Gender differences in salaries: an application to academe.

• Author: Lindley, James T.

1. Introduction

   There is a lengthy body of literature investigating differences between male and female salaries in academe. Most of these studies have two common characteristics: they examine salary levels but not raises, and they lack information about research productivity. Because of that, they tell us much about the differences between male and female salary levels but little about salary increments for research production. Yet the allocation of raises is an important element in the study of gender differences in compensation. Even if males and females have equal starting salaries, raises which do not fully reflect productivity would soon lead to salary differentials. The purpose of this study is to fill this void by analyzing longitudinal data, including research output, from the University of Alabama for the years 1981-1985.(1)

   We do not find that females receive lower salaries or lower raises than males given differences in human capital, academic discipline, rank, and research productivity. On the contrary, there is some evidence that females receive higher salaries and raises than their human capital, academic discipline, rank, and research productivity would warrant. These interesting, yet controversial, findings are explored more fully in this paper.

   This paper proceeds as follows. In section II, a short background of the problem is presented and the contradictory nature of the findings of various studies are discussed. Section III contains a description of the data set used in this paper and a discussion of the statistical methodology. Section IV and section V include discussions of the results and conclusions, respectively.

2. Background

   Taken collectively, previous studies present a varied picture of the gender differences in academic salaries. There is still uncertainty as to whether the salary differences reflect discrimination by universities or reflect human capital differences, productivity, or choices on the part of the suppliers of labor.

   Johnson and Stafford |21~ maintain that over one-half of the academic year salary differentials found between full-time female and male Ph.D. faculty members at American universities are due to gender. They attribute this difference to voluntary decisions by women to interrupt their careers. However, Strober and Quester |31~ show that only a small fraction of full-time female faculty interrupt their careers. Barbezat |2, 428~ also found that marital and parental variables have little affect on women's salaries.

   There is no consensus on whether the differences occur at entry level or become evident with experience and rank. Hirsch and Leppel |17~ find women's salaries initially lower but steeper and more concave than men's salaries over time. That is, women faculty receive lower entry salaries but they are treated the same as their counterparts over time. Megdal and Ransom |25~ find a comparable situation at the University of Arizona for the years 1972, 1977, and 1982. They find women's initial salaries to be lower than men's but growing at a higher rate than men's.

   Gordon, Morton, and Braden |14~ record a female-male differential which they say is explained by differences in individual characteristics such as age, seniority, education, rank, race, and discipline. Hoffman |18~ adjusts the Gordon, Morton, and Braden model by recognizing that "sex discrimination may occur through slower promotion rates for females in which case rank itself would reflect discrimination." Weiler |37~ suggests that rank and seniority are correlated for men, but not for women.

   Raymond, Sesnowitz, and Williams |29~ in a study of 1983-1984 Kent State University data find raw salary differentials by sex. However, the differentials disappear after adjusting for experience, degrees, specialties, scholarly publications, and graduate faculty standing. Moreover, they find that, "reverse discrimination appears when the rank variables are added ..." |29, 48~.

   While these studies provide insights |13~, their results have not been subjected to statistical tests of significance. By employing statistical tests omitted from previous studies, we can evaluate results with a higher degree of confidence. In addition, by analyzing raises across time we can gain new insights into how productivity affects salary differences between males and females.

3. Description of Data and the Model

   Data Description

   The data used in this study were collected from The University of Alabama administration, faculty, budget documents and directories. The data set contains all faculty who were employed at the university consecutively across the period 1981 through 1985. The difference in the logarithm of salary between 1981 and 1985 salaries constitutes the rate of salary increase.

   Human capital variables include years of service at the university, administrative service, prior experience, and degree held. Academic discipline variables are included to take into account the variation in salaries between disciplines. Since distinguishing between disciplines a priori is somewhat subjective, the categories of disciplines used in this paper are similar to those used by Hirsch and Leppel |17~. Productivity variables include numbers of books, refereed articles, and exhibitions-performances.(2) No distinction is made between single authors and coauthors. Faculty teaching and service are not included. A problem inherent in using research productivity measures is the aspect of placing weights on publications. Weights have been used in studies which evaluated research output |4~; however, there is no consensus on an appropriate weighting scheme. In this paper, the number of books, refereed articles, and exhibitions-performances are not weighted.

   The primary focus of this paper is on raises and research productivity. However, we also are interested in salary level differences between male and female faculty. Therefore, both salary level and raises are examined. In this study, a variation of the traditional human capital model employed in the previous work of Blinder |3~, Oaxaca |26~, Ferber, Loeb and Lowry |10~, Hirsch and Leppel |17~, and Jackson and Lindley |19~ is developed.

   Model

   Following the human capital view of wage determination, we posit a traditional behavioral model to analyze the determinants of 1985 salary levels:

   |Mathematical Expression Omitted~

   where y is an (n x 1) vector of observations on the natural logarithm of yearly (9 month) salary, X is an (n x k) matrix of observations taken by the k human capital, rank, and academic discipline variables discussed above, |beta~ is a (k x 1) vector of unknown coefficients measuring the response of the wage rate to these determinants, and e is a stochastic disturbance vector (n x 1). To aid in the discussion to follow, we rewrite equation (1) more explicitly:

   |Mathematical Expression Omitted~

   where H|C.sub.j~ (j = 1,...,5) are five human capital measures, A|D.sub.k~ (k = 1,...,11) are eleven academic discipline categories, and |R.sub.m~ (m = 1,...,3) represent three academic ranks. Within the context of equation (1), the variables in equation (2) are

   |Mathematical Expression Omitted~

   |Mathematical Expression Omitted~

   |Mathematical Expression Omitted~

   and

   |Mathematical Expression Omitted~.

   Thus k, the number of parameters to be estimated (including the constant term), in equation (1) is equal to 20 in the salary level analysis to follow.

   While using Blinder's method to decompose a wage equation of the form of (1) or (2) is a traditional method of investigating gender differences in academic (or most other types of) salaries, employing a similar approach to investigate gender differences in salary changes over time, i.e., raises, is not so traditional. Ideally, if data were available on all variables for, say, the years 1981 and 1985, we would augment equation (1) with a gender dummy and a full set of gender-interaction variables. We would then pool the 1981 and 1985 samples, add a time dummy with a full set of time-interaction variables, and estimate this further-augmented model by OLS regression techniques. The resulting model would be a full four-way analysis of covariance model (male and female equations for 1981 and male and female equations for 1985) so...