Unemployment Risk and Precautionary Wealth:
Evidence from Households' Balance Sheets
Christopher D. Carroll Karen E. Dynan Spencer D. Krane
Department of Economics Mail Stop 80 Mail Stop 80
The Johns Hopkins University Federal Reserve Board Federal Reserve Board
3400 Charles Street Washington, DC 20551 Washington, DC 20551
Baltimore, MD 21218 (202) 452-2553 (202) 452-3702
(410) 516-7602 kdynan@frb.gov skrane@frb.gov
ccarroll@jhu.edu
April 1999
Abstract
Recent empirical work on the strength of precautionary saving has yielded widely
varying conclusions. The mixed findings may reflect a number of difficulties in proxying
uncertainty, executing instrumental variables estimation, and incorporating theoretical
restrictions into empirical models. For each of these problems, this paper uses existing best-
practice techniques and some new strategies to relate unemployment probabilities from the
Current Population Survey to net worth data from the Survey of Consumer Finances. We
find that increases in unemployment risk do not boost saving by households with relatively
low permanent income, but that a statistically significant precautionary effect emerges for
households at a moderate level of income. This finding is robust to certain restrictions on the
sample, but not robust across measures of wealth: We generally find a significant
precautionary motive in broad measures of wealth that include home equity, but not in
narrower subaggregates comprising only financial assets and liabilities.
We are grateful to Dan Bergstresser, Martin Browning, Eric Engen, Steve Lumpkin, Martha
Starr-McCluer, Valerie Ramey, and seminar participants at the American Economic
Association Annual Meetings, Johns Hopkins University, the NBER Summer Institute, and
Georgetown University for helpful comments. We also thank Dan Bergstresser and Byron
Lutz for excellent research assistance and Arthur Kennickell, Martha Starr-McCluer, and
Gerhard Fries for help with the SCF. The views expressed are those of the authors and not
necessarily those of the Federal Reserve Board or its staff.
The coefficient of relative risk aversion estimated by Gourinchas and Parker (1997)
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also implies a substantial precautionary response.
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I. Introduction
I.1. Overview
Many recent studies have noted the potential economic importance of precautionary
saving. Caballero (1990) and Normandin (1994) have pointed out that precautionary saving
may be able to explain certain stylized facts about aggregate consumption such as its excess
sensitivity to movements in income. Carroll (1992) and Carroll and Dunn (1997) have argued
that precautionary behavior is an important driving force for consumption-led business cycles.
And simulations in Hubbard, Skinner, and Zeldes (1994) suggest that precautionary saving
could account for almost half of the aggregate capital stock. Yet the empirical evidence
regarding precautionary saving is mixed: Kuehlwein (1991), Dynan (1993), Guiso, Jappelli,
and Terlizzese (1992) and Starr-McCluer (1996) find little or no precautionary saving,
whereas Carroll (1994), Carroll and Samwick (1997, 1998), Engen and Gruber (1997), and
Lusardi (1997, 1998) find evidence of a significant precautionary motive.
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The mixed findings may reflect a number of difficulties in testing for precautionary
saving. The problems fall into three general categories: the method of proxying uncertainty,
the instrumental variables strategy, and the incorporation of restrictions and insights provided
by a theoretical model. In each of these categories, this paper either builds on best-practice
techniques and other insights from the existing literature or brings to bear new strategies.
I.2. Proxying Uncertainty
Precautionary wealth is defined as the difference between the wealth that consumers
would hold in the absence of uncertainty and the amount they hold when uncertainty is
present (Kimball 1990). However, the most appropriate empirical measure of uncertainty is
not obvious. Many previous studies have proxied uncertainty with either the variability in a
household's income (Carroll, 1994 and Carroll and Samwick, 1997, 1998) or the variability in
its expenditures (Dynan, 1993, and Kuehlwein,1991). But, as Guiso, Jappelli, and Terlizzese
and Lusardi (1997, 1998) have pointed out, variability measures may be poor proxies for
uncertainty because they can contain large controllable elements in them. For example, a
Lusardi (1998) and Engen and Gruber also use measures of the probability of job loss
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in their analyses. Lusardi finds significant precautionary wealth accumulation using the
household's reported perception of job-loss risk. Engen and Gruber find that the effect of
unemployment insurance on precautionary wealth is significantly more pronounced at higher
unemployment rates.
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tenured college professor who, by choice, works only every other summer may have much
more variable annual income than a factory worker, but does not face the uncertainty of being
laid off during a recession. Similarly, differences in the variation in quarterly expenditures
between two households may simply reflect differences in the families' preferences towards
regular seasonal outlays such as summer vacations or school tuition.
Our measure of uncertainty is the probability of job-loss. Specifically, we estimate the
probability that a consumer who currently is employed will be unemployed one year hence.
2
Future job loss represents a potential major interruption to income over which households
generally have little influence, and thus it should provide a much cleaner signal of the
uncertainty faced by a household than variation in income or expenditures.
Unfortunately, no single source contains high-quality information on household-level
income, wealth, and job-loss risk. Our solution is to use a source of good data on
employment and unemployment, the Current Population Survey (CPS), to estimate job-loss
risk based on observable household characteristics. We then take the results from the CPS
estimation and apply them to predict job-loss risk for households in a data set that contains
good information on income and wealth, the Survey of Consumer Finances (SCF). Finally,
we relate the resulting predicted unemployment risk to household net worth.
I.3. Instrumental Variables Strategy
Because uncertainty is measured with significant error, most studies instrument for
their uncertainty proxy using variables such as the consumer's occupation, education, industry
of employment, and demographic characteristics. Econometric identification requires that at
least one instrument be related to the dependent variable (wealth, in our case) solely through
that instrument's correlation with uncertainty; this instrument can then legitimately be
excluded as an independent variable in the second-stage regression of wealth on instrumented
Lusardi (1997) emphasizes the link between occupational choice and risk aversion in
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the 1989 Italian Survey of Household Income and Wealth, in which one-half of households
mention job security as a reason for choosing their jobs.
A related problem occurs in the branch of the literature that proxies uncertainty with
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insurance coverage: Risk-averse households may both save more and obtain more insurance,
biasing the IV coefficient estimates. In a study focusing on households’ health insurance
coverage, Starr-McCluer addresses this problem by instrumenting coverage with the percent of
the local workforce employed by large firms. Elsewhere, Engen and Gruber consider
unemployment insurance coverage, which is determined by state policy makers and thus
probably largely exogenous to the individual household's saving behavior.
Engen and Gruber provide a forceful discussion of issues concerning instrument
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validity; they also argue that regional variables are good candidates for instrumenting
uncertainty because they likely satisfy exogeneity requirements.
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uncertainty.
Finding an appropriate instrument to exclude is problematic. For example, suppose
that more risk averse consumers both hold more precautionary wealth and choose occupations
with lower job-loss risk. Then occupation may be a good predictor of job-loss risk, but, if it
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is excluded from the second-stage regression, the coefficient estimate on the uncertainty
variable will be biased because of the correlation between instrumented job-loss risk and the
unmeasured risk-aversion portion of the error term. Similar arguments can be made for
educational choice and industry choice, and we find some empirical evidence that these
concerns may be warranted.
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To avoid this identification problem, we include all of the usual variables (occupation,
education, etc.) that are used as instruments for uncertainty as independent controls in our
second-stage equation. This requires us to find some other instrument that is correlated with
job-loss risk and can be excluded from the second-stage regression. We use the region where
the household resides. The large variation in regional economic conditions suggests that
region will be significantly correlated with an individual’s job-loss risk. In addition, based on
the assumption that,
ex ante
, most households do not choose where to live on the basis of
regional differences in job-loss risk, region should be uncorrelated with unobserved risk-
related determinants of wealth.
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I.4. Insights From a Structural Model
We thank Martin Browning for suggesting this transformation.
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We solve a theoretical model of precautionary saving, which implies important
restrictions on the empirical work. First, because a spell of unemployment causes a
household to run down precautionary balances, and because high-risk households are more
likely to have recently experienced unemployment spells, at a given point in time we may
observe high-risk households holding less wealth than their low-risk counterparts, even though
high-risk households would hold higher balances in the steady state. Thus, our specification
controls for recent shocks that may have depleted precautionary reserves.
Our model also provides guidance on how to transform wealth data. To deal with the
extreme skewness of the wealth distribution, many empirical papers have used the logarithm
rather than the level of wealth as the dependent variable. Of course, this requires dropping or
making ad hoc adjustments to observations with nonpositive wealth. However, a non-trivial
proportion of households hold zero or negative net worth--a fact that we show is consistent
with optimizing behavior in our model because of the existence of unemployment insurance
and differential borrowing and lending rates. To avoid eliminating these households from our
empirical work, we instead transform wealth using the inverse hyperbolic sine function
suggested by Burbidge, Magee and Robb (1988). Like the log, the inverse hyperbolic sine
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downweights large values, but unlike the log it can be applied to positive, zero, and negative
numbers; it also allows elasticities to vary with the level of wealth, another property of
precautionary behavior implied by standard theoretical models like ours but not allowed by a
log transformation.
I.5. Results
Our empirical results provide some support for the proposition that precautionary
saving is important. We find that increases in unemployment risk do not cause households
with relatively low permanent income to significantly boost their net worth, but that a
statistically significant and economically sizable precautionary effect emerges for households
at moderate and higher levels of income. These results are robust to a number of changes in
the specification, but not across subcomponents of wealth: We generally find a significant
precautionary motive in broad measures of wealth that include home equity, but not in
