Job Market Paper

Job Preferences, Labor Market Power, and Inequality
This Version: November 2024; Supplemental Materials

I analyze how a firm’s labor market power shapes, and is shaped by, its workforce, and I evaluate the implications for welfare and inequality. Using matched worker-firm panel data from Norway (1995-2018), I develop, identify, and estimate an equilibrium model of the labor market where firms compete with one another for workers who are heterogeneous in both their skills and preferences over wages versus non-wage job amenities. I allow the wage-amenity trade-offs to be correlated with skills, while also varying among equally skilled workers. When a firm adjusts its wages, the composition of its workforce shifts, and these compositional changes, in turn, affect the labor supply curve to the firm. As a result, the firm’s wage-setting power varies based on which types of workers it employs. I find that this variation leads to large allocative inefficiency, with welfare losses from imperfect competition estimated at 9.5% relative to the competitive benchmark.

Working Papers

Discrete Choice with Generalized Social Interactions
Revision Requested at Econometrica; Supplemental Materials

This paper explores how identity influences group behavior through social interactions. I study a discrete choice model where people wish to conform to the actions of some members of their network, while deviating from the actions of others. Under this generalized framework, I explore what aggregate outcomes arise from noncooperative decisionmaking. I characterize the uniqueness and stability of equilibria, and I discuss implications of negative spillovers for welfare and inefficiency. Additionally, I demonstrate how the model may be taken to data. I introduce a novel identification strategy that accounts for unobserved network effects by leveraging within-network variation in individual characteristics. I also construct internal instruments to overcome the issue of measurement error, which is a primary source of endogeneity in network-based models with incomplete information. Finally, I apply this methodology using data from the large-scale education experiment Project STAR, where I find robust evidence of gender differences in peer effects.

Presentations: North American UEA Meeting (2022)

Peer Effects in Linear-in-Means Models with Heterogeneous Interaction Effects
(with Magne Mogstad and Alex Torgovitsky)

We study peer effects in linear-in-means models with heterogeneous interaction effects. The classical linear-in-means model imposes strict homogeneity on the interaction effects, yielding testable implications that can easily be examined in data. We relax these restrictions to allow for both positive and negative interaction effects that vary within and across groups. These extensions make the linear-in-means model suited to study a wide range of economic behaviors in addition to peer effects, including joint labor supply decisions within households and strategic interactions among firms. We analyze what can and cannot be learned from frequently used OLS and IV estimands for linear-in-means models under heterogeneous interaction effects. While these estimands do not lead to point identification, they can still be used to draw inferences about key economic quantities. We apply these results to two economic applications: classroom peer effects in Kenyan primary schools and strategic pricing decisions among cocoa traders in Sierra Leone. In each application, we reject homogenous interaction effects. Yet, we still draw meaningful inferences about endogenous interactions and social multipliers while allowing for heterogeneous interaction effects.

Linear Supply and Demand in Heterogeneous Markets
(with L. Henry de Frahan, Ingvil Gaarder, Magne Mogstad, and Alex Torgovitsky)

We modify the classic linear supply and demand system to allow for the coefficients on price to be unobservable random variables that vary across heterogeneous markets. Known conditions for point identification place strong requirements on the available instruments. We show how to construct and estimate bounds on scalar target parameters that are valid for any type of instrument, or even with no instrument at all. Numerical simulations calibrated to a well-known data set show that the model is not point identified. However, the bounds can be remarkably informative even under limited instrument variation. We apply our approach to study the welfare effects of sales tax.