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Gender, beauty and support networks in
academia: evidence from a field
experiment
Magdalena Smyk
Michał Krawczyk
Warsaw Economic Seminar 2016
Group for Research in Applied Economics
2
Motivation
Gender differences in academic productivity – large
unexplained component
Social support networks and gender inequalities in academia
Participation in social network increase probability of
receiving job offer (McDonald, 2011) and scientific
productivity (Reagans and Zuckerman, 2001).
Differences in experience between women and men:
 Mentoring (Chandler, 1996)
 Collaboration (Gersick et al. 2000)
3
Can we blame „ old-boys network”?
Do scholars prefer to „lend a hand” to male researcher
rather than female?
Study I (data request)
• 247 papers (with 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).
• After three weeks - reminder
4
5
Study I – measures of success
Two measures:
Response rate = number of responses we received/
number of e-mails sent (succesfully)
Compliance rate = number of datasets we
received/ number of e-mails sent
6
Study I (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.
7
Study II
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
8
Physical attractiveness
Pre-study: Pictures with the highest and the lowest average
rank were chosen.
9
Requestor website and Google+ account
10
Subjects in Study II
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 (without non-English writers
and scholars without papers)
Meeting Treatment – 1488 scholars
Lack of gender balance in the sample (much more males)
11
Study II – measures of success
Response rate = number of responses we received/
number of e-mails sent (succesfully)
Article Treatment:
Compliance rate = number of full papers we
received/ number of e-mails sent
Meeting Treatment:
Compliance rate = number of meeting
aggrement or proposition/ number of e-mails
sent
12
Study II – 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
Study II – 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
Study II – 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
Robustness check and additional dimensions
Interactions: gender of the subject and treatment –
insignificant = lack of „old-boys network” signs
Stronger results (higher marginal effect) in subsample of
subjects who has G-Talk option available
Lack of specific field 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 Meeting Treatment:
 55/124 (males) to 34/111 (females) negative e-mail with explanation why
someone cannot meet with reqeustor
16
Conlusions
 GOOD NEWS :
No gender bias in responding to or fullfilling requests
Result was strong and robust in both studies (and in many
different fields)
 BUT…
Attractivness can play a role – but only in the case of female
students
Thank you for your attention!
Author: Magdalena Smyk, Michał Krawczyk
e-mail: msmyk@wne.uw.edu.pl
More about our research on
http://grape.uw.edu.pl
Twitter: @GrapeUW

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Request experiment at WES

  • 1. Gender, beauty and support networks in academia: evidence from a field experiment Magdalena Smyk Michał Krawczyk Warsaw Economic Seminar 2016 Group for Research in Applied Economics
  • 2. 2 Motivation Gender differences in academic productivity – large unexplained component Social support networks and gender inequalities in academia Participation in social network increase probability of receiving job offer (McDonald, 2011) and scientific productivity (Reagans and Zuckerman, 2001). Differences in experience between women and men:  Mentoring (Chandler, 1996)  Collaboration (Gersick et al. 2000)
  • 3. 3 Can we blame „ old-boys network”? Do scholars prefer to „lend a hand” to male researcher rather than female?
  • 4. Study I (data request) • 247 papers (with 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). • After three weeks - reminder 4
  • 5. 5 Study I – measures of success Two measures: Response rate = number of responses we received/ number of e-mails sent (succesfully) Compliance rate = number of datasets we received/ number of e-mails sent
  • 6. 6 Study I (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.
  • 7. 7 Study II 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
  • 8. 8 Physical attractiveness Pre-study: Pictures with the highest and the lowest average rank were chosen.
  • 9. 9 Requestor website and Google+ account
  • 10. 10 Subjects in Study II 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 (without non-English writers and scholars without papers) Meeting Treatment – 1488 scholars Lack of gender balance in the sample (much more males)
  • 11. 11 Study II – measures of success Response rate = number of responses we received/ number of e-mails sent (succesfully) Article Treatment: Compliance rate = number of full papers we received/ number of e-mails sent Meeting Treatment: Compliance rate = number of meeting aggrement or proposition/ number of e-mails sent
  • 12. 12 Study II – 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 II – 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 II – 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 Interactions: gender of the subject and treatment – insignificant = lack of „old-boys network” signs Stronger results (higher marginal effect) in subsample of subjects who has G-Talk option available Lack of specific field 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 Meeting Treatment:  55/124 (males) to 34/111 (females) negative e-mail with explanation why someone cannot meet with reqeustor
  • 16. 16 Conlusions  GOOD NEWS : No gender bias in responding to or fullfilling requests Result was strong and robust in both studies (and in many different fields)  BUT… Attractivness can play a role – but only in the case of female students
  • 17. Thank you for your attention! Author: Magdalena Smyk, Michał Krawczyk e-mail: msmyk@wne.uw.edu.pl More about our research on http://grape.uw.edu.pl Twitter: @GrapeUW