Harnessing Naturally Occurring Data to Measure the Response of Spending to Income

Gelman, M., S. Kariv, M.D. Shapiro, D. Silverman, and S. Tadelis. "Harnessing Naturally Occurring Data to Measure the Response of Spending to Income." Science 345, no. 11 (2014). DOI: 10.1126/science.1247727, available at http://www.sciencemag.org/content/345/6193/212.full.
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. Link to data at Berkeley Econometrics Lab (EML): https://eml.berkeley.edu/cgi-bin/HarnessingDataScience2014.cgi