A number of researchers have examined the bivariate relationship between average living standards, measured using real GDP per capita in PPP US$ terms, and the incidence of child labor force participation across regions and countries (see, Krueger 1996, Dehejia and Gatti 2002, Gunnersson _et al_ 2005, and Edmonds and Pavcnik 2005). The evidence appears to be unusually clear-cut: across various model specifications of the relationship between the two variables, and irrespective of the time period under consideration or whether the relationship is conditioned on other covariates or not, the literature consistently finds a strong negative convex relationship between child economic activity rates and per capita national incomes, that is, child economic activity rates appear to be decreasing at a decreasing rate with per capita national incomes.

Furthermore, GDP per capita appears to be an especially strong correlate of child labor force participation rates across countries. For example, using data for 1990, Edmonds and Pavcnik (2005) estimate the relationship between child economic activity rates and a cubic polynomial of GDP per capita via ordinary least squares and find that 73 percent of the cross-country variation in child economic activity rates is explained by the cross-country variation in GDP per capita. Similarly, Gunnersson _et al_ (2005) report a simple correlation coefficient of -0.82 between child economic activity rates and GDP per capita across countries.

As a starting point, mainly to check if we can reproduce the findings from these previous studies, we examine the cross-country relationship between per capita national incomes and child economic activity rates. As the scatter plot of country observations in Figure 11 suggests, the share of children working appears to inversely related to GDP per capita.

This finding is reinforced by the predicted curve from estimating an unconditional linear regression model of log child economic activity rates as a quadratic function of GDP per capita using ordinary least squares. 25 To be precise, GDP per capita appears to have a diminishing negative effect on the share of children working up to around $5,800 after which GDP per capita appears to have an increasing positive effect on the share of children working. However, this upturn in the predicted curve in the upper quartile of the GDP per capita distribution can be overlooked given the size of the prediction interval.26 In terms of the magnitude of the effect, at the 25th percentile of GDP per capita (roughly at $1,200), a $100 increase in GDP per capita is associated with a 4.7 percent decrease in the share of children working, while at the 75th percentile of GDP per capita (roughly at $4,600), a $100 increase in GDP per capita is associated with a 1.3 percent decrease in the share of children working.

> 25 In order to determine whether we should specify GDP per capita in level or logarithmic form, we conduct a test of non-nested models with identical dependent variables using the method proposed by Davidson and MacKinnon (1981).  The test results indicate that neither model can be rejected at standard significance levels.  Comparing adjusted _R_ -squared statistics from estimating the two models however suggests that the logarithmic form of GDP per capita fits our data better.  We also test what order polynomial of log GDP per capita fits the data best.  Results from the Ramsey (1969) test of functional form suggests that a second order of log GDP per capita belongs in the model specification but that higher order terms do not contribute anything additional.  In light of these results, we specify child economic activity rate as a quadratic function of log real GDP per capita.  This specification of the relationship between child economic activity rates and GDP per capita is in line with Dehejia and Gatti (2002) and Gunnersson _et al_ (2005).

> 26 The upturn in the predicted function appears to be due to use of the child population weights in the estimation. The predicted function obtained from estimating an unweighted regression model is negatively sloped over the entire range of GDP per capita.

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