The most prestigious institutions tend to do least well in recruiting female faculty. “The higher up the academic-prestige ladder a university is, the fewer women it usually has in tenured faculty positions. Research released showed that while the nation is doing a good job of turning out women with research doctorates, the top 50 institutions in research spending are not doing such a good job of hiring them” (Wilson, 2004).[122]
The under representation of women in the most prestigious departments could result either from a lack of demand for female faculty in these departments or from a lack of supply of female candidates. Potential faculty may be likely to consider the reputation of both the department and the institution in deciding which jobs openings they will apply for. Some argue greater prestige may not always be seen as a positive attribute by female applicants. “Women just are not applying, “says
Geraldine L. Richmond, who holds an endowed chair in chemistry at the University of Oregon. She argues “many top-notch science departments have ‘toxic atmospheres’ that suffocate women’s enthusiasm for their work and steer them away from research careers. But women are also rejecting elite research universities for other reasons, like the fear that they will not have enough time for their families” (Wilson, 2004).
Kulis and Miller-Loessi (1992) offered a different rationale: higher prestige institutions seek to attract high-powered researchers. In the past, those would more likely be men. The authors noted women have been located outside informal prestige networks, making it harder for women to be recognized and recruited.
Steinpreis et al. (1999) simulated a hiring situation by sending 238 male and female academic psychologists one of four randomly selected versions of curriculum vitae (CV) along with a questionnaire about the qualifications of the candidate. The CV was drawn from a real-life, female scientist. Some versions of the CV contained a traditional male name; other versions, a traditional female name. The authors found “both male and female academicians were significantly more likely to hire a potential male colleague than an equally qualified potential female colleague. Furthermore, both male and female participants were more likely to positively evaluate the research, teaching, and service contributions of a male job applicant than a female job applicant with an identical record” (p. 522).
Several other studies reach the similar conclusion that female candidates may be at a disadvantage in both academic and nonacademic labor markets:
• Cole et al. (2004) randomly sent business school students’ resumes to 40 employers, who were asked to rate the resumes on a number of criteria. They found male reviewers rated male applicants as having slightly more work experiences than female applicants (not statistically significant), while female reviewers rated male applicants as possessing significantly more work experience.
• Studies suggest women’s professional work is discounted more so than for men. For example, a study of the outcomes of the peer-review system of the Swedish Medical Research Council for postdoctoral fellowships found the success rate for female applicants was less than half that of male applicants (Wenneras and Wold, 1997).
The situation applies not just to female versus male names as triggers, but also to female versus male appearance. In the music world, very few women were playing with top orchestras in the 1970s. Then orchestras changed how the audition occurred: the musician was hidden behind a screen and the stage was carpeted. The number of women successfully auditioning rose significantly (Koretz, 1997; Goldin and Rouse, 2000). Women seem to get rated harder than men do, both by men and women. However, one study did not find a disparity. In a review of editors, reviewers, and authors regarding manuscripts submitted to JAMA in 1991, the authors found that there were gender differences in how editors worked and how reviewers made recommendations, but they found final “manuscript acceptance rates did not differ across author gender and editor gender combinations” (Gilbert et al., 1992). Another study by Swim et al. (1989)—where the authors conducted a meta-analysis on studies drawing on the influential experiment conducted by Goldberg in 1968—demonstrated that women rated publications perceived to have been written by female authors less favorably than those thought to have been written by males.
This bias could occur because of at least two different kinds of stereotypes about women (Cole et al., 2004). Evaluators could have descriptive stereotypes. For example, they could believe women “don’t have what it takes to succeed in competitive situations.” Alternatively, evaluators could have prescriptive stereotypes. A woman perceived as behaving in an unfeminine way to get an academic position could be negatively evaluated for her behavior. In addition to broad gender stereotypes, gender stereotypes specific to the academic world, such as a perception that women are less mobile or less committed to the profession, may affect invitations to interview. Differences in the level of socialization among male and female graduate students and postdocs may further impact an aspiring faculty member by affecting the quality of letters of reference. This may be a significant problem. Trix and Psenka (2003), for example, found recommendation letters for women for medical faculty positions were shorter, less favorable, and focused more on women’s teaching abilities than the letters for men.[123] In general, perceptions regarding women, held by both men and women, may have a detrimental effect on hiring or career advancement (Valian, 1998).
Estimated Adjusted Mean Effects and Differences for the Probability That There Are No Female Applicants0
|
Differences Across Effect Levels |
Estimated Mean Difference (Lower 95%, Upper 95% Confidence Limits) |
|
Biology — Chemistryb |
0.22 (-0.08, 0.51) |
|
Biology — Mathematics |
0.50 ( 0.01, 0.99) |
|
Biology — Electrical engineering |
0.23 (-0.12, 0.57) |
|
Biology — Physics |
0.22 (-0.11, 0.54) |
|
Biology — Civil engineering |
0.13 (-0.07, 0.34) |
|
Tenured — Tenure-track |
0.81 (0.71, 0.92) |
|
Private institution — Public institution |
0.66 (0.49, 0.84) |
|
Top 10 department — Next 10 depts. |
0.27 (0.10, 0.44) |
|
Next 10 departments — Remaining depts. |
0.81 (0.59, 1.03) |
|
M — F search committee chair |
0.24 (-0.16, 0.63) |
a The sample size used to fit this model was 667. The effects fit were: (1) indicator variables for discipline (Biology, Chemistry, Civil Engineering, Electrical Engineering, Mathematics, and Physics, (2) indicator variables for Tenured, Tenure-track, (3) indicator variables for private institution, public institution, (4) indicator variables for top ten departments, second ten departments, and remainder, and (5) an indicator variable as to whether the committee chair was female.
b The estimated adjusted mean differences can be interpreted using Biology — Chemistry as an example. For those individuals in Biology, there is an estimated probability of having no female applicants given, or conditional on, the values for the remaining predictors in the logistic regression model. There is an analogous set of estimated conditional probabilities for Chemistry, again conditional on the predictors in the model. For each set of predictors, one can compute the difference of the estimated probabilities, and then one can average these differences in estimated probabilities over the estimated distribution of the predictors. The result is an estimated average difference of probabilities.
SOURCE: Departmental survey conducted by the Committee on Gender Differences in Careers of Science, Engineering, and Mathematics Faculty.
|
Effects |
Ratios of Means (Lower, Upper 95% Confidence Limits) |
|
Differences across disciplines |
|
|
Biology — Chemistry |
1.36 (1.10, 1.62) |
|
Biology — Mathematics |
1.21 (1.05, 1.37) |
|
Biology — Electrical engineering |
2.44 (1.61, 3.27) |
|
Biology — Physics |
2.30 (1.91, 2.69) |
|
Biology — Civil engineering |
1.80 (1.29, 2.32) |
|
Chemistry — Mathematics |
0.89 (0.69, 1.08) |
|
Chemistry — Electrical engineering |
1.79 (1.18, 2.39) |
|
Chemistry — Physics |
1.69 (1.39, 1.98) |
|
Chemistry — Civil engineering |
1.32 (0.92, 1.73) |
|
Mathematics — Electrical engineering |
2.02 (1.35, 2.68) |
|
Mathematics — Physics |
1.90 (1.57, 2.24) |
|
Mathematics — Civil engineering |
1.49 (1.06, 1.93) |
|
Electrical Engineering — Physics |
0.94 (0.64, 1.25) |
|
Electrical — Civil engineering |
0.74 (0.47, 1.01) |
|
Physics — Civil engineering |
0.78 (0.58, 0.99) |
|
Type of position |
|
|
Tenured — tenure-track |
1.00 (0.88, 1.12) |
|
Type of institution |
|
|
Private — public |
1.03 (0.87, 1.19) |
|
Prestige of institution |
|
|
Top 10 — second 10 |
1.12 (0.75, 1.48) |
|
Top 10 — remaining |
1.08 (0.94, 1.22) |
|
Second 10 — remaining |
0.97 (0.69, 1.25) |
|
Gender of search committee chair |
|
|
Female — male chair |
1.17 (1.01, 1.32) |
a The sample size used to fit this model was 667. b The same effects were fit as in the table in Appendix 3-2. c See note b in the table in Appendix 3-2.
SOURCE: Departmental survey conducted by the Committee on Gender Differences in Careers of Science, Engineering, and Mathematics Faculty.
Estimated Adjusted Mean Effects and Differences Based on the Modeled Probability of at Least One Female Candidate Interviewed0
Mean Odds Ratios
Effects (Lower, Upper 95% Confidence Limits)
|
Disciplines
|
Ratio Of Mean Odds Ratios
Effects (Lower, Upper 95% Confidence Limits)
|
Differences across disciplines
|
a The sample size used to fit this model was 667. For differences across effect level, the mean represents the ratio of odds ratios between the two factor levels. b The same effects were fit as in Appendix 3-2.
SOURCE: Departmental survey conducted by the Committee on Gender Differences in Careers of Science, Engineering, and Mathematics Faculty.
Doctoral Degrees Awarded by All Doctoral-Granting Institutions, by Field, Gender, and Year
|
Field |
Gender |
1999 |
2000 |
2001 |
2002 |
2003 |
|
Civil engineering |
Female |
89 |
88 |
111 |
120 |
125 |
|
Civil engineering |
Male |
495 |
466 |
482 |
504 |
544 |
|
Civil engineering |
Percent Female |
15.2% |
15.9% |
18.7% |
19.2% |
18.7% |
|
Electrical |
Female |
155 |
195 |
203 |
163 |
179 |
|
engineering |
||||||
|
Electrical |
Male |
1,310 |
1,339 |
1,372 |
1,223 |
1,276 |
|
engineering |
||||||
|
Electrical |
Percent Female |
10.6% |
12.7% |
12.9% |
11.8% |
12.3% |
|
engineering |
||||||
|
Chemistry |
Female |
632 |
624 |
628 |
647 |
647 |
|
Chemistry |
Male |
1,493 |
1,361 |
1,349 |
1,275 |
1,385 |
|
Chemistry |
Percent Female |
29.7% |
31.4% |
31.8% |
33.7% |
31.8% |
|
Physics |
Female |
160 |
163 |
160 |
177 |
193 |
|
Physics |
Male |
1,103 |
1,040 |
1,036 |
946 |
882 |
|
Physics |
Percent Female |
12.7% |
13.5% |
13.4% |
15.8% |
18.0% |
|
Mathematics and |
Female |
277 |
259 |
276 |
265 |
263 |
|
Statistics |
||||||
|
Mathematics and |
Male |
803 |
790 |
731 |
650 |
729 |
|
Statistics |
||||||
|
Mathematics and |
Percent Female |
25.6% |
24.7% |
27.4% |
29.0% |
26.5% |
|
Statistics |
||||||
|
Biological |
Female |
2,394 |
2,622 |
2,549 |
2,544 |
2,598 |
|
sciences |
||||||
|
Biological |
Male |
3,171 |
3,226 |
3,133 |
3,142 |
3,083 |
|
sciences |
||||||
|
Biological |
Percent Female |
43.0% |
44.8% |
44.9% |
44.7% |
45.7% |
|
sciences |
|
SOURCE: NSF, WebCASPAR. |
|
|
 |
|
 |
|
 |
|