decision making processes such as whether some children may at times be working as their own choice to be with family members, because they want to work, or even because they dislike school. Multiple children interviewed in 2010 indicated that they were beaten at times by their teacher in class for arriving late, not completing homework or the like.<sup>27</sup> They include differences in levels of laziness or intrinsic motivation while working which are likely to influence children’s energy levels for example in school or to complete homework. They also include differences in parental knowledge about anti-child labor legislation and the level of potential enforcement (if any) in their community, among many other factors. Such omitted variables can reduce the predictive power of existing coefficients as they can at times be correlated with independent variables, so that there would be a correlation between the error term and independent variables. The regression models here, while acknowledging the important methodological limitations of trying to model such complex human behaviour, focus on exploring those quantifiable factors that can help influence decisions about child labor found in GLSS survey data.<sup>28</sup> It is important to stress that the estimated results do not reflect causal effects, that they do not go beyond correlations.

The first model assesses potential effects of demand variables (at the household level) on influencing individual choices in favor of or against child labor and schooling, while the second model also takes into account supply variables (at the government level) such as distance to the nearest school facility and total school expenditure. The results of these two regression models are presented in Table 3, while only the last column reflects basic descriptive statistics of the explanatory variables applied (for the full sample). Results for models 1 and 2 are similar due to the rather limited reported effects of supply variables and the results for model 2 are described in the following for simplification purposes.

In terms of gender, the regression results show that overall the difference between 7-14 year old boys and girls is not significant in their probability of being engaged in work. This result is consistent with previous research in Ghana (Canagarajah and Coulombe 1997). In relation to fathers without any schooling (the reference group), those who have at least primary education are an estimated 4 to 5% less likely to have their children working while they are an estimated 3 to 6% more likely to send their children to school, with controls for individual, household, community and cluster factors (Table 3).<sup>29</sup> Similar trends are found internationally (Brown _et al._ 2001). It has been argued that the age and presence of siblings can affect working patterns of other children in the household (Chernichovsky 1985). This influence seems to exist in Ghana but is small, with the odds of children working increasing by about one percent with each additional household member aged 5 or below.

A child is estimated to be 7% less likely to work if he or she falls into the richest quintile compared to the poorest quintile (the reference group). This does not directly imply that poverty is influencing these potential effects but rather—as Bhalotra (2007) points out—that some more affluent households may simply place a greater importance on children’s leisure

> 27 The assumption in the literature however is that adults make the decisions related to their children’s work. 28 However, further qualitative research with children, parents and teachers on these unobservables could provide additional insight into the non-quantifiable factors that can influence child labor.

> 29 While levels of parental education often correlate with levels of household wealth, collinearity is not a direct issue in the models as wealth quintiles reflect household consumption expenditure (not income from employment) and as most Ghanaians are self-employed in agriculture, for which the completion of primary or lower secondary education yields no higher returns relative to no schooling (Table 1). On the other hand, existing studies often do not control for parental education and thus tend to overestimate the potential effect of wealth on child labor, as for example knowledge acquired through education can also influence social norms about child labor.

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