or their schooling, for instance. Other factors are also linked to wealth that influence its correlation with child labor. Wealthier households and communities are for example more likely to use more sophisticated technology and thus be in localities with higher returns to education, with the inclusion of such a variable into the model (see section 4.3) reducing the reported wealth effect.

Estimated effects of conditions associated with geographic location on labor participation appear strong: children who live in the Ashanti region are an estimated 7.5% less likely to be working compared to those in the Northern region (the reference group) while those in the Upper West region are an estimated 13.6% more likely to work, reflecting a cross-regional difference of 21.1% in the likelihood of working, with other factors such as household wealth levels held constant. Children in the Ashanti region are an estimated 4.4% more likely to attend school in comparison to those in the Northern region (Table 3). Overall, geographic location appears to influence child labor much more strongly than it does school participation which is likely linked to the high concentration of child laborers in the North.

An important result is that household wealth in Ghana appears to have weaker effects on the prevalence of child labor and schooling than the conditions associated with geographic location particularly in the North.<sup>30</sup> This casts further doubt on the common perception that it is mainly just poverty pushing children prematurely into employment and it highlights the policy importance of geographic targeting to the North. Given these regression results (Table 3) together with the descriptive data on this relationship (Figure 1) and qualitative results, this paper breaks away from the conventional thinking that child labor is nearly entirely explained by monetary poverty. Since the models already control for the lower levels of parental education and household wealth of Northerners and, among other factors, their high levels of household engagement in agriculture, the strong reported effects of Northern residency largely capture the combination of remaining conditions (beyond the control variables available in the GLSS survey) associated with living in this part of the country. The estimated effects can reflect lower labor mobility into formal sector jobs and higher levels of social acceptance of child labor (than in the country as a whole) as the vast majority of households are active in agriculture here. The North also has a higher prevalence of drought, lower soil fertility and more unpredictable water and resource availability which typically generates lower agricultural productivity and output than in southern areas (World Bank 2010) and can increase the demand for child labor.

Children living in households with one or both parents employed in agriculture (the reference group) are an estimated 2-3% more likely to be working themselves compared to children with parents employed in the manufacturing or service sector. This reported effect is low likely because the very large reported effects of geographic location in the North already partly capture these children’s residency in rural areas.

Given that data on household expenditure for education and on distance to school are only provided for students (i.e. not for children who do not attend school), these two supply variables are calculated here creating the cluster median (the median for a given geographic enumeration area used for the national census). The assumption is thus made here that educational expenses for households and school distance would be similar within a given cluster which the data confirms. These cluster-level variables also reduce potential

> 30 Testing the model with an urban location dummy (1 or 0) instead of 10 regional dummies shows that the estimated effects of urban/rural location are also much stronger than those of all other factors including household wealth.

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