The Effect of Parental Presence, Parents’ Education, and Household Headship on Adolescents’...

The Effect of Parental Presence, Parents’Education, and Household Headship on Adoles-cents’ Schooling and Work in Latin America

Mary Arends-KuenningUniversity of Illinois at Urbana-Champaign

Suzanne DuryeaInter-American Development Bank

ABSTRACT: We investigate how the presence and education of parents affect ado-lescents’ school attendance, work participation, and school attainment in Brazil, Ecua-dor, Nicaragua and Panama. Across the four countries, approximately 20% ofadolescents live in single-mother families and 4% in single-father families. Adolescentswho live in single-mother families have significantly lower school attendance andattainment than adolescents who live with both parents. However, the effects of living ina single-mother family are small relative to the effects of parents’ education. Adolescentswho live in single-mother families are not more likely to work than adolescents in two-parent families. Finally, targeting benefits to children in single-mother families wouldreach more children at risk of poor school outcomes than targeting children in female-headed households.

KEY WORDS: child labor; education; female-headship; Latin America; single parents.

While the impact of single-parent families has been the subject ofan extensive literature in the United States, few studies have exam-ined this question in Latin America. Until the World Bank’s LivingStandards Measurement Surveys (LSMS) were available, few datasets in Latin America included questions asked directly about chil-dren’s parents, including their education. This study uses the LSMSdata sets from Brazil (1996–1997), Ecuador (1995) Nicaragua (1998),and Panama (1997) to examine three questions. First, how are ado-lescents’ time allocation decisions, specifically to work and to attendschool, affected by living in a single-parent family? Do these effectsdiffer by the parent’s gender? Second, how is school attainment af-fected by living in a single-parent family? Third, for policy targeting

Mary Arends-Kuenning, University of Illinois at Urbana-Champaign, 408 MumfordHall, MC-710, 1301 W. Gregory, Urbana, IL 61801; e-mail: [email protected].

Suzanne Duryea, Inter-American Development Bank, 1300 New York Avenue, N.W.,Washington, DC 20577, USA; e-mail: [email protected].

Journal of Family and Economic Issues, Vol. 27(2), Summer 2006 � 2006 Springer Science+Business Media, Inc. 263DOI: 10.1007/s10834-006-9011-1

purposes, we consider whether living in a female-headed householdserves as an adequate indicator for an adolescent’s risk of droppingout of school.

Previous Research

The effects of rising divorce rates and increasing incidence of single-parent families on children’s outcomes have received much attentionin the United States. The literature using U.S. data found that chil-dren who lived with single parents had lower educational attainmentthan children who lived with both parents, even after controlling forincome (Couch & Lillard, 1997; Krein & Beller, 1988; McLanahan &Astone, 1991).

Single-parent households were poorer than two-parent households,which is one explanation for why children in single-parent house-holds did worse than children in two-parent households (Jeynes,2002). In Latin America, where households are poorer on averagethan in the U.S., one would expect growing up in a single-parentfamily to have larger negative impacts on children, and especially onboys. Poor families often send their children to work to make-up forincome shortfalls, and boys can earn higher wages in the market thangirls.

In addition to having lower income, single parents may not be ableto provide as much child supervision as two-parent households, even ifextended family members are present (Jeynes, 2002). Therefore,children may be more able to shirk doing homework or attendingschool in single-parent households than in two-parent households.

Studies that compared single-parent households where the parentwas the father to single-parent households where the parent was themother found that the presence of the mother had stronger positiveeffects on children’s schooling than the presence of the father, con-trolling for socioeconomic status and household resources. Studies in avariety of settings have found this result, including the U.S. (Biblarz &Raftery, 1999), Mexico (Levison, Moe, & Knaul, 2001), Jamaica(Handa, 1994), and seven sub-Saharan African countries (Lloyd &Blanc, 1996). These findings were consistent with the literature onintrahousehold distribution, which typically found that resourcescontrolled by mothers had larger impacts on child schooling than re-sources controlled by fathers (Haddad, Hoddinott, & Alderman, 1997;Thomas, 1994).

264 Journal of Family and Economic Issues

Data

We use LSMS data from Brazil, Ecuador, Nicaragua, and Panama.These four countries represent a broad range of Latin Americaneconomies, from Brazil, the region’s largest economy to Nicaragua, oneof its smallest. They also represent a broad range of educational sys-tems, with Panama considered a relatively high quality system andBrazil and Nicaragua regarded as lower quality.

The LSMS data sets were designed by the World Bank in consul-tation with the statistical agencies of developing countries. The Brazildata set is the Pesquisa Sobre Padroes de Vida 1996–97, collected bythe Fundacao Instituto Brasileiro de Geografia e Estatıstica. Thesample covered 10 geographic areas, including the largest cities inBrazil. The other data sets are, for the most part, nationally repre-sentative. The Ecuador data set is the Encuesta de Condiciones deVida, collected by the Instituto Nacional de Estatıstica y Censos. TheNicaragua data set is the Encuesta Nacional de Hogares SobreMedicion de Niveles de Vida 1998, collected by the Instituto Nacionalde Estatısticas y Censos. Finally, the Panama data set is the Encuestade Niveles de Vida 1997 collected by the Ministerio de Planificacion yPolıtica Economica.

The LSMS data sets are unique for their high quality of data aboutparental characteristics. They include parent codes, so children can bematched with their parents within the household, regardless of howthey are related to the household head. Information about the parents’education is also available, regardless of whether the parents live withthe children or not.

The analyses include adolescents aged 14–16 because these arecrucial ages for decisions about work and school. In addition, girls aremaking decisions about whether to marry, especially in Nicaragua. Ineach country, school attendance was very high at age 12 for both girlsand boys. School attendance started to drop off at age 14 in eachcountry except Brazil, where attendance was still above 85% for 14-year old girls and boys. By the age of 16, attendance rates had fallen sothat about 75% of boys were attending school in Brazil, and about halfof boys in Ecuador, Nicaragua, and Panama. At age 16, about 80% ofgirls in Brazil were attending school, compared to about 60% of girls inEcuador and Nicaragua, and 75% in Panama.

We exclude girls who are married and adolescents who are identi-fied as non-relatives, such as boarders, domestic servants, and rela-tives of domestic servants from the analyses. By excluding theseadolescents, we are likely underestimating the effect of growing up in

Mary Arends-Kuenning and Suzanne Duryea 265

a single-parent family on schooling and work. Few of the girls aged14–16 are married, so the bias from excluding married girls is likely tobe low. Further discussion of this issue can be found in the appendix.

Background Information about the Four Countries

The countries represent a wide range of experiences with respect toschooling, child labor, household structure, and the size of the economy.As shown in Table 1, the completed years of schooling of 18-year-oldswere higher for girls than for boys in each country. Although Brazil hasthe most important economy in South America, and Nicaragua is one ofthe poorest countries in Latin America, both countries had approxi-mately equal levels of school attainment for boys. The average level of

TABLE 1

Schooling, Work, and Socioeconomic Indicators

Brazil Ecuador Nicaragua Panama

Schooling and work+

School attainment of 18-year-old girls 7.26 8.76 6.00 8.35School attainment of 18-year-old boys 5.38 7.88 5.72 7.99Proportion of boys aged 14–16

who attend school.82 .60 .56 .67

Proportion of girls aged 14–16who attend school

.86 .61 .63 .72

Proportion of boys aged 14–16who work

.45 .65 .48 .32

Proportion of girls aged 14 to 16who work

.20 .40 .15 .15

Socioeconomic indicatorsGDP per capita, 1995 $5928 $4602 $1837 $6023Poverty rates N/A .35a .48b .25c

Percentage of labor force inagriculture, 1990

.23 .33 .29 .26

Percentage of population living inurban areas, 1995

.78 .59 .62 .56

Note. +Data are from World Bank LSMS data sets collected in 1996–1997 for Brazil, 1995for Ecuador, 1998 for Nicaragua, and 1997 for Panama.Source for GDP per capita, percentage of labor force in agriculture, and percentage ofpopulation living in urban areas is 1998 UNDP Human Development Report.Source for percentage of population from indigenous groups is CIA The World Fact Book.aData from 1994 from World Bank World Development Indicators Online.bData from 1998 from World Bank World Development Indicators Online.cData from 1996 from World Bank (1996b).


schooling that Brazilians completed was much lower than would bepredicted from Brazil’s relatively high GDP per capita (Behrman &Schneider, 1996). Ecuador’s school attainment was higher than Bra-zil’s, although Ecuador’s GDP per capita was relatively low. Schoolattainment appears to be not very closely related to GDP.

Looking at school attendance rates (Table 1), Brazil had a highattendance rate for both boys and girls, although school attainment wasrelatively low. This indicates that in Brazil, children’s progress throughthe school system was more delayed than it is in Ecuador. In Ecuador,14% of 1st graders repeated the grade in 1990 (Garcia-Moreno, 1998),whereas in Brazil, 27% of 1st graders repeated the grade in 1991(Ministry of Education, Brazil, 2003).

Rates of child labor differed across countries.1 Ecuador is notable forthe proportion of boys and girls who work, relative to the othercountries. One explanation for the high proportion of adolescentsworking in Ecuador is that the country had a high proportion of peopleworking in agriculture and a high proportion of indigenous people,relative to the other countries (see also Garcia-Moreno, 1998). InBrazil and Ecuador, boys were more likely to combine school and workthan in Nicaragua and Panama. In each country, girls were less likelyto work and more likely to be attending school than boys. Girls werealso more likely to be reported as neither working nor attending schoolprobably because they were engaged in domestic work.

Another possible explanation for high rates of child labor in Ecuadorwas the economic situation. Of the four countries, Ecuador was theonly one that experienced a negative GDP per capita growth rate of).25% in 1995, the year that the LSMS data were collected (WorldBank, 2005). In contrast, the other countries were doing well eco-nomically. Between 1996 and 1997, GDP per capita grew about 2% inBrazil. GDP per capita grew by 1% in Nicaragua in 1998 and by 4.6%in Panama in 1997 (World Bank, 2005).

The four countries differed in the importance of agriculture in thelabor market. In Ecuador, about a third of the labor force worked inagriculture. Brazil had the lowest proportion of the labor force inagriculture, at less than a quarter. Child labor is often found to behigher in rural areas than in urban areas because children have tra-ditionally provided farm labor (Ashagrie, 1998; Kassouf, 2004; Post,2001).

The countries also differed in their degree of urbanization, withBrazil being the most urban and Panama the most rural. Buildingschools and hiring teachers in rural areas is more difficult than inurban areas because of low population densities and poorer


infrastructure. Therefore, we would expect schooling attainment to belower in countries where a higher proportion of the population lives inrural areas.

Differences in child labor across countries seem to be better ex-plained by variables such as GDP per capita, percentage of labor forcein agriculture and percentage of population in urban areas than dodifferences in schooling. We would expect Ecuador to have a high levelof child labor and low schooling, and Brazil to have low levels of childlabor and high schooling. Institutional differences in educationalsystems also play an important role.

Poor quality public schools have been identified as the mostimportant educational problem in Brazil (Birdsall, Bruns, & Sabot,1996), Ecuador (World Bank, 1996a), and rural areas of Panama(World Bank, 1995). Nicaragua had unique problems as the govern-ment transitioned from a socialist state to a capitalist one, includinghigh levels of debt and weak educational indicators (Ureta, 2005). Inall four countries, poor quality schools led to high levels of graderepetition and school dropout.

In Latin America, the 1990s were a time of great experimentation inpublic education, especially in Brazil, Ecuador, and Nicaragua. Thesethree countries all decentralized their schooling systems so that par-ents and local government officials had more control over school pol-icies and decisions (Barros & Mendonca, 1998; Garcia-Moreno, 1998;King, Ozler, & Rawlings, 1999). Brazil enacted far-reaching reforms in1996 including increased teacher pay, new standards for teachers, andmandatory minimum levels of education spending per student (Kas-souf, Nunes de Almeida, Pontili, & Rodrigues, 2004). Ecuador enactedcurriculum reform (Garcia-Moreno, 1998). Panama passed a law in1995 that increased expenditures on education, investment in infra-structure, and teacher training (World Bank, 1996b).

Governments also enacted new programs to increase children’sschool attendance and attainment and increased resources spent oneducation. Brazil instituted innovative plans that paid poor families inexchange for their children attending school. The two best-knownwere the Programa de Eradicacao do Trabalho Infantil (PETI), whichtargeted children who worked in dangerous occupations in rural areasstarting in 1996, and Bolsa Escola, which was a program institutedand administrated at the municipal level starting in 1995 (Kassoufet al., 2004). In 1997, the year of the LSMS, about 38,000 childrenwere involved with PETI (Kassouf et al., 2004) and 150,000 childrenwith Bolsa Escola.2 Ecuador started a school lunch program in 1990which was supposed to reach 500,000 children in 1995 (World Bank,


1996a). Nicaragua created the Emergency Social Fund in 1991. Newschools were financed through the fund, which accounted for half ofthe investment in Nicaragua’s schools between 1991 and 1998. A re-cent study concluded that the Fund’s investments reached the poorand increased school enrollments, especially for girls (Pradhan &Rawlings, 2002). In Panama, at the time of the LSMS data collectionin 1997, the Inter-American Development Bank had just approved a$58 million loan to improve the quality of primary and secondaryeducation.

Cross-Country Comparisons with LSMS Data

The family structure of 14–16-year-old unmarried adolescents variedacross the countries. In Nicaragua, 27% of these adolescents lived insingle-mother families. The proportion was lower in Ecuador andPanama, where 19% and 18% of 14–16-year-olds lived in single-motherfamilies. Single-father families were rare, with 5% or less in this type ofliving arrangement in all four countries. About 10% of 14–16-year-oldsin each country lived with neither their mother nor their father. InNicaragua, only 58% of adolescents lived with both parents, and inPanama, 68% of adolescents lived with both parents. The countrieswith a high incidence of single-mother families tended to also haverelatively low school attainment.

A high proportion of adolescents who lived in a single-mother familyalso lived in a male-headed household. This proportion varied from .24in Nicaragua to .34 in Panama. Although the adolescent’s father wasnot present, another male relative was the household head. If living ina single-mother family puts children at risk of dropping out of school,then targeting female-headed households is not necessarily a good wayto reach these children. Whether a male or female heads a householdis endogenous. Single mothers who are able to live on their own andhead the household might have better work opportunities than singlemothers who opt to live with their parents, brothers, or other relatives(Light & Ureta, 2004). Female headship is commonly used as a controlfor the risk-status of children both within technical literature as wellas in means-proxy eligibility tests for social assistance.3 While thismeasure seems to work well in the U.S. where female-headship iscorrelated with less-educated mothers as well as the absence of thefather in the household, this standard measure may not be a goodproxy of the risk-status of children in Latin America.


Econometric Approach

We are interested in both the long-term and the short-term impactsof living in a single-parent family on children’s schooling. Therefore,we estimate both a model of current time allocation and of schoolattainment. The first analysis uses a bivariate probit approach to lookat the decisions made by parents to send their children to school, sendthem to work, or to do both at the same time. This model represents aflow of time invested to achieve a given level of schooling. The type offamily is contemporaneous with the time allocation outcome.

In contrast, school attainment represents a stock of human capital,determined by past time allocation decisions. A drawback to looking atschool attainment is that we do not know which years the child lived ina single-parent home or what the household permanent income levelwas in the past. The greater the extent to which current income iscorrelated with past income and current single-parent status is cor-related with past single-parent status, the less biased the result willbe.

We estimate bivariate probit models, which make the relationshipbetween the decisions to send adolescents to work or to school explicit.The outcome variables are whether or not the adolescent attends schooland whether or not the adolescent works. The regression equationsinclude variables such as education of the father and the mother,whether the adolescent lives in a single-parent family, whether theadolescent lives in a female-headed household, and controls for regionswithin the countries. In Ecuador, Nicaragua, and Panama, theregressions include per capita household consumption because thevariable is a better proxy for permanent income than the incomemeasured in LSMS surveys.4 In Brazil, household consumptionaggregates are not readily available, so household income net of theadolescent’s income is calculated and used instead. In cases where aparent’s education was missing, we set that parent’s education equal tozero and created a dummy variable equal to 1 if the parent’s educationwas missing, and zero otherwise. All of the regression results havestandard errors that are corrected for clustering at the household level.

Following Cameron and Heckman (1998) we use an ordered probitmodel, to examine school attainment where the outcomes are the yearsof schooling completed. Other examples of this method can be found inChevalier and Lanot (2002), Lauer (2003), and Maitra (2003).

Two potential endogeneity problems arise with this approach. Thefirst is that per capita household consumption might be affected by childlabor. The second is that whether a household is headed by a male or


female might also be endogenous. These issues are also discussed in theappendix.

Results

Bivariate Probit Models of School Attendance and Labor ForceParticipation

The results for the bivariate probit estimations are shown in Table 2for the school attendance outcome and Table 3 for the work outcome.When discussing the results, we also present predicted probabilities ofworking and of attending school by presence of parents and bymothers’ education. To calculate the predicted probabilities, the vari-able of interest is changed, holding the other variables at their actualvalues. For each individual observation, a predicted probability iscalculated and then an average is taken over all of the observations.

In all four countries, the results indicate that adolescents who live insingle-mother families are statistically significantly less likely to at-tend school than adolescents who live with both parents (Table 2). Theeffect is not merely due to lower income or consumption levels when aworking parent is missing. Figure 1 shows that the effect is especiallystrong in Nicaragua, where the predicted probabilities of attendingschool are .67 in a two-parent family and .55 in a single-mother familyand in Ecuador, where these probabilities are .65 in a two-parentfamily and .53 in a single-mother family. The effects of living in asingle-father family and living in a two-parent family on schoolattendance are statistically indistinguishable in all four countries.Living with neither mother nor father also has a smaller negativeimpact on school attendance than living in a single-mother family,except in Ecuador where the effects are about equal. The effect ofliving with neither parent is negative and statistically significant inEcuador and Nicaragua but is not statistically significant in Braziland Panama. The negative effects of living in a single-mother familyand living with neither parent are greatest in magnitude in the twopoorest and most rural countries.

Adolescents who live in female-headed households are at no greaterrisk of not attending school or joining the work force than adolescentswho live in male-headed households (Table 2 and Table 3) after con-trolling for the educational and income environment of the householdas well as the parental structure of the family. The impact of living ina female-headed household is not statistically significant, except in


Ecuador, where adolescents who live in such households are morelikely to attend school than adolescents who live in male-headedhouseholds. This result suggests that the important risk factor is liv-ing in a single-mother family. Targeting school interventions to ado-lescents who live in female-headed households will miss a significant

TABLE 2

Bivariate Probit Model, School Attendance Decision, Adolescents Aged 14–16

Brazil Ecuadora Nicaragua Panama

Age 14 – – – –Age 15 .028 ).246*** ).230** ).258**

Age 16 ).340** ).324*** ).521*** ).564***

Female .163 .055 .371*** .243**Single mother ).476** ).435*** ).465*** ).356**Single father ).174 ).227 ).166 .067Lives with neither

mother nor father).216 ).416*** ).260* ).224

Female-headed household .270 .400*** ).018 .173

Mother’s educationSome primary .167 – .229** .009Complete primary .716*** .220** .656*** .196Complete secondary .820*** .867*** 1.823*** .492*

Father’s educationSome primary .287 – .225** .261*

Complete primary .384 .288** .306** .521***

Complete secondary .240 .868*** .766*** .626***

Mother’s ed. Missing ).500 .166 .194 .339Father’s ed. Missing ).090 .542** .290 ).070Net household income (‘000s) .211** – – –Net household income

missing).428 – – –

Household per capitaconsumption (‘000s)

– .004*** ).013* .684***

Household per capitaconsumption missing

– ).493* – –

Urban .243 .586*** .648*** .598***

Rho ).170** ).311*** ).444*** ).601***

N 1314 1927 1605 1325Log likelihood )5962507.1 )200.69 )33344.03 )859262.41

Note. Regressions also include controls for region.*p < .10, **p < .05, ***p < .01.aFor Ecuador, parents’ education is classified as primary, secondary, and tertiary, be-cause the questionnaire has only these categories for parents’ education. For parentswho are not resident in the household, it is not possible to know whether the parentcompleted the level of schooling or not. For parents who live in the same household asthe child, parents who completed at least one year of primary school are classified asprimary, parents who completed at least one year of secondary school or at least one yearof tertiary school are classified as secondary.


number of them who are at risk, because approximately one-quarter ofadolescents without fathers in the household have a male designatedas the household head.

Parents’ education is an important determinant of school atten-dance and work. The results differ across countries, so we discuss eachcountry’s results separately.

In Brazil, adolescents whose mothers have completed primary orcompleted secondary are significantly more likely to attend school

TABLE 3

Bivariate Probit Model, Work Decision, Adolescents Aged 14–16


Age 14 – – – –Age 15 .240** .138* .004 .008Age 16 .511*** .221*** .141 .365***

Female ).840*** ).767*** ).949*** ).741***

Single mother .063 .174 .199 .133Single father .042 ).018 .189 .080Lives with neither

mother nor father.095 ).010 .171 .166

Female-headed household .214 ).131 ).067 .191

Mother’s educationSome primary .056 – ).292*** .430***

Complete primary ).114 ).314*** ).520*** .144Complete secondary ).376 ).900*** ).569** ).124

Father’s educationSome primary ).237 – ).160 ).069Complete primary ).549** ).065 ).304** ).035Complete secondary ).468 ).404** ).592*** .050Mother’s ed. Missing ).237 ).477 ).763*** .881*

Father’s ed. Missing ).112 ).643** ).175 ).112Net household income

(‘000s)).063 – – –

Net household incomemissing

.308 – – –

Household per capitaconsumption

– ).001** .006*** ).296***


– .172 – –

Urban ).239 ).478*** ).278*** ).379***

Rho ).170** ).311*** ).444*** ).601***

N 1314 1927 1605 1325Log likelihood )5962507.1 )2000.69 )333440.03 )859262.41

Note. Regressions also include controls for region.*p < .10, **p < .05, ***p < .01.aSee footnote 1 for Table 2.


than adolescents whose mothers have no education (Table 2). Themothers’ education variables are also jointly significant at the 5% le-vel. The fathers’ education variables are neither significant individu-ally nor jointly. The correlation of household income with fathers’education does not explain the lack of significance of fathers’ educa-tion. Figure 2 shows that the magnitudes of mother’s education effectsare similar for no education and for some primary education, and aresimilar for completed primary and completed secondary. The predictedprobability that an adolescent would attend school went from .81 if themother had no schooling to .95 if the mother had completed secondary.Although the effect of mothers’ education seemed stronger than theeffect of fathers’ education, the difference between mothers’ and fa-thers’ education was not statistically significant at any level of edu-cation.

In Ecuador, parents’ education is not broken down into as manycategories as in the other countries—it is only possible to distinguishamong parents having no schooling, primary, secondary and tertiaryschooling. We included tertiary schooling with secondary schooling.All levels of parents’ schooling have statistically significant impactson adolescents’ school attendance relative to having no schooling(Table 2). The mothers’ schooling variables are also jointly significant,as are the fathers’ schooling variables. The effects of mother’s edu-cation and father’s education are very similar and statistical tests

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1


Single MotherTwo ParentsSingle FatherNeither Parent

FIGURE 1

Predicted Probabilities that an Adolescent Aged 14–16 Attends School byPresence of Parents. Calculation Uses Coefficients and N’s from Table 2.


cannot reject that the effect of mother’s education is equal to father’seducation. Compared to Brazil, the magnitudes of the effects ofparental education are large. The predicted probability that an ado-lescent will attend school goes from .53 if the mother has no educationto .79 if the mother has secondary schooling (Figure 2). For fathers,the predicted probability of attending school goes from .52 if the fa-ther has no education to .78 if the father has secondary schooling.

In Nicaragua, where the incidence of single-mother families ishighest, mother’s education has a larger impact on adolescents’ schoolattendance than father’s education (Table 2). All of the parentaleducation variables are statistically significant, and both the mother’seducation variables and the father’s education variables are jointlyhighly statistically significant. Having a mother who has completedsecondary schooling has a larger effect on adolescents’ school atten-dance than having a father who has completed secondary schooling,and the difference between the two effects is statistically significant.Figure 2 shows that the probability that an adolescent attends schoolincreases from .54 to .94 as a mother’s schooling level increases fromno education to completed secondary. The magnitude of the effect issmaller for father’s education, where the probability that an adoles-cent attends school increases from .58 to .79 as a father goes fromhaving no schooling to having completed secondary. Also notable is

NoneSome PrimaryCompleted PrimaryCompleted Secondary

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

Brazil Ecuador* Nicaragua Panama

FIGURE 2

Predicted Probabilities that an Adolescent Aged 14–16 Attends School byMother’s Education Level. Calculation Uses Coefficients and N’s from Table

2.


that Nicaragua is the poorest country and the magnitude of the impactof mother’s schooling on adolescents’ school attendance is the greatestof the four countries.

In Panama, only mothers’ schooling at the secondary level is mar-ginally statistically significant, and the mother’s schooling variablesare not jointly statistically significant. All levels of fathers’ schoolingare statistically significantly different from no schooling and the fa-ther’s schooling variables are also jointly highly significant (Table 2).However, we cannot reject the hypothesis that the effect of father’sschooling is equal to mother’s schooling at any of the three levels ofschooling. In terms of predicted probabilities, the effects of having amother with completed secondary school are about equal to the effectsof having a father with completed secondary school, with the effectslightly larger for fathers. An adolescent with a mother with no edu-cation has a predicted probability of.67 of attending school, comparedto a predicted probability of .79 if the mother has completed secondary(Figure 2).

The impact of parents’ schooling on adolescent labor is presented inTable 3. We focus discussion on the results for Ecuador and Nicaraguabecause the impacts of parents’ education on work are stronger than inBrazil and Panama. Also, Ecuador and Nicaragua are the two poorestcountries and have the two highest rates of adolescent boys’ partici-pation in work.

In Ecuador, the mother’s education variables in the work regressionare jointly statistically significant at the 1% level, as are the father’seducation variables (Table 3). Mother’s education has a strongernegative impact on the probability that an adolescent will work thanfather’s education does. The probability that an adolescent will workfalls from .65 to .36 as mother’s education goes from no education tosecondary schooling (Figure 3) and falls from .57 to .44 as father’seducation goes from no education to secondary schooling. Ecuador hasthe highest predicted probabilities that adolescents will work of thefour countries. Even adolescents who have parents with secondaryschooling have relatively high predicted probabilities of working.

Similar to Ecuador, in Nicaragua, mother’s education has a highlysignificant and negative impact on the probability that an adolescentwill work. All of the mother’s education variables are significantlydifferent from having a mother with no education (Table 3), and themother’s schooling variables are also jointly significant at the 1% level.The magnitude of the effect is to cut the probability by about half thatan adolescent will work from .40 if the mother has no education to .23if a mother has completed secondary education (Figure 3). Father’s


education is negative and statistically significantly different from noeducation at the completed primary and completed secondary levels(Table 3). The father’s education variables are also jointly statisticallysignificant, but at the 5% level. The magnitude of the effect of father’seducation is statistically equal to the effect of mother’s education, withthe predicted probability that an adolescent will work falling from .38if the father has no education to .21 if the father has completed sec-ondary. Therefore, in the two poorest and most rural countries, theimpacts of parents’ schooling on the probability that an adolescent willwork are large and statistically significant.

To generalize, parents’ education has a stronger impact on childlabor in countries with lower levels of GDP and with higher levels ofchild labor. The impact is apparent at lower levels of schooling in thepoorer countries than in the richer countries.

Ordered Probit Models of School Attainment

The independent variables do a better job of explaining schoolattainment than explaining time allocation. Most of the variables arehighly significant in all four countries. The results are presented inTable 4.

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

Brazil Ecuador* Nicaragua Panama

NoneSome PrimaryCompleted PrimaryCompleted Secondary

FIGURE 3

Predicted Probabilities that an Adolescent Aged 14–16 Works by Mother’sEducation Level. Calculation Uses Coefficients and N’s from Table 3.


TABLE 4

Ordered Probit Results, Years of Schooling, Adolescents Aged 14–16


Age 14 – – – –Age 15 .246*** .322*** .443*** .456***

Age 16 .872*** .768*** .621*** .971***

Female .544*** .145*** .485*** .280***

Single mother ).468*** ).243** ).351*** ).047Single father ).358 ).208 ).143 ).031Lives with

neither parent).493*** ).407*** ).155 .083

Female-headedhousehold

.309** .280*** .121 ).016

Mother’s educationSome primary .403*** – .415*** .589***

Complete primary .791*** .575*** .916*** .863***

Complete secondary 1.301*** 1.108*** 1.255*** 1.299***

Father’s educationSome primary .567*** – .154* .205*

Complete primary .791*** .341*** .281*** .511***

Complete secondary 1.027*** .678*** .490*** .603***

Mother’s ed. Missing ).357 .556*** .599*** .611***

Father’s ed. Missing .192 .447** .204 .278Net household

income (‘000s).020 – – –

Net householdincome missing

).339** – – –

Household per capitaconsumption

– .001*** .015*** .157***


– ).120 – –

Urban .194 .453*** .487*** .424***

Cut points for years of schooling1 ).572 ).549 ).026 ).3422 ).013 ).409 .129 ).3073 .354 ).234 .506 ).1824 .716 .027 .860 .0725 1.319 .258 1.181 .3716 1.875 .538 1.574 .6507 2.313 1.723 2.281 1.7538 2.960 2.110 2.825 2.1979 3.856 2.682 3.506 2.941

10 4.681 3.538 4.290 3.86711 6.645 4.423 5.145 5.12712 – 5.375 5.825 –N 1314 1927 1605 1325Log likelihood )13173831 )3458.16 )680396 )1772435.9

Note. Regressions also include controls for region.*p < .10, **p < .05, ***p < .01.aSee footnote 1 for Table 2.


We calculated the predicted probabilities that an adolescent hascompleted at least seventh grade in Brazil and at least eighth grade inthe other countries. This cut off is based upon the years of schoolingthat a 14-year-old will have completed if he entered school at the agethat compulsory schooling begins and did not repeat a grade.5 InBrazil, school begins at age seven and in the other countries, at agesix. These cut-off points allow us to compare school attainment by ages14–16 across countries.

As was the case for the time allocation estimation, adolescents wholive in a single-mother family have lower school attainment thanadolescents who live with both parents. The effect is highly statisti-cally significant, except in Panama, where there is no significant dif-ference between adolescents who live in a single-mother family andadolescents who live with both parents. Living in a single-fatherfamily has no significant effect on the schooling attainment of ado-lescents in any of the countries. However, when we directly test for theequality of effects of living in a single-mother family and living in asingle-father family, we cannot reject the null hypothesis that they areequal in any of the countries.

Figure 4 indicates that school attainment is highest in Panama andlowest in Nicaragua. The negative effects of growing up in a single-mother family are especially large in Nicaragua, where adolescents

0

0.1

0.2

0.3

0.4

0.5

0.6


Single momTwo-parentSingle dadNeither

FIGURE 4

Predicted Probabilities that an Adolescent Aged 14–16 will have CompletedGrade 8 by Presence of Parents. Calculation Uses Coefficients and N’s from

Table 4


who live in a single-mother family have only a .18 probability ofhaving completed eighth grade, compared to a .24 probability foradolescents who live with both parents, and in Brazil, where adoles-cents who live in a single-mother family have only a .27 probability ofhaving completed eighth grade, compared to a .39 probability foradolescents who live with both parents.

In Brazil and in Ecuador, living with neither parent has a negativeand highly statistically significant effect on adolescents’ schoolattainment, which is statistically indistinguishable from the effect ofliving in a single-mother family. This result is expected if mothers andfathers put a higher weight in their utility function on their children’seducation than more distant relatives and friends do. In Ecuador(Figure 4), adolescents who live with neither parent have a predictedprobability of .33 of having completed eighth grade, compared to .38for adolescents who live with a single mother and .45 for children wholive with both parents. In Nicaragua and Panama, adolescents wholive with neither parent do not have a significantly lower schoolattainment than adolescents who live with both parents.

Consistent with the intrahousehold allocation literature (Haddadet al., 1997; Thomas, 1994), adolescents who live in female-headedhouseholds tend to have higher educational attainment than adoles-cents who live in male-headed households after controlling forparental presence and other factors. The variable has a positive andstatistically significant effect in Brazil and Ecuador, and is not sta-tistically significant in Nicaragua and Panama. In Brazil and Ecuador,these effects are large in magnitude. In Brazil, an adolescent who livesin a female-headed household has a predicted probability of .41 ofhaving completed seventh grade, compared to a predicted probabilityof .33 for an adolescent who lives in a male-headed household. InEcuador, an adolescent who lives in a female-headed household has apredicted probability of .51 of having completed eighth grade, com-pared to a predicted probability of .41 for an adolescent who lives in amale-headed household. Again, this indicates that targeting female-headed households does not reach children who are at risk of droppingout of school.

As expected, mothers’ education has a large and statistically sig-nificant impact on adolescents’ school attainment. The effect ofmothers’ education is larger than the effect of fathers’ education. Thedifference between mothers’ education and fathers’ education wasfound to be statistically significant at at least the 5% level at theeducational levels of secondary schooling in Ecuador, some primary,completed primary, and completed secondary in Nicaragua, and


completed secondary in Panama. In the two Central American coun-tries, we found the strongest evidence that the mother’s schooling hada larger effect on adolescents’ school attainment than the father’sschooling. In Nicaragua, with its high levels of marital instability, menwho are identified as children’s fathers might be their stepfathers. Themothers have typically been with the children all of the children’slives, whereas the men might be transient members of the family.

In all four countries, the patterns of the impact of parents’ schoolingon adolescents’ school attainment are very similar. For mother’seducation, the effects are large in magnitude. For example, in Nica-ragua, the effects of increasing the level of mother’s education from noeducation to completed secondary are to almost quadruple the prob-ability that an adolescent will have completed eighth grade from .11 to.40. Even in Panama, the magnitudes of the effects are large, with thepredicted probability that an adolescent will complete at least eighthgrade increasing from .23 if the mother has no schooling to .63 if themother has completed secondary. The marginal impact of having amother who has achieved a given level of education (for example, goingfrom some primary to completed primary) has sizable impacts at eachlevel of education. For example, in Nicaragua, raising mother’s edu-cation from some primary to completed primary increases the proba-bility that an adolescent will complete eighth grade by 76%, from .17 to.30. The patterns for father’s education are similar to those for mo-ther’s education, although the predicted probabilities are lower.

Conclusions

Using the World Bank LSMS data sets, we were able to distinguishamong the effects of parents’ presence and parents’ education onschool attendance, child labor, and school attainment.

Living in a single-mother family has negative and significant im-pacts on the probability that an adolescent will be enrolled in schooland on an adolescent’s schooling attainment in all four countries. Theeffects of living in a single-mother family are larger in magnitude thanliving in a single-father family, although the differences are not sta-tistically significant. The probability that an adolescent will work isnot significantly affected by the presence of parents.

Parents’ education has positive and significant impacts on theprobability that an adolescent is enrolled in school and on schoolattainment. The effects are stronger for school attainment than forschool enrollment, indicating the importance of repetition rates. In the


school attainment analysis, effects of mothers’ schooling are larger inmagnitude than effects of fathers’ schooling.

The effects of parental education on the probability that an adoles-cent will work are stronger in the poorest and most agricultural coun-tries in the sample, Ecuador and Nicaragua. In Brazil, the most urbancountry, only the fathers’ education at the level of some secondaryschooling or higher has a statistically significant impact on the proba-bility that an adolescent will work. In Panama, adolescents of motherswith some primary are more likely to work than adolescents of motherswith no education. The results show that the relationship between childlabor and parental education depends on the country context.

Across the four countries, approximately 20% of adolescents live insingle-mother families and 4% in single-father families. Adolescentswho live in single-mother families are less likely to attend school andhave lower school attainment than adolescents who live with bothparents. Our results suggest that targeting benefits to female-headedhouseholds, however, is not an effective strategy. First, approximatelyone-quarter of adolescents living in single-mother families are missedby the female headship classification. Second, we find no additionalrisk factor of female-headship after controlling for single-mother sta-tus and income. In fact, in Brazil and Ecuador, adolescents in female-headed households have better schooling outcomes than those who livein male-headed households. A more effective strategy to encouragedisadvantaged adolescents to attend school is to target benefits tothose who are growing up in single-mother families, provided thatsuch status is inexpensive to discern.

The results have implications for understanding the intergenera-tional transmission of poverty. Growing up in a single-mother familylowers educational attainment in all four countries, even after con-trolling for household consumption and income. However, the mag-nitudes of the effects of growing up in a single-mother family are smallcompared to the impacts of mothers’ education on educationalattainment. The effects of growing up in a single-mother family onchild labor operate through income effects.

Appendix

Marriage Rates of 14–16-year-olds and Bias

One problem with studying 14–16-year-olds is that girls are startingto get married at these ages. These girls often do not live with their


fathers or mothers and are also less likely to be in school. To avoid aspurious correlation between being in school and living with parents formarried girls, we exclude them from the analysis. To the extent thatgirls who grew up in single-parent families are less likely to attendschool and more likely to be married young, by excluding married 14–16-year-olds, we are underestimating the impact of growing up in a single-parent family on schooling. This bias will be highest in Nicaragua,where 14% of girls aged 14–16 and 26% of 16-year-old girls are married.For the other countries, marriage rates are still low at these ages and sothe bias from excluding married adolescents will be low. Looking at theentire 14–16-year-old age group, 6% or fewer of the girls or boys in theage group are married.

Endogeneity Issues

Two endogeneity problems might arise with this approach. The firstis that ideally, the regressions should include controls for permanentincome. Instead, current income net of the child’s income is availablefor Brazil and per capita household consumption for Ecuador, Nica-ragua, and Panama. Consumption is probably a better proxy for per-manent income than current income is, but if child labor contributes tohousehold consumption levels, endogeneity bias could result. Unob-servable characteristics such as parental preferences could affect bothconsumption levels and children’s time allocation. If parents preferconsumption now to consumption in the future, they might send theirchildren to work now. Consumption would then tend to be negativelyrelated to school attendance. We argue that getting a good control forpermanent income by using consumption is more important than thepotential endogeneity bias that results. We are interested in whetherthe effects of single-parent status on the outcome variables persistafter controlling for income.

Another problem is that family composition might also be endoge-nous. For example, a father might be more likely to leave the family ornot marry the mother of his child if a daughter is born compared to ason as demonstrated using U.S. data in Lundberg and Rose (2003). Asa preliminary step to determine if girls are more likely to live in single-parent families than boys, we estimated probit regressions where theoutcome variable is whether or not the child lives with the father andwhether or not the child lives in a single-parent family and the onlyindependent variable is whether the child is female. For the most part,girls are no more likely to live in a single-mother family or to live apartfrom their fathers than boys are. In Ecuador, girls are significantly


less likely (at the 5% level) to live in a single-mother family than boys.Panama is an exception that is a closer fit to the Lundberg and Rosestory. In Panama, a girl is marginally significantly less likely (at the10% level) to live with her father than a boy is. The results suggestthat there is no gender bias in terms of whether the father lives withthe family or not.

Acknowledgments

The authors would like to thank Claudia Buchmann and seminarparticipants in the Department of Agricultural and Consumer Eco-nomics for very helpful comments on an earlier draft of this paper.This material is based on work supported by Research Board of theUniversity of Illinois and the Cooperative State Research, Education,and Extension Service, U.S. Department of Agriculture, under ProjectNo. ILLU-05-0310. Any opinions, findings, conclusions, or recom-mendations expressed in this publication are those of the authors anddo not necessarily reflect the view of the U.S. Department of Agri-culture. This document reflects the opinion of the authors and does notrepresent the opinions of the Inter-American Development Bank or itsboard of Directors.

Notes

1. The definitions of work are more comparable for Ecuador, Nicaragua, and Panamathan for Brazil. In the three countries, a child is defined as working if he workedat least 1 hour or if he had a job in the last week, although he did not work. Theprobes for the question include working for a family business, working as anapprentice, or helping out on the family farm. In the case of Brazil, a child isclassified as working if she worked in the last week or had a job, but did not work.The questionnaire does not include the probes included in the LSMS question-naires for the other three countries.

2. This number is a very rough estimate based on numbers presented in World Bank(2001) Tables 1 and 2, which presents data by municipality on the number of par-ticipants in Bolsa Escola programs in 1999 and when the programs started. Weadded up the 1999 numbers for municipalities that had programs in 1997, assum-ing that the number of beneficiaries has been constant from 1997 to 1999. Becausethe programs were administered at the municipal level, national data are notavailable.

3. World Bank (2001) reports that 19 of 58 municipalities in Brazil gave preferenceto female-headed households in determining Bolsa Escola benefits. Morley and Co-ady (2003) discuss targeting to female-headed households and note that the PRAFprogram in Honduras originally limited benefits only to female-headed households.

4. According to the World Bank Ecuador Poverty Report (1996a), in cases where dataabout household purchases are incomplete or missing, the World Bank researchersimputed responses at the level of cities or regions. Two percent of observations in


Ecuador are missing data on consumption because the data set with the consump-tion aggregates did not match with the household data sets. For Nicaragua andPanama, consumption values were imputed when categories like food and housingwere missing. Observations were discarded if more than 25% of the value of con-sumption was imputed. In Panama 7 of 4945 households were excluded due tomissing data on consumption and in Nicaragua 169 of 4209 households were ex-cluded (World Bank documentation).

5. Fifteen-year-olds should have completed one more year of schooling and 16-year-olds two more years of schooling. To keep the exposition simple, we focus on thesame grade for all three ages.

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