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Gender differences in agency head salaries: the case of public education.

Nearly 40 years have gone by since the passage of the Equal Pay Act, the first modern statute directed at protecting workers against wage discrimination. The Equal Pay Act of 1963 prohibits unequal pay for equal or "substantially equal" work performed by men and women. (1) This legislation was quickly followed by the Civil Rights Act of 1964 which prohibited wage discrimination on the basis of race, color, sex, religion, or national origin, Together, these laws have revolutionized the American workplace. Despite the advancements made by women in the workforce, however, sex-based wage discrimination has persisted. Indeed, the Department of Labor reports that in 1999, women earned approximately 77 percent as much as men did, up little more than a dime since 1963. African American and Latina women fare worse at 65 percent and 59 percent, respectively (DOLL 1999). (2)

Although women are making great strides in certain labor sectors (Blau and Kahn 1994), many problems remain. A preponderance of studies on the public employment distribution of women and men provides evidence that women often face glass ceilings and glass walls at the federal and state levels (Baron and Newman 1989; Bullard and Wright 1993; Cornwell and Kellough 1994; Crampton, Hodge, and Mishra 1997; Crum and Naif 1997; Guy 1992; Kellough 1989; Lewis and Emmert 1986; Lewis and Nice 1994; Mani 1997, 1999; Naif and Thomas 1994; Newman 1994; Pfeffer and Davis-Blake 1987; Reid, Kerr, and Miller 2000). In this work, we extend the analysis to examine gender differences in salary among a set of administrators who have reached the top of the organizational ladder, school superintendents.

Researchers have predicted that as more women occupy line positions in school districts (such as assistant superintendents or principals), we will see more women become superintendents (Schmuck 1992). This, however, has not been the case. Nationally, only about 4 percent of district superintendents are women, while more than 20 percent of line district office positions are filled by women (Schuster and Foote 1990). In her study on the promotion of teachers to administrative positions, Joy (1998) finds that men are more likely than women to be selected for promotion during the school year, even when the teacher's desire for promotions and credentials are considered. Examining the explanations for the small percentage of women superintendents is beyond the scope of this work, but we take an important step in assessing sex-based wage disparities among individuals who have become superintendents. (3)

Two objectives guide this article. The first is whether gender has any unique effect on superintendent salaries, above and beyond the effects of income-related factors such as human capital, local resources, and job performance. To address this question, we assess the salaries of male and female school superintendents in Texas over time (1995-98) to determine whether sex-based wage disparities exist. Superintendents are an interesting case because well-educated women have long been employed by schools, yet few have become superintendents. The second objective is to illustrate how gender differences should be assessed, thus creating a template for future researchers and practitioners seeking to examine this question in other public organizations.

Prior Studies of Gender Discrimination in Salaries

Numerous studies of sex-based salary disparities have demonstrated unequivocally the existence and persistence of salary disparities in both the private and public sectors. Although the private sector has made some progress toward pay equity (Furchgott-Ruth and Stolba 1996; O'Neill 1985; O'Neill and Polachek 1993), significant sex-based pay gaps continue (Groshen 1991; Macpherson and Hirsch 1995; Sorensen 1987; Hultin and Szulkin 1999). In these studies, the gaps remain even after researchers control for human capital differences such as education, years of experience, tenure in current job, and the education level of the employee.

Similar research in the public sector has produced analogous results. Several studies using aggregate data on public-sector wages provide strong evidence of sex-based pay disparities at all levels of government (Bullard and Wright 1993; Lewis and Emmert 1986; Miller, Kerr and Reid 1999; Lewis and Nice 1994; Pfeffer and Davis-Blake 1987; Reid, Kerr and Miller 2000). These disparities have been linked to gender composition at the occupational level (Lewis and Nice 1994; Pfeffer and Davis-Blake 1987), at the organizational level (Lewis and Emmert 1986; Blau and Kahn 1999), and at the job level (Treiman and Hartmann 1981). Pfeffer and Davis-Blake (1987) demonstrate in their study of college and university administrators that the proportion of female incumbents depressed wages for both male and female administrators. In recent research on glass ceilings in U.S. state-level bureaucracies, Reid, Kerr and Miller (2000) conclude that women are underrepresented in higher-paying positions (in proportion to their numbers in the agency). This previous research provides a rich foundation for our inquiry into whether gender has any unique effect on superintendent salaries. (3)

One frequent difference between studies of the private sector and the public sector concerns the level of aggregation. Private-sector studies often examine gender discrimination at the individual level, whereas public-sector studies generally aggregate data to examine groups of jobs. (4) Public-sector studies are generally aggregate because public classification systems establish pay levels for specific jobs rather than for individuals. The present study is relatively unique, therefore, in using individual data for public agency heads in numerous organizations.

Previous research on sex-based wage disparity followed a standard human capital approach for determining whether a pay gap exists. This approach regresses wages on the sex of the employee and those factors thought to legitimately influence earnings, such as education, job performance, and organizational resources. Although we start with this standard approach, our research goes a step further to consider how salaries are affected by turnover and replacement. This more subtle form of analysis will illustrate that other studies in the literature may not have teased out all of the sex-based wage disparity that exists in organizations.

Data and Methods

The data base used for analysis contains all full-time Texas school superintendents during 1995-98. Texas has more than 1,000 superintendents, approximately 8 percent of all superintendents nationwide; the total number of cases for analysis is 4,103. All data were provided by the Texas Educational Agency and were cleaned of obvious errors. Because these are pooled time-series data, we include a set of year-dummy variables to adjust for serial correlation.

The market for school superintendents may differ from that for other public agency heads. The market can be characterized as competitive with full information; that is, all positions are announced and individuals know the salary of the previous superintendent and salaries that similar-sized districts pay. Under such circumstances, paying below-market wages based on ascriptive characteristics such as gender is more difficult.

Dependent Variable

The dependent variable for analysis is the annual salary for the district superintendent. This figure includes salary and benefits from official sources and may not include perquisites such as club memberships, transportation allowances, and similar factors. The mean salary is $68,400 and is positively skewed. To adjust for the skew and to facilitate interpretation, a log transformation of the salary figure was taken. (5)

Independent Variables

Assessing gender discrimination in salaries requires that one control for all other factors likely to affect salary rates for managers, including the scope of the job, local resources, job performance, and personal investments in human capital (Ehrenberg, Chaykowski, and Ehrenberg 1988). Quite clearly, the major factor in determining a superintendent's salary is the scope of the job, that is, how much responsibility and authority the superintendent has. We measure this using the total revenue (operating capital) of the school district. (When controlling for budgets, student enrollments and other similar factors are uncorrelated with salary.) This variable was also positively skewed and was subjected to a log transformation.

Local resources were measured by the percentage of revenues from local (rather than state or federal) sources. Texas's state funding formula is redistributive in nature, so a larger percentage of a district's money raised locally is an indicator of district wealth. This factor can influence salaries in two ways: First, the percentage of local funds is positively correlated with per student educational expenditures, and thus it indicates a more ample budget. Second, local wealth is also related to the cost of living in a community, implying that such communities will need to compensate employees better (Eller, Meier, and Doerfler 2000).

Job performance should be related to salaries. Schools have multiple goals; as a result, measuring superintendent performance is difficult and likely contains many subjective judgments. One aspect of performance, however, might be amenable to quantitative measurement. Texas relies heavily on standardized tests for school; and test results are front-page news throughout the state. The importance of the state standardized test (known as TAAS) suggests that superintendents may be rewarded for high scores. The indicator used is the percentage of the district's students who passed the TAAS exam in the previous year. (6)

Human capital is the experience and skills that an individual brings to the job. Four measures of human capital are available--years of administrative experience, age, tenure in the current job, and whether the individual holds a doctoral degree. (7) Each should be positively related to salary.

In addition to scope of the job, performance, and human capital, we include three dummy variables to indicate whether the superintendent: is female, African American, or Latino. Our concern is with gender discrimination, but giver, the relative scarcity of African American and Latino administrators, one also needs to control for race and ethnicity in the models.


School districts are classic glass-ceiling organizations. In our set of districts, women comprise 75 percent of the teachers, 51.3 percent of the assistant principals, 47 percent of the principals, and 35.8 percent of the assistant superintendents, but only 8.4 percent of the superintendents. Table 1 presents a demographic comparison of male and female superintendents. On average, women superintendents are paid slightly more than male superintendents, but they also oversee larger school districts with bigger budgets. Descriptive data such as this, while interesting, tell us very little about whether gender discrimination exists in salaries, simply because the comparisons do not control for other factors that influence salaries.

Gender discrimination in employment can have two different characteristics. In one case, women could be paid a constant percentage less than men at all levels of experience, skills, and performance. This situation would be indicated by a significant negative coefficient for the gender variable. (8) In the second case, women might be rewarded less for a given level of experience, skills, or performance. For example, an earned doctorate might be worth a 6 percent salary increase for a male but only a 3 percent salary increase for a female. This study examines both possibilities.

The first two columns of table 2 show the regression equation to determine whether women are paid less at all levels of experience, skill, and performance. The overall level of prediction (79 percent) compares favorably with other studies of school superintendent salaries (see Ehrenberg, Chaykowski, and Ehrenberg 1988). The coefficient for gender (.0042) is both small and statistically insignificant, suggesting that women are paid 0.42 percent more than men, all other things being equal (about $300 at average salaries). The results suggest no discrimination on the basis of gender.

Other coefficients in this regression also merit some discussion. The major factor in determining superintendent salaries is the scope of the job; the contribution of the district's budget is far greater than any other single factor or set of factors. A 1 percent increase in the district's budget is associated with a 0.15 percent increase in superintendent's salary, all other things being equal. Local revenues also matter. Because the independent variable is not logged in this case, the coefficient can be multiplied by 100 and interpreted as a percentage (see Tufte 1974). In this case, a 1 percentage point increase in the local funding percentage is associated with a 0.1 percent increase in salary. Because the standard deviation for the percentage of local funds is 23 percent, the overall impact of this variable could be as high as 10 percent in total salary.

Each of the human capital factors is significant and in the predicted direction. The coefficients are relatively small, however, except for the doctoral degree. The possession of a doctorate is associated with a 5.95 percent increase in salary, all other things being equal.

Performance matters, but not a great deal. An increase of 1 percentage point in the share of students passing the TAAS is associated with a salary increase of 0.09 percent, all other things being equal. A standard deviation increase (about 12.3 percent) translates into a 1.35 percent increase in salary. Race matters, but ethnicity does not. The coefficient for Latinos is essentially zero, but the coefficient for African Americans suggests that a premium of approximately 11 percent is paid to African American superintendents. Although an investigation of this phenomenon is beyond the scope of this article, African American superintendents are few in number, and this premium likely reflects an imbalance in supply and demand. (9)

The third and fourth columns of table 2 show the regression for determining whether gender interacts with other variables to adversely affect women's salaries. The top set of coefficients in the table can be interpreted as the relationships for male superintendents; the bottom set of coefficients are essentially how much different the relationship is for women. To illustrate, the doctorate coefficient indicates that possession of the degree is worth approximately a 6 percent increase in salary for men. The corresponding coefficient for women (-.0206) indicates that a doctorate for women translates into only about a 4 percent salary increase (2.06 percent less). This difference, while interesting, does not meet standard levels of statistical significance (t > 1.96).

Two aspects of the regression merit examination. The first is whether the coefficients for women as a group are systematically different from those for men. This is done with a joint f-test that simultaneously determines whether all the coefficients for women could be zero (and thus do not differ; see Pindyck and Rubinfeld 1991; 110-12). The joint f-test at the bottom of the table is highly significant, indicating that women's coefficients are significantly different.

The joint test, however, cannot reveal discrimination because positive coefficients on one factor might be compensated for by negative findings on another; it shows only that there are systematic differences. The individual coefficients need to be examined to determine what those factors might be and if there is cause for concern. In many cases, individual coefficients may not be significant because interaction equations such as this one induce a great deal of collinearity. This is especially a problem when the number of cases is small; but with more than 4,000 cases in this study, it is not a problem here.

The bottom set of coefficients has only a single coefficient that meets traditional levels of statistical significance, that for percentage of local funding. For each 1 percentage point increase in funds coming from local sources, men are paid 0.11 percent more and women are paid 0.06 percent (0.11 - 0.17 = -0.06) less, all other things being equal. Although these figures appear small, a one standard deviation change in the percentage of local funding (23 percent) is associated with a gender wage gap of 3.9 percent. Particularly in relatively wealthy school districts (those with a high percentage of local funds), gender differences in superintendent salaries can be substantial. (10) The remaining coefficients in the bottom half of the table should be considered statistically indistinguishable from zero, with the result that no inferences should be drawn.

Although table 2 attempts to control for all relevant factors that may influence salaries other than gender, there is always a possibility that something has been omitted. As a result, some assessments of gender discrimination take a different tack. They compare how the salary for a given job changes when a woman replaces a man to how the salary changes when a man replaces a woman. Because we have data over a four-year period, we have 500 cases in which new superintendents were hired. In 60 cases, a male superintendent was replaced by a female superintendent; in 38 cases, a female superintendent was replaced by a male. In all other cases, the gender of the superintendent remained the same.

Table 3 presents a regression equation in which the dependent variable is the change in logged salary from one year to the next. The intercept can be interpreted as the percent change in salary if there is no change in superintendent (4.11 percent). In general, when a new superintendent is hired, he or she is paid about 2.63 percent less than the previous superintendent. Subtracting the other coefficients from this base gives the change in salary when there is a change in gender. When a male is hired to replace a female superintendent, the salary remains virtually the same (-.0263 + .0206 = -.0057). When a female is hired to replace a male superintendent, the salary drops by 7.5 percent (-.0487 -.02;63 = -.0750). These findings are relatively strong evidence that, at least in some cases, gender discrimination exists.

This exploration of superintendent changes and the assessment of individual factors suggests that we reformulate our base model of salaries to include the interaction of local funds with gender and the replacement of a male superintendent with a female one. The results of this regression are shown in table 4. This regression yields a more specific conclusion about gender and salaries. There appear to be preferences in terms of gender that are reflected in salary differences in specific situations. First, all other things being equal, a female superintendent who replaces a male superintendent is paid an estimated 5.5 percent less in salary. Second, gender preferences interact with local funding in an interesting pattern. One can combine the interaction coefficient (-.0015) with the gender coefficient (.0746) to find gender differentials at different levels of local funding using the following formula:

Salary Differential = .0746 - .0015 (Local Funding)

At 90 percent local funding (71 districts have at least 90 percent local funding), the coefficient becomes -.0604: Women are paid approximately 6.0 percent less than men, all other things being equal. At 10 percent local funding (82 districts meet this criterion), the coefficient becomes .0596, meaning that women are paid approximately 6 percent more than men. Gender discrimination affects both men and women when local control is considered. Third, the coefficient for gender by itself is now positive and significant. Women superintendents make 7.5 percent more than men, all other things being equal (including the percentage of local funds and replacement of a male superintendent).

Overall, the relationships show a complex pattern; there is some evidence of discrimination in salaries in specific situations. In some cases, women are disadvantaged, such as when they replace a male superintendent or in relatively wealthy districts. In other cases, males are at a disadvantage in districts that are relatively poorer and in general.

Whether these differences constitute discrimination based on gender depends on the specific situation. Salaries are legitimately determined by a wide variety of factors, including the track record of the superintendent in managing the district; such factors need to be considered in individual cases. Regression analyses such as this one cannot provide evidence of actual discrimination; it can only provide information about salary differences. To conclude that discrimination exists requires the examination of the specific case involved.


The study presents a template for how to conduct studies of salary discrimination at the individual level. Substantively, the gender differences that we found were subtle rather than systematic. Such small differences are likely the result of a market for agency heads that relies on open competition and full information. Whether the individual differences constitute discrimination can be resolved only by examining the individual cases. This techniques tells the public manager where to look, but is not a substitute for careful assessment at the individual level. The technique that we use is likely to be useful in other situations in which agency head salaries are not set by law, such as city managers or local agency heads (public works administrators, police chiefs, etc.).
Table 1 A Comparison of Male and Female

Variable                     Females      Males

Years of experience              21.7       24.3 *
Age                              50.4       50.2
Tenure (years)                    5.7        6.8 *
Master's degree (percent)        67.1       74.4 *
Doctorate (percent)              32.9       23.2 *
Student enrollment            4,570      3,543
Budget (millions)                12.7        9.8
Salary                       70,015     68,225

* Differences significant at p < .05.
Table 2 Gender and Salaries
Dependent variable = log (salary)

                                   Intercept only

Independent variable             Slope      t-score

Budget size (logged)              .1558     102.53
Local revenue percent             .0010      11.52
Human capital factors
  Experience (years)              .0025       8.66
  Age                             .0009       3.79
  Tenure                          .0006       2.43
  Doctorate                       .0595      12.59
Performance (previous years)      .0009       5.21
African American                  .1099       5.59
Latino                           -.0001        .02
Female                            .0042        .63
Female x budget                    --         --
Female x local revenue             --         --
Female x experience                --         --
Female x age                       --         --
Female x tenure                    --         --
Female x doctorate                 --         --
Female x performance               --         --
Female African American            --         --
Latina                             --         --
[R.sup.2]             .79          .80
Standard error        .1175        .1171
F                1,213.48       723.67
N                4,103        4,103

                                  Full interaction

Independent variable             Slope      t-score

Budget size (logged)              .1554      96.72
Local revenue percent             .0011      12.55
Human capital factors
  Experience (years)              .0026       8.47
  Age                             .0009       3.53
  Tenure                          .0007       2.64
  Doctorate                       .0600      12.05
Performance (previous years)      .0009       5.17
African American                  .0970       4.39
Latino                            .0028        .32
Female                           -.0513        .51
Female x budget                   .0073       1.41
Female x local revenue           -.0017       5.38
Female x experience              -.0011       1.07
Female x age                      .0007        .59
Female x tenure                  -.0019       1.83
Female x doctorate                .0206       1.29
Female x performance              .0003        .54
Female African American           .0655       1.33
Latina                           -.0330       1.30
Standard error

Joint f-test (9, 4085) = 4.09 p = .0001

Dummy variables for individual years not reported.
Table 3 Changes in Salary: New Superintendent

Dependent variable = first difference of logged (salary)

Independent variable                Slope    t-score

Intercept                           .0411     25.42
New superintendent                  -.0263    5.16
Female replaces male                -.0487    4.40
Male replaces female                .0206     1.48
[R.sup.2]                   .02
Standard error              .0084
F                         18.20
N                      3,045
Table 4 Gender and Salaries: The Impact of Gender
Change and Local Wealth

Dependent variable = log (salary)
Intercept only
Independent variable         Slope      t-score

Budget size (logged)        .1558       102.99
Local revenue percent       .0011        12.59
Human capital factors
  Experience (years)        .0025         8.56
  Age                       .0009         3.77
  Tenure                    .0006         2.35
  Doctorate                 .0583        12.39
Performance (last years)    .0009         5.21
African American            .1082         5.52
Latino                     -.0013          .15
Female                      .0746         5.41
Female replaces male       -.0551         3.38
Female x local revenue     -.0015         5.10
[R.sup.2]              .80
Standard error         .1170
F                 1,063.73
N                 4,103


All data and documentation necessary to replicate this analysis are available from the authors. Financial support for the analysis was provided by the George Bush School of Government and Public Service and the Department of Political Science at Texas A&M University.


(1.) Exceptions to the act include seniority, merit, and differences in the quantity and quality of output.

(2.) Raw comparisons such as these omit any controls for human capital or tastes for leisure, and thus they may over- or under-estimate the actual wage gap.

(3.) We considered using a Heckman selection bias correction, in the event some districts simply were unlikely to hire women superintendents. The selection bias equation predicted poorly, suggesting that salary levels are not affected by whether the district will hire a female superintendent.

(4.) An exception to this generalization is the research agenda of Greg Lewis (1986, 1996) which uses samples from the federal central personnel data file.

(5.) The log transformation, a standard practice in human capital equations, allows us to interpret the coefficients in terms of percentage increases or decreases.

(6.) We use the previous year's test score because it would be known at the time the school board sets the superintendent's salary for the year.

(7.) The key educational distinction is between individuals with a master's degree and those with a doctorate. Table 1 shows that virtually all superintendents have at least a master's degree.

(8.) This is known as a test for a change in intercept. See Jacobsen (1998,293) for a lucid discussion of these models.

(9.) Inner-city school districts are especially likely to hire African American superintendents. Texas has several large cities, and many of these have more than one inner-city school district. There have never been more than 10 African American superintendents in any given year.

(10.) Another way to illustrate this relationship is to split the sample at the median, 37 percent local funding, and rerun the first equation from table 2. For districts with less than 37 percent in local funds, women superintendents are paid 3.3 percent more than men, all things being equal; in districts with more than 37 percent local funding, women are paid 2.3 percent less than men, all things being equal. Both coefficients are statistically significant.


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Kenneth J. Meier is the Charles Puryear Professor of Liberal Arts and the Sara Lindsey Professor of Government at Texas A&M University. He teaches in the Department of Political Science and is the director of the Center for Presidential Studies, Policy, and Governance in the George Bush School of Government and Public Service. His current research focuses on empirical studies of public management, empirical theories of public organizations, and new methods for public administration. Email:

Vicky M. Wilkins is a doctoral candidate in the Department of Political Science at the University of Missouri-Columbia. Her research interests include representation in American political institutions, representative bureaucracy, welfare policy, and public administration. Email:
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Author:Meier, Kenneth J.; Wilkins, Vicky M.
Publication:Public Administration Review
Article Type:Statistical Data Included
Geographic Code:1USA
Date:Jul 1, 2002
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