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Explaining Variation in Child Labor Statistics

Author

Listed:
  • Dillon, Andrew

    () (Michigan State University)

  • Bardasi, Elena

    () (World Bank)

  • Beegle, Kathleen

    () (World Bank)

  • Serneels, Pieter

    () (University of East Anglia)

Abstract

Child labor statistics are critical for assessing the extent and nature of child labor activities in developing countries. In practice, widespread variation exists in how child labor is measured. Questionnaire modules vary across countries and within countries over time along several dimensions, including respondent type and the structure of the questionnaire. Little is known about the effect of these differences on child labor statistics. This paper presents the results from a randomized survey experiment in Tanzania focusing on two survey aspects: different questionnaire design to classify children work and proxy response versus self-reporting. Use of a short module compared with a more detailed questionnaire has a statistically significant effect, especially on child labor force participation rates, and, to a lesser extent, on working hours. Proxy reports do not differ significantly from a child’s self-report. Further analysis demonstrates that survey design choices affect the coefficient estimates of some determinants of child labor in a child labor supply equation. The results suggest that low-cost changes to questionnaire design to clarify the concept of work for respondents can improve the data collected.

Suggested Citation

  • Dillon, Andrew & Bardasi, Elena & Beegle, Kathleen & Serneels, Pieter, 2010. "Explaining Variation in Child Labor Statistics," IZA Discussion Papers 5156, Institute for the Study of Labor (IZA).
  • Handle: RePEc:iza:izadps:dp5156
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    References listed on IDEAS

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    1. Eric V. Edmonds & Norbert Schady, 2012. "Poverty Alleviation and Child Labor," American Economic Journal: Economic Policy, American Economic Association, vol. 4(4), pages 100-124, November.
    2. Sonia Bhalotra & Christopher Heady, 2003. "Child Farm Labor: The Wealth Paradox," World Bank Economic Review, World Bank Group, vol. 17(2), pages 197-227, December.
    3. Bardasi, Elena & Beegle, Kathleen & Dillon, Andrew & Serneels, Pieter, 2010. "Do labor statistics depend on how and to whom the questions are asked ? results from a survey experiment in Tanzania," Policy Research Working Paper Series 5192, The World Bank.
    4. Eric V. Edmonds, 2005. "Does Child Labor Decline with Improving Economic Status?," Journal of Human Resources, University of Wisconsin Press, vol. 40(1).
    5. Basu, Kaushik & Van, Pham Hoang, 1998. "The Economics of Child Labor," American Economic Review, American Economic Association, vol. 88(3), pages 412-427, June.
    6. L. Guarcello & I. Kovrova & S. Lyon & M. Manacorda & F. C. Rosati, 2010. "Towards consistency in child labour measurement: Assessing the comparability of estimates generated by different survey instruments," UCW Working Paper 54, Understanding Children's Work (UCW Programme).
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    Citations

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    Cited by:

    1. Zezza, Alberto & Federighi, Giovanni & Adamou, Kalilou & Hiernaux, Pierre, 2014. "Milking the data : measuring income from milk production in extensive livestock systems -- experimental evidence from Niger," Policy Research Working Paper Series 7114, The World Bank.
    2. David N Margolis, 2014. "By Choice and by Necessity: Entrepreneurship and Self-Employment in the Developing World," The European Journal of Development Research, Palgrave Macmillan;European Association of Development Research and Training Institutes (EADI), vol. 26(4), pages 419-436, September.
    3. 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.
    4. Samphantharak, Krislert & Townsend, Robert M., 2012. "Measuring the return on household enterprise: What matters most for whom?," Journal of Development Economics, Elsevier, vol. 98(1), pages 58-70.
    5. Alwang, Jeffrey & Larochelle, Catherine & Barrera, Victor, 2017. "Farm Decision Making and Gender: Results from a Randomized Experiment in Ecuador," World Development, Elsevier, vol. 92(C), pages 117-129.
    6. Moana S. Simas & Laura Golsteijn & Mark A. J. Huijbregts & Richard Wood & Edgar G. Hertwich, 2014. "The “Bad Labor” Footprint: Quantifying the Social Impacts of Globalization," Sustainability, MDPI, Open Access Journal, vol. 6(11), pages 1-27, October.
    7. Virginie Comblon & Anne-Sophie Robilliard, 2015. "Are female employment statistics more sensitive than male ones to questionnaire design? Evidence from Cameroon, Mali and Senegal," Working Papers DT/2015/22, DIAL (Développement, Institutions et Mondialisation).
    8. Vimefall, Elin, 2015. "Income diversification and working children," Working Papers 2015:8, Örebro University, School of Business.
    9. repec:pal:eurjdr:v:30:y:2018:i:2:d:10.1057_s41287-017-0079-2 is not listed on IDEAS
    10. Carletto, Calogero & Gourlay, Sydney & Murray, Siobhan & Zezza, Alberto, 2015. "Welcome to Fantasyland: Comparing Approaches To Land Area Measurement In Household Surveys," 2015 Conference, August 9-14, 2015, Milan, Italy 211849, International Association of Agricultural Economists.
    11. Zezza, Alberto & Federighi, Giovanni & Kalilou, Amadou Adamou & Hiernaux, Pierre, 2016. "Milking the data: Measuring milk off-take in extensive livestock systems. Experimental evidence from Niger," Food Policy, Elsevier, vol. 59(C), pages 174-186.
    12. Charles Kenny, Jonathan Karver, and Andy Sumner, 2012. "MDGs 2.0: What Goals, Targets, and Timeframe? - Working Paper 297," Working Papers 297, Center for Global Development.

    More about this item

    Keywords

    child labor; survey design; Tanzania;

    JEL classification:

    • J21 - Labor and Demographic Economics - - Demand and Supply of Labor - - - Labor Force and Employment, Size, and Structure
    • C81 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Methodology for Collecting, Estimating, and Organizing Microeconomic Data; Data Access
    • C93 - Mathematical and Quantitative Methods - - Design of Experiments - - - Field Experiments

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