Results from the Instrumental Variable Analysis

This section presents the results of the instrumental variable analysis using the two-stage residual inclusion approach introduced by Terza et al. (2008). In particular, I create an indicator variable equal to one if a household faced a shock in income over the past 12 months due to a death of a working member or illness/serious accident, and zero otherwise. As explained in section 4, I also create a wealth index and add it to the set of control variables to account for the differences between the poor and the rich in the probabilities of death and sickness. The results of the first stage and second regressions are shown in Tables 23 and 24, respectively. The first two columns in each table present the results without controlling for the wealth index; whereas, the last two columns control for the wealth index. As can be seen, controlling for wealth slightly reduces the magnitude and the significance of the coefficients but does not change the main results. The discussion in this section is focused on the findings of the IV regressions controlling for the wealth index to account for potential selection into illness, serious accidents, or death.

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Table 23 shows that, holding other things constant, a household with a working member who was sick, had a serious accident, or died last year experienced 14 percent reduction in its monthly income in comparison to a household who did not experience this shock. Controlling for wealth reduces this estimate from 15.6 percent to 14 percent. Both estimates are statistically significant at the 99 percent confidence level.

Table 23: First Stage Regression: the Effect of a Recent Shock on Household Income

Without controlling for wealth Controlling for wealth Estimate -0.156*** -0.140*** S.E. (0.022) (0.022) P-value [0.000] [0.000] R-squared 0.1459 0.177 No. of observations 54,994 54,994

I control for child gender, child age, an urban dummy, family size, education of the head of the household, a dummy for whether the head of the household was a child laborer, the age of the head of the household, a dummy if a child’s mother works, a dummy for single-parent household, a dummy for whether a household owns livestock, size of agricultural land, the age of the head of the household, and the highest four quintiles of the wealth index. Standard errors are shown in parentheses *** refers to the 99 percent confidence level, ** refers to the 95 percent confidence level, and * refers to the 90 percent confidence level.

The results of the bivariate Probit regressions using the instrumental variable are shown in Table 24. As can be seen from the aggregate analysis of all working children, each one percent increase in household monthly income reduces the probability of child’s work by 0.101

percentage points, which is more than five times bigger than the estimate (0.018 percent) obtained from the baseline bivariate Probit model in Table 22. This estimate is statistically significant at 99 percent confidence level. The subpopulation analysis is presented in panel (1) through panel (4). I focus the analysis in this section on four work dimensions: work intensity,

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hazard exposure, work pattern, and the type of employer. As discussed before, the age at first job dimension is excluded from this analysis because children had entered the labor market before the occurrence of the income shock and therefore, the exogenous variation is not appropriate to predict the variations in this outcome.

Disaggregating the population of working children in panels 1 through 4 shows that work intensity and hazard exposure are the most significant dimensions where the effect of income differs. In particular, panel (1) shows that each one percent increase in income reduces the probability that a child is engaged in a heavy workload (Child labor –ILO) by 0.094 percentage points, which is statistically significant at 95 percent confidence level. The increase in income has an insignificant effect on the probability that a child is engaged in a light economic activity. In particular, the effect of income on child labor-ILO is almost seven times as large as the effect on light economic activity. Panel (2) shows the results of the subpopulation analysis by hazard exposure. The results show that each one percent increase in income reduces the probability that a child is engaged in a hazardous work by 0.104 percentage points, which is again statistically significant at 95 percent confidence level. The increase in income, however, has a small and insignificant effect on the probability of engaging a child in nonhazardous work.

It is worth mentioning that the effect of income on the probability of school attendance is statistically insignificant in the regression of all working children and for all subpopulations regressions. One potential explanation might be related to the nature of the instrumental variable that is being used to create the exogenous variation in household income. In particular, I use recent accidents (illness, serious accidents, or death) occurred to working members in households as the source of exogenous variation in household monthly income. Given the universal primary school enrollment in Egypt, parents are less likely not to enroll their children in school or

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withdraw them out of the school in a response to a recent income shock that might be temporarily. That is, school attendance is nonresponsive to income shocks in the short run. Another explanation is that the effect of income on school attendance might have opposite effects for subpopulations of children (males versus females; urban versus rural), that are canceled out at the aggregate level. To investigate this explanation and to further explore the heterogeneous effects of income on child’s work, the next sections present the results of separate analyses by child’s gender and region.

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Table 24: Bivariate Probit Model (with IV): AME of Household Monthly Income Without controlling

for wealth

Controlling for wealth

Sample Work School Work School

All Working children (N=54,994) -0.105*** 0.025 -0.101** 0.008

S.E. (0.036) (0.029) (0.040) (0.033) P-value [0.004] [0.382] [0.011] [0.818] 1) Work intensity Light work (n=49,687) -0.014 0.010 -0.014 -0.005 S.E. (0.019) (0.024) (0.022) (0.027) P-value [0.459] [0.662] [0.530] [0.854]

Child laborers (ILO) (n=53,751) -0.101*** 0.023 -0.094** 0.030

S.E. (0.031) (0.029) (0.036) (0.037) P-value [0.001] [0.427] [0.010] [0.417] 2) Hazard exposure Nonhazardous work (n=50,581) -0.008 0.005 -0.004 -0.012 S.E. (0.023) (0.025) (0.027) (0.028) P-value [0.728] [0.854] [0.873] [0.673] Hazardous work (n=52,857) -0.103*** 0.030 -0.104*** 0.014 S.E. (0.029) (0.028) (0.034) (0.031) P-value [0.000] [0.282] [0.002] [0.657] 3) Work pattern Summer work (n=48,933) 0.009 0.009 0.012 -0.005 S.E. (0.012) (0.023) (0.014) (0.025) P-value [0.450] [0.699] [0.383] [0.832]

Work all the year (n=51,597) 0.008 0.019 0.017 -0.001

S.E. (0.027) (0.028) (0.031) (0.031)

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Table 24: Bivariate Probit Model (with IV): AME of Household Monthly Income (cont’d) Without controlling

for wealth

Controlling for wealth

Sample Work School Work School

4) Type of employer

Unpaid Family work (n=52,486) -0.065** 0.037 -0.066* 0.027

S.E. (0.029) (0.025) (0.034) (0.029)

P-value [0.028] [0.137] [0.050] [0.353]

Market work (n=50,952) -0.044* 0.001 -0.040 -0.020

S.E. (0.022) (0.026) (0.025) (0.030)

P-value [0.044] [0.974] [0.108] [0.506]

This model is estimated using the two-stage residual inclusion approach introduced by Terza et al. (2008) (see the text). I control for child gender, child age, an urban dummy, family size, education of the head of the household, a dummy for whether the head of the household was a child laborer, the age of the head of the household, a dummy if a child’s mother works, a dummy for single-parent household, a dummy for whether a household owns livestock, size of agricultural land, the age of the head of the household, the highest four quintiles of the wealth index, and the first-stage residuals from Eq.9. Standard errors are shown in parentheses. *** refers to the 99 percent confidence level, ** refers to the 95 percent confidence level, and * refers to the 90 percent confidence level.