Conference Paper Surrounded by Women: How Changes in the Gender Composition Affect Student Performance and Careers in Academia

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1 econstor Der Open-Access-Publikationsserver der ZBW Leibniz-Informationszentrum Wirtschaft The Open Access Publication Server of the ZBW Leibniz Information Centre for Economics Zölitz, Ulf Conference Paper Surrounded by Women: How Changes in the Gender Composition Affect Student Performance and Careers in Academia Beiträge zur Jahrestagung des Vereins für Socialpolitik 2015: Ökonomische Entwicklung - Theorie und Politik - Session: Understanding the nature of peer effects, No. E02-V2 Provided in Cooperation with: Verein für Socialpolitik / German Economic Association Suggested Citation: Zölitz, Ulf (2015) : Surrounded by Women: How Changes in the Gender Composition Affect Student Performance and Careers in Academia, Beiträge zur Jahrestagung des Vereins für Socialpolitik 2015: Ökonomische Entwicklung - Theorie und Politik - Session: Understanding the nature of peer effects, No. E02-V2 This Version is available at: Nutzungsbedingungen: Die ZBW räumt Ihnen als Nutzerin/Nutzer das unentgeltliche, räumlich unbeschränkte und zeitlich auf die Dauer des Schutzrechts beschränkte einfache Recht ein, das ausgewählte Werk im Rahmen der unter nachzulesenden vollständigen Nutzungsbedingungen zu vervielfältigen, mit denen die Nutzerin/der Nutzer sich durch die erste Nutzung einverstanden erklärt. Terms of use: The ZBW grants you, the user, the non-exclusive right to use the selected work free of charge, territorially unrestricted and within the time limit of the term of the property rights according to the terms specified at By the first use of the selected work the user agrees and declares to comply with these terms of use. zbw Leibniz-Informationszentrum Wirtschaft Leibniz Information Centre for Economics

2 Surrounded by Women: How Changes in the Gender Composition Affect Student Performance and Careers in Academia * February, 2015 Ulf Zölitz A IZA and Maastricht University Abstract This paper shows how the classroom gender composition and teacher gender affect achievement in university education. Identification is based on random assignment of staff and students to teaching sections. A higher fraction of females in the classroom increases academic performance of females, but only when instructors are female as well. Consistent with the idea of gender role models female staff raises female students course performance. Both female and male students perform better when the best student in class has the same gender. Strikingly exposure to a higher share of female instructors increases female s probability to enroll in a PhD program. Keywords: gender composition, teacher gender, higher education JEL classification: I21, I24, J24 * I thank Sarah Cannon, Jan Feld, David Figlio, Nina Weschenfelder and participants at various seminars and conferences for helpful discussions and comments. I further like to thank Joël Castermans, Sanne Klasen and Kim Schippers from the SBE Scheduling Department, Sylvie Kersten from the SBE Exams Office, and Jeannette Hommes and Paul Jacobs from the Educational Research and Development Department for providing data and valuable background information. A Institute for the Study of Labor (IZA), P.O. Box 7240, Bonn, Germany.

3 1 Introduction Women are increasingly outperforming men in school achievements, college enrollment rates and study performance. In most developed countries the gender gap in education as reversed and continues to grow. Throughout the last decades female performance and the fraction of female students in higher education has steadily increased. Does this change in the academic gender composition affect female career trajectories? Not quite, controversy still only few female graduates choose for a career in academia creating a lack of female role models in many study fields. This lack of female professors and university instructors itself may have an effect female achievement and career choices. This paper investigates how changes in the gender composition in higher education affect students study performance and PhD enrollment. I use data from the Maastricht University School of Business and Economics (SBE) a non-selective, in many ways typical European university located in the Netherlands. The key institutional feature of the SBE is that within courses both students and teachers are randomly assigned to teaching sections of 10 to 15 students. Exploiting this random variation allows me to identify whether the fraction of female classroom peers and the gender of the classroom instructor affects the performance of female and male students differentially. To assess whether treatment effects persist I follow students and investigate whether exposure to female peers and staff affects the probability to enroll in a PhD program after graduation. The results on study performance show that being assigned to a section with a higher fraction of female students increases course grades of females. While male students appear largely unaffected, in particular above median ability females profit from exposure to female peers. Surprisingly this effect is entirely driven by teaching sections with female instructors and -1-

4 is non-existent for sections taught by male staff. I find that a 10 percent increase in the share of female peers causes a 2 percent increase in standardized course grades in sections with female instructors. Next I test whether the presence of female role models affects achievements and find mixed evidence. Being taught by female staff raises course grades of female students by 3.3 percent of a standard deviation, but does not affect male students. When testing for whether also high performing classmates can serve a role models I find that students performance increases by percent of a standard deviation if the gender of the best student in class matches the own gender. Finally I investigate whether exposure to both female staff and peers are persistent and affect medium run career choices, namely enrollment in PhD graduate programs. I find that being randomly exposed to sections containing more female students increases females probability to enroll in a PhD program. The key finding of this paper is that exposure to female instructors has a large effect on the probability of female students to start a PhD. A 10 percent increase in the share of female instructors in obligatory first year bachelor causes a 10 percent increase in the probability of female students to enroll in a doctoral program. My results suggest that policies of hiring more female professors and university instructors would raise female study performance and increase the number of women starting a career in academia while male students would remain largely unaffected. The evidence presented in this article makes three distinct contributions to the existing literature. First, this study uses large and representative administrative data that combines random assignment of both students and staff which provides well-identified causal estimates for the effects of gender composition and staff gender. Second this work provides evidence that gender composition effects are strongly linked to instructor gender and should not be evaluated -2-

5 independently. Third this paper is able to follow the academic career of students and is the first to provide causal evidence that exposure to female university staff increases women s probability to enroll in a PhD program. While there is a substantial amount of papers on gender effects in education, there are only very few studies exploiting random assignment. Carell, Page and West (2010) look at the impact of instructor gender at the U.S. air force academy where students were randomly assigned to Professors. They show that female professors increase females probability to take math and science classes and to graduate with a STEM major. While the work of Carell et al. (2010) has outstanding internal validity due to the random assignment, it is not clear whether results would be similar at a non-selective, non-military college environment. The remainder of the paper is structured as follows. Section 2 describes the institutional environment and the assignment procedure of students and staff to sections. Section 3 discusses the dataset. Section 4 provides the empirical strategy and shows evidence that the assignment to sections is random, conditional on scheduling constraints. Section 5 shows the results. Section 6 concludes the article. 2 Background 2.1 Institutional Environment In this paper I use data collected at the Maastricht University School of Business and Economics (SBE). 1 Maastricht University is located in Maastricht, a city in the south of the Netherlands and in the center of Europe. Currently there are about 4,400 students at the SBE enrolled in Bachelor, Master and PhD programs. Because of its proximity to Germany, it has a large German student 1 For more detailed information on the institutional environment see Feld & Zölitz (2014). -3-

6 population (49 percent) mixed with Dutch (33 percent) and other nationalities. About 38 percent of the students and 36 percent of teaching staff are female. The academic year at the SBE is divided into four regular teaching periods of two months and two skills periods of two weeks. Students usually take two courses at the same time in the regular periods and one course in the skills period. I exclude courses in non-regular skills periods from the analysis since these are often not graded and usually include multiple staff members which I could not always separately identify in the data. At the SBE courses are organized by course coordinators, mostly senior staff. Almost all courses have lectures which are followed by all students of the course and are usually given by the course coordinator or other senior staff. In addition to the lectures each course is divided into a number of teaching sections containing up to 16 students. Most of the actual teaching takes place in these small classes that meet in two weekly sessions of two hours each. My analysis focuses on these sections and the staff members allocated to them. 2.2 Teachers at Maastricht University The teachers at the section level present in the classroom can either be student teaching assistants, PhD students, Post-Docs, full time lecturers, Assistant-, Associate- or Full professors. 2 Depending on their contract type teachers at Maastricht University spend between hours per academic year in the classroom with students. Teachers usually teach courses offered by the department they are working in. Teachers usually have an influence on the type of course they 2 At Maastricht University, all PhD students are required to teach classes. When the SBE has not enough teachers for sections at the bachelor level master students are recruited as student teaching assistants. -4-

7 are teaching, but within a given course they have no influence on the sections they get assigned to. 2.3 Assignment of Teachers and Students to Sections The Scheduling Department of the SBE assigns teachers to sections, students to sections and sections to time slots. Before each period, there is a time period in which students can register online for the courses they want to take. After the registration deadline, the scheduler gets a list of registered students for each course. The scheduler then allocates the students to sections with the program Syllabus Plus Enterprise Timetable using the allocation option allocate randomly (see Figure F1 in the Appendix for a screenshot from the scheduling program). 3 This assignment mechanism to sections was introduced in the academic year 2008/09 for all Bachelor and Master courses at SBE in order to simplify student allocation. After the assignment of students to sections, the sections are assigned to the available teaching staff, time slots and rooms. 4 After this assignment the program Syllabus Plus Enterprise Timetable indicates scheduling conflicts. 5 Scheduling conflicts arise for about 5 percent of the initial assignments. If the computer program indicates a scheduling conflict the scheduler 3 About ten percent of the slots in each section are initially left empty and are filled with students who register late. This procedure balances the amount of late registration students over the sections. In addition to this all Bachelor sections are stratified by nationality with the computer program SPASSAT since the academic year 2010/11. 4 About ten percent of teachers indicate time slots when they are not available for teaching. This happens before they are scheduled and requires the signature of the department chair. This is not a thread to my identification since students are still randomly assigned to teachers conditional on the students scheduling conflicts. 5 There are three reasons for students scheduling conflicts: (1) the student takes another course at the same time. (2) The student is also teaching assistant and needs to teach at the same time. (3) The student indicated non-availability for evening education. By default all students are recorded as available for evening sessions. Students can opt out of this by indicating this in an online form. Evening sessions are scheduled from 6 p.m. to 8 p.m. and about three percent of all sessions in our sample are scheduled for this time slot. -5-

8 manually moves students between different sections until all scheduling conflicts are resolved. The next step in the scheduling procedure is that the section and teacher assignment is published. After this, the scheduler receives information on late registering students and allocates them to the empty spots. Schedulers do not know the students and do not observe the previous grades or the gender of the students in their scheduling program. There are a few exceptions to this general procedure which I list in detail in section A.1 in the appendix. After removing these exceptions, in my estimation sample neither teachers, nor students, nor schedulers, and not even course coordinators influenced the assignment of teachers or the gender composition of sections. Within each course the gender composition of a tutorial and the gender of the assigned staff are fully random and exogenous to the outcomes I investigate. 3 Data The data I am using in this paper contains information on all students taking courses at the SBE over the course of four academic years between 2009/2010 and 2012/2013. Scheduling data on section composition and teacher assignment was provided by the Scheduling Department of the SBE. In total I observe 686 teaching staff members, 7,474 students, 628 courses 6, 5,359 sections and 53,838 course grades in the estimation sample. The course size ranges from 1 to 638 students and there are 1 to 43 sections per course. The data on student grades and student background characteristics were provided by the Examinations Office of the SBE. The Dutch grading scale ranges from 1 to 10, with 5.5 usually 6 In this paper I refer to each course-year combination as separate course. That means that I count a course with the same course code that takes place in four years as four separate courses. -6-

9 being the lowest passing grade. Figure 1 shows the distribution of course grades in the estimation sample. If the grade of a student is lower than 5.5, the student fails the course and has the possibility to take a second attempt at the exam. I observe grades after the first and second attempt separately. For my analysis I only use the final grade after the first exam attempt as an outcome measure, since first and second attempt grades are not comparable. For the construction of the student GPA final grades after the last attempt were used. 7 Table 1 provides an overview on descriptive summary statistics. Panel A shows the student level variables, panel B the measures for student performance and panel C the teaching staff characteristics. The sample contains 58,057 student course registrations. Out of these 4,219 (7.3 percent) dropped out of the course throughout the course period. 8 I therefore observe 53,838 course grades after the first sit. In order to provide a more formal test of the described random assignment of students to sections within courses I regress instructor gender on individual student characteristics. Table 2 and table 3 show the results of this randomness check. The tables show that the assignment has the properties we expect from random assignment. The included individual covariates are not jointly significant in any of the models. 4 Empirical Strategy 4.1 Identification of Role Model Effects 7 I decided to use the GPA calculated from final grades because this is closer to the common understanding and interpretation of GPA. 8 Dropping out of a course means that a student was registered for a course but did not receive a final course grade. -7-

10 I start testing whether role models affect student outcomes first looking at the direct impact of instructor s gender on grades, shown in equation (1). (1) The dependent variable is the grade of student, in a course-specific section, at time. is a constant. is an indicator variable for whether the section instructor is female and is a dummy for the gender of student. Since instructor gender may affects female and male students differentially I allow the coefficient to vary by the gender of student. This is captured by the interaction term. Since students are randomly assigned to sections and instructors the estimates of and can directly be interpreted as causal effects. will capture the general gender performance gap. I test whether the gender of the best peer in the classroom has an impact on grades in a similar way by applying equation (2). (2) Equations (1) and (2) also include, a vector of additional controls including the GPA (grade point average) of the student, the number of students in the classroom, year-course-period fixed effects and fixed effects for the parallel courses taken at the same time. is an error term with the usual properties. The included year-course-period fixed effects control for mean differences in outcomes across courses and time. This takes into account different grade levels in different years and courses with differing degrees of difficulty. In all specifications, I also -8-

11 include parallel-course fixed effects i.e., fixed effects for the other course taken at the same time. Conceptually, including parallel-course fixed effects should pick up all leftover nonrandom variation in section assignment that is due to conflicting schedules. 9 To allow for correlations in the outcomes of students within each course, I cluster the standard errors at the course-year-period level in all specifications. I standardized the dependent variable, the grade of student to mean zero and unit variance over the estimation sample to simplify the interpretation of the coefficients. 4.2 Identification of Classroom Gender Composition Effects Next I investigate the general effect of the classroom gender composition on university performance by using the following model: (3) In equation (3) section excluding student refers to the fraction of female students in the 10 To take into account that the section gender composition may affect female and male students differentially I allow the coefficient to vary by the gender of student. This is captured by the interaction control for the gender of student. In equation (3) the estimate of. Additionally I will capture how men s performance is affected by the fraction of female peers. will inform us about whether changes 9 In practice point estimates are almost identical whether parallel-course fixed effects are included or not. 10 I exclude student from the calculation of the fraction of females in the section to avoid the reflection problem described by Manski (1993). -9-

12 in the fraction of females affect females and males symmetrically and captures the general gender gap in university grades. and have the same properties as describes in section 4.1. Since the gender composition effects estimated in equation (3) may also depend on the gender of the section instructor I also estimate equation (3) separately for sections with female and male instructors to test whether the instructor gender is a relevant mediator Identification of Long Run Effects In order to test whether exposure to female peers and female instructors has effects beyond course grades and affects long run outcomes I estimate the following equations: (4) (5) The dependent variables for which I estimate equations (4) and (5) are end-of-study-gpa and enrollment in a PhD program. In equation (4) refers to the fraction of female peers student faced in all sections together over the total course of the study. The vector includes course fixed effects to take into account that students may have conscientiously chosen courses with more female students. 11 I have chosen to report the split-sample results instead of triple interaction to allow for a more straightforward interpretation of the regression results. For readers who prefer the latter I also report models using triple interaction between student gender, instructor gender and fraction females in the appendix. It can be seen that all main results are very similar. -10-

13 In equation (5) refers to the fraction of female instructors student faced during the eight compulsory first year bachelor courses. In an alternative model I use the fraction of female instructors over the total course of study. Since students at Maastricht University can choose between a limited number of courses in their second and third year of bachelor studies includes fixed effects for all the chosen courses. This should account for the fact that students may go shopping for courses with a higher fraction of female instructors at the course level. As an additional robustness check I also estimate equation (5) using only the fraction of female instructors in compulsory first year courses. 5 Results 5.1 Direct Effects on Course Performance Table 4 shows the estimation results for equation (1). Column (1) shows that being taught be a female instructor increases female students course grades by 3 percent of a standard deviation. The gender effect for male students is close to zero and not statistically significant. When adding parallel course fixed effects and teacher fixed effects in column (2) and (3) point estimates remain similar. When splitting the data into section taught by senior staff (full, associate and assistant professors) and junior staff (graduate students) in columns (4) and (5) we observe that most of the gender specific effect appears to be driven by senior staff. Females in sections with senior female staff achieve 5.9 percent higher standardized grades This effect is unlikely to be driven by female favoritism in exam grading, since the person who grades the final exam is not necessarily one of the section teachers. In addition to this exams are most of the time graded by multiple staff members who usually grade specific exam questions for all students and not only their own students. -11-

14 Conceptually there are at least two different channels through which this effect could work. First female teachers might be more effective in teaching female students and adjust their teaching speed and style more towards the needs of their female students. Second, female staff may serve as a role model for female student which could increase female students class involvement and effort put into the coursework. Next I test whether excellent peers may serve as gender role models and affect the performance of their fellow classmates. Table 5 shows the estimation results for equation (2). Colum (1) shows that the gender of the best peer has no significant on grades. Columns (2) and (3) show that there is also no effect when splitting the sample based on instructor gender. When splitting the sample at the median student GPA in columns (4) and (5) we can see that these average results hide some heterogeneity. Above median GPA females perform 3.6 percent of a standard deviation higher when their best peer is female relative to male students. In contrast to this male students appear to be harmed by top female peers. Above median GPA males perform 2.6 percent lower when in a section where the best peer is female. This means that both female and male students with an above median past performance obtain higher grades when their best classmate matches their own gender. Whether this is a result of peer interaction, classroom completion dynamics within the own gender group or performance dependent role model effects cannot be specified at this stage, but will be discussed later. 5.2 Classroom gender composition and course performance Table 6 shows the estimation results for equation (1) on the impact of the classroom gender composition on student performance. Colum (1) suggests that the fraction of female students in the section does not significantly course grades. When looking at columns (2) and (3) we observe -12-

15 that this result masks substantial heterogeneity. Column (2) shows that in sections with female instructors the fraction of female peers has a significant impact on performance of female students. A 10 percent increase in the fraction of females causes a relative gain of 1.9 percent in standardized grades. Column (2) shows that the absolute gain is about 1.2 percent (1.98 percent minus 0.77 percent). Column (3) shows the effects for sections with male instructors. In these sections we observe that male student grades improve as the fraction of female students increases. 5.3 Long run effects: PhD enrollment Looking at the effects of female peers and instructors on course grades can provide interesting insights on how the gender composition and gender role models may affect short run performance. One important question however is whether the effects are persistent and translate into long-run outcomes. For most students this is the GPA at the time of graduation which affects the admission probability to master and PhD programs at different institutions. For students that enter the job market the graduation GPA is one characteristic frequently used in the application process. It is likely to affects job search length, and labor market outcomes. Graduation GPA is correlated with wages 1.5 years after graduation. Table 8 shows the estimation results for equation (6). The dependent variable is a dummy indicator for whether the student started a PhD at Maastricht University after study competition. The table shows that females who faced a higher share of female classmates during their study time become significantly more likely to enroll in a PhD program. A 10 percent increase in the -13-

16 share of females increases the take-up probability by X percent. There is no effect for male students. Next I investigate whether exposure to female instructors has similar effects on PhD enrollment. Table 9 shows that exposure to a higher fraction of female instructors increases the probability to take up a PhD dramatically. Taking into consideration the baseline take-up rates a 10 percent increase in the share of female instructors increases the take-up probability by 10 percent. Female instructors may provide a role model for female students to start a career in academia. Female instructors may also be more likely to encourage female students to apply or write them letters of recommendation. Female professors may also directly recruit their PhD students from the pool of talented students they observe in their classes. 6 Conclusion In this paper, I investigate gender interactions between university students, peers and teachers to explain university achievements and enrollment into a PhD program. Exploiting random assignment of students to section and instructors I show that female students obtain better course grades when assigned to a more feminine classroom but only when the instructor is female as well. This suggests that female instructors adjust their teaching style to the needs of female students when their relative share in class is large. Somewhat surprising male student s performance seems to be independent of the gender composition. To understand whether gender role models affect outcomes I test whether instructor s gender and the gender of best section peer affects performance. I find that females perform better when assigned to female teachers. I also find that students perform better when their classmate with the highest GPA has the same gender. -14-

17 This paper uses relatively small variation in the fraction of female students to identify effects. Whether we can extrapolate from these findings and whether more dramatic changes in the student composition would have symmetric effects is currently not clear. The findings of this paper are important to understand how policies introducing female quotas in both student admissions and hiring of university faculty would affect outcomes and gender differences in university attainment and entry into scientific careers. Taken together the findings suggest that an increasing feminization of academia in terms of students and staff will benefit females and leave male students absolute performance largely unaffected. Relative performance differences the gender performance gap will increase. Strikingly, and perhaps more important from a societal perspective, the results suggest that hiring more female academic staff will also increase the number of females enrolling in PhD programs and therefore create a reinforcing mechanism that will gradually attract more and more women to academic careers. -15-

18 References Beaman, L., Chattopadhyay, R., Duflo, E., Pande, R., & Topalova, P. Forthcoming. Powerful women: Does exposure reduce bias. Quarterly Journal of Economics, 124(4). Bettinger, E. P., & Long, B. T. (2005). Do faculty serve as role models? The impact of instructor gender on female students. American Economic Review, Carell S., M. Page & J. West (2010). Sex and Science: How Professor Gender Perpetuates the Gender Gap, Quarterly Journal of Economics, 125(3), Dee, T. S. (2007). Teachers and the gender gaps in student achievement. Journal of Human Resources, 42(3), Ginther, D. K., & Kahn, S. (2004). Women in economics: moving up or falling off the academic career ladder? Journal of Economic Perspectives, 18, Hoffmann, F., & Oreopoulos, P. (2009). A professor like me The influence of instructor gender on college achievement. Journal of Human Resources, 44(2), Manski, C. F. (1993). Identification of endogenous social effects: The reflection problem. The Review of Economic Studies, 60(3),

19 0.05 Fraction.1.15 Tables and Figures Figure 1: Distribution of Course Grades Grade first-sit Note: Based on 53,838 course grades. -17-

20 0 Fraction Figure 2: Fraction of female students in section Fraction female Note: Based on 5,397 sections containg usually between students. -18-

21 Figure 3: Percentage of Female University Graduates and Doctoral Degrees in the Netherlands (1) and the US (2) (1) Source: (2) Source: U.S. Department of Education, National Center for Education Statistics (NCES) -19-

22 Figure 4: Percent of U.S. Adults Ages With a Bachelor's Degree or Higher, Source: PRB analysis of data from U.S. Census Bureau. -20-

23 Table 1: Summary statistics Panel A: (1) (2) (3) (4) (5) Student level characteristics N Mean SD Min Max Female 7, Age 7, Dutch 7, German 7, Bachelor student 7, BSc economics 7, BSc business 7, BSc other 7, Courses taken 7, Final GPA 7, Student enrolls in PhD program 7, Panel B: (1) (2) (3) (4) (5) Student performance N Mean SD Min Max Course dropout 58, Grade first-sit 53, Passed course 53, Panel C: All teaching staff Female staff Male staff Staff characteristics (1) (2) (3) (4) (5) (6) Mean SD Mean SD Mean SD Female staff Full professor Associate professor Assistant professor Lecturer Postdoc PhD student Student teacher Staff has research duties N

24 Table 2: Randomness check: Determinants of Staff Gender (1) (2) (3) (4) Female staff Female staff Female staff Female staff Female *** (0.004) (0.003) (0.003) (0.003) Female staff Age * * (0.001) (0.001) Dutch (0.004) (0.004) German (0.004) (0.004) BSc economics (0.013) (0.015) BSc business (0.012) (0.013) Observations 72,241 72,241 68,383 68,383 R-squared Course FE NO YES YES YES Parallel course FE NO NO NO YES P-value: Joined significance of individual covariates < Note: Robust standard errors clustered at the tutorial level in parentheses. All models control for the fraction of females of females at the course level. *** p<0.01, ** p<0.05, * p<

25 Table 3: Randomness Check: Determinants of the Fraction Female Classmates (1) (2) (3) (4) Fraction female Fraction female Fraction female Fraction female Female (0.002) (0.002) (0.002) (0.002) Female staff (0.004) (0.005) (0.005) Age (0.000) (0.000) Dutch (0.002) (0.002) German * * (0.001) (0.001) BSc economics (0.005) (0.006) BSc business (0.005) (0.006) Observations 72,178 72,178 68,320 68,320 R-squared Course FE YES YES YES YES Parallel course FE NO NO NO YES P-value: Joined significance of individual covariates Note: Robust standard errors clustered at the tutorial level in parentheses. All models control for the fraction of females of females at the course level. *** p<0.01, ** p<0.05, * p<

26 Table 4: Role models? - The Effect of Staff Gender on Grades Junior staff Senior staff (1) (2) (3) (4) (5) Std. Grade Std. Grade Std. Grade Std. Grade Std. Grade Female * Female staff ** * ** ** (0.015) (0.015) (0.015) (0.018) (0.028) Female staff (omitted) (omitted) (omitted) (0.010) (0.010) Female ** ** * ** (0.008) (0.008) (0.008) (0.011) (0.013) GPA *** *** *** *** *** (0.005) (0.005) (0.005) (0.006) (0.008) Observations 53,838 53,838 53,838 31,171 22,667 R-squared Course FE YES YES YES YES YES Parallel course FE NO YES YES YES YES Teacher FE NO NO YES YES YES Note: Robust standard errors clustered at the class level are in parentheses. The dependent variable is the standardized grade. All regressions include course fixed effects and fixed effects for the parallel course taken at the same time as well the students GPA and the number of student in the classroom. * p<0.1, ** p<0.05, *** p<0.01. Table 5: Role models? - The Effect of the Best Classmate s Gender on Grades Female staff Male staff Below median GPA Above median GPA (1) (2) (3) (4) (5) Std. Grade Std. Grade Std. Grade Std. Grade Std. Grade Best peer is female * Female ** (0.014) (0.026) (0.017) (0.023) (0.016) Best peer is female ** (0.010) (0.018) (0.011) (0.014) (0.012) Female ** ** *** (0.009) (0.017) (0.011) (0.015) (0.010) GPA *** *** *** *** *** (0.005) (0.010) (0.006) (0.010) (0.009) Observations 53,838 18,224 35,614 27,169 26,669 R-squared Course FE YES YES YES YES YES Parallel course FE YES YES YES YES YES Note: Robust standard errors clustered at the class level are in parentheses. The dependent variable is the standardized grade. All regressions include course fixed effects and fixed effects for the parallel course taken at the same time. Additional controls are number of female students, the total number of student in the classroom and a dummy indicator for whether the student is the best herself. * p<0.1, ** p<0.05, *** p<

27 Table 6: Gender composition: The Effect of Fraction of Female Students on Course Grades Female staff Male staff Below median GPA Above median GPA (1) (2) (3) (4) (5) Std. Grade Std. Grade Std. Grade Std. Grade Std. Grade Female * Fraction female *** (0.040) (0.074) (0.047) (0.069) (0.041) Fraction female ** (0.027) (0.049) (0.033) (0.043) (0.029) Female ** (0.016) (0.030) (0.019) (0.027) (0.017) GPA *** *** *** *** *** (0.005) (0.009) (0.006) (0.009) (0.007) Observations 53,838 18,224 35,614 25,929 27,909 R-squared Course FE YES YES YES YES YES Parallel course FE YES YES YES YES YES Note: Robust standard errors clustered at the class level are in parentheses. The dependent variable is the standardized grade. All regressions include course fixed effects and fixed effects for the parallel course taken at the same time as well the students GPA and the number of student in the classroom. * p<0.1, ** p<0.05, *** p<0.01. Table 7: The Effect of Female Instrcutors on Performance in Consecutive Courses (1) Std. Grade Female * Fraction female instructors in first year * (0.067) Fraction female instructors in first year (0.045) Female (0.025) GPA *** (0.010) Observations 16,844 R-squared Course FE YES Note: Robust standard errors clustered at the class level are in parentheses. The dependent variable is the standardized grade. The regressions includes course fixed effects and fixed effects for all courses taken in the past. -25-

28 Table 8: The Effect of Female Exposure on PhD enrolment Only first year BSc compulsory courses (1) (2) (3) (4) PhD enrolment PhD enrolment PhD enrolment PhD enrolment Female * Fraction female staff *** * *** *** (0.007) (0.006) (0.005) (0.005) Fraction female staff ** (0.004) (0.004) (0.003) (0.003) Female (0.003) (0.003) (0.002) (0.002) Average female staff fraction *** available in all courses (0.005) (0.008) Observations 7,474 6,940 3,854 3,852 R-squared Note: Robust standard errors clustered at the individual level are in parentheses. The dependent variable is a dummy variable for whether the student started a PhD at Maastricht University. -26-

29 APPENDIX: A.1 Exceptions from the conditional random assignment A number of special courses and sections where the section assignment was not random were excluded from the estimation sample. For some Bachelor courses there are special sections consisting mainly of repeating students. Whether a repeater section is created depends on the preference of the course coordinator and the number of repeat students. I excluded 34 repeater sections from the analysis. When the number of late registering student exceeds the number of empty spots, the scheduler creates a new section which mainly consist of late registering students. I excluded eight late registration sections from the analysis. In some Bachelor courses students who are part of the Maastricht Research Based Learning (MARBLE) program are assigned to separate sections where they often are assigned to more experienced teacher. Students of this program are typically the highest performing students of their cohort. I excluded 15 sections that consist of MARBLE students from the analysis. In six courses the course coordinator or other education staff influenced the section composition or staff assignment. I therefore excluded these courses from our analysis. Some Master tracks have part time students. Part time students are scheduled mostly in evening classes and there are special classes with only part time students. I excluded 95 part time students from the analysis. In addition to this I also excluded first year first period courses of the two largest Bachelor programs (International Business and Economics) because in these courses only particular students, such as repeating student, have previous grades which are necessary to construct the student GPA. -27-

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