"Tracking the COVID-19 Crisis with High-Resolution Transaction Data" (with Vasco M. Carvalho, Juan R. Garcia, Stephen Hansen, Álvaro Ortiz, Tomasa Rodrigo, and Pep Ruiz)

Abstract:

Financial and payments systems throughout the world generate a vast amount of naturally occurring, and digitally recorded, transaction data, but national statistical agencies mainly rely on surveys of much smaller scale for constructing official economic series. This paper considers billions of transactions from credit- and debit-card data from BBVA, one of the largest banks in the world, as an alternative source of information for measuring consumption, a key component of GDP. We show, through extensive validation exercises against official consumption measures, that transaction data can usefully complement slow-moving national accounts and consumption surveys. We show that this holds (i) over time, as a high frequency consumption proxy both at national and subnational levels; (ii) over consumption categories, rendering it a naturally occurring consumption survey and (iii) over space, as a covariate-rich mobility dataset. We apply the idea of card spending as a real-time, geographically resolved and covariate-rich consumption proxy to the COVID- 19 crisis, where we present four findings: (1) a strong consumption reaction to lockdown enactment and a rapid, V-shaped consumption recovery at the global, national and subnational levels; (2) differential responses of expenditure to alternative lockdown policies at a subnational level; (3) an adjustment to the average consumption basket during lockdown towards the goods basket of low-income house- holds; (4) a divergence in mobility patterns during lockdown according to income in which poorer households travel more during the workweek. We conclude that transaction data provides high-quality information about household consumption and mobility, rendering it a potentially important input into national statistics and research on household consumption.