{\rtf1\ansi \deflang1033\deff0{\fonttbl {\f0\fnil \fcharset0 \fprq2 Times New Roman;}{\f1\fnil \fcharset0 \fprq2 WP MathB;}{\f2\fnil \fcharset0 \fprq2 Courier;}}{\colortbl \red0\green0\blue0;\red0\green0\blue255;} {\stylesheet{\fs20 \snext0 Normal;} {\s1 \qc\sl480 Title;} {\*\cs2 \additive Footnote Ref;} {\*\cs3 \additive\cf1 Hyperlink;} {\*\cs4 \additive\fs20 Page Number;} {\s5 \sl0\tx0\tqc\tx4320\tqr\tx8640\tx9360 Footer;} {\s6 Normal (Web);} {\*\cs7 \additive\fs20 Default Para;} }{\info{\doccomm Gender Differences in Agency Head Salaries:}} \margl1440\margr1440\ftnbj\ftnrestart\aftnnar \sectd \sbknone {\*\pnseclvl1\pndec\pnstart1{\pntxta .}} {\*\pnseclvl2\pnlcltr\pnstart1{\pntxta .}} {\*\pnseclvl3\pnlcrm\pnstart1{\pntxta .}} {\*\pnseclvl4\pndec\pnstart1{\pntxtb (}{\pntxta )}} {\*\pnseclvl5\pnlcltr\pnstart1{\pntxtb (}{\pntxta )}} {\*\pnseclvl6\pnlcrm\pnstart1{\pntxtb (}{\pntxta )}} {\*\pnseclvl7\pndec\pnstart1{\pntxta .}} {\*\pnseclvl8\pnlcltr\pnstart1{\pntxta .}} {\*\pnseclvl9\pnlcrm\pnstart1} \pard \sl0 {\plain \par }{\plain \par }\pard \qc\sl0 {\plain \b\fs28 Gender Differences in Agency Head Salaries:\par }{\plain \b\fs28 \par }{\plain \b\fs28 The Case of Public Education}{\plain \b }{\plain \par }\pard \sl0 {\plain \par }{\plain \par }{\plain \par }{\plain \par }{\plain \par }{\plain \par }{\plain \par }{\plain \par }{\plain \par }\pard \qc\sl0 {\plain Kenneth J. Meier\par }{\plain \par }\pard \s1\qc {\plain }{\plain \b }{\plain Department of Political Science\par }\pard \qc {\plain and \par }{\plain George Bush School of Government\par }{\plain Texas A&M University\par }{\plain 4348 TAMUS\par }\pard \s1\qc {\plain }{\plain \b }{\plain College Station TX 77843-4348\par }\pard \s1\qc {\plain }{\plain \b }{\plain \par }\pard {\plain \par }\pard \qc {\plain Vicky M. Wilkins\par }{\plain \par }{\plain Dept. of Political Science\par }{\plain University of Missouri \'96 Columbia\par }{\plain Columbia, MO 65211\par }\pard {\plain \par }{\plain \par }{\plain \par }{\plain \par }{\plain \par }{\plain \par }{\plain \par }{\plain \par }{\plain \par }{\plain \par }{\plain \par }{\plain \par }\pard \s1 {\plain }{\plain \b }{\plain 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.}{\plain \par }\pard \s1\qc\sl480 \pard\page {\plain \b\fs28 Gender Differences in Agency Head Salaries:\par }\pard \s1\qc\sl480 {\plain \b\fs28 The Case of Public Education}{\plain \b \par }\pard {\plain \b }{\plain \fs28 \par }\pard \qc {\plain Abstract\par }\pard {\plain \par }{\plain \tab \tab \tab \tab \tab \par }\pard \sl480 {\plain \tab This study demonstrates a quantitative approach to assessing gender discrimination in public salaries at the individual level. Using data from 1000+ school districts in Texas over a period of 4 years, the results show that gender differences in superintendent\'92s salaries are subtle rather than systematic. Female superintendents who replace male superintendents receive lower compensation. Local district wealth also interacts with gender to affect salaries.\par }{\plain \par }\pard\page \sect \sectd \sbknone\pgnstarts0\pgnrestart\pgndec\pgnx6120\pgny15120 {\*\pnseclvl1\pndec\pnstart1{\pntxta .}} {\*\pnseclvl2\pnlcltr\pnstart1{\pntxta .}} {\*\pnseclvl3\pnlcrm\pnstart1{\pntxta .}} {\*\pnseclvl4\pndec\pnstart1{\pntxtb (}{\pntxta )}} {\*\pnseclvl5\pnlcltr\pnstart1{\pntxtb (}{\pntxta )}} {\*\pnseclvl6\pnlcrm\pnstart1{\pntxtb (}{\pntxta )}} {\*\pnseclvl7\pndec\pnstart1{\pntxta .}} {\*\pnseclvl8\pnlcltr\pnstart1{\pntxta .}} {\*\pnseclvl9\pnlcrm\pnstart1} \pard \s1\qc\sl480 {\plain }{\plain \b\f1 }{\plain \f1 }{\plain \b\fs28 Gender Differences in Agency Head Salaries:\par }\pard \s1\qc\sl480 {\plain \b\fs28 }{\plain }{\plain \b\fs28 The Case of Public Education}{\plain \b \par }\pard \sl480 {\plain \b }{\plain \tab Nearly 40 years have passed since the passage of the }{\plain \i Equal Pay Act}{\plain , the first modern statute directed at protecting workers against wage discrimination. }{\plain \i The Equal Pay Act of 1963}{\plain prohibits unequal pay for equal or \'93substantially equal\'94 work performed by men and women.}{\plain \super 1}{\plain This legislation was quickly followed by the }{\plain \i Civil Rights Act of 1964}{\plain prohibiting wage discrimination on the basis of race, color, sex, religion, or national origin. Together, these laws 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 a little more than a dime since 1963. African-American and Latino women fare worse at 65 percent and 59 percent, respectively (Department 1999).}{\plain \super 2}{\plain \par }{\plain \tab 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 Naff 1997; Guy 1992; Kellough 1989; Lewis and Emmert 1986; Lewis and Nice 1994; Mani 1997; Mani 1999; Naff and Thomas 1994; Newman 1994; Pfeffer and Davis-Blake 1987; Reid, Kerr and Miller 2000). In this work, we extend the analysis to an examination of gender differences in salary among a set of administrators who have reached the top of the organizational ladder, school superintendents.\par }\pard \fi720\sl480 {\plain Researchers have predicted that as more women occupy line positions in school districts (such as assistant superintendents or principals), we would see more women become superintendents (Schmuck 1982). 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) found that men are more likely than women to be selected for promotion during the school year, even when the teacher\'92s 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 become superintendents.{\super \chftn {\footnote \pard \sa240 {\plain \super \chftn }{}{\plain \fs20 }{\plain We considered using a Heckman selection bias correction in case some districts simply were unlikely to hire women superintendents. The selection bias equation predicted poorly suggesting that salary levels are not affected by whether or not the district will hire a female superintendent.}}} }{\plain \par }{\plain \tab Two objectives guide this paper. The first is whether gender has any unique effect on superintendent salaries above and beyond the effects of such suspected income-related factors 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 (1995-1998) 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.\par }\pard \qc\sl480 {\plain }{\plain \b\fs28 Prior Studies of Gender Discrimination in Salaries}{\plain \b }{\plain \par }\pard \sl480 {\plain \tab 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-Roth and Stolba 1996; O\'92Neill 1985; O\'92Neill and Polachek 1993), significant sex-based pay gaps continue (Groshen 1991; Macpherson and Hirsch 1995; Sorensen 1994; 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.\par }{\plain \tab 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 1985; Blau and Kahn 1999), and at the job level (Treiman and Hartmann 1981). Pfeffer and Davis-Blake (1987) demonstrated in their study of college and university administrators that the proportion of female incumbents depressed the wages for both male and female administrators. In recent research on glass ceilings in U.S. state-level bureaucracies, Reid, Kerr and Miller (2000) concluded 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 of whether gender has any unique effect on superintendent salaries.}{\plain \super 3}{\plain \par }{\plain \tab One frequent difference between the private sector and the public sector studies concerns the level of aggregation. Private sector studies often examine gender discrimination at the individual level; public sector studies generally aggregate data to examine groups of jobs.}{\plain \super 4}{\plain 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.\par }\pard \qc\sl480 {\plain \tab The previous research on sex-based wage disparity follows 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 tease out all of the sex-based wage disparity that exists in organizations. }{\plain \b\fs28 Data and Methods}{\plain \b }{\plain \par }\pard \fi720\sl480 {\plain The data base used for analysis contains all full-time Texas school superintendents from 1995-98. Texas has over 1000 superintendents, approximately 8 percent of all superintendents nationwide; the total number of cases for analysis is 4103. 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.\par }\pard \fi720\sl480 {\plain The market for school superintendents may differ from that for other public agency head positions. 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. \par }{\plain }{\plain \b Dependent Variable}{\plain \par }\pard \fi720\sl480 {\plain 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 also to facilitate interpretation, a log transformation of the salary figure was taken.\pard \fi720\sl480 }{\plain \*\cs2 }{\plain \super 5}{\plain \par }{\plain \b Independent Variables}{\plain \par }\pard \fi720\sl480 {\plain Assessing gender discrimination in salaries requires that one control for all other factors likely to affect salary rates for managers including 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\'92s 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 and 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.\par }\pard \fi720\sl480 {\plain Local resources were measured by the percentage of revenues from local (rather than state or federal sources). Texas\'92 state funding formula is redistributive in nature, so a larger percentage of a district\'92s money raised locally is an indicator of district wealth. This factor can influence salaries in two ways. First, percent local funds is positively correlated with per student educational expenditures and thus 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 (see Eller, Meier and Doerfler 2000).\par }\pard \fi720\sl480 {\plain Job performance should be related to salaries. Schools have multiple goals; and as a result, measuring superintendent performance is difficult and likely contains many subjective judgements. One aspect of performance, however, might be amenable to quantitative measurement. Texas relies heavily on standardized tests for schools; and test results are front-page news through out 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\'92s students who passed the TAAS exam last year.}{\plain \*\cs2 }{\plain \super 6}{\plain \par }\pard \fi720\sl480 {\plain Human capital is the experience and skills that an individual brings to the job. Four measures of human capital are available\'96years of administrative experience, age, tenure in the current job, and whether or not the individual holds a doctoral degree.}{\plain \*\cs2 }{\plain \super 7}{\plain Each should be positively related to salary.\par }\pard \fi720\sl480 {\plain In addition to scope of the job, performance, and human capital, we include three dummy variables to indicate whether or not the superintendent is female, African American, or Latino. Our concern is with gender discrimination, but given the relative scarcity of African American and Latino administrators, one also needs to control for race and ethnicity in the models.\par }{\plain \b Results}{\plain \par }\pard \fi720\sl480 {\plain 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, 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 or not gender discrimination exists in salaries simply because the comparisons do not control for other factors that influence salaries.\par }\pard \qc\sl480 {\plain [Table 1 About Here] \par }\pard \fi720\sl480 {\plain Gender discrimination in employment can have two different characteristics. In one case, women could be paid 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.}{\plain \*\cs2 }{\plain \super 8}{\plain 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 3 percent salary increase for a female. This study examines both possibilities.\par }\pard \fi720\sl480 {\plain The first two columns of table 2 shows the regression equation to determine if 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; it suggests that women are paid 0.42 percent more than men all other things being equal (or about $300 at average salaries). The results suggest no discrimination on the basis of gender.\par }\pard \qc\sl480 {\plain [Table 2 About Here]\par }\pard \fi720\sl480 {\plain The 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\'92s budget is far greater than any other single factor or set of factors. A one percent increase in the districts budget is associated with a 0.15 percent increase in superintendent\'92s salary, all other things being equal. Local revenues also matter. Since the independent variable is not logged in this case, the coefficient can be multiplied by 100 and interpreted as a percent (see Tufte 1974). In this case, a one percentage point increase in the local funding percentage is associated with a 0.1 percent increase in salary. Since the standard deviation for percent local funds is 23 percent, the overall impact of this variable could be as high as 10 percent in total salary.\par }\pard \fi720\sl480 {\plain Each of the human capital factors is significant and in the predicted direction. The coefficients are relatively small, however, except for the doctorate degree. The possession of a doctorate is associated with a 5.95 percent increase in salary, all other things being equal. \par }\pard \fi720\sl480 {\plain Performance matters but not a great deal. An increase of one percentage point in 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 approximately an 11 percent premium is paid to African American superintendents. Although an investigation of this phenomenon is beyond the scope of this paper, African American superintendents are few in number, and this premium likely reflects an imbalance in supply and demand.}{\plain \*\cs2 }{\plain \super 9}{\plain \par }\pard \fi720\sl480 {\plain The third and fourth columns of Table 2 show the regression for determining if gender interacts with other variables to adversely affect women\'92s 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) indicate that a doctorate for women translates into only about a 4 percent salary increase (or 2.06 percent less). This difference while interesting does not meet standard levels of statistical significance (i.e., t > 1.96).\par }\pard \fi720\sl480 {\plain Two aspects of the regression merit examination. The first is whether or not 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 if all the coefficients for women could be zero (and thus do not differ; see Pindyck and Rubinfeld 1991: 110-2). The joint f-test at the bottom of the table is highly significant indicating that the women\'92s coefficients are significantly different from the men\'92s. \par }\pard \fi720\sl480 {\plain The joint test, however, cannot reveal discrimination since positive coefficients on one factor might be compensated for negative findings on another; it only shows 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 4000+ plus cases in this study, it is not a problem here. \par }\pard \fi720\sl480 {\plain The bottom set of coefficients has only a single coefficient that meets traditional levels of statistical significance, that for percent local funding. For each increase of one percentage point of 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. While these figures appear small, a one standard deviation change in percent 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 superintendent salaries could be substantial.}{\plain \*\cs2 }{\plain \super 10}{\plain 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.\par }\pard \fi720\sl480 {\plain Although Table 2 attempts to control for all relevant factors that could 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. Since we have data over a four-year time period, we have 500 cases where new superintendents were hired. In sixty 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. \par }\pard \fi720\sl480 {\plain Table 3 presents a regression equation where 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 -.0263 = -.0750). These findings are relatively strong evidence that at least in some cases gender discrimination exists.\par }\pard \qc\sl480 {\plain [Table 3 About Here]\par }\pard \fi720\sl480 {\plain 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:\par }\pard \fi720\sl480 {\plain \tab \tab Salary Differential = .0746 - .0015 (Local Funding)\par }{\plain At 90 percent local funding (71 districts have at least 90 percent local funding), the coefficient becomes -.0604 or 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 or 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 percent local funds and replacement of a male superintendent).\par }\pard \fi720\sl480 {\plain 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 replacing 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.\par }\pard \fi720\sl480 {\plain Whether or not 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.\par }\pard \qc\sl480 {\plain }{\plain \b\fs28 Conclusion}{\plain \par }\pard \sl480 {\plain \tab The study presented 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 found constitute discrimination or not can only be resolved by examining the individual cases. This techniques tells the public manager where to look but is not a substitute for a careful assessment at the individual level. The technique that we use is likely to be useful in other situations where agency head salaries are not set by law such as city managers or local agency heads (e.g., public works administrators, police chiefs, etc.). \par }{\plain \par }\pard\page \pard \qc\sl480 {\plain \fs28 }{\plain \b\fs28 Notes}{\plain \par }\pard \sl480 {\plain }{\plain \super 1}{\plain Exceptions to the act include seniority, merit, and differences in quantity and quality of output.\par }{\plain \super 2}{\plain Raw comparisons such as these omit any controls for human capital or tastes for leisure and thus may over or under estimate the actual wage gap.\par }{\plain \super 3}{\plain We considered using a Heckman selection bias correction in case some districts simply were unlikely to hire women superintendents. The selection bias equation predicted poorly suggesting that salary levels are not affected by whether or not the district will hire a female superintendent.\par }{\plain \super 4}{\plain An exception to this generalization is the research agenda of Greg Lewis using samples from the federal central personnel data file (e.g., Lewis 1986; 1996)\par }{\plain \super 5}{\plain The log transformation, a standard practice in human capital equations, allows us to interpret the coefficients in terms of percentage increases or decreases. \par }{\plain \super 6}{\plain We use last year\'92s test score because that would be known at the time the school board sets the superintendent\'92s salary for the year.\par }{\plain \super 7}{\plain The key educational distinction is between individuals with a master\'92s degree and those with a doctorate. Table 1 shows that virtually all superintendents have at least a masters degree.\par }{\plain \super 8}{\plain This is known as a test for a change in intercept. See Jacobsen (1998: 293) for a lucid discussion of these models. \par }{\plain \super 9}{\plain 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. \par }{\plain \super 10}{\plain 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.\par }\pard\page \pard \qc {\plain }{\plain \b\fs28 References}{\plain \par }\pard \qj {\plain \par }\pard \fi-720\li720 {\plain Baron, James N. and Meredith Ann Newman. 1989. Pay the Man: Effects of Demographic Composition on Prescribed Wage Rates in the California Civil Service. In }{\plain \i Pay Equity: Empirical Inquiries, }{\plain eds. Robert T. Michaels, Heidi I. Hartmann, and Brigid O\'92Farrell. Washington, DC: National Academy Press.\par }\pard {\plain \par }\pard \fi-720\li720 {\plain Blau, Francine D. and Lawrence M. Kahn. 1994. Rising Wage Inequality and the U.S. Gender Gap. }{\plain \i American Economic Review }{\plain 84 (2) :23-28.\par }\pard {\plain \par }\pard \fi-720\li720 {\plain Blau, Francine D. and Lawrence M. Kahn. 1999. Analyzing the Gender Pay Gap. }{\plain \i The Quarterly Review of Economics and Finance }{\plain 39 (Special Issue):625-646.\par }\pard {\plain \par }\pard \fi-720\li720 {\plain Bullard, Angela M. and Deil S. Wright. 1993. Circumventing the Glass Ceiling: Women Executives in American State Governments. }{\plain \i Public Administration Review }{\plain 53 (3) :189-202.\par }\pard {\plain \par }\pard \fi-720\li720 {\plain Cornwell, Christopher and J. Edward Kellough. 1994. Women and Minorities in Federal Government Agencies: Examining New Evidence from Panel Data. }{\plain \i Public Administration Review }{\plain 54 (3) :265-276.\par }\pard {\plain \par }\pard \fi-720\li720 {\plain Crampton, Suzanne M., John W. Hodge and Jitendra M. Mishra. 1997. The Equal Pay Act: the First 30 Years. }{\plain \i Public Personnel Management}{\plain 26 (3): 335-345.\par }\pard {\plain \par }\pard \fi-720\li720 {\plain Crum, John and Katherine C. Naff. 1997. 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Arlington, VA: Independent Women\'92s Forum.\par }\pard {\plain \par }\pard \fi-720\li720 {\plain Groshen, Erica L. 1991 The Structure of the Female/Male Wage Differential. }{\plain \i The Journal of Human Resources }{\plain 26 (3): 457-472.\par }\pard {\plain \par }{\plain Guy, Mary E. 1992. }{\plain \i Women and Men of the States}{\plain . Armonk, NY: M.E. Sharpe, Inc.\par }{\plain \par }\pard \fi-720\li720 {\plain Hultin, Mia and Ryszard Szulkin. 1999. Wages and Unequal Access to Organizational Power: An Empirical Test of Gender Discrimination. }{\plain \i Administrative Science Quarterly}{\plain 44 (3) :453-475.\par }\pard {\plain \par }\pard \fi-720\li720 {\plain Institute for Women\'92s Policy Research. 1999. }{\plain \i Status of Women in the States Project. }{\plain Available: www.iwpr.org}{\plain \ul .\par }\pard {\plain \par }\pard \fi-720\li720 {\plain Jacobsen, Joyce P. 1998. }{\plain \i The Economics of Gender Discrimination}{\plain . 2}{\plain \super nd}{\plain Edition. Malden, MA: Blackwell Publishers.\par }\pard {\plain \par }\pard \fi-720\li720 {\plain Joy, Lois. 1998. Why Are Women Underrepresented in Public School Administration? An Empirical Test of Promotion Discrimination. }{\plain \i Economics of Education Review}{\plain 17 (2):193-204.\par }\pard {\plain \par }\pard \fi-720\li720 {\plain Kellough, J. Edward. 1989 }{\plain \i Federal Equal Employment Opportunity Policy and Numerical Goals and Timetables. }{\plain New York: Praeger.\par }\pard {\plain \par }\pard \fi-720\li720 {\plain Lewis, Gregory B. 1986. Gender and Promotions: Promotion Chances of White Men And Women in Federal White-Collar Employment. }{\plain \i Journal of Human Resources}{\plain 21 (3): 406-420.\par }\pard {\plain \par }\pard \fi-720\li720 {\plain Lewis, Gregory B. 1996. Gender Integration of Occupations in the Federal Civil Service: Extent and Effects on Male-Female Earnings. }{\plain \i Industrial and Labor Relations Review}{\plain 49 (3): 472-439.\par }\pard {\plain \par }\pard \fi-720\li720 {\plain Lewis, Gregory B. and Mark A. Emmert. 1986. The Sexual Division of Labor in Federal Employment. }{\plain \i Social Science Quarterly }{\plain 67 (1):143-156.\par }\pard {\plain \par }\pard \fi-720\li720 {\plain Lewis, Gregory B. and David Nice. 1994. Race, Sex, and Occupation Segregation in State and Local Governments. }{\plain \i American Review of Public Administration }{\plain 24 (4): 393-410.\par }\pard {\plain \par }\pard \fi-720\li720 {\plain Macpherson, David A. and Barry T. Hirsch. 1995. Wages and Gender Composition: Why Do Women\'92s Jobs Pay Less? }{\plain \i Journal of Labor Economics }{\plain 13 (3) :426-471.\par }\pard {\plain \par }\pard \fi-720\li720 {\plain Mani, Bonnie G. 1999. Challenges and Opportunities for Women to Advance in The Federal Civil Service: Veterans\'92 Preference and Promotions. }{\plain \i Public Administration Review}{\plain 59 (6): 523-541.\par }\pard {\plain \par }\pard \fi-720\li720 {\plain Mani, Bonnie G. 1997, Gender and the Federal Senior Executive Service: Where is the Glass Ceiling? }{\plain \i Public Personnel Management }{\plain 26 (4): 545-558.\par }\pard {\plain \par }\pard \fi-720\li720 {\plain Miller, Will, Brinck Kerr, and Margaret Reid. 1999. A National Study of Gender-Based Occupational Segregation in Municipal Bureaucracies: Persistence of Glass Walls? }{\plain \i Public Administration Review }{\plain 59 (3): 218-230.\par }\pard {\plain \par }\pard \fi-720\li720 {\plain Naff, Katherine C. and Sue Thomas. 1994. The Glass Ceiling Revisited: Determinants of Federal Job Advancement. }{\plain \i Policy Studies Review}{\plain 13(3-4):249-272.\par }\pard {\plain \par }\pard \fi-720\li720 {\plain National Committee on Pay Equity. 2000. }{\plain \i The Wage Gap. }{\plain Available: www.feminist.com/fairpay/\par }\pard {\plain \par }\pard \fi-720\li720 {\plain Newman, Meredith Ann. 1994. Gender and Lowi\'92s Thesis: Implications for Career Advancement. }{\plain \i Public Administration Review }{\plain 54(3):277-284.\par }\pard {\plain \par }\pard \fi-720\li720 {\plain O\'92Neill, June. 1985. The Trend in the Male-Female Wage Gap in the United States. .}{\plain \i Journal of Labor Economics}{\plain 11(4):S91-S116.\par }\pard {\plain \par }\pard \fi-720\li720 {\plain O\'92Neill, June and Solomon Polachek. 1993. Why the Gender Gap in Wages Narrowed in the 1980s. }{\plain \i Journal of Labor Economics }{\plain 11(1):205-229.\par }\pard {\plain \par }\pard \fi-720\li720 {\plain Pfeffer, Jeffrey and Alison Davis-Blake. 1987. The Effect of the Proportion of Women on Salaries: The Case of College Administrators. }{\plain \i Administrative Science Quarterly }{\plain 32(1):1-24.\par }\pard {\plain \par }\pard \fi-720\li720 {\plain Pindyck, Robert S. and Daniel L. Rubinfeld. 1991. }{\plain \i Econometric Models and Economic Forecasts}{\plain . 3}{\plain \super rd}{\plain Edition. New York: McGraw-Hill.\par }\pard {\plain \par }\pard \fi-720\li720 {\plain Reid, Margaret, Brinck Kerr, and Will Miller. 2000. A Study of the Advancement of Women in Municipal Government Bureaucracies: Persistence of Glass Ceilings? }{\plain \i Women & Politics }{\plain 21(1):35-53.\par }\pard {\plain \par }\pard \fi-720\li720 {\plain Schmuck, Patricia A. 1992. }{\plain \i The Oregon Story. }{\plain Wellesley, MA: Education Development Corporation.\par }\pard {\plain \par }\pard \fi-720\li720 {\plain Schuster, D.J. and T.H. Foote. 1990. Differences Abound Between Male and Female Superintendents. }{\plain \i The School Administrator}{\plain 47(2):14-19.\par }\pard {\plain \par }\pard \fi-720\li720 {\plain Sorenson, Elaine. 1987. }{\plain \i Comparable Worth: Is It a Worthy Policy? }{\plain Princeton, NJ: Princeton University Press.\par }\pard {\plain \par }\pard \fi-720\li720 {\plain Treiman, D. and H. Hartmann. 1981. }{\plain \i Women, Work, and Wages: Equal Pay for Jobs of Equal Value. }{\plain Washington, DC: National Academy Press.\par }\pard {\plain \par }\pard \fi-720\li720 {\plain Tufte, Edward. 1974. }{\plain \i Data Analysis for Politics and Policy}{\plain . Englewood Cliffs: Prentice-Hall.\par }\pard {\plain \par }\pard \fi-720\li720 {\plain U.S. Department of Labor. 2000. }{\plain \i Highlights of Women\'92s Earnings in 1999}{\plain . Bureau of Labor Statistics. May (Report 943). Washington, DC: Government Printing Office.\par }\pard {\plain \par }\pard\page \pard \qc {\plain \f2 }{\plain \b\f2 Table 1. A Comparison of Male and Female Superintendents\par }\pard {\plain \b\f2 }{\plain \f2 \par }{\plain \f2 }{\plain \ul\f2 Variable Females Males }{\plain \f2 \par }{\plain \f2 \par }{\plain \f2 Years of Experience 21.7 24.3*\par }{\plain \f2 \par }{\plain \f2 Age 50.4 50.2\par }{\plain \f2 \par }{\plain \f2 Tenure (years) 5.7 6.8*\par }{\plain \f2 \par }{\plain \f2 Masters Degree % 67.1 74.4*\par }{\plain \f2 \par }{\plain \f2 Doctorate % 32.9 23.2*\par }{\plain \f2 \par }{\plain \f2 Student Enrollment 4570 3543\par }{\plain \f2 \par }{\plain \f2 Budget (millions) 12.7 9.8\par }{\plain \f2 \par }{\plain \f2 Salary 70,015 68,225\par }{\plain \f2 \par }{\plain \f2 \par }{\plain \f2 *Differences significant at p < .05.\par }{\plain \f2 \par }\pard\page \pard \qc\sl-192 {\plain \f2 }{\plain \b\f2 Table 2. Gender and Salaries\par }{\plain \f2 Dependent Variable = Log (Salary)\par }\pard \sl-192 {\plain \f2 \par }\pard {\plain \f2 Intercept Only Full Interaction \par }{\plain \ul\f2 Independent Variable Slope t-score Slope t-score\par }\pard \sl-192 {\plain \ul\f2 }{\plain \f2 \par }{\plain \f2 Budget Size (logged) .1558 102.53 .1554 96.72\par }{\plain \f2 \par }{\plain \f2 Local Revenue Percent .0010 11.52 .0011 12.55\par }{\plain \f2 \par }{\plain \f2 Human Capital Factors\par }{\plain \f2 \par }{\plain \f2 Experience (Years) .0025 8.66 .0026 8.47 \par }{\plain \f2 \par }{\plain \f2 Age .0009 3.79 .0009 3.53\par }{\plain \f2 \par }{\plain \f2 Tenure .0006 2.43 .0007 2.64\par }{\plain \f2 \par }{\plain \f2 Doctorate .0595 12.59 .0600 12.05\par }{\plain \f2 \par }{\plain \f2 Performance (last years) .0009 5.21 .0009 5.17\par }{\plain \f2 \par }{\plain \f2 African American .1099 5.59 .0970 4.39\par }{\plain \f2 \par }{\plain \f2 Latino -.0001 .02 .0028 .32 \par }{\plain \f2 \par }{\plain \f2 Female .0042 .63 -.0513 .51\par }{\plain \f2 \par }{\plain \f2 Female x Budget ---- -- .0073 1.41\par }{\plain \f2 \par }{\plain \f2 Female x Local Revenue ---- -- -.0017 5.38\par }{\plain \f2 \par }{\plain \f2 Female x Experience ---- -- -.0011 1.07\par }{\plain \f2 \par }{\plain \f2 Female x Age ---- -- .0007 .59\par }{\plain \f2 \par }{\plain \f2 Female x Tenure ---- -- -.0019 1.83\par }{\plain \f2 \par }{\plain \f2 Female x Doctorate ---- -- -.0206 1.29\par }{\plain \f2 \par }{\plain \f2 Female x Performance ---- -- .0003 .54\par }{\plain \f2 \par }{\plain \f2 Female African American ---- -- .0655 1.33\par }{\plain \f2 \par }{\plain \f2 Latina ---- -- -.0330 1.30\par }{\plain \f2 ________________________________________________________________\par }{\plain \f2 \par }{\plain \f2 R-Squared .79 .80\par }{\plain \f2 \par }{\plain \f2 Standard Error .1175 .1171\par }{\plain \f2 \par }{\plain \f2 F 1213.48 723.67\par }{\plain \f2 \par }{\plain \f2 N 4103 4103\par }{\plain \f2 \par }{\plain \f2 Joint F-test (9, 4085) = 4.09 p = .0001\par }\pard {\plain \f2 ________________________________________________________________\par }{\plain \f2 Dummy variables for individual years not reported.\par }{\plain \f2 \par }\pard\page \pard \qc {\plain \f2 }{\plain \b\f2 Table 3. Changes in Salary: New Superintendent}{\plain \f2 \par }\pard {\plain \f2 \par }{\plain \ul\f2 Dependent Variable = First Difference of Logged (Salary) \par }{\plain \f2 \par }{\plain \f2 Independent Variable Slope t-score \par }{\plain \f2 \par }{\plain \f2 Intercept .0411 25.42\par }{\plain \f2 \par }{\plain \f2 New Superintendent -.0263 5.16\par }{\plain \f2 \par }{\plain \f2 Female Replaces Male -.0487 4.40\par }{\plain \f2 \par }{\plain \f2 Male Replaces Female .0206 1.48\par }{\plain \f2 ________________________________________________________________\par }{\plain \f2 \par }{\plain \f2 R-Square .02\par }{\plain \f2 \par }{\plain \f2 Standard Error .0084\par }{\plain \f2 \par }{\plain \f2 F 18.20\par }{\plain \f2 \par }{\plain \f2 N 3045 \par }\sect \sectd \sbknone\marglsxn1800\margrsxn1800 {\*\pnseclvl1\pndec\pnstart1{\pntxta .}} {\*\pnseclvl2\pnlcltr\pnstart1{\pntxta .}} {\*\pnseclvl3\pnlcrm\pnstart1{\pntxta .}} {\*\pnseclvl4\pndec\pnstart1{\pntxtb (}{\pntxta )}} {\*\pnseclvl5\pnlcltr\pnstart1{\pntxtb (}{\pntxta )}} {\*\pnseclvl6\pnlcrm\pnstart1{\pntxtb (}{\pntxta )}} {\*\pnseclvl7\pndec\pnstart1{\pntxta .}} {\*\pnseclvl8\pnlcltr\pnstart1{\pntxta .}} {\*\pnseclvl9\pnlcrm\pnstart1} \pard {\plain \f2 \par }\pard\page \pard \qc {\plain \f2 }{\plain \b\f2 Table 4. Gender and Salaries: The Impact of Gender Change and Local Wealth\par }\pard {\plain \b\f2 }{\plain \f2 \par }\pard \qc {\plain \f2 Dependent Variable = Log (Salary)\par }\pard {\plain \f2 \par }{\plain \f2 Intercept Only \par }{\plain \ul\f2 Independent Variable Slope t-score \par }{\plain \f2 \par }{\plain \f2 Budget Size (logged) .1558 102.99 \par }{\plain \f2 \par }{\plain \f2 Local Revenue Percent .0011 12.59 \par }{\plain \f2 \par }{\plain \f2 Human Capital Factors\par }{\plain \f2 \par }{\plain \f2 Experience (Years) .0025 8.56 \par }{\plain \f2 \par }{\plain \f2 Age .0009 3.77 \par }{\plain \f2 \par }{\plain \f2 Tenure .0006 2.35 \par }{\plain \f2 \par }{\plain \f2 Doctorate .0583 12.39 \par }{\plain \f2 \par }{\plain \f2 Performance (last years) .0009 5.21 \par }{\plain \f2 \par }{\plain \f2 African American .1082 5.52 \par }{\plain \f2 \par }{\plain \f2 Latino -.0013 .15 \par }{\plain \f2 \par }{\plain \f2 Female .0746 5.41 \par }{\plain \f2 \par }{\plain \f2 Female Replaced Male -.0551 3.38\par }{\plain \f2 \par }{\plain \f2 Female x Local Revenue -.0015 5.10\par }{\plain \f2 ___________________________________________________________\par }{\plain \f2 \par }{\plain \f2 R-Squared .80\par }{\plain \f2 \par }{\plain \f2 Standard Error .1170\par }{\plain \f2 \par }{\plain \f2 F 1063.73\par }{\plain \f2 \par }{\plain \f2 N 4103\tab \tab \tab \tab \tab \par }{\plain \f2 \par }{\plain \f2 }{\plain \par }\pard \sl480 {\plain \par }{\plain \par }{\plain \pard\page Biographies\par }{\plain Kenneth J. Meier is the Charles Puryear Professor of Liberal Arts and 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 & 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.}{\plain \par }{\plain \par }{\plain 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. \par }{\plain \pard \sl480 }}