Measuring Markups with Revenue Data

29 Pages Posted: 31 Aug 2021 Last revised: 18 Oct 2023

See all articles by Ivan Kirov

Ivan Kirov

University of Chicago - Department of Economics; Analysis Group, Inc.

Paolo Mengano

ESADE Business School

James Traina

Federal Reserve Bank of San Francisco

Date Written: October 1, 2023

Abstract

When output prices are unobserved, standard production-based markup estimators are biased and inconsistent because they are unable to distinguish whether firms have higher revenues due to higher prices or higher quantities. Building on work designed for competitive environments, we propose a novel method that solves this problem using only revenue data. We flexibly model markups as a specified function of observables and fixed effects, supporting a broad class of variable-markup frameworks. We explicitly adopt a Markovian revenue productivity process, a commonly implicit assumption in the literature. Our suggested two-step approach is simple in concept and implementation, requiring only common regression techniques.

Keywords: Markups, Revenue, Omitted Price Bias, Production Functions, Productivity

JEL Classification: C14, C33, D24, D43, L11

Suggested Citation

Kirov, Ivan and Mengano, Paolo and Traina, James, Measuring Markups with Revenue Data (October 1, 2023). Available at SSRN: https://ssrn.com/abstract=3912966 or http://dx.doi.org/10.2139/ssrn.3912966

Ivan Kirov

University of Chicago - Department of Economics ( email )

1126 East 59th Street
Chicago, IL 60637
United States

Analysis Group, Inc. ( email )

111 Huntington Avenue
10th floor
Boston, MA 02199
United States

Paolo Mengano

ESADE Business School ( email )

Av. de Pedralbes, 60-62
Barcelona, 08034
Spain

James Traina (Contact Author)

Federal Reserve Bank of San Francisco ( email )

101 Market Street
San Francisco, CA 94105
United States

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