Teenage Night Owls: Does Going to School at Night Impact on Risk Behavior?

Transcription

1 Teenage Night Owls: Does Going to School at Night Impact on Risk Behavior? Ana María Reynoso Yale University and Martín A. Rossi Universidad de San Andrés First draft: May 2011 This draft: June 2012 Abstract We study if adolescents that have a higher probability of developing activities during the night hours engage later in more risk behavior. To address this question we evaluate if adolescents attending high school during the evening behave differently than those attending high school during the morning or the afternoon. Given that preference for nightlife is endogenous in a model of risk behavior, we take advantage of the lottery allocation of school shifts in a public high school in the city of Buenos Aires. We find that women attending high school in the evening present a higher probability of getting an abortion and start to have sex at a younger age. We also find that attending high school in the evening is associated to a higher probability of using hard drugs. JEL Classification: D12, D71, I25, J13. Keywords: risk behavior, abortion, hard drugs, alcohol. Ana María Reynoso, Yale University, Economic Department, 28 Hillhouse Avenue, 06511, New Haven, Connecticut, USA, ana.reynoso@yale.edu; Martín Rossi, Universidad de San Andrés, Vito Dumas 284, B1644BID, Victoria, Buenos Aires, Argentina, mrossi@udesa.edu.ar. We thank María Teresa Casparri, María del Cármen Rodríguez de Ramirez, Leandro Rodríguez, Luis Jorge Conti, María Lorenzo, Aldana Bartolomeoli, and Gustavo Zorzoli for helping us to obtain the data. We also thank Irene Brambilla, Sebastián Galiani, Juan Carlos Hallak, Esteban Peralta, Lorena Pompilio, Christian Ruzzier, and Micaela Sviatschi for many valuable comments and suggestions.

2 I. Introduction The initiation in activities related to risk behavior such as having unsafe sex or consuming substances is, typically, an adolescent phenomenon. 1 Therefore, the study of the determinants of entry into risk behavior should pay particular attention to events affecting the young. One of such events is high school. In this paper we study if adolescents that attend high school at night are more prone to engage later in risk behavior. Addressing the causal relationship between high school shift and risk behavior is challenging, since the preference for nightlife is endogenous in a model of risk behavior. In order to overcome this endogeneity problem we take advantage of a natural experiment in Argentina. The public high school Escuela Superior de Comercio Carlos Pellegrini, in the city of Buenos Aires, randomly assigns high school students to morning, afternoon, and evening shifts, thus generating an exogenous variation in the time students go to high school. Our research is related to the literature exploring the causes for risk behavior. There is agreement that the decision to engage in risk behavior is a social phenomenon (Akerlof 1997), and many papers have addressed the effects of peers and family on risk behavior. Soetevent (2006) surveys empirical papers identifying peer effects in the consumption of drugs, alcohol, and cigarettes. He observes that evidence of peer effects on alchohol and cigarrettes consumption are always large and positive, while the peer effects on drug use are sometimes positive and sometimes negative. Peer groups in the papers surveyed by Soetevent (2006) include neighborhood (Case and Katz 1991), school mates and school 1 For example, according to the National Center on Addiction and Substance Abuse at Columbia University (2005) someone who does not initiate substance consumption by the age of 21 is almost certain never to do so. 2

3 environment (Clark and Lohéac 2005 and Gaviria and Raphael 2001), and classmates (Soetevent and Kooreman 2006). Card and Giuliano (2011) investigate the influence of best friends on sex and substance use initiation, concluding that having a close friend who initiated a risky activity increases the probability of taking up such activity. In a recent contribution, Altonji, Cattan, and Ware (2010) find that older siblings have a positive effect on younger siblings consumption of cigarettes, alcohol, and marijuana. 2 Finally, the National Center on Addiction and Substance Abuse (CASA) at Columbia University annual reports have consistently showed that having family dinners more than twice a week is associated with a lower probability of drinking, smoking, or using drugs. 3 Our paper makes two original contributions to the related risk behavior literature. First, we provide empirical evidence of an additional cause for risk behavior; namely, studying at night when adolescent. We estimate the impact of attending high school at night on the probability of getting an abortion, the age of intercourse initiation, and the probability of using substances. To the best of our knowledge, this treatment effect has not been studied before. Our second original contribution is that we follow an experimental approach that allows identification of a causal relationship. As Soetevent (2006) points out, identification is a major problem of empirical papers in this literature. Experimental approaches are used in the literature that analyzes peers influences on academic performance and crime (see, for example, Sacerdote 2001; Kremer and Levy 2008; and Duflo, Dupas, and Kremer 2008). 2 Previous papers reporting strong positive correlations between family members and substance consumption include Slomkowski, Rende, Novak, Lloyd-Richardson, and Niaura (2005) on sibling effects and Amuedo- Dorantes and Mach (2002) and Windle (2000) that focus on parent effects. 3 CASA s reports were accessed at 3

4 However, to the best of our knowledge, the experimental approach has not been used yet to study the causes of risk behavior. We find that women attending high school in the evening present a higher probability of getting an abortion and start to have sex at a younger age. We also find that attending high school in the evening is associated to a higher probability of using hard drugs and alcohol. Our identification strategy suggests that the link between high school shift and risk behavior is causal. The rest of the paper is organized as follows. Section II describes the natural experiment. Section III describes the survey. Section IV explains the identification strategy and estimation methodology. Section V displays and analyses our main results. Section VI presents robustness checks. Finally, section VII concludes. II. The natural experiment The Escuela Superior de Comercio Carlos Pellegrini (hereafter ESCCP) is a very prestigious public high school in Buenos Aires City. This school directly depends on Universidad de Buenos Aires, the largest university in Argentina. As every high school in Buenos Aires, ESCCP has a five-year curriculum. Courses are taught in three shifts: the morning shift (from 07:30 am to 12:05 pm), the afternoon shift (from 12:30 pm to 5 pm), and the evening shift (from 05:20 pm to 09:40 pm). Because of its renowned quality of education and because tuition is free, every year the school faces an excess demand of applicants. Among all applicants, eligible students are chosen according to their performance on admission examinations. Every student who wishes to pursue her secondary degree at ESCCP is required to attend an admission course and to sit for a series of Language and Literature, Mathematics, History, and Geography admission 4

5 examinations. An applicant is admitted to the school if her total punctuation in the set of examinations exceeds a certain threshold that is determined each academic year according to the established quota of students the school can receive. Once admitted students are known, school authorities split them equally among the opening classes so that class size is homogeneous. This process is made in two steps. The first step is to allocate each student to one of the three school shifts. For the sake of transparency, shift allocation has traditionally been made at a public lottery session held at the beginning of every academic year before classes begin. The principal of the school opens the lottery session giving a speech to welcome incoming students and relatives, and to give relevant information about the school and its curricula. After that, the principal announces the beginning of the lottery for shift assignment reminding the audience that once shift is allocated, it is not possible to change shift assigned. Three administrative and/or academic authorities of the school are in charge of conducting the lottery session. One of them holds a hosting role making relevant announcements out loud, while the remaining two supervise all the procedures. The school authorities bring the names of incoming students to be drafted printed in strips and when the session begins they insert the strips into an opaque urn. The host announces the amount of students to be drafted to each of the three shifts and starts the lottery by saying: we are going to draft x students to the morning shift. After the announcements, the host asks a student from the crowd to voluntarily approach the stage and extract strips from the urn one at a time. As the student hands in the strips to the host, the host reads the name of the drafted student out loud and hands in the strip to other authority in charge of accounting for the number of drafted units and corresponding shift. Once the morning shift is complete, the host asks a 5

6 parent from the crowd to voluntarily approach the stage in order double-check the count of students drafted to the morning shift. This procedure is replicated for the afternoon shift. Students whose names remain in the urn after the morning and afternoon drafts are automatically assigned to the evening shift. A volunteer parent is invited to supervise the count of students assigned to the evening shift as well. Not every incoming student is drafted, and the rules for determining which students are exempt from the draft have changed over time. For some years students with the highest marks in the admission examinations were allowed to choose the shift they would attend. In other years, students with older siblings in the school or with parents working at the school have been allowed to attend the same shift as their siblings or the shift in which their parents work. Once students have been assigned to the shifts school authorities in each shift split their set of students among classes so that class size, gender, and surname initial is balanced among classes. For some years in the past, class assignment in each shift was determined according to an English examination, so that classes differed in the aggregate English level. III. Data This paper relies on an original database obtained through a voluntary and anonymous on-line survey that provides information on treatment assigned, treatment received, outcomes related to risk behavior, and a set of pre-treatment characteristics. The survey was released by by the ESCCP Graduates Office to all the school graduates. 4 The survey was answered by 405 students that graduated between 1983 and Our sample size represents a 3.7 percent of total graduates in the considered cohorts, a figure 4 The survey was administered through an appropriate Google Docs gadget, the Google Docs Forms. 6

7 that raises a concern on the representativeness of the sample. A necessary condition for the sample to be representative is that the proportion of respondents is equal among treatment status. In Table 1 we report a test of differences in proportions of respondents by shift attended. 5 When gathering all the cohorts together, the test suggests that the number of respondents is balanced among shift attended: 3.5 percent of total graduates from the morning or afternoon shifts and four percent of total graduates from the evening shift participated in the on-line survey, and the difference is not significant at the usual levels of confidence. When considering each cohort separately, only three out of 27 cohorts show a significant difference in the number of respondents. Nevertheless, the direction of the difference is not the same in all cohorts: in the 1983 and 2006 cohorts, the proportion of respondents in the morning or afternoon shifts exceeds the proportion of respondents in the evening shift; while in the 1996 cohort the proportion of respondents in the evening shift exceeds the proportion of respondents in the morning or afternoon shifts. Overall, these results suggest that the necessary condition for the sample to be representative is satisfied. We exclude from the analysis the students that were not drafted and those for whom treatment status was missing. The final dataset includes 300 graduates that were shiftdrafted. Information about treatment assigned and treatment received was gathered through a series of questions. First, we asked graduates what shift they were drafted to in the shift lottery session. Multiple mutually exclusive choices were I was not drafted, morning, afternoon, and evening. This question allowed us to identify those units that were not 5 The ESCCP Graduate Office provided data on the population size by cohort and shift attended. 7

8 required to be drafted. Second, we asked respondents what shift they actually attended in the first academic year and the cause of assignment to that shift. Available choices for the latter were lottery draft, I had relatives in the school attending that same shift, merit, I was waitlisted and eventually admitted to the evening shift, and other. Last, graduates were asked if they had moved to another shift and if so, in which academic year. 200 of the respondents that participated in the lottery were drafted to the morning or afternoon shift. Compliance in the morning and afternoon shifts was high: percent of students drafted to the morning and percent of students drafted to the afternoon complied with shift assigned. The remaining 100 participants of the lottery were drafted to the evening shift. Compliance to the evening shift was 76 percent. Pre-treatment characteristics captured by the survey include Female (a dummy that takes value one if the graduate is a female and cero otherwise); Primary school morning, Primary school afternoon, and Primary school full time (three dummies that take the value one if the graduate attended primary in the morning shift, in the afternoon shift, or full time- respectively- and cero otherwise); Primary school public (a dummy that takes value one if the graduate attended high school at a public institution and cero otherwise); Mother has higher education (a dummy that takes value one if the graduate s mother holds a tertiary or university degree and cero otherwise); Father has higher education (a dummy that takes value one if the graduate s father holds a tertiary or university degree and cero otherwise); and Age at school entry. The outcomes in the dataset are Abortion (a dummy that takes value one if the graduate is a female who has ever gotten an abortion and cero otherwise); Sexual initiation (a count variable that captures the age at which the graduate had sex for the first time); and 8

9 Uses hard drugs (a dummy that takes value one if the graduate has ever tried and currently consumes cocaine or ecstasy pills or alike, and cero otherwise). Table 2 displays summary statistics of pre-treatment characteristics and outcomes for the whole sample, for the sub-sample of graduates that attended the morning or afternoon shifts, and for the sub-sample of graduates that attended the evening shift. Descriptive statistics of main outcomes indicate that female abortion rate in our sample is 9 percent. The probability of getting an abortion is 18 percent for women in the evening shift, 11.5 percent points higher than the probability of getting an abortion for women in other shifts. Moreover, the average graduate in our sample starts to have sex between 17 and 18 years old, but graduates from the evening shift initiate earlier than graduates from other shifts. The average women initiates later than the average graduate. On average, women in the evening shift start to have sex almost a year before women in other shifts. The median intercourse initiation age is between 16 and 17 for women in the evening shift, while it is between 17 and 18 for women in the morning or afternoon shifts. As regards to substance use, five percent of graduates in our sample currently use hard drugs. While in the evening shift the probability of using hard drugs is 12 percent, in the morning or afternoon shifts the probability of using hard drugs is less than 3 percent, implying a difference of almost nine percent points among treatment status. IV. Econometric methods Our main objective in this paper is to capture the causal relationship between studying at night and individual risk behaviour. To do so, we evaluate the impact of attending high school at evening on the probability of getting an abortion, consuming 9

10 substances, and abusing of substances. Formally, we aim at estimating the following regression model: Risk Behavior Evening X (1) icj icj icj c j i where Risk Behavior icj is any of the outcomes of interest (the probability of getting an abortion, the age at intercourse initiation, and the probability of using substances) for graduate i, in class c, and cohort j; Evening icj is a continuous variable that captures the percent of academic years the graduate attended the evening shift., is the casual parameter of interest, X icj is a matrix of graduate i pre- treatment characteristics; c is a class effect that accounts for differential foreign language level between classes; cohort effect; and is an error term. j is a Shift attended is potentially endogenous in a model of risk behavior. Adolescents with higher propensity to engage in risk behavior might have a preference for nightlife or for having less parental control and, therefore, might self-select into the evening shift. The natural experiment described in the previous section provides for a source of exogenous variation for shift attended. Although ESCCP authorities make great efforts to discourage parents and incoming students from asking for a shift change after observing the lottery result, every year exceptions are made and some students manage not to comply with the shift assigned by the lottery. To account for the presence of non-compliance, we use the randomly assigned shift as an instrument for shift attended. As shown in Angrist, Imbens, and Rubin (1996), the Two Stages Least Squares (2SLS) estimator can only recover the Local Average 10

11 Treatment Effect (LATE), a parameter that estimates the effect of shift attended on those students whose shift attended is influenced by the lottery assignment. Although instrumental variables estimation allows controlling for the noncompliance bias, there is still the concern that students might self select into attending or not the ESCCP School after learning the shift they were drafted to. According to the school authorities, cases of students deciding not to attend this prestigious institution upon observation of shift assigned is extremely rare. V. Main Results Table 3 reports the first stage. Columns (1) to (3) show estimates for the whole sample, while columns (4) to (6) show estimates for the sub-sample of women. The estimates indicate that being randomly assigned to the evening shift increases the probability of actually attending the evening shift in about 67 to 70 percent, depending on the specification. In all cases the correlations between treatment assigned and treatment received are highly significant, suggesting that shift assigned is a strong instrument for shift attended. Although the shifts were randomly assigned, it is useful to examine whether, ex post, the pre-treatment characteristics of the students are correlated with shift assignment. Table 4 shows the results of the test of differences in pre-treatment characteristics means, by treatment assigned. The first eight rows show the results for the whole sample and the following rows show the results for the sub-sample of women. In the two cases those graduates drafted to the evening shift are not significantly different in most of the pretreatment characteristics to those graduates drafted to the morning or afternoon shifts, suggesting that the randomization was successful in ensuring orthogonality between 11

12 covariates and treatment assignment. The only exceptions are the proportion of graduates that had attended primary school full time and the proportion of graduates whose father has a tertiary or university degree. In addition, the main results in the paper do not change substantially if the set of pre-treatment characteristics are included as controls. Abortion and sex initiation The estimates of the model for female Abortion are shown in Table 5. Besides the estimates, we report robust standard errors and standard errors clustered at the cohorttreatment level. Columns (1) and (2) show the ITT estimates; columns (3) and (4) show the OLS estimates; and columns (5) and (6) show the 2SLS estimates. All regressions include class and cohort dummies, and columns (2), (4), and (6) additionally include pre-treatment characteristics as controls. We performed the Hausman specification test and we cannot reject the null hypothesis of equality of coefficients (p-value 0.38 and 0.99 depending on the model specification). Our results suggest that attending high school in the evening causes female graduates to have a higher probability of getting an abortion. Our ITT estimates suggest that being a female drafted to the evening shift increments the probability of getting an abortion in between 11 percent and 12.5 percent respective to being drafted to the morning or afternoon shifts. OLS and 2SLS estimates are also positive and significant: female graduates from the evening shift have a probability of getting an abortion that is between 16 percent and 18 percent higher than female graduates from the morning or afternoon shifts. All our estimates are highly significant. Table 6 shows the estimates of the impact of studying at night on the age at which students have their first sexual experience. While columns (1) to (6) display the results for 12

13 the whole sample of graduates, columns (7) to (12) show the results for the sub- sample of women. Columns (1), (2), (7), and (8) show the ITT estimates; columns (3), (4), (9), and (10) show the OLS estimates; and columns (5), (6), (11), and (12) show the 2SLS estimates. All regressions include class and cohort dummies, and even columns additionally include pre-treatment characteristics as controls. When considering all graduates, all the estimations of the considered effect are negative, and generally significant. When considering the sub- sample of women, the effect is also always negative, but only OLS estimations are significant at the 10 percent level. This might be explained by the fact that Hausman test could not reject the null hypothesis that OLS and 2SLS are not systematically different, implying that our 2SLS estimator is inefficient. Relying on Hausman test results (p-value between 0.56 and 0.99 depending on our model specification), we should interpret OLS estimator as the causal effect of interest. The very high p-values associated to the Hausman tests suggest that the impossibility to reject the null is not arising from lack of statistical power. Therefore, we conclude that women that attend the evening shift start having sex almost a year before women in other shifts. All in all, our results for the model of female abortion and sex initiation suggest that women that were randomly assigned to attending school at night might take more risks when experiencing intercourse relationships than women that were assigned to attending school during daytime. Substance use Table 7 shows estimates of the model for current substance consumption, for the whole sample. We also report robust standard errors and standard errors clustered at the cohort-treatment level. Columns (1) and (2) show the ITT estimates; columns (3) and (4) 13

14 show the OLS estimates; and columns (5) and (6) show the 2SLS estimates. All regressions include class and cohort dummies, and columns (2), (4), and (6) additionally include pretreatment characteristics as controls. Our findings indicate that shift-drafted graduates from the evening are more likely to use hard drugs. This positive effect is only significant at the 5% and 10% levels when estimated through OLS. Again, this might be explained by the fact that we are under Hausman test null hypothesis (p-value between 0.39 and 0.99 depending on the model specification), where 2SLS is inefficient. As a result, we would rather trust OLS estimates that are both, consistent and efficient. According to our evidence, graduates from the evening have a probability of using hard drugs between 11% and 12% higher than graduates from the morning or the afternoon shifts. 6 A potential caveat could arise when interpreting our results and it has to do with the persistence of the Evening effect. Our sample is composed by people that at the moment of answering the survey were between 18 and 46 years old. It could be argued, hence, that only if the Evening effect is highly persistence it makes sense to analyze the link between studying at night during adolescence and adulthood consumption of drugs. However, CASA s (2005) evidence that risk behavior is with high probability taken up during adolescence is confirmed by our sample of graduates: 87.5 percent of current hard drugs 6 We estimated model (1) for other substance use outcomes. The estimates of Evening in the model of alcohol abuse are significant when estimated through ITT and 2SLS, for the specifications with controls. Our estimations suggest that drafted graduates that went to the evening shift might abuse of alcohol between 1 percent and 15 percent more than drafted graduates that went to high school during daytime. As Hausman test cannot reject the null hypothesis of equality between OLS and 2SLS, we are suspicious of these results. We did not find significant effects of Evening in the models of the probability of smoking, smoking in excess, using marijuana, or abusing of hard drugs. While the estimated effects are negative for cigar and marijuana use, the coefficients are positive in the model for hard drugs abuse. Finally, our estimates of the model of marijuana abuse are negative and significant when estimated through OLS: being shift-drafted and graduated from the evening reduces the probability of smoking marijuana in excess by between 5.6% and 6.6%. 14

15 users in our sample had tried hard drugs by the age of 21, and all of them by the age of 25. Moreover, 96 percent of current smokers in our sample had tried cigars by the age of 21, and 95 percent of current marijuana users in our sample had tried marijuana by the age of 22. In addition to this, we control for age in all regression exercises as we include cohort dummies. Given that risk behavior is initiated during adolescence, adults exposure to nightlife during adolescence might be relevant for explaining their current drug consumption habits. VI. Robustness check: isn t it classroom peer effects? In this section we study if our previous findings are due to within-class peer effects instead of night-shift effect. The motivation for this is that most of the incoming students that are allowed to choose shift, choose the morning shift. It might be argued that those students that are allowed to choose shift are the ones that are idioscyncratically better students (because they earn higher marks in the admission examinations and this excludes them from the lottery). Alternatively, it could be argued that those students that are allowed to choose shift have better genes (because they already have older siblings in this prestigious school). All in all, it could be the case that our previous results are evidence of classroom peer effects, instead of night-shift effect, which calls for clarification. In order to account for this, we replicate our econometric exercises excluding students from the morning. In other words, we compare graduates from the evening against graduates from the afternoon. Results are shown in Tables 8 to 10. Our main conclusions are confirmed when we exclude the group of students that is most likely to be affected by positive peer classroom effects, thus strengthening our results about evening-shift effect. 15

16 References Akerlof, G. A. (1997). Social Distance and Social Decisions. Econometrica 65 (5), Altonji, J. G., Cattan, S., and Ware, I. (2010). Identifying Sibling Influence on Teenage Substance Use. NBER Working Paper Series, WP Amuedo-Dorantes, C., and Mach, T. (2002). The Impact of Families on Juvenile Substance Use. Journal of Bioeconomics 4 (3), Angrist, J. D., Imbens, G. W., and Rubin, D. B. (1996). Identification of Causal Effects Using Instrumental Variables. Journal of the American Statistical Association 91 (434), Banerjee, A. V., Cole, S., Duflo, E., and Linden, L. (2007). Remedying Education: Evidence from Two Randomized Experiments in India. The Quarterly Journal of Economics 122 (3), Calvó-Armengol, A., Patacchini, E., and Zenou, Y. (2009). Peer Effects and Social Networks in Education. Review of Economic Studies 76 (4), Card, D., & Giuliano, L. (2011). Peer Effects and Multiple Equilibria in the Risk Behavior of Friends. NBER Working Papers Series, WP CASA. (2003). The Importance of Family Dinners. The National Center on Addiction and Substance Abuse at Columbia University. CASA. (2005). The Importance of Family Dinners II. The National Center on Addiction and Substance Abuse at Columbia University. CASA. (2006). The Importance of Family Dinners III. The National Center on Addiction and Substance Abuse at Columbia University. 16

17 CASA. (2007). The Importance of Family Dinners IV. The National Center on Addiction and Substance Abuse at Columbia University. CASA. (2009). The Importance of Family Dinners V. The National Center on Addiction and Substance Abuse at Columbia University. CASA. (2010). The Importance of Family Dinners VI. The National Center on Addiction and Substance Abuse at Columbia University. Case, A., and Katz, L. F. (1991). The Company You Keep: The Effects of Family and Neighborhood on Disadvantaged Youths. NBER Working Paper Series, WP Clark, A., and Lohéac, Y. (2005). 'It Wasn't Me, It Was Them!' Social Influence in Risky Behavior by Adolescents. Institute for the Study of Labor (IZA), IZA Discussion Papers Duflo, E., Dupas, P., and Kremer, M. (2008). Peer Effects, Teacher Incentives, and the Impact of Tracking: Evidence from a Randomized Evaluation in Kenya. NBER Working Paper Series, WP Galiani, S., and Schargrodsky, E. (2010). Property Rights for the Poor: Effects of Land Titling. Journal of Public Economics 94, Galiani, S., Rossi, M., and Schargrodsky, E. (2011). Conscription and Crime: Evidence from the Argentine Draft Lottery. American Economic Journal: Applied Economics 3 (April), Gaviria, A., and Raphael, S. (2001). School-Based Peer Effects and Juvenile Behavior. The Review of Economics and Statistics 83 (2),

18 Graham, B., Imbens, G. W., and Ridder, G. (2005). Measuring the Average Outcome and Inequality Effects of Segregation in the Presence of Social Spillovers. Working Paper. Hausman, J. A. (1978). Specification Tests in Econometrics. Econometrica 46 (6), Kremer, M., and Levy, D. (2008). Peer Effects and Alcohol Use Among College Students. Journal of Economic Perspectives 22 (3), Sacerdote, B. (2001). Peer Effects with Random Assignment: Results form Dartmouth Roommates. The Quarterly Journal of Economics 116 (2), Slomkowski, C., Rende, R., Conger, K. J., Simons, R. L., and Conger, R. D. (2001). Sisters, Brothers, and Delinquency: Evaluating Social Influence during Early and Middle Adolescence. Child Development 72 (1), Soetevent, A. R. (2006). Empirics of the Identification of Social Interactios; an Evaluation of the Approaches and their Results. Journal of Economic Surveys 20 (2), Soetevent, A., and Kooreman, P. (2006). A Discrete Choice Model with Social Interactions: an Analysis of High School Teen Behavior. Journal of Applied Econometrics 22 (3), Windle, M. (2000). Parental, Sibling, and Peer Influences on Adolescent Substance Use and Alcohol Problems. Applied Developmental Science 4 (2),

19 Table 1. Graduates attrition by treatment received Cohort Proportion of non-attritors Difference Evening=0 Evening= (0.004) (0.009)* (0.014) (0.023) (0.029) (0.003) (0.021) (0.025) (0.027) (0.005) (0.028) (0.018) (0.020) (0.021) (0.029)** (0.026) (0.025) (0.022) (0.013) (0.013) (0.017) (0.016) ,044 0,047-0,002 (0,022) (0.026) (0.025)** (0.019) (0.020) (0.025) Notes: Standard errors are in parentheses. **Significant at the 5% level. *Significant at the 10% level. 19

20 Pre-treatment Table 2. Summary statistics Drafted units (300) Evening=0 (222) Evening=1 (78) (1) (2) (3) (4) (5) (6) (7) (8) (9) Obs. Mean Std.Dev. Obs. Mean Std.Dev. Obs. Mean Std.Dev. Female Primary school full time Primary school morning Primary school afternoon Primary school public Mother has higher education Father has higher education Age at school entry Outcomes Abortion (women) Sexual initiation Sexual initiation (women) Uses hard drugs Notes: Columns (1) to (3) show statistics for the 300 units that were drafted to shift. Columns (4) to (6) show descriptive statistics for the 222 graduates that never attended the evening shift during their academic years at school. Columns (7) to (9) show descriptive statistics for the 78 graduates that attended the evening shift sometime during their academic years at school. Questions about pre- treatment characteristics were made to every participant in the survey. Questions about female abortion and female sexual initiation were made to the 166 women drafted to shift. Questions about sexual initiation and were made to every participant in the survey. Questions about hard drugs use were made to the 189 graduates that reported having ever tried substances. 20

21 Table 3. First Stage Whole sample Sub-sample of women (1) (2) (3) (4) (5) (6) Dependent variable: Treatment Received: Evening Treatment Assigned: Evening (0.045)*** (0.046)*** (0.047)*** (0.062)*** (0.066)*** (0.070)*** Constant (0.005) (0.062)*** (0.089) (0.000)*** (0.145) (0.102) F-Statistic Class and Cohort dummies No Yes Yes No Yes Yes Controls No No Yes No No Yes Observations Notes: Treatment Received: Evening is a continuous variable that captures the percent of academic years the graduate attended the evening shift. Controls are Female (a dummy that takes value one if the graduate is a female and cero otherwise, only included in columns (1) to (3)); Primary school full time, morning, or afternoon (three dummies that takes value one if the graduate attended primary in the morning shift, in the afternoon shift, or full time- respectively- and cero otherwise); Primary school public (a dummy that takes value one if the graduate attended high school at a public institution and cero otherwise); Mother has higher education (a dummy that takes value one if the graduate s mother holds a tertiary or university degree and cero otherwise); Father has higher education (a dummy that takes value one if the graduate s father holds a tertiary or university degree and cero otherwise); and Age at school entry. Robust standard errors are in parentheses. ***Significant at the 1% level. *Significant at the 10% level. 21

22 Table 4. Balancing of pre-treatment characteristics, by treatment assigned Mean Difference Drafted to Evening=0 Drafted to Evening=1 Whole sample Female (0.035) (0.050) (0.061) Primary shift: full time (0.034) (0.051) (0.060)* Primary shift: morning (0.035) (0.049) (0.061) Primary shift: afternoon (0.028) (0.038) (0.048) Primary school was public (0.035) (0.050) (0.061) Mother has higher education (0.035) (0.050) (0.060) Father has higher education (0.035) (0.050) (0.061)** Age at school entry Sub-sample of women (0.042) (0.057) (0.072) Primary shift: full time (0.043) (0.070) (0.080)* Primary shift: morning (0.047) (0.068) (0.083) Primary shift: afternoon (0.038) (0.051) (0.066) Primary school was public (0.045) (0.070) (0.082) Mother has higher education (0.045) (0.069) (0.081) Father has higher education (0.046) (0.069) (0.082)** Age at school entry (0.053) (0.072) (0.093) Notes: Standard errors are in parentheses. **Significant at the 5% level. *Significant at the 10% level. 22

23 Table 5. Probability of getting an abortion Sub-sample of women (1) (2) (3) (4) (5) (6) Treatment Received: Evening Treatment Assigned: Evening (0.053)** (0.057)** [0.048]** [0.051]** (0.065)** (0.070)** (0.077)** (0.082)** [0.053]*** [0.058]*** [0.098]** [0.070]** Controls No Yes No Yes No Yes Observations Estimator ITT ITT OLS OLS 2SLS 2SLS Notes: Questions about female abortion were made to the 166 women drafted to shift. All regressions include class and cohort dummies. Treatment Received: Evening is a continuous variable that captures the percent of academic years the graduate attended the evening shift. Controls are Primary school full time, morning, or afternoon (three dummies that takes value one if the graduate attended primary in the morning shift, in the afternoon shift, or full time- respectively- and cero otherwise); Primary school public (a dummy that takes value one if the graduate attended high school at a public institution and cero otherwise); Mother has higher education (a dummy that takes value one if the graduate s mother holds a tertiary or university degree and cero otherwise); Father has higher education (a dummy that takes value one if the graduate s father holds a tertiary or university degree and cero otherwise); and Age at school entry. Robust standard errors are in parentheses. Standard errors clustered at the cohort and treatment level are in square brackets. Hausman test cannot reject the null hypothesis with p-value of **Significant at the 5% level. *Significant at the 10% level. 23

24 Table 6. Age at first time to have sex (1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11) (12) Treatment Received: Evening (0.326)* (0.338)* (0.427) (0.441) (0.498)** (0.513)** (0.672) (0.700) [0.235]** [0.239]** [0.311]* [0.301]** [0.484]** [0.508]** [0.669] [0.701] Treatment Assigned: Evening (0.299) (0.303) (0.497) (0.512) [0.208]** [0.194]** [0.486] [0.500] Controls No Yes No Yes No Yes No Yes No Yes No Yes Observations Estimator ITT ITT OLS OLS 2SLS 2SLS ITT ITT OLS OLS 2SLS 2SLS Notes: Questions about sexual initiation were made to every participant in the survey. Questions about female sexual initiation were made to the 166 women drafted to shift. All regressions include class and cohort dummies. Treatment Received: Evening is a continuous variable that captures the percent of academic years the graduate attended the evening shift. Controls are Female (a dummy that takes value one if the graduate is a female and cero otherwise, only included in columns (1) to (6)); Primary school full time, morning, or afternoon (three dummies that takes value one if the graduate attended primary in the morning shift, in the afternoon shift, or full time- respectively- and cero otherwise); Primary school public (a dummy that takes value one if the graduate attended high school at a public institution and cero otherwise); Mother has higher education (a dummy that takes value one if the graduate s mother holds a tertiary or university degree and cero otherwise); Father has higher education (a dummy that takes value one if the graduate s father holds a tertiary or university degree and cero otherwise); and Age at school entry. Robust standard errors are in parentheses. Standard errors clustered at the cohort and treatment level are in square brackets. Hausman test cannot reject the null hypothesis with p-value of *Significant at the 10% level.

25 Table 7. Probability of using hard drugs (1) (2) (3) (4) (5) (6) Treatment Received: Evening Treatment Assigned: Evening (0.043) (0.040) [0.045] [0.039] (0.059)** (0.057)* (0.069) (0.068) [0.070]* [0.065] [0.071] [0.066] Controls No Yes No Yes No Yes Observations Estimator ITT ITT OLS OLS 2SLS 2SLS Notes: Questions about hard drugs use were made to the 189 graduates that reported having ever tried any substance. All regressions include class and cohort dummies. Treatment Received: Evening is a continuous variable that captures the percent of academic years the graduate attended the evening shift. Controls are Female (a dummy that takes value one if the graduate is a female and cero otherwise); Primary school full time, morning, or afternoon (three dummies that takes value one if the graduate attended primary in the morning shift, in the afternoon shift, or full time- respectively- and cero otherwise); Primary school public (a dummy that takes value one if the graduate attended high school at a public institution and cero otherwise); Mother has higher education (a dummy that takes value one if the graduate s mother holds a tertiary or university degree and cero otherwise); Father has higher education (a dummy that takes value one if the graduate s father holds a tertiary or university degree and cero otherwise); and Age at school entry. Robust standard errors are in parentheses. Standard errors clustered at the cohort and treatment level are in square brackets. Hausman test cannot reject the null hypothesis with p-value of **Significant at the 5% level. *Significant at the 10% level.

26 Table 8. Evening vs. Afternoon: Probability of getting an abortion Sub-sample of women (1) (2) (3) (4) (5) (6) Treatment Received: Evening Treatment Assigned: Evening (0.068)** (0.074)* [0.063]** [0.074]* (0.075) (0.087) (0.088)** (0.096)* [0.055]* [0.073]* [0.085]** [0.097]* Controls No Yes No Yes No Yes Observations Estimator ITT ITT OLS OLS 2SLS 2SLS Notes: These estimations exclude graduates that attended the morning shift. Hence, estimates about female abortion apply to the 95 women drafted to shift and that did not attend the morning shift. All regressions include class and cohort dummies. Treatment Received: Evening is a continuous variable that captures the percent of academic years the graduate attended the evening shift. Controls are Primary school full time, morning, or afternoon (three dummies that takes value one if the graduate attended primary in the morning shift, in the afternoon shift, or full time- respectively- and cero otherwise); Primary school public (a dummy that takes value one if the graduate attended high school at a public institution and cero otherwise); Mother has higher education (a dummy that takes value one if the graduate s mother holds a tertiary or university degree and cero otherwise); Father has higher education (a dummy that takes value one if the graduate s father holds a tertiary or university degree and cero otherwise); and Age at school entry. Robust standard errors are in parentheses. Standard errors clustered at the cohort and treatment level are in square brackets. Hausman test cannot reject the null hypothesis with p-value of **Significant at the 5% level. *Significant at the 10% level. 26

27 Table 9. Evening vs. Afternoon: Age at first time to have sex Whole sample Women only (1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11) (12) Treatment Received: Evening (0.379) (0.387) (0.479) (0.504) (0.659) (0.627) (0.923) (0.888) [0.295] [0.302]* [0.460] [0.504] [0.564] [0.612] [1013] [0.986] Treatment Assigned: Evening (0.412) (0.430) (0.801) (0.769) [0.397] [0.430] [0.882] [0.859] Controls No Yes No Yes No Yes No Yes No Yes No Yes Observations Estimator ITT ITT OLS OLS 2SLS 2SLS ITT ITT OLS OLS 2SLS 2SLS Notes: These estimations exclude graduates that attended the morning shift. Hence, estimates about sexual initiation apply to every participant in the survey that did not attend the morning shift. Questions about female sexual initiation apply to the 95 women drafted to shift and that did not attend the morning shift. All regressions include class and cohort dummies. Treatment Received: Evening is a continuous variable that captures the percent of academic years the graduate attended the evening shift. Controls are Female (a dummy that takes value one if the graduate is a female and cero otherwise, only included in columns (1) to (6)); Primary school full time, morning, or afternoon (three dummies that takes value one if the graduate attended primary in the morning shift, in the afternoon shift, or full time- respectively- and cero otherwise); Primary school public (a dummy that takes value one if the graduate attended high school at a public institution and cero otherwise); Mother has higher education (a dummy that takes value one if the graduate s mother holds a tertiary or university degree and cero otherwise); Father has higher education (a dummy that takes value one if the graduate s father holds a tertiary or university degree and cero otherwise); and Age at school entry. Robust standard errors are in parentheses. Standard errors clustered at the cohort and treatment level are in square brackets. Hausman test cannot reject the null hypothesis with p-value of *Significant at the 10% level.

28 Table 10. Evening vs. Afternoon: Probability of using hard drugs (1) (2) (3) (4) (5) (6) Treatment Received: Evening Treatment Received: Percent at Evening (0.048)* (0.039) [0.044]** [0.029]* (0.055)** (0.048)** (0.059)* (0.050) [0.063]* [0.051]* [0.053]** [0.038]* Controls No Yes No Yes No Yes Observations Estimator ITT ITT OLS OLS 2SLS 2SLS Notes: These estimations exclude graduates that attended the morning shift. Hence, estimates apply to the 104 graduates that reported having ever tried any substance and did not attend the morning shift. All regressions include class and cohort dummies. Treatment Received: Evening is a continuous variable that captures the percent of academic years the graduate attended the evening shift. Controls are Female (a dummy that takes value one if the graduate is a female and cero otherwise); Primary school full time, morning, or afternoon (three dummies that takes value one if the graduate attended primary in the morning shift, in the afternoon shift, or full timerespectively- and cero otherwise); Primary school public (a dummy that takes value one if the graduate attended high school at a public institution and cero otherwise); Mother has higher education (a dummy that takes value one if the graduate s mother holds a tertiary or university degree and cero otherwise); Father has higher education (a dummy that takes value one if the graduate s father holds a tertiary or university degree and cero otherwise); and Age at school entry. Robust standard errors are in parentheses. Standard errors clustered at the cohort and treatment level are in square brackets. Hausman test cannot reject the null hypothesis with p-value of **Significant at the 5% level. *Significant at the 10% level.