From Transactions Data to Economic Statistics: Constructing Real-time, High-frequency, Geographic Measures of Consumer Spending

Abstract

Access to timely information on consumer spending is important to economic policymakers. The Census Bureau's monthly retail trade survey is a primary source for monitoring consumer spending nationally, but it is not well suited to study localized or shortlived economic shocks. Moreover, lags in the publication of the Census estimates and subsequent, sometimes large, revisions diminish its usefulness for real-time analysis. Expanding the Census survey to include higher frequencies and subnational detail would be costly and would add substantially to respondent burden. We take an alternative approach to fill these information gaps. Using anonymized transactions data from a large electronic payments technology company, we create daily estimates of retail spending at detailed geographies. Our daily estimates are available only a few days after the transactions occur, and the historical time series are available from 2010 to the present. When aggregated to the national level, the pattern of monthly growth rates is similar to the official Census statistics. We discuss two applications of these new data for economic analysis: First, we describe how our monthly spending estimates are useful for real-time monitoring of aggregate spending, especially during the government shutdown in 2019, when Census data were delayed and concerns about the economy spiked. Second, we show how the geographic detail allowed us quantify in real time the spending effects of Hurricanes Harvey and Irma in 2017.

The Economic Impacts of COVID-19: Evidence from a New Public Database Built Using Private Sector Data

“…To mitigate selection biases that can arise from this approach, we use data from companies that have large samples (e.g., at least one million individuals), span well-defined sectors 1 or subgroups (e.g., small businesses, bottom-income-quintile workers), and track publicly available benchmarks in historical data. Although there is no guarantee that the statistics from such data sources capture total economic activity accurately, we believe they contain useful information because the shocks induced by major crises such as COVID-19 are large relative to plausible biases due to non-representative sampling, as shown e.g., by Aladangady et al (2019) and Dunn, Hood, and Driessen (2020).…”

Changes in consumer behaviors and financial well-being during the coronavirus pandemic: results from the U.S. Household Pulse Survey

“…Our work builds on two literatures: a longstanding literature on macroeconomic measurement and a nascent literature on the economics of pandemics. In the macroeconomic measurement literature, our work is most closely related to recent studies showing that private sector data sources can be used to forecast government statistics (e.g., Abraham et al 2019, Aladangady et al 2019, Ehrlich et al 2019, Cajner et al 2019, Gindelsky, Moulton, and Wentland 2019, Dunn, Hood, and Driessen 2020. In the COVID-19 pandemic literature, several recent papers -whose results we compare to ours in the course of our analysis below -have used confidential private sector data to analyze consumer spending (e.g., Baker et al 2020, Chen, Qian, and Wen 2020, Cox et al 2020, business revenues (e.g., Alexander and Karger 2020), and labor market trends (e.g., Bartik et al 2020, Cajner et al 2020, Kurmann, Lalé, and Ta 2020, Forsythe et al 2020.…”

Using big data for impact evaluations

“…The validity of using nightlights as a proxy for economic activity can be tested by considering the GDP at the sub-national level calculated based on the conventional measures. Similarly, a research team 10 used anonymised transaction data from a large electronic payment technology company to estimate the impact of hurricanes on consumer spending. In order to test the validity of this measure, they compared the new data with a conventional consumer spending index based on the US Census Bureau's monthly retail trade survey.…”