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Where Child Labor Supply Finds Its Demand

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Where Child Labor Supply Finds Its Demand Where child labor supply finds its demand Krisztina Kis–Katos and Gunther¨ G. Schulze ∗ May 17, 2006 Abstract We analyze child labor incidence in small industries in Indonesia at the village level. Using a data set of all Indonesian villages and urban neighborhoods we find that child labor is significantly associated with poverty, natural and epidemic disasters, and with unemployment. It is negatively associated with credit and school availability, only if we correct for the existence of small industries in that village. Our results thus confirm the importance of school availability and credit provision as policy instruments to reduce child labor. They also show that it is imperative to look at the interplay of demand and supply determinants of child labor to arrive at unbiased results and correct policy conclusions. JEL Classification: J8, I3 Keywords: Child labor, demand effects, SMEs, credit constraints, Asia, Indonesia ∗Institute for Economic Research, Department of International Economics, University of Freiburg, Germany. Contact: [email protected]. We are grateful to the Volkswagen Foundation for financial support and Fitria Fitrani for excellent research assistance. All remaining errors are ours. 1 1 Introduction In 2000 186 million children worked as laborers worldwide, 110 million were under the age of 12 (ILO 2002); in Indonesia approximately 1,7 million children were part of the labor force (ILO 1985-2005). This is reason for great concern, not only because of negative consequences for the development of the affected children; child labor also diminishes human capital formation, which may have high rates of return (see e.g., Duflo 2001).1 Empirical evidence suggests that child labor is associated with poverty. In cross country analyzes GDP per capita turns out as a very powerful determinant of child labor (e.g., Krueger 1996, Cigno, Rosati and Guarcello 2002); Edmonds and Pavcnik (2004) show that open economies have less child labor due to the income gains from trade. At the household level, direct evidence on the effect of income or wealth on child labor is less conclusive, partly because of the endogeneity of income or expenditure measures to the child labor decision (Bhalotra and Tzannatos 2003). Edmonds (2005) shows in a panel approach for Vietnam that the favorable income effects of a trade liberalization reduced child labor. Bhalotra (2003) estimates a negative own wage elasticity of child labor supply for Pakistani boys (though not for girls); apparently, the income effect from rising child wages outweighs any substitution effect.2 These findings support the notion that parents send their children to work only if they are forced to do so in order to meet subsistence 1Even if most children who work attend school, their educational attainment suffers from child labor (Beegle, Dehejia and Gatti 2004, Heady 2003, Orazem and Gunnarsson 2004). Out of the thirty percent of children worldwide aged 5-14 who do not attend school almost 60 percent are working (Edmonds and Pavcnik 2005). 2Another possibility to overcome the endogeneity problem for income is to examine exogenous income transfers provided by different school incentive programs like the Food for Education Program in Bangladesh (Ravallion and Wodon 2000), the Mexican PROGRESA (Schultz 2004), or Brazil’s Bolsa Escola program (Bourguignon, Ferreira and Leite 2003). 1 needs.3 Yet, a large number of studies fails to find a positive relationship between poverty and child labor (e.g., Ray (2000, Pakistan), Canagarajah and Coulombe (1999, India), Psacharopoulos and Patrinos (1997, Peru)). Bhalotra and Heady (2003) and Kanbargi and Kulkarni (1991) even find a positive relationship between household wealth and child labor for agricultural households in Pakistan and Ghana, and in Karnataka/ India, respectively, which they explain by imperfect agricultural markets for land and labor. Thus, at the microeconomic level the role of wealth and income for child labor is still not entirely clear. If investment in human capital is profitable, the poor could, in principle, borrow against future earnings and send their children to school.4 Yet, in many developing countries the poor are thought to have insufficient access to credit, which gives rise to child labor and a misallocation of resources (Baland and Robinson 2000, Ranjan 2001). The empirical problem in testing this hypothesis is that (insufficient) access to credit is typically not directly observable and therefore has to be inferred from reactions to income shocks. Dehejia and Gatti (2002) show that income volatility affects child labor more strongly in countries with low financial development. Jacoby and Skoufias (1997) show for rural India that school attendance declines in response to unanticipated seasonal fluctuations in household income. Beegle, Dehejia and Gatti (2003) find that Tanzanian farmers react to transitory income shocks with increased child labor, but that this increase is lower for farmers with durable assets, who can be assumed to have better access to credit due to their collateral. Edmonds (2004) shows that the introduction of a large pension scheme for black South Africans reduced work of those children living with recipients of the pension, but not of those living with future pensioners. These findings suggest that credit constraints are binding. 3This has been coined the ’luxury axiom’ (Basu and Van 1998). 4Child labor and school attendance need not be mutually exclusive; still credit would allow households to optimize the trade off between school attainment and child labor income. 2 Contrary to this, Manning (2000), Cameron (2001), and Suryahadi, Priyambada and Sumarto (2005) do not find any significant increase in Indonesian child labor in response to the sharp decline in income during the Asian crisis 1997-1998. Manning (2000) argues (but does not show explicitly) that high unemployment has made it hard for children to find jobs; in other words, negative income shocks may increase the supply of child labor and decrease the demand for it at the same time. We observe only the net effect. Although these findings are suggestive they are necessarily indirect. An ideal empirical strategy would seek to measure credit constraints directly, but data limitations typically do not allow for that.5 Moreover, it would distinguish between supply and demand factors in explaining child labor incidence and explicitly analyze the role of unemployment for child labor. Yet, most microeconometric studies have focused on the determinants of child labor sup- ply, given by household characteristics (income and/or wealth, parental education, fam- ily structure, etc.), but did not systematically control for demand factors (c.f. Bhalotra and Tzannatos 2003). They also did not condition on (local) unemployment, presumably because geographical coverage was too small to allow for cross-section variation in unem- ployment. Changes in unemployment over time are correlated with income shocks, thereby making an identification strategy difficult, if not impossible. Only Kambhampati and Ra- jan (2005) analyze supply and demand determinants of child labor. They use the rate of growth of regional GDP and the proportion of agriculture in state’s GDP as proxies for child labor demand and consider the level of state GDP (as well as traditional household vari- ables) as supply determinants. It is not entirely clear though that the level of regional GDP 5The only exception is Guarcello, Mealli and Rosati (2003) who measure credit restriction directly through a survey recording credit history and find that credit constrained families in Guatemala are less likely to send their children to school and more likely to increase child labor in response to income shocks. 3 proxies supply factors and its change proxies demand factors.6 In other words, although their approach is a major improvement over existing studies, their proxies for child labor demand may be less than ideal. Our data set contains more direct measures. Thanks to a unique data set we are able to directly and separately measure the effect of credit constraints, (local) unemployment, income shocks, and poverty. Our data set is the Village Potential Statistics (PODES, Potensi Desa), which covers all villages and urban neighborhoods in Indonesia (more than 68 thousand). It provides information about child labor incidence in small industry at the village level, credit availability, income shocks, a wide range of poverty-related variables, local unemployment, and the economic structure of villages and urban neighborhoods. This allows us a richer specification of supply and demand factors such as size and structure of the small industries, and infrastructure vari- ables, including the availability of schools. Unlike most other data sets, it also allows to identify the determining factors of child labor separately and more comprehensively.7 Our paper thus contributes to the understanding of the role of poverty, credit constraints, in- come shocks, and unemployment on child labor and underlines the importance of demand factors for child labor. We find that child labor in small and medium industry is significantly associated with poverty, negative income shocks, and unemployment. Credit availability increases child labor, but only through a demand side effect: It increases the likelihood of small industry locating in a village, and if so, the number of small firms in that village and thereby the 6Other things being equal, the higher the level of economic activity, the higher the demand for labor, including child labor. In addition, the level of economic activity may be endogenous to child labor. 7For instance, credit constraints may increase child labor also in the absence of income shocks, but have been measured only in connection with income shocks due to the identification problem described above. Likewise, credit constraints will undoubtedly exacerbate the impact of a shock, but shocks may have an impact on child labor also in the absence of credit constraints. Furthermore, the omission of unemployment may lead to an omitted variable bias if it is correlated with the error term. 4 demand for child labor.
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