Firms and Labor Market Inequality: Evidence and Some Theory

Abstract

Standard-Nutzungsbedingungen:Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden.Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen.Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. We review the literature on firm-level drivers of labor market inequality. There is strong evidence from a variety of fields that standard measures of productivity -like output per worker or total factor productivity -vary substantially across firms, even within narrowlydefined industries. Several recent studies note that rising trends in the dispersion of productivity across firms mirror the trends in the wage inequality across workers. Two distinct literatures have searched for a more direct link between these two phenomena. The first examines how wages are affected by differences in employer productivity. Studies that focus on firm-specific productivity shocks and control for the non-random sorting of workers to more and less productive firms typically find that a 10% increase in value-added per worker leads to somewhere between a 0.5% and 1.5% increase in wages. A second literature focuses on firm-specific wage premiums, using the wage outcomes of job changers. This literature also concludes that firm pay setting is important for wage inequality, with many studies finding that firm wage effects contribute approximately 20% of the overall variance of wages. To interpret these findings, we develop a model where workplace environments are viewed as imperfect substitutes by workers, and firms set wages with some degree of market power. We show that simple versions of this model can readily match the stylized empirical findings in the literature regarding rent-sharing elasticities and the structure of firm-specific pay premiums. (Tirole, 1988;Pakes, 2016). Though economists seem to agree that part of the variation in the prices of cars and breakfast cereal is due to factors other than marginal cost, the possibility that wages reect anything other than skill remains highly controversial. Terms of use: Documents inThe growing availability of matched employer-employee datasets has created new opportunities to disentangle the eects of worker and rm heterogeneity on wage inequality. Nevertheless, many of the fundamental issues that economists have long debated about dierences in the characteristics of the workers at dierent rms, and the nature of the jobs at dierent workplaces, carry over to these new datasets. This reviewsummarizes what has been learned so far from these new datasets about the importance of rms in wagesetting, and what challenges remain.

The Role of Firms in the Gender Wage Gap

“…There are several papers performing these estimates. Card et al (2018) summarise that 20% of the variation in wages can be assigned to variations in firm-specific wage premiums. This implies that workers with an equal set of characteristics can get substantial premiums from working for high-wage firms compared to low-wage firms.…”

“…The advantage of our data is that the whole population of employees and employers is covered, and that we can link employee and employer data using their unique identifiers. The former implies that our data is less prone to limited mobility bias, which can originate from small subsamples of the population (see the discussion in Card et al 2018). 2 Many similar studies proceed only from the subsample of employees (e.g.…”

“…The key assumption of the two-way fixed effects approach used in this paper is that worker mobility is exogenous, otherwise firm and worker fixed effects estimates would be biased (see e.g. discussion by Card et al (2018) and Section 2.2 of our paper). The idea is that "worker mobility is uncorrelated with the time-varying residual components of wages" (Card et al 2018, p. 31).…”

“…2013. Our data is less prone to limited mobility bias, which can originate from small subsamples of the population (Card et al 2018). The data are from Estonia, which is an excellent case to study the drivers of the gender wage gap.…”

Leave-out Estimation of Variance Components

“…Consider again the problem of estimating variance components in a two-way fixed effect model of wage determination. Card et al (2018) note that plug-in wage decompositions of the sort introduced by AKM typically attribute 15%-25% of overall wage variance to variability in firm fixed effects.…”