High wage workers and low wage firms: negative assortative matching or limited mobility bias?
By Martyn Andrews, L. Gill, Thorsten Schank, R. Upward. 2008
304 citations recorded by the source corpus.
Subfields: Economics and Econometrics, Political Science and International Relations
View Paper: https://www.semanticscholar.org/paper/93edeb8ad5fbd48173fc4fd63bf6da0f21c8bf5e
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
- Thibaut Lamadon
- Bradley Setzler
- Patrick M. Kline
- Magne Mogstad
- Ronald Wolthoff
- Ioana Marinescu
- David Card
- Jörg Heining
In Political Science and International Relations
- Thibaut Lamadon
- Patrick M. Kline
- David Card
- Magne Mogstad
- Jesse M. Rothstein
- S. Bonhomme
- Elena Manresa
- Jens Suedekum
Citations in the corpus
- Imperfect Competition, Compensating Differentials and Rent Sharing in the U.S. Labor Market (2019, methodology): “In contrast to much of the existing work, we also attempt to address the concern that estimates of firm effects will be biased upward and estimates of worker sorting will be biased downward in finite samples, with the size of the bias depending inversely on the degree of worker mobility among firms ”
- Workplace Heterogeneity and the Rise of West German Wage Inequality (2013, methodology): “To quantify the separate contributions of rising dispersion in person and establishment effects, and increases in assortative matching, we conduct a simple variance decomposition based on equation (6) in each interval.”
- Opening the Black Box of the Matching Function: The Power of Words (2020, background): “the empirical side, the literature that decomposes the wage variance (e.g. Abowd et al., 1999; Woodcock, 2007; Abowd et al., 2002; Andrews et al., 2008; Iranzo et al., 2008; Woodcock, 2008) focuses on realized wages.”
- Leave‐Out Estimation of Variance Components (2018, unknown): “Table 2 reports the results of applying to our leave-one-out samples three estimators of the AKM variance decomposition: the traditional plug-in (PI) estimator θ̂PI, the homoscedasticity-only (HO) estimator θ̂HO of Andrews et al. (2008), and the leave-out (KSS) estimator θ̂.”
- Firms and the Decline in Earnings Inequality in Brazil (2017, unknown): “Similar conclusions have been reached among others for Germany (Andrews et al. 2008; Card, Heining, and Kline 2013), Italy (Iranzo, Schivardi, and Tosetti 2008), Portugal (Card, Cardoso, and Kline 2016), Brazil (Lopes de Melo, forthcoming), Sweden (Bonhomme, Lamadon, and Manresa 2016), Denmark (Bagg”
- Firms and Labor Market Inequality: Evidence and Some Theory (2018, unknown): “While Andrews et al. (2008) and Gaure (2014) provide approaches to correcting for this downward bias in the correlation (and the upward biases in the estimated variances of person and firm effects), their procedures require a complete specification of the covariance structure of the time-varying…”