ANN ARBOR — Researchers at the University of Michigan, University of California Berkeley, and Arizona State University have developed a new data infrastructure for measuring economic activity.
The infrastructure uses aggregated and de-identified data on transactions and account balances from Check, a mobile payments app, to produce accurate and comprehensive measures of consumers’ spending and income on a daily basis.
In a recent issue of Science, economists Michael Gelman, Shachar Kariv, Matthew Shapiro, Dan Silverman, and Steven Tadelis report precise measures of how spending responds to the arrival of paychecks and government benefits.
They find that relative to other days of the month, spending jumps on the day that payments arrive and remains high for several subsequent days. For most people this jump is not because they have difficulty managing their money; instead it reflects the convenience of paying large, recurring bills right around paydays. The data reveal that non-recurring spending, especially at fast food restaurants and coffee shops, is quite smooth across days of the month.
The research, which was supported by the Alfred P. Sloan Foundation, aims to use data generated by households and businesses in the course of their normal activities to produce economic and demographic measurements that can augment existing measures that are currently generated from surveys. These innovative measurements may improve the timeliness, frequency, accuracy, and scope of data available for understanding economic activity.