drug expen­
diture increased by an additional 17.9 percent in 1999,
22.2 percent in 2000, and is estimated to increase by

3Q/2002, Economic Perspectives

the size of the medically dependent population. However,
this relationship should not be overstated. When I per­
formed simple regressions of the recipient population
between 1972 and 1998 on the civilian unemployment
rate, a time trend, and dummies controlling for legis­
lated changes in eligibility in 1990 and 1996,1 only
found a statistically significant relationship between
the number of adult recipients and the unemployment
rate. For all other recipient categories, there is no dis­
cernible relationship between the unemployment rate
and the size of the recipient population. Because spend­
ing on adults is such a small part of total Medicaid
expenditure, this cyclical factor is not a huge part of
the Medicaid spending story.
Even though Medicaid spending is not very cy­
clical, worries about costs tend to be most common
when the economy is weakest. This is because state
budgetary problems become more acute during down­
turns and Medicaid is such a significant spending area.
This is no exception during the current economic cli­
mate. I discuss potential cost saving measures for
Medicaid in a later section.

an additional 19.7 percent and 14.9 percent in 2001
and 2002, respectively (NASBO, 2001a).
In addition to this general upward trend, Medicaid
spending does have a cyclical component as well,
because the loss ofjobs and health insurance increases

Federal Reserve Bank of Chicago

Trends in education spending
Education spending, specifically spending for
elementary and secondary education, represents the
single greatest expenditure category for state govern­
ments. About $0.36 out of every $1.00 in general ex­
penditure is spent on education. Between 1992 and
1999, state education spending increased by 32 percent.
States cover just under half of total education expen­
diture, with local governments funding 40 percent to
45 percent and the federal government paying the re­
mainder (U.S. Department of Commerce, Bureau of
the Census, 2002c; U.S. Department of Education,
National Center for Education Statistics, 2001).
The growth in school expenditure results from a
number of sources. First, there was an increase in the
number of pupils in elementary and secondary schools.
Between 1992 and 1999, the number of pupils increased
by 9.4 percent. However, as this percentage increase
is less than one-third of the percentage increase in ex­
penditure, other forces are needed to explain the total
increase in costs. Second, over the same period, the num­
ber of teachers increased by 18.2 percent. The result­
ant increase in the teacher-pupil ratio represents the
continuation of a long-standing trend in education.
Costs for instruction (teachers and textbooks) represent
53 percent of total education expenditure, so the growth
in teachers, combined with the increase in teacher sala­
ries discussed earlier, goes a long way toward explain­
ing the increase in total expenditures.9 Third, during the
boom, states increased spending on school-related

7

capital projects. Capital expenditure jumped from
7.6 percent of school expenditures in 1990 to 9.9
percent in both 1999 and 2000 (U.S. Department of
Commerce, Bureau of the Census, 2002c). This
increase in capital spending was needed to help shore
up deteriorating school buildings and assure compli­
ance with federal mandates regarding accessibility
and health hazards (U.S. Congress, General Account­
ing Office, 1995). Despite this increase in capital
spending, school buildings continue to be in poor
shape, with 50 percent reporting at least one inade­
quate building feature as of 1999 (U.S. Department
of Commerce, Bureau of the Census, 2002c). Finally,
although precise national statistics on special educa­
tion spending are not available, there is a general
consensus that increases in the number of students
with diagnosed disabilities have challenged the re­
sources of school districts. Between the 1992-93 and
1998-99 school years, the percentage of students
with a disability increased from 11.8 percent to 13.0
percent. In 1976-77, only 8.3 percent of students
were diagnosed with a disability.
School expenditure holds a privileged position in
the debate over state expenditure. While it is the larg­
est single expenditure area, it is also very politically
popular and somewhat sacred. Discussions of school
spending on the state level often take place outside the
general budget debate and it is the area most frequently
exempted from across-the-board budget cuts.
Overall spending growth is driven by a number
of factors, but the two largest programmatic areas—
Medicaid and education—go a long way toward ex­
plaining the overall condition of state budgets. Spending
in both of these areas increased throughout the expan­
sion. And as budgets tighten, much of the debate in­
evitably focuses on these two areas.
One other feature of state expenditure deserves
attention. During an expansion, when state expenditure
rises, increases for programs are debated and specifi­
cally funded. However, during budget crises, cuts tend
to be across the board. (I discuss this issue in greater
detail below). In other words, budget cuts are neither
specific nor particularly debated. As a result, state agen­
cies have an added incentive to maximize their bud­
get by adding items that will be easy to cut in time of
crisis. So, if individual agencies are concerned about
the economic cycle, it is in their best interest not to
save now for later, but to spend more.

Tax cutting
Throughout the economic boom, states reduced
the tax obligations of businesses and individuals with­
in their borders. The federal program that sent tax

8

rebate checks to households in 2001 disbursed a total
of $38 billion. Combined, the 50 states reduced taxes
between 1995 and 2000 by a similar amount, $36 bil­
lion in 2001 dollars (NCSL, 2001c).10 Some of the
state reductions are permanent, such as legislated re­
ductions in income tax rates. Other reductions were
one-time events, such as tax rebates and refunds.
States also reduced tax burdens further by providing
funding to localities to reduce property tax burdens.
These tax reductions served to bolster the already ro­
bust macroeconomy by returning funds to individu­
als at the same time as other forces were serving to
increase personal income. The tax reductions were
widespread, occurring every year between 1995 and
2001 and occurring in some manner in all 50 states.
Figure 5 graphs net yearly state tax changes as a
percent of the previous year’s tax collections against
the year-over-year percentage change in second quar­
ter gross domestic product (GDP is seasonally adjust­
ed at annual rates). I use second-quarter GDP because
most state fiscal years end at the end of the second
quarter. Therefore, the two lines correspond to simi­
lar periods. Most tax reductions take effect in the year
after the year of passage. The figure shows that as the
percentage change in GDP turned positive in 1992,
enacted tax increases began to fall, finally turning neg­
ative (into a net tax decrease) in 1995. The correlation
between the two sets of numbers is a striking -0.8,
showing the close connection between GDP growth
and tax cuts. The figure also shows that the tax cut­
ting continued in earnest until 2000. The preliminary
2001 number shows a continued decline in taxes dur­
ing the 2001 legislative session as well.
These data do not distinguish between one-time
tax rebates and permanent changes in taxes. Therefore,
this figure only accurately depicts the change from one
year to the next and does not show aggregate changes
over a number of years. Many of the enacted changes
represented permanent changes and, therefore, the to­
tal tax reductions over time are greater than the sim­
ple sum of the numbers presented in the figure.
As the figure shows, extensive tax cutting began
in 1995—the first year since 1985 that states engaged
in a net tax reduction (Mackey, 1999). In the 1995
legislative session, states reduced the taxes to be col­
lected in fiscal 1996 by $3.3 billion—0.9 percent of
the previous year’s tax collections. Most reductions
occurred in the traditionally unpopular personal in­
come tax. Personal income tax reductions represent­
ed $1.1 billion of the decline. Reflecting on the 1995
tax reduction, Scott Mackey of the National Confer­
ence of State Legislators wrote: “There are several
reasons to think that state tax cutting activity may have

3Q/2002, Economic Perspectives

other tax changes are difficult to generalize. One ex­
ception to this general pattern, not shown in the fig­
ure, is that throughout the decade, “sin” taxes on alcohol
and tobacco were stable or increasing. In fact, tobacco
taxes increased every year between 1995 and 2001,
except 1998 when they remained unchanged.

peaked during 1995. First, federal budget cuts that
affect state budgets are a virtual certainty in 1995
and beyond, making states cautious about reducing
revenues. Second, the strong revenue growth that
states enjoyed in fiscal year (FY) 1994 and FY1995
appears to be returning to more modest levels. Finally,
local property tax relief may be a higher priority than
reducing state taxes” (Mackey, 1999). Mr. Mackey’s
prediction proved wide of the mark. In 1996, states
reduced taxes again. It was the first time that states had
cut taxes in two consecutive years since FY1979-80.
Further tax reductions occurred in the next five years.
Figure 6 shows the reduction in taxes by year for
the major tax categories—personal income tax, corporate
income tax, sales and use taxes, and others. Other tax­
es include health care, motor fuel, cigarette, alcohol,
and miscellaneous taxes. As the figure demonstrates,
the tax cuts throughout the period tended to follow a
general pattern. Every year, the main focus of cuts was
personal income tax. Personal income tax was cut
across numerous dimensions—rates were reduced in
some cases, in others the base was narrowed, while
other states chose to increase standard deductions or
exemptions, or issue refunds. Corporate income taxes
were also reduced, but not to as great an extent as per­
sonal income taxes. Sales and use taxes were largely
stable, with some increases in exemptions for food,
drugs, and other necessities. Figure 6 shows that oth­
er taxes were also cut throughout the period. Most of
these declines in other taxes represent changes in statespecific tax programs such as Florida’s 1997 enact­
ment of a $411 million freeze in the special assessment
for the special disability trust fund. As a result, these

Federal Reserve Bank of Chicago

7ax cutting in the Midwest
Tax cutting was persistent, across the board, and
widespread throughout the second half of the decade.
The behavior of the midwestem states was represen­
tative of this overall pattern. Faced with unexpectedly
high revenues, state governors and legislators chose
to return some monies to state residents and resident
corporations. In this section, I briefly detail the major
revenue actions undertaken in Indiana, Illinois, Iowa,
Michigan, and Wisconsin during this period.
Michigan was one of the most aggressive tax cut­
ters, legislating significant tax reductions on numerous
occasions during the second half of the 1990s. As
was the case with the overall pattern of tax cuts, the
major source of cuts was the personal income tax. In
1995, Michigan increased personal exemptions and
standard deductions. This was followed by a tax cut
passed in 1999 that cut income tax rates and expand­
ed personal exemptions even further. This second cut
reduced taxes by $218 million dollars. Prior to the
start of the expansion, businesses in Michigan were
heavily taxed, relative to corporations in other states.
As of 1992, corporate income taxes represented 7.8
percent of Michigan’s general revenues, compared
with 3.6 percent across all states. In order to increase
state competitiveness, the Michigan legislature sig­
nificantly reduced business taxes in 1995 and 1999.

9

The reductions in personal and corporate income tax­
es continued to be phased in through 2002. Michigan
was also typical in that any taxes that were increased
were excise taxes. In 1997, motor fuel taxes were in­
creased, and in 1999 there was a major increase in
cigarette taxation, bringing in an additional $95.2
million in revenues.
Illinois’s tax reductions were similar in direction
to those in Michigan, but smaller in magnitude. The
main tax cut was a three-year doubling of the personal
income tax exemption passed in 1998. This was viewed
as a welcome change in Illinois’s historically regres­
sive income tax policies. Illinois also slightly reduced
corporate income taxes, but the change was not as sig­
nificant as in Michigan. In 1999, Illinois engaged in
a significant excise tax hike. In order to fund a major
state public works program, state leaders increased
motor vehicle and liquor taxes. The program, termed
“Illinois FIRST,” was passed as a five-year, $12 billion
program. Two other tax reductions were a 2000 prop­
erty tax rebate program and an increase in the state
earned income tax program.
Wisconsin’s most notable tax reductions were en­
acted in 1999. The state rebated $700 million in excess
sales tax revenues to taxpayers who had filed income
tax returns in 1998. The state also reformed the per­
sonal income tax by increasing standard deductions,
reducing rates, and raising credits for married couples.
These changes saved taxpayers $655 million.
Indiana’s major tax reduction was passed during
the 1999 legislative session when a major property tax
decrease was coupled with an increase in the dependent
child exemption to $1,000 per child. The state also
changed excise taxes, reducing the unpopular automo­
bile excise tax in 1996, while increasing motor vehi­
cle license fees in 1998.
Although Iowa is the least populous midwestern
state, it was one of the most aggressive tax cutters. The
major tax changes in Iowa were almost exclusively in
the personal income tax. In 1995, personal exemptions
and standard deductions were increased, while in 1997
personal income tax rates were reduced.
Overall, tax changes in the midwestern states were
fairly representative of those taking place across the
nation. The major source of cuts was personal income
tax. States both increased exemptions and deductions
and lowered tax rates. Some states also decreased cor­
porate income taxes, but not to nearly as large an ex­
tent. The states only engaged in minor changes in excise
taxes. Some states also acted to reduce or rebate some
of the perennially unpopular property tax.
We have seen how during the expansion, states
used some of their windfall revenues to engage in the

io

very popular activity of cutting taxes. However, they
also used some of these excess funds to prepare for
future economic contingencies by shoring up their
reserve funds. In the next section, I explore the con­
dition of state rainy day funds and other reserves and
the extent to which states prepared for a downturn in
the economic cycle.

Reserve funds
In order to confront unexpected shortfalls and eco­
nomic downturns, states maintain reserves. These re­
serves may be in the form of ending balances in the
general fund, monies in a budget stabilization fund,
or monies in a diverse array of other emergency funds.
Specific rules govern when states may access the
monies in budget stabilization or “rainy day” funds.
By contrast, access to general fund ending balances
is controlled by the same type of legislation that reg­
ulates other general fund appropriations. As a result,
it is politically more complicated for states to access
rainy day balances in the absence of an obvious need.
As in the case of withdrawals, deposits for rainy day
funds are controlled by specific provisions.
All but three states have budget stabilization funds,
which may be budget reserve funds, revenue shortfall
accounts, or cash flow accounts. Those states without
rainy day funds maintain all reserves as ending balances
in their general fund accounts. In 2000, just under half
of all reserves were maintained in rainy day funds, the
other half remaining as general fund ending balances.
Three-fifths of states limit the size of rainy day fund
balances to between 3 percent and 10 percent of appro­
priations. Funds above those permitted in the budget
stabilization fund remain in the state’s ending balance
(NASBO, 2001a).
Reserves, whether in rainy day funds or as general
fund ending balances, offer states an important source
of funds when unexpected contingencies threaten to
disrupt fiscal functions. It is frequently cited that Wall
Street views any total level of reserves in excess of 5
percent of expenditures as adequate. Figure 7 depicts
total state reserves as a percent of total state expendi­
tures from FY1979 to FY2002. The figure also depicts
the year over year percentage change in U.S. real GDP
as of the second quarter (the end point for most state
fiscal years). The data demonstrate a number of impor­
tant patterns concerning reserves. First, reserves have
been quite strong. According to the data displayed in
the figure, by 1998, state reserve fund balances ex­
ceeded the heights they had attained in 1980. Fiscal
year 2002 is projected to be the ninth consecutive year
with total state reserves above 5 percent of expendi­
tures. As of June 2001, 2002 reserves were anticipated

3Q/2002, Economic Perspectives

FIGURE 7

Total reserve fund balances vs. changes in GDP

Note: Total balances include both ending balances in the general
fund and amounts in budget stabilization funds.
Sources: GDP data from U.S. Department of Commerce,
Bureau of Economic Analysis, 2002a: balances from National
Association of State Budget Officers, 2001a.

to be 5.9 percent of expenditures. However, while
reserves remain high relative to their historical patterns,
they fell between 2000 and 2001 and are expected to
continue falling in fiscal 2002. Total reserve hind bal­
ances reached a high of 10.1 percent of expenditures
in 2000 and were expected to decline to 5.9 percent by
the end of FY2002. This projection for 2002, based
on governors’ recommended budgets, is probably op­
timistic, because these estimates were published in June
2001 when the economic outlook was better. Even these
optimistic forecasts predict that fiscal 2002 reserve
balances as a percentage of expenditures will be low­
er than they have been in the past seven years. These
averages mask significant variety across states. While
22 states anticipated total reserve balances below 5 per­
cent of expenditures for 2002, four predicted balanc­
es would exceed 10 percent. The decline since 2000 is
widespread. In 2000,21 states had reserve hind balanc­
es above 10 percent and 11 had reserve fund balances
below 5 percent. Reserve fund balances have declined
over the past two years due to additional tax cuts, in­
creases in spending especially in the areas of health
care and education, and the slowing of the economy.
The data also show how quickly reserves can fall
in responses to economic difficulties. Between 1989
and 1991, reserves fell from 4.8 percent of expendi­
tures to 1.1 percent of expenditures. So, while reserves
were nearly adequate according to the oft-cited Wall
Street rule of thumb prior to the early 1990s downturn,
they nearly evaporated in just two difficult years.
This begs the question whether the reserves that
states built up during the booming 1990s are sufficient

Federal Reserve Bank of Chicago

to help them weather the current economic storm. The
news reports from state governments suggest (discussed
in detail below) that these reserves are not sufficient
to allow states to endure the current economic situa­
tion without cutting spending or raising taxes.
The inadequacy of state reserves to maintain ser­
vices in the event of a downturn was addressed in an
article by the Center on Budget and Policy Priorities
(CBPP) in March 1999 and updated in May 2000 (Lav
and Berube, 1999, and Zahradnik and Lav, 2000). The
authors calculate the amount of reserves each state would
need to endure a recession without cutting programs
dramatically or enacting significant tax increases. They
then compare this level of needed reserves to the level
available. In their calculations, the authors assume that
states would face a fall in the growth rate of revenues
between FY2000 and FY2003 similar to the decline
experienced between FYI989 and FYI992 that cor­
responded to the 1990 recession. This methodology
leads them to assume that the growth rate of revenues
would be 43 percent of the growth rate from FYI 993
and FYI998. At the same time, they assume that state
expenditures would grow at the same pace as they
did between 1989 and 1998. The authors calculate
the needed reserves as the gap between expenditures
and revenues over the three-year period.11 These cal­
culations yield a conservative estimate of necessary
reserves, because the 1990 recession was relative short­
lived and mild. In addition, as mentioned above, the
demand for government services tends to increase
slightly more rapidly during a recession, suggesting
the growth rate in expenditures (absent government
action) would be greater than experienced between
1989 and 1998. The authors conclude that only eight
states (Delaware, Indiana, Iowa, Maine, Massachu­
setts, Michigan, Minnesota, and North Dakota) had
adequate reserves on hand to combat a relatively mild
recession. Other states had reserves that were lower
than needed. In fact, they find that in most states re­
serves on hand were more than 10 percent of expen­
diture below what was required to maintain services.
In their follow-up report, the authors noted that five
of those original eight states (Delaware, Indiana,
Massachusetts, Michigan, and Minnesota) had enacted
tax cuts since the previous publication that left them
without sufficient reserves. The CBPP report also ar­
gues that the statistic that 5 percent of expenditures is
sufficient, while frequently cited, is “of uncertain ori­
gin and even more questionable validity.” They argue
that reserves equal to 5 percent of expenditures are
insufficient for managing recessions in all but a couple
of states. The report correctly points out that needed
reserves vary from state to state. States that depend

11

heavily on cyclical sources of revenue, especially in­
come taxes, need a greater level of reserves. The 5 per­
cent statistic better represents the level needed on hand
for unpredictable emergencies, perhaps an event like
September 11, than the level required to counteract
revenue losses caused by the business cycle.
This leads one to question why states did not take
advantage of the strong economy and move to build
adequate reserves, and why those few states with suf­
ficient reserves had spent them. The simple answer to
both of these questions would be that it is far easier
to spend money than to save it. One might say state
leaders are either myopic and do not worry about fu­
ture economic difficulties or are overly optimistic and,
thus, did not believe another recession was likely. How­
ever, such thinking misses the important point that states
do not view reserve funds as designed to allow them
to maintain services in a downturn. Rather they view
these funds as allowing a window during which they
can adjust their budgets and cut services or raise taxes
in an orderly fashion. In other words, reserve funds
are designed to allow states time to build the ark; they
are not designed to carry them through the deluge.
The evidence for this distinction is widespread.
First, most states cap the amount of money allowed
in the reserve fund. Thirty-three of the 46 states with
rainy day funds cap the amount allowed in the fund.
Most of the caps are at or below 5 percent of expen­
ditures. If states wanted these funds to counteract the
fiscal effects of recession, they would not cap them at
such a low level. Second, the language used by states
when discussing their reserves tends to be based on
concern for unexpected or short-term disruptions, not
prolonged economic problems.
For example, Illinois passed Rainy Day Fund
legislation in April 2000. The state controller made the
first deposit into the new fund on July 1, 2001. The
Illinois fund is capped at $600 million (2.6 percent
of 2000 state general fund expenditure). Previously,
the entire ending balance had been left in the general
fund. In a press release praising the legislature’s ac­
tion, Governor George Ryan stated that the fund was
“for use at the discretion of the governor and General
Assembly in the event of an unseen economic down­
turn that threatens state services (State of Illinois,
2000).” The language used by NASBO in explaining
reserve funds is similar. They write, “[Tjotal balanc­
es reflect the funds states may use to respond to un­
foreseen circumstances after budget obligations have
been met” (NASBO, 2001a).
State balanced budget requirements and debt re­
strictions limit the ability of states to borrow to meet

12

short-term needs. In lieu of access to short-term credit
markets, states maintain reserves, permitting them to
dip into savings rather than borrow. These reserve
funds buy states time, giving them the opportunity and
flexibility to adjust their budgets in a deliberate, sen­
sible manner. These funds help states avoid fiscal gim­
mickry to affect budget balances. However, the reserve
funds do not allow states to emerge unscathed from
recessions. For better or worse, state governments cling
firmly to their balanced budget requirements and be­
lieve that they ought to spend in one year what they
receive in that year (or over two years in states with
biennial budget cycles). That said, overall state fiscal
health would improve if reserve funds were adequate
to allow states to maintain, or even increase, spend­
ing without increasing taxes during economic down­
turns. Preserving balances for this purpose would
require a change in thinking about state budgeting.

Midwestern states ’ reserves
Table 2 shows the level of reserves as a percent
of general fund expenditures in the midwestern states
and for the nation as a whole from FYI998 to FY2002.
The data show that reserves among this group of states
have been fairly typical of the U.S. averages—reach­
ing high levels over the past five years, though fall­
ing more recently. With the exception of Wisconsin
in 2001-02, all midwestern states have maintained
reserves above the 5 percent threshold. The final col­
umn of the table also displays the level of reserves
needed to survive a mild recession, according to the
CBPP report. These numbers tend to vary quite dra­
matically across states.
Wisconsin’s reserves are the weakest of the group.
They declined dramatically between 2000 and 2001,
principally due to tax rebates passed in 1999. At the
same time, Wisconsin’s required recession reserves
are the highest of the Midwest states and the eighth
highest among the 50 states.
Illinois’s reserves have been quite stable over the
past five years, hovering close to the 5 percent mark.
By contrast, required reserves are quite high, suggesting
that Illinois will face fiscal difficulties.
Both Iowa and Indiana have reserves that have been
declining over time but are slightly above the nation­
al average. Their required reserves are lower than the
national average, but still double the level of reserves
on hand.
Michigan’s reserves have proven the strongest
among this group of states, exceeding 10 percent in
all five years presented in the table. As of 1999,
Michigan exceeded the reserves required to withstand
a recession by 10 percent. However, significant tax

3Q/2002, Economic Perspectives

TABLE 2

Reserve balances as percent of expenditures
1998

1999

2000

2001

2002

Required reserves

(as of 1999)
Illinois
Indiana
Iowa
Michigan
Wisconsin
5 state total
50 state total

6.10
23.00
19.60
12.20
5.70
10.90
11.00

6.30
20.60
16.00
15.40
7.00
11.00
8.90

6.60
18.30
13.40
15.10
7.40
10.50
11.90

5.70
9.80
9.80
12.90
2.70
7.30
9.10

5.60
8.30
8.20
12.90
2.00
6.70
6.30

22.90
14.90
16.30
5.10
27.00
18.60

Note: Data for 2001 are estimated and data for 2002 are from recommended budgets.
Sources: Required reserves from Lav and Berube, 1999; reserves from National Association of State Budget Officers, 1999 and 2001a.

cuts enacted during the last years of the expansion
have increased the state’s required reserves from the
5.1 percent reported in the table to 25.0 percent.
Therefore, after aggressive tax-cutting, Michigan’s
reserves were quite low relative to required levels.
The experience of the midwestem states has been
fairly typical. Reserves are high relative to historical
levels, but low relative to cyclical requirements. The
only states with low reserves as a group are a number
of southern states—Alabama, Arkansas, Kentucky,
Louisiana, North Carolina, and Tennessee—that did
not benefit as much from the economic expansion as
states in other regions.

Indebtedness
In addition to lowering taxes, increasing spend­
ing, and bolstering reserves, states also had the option
of using their new-found revenues in the 1990s to re­
duce their indebtedness. States are not major debtors.
Total outstanding state debt at the end of FYI 999 of
$510 billion represented just 51 percent of total an­
nual state expenditure (U.S. Department of Commerce,
Bureau of the Census, 2002a). By comparison, the
federal government’s indebtedness is over three times
its annual expenditures (U.S. Department of Commerce,
Bureau of the Census, 2002c). On balance, real state
indebtedness actually increased by 20 percent between
1992 and 1999, but this was less than the percentage
increase in expenditure. Because most debt is long
term and funds specific capital projects, it is not sur­
prising that debt increased at a time that capital spend­
ing was also increasing. While states could have used
their surplus funds to pay down their debt or fund
more capital projects out of current funds, they did
not do so. The only kind of debt that declined during
the expansion was short-term debt, which was 21 per­
cent lower at the end of 1999 than at the end of 1992.

Federal Reserve Bank of Chicago

This type of debt that matures in one year or less com­
prises bond and tax anticipation notes and is a barom­
eter of the health of state finances.

The bust: Revenues
The first real signs of the deterioration in state bud­
gets were seen in the third quarter of2000. These signs
could be seen in the revenue numbers being reported
by state governments. Figure 8 shows year-over-year
changes in quarterly tax revenues both in total and
for sales taxes. As the figure shows, between the third
quarter of 1999 and the third quarter of 2000, state
tax revenues, adjusting for tax changes and inflation,
had grown by only 4.1 percent. These data for total
tax revenues are estimates of what state revenues would
have been had legislated tax changes not occurred.
State revenue growth had slowed for the first time in
a year and was only half the growth rate reported in
the previous quarter. The changes in actual sales tax
revenues were even more dramatic relative to their
historical trend. Sales tax revenues grew by 4.7 per­
cent that quarter, the lowest growth rate reported since
the first half of 1997. These sales tax revenue numbers,
unadjusted for inflation or tax changes, are probably
the most reliable indication of the revenue situation.
Major legislated changes in the sales tax are rare and,
as a result, these numbers do not rely upon predictions
of the effects of legislated changes.
At this juncture, there was a great deal of variation
in the financial situation confronting different states and
regions. Figure 9 displays changes in year-over-year
sales tax revenues, by quarter, for the various regions.
The figure shows that, as of the second quarter of 2000,
some regions were doing far better and others far worse
than the national averages. In particular, sales tax reve­
nues in the Far West were growing well above the na­
tional average, while revenues in the Mid-Atlantic,

13

Great Lakes, and Southeast were lagging the national
average. Between the second and third quarter, growth
rates dropped off in all areas, with the exception of the
Mid-Atlantic and Rocky Mountain states, where they
were flat. Revenues in these two regions fell the fol­
lowing quarter. Some states were still well entrenched
in the impressive expansion, while others were falling
quickly into revenue troubles.
The decline in the growth rate of tax revenues
first hinted at in the third quarter of 2000 accelerated
in all the subsequent quarters for which data are avail­
able except one. In the third quarter of 2001, overall
tax revenues and sales tax revenues fell. Between the
third quarter of 2000 and the third quarter of 2001,
revenues had fallen in nearly every region, exceptions
being the Southeast and Southeast where revenues
were close to flat. We see a similar picture when we
look at the revenue growth rates for the second quar­
ter of 2001. Although still positive, revenue growth
rates were weak in all regions. The state revenue sit­
uation was rapidly deteriorating in advance of the
September 11, 2001, terrorist attacks. Although it is
difficult to distinguish the effects of the attacks from
the effects of a continuing secular decline in revenues,
there is little doubt that state finances were in trouble
before September 2001.
Next, I look at personal income tax revenues.
Because of the frequency of legislated changes in
this tax, I present numbers that are unadjusted for
legislated tax changes, as well as adjusted numbers.
Unadjusted revenues are the revenues actually re­
ceived by the state government. Adjusted numbers

display estimates of what receipts would have been
had the legislature not changed the tax code. While
income tax revenues, adjusting for legislated tax
changes, held up through the end of 2000, the income
tax situation was also poor by mid-2001. Figure 10
depicts the quarterly change in personal income tax
revenues by quarter, both adjusted for legislated tax
changes and unadjusted. Because nearly all income
tax changes lowered taxes, the unadjusted line (reve­
nues actually received) lies almost entirely below the
adjusted line. Income tax revenue changes are more
difficult to interpret than changes in sales tax reve­
nues for two principal reasons. First, the income tax
is frequently changed and even thorough estimates of
the effects of legislated tax changes are bound to be
imprecise. Second, because taxes must be filed prior
to April 15, there is a high level of seasonality in in­
come tax revenues and, as a result, comparisons across
quarters are quite difficult. The fall in income tax
revenues is more easily understood by looking at the
changes in year-over-year revenues by quarter as
show in figure 11. This figure shows that in every
quarter since 2000 :Q4, revenue growth rates have
fallen below their level from a year earlier. The fact
that income tax revenues fell slightly later than sales
tax revenues suggests that the more sales-tax-dependent states were likely confronted with revenue is­
sues earlier than more income-tax-dependent states.
When looked at from numerous angles, the state
revenue situation appears poor. Revenue growth rates
slowed early relative to the slowdown in GDP and have
continued to decline. Revenue numbers also contin­
ued to fall below already reduced expectations and,
by November 2001, 43 states were reporting that
revenues had come in below what they had anticipat­
ed for FY2002. By April 2002, this number had risen
to 48 states. (NCSL, 2001b; NCSL 2002)

Expenditures
Unsurprisingly, this decline in revenues has not
coincided with a decline in the demand for state ser­
vices. On the contrary, among the 43 states reporting
revenue shortfalls for FY2002 in November 2001
(NCSL, 2001b), 20 were also reporting that spending
was exceeding levels anticipated when fiscal 2002 bud­
gets were passed. By April 2002, 33 states were re­
porting spending overruns (NCSL, 2002). Predictably
enough, the main source of spending overruns and con­
cerns involved the Medicaid program. Nearly every
state that reported spending problems, along with some
that reported that spending remained on target, high­
lighted Medicaid spending as problematic.

14

3Q/2002, Economic Perspectives

FIGURE 9

Year-over-year changes in sales tax revenues, by region and quarter

Sources: Jenny, 2001, and Jenny and Boyd, 2001.

Next, I look at how states might respond to this
budget predicament. To do so, I examine how the states
reacted to previous downturns, how the states hit ear­
liest by the current recession have reacted so far, and
the projections and pronouncements coming from
state capitols.

State reactions: The 1991 recession
The 1991 recession was mild and relatively short
compared with previous downturns, but it hit the states
very hard. States dramatically cut services and enacted
large tax hikes (see figure 5). Many of these changes
occurred in the middle of the fiscal year. In many cases,
states were compelled to change their enacted budget
mid-year to avoid running foul of their balanced bud­
get provisions. Reducing enacted budgets is a sign that
the economy is worse than was anticipated when the
original budget was passed. Thirty-five states faced a
potential budget deficit at one point from 1990 to 1992,
and 20 or more states acted to reduce enacted budgets
during each year from fiscal 1990 to fiscal 1993. The
worst year was 1991, when 30 states faced a mid-fiscal-year deficit of nearly $15 billion (2.7 percent of
general expenditures). In 1991, states drew down their
reserve balances. Balances at the start of the downturn
were reasonably healthy, totaling 4.8 percent of ex­
penditures in 1989. However, by 1991 balances had
fallen to 1.1 percent of expenditures. A similar pattern

Federal Reserve Bank of Chicago

existed during the 1980s recession. For example, bal­
ances declined from 9 percent to 4.4 percent in the oneyear period from fiscal 1980 to fiscal 1981 (shown in
figure 7). In 1991, states were also forced to cut their
budgets by $7.6 billion.
Because balances had been used to deal with the
1991 fiscal situation, these excess funds were no long­
er available in FYI992 and FYI993. With little avail­
able reserves, states were forced to reduce current

15

year budgets further and raise taxes. In 1992 and 1993,
35 states and 23 states, respectively, reduced currentyear budgets, and states raised taxes by a total of $25
billion. If spending cuts and tax increases were insuf­
ficient, states resorted to fiscal gimmickry to affect
budget balances. The most popular form of gimmick­
ry is for states to postpone payments to vendors, em­
ployees, and other recipients of state funds. Illinois
was one of the main practitioners, increasing the time
between the receipt and payment of bills. States could
also speed up the collection of revenues by forcing
vendors to remit payments to the states more quickly.
Enacted budget reductions are very disruptive to
service provision, because budget changes need to go
into effect almost immediately (and sometimes even
retroactively) leaving state agencies and their clients
little time to anticipate and adapt to the changes. While
declines in service provision can take effect almost im­
mediately, tax increases take longer. Most tax increas­
es go into effect in the fiscal year following the year
of passage.
States can only cut spending mid-year for a select
range of programs. Many programs are nearly impos­
sible to cut mid-year. The largest item in state budgets,
elementary and secondary education, is hard to reduce
once teacher contracts have been signed. Also, school­
ing involves significant start-up costs that occur in the
beginning of the school year, which is fairly early in
the fiscal year in most states. By mid-fiscal year, school
expenditure is fairly inflexible. In the early 1990s, there­
fore, states cut spending on those programs where
cuts were possible mid-year, which tended to be pro­
grams that serve the poor (Lav and Berube, 1999).

16

The largest of these programs—then AFDC, now
TANF—has undergone a series of changes that will
greatly limit the states’ ability to cut funds in the future.
In 1996, the program changed from an entitlement pro­
gram where the states and federal government split
payments to a discretionary program where specific
amounts were block granted to each state. States
were given more control over the structure of their pro­
grams, with the exception that they needed to maintain
spending at or above 75 percent of their 1994 spending
level if they met work requirement provisions, and at
or above 80 percent of their 1994 spending level if
these work requirements were not met. These “main­
tenance of effort” provisions prohibit states from re­
ducing their expenditure below a certain level.
Throughout the recent economic boom, caseloads
have dramatically dropped and the maintenance of
effort provisions has proved to be binding in a number
of cases. In federal fiscal year 2000, only 11 states spent
more than 80 percent of their 1994 baseline, with 15
states spending exactly 75 percent of their 1994 level
and five states spending exactly 80 percent. The com­
bination of relative fixed funding levels and smaller
case loads has meant that states have been able to sup­
port their dependent populations with a wide array of
benefits and services in addition to cash grants. Such
additional programs include work transportation, child­
care, and housing assistance. In the face of significant
fiscal pressures, states will not be able to decrease
funding levels below their maintenance of effort require­
ments. However, they may well keep funding at, or
lower funding to, the maintenance of effort levels.
A number of states will actually be able to increase
total TANF funding dining an economic downturn
without harm to their budget situation, because these
states have not spent their entire block grants, leav­
ing excess amounts on account with the federal gov­
ernment. The TANF legislation explicitly allows states
to reserve part of their block grant for future spend­
ing. Funds reserved with the federal government can
be spent in subsequent years on “assistance.” While
states will not be able to spend these monies on the
wide array of “non-assistance” purposes that TANF
has funded, these funds will allow greater expenditure
on cash benefits.
Unspent TANF funds are categorized in one of two
ways for federal reporting purposes—as either unob­
ligated funds or as unliquidated obligations. Unobli­
gated hinds are monies neither committed nor expended;
these funds would be available for additional cash as­
sistance spending during a period of economy hardship.
Unliquidated obligations are payments that have been
committed by state governments, but not yet spent.

3Q/2002, Economic Perspectives

Additionally, in some states portions of unliquidated
obligations are not truly committed and would also
be available during a downturn.12 The true measure­
ment of available funds lies somewhere between the
level of unobligated funds and the sum of both types
of unspent funds.
As of the end of federal FY2000, the 50 states had
$2.7 billion in unobligated funds on account at the
federal government and $8 billion in total unspent
funds. This represents 9.7 percent and 28.6 percent,
respectively, of total required state and federal TANF
spending in federal FY2001, where required state and
federal spending is defined as the sum of the 2001 TANF
grant and the 80 percent maintenance of effort provi­
sion. There is a great deal of variation in the amounts
available to different states. While 11 states have less
than 10 percent of one year’s funding unspent, nine
states have more than 50 percent of a year’s funding
unspent (Lazere, 2001).
These unused block grants are an additional form
of rainy day reserves, providing states with an added
cushion as the economy declines. Therefore, TANF
spending changes will be more complicated in the com­
ing days. On the one hand, the saved block grants will
make it easier for states with saved amounts to increase
spending, and the maintenance of effort provisions
will not allow states to cut spending below a certain
threshold. On the other hand, states have added flexi­
bility to cut expenditure to the level of their mainte­
nance of effort requirement, because the program is
no longer an entitlement program.
If the dependent population increases, as occurs
with a deteriorating economy, and funding levels stay
relatively fixed or increase only slightly, benefits and
services will inevitably be cut. The most likely targets
for cutting will be those same creative new benefits
in transportation and childcare that have characterized
the very successful first years of TANF.13
If state behavior during the current downturn par­
allels that taken during the 1991 recession, we will
see states begin by drawing down their balances and
cutting budgets and then progress to cutting spending
more dramatically and increasing taxes.

The 2001 recession: Action thus far
The combination of lower revenues and high or
stable spending has meant that state budgets are com­
ing increasingly underpressure. State governments have
taken various actions to confront these budget issues
and bring their FY2002 budgets into balance. State
governors have also begun to put forward their FY2003
budgets. As of November 2001, 36 states had cut their
budgets for fiscal 2002, 24 had decided to use some

Federal Reserve Bank of Chicago

of their reserves, and 22 had turned to other measures,
explained by the NCSL (2001b) as including “hiring
freezes, capital project cancellations, and travel restric­
tions.” By April 2002, 40 states had reduced or were
planning to reduce their budgets, 26 had turned to
their rainy day funds, and 17 were eyeing tobacco
settlement dollars (NCSL, 2002).
Mid-year budget cuts are often across the board,
with nearly all departments faced with funds a few
percentage points below previously budgeted levels.
In many states some sacrosanct departments, particu­
larly K-12 education, are spared from these cuts. Across
the board spending cuts are common perhaps because
they are the easiest to implement quickly. Because states
need to bring their budgets back into balance quickly,
they lack the ability to carefully determine areas where
budget reductions would be least damaging. It then
falls to the individual state agencies to choose the ex­
act programs where the reductions will be implement­
ed. More specific budgetary debate has accompanied
the early discussions concerning 2003 budgets.
As states have debated spending cuts, attention has
inevitably turned to the Medicaid program. Through­
out the 1990s expansion, there was little broad discus­
sion of the problems with Medicaid spending. Although
health care expenditure specialists did debate the is­
sue, debate was not widespread. As the economic sit­
uation has deteriorated, Medicaid spending has once
again come to the fore. This is not surprising, given
that it is a quickly growing program that already ac­
counts for nearly 20 percent of all state expenditures.
To reduce costs, states have both restricted eligibility
further and tried to cut costs per eligible recipient. Cost
cutting can take many forms. States have limited ac­
cess to services and drugs by increasing the need for
pre-approval and by reducing optional benefits. States
have also contemplated increases in co-payments, shift­
ing more of the costs onto recipient families. In addi­
tion, states have reduced payments to service providers.
This strategy is successful in reducing costs, but may
lead more providers to refuse to serve Medicaid pa­
tients. For example, state proposals for reductions in
prescription payments have led drugstores to threaten
to stop filling Medicaid prescriptions (Associated Press,
2002). States have also turned to drug companies and
asked for larger volume discounts on Medicaid drug
purchases. Finally, states have asked the federal gov­
ernment for an increase in the matching rate. One jus­
tification for this request is that some of the increase
in Medicaid costs derives from federally legislated
increases in the eligible population.
In addition to cutting spending, states have also
drawn down their reserve fund balances. As shown in

17

figure 7 and discussed earlier, balances were expect­
ed to fall from 10.1 percent of expenditure in 2000 to
5.9 percent by the end of fiscal 2002. As of Novem­
ber 2001, seven states indicated that they would defi­
nitely be using reserves to balance their budgets, and
17 additional states were contemplating using reserves
to balance their 2002 budgets (NCSL, 2001b).
Thus far, there has been little movement, espe­
cially among state governors, to raise taxes. In 2001,
only six states passed substantial increases in taxes.
And only in North Carolina was the increase viewed
as a response to revenue problems caused by the re­
cession (Jenny, 2002) One lesson that was reinforced
during the early 1990s recession was the political un­
popularity of raising taxes. Governors who had en­
acted significant tax increases were almost universally
voted out of office in favor of politicians promising
to lower taxes. In many cases, these changes in lead­
ership coincided with the improving economy and
new governors were able to keep their campaign
promises. A number of the governors and legislatures
that came to power in the early 1990s will soon face
a similar dilemma to that confronted by their unfor­
tunate predecessors.
So far, the limited discussion of tax increases
has revolved around the cigarette and alcohol taxes.
Oregon’s governor proposed increasing these two tax­
es in order to balance the budget. Similarly, Indiana’s
governor has proposed hiking taxes on cigarettes and
gambling. These taxes are politically the easiest to hike,
although they are not the most lucrative revenue sources.
However, for the most part, governors have chosen
to speak out vociferously against tax increases. For
example, New Jersey Governor McGreevey stated that
he was “ruling out a tax increase” as a way to solve
budget problems (Herszenhorn, 2001). This sentiment
has been echoed by numerous other governors and
legislative leaders across the country. That said, judg­
ing from the experience of the 1990s, tax hikes tend
to occur late in a decline after other, easier avenues
of budget balance have been exploited.
The debate over 2003 budgets is quite similar to the
debate over 2002 budgets. While this discussion is occur­
ring in a less panicked environment, the policy decisions
closely parallel the decisions made concerning FY2002
budgets. In particular, states are relying on spending
cuts and reserve funds rather than on tax increases.
If revenue estimates for FY2003 prove too opti­
mistic, more states will likely turn to discussion of
tax hikes. Tax increases during FY2003 may prove
particularly politically challenging. If the trend in
positive national economic news continues, state

18

leaders will need to justify tax increases at the same
time that voters are hearing more about the overall
health of the macroeconomy.

Actions in the midwestern states
Midwestern states were among the first hit by the
downturn. Returning to figure 9, we see that the Great
Lakes states had either the weakest or close to the
weakest tax growth in all the quarters pictured. The
midwestern (and southeastern) states had tax collec­
tions significantly below projections in 2001. Most
other states did not begin experiencing revenue prob­
lems until FY2002 (see NASBO, 2001a).
The midwestern states were among those hit ear­
liest by falling revenues. One principal reason for this
was that a downturn in manufacturing production
preceded the downturn in overall GDP growth. Fig­
ure 12 shows the trend in manufacturing relative to
the trend in GDP, while table 3 details the percentage
of state employment in manufacturing both overall
and for the midwestern states.

Illinois
As of November 2001, Illinois was facing a $500
billion deficit in the FY2002 budget. This deficit was
principally caused by lower-than-expected state reve­
nues. As of October 2001, FY2002 revenues were $262
million below the level collected over the same peri­
od the previous year. In order to confront the deficit,
the government called on state agencies to reduce their
spending by 2 percent and instituted travel restrictions,
a hiring freeze, and a one-day furlough program for
state workers. (In the end, the furlough program was pre­
vented by the state employees’ union.) The governor
also cut Medicaid payments to some hospitals, although
some of the original cuts were subsequently restored.
The 2003 budget proposed by the governor in late
February 2002 appropriated $22.7 billion from the state
general fund, representing a decline in $700 million
from the previous year’s appropriations. The proposed
budget included no tax increases, but further across
the board agency cuts of 3 percent. Additionally, the
governor proposed a cut in the state work force of 3,800
workers, principally through an early retirement pro­
gram. Furthermore, some state penal mental health
facilities were to be closed or have their opening de­
layed (State of Illinois, 2002).
Indiana
As of November, Indiana’s revenues were antici­
pated to be $540 million below the original forecast
for FY2002. By April, the state was facing a deficit
of $1.3 billion in the fiscal 2002-03 biennial budget.

3Q/2002, Economic Perspectives

is $48 million that had been slated for the state rainy
day fund and an additional $42 million from the ex­
isting rainy day fund balance. Competing budget
plans from senate Republicans (the governor is a
Democrat) propose reducing spending more dramati­
cally, including spending on education, and relying
less on emergency funds (Okamoto, 2002).

The governor dealt with this shortfall by freezing a
series of state capital projects, instituting a hiring
freeze (both in September), and calling on agencies
to reduce expenditures by 7 percent. He also proposed
increases in a number of different taxes, including tax­
es on cigarettes and casinos and further cuts in agency
budgets. The legislature failed to enact tax increases
before adjourning in March, and the governor put
spending cuts directly into effect and recalled the leg­
islature for May. School funding was among the areas
cut. Indiana is the only one of the midwestern states
that is seriously considering tax increases. However,
the discussion concerning tax increases is taking part
in the context of a general tax restructuring caused
by a court-ordered change in the property tax.
Iowa
Through the end of December 2001, Iowa’s rev­
enues were $200 million below original projections.
In order to confront the resulting deficit, the governor
implemented a 4.3 percent across-the-board spending
cut. Subsequently, funding was restored for a selection
of programs, including elementary and secondary ed­
ucation. Further bad news in February was met by an
additional 1 percent cut in the 2002 budget, use of
state emergency and tobacco settlement funds, and a
furlough program for state workers.
The governor’s proposed (revised) 2003 budget
continues to avoid tax increases but proposes to bal­
ance the budget using a further 3 percent cut to agency
budgets and funds from a number of state reserves.
Education would continue to be shielded from cuts.
Included in the reserves the governor proposes to use

Federal Reserve Bank of Chicago

Michigan
As of November, Michigan’s general fund reve­
nues were projected to be $462 million below original
estimates and overall revenues 2.5 percent below fis­
cal 2001 collections. The state made up for this short­
fall by canceling a series of capital projects, enacting
spending cuts, and using money from outside the gen­
eral fund, including tobacco settlement money and
money from the contingency fund. Spending cuts fo­
cused on health, welfare, and corrections, but K-12
education spending was not cut. The state considered
delaying or canceling previously enacted income and
business tax cuts, but chose not to do so.
The governor’s proposed 2003 budget plans to
make up for a $ 1 billion shortfall using additional spend­
ing cuts, in particular a freeze in the state-local reve­
nue sharing program and significant withdrawals from
the rainy day fund and other state reserves. He does
not recommend tax increases, aside from a small in­
crease in diesel taxes. The budget shields schools
from cuts, in part by moving the timing of school tax
payments (Cain et al., 2002).

Wisconsin
Wisconsin was facing a $1.1 billion deficit in its
biennial budget covering FY2002 and FY2003. The
deficit is primarily a result of lower than anticipated
income tax collections. Because of the biennial bud­
get cycle, the state needed to confront 2002 and 2003
issues together. The governor’s proposed budget plan
includes 3.5 percent and 5 percent reductions in
TABLE 3

Percent of employment in manufacturing
by state, 2000

Illinois
Indiana
Iowa
Michigan
Wisconsin
U.S.

Manufacturing %

50 state ranking

16.1
23.2
17.8
21.6
22.2
14.3

17
1
12
4
2

Source: U.S. Department of Commerce. Bureau of the Census,
2002c.

19

agency spending in 2002 and 2003, respectively,
modest cuts in university spending, and a phase out
of the provision of discretionary moneys or “shared
revenues” to local governments. Education and state
programs serving the needy were for the most part
shielded from cuts. The governor also proposes to
borrow $794 million from the state tobacco settle­
ment fund to fund the shared revenue program while
it is being phased out (McCallum, 2002).
Each of the midwestern states has chosen a dif­
ferent package of changes to address budget deficits
for the current fiscal year. These states have also be­
gun debating how to ensure that the budgets for FY2003
will be balanced. While the choices made have been
different, a general pattern emerges with the states en­
acting the least painful changes first and evolving to
harder decisions as the budget situation has continued
to deteriorate. Hiring freezes and travel restrictions
have been followed by across the board spending cuts
and a drawing down of reserve funds. States have re­
lied on reserves not only in their rainy day fund, but
also funds from the tobacco settlement, and other more
obscure places. While tax increases have largely been
avoided, if state revenues continue to disappoint, fur­
ther agency cuts may prove too painful, reserves will
be largely spent, and the states may have to resort to
tax hikes to balance their FY2003 budgets.

Conclusion: Lessons learned
After the 1991 recession, many observers hoped
that states had learned about the dangers inherent in
their budget situations and would react in subsequent
booms in ways that would prevent a recurrence of fis­
cal crisis. The current fiscal situation indicates that
many of these lessons were inadequately learned.
The biggest problem states face is the combination
of cyclical revenues with acyclical or even counter­
cyclical obligations and institutions that are not per­
mitted to use financial markets to deal with this
disjoint. States have acted in ways that exacerbate this
mismatch. For example, while the reduced sales tax
rates on food and prescription drugs are motivated by
understandable, even admirable, policy objectives,
these serve to increase the sensitivity of revenues to
the business cycle.
How can states deal with this problem?
Rainy day funds
While states’ balanced budget requirements pro­
hibit them from borrowing, they are permitted to save
money. The principal ways this is done is through
rainy day funds and cash balances in the general ac­
count. States should increase the levels of these

20

funds during booms to prepare for the inevitable de­
cline in revenues when the economy sours. As men­
tioned earlier, rainy day balances have been rising
over recent decades. States should continue this trend.
In fact, if states are successful in managing the
current downturn without resorting to significant tax
hikes, research may ultimately attribute this success
to the health of reserves at the start of the recession.
One issue regarding rainy day funds is that they
are perceived as funds to cover short-term adjustment
needs rather than longer-term revenue shortfalls. They
are preparing states to manage for a rainy day rather
than for the rainy season, or several seasons, that an
economic downturn represents. State leaders would
need to change their perception of these funds in order
to allow them to grow to the levels needed to main­
tain services in the face of widespread economic dif­
ficulties.
In keeping with the increased role of reserves,
state legislatures would need to increase the permit­
ted size of reserve funds. As mentioned above, many
states limit the level of reserves.
Tax cuts
The current situation, where taxes are cut during
a boom and increased during a recession, both exac­
erbates the economic cycle and means that consider­
able energy is being expended in debating changes
that are soon reversed. Given the political popularity
of tax cuts, it would be idealistic to suggest that states
should not cut taxes when the economy is booming
and instead maintain all excess funds as reserves. At
the same time, the political unpopularity of tax in­
creases means that needed tax increases occur late in
the economic cycle after considerable damage has
been done in terms of interruptions to state-provided
services. In order to deal with this problem, states
should consider enacting tax cuts that do not require
offsetting legislation to be reversed in subsequent
years. In particular, they might consider tax rebates
and refunds rather than legislated reductions in rates.
In this way, states could return money to taxpayers
without jeopardizing the finances of the government
during economic difficulties any more than is done
by the contingencies of the economic cycle itself. Many
of the tax cuts enacted during the expansion were re­
bates. More states should consider these during fu­
ture surplus years.

Expenditure patterns
One reason some observers argue against higher
reserve balances is that they believe that governments
will see these balances and find wasteful ways to spend

3Q/2002, Economic Perspectives

them. By returning money to taxpayers instead,
government leaders are relieved of this temptation. In
other words, these commentators believe that taxpay­
ers are better stewards of resources than legislators.
The long-term spending trends in the states justi­
fy this worry. State spending has been on an upward
trajectory relative to personal income for quite some
time. Governors and legislators should work to con­
front the spending demons by carefully reexamining
spending priorities. Spending appears only to be care­
fully controlled during fiscal crisis and not during
calmer times. States should look closely at how agen­
cies confront across-the-board spending cuts to deter­
mine where excess fat may be in the system.
Additionally, when the economy improves, the states
should continue the scrutiny of the Medicaid pro­
gram that is occurring during budget discussions.
Medicaid spending is particularly problematic be­
cause its rate of growth shows no sign of abating.
Also, major adjustments in the program are likely to
be slow to develop because they would require the
cooperation of state and federal authorities.
Additionally, states should consider public rela­
tions programs that educate the public about the valu­
able services they provide. For example, do taxpayers
know that states are the largest providers of school
funding or do they believe that this service is princi­
pally funded locally?

Leaning on the federal government
States should not expect the federal government
to bail them out when the economy sours. While the
states need to act quickly to affect budget balances dur­
ing recessions, the federal government makes policy
in a slow and considered fashion. The recent experi­
ence with Medicaid spending demonstrates the prob­
lems of relying on the federal government. While the
states have been requesting additional funds for over
six months, stimulus packages containing Medicaid
relief for states have consistently stalled in Congress.
While states may well get their additional Medicaid

Federal Reserve Bank of Chicago

support eventually, it will not come quickly enough
to ameliorate the last-minute budget crises. The prob­
lems underlying the requests for added Medicaid funds
are part of long-standing trends. The federal govern­
ment may have been more receptive to these requests
during more robust economic times.

Reversing balanced budget restrictions
One additional option for the states would be to
reverse their long-standing balanced budget restrictions
and debt limits. This would allow states to borrow
from financial markets when the economy deteriorates
and (presumably) to pay the money back as the econ­
omy improves. There are two principal arguments
against such a suggestion. First of all, such a recom­
mendation is impractical. Governors and legislators
are very proud of their balanced budgets. Even the
suggestion that these rules be reversed would be po­
litical suicide. Second, and more importantly, without
these restrictions, states would be less compelled to
make difficult spending decisions. As a result, state
spending would likely get even more out of hand.
Balanced budget restrictions mean that budgets are
balanced both from year to year and (as a result) on
average. While yearly balanced budgets are troubling
because of the business cycle, the fact that states are
not major debtors is an important strength of the state
fiscal process.
The current fiscal condition of the states and the
difficult budget negotiations states are engaged in have
come alarmingly soon after a long period of windfall
revenues. While state leaders used these revenues to
cut taxes and increase spending, they did not use them
to plan adequately for a weak economy. After two simi­
lar fiscal crises only a decade apart, one might hope
that states will understand the need to plan for future
recessions. States could better prepare for recession
by relying more completely on their reserve funds.
This would allow them to escape their historical pat­
tern of increasing taxes when citizens are poorest and
cutting services when they are most needed.

21

NOTES
The majority of state budgets pertain to a fiscal year that starts
on July 1 and ends on June 30. A small number of states use a
different fiscal year. While most states operate annual budgets, 21
have biennial budget cycles. In some smaller states, this is in con­
junction with a legislature that meets every other year. In states
with biennial budgets, full-blown budgets are only authorized every
other year, but supplemental budget bills are often passed in off
years to cope with unplanned contingencies.
2States themselves rely on different fund definitions than the Cen­
sus Bureau. I use Census Bureau definitions because these guar­
antee comparability across states.

Throughout this article, I use 1992 as a dividing point for data com­
parisons because it was the first year of positive economic growth
during the recent expansion. The last year of data used depends
on data availability. I use the most recent year for which histori­
cally comparable data is available. All real numbers are calculated
using the gross domestic product implicit price deflator from the
Executive Office of the President, Council of Economic Advisers,
2002. Average yearly growth rates are based on compounding.

California does collect these data. Income from options and capi­
tal gains in the state grew from $25 billion in 1994 to $200 billion
in 2000 then fell to $70 billion in 2001 (Sterngold, 2002).
5Due to the particulars of the settlement, no monies were due for
1999. However, states received the 1998 funds in 1999.
6The levels of expenditure from the NASBO and Census data sets
are quite different; however, historical comparisons find that the rates
of expenditure growth tend to be very similar (Merrimam, 2000).
7Administrative costs are not included in any of these categories.
The underlying numbers include only Medicaid payments on be­
half of recipients.

9Instruction represents 61.7 percent of current education expenditure,
and current expenditure represents 85 percent of total expenditure.
The 61.7 percent number is frequently cited as the percentage of
spending on teachers, but this excludes non-current spending.

10This figure actually underestimates the net effects of the decline
in state taxes because it treats each reduction as only reducing
one year’s taxes. Many reductions were permanent and therefore
reduced taxes in all subsequent years.
nThis is a reasonably quick way to get an approximate calcula­
tion. One problem with this measure is that it leads to the predic­
tion that those states with the fastest growth rates of revenues from
FY1993 to FYI998 would continue to face the fastest growth rates
in the future. However, if revenues grew more quickly because
some states rely on more cyclical forms of revenue, one would
expect revenues to be slowest in those states that grew most quickly
during the expansion. A more precise estimate of necessary re­
serves would be based on the cyclicality of the specific revenue
sources relied on by each state. In a paper in 1998, Dye and McGuire
provide estimates of revenue cyclicality by state, but do not esti­
mate required reserves. The correlation between the estimates of
needed reserves and the cyclicality of revenues is -0.26. In other
words, those states that CBPP calculate as needing the most re­
serves (as a percent of their budget) to withstand a recession are
the states that Dye and McGuire find rely on least sensitive rev­
enue sources.

12For further information on the distinction between reported un­
liquidated obligations and unobligated funds, see Fazere, 2001.
13TANF will need to be reauthorized in 2002. Significant changes
are not anticipated because the program has been widely perceived
as being successful.

8There have been numerous changes relating to the eligibility of
children since 1986. For a full discussion, see U.S. Congress, House
Committee on Ways and Means, 2002.

22

3Q/2002, Economic Perspectives

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Cain, Charlie, Mark Hornbeck, and Gary Heinlein, 2002, “Critics argue Engler takes from future
to fix budget,” Detroit News, available on the Inter­
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Federal Reserve Bank of Chicago

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“A second quarter of slow tax revenue growth,”
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Lav, Iris J., and Alan Berube, 1999, When it Rains
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McCallum, Scott, 2002, “State of Wisconsin budget
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23

__________ , 2001a, Fiscal Survey of the States:
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__________ , 2001b, Fiscal Survey of the States:
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State of Illinois, 2002, “Governor Ryan proposes
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24

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Federal Reserve Bank of Chicago

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25

The 2001 recession and the Chicago Fed National Activity
Index: Identifying business cycle turning points

Charles L. Evans, Chin Te Liu, and Genevieve Pham-Kanter

Introduction and summary
On March 5, 2001, the Federal Reserve Bank of
Chicago first released publicly the Chicago Fed Na­
tional Activity Index (CFNAI), a single, summary
measure of real economic activity that is based on a
weighted average of 85 economic indicators. This in­
augural CFNAI release explicitly mentioned the pos­
sibility that the U.S. economy had begun to slip into
a recession. On November 26, 2001, the National
Bureau of Economic Research’s (NBER) Business
Cycle Dating Committee “determined that a peak in
business activity had in fact occurred in the U.S. econ­
omy in March 2001 (NBER, 2001).’’ As the eightmonths lag of the NBER report indicates, business
cycle turning points are typically only recognized many
months after the event; thus, the ability of the CFNAI
to identify the recession in approximately real time is
important—since early recognition of business cycle
turning points will enable more timely monetary pol­
icy responses.
Although one of the first uses of the CFNAI was
to gauge inflationary pressures (Fisher, 2000), there
is a strong statistical relationship between this index
of economic activity and business cycle movements.
We can see this in figure 1, which displays the threemonth moving average index (CFNAI-MA3) from
1986 through 2001. Whenever the three-month mov­
ing average of this index falls into the range of -0.70
to -1.00, there is an increasing probability that the U.S.
economy is in a recession. The substantial fall in the
index to -1.50 in late 1990 corresponds to the 1990-91
recession. Similarly, the 2001 recession (see figures 2
and 3) is clearly evident as the index fell below -1.00.
Prior to the current recession, there were five reces­
sions over the 1967-2000 period. In six cases, the
CFNAI-MA3 fell below -0.70, after having previous­
ly been above zero when the economy was expanding.
On five of these occasions, the U.S. economy had
just entered a recession as determined later by the

26

NBER. Taken at face value, this is an 83 percent suc­
cess rate for the CFNAI.
To further our understanding of the CFNAI and
its role as a business cycle indicator, we address two
main questions in this article. First, what is the general
relationship between the CFNAI and economic reces­
sions? While economic downturns are clearly evident
in the sharp reductions in the CFNAI, how much more
information do we gain beyond what we would learn
by simply focusing on single indicator measures of
economic activity like industrial production, personal
consumption expenditures, and others? We offer a
graphical analysis of the data to answer this question.
Second, what probabilistic statements about economic
performance can we attach to specific values of the
CFNAI-MA3? When the CFNAI-MA3 plunges to val­
ues below -0.70, what is the probability that the U.S.
economy has entered a recession? We adopt a statistical
approach to modeling the dynamic evolution of the 85
economic indicators in order to answer this question.
To summarize our findings, our graphical analy­
sis indicates that individual economic indicators ap­
pear to predict the onset of economic recessions almost
as well as the CFNAI-MA3. Indeed, many business
cycle analysts prefer a relatively small number of eco­
nomic indicators to guide their analysis. For example,
the NBER November 2001 committee report makes
clear the importance of four monthly coincident eco­
nomic indicators of real activity; payroll employment,
industrial production, real personal income less

Charles L. Evans is a vice president and senior economist
and Chin Te Liu is an associate economist at the Federal
Reserve Bank of Chicago. Genevieve Pham-Kanter, formerly
an associate economist at the Federal Reserve Bank of
Chicago, is a graduate student at the University of Chicago.
Throughout this project, the authors have benefited from
the helpful comments of Larry Christiano, David
Marshall, Ken Matheny, and Mark Watson.

3Q/2002, Economic Perspectives

transfer payments, and manufacturing and trade sales
in real terms.1 However, while all of the economic in­
dicators were signaling that the real economy was
growing well below trend throughout this period, they
conveyed different information about the timing of the
business cycle peak. Essentially, the NBER selected
the business peak based upon a peak in one very im­
portant indicator, total payroll employment. Visual
inspection of the co-movements between industrial
production, employment, and the CFNAI-MA3 sug­
gests that perhaps the gain in computing the index of
85 indicators is fairly small.
However, the small number of economic reces­
sions since 1967 makes this assessment misleading.
Using Monte Carlo simulations, a more careful eval­
uation of the statistical properties suggests substantial
improvements from using the CFNAI-MA3 over in­
dividual indicators. For example, when the CFNAI-MA3
falls to -0.70, the probability that the economy has
entered a recession is around 70 percent. When simi­
larly normalized three-month moving average index­
es of industrial production and personal consumption
expenditures fall below -0.70, the probabilities are
50 percent and 35 percent, respectively. This quanti­
tative analysis indicates that the CFNAI-MA3 is use­
ful for detecting the onset of economic recessions.
In the following sections, we explain the develop­
ment and construction of the CFNAI. Then, we exam­
ine how quickly and how well the index has historically
identified business cycle turning points. We examine
whether it is possible to accurately reflect the economy
with fewer indicators or whether more than 85 indi­
cators may be necessary to average out the idiosyn­
cratic noise from the underlying economic signals.
Finally, we use a statistical technique, Monte Carlo
simulations, to analyze the index’s performance. This
statistical approach provides us with a far greater num­
ber of observations than the five recessions that have
actually occurred since 1967. In particular, we focus
on the index’s ability to correctly identify the onset
of recession in the simulated economy.

Origins of the CFNAI
The Chicago Fed National Activity Index is based
upon an index designed by James Stock and Mark
Watson in their Journal ofMonetary Economics article
on “Forecasting Inflation’’ (Stock and Watson, 1999a).
Stock and Watson’s Activity Index summarizes the
information of 85 data series in a single index value.
This is accomplished using the well-known method
of principal component analysis (see box 1 for an ex­
planation). The current version of the CFNAI attempts
to implement their monthly data selections as closely

Federal Reserve Bank of Chicago

as possible in real time. The CFNAI is the first prin­
cipal component of these data series, accounting for
the largest independent variation among the econom­
ic indicators in the data set. Equivalently, the CFNAI
is a weighted average of the 85 economic indicators.
For example, a principal component index v based on
three series can be expressed as yt =
x xlf + w2 x
x2( + w3 x x3f, where xp x2, and x, are the three original
series, and
w2, and w3 are the weights assigned to
the data series. In practice, the weights measure the
relative importance of each series in the index.
An index like the CFNAI can be used in several
ways. One approach is to use the index as an explan­
atory variable in estimating linear relationships. By
computing a single index value for a large data set, the
gains from data reduction allow the analyst to specify
parsimonious forecasting relationships. This approach
has been used by Stock and Watson (1999a, 1999b),
Fisher, Liu, and Zhou (2002), and Bernanke and Boivin
(2002). Another approach is to use the index to iden­
tify non-linear regime switches. For example, Fisher
(2000) describes how movements in the activity in­
dex relate to broad accelerations in inflation during
certain time periods. In this article, we focus on non­
linear regime switches from economic expansions to
recessions, as in Hamilton (1989) and Diebold and
Rudebusch (1996).

Constructing the CFNAI
One of Stock and Watson’s (1999a) findings is
that the first principal component of our 85-variable
dataset captures aggregate real activity in the United
States. When we double the dataset by including var­
ious inflation rates, monetary aggregates, interest

27

BOX 1

Construction of the CFNAI
Data transformations and the principal
components method
The CFNAI is the first principal component of a
dataset consisting of 85 economic indicators. Back­
ground on the method of principal components may
be found in most advanced statistics and economet­
rics books. Henri Theil’s (1971) classic text Princi­
ples of Econometrics provides an excellent overview
of this method; we use Theil’s exposition and nota­
tion in the following discussion.
Let x denote the 1 x 85 row vector consisting of
observations at time t of the 85 data series. Let XT denote
the T x 85 stacked matrix of data vectors, that is,

XT =

where T is the total number of observations.

By this construction, each column of XTcontains T
observations of an individual economic indicator.
Each of the 85 raw series used to compute the
CFNAI has already been inflation adjusted and, if
necessary, seasonally adjusted by the original data
provider. After obtaining these raw data series from
HaverAnalytics, we first assess each series for its
stationarity properties. If a series is determined to be
non-stationary, we apply an appropriate transforma­
tion to render the series stationary. In most instanc­
es, the data are log-differenced so that the indicator
series are transformed into growth rates. This is the
case, for example, with employment and industrial
production data. In some cases, such as the Institute
for Supply Management’s Purchasing Managers In­
dexes, the data require no transformation.
Second, each stationary series is adjusted for
outlying observations. We define an outlier to be an
observation whose distance away from the median is
greater than six times the interquartile range of the
series. That is, ,v.—the observation at time t of series
i—is an outlier if |.vIf -xf°| >6(.v/5 -xf), where

xf, xf, and xf are the 25th, 50th, and 75th percentiles
of series x.. An outlier that is above the median has
its original value replaced with xf + 6(.r’5 - xf),
while an outlier that is below the median has its origi­
nal value replaced with xf - 6(xf - xf).
Finally, we rescale each series to have a mean of
zero and standard deviation of one. These standardized

rates, commodity prices, and equities, the first princi­
pal component is essentially unchanged. That is, a
measure of real activity continues to account for the
largest independent, common variation in each of the

28

series are the indicator series used in XT for the prin­
cipal component calculation.
In general, a principal component of XT is deter­
mined by a specific eigenvalue of the second-moment
matrix XfXT. Computing the first principal component
of XT requires calculating the eigenvector associated
with the largest eigenvalue of XfXT. Consequently,
since the CFNAI is the first principal component of
X , it is simply a particular weighted average of the
85 economic indicators. In particular, CFNAIt = xfa,
where a is an 85 x 1 vector of weights. Although the
weights in the vector a correspond to the elements
of the eigenvector associated with the largest eigen­
value of XfX-r , the vector a is re-scaled such the re­
sulting CFNAI has a mean of zero and standard
deviation of one. Since we estimate a single set of
weights over the entire sample period, this vector of
weights remains fixed for a given set of data XT.

Revisions to the CFNAI
There are two main sources of revisions in the
CFNAI. Firstly, because the CFNAI is designed to be
released in a timely way and because indicator data are
released at different times, not all of the indicators
are available in time for a particular month’s CFNAI
release. For example, employment data are usually
available within one week of a month’s end, but in­
flation-adjusted retail inventory data are typically
not available until another five weeks have elapsed.
For any given CFNAI release in 2001, approxi­
mately one-third of the indicators will have had their
latest monthly values forecast. In other words, the re­
ported CFNAI is based on the latest observed values
for two-thirds of the 85 series and based on forecast
values for the remaining one-third. In the following
month’s CFNAI release, the data for the “lagging” series
will have become available, and the previous month’s
CFNAI value will be revised based on this data. In this
way, forecast error is a source of revision in the CFNAI.
Secondly, throughout the calendar year, the 85
monthly series are systematically revised by the origi­
nal reporting institutions. These revisions will alter the
underlying monthly data, resulting in a change in the
value of CFNAI. Although both sources of revision
will also result in a change in the weighting vector a,
we expect this and the re-normalization of the under­
lying data to have a negligible effect on the index.

data series. So using 166 indicators rather than our
85 would lead to negligible changes in our threshold
analysis below. Consequently, we focus on real eco­
nomic indicators in computing our index.

3Q/2002, Economic Perspectives

The CFNAI is constructed from 85 coincident
economic series that are drawn from five categories
of economic activity. Table A3 in the appendix lists
all 85 series. The five categories are:
1. Production and income—These data include in­
dustrial production growth for several industries
and product classifications; component indexes
from the Institute for Supply Management’s (ISM)
Purchasing Managers Index (PMI); capacity utili­
zation measures; and real income growth mea­
sures (21 series).

2. Employment, unemployment, and labor hours—
These data include employment growth rates for
several industries from the Payroll Survey; the
employment component of the PMI; changes in
unemployment rates for several demographic
groups from the Household Survey; initial claims
for state unemployment insurance; growth rates
of production hours; and changes in help-wanted
measures (24 series).
3. Personal consumption and housing—These data
include the growth rate of real personal consump­
tion expenditures for several categories; housing
starts nationally and by region; building permits
for new housing units; and shipments of mobile
homes (13 series).

4. Manufacturing and trade sales—These data include
growth rates of real sales measures for manufac­
turing industries; several categories of wholesale
trade sales; and several categories of retail trade
sales (11 series).
5. Inventories and orders—These data include com­
ponents of the PMI related to new orders and ven­
dor performance; the growth rate of inventories
and inventory-sales ratios by manufacturing and
trade categories; and the growth rate of new or­
ders for durable goods manufacturing and nonde­
fense capital goods (16 series).

Basic properties of the CFNAI
Figure 2 displays the monthly CFNAI from 1967
through 2001. By construction, the monthly index
has an average value of zero and a standard deviation
of one. Since many data series are deviations of
growth rates from their sample average, the monthly
index can be interpreted as the deviation of national
activity growth from its trend rate. Consequently, an
index value of zero is associated with trend rates of
growth. Another reaction to figure 2 is that the index
is quite volatile from month to month. Although in­
dex values above zero tend to stay above zero for a
period of time, there are many reversals of sharp spikes
from month to month. The monthly index, therefore,
appears to track broad movements in the economy,
but contains transitory noise. Consequently, taking a
moving average of the monthly series would average
out the transient noise while leaving the underlying
signal in place.
Figure 3 displays the trailing three-month moving
average of the monthly index. We refer to this moving
average as the CFNAI-MA3. Clearly, much of the
transient noise in the monthly index has been filtered
out. Now it is easier to see the persistent movements
of the index over time. Since the index is a weighted
average of 85 economic indicators, movements in each
of the components contribute to movements in the
CFNAI-MA3. Large positive or negative index values
tend to arise when most of the individual indicators
are moving together. This is especially evident during
periods of economic contraction. In figure 3, sharply
negative values of the CFNAI-MA3 correspond to of­
ficial NBER recessions. From 1967 through 2000, the
moving average index fell below -0.70 after having

Prior to constructing the activity index, the indi­
vidual data series are transformed to be stationary as
denoted in table A3. In practice, this means that trend­
ing variables are often measured as growth rates, while
variables without trends are often left untransformed.
These transformed data are then each de-meaned and
standardized to have a unit variance. We then com­
pute the CFNAI as the first principal component of
the 85 data series. Box 1 presents the formal details
of the methodology used to construct the index.

Federal Reserve Bank of Chicago

29

previously been above zero on six occasions, and
five of those were associated with recession.
Perhaps it should not be surprising that a basket
of economic indicators can provide a useful guide to
the state of aggregate economic activity. In financial
markets, individual stock prices reflect both market
and company-specific risk. A portfolio of stocks, like
the Standard & Poor’s 500, provides diversification
of the idiosyncratic risks for individual stocks, leaving
in place the undiversifiable market risk. Movements
in the stock index provide indications of how the
stock market is evolving. Similarly, the CFNAI-MA3
is a portfolio of economic indicators. Thus, movements
in the CFNAI-MA3 are reflective of how the econo­
my is evolving. Over the period 1967 to 2000, the
CFNAI-MA3 fell substantially whenever the U.S.
economy was in a recession.

Identifying business cycle turning points
with the CFNAI
The construction of the CFNAI highlights its prop­
erties as a coincident indicator of economic expansions
and contractions. There are many ways to evaluate an
indicator. Fisher, Liu, and Zhou (2002) examine how
the CFNAI contributes to the out-of-sample explana­
tory power in linear models for forecasting inflation
over the last 15 years. In this article, we focus on how
quickly and how well the CFNAI aids in identifying
business cycle turning points. Applying a simple thresh­
old criterion, we examine how accurately the histori­
cally constructed CFNAI would have identified past
recessions and recoveries.

30

Identifying recessions
During the period 1967-2000, there were five
economic recessions, as identified by the NBER;
these occurred in 1970, 1973-75, 1980, 1981-82,
and 1990-91.2 Figure 3 shows the movements in the
CFNAI-MA3 in the context of the NBER recession
episodes, which are the shaded regions. As we men­
tioned earlier, figure 3 suggests that the CFNAI-MA3
may be a useful guide for identifying whether the
economy has slipped into and out of a recession. Spe­
cifically, note that, in each of the five recessions, the
smoothed CFNAI-MA3 fell below -0.70 (the dashed
negative horizontal line) very near the onset of the
recession. If we designate -0.70 as our recession thresh­
old, we see that during the 1970, 1981-82, and 199091 recessions, the index first fell below the threshold
during the first month of the recession. During the
1973-75 and 1980 recessions, the index first fell below
the -0.70 threshold during the third and second months
of the recessions, respectively. Thus, during the period
1967-2000, the CFNAI-MA3, using the -0.70 thresh­
old, gave a signal of the economy being in a recession
within the first three months of the recession.
The -0.70 recession threshold generated one false
alarm during the 1967-2000 period. Specifically, in
July 1989, the CFNAI-MA3 fell to -0.94, but no re­
cession materialized. One explanation for the signifi­
cant dip in the CFNAI-MA3 is that, from mid-1988
through spring 1989, the Federal Open Market Com­
mittee pursued a contractionary monetary policy in an
attempt to reduce inflation. This tight policy was re­
flected in an increase in the federal funds rate to 9.75
percent. During this time, the smoothed CFNAI exhib­
ited a steady decline, reaching its low level in July
1989, before returning above the -0.70 threshold.
This brief analysis highlights some problems
with using the CFNAI-MA3 and a simple threshold
rule to identify recessions. In particular, having a thresh­
old low enough to prevent false signals of recessions
will delay the date at which a true recession can be
identified. The threshold value of -0.70 identified all
five of the true recessions, but falsely signaled a sixth
recession. Using a lower recession threshold of-1.50
would have eliminated the false alarm, but the true
recessions would not have been identified until many
months into the recession. Indeed, the 1970 recession
would not have been identified until its twelfth
month—the last month of this recession.
Identifying historical recoveries
The tension between identifying turning points
early and minimizing the number of false signals also
arises when we try to determine when the economy

3Q/2002, Economic Perspectives

has successfully pulled out of a recession. In this case,
we start with the rule that, when the CFNAI-MA3 first
crosses the +0.20 threshold level from below, the reces­
sion has ended; this threshold is indicated in figure 3
by the dashed positive horizontal line.
We see that, for four of the last five recessions,
the CFNAI-MA3 crossed +0.20 from below within
five months of the NBER-identified trough (official
end of the recession). Following the 1970 recession,
the smoothed index exceeded +0.20 two months after
the trough. For the 1973-75, 1980, and 1981-82 re­
cessions, the threshold was crossed in the fifth, third,
and fourth months, respectively, following the offi­
cial trough.
For the 1990-91 recession, however, the smoothed
CFNAI did not provide an early indication of the re­
cession’s end. Specifically, the CFNAI-MA3 crossed
+0.20 in November 1993, even though the trough was
retrospectively identified by the NBER as March 1991.
In part, high levels of corporate debt and financial in­
stitutions’ reduced ability to extend new financing
slowed the recovery from the 1990-91 recession. To
mitigate the effects of these financial headwinds, the
monetary policy response was to keep the federal funds
rate at 3 percent until February 1994. The CFNAI
signal was further delayed by the choppy nature of
the recovery. The halting movements of the activity
index, seen in figure 3, are consistent with contempo­
raneous economists’ accounts of double- and triple­
dips in economic activity during this period. Indeed,
this recovery was so difficult to discern that the NBER
only declared an end to this recession almost two
years after the trough had passed.
A more lax recovery threshold of +0.00, or return
to trend growth, would have identified the end of the
1990-91 recession earlier. Had this threshold been in
effect, the recovery would have been signaled in April
1992, or 19 months prior to the +0.20 threshold date.
On the other hand, the weaker recovery threshold
would also have generated false signals. In particular,
a +0.00 threshold would have prematurely (by 11
months) signaled the end of the 1973-74 recession.
Overall, then, the CFNAI-MA3 with a recovery
threshold of +0.20 was able to identify all of the re­
coveries, signaling four out of the five recoveries within
the first five months. Its identification of the erratic
1990-91 recovery, however, did not come until 32
months after the actual trough.
The CFNAI during the 2001 recession
Up until the 2001 recession, the evidence in favor
of using the CFNAI as a barometer for detecting the
onset of recessions was all historical. But with the

Federal Reserve Bank of Chicago

inaugural publication of the January 2001 CFNAI on
March 5, 2001, the evaluation process moved from
the sterile laboratory setting of a computer to a field
test using real-time data.
In the spring of 2000, the U.S. economy was con­
tinuing to expand at a rate that was above its potential
growth rate. Second-quarter real gross domestic prod­
uct (GDP) growth was 5.7 percent. Monetary policy
had shifted to a relatively tight stance. The federal
funds rate began its initial increase in June 1999 from
4.75 percent to 6.50 percent in May 2000. By the sum­
mer of 2000, business analysts’ were expecting the
economy to begin a transition from above-trend growth
to a period of below-trend growth. Growth rates of
industrial production turned negative beginning in
July 2000, while other indicators began to cool notice­
ably. The January 2001 release of the CFNAI report­
ed that the CFNAI-MA3 had fallen below zero in July
2000. As zero represents the economy growing at trend,
the index captured the transition that business ana­
lysts in the press had been discussing.
Much of the initial drop in the CFNAI-MA3 comes
from industrial production and the ISM Purchasing
Managers Index data. These components provided
strongly negative weight to the other index components
that were more evenly spread around trend growth
behavior. Figure 4 displays category indexes represent­
ing each of the five data categories of the CFNAI from
1986 through 2001. The category indexes are construct­
ed by summing only the weighted series in each re­
spective category. Each category index is then re-scaled
to have a standard deviation of one. With this trans­
formation, if any of the category indexes captured all
of the movements of the CFNAI, the two would lie
on top of each other in figure 4.
The production/income (panel A), employment
(panel B), and inventory/orders (panel E) categories
track the initial decline in economic activity pretty
well for the second half of 2000. Manufacturing/trade
(panel D) captured some of this decline, while the con­
sumer (panel C) category did not fall below zero at
all in 2000. Once the recession began in March 2001,
according to the NBER business cycle dating com­
mittee, the categories began to diverge to a greater
extent. The production/income and inventory/orders
categories moved with the total CFNAI-MA3 during
this period. The employment category, however, fell
much more sharply, particularly beginning in March
2001. This latter observation is not surprising in the
context of the NBER’s announcements regarding the
selection of March 2001 as the most recent business
cycle peak. The dating committee mentioned that
movements in payroll employment were decisive in

31

FIGURE 4

CFNAI category indexes—MA3 and business cycles (1967-2001)

Notes: Shaded areas indicate NBER recessions. The black
line indicates the CFNAI-MA3, and the colored line indicates
the respective category index.

picking the date, and very important in the overall de­
termination that the economy had entered recession.
Within the full index, figure 4 displays some de­
gree of heterogeneity among the category indexes
during the recession of 2001. Manufacturing/trade fell
less than the overall index. And the consumer catego­
ry hardly registered any negative values. Simply us­
ing the consumer category as a proxy for the CFNAI
would clearly result in different inferences. The pro­
duction and employment categories move much more

32

strongly with the full index, although there are peri­
odic differences in magnitude.

Is there value in diversifying the basket
of economic indicators?
The previous discussion raises the issue: How
many indicators are necessary to provide an accurate
description of the state of economic activity? Does it
take a large number of economic indicators to filter
out the idiosyncratic noise, or can a single, favorite

3Q/2002, Economic Perspectives

indicator do the trick? Most analysts’ first approach
to answering this question would involve producing
large numbers of graphs and staring. Figure 5 (on
page 34) provides an abbreviated tour of the data.
Figure 5 displays graphs of several baskets of
economic indicators, as well as individual indicators.
Each panel graphs a three-month moving average of
the indicator against the CFNAI-MA3. We consider
two questions here: 1) How does the individual indi­
cator compare with the CFNAI-MA3?; and 2) How
well does the individual indicator perform at detect­
ing recessions and expansions using thresholds like
-0.70 and +0.20?
Are 85 real indicators enough? The CFNAI con­
tains only 85 indicators. Perhaps worse still, none of
these are financial indicators that have a proven track
record of periodically signaling turning points. An al­
ternative approach would construct an economic index
using a larger set of data that include financial, mon­
etary, and price variables. Figure 5, panel A displays
an analogous index employing 166 economic, finan­
cial, monetary, and price indicators. The striking fea­
ture of panel A is that the two measures are nearly
identical. Apparently, the first principal component
of this larger dataset is essentially an activity index.
This observation was originally made by Stock and
Watson (1999a).
Do we really need 85 indicators? In our earlier
analysis of the 2001 recession, we found that subcom­
ponents of the CFNAI performed reasonably well in
tracking the economic downturn. Perhaps a smaller
index performs just as well as the CFNAI. Figure 5,
panels B and C display the production and employ­
ment category indexes, respectively. The production
index follows the CFNAI-MA3 quite closely, and there
appear to be few differences in inference about busi­
ness cycle turning points. Both suffer from the false
recession signal in July 1989. The production index
has an additional false positive prior to the onset of
the 1980 recession. The CFNAI-MA3 was in nega­
tive territory in 1979 prior to the recession, but it did
not cross the -0.70 threshold until 1980. Following
the 1990-91 recession, the production index signals
the end of the recession much sooner than the full in­
dex. However, the slow expansion in 1991-92 makes
this virtue a bit hollow. In 1991, the production index
has a close brush with calling a second recession. The
CFNAI-MA3 moved lower in 1991 than the produc­
tion index; but since the index had not determined
the end of the recession, it would not be a second re­
cession. In both cases, the 1990-92 period is a diffi­
cult one. The employment component in figure 5,

Federal Reserve Bank of Chicago

panel C also performs reasonably well. On the plus
side, this component did not falsely signal a recession
in July 1989. On the downside, it did not capture the
1973-75 recession until late 1974, almost at its end.
In summary, smaller component indexes may perform
about as well as the CFNAI, but more experience is
required to sort this out.
Many individual indicators provide false signals.
As the focus narrows to individual economic indica­
tors, it is not surprising that many series provide false
readings on the state of the aggregate economy. The
discussion of the consumption and manufacturing/
trade categories of the CFNAI suggests that many of
these data are poor candidates by themselves. Figure
5, panels D, E, and F display real retail sales growth,
housing starts, and the PMI New Orders Index, re­
spectively. Each series has been transformed to be
mean zero and unit standard deviation and is a threemonth moving average. Retail sales growth is quite
volatile and often falls below the -0.70 threshold when
the economy is not in recession. Housing starts tend
to be low during most recessions. The slow recoveries
following the 1973-75 and 1990-91 recessions sug­
gest that this indicator does not provide a quick indi­
cation of economic recovery. In addition, there were
false recession warnings in 1967 and 1996; and the
2001 recession has been missed completely. Similarly,
the PMI New Orders Index captures the five reces­
sions prior to 2001, as well as the current one. How­
ever, there are several false warnings: 1967, 1996,
and 1998. Many single economic indicators contain
transient fluctuations that are not related to the state
of the economy.
Some individual indicators do pretty well. Be­
cause the CFNAI gives substantial weight to data on
industrial production and employment, it may be the
case that single indicators in this category provide
similar information to the CFNAI. Figure 5, panels
G and H display growth rates of industrial produc­
tion and private payroll employment. Similar to the
CFNAI category measures, these indicators do pretty
well. For both of them, recessions are periods when
industrial production and employment are low and
below -1.00. Using a recession threshold of -0.70 for
industrial production admits a couple of false reces­
sion warnings, but the performance improves if the
lower threshold is decreased further to -1.00. Em­
ployment seems to do better than industrial produc­
tion. This may be because the NBER has tended to
focus the recession determination on employment
data more than on industrial production, at least in
recent years.

33

FIGURE 5

CFNAI-MA3 versus select economic indicators, three-month moving average (1967-2001)
E. Housing starts

B. Production and Income Category Index

F. PMI: Manufacturing New Orders Diffusion Index

C. Employment, Unemployment, and Labor Hours Category Index

G. Industrial production

D. Real retail sales

Note: Shaded areas indicate NBER recessions. The black line ii
respective economic indicator.

34

the CFNAI-MA3, and the colored line indicates the

3Q/2002, Economic Perspectives

Summary of analysis
To sum up, our visual inspection of individual
data series suggests that the CFNAI-MA3 does not
perform appreciably better than the workhorse NBER
coincident economic indicators like industrial produc­
tion and private payroll employment. But an essential
question is: How much of this has been the result of
data-mining from the small number of recessions un­
der examination? The idiosyncratic statistical noise
in individual data series may have simply been small
enough over this period to make a couple of data series
work. In pooling 85 economic indicators for the CFNAI,
the method is purchasing an insurance policy against
statistical noise. And just like a home insurance poli­
cy, the mere fact that a house hasn’t burned down in
the ten years that it has been insured does not mean
that the insurance was unnecessary. To address this
issue over a longer period, we now turn to simulation
results of an empirically relevant statistical model.

Statistical approach and Monte Carlo
simulations
The preceding historical analysis provides only a
limited assessment of the CFNAI because the five-recession sample during the 1967-2000 period is small
in statistical terms. For this reason, we have developed
and analyzed Monte Carlo simulations of a business
cycle index, 85 economic indicators, and the activity
index to assess patterns based on a larger number of
simulated observations.
The challenge is to estimate an empirically rele­
vant set of business cycles and 85 equations for the
economic indicators. For the business cycle indicator,
we adopt a nonlinear Markov-switching process de­
veloped by Hamilton (1989). This model states that
real activity transitions exogenously between expan­
sionary and contractionary rates of growth, while cap­
turing the historical average duration of business cycle
expansions and recessions. Diebold and Rudebusch
(1996) have also studied a system like this. For the
economic indicator equations, we follow the unob­
served component model studied by Stock and Watson
(1989). This specification states that each indicator is
related to the business cycle index but also is contami­
nated by independent statistical noise. This captures
the idea that each of the indicators has an idiosyncratic
component that is not related to aggregate activity.
Precise details on these specifications and the estima­
tion strategy are reported in the appendix.
Given estimates of our statistical model from
the 1967-2000 period, we conduct Monte Carlo sim­
ulations for the economy over a period of approximate­
ly 2,000 years. During this period, the nonlinear

Federal Reserve Bank of Chicago

Markov-switching model generates 404 recessions.
We can use the simulated data for the 85 economic
indicators to compute a CFNAI index over the 2,000year period. With these data, we can repeat the exer­
cise of using the CFNAI to decide if the economy
has entered a recession. Specifically, for any given
recession threshold of -r, we calculate whether the
CFNAI-MA3 indicates the economy is in recession.
The procedure works as follows.
1. Begin with the economy in an expansionary state.
2. If the CFNAI-MA3 falls below -r from above,
then the economy is in a “CFNAI recession.’’
3. The CFNAI recession continues until the CFNAIMA3 rises above +0.20, and then the economy is
in a “CFNAI expansion.’’

Repeating steps 1 through 3 until the data sample
is exhausted provides a long time-series of business
cycle dates according to the CFNAI-MA3 criterion.
The advantage of the laboratory environment is
that the experimental design allows us to know at any
date whether the true state of the simulated economy
is expansion or contraction. We can tabulate what per­
centage of the time a CFNAI recession is in fact a true
recession, and also what percentage of recessions are
missed by the CFNAI using a threshold of -r. For ex­
ample, using a threshold of -0.70, the CFNAI-MA3
criterion determined that 394 recessions occurred
during the 2,000 years of simulations. On the date
that the recession call occurred, the true state of the
business cycle was a recession in only 285 of the 394
recession calls. At this threshold, the frequency of
success in calling a recession was 72 percent. This
frequency can also be thought of as the probability
that a “recession call” is correct. Notice that 119 re­
cessions were missed (404 true recession minus 285
correct recessions). Therefore, the frequency of fail­
ing to call a recession was 29 percent when the thresh­
old criterion insisted that the moving average index
fall below -0.70.3 These are cases where the true econ­
omy was in recession, but the severity of the down­
turn was relatively modest.4
Before turning to the overall simulation results,
notice that we can also compute the success of other
indicators in calling recessions. We consider three
additional gauges. First, given the visual success of
the industrial production three-month moving index,
we have tabulated the success frequencies for a mea­
sure we call IP3. Second, given the visual failure for
consumption measures, we tabulated success frequen­
cies based upon a real personal consumption expen­
diture measure, referred to as CON3. Third, in order
to assess the overall accuracy of the CFNAI-MA3

35

measure, we tabulated a success frequency for an un­
observed measure of the business cycle that is com­
mon to all 85 economic indicators. By construction,
this measure has no indicator-specific idiosyncratic
noise. In some sense, this is a virtually ideal indicator
of the business cycle. We refer to this as ZSIM3, and
it corresponds to observing z directly (as defined in
the appendix).
Table 1 reports the simulation frequency results
for all four moving average indexes, using thresholds
from -0.70 to -2.20. Recall that during the 2,000-year
period of the exercise, the simulated business cycle
generates 404 “true” recessions.
First, consider the results for the CFNAI-MA3.
As we mentioned above, with a threshold of -0.70,
the probability that the economy has moved into re­
cession is 0.72. Apractical application of this can be
seen from the March 2001 release of the CFNAI. As
reported on May 31, 2001, the March 2001 CFNAIMA3 was -0.80. According to the simulation results
here, that corresponds to approximately a 75 percent
probability that the economy was in recession. In fact,
the NBER reported in November that the economy
entered a recession in March, but the Business Cycle
Dating Committee also mentioned that without the ter­
rorist attacks of September 11, the economy might
not have gone into recession. Our calculations indi­
cate a relatively high likelihood, three out of four
chances, that the economy was in recession prior to

the terrorist attacks. Next, notice that as the threshold
for calling a recession becomes more stringent, the prob­
ability of making a false recession call becomes less
likely. The CFNAI releases during 2001 pointed out
that every economic recession since 1967 had breached
the -1.50 level, and most had declined much more.
The Monte Carlo simulations attach a 0.95 probability
to a threshold of-1.50, which seems consistent with
these observations. Not surprisingly, this increased
reliability comes at a higher cost. As the threshold
tightens, more true recessions are missed because they
are not sufficiently deep. Taken to an extreme, a very
large, negative threshold would likely guarantee that
recessions as deep as the Great Depression would be
captured, but perhaps at the cost of missing large re­
cessions such as 1973-75 or 1981-82.
The simulation results indicate that the CFNAI MA3 filters out almost all of the idiosyncratic noise
from the individual 85 economic indicators. Specifi­
cally, if the aggregate indicator z were directly ob­
servable, then the ZSIM3 measure could be constructed.
At a threshold of -0.70, whenever ZSIM3 crossed
this threshold, there would be a 74 percent probabili­
ty that the economy was in recession, compared with
the CFNAI-MA3 probability of 72 percent. Across
the range of thresholds considered, these differences
are essentially negligible.
Looking at the performance of IP3, however,
the differences appear to be more substantial at first

TABLE 1

Simulation frequency results for all four moving average indexes
Probability that recession call is correct

Probability that recession is missed

ZSIM3

CFNAI-MA3

IP3

0.35

0.27

0.29

0.23

0.26

0.38

0.27

0.30

0.25

0.37

0.27

0.30

0.25

0.48

0.28

0.29

0.29

0.57

0.30

0.30

0.34

0.67

0.57

0.33

0.33

0.39

0.77

0.81

0.57

0.37

0.36

0.45

0.85

0.94

0.84

0.57

0.40

0.40

0.53

0.90

0.96

0.95

0.89

0.65

0.43

0.45

0.57

0.93

0.97

0.96

0.94

0.70

0.49

0.50

0.65

0.96

-1.70

0.98

0.97

0.96

0.67

0.56

0.54

0.71

0.98

-1.80

0.99

0.99

0.98

0.62

0.60

0.60

0.78

0.99

-1.90

0.99

0.99

0.99

N/A

0.66

0.65

0.83

N/A

-2.00

0.99

0.99

0.98

N/A

0.72

0.71

0.87

N/A

-2.10

0.99

0.99

0.97

N/A

0.78

0.76

0.92

N/A

-2.20

0.99

0.99

0.96

N/A

0.81

0.80

0.94

N/A

ZSIM3

CFNAI-MA3

-0.70

0.74

0.72

0.51

-0.80

0.78

0.76

0.58

-0.90

0.83

0.81

0.67

0.44

-1.00

0.86

0.85

0.70

0.46

-1.10

0.89

0.88

0.73

0.55

-1.20

0.92

0.92

0.77

-1.30

0.93

0.94

-1.40

0.94

-1.50
-1.60

Threshold

IP3

C0N3

C0N3

Note: N/A indicates not applicable.

36

3Q/2002, Economic Perspectives

glance. At a threshold of -0.70, the IP3
measure has a recession success proba­
bility of only 51 percent. However, in­
dustrial production exhibits deeper
reductions during recessions, so a lower
threshold may be more accurate. In fact,
at -1.00, the IP3 success rate is 70 per­
cent, with only a missed probability of
29 percent. This is essentially the same
performance as the CFNAI-MA3 at the
-0.70 threshold.
A cleaner comparison of these per­
formances is to graph the probability
trade-offs for each indicator on the same
graph. For each indicators’ threshold, fig­
ure 6 plots a pair of probabilities: the prob­
ability that “a recession was called” when
no recession occurred versus the proba­
bility that “a recession occurred” but
wasn’t called. The most efficient indica­
tors will minimize both of these proba­
bilities and exhibit a probability frontier,
which is concentrated in the southwest
portion of the figure.
Figure 6 clearly displays two useful properties of
the CFNAI-MA3 as a business cycle indicator. First,
the performance of CFNAI-MA3 closely follows the
performance of the unobservable index ZSIM3. By
using the CFNAI to filter out idiosyncratic noise in
the 85 economic indicators, we lose little by not ob­
serving the z indicator. Second, the IP3 indicator only
performs better than the CFNAI-MA3 at levels of
false positives that most analysts would deem unac­
ceptable. As long as the probability of successfully
calling a recession exceeds 65 percent, the CFNAIMA3 and ZSIM3 provide lower rates of failing to
call a recession than the IP3 index. Finally, focusing
on consumption indicators alone is unlikely to pro­
vide useful indicators of business cycle turning points.
The CON3 frontier is dominated by the other indica­
tors for all reasonable probabilities. This is not really
surprising, considering that household spending indi­
cators do not always turn down dramatically during
recessions. During the 2001 recession, which is not
included in the simulated business cycle analysis here,
the economy continued to experience strong growth
in consumer spending during much of the downturn.

Federal Reserve Bank of Chicago

Conclusion
The Chicago Fed National Activity Index was
launched on March 5, 2001, and was promptly tested
by the deceleration of U.S. economic growth that be­
gan in the summer of 2000. Throughout this period,
the CFNAI release discussed how the low index val­
ues below -0.70 had previously been associated with
economic recessions, and each successive month in
2001 was showing an increasing probability that the
economy was already in recession. The NBER later
declared that the economy officially entered recession
in March 2001. The statistical analysis presented in
this article indicates that the March 2001 CFNAI-MA3
of -0.80 was associated with a 75 percent probability
that the economy was in recession at that time. The
March CFNAI was released on May 2, 2001, com­
pared with the NBER report in November 2001. Con­
sequently, the real-time experience with the index
and the statistical analysis here seem consistent with
the view that the Chicago Fed National Activity In­
dex is a good early warning indicator of current eco­
nomic conditions.

37

NOTES
'See NBER (2001).

'More specifically, the NBER recession periods were December
1969-November 1970. November 1973-March 1975. January
1980-July 1980. July 1981-November 1982. and July 1990March 1991. According to the NBER definition of contraction
(recession), the first date of each recession period indicates the
peak of a business cycle, and the second date indicates the subse­
quent trough of the cycle.

4The Monte Carlo simulations of the Hamilton-Markov switching
process include a restriction that recessions and expansions can
be no shorter than six months. The stochastic process described
in the appendix has the property that the economy could shift to
recession for a single month. Although this is not likely, short re­
cessions do occur over a 2.000-year simulation. In the simula­
tions. if the economy shifted from one state to another before six
months had elapsed, we assumed that the shift happened at the
six-month mark. This had no noticeable effect on the average du­
ration of recessions in the simulations.

'Notice that given knowledge of the total number of true recessions.
404. the two percentages are sufficient to recover the number of
“recessions called.” “correctly called recessions.” and “recessions
not called.”

APPENDIX: MONTE CARLO SIMULATION METHODS
The Monte Carlo simulation consists of four parts. First,
we specify two models: a model of the business cycle
index and a model of the relationship between the (un­
observed) business cycle index and the observable parts
of the economy. Second, we estimate the parameters re­
quired for the models using data from historical indica­
tors. Third, using the estimated parameters, we simulate
the unobservable business cycle index and the 85 observ­
able series. Finally, we compute the simulated CFNAI
index as a weighted average of these 85 simulated series,
and we evaluate the ability of the activity index to sig­
nal turning points in the simulated business cycle index.

S G {0,1} is the binary random variable identifying
the state/regime, where 5}= 0 designates an
expansion and S = 1 designates a recession, and
the switching between expansion and recession
states is determined by Markov transition probabil­
ities; and
Z is a noise term that follows an AR( 1) process,
namely

2)

where

Model of the business cycle and
observable series
Heuristically, a single index of the business cycle
indicates whether the aggregate economy is expanding
or contracting. Because of the complexities of large,
dynamic, and decentralized economies, individual eco­
nomic indicators will be correlated with this latent in­
dex of the business cycle, but are measured with
idiosyncratic noise.
We use the Hamilton regime-switching model
(Hamilton, 1989, and Diebold and Rudebusch, 1996)
to formalize the behavior of the business cycle index.
In particular, we express unobservable real activity as:
1)' z t= Lt• exp+Wr recessS+z,
t
i

? = cp?_1 + yt,

v( - )V(0,cr), is independent and identically
distributed (i.i.d.). £ is a deviation of current
growth from trend growth, which is independent
of the business cycle state .S'.
In this way, latent economic activity is modeled as
a non-linear process with cyclical noise.
We model the observable variables ,v to be noisy
measures of zt- In particular, we specify that each of the
85 observable variables is determined by:

3)

A, =Y,Z,

where
,v.It is the observable indicator,7 where 1 < i < 85,7 and

ii.t is a noise term that follows an AR( 1) process:

where
Z is the (unobservable) growth in economic
activity;

4)

pi is the growth of economic activity during
expansions;

Similar specifications have been studied in many
aggregate time series studies; see Stock and Watson
(1989) for an extended example and additional refer­
ences. Combining equations 1 and 3, we have:

li
+ Lt is the growth of economic activity
during recessions;

5)

u = du

+ e, where e - A(0, )

is i.i.d.

A, =YMxP+Rra™5',) + w,,,

where

38

3Q/2002, Economic Perspectives

TABLE A1

Estimated business cycle parameters
Aexp = 4-4%
= -1-6%

Pr{S, = 0 | S,_2 = 0} = 0.98
Pr{S; = 11 S(_± = 1} = 0.91

(p = 0.94

6)

= 0.653

w’,7 = Y, I, + “«•

It is important to recognize that S, i, and u are mu­
tually independent. Similar assumptions are typically
employed in the empirical literature, as in Hamilton
(1989), Diebold and Rudebusch (1996), and Stock and
Watson (1989).

months and 5 = 1 during recession months.1 According
to equation 5, this regression gives us y; and wy for
each of the i indicators.
To estimate the remaining parameters, we make an
additional assumption that allows us to use a simple
method of moments estimator. The additional assump­
tion is that there are two “instrument” indicators that
reflect the business cycle to the same degree. Specifi­
cally, let x, and x, be as follows:

7)

Ji, = YN, +«,, =Y,(RexP +R««sA) + wi, >

and

8)

X2, = Y2Z, + «2, = Y2 (Rexp +

) + W2, ,

with the restriction that

Estimation of parameters
To provide empirically interesting simulation ex­
periments, we need estimates of the model parameters
li , li
, the Markov transition probabilities, cp, cr ,
y.,
d.,
and
crV for each indicator ,v.z.
*z’ z’
We estimate lx and lx
from the growth rates of
real GDP from 1947 to 2000. Specifically, we compute
the growth rate of real GDP during each expansionary
period and each recessionary period (as identified by the
NBER). We then set the parameter p equal to the aver­
age GDP growth rate over all expansions, and lx
+
p equal to the average growth rate over all recessions.
Table A1 lists the estimated values of these parameters.
The transition probabilities are calibrated so that
they correspond to the average length of historical
expansions and recessions. In particular, we use the re­
lations that the duration of an expansion = 1/(1 - proba­
bility of remaining in an expansion), and the duration
of a recession = 1/(1- probability of remaining in a
recession). That is, the mean duration of an expansion
= 1/(1 - Pr{.S'( = 01 St= 0}), and the mean duration of
a recession = l/(l-Pr{S, =11 S,_, =1}). Based upon
the NBER business cycle dates for 1947-2000, the
mean duration of an expansion has been 50 months and
the mean duration of a recession has been 12 months.
The transition probabilities based on these calculations
are also shown in table A1.
To find estimates of y., we regress each of the 85
actual indicators on pexp + pm.„s5f for the period 1967—
2000, where .S' is the binary variable indicating expan­
sionary and recessionary periods. This regression is
well specified, given the independence of 5, c, and u.
For values of S, we note that Hamilton (1989) finds that
his Kalman-smoothed inferences of the latent business
cycle index correspond reasonably closely with the
NBER business cycle dates. Let 5 = 0 during expansion

Federal Reserve Bank of Chicago

9)

Y, = Y, = Y-

As noted earlier, the disturbance terms w and w,
are independently distributed. This restriction is most
likely to be satisfied when two indicators attempt to
measure the same economic phenomenon but are from
different source data. An example of this is in Prescott
(1986), when he proposes a probability model of mea­
sured employment hours based upon the establishment
survey and the household survey. Another example is
where the two indicators have a similar relationship to
the business cycle index, but are measured with differ­
ent levels of precision. For example, equation 9 might
hold even when one indicator was a measure of output
and another was a measure of input. In any event, the
two instrument indicators used in this study are Private
Payroll Employment growth from the BLS and the In­
stitute for Supply Management’s Purchasing Managers
Index of New Orders. The source data for these two se­
ries are clearly independent. In addition, the restriction
in equation 9 is not rejected by the parameter estimates
for these two indicators.
We use the restriction in equation 9 to estimate
cp and cr, and (for each indicator) d. and <r. Using
equation 6, we first note that:

10) w;.,- — w2f =
Y2

.
y2

To find an estimate of d., we use equations 4, 6,
and 10 and find that:

11) £[(wfl- —w21)wfl] = £[z/2] = cl2 ;
Y2

39

TABLE A2

Estimated parameters of selected indicator equations
Indicator
PMI, new orders
Employment, private
Employment, nonagriculture
Employment, goods industry
Unemployment rate
Help-wanted ads
PMI, production
Industrial production
IP, manufacturing
Capacity utilization, manufacturing
Personal income, transfers
Housing permits
Housing starts, Midwest
Housing starts, West
Manufacturing and trade sales
Retail sales (real)
Personal consumption expenditures
New orders, construction, and materials
Manufacturing and trade, inventory/sales

R2

4

Y/

0.282
0.272
0.268
0.278
-0.232
0.197
0.288
0.229
0.236
0.221
0.197
0.256
0.213
0.253
0.153
0.081
0.092
0.130
-0.117

0.715
0.297
-0.244
-0.190
-0.436
-0.431
0.724
-0.184
-0.243
-0.195
-0.128
0.915
0.765
0.839
-0.392
-0.271
-0.312
-0.250
-0.238

0.399
0.578
0.601
0.566
0.686
0.731
0.372
0.725
0.701
0.726
0.837
0.273
0.500
0.383
0.841
0.949
0.930
0.909
0.933

0.673
0.636
0.619
0.670
0.426
0.341
0.709
0.458
0.479
0.451
0.296
0.542
0.403
0.494
0.169
0.036
0.050
0.128
0.083

Note: The variables being measured in this table are derived from equation 3—x.f = YjZt+ujt—and equation 4—-uj=d.ujtl+ ejt—where e/f ~ MO, a2).

12)

From equations 15 and 16, we see that:

- — w2,= diE[u;t_i\ = dtf .
Y2

From equations 11 and 12, it is clear that:
E[(wi(-Ti-W2()w.( J

13) d,=----------- .

E[(h;7 - —h2,)h',,]
y2
Because we have estimates of y and w., we can use
equation 13 to find d .
1
2
To find estimates of CT" , we note that equation 4
implies the following relationship between the varianc­
es of e and

14) o2=(l-d2)o2.
Since o2 can be estimated using equation 11,
we can easily obtain estimates of o2 using equation
14. Similarly, we obtain an estimate of cp by noting that
equations 2 and 6 imply:

17)

■E[w„w2,-,]

To obtain an estimate of cr, we note that equation
2 implies:

18) a2=(l-cp2)a2. where cr; = —£[w’„w’,,]

r

Finally, in estimating parameters for a particular
.v , the choice of instrument indicator (,v,f) will depend
on whether ,v.It is from the ISM data release or not. For
example, if .v corresponds to a component of the Pur­
chasing Managers Index, then ,v,f will be payroll employ­
ment; otherwise, it will be the PMI New Orders Index.
Table A2 presents a partial listing of the indicator param­
eter estimates. Table A3 lists all of the component data
series in the CFNAI.
'NBER recessions are designated from peak to trough. For these
exercises, we consider the peak and trough months as part of the
recession period.

15) £[hi,h2,] = y2Oj
and

16) E[w-1,u-2,_1] = Y2cpcr2.

40

3Q/2002, Economic Perspectives

TABLE A3

CFNAI component data series
Production and income (21 series)
CUMFG
DLV
Capacity utilization: Manufacturing SA, percent of capacity
IP
DLN
Industrial Production Index SA, 1992=100
DLN
IP51
Industrial Production: Consumer goods SA, 1992=100
DLN
IP5102
Industrial Production: Durable consumer goods SA, 1992=100
DLN
Industrial Production: Nondurable consumer goods SA, 1992=100
IP51021
DLN
IP52001
Industrial Production: Business equipment SA, 1992=100
DLN
IP53
Industrial Production: Materials SA, 1992=100
DLN
Industrial Production: Materials: Durable SA, 1992=100
IP53011
DLN
IP53017
Industrial Production: Materials: Nondurable SA, 1992=100
DLN
IP54
Industrial Production: Intermediate products SA, 1992=100
DLN
IPDG
Industrial Production: Durable manufacturing SA, 1992=100
DLN
IPFP
Industrial Production: Final products SA, 1992=100
DLN
IPMFG
Industrial Production: Manufacturing SA, 1992=100
IPMIN
DLN
Industrial Production: Mining SA, 1992=100
IPND
DLN
Industrial Production: Nondurable manufacturing SA, 1992=100
DLN
IPTP
Industrial Production: Products SA, 1992=100
DLN
IPUTI
Industrial Production: Utilities SA, 1992=100
NAPMC
LV
Institute for Supply Management: Manufacturing: Composite Index SA, percent
NAPMOI
Institute for Supply Management: Manufacturing: Diffusion Index, Production SA, percent
LV
YPDHM
DLN
Disposable personal income SAAR, billions of chained 1996$
DLN
YPLTPMH
Real personal income less transfer payments SAAR, billions of chained 1996$

Employment, unemployment and labor hours (24 series)
A0M005
DLV
Weekly initial claims for unemployment insurance SA, thousands
LACONSA
DLN
All employees: Construction SA, thousands
LADURGA
DLN
All employees: Durable goods manufacturing SA, thousands
LAFIREA
DLN
All employees: Finance, insurance, and real estate SA, thousands
LAGOODA
DLN
All employees: Goods-producing industries SA, thousands
LAGOVTA
DLN
All employees: Government SA, thousands
LAMANUA
DLN
All employees: Manufacturing SA, thousands
LAMINGA
DLN
All employees: Mining SA, thousands
LANAGRA
DLN
Employees on nonfarm payrolls SA, thousands
LANDURA
DLN
All employees: Nondurable goods manufacturing SA, thousands
LAPRIVA
DLN
All employees: Private nonfarm payrolls SA, thousands
LARTRDA+LAWTRDA DLN
All employees: Retail and wholesale trade SA, thousands
LASERPA
DLN
All employees: Service-producing industries SA, thousands
LASRVSA
DLN
All employees: Services SA, thousands
LATPUTA
DLN
All employees: Transportation and public utilities SA, thousands
LE
DLN
Civilian employment: Sixteen years & over SA, thousands
LENA
DLN
Civilian employment: Nonagricultural industries SA, thousands
LHELP
DLN
Index of help-wanted advertising in newspapers SA, 1987=100
LHELPR
DLN
Ratio: Help-wanted advertising in newspapers/number unemployed SA
LOMANUA
DLV
Average weekly overtime hours: Manufacturing SA, hours
LR
DLV
Civilian unemployment rate SA, percent
LRM25
DLV
Civilian unemployment rate: Men, 25-54 years SA, percent
LRMANUA
DLV
Average weekly hours: Manufacturing SA, hours
NAPMEI
LV
Institute for Supply Management: Manufacturing: Diffusion Index, Employment SA, percent
Personal consumption
CBHM
CDBHM
CDMNHM
CNBHM
C0ND09
CSBHM
HPT
HSM
HST
HSTMW
HSTNE
HSTS
HSTW

and housing (13 series)
DLN
Personal consumption expenditures SAAR, billions of chained 1996$
DLN
Personal consumption expenditures: Durable goods SAAR, billions of chained 1996$
DLN
Personal consumption expenditures: New autos SAAR, millions of chained 1996$
DLN
Personal consumption expenditures: Nondurable goods SAAR, billions of chained 1996$
LN
Construction contracts, millions of square feet
DLN
Personal consumption expenditures: Services SAAR, billions of chained 1996$
LN
Housing units authorized by building permit SAAR, thousands of units
LN
Manufacturers’ shipment of mobile homes SAAR, thousands of units
LN
Housing starts SAAR, thousands of units
LN
Housing starts: Midwest SAAR, thousands of units
LN
Housing starts: Northeast SAAR, thousands of units
LN
Housing starts: South SAAR, thousands of units
LN
Housing starts: West SAAR, thousands of units

Federal Reserve Bank of Chicago

41

TABLE A3 (continued)

CFNAI component data series
Manufacturing and trade sales (11 series)
NAPMVDI
LV
Institute for Supply Management: Manufacturing: Diffusion Index, Vendor Deliveries SA, percent
RSDH
DLN
Real retail sales: Durable goods SA, millions of chained 1996$
RSH
DLN
Real retail sales SA, millions of chained 1996$
RSNH
DLN
Real retail sales: Nondurable goods SA, millions of chained 1996$
TSMDH
DLN
Sales: Manufacturing: Durable Goods SA, millions of chained 1996$
TSMH
DLN
Sales: Manufacturing SA, millions of chained 1996$
TSMNH
DLN
Sales: Manufacturing: Nondurable goods SA, millions of chained 1996$
TSTH
DLN
Real manufacturing and trade: Sales SA, millions of chained 1996$
TSWDH
DLN
Sales: Wholesale: Durable goods SA, millions of chained 1996$
TSWH
DLN
Sales: Merchant wholesalers SA, millions of chained 1996$
TSWNH
DLN
Sales: Wholesale: Nondurable goods SA, millions of chained 1996$

Inventories and orders (16 series)
A0M007
DLN
Real manufacturers' new orders: Durable goods industries, billions of chained 1996$
A0M008
DLN
Real manufacturers' new orders: Consumer goods & materials SA, millions of 1996$
A0M020
DLN
Contracts and orders for plant and equipment, billions of chained 1996$
A0M027
DLN
Real manufacturers' new orders: Nondefense capital goods industries SA, millions of 1996$
NAPMII
LV
Institute for Supply Management: Manufacturing: Diffusion Index, Inventory SA, percent
NAPMNI
LV
Institute for Supply Management: Manufacturing: Diffusion Index, New orders SA, percent
TIMDH
DLN
Inventories: Manufacturing: Durable goods EOPSA, millions of chained 1996$
TIMH
DLN
Inventories: Manufacturing EOP SA, millions of chained 1996$
TIMNH
DLN
Inventories: Manufacturing: Nondurable goods EOP SA, millions of chained 1996$
TIRH
DLN
Inventories: Retail trade EORSA, millions of chained 1996$
TITH
DLN
Real manufacturing & trade inventories EOPSA, millions of chained 1996$
TIWH
DLN
Inventories: Merchant wholesalers EOP SA, millions of chained 1996$
TRMH
DLV
Inventory/sales ratio: Manufacturing SA, chained 1996$
TRRH
DLV
Inventory/sales ratio: Retail trade SA, chained 1996$
TRTH
DLV
Real manufacturing and trade: Inventory/sales ratio SA, chained 1996$
TRWH
DLV
Inventory/sales ratio: Merchant wholesalers SA, chained 1996$
Notes: The variable mnemonics are those from HaverAnalytics. For a series yt, the stationary transformations are as follows: LV: xt = y(; DLV:
xt = yt- yt_±', LN: xt = ln(y;); and DLN: xt = ln(yt) - ln(yF1). SA is seasonally adjusted. SAAR is seasonally adjusted annual rate. EOP is end
of period.

42

3Q/2002, Economic Perspectives

REFERENCES

Bernanke, Ben, and Jean Boivin, 2002, “Monetary
policy in a data-rich environment,” Journal of Mone­
tary Economics, forthcoming.
Conference Board, The, 2001, “Leading economic
indicators and related composite indexes: Methodology,
revisions, and other information,” New York, report.

Diebold, F., and G. Rudebusch, 1996, “Measuring
business cycles: A modern perspective,” Review of
Economics and Statistics, Vol. 78, February, pp. 67-77.
Fisher, Jonas, 2000, “Forecasting inflation with a lot
of data,” Chicago Fed Letter, Federal Reserve Bank
of Chicago, No. 151, March.
Fisher, Jonas D. M., Chin Te Liu, and Ruilin
Zhou, 2002, “When can we forecast inflation?,” Eco­
nomic Perspectives, Federal Reserve Bank of Chica­
go, Vol. 26, No. 1, First Quarter, pp. 32M4.

Prescott, Edward C., 1986, “Theory ahead of busi­
ness cycle measurement,” in Real Business Cycles,
Real Exchange Rates and Actual Policies, CarnegieRochester Conference Series on Public Policy, K.
Brunner and A. H. Meltzer (eds.), Vol. 25, Autumn,
pp. 11—44.

Stock, James, and Mark Watson, 1999a, “Forecast­
ing inflation,” Journal of Monetary Economics, Vol. 44,
pp. 293-335.
__________ , 1999b, “Diffusion indexes,” Harvard
University, Kennedy School of Government, unpub­
lished manuscript.
__________ , 1989, “New indexes of coincident and
leading economic indicators,” NBER Macroeconom­
ics Annual, pp. 351-394.
Theil, Henry, 1971, Principles of Econometrics,
New York: John Wiley and Sons.

Hamilton, James D., 1989, “A new approach to the
economic analysis of nonstationary time series and the
business cycle,” Econometrica, Vol. 57, pp. 357-384.
National Bureau of Economic Research, 2001,
“The business cycle peak of March 2001,” release,
Washington, DC, available on the Internet at
www.nber.org/cycles/november2001/, November 26.

Federal Reserve Bank of Chicago

43

Why do we use so many checks?

Sujit Chakravorti and Timothy McHugh

Introduction and summary
The primary question we address in this article is why
consumers, merchants, and financial institutions are
reluctant to embrace electronic payments even though
electronic payment networks, such as the credit card
and automated clearinghouse (ACH) networks, have
existed for more than 25 years. While most Internetbased transactions are primarily processed via credit
card networks, most noncash off-line payments by
both consumers and businesses in the United States
are made with checks.
In the United States, there are over 15 checks
written per month per person.1 This is more than three
times the number of checks written per person in
Canada or the United Kingdom and at least 15 times
more per person than in Germany, Italy, Belgium, the
Netherlands, Sweden, or Switzerland (Bank for Inter­
national Settlements, 2000, and Federal Reserve Sys­
tem, 2001).2-3
In this article, we incorporate various strands of
the payment literature to provide a more integrated
view as to why payment system participants are reluc­
tant to use electronic payments. Brito and Hartley (1995),
Hirschman (1982), Mantel (2000), Murphy (1988),
and Whitesell (1992) focus on consumer choice issues.
Radecki (1999) and Wells (1996) discuss the revenue
earned and cost to financial institutions from provid­
ing check services. Food Marketing Institute (1994,
1998, and 2000), Chakravorti and To (1999), and
Murphy and Ott (1977) concentrate on the merchants’
perspectives. McAndrews (1997) and Weinberg
(1997) investigate the network issues. Connolly and
Eisenmenger (2000), Benston and Humphrey (1997),
Green and Todd (2001), Guynn (1996), and Lacker
and Weinberg (1998) discuss the Federal Reserve’s
role in the payment system. A more integrated analy­
sis of the underlying incentives of various payment
system participants has been developed by Baxter

44

(1983), Chakravorti and Emmons (2001), Chakravorti
and Shah (2001), Rochet and Tirole (2000), and
Wright (2000).
We study the incentives underlying the payment
network to examine why, unlike several other indus­
trialized countries, the United States has been slow to
abandon checks. Many observers claim that electronic
payments are less expensive than checks. However,
these social cost comparisons usually ignore transition
costs and the underlying incentives to each payment
participant. Furthermore, the provision and usage of
payment services exhibit network effects, more com­
monly referred to as the chicken-and-egg problem,
which may impede the adoption of new payment tech­
nologies. Even if electronic payments are less expen­
sive and they can overcome the chicken-and-egg
problem, consumers, merchants, and financial insti­
tutions may still be reluctant to move to electronic
payments. We analyze why this is so. In addition,
we explore actions by the Federal Reserve to improve
the check processing system and whether this could
possibly hinder the migration away from checks.
Finally, we discuss potential drivers to the adoption
of electronic payments.

Check usage
We use two different sources of check data in this
article. The first source is the annual payments data
published by the Bank for International Settlements

Sujit Chakravorti is a senior economist and Timothy
McHugh is a senior analyst in the Emerging Payments
and Policy Department at the Federal Reserve Bank of
Chicago. The authors would like to thank David Allardice,
Ed Green, Harvey Rosenblum, and Fiona Sigalla for
numerous helpful discussions. They also thank Eve
Boboch, Tom Ciesielski, David Marshall, Ann Spiotto,
Victor Stango, and Kristin Stanton for comments on
previous drafts.

3Q/2002, Economic Perspectives

(BIS). The second source of data is a comprehensive
review of the retail payment systems by the Federal
Reserve System (Fed) (2001). The Fed study indicated
that the total volume of check payments in the United
States was significantly lower than previously estimat­
ed. However, the data published by the BIS still pro­
vide valuable insights into check usage in other countries
and check usage trends in the United States. We rely
on the new Fed study for current check values and
volumes and use the older data reported to the BIS
by the Fed for trends in check values and volume.
According to the new Fed benchmarking study
released in November 2001, 49.6 billion checks were
written in the U.S. in 2000, valued at $47.7 trillion
(Federal Reserve System, 2001).4 Checks represented
around 60 percent of non-cash consumer transactions.
The Fed study estimates that consumers wrote around
51 percent of checks but only accounted for 19 per­
cent of the total value. According to the BIS (1991—
2000), per capita check volume grew at a compounded
annual growth rate (CAGR) of 1.13 percent, while
per capita check value grew at a 1.91 percent CAGR
from 1991 to 1999.5
Unlike most other industrialized countries, the
U.S. seems to have experienced growth in total check
volume and value during the 1990s. For every Group
of Ten (G-10) country except the United States, the
volume of check usage (see figure 1) and value (see
figure 2) declined during the 1990s.6 Among the rea­
sons that have been cited to explain U.S. check vol­
ume growth are differences in financial institutions
per capita, cash usage, laws and regulations, and pric­
ing of financial services (see BIS, 1999 and 2000,
and Humphrey, Pulley, and Vesala, 2000).
By increasing the price of checks vis-a-vis other
payment options, financial institutions in Scandinavian
countries have been successful in decreasing check
usage. For example, in Finland, during the mid-1980s,
banks began implementing a small per-check fee of
about 10 cents. Palva (2000) states that this pricing
policy coincided with a drastic reduction in the use
of checks (see figure 3).
Adopting similar policies, Norwegian banks also
successfully decreased check usage. Humphrey, Kim,
and Vale (2001) found that a 1 percent increase in the
price of checks resulted in a 1.07 percent decrease in
check usage. They also found that online debit cards
were a close substitute for checks at the point of sale.7
As a result, check usage in Norway decreased from
72 million checks in 1988 to only 6.2 million in 2000.
Furthermore, the volume of payments made by pay­
ment cards, primarily debit cards, was 62 times that
of checks in 2000 (Bank of Norway, 2000).

Federal Reserve Bank of Chicago

Other countries have used different approaches
to reduce check usage. For example, Canadian banks
give check payees immediate credit and availability of
their funds (see Humphrey, Pulley, and Vesala, 2000).
Furthermore, checks are backdated to remove any float
benefit to paying banks. In addition, corporations are
charged for the float when the distance is significant
between the bank where the check is drawn and where
it is first deposited.
The decline in check usage across most countries
indicates that, given market incentives, there is a move­
ment toward electronic instruments. Electronic alter­
natives to accessing transaction accounts for purchases
are held to be less expensive than checks. Humphrey
and Berger (1990) were the first to calculate the total
social cost of each instrument in the United States.
Social cost is the sum of the real resource cost borne
by each participant to convert a given payment into
good funds. They found the social cost of a cash trans­
action to be the lowest at 4 cents and a credit card trans­
action to be the highest at 88 cents. An ACH payment,
an online debit transaction, and a check transaction
have social costs of 29 cents, 47 cents, and 79 cents,
respectively. Wells (1996) updated Humphrey and
Berger’s study and found that ACH payments cost
between one-third to one-half as much as a check
payments but found significantly higher social cost
estimates for both checks and ACH payments.
A difficulty with comparing social cost among
payment instruments is that a given payment instrument
may not be preferred for both small and large trans­
actions. While cash transactions outnumber all other
types of transactions, the average transaction size is
relatively small compared with other payment instru­
ments. Consumers tend to prefer checks for larger trans­
actions. The average consumer check transaction is
estimated at $364 (Federal Reserve System, 2001).
Furthermore, consumers may not be able to use all pay­
ment instruments for all types of transactions. For
example, cash cannot be used for bill payment via
mail and checks are difficult to use for Internet trans­
actions. Additionally, these social cost calculations
may not adequately adjust for the risk of not being
able to convert the payment into good funds that may
play a role in the acceptance of certain payment instru­
ments. Some characteristics of payment instruments
are difficult to quantify, such as the convenience and
comfort levels enjoyed by the participants.
These estimates also ignore transition costs and
network effects. Consumers, merchants, and financial
institutions may be unwilling to invest in emerging
payment technologies due to uncertainty about wheth­
er they will be widely accepted in the marketplace.

45

FIGURE 1

Percent change in check volume (1990-99)
percent

Belgium

Canada

Germany

Italy

Netherlands

Sweden

Switzerland

Kingdom

States

Source: BIS (1991-2000).

46

3Q/2002, Economic Perspectives

FIGURE 3

Electronic payments in Finland

Source: Finnish Bankers’ Association and Palva (2000).

U.S. smart card trials demonstrated that consumers
and merchants may not be willing to adopt new forms
of payment rapidly.8

Lack of incentives
In this section of the article, we analyze the cost
and incentive structure faced by each participant in
the payment network. We address two fundamental
questions for consumers, merchants, and financial in­
stitutions. First, are electronic payment alternatives
less expensive than checks for each participant? Sec­
ond, if electronic payment forms are less expensive,
are participants reluctant to abandon checks because
they lack the right incentives to adopt alternative pay­
ment instruments?
Consumers
While checks might be more costly to society as a
whole, several studies point out that consumers may
view the marginal cost to use a check to be zero.9 Re­
cently, several banks have reintroduced free checking
accounts to entice new customers.10 Humphrey, Pul­
ley, and Vesala (2000) state that most U.S. consumers
prefer accounts with fixed monthly fees or no fees
with minimum balance requirements to those with
per-check transaction fees. Furthermore, merchants
rarely impose additional fees for check payments.

Federal Reserve Bank of Chicago

Moreover, some consumers still view check float
as a major benefit. Today, most checks are processed
overnight and interest rates on transaction accounts,
if they are offered, are quite low, resulting in low float
benefits. Wells (1996) calculated that float is no longer
significant for consumer check payments. Nonetheless,
some consumers still may perceive significant float
benefits.
Ironically, checks do not have a built-in feature
that automatically declines a transaction if the custom­
er’s account does not have sufficient funds. While
non-sufficient-funds fees are relatively high and may
lead to several other checks bouncing, most consum­
ers seem to ignore these costs. However, non-sufficient
funds fee income is large for financial institutions,
potentially reducing banks’ incentive to promote
some electronic payment alternatives.
Independent of the cost of check payments, we
can identify three key reasons consumers have resist­
ed abandoning checks. First, checks are easy to use.
The 1998 Survey of Consumer Finances indicated that
about 87 percent of U.S. households had checking
accounts, making checks the most accessible noncash
payment instrument.11 Checks are also one of the most
widely accepted forms of payment by merchants at
the point of sale.12 For bill payments, checks are the
most popular instrument because, unlike other forms
of payment, they are almost always accepted by billers.13

47

Second, consumers are reluctant to switch to elec­
tronic alternatives unless they offer superior benefits
to checks. While consumers may believe that electronic
payments are less expensive overall, they are reluctant
to change unless they view the shift as beneficial to
them. Credit card issuers often offer additional services
such as extended warranties, dispute resolution servic­
es, and frequent-use awards, along with interest-free
short-term loans to those who pay off their balances
each month.
Third, some consumers feel checks give them great­
er control over the timing of their payments, leading
to better budgeting. Hirschman (1982) argues that some
consumers believe that checks may enhance their abil­
ity to track, budget, and control spending better than
other payment instruments. Mester (2000) argues that
checks give consumers more control over when to pay
bills than pre-authorized ACH payments and can more
easily attach remittance information. Yet, consumers
can also access their checking accounts via their deb­
it cards and maintain budgeting, tracking, and control
over their funds. When using debit cards, consumers
cannot overdraw their accounts unless previous credit
lines have been established. However, debit card us­
age in bill payment is relatively low, given the slow
adoption of the necessary infrastructure.
Because consumers perceive checks to be a low­
cost payment instrument and are comfortable with them,
they are reluctant to change unless there are strong
incentives to do so. From a cost standpoint, checks are
relatively inexpensive if one ignores non-sufficientfiinds fees. As we noted earlier, explicit per-check charg­
es by financial institutions in other countries have been
effective in changing consumers’ payment habits.

merchants 80 cents per $ 100 in sales, though this cost
varies widely depending on whether the check was
verified.
Given the rapid increase in the use of check veri­
fication systems during the last decade, it is important
to analyze check costs using this technology. Nilson
(2001a) estimates that 9.14 billion checks were veri­
fied at the point of sale. Using Federal Reserve System
(2001) point-of-sale check numbers, we estimate that
between 75 percent and 97 percent of checks written
at the point of sale were verified in 2000.14 The typi­
cal cost for these services ranges from 2 cents to 20
cents per check (Nilson, 1997b). Nilson reports an
average cost of 3 cents in 1998.15
Merchants have found that check verification ser­
vices significantly reduce the risk that they will not
receive good funds. As a result, they have been able
to lower their costs by over 23 percent and to reduce
losses from exception items from 0.50 percent of the
value of the check to 0.05 percent of the value of the
check (FMI, 1998).16
According to FMI (2000), a verified check pay­
ment is actually the cheapest form of payment for the
merchant to accept, costing a merchant 60 cents per
$100 in sales versus $3.00 per $100 in sales for an
unverified check.17 Since the majority of checks at
the point of sale appear to be verified, it is important
to concentrate on the cost of verified checks. According
to FMI (2000), the cost of a verified check per $100 in
sales is significantly less than cash, off-line debit cards,
and credit cards. Although FMI (2000) did not report
a cost for ACH-based debit card transactions, FMI
(1998) reported an average cost of 82 cents in 1997.
The cost difference between a verified check trans­
action and an online debit card transaction might also
be growing. Between 1997 and 2000, FMI (1998 and
2000) found that the cost of online debit cards increased
by 14 percent, or 10 cents per $100 in sales.18 Recent­
ly, several networks have announced plans to increase
their fees significantly.19
Even if checks are more expensive than electronic
alternatives, merchants may continue to accept checks

Merchants
A primary issue for merchants is the cost of pay­
ments. There are significant differences in the cost of
accepting alternative payment instruments. The Food
Marketing Institute (FMI) (2000) estimates the mer­
chant’s cost to accept each payment instrument (see
table 1). Online debit cards are the second least ex­
pensive payment instrument for merchants
to accept at 80 cents per $100 of transac­
TABLE 1
tions. They offer merchants immediate
Merchant
costs
to
accept
a payment instrument
funds, low per-transaction fees, and little,
if any, settlement risk. While cash pro­
Cost per
Check
cessing costs are low at 90 cents per $100
transaction
not
Check
Online Off-line
(dollars)
Cash
verified
verified
Credit debit
debit
of sales, most consumers are reluctant to
use cash for larger purchases. Credit card
Cost per
$100 sales
.90
3.00
.60
1.80
.80
1.80
and off-line debit card transactions cost
merchants an average of $1.80 per $100 in
Source: Food Marketing Institute, 2000.
sales. The average check transaction costs

48

3Q/2002, Economic Perspectives

FIGURE 4

U.S. debit card use at point of sale

Source: Nilson (1996-2001), Nos. 737, 726, 705. 678. 654. and 627.

for three reasons. First, the potential cost savings from
electronic alternatives might not be large enough to
justify the transition costs to make this change and/or
risk movement to more expensive payment vehicles.
Second, cheaper electronic payment alternatives at
the point of sale have only recently flourished. Last,
checks might offer merchants some level of benefits
that they are willing to “pay” for.
Given the slow movement away from checks,
merchants, banks, and third-party providers have start­
ed to convert check transactions to ACH transactions
at the point of sale to reduce costs.20 However, this
may involve high initial set-up costs related to imple­
menting a new system, purchasing equipment, and
training staff. Furthermore, merchants may be reluc­
tant to make large investments in new payment tech­
nologies with uncertain futures.
Furthermore, even though online debit cards are
a relatively inexpensive payment option, they have be­
come widespread only recently. Annual per capita trans­
action volume in the United States increased from 0.76
transactions per year in 1990 to 11.3 transactions in
2000 (BIS, 1991-2000, and Thomson Financial, 2001).
Figure 4 shows the rapid increase in debit card trans­
actions over the last five years. Figure 5 shows the
increase in online debit card terminals installed by mer­
chants, indicating substantial growth in merchant ac­
ceptance of online debit cards over the last 13 years.

Federal Reserve Bank of Chicago

Lastly, some evidence suggests that merchants are
willing to accept high-cost payment instruments be­
cause they offer benefits not offered by other instru­
ments. Credit cards generate sales to illiquid consumers
who may not otherwise be able to purchase goods and
services.21 In some instances, merchants may choose
to accept certain instruments because they are tied to
other instruments that they choose to accept. For ex­
ample, merchants accepting Visa or MasterCard credit
cards are required to accept their off-line debit card prod­
ucts. A large group of retailers led by Wal-Mart has sued
the credit card associations, alleging that this tying of
their credit card and debit card products is illegal.
Available U.S. data do not indicate a significant
cost reduction if merchants move toward electronic
payments. Furthermore, the benefits of accepting
electronic payment instruments may not outweigh
the investment that may be required. Merchants may
be willing to accept relatively expensive payment in­
struments because they offer benefits such as the po­
tential to increase sales and profits.

Financial institutions
Although electronic payments are generally per­
ceived to be less expensive than paper-based pay­
ments for financial institutions, several U.S. studies
indicate the costs of processing ACH and check pay­
ments are not very large for financial institutions.

49

FIGURE 5

EFT point-of-sale terminals

The Federal Reserve Board’s 1994 Functional Cost
Analysis estimated that the average cost per transaction
for an ACH payment was 14 cents and the average cost
for a check payment was 14.6 cents. Wells (1996) es­
timated that the cost of processing a check ranged from
15 cents to 43 cents, while the cost of processing an
ACH payment ranged from 12 cents to 45 cents. Guynn
(1996) questions whether ACH payments are really less
expensive for banks to process than checks. However,
evidence from Norway indicates that check transactions
cost banks two to three times as much as electronic
giro services and electronic funds transfer point-ofsale transactions (Robinson and Flatraaker, 1995).
Furthermore, financial institutions earn significant
revenue from the provision of check services. On aver­
age, they charge customers 21 cents and merchants 5
cents to process each check.22 In order to spur adop­
tion, online debit cards should offer financial institu­
tions similar revenue opportunities assuming similar
cost structures, or similar profit opportunities result­
ing from offsetting cost savings. Online debit cards
provide a potentially lucrative revenue stream in the
form of the fee paid by the merchant’s bank to the cus­
tomer’s bank, commonly referred to as the interchange
fee. Yet, as of the end of 2001, no online debit card
network had interchange fees higher than 20 cents.23
Recently, a few online debit card networks have sig­
nificantly increased their processing fees. Several in­
stitutions have also implemented per-transaction fees

50

for using personal identification number (PIN) based
debit cards at the point of sale.24
Financial institutions also earn significant revenue
from fees related to overdrafts to checking accounts.
According to Bank Administration Institute and PSI
Global (1998), in 1995, banks earned $8.1 billion from
non-sufficient-funds check fees. The Board of Gov­
ernors of the Federal Reserve System (1996) estimated
that banks’ losses from check fraud amounted to
$615 million in 1995, $215 million of which was

3Q/2002, Economic Perspectives

eventually recovered. Debit cards could significantly
reduce or eliminate overdrawn accounts, because
transactions are only processed if funds are available.
However, by promoting online debit cards, financial
institutions would reduce their revenue from nonsufficient-funds fees.25
If electronic payment instruments were less ex­
pensive than checks, financial institutions might be
able to influence consumer usage and merchant accep­
tance of electronic payments as evidenced in Norway
and Finland. However, U.S. financial institutions may
be reluctant to impose explicit per-check usage fees
due to competitive pressures.
Some financial observers have argued that the large
number of payment providers in the United States might
prevent financial institutions from implementing price
increases and cost-saving measures. One initiative by
a Midwest bank to charge consumers for using bank
tellers initially met with consumer resistance and little
support from other financial institutions. Stavins (1999)
found that regional competition prevented banks from
not returning canceled checks because they feared
customers might switch to a rival bank that returned
checks. Yet, some financial institutions were eventual­
ly successful in implementing both of these policies.
Whether electronic payments are less expensive
to process than checks for financial institutions is de­
batable. However, even if electronic payments are less
expensive, the potential revenue from checks, especial­
ly in the form of non-sufficient-funds fee income, is
difficult for financial institutions to forgo.

The Federal Reserve
Central banks differ in the roles they play in the
operation and oversight of their domestic payment
system (see Bank for International Settlements, 1999
and 2000). Most central banks of industrialized coun­
tries are involved in the settlement of retail payment
transactions and some also play a clearing role. In the
United States, the Fed is a provider of interbank check
clearing services and is the largest ACH operator.
Whether there remains a compelling need for the
Fed to provide check-processing services is debatable,
given technological and regulatory changes. When
creating the Fed, the U.S. Congress stipulated that it
should improve the efficiency and safety of the pay­
ment system.26 At the time the Fed was created in 1913,
checks were the primary means of interbank funds
transfer. Today, large-value domestic transactions are
processed electronically via Fedwire, which is oper­
ated by the Fed. In addition, electronic retail payment
alternatives, such as credit cards, debit cards, and
ACH payments, are increasing their market shares.27

Federal Reserve Bank of Chicago

The Federal Reserve has historically played an im­
portant role in the development of check processing.
However, some have questioned this role. Green and
Todd (2001) argue that as the United States transitions
to the next generation of payment instruments, the
Fed should promote “efficiency, integrity and acces­
sibility primarily by means other than direct service
provision—such as participation in the setting of stan­
dards, the drafting of model legislation and the regu­
lation of payment services markets” (Green and Todd,
2001, p. 1). They further argue that “encroaching on
activities that the private sector can perform efficiently
and equitably” may erode the Fed’s reputation as a
trustworthy and neutral institution.
The Fed’s role in check processing has declined
over the past two decades, partly due to regulatory
changes such as the Monetary Control Act of 1980
(MCA), the removal of interstate branching restrictions,
and changes to Regulation CC to allow banks to settle
checks in same-day funds. In the five years following
the passage of MCA—under the terms of which the
Fed had to price its financial services and make them
available to all financial institutions—the Fed’s share of
interbank check clearing decreased sharply.28 Summers
and Gilbert (1996) claim that the Fed’s share of check
volume decreased from 61.0 percent in 1980 to 49.8
percent in 1985.29 For the same period, its share of check
value decreased from 48.5 percent to 31.7 percent.
The Fed also experienced a sharp decrease in the
number of checks handled from 1993 to 1995. Given
the lack of reliable non-Fed check volume data for this
period, it is difficult to determine the cause of this de­
crease. Some financial observers claim that changes
in Regulation CC and the removal of interstate branch­
ing restrictions were at least partially responsible. In
1994, Regulation CC was changed to allow banks to
settle checks in same-day funds. These changes may
have resulted in more institutions using private clear­
inghouses. We would also expect that as banks merged,
the ratio of on-us transactions would increase. How­
ever, most estimates indicate that on-us check volume
has remained constant at around 30 percent.30
Alternatively, total check volume may have de­
creased during this period as a result of the adoption
of electronic alternatives. Therefore, the drop in Fed
volume would be attributable to a decrease in overall
check volume and not a change in market share. Per­
haps a more plausible explanation is that both a shift
to private-sector alternatives and substitution of other
payment instruments were responsible for the reduc­
tion in check volume. However, as stated before, in
the absence of reliable data, we can not determine the
magnitude of each effect.

51

According to Federal Reserve System (1998), the
Fed processes a greater proportion of checks for smaller
depository institutions than private sector providers.
During the Committee on the Federal Reserve in the
Payments Mechanism (also known as the Rivlin Com­
mittee) public forums, several community banks in­
dicated that they feared that private entities would
not process checks at similar prices to those currently
charged if the Fed left the check processing industry.
Several banks and clearinghouses “freely admit that
they would charge more to clear these items than the
Federal Reserve now does, citing the higher costs in­
volved in serving these endpoints” (Federal Reserve
System, 1998). These institutions suggested that the
Fed subsidizes small institutions, especially those lo­
cated in remote locations.
The Fed has denied cross-subsidizing across its
priced-services product. According to Rivlin (1997,
p. 5), then vice-chair of the Board of Governors of
the Federal Reserve System, each product in a multi­
product firm “should recover at least its incremental
production cost” to ensure that no individual product
is being subsidized.31 Using this standard, the Fed
does not subsidize any of its products. Similar argu­
ments could be made about the allocation of fixed costs
among customers that may result in certain custom­
ers paying a higher proportion of fixed costs. How­
ever, some distributions of allocating fixed costs
among customer segments may deter competitors
from entering industries where economies of scale
and scope are present.
Chakravorti, Gunther, and Moore (1999) suggest
that private-sector providers could cherry-pick the prof­
itable customers and leave the higher cost ones with
the Fed if low-cost customers pay more than the mar­
ginal cost to serve them. They argue that as low-cost
customers find other less-expensive check processors,
the price charged should rise for those remaining cus­
tomers. Eventually, the rise in cost may result in an
exit strategy for the Fed. Thus, these more expensiveto-serve customers may eventually choose to promote
non-check payment alternatives by charging higher
check fees to their customers.
However, if the Fed improves check-processing
technology, such incentives would be reduced. Recent­
ly, the Fed has made large investments in improving
check-processing technology to lower its costs.32 It
continues to promote electronic check presentment.33
In 2000, the Fed electronically presented about 20
percent of the checks it handled. From 1995 to 2000,
the number of checks presented electronically grew
from 1 billion to 3.5 billion, or a 28.5 percent com­
pound annual growth rate (CAGR). Though the

52

physical checks still followed, this program allowed
for faster presentation of the check for payment. The
Fed also participated in projects where the check was
imaged or truncated at either the payee bank or at the
Fed and no paper was sent to the paying bank. More
than 7 percent of the checks the Federal Reserve han­
dled were processed in this manner in 2000.
While these changes are aimed at decreasing check­
processing costs, they may also affect the migration
to electronic alternatives. Specifically, a less-expen­
sive check-processing system may reduce the incen­
tives for financial institutions to migrate to electronic
alternatives. Lacker and Weinberg (1998, p. 19) argue
that “ECP (electronic check presentment) could be
viewed as an attempt to stem the expected decline in
check use. By reducing the cost of paper checks, ECP
could slow the transition to fully electronic payment
instruments that are even more beneficial.”
Benston and Humphrey (1997) suggest that the
Fed may not have sufficient reason to continue in the
check-processing business. Furthermore, they point
out that the Fed is virtually alone among central banks
of developed countries in the provision of check-pro­
cessing services to financial institutions. Bullock and
Ellis (1998) suggest that the “heavy involvement” of
the Fed in check processing, along with its role in reg­
ulating the industry, has kept the check competitive
with other payment instruments.
On the other hand, improvements in check pro­
cessing may allow for the electronification of checks,
while maintaining some features not presently avail­
able in competing payment instruments in the United
States, ultimately facilitating the movement to electron­
ic alternatives. Connolly and Eisenmenger (2000) ar­
gue that in some instances there is a need for the Fed
to take an operational role to improve the processing
of checks where the private sector may not be willing
to invest the necessary funds initially to adopt the
necessary infrastructure. Guynn (1996) suggests that
certain improvements in check processing may actu­
ally improve the adoption of electronic instruments.

Drivers to change
Given today’s underlying incentive structure, it
would appear that U.S. consumers, merchants, and
financial institutions are not likely to change their pay­
ment preferences in the near future. However, given
technological enhancements and competition from
nonbank payment providers, the incentives for pay­
ment system participants to use electronic alterna­
tives will increase. Financial institutions, along with
merchants, have started to “electronify” checks by
converting them to ACH payments. Given the right

3Q/2002, Economic Perspectives

incentives, consumers may also increase their use of
electronic payment instruments. In this section, we dis­
cuss drivers that might aid the transition away from
checks in the future.
U.S. consumers have only recently had electron­
ic alternatives for different types of payments. Credit
cards have surpassed checks as the most popular in­
strument used for point-of-sale transactions.34 Debit
cards are becoming increasingly popular at the point
of sale, and ACH payments continue to gain popular­
ity for recurring bill payment and payroll disbursements.
In addition, third-party providers are using new tech­
nologies to deliver payment card products and ACH
payments to new market segments.
Debit cards show the greatest promise to decrease
check volume at the point of sale. Debit cards offer con­
sumers access to their transaction account like checks
and allow merchants to receive their funds relatively
quickly, incurring little, if any, settlement risk. Mer­
chant acceptance of debit cards and the number of con­
sumers holding debit cards are growing rapidly. From
1995 to 1998, the number of households with a debit
card increased by a 27 percent CAGR. The success
of promoting debit card usage is partly due to the le­
veraging of existing credit card and automated teller
machine (ATM) networks and financial institutions’
ability to easily put the product in the wallets of their
consumers by increasing the functionality of their
ATM cards.35
Earlier, we noted that financial institutions may
not have sufficient incentives to promote online debit
cards. Recently, several electronic funds transfer net­
works have consolidated. In the process, several of
the largest networks have publicly stated that they in­
tend to increase the revenue to financial institutions
for participating in their networks (Breitkopf, 2001a).
Two of the most prominent networks announced their
plans to almost double their interchange fees (Breitkopf,
2001b). However, these plans were delayed more than
six months after several large retailers indicated that
they would discontinue processing transactions over
the networks if the rate increases went through.
Off-line debit card usage has also increased rapidly
and these cards now outnumber their online counterparts
in the United States. These cards provide financial in­
stitutions with similar levels of interchange fees to those
offered by credit cards, usually a percentage of the
purchase price. A few years ago, a group of merchants
filed a lawsuit against Visa and MasterCard, challenging
the “honor-all-cards” rules of the card associations. 36
These rules stipulate that if merchants accept one of
the card association’s products such as credit cards, they
must accept all of the association-branded products. 37

Federal Reserve Bank of Chicago

The merchants claim that few alternatives exist to the
general-purpose credit cards. Therefore, they are un­
willing to stop accepting credit cards but want the abili­
ty to decline the associations’ off-line debit cards.
A significant but not often discussed payment seg­
ment is the person-to-person (P2P) payment segment.38
The Federal Reserve System (2001) estimates that 11.2
percent of total check volume and 6.7 percent of total
check value in 2000 were consumer-to-consumer pay­
ments. Individuals are usually unable to accept ACH
payments or credit or debit card transactions. In the
last two years, banks and nonbanks have started to
enable individuals to accept these payment instruments.39
While the initial impact of P2P has been mostly lim­
ited to the online auction community, recent initiatives
by P2P providers have been geared toward capturing
a larger share of the non-auction online transactions.40
Some small businesses have begun to use P2P payment
services as a means to accept payments both within
and outside the auction community.
In the bill payment arena, depository institutions
(Dis) are facing competitive pressures from both thirdparty providers and non-depository financial institu­
tions. Third-party providers began promoting the use
of electronic payments to many of the most profitable
customers for Dis. Brokerage firms and credit-card
banks, which already have a connection to many of
the Dis’ high-net-worth customers, have also been
actively promoting electronic bill payment services.
Depository institutions risk losing these integral rela­
tionships if they do not match or exceed the services
offered by these competitors. Thus, other providers ap­
pear to have accelerated the incentives for Dis to pro­
mote some forms of account-based electronic payments.
While checks continue to dominate the propor­
tion of non-cash payments, we have discussed several
drivers that should facilitate the migration to electronic
alternatives. Leveraging existing networks, debit cards
have gained significant market share, and financial
observers believe that debit card growth rates will con­
tinue to be higher than those of other established pay­
ment media. As more commerce is conducted remotely,
such as via the Internet, electronic payments’ share of
total payments will also increase, since paper instru­
ments may not be appropriate for these environments.
Furthermore, new payment providers, especially non­
bank providers, have started to leverage existing net­
works, such as debit and ACH networks, to allow
electronic person-to-person payments. As consumers
and merchants become comfortable with electronic pay­
ments in certain payment segments, there will likely
be spillover effects to other payment segments that
have traditionally been dominated by checks.

53

Conclusion
In this article, we have examined why U.S. con­
sumers, merchants, and financial institutions have been
unwilling to significantly reduce their check usage. Con­
sumers lack incentives to change their habits. In the
United States, credit card issuers have been success­
ful in gaining point-of-sale market share by offering
incentives such as frequent-use awards and interest-free
short-term loans if monthly balances are paid in full.
However, in some environments, such as online pur­
chases, consumers may have little choice but to use
electronic alternatives. Consumers may become more
comfortable with electronic alternatives the more
they use them, resulting in faster market adoption.
For merchants, the cost to process checks, includ­
ing the risk of not being able to convert a payment to
good funds, may not be significantly greater than for
electronic alternatives such as online debit cards. How­
ever, merchants are also realizing the benefits of on­
line debit cards as evidenced by the rapid deployment
of point-of-sale terminals and the merchants’ antitrust
suit against the credit card associations. Merchants
may be gaining sufficient bargaining power with pay­
ment providers to impact existing cost structures in
ways that may increase usage of electronic alternatives.
Financial institutions in Finland and Norway
have been successful in convincing consumers to sig­
nificantly curtail their check usage and increase their
use of electronic payment forms by imposing per-check
fees. However, checks remain a substantial source of
revenue for U.S. financial institutions, especially in
terms of non-sufficient-funds fees. Electronic alterna­
tives, such as online debit cards, may not have been as

54

financially attractive, but new pricing policies by the
online debit card networks may entice financial insti­
tutions to promote them more heavily.
Some have questioned the role of the Fed in the
retail payments arena. While the Federal Reserve is ac­
tively promoting electronic alternatives, it continues
to improve check processing. Such improvements may
distort the market incentives to move to electronic pay­
ments. Even though the United States lags behind other
industrialized countries in its continued high usage of
checks, no studies to date have concluded that the mi­
gration to electronic substitutes for checks is welfare
enhancing in the U.S. context.
In this article, we have identified several poten­
tial drivers of electronic payments, such as greater choice
of payment instruments for consumers for different
payment segments, greater non-face-to-face shopping
opportunities, competition from non-bank payment
providers, and a greater role by merchants to offer the
low-cost payment alternatives. Anecdotal evidence sug­
gests that U.S. consumers are slowly changing their pay­
ment habits, and we would expect this trend to continue.
Further research is warranted as to why the Unit­
ed States lags other industrialized countries in adopt­
ing electronic alternatives. We have suggested that
changes need to occur in the underlying incentive
structure to convince all payment participants to mi­
grate to electronic payments. U.S. consumers, mer­
chants, and financial institutions are more likely to
make the transition to electronic payments, given the
growth in remote purchases, developments in tech­
nology, and greater market-based incentives to use
electronic alternatives.

3Q/2002, Economic Perspectives

NOTES
JThe number of checks written includes consumer, business, and
government checks. We focus only on consumer checks in this ar­
ticle. We use U.S. check data for 2000 from the new Federal Re­
serve System check survey and 1999 figures from BIS for the
other countries (Federal Reserve System, 2001, and BIS, 2000).

2The level of confidence in the payment statistics published by
BIS is questionable. Therefore, a great amount of care should be
exercised in interpreting them.
3A portion of the difference in per-capita check usage might be
attributable to higher levels of cash use in some of these countries.
Some countries with low check usage may have a high level of
cash usage, such as Japan. See BIS (1999) for a more detailed
discussion of these differences.
determining the volume and value of checks is difficult. While
the Federal Reserve knows the number and value of checks it pro­
cesses, it estimates the volume of checks processed by others. In
1999, Nilson (1996-2001), BIS (2000), and Green (1999) esti­
mated total U.S. check volume between 64 billion and 69 billion,
accounting for $47 trillion to $83 trillion. Prior to the 2001 study,
the Federal Reserve’s last benchmarking study was conducted in
1979. For more recent years, non-Fed check volume was extrapo­
lated from the 1979 study.

5We would expect that these numbers overestimate the growth rate
in check usage, given the 2001 Fed study, but no reliable evidence
indicates that growth has been negative during this period.
6The G-10 countries are Belgium, Canada, France, Germany, Italy,
Japan, the Netherlands, Sweden, Switzerland, the United Kingdom,
and the United States. Japan is not included in the figure because
check usage in Japan is extremely limited, and France is not in­
cluded because it did not report check data for 1999.
7There are two types of debit cards—online and off-line. Online
debit cards use ATM networks to authorize and process transac­
tions and require customers to use a PIN (personal identification
number) code. Off-line debit cards use credit card networks and
are signature-based.
8For a discussion of why stored-value did not succeed in the
United States, see Chakravorti (2000).
9However, there are fixed costs such as the opportunity cost of
holding funds in a zero- or low-interest bearing account and po­
tential monthly fees.

10Bank of America, Washington Mutual, Bank One, Harris Bank,
and Fifth Third have recently started to promote free checking
accounts (Thomson Media, 2001).
HIn comparison, 72.5 percent of households had a credit card and
34.5 percent of households had a debit card in 1998 (a substantial
increase from 17.6 percent of households in 1995).

12However, there are several types of merchants, such as gas sta­
tions and restaurants, that do not usually accept checks.
13For more on bill payment, see Andreeff et al. (2001).
14Federal Reserve System (2001) estimates that of the 49.6 billion
checks written in 2000, 19 percent were written at the point of
sale and 12 percent were written at either the point of sale or for
remittance. Therefore, between 9.42 billion and 12.4 billion
checks were written at the point of sale.

Federal Reserve Bank of Chicago

15In addition, merchants used third parties to guarantee another 2
percent of the total check volume. According to Nilson (1999b),
the cost for check guarantee services averaged 1.56 percent of the
value of the check in 1999. Note that guaranteed check costs are
significantly higher than those of online debit cards and may be
more than off-line debit cards and credit cards.
16FMI (2000) does not break out the cost of fraud for verified and
unverified checks, so we are using FMI (1998) for this portion of
the analysis.
17We should note that based on the average cost per transaction,
FMI (2000) shows that online debit cards are still the cheapest means
of payment. On a per transaction basis, online debit cards cost $0.34,
verified checks cost $0.36, and unverified checks cost $0.38.

^Comparatively, on a per transaction basis, the cost of debit cards
rose from an average of $0.30 in 1994 and $0.29 in 1997 to $0.34
in 2000.
19Both the Star and NYCE networks have increased their maximum
fees for supermarkets to 19 cents, while Interlink has raised this
fee to 20 cents.
20In 2000, 32 million checks were converted to ACH payments at
retail locations (National Automated Clearing House Association,
2001).
21For a theoretical exposition of credit extensions and their ben­
efits to merchants, see Chakravorti and To (1999).
22The Board of Governors of the Federal Reserve System (2001)
states that the average per check fee was about 21 cents for inter­
est-bearing accounts. Check revenue would be similar for accounts
that did not charge per-check fees if the average account holder
wrote 9.5 checks. See FMI (1998) for merchants’ fees from checks.
23As of the end of 2000, Interlink charged the highest online debitcard-processing fee of 20 cents for point-of-sale transactions. Af­
ter recent price increases, Interlink still charges the highest fees
with a maximum of 45 cents for point-of-sale transactions.
24According to a recent survey by Dove Consulting and Pulse EFT,
6 percent of financial institutions charge extra fees for using PINbased debit cards at the point of sale. The study found that an av­
erage fee of $1 is being charged to consumers because the lowinterchange offered on PIN-based debit transactions does not ad­
equately cover processing costs. See Breitkopf (2002).

25Off-line debit cards offer issuers significant revenues in the form
of interchange fees that could offset the decrease in insufficientfunds fees. This lucrative interchange revenue may be partly re­
sponsible for the increase in popularity of debit cards since their
introduction in the early 1990s (see figure 4).
26For a historical perspective on the Fed’s role, see Gilbert (1998)
and Summers and Gilbert (1996).
27See Federal Reserve System (2001).

28For a discussion of the private sector response to Fed pricing
polices resulting from the MCA, see Frodin (1984). For a histori­
cal perspective on retail payment services and the MCA, see
Kuprianov (1985).

55

29The Fed’s share is taken as a percentage of interbank check vol­
ume ignoring any changes in on-us volume.
30The last comprehensive study of the payment system by the
Federal Reserve prior to the 2001 study placed the share of on-us
check volume at 30 percent in 1979. The 2001 Fed study also put
the on-us check share at 30 percent.
31For more discussion of cross-subsidization, see Faulhaber (1975).
32By mandate, the Fed must recover costs from the financial services
it provides. Investments in equipment are amortized over years.
Thus, it appears that the Fed expects the demand for its check­
clearing services will not decline significantly in the near future.
33A number of other private-sector initiatives have also been un­
dertaken to truncate checks at the point of sale and through lock
boxes. In most cases, these initiatives take the magnetic ink char­
acter recognition (MICR) information and turn the payment into
an ACH transaction.

35There are differences in consumer liability across payment instru­
ments. For a discussion, see Spiotto (2001). For a discussion of how
evolving payment instruments and applications have leveraged the
existing payment infrastructure, see Mantel and McHugh (2002).
36This case is currently awaiting trial.
37See Evans and Schmalensee (1999) for a discussion of network
rules and history.

38For more details on online P2P systems, see Kuttner and
McAndrews (2001) and McHugh (2002).
39In some cases, P2P providers have created their own medium of
exchange, but most also allow consumers to easily convert the
value into good funds.
40PayPal, the leading provider of electronic person-to-person pay­
ments, indicated that 66.9 percent of its payment volume in 2001
originated from online auctions. See McHugh (2002) for a discus­
sion of PayPal and its services.

34Some observers do not categorize credit cards as electronic pay­
ments, because in most instances the statement is provided on paper
and most payments are made by check. However, for most mer­
chants, credit cards are processed, cleared, and settled electronically.

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59

Analyzing the relationship between health insurance, health
costs, and health care utilization

Eric French and Kirti Kamboj

Introduction and summary
In this article, we provide an empirical analysis of the
determinants of whether an individual purchases health
insurance coverage. We describe the relationship be­
tween health insurance, health costs, and health care
utilization of the elderly, using data from the Health
and Retirement Survey and the Assets and Health
Dynamics among the Oldest Old. We show how health
costs and health care utilization depend upon access
to health insurance for individuals aged 50 and older.
Given the public interest in extending health in­
surance coverage to those who are currently unin­
sured, it seems worthwhile to better understand why
some people do not purchase health insurance. For
example, 2000 Democratic presidential candidate A1
Gore advocated that individuals aged 55-64 be allowed
to “buy in” to Medicare. The idea was that eligible
individuals would have to pay for Medicare coverage,
but would potentially pay less than the price of pri­
vately available insurance. Medicare would potentially
be cheaper because of the cost advantages associated
with the group coverage that Medicare provides. By
understanding the determinants of the health insurance
purchase decision, we can better understand how pro­
posed reforms may affect health insurance coverage.
First, we investigate the factors influencing a per­
son’s decision to purchase health insurance. A General
Accounting Office study found that in 1998, private
health insurance premiums for a family of four ranged
from $3,000 to $ 14,000 per year. Although health care
coverage can be expensive, very few households are
unable to buy private health insurance. Nevertheless,
many households choose to be uninsured rather than
purchase private health insurance.1 Therefore, we as­
sume that even low-income households are able to
buy basic health insurance.
Given that almost all individuals in our data are
able to purchase health insurance, the most likely

60

reason that they remain uninsured is that they expect
their health costs without insurance to be significant­
ly lower than their health costs with insurance. We test
four potential reasons why this might be the case:
1) adverse selection in the insurance market—because
insurers cannot distinguish between high-cost and low­
cost individuals in a group—leading to potentially
prohibitive costs of health insurance for healthy indi­
viduals; 2) moral hazard—the idea that if the price of
something is low, people use more of it—leading to
potentially prohibitive costs of general insurance;
3) potentially prohibitive administrative costs of pro­
viding health insurance for private individuals; and
4) many of the uninsured already receive explicit insur­
ance through Medicaid and implicit insurance through
hospitals that will treat indigent patients, which may
obviate the need for them to purchase additional
health insurance.
Most studies of the health insurance purchase
decision focus on the importance of adverse selection
and moral hazard as potential reasons why individu­
als may not purchase insurance. Our results provide
evidence that neither adverse selection nor moral
hazard is the key determinant of the health insurance
purchase decision. We find no evidence that adverse
selection makes private insurance too expensive and
only moderate evidence that moral hazard may make
private health insurance prohibitively expensive. How­
ever, we find significant evidence that high adminis­
trative costs drive up the price of private insurance.
Moreover, we find a large amount of evidence that
the existence of Medicaid and implicit insurance ob­
viates the need for individuals to purchase additional
health insurance. This last result suggests that changes

Eric French is an economist at the Federal Reserve Bank
of Chicago. Kirti Kamboj is a graduate student at the
University of Chicago.

3Q/2002, Economic Perspectives

in government-provided health insurance, such as al­
lowing younger individuals to “buy in” to the Medicare
program, would likely have a small effect on the health
insurance coverage of older Americans. The data show
that many of those currently “uninsured” already have
access to low- or no-cost health care coverage from
the government and hospitals.

Data: Health and Retirement Survey and
Assets and Health Dynamics among the
Oldest Old
We use data from the Health and Retirement Sur­
vey (HRS) and Assets and Health Dynamics among
the Oldest Old (AHEAD). These two datasets are
collected by the same organization and have a similar
sample design for much of the sample period. Both
contain detailed information on health costs, health
insurance, and demographics.
The HRS is a sample of non-institutionalized2
individuals aged 51-61 in 1992. Spouses of these in­
dividuals were also interviewed, regardless of the
spouse’s age. The HRS includes both a nationally rep­
resentative core sample and an additional sample of
blacks, Hispanics, and Florida residents. A total of
12,652 individuals in 7,608 households were inter­
viewed in 1992 and re-interviewed in 1994, 1996,1998,
and 2000, creating up to five separate responses for
each individual.
The AHEAD is a nationally representative sample
of non-institutionalized individuals aged 70 and older
in 1993. Like the HRS, spouses ofAHEAD respondents
were also interviewed, regardless of age. Also like the
HRS, the AHEAD dataset includes both a nationally
representative core sample and additional samples of
blacks, Hispanics, and Florida residents. A total of
8,222 individuals in 6,047 households were interviewed
in 1993. These individuals were interviewed again in
1995, 1998, and 2000, creating up to four separate
responses for each individual.3
In order to assess the quality of the HRS/AHEAD
data, we present means of several key variables of in­
dividuals aged 50 and older and compare them with
aggregated statistics from other sources.4
Consider sources of insurance first. Table 1 shows
that most individuals receive employer-provided in­
surance, including insurance from current employers,
past employers, and unions, as well as from the spouse’s
current employer, past employers, and unions.5 Almost
all individuals over age 65, as well as those who draw
disability insurance, are eligible for Medicare. Individ­
uals with low incomes and asset levels are also eligible
for Medicaid. Those not eligible for any of the above
forms of insurance are faced with either purchasing

Federal Reserve Bank of Chicago

private health insurance or having no insurance at all.
Table 1 shows that many individuals who do not have
access to government- or employer-provided health
insurance choose not to purchase insurance on the pri­
vate market. Of our sample, 17 percent have private
insurance, while 7 percent have no insurance. Much
of the remainder of this article is devoted to under­
standing the health insurance purchase decision for
people who are neither covered by employers nor by
the government.
The central variable of interest in our study is the
level of health costs paid by the household. For sin­
gle households, we compute this as the individual’s
health costs. For married households, it is the sum of
the husband’s and wife’s health costs. Health costs are
the sum of insurance premiums, drug costs, and costs
for hospital, nursing home care, doctor visits, dental
visits, and outpatient care. See the appendix for a more
detailed description of these variables. For our sam­
ple, mean household out-of-pocket health costs are
$2,527 per year and mean health costs for those aged
65 and older are $2,716. The U.S. per capita average
is $2,831 for non-institutionalized households headed
by an individual aged 65 or older (Federal Interagen­
cy Forum, 2000). This means that health costs in the
HRS/AHEAD are likely significantly below the na­
tional average when accounting for the institutional­
ized population.
One important reason why average health costs
in the HRS/AHEAD data are below the national av­
erage is that individuals in the HRS/AHEAD spend
far fewer nights in a nursing home. Households head­
ed by someone aged 65 or older spend 7.2 nights in a
nursing home per year in our sample versus 15.8 nights
in the aggregate statistics (National Center for Health
Statistics, 1999).6 Selden et al. (2001) find that 9 per­
cent of total aggregate health costs and 13 percent of
costs paid out of pocket arise from nursing home visits.

Why is there a market for health insurance?
In the next two sections of this article, we describe
some of the important determinants of the health in­
surance purchase decision. Then, we provide empiri­
cal evidence on these issues.
The most obvious reason people purchase health
insurance is to limit uncertainty associated with cata­
strophic health costs.7 The idea behind health insurance
is that uncertain health expenditures are diversifiable
risks. That is, health insurers provide health insurance
to many individuals. While there is a great amount of
uncertainty about how much insurers must pay out for
any individual, there is very little uncertainty about aver­
age medical expenses for a large pool of individuals.

61

TABLE 1

Descriptive statistics
Mean

Standard
deviation

Observations

Fraction with insurance plan
Employer-provided
Private
None
Medicaid
Medicare

0.50
0.17
0.07
0.10
0.17

0.50
0.37
0.25
0.29
0.38

46,991
46,991
46,991
46,991
46,991

Medical costs (1998 dollars)
Out of pocket costs
Drug costs
Insurance premiums
Total household expenses

744
753
1,085
2,527

Variable

actuarially fair insurance. As we noted in
table 1, however, many people do not pur­
chase insurance. The most common ex­
planation why people do not buy insurance
is that it is impossible to buy actuarially
fair insurance. Next, we examine why
this is so.

Why doesn’t everyone purchase
health insurance?

Above, we argued that people should
purchase health insurance to reduce un­
certainty if their expectation is that they
will pay the same amount for health care
Health care utilization
whether or not they are insured. However,
Nights in nursing home
4.09
34.65
42,638
insured individuals are, on average, likely
Nights in hospital
1.69
6.13
42,418
Doctor visits
6.95
9.99
41,757
to pay more than the uninsured. Below,
Had outpatient surgery
0.13
0.24
42,663
we
highlight four reasons for this and cite
Saw a dentist
0.65
0.48
36,315
existing evidence for each of the reasons.
Did not take prescribed drugs
0.09
0.28
36,316
First, prices of health insurance may
Demographics
Fraction married
0.54
0.50
46,953
be potentially high because of adverse
Good health
0.69
0.46
41,606
selection. Adverse selection occurs when
Economic resources
there are high health cost individuals and
Assets <$50,000
0.32
0.47
45,627
low health cost individuals in a group,
Assets >$50,000<$200,000
0.34
0.47
45,627
Assets >$200,000
0.34
0.48
45,627
but health insurers cannot distinguish be­
Income <$5,000
0.05
0.22
45,874
tween
the two.
Income >$5,000<$30,000
0.49
0.50
45,874
Income >$30,000
0.46
0.50
45,874
Recall that if markets are competi­
Working
0.38
0.48
46,442
tive and there are no administrative costs,
insurers will set the price of health insur­
Sources: HRS/AHEAD data and authors’ calculations.
ance equal to the average medical expen­
diture of individuals who purchase health
Therefore, even if the health insurer is risk averse, by
insurance. If individuals with low health costs are able
pooling health costs of many individuals together, the
to reveal that, on average, they will have low health
insurer faces very little risk. As a result, the insurer
costs, health insurers will charge those individuals low
cares only about expected medical expenditures of
insurance premiums.
However, in practice it is very difficult for insur­
the individual when setting the insurance premium.
ers to distinguish between the two groups. Individuals
Suppose that the firm’s only cost of providing
health insurance is medical expenditures. In other
may know whether they are “high cost.” However, this
words, we ignore administrative costs to the insurer.
information is not available to the insurer of a group
Also, assume that there are a large number of indi­
plan. For example, Blue Cross/Blue Shield health in­
viduals in the market, and that all of these individuals
surance merely requests home address, date of birth,
face the same distribution of health costs. If markets
sex, whether the individual smokes, and whether the
are perfectly competitive, the firm’s expected profit
individual wants maternity coverage.
is zero. If the insurer makes profits, new health insur­
As a result of not being able to distinguish between
ance providers will enter the market and bid down
high-cost and low-cost individuals, insurers charge
insurance premiums to the expected health costs of
everyone (conditional on the information listed imme­
the individual. Therefore, insurers will offer insurance
diately above) the same price for health insurance.8 If
only high health cost individuals purchase health in­
to individuals at “actuarially fair” prices, that is, prices
equal to the expected health costs that individuals face.
surance, and health insurers charge premiums equal
Assuming that individuals are risk averse, they
to average health costs of people who buy health in­
would rather pay their expected health costs than
surance, then the cost will be relatively high. Although
face the possibility of extremely high health costs.
low health cost individuals may value health insurance
Therefore, individuals will be better off purchasing
at more than the cost to insurers of providing it to

62

2,516
2,523
3,197
5,057

41,876
41,807
34,251
33,005

3Q/2002, Economic Perspectives

them, since they are risk averse, they may value it at
less than what insurers charge to provide health in­
surance to high health cost people. In this scenario,
the low health cost individuals will not purchase
health insurance.9
If insurers could distinguish between high-cost
and low-cost individuals, they would provide insur­
ance to low health cost individuals at a price equal to
their expected health costs. This would make low­
cost individuals better off. Insurers would still charge
high-cost individuals their expected health costs,
making them no worse off.
Cutler and Zeckhauser (2000) survey the evidence
on adverse selection. They argue that empirical work
has repeatedly documented its importance when com­
paring insurers that offer multiple plans. For example,
individuals who opt for Medicare health maintenance
organizations (HMOs) (that offer less generous ser­
vice than most Medicare plans but cover some servic­
es, like drug costs, that most Medicare plans do not
cover) are more likely to have consumed few medical
services in the past than those who do not opt for
Medicare HMOs.
Many researchers also cite the high price of pri­
vately provided health insurance as evidence that ad­
verse selection does drive up the price of health
insurance. For example, Gruber and Madrian (1995)
document that Blue Cross/Blue Shield health insur­
ance for a family of four in New England costs
$10,310 in 1998 dollars.10
However, it is not clear in the above example
that individuals who buy Blue Cross/Blue Shield are
any less healthy than those who decide not to pur­
chase insurance. Moreover, many studies that consid­
er the health insurance purchase decision have found
relatively little evidence that adverse selection exists
in the market for health insurance (see Cardon and
Hendel, 2001, for example).
The second reason insurance may be so expen­
sive is the cost of administering plans for large em­
ployers. Administrative costs account for 10 percent
to 15 percent of the costs of the health plans (Cutler
and Zeckhauser, 2000). However, these costs are po­
tentially higher for insurance plans administered to
small groups of people. For example a Congressional
Budget Office (U.S. Congress, 1988) study found that
large firms (10,000+ workers) pay 35 percent less than
small firms (one to four workers). Gruber and Madrian
(1995) argue that this price difference reflects some
combination of adverse selection and administrative
costs. Given that it is not obvious that adverse selec­
tion is more serious for small employers than large em­
ployers, it is likely that the cost difference is largely

Federal Reserve Bank of Chicago

from the lower administrative costs at large firms.
Pauly (1986) finds that administrative costs may ac­
count for 50 percent of the cost of “Medigap” health
insurance plans.11
Moral hazard is the third reason health insurance
costs are high. Moral hazard is a consequence of
downward sloping demand curves: If the price of a
good becomes cheaper, people buy more of that
good. People purchase health insurance to reduce the
costs of medical procedures. For example, many “in­
demnity” plans like Blue Cross/Blue Shield allow
people to obtain whatever health care they wish, but
the insurer pays most of the price. If individuals have
a 20 percent co-payment, then the price of medical
services is only 20 percent of what it would be with­
out insurance. This potentially leads people to use
medical services that are of very little value to them.
Recall that if markets are competitive, then the price
of health insurance is equal to expected medical ex­
penses of purchasers of health insurance plus admin­
istrative costs. The high level of medical services
consumed by insured individuals will be reflected in
the price of health insurance.
Evidence from the RAND Health Insurance Ex­
periment (Manning et al., 1987) suggests that a 1 per­
cent rise in the price of health care services results in
a .2 percent reduction in the quantity of health care
services consumed, or a price elasticity of .2. Given
that the price of health care services differs greatly
between those with and without insurance, moral
hazard potentially leads insured individuals to con­
sume far more medical services than is ideal, leading
to expensive medical insurance.
A final reason many people may find private medi­
cal insurance expensive is that they already receive
insurance from the government or through hospitals.
Medicaid provides insurance to individuals with low
income and assets. Moreover, hospitals that receive
federal funding cannot turn away indigent patients.
Therefore, individuals with low income and assets do
not need to purchase insurance. They already have it
provided explicitly by Medicaid or implicitly by hos­
pitals. This explanation has received less attention than
the other explanations (see Cutler and Gruber, 1996,
for an exception). However, as shown in the empiri­
cal work below, this may be an important oversight.
With these explanations in mind, table 2 describes
the problems associated with universal government
insurance relative to employer-provided health insur­
ance and private insurance. The main advantage of
nationalizing health insurance, such as expanding
Medicare to all individuals aged 55 and older, is to
overcome adverse selection problems.12 Because the

63

plans. The Employee Benefit Research Institute (1999)
reports that employers contribute an average of $3,288
to their employees’ health insurance. Therefore, the
total cost of employer-provided insurance premiums
is the sum of the employee contribution plus the em­
ployer contribution, or $4,442. Compare this amount
with insurance premiums for households headed by
individuals aged 50-64 with private insurance. These
households spend $4,067, on average.
This would imply that the total cost of a private
plan is slightly less than the cost of an employer-pro­
vided plan.13 However, not only do households with
private insurance spend more on insurance premiums
than households with employer-provided insurance,
they also have higher out-of-pocket expenses. This may
reflect the higher deductibles and co-pays of private
health insurance policies. When we sum up the insur­
ance premiums paid by the individual and the firm
plus what the individual pays out of pocket, the total
Health insurance coverage, health costs,
health cost for households with employer-provided
and health care utilization
insurance is $5,489 (a $3,288 employer contribution
In this section we provide some new empirical
plus total household expenses of $2,201) and the total
evidence on the four potential reasons individuals do
cost for private insurance is $5,871, a difference of 7
not purchase health insurance We find no evidence of
percent. Given that administrative costs constitute
adverse selection and limited evidence in favor of moral
about 13 percent of insurance costs for employer-pro­
hazard and high administrative costs. Instead, we be­
vided plans, these costs make up 20 percent of private
lieve the main reason some individuals do not purchase
insurance costs.
insurance is that they are already receiving insurance,
Moreover, table 3 shows that households with
either through the government or implicitly through
private insurance receive fewer medical procedures
hospitals.
than households with employer-provided health insur­
In order to assess the importance of administra­
ance. This may reflect the fact that private insurance
tive costs, we compare individuals with private insur­
does not usually cover pre-existing conditions. Given
ance with individuals with employer-provided insurance.
that those with private health insurance consume few­
Recall that Cutler and Zeckhauser (2000) find that
er medical services than those with employer-provid­
administration accounts for 10 percent to 15 percent
ed insurance, the cost (net of administrative cost) of
of the total cost of health insurance at large firms. Our
private health insurance is likely lower than the cost
goal is to find out whether individuals who purchase
(net of administrative cost) of employer-provided in­
private insurance face significantly higher administra­
surance. Therefore, the calculation of administrative
tive costs than those who receive health insurance
costs above likely understates the administrative cost
through their employer.
of private health insurance.
Table 3 shows household health costs by age group
The second potential reason people do not purchase
and health insurance type. For households headed by
health insurance is adverse selection, which implies
someone aged 50-64, health insurance premiums are
that only the most unhealthy purchase private insurance,
$ 1,154 per year for those with employer-provided
which makes premiums prohibitively expensive for
healthy people. However, the evidence
presented in table 3 refutes this explana­
TABLE 2
tion. Fully 81 percent of people aged
Problems with health insurance, by payment system
50-64 with private insurance report that
Employerthey are in good health. However, only
Nationalized
provided
Private
65 percent of the uninsured do likewise.
Therefore,
the uninsured are more likely
Administrative costs
yes
yes
yes
Moral hazard
yes
yes
yes
to be unhealthy than those who purchase
Adverse selection
no
some
yes
private insurance. Comparing those older
government would expand coverage to everyone, both
the healthy and unhealthy would be covered. Indeed,
Akerlof (1970) points out that most individuals aged
65+ were uninsured before Medicare was passed into
law and argues that adverse selection was one reason
for the low insurance rates of these people. However,
nationalizing health care would do little if anything
to overcome high administrative costs or moral hazard.
Administrative costs would potentially be the same
for insurance plans administered by large private in­
dustries and the government. And moral hazard is in­
herent in the very nature of insurance contracts and is
not specific to the insurance provider. Therefore, ar­
guments in favor of nationalizing health insurance must
rest on the assumption that adverse selection exists in
the marketplace for health insurance coverage and
partly on the assumption that, because of risk aver­
sion, health insurance makes people better off.

64

3Q/2002, Economic Perspectives

TABLE 3

Descriptive statistics by age group
Employerprovided

A. Ages 50-64
Fraction with insurance plan
Medical costs (1998 dollars)
Non-drug out-of-pocket costs
Drug costs
Insurance premiums
Total household expenses
Health care utilization
Nights in nursing home
Nights in hospital
Doctor visits
Had outpatient surgery
Saw a dentist
Did not take prescribed drugs
Demographics
Fraction married
Good health
Economic resources (1998 dollars)
Assets <$50,000
Assets >$50,000<$200,000
Assets >$200,000
Income <$5,000
Income >$5,000<$30,000
Income >$30,000
Working
B. Ages 65-79
Fraction with insurance plan
Medical costs (1998 dollars)
Non-drug out-of-pocket costs
Drug costs
Insurance premiums
Total household expenses
Health care utilization
Nights in nursing home
Nights in hospital
Doctor visits
Had outpatient surgery
Saw a dentist
Did not take prescribed drugs
Demographics
Fraction married
Good health
Economic resources (1998 dollars)
Assets <$50,000
Assets >$50,000<$200,000
Assets >$200,000
Income <$5,000
Income >$5,000<$30,000
Income >$30,000
Working

None

0.67

0.09

0.12

0.07

0.04

659
513
1,154
2,201

1,049
709
4,067
5,871

599
585
110
1,277

224
465
53
712

994
1,354
366
2,792

0.241
1.067
6.415
0.133
0.806
0.061

0.008
0.809
5.702
0.115
0.769
0.084

0.627
0.932
4.532
0.055
0.487
0.209

5.350
3.146
10.492
0.094
0.385
0.205

1.527
2.791
9.608
0.118
0.469
0.311

0.683
0.822

0.583
0.809

0.451
0.646

0.201
0.235

0.454
0.332

0.229
0.392
0.379
0.020
0.266
0.714
0.729

0.197
0.252
0.550
0.058
0.320
0.622
0.653

0.572
0.285
0.143
0.189
0.552
0.257
0.554

0.857
0.122
0.019
0.275
0.692
0.031
0.071

0.631
0.265
0.102
0.148
0.706
0.146
0.072

0.37

0.22

0.01

0.10

0.30

732
669
1,255
2,601

651
1,394
2,408
4,329

605
880
211
1,657

333
585
149
1,032

647
1,055
433
2,088

1.135
1.845
7.776
0.174
0.762
0.040

0.798
1.835
7.260
0.159
0.701
0.095

0.693
2.133
6.336
0.077
0.463
0.136

10.005
3.394
9.305
0.102
0.384
0.139

1.921
1.690
6.650
0.120
0.594
0.110

0.662
0.735

0.581
0.723

0.492
0.583

0.273
0.396

0.509
0.650

0.153
0.357
0.489
0.010
0.429
0.561
0.201

0.156
0.343
0.501
0.012
0.528
0.460
0.221

0.468
0.321
0.205
0.174
0.626
0.200
0.176

0.760
0.186
0.049
0.122
0.831
0.047
0.059

0.323
0.369
0.306
0.031
0.661
0.308
0.170

than 65 who purchase private Medigap health insur­
ance with those who only have Medicare or who have
no health insurance at all, again we see that those who
purchase private insurance are healthier than those
with no insurance other than Medicare.14
The third potential reason individuals may not
purchase health insurance is the moral hazard prob­
lem. Those who are insured face a low price of health
care services, so they tend to consume more, which
drives up the price of premiums. Therefore, controlling

Federal Reserve Bank of Chicago

Medicare

Private

Medicaid

for health status, those who have private health insur­
ance should consume more health care services than
those who have no insurance. Table 3 shows that for
households headed by someone aged 50-64, those with
private health insurance are the least likely to spend a
night in a nursing home or a hospital. Those 50-64
with private insurance spend .01 nights in a nursing
home and .8 nights in a hospital per year, on average.
Those without insurance spend .6 nights in a nursing
home and .9 nights in a hospital per year, on average.

65

TABLE 3 (continued)

Descriptive statistics by age group
Employerprovided
C. Ages 80 and older
Fraction with insurance plan
Medical costs (1998 dollars)
Non-drug out-of-pocket costs
Drug costs
Insurance premiums
Total household expenses
Health care utilization
Nights in nursing home
Nights in hospital
Doctor visits
Had outpatient surgery
Saw a dentist
Did not take prescribed drugs
Demographics
Fraction married
Good health
Economic resources (1998 dollars)
Assets <$50,000
Assets >$50,000<$200,000
Assets >$200,000
Income <$5,000
Income >$5,000<$30,000
Income >$30,000
Working

Medicare

Private

None

0.23

0.28

0.01

0.16

0.32

1,773
765
832
3,198

1,241
1,230
2,033
4,431

477
1,086
73
1,141

646
391
165
1,123

1,018
944
345
2,281

16.168
2.638
7.446
0.151
0.631
0.019

12.622
2.366
7.094
0.132
0.558
0.055

25.198
1.984
6.055
0.102
0.422
0.094

44.476
3.275
8.442
0.080
0.274
0.060

12.279
1.975
6.146
0.099
0.454
0.066

0.369
0.560

0.309
0.592

0.294
0.564

0.140
0.359

0.266
0.586

0.214
0.375
0.410
0.010
0.640
0.350
0.018

0.241
0.376
0.382
0.024
0.719
0.257
0.044

0.433
0.328
0.239
0.209
0.657
0.134
0.029

0.804
0.154
0.039
0.123
0.867
0.010
0.008

0.391
0.341
0.265
0.056
0.789
0.155
0.040

Medicaid

Sources: HRS/AHEAD data and authors’ calculations.

These findings are not consistent with the moral haz­
ard explanation. However, those with private insurance
on average have more doctor visits, are more likely
to have outpatient surgery, are more likely to see a
dentist, and are less likely to not take prescribed drugs
than those without insurance. These findings are con­
sistent with moral hazard. These patterns hold when com­
paring those older than 65 with private insurance with
those older than 65 who have either Medicare insurance
or no insurance. The privately insured older than 65 are
less likely to spend time in a nursing home or in a
hospital, but have more doctor visits, are more likely
to have outpatient surgery, are more likely to see a
dentist, and are less likely to not take prescribed drugs
than those without insurance or those who only have
Medicare.
One possible reason those with private insurance
are less likely to spend the night in a nursing home or
a hospital than those who are uninsured is that the pri­
vately insured are healthier. For two people with equal
health, the person with private insurance is potential­
ly more likely to spend time in a nursing home or a
hospital than the person without insurance. We return
to this issue when conducting our multivariate analy­
sis in the next section. Another possible reason that
complements the previous explanation is that hospitals
may have a difficult time turning away those without

66

insurance who are very ill. However, it is easy for
dentists and doctors offering elective surgery to turn
away the uninsured.
The final explanation why people do not purchase
health insurance is that they receive implicit insurance
through the government and hospitals. One testable
implication of this hypothesis is that those without
insurance pay less for a unit of health care services
than those with insurance. As discussed previously,
table 3 provides evidence that those without health
insurance consume only slightly fewer health care
services than those with private insurance. Note, how­
ever, that those aged 50-64 with no insurance spend only
$ 1,277 per year on health care, versus $5,871 per year
for those with private insurance. This difference in
costs is not completely an artifact of differences in in­
surance premiums either. Those who have no insur­
ance spend less on out-of-pocket expenses such as
drugs and co-pays than those with private insurance.
We also note that households with no insurance
are more likely to have low assets and low income
than those with private insurance. This is important
for two reasons. First, if an individual is indigent,
public and non-profit hospitals must treat them. There­
fore, low-asset individuals have implicit insurance
through hospitals. Second, individuals with low assets
are potentially eligible for Medicaid.

3Q/2002, Economic Perspectives

Individuals receiving Medicaid insurance con­
sume more medical services and spend less on health
care than any other group. That expenditures by
Medicaid beneficiaries, who have low income and
assets, are low is not surprising. After all, the govern­
ment spent $10,243 per Medicaid beneficiary aged
65 or older and $9,097 per blind or disabled individ­
ual in 1998 (U.S. House of Representatives, 2000).
One way to test whether Medicaid is a significant
source of insurance for the uninsured is to estimate
the probability that a household that is uninsured be­
comes covered by Medicaid health insurance two years
later. For uninsured households headed by someone
aged 50-64, there is a 9.2 percent probability that they
will be covered by Medicaid two years later. For house­
holds that purchase private insurance, there is only a
2.6 percent probability that they will be covered by
Medicaid two years later. This shows that individuals
with “no” insurance are more likely to be covered in
the near future. This may mean that individuals who
believe that they will be eligible for Medicaid in the
event of a household emergency feel less compelled
to purchase private health insurance than those who
do not believe that they will be eligible for Medicaid.

Multivariate analysis of determinants of
health costs
As noted above, we find that the total cost of
employer-provided insurance plans (that is, the sum
of costs paid by both employees and employers) is
similar to that of private plans for households headed
by individuals younger than 65. We also find that those
with no insurance pay much less for medical care than
those with private insurance. However, these compari­
sons are difficult to interpret because there may be
important differences in the quality of care provided
across health care plans. Even though having private
insurance leads to higher health costs than having no
insurance, having private insurance may also lead to
a significantly higher quality of care. In this section,
we use multivariate regressions to control for the
quality of health care received. Although we cannot
control for all aspects of health care quality, we can
control for many of the determinants of health costs,
such as nights in a hospital or nursing home. In this
analysis, we aim to explain differences in costs of
different types of health insurance, controlling for
health care utilization.
Table 4 presents estimates of some of the deter­
minants of health costs for the three age groups.
Each age group has two columns, the first one with
the health care utilization and health status measures
and the second one without. By controlling for health

Federal Reserve Bank of Chicago

care utilization in the regressions, we can assess whether
differences in health care utilization explain differ­
ences in health costs among households with differ­
ent types of insurance.
First, we need to infer administrative costs. Re­
call that although the total cost of employer-provided
insurance is similar to that of private plans for house­
holds headed by an individual younger than 65, house­
holds with employer-provided insurance consume more
health care services than households with private in­
surance. Here, we assess whether controlling for the
quantity of health care services consumed affects the
estimated cost differences for the two groups. We are
interested in whether the total cost of private insurance
is greater than the total cost of employer-provided in­
surance, holding utilization constant. Column 2 in
each category of table 4 provides evidence on this.
Controlling for the health utilization variables
those with employer-provided insurance pay $324 more
than those with no insurance.15 Those with private
health insurance plans pay $4,132 more than those who
are uninsured and ($4,132 - $324 =) $3,808 more
than those with employer-provided health insurance.
Recall that firms contribute about $3,288 toward em­
ployees’ health insurance plans. Therefore, the total
cost of obtaining private health insurance is only $520
greater than the cost of obtaining private health insur­
ance. This estimate is not much different from the
difference in mean health costs described in the pre­
vious section. Therefore, our estimate of administra­
tive costs of private health insurance above is little
changed by the multivariate analysis.
Another finding in the previous section is that those
who are uninsured pay much less for health care than
those who purchase private insurance. However, those
who purchase private insurance are also more likely
to consume certain medical services, such as dentist
visits. Controlling for assets, income, education, race,
marital status, age, and health care utilization does
not affect the difference in health costs between those
who are insured and those who are uninsured. The gap
between health costs for households that are privately
insured and uninsured is $4,132, almost the same as the
difference in mean health costs shown in table 3. There­
fore, our central finding, that the uninsured are implic­
itly insured by hospitals and the government, is not
overturned by the multivariate regression analysis.
As further evidence on the hypothesis that “im­
plicit” insurance is important, it appears that greater
household resources lead to greater health costs, even
after controlling for health care utilization and health
insurance. This is true when the proxy for household
resources is assets, income, or education. In other words,

67

TABLE 4

Determinants of medical costs
Ages 65-79

Ages 50-64
1

2

Insurance type
Employer-provided
430 (99)
324 (122)
Private
4,057 (135) 4,132 (160)
Medicaid
-172 (140)
-550 (165)
Medicare
1,237 (168)
927 (197)
Demographics
High school graduate
225 (80)
161 (98)
College graduate
528 (105)
361 (130)
Age
58
(9)
48 (11)
Black
-34 (83)
7 (100)
Married
1,751 (72) 1,623 (90)
Good health
159 (83)
316 (101)
Economic resources (1998 dollars)
Assets >$50,000<$200,000
531 (97)
736 (120)
Assets >$200,000
32 (126)
26 (152)
Income >$5,000<$30,000
55 (144)
11 (175)
Income >$30,000
-260 (77)
12 (95)
Working
-2,937 (555)
-2
(2)
Health care utilization
Nights in nursing home
66
(8)
Nights in hospital
28
(4)
Doctor visits
860 (176)
Had outpatient surgery
185 (92)
Saw a dentist
830 (126)
Did not take prescribed drugs
-2,784 (684)
Constant
R2
0.148
0.180

1

Ages 80+
1

2

2

240
2,037
-241
110

(418)
(421)
(426)
(416)

161
1,892
-653
-26

(435)
(437)
(442)
(431)

1,423
2,681
47
929

(911) 1,121
(905) 2,524
(912) -681
(901)
787

(937)
(932)
(940)
(927)

241
521
46
-130
1,848
-32

(107)
(153)
(10)
(132)
(100)
(120)

228
490
31
-89
1,499
70

(111)
(159)
(11)
(137)
(108)
(125)

580
461
114
-794
1,987
-353

(196)
400
(332)
380
(20)
78
(251) -783
(231) 1,358
(216)
-37

(196)
(329)
(20)
(250)
(243)
(215)

344
30
376
-38
-2,455

(142)
(247)
(272)
(122)
(874)

484
-67
247
45
19

(149)
(255)
(281)
(126)
(2)

-516 (268) -231
580 (387)
550
2,038 (477) 1,854
-1,065 (499) -680
-9,237 (1,935)
17

(267)
(387)
(474)
(485)
(1)

23
41
662
421
1,216
-1,931

(7)
(4)
(196)
(107)
(162)
(904)

0.091

0.121

44
(14)
77
(11)
259 (389)
211 (185)
1,531 (377)
-6,989 (1,961)
0.065

0.117

Notes: For each age category, column 1 shows results with no health utilization controls, while column 2 shows results with controls.
Numbers in parentheses are standard errors.
Sources: HRS/AHEAD data and authors’ calculations.

poor people seem to pay less than rich people for the
same medical services. One caveat to this last finding
is that more affluent households may be paying more
for medical care because they are purchasing better
care. For example, they are potentially spending nights
in better hospitals and seeing better doctors.

Conclusion
This article provides an empirical analysis of the
determinants of whether an individual purchases health
insurance coverage. Using data from the Health and
Retirement Survey and the Assets and Health Dynam­
ics among the Oldest Old, we document the distribu­
tion of health costs and health care utilization of
individuals aged 50 and older. We show how health
costs and health care utilization depend upon access
to health insurance.

68

Of our sample, 6.5 percent are uninsured. We con­
sider four potential reasons for this. We show that ad­
verse selection is unlikely to be an important factor in
driving health insurance costs. There is some evidence
that administrative costs and overconsumption due to
moral hazard raise the cost of health insurance. How­
ever, we find a large amount of evidence that the ex­
istence of Medicaid and implicit insurance obviates
the need for individuals to purchase additional health
insurance. Given that many of the “uninsured” already
have access to low- or no-cost health care coverage
from the government and hospitals, we argue that
Medicare buy-in proposals (that is, proposals to
allow younger individuals to pay a premium to join
Medicare) would most likely have a small effect on
the health insurance coverage of older Americans.

3Q/2002, Economic Perspectives

APPENDIX: CODING TWO HEALTH COST VARIABLES

This appendix describes the coding of the two main
health cost variables used in our analysis: medical costs
paid by the individual and total costs of medical treat­
ment. Medical costs paid by the individual are equal
to the sum of drug costs, out-of-pocket expenses on
items other than drugs, and insurance costs. The total
cost of medical treatment is the total cost of medical
services (whether or not the individual pays for those
services) plus drug costs.
During waves one and two, members of the HRS
and AHEAD samples were asked different sets of ques­
tions. Wave three includes only HRS respondents. Dur­
ing waves four and five, HRS and AHEAD respondents
were asked the same questions.
Health cost information for wave one of the HRS
is limited to insurance premiums. The insurance pre­
mium question only refers to insurance purchased
directly from an insurance company or through a mem­
bership organization, such as the American Associa­
tion of Retired Persons (AARP). It does not include
employee contributions to employer-provided insur­
ance plans. Given that the information is incomplete,
we do not include wave one information. From wave
two onward of the HRS, however, we are able to com­
pute the total cost of medical treatment, the out of
pocket expenses, the drug costs, the cost of the insur­
ance, and the total medical costs the individual pays.
In wave two, the insurance premium question in­
cludes employee contributions to employer-provided
insurance plans and insurance directly purchased or
through a membership organization.
In wave two, respondents were asked whether they
had any hospital stays, nursing home stays, or visits
to a doctor. If they answered yes to any of these ques­
tions, they were then asked both the total cost and
out of pocket cost for the visit or stay.
Also in wave two, respondents were asked wheth­
er they purchased medicines prescribed by a doctor.
If they did, they were asked how much these medicines

Federal Reserve Bank of Chicago

cost per year. It is not clear whether the cost measure
refers to the cost paid by the individual or what the
pharmacy charges the individual and the insurer.
Nevertheless, we use this variable to determine the
drug costs variable, which is also added into the total
costs variable.
For wave three onward, total health costs and
out of pocket costs are clarified to include the amount
paid for doctors, hospitals, nursing homes, outpatient
surgery, dental expenses, in-home medical care, and
special facilities and services. Note that the costs (both
out-of-pocket and total) of outpatient surgery, dental
expenses, in-home care, and special facilities and ser­
vices are not included in wave two.
The procedure for determining the insurance costs
also changes for HRS waves three through five. The
insurance premium variable is now the sum of premi­
ums of all employer-provided insurance, Medicare
through HMO plans, supplemental plans, private/AARP/
professional coverage, and long-term care plans. Note
that in wave two, Medicare insurance costs are missing.
For wave one of the AHEAD dataset, we can
determine the out-of-pocket and total costs. The outof-pocket costs include the costs of nursing home stays
as well as “any part of hospital and doctor bills and
any other medical or dental expenses in the last 12
months.” We infer that drug costs are included in this
measure, although respondents were not asked directly
about drug costs. AHEAD wave one also asked what
policies besides Medicare respondents have, includ­
ing long-term care policies, and how much they paid
yearly for such policies. From this, we determine the
insurance costs of the respondent. We do not use
wave one in our analysis in this article, however, be­
cause the source of health insurance is incomplete.
For wave two of the AHEAD dataset, imputation
procedures for total costs, and out of pocket costs, in­
surance costs, drug costs, and medical costs are the
same as in waves three through five of the HRS.

69

NOTES
’Blue Cross/Blue Shield is willing to cover most people, although
its plans often do not cover pre-existing conditions.

institutionalized individuals include individuals in nursing homes.
3In 1998 and 2000, individuals in the HRS and AHEAD (as well
as an additional sample of older individuals) were asked the same
questions. In the HRS and AHEAD waves before 1998, many of
the questions asked were the same across the two datasets, allow­
ing us to merge the datasets. Because the health insurance and
health cost data are incomplete in wave one of both datasets, we
use waves two through five in our analysis here.

"Health costs and health care utilization in the survey instrument
are for the past two years or since the individual was last inter­
viewed. We divide health costs and health care utilization mea­
sures by the number of years since the individual was last interviewed,
or by two if the individual was never previously interviewed. We
dropped individuals with missing information on their health in­
surance coverage. This reduced the original sample from 49,843
person-year observations to 46,991 person-year observations. How­
ever, we kept individuals with missing information on other vari­
ables. There are 33,005 observations with health cost information.
5Employer-provided insurance includes individuals with federally
provided health insurance plans through the Veterans Administra­
tion and the Post Office. It does not include Medicare or Medicaid.
Individuals with federally provided health insurance also have
rather similar characteristics to people with employer-provided
insurance. The main difference is that households with federal
insurance tend to spend less on insurance premiums.
6B ecause the HRS/AHEAD sample was drawn from the non-institutionalized population—which excludes individuals in nursing
homes—it is not surprising that the number of nights in a nursing
home is lower in the HRS/AHEAD sample than the national aver­
age. Nevertheless, many HRS/AHEAD household members do
enter a nursing home after they are initially interviewed.
7There are other explanations for why people purchase health in­
surance. One is that people who purchase health insurance through
their employer pay for it with pre-tax dollars. If individuals face a
30 percent tax rate, they will be indifferent between spending $1,000
on medical expenses pre-tax and $700 post-tax. Unless they have
very large medical expenses, making them eligible to deduct medi­
cal expenses on taxes, they will pay for any medical procedures
not covered by employer-provided health insurance on a post-tax

70

basis. Therefore, there are important tax advantages to employerprovided insurance. Another reason is that health maintenance
organizations potentially have market power in the market for
medical services and thus can bargain with hospitals for lower prices.

^ote, however, that pre-existing conditions, such as cancer, are
usually not covered by private health insurance plans.

9Note that if everyone purchased health insurance, average medi­
cal expenses would fall. Therefore, if low-cost individuals value
health insurance at more than the cost of providing insurance to
them, two equilibriums potentially exist: one in which health in­
surance is purchased only by high-cost individuals (resulting in
high health insurance premiums), the other in which health insur­
ance is purchased by everyone and the price of health insurance
premiums lies between the costs of high- and low-cost individuals.
10Gruber and Madrian (1995) document that Blue Cross/Blue Shield
was $8,640 in 1993. We adjusted this number to 1998 dollars us­
ing the medical care component of the Consumer Price Index.

1’Medigap plans are private health insurance plans that cover health
costs that Medicare does not cover, such as co-pays. Note that
Medigap plans likely have high administrative costs because Medigap
only pays for the smaller expenses that Medicare does not cover.
12However, Gore’s “buy in” proposal still might have had problems,
given that the buy in would be voluntary. Healthy individuals would
potentially not buy into Medicare.

^Nevertheless, $4,067 is surprisingly small given that Gruber and
Madrian (1995) found that Blue Cross/Blue Shield charged over
$10,000 in 1998 dollars for private health insurance for a family
of four in New England.
14Note that differences in health costs amongst the different health
insurance groups fall greatly after age 65. Private health insurance
premiums fall from $4,067 for those aged 50-64 to $2,408 for
those aged 65-79. This is not surprising given that after age 65,
Medicare becomes the primary source of health insurance. Private
insurance pays many of the costs that Medicare does not pay. This
is why these health insurance plans are also often referred to as
“Medigap” plans.

15The omitted category in these regressions is an uninsured, lowasset, low-income, white individual, who dropped out of high school.

3Q/2002, Economic Perspectives

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3Q/2002, Economic Perspectives