Gender Discrimination in Early Stages of Academic Careers

Vincent Chandler 1

vincent.chandler@smu.caSaint Mary’s University

Abstract

This paper studies gender discrimination in early stages of academic careers by takingadvantage of individual scores given by 105 evaluators to 3,500 students who applied fora doctoral scholarship. I find no evidence that evaluators give a higher score to candidatesfrom their own gender in general. However, I do find some evidence of gender discriminationin three specific cases. First, gender representation within the discipline affects evaluators.Female evaluators from disciplines in which they are severely under-represented give lowerscores to female candidates. Second, male evaluators react to the gender composition of theirsubcommittee. They give higher scores to male candidates when they are the only male intheir subcommittee. Third, candidates in the tails of the distribution are affected by genderdiscrimination. Male evaluators give higher scores to relatively stronger male candidates, andfemale evaluators give lower scores to relatively weaker female candidates. These results sug-gest that gender quotas in evaluation committees will favour female candidates only in veryspecific cases.

JEL Codes: I23Keywords: gender discrimination, academia, doctoral candidates

January 2017

1The author wishes to thank SSHRC for accepting to share information and is particularly indebted to MargaretBlakeney, Andreas Reichert, Matthew Lucas and Jack Mintz for their help. The usual caveat applies.

1 Introduction

While females represent 59% of students obtaining an undergraduate degree, they earn only 47% of

doctorates (Statistics Canada, 2011). A similar trend can be observed in academic positions: 46%

of assistant professors are female, but they occupy only 23% of full professor positions (CAUT,

2014). The under-representation of females in academia is observed in Europe (European Com-

mission, 2016) and in the US (National Research Council, 2009).

Gender discrimination could explain this phenomenon2. At the begin of their academic ca-

reers, females could face hostile evaluation committees composed mostly of males with gender

bias. Such committees may prefer male candidates making it difficult for females to secure fund-

ing and ultimately to enter academia. Even though past research has shown serious issues relating

to gender bias in academia (e.g. Wold and Wenneras, 1997 or Moss-Racussin et al., 2012), more

recent and comprehensive work has shown no such evidence. Bagues, Sylos-Labini and Zinovyeva

(forthcoming), for example, show no evidence of gender discrimination in the promotion of assis-

tant and associate professors in Italy and Spain using individual evaluations for more than 100,000

candidates and 8,000 evaluators. Similarly, Williams and Ceci (2015) show in a randomized con-

trolled trial that 873 tenure-track faculty in male-dominated disciplines tend to prefer female PhD

holders when hiring assistant professors. These new results force us to reconsider gender discrim-

ination in academia. Has academia really freed itself from gender bias as suggested by Ceci and

Williams (2011)?

This paper estimates gender bias by studying the scoring decisions of 105 evaluators assessing

3,500 students applying for doctoral scholarships in the humanities and social sciences. As in the

literature, I do not find any evidence that male/female evaluators give on average higher scores to

male/female candidates. However, I do find some evidence of gender bias in three specific situa-

tions. First, the gender composition of a discipline affects female evaluators. Female evaluators

from disciplines in which fewer than 35% of professors are female tend to give 0.350 fewer points

2Other explanations include the lack of research network (Blau, Currie, Croson and Ginther, 2010), issues relatingto fertility decisions (Ceci and Williams, 2011), and a female distaste for competition (Niederle and Vesterlund, 2014)and bargaining (Small et al., 2006).

1

(18.5% of a standard deviation) to female candidates. Second, male evaluators seem to react to

the gender composition of the subcommittee. Indeed, when an evaluator is the only male in the

subcommittee, he gives male candidates 0.151 points (8.0% of a standard deviation) more than do

female evaluators accounting for candidate fixed effects. There is no evidence of such an effect for

female candidates. Bagues, Sylos-Labini and Zinvyeva (forthcoming) also find that male evalua-

tors react to the gender composition of an evaluation committee. Third, there is also evidence for

gender discrimination in the tails of the distribution. Indeed, relatively stronger male candidates

receive 0.439 points (23.2% of a standard deviation) more from male evaluators, while relatively

weaker female candidates receive 0.228 points (12.1% of a standard deviation) lower scores from

female evaluators. As far as I know, this is the first result suggesting that gender discrimination

may vary across the distribution of skills.

By affecting the score of a candidate, gender discrimination also affects the allocation of schol-

arships. I find that candidates who are evaluated by a subcommittee composed of a minority of

evaluators from their own gender have a 11.4 percentage point higher probability of receiving a

scholarship than candidates facing a committee in which the majority of evaluators are of their

own gender. This result is reminiscent of the one from Bagues and Esteve-Volart (2010) and sug-

gests that increasing the share of females in evaluation committees could be detrimental to female

candidates.

This paper contributes to the literature in three ways. First, it studies an evaluation setting

in which there is scarce information about candidates thus leaving more room for bias. Previous

studies rejecting gender bias have focused on situations in which evaluators have ample informa-

tion on the candidates who were assistant/associate professors (Bagues, Labini and Zinovyeva,

forthcoming) or postdocs (Williams and Ceci, 2015). In such cases, evaluators can rely on the

information and make reflected decisions. Assessing the research potential of a student who has

barely completed his/her undergraduate degree is much more difficult. When a task is hard and

must be completed quickly, an evaluator may have a tendency to refer to system 1, which is prone

to bias and heuristic judgement (Kahneman, 2011). This evaluation setting is also interesting, be-

2

cause there is evidence that graduate scholarships have a causal impact on the careers of recipients

(Chandler, 2016). Gender discrimination in the allocation of graduate scholarships could therefore

have long lasting consequences.

Second, contrary to previous work studying binary decisions (e.g. hire/not hire or promote/not

promote), evaluators in my setting can give any score between 0 and 10. The binary decisions

studied previously could understate variance in perceptions. For example, male evaluators could

strongly support the applications of male candidates, but only only weakly support those of female

candidates. Since researchers only observe support in both cases, they would conclude there is no

gender discrimination, even though there is an unobservable difference. In a committee discussion,

this difference in the intensity of the individual support would matter for a group decision. My

setting offers richer data and therefore more opportunity to detect bias.

Third, this paper is the first to study gender discrimination across the skill distribution by using

candidate fixed effects and total scores. Any applied economist would be weary to separate the

sample based on a dependent variable. Usually, such a decision leads to biased estimators, because

the error term impacts the separation of the sample. For example, observations with large positive

error terms would have a higher probability of being in the right tail of the distribution. A dummy

variable identifying such observations would therefore be positively correlated with the error term.

When controlling for candidate fixed effects, however, the error term of the individual scoring

decision has no impact on the total score of the candidate. Indeed, the individual error terms sum

to zero for each candidate. It is therefore possible to identify the impact of gender discrimination

through the distribution of candidates.

The rest of the paper is structured in the following way. I first introduce the selection pro-

cedure used by the Social Science and Humanities and Research Council (SSHRC) to allocate

scholarships. Second, I present the data. Third, I discuss the methodology and show the results

of the regressions. Finally, I discuss some of the possible mechanisms used to explain gender

discrimination in light of the results.

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2 SSHRC and the Selection Procedure

The Social Science and Humanities Research Council (SSHRC) is a Canadian federal agency that

promotes and supports postsecondary-based research and training in the humanities and social

sciences. One of the ways through which SSHRC achieves this goal is by awarding yearly approx-

imately $66 million 3 in scholarships to doctoral students.

Candidates — Canadian citizens or Canadian permanent residents — apply in the fall with

the following documentation: a project proposal, a CV, two reference letters from faculty, and

all their university transcripts. While students enrolled at a Canadian university apply to their

home university pre-selection committee, those matriculated at foreign universities do so directly

at the preliminary competition at SSHRC. The top-ranked candidates from each pre-competition

are forwarded to the national competition. The data used in this study stem from the 2004-2005

and 2005-2006 national competitions.

Applications forwarded to the national competition are sorted into one of the five committees

based on the discipline of the project proposal as shown in table 1. Within each committee, ap-

plications are then allocated to one of the 3 or 4 subcommittees according to the candidate’s last

name4. Overall, there are 35 subcommittees 5. On average, a subcommittee had 100 applicants

and allocated $3.76 million CAD in scholarships.

Each subcommittee is comprised of three associate or full professors in one of the disciplines

included in the committee6. These professors are active researchers who have previously received

a research grant from SSHRC. Each evaluator gives a score between 0 and 10 to each candidate in

3SSHRC awarded $65,928,665 CAD for the 2004-2005 competition and $65,775,00 CAD for the 2005-2006 com-petition. Approximately $50 mio USD.

4Table 2 shows the subcommittees of committee 5 for both competition years as an example. Candidates with lastnames starting with letters A to F usually went to the first subcommittee; those with names starting with letters fromG to M, to the second subcommittee; the remaining candidates to the 3rd subcommittee. Exceptions can be explainedby the fact that evaluators cannot assess students from their own university and that French applications all went to asubcommittee in which all evaluators were fluent in French. It is important to note that this table was created usingonly scholarship recipients, because I do not know the identity of non-recipients.

5In 2004, there were 16 subcommittees (5 committees with each 3 subcommittees except psychol-ogy/education/social work which had 4 subcommittees) and in 2005, there were 19 subcommittees (5 committeeswith each 4 subcommittees except the economics/management/political science committee which had 3 subcommit-tees).

6See table 1 for a list of disciplines per committee.

4

the subcommittee based on past academic results, the potential contribution to the advancement of

knowledge of the program of study, and relevant professional and academic experience. Evaluators

know the identity of the two other evaluators in their subcommittee when assessing candidates, but

the assessment is done individually. They meet once they have evaluated all candidates to discuss

cases where the scores awarded are very different. The final score of a candidate is simply the sum

of the three individual scores.

Students are informed in April/May whether their application was successful. There is no

appeal procedure. The candidates with the highest scores starting first or second year at a Canadian

university receive the Canadian Graduate Scholarship (CGS). This scholarship provides recipients

with three annual payments of $35,000. The second-tier candidates receive the SSHRC Doctoral

Fellowship (SDF) which represents a yearly payment of $20,000 until the fourth year of doctoral

studies7. The last tier receives no scholarship from SSHRC but could still be awarded scholarships

from their own university or from other agencies.

3 Data

In 2004 and 2005 competitions, 3,500 doctoral candidates were assessed in the national competi-

tions by 105 evaluators grouped in 35 subcommittees. Overall, the data set contains 10,500 scores.

For all candidates, I have information on gender, discipline, year of study, subcommittee, and type

of award received. Overall, 62.4 percent of candidates are females as indicated in table 3. Table

4 shows the distribution of candidates into the 27 disciplines defined by SSHRC. Literature and

psychology are clearly the most popular disciplines with each more than 450 candidates. Industrial

relations, folklore and demography are the least popular with each fewer than 15 candidates.

Of these 3,500 candidates, 65.9 percent received total scores above the funding threshold of

their subcommittee, which is defined as the lowest score of the subset of candidates who received7Students awarded the scholarship in their first year of doctoral studies will receive $80,000. Similarly, students

awarded a scholarship in 4th year will only receive one payment of $20,000.

5

a scholarship8. However, only 54.3 percent of all candidates actually received a scholarship9.

As to the evaluators, I know their gender and their discipline. Table 3 shows that SSHRC

attempts to give equal representation to both genders with 47.6 % of evaluators being females.

It is clearly below the share of female candidates (62.4%), but it is above the share of female

associate professors in the social sciences in 2014 (43.1%). In terms of discipline, the distribution

of evaluators is similar to the distribution of candidates (see table 4), even though some fields are

under-represented (e.g. psychology) and others are over-represented (e.g. law).

Each evaluator assigns a score between 0 and 10 to each candidate in the subcommittee. Figure

1 shows the distribution of individual scores which has an average of 6.03 and a standard deviation

of 1.89. Figure 2 shows the distribution of total scores — the sum of the individual scores of the

three evaluators in a subcommittee — which has an average of 18.1 and a standard deviation of

4.7.

The representation of males and females varies by subcommittee. Table 5 shows that in 6 from

35 subcommittees, there was only one gender represented (2 subcommittees composed of only

females and 4 subcommittees composed only of males). Overall, there were 719 candidates who

were evaluated by single-gender committees.

8Thresholds vary by subcommittee with a minimum at 13.7, a maximum at 18.4, and a mean of 16.15. Thesevariations can be explained in two ways. First, there may be systematic differences in the mean score given byevaluators. A subcommittee composed of three generous evaluators will have a higher threshold than a subcommitteecomposed of three harsher evaluators. Second, the covariance of the scores within a subcommittee could affect thevalue of the threshold. If there is positive covariance between scores (ie evaluators tend to agree), good candidateswill have very high scores and weak candidates will have very low scores. If more than 50% of applicants are abovethe threshold, the threshold will be relatively low. If there is negative covariance (ie evaluators tend to disagree), therewill be much density around the average. If more than 50% of applicants are above the threshold, it will be relativelyhigh.

9Overall, 407 candidates had total scores above the subcommittee threshold, but did not receive the scholarship.Of these candidates, 315 applied in 2004 and 92 did in 2005. Candidates could have been offered a scholarshipbut simply declined it, because they no longer intended on pursuing a PhD. This reason, however, cannot justify thedifference between 2004 and 2005 knowing that there were as many applicants in both years (1,748 in 2004 and 1,752in 2005). An administrative change could explain the variation between the years. The Canadian Graduate Scholarship(CGS) is only awarded to doctoral students entering first or second year at a Canadian university. In 2004, there wasa fixed number of CGS per subcommittee. When a candidate declined a CGS, it went to the next candidate in thesubcommittee who was eligible. There could therefore be candidates who were not eligible who were skipped to givea CGS to a lower-ranked candidate who was eligible. These ineligible candidates were never offered an award, eventhough their score was above the funding threshold as I defined it. This policy was replaced the next year. As of 2005,if a student declines a CGS, the next person receives an award whether the person is eligible for CGS or not. Themoney is simply split and CGS awards can change subcommittees. The data does not make it possible to distinguishbetween a candidate who was offered a scholarship and declined it and a candidate who was not offered a scholarship.

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4 Methodology and Results

To determine whether there is any gender bias, I first study the impact of gender on scoring de-

cisions using random effect and then fixed effects models. Both types of models allow different

types of comparisons. While random effect models compare, for example, male evaluators across

different settings or candidates, fixed effect models compare male to female evaluators for a given

candidate. Certain comparisons are only possible in one of the two types of models. Finally, I turn

my attention to the impact of the gender composition of a subcommittee on whether a candidate

received a scholarship.

4.1 Random Effect Models Explaining Scores

Random effect models provide consistent estimators if the candidate specific error term is uncorre-

lated with the independent variables. Since the independent variables are related to the composition

of the subcommittee, we need to think about the allocation of candidates to subcommittees. Table

2 shows that candidates are allocated to subcommittees based on the first letter of their last name.

There will be no correlation between the error term and the independent variables if one of these

two assumptions is verified: 1) academic potential – unobserved variable responsible for the score

– is not related to the first letter of a person’s last name or 2) the gender composition of a sub-

committee is not related to the order of the subcommittee10. It would be difficult to verify the first

assumption, but table 6 shows the gender composition of a subcommittee does not seem related to

its order.

The basic regression is simply:

10If applicants whose names start with “W” are generally better than those whose names start with “C” and ifsubcommittees evaluating candidates whose names start with “W” are more often composed of females, then thecoefficient for the female evaluator variable could be biased.

7

scoreij =β0 + β1Female Candidatei + β2Female Evaluatorj

+ β3Female Evaluator*Female Candidateij + uij

(1)

where all independent variables are dummies. The female candidate variable takes the value 1

if the candidate is a female and is otherwise 0. The female evaluator variable takes the value 1 if

the evaluator is a female and is otherwise 0. The variable “Female Evaluator*Female Candidate”

takes the value 1 if both the candidate and evaluator are female. The standard errors are clustered

at the subcommittee level.

The coefficient β1 would provide evidence of systematic gender discrimination, β2 would sug-

gest that female evaluators are different from male evaluators, and β3 would show that female

evaluators treat female candidates differently. Column 1 in table 7 shows that none of these co-

efficients is statistically significant when explaining the score given by evaluator “j” to candidate

“i” as in Bagues, Sylos-Labini and Zinovyeva (forthcoming). Even though there is no blunt gen-

der discrimination, we will consider three other situations in which gender discrimination could

happen.

First, a female evaluator may assess female candidates differently is she is the only female

evaluator in a subcommittee to compensate for her minority situation. The following regression

addresses this possibility:

scoreij =β0 + β1Female Candidatei + β2Female Evaluatorj (2)

+ β3Female Evaluator*Female Candidateij

+ β4Female Eval.*Fem. Cand.*Only 1 Fem. Eval.ij + uij

8

where the variable “Female Eval.*Fem. Cand.*Only 1 Fem. Eval.” takes the value 1 if a

female evaluator assesses a female candidate and if she is the only female in the subcommittee.

The standard errors are clustered at the subcommittee level. Column 2 in table 7 shows that this

variable is also not statistically significant.

Second, female evaluators may evaluate a female candidate differently if both are in the same

discipline. The following regression estimates this possible relationship:

scoreij =β0 + β1Female Candidatei + β2Female Evaluatorj (3)

+ β3Female Evaluator*Female Candidateij + β4Same Disciplineij

+ β5Female Eval.*Fem. Cand.*Same Disciplineij + β6Xi + uij

where the variable “Female Eval.*Fem. Cand.*Same Discipline” takes the value 1 if both

evaluator and candidate are females and share the same discipline. The standard errors are clustered

at the subcommittee level. Column 3 in table 7 shows some weak evidence that female evaluators

may give higher scores to female candidates in their discipline with a p-value of 0.101. Column 4

shows the coefficient loses significance (p-value = 0.125) when controlling for year of study (4),

candidate discipline dummies (27) and subcommittee dummies (35)11. The standard errors are

clustered by subcommittee.

Finally, the share of females in a discipline could affect how female evaluators perceive female

candidates. The following regression considers this possibility:

11The results are the same if I include the interactions “female candidate * same discipline” and “female evaluator* same discipline”.

9

scoreij =β0 + β1Female Candidatei + β2Female Evaluatorj (4)

+ β3Female Evaluator*Female Candidateij + β4Low Female Rep. Disciplineij

+ β5Fem. Eval.*Low Female Rep. Discij + β6Fem. Cand.*Low Female Rep. Discij

+ β7Fem. Eval.*Fem. Cand.*Low Female Rep. Disciplineij + β8Xi + uij

The variable “Low Female Rep. Discipline” takes the value 1 if in the evaluator’s disciplines

males represent more than 65% of professors in Canada12. The standard errors are clustered by

subcommittee.

Columns 4 and 6 of table 7 show that female evaluators tend to give 0.347 fewer points to

female candidates when the female evaluator comes from a male-dominated discipline (more than

65% of professors are male) or 0.350 fewer points after controlling for year of study (4), the candi-

date discipline (27) and the subcommittee (35). Table 8 provides more robustness to these findings

by varying the threshold to define a male-dominated discipline. Even though the coefficients of

interest are not statistically significant at all thresholds, they have the same sign for all thresholds

between 61 and 69%. Moreover, coefficients for thresholds between 63 and 67% are statistically

significant at a threshold of 10%.

A similar regression analysis can be performed for males. To avoid repetition, I will only

present the specification for which the coefficients are statistically significant:

12Females represent fewer than 65% of professors in Canada in the following disciplines: archaeology, classics,economics, geography, philosophy, political science and religious studies (CAUT, 2014). Table 4 reports the share ofmales for all disciplines.

10

scoreij =β0 + β1Male Candidatei + β2Male Evaluatorj

+ β3Male Evaluator * Male Candidateij

+ β4Male Evaluator*Male Candidate*Only 1 Male Evaluatorij

+ β5Xi + uij (5)

The variable “Male Evaluator*Male Candidate*Only 1 Male Evaluator” takes the value 1 if a

male evaluator assesses a male candidate and is the only male in the subcommittee. The control

variables are year of study (4), discipline of candidate (27) and subcommittee (35). The standard

errors are clustered at the subcommittee level.

Columns 2 and 5 of table 9 show that male evaluators give male candidates 0.248 (without

control variables) to 0.286 (with control variables13) points more when they are the only male in a

subcommittee in comparison to males in subcommittees with a larger proportion of males.

The other columns of table 9 show no evidence that male evaluators give higher scores to male

candidates in general (column 1), when they share the same discipline (columns 3 and 6) and when

the evaluator is from a male-dominated discipline (columns 4 and 7).

4.2 Fixed Effects Models Explaining Scores

To add robustness to some of the results of random effect models, I perform a similar analysis with

fixed effects. By doing so, I can also study the impact of discrimination throughout the distribution.

I define the relative strength of students based on their total score. Generally, a dummy variable

based on the value of a dependent variable would lead to biased estimators due to the correlation

between the dummy variable and the error term. For example, if I define a dummy variable that

takes the value 1 for observations above a certain threshold and otherwise 0, observations with13By including subcommittee dummies, I am also controlling for the fact that all evaluators in subcommittees with

only one male behave differently.

11

a large error term would have a higher probability of being above this threshold. Conversely, an

observation with a negative error term would have a lower probability of being above this threshold

and therefore of having a value of 1 for the dummy variable. This dummy variable would therefore

be positively correlated with the error term.

This issue does not arise in a fixed effect model using total score as indication of relative

strength. A general fixed effect regression can be expressed as a demeaned regression:

yij − yi = β(xij − xi) + uij (6)

whereby “i” represents the candidate and “j”, the evaluator.

The term uij has no influence on whether an observation is above or below the total score.

Indeed, the sum of uij for a given candidate is zero for all candidates since∑

j∈J(yij− yi) = 0 and∑j∈J(xij − xi) = 0 where J is the set of evaluators assessing candidate “i”. A positive uij simply

means that there must be a negative uij for another evaluator-candidate match for this candidate. I

can therefore perform the following regression with unbiased estimators:

scoreij =β1Male Evaluator * Male Candidate * High Total Scoreij

+ β2Male Evaluator * Male Candidate * Low Total Scoreij

+ β3Female Evaluator * Female Candidate * High Total Scoreij

+ β4Female Evaluator * Female Candidate * Low Total Scoreij + λi + uij (7)

The four independent variables are dummies. The first one takes a value 1 if both the evaluator

and the candidate are males and if the candidate’s total score was above a certain threshold. Oth-

erwise, the variable takes the value zero. The second one takes a value 1 if both the evaluator and

the candidate are males and if the candidate’s total score is below a certain threshold. The last two

variables follow the same logic but for female candidates and evaluators.

The first two columns of table 10 confirm the results of table 7 and 8: there is no statistical

12

evidence that male/female evaluators treat the average male/female candidate differently. Column

3 revisits the idea that a male evaluator may treat male candidates differently if he is the only male

evaluator in the subcommittee. The coefficient on the variable “Male*Male*Only 1 Male Eval.”

is still significant at the 5% level showing that male evaluators are the only male evaluator in a

subcommittee, they give scores that are 0.151 points higher than those given by female evaluators

to male candidates. This result does not identify the same effect as the one in table 7 which com-

pared male evaluators in a subcommittee with only one male to male evaluators in a subcommittee

with at least 2 male evaluators. In table 9, I am comparing male evaluators in a subcommittee with

only one male with female evaluators in a subcommittee with only one male. Even though both

results are identified differently, they both show that male evaluators seem to react to the gender

composition of the subcommittee, while there is no such effect for female evaluators.

Column 4 of table 10 offers another perspective on gender discrimination by showing gender

discrimination throughout the distribution. Male candidates with scores at or above 24 receive

0.439 points (23% of a standard deviation) more from male evaluators14. Moreover, female evalu-

ators give 0.228 fewer points (12% of a standard deviation) to female candidates with a total score

at or below 1415.

Table 11 reproduces the results of column 4 of table 7 with a large variety of thresholds to

provide more robustness. The positive coefficient for male candidates being assessed by male

evaluators starts being statistically significant at the 10% level at the threshold of 20.5 and remains

significant until a threshold of 24.5. As for the dummy coefficient indicating that a female evaluator

is assessing a female candidate below a certain threshold, it starts being statistically significant at

the 5% level at a threshold of 13.5 and remains so until a threshold of 15.5. As the threshold grows,

the coefficient loses its statistical significance but it keeps its sign and a similar magnitude. Figure

2 shows the distribution of total scores to visualize the share of candidates below or above certain

thresholds.14There are 147 male candidates with scores at or above 24.15There are 399 female candidates with scores at or below 14

13

4.3 Impact of Gender Discrimination on the Allocation of Scholarships

Scores are important to identify gender discrimination, but they also have a real impact: they

determine who receives a scholarships. Following Bagues and Esteve-Volart (2010), I estimate

the following linear probability model to determine whether the composition of the subcommittee

affects who receives a scholarship:

Scholarshipi = β0 + β1Own Gender in Minorityi + β2Gender Candidatei + β3Year of Study Dummiesi

+ β3Discipline Dummiesi + β4Subcommittee Dummiesi + ui (8)

whereby the variable Scholarshipi is a dummy variable that takes the value 1 if the candidate

holds16 an award and 0 otherwise. The key independent variable is whether there is a minority of

evaluators in a subcommittee who share the gender of the candidate. This variable takes the value

1 if a male (female) candidate is evaluated by a subcommittee composed of either no male (female)

or 1 male (female) evaluator and otherwise 0. I also control for the gender, the year of study, the

discipline and the subcommittee of the candidate.

Coefficient β1 will be consistent if the error term is uncorrelated with the dummy variable “Own

Gender in Minority”. This assumption is similar to the one needed for unbiased estimator in the

random effect model: either research potential of candidates is not correlated with the first letter of

their last names or the gender composition of evaluators in a subcommittee is not correlated with

the order of the subcommittee. For example, the coefficient would be biased if male candidates

with names starting by a letter between A and G are systematically better than the other ones and

if these candidates in the first subcommittee were more often evaluated by a committee with a

minority of males. Table 6 shows no evidence that the order of subcommittees is related to their

gender composition. The third subcommittee does seem to have a higher probability of having a

majority of male evaluators, but given the small sample, it is not particularly alarming.

16Holding a scholarship means being given a scholarship and having accepted it. The dataset is unable to differen-tiate between candidates who decline an award and those who were never given one.

14

Columns 1 to 3 in table 11 show that students have a 8.02 to 15.8 percentage point (16% to

32% of a standard deviation) higher probability of receiving a scholarship when there is a minority

of evaluators from their own gender. Knowing that 54.3% of students receive a scholarship, the

magnitude of this effect is substantial.

To provide more robustness, I also conduct the regression using whether a candidate is above

the subcommittee funding threshold17 as dependent variable. As discussed in the data section (see

footnote 9), some students not holding a scholarship may have declined it or may have never been

offered one. The coefficients in columns 4 to 6 have the same signs and magnitudes as those in

columns 1 to 3.

5 Discussion and conclusion

This paper studies gender discrimination in the allocation of graduate scholarships and finds no ev-

idence of blunt gender discrimination: the gender of the average candidate has no impact on his/her

score and on average male/female evaluators do not give a preferential treatment to candidates of

their own gender. This paper does however find some subtle forms of gender discrimination.

First, there is evidence that the presence of a majority of female evaluators affects male evalu-

ators. In a position of minority, the male evaluator may feel the need to protect a male candidate

from possible discrimination from female evaluators or they may want to express his support for

his male identity in a situation of minority (Akerlof and Kranton, 2000). Second, the gender com-

position of a discipline affects the scoring decisions of evaluators within those disciplines. I show

that female evaluators in male-dominated disciplines give lower scores to female candidates than

do females in more gender balanced disciplines.

Third, gender discrimination seems to affect candidates in the tails of the distribution. I find

that male evaluators give higher scores than females to relatively strong male candidates 18. This

17This variable is defined as the lowest score of a student who received a scholarships in a subcommittee18Similarly, female evaluators may be particularly hard on strong male candidates. A dummy variable can only

inform us that there is a difference, it cannot tell us who is being generous and who is being harsh.

15

result is consistent with evaluators identifying with candidates along gender lines. Evaluators

were doctoral candidates 10 or 20 years ago, and may see themselves in the candidates they are

evaluating, just like mentors can see proteges as younger versions of themselves (Humberd and

Rouse, 2015). Evaluators would identify with relatively stronger candidates, because they probably

were probably strong candidates when they applied for scholarships19. Interestingly, this type of

identification is only present for male evaluators perhaps because male candidates are in a minority

position.

I also find that female evaluators give lower scores than male evaluators to relatively weaker fe-

male candidates. These results are consistent with a variation of the Queen Bee syndrome (Staines,

Jayaratne and Tavris, 1973) which is used to characterise a woman in a position of authority who

treats female subordinates more critically. There is no overall evidence of this syndrome in the

sample, but it is notsurprising to find evidence of this type of syndrome for relatively weaker

female candidates who may not stand up to the standards of academically distinguished female

evaluators.

Finally, gender bias in the scoring decisions also has an impact on the allocation of scholar-

ships. Male (female) candidates facing a subcommittee in which evaluators of their gender are in

a minority have a higher probability of receiving a scholarship. The fact that male evaluators give

higher scores to male candidates when they are the only male evaluator, the fact that female eval-

uators from male-dominated disciplines give lower scores to female candidates and the fact that

female evaluators are particularly harsh on relatively weaker female candidates20 could explain the

impact of gender composition on the scholarship granting decision.

These results provide very little evidence that gender quotas would help the representation

of women in academia. First, if a larger presence of female evaluators makes male evaluators

more sexist, gender quotas could lead male evaluators to develop gender bias and ultimately have

19It would be interesting to further research this topic, and identify evaluators who received scholarships as graduatestudents and those who didn’t. Perhaps evaluators who did not receive a scholarship would identify with relativelyweaker looking candidates who could have a lot of potential, just like themselves. Unfortunately, without knowing theidentity of the evaluators, this research is impossible in the present project.

20One must keep in mind that 54% of candidates receive a scholarship, meaning that candidates close to the fundingthreshold are relatively weak.

16

a negative impact on female candidates. Second, if policy-makers implement gender quotas in

male-dominated disciplines to favour female candidates in these disciplines, my result suggests

that such a policy will hurt female candidates. Third, if a large proportion of candidates receive

the scholarship and that marginal candidates – those close to the funding threshold – are relatively

weak, female evaluators will give lower scores to female candidates than do male evaluators.

On a more positive note, gender quotas could be effective under two conditions. First, if policy-

makers provide male evaluators evidence that female evaluators are not biased against male candi-

dates, male evaluator may not feel the need to give higher scores to male candidates when they are

the only male in the subcommittee. Second, if the marginal candidates – those close to the funding

threshold – are relatively strong, female evaluators will not give female candidates lower scores

and there will be fewer male evaluators who will give higher scores to male candidates.

17

6 BibliographyAkerlof, G. A., Kranton, R. E. (2000). Economics and identity. Quarterly journal of Economics,715-753.

Bagues, M. F., Esteve-Volart, B. (2010). Can gender parity break the glass ceiling? Evidencefrom a repeated randomized experiment. The Review of Economic Studies, 77(4), 1301-1328.

Bagues, M., Sylos-Labini, M. and Zinovyeva, N. (forthcoming). Does the Gender Compositionof Scientific Committees Matter?, American Economic Review.

Blau, F. D., Currie, J. M., Croson, R. T., Ginther, D. K. (2010). Can Mentoring Help FemaleAssistant Professors? Interim Results from a Randomized Trial. American Economic Review,100(2), 348-52.

Canadian Association of University Teachers CAUT (2014). CAUT Almanach of Higher Edu-cation 2013-2014, table 2.12.

Ceci, S. J., Williams, W. M. (2011). Understanding current causes of women’s underrepresen-tation in science. Proceedings of the National Academy of Sciences, 108(8), 3157-3162.

Chandler, V. (2016). Short and Long-term Impacts of an Increase in Graduate Funding. Work-ing paper.

European Commission (2016), She Figures 2015: Gender in Research and Innovation, Luxem-bourg: Publications Office of the European Union.

Humberd, B., Rouse, E. (2016). Seeing you in me and me in you: Personal identification inthe phases of mentoring relationships. Academy of Management Review, 41(3), 435-455.

Moss-Racusin, C. A., Dovidio, J. F., Brescoll, V. L., Graham, M. J., Handelsman, J. (2012).Science facultys subtle gender biases favor male students. Proceedings of the National Academyof Sciences, 109(41), 16474-16479.

National Research Council (2009), Gender Differences at Critical Transitions in the Careers ofScience, Engineering, and Mathematics Faculty, Washington D.C.: The National Academies Press.

Niederle, M., Vesterlund, L. (2007). Do women shy away from competition? Do men competetoo much?. The Quarterly Journal of Economics, 1067-1101.

Small, D. A., Gelfand, M., Babcock, L., Gettman, H. (2007). Who goes to the bargainingtable? The influence of gender and framing on the initiation of negotiation. Journal of personalityand social psychology, 93(4), 600.

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Figure 1: Distribution of Individual Scores

Staines, G. Tavris. C. Jayaratne, TE (1973). The Queen Bee Syndrome. The Female. Experi-ence edited by Tavris, C. CRM Books. Del Mar. CA.

Statistics Canada (2011). Education in Canada: Attainment, Field of Study and Location ofStudy. Accessed at: https://www12.statcan.gc.ca/nhs-enm/2011/as-sa/99-012-x/99-012-x2011001-eng.cfmon December 30th 2016.

Williams, W. M., Ceci, S. J. (2015). National hiring experiments reveal 2: 1 faculty preferencefor women on STEM tenure track. Proceedings of the National Academy of Sciences, 112(17),5360-5365.

Wold, A., Wenners, C. (1997). Nepotism and sexism in peer review. Nature, 387(6631), 341-343.

7 Figures

8 Tables

19

Figure 2: Distribution of Total Scores

Table 1: Disciplines by CommitteeFirst committee Fine arts, literature (all types)

Second committee Classical archaeology, classics, classical and dead languages,history, mediaeval studies, philosophy, religious studies

Third committee Anthropology, archaeology (except classical archaeology),archival science, communications and media studies,criminology, demography, folklore, geography,library and information science, sociology,urban and regional studies, environmental studies

Fourth committee Education, linguistics, psychology, social work

Fifth committee Economics, industrial relations, law, management,business, administrative studies, political science

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Table 2: Distribution of students by first letter of last name - Committee 5Subcommittees 2004 Subcommittees 2005

First letter of last name 1 2 3 1 2 3of candidateA 6 0 0 4 0 2B 10 1 0 21 0 1C 9 0 1 12 0 1D 6 0 0 5 0 3E 3 0 0 0 0 0F 4 1 0 2 3 2G 0 2 1 2 12 1H 0 4 0 0 4 0I 0 0 0 0 1 0J 0 3 0 0 0 0K 0 3 0 1 5 1L 0 12 2 1 8 0M 0 15 4 1 8 4N 0 0 2 1 0 2O 0 0 3 0 0 0P 1 0 2 0 1 9Q 0 0 0 0 0 0R 0 0 8 0 0 7S 1 0 8 0 2 9T 1 0 2 0 1 2U 0 0 0 0 0 0V 0 0 4 0 1 0W 0 0 4 0 0 4X 0 0 0 0 0 0Y 0 0 1 0 0 2Z 0 0 0 0 0 0Total 41 41 42 50 46 51

Note: This table reflects only information concerning candidates who received and accepted a scholarship. I onlyhave information on the last name for these candidates. The students who are not allocated to the subcommitteecorresponding to the first letter of their last name represent one of the two exceptions: 1) evaluators cannot assessstudents from their university and 2) some subcommittees may not be able to assess applications written in French.

Table 3: Gender Distribution of Candidates, Recipients and EvaluatorsGender Candidates Recipients EvaluatorsMale 1,310 707 55

Female 2,190 1,193 50Total 3,500 1,900 105

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Table 4: Distribution of Candidates and Evaluators by DisciplineDiscipline Number of Candidates Number of Evaluators Share of Male in GeneralAnthropology 132 3 48.2Archaeology 53 2 75Classics 39 0 68.3Communications 100 4 58.0Criminology 28 1 54.7Demography 13 0 .Economics 86 3 78.9Education 278 9 42.6Fine Arts 185 6 57.2Folklore 12 0 .Geography 85 3 69.8History 301 10 62.8Industrial Relations 9 0 .Interdisciplinary 89 2 55.3Law 79 5 55.2Library and Info. Science 17 2 37.5Linguistics 81 7 45.0Literature 467 13 48.9Management 95 3 64.1Mediaeval Studies 15 0 .Philosophy 192 7 71.4Political Science 233 7 68.9Psychology 479 7 54.3Religious Studies 103 4 65.6Social Work 40 2 35.6Sociology 228 4 51.0Urban & Regional Studies 61 1 56.6Total 3,500 105

Note: The column “Share of Male in General” shows the share of male in the discipline as shown in Caut (2014). Forsome small disciplines, it was impossible to determine the share of males. Notice that there is no evaluator from thesesmall disciplines.

Table 5: Number of Candidates by Type of SubcommitteeType of subcommittee Number of candidates Number of subcommittees3 female evaluators 250 2

2 female evaluators 1,441 16

1 female evaluator 1,340 13

No female evaluator 469 4Total 3,500 35

22

Table 6: Gender Representation of Evaluators by Order of Subcommittee1 2 3 4 Total

Male Majority 4 4 7 2 17Female Majority 6 6 3 3 18Total 10 10 10 5 35

Note: Candidates in a given committee are allocated to a certain subcommittee based on the first letter of their lastname. This table shows that there does not seem to be any correlation between the order of subcommittees and theshare of male/female evaluators. In the 2004 competition, only the fourth committee had 4 subcommittees. In the2005 competition, all committees had 4 subcommittees except the fifth one.

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Table 7: Impact of Gender of Candidates and Evaluators on Scores - Focus Females(1) (2) (3) (4) (5) (6)

Female Cand. 0.0708 0.0636 0.0693 0.0235 0.0474 0.00177(0.357) (0.398) (0.368) (0.780) (0.550) (0.984)

Female Eval. -0.0323 -0.0322 -0.0330 -0.0621 -0.0322 -0.0585(0.610) (0.612) (0.605) (0.427) (0.627) (0.475)

Fem. Eval.*Fem. Cand. 0.0130 0.0521 -0.0282 0.0876 -0.0292 0.0834(0.860) (0.493) (0.707) (0.326) (0.697) (0.355)

Fem.*Fem.*One Fem. Eval. -0.0960(0.388)

Same Discipline 0.0270 0.0269(0.664) (0.679)

Fem.*Fem.*Same Discipline 0.137 0.129(0.101) (0.125)

Low Fem. Rep. Discipline -0.0576 -0.0481(0.414) (0.513)

Fem. Eval.*Low Fem. Rep. 0.101 0.0923(0.359) (0.419)

Fem. Cand.*Low Fem. Rep. 0.159 0.161(0.137) (0.142)

Fem.*Fem.*Low Fem. Rep. -0.347∗∗ -0.350∗∗

(0.015) (0.014)

Year Dummies No No No No Yes Yes

Discipline Dummies No No No No Yes Yes

Subcommittee Dummies No No No No Yes Yes

Constant 5.996∗∗∗ 5.996∗∗∗ 5.988∗∗∗ 6.018∗∗∗ 6.330∗∗∗ 5.680∗∗∗

(0.000) (0.000) (0.000) (0.000) (0.000) (0.000)N 10500 10500 10500 10500 10500 10500p-values in parentheses∗ p < 0.10, ∗∗ p < 0.05, ∗∗∗ p < 0.01

These models explain the score received by candidate “i” from evaluator “j” using random effects. Eachcandidate receives three scores. The variable “Fem.*Fem.*One Fem. Eval.” takes the value 1 only if afemale candidate is assessed by a female evaluator who is the only female evaluator in the subcommittee.The variable “Fem*Fem*Same Discipline” takes the value 1 only if a female candidate is assessed by afemale evaluator who is from her discipline. The variable “Fem*Fem*Low Fem Rep” takes the value 1only if a female candidate is assessed by a female evaluator who comes from a discipline in which womenrepresent 35% or fewer of professors (archeology, classics, economics, geography, philosophy, politicalscience and religious studies). The standard errors are clustered at the subcommittee level (35).

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Table 8: Scores given by Female Evaluators from Disciplines with Low Female Representation toFemale Candidates: Robustness Check

(1) (2) (3) (4) (5)61% Male 63% Male 65% Male 67% Male 69% Male

Female Candidate 0.0799 0.0222 0.0235 0.0353 0.0688(0.391) (0.804) (0.780) (0.662) (0.408)

Female Evaluator 0.0120 -0.0673 -0.0621 -0.0551 -0.0281(0.880) (0.423) (0.427) (0.464) (0.694)

Fem. Cand. * Fem. Eval. 0.00455 0.0895 0.0876 0.0685 0.0259(0.963) (0.345) (0.326) (0.429) (0.757)

Low Fem. Rep. Discipline 0.0426 -0.0611 -0.0576 -0.0701 0.00471(0.613) (0.401) (0.414) (0.305) (0.941)

Fem. Eval.*Low Fem. Rep. -0.101 0.106 0.101 0.0836 -0.0232(0.447) (0.348) (0.359) (0.457) (0.855)

Fem. Cand.*Low Fem. Rep. -0.00442 0.136 0.159 0.132 0.00846(0.969) (0.166) (0.137) (0.284) (0.935)

Fem.*Fem.*Low Fem. Rep. -0.0364 -0.328∗∗ -0.347∗∗ -0.296∗ -0.115(0.822) (0.018) (0.015) (0.059) (0.501)

cons 5.973∗∗∗ 6.023∗∗∗ 6.018∗∗∗ 6.017∗∗∗ 5.995∗∗∗

(0.000) (0.000) (0.000) (0.000) (0.000)N 10500 10500 10500 10500 10500p-values in parentheses∗ p < 0.10, ∗∗ p < 0.05, ∗∗∗ p < 0.01

Note: This table provides robustness to the results in columns 4 and 6 of table 7. It shows the coefficients for thevariable “Fem*Fem*Low Fem Rep” which takes the value 1 if a female evaluator from a discipline with a low shareof female professors assesses a female candidates. Column 1 shows the results when “low” is defined as less than31%, column 2 shows the results when “low” is defined as less than 33% etc. Standard errors are clustered at thesubcommittee level.

25

Table 9: Impact of Gender of Candidates and Evaluators on Scores - Focus on Males(1) (2) (3) (4) (5) (6) (7)

Male Cand. -0.0829 -0.0572 -0.0852 -0.0807 -0.0302 -0.0596 -0.0567(0.138) (0.315) (0.128) (0.153) (0.610) (0.298) (0.324)

Male Eval. 0.0194 0.0194 0.0198 0.0213 0.0164 0.0227 0.0242(0.636) (0.636) (0.620) (0.598) (0.713) (0.591) (0.580)

Male*Male 0.0115 -0.0947 0.0560 -0.0268 -0.117 0.0489 -0.0340(0.876) (0.321) (0.516) (0.757) (0.250) (0.576) (0.699)

Male*Male*1 Male Eval. 0.248∗∗ 0.286∗∗

(0.036) (0.034)

Same Discipline 0.0990 0.0940(0.120) (0.142)

Male*Male*Same Disc. -0.140 -0.129(0.137) (0.168)

High Male Rep. Disc. 0.0151 0.0225(0.607) (0.469)

Male*Male*High Male Rep. -0.0717 -0.0763(0.418) (0.409)

Year Dummies No No No No Yes Yes Yes

Discipline Dummies No No No No Yes Yes Yes

Subcommittee Dummies No No No No Yes Yes Yes

Constant 6.047∗∗∗ 6.047∗∗∗ 6.017∗∗∗ 6.055∗∗∗ 5.702∗∗∗ 5.665∗∗∗ 5.696∗∗∗

(0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000)N 10500 10500 10500 10500 10500 10500 10500p-values in parentheses∗ p < 0.10, ∗∗ p < 0.05, ∗∗∗ p < 0.01

These models explain the score received by candidate “i” from evaluator “j” using random effects.Each candidate receives three scores. The variable “Male*Male*Only 1 Male Eval.” takes the value 1if a male candidate is assessed by a male evaluator who is the only male evaluator in the subcommitteeand otherwise 0. The variable “Male*Male*Same Disc” takes the value 1 if a male candidate is assessedby a male evaluator and both share the same discipline, otherwise it takes the value 0. The variable“male*male*High Male Rep.” takes the value 1 if a male candidate is assessed by a male evaluator whosediscipline has a high (more than 65%) proportion of male professors (archeology, classics, economics,geography, philosophy, political science and religious studies). The standard errors are clustered at thesubcommittee level (35).

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Table 10: Impact of Gender on Scores using Fixed Effects(1) (2) (3) (4)

Same Gender Dummy 0.00546(0.867)

Male Cand.*Male Eval. 0.0649(0.246)

Female Cand.*Fem. Eval. -0.0291(0.468)

Male*Male*Only 1 Male Eval. 0.151∗∗

(0.043)

Male*Male*Many Male Eval. -0.0236(0.771)

Female*Female*Only 1 Fem. Eval. -0.0245(0.668)

Female*Female*Many Fem. Eval. -0.0333(0.466)

Male*Male*Total Score Above 24 0.439∗∗∗

(0.005)

Male*Male*Total Score Below 24 0.0202(0.734)

Female*Female*Total Score Above 14 0.0163(0.718)

Female*Female*Total Score Below 14 -0.228∗∗∗

(0.008)

Constant 6.026∗∗∗ 6.024∗∗∗ 6.035∗∗∗ 6.023∗∗∗

(0.000) (0.000) (0.000) (0.000)N 10,500 10,500 10,500 10,500r2 0.00000470 0.000333 0.000787 0.00223p-values in parentheses∗ p < 0.10, ∗∗ p < 0.05, ∗∗∗ p < 0.01

The dependent variable is the score given by evaluator “j” to candidate “i”. All independent variables are dummies.The variable “Same Gender Dummy” takes the value 1 if both evaluator and candidate have the same gender andotherwise 0. The variable “Male Cand.*Male Eval.” takes the value 1 if a male candidate is evaluated by a maleevaluator and otherwise 0. The variable “Male *Male*Only 1 Male Eval. ” takes the value 1 if a male candidate isevaluated by a male evaluator who is only male in the subcommittee and otherwise 0. The variable “Male*Male*TotalScore Above 24” takes the value 1 if a male candidate with a total score at or above 24 is evaluated by a male evaluator.All models use candidate fixed effects (3,500). The standard errors are clustered by subcommittees (35). The averagetotal score is 18.1 and the distribution of total scores is shown in figure 2.

27

Table 11: Robustness of Gender Discrimination in the Tails with Different ThresholdsThresholds to Define “Above” and “Below” for the Total Score of a Candidate

13.5 14.5 15.5 16.5 20.5 21.5 22.5 23.5 24.5(1) (2) (3) (4) (5) (6) (7) (8) (9)

Male*Male*Above 0.0709 0.0920 0.0945 0.137∗ 0.203∗ 0.226∗ 0.274∗∗ 0.408∗∗ 0.380∗

(0.181) (0.100) (0.126) (0.074) (0.054) (0.051) (0.045) (0.016) (0.065)

Male*Male*Below 0.0382 -0.0246 -0.00498 -0.0473 0.00278 0.00744 0.0148 0.0147 0.0329(0.803) (0.865) (0.967) (0.692) (0.974) (0.919) (0.834) (0.835) (0.618)

Female*Female*Above 0.00849 0.0129 0.0148 0.0106 0.0440 0.0405 0.0106 -0.0234 0.0220(0.842) (0.779) (0.785) (0.858) (0.652) (0.726) (0.938) (0.883) (0.901)

Female*Female*Below -0.228∗∗ -0.178∗∗ -0.135∗∗ -0.0910 -0.0634 -0.0518 -0.0390 -0.0310 -0.0352(0.018) (0.027) (0.045) (0.145) (0.142) (0.185) (0.312) (0.407) (0.332)

Constant 6.024∗∗∗ 6.024∗∗∗ 6.024∗∗∗ 6.024∗∗∗ 6.025∗∗∗ 6.025∗∗∗ 6.025∗∗∗ 6.025∗∗∗ 6.025∗∗∗

(0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000)N 10,500 10,500 10,500 10,500 10,500 10,500 10,500 10,500 10,500r2 0.00110 0.00111 0.000925 0.00106 0.00109 0.00104 0.000992 0.00135 0.000959p-values in parentheses∗ p < 0.10, ∗∗ p < 0.05, ∗∗∗ p < 0.01

28

Table 12: Impact of Having a Minority of Evaluators of One’s with the Same Gender as Candidateon the Probability of the Candidate Receiving a Scholarship

Scholarship Above Funding Threshold(1) (2) (3) (4) (5) (6)

Minority Own Gender Dummy 0.0802∗∗ 0.0898∗∗ 0.158∗∗ 0.0997∗∗∗ 0.109∗∗∗ 0.114∗∗

(0.033) (0.019) (0.011) (0.004) (0.002) (0.015)

Male Candidate Dummy -0.00548 0.000592 0.00169 0.00962 0.00878 0.00490(0.730) (0.969) (0.929) (0.586) (0.607) (0.779)

Year of Study Dummies (4) X X X X X X

Discipline Dummies (27) X X X X

Subcommittee Dummies (35) X X

Constant 0.460∗∗∗ 0.422∗∗∗ 0.397∗∗∗ 0.572∗∗∗ 0.599∗∗∗ 0.656∗∗∗

(0.000) (0.000) (0.000) (0.000) (0.000) (0.000)N 3500 3500 3500 3500 3500 3500r2 0.0116 0.0274 0.0612 0.0133 0.0274 0.0630p-values in parentheses∗ p < 0.10, ∗∗ p < 0.05, ∗∗∗ p < 0.01

Note: All regressions are linear probability models. Models 1 to 3 explain whether a candidate received ascholarship, and models 4 to 6 explain whether a candidate was above the funding threshold. The difference isimportant, because some candidates may have be granted a scholarship, but declined it. Minority Own GenderDummy takes the value 1 if there is none or one evaluator in the subcommittee who has the same gender as thecandidate and it takes the value 0 if there are two or three evaluators in the subcommittee who share the same genderas the candidate. The standard errors are clustered at the subcommittee level.

29