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Employment Quotas for Minorities

Authors: Welch, Finis;

Employment Quotas for Minorities

Abstract

Reactions to employment quotas are seldom mild. Protagonists argue that they create incentives for minority-majority convergence in skill distributions. In their view, quotas, unlike simple transfers, promote income equality via equality of worker productivities. Antagonists argue that quotas are socially costly; they result in both sacrificing standards and inefficient use of resources. My objective is to present an analytical framework showing that, in this as in most cases, effects are not necessarily what they seem. I assume that quotas are target minority-majority employment ratios for skilled or highly paid occupations. I also assume that in covered occupations firms must pay the same wage to all workers who have the same job title. The presumption is that target ratios are not easily satisfied with available supplies of skilled minority workers, so that quotas create an effective shortage of these workers. Associated with this shortage is the incentive to distinguish between demographic (color or sex) and skill

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    influence
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Powered by OpenAIRE graph
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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!
33
Top 10%
Top 10%
Average
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