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International Journal of Behavioral Development
Int J Behav Dev. 2013 Jul; 37(4): 298–308.
PMCID: PMC3785225
PMID: 24089582
A psychosocial and behavioural study in the slums of Nairobi
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Abstract
Home-leaving is considered an important marker of the transition to adulthood and isusually framed as an individual decision. We move beyond this limited assumption toexamine a broader conceptualization that might better illuminate home-leaving among youthin impoverished circ*mstances. We adopt the Problem Behavior Theory-framework toinvestigate the association of home-leaving with behavioral and psychosocial variables andwith other transitions. We use data on adolescents aged 14–22 years from a three-wavestudy conducted between 2007 and 2010. We used variable- and person-centeredcross-sectional analyses, as well as predictive analysis of home-leaving by subsequentwaves. Parental controls protection predicted home-leaving by subsequent waves. Overall,protective factors moderated the association of problem behavior involvement with leavinghome in Nairobi’s slums.
Keywords: leaving home, psychosocial factors, protective factors, risk factors, transition to adulthood
In this article, we explore “home-leaving” (establishing independentresidence) among young people in two informal settlements (slums) in Nairobi, Kenya’s capitalcity. We seek to understand home-leaving as one indicator of the transition to adulthoodwithin resource-poor informal settlements. Specifically, we investigate the associationbetween the occurrence and timing of home-leaving and socio-demographic, contextual, andpsychosocial characteristics..
The transition to adulthood is a period of significant developmental changes that shape thenature and quality of young people's future lives (Lloyd, 2005). Independence is considered animportant hallmark of adulthood. Consequently, the act of leaving the parental home andestablishing an independent residence is considered an important marker of the transition toadulthood (; Koc,2007; ). For example, a study by Rusconi (2000) in Germany and Italy, indicates that becoming residentiallyindependent is considered indexical of economic and individual autonomy from the household oforigin. Similarly, a study conducted in Zambia highlights home-leaving as a focal point forother critical developmental tasks and transitions (Benefo, 2004). Investigations of the dynamics ofhome-leaving in Italy have shown that economic resources play a key role in young people’stransition into independent living (). Studies in the United States also show that the highera young person’s income level, the more likely she or he is to be living independently (;; ). In the UK, Ermisch(1999) found that the cost of housing also influences young people to leave theirparental home. Specifically, some youth delay home-leaving, while others may return to theirparental home after a stint of living independently because of financial constraints. Somestudies in The Netherlands and China have shown that young people leave home earlier when theparental household has a high level of transferable material resources (e.g., income andproperty) and that non-transferable material resources (e.g., living space, help with mealpreparation and housework, etc.) delay home-leaving (; ; Laferrère, 2005). There is alsoevidence that family size can influence leaving the parental home. For example, it has beenfound that a higher number of siblings increases the likelihood of leaving home for unionformation and employment reasons; however, it decreases the likelihood of leaving home forfurthering education (). Overall, most theorizations of home-leaving frame home-leaving as a personalchoice or an independent decision of the young person concerned. In this article, we movebeyond this limited assumption to examine a broader conceptualization that takes into accountboth contextual and individual-level constructs and that might better illuminate home-leavingamong young people in impoverished circ*mstances.
Leaving home is also an important event because of its interdependencies and consequences(Aassve et al., 2003). Thus, inaddition to exploring the dynamics of home-leaving in the slums, we will examine theassociation between independent living and other transition behaviors (e.g., sexual initiationand marriage), some of which can also represent a claim on a more mature status. Since some ofthese other transition behaviors (e.g., early sexual initiation) can be viewed as riskbehaviors, we have engaged a well-established explanatory framework, Jessor’s Problem BehaviorTheory (Costa et al., 2005; Jessor, 1991; Jessor et al., 2003), to illuminate the interlinkagesbetween home-leaving and other markers of the transition to adulthood. The explanatoryframework involves psychosocial protective factors, for example, informal social controls andsupports that lessen the likelihood of engaging in risk behavior, and psychosocial riskfactors, such as, models risk and vulnerability risk, that enhance the likelihood of engagingin risk behavior.
Problem Behavior Theory posits that behavior is influenced by both protective and riskfactors. The theory outlines three types of protective factors: models protection, controlsprotection, and support protection; and three types of risk factors: models risk, opportunityrisk, and vulnerability risk (Jessor etal., 2003). Protective factors promote pro-social behaviors while risk factorsincrease the likelihood of risk behaviors. Protective factors may also moderate the impact ofrisk factors on behavior. According to the theory, models risk includes measures of models forrisk behavior (e.g., friends who engage in substance use may serve as behavioral models).Opportunity risk refers to situational factors that provide an opportunity to engage in riskbehaviors (e.g., presence of alcohol in the household may provide an opportunity to consumealcohol). Lastly, vulnerability risk refers to individual characteristics, such as lowself-esteem, that increase the likelihood of engaging in risk behavior. Models protection, onthe other hand, includes measures of parent and peer models for pro-social behavior (e.g.,friends who value education). Controls protection are informal regulatory controls that areeither individual-level (e.g., high religiosity) or social-environmental (e.g., parentalmonitoring). Finally, support protection refers to contextual supports for pro-social behavior(e.g., having a supportive parent). In this study, we posit that young people reporting highlevels of protective factors will be less likely to leave home and will report lower levels ofengagement in risk behavior even if they are living independently. In contrast, young peoplereporting higher levels of risk factors will be more likely to report independentresidence.
In addition to the associations postulated by Problem Behavior Theory, we also posit that theexperience of other markers of the transition to adulthood, and whether a young person is amigrant or not, will be associated with home-leaving. First, in most societies, marriedcouples are expected to establish an independent household. Consequently, we expect to findthat married youth will live independently. In addition, early pre-marital pregnancy may alsoresult in the transition to independent living. Further, young people with an income may bemore likely to live independently because they have the resources to support an independenthousehold. Living independently also may give young people the freedom to engage in riskbehavior. Conversely, young people who engage in risk behavior may desire to live alone inorder to have more freedom. Finally, with respect to migration, we consider that home-leavingmay take several pathways; those who migrate from their rural areas to start their ownindependent living in the city and those who leave their parental homes in the same slum orother parts of the city to form their own independent living in the slum.
The overall conceptual framework for the study is shown in Figure 1.
Study context
It is noteworthy that studies of the dynamics of residential independence have primarilyfocused on youth living in the global North. Conversely, little is known about home-leavingin sub-Saharan Africa, where different cultural factors may have substantial effects onhome-leaving—an important marker of transition to adulthood in African settings. Nairobi’sinformal settlements (or “slums”) provide a unique context for examining residentialindependence for three main reasons. First, overcrowding and inadequate dwelling spacestypify these slums. Dwelling units have average measurements of 10 by 10 ft and areconstructed with substandard materials such as iron sheets or mud and timber. Severalscholars (; ) have investigated the association between the lack of space and the sexualbehavior of youths resident in the slums. However, space constraints in informal settlementsare also likely to be linked to the timing of residential independence among young people.In other words, in addition to other reasons, leaving home is likely to be a function ofpragmatic considerations: youths in the slums may be prompted to establish a separateresidence from that of their parents simply because sufficient space in their household oforigin cannot be taken for granted.
The second reason for which Nairobi’s slum settlements constitute a unique study site hasto do with the fact that they are home to a diversity of ethnic groups. This diversity mayalso play a role in influencing the establishment of independent households by young people.The traditional expectation for certain ethnic groups (the Kikuyu, for instance) is thatboys will live on their own once they have undergone circumcision, a rite that symbolizesthe transition to adulthood for some sub-cultures in Kenya and that is performed on boysaround the age of 13 years. Of significance is the fact that this sort of culturalexpectation has little to do with the youth’s personal choice or desire to leave home ornot. Rather, it is more of an obligation to which male youth must adhere. Third, while manytheorizations of home-leaving center on the economic resources of the home-leaver toestablish an independent residence, the slum setting (which is characterized by high levelsof poverty and unemployment) raises questions about the centrality of economics toresidential independence among young people in the slums of Nairobi. The slums of Nairobiare characterized by a high unemployment rate and a shortage of productive investment. Basicpublic services such as affordable and clean water, access to electricity, and stablesources of income are lacking. The realities of the dire economic challenges in the slumscreate a situation in which leaving home may be realized through unique living arrangements.For instance, while some young people who have left home may be living a fully(economically) independent life, others may have “left home” in that they live in, and areresponsible for paying for, their own independent residence, but they continue to besupported in other ways by their household of origin. For example, food and educationalexpenses may be borne by their parents or caregivers. Other young people will live withtheir peers.
The present study examines the dynamics and consequences of home-leaving in two informal“slum” settlements in Nairobi. The study seeks to address the following three questions: 1)is home-leaving related to other transition-to-adulthood markers, including first sexualintercourse, marriage, childbearing, and involvement in income-generating activities?; 2) dopsychosocial protective and risk factors, as well as sociodemographic characteristics,explain the occurrence and timing of the home-leaving transition?; and 3) do protectivefactors moderate the impact of risk factors on adolescents’ home-leaving?
Methods
Study design, participants and procedures
The data used in this study are drawn from the baseline (Wave 1) and the follow-up (Waves2 and 3) surveys of the Transition-To-Adulthood (TTA) project, a component of the 5-yearUrbanization, Poverty and Health Dynamics (UPHD) project conducted by the AfricanPopulation and Health Research Center (APHRC) in two slums in Nairobi. The study is nestedin the Nairobi Urban Health and Demographic Surveillance System (NUHDSS), which collectsroutine health and demographic data from about 76,094 people in 29,900 households (as atthe end of 2009) in the two slums (African Population and Health Research Center, 2009). During the first wave ofdata collection, about 4,057 youths were interviewed using a structuredinterviewer-administered questionnaire between October 2007 and June 2008. In the secondwave (March 2009–August 2009), 2,527 youths were re-interviewed and 1,629 youths werere-interviewed in the third wave (April 2010–August 2010).
The questionnaire included questions covering social demographic characteristics (e.g.,independent housing and schooling), and living arrangements, as well as other psychosocialand behavioral factors. The questionnaire was developed and reviewed by a team of expertsin youth issues and was pilot tested among a group of young people living in villagesadjacent to the Demographic Surveillance Area (DSA). The complete questionnaire wastranslated from English to Kiswahili and administered in Kiswahili, the language mostspoken in the study area.
Measures
Outcome variables
Independent housing (residential status) was assessed based on the response to a singlequestion: “Have you ever owned or rented your own residence, such as a structure orhouse?” This variable was used as proxy for the event status of leavinghome. Fieldworkers were trained to ensure that respondents understood thatresidential independence referred to being primarily responsible for paying rent orbeing the head of household. Respondents who had lived independently were also asked atwhat age they first lived independently; and in what month and year they first owned orrented their residence. A variable denoting the timing of first independenthousing was derived from this second question. The outcome criterion measureis the dichotomous variable indicating whether or not an individual had ever livedindependently. Analyses of this criterion in this paper adopted three approaches:variable-centered analysis, predictive analysis of leaving home, and person-centeredanalysis. The variable-centered analysis focused on the association between thepsychosocial and behavioral explanatory variables in the conceptual framework andresidential status at Wave 1. The second approach was a predictive analysis to establishwhether the explanatory variables, measured at Wave 1, predicted home-leaving by Wave 2,for the cohort that had not left home by the first round of survey. Third, theperson-centered analysis involved the creation of subgroups, based on transitions made,and then comparing predictor variables among the groups, again based on Wave 1 data.
Socio-demographic variables
Socio-demographic measures included respondents’ sex and schooling status (whether ornot a respondent was in school at Wave 1), youth sexual behavior, employment status,migration status and marriage. Schooling status was included as an independent variablesince being in or out of school may influence the decision to move out of the parent’shome. Migration status comprised two categories: whether or not the respondent was bornin the study area. Migration is controlled for in this case because those who move intotheir study area without their families are thought to be more likely to acquireindependent housing than those who were born there.
Marital status was assessed using the responses to three questions. Respondents wereasked, “Have you ever been married or lived together with a man/woman as if married?” Ifthey responded “yes”, they were asked, “Are you currently married or living togetherwith a man/woman as if married?” If they gave an affirmative response, they were askedabout the month and year when they first got married/started living with a partner, andwhere the date was unknown, they reported the age when they first got married or startedliving with a partner. Sexual behavior was assessed by asking the respondents, “Have youever had sexual intercourse?” If their response was in the affirmative, they were askedabout the age when they had their first sexual intercourse. Respondent’s pregnancyhistory was derived from the questions “Have you ever been pregnant?” for girls and“Have you ever made someone pregnant?” for boys. The date when this first happened wasalso recorded. The age or date when these events happened were collected to determinewhether they happened before or after leaving the parental home. Respondents were alsoasked about their involvement in income-generating activities (IGA). Involvement in IGAis considered as a measure of economic independence and the ability to affordindependent living (Aassve et al.,2003; Rusconi, 2000).
A socio-economic index was constructed using data on household characteristics andpossessions collected under the Demographic Surveillance System. Principal ComponentsAnalysis (PCA) was used to construct the socio-economic index using information on assetownership, access to utilities and infrastructure (e.g., source of water), and housingcharacteristics (e.g., building material) were used. Descriptive analysis (frequencies)was performed to guide in deciding which variables to include in the analysis. If mostor very few households owned the asset then these variables were dropped from theanalysis. The variables that were excluded are vehicle, car, motorcycle, refrigerator,mattresses, fan, blankets, and roof material which had less than 1% of households owningthem. Variables with many categories or low frequencies were combined and recoded intobinary variables. A continuous score obtained from (PCA) was grouped into tertiles ofpoorest, poor, and least poor.
Measures of psychosocial and behavioral protective and risk factors
We constructed composite measures of three key psychosocial protective factors(controls protection, support protection, and behavior protection), and three keypsychosocial risk factors (models risk, vulnerability risk, and problem behavior risk)from the Problem Behavior Theory framework. Composite measures of protection and of riskwere generated by averaging all the equally weighted items in the component subscalesand standardizing them with mean of zero. The alpha reliabilities of the compositemeasures of risk and protective factors, and of their component subscales, are presentedin Table 1. The compositeprotection and risk measures were generated to assess the relationship of overallprotection and overall risk with the home-leaving criterion measure.
Table 1.
Psychosocial and behavioral protective and risk factor composite measures,component subscales, and alpha reliabilities.
| Alpha | |
|---|---|
| Protective factors | |
| Controls protection | 0.83 |
| Parental controls (10) | 0.88 |
| Personal controls (6) | 0.69 |
| Friends controls (3) | 0.76 |
| Support protection (6) | 0.67 |
| Pro-social behavior protection (8) | 0.61 |
| Risk factors | |
| Models risk | 0.68 |
| Sibling models (4) | 0.74 |
| Peer models (pressure)(1) | – |
| Vulnerability risk (6) | 0.59 |
| Problem behavior involvement | 0.82 |
| Delinquency (7) | 0.75 |
| Substance use (8) | 0.87 |
The controls protection composite comprised items in threemultiple-item subscales that assess parental, personal, and friends’ controls.Parental controls were measured using 10 items that assessed therespondent’s perception of how much their parents or guardians know about therespondent’s daily activities (e.g., “Where you spend time in the evenings on weekdays,or who your friends are”) and parental sanctions (e.g., “How often does your parentscold or reprimand you when you do something wrong?”). Personalcontrols included individual reliance on religious beliefs (e.g., “Howimportant is it to you to be able to rely on religious teachings when you have aproblem?”) and individual-level intolerance for normative transgressions (e.g., “Youngwomen/men should remain virgin until they marry”). Peer controlsincluded peers’ approval of pro-social behavior (e.g., “How important is it to yourfriends that you do well in school?”) and peers’ feelings about substance use (e.g.,“How do most of your friends feel about someone your age drinking alcohol, usingmarijuana or other drugs?”). The support protection composite comprisedsix items assessing parental support using questions (e.g. “How often does yourfather/mother teach you things?”, “How often do you share secrets with yourfather/mother?” and “How often does your father/mother try to help you?”).Pro-social behavior protection included involvement in positivecommunity activities (e.g. “Do you belong to a religious group, drama/dance/choir group,anti-AIDs club, anti-drugs club or self help group?”).
The models risk composite comprised four items related to siblings anda single item related to peers (e.g., “How much pressure is there on people your age tohave sex?” and “Have any of your brothers or sisters ever had premarital sex, smoked,drunk alcohol?”). Vulnerability risk was measured using a six-itemscale of self-esteem including the following questions: “How well do you get along withothers?”, “How well do you live up to what is expected of you?”, “What is your abilityto do well in school?”, “How attractive do you think you are?”, “How satisfied are youwith yourself?”, “How well do you resist peer pressure from the rest of the group?” Thecomposite measure of problem behavior involvement comprised twomulti-item subscales; delinquent-type behavior and substance use. Delinquency wasassessed using seven items that measured the frequency with which the respondent engagedin delinquent behaviors, for example, staying away from home for at least one nightwithout parental permission. Eight items assessing cigarette smoking, alcohol drinking,and use of other recreational drugs were used to generate a scale for substance use.
Statistical analyses
Descriptive characteristics of the sample are presented by residential status (see Table 2). Two analytic approacheswere used to examine the relation of our psychosocial and behavioral variables tohome-leaving. First, a variable-centered approach was used to examine the association ofthe explanatory measures with the home-leaving measure using logistic regression. Weexpect the three protective factor measures to be associated with a lower likelihood ofleaving the parental home; conversely, we expect the three risk factor measures to beassociated with a higher likelihood of leaving home. Second, person-centered analysis,based on leaving home sub-groups, was employed to address the hypothesis that problembehavior involvement will be higher among adolescents with low protection and high riskwho left their parental home.
Table 2.
Percentage distribution of socio-demographic characteristics by residentialstatus.
| Has respondent ever owned or rented house? | |||
|---|---|---|---|
| Yes (%) | No (%) | n | |
| Study site | |||
| Korogocho | 29.1 | 70.9 | 1589 |
| Viwandani | 37.9 | 62.1 | 1648 |
| Respondent’s sex | |||
| Male | 43.1 | 56.9 | 1618 |
| Female | 24.0 | 76.0 | 1619 |
| Age group | |||
| 14–17 | 13.2 | 86.8 | 1472 |
| 18–22 | 50.5 | 49.5 | 1765 |
| Where migrant lived before DSA | |||
| Nairobi | 29.9 | 70.1 | 2085 |
| Rural Kenya | 40.6 | 59.4 | 1106 |
| Involved in income-generating activity (IGA) | |||
| Yes | 74.3 | 25.7 | 412 |
| No | 27.6 | 72.4 | 2779 |
| Wealth index | |||
| Poorest | 36.5 | 63.5 | 1253 |
| Poor | 34.5 | 65.5 | 1020 |
| Least poor | 28.6 | 71.4 | 918 |
| Schooling status | |||
| Still in school | 15.1 | 84.9 | 1557 |
| Out of school | 51.2 | 48.8 | 1634 |
Bivariate analyses were performed to assess the association between each independentvariable and the criterion measure of home-leaving. Multivariable analyses (logisticregression) were then conducted to assess the combined effects of the explanatoryvariables on the odds of home-leaving. Socio-demographic variables and othertransition-to-adulthood variables that were significantly associated with leaving homewere included in the multivariate model to control for their effect in assessing the roleof the psychosocial and behavioral protective and risk factors. The final model wasobtained through stepwise model selection, keeping all the psychosocial variables in themodel. The final model was fitted for the overall sample, and then stratified by sex andage group. The stratified analysis by sex and age was performed since home-leaving amongadolescents may differ by sex and age. The moderating effect of protective factors on theimpact of risk factors on home leaving was assessed through examining interaction effectsbetween protective and risk factors. The approach used for the predictive analysis alsoemployed logistic regression. The predictive analysis assessed whether the psychosocialand behavioral protective and risk factors, at Wave 1, predicted home-leaving by eitherWave 2 or Wave 3, for those who had not left the parental home at Wave 1. To make moreapparent the overall effect of the risk and protective factors, analyses of compositepsychosocial measures were also undertaken.
Results
Descriptive findings about home-leaving
The study used data on 3,237 youths aged 14–22 years (excluding 820 adolescents aged12–13 years) with about equal number of male and female respondents (1,618 males; 1,619female). The study excluded youth aged 12 and 13 years because this group is less likelyto experience any of the transition to adulthood markers. Only 2% of 12–13-year-olds hadmoved out of the parental home. Table2 presents the proportion that ever rented or owned a house by socio-demographiccharacteristics. The ethnic groups in the study area include Kikuyu (34%), Kamba (17%),Luhya (12%), Luo (17%), and other groups (18%). The data from this study show that Kikuyuand Kamba were more likely to leave the parental home compared to the other groups. Amongthe adolescents interviewed, 34% had ever owned or rented a house. The proportion everowned or rented a house in Korogocho was 29% and is 38% in Viwandani. Of the femaleyouths, 24% reported to have ever lived independently, while 43% of males had moved out oftheir parental home. About 50% of those aged 18–22 years had moved out of their parentalhome compared to 13% among those aged 14–17 years. About 41% of youths who migrated fromrural Kenya to the study areas reported that they had rented or owned a house compared toabout 30% of those who were born in the area or came from other parts of Nairobi. Of thoseinvolved in income-generating activity, 74% reported ever moved out, compared to about 28%of those who were not involved in income-generating activity. Table 2 shows that 37% of the adolescents frompoorest households moved to independent houses compared to 35% from poor households, and29% from the least poor households. The table also shows that 51% of those out of schoolhad lived independently as compared to 15% of those still in school.
With regard to our first objective, the exploration of the relations of the home-leavingtransition to other transition markers, Table 3, presents percentages indicating theinter-relationships among the various markers of transition to adulthood. What is apparentis the clear bi-directional relation between pregnancy status and marital status. The restof the relationships show that one marker is more an outcome of other markers.Home-leaving is more likely to occur as a result of involvement in income-generatingactivities (IGA) as opposed to the reverse: 74% of those involved in IGA reported havingleft their parental home, and 29% of those who left their parental home reportedinvolvement in IGA. A similar relationship is observed between home-leaving and othertransition-to-adulthood markers, except engaging in sexual intercourse. The data makeclear that the transition of home-leaving is significantly related to other markers of thetransition to young adulthood. In that regard, they support the application of the ProblemBehavior Theory-framework to illuminating the leaving-home transition.
Table 3.
Inter-relationships among transition-to-adulthood markers in terms of percentages:Considering columns as outcome and rows as exposure.
| Ever had sex | Ever been pregnant | Ever given birth | Ever married | Involved in IGA | Leaving home | |
|---|---|---|---|---|---|---|
| Ever had sex | – | – | – | – | 19.6 | 51.8 |
| Ever been pregnant | – | – | 58.7 | 72.3 | 21.1 | 56.8 |
| Ever given birth | – | – | – | 80.4 | 19.3 | 55.2 |
| Ever married | – | 84.6 | 55.2 | – | 22.9 | 64.5 |
| Involved in IGA | 82.8 | 39.6 | 21.1 | 36.7 | – | 74.3 |
| Leaving home | 84.0 | 40.9 | 23.4 | 39.7 | 28.5 | – |
Note. The missing cells are for those obvious outcomes that wouldbring the results to 100%.
Accounting for home-leaving: Findings from cross-sectional, variable-centeredanalysis
Models were fitted for the overall sample, for younger adolescents (14–17 years), forolder adolescents (18–22 years), and for males and females separately. A target sample of3,237 adolescents formed the analytical sample for this cross-sectional analysis. Theactual number reported for the different models is less than 3,237 because of the excludedobservations with missing information on some of the predictors. The results of thecombined sample show, in Table4, that, as expected, older adolescents (18–22 years) were more likely to leavetheir parental home compared to the younger adolescents, regardless of sex. Generally,female youths were less likely to leave their parental home compared to male youths.Adolescents who reported having ever married were more likely to move out of theirparent’s home compared to those who never married, and a similar effect was observedacross sex and age. Sexual intercourse-experience was significantly associated withleaving home, regardless of sex and age. Though ever being married or being sexuallyexperienced were associated with a greater likelihood of home-leaving, the association wasgreater among males and younger youths. Involvement in income-generating activity andbeing out of school both increased the chance of young adolescents moving into independenthousing across sex and age. Residents in Viwandani were more likely to have livedindependently compared to Korogocho residents, though this association was not significantfor young adolescents. This difference may be due the fact that more residents inViwandani are employed, thus increasing the likelihood of leaving the parental homebecause of economic independence. Indeed, Viwandani had a higher proportion of youths whowere involved in income-generating activities (19%) compared to Korogocho (7%). Migrationstatus was not found to have an influence on leaving the parental home after controllingfor other factors. Household socio-economic status had a negative effect on leaving homein that adolescents from wealthier households were less likely to leave their parentalhome.
Table 4.
Association of psychosocial and behavioral protective and risk factor componentmeasure with home-leaving among adolescents.
| −1 | −2 | −3 | −4 | −5 | |
|---|---|---|---|---|---|
| Overall | Aged 14–17 | Aged 18–22 | Males | Females | |
| Aged 18–22 (ref: 14–17) | 1.00*** | 0.94*** | 1.16*** | ||
| Females | −1.85*** | −2.30*** | −1.75*** | ||
| Socio-economic status | |||||
| Poor | −0.26** | −0.46* | −0.19 | −0.16 | −0.37* |
| Least poor | −0.49*** | −0.42* | −0.50*** | −0.61*** | −0.39* |
| Out of school (ref: still in school) | 0.71*** | 0.57** | 0.65*** | 0.81*** | 0.57** |
| Viwandani (ref: Korogocho) | 0.30*** | 0.04 | 0.41*** | 0.26* | 0.41** |
| Involved in IGA | 0.95*** | 1.50*** | 0.89*** | 0.83*** | 1.03*** |
| Ever married | 1.44*** | 2.28*** | 1.33*** | 2.32*** | 1.36*** |
| Ever had sexual intercourse | 1.16*** | 1.63*** | 0.87*** | 1.30*** | 0.86*** |
| Controls protection | −0.06 | −0.59*** | 0.09 | −0.22* | 0.13 |
| Parental support protection | −0.26*** | −0.05 | −0.32*** | −0.36*** | −0.19 |
| Pro-social behavior protection | 0.27*** | 0.25 | 0.27** | 0.40*** | 0.06 |
| Models risk | 0.03 | 0.10 | −0.01 | 0.01 | 0.05 |
| Vulnerability risk | 0.01 | 0.00 | −0.02 | −0.09 | 0.09 |
| Problem-behavior risk | 0.03 | −0.17 | 0.09 | −0.09 | 0.33* |
| Constant | −2.14*** | −2.23*** | −0.92*** | −2.25*** | −3.73*** |
| Observations | 3,074 | 1,433 | 1,641 | 1,538 | 1,536 |
*** p < 0.01; ** p < 0.05; *p < 0.1.
After controlling for these characteristics, the model results show that psychosocial andbehavioral protective factors were associated with a lower likelihood of leaving home.This association varied with both age and sex. Higher controls protection was associatedwith a lower likelihood of leaving home for young and male adolescents only, whileparental support was associated with a lower likelihood of leaving the parental home forolder and male adolescents. Unexpectedly, high pro-social behavior was significantlyassociated with a higher likelihood of leaving the parental home for male and olderadolescents. This result is contrary to what was theoretically expected. Neither modelsrisk nor vulnerability risk was significantly associated with home-leaving, aftercontrolling for other factors, except for problem-behavior involvement risk which wasfound to increase the likelihood of home-leaving, as expected, but only among femaleadolescents. There was no significant interaction between controls protection and any ofthe risk measures.
The second model considered the relation of overall protection (controls + support +pro-social-behavior involvement) and of overall risk (models + vulnerability +problem-behavior involvement) to home leaving, controlling for socio-demographiccharacteristics and other transitions. In this model, there was no change in the relationof socio-demographic characteristics and of the other transition-to-adulthood markers. Therelation of the measure of overall protection is the same as that shown in the Table 4 model, which is based onthe individual components of the protection measure: Protection is associated with a lowerlikelihood of independent living for male and younger youths. The measure of overall riskwas significantly associated with home-leaving, but only for older youths. The interactionbetween the overall protection measure and the overall risk measure was significant—asexpected, overall protection moderated, buffered or reduced the association betweenproblem-behavior involvement and home-leaving (Table 5).
Table 5.
Association of overall psychosocial and behavioral protection and risk withhome-leaving among adolescents.
| −1 | −2 | −3 | −4 | −5 | |
|---|---|---|---|---|---|
| Overall | Aged 14–17 | Aged 18–22 | Males | Females | |
| Aged 18–22 (ref: 14–17) | 0.96*** | 0.95*** | 1.03*** | ||
| Females | −1.85*** | −2.26*** | −1.76*** | ||
| Socio-economic status | |||||
| Poor | −0.34*** | −0.46* | −0.30** | −0.24 | −0.44** |
| Least poor | −0.54*** | −0.43* | −0.56*** | −0.66*** | −0.43** |
| Out of school (ref: still in school) | 0.68*** | 0.67*** | 0.59*** | 0.75*** | 0.58*** |
| Viwandani (ref: Korogocho) | 0.41*** | 0.06 | 0.56*** | 0.36** | 0.50*** |
| Involved in IGA | 0.99*** | 1.46*** | 0.93*** | 0.82*** | 1.10*** |
| Ever married | 1.44*** | 2.23*** | 1.34*** | 2.26*** | 1.38*** |
| Ever had sexual intercourse | 1.12*** | 1.67*** | 0.78*** | 1.23*** | 0.87*** |
| Overall protection measure | −0.27* | −0.56** | −0.16 | −0.44** | −0.15 |
| Overall risk measure | 0.15 | −0.16 | 0.24* | 0.09 | 0.22 |
| Overall protection measure × overall risk measure interaction | 0.43** | −0.07 | 0.60** | 0.63*** | −0.07 |
| Constant | −2.02*** | −2.21*** | −0.79*** | −2.10*** | −3.66*** |
| Observations | 3,191 | 1,455 | 1,736 | 1,589 | 1,602 |
*** p < 0.01; ** p < 0.05; *p < 0.1.
Accounting for home-leaving: Findings from predicting home-leaving over time
The target sample for this predictive analysis was 2,150 adolescents who had not movedout at Wave 1, of which 1,780 adolescents had information at the subsequent waves eitherat Waves 2 or 3. Therefore, the analytical sample for predictive analysis was 1,780, andthe actual number used for analysis (reported in Tables 6 and and7)7) is less than 1,780 because of observations withmissing information on some of the predictors. A predictive analysis of home-leaving forthose adolescents who had not yet left home by the first wave of data collection showsthat older adolescents were more likely to leave home compared to younger adolescents,while female respondents were less likely to leave regardless of their age. Adolescentswho reported ever having been in a marital union were more likely to leave home by thesecond wave of data collection. Among the psychosocial variables, controls protectionconferred a delaying effect on home-leaving: the higher the controls-protection score, theless likely they were to leave home, controlling for demographic factors. Neither themeasures of pro-social behavior-involvement protection nor of problem-behavior involvementrisk nor of models risk were significant predictors of home-leaving after controlling forother factors. These predictive results for the component measures are presented in Table 6. The composite measures ofoverall protection and risk, shown in Table 7, reinforce the importance of the overall protection composite assignificantly associated with a reduced likelihood of a home-leaving transition over thesubsequent time interval.
Table 6.
Psychosocial and behavioral protective and risk factor component measures aspredictors of home-leaving over time (Wave 1 to Wave 2 or Wave 3).
| (1) | (2) | (3) | |
|---|---|---|---|
| Overall | Aged 14–17 | Aged 18–22 | |
| Aged 18–22 (ref: 14–17) | 1.29*** | ||
| Females | −1.66*** | −2.06*** | −1.52*** |
| Ever married | 1.68*** | 2.84*** | 1.26*** |
| Controls protection | −0.46*** | −0.91*** | −0.29* |
| Parental support protection | −0.05 | −0.09 | −0.00 |
| Pro-social behavior protection | −0.01 | 0.14 | −0.15 |
| Models risk | 0.09 | 0.06 | 0.05 |
| Vulnerability risk | 0.03 | 0.16 | −0.12 |
| Problem-behavior risk | 0.04 | 0.19 | −0.03 |
| Constant | −2.58*** | −1.25*** | −0.02 |
| Observations | 1,751 | 1,018 | 733 |
*** p < 0.01; * p < 0.1.
Table 7.
Overall psychosocial and behavioral protection and risk predicting home-leaving overtime (Wave 1 to Wave 2 or Wave 3).
| (3) | (4) | (5) | |
|---|---|---|---|
| Overall | Aged 14–17 | Aged 18–22 | |
| Aged 18–22 (ref: 14–17) | 1.24*** | ||
| Females | −1.62*** | −1.88*** | −1.52*** |
| Ever married | 1.57*** | 2.60*** | 1.22*** |
| Composite protection measure | −0.70*** | −1.19*** | −0.47** |
| Composite risk measure | 0.16 | 0.41 | −0.00 |
| Constant | −2.52*** | −1.41*** | 0.01 |
| Observations | 1,781 | 1,031 | 750 |
*** p < 0.01; ** p < 0.05.
Accounting for home-leaving: Findings from person-centered analysis
The person-centered analysis considered two sub-groups of adolescents: those whosetransition event was leaving home only, and those who had not experienced anytransition-to-adulthood event. Figure2 illustrates the relationship between controls protection, models risk, and anindex of involvement in problem behavior. The distribution of the controls protectionscore was dichotomized to define groups as low (L) and high (H) in protection; thedistribution of the models risk score was categorized to define groups as low, medium, andhigh risk. The problem behavior index was used as a continuous score, with a high scoreassociated with high problem behavior involvement, that is, with high engagement indelinquent behavior and substance use.
Moderation of models risk by controls protection for sub-groups of those who left theparental home only and those who made no transition (LP: Low controlprotection; HP: High control protection; LH: Left home;NT: No transition).
Figure 2 shows the mean problembehavior involvement score for participants with low protection scores (LP) and highprotection scores (HP) in subgroups at low, medium, or high model risk scores,respectively. Results show that among those who have left home (LH), those who had lowprotection (LP) also had high involvement in problem behavior. In contrast, among thosewho left home (LH) with high protection (HP), their problem-behavior involvement was low,and it remained low, that is, it did not vary as risk went from low to high. As shown inthe figure, among those who made no transition, the role of variation in protection is thesame; those with low protection have high problem behavior scores, scores increasing asrisk goes from low to high, while those with high protection have low problem behaviorscores irrespective of the level of risk.
Discussion
In this article, we explored the concept of home-leaving (establishing independentresidence) as a transition to adulthood among young people in two informal settlements(slums) in Nairobi, Kenya’s capital city. In addition, we examined the usefulness of anexplanatory framework incorporating psychosocial and behavioral risk and protectivefactors.
Our first objective was to examine whether home-leaving is related to othertransition-to-adulthood markers, including first sexual intercourse, marriage, childbearing,involvement in income generating activities. We observed a strong association betweenhome-leaving, marital status, sexual experience, involvement in an income-generatingactivity, and schooling status in the cross-sectional, variable-centered analysis. Asexpected, young people who were married were more likely to be living independently. Theassociation with sexual experience can be explained in two ways; sexually-active youth mayleave home in anticipation of greater freedom and privacy, given the crowded living space inslum dwellings; youth who are living independently have more chance to engage in sex becauseof the absence of social controls such as parental monitoring. The bi-directionalrelationship between involvement in IGA and home-leaving reinforces the role of having anincome in the transition to independent residence. Previous studies have documented the roleof economic resources in the attainment of independent residence among young people (Avery et al., 1992; Donald et al., 1993; ). Asexpected, we also observed that young people who were not in school were more likely to beout of their parental homes. As explained below, the opportunity to attend school may beregarded as a non-transferable resource within the parental household which in effect delayshome-leaving.
Our second objective was to explore the role of psychosocial protective and risk factors inexplaining the occurrence and timing of the home-leaving transition, while accounting forsocio-demographic characteristics. With respect to socio-demographic characteristics, wefound that females leave home later than males, a finding that is counter to studiesconducted in parts of Europe (; Mulder, 2000; Rusconi, 2000). Cultural practices thatfavor early male residential independence while expecting females to leave the parental homeupon marriage may underlie this observation. As noted by Kuate-Defo, (2006) in most sub-Saharan Africansocieties, girls are granted less autonomy and given greater parental monitoring. Therefore,parents may be less willing to let their daughters move into independent housing as comparedto sons. In contrast, as noted earlier, cultural expectations of male independence may alsotrigger their leaving home earlier than girls. However, females may also be less likely tomove out because they lack the financial means to do so.
Although socioeconomic status at Wave 1 was not associated with residential status atsubsequent waves, the results of the cross-sectional, variable-centered analyses suggestthat in low resource settings, such as urban slums, young people living in better resourcedhouseholds may delay home-leaving compared to their counterparts living in the mostresource-strained households. This is in contrast to some studies conducted in the globalNorth (Aquilino, 1991) wherescholars have found the opposite association—higher socioeconomic status is associated withhome-leaving. As noted by An, Mertig, andLiu (2003), in wealthier households in resource-constrained settings, access tonon-transferable resources within the parental household, such as availability of food oropportunities for schooling, among others, may lead youth in wealthier households to delayhome-leaving, while those from poorer households may be forced to move out to look foralternative sources of livelihood.
The risk-protection framework of Problem Behavior Theory employed in this study explainedsubstantial variation in residential status. There were observed differences in theassociation of the theoretical concepts of risk and protection with residential status,depending on age and sex. Unlike the study by Juang, Silbereisen, and Wiesner (1999) in Germany, wedid not observe an association between home-leaving and engagement in problem behavior.However, we observed that the theoretical measure of controls protection moderated orbuffered the likelihood that the home-leaving transition will be accompanied by involvementin problem behavior. In other words, these analyses are uniquely important in revealing thatthere are (at least) two kinds of home-leavers; those whose home-leaving is associated withinvolvement in problem behavior, and those whose home-leaving does not implicate problembehavior, the difference being due to variation in the magnitude of protection. Protectionemerges from this study as a key factor, not only in the likelihood of occurrence ofhome-leaving, but also in the factors associated with it.
The findings that models risk was not associated with home-leaving among adolescents andthat engagement in pro-social activities such as participation in religious, drama, andother groups was associated with a higher likelihood of leaving home, were unexpected. Aspostulated by Juang et al.(1999), young people’s development is affected not only by proximal factors, such aspeer influence, but also by more distal, macro-level factors, including poverty levels. Assuch, it is plausible that although having peers who engage in risk behavior may increasethe likelihood that young people engage in risk behavior and subsequently cause parent-childconflicts, in resource-constrained settings, such as urban slums, the lack of financialresources to support independent living may reduce the likelihood of home-leaving. Withrespect to the observed association between engagement in pro-social activities andhome-leaving, participation in pro-social activities may reflect the young person’s level ofmaturity and readiness for independence, which may be directly associated with timing ofhome-leaving.
Overall, the cross-sectional and predictive variable-centered analyses, and thecross-sectional, person-centered analyses highlight the association of psychosocial andbehavioral factors with leaving home among adolescents in resource-limited settings such asthe slums surrounding Nairobi. Therefore, beyond individual socio-demographiccharacteristics, it is evident that protective factors such as informal social and personalcontrols regulate and reduce the likelihood of early adolescent transitions, whetherinvolvement in risk behaviors or the likelihood of leaving the parental home.
There are several limitations that must be considered when interpreting the findings ofthis study. First, the study did not collect information on the main reasons why youngpeople leave home in the study communities. Therefore, further qualitative studies may behelpful in this respect because they may shed light on the variety of actual experiencesthat lead to home-leaving among youth. Second, although the inclusion of psychosocialvariables such as protective and risk factors advances the understanding of the concept ofhome-leaving, most of these psychosocial variables did not capture parental, peer, orindividual attitudes and beliefs about the desireable timing of independent living thatmight be more directly linked with residential status. Third, parental and peer psychosocialfactors were obtained from the perceptions of adolescents themselves; this could introducebias in the reporting of peer and parental orientations. Attrition may also be a concern forthe predictive analysis, though we looked at how the factors at Wave 1 predict home-leavingby either Wave 2 or Wave 3. This reduced the attrition rate from about 60% to about 34%. Wechecked how sensitive our results might be to the attrition by fitting the model afterimputing all missing data with either 0 or 1 for the outcome variable of home-leaving; therewas no contradiction to our conclusions when compared to the model without imputation(results not shown). Based on these findings, attrition does not appear associated with theoutcome of interest, home-leaving.
Despite these limitations, the study has provided enlarged understanding of home-leavingamong youth in informal settlements and underscored the role of the social and economiccontext in determining home-leaving among young people in resource-poor settings. Thesefinding may have implications in initiatives to ensure positive youth development especiallythose in poverty as noted by Lloyd(2005). Although the prevalence and timing of home-leaving may differ in moreaffluent and representative sections of the region, the present account of home-leaving bypsychosocial risk and protective factors, based as it is on theory, should havegenerality.
Acknowledgments
The authors would like to thank Dr. Chi-Chi Undie for her valuable contribution in theinitial conceptualization of the paper. We are grateful to the colleagues at APHRC whoworked on the project and to the youth in the study communities for participating in thisstudy.
Appendix
The list of items forming different psychosocial domains
| Parental controls protection |
| How much would you say your parents/guardians really know about the followingthings about you? |
| Where you spend time in the evenings on weekdays |
| Who you spend time with in the evenings on weekdays |
| Where you spend time on weekends |
| Who you spend time with on weekends |
| What you do during your free time |
| How you spend your money |
| Whether you have or do homework |
| What TV programs, videos, or films you watch |
| Who your friends are |
| How often does your [PARENT(S)/GUARDIAN(S)] scold or reprimand you when youdo something wrong? For example, if you come home late, don’t do your chores,watch too much TV |
| Personal controls protection |
| How important is it to you [READ STATEMENT]? |
| To be able to rely on religious teachings when you have a problem? |
| To believe in God? |
| To rely on your religious beliefs as a guide for day-to-day living? |
| To be able to turn to prayer when you’re facing a personal problem? |
| Young women should remain virgins until they marry [response categories:agree, disagree, don't know] |
| Young men should remain virgins until they marry [response categories: agree,disagree, don't know] |
| Friends controls protection |
| If you are currently in school, how important is it to your friends that youdo well in school? Would you say [Not too important, important, very important,not in school]? |
| How do most of your friends feel about someone your age drinking alcohol?Would you say [They strongly disapprove, they disapprove, they approve, theystrongly approve, don't really care]? |
| How do most of your friends feel about someone your age using marijuana orother drugs? Would you say [They strongly disapprove, they disapprove, theyapprove, they strongly approve, don't really care]? |
| Parental support protection |
| How often does your [FATHER/FATHER FIGURE] teach you things you didn’tknow? |
| How often do you share your secrets and private feelings with your[FATHER/FATHER FIGURE]? |
| How often does your [FATHER/FATHER FIGURE] try to help you when you needsomething? |
| How often does your [MOTHER/MOTHER FIGURE] teach you things you didn’tknow? |
| How often do you share your secrets and private feelings with your[MOTHER/MOTHER FIGURE]? |
| How often does your [MOTHER/MOTHER FIGURE] try to help you when you needsomething? |
| Pro-social behavior protection (Do you belong to a[GROUP]?) |
| Religious group |
| Drama group/Dance group/Choir |
| Anti-AIDS club |
| Anti-drugs club |
| Girl guides/boy scouts |
| Wildlife society |
| Self-help group |
| Other |
| Models riskSiblings |
| Have any of your brothers or sisters ever had to drop out of school for anyreason |
| Have any of your brothers or sisters ever had premarital sex? |
| Have any of your brothers or sisters ever smoked or do any currently smokecigarettes? |
| Have any of your brothers or sisters ever drunk or do any currently drinkalcohol? |
| Peer models (pressure) |
| How much peer pressure is there on people your age to have sex? Would you say[None, a little, a fair amount, a lot]? |
| Vulnerability risk |
| How well do you get along with others your age? Would you say very well,pretty well, not too well, or not well at all? |
| How well do you live up to what other people expect of you? Would you sayvery well, pretty well, not too well, or not well at all? |
| What about your ability to do well in school (even if you are not in schoolcurrently). Would you say you are very able, pretty able, not too able, or notat all able to do well in school? |
| How attractive do you think you are? Would you say very attractive, fairlyattractive, not too attractive, or not attractive at all? |
| On the whole, how satisfied are you with yourself? Would you say verysatisfied, pretty satisfied, not too satisfied, or not satisfied at all? |
| How well do you resist peer pressure from the rest of the group? Would yousay [Very well, pretty well, not too well, not well at all]? |
| Problem-behavior riskDelinquency |
| You stayed away from home for at least one night without your parent’spermission |
| You started a fight with your peers |
| You took or tried to take something that belonged to someone else, withouttheir knowledge |
| You carried a knife, gun, or other weapon |
| You hit or threatened to hit a peer or adult |
| You delivered or sold drugs (e.g., bhang, miraa, glue) |
| You delivered or sold alcohol (e.g., chang’aa, busaa, beer) |
| Substance use |
| Have you ever smoked a cigarette (not just a few puffs)? |
| Have you smoked a cigarette in the past 4 months? |
| During the past month, how many cigarettes have you smoked on an averageday? |
| Have you ever had a drink of beer, wine, chang’aa, kumi kumi, muratina,busaa, etc., more than two or three times in your life? Not just a sip or tasteof someone else drink? |
| During the past 4 months, how often did you drink alcohol? |
| Over the past 4 months, how many times did you drink four ormore drinks of beer, wine, chang’aa, kumi kumi, muratina or busaa at one time oron the same occasion? |
| How often have you gotten drunk or very high from drinking alcohol in thelast four months? |
| During the past year, have you used [NAME ITEM] to get high? (pills, bhang,miraa, cocaine, petrol, glue, kuber, other) |
Footnotes
Funding: This work uses data from the Transitions To Adulthood study, which was part of theUrbanization, Poverty and Health Dynamics project funded by the Wellcome Trust (GrantNumber 078530/Z/05/Z) from 2006 to 2010. Analysis and writing of the manuscript wassupported by the general support grants from the William and Flora Hewlett Foundation(Grant Number 2009-4051) and the Rockefeller Foundation (Grant Number 2009 SCG 302).
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