This article estimates worker switching costs and how much the employer switching of experienced engineers responds to outside wage offers. I use data on engineers across Swedish private sector firms to estimate the relative importance of employer wage policies and switching costs in a dynamic programming, discrete choice model of employer choice. The differentiated firms are modeled in employer characteristic space, and each firm has its own age‐wage profile. A majority of engineers have moderately high switching costs and a minority of experienced workers are responsive to outside wage offers. Younger workers are more sensitive to outside wage offers.

I present the fact that wage gaps due to firm size increase with job responsibility. I use Swedish data to determine whether wage gaps increase with a direct measure of job responsibility, to compare the age patterns of the wage gaps for blue‐ and white‐collar workers, and to compare wages by job responsibility and spans of control. With U.S. data, I compare supervisory to nonsupervisory occupations and find that wage gaps increase with job responsibility for most occupational ladders. This fact is consistent with hierarchical matching models in which the larger number of subordinates amplifies managerial talent.

Structural estimation of matching games with transferable utility, including matching games of trading networks and many-to-many matching, is increasingly popular in empirical work. I explore several modeling decisions that need to be made when specifying a structural model for a matching game. One decision is the choice of a game theoretic solution concept to impose in the structural model. I discuss pairwise stability, competitive equilibrium, and noncooperative games such as auctions. Another decision is whether to work with a continuum of agents or a finite number of agents. I explore other issues as well.

Linear Regression Estimation of Discrete Choice Models with Nonparametric Distributions of Random Coefficients by Patrick Bajari, Jeremy T. Fox and Stephen P. Ryan. Published in volume 97, issue 2, pages 459-463 of American Economic Review, May 2007

Agents in two-sided matching games vary in characteristics that are unobservable in typical data on matching markets. We investigate the identification of the distribution of unobserved characteristics using data on who matches with whom. In full generality, we consider many-to-many matching and matching with trades. The distribution of match-specific unobservables cannot be fully recovered without information on unmatched agents, but the distribution of a combination of unobservables, which we call unobserved complementarities, can be identified. Using data on unmatched agents restores identification.

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