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SSRN Electronic Journal
Article . 2009 . Peer-reviewed
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The Review of Economic Studies
Article . 2011 . Peer-reviewed
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SSRN Electronic Journal
Article . 2009 . Peer-reviewed
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EconStor
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Identifying Sorting - In Theory

Authors: Jan Eeckhout; Philipp Kircher;

Identifying Sorting - In Theory

Abstract

Assortative Matching between workers and rms provides evidence of the complementarities or substitutes in production. The presence of complementarities is important for policies that aim to achieve the optimal allocation of resources, for example unemployment insurance. We argue that using wage data alone, it is virtually impossible to identify whether Assortative Matching is positive or negative. Even though we cannot identify the sign of the sorting, we can identify the strength, i.e., the magnitude of the cross-partial, and the associated welfare loss. We show rst that the wage for a given worker is non-monotonic in the type of his employer. This is due to the fact that in a sorting model, wages reect the opportunity cost of mismatch. We show analytically that this non-monotonicity prevents standard rm xed eects to correlate with the true type of the rm. We then propose an alternative procedure that measures the strength of sorting in the presence of search frictions. Knowing the strength of sorting facilitates the measurement of the output loss due to mismatch.

Country
United Kingdom
Keywords

Lohnniveau, sorting, assortative matching, identification, linked employer-employee data, interpretation of fixed-effects, Identification, Assortative matching, Arbeitsmarkt, interpretation of fixed-effects, Matching, Messung, linked employer-employee data, J31, Arbeitsangebot, C78, assortative matching, ddc:330, Sorting, Arbeitsnachfrage, sorting; assortative matching; complementarities; supermodularity; identification, Complementaries, identification, Theorie, Supermodularity, jel: jel:J60, jel: jel:C78, jel: jel:J31, jel: jel:J0

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    selected citations
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    151
    popularity
    This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
    Top 1%
    influence
    This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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    impulse
    This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
    Top 10%
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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
151
Top 1%
Top 1%
Top 10%
Green
bronze