especially adult labor shortages are more likely to arise.<sup>25</sup> A study by Fentiman _et al._ (1999) found that in rural Ghana some children who are enrolled miss school throughout seasons when families need additional assistance on the farm. Likewise, a study in Nigeria observed that lower school attendance is common throughout harvest seasons and on days of local markets (Francis _et al._ 1998).

# **4. Regression results**

## _4.1 Regression model and basic results_

Research that attempts to identify potential sources of child labor faces the challenge of trying to isolate those factors that influence child labor without simultaneously influencing schooling. It is likely that child labor and schooling decisions are typically jointly determined, as these are interrelated choices about children’s restricted time allocation. Given that working and schooling are interdependent decisions (and not mutually exclusive as descriptive data show above), bivariate probit regressions are applied here which can permit interconnected relationships between these two binary dependent variables. A bivariate probit model allows for correlation in the error terms of the child labor regression and the schooling regression. This modeling approach – while acknowledging that every statistical model has important methodological limitations – may be better suited compared to the most common method of analyzing child labor independently using for example a logit or probit model (see Edmonds 2008). Some researchers apply a sequential probit, see for example Grootaert and Patrinos (1998), who make a strong assumption that households first decide if a child attends school and then make decisions about a child’s further time allocation.

The bivariate probit regressions conducted here control for the presence of a number of individual, parent, household, regional and cluster-level factors, and can help provide some insight into potential sources that can influence decisions about child labor—in part building on and expanding a model employed in Ghana using older data from the late 1980s and early 1990s (Canagarajah and Coulombe 1997). The potential correlates of working and schooling are examined applying both supply- and demand-side variables. Independent variables explored range from sex, parental levels of education, and household wealth,<sup>26</sup> to the number of household members, household occupation in agriculture/non-agriculture, region of residence and, among other factors, education supply indicators (for further information on variables and methodology applied, see Table 3).

It is important to note that a number of other factors can influence child labor that cannot all be easily measured or captured in household survey data and thus in the regression models. These unobservables include factors such as social norms and attitudes about child labor, while their importance is highlighted above in the qualitative analysis. They include child

> 25 Basu and Van (1998) include a substitution axiom in their model that suggests that, from a firm’s point of view, child labor can substitute adult labor, and vice versa.

> 26 Household wealth is calculated based on annual household consumption expenditure, thereby reducing endogeneity in the child labor equation which can arise when using income from employment as a control variable as some children receive income for their work. Many child labor studies nonetheless use income as a control variable and suffer from greater endogeneity (see e.g. Deb and Rosati 2004; Edmonds 2008). Even using household consumption instead of income can lead to some biased results as nearly all child laborers work on the farm and thus household food consumption can be endogenous to household food production – this is another important methodological constraint in trying to identify potential sources of child labor that is not commonly recognized in the literature.

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