Primary school student employment and academic achievement in Chile, Colombia, Ecuador and Peru

International Labour Review, Vol. 150 (2011), No. 3–4

Copyright © The author 2011Journal compilation © International Labour Organization 2011

Primary school student employmentand academic achievement

in Chile, Colombia, Ecuador and Peru

David POST*

Abstract. Across Latin America, large numbers of students engage in non-schoolwork during their years of compulsory education, either in the home or in an outsidejob. Drawing upon newly available data, this article uses OLS and multi-level modelsto detect associations between different intensities and locations of employment andstudent achievement in mathematics and reading in the final year of primary school infour Andean countries. Even after controlling for the selection of working studentsinto worse schools, employment is found to have a detrimental impact on achieve-ment, especially when students work four or more hours per day.

ollowing the international ratifications of the United Nations ConventionF on the Rights of the Child 20 years ago, and in the wake of the Educationfor All (EFA) movement, universal access to basic, quality education becameaccepted both as a human right and a priority for public policy. Latin Americancountries – including the four discussed in this article – subsequently witnessedan increase in school enrolment among populations of working children, at leastthrough the completion of primary schooling. However, this shift clearly does notmean that all of the children concomitantly abandoned the world of work whilethey were studying. Many children today combine schooling with part-timeemployment, even in the age ranges when such work is prohibited under ILO’sMinimum Age Convention, 1973 (No. 138), and local labour laws. This pattern isparticularly evident in Latin America. It is doubtful that a rights-based approachto universal education – as expressed by the EFA movement and the Conventionon the Rights of the Child – can entirely eradicate child labour any more thansuch an approach can eliminate the dependence of many families on their chil-dren’s earnings.

* Penn State University, email: [email protected]. The author wishes to acknowledge the use-ful comments he received from Ernesto Treviño and Gilbert Valverde on earlier versions of thisarticle. He is also grateful to colleagues in FLACSO–Quito and UNESCO–Santiago, and to PaulaRazquin, for help in accessing the employment survey and SERCE data.

Responsibility for opinions expressed in signed articles rests solely with their authors, andpublication does not constitute an endorsement by the ILO.

256 International Labour Review

While complementing studies that emphasize the right of children to attendschool, this investigation views school success through a different lens: the effectsof after-school work on the academic achievement of children who are forced tocombine their schooling with employment. Many such studies have previouslyfocused on teens and adolescents during secondary schooling, a stage when manyworking children have already left school. In this article, however, I use newlyavailable testing data on children in their last year of primary school, completionof which is compulsory for all. These data make it possible to assess the impact ofwork intensity (hours per day) and work location (at home or outside the home)on the academic achievement of working and non-working children. Who is mostadvantaged, and who is most disadvantaged, in terms of mathematics and read-ing-level proficiency? Those who work longer or shorter hours? Children whosework is performed at home, or those working outside their home? In which coun-tries are working children at greatest risk? This article addresses these questionswith a focus on the educational systems of four Andean countries where childlabour is still a serious obstacle to universal secondary schooling.

In the four countries studied here, there could be immediate personal andpublic benefits from reducing child labour. Especially in Colombia, Ecuadorand Peru, educators and professional teacher associations and unions could buildpowerful arguments against child labour based on the benefits to children ofreducing their hours of work and increasing their educational attainment. In allof the countries for which evidence is available – including the four countriesexamined here – children’s intensive employment is seen to diminish the likeli-hood of their success at school, thereby also hampering the productivity anddevelopment of their societies. In Latin America, concern with human rightslong predates the current campaigns against child labour and for quality educa-tion, though it was that concern which provided the foundation for recent mobil-izations to protect children (see, for example, Miró Quesada, 1986; Salazár, 1998;García Méndez, 1998). While UNICEF can probably be identified as the leadorganization in the current movement to protect children by guaranteeing theirright to quality education, the ILO coordinates national-level campaigns in thefour countries of this study to eliminate most forms of employment duringthe time that children are enrolled in primary school.

Investigations and advocacy based on the consequences of part-time em-ployment among young students are more ethically complex than those taking arights-based approach. Academic consequences are also difficult to assessbecause the connection between child labour and community welfare is notalways a causal relationship. Being in school does not invariably reduce the like-lihood of employment among children; and employment does not always lessentheir chances of success at school. Often, the allocation of time and energy toschooling and work is simultaneously determined as families and children weighthe current and future costs against the benefits of different uses of the child’stime. In particular, their decisions take account of the availability of qualityeducation and its affordability. Education can be very costly (in terms of books,uniforms, forgone earnings), even when the direct fees are low. These costs are

Primary school student employment and academic achievement 257

immediately apparent to the families of working children. But the material bene-fits from education are not immediately or equally obvious to families (thoughthere are also normative and symbolic benefits of schooling for family status).Apart from the immediate benefits of school feeding programmes, the materialbenefits of education may come far in the future.

One goal of public policy should therefore be to ensure that families makethe kinds of choices they would make (and that the child would want) if completeinformation about the full benefits and costs were available to all, such that fam-ilies and children could make fully informed decisions. In this sense, it is neces-sary to consider the individual and community benefits of education becausesuch benefits might be invisible to struggling families. If families and commu-nities appreciated the full benefits of schooling, they might support full-timeschooling more enthusiastically. If parents and teachers understood the negativeconsequences of children’s employment during primary school, they might dis-courage children from working at an early age.

What is “student employment”?The label of “child labour” has come to have a negative connotation, and mostteachers avoid this term when referring to students who help with family respon-sibilities in business or in agriculture. The meaning of student work has been con-structed by a combination of historical contingencies, global movements, andinternational as well as national politics. Public norms and popular visions ofchildhood have been converging over the past 100 years, since well before theadvent of “children’s rights” (see Zelizer, 1985; Horn, 1995).1 Today, children’semployment in paid and unpaid jobs is publicly debated by United Nations agen-cies and organizations at the national and international levels. The cacophony inthese debates, however, reflects the inconsistency of definitions of “child labour”across countries.

The international legal backdrop to this investigation of the connectionbetween employment and academic achievement is the strategic shift of the ILOand some countries over the past decade as they “de-emphasized” the ILO’sMinimum Age Convention, 1973 (No. 138), in favour of the Worst Forms ofChild Labour Convention, 1999 (No. 182). Convention No. 138 includes blanketprohibitions against nearly all forms of child labour during the age when schoolis compulsory. This Convention, however, was not ratified by many of the devel-oping countries where child labour is widespread, and a new campaign based ona different strategy led to the adoption of Convention No. 182 in 1999. Themobilization and attention generated through the national campaigns to ratifyConvention No. 182 have enabled proponents to secure the ratification ofConvention No. 138 as well. Chile, Ecuador, Colombia and Peru, for example, all

1 See Weiner (1991) for a comparative perspective on a case of national orientation that hasresisted the global convergence around labour norms for children (to their detriment, according tothe author). The forces at play in the regulation of children by states, families and international laware further described by Nieuwenhuys (1996) and White (1999).


ratified Convention No. 138 in rapid sequence beginning ten years ago. Yet,despite the fact that Convention No. 138 prohibits most forms of employmentduring the age range of compulsory education, the ILO’s emphasis in recentyears has been on the most harmful forms of child labour, as opposed to the typesthat are supposed to be incompatible with school attendance (Matz, 2003).

Benefits vs harm: What is a “safe” levelof student employment?How harmful must a type of child labour be in order to qualify as one of the formsthat should be eradicated? Under Convention No. 138, any work that impedeschildren’s schooling is, by definition, illegal. The ILO’s Minimum Age Recom-mendation, 1973 (No. 146), ties the minimum age of legal employment to the ageof completion of compulsory education. Convention No. 182 is less clear on thispoint. It seems to sanction work that impedes children’s education only if missingor failing school harms the child, so the definition of the “worst” forms of childlabour hinges on the empirical questions of how much educational harm comesfrom particular types of labour and how much harm occurs beyond a possiblethreshold level of time-intensity of employment.

In this policy context, international investigations have begun to examinethe consequences of children’s labour for their academic attainment. Althoughthere may be situations in which paid employment benefits academic achieve-ment, the few comparative studies that have examined this relationship tend tofind detrimental effects, even after controlling for the family resource effectsknown to increase either achievement or the likelihood of child labour (seeHeady, 2000; Knaul, 2001; Post and Pong, 2000; Gunnarsson, Orazem andSanchez, 2006; Ray and Lancaster, 2005).

Studies of the individual and community consequences of employment forthe schooling of young children are rare. Most researchers find that workimpedes the academic achievement of older children – at least past a certainpoint in hours and intensity. Lower academic achievement, in turn, is likely toreduce children’s opportunities as adults. The exact types of work associated withnegative learning outcomes, and the threshold points at which these effects ulti-mately appear, seem to vary across and within countries. Less healthy and aca-demically weaker children are more likely to work, and they are less likely toattend school. Their self-selection makes the consequences of work unclear attimes, and this presents difficulties for researchers. When taking into account theendogeneity of school attendance among students, negative consequences ofchild labour have been found in terms of both individual student health in Indo-nesia and academic achievement in Indonesia, the Philippines and other coun-tries (see Post and Pong, 2009; Wolff and Maliki, 2008).2

2 For previous research on Latin America, using the same cross-sectional UNESCO data asthat analysed in the present article (though from 1997, and for fourth-graders), see Sánchez, Orazemand Gunnarsson (2009).


Lower-quality schools are more likely to push students and families to seekalternatives. When children are needed to supplement scarce family income,then low-quality schools provide no countervailing incentive for families tomaintain their children in school (Schiefelbein, 1997; Boyden, Ling and Myers,1998). By contrast, high-quality and close-by schools can reduce the likelihoodthat children will work, either exclusively or in addition to their time in school.Free breakfasts and lunches not only promote children’s health directly, but theyalso help to retain them in school and lessen the likelihood that they will need towork. The two-way relationship between child labour and school quality isimportant in discussions of child labour because child-friendly and high-qualityeducation will ultimately promote school attendance.

What about the community effects of education as an alternative to childlabour? In the aggregate, a better-educated adult population is likely to embracemore progressive and less traditional views on childhood and to embrace its ownchildren’s right to an education. The acceptance of global norms and the expec-tations for children can also work in a feedback loop to improve educationalquality. National and global campaigns to provide quality education for all chil-dren can press local governments to improve schools and, thereby, lower the inci-dence of harmful child labour. The EFA movement has forced governments toreport on the progress they make toward this goal, and the global public discus-sion may positively affect the willingness of governments to back rhetoric withresources. The mediating role of schooling in the relationship between children’sindividual well-being and community well-being thus goes beyond the curricu-lum and pedagogy. The presence or absence of quality schools affects the waysthat families allocate their children’s time, while their choices simultaneouslydetermine whether or not children receive an education.

Many material and symbolic incentives also have been used to induceschool attendance in place of child labour. A partial list would include cash pay-ments, school feeding programmes, payment of direct costs, voucher systems ofschool finance, apprenticeship programmes that pay students a wage along withtheir schooling, and incentives to school administrators. In addition to suchmaterial incentives, governments and NGOs can increase the subjective valu-ation of education relative to work by sending a message about what types ofbehaviour are normative for families and communities. In parallel with scholar-ship on the norms and institutions that have shaped childhood, a large body ofresearch has examined the impact of conditional cash transfers aimed at influen-cing household behaviour by substituting for children’s earnings (on LatinAmerica, see for example, Attanasio et al., 2006; Schultz, 2004; Ponce and Bedi,2010).

Working children in Chile, Peru, Ecuadorand ColombiaChile has been a leader in the areas of workers’ rights and the abolition of childlabour in Latin America – a position associated with the election of Juan Somavia


as Director-General of the ILO in 1998. In the following year, Chile became thefirst of the four countries under study in this article formally to ratify ConventionNo. 138, establishing a minimum age of 15 for employment. In 2001, rather thanmerely protect working children, Chile adopted one of the region’s most ambi-tious legal frameworks aimed at eliminating child labour. Over the past decade,Chilean groups united against child labour have included political parties, tradeunions, religious orders, NGOs and – most significantly for our purposes here –the teaching profession.3 As will be shown below, these movements achievedresults: by 2006, Chile had far fewer sixth-graders in employment than most otherLatin American countries, and those who did work were employed for fewerhours per day.

In Peru, educational leaders have had a more ambivalent relation withcampaigns to eliminate child labour. Promulgated in 1992, Peru’s Code of Chil-dren and Adolescents created a new legal category by granting minors aged12–17 the right to work, albeit subject to safeguards.4 Peru was also the centre ofa continental movement led by working children themselves, who argued thattheir right to participation – as recognized by the Convention on the Rights of theChild – gave them the right to formulate policies aimed at protecting them and towork whenever necessary for their survival (Schibotto and Cussiánovich, 1994;Verdera, 1995; Rodríguez and Abler, 1998). Perhaps partly as a result of suchambivalence, all previous evidence suggests both that high proportions of Lima’schildren combine their schooling with employment, and also that the competi-tion for children’s time affects the quality of their education.5

During the 1990s, Colombian advocates for children, like their Peruviancounterparts, attended to children’s agency (protagonismo infantil) in the formu-lation of public policy (see Muñoz Vila and Palacios, 1980; Salazár and AlarcónGlasinovich, 1998; Muñoz Vila, 1996; Rojas, 1999). In more recent years, how-ever, the ILO has led a successful campaign to involve institutions – including theschool system – in a national plan. The proportions of working children and ado-lescents subsequently declined.6

Ecuador has implemented numerous child protection and school attend-ance reforms since the introduction of the ILO’s International Programme onthe Elimination of Child Labour (IPEC) in 1997. One programme attempted toencourage attendance by providing school supplies to poor children (mochila es-colar). All school fees have been suspended. An experiment with decentralized

3 On the policy, see Chile (2001); for historical background, see Gajardo and de Andraca(1998); and for more recent explorations, see del Rio and Cumsille (2008), Sapelli and Torche (2004),Garcés (2006) and Peralta and Muñoz (2006).

4 See Legislative Decree No. 26102, of 24 December 1992, in El Peruano, No. 4556, 29 Decem-ber 1992, pp. 111558–111573.

5 For recent explorations, see Rodríguez and Vargas (2008, p. 86) and Ponce (2007). On thehigh proportions of Peruvian students who have historically combined work with schooling (espe-cially compared with Mexico and Chile), see also Post (2001).

6 For recent developments, see the assessments contained in Colombia’s (2008) NationalStrategy for the Prevention and Elimination of the Worst Forms of Child Labour and the Protectionof Young Workers.


school governance also created incentives to enrol greater numbers of rural chil-dren. Targeted cash transfers have attempted to substitute for children’s earnings(“bono de desarrollo humano”). During the past decade, a network of labour andchildren’s rights organizations has also mobilized against the employment of chil-dren during their primary-school years. While the results of each of these inter-ventions are difficult to disentangle, the net consequence for children has beenvery clear (Ponce Jarrín, 2008).7 In collaboration with child rights organizationsand trade unions, Ecuador’s 2003 Code for Childhood and Adolescence un-equivocally sets the goal of “eradicating” work among all children younger than15 years. Probably more than other countries, Ecuador has reduced the propor-tions of young people who work, either as an alternative to schooling or in com-bination with school attendance. Annual national employment surveys ofhouseholds show a consistent trend (see figure 1). At the time of the SecondRegional Comparative and Explanatory Study (SERCE) of Ecuadorian stu-dents, in 2006, rates of school participation were already rising and this trend hascontinued. According to a 2006 employment survey based on a national sampleof households, however, about 15 per cent of children aged 10–14 simultaneouslyworked while attending school. This figure probably understates the truthbecause it is based on interviews, not with the children themselves, but withhousehold heads (who may be reluctant to acknowledge their children’s out-of-school activities).

7 For attempts to disentangle the many factors associated with the decline of child labour, seeVelasco Abad (2010) and ODNA/UNICEF/OSE (2006).


Theoretical frameworks: Pathways and trade-offsin the work–achievement relationshipFrom one theoretical perspective, children who allocate their time to paid em-ployment, as opposed to school-related assignments and extra-curricular activ-ity, thereby “choose” to acquire fewer of the skills that are valued by the schoolsystem. In this view, intensive employment interferes with academic success.Others have suggested that employment makes a positive contribution to theearly development of children’s sense of responsibility, independence and self-improvement, all of which are essential in the process of socialization to adult-hood. Although the data used in this study offer no detailed information aboutchildren’s work environments, in much of Latin America the work performed byyoung people is not age-segregated, and children have the opportunity to workalongside adult role models. Such opportunities could well serve to support chil-dren’s development and their integration into the wider community. In addition,some types of commercial work could foster the development of numeracy andliteracy in ways which counterbalance the drain that jobs place on the child’stime. Can work experiences in some Latin American countries actually promotecognitive development as effectively, or perhaps even more effectively, thanschool experiences? There may indeed be situations in which some paid employ-ment benefits academic achievement.8

Despite this possibility, the few international studies that have looked atemployment and achievement tend to find detrimental effects, even after con-trolling for family resources. Such detrimental effects also show up in pastresearch on Latin America (Sánchez, Orazem and Gunnarsson, 2009).9 How-ever, there has been little research on the effect of unpaid family work on chil-dren’s overall development or, more specifically, whether such work helps orhinders their academic achievement. Many of the arguments applied to theadvantages and disadvantages of paid employment outside the home can also beused to analyse unpaid chores. The chief policy difference, obviously, is that nocountries have attempted to regulate the non-market work performed by chil-dren in their own homes.

Regardless of the reasons that parents and students may have for the lat-ter’s part-time work after school, the hypothesis underlying this article is thatthere will be consequences for academic achievement. But there are two distincttheories that lead to this hypothesis. The first stems from the perspective ofhousehold economics: the time and energy that students devote to their employ-ment detract from the finite reserve of resources at their disposal for the develop-ment of their academic proficiency. It can thus be hypothesized that children who

8 There is, of course, a voluminous literature on the developmental consequences of earlyemployment, though most is focused on children in wealthier countries, where the motivationfor work is often to obtain individual earnings as opposed to providing household support (see, forexample, Greenberger and Steinberg, 1986; Schoenhals, Tienda and Schneider, 1998; Warren,LePore and Mare, 2000; Marsh and Kleitman, 2005; Tyler, 2003; Staff and Mortimer, 2007).

9 According to Ilahi, Orazem and Sedlacek (2009), the achievement gap between employedand non-employed students may be a major reason for wage inequalities among adults.


work have less opportunity and less incentive for academic achievement. Admit-tedly, some types of work are more compatible with academic success thanothers. Work that has flexible hours (as does most work done within the home)is less likely to conflict with the demands of schooling than work with fixed hoursoutside the home. From the perspective of a time budget, also it can be hypothe-sized that students who work for pay have lower academic achievement whenthey work many hours each day, but that the impact on achievement would besmaller among children who work for pay only a few hours per day.

The second theoretical perspective from which early labour force partici-pation would seem to affect academic proficiency – independently of the self-selection of less proficient students as part-time workers – is that of identity. Asyoung people develop their identities, they are oriented by the values of peers.Irrespective of the amount of actual time that students spend in school and on thejob, those who engage in part-time work may tend to see themselves differentlyas compared with the self-conceptions of students who focus only on schoolwork. Students’ self-conceptions are likely to depend on their associations withworkers and/or with students. In work experiences where students find linkagesbetween academic success and success on the job, the after-school workers wouldhave incentives to achieve greater academic proficiency. But if school work is feltto be irrelevant to success on the job, students will have little incentive for aca-demic achievement. From this perspective, working in a job (as opposed to work-ing at home) affects students primarily via their identification with the role ofworker versus that of student. In other words, the net impact of having a jobwould not derive primarily from the hours worked. Rather, it would result simplyfrom the fact that the student works at all and self-identifies as an employee.From this perspective, one would therefore expect work in the home to have lessof an impact.

The relationships between the home, the school/work choice and academicproficiency are also dependent on the national context in which these relation-ships develop. There are two ways in which the national context can affect theserelationships. In economies which depend heavily on child labour, where familiesmust use their children’s time for meeting basic welfare needs, children who arelucky enough to attend school will nevertheless face both opportunities anddemands for part-time work. In such circumstances, the type of work that chil-dren do may include arduous and sometimes even dangerous physical labour. Inother words, the mere hours that children work after school cannot be expectedto impact their academic development in the same way in every economy. InChile, with a system of labour protections more firmly in place than in the otherAndean countries of this study, working for a given number of hours could leadto less detrimental consequences than elsewhere.

There is another contextual factor that complicates the relationshipbetween school achievement and children’s work. Children’s work-related deci-sions are likely to be influenced not only by the supply of local work opportuni-ties, by government regulation of employment and by families’ demands for theirchildren’s time. In addition, the school itself may also play an important role.


Schools – like families – place demands on children’s time by requiring them toattend regularly and by assigning out-of-school readings and exercises. The qual-ity of the school and of its teaching could thus also influence the amount of non-school work children perform as well as children’s academic achievement. Thiswould be of less concern for researchers if working and non-working childrenwere randomly assigned to schools of better and worse quality. But it is under-stood that poorer families, which depend on their children’s labour, are likely toplace their children in lower-quality schools because they can neither affordto pay for private schooling nor relocate to areas with higher-quality free school-ing. Because the amount of school resources and family resources influencingwork-related decisions and achievement are related, it is necessary to differenti-ate the individual-level effects of student employment from the net effects of theschools themselves. Figure 2 offers a graphical representation of the identifiable(though not always measurable) factors that would, in theory, lead us to hypothe-size a relationship between non-school work and academic achievement. Thefigure also represents the possibility that students’ proficiency in math and read-ing will be affected differently depending on whether they work after school intheir home or in a job. The variation in this effect could stem from differencesin the intensity of the work children perform inside the home as opposed to workfor an external employer or in family agriculture, and these differences would bereflected in the number of hours of work. Such differences may also stem from


the economic and political context of their work, since certain work locations areless likely than others to be inspected by labour administration officials or schoolauthorities.

SERCE data and summary statisticsConducted under a UNESCO project entitled Laboratorio Latinoamericano deEvaluación de la Calidad de la Educación (LLECE), the Second Regional Com-parative and Explanatory Study (SERCE in Spanish) surveyed 16 countries ofLatin America in 2006. In addition, the Mexican state of Nuevo León partici-pated independently. In all participating countries, students in the fourth andsixth years of primary school were assessed for their proficiency in language(reading and writing) and mathematics (several countries also included naturalscience assessments). The questions used to assess students were based oncurricular components common to all participating countries, with input fromcurriculum specialists in each nation’s ministry.

The 2006 SERCE was not a simple repeat of the first study of achievementin Latin America, which LLECE had conducted among third- and fourth-gradestudents in 1997. Not only were different grade levels selected for the secondstudy, but there was also stronger emphasis on applied knowledge and life skills.Most of the questions included reasoning and thinking problems that requiredstudents to use skills acquired in the classroom. SERCE included students from3,065 schools across all of the participating countries; 4,227 sixth-grade class-rooms were selected, comprising a total of 95,288 sixth-grade students. Not onlywere students assessed on their academic skills, but SERCE also administeredsix questionnaires. These were answered by the child, the child’s parent or par-ents, the school director and the child’s teacher,10 with separate questionnairesfor the directors and teachers asking for individual professional information andabout characteristics of the school (for directors) and classroom pedagogy (forteachers). Following extensive work on questionnaire and sample design from2002 to 2005, SERCE conducted a pilot survey in late 2005. Then, after process-ing the data and developing an internationally consistent coding scheme, thestudy was applied across Latin America in late 2006. The findings on national lev-els of achievement were extensively reported across Latin America, and in manycases they were consistent with the results of LLECE’s first study. As in 1997,Cuban students achieved the highest scores in all subject areas. Of the four coun-tries investigated in this article, Chile was among the higher-scoring participants,Ecuador was among the lower-scoring participants, while Colombia and Peruwere in a middle group.

To assess the impact of after-school employment among sixth-grade stu-dents in each of the four countries, I used control variables originating from various

10 In part because Mexico was simultaneously participating in the OECD’s Programme forInternational Student Assessment (PISA), Mexico omitted the parental component of the SERCE.Therefore, no information was available to create the index of socio-economic status in Mexico as awhole. Nuevo León, participating separately, did include this component.


components of the SERCE. Household resources and parental socio-economicstatus have been indexed by UNESCO, based on parental responses to thehousehold survey. As another control, I used information about how many chil-dren were living at home with the student. As a school level control, I drew uponinformation provided by the school director about the school infrastructure tocreate a scale of facilities that were known to promote student achievement. Asfurther control variables, I also used the location and governance of the schooland two dummy variables to control for whether or not the school was urban pri-vate (as opposed to urban public) or rural public (as opposed to urban public). Asteacher-level control variables, I included information about teacher morale andinterest. SERCE asked the children’s teachers whether they would prefer towork in a different school. As we will see, the teachers’ responses to this questionwere an important factor in their students’ proficiency. I also took account ofeach teacher’s number of years of professional experience in the school becausemore experienced teachers are associated with higher-achieving students.

From the student questionnaire, I selected only four items in addition tothe child’s reading and math scores, which are the dependent variables. As a stu-dent-level control, I used the student’s age, which is important in cross-nationalcomparisons because Colombian students averaged about six months older thantheir peers in the other three countries. I also included information on whetherthe student was female or male. Finally, for the specific purposes of this study, Ilooked at information on after-school employment, using the responses to two ofthe five questions SERCE asked about after-school employment, namely, ques-tions 15 and 19. Question 15 asked: “In addition to attending school, do youwork?” The three possible responses were (a) “No”, (b) “Yes, at home”, and(c) “Yes, outside the home”. Question 19 asked students who responded thatthey worked outside of school: “How many hours each day do you work?” Thepossible responses were “1 hour”, “2 hours”, “3 hours”, and “4 or more hours”.

Table 1 gives the means, standard deviations and numbers for the sixth-gradestudents on whom information was available. These statistics are presented separ-ately for Chile, Colombia, Ecuador and Peru. Apart from the wide differences instudent achievement, which have already been widely discussed in the popularmedia of Latin America, there are other notable differences between countries.Whereas the teachers of Colombia and Peru averaged 16 years of teachingexperience, those in Chile and in Ecuador had many more years of experience.About 45 per cent of Peruvian teachers would prefer to work in a different school,in contrast with only 13 per cent of Chilean teachers. I created an index composedof 12 separate school resources that together indicate overall quality of infrastruc-ture. Based on this index, table 1 shows large international differences in the facil-ities available in schools. On average, Chilean schools possessed more than ten ofthe 12 facilities, whereas Ecuadorian schools had fewer than six.

Important differences are also apparent as to locus of employment andwork intensity. Although roughly equal proportions of sixth-graders worked out-side the home in each country, many more Peruvian students reported that theyworked for their families than did children in the other three countries. And,


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60.

510.

504,

509

Sch

ool i

s ur

ban

priv

ate

(not

urb

an p

ublic

)0.

320.

476,

912

0.27

0.45

6,02

60.

200.

405,

376

0.13

0.33

4,66

2

Sch

ool i

s ru

ral p

ublic

(not

urb

an p

ublic

)0.

100.

306,

912

0.21

0.41

6,02

60.

240.

435,

376

0.15

0.36

4,66

2

Num

ber

of y

ears

of t

each

er’s

exp

erie

nce

23.1

511

.95

5,02

016

.01

10.1

65,

738

20.0

910

.37

4,65

516

.60

7.39

4,42

1

Teac

her

wou

ld p

refe

r to

wor

k in

diff

eren

t sch

ool

0.13

0.34

4,74

70.

200.

405,

361

0.23

0.42

4,43

00.

450.

504,

215

Inde

x of

sch

ool f

acilit

ies

avai

labl

e*10

.25

1.49

6,17

27.

601.

965,

473

5.84

1.91

2,98

06.

801.

984,

204

Wor

ked

afte

r sc

hool

with

fam

ily0.

220.

425,

870

0.26

0.44

5,21

50.

270.

444,

785

0.37

0.48

4,09

0

Wor

ked

afte

r sc

hool

out

side

hom

e0.

070.

265,

870

0.09

0.28

5,21

50.

100.

304,

785

0.11

0.31

4,09

0

Wor

ked

1 ho

ur/d

ay0.

100.

306,

912

0.08

0.27

6,02

60.

080.

275,

376

0.13

0.34

4,66

2

Wor

ked

2 ho

urs/

day

0.05

0.22

6,91

20.

050.

236,

026

0.06

0.23

5,37

60.

080.

284,

662

Wor

ked

3 ho

urs/

day

0.03

0.16

6,91

20.

050.

216,

026

0.05

0.22

5,37

60.

070.

264,

662

Wor

ked

4 or

mor

e ho

urs/

day

0.06

0.24

6,91

20.

110.

326,

026

0.14

0.34

5,37

60.

150.

364,

662

* Th

e “in

dex

of s

choo

l fac

ilitie

s” is

the

sum

of

dire

ctor

s’ r

espo

nses

to

12 y

es/n

o qu

estio

ns a

s to

whe

ther

the

sch

ool h

as t

he f

ollo

win

g ite

ms:

ele

ctric

ity,

runn

ing

wat

er,

indo

or p

lum

bing

, a

tele

phon

e, s

uffic

ient

bath

room

s, a

kitc

hen,

a lu

nchr

oom

, a li

brar

y, n

utrit

iona

l fee

ding

pro

gram

mes

, med

ical

ser

vice

s, tr

ansp

orta

tion,

and

free

text

book

s.

Not

e: S

.D. =

sta

ndar

d de

viat

ion.


whereas only 6 per cent of Chilean students reported working four or more hourseach day, 15 per cent of Peruvian students and 14 per cent of Ecuadorean stu-dents stated that they did so.

Ordinary least squares and hierarchicallinear modellingCross-sectional data are limited in their ability to illuminate causal relationsbetween student employment and achievement. A student’s sixth-grade achieve-ment is a cumulative measure of all prior learning in the classroom, at home, andin the community. But student questionnaires typically ask only whether the stu-dent is currently employed, at the time of the survey. Whatever the effects ofemployment may be, a student who began to work recently would be affectedless than a student who has worked since the first grade. A related problem is thatof self-selection. Families with scarce resources may allocate more able studentsto full-time study, while permitting weaker or less-motivated students to work athome or in a job. This allocation could create reverse causality in the work–achievement relationship, as students with lower achievement become over timemore likely to be employed. Although these two problems raise important theor-etical concerns, past research has not found them to be insurmountable for inves-tigations of the independent effects of employment. As discussed previously,when researchers have addressed these issues by including instrumental vari-ables or prior information about student achievement as a control, the effects ofemployment persist. These earlier studies suggest that one would find similarresults if we had the right types of controls for prior achievement in SERCE or aninstrumental variable (such as local employment rates) predicting studentemployment but not predicting achievement.

There is a different type of self-selection, discussed previously, that can beaddressed with the SERCE data by using multi-level models to differentiate theschool factors from individual factors of achievement. It is frequently the casethat working-class students attend worse, less productive schools than middle-class children, who then doubly benefit because they are freer from work respon-sibilities. Typically, investigations of work–achievement effects do not attempt toaccount for this type of self-selectivity, and they may thus erroneously attributethe lower level of employed student achievement to the work activity itself, whenthe fault actually lies with the student worker’s low-quality school. In otherwords, some of the lower achievement observed among working students maystem from the fact that they attend bad schools, not from the fact that they work.School quality can be measured, in part, by the individual-level controls thatwere discussed previously and presented in table 1. Over and above these indi-vidual-level variables there is the net quality of the school as measured throughthe achievement of all students. Overall achievement is likely to be lower inschools that enrol many working students. To control for these organizationaleffects on school achievement, I estimated both ordinary least squares (OLS) re-gressions and hierarchical linear models (HLM).


Table 2 presents the results of both types of regression analysis for mathand reading proficiency in all four countries combined (using dummy variablesfor Peru, Ecuador and Colombia, with Chile as the excluded reference category).The employment variable is specified in two different ways. In the first four mod-els (left side of table 2), the intensity of after-school employment is measuredusing dummy variables for the numbers of hours worked each day. The omittedreference category is “working zero hours” (i.e. not working at all). Then, in thefour columns on the right side of table 2, the locus of after-school employment ismeasured with dummy variables representing working at home and working out-side the home (as opposed to the omitted reference category of not working atall).

Aside from the apparent effects of sixth-graders’ employment, the controlvariables also reveal some important information that should be briefly noted.The effects of student age, socio-economic status, and number of other younghousehold members are all as would be expected, and consistent with much pre-vious demographic research on educational achievement. Over-age studentshave often repeated a year of school, and this may indicate lower ability as com-pared to students who are in the modal grade level for their age. Higher parentaleducation, occupational prestige and home resources have been found univer-sally to lead to higher levels of proficiency among children. Notice, however, thatthe coefficients for socio-economic status are much lower in the HLM estima-tions than they are in the OLS regressions. This illustrates the point that part ofthe benefit of higher socio-economic status is indirect. Parents with greater cul-tural and material resources are able to select good schools for their children, andgood schools tend to produce higher overall academic achievement. After takingaccount of overall school achievement, the direct effects of socio-economic statusare still highly significant, but much smaller. The negative coefficients for beingfemale are consistent with LLECE reports for sixth-graders in the four countriesof this study. However, the negative coefficients for reading proficiency mayseem surprising. After controlling for the effects of work, girls actually scoredlower than boys in reading. This finding can be explained by the fact that boyswere far more likely than girls to work after school, and by the fact that after-school work negatively affects reading and math proficiency (as also seen intable 2). In other words, some of the disadvantages in reading for boys (relativeto girls) that are evident in the bivariate reports publicized by the LLECE reflectthe fact that more boys than girls work after school (see LLECE, 2008, p. 149, fig-ure 6.11). Controlling for after-school activities, sixth-grade boys outperformgirls in reading as well as in math.

It is important to note that students in urban private schools displayedhigher proficiencies in math and reading than those in urban public schools, evenafter taking account of family socio-economic status and school facilities. It isalso important to observe that students in rural public schools are not inherentlydisadvantaged relative to their urban public-school peers, once demographic andschool quality indicators are controlled. The coefficients for rural school attend-ance are not statistically significant in any of the eight regression models.


Tab

le 2

.A

fter

-sch

ool e

mp

loym

ent

and

aca

dem

ic p

rofic

ienc

y am

ong

sixt

h-gr

ade

stud

ents

in C

hile

, Col

omb

ia, E

cuad

or a

nd P

eru:

Effe

cts

of h

ours

and

locu

s of

wor

k (O

LS a

nd H

LM r

egre

ssio

n co

effic

ient

s)

Dum

mie

s fo

r ho

urs

per

day

Dum

mie

s fo

r w

ork

at h

ome/

outs

ide

Mat

hR

eadi

ngM

ath

Rea

ding

OLS

HLM

OLS

HLM

OLS

HLM

OLS

HLM

Stu

dent

’s a

ge a

t tim

e of

SE

RC

E–5

.16

–4.7

2–6

.05

–5.3

3–4

.88

–4.2

5–6

.02

–5.3

9(5

.82)

**(5

.54)

**(7

.43)

**(6

.59)

**(5

.26)

**(4

.77)

**(7

.09)

**(6

.38)

**

UN

ES

CO

Inde

x of

soc

io-e

cono

mic

sta

tus

13.7

26.

3815

.58

9.93

14.3

36.

6916

.00

10.3

3(1

9.2)

**(8

.54)

**(2

3.5)

**(1

3.9)

**(1

9.2)

**(8

.51)

**(2

3.0)

**(1

3.8)

**

Num

ber

of h

ouse

hold

mem

bers

und

er a

ge 1

8–2

.48

–1.5

7–3

.44

–2.6

9–2

.53

–1.6

1–3

.37

–2.7

0(4

.79)

**(3

.20)

**(7

.20)

**(5

.76)

**(4

.64)

**(3

.14)

**(6

.73)

**(5

.52)

**

Stu

dent

is fe

mal

e (n

ot m

ale)

–13.

92–1

6.76

–3.6

1–5

.51

–13.

97–1

6.67

–3.2

1–5

.29

(8.0

7)**

(10.

1)**

(2.2

7)*

(3.4

7)**

(7.6

6)**

(9.4

9)**

–1.9

1(3

.16)

**

Sch

ool i

s ur

ban

priv

ate

(not

urb

an p

ublic

)37

.40

44.1

931

.02

34.0

838

.15

44.6

630

.93

33.5

9(1

5.7)

**(7

.39)

**(1

4.1)

**(7

.25)

**(1

5.3)

**(7

.2)*

*(1

3.4)

**(7

.24)

**

Sch

ool i

s ru

ral p

ublic

(not

urb

an p

ublic

)0.

15–3

.56

–3.0

4–6

.88

0.16

–3.6

4–1

.85

–5.5

6(–

0.06

)(–

0.62

)(–

1.35

)(–

1.52

)(–

0.06

)(–

0.62

)(–

0.78

)(–

1.24

)

Num

ber

of y

ears

teac

her

wor

ked

in s

choo

l0.

460.

600.

240.

190.

460.

610.

260.

23(4

.96)

**(4

.24)

**(2

.80)

**(–

1.50

)(4

.70)

**(4

.14)

**(2

.91)

**(–

1.74

)

Teac

her

wou

ld p

refe

r to

wor

k in

diff

eren

t sch

ool

–6.6

6–3

.08

–10.

87–8

.73

–6.9

9–2

.44

–10.

95–8

.55

(3.1

1)**

–0.9

7(5

.50)

**(3

.07)

**(3

.11)

**(–

0.74

)(5

.30)

**(2

.94)

**

Inde

x of

sch

ool f

acilit

ies

avai

labl

e2.

122.

973.

004.

122.

053.

072.

903.

99(4

.60)

**(2

.53)

*(7

.03)

**(4

.44)

**(4

.23)

**(2

.51)

*(6

.52)

**(4

.36)

**

Col

ombi

a (d

umm

y: n

ot C

hile

)–1

4.80

–8.5

1–1

2.06

–10.

48–1

4.67

–8.2

7–1

1.52

–10.

32(5

.65)

**(–

1.19

)(4

.98)

**(–

1.88

)(5

.28)

**(–

1.11

)(4

.51)

**(1

.87)

Ecu

ador

(dum

my:

not

Chi

le)

–38.

93–4

5.13

–69.

61–7

4.32

–41.

05–4

5.62

–70.

82–7

4.88

(14.

1)**

(6.2

2)**

(27.

2)**

(13.

1)**

(14.

1)**

(6.1

)**

(26.

5)**

(13.

3)**

Per

u (d

umm

y: n

ot C

hile

)12

.86

3.02

–24.

48–3

1.34

11.0

91.

98–2

6.59

–33.

10(5

.12)

**–0

.44

(10.

6)**

(5.8

6)**

(4.2

0)**

(–0.

28)

(10.

9)**

(6.3

)**


Tab

le 2

.A

fter

-sch

ool e

mp

loym

ent

and

aca

dem

ic p

rofic

ienc

y am

ong

sixt

h-gr

ade

stud

ents

in C

hile

, Col

omb

ia, E

cuad

or a

nd P

eru:

Effe

cts

of h

ours

and

locu

s of

wor

k (O

LS a

nd H

LM r

egre

ssio

n co

effic

ient

s) (c

oncl

.)

Dum

mie

s fo

r hou

rs p

er d

ayD

umm

ies

for

wor

k at

hom

e/ou

tsid

e

Mat

hR

eadi

ngM

ath

Rea

ding

OLS

HLM

OLS

HLM

OLS

HLM

OLS

HLM

Wor

ked

1 h

our/

day

(rat

her

than

non

e)–1

2.96

–10.

59–1

3.58

–11.

78—

——

—(4

.47)

**(3

.84)

**(5

.06)

**(4

.47)

**—

——

—

Wor

ked

2 h

ours

/day

(rat

her

than

non

e)–9

.07

–10.

37–1

3.54

–13.

92—

——

—(2

.54)

*(3

.07)

**(4

.11)

**(4

.32)

**—

——

—

Wor

ked

3 h

ours

/day

(rat

her

than

non

e)–1

6.95

–11.

36–1

7.95

–15.

34—

——

—(4

.31)

**(3

.05)

**(4

.95)

**(4

.32)

**—

——

—

Wor

ked

4 o

r m

ore

hour

s/d

ay–2

2.02

–16.

34–2

2.16

–18.

66—

——

—(7

.58)

**(5

.85)

**(8

.26)

**(7

.02)

**—

——

—

Wor

ked

aft

er s

choo

l with

fam

ily—

——

—–1

4.94

–12.

86–1

8.30

–17.

10

——

——

(7.0

7)**

(6.2

3)**

(9.4

1)**

(8.7

2)**

Wor

ked

aft

er s

choo

l out

sid

e ho

me

——

——

–20.

82–1

6.59

–20.

42–1

7.69

——

——

(6.2

6)**

(5.2

9)**

(6.6

8)**

(5.9

2)**

Con

stan

t63

3.95

571.

4867

5.45

623.

0463

7.47

568.

7967

9.91

628.

90(5

2.09

)**

(36.

09)*

*(6

0.42

)**

(44.

97)*

*(5

0.09

)**

(34.

39)*

*(5

8.42

)**

(44.

32)*

*

Num

ber

of s

tude

nts

10,8

6610

,866

10,9

1410

,914

9,83

09,

830

9,86

89,

868

R-s

quar

ed0.

180.

290.

190.

30

Num

ber

of s

choo

ls38

939

038

939

0

Not

e: A

bsol

ute

T-Va

lues

in p

aren

thes

es.

**

Sig

nific

ant a

t the

.01

leve

l.

* S

igni

fican

t at t

he .0

5 le

vel.


Teacher characteristics and school facilities exert an important influenceon students’ academic proficiency in most models. The positive coefficient forthe number of years that the teacher has worked in the child’s school can be seenboth as an effect of teaching experience and as an indicator of the teacher’s com-mitment to the school. The importance of teacher commitment to the school isalso reflected in the negative effect of the teacher’s wish to transfer to a differentschool. In six of the eight regression equations, this wish to transfer is associatedwith significantly lower academic proficiency. As regards the school’s ownresources and infrastructure, positive effects can be observed in all equations forthe index of 12 facilities reported by the school director (see table 1 for details onthese resources).

It has been widely reported that Chilean students outperform students inthe other three countries of this study. Indeed, even after controlling for demo-graphic factors, teacher commitment and school facilities, Ecuadorean studentshave much lower test scores than Chilean students, as reflected in the very largeand statistically significant negative coefficients for Ecuador in all eight regres-sion models. However, the coefficients for Peru and Colombia (with Chile as theomitted reference category) reveal a more complex picture. In two OLS models,being Peruvian positively affects math proficiency, while two HLM estimationsshow that being Peruvian has no significant impact on math proficiency. ThePeru coefficients for reading proficiency are consistently negative, indicating thateven after controlling for student, teacher and school characteristics, Peruviansixth-graders lag behind their Chilean peers. While the focus of this study is onthe impact of student employment, the results of international comparisons(shown in table 2) underscore the importance of controlling for school, teacherand student effects when assessing possible differences in student achievement.

Turning to the effects of student employment, we should first note thatregardless of how children’s labour is modelled (with OLS or HLM estimations)and regardless of how it is measured (either by intensity or by locus of work), ithas negative coefficients in each of the eight regression equations. As expected,however, the coefficients are smaller in the HLM models, indicating that some ofthe poor performance of working students comes from the poor schools theyattend. Importantly, the effect of working longer hours is more detrimental thanthat of working fewer hours. This seems to support a time budget explanation, asopposed to a social identity explanation. In other words, it is not the mere fact ofworking that is associated with lower math and reading proficiency, but the factof working many hours each day. The right side of table 2 shows the impact of thelocus of work. In terms of math proficiency, working outside the household car-ries a higher “cost” to sixth-grade learning than does working for the family. Asregards reading proficiency, however, there appears to be little difference in thenegative coefficients as between working at home and working outside the home.

For a closer look at the impact of children’s work within each of the fourcountries, I repeated the HLM analysis separately for each country. Table 3reports the estimates of HLM models for math and reading proficiency inChile, Peru, Ecuador and Colombia. Table 3 presents only the coefficients ofthe employment variables. Coefficients that are not statistically significant are


printed in regular font, and those that are statistically significant appear in bold.In the first model it is evident that working greater numbers of hours is associ-ated with a lower level of proficiency in math and reading in every country. Inthe case of Chile, however, the most negative effects are found among childrenwho worked three hours each day, not those working four or more hours daily.This is difficult to explain, given that in the 1990s Chile instituted a full day ofschooling (from 8:30 a.m. to 4:30 p.m.). Chile also differs from the other threecountries in that children’s work outside the household seems to have no detri-mental impact on their achievement at school. It could be speculated that Chilehas put sufficient protections into place to prevent employment from interfer-ing with schooling, whereas Colombia, Ecuador and Peru have been less suc-cessful in regulating employers or preventing children’s work from interferingwith their schooling. Peru is the country with the most flexible employmentlaws for young people, and also the one that displays the greatest sympathy forstudent employment and even acceptance of work by students.11 Indeed,Peruvian regulations and attitudes have probably helped many working children

11 Peru’s 1992 Code of Children and Adolescents embodies a widely held belief. One Limaprincipal responded, when asked about his working students: “There are dropouts here in this school– more or less 3 per cent – and that is low in comparison with other schools. There is also a group ofstudents who work, and this affects their performance and their attendance. It’s something that can’tbe controlled, all that we can do is to help them continue, give them their exams, their grades, andgive them some extra help so they can continue because it’s so painful. These are boys who disappearfor a week, and come and say ‘Sir, I’ve been working’; ‘I work in a gas station’; ‘I work in such andsuch, selling papers’; ‘I help out my parents’. And what do you want me to say to them? No? That Ican’t help them? I have to help the boy to get ahead!” (for details, see Post, 2001).

Table 3. Effects of after-school employment on sixth-grade math and readingproficiency

Chile Colombia Ecuador Peru

Math Reading Math Reading Math Reading Math Reading

Model 1Worked 1 hour/day(rather than none) –8.40 –11.89 –9.67 –12.78 –9.13 –0.77 –15.79 –17.89

Worked 2 hours/day(rather than none) –8.40 –16.09 –6.84 –9.09 –10.98 –13.63 –14.36 –17.76

Worked 3 hours/day(rather than none) –23.01 –28.85 –4.86 –8.33 –3.13 –12.42 –17.67 –17.90

Worked 4 or more hours/day –3.93 –18.71 –11.23 –16.57 –22.86 –10.88 –23.17 –25.60

Model 2Worked after school with family –13.32 –22.85 –11.14 –14.55 –13.39 –11.95 –13.86 –18.17

Worked after school outside home –8.85 –10.47 –13.55 –19.98 –16.66 –8.15 –25.24 –26.08

Note: Each model also includes all other control variables entered in table 2 from HLM regressions. Coefficientsprinted in bold are significant at the .05 level.



remain in school. Unsurprisingly, however, this has led to wide inequalities inlearning between working and non-working students.

Figure 3 illustrates the impact of after-school hours of work on math andreading proficiency, based on the HLM model of table 2 (i.e. taking into accountthe combined influences of family, school and teachers). Since there are no inter-action terms used in the equations estimated for table 2, the effect of work inten-sity is not equally significant in all countries, so figure 3 shows only the generaltendency.

Conclusions and policy directionsThe results of this analysis of SERCE data on four Latin American countries will,it is hoped, lend support to an already vital movement to advocate for children inways that go beyond gaining them mere access to schools. Cross-national testingsupports the general conclusion that employment and classroom learning are notperfectly compatible – at least for children in primary school. There are trade-offsthat must be considered by education leaders and child welfare advocates.Beyond generalization there are indeed nuances that call for consideration. Inpart, the effects of employment are due to intensive work of four or more hoursper day. This finding suggests a need for further parent education about theimportance of allowing children sufficient time to be students, in addition to theirafter-school work. The SERCE data also confirm that working children tend togo to worse schools, where overall math and reading achievement is lower than inschools where few students work. This finding should support efforts by educa-tion ministries and international agencies to prioritize assistance to schools inwhich overall achievement scores are low. The case of Peru – where concertedefforts have been made to permit working children to remain in school – serves asa note of caution that attendance alone is never enough to ensure success. Amongthe four countries of this investigation, Peru’s student workers fare the worst.

Many educators already assist working children who try to attend and suc-ceed in school. But altruism makes a weak foundation for the construction ofstrong programmes, so consideration should be given to the benefits of a schoolfinance system that rewards not only the enrolment but the success of workingchildren. Broader discussion of the benefits of school success would incentivizefamilies and children to dedicate their energy to schooling and away from work-ing. Where parents have discounted education as an alternative because schoolsare of poor quality, too unfriendly or not accessible, greater awareness of thehealth benefits of quality education could also cause families to exert politicalpressure to improve schools and promote access for all. As schools improve, sowill community and child health, making more visible the connection betweenthe two. This feedback loop could create a virtuous cycle, ultimately diminishingthe reliance on child labour even without further legal regulation by local govern-ments. To promote this virtuous cycle, countries would be well-advised to publi-cize the quality-of-life returns to successful schooling: longevity, maternal andinfant survival, and meaningful, safe and productive employment.


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Documents

Primary school student employment and academic achievement in Chile, Colombia, Ecuador and Peru