to be doing household chores than Northern children (see also Figure 4). That is, in the North, where agricultural livelihoods are most present, children’s time is biased towards working for economic gain on the farm whereas in the South, where agricultural livelihoods are least present, children’s time is biased towards assisting with household chores.

To assess factors across survey years for why children work for economic gain, Table 5 presents the regression results for the demand model and the demand and supply model using the GLSS survey from 1998/99 and compares the results with those from the 2005/06 survey. In describing the results for the demand and supply model, conditions associated with geographic location in the Northern region relative to different regions of the South appear to have the strongest and most consistent effects on increased child labor and reduced school participation in the country over both survey years. Compared to 2005/06, the estimated effects of household wealth on child labor were smaller and non-significant across the bottom four quintiles in 1998/99, with only children in the richest quintile being less likely to work.

An additional exercise with the 2005/06 survey is conducted using a probit regression with labor participation as the dependent variable and including all control variables in the demand and supply model while also including school attendance. To reduce endogeneity associated with such a model (the interdependencies between schooling and working), a child’s actual school attendance is not used but rather school attendance rates for the cluster mean are calculated and then divided into terciles. Results suggest that there are no large differences in the estimated effects of the remaining parameters. Relative to children living in clusters with the lowest school attendance rates (tercile 1), those in clusters with the middle or highest school attendance rates are an estimated 4 to 8% less likely to be working, respectively. The reported school attendance effect on reduced child labor appears thus larger than that of levels of household wealth and fathers’ education. In addition, a probit regression with interaction terms is conducted for children involved in harmful child labor (i.e. children attending school are omitted from the sample), with boys living in an agricultural household in the North being most strongly and significantly affected by harmful child labor. The point at which child labor and schooling are thus more likely to become substitutes rather than complements is especially when children have these three traits. After discussing the regression results, the next section outlines policy and reform options.

# **5. Policy implications**

This paper, first, tries to identify which potential constraints and enabling factors of child labor beyond monetary poverty may be critical and, second, which policy and reform approaches may then be best in reducing harmful child labor. Each step is incomplete without the other. Together they are crucial for planners and policymakers. Yet linking applied research to policy design is often lacking or rather rudimentary in child labor research, particularly context-specific policy design (cf. Bhalotra and Tzannatos 2003; Edmonds 2008).

Results suggest that demand-side variables and thus policies (that influence household demand) appear currently more important than school supply variables (school expenditure and distance) which often have a more limited or non-significant estimated effect on influencing labor and school participation in the country on a whole. Because access to basic education is often seen in the literature as the most effective way to reduce child labor (e.g. UNICEF 2007; Overhoff 2006), this paper provides alternative evidence, especially as most child laborers are already in school.

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