How Much Should we Trust Estimates of Firm Effects and Worker Sorting?
StÃ©phane Bonhomme (),
Kerstin Holzheu (),
Elena Manresa (),
Magne Mogstad () and
Bradley Setzler ()
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StÃ©phane Bonhomme: University of Chicago - Department of Economics
Kerstin Holzheu: Sciences Po
Elena Manresa: New York University - Department of Economics
Bradley Setzler: University of Chicago - Department of Economics
No 2020-77, Working Papers from Becker Friedman Institute for Research In Economics
Many studies use matched employer-employee data to estimate a statistical model of earnings determination where log-earnings are expressed as the sum of worker effects, firm effects, covariates, and idiosyncratic error terms. Estimates based on this model have produced two influential yet controversial conclusions. First, firm effects typically explain around 20% of the variance of log-earnings, pointing to the importance of firm-specific wage-setting for earnings inequality. Second, the correlation between firm and worker effects is often small and sometimes negative, indicating little if any sorting of high-wage workers to high-paying firms. The objective of this paper is to assess the sensitivity of these conclusions to the biases that arise because of limited mobility of workers across firms. We use employer-employee data from the US and several European countries while taking advantage of both fixed-effects and random-effects methods for bias-correction. We find that limited mobility bias is severe and that bias-correction is important. Once one corrects for limited mobility bias, firm effects dispersion matters less for earnings inequality and worker sorting becomes always positive and typically strong.
Keywords: Earnings inequality; firm effects; worker sorting; bias correction; fixed effects; random effects; matched employer employee data (search for similar items in EconPapers)
JEL-codes: C23 J31 J62 (search for similar items in EconPapers)
Pages: 75 pages
New Economics Papers: this item is included in nep-bec and nep-ltv
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Working Paper: How Much Should we Trust Estimates of Firm Effects and Worker Sorting? (2020)
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