Collecting Compensation Data from Employers by National Research Council

Author:National Research Council
Language: eng
Format: epub
Tags: Industry and Labor: Economics
ISBN: 9780309264112
Publisher: The National Academies Press
Published: 2012-06-04T00:00:00+00:00

Utility of the Data Items for Statistical Analysis

In this section we consider how the EEOC could develop a statistical model for use in screening individual employers for possible violations of pay discrimination. There are several key considerations here. First, the data to be used in this model would, of course, be reported by each individual employer. In addition to the information already requested for the EEO-1 report (e.g., employment by occupation, sex, and race/ethnicity), a form would collect pay (measured as discussed in Chapter 3) and possibly other information, such as employees’ years of service. Given these data, one could conduct a multiple regression analysis of pay in relation to demographic variables (e.g., the EEO-1’s 14 sex and race/ethnicity groups) and other characteristics, usually called “control variables,” such as occupational category and years of service. More complex models might include controls for occupation or job categories or more elaborate controls for education and labor force experience. Still more complex models might include more detailed occupational or job categories and more elaborate controls for previous experience and qualifications.

There are a large number of potential control variables that could be included in such regression models, and, especially for employers with small numbers of employees, there would be benefits from keeping the number of covariates in such models relatively small. To do that, there are a variety of statistics, including Mallows’ Cp, Akaike Information Criterion (AIC), and Bayesian Information Criterion (BIC) that could be employed to remove control variables that were not contributing substantially to the fit of the model.

While there is substantial disagreement over the most appropriate models to use for establishing a reasonable claim of possible wage discrimination, or defending one, it is not necessary to have a definitive model to assess the potential quality of certain basic statistical tests that might be reasonably performed by EEOC. We undertake such an analysis here. We emphasize that the regression model we describe below is intended, first and foremost, as an illustrative example of a methodology for undertaking some of these basic statistical tests. For this purpose, we need to provide enough specifics to allow a clear and straightforward discussion of the general nature of the issues that would arise in such an exercise.

The regression model we use is a general linear model of the form:

yi = β0 + di β1 + xi β2 + ò1

Here, yi is the logarithm of the wage measure for individual i, di is the vector of design variables that indicate the EEO-1 categories occupied by individual i, xi is a vector of control variables, ò1 is the statistical error, β0 is the intercept, β1 is the vector of EEO-1 log wage differentials from a specified reference group (usually white, non-Hispanic males), β2 is the vector of effects associated with the control variables, and i = 1,...,N, where N is the total number of employees in the analysis.1

For an agency such as EEOC or OFCCP, the results from this kind of regression analysis that will be of greatest

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