Projects Funded for Tengda Gong
Determinants of Markups: Evidence Using a New Big Data Analysis for U.S. Groceries
Bulat Gafarov and Tengda Gong
1. Our objective is to introduce and use a new big-data methodology to obtain estimates of good-specific markups for US grocery stores.
Specifically, we propose to estimate markups based on a standard differentiated-good model of profit-maximizing sellers facing downward-sloping demand. This approach does not require observations of unit costs, which allows us to use a comprehensive data set that covers the entire United States over a long sample periods and which includes a large variety of good categories.
2. Our approach allows us to identify and study determinants of heterogeneity and time variation of markups.
In particular, our estimates can be used to capture the effect of market concentration on markups and measure the effects of local wealth and unemployment on the market structure.
Summary of Results:
We have completed a working paper (Gafarov et al., 2023). This paper documents substantial time variation in price elasticities of demand and therefore markups. We propose a two-step procedure to identify time-varying markups. Using data of US grocery stores from 2001-2020 we first estimate elasticities at the market-good-year level. We then efficiently aggregate these data by year to estimate a common trend and cyclical variation in elasticities. We estimate (i) a secular increase in U.S. grocery store markups of 3.9% per year over the sample period and (ii) a 13.6% cyclical decline at times of aggregate demand contractions. Our results imply pro-cyclical changes in markups. Across markets, elasticities vary with market-wide factors that we expect to influence preferences and market structure—real GDP, unemployment and market concentration.