cover image: Nonlinear Pricing and Misallocation

Nonlinear Pricing and Misallocation

7 Nov 2024

This paper studies the effect of nonlinear pricing on markups and misallocation. We develop a general equilibrium model of firms that are allowed to set a quantity-dependent pricing schedule—contrary to the typical assumption in macroeconomic models. Without the restriction to linear pricing, markup heterogeneity is no longer a sign of misallocation. Larger firms charge higher markups, yet the allocation of resources across firms is efficient. Further, we point to a new source of misallocation. In general equilibrium, high-taste consumers are allocated too much of each good, low-taste consumers too little. When labor supply is elastic, firms’ market power depresses aggregate labor, but this effect is independent of the level of the aggregate markup in the economy. Using micro data from the retail sector, we show that nonlinear pricing is prevalent and quantify the model. We find that the welfare losses from misallocation across consumers under nonlinear pricing are substantially larger than those from misallocation across firms under linear pricing.
industrial organization macroeconomics microeconomics growth and productivity economic fluctuations and growth market structure and firm performance development and growth consumption and investment market structure and distribution

Authors

Gideon Bornstein, Alessandra Peter

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Acknowledgements & Disclosure
We would like to thank Scott Baker, Hugo Hopenhayn, Pete Klenow, Erik Madsen, Virgiliu Midrigan, Alessandro Pavan, Ivan Werning, and EAGLS, as well as participants at VMACS, IIES Macro Lunch, Stony Brook, Wharton Macro Lunch, World Bank Research Seminar, Insper, USC Marshall Macro Day, NYU Macro Lunch, MIT, UBC, Harvard, UT Austin, Barcelona Summer Forum, SED, the EFG at NBER SI, UCL, Columbia, Queen Mary University, LSE, the Minneapolis Fed, the Richmond Fed, UW Madison, EIEF, Michigan, the Atlanta Fed, and UCLA for insightful comments. The paper benefited from thoughtful discussions by Michael Peters, Kieran Larkin, and Joel David. We also thank Tanvi Jindal and Paige Stevenson, who provided excellent research assistance. Researchers’ 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 researchers 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 authors declare that they have no relevant or material financial interests that relate to the research described in this paper. The views expressed herein are those of the authors and do not necessarily reflect the views of the National Bureau of Economic Research.
DOI
https://doi.org/10.3386/w33144
Pages
84
Published in
United States of America

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