Sex and Pay in the Federal Government
Using Job Evaluation Systems to Implement Comparable Worth
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This study focuses on the job evaluation procedures used in the federal government to evaluate all white-collar non-supervisory occupations. It examines the factor and factor weighing methodologies developed by the Civil Service Commission to provide the basis for institutionalized standards used to establish existing pay differences. The Factor Evaluation System (FES) appears responsive to recommendations of comparable worth advocates that the criteria for determining job worth be made explicit and as bias-free as possible. The volume provides an extensive analysis of the new FES in an effort...
This study focuses on the job evaluation procedures used in the federal government to evaluate all white-collar non-supervisory occupations. It examines the factor and factor weighing methodologies developed by the Civil Service Commission to provide the basis for institutionalized standards used to establish existing pay differences. The Factor Evaluation System (FES) appears responsive to recommendations of comparable worth advocates that the criteria for determining job worth be made explicit and as bias-free as possible. The volume provides an extensive analysis of the new FES in an effort to determine fully its usefulness from the standpoint of such advocacy. The study addresses whether the new FES is more beneficial to female-dominated jobs than the old narrative classification system. Female-dominated jobs, it is discovered, were rated lower on all factors used in the federal government's job evaluation system. Dr. Werwie then goes on to explore why this was the case and whether changing the weights assigned to job factors under the new system would alter the pay relationship between male-and female-dominated jobs. Also examined is the extent to which the factors, dimensions and operational indicators of the FES and other evaluation systems adequately define and measure the job content of female-dominate occupations. The results provide insights which will be useful to administrators and researchers interested in moving current job evaluation systems closer toward the goal of a bias-free evaluation system.
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