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Measuring Farm Labor : Survey Experimental Evidence from Ghana


  • Gaddis,Isis
  • Siwatu,Gbemisola Oseni
  • Palacios-Lopez,Amparo
  • Pieters,Janneke


This study examines recall bias in farm labor by conducting a randomized survey experiment in Ghana. Hours of farm labor obtained from a recall survey conducted at the end of the season are compared with data collected weekly throughout the season. The study finds that the recall method overestimates farm labor per person per plot by about 10 percent, controlling for observable differences at baseline. Recall bias in farm labor per person per plot is accounted for by the fact that households in the recall group report fewer marginal plots and farm workers, denoted here as listing bias. This listing bias also creates a countervailing effect on hours of farm labor at higher levels of aggregation, so that the recall method underestimates farm labor per plot and per household and overestimates the labor productivity of household-operated farms. Consistent with the notion that recall bias is linked to the cognitive burden of reporting on past events, the study finds that recall bias in farm labor has a strong educational gradient.

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  • Gaddis,Isis & Siwatu,Gbemisola Oseni & Palacios-Lopez,Amparo & Pieters,Janneke, 2019. "Measuring Farm Labor : Survey Experimental Evidence from Ghana," Policy Research Working Paper Series 8717, The World Bank.
  • Handle: RePEc:wbk:wbrwps:8717

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    References listed on IDEAS

    1. Daniel LaFave & Duncan Thomas, 2016. "Farms, Families, and Markets: New Evidence on Completeness of Markets in Agricultural Settings," Econometrica, Econometric Society, vol. 84, pages 1917-1960, September.
    2. Kjellsson, Gustav & Clarke, Philip & Gerdtham, Ulf-G., 2014. "Forgetting to remember or remembering to forget: A study of the recall period length in health care survey questions," Journal of Health Economics, Elsevier, vol. 35(C), pages 34-46.
    3. Amparo Palacios-L�pez & Ram�n L�pez, 2015. "The Gender Gap in Agricultural Productivity: The Role of Market Imperfections," Journal of Development Studies, Taylor & Francis Journals, vol. 51(9), pages 1175-1192, September.
    4. McCullough, Ellen B., 2017. "Labor productivity and employment gaps in Sub-Saharan Africa," Food Policy, Elsevier, vol. 67(C), pages 133-152.
    5. Gbemisola Oseni & Paul Corral & Markus Goldstein & Paul Winters, 2015. "Explaining gender differentials in agricultural production in Nigeria," Agricultural Economics, International Association of Agricultural Economists, vol. 46(3), pages 285-310, May.
    6. H. Elizabeth Peters, 1988. "Retrospective Versus Panel Data in Analyzing Lifecycle Events," Journal of Human Resources, University of Wisconsin Press, vol. 23(4), pages 488-513.
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    1. Rebecca Pietrelli & Marco d’Errico & Kate Dassesse, 2021. "Measuring household food security through surveys: Do the characteristics of the enumerators matter?," Development Policy Review, Overseas Development Institute, vol. 39(6), pages 911-925, November.
    2. 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.
    3. Fiala, Nathan & Rose, Julian & Aryemo, Filder & Peters, Jörg, 2022. "The (very) long-run impacts of cash grants during a crisis," Ruhr Economic Papers 961, RWI - Leibniz-Institut für Wirtschaftsforschung, Ruhr-University Bochum, TU Dortmund University, University of Duisburg-Essen.
    4. Joachim De Weerdt & John Gibson & Kathleen Beegle, 2020. "What Can We Learn from Experimenting with Survey Methods?," Annual Review of Resource Economics, Annual Reviews, vol. 12(1), pages 431-447, October.
    5. Dervisevic, Ervin & Goldstein, Markus, 2023. "He said, she said: The impact of gender and marriage perceptions on self and proxy reporting of labor," Journal of Development Economics, Elsevier, vol. 161(C).
    6. Mahajan, Kanika, 2019. "Back to the plough: Women managers and farm productivity in India," World Development, Elsevier, vol. 124(C), pages 1-1.
    7. Calogero Carletto, 2021. "Better data, higher impact: improving agricultural data systems for societal change [Correlated non-classical measurement errors, ‘second best’ policy inference, and the inverse size-productivity r," European Review of Agricultural Economics, Oxford University Press and the European Agricultural and Applied Economics Publications Foundation, vol. 48(4), pages 719-740.
    8. Sangwan, Nikita & Kumar, Shalander, 2021. "Labor force participation of rural women and the household’s nutrition: Panel data evidence from SAT India," Food Policy, Elsevier, vol. 102(C).
    9. Jose Galdo & Ana C Dammert & Degnet Abebaw, 2021. "Gender Bias in Agricultural Child Labor: Evidence from Survey Design Experiments," The World Bank Economic Review, World Bank, vol. 35(4), pages 872-891.
    10. Wollburg, Philip & Tiberti, Marco & Zezza, Alberto, 2021. "Recall length and measurement error in agricultural surveys," Food Policy, Elsevier, vol. 100(C).
    11. Helfand, Steven M. & Taylor, Matthew P.H., 2021. "The inverse relationship between farm size and productivity: Refocusing the debate," Food Policy, Elsevier, vol. 99(C).
    12. 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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    14. Amadu, Festus O. & McNamara, Paul E. & Miller, Daniel C., 2020. "Yield effects of climate-smart agriculture aid investment in southern Malawi," Food Policy, Elsevier, vol. 92(C).

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    Food Security; Educational Sciences; Gender and Development; Labor Markets;
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