Gender Ratios in Top PhD Programs in Economics

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NES 20th Anniversary Conference, Dec 13-16, 2012
Gender Ratios in Top PhD Programs in Economics (based on the article presented by Galina Hale at the NES 20th Anniversary Conference).
Authors: Galina Hale, Tali Regev

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Gender Ratios in Top PhD Programs in Economics

  1. 1. Gender Ratios in Top PhD programs in Economics Galina Hale Tali Regev Opinions are our own and do not necessarily represent those of the Federal Reserve Bank of San Francisco or Federal Reserve SystemMoscow 12/15/2012
  2. 2. Our paper is motivated by the importance of gender segregation  At least 45% of the gender wage gap is accounted by segregation of women into lower paying occupations, industries, establishments and jobs.  Women are underrepresented in high status occupations, such as science and engineering  And economics  Do gender policies, such as affirmative action, have desired effects?Moscow 12/15/2012 2
  3. 3. Reasons for gender segregation  Employer discrimination  Dynamic effects of gender composition: occupations with larger female share tend to attract more females  Political power: females advocate for females (D).  Learning by employers: females perform well, reducing the bias (D).  Mentoring and social environment: female entrants prefer to work around females (S).  We find the presence of both mechanisms in econ departments – higher share of female faculty leads to more women in graduating PhD class  At this stage we can’t identify the exact mechanism of the causal effect  In our sample 11% of female students and only 4% of male students have female advisors.Moscow 12/15/2012 3
  4. 4. What do we already know: Gender segregation exists  Carrington and Trosky 1995, Petersen and Morgan 1995, Bayard, Hellerstein et al 2003. There are gender differences in academic career paths of economists  Kahn 1993, Kahn 1995, McDowell, Singell et al 1999, Ginther and Kahn 2004, Lynch 2008. There is evidence of gender discrimination: audit studies and sex-blind hiring  Neumark 1996, Goldin and Rouse 2000. Female students tend to work with female faculty  Neumark and Gardecki 1998, Hilmer and Hilmer 2007, Hoffmann and Oreopoulos 2009, Blau, Currie et al 2010, Bettinger and Terry Long 2004, Zinovyeva and Bagues 2010.Moscow 12/15/2012 4
  5. 5. We study gender composition of faculty and grad students in top econ departments  There is a positive correlation between share of women on the faculty and share of women among PhD students  Some of it is attributable to unobservable gender bias  Some of it is attributable to path dependence that is causal: more women on faculty => more female PhD students graduate  Challenge in establishing causality: share of female faculty is endogenousMoscow 12/15/2012 5
  6. 6. Plan of the talk  Describe the data  Present the correlation results (OLS) and gender bias effects  Present evidence of causal effects of female faculty share on share of women among PhD students (IVs)Moscow 12/15/2012 6
  7. 7. Sample: ten top econ departments 1983-2007  All ladder faculty:  gender and academic career, including tenure, publication history and PhD institution.  Sources: Faculty lists, CV’s, Harzing’s Publish or Perish  Departments chosen by data availability (not gender related).  750 economists, 98 of these women.  All students awarded PhDs between 1983-2006:  Gender , institution (field, academic advisor)  Sources: NSF Survey of Earned Doctorates, Proquest, Dissertation abstracts at institutions’ libraries.  Only those who ended up getting a PhD – not all admissions  Race composition of the econ department’s entering PhD class  Gender composition of entering PhD class at the University levelMoscow 12/15/2012 7
  8. 8. Female faculty share in top econ departments is still very lowMoscow 12/15/2012 8
  9. 9. Increase in female share is uneven across institutions Inst 1 Inst 2 Inst 3 Inst 4 Inst 5 Inst 6 Inst 7 Inst 8 Inst 9 Inst 10Moscow 12/15/2012 9
  10. 10. Share of women in entering PhD class is higherMoscow 12/15/2012 10
  11. 11. And also uneven across institutions Inst 1 Inst 2 Inst 3 Inst 4 Inst 5 Inst 6 Inst 7 Inst 8 Inst 9 Inst 10Moscow 12/15/2012 11
  12. 12. In most institutions female shares are trending up, but there is sufficient heterogeneity for analysisMoscow 12/15/2012 12
  13. 13. We measure correlation between these shares using OLS regressionMoscow 12/15/2012 13
  14. 14. Female faculty share has a large “effect” on female student shareMoscow 12/15/2012 14
  15. 15. There are time-invariant differences across departments: Institution FEs Inst 1 Inst 2 Inst 3 Inst 4 Inst 5 Inst 6 Inst 7 Inst 8 Inst 9 Inst 10Moscow 12/15/2012 15
  16. 16. This correlation is robustMoscow 12/15/2012 16
  17. 17. Strong evidence of positive correlation – summary  Magnitude of coefficient is ~ 1:  1 pp increase in share faculty is associated with 1 pp higher share of students.  Doubling the 2000 faculty share from 0.1 to 0.2, is associated with an increase of student share from 0.2 to 0.3.  OLS results are robust to:  including faculty share in female friendly fields.  Non-linear effects of faculty share (found that effect is linear).  Differences in tenure of female faculty  Institution-specific trend.Moscow 12/15/2012 17
  18. 18. What about causality? IV!  Omitted variables might be responsible for both shares, producing spurious OLS results  Need an instrument which changes the female faculty share but uncorrelated with student share in any other way.  Instrument: male exits.  Mechanically increase the female faculty share  female share = #females/ (#females+#males)  True for lagged exits as well if not replaced immediatelyMoscow 12/15/2012 18
  19. 19. Find causal effect of female faculty share of female student shareMoscow 12/15/2012 19
  20. 20. Testing the exclusion restriction: exits of male faculty have no direct effect on female student share  Share of female faculty does not predict male exits (no reverse causality) – specification test  Limit to exogenous male exits – robustness test  Predict male exits with individual-level exogenous variable – robustness testMoscow 12/15/2012 20
  21. 21. Female faculty share does not predict male exitsMoscow 12/15/2012 21
  22. 22. Are male exits exogenous? Male exits could be due to  retirement (older) – could be anticipated  transfers (young and old, move within sample) – could be associated with gender policies  failure at tenure (young - non tenured, move out of sample) – most likely exogenous and unanticipatedMoscow 12/15/2012 22
  23. 23. First stage with alternative instruments and alternative sets of controlsMoscow 12/15/2012 23
  24. 24. Second stage largely unaffectedMoscow 12/15/2012 24
  25. 25. What if male exits are still endogenous to gender policies? Instrument them!  Predict exits at individual level using age and publications as instruments:  Young male exit due to poor chance at (or denied) tenure  Older male exit due to retirement  Two equations for “zero” stageMoscow 12/15/2012 25
  26. 26. The result still holdsMoscow 12/15/2012 26
  27. 27. Conclusion  We find that higher share of female faculty leads to more female graduate students  We are pretty sure of this being a causal effect  We cannot tell exactly which mechanism is driving it  We do find some evidence of a time-varying gender bias  We also find an effect of quality of female faculty  We also find that female students are more likely than male students to be advised by female facultyMoscow 12/15/2012 27
  28. 28. Thank you!!!Moscow 12/15/2012

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