Gender Wage Differentials, Affirmative Action, and Employment Growth on the Industry Level

Posted: 9 Jul 1997

See all articles by Judith Fields

Judith Fields

Bard College

Edward N. Wolff

New York University (NYU) - Department of Economics; National Bureau of Economic Research (NBER); Bard College - Levy Economics Institute

Date Written: March 1997

Abstract

The present study examines factors that might explain the difference between female and male industry wage premia. It focuses on three industry characteristics in particular--the extent to which firms in each industry were likely to be targeted for Affirmation Action compliance review or investigation, industry employment growth, and industry profitability. We find strong evidence that all three factors help narrow the gender gap in industry wage premia. Other characteristics that we have looked at including average plant size, the capital intensity of the production process, both the average level and variance in work education, and changes in overall sales and wage levels were statistically much less important. Our principal finding is that Affirmation Action, employment growth, and profitability each leads to a narrowing of the gender gap in industry wage premia. These effects act independently of each other. With regard to the Affirmation Action variable, our results contrast sharply with those of Leonard (1996), who concludes that Affirmative Action had lost its effectiveness as a measure to reduce the gender wage gap in the 1980s. The difference in results is likely attributable to the fact that the dependent variable in his regression analysis is the gender gap in earnings, whereas ours is the gender gap in industry wage premia. In terms of policy implications, our results provide new support to the recent effectiveness of the Affirmative Action program, which is currently under fire from so many sources and has been greatly diminished in size and is also clear that import-competing industries, for which output prices and sales declined during this period, showed smaller wage increases for all workers during the 1980s. This would narrow the gender gap in wage premia if males had been more likely than females to benefit from rent sharing before the decline in demand for the industry's output or if males at the top of the wage ladder were more likely than others, of both genders, to have left these industries when demand and pay declines. Sachs and Shatz (1996) support this argument with the observation that the overall gender gap in wages tended to narrow in these industries. The average level of education of workers within the industry (MEANEDUC) is generally negatively related to GGIWP, but the coefficient is significant in only one of the four regressions (and then at only the ten percent level). As such, we must conclude that average education does not have much impact on the GGIWP. We include this variable because Dickens and Katz (1987) find education to be the most important variable in explaining inter-industry variations in average wages (including both genders). Bound and Johnson (1992), too, cite a substantial increase in the relative wages of highly educated workers during the 1980s, presumed to be brought about by shift in the skill structure of labor demand during this period. The standard deviation of education (STDEDUC) is not significant in any of the four regressions. We include this variable as a proxy for the range in the occupation distribution of workers within an industry. As noted above, one of the reasons why males and females in the same industry might receive different wage premia is that the occupational distributions of male and female workers within that industry might differ. Further, this might reflect budget since the late 1970s. Our results are particularly important because they refer to industry wage premia and thus control for differences in male and female productivity and more accurately reflect discrimination effects than the female-male wage gap. Indeed, in our 1995 study, we estimated that gender differences in industry wage premia might have explained as much as 22 percent of the overall male-female wage gap. The very strong evidence we have produced on the subject of industry employment growth is indirectly related to the Affirmative Action program. As Leonard (1986b) observes, it is more fruitful to target such programs, particularly compliance reviews, at fast-growing industries, since these are the ones increasing both female wages and female employment opportunities of existing male employees. In stagnant industries, in contrast, such programs will take the form of a zero-sum game in which raising female wages and employment opportunities will occur at the expense of males. So, too, are the implications of industry profitability. Though we cannot conclude from our study here which way the direction of causality runs--firms either increase their profits by paying females more equitably (if the premia represent efficiency wages) or spread their excess profits more--either case, government policy should be aimed at educating firms as to the jobs where efficiency wages are relevant. Moreover, equal opportunity programs should be strongly targeted on firms in high profit industries.

JEL Classification: J16, J31, J38, J71, J78

Suggested Citation

Fields, Judith and Wolff, Edward N., Gender Wage Differentials, Affirmative Action, and Employment Growth on the Industry Level (March 1997). Available at SSRN: https://ssrn.com/abstract=8622

Judith Fields (Contact Author)

Bard College

Blithewood
Annandale-on-Hudson, NY 12504
United States
845-758-7700 (Phone)
845-758-1149 (Fax)

Edward N. Wolff

New York University (NYU) - Department of Economics ( email )

269 Mercer Street, 7th Floor
New York, NY 10011
United States
212-998-8917 (Phone)
212-995-4186 (Fax)

National Bureau of Economic Research (NBER)

1050 Massachusetts Avenue
Cambridge, MA 02138
United States

Bard College - Levy Economics Institute

Blithewood
Annandale-on-Hudson, NY 12504
United States

Do you have negative results from your research you’d like to share?

Paper statistics

Abstract Views
1,471
PlumX Metrics