metadata: authors: 0: author: Abman,Ryan Michael
1: author: Lundberg,Clark Christopher
2: author: Mclaren,John Edward
3: author: Ruta,Michele
count: World
docna: 0: docna: Child Labor Standards in Regional Trade
            Agreements : Theory and Evidence
docty: Policy Research Working Paper
abstracts: cdata!: This paper studies the impact of child
            labor standards in regional trade agreements on a variety of
            child labor market outcomes, including employment,
            education, and household inequality. It develops a stylized
            general equilibrium model of child labor in an economy open
            to international trade and considers the impact of regional
            trade agreements with and without child labor bans. The
            paper empirically investigates the effects of these clauses
            in trade agreements in a broad international panel of 101
            developing countries, using harmonized survey microdata.
            Exploiting quasi-experimental methods to obtain plausibly
            causal estimates, the analysis finds that regional trade
            agreements without child-labor bans lead to reductions in
            child employment and increases in school enrollment,
            particularly for older children aged 14&#x2013;17 years.
            Child labor bans in regional trade agreements perversely
            increase employment of children aged 14&#x2013;17 years
            and decrease school enrollment for both young and older
            children. These effects appear to decrease inter-household
            income inequality through increased child earnings. The
            findings are consistent with the theoretical predictions
            from the model and the literature on child labor bans.
display_title: Child Labor Standards in Regional
            Trade Agreements : Theory and Evidence
listing_relative_url: /research/2023/02/34010662/child-labor-standards-regional-trade-agreements-theory-evidence
topicv3: Child Labor m1326042 1859,Trade Agreements and Negotiations m1327641 779,Trade and Labor Markets m1504261 2553,Trade and Employment m1504263 2555
publishtoextweb_dt: 2023-02-28T00:00:00Z
dois: 10.1596/1813-9450-10331
origu: Off of Sr VP Dev Econ/Chief Econ (DECVP)
author_keys: 0: author_key: 1891115
1: author_key: 1888463
2: author_key: 1015950
3: author_key: 1245806
model_extractions: Policy Research Working Paper 10331 Child Labor Standards in Regional Trade Agreements

# Theory and Evidence

_Ryan Abman Clark Lundberg John McLaren Michele Ruta_ Development Research Group & Macroeconomics, Trade and Investment Global Practice February 2023
Policy Research Working Paper 10331

# **Abstract**

This paper studies the impact of child labor standards in regional trade agreements on a variety of child labor market outcomes, including employment, education, and household inequality. It develops a stylized general equilibrium model of child labor in an economy open to international trade and considers the impact of regional trade agreements with and without child labor bans. The paper empirically investigates the effects of these clauses in trade agreements in a broad international panel of 101 developing countries, using harmonized survey microdata. Exploiting quasi-experimental methods to obtain plausibly causal estimates, the analysis finds that regional trade agreements without child-labor bans lead to reductions in child employment and increases in school enrollment, particularly for older children aged 14–17 years. Child labor bans in regional trade agreements perversely increase employment of children aged 14–17 years and decrease school enrollment for both young and older children. These effects appear to decrease inter-household income inequality - through increased child earnings. The findings are consis tent with the theoretical predictions from the model and the literature on child labor bans.

This paper is a product of the Development Research Group, Development Economics and the Macroeconomics, Trade and Investment Global Practice. 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://www.worldbank.org/prwp. The authors may be contacted at rabman@sdsu.edu, clundberg@sdsu.edu, jem6x@virginia.edu, and mruta@imf.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
# Child Labor Standards in Regional Trade Agreements: Theory and Evidence<sup>∗</sup>

Ryan Abman<sup>†</sup> , Clark Lundberg<sup>‡</sup> , John McLaren<sup>§</sup> , and Michele Ruta<sup>¶</sup>

**Keywords:** Child labor, Regional Trade Agreements, Trade Liberalization **JEL Classification Codes:** F14, F15, F16, J13, J22, J82

> ∗We are grateful to discussion from Bob Rijkers and participants in the 2022 World Bank Deep Trade Agreements Conference and to Erhan Artu ̧c for crucial advice. This paper has benefited from support from the World Bank’s Umbrella Facility for Trade trust fund financed by the governments of the Netherlands, Norway, Sweden, Switzerland and the United Kingdom. All errors are our own.

> †Department of Economics, San Diego State University: rabman@sdsu.edu.

> ‡Department of Economics, San Diego State University: clundberg@sdsu.edu.

> §Department of Economics, University of Virginia: jem6x@virginia.edu.

> ¶International Monetary Fund: mruta@imf.org.
# **1 Introduction**

Over the last 30 years, regional trade agreements (RTAs) have become the de facto policy tool in trade liberalization. The bilateral nature of RTA negotiations has introduced opportunities to broaden the scope of trade policy well beyond liberalization in tariff rates and non-tariff trade barriers, with contemporary RTAs frequently including binding commitments in areas such as investment, intellectual property, labor and environmental policy, among others (Mattoo _et al._ , 2020). Such policy interdependencies are referred to as “issue linkages” in the literature (e.g. Maggi, 2016; Lim ̃ao, 2005, 2007, 2016). In particular, concerns about undesirable labor practices such as the exploitation of child labor have increasingly dominated trade debates and motivated the inclusion of provisions banning child labor in trade agreements. Figure 1 displays the evolution of child labor content in RTAs over time.<sup>1</sup> Though sparse prior to 2000, child labor provisions became increasingly common through the 2000s and 2010s. Despite their increasing use, there is no systematic evidence on their impacts on labor outcomes, both intended and unintended.

In this paper we investigate the effect of child labor provisions in regional trade agreements on a variety of child labor market outcomes in developing countries. To structure our analysis, we develop a general-equilibrium model of an open economy in which both children and adults can supply labor as part of a household-level joint labor-supply decision. We show that a trade agreement is likely to decrease the incidence of child labor in developing countries through income effects. We find that trade agreements that include provisions banning household supply of child labor will lead to reductions in employment of the youngest children (i.e. those under 14 years of age that are directly affected by the ban), but increases in employment of older children (i.e. those above 14 years of age that are not included in the ban) via intra-household substitution effects. If instead a trade agreement includes prohibitions on child labor that are implemented as a finebased restriction on hiring children below 14 years of age, employment of the youngest children is ambiguous—if young children contribute a non-negligible share of household earnings, their labor supply is likely to increase when exposed to a fine-based restriction. We find that employment of

> 1See Table A.1 for the list of RTAs that have included such provisions.

1
older children is again likely to increase in developing countries via intra-household substitution effects, even with demand-side implementations.

To empirically study this question we bring to bear harmonized income survey microdata from 170 countries covering response years between 1960 and 2020. The data include over 1,700 unique nationally representative survey years covering over 180 million respondents. We combine these surveys with detailed data on trade agreements reported to the WTO that include non-trade characteristics such as whether an agreement includes provisions banning the use of child labor and how said provisions are enforced. Because child labor is less of a concern in developed countries, we focus our analysis on a sub-sample of 101 developing countries. We estimate a variety of difference-indifferences (DiD) and triple DiD models on this developing country sample to assess within-country changes in child labor market outcomes before and after a given trade agreement enters into force, and whether these effects differ for trade agreements with the child labor provisions compared to those without.

We find that, consistent with our model predictions, child labor—especially employment of 14 to 17 year olds—declines following entry into force of RTAs that do not include child labor protections with corresponding increases in school enrollment rates. However, in trade agreements that include provisions banning child labor, those provisions perversely _increase_ child employment for both young (under 14 years of age) and, especially, 14 to 17 year olds. Consistently with this finding, we also observe a decrease in school enrollment rates for both young and older children. Finally, we find evidence that these provisions decrease inter-household income inequality for households with children, presumably through increases in child labor income.

This is the first paper to establish systematic evidence on the impacts of child labor protections in RTAs on child labor and related outcomes. In doing so, we also make significant contributions to the literature on the impacts of free trade on child labor more broadly. Our empirical setting offers credibly exogenous variation in trade openness applied to a broad panel of developing countries and improves upon existing identification strategies in the literature that leverage cross-country differences in trade and child labor. Our study also provides new causal evidence on the impacts of child labor bans on child labor, a literature which has largely focused on domestic labor bans 2
examined through single-country analysis. Ours is the first study, to our knowledge, that investigates the impact of child labor bans across a broad sample of developing countries. This paper also contributes to the nascent literature on the non-trade effects of provisions in trade agreements that have non-trade objectives, such as labor and environmental protection.

Our findings contribute to a rich literature on child labor in economics. On the theoretical side of the literature, Basu and Van (1998) propose a simple equilibrium model in which households choose to send their children into market work only when necessary to meet a basic standard of consumption. Child workers and adult workers are perfect substitutes, so their wages move together; when the adult wage is above a subsistence threshold, no children are sent to work. This can create two equilibria: One in which wages are low, and there is child labor; and another in which wages are high, and there is no child labor. Basu (2005) shows how child-labor bans in such a model can also paradoxically increase child labor if they are implemented as fines on employers. Baland and Robinson (2000) and Ranjan (2001) both study models in which child labor is inefficient, either because children cannot credibly commit to repaying parents in the future for investments in schooling or because of credit constraints more generally. Basu (1999) surveys the economics literature on the subject, including a discussion (Section 8) of the scope for international coordination to deal with the problem.

The empirical relationship between trade and child labor is examined by Edmonds and Pavcnik (2005), who show that when Vietnam lifted export restrictions on rice, rural locations where the rice price rose the most had the most rapid reductions in child labor. This appears to suggest that the rising income effect of the trade liberalization facilitated a drop in the need for child labor. This conclusion is supported by cross-country evidence in Edmonds and Pavcnik (2006). As a matter of theory, Ranjan (2001) shows that trade sanctions against a country with child labor can be counterproductive, by increasing income inequality and thus the credit constraint problem. We contribute to this literature by looking specifically at the role of trade _agreements_ , and labor clauses within those agreements, in addressing child labor.

We also draw on studies of explicit child-labor bans. Bharadwaj _et al._ (2020) study a childlabor ban in India in 1986, which imposed a fine on employers found in violation, and find that 3
it paradoxically _increased_ child labor, as predicted in Basu (2005). In a similar vein, Lakdawala _et al._ (2022) study a policy change that legalized certain forms of child labor in Bolivia, with the surprising result that use of child labor declined. These are consistent with our own results, as we will point out. Finally, Doran (2013) studies the effects of the PROGRESA program in Mexico, which paid parents to keep their children in school, and finds that the program resulted in less use of child labor and more employment of adult labor. The contrast between the Indian policy, which worked on the labor-demand side, and the Mexican policy, which worked on the labor-supply side, will be important in our interpretations.

Child labor is also closely linked to education, since the most important opportunity cost of children’s time is schooling. Several authors have explored the effect of trade liberalization on educational attainment. Leight and Pan (2021) show that areas in China most affected by the boom in low-skill-intensive manufactured exports saw relative reductions in educational attainment. Greenland and Lopresti (2016) show that locations in the US most affected by low-skilled-manufacturing import competition from China saw relative _increases_ in high-school graduation. Atkin (2016) shows that access to low-skill export manufacturing jobs provided by multinational firms in Mexico is correlated with reduced high-school attainment. Edmonds _et al._ (2010) show that in Indian trade liberalization, import competition shocks hitting industries important to local employment were correlated with reduced school enrollment. Blanchard and Olney (2017) show in a panel of countries that increased low-skill-labor-intensive exports are correlated at the national level with reduced high-school enrollment, with the opposite effect for high-skill-intensive exports. These results together suggest that a trade agreement on its own, even without a labor clause, could have an effect on child labor by raising or lowering the returns to schooling. This is a question that we explore empirically.

Our work also contributes to the large literature studying the effects of trade agreements. This literature has consistently found that trade agreements significantly increase trade flows between member countries.<sup>2</sup> The more recent strand of this work has emphasized the importance of the varying content of RTAs in assessing their trade effects. Baier _et al._ (2014) find evidence of dif-

> 2Seminal works include Baier and Bergstrand (2007); Egger _et al._ (2011); Bergstrand _et al._ (2015). See Lim ̃ao (2016) for a survey.

4
ferential trade effects from different types of agreements (e.g. partial scope agreements, free trade agreements or custom unions), while Mattoo _et al._ (2017) show that “deep” agreements (i.e. RTAs that cover a larger number of policy areas beyond tariff reduction) are associated with a stronger trade impact.<sup>3</sup> The literature on trade agreements that focuses on non-trade outcomes such as labor and the environment is mostly theoretical and focuses on the conditions under which issue linkage in trade agreements is beneficial (e.g. Ederington, 2002; Lim ̃ao, 2007; Maggi, 2016). Notable exceptions include Abman _et al._ (2021) who find that environmental provisions in RTAs effectively mitigate deforestation that otherwise arises from trade liberalization, Baghdadi _et al._ (2013) that study the impact of RTAs with environmental clauses on emissions, Brandi _et al._ (2020) that investigate whether environmental provisions in RTAs make exports from developing countries greener, and Lundberg _et al._ (2022) that find linking RTAs to international environmental agreements can improve participation and strengthen enforcement.

The remainder of the paper is structured as follows: in Section 2 we develop our theoretical model. In Section 3 we elaborate on our data an empirical strategy. We discuss our empirical findings in Section 4 and offer some concluding remarks in Section 5.

# **2 Theory**

We present here a simple, stylized general-equilibrium model of an open economy in which both children and adults supply labor as part of a household-level joint labor-supply decision.<sup>4</sup> Suppose that the economy produces two goods, _X_ , which is an export, and _Y_ , which is import-competing. Good _Y_ is the numeraire. Denote the domestic price of _X_ as _p_ . Each good _j_ is produced with low-skilled labor _L_<sup>_j_</sup> , high-skilled labor _H_<sup>_j_</sup> , and a specific factor _K_<sup>_j_</sup> , by a constant-returns-to- _<u>σ</u>_<sup>_j_</sup> _σ_<sup>_j_</sup> _−_ 1 _σ_<sup>_j_</sup> _−_ 1 _σ_<sup>_j_</sup> _−_ 1 _σ_<sup>_j_</sup> _−_ 1 scale production function _f_<sup>_j_</sup> ( _L_<sup>_j_</sup> _, H_<sup>_j_</sup> _, K_<sup>_j_</sup> ) = _β_<sup>_jL_</sup> ( _L_<sup>_j_</sup> ) _σ_<sup>_j_</sup> + _β_<sup>_jH_</sup> ( _H_<sup>_j_</sup> ) _σ_<sup>_j_</sup> + _β_<sup>_jK_</sup> ( _K_<sup>_j_</sup> ) _σ_<sup>_j_</sup> , � �

> 3Carr`ere _et al._ (2022) study how labor provisions in RTAs affect bilateral trade flows and find that on average these clauses do not have a statistically significant impact. Robertson (2021) finds that specific labor clauses in trade agreements, such as child labor standards, are associated with an increase in trade, most likely as they boost demand from reputation-conscious buyers (Maskus, 1997).

> 4The model presented here is static, close to the spirit of Basu and Van (1998) and the theory model of Edmonds and Pavcnik (2005). The important intertemporal considerations raised in approaches such as Baland and Robinson (2000), Ranjan (2001), and Section 6.4 of Basu (1999) are beyond the scope of this paper.

5
where _σ_<sup>_j_</sup> _>_ 0 is the elasticity of substitution between the three factors. The specific factors _K_<sup>_j_</sup> are inelastically supplied. Labor is supplied by households, indexed by _i ∈_ [0 _, N_<sup>_L_</sup> ] for low-skilled workers and _i ∈_ [0 _, N_<sup>_H_</sup> ] for high-skilled.

Each household is either exogenously low skilled, in which case all of its members are low-skilled workers, or high skilled, in which case all of its members are high-skilled workers. Household _i_ has _k_ ̄( _i_ ) members, each of whom can work for wages or do something else such as household work or education. A worker of age _a ∈_ [1 _,_ 2 _, . . . ∞_ ) in household _i_ produces _A_ ( _i_ ) _e_ ( _a_ ) _∈_ [0 _, ∞_ ) units of effective labor if employed in the labor market, where _A_ ( _i_ ) is a productivity factor that measures household-wide ability and _e_ ( _a_ ) captures the portion of ability that is an increasing function of age. Household member _k_ in household _i_ has age _a_ ( _k, i_ ). Without loss of generality, number household members so that _a_ ( _k, i_ ) is increasing in _k_ .

For a worker of age _a_ , there is a cost _v_ ( _L, a_ ) of working _L_ hours in the labor market. This can be the opportunity cost of the time that would otherwise be spent in school, with family, at play, in household production, and so on, as well as drudgery and direct disutility of work. It is a convex, differentiable function with _v_ 1 _>_ 0 _, v_ 11 _>_ 0 over its whole domain, and _v_ 1( _L, a_ ) _> v_ 1( _L, a_<sup>_′_</sup> ) if _a < a_<sup>_′_</sup> so work is costly at the margin—with an increasing marginal cost and a marginal cost that is greater for younger workers.

Each household has a head who acts as a benevolent dictator, choosing labor supply and allocation of consumption to maximize household welfare. The household head chooses the market labor supply _Lk ∈_ [0 _, L_<sup> ̄</sup> _k_ ] for each member _k_ . The wage per unit of effective labor for a worker of type _t_ is denoted _w_<sup>_t_</sup> , where _t_ = _L, H_ indicates low-skilled or high-skilled, and so the total household income ̄ is equal to _I_ ( _i_ ) = _w_<sup>_t_</sup> Σ _k_ =1 _,...,k_ ( _i_ ) _A_ ( _i_ ) _e_ ( _a_ ( _k, i_ )) _Lk_ .

The benefit of market work to the household depends on per-capita labor income. Specifically, the benefit to household _i_ of labor income _I_ is given by _u_ ( _c_ ( _i_ )), where _c_ ( _i_ ) is per-capita consumption and _u_ ( _·_ ) is strictly increasing, concave, and differentiable. Total consumption is an aggregate of _X_ and _Y_ consumption, with _c_ =<sup>_h_</sup><sup><u>(</u></sup><sup>_X_</sup> _k_ <u> ̄</u><sup>_<u>,Y</u>_</sup><sup><u>)</u></sup> , where _h_ ( _·, ·_ ) is a linear-homogeneous function. The consumer price index _P_ is the unit cost of consumption derived from _h_ ( _·, ·_ ), which can be written as a function _φ_ ( _·_ ) of the price _p_ of _X_ given that _Y_ is the numeraire: _P_ = _φ_ ( _p_ ). Therefore, household consumption 6
per capita can be written as _c_ ( _i_ ) =<sup>_I_</sup> _P_<sup><u>(</u></sup><sup>_i_</sup> _k_<sup><u> ̄)</u>.</sup> Given this structure, the household head will choose the labor supply _Lk_ of each household member _k_ to maximize: The first-order condition for the choice of _Lk_ in the event of an interior solution is: Clearly, within a household, younger household members will be assigned less market work, since _e_ ( _a_ ) is lower and _v_ 1( _L, a_ ) is higher for younger members.

We shall assume that in any equilibrium _w_<sup>_H_</sup> is high enough, and the opportunity cost of labor for children in high-skilled households is high enough, that no high-skilled household uses child labor.

# **2.1 Labor Supply Behavior**

To analyze the effects of trade agreements and their labor clauses on child labor, it is important to understand how labor supply responds to changes in the environment. We will analyze two interpretations of a child-labor ban. The first is a ban that prohibits households from sending their children under the age of 14 to work in the labor market. This type of ban is a labor-supply shock. Alternatively, the ban could take the form of a fine imposed on employers that are caught with under-age workers. This type of ban is a labor-demand shock. We will see that these two types of ban have very different implications, for welfare and for empirical predictions.

To simplify the analysis, consider the following special case. Suppose that each household _i_ has adults whose marginal opportunity costs are always low enough that they always work the maximum _L_<sup> ̄</sup> _k_ , and whose effective labor sums to _Ei_ . In addition, each household has one child 7
below the age of 14, whose index is _k_ = 1, and one child above the age of 14, whose index is _k_ = 2.

There are two important effects on labor supply to identify: The effect of a change in the real low-skilled wage and the effect of a labor clause in the trade agreement. For the former, it will be important to distinguish between cases in which: holds and those in which it does not. Condition (3) states that the utility function is sufficiently concave that _cu_<sup>_′_</sup> ( _c_ ) is a decreasing function of _c_ . This ensures that the income effect of a wage change dominates the substitution effect for labor supply, so that child labor is less likely to be used when low-skilled wages are high, as would be consistent with the empirical findings of Edmonds and Pavcnik (2005, 2006). The main result is:

**Proposition 1.** _An increase in w_<sup>_L_</sup> _will lower the labor supply in the labor market of all children in the household if (3) is satisfied, and will increase their labor supply otherwise._

# **Proof: See appendix.**

The general equilibrium impacts of a trade agreement can be determined as follows. The endogenous variables are low- and high-skilled wages _w_<sup>_L_</sup> and _w_<sup>_H_</sup> , and the rental prices _r_<sup>_X_</sup> and _r_<sup>_Y_</sup> of the specific factors. These can be determined by the zero-profit conditions for the two sectors, plus the requirement that the total demand for each of the four factors equals its supply. Since good _X_ is the export good, the effect of the trade agreement can be modelled as an increase in _p_ , as foreign import barriers are lowered, making it easier to export _X_ (and as domestic import barriers are lowered, lowering the domestic relative price of imported good _Y_ ).

An important technical issue needs to be raised. Basu and Van (1998) showed that a model with endogenous choice of child labor by households can create multiple equilibria if it results in a downward-sloped supply of low-skilled labor that is sufficiently elastic. The main results below will depend on the elasticity of labor supply either being positive or being negative with an elasticity that is not too large in magnitude, which avoids those complications. Even in the Basu and Van 8
(1998) model, labor supply is inelastic _locally_ at the equilibrium, so the sort of marginal analysis we use here would apply.

The first result is that the trade agreement itself, apart from any labor clause, has effects on the labor market, income distribution, and child labor which depend on the nature of the export sector that the agreement promotes relative to the import-competing sector:

**Proposition 2.** _If the trade agreement is sufficiently low-skilled-labor biased, meaning if the production-function parameters β_<sup>_XH_</sup> _and β_<sup>_Y L_</sup> _are sufficiently small, then the trade agreement, by raising p, will increase real low-skilled wages_<sup>_<u>w</u>_</sup> _P_<sup>_L_</sup> _and lower high-skilled real wages_<sup>_<u>w</u>_</sup> _P_<sup>_H,aslongas_</sup> _the supply elasticity of low-skilled labor is not too negative. If (3) holds, this change in_<sup>_<u>w</u>_</sup> _P_<sup>_L_</sup> _will lower the use of child labor._

_If the trade agreement is sufficiently high-skilled-labor biased, meaning β_<sup>_XL_</sup> _and β_<sup>_Y H_</sup> _are sufficiently small, then the trade agreement will have the opposite effect on all counts._

## **Proof: See appendix.**

In most cases of a developing economy signing a trade agreement with a high-income country, the export sector that will be promoted will be low-skill-labor-intensive manufactures, meaning that the first of these two cases will be more relevant.

# **2.2 Supply-Side Implementation of a Child Labor Ban**

We now consider the effects of a child labor ban implemented as a supply restriction on households. This typically affects younger children directly, but it can have an indirect effect on older children whose labor is not explicitly banned:

**Proposition 3.** _Holding w_<sup>_L_</sup> _constant, an enforced ban on children under the age of 14 working in the labor market will increase the market labor supply of children above the age of 14 but lower the total supply of low skilled labor._

## **Proof: See appendix.**

9
Banning the market labor of younger children induces households to increase the labor supply of older children as a _partial_ compensation for the lost income. An enforced child-labor clause will also have effects on the labor-market equilibrium:

**Proposition 4.** _A ban on the labor of children under the age of 14 will result in an increase in the low-skilled wage either if the supply of low-skilled labor is increasing in the low-skilled real wage, or if it is decreasing in the low-skilled real wage with an elasticity that is not too large._

## **Proof: See appendix.**

Banning child labor reduces the supply of low-skilled labor, raising its price. Note that putting together Propositions 1, 3 and 4, we see two separate effects of a child labor supply ban on the employment of 14-17-year-olds: Holding _w_<sup>_L_</sup> constant, the ban will increase the labor supply of 14-17-year-olds, but if (3) holds, the resulting increase in _w_<sup>_L_</sup> will tend to _reduce_ the labor supply of 14-17-year olds. The net effect is ambiguous.

# **2.3 Demand-Side Implementation of a Child Labor Ban**

Now, consider the possibility that the child-labor ban acts on the employers rather than the households, creating a labor-demand shock rather than a labor-supply shock. Suppose that in a country that has a child-labor ban in effect as specified in a trade agreement, if an employer is caught employing children under the age of 14, it will need to pay a fine. There is some probability of getting caught, and the expected fine taking into account this probability is _t_ times the wages paid to the children workers employed.<sup>5</sup> In this situation, an employer will be willing to employ those under-age workers only if their wage is sufficiently lower than the wages of other workers to compensate for the anticipated fine, so if the wage per effective unit of low-skilled labor is _w_<sup>_L_</sup> , the wage paid to under-age labor in the presence of a child-labor ban will be (1 _− t_ ) _w_<sup>_L_</sup> . Henceforth, we will use _w_<sup>_L_</sup> to denote the full wage

> 5This specification of an _ad valorem_ fine is certainly not the way such fines would likely be implemented in practice. We use it for convenience, to provide an example that is easy to understand, and we conjecture that the result of a per-child fine would be similar.

10
as viewed by the employer, inclusive of the expected fine, and (1 _− t_ ) _w_<sup>_L_</sup> to denote the wage received by the under-age worker.

To analyze the effects of such a policy, consider the special case of Proposition 1, where each household has adults with inelastic labor supply plus one child ( _k_ = 1) under the age of 14 and one child ( _k_ = 2) in the 14-17 range. There is no child labor among high-skilled households. In this case, we can summarize the equilibrium by a supply curve for low-skill labor, the aggregate of all low-skill household decisions for any given value of _w_<sup>_L_</sup> , and a demand curve for low-skill labor as derived in the proof of Proposition 4.<sup>6</sup> If (3) holds, the labor-supply curve will be downward sloping (in which case we will assume the stability condition that it is steeper than the labor-demand curve), and otherwise it will be upward sloping.

First, we need to see how a household responds to the imposition of a fine, for a given value of

# _w_<sup>_L_</sup> :

**Proposition 5.** _Holding w_<sup>_L_</sup> _fixed, an increase in the fine t will increase the labor of the older child in each household. The labor supply of the younger child will fall if u_<sup>_′_</sup> ( _c_ )+ _χcu_<sup>_′′_</sup> ( _c_ ) _>_ 0 _, where χ ≡ w_<sup>_L_</sup> Σ _k_ =1 _w_<sup>_L_</sup> _,...,A_ <u>(</u> _k_  ̄ _i_ () _ie_ )( _Aa_ (( _i_ 1) _<u>,ei</u>_ () _aL_ (1 _k,i_ )) _Lk_<sup>_isthefractionofthehousehold’sincomederivedfromtheyounger_</sup> _child’s labor._

# **Proof: See appendix.**

Holding the low-skill wage constant, an increase in _t_ lowers the wage only for under-age child workers. Therefore, it does not change the opportunity cost of leisure time for any household member other than the youngest worker, and so the usual tension between income and substitution effects is absent. For workers other than under-age children, there is only the income effect: Those workers work more in order to recoup some of the lost income. For the under-age worker, however, the reduction in wage does create both an income and substitution effect, and therefore an ambiguity in the effect on labor supply. Note that the income effect is larger, the larger is under-age child labor in the family’s income, and so the condition given in the proposition is a statement that either the utility function is not too concave, or that _χ_ , the share of under-age labor in total income, is

> 6In both cases, it should be underlined that these are not partial-equilibrium labor-supply and labor-demand curves. In particular, as we move along the labor-demand curve by exogenously increasing the effective supply of low-skill labor, the other factor prices are adjusting to maintain zero profits.

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not too large.

**Proposition 6.** _Starting from t_ = 0 _, a small increase in t will shift the low-skill labor-supply curve to the right if_

# _for each low-skilled household i._

# **Proof: See appendix.**

In this case, the introduction of the fine will lower the low-skill wage; lower the wage more for under-age workers; and increase the total labor supply of low-skill household members other than under-age workers (with the effect on the latter ambiguous).

The condition (4) is a very strong sufficient condition, and the rightward shift in the labor-supply curve will likely occur under much weaker conditions as well. Note that (4) is strictly stronger than (3) because _χ_ ( _i_ ) _<_ 1. It amounts to a requirement that _RRA∗χ_ ( _i_ ) _>_ 1, where _RRA_ is the coefficient of relative risk aversion. Estimates of _RRA_ in the developing world vary widely. Cardenas and Carpenter (2008) survey numerous studies and find values from 0.05 to 2.57. Yesuf and Bluffstone (2009) provide estimates from experiments in Ethiopia that range from close to 0 up to over 15, with an average around 4. Whether (4) would be satisfied or not in practice is not clear. On the other hand, Basu (2005) and Bharadwaj _et al._ (2020) show that, regardless of _RRA_ , imposing a fine on child labor can result in both an increase in child labor and in total unskilled labor in a model with a household subsistence consumption constraint, so the possibility of such an effect is much broader than (4) would suggest.<sup>7</sup> The contrast between the implications of the supply-side implementation and the demandside implementation of the child-labor ban is striking. The demand-side implementation implies much more adverse effects on low-skilled households. From Proposition 4, a supply-side ban, by withdrawing low-skilled labor from the market, tends to raise unskilled wages, but by Proposition 6, a demand-side ban tends to lower them, and lower them especially for the under-age child workers

> 7Indeed, the assumption that there is a minimum necessary consumption requirement, and that this constraint binds in households that use child labor, is equivalent to assuming a locally infinite _RRA_ . Under that interpretation, the estimates of average values of _RRA_ mentioned above are not relevant; only the value for child-labor-using households would matter, and that value may be much larger than the average.

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themselves, who may still be employed in the market despite the ban. This latter effect is both through the direct effect on labor demand of penalizing employers for hiring a certain type of lowskilled worker, but it can also work through the indirect effect of increasing low-skilled labor supply through income effects.<sup>8</sup> Finally, under the supply-side model, the ban can only lower the incidence of child labor, while in the demand-side interpretation it is ironically possible that labor supply by the under-age child workers themselves may rise because of the ban. This is due to income effects, and is made the more likely by the possibility that unskilled wages will fall due to the ban, further lowering the incomes of low-skilled households.

# **2.4 Testable Hypotheses**

Our theory yields a number of testable hypotheses that we summarize below:

1. A trade agreement itself can either increase or decrease child labor even without a labor clause, depending on the nature of trade that it promotes. In the likely case that it promotes lowskilled-labor-intensive manufactures from a low- or middle-income country, and (3) holds, it is likely to reduce child labor. In our regressions, we aim to measure the effect of an agreement with no labor clause on the incidence of child labor.

2. A child labor clause that prohibits households from sending children below the age of 14 into the labor market (a supply-side ban) could either raise or lower the employment of 14-andover children. For a given real low-skilled wage, the removal of under-14 children from the labor market will tend to increase the use of 14-and-over children to replace the lost income, but if (3) holds, the increase in low-skilled wages will work in the opposite direction. In our regressions, we look both for effects of the labor clauses on the labor supply of children under 14, who are typically explicitly covered by such clauses, and also for the indirect effect on children aged 14 and up. If the latter is positive, the interpretation will be that the replacement-of-lost-income effect dominates any induced wage effect, and _vice versa_ if it is

> 8It may be surprising that modeling the ban as a labor-demand-side policy works through shifting the laborsupply curve. This arises because, as noted above, these are not partial-equilibrium supply and demand curves. In particular, the labor-supply curve here shifts because of a change in household income.

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negative.

3. In contrast, a child-labor ban that takes the form of a fine imposed on employers caught with under-age workers (a demand-side ban) will tend to _lower_ unskilled wages, lowering them more for underage workers than for other low-skill workers, and unambiguously increase the labor supply of the rest of the household, including children aged 14-17. In addition, it will not stop under-age child labor and can even increase it.

4. A labor clause that prohibits households from sending children below the age of 14 into the labor market is likely to raise low-skilled wages, lowering wage inequality. However, the direct effect of the lost labor on income of low-income households may tend to push inequality upward. For example, if high-skilled households—that never use child labor—are in the upper household income quantiles and low-skilled households—that would use child labor when it is allowed—are in the lower quantiles, income inequality will rise if the drop in<sup>_<u>w</u>_</sup> _w_<sup>_HL_dominates</sup> the direct loss of labor income to low-skill households from loss of child labor earnings.

# **3 Data and Empirical Strategy**

Our labor market data comes from the World Bank’s International Income Distribution Database (I2D2) which harmonizes income survey microdata from 170 countries covering response years (nonuniformly) between 1960 and 2019. The data include 1,777 unique nationally representative survey years covering 180,027,049 respondents. The underlying survey data comes from a variety of survey modules that cover income-related questions, including respondent age, employment status, wages, household income, etc. Because the incidence of and concerns over child labor are concentrated on developing countries, we likewise restrict our analysis to developing countries in the I2D2 data, resulting in a sample of 101 countries.

The temporal resolution varies by country and surveys are repeated cross-sections—respondent identification is unique only within survey-country-year, which precludes linking any repeatedly sampled individuals across time. To circumvent this limitation, we aggregate the individual microdata to the country-year. Because the surveys are nationally representative, this aggregation 14
yields an unbalanced pseudo-panel at our unit of analysis: country years. Notably, the pseudo-panel imbalance is not systematically related to country trade policy or labor market outcomes and is instead a function of pre-determined survey schedules. As surveys are nationally representative, we use expansion weights in our calculation of country-level aggregates.

Several of our aggregated variables warrant more detailed explanation. We calculate employment rates for children under 14 and children 14–17 as the share of the surveyed age group reporting employment. We separately calculate under 14 and 14–17 employment for several interrelated reasons. First, we are concerned that survey respondents might misreport employment by young children due to social, religious, cultural, or even legal prohibitions, since 14 is frequently the legislated minimum age for employment and hence misreporting or mismeasurement of employment rates for 14–17 year olds is far less likely.<sup>9</sup> Second, our theoretical model explicitly predicts different responses in younger child labor markets and older child labor markets. By leveraging our rich survey microdata we are able to separately calculate child employment by age ranges and test our theoretical hypotheses. Finally, 14 years is commonly the age that differentiates primary and secondary schooling. Our differentiated child employment rates will roughly correspond to primaryand secondary-school-aged children.

For each country at each date, we calculate the relative income of households with children as the median household per capita income weighted by the share of the household members that are children, divided by the median household per capita income for the whole sample. We compute two related versions of this measure. In the first, we consider households with _any_ children (i.e. under 18 years of age) and weight household observations by the share of the household members under 18. In the second measure we only consider households that have children in both of our age ranges (younger than 14 and 14–17) to better capture intra-household child labor substitution effects predicted by our theoretical model. Both measures will approximate income inequality impacts on households that contain children accounting for how exposed a household is to potential child labor bans.<sup>10</sup>

> 9For example, the United States Fair Labor Standards Act establishes the minimum age for non-agricultural employment at 14.

> 10Survey weights are for individual respondents, not households. Consequently we do not use expansion weights when computing these aggregate household income equality measures.

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We also consider primary and secondary school enrollment rates using data from the United Nations Educational, Scientific and Cultural Organization (UNESCO) Institute for Statistics. Because education is one of the most important opportunity costs of child employment, child labor market changes are likely to be reflected in school enrollment rates. Furthermore, primary school enrollment rates in particular should reflect changes in under 14 employment without potential misreporting due to cultural stigma or legal prohibitions on child labor. School enrollment rates should also capture child labor market changes at both the intensive and extensive margin. Our child employment rates only capture changes in child labor at the _extensive_ margin—entry and exit from employment—while changes in the intensity of child employment might also be reflected in school enrollment rates.

We also include in the Appendix estimates on what we refer to as wage “premia” for these age groups. We calculate these child wage “premia” as the median wage for the age group divided by the median wage over the entire population.<sup>11</sup> As with our relative household equality measures discussed above, these relative wage measures will avoid confounded results driven by cross-sectional income or exchange rate differences. In addition to these child wage premia, we also calculate a skill premium as the median wage for survey respondents who completed secondary education divided by the median wage for respondents who did not complete secondary schooling.

From this pseudo-panel, we construct as our analysis data set a “stacked” country panel with a _±_ 3 year event window around entry into force of any RTAs. To do so we consider every year an RTA enters into force for a country and include three years before and three years after. If a country has multiple RTAs enter into force in adjacent years, each of _±_ 3 year event windows enter into the analysis dataset separately and are “stacked” or pooled.<sup>12</sup> We report summary statistics from this analysis dataset in Table 1.

Our data on RTAs and agreement content have been collected as part of the broader World Bank

> 11Here we condition on strictly positive wages. Numerous respondents report a wage of zero despite reporting employment. The majority of these respondents are employed in the agriculture sector or the “commerce” sector; we speculate these individuals are employed by family-owned businesses in which household members do not draw individual wage compensation. Including such zero wages will severely bias aggregated wages to zero in our pseudopanel.

> 12Given our focus on child employment, we filter our data on country-year observations for which aggregated child employment rates (both under 14 and 14–17) are not missing.

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project on the content of trade agreements. These data are described in Mattoo _et al._ (2020) with labor market provisions in particular being discussed in detail in Raess and Sari (2020). We focus in particular on trade agreements that include reference to the “abolition of child labor.” We also consider whether such provisions are legally binding. The trade agreement content coded in Raess and Sari (2020) does not differentiate between non-derogation child labor provisions—provisions that reinforce existing child labor bans under national laws—from child labor bans introduced via issue linkage in the trade agreement. Regardless of whether such provisions reinforce existing national laws or introduce new legal commitments via the trade agreement, issue linkage under the RTA exposes non-compliant countries to potential trade retaliation under the RTA, regardless of national legislation. Additionally, our trade agreement data does not record how child labor provisions are implemented at the national level. As a result, we cannot differentiate child labor supply shocks (labor supply bans) from child labor demand shocks (employer-based fines) and, hence, we cannot directly test for heterogeneous impacts of different implementation schemes as predicted in our theoretical model. Our estimates of the effects of child labor provisions will therefore reflect average effects across different unobserved implementation schemes. Table A.1 provides the list of RTAs notified to the WTO and currently in force that have included child labor provisions and the legal enforceability of such clauses.

# **3.1 Methodology**

We identify both the effects of trade liberalization on our target labor market outcomes as well as the impact of child labor provisions in RTAs with the following triple-difference model on our stacked country panel: where _i_ indexes countries, _t_ indexes time, and _g_ indexes RTAs with _yigt_ as the outcome of interest. _RTAigt_ is an indicator equal to 1 if year _t_ is later than the year that RTA _g_ enters into force and zero prior, and _Childig_ is in indicator equal to 1 if RTA _g_ includes the child labor provision. We include 17
country-RTA fixed effects with _αig_ . By including _αig_ we normalize the pre-RTA level of output to 0 for each country-RTA “stack” in our dataset—estimated triple difference coefficients describe outcome changes at the country level relative to the pre-period within country and within RTA. Country characteristics such as political, legal and religious institutions, persistent demographic profiles, and time-invariant comparative advantages (e.g. driven by population size or geography) are accounted for via the inclusion of _αig_ . Critically, _αig_ also controls for all RTA-level factors that might lead to endogenous inclusion of child labor provisions, including relative characteristics across signatory countries (e.g. legal, religious, demographic, comparative advantages, etc.). We also include time fixed effects with _δt_ . We consider two approaches to controlling for common changes across time: calendar-year and event-time fixed effects. Calendar-year fixed effects control for common, cross-country shocks that affect the global economy while event-time fixed fixed effects account for common dynamics around trade liberalization. The inclusion of event-time fixed effects does not allow us to separately identify _β_ 1 in equation (5) from the event-time fixed effects. Hence in models that include event-time fixed effects we only identify the differential effects of child labor provisions _relative to trade agreements that do not include them_ .<sup>13</sup> We also note that since our event window is fixed in time for each RTA and country, our country-RTA fixed effects in conjunction with event time fixed effects control for a large share of variation in country-year outcomes.

We report two-way, cluster-robust standard errors clustered at the country-RTA level—to account for temporal autocorrelation within treatment units—and at the country-year level—to account for correlation across RTA “stacks” that might arise from overlapping, adjacent RTA treatment at the country level.

As argued in Abman and Lundberg (2020) and Abman _et al._ (2021), entry into force of RTAs requires separate, independent ratification from multiple signatory countries— countries cannot unilaterally enact the RTA which creates plausibly exogenous variation in the timing of RTA policy exposure.<sup>14</sup> Hence, our triple difference parameters recover plausibly causal impacts of trade

> 13Our event stacking approach yields an analog to two-way FE triple difference models with common treatment timing. In such models, only the triple-difference coefficient is identifiable

> 14Some RTAs enter into force upon independent ratification by _all_ signatory countries while other RTAs only require ratification by a threshold share of signatories. Regardless, the multilateral ratification process creates plausible exogeneity in the timing an RTA enters into force.

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liberalization. In Model (5), _β_ 1 captures the changes in country outcomes after the enactment of an RTA without labor protections while _β_ 2 captures the differential effect from RTAs with relevant child labor provisions. Thus, entry into force of RTAs that include child labor provisions will lead to an estimated _β_ 1 + _β_ 2 increase in the outcome variable.

In addition to these main triple-difference models, we also estimate a modified two-way fixed effect synthetic difference-in-difference (DiD) model from Arkhangelsky _et al._ (2021). The synthetic DiD model combines elements of the traditional DiD framework with elements from the synthetic control framework. Observations are weighted to align pre-trends in outcomes between treated and untreated units—effectively “parallelizing” pre-trends. Observations are also weighted to balance pre-treatment time periods with post-treatment time periods. The weighted observations are then used in a two-way fixed effect model to estimate treatment effects. Our particular application of this model requires some explanation and additional data processing. Arkhangelsky _et al._ (2021) develops the model for a balanced panel with homogeneous treatment timing. To facilitate identification in our setting, we balance our stacked treatment window panel by backward interpolation at the country-RTA level. Our interpolation scheme is constant—if observation _yigt_ is missing, we interpolate it to _yigt′_ where _t_<sup>_′_</sup> _> t_ is the first non-missing observation for country-RTA _ig_ . While treatment years are not homogeneous, our windowed treatment approach yields homogeneous treatment timing in _event time_ , which we index by _s_ = _−_ 3 _, −_ 2 _, −_ 1 _, . . . ,_ 3. The cross-sectional observational unit in our application is the country-RTA level ( _ig_ ) while the temporal observational unit in our synthetic DiD application is event-time units ( _s_ ). Hence, our synthetic DiD model estimates the following: where _ω_ ˆ _ig_<sup>_sdid_</sup> are the unit weights that parallelize pre-trends and _λ_<sup>ˆ</sup><sup>_sdid_</sup> _gs_ are the time weights that balance pre/post treatment (Arkhangelsky _et al._ , 2021). _αig_ and _δs_ are unit and time fixed effects respectively and our estimate _β_<sup>ˆ</sup> 2 from Equation (6) is the synthetic DiD treatment effect of child labor provisions in RTAs relative to RTAs without child labor provisions. As in the model with 19
event-time fixed effects described above, we cannot separately identify the effect of an RTA without a provision on our outcomes of interest from the event-time fixed effects.

# **4 Results**

We present estimates from triple difference-in-differences and synthetic difference-in-differences models on youth employment rates in Table 2. In Panel A we report employment rates among children under 14 years of age and in Panel B examine employment rates among children aged 14 to 17. In both panels, column (1) reports estimates from triple DiD models country-RTA fixed effects. Column (2) reports estimates from a DiD-analog with country-RTA fixed effects and event time FE. Because we cannot separately identify event time FE and the post-RTA coefficient ( _β_ 1 from equation (5)), Column (2) only reports the marginal impact of child labor provisions ( _β_ 2 from equation (5)). Column (3) reports estimates from a triple-DiD model with country-RTA fixed effects and calendar year FE. Columns (4)–(6) report estimates from models equivalent to those in Columns (1)–(3) but using our balanced, backward interpolated panel. Finally, Column (7) reports estimates from our synthetic DiD model.

Our estimates on the under-14 employment rate in Panel A are generally imprecise. We find no statistically significant response to trade liberalization or child labor provisions in under 14 employment. We find some suggestive support for the type of income effect documented in the literature (e.g. Edmonds and Pavcnik, 2005, 2006) with the under-14 employment rate in developing countries falling by .02–.2 percentage points following entry into force of an RTA. We find robust but imprecise evidence suggesting that child labor provisions in RTAs lead to small _increases_ in under 14 employment of 0.1–0.4 percentage points. Although statistically imprecise, these estimates are economically significant and large in magnitude, corresponding to increases of 5%–20% relative to the mean under-14 employment rate of 2%.

In contrast, our estimates on the 14–17 employment rate in Panel B are highly significant and generally consistent across specifications. We find additional evidence of an income effect from the literature, with reductions in the 14–17 employment rate of between 0.3 and 1.3 percentage 20
points which correspond to approximately 2%–9% declines relative to a mean of 15%, but these estimates become insignificant upon inclusion of time controls. We find that child labor provisions significantly increase the 14–17 employment rate by 0.9–1.3 percentage points. This findings are statistically significant at the 5% level or smaller across all model specifications and correspond to an increase in 14–17 employment by between 6%–9% relative to the mean.

Overall, we find empirical support for income effects of trade liberalization driving down child employment, consistent with both the literature (e.g. Edmonds and Pavcnik, 2005, 2006) as well as our theoretical model. In contrast, we find that provisions in RTAs prohibiting child labor _increase_ employed children, especially for older children. This paradoxical finding is consistent with empirical and theoretical findings in the literature (e.g. Bharadwaj _et al._ , 2020; Basu, 2005), in which the ban reduces incomes of low-income households and induces them to increase child labor supply.<sup>15</sup> These results are also consistent with our theoretical model which suggests that a ban on the youngest children working leads to intra-household substitution towards older children. We identify imprecisely estimated but positive effects of child labor bans on under-14 employment which is consistent with the fine-based, demand-side implementation of the ban but not with a household supply-side implementation. We note, however, that there are reasons to believe effects on under-14 employment might be difficult to identify in the presence of mismeasured outcomes. For the arguably better measured older-child employment (14–17) rates, our findings are robust across model specifications, statistically significant, and large in economic magnitude.

We turn our attention to educational enrollment rates in Table 3. We report primary school enrollment rates in Panel A. As above, Columns (1)–(3) report models with country-RTA fixed effects and no time fixed effects, event time fixed effects, and calendar year fixed effects, respectively. Columns (4)–(6) report analogous models using our balanced, backward interpolated panel and Column (7) reports synthetic DiD estimates. We find small imprecisely estimated positive impacts on primary school enrollment rates following trade liberalization that are statistically and economically insignificant. While we cannot differentiate these estimates from zero, this weak evidence is

> 15The results in Lakdawala _et al._ (2022) are somewhat more ambiguous. The policy change studied there is a law in Bolivia that made certain types of child labor legal, but subject to rules that were enforceable by fine. Overall it was a relaxation of rules imposed on employers, which resulted in a reduction in the use of child labor.

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at least consistent with the reductions in primary-school-age child employment (under 14) that we observe in Table 2. We find highly significant reductions in primary school enrollment rates with exposure to child labor provisions. Our estimates range from 0.3–1.2 percentage point drops which correspond reductions in primary school enrollment by 0.3%–1.3% of the mean. These findings are consistent with our weak, statistically insignificant findings from Table 2 which suggests that under 14 employment increases following exposure to child labor provisions in RTAs.

Two points on these primary school results warrant further discussion. First, primary school enrollment rates are unlikely to suffer the same misreporting issues that direct enumeration of under-14 workers in our survey microdata might suffer from. Consequently, these more precisely estimated drops in primary school enrollment rates might reflect stronger employment effects than we are able to directly identify with under-14 employment data. Second, primary school enrollment results might reflect under-14 labor market effects at both the intensive and extensive margin. Our child employment rates capture changes in child labor only at the _extensive_ margin—entry and exit from employment. But there may also be intensive margin effects of child labor provisions in RTAs, in which under-14 children who are employed end up working more hours on average. Such intensive margin effects would not be reflected in changes in country-level under-14 employment rates but may be reflected in lower primary school enrollment rates as work and school together become unfeasible.

We find similar patterns with secondary school enrollment rates in Panel B of Table 3, however the income effects from trade liberalization are now statistically and economically significant in some specifications. We estimate increases in secondary school enrollment rates of up to 3% of the mean following entry into force of RTAs without child labor provisions. As above, Columns (1)–(3) report models with country-RTA fixed effects and no time fixed effects, event time fixed effects, and calendar year fixed effects, respectively. Columns (4)–(6) report analogous models using our balanced, backward interpolated panel and Column (7) reports synthetic DiD estimates. Inclusion of child labor provisions reduces secondary school enrollment rates by 0.2–0.9 percentage points, up to 1.2% of the mean. These findings are significant across a number of specifications, notably at the 5% level in our synthetic DiD specification. In general, these findings lend credence to our findings 22
on 14–17 employment rates in Panel B of Table 2—increases in 14–17 employment correspond to decreases in secondary school enrollment rates.

Finally, we consider impacts of trade liberalization on household (in)equality in Table 4. Consistent with our other tables, Columns (1)–(3) report models with country-RTA fixed effects and no time fixed effects, event time fixed effects, and calendar year fixed effects, respectively. Columns (4)–(6) report analogous models using our balanced, backward interpolated panel and Column (7) reports synthetic DiD estimates. In Panel A we consider per capita incomes of households with children relative to all households in a country, year. We weight household incomes in the numerator by the share of the household that is under 18 since households with a larger share of children will be more strongly exposed to child labor provisions. For the most part, our findings are qualitatively consistent with our findings on under 14 employment. We do not find consistent (or even suggestive) evidence of an impact from RTAs without child labor provisions—in some specifications we find small, imprecise negative impacts and in some specifications small, imprecise positive impacts. The inclusion of child labor provisions in general tends to lead to increases in the income of households with children relative to the household population. Notably, this positive effect is significant at the 1% level in our synthetic DiD specification which corresponds to an increase in relative incomes of households with children of approximately 1.6% at the mean. These findings are again consistent with patterns and findings on child employment and school enrollment rates—the inclusion of child labor provisions tends to increase child employment, decrease school enrollment and, as a result, raise incomes in households with children relative to the population of households. We are careful to note that this last finding does not follow trivially from our findings on child employment. Our theoretical model makes clear that these policies can push down wages for child workers and increase their labor supply at the same time, creating competing forces on total household income.

In Panel B of Table 4 we focus on the role of substitutability of under-14 and 14–17 labor in these household income effects. As above, we consider per capita incomes of households with children relative to all households in a country, year but here limit our attention in the numerator to households that have _both_ under 14 and 14–17 year old children. We continue to weight household 23
incomes in the numerator by the share of the household that is under 18. Columns (1)–(3) report models with country-RTA fixed effects and no time fixed effects, event time fixed effects, and calendar year fixed effects, respectively. Columns (4)–(6) report analogous models using our balanced, backward interpolated panel and Column (7) reports synthetic DiD estimates. We find similar patterns to those documented in Panel A but with much stronger and more precisely estimated effects. We find significant reductions in relative incomes of households with both types of children. We note that these are _relative_ income effects and, as such, are still entirely consistent with income effects driving child labor market response to RTAs without child labor prohibitions. It is likely the case that household incomes across the population rise in response to trade liberalization but that for households with children, this facilitates pulling those children out of the labor market which reduces per capita earnings relative to the population. These relative income effects are significant at the 1% level in some specifications and correspond to a more than 3% reduction in household equality relative to the mean.

We find robust evidence of increases in relative household incomes with the inclusion of child labor provisions with significantly positive increases in relative incomes across most specifications of up to 3.3%. These positive impacts are entirely consistent with increases in child employment and decreases in school enrollments that we document in Tables 2 and 3. Our theoretical model suggests that a demand-side restriction on child labor implemented as a fine on employers of underage children will drive down wages earned by the youngest children and, ceteris paribus, lower household income. This pushes 14-17 year old children into employment with potentially ambiguous results for under-14 children for which the provisions create both income and substitution effects. Hence, the ex ante impact of child labor provisions on per capita income for households with both younger and older children is ambiguous. Panel B of Table 4 suggests that the net effect on household incomes is positive—child labor provisions lead to income equality between households with children and the general population, but apparently through increases in child employment. We also note that our identification strategy cannot identify the longer-run impacts that will arise from changes in schooling behavior. Reductions in primary and secondary school enrollment rates attributable to child labor provisions are likely to have longer-term negative impacts on household 24
inequality that we are unable to capture with our relatively narrow event window.

We present additional evidence supporting our identification strategy with fully-specified event study models in Figure 2. We extend our triple DiD model with country-RTA fixed effects and event time fixed effects (Columns (2) and (4) in Tables 2–4) by interacting our treatment variable _Childig_ with a full set of event-time dummy variables rather than a simple dichotomous post treatment variable as in Equation (5). We omit the dummy variable for the year before an RTA enters into force as a reference year. As in our triple DiD models with event time fixed effects, we cannot separately identify trade liberalization dynamics with this event study specification. Instead, our estimated event study coefficients will reflect the dynamics of outcomes in RTAs with child labor provisions relative to RTAs without, normalized to the year before the RTA with provisions enters into force. Violation of the parallel trends identifying assumption should be evident as pre-trends in these event studies. We present event study coefficient estimates and 95% confidence intervals in Figure 2.<sup>16</sup> We find no evidence that our results are spuriously driven by differential trends.<sup>17</sup> We also consider the role that enforceability plays in child labor outcomes which we report in the Appendix. To do so we add an additional differencing dimension (i.e. a quadruple difference model) capturing whether provisions are legally binding and enforceable under the RTA. Coefficient estimates of the added interaction effect are generally imprecise as the majority of the RTAs with provisions in our sample have enforceable provisions (21/29). For some outcomes, it seems the dimension of enforceability may attenuate the impacts from the provisions themselves (child employment, primary education) however, for secondary school enrollment, the larger effect is driven by the agreements with enforceable child labor provisions. While we cannot reject the hypothesis that there is no difference between the two types of provisions, our estimates of the differential effect lack precision. We present additional results on child wage premia, the skill premium in the Appendix.

> 16Confidence intervals are derived from two-way cluster robust standard errors clustered by country-RTA and country-year.

> 17In addition to this event study evidence that supports our identifying assumptions, our synthetic DiD approach is uniquely designed to address failures in parallel trends by “parallelizing” pre-trends.

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# **5 Concluding Remarks**

In this paper, we study the impact of labor provisions in trade agreements aimed at eliminating the use of child labor. Such provisions have become more common over time as trade agreements have become more expansive in scope and increasingly cover non-trade areas. Using a large set of survey data from across 101 developing countries, we employ a variety of DiD and triple DiD regressions to identify the effect of RTAs without a child-labor provisions on child labor outcomes, as well as the differential effect of an agreement that does have such a provision.

We find evidence that a trade agreement with no child-labor provision reduces the fraction of 14-17-year-olds in the labor force by a statistically and economically significant amount with corresponding increases in secondary school enrollment. We find similar effects for children under the age of 14 but these estimates are imprecise and not statistically significant. The pattern is consistent with an interpretation that a developing economy joining a trade agreement opens up possibilities for low-skill export employment and is consistent with the empirical findings of Edmonds and Pavcnik (2005) and Edmonds and Pavcnik (2006) that opening trade can reduce child labor in developing economies.

On the other hand, we find that the inclusion of child labor prohibitions in RTAs perversely _increases_ child labor—the opposite of their intended effect. We find statistically and economically significant increases in the employment of 14-17 year old children and statistically imprecise but economically significant increases in under-14 labor as well. We find corresponding drops in both primary and secondary school enrollment rates following entry into force of RTAs with child labor prohibitions.

Our results are strikingly similar to those predicted in Basu (2005) and found empirically in Bharadwaj _et al._ (2020)—a case study of the 1986 ban in India. As in these works and in our theoretical model, if the ban is enforced through a fine imposed on employers, it will tend to depress child wages, inducing the household to increase total labor supply in order to make up the lost income—including the household child labor supply. We find a strong, significant increase in employment for 14-17-year-olds but none for under-14-year-olds, which is consistent with the model 26
prediction of an ambiguous effect on labor supply for the younger children who are the targets of the ban. But it is also possible that employment effects are not well measured for the younger children, since families with children working illegally may be reluctant to report that information to survey-takers. The strongly significant reduction in school enrollment rates for both age ranges, which is larger for elementary school enrollment, suggests that there may be an employment effect for the younger children that is missed in the data.

The policy implications of this work are subtle. On the one hand, the findings suggest that labor provisions in RTAs are effective, as they impact child labor outcomes and other relevant non-trade outcomes such as schooling and household inequality. On the other hand, the evidence suggests that in a developing-country context, opening opportunities for low-skill intensive exports is more effective at reducing child labor than an outright ban. Child labor bans in RTAs tend to be counterproductive because they push down the income of households who use child labor as an income source, requiring them to double down on the use of child labor. While beyond the scope of this paper, the analysis seems to suggest that provisions in RTAs aiming at eliminating child labor should consider approaches other than bans, such as the introduction of direct payments to households for school attendance, as discussed in Doran (2013).

While we bring new information on these relationships, there are many areas outside the scope of this paper that present valuable avenues for future research. The narrow time window used in our analysis (+/- 3 years around entry into force) limits our ability to study medium- and long-run impacts of child labor provisions on these outcomes. The increases in school enrollment we observe following trade agreements without labor clauses might increase human capital accumulation and increase incomes later in affected children’s households. We also do not address sector-specific trade shocks or heterogeneity by sectors that may employ more or less child/youth labor. There may be complementarities arising from child labor provisions included in RTAs that lower tariffs on exports from industries most likely to use child labor. Finally, our broad dataset limits our ability to explicitly test underlying mechanisms driving the results that we find. We believe all of these areas offer important avenues for future work.

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