Paper Detail
Aditya Aladangady, Ricardo Duque Gabriel, Carlo Wix
We introduce a novel monthly county-level consumption dataset constructed from spending data on over 350 million credit cards in the Federal Reserve's Y-14M reports, covering over 3,000 U.S. counties since 2014. We first show that the data closely approximate traditional consumption measures, explaining 92 percent of the variation in monthly adjusted personal consumption expenditures (PCE) growth at the national level and capturing meaningful cross-sectional variation in annual adjusted PCE growth at the state level. As a proof of concept, we use the county-month panel to estimate heterogeneous consumption responses to monetary policy shocks across the county-level income distribution, an analysis infeasible with traditional consumption data. We find that low-income counties exhibit larger spending declines than high-income counties, consistent with heterogeneous agent New Keynesian models. Finally, we provide practical guidance for researchers working with similar data, discussing coverage, sample composition, and the approximation of credit card spending from credit bureau data.
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@article{aladangady2026measuring,
title = {Measuring Consumption with Credit Card Data: Benchmarking and Beyond},
author = {Aditya Aladangady and Ricardo Duque Gabriel and Carlo Wix},
year = {2026},
abstract = {We introduce a novel monthly county-level consumption dataset constructed from spending data on over 350 million credit cards in the Federal Reserve's Y-14M reports, covering over 3,000 U.S. counties since 2014. We first show that the data closely approximate traditional consumption measures, explaining 92 percent of the variation in monthly adjusted personal consumption expenditures (PCE) growth at the national level and capturing meaningful cross-sectional variation in annual adjusted PCE grow},
url = {https://arxiv.org/abs/2607.08759},
keywords = {econ.GN},
eprint = {2607.08759},
archiveprefix = {arXiv},
}
{}