Working Papers

Job Preferences, Labor Market Power, and Inequality
Job Market Paper; Updated: May 2026

This paper examines how a firm's labor market power shapes, and is shaped by, its workforce, and evaluates the implications for wage inequality and welfare. 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 for workers who are heterogeneous in both their skills and preferences over wages versus non-wage job amenities. When a firm adjusts its wages, the composition of its workforce shifts, which in turn affects the slope of its labor supply curve. As a result, a firm's wage setting power varies based on which workers it employs. I use the model to draw inference about the incidence of wage markdowns and rents within and across firms, and the implications for wage inequality and sorting. Eliminating market power widens within-firm skill premia by 1.3% while compressing wage differences between firms by 16%, leading to a 4% reduction in total wage inequality and a 3.3 percentage-point (23% of the baseline) decline in the gender pay gap. Variation in wage setting power across firms also generates large allocative inefficiency, with welfare losses from labor market power estimated at 9.6% relative to the competitive benchmark.

Discrete Choice with Generalized Social Interactions
Revise and Resubmit at Econometrica (2nd Round); Supplemental Materials

This paper examines how individual identity influences group behavior through social interactions. I study a discrete choice model in which people are affected differently by different members of their network, conforming to the actions of some peers while deviating from the actions of others. Under this generalized framework, I explore what aggregate outcomes arise from noncooperative decisionmaking. I analyze uniqueness and stability of equilibria, and I characterize how negative spillovers impact social welfare. I then show how to take the model to data, introducing a novel identification strategy that leverages within-network variation in individual characteristics to account for unobserved network effects. I also show how to construct internal instruments to overcome the issue of measurement error, which is a primary source of endogeneity in models with incomplete information. Lastly, I apply my method to data from the large-scale education experiment Project STAR, where I find strong evidence that classroom peer effects differ by gender.

Supply and Demand with Market Heterogeneity
(with L. H. de Frahan, Ingvil Gaarder, Magne Mogstad, and Alex Torgovitsky)

We revisit the classic identification problem of separating supply and demand for a homogeneous good using data from multiple markets. We allow markets to be heterogeneous according to unobservables, a feature that arises if there are unobservable differences in consumer preferences or firm technology. We develop a new identification analysis based on hypothetical market types. We use this analysis to show how nonparametric, economically motivated assumptions carry empirical restrictions for a wide range of target parameters, including elasticities, but also welfare parameters, such as consumer surplus. Then, we develop computationally tractable methods for implementing partially identified linear random coefficients models in which the slopes of supply and demand are heterogeneous. We apply these methods to estimate the welfare impact and incidence of sales taxes in the United States.

The Linear-in-Means Model with Heterogeneous Interactions
(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 be readily examined in data. We relax these restrictions to allow for both positive and negative interaction effects that vary within and across groups. This extension makes 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.