Firms and Labor Market Inequality: Evidence and Some Theory
By David Card, Ana Rute Cardoso, Joerg Heining, Patrick M. Kline. Journal of Labor Economics, 2018
799 citations recorded by the source corpus.
We synthesize two related literatures on firm-level drivers of wage inequality. Studies of rent sharing that use matched worker-firm data find elasticities of wages with respect to value added per worker in the range of 0.05–0.15. Studies of wage determination with worker and firm fixed effects typically find that firm-specific premiums explain 20% of overall wage variation. To interpret these findings, we develop a model of wage setting in which workers have idiosyncratic tastes for different workplaces. Simple versions of this model can rationalize standard fixed effects specifications and also match the typical rent-sharing elasticities in the literature.
Subfields: Economics and Econometrics, Gender Studies
View Paper: https://www.semanticscholar.org/paper/5f5628d122db3e77ac989362eca4d7ef70bd63f3
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Economics and Econometrics
- Alan Manning
- A. Lindner
- Christian Dustmann
- Arindrajit Dubé
- Silvia Massini
- Bradley Setzler
- Thibaut Lamadon
- David Seim
In Gender Studies
- Alan Manning
- Arindrajit Dubé
- David Seim
- Christian Dustmann
- Magne Mogstad
- Thibaut Lamadon
- Suresh Naidu
- A. Lindner
Citations in the corpus
- Reallocation Effects of the Minimum Wage (2021, background): “Building on Card et al. (2018), we set up a simple yet insightful model (discussed in detail in Online Appendix D) where nonpecuniary preferences toward a workplace—such as distance to home—give establishments the power to set wages.”
- Imperfect Competition, Compensating Differentials and Rent Sharing in the U.S. Labor Market (2019, methodology): “A corollary of this result is that tax wedges are the only source of allocative inefficiencies if one assumes a standard logit structure on the distribution of ~ it (as in, for example, Card et al., 2018).”
- Monopsony in Labor Markets: A Review (2020, methodology): “This pass-through is still less than 1, predicted by the simple theory presented above and rationalized in the model by the use of a labor supply function similar to that in Card et al. (2018).”
- Protecting Competition in the American Economy: Merger Control, Tech Titans, Labor Markets (2019, background): “2017; Card et al. 2018). Some local US labor markets are highly concentrated on the employer side, but that is not the situation for most workers. Azar, Marinescu, and Steinbaum (2017) use data from CareerBuilder.com to calculate labor market concentration in some 8,000 selected labor markets in the”
- Payroll Taxes, Firm Behavior, and Rent Sharing: Evidence from a Young Workers' Tax Cut in Sweden (2019, positive): “There is a growing body of evidence in labor economics that points to the role of individual firms in setting wages and in generating wage dispersion between similar workers, which may emerge in frictional labor markets (see, e.g., Card, Heining, and Kline 2013; Card et al. 2018).”
- Digital technologies, innovation, and skills: emerging trajectories and challenges (2021, background): “Some researchers have found that differences between firms explain two-thirds of the earning variability across workers (Card et al., 2018; Song et al., 2019), suggesting that this outcome is based, in part, on interfirm competition.”
- Exploitation of labor? Classical monopsony power and labor's share (2019, positive): “ies focusing on the United States, including Card et al. (2018), Gouin-Bonenfant (2018), Lamadon, Mogstad and Setzler (2017), and Berger, Herkenhoff and Mongey (2018). The first three examine the sharing of rents in matching or search labor markets. We study the exertion of classical market power in”
- Monopsony in Online Labor Markets (2020, methodology): “This evidence thus provides us with an estimate of wage-setting (monopsony) power facing task requesters (Manning (2003); Card et al. (2016)). We isolate plausibly exogenous variation in rewards using a double-machine-learning (Chernozhukov et al. (2017)) method, which controls for a highly predicti”