organizations and inequality: sources of earnings differences between male and female faculty

Organizations and Inequality: Sources of Earnings Differences Between Male and FemaleFacultyAuthor(s): Pamela S. TolbertSource: Sociology of Education, Vol. 59, No. 4 (Oct., 1986), pp. 227-236Published by: American Sociological AssociationStable URL: http://www.jstor.org/stable/2112349 .

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ORGANIZATIONS AND INEQUALITY: SOURCES OF EARNINGS DIFFERENCES BETWEEN

MALE AND FEMALE FACULTY

PAMELA S. TOLBERT

Cornell University

Sociology of Education 1986, Vol. 59 (October):227-235

This paper examines the relationship between organizational characteristics and earnings differences between male and female faculty from two theoretical perspectives. Two general sets of organization variables are considered: (1) characteristics that index an organization's power and autonomy in environmental relations, and (2) demographic characteristics. Results show that these characteristics define important aspects of the organizational context in which earnings differences between males and females are most likely to occur and to be maintained. The implications of this research for further research on organizational sources of earnings differences are discussed.

The existence of a sizeable and persistent earnings gap between men and women in the U.S. has been well documented (Treiman and Terrell 1975; Blau 1978). Within the last decade, an enormous quantity of research on the sources of this gap has been produced. Early work on this problem focused on differences in individuals' skills and education (Cohen 1971; Suter and Miller 1973; Mincer and Polachek 1974), but recent research has emphasized structural sources of earnings differences, particularly the segregation of females within certain occupations and industrial sectors (Bibb and Form 1974; Blau and Hendricks 1979; Beck, Horan, and Tolbert 1980). There has been a curious neglect in this literature, however, of organizational-level analysis. This is particularly striking because, as a number of analysts have indicated, rates of pay and promotion are determined by the organization; inequalities at occupational and industrial levels are largely reflections of organizational policies and practices (Baron and Bielby 1980, 1984; Roos and Reskin 1984).

This study addresses this problem by examining the organizational sources of differences between the salaries of male and female faculty in institutions of higher education. Average salaries of males and females, and the magnitude of the difference between these, are treated as characteristics of the organization and are expected to be predictable from other organizational characteristics. A major assumption of this approach is that the organizational context in which pay decisions

are made has an important effect on the likelihood of discriminatory outcomes. Two theoretical perspectives are relevant to this "demand side" approach. One, drawn from economics, emphasizes the degree to which an organization's ability to dominate a market or its environment provides the latitude for indulging in discriminatory "tastes" (Becker 1957). Organizations that are less driven by competitive pressures and that have greater slack resources can more easily afford to recruit and pay higher wages to a preferred group. The second perspective, reflecting a social-psycholog- ical emphasis, suggests that the demographic composition of an organization can affect the nature of intergroup relations and the occurrence of discriminatory perceptions and practices (Kanter 1977; Pfeffer 1983; South et al. 1982). Of course, these perspectives are not mutually exclusive; the exercise of discriminatory tastes may lead to a structure of group relations that serves to reinforce those preferences. Organizational characteristics suggested by both perspectives are examined as predictors of the compensation practices of colleges and universities.

In the next section, I review previous research on individual and structural sources of earnings inequalities and discuss the need for organizational- level analysis. In the following section, I consider the aspects of organizations that are likely to be related to compensation policies, focusing upon characteristics of colleges and universities. In the fourth section, I describe the data and analytic procedures, and in the fifth section, I present the results. In the concluding section, I consider the implications and problems of this approach and directions for future research.

INDIVIDUAL AND STRUCTURAL DETERMINANTS OF EARNINGS

Research on sex differences in earnings reflects a number of very different theoretical assumptions

For helpful criticisms and comments on earlier drafts, I thank Ron Breiger, Paul DiMaggio, Ron Ehrenberg, Olivia Mitchell, Jeff Pfeffer, Pat Roos, Sara Rynes, and Bob Stem. The research assistance of Jeff Arthur and Beth Florin is also appreciated. Address all correspon- dence to Pamela S. Tolbert, New York State School of Industrial and Labor Relations, Comell University, P.O. Box 1000, Ithaca, NY 14853.

227



228 TOLBERT

about the sources of such differences. One approach to this problem, derived from neoclassi- cal economics, emphasizes the differing amounts of human capital-education, training, work experience-acquired by males and females (Mincer and Polachek 1974; Johnson and Stafford 1974). It is argued that females have traditionally invested fewer resources in acquiring such human capital and are therefore less productive than males. Such productivity differences are reflected in earnings differences. Although the economic rationale is not as clearly specified, the same type of reasoning underlies much of sociologists' research on status attainment processes (e.g., Blau and Duncan 1967).

The logical and empirical problems with this approach have been noted. First, it ignores the possibility that the failure of women to acquire human capital, particularly job-related training and experience, may result less from their unwillingness to invest in such capital than from organizations' unwillingness to invest in the training and promotion of women. In other words, the causal relationships are likely to be more complex than this approach suggests (Stevenson 1978). Second, research using measures of human capital has typically been able to account for only about half of the difference between male and female earnings. Thus, a substantial amount of the earnings gap remains unexplained (Goldfarb and Hosek 1976; Corcoran 1978; Sandell and Shapiro 1978; England 1982).

Partly because of these limitations, researchers have increasingly turned their attention to structural sources of wage and salary differences. This research emphasizes the effects of the clustering of women in low-status, low-paying occupations (e.g., Blau 1978; Rytina 1981) and in peripheral industries in which the economic returns to education, experience, etc., are much lower than they are in core industries (Reich, Gordon, and Edwards 1973; Bibb and Form 1974; Beck et al. 1978; Kalleberg and Griffin 1980). Earnings differences are viewed as a result of the different locations of males and females in the labor market.

This research is also limited in a number of respects. Work emphasizing core/periphery distinc- tions has produced contradictory and anomalous results. This may be due to the inconsistency of the sectoral measures used by different researchers, which, in turn, suggests problems in the concep- tual development of this approach (Zucker and Rosenstein 1981). Moreover, both occupations and industries encompass a heterogeneous set of organizations and jobs. Thus, the use of occupational- or industrial-level data is apt to obscure how and the extent to which segregation produces earnings differences (Grimm and Stern 1974). Most importantly, promotion and pay policies are established by organizations, not by occupations or industries (Baron and Bielby 1984; Bielby and

Baron 1986). This is implicitly recognized in most sectoral analyses that are based largely on arguments about the nature of employing organizations within different sectors; typically, sectors characterized by large oligopolistic firms are contrasted with those containing small, competitive firms. And though occupational segregation is clearly an important source of aggregate differences between male and female earnings (Roos 1983), it is also evident that even within very narrowly defined occupational categories, levels of pay can vary substantially. For example, in 1980, average salaries of registered nurses ranged from $14,500 to $19,690, depending on the medical organization in which they worked, and average salaries of secretaries ranged from $9,932 to $19,812 (U.S. Department of Labor 1982).1

This suggests that the study of the structural sources of earnings differences requires identification of the features of organizations that are associated with larger or smaller pay differences. Only a handful of studies have addressed the relationship between organizational characteristics and compensation policies, and even fewer have considered the effects on differences between male and female earnings (Bridges and Berk 1974; Cox and Astin 1977; Talbert and Bose 1977; Borjas 1980; Baron and Bielby 1984; Bielby and Baron 1986). In the following section, I consider two theoretical perspectives on the organizational determinants of earnings differences and discuss the characteristics of institutions of higher education that predict these differences.

ORGANIZATIONAL SOURCES OF EARNINGS DIFFERENCES

Environmental Relations

One relevant theoretical perspective, developed by economists interested in interfirm differences in wage rates, assumes the existence of discriminatory tastes or preferences (Becker 1957; Alchian and Kessel 1962; Williamson 1963; Welch 1967). Depending on the intensity of these preferences, individuals may be willing to pay a premium for the privilege of associating with members of one group and not another. The existence of significant preferences is indicated by a discrepancy in the wage rates paid to different, substitutable groups in

I Industry-wide union contracts may limit the amount of discretion that firms exercise in setting wage and promotion policies. However, since less than 20 percent of the labor force is unionized, the effects of such contracts are confined to a relatively small segment of employees. The present study focuses upon four-year institutions of higher education, a minority of which have faculty unions. Moreover, the ability of these unions to determine wage policies is extremely limited (Ladd and Lipset 1973, pp. 97-99).



ORGANIZATIONS AND INEQUALITY 229

a labor market. This discrepancy represents the costs of discrimination to an employer.

The ability of members of an organization to indulge in discriminatory preferences and thus to incur these costs is assumed to depend on the amount of competitive pressure confronting the organization. Larger, wealthier organizations in less competitive environments are more likely to have the slack resources required for the exercise of discriminatory preferences. Consistent with these arguments, a number of studies have indicated that larger firms and firms in an oligopolistic or monopolistic position have higher levels of compensation (Williamson 1963; Lester 1967) and hire significantly fewer minorities (Becker 1957; Alchian and Kessel 1962). This approach was formulated by economists to explain the behavior of for-profit organizations, but the behavior of nonprofit organizations is likely to be similarly affected by the existence of slack resources (see Cyert and March 1963), and many of the indicators of slack in for-profit firms (Bourgeois 1981) are apparent in nonprofit firms as well.

In the context of higher education, if male faculty are preferred over female faculty (see Abrahamson 1975; Cole 1979), institutions that indulge in such preferences are likely to hire more males and to pay them higher wages, since higher demand will drive up males' wages relative to females' wages. This effect will be most visible among institutions that are able to dominate their environment and hence exercise more latitude in setting wage levels.

There are a number of characteristics that may be taken as indicators of the organization's dominant position. One such indicator is size (see Baron and Bielby [1984] for empirical evidence of the link between size and other indicators of environmental dominance). Larger institutions, by definition, experience a higher demand for their products and services. Phelps-Brown (1972) has argued that high levels of demand enable organizations to absorb the costs of higher wages through product pricing. Larger colleges and universities are also likely to devote proportionately less resources to the recruitment of students. Finally, large organizations are able to capture the benefits of economies of scale. Hence, size affects the level of slack resources in a number of ways (Pfeffer and Salancik 1978). A second indicator of dominance is the selectivity of the institution in admitting students. Selectivity is also associated with increased demand; lower standards are more likely to be associated with strong competitive pressure to attract students. Thus, the costs of higher wages can be offset by higher tuition and fees. A third indicator of dominance is the wealth of the

institution, or the relative size of its revenue base.2 Larger, wealthier, more selective institutions

can better afford to exercise discriminatory preferences because they have access to a greater and more secure level of resource support. Thus, even if the members of these institutions were only slightly inclined to discriminate, discrimination would be more evident because the institution would be less constrained by resource limitations. Because these institutions have more discretionary resources, they are expected to have a higher proportion of males on their faculty and to pay premium wages to males. Female faculty might also benefit from the generally higher wage structure of these institutions, but to a lesser extent than males. Thus, I hypothesize that

1. the larger the college or university, the greater the difference between average male and female wages;

2. the more selective the institution, the greater the difference between average male and female wages;

3. the wealthier the institution, the greater the difference between average male and female wages.

The ability to exercise discriminatory preferences may also be affected by the nature of organizational control, public or private. Because public institutions are chartered as representatives of general collective interests, they are subject to pressures to conform to and embody basic social norms and values, such as equality of opportunity. More importantly, budgetary and salary information is usually publicly available and subject to scrutiny by legislative committees and other agencies. Earnings differences in public institutions, therefore, are likely to be more difficult to maintain. Thus, I hypothesize that

4. public institutions have smaller earnings differences than private institutions.

2 State legislatures sometimes place restrictions on the admissions criteria used by public schools, and by limiting funding, they also implicitly set limits on admissions. Consequently, well-known public schools with a large number of applicants can be as selective as private schools. Moreover, although lower tuition rates at public schools may increase demand to some extent, public schools can still face the problems of insufficient demand and declining enrollments-the same problems faced by private schools (Clark 1956). Lacking demand and, hence, a viable political constituency, these institutions are vulnerable to reduced support by state legislatures. Thus, while the availability of slack resources in public institutions is largely mediated by the actions of govemment agencies, it varies along the same dimensions in both types of institution.



230 TOLBERT

Group Relations Within the Organization

The second theoretical perspective that addresses the organizational sources of earnings differences focuses upon the effects of demographic characteristics, particularly the unequal representation and distribution of minority groups within an organization (Kanter 1977; Spangler, Gordon, and Pipkin 1978; South et al. 1982; Pfeffer 1983). Kanter has suggested that the entry of a small number of females into male-dominated fields or jobs is associated with a number of small-group processes: increased scrutiny and consciousness of the minority members, greater identification and solidarity among majority members, and increased reliance on minority stereo- types in interactions. Kanter (1977) has hypothe- sized that these processes, and the associated pressures on minority members, increase the likelihood of discriminatory perceptions and behavior.

Some support for this argument is provided by Spangler et al. (1978) in a study of female law students. They found that women in schools with a very small proportion of females experienced more difficulty and had lower levels of achievement than women in schools with a relatively high proportion of females. Research by South et al. (1982), on the other hand, suggests that the larger the proportion of women in an organization, the more likely are males to perceive them as a competitive threat and thus to respond to them negatively. One important possible source of discrepancy in these studies is the dependent variable that each uses: Spangler et al. (1978) focus on behavioral outcomes, and South et al. (1982) focus on perceptions of discrimination. In this paper, I follow Kanter's arguments and hypothesize that

5. the smaller the proportion of females in an institution, the greater the likelihood of discriminatory perceptions and, hence, the larger the differences between male and female earnings.

Greater segregation of women relative to men in lower academic ranks will directly affect the magnitude of salary differences, since rank is closely associated with salary. In addition, the segregation of women among certain positions or statuses within the organization is also likely to reduce the amount of interaction between males and females and thus contribute independently to the process described by Kanter, leading ultimately to larger earnings differences. Thus, I hypothesize that

6. the greater the segregation of females in lower ranks, the larger the earnings difference between males and females.

DATA AND ANALYSIS

The institutions in this study were selected from the set of all four-year higher education institutions classified by the Carnegie Commission as having a basic liberal arts curriculum and at least one professional program (National Center for Educa- tion Statistics [NCES] 1980). After stratifying the institutions by public and private control, a random sample was drawn. The final sample consisted of 309 institutions, 167 public and 139 private.

The data on these institutions were obtained from the 1976 Higher Education General Informa- tion Survey (HEGIS), a national survey of two- and four-year institutions in the U.S. conducted annually by the NCES (1980), and from the American Association of University Professors (1976). Missing data reduced the final sample to 282 institutions.

Measures

I examined three dependent variables: (1) the average salary of male faculty in an institution, (2) the average salary of female faculty, and (3) the proportionate difference between these. I constructed the first two variables by taking the annual expenditures for male and female salaries and dividing by the total number of male and female faculty members, respectively.3 I constructed the third variable by subtracting the average female salary from the average male salary and then dividing by the average male salary to normalize it.

Selectivity was measured by the average SAT score of entering freshmen. This has been shown to be closely related to traditional rating scales and other measures of prestige (see Astin bnd Henson 1977). Size was measured by the total number of students enrolled, logged to normalize the distribution. The wealth of an institution was measured by the total revenues of the institution, residualized on the number of students. Control was represented by a dummy variable, coded 1 for public institutions.

The representation of females in an institution was measured as a ratio variable: the number of female faculty relative to the number of all faculty. Finally, the segregation of women within major ranks-assistant, associate, and full professor- was measured by an index of dissimilarity, a measure that indicates the proportion of one group that would have to change ranks for both groups to

3 More precisely, the expenditures for males and females who were paid on a nine-month basis were divided by the number of males and females on nine-month contracts. This procedure was repeated for males and females with twelve-month contracts. The salaries of males on a nine- and twelve-month basis were then averaged to get the mean salary of males in an institution. The same was done for females.




have proportionally equal distributions (Duncan and Duncan 1955).

Analysis

I used ordinary least squares analysis to examine the effects of size, selectivity, wealth, institutional control, percentage of the faculty who are women, and the segregation measure on each of the dependent variables. Organizational characteristics may generate earnings differences by increasing male salaries relatively more than female salaries or by increasing male salaries and decreasing female salaries. Thus, separate analyses of average male and female salaries permit closer examination of the way in which earnings differences are produced.

Theoretically, it seems most plausible that the indicators of environmental relations are prior to the demographic characteristics. Moreover, it may be argued that these are in fact the critical determinants of male and female earnings. To examine this, I estimated two models. Model 1 includes the indicators of environmental relations. Model 2 includes the variables in model 1 plus the demographic variables. I used a significance test of the increment in R2 to determine the relative contributions of the latter set of variables to the predictions. Changes in the regression coefficients indicate the extent to which the effects of the measures of environmental relations are direct or are mediated by demographic measures.

Results

Table 1 presents descriptive information on the independent and dependent variables. As can be seen, the mean annual male salary exceeds the mean annual female salary by approximately $3,500. As the correlations suggest, the difference may indicate that institutions with a higher proportion of female faculty tend to be smaller and less prestigious; institutions of this type have lower salary levels in general. It has been suggested that the clustering of females in nonresearch institu-

tions, where the work load is assumed to be more compatible with family commitments, is one of the most important sources of faculty earnings differences (Johnson and Stafford 1977). However, it is by no means a complete explanation of the differences, as the data in Table 2 suggest.

The institutional types in Table 2 are based on the Carnegie Commission's classification of colleges and universities (NCES 1980). Research universities are defined by the amount of federal support provided for academic research; this criterion is used to distinguish between the top 50 institutions and the next top 100 institutions. Doctorate-granting institutions are those that do not fall into either of the first two categories but award 20 or more doctorates in at least five separate fields. Comprehensive colleges and universities are those that administer one or more professional programs, in addition to a basic liberal arts curriculum.

As Johnson and Stafford suggest, the latter institutions, which are the least research-oriented, have the lowest average wages and a much larger proportion of female faculty. This argument, however, does not tell us why the magnitude of earnings differences varies by type of institution, i.e., why more-elite institutions have progressively larger differences than less-elite institutions.4

I argue that these results are due to the characteristics of the institutions within each category. Elite research institutions are more likely to be larger, wealthier, and more selective and to have a different demographic composition: i.e., they are more likely to have discriminatory perceptions and preferences that are easier to sustain. To examine more closely the effects of these organizational characteristics on earnings

Table 1. Correlations, Means, and Standard Deviations of Independent and Dependent Variables

1 2 3 4 5 6 7 8 9

1. Log size 1.00 .17 .14 .50 -.57 -.03 .38 .71 .54 2. Selectivity 1.00 .49 -.27 -.32 .23 .28 .31 .12 3. Per-student revenues 1.00 -.24 -.36 .29 .31 .38 .18 4. Control (1 = public) 1.00 -.21 -.15 .05 .28 .30 5. Percentage female 1.00 -.16 -.38 -.50 -.27 6. Dissimilarity index 1.00 .26 .11 -.10 7. Earnings difference 1.00 .55 -.17 8. Log male salary 1.00 .73 9. Log female salary 1.00 Mean 1.95 999.16 4,694.20 .55 19.28 27.70 17.43 18,911.O0a 15,441.00a SD 1.06 173.57 3,622.64 .50 10.54 11.23 10.35 3,168.00a 2,435.00a

a These are not logged values.

'Johnson and Stafford (1974, 1977) have been criticized for failing to provide appropriate evidence for their arguments (see Gordon, Morton, and Braden 1974; Hoffman 1976; Strober and Quester 1977). A different point is made here. Apart from the empirical validity of their analysis, the theoretical perspective they adopt leaves many questions unanswered.



232 TOLBERT

Table 2. Average Male and Female Salaries and Percentage of Faculty who are Women, by Type of Institution

Average Average Proportionate Percentage of Type of Male Female Earnings Faculty who Institution Salary Salary Difference are Women

Research universities I $21,078 $15,787 24.86 11.09 (N=45) (1,877) (1,486) (6.38) (4.07) Research universities II 19,418 15,115 21.77 12.30 (N=41) (2,181) (1,683) (7.49) (3.91) Doctorate-granting schools 18,329 14,994 17.84 16.49 (N= 58) (2,089) (2,425) (11.43) (7.63) Comprehensive schools 15,097 13,538 14.18 24.34 (N= 130) (2,501) (2,025) (10.02) (10.34)

NOTE: Standard deviations are in parentheses.

differences, I present the results of the regression of the proportionate earnings difference on the independent variables in Table 3.

The effects of these variables on earnings differences are all in the predicted direction and, with two exceptions, are highly significant. Larger and wealthier institutions with a smaller proportion of female faculty are likely to have larger earnings differences. Though selectivity is an important predictor in model 1, its effects appear to be largely mediated by the demographic variables in model 2. Public institutions have somewhat smaller differences, though the effects of this control are slight.

Perhaps the most surprising result of this analysis is that the effects of most variables persist even after segregation by rank is taken into account. Since such segregation is likely to be the primary mechanism by which earnings differences are produced, we might expect the effects of the other variables to be substantially reduced once this variable is introduced into the equation. The failure of the segregation index to account for a larger part of the variance may be due in part to the inability to take into account within-rank differences (i.e., length of time within the rank). However, after controlling for segregation, the

strong and persisting effects of the other variables suggest that they do define important aspects of the context in which salaries are assigned.

Another way to approach this problem is to examine the effects of these measures on the constituent parts of the dependent variable: average male and female salaries. This permits us to determine whether certain characteristics increase male salaries but not female salaries or whether there are also differences in the direction of the effects. The results of the regression of average male and female salaries on these variables are presented in Table 4.

By far, the strongest predictor of salary levels for both males and females is the size of the institution. Larger institutions pay both males and females higher salaries, although a comparison of the unstandardized coefficients suggests that males benefit considerably more from being in larger institutions than females. The same is true for the effects of the wealth of the institution. In general, wealthier colleges and universities compensate their faculty better, but this appears to affect the wages of males much more than the wages of females. Being in a selective institution also increases males' salaries significantly. This is not the case for females, for whom the difference in

Table 3. Standardized and Unstandardized Coefficients from OLS Regressions of Earnings Differences on Organizational Characteristics

Model 1 Model 2a

a b S.E. a b S.E.

Log size .356 3.439*** .658 .311 3.045*** .718 Selectivity .188 .689* .381 .074 .442 .377 Per-student revenues .188 5.328*** 1.756 .112 3.168* 1.801 Control (1 =public) -.051 -1.061 1.434 -.056 -1.165 1.401 Percentage female - - - -.127 - 1.245* .666 Segregation by rank - - - .188 - 1.737*** .501 Intercept 1.842 3.840 R 2 .22 .27

a Increment in R2, F = 9.34, p < .01. *p < .10.

** p < .05. ***p < .01.




Table 4. Standardized and Unstandardized Coefficients from OLS Regressions of Average Male and Female Salaries on Organizational Characteristics

Log Male Salary Log Female Salary

Model 1 Model 2a Model 1 Model 2b

13 b S.E. 1 b S.E. 1 b S.E. 1 b S.E.

Log size .632 .107*** .008 .629 .106*** .009 .462 .066*** .009 .496 .071*** .010 Selectivity .086 .884* .478 .079 .816* .487 .000 .020 .526 .028 .242 .529 Per-student

revenues .259 .126*** .022 .246 .120*** .023 .134 .055*** .024 .184 .076*** .025 Control (1 =

public) .051 .018 .018 .051 .018 .018 .103 .031 .019 .106 .033* .019 Percentage

female - - - -.012 -.020 .086 - - - .086 -.123 .094 Segregation

by rank - - - .040 .064 .065 - - - -.123 -.166** .071 Intercept 9.396 9.393 9.389 9.368 R 2 .59 .59 .30 .32

a Increment in R2, F = .53, n.s. b Increment in R2, F = 3.394, p < .05. *p < .10.

**p < .05. ***p < .01.

salaries between selective and nonselective institutions is nonsignificant. Thus, those institutions whose market or environmental position enables them to pay higher wages do so, but this ability is more likely to be exercised in behalf of male faculty than female faculty. Interestingly, public control becomes a significant predictor of female salary after the demographic variables are entered into the equation (model 2). This may reflect the relatively higher wages paid to female assistant professors in public institutions (American Associ- ation of University Professors 1976).

The demographic characteristics of the institution have a much greater impact on female salaries than on male salaries. Their entry into the equation for male salaries neither increases the amount of explained variance nor affects the coefficients of the indicators of environmental relations. In contrast, the demographic variables contribute significantly to the amount of explained variance in female salaries. Not surprisingly, segregation by rank has a significant, negative effect on women's earnings. An increase in the proportion of females has a marked (though nonsignificant at the .05 level) positive effect on average salaries of females. In contrast, its effect on male salaries is weak and slightly negative. Thus, segregation increases the earnings difference in an institution primarily by depressing the average salary of females, and an increase in the proportion of females decreases the difference largely by increasing the average salary of females.

DISCUSSION

This research has explored the organizational sources of the difference in salaries between male and female faculty. Following earlier research, it

has shown that one source of lower female earnings is the clustering of women within academic markets in which the average levels of compensation for both males and females is lower. However, this provides only a partial explanation of earnings differences, since substantial differences in compensation remain even within these markets, and since the size of the difference is linked to the type of market. In top research institutions, the average female salary is 75 percent of the average male salary; in comprehensive institutions it is 86 percent. This directs attention to the specific institutional characteristics that generate larger salary differences and the ability to maintain these differences.

These characteristics have been the primary focus of this study. Drawing upon theoretical perspectives that emphasize organizational sources of inequality, I have examined two general sets of characteristics: (1) characteristics linked to environmental relations and (2) demographic characteristics. One perspective, which stresses the effect of the presence or absence of constraints on the exercise of discriminatory preferences by members of an organization, suggests that discrimination is more likely to occur in organizations that are able to dominate their environments (Becker 1957). I explored the relationships between a number of indicators of environmental dominance and the difference between average male and female salaries. The results are consistent with the theory that dominant institutions are more likely to exercise discriminatory preferences by hiring more males and paying them higher salaries than females.

I also found support for the second perspective, which suggests that reduced exposure to a minority group, as a consequence of minority group size and



234 TOLBERT

segregation, can foster discriminatory perceptions and behavior. The results show that demographic characteristics of institutions are related to the magnitude of earnings differences, primarily through their effects on average female salaries. These two perspectives are not incompatible. Indeed, the results indicate that the effects of the indicators of environmental relations operate in part through the demographic variables, as well as independently.

This research does not take into account individual productivity differences that may also influence earnings differences. However, a number of studies have shown that when disciplinary and institutional differences are controlled, there are no significant differences in productivity between male and female faculty, as measured by number of publications, number of presentations, etc. (Ferber 1974; Katz 1973; Simon, Clark, and Galway 1975; Ferber and Kordick 1978; National Research Council 1979). When productivity measures are held constant, significant differences between the salaries of individual males and females remain (Bayer and Astin 1968; Astin and Bayer 1973; National Research Council 1981).

Moreover, individual-level explanations do not account for the observed patterning of larger and smaller earnings differences among institutions. If top-ranked institutions hired only top-ranked aca- demicians, and if more males than females fell into this latter category, then we would expect these institutions to have a relatively small proportion of female faculty. (See Arrow [1971] for a similar argument on group productivity differences and interorganizational segregation.) This argument does not, however, explain why elite institutions have a larger earnings difference than less elite institutions, nor why the average earnings of males at lower-ranked institutions remain higher than the average earnings of females at top-ranked institutions.

Another possible influence on the earnings difference is the distribution of academic departments in an institution. Certain areas of specialization (e.g., engineering) are typically characterized by both higher salary levels and a high proportion of male faculty. Thus, we may expect institutions with these departments to have a larger earnings gap. To examine this possibility, I determined for each institution the presence and relative size of four academic specializations: medicine, law, business, and engineering. These fields have traditionally been dominated by males and, on the average, have somewhat higher salaries (Chronicle of Higher Education 1981; NCES 1981; National Science Foundation 1984). The relative size of each program was measured by the number of degrees awarded in the program in 1974, divided by the total number of students (NCES 1975).

The coefficient of this variable in the regression analysis of differences between average male and female salaries was both positive and significant at

the .10 level, indicating that the disciplinary composition does contribute to the earnings gap. It is a fairly strong, significant predictor in the equation for average male salary; however, it fails to approach significance in the equation for average female salary. Since these are male- dominated areas, these results are not surprising. However, the coefficients of the other variables were not substantially affected by the inclusion of this measure in any of the analyses. Thus, the results do not support the argument that earnings differences can be attributed solely or largely to differences in academic specialization. (The tables can be obtained from the author.)

Earnings differences between males and females involve a complex interaction between individual characteristics and the organizational context in which pay and promotion decisions are made (Stolzenberg 1978). This research has attempted to further our understanding of this problem by focusing on aspects of organizations that are relevant to the decision-making context. An important step for future research would be to assess the effects of these contextual factors, independent of and in interaction with individual characteristics, on earnings inequalities.

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Alchian, Armen, and Reuben Kessel. 1962. "Competi- tion, Monopoly and the Pursuit of Money." Pp. 157-83 in Aspects of Labor Economics. Princeton: Princeton University Press.

American Association of University Professors. 1976. "Report on the Economic Status of the Profession, 1975-76." AAUP Bulletin 62:268-78.

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organizations and inequality: sources of earnings differences between male and female faculty

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