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The Retail Transformation

Paper Session

Saturday, Jan. 4, 2025 2:30 PM - 4:30 PM (PST)

Hilton San Francisco Union Square, Yosemite A
Hosted By: American Economic Association & Committee on Economic Statistics
  • Chair: Zhonglin Li, National University of Singapore

Distributional Impacts of the Changing Retail Landscape

Jessie Handbury
,
University of Pennsylvania and NBER
Yue Cao
,
Chinese University of Hong Kong
Judith Chevalier
,
Yale University and NBER
Hayden Parsley
,
University of Texas-Austin
Kevin R. Williams
,
Yale University and NBER

Abstract

We consider the common empirical tactic of estimating consumer value for a good or service by examining consumer willingness-to-travel. We argue that distance to suppliers is endogenous because suppliers strategically choose locations to target consumers; we introduce a novel instrument to address this form of endogeneity. Using geolocation data from millions of smartphones, we estimate preferences for specific retail chains across income groups and regions and show that accounting for distance endogeneity significantly alters willingness-to-travel measures. Contrary to the prevailing “retail apocalypse” narrative, we find that consumer surplus per trip to general merchandise stores did not significantly decline from 2010 to 2019. For the lowest income consumers, the expansion of national chains, particularly dollar stores, nearly compensates for the closure of traditional department stores and regional chains. Notably, failing to account for distance endogeneity leads to the erroneous conclusion that lower-income households experienced statistically significant consumer surplus declines.

Productivity Growth and Structural Change in Retail Trade

Dominic Smith
,
U.S. Bureau of Labor Statistics
G. Jacob Blackwood
,
U.S. Census Bureau and Amherst College
Michael Giandra
,
U.S. Bureau of Labor Statistics
Cheryl Grim
,
U.S. Census Bureau
Jay Stewart
,
U.S. Bureau of Labor Statistics

Abstract

Official Bureau of Labor Statistics (BLS) estimates of productivity growth in the retail trade sector indicate that productivity has grown at a moderate rate of 2.8 percent per year between 1987 and 2017, and that there is considerable variation in growth rates across 4-digit industries. But the official data, which can be thought of as weighted averages of establishment-level productivity, tell us nothing about what goes on within industries. Given the transformation of retail trade over the past three decades, this information could provide more insight. In this paper, we present productivity dispersion statistics for industries in the retail trade sector. These statistics are similar to the BLS-Census Bureau Dispersion Statistics on Productivity (DiSP) for manufacturing industries and complement the official BLS industry-level productivity statistics. We find that from 1987 through 2017, productivity dispersion increased slightly on average. Surprisingly, the tails of the retail productivity distribution have similar dispersion as we find in the middle. Firm dispersion has increased more than establishment dispersion. In results undergoing Census disclosure review, we relate these changes to productivity growth and the increasing importance of national retailers and e-commerce.

Markups and Cost Pass-through along the Supply Chain

Santiago Ernesto Alvarez-Blaser
,
University of Basel
Alberto Cavallo
,
Harvard Business School
Alexander Mackay
,
Harvard Business School
Paolo Mengano
,
Harvard Business School

Abstract

We study markups and pricing strategies along the supply chain leveraging a unique dataset that combines detailed product price and cost information from a major international manufacturer with retail prices. Our analysis reveals a similar level of consumer markup across countries yet uncovers substantial heterogeneity in the division of the markup between the manufacturer and retailers across different countries. Perhaps surprisingly, these consumer markups appear to remain stable over the recent high inflation period. A detailed examination of pricing strategies indicates that manufacturers engage in quality differentiation, a factor not considered by retailers in their pricing. Our analysis reveals divergent pricing behaviors in response to a cost shock: the manufacturer adjusts prices quickly and fully, while retailers exhibit a more gradual reaction. This paper contributes to the understanding of pricing dynamics along the supply chain, highlighting the roles of different actors and their strategies in price setting and adjustment.

Sales versus Margins: Alternative Measures of Output and Productivity for Retail Trade

Jennifer Kim
,
U.S. Bureau of Labor Statistics
Charles Myers III
,
U.S. Bureau of Labor Statistics
Jenny Rudd
,
U.S. Bureau of Labor Statistics
Jennifer Price
,
U.S. Bureau of Labor Statistics
Kandi Miller
,
U.S. Bureau of Labor Statistics
Amanda Robic
,
U.S. Bureau of Labor Statistics

Abstract

The retail trade sector has experienced dramatic changes in the types of establishments and goods and in the ways these goods are distributed and sold. We introduce new margin-based measures of output that complement existing sales-based output measures and identify what affects the differences between these measures of output, including prices, the product mixes, and industry dynamics. From 2007 to 2022, sales productivity outpaces margin productivity in most instances. We look closely at three representative industries with different trends and the role prices may play. In electronics and appliance stores, sales productivity showed robust growth while margin productivity grew more slowly. In beer, wine, and liquor stores, the two measures have similar trends. For pharmacies and drug stores, margin productivity growth is three times the rate of sales productivity. Comparing these measures gives a better understanding of the distinct effects of quantity of goods sold and changes to retail services on output and productivity.

Discussant(s)
Brett Hollenbeck
,
University of California-Los Angeles
Zhonglin Li
,
National University of Singapore
Brian Wheaton
,
University of California-Los Angeles
James Traina
,
Federal Reserve Bank of San Francisco
JEL Classifications
  • C8 - Data Collection and Data Estimation Methodology; Computer Programs
  • D2 - Production and Organizations