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Measuring child labor: Comparisons between hours data and subjective measures

Author

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  • Dillon, Andrew
Abstract
"This paper examines a subjective measure of child labor as an alternative to hours data for eliciting the distribution of children's time between work, school, and leisure. The subjective child labor questions that were developed have two primary advantages. First, the subjective measures avoid proxy respondent bias in child labor reports made by parents in a standard hours module. Second, the subjective child labor module scales responses to elicit the relative distribution of the shares of children's time without relying on hours data which are prone to severe outlier problems. Adult, proxy respondents are found to produce uniformly lower reports of children's time allocated to work and school than the child's own subjective responses. Conditional labor supply functions are also estimated to examine the marginal effects of child, parent, household and school characteristics between the two types of data. Children's subjective responses are found to increase the magnitude of the marginal effects for child's age, parental education, and school availability with limited differences between household composition and asset variables." from authors' abstract

Suggested Citation

  • Dillon, Andrew, 2009. "Measuring child labor: Comparisons between hours data and subjective measures," IFPRI discussion papers 879, International Food Policy Research Institute (IFPRI).
    • Handle: RePEc:fpr:ifprid:879
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    References listed on IDEAS

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    Cited by:

    1. Dammert, Ana C. & Galdo, Jose, 2013. "Child Labor Variation by Type of Respondent: Evidence from a Large-Scale Study," World Development, Elsevier, vol. 51(C), pages 207-220.
    2. André, Pierre & Delesalle, Esther & Dumas, Christelle, 2021. "Returns to farm child labor in Tanzania," World Development, Elsevier, vol. 138(C).
    3. Aurelia Lepine & Yah Ariane Bernadette N’Djore & Carole Treibich & Henry Cust & Laurent Foubert & Megan Passey & Selina Binder, 2024. "Estimating the prevalence of child labour in the cocoa industry via indirect elicitation methods: a mixed-methods study," Journal of Population Economics, Springer;European Society for Population Economics, vol. 37(4), pages 1-29, December.
    4. F. Blanco & C. A. Valdivia, 2006. "Child labour in Venezuela: children's vulnerability to macroeconomic shocks," UCW Working Paper 52, Understanding Children's Work (UCW Programme).
    5. Dillon, Andrew & Bardasi, Elena & Beegle, Kathleen & Serneels, Pieter, 2012. "Explaining variation in child labor statistics," Journal of Development Economics, Elsevier, vol. 98(1), pages 136-147.
    6. Deborah Levison & Deborah S. DeGraff & Esther W. Dungumaro, 2018. "Implications of Environmental Chores for Schooling: Children’s Time Fetching Water and Firewood in Tanzania," The European Journal of Development Research, Palgrave Macmillan;European Association of Development Research and Training Institutes (EADI), vol. 30(2), pages 217-234, April.
    7. Marine JOUVIN, 2021. "Addressing social desirability bias in child labor measurement : an application to cocoa farms in Côte d’Ivoire," Bordeaux Economics Working Papers 2021-08, Bordeaux School of Economics (BSE).

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    More about this item

    Keywords

    Child labor; Questionnaire design; Development strategies; Childcare and work; Gender;
    All these keywords.

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