Augmenting the Human Capital Earnings Equation with Measures of Where People Work

Abstract

We augment standard log earnings equations for workers in US manufacturing with variables reflecting measured and unmeasured attributes of their employer. Using panel employee-establishment data, we find that establishment-level employment, education of coworkers, capital equipment per worker, and firm-level R&D intensity affects earnings substantially. Unobserved characteristics of employers captured by employer fixed effects also contribute to the variance of log earnings, although less than unobserved characteristics of individuals captured by individual fixed effects. The observed and unobserved measures of employers mediate the effects of individual characteristics on earnings and increase earnings inequality through sorting of workers among establishments.

The Productivity-Wage Premium: Does Size Still Matter in a Service Economy?

“…This relationship, called the "size-wage premium", was discovered by Moore (1911) and then confirmed not only for the US (Brown and J. Medoff, 1989;Bayard and K. R. Troske, 1999;K. Troske, 1999;Oi and Idson, 1999;Barth et al, 2018), but also for other industrialised countries (see Morissette, 1993, for Canada, Main andReilly, 1993, for Great Britain, Albaek et al, 1998, for the Scandinavian countries, and Schmidt and Zimmermann, 1991, for Germany).…”

The productivity-wage premium

“…This relationship, called the "size-wage premium", was discovered by Moore (1911) and then confirmed not only for the US (Brown and J. Medoff, 1989;Bayard and K. R. Troske, 1999;K. Troske, 1999;Oi and Idson, 1999;Barth et al, 2018), but also for other industrialised countries (see Morissette, 1993, for Canada, Main and Reilly, 1993, for Great Britain, Albaek et al, 1998, for the Scandinavian countries, and Schmidt and Zimmermann, 1991, for Germany).…”

What’s in an Occupation? Investigating Within-Occupation Variation and Gender Segregation Using Job Titles and Task Descriptions

“…If men and women with the same job titles tend to work in different establishments, gender segregation is underestimated. Because the growth in withinoccupation inequality is generally attributed, at least in part, to firm-level differences (Abowd, McKinney, and Zhao 2018;Barth, Davis, and Freeman 2018;Card et al 2018;Song et al 2018), the ideal dataset would include both job title and firm information, as in Bielby and Baron (1986) (although their data were limited to a nonrepresentative set of firms over a relatively short time span).…”