metadata: authors: 0: author: Piza,Caio
1: author: Portela Souza,André
count: Brazil
docna: 0: docna: The causal impacts of child labor law in
            Brazil : some preliminary findings
docty: Policy Research Working Paper
subtopic: Child Labor Law,Labor Markets,Child Labor,Labor Standards,Gender and Development,Rural Labor Markets
teratopic: Law and Development,Social Protections and Labor
abstracts: cdata!: This paper investigates the causal
            impact of the change in Brazil’s child labor law of December
            1998. The change increased the minimum legal age of entry
            into the labor force from 14 to 16 years. The analysis uses
            a difference-in-differences approach to estimate the impact
            of this change in the law on labor force participation rates
            as a whole, as well as for the formal and informal sectors
            separately. The results show that the ban reduced
            participation rates for boys by 4 percentage points and that
            this effect was mostly driven by the informal sector. No
            effect is found for girls.
geo_reg_key: 119228,517191
repnme: repnme: The causal impacts of child labor law in
            Brazil : some preliminary findings
display_title: The causal impacts of child labor
            law in Brazil : some preliminary findings
listing_relative_url: /research/2015/10/25167333/causal-impacts-child-labor-law-brazil-some-preliminary-findings
topicv3: Child Labor m1326042 1859,Informal Sector m1326258 1168,Labor Markets m1326488 855,Gender m1327621 615,Labor Force Participation m1504050 2746
publishtoextweb_dt: 2015-10-19T00:00:00Z
historic_topic: Law and Development,Social Protections and Labor
no_of_pages: 11
author_keys: 0: author_key: 1239892
model_extractions: WPS7444 Policy Research Working Paper 7444 The Causal Impacts of Child Labor Law in Brazil Some Preliminary Findings _Caio Piza André Portela Souza_ Development Research Group Impact Evaluation Team October 2015
Policy Research Working Paper 7444

# **Abstract**

This paper investigates the causal impact of the change in Brazil’s child labor law of December 1998. The change increased the minimum legal age of entry into the labor force from 14 to 16 years. The analysis uses a difference-indifferences approach to estimate the impact of this change in the law on labor force participation rates as a whole, as well as for the formal and informal sectors separately. The results show that the ban reduced participation rates for boys by 4 percentage points and that this effect was mostly driven by the informal sector. No effect is found for girls.

This paper is a product of the Impact Evaluation Team, Development Research Group. 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 caiopiza@worldbank.org.

_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
# The Causal Impacts of Child Labor Law in Brazil: Some Preliminary Findings

## _Caio Piza_

The World Bank Development Research Group Contact email: <u>caiopiza@worldbank.org</u>

## _André Portela Souza_

Professor of Economics at Sao Paulo School of Economics (EESP-FGV) Contact email: <u>andre.portela.souza@fgv.br</u> Keywords: Child labor ban, child labor, participation rates, and treatment effects. **JEL** : J08, J22, J23.
# **INTRODUCTION**

Literature on child labor has grown considerably over the last 15 years, and this is not just because of increasing data availability. Child labor has fallen over the years, but the worldwide figures are still alarming. According to the International Labour Organization (ILO), 264 million children aged 5 to 17 participated in the labor market in 2012. Of this, 168 million were child labor, that is, under the minimum legal age and/or working in hazardous activities.

Due to the negative externalities associated with children’s participation in the labor force, it is argued that the public sector could intervene by changing the circumstances that cause parents to send their children to work (see Basu and Van, 1998). In fact, many countries have adopted bans or other mechanisms to break down the ‘intergenerational child labor trap’ (see Emerson and Souza 2003 and Edmonds 2008 for a survey).

Basu and Van (1998), for instance, argue that a parent’s decision to send a child into the labor force might be seen as a rational choice in a poor household facing many constraints. Given a few assumptions, the labor market may have multiple stable equilibria, one characterized by children participating in the labor force and depressed adult wages, and another in which children do not participate in the labor force and adult wages are higher. Because these two equilibria are stable, the authors argue that if children participate in the labor force, a ban could be put in place by the government to shift the economy from this equilibrium to one without child labor.<sup>1</sup> Although many policies have been put in place to fight child labor, too little is known about the causal impact of such interventions, particularly in developing countries where empirical evidence is almost nonexistent.<sup>2</sup> This paper helps fill this gap by delving into the consequences of two recent federal legislations in Brazil aimed at children of a certain age range.<sup>3</sup> In December 1998, Brazil increased the minimum employment age from 14 to 16. This policy change gave rise to a natural experiment that we will investigate in this paper.<sup>4</sup> We used a difference-in-differences (DD) approach to estimate the impact of the law passed in December 1998. The results suggest that the change in law reduced labor force participation rates of 14-year-old boys as a whole, but not of girls, who seem to have shifted to the informal sector. The results for boys are interesting as they indicate

> 1 The two main assumptions stemming from this result are that the rise in adult wages that results from a ban has to be high enough to compensate for the children’s ‘forgone’ income; and that it allows parents to consume children’s leisure. That means: (i) that child and adult labour inputs are perfect substitutes, and (ii) that children’s leisure is a normal good for the parents. This theoretical framework therefore suggests that net benefit for households from a child labour ban is ultimately an empirical question.

> 2 The only study we are aware of for a developing country is Bharadwaj et al. (2013) for India.

> 3 The available evidence for such policies comes almost exclusively from the U.S., mainly the impact of minimum legal age legislations at the beginning of the last century. See, for instance, Moehling (1999), Margo and Finegan (1996), Lleras-Muney (2002), Tyler (2003), and Manacorda (2006).

> 4 Ferro and Kassouf (2005) studied the impact of the law by looking at cohorts of 14 and 15 year-old children between 1995 and 2003. Their analysis is akin to a before-and-after analysis without a comparison group.

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that a law can be a powerful instrument that affects individuals, including those working informally.<sup>5</sup> The remaining part of this paper is organized as follows: The second section briefly discusses the change in law and provides the rationale as to how this law might affect children’s time allocation. The third section presents the data, while the fourth presents the identification strategy and the results. The conclusion highlights the main findings of the paper and outlines some policy recommendations.

# **2. THE CHANGE IN LAW OF DECEMBER 1998**

The Brazilian Constitution of 1988 set the minimum legal age of entry into the labor market at 14. In 1990, a federal rule named ‘The Statute of Children and Adolescents’<sup>6</sup> established the rights of children and youth. Complementary to the Constitution of 1988, the statute is considered the legal framework for children and youth in the labor market.<sup>7</sup> From 1988 to November 1998, the minimum legal working age in Brazil was 14 and individuals below age 17 were prohibited from working in hazardous conditions.

As a consequence of comprehensive modifications approved for the pension system on December 15, 1998, Constitutional Amendment No. 20 increased the minimum legal age for entry into the labor market from 14 to 16.<sup>8</sup> This change in law did not affect children who turned 14 before the law was passed; but those who turned 14 after the change were banned from the formal labor force.

The relatively large informal sector in Brazil can cast doubts on the effectiveness of the law. However, the effect of this intervention on participation rates of the treatment group depends on its enforceability and also on the size of the problem it is trying to fix. If some of the children participating in the formal sector simply shifted to the informal sector after the ban, the effect of the law on children’s participation rates would be negligible or even positive. But, if some employers decided to no longer employ children below age 16 to avoid legal consequences (such as paying fines), the law would probably reduce participation rates in the informal sector as well.

> 5 We are working on regression discontinuity regressions and looking at a wider range of outcomes. Some preliminary results on participation rates are aligned with the main findings discussed here. However, we also find that the ban affected school attendance of girls and had heterogeneous long-term effects on white and non-white males.

> 6 _Lei do Estatuto e do Adolescente_ , Law No.8069 from July 13, 1990.  Complementary to the Constitution of 1988, the statute is considered the legal framework for children and youth in the labour market. 7 ILO considers an individual 17 years or younger as a child.  In this paper, the terms children, teenagers, and youth are used interchangeably.

> 8 The law was passed on December 15, 1998 and came into effect the following day.

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# **3. EMPIRICAL STRATEGY**

To estimate the effect of the law of 1998, children who turned age 14 after the law change of December 1998 (treatment) were compared with those who turned age 14 before the change in law (comparison). As differences in labor force participation can arise from differences in ages or cohorts, we applied two DD estimates as control for these potential confounding effects. One estimate uses as counterfactual the difference in participation rates between those who turned 14 after December 1997 and those who turned 14 before December 1997. This is the different-cohorts-and-same-ages DD. In the second estimate, the counterfactual is the difference in participation rates between those who turned 13 after December 1997 and those who turned 13 on or before December 1997. This is the different-ages-and-same-cohorts DD. Table 1 shows how the ‘treatment’ and ‘comparison’ groups are defined for these two estimates.

Since we controlled for age (in weeks) linearly, the first difference in the DD exercise can be interpreted as a parametric regression discontinuity design.

<u>Table 1 – Definition of the Eligible and Comparison Groups for the DD Estimates</u>

||PNAD 1998|PNAD 1999|
|---|---|---|
|Treatment Group|_Different coho_<br>Turned 14 between<br>January and June, 1998|_rts and same ages_<br>Turned 14 between January<br>and June, 1999|
|Comparison Group|Turned 14 between July<br>and December,1997|Turned 14 between July and<br>December,1998|
||_Different ages_|_and same cohorts_|
|Treatment Group|Turned 13 between<br>January and June, 1998|Turned 14 between January<br>and June, 1999|
|Comparison Group|Turned 13 between July<br>andDecember,1997|Turned 14 between July and<br>December,1998|

The identification strategy for the DD depends on two assumptions: (1) any difference in labor force participation between the treatment and control groups exists in level but not in difference. That means the groups would show a common trend in the absence of the changes in law; and (2) all unobserved characteristics that can be correlated with the eligibility status of the individual or other covariates are additive and timeinvariant. In our first regression model, we assumed that any differences in participation rates between treated and comparison groups that are due to age effects, is constant over time. Whereas in the second regression model, we assumed a cohort effect that is fixed over time (see Meyer 1995).

The estimation of the impact of the law of 1998 on the outcomes of interest is conducted through the linear probability regression model:

# ��� ��� ����� �� ����� ������ ������� ����

where ��� is the outcome variable of individual _i_ in time _t_ , ��� is the vector of observed characteristics of individual _i_ in time _t_ . The vector includes dummies gender and ethnicity, the years of schooling of the household head, the age of the household head, the gender of the household head, and dummy variables for regions and the metropolitan region. In our first regression, �� is a dummy variable that equals 1 if individual _i_ turned aged 14 in the first semester of 1998 or 1999, and 0 if individual _i_ turned 14 in the second semester of 1997 or 1998, ��� is a year dummy that takes value 1 in 1999 (after the law was passed) 4
and zero for 1998 (before the law was passed), and ��� denotes an idiosyncratic error term. For this regression, we also include as control the individual ́s age (in weeks). In our second regression, �� takes the value of 1 if individual _i_ in cohort c turned age 14 in the first semester of 1999 and 0 if (s)he belongs to the cohort that turned 14 in the second half of 1998. Table 1 shows the groups under comparison in each case. Because the data provides exact dates of birth, we run the regressions using a 26 week-bandwidth (6month) as can be inferred from table 1.

In both regression models, the parameter of interest,  , provides the intent-totreat (ITT) effect. In other words, the impact of the ban on the cohort hindered from participating in the formal labor force at age 14.

# **4. DATA & RESULTS**

The sample used in this paper is drawn from the Brazilian household surveys ( _Pesquisa Nacional por Amostra de Domicilios_ – PNAD) of 1998 and 1999. The PNAD is an annual household survey that covers around 100,000 households and about 320,000 individuals. It is a major source of microdata in Brazil, and is a nationally representative survey that contains detailed information on each household’s socio-economic characteristics, demographic data, household income, and labor force status. The Brazilian Bureau of Statistics (IBGE) provides the exact reference date for when data collection is carried out and we used that information to identify the treatment and comparison groups.<sup>9</sup> Our results are exclusive for urban areas.

We used three measures of child labor: labor force participation rates as a whole without distinguishing between sectors, participation in the formal labor force, and participation rates in the informal labor force.<sup>10</sup> Table 2 shows ITT estimates on child labor incidence and reports the mean for the comparison group before the change in law. As can be seen, participation rates among boys are much higher compared to girls, and predominantly informal. We could therefore expect a higher impact of the change in law on boys. The first block of table 2 compares different cohorts keeping the age differential constant, whereas the second block compares the same cohort before and after the change in law. The estimates are very similar and point to a reduction in child labor of 4 percentage points for boys. Compared to a different cohort, the fall in participation rates was of 36 percent over the comparison group. Instead, if we used participation rates of the comparison group one year before, the drop in participation rates approached 80 percent. In both cases, the effect is almost fully driven by a reduction in participation rates in the informal sector. We found no impact on girls.

# **CONCLUSION**

This paper contributes to the literature on the impact of child labor laws by delving into the short-term impacts of the Brazilian Constitutional Amendment of December

> 9 According to IBGE, the reference date is the last week of September. There can be some variability in the exact day, but it is usually September 26 or 27.

> 10 The dummy variable for labour force participation rates takes the value of 1 if the individual is either (1) employed or looking for a job or was an active worker in the week of reference but was prevented from working due to external causes in the week of reference, or (2) worked in the last 12 months, and zero otherwise. A formal worker is someone working with a work permit ( _carteira assinada_ ) issued by the Brazilian Ministry of Labour and zero otherwise, whereas an informal worker is someone who works without such permission. This definition does not include domestic servants. In Brazil, domestic servants are covered by a separate legislation.

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1998, which increased the minimum legal age of entry into the labor force from 14 to 16. Unlike other estimates available in the literature, we present estimates for both formal and informal sectors separately. DD estimates show that the ban reduced the labor force participation rate for boys only. The impact is almost fully explained by the fall in participation rates in the informal sector. We found no impact on girls.

These preliminary findings suggest that the law was biding and general measures are more effective when the incidence is somewhat pervasive, as in the case of boys. One may conjecture the reasons why there is a strong effect among informal workers. It is possible that the child labor ban reduced verification costs by local authorities and increased chances of firms getting caught for breaking the law. Before the change in law, a child aged 14 could work formally. The cost of verification of a worker hired formally or informally might be greater than the simpler verification of whether a child is working at all. The firms seem to have responded to the change in the law by reducing the number of working children. Policy makers thus should have a broader perspective when they pass such laws, and take into account their heterogeneous effects on children’s time allocation.

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|Table 2–Difference-in-Diffe|rences Esti<br>|mates for the<br>|Impact of th<br>|e Law of 1998<br>|on Extensiv<br>|e Margin of<br>|Labor Supply<br>|<br>||
|---|---|---|---|---|---|---|---|---|---|
||Labor Fo|rce Participatio|n Rates|Participation R|ates – Formal|Labor Force|Participation|Rates – Informa|l Labor Force|
||All|Boys|Girls|All|Boys|Girls|All|Boys|Girls|
|||||_Different_<br>|_cohorts and sa_<br>|_me ages_<br>||||
|Eligible*D99(_DD_)|-0.019**|-0.040***|0.0035|-0.0048|-0.0074|-0.0016|-0.014*|-0.032**|0.0051|
||(-2.40)|(-2.89)|(0.45)|(-1.42)|(-1.27)|(-0.47)|(-1.94)|(-2.55)|(0.73)|
|Eligible|-0.017***|-0.024***|-0.011**|-0.0091***|-0.016***|-0.0032|-0.0076*|-0.0089|-0.0076*|
||(-3.46)|(-2.87)|(-2.26)|(-4.41)|(-4.26)|(-1.58)|(-1.73)|(-1.14)|(-1.75)|
|D99(1998=0, 1999=1)|0.040***|0.068***|0.012**|0.0078***|0.011**|0.0046|0.032***|0.057***|0.0077|
||(6.53)|(6.25)|(2.05)|(2.59)|(2.10)|(1.58)|(5.88)|(5.81)|(1.45)|
|_Mean of the dependent variable_||||||||||
|_Comparison Group in 1998 _|_0.076 _|_0.112_|_0.040_|_0.021_|_0.032_|_0.010_|_0.055_|_0.08_|_0.030_|
|Observations|14641|7223|7418|14641|7223|7418|14641|7223|7418|
|AdjustedR2|0.035|0.026|0.008|0.010|0.013|0.001|0.030|0.024|0.005|
|||||_Different_|_ages and sam_|_e cohorts_||||
|Eligible*D99(_DD_)|-0.025***|-0.042***|-0.0067|-0.0039*|-0.0056|-0.0021|-0.020**|-0.035**|-0.0037|
||(-2.77)|(-2.66)|(-0.78)|(-1.86)|(-1.53)|(-0.97)|(-2.29)|(-2.33)|(-0.45)|
|Eligible|-0.0032|-0.0098|0.0021|-0.0010|-0.0022|0.0001|-0.0033|-0.0088|0.0011|
||(-0.63)|(-1.04)|(0.46)|(-1.37)|(-1.41)|(0.0001)|(-0.66)|(-0.95)|(0.25)|
|D99(1998=0, 1999=1)|0.036***|0.052***|0.020***|0.0050**|0.0067*|0.0032*|0.032***|0.047***|0.017***|
||(5.28)|(4.22)|(3.21)|(2.53)|(1.92)|(1.73)|(4.83)|(3.94)|(2.82)|
|_Mean of the dependent variable_||||||||||
|_Comparison Group in 1998 _|_0.031_|_0.053_|_0.010_|_0.002_|_0.004_|_0.001_|_0.028_|_0.048_|_0.01_|
|Observations|7354|3597|3757|7354|3597|3757|7354|3597|3757|
|AdjustedR2|0.027|0.024|0.005|0.003|0.005|0.000|0.026|0.024|0.004|

Note: Robust T-statistics in parentheses. *, **, *** Statistically significant at 10 percent 5 percent, and 1 percent respectively. The controls include age (in weeks), dummy variables for gender (male), ethnicity (white), head’s years of schooling, age, and gender (=1 if male), dummy for states and metropolitan area.

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