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Measuring Farm Labor: Survey Experimental Evidence from Ghana

Author

Listed:
  • Isis Gaddis
  • Gbemisola Oseni
  • Amparo Palacios-Lopez
  • Janneke Pieters

Abstract

This study examines recall bias in farm labor through a randomized survey experiment in Ghana, comparing farm labor estimates from an end-of-season recall survey with data collected weekly throughout the agricultural season. Recall households report 10 percent more farm labor per person-plot, which can be explained by recall households’ underreporting of “marginal” plots and household workers. This “selective” omission by recall households, denoted as listing bias, alters the composition of plots and workers across treatment arms and inflates average farm labor hours per person-plot in the recall group. Since listing bias, in this setting, dominates other forms of recall bias at higher levels of aggregation (i.e., when farm labor per person-plot is summed at the plot, person, or household level), farm labor productivity is overestimated for recall households. Consistent with the notion that recall bias is linked to the cognitive burden of reporting on past events, there is no recall bias among more educated households.

Suggested Citation

  • 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.
  • Handle: RePEc:oup:wbecrv:v:35:y:2021:i:3:p:604-634.
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    File URL: http://hdl.handle.net/10.1093/wber/lhaa012
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    Cited by:

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    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).
    13. 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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