Data publikacji: 01 lut 2022
Zakres stron: -
Przyjęty: 02 lis 2021
DOI: https://doi.org/10.2478/izajole-2022-0001
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© 2022 Nicolas Pistolesi, published by Sciendo
This work is licensed under the Creative Commons Attribution 4.0 International License.
At the completion of high school, senior students must make important decisions on their post-secondary education. These choices have long lasting effects, determining job opportunities and level of wages throughout their working career. However, high school students can sometimes make poor educational decisions (Bettinger et al., 2012; Hoxby and Avery, 2012; Oreopoulos and Dunn, 2013; Wiswall and Zafar, 2015; Hastings et al., 2016). The literature on the determinants of schooling choices has mostly concentrated on the role of expected earnings, the influence of perceived ability, or gender specific preferences. See Altonji et al. (2016) for a review of the literature on the determinants of higher education choices. Berger (1988) studies the role of expected earnings, Arcidiacono (2004) studies perceived ability, and Zafar (2013) examines gender specific preferences among others.
In this article, we study whether or not high school students are influenced by their classmates in their decision to study at university. In the period under study, when French students apply for higher education, they receive feedback from the university on their application. This feedback is intended to assist them in selecting the institute for their post-secondary education. We use the feedback received by the high school classmates to measure the peer effects on enrollment. This measure encompasses the two channels of social influence previously mentioned. First, it is directly related to the students’ academic level, as universities use high school grades to advise their applicants. Second, the feedback received represents relevant information for students to use to update their individual strategy when choosing higher education. Overall, with this measure we can assess the joint impact of both channels of social influence, among high school classmates on their decision to enroll at university.
We exploit a policy called
Using French administrative data on access to university, we observe the applications and the enrollment decisions of the universe of high school students applying to a given university for seven consecutive cohorts, representing around 20,000 individual applications. Our data provide individual characteristics for all applicants to that university. It also indicates their school and class identifiers and the total number of students in each class. However, the data does not include the students that do not apply to this university. As a consequence, the group of peers is partially observed: among the peers of individuals applying to this university, we observe only those applying to the same institution. To investigate the consequences of this important data characteristic, we test whether our results change between the classes that are fully observed and those that are not. As detailed below, the results remain unchanged between these two groups. Finally, our data is representative of the population of high school students applying to university. From these data, we observe that students in a class with a larger share of peers receiving C feedback are less likely to register in the same university.
Assessing whether or not this relationship is causal is a challenging issue: indeed, the endogenous sorting of peers into high schools is particularly challenging in this setting. We use the idiosyncratic variations in the share of peers receiving C feedback across different classes, within the same high school major and during the same academic year. In France, high schools offer three different types of majors, with each major having several classes. In a high school and within a major, there is no tracking by ability between the different classes; however, there is a clear selection of students between high school majors. Using major-by-high-school-by-year fixed effects, we correct the non-random allocation of students between peer groups and control for unobserved factors that are correlated with the choice of high school, the choice of high school major, and the decision to enroll at university. Therefore, our analysis is valid only if there is a random allocation of students within major, high school, and year. We provide an empirical test and a discussion of that assumption.
Our paper stands at the crossroads of two bodies of literature. The first studies ability peer effects. There is a large literature on this topic surveyed in Epple and Romano (2011) and Sacerdote (2011). Hoxby (2000) measures peer quality by fixed students’ characteristics and exploits within-school variation in these characteristics across cohorts. Fruehwirth (2013) studies the effect of a policy that aims to assist low-achieving students to improve their effort input. Lavy et al. (2012) consider the variation of low-ability students within high school and across cohorts, and find that the proportion of academically weaker students has a negative effect on the performance of average students. Other related articles on ability peer effects are those of Hanushek et al. (2003), Zimmerman (2003), Withmore (2005), Foster (2006), Stinebrickner and Stinebrickner (2006), Vigdor and Nechybba (2007), Kang (2007), Graham (2008), Ammermueller and Pischke (2009), Carell et al. (2009), Carell and Hoekstra (2010), Lavy and Schlosser (2011), Lavy et al. (2012), Arcidiacono et al. (2012), Burke and Sass (2013), Sojourner (2013), and Patacchini et al. (2017), among others.
Our study complements the previously mentioned works in at least three important dimensions. First, we investigate the influence of peers on the decision to enroll at the same university. As discussed, the transition from secondary to higher education is a key determinant of labor market outcomes. In France, there is a large difference in unemployment rates between individuals with and without postsecondary education. Figure 1 displays the unemployment gap in France between these two groups from 1986 to 2017. It has held at 8.5 percentage points (5.7 Another type of institution in France that provides higher education is called the Sacerdote (2001) uses data from Dartmouth College, an elite institution in the U.S., Arcidiacono and Nicholson (2005) examine students in medical schools, and De Giorgi et al. (2010) collect data from Bocconi University, a top European institution.
Figure 1
Unemployment gap by education in France, 1987–2017.
Our results show that there are negative peer effects in enrollment choices. The size of the estimated coefficient is small but negative and significant. The estimated effect is larger within groups of similar observable characteristics, such as gender, or by social origin. The difference between the groups is not always significant. We find that individuals in the top quartile of the ability distribution are less impacted by their peers than the rest of the students. We test whether the results vary with the number of observed peers or whether the full group of peers is observed; however, we find no differences in the results between the groups.
The paper is organized as follows. In Section 2, we present the institutional background and our data. In Section 3, we detail the empirical strategy. Section 4 provides the results, and Section 5 concludes.
In France, from the second year of high school, students must choose a subject major. Each major gives a different weight to the variety of topics on offer for the
Each year in April, senior high school students must select their higher education path using a website called Application Post Bac (APB). The detailed procedure to gain access to higher education in France is described in Appendix A.I. In 2018, the web site has changed to
In 2009, a new policy, called Orientation Active (OA), was enacted with the objective of helping students to select their higher education pathway. For any choice the student has selected on APB, the university must deliver individual feedback on the student's likelihood of graduating from that field of study. This feedback is determined by the student's previous academic records, and is one of the three possible responses: first, a positive response, (henceforth called “A”), stating that the student is encouraged to register in that institution, and is likely to graduate, given their past academic records; and, second, a “B” or neutral response states that the student should succeed but will have to work conscientiously in order to graduate; and third, a negative response (henceforth called “C”), which states that based on the student's academic records, it would be advisable to amend the choice of postsecondary education. Feedback is sent out in May prior to the students graduating from high school. Whatever the feedback received, students are able to register in their choice of university and in their field of choice. In previous work (Hestermann and Pistolesi, 2017; Pistolesi, 2017), we demonstrate the impact of this policy on the students’ choices of higher education: the students receiving C feedback are less likely to register and this relationship is causal. In this article, we investigate whether the share of classmates receiving C feedback from their local university has an impact on the individual probability of enrolling in the same university.
We use administrative records collected by the APB website on all applicants at a large French university for seven consecutive cohorts from 2009 to 2015. The data contains individual characteristics, such as a unique student identifier, gender, age, place of residence, nationality, and whether or not she received a needs-based scholarship. A proxy for coming from a low socio-economic background.
The university that provided the data used in this paper is a large research public institution. Around 25,000 individuals study in this university in the fields of economics, law, management, and computer sciences. At the graduate level, it attracts students from all over the country and from other European and non-European countries. It offers professional and research Master degrees in each of these fields, as well as PhD programs. At the undergraduate level, most students are French and come from within the local district (as is the case for most other French universities). As shown in the descriptive statistics below 92% of the applicants in a first-year undergraduate programme of that university are French.
The data sample is restricted to high school students living in the same region as the university providing the data. Those living abroad face very different costs and opportunities. We drop observations from vocational high schools. These students turn to vocational studies. These are short programs having a duration of 2–3 years. Moreover, vocational studies take place in institutions other than universities.
One can wonder whether observing only the university applicants affects the internal and external validity of our study. First, as regards external validity (as explained above) the vast majority of students in France choose to apply to the university within their region of residence. By law, when applying to their local university, students are prioritized for enrollment. Moreover, universities are the single type of higher education institutions that are non-selective. Universities have capacity constraints and some fields can be oversubscribed, such as medicine, sports, or psychology. None of these programmes are offered in the university providing the data for this study. If capacity constraints are reached, by law, students who live within the same region are favored over students from a different region, making it highly unlikely that the student is not admitted. As explained below, even if we do not observe the individual characteristics of non-applicants, the data indicates the total number of students in the class. Therefore, we know—for each peer group—how many students do not apply to university.
Table 1 provides descriptive statistics for our sample, which includes 19,181 individual observations. Panel A shows individual observable characteristics: for example male students represent a smaller share (41%) of university applicants in our data; a large majority of students (92%) are French; and in high school, 14% of the students come from families with a low socio-economic background. Several measures of academic level in high school are available. First, the mean numerical score over the different topics can range from 0 (worst) to 20 (best). We observe a large variance in achievement, with low-achievers scoring 3.25, while high achievers score 19.33. Second, 32% are potentially high-achievers. These students have chosen to take elective courses that require additional work, such as Latin or Greek.
Descriptive statistics
| Age | 18.04 | 1.03 | 16.69 | 23.18 | 19,181 |
| Male | 0.41 | 0.49 | 19,181 | ||
| French nationality | 0.92 | 0.25 | 19,181 | ||
| Mean grade in h-school | 11.81 | 2.06 | 3.25 | 19.33 | 19,181 |
| Held back in h-school | 0.07 | 0.26 | 19,181 | ||
| Low social background | 0.14 | 0.35 | 19,181 | ||
| High achievers | 0.32 | 0.46 | 19,181 | ||
| Major in h-school: economics | 0.44 | 0.49 | 19,181 | ||
| Major in h-school: sciences | 0.37 | 0.48 | 19,181 | ||
| Major in h-school: literature | 0.18 | 0.38 | 19,181 | ||
| Public h-school | 0.81 | 0.38 | 19,181 | ||
| C feedback at univ. application | 0.27 | 0.30 | 19,181 | ||
| Enrolled at university | 0.50 | 0.50 | 19,181 | ||
| Mean class age | 18.05 | 18.16 | 18.38 | 18.53 | 19.33 |
| Class share of males | 0.05 | 0.33 | 0.42 | 0.50 | 0.70 |
| Class share of French | 0.75 | 0.92 | 0.96 | 1.00 | 1.00 |
| Class size | 22 | 24 | 28 | 31 | 35 |
| Mean class grade | 10.10 | 11.25 | 11.79 | 12.29 | 14.17 |
| Class share held back | 0.00 | 0.00 | 0.05 | 0.09 | 0.38 |
| Class share of low soc. back. | 0.00 | 0.05 | 0.12 | 0.18 | 0.50 |
| Class share high achievers | 0.00 | 0.09 | 0.26 | 0.43 | 0.92 |
| Class share enrolled at university | 0.08 | 0.42 | 0.57 | 0.87 | 1.00 |
| Class share observed | 0.62 | 0.77 | 0.88 | 1.00 | 1.00 |
In panel B, we compute the mean value of various class characteristics, and study their distribution across classes. We observe a median class size of 28 students. The composition of the class varies largely between the different classes: the share of male students varies from 5% to 70% of the class and the share of students from low socio-economic backgrounds varies from 0% to 50% of the class. Importantly, the mean academic level of the class is heterogeneous, since it ranges from 10.10 to 14.17. The last row indicates the distribution, of the share of peers that register in this university. In the classes in the lower part of the class distribution (P25), 42% of the students register at this university, while for classes in the higher part of the class distribution (P75), this proportion increases to 87% of the class. Overall, this panel shows that the composition of the classes changes considerably from one class to another. Peer groups are mostly heterogeneous in this sample.
Table 2 provides descriptive statistics on the type of feedback received and the individual probability to register. Panel A displays the individual probability of attending this university by the type of feedback received: 51% of students receiving A or B feedback are likely to register, There is no statistically significant difference in enrolment between the group of students receiving A feedback and those receiving B feedback.
OA policy and enrollment
| Enrollment | 0.51 | 0.47 | ||
| (0.01) | (0.01) | |||
| Share of peers with C feedback | 0.2 | 0.4 | 0.6 | 0.8 |
| Enrollment | 0.69 | 0.57 | 0.42 | 0.35 |
| (0.01) | (0.01) | (0.01) | (0.01) | |
Figure 2 graphically depicts the relationship between the share of students enrolling in this university and the proportion of classmates receiving C feedback. To draw this figure, we regress the enrolment dummy variable on a polynomial function of order three for the share of peers receiving C feedback and we compute the predicted value. The regression results are displayed in the Appendix Table A3.
Figure 2
University Enrollment and Share of Peers with C Feedback.
Endogenous sorting of peers into high schools and into high school majors is the biggest challenge faced when measuring peer effects with these data. In most countries, the self-selection of students into schools, within academic tracks and classes, is a widely observed. As discussed, there are strong significant differences in France in terms of academic ability by high school and by high-school major. To deal with this issue, we use major-by-high-school-by-year fixed effects, comparing students between classes within the same major and the same high school during the same academic year. In the next section, we present in more detail the econometric specification used in this paper. We discuss the validity of the empirical strategy in Section 3.2.
Using repeated cross-sectional data, we estimate the following reduced-form equation, in order to explore how the feedback received by classmates in high school affect an individual's choices for post-secondary education: This model is called a As explained in Section 2.1, a school-major is a collection of different classes, usually between two and four classes, within a high school following the same curriculae. In most high schools three types of majors are proposed: science, language and literature, and economics and social sciences.
Estimating the enrollment equation by OLS regression without the major-by-high-school-by-year fixed effects is unlikely to deliver credible peer effect estimates, since the composition of the peer group is not random. Using major-by-high-school-by-year fixed effects, we focus on the random assignment of students to classes within high school major-year and use the variation between classes of the same major, school, and year. We discuss the validity of this assumption in the next section.
First, we discuss the self-selection of students into peer groups. Studies using random assignment of peers include Sacerdote (2001), Zimmerman (2003), Foster (2006) Stinebrickner and Stinebrickner (2006), Kang (2007), Carrell, Fullerton and West (2009), Duflo et al. (2011), Sojourner (2013), and Zölitz and Feld (2018) among others. Studies using fixed effects include Hoxby (2000), Hanushek et al. (2003), Betts and Zau (2004), Lavy and Scholsser (2011), Lavy et al. (2012), Patacchini et al. (2017), Anelli and Peri (2017), and Brenøe and Zölitz (2020), among others.
We now present evidence of the validity of the fixed effect identification strategy. First, in Appendix AII, we check that the peers’ mean variables have enough variation within high school majors, schools, and years. Table A1 presents the decomposition within and between high school major by school and by year components and shows that, on average, the within component of the peers’ mean variables represents around 45% of the variance. Second, in Table 3, we test whether the change in the proportion of peers receiving C feedback within a high school major, a school, and a year is correlated with the changes in students’ background characteristics. Figure 3 presents the results graphically. The first column of Table 3 displays the OLS regression coefficients. In this column, we see that several observable characteristics are correlated with the share of peers receiving C feedback: males, individuals with lower academic records, those who have been academically held back in the past, and those with a larger share of peers receiving C feedback. In column (2), we introduce major-by-high-school-by-year fixed effects and find that the coefficients are closer to zero. Only two variables are still significant: the dummy for males and the one for French nationality. In column (3), we add peer mean characteristics and find that none of the coefficients are now significant. From the results of column (3), we conclude that the variation in the share of peers receiving C feedback is exogeneous after taking out major-by-high-school-by-year and peer mean characteristics. Finally, to complement this analysis, in Appendix AII.2, we test whether or not the assignment of students between classes within a high school major, a school, and a year is randomly determined. The empirical test presented here validates this assumption.
Figure 3
Balancing tests for the proportion of peers with C feedback.
Balancing tests for the proportion of peers with C feedback
| Dependent variable | |||
| Age | 0.013 (0.062) | −0.060 (0.069) | 0.035 (0.079) |
| Male | 0.352*** (0.040) | 0.157*** (0.052) | 0.087 (0.067) |
| Low soc. background | −0.068*** (0.032) | −0.040 (0.033) | −0.034 (0.046) |
| Mean grade in h-school | −0.054*** (0.020) | 0.034 (0.025) | 0.027 (0.028) |
| Optional course in h-school | −0.103*** (0.050) | −0.060 (0.054) | −0.016 (0.050) |
| Held back in h-school | 0.058*** (0.026) | 0.004 (0.028) | 0.005 (0.034) |
| French nationality | 0.002 (0.018) | 0.041* (0.025) | 0.021 (0.033) |
| Major-by-high-school-by-year fixed effects | ✓ | ✓ | |
| Peers characteristics | ✓ | ||
| Observations | 19,181 | 19,181 | 19,181 |
Our major-by-high-school-by-year fixed effects strategy controls for the most obvious potential confounding factor: the endogenous sorting of students across majors and across schools based on their socio-economic background. It is still possible that unobservable factors varying across classes and within high school majors that are correlated with the proportion of peers receiving C feedback may generate some spurious correlation within the group of peers. In this respect, the major potential threat to our strategy comes from the potential difference in teacher quality. A class can have a particularly effective teacher, which reduces the share of the class receiving C feedback. Admittedly, our strategy does not completely prevent that threat. However, high school students are allowed to apply to university whatever their score during the high school academic year. The single requirement in applying to university is to succeed at the
In Section 4.1, we detail the results of the regression estimations. In Section 4.2, we investigate whether the results are heterogeneous over different sub-samples. In Section 4.3, we present the falsification tests.
Table 4 reports the results of regressions of individual enrollment at university on the proportion of high school classmates receiving C feedback from their application in the same institution during the same year. The dependent variable is a dummy taking the value 1 if the individual registers and 0 otherwise. These regressions are linear probability models. In any of these regressions, standard errors are clustered at the major-by-high-school-by-year level. We have attempted to cluster standard errors at the school-major-year-class level, as advised by Abadie et al. (2017), with very similar results.
Regressions of individual enrollment on peers with C feedback from university application
| Prop. of peers with C feedback | −0.153*** (0.048) | −0.094** (0.037) | −0.095** (0.037) | −0.055 (0.040) | −0.065 (0.041) | −0.068* (0.041) |
| Individual characteristics | ✓ | ✓ | ✓ | ✓ | ||
| Peers characteristics | ✓ | ✓ | ||||
| Major-by-high-school-by-year fixed effects | ✓ | ✓ | ✓ | |||
| Observations | 19,181 | 19,181 | 19,181 | 19,181 | 19,181 | 19,181 |
| 0.001 | 0.067 | 0.073 | 0.001 | 0.019 | 0.023 | |
| Mean dep. var. | 0.507 | 0.507 | 0.507 | 0.507 | 0.507 | 0.507 |
From the regression in column (6), we document that an increase of 10 percentage points of the share of peers receiving C feedback is associated with a smaller probability to register at university of around 0.68 percentage points, indicating that there is a negative peer effect of being in a group of academically weaker students on the choice of gaining access to university. While the effect may seem modest, the average probability to register to university is 0.5, and therefore we need alternative ways to measure its importance. Alternatively, we compute standardized coefficients: the effect of a one standard deviation change in the share of peers receiving C feedback. These regressions indicate that an increase by a one standard deviation of the share of peers receiving C feedback results in a decrease in individual registration of around −0.01 in standard deviation unit, compared to −0.02 with the OLS coefficients with the fixed effects. The standard deviation of the share of peer receiving C feedback is 0.1059, and the standard deviation of the individual registration rate is 0.499. Therefore, the standardized coefficient stands at −0.068*0.1059/0.499 = −0.014.
It is difficult to compare our estimates to the literature, since we are not aware of any study of the effect of academically low achieving peers on the choice to enroll at university, as previously mentioned. Most of the literature on peer effects in higher education estimates peer effects in the choice of field of study, conditional on enrollment. In the next section, we test whether the previous estimates are homogeneous over the population.
Heterogeneous effects of the proportion of peers receiving C feedback are explored in Table 5. In this table, we report the results of OLS regressions of individual enrollment on the share of peers receiving C feedback for different subsamples. In panel A, we study whether there are heterogeneous effects by gender. Columns (1) and (2) show the results for male students, columns (3) and (4) display results for female students, and column (5) presents results for male and female students together. We provide mean peer effects’ estimates as well as estimates distinguishing female and male peers.
Heterogeneous effects: Regressions by gender and by social background
| Prop. of peers with C feedback | −0.122* (0.070) | −0.034 (0.063) | |||
| Prop. of male peers with C feedback | −0.110** (0.054) | −0.013 (0.035) | |||
| Prop. of female peers with C feedback | −0.032 (0.035) | −0.035 (0.045) | |||
| Prop. of same gender peers with C feedback | −0.044* (0.024) | ||||
| Prop. of other gender peers with C feedback | −0.028 (0.024) | ||||
| Individual Characteristics | ✓ | ✓ | ✓ | ✓ | ✓ |
| Peers Characteristics | ✓ | ✓ | ✓ | ✓ | ✓ |
| Major-by-high-school-by-year fixed effects | ✓ | ✓ | ✓ | ✓ | ✓ |
| Observations | 7,891 | 7,891 | 11,290 | 11,290 | 19,181 |
| 0.026 | 0.026 | 0.022 | 0.022 | 0.022 | |
| Mean dep. var. | 0.509 | 0.509 | 0.506 | 0.506 | 0.507 |
| Prop of peers with C feedback | −0.105* (0.062) | −0.027 (0.050) | |||
| Prop of low soc. back. peers with C feedback | −0.053* (0.029) | 0.021 (0.021) | |||
| Prop of high soc. back. peers with C feedback | −0.053 (0.044) | −0.019 (0.039) | |||
| Prop of same soc. back. peers with C feedback | −0.034 (0.022) | ||||
| Prop of other soc. back. peers with C feedback | 0.002 (0.018) | ||||
| Individual characteristics | ✓ | ✓ | ✓ | ✓ | ✓ |
| Peers characteristics | ✓ | ✓ | ✓ | ✓ | ✓ |
| Major-by-high-school-by-year fixed effects | ✓ | ✓ | ✓ | ✓ | ✓ |
| Observations | 5,068 | 5,068 | 14,113 | 14,113 | 19,181 |
| 0.027 | 0.027 | 0.018 | 0.018 | 0.022 | |
| Mean dep. var. | 0.535 | 0.535 | 0.497 | 0.497 | 0.507 |
From these regressions, we see that mean peer effects are larger for males than for females (−0.122 The share of males receiving C feedback is slightly larger than the share of females receiving C feedback (0.30
In panel B, we repeat the same exercise, but split the sample by social background. A low socio-economic background is measured by receiving a needs-based scholarship in high school. A privileged or high socio-economic background represents those who do not receive such a scholarship. The share of students receiving C feedback is very close between these two groups (0.27 and 0.26).
In Table 6, we study peer effects for different groups of students defined by ability level We use the average numerical score over the different topics in the senior year of high school in order to measure ability. These raw scores are not standardized across schools. The raw scores are difficult to interpret since they depend on the individual school's grading policy. We build a measure of ability relative to the group of peers, computing each individual's rank within the score distribution of her class. Then, we translate these ranks into within-classroom percentiles.
Heterogeneous effects: Regressions by ability level
| Prop. of peers with C feedback | −0.068* (0.041) | −0.071* (0.041) | −0.071* (0.043) | −0.071* (0.041) | −0.092** (0.043) |
| Top 50% in ability distribution | 0.010 (0.009) | 0.010 (0.009) | |||
| Top 50% × Prop. of peers with C feedback | 0.001 (0.121) | ||||
| Bottom 25% in ability distribution | −0.081*** (0.009) | −0.090*** (0.011) | |||
| Top 25% in ability distribution | −0.079*** (0.009) | −0.082*** (0.009) | |||
| Bottom 25% × Prop. of peers with C feedback | 0.023 (0.014) | ||||
| Top 25% × Prop. of peers with C feedback | 0.048* (0.026) | ||||
| Individual characteristics | ✓ | ✓ | ✓ | ✓ | ✓ |
| Peers characteristics | ✓ | ✓ | ✓ | ✓ | ✓ |
| Major-by-high-school-by-year fixed effects | ✓ | ✓ | ✓ | ✓ | ✓ |
| N | 19,181 | 19,181 | 19,181 | 19,181 | 19,181 |
| 0.023 | 0.023 | 0.023 | 0.030 | 0.030 | |
| Mean dep. var. | 0.507 | 0.507 | 0.507 | 0.507 | 0.507 |
| P-value F-test | 0.033 | ||||
Finally in column (5), we interact the dummy variables for the first and last quartile of the ability distribution with the proportion of peers receiving C feedback. The coefficient of the interaction is positive and significant for the high academic ability students. For this group, the negative effect of having academically low performing peers represents two-thirds of the effect estimated for the overall sample: these results, therefore, provide some insight into how social interactions impact individual enrollment. A larger fraction of academically low performing peers is less detrimental to the students that are in the higher quartile of the ability distribution. A potential explanation could be that those receiving C feedback interact less with their academically high achieving peers.
In Table 7, we investigate whether the peer effects’ estimates change with the number of observed peers. The first column presents our main result from Table 4 for comparison. In column (2), we add a dummy variable taking the value 1 if the number of observed peers in the class of individual
Heterogeneous effects: Regressions by number of observed peers
| Prop. of peers with C feedback | −0.068* (0.041) | −0.080 (0.051) | −0.061 (0.042) | −0.075* (0.043) |
| Nb of obs. peers above the median | −0.002 (0.013) | |||
| Prop. of obs. peers with C feedback × Above median | 0.044 (0.081) | |||
| Total class observed | −0.021 (0.022) | |||
| Prop. of obs. peers with C feedback × Total Class | −0.057 (0.188) | |||
| Prop. obs. peers >80% | −0.014 (0.016) | |||
| Prop. of obs. peers with C feedback × Observed peers >80% | 0.087 (0.051) | |||
| Individual characteristics | ✓ | ✓ | ✓ | ✓ |
| Peers characteristics | ✓ | ✓ | ✓ | ✓ |
| Major-by-high-school fixed effects | ✓ | ✓ | ✓ | ✓ |
| N | 19,181 | 19,181 | 19,181 | 19,181 |
| 0.023 | 0.023 | 0.023 | 0.023 | |
| Mean dep. var. | 0.507 | 0.507 | 0.507 | 0.507 |
So far, the peer groups have been defined in relation to university applicants only. In this setting, the group of peers is partially observed when some students in the class do not apply to this university; however, it is fully observed when every student in the class does so. To test if the peer effects estimate changes between these two groups, we use the information provided in the data for each applicant on the total number of students in the class. We define a dummy variable taking the value 1 if the number of observed peers plus one is equal to the total number of students in the class We observed the full group of peers for 31% of observations.
In Table 8, we perform some falsification tests, changing the proportion of students that register at university in cohort
Placebo regressions
| Prop. of peers with C feedback | −0.029 (0.048) | −0.048 (0.051) | −0.054 (0.049) | −0.056 (0.053) |
| Individual characteristics | ✓ | ✓ | ||
| Peers characteristics | ✓ | ✓ | ||
| Major-by-high-school-by-year fixed effects | ✓ | ✓ | ✓ | ✓ |
| 14,402 | 14,402 | 13,544 | 13,544 | |
| Mean dep. var. | 0.508 | 0.508 | 0.505 | 0.505 |
The results of these four regressions point to no clear relationship between the adjacent cohorts and the university enrollment choices of the current cohort. None of the four estimated coefficients is significant at the usual levels. We interpret the estimates from these regressions as evidence that the results from Tables 4–7 do not come from a correlation between the share of students enrolling at university and time-varying high school major unobserved characteristics.
In this paper, we empirically measure the extent of peer effects on high school students’ choices to attend university. Focusing on the impact of the share of peers advised to change their educational choice, we can aggregate the two major channels of social influence in the classroom: first, the level of academic achievement of the peers; and second, the possibility of them sharing relevant information about future study options. We use a unique and rich dataset on the universe of applicants to a large university over seven consecutive cohorts. These data allow us to study students’ enrollment choices and compare them to the share of their classmates that do not receive a positive reply to their application to the same university. In this respect, we develop an empirical strategy using major-by-high-school-by-year fixed effects to deal with the endogenous sorting of students into peer groups.
Our results suggest that having peers with a lower academic record has a slight but negative impact on the decision to enroll at university. An increase in the share of classmates receiving C feedback when they apply to university decreases the likelihood that an individual registers at the same institution by between 0.01 and 0.02 in standard deviation units, which is small but significant. The effect is larger within groups of individuals sharing similar observable characteristics, such as gender or socio-economic background, though the difference is not always significant. Moreover, we find that individuals in the top quartile of the academic ability distribution are less impacted by their weaker peers in their choices than those in the lower part of the ability distribution. Different mechanisms may be at play here: students may learn about their ability as in Stinebrickner and Stinebrickner (2012); they may also learn about the level of difficulty of the university program, as in Altonji et al. (2012); and furthermore, the university application feedback policy may alter the students’ beliefs about their own ability (Wiswall and Zafar, 2015). It would be interesting to distinguish between the relative importances of each of these potential mechanisms; however, the available data limits this ability. These results show that when designing a policy to improve access to higher education, an individual's peer group is a factor in the selection decision of future academic or vocational choices.
Several caveats are necessary so as not to over-interpret the results of our study. First, as already discussed, the data provide information on university applicants only. Among the group of class peers, we do not observe students who decide not to apply to university. In France, 70% of high school graduates start studying at university (Inan, 2017). Therefore, the difference between university applicants and their group of peers is slight: we cannot claim that our sample is representative of the full population of high school students, but it does remain representative of the population of university applicants. Second, our empirical analysis compares classes of students in the same high school major in the same high school during the same year. It does not prevent from shocks that would be class-specific and related to the unobserved determinants of university enrollment. While we are not able to see where these class-specific shocks would come from, the results presented here rely on the assumption that common shocks do not prevail.
A large empirical literature has already documented that educational attainment is partly determined by the academic records of a student's peers. Our results complement these studies, showing that an individual's choices upon entering higher education are also subject to the academic achievement level of their peers. Our paper also complements the literature showing peer effects in the choice of subject major in higher education. Since most of the literature is focused on the intensive margin, while we focus on the extensive margin, future studies could extend this work by investigating alternative schooling choices, such as the choice between vocational or academic tracks. In this situation, the choices have long-term consequences and more research is needed to assess whether or not the results presented here hold in these alternative contexts.