JSTOR is a not-for-profit service that helps scholars, researchers, and students discover, use, and build upon a wide range of content in a trusted digital archive. We use information technology and tools to increase productivity and facilitate new forms of scholarship. For more information about JSTOR, please contact support@jstor.org. This content downloaded from 128.235.251.160 on Mon, 30 Mar 2015 05:34:48 UTC All use subject to JSTOR Terms and Conditions CULTURE, TRADITION, AND OVERT DISCRIMINATION tend to make restrictive the terms by which women may participate in the labor force. These influences combine to generate an unfavorable occupational distribution of female workers vis-a-vis male workers and to create pay differences between males and females within the same occupation. The result is a chronic earnings gap between male and female full-time, year-round workers. Unfortunately, explanations at this level of generality are mainly descriptive. It is the purpose of this paper to estimate the average extent of discrimination against female workers in the United States and to provide a quantitative assessment of the sources of male-female wage differentials.

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The standard wage decomposition methodology produces arbitrary results when attempting to estimate the separate contributions of sets of dummy variables to the unexplained portion of the wage decomposition: the estimates are not invariant with respect to the choice of reference groups. However, the estimated separate contributions of sets of dummy variables to the explained portion and the overall decomposition are shown not to be dependent upon the choice of left-out reference groups. A similar identification problem applies to continuous variables, although this may not be as likely to cause problems in practice.

This note formalizes bias and inconsistency results for ordinary least squares (OLS) on the linear probability model and provides sufficient conditions for unbiasedness and consistency to hold. The conditions suggest that a btrimming estimatorQ may reduce OLS bias. D

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