Field experiment on willingness to help a young researcher according to gender

1. Gender, beauty and support networks
in academia: evidence from a field
experiment
Magdalena Smyk, Michał Krawczyk
The Choice Lab, Bergen
March 17, 2016
GendEqU project is sponsored by EEA/Norway Grants
Group for Research in Applied Economics

2. 2
Broad motivation
Huge gender gap at higher academic positions…
This is partly due to gender differences in academic
productivity…
…which tends to be difficult to explain in terms of abilities or
preferences

3. 3
Support networks in the academia
 Participation in social networks increase probability of receiving job
offer (McDonald, 2011) and scientific productivity (Reagans and
Zuckerman, 2001).
 Colussi (2015): editor’s former PhD students and faculty colleagues
improve their publication outcomes
 Balliet et al. (2001) meta analysis: more cooperation in male-male
interactions
 Differences in experience between women and men:
 Mentoring (Chandler, 1996)
 Possibly collaboration (Gersick et al. 2000) (although Long (1992) Van
Rijnsoever et al. (2008) found no differences and McDowell et al. (2006) only
in historical data. ). Note: Ynalvez and Schrumb (2011) claim that networks
matter NOT via formal collaborative projects.

4. 4
Can we blame the ``old-boys network”?
Are (male) scholars more willing to ``lend a hand’’
to a male researcher than a female?

5. Study 1 (data request)
• 247 papers (recent EE, JEBO, GEB papers reporting
experiments that meet certain criteria)
• Ask for raw data from their experiments
• E-mails from two accounts:
– Female student
– Male student
• Randomly chosen samples of subjects:
– equal distribution of male and female subjects
– three geographical regions (Europe, Australia and Asia,
Americas).
• A reminder after three weeks
5

6. 6
Study 1 – measures of success
Response rate = number of responses we received/
number of e-mails sent (successfully)
Compliance rate = number of datasets we
received/number of e-mails sent

7. 7
Study 1 (data request): RESULTS
Female Student Male Student
No. of requests 100 105
Response rate 75% 74.3%
MWW test (p-value) 0.91
Marginal effects* -0.01 (insignificant)
Compliance rate 34% 35.2%
MWW test (p-value) 0.85
Marginal effects* -0.02 (insignificant)
Notes: *probit regression; gender, university region, fixed effects of journal, date of sending
the request and number of datasets we asked for.

8. 8
Study 2
Extension:
10 fields of study: psychology, sociology, economics,
mathematics, law, computer science, philosophy, medicine,
physics and chemistry
two types of request (much smaller):
Article treatment – we ask for full text of subject’s paper
Meeting treatment – we ask for a meeting during office
hours or Skype/phone call to discuss possible mentoring
for graduate studies
additional dimension: physical attractiveness

9. 9
Physical attractiveness
Pre-study: Pictures with the highest and the lowest average
rank were chosen.
Gmail picture + website link

10. 10
Sampling for Study 2
One hundred top faculties from QS World University Rankings
Four (randomly chosen) scholars from each faculty
Faculties without websites or without list of employees –
excluded
Article Treatment – 1287 scholars (discarding those with no
known papers in English)
Meeting Treatment – 1488 scholars
No gender balance in the sample (male majority)

11. 11
Study 2: dependant variables
Response rate = number of responses we received/
number of e-mails sent (successfully)
Article Treatment:
Compliance rate = number of full texts we
received/ number of e-mails sent
Meeting Treatment:
Compliance rate = number of meetings
scheduled or offered/ number of e-mails sent

12. 12
Study 2: results (Article Treatment)
Attractive
Female
Less
Attractive
Female
Attractive
Male
Less
Attractive
Male
No. of requests 343 307 337 300
Response rate 56.6% 67.1% 63.2% 62.4%
MWW test p-value
(vs. attractive female) 0.006 0.08 0.08
(vs. less attractive female) 0.3 0.33
(vs. attractive male) 0.97
Compliance rate 49% 60% 56.7% 54.8%
MWW test p-value
(vs. attractive female) 0.005 0.04 0.2
(vs. unattractive female) 0.4 0.14
(vs. attractive male) 0.5

13. 13
Study 2: results (Meeting Treatment)
Attractive
Female
Less
Attractive
Female
Attractive
Male
Less
Attractive
Male
No. of requests 370 378 374 366
Response rate 45.7% 47.6% 43.9% 44.3%
MWW test p-value
(vs. attractive female) 0.59 0.62 0.7
(vs. less attractive female) 0.3 0.36
(vs. attractive male) 0.91
Compliance rate 29.2% 34.4% 27% 27.6%
MWW test p-value
(vs. attractive female) 0.13 0.51 0.63
(vs. unattractive female) 0.03 0.05
(vs. attractive male) 0.86

14. 14
Study 2: results (probit)
Article
treatment
(1)
Article
treatment
(2)
Meeting
treatment
(1)
Meeting
treatment
(2)
response compliance response compliance
attractive female -0.08* -0.11*** 0.02 0.02
less attractive female 0.03 0.18 0.05 0.09***
less attractive male -0.02 -0.06 -0.005 0.001
female scholar -0.05 -0.07** -0.09*** -0.09***
Observations 1287 1287 1488 1488
Notes: Marginal effects from probit regressions; reference category is attractive male; regressions include
subjects’ characteristics (gender, university region, university ranking position, field of study), date of sending
the request and year of the paper publication (in Article treatment); *** p<0.01, ** p<0.05, * p<0.1.

15. 15
Robustness check and additional dimensions
No interaction of genders
Stronger results (higher marginal effect) in the subsample
of subjects who has G-Talk option available
Lack of field-specific effects
Nr of unique vistors on websites = 44% of the nr of subjects
Attractive senders websites more popular by 10 pp on average
Refusals in the Meeting Treatment:
 55/124 (males) to 34/111 (females) negative e-mail with explanation why
someone cannot meet the reqeustor

16. 16
Conclusions
GOOD NEWS!
No gender bias in responding to or fullfilling requests
This result seems robust across fields and treatments
BUT…
Attractivness can play a role – but only in the case of female
students
There seems to be an interaction with treatment
Cautios interpretation: female students considered less
competent but more likable

17. Thank you for your attention!
Authors: Magdalena Smyk, Michał Krawczyk
e-mail: mkrawczyk@wne.uw.edu.pl
More about our research on
http://grape.uw.edu.pl
Twitter: @GrapeUW