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 System

12/15/2012 Moscow

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?

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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. 12/15/2012 Moscow 3

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.

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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 endogenous

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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)

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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 level

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Female faculty share in top econ

departments is still very low

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Increase in female share is uneven

across institutions

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Inst 1

Inst 2

Inst 3

Inst 4

Inst 5

Inst 6

Inst 7

Inst 8

Inst 9

Inst 10

Share of women in entering PhD

class is higher

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And also uneven across

institutions

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Inst 1

Inst 2

Inst 3

Inst 4

Inst 5

Inst 6

Inst 7

Inst 8

Inst 9

Inst 10

In most institutions female shares are trending

up, but there is sufficient heterogeneity for

analysis

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We measure correlation between

these shares using OLS regression

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Female faculty share has a large

“effect” on female student share

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There are time-invariant differences

across departments: Institution FEs

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Inst 1

Inst 2

Inst 3

Inst 4

Inst 5

Inst 6

Inst 7

Inst 8

Inst 9

Inst 10

This correlation is robust

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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.

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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 immediately

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Find causal effect of female faculty share

of female student share

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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 test

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Female faculty share does not predict

male exits

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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 unanticipated

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First stage with alternative instruments and

alternative sets of controls

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Second stage largely unaffected

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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” stage

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The result still holds

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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 faculty

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Thank you!!!

12/15/2012 Moscow