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Geospatial Heterogeneity in Inflation: A Market Concentration Story

Photo potrait of Seula Kim
Seula Kim Pennsylvania State University
Headshot of Michael Navarrete
Michael Navarrete Assistant Policy Adviser and Economist

Summary

Examining how inflation varies across regions with different income levels, the authors show that from 2006 to 2020, poorer metropolitan statistical areas (MSAs) experienced higher food inflation compared to wealthier MSAs. They authors of this working paper identify a causal link between market concentration and inflation by exploiting the 2014–15 bird flu episode.

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Working Paper 2025-15a

Abstract: We study spatial heterogeneity in inflation across regions with different income levels and the role of retailer market structure. Using NielsenIQ Retail Scanner and Business Dynamics Statistics data, we document new stylized facts on food inflation and local retail concentration. From 2006 to 2020, poorer MSAs experienced annualized food inflation that was 0.46 percentage points higher than richer MSAs, implying a cumulative gap of 8.8 percentage points. Poorer areas also exhibit fewer products, fewer retailers, and higher market concentration. To identify causal effects, we exploit two exogenous cost shocks. We use the 2014–15 avian influenza outbreak in a triple-difference design and global coffee price fluctuations in a difference-in-pass-through design that interact cost shocks with local retail concentration. We develop a heterogeneous-firm model with customer capital accumulation that rationalizes these patterns. Firms with larger market shares accumulate more customer capital and market power, which leads to higher pass-through of cost shocks.

JEL classification: E31, I31, L11, L81, R12

Key words: inflation, retailer market structure, market concentration, market power, passthrough of cost shocks, spatial inequality

https://doi.org/10.29338/wp2025-15


Seula Kim is with Penn State University. Michael A. Navarrete is with the Research Department of the Federal Reserve Bank of Atlanta. The authors thank Miguel Ampudia, Boragan Aruoba, Ben Bernanke, Marcus Casey, Thesia Garner, John Haltiwanger, Judy Hellerstein, Colin Hottman, David Johnson, Ethan Kaplan, Munseob Lee, Giacomo Ponzetto, John Shea, Lumi Stevens, Nico Trachter, and participants at several seminars at the BLS, University of Maryland, Federal Reserve Board, Federal Reserve Bank of Atlanta, and various conferences including the NBER Competition in the U.S. Agricultural Sector Meeting, BSE Summer Forum, UChicago-SNU Inequality conference, BdE-BOC-ECB-Fed Conference, NASMES Meeting, DC Urban Economics Workshop, Midwest Macro Meeting, and SED. They also thank the RESET team for providing the infrastructure to address our research project. This research is funded by the Washington Center for Equitable Growth. Researcher(s)' own analyses calculated (or derived) based in part on data from Nielsen Consumer LLC and marketing databases provided through the NielsenIQ Datasets at the Kilts Center for Marketing Data Center at the University of Chicago Booth School of Business. The conclusions drawn from the NielsenIQ data are those of the researcher(s) and do not reflect the views of NielsenIQ. NielsenIQ is not responsible for, had no role in, and was not involved in analyzing and preparing the results reported herein. The views expressed here are those of the authors and do not necessarily reflect the views of the Federal Reserve System, Board of Governors, or their staff. All errors are our own. Any opinions and conclusions expressed herein are those of the authors and do not necessarily represent the views of the Federal Reserve System, the Board of Governors, or its staff.

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