MERIT-BASED FINANCIAL AID TO STUDENTS FROM LOW-INCOME FAMILIES AND ITS EFFECTS ON ACADEMIC PERFORMANCE

MERIT-BASED FINANCIAL AID TO STUDENTS FROM LOW-INCOME FAMILIES AND ITS EFFECTS ON ACADEMIC PERFORMANCE Antonio Schizzerotto IRVAPP & University of Trento Loris Vergolini IRVAPP Nadir Zanini IRVAPP July 2012 Paper for the Espanet Conference Risposte alla crisi. Esperienze, proposte e politiche di welfare in Italia e in Europa Roma, 20-22 Settembre 2012 Very preliminary and incomplete. Please do not quote. Corresponding author: Loris Vergolini, IRVAPP Research institute for the Evaluation of Public Policies, via Santa Croce 77 38122, Trento. Mail: vergolini@fbk.eu.

MERIT-BASED FINANCIAL AID TO STUDENTS FROM LOW-INCOME FAMILIES AND ITS EFFECTS ON ACADEMIC PERFORMANCE Antonio Schizzerotto, Loris Vergolini & Nadir Zanini IRVAPP -Research Institute for the Evaluation of Public Policies- Abstract There is a wider debate about the role of liquidity constraints on university enrolment and completion. This paper empirically investigates the impact of merit-based financial aid to students from low income families recently introduced in a well-defined area in the North-East of Italy, the administrative province of Trento. Exploiting data from a unique dataset resulting from ad hoc longitudinal survey, we employ a fuzzy Regression Discontinuity Design in order to disentangle the effect of the aid from other confounding factors (e.g. merit and income). Our main preliminary findings are that recipient students early in their studies are (i) less inclined to drop-out from university, (ii) they successfully completed a higher number of courses, though (iii) they obtained lower grades. Keywords: higher education, achievement, regression discontinuity, instrumental variables * Acknowledgements. We would like to thank Silvia de Poli for excellent research assistance.

1. Introduction Students from low-income families face several problems in the Higher Education system. They are at greater risk than other students of not enrolling or completing university and they typically show lower academic performance than students from wealthy families. Our aim is to understand if and how means-tested and merit based financial aids can help students from lowincome families in improving their academic achievement in terms of average grade, number of credits achieved and drop-out reduction. The effects of family income and monetary incentives on educational attainment have been issues of longstanding concern in social sciences. In the empirical literature there are a large amount of studies aimed at investigating the effects of financial assistance on university enrolment and completion (Hansen 1983; Kane 1994; Dynarski 2000, 2003; Van der Klaauw 2002; Goodman 2008), but much less is known about the effects of public support on the performance and behaviour of students. The recent introduction of the Grant 5B by the local government of Trento an autonomous province in the North of Italy providing merit-based financial incentives to students from low income families allows us to fill this gap. The aim of this paper is therefore to assess the programme with respect to its effects on drop-out and academic performance. The effect of financial aid has been widely analysed in relation to college choices, but there are only few studies that try to assess the impact of public support on academic performance. More precisely, Leuven et al. (2003), analysing the case of the University of Amsterdam, find no effects of financial aids in the number of the collected credits and in the drop-out rate. On the contrary, Belot et al. (2007) exploit a major reform in the Dutch higher education system in order to identify the effect of student support on academic performance and students time allocation. They find that there is a small positive effect on the grades (about 0.13 points on a ten point scale), but drop-out and time allocation of students (hours spent on study and work, and incidence of jobs on the side) remain basically unchanged. With reference to the US case, Bettinger (2004) stress that in Ohio a programme of means-tested financial assistance exerts a remarkable reduction (about 9% points) in the drop-out rate. Dynarski (2005), using data from thirteen US states, finds that merit programmes increase college completion by 3 to 4 percentage points. This result is quite remarkable, because the share of the affected population with a college degree is about 26%. Cornwell at al. (2003) analyse the case of HOPE programme in Georgia (US) and they find that the shift from need- to merit-based aid increase the probability to withdraw and reduce the average completed credits. Richburg-Hayes et al. (2009) analyse a programme implemented in New Orleans area showing how financial aid increase the number of credits earned and increase the persistence in the university. A similar result 1

has been found also by Miller at al. (2011) who show some preliminary results from a new programme established by the University of New Mexico. More precisely, they find that the intervention encouraged students to earn more credit, they are 8.8 percentage points more likely to have gained 30 or more credits by the end of the first year. Eventually, Scott-Clayton (2011), using data from West Virginia, stresses how financial aids can have a slightly effect also on the average grade, but she does not find any influence on drop-out rate. Analysing the Canadian case Angrist et al. (2009) find sizeable effects only for women who were offered of both financial incentives and students services. Garibaldi et al. (2012) find that in an Italian institution the gradual increase in the tuition payments as response to delay graduation exert a remarkable effect on on-time completion rate. Mealli and Rampichini (2012) analyse data regarding four Italian universities and they show how university grants (the so called Right to Study ) prevent students from low income families from dropping out, but for poorer students the effects are not significant. As shown there is still a small literature that try to understand the effectiveness of financial measures on academic performance and the evidence produced is controversial. In this paper we wish to enrich this debate showing the results of a local studies carried out in the Italian context. Our research question can be summarised as follow: Do financial contribution stimulate students effort and improve their academic performance? To do this we rely on previous studies by Covizzi et al. (2012) and Vergolini and Zanini (2012a) demonstrating that this intervention do not affect the university enrolment decision and so that there is no a sample selection of students attending higher education. We move from this result and exploit a quasi-experimental method in order to disentangle the effects of the monetary incentives from other confounding factors (social origins, family background, etc.). We consider the drop-out rate the average mark and number of achieved credits at the end of the first year of university attendance and we find no any significant effect of the Grant 5B, suggesting that students do not change their time allocation in order to improve their performance. The rest of the paper is organised as follow. The next section outlines the main features of the programme under scrutiny and Section 2 introduce some hypothesis about its possible effects. In section 3 we describe the data used in the analyses, while in section 4 we discuss the identification strategy employed in order to estimate the causal effect of the grant on academic performance. Section 5 reports our main findings and we discuss their implications and limitations in section 6. 2. The Grant 5B: basic provisions and questions about its effectiveness Drawn by the recent economic crisis, in autumn 2009 the local government of the autonomous Province of Trento (PaT henceforth), a small area in the North-East of Italy, has begun to play a 2

leading role in encouraging students from low income families to obtain higher education. Although PaT has a good economic situation witnessed by higher GDP per capita and lower unemployment rate than in the rest of the country 1, in the last years it has experienced a decline in the enrolment rate at university (see Vergolini and Zanini, 2012b: p. 5, figure 2). In order to reverse this negative trend, a generous financial incentive known as Grant 5B has been introduced to assign merit-based monetary aid to students from low income families. Specifically, this scholarship will be paid to those students who have successfully completed the last year of secondary school (diploma di maturità) obtaining a final score which fall above 93/100 and whose family income is below a predetermined income threshold. The financial need of students and their families is measured by a mixture of family income and assets. The threshold is set to 30.000 of equivalent income. 2 In addition, to be eligible students are expected to have been residing in the PaT for at least 3 years. The amount of the scholarship varies between 1,200 and 6,000, depending on family income and geographic location of the chosen university. Anyway, more than 80% of Grant 5B recipients receive an amount larger than 4,500 per year, i.e. a monthly scholarship in the range 375 500. It is worth noting that this monetary transfer can be considered as a top-up of the coverage of university attendance direct costs provided by each university and known as Right to Study. The underline idea of policy makers setting this policy was to provide a financial support to worth students willing to attend university but with tight liquidity constraints which stop them from continuing to higher education and boost them in the labour market. Therefore, it is clear that the Grant 5B aims at covering the opportunity cost of going to university the money they would have earned if they worked instead allowing worth students from low-income families to attend university. The first target population was composed by those students have successfully completed high school in the year 2008/2009 and prospective freshmen in the academic year 2009/2010. The measure has been confirmed for the following two cohorts of high schools graduates, though empirical analysis reveal that Grant 5B has been not effective in reversing the negative trend of the enrolment rate. Covizzi et al. (2012) compare a group of students eligible for both merit and need to a group of students with the same economic condition but just below the 93/100 threshold on the 1 In case of introduction of a similar measure at national level, although our main results would reasonably generalize to most of Northern and Central Italy, which feature not so dissimilar economic conditions and educational levels, by no means they can be taken as representative of the whole of Italy. 2 The financial need is measured by an ad hoc index called ICEF which summarise incomes and assets of each family using a certain scale of equivalence that is not far from to the OECD one. But, since it takes into account family components assets as well as income, the interpretation of this indicator is far from straightforward. However, a family with 30,000 of equivalent income measured by ICEF is not a proper poor family, since the sixty percent of the median incomes in the Province of Trento is around 8.300. 3

final score. Performing a quasi-experimental design to avoid selection bias issue, they demonstrate that being eligible for the Grant 5B have not affected students choice to continue to higher education. Exploiting the same data, Vergolini and Zanini (2012a) confirm this result and find that the financial aid provided by PaT exerts a large effect (about 40%) in the probability of redirecting students already bound for university to enrol outside the Province of Trento. They show that, given lower liquidity constraint, eligible students are more inclined to choose field of studies (such as medicine or veterinary) not present in the local University of Trento. The monetary aid is not just provided during the first year of university attendance. It is renewable upon determining whether or not a student is still eligible to qualify for help from the programme. In order to be eligible for the renewal, students must demonstrate that family income is still below the threshold as well as a certain achievement measured in terms of ECTS credits. 3 To obtain the renewal a student has to achieve: a) at least 50 credits if the maximum number of expected CFU is equal or above 60; b) at the least the 85% of the credits if the maximum number of the expected CFU is between 46 and 59; c) all the credits if the maximum number of the expected credits is less than 45. 3. Theory and hypothesis Theoretically, our argumentation is based on the way in which individuals respond to incentives. Relying on economic theory we can suppose that monetary aids should act as incentives promoting effort and scholastic performance (Lazear 2000). In this sense, the economic reward works as a positive reinforce for the desired behaviour. Moreover, The idea underlying Grant 5B consists in the reduction of the indirect costs connected to the Higher Education, while the direct costs are demanded to the so called Right to Study. This way, students from lower social background can cumulate Right to Study and Grant 5B in order to have a sizeable reduction in the educational costs. As a consequence this kind of programmes should change recipients time allocation. It may be the case that, the monetary transfer save them from financing their studies through occasional or part-time jobs, spending more time on their coursework. Hence, they could achieve better results at university: reduce the risk of drop-out (particularly high during the first year), improve their grades and the number of gained credits as well as accelerate the progress towards college completion. Moreover, as stressed in the previous section, the renewal of Grant 5B 3 In 1999 the Italian University Reform based on the Bologna Process has introduced a credit system based to facilitate mutual recognition of degrees. The credits represent the student's total workload (class time, individual study, exam preparation, practical work, etc.) and are earned once the student has passed the assessment for each course or activity. The average full-time workload for one academic year is 60 credits which is equivalent to 1,500 hours of work. However, the teaching regulations of each university can provide for regular reassessment of credit allocation and indicate the minimum number of credits that must be achieved each year. 4

is based only on the number of the achieved credit. As a consequence, we suppose that, given the renewal constrains, Grant 5B will exert a positive effect on number of credits achieved, but a null or negative effect on the average grade. At the same time, given the results from the previous studies and given the reduction in opportunity costs, we expect to find a decline in the drop-out risk for the grant holders. If empirical evidence confirms these hypotheses, the public spending for the Grant 5B would be justified, though not effective in favouring the access to tertiary education. However, so far there is no evidence about the impact of the programme in terms of students academic performance. In psychology the role of incentives is much more controversial. This literature is based on experimental evidence (Deci 1975, Kohn 1993) indicating that the extrinsic motivation due to the rewards could in some cases conflict with the intrinsic motivation that is the personal desire to study or to complete a given task for its own sake. More precisely, following Ryan and Deci (2000: 56) the intrinsic motivation is defined as the doing of an activity for its inherent satisfactions rather than for some separable consequence. When intrinsically motivated a person is moved to act for the fun or challenge entailed rather than because of external prods, pressures, or rewards. On the other side, extrinsic motivation is a construct that pertains whenever an activity is done in order to attain some separable outcome. (Ryan and Deci 2000: 60). Following this approach Benabou and Tirole (2003) propose a theoretical model in which incentives and rewards may act as weak reinforces and having modest impact on performance and only in the short time. Moreover, there is the risk that they have hidden costs, in the sense that they may become negative reinforces once they are withdraw. If this approach is reliable, we should find a small or a null effect of the Grant5b on the academic performance and we can also test the question of hidden cost looking at the students that do not respect the renewal criteria loosing the eligibility to Grant 5B. In this way the analysis of the effect of the Gran 5B may be used as a case study to test the validity of two antagonist theories about the role of incentives and rewards in influencing human behaviour. 4. Data and descriptive evidence In order to understand if the Grant 5B affects students academic achievement, we carry out our analyses on data resulting from the linkage between the archives of Opera Universitaria the agency in charge of the administration of the programme and an ad hoc longitudinal survey specifically designed for our aim. 4 As shown in Figure 1, we administered a baseline censual survey 4 Data collection has been carried out by IRVAPP and University of Trento and the questionnaire were administered by CATI (Computer-Assisted Telephone Interviewing) and CAWI (Computer-Assisted Web Interviewing) procedures. 5

in November on those students had successfully completed high school in July of the same year, and so that prospective freshmen in the following academic year. Relying on the fact that in Italy the deadline of university enrolment is in late September, this way we are able to know whether each student is enrolled at university or not as well as gather information about socio-demographic characteristics, social origins, and previous school career. 5 After the end of the first and second academic year, in October, we contacted all respondents for follow-up surveys, in order to ask them if they are still attending university and if so to collect information about their academic achievement, basically in terms of average grade and number of CFU. In this way we get a rich panel dataset which allows us to study the effects of the Grant 5B in terms of drop-out rate, average mark and number of courses successfully attended at the end of the first and second year. Table 1 reports a set of information about the response rate. For the first cohort the reference population consists of the 3,281 high school graduates in the year 2008/2009 who could enrol at the university in the academic year 2009/2010. We interview 2,760 students with a high response rate (84%). As for the second cohort, we gathered information on 2665 students, the 81% of the reference population (3278). Moreover, it emerges that attrition is not a huge problem in our data, in fact we are able to interview more than 90% of enrolled students in the follow-up surveys. Table 2 shows that among interviewed individuals about the 70% has continued their studies at university 1915 and 1897 students in the first and second cohort respectively and 37% and 39% of them choose to study outside Trento. 5. Identification strategy We refer to the so-called counterfactual model of causality and throughout the remainder of this paper we follow the notation of the potential outcome approach to causal inference. 6 As Morgan and Harding (2006) clearly pointed out, the primary goal of the causal analysis is the investigation of selected effects of a particular cause rather than the search for all possible causes of a particular outcome, i.e. the academic performance in the case at hand. The approach foresees the effect of the financial aid is evaluated as the difference between a recipient educational outcomes compared to what would have been happened if s/he do not receive the scholarship. Obviously this difference is not observable at the individual level, since a certain student can either being a recipient or a non-recipient, but not both. However, by exploiting the institutional rules of the Grant 5B, we can identify the causal effect of receiving the monetary aid. To do this we 5 Table 3 reports the descriptive statistics for these variables. 6 The counterfactual model of causality is widespread in the social sciences. See for example: Heckman et al. (1999), Pearl (2000), Shadish et al. (2002), Morgan and Winship (2007), Imbens and Wooldridge (2009). As for a deeper explanation of the potential outcome approach see Rubin (1974), Heckman (1979) and Holland (1986). 6

estimate a fuzzy Regression Discontinuity Design (RDD), conditioning on the subpopulation eligible to the aid for income. In particular, we rely on the treatment discontinuity at the merit threshold to disentangle the causal impact of the Grant 5B on academic achievement from the pure merit effect and other confounding factor. The basic idea underlying RDD is that since little changes in the assignment variable do not have any significant impact on the individual s behaviour, we could compare subjects immediately below and just above a given threshold, because we can consider them equivalent excepted for the eligibility to the treatment. 7 As shown in Figure 5, the probability of being recipients of this incentive does not jump from 0 to 1 at the threshold, since a non negligible fraction of eligible students have not applied for the Grant 5B, so that the take-up rate is some.70 (Table 4, first coloumn and Figure 4). However, this discontinuity helps to solve the endogeneity problem due to the different composition of students above and below the threshold and shown above. In this setting the average causal effect can be retrieved for those students around the 93 threshold, and therefore we can refer to this quantity as the Local Average Treatment Effect (LATE). Let Y be the outcome variable of interest (drop-out status, average mark, etc.) and D be the binary variable denoting the recipient status indicator (D=1 for recipients and D=0 otherwise). It is possible to demonstrate (see Hahn et al. 2001; Battistin and Rettore 2008) that the Wald ratio: LATE = β Wald = E[Y final score=93 + ] E[Y final score=93 ] = β IV (1) E[D final score=93 + ] E[D final score=93 ] is an unbiased estimator for LATE. It would be estimated non parametrically with Local Linear Regressions or parametrically in an instrumental variables setting considering as an instrument for D the eligibility status Z, defined according to the deterministic rule 8 : 1 final _ score 93 Z, (2) 0 final _ score 92 using two-stages least-squares (2SLS). Instrumental variable estimation, performed using as covariates the individual characteristics mentioned before, also allows us to avoid a possible source of bias given by the self-selection of students among those eligible claiming for the scholarship. It is worth mentioning that a possible threat to the identification strategy takes place, in principle, when the Grant 5B affect the probability of enrolment at university. If so, students above the threshold result different from those below. However, previous results demonstrate 7 See also Vergolini and Zanini (2012a) for a deeper explanation of the RDD applied in this case. 8 Note that Z and D are different because just a fraction of all eligible students claim for the grant, indeed the take-up rate is not equal to 1. 7

that this is not an issue in the case at hand (Covizzi et al. 2012; and Vergolini and Zanini 2012a). 6. Empirical results Preliminary results of our investigation are summarised in Table 4 and are referred to the pooled sample of students from both the first and second cohort. The IV estimates reported in the top part of the table reveal that at the 93 threshold recipient students are less inclined (some 5%) to drop-out during the first year, even though this effect is not statistically significant. As for the academic performance, the effects are negative but are not statistically different from zero. In particular, it is worth noting that neither the number of achieved credits nor the dummy variable which is equal to one if the students achieved at least the number of credits for the renewal of the Grant 5B are significant. Non parametric estimates reported in Table 5 confirm these results. We have also carried out robustness checks to ensure the reliability of these estimates. Moreover, the validity of the identification strategy is verified by the Figure 5, which show the distributions of students according to their final score. The charts show clearly that there is not any significant discontinuity at the 93 threshold, suggesting that there is not manipulation of the score variable (MacCrary 2008). 7. Conclusions The aim of the expected outcomes of our study is twofold. On one hand we intend to participate at the debate on whether financial constraints affect students academic performance which should have direct implications on social stratification and career mobility. On the other, from a policy perspective, we will provide evidence about the effects of this kind of policies on academic achievement in order to provide recommendation for policy makers to improve its efficiency. In general we find that Grant 5B does not have any impacts on the various dimension of academic performance considered (drop-out, average mark and credits achieved). Hence, the preliminary empirical evidence presented in the paper does not support the positive view of monetary incentives that we derive from economic theory. For future development we aim to consider more information in order to reduce the heterogeneity of the results and to collect more data for completing the longitudinal structure of the survey design. In this way we will be able to take into account also the time to complete the degree. 8

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Tables and Figures Cohort Table 1: Sample size and cohorts. High school graduates (Reference population) Baseline survey Enrolled 1 st follow-up survey 2 nd follow-up survey 2009 N 3281 2760 1915 1762 1694 % 100% 84% % 100% 92% 88% % 100% 96% 2010 N 3278 2665 1897 1774 --- % 100% 81% % 100% 94% --- Table 2: Enrolment at the university. Cohort High school graduates interviewed (baseline survey) Enrolled Outside Trento 2009 N 2760 1915 743 % 100% 70% % 100% 39% 2010 N 2665 1897 696 % 100% 71% % 100% 37% 11

Table 3: Descriptive statistics of students enrolled at university by cohort. Cohort 2009 Cohort 2010 Mean S.E. Mean S.E. Outcome variables Drop-out 0.048 0.006 0.059 0.006 Average mark 24.616 0.097 24.664 0.106 Credits achieved 43.960 0.449 42.517 0.452 School switch 0.120 0.008 0.124 0.009 Student information Grant 5B holder 0.069 0.006 0.060 0.005 Sex Male 0.422 0.011 0.415 0.011 Female 0.578 0.011 0.585 0.011 High school track Academic track 0.524 0.011 0.564 0.011 Vocational and technical track 0.476 0.011 0.436 0.011 Grade at high school final exam 79.142 0.252 78.179 0.253 Type of schools and faculties Mathematical, physical, natural sciences 0.110 0.007 0.141 0.008 Chemistry and pharmacy 0.019 0.003 0.018 0.003 Medical 0.095 0.007 0.078 0.006 Engineering 0.162 0.009 0.136 0.008 Architecture 0.021 0.003 0.022 0.003 Agricultural 0.016 0.003 0.016 0.003 Economics and statistics 0.157 0.008 0.146 0.008 Social and political sciences 0.086 0.006 0.092 0.007 Law 0.065 0.006 0.067 0.006 Humanities 0.140 0.008 0.135 0.008 Foreign languages 0.026 0.004 0.025 0.004 Education 0.051 0.005 0.054 0.005 Psychology 0.051 0.005 0.069 0.006 Family information Parental social class Service class 0.337 0.012 0.358 0.011 White collars 0.310 0.011 0.291 0.011 Self-employed 0.091 0.007 0.110 0.007 Working class 0.263 0.011 0.241 0.010 Parental education Primary 0.006 0.002 0.005 0.002 Lower secondary 0.302 0.011 0.311 0.011 Upper secondary 0.453 0.012 0.449 0.012 University 0.240 0.010 0.235 0.010 Number of siblings 0.909 0.017 1.078 0.018 Family structure One parent 0.152 0.008 0.103 0.008 Both parents 0.848 0.008 0.897 0.008 Parental supervision Yes 0.395 0.011 0.386 0.011 No 0.605 0.011 0.614 0.011 12

Table 4: Fuzzy RDD parametric estimates of the treatment effect on drop-out rate, average mark and credits. First Stage Drop Out Average Score LATE Credits Enough Credits Coeff. 0.74-0.05-0.92-0.66-0.01 S.E. 0.06 0.08 10.12 45.63 0.14 T-test 12.34-0.57-0.91-0.15-0.09 N 549 493 458 470 470 Note: Instrumental variable (2SLS) estimates, robust standard errors, t-test statistics are reported. Other covariates presented in Table 3 omitted. Estimates are referred to the subsample of students with final score 85. Table 5: Fuzzy RDD non-parametric estimates of the treatment effect on drop-out rate, average mark and credits. Drop Out Average Score Wald ratio Credits Enough Credits Coeff. -0.12-0.09-0.13-0.13 S.E. 0.12 0.12 0.13 0.13 T-test -1.04-0.76-1.04-1.00 N 493 493 493 493 Note: Wald ratios estimates, bootstrapped (replication 500) standard errors, and t-test statistics are reported. Estimates are referred to the subsample of students with final score 85. 13

Figure 1: Survey design. 2009 2010 2011 July September October July September October July September October First cohort High-school graduation Enrolment at university Baseline survey 1 st follow-up survey 2 nd follow-up survey Second cohort High-school graduation Enrolment at university Baseline survey 1 st follow-up survey 14

Figure 2: Mean and confidence intervals of various outcomes by cohort and other characteristics. Cohort 2009 Cohort 2010 All Drop-out School switch Drop-out School switch Under Icef Above Icef Grant No Grant Yes.08.1.12.14.16.18.1.15.2.25 0.02.04.06.08.1.12.14.16.18.05.1.15.2.25 All Total credits Average mark Total credits Average mark Under Icef Above Icef Grant No Grant Yes 35 38 41 44 47 50 24 25 26 27 35 38 41 44 47 50 53 56 24 25 26 27 28 15

Figure 3: Academic achievement according to final score at high school exit exam by cohort. Cohort 2009 Cohort 2010 Drop-out School switch Drop-out School switch 0.1.2.3.4 0.1.2.3.4.5 60 70 80 90 100 60 70 80 90 100 Total credits Average mark 20 30 40 50 60 18 20 22 24 26 28 0.1.2.3.4 0.1.2.3.4.5 60 70 80 90 100 60 70 80 90 100 Total credits Average mark 20 30 40 50 60 18 20 22 24 26 28 60 70 80 90 100 60 70 80 90 100 60 70 80 90 100 60 70 80 90 100 16

Figure 4: Probability being recipient of the Grant 5B according to final score by cohort. First cohort: 2009 high school graduates Second cohort: 2010 high school graduates 0.2.4.6.8 1 0.2.4.6.8 1 85 90 95 100 Final score 85 90 95 100 Final score Figure 5: Distribution of students enrolled at university according to final score and by cohort (McCrary test). First cohort: 2009 high school graduates Second cohort: 2010 high school graduates -20 0 20 40 60 80 85 90 95 100 Final score 0 20 40 60 80 85 90 95 100 Final score 17