metadata: authors: 0: author: Bagby,Emilie
1: author: Cunningham,Wendy
count: Latin America
docna: 0: docna: Early identification of at-risk youth in
            Latin America : an application of cluster analysis
docty: Policy Research Working Paper
subtopic: Health Care Services Industry,Educational Sciences,Gender and Development,Inequality
teratopic: Governance,Health, Nutrition and Population,Gender
abstracts: cdata!: A new literature on the nature of and
            policies for youth in Latin America is emerging, but there
            is still very little known about who are the most vulnerable
            young people.  This paper aims to characterize the
            heterogeneity in the youth population and identify ex ante
            the youth that are at-risk and should be targeted with
            prevention programs.  Using non-parametric methodologies and
            specialized youth surveys from Mexico and Chile, the authors
            quantify and characterize the different sub-groups of youth,
            according to the amount of risk in their lives, and find
            that approximately 20 percent of 18 to 24 year old Chileans
            and 40 percent of the same age cohort in Mexico are
            suffering the consequences of a range of negative behaviors.
            Another 8 to 20 percent demonstrate factors in their lives
            that pre-dispose them to becoming at-risk youth - they are
            the candidates for prevention programs.  The analysis finds
            two observable variables that can be used to identify which
            children have a higher probability of becoming troubled
            youth:  poverty and residing in rural areas.  The analysis
            also finds that risky behaviors increase with age and differ
            by gender, thereby highlighting the need for program and
            policy differentiation along these two demographic dimensions.
repnme: repnme: Early identification of at-risk youth in
            Latin America : an application of cluster analysis
display_title: Early identification of at-risk
            youth in Latin America : an application of cluster analysis
listing_relative_url: /research/2007/10/8474831/early-identification-at-risk-youth-latin-america-application-cluster-analysis
topicv3: Poverty m1326687 695,Education m1327048 470,Gender m1327621 615,Health m1504661 3480,Psychiatry, Psychology m1504686 3613
publishtoextweb_dt: 2010-07-01T00:00:00Z
historic_topic: Governance,Health, Nutrition and Population,Gender
no_of_pages: 57
author_keys: 0: author_key: 891664
1: author_key: 145073
model_extractions: WPS4377 Policy ReseaRch WoRking PaPeR 4377 Early Identification of At-Risk Youth in Latin America: An Application of Cluster Analysis _Emilie Bagby Wendy Cunningham_ The World Bank Latin America and the Caribbean Region Human Development Department October 2007
Policy ReseaRch WoRking PaPeR 4377

# **Abstract**

A new literature on the nature of and policies for youth in Latin America is emerging, but there is still very little known about who are the most vulnerable young people. This paper aims to characterize the heterogeneity in the youth population and identify ex ante the youth that are at-risk and should be targeted with prevention programs. Using non-parametric methodologies and specialized youth surveys from Mexico and Chile, the authors quantify and characterize the different subgroups of youth, according to the amount of risk in their lives, and find that approximately 20 percent of 18 to 24 year old Chileans and 40 percent of the same age cohort in Mexico are suffering the consequences of a range of negative behaviors. Another 8 to 20 percent demonstrate factors in their lives that pre-dispose them to becoming at-risk youth – they are the candidates for prevention programs. The analysis finds two observable variables that can be used to identify which children have a higher probability of becoming troubled youth: poverty and residing in rural areas. The analysis also finds that risky behaviors increase with age and differ by gender, thereby highlighting the need for program and policy differentiation along these two demographic dimensions.

This paper—a product of the Latin American and Caribbean Region , Human Development Department—is part of a larger effort in the department to understand the challenges facing at-risk youth in the region and to design effective policy to support them. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The author may be contacted at Wcunningham@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
# **EARLY IDENTIFICATION OF AT-RISK YOUTH IN LATIN AMERICA: AN APPLICATION OF**

# **CLUSTER ANALYSIS**

# **Emilie Bagby and Wendy Cunningham**<sup>**1**</sup>

1 University of Illinois Urbana-Champaign and World Bank, respectively.
# **I. Introduction**

The specific needs of young people are an increasingly debated subject in the development community. A large literature has emerged to inform the debate in Latin America, principally by mapping youth behaviors, positing policy and program interventions to address the behaviors, and, to a lesser extent, to identifying the (economic) factors driving young people’s decisions (Lloyd 2005, World Bank 2006). A primary shortcoming of the research is that it treats youth as a homogenous group and reports average behaviors, thereby not capturing the complexities of the youth population across its many dimensions.

Even if we do understand the heterogeneity in the youth population, appropriate policy requires an understanding of why young people make the decisions they do. The evidence for Latin American youth is limited. The role of economic incentives and budget constraints affecting decision-making by young people in developing countries has been examined (World Bank 2006 and the sources within), building on Gruber’s (2001) work in the United States. A second line of work considers the broader context in which young people form their preferences and make their decisions (World Bank 2003, 2007), building on the public health and psychology research in the United States. While this literature gives good insights as to _how_ to modify behaviors, it is silent on _whom_ prevention programs should target.

This paper aims to construct a more complete picture of the heterogeneous youth population in LAC by quantifying and describing the at-risk youth population and by identifying easily observable variables can be used to identify, ex ante, those youth who have a higher propensity of engaging in risky behaviors. We use non-parametric methodologies and special cross sectional youth surveys from Mexico and Chile that permit us to identify the factors that today’s risk-taking youth had in their childhoods, which are then used to identify who should be the target of prevention policies and programs.<sup>2</sup> The paper finds that over 20 percent of 18 to 24 year olds in Chile and 40 percent in Mexico have influences in their early and current lives that predispose them to negative behaviors, they have engaged in these behaviors, and they are suffering the consequences. Conversely, 40 percent of Chileans age 18 to 24 and 16 percent of Mexicans of the same age do not display any of these risks, behaviors, or consequences. While many of the factors that identify if a person will engage in risky behavior are unobservable, we find two observable variables that are highly correlated with these underlying factors and can be used

> 2 Causality can only be determined using panel data that includes information on behaviors and household factors, which do not exist in LAC, to the best of our knowledge.

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to identify which children have a higher probability of becoming troubled youth: poverty and residing in rural areas. While self-identifying as indigenous is correlated with risky behaviors, the relationship is not statistically significant. And, we find that risky behaviors increase with age and differ by gender, thereby highlighting the need for program and policy differentiation along these two dimensions.

There are five sections following this introduction. Section II presents the conceptual framework and provides a brief review of the literature. Section III describes the methodology and Section IV discusses the data. The results are presented in section V and section VI concludes.

# **II. Review of the Literature and Conceptual Framework**

The issue of youth development is relatively new in the field of economics. Perhaps the most extensive study in the economic literature is Gruber (2001), which investigates the determinants and implications of nine different behaviors among US youth – smoking, risky driving, sexual activity, suicide, marijuana use, crime, alcohol use, school dropout, and misnutrition – using both cross sectional and time series data. The rational addiction model developed by Becker and Murphy (1988) is expanded to allow for youth-specific characteristics identified in the psychology and human development literature, namely myopia, time inconsistent preferences, and projection bias.<sup>3</sup> The book concludes that youth respond to incentives such as age-specific legal penalties, prices, and income and that the marginal cost to additional risk taking is small once participation in risk behavior has begun. Recent studies have tested these conclusions in the context of developing countries, finding similarities and differences with the US<sup>4</sup> .

A shortcoming of the economic literature is the absence of a discussion about preference formation and that early experiences vary across the youth population, leading to heterogeneity in decision-making during the youth years.<sup>5</sup> In contrast, an extensive literature in the public health and psychology fields start from the assumption that preference formation and constraints before the youth years, as well as during them, explain a significant portion of the variation in youth behaviors. The ecological risk framework posits that youth are a product of individual (personal), micro-, and macro-environmental factors (Bronfenbrenner 1979). The individual factors are those skills, behaviors, and ideas that are

> 3 “Projection bias” is understood as today’s preferences may not be representative of future adult preferences. 4 See Lloyd ed. (2005), World Bank (2006), and Attanasio et al. (2005) for example.

> 5 Gruber (2001) discusses preferences, but it focuses on the time-inconsistency of preferences between the youth period and adulthood.  It does not investigate why different youth have different preferences.

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“hardwired”, rather than formed, such as rage, optimism, or general health. The micro factors include preferences taught and formed by the family, peers, community, and local institutions and the constraints imposed by the same, including household poverty. The macro factors include more general influences and constraints, such as gender/race discrimination, armed conflict, poverty and economic inequality. These factors are commonly classified into two groups: the set of personal, macro, and micro factors that increase the risk of negative behaviors (risk factors) and the set that prevent youth from engaging in negative behaviors, commonly called protective factors. Each person has a set of risk and protective factors that influence preference formation, constraints, and thus behaviors.

Most of the empirical testing of the ecological risk framework depends on correlations between current risky behaviors and personal, micro, and macro factors of the youth population. The literature in the US primarily focuses on identifying factors related to single risky behaviors (e.g. substance use).<sup>6</sup> Recent US literature has begun to take advantage of longitudinal data available in the US<sup>7</sup> to demonstrate that many of those factors correlated with risk taking behaviors are actually causal factors.

The few studies that have tested the model using data from Latin American and Caribbean find similar results to those in the US. Blum (2004) uses data collected in 11 Caribbean countries and finds that a positive relationship with a caring adult, whether in the family or in school, is a key factor that is positively correlated with less risky sexual behavior (sexual debut and condom use), contraception, pregnancy and childbearing. Lloyd (2005) identifies the positive effects of schooling and health on transitions to risk-free adulthood

> 6 There is an extensive literature in the US which is too large to cover here.  The US National Library of Medicine and National Institute of Health maintains a web page (http://www.ncbi.nlm.nih.gov/sites/entrez), that lists hundreds of published articles that have used this methodology.  A few of the papers that motivated this paper include: Resnick et al. (2004) look at risk and protective factors related to youth violence; Blum et al. (2002) describe the ecological risk framework and provide empirical evidence for three risky behaviors (weapon related violence, ever had sexual intercourse and ever used cocaine); Scal et al. (2003) look at risk and protective factors related to smoking; Zweig et al (2002) identify methods of predicting risk profiles using risk and protective factors such as psychosocial development, school and family characteristics using OLS and multinomial logit regressions with cross sectional data; and Bernat and Resnick (2006) provides a comprehensive review of the resiliency framework and additional empirical support for this framework in promoting healthy youth development.

> 7These studies, which also appear on the NIH web site, use the National Longitudinal Survey of Adolescent Health (ADDHEALTH) through the University of North Carolina, Carolina Population  Center, surveys youth in grades 7-12 with the first wave of interviews in 1994 (http://www.cpc.unc.edu/projects/addhealth/design). Follow-up waves were in 1996 and 2002, the latter enabling more detailed analysis.  This survey is school based and asks about risk and protective factors as well as behaviors.  The National Longitudinal Survey of Youth (NLSY) 1997 from the Bureau of Labor Statistics, surveyed males and females born in 1980-1984 (http://www.bls.gov/nls/nlsy97.htm) and focuses primarily on educational and employment outcomes. The previous NLSY was from 1979.

4
using DHS surveys from developing countries. World Bank (2007) finds that low selfesteem, spirituality, school connectedness, abuse in the home, abuse in the community, connectedness to institutions, poverty and gender are the factors most associated with risky behaviors and negative outcomes in Brazil.<sup>8</sup> Youth in the English-speaking Caribbean who have lower risky behaviors<sup>9</sup> are also those who are more connected to parent/family, attending religious services, feel little rage, have not been abused or witnessed parental violence, do not have family members who have attempted suicide, have mentally healthy parents, and have households free of illegal substances (World Bank 2003). A series of papers by Brook, et. al. (2001, 2002a, 2002b) find similar correlations between household factors, poverty, and community factors for explaining substance use and violence among Colombian youth, while Hutz and Silva (2003) find that young Brazilian men who have been incarcerated for violence are disproportionately the sons of poor, uneducated, and violent fathers.

The ecological risk framework can be stated more formally. A person _i_ has a set of behaviors,<sup>_B_</sup> _i_<sup>, that are determined by a vector of risk factors,</sup><sup>_r_</sup> _i_<sup>, and protective factors,</sup><sup>_p_</sup> _i_<sup>,</sup> determined at the individual (<sup>_I_</sup> _i_<sup>), micro (</sup><sup>_c_</sup> _i_<sup>), and macro (</sup> _M i_ ) levels.

If an element in any of the vectors<sup>_I_</sup> _i_<sup>,</sup><sup>_c_</sup> _i_<sup>, or</sup> _M i_ leads to a positive behavior in<sup>_B_</sup> _i_<sup>, it will</sup> take a positive value in _p_ ( _I i_ , _ci_ , _M i_ ) and a 0 in _r_ ( _I i_ , _ci_ , _M i_ ) . Likewise, an element that leads to risky behavior in<sup>_B_</sup> _i_<sup>will take a 0 value in</sup> _p_ ( _I i_ , _ci_ , _M i_ ) and a positive value in _r_ ( _I i_ , _ci_ , _M i_ ) . A weighted average of the risk (<sup>_r_</sup> _i_<sup>) and protective factors (</sup><sup>_p_</sup> _i_<sup>) specific to each</sup> person will predict the behavior elements in the vector<sup>_B_</sup> _i_<sup>. Behaviors include elements such</sup> as unprotected sex, school truancy, or substance use.

The outcomes of these behaviors are a function of the behaviors, the individual, micro, and macro environments, and luck ( δ ). The outcome, _Oi_ , is given by

> 8 Behaviors and outcomes studied include: grade repetition, early labor force entry, early sexual initiation, risk taking sexual practices, alcohol use tobacco use, illegal drug use, violence and suicide attempt.

> 9 Behaviors and outcomes studied include: perception of general health, ever had sexual intercourse, ever attempted suicide, violent behavior, problems due to alcohol and drugs.

5
Outcomes can be good (school completion, youth participation) or negative (school dropout, exclusion). Risk and protective factors are included in the function since they can magnify or mitigate the outcomes of a behavior. We assume a distribution of δ that is constant across people, but instrumental in determining if behavior<sup>_B_</sup> _i_<sup>becomes outcome</sup> _Oi_ .

Equations 1 and 2 can be used to link the concepts of risk/protective factors, behaviors, and outcomes to levels of risk and give insight to policy. A person with high values of the elements in _r_ ( _I i_ , _ci_ , _M i_ ) , low values of the elements in _p_ ( _I i_ , _ci_ , _M i_ ) , and a<sup>_B_</sup> _i_ and _Oi_ that displays few negative behaviors and negative outcomes, is defined as being type I risk. More generally, a person classified as type I risk has many risk factors and few protective factors, indicating that they have a predisposition to engage in negative behaviors, but the person has not undertaken any risky behaviors. Prevention programs would be targeted to this group.<sup>10</sup> A person with high values of the individual elements in _r_ ( _I i_ , _ci_ , _M i_ ) , low values of the individual elements in _p_ ( _I i_ , _ci_ , _M i_ ) , a<sup>_B_</sup> _i_<sup>that displays many</sup> negative behaviors, and an _Oi_ , with few negative outcomes would be classified as type II risk, where risk factors are present, protective factors are few, and the young person has engaged in risk-taking behaviors without having experienced any negative consequences. These youth are at-risk of suffering consequences and can thus benefit from prevention or second chance programs. Youth with positive values in the _Oi_ vector are those who are suffering the consequences of their behaviors and are thus categorized as type III risk. These youth tend to have high risk factors, poor protective factors, and many negative behaviors, many of which might be identified prior to their suffering the consequences of their risky behaviors. They are candidates for second chance (remedial or rehabilitation) programs.

# **III. Methodology**

To understand heterogeneity in the youth population, we use cluster analysis to identify different groups of youth based on the observable elements in the vectors<sup>_I_</sup> _i_<sup>,</sup><sup>_c_</sup> _i_<sup>,</sup> _M i_ ,<sup>_B_</sup> _i_<sup>, and</sup> _Oi_ . Classifying these groups along the risk profiles will allow us to measure the share of the youth population that is at different levels of risk and to identify observable

> 10 See Blum (1998) for a discussion of resiliency based intervention programs.

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factors that can be used by policymakers to target programs to youth with type I, II, or III risk.

# **A. Cluster Analysis**

Cluster analysis is a means to identify correlations across large data sets without imposing, a priori, a structure on the data. Observations are grouped based on minimizing a distance measure between each variable for each observation, i.e. the observations in a cluster share a set of common variables. By comparing the mean values of various variables across clusters, we can characterize each cluster.

Ward’s method (minimum-variance) of testing was selected since it provided the most distinct and interpretable clusters.<sup>11</sup> Ward’s method uses the error sum of squares criteria.<sup>12</sup> The variance is minimized by calculating the sum of squared errors from the mean of the cluster for each of the _m_ variables for each observation: _i_ = 1,...., _n_ observations, _j_ = 1,..., _m_ variables, and _k_ = 1,..., _l_ clusters Initially, each of the n observations forms its own cluster. The first merge is identified by calculating the sum of squares for each pair of cluster. The pairing with the smallest sum of squares is identified and those clusters are joined, leaving n-1 clusters. The second grouping calculates the sum of squared errors again and pairs the two clusters that have the smallest value, leaving n-2 clusters. The process is repeated until the optimal number of clusters has been reached.

Three tools were used to determine the optimal number of clusters. First, stop commands following two possible rules (Calinsky-Harabasz and Duda-Hart) were used to find criterion for each cluster possibility.<sup>13</sup> The Calinski and Harabasz method suggests the optimal number of clusters (g) that maximizes an index C(g) which uses the pooled withincluster covariance matrix (W) and the between-cluster covariance matrix (V), where The Duda and Hart method maximizes D(g), where

> 11 There are many different ways to perform cluster analysis, and no particular method is considered the best. Ward’s linkage cluster analysis is a commonly used agglomerative hierarchical method.

> 12 An attractive feature of the Ward’s method is that it performs well with groups that are of unequal size, which, as will be shown in the results, strongly characterizes the data.  See Everitt et al. (2001) for a theoretical discussion of Cluster Analysis and Ward’s criterion.  See Cunningham and Maloney (2001) for an application. 13 These two methods are implemented in STATA.  They were identified as the two best methods available (out of 30) by Milligan and Cooper (1985) and are discussed in Everitt et al. (2001).

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(5) D(g) = Je(2)/Je(1) Je(2) is the sum of the within cluster sum of squared distances between the objects and centroid if the cluster is split into two and Je(1) is the within cluster sum of squared distances. The local criteria calculated in equations 4 and 5, C(g) and D(g), are then combined with test statistics for each clustering option to suggest the optimal number of clusters. Larger values of both methods indicate that the clusters are more distinct from each other while lower values indicate that the clusters are not very different from each other and therefore artificially sub-divided.

Second, dendrograms were used to select among the multiple “right” clusters that the other methods may give. Dendrograms graphically depict the hierarchical relationship between the clusters by showing the order in which clusters are merged as well as the distance between the clusters. At each level of the cluster formation process, a dendrogram can be generated to view the relationships between the clusters. The dendrogram changes as clusters are grouped and un-grouped, thus enabling the researcher to optimally choose the clustering level.

Third, once the optimal number of clusters is suggested and the dendrograms generated, the clusters themselves are observed and the means of the variables are compared across clusters. The differences found between the different clusters are used to ultimately determine the optimal number of clusters. For instance, if 5 clusters were suggested, 6 clusters were investigated to see if there was an interpretable difference. If not, 5 clusters were used, however if so, 6 clusters were used. This process could then repeat.

The cluster analysis is performed for 10 cohorts, identified by age, gender, and country. The sample for each country is divided ex-ante by age and sex since, when pooling the sample, these two variables dominated the clusters to such an extent that the risk and protective factors of interest played a very small role. Six cohorts from Mexico are analyzed: female ages 12 to 14, female ages 15 to 17, female ages 18 to 24, and males in each of the three age groups. Only the four older cohorts from Chile are analyzed since youth age 12 to 14 were not included in the sample.

While the objective of a cluster analysis is to identify which variables move together, a decision was made to treat some variables endogenously and others exogenously. For example, a hypothesis is that poverty status is a good indicator for a youth being “atrisk”. If we use this variable to create the clusters, it is possible that poverty is such a strong factor that it drives the clusters and renders the other variables meaningless. Thus, for these 8
type variables, we carried out the analysis treating them as both endogenous and exogenous and found little difference. We thus report only the results for treating them exogenously.

The advantage of cluster analysis is that the only priors required are in the variable construction, such that they range between 0 and 1. For continuous variables, the value was normalized: Binary variables were assigned a 0 or 1 and discrete variables were assigned a value between 0 and 1 based on the ordering of the responses. A variable takes a value of 1 the closer it is to the variable being described. For example, the variable “abuse” takes a value of 1 if there is abuse in the household and a 0 if there is not while the variable “connected” takes a value of 1 if the respondent reports that they reach out for help all of the time if they have problems, a value of 0 if they never reach out, and a value in between depending on how frequently they reach out. Variables that could not be ordered in a logical way were not included in the analysis.

# **B. Identification of Correlation between Factors and Behaviors**

Using the generated clusters, we use parametric methods to test the relationship between behaviors/outcomes and factors. First, we graph the clusters using bubble graphs to show the relationships between risk and protective factors with positive behaviors<sup>14</sup> then we regress the average level of positive behaviors from each cluster against the average level of risk factors and protective factors from each cluster,<sup>15</sup> using OLS to identify if there is a significant relationship between the behavior and the risk or protective factors. We consider the relationships in both countries for each age cohort and for the sample as a whole. We also test the correlation between positive behaviors and three easily observable variables that seem correlated with good behavior: low poverty, ethnic majority, and urban residence.

> 14 The size of the bubble represents the sample size for each cluster within the age/sex groups.

> 15 For this exercise, we need to assign a “good” or “bad” subjective ranking to each I, c, M, B, and O.  This is straightforward for most cases, but in three instances, the variable changed value from 0 to 1 and vice versa depending on whether the youth is younger than 18 years of age or not: parenthood, working, and marriage. The case can be made that for those under 18, it is not optimal to be a parent, to be working, or to be married. Indeed, this opinion is supported by the large body of research attempting to explain how and why youth arrive at these adult outcomes at an early age.  At the age of 18, however, it is inappropriate to consider a person with these characteristics to be necessarily at-risk.  Therefore, for those under 18 years of age, the indicators for being a parent, being married and working take a 0, whereas for those 18 to 24 the indicators take a value of 1.

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# **IV. Data Description**

# **A. Data**

As youth departments and governments become more sophisticated in their efforts to understand youth, several LAC countries have developed specialized youth surveys. We use two surveys in this paper: the 2003 National Youth Survey ( _Encuesta Nacional de Juventud_ ) from Chile and the 2000 National Youth Survey from Mexico. Youth are defined as being between the ages of 12 and 24 for this analysis, consistent with World Bank (2006). Chile first implemented their National Youth Survey in 1994 and has subsequently repeated it every three years. This paper uses the most recent data set that could be accessed, which was collected in 2003. A nationally representative sample of youth aged 15 to 29 is surveyed, reaching all regions (urban and rural) in the country, with a sampling error of 2.1% and a 95% confidence level. The sample size for youth aged 15 to 24 used in the analysis is 5321<sup>16</sup> .

The survey questions cover socioeconomic status, youth behaviors and opinions, and family backgrounds. It includes many questions about risk factors such as family cohesion, neighborhood violence, and social exclusion. Many protective factors are not covered, but there is information about trust in institutions, connectedness, and relationship with parents. The behaviors and outcomes are limited to employment, schooling, sexual health, victimization, discrimination and participation in activities. It does not include questions about whether the respondents take drugs or commit violent acts. There are very limited health and health access data.

Mexico carried out its National Youth Survey in 2000, through the National Institute for Statistics, Geography, and Information ( _Instituto Nacional de Estadística, Geografía e Informática_ , INEGI).<sup>17</sup> The survey sample is nationally representative and was performed in two stages: the entire household was surveyed and asked basic household characteristics, and, at a later date, youth aged 12 to 29 were asked a set of youth-specific questions. Only those aged 12 to 24 were included in this analysis, resulting in a sample of 37,979 respondents<sup>18</sup> .

> 16 For this analysis, 195 observations out of 7,189 (2.7% of sample) for youth aged 15 to 29 were dropped due to missing data.  We then restricted the sample to youth aged 15 to 24 dropping another 1674 observations.  In many cases, missing responses could be coded based on responses to related questions so as to maintain a larger sample size.  For instance, if someone does not respond as to whether they attend church, after they have already indicated that they do not believe in God, then we assume that they do not attend church.  It would be useful to do analysis using censored data tools.

> 17 The survey was repeated in 2005, but the data was not available at the time this paper was under preparation. 18 Of the almost 60,000 youth respondents, about 10,000 youth aged 12-29 were not surveyed the second time, and were dropped from the sample used for the analysis. The reasons for not interviewing these youth were

10
These data are similar to those from Chile; however they are slightly stronger on protective factors and more limited on risk factors. The Mexican dataset does not have information about connectedness with other adults, abuse in home, victimization or violence in community, but it does have questions on attitudes towards drugs, the number of parents in the household and parental response to good/bad behavior. As with the Chile survey, there is information on individual earnings and three family poverty variables – parental earnings, parental education and durable goods in household – as well as regional/community level information. Although youth aged 12 to 29 were surveyed, those aged 12 to 14 were not asked questions about sexual activity or practices.

To the extent possible, similar variables are used in our analysis for both countries, although the Chilean data are more robust with the protective factors and the Mexican data include more risk factors. Table 1 describes each variable and identifies which variables are used in which clustering exercise and which are treated exogenously.

# **B. Descriptive Statistics**

Descriptive statistics for continuous and binary variables are provided for risky behaviors and factors in Table 2.<sup>19</sup> Sample means for all of the normalized variables are presented in Annex A to provide a baseline of the average status of the population against which to compare the variable values in each distinct cluster. The Chileans are 1.7 years older in the sample than the Mexicans, but this is primarily due to the inclusion of 12 to 14 years olds in the Mexican survey. Thus, it is important to compare characteristics by age group, as presented in Table 2. Both surveys are slightly more weighted toward women. About 11 percent of the Chilean sample self-identifies as indigenous; ethnicity was not

> tracked in the dataset:  they did not want to participate, were not at home at the time of the interview and would not return within the week, were on vacation, were working or at school in another city, were disabled, and other.  Comparing the poverty variables (education level and monthly earnings of heads of households) and rural means of this dataset before and after dropping the data showed no significant difference at α=.01 (However, at α=.05, the difference between the education level of heads of households the original and remaining datasets is not zero). An additional 5% of the observations were dropped in creation of the variables. Since this was such a small amount of the sample, the resulting dataset was not statistically different from the original. Finally, the data were restricted to youth aged 12 to 24, thus further decreasing the sample size by 8903.

> 19 Variables with categorical responses cannot be summarized, but the mean of the variables created from the categorical responses can be.  Thus, the categories are not summarized in Table 2, but the mean of the variable created from the categorical responses is summarized in Annex A.

11
identified in the Mexican survey. In Chile, about 13% of the sample is considered rural<sup>20</sup> while in Mexico about 24% is rural.

In both countries, a significant percentage of youth in both countries are dropping out of school early and not working after age 18. In the survey, 29.9% of Mexican and 14.2% Chilean youth have dropped out of school prior to completing high school, but 41% of Chileans age 18 to 24 and 27.5% of Mexicans in the same age range are in school.<sup>21</sup> While 20 to 30% of Mexican and Chilean youth are inactive after age 18, 12.8% of Mexican and 6.7% Chilean youth are inactive before age 18.

Chileans have earlier and riskier sex than Mexicans. While only 13% of Mexicans age 15 to 17 report having had their first sexual experience, 27% of Chileans in this age group report the same. Chileans are 10 percentage points less likely to use condoms than are Mexicans, which reflects that 2.5 percent of Mexicans and nearly 4 percent of Chileans age 15-17 have had their first child. Marriage rates in Chile are less than one-third of those in Mexico for each age group.<sup>22</sup> In terms of the risk and protective factors at the micro-level, Mexican and Chilean youth report a high level of personal connections with someone. Mexican youth report a wide range of topics that they communicate with their parents about, from school and work to politics and religion. While Chileans report good relationships with their parents, ten percent of Chileans also note important relationships with other adults. On the negative side, six percent of Chileans have suffered abuse in the home and 8.5 percent report substance abuse in their homes.

Mexican youth tend to trust government institutions less than they trust local institutions. They feel that school quality is good on average, although half say they do not have access to health services. There is a connection to religious institutions as 21 percent of Chilean youth and 9 percent of Mexican youth attend church weekly, with 66 percent of Mexican youth attending church at least once in the past month.

Of the questions on individual risk/protective factors, more than 85 percent of Chilean youth are optimistic about the future, similar to rates in Brazil (Instituto Cidadania

> 20 The sampling was done in communities with at least 2000 inhabitants, so a rural indicator means that the respondent comes from a community with between 2000 and 5000 inhabitants.  An urban respondent lives in a community of at least 5000 people.

> 21 If there is no repetition, youth would finish high school at age 17 or 18 in both countries.

> 22 In Chile and Mexico, the legal age of marriage age is 18, but Mexican youth can marry as early as age 16 with parental consent.

12
2004), and an even greater share, 98%, of Mexicans have a sense of well-being. However, half of Mexican youth and one-fifth of Chilean youth feel socially excluded.

There are few questions on macro factors, but the data do show that over half of the Mexican youth are from families whose parents have less than a primary education, while over one-third of Chilean youth have parents with less than a primary education. In addition, over half of Chilean youth have felt discriminated against at some time.

# **V. Results**

The clustering methodology identified four to seven sub-groups within each of the 10 age-sex-country cohorts, resulting in 53 (23 from Chile, 30 from Mexico) distinct groups of youth. To simplify the discussion, the commonalities across the clusters are presented in this section. The results are presented by risk category to allow us to characterize the heterogeneity in the youth population and to create a picture of youth in each category, based on the findings from the 53 clusters. Tables 3a and 3b present a summary of youth in the different risk categories in Mexico and Chile respectively while the full cluster results are presented in Tables 4a-4j. Annex B gives a brief description of each of the 53 clusters.

# **A. Who Are the Youth at Risk in Mexico and Chile?**

# **Type III Risk**

Thirty-three percent of Mexican youth can be classified as type III risk, meaning that they are coping with the outcomes, or consequences, of their risky behavior, while 16.8% of Chilean youth fall into this risk category (Tables 3a and 3b). Considering separately the agegender categories better characterizes type III risk youth in Mexico and Chile.

Fourteen percent of Mexican youth aged 12 to 14 are in this risk category. They have almost all dropped out of school, resulting in an average education level of primary school. Few are involved in organized activities and a large proportion is working (49% of boys and 27% of girls). We cannot report on the sexual activity of these youth since they were not asked these types of questions. All youth who are illiterate are in this category (Tables 4a and 4b, column 1).

Eight percent of Chileans age 15 to 17 and almost 40 percent of Mexicans in the same age range are classified as risk type III (Tables 3a and 3b). Almost all have dropped out of school prior to completing secondary education, many are inactive, most engage in risky sexual behavior and feel socially excluded. Among the women, a strong at-risk mother 13
group emerges, comprised of women who engaged in risky sexual behavior at a young age, left school, had their child, and largely remain unmarried.<sup>23</sup> The males report unsafe sexual activity, low education level and idleness. They also report an early working age as well as an early age at onset of sexual activity (Tables 4c, 4g, 4h column 1; Table 4d columns 1-3).

Almost half of Mexican youth (43%) and almost one-quarter of Chilean youth (22%) aged 18 to 24 can be classified as risk type III. These young people have had significantly more time to engage in risky behaviors and thus demonstrate a wider range of negative behaviors and outcomes. They break into two general categories: inactive youth and parents. The inactive males dropped out of school early and are not working – these are the stereotypical youth at-risk. Inactive females tend to be mothers who dropped out of school early and continue to engage in unsafe sexual activities. Among male parents, the fathers that are working typically dropped out of school prior to completing secondary education. Some but not all of these youth engage in risky sexual practices (Tables 4e, 4f, 4i, 4j).<sup>24</sup> Several of the risk factors clustered with the groups described above, and protective factors were notably absent. The 12-14 year old Mexicans in this category come from the poorest households, tend to live in more rural areas, come from abusive households, had little parental control/influence on their behavior related to alcohol and smoking, had low access to healthcare and high social exclusion. The youth in these clusters report a low to average level of spirituality and church attendance (Tables 4a and 4b).

Risk factors for the 15 to 17 year olds include low parental education, low level of connectedness, and non-positive family backgrounds. Thirty-three percent of the males in the Mexico type III risk clusters are from rural areas, but the Chilean type III clusters are not as rural with 25 to 27 percent living in rural areas<sup>25</sup> . Since Chile is much less rural overall than Mexico (13% compared to 25%), we can conclude that living in a rural area in Chile is more risky compared to living in a rural area in Mexico (Tables 4c, 4d, 4g, 4h).

The 18 to 24 year olds in this age category come from disadvantaged backgrounds, with parents having below a secondary education on average. Twenty-three percent of the Chilean women in this category and age group live in rural areas, whereas about 19% of

> 23 While the girls and boys in this cluster have similar situations, the data do not well capture fatherhood, so the most risky boy clusters will not report children, pregnancy or marriage, where the girls do.

> 24 The males age 18-24 clusters that are classified as risk type III are largely defined by school and work variables, while the women’s clusters are also defined by sexual behavior.  This is largely driven by the data in two ways.  First, the data about risky behavior sexual is largely targeted toward women.  Second, the main risky behaviors that men engage in – violence and substance abuse – are not well captured in the data.

> 25 Note: the tables for Mexico rural use 3 levels of rurality/urbanity: 1 = rural,, 0.5=from a small town,

> 0=urban.  The information for a binary rural variable is reported above for comparison to Chile.

14
Chilean males in this age group and of this type live in rural areas. The “at-risk parents” are more rural than other groups (23% Chilean female cluster and 31% Mexican women are rural). The Chilean mothers that are early dropouts in this group report a high level of social exclusion, average family cohesion and are made up of 13% who self identify as indigenous. The “at-risk and inactive” clusters have the worst outcomes being idle (Tables 4e, 4f, 4i, 4j).

A small group of males – 7.5% of Chileans and 11.4% of Mexicans – categorized as risk type III come from relatively good backgrounds. The Chileans tend to be in the 18 to 24 age range, have caring families and good behaviors in general (best sexual behaviors of all the clusters) and yet are idle (Table 4i, column 2). None are married and few are registered to vote. These idle youth did not drop out of secondary school and are the youngest cluster in this cohort, being under 20 on average. Perhaps this reflects the difficulty some youth have in finding a job to settle into when first graduating– a closer look at the question that generated the work variable shows that 60% of these youth are looking for work. Overall, they are not very optimistic about the future, and do not feel very prepared for work. The Mexican men dropped out of school early, but most are working (99%). They were younger than most other clusters when they first started working and when they started their sexual activity and had children. Seventy-one percent are fathers and 99% are married (well above the other clusters), yet they are slightly more likely to have sexual relations with more than one person in the past year than they other clusters (Table 4e, column 3). Notably, this group is very small, suggesting that youth who have backgrounds with numerous protective factors can experience negative outcomes, but this is the exception; most youth who experience negative outcomes also have many risk factors in their lives.

Figures 1a and 1b show the average behavior and factor levels for each cluster type. First considering Type III, we see the clusters of this type have lower averages for those variables that might be considered protective factors and high values for those variables that might be considered risk factors.

# **No Risk**

At the other end of the spectrum is 20-45 percent of the youth population that has neither engaged in risky behaviors, nor demonstrated a high level of potentially risky factors. There are fewer youth in this category in the older age groups than in the younger age groups (Tables 3a and 3b). In addition to the low presence of risk factors, these youth have a higher level of protective factors and good behaviors than those with type II or III risk levels (Figures 1a and 1b). All of the 12-14 and 15-17 years old youth with type 0 risk are in 15
school, few are working, none have children, and all initiated sexual behavior at a later age - indeed most have not initiated sexual behavior - than those in groups classified as risk types I, II, or III. Among the 18-24 year old groups, many with risk type 0 are still in school, fewer are working than in the other risk type groups, and fewer have started families. Generally speaking, these youth can be considered more economically advantaged than other youth, while youth in this group who are not from wealthy households have strong emotional connections with their families and feel connected to adults who care about them. They tend to have fewer risk factors, in terms of household abuse or exposure to drugs (Tables 4a-4j, last columns in each table).

# **Type I Risk**

Those with a higher risk than type 0 but much better than risk type III are the 9 to 20 percent of the Chilean and Mexican samples that have many risk factors, but they are not engaged in risky behaviors nor suffering negative consequences, i.e. they can be classified as type I risk (Tables 3a and 3b). Their behaviors are similar to those with no risk despite having a higher level of risk factors. In some cases this is perhaps because of the level of protective factors in their lives, such as connectedness with adults outside of the household, extracurricular activities or spirituality, and in others resiliency is likely playing a part while others are on their way to graduating to type II risk. One group of Chilean males aged 18 to 24 (9.3% of the cohort) is resilient and yet has poor protective factors to make up for the poor family cohesion with some substance abuse and abuse in the home. This group did not drop out of school early, half are still in school, yet about 20% are idle and 10% report victimization (Table 4i, column 5). Rural youth are over represented in some clusters, and underrepresented in others. Across age groups, there are fewer youth in this category in the older age groups than in the younger age groups as seen in Tables 3a and 3b. These youth are either too young to have begun engaging in risky behaviors, or they may be considered resilient.

Some youth in this risk level (across all age groups, genders and countries) report a high level of social exclusion and can be considered loners. Those that report a high level of social exclusion and have good behaviors tend to report a higher level of connectedness with their families (better relationships, doing more activities with their parents, better cohesion, etc.), suggesting that being socially excluded from their peers could indeed be a protective factor in some cases. For others, though, the social exclusion may lead to deviant behaviors, as demonstrated by very high levels of social exclusion among older youth of risk type III.

16
The level of risk factors, protective factors and behaviors for this type of risk can be seen in Figures 1a and 1b for Mexico and Chile respectively. Behaviors are better than for types II and III risk and comparable to type 0 risk. Risk factors are worse than those with type 0 risk, but better than those with types II and III risk. Protective factors in Mexico are comparable to those with no risk; however those who are classified as type I risk in Chile have the fewest protective factors of any risk type, suggesting that this is a temporary phase with potentially worse to come.

# **Type II Risk**

The most heterogeneous of the risk groups are the youth in clusters of risk type II who are 28% of the Chile sample and 25% of the Mexico sample. In Mexico, they are younger than the risk type III youth, with 33 percent of Mexicans age 12 to 14, 12 percent of Mexicans age 15 to 17, and 26.8 percent of Mexicans age 18 to 24 in this group. In contrast, about 20 percent of Chileans in the 15 to 17 age group and 33 percent in the 18 to 24 age group fit in this category. These youth are engaging in risky behaviors but are not suffering the consequences of their behaviors (Tables 3a and 3b). Figures 1a and 1b show that these youth have worse behaviors and outcomes compared to those with types 0 and I risk, however are not nearly as bad as those with type III.

The types of behaviors vary amongst the different age groups and genders. The 12 to 14 year olds have a high level of early workers yet are still in school, in contrast to the most at-risk groups which are not in school. The boys falling into this category started work on average much earlier than in the other clusters, including the type III youth. For these youth, early work does not appear to influence their other behaviors but since they are younger than type III youth, there is a chance that some will “graduate” to risk type III. On the other hand, the girls in this category did not start work as early as the most at-risk cluster, and not as many of them are working (Tables 4a and 4b, columns 2).

Among the 15-17 year olds, those with risk type II started sexual activity and work (more so for males) at a young age. They are still in school and are not parents, but their risky behaviors increase the likelihood that they will dropout or have children (Tables 4c, 4d, 4g, and 4h).

Among the 18 to 24 year olds, a wide range in behavior for youth with type II risk is observed. Some have high levels of many risky activities, while others strongly display only one risky behavior (social exclusion or risky sex). A special group that breaks out can be identified as “resilient” youth who display type II risk, but have the luck, genetics, or 17
psychological make-up such that they have not suffered the consequences of their actions. For example, the resilient groups might have started work at an early age and yet managed to graduate from high school (Tables 4e, column 5; Table 4f column 5; Table 4j column 2).

These youth tend to come from families with low parental education (though not as low as the most at-risk clusters), limited access to healthcare, and poor relationships with their parents. The 12 to 14 year olds have parents with low education and have poor relationships with their parents, but they have better relationships with their parents and a higher level of spirituality than those classified as risky type III. These youth may be on their way to the risk type III later in life or they may be resilient to the type I and II risk present in their lives. In the 15 to 17 age range, the different sub-groups have high levels of risk factors (low parental education, limited access to healthcare, and poor family cohesion) and medium levels of protective factors. The females tend to come from particularly difficult family backgrounds but have a high level of connectedness, often with an adult outside the family. The youth in the 18 to 24 age group tend to be more connected or resilient than those with type III risk, or have a significantly lower level of risk factors present (Tables 4a4j).

# **B.  Correlations among Risk Factors, Protective Factors, Risky Behaviors, and Outcomes**

Plotting risk or protective factors against behaviors and estimating the correlation confirms that young people with worse personal, micro, and macro influences in their lives are more at-risk than those who have positive influences. To keep this discussion short, we will discuss these relationships only for the 18 to 24 year old cohort in each country. The general trends described here are also observed when analyzing the younger cohorts as well as when grouping the clusters by each variation of age/gender groups.<sup>26</sup> Good behaviors are positively correlated with protective factors in both Mexico and Chile as seen in Figures 2a and 2b respectively. The relationship is significant in Mexico for all age cohorts individually as well as when grouped together, however is not significant in Chile. The weaker results in Chile are potentially due to the lower number of available protective factors in its dataset. The analysis was also performed on youth aged 12 to 29 in

> 26 These results are available from the authors upon request.  While the relationships are not significantly different from zero in all cases due to the limited number of data points once disaggregating by gender and age, the relationships between factors and behaviors are clearer.  Similarly, when grouping all clusters together for all ages and both genders, the results are not always interesting due to the wide difference in behaviors amongst age groups.

18
both countries as a robustness check<sup>27</sup> . Results were significantly different than 0 (at a 5% level) with this wider age range in both countries, suggesting the importance of age in some behaviors and outcomes.

As suggested by the ecological framework, the opposite results are observed when considering the correlation between risk factors and positive behaviors (Figures 3a and 3b) for 18-24 year old youth. The relationships are significant in Mexico at a 10% level and not significant in Chile at the 10 percent level. The relationship is strengthened (1% level) in Mexico when grouping all clusters together rather than breaking up the different age groups. This is partially due to few risk factors in the Mexican data as compared to protective factors. The Chilean dataset, which is richer in risk factors than in protective, shows a negative and significant relationship at 1% between risk factors and positive behaviors in the 15 to 17 year old age group, but only at 15% in the 18 to 24 year olds<sup>28</sup> .

# C **.  Proxies for At-Risk**

Due to the nature of the data used in this analysis, we can use a large set of risk and protective factors to predict the level of risky behavior that will be displayed by youth. Some of our variables are easily observable characteristics and are regularly found in most household data sets, but most of these data are unique and not available for most countries and youth. Thus, it would be useful to identify a youth’s risk level by a single or small set of easily observable characteristics. We test a set of observable variables to determine their usefulness in predicting risky behavior: poverty, living in a rural area, ethnicity, gender, and age.

_Poverty_ is a fairly good proxy to identify youth at-risk. Tables 3a and 3b summarize the information from the clusters and reveal that as the risk type rating of a group increases, the average level of parental education falls. Plotting the clusters against wealth proxies and estimating the slope of the line confirms this relationship.<sup>29</sup> Figure 4a shows that in Mexico, the more at-risk clusters have lower parental education, and the relationship is statistically significant at the 1% level. The same result emerges with each Mexico age group as well as when merging the Mexico age groups together, with a negative and significant coefficient on

> 27 Results are available upon request.

> 28 The relationship was significant at 10% and negative for the 18 to 29 year old cohort.

> 29 Since poverty level is an exogenous variable, we test these relationships for all poverty indicators: education level of household head, household ownership of luxury or durable goods, or monthly earnings of heads of households.  Since there are no significant differences between the various proxies for poverty, we chose parental education level since it is a widely available and observable indicator.

19
poverty for all age groups. The coefficients are larger negative values for the younger age groups. Similarly, Chile’s graph of poverty against positive behaviors in Figure 4b shows a statistically significant downward trend of behavior against poverty (proxied by parental education).<sup>30</sup> This negative trend is significant for the younger cohort as well, with an even larger magnitude.

While poverty is a reasonable candidate as an indicator in identifying at-risk groups,<sup>31</sup> we cannot conclude that economic class is the sole source of clustering since a close look at the clusters shows that not everyone in the at-risk groups is poor. The figures in Annex C use the density of the various possible general levels of education of heads of households for each cluster and demonstrate that poverty is correlated with risk level, however each risk cluster has youth with parents of varying education levels. Furthermore, we do not suggest that poverty causes risky behavior, but, poverty, for whatever reason, can be used to identify ex ante which youth have a higher propensity for engaging in risky behaviors.

_Living in a rural area_ is a candidate for a proxy for risk; in both countries, rural youth are over 10 percentage points more likely to be in risk types I, II, and III than are urban youth, and ten percentage points less likely to be risk-free (Tables 3a and 3b). Figures 5a and 5b confirm this correlation, demonstrating that living in a rural area is negatively correlated with positive behaviors and outcomes at the 1% level. That said it is important to note that not all rural youth are at risk, and that most at-risk youth live in urban areas, since more of the general population lives in urban areas.<sup>32</sup> Again, in Chile living in a rural area may be more risky than in Mexico, since the more at-risk clusters in Chile are more rural compared to the national average than Mexico.

Ordered logistic regressions relating the risk level to poverty level and living in a rural area (when controlling for age and gender as well as when not doing so) demonstrate a large, significant and negative relationship for both variables as shown in Table 5. This

> 30 Similarly Chile had two variables available to represent poverty: parent/caregiver education level and the economic class variable from the survey data (nse – nivel socio-economico).  The “nse” variable was constructed by the Chilean National Institute of Youth using parental/caregiver education and occupation or an average of six durable goods in the event that the other information was not available.

> 31 Indeed, Blakely et al. (2005) used income level to estimate relative risk levels.  Their risk factors are: tobacco, drinking alcohol, not having access to safe water/sanitation, indoor air pollution exposure and obesity. 32 In Mexico, the rural indicator was constructed to equal 1 if rural (< 2500 inhabitants), 0.5 if living in a small to medium sized town (2500 to 99,000) and 0 if living in a large urban setting.  The rural indicator in Chile equals 1 if rural with more than 2000 and less than 5000 inhabitants and 0 if urban with more than 5000 inhabitants.

20
suggests that using both poverty as well as rurality might prove useful in identifying the atrisk youth.

_Ethnic identity_ is not a good proxy for youth at-risk, although indigenous youth are over-represented among clusters defined as risk type III (Table 3b). A closer look at the indigenous youth in the Chile sample shows that while indigenous youth have worse risk factors and behaviors, they have better protective factors.<sup>33</sup> The more at risk clusters are about 5 percentage points more indigenous than the average, but the negative relationship between risk factors and positive behaviors is slight and insignificant as seen in Figure 6.<sup>34</sup> We must remember, however, that this is a self-reported survey, with the youth indicating if they identify with a particular indigenous group.<sup>35</sup> Overall levels of risky behavior increase with age, as seen in Tables 3a and 3b. The bubble graphs by age confirm this observed correlation.<sup>36</sup> Older youth have lower levels of positive behaviors across the board. On the other hand the poverty level for the most at risk clusters is higher for the younger cohorts than the older, indicating that with age a wider variety of background characteristics are involved in risky behaviors.

In Mexico, gender does not seem to matter (Table 3a) while in Chile women seem to be less at-risk (Table 3b). A closer look at the data reveals that male risky behavior is related to education and employment and females are more at risk for poor sexual behavior at younger ages. The females participating in risky behaviors at young ages tend to exhibit many risky behaviors, more than the males in the same cohort. Unfortunately, we were not able to look at violent or criminal behavior for this study, which is generally considered to be more common among men. In both Chile and Mexico, the most at-risk groups of females are more rural than males, despite the fact that similar percentages of males and females (across all age ranges) are rural. So being female and living in a rural area is more risky than being male and living in a rural area<sup>37</sup> .

Finally, we see a larger percentage of youth at risk in Mexico than in Chile, perhaps reflecting overall macroeconomic growth or government policy.

> 33 Further details available upon request.

> 34 Youth 15 to 17 years of age have a similar plot which is available upon request.

> 35 See Hall and Patrinos (2006) for documentation of the limitations of using self-reported ethnicity.

> 36 This is seen with both the Chile and the Mexico clusters by comparing the different age cohorts.  We do not present the graphs here but they are available upon request.

> 37 Approximately 13% of females and males live in rural areas in Chile and 24.5% in Mexico. Of those with type III risk, 20% of males and 23.6% of females in Chile and 29.7% of males and 33.7% of females in Mexico live in rural areas.

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# **VI. Conclusion**

Based on unique youth surveys in Chile and Mexico, we find that more than half of young people in these two countries can be considered at-risk. In Mexico, nearly one-third of people age 12-24 are suffering the consequences of negative youth behaviors – adolescent mothers, early school dropout, not working – while 17 percent of Chilean youth are in this situation. They also come from the poorest families and have the fewest social bonds. Another one-quarter of Mexican and Chilean youth are engaging in negative behaviors and on their way to the worst-off category. A younger group in Mexico (20 percent) and Chile (8 percent) are not engaging in risky behaviors, but they have factor in their lives that suggest that they may be graduating to these more harmful groups before long since they also lack the social supports and mental health that the no-risk group boasts. These findings confirm the U.S. literature that negative factors in a young person’s life is highly correlated with negative behaviors. However, further analysis, preferably with panel data, is needed to understand the transition more clearly.

Some factors repeatedly arise in the best-off or worst-off groups. Positive relationship with the family is a recurring protective factor across the clusters. The clusters show that youth who live(d) with both parents have a lower incidence of all risky behaviors. Those youth who feel connected to a parent, i.e. those who feel that they can relate to a parent, the parent cares for them, they can depend on the parent, etc, also have lower risky behavior and negative outcomes than youth who do not feel connected. Connection with non-family members can partially compensate for absent parental connection, as shown by youth who are connected to non-family members having less risky behavior than those who do not have connections with anyone. Conversely, family abuse, substance abuse and lack of family cohesion cluster with victimization in Chile, while non-positive feedback from parents is correlated with risky behaviors in Mexico.

Positive institutional factors are also correlated with positive behaviors. School quality matters in Chile, and an individual’s relationship with his/her community (trust, feeling of school quality) is positively correlated with voter registration. Low spirituality/church attendance clusters loosely with the risk type III groups. Surprisingly, social exclusion is rarely correlated with negative behaviors. Instead, the “loner” youth usually showed up as sub-groups in the less at-risk clusters.

Negative behaviors (or outcomes) cluster together. An early age of starting work clusters loosely with an early age at onset of sexual activity for boys and girls. Other risky behaviors by females cluster with risky sexual behavior while the pattern is weaker for males: 22
For those 12 to 17 and for all females, inactivity and early school drop cluster, which is not the case for the older males. For women 18 to 24 years of age, inactivity and marriage cluster together with a high incidence of having a child, however in this age range this is not risky behavior.

There is no simple answer as to which variables can be used to proxy being at-risk, however poverty is the best option of those available. Poor youth are more at risk on average. Rural youth are more at risk on average than urban youth.

When considering prevention programs to target at risk youth, it is important to remember that risk profiles vary with youth development. Young youth not exhibiting any risky behavior are not out of danger. Therefore age appropriate programs targeting those with type I risk could potentially prevent risky behaviors later in life.

In addition, intervention plans should take gender into account. Gender appears to matter more so with regards to sexual health as younger at-risk females tend to participate in all risky behaviors available in our dataset, while young males exhibit some risky behaviors but not all. The public health literature suggests that perhaps violent behavior is more important for boys however this could not be tested with these data used in this study.

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# Table 1 – Construction of Variables

||**Risk Type**<br>**I, II, III**|**If variabl**<br>**in the**|**e appears**<br>**data set**|**Used in**<br>**the**|
|---|---|---|---|---|
|||**Chile**|**Mexico**|**cluster**<br>|
|||||**analysis**|
|**Behaviors/outcomes**|||||
|Not idle – either in school nor working|II, III|X|X|X|
|No early school dropout (i.e. completed secondary school)|II|X|X|X|
|Literate (can read and/or write a message)|III||X|X|
|In school|II, III|X|X||
|Years of education completed|I, III|X|X||
|<br>Working (Not Working<sup>c</sup>)|<br>II, III|X|X||
|Older age when started working|II, III|X|X|X|
|Safe sex (among the sexually active)|II|X|X||
|Low number of sexual partners in the past year|II||X<sup>a</sup>||
|<br>Older age at onset of sexual activity|II|X|X<sup>a</sup>|X|
|<br>Older age at first pregnancy/child|III|X|X<sup>a</sup>|X|
|<br>Has at least one child (Does not have at least one child<sup>c</sup>)|III|X|X||
|<br>Married (Not Married<sup>c</sup>)|I, III|X|X|X|
|<br>Participate in extracurricular activities|II|X|X|X|
|<br>Registered to vote, planning on voting in the next election,|III|X|X<sup>b</sup>|X|
|desires to vote when of age (if under 18 years old)<br>Has not been a victim of a crime (proxy for criminal activity)|II|X||X|
|Attitude towards drugs (respondent can justify using drugs)|II||X<sup>a</sup>||
|<br>Attitude towards alcohol(respondent canjustify gettingdrunk)|II||X<sup>a</sup>||
|**Protective Factors**|||||
|Trust in governmental institutions|I|X<sup>d</sup>|X<sup>a,e</sup>|X|
|<br>Trust in community institutions|I|X<sup>f</sup>|X<sup>a,g</sup>|X|
|<br>Connected (whether youth reaches out – for talk or help- to|I|X|X|X|
|someone when they have problems)|||||
|<br>Living with both, one or no parents|I||X|X|
|<br>Positive relationship with father|I|X<sup>h</sup>|X<sup>i</sup>|X|
|Positive relationship with mother|I|X<sup>j</sup>|X<sup>k</sup>|X|
|<br>Connected with an adult other than parents|I|X||X|
|Communication with parents (talk to parents when facing a|I||X||
|personal problem)|||||
|<br>Church attendance|I|X|X|X|
|Spiritual influence in beliefs, opinions and attitudes|I||X||
|<br>School quality<sup>l</sup>|I||X|X|
|<br>Feeling optimistic about future work|I|X|||
|Feeling prepared for future employment|I|X|||
|<br>Sense of wellbeing (level of happiness reported bytheyouth)|I||X<sup>a</sup>||
|**Risk Factors**|||||
|Have felt discriminated against|I|X|||
|<br>Parental education – this is a good proxy for poverty|I|X|X|X|
|Household ownership of durable/luxury goods<sup>m</sup>|I||X||
|Monthly earnings of heads of household|I||X||
|<br>Limited access to healthcare|I||X|X|

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|Rural residence (versus urban)|I|X|X||
|---|---|---|---|---|
|Social exclusion|I, III|X|X|X|
|Poor family cohesion|I|X||X|
|Abuse in the home|I|X||X|
|Substance abuse in the home|I|X||X|
|Level of perceived violence in the neighborhood|I|X||X|
|Indigenous (self-identifying with an indigenous group)|I|X|||
|Parental influence with regards to smoking and alcohol<sup>n</sup>|I||X|X|
|Parental response to misbehavior<sup>o</sup>|I||X||
|Parental response togood behavior<sup>p</sup>|I||X||

a for respondents age 15 or older b for respondents age 18 or older c for respondents under age 18 d indicates level of confidence in government, congress, city government, political parties, judicial system e indicates level of confidence in politicians, judges, the police, and the military f level of confidence in hospitals, the Catholic Church, schools, universities, and family g indicates level of confidence in teachers, doctors, shop owners, union leaders and priests h quality of relationship with father on various attributes (communication, demonstration of love or affection, understanding and help with problems, respect for private life of youth, the time spend with father) i variety of topics that the youth communicates with the father about (school, politics, religion, sexual relations, work, and other topics) j indicates quality of relationship with mother on various attributes (communication, demonstration of love or affection, understanding and help with problems, respect for private life of youth, the time spend with mother) k variety of topics that the youth communicates with the mother about (school, politics, religion, sexual relations, work, and other topics) l rank of the overall quality of the youth’s current/past school as reported by youth (physical building, scholastic materials, teachers preparation, content of courses and teachers assistance) m includes radio recorder, CD burner, TV, cable, VCR, game console, telephone, computer, internet, car/truck/van - could be used instead of household education level to indicate economic class n indicates level of control parents attempt to control children’s behaviors (do they forbid smoking/drinking, grant periodic permission, allow the child to make his/her own decision) o how parents respond when child bothers/angers them (0=by talking with their child, 0.5 = punishing, 1 = beating/hitting, insulting, accusation in front of others, stop talking) p indicates frequency with which parents use positive feedback (words of encouragement, hug/kiss, give a gift, concede to something) when child does something good/correct (0 = always, 0.5 = sometimes, 1 = never) 25
Table 2 – Descriptive Statistics

|||**Mex**|**ico**|||**Chile**||
|---|---|---|---|---|---|---|---|
||All|12 to|15 to|18 to|All|15 to|18 to|
|**Actualpercentages**|ages|14|17|24|ages|17|24|
|Average age|17.19|13.0|16.0|20.8|18.9|15.95|20.8|
|Percent female|53.5|50.65|52.9|55.9|53.0|50.5|54.6|
|Percent indigenous|-|-|-|-|10.8|11.5|10.4|
|<br>Percent rural|24.7|28.1|26.0|21.6|13.2|13.3|13.1|
|**Behaviors and Outcomes**||||||||
|Share not completing high school|29.9|10.0|33.0|42.3|14.2|8.4|17.8|
|Share in school|45.3|85.9|61.8|27.5|60.5|91.4|41.0|
|Share idle/inactive|20.3|8.9|17.7|29.9|27.3|6.7|35.9|
|Share working|41.9|17.2|35.1|35.1|21.4|4.7|32.0|
|<br>Share having sex|52.1|-|13.3|56.0|59.1|26.9|79.4|
|Share of the sexually active using protection|50.6|-|54.0|52.5|61.9|59.9|62.3|
|Share reporting at least 1 child|19.4|-|2.5|28.9|18.6|3.9|27.9|
|Share married|15.7|0.4|4.1|32.9|4.8|0.1|7.7|
|**Risk and Protective Factors**||||||||
|Share reporting physical or psychological abuse<br>in home|-|-|-|-|6.3|6.2|6.4|
|Share reporting problems arising from substance|-|-|-|-|8.5|7.0|9.5|
|abuse in home||||||||
|Share without access to medical services|49.4|50.2|50.7|48.0|-|-|-|
|Share attending church weekly|9.3|11.3|10.3|7.3|21.6|20.9|18.1|
|Share attending church at least once in the past<br>month|66.3|72.0|65.9|62.5|-|-|-|
|Share believing in God|-|-|-|-|94.8|95.1|94.6|
|Share optimistic about the future|-|-|-|-|87.7|88.2|87.3|
|<br>Share reporting being happy (sense of well-|98.2|-|98.0|98.4|-|-|-|
|being)||||||||
|Share reporting social exclusion|53.2|66.8|53.9|43.1|17.7|10.1|22.5|
|Share with parents who have a primary degree or|56.1|56.3|58.0|55.0|38.1|38.8|37.7|
|less||||||||
|Share reporting they have felt discriminated<br>against|-|-|-|-|54.7|54.2|55.1|

26
Table 3a – Mexico Cluster analysis overview

||Type III|Type II|Type I|None|
|---|---|---|---|---|
|Percent of total sample|33.1|25.1|20.2|21.6|
|Percent of 12-14 year olds|14.1|33.1|24.3|28.5|
|Percent of 15-17 year olds|38.9|11.9|27.5|21.7|
|Percent of 18-24 year olds|43.4|26.8|13.1|16.7|
|Percent of males|31.9|23.2|22.2|22.7|
|Percent of females|34.2|26.7|18.4|20.7|
|Average Parental<br>Education (poverty proxy)|Primary graduate,<br>some secondary|Primary graduate,<br>some secondary|Secondary<br>graduate|Secondary<br>graduate|
|Percent of rural|42.9|24.4|18.1|14.6|
|Percent of urban|29.9|25.3|20.8|23.9|

Table 3b – Chile Cluster analysis overview

||Type III|Type II|Type I|None|
|---|---|---|---|---|
|Percent of total sample|16.8|28.0|8.7|46.5|
|Percent of 15-17 year olds|8.3|20.4|15.9|55.5|
|Percent of 18-24 year olds|22.2|32.8|4.2|40.8|
|Percent of males|23.7|19.1|13.2|44.1|
|Percent of females|10.8|4.7|35.9|10.8|
|Average Parental|Primary Graduate|Some Secondary|Some Secondary|Secondary|
|Education (poverty proxy)||Education|Education|Graduate|
|Percent of rural|27.2|29.5|7.0|36.3|
|Percent of urban|15.3|27.7|9.0|48.0|
|Percent of indigenous|21.7|26.3|8.7|43.3|
|Percent of non-indigenous|16.3|28.2|8.7|46.9|

27
Table 4a: Cluster analysis results: Mexican Males Aged 12 to 14

|**Cluster name**|**at-risk**|**very early**<br>**workers**|**advantaged**<br>**loners**|**advantaged**<br>**youth**|
|---|---|---|---|---|
|age|13.25|13.07|12.87|13.05|
|**Behaviors Average **|**0.41**|**0.69**|**0.83**|**0.77**|
|older age when first started working|0.42|0.11|1.00|0.62|
|not idle|0.49|1.00|1.00|1.00|
|no early school dropout|0.01|1.00|1.00|1.00|
|<br>literacy|0.91|1.00|1.00|1.00|
|not married|0.99|1.00|1.00|1.00|
|participate in activities|0.14|0.29|0.17|0.23|
|not working|0.51|0.58|0.99|0.81|
|in school|0.01|1.00|1.00|1.00|
|years of education completed|0.20|0.28|0.27|0.28|
|**Protective Factors Average **|**0.54**|**0.58**|**0.60**|**0.58**|
|relationship with father|0.37|0.42|0.40|0.40|
|relationship with mother|0.42|0.48|0.46|0.46|
|connected|0.96|0.84|1.00|1.00|
|live with both parents|0.87|0.90|0.93|0.90|
|church attendance|0.36|0.41|0.39|0.39|
|school quality|0.75|0.81|0.83|0.82|
|communication with parents|0.49|0.52|0.55|0.51|
|spiritual influence|0.15|0.22|0.15|0.17|
|**Risk Factors Average **|**0.57**|**0.57**|**0.54**|**0.41**|
|parental influence (alcohol & smoking)|0.44|0.38|0.35|0.37|
|social exclusion|0.58|1.00|1.00|0.00|
|limited access to healthcare|0.63|0.52|0.45|0.47|
|parental response to misbehavior|0.38|0.31|0.29|0.32|
|parental response to good behavior|0.58|0.52|0.51|0.53|
|rural|0.55|0.49|0.45|0.43|
|parental education|0.80|0.70|0.67|0.67|
|household ownership of goods|0.84|0.79|0.79|0.77|
|monthly earnings household heads|0.85|0.80|0.78|0.78|
|n|758|1193|1893|1989|
|Risk Type|III|II|I|-|
|Percent of sample|13.0%|20.5%|32.5%|34.1%|

_Notes:_ The highlighted rows indicate that the variables were not included in the clustering exercise. They are exogenous veriables.

28
Table 4b: Cluster analysis results: Mexican Females Aged 12 to 14

|**Cluster name**|**at-risk**|**early workers**|**advantaged**<br>**loners**|**advantaged**<br>**youth**|
|---|---|---|---|---|
|Age|13.33|12.98|12.97|13.13|
|**Behaviors Average **|**0.43**|**0.79**|**0.83**|**0.80**|
|older age when first started working|0.66|0.74|1.00|0.77|
|not idle|0.28|1.00|1.00|1.00|
|No early school dropout|0.01|1.00|1.00|1.00|
|literacy|0.92|1.00|1.00|1.00|
|not married|0.97|1.00|0.99|1.00|
|participate in activities|0.13|0.24|0.24|0.25|
|<br>not working|0.73|0.87|0.99|0.89|
|in school|0.01|0.99|1.00|1.00|
|years of education completed|0.20|0.27|0.29|0.29|
|**Protective Factors Average **|**0.56**|**0.59**|**0.61**|**0.59**|
|relationship with father|0.36|0.39|0.42|0.40|
|<br>relationship with mother|0.45|0.50|0.52|0.51|
|Connected|0.95|0.93|1.00|1.00|
|live with both parents|0.86|0.88|0.93|0.90|
|church attendance|0.45|0.44|0.42|0.45|
|school quality|0.77|0.82|0.81|0.81|
|communication with parents|0.45|0.50|0.55|0.48|
|spiritual influence|0.18|0.17|0.17|0.18|
|**Risk Factors Average **|**0.66**|**0.65**|**0.48**|**0.48**|
|parental influence (alcohol & smoking)|0.40|0.48|0.06|**0.34**|
|social exclusion|0.73|1.00|**1.00**|0.00|
|limited access to healthcare|0.64|0.69|0.00|**0.43**|
|parental response to misbehavior|0.30|0.27|0.26|0.27|
|parental response to good behavior|0.59|0.51|0.47|0.51|
|Rural|0.59|0.49|0.35|0.40|
|parental education|0.81|0.71|0.60|0.65|
|household ownership of goods|0.86|0.81|0.76|0.76|
|monthly earnings household heads|0.87|0.81|0.74|0.75|
|N|908|2723|982|1376|
|Risk Type|III|II|I|-|
|Percent of sample|15.2%|45.5%|16.4%|23.0%|

_Notes:_ The highlighted rows indicate that the variables were not included in the clustering exercise. They are exogenous veriables.

29
Table 4c: Cluster analysis results: Mexican Males Aged 15 to 17

|**Cluster name**|**at-risk**|**becoming at**<br>**risk**|**loners**|**advantaged**<br>**youth**|
|---|---|---|---|---|
|age|16.16|15.89|15.88|15.97|
|**Behaviors Average **|**0.64**|**0.84**|**0.85**|**0.84**|
|older age when first started working|0.25|0.41|0.52|0.48|
|not idle|0.74|1.00|1.00|1.00|
|no early school dropout|0.06|1.00|1.00|1.00|
|<br>literacy|0.97|1.00|1.00|1.00|
|older age at onset of sexual activity|0.85|0.92|0.90|0.88|
|<br>older age at first pregnancy|0.98|1.00|1.00|1.00|
|<br>not married|0.97|1.00|1.00|1.00|
|participate in activities|0.17|0.27|0.27|0.32|
|not working|0.30|0.63|0.73|0.73|
|in school|0.06|0.99|0.99|1.00|
|years of education completed|0.28|0.39|0.42|0.42|
|safe sex|0.85|0.93|0.91|0.90|
|low number of sexual partners in past year|0.96|0.98|0.98|0.97|
|<br>does not have a child|0.99|1.00|1.00|1.00|
|attitude towards drugs|0.97|0.97|0.96|0.95|
|attitude towards alcohol|0.88|0.89|0.91|0.85|
|**Protective Factors Average **|**0.54**|**0.58**|**0.59**|**0.58**|
|Connected|0.91|1.00|1.00|1.00|
|live with both parents|0.84|0.88|0.89|0.88|
|<br>relationship with father|0.38|0.42|0.44|0.43|
|<br>relationship with mother|0.44|0.47|0.50|0.49|
|<br>trust in governmental institutions|0.35|0.36|0.36|0.38|
|trust in community institutions|0.58|0.60|0.60|0.61|
|church attendance|0.32|0.35|0.35|0.32|
|school quality|0.78|0.81|0.81|0.82|
|communication with parents|0.44|0.48|0.51|0.47|
|<br>spiritual influence|0.19|0.19|0.18|0.18|
|sense of wellbeing|0.82|0.85|0.89|0.87|
|**Risk Factors Average **|**0.54**|**0.57**|**0.45**|**0.29**|
|parental influence (alcohol & smoking)|0.54|0.46|0.42|0.45|
|social exclusion|0.46|0.48|1.00|0.00|
|limited access to healthcare|0.61|1.00|0.04|0.00|
|parental response to misbehavior|0.29|0.27|0.21|0.24|
|<br>parental response to good behavior|0.60|0.55|0.50|0.53|
|rural|0.51|0.49|0.35|0.29|
|parental education|0.78|0.72|0.59|0.56|
|household ownership of goods|0.82|0.78|0.73|0.69|
|monthlyearnings household heads|0.85|0.82|0.74|0.70|
|n|1765|1120|681|869|
|Risk Type|III|II|I|-|
|Percent|39.8%|25.3%|15.4%|19.6%|

_Notes:_ The highlighted rows indicate that the variables were not included in the clustering exercise. They are exogenous veriables.

30
Table 4d: Cluster analysis results: Mexican Females Aged 15 to 17

|**Cluster names**|**early**<br>**workers"**|**at-risk**<br>**mothers**<br>**& wives**|**at-risk**<br>**idle**<br>**dropouts**|**resilient**<br>**loners**|**advantaged**<br>**loners**|**advantaged**<br>**youth**|
|---|---|---|---|---|---|---|
|age|16.20|16.42|16.07|15.85|15.85|15.92|
|**Behaviors Average **|**0.66**|**0.46**|**0.68**|**0.86**|**0.88**|**0.88**|
|older age when started working|0.26|0.43|0.65|0.62|0.71|0.67|
|<br>not idle|1.00|0.21|0.02|1.00|1.00|1.00|
|no early school dropout|0.00|0.07|0.00|0.96|0.99|1.00|
|literacy|1.00|0.96|0.95|1.00|1.00|1.00|
|<br>older age at onset of sexual<br>|0.97|0.29|0.97|0.99|0.98|0.97|
|activity|||||||
|<br>older age at first pregnancy|1.00|0.43|0.99|1.00|1.00|1.00|
|not married|0.99|0.34|0.99|1.00|0.99|1.00|
|participate in activities|0.16|0.07|0.15|0.25|0.27|0.31|
|not working|0.00|0.83|0.98|0.77|0.86|0.83|
|in school|0.00|0.06|0.00|0.95|0.99|1.00|
|years of education completed|0.27|0.27|0.26|0.38|0.42|0.42|
|safe sex|0.96|0.19|0.96|0.99|0.98|0.97|
|low number of sexual partners in|0.99|0.89|0.99|1.00|1.00|0.99|
|past year|||||||
|<br>does not have a child|1.00|0.46|0.99|1.00|1.00|1.00|
|attitude towards drugs|0.98|0.97|0.97|0.97|0.97|0.96|
|<br>attitude towards alcohol|0.94|0.94|0.94|0.95|0.93|0.89|
|**Protective Factors Average **|**0.57**|**0.49**|**0.57**|**0.57**|**0.62**|**0.60**|
|connected|1.00|0.85|0.96|0.82|1.00|1.00|
|live with both parents|0.85|0.27|0.87|0.85|0.91|0.86|
|<br>relationship with father|0.43|0.41|0.45|0.48|0.52|0.45|
|<br>relationship with mother|0.39|0.35|0.36|0.41|0.44|0.42|
|<br>trust in governmental institutions|0.35|0.32|0.34|0.33|0.36|0.37|
|<br>trust in community institutions|0.59|0.57|0.58|0.58|0.60|0.62|
|<br>church attendance|0.20|0.16|0.18|0.22|0.20|0.21|
|school quality|0.82|0.79|0.78|0.82|0.82|0.80|
|communication with parents|0.51|0.46|0.46|0.53|0.57|0.54|
|spiritual influence|0.42|0.31|0.45|0.45|0.43|0.42|
|sense of wellbeing|0.81|0.85|0.79|0.82|0.89|0.86|
|**Risk Factors Average **|**0.54**|**0.53**|**0.57**|**0.62**|**0.43**|**0.35**|
|parental influence (alcohol &|0.43|0.43|0.41|0.40|0.38|0.39|
|smoking)|||||||
|social exclusion|0.60|0.39|0.68|1.00|1.00|0.00|
|limited access to healthcare|0.62|0.63|0.64|0.92|0.01|0.39|
|parental response to misbehavior|0.26|0.38|0.28|0.25|0.22|0.24|
|parental response to good|0.59|0.58|0.58|0.52|0.47|0.49|
|<br>behavior|||||||
|rural|0.48|0.50|0.61|0.50|0.33|0.32|
|parental education|0.78|0.77|0.80|0.72|0.60|0.60|
|household ownership of goods|0.83|0.84|0.84|0.80|0.74|0.72|
|monthly earnings household|||||||
|<br>heads|0.85|0.85|0.86|0.83|0.74|0.73|
|n|603|363|932|957|947|1178|
|Risk Type|III|III|III|I|I|-|
|Percent|12.1%|7.3%|18.7%|19.2%|19.0%|23.7%|

_Notes:_ The highlighted rows indicate that the variables were not included in the clustering exercise. They are exogenous veriables.

31
Table 4e: Cluster analysis results: Mexican Males Aged 18 to 24

|**Cluster name**|**idle**|**drinkers**|**working**<br>**dads**|**loners**|**resilient**<br>**loners**|**advantaged**<br>**students**|
|---|---|---|---|---|---|---|
|age|20.58|19.60|22.28|21.19|20.96|20.15|
|**Behaviors Average **|**0.47**|**0.56**|**0.64**|**0.58**|**0.63**|**0.69**|
|older age when started working|0.42|0.30|0.30|0.32|0.40|0.46|
|<br>not idle|0.00|0.98|1.00|1.00|1.00|1.00|
|no early school dropout|0.27|0.51|0.11|0.00|0.25|1.00|
|literacy|0.99|0.90|1.00|1.00|1.00|1.00|
|older age at onset of sexual activity|0.66|0.69|0.50|0.62|0.71|0.68|
|older age at first<br>|0.91|0.92|0.69|0.92|0.95|0.98|
|pregnancy/parenthood|||||||
|married|0.14|0.14|0.99|0.05|0.06|0.01|
|participate in activities|0.19|0.18|0.18|0.20|0.24|0.31|
|<br>registered to vote|0.95|0.95|0.98|0.96|0.96|0.93|
|working|0.00|0.81|0.99|0.91|0.78|0.56|
|in school|0.00|0.30|0.07|0.20|0.38|0.74|
|years of education completed|0.36|0.34|0.37|0.35|0.44|0.69|
|<br>safe sex|0.57|0.61|0.22|0.51|0.63|0.64|
|low number of sexual partners in|0.89|0.90|0.87|0.87|0.90|0.89|
|<br>past year|||||||
|<br>has a child|0.16|0.14|0.71|0.15|0.11|0.03|
|attitude towards drugs|0.82|0.84|0.86|0.82|0.85|0.77|
|attitude towards alcohol|0.73|0.06|1.00|1.00|1.00|1.00|
|**Protective Factors Average **|**0.54**|**0.53**|**0.53**|**0.55**|**0.55**|**0.59**|
|relationship with father|0.46|0.45|0.48|0.47|0.49|0.50|
|<br>relationship with mother|0.40|0.40|0.43|0.42|0.43|0.48|
|connected|0.96|0.95|0.99|1.00|0.81|1.00|
|live with both parents|0.75|0.73|0.34|0.73|0.79|0.83|
|trust in governmental institutions|0.34|0.36|0.35|0.34|0.38|0.36|
|trust in community institutions|0.59|0.60|0.62|0.61|0.63|0.64|
|church attendance|0.30|0.28|0.31|0.30|0.31|0.31|
|school quality|0.79|0.76|0.78|0.80|0.80|0.81|
|communication with parents|0.47|0.45|0.48|0.48|0.49|0.53|
|spiritual influence|0.19|0.20|0.19|0.17|0.20|0.19|
|sense of wellbeing|**0.82**|**0.83**|0.91|0.85|0.84|0.89|
|**Risk Factors Average **|**0.58**|**0.56**|**0.50**|**0.51**|**0.60**|**0.45**|
|parental influence (alcohol &|0.66|0.66|0.68|0.74|0.73|0.72|
|smoking)|||||||
|social exclusion|0.37|0.36|0.10|0.00|1.00|0.16|
|limited access to healthcare|0.61|0.52|0.38|0.46|0.40|0.44|
|parental response to misbehavior|0.28|0.29|0.31|0.29|0.24|0.22|
|parental response to good behavior|0.59|0.60|0.59|0.60|0.56|0.54|
|rural|0.48|0.44|0.38|0.36|0.38|0.24|
|parental education|0.74|0.73|0.66|0.73|0.69|0.54|
|household ownership of goods|0.78|0.80|0.80|0.76|0.75|0.66|
|monthlyearnings household heads|0.84|0.83|0.80|0.82|0.81|0.71|
|n|743|1511|837|1778|1335|1142|
|Risk Type|III|III|III|II|I|None|
|Percent|10.1%|20.6%|11.4%|24.2%|18.2%|15.5%|

_Notes:_ The highlighted rows indicate that the variables were not included in the clustering exercise. They are exogenous veriables.

32
Table 4f: Cluster analysis results: Mexican Females Aged 18 to 24

|**Cluster name**|**at risk,**<br>**idle**<br>**married**<br>**mothers**|**at risk,**<br>**idle, and**<br>**excluded**<br>**married**<br>**mothers**|**poor**<br>**working**<br>**early**<br>**dropouts**|**somewhat**<br>**advantaged**<br>**married**<br>**moms**|**resilient**<br>**poor**|**advantaged**<br>**students**|
|---|---|---|---|---|---|---|
|age|21.35|21.09|21.27|21.48|19.02|20.27|
|**Behaviors Average **|**0.50**|**0.51**|**0.63**|**0.63**|**0.62**|**0.72**|
|older age when started working|0.53|0.56|0.47|0.46|0.48|0.63|
|<br>not idle|0.00|0.07|1.00|1.00|0.95|0.89|
|no early school dropout|0.07|0.19|0.00|0.10|0.62|1.00|
|literacy|0.99|0.92|1.00|0.98|1.00|1.00|
|older age at onset of sexual activity|0.58|0.67|0.85|0.70|0.92|0.91|
|older age at first|0.60|0.68|0.87|0.76|0.97|0.96|
|pregnancy/parenthood|||||||
|<br>married|0.63|0.40|0.12|0.45|0.03|0.05|
|participate in activities|0.11|0.11|0.18|0.18|0.22|0.29|
|registered to vote|0.98|0.98|0.97|0.98|0.97|0.97|
|working|0.00|0.05|0.83|0.82|0.60|0.40|
|in school|0.00|0.02|0.29|0.26|0.48|0.69|
|years of education completed|0.29|0.29|0.35|0.39|0.40|0.71|
|<br>safe sex|0.36|0.50|0.75|0.54|0.88|0.86|
|low number of sexual partners in|0.90|0.93|0.96|0.93|0.98|0.98|
|<br>past year|||||||
|<br>has a child|0.68|0.56|0.23|0.40|0.05|0.06|
|attitude towards drugs|0.96|0.95|0.93|0.90|0.92|0.88|
|attitude towards alcohol|0.76|0.73|1.00|0.93|0.00|0.99|
|**Protective Factors Average **|**0.53**|**0.51**|**0.57**|**0.56**|**0.58**|**0.61**|
|relationship with father|0.43|0.43|0.46|0.45|0.46|0.51|
|<br>relationship with mother|0.37|0.36|0.41|0.41|0.41|0.48|
|<br>connected|1.00|0.64|0.85|1.00|1.00|0.95|
|live with both parents|0.26|0.52|0.74|**0.47**|0.80|0.80|
|trust in governmental institutions|0.35|0.35|0.35|0.35|0.34|0.36|
|trust in community institutions|0.61|0.61|0.62|0.62|0.61|0.63|
|<br>church attendance|0.37|0.39|0.39|0.36|0.41|0.39|
|school quality|0.80|0.76|0.81|0.81|0.80|0.82|
|communication with parents|0.49|0.48|0.54|0.55|0.54|0.60|
|spiritual influence|0.18|0.20|0.21|0.22|0.22|0.25|
|<br>sense of wellbeing|0.89|0.82|0.82|0.90|0.82|0.89|
|**Risk Factors Average **|**0.51**|**0.65**|**0.59**|**0.42**|**0.54**|**0.46**|
|parental influence (alcohol &|0.47|0.49|0.55|0.53|0.50|0.59|
|smoking)|||||||
|<br>social exclusion|0.00|0.97|0.80|0.01|0.52|0.42|
|limited access to healthcare|0.51|0.65|0.57|0.14|0.54|0.36|
|parental response to misbehavior|0.36|0.33|0.28|0.30|0.26|0.21|
|parental response to good behavior|0.58|0.59|0.55|0.55|0.54|0.49|
|<br>rural|0.45|0.54|0.40|0.26|0.40|0.24|
|parental education|0.71|0.75|0.74|0.63|0.72|0.55|
|<br>household ownership of goods|0.82|0.83|0.79|0.76|0.78|0.68|
|monthlyearnings household heads|0.84|0.86|0.84|0.75|0.82|0.72|
|n|1810|2324|1693|999|850|1644|
|Risk Type|III|III|II|II|I|-|
|Percent|19.4%|24.9%|18.2%|10.7%|9.1%|17.6%|

33
_Notes:_ The highlighted rows indicate that the variables were not included in the clustering exercise. They are exogenous veriables.

Table 4g: Cluster analysis results: Chilean Males Aged 15 to 17

|**Cluster name**|**at-risk**|**risky**<br>**sex**|**doing ok**|**loners**|**advantaged**<br>**youth - some**<br>**sexual activity**|**advantaged**<br>**youth**|
|---|---|---|---|---|---|---|
|age<br>|16.31|16.01|15.90|15.95|15.78|15.72|
|**Behaviors/Consequences Average**|**0.53**|**0.77**|**0.82**|**0.78**|**0.84**|**0.86**|
|older age when first working|0.39|0.52|0.67|0.71|0.74|0.97|
|not idle|0.36|1.00|1.00|1.00|1.00|1.00|
|no early school dropout|0.00|1.00|1.00|0.98|1.00|1.00|
|older age at onset of sexual activity|0.60|0.63|0.83|0.73|0.86|0.98|
|older age at first pregnancy/child|0.97|0.99|1.00|0.98|1.00|1.00|
|not married|1.00|1.00|1.00|1.00|1.00|1.00|
|Participate in activities|0.09|0.16|0.18|0.11|0.23|0.14|
|not a victim|0.92|0.91|0.91|0.91|0.93|0.94|
|not working|0.64|0.92|0.95|0.92|0.98|0.99|
|in school|0.00|1.00|1.00|0.98|0.99|1.00|
|years of education completed|0.31|0.46|0.45|0.46|0.45|0.45|
|<br>safe sex|0.58|0.62|0.82|0.72|0.87|0.98|
|does not have a child|0.96|0.99|1.00|0.97|1.00|0.99|
|desires to vote|0.53|0.55|0.63|0.44|0.64|0.59|
|**Protective Factors Average **|**0.59**|**0.62**|**0.75**|**0.50**|**0.76**|**0.66**|
|relationship with father|0.65|0.62|0.70|0.70|0.92|0.86|
|relationship with mother|0.85|0.88|0.79|0.86|0.95|0.93|
|Connected to other adults|0.05|0.02|0.93|0.00|0.00|0.02|
|Connected|0.72|0.96|1.00|0.00|1.00|1.00|
|trust in government|0.31|0.33|0.37|0.28|0.44|0.37|
|trust in community|0.74|0.79|0.83|0.74|0.84|0.81|
|Spirituality|0.26|0.21|0.34|0.23|0.97|0.22|
|**Risk Factors Average **|**0.35**|**0.31**|**0.27**|**0.26**|**0.25**|**0.24**|
|poor family cohesion|0.38|0.38|0.28|0.35|0.19|0.26|
|abuse in the home|0.09|0.09|0.00|0.01|0.00|0.01|
|substance abuse in the home|0.08|0.19|0.00|0.00|0.00|0.00|
|social exclusion|0.13|0.01|0.00|0.11|0.00|0.17|
|community violence|0.15|0.21|0.25|0.18|0.29|0.19|
|felt discriminated against|0.61|0.63|0.51|0.50|0.46|0.48|
|live in a rural area|0.27|0.13|0.19|0.08|0.13|0.11|
|indigenous|0.20|0.12|0.13|0.10|0.17|0.08|
|<br>low economic class (nse)|0.85|0.71|0.74|0.71|0.67|0.63|
|lowparental education|0.75|0.59|0.61|0.59|0.56|0.50|
|N|75|359|88|105|126|265|
|Risk Level|III|II|I|I|-|-|
|Percent of sample|7.4%|35.3%|8.6%|10.3%|12.4%|26.0%|

_Notes:_ The highlighted rows indicate that the variables were not included in the clustering exercise. They are exogenous veriables.

34
Table 4h: Cluster analysis results: Chilean Females Aged 15 to 17

|**Cluster name**|**at-risk**|**high risk but**<br>**connected**|**loners**|**advantaged**<br>**youth**|**connected**|
|---|---|---|---|---|---|
|age|16.26|16.15|15.91|15.94|16.00|
|**Behaviors/Consequences Average**|**0.44**|**0.79**|**0.81**|**0.83**|**0.82**|
|older age when started working|0.59|0.78|0.84|0.86|0.88|
|not idle|0.07|1.00|1.00|1.00|0.99|
|no early school dropout|0.01|1.00|1.00|1.00|1.00|
|<br>older age at onset of sexual activity|0.37|0.67|0.84|0.89|0.87|
|older age at first parenthood|0.61|0.99|0.96|0.99|0.99|
|not married|0.98|1.00|1.00|1.00|1.00|
|participate in activities|0.02|0.12|0.08|0.11|0.11|
|not a victim|0.92|0.91|0.94|0.93|0.92|
|not working|0.93|0.93|0.99|0.99|0.98|
|in school|0.00|1.00|1.00|1.00|0.99|
|years of education completed|0.31|0.45|0.47|0.46|0.47|
|<br>safe sex|0.28|0.63|0.82|0.88|0.84|
|does not have a child|0.51|0.98|0.95|0.98|0.99|
|desires to vote|0.62|0.55|0.50|0.55|0.49|
|**Protective Factors Average **|**0.61**|**0.60**|**0.55**|**0.66**|**0.74**|
|relationship with father|0.59|0.55|0.57|0.75|0.71|
|relationship with mother|0.75|0.71|0.85|0.91|0.86|
|connected to other adults|0.12|0.20|0.02|0.00|1.00|
|connected|0.84|1.00|0.41|1.00|1.00|
|trust in government|0.38|0.24|0.30|0.31|0.29|
|trust in community|0.79|0.70|0.70|0.81|0.77|
|spirituality|0.28|0.36|0.32|0.42|0.42|
|**Risk Factors Average **|**0.39**|**0.48**|**0.34**|**0.27**|**0.29**|
|poor family cohesion|0.39|0.69|0.39|0.33|0.38|
|abuse in the home|0.14|1.00|0.05|0.00|0.00|
|substance abuse in the home|0.13|0.20|0.05|0.05|0.06|
|social exclusion|0.55|0.10|0.53|0.00|0.07|
|community violence|0.25|0.29|0.34|0.31|0.33|
|felt discriminated against|0.52|0.82|0.55|0.51|0.58|
|live in a rural area|0.25|0.18|0.08|0.12|0.10|
|indigenous|0.11|0.20|0.12|0.10|0.08|
|low economic class (nse)|0.85|0.73|0.73|0.69|0.71|
|lowparental education|0.72|0.59|0.59|0.56|0.58|
|n|95|60|133|661|90|
|Risk Level|III|II|I|-|-|
|Percent of sample|9.1%|5.8%|12.8%|63.6%|8.7%|

_Notes:_ The highlighted rows indicate that the variables were not included in the clustering exercise. They are exogenous veriables.

35
Table 4i: Cluster analysis results: Chilean Males Aged 18 to 24

|**Cluster name**|**idle**<br>**dropout**<br>**(dads)**|**idle high**<br>**school**<br>**graduates**|**working**<br>**(fathers)**<br>**- socially**<br>**excluded**|**connected**|**not well**<br>**protected**|**advantaged**|**advantaged**<br>**students**|
|---|---|---|---|---|---|---|---|
|age|20.99|19.98|21.02|20.65|20.43|20.41|21.72|
|**Behaviors/Consequences**<br>**Average**|**0.37**|**0.37**|**0.48**|**0.48**|**0.48**|**0.51**|**0.56**|
|older age when started working|0.38|0.60|0.48|0.49|0.53|0.57|0.56|
|not idle|0.62|0.00|0.80|0.79|0.79|0.93|0.81|
|no early school dropout|0.28|1.00|0.80|0.85|0.99|0.98|0.93|
|<br>older age at onset of sexual activity|0.41|0.54|0.53|0.48|0.47|0.51|0.48|
|older age at first parenthood|0.90|0.97|0.90|0.96|0.96|0.95|0.98|
|married|0.00|0.00|0.08|0.04|0.02|0.05|0.00|
|participate in activities|0.08|0.11|0.07|0.16|0.14|0.12|0.12|
|registered to vote|0.02|0.01|0.14|0.17|0.01|0.01|1.00|
|not victim|0.93|0.92|0.92|0.90|0.90|0.91|0.91|
|working|0.53|0.00|0.57|0.42|0.38|0.39|0.38|
|in school|0.13|0.00|0.28|0.44|0.52|0.64|0.56|
|years of education completed|0.43|0.61|0.55|0.61|0.62|0.65|0.68|
|safe sex|0.22|0.37|0.35|0.33|0.30|0.35|0.32|
|has a child|0.22|0.06|0.25|0.12|0.09|0.13|0.06|
|**Protective Factors Average **|**0.58**|**0.66**|**0.62**|**0.78**|**0.50**|**0.63**|**0.66**|
|relationship with father|0.56|0.85|0.69|0.74|0.61|0.68|0.72|
|relationship with mother|0.81|0.86|0.85|0.87|0.79|0.87|0.87|
|connected to other adults|0.02|0.00|0.02|1.00|0.00|0.03|0.00|
|connected|0.75|1.00|0.81|1.00|0.00|1.00|0.85|
|trust in government|0.27|0.34|0.29|0.33|0.28|0.29|0.39|
|trust in community|0.72|0.81|0.73|0.75|0.72|0.74|0.76|
|spirituality|0.19|0.32|0.28|0.39|0.21|0.27|0.35|
|**Risk Factors Average**|**0.36**|**0.25**|**0.38**|**0.27**|**0.28**|**0.25**|**0.25**|
|poor family cohesion|0.42|0.25|0.31|0.31|0.41|0.31|0.29|
|abuse in the home|0.05|0.00|0.02|0.00|0.09|0.08|0.01|
|substance abuse in the home|0.42|0.00|0.04|0.04|0.15|0.03|0.08|
|social exclusion|0.09|0.00|1.00|0.13|0.01|0.01|0.00|
|community violence|0.18|0.18|0.23|0.19|0.20|0.20|0.18|
|felt discriminated against|0.55|0.47|0.56|0.56|0.55|0.54|0.59|
|live in a rural area|0.22|0.16|0.17|0.09|0.09|0.10|0.11|
|indigenous|0.14|0.10|0.17|0.12|0.09|0.09|0.16|
|low economic class (nse)|0.83|0.75|0.74|0.68|0.69|0.65|0.59|
|lowparental education|0.71|0.62|0.58|0.59|0.55|0.53|0.47|
|n|237|111|169|117|137|576|134|
|Risk Level|III|III|III|II|I|-|-|
|Percent of sample|16.0%|7.5%|11.4%|7.9%|9.3%|38.9%|9.0%|

_Notes:_ The highlighted rows indicate that the variables were not included in the clustering exercise. They are exogenous veriables.

36
Table 4j: Cluster analysis results: Chilean Females Aged 18 to 24

|**Cluster name**|**early drop**<br>**out**<br>**mothers**|**resilient**|**spiritual**|**connected**|**advantaged**<br>**students**|
|---|---|---|---|---|---|
|age|21.74|20.80|20.83|20.86|20.49|
|**Behaviors/Consequences**<br>**Average**|**0.31**|**0.44**|**0.45**|**0.48**|**0.56**|
|older age when first started working|0.54|0.57|0.60|0.60|0.71|
|not idle|0.12|0.55|0.36|0.63|0.93|
|no early school dropout|0.00|0.73|0.92|0.88|1.00|
|<br>older age at onset of sexual activity|0.38|0.52|0.58|0.58|0.69|
|older age at first parenthood|0.50|0.75|0.78|0.80|0.93|
|married|0.30|0.05|0.18|0.09|0.01|
|participate in activities|0.02|0.04|0.04|0.07|0.07|
|registered to vote|0.05|0.05|0.05|0.09|0.16|
|not victim|0.94|0.91|0.93|0.93|0.93|
|working|0.12|0.24|0.24|0.33|0.29|
|in school|0.00|0.38|0.16|0.36|0.75|
|years of education completed|0.36|0.56|0.60|0.61|0.68|
|<br>safe sex|0.12|0.30|0.34|0.39|0.53|
|has a child|0.88|0.46|0.45|0.39|0.15|
|**Protective Factors Average **|**0.58**|**0.59**|**0.61**|**0.72**|**0.64**|
|relationship with father|0.57|0.53|0.64|0.61|0.72|
|relationship with mother|0.72|0.73|0.84|0.82|0.86|
|connected to other adults|0.02|0.13|0.00|1.00|0.01|
|connected|0.86|0.89|0.79|1.00|1.00|
|trust in government|0.33|0.24|0.27|0.30|0.30|
|trust in community|0.75|0.73|0.75|0.74|0.76|
|spirituality|0.30|0.32|**0.37**|0.34|0.33|
|**Risk Factors Average**|**0.36**|**0.48**|**0.31**|**0.30**|**0.25**|
|poor family cohesion|0.32|0.59|0.31|0.30|0.30|
|abuse in the home|0.01|0.56|0.02|0.01|0.00|
|substance abuse in the home|0.00|0.67|0.01|0.01|0.00|
|social exclusion|0.58|0.33|0.46|0.34|0.02|
|community violence|0.32|0.29|0.27|0.29|0.29|
|felt discriminated against|0.52|0.70|0.55|0.58|0.51|
|live in a rural area|0.23|0.16|0.15|0.11|0.08|
|indigenous|0.13|0.10|0.08|0.09|0.10|
|low economic class (nse)|0.80|0.75|0.71|0.70|0.65|
|lowparental education|0.66|0.62|0.58|0.56|0.52|
|n|209|210|582|160|622|
|Risk Level|III|II|II|II|-|
|Percent of sample|11.7%|11.8%|32.6%|9.0%|34.9%|

_Notes:_ The highlighted rows indicate that the variables were not included in the clustering exercise. They are exogenous veriables.

37
Table 5 – Ordered logistic regression for Risk Level

|**Mexico**|High Risk<br>Level|High Risk<br>Level|
|---|---|---|
|Low parent education (1)|0.48<br>(0.012)**|0.485<br>(0.012)**|
|Being rural (rural =1, urban=0)|0.401<br>(0.023)**|0.303<br>(0.023)**|
|Age||0.135<br>(0.003)**|
|Gender (female=2, male=1)||0.128<br>(0.019)**|
|Observations|37903|37903|

Standard errors in parentheses * significant at 5%; ** significant at 1%

# **Figures**

Figure 1a. Cluster Group Means – Mexico <!-- Start of picture text --> Mexico Clusters<br>Type III Type II Type I Type 0<br>0.80<br>0.60<br>0.40<br>0.20<br>0.00<br>Behaviors Protective Risk Factors Rural (binary) Low parental<br>Average Factors Average Average Factors education<br>Group Means<br><!-- End of picture text --> Figure 1b. Cluster Group Means – Chile <!-- Start of picture text --> Chile Clusters III II I 0<br>0.80<br>0.60<br>0.40<br>0.20<br>0.00<br>Behaviors / Protective Risk Factors live in a rural indigenous low economic low parental<br>Consequences Factors Average area class (nse) education<br>Factors<br>Average Average<br>Group Means<br><!-- End of picture text --> _Note_ : The values of the group means are on a 0 to 1 scale as described in the variable construction section. Thus, the relative scale of the means between the different x values (primarily factors) is not interpretable. What is important is the relationship between the graphs for each risk level. As seen above, the Type I and 0 behaviors have a higher mean than the Type II and the Type II behaviors have a higher mean than the Type III.

38
Figure 2a. Protective Factors and Positive Behaviors across Clusters of Youth Aged 18 to 24 – Mexico

**Mexico - 18 to 24 year olds**

<!-- Start of picture text --> y = 2.3288x - 0.6923<br>1.00<br>R2 = 0.6628<br>t = 4.4<br>0.80<br>0.60<br>0.40<br>0.20<br>0.00<br>0.40 0.45 0.50 0.55 0.60 0.65 0.70<br>Protective Factors<br>Positive Behaviors<br><!-- End of picture text --> Figure 3a. Risk Factors and Positive Behaviors across Clusters of Youth Aged 18 to 24 – Mexico

**Mexico - 18 to 24 year olds**

<!-- Start of picture text --> y = -0.6138x + 0.9237<br>1.00<br>R2 = 0.3027<br>t = -2.08<br>0.80<br>0.60<br>0.40<br>0.20<br>0.00<br>0.20 0.30 0.40 0.50 0.60 0.70 0.8<br>Risk Factors<br>Positive Behaviors<br><!-- End of picture text --> Figure 2b. Protective Factors and Positive Behaviors across Clusters of Youth Aged 18 to 24 – Chile <!-- Start of picture text --> Chile - 18 to 24 year olds<br><!-- End of picture text --> <!-- Start of picture text -->
1.00 y = 0.3087x + 0.2621<br>R2 = 0.0819<br>t = 0.94<br>0.80<br>0.60<br>0.40<br>0.20<br>0.00<br>0.40 0.50 0.60 0.70 0.80 0.90<br>Protective Factors<br>Positive Behaviors<br><!-- End of picture text -->

Figure 3b. Risk Factors and Positive Behaviors across Clusters of Youth Aged 18 to 24 – Chile

**Chile - 18 to 24 year olds**

<!-- Start of picture text --> y = -0.487x + 0.6085<br>1.00<br>R2 = 0.2003<br>t = -1.58<br>0.80<br>0.60<br>0.40<br>0.20<br>0.00<br>0.10 0.20 0.30 0.40 0.50 0.60<br>Risk Factors<br>Positive Behaviors<br><!-- End of picture text --> 39
Figure 4a. Poverty and Positive Behaviors across Youth Aged 18 to 24 – Mexico <!-- Start of picture text --> Mexico - 18 to 24 year olds<br>y = -0.819x + 1.158<br>1.00<br>R2 = 0.5993<br>t = -3.87<br>0.80<br>0.60<br>0.40<br>0.20<br>0.00<br>0.40 0.50 0.60 0.70 0.80 0.90<br>Poverty as a Risk Factor (parents with post graduate<br>education = 0, no education = 1)<br>Positive Behaviors<br><!-- End of picture text --> Figure 4b. Poverty and Positive Behaviors across Youth Aged 18 to 24 – Chile <!-- Start of picture text --> Chile - 18 to 24 year olds<br>y = -1.06x + 1.0725<br>1.00<br>R2 = 0.7987<br>0.80 t = -6.298<br>0.60<br>0.40<br>0.20<br>0.00<br>0.40 0.50 0.60 0.70 0.80<br>Poverty as a Risk Factor (parents with post<br>graduate education = 0, no education = 1)<br>Positive Behaviors<br><!-- End of picture text --> Figure 5a. Rural and Positive Behaviors across Youth Aged 18 to 24 – Chile <!-- Start of picture text --> Mexico - 18 to 24 year olds<br><!-- End of picture text --> <!-- Start of picture text --> y = -0.6895x + 0.8612<br>1.00<br>R2 = 0.7425<br>t = -6.11<br>0.80<br>0.60<br>0.40<br>0.20<br>0.00<br>0.00 0.20 0.40 0.60 0.80<br>Rural as a Risk Factor (rural=1, 0.5 = mid sized town,<br>urban=0)<br>Positive Behaviors<br><!-- End of picture text --> Figure 5b. Rural and Positive Behaviors across Youth Aged 18 to 24 – Chile <!-- Start of picture text --> Chile - 18 to 24 year olds<br>y = -1.3162x + 0.6401<br>1.00<br>R2 = 0.7465<br>t = -5.426<br>0.80<br>0.60<br>0.40<br>0.20<br>0.00<br>0.00 0.05 0.10 0.15 0.20 0.25 0.30<br>Rural as a Risk Factor (rural=1, urban=0)<br>Positive Behaviors<br><!-- End of picture text --> Figure 6. Chile Indigenous and Positive Behaviors across Youth aged 18 to 24

**Chile - 18 to 24 year olds**

<!-- Start of picture text --> y = 0.1454x + 0.4203<br>1.00<br>R2 = 0.1746<br>t = -0.186<br>0.80<br>0.60<br>0.40<br>0.20<br>0.00<br>0.00 0.05 0.10 0.15 0.20 0.25<br>Indigenous as a Risk Factor (indigenous = 1, non-<br>indigenous=0)<br>Positive Behaviors<br><!-- End of picture text --> 40