# _4.2 Running split regressions_

Most papers pool data on child labor in household farming with labor for outside employers and also pool data for rural and urban sectors within the economy, for children of different age groups, across wealth levels, and over different seasons of the year. Because such aggregation can hide potential effects that influence some subgroups but not others, the following model specifications address and disaggregate these issues. Table 3 shows the regression results applying the variables in the second model (demand and supply model) but only for children in rural areas as well as only for children in agricultural households (in which either one or both parents are employed in agriculture). Relative to the results for the full sample in the second model, results only for rural children illustrate that the likelihood to be working increases (and schooling decreases) for rural boys in general and for all rural children living in the North compared to the South. These differences in reported effects for rural children are similar to the differences in reported effects for children in agricultural households. An important result is that the estimated effects of household wealth on child labor in agricultural households and in rural areas (where nearly all children work) become very small and non-significant across all wealth quintiles except for the richest 20% of households, providing further evidence against the ‘luxury axiom’ particularly for rural and agricultural areas.

Table 4 presents the estimated results of split regressions for children by household wealth levels, by season of the year and by age (see appendix). Results only for children in the poorest quintile illustrate that there is a bias against poor boys, as they are much more likely to be working and much less likely to be schooling. Limited research exists on the role of seasonal child labor. With GLSS 2005/06 collecting monthly data from the 4th of September 2005 to the 3rd of September 2006, Table 4 presents the results for the subgroup of children surveyed during the harvesting and planting seasons from November through April (i.e. during the months with greater levels of child labor, see Figure 3). Results show that children become even more likely to work in the Northern region relative to the regions across the South during this period. On the other hand, the estimated effects of geographic location on schooling become smaller. Among wealth quintiles, only households in the richest quintile appear to have a statistically significant effect on reducing child labor, while there is no significant wealth effect on children’s schooling, which may both illustrate the stronger overall demand for children to assist on the farm during this period of the year. Other estimates of sub-groups for the full model, for example by geographic location in the North and South, constrain the sample and are not possible.

_4.3 Testing other variables, assessing potential sources over time, and conducting further analyses_ A range of other variables are individually included into the second model to test whether they may influence the decision for children to engage in work or go to school. In including variables to indirectly capture an aspect of social norms, results show that the potential effect of different religious affiliations or ethnicity on working is not significant. Yet results suggest that Muslims and Christians may possibly place a greater value on formal education as children in these households are an estimated 4 to 9% more likely to attend school, respectively, relative to Animists, the reference group (for similar results, see Canagarajah 18