USAID/HONDURAS

PROPONTE MÁS

REPORT: RESULTS OF THE APPLICATION OF THE INSTRUMENTO DE MEDICION DEL COMPORTAMIENTO-IMC AT THE NATIONAL LEVEL

May 20, 2020 This publication was produced for review by the United States Agency for International Development and prepared by Creative Associates International INC.

USAID|HONDURAS PROPONTE MÁS REPORT: RESULTS OF THE APPLICATION OF THE IMC AT THE NATIONAL LEVEL

MAY 2020

SUBMITTED BY:

Creative Associates International 5301 Wisconsin Ave., NW Suite 700 Washington, DC 20015

May 20, 2020

TABLES OF CONTENTS EXECUTIVE SUMMARY 4 INTRODUCTION REPORT: RESULTS OF THE APPLICATION OF THE IMC AT THE NATIONAL LEVEL 5 DEVELOPMENT AND VALIDATION OF THE IMC 6 NATIONAL SAMPLE 6 I. Selection of the Data Collection Company 7 II. The IMC Tool 7 III. Administration guidelines of the IMC 9 Community entry strategy 9 Household selection 9 Obtaining consent 9 Georeferencing of the house 9 Control and verification mechanisms during and after administration 9 IV. Administration process of the IMC 10 ANALYSIS OF IMC RESULTS 10 I. Levels of Risk 11 II. Results 12 Socioeconomic and Demographic Characteristics 12 Migration 12 Risky Behaviors 12 Drivers of Risk and Intention to Migrate 15 Regression Results 16 Matching Results 19 CONCLUSIONS 20 ANNEX A- METHODOLOGICAL APPROACH 22

EXECUTIVE SUMMARY Proponte Mas conducted a nationally representative sample of 4,009 youths between 8 and 17 years old to assess their levels of risk using the Risk Behavior Measurement Instrument (IMC). Proponte Mas calculated the cut points for the full sample of the national IMC and the results show that 57.4% of youth are at a primary level of risk, 41.1% are at a secondary level of risk, and 1.4% are at a tertiary level of risk.

Figure 1. Percentage of Respondents by Risk Factor Differentiation

Source: National IMC Survey

The result disaggregated by gender, age group, race, place or birth (urban/rural), and whether or not the youth is currently attending school. The results are presented in Table 1 below and show that:

● Males are at a higher risk than females with 42.3% of respondents being at a secondary risk compared to 39.6% of females. ● Youth 15 to 18 years old and “mestizo” youth are at a higher risk compared to their peers with 58.1% and 44%, respectively. ● Youth who are out of school are at a higher risk compared to youth who are in school with 65% at a secondary risk compared to 37% of those currently attending school. ● Youth from urban environments are at a higher level of risk with 43.2% of respondents in urban areas compared to 33.5% of youth in rural areas. Finally, we conducted additional statistical analyses on the drivers of risky behavior as well as whether being at risk is correlated with a youth’s intention to migrate. The results suggest that:

● Males are four percentage points more likely to be at risk than females. ● Older youth (15-18) are 15 percentage points more likely to be at risk than their younger peers. ● Youth who self-identify as “Mestizos” are five percentage points more likely to be at risk compared to their indigenous/afro-descendant peers. ● Youth who have been born in urban areas are five percentage points more likely to be at risk than their peers who were born in rural areas. ● Youths who are currently studying are 25 percentage points less likely to be at risk than their peers who are not studying. ● Youth who stay active, whether participating or engaging in cultural activities and doing sports is associated with a lower likelihood of being at risk—seven and four percentage points, respectively. ● Finally, those youth whose parents are still alive are six percentage points less likely of being at risk compared to their peers whose parents (one or both) are not alive.

The results of the regression analyses on the intention to migrate measures, and, on average, indicate the following:

● Those youth being at risk appear less likely to migrate, all else equal. The coefficients are negative, strong, and statistically significant with a 99% level of confidence. ● Mestizos appear more likely to migrate compared to indigenous and afro descendants, all else equal. ● Those with more education appear (12 years) appear less likely to intend to migrate compared to their peers with less education, all else equal. ● Surprisingly, a youth being born in an urban area is associated with a lower likelihood of migrating, all else equal.

INTRODUCTION REPORT: RESULTS OF THE APPLICATION OF THE IMC AT THE NATIONAL LEVEL Proponte Más is a program funded by the United States Agency for International Development (USAID). It is part of initiatives focusing on secondary violence prevention in the Northern Triangle of Central America within the framework of the 2015 - 2019 Honduras Country Development Cooperation Strategy, aiming to increase citizen security in urban populations with high crime rates.

Proponte Más' main objectives is to implement a family-based prevention approach that offers new solutions to reduce youths' risk of joining gangs/maras and becoming victims of gang-related violence. Furthermore, the work extends to the strengthening of the Juvenile Justice system and the empowerment of community structures whose activities contribute to violence prevention.

Between 2016 and 2019, Proponte Más implemented various actions under an evidence-based intervention approach through its five intervention results. One of the core elements of the project's intervention focused on using the Behavior Measurement Instrument (IMC), which allows identifying youth between the ages of 8 and 17 at the highest risk of exhibiting violent behavior or engaging in criminal activities and referring them and their families to prevention services.

Proponte Mas's strategy included calibrating the IMC for its effective utilization by USAID-funded activities in five targeted municipalities (Tegucigalpa, San Pedro Sula, Choloma, Tela and La Ceiba) for a youth population between 8 and 17 years old. The IMC is a tool that measures the risk in adolescents of being associated with violent or problematic behaviors, including 38 risk factors and 7 categories of behavior. In December 2019, the project received a three-month extension to be able to expand the scope of the project and thereby collect a national sample of the IMC. The administering of 4,009 instruments yielded data at the national level through two representative samples. One sample was at the national level and the other encompassed ten municipalities with the highest risk of irregular migration per data obtained from the National Migration Observatory and USAID.

It is worth emphasizing the following to the reader: ● The analyses and the findings presented here are considered preliminary. While the cut points calculated by the Proponte Mas team are for the full sample, given the existing diversity in Honduras, cut points for youth in urban vs. rural environments, ethnicity, and others, this additional data should be considered to obtain a more accurate understanding of the youth at risk in Honduras. ● Although the report presents results using methodologies that are often applied in impact evaluations with observable data, these results are not and cannot be interpreted as causality (X causes Y). The reader should interpret them as associations or correlations of an explanatory variable X on Y. ● More research is needed to understand the magnitude and effects of socioeconomic, demographic, and family characteristics on youths’ risky behaviors in Honduras.

This report describes the process to administer the IMC to these two samples, provides details on the methodology, the administration process in some 62 municipalities, the preliminary results from this process as well as recommendations for using these data in the future.

DEVELOPMENT AND VALIDATION OF THE IMC The IMC derives from the Youth Services Eligibility Tool (YSET). It emerged from the Gang Risk of Entry Factors (GREF) assessment tool, which was the “generic version” of the YSET that was originally developed by Karen Hennigan and her colleagues at the University of Southern California (for more information, see Hennigan et al., 2015 and Hennigan et al., 2014) through the establishment of a secondary gang prevention program to address high-risk youth in Los Angeles, California (Katz , Cheon , & Zheng, 2019 , p. 3). The goal of the assessment tool was to identify youth who were at high risk for gang joining using nine risk factors.

The YSET was transferred to Honduras in 2013 through a USAID pilot program implemented in five communities in Tegucigalpa, an experience that helped to adapt the tool's language. In 2016, the USAID Proponte Más project implemented the intervention model on a large scale and conducted the process to adapt, calibrate and contextualize the tool which led to the creation of the IMC. The IMC built upon YSET elements and various existing study frameworks dealing with risk factors, problem behavior and protective factors in developing countries. Under this framework, the tool was adapted, and some 5,000 instruments were administered to youth between the ages of 8 and 17 in the communities most vulnerable to violence in five municipalities in Honduras. These data were used to calibrate the tool, a process that took approximately 10 to 12 months.

The Honduran IMC contains various questions about youth including basic demographics (e.g., age and gender), school attendance and perception, attitudes about police, horizontal family dynamics, vertical family dynamics, parental warmth and support, and victimization. In particular, the IMC includes 173 items that measure 38 factors (11 protective factors and 27 risk factors) within four domains (i.e., community, school, family, peer/individual) (Katz, Cheon , & Zheng, 2019 , pág. 5).

Expanding the use of the IMC through a nationwide sample and an oversample of municipalities with high irregular migration rates responds to the need to continue to increase opportunities that allow calibrating the tool for a future national context. Until now, the IMC has been used in urban contexts highly vulnerable to violence; however, it is unknown whether its mechanisms will be useful in identifying risk in other Honduran contexts such as rural areas and municipalities with little presence of gang and other forms of crime and violence.

NATIONAL SAMPLE To obtain the two samples, PM was to subcontract the administration of 4,009 Risk Behavior Measurement Instrument (IMC) interviews in low-income communities across the country under the following scheme:

Two representative samples were to be obtained: One sample at the national level (49 municipalities) and another sample comprising 12 municipalities at higher risk for irregular migration, for a total sample of 4,009 individuals between the ages of 8 and 17. The sample in the 12 municipalities with a higher probability of irregular migration consisted of 1,929 individuals, while the representative sample (rural and urban stratum) for the rest of the country (the remaining 49 municipalities) consisted of 2,080 individuals. Communities addressed by the project in municipalities where PM carried out the intervention were to be excluded in order to avoid interviewing the same respondents again.

I. Selection of the Data Collection Company In January 2020, Proponte Más launched terms of reference in search of a company to carry out the IMC administration process in the 61 selected municipalities. The project received approximately ten proposals from various companies. The technical review process included an assessment of 1) field knowledge; 2) administration methodology; 3) coordination plans in the field; 4) work schedule; and 5) the structure of the personnel who would be administering the instrument.

The company that offered the best competitive advantages for this process was CONFIE, as it proposed a 19-day administration process, a strategic territorial distribution strategy that allowed it to meet its objectives under a continuous supervision scheme, and could start the process right away. This made it the suitable entity to perform the administration considering the project's timeframe for data collection.

The first coordination meeting with CONFIE was held to review the instrument and the entering of information into https://www.kobotoolbox.org/, a free open-source platform that CONFIE would use to conduct the interviews via tablets. Subsequently, a two-day training workshop was provided to the data collection teams to introduce the tool and explain the use of informed consent and interview protocols. After the workshop, a pilot trial was carried out in Tegucigalpa to test the tool and identify areas for improvement prior to start-up of the official survey in the field, which began two days after the pilot. CONFIE deployed 46 interviewers divided into eight field teams, with one supervisor per team.

An additional element of this application was the inclusion of a set of migration questions, for which the project developed meetings with UNAH experts, through coordination with DICU. This process carried out in November and December 2019 allowed the inclusion of a set of questions derived from the theory of planned action, for which sources of literature specialized in the topic were used to construct this new section included in the IMC.

II. The IMC Tool The IMC uses 38 measurement scales, one for each risk or protective factor. Community Risk Factors Transitions and Mobility Low Neighborhood Attachment Community Disorganization Laws and Norms Favorable to Drug Use Perceived Availability of Drugs Community Protective Factors Opportunities for Prosocial Involvement Rewards for Prosocial Involvement School Risk Factors Academic Failure Low Commitment to School School Protective Factors Opportunities for Prosocial Involvement Rewards for Prosocial Involvement

Family Risk Factors Family History of Antisocial Behavior Parental Attitudes Favorable Toward Drug Use Poor Family Management Family Conflict Weak Parental Supervision Family Gang Influence Family Protective Factors Attachment Opportunities for Prosocial Involvement Rewards for Prosocial Involvement Peer/Individual Risk Factors Rebelliousness Rewards for Antisocial Involvement Favorable Attitudes Toward Drug Use Favorable Attitudes Toward Antisocial Behavior Perceived Risks of Drug Use Friends’ Use of Drugs Interaction with Antisocial Peer Intentions to Use Antisocial Tendencies Critical Life Events Impulsive Risk Taking Neutralization of Guilt Negative Peer Influence Peer Delinquency Peer/Individual Protective Factors Belief in the Moral Order Rewards for Prosocial Involvement Interaction with Prosocial Peers Social Skills

Each scale is made up of several elements that youth are asked to respond to during an introductory interview. Most of the scales consist of questions with five answer options, ordered from 1 to 5. To obtain a respondent's score in any scale, the answers to items in a given scale are weighted by converting the responses to a scale from 0 to 100 using the Percent of Maximum Possible formula. The respondent's score in each scale is calculated by determining the average of the items of all the scales. Scores will always be a number between 0 and 100.

The score is compared with a scale's risk threshold to determine whether the youth is at risk for that factor or scale. Since the IMC was calibrated for two populations: in and out-of-school youth, the thresholds for each scale are different for each population.

Finally, youth are deemed eligible or ineligible for a family-centered intervention based on the number of risk factors/scales in which the youth scored above the risk threshold. After determining the risk factors, these are aggregated to determine eligibility.

Annex A includes information on the methodological approach and the municipalities where the IMC survey was conducted.

III. Administration guidelines of the IMC The following operational guidelines were used to administer the IMC:

Community entry strategy The field officer and each team supervisor identified and coordinated with authorities and staff from social programs in targeted communities in order to explain the reason for the visit and the goal of the consultancy. This was backed by an accreditation letter. This activity made it easier for the teams to identify and analyze the security situation, identifying the areas within a community in which it was possible to work, the most suitable times to conduct this action, and to take measures in communities with citizen security issues.

Household selection Households were selected systematically and continuously after the first household, which was defined by the team supervisor based on eligibility (households with youths between the ages of 8 to 17 and the presence of a responsible adult to authorize administration) and security criteria.

Obtaining consent Once a household with an eligible youth was found, the interviewer explained the purpose of administering the IMC tool, read and explained the informed consent, had the parent or guardian sign the consent form, and proceeded to explain the purpose of the interview to the youth.

Georeferencing of the house Once the team supervisor had reviewed the consent forms and checked that they had been signed by both the parent or guardian and the youth and that these included the youth's date of birth, the supervisor noted the dwelling’s GPS coordinates and assigned an interviewer code and a respondent code.

Control and verification mechanisms during and after administration Administration included control and verification mechanisms to ensure the quality of the information. The team supervisor and the field officer were the people responsible for implementing these verification and control mechanisms during and after the IMC tool was administered, which included:

a) Assigning codes to interviewed youths for greater control of the administration. b) Proper household selection, which consisted of verifying whether the selected household had eligible youth between the ages of 8 to 17 and that the proper consent had been obtained. c) Observation while the IMC was being administered to ensure appropriate privacy, to the extent allowed by parents or guardians. d) Cross-checking georeferencing reports against the information registered in the KoBoToolbox platform.

e) Verification of geo-referencing using Qgis and Google Earth software, to ensure compliance with the route and zoning plan. f) Verification of consents against geo-referencing reports, and classification by municipality.

IV. Administration process of the IMC Below are the issues that arose during administration and the corrective action taken:

Issue Actions Entry was denied to some neighborhoods Coordination with leaders in some due to citizen insecurity issues being communities, including church leaders, experienced in the country. women's groups, etc., or changed to another neighborhood nearby.

Eligible youths were not at home during The team came to the school to speak the visit because they were in school with school authorities to explain the reason for the visit and showed them the consent forms signed by the youths' parents or guardians, after which the youths' consent was obtained to administer the IMC. Pre-identified areas had changed from a residential to a commercial zoning status, Changed to another neighborhood as was the case in Comayagua, for nearby example. Improved financial situation of Changed to areas with the initially population in some neighborhoods (high- defined characteristics. income social stratum)

Other comments: ● The presentation that the staff makes to a youth's parent or guardian to obtain their consent is extremely important, as is making them aware which program it is and who is funding the research. Mentioning that the study was for the USAID Proponte Más project made it easier to get parents and guardians to sign the consent form. ● Future administration of the IMC in the Gracias a Dios area, or anywhere with a strong ethnic presence, will require having staff with extensive knowledge of the language. One of the issues in this area was that certain communities did not understand Spanish very well. Coordination with key actors is a determining factor to apply the IMC since it makes it much easier to move around, especially in urban areas with high vulnerability to violence.

ANALYSIS OF IMC RESULTS

I. Levels of Risk The manual produced by Arizona State’s University showed that the cut point to identify risk levels in the Proponte Mas’s sample was as follows:

Primary level of risk: Youth presenting fewer than a total score of 14.5 in the number of risk factors Secondary level of risk: Youth who present 14.5 or more risk factors/scales. For secondary prevention eligibility levels, the total number of risk factors present will determine the specific behaviors that youth are at risk of developing. Predictable behaviors include: ● violence ● property crime ● gang involvement ● drug/alcohol use ● drug sales ● weapon carrying ● truancy (not applicable to out-of-school population).

Tertiary level of risk: Youth presenting 14.5 or more risk factors/scales, who score above the risk threshold, and answer yes to any of the following three questions: • In the past 6 months, have you been a member of a gang? • Is your group of friends in a gang? • Are you currently in a crew, clique, or associated with a gang?

Thus, a combination of 14.5 or more risk factors and answering yes to any of the questions above would place youth at the tertiary eligibility level. For the purposes of this report, Proponte Mas calculated the cut points for the full sample of the national IMC as well as the cut points for those in school and those out of school.

For the purposes of this report, we conducted a preliminary cut point analysis for the full sample (4,009 individuals) and for those youth in school and out of school. These analyses were done with the following steps.

• Recalibrate the tool in order to identify new cut points that are consistent with the national reality of urban and rural diversity, including municipalities with the highest migration rates. • We conducted exploratory factor analysis and a confirmatory factor analysis to analyze whether the different risk scales fit well with the sub-sample of youth in school and those out of school. • Once the different scales in the tool were calibrated, we applied ROC1 (Receiver Operating Characteristic) curve analysis to measure specificity and sensitivity in order to identify the optimal cut points.

1 The ROC curve provides a comprehensive performance profile of a diagnostic test (Green and Swets, 1996). The ROC methodology is derived from the signal detection theory where it is used to determine if an electronic receiver is able to distinguish between the correct signal and noise (Fan et al., 2006). We applied this methodology to assess the effectiveness of the IMC tool in distinguishing between at-risk and non-at-risk youth for various problem behaviors

II. Results This section of the report presents a summary of the results of the IMC for the full sample; that is, the sample including municipalities where there is a higher incidence of out-migration and those municipalities in the rest of the country.

Socioeconomic and Demographic Characteristics In terms of gender, 57.5% of the respondents were males compared to 42.5 % of females. Most of the respondents were between 8 and 14 years old (82%) compared to 18% who were between 15 and 17 years old. Most respondents were born in urban areas (81.5%) compared to those who were born in rural areas (18.4%). About 75% of the respondents self-identify as “mestizos” compared to 26% who are indigenous or afro descendants. Migration The IMC asked a battery of questions related to the intention and attitude towards migration as well as questions related to having family members living overseas. Because the set of questions asked to respondents are Likert scales, we first examine the distribution of responses in each scale and then a measure that captures a dichotomic response2 on whether the respondent has intentions to migrate. The results show that 45% of respondents are eager to go to another country to work and earn money compared to 54% who do not. By contrast, 65% of respondents are planning to move to another country. This is a bit of a surprising finding that over two thirds of respondents are planning to leave the country. To further examine this result, we disaggregate the question by age group (14 and younger and 15 to 18 years old) and it indicates that a higher percentage (67.7%) of youth in the younger age cohort are planning to leave compared to 56% of respondents in the older age cohort who are planning to leave the country. It is worth noting that these differences are statistically significant (p<.05).

Risky Behaviors As noted above the overall objective of the IMC is to be able to identify those youth who are at risk of engaging in risky and delinquent behavior and/or at risk of joining a gang. During the implementation of the project, Proponte Mas, with the assistance of Arizona State University, estimated the cut points to identify who is at risk of risky and violent behavior and who is not. However, these cut points were calculated using a previous version of the IMC and with a non- representative sample of youth from five municipalities identified by USAID as being most at risk of crime and violence. These municipalities included Choloma, La Ceiba, San Pedro Sula, Tegucigalpa, and Tela. These are primarily urban centers in Honduras. Therefore, the estimated cut points may not be suitable in a nationally representative sample that includes more diversity in the sample such as urban/rural environments and indigenous and afro-descendent populations3.

2 This measure is calculated by collapsing the Likert scales to the median. 3 It is worth noting that Proponte Mas worked with Garifuna communities, but these populations were not the main objective of the program.

Proponte Mas calculated the cut points for the full sample of the national IMC using the same approach as ASU’s scoring manual shared with local institutions. We calculated the cut point for each of the 38 risk and protective factors by problem behavior (see Levels of Risk) and the lowest score was used to estimate the cut point between being at primary risk and secondary or tertiary risk. After estimating the new cut points, the percentage of youth at risk in Honduras is illustrated in Figure 2 below. The results indicate that 57.4% of youth are at a primary level of risk, 41.1% are at a secondary level of risk, and 1.4% are at a tertiary level of risk. These results, for example, contrast with the findings of Proponte Mas’s cut points. The estimated cut points in Proponte Mas’s sample are significantly different than those found in the national IMC sample. In the Proponte Mas’s sample, the percentage of youth who were at a secondary level of risk was between 20% and 25% compared 41.1% in the national sample. Figure 2. Percentage of Respondents by Risk Factor Differentiation

Source: National IMC Survey. It is important to note to the reader, however, that the association of the various dimensions of risk and protective factors with each problem behavior differs from that of Proponte Mas’s sample analyses. For example, compared to Proponte Mas, the ROC analyses suggest that some of the risk and protective factor dimensions do not predict problem behaviors as well. Also, the analysis undertaken in the Proponte Mas’s sample included a cut point analysis for the sample of children and youth in school and those out of school. We conducted the same estimations in addition to the estimation of cut points for the full sample. This underscores the importance of continuing to evaluate this tool and identify cut points for youth living in rural areas compared to those living in urban areas.

Figure 3 presents the ROC curve analysis for the mean score of the risk cut points for all problem behaviors. The resulting cut point is 9.5 risk factors and the Area Under the Curve (AUC) is 0.65, which indicates a low precision4 (Fischer, Bachman, and Jaeschke, 2003), but in line with previous

4 The ASU’s manual indicates the following: “ The greater the AUC, the better the test. Theoretically, the maximum value for the AUC, which is 1.0, indicates a perfect diagnostic test (i.e., 100% of sensitivity and 100% of specificity). An AUC value of 0.5 indicates no discriminative value (i.e., 50% of sensitivity and 50% of specificity). In general, ROC curves with an AUC greater than 90% indicates high accuracy, while 70% - 90%

research on this topic5. Thus, a youth’s risk factors cut point equal or greater than 9.5 places at a secondary level of risk (see Figure 2 above) and a youth’ s cut point equal or greater than 9.5 and who answered yes to any of the questions related to gang membership in the levels of Risks section is considered to be at a tertiary level of risk.

Figure 3. ROC Curve Analysis

Source: National IMC Survey. We then disaggregate the cut points by gender, age group, race, place or birth (urban/rural), and whether or not the youth is currently attending school. The results are presented in Table 1 and show that: ● Males are at a higher risk than females with 42.3% of respondents being at a secondary risk compared to 39.6% of females. ● Youth in the older age cohort (15-18) and “mestizo” youth are at a higher risk compared to their peers with 58.1% and 44%, respectively. ● As expected, youth who are out of school are at a higher risk compared to youth who are in school with 65% at a secondary risk compared to 37% of those currently attending school. ● Finally, youth living in urban environments are at a higher level of risk with 43.2% of respondents in urban areas compared to 33.5% of youth in rural areas.

Table 1: Disaggregation by Risk Levels shows moderate accuracy, 50%-69% demonstrates low accuracy (Fischer, Bachman, and Jaeschke, 2003). Given this, optimal cut points were selected as the point maximizing AUC value over all possible cut-point values”. 5 As a point of reference Singh et al. (2011) reported among multiple prior studies the median area under the curve for the following nine risk assessment tools: SVR-20=.78; SORAG=.75; VRAG=.74; SAVRY=.71; HCR- 20=.70; SARA=.7-; Static-99=.70; LSI-R=.67; and PCL-R=.66) (ASU, 2019).

Primary Secondary Tertiary Gender Male 56.3 (1,292) 42.27 (970) 1.44 (33) Female 58.93 (1,010) 39.61 (679) 1.46 (25) Age Group 8-14 62.73 (1,961) 36.34 (1,136) 0.93 (29) 15-18 38.62 (341) 58.1 (513) 3.28 (29) Race/Ethnicity Mestizo 53.77 (749) 44.01 (613) 2.23 (31) Indigenous/Afro-Descendants 59.37 (1,553) 39.6 (1,036) 1.03 (27) Currently Studying Yes 61.11 (2,153) 37.84 (1,333) 1.05 (37) No 30.66 (149) 65.02 (316) 4.32 (21) Place of birth: Rural-Urban Urban 55.41 (1,750) 43.19 (1,364) 1.39 (44) Rural 64.82 (36) 33.54 (18) 1.63 (1) Source: National IMC Survey.

Notes: For the purposes of this report the percentages are calculated by disaggregating the cut points for the full sample by gender, age group, race, urban/rural, and school attendance. Ideally, and depending on the interest of the program implementer, a cut point should be identified for one or various groups. As indicated above, Proponte Mas estimated the cut points for the full sample – used in this report—and for those youth in and out of school. Differences between gender, age group, race/ethnicity, currently studying, and place of birth are statistically significant (p<.05).

Drivers of Risk and Intention to Migrate In addition to collecting data on risk at protective factors at the national level, another key objective of the IMC was to collect information on children and youths’ intentions, attitudes, and subjective norms about migration. This section presents the results of the econometric analyses on the factors that drive children and youths’ risky behaviors as well as their intention to migrate irregularly from Honduras. The empirical approach for the analyses is as follows: For the analysis of the drivers of risk we model it by creating a dichotomic variable of who is at risk and who is not. We then run a probit model to ascertain the effect of social and demographic variables on being at risk. For the migration analysis, the empirical approach is in two stages. The first stage uses Likert scale type questions on the intention to migrate as the dependent variables in two different models—OLS and Ordered probit. The reason behind this approach is twofold. First, some scholars argue in favor of using the sum or the average of the items in a scale and use it as a dependent variable in a linear regression model. Secondly, using an ordered probit model seems more appropriate because the “distance” between the responses, which range from “Completely Agree” to “Completely Disagree” may not be the same and it is important to tease out the effect that explanatory variables have on each of the item responses. Thus, for the purposes of this report we use two methodological approaches: one that uses an ordered probit model with the Likert type scale of the following questions as a dependent:

-Tengo ganas de irme a otro país para ganar dinero trabajando – I have the urge to go to another country to make money. - Me gustaría irme a otro país donde haya mejores condiciones para vivir – I would like to go to another country where living conditions are better. - Estoy planeando irme de Honduras – I am planning to leave Honduras. The second model specification uses the average of the scale items to create a new scale that becomes the dependent variable in a linear regression model. The second stage of the empirical approach uses a range of matching methods to create a sample of youth who intend to migrate and youth who do not which are similar in observable characteristics such as age, gender, race, educational status, and place of birth. By matching individuals based on observable characteristics, we end up with a sample of youth that is similar between those who are at risk and those who are not. Therefore, any differences in the “intention to migrate” could be more likely be attributed to whether or not the individual is at risk6. Then, we proceed to estimate the effect of an individual being at risk –above primary level of risk—on the intention to migrate as our “outcome” of interest in our analyses. This variable was created by creating a dichotomous (0/1) measure using the Likert type question “I’m planning to leave Honduras” after collapsing it at the median7. For the purposes of this report, we only present stylized facts and do not provide an in-depth discussion (nor we speculate) on the reasons that may be driving these results. To do this would require additional analyses.

Regression Results Table 2 presents the regression results of the drivers of risk. The estimates suggest that, all else, equal: ● Males are four percentage points more likely to be at risk than females. ● Older youth (15-18) are 15 percentage points more likely to be at risk than their younger peers. ● Youth who self-identify as “Mestizos” are five percentage points more likely to be at risk compared to their indigenous/afro-descendant peers. ● Youth who have been born8 in urban areas are five percentage points more likely to be at risk than their peers who were born in rural areas. ● Youths who are currently studying are 25 percentage points less likely to be at risk than their peers who are not studying. ● Youth who stay active, whether participating or engaging in cultural activities and doing sports is associated with a lower likelihood of being at risk—seven and four percentage points, respectively. ● Finally, those youth whose parents are still alive are six percentage points less likely of being at risk compared to their peers whose parents (one or both) are not alive.

6 The reader should note that this is not an impact evaluation and the results should not be interpreted as such. 7 The value of the median of this Likert scale (1 to 5) is 4. 8 It is likely that this question also captures the place the respondent currently lives.

Table 2. Regression Results-Being at risk of various problem behaviors

(1) VARIABLES Marginal effects- Risk

Gender 0.0439*** (0.0168) Age Group 0.157*** (0.0183) Race/Ethnicity 0.0501*** (0.0175) Currently Studying -0.253*** (0.0245) Urban/Rural 0.0526** (0.0221) Cultural Activities -0.0739*** (0.0170) Doing Sporting Activities -0.0419** (0.0166) Alive parents -0.0616*** (0.0114)

Observations 3,847

Pseudo-R2 0.166 Standard errors in parentheses; *** p<0.01, ** p<0.05, * p<0.1 Notes: Probit model with fixed effects to control for time invariant unobserved heterogeneity at the community level. The estimated coefficients capture the marginal the discrete change of dummy variable from 0 to 1.

Table 3 presents the results of the regression analyses on the intention to migrate measures, and, on average, indicate the following: ● Those youth being at risk appear less likely to migrate, all else equal. The coefficients are negative, strong, and statistically significant with a 99% level of confidence. ● Mestizos appear more likely to migrate compared to indigenous and afro descendants, all else equal. ● Those with more education appear (12 years) appear less likely to intend to migrate compared to their peers with less education, all else equal. ● Surprisingly, a youth being born in an urban area is associated with a lower likelihood of migrating, all else equal.

Table 3. Regression Results-Intention to Migrate

(1) (2) (3) (4) VARIABLES Intention to Intention to Intention to Intention to Migrate Migrate Migrate Migrate

Being at Risk = 1, at Risk -0.290*** -0.295*** -0.238*** -0.308*** (0.0383) (0.0390) (0.0376) (0.0322) Gender = 1, Male -0.0956** -0.0207 -0.0435 -0.0509 (0.0381) (0.0371) (0.0369) (0.0329) Age Group= 15-18 -0.00437 -0.0434 -0.127* -0.0661 (0.0827) (0.0696) (0.0748) (0.0681) Race/Ethnicity= 0.0803** 0.127*** 0.0941** 0.108*** “Mestizo” (0.0388) (0.0420) (0.0394) (0.0359) Year of School = 2 -0.179 0.134 -0.102 -0.106 (0.278) (0.218) (0.204) (0.197) Year of School = 3 -0.138 0.182 0.0852 0.0241 (0.259) (0.209) (0.190) (0.189) Year of School = 4 -0.0787 0.184 0.132 0.0445 (0.258) (0.204) (0.191) (0.186) Year of School = 5 -0.215 0.0186 0.140 -0.0662 (0.270) (0.209) (0.190) (0.191) Year of School = 6 -0.294 -0.0703 0.00704 -0.177 (0.269) (0.208) (0.189) (0.191) Year of School = 7 -0.192 -0.0817 0.0783 -0.119 (0.262) (0.208) (0.189) (0.187) Year of School = 8 -0.196 -0.202 0.0100 -0.193 (0.266) (0.204) (0.196) (0.190) Year of School = 9 -0.181 -0.163 0.140 -0.128 (0.270) (0.207) (0.203) (0.196) Year of School = 10 -0.346 -0.307 0.00362 -0.297 (0.276) (0.219) (0.195) (0.198) Year of School = 11 -0.449 -0.283 0.0764 -0.308 (0.280) (0.236) (0.212) (0.208) Year of School = 12 -0.474* -0.418* 0.0140 -0.384* (0.284) (0.239) (0.225) (0.216) Urban/Rural -0.0415 -0.0813* -0.0689 -0.0871** (0.0500) (0.0492) (0.0506) (0.0426) Parents Alive 0.0148 0.00156 0.00617 0.0137 (0.0302) (0.0292) (0.0318) (0.0263) /cut1 -1.529*** -1.296*** -1.742*** (0.283) (0.230) (0.208) /cut2 -0.501* -0.0114 -0.836*** (0.278) (0.224) (0.207) /cut3 -0.259 0.265 -0.494** (0.279) (0.225) (0.208) /cut4 0.915*** 1.299*** 0.911*** (0.280) (0.223) (0.206) Constant 3.354*** (0.202)

Observations 3,476 3,476 3,476 3,476 R-squared 0.054 Robust standard errors in parentheses; *** p<0.01, ** p<0.05, * p<0.1 Notes: Columns 1-3 present the results of an ordered probit model and column 4 presents the results of an OLS

model.

Matching Results Figure 4 presents the results of the sample before and after matching based on the propensity score of being at risk conditional on social and demographic characteristics which include age, education, gender, race/ethnicity, place of birth (urban/rural), and whether or not their parents are alive. The figure to the left (raw) shows the density plot before the individuals in the sample were matched; that is, unconditional on any observable characteristics. The reader could interpret that as the individuals in the same sample used in the regression analyses presented in Table 2. The blue line are those who are at risk of engaging in delinquent behavior based on the previously identified cut point—those who are at secondary and tertiary risk-- and the red line are those who are not at risk based on the same cut point—those who are at a primary risk level. After the matching exercise, the density plot on the right-hand side of Figure 4 (Matched (ATE)) shows the sample of youth who were matched conditional on the observable characteristics described above. As the reader can see, there are no apparent observable differences between both groups.

Figure 4. Propensity Score Matching Results

Table 4 presents the results of the matching analyses. The results are similar to those in Table 3 in that there is a negative and statistically significant association between being at risk and the intention to migrate, but the size of the coefficients is smaller ranging from 10 to about 12 percentage points. In other words, youth being at risk (secondary or tertiary) are less likely to wanting to migrate. This is a somewhat surprising finding and more research would be needed to tease out the mechanism through which this effect takes place.

Table 4- Matching Results (1) (2) (3) (4) (5) (6) MD PS RA DWPS RM CEM ATE -0.115*** -0.119*** -0.110*** -0.116*** -0.108*** -0.104*** (0.0171) (0.0183) (0.0182) (0.0175) (0.0179) (0.0163) Observations 3476 3476 3476 3476 3476 3551 Standard errors in parentheses; * p < 0.05, ** p < 0.01, *** p < 0.001 Notes: MD = Mahalanobis Distance Matching PS = Propensity Score Matching-Nearest Neighbor RA = Regression Adjustment Matching DWPS = Doubly Weighted Propensity Score Matching RM = Ridge Matching CEM = Coarsened Exact Matching

CONCLUSIONS

This report presents stylized facts on the implementation of the Instrumento de Medicion del Comportamiento (IMC) at the national level in Honduras. The results indicate that at least 41% of the youth at are secondary or tertiary risk of engaging in problem behaviors.

Additional analyses suggest that demographic factors explain whether or not a youth is at risk of problem behaviors. We found that gender, age, racial/ethnic identification and being born in an urban area predict a higher probability of being at risk. Conversely, going to school, staying active and participating in cultural activities, having parents still alive are correlated with a lower probability of being at risk, all else equal. We also find that being at risk is correlated with a lower probability of intending to migrate, all else equal.

Although it is difficult to draw meaningful comparisons, these results suggest that the percentage of youth who are at risk is higher compared to the Proponte Mas’s sample. There could be many factors that explain these results.

First, the cut point estimated for this report is based on analyses for full sample whereas the cut point estimated in Proponte Mas is based on the lowest estimated cut point of all problem behaviors for both the sample of youth in school and youth out of school. Secondly, in a country as diverse as Honduras, it is likely that the areas where the youth live (rural vs. urban), level of schooling, and their ethnic/racial identification also plays a role in determining the whether or not a youth is at risk. In fact, the regression results (Table 2) provide, in anything, an indication on how these factors can influence risky behavior. Third, potential differences in risk profiles based on different cut points merit additional analyses. Thus, it will be the responsibility of the researcher/policy maker to decide which type of risk differentiation to use.

Of course, there are limitations in the results presented in the report. These are outlined below:

• The results presented here are based on an optimal cut point calculated for the full sample. As a result, it does not capture differences between youths living in rural/urban contexts and in greater migration rates. Additional analyses should calculate various cut points to capture these differences and, thus, obtaining a more accurate picture of the risk profile of youths living in different environments. • The findings of the regression models are preliminary and additional research is needed to tease out the drivers of these results. • The richness of the sample is such that additional analyses can yield very useful information for both programming and policymaking on youth at risk issues.

ANNEX A- METHODOLOGICAL APPROACH

To calculate the national sample, the probability of finding a young person in a household (which is 0.67) was calculated based on INE data, and the number of surveys to be carried out at the national level is calculated based on that. The number of municipalities in which the IMC was to be administered to have a representative sample is derived from the total survey. Once the number of surveys has been identified, the municipalities for the national sample (not the migration sample) are chosen at random. In the case of the oversampling for migration, the municipalities were identified based on data provided by USAID. The administration of samples was distributed as follows:

Table 1 National Administration of the IMC - 2,080 tools Number of Number of Department Municipality administrations administrations defined by ToRs performed Atlántida Jutiapa 40 40 Atlántida Arizona 40 40 Comayagua San Jerónimo 40 40 Comayagua Ajuterique 40 40 Comayagua Minas de Oro 40 40 Copán San Antonio 40 40 Copán Dulce Nombre 40 40 Copán Nueva Arcadia 40 40 Cortés San Antonio de 40 40 Cortés Cortés San Francisco de 40 40 Yojoa Cortés Potrerillos 40 40 Cortés San Manuel 40 40 Choluteca San Antonio de 40 40 Flores Choluteca San Marcos de 40 40 Colón Choluteca Namasigüe 40 40 Choluteca Morolica 40 40 Choluteca Orocuina 40 40 Choluteca San José 40 40 El Paraíso Güinope 40 40 El Paraíso Danlí 40 40 El Paraíso Soledad 40 40 Francisco Vallecillo 40 40 Morazán Francisco Maraita 40 40 Morazán Francisco Tatumbla 40 40 Morazán Francisco San Juan de Flores 40 40 Morazán Francisco Nueva Armenia 40 40 Morazán Gracias a Dios Puerto Lempira 160* 160* Intibucá Intibucá 40 40 Intibucá Dolores 40 40 Intibucá La Esperanza 40 40 La Paz Opatoro 40 40 La Paz Santa María 40 40 La Paz San Pedro de 40 40 Tutule La Paz La Paz 40 40 La Paz Aguanqueterique 40 40 La Paz Santiago de 40 40 Puringla Lempira Talgua 40 40 Lempira La Unión 40 40 Lempira Virginia 40 40 Lempira La Iguala 40 40 Ocotepeque San Fernando 40 40 Olancho Esquipulas del 40 40 Norte Olancho Patuca 40 40 Santa Bárbara Nueva Frontera 40 40 Santa Bárbara Chinda 40 40 Santa Bárbara San Marcos 40 40 Valle Goascorán 40 40 Valle San Lorenzo 40 40 Valle Amapala 40 40 Total 2,080 2,080 *In the case of the municipality in Gracias a Dios, it was decided to perform one single administration in the department's capital city, as it was not possible to administer the instrument in the other three municipalities due to distance and access issues. The sample for this municipality was therefore centralized in the department's capital.

Table 2 Distribution of sample in municipalities with high migration rates Number of Number of Department Municipality administrations administrations defined by ToRs performed

Cortés Choloma 147 147 Comayagua Comayagua 83 83 Francisco Distrito central 709 709 Morazán Yoro El Progreso 115 115 Atlántida La Ceiba 123 123 Cortés Puerto Cortés 79 79 Cortés San Pedro Sula 473 473 Comayagua Siguatepeque 56 56 Colón Trujillo 32 32 Colón Tocoa 46 46 Cortés La Lima 41 41 Yoro Morazán 25 25 Total 1,929 1,929