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Gender Bias in Agricultural Child Labor: Evidence from Survey Design Experiments


  • Galdo, Jose C.

    (Carleton University)

  • Dammert, Ana C.

    (Carleton University)

  • Abebaw, Degnet

    (Ethiopian Economic Policy Research Institute)


Agricultural labor accounts for the largest share of child labor worldwide. Yet, measurement of farm labor statistics is challenging due to its inherent seasonality, variable and irregular work schedules, and the varying saliences of individuals' work activities. The problem is further complicated by the presence of widespread gender stratification of work and social lives. This study reports the findings of three randomized survey design interventions conducted over the agricultural coffee calendar in rural Ethiopia to address whether response by proxy rather than self-report has effects on the measurement of child labor statistics within and across seasons. While the estimates do not report differences for boys across all seasons, the analysis shows sizable self/proxy discrepancies in child labor statistics for girls. Overall, the results highlight concerns on the use of survey proxy respondents in agricultural labor, particularly for girls. The main findings have important implications for policymakers about data collection in rural areas in developing countries.

Suggested Citation

  • Galdo, Jose C. & Dammert, Ana C. & Abebaw, Degnet, 2020. "Gender Bias in Agricultural Child Labor: Evidence from Survey Design Experiments," IZA Discussion Papers 13826, Institute of Labor Economics (IZA).
  • Handle: RePEc:iza:izadps:dp13826

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    References listed on IDEAS

    1. Isis Gaddis & Gbemisola Oseni & Amparo Palacios-Lopez & Janneke Pieters, 2021. "Measuring Farm Labor: Survey Experimental Evidence from Ghana," The World Bank Economic Review, World Bank, vol. 35(3), pages 604-634.
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    3. Arthi, Vellore & Beegle, Kathleen & De Weerdt, Joachim & Palacios-López, Amparo, 2018. "Not your average job: Measuring farm labor in Tanzania," Journal of Development Economics, Elsevier, vol. 130(C), pages 160-172.
    4. Palacios-Lopez, Amparo & Christiaensen, Luc & Kilic, Talip, 2017. "How much of the labor in African agriculture is provided by women?," Food Policy, Elsevier, vol. 67(C), pages 52-63.
    5. Das, Jishnu & Hammer, Jeffrey & Sánchez-Paramo, Carolina, 2012. "The impact of recall periods on reported morbidity and health seeking behavior," Journal of Development Economics, Elsevier, vol. 98(1), pages 76-88.
    6. Dammert, Ana C. & de Hoop, Jacobus & Mvukiyehe, Eric & Rosati, Furio C., 2018. "Effects of public policy on child labor: Current knowledge, gaps, and implications for program design," World Development, Elsevier, vol. 110(C), pages 104-123.
    7. repec:pri:rpdevs:hammer_the_impact_of_recall_periods_on_reported_morbidity_and_health_seeking_behavior.pdf is not listed on IDEAS
    8. 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.
    9. Bound, John & Brown, Charles & Mathiowetz, Nancy, 2001. "Measurement error in survey data," Handbook of Econometrics, in: J.J. Heckman & E.E. Leamer (ed.), Handbook of Econometrics, edition 1, volume 5, chapter 59, pages 3705-3843, Elsevier.
    10. Deborah LEVISON & Jasper HOEK & David LAM & Suzanne DURYEA, 2007. "Intermittent child employment and its implications for estimates of child labour," International Labour Review, International Labour Organization, vol. 146(3-4), pages 217-251, September.
    11. 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).
    12. Sarah A. JANZEN, 2018. "Child labour measurement: Whom should we ask?," International Labour Review, International Labour Organization, vol. 157(2), pages 169-191, June.
    13. Beegle, Kathleen & Galasso, Emanuela & Goldberg, Jessica, 2017. "Direct and indirect effects of Malawi's public works program on food security," Journal of Development Economics, Elsevier, vol. 128(C), pages 1-23.
    14. Jungmin Lee & Sokbae Lee, 2012. "Does it Matter WHO Responded to the Survey? Trends in the U.S. Gender Earnings Gap Revisited," ILR Review, Cornell University, ILR School, vol. 65(1), pages 148-160, January.
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    Cited by:

    1. Nicola S. Pocock & Clara W. Chan & Cathy Zimmerman, 2021. "Suitability of Measurement Tools for Assessing the Prevalence of Child Domestic Work: A Rapid Systematic Review," IJERPH, MDPI, vol. 18(5), pages 1-20, February.
    2. Galdo, Jose C., 2021. "Using Bank Savings Product Design for Empowering Women and Agricultural Development," IZA Discussion Papers 14523, Institute of Labor Economics (IZA).
    3. 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).
    4. Dammert, Ana C. & Galdo, Jose, 2021. "Assessing adult farm labor statistics: Evidence from a survey design experiment in Ethiopia," Economics Letters, Elsevier, vol. 203(C).
    5. Koolwal, Gayatri B., 2021. "Improving the measurement of rural women's employment: Global momentum and survey priorities," World Development, Elsevier, vol. 147(C).

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    More about this item


    survey design; farm labor; gender; labor statistics; child labor;
    All these keywords.

    JEL classification:

    • C8 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs
    • J22 - Labor and Demographic Economics - - Demand and Supply of Labor - - - Time Allocation and Labor Supply
    • O12 - Economic Development, Innovation, Technological Change, and Growth - - Economic Development - - - Microeconomic Analyses of Economic Development
    • Q12 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Agriculture - - - Micro Analysis of Farm Firms, Farm Households, and Farm Input Markets

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