The National Academies | 500 Fifth St. N.W. | Washington, D.C. 20001
Copyright © National Academy of Sciences. All rights reserved.
Terms of Use and Privacy Statement



Below are the first 10 and last 10 pages of uncorrected machine-read text (when available) of this chapter, followed by the top 30 algorithmically extracted key phrases from the chapter as a whole.
Intended to provide our own search engines and external engines with highly rich, chapter-representative searchable text on the opening pages of each chapter. Because it is UNCORRECTED material, please consider the following text as a useful but insufficient proxy for the authoritative book pages.

Do not use for reproduction, copying, pasting, or reading; exclusively for search engines.

OCR for page 97
Section 2 Selected Workshop Papers Donna Ginther The Economics of Gender Differences in Employment Outcomes in Academia Diane Halpern Biopsychosocial Contributions to Cognitive Performance Janet Shibley Hyde Women in Science and Mathematics: Gender Similarities in Abilities and Sociocultural Forces Sue V. Rosser Creating an Inclusive Work Environment Joan C. Williams Long Time No See: Why Are There Still So Few Women in Academic Science and Engineering? Yu Xie Social Influences on Science and Engineering Career Decisions 97

OCR for page 97

OCR for page 97
99 SECTION 2: SELECTED WORKSHOP PAPERS 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 be- tween 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 per- sist 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 mari- tal 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 chil- dren, 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 promo- tion 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 ex- plained 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 De- cember 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.

OCR for page 97
100 COMPONENTS OF SUCCESS FOR WOMEN IN ACADEMIC SCIENCE & ENGINEERING between male and female faculty persist despite the increasing proportion of women in the academic profession” (Benjamin, 1999). While the evidence pre- sented by AAUP is striking, the gender comparisons of salaries do not control for characteristics that contribute to pay differentials such as academic field or publi- cation 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 cus- tomer 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 aca- demic 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, differ- ences 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.

OCR for page 97
101 SECTION 2: SELECTED WORKSHOP PAPERS 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 differ- ent 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 out- comes using one of the above explanations, then the residual gender difference in hiring, promotion, or salary may be attributed to discrimination. Statistical dis- crimination 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 statis- tical 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 inter- related. 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 differ- ences 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

OCR for page 97
102 COMPONENTS OF SUCCESS FOR WOMEN IN ACADEMIC SCIENCE & 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 work- force 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 char- acteristics, 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 publi- cations 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 repre- sentation 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 1Specifics of the data creation may be found in Ginther (2001) and Ginther and Kahn (2005).

OCR for page 97
103 SECTION 2: SELECTED WORKSHOP PAPERS Science 60 Life Science Physical Science Engineering Social Science Percentage Female 40 20 0 1970 1980 1990 2000 Year 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. Physi- cal 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) exam- ine 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

OCR for page 97
104 COMPONENTS OF SUCCESS FOR WOMEN IN ACADEMIC SCIENCE & ENGINEERING 60 Science Life Science Physical Science Engineering Social Science Percentage Female 40 20 0 1975 1980 1985 1990 1995 2000 Year 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 num- bers 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. Num- bers 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.

OCR for page 97
105 SECTION 2: SELECTED WORKSHOP PAPERS 25 20.6 20 16.7 15.6 15 Probability Female 10.8 10 7.2 5 0 0 -0.2 -5 -3.8 -4.1 -5.7 -10 Physical Engineering Social Science Life Science Science Science No Controls Marital Status Female x Marital Status 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. 5 0 Probability Female Tenured 2.2 3.9 -1.4 -2.8 -5 -8.4 -10 -8.1 -15 -20 -21 -25 Science Engineering Humanities Social Physical Life Economics Science Science X Science 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).

OCR for page 97
106 COMPONENTS OF SUCCESS FOR WOMEN IN ACADEMIC SCIENCE & 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 aca- demic 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 get- ting 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 like- lihood 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 employ- ment 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 prob- lem 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 invest- ing 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.

OCR for page 97
107 SECTION 2: SELECTED WORKSHOP PAPERS 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 differ- ences 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 sig- nificant 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, mar- riage 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-

OCR for page 97
164 COMPONENTS OF SUCCESS FOR WOMEN IN ACADEMIC SCIENCE & ENGINEERING AH Eagly and SJ Karau (2002). Role incongruity theory of prejudice toward female leaders. Psychol- ogy Review 109:537-598. AH Eagly and VJ Steffen (1986). Gender stereotypes, occupational roles, and beliefs about part-time employees. Psychology of Women 10:252-262. T Eckes (2002). Paternalistic and envious gender prejudice: testing predictions from the stereotype content model. Sex Roles 47:99-114. Equal Pay Act. 29 USC § 206 (d)(1). Equal Protection. Section 1983, 42 USC § 1983. C Etaugh and G Gilomen (1989). Perceptions of mothers: effects of employment status, marital status, and age of child. Sex Roles 20:59-70. C Etaugh and C Moss (2001). Attitudes of employed women toward parents who choose full-time or part-time employment following their child’s birth. Sex Roles 44:611-619. Family and Medical Leave Act of 1993. (29 U.S.C. § 2601). H Georgi (2000). Is their an unconscious discrimination against women in science? The American Physical Society News 9(1):27-30. http://www.aps.org/apsnews/0100/010016.cfm. P Glick and ST Fiske (2002). A model of (often mixed) stereotype content: competence and warmth respectively follow from perceived status and competition. Journal of Personality and Social Psychology 82:878-902. P Glick and ST Fiske (2001). An ambivalent alliance: hostile and benevolent sexism as complemen- tary justifications for gender inequality. American Psychology 58:109-118. P Glick and ST Fiske (1999). (Dis)respecting versus (dis)liking: Status and interdependence predict ambivalent stereotypes of competence and warmth. Journal of Social Issues 55:473-489. Griggs v. Duke Power Co., 401 U.S. 424 (1971). American Management Association survey cited in KD Grimsley (1996). Co-Workers Cited in Most Sexual Harassment Cases; Management Group’s Study Disputes Stereotype. The Washington Post, June 14, p. D01. L Haak (1999). Women in Neuroscience: The first twenty years. Journal of the History of Neuro- science 11:70-79. S Hewlett (2002). Creating a life: professional women and the quest for children. New York: Talk Miramax Books. ME Heilman (2001). Description and prescription: How gender stereotypes prevent women’s ascent up the organizational ladder. Journal of Social Issues 57:657-674. ME Heilman (1995). Sex stereotypes and their effects in the workplace: What we know and what we don’t know. Journal of Social Behavior and Personality 10 (3):3-26. ME Heilman (1993). Sex bias in work settings: The lack of fit model. In Research in Organizational Behavior, eds. LL Cumings and BM Staw. Greenwich, CT: JAI Press. A Kalev, F Dobbin, and E Kelly (2005). Best Practices or Best Guesses? Diversity Management and the Remediation of Inequality. Working Paper. Department of Sociology, Harvard University. I Kennelly (1999). That single mother element: How white employers typify black women. Gender and Society 14:168-192. D Kobrynowicz and M Biernat (1997). Decoding subjective evaluations: How stereotypes provide shifting standards. Journal of Experimental Social Psychology 33:579-601. LH Krieger (1995). The content of our categories: A cognitive bias approach to discrimination and equal employment opportunity. Stanford Law Review 47:1161-1248. Lam v. University of Hawaii, 59 Fair Empl. Prac. Cas. (BNA) 113 (1991). T Lewin (2002). “Collegiality” as a tenure battleground. New York Times, July 12, p. A12. Lovell v. BBNT Solutions, LLC, 295 F. Supp. 2d 611 (E.D. Va. 2003). Lust v. Sealy, 383 F.3d 580 (7th Cir. 2004). C MacKinnon (1987). Feminism unmodified: discourses of life and law. Cambridge, MA: Harvard University Press. MA Mason (2003). UC Berkeley faculty work and family survey: preliminary findings, http:// universitywomen.stanford.edu/reports/UCBfacultyworknfamilysurvey.pdf.

OCR for page 97
165 SECTION 2: SELECTED WORKSHOP PAPERS MA Mason and M Goulden (2002). Do babies matter? The effect of family formation on the lifelong careers of academic men, and women. Academe, November-December. http://www.aaup.org/ publications/Academe/2002/02nd/02ndmas.htm. Massachusetts Institute of Technology (1999). A study on the status of women faculty in science at MIT. The MIT Faculty Newsletter 11 (4):14-26, http://web.mit.edu/fnl/women/women.html. DM McCracken (2000). Winning the talent war for women: Sometimes it takes a revolution. Harvard Business Review, http://hbswk.hbs.edu/item.jhtml?id=1840&t=organizations. L McNeil (1999). Dual-career-couples: Survey results. http://www.physics.wm.edu/~sher/survey3.html. J Mervis (2002). Can equality in the sports be repeated in the lab? Science 298:356. National Science Foundation (2005). All in a week’s work: average work weeks of doctoral scientists and engineers, http://www.nsf.gov/statistics/infbrief/nsf06302/nsf06302.pdf. N Pedriana and R Stryker (1997). Political culture wars 1960s style: Equal employment opportunity- affirmative action law and the Philadelphia plan. American Journal of Sociology 103(3):633-691. Pregnancy Discrimination Act 42 USC § 2000e (k). Price Waterhouse v. Hopkins, 490 U.S. 228 (1989). BR Sandler, LA Silverberg, and RM Hall (1996). The Chilly Classroom Climate: A Guide to Improve the Education of Women. Washington, DC: The National Association of Women in Education. LC Sayer (2001). Time, use, gender, and equality: Differences in men’s and women’s market, non- market, and leisure time. Unpublished doctoral dissertation, University of Maryland, College Park. A Schneider (2000). University of Oregon settles tenure lawsuit over maternity leave. The Chronicle of Higher Education (July 21), p. A12. Shafer v. Board of Public Education, 903 F.2d 243 (3rd Cir. 1990). SR Sommers and PC Ellsworth (2001). White juror bias: An investigation of racial prejudice against Black defendants in the American courtroom. Psychology, Public Policy, and Law 7:201-229. Stanford University (1993). Report of the Committee on the Recruitment and Retention of Women Faculty at Stanford. M. Strober, Chair. M Still (2005). E-mail to Joan C. Williams, December 7. AV Sullivan (2005). Breaking anonymity: The chilly climate for women faculty by the chilly collec- tive, http://fcis.oise.utoronto.ca/~hep/Sullivan.html. JK Swim and LJ Sana (1996). He’s skilled, she’s lucky: a meta-analysis of observers’ attribution for women’s and men’s successes and failures. Personality and Social Psychology Bulletin 22(5):507-519. DJ Swiss and JP Walker (1993). Women and the Work/Family Dilemma. New Jersey: John Wiley and Sons. SE Taylor (1981). A categorization approach to stereotyping. In Cognitive Processes in Stereotyping and Intergroup Behavior, ed. DL Hamilton. New Jersey: Lawrence Erlbaum Associates. CA Trower and RP Chait (2002). Faculty diversity: Too little for too long. Harvard Magazine (March- April):33-38. http://www.harvardmagazine.com/on-line/030218.html. V Valian (1998). Why so slow? The advancement of women. Cambridge, MA: MIT Press. Weinstock v. Columbia University, 224 F.3d 33 (2nd Cir. 2000) M West (1994). Gender bias in academic robes: The law’s failure to protect women faculty. Temple Law Review 67:68-178. JC Williams (2004). Hitting the maternal wall. Academe, http://www.aaup.org/publications/Academe/ 2004/04nd/04ndwill.htm. JC Williams and HC Cooper (2004). The public policy of motherhood. Journal of Social Issues 60(4):849-865. JC Williams and N Segal (2003). Beyond the maternal wall: relief for family caregivers who are discriminated against on the job. Harvard Women’s Law Journal 26:77-162. JC Williams (2003). The social psychology of stereotyping: Using social science to litigate gender discrimination cases and defang the “cluelessness” defense. Employee Rights and Employment Policy Journal 7(2):401-458.

OCR for page 97
166 COMPONENTS OF SUCCESS FOR WOMEN IN ACADEMIC SCIENCE & 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 seg- ments of a science/engineering (S/E) career, we studied the entirety of a career trajectory. Second, we analyzed seventeen large, nationally rep- resentative 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 methodologi- cal perspectives which recognize the following phenomena: a. 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. b. 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. c. Individual-level variations in career tracks resulting from differ- ences among individuals, even those with the same demographic characteristics. d. 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.” Be- cause of path dependency, small differences at particular points in time can deflect trajectories and subsequently generate large differences in career outcomes. 1This 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 De- cember 9, 2005, in Washington, DC.

OCR for page 97
167 SECTION 2: SELECTED WORKSHOP PAPERS 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 inter- ventions 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. How- ever, President Summers failed to cite the following finding: gender differences in neither average nor high achievement in mathematics explain gender differ- ences in the likelihood of majoring in science/engineering fields.2 2See Xie and Shauman (2003). Ibid, Chapters 3 and 4.

OCR for page 97
High school diploma + S/E Bachelor’s S/E Master’s 168 Grades 7–12 Post-MS and Post-PhD Career Years 6 years Degree + 2 years Degree + 2 years Chapter 2: Chapter 4: Chapter 6: Chapter 7: Chapter 9: Gender differences Gender differences in Gender differences Demographic and The research in math and science the attainment of a in career choice labor force productivity achievement science/engineering after attainment profiles of men puzzle revisited Bachelor’s degree of a Master’s Degree and women in Data Sources: Data Sources: in S/E science and NLS-72, HSBSr, Data Source: Carnegie -1969, engineering HSBSo, LSAY1, HSBSo ACE-1973, Data Source: LSAY2, NELS NSPF-1988, Data Sources: NES 1960-1990 NSPF-1993 Census PUMS, SSE Chapter 10: The intersection of immigration Chapter 3: Chapter 5: Chapter 8: and gender: Geographic Gender Beyond the immigrant mobility of men differences in the science women scientists/ and women in expectation of an baccalaureate: engineers science and S/E college major gender differences engineering Data Sources: among high in career choice 1990 Census Data Source: school seniors after degree PUMS, 1990 Census attainment Data Source: SSE PUMS NELS Data Sources: NES, B&B FIGURE 2-12 Synthetic cohort life course, career processes, and outcomes examined, and data sources.

OCR for page 97
169 SECTION 2: SELECTED WORKSHOP PAPERS TABLE 2-5 Standardized Mean Gender Difference of Math Achievement Scores Among High School Seniors by Cohort School Cohort Mean Difference (d) Data Source –0.25*** 1960 NLS-72 –0.22*** 1968 HSBSr –0.15*** 1970 HSBSo –0.13** 1978 LSAY1 –0.09*** 1980 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 0.45*** 1960 NLS-72 0.47*** 1968 HSBSr 0.48*** 1970 HSBSo 0.25*** 1978 LSAY1 0.60*** 1980 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 “pipe- line” paradigm. According to this paradigm, the process of becoming a scientist can be conceptualized as a pipeline, called the “science pipeline,” which is essen- tially 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.

OCR for page 97
170 Educational Educational status, Educational status, Educational status, t expectations, Fall 1982 1984 1986-1988 Spring 1982 Not in College Bachelor’s Degree or in S/E Field Non-S/E Major in College by Pathway: females: 0.063 Prob. of exit: Reentry: males: 0.046 females: 0.821 females: 0.004 males: 0.541 males: 0.004 Educational State (k) Complete S/E Major in College: S/E Major S/E Major Persistence: females: 0.075 in College in College females: 0.008 males: 0.149 males: 0.039 females: 0.865 females: 0.603 females: 0.207 males: 0.919 males: 0.566 males: 0.500 FIGURE 2-13 Sex-specific probabilities for selected pathways to an S/E baccalaureate.

OCR for page 97
171 SECTION 2: SELECTED WORKSHOP PAPERS 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 addi- tion, women experience a much larger attrition from the science/engineering edu- cational 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 transi- tion rates to attaining degrees in science and engineering. The “Productivity Puzzle” In an influential paper, Cole and Zuckerman (1984) state that “women pub- lished 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 “produc- tivity puzzle.” Later, Long (1992), after considering possible explanations, reaffirms this characterization with the observation that “none of these explana- tions 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 3See 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.

OCR for page 97
172 1 0.817 # Papers Published by Women/ 0.8 # Papers Published by Men 0.695 0.632 0.58 0.6 0.4 0.2 0 1969 1973 1988 1993 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 counter- parts, 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 attribut- able 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. 4Xie and Shauman (2003). Ibid, Chapters 5 and 6. 5Xie and Shauman (2003). Ibid, Chapter 7. 6Xie and Shauman (2003). Ibid, Chapter 8.

OCR for page 97
173 TABLE 2-8 Female-to-Male Odds Ratio of Post-Baccalaureate Career Paths by Family Status Grad School Grad School Family Status or Work 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/engi- neering. 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 7Xie and Shauman (2003). Ibid, Chapter 7.

OCR for page 97
174 COMPONENTS OF SUCCESS FOR WOMEN IN ACADEMIC SCIENCE & ENGINEERING TABLE 2-9 Comparison Between Conventional Thinking and Our Findings Conventional Wisdom Our Findings • Math deficiency • Gender gap in mathematics is small • “Pipeline” paradigm • Career processes are fluid and dynamic • “War of the sexes” within marriage • Being married and having children matter • Low rates of research productivity • Sex differences in research productivity declined • Some “key” factor 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, cul- tural, and economic roots that will not be transformed by a few isolated policy interventions or programs. Increasing women’s representation in science/engi- neering requires many social, cultural, and economic changes that are large-scale and interdependent. After spending ten years searching for explanations, our re- search 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.