Psychosocial competencies and risky behaviours in Peru

2.1 Psychosocial competencies and cognitive skills as predictors of risky behaviours: evidence from policy and research

Many studies in the economic literature find evidence of contemporaneous correlation between different risky behaviours (Chaloupka and Laixuthai 1997; Dee 1999; DiNardo and Lemieux 2001; DuRant et al. 1999; Farrelly et al. 2001; Model 1993; Wiefferink et al. 2006). Those evidence support the ‘bad seed’ hypothesis, as described by Gruber (2001). The hypothesis is that there is a certain segment of the youth population that is predisposed towards risky activities, while others are not. In that case, policies targeting the segment of population at risk should work effectively. An alternative hypothesis in psychological literature is that there is a certain amount of risk that youths have the tendency to take (‘conservation of risk’ hypothesis). Reducing risky activity in one area would have a substitution effect by increasing risky activities in another. To date, most intervention programmes have been targeting specific groups of the population considered at risk, mainly by targeting single risk behaviours. Most recently, there are examples of interventions taking a broader approach and target more than one risky behaviour at time. More specifically, they aim to address some underlying determinants of risky behaviours which are believed to protect young people from, or predispose them to, distinct risky behaviours. Therefore, a better understanding of which childhood traits predict risky behaviours is crucial from a policy perspective.

Empirical evidence suggest that interventions focusing on improving cognitive skills or aimed at improving soft skills are effective in reducing risky behaviours. An example of an intervention aimed at improving opportunities for children coming from poor backgrounds is the well-known Perry Preschool Programme, an intervention targeting a sample of 3–4-year-old African–American children living in poverty and assessed to be at high risk of school failure. Although the literature originally focused on the cognitive impact of the intervention, long-term effects have in fact been more persistent in non-cognitive areas. Heckman et al. (2010) and Conti et al. (2015) show that Perry significantly enhanced adult outcomes including education, employment, earnings, marriage, participation in healthy behaviours, and reduced participation in crime teen pregnancy, and welfare dependency later in life. Interestingly, although the programme initially boosted the IQs of participants, this effect soon faded. A persistent effect of the programme has been found on improvements in personality skills (e.g. it reduces aggressive, antisocial, and rule-breaking behaviours). On the other side, Hill et al. (2011) show that several interventions that focus on personality rather than only on cognitive skills were effective at reducing delinquency and traits related to delinquency.

Few economic papers analyse the role of personality traits and non-cognitive skills on criminal activities, or more generally, risky behaviours. Heckman et al. (2006) find that self-esteem and locus of control measured during adolescence are as powerful as cognitive abilities in predicting adult earnings. Moreover, they find that personality factors for men affect the probability of daily smoking more than cognitive factors and the opposite is true for women. Similarly, Cunha et al. (2010) show that personality traits are relatively more important in predicting criminal activity than cognitive traits are. Further, Conti and Heckman (2010) suggest that personality and health status measured during adolescence explain more than 50% of the difference in poor health, depression and obesity at age 30. For males, personality traits and health endowments are more predictive than cognitive skills while for women they are equally predictive.

The role of self-efficacy and self-esteem as predictors of risky behaviours has been discussed in the psychological literature, particularly its role during the adolescence period. This is because during this stage individuals commonly start experimenting with risky activities (including alcohol abuse, smoking, drug use, and unprotected sex). Bandura et al. (2001) state that perceived self-efficacy (in the areas of academic, social, and self-regulatory efficacy) is important to resist peer pressure for transgressive activities, a view also shared by other authors (e.g. Wills 1994). Empirical evidence shows a negative relationship between self-efficacy and risky or delinquent behaviours, including use of alcohol and drugs, physical and verbal aggression, theft, cheating and lying (Bandura et al. 2001; Bandura et al. 2003). In addition, self-efficacy is thought to be important to change unhealthy behaviours, such as smoking (Schwarzer 2001).

In the case of self-esteem, a negative relationship with risky behaviours is expected (Donnellan et al. 2005). First, people with low self-esteem perceive that they have less social ties (Rosenberg 1965), which in turn decrease conformity to social norms and increase delinquency. Second, it is theorized that aggression and antisocial behaviour are motivated by feelings of inferiority rooted in early childhood experiences. In addition, it is thought that self-esteem mediates the impact of stress, which is of a subjective nature (Baumeister et al. 2003). People with high self-esteem are likely to experience less stress because they interpret negative events more benignly, are more optimistic about their coping abilities, and perceive they have more control compared to people with low self-esteem.

Notwithstanding these arguments, others have argued that a positive relationship could arise, as noted by Baumeister et al. (2003). While it is true that young people with low self-esteem might be more prone to engage in risky behaviours—for solace when they feel bad about themselves, young people with arguably high self-esteem might have biases in their interpretation of events that allow them to feel better about themselves, either by minimizing their own vulnerability or by distorting how their parent will react. This is likely to be the case in particular for people with unrealistically high self-esteem, close to narcissism (Donnellan et al. 2005).

Baumeister et al. (2003) provide a review of the literature about the role of self-esteem on several life outcomes, including smoking, alcohol and drug abuse, and unprotected sex. They conclude that evidence linking low self-esteem to risky behaviours during the adolescence is mixed and inconclusive, with positive, negative and zero effects found, particularly in the case of alcohol use, whereas in the case of smoking, the relation is mainly negative. On the other hand, Donnellan et al. (2005) use data from three different datasets which strongly support the notion that low self-esteem is related to aggressive behaviour.

An important aspect is whether self-esteem and self-efficacy can be measuring similar dimensions of a person self-concept. In fact, some authors (Dercon and Krishnan 2009; Epstein et al. 2004) suggest that self-efficacy can be treated as a determinant of self-esteem. Wills (1994) shows empirical evidence that supports the notion that self-efficacy might be a more important factor than self-esteem, and suggests that, in absence of a control for self-efficacy, previous studies might have overstated the importance of self-esteem. Overall, what this seems to suggest is that it is important to control for both psychosocial dimensions in order to estimate the individual contribution of each.

At the heart of the traditional opportunity cost approach to risky behaviours and of intertemporal choice models described above, are individual expectations. As mentioned, people make decisions taking into account the present utility, their expectations about future utility. Present-biased time preferences are likely to be more frequent among people who are pessimistic about their future. Consistently with the ‘opportunity cost’ argument in the risky behaviour literature, if an outcome is perceived as inaccessible, people might believe that they have little to lose by engaging in risky behaviours. There is a considerable body of economic literature investigating the role of aspirations and subjective expectations for contraceptive choices (Delavande 2008), (sexual) risky behaviour (De Paula et al. 2013; Shapira 2013) and non-marital childbearing choices (Wolfe et al. 2007). As Dalton et al. (2016) argue, how far people aspire depends on their own beliefs about what they can achieve with effort, i.e. their own expectations. People would not aspire to an outcome that is perceived as inaccessible. However, given the endogenous nature of aspirations, the empirical distinction between aspirations and expectations is hard to achieve in a non-experimental setting and often aspirations are used interchangeably with expectations.

Finally, the decision making model described above, yields several important implications regarding the role that cognition plays for the probability to engage in risky behaviours. There are a number of mechanisms through with cognitive skills might affect individual decision making, some of which can be amplified by the interaction between cognitive skills and schooling. First, individuals with higher cognitive skills might be more able to access information and more efficient at interpreting it. Second, cognitive skills are likely to shape preferences. As argued by Dohmen et al. (2010), people with better cognition appear to be more patient. They are also more willing to take risks. One potential explanation is that they are better able to envision future consequences and somehow reduce ambiguity about the future. In this sense, increased cognitive ability favourably influences behaviours, particularly when information is limited or idiosyncratic.

Schooling is also considered a protective factor against risky behaviours (see for example Cutler and Lleras-Muney 2010). First, education promotes the accumulation of both cognitive and socio-emotional skills, which affects the way individuals process information and behave. Second, education shapes the nature of the social network available to the individual, which can have either a positive or a negative effect (Behrman 2015; Peters et al. 2010). Third, education might shape time preferences, e.g. because schooling focuses students’ attention on the future (Becker and Mulligan 1997; Fuchs 1982). Fourth, people with more education might be better informed about negative health consequences, either because they learned about these consequences in school, or because better educated people find it easier to obtain and evaluate such information (De Walque 2007; Kenkel 1991). Fourth, education could also influence behavior by increasing the opportunity cost of engaging in risky behaviours, i.e. by increasing future income.

2.2 Other predictors of risky behaviours

The importance of family environment is well recognized by developmental research. Numerous studies show that children who grow up in single-parent families are more at risk of engaging in risky behaviours (see for example Evans et al. 1992). Adolescents from intact two-parent families tend delay the start of sexual activity relative to those in disrupted families (see for example Meschke and Silbereisen 1997). Clark and Loheac (2007) examine the consumption of tobacco, alcohol and marijuana in the USA and find that marijuana use is more widespread in single-parent families. They also find that smoking is more frequent amongst recent movers. Migration indeed might be potential source of instability. Gaviria and Raphael (2001) using secondary school data from the USA suggest that recent movers may be more susceptible to peer group pressure, at least with respect to the consumption of marijuana and cocaine.

Similarly, children who have older siblings have a higher probability of engaging in risky behaviours (Averett et al. 2011), and there might be a number of plausible explanations for that. It might be that older siblings affect their younger siblings’ behaviours indirectly, by being a role model to them and directly by proving them more opportunities to interact with a different group of older friends. An alternative explanation might be that parents spend less time in supervising their younger offspring (Aizer 2004).

It is worth to highlight that single parenthood as well as some other socio-economic characteristics frequently associated with poverty are some of the stronger predictors of risky behaviours. Risky sexual behaviours are often a manifestation of lack of opportunities, deprivation and poverty. Nevertheless, although risky behaviours are generally more prevalent in deprived socio-economic contexts, there is no consensus in the literature about the relative importance of different socio-economic indicators as independent determinants of adolescent risky behaviours. Some researchers have argued that parental educational attainment is a stronger predictor than other socio-economic indicators, such as household income or parental occupation (Goodman 1999).

Furthermore, it is not clear through which mechanisms the various indicators of socio-economic status might operate in affecting adolescents’ behaviours. Skills and competencies formation might be one of them. For example, it has been observed that scarce family resources are associated with low self-esteem (Amato and Ochiltree 1986) which in turn might be related with a higher risk of engaging in criminal and health-detrimental activities. Also, children who live in single-parent families show more behavioural problems and lower self-confidence (see for example Steinberg 1987).

4.1 Data

The data used in this paper comes from the Young Lives Panel Survey, a longitudinal study that follows 12,000 children in Ethiopia, India (Andhra Pradesh and Telangana), Peru and Vietnam over 15 years. The sample in each country consists of two cohorts—the older cohort who were born in 1994/95, and the younger cohort who were born in 2001/03. The first wave of the study was in 2002 (round 1), which was then followed by three subsequent rounds in 2006 (round 2), 2009 (round 3) and 2013 (round 4). The attrition rate across all four rounds is relatively low compared to other longitudinal studies, particularly for Peru where the attrition rate is 6.3% for the younger cohort and 10.3% for the older cohort.

The Young Lives sample for Peru gathers information for approximately 700 older cohort children and 2,000 younger cohort children with an over-sampling of poor areas. The original sample was spread over 20 clusters in different geographical regions.³ More specifically, the 20 clusters were randomly selected from the universe of districts in 2002, excluding the wealthiest 5%. Each district was given a probability of being selected proportional to its population size. Then, within each selected district, an area was randomly selected and families with children aged 6 to 18 months and 7 to 8 years were selected to be part of the younger cohort and older cohort respectively. Although Young Lives is not intended to be nationally representative, it is worth highlighting that because of the sampling procedure used, the Young Lives sample for Peru has been found to optimally reflect the diversity of children and families in Peru, excluding the wealthiest 5%.⁴ In the present analysis, we use the older cohort data aged 8 years old in 2002, 15 years old in 2009 and 19 in 2013. For this cohort, data on risky behaviours was collected in both round 3 and 4 (ages 15 and 19).

A key challenge involved in the collection of risky behaviours data is the danger of substantial under-reporting, both due to cultural reasons as well as legal reasons in the case of the consumption of illegal drugs. This problem is particularly acute in face-to-face interviews. Although Young Lives administers face-to-face interviews for both the child and her family, the information on risky behaviours comes from a self-administered questionnaire which includes a set of questions about alcohol, cigarettes and drug consumption together with sexual behaviours, contraceptives use and knowledge about sexual and reproductive health. This questionnaire was applied in Rounds 3 and 4 following a meticulous protocol with the aim of minimizing under-reporting.

The protocol of the self-administered questionnaire, which is typically applied at the end of the visit, is as follows. The interviewer explains to the child that she will be asked a number of questions about aspects that might be considered sensitive, such as consumption of alcohol, cigarettes and even drugs, together with sexual behaviours, contraceptives use and knowledge about sexual and reproductive health. She is told that she is free to choose to complete the questionnaire or not, and she is free to leave questions blank if she wishes to do so. Then the interviewer mentions that all her answers will remain confidential and that, once she completes the questionnaire, he will put the paper in a sealed envelope, and that neither the questionnaire nor the envelope will contain her name, but a code. Once the interviewer gives this information, the child is asked whether she wants to complete the questionnaire. If she agrees, she is left alone for 15 minutes. Finally, once she completes the questionnaire, this questionnaire will be sealed in an envelope with the code that corresponds to the child.

The aim of the protocol previously described is to assure the child that her answers will remain confidential (as it is indeed the case). Following this procedure, the percentage of children that decided to answer the questionnaire was very high. In Round 4, only 3.8% of the sample refused to complete the self-administered questionnaire. From those that agreed to answer, 1.6% left all the self-administered questions blank. Among those that decided to go on, the proportion of missing answers is relatively small, especially for questions related to smoking, drinking, possession of a weapon, and criminal behaviours (1.3% of missing answers in questions related to the consumption of cigarettes; 0.5% for alcohol consumption; up to 1.0% for alcohol consumption and engagement in risky activities; 0.5% for possession of a weapon; and, up to 1.5% for questions related to criminal behaviours).The proportion is slightly larger for sexual relations (4.0% for questions related to use of condoms during last sexual intercourse) and consumption of illegal drugs (up to 5.8%). Although there can be some level of under-reporting hidden in the answers that were left blank, the fact that a small proportion of children answered this way leads us to believe that this is unlikely to cause a meaningful bias in our results, particularly because we do capture a high proportion of adolescents engaged in risky behaviours, as will be shown in the following sections.

4.2 Risky behaviours: definition and statistics using Young Lives data

Smoking participation (‘smoking’ variable) is defined as a dummy variable equal to 1 for those individuals who reported to smoke cigarettes at least once per month. At the age of 19, about 19% of our sample report to be smoking (Table 3). On average they smoke their first cigarette at the age of 16. Most of them (89%) report to smoke only one cigarette (or less) per day (see Table 13 in the Appendix).

With respect to alcohol consumption, there is a growing public concern about how much alcohol young people are drinking in their teenage years. According to our data, drinking is the activity with the highest prevalence at both age 15 and 19. At the age of 15, about 65% of the sample report that they never drink alcohol and, among those who do, most of them drink exclusively in special occasions or very sporadically. By the age of 19, the number of young people drinking increase tremendously, although most of them do not drink on a regular basis.⁵

Although the proportion of 19-year-old teenagers who drink regularly is quite low (less than 1% drink alcohol on a daily basis and 3% at least once per week), alcohol consumption increases tremendously between age 15 and 19 as for the number of times they abuse from alcohol consumption. The excessive alcohol consumption not only puts their own health at risk but also makes them more likely to get involved in anti-social behaviours.

We defined two variables for the excess of alcohol consumption: the ‘drinking’ variable equal to 1 for those adolescents who got drunk at least once in their life (and 0 otherwise) and the ‘drinking and violence’ variable equal to 1 for those who engaged in risky behaviours (either having sex, engaging in a fight, feeling sick or drunk) while drinking. By the age of 19, about 34% of the sample report of having been drunk and 41% of having engaged in risky behaviours while drinking (Table 3).

With respect to sexual behaviours, about 67% of our sample had sex by the age of 19, on average having the first sexual relation at the age of 16 (see Table 13 in the Appendix). We define a variable to capture those young people at risk of sexual transmitted diseases (STDs).⁶ The ‘unprotected sex’ variable is equal to 1 for those who did not use a condom in the last sexual relationship (including also those who used other birth control methods or emergency contraception) and 0 for those who had protected sex (using condoms) or never had sex.

For drug consumption, we define an indicator to identify those who ever tried any drugs; about 13% by the age of 19.

Similarly, the dummy variable for weapon possession is equal to 1 for those who during the last 30 days carried a weapon at least once.

Overall, looking at the incidence of risky behaviours by age, we notice that risky behaviours increase significantly between the age of 15 and the age of 19, in correspondence with the transition from childhood to adolescence (Table 3). It is worth to note that by the age of 19 male engagement in risky behaviours is about two to three times that of females in smoking, drinking and taking drugs and criminal behaviours. There is also an urban-rural difference in drinking, where adolescents living in urban areas drink more (10% by the age of 15 and 38% by the age of 19) relative to those in rural areas (5% by the age of 15 and 24% by the age of 19) and by the age of 19 are more likely to engage in risky behaviours while drinking.

However, not only the prevalence but also the intensity of risky behaviours increases over time. We define a variable counting the number of risky activities the young people have been involved in by the age of 15 and 19.⁷ By the age of 15, about 22% of young people have engaged with at least one risky behaviour. By the age of 19, slightly more than one out of two had engaged in at least one type of risky behaviour, with a distinctive pro-male bias (67% among males, 43% among females). While 26% of the population engaged in only one risky activity, a consistent segment of the youth population (29%), mainly male population, undertakes more than one of these activities.

Our data reports quite a remarkable diffusion of risky behaviours among Peruvian adolescents and a worrisome predisposition towards risky activities for the relevant part of them. Although our data do not provide full support to either the ‘bad seed’ or the ‘conservation of risk’ hypothesis, it is worth to note that there is evidence of a certain persistence (or recidivism) in risky behaviours. Those who engage in risky behaviours at the age of 15 are indeed more likely to engage in risky behaviours at the age of 19. The average ‘number of risky behaviours’ at the age 15 is strongly correlated with the same measured 4 years later (standardized correlation coefficient of 0.6). Recidivism is more evident in some risky behaviours than others, particularly in drug consumption, drinking and smoking. In fact, adolescents who consume drugs at age 15 are 64 percentage points more likely to consume drugs at age 19. Similarly, drinking (smoking) at age 15 increases the probability of smoking (drinking) at age 19 by 38 percentage points (39 percentage points).

In the next section we characterize further who are these young people, what is their history, their past experience, their ability and psycho-social well-being and where do they live using a multivariate approach.

4.3 Psycho-social competencies and cognitive skills in Young Lives data

In this section we briefly define the core predictors of risky behaviours used in the analysis.

As discussed in Section 2, psychosocial competencies have been identified as important factors in predicting risky behaviours. In our data, we capture them through two indicators that have been administered in the last three rounds of the Young Lives survey: the self-esteem scale and the self-efficacy scale. In the Young Lives database, these scales are referred as the pride index and the agency index, respectively. The self-esteem scale builds on the self-esteem concept by Rosenberg (1965). The objective is to measure a child’s overall evaluation of his or her own worth. In turn, the self-efficacy scale builds on the concept of locus of control by Rotter (1966) and self-efficacy by Bandura (1993). In this case, the objective is to measures a child’s sense of agency or mastery over his/her own life. Each scale is measured based on respondents’ degree of agreement or disagreement with a number of positive and negative statements measured on a 4-point Likert scale (the full list of statements are reported in Table 2). Statements are adapted to measure specific dimensions of the children’s living circumstances. In order to calculate each scale, all statements were recoded to be positive outcomes, standardized (normalized to z-scores), and then averaged. The internal consistency of these scales is shown in (Dercon and Krishnan 2009).⁸

Another core predictor for risky behaviours investigated in this analysis is individual aspirations. The measure of aspirations considered in this study reflects a combination of aspirations and beliefs about the likelihood of achieving the aspired outcomes. Specifically, Young Lives collects information about educational aspirations by asking the child the following question: ‘Imagine you had no constraints and could study for as long as you liked, or go back to school if you have already left. What level of formal education would you like to complete?’. In this study, we define a dummy variable equal to 1 for individuals with high aspirations, i.e. for those children who aspire to go to university, and 0 otherwise. It is important to note that aspirations are understood to be shaped to a large extent by self-efficacy (Bandura et al. 2001).

Finally, we look at a set of predictors relating to education, namely school enrollment and cognitive development. Related to the latter, we include an indicator of literacy measured by the Peabody Picture Vocabulary Test (PPVT), a test of receptive vocabulary. The task of the test taker is to select the picture that best represents the meaning of a stimulus word presented orally by the examiner. The items used were validated independently for local teams in each country and are age-standardized. The PPVT was administered since round 2, when the children were 8 years old. To measure early age cognitive skills, in round 1 Young Lives administer the Raven test, a widely used test of abstract reasoning and regarded as a non-verbal estimate of fluid intelligence.

Besides the PPVT and the Raven test, a numeracy test was administered. Given that Young Lives is not a school-linked study, numeracy assessments are not aligned with school curricula; however, the contents of the tests could be linked with learning that should be emphasized in schools. In order to account for wide variations in the grade and skill levels of individuals both within and across countries, the tests incorporated questions with differing levels of difficulty: at the basic level the tests included questions assessing basic number identification and quantity discrimination; at the intermediate level, questions on calculation and measurement; and at the advanced level, questions related to problem solving embedded in hypothetical contexts that simulate real-life situations (e.g. tables in newspapers). The numeracy skills indicator used in this analysis is the number of correct answers in the Math test. Notably, the cognitive tests have been collected for all children regardless whether they are attending school or not. This feature of the data avoids any selection problem which commonly arises using school-based data. A validation of the psychomethric properties of the PPVT and Math scores can be found in Cueto and Leon (2012) in and Cueto et al. (2009).

6.1 Main results

The four most consistent predictors of risky and criminal behaviours are gender, age, self-esteem and whether the individual comes from a single-parent household. The fact that there are differential patterns by gender and age was already evident in the descriptive statistics. The probability of smoking, drinking and engaging in drinking and violence increases respectively by 22, 23 and 25 percentage points for males compared to females. Similarly, males are 13 percentage points more likely than females of consuming drugs and engaging in criminal behaviours. Although the average age is 19, many individual were aged 18 at the moment of the interview. We find that moving from 18 to 19 years old increases the likelihood of smoking, drinking and drinking and violence by around 10 percentage points in all cases.

Beyond the role of gender and age, our main finding is related to the association between self-esteem and risky and criminal behaviours. Given that the estimation controls for demographic and socio-economic characteristics, as well as for schooling achievement and time-invariant community characteristics, among other aspects, the estimated parameter can be interpreted as a robust association. Keeping other factors constant, a 1 standard deviation increase in self-esteem at age 15 reduces the likelihood of engaging in smoking, drinking, and drinking and violence by 7, 6 and 8 percentage points (respectively); it also reduces the likelihood of criminal behaviours and carrying a weapon by 14 percentage points and 4 percentage points. In contrast, the association with self-efficacy is not statistically significant, though it is interesting to observe that the estimated coefficients have the expected (negative) sign.

About the role of family structure, a specific dimension that plays a role is whether the individual comes from a single-parent household, which increases the likelihood of engaging in risky sex, in criminal behaviours and carrying a weapon by 14, 17 and 5 percentage points, respectively. In addition, the number of siblings is positively associated with criminal behaviours.

It is interesting to observe that living in a rural area reduces drinking by 27 percentage points and drinking and violence by 23 percentage points.

Finally, keeping other factors constant, we find a strong negative association between school enrollment and criminal behaviours. Also, an increase of one standard deviation in the PPVT test is associated with a reduction in smoking of 5 percentage points.

From the factors previously mentioned, gender, age, self-esteem and living in a single-parent household stand out as factors that systematically predict risky and criminal behaviours. These are also the factors that predict the (overall) number of risky behaviours in which the individual has engaged.

Also, it is interesting to observe that psychosocial competences do not play any role in predicting risky sexual behaviours. This is quite surprising given that previous literature suggest self-efficacy (or self-confidence) to be one of the key factors for contraceptive uses and particularly for the use of condom which, particularly for girls, requires negotiating its use with the partner (see for example Salazar et al. 2005).

More generally, unprotected sex is the behaviour for which fewer predictors turn out to be statistically significant (only one, coming from a single-parent household) which suggest that other important predictors might have been neglected. Factors such as being born to a teenage mother, knowledge on sexual and reproductive health, access to contraceptive methods, age of the sexual debut and relationship status are some of the factors commonly correlated with teenage pregnancy and motherhood (see for example Azevedo et al. 2013; Ermisch and Pevalin 2003). Furthermore, being married or in a stable relationship might influence the decision to use of using contraceptive methods. Nevertheless, these factors have been not included in the analysis mainly for two reasons: first, to preserve comparability across the different risky behaviours considered; second, some of those variables are only collected at Round 4.¹⁰

In Table 6 we report the results for the risky behaviours models including educational aspirations. Keeping everything else constant, in this model we observe that aspiring for higher education reduced the likelihood of engaging in criminal behaviours by 23 percentage points. Higher aspirations are also negatively correlated with the total number of risky behaviours. The role played by the other predictors (the same as in the previous model) remains very similar. One noticeable difference is that, once aspirations are controlled for, school enrollment does not predict criminal behaviours which suggest that aspirations measured and school enrollment both measured at the age of 15 are strongly correlated.

So far we show that psychosocial competencies and aspirations measured at the age of 15 predict many risky behaviours that occur at the age of 19. An empirical questions is whether this correlation is constant over time and psychosocial competencies and aspirations measured earlier in life similarly predict later behaviours.

6.2 Fixed effects estimates

In Table 11, we report the results for the individual fixed effects estimates using self-esteem and self-efficacy as predictors of risky behaviours. The results are qualitatively similar to the ones discussed above. An increase in self-esteem is negatively correlated to the the prevalence of risky behaviours over time. More specifically, one standard deviation increase in self-esteem reduces smoking, drinking and engaging in drinking and violence by 5, 10 and 9 percentage points respectively; it does not predict the likelihood of carrying a weapon, but the point estimate is very similar (3 percentage points). In contrast to the results in the previous model, self-efficacy is predictive of carrying a weapon. One standard deviation increase in self-efficacy reduces the probability of carrying a weapon by 5 percentage points. Besides this, in this set of estimations schooling achievement is found to play a more prominent role. School enrollment reduces the likelihood of drinking, drugs consumption, and risky sex. A similar role is played by vocabulary and math achievement. In addition, coming from a single-parent household, area of location and migration remain as important predictors of risky behaviours.

In Table 11, we report the results for the individual fixed effect model including aspirations among the predictors. However, in this case we are not able to detect a relationship between aspirations and the outcomes of interest.

To summarize, the fixed effects estimates show that the relationship between self-esteem and risky behaviours is very robust, whereas the relationship between self-efficacy, aspirations and risky behaviours is not. In addition, there seems to be a lot of meaningful variation over time in the control variables, which allows us to show that coming from a single-parent household, area of location, migration and schooling achievement are also important factors that play a role in the determination of risky and criminal behaviours.