and avoiding the bad consequences of temporary illiquidity. Given that we defined recurring
spending conservatively (i.e., required that it be
the same amount to the cent), this estimate is
probably a lower bound on how much accounting for it reduces excess sensitivity.
Figure 2C provides still more evidence that
the benchmark theory is a better description
of behavior than the total spending estimates
would suggest. For this imminently divisible and
easily smoothed discretionary spending, we observe very modest excess sensitivity to the arrival
of predictable income.
We find evidence of individual heterogeneity
of excess sensitivity that is consistent with the
theory that predicts such behavior among those
with insufficient liquidity or available credit,
perhaps due to imperfections in credit markets.
Figure 3 plots estimates of bkc for nonrecurring
spending by terciles of liquidity. We define liquidity for each user as the average daily balance
of checking and savings accounts over the entire sample period, normalized by the user’s average daily spending. The average user in the
lowest tercile has 5 days of spending in cash on
hand; the average user in the highest tercile
has 159 days. The estimates show that excess
sensitivity is significantly more pronounced
among those in the lowest tercile of the liquidity
Figure S2 plots estimates of excess sensitivity by terciles of the available credit utilization
distribution. Excess sensitivity is concentrated
among users near the limit of their ability to
borrow with credit cards. Those who have little
liquidity or take their debt levels very close to
their limits may be poor at planning or optimizing. The evidence indicates that differences in
liquidity and constraints drive heterogeneity of
excess sensitivity among Check users.
Many prior studies of spending responses to
income have used the CEX quarterly retrospective survey, which records self-reports of income
but does not measure its timing precisely. Souleles,
for example, uses it to estimate the spending
response to the arrival of income tax refunds
and overcomes the lack of timing information
by calculating from aggregate statistics the likelihood of receiving a refund at various dates (1).
Parker takes a similar approach and exploits anticipated changes in take-home pay when workers
hit the annual cap on the Social Security payroll
tax (2). Johnson et al. and Parker et al. measure
the timing of some income more precisely by
adding special questions to the CEX about tax
rebates (3, 4).
Some studies use higher-frequency data to
estimate spending responses to income. The
CEX diary survey records spending daily for 2
weeks but does not collect high-frequency in-
come data. Stephens overcomes this limitation
by studying the spending response to the re-
ceipt of Social Security benefits, which used to
arrive on the same day of each month (5). The
UK’s Family Expenditure Survey collects the
most recent paystub of respondents and asks
them to track spending for 2 weeks. Stephens
uses the paystub to infer the amount and
timing of paychecks and estimates the spending
response to them (6).
These prior studies use a variety of methods, but
share an interest in estimating either an elasticity,
defined as ∂logðspendingÞ ∂logðincomeÞ , or a marginal propensity
to consume (MPC), defined as ∂ðspendingÞ ∂ðincomeÞ . Table S3
summarizes the key features of these prior estimates and compares them to analogous aspects
of our study.
The studies differ in the time frame over which
they measure spending changes in response to a
change in income. This makes the levels of their
estimated elasticities or MPCs difficult to compare. For our study, we present the point estimate
of effects on the first day after the income arrives;
that is b1c from Eq. 1 for the elasticity of spending
in category c. For the MPC we present the g1c
from the equation
xict ¼ aic þ ∑
djc þ ∑
gkc Paymentic,t−k þ eict
where xict is the ratio of spending of individual i
to i’s average daily spending in category c, at
date t; djc is a day-of-week fixed effect; aic is a
user fixed effect; and Paymentic,t−k is the ratio of
the amount of the payment received by individual i divided by i’s average daily spending in
category c, at date t−k. Analogously, table S3
presents only the shortest-run effects reported
in all the other studies. Although our and other
studies estimate larger impacts at longer horizons, the central conclusion of table S3 about the
relative precision of the estimates is not affected
by the choice of horizon.
The prior estimates are important and influential but, as table S3 shows, they often lack
precision. Among studies of the quarterly CEX
data, Hsieh is unusual in its precision (7). The
last four rows of table S3 include the confidence intervals for our estimates of both the
elasticity and the MPC. These intervals are
small, both economically and relative to other
studies. Only Broda and Parker provides estimates that are as precise as those from the
Check data (8). That paper uses Homescan data
and estimates precisely an MPC out of tax rebates near 0. These estimates rely on surveys to
determine receipt of the rebate, however, and
would be attenuated if those reports are subject
to error. The Homescan spending data are also
limited in scope, largely capturing only goods
with Universal Product Codes. Moreover, the
Check data allow estimates of the response to
routine payments such as paychecks and Social
Security payments, not just particular payments
such as tax rebates.
Related studies of administrative data also
provide accurate measures of spending but
do not cover it comprehensively. For example,
Agarwal et al. use data from a single credit card
company to study the spending response to tax
rebates; they can thus track the effects of the
rebate on a single account but not on overall
spending (9). Kuchler makes use of more com-
prehensive administrative data collected from a
debt management Web site, but the number of
users (556) is relatively small (10). The financial
application Mint ( https://www.mint.com/) has
a complementary data infrastructure that it is
using to construct monthly time series of spend-
ing by types of goods (11). It has not been used
for research along the lines of the estimates in
In policy discussions before the 2008 tax
rebates, the Congressional Budget Office and
others cited the point estimates of the effect
of the 2001 rebate from Parker, Johnson, and
Souleles, but not the substantial uncertainty
about that estimate documented in that paper
and in table S3 (3, 12). More generally, estimates of spending rates from different changes
in income play a key role in the evaluation of
the American Recovery and Reinvestment Act
(13), making the stakes in getting credible and
precise estimates of these parameters very high.
This paper shows how economic theory and
policy can benefit from analysis made possible
with naturally occurring data such as those
provided by Check.
REFERENCES AND NOTES
1. N. S. Souleles, Am. Econ. Rev. 89, 947–958 (1999).
2. J. A. Parker, Am. Econ. Rev. 89, 959–973 (1999).
3. D. S. Johnson, J. A. Parker, N. S. Souleles, Am. Econ. Rev. 96,
4. J. A. Parker, N. S. Souleles, D. S. Johnson, R. McClelland,
Am. Econ. Rev. 103, 2530–2553 (2013).
5. M. Stephens Jr., Am. Econ. Rev. 93, 406–422 (2003).
6. M. Stephens Jr., Econ. J. 116, 680–701 (2006).
7. C.-T. Hsieh, Am. Econ. Rev. 93, 397–405 (2003).
8. C. Broda, J. A. Parker, “The economic stimulus payments
of 2008 and the aggregate demand for consumption”
(manuscript, Sloan School of Management, Cambridge,
9. S. Agarwal, C. Liu, N. S. Souleles, J. Polit. Econ. 115, 986–1019
10. T. Kuchler, “Sticking to your plan: Hyperbolic discounting and
credit card debt paydown” (manuscript, Stern School of
Business, New York, 2013).
11. Intuit, Intuit consumer spending index (Intuit, Mountain View,
12. Congressional Budget Office, Options for Responding to
Short-Term Economic Weakness (U.S. Congressional Budget
Office, Washington, DC, 2008).
13. Congressional Budget Office, Estimated Impact of the
American Recovery and Reinvestment Act on Employment
and Economic Output from July 2010 Through September
2010 (U.S. Congressional Budget Office, Washington,
This research was supported by a grant from the Alfred P. Sloan
Foundation. M.D.S. acknowledges additional support through
the Michigan node of the NSF-Census Research Network
(NSF grant SES 1131500). This paper has benefited from
suggestions by the participants of the NBER Summer Institute,
the Conference on Economic Decisionmaking (Aspen, Colorado),
and several seminars. A data set for replicating the results
of this paper is available through the University of California
Berkeley Econometrics Lab (EML) at https://eml.berkeley.edu/
cgi-bin/ HarnessingDataScience2014.cgi. To access the data,
The data set contains no personal or account identifiers.
The data are aggregated and transformed so that they reveal
no personally identifying information.
28 October 2013; accepted 13 June 2014