Abstract
This study considers sibling differences in child labor in Nepal. The data are consistent with a model where parents care equally for all children but siblings differ in comparative advantage in household production, although parental preferences and credit constraints could also be important. Girls, especially older girls, tend to work more than their brothers. This extra work increases with the number of younger siblings and the spacing between siblings. The extra work performed by girls is such that, at modal birth spacing, the younger girl actually spends significantly more time working than her older brother.
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Notes
The model has been written without explicitly allowing transfers between consumption c and the present value of future earnings. Removing credit constraints is simply a matter of adding a transfer B into the household production function and subtracting the cost of that transfer from the return to education function. Allowing borrowing between c and w changes the equilibrium value of Eq. 2, but Eq. 3 still follows.
The degree of substitutability or complementarities between types of labor in household production technology can affect the relationship between sibling composition and child labor. Consider an example where children i and j have the same return to educational investments. The child with comparative advantage in household production works more. Assume that child i is the older child who has comparative advantage in household production. If child j's marginal product of labor in household production improves with increases in child i's labor in household production, sibling differences owing to age rank can be mitigated.
The model in Section 2 describes the allocation of child time for resident children, and I follow this approach in the empirical work. The NLFS does not provide information about nonresident children. In a separate analysis, I use data from a nationally representative, multipurpose household survey from Nepal in 1996 to compare resident children to children ever born to the household head. Forty-five percent of the ever-born children who are not residents are deceased. Of the living, nonresident, ever-born children, 97% are over the age of 15 years and likely economically independent. Fifty percent of the remaining nonresident, living, ever-born children are females.
In Table 4, I find that nonlinearities are important in the relationship between age rank and child labor. To check for nonlinearities in the relationships in Table 5, I include dummy variables for the number of younger sisters and the number of younger brothers, allowing boys and girls to have different means in the presence of additional siblings of a specific gender. The results of this more flexible functional form do not differ from those reported in Table 5 and hence are not reported here.
This statement requires a caveat because my data are limited to surviving resident children rather than children ever born. It is possible that the genders differ in their mortality rates (I find no evidence of this in the supplementary analysis described in footnote 12); thus, the gender ordering of two equally spaced siblings would reflect some household characteristics. However, with age gaps less than or equal to 3 years (as with 80% of the sample), it is unlikely that this differential mortality could seriously impact my estimates of means given the natural period of infertility that accompanies pregnancy and birth.
I prefer the community fixed-effects specification because I then report means after controlling for community average sibling differences. Regression coefficients barely change if I do not include community fixed effects. I do not include household fixed effects because I am already differencing out the household fixed effect. Also, inclusion of the household fixed effect (a fixed effect in the difference) would limit my identifying sample to households with three or more children.
If, instead of constraining the age gap to enter additively, I include a dummy variable for each year (not shown), I cannot reject that the increment between each year is 1.2. Similarly, if I allow the association between the age gap and differences in hours work to vary by sibling size, I cannot reject the hypothesis that an additional year of age gap is associated with 1.2 h of additional work for all sibling sizes.
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Acknowledgements
This research has benefited from the comments of Deborah Cobb-Clark, three anonymous referees, Douglas Miller, Nina Pavcnik, and Bruce Sacerdote, as well as seminar participants at the Dartmouth College and the Northeast Universities Development Consortium. I am grateful to Andreea Gorbatai for her able research assistance, and I appreciate Keshev Karmacharya's help in acquiring the data used in this paper.
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Edmonds, E.V. Understanding sibling differences in child labor. J Popul Econ 19, 795–821 (2006). https://doi.org/10.1007/s00148-005-0013-3
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DOI: https://doi.org/10.1007/s00148-005-0013-3