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Project Citation: 

Kokovin, Sergey, Ozhegova, Alina, Sharapudinov, Shamil, Tarasov, Alexander, and Ushchev, Philip. Data and code for: A Theory of Monopolistic Competition with Horizontally Heterogeneous Consumers. Nashville, TN: American Economic Association [publisher], 2024. Ann Arbor, MI: Inter-university Consortium for Political and Social Research [distributor], 2024-03-29. https://doi.org/10.3886/E186482V1

Project Description

Summary:  View help for Summary
Our novel approach to modeling monopolistic competition with heterogeneous firms and consumers involves spatial product differentiation. Space can be interpreted either as a geographical space or as a space of characteristics of a differentiated good. In addition to price setting, each firm also chooses its optimal location in this space. We formulate conditions for positive sorting: more productive firms serve larger market segments and face tougher competition; and for the existence and uniqueness of the equilibrium. To quantify the role of the sorting mechanism, we calibrate the model using cross-sectional haircut market data and perform counterfactual analysis. We find that inequality in the distribution of the gains among consumers caused by positive market shocks can be substantial: the gains of consumers from more populated locations are 3-4 times higher.

Scope of Project

Subject Terms:  View help for Subject Terms Monopolistic Competition; Spatial economics; Spatial data
JEL Classification:  View help for JEL Classification
      F10 Trade: General
      L11 Production, Pricing, and Market Structure; Size Distribution of Firms
      L13 Oligopoly and Other Imperfect Markets
Geographic Coverage:  View help for Geographic Coverage Bergen, Norway
Time Period(s):  View help for Time Period(s) 2020 – 2021
Collection Date(s):  View help for Collection Date(s) 8/1/2021 – 8/20/2021
Universe:  View help for Universe Hairdressers in Bergen, Norway
Data Type(s):  View help for Data Type(s) aggregate data; geographic information system (GIS) data; observational data

Methodology

Unit(s) of Observation:  View help for Unit(s) of Observation firm

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