metadata: authors: 0: author: Emerson, Patrick M.
1: author: Ponczek, Vladimir
2: author: Portela Souza, Andre
count: Brazil
docna: 0: docna: Child labor and learning
docty: Policy Research Working Paper
subtopic: Child Labor Law,Health Care Services Industry,Labor Markets,Child Labor,Labor Standards,Skills Development and Labor Force Training,Educational Sciences,Rural Labor Markets
teratopic: Governance,Social Protections and Labor,Urban Development,Education
abstracts: cdata!: This paper uses a unique micro panel
            dataset of Brazilian students to investigate the impact of
            working while in school on learning outcomes. The potential
            endogeneity is addressed through the use of
            difference-in-difference and instrumental variable
            estimators. A negative effect of working on learning
            outcomes in math and Portuguese is found. The effects of
            child work range from 3 to 8 percent of a standard deviation
            decline in test score, which represents a loss of about a
            quarter to a half of a year of learning on average.
geo_reg_key: 80475,80450,80469
repnme: repnme: Child labor and learning
display_title: Child labor and learning
listing_relative_url: /research/2014/06/19611209/child-labor-learning
topicv3: Child Labor m1326042 1859,Labor Markets m1326488 855,Education m1327048 470,Human Capital Accumulation m1504139 2816,Health m1504661 3480
publishtoextweb_dt: 2014-06-01T00:00:00Z
historic_topic: Governance,Social Protections and Labor,Urban Development,Education
no_of_pages: 47
model_extractions: WPS6904 Policy Research Working Paper 6904

# Child Labor and Learning

_Patrick M. Emerson Vladimir Ponczek André Portela Souza_ The World Bank Development Economics Vice Presidency Office of the Chief Economist June 2014
Policy Research Working Paper 6904

# **Abstract**

This paper uses a unique micro panel dataset of Brazilian students to investigate the impact of working while in school on learning outcomes. The potential endogeneity is addressed through the use of difference-in-difference and instrumental variable estimators. A negative effect of working on learning outcomes in math and Portuguese is found. The effects of child work range from 3 to 8 percent of a standard deviation decline in test score, which represents a loss of about a quarter to a half of a year of learning on average.

This paper is a product of the Office of the Chief Economist, Development Economics Vice Presidency. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The authors may be contacted at patrick.emerson@oregonstate.edu, vladimir.ponczek@fgv.br, and andre.portela.souza@fgv.br.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
# Child Labor and Learning<sup>_⇤_</sup>

**Patrick M. Emerson**<sup>_†_</sup> **Vladimir Ponczek**<sup>_‡_</sup> **Andr ́e Portela Souza**<sup>_§_</sup>

_JEL classification_ : J13,I21. _Keywords_ : Child Labor; Learning; Proficiency; Education.

> _⇤_ Acknowledgements: For useful comments and advice, we thank seminar participants at the World Bank and S ̃ao Paulo School of Economics. For outstanding research assistance we thank Eduardo Tillman. We thank the Funda ̧c ̃ao de Amparo `a Pesquisa do Estado de S ̃ao Paulo (FAPESP) for financial support.

> _†_ Oregon State University, IZA & S ̃ao Paulo School of Economics, FGV patrick.emerson@oregonstate.edu. phone: 541-737-1479. Fax: 541-737-5917

> _‡_ S ̃ao Paulo School of Economics, FGV & CNPq. vladimir.ponczek@fgv.br

> _§_ S ̃ao Paulo School of Economics, FGV, & CNPq. andre.portela.souza@fgv.br
# **1 Introduction**

Though the global trend is downward, the incidence of child labor remains very high in developing countries. For example, in 2000 the International Labour Organization estimated that 246 million, or almost 16 percent, of the world’s children between the ages of 5 and 17 were child laborers. By 2008 this had fallen to 215 million - a dramatic decline but still representative of 13.6 percent of the world’s children. In response, many governments have proposed or implemented policies designed to reduce the incidence either in their own countries, through labor laws that restrict or prohibit children working, or in other countries, through policies such as restricting the importation of goods that use children in some part of their production.

Though the goal of protecting children from harmful work is a recognized aim of the rights-based approach to child labor, and while the worst-forms of child labor are prima facie intolerable, there is much to be learned about what types of activities and their intensities constitute harmful work. In order to make e↵ective and responsible policy it is important to understand the consequences working as a child on participants and the interaction of child work with other activities such as schooling. Doing so will add clarity to questions such as how young is too young to work, how much is too much work at given ages and does working interfere with human capital accumulation. This study contributes to this understudying by demonstrating that working while attending school has a large negative and significant impact on the learning progress of schoolchildren in Brazil. This finding is also consistent with most theoretical treatments of child labor that stress the trade o↵between work and human capital accumulation.

While much is known about the incidence and the determinants of child labor, surprisingly little is known about the consequences of child work on participants. Important policy 2
questions such as how young is too young to work, are some work activities better or worse than others, does work impair the health of children and does combining work and school hinder learning remain largely unanswered. This paper seeks to provide an answer the last of these questions and contributes to the literature by assessing the impact of working while in school on learning as measured by the proficiency of students in the S ̃ao Paulo municipal school system through their performance on standardized exams.

Perhaps the main reason for the dearth of received evidence on the impact of child work is the inherent difficulty in uncovering causal linkages between the activities of children and their subsequent outcomes. The e↵ects of confounding and unobserved variables are persistent problems that are difficult to overcome. In this study, we utilize a unique panel data set of the standardized test scores of Brazilian children in the metropolis of S ̃ao Paulo that allow us to explore the causal link between the work activity of children and its e↵ect on their exam performance.

We find that, controlling for individual time-invariant unobservable characteristics, working while remaining in school has a depressing e↵ect on their proficiency test scores. The magnitude of these e↵ects range from 3% of a standard deviation in test scores to 8% which represents from one quarter to one half of a year of lost learning. Additionally, we find that the magnitude of the negative impact increases with a students ability and that there are both lingering and cumulative negative e↵ects from working while in school. These results provide valuable information to policy makers who wish to understand what types of child work to target for elimination and the e↵ect of child labor on human capital accumulation. As these are short-run e↵ects, it is likely that the long-run impact of the will be quite a bit larger. Additionally these results apply only to those who remain in school, factoring in drop-out and grade repetition would certainly increase the magnitude of the impact of 3
working while in school.

The results are robust to idiosyncratic preferences and we perform robustness checks to show that the results are not due to idiosyncratic trends or shocks at the household level. We implement placebo tests and control for idiosyncratic economic shocks at the household level. Moreover, we implement a LATE strategy using the minimum legal age to entry in the labor market to induce an exogenous variation in child labor status. The robustness check results are consistent with our main findings. Finally, we find possible channels through which child labor can impact learning as participants in the labor market are more likely to report that they miss school days, turn homework in late and complete homework while in school rather than at home.

In recent years, a large body of theoretical and empirical research has emerged that has studied the economics of child labor (see Edmonds, 2008; Edmonds and Pavcnik, 2005; Basu and Tzannatos, 2003; Basu, 1999, for extensive literature reviews). Much of the received empirical work has focused on the determinants of child labor while questions of the consequences of child labor have been largely contained in the theoretical literature (see e.g. Edmonds, 2008; Edmonds and Pavcnik, 2005; Basu and Tzannatos, 2003; Basu, 1999; Emerson and Knabb, 2012, 2007, 2006; Horowitz and Wang, 2004; Ejrnæs and P ̈ortner, 2004; Basu, 2002; Dessy and Pallage, 2001; Baland and Robinson, 2000; Dessy, 2000; Basu and Van, 1998). The theoretical literature has long emphasized the trade-o↵between child labor and human capital accumulation to justify policy interventions assuming depressing impacts from child labor. As noted above, the empirical foundations to support these assumptions are weak.<sup>1</sup> Though evidence of the e↵ects of child work on participants is still relatively scarce, 1(See also Ponczek and Souza, 2012; Emerson and Souza, 2003; Ilahi et al., 2001; Psacharopoulos and Patrinos, 1997) 4
there is a new and growing literature that has begun to fill in the lacuna. Beegle et al. (2009), studies a five year panel of school children in Vietnam and finds that child labor has negative consequences on school participation and educational attainment. Orazem and Lee (2010) uses Brazilian household data to examine the impact of working as children on self-reported health outcomes and finds negative impacts of child labor. Emerson and Souza (2011) examines retrospective data from Brazil and finds that child work before 13-14 years old negatively a↵ects adult incomes, but that this a↵ect turns positive after these ages. Interestingly, this study finds that the e↵ect of child work on earnings remain even when controls for years of education are included, raising the possibility that child labor may a↵ect the learning of those workers who remain in school, which provides a motivation for the current study.

Research on the consequences of child work on education have mainly focused on attendance rather than learning (e.g. Ravallion and Wodon, 2000; Assaad et al., 2001; CanalsCerda and Ridao-Cano, 2004; Beegle et al., 2008, 2006; Assaad et al., 2005) and have found modest negative e↵ects of child work. But as Emerson and Souza (2011) suggest, school enrollment may not be the only important measure, especially in countries like Brazil where combining work and school are common.

The direction of the expected impact of child work on learning is unclear. Working requires time and energy that could hamper a student’s ability to learn, but some work activities could involve tasks that are either directly related to learning (like reading, writing and math) or indirectly related but still involve use of these skills. If a work activity involves learning-by-doing or is otherwise positively correlated to learning the skills tested in school (in our case math and Portuguese), work could, in fact, have a positive impact. In the end the true nature of the relationship between work and learning, if the two are substitutes or 5
complements, is an empirical issue.<sup>2</sup> Understanding this relationship is extremely important as previous research has shown a very strong connection between educational proficiency and adult income and economic growth, and that proficiency is a stronger determinant than completed years of schooling [see, e.g. Hanushek and Zhang (2009) and Hanushek and Kimko (2000)].

We are aware of three previous studies that have examined the impact of child work on student proficiency. The first paper, closely related to the present study is Bezerra et al. (2009), which uses cross-sectional Brazilian data to test the impact of working on the performance on similar exams. The authors find that working has a negative impact on the performance of participants. Another closely related study is Dumas (2012) which exploits retrospective data from Senegal to examine the e↵ects of child work on the test scores of Senegalese children and finds some evidence of positive e↵ects of child work. Finally Gunnarsson et al. (2006) uses data from nine Latin American countries (including Brazil) and finds negative and significant impacts of working on student test scores. All three studies are constrained by the inherent difficulties in overcoming the potential endogeneity of child labor and all three implement instrumental variables strategies in an attempt to overcome the problem. In all three cases, the challenge of finding sources of variation that are correlated with the decision to work but uncorrelated with the unexplained variation in school performance is severe, leading to questions of the validity of the instruments themselves and thus the results. In our case the use of time-series data presents a huge advantage in our ability to control for both the endogeneity of child labor and the presence of other unobservables (e.g. parental preferences) that are potentially correlated with both the decision to work and the aptitude for, and attitude toward, school. We are also able to explore the lingering and 2 We use the terms ’learning’ and ’proficiency’ synonymously, but for both we are referring specifically to only those aspects of learning that are measured by standardized tests in Portuguese and Math.

6
cumulative impacts of child labor as well as explore the heterogeneous e↵ects on student of di↵erent ages and abilities. In addition, through the time-use information available to us, we are able to explore the potential channels through which child labor may interact with the process of learning and we are thus able to shed light on the mechanisms involved.<sup>3</sup> This paper proceeds as follows: In section 2 we describe the data used in the study. In section 3 we describe the general child labor and educational environment in the City of S ̃ao Paulo. In section 4 we explain the empirical strategy, how we identify our model and the robustness checks we employ. In section 5 we present and discuss the results of the empirical investigation. In section 6 we summarize the paper and discuss the policy implications of the results of the empirical investigation.

# **2 Data, Sample Selection and Descriptive Statistics**

In 2007, the City of S ̃ao Paulo started an evaluation system for students enrolled in municipal schools involving a set of proficiency exams in mathematics and Portuguese. These exams were accompanied by a questionnaire that was given to each student taking the exams as well as an additional questionnaire that was given to the parents of the students taking the exams about the socio-economic characteristics of the family, although the parents questionnaire was not administered in 2008.<sup>4</sup> The exam is called the _Prova S ̃ao Paulo_ (S ̃ao Paulo Exam) and was implemented annually until 2013 when it was discontinued. The microdata from the 2007 to 2010 exams are available and used in this paper. In 2007 all students in the even grades (2<sup>_nd_</sup> _,_ 4<sup>_th_</sup> _,_ 6<sup>_th_</sup> and 8<sup>_th_</sup> ) took the exam. In 2008, all students in the 2<sup>_nd_</sup> _,_ 4<sup>_th_</sup> and 6<sup>_th_</sup>

> 3Also related are studies from cross sectional data such as Heady (2003) which finds large and negative impacts of child work on educational achievement and Akabayashi and Psacharopolous (1999) which examines the correlation between work and subjective measures of ability, and finds a negative impact from child work. 4Questionnaires were also given to the principals which asked them to answer questions about themselves, their school, the teachers and supervisors, and the student population, but it was not administered in 2009.

7
grades took the exam, and randomly selected students in 3<sup>_rd_</sup> _,_ 5<sup>_th_</sup> , 7<sup>_th_</sup> and 8<sup>_th_</sup> grades took the exam (one class per grade per school was randomly selected to take the exam). From 2009 and on, all students in the even grades and randomly selected students in the odd grades (35 students per grade per school) took the exam.<sup>5</sup> In each year, around 500,000 students in 8,000 classes in 500 schools took the exam. Importantly, the S ̃ao Paulo Exam was structured based on the Item Response Theory (IRT) so that the results are comparable across grades and across years<sup>6</sup> .

We have information from the students’ questionnaires for all years on the students’ working status. Only students in the fifth grade and above answer the question about child labor, which restricts the population of analysis.<sup>7</sup> The precise wording of the question about working status is as follows (translation ours): _“During school days, do you work?_

- _(A) Yes, outside of the house; (B) Yes, at home helping with the chores; (C) No, I only study.”_

Respondents were limited to one answer only. We set the indicator variable _MarketLabor_ equal to 1 whenever a student answered ”A”, and to 0 otherwise so we are comparing those that work outside of the home to those who work at home on chores and those who do not work at all.

> 5All students in the fifth grade who scored below 150 points in the previous year are also included in sample.

> 6There are common items in the tests over time and across grades that can be used as linking items in constructing comparable scores. Please, check `http://portalsme.prefeitura.sp.gov.br/Projetos/ nucleo/AnonimoSistema/MenuTexto.aspx?MenuID=44&MenuIDAberto=38formoredetails` .

> 7In 2008, students in the fifth grade answered a version of the questionnaire that was distributed to the younger students in second to fourth grade. In this questionnaire, there is no question about the child labor status. Together with the fact that fifth graders are not randomly selected, we decided to exclude them from our sample.

8
From the parent’s questionnaire we collected information about the father’s employment status for 2007, 2009 and 2010.

The student questionnaire also asks students about their studying habits such as whether they miss classes, hand in homework late, prepare for exams in advance, and complete homework at school. From the responses to these questions we created four indicator variables.

We construct three di↵erent samples for this study. The first includes all observations of students in the 6<sup>_th_</sup> _,_ 7<sup>_th_</sup> and 8<sup>_th_</sup> grades in 2007, 2008, 2009 and 2010. This sample constitutes an unbalanced panel of 473,051 observations of 313,297 students (158,180 boys and 155,117 girls).<sup>8</sup> We call this the “full sample”. The second sample encompasses all students that were in 6<sup>_th_</sup> grade in 2007 or in 2008 and were found two years later from the first observation. Therefore, we have exactly two observations for each student. This balanced panel has 48,009 boys and 48,161 girls. We call this the “paired sample”. The third sample also encompasses all that were in 6<sup>_th_</sup> grade in 2007 or in 2008, but we include only those who were also found in next two consecutive years after the first observation. Depending on the exercise, we use the first and third observations or the first and second observation for each students. Therefore, we have exactly two observations for each student. The sample contains observations on 6,563 boys and 6,630 girls and we call this the “3 period sample.”<sup>9</sup> Tables 1 to 3 presents some descriptive statistics of the three samples separately for boys and girls. The incidence of child labor in the full sample is around 12% for boys and 6% for girls. The average age is 14 years old and, on average, boys outperform girls in math and the reverse occurs in Portuguese.

> 8We also dropped 994 students aged nine years old or below. We believe those are measurement error, since the regular age for the 6<sup>_th_</sup> grade in Brazil is 12 years old. Nevertheless, none of the deleted students appear more than once in the sample, therefore the trimming does not change the estimate of the parameter of interest in the fixed e↵ect specification.

> 9No student was age trimmed in the paired and 3 period samples

9
Since we use fixed e↵ect estimators, it is necessary for identification to have transitions into and out of market labor. Table 4 shows the transition matrix of the market labor variable. We can see that for both boys and girls we have a sufficient number of students transiting in and out of working status. For instance, 4,500 boys and 2,300 girls change their status from not working to working in one year.

It is worth noting here that we observe only those who are enrolled in the S ̃ao Paulo municipal school system and who remain in the school system. There is no forced repetition of grades in S ̃ao Paulo municipal schools for the 6<sup>_th_</sup> _,_ 7<sup>_th_</sup> and 8<sup>_th_</sup> grades, but there can be drop-out, movement to state or private schools or movement out of the area. As drop-out and delay are likely two other important e↵ects of child labor, it is important to note that we are not examining these ancillary e↵ects of child labor.

# **3 Child Labor and Students in the City of S ̃ao Paulo**

This section describes the incidence of child labor and school attendance in the City of S ̃ao Paulo.<sup>10</sup> The data used in this study come from the _2010 Demographic Census_ collected by the Brazilian Census Bureau (IBGE). It contains information on socio-demographic characteristics, fertility, migration, and time allocation for all individuals sampled. It is a sample of the entire population representative at the municipality level. Most important for the present study, it contains information about school attendance, labor force participation and occupation in the reference month of the survey (July). The information for labor market outcomes are available for individuals aged 10 years old and above.

> 10S ̃ao Paulo is the name of both the City and the State in which it resides and there are both state and municipal schools inside the City of S ̃ao Paulo

10
# **3.1 Child Laborers**

Table 5 below presents the figures for the time allocation of individuals aged 10 to 17 living in S ̃ao Paulo City in 2010 for male and female individuals, separately. There are around 1.4 million individuals in this category and the majority attend school. In fact, 85.4% (86.6%) of boys (girls) attend school only; and 6.2% (5.4%) of the boys (girls) divide their time between school and work. Thus around 92% (93%) of boys (girls) attend school. Conversely, 2.5% (1.7%) of boys (girls) work only; and 5.9% (6.3%) of boys (girls) neither work in the labor market nor attend school. Summing up those that work only and those that work and attend school, the incidence of child and adolescent work among boys is 8.7% and among girls it is 7%. Note that of all boys working in the labor market, 71% attend school, and for girls the figure is 76%. On the other hand, of all boys attending school, 6.8% work in the labor market, and of all girls attending school, 5.8% work in the labor market.

# **3.2 Students**

Our data encompass students enrolled in the 6<sup>_th_</sup> _,_ 7<sup>_th_</sup> and 8<sup>_th_</sup> grades at S ̃ao Paulo municipal schools. According to the _2010 Educational Census_ from the Brazilian Ministry of Education, there are around 580,000 students enrolled in these grades in the City of S ̃ao Paulo in 2010. Table 6 shows their distribution across grades and school systems. Of all of them, 49.6% are enrolled in the public municipal schools, 31.5% are enrolled in public state schools and 18.8% are enrolled in private schools and these proportions are similar for all grades. Since our data are of municipal school children only, we observe roughly half of the 6<sup>_th_</sup> _,_ 7<sup>_th_</sup> and 8<sup>_th_</sup> grade students in S ̃ao Paulo City, a population of around 290,000 students.

The IBGE Demographic Census has information about the type of school system in which the student is enrolled as well. It classifies schools as public or private but does 11
not distinguish between municipal and state public schools. Table 7 below presents the distribution of 6<sup>_th_</sup> _,_ 7<sup>_th_</sup> and 8<sup>_th_</sup> grade students across public (municipal and state) and private schools. These figures are presented for students who work and who do not work separately.

According to the census, there are around 540,000 individuals living in the City of S ̃ao Paulo in 2010 that reported that they attend 6<sup>_th_</sup> _,_ 7<sup>_th_</sup> and 8<sup>_th_</sup> grades. Of all of them, 82% attend public schools. Of all middle school students, around 11% work in the labor market. However, these figures are sharply di↵erent between public and private school students. Among public school students, 12.8% work in the labor market, whereas among private school students, 2.4% work in the labor market. As expected, the incidence of child and adolescent work increase with grade. Among 6<sup>_th_</sup> _,_ 7<sup>_th_</sup> and 8<sup>_th_</sup> graders in public schools, the incidences of working the labor market are 7%, 12.6%, and 20.4%, respectively.<sup>11</sup> If the proportion of child workers among 6<sup>_th_</sup> _,_ 7<sup>_th_</sup> and 8<sup>_th_</sup> grade students is similar between municipal and state school students, then there are roughly 37,000 municipal middle school students working in the labor market of the City of S ̃ao Paulo in 2010.

What do these working students do? Table 8 below presents the occupational distribution of the working public school students in 6<sup>_th_</sup> _,_ 7<sup>_th_</sup> and 8<sup>_th_</sup> grade in the City of S ̃ao Paulo according to the _2010 Demographic Census_ .

Most students who work, work in the service sector. Indeed, 26.1% of them work as domestic servants, street vendors, car washers, and others; 25.4% work as service and retail vendors; 14.8% are in office work; and 10.2% are in military service occupations.

> 11In our pooled sample the averages (across all three grades) for the market labor variable are 12% for boys and 6% for girls.

12
# **4 Empirical Strategy**

The main challenge in estimating the impact of child labor on learning is overcoming the potential endogeneity of child labor. The decision about the child’s time allocation could be made based on unobservable characteristics of the individual that also determine her proficiency. It is very likely that _ability_ is correlated with proficiency and the parents’ perception of the value of education which determines the child’s time allocation. In this case, a simple OLS estimator for child labor and proficiency will be biased. Depending on the correlation between the unobservables and time allocation decisions and between the unobservables and proficiency, the OLS estimator could be upward or downward biased.

In the above example, if there is a positive relationship between _ability_ and the perception of the value of education, meaning parents with high ability children are more likely to prioritize schooling, we would expect that a naive approach would overestimate the actual impact of child labor on proficiency. On the other hand, one can imagine that more able children have better opportunities in the labor market. In this case, the OLS estimator would underestimate the e↵ect of child labor on proficiency.

Therefore controlling for such unobservable characteristics is essential to consistently estimate the impact of child labor on proficiency. The longitudinal dataset of _Prova S ̃ao Paulo_ allows us to control for unobservable characteristics that are over time.

Our ‘benchmark’ strategy is, therefore, a fixed e↵ect estimator, we run the following regression separately for boys and girls and for math and Portuguese: where _Tigt_ is the math or Portuguese language proficiency test score of student _i_ in grade _g_ , 13
and year _t_ . _MarketLaborigt_ is an indicator variable that assumes 1 if the student _i_ , in grade _g_ is working at year _t_ . _✓i_ is the individual fixed e↵ect, _λt_ is a time-specific e↵ect, and _γg_ is the grade fixed e↵ect; _✏isct_ is the error term with school clustered variance-covariance matrix. In this case, _β_ 1 is the parameter of interest. Note that by including both and age and grade fixed e↵ects we are estimating the impact of working on students who are the same age and in the same grade, thus we are deliberately netting out the potential e↵ects of drop out and delay as mentioned previously.

This strategy is consistent even if there are unobservable attributes that simultaneously determine child labor and proficiency as long as those characteristics are constant over time.<sup>12</sup> We run the benchmark specification using both full and paired samples.

The identification strategy in our panel structure requires some individuals to transit in and out of the labor market. In the above estimations we implicitly assume that the e↵ects of these transits are the same for all individuals regardless of age, ability, and whether they are entering into child labor or exiting out of child labor. It is likely that these e↵ects are not the same - that there is heterogeneity based on age ability and the direction of the transit - and we therefore conduct four tests of the possibility of heterogeneous e↵ects using the paired sample.

First, we test if younger students su↵er more from working than older students.<sup>13</sup> Second, we test if students with di↵erent ability levels, as measured by first year test scores, su↵er di↵erential impacts of working while studying.

To conduct these two tests we estimate the two specifications below: 12 Indeed, we need only that the variation of the unobservable attributes is not jointly correlated with the child labor and proficiency variation.

13Emerson and Souza (2011) have shown the presence of heterogeneous e↵ects of child labor on adult labor market outcomes depending the age the child enter in the labor market in Brazil.

14
_Tigt_ = _β_ 0+ _β_ 1 _MarketLaborigt_ + _β_ 2 _MarketLaborigt⇥Ageig_ 1+ _β_ 3 _Ageigt_ + _β_ 4 _Age_<sup>2</sup> _igt_<sup>+</sup><sup>_✓i_+</sup><sup>_λt_+</sup><sup>_γg_+</sup><sup>_✏igt_</sup> (2) _Tigt_ = _β_ 0+ _β_ 1 _MarketLaborigt_ + _β_ 2 _MarketLaborigt⇥Tig_ 1+ _β_ 3 _Ageigt_ + _β_ 4 _Age_<sup>2</sup> _igt_<sup>+</sup><sup>_✓i_+</sup><sup>_λt_+</sup><sup>_γg_+</sup><sup>_✏igt_</sup> (3) where _MarketLaborigt ⇥Ageig_ 1 and _MarketLaborigt ⇥Tig_ 1 are interaction terms between child labor status and age and test score at the first year the student is observed in the sample, respectively. A negative coefficient estimate associated with the interaction between child labor and age suggests that child labor e↵ects younger students more than older students; while a negative coefficient associated with the interaction between test score indicates that higher scoring students are more harmed by working.

In order to account for the possibility of heterogeneous transition e↵ects, we estimate specification (1) separately for those entering and those exiting the labor market using the paired sample. Thus our third test of heterogeneous e↵ects is on those who enter into child labor compared to those who never worked, and our fourth test is on those who exit out of child labor compared to those who work in both periods. Notice that this is a di↵erent comparison to the benchmark test which compares those who transit into or out of market labor to those who not change their status (both not working in all periods and working in all periods of observation).

Next, we ask whether child labor has cumulative and lingering e↵ects. To test for these, we analyze whether the impact of work on learning depends on the length of time spent working. For these exercises, we use the 3 period sample restricted to students that were 15
not working in the first observation _t_ .

To check for the presence of cumulative e↵ects we compare the evolution of test scores between _t_ and _t_ + 2 among three groups of students. Students in the comparison group have not worked in all three periods. Students of group 1 work at period _t_ + 2 only. Students of group 2 started working in _t_ +1 and remain working in _t_ +2. Therefore, we run the following specification: _Tigt_ = _β_ 0+ _β_ 1 _MarketLaborigt_<sup>1</sup><sup>_t_+</sup><sup>_β_2</sup><sup>_MarketLabor_</sup> _igt_<sup>2</sup><sup>_t_+</sup><sup>_β_3</sup><sup>_Ageigt_+</sup><sup>_β_4</sup><sup>_Age_2</sup> _igt_<sup>+</sup><sup>_✓i_+</sup><sup>_λt_+</sup><sup>_γg_+</sup><sup>_✏igt_(4)</sup> where _Market Laborigt_<sup>1</sup><sup>_t_indicateswhetherthestudenthasbeeninthelabormarketfor</sup> one year (started worked in _t_ + 2) and _Market Laborigt_<sup>2</sup><sup>_t_indicateswhetherthestudenthas</sup> been in the labor market for two years (started worked in _t_ +1 and remains working in _t_ +2). We then test if _β_ 1 = _β_ 2.

To test for the presence of lingering e↵ects we compare the evolution in scores from _t_ to _t_ + 2 between two groups of students: a comparison group (worked in no periods), and a treatment group of students who have only worked in _t_ + 1 and have stopped working in _t_ + 2.

If the time variation of the relevant unobservables is correlated with the child labor and proficiency variations, the fixed-e↵ect estimator is inconsistent.<sup>14</sup> Therefore, we perform several robustness checks in order to validate our identification assumption. The robustness checks use information from students that appear at least three years in our sample. We compare students with the same working history in the first two years we observe them, but

> 14 Ideally, we would observe the same individual’s proficiency at the same time: working and not working. In this hypothetical situation, the mean proficiency di↵erential would be a clear and immediate indicator of the impact of child labor on proficiency.

16
with a di↵erent working status in the third year. The idea is that the outcome in the second year should not be impacted by a future (third year) working event. If this were so, then the assumption of identical trend for treatment and comparison groups would be invalid. Therefore, we estimate specification (1) for the students that: (i) are in the 3 Period sample (i.e. appear in three consecutive years); and (ii) have the same working status history in the first two years in the sample.

Though our empirical strategy allows us to control for all time-invariant individual and family characteristics there may still be some time-variant characteristics that are important. For example, it is likely that some transitions into and out of child labor are correlated with idiosyncratic transitory shocks at the household level and, if so, there may be a direct e↵ect of such shocks on learning.<sup>15</sup> In this case our estimates would be biased upward as we would attribute to child labor the direct e↵ect of the shock itself. We believe that it is likely that most of the impact of a shock that causes a child to enter the labor market is through the interference that the labor itself has on the time allocation of the student leading to less studying, fatigue, etc. Direct e↵ects from, for example, the additional stress of an employment or health shock in the household, would likely be small relative to the disruption the child experiences from working itself.

Nevertheless, we are able to perform a robustness check to see if such transitory shocks are biasing our estimates by including a control for the employment status of the father. Since we do not have this information from 2008 we estimate our benchmark model on a sample from 2007, 2009 and 2010 for observations that include the father’s employment status with and without the father’s employment status included as a control.

Moreover, other sources of shocks may be both correlated to child labor transitions and

> 15For instance Duryea et al. (2007) finds that household economic shocks are important determinants of transit into and out of child labor in metropolitan Brazil.

17
changes in learning. For instance, it is possible that changes of the parents marital status or health problems of a family member may bias our results. Therefore, we implement an IV strategy to attempt to address this potential problem. Specifically, we explore the transitions in child labor status induced by the change in age that turn the individual eligible to work. In Brazil the minimum legal age to participate in the labor market is sixteen years old. Therefore, we will estimate a Local Average Treatment E↵ect (LATE) as explained by Imbens and Angrist (1994).

Finally, if child labor does have a significant e↵ect on learning it would be useful to understand the nature of this e↵ect. To do so we identify some channels through which working could a↵ect student performance that are related to the time use and study habits of the students. Specifically, we use the full sample and run specification (1) to test the impact of child labor on four di↵erent outcomes: missing classes, preparing for exams in advance, completing homework at school, and turning in homework late.

All regressions use robust standard errors.

# **5 Results**

## **5.1 Benchmark model**

To assess the impact of working while in school on the learning outcomes of S ̃ao Paulo city school children we begin by estimating the benchmark model on both the full pooled sample (an unbalanced panel) and the paired sample (a balanced panel).

Table 9 presents the results of these regressions. The first four columns present the pooled sample estimation results for the math and Portuguese test scores for boys and girls sepa18
rately. The second four columns present the same estimation results for the paired sample. In all eight regressions the coefficient estimate on the child labor dummy variable is negative and significant at the one percent level. The point estimates range from -1.278 (boys math) to -3.951 (girls Portuguese). The results suggest that working while in school negatively impacts the students’ performance on standardized exams in both math and Portuguese. For math for boys and girls and for Portuguese for boys the coefficient estimates translate to around 3% to 3.5% of a standard deviation decrease in test scores. For Portuguese for girls the coefficient estimates translate to a 6.7% of a standard deviation decrease in the pooled sample and an 8.1% decrease in the paired sample. We can interpret those coefficients with the average proficiency gain a student obtain of one extra year of schooling. The average annual increase is 11 points in math and 12 in Portuguese which suggests the impact is a loss of from around 10% to 40% of a year of learning.

The students’ age coefficients are all positive and significant except for boys and Portuguese suggesting that the older a child is (in a given grade) the better he or she generally does on standardized tests with the exception of boys and Portuguese. The squared age coefficient estimates are all negative and significant but very small suggesting that the age e↵ect is slightly non-linear but not enough to turn the net e↵ect negative.

# **5.2 Heterogeneity**

Tables 10 through 13 present the results of the tests of heterogeneity, all of which use the paired sample.

19
# **5.2.1 Marginal impacts of age and proficiency**

Table 10 presents the results of the estimation with the child labor indicator variable interacted with age at the first observation. The results from this regression suggest that, after controlling for grade and year, the e↵ect of child labor does not significantly change with the student’s age.

Table 11 presents the results of the estimation with the child labor indicator variable interacted with test score. In contrast to age, the e↵ect of child labor does vary depending on test score: students are more negatively impacted by child labor the higher their initial test score. This may be perhaps because better students are more prone to study at home where child labor might interfere more or perhaps fatigue has a larger marginal impact on better students.<sup>16</sup>

# **5.2.2 Isolating transitions into and out of child labor**

Tables 12 and 13 present coefficient estimates on the paired sample that attempt to isolate the e↵ect of a child starting to work and a child stopping working. In order to accomplish this, the estimation presented in Table 12, ’Ins’, selects all those who did not work in the first period of observation ( _t_ ) and compares those that continued without working in the next window of observation ( _t_ + 2) to those were working in the next window of observation ( _t_ + 2). Table 13, ’Outs’, does the opposite: it considers all those that were observed to be working in the first period ( _t_ ) and compares those that remained working in the next period of observation ( _t_ + 2) to those that were no longer working in the next period of observation. The coefficient estimates for the child labor variable presented in Table 12 for the ’ins’ are all negative and significant at the one percent level, similar to Table 9, but

> 16The negative e↵ect dominates for those whose test scores are above: 171.1 for boys math; 187.9 for girls math; 173.1 for boys Portuguese; 113.8 for girls Portuguese. All well below their respective means.

20
the point estimates are larger.<sup>17</sup> The marginal impact of these new estimates now range from 6.2% (girls math) to 10.6% (girls Portuguese) of a standard deviation decline in test score. Interpreting these in terms of average annual increases in test scores reveals that the impact of starting to work is equivalent to roughly one half to a whole year of learning loss. The coefficient estimates for the child labor variable presented in Table 13 for the ’outs’ are all negative and significant at the one percent level, similar to Table 9, but again the point estimates are larger. The marginal impact of the estimates now range from 6.7% (boys math) to 19.0% (girls Portuguese) of a standard deviation decline in test score of roughly one half to almost two years of learning loss for those that remain in the labor market compared to those who exit.

The ‘In’ and ‘Out’ coefficient estimates have similar magnitudes and we cannot reject that they are statistically equal to each other. Therefore there is no evidence of heterogeneity between moment into or out of the labor market. This could suggest that transitions into and out of the labor market may be due to individual idiosyncratic shocks that are orthogonal to proficiency.

# **5.3 Exposure and lingering e↵ects of child labor**

We now turn to two questions that demand the use of the third ’three period’ sample. Recall that this sample takes all of the children we observe in 6<sup>_th_</sup> grade in either 2007 or in 2008 and whom we observe in the next two consecutive years (regardless of progression through the grades) and we compare scores in _t_ and _t_ + 2. Table 14 presents estimates of ’exposure e↵ects’ and takes all children who are not working in the first observation year and compares

> 17It is important to understand that these estimates are from an entirely di↵erent model and are thus not directly comparable. Whereas the baseline model imposes homogeneous e↵ects and estimates the average impact, this model estimates the impact of working conditional on not working in period _t_ .

21
those who only work in the third year (group 1) to those who work in the second and third years (group 2) to see if consecutive years of exposure have increasing or decreasing marginal e↵ects on the students’ test scores. For boys math scores the coefficient estimates on both the indicator variable for one year working and two years working are negative and significant. Interestingly, the two year indicator variable coefficient is almost double the one year estimate, suggesting that the e↵ect is essentially linear. Each year of working leads to about a 3.1 point drop in test scores. For the other regressions the e↵ects could not be separately identified perhaps due to the fairly small sample size we are now working with.

Another question we seek to address using the three period sample is the question of ’lingering e↵ects.’ In Table 15 we present coefficient estimates of regressions where we again start with those that initially do not work and compare those that remain not working for all three years to those that work in the second year but do not work in the third. The question is if having worked in the past continues to depress a child’s test score or do they ‘catch up?’ From the results in Table 15 we find some evidence that the e↵ects do linger for boys as for math and Portuguese the coefficient estimates are negative and significant (at the 10% level for math). For girls the point estimate for the math coefficient is negative and of a similar magnitude to previous regressions but has a large standard error and is not statistically significant while the Portuguese coefficient estimate is both very small and insignificant.

# **5.4 Robustness checks**

## **5.4.1 Preexistent di↵erent trends**

We next conduct a series of robustness checks. The first set are described in Table 16: using the three period sample we conduct a series of di↵erence-in-di↵erence estimates on the 22
relative test score increase over the first two years for kids with identical work experiences and condition on their third year (post evaluation) work experience. We expect to find no impact of working in the third year for those with identical work histories.<sup>18</sup> Below we show the four subsamples we use to conduct the robustness checks. Those who did not work in year _t_ or _t_ + 1 (Treatment 001), those who worked in both year _t_ and year _t_ + 1 (Treatment 111), those who worked in year _t_ but did not work in year _t_ + 1 (Treatment 101)and those that did not work in year _t_ but did work in year _t_ + 1 (Treatment 011). We will compare the score trajectory between _t_ and _t_ + 1 for all subsamples.<sup>19</sup> In each case we estimate our model for both boys and girls and for both math and Portuguese for a total of 16 robustness tests. The results for boys are presented in Table 17 and the results for girls are presented in Table 18. In all cases but one the robustness test yields the expected result: no di↵erence in the test score progression. In one case the math scores for boys is negative and significant. This could suggest that there could be some selection on trends in scores: boys who observe their score progressing slowly select into child labor or perhaps some correlation with unobserved prior work history.

# **5.4.2 Idiosyncratic shocks**

The di↵erence in di↵erence estimators may be biased if there are time variant idiosyncratic shocks correlated to both child labor transitions and variation in test scores such as an unemployment shock that causes a child to begin working. To the extent that the e↵ects of a shock on learning are through the physical, mental and time demands that are a consequence

> 18We can only identify those with identical _observed_ previous work histories, therefore we cannot completely exclude the possibility that unobserved work histories are correlated with future work histories. This could happen for children in households particularly sensitive to income shocks, for example.

> 19Note that all 16 regressions are robustness checks of the baseline regressions but only some are relevant to the ins and outs regressions.

23
of working itself, our estimates are unbiased. It is likely that direct e↵ects from the event itself are small in comparison. We can test this supposition using the information of the timevariant employment status of the father. As there are many missing observations and we do not have information on the parents for 2008, the sample size has decreased considerably from the benchmark case on the full pooled sample. For this reason we estimate the benchmark regression both without and with the father’s employment status variable as a control using this sample in order to gauge the extent of this potential bias.

Table 19 presents the result of the estimations of the benchmark model on the sample of observations for which we have information on the employment status of the father both including and excluding the father’s employment status as proxy for idiosyncratic economic shocks to the household. We find that for boys the point estimates for both math and Portuguese are statistically significant and larger than in the benchmark regression on the pooled sample when father’s employment status is not included as a control. However, there is almost no change in the size or significance of the variable when we control for father’s employment status suggesting the fixed-e↵ect estimator is not biased upward. For girls the change in sample size causes the point estimates for both math and Portuguese to shrink and loose significance relative to the benchmark case on the full sample, but again, there is virtually no di↵erence between the coefficient estimates from the regressions where father’s employment status is excluded and included. These results suggest that our coefficient estimates are not biased due to correlation with time variant idiosyncratic employment shocks to the household.<sup>20</sup>

> 20We have information on the mother’s employment status as well and we ran regressions using mother’s employment status and both mother’s and father’s employment status and found similar results, but using mother’s employment status reduces sample size even further.

24
# **5.5 IV estimator**

One may argue that these robustness checks are not sufficient since unobservable individual characteristics or other time variant idiosyncratic shocks beyond father ́s unemployment may be both correlated to labor market transitions and changes in test scores. For instance, a family break up or health problems of a family member may not only send a child to the labor maker but might also a↵ect learning. It is important to note that to the extent that such shocks a↵ect learning through the disruptions caused by the child starting to work, there is no bias concern as we control for child labor. It is only if there are e↵ects over-and-above the work itself (perhaps stress) that there may be the potential of bias.

In order to further investigate the potential bias of our fixed e↵ect estimators, we proceed with an instrumental variable strategy. Brazilian law establishes a minimum legal age to participate in the labor market.<sup>21</sup> No firm can formally hire a worker younger than sixteen years old and parents are also subject to legal punishments if an under age child works in the labor market. Because of this Brazilian firms rarely hire formal workers under age of sixteen. However, due to lax enforcement in the informal sector there remains much employment of children younger than sixteen. Hence, our instrumental variable of the labor market transition is a variable the indicates whether the individual is legally allowed to work (older than 192 months old). We estimate the following system of equations: _Market Laborigt_ = _↵_ 0 + _↵_ 1 _Minimum Ageigt_ + _↵_ 2 _Ageigt_ + _⌘i_ + _⌫t_ + _⇡g_ + _υigt_

> 21Constitutional Amendment No. 20 on December 16th, 1998 which increased the minimum legal age for entry to labor market from 14 to 16.

25
Where _MinimumAgeigt_ is the indicator variable that it is equal to one if the individual is sixteen years or older at time _t_ and zero otherwise. Note that we are controlling for individual _i_ , year _t_ , and grade _g_ fixed e↵ects, and also for age measured in months. Therefore, our identification strategy comes from the variation on age from not allowed to allowed to work over and above the direct e↵ect of age on the probability of working.

The identification assumption is that there is no direct e↵ect of turning sixteen on learning over and above the directly linear e↵ect of age controlled for in the regression. This is the Local Average Treatment E↵ect (LATE) described by Imbens and Angrist (1994) and this is the e↵ect of child labor on proficiency among a sub-population of those students induced to work due to changing their legal status of work (i.e., turning sixteen years old). We perform this exercise with the paired sample. In this sample, around 8.7% of the individuals move from not allowed to allowed to work within two years.

The LATE estimator requires that the instrument is valid plus the monotonicity assumption. The instrumental variable is correlated with the potential endogenous variable (this is shown in Table 20 below). Also, it must not be correlated with the error term of the outcome equation. This seems reasonable once we control for the individual fixed e↵ect and age in months. Finally, the monotonicity assumption looks reasonable as well. It requires that all individuals change their probability to work in the same direction when they turn sixteen. It does not seem likely that some individual will decrease their probability to work because they turned sixteen.

Table 20 below presents the results for boys and girls separately. The first stage regressions show that boys and girls are more likely to work when they turn sixteen over and above the linear age e↵ect. In fact, boys (girls) are around 6.5 (4.0) percentage points more likely to work when they became sixteen years of age. The results of the second stage re26
gressions show strong negative impacts on proficiency. A working boy (girl) decreases his proficiency in math by 94.7 (39.4) proficiency points. For Portuguese, the e↵ects are -91 and -137 proficiency points for boys and girls, respectively. The standard deviations of the proficiency scores among individuals aged sixteen years or older ranges from 73.6 for boys in Portuguese to 80.2 for boys in Math. The standard deviations for girls are 76.7 and 76.8 for math and Portuguese, respectively. The results implies that the child labor e↵ect for this subpopulation ranges from 0.5 (1.2) of a standard deviation for girls (boys) in math to 1.8 (1.2) of a standard deviation for girls (boys) in Portuguese.

The IV point-estimates are much larger than the di↵erence in di↵erence estimators. This may suggest that the fixed e↵ect estimators are attenuated (in absolute terms) due to measurement error of the market labor variable. It is even more likely that the LATE e↵ects are for a very particular subpopulation of those induced to work due to becoming able to work legally. Because of the sixteen year old threshold, these are individuals who have already experienced delay in their progression in school and who are, presumably, mostly going into formal sector work.<sup>22</sup> Thus there could be heterogenous e↵ects such that the impact of child labor on this subpopulation is much stronger than for working students in general. Another likely explanation is the classical problem of weak instruments. In this case, even though the correlation is significant it is relatively small as the law directly a↵ects very few children.

# **5.6 Channels**

Finally we explore potential channels through which child labor could be causing the suppression of test scores. Table 21 has the results of the OLS regression of the answers to the four channels questions on the child labor indicator variable. For three of the variables

> 22For example, in the formal sector there is a system of apprenticeships that are expected to lead to permanent employment and thus may induce particular detachment from schoolwork.

27
there is a positive and significant coefficient estimate for both boys and girls: missing classes, completing homework at school, and turning in homework late. Boys (girls) are 6.4 (3.9) percentage points more likely miss classes if they work while in school compared to students who do not work, which translates to 29% (14%) more likely to miss classes. Similarly both boys and girls are 2.2 percentage points more likely to complete their homework at school (rather than at home) if they work, which translates to 8% (10%) more likely to complete homework at home. Finally, boys (girls) are 3.1 (4.4) percentage points more likely to turn in homework late if they work while in school, which translates to 5% (9%) more likely to turn in homework late. These results suggest that the time burden of working while in school is interfering with attendance and the careful and timely completion of assignments.

# **6 Discussion and Conclusion**

Working while in school has negative and lasting consequences for children who participate in the labor market compared to those who do not. This paper finds negative and significant impacts of working while in school on the math and Portuguese proficiency scores of children enrolled in S ̃ao Paulo municipal schools. The impacts are economically significant. The lower bound of the average e↵ects of working while in school hovers around 3 percent of a standard deviation in math scores for boys and 6% for girls, and 5% for Portuguese for boys and 7% for girls. When we isolate the e↵ects for just those students who transition into child labor we find negative e↵ects of over 6% to over 10%. Extrapolating from the year to year average gain in proficiency scores the average e↵ect of transitioning to work while in school the e↵ect of working is equivalent to one quarter to an entire year of learning.

We show that these results are robust to idiosyncratic preferences and perform robustness 28
checks to rule out selection on idiosyncratic trends and shocks at the household level. We find some evidence that the e↵ect of working while in school is cumulative and that the e↵ect lingers over time. We also find evidence that the negative e↵ect of child labor while in school operates through the interference in students’ study time allocation and habits such as attending class, doing homework outside of class and turning in homework on time.

This is not to say that students that work are not optimizing. Their behavior could be the consequence of an optimal decision including the cases where they have large discount rates, are myopic about the future returns and simply prioritize current consumption as some sociologists in Brazil have suggested. It is also possible that students lack information about the true returns to learning in the adult labor market. Another explanation could be that individuals are credit constrained and are unable to borrow against future earnings. However, this individually rational (or boundedly rational) behavior of working while in school is likely inefficient in the sense that the real cost to the individual exceeds the benefits particularly in developing countries like Brazil where returns to education are very high. Whatever the reason as student learning is highly correlated with adult outcomes as well as economic growth, working while in school is likely inefficient.

Though it is tempting to suggest that the policy prescription is to prohibit working for students, one must proceed with caution. It is possible that without the ability to work while in school these students would drop out of school entirely. In general the findings of this paper show that while learning is impaired by working, learning still occurs even when the child works at the same time. This suggests that working and going to school is better than not going to school at all. We are also unable to comment on other e↵ects of working while in school such as grade repetition and dropping out as we are able to study only those that remain in school. Nevertheless, policy interventions that manage to keep kids in school 29
while curtailing their work activity have the potential of producing a dramatic improvement in their academic achievement. As the channels results indicate, it appears the time cost of work activity interferes with the process of learning, so policies that minimize the time spent in work activities and promote study at home have the potential of significantly improving learning outcomes.

30