Sexual Risky Behavior Patters-Version 9

MASTER IN INDUSTRIAL ECONOMICS

TESINA

Patterns of Risk Exposure in Teenage Sexual Behaviour

Author: Karen Olivo Santana

Director: Ricardo Mora

Promocion: 2010-2012

Getafe (Madrid), September 2012

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Patterns of Risk Exposure in Teenage Sexual Behaviour

Karen Olivo Santana

September 10, 2012

AbstractThis papers analyses the key drivers of Risky Sexual Behavior among teenagers in 32 provinces

of Dominican Republic for 2007. The analysis shows that family structure is a relevant factor in

teenager�s sexual activities, as well as, the socio-demographic and personal characteristics. The

results, also suggest that the identification method of “risk taking population” and “no risk- taking

population” can affect the estimation results, given the assignation technique of non-identified

individuals.

1. Introduction

In recent years professional and public attention has been directed to the risks of unsafe sexualbehavior among adolescents. Engaging in risky sexual behavior inherently puts teenagers at higherrisk of unintended pregnancy and Sexually Transmitted Diseases (STDs) (Miller 2001, Markowitz etal 2005). Teenage pregnancy is a social problem with high economical, medical, and educational costs.Among the negative social and medical consequences of teenage pregnancy researchers find: increases inabortion rates, increases in school drop outs, and decreases in women labor force participation ( Miller2001, Gilmore et al 1992, Klein et al 2008). Children from adolescent women tend to develop moremedical complications and have larger probabilities of suffering Suddent Infant Death Syndrom (SIDS),malnutrition, and low weight in comparison to children from older mothers. Sexually TrasmittedDisease (STD’s) exposure implies many health problems, such as, infertility, brain damage, cervicalcancer, and other serious complications (Stevens-Simmons et al 1996). Our definition of risky sexualbehavior (RSB) focus on sexual intercourse among teenagers without the use of any contraceptiveprecauction.The idea is to focus the study group among the sample selected, not including other RSBconsequences as STDs. This essay is intended to obtain evidence of the association between familystructure and the propensity among teenagers to engage in risky sexual behavior which leads to higherincidence in teen pregnancy. Using cross-sectional data collected in 2007, this work adresses one mainquestion: is there any linkages between family structure with risky sexual behavior and subsequentpregnancy among teenagers? The results shows that in general family structure and domestic violencehas a significant effect on a teenager’s sexual behavior.

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Previous research shows that risky sexual behavior plays a major role in adolescent pregnancy.Using data from the United States, Kershaw et al (2005), find that adolescent females tend to un-derestimate the effects of their unsafe sexual practices and to replicate them in the long run, leadingto an increase in pregnancy rates among teenagers. Individuals carry out RSB, knowing that thereis some probability of becoming pregnant and that the probability will decrease if contraceptives areused (Arcidiacono et al (2010), Hotz and Miller 1998,1993). Individuals who engage in unprotectedsex, mostly change their behavior if they become pregnant. Manlove et al. (2008), analyzes religiousfamilies and find that the social context is relevant for determining the propensity of teenagers engag-ing in risky sexual activities. Vasquez et al. (2003) explore the links between decision-making processregarding sexual risky behavior, and external influence, finding that family structure indirectly pre-dicts sexual risky behavior through family economic class, parental involvement and family prosocialactivities. Similarly, social perception and perspectives of adolescents influence their sexual choices.Campo-Arias et al. (2010) find that teenagers with a bad perception of academic performance, aremore propense to unintended pregnacy due to engagement in risky sexual behavior. Jones et al (2010),using the experiences and perceptions of 15 pregnant adolescents, analyzes the perspective of adoles-cents on sexual risk-taking based on the idea that pregnant teenagers can provide an insider viewpointof the antecedents for adolescent sexual risk-taking behaviors. The authors identify as the main driversof risky sexual behavior: the partner pressure, pressures from peers, and media influence.

This literature has been very useful for developing our model. However, in the case of Jones et al(2010), assuming that an ex-post survey will lead to a more precise identification, is not fully supportedin the theoretical framework of RSB. Furthermore, these surveys are not available for many countries,which will not allow to extrapolate conclusions to the general population of teenagers. The data usedhas rich information of the family structure of each individual but it has incomplete information onthe degree of riskiness that teenagers have. We deal with this problem by, first, inputting a RBSvariable from the dataset, reaching an identification level of the 80% of the sample. Second, estimatinga naive aproximation of the initial model, and , third, by generating a robustness analysis. The paperis organized as follows: section 2 gives an overview of the sexual risk behavior links to pregnancy andsocial environment in literature. Section 3 describes the dataset and the variables. In section 4 wepresent the basis of the binary outcome model definition of risky sexual behavior. Section 5describesthe results, and in section 6 conclusions.

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2. Literature Review

Sexual risky behavior, like many other youth problematic behaviors, has been studied for quitesome time1. Studies designed to explore and recognize the drivers behind adolescent risky sexualbehavior and pregnancy have followed a similar structure. For example, Rocca et al. (2010) analyze213 latin american adolescents in San Francisco and their perspectives about pregnancy consequencesand sexual behavior. Between the groups identified in the study, having a mother who had given birthas a teenager was associated with elevated pregnancy risk but not with pregnancy wantedness, whichwas also identified as a determinant of relaxing conceptive precautions. The authors, also includecultural factors, pregnancy history, and enrollment in romantic relationships as determinants of highrisky sexual activities. Additionally, educational aspirations and negotiation power of contraceptivemethods with sexual partners was identified as the main barrier to pregnancy in the selected population.Manlove et al. (2008), includes religiousity as mechanism for avoiding risky sexual activities duringadolescence, which generates a decrease in pregancy rates. The authors find that the propensity ofinvolvement in sexual activities in a highly religious population tends to be lower compared to non-religious adolescents, this implies that a religious environment is more closed and that the teenage peerstend to share the same principles and awareness about sexual activities, pregnancy and abortion. Theauthors uses data from the 1997 cohort of the National Longitudinal Survey of youth for adolescentsin the United States in 1980-1984, who were age 12-16 on december 31 of 1996. Their findingsare consistent with the idea that religiosity was directly associated with sexual activity beliefs andpreferences.

In this context, the work of Campo-Arias Ceballo and Herazo (2010), tries to establish the preva-lence and factors associated with the pattern of risky behavior, among secondary education students inSanta Marta, Colombia. The authors, identify the use of illegal substances, alcohol drinking, and go-ing to public schools, as the main risky behavior drivers for adolescents interviewed. Additionally, theauthors highlights that academic performance perception contributes to a risky sexual pattern, whichcould indicates teenager overconfidence about their knowledge of contraceptive methods. Gilmore et al.(1992), previously developed a study of the relationship between substance use and risky sexual behav-ior, among 241 unmarried and pregnant adolescents, finding that as in Campo-Arias et al (2010) theuse of substances was directly related to risky sexual behavior, high pregnancy, and STD prevalence inthe population analyzed. Additionally, authors point out others determinants, such as, parental moni-toring and high self-esteem both which have negative impacts on teenager sexual behavior preferences.Higher parental control over adolescent extra-curricular actvities reduces the propensity of involve insexual activities. Family and social environments play a important role because these are the firstsocial interrelation that teenagers experience, where attitudes, believes, and preferences learnt within

1Kotchick et al (2001)

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them permantly influence their future social choices 2. Tanner et al .(2008), explore how parental per-ception of pregnancy and sexual practices influences adolescent choices and risk taking behaviour. Theauthors find that parents exposed to pregnancy preventive campaings are more prepared to translateknowledge and right practices to their teenage children, than parents not exposed to these kinds ofcampaings. Weiss et al. (2010), analyses this idea by interviewing 20 adolescents from different publicschools in the United States. The authors indicate that a desire for future success, receiving inspiringmessages from “point persons”, and awareness of the consequences of risky sexual behaviours are themain determinants for a risk averse actitude in the context of first sexual intercourse.

Recent literature of teenage pregnancy addresses two opposite positions related to adolescentspregnancy choices. First, factors affecting pregnancy and childbirth during adolescence are not relatedto a conscious decision-making process. Second, the drivers of teenager pregnancy, are a result ofa decision-making process under uncertainty and a risk-taking aptitude. For example, Brooks-Gunnand Paikoff (1997), analyze adolescents who are already mothers and evaluate this issue under therisk-adjustment models, and the family system. The authors examine two elements, the ones affectingteenage pregnancy, and the survival mechanisms that the teenager has while pregnant, in fact, thefamily environment and conditions are fundamental to assume teen motherhood. According to thisperspective, for example, a teenager, may feel social pressure from their peers to have sex. Addition-ally,family income level, and educational level will determine if the girl decides to engage in sexualrelations, or not. In this context, the family will become the fundamental element behind the decisionof whether to be involved in sexual activities3.

Oettinger (1999), provides a theoretical model which assumes that teen women decide to havesex based on a complex cost-benefit analysis thinking structure. The author indicates, distributingcontraceptives to teenagers increase teen sexual activity and pregnancy, as knowledge increases withthe frequency of sexual activity. The author concludes, that implementation of this policy, reducesteenage pregnancies in the short term but, increases them in the long-term. This is consistent withthe idea that, with access to contraceptives, the teen-age population has the opportunity to increasetheir sexual activity, and psychological and moral barriers that previously prevented their incursioninto sexual activities, are weakened. This effect is greater in younger adolescents cohorts.

Seeking to explain the differences in rates of teenage pregnancy among states in the U.S. Kearnyand Levine (2011), argue that the decision of childbearing among adolescent women depends partiallyin the "culture" that surrounds them, where "culture" refers to social conditions in which poor peoplelive. The authors, analyzes the hypothesis that girls who are in the lower socio-economic level decide tohave children during adolescence, and outside of marriage because: do not perceive that in the futuremay increase their level of "utility" (income, education level and living standards in general), and do

2Campo-Arias et al (2010)3Brooks-Gynn et al (1997)

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not perceive, that they can improve their living conditions through marriage4. Finally, authors,5 in themost of the researchs, coincides in identifying, environmental, familiar, social and economic factors,as the main components of the preferences and perceptions amog teeangers towards pregnancy, STDs,and sexual behavior.

2.1 Relevant Variables in Literature

The aim of this work is to analyze the factors and key drivers of risky sexual behavior and un-intended pregnancy, among teenagers. The majority of authors6 in literature, identify a wide set offactors as the principal elements of adolescent sexual behavior preferences, even though the proposedtheoretical models are very similar, we can classify variables included in our analysis in three maingroups:

• Socio-Demographic Variables: The variables considered in this set, are those related to the familyand educational environment of adolescents, which influence perspectives about pregnancy andother risks related to sexual behavior. Previously analyzed authors consider these factors themain determinants of the adolescents 7 positive/negative preferences and perceptions of the effectsof risky sexual behavior. Among this set of variables we are analyzing, age, age at first sexualintercourse, educational level, cultural perceptions of pregnancy and early sexual activities, a setof dummy variables indicating if the adolescent lives in an urban or rural environment, householdincome, as well as the availability of water and electricity facilities near the household.

• Family Environment: The variables included, are those related to the number of family members,age of household head, domestic violence and family leadership structure. Additionally we includethe number of first line relatives and second line relatives ( as aunts and cousins). Some authorsconsider the family environment as a relevant baseline for analyzing teenager’s sexual preferencesand propensity to engage in certain activities, because the family structure influences the levelof monitoring and guidance that teenagers receive.

• Individual and Personal Characteristics: The variables included, describe attitudes and percep-tions of their negotiation power within their social environment as well as their negotiation powerwith their partners, their future perceptions and suceptibility to domestic violence. Additionally,we include contraceptive methods and STDs knowledge use as singular activities creating kindof stratification among the teenagers and their preferences according to sexual behavior choices.This additional set of non conventional theorical variables are considered in many empirical stud-

4Kearny and Levine (2011)5Rocca et al (2010), Manlove et al (2008), Campo-Arias et al (2010), Gilmore et al (1992),Weiss et al (2010), Klein et al (2008), Penfold et al (2009), and , Kershaw et al (2005).6Rocca et al (2010), Manlove et al (2008), Campo-Arias et al (2008), Gilmore et al (2010), and Weiss et al (2010)7Kershan et al (2005) , Penfold et al (2009) and Klein et al (2008)

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ies, as in Kersahw et al. (2005), which using multivariate analytical methods conclude that thedanger perception scale in STDs and pregnancy plays a major role in determining preferences andtheir subsequent sexual behavior, which can oscillate from non-risky to highly risky dependingon their danger perception8.

3. Database Description

3.1 General Database Overview

The empirical analysis of the present paper, is based on data from the Demographic Health Surveyof the Dominican Republic9, designed to collect data related to marriage, fertility, family planning,reproductive health, child health , and HIV/AIDS, on women of reproductive age (15-49), from anationally representative sample. The survey is structured in three main informational groups: house-hold, women, and men, answering two standard questionnaires: the household questionnaire, and thewomen questionnaire, all questionnaires contain questions on respondent’s background characteristics,education, knowledge and use of contraceptives, recent sexual activity, and contraceptive behavior.The survey focus more on STD prevalence and ITS effects among the population than risk sexual be-havior, this leads us to look for alternative estimation procedures and variables that also don�t affectthe probability of missing values (not identified individuals) in the sample.

We concentrate on a broad definitions of risky sexual behavior, given the fact that sexual risktaking as an event is not observable in the dataset. The variables needed for the present analysisare contained in the women sample. This sample, contains information on 27,195 individuals, with aresponse rate of 93%. There are two main subset of women selected from the initial database. In thefirst one, about 5,400 individuals, is asked to compile a specific questionnaire, containing informationabout contraceptive behavior, and contraceptive use. In the second subset, about 58,037 individualsamong mothers and children, are asked to compile a specific questionnaire containing questions aboutchildren health status, and nutrition.

3.2 Measures of Sexual Risky Behavior

We evaluate the impact family structure on in risky behaviors using a very rich cross sectionaldataset of women between 15-49 of age, interviewed once in 2007. These data have a number of

8Kershaw et al (2005)9The Demographic Health Survey Program is sponsored by the United States Agency for National Development

(USAID), and carried out by the Health Ministry of Dominican Republic in 2007.

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advantages over those used in some of the existing literature. First, we can identify pregnancy age ofthe population for the first child, as well as pregnancy key factors. As such, we avoid one aspect ofthe decision problem: while the risk is not directly observed, we can document one possible outcomeof risky sexual behavior and condition the risk to the prevalence of teenage pregnancy. Second, thecomposition of the dataset, which is based here on a representative sample of all income levels andprovinces in the country, allows us to segment the population according to the political division of thecountry.

As we mentioned previously, we define RSB as being involved in sexual intercourse without condomuse and being between 14-18 years of age. Since the survey doesn�t directl measure RSB, in order toestimate family and social structure effects, we need to use a broad definition to build a risky sexualbehavior proxy. The idea behind this is to be able to capture the entire population surveyed involvedin RSB by using an innovative procedure to define the output variable. First, we analyze and selectthe controls that condition the apperance of RSB among the target population. Second, we developthe identification procedure by conditioning the output of the dependent variable on these controls,through an iterative process. The definition of the RSB output variable can be summarized as: Yi = 1

if X = xi, and Yi = 0 if X � xi .

We define the individuals not identified in this risk definition as missing values, which allow us tostrictly identify the population involved in risky sexual behavior. The RSB definition involves controlsrelated to knowledge and use of contraceptive measures, sexual history, miscarrige and abortion history,and the consistence of condom use during the first and last intercourse. This vector of variables usedin the definition are different from those included as regressors in the subsequent logit estimations.WeInput RSB by implementing the following conditions to identify when an individual has been involvein RSB. a) Under 18 and mother of children. b)Under 18 and had a termination or miscarrige. c) Hashad sex, never used contraception and under 18. d) Ever Married, never used contraceptive and under18.e) Pregnant under 18. We also implement the following sufficient conditions to identify when anindividual has not been involve in RSB. a)Never had sex. b) Only had sex after 18. c) Using effectivemethod now . d) No child, no misscarriage, not pregnant, no std in last 12 months . e)Always usedcondom with only partner, under 18. f)Used always condom with only partner, started before 18.g)Always used condom with the only 2 partners, under 18. h)Always used condom with 2 partners,started before 18. i)Always used condom with the 3 only partners, under 18. j)Always used condomwith only 3 partners, started before 18.

As an additional contribution of our analysis we develop logit regressions using had sex as anoutput variable in order to establish control set comparability among the two actions, which if related,do not imply the same consequences. The had sex variable is already defined in the dataset and doesnot require a broad definition to be included in the estimation. So, we will be able to provide resultsnot only on the key drivers of RSB, but additionally be able to verify if these drivers imply the samepropensity of being involved in sexuac activities in first place. This inputting of the risk variable, is more

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sophisticated than using pregnancy as a proxy of RBS, because it allows us to define more precisely thepopulation who is actually involved in RSB but doesn’t reflect the observable output of teen pregnacy.However this inputting doesn’t guarantee that all individuals involve in RSB are identified,for examplea teenager below 18 years of age all who is having intercourse with no contraception but hasn’t gottenpregnant, or otherwise, a teenager who is taking contraception or using condoms during intercoursesbut gets pregnant.Table 2 shows the results of the inputting procedure, as well as the distribution ofthe variable had_sex distribution in the dataset.10

10Regarding the miscarrige, we have assumed that previous termination for which year is not reported in the databasehappened just once. Additionally, we have developed a second risky sexual behavior definition, which also controls forthe condom use in the first intercourse. This allows us to establish a base line regarding contraceptive methods amongteenagers in the population, which some authors have found relevant to explain the sexual behavior among teenagers.

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Table 2: Risky and Sexual Habits Descriptives

% of Missing Values % of No missing ValuesRisk Had_Sex Risk Had_sex

All Data 21.64% 0.43% 78.36% 99.57%Age

Below 18 Age 4.90% 16.00% 95.10% 84.00%Below 25 Age 15.84% 4.90% 84.16% 95.10%Below 30 Age 18.37% 27.00% 81.63% 73.00%

Civil Estatus

Never Married 19.66% 70.00% 80.34% 30.00%Married 11.00% 4.63% 89.00% 89.00%

Income Level

Low Income Level 23.53% 0.50% 76.47% 99.50%Medium Income Level 22.43% 0.33% 77.57% 99.67%

High Income Level 18.42% 0.39% 81.58% 99.61%Additional

Below 18 Age and NeverMarried

1.77% 0.11% 98.23% 99.89%

Below 18 Age and LowIncome Level

4.84% 0.17% 95.16% 99.83%

Below 18 Age andMedium Level

4.96% 0.20% 95.04% 99.80%

Below 18 Age and HighLevel

4.93% 0.13% 95.07% 99.87%

Below 25 Age and NeverMarried

4.32% 9.00% 95.68% 91.00%

Below 25 Age and LowIncome Level

16.62% 0.09% 83.38% 99.91%

Below 25 Age andMedium Level

16.01% 0.09% 83.99% 99.91%

Below 25 Age and HighLevel

14.56% 11.00% 85.44% 89.00%

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3.3 Drivers of Risky Sexual Behavior

We have identified three different types of variables: the ones directly related to the effects and keydrivers of risky sexual behavior, the ones related only to sexual risk taking behavior, and variables thatcondition sexual risk perception but are not directly related. This means that the selected variablesmay be classified according to themes. First we include, family characteristic variables which arerelevant or establishing a pattern of behavior among teenagers, less likely of being involved in sexualactivities at an early age when coming from a traditional family structure, Gilmore et al (1992).

Inside this group we include a set of continous variables: Age of Household head, Number of House-hold Members, Number of Siblings , and discrete variables: Sex of household head, and Antecedents ofDomestic Violence. Second, we include a set of variables providing economic and welfare informationabout the households of teenagers, using a set of dummy variables for the Wealth Factor Index oflow, mediam and high11. The third set of variables are related to teenager’s individual characteristics.In this set we include continous variables: current age of teenagers, and discrete variables: alcoholconsumption during the last intercourse, risk of AIDS perception, Knowing somebody who has diedof AIDS and educational attaitment. This last set of variables affect the risk perception of teenagers,making them less propense to enroll in high risk sexual activities as not using condom at intercourse, oravoiding contraceptive methods, Penfold et al (2009). Finally, the fourth set of variables are related tothe characteristics of the teenager’s partner, here we use one continous variable regarding the partner’sage, and a discrete variable regarding partner’s education level12.

4. Model Description

Our empirical strategy aims to establish wether individual propensity to engage in risky sexualbehavior is affected by the family struture. We evaluate teenagers’s participation in risky sexualbehavior, using a very rich cross sectional dataset of women between 15-49 years of age , interviewedin 2007. These data have a number of advantages over those used in similar analysis. First, we cancollect information, about the household structure and composition of teenagers. Second, given thedataset extension we can identify different sexual risky behavior key factors and consequences such aspregnancy and STD prevalence among the population. These will control for unobserved characteristics

11In the posterior estimation Wealth Index Dummys are used indistinctly among Total Population Sample and theother subsamples, based in the assumption that the income level of the teenagers actually is not very different to theirparent’s income level, due to the small social movility present in the geographical area of the dataset.

12We also include a regional dummy in all specifications. We also included the Knowledge of contraception in theset of control variables. However we can not addrese this variable in the estimations because in our sample, the vastmajority od the teenagers says that they know contraception in the 99.2% of the cases. So the main question would bewhy if they know contraception, they don’t use it effectively.

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that are shared by adolescents living in the same region. In addition, as in Arcidiacomo et al (2011),we can identify adolescent sex and contraceptive decisions, and we will use this information to buildthe risky sexual behavior pattern among the population. Additionally, since we have information ondifferent regions and provinces of the same country, we are able to identify, where the key factors ofrisky sexual behavior are strong and more persistent. Finally, we have information about the parentsof teenagers (as reported by the parents themselves). In particular, we consider the parent age, andthe sex of the household head. We model the propensity to enroll in risky sexual behavior through thefollowing expression:

Prob (y = 1 | x) = F (xi�) = � (x0�)

The F (xi�) represents the cumulative probability function of the probit model. The y is a binaryoutcome, which takes a value of one if risky sexual behavior is assumed, zero in case of no risky sexualbehavior and missing values for those members of the population who can not be identified by ourrisky sexual behavior definition . X is a vector of social , family, and individual characteristics. Theset of models to be estimated can be summarized as follows:

yi = ↵+ xi�i + xj�j + xh�h + e

zi = ↵+ xi�i + xj�j + xh�j + e

Where the xi � xh variables identifies the three types of controls mentioned in previous section.The research framework developep here has implications that would be harder to examine using otherparadigms. First, the model is able to match both the fraction of teenagers involved in risky sexualbehavior , as well as, the proportion of pregnant teenagers with 18 years. In the second part of theresults, we develop three consistency tests for each of the models, which alters the missing valuesassignation in the dependent variable. Likewise, the model can give predictions on the fraction ofteenagers becoming pregnant after age 18, as a result of engaging in risky sexual behavior. Second, themodel can match the risky sexual behavior effects for sexually active teenagers who use contraception,but don�t get pregnant. Additionally, the model control for the sexual behavior of teenagers byverifying their conduct with their last two sexual partners, as well as, the age of their last two sexualpartners. Modeling the average number and the dispersion of sexual partners is likely to be importantfor sheeding light on the spread of sexually trasmitted diseases (STD’s)13.

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Greenwood and Guner (2010)

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5. Estimation Results

In Table 2 we present estimation results for risky sexual behavior. Columms (1) to (3) report theresults for a model using the subsample of those under 25, single and living with parents. We findthat the level of education is relevant in reducing the propensity of risky sexual behavior among thispopulation by around 2%. Additionally, family structure characteristics such as the sex of the houseoldhead, show significant but opposite effects. It’s interesting to verify that education effects tends to belarger when we separate the effect into primary, secondary and higher education. Larger effects areobserved on primary education which reduces risky sexual behavior propensity by 49%. In columns(4) and (5) we present the estimates of the basic model for the subsample until 18 , single and livingwith parents. The idea behind this subset of data is to test the validity of the assumptions madewithin the model�s focus group. We find similar effects across groups, but in this subset the householdhead age is relevant in determining the propensity to engage in risky sexual behavior. This could berelated with the intuition that older household heads are more aware of the consequences related torisky sexual behavior, and could also have greater supervision over their teenagers. Additionally weverify that the rural/urban dummys is not relevant for the subsample under 25 years but it’s relevantin the subsample of age under 18. In this context, geographic locations plays a major role in teenagersexual conduct, because the level of training and information to which they are exposed is directlyrelated to community size and capital of the country proximity , so rural and urban location effectsshould differ across groups14.

In Table 3 we present estimates of the basic and extended model, for both subsamples, includingcontrols for community size across subsamples. In columns (1) to (3) we report the results for the until25 years subsample, here we can observe that educational level, demographics and family structureaffect the propension to engage in RSB . The estimated effects of demographic dummies for mediumand large cities in the Dominican Republic reduce the propensity to engage in RSB between 4%-7%. Regading the education level effects, when we introduce level dummys, they have significant andnegative effects for the population under 25. The effects tends to be lower in the case of higher andsuperior education, which reduces the propensity by 23%-22%. Family structure controls are relevantand positively influence the propensity to engage on RSB. It’s relevant that the estimated effect of thesex runs from 5% in the basic model specification to 17% in the long version of the model , reportedin colum (3). In columns (4) and (5) we report the results for the subsample of age 18 & under andliving with parents. In this subsample the educational level effects are pretty similar to the previousestimation. Is important to mention that household head has a significant and negative effect on theprobability of engaging in RSB only for the under 18 years subsample.

We are concerned about the consistency of the model when other segments of the population are14In the following tables are presented the coefficients of each estimation as well as the marginal effec estimated

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included. We have analyzed a third subset of the population, composed of married women, in orderto verify how the effects of the control differ when the estimation is implemented. In Table 4, wereport the results for the basic and extended model in the married women subsample. We observe thatvariables related to age and education are relevant but as expected in signs opposite to those in othersamples, which could indicate that married women have a background that allows them once marriedto be more precauctions as they get older. As expected, the variables controling for house structure,are not significant which is consistent with the idea that married woman’s propensity to engage inRSB is affected more by economic factors, and the family structure scheme is not relevant. Given thestructure of the dataset used and the missing values, we develop a robustness analysis, changing thedefinition of the dependent variable and re-estimating the basic specification. the definitions used forthe robustness analysis are:

i) Naive 1: We replace missing values with zeros in the RSB definition.ii) Naive 2: We replace missing values with ones in the RSB definition.ii Naive 3: We replace missing values with one with probability of the relative frequency of ones in

the non-missing sample and with zero probability the relative frequency of zeros.

This robustness analysis will helps us to identify the possible bias effect related to the missingvalues initially present in our RSB definition. The first and second scenario, assume that the missingvalues which implies some individuals missppecification is not a real problem and that the resultsshould not be alter. The third scenario assumes that the misspecification of individuals is relevant andthat requires some level of treatment. From our perspective, results are sensible to specification biasdue to the treatment of missing values .For example, a girl who has been involved in RSB but has notgotten pregnant and comes from rural areas will not be included as part of RSB population in ourpopulation, arbitrarly assigning this observation to our RSB population, will bias estimation results ofthe marginal effects of controls by inputting them upwards or downwards depending on the assignationprocedure. Essentially, the likelihood of making inputation errors in the first two additional scenariosproposed is likely to be positive and larger than in the third proposed scenario. However, given thatthe missing values distribution is not conditioned on any control, the third scenario is more assertiveassuming that missing observations can be ignored without creating any sample selection bias.

In Table 5 we report estimations for the two subsamples using this procedure. In column (1)we present the results for population until 25 years, using the Naive 1 definition. The sign of theeducational level effects is consistent with the basic model in Table 1, but the estimated effect aresignificantly lower by around 0.1%. The estimated effects of family structure tends to be significantlylower than the basic model for the same subsample. Additionally, the estimated effects of the placeplace residence results are not significant for this subsample. In columns (2) and (3) we report theresults of the until 25 years subsample, for the Naive 2 and Naive 3 scenario. The estimated effectstend to be higher in the Naive 2 especification than in the Naive 1 and 3, this is due to the effec of the

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over estimation of the missing values, when assumed to be in the population of RSB. In this context,we are including non identified individuals in the RSB population, which incide in the marginal effectestimation of the model, making them inconsistent. Regarding the subsample until 18 years, the resultsfor the three especifications are reported in columns (3)-(6), where we find over-estimation effects notonly with Naive 2 specification, but also Naive 3 are regarding current age and place of residence type,which are more relevant when including the non-identified population in the RSB group, due to thehigher prevalence of unsafe practices in rural areas of teenagers below the age of 18 years. Additionally,we verify the links among the controls used previously and the propension to have sex, in order tocompare not only the drivers of unsafe practices, but also the preferences determinants which leadsto RSB initially among the same population. In Table 6, we present the results of the basic modelusing as dependent variable Had sex which takes on a value of 1 if the event has taken place and 0otherwise. The results for the subsample until 25 years are found in the columns (1) and (2). Weobserve that educational level variables when introduced as a dummies have a positive and significanteffect on the propensity to have sex, which could indicate that educational level is not a relevant asa factor in teenager’s decision to have sex. Family structure variables are significant and positivelyrelated to the propensity of having intercourse , except household head age which has a negative andsignificant effect on the propensity of having intercourse, reducing it by 0.1%.

The extended version of the model is presented in table (7). We observe a similar behavior forthe variables included in the basic specification. Aditionally, dummies for place of residence shownegative and significant effects. Regarding the welfare factors, we fond beloging to middle and highclass reduces the probability of having sex by around 3%-5%. Family structure factors are significantand relevant, especially household head sex. When the household head is a woman the propensityto have sex increases, in the two subsamples by between 7%-10%. Finally, we present the results ofthe robustness analysis changing the definition of the dependent variable and reestimating the basicspecification. the definitions used for the robustness analysis follows tha same structure as the onesused for the RSB estimations:

i) Naive 1: We change missing values for zeros in the had sex definition.ii) Naive 2: We change missing values for ones in the had_sex definition.ii Naive 3: We replace missing values with one with probability of the relative frequency of ones in

the non-missing sample and with zero probability the relative frequency of zeros.We observe that, educational effects when measured as education years results, shows a significant

and negative effect across groups. In columns (2) and (4) are reported the results of the Naive 2definition for the until 25 years sample and the until 18 years sample. Basically, the current age ffectson the propension to have sex tends to be around 2% larger than in the basic model specification ofpropensity to have sex. Regarding the family structure variables, it’s relevant to note that the age ofthe household head results significant and have a negative effect on the propensity to have sex amongteenagers, which could be related to higher levels of monitoring teenagers behavior usually establish

15

among older parents. The place of residence, specifically in the rural areas increases the propensity ofhaving sex.

6. Conclusions

We used Health Survey data to investigate the effect of family structure on RSB among teenagers.Our use of the data has allowed us to carry out the analysis with the many identification problemsthat arise in this kind of analysis. In addition, the extensive information included in the dataset hasallowed us to control for different teenager characteristics such as: age, domestic violence presence,among others. Our analysis additionally contains a sensitivity analysis of bias selection due to theassignment of missing values in the output variable.

We find significant and relevant effects of family structure on RSB specially among the age 18 andunder subsample , where the effects are more persistent. There is evidence, that the number of siblingspositively affect the propensity among teenagers to engage in RSB, as well as young female householdhead. Education level is of the most relevant factors identifying a high or low propensity to engagein RSB, specially for those teenagers who lives in rural areas. Place of residence effects are relevant.We find that living in “Rural” areas against “urban” areas increase the propensity to involve in RSBamong the age 18 and under 18 subsample, but is not relavant for the age 25 and under subsample. Inthis segment of the population the place of residence effects are more evident when classified accordingto the size of the community in which they live. Further results indicates that the household welfarelevel influences the sexual choices of teenagers, influencing t he propensity to engage in RSB but is notrelevant in the decision of having sex.

Regarding the propensity among teenagers to be involved in sexual activities family structure playsa major role in the attitudes of girls toward these activities. Further results show that household headage reduces the propensity of participating in sexual activities among teenagers around 0.1%. Eventhough this effect is not significant in the propensity to be involve RSB, which may indicate that themonitoring effect that older household heads have over teenagers is not relevant, once they have alreadybeen involved in sexual activities. Our findings suggest that the factors determining participationin sexual intercourse are distinct from those determining participation in RSB, even though it maycoincide in some cases. For example the estimated effects of place of residence, household income level,and the household head age, influences the propensity of engaging in RSB but their effects on thepropensity of having intercourse are less relevant.

Finally, we have developed an additional robustness analysis, which helps us to verify the defini-tion of RSB. The results provided indicates that the initial definition of RSB provides more efficientestimates that the others explored attempts. Our study is an attempt to understand this complexity

16

so that future research may focus on the factors that may be most relevant in the identification ofindividuals involved in RSB.

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20

Tables and Graphs

Table 1.a: Summary StatisticsVariables Number obs Mean SD Min Max

current_age 27195 29.65582 10.07187 15 49province 27195 16.81577 9.364838 1 32

high_educ 27195 1.663541 0.8114836 0 3educ_years 27151 8.533277 4.386694 0 19

hous_members 27195 4.832322 2.062078 1 24educ_atta 27195 2.576466 1.56631 0 5

age_household head 27195 43.65398 13.60026 15 99smokes_cig 27195 0.070674 0.3405693 0 9

fecund_level 27195 0.0209597 0.4235325 0 9age_first_intercourse 27195 25.25666 29.26703 0 98

knows_some~S 26912 0.4600922 0.8699189 0 9ever_tested for STD 27195 0.7550653 0.9485843 0 9

condom_use last partner 1928 1.120332 1.700466 0 9age_last_sexual partnet 1703 29.94656 16.21746 15 99

alco_last_sexualintercourse

20038 0.4125661 1.191004 0 9

num_sexual partners 22540 3.647649 12.26253 1 99can_ask_condom use to

partner15872 1.087954 1.244615 0 9

num_siblings 27195 5.38952 5.231139 0 99drug_use in last sexual

intercourse20038 0.1124364 0.9588968 0 9

risk_aids perception 26912 2.818817 1.362102 1 9type_place_residence 27195 0.6021695 0.4894591 0 1

sex_household head 27195 0.3644788 0.4812925 0 1domestic_violence 9873 0.1543604 0.3613122 0 1

Know_contraception 27195 0.9912484 0.0931415 0 1risk 21309 0.3341311 0.4716969 0 1

had_sex 27078 0.8280892 0.3773098 0 1ever_had_children 27195 0.7185512 0.4497141 0 1

had_std_ in last 12months

27084 0.0196057 0.1386434 0 1

risk_aids 26912 0.0957565 0.3245286 0 8

21

Table 2: RSB Extimations

Dependent VariableRisk

Populationuntil 25 andLiving with

Parents


Parents


Parents

PopulationUntil 18 andLiving with

Parents


Parentscurrent_age .1247*** .1044*** -.1619*** 0.3744*** .380***

0.0266 0.0230 -0.0589 0.0720 0.0743Deduc1 1.6314*** 1.6187***

0.4948 0.4435Deduc2 1.1181*** 1.0384***

0.2343 0.1670Deduc3 0.7744*** 0.9423***

0.2199 0.2731wfm -.1014* -0.0707 0.4360 -0.209*** -.1839**

-0.0209 -0.0152 0.1474 -0.0375 -0.0338wfh -.2820*** -.2903*** 0.6748 -.2903*** -.2822***

-0.0578 -0.0615 0.2216 -0.0530 -0.0525diff_household

members .06634*** .07124*** 0.3428*** 0.0649*** .066***

0.0141 0.0156 0.1248 0.0125 0.0130num_siblings .05367*** .06010*** .3161*** 0.0666*** .066***

0.0114 0.0132 0.1151 0.0128 0.01296sex_household head 0.2359*** .23339*** 0.4714 0.2724*** .263***

0.0516 0.0527 0.1689 0.0545 0.0535age_household head -0.0019 -0.0014 0.0158 -0.0046** -0.0035

-0.0004 -0.0003 0.0057 -0.0008 -0.0006type_place_residence 0.0632 0.0700 -0.4020 .1157* .1032*

0.01337 0.0152 -0.1453 0.0219 0.01993dummy_N -0.0360 -0.0310 -0.5643* -0.0206 -0.0208

-0.0076 -0.0068 -0.2021 -0.0039 -0.0040dummy_SE -0.0880 -0.0651 -0.3397 -0.0795 -0.0505

-0.0183 -0.0141 -0.1286 -0.0149 -0.0097educ_years -.1293*** -.1801*** -.121***

-0.0284 -0.0656 -0.0237risk_aids 0.6843

0.2093alco_last_intercourse 0.1487

0.0541partner_age 0.0043

0.0015knows_some_died of

AIDS 0.0488

0.0177domestic_violece 0.6042*

0.1976_cons -4.604*** -2.021*** 2.3478* -8.601*** -6.528***

legend: * p<.1; ** p<.05; *** p<.01

22

Table 3: RSB Estimations

Variables


Parents


Parents


Parents


Parents


Parents

current_age 0.1243*** 0.1039*** -0.149*** 0.3744*** 0.3801***0.026453 0.0228 -0.0540 0.0720 0.0742

Deduc1 1.6388*** 1.6193***0.4967 0.4436

Deduc2 1.1267*** 1.0397***0.2355 0.1670

Deduc3 .7854*** 0.9428***0.2233 0.2732

wfm -.10264* -0.0714 0.5457 -0.210*** -0.1844**-0.0211 -0.0153 0.1783 -0.0376 -0.0338

wfh -0.305*** -0.311*** 0.6780 -0.298*** -0.291***-0.0623 -0.0657 0.2200 -0.0545 -0.0541

diff_household members .06738*** 0.0721*** .3546*** 0.0651*** 0.0667***0.0143 0.0158 0.1280 0.0125 0.0130

num_siblings 0.0553*** 0.0616*** 0.3163*** 0.0672*** 0.0669***0.0117 0.0135 0.1142 0.0129 0.0130

sex_household head 0.2363*** 0.2336*** 0.4974 0.2719*** .2628***0.0516 0.0526 0.1765 0.0544 0.0533

age_househ -0.0018 -0.0013 0.0127 -0.0045** -0.0035-0.0003 -0.0003 0.0046 -0.0008 -0.0006

dummy_N -0.0317 -0.0269 -0.5769* -0.0191 -0.0191-0.0067 -0.0059 -0.2047 -0.0036 -0.0037

dummy_SE -0.1517** -0.1233** 0.3398 -0.1022 -0.0752-0.0311 -0.0263 0.1151 -0.0191 -0.0144

facto_place of residence2 -0.2449*** -.2206** 2.5191*** -0.0973 -0.1045

-0.0526 -0.0489 0.7311 -0.0187 -0.0205facto_place of residence

3 -0.295*** -.2791*** 2.8053*** -0.2077 -0.2021

-0.0603 -0.0590 0.7941 -0.0389 -0.0385educ_years -.1294*** -0.190*** -.1215***

-0.0284 -0.0688 -0.0237risk_aids 1.3221***

_cons -4.309*** -1.732*** -0.4666 -8.394*** -6.325***legend: * p<.1; ** p<.05; *** p<.01

23

Table 4: RSB Estimation Married Women Sample

current_age -0.028*** -.030*** -0.029***-0.0106 -0.0110 -0.0106

Deduc1 1.5965***0.5503

Deduc2 .8878***0.3381

Deduc3 0.3289***0.1253

wfm -0.0621 0.0074 -0.0789-0.0225 0.0027 -0.0286

wfh -0.199*** -0.150*** -0.218***-0.0719 -0.0547 -0.0786

diff_household members 0.0173** 0.0140* 0.01500.0063 0.0051 0.0055

num_siblings 0.0139 0.0112 0.01070.0051 0.0041 0.0039

sex_household head 0.0769** 0.0805** 0.1277**0.0284 0.0298 0.0475

age_household head 0.0030* 0.0022 -0.00180.0011 0.0008 -0.0006

type_place_residence 0.0319 0.0514 0.06930.0117 0.0188 0.0253

dummy_N -0.184*** -0.127*** -0.0697-0.0673 -0.0466 -0.0255

dummy_SE -0.0978** -0.0537 -0.0910-0.0355 -0.0196 -0.0330

educ_years -0.133*** -0.137***-0.0490 -0.0505

risk_aids -0.0301-0.0110

alco_last_sexualintercourse

0.0685***

0.0251partner_age 0.0111***

0.0040knows_some~S 0.0809**

0.0296domestic_v~e -0.0677

-0.0245_cons -.3504*** 1.7179*** 1.379***

legend: * p<.1; ** p<.05; *** p<.01

24

Table 5: Robustness Analysis of RSB Estimation

DependentVariable:

Risk

Poulationuntil 25 andLiving with

Parents


Parents


Parents


Parents


Parents


Parents

current_age 0.0651*** .1591*** 0.0796*** 0.3212*** 0.4884*** 0.3435***0.0134 0.048 0.0180 0.0625 0.1064 0.0684

educ_years -0.110*** -0.109*** -.1059*** -0.112*** -0.120*** -0.111***-0.0228 -0.0330 -0.0239 -0.0218 -0.0263 -0.0222

wfm -0.0628 -0.0732 -0.0547 -0.189*** -0.1443** -0.202***-0.0127 -0.0217 -0.0121 -0.0346 -0.0300 -0.0377

wfh -0.271*** -0.207*** -0.217*** -0.286*** -0.202*** -0.272***-0.0540 -0.0613 -0.0477 -0.0530 -0.0426 -0.0517

diff_householdmembers 0.0628*** 0.0608*** 0.0617*** 0.0610*** 0.0634*** 0.0590***

0.0129 0.0183 0.0139 0.0118 0.0138 0.0117num_siblings 0.0552*** 0.0509*** 0.0548*** 0.0590*** 0.0706*** 0.0605***

0.0114 0.0153 0.0123 0.0114 0.0154 0.0120sex_househ~h 0.2023*** 0.2137*** 0.1802*** 0.2307*** 0.2748*** 0.2284***

0.0427 0.0653 0.0414 0.0464 0.0620 0.0469age_househ~h -0.0006 -0.0024 -0.0006 -0.0034 -0.0034 -0.0036

-0.0001 -0.0007 -0.0001 -0.0006 -0.0007 -0.0007type_place_r 0.0644 0.0978** 0.0763* 0.0949 0.1107* 0.1202**

0.0131 0.0292 0.0170 0.0182 0.0238 0.0235dummy_N -0.0486 -0.0064 -0.0571 -0.0143 -0.0350 -0.0119

-0.0100 -0.0019 -0.0128 -0.0027 -0.0076 -0.0023dummy_SE -0.0848 -0.0282 -0.0824 -0.0615 -0.0536 -0.0501

-0.0171 -0.0084 -0.0182 -0.0117 -0.0115 -0.0098_cons -1.538*** -2.863*** -1.789*** -5.616*** -8.282*** -5.978***

legend: * p<.1; ** p<.05; *** p<.01

25

Table 6: Had Sex Estimations

Dependent VariableHad_sex


Parents


Parents


Parents


Parents

current_age .2892*** .2860*** 0.4706*** 0.4843***0.1098 0.1087 0.1235 0.1277

Deduc1 0.9959*** 1.0811**0.3813 0.3384

Deduc2 0.5796*** 0.5299***0.2166 0.1282

Deduc3 0.5304*** 0.5445***0.2079 0.1725

wfm -0.1392*** -0.1031** -0.178*** -0.1504**-0.0521 -0.0388 -0.0446 -0.0381

wfh -0.136*** -0.127*** -0.177*** -0.1572**-0.0514 -0.0481 -0.0454 -0.0405

diff_household members 0.0600*** 0.0607*** 0.0530*** 0.0537***0.0227 0.0230 0.0139 0.0141

num_siblings 0.0550*** .0554*** 0.0552*** 0.0535***0.0209 0.0210 0.0145 0.0141

sex_household head 0.2419*** 0.2304*** 0.3032*** 0.2893***0.0922 0.0879 0.0821 0.0785

age_household head -0.004*** -0.004*** -0.0047** -0.0042**-0.0016 -0.0016 -0.0012 -0.0011

type_place_residence 0.0825** 0.0797** 0.1023* 0.09050.0312 0.0302 0.0265 0.0236

dummy_N -0.0188 -0.0184 -0.0012 -0.0013-0.0071 -0.0070 -0.0003 -0.0003

dummy_SE -0.0083 0.0072 -0.0480 -0.0236-0.0031 0.0027 -0.0124 -0.0062

educ_years -0.096*** -0.11***-0.0365 -0.0293

_cons -6.39*** -4.856*** -9.40***

legend: * p<.1; ** p<.05; *** p<.01

26

Table 7: Had_sex Estimations

Variables


Parents


Parents


Parents

current_age .2889*** 0.2857*** 0.4844***0.1097 0.1085 0.1277

Deduc1 0.9972***0.3818

Deduc2 0.5825***0.2177

Deduc3 0.5352***0.2098

wfm -0.1401*** -0.1038** -0.1507**-0.0525 -0.0390 -0.0382

wfh -0.149*** -0.139*** -0.1635**-0.0563 -0.0526 -0.0421

diff_household head 0.0605*** 0.0611*** 0.0539***0.0229 0.0232 0.0142

num_siblings 0.0560*** 0.0563*** 0.0540***0.0212 0.0214 0.0142

sex_household head 0.2420*** 0.2307*** 0.2890***0.092 0.0880 0.0784

age_household head -0.004*** -0.004*** -0.0042**-0.0016 -0.0015 -0.001

dummy_N -0.0159 -0.0158 -0.0000-0.0060 -0.0060 -0.00001

dummy_SE -0.0477 -0.0295 -0.0420-0.0180 -0.0111 -0.0109

facto_plac~2 -0.1427** -0.1334* -0.0758-0.0542 -0.0507 -0.0200

facto_plac~3 -0.217*** -0.206*** -0.1622-0.0818 -0.0776 -0.0421

educ_years -0.096*** -0.11***-0.0365 -0.0293

_cons -6.174*** -4.6437*** -7.8629***legend: * p<.1; ** p<.05; *** p<.01

27

Table 8: Robustness Analysis of Had Sex EstimationsPoulation

until 25 andLiving with

Parents


Parents


Parents


Parents


Parents

current_age 0.2840*** 0.2862*** 0.4829*** 0.4861*** 0.4848***0.1078 0.1088 0.1082 0.1284 0.1280

educ_years -0.095*** -0.096*** -0.110*** -0.111*** -0.111***-0.0362 -0.036 -0.0363 -0.0295 -0.0294

wfm -0.0977** -0.1050** -0.1487** -0.1527** -0.1491**-0.0367 -0.0395 -0.0380 -0.0387 -0.0378

wfh -0.1242*** -0.1276*** -0.1569** -0.1578** -0.1537**-0.0469 -0.048 -0.0472 -0.0408 -0.039

diff_household members 0.0606*** 0.0605*** 0.0540*** 0.0535*** 0.0539***0.0230 0.0230 0.0229 0.0141 0.0142

num_siblings 0.0554*** .0550*** 0.0538*** 0.0527*** 0.0538***0.0210 0.0209 0.0209 0.0139 0.0142

sex_household head 0.2284*** 0.2296*** 0.2892*** 0.2886*** 0.2865***0.0871 0.0876 0.0868 0.0785 0.0778

age_household head -0.004*** -0.004*** -.0044** -0.0041** -0.0043**-0.0016 -0.0015 -0.0016 -0.0011 -0.0011

type_place_r 0.0747* 0.0804** 0.0896 0.0904 0.08830.0282 0.0305 0.0289 0.0236 0.0231

dummy_N -0.0152 -0.0200 0.0005 -0.0049 -0.0054-0.0057 -0.0076 -0.0074 -0.0013 -0.0014

dummy_SE 0.0125 0.0058 -0.0233 -0.0252 -0.02910.0047 0.0022 0.0025 -0.0066 -0.0076

_cons -4.82* -4.857*** -8.002*** -8.045*** -8.022***

legend: * p<.1; ** p<.05; *** p<.01

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Sexual Risky Behavior Patters-Version 9

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Transcript of Sexual Risky Behavior Patters-Version 9