REFERENCES AND NOTES
1. D. Bell-Pedersen et al., Nat. Rev. Genet. 6, 544–556 (2005).
2. I. M. Axmann, S. Hertel, A. Wiegard, A. K. Dörrich, A. Wilde,
Mar. Genomics, (2014).
3. H. Ito et al., Proc. Natl. Acad. Sci. U.S. A. 106, 14168–14173
4. E. R. Zinser et al., PLOS ONE 4, e5135 (2009).
5. K. Whitehead, M. Pan, K. Masumura, R. Bonneau, N. S. Baliga,
PLOS ONE 4, e5485 (2009).
6. J. F. Flint, D. Drzymalski, W. L. Montgomery, G. Southam,
E. R. Angert, J. Bacteriol. 187, 7460–7470 (2005).
7. A. M. Wier et al., Proc. Natl. Acad. Sci. U.S.A. 107, 2259–2264
8. J. M. Gasol et al., Mar. Ecol. Prog. Ser. 164, 107– 124 (1998).
9. C. Winter, G. J. Herndl, M. G. Weinbauer, Aquat. Microb. Ecol.
35, 207–216 (2004).
10. M. Galí et al., Global Biogeochem. Cycles 27, 620–636 (2013).
11. S. M. Gifford, S. Sharma, M. Booth, M. A. Moran, ISME J. 7,
12. E. A. Ottesen et al., Proc. Natl. Acad. Sci. U.S.A. 110,
13. E. A. Ottesen et al., ISME J. 5, 1881–1895 (2011).
14. Materials and methods are available as supplementary
materials on Science Online.
15. C. M. Preston et al., PLOS ONE 6, e22522 (2011).
16. J. Tomasch, R. Gohl, B. Bunk, M. S. Diez, I. Wagner-Döbler,
ISME J. 5, 1957–1968 (2011).
17. N. Li et al., PLOS ONE 5, e9894 (2010).
18. L. Steindler, M. S. Schwalbach, D. P. Smith, F. Chan,
S. J. Giovannoni, PLOS ONE 6, e19725 (2011).
19. Z. Liu, W. Hsiao, B. L. Cantarel, E. F. Drábek, C. Fraser-Liggett,
Bioinformatics 27, 3242–3249 (2011).
20. J. Okansen et al., Package “vegan”: Community ecology
package (2012); http://cran.rproject.org/web/packages/
We thank the officers and crew of the Kilo Moana, J. Robidart,
S. Wilson, and the ESP engineering and science team (J. Ryan,
J. Birch, C. Preston, G. Massion, S. Jensen, and B. Roman) for all
of the able assistance. This work was supported by grants from
the Gordon and Betty Moore Foundation GBMF nos. 492.01 and
3777 (E.F.D.) and NSF grant EF0424599 (E.F.D.). Development
of the ESP was supported by NSF grant OCE-0314222 (to C.A.S.),
NASA Astrobiology grants NNG06GB34G and NNX09AB78G
(to C.A.S.), the Gordon and Betty Moore Foundation nos. 731 and
2728 (C.A.S.), and the David and Lucile Packard Foundation.
This work is a contribution of C-MORE. Sequences reported in this
paper have been deposited in the GenBank database (accession
no. SRP041215). The Monterey Bay Aquarium Research Institute
holds rights to C. A. Scholin et al., U.S. Patent 6187530 (2001).
C.A.S. is disqualified from receiving any royalties that might arise
from licensing agreements. The ESP is available commercially from
Spyglass Technologies and MacLane Research Laboratories; C.A.S.
has no financial interest in either company and is not compensated
in any way for giving advice on ESP technology transfer.
Materials and Methods
Figs. S1 to S14
Tables S1 to S4
20 February 2014; accepted 22 May 2014
Harnessing naturally occurring
data to measure the response
of spending to income
Michael Gelman,1 Shachar Kariv,2 Matthew D. Shapiro,1,3 Dan Silverman,3,4 Steven Tadelis3,5
This paper presents a new data infrastructure for measuring economic activity. The
infrastructure records transactions and account balances, yielding measurements with
scope and accuracy that have little precedent in economics. The data are drawn from a
diverse population that overrepresents males and younger adults but contains large
numbers of underrepresented groups. The data infrastructure permits evaluation of a
benchmark theory in economics that predicts that individuals should use a combination of
cash management, saving, and borrowing to make the timing of income irrelevant for the
timing of spending. As in previous studies and in contrast to the predictions of the theory,
there is a response of spending to the arrival of anticipated income. The data also show,
however, that this apparent excess sensitivity of spending results largely from the
coincident timing of regular income and regular spending. The remaining excess sensitivity
is concentrated among individuals with less liquidity.
Economic researchers and policy-makers have long sought high-quality measures of indi- vidual income, spending, and assets from large and heterogeneous samples. For ex- ample, when policy-makers consider whether
and how to stimulate the economy, they need to
know how individuals will react to changes in
their income. Will individuals spend differently?
Will they save at a different rate or reduce their
debt, and when? There are many obstacles to
obtaining reliable answers to these important
questions. One obstacle is that existing data
sources on individual income and spending have
substantial limits in terms of accuracy, scope,
This paper advances the measurement of income and spending with new high-frequency
data derived from the actual transactions and
account balances of individuals. It uses these
measures to evaluate the predictions of a benchmark economic theory that states that the timing
of anticipated income should not matter for
spending. Like previous research, it finds that
there is a response of spending to the arrival
of anticipated income. The data show that, on
average, an individual’s total spending rises
substantially above average daily spending on
the day that a paycheck or Social Security check
arrives, and remains high for at least the next
4 days. The data also allow the construction of
variables that show, however, that this apparent
excess sensitivity of spending results in large part
from the coincident timing of regular income and
regular spending. The remaining excess sensitiv-
ity is concentrated among individuals who are
likely to be liquidity-constrained.
Traditionally, researchers have used surveys
such as the Consumer Expenditure Survey (CEX)
to measure individual economic activity. Such
surveys are expensive to implement and require
considerable effort from participants and are
therefore fielded infrequently, with modest-sized
samples. Researchers have recently turned to administrative records, which are accurate and can
be frequently refreshed, to augment survey research. So far, however, the administrative
records have typically represented just a slice
212 11 JULY 2014 • VOL 345 ISSUE 6193 sciencemag.org SCIENCE
1Department of Economics, University of Michigan, Ann
Arbor, MI 48109, USA. 2Department of Economics, University
of California, Berkeley, Berkeley, CA 94720, USA. 3National
Bureau of Economic Research (NBER), Cambridge, MA
02138, USA. 4Department of Economics, Arizona State
University, Tempe, AZ 85287, USA. 5Haas School of
Business, University of California, Berkeley, Berkeley, CA
*Corresponding author. E-mail: firstname.lastname@example.org
Table 1. Check versus ACS demographics
(percent). The sample size for Check is 59,072,
35,417, 28,057, and 63,745 for gender, age, education,
and region, respectively. The sample size for ACS is
2,441,532 for gender, age, and region and 2, 158,014
Male 59.93 48.59
Female 40.07 51.41
18–20 0.59 5.72
21–24 5.26 7.36
25–34 37.85 17.48
35–44 30.06 17.03
45–54 15.00 18.39
55–64 7.76 16.06
65+ 3.48 17.95
Less than college 69.95 62.86
College 24.07 26.22
Graduate school 5.98 10.92
Census Bureau region
Northeast 20.61 17.77
Midwest 14.62 21.45
South 36.66 37.36
West 28.11 23.43