Michigan State University Extension
Diversity and Pluralism - 02080139
12/95/
ERIC TITLE NUMBER: ED147394 AUTHOR: Welch, Finis
TITLE: Employment Quotas for Minorities.
YEAR PUBLISHED: 1976
NOTE: 64 p.
ABSTRACT: This report, part of Rand's Labor and Population
Studies Program, delves into sources of black/white income
differentials. This report has as its purpose, the use of
employment quotas as an analytical device for devising a
priori notions of what the effects of government attempts
to reduce employment discrimination might be. Following a
formal solution to both a one-sector and a two-sector model
of quotas, wherein the two-sector case quotas are imposed
on only one sector, a number of simulations are presented.
The simulations are an arithmetical exercise conducted to
give order of magnitude estimates of the social cost of
quota induced income redistribution. The main conclusion is
that without skill bumping quotas are expensive means of
redistributing income. With skill bumping, quotas have the
potential of redistributing income at costs that appear
trivial, but there is a problem. Skill bumping presumes
that workers are upgraded into better paying jobs than they
would otherwise hold. Protagonists of affirmative action
have argued that quotas for skilled minority workers create
incentives for increasing minority skills and therefore
provide longer run incentives for eliminating the skill
component of majority/minority income differentials. That
these effects are in fact ambiguous is established by
examination of a very simple one-sector model in which
quotas only redistribute income without social costs. In
sum, the analysis presented suggests that the antagonists'
fears of quota programs with high social costs alongside
widespread sacrifices of employment standards may be ill
founded. On the contrary, it is the flexibility of
standards that can dampen costs. As in many other cases,
effects of quotas seem to be largely an empirical question.
(Author/AM)
KEY DESCRIPTORS: Affirmative-Action; Employment-Services;
Models-
KEY DESCRIPTORS: *Data-Analysis; *Employment-Practices;
*Equal-Opportunities-Jobs; *Income-; *Minority-Groups;
*Quotas-
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