Women in European Economics∗

Emmanuelle Auriol† Guido Friebel‡ Sascha Wilhelm§

First draft, May 2019

Abstract

This paper presents results from the first comprehensive data collectionof all research institutions in economics in Europe. We built a web-scrapingtool that collects publicly available information about the proportion ofwomen in different positions in universities, business schools, and other in-stitutions. We find a similar picture as the one from the U.S. (which, how-ever, is gathered from surveys, and not web resources), but across Europeancountries and regions, there are substantial differences. We also documentthat institutions that rank higher in terms of their research output tend tohave less women in senior positions, in line with the “leaky pipeline” hy-pothesis. Moreover, we also find that higher ranked institutions also tendto have less women on the junior level. We suggest avenues for further datacollection and research.

Keywords: Gender equality, academic hierarchies, leaky pipeline

∗We would like to thank Christian Zimmermann for providing data from RePEc’s databaseand the European Economic Association, in particular, the Women in Economics Committeeand its members, seminar and conference audiences. Moreover, we thank Pascal Scheu, TristanStahl, and Alisa Weinberger for excellent research assistance.

†Toulouse School of Economics (University of Toulouse I) and CEPR. E-mail: emmanuelle.auriol@tse-fr.eu‡Goethe University Frankfurt, CEPR, and IZA. E-mail: gfriebel@wiwi.uni-frankfurt.de§Goethe University Frankfurt. E-mail: wilhelm@econ.uni-frankfurt.de

1 Introduction

In many realms of society, and, in particular, in key positions such as top manage-ment, politics, and science, women are still under-represented. These professionsrequire high skills and effort, but career outcomes are very risky. One of theseprofessions, which has recently received a fair amount of interest, is the one ofacademic economists (Lundberg and Stearns, 2019). In the U.S., for instance, in2017, only 13.9 percent of full professors were female (CSWEP, 2017). This lowlevel could be explained by exogenous differences in taste between genders becausewomen are under-represented at the undergraduate level already with less than30% of the bachelor degrees in economics in the U.S. being awarded to women.However, conditional on the fact that they study economics, more women start aPh.D. than men, and women also tend to complete their Ph.D. more often thanmen. In other words, women are initially less attracted to economics than men,but when they choose to study it they tend to succeed better up to the Ph.D.level.1 Over the last decade, between 30% and 35% of Ph.D.s in economics in theU.S. have hence been earned by women (CSWEP, 2017).

The big gap between the percentage of women holding a Ph.D. (roughly one third)and those who eventually are promoted to full professors (less than 15 percent)has been interpreted as evidence for a “leaky pipeline”, in which, over the differentstages of a career, the attrition of women is higher than the one of men.2 This gapcould be due to cohort effects, a lag effect between the time of Ph.D. completionand the time of promotion to full professorship. However, the gap has been stableover the last two decades making cohort effects appear unlikely. The puzzle ofthe persistence of the leaky pipeline has therefore attracted a lot of research andmedia attention lately.3

1This is true also in Britain, where undergraduate women who stay in economics get bettergrades than their male classmates (The Economist “Women and economics”, Print edition |Christmas Specials 2017 by Soumaya Keynes).

2For instance, in 2017 in the U.S., new doctorates in economics were 32.9% female, fallingto 28.8% for assistant professors, to 23.0% for tenured associate professors and to 13.9% for fullprofessors (CSWEP, 2017).

3See for instance the various papers in The New York Times and the Financial Times as wellas in The Economist:The New York Times (2019): "Female Economists Push Their Field Toward a ]#MeToo

Reckoning"The New York Times (2018): "Why Women’s Voices Are Scarce in Economics"The New York Times (2017): "Evidence of a Toxic Environment for Women in Economics"The New York Times (2018): "Star Economist at Harvard Faces Sexual Harassment Com-

plaints"

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An important concern with the leaky pipeline hypothesis is that most of its evi-dence comes from the U.S. The situation could well be different in other regions ofthe world, for taste reasons, existence of norms, or more female-friendly policies.Existing studies suggest a leaky pipeline in Europe too, as under-representationof women in tenured positions has been found in Sweden (Persson, 2003), in Italy(Corsi et al., 2014) and in the United Kingdom (Blackaby et al., 2005), but welack systematic evidence.

Our paper seeks to provide an answer to the question of whether the situation inEurope is similar to the U.S. or whether there are important differences. Second,we aim to investigate to what extent there are important differences within Europe.One common a priori is that in the Nordic countries and maybe the Beneluxcountries, there are more women in academic careers because of different normsand different social policies. Third, we want to study when exactly the leakypipeline starts. This is important for policy purpose and to understand whichmechanisms are at hand. Most studies on the topics have focused on showing theexistence of a glass ceiling in promotion to full professor and a wage gap for femaleeconomist, and more recently also started to explore its origin.

The data set and the underlying technicalities are described in detail in Friebeland Wilhelm (2019). The most important facts are as follows: We designed analgorithm to monitor on a daily basis all known URLs of European institutionsthat contribute to research in economics. The algorithm identifies the individu-als listed on these websites and, where available, records the position titles theseindividuals hold. Gender is identified through first names, and a gender identifi-cation software analyzing pictures of the individuals. For the top 300 Europeanresearch institutions (in terms of research output in RePEc), these algorithms arecomplemented by our additional research classifying the obtained position titlesinto a generally accepted hierarchy of positions. Finally, we contacted the peopleresponsible for managing the institutions and websites to verify the results of ourwork and provide us with feedback.

RePEc4, a bibliographic database, provided us with a dataset of 4,414 institutions’contributing to the economic literature until December 2017. We manually iden-

The New York Times (2015): "Even Famous Female Economists Get No Respect"Financial Times (2018): "Where are all the female economists?"Financial Times (2019): "The way to fix bias in economics is to recruit more women"4Accessible by http://repec.org/.

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tified all the institutions’ websites containing a summary of affiliated researchers.5

Importantly, we rely on RePEc’s definition of institutions contributing to the fieldof economics. Therefore, in the data set, we do not only have institutions that pri-marily contribute to economics but also to neighboring research areas like finance,management, marketing or psychology.

Our data reveal that in Europe, there is a leaky pipeline too. In general, andin all countries, the proportion of female researchers on all levels is much higherthan on the full professor level. There are substantial differences though, withthe Nordic countries and France scoring much higher on gender equality than, forinstance, Germany and the Netherlands. Partly, this may be owing to historicaland institutional reasons (the formerly socialist countries, for instance, score par-ticularly high, possibly for historical reasons, economics being a rather “female”occupation during socialist times). Partly, this may however also be driven byother factors, such as recruitment policies related to the ranking of the researchinstitution, which we measure through research output from RePEc.

Comparing the better ranked half of the top 300 institutions in terms of researchoutput with the lower ranked half, we find that at full professor level, the betterinstitutions have fewer female researchers. This could be interpreted as evidencefor the leaky pipeline – women do not get promoted, move to less prestigious in-stitutions, or drop out because of the double burden of family and work. However,we also find on the junior (entry) level, the more prestigious research institutionsin Europe hire significantly less women than the less prominent institutions, sug-gesting that the leaky pipeline is only one part of the story, or that the leakypipeline may start much earlier than it is usually considered: at the transitionbetween graduation and the first job. To the best of our knowledge, this resulthas never been documented in the literature. We hypothesize in the conclusion ofthis paper about what may be going on to prepare the stage for a deeper under-standing of the persistence of female under-representation in an important sectorof the research landscape.

We hope that our data set and these first results will prove to be helpful andinteresting not only for the research community, but also for university presidents,deans and chairpersons. We also wish to contribute to a better informed debate

5Some institutions do not provide a comprehensive overview of researchers and cannot bemonitored. In the majority of cases, these institutions are inactive or not related to the academicresearch.

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about the state of women in European economics and what could and should bedone about it.

We first summarize the existing knowledge, and then present an overview by coun-tries. We next discuss some descriptive statistics and simple regressions about thedifference between the higher and lower ranked research institutions. A shortdiscussion about open questions concludes.

2 What Do We Know so Far?

The literature on gender balance in the economics profession is mainly focused ondocumenting and explaining the leaky pipeline between junior and senior ranks.A first set of studies rely on microdata to test for gender differences in promotionor salary. They typically predict a probability of promotion or the amount ofsalary based on observable, including a sex dummy. They usually find that partof the wage or promotion gap can be explained when controlling for observedcharacteristics, unobserved heterogeneity and self-selection. Nevertheless, somegender differences remain unexplained (Kahn, 1995; Broder, 1993; McDowell et al.,1999, 2001; Ward, 2001; Bandiera, 2016).

One could think that the gender gap in promotion to tenure is not specific toeconomics and applies to all fields of science, which is true to some extent. Yet,Ginther and Khan (2014) show that the gender gap in tenure and promotion tofull professor rates in economics, 20% and 50% respectively, are much greater (byalmost the double) than those in the social sciences overall. Since economics relieson analytical skills, and the mastering of mathematics and statistics, the gendergap could reflect some general bias in science. Ginther and Khan (2014) comparethe career path of men and women in engineering, statistics, physical sciences,life science, political science and economics. They control for many observableincluding publications and citation index. They show that, even after accountingfor differences in productivity (women in these disciplines publish on average lesspapers than men) and the effect of children on promotion, women in economicsare still substantially less likely to get tenure and take longer to achieve it thanmen and than women in other disciplines. Ceci et al. (2014) find that across mostfields with a heavy focus on math skills, “the research indicates no significantsex differences in promotion to tenure and full professor.” They conclude that

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“Economics is an outlier, with a persistent sex gap in promotion that cannot bereadily explained by productivity differences.” Moreover, Ceci et al. (2014) findthat female full professor salaries in economics as a proportion of male salariesdropped from 95 percent in 1995 to less than 75 percent in 2010. Unsurprisingly,women in economics are less happy than the men they work with, and less happythan women working in other disciplines. The gap is quite big and growing largerover time.

Even if this empirical evidence is compelling, it does not explain where the promo-tion/pay gap comes from. More recently, there has hence been a surge of papersaddressing the problem from different angles. These papers complement the classicliterature on promotion and wages determinants. They point out to a bias againstwomen in the economics profession. Some of them have made the headlines.

Milkman et al. (2015) conducted a field experiment study in academia. Over 6,500professors at top U.S. universities drawn from 89 disciplines and 259 institutionswere contacted by email by a fictional prospective student seeking to discuss re-search opportunities prior to applying to a doctoral program. Names of studentwere randomly assigned to signal gender and race (White, Black, Hispanic, Indian,Chinese), but messages were otherwise identical. Faculty were significantly moreresponsive to Caucasian males than to all other categories of students, particu-larly in higher paying disciplines and private institutions. Interestingly enough,the representation of women/minorities and discrimination were uncorrelated –a finding that suggests that both male and female evaluators are influenced bysocial stereotypes about women and minorities.

Wu (2017) mined more than a million posts from the Economics Job Market Ru-mors, an anonymous online message board frequented by economists, particularlyby junior ones, in search of tips for their career. After having determined the gen-der of the subject of each post, she applied machine-learning techniques to findout the terms most uniquely associated with posts about men and about women.The result is disturbing as it reveals strong gender stereotyping and sexism in aprofessional forum dedicated to higher education job market.6 And in fact, many

6The 30 words most uniquely associated with discussions of women are: hotter, pregnant,plow, marry, hot, marrying, pregnancy, attractive, beautiful, breast, dumped, kissed, misogynis-tic, feminist, sexism, dated, whore, sexy, raped, attracted, slept, blonde, unattractive, gorgeous,assaulted, cute, vagina, date, dating, ugly. For men there are: homo, testosterone, chapters,satisfaction, fieckers, macroeconomics, cuny, thrust, nk, macro, fenance, founding, blog, moun-tains, grown, frat, handsome, nba, lyrics, ferguson, wasn, supervisor, rfs, adviser, minnesota,hero, gay, Puerto, nobel, Keynesian.

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women in economics report experiencing inappropriate behavior in job interviews,seminars, meetings, and at conferences (Shinall, 2018).

Several papers show that women are held to higher editorial standards than men ineconomics. Krawczyk and Smyk (2016) show in a controlled lab experiment thatfemale-authored manuscripts are evaluated more critically by participants thanthose authored by men. Hengel (2017), using objective readability scores, showsthat female-authored abstracts in a top five journal are better written than equiv-alent papers by men and that the gap is almost two times higher in publishedarticles than in draft versions of the same papers. The paper also shows thatfemale-authored papers take half a year longer in peer review process, presumablybecause of higher scrutiny. Hengel (2017) concludes that the quantity/qualitytradeoff imposed by higher standards could partly explain the persistently lowerfemale productivity, especially if female economists update their beliefs about ref-erees’ standards and increasingly meet those standards before peer review. If itis true that women are held to higher standards so that on average their pa-pers are better written, then they should attract more citations. Grossbard et al.(2018) show that papers in demographic economics journals with female authorsreceive more citations. Similarly, Card et al. (2018), studying referee decisionsat four leading economics journals, show that, independently of the referees’ gen-der, female authors appear to be held to higher standards as measured by cita-tion counts than men, resulting in a substantial difference in the probability thatfemale-authored papers receive a revise and resubmit.

The higher standards for women seem to carry over to the tenure decision level.Sarsons (2015) investigated the effects of co-authorship on the probability to gettenure. After having collected data from the C.V.s of economists who were up fortenure between 1975 and 2014 at the top 30 Ph.D.-granting U.S. universities, sheshows that when researchers write papers on their own, women see their chancesof promotion rise by roughly the same amount as men do. However, while anadditional coauthored paper for a man has the same effect on the likelihood oftenure as a solo-authored paper, women suffer a significant penalty for coauthoringwhen their coauthors are men. To be more specific, when a man is co-authoring apaper, his chances of getting tenure rise by 8%, while for a woman this is only 2%.As a result, Sarsons (2015) finds that women are 17 percentage points less likelyto get tenure than men with similar publication records. As women update beliefsabout tenure committee standards, the latter reduces women’s output further as

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they have to refrain from working in teams if they want to increase their chances ofgoing through. And, in fact, Boschini and Sjögren (2007) find that women singleauthor significantly more than men in top journals (i.e., AER, QJE, JPE).

These results shed new light on the productivity gap and the leaky pipeline inpromotion puzzles. First, women have a lower publication count than men, whichseems to be in part due to the fact that they are held to higher standards. Second,their coauthored publications do not count fully for their promotion.

If it was true that women are discriminated against, a cynical question is: whyshould we care (apart for the obvious fairness concern)? There are two mainreasons. First, if mainly the male ability distribution is considered in the employ-ment/promotion situation, the female potential is neglected and, therefore, facultywill tend to be hired relatively low in the male ability distribution which meansthat universities forego or loose potentially abler employees. Second, if the topicsfavored by women in research are different than those favored by men, the weakrepresentation of women in the most prestigious and powerful positions impliesless means dedicated to these topics and less publicity around the results. Now, ifthese topics are relevant to society, it would mean that we systematically underin-vest in them which will reduce their policy impact. May et al. (2013), looking ata survey of 143 AEA members with doctoral degrees from US institutions, foundthat male and female economists have different views on economic outcomes andpolicies, even after controlling for the year of Ph.D. and type of employment7.They find similar results among European economists. Since opinions about pol-icy vary with gender, the lack of women biases the prevailing range of views amongeconomists and policy makers. Similarly, the topics chosen by women in researchare statistically different from those picked by men. Focusing on articles pub-lished in top three economics journals (AER, JPE, QJE), Boschini and Sjögren(2007) find large differences in the share of women across research fields: “femalepresence is roughly three times higher in Health, Education, and Welfare thanin Macroeconomics and Monetary Economics”.8 More generally looking at Ph.D.JEL code by gender, Lundberg and Stearns (2019) find that women in the US are

7Female economists are 21 percentage points more likely to disagree that the U.S. has excessivegovernment regulation, 32 percentage points more likely to agree with making the distributionof income more equal, 30 percentage points more likely to agree that the United States shouldlink import openness to labor standards, 42 percentage points more likely to disagree that labormarket opportunities are equal for men and women (May et al., 2013).

8A similar pattern has been found by (Corsi et al., 2014) on the universe of Ph.D. defendedin Italy between 2003 and 2006, which is far less elitist than the focus on top three economicsjournals.

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more likely than men to study topics in labor and public economics and less likelyto study topics in macro and finance, and this difference is stable over the period1990-2017.

3 The Situation in European Economics

While literature on gender distributions in U.S. universities is available for morethan the last decade in the report of Lundberg and Stearns (2019), informationon the number of females in European research institutions are not available. Wesegment our results by hierarchical levels and by countries in the next two subchapters.

The following figures and tables provide an overview over the proportion of fe-males in all academic positions of all European institutions. After web-scrapingthe URLs, we carefully separated the data entry of non-academic from academicstaff, and then translated the multitude of different titles (more than 1000) into asimple hierarchy of positions in descending order: (Full) Professor, Associate Pro-fessor, Assistant Professor, Lecturer, Research Fellow, Research Associate. Thesedistinctions are at times blurred, and the titles are often language- and institution-specific giving rise to some ambiguity. To provide a few examples: the positionMaître de Conférences in France is a tenured position at the entry level, hence com-parable to an assistant professor or lecturer. Some researchers, however, translatethe title into associate professor. In turn, lecturers can be members of faculty orbe adjunct faculty. Research fellows represent researchers who are full-time active,for instance in the French CNRS, or represent emeritus or part-time researchers.

Almost inevitably, this leads to imperfect compatibility, but we have done our bestto bring down measurement errors wherever possible. Most importantly, whereverpossible for the top 300 institutions, we contacted the persons responsible forthe departments, and sent them a clickable list of the positions and persons weidentified, asking them to verify what we found. A large proportion of the personswe contacted responded, and we changed the data according to their requests.Hence, while the data may be subject to some remaining measurement error, weare confident that the big picture is quite accurate.

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Table 1: Female Position Share Across Europe

Hierarchical Level All Top 300 Top 100Research Associate 40.7% 38.8% 38.1%Entry Level 40.4% 39.7% 38.8%Associate Professor 37.0% 36.4% 38.5%Research Fellow 37.5% 33.3% 33.0%Professor 22.1% 21.5% 21.7%Total 34.3% 33.0% 32.8%

3.1 Broken Down by Levels

Table 1 lists the share of females across Europe by hierarchical levels. Further-more, we present information on the entire population of institutions, and on thetop 300 and top 100 institutions in each country, respectively, to compare ournumbers with the results of Lundberg and Stearns (2019), who analyze positioninformation on 127 institutions. The authors report a female’s share of 31.7%for research associates.9 In comparison, European institutions contain a highershare of females in their research institutions of 38.1% and 40.7%, depending onthe analyzed ranking sample. For full professors, Lundberg and Stearns (2019)report a female share of 13.1% while more than every fifths professor is female inEuropean institutions. Some of this, in comparison to the U.S. surprisingly, highnumber of females, originates from Russian institutions that consist of relativelymany females and do also have a huge impact on the results as their faculties areone of the largest of European institutions. Therefore, we exclude Russian insti-tutions in table 2. The exclusion decreases the level of females for all levels. Thepattern that better-ranked institutions employ fewer females and that males holdhigher hierarchical positions intensives, however. This raises the question whetherself-selection effects, the “leaky pipeline” story, or even discrimination drive thisresult. Therefore, we discuss these explanations in the regression analysis section.

9The equivalent title for research associates is Ph.D. student in the analysis of Lundberg andStearns (2019).

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Table 2: Female Position Share Across Europe (Russia Excluded)

Hierarchical Level All Top 300 Top 100Research Associate 39.4% 37.2% 35.3%Entry Level 39.3% 38.2% 34.5%Associate Professor 34.5% 33.4% 31.7%Research Fellow 35.5% 29.7% 26.7%Professor 21.4% 20.6% 19.7%Total 32.9% 31.0% 28.4%

3.2 Results by Country

Figure 1 depicts an interesting situation that is straightforward to describe. First,Eastern European countries tend to have the highest proportion of females in re-search institutions (Poland, Russia, and Romania). However, it should be notedthat among the Top 300, there are only four Russian and two Romanian institu-tions. Austria, the Czech Republic, Germany, Greece, Hungary, and the Nether-lands, representing more than a quarter number of institutions in our data set,have lower female representation than in the rest of Europe. In Western andNorthern Europe, more than one third of the positions are held by women, whilein the other countries it is a quarter to 30%. We include a list of the top 300institutions and their statistics, which is also available on the website.

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Figure 1: Proportion of Females in All Academic Positions

The next figure plots the proportion of females in (full) professor positions andshows a situation in line with what would be expected from the leaky pipelinehypothesis.

It appears that in almost all countries, the proportion of females is much lower onthe full professor level than on all academic levels. Particularly low levels can befound in Germany, the Netherlands, Portugal, and Switzerland, with 20% or lessof women in the top position. However, even in the Nordic countries, only aroundone quarter of these positions are filled with women. France reaches 30% and isleading the large countries that are well represented in our sample.

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Figure 2: Proportion of Females, Full Professors only

In section 5, we investigate whether the lower representation of women on the fullprofessor level can be explained by the effect of cohorts from graduate schools inthe past, a time at which there were less female graduates than today.

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4 Research Rank and Percentage of Female Re-searchers

While there is heterogeneity across countries and regions in Europe, it is not ev-ident that this is (entirely) driven by institutions, norms and policies. It is wellpossible that the observed heterogeneity is driven by the demands that differentinstitutions may have on their faculty. Publishing in international journals is atime-consuming and very risky activity and (besides luck) it needs skills, net-working, and commitment. While there is no reason to believe that female Ph.D.graduates would be less qualified, one commonly advanced explanation for theleaky pipeline is that women may find it hard to supply the substantial effortneeded for high-level research when they enter parenthood. In line with this idea,we expect the following:

E1. Higher ranked research institutions should hire female researchers on the entrylevel at the same rate as lower ranked institutions.

E2. Higher ranked research institutions should have a smaller proportion of femaleresearchers on the full professor level.

To test these expectations, we use RePEc’s ranking of European institutions.Zimmermann (2013) describes the methodology how research institutions’ researchoutput is measured and ranked using widely accepted journal rankings. Table Ain the appendix provides a list of the top 300 institutions.

We first plot simple kernel graphs for a sample split of these data in the next figure.The first graph plots (full) professors only, the second one plots all positions exceptfor the (full) professors, and the last one plots only the entry level. It appears thatthe mode for the lower ranked half of the top 300 research institutions is muchhigher (around one third) than for the higher ranked half (around 15%) for thefull professorship level. Surprisingly, the gap is of similar magnitude for the entrylevel.

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Figure 3: Kernel Density Estimates by Level

Notes: For institutions having at least five positions on each level.

It hence appears that there is a significant difference between the top universitiesand the second half of the top 300 research institutions. This can be furtherexplored by simple regressions. We run three specifications with the percentageof females on full professor level, all other positions, and on the entry level on theranking of the research university. For each of the regressions we also include in onespecification the country fixed effect. To have meaningful regressions, we excludeinstitutions that do not have at least 5 positions on each level.10 Two remarks: (i)in the following table, Ranking is reversely coded, that is, a higher ranked researchinstitution, say LSE, has rank 1, and lower ranked universities have rank 2 to 300;(ii) the regression is purely descriptive: what we find is correlation, not causation.

10The restriction on the minimum number of researchers is necessary as standard errors in-crease when including institutions with a very low number of positions. Institutions with oneperson at the level, for example, can only have a female proportion of 0% and 100% and causea high standard deviation. The restriction does not affect point estimates. Our results remainstable requiring at least 3 positions for each level, and also persist at an increasing minimalnumber of positions.

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Table 3: Percentage Females on Ranking (At Least 5 Identified Positions)

(1) (2) (3) (4) (5) (6)Full

ProfessorsFull

ProfessorsOtherPosi-tions

OtherPosi-tions

EntryLevel

EntryLevel

Ranking 0.0204** 0.0208*** 0.0280*** 0.0231*** 0.0302** 0.0153(0.00874) (0.00686) (0.00909) (0.00678) (0.0131) (0.0122)

Constant 16.75*** 16.69*** 30.58*** 31.29*** 31.99*** 34.15***-1.396 -1.030 -1.358 -1.003 -2.058 -1.770

Observations 237 237 257 257 180 180Adjusted R2 0.015 0.017 0.035 0.026 0.027 0.004Country FE 25 25 23

Robust standard errors in parentheses. * p<0.10, ** p<0.05, *** p<0.01

What we find is that across all hierarchical levels, higher ranked research institu-tions have a smaller percentage of women. Our expectation E2 – less women infull professor levels in better research institutions – is met by the data, but E1 isnot met. Actually, higher-ranked research institutions also have less females onthe entry level, and the effect is sizable. Our regressions imply that an institu-tion that is ranked 100 places higher than another one would be expected to have2 percentage points less women as full professors, and 3 percentage points lessfemales on the entry level. This is quite substantial (compare the constant).

It hence seems that the leaky pipeline may begin much earlier than expected, andsomehow at the transition from graduate school to the first job. We will discussthis in the last section.

It is also noteworthy that under the inclusion of 23 to 25 country fixed effects, theeffects remain stable, except for the one on the entry level where the estimatedcoefficient is statistically not significant. We explore this further in the next setof regressions.

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Table 4: Percentage Females on Rank and Regions

VARIABLES Full Pro-fessors

Full Pro-fessors

OtherPositions

EntryLevel

Ranking 0.0197**(0.00886)

Northern Europe -3.882 -35.72*** -40.60*** -44.51***(6.676) (11.86) (11.53) (15.79)

Southern Europe -1.382 -40.41*** -28.42** -26.42(6.624) (11.96) (11.31) (15.99)

Western Europe -0.641 -36.29*** -33.34*** -40.47***(6.441) (11.53) (11.04) (15.07)

Rank Central-Eastern Europe -0.218** -0.175** -0.195*(0.0868) (0.0792) (0.111)

Rank Northern Europe -0.00378 0.0628** 0.0323(0.0195) (0.0260) (0.0354)

Rank Southern Europe 0.0436* 0.00463 -0.00669(0.0223) (0.0172) (0.0332)

Rank Western Europe 0.0240** 0.0241** 0.0314**(0.0105) (0.0110) (0.0140)

Constant 17.87*** 52.89*** 64.04*** 70.97***(6.590) (11.42) (10.94) (14.88)

Observations 232 232 250 175Adjusted R2 0.008 0.049 0.055 0.097

Robust standard errors in parentheses. * p<0.10, ** p<0.05, *** p<0.01

In table 4, we interact rankings with regions in Europe. We use the geographicalsub-regions of Europe defined by the EuroVoc11 of the publications Office of theEuropean Union. In the first column, we find that the region does not explain themagnitudes significantly while the ranking coefficient remains significant. In theremaining columns, we estimate an individual ranking slope for each region. Theresults indicate that it is the Southern and Western European regions that drivethe correlation between rankings and proportions of females (although it shouldbe noted that the point estimate for Central and Eastern Europe for entry levelis also quite high).

The picture thus seems to be quite nuanced. Higher ranked universities tend tohave less females in their institution, but this heterogeneity is particularly strong

11We provide a table with the exact list of countries belonging to these regions in table D inthe appendix.

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for Western and Southern Europe.

Table 5: Percentage Females on Rank, Regions and Countries

(1) (2)VARIABLES Full Professors Full Professors

Ranking 0.00299(0.0117)

Ranking Eastern and Central Europe -0.0545(0.0882)

Ranking Northern Europe -0.0108(0.0202)

Ranking Southern Europe -0.00587(0.0421)

Ranking Western Europe 0.00909(0.0143)

Ranking Austria 0.115** 0.115**(0.0542) (0.0539)

Ranking Belgium 0.0300** 0.0300**(0.0120) (0.0120)

Ranking Italy 0.0601* 0.0601*(0.0339) (0.0338)

Ranking Portugal 0.0814** 0.0814**(0.0390) (0.0388)

Ranking Spain 0.0725** 0.0725**(0.0316) (0.0315)

Ranking United Kingdom 0.0304 0.0304(0.0187) (0.0187)

Constant 6.106 6.106(7.748) (7.716)

Observations 232 237Country Fixed Effects 24 24Adjusted R2 0.106 0.106

Robust standard errors in parentheses. * p<0.10, ** p<0.05, *** p<0.01

To explore the relation between ranking and proportion of females, we present re-gressions with more details in table 5. We explore whether the correlation is drivenby specific countries by running a regression with interaction terms and interceptsfor every country with at least two observations. We find that the intercept is onlystatistically significant for five countries: Austria, Belgium, Italy, Portugal, andSpain. Regressing female ratios on ranking with individual slope for each of these

18

countries yields an insignificant coefficient for the remaining countries. Hence,only a handful of countries are responsible for the estimated relation. While Aus-tria, Belgium and Portugal contain only between 4-6 institutions and the resultsfor these countries may result from overfitting issues, Spain (13) and Italy (28)consist of more observations. Hence, the regressions indicate that better rankedinstitutions from Spain and Italy have fewer female tenured faculty members whileother countries do not indicate such a relation.

The results are also robust to specifications that are more restrictive. The es-timates remain quantitatively similar by restricting the sample to the top 250institutions, by removing the top 25, and by removing institutions with femalesbelow the 10% and above the 90% percentile as well as removing 1.5 interquartileranges below the first quartile and 1.5 interquartile ranges above the third quartile.

A weighted least squares regression yields insignificant results for the researchrank regressions. The Russian National Research University Higher School ofEconomics, representing the largest number of 545 full professors in the dataset, drives this. As we do not observe an inverse relation between ranking andfemales for Eastern and Central European institutions, the correlation vanisheswith weighting. An exclusion of this institution yields the same results as before.Hence, this does not go against our hypothesis, as we find that institutions in Italyand Spain are the driver for this relation.

As mentioned before, we restrict our analysis to institutions which report at leastfive researchers at the analyzed level. Our results remain stable requiring at leastthree positions for each level, and persist when restricting on at least 20 availablepositions at the level. For restrictions below three researchers, point estimatesremain, while standard errors increase such that the estimated coefficients becomepartly insignificant.

19

5 Cohort Effects Hypothesis

One argument to explain the current low number of tenured females in academicsare cohort explanations. As the number of female academic job market entrantswas low in the last decades, the previous, mostly male entrants, are still occupyingthe professorships. The argument implies that interventions are not necessary, astime will erase the observed inequality automatically.

To explore whether the cohort explanation can entirely explain the observed ratios,we merge our results with data from Lundberg and Stearns (2019). In their study,they observe a stable female’s ratio of around 28% for Ph.D. graduates since 1993.Furthermore, we observe 19.7% female professors in European institutions (Russiaexcluded) in 2019.

We can sketch a timeline and calculate the necessary ratio of entering femalesbetween 1979 and 1993 such that the cohort explanation was able to rationalizethe current female’s ratio for professors by using the following assumptions:

1. Ph.D. graduates equally enter the academic market at the age of 25 years.

2. It takes at least 5 years to become full professor (age of 30 years).

3. Tenured positions are kept for 35 years until retirement at the age of 65years.

4. The number of staffed positions is constant over time.

5. There are as many female market entrants as on the U.S. job market.

20

Figure 4: Timeline of the Cohort Explanation

Figure 4 visualizes the relevant years to explain the ratios with persistent cohorteffects. The oldest observed person in our data set became full professor in 1984and graduated in 1979. The youngest full professor in our data set graduated in2014. On average 28% of Ph.D. graduates between 1993 and 2014 that had becomefull professors between 1998 and 2019 were female. Hence, if institutions equallystaffed full professorships, the necessary gender distribution of Ph.D. graduates toexplain the current share of females would have been on average 10.2% between1984 and 1998. This number is much lower than the reported shares of femalesin the literature. For instance, Hale and Regev (2011) collected information onfemale graduates for ten U.S. institutions and determine a female’s share of 23.4%for the period between 1988 and 1993.

How would our estimate change without our assumptions? To get an understand-ing for that question, we release each assumption by its own and conclude thatassumptions that are more realistic would lead to a lower number than our esti-mate and strengthen our argument. First, if Ph.D. students enter the job marketlater than by 25 years or if it took more than five years to become a full professor,we would have to shorter average unknown period before 1993, which decreasesthe necessary share. If any, the number of vacant positions in academia has in-creased during the past 35 years, hence, relaxing this assumption would yield to ahigher weighting of the last years, and lowers necessary graduation shares for fe-males. The number of graduates is, of course, different between the European andU.S. market, but, as we observe more female Ph.D. students in the European jobmarket today, we expect a similar relation for the past, leading to lower necessaryfemale ratios before 1993.

21

Hence, the cohort explanation is not able to explain the current low share of fe-males in the economic profession entirely. Therefore, the leaky pipeline hypothesishas appeal as our data is consistent with it. Indeed, higher ranked institutionshave a lower females ratio in full professorships. As our regressions show, therealso seems to be a problem at the hiring stage.

6 What Next?

In many countries, there has been a rising scholarly attention to the status ofwomen in the economics profession. Some economic associations have taken ex-plicit measures with the aim of promoting the careers of female economists. In theU.S., the American Economic Association in 1972 inaugurated CSWEP, the Com-mittee for the Status of Women in the Economics Profession. In Great Britain, agroup within the Royal Economic Society has been working on similar tasks since1996. In Canada, female economists have had a network of their own since 1990.In 2002, the Economic Society of Australia established the Committee for Womenin Economics. The EEA Standing Committee on Women in Economics, WinE,was established on the request of EEA President, Brigit Grodal, in 2003, and wascreated by the Executive Committee and the Council of the EEA at the 20th EEACongress held in Amsterdam, 2005.

We hope that our data help to advance the debate about women in Europeaneconomics and more generally. Our data reveal that in Europe, there is a leakypipeline. As our calculations show, the cohort explanation is not able to explainthe current numbers entirely. Furthermore, it does not explain why economicsis an outlier compared to other social sciences and STEM, fields with similarrequirements. We would argue, though, that it may have to be complemented,because already on the entry level, we see that higher ranked universities that arelikely to put higher standards in terms of publication are employing females to alesser degree than lower ranked universities.

How can this be explained? It is indeed hard to believe that women are less “good”than men when graduating (if anything, they succeed better). Hence, the earlydifference is likely to be caused by or during the matching process of graduates toresearch institutions. The evidence reviewed in section 2 may suggest that part ofit could be driven by discrimination. Another possibility is that women may tend

22

not to apply for the best academic positions, because they may lack confidenceor encouragement by placement officers and their advisors. In fact, letters of rec-ommendation written for individuals applying for academic positions use differentadjectives to describe men and women, and those used to describe women areviewed more negatively in hiring decisions (Madera et al., 2009; Schmader et al.,2007). To find out whether this is the case in our profession in Europe, we wouldneed data from the hiring committees of as many research institutions as possible,a hard but not impossible task. Another possibility is that women apply but donot get selected by the good research institutions, which again, could be testedwith such data.

For all of these explanations it may be relevant to think deeper about the hiringprocess. In Europe, only the best institutions hire through the international jobmarket, which uses very specific and, arguably, stressful mechanisms that maykeep women from applying or obstruct their performance. EEA has organizedits own job market, which, to date, has attracted less than one third of women,despite its efforts in coaching and monitoring job candidates. The lower rankedinstitutions hire through different mechanisms, for instance, nationwide compe-titions like in France, referral-based or internal hiring. The fact that the lowerfemale percentages on the entry level seems to be driven by Western and SouthernEurope could be an indicator of a sorting effect – women applying and succeedingin less good places. A third possibility is that women on the junior level in goodinstitutions drop out quickly after being hired, and potentially move to less goodinstitutions.

We are not yet in the position to judge these alternatives, and would hope thatwe can collect more data, potentially through the job market organizations. Con-sequently, we would also want to abstain from the intricate question of what canbe done besides the many coaching and mentoring activities. It is undoubtful thatmany institutions undertake efforts to reach more gender balanced hiring and pro-motion decisions, but there is also some evidence that seemingly female-friendlypolicies may not result in desired outcomes.12 Consequently, we may need to

12Antecol et al. (2018) examine the effect of gender-neutral tenure-clock stopping policies,which allow assistant professors who have children to extend their tenure clock. They findthat such policies substantially increase the probability that men get tenure in their first job,but reduce the probability that women get tenure. Observed publishing outcomes suggest thatmen use the additional time on the tenure clock to continue to work and publish, while womendo not. Moreover, this study also finds that a large and significant gap in the probability oftenure remains even when controlling for the number of publications in top-five and non-top-fivejournals.

23

continue analyzing and looking carefully at more micro-level data to get the fullpicture.

24

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Grossbard, S., T. Yilmazer, and L. Zhang (2018): “The Gender Gap inCitations: Lessons from Demographic Economics Journals,” HCEO WorkingPaper 2018-078.

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27

App

endix

TableA:F

emales

inReP

Ec’s

Top300Ran

kedInstitu

tions

Ran

kIn

stit

utio

nC

ount

ryR

esea

rch

Pos

itio

nsFe

mal

eP

o-si

tion

sP

rofe

ssor

sFe

mal

eP

rofe

ssor

sA

sof

1Lo

ndon

Scho

olof

Econ

omics(L

SE)

UnitedKingd

om214

24%

9018%

2018-07-02

2Dep

artm

ento

fEcono

mics,OxfordUni-

versity

UnitedKingd

om156

31%

3312%

2018-08-07

3Pa

risScho

olof

Econ

omics

Fran

ce145

30%

124

31%

2018-04-13

4To

ulou

seScho

olof

Econ

omics(T

SE)

Fran

ce136

26%

7920%

2018-05-16

5Università

Com

mercialeLu

igiB

occoni

Italy

384

29%

111

10%

2018-04-16

6Eu

rope

anCentral

Ban

kInternationa

lOrga-

nizatio

n77

17%

1315%

2018-10-13

7Institu

tefortheStud

yof

Labo

r(IZA

)German

y19

21%

40%

2018-10-27

8CentreforEc

onom

icPo

licy

Research

(CEP

R)

Internationa

lOrga-

nizatio

n1074

21%

2018-10-13

9Dep

artm

entof

Econ

omics,

University

College

Lond

on(U

CL)

UnitedKingd

om66

30%

2917%

2018-04-12

10Barcelona

Gradu

ate

Scho

olof

Eco-

nomics(B

arcelona

GSE

)Sp

ain

164

27%

2512%

2018-05-07

11Ban

kfor

Internationa

lSe

ttlements

(BIS)

Switz

erland

122

20%

2019-01-04

12Scho

olof

Econ

omicsan

dMan

agem

ent,

Universite

itvanTilb

urg

Nethe

rland

s258

24%

9013%

2018-04-13

13W

irtscha

ftsw

issen

scha

ftliche

Faku

tät,

Universitä

tZü

rich

Switz

erland

337

28%

8221%

2018-06-29

28

Ran

kIn

stit

utio

nC

ount

ryR

esea

rch

Pos

itio

nsFe

mal

eP

o-si

tion

sP

rofe

ssor

sFe

mal

eP

rofe

ssor

sA

sof

14Dep

artm

entof

Econ

omics,

University

ofWarwick

UnitedKingd

om68

26%

3116%

2018-04-13

15Organ

isatio

nde

Coo

pératio

net

deDévelop

pementÉc

onom

ique

s(O

CDE)

Fran

ce

16Fa

culty

ofEc

onom

ics,

University

ofCam

bridge

UnitedKingd

om118

21%

119%

2018-06-19

17ifo

Institu

t-

Leibniz-Institu

tfür

Wirt

scha

ftsforschu

ngan

der

Univer-

sität

Mün

chen

e.V.

German

y195

33%

679%

2018-10-13

18Ban

cad’Italia

Italy

19So

lvay

Brussels

Scho

olof

Econ

omics

and

Man

agem

ent,

Université

Librede

Bruxelle

s

Belgium

9031%

6522%

2018-03-30

20Sc

ienc

esécon

omique

s,Sc

ienc

esPo

Fran

ce38

21%

1613%

2018-06-15

21DIW

Berlin

(Deu

tsches

Institu

tfür

Wirt

scha

ftsforschu

ng)

German

y48

23%

729%

2018-04-16

22Institu

tforØkono

mi,

Aarhu

sUniver-

sitet

Den

mark

202

24%

4023%

2018-04-13

23Han

delsh

ögskolan

iStockho

lmSw

eden

194

26%

4423%

2018-04-13

24Centrede

Reche

rche

enÉc

onom

ieet

Statist

ique

(CRES

T)

Fran

ce223

30%

6719%

2018-06-15

25Scho

olof

Econ

omics,University

ofNot-

tingh

amUnitedKingd

om79

27%

248%

2018-04-17

26Éc

ole

des

Scienc

esÉc

onom

ique

sde

Louv

ain,

Université

Catho

lique

deLo

u-vain

Belgium

7011%

4112%

2019-02-18

29

Ran

kIn

stit

utio

nC

ount

ryR

esea

rch

Pos

itio

nsFe

mal

eP

o-si

tion

sP

rofe

ssor

sFe

mal

eP

rofe

ssor

sA

sof

27Fa

culte

itEc

onom

ieen

Bed

rijfsku

nde,

Universite

itvanAmsterda

mNethe

rland

s386

25%

747%

2018-10-13

28Ban

kof

Englan

dUnitedKingd

om94

29%

2019-01-31

29Fa

culte

itEc

onom

ieen

Bed

rijfsweten

-scha

ppen

,KU

Leuv

enBelgium

149

28%

6525%

2018-04-17

30Dep

artm

entof

Econ

omicsan

dRelated

Stud

ies,

University

ofYo

rkUnitedKingd

om54

30%

1828%

2018-04-17

31Aix-M

arseille

Scho

olof

Econ

omics

(AMSE

)Fran

ce95

21%

4823%

2018-06-15

32Lo

ndon

BusinessScho

ol(L

BS)

UnitedKingd

om129

26%

4124%

2019-02-20

33Fa

culte

itde

rEcono

misc

heWeten

scha

p-pe

n,Er

asmus

Universite

itRotterdam

Nethe

rland

s232

23%

526%

2018-04-23

34CES

ifoGerman

y1480

16%

2019-02-22

35Centro

deEs

tudios

Mon

etariosy

Fi-

nanc

ieros(C

EMFI

)Sp

ain

4821%

80%

2018-10-16

36Økono

misk

institu

tt,

Universite

tet

iOslo

Norway

7323%

2110%

2018-04-23

37Økono

misk

Institu

t,Køb

enha

vnsUni-

versite

tDen

mark

110

21%

244%

2019-02-20

38Institu

tefor

Internationa

lEc

onom

icStud

ies(IIE

S),S

tockho

lmsUniversite

tSw

eden

4730%

617%

2019-02-20

39Ban

quede

Fran

ceFran

ce97

29%

2019-01-04

40Fa

culte

itEc

onom

ieen

Bed

rijfsku

nde,

Rijk

suniversite

itGroning

enNethe

rland

s76

13%

6913%

2018-06-15

30

Ran

kIn

stit

utio

nC

ount

ryR

esea

rch

Pos

itio

nsFe

mal

eP

o-si

tion

sP

rofe

ssor

sFe

mal

eP

rofe

ssor

sA

sof

41Dipartim

ento

diSc

ienz

eEc

onom

iche

,Alm

aMater

Stud

iorum

-Università

diBologna

Italy

140

29%

4217%

2018-04-21

42Fa

culte

itde

rEcono

misc

heWeten

scha

p-pe

nen

Bed

rijfsku

nde,Vr

ijeUniversite

itNethe

rland

s384

23%

8115%

2018-06-15

43Scho

olof

Business,

Man

agem

entan

dEc

onom

ics,

University

ofSu

ssex

UnitedKingd

om315

38%

5721%

2018-04-23

44Tinbe

rgen

Institu

utNethe

rland

s231

14%

963%

2018-04-23

45Institu

teforFiscal

Stud

ies(IFS

)UnitedKingd

om198

38%

1346%

2018-06-15

46Istit

utoEina

udip

erl’E

cono

mia

ela

Fi-

nanz

a(E

IEF)

Italy

3033%

30%

2018-06-15

47Ban

code

Espa

ñaSp

ain

9332%

2018-10-13

48Scho

olof

Econ

omics,Fina

ncea

ndMan

-agem

ent,University

ofBris

tol

UnitedKingd

om155

31%

3424%

2018-04-13

49Vo

lksw

irtscha

ftliche

Faku

ltät,

Ludw

ig-

Max

imilian

s-Universitä

tMün

chen

German

y50

16%

2612%

2018-04-23

50CassBusinessScho

ol,C

ityUniversity

UnitedKingd

om189

24%

7519%

2018-04-23

51Scho

olof

Business

and

Econ

omics,

Maastric

htUniversity

Nethe

rland

s65

15%

5417%

2019-02-22

52Dep

artamento

deEc

onom

ía,Universi-

dadCarlosIIIde

Mad

ridSp

ain

6219%

2921%

2019-03-14

53Fa

culté

desHau

tesÉt

udes

Com

mer-

ciales

(HEC

),Université

deLa

usan

neSw

itzerland

299

29%

7031%

2018-06-15

54Fa

chbe

reich

Wirt

scha

ftsw

issen

scha

ft,

GoetheUniversitä

tFran

kfurtam

Main

German

y67

18%

4912%

2018-04-11

31

Ran

kIn

stit

utio

nC

ount

ryR

esea

rch

Pos

itio

nsFe

mal

eP

o-si

tion

sP

rofe

ssor

sFe

mal

eP

rofe

ssor

sA

sof

55Acade

mia

deStud

iiEc

onom

icedinBu-

curesti

Rom

ania

396

56%

134

46%

2018-06-15

56Ec

onom

icsDep

artm

ent,

University

ofEs

sex

UnitedKingd

om52

25%

1625%

2018-05-17

57Scho

olof

Econ

omics,

University

ofEd

-inbu

rgh

UnitedKingd

om44

25%

1414%

2018-05-17

58Scho

olof

Econ

omics

and

Fina

nce,

Que

enMary

UnitedKingd

om100

25%

244%

2018-05-17

59NorgesHan

delsh

øyskole(N

HH)

Norway

188

24%

8520%

2018-07-02

60Fa

kultä

tfür

Wirt

scha

ftsw

is-senschaften,

Universitä

tW

ien

Austria

5122%

329%

2018-05-17

61Internationa

lEc

onom

icsSe

ction,

The

Gradu

ateInstitu

teof

Internationa

land

Develop

mentStud

ies

Switz

erland

3421%

220%

2018-06-15

62Nationa

lResearch

University

Higher

Scho

olof

Econ

omics

Russia

nFe

deratio

n2852

53%

545

35%

2018-06-15

63W

irtscha

ftsw

issen

scha

ftliche

rFa

ch-

bereich,

Rhe

inisc

heFriedrich-

Wilh

elms-Universitä

tBon

n

German

y78

14%

307%

2019-02-22

64Deu

tscheBun

desban

kGerman

y81

26%

2018-12-20

65Abteilung

für

Volksw

irtscha

ftsle

hre,

Universitä

tMan

nheim

German

y61

21%

2010%

2018-04-12

66United

Nations

University

-Maastric

htEc

onom

icResearchInstitu

teof

Inno

va-

tionan

dTe

chno

logy

(UNU-M

ERIT

)

Nethe

rland

s195

41%

2623%

2018-06-15

32

Ran

kIn

stit

utio

nC

ount

ryR

esea

rch

Pos

itio

nsFe

mal

eP

o-si

tion

sP

rofe

ssor

sFe

mal

eP

rofe

ssor

sA

sof

67Institu

tet

för

Näringsliv

sforskning

(IFN

)Sw

eden

3010%

70%

2018-06-15

68Fa

coltà

diEc

onom

ia,Università

degli

Stud

idiR

oma"T

orVe

rgata"

Italy

134

31%

5217%

2018-06-15

69Centro

Stud

idi

Econ

omia

eFina

nza

(CSE

F)Italy

5426%

2018-10-13

70Sa

ïdBusinessScho

ol,Oxford

Univer-

sity

UnitedKingd

om142

32%

3113%

2018-05-17

71Cop

enha

genBusinessScho

olDen

mark

1907

32%

167

25%

2018-05-17

72Han

delsh

ögskolan

,Göteb

orgs

Univer-

sitet

Swed

en373

41%

6033%

2018-05-22

73Dep

artm

ent

ofMan

agem

ent,

Tech-

nology

and

Econ

omics

(D-M

TEC

),Eidg

enössis

che

Technische

Hochschule

Züric

h(E

THZ)

Switz

erland

402

30%

195%

2018-05-22

74Ek

onom

ihögskolan

,Lun

dsUniversite

tSw

eden

267

34%

4821%

2018-05-22

75Fa

chbe

reichW

irtscha

ftsw

issen

scha

ften

,Universitä

tKon

stan

zGerman

y43

33%

1822%

2018-06-15

76Scho

olof

Econ

omics,

University

ofMan

chester

UnitedKingd

om111

14%

3027%

2018-05-22

77WarwickBusinessS

choo

l,University

ofWarwick

UnitedKingd

om36

25%

176%

2018-05-22

78Ze

ntrum

fürEu

ropä

ische

Wirt

scha

fts-

forschun

g(ZEW

)German

y106

32%

540%

2018-06-18

79Athen

sUniversity

ofEc

onom

ics

and

Business(A

UEB

)Greece

257

25%

7411%

2018-10-13

33

Ran

kIn

stit

utio

nC

ount

ryR

esea

rch

Pos

itio

nsFe

mal

eP

o-si

tion

sP

rofe

ssor

sFe

mal

eP

rofe

ssor

sA

sof

80Dipartim

enti

eIstit

utid

iScien

zeEc

o-no

miche

,Università

Cattolic

ade

lSacro

Cuo

re

Italy

108

40%

2532%

2018-06-18

81de

Ned

erland

sche

Ban

kNethe

rland

s49

20%

2018-06-18

82Ada

mSm

ithBusinessScho

ol,Univer-

sityof

Glasgow

UnitedKingd

om165

38%

4726%

2018-05-22

83W

UW

irtscha

ftsuniversitä

tW

ien

Austria

143

27%

119

27%

2018-04-19

84Dep

artm

entVo

lksw

irtscha

ftlehre,

Uni-

versitä

tBern

Switz

erland

7222%

90%

2018-05-22

85Scho

olof

Econ

omicsan

dPo

litical

Sci-

ence,U

niversitä

tSt.Gallen

Switz

erland

123

33%

2110%

2018-06-18

86Sv

eriges

Riksban

kSw

eden

3135%

2018-06-18

87HEC

Paris

(École

desHau

tesÉt

udes

Com

merciales)

Fran

ce399

21%

137

18%

2018-10-13

88Dep

artm

ent

ofEc

onom

ics,

Europe

anUniversity

Institu

teItaly

1225%

1225%

2018-06-18

89Fa

culta

td’Ec

onom

iaiEm

presa,

Uni-

versita

tde

Barcelona

Spain

295

38%

8128%

2019-02-23

90Nationa

lekono

misk

aInstitu

tione

n,Up-

psalaUniversite

tSw

eden

113

29%

1020%

2019-03-06

91Dep

artm

entof

Econ

omics,Tr

inity

Col-

lege

Dub

linIrelan

d18

33%

250%

2018-05-22

92BusinessScho

ol,Impe

rialC

ollege

UnitedKingd

om111

30%

307%

2018-04-24

93W

irtscha

ftsw

issen

scha

ftliche

Faku

ltät,

Hum

boldt-Universitä

tBerlin

German

y123

27%

5020%

2018-04-13

34

Ran

kIn

stit

utio

nC

ount

ryR

esea

rch

Pos

itio

nsFe

mal

eP

o-si

tion

sP

rofe

ssor

sFe

mal

eP

rofe

ssor

sA

sof

94SK

EMA

BusinessScho

olFran

ce115

43%

115

43%

2018-10-13

95Sw

issFina

nceInstitu

teSw

itzerland

607%

415%

2018-05-22

96Scho

olof

Econ

omics,

University

Col-

lege

Dub

linIrelan

d29

41%

714%

2018-05-22

97Centre

d’Éc

onom

iede

laSo

rbon

ne,

Université

Paris

1(P

anthéon-

Sorbon

ne)

Fran

ce94

41%

2623%

2018-05-22

98Nationa

lekono

misk

ainstitu

tione

n,Stockh

olmsUniversite

tSw

eden

8637%

1619%

2018-06-18

99Man

agem

entS

choo

l,La

ncasterU

niver-

sity

UnitedKingd

om215

30%

7718%

2018-06-18

100

Dipartim

ento

diEc

onom

iaeStatist

ica

"Cogne

ttid

eMartiis",U

niversità

degli

Stud

idiT

orino

Italy

4347%

1040%

2018-05-22

101

Österreichische

sInstitu

tfür

Wirt

scha

ftsforschu

ng(W

IFO)

Austria

6431%

714%

2018-05-22

102

Wirt

scha

fts-

und

Sozialwis-

senschaftlicheFa

kultä

t,Universitä

tzu

Köln

German

y100

22%

7613%

2018-05-22

103

Institu

tulNationa

lde

Cercetari

Eco-

nomice(INCE)

,Acade

mia

Rom

ana

Rom

ania

111

66%

1250%

2018-10-13

104

Institu

tfürWeltw

irtscha

ft(IfW

)German

y91

35%

2020%

2019-05-29

105

Scho

olof

Econ

omics,University

ofSu

r-rey

UnitedKingd

om38

26%

1323%

2018-05-22

35

Ran

kIn

stit

utio

nC

ount

ryR

esea

rch

Pos

itio

nsFe

mal

eP

o-si

tion

sP

rofe

ssor

sFe

mal

eP

rofe

ssor

sA

sof

106

Dipartim

ento

diEc

onom

ia,

Man

age-

menteMetod

iQua

ntita

tivi(

DEM

M),

Università

degliS

tudi

diMila

no

Italy

5338%

1911%

2018-05-22

107

Econ

omiX

,Université

Paris

Oue

st-

Nan

terrela

Défense

(Paris

X)

Fran

ce169

37%

3546%

2018-05-22

108

Institu

tEu

ropé

end’Adm

inist

ratio

n(INSE

AD)

Fran

ce232

18%

514%

2018-05-22

109

Dep

artm

entof

Internationa

lDevelop

-ment(

Que

enElizab

ethHou

se),Oxford

University

UnitedKingd

om61

49%

944%

2018-05-22

110

Université

Paris

-Dau

phine(P

aris

IX)

Fran

ce341

43%

142

32%

2018-07-02

111

CardiffBusinessS

choo

l,CardiffUniver-

sity

UnitedKingd

om201

34%

6314%

2018-06-18

112

Dep

artm

entof

Econ

omics,

University

ofBirm

ingh

amUnitedKingd

om51

27%

1421%

2018-05-22

113

Dipartim

ento

diEc

onom

ia,Università

Ca’

FoscariV

enezia

Italy

130

40%

2821%

2018-07-13

114

Scho

olof

Businessa

ndEc

onom

ics,Uni-

versidad

eNovade

Lisboa

Portug

al157

30%

190%

2019-05-29

115

Scho

olof

Econ

omics,

University

ofKent

UnitedKingd

om35

23%

813%

2018-05-22

116

Institu

teforPr

ospe

ctiveTe

chno

logical

Stud

ies(IPT

S),J

oint

ResearchCentre,

Europe

anCom

miss

ion

Internationa

lOrga-

nizatio

n185

32%

2018-10-13

117

Facu

ltéde

droit,

d’écon

omie

etde

fi-na

nce,

Université

duLu

xembo

urg

Luxembo

urg

136

25%

8721%

2018-06-18

36

Ran

kIn

stit

utio

nC

ount

ryR

esea

rch

Pos

itio

nsFe

mal

eP

o-si

tion

sP

rofe

ssor

sFe

mal

eP

rofe

ssor

sA

sof

118

IESE

BusinessScho

ol,Universidad

deNavarra

Spain

204

11%

679%

2019-01-31

119

Econ

omics

Division

,University

ofSo

utha

mpton

UnitedKingd

om42

19%

714%

2018-04-16

120

Econ

omicsDep

artm

ent,

University

ofStrathclyd

eUnitedKingd

om50

18%

2223%

2018-05-22

121

Faku

ltätfürW

irtscha

fts-

und

Sozial-

wiss

enscha

ften

,Rup

recht-Karls-

Universitä

tHeide

lberg

German

y53

21%

3722%

2018-07-02

122

Labo

ratory

ofEc

onom

ics

and

Man

-agem

ent

(LEM

),Sc

uola

Supe

riore

Sant’A

nna

Italy

2025%

520%

2018-05-22

123

Institu

tfür

Volksw

irtscha

ftsle

hre,

Joha

nnes-K

epler-Universitä

tLinz

Austria

5326%

813%

2018-06-18

124

Facu

lteit

Econ

omie

enBed

rijfsku

nde,

Universite

itGent

Belgium

284

34%

2811%

2018-06-21

125

Ban

code

Portug

alPo

rtug

al58

47%

2018-06-18

126

Dep

artm

entof

Econ

omics,

Royal

Hol-

loway

UnitedKingd

om51

14%

248%

2018-05-22

127

Econ

omican

dSo

cial

ResearchInstitu

te(E

SRI)

Irelan

d69

42%

1844%

2018-05-22

128

Fachbe

reich

Wirt

scha

ftsw

issen

scha

ft,

FreieUniversitä

tBerlin

German

y78

17%

5012%

2018-05-22

129

Dipartim

ento

diSc

ienz

eEc

onom

iche

"Marco

Fann

o",Università

degliStud

idi

Pado

va

Italy

5623%

186%

2018-05-22

37

Ran

kIn

stit

utio

nC

ount

ryR

esea

rch

Pos

itio

nsFe

mal

eP

o-si

tion

sP

rofe

ssor

sFe

mal

eP

rofe

ssor

sA

sof

130

Institu

tetförSo

cial

Forskn

ing(SOFI

),Stockh

olmsUniversite

tSw

eden

8046%

1118%

2018-05-22

131

University

ofPiraeu

sGreece

156

22%

6614%

2018-10-13

132

Center

for

Econ

omic

Research

and

Gradu

ateEd

ucationan

dEc

onom

icsI

n-stitu

te(C

ERGE-

EI)

Czechia

5728%

40%

2019-05-30

133

Facu

ltadde

Cienc

iasE

conó

micas

yEm

-presariales,

Universidad

delP

aísVa

sco

-Euska

lHerrik

oUnibe

rtsit

atea

Spain

258

51%

425%

2018-06-18

134

Dep

artm

ent

ofEc

onom

ics,

Leicester

University

UnitedKingd

om151

36%

229%

2018-05-22

135

Dep

artm

entof

Econ

omics,

Mathe

mat-

icsan

dStatist

ics,

Birk

beck

College

UnitedKingd

om33

27%

838%

2018-05-22

136

Dep

artm

entof

Econ

omics,

University

ofSh

effield

UnitedKingd

om31

23%

1127%

2018-05-22

137

Facu

ltadde

Econ

omía,U

niversidad

deVa

lència

Spain

429

39%

250%

2018-06-18

138

Wirt

scha

ftsw

issen

scha

ftliche

sZentrum

,Universitä

tBasel

Switz

erland

5525%

1916%

2019-01-05

139

Schw

eizeris

cheNationa

lban

k(SNB)

Switz

erland

140

Euro-area

Econ

omy

Mod

ellin

gCen

-tre,

Dire

ctorate-Gen

eral

JointR

esearch

Centre,

Europe

anCom

miss

ion

Italy

141

CollegioCarlo

Alberto,U

niversità

degli

Stud

idiT

orino

Italy

5636%

40%

2018-06-18

38

Ran

kIn

stit

utio

nC

ount

ryR

esea

rch

Pos

itio

nsFe

mal

eP

o-si

tion

sP

rofe

ssor

sFe

mal

eP

rofe

ssor

sA

sof

142

Max

-Planck-Institu

tzu

rEr

forschun

gvonGem

einschaftsgü

tern,M

ax-P

lanck-

Gesellsc

haft

German

y65

34%

138%

2018-06-18

143

Dép

artementScienc

esSo

ciales,Agri-

cultu

reet

Alim

entatio

n,Es

pace

etEn

-vironn

ement(SAE2

),Institu

tNationa

lde

laReche

rche

Agron

omique

(INRA)

Fran

ce

144

BusinessScho

ol,U

niversity

ofNottin

g-ha

mUnitedKingd

om149

42%

3631%

2018-04-17

145

Facu

ltyof

Econ

omicsa

ndMan

agem

ent,

University

ofCyp

rus

Cyp

rus

8026%

2015%

2018-06-18

146

Dep

artm

entof

Econ

omics,Central

Eu-

rope

anUniversity

Hun

gary

5822%

1010%

2019-02-22

147

Wirt

scha

ftsw

issen

scha

ftliche

Faku

ltät,

Heinriche

-Heine

-Universitä

tDüsseldorf

German

y34

24%

1921%

2018-04-23

148

Scho

olof

Econ

omics,University

ofEa

stAng

liaUnitedKingd

om1477

24%

284

0%2018-06-18

149

Institu

tekon

omický

chstud

ií,Uni-

verzita

Karlova

vPr

aze

Czechia

5227%

80%

2018-05-09

150

BIHan

delsh

øyskolen

Norway

404

26%

102

26%

2018-06-18

151

Suom

enPa

nkki

Finlan

d52

31%

2133%

2018-06-18

152

Facoltà

diEc

onom

ia"G

iorgio

Fuà",

Università

Politecnica

delle

Marche

Italy

8233%

3424%

2018-05-09

153

Institu

tfür

Volksw

irtscha

ftsle

hre,

Christ

ian-Albrechts-U

niversitä

tKiel

German

y16

19%

1619%

2018-06-18

39

Ran

kIn

stit

utio

nC

ount

ryR

esea

rch

Pos

itio

nsFe

mal

eP

o-si

tion

sP

rofe

ssor

sFe

mal

eP

rofe

ssor

sA

sof

154

Dipartim

ento

diEc

onom

iaeFina

nza

(DEF

),Libe

raUniversità

Inter-

nazion

ale

degli

Stud

iSo

ciali

Guido

Carli(L

UISS)

Italy

2214%

1010%

2019-02-22

155

Facu

ltadde

Cienc

iasE

conó

micas

yEm

-presariales,

Universidad

Com

pluten

sede

Mad

rid

Spain

321

36%

166

36%

2018-06-18

156

Wirt

scha

ftsw

issen

scha

ftliche

Faku

ltät,

LeibnizUniversitä

tHan

nover

German

y26

15%

2615%

2018-06-18

157

Nationa

leBan

kvanBelgië/Ban

quena

-tio

nald

eBelqiqu

e(B

NB)

Belgium

158

Stiftelsen

Frisc

hsenteret

for

sam-

funn

søkono

misk

forskn

ing,

Univer-

sitetet

iOslo

Norway

3617%

80%

2018-06-18

159

Center

for

Research

inEc

onom

ics,

Man

agem

entan

dtheArts(C

REM

A)

Switz

erland

3333%

2433%

2018-06-18

160

Group

ed’Ana

lyse

etde

Théorie

Écon

omique

Lyon

St-É

tienn

e(G

ATE

Lyon

St-É

tienn

e),Fa

culté

deSc

ienc

esÉc

onom

ique

set

deGestio

n,Université

Lumière

(Lyon2)

Fran

ce51

41%

2446%

2018-05-15

161

Institu

toSu

perio

rde

Econ

omia

eGestão(ISE

G),Universidad

ede

Lisboa

Portug

al284

41%

4319%

2018-06-18

162

BusinessScho

ol,U

niversity

ofEx

eter

UnitedKingd

om184

40%

3617%

2018-06-18

163

Lille

Écon

omie

etMan

agem

ent(L

EM)

Fran

ce74

27%

1421%

2018-06-18

40

Ran

kIn

stit

utio

nC

ount

ryR

esea

rch

Pos

itio

nsFe

mal

eP

o-si

tion

sP

rofe

ssor

sFe

mal

eP

rofe

ssor

sA

sof

164

Gen

evaScho

olof

Econ

omicsan

dMan

-agem

ent,Université

deGen

ève

Switz

erland

7630%

3123%

2018-06-18

165

Essex

Business

Scho

ol,University

ofEs

sex

UnitedKingd

om109

36%

2623%

2018-04-23

166

Kau

ppakorkeak

oulu,A

alto-ylio

pisto

Finlan

d109

28%

4522%

2018-05-15

167

Centre

d’étud

esprospe

ctives

etd’inform

ations

internationa

les(C

EPII)

Fran

ce38

37%

333%

2018-10-13

168

Thé

orie

Écon

omique

,Mod

élisa

tion,

App

lication

(THEM

A),

Université

deCergy

-Pon

toise

Fran

ce64

34%

2227%

2018-08-29

169

Wyd

zial

Nau

kEk

onom

icznych,

Uniwer-

sytetWarszaw

ski

Poland

109

36%

1010%

2018-10-13

170

Türkiye

Cum

huriy

etMerkezBan

kasi

Turkey

171

Rim

iniCentreforEc

onom

icAna

lysis

(RCEA

)Italy

9916%

2018-10-13

172

Oesterreichisc

heNationa

lban

kAustria

3933%

2018-10-13

173

Bruegel

Belgium

3622%

250%

2018-06-19

174

Rhe

inisc

h-Westfälisc

hes

Institu

tfür

Wirt

scha

ftsforschu

ng(R

WI)

German

y83

31%

195%

2018-06-19

175

Institu

tfürArbeitsmarkt-un

dBerufs-

forschun

g(IAB)

German

y35

20%

2719%

2018-06-19

176

Institu

teforSo

cial

andEc

onom

icRe-

search

(ISE

R),

University

ofEs

sex

UnitedKingd

om53

60%

1567%

2018-06-19

177

Dipartim

ento

diEc

onom

ia"M

arco

Bi-

agi",

Università

degliS

tudi

diMod

ena

eReggioEm

ilia

Italy

107

36%

3023%

2018-06-19

41

Ran

kIn

stit

utio

nC

ount

ryR

esea

rch

Pos

itio

nsFe

mal

eP

o-si

tion

sP

rofe

ssor

sFe

mal

eP

rofe

ssor

sA

sof

178

Ceská

Národ

níBan

kaCzechia

2110%

714%

2018-10-13

179

Hen

ley

BusinessScho

ol,University

ofReading

UnitedKingd

om105

49%

2627%

2018-06-19

180

Group

ede

Reche

rche

enÉc

onom

ieThé

orique

etApp

liqué

e(G

RET

hA),

Université

deBorde

aux

Fran

ce128

28%

2018-10-13

181

Institu

tSu

périe

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onom

ieet

Man

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ent(ISE

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Université

deNice-

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iaAntipolis

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ce52

48%

1839%

2018-05-15

182

Fachbe

reichVo

lksw

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ftsle

hre,Uni-

versitä

tHam

burg

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y28

25%

2025%

2018-06-19

183

New

Econ

omic

Scho

ol(N

ES)

Russia

nFe

deratio

n126

32%

140%

2018-06-19

184

BusinessScho

ol,D

urha

mUniversity

UnitedKingd

om168

34%

4721%

2018-04-23

185

Institu

ttforØkono

mi,

Universite

teti

Bergen

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3818%

157%

2018-04-23

186

Scuo

ladi

Econ

omia

eStatist

ica,

Uni-

versità

degliS

tudi

diMila

no-B

icocca

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7842%

1729%

2019-02-01

187

Rotterdam

Scho

olof

Man

agem

ent

(RSM

Erasmus

University

),Er

asmus

Universite

itRotterdam

Nethe

rland

s212

25%

518%

2018-06-19

188

Judg

eBusinessScho

ol,University

ofCam

bridge

UnitedKingd

om68

18%

2114%

2018-05-15

189

Institu

tfürHöh

ereStud

ien(IHS)

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3333%

1136%

2019-01-04

190

Facu

ltadde

Econ

omía

yEm

presa,

Uni-

versidad

deZa

ragoza

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119

44%

157%

2019-01-04

42

Ran

kIn

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191

Ban

gorBusinessScho

ol,Ban

gorUni-

versity

UnitedKingd

om47

34%

1921%

2018-06-19

192

Wirt

scha

ftsw

issen

scha

ftliche

Faku

ltät,

Georg-A

ugust-Universitä

tGöttin

gen

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y32

25%

3027%

2018-06-19

193

Rob

ertSchu

man

CentreforAdv

anced

Stud

ies(

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urop

eanUniversity

Institu

te

Internationa

lOrga-

nizatio

n53

21%

5221%

2018-10-13

194

Iktis

adiv

eIdariB

ilimlerF

akültesi,

Koç

Üniversite

siTu

rkey

7943%

2348%

2018-06-19

195

Institu

tde

Prép

aration

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ratio

net

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Gestio

n(IPA

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Fran

ce63

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2018-06-19

196

Dire

ctorate-Gen

eral

Econ

omic

andFi-

nanc

ialA

ffairs

,Europ

eanCom

miss

ion

Belgium

197

Statist

iskSe

ntralbyrå

Norway

5531%

367%

2018-06-19

198

Ban

kof

Greece

Greece

199

Centrede

Reche

rche

enÉc

onom

ieet

Man

agem

ent(C

REM

)Fran

ce105

37%

4122%

2018-05-15

200

Center

for

Econ

omics

and

Neu

ro-

scienc

e,Rhe

inisc

heFriedrich-W

ilhelms-

Universitä

tBon

n

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y8

50%

20%

2018-06-19

201

Institu

tfürW

irtscha

ftsforschu

ngHalle

(IW

H)

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y99

21%

60%

2018-06-19

202

École

d’Éc

onom

ie,

Université

d’Auv

ergn

e(C

lerm

ont-Fe

rran

d1)

Fran

ce46

46%

1741%

2018-10-13

43

Ran

kIn

stit

utio

nC

ount

ryR

esea

rch

Pos

itio

nsFe

mal

eP

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sP

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eP

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sA

sof

203

Dep

artm

ento

fEcono

micsa

ndFina

nce,

Brune

lUniversity

UnitedKingd

om29

31%

60%

2018-06-19

204

Wirt

scha

ftsw

issen

scha

ftliche

Faku

ltät,

Friedrich-Schille

r-Universitä

tJena

German

y122

20%

408%

2018-06-19

205

Institu

tfor

Virk

somhe

dslede

lseog

Økono

mi,Sy

ddan

skUniversite

tDen

mark

6818%

186%

2018-10-13

206

Faku

ltätfür

Volksw

irtscha

ftun

dStatis-

tik,L

eopo

ld-Franz

ens-Universitä

tInn

s-bruck

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3030%

1625%

2018-06-19

207

Wirt

scha

fts-

und

Sozialwis-

senschaftliche

Faku

ltät,

Friedrich-

Alexa

nder-U

niversitä

tEr

lang

en-

Nürnb

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y48

27%

4827%

2018-06-19

208

Wirt

scha

ftsw

issen

scha

ftliche

nFa

kultä

t,Eb

erha

rd-K

arls-

Universitä

tTüb

ingen

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y35

40%

2124%

2018-06-19

209

Bureau

d’Éc

onom

ieThé

orique

etAp-

pliqué

e(B

ETA)

Fran

ce119

31%

3135%

2018-06-19

210

École

Supé

rieure

deCom

merce

deMon

tpellie

rFran

ce76

39%

1010%

2018-10-13

211

Nationa

lInstit

uteof

Econ

omic

andSo

-cial

Research(N

IESR

)UnitedKingd

om38

37%

1323%

2018-06-19

212

Institu

ttforSa

mfunn

søkono

mi,Norges

tekn

isk-naturvitenska

plige

universit

et(N

TNU)

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2425%

1513%

2018-06-19

44

Ran

kIn

stit

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ount

ryR

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rch

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itio

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mal

eP

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sA

sof

213

Scho

olof

Fina

nce,

Universitä

tSt.

Gallen

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erland

244%

80%

2019-02-23

214

Gaida

rInstitu

teforEc

onom

icPo

licy

Russia

nFe

deratio

n110

54%

2638%

2018-10-13

215

Central

Ban

kof

Irelan

dIrelan

d30

47%

10%

2019-01-10

216

Dep

artm

entof

Econ

omics,

University

ofBath

UnitedKingd

om43

19%

80%

2018-06-19

217

Közgazd

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yiIntézet,

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lisTu

domán

yiKutatóközpo

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rTu

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Hun

gary

8729%

70%

2018-06-19

218

Scho

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Man

agem

entan

dBusiness,

King’sCollege

UnitedKingd

om94

38%

3023%

2018-06-19

219

Iktis

atBölüm

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entÜniversite

siTu

rkey

2335%

30%

2018-06-19

220

Dipartim

ento

diSc

ienz

eper

l’Econo

mia

el’Impresa,

Università

degliStud

idi

Firenz

e

Italy

103

32%

2917%

2018-06-19

221

Arbetsm

arkn

adsdepartementet

Swed

en222

Centrefor

Researchan

dAna

lysis

ofMi-

gration

(CReA

M),

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College

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on(U

CL)

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om29

41%

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2018-04-24

223

Facu

ltéde

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s,So

-ciales

etde

Gestio

n(F

SESG

),Univer-

sitéde

Nam

ur

Belgium

1118%

729%

2019-01-04

224

NorgesBan

kNorway

3017%

60%

2018-10-13

225

Facu

ldad

ede

Econ

omia,Universidad

edo

Porto

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al57

42%

617%

2018-06-19

45

Ran

kIn

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mal

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sof

226

Scho

olof

Econ

omics,

Universite

itUtrecht

Nethe

rland

s102

32%

1724%

2018-06-21

227

Facoltà

diEc

onom

ia,Università

degli

Stud

idiV

eron

aItaly

3631%

1020%

2018-06-19

228

Scho

olof

Man

agem

entan

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ngua

ges,

Heriot-WattUniversity

UnitedKingd

om22

45%

956%

2018-10-13

229

Szkola

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Han

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aw

Warszaw

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land

230

Group

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tes

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merciales

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Fran

ce50

30%

2124%

2018-06-19

231

Geary

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te,

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College

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linIrelan

d69

39%

2730%

2018-06-19

232

Facu

ltade

deCienc

ias

Econ

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eEm

presariais,

Universidad

ede

Vigo

Spain

9848%

2938%

2018-10-13

233

Gutenbe

rgScho

olof

Man

agem

ent

and

Econ

omics,

Joha

nnes

Gutenbe

rg-

Universitä

tMainz

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y28

11%

239%

2018-06-19

234

Dep

artm

entof

Land

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omy,

Univer-

sityof

Cam

bridge

UnitedKingd

om77

35%

1315%

2018-07-06

235

Russia

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ofNa-

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Russia

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236

Rechts-

und

Wirt

scha

ftsw

is-senschaftliche

Facu

ltät,

Universitä

tBayreuth

German

y128

12%

9013%

2018-06-28

46

Ran

kIn

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Pos

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237

Erasmus

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Man

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asmus

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rland

s268

23%

847%

2018-05-09

238

CentroRicercheNordSu

d(C

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oS)

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2330%

922%

2018-06-19

239

Dipartim

ento

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onom

ico-

SocialieMatem

atico-Statist

iche

,Uni-

versità

degliS

tudi

diTo

rino

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150

33%

1729%

2018-06-19

240

Facoltà

diEc

onom

ia,Università

degli

Stud

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oma"LaSa

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za"

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131

40%

131

40%

2019-01-04

241

Faku

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Wirt

scha

fts-

und

Sozialwis-

senschaften,

Universitä

tHoh

enhe

imGerman

y45

31%

4330%

2018-06-19

242

Facu

ltéde

sciences

écon

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s,Uni-

versité

deMon

tpellie

rI

Fran

ce13

23%

40%

2018-06-19

243

BusinessScho

ol,N

ewcastle

University

UnitedKingd

om166

42%

3113%

2018-06-19

244

Facoltà

diEc

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ia/W

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ftsw

is-senschaftlicheFa

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raUniver-

sità

diBolzano

/Freie

Universitä

tBozen

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5439%

1613%

2018-06-19

245

Dep

artm

ento

fEcono

mics,Fina

ncean

dAccou

nting,

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noothUniversity

Irelan

d23

52%

333%

2018-06-19

246

Facu

ltadde

Cienc

iasE

conó

micas

yEm

-presariales,

Universidad

deAlic

ante

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287

38%

8633%

2019-01-04

247

Institu

tfürVo

lksw

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ftsle

hre,

Carl

vonOssietzky

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tOldenbu

rgGerman

y16

31%

60%

2018-06-19

248

Facu

ltéde

droit,

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ges-

tion,

Université

d’Orlé

ans

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ce159

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2018-06-19

47

Ran

kIn

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mal

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249

Max

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va-

tion

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Wettbew

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Max

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y57

28%

714%

2019-01-04

250

Facu

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Cienc

iasE

conó

micas

yEm

-presariales,

Universidad

Autón

omade

Mad

rid

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304

33%

2417%

2018-06-19

251

Facu

ltade

deCienc

ias

Econ

ómicas

eEm

presariais,

Universidad

ede

Santiago

deCom

postela

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9639%

922%

2018-12-21

252

Dipartim

ento

diEc

onom

ia,Università

degliS

tudi

Rom

aTr

eItaly

5740%

2552%

2018-06-19

253

Stirling

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agem

entScho

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sityof

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UnitedKingd

om82

28%

2214%

2018-06-19

254

Fond

azione

ENIE

nricoMattei(FE

EM)

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4243%

30%

2018-10-13

255

Dipartim

ento

diEc

onom

iaeMan

age-

ment,Università

degliS

tudi

diBrescia

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2524%

2524%

2018-06-19

256

Faku

ltät

für

Wirt

scha

ftsw

issen

scha

ft,

Ruh

r-Universitä

tBochu

mGerman

y41

24%

3027%

2018-10-13

257

Centre

d’Éc

onom

iede

l’Université

Paris

-Nord,

Université

Paris

-Nord

(Paris

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Fran

ce70

33%

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2018-06-19

258

Scho

olof

Business

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omics,

Loug

hborou

ghUniversity

UnitedKingd

om157

31%

5311%

2018-05-07

259

Dep

artm

ent

ofPo

litical

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omy,

King’sCollege

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om52

25%

1429%

2018-05-09

48

Ran

kIn

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260

Wiene

rInstitu

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leW

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ftsvergleiche(W

IIW

)Austria

229%

20%

2018-10-13

261

Han

sBöckler

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65%

743%

2019-01-04

262

Fachbe

reichW

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,Universitä

tDuisburg-Es

sen

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y42

10%

346%

2018-06-19

263

Scho

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dFina

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Uni-

versity

ofSt.And

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UnitedKingd

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24%

1010%

2018-05-09

264

Wirt

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und

Sozialwis-

senschafl

tiche

Faku

ltät,

Universitä

tPo

tsda

m

German

y38

45%

3244%

2018-06-19

265

Iktis

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ilimlerF

akültesi,

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Üniversite

siTu

rkey

2245%

60%

2019-01-04

266

Faku

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für

Wirt

scha

ftsw

issen

scha

ft,

Otto-von-Gue

ricke-U

niversitä

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rg

German

y27

22%

2218%

2018-06-19

267

Dep

artament

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onom

iaAplicad

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tAutòn

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Barcelona

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5332%

425%

2018-06-19

268

Europe

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kforR

econ

structionan

dDevelop

ment(E

BRD)

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lOrga-

nizatio

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20%

2018-12-21

269

Politiik

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2018-10-13

270

Faku

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Wirt

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ftsw

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scha

ften

,Le

upha

naUniversitä

tLü

nebu

rgGerman

y31

29%

3129%

2018-06-19

271

Fran

kfurtScho

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ncean

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-agem

ent

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y72

18%

383%

2019-05-29

49

Ran

kIn

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272

Man

agem

entScho

ol,Que

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Univer-

sity

UnitedKingd

om80

35%

1225%

2018-05-14

273

Facu

ltat

deCiènc

ies

Econ

òmiques

iEm

presarials,

Universita

tRovira

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giliTa

rragon

a

Spain

161

42%

1331%

2018-06-19

274

Leerstoelgroep

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gsecon

omie,

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ingen

Universite

iten

Research-

centrum

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rland

s16

25%

30%

2018-06-19

275

Dep

artm

ento

fEcono

mics,City

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sity

UnitedKingd

om25

44%

933%

2018-04-19

276

Senter

fortekn

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vasjon

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IK),

Universite

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sloNorway

3959%

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2018-04-19

277

Écolede

sHau

tesÉt

udes

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ienc

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ciales

(EHES

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2019-01-04

278

Centre

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ience

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vatio

nStud

ies,

Kun

gligaTe

knisk

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Swed

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33%

714%

2018-06-19

279

Wiss

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ftszentrum

Berlin

für

Sozialforschun

g(W

ZB)

German

y12

25%

1225%

2018-06-19

280

Facu

ltadde

Cienc

iasE

conó

micas

yEm

-presariales,

Universidad

deNavarra

Spain

1513%

520%

2019-01-01

281

Man

agem

entScho

ol,U

niversity

ofLiv-

erpo

olUnitedKingd

om159

39%

3732%

2018-05-08

282

Dép

artementde

Scienc

esÉc

onom

ique

set

deGestio

n,Université

Panthé

on-

Assas

(Paris

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Fran

ce63

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3132%

2018-06-19

50

Ran

kIn

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rch

Pos

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283

Dép

artementd’Éc

onom

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olePo

ly-

techniqu

eFran

ce22

14%

2214%

2018-05-08

284

Scho

olof

BusinessM

anagem

ent,Que

enMary

UnitedKingd

om87

48%

2343%

2018-04-16

285

ESSE

CBusinessScho

olFran

ce165

30%

7229%

2018-04-18

286

Dep

artm

entof

Econ

omics,

University

ofCrete

Greece

2619%

80%

2019-01-31

287

Iktis

adi

veIdari

Bilimler

Fakü

ltesi,

OrtaDoguTe

knik

Üniversite

siTu

rkey

8853%

3135%

2018-04-18

288

Collège

duMan

agem

entd

elaTe

chno

lo-

gie,

École

Polytechniqu

eFé

dérale

deLa

usan

ne(E

PFL)

Switz

erland

195%

1010%

2018-06-20

289

Dep

artm

entof

Econ

omics,

University

ofThe

ssaly

Greece

1921%

50%

2018-04-18

290

CentreInternationa

lde

Reche

rche

sur

l’Env

ironn

ementet

leDévelop

pement

(CIR

ED)

Fran

ce25

20%

911%

2018-06-20

291

Institu

tdeFina

ncede

Strasbou

rg,U

ni-

versité

deStrasbou

rgFran

ce39

31%

1619%

2018-06-20

292

Dep

artm

entof

Econ

omics,

University

ofMaced

onia

Greece

2223%

1921%

2018-05-08

293

CentraalP

lanb

ureau(C

PB)

Nethe

rland

s120

23%

1315%

2019-02-04

294

Facoltá

discienz

eecon

omiche

,Univer-

sitáde

llaSv

izzera

Italiana

(USI)

Switz

erland

134

24%

1911%

2019-02-22

295

Facu

ldad

ede

Econ

omia,Universidad

edo

Coimbra

Portug

al121

35%

1118%

2018-08-29

51

Ran

kIn

stit

utio

nC

ount

ryR

esea

rch

Pos

itio

nsFe

mal

eP

o-si

tion

sP

rofe

ssor

sFe

mal

eP

rofe

ssor

sA

sof

296

Han

delsh

øgskolen

,Universite

tetiSta-

vang

erNorway

7726%

2818%

2018-04-16

297

HEC

Écolede

Gestio

n,Université

deLiège

Belgium

271

43%

4330%

2018-08-29

298

NetworkforS

tudies

onPe

nsions,A

ging

andRetire

ment(N

etSP

AR)

Nethe

rland

s13

15%

2018-06-20

299

Allian

ceMan

chesterBusinessScho

ol,

University

ofMan

chester

UnitedKingd

om120

29%

8028%

2018-04-18

300

Équipe

deReche

rche

surl’U

tilisa

tion

desDon

nées

Individu

ellesen

lienavec

laThé

orie

Écon

omique

(ERUDIT

E),

Université

Paris

-Est

Fran

ce36

39%

1315%

2018-04-23

52

TableB:P

ercentageof

Wom

enon

DifferentLe

vels,

byCou

ntry

Cou

ntry

Full

Pro

-fe

ssor

Ass

ocia

teP

ro-

fess

or

Ent

ryLe

vel

Res

earc

hFe

llow

Res

earc

hA

sso-

ciat

e

All

leve

lsO

bs.

Inst

i-tu

tion

s

Aus

tria

21.7%

29.7%

25.4%

311

6B

elgi

um23.6%

36.6%

45.0%

39.6%

34.8%

33.6%

979

7C

zech

Rep

ublic

30.6%

30.6%

623

Den

mar

k20.7%

34.6%

29.5%

31.9%

25.5%

28.7%

677

7F

inla

nd24.2%

43.1%

30.3%

30.6%

235

3Fr

ance

27.6%

33.0%

42.1%

34.9%

25.3%

32.2%

3583

36G

erm

any

17.5%

30.4%

34.7%

27.0%

21.2%

3708

101

Gre

ece

12.7%

30.3%

25.2%

20.9%

382

5H

unga

ry25.7%

25.7%

702

Inte

rnat

iona

lO

rgan

izat

ion

20.0%

22.9%

22.8%

1358

4

Irel

and

35.2%

44.1%

40.4%

218

7It

aly

21.4%

36.4%

46.8%

42.4%

32.2%

33.4%

2117

33Lu

xem

bour

g21.7%

21.7%

921

Net

herl

ands

11.1%

20.5%

30.8%

38.2%

32.9%

24.7%

2688

27N

orw

ay22.7%

31.2%

29.9%

42.4%

34.9%

29.8%

1197

17P

olan

d34.4%

41.8%

52.6%

51.1%

1405

40P

ortu

gal

16.3%

37.6%

38.1%

33.9%

41.6%

35.7%

1158

10R

oman

ia50.5%

61.1%

61.4%

63.4%

57.9%

558

12R

ussi

a34.3%

53.7%

59.3%

63.6%

55.0%

52.0%

3042

4Sp

ain

30.0%

38.8%

43.3%

34.0%

41.4%

39.1%

2835

25Sw

eden

24.6%

40.1%

37.8%

50.6%

37.0%

36.3%

1780

22Sw

itze

rlan

d16.6%

21.1%

22.2%

37.3%

25.9%

25.9%

2188

30T

urke

y28.6%

51.5%

45.5%

41.9%

215

6U

nite

dK

ingd

om22.0%

33.0%

41.7%

44.5%

40.7%

35.8%

9573

129

Tot

al22.1%

37.2%

40.6%

40.0%

37.5%

34.0%

40431

537

Not

es:Werepo

rtcells

whe

rethelevelisrepresentedby

atleast50

posit

ions.

53

TableC:P

ercentageof

Wom

enon

DifferentLe

vels,

byCou

ntry,T

op300Only

Cou

ntry

Full

Pro

-fe

ssor

Ass

ocia

teP

ro-

fess

or

Ent

ryLe

vel

Res

earc

hFe

llow

Res

earc

hA

sso-

ciat

e

All

leve

lsO

bs.

Inst

i-tu

tion

s

Aus

tria

21.5%

29.9%

25.7%

269

3B

elgi

um21.5%

36.6%

45.6%

35.8%

39.9%

33.5%

892

5C

zech

Rep

ublic

31.4%

31.4%

511

Den

mar

k19.6%

31.9%

25.0%

27.5%

25.9%

526

4F

inla

nd25.0%

43.1%

31.9%

31.6%

215

2Fr

ance

27.4%

33.5%

42.4%

25.6%

34.9%

32.1%

3460

32G

erm

any

14.7%

32.8%

27.3%

34.0%

22.0%

2238

37G

reec

e12.7%

30.3%

25.2%

20.9%

382

5H

unga

ry25.7%

25.7%

701

Inte

rnat

iona

lO

rgan

izat

ion

20.0%

22.8%

22.7%

1355

3

Irel

and

33.3%

43.8%

39.2%

130

4It

aly

19.4%

34.7%

46.2%

30.5%

42.2%

31.9%

1857

24Lu

xem

bour

g20.7%

20.7%

871

Net

herl

ands

10.0%

20.1%

30.7%

28.5%

38.1%

23.8%

2446

13N

orw

ay20.0%

30.2%

27.1%

27.3%

25.6%

891

7P

olan

d42.0%

42.0%

881

Por

tuga

l10.4%

37.2%

38.0%

34.5%

34.5%

919

5R

oman

ia48.5%

62.4%

60.8%

63.4%

57.9%

520

2R

ussi

a34.3%

53.7%

59.3%

55.0%

63.6%

52.0%

3037

3Sp

ain

29.9%

36.7%

44.2%

36.6%

30.3%

37.7%

2185

12Sw

eden

22.3%

26.5%

36.4%

33.2%

48.8%

33.1%

1237

10Sw

itze

rlan

d18.3%

21.4%

23.6%

24.8%

36.9%

27.1%

1574

12T

urke

y33.9%

51.5%

48.5%

45.3%

190

4U

nite

dK

ingd

om20.2%

32.9%

39.1%

37.2%

36.5%

32.8%

5453

53

Tot

al21.6%

36.6%

39.9%

33.4%

38.5%

32.7%

30072

244

Not

es:Werepo

rtcells

whe

rethelevelisrepresentedby

atleast50

posit

ions.

54

TableD:E

uroV

ocDefintionof

Regions

Central

andEa

sternEu

rope

NorthernEu

rope

Southern

Europe

Western

Europe

Alban

iaDenmark

Cyp

rus

And

orra

Arm

enia

Estonia

Greece

Austria

Azerbaijan

Finlan

dHolySee

Belgium

Belarus

Icelan

dItaly

Fran

ceBosniaan

dHerzegovina

Latvia

Malta

German

yBulgaria

Lithua

nia

Portug

alIrelan

dCzech

Repub

licNorwa

ySa

nMarino

Liechtenstein

Croatia

Sweden

Spain

Luxembo

urg

Georgia

Mon

aco

Hun

gary

Netherla

nds

Moldo

vaSw

itzerland

Mon

tenegro

UnitedKingd

omNorth

Macedon

iaPo

land

Rom

ania

Russia

Serbia

Slovak

iaSlovenia

Ukraine

Source:https:/

/pub

lications.europ

a.eu

/en/

web

/eu-vo

cabu

larie

s/th-con

cept-schem

e/-/resource/eurovoc/100277,

accessed

May

9,2019.

55