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Biological, Social, and Organizational Components of Success: For Women in Academic Science and Engineering Section 2 Selected Workshop Papers The Economics of Gender Differences in Employment Outcomes in Academia Donna Ginther Biopsychosocial Contributions to Cognitive Performance Diane Halpern Women in Science and Mathematics: Gender Similarities in Abilities and Sociocultural Forces Janet Shibley Hyde Creating an Inclusive Work Environment Sue V. Rosser Long Time No See: Why Are There Still So Few Women in Academic Science and Engineering? Joan C. Williams Social Influences on Science and Engineering Career Decisions Yu Xie
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Biological, Social, and Organizational Components of Success: For Women in Academic Science and Engineering THE ECONOMICS OF GENDER DIFFERENCES IN EMPLOYMENT OUTCOMES IN ACADEMIA* Donna K. Ginther Department of Economics University of Kansas Abstract This paper summarizes research that examines the relationship between hiring, promotion, and salary for tenure track science and social science faculty using data from the Survey of Doctorate Recipients (SDR). Gender differences in hiring and promotion can be explained by observable characteristics. However, gender differences in salaries persist at the full professor rank. In particular, women in science and social science are less likely to have tenure track jobs within five years of the doctorate when compared with men. However, when controls for marital status and children are included in the analysis, the research finds that unmarried women are significantly more likely to have tenure track jobs than unmarried men. Marriage provides a significant advantage for men relative to women. Presence of children, especially young children, significantly disadvantages women while having no impact on men in obtaining tenure track jobs. The research also finds no significant gender differences in the probability of obtaining tenure in life science, physical science, and engineering. These results also hold for promotion to full professor. However, significant gender promotion differences are evident in the social sciences, in particular, economics. Finally, the research finds large gender differences in salaries are partially explained by academic rank. However, gender salary differences for full professors, on the order of 13% in the sciences, are not fully explained by observable characteristics. In his examination of the salaries and appointments of men and women in academia, the Director of Research at the American Association of University Professors (AAUP) observes: “Substantial disparities in salary, rank, and tenure * Paper presented at the National Academies Convocation on Maximizing the Success of Women in Science and Engineering: Biological, Social, and Organizational Components of Success, held December 9, 2005, in Washington, DC. I thank the National Science Foundation for granting a site license to use the data and Kelly Kang of the NSF for providing technical documentation. Ronnie Mukherjee provided research assistance. The use of NSF data does not imply NSF endorsement of the research, research methods, or conclusions contained in this report. Financial support was provided from NSF grant SES-0353703. Any errors are my own responsibility.
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Biological, Social, and Organizational Components of Success: For Women in Academic Science and Engineering between male and female faculty persist despite the increasing proportion of women in the academic profession” (Benjamin, 1999). While the evidence presented by AAUP is striking, the gender comparisons of salaries do not control for characteristics that contribute to pay differentials such as academic field or publication record. Simply comparing salaries of male and female academic scientists without taking into consideration these factors could overstate the gender salary gap. Disentangling the causes of gender disparities in employment outcomes requires an in-depth examination of the data. This report summarizes research that examines the relationship between hiring, promotion, and salary for tenure track faculty using data from the Survey of Doctorate Recipients (SDR). The Economic Perspective Economic theory provides the underpinnings of this research. I start by assuming that employment outcomes are determined by market forces. Wages and hiring are determined by the supply of and demand for PhD scientists. Equally productive workers irregardless of gender will be paid the same and hired in similar numbers given market forces. Given these assumptions, one should not observe hiring, promotion, and salary differences for equally productive workers of either gender. However, persistent gender wage and employment differentials persist on average in the market as a whole (Altonji and Blank, 1999) and for scientists in particular (Ginther, 2001). I use economic theory to explain observed gender differences in hiring, promotion and salary. Beginning with Becker’s seminal work on discrimination (Becker, 1971), economists have developed models to understand gender and racial disparities in employment outcomes. Becker argues that taste-based discrimination (prejudice) will be eliminated by competitive forces. Given employer, employee, or customer prejudice, those firms that pay premiums to favored workers will have higher costs. Thus, the nondiscriminating firm will have a competitive advantage by hiring women or minorities, and the market will eventually compete away the discriminating wage differential. Becker’s prediction relies on the assumption that markets are perfectly competitive—an assumption one can reject for academic institutions. Given Becker’s results, economic theory has developed other explanations besides discrimination to account for observed gender differences in employment outcomes. These explanations may be divided into differences in “preferences” or choices and other factors. The preference-based explanations argue that gender differences in employment outcomes result from choices, in particular, differences in productivity. Economic theory holds that equally productive workers will be paid the same, thus, gender salary differences are the result of differences in productivity. A second preference-based explanation is that women chose to marry and have children, which in turn affects their attachment to their careers and overall productivity.
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Biological, Social, and Organizational Components of Success: For Women in Academic Science and Engineering Other theoretical explanations include monopsony models of the labor market. A monopsonist is a single employer of labor that has more bargaining power in the employment contract than the worker. Monopsonists pay workers less than the competitive wage and may be able to pay different wages to different types of workers depending upon their relative mobility. Thus if female faculty have fewer outside job opportunities, this will generate a gender wage differential. One may convincingly argue that academic institutions have monopsony power relative to faculty in most fields. However, for monopsony to explain gender employment disparities, women would need to be less mobile than men. Job-matching models may also explain gender differences in employment outcomes. In this model workers who are the best match for the job earn the highest salaries. In loose terms, the job-matching model suggests that women are paid less because they are not as capable (not as good of a match) in science compared to men. If the researcher cannot explain the gender differences in employment outcomes using one of the above explanations, then the residual gender difference in hiring, promotion, or salary may be attributed to discrimination. Statistical discrimination suggests that imperfect information on the part of employers generates wage differentials. In this model, an employer attributes the average characteristics of a group to an individual member of this group—essentially the employer uses a stereotype in making hiring decisions or setting wages. As a result, we observe gender differences in employment outcomes. However, direct measures of statistical discrimination are difficult to come by. Thus, discrimination may be inferred when other plausible explanations have been ruled out. Using economic theory as a guide, the research summarized in this report is organized using three basic principles. First, there is no single scientific labor market. As a result, this research disaggregates the data by scientific field. Second, gender differences in employment outcomes need a context in order to make meaningful comparisons. Thus, the research compares employment outcomes across academic fields in order to ascertain the relative status of women in academic science and social science. Finally, employment outcomes are interrelated. One cannot understand gender differences in salary without considering related outcomes of hiring and promotion. Given these principles, my research poses the question: Does science discriminate against women? I evaluate gender differences in hiring, promotion, and salary and can largely explain the first two outcomes using observable characteristics. However, I find large gender differences in the salaries of full professors that I cannot explain as a function of productivity or other choices. Data and Methods This study uses data from the Survey of Earned Doctorates (SED) and the Survey of Doctorate Recipients (SDR) to examine the distribution of women
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Biological, Social, and Organizational Components of Success: For Women in Academic Science and Engineering across scientific fields and gender differences in salary. The SED is a census of doctorates awarded in the United States each year. I use the 1974–2004 waves of the survey to evaluate changes in the distribution of women in scientific fields. The SDR is a nationally representative sample of PhD scientists in the United States used by the National Science Foundation to monitor the scientific workforce and fulfill its congressional mandate to monitor the status of women in science. This study uses data from the 1973-2001 waves of the SDR. The SDR collects detailed information on doctorate recipients including demographic characteristics, educational background, employer characteristics, academic rank, government support, primary work activity, productivity, and salary. Although the SDR has comprehensive measures of factors that influence academic salaries, the data lack information on some quantitative measures, such as laboratory space and extensive measures of publications. Measures of academic productivity are largely missing from the SDR data, but the SDR does ask questions about publications in the 1983, 1995, and 2001 surveys. I use these data to create rough measures of productivity for each year following the doctorate.1 Academics in the life sciences, physical sciences, engineering, and social science are included in the analysis. Life science includes biological sciences and agriculture and food science. Physical science includes mathematics and computer science, chemistry, earth science and physics. Social science includes economics, psychology, sociology and anthropology, and political science. Engineering includes all engineering fields. The SDR collected information on doctorate recipients in the humanities between 1977 and 1995. In some of the analysis that follows, I include comparisons across the three broad disciplines of humanities, sciences, and social sciences. I begin the analysis by analyzing the percentage of doctorates awarded and the percentage of tenured faculty who are female. Figures 2-1 and 2-2 indicate that women are not equally distributed across scientific fields. Figure 2-1 graphs the percentage of doctorates awarded to females between 1974 and 2004 using data from the SED. If we consider only life science fields, we may conclude, like the National Research Council (2001), that women have indeed moved ‘from scarcity to visibility’ in terms of doctorates granted. By 2004 almost half of all doctorates in life science and more than half of all doctorates in social science were awarded to women. However, both physical science and engineering awarded less than one-third of doctorates to women. In the year 2004, less than 18% of engineering doctorates and less than 27% of physical science doctorates were granted to women. Despite the increasing numbers of doctorates awarded to women, the representation of women among tenured academic scientists remains quite low. Figure 2-2 uses data from the 1973–2001 waves of the SDR to graph the percentage of 1 Specifics of the data creation may be found in Ginther (2001) and Ginther and Kahn (2005).
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Biological, Social, and Organizational Components of Success: For Women in Academic Science and Engineering FIGURE 2-1 Percentage of doctorates granted to females, 1974–2004. SOURCE: 1974-2004 Survey of Earned Doctorates. tenured faculty who are female in life science, physical science, social science, and engineering. As expected, social science and life science have the highest percentages of tenured female faculty at 28 and 25% respectively in 2001. Physical science and engineering have far fewer tenured female faculty at 11 and 5%, respectively. Given the large differences between the percentages of doctorates awarded to women and the percentages of tenured faculty who are women, I turn to potential explanations. Gender Differences in Hiring and Promotion Hiring The underrepresentation of women in tenured academic ranks may result from gender differences in hiring or promotion. Ginther and Kahn (2005) examine gender differences in hiring by evaluating whether women in science are more or less likely than men to get tenure track jobs within five years of receiving their doctorate. Women and men who leave academia immediately following the doctorate are dropped from the sample. Figure 2-3 shows three sets of estimates of the effect of being female on getting a tenure track job using samples of over
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Biological, Social, and Organizational Components of Success: For Women in Academic Science and Engineering FIGURE 2-2 Percentage of tenured faculty who are female, by discipline, 1973–2001. SOURCE: 1973-2001 Survey of Doctorate Recipients. 12,000 scientists and over 3,000 social scientists from 1973–2001. Negative numbers indicate that women are less likely whereas positive numbers indicate that women are more likely to get a tenure track job within five years of PhD. Numbers that are underlined are statistically significant at the 5% level. The first bar in Figure 2-4 shows that women are between 4 to 6% less likely than men to have tenure-track jobs in all science fields combined, social science, and life science. There is no significant difference between men and women getting a tenure-track job in physical science and engineering. The second bar in Figure 2-4 includes controls for academic field, race, age at PhD, year of PhD, marital status, and children. The estimated gender gap falls for all science and social science fields but does not change appreciably for the disaggregated science fields. The third bar includes controls that interact female with marital status and children. These interaction terms allow the impact of marriage and children to be different for men and women in the model. The estimates are strikingly different.
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Biological, Social, and Organizational Components of Success: For Women in Academic Science and Engineering FIGURE 2-3 Gender differences in tenure-track job within 5 years of PhD. Notes: Estimates from Ginther and Kahn (2005) using 1973-2001 Survey of Doctorate Recipients. FIGURE 2-4 Gender differences in promotion to tenure 10 years past PhD. Notes: Estimates from Ginther and Kahn (2004) and Ginther and Hayes (2003). Science and Social Science estimates from 1973-2001 SDR. Humanities estimates from 1977-1995 SDR. Economics, humanities, and social science X (excluding economics) are statistically significant (p = 0.01).
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Biological, Social, and Organizational Components of Success: For Women in Academic Science and Engineering Women are between 7 to 21% more likely than men to get a tenure-track job within 5 years of PhD provided they are unmarried and do not have children. These results indicate that much of the underrepresentation of women in academic science is the result of having children. Single women are 16% more likely in science and 17% more likely in social science to get tenure-track jobs than single men. Marriage has a positive and significant impact of 22% on men getting a tenure-track job whereas the effect of marriage on women ranges between 0 and 8% for all science, life science, and social science fields. The exception is engineering where marriage increases women’s chances of having a tenure-track job by 23%. Children, especially young children, significantly decrease the likelihood of women obtaining a tenure-track job between 8 to 10% in all science fields, life science, and social science while having no significant impact on men. The positive impact of marriage and children on men’s tenure-track employment echoes the positive impact of men’s marriage and children on wages and promotion in the labor market as a whole. The negative impact of children on women’s tenure-track employment may result from a number of factors. Women may choose to have children instead of pursuing an academic career because of the coincident timing of the tenure and biological clocks. The dual-career problem may also play a role. Career hierarchies in marriage often result in the husband’s career taking precedence over the wife’s career. If it is difficult to obtain two tenure-track jobs, she may choose to have children instead of investing in her career. Furthermore, women are often the primary caregivers of children and this may hamper investments in their careers. The availability of tenure-track jobs may be limited to such an extent that women choose to invest more in marriage and family than in their careers. I suggest that the relative lack of academic jobs may be playing a significant role. By way of example, approximately half of all medical students are women and increasing numbers of women are practicing medicine. The demand for doctors is much higher than the demand for academic scientists, and this demand results in more women practicing medicine. It follows that the lack of academic jobs may be contributing to women’s underrepresentation in academic science. Finally, the timing of women’s departure from academia may also indicate problems with the post-doctoral system in academic science. Studies suggest that the post-doctoral process is taking longer because the number of post-doctoral positions has expanded without a similar expansion of academic jobs (Davis, 2005). These results suggest that some combination of factors at the early stages of women’s careers are affecting married women’s choice of or access to tenure-track jobs. I now examine what happens to women as they progress through the tenure track.
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Biological, Social, and Organizational Components of Success: For Women in Academic Science and Engineering Promotion Once women have tenure-track jobs, their prospects for getting tenure in science are very promising but less so in social science. Figure 2-4 is derived from estimates in Ginther and Kahn (2004, 2005) and Ginther and Hayes (2003). It shows gender differences in the promotion to tenure 10 years past the doctorate in the fields of science, social science excluding economics, life science, physical science, engineering, humanities, and economics. These latter two disciplines are included to provide a context for the remaining fields. Women are between 1 to 3% less likely to get tenure in all scientific fields combined and in physical science 10 years past the doctorate. Women are between 2 and 4% more likely to get tenure in life science and engineering. These results indicate that gender differences in promotion to tenure are small for women in scientific fields. This is not true for social science (excluding economics) and the humanities where women are 8% less likely than men to get tenure. Economics is the outlier—women are 21% less likely to get tenure than men 10 years past the doctorate. These differences in economics cannot be fully explained by gender differences in productivity, marital status, or presence of children (Ginther and Kahn, 2004). Ginther and Kahn (2005) estimate gender differences in promotion to tenure and promotion to full professor in scientific fields. They find no statistically significant gender differences in promotion to either rank. Thus, we can conclude that gender differences in promotion in science are negligible. However, gender differences in promotion in social science are large, especially in economics. I now consider gender differences in salaries. Gender Differences in Salaries There are several factors that affect the salaries of academics. Demographic characteristics such as race, marital status, fertility, and years of work experience may have a positive or negative effect on salaries. For example, on average, marriage increases male salaries while having a negative effect on female salaries. Employer characteristics such as working at a public or private institution, liberal arts or a doctoral institution, and the Carnegie ranking of the employer may also affect salaries. Top research institutions pay more than liberal arts colleges. Public institutions have state-mandated salary scales that tend to be more restrictive than those at private institutions. Employee characteristics such as the academic rank and tenure status of the individual also influence salaries, with salaries increasing with academic rank and tenure. Measures of productivity also affect salaries. These include factors such as whether the individual receives government support, primary work activities, and publications. If men are more likely to work at top-ranked research universities, the gender salary gap will be larger. Salary differences may also result from dif-
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Biological, Social, and Organizational Components of Success: For Women in Academic Science and Engineering JC Williams (2000). Unbending gender: Why work and family conflict and what to do about it. New York, NY: Oxford University Press. JD Yoder (1994). Looking beyond numbers: the effect of gender status, job prestige, and occupational gender-typing on tokenism processes. Social Psychology Quarterly 57:150-159. Zahorick v. Cornell University, 729 F.2d 85, 89-90 (2d Cir. 1984) SOCIAL INFLUENCES ON SCIENCE AND ENGINEERING CAREER DECISIONS1 Yu Xie University of Michigan Abstract Our study on the career processes and outcomes of women in science has four major components. First, rather than focusing on specific segments of a science/engineering (S/E) career, we studied the entirety of a career trajectory. Second, we analyzed seventeen large, nationally representative datasets. Third, we tried to be as objective and “value-free” as possible and to emphasize empirical evidence. Finally, we based the book on a life-course approach, a combination of special methodological perspectives which recognize the following phenomena: Interactive effects across multiple levels, such as the individual level, the family level, and the school level. Individuals do not live or work in isolation from one another. Interactive effects across multiple domains, such as education, family, and work. What we do in one domain of our lives affects what we do in other domains. Individual-level variations in career tracks resulting from differences among individuals, even those with the same demographic characteristics. The cumulative nature of the life course. What happened before affects what happens now, and what is happening now affects what comes next. This is also called “path-dependency.” Because of path dependency, small differences at particular points in time can deflect trajectories and subsequently generate large differences in career outcomes. 1 This presentation is based on the book Yu Xie co-authored with Kimberlee Shauman entitled, Women in Science: Career Processes and Outcomes, published by Harvard University Press in 2003. *Paper presented at the National Academies Convocation on Maximizing the Success of Women in Science and Engineering: Biological, Social, and Organizational Components of Success, held December 9, 2005, in Washington, DC.
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Biological, Social, and Organizational Components of Success: For Women in Academic Science and Engineering The life course approach places a high demand on data. Ideally, we would like to have longitudinal data over the entire career of many scientists and non-scientists. We looked very hard but were not able to find a perfect data set. Lacking such a data set, we were still able to carry out our study by piecing together many datasets to paint a composite picture of gender differences in science careers, a method which is called synthetic cohort in demography. Figure 2-12 shows the data sets that we used to look at different segments of science/engineering careers. Needless to say, our study contains complicated and nuanced analyses. These analyses led us to conclude that women’s severe underrepresentation in science and engineering is an extremely complex social phenomenon that defies any attempt at simplistic explanations. Due to the complex and multi-faceted nature of women scientists’ career processes and outcomes, especially how these processes and outcomes affect, and are affected by, other life course events such as marriage and childbearing, we were uncomfortable recommending concrete policy interventions intended to increase women’s representation in science and engineering No single explanation or hypothesis testing should or could substitute for the richness of the empirical results from these analyses, though we did consider and reject several widely accepted hypotheses, as the following discussion shows. The “Critical Filter” Hypothesis One longstanding hypothesis in the literature is that women are less likely to pursue science/engineering careers because they are handicapped by deficits in high school mathematics training. In a classic statement of this position, Sells (1980) claims that “[a] student’s level of high-school mathematics achievement acts as a critical filter for undergraduate college admission for blacks and limits choices of an undergraduate major for women in general once they are admitted to college.” This hypothesis is appealing for its simplicity and the clear remedy it implies. From our research, we find that the gender gap in average mathematics achievement is small and has been declining since the 1960s (see Table 2-5). The numbers in Table 2-1 are mean gender differences in math achievement scores (in standard deviation units). The declining trend shown in Table 2-5 casts doubt on the interpretation that the gender gap in math achievement reflects innate, perhaps biological, differences between the sexes. We also find that the gender gap in representation among top achievers remains significant (see Table 2-6). This finding was cited by Harvard President Larry Summers in his remarks at an NBER conference on January 14, 2005, which made international news. However, President Summers failed to cite the following finding: gender differences in neither average nor high achievement in mathematics explain gender differences in the likelihood of majoring in science/engineering fields.2 2 See Xie and Shauman (2003). Ibid, Chapters 3 and 4.
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Biological, Social, and Organizational Components of Success: For Women in Academic Science and Engineering FIGURE 2-12 Synthetic cohort life course, career processes, and outcomes examined, and data sources.
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Biological, Social, and Organizational Components of Success: For Women in Academic Science and Engineering TABLE 2-5 Standardized Mean Gender Difference of Math Achievement Scores Among High School Seniors by Cohort School Cohort Mean Difference (d) Data Source 1960 –0.25*** NLS-72 1968 –0.22*** HSBSr 1970 –0.15*** HSBSo 1978 –0.13** LSAY1 1980 –0.09*** NELS *p<.05 **p<.01 ***p<.001 (two-tailed test), for the hypothesis that there is no mean difference between males and females. TABLE 2-6 Female-to-Male Ratio of the Odds of Achieving in the Top 5% of the Distribution of Math Achievement Test Scores Among High School Seniors by Cohort School Cohort Achievement Ratio Data Source 1960 0.45*** NLS-72 1968 0.47*** HSBSr 1970 0.48*** HSBSo 1978 0.25*** LSAY1 1980 0.60*** NELS *p<.05 **p<.01 ***p<.001 (two-tailed test), for the hypothesis that there is no mean difference between males and females. The Pipeline Paradigm A dominant perspective in the literature on women in science is the “pipeline” paradigm. According to this paradigm, the process of becoming a scientist can be conceptualized as a pipeline, called the “science pipeline,” which is essentially a developmental process. Change in the developmental process along the life course is unidirectional—leaving science versus staying in science. However, we find career processes to be fluid and dynamic. Exit, entry, and reentry are real possibilities. Many persons, especially women, become scientists through complicated processes rather than by just staying in the pipeline. Also, we show that participation gaps are greatest at the transition from high school to college. This is illustrated in Figure 2-13.
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Biological, Social, and Organizational Components of Success: For Women in Academic Science and Engineering FIGURE 2-13 Sex-specific probabilities for selected pathways to an S/E baccalaureate.
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Biological, Social, and Organizational Components of Success: For Women in Academic Science and Engineering In Figure 2-13, we observe that in the senior year of high school, women are much less likely than men to plan a science/engineering major in college. In addition, women experience a much larger attrition from the science/engineering educational trajectory than men do at the transition from high school to college. In the later college years, however, we find women and men to have similar transition rates to attaining degrees in science and engineering. The “Productivity Puzzle” In an influential paper, Cole and Zuckerman (1984) state that “women published slightly more than half (57%) as many papers as men.” They found that the gender gap had persisted for many decades at this level and could not find any explanations for it. Out of despair, they called this gender difference the “productivity puzzle.” Later, Long (1992), after considering possible explanations, reaffirms this characterization with the observation that “none of these explanations has been very successful.” We analyzed data from four nationally representative surveys of faculty in postsecondary institutions in 1969, 1973, 1988, and 1993.3 Two major findings emerged from our work about the puzzle. First, sex differences in research productivity declined sharply between the 1960s and the 1990s, even without any controls. This is shown in Figure 2-14. Women scientists’ research productivity has improved because their overall structural positions, such as institutional affiliation, have improved. This improvement in women’s productivity relative to men’s suggests that the large gender gap observed for earlier decades should not be attributed to innate biological differences between men and women. Second, most of the observed sex differences in research productivity can be attributed to sex differences in background characteristics, employment positions and resources, and marital status. This is shown in Table 2-7. The first line of Table 2-7 reproduces the observed trends presented earlier in Figure 2-14. In lower lines, we included statistical adjustments for the fact that women and men differ in relevant characteristics, such as rank, year from a bachelor’s degree to PhD, and institutional affiliation. Thus, even in the earlier decades, the observed sex differences in productivity can be explained once these relevant attributes are controlled for. Family Life and Women Scientists’ Careers A common theme is the importance of considering the family in studies of women in science. In particular, we find that it is not marriage per se that hampers women’s career development. Married women appear to be disadvantaged only if 3 See also Y Xie and KA Shauman (1998). Sex differences in research productivity revisited: new evidence about an old puzzle. American Sociological Review 63:847-870.
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Biological, Social, and Organizational Components of Success: For Women in Academic Science and Engineering FIGURE 2-14 Trends in female-male ratio of publication rate. TABLE 2-7 Estimated Female-to-Male Ratio of Publication Model Description 1969 1973 1988 1993 (0): Sex 0.580*** 0.632*** 0.695** 0.817 (1): (0) + Field + Time for PhD + Experience 0.630*** 0.663*** 0.800 0.789* (2): (1) + Institution + Rank +Teaching + Funding + RA 0.952 0.936 0.775 0.931 (3): (2) + Family/Marital Status 0.997 0.971 0.801 0.944 *p<.05 **p<.01 ***p<.001 (two-tailed test), for the hypothesis that there is no mean difference between males and females. they have children. For example, we show that, relative to their male counterparts, married women with children are less likely to pursue careers in science and engineering after the completion of science/engineering education4 less likely to be in the labor force or employed, less likely to be promoted,5 and less likely to be geographically mobile.6 Although some of the gender differences are attributable to the advantages that marriage and parenthood bestow upon men, they clearly suggest that being married and having children create career barriers that are unique to women—as opposed to men—scientists. 4 Xie and Shauman (2003). Ibid, Chapters 5 and 6. 5 Xie and Shauman (2003). Ibid, Chapter 7. 6 Xie and Shauman (2003). Ibid, Chapter 8.
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Biological, Social, and Organizational Components of Success: For Women in Academic Science and Engineering TABLE 2-8 Female-to-Male Odds Ratio of Post-Baccalaureate Career Paths by Family Status Family Status Grad School or Work Grad School Grad School in S/E Work in S/E Single 0.90 1.02 0.77 0.78** Married without children 0.28*** 0.67 0.11** 0.72** Married with children 0.05*** 0.35* 0.39*** *p<.05 **p<.01 ***p<.001 (two-tailed test), for the hypothesis that there is no mean difference between males and females. Table 2-8 presents the female-to-male odds ratio of post-baccalaureate career paths by family status. There are five destinations for graduates with a bachelor’s degree in science and engineering: (1) out of work and school altogether, (2) graduate school in science/engineering, (3) graduate school in nonscience/ engineering, (4) work in science/engineering, and (5) work in nonscience/engineering. For the five outcomes, we made four contrasts and found that in all four, married women with children are disadvantaged in terms of science/engineering careers. Column 1 shows that married women with children are less likely than men to either work or attend graduate school. In column 2, we see that they are less likely than men to be in graduate school rather than working. Furthermore, married women with children are less likely than men to be in science/engineering, either in work (column 3) or in graduate school (column 4). Similarly, we also find married women with children disadvantaged in terms of other labor force outcomes.7 Summary While the conventional wisdom often draws on casual analyses of non-representative data, our tentative conclusions are based on very good data and careful analyses. Table 2-9 shows the contrast between conventional wisdom and our findings. There appear to be two types of simplistic explanations. At one extreme, some observers claim that gender differences in science are all due to innate biological differences between men and women. At the other extreme, some scholars are tempted to make a sweeping claim that all gender differences are due to discrimination against women in school and at work. Our research shows that both positions are wrong. Otherwise, it would not be possible to explain either the rapid improvement of women’s position in science, which cannot be attributed to 7 Xie and Shauman (2003). Ibid, Chapter 7.
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Biological, Social, and Organizational Components of Success: For Women in Academic Science and Engineering TABLE 2-9 Comparison Between Conventional Thinking and Our Findings Conventional Wisdom Our Findings Math deficiency “Pipeline” paradigm “War of the sexes” within marriage Low rates of research productivity Some “key” factor Gender gap in mathematics is small Career processes are fluid and dynamic Being married and having children matter Sex differences in research productivity declined and can be attributed to differences in personal characteristics and structural features of employment Deep social, cultural and economic roots change in biological differences between the sexes, or the interaction between gender and parental status, which suggests that factors outside educational and work settings play an important role. Women’s underrepresentation in science/engineering has deep social, cultural, and economic roots that will not be transformed by a few isolated policy interventions or programs. Increasing women’s representation in science/engineering requires many social, cultural, and economic changes that are large-scale and interdependent. After spending ten years searching for explanations, our research indicates we should stop looking for simple explanations and easy fixes, as attractive as they may be to us as human beings. Instead, we should look at the actual social processes that generate gender differences in science, and base policy interventions on empirical knowledge about these processes. Finally, while there may be policy changes that could address some of the complex reasons for women’s underrepresentation, we should not expect any individual policy change to bring about gender equity in science overnight. References JR Cole and H Zuckerman (1984). The productivity puzzle: Persistence and change in patterns of publication of men and women scientists. Advances in Motivation and Achievement 2:217-258. JS Long (1992). Measures of Sex Differences in Scientific Productivity. Social Forces 71:159-178. LW Sells (1980). The mathematics filter and the education of women and minorities. In: Women and the Mathematical Mystique, eds. L Fox, L Brody, and D Tobin. Baltimore, MD: Johns Hopkins University Press. Y Xie and KA Shauman (1998). Sex differences in research productivity revisited: New evidence about an old puzzle. American Sociological Review 63:847-870. Y Xie and KA Shauman (2003). Women in Science: Career Processes and Outcomes. Cambridge, MA: Harvard University Press.
Representative terms from entire chapter: