Page images
PDF
EPUB

In pursuing the implications of possibly having em

ployees listed as professionals who were, in fact, non

professionals, the Task Force developed a set of data which removed all of these questionable employees from the pro25/

fessional classification."

The status of minorities and

women in this revised set of data changes somewhat, but does not differ substantially from the previous findings. In comparison with these results, the new set of data show that blacks and women professionals have a slightly lower representation, and that the difference between their average salary and that of all professionals decreases a little. In addition, the length of service for black and female professionals declines a little, while educational

attainment increases somewhat. We find that even if all the

professionals who might possibly be nonprofessionals were listed as such, the results of the statistical study re

main basically unchanged. 26/

25/

Professionals in grades GS-5, 7, and 9 with more than eighteen months of time-in-grade, and professionals in grades GS-6, 8, and 10 were listed as nonprofessionals.

26/

The set of data excluding all the professionals who might possibly be nonprofessionals from the professional classification is available in the EEO office.

Women

In terms of mobility the time-in-grade data disclose

that, over the last year, female professionals in the De

partment have been entering the higher grade positions at

a slower rate than all professionals.

There was no progress

for women in the National Office or the Field toward achiev

ing parity, or even an equitable distribution. and 18, pp. 65-66).

(Charts 17

The overall pattern of time-in-grade for female professionals is very similar to that for male professionals. A slightly larger proportion of women professionals in the higher grades have more than a year of time-in-grade. Certainly the absence of sufficient time-in-grade would not present any obstacle to women having a greater entry rate than all professionals. (Tables 19-90, p. 433-68).

Nonblack minorities

The time-in-grade data do not disclose any new information concerning the status of the nonblack minorities.

This is because most of the underutilization of the nonblack

minorities is in terms of their under-representation in the professional ranks, rather than according to their distribution

[blocks in formation]

This section has analyzed the nature of various factors that determine an employee's status and has examined how these explanatory factors are related to the underutilization of minorities and women. This analysis has disclosed several main points. The explanatory factors account for some of the underutilization of minorities and women. However, this represents a small portion The major part of their underutilization remains unexplained by factors other than race or sex. The correlation analysis will specify this further. In addition, present rates of hiring and promoting

of the underutilization.

minorities and women are not sufficient to overcome their

underutilization within a decade.

2.

Correlation Analysis on Salary

In previous sections, we considered whether the potentially explanatory factors of age, education, and

length of government service could account for the differences in average salaries between minorities and all em

ployees and between women and all employees. Each of these

factors was analyzed individually to determine its affect

upon salary within the various racial-sexual groups and to identify differences in the affect from one group to another. Each factor was examined to see to what extent

differences in its values across racial-sexual groups could be said to explain the salary differences across groups. That is, did minorities and women have, on the average, lower levels of education or age or experience than all employees and did such differences, if existent, account for their lower salaries? The analysis of the earlier sections demonstrates conclusively that age, education, and length of service when considered individually do not

account for the major portion of salary differences existing in the Department. whether these factors, if considered simultaneously, might explain considerably more of the differences in salaries. Clearly, this is an important issue.

The question remains, though, as to

a.

Use of Correlation Analysis

To approach this question, a correlation analysis was
Correlation analysis, or regression analysis,

utilized.

is a powerful research tool for investigating the relationship between a given dependent variable and certain independent variables believed to be significant in explaining

variation in the dependent variable. In this analysis, actual salary is the dependent variable, and the independent variables thought to explain salary variations are race, sex, age, education, years of government service, geographic location, and organizational unit. These are postulated as determiners of salary in the Labor Department. The correlation analysis yields an equation for salary as a function of the independent variables identified above. This equation can be used as a predictor model for salaries in the Department by plugging in specific values for race, sex, age, education, and the other independent variables and then calculating salaries.

b. The Dependent and Independent Variables

The dependent variable in all of the models developed was actual salary as extracted from the current file of the Department's computerized personnel records. The independent variables varied with different models, and, except for the education data, they also derived from the current computer file. As the Department's computerized records do not presently contain data on educational attainment, such data must be obtained from official personnel records. The enormity of such a task dictated that

« PreviousContinue »