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Could the debate be over ? errors in farmer-reported production and their implications for the inverse scale-productivity relationship in Uganda

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  • Gourlay,Sydney
  • Kilic,Talip
  • Lobell,David
  • Gourlay,Sydney
  • Kilic,Talip
  • Lobell,David

Abstract

Based on a two-round household panel survey conducted in Eastern Uganda, this study shows that the analysis of the inverse scale-productivity relationship is highly sensitive to how plot-level maize production, hence yield (production divided by GPS-based plot area), is measured. Although farmer-reported production-based plot-level maize yield regressions consistently lend support to the inverse scale-productivity relationship, the comparable regressions estimated with maize yields based on sub-plot crop cutting, full-plot crop cutting, and remote sensing point toward constant returns to scale, at the mean as well as throughout the distributions of objective measures of maize yield. In deriving the much-debated coefficient for GPS-based plot area, the maize yield regressions control for objective measures of soil fertility, maize genetic heterogeneity, and edge effects at the plot level; a rich set of plot, household, and plot manager attributes; as well as time-invariant household- and parcel-level unobserved heterogeneity in select specifications that exploit the panel nature of the data. The core finding is driven by persistent overestimation of farmer-reported maize production and yield vis-à-vis their crop cutting?based counterparts, particularly in the lower half of the plot area distribution. Although the results contribute to a larger, and renewed, body of literature questioning the inverse scale-productivity relationship based on omitted explanatory variables or alternative formulations of the agricultural productivity measure, the paper is among the first documenting how the inverse relationship could be a statistical artifact, driven by errors in farmer-reported survey data on crop production.

Suggested Citation

  • Gourlay,Sydney & Kilic,Talip & Lobell,David & Gourlay,Sydney & Kilic,Talip & Lobell,David, 2017. "Could the debate be over ? errors in farmer-reported production and their implications for the inverse scale-productivity relationship in Uganda," Policy Research Working Paper Series 8192, The World Bank.
  • Handle: RePEc:wbk:wbrwps:8192
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    Cited by:

    1. Abay, Kibrom A. & Abate, Gashaw T. & Barrett, Christopher B. & Bernard, Tanguy, 2019. "Correlated non-classical measurement errors, ‘Second best’ policy inference, and the inverse size-productivity relationship in agriculture," Journal of Development Economics, Elsevier, vol. 139(C), pages 171-184.
    2. Aragón, Fernando M. & Restuccia, Diego & Rud, Juan Pablo, 2022. "Are small farms really more productive than large farms?," Food Policy, Elsevier, vol. 106(C).
    3. Ayala Wineman & Thomas S. Jayne, 2021. "Factor Market Activity and the Inverse Farm Size-Productivity Relationship in Tanzania," Journal of Development Studies, Taylor & Francis Journals, vol. 57(3), pages 443-464, March.
    4. Senne Vandevelde & Bjorn Van Campenhout & Wilberforce Walukano, 2018. "Spoiler alert! Spillovers in the context of a video intervention to maintain seed quality among Ugandan potato farmers," LICOS Discussion Papers 40718, LICOS - Centre for Institutions and Economic Performance, KU Leuven.
    5. Douglas Gollin & Christopher Udry, 2021. "Heterogeneity, Measurement Error, and Misallocation: Evidence from African Agriculture," Journal of Political Economy, University of Chicago Press, vol. 129(1), pages 1-80.
    6. 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.
    7. Desiere, Sam & Jolliffe, Dean, 2018. "Land productivity and plot size: Is measurement error driving the inverse relationship?," Journal of Development Economics, Elsevier, vol. 130(C), pages 84-98.
    8. Kibrom A. Abay & Leah E. M. Bevis & Christopher B. Barrett, 2021. "Measurement Error Mechanisms Matter: Agricultural Intensification with Farmer Misperceptions and Misreporting," American Journal of Agricultural Economics, John Wiley & Sons, vol. 103(2), pages 498-522, March.
    9. Basile Boulay, 2018. "Revisiting the old debate: on the relationship between size and productivity in Tanzania," Discussion Papers 2018-02, University of Nottingham, CREDIT.
    10. Liu, Yanyan & Barrett, Christopher B. & Pham, Trinh & Violette, William, 2020. "The intertemporal evolution of agriculture and labor over a rapid structural transformation: Lessons from Vietnam," Food Policy, Elsevier, vol. 94(C).
    11. Wollburg, Philip & Tiberti, Marco & Zezza, Alberto, 2021. "Recall length and measurement error in agricultural surveys," Food Policy, Elsevier, vol. 100(C).
    12. Steven Helfand & Matthew Taylor, 2018. "The Inverse Relationship between Farm Size and Productivity: Refocusing the Debate," Working Papers 201811, University of California at Riverside, Department of Economics.
    13. Bevis, Leah EM. & Barrett, Christopher B., 2020. "Close to the edge: High productivity at plot peripheries and the inverse size-productivity relationship," Journal of Development Economics, Elsevier, vol. 143(C).
    14. Rada, Nicholas E. & Fuglie, Keith O., 2019. "New perspectives on farm size and productivity," Food Policy, Elsevier, vol. 84(C), pages 147-152.
    15. Emerick, Kyle & Burke, Marshall & Maue, Casey, 2020. "Productivity dispersion and persistence among the world’s most numerous firms," CEPR Discussion Papers 14553, C.E.P.R. Discussion Papers.
    16. Anthony Harris & Anthony D'Agostino & Sara Litke-Farzaneh & Beryl Seiler & Matt Sloan, "undated". "Morocco Land Productivity Project: Evaluation Design Report," Mathematica Policy Research Reports f3fc788501b64608b17e1cb23, Mathematica Policy Research.
    17. Robertson R.B. Khataza & Atakelty Hailu & Graeme J. Doole & Marit E. Kragt & Arega D. Alene, 2019. "Examining the relationship between farm size and productive efficiency: a Bayesian directional distance function approach," Agricultural Economics, International Association of Agricultural Economists, vol. 50(2), pages 237-246, March.
    18. Maue, Casey C. & Burke, Marshall & Emerick, Kyle, 2020. "Productivity dispersion and persistence among the world's most numerous firms," 2020 Annual Meeting, July 26-28, Kansas City, Missouri 304287, Agricultural and Applied Economics Association.
    19. David B Lobell & George Azzari & Marshall Burke & Sydney Gourlay & Zhenong Jin & Talip Kilic & Siobhan Murray, 2020. "Eyes in the Sky, Boots on the Ground: Assessing Satellite‐ and Ground‐Based Approaches to Crop Yield Measurement and Analysis," American Journal of Agricultural Economics, John Wiley & Sons, vol. 102(1), pages 202-219, January.
    20. Ayala Wineman & C. Leigh Anderson & Travis W. Reynolds & Pierre Biscaye, 2019. "Methods of crop yield measurement on multi-cropped plots: Examples from Tanzania," Food Security: The Science, Sociology and Economics of Food Production and Access to Food, Springer;The International Society for Plant Pathology, vol. 11(6), pages 1257-1273, December.
    21. Joshua W. Deutschmann & Maya Duru & Kim Siegal & Emilia Tjernström, 2019. "Can Smallholder Extension Transform African Agriculture?," NBER Working Papers 26054, National Bureau of Economic Research, Inc.
    22. Wossen, Tesfamicheal & Alene, Arega & Abdoulaye, Tahirou & Feleke, Shiferaw & Manyong, Victor, 2019. "Agricultural technology adoption and household welfare: Measurement and evidence," Food Policy, Elsevier, vol. 87(C), pages 1-1.

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