High wage workers match with high wage firms: Clear evidence of the effects of limited mobility bias

Andrews, Matt; Gill, Len; Schänk, Thorsten; Upward, Richard

doi:10.1016/j.econlet.2012.07.033

Cited by 80 publications

(80 citation statements)

References 15 publications

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“…11 Andrews et al (2012) show convincingly that this limited-mobility bias can be substantial. In sampling experiments they nd that the correlation of the estimated e ects becomes more negative when the AKM model is estimated on smaller subsets of the available data.…”

Section: Worker-firm Sorting and Limited Mobility Biasmentioning

confidence: 77%

See 1 more Smart Citation

Firms and Labor Market Inequality: Evidence and Some Theory

Card¹,

Cardoso²,

Heining

et al. 2018

Journal of Labor Economics

491

173

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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. Does where you work determine how much you earn? In the standard competitive labor market model rms take market wages as given and rm-speci c heterogeneity in uences who is hired, but not the level of pay of any particular worker. The pervasive in uence of this perspective is evident in major reviews of the wage inequality literature (Katz and Autor, 1999;Goldin and Katz, 2009;Acemoglu and Autor, 2011), which focus almost exclusively on the role of market-level skill prices in driving inequality trends. 1 This view stands in stark contrast to the Industrial Organization literature, which typically models markets as imperfectly competitive (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 re ect anything other than skill remains highly controversial. Terms of use: Documents inThe gr...

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Section: Worker-firm Sorting and Limited Mobility Biasmentioning

confidence: 77%

“…They nd that the corrections have little e ect on the estimated correlation between worker and rm e ects. However, subsequent results by Andrews et al (2012) show large biases in the estimated correlation when the AKM model is estimated on subsamples as large as 30% of the data.…”

mentioning

confidence: 99%

Firms and Labor Market Inequality: Evidence and Some Theory

Card¹,

Cardoso²,

Heining

et al. 2018

Journal of Labor Economics

491

173

View full text Add to dashboard Cite

show abstract

“…In both cases, the negative correlation between individual fixed effects and department effects increases to −0.19 in the first case and to −0.41 in the second case, for instance, for the quantity of publications (see full results in Bosquet and Combes, 2017 ). As Andrews et al (2012) emphasise, this is a sign of possible lack of identification power.…”

Section: Productive Academics: Individual Abilities Versus Departmentmentioning

confidence: 96%

“…A slight positive sorting of academics with the best observed characteristics into departments that generate higher positive externalities is observed if individual fixed effects are not considered. When considering individual fixed effects, sorting on unobservables is slightly negative, which, in the literature, is considered a possible sign of lack of identification power (see Abowd et al, 2004;Andrews et al, 2012 ). Therefore, it is not possible to be sure that the model with both individual and department-time fixed effects is correctly identified.…”

Section: Introductionmentioning

confidence: 95%

Sorting and agglomeration economies in French economics departments

Bosquet

Combes

2017

Journal of Urban Economics

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a b s t r a c tAre agglomeration and peer effects at stake in academic research? To tackle this question, we study how departments' characteristics affect the quantity and quality of academics' publications in economics in France, controlling for individual time-varying characteristics and individual fixed effects. Department characteristics have an explanatory power at least equal to a quarter of that of individual characteristics and possibly as high as theirs. The quantity and quality of an academic's publications in a field increase with the presence of other academics specialised in that field and with the share of the department's publications output in that field. In contrast, department size, proximity to other large departments, homogeneity in terms of publication performance, presence of colleagues with connections abroad, and composition in terms of positions and age matter for some publication measures but only if not controlling for individual fixed effects.

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“…A positive correlation indicates that high quality workers are employed in more productive firms. In fact, the few results that are available point to issues of downward bias in the estimation of the correlation between worker and firm effects, finding a spurious negative correlation in many data sets (Andrews, Gill, Schank, andUpward (2008, 2012)), and upward bias in the estimator of the variance of teacher effects (Rockoff (2004)). Nimczik (2018) reports the whole distribution of the estimated worker and firm effects.…”

Section: Introductionmentioning

confidence: 99%

Fixed‐Effect Regressions on Network Data

Jochmans

Weidner

2019

ECTA

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This paper considers inference on fixed effects in a linear regression model estimated from network data. An important special case of our setup is the two-way regression model. This is a workhorse technique in the analysis of matched data sets, such as employer-employee or student-teacher panel data. We formalize how the structure of the network affects the accuracy with which the fixed effects can be estimated. This allows us to derive sufficient conditions on the network for consistent estimation and asymptotically valid inference to be possible. Estimation of moments is also considered. We allow for general networks and our setup covers both the dense and the sparse case. We provide numerical results for the estimation of teacher value-added models and regressions with occupational dummies.

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High wage workers match with high wage firms: Clear evidence of the effects of limited mobility bias

Cited by 80 publications

References 15 publications

Firms and Labor Market Inequality: Evidence and Some Theory

Firms and Labor Market Inequality: Evidence and Some Theory

Sorting and agglomeration economies in French economics departments

Fixed‐Effect Regressions on Network Data

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