It's Where You Work: Increases in Earnings Dispersion Across Establishments and Individuals in the U.S

By Erling Barth, Alex Bryson, James C. Davis, Richard B. Freeman. Social Science Research Network, 2014

165 citations recorded by the source corpus.

This paper links data on establishments and individuals to analyze the role of establishments in the increase in inequality that has become a central topic in economic analysis and policy debate. It decomposes changes in the variance of ln earnings among individuals into the part due to changes in earnings among establishments and the part due to changes in earnings within-establishments and finds that much of the 1970s-2010s increase in earnings inequality results from increased dispersion of the earnings among the establishments where individuals work. It also shows that the divergence of establishment earnings occurred within and across industries and was associated with increased variance of revenues per worker. Our results direct attention to the fundamental role of establishment-level pay setting and economic adjustments in earnings inequality.

Subfields: Economics and Econometrics, Sociology and Political Science

View Paper: https://www.semanticscholar.org/paper/4753a71dd26a5b8f72cd812b000418317d9dc053

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

In Sociology and Political Science

Citations in the corpus

The Expanding Gender Earnings Gap: Evidence from the LEHD-2000 Census (2017, methodology): “An important literature has developed around the increase in earnings inequality across establishments and the increase in the sorting of workers by firms (see Barth et al. 2016 and Song et al. 2016 on the United States).”
Earnings Inequality and the Minimum Wage: Evidence from Brazil (2017, methodology): “Although not within an AKM framework, Barth et al. (2014) and Song et al. (2015) argue that changes in pay across firms were important in understanding the increase in wage dispersion in the U.S. during the last decades.”
A Distributional Framework for Matched Employer Employee Data (2019, methodology): “2010, Lin and Ng, 2012, Bonhomme and Manresa, 2015). The k-means clustering algorithm we use to classify firms is widely used in a number of fields, and efficient computational routines are available (Steinley, 2006). Here we use such an approach in models with two-sided heterogeneity. Our approach ”
Rethinking Measurement of Pay Disparity and Its Relation to Firm Performance (2019, background): “21 Barth et al. (2014) provides evidence that the growth in pay dispersion in the United States has been due to changes in dispersion between establishments, as opposed to within establishments.”
The Multiprod project: A comprehensive overview (2017, background): “(2014) and Barth et al. (2014) one could employ an instrument variable approach where average sales per worker of a firm in the same narrow defined industry but in a different region is an instrument for sales per worker for a firm.”