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Estimating Farm Production Parameters with Measurement Error in Land Area

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  • Alex Cohen

Abstract

I provide a new method for correcting and assessing bias from measurement error in land area and apply it to estimating two important sets of parameters: the relationship between farm size and productivity and farm production function coefficients. Traditionally, researchers have measured area using farmer self-reporting, which is prone to (nonclassical) measurement error. In response, recent papers use area estimates from global positioning system (GPS) devices. However, GPS estimates may also have errors. I show that instrumenting GPS estimates with self-reported area resolves bias from measurement error in both measures, provided that (log) GPS estimates have classical measurement error. Applying this approach to data from Tanzania, I find that using either GPS estimates or self-reported area leads to bias, and the bias is actually worse when using GPS estimates. Measurement error in GPS-estimated area biases the farm size-productivity relationship by 22%–26% (4%–10% for self-reported), though the inverse farm size-productivity “puzzle” remains even when I correct this bias using my instrumental variables approach. In production function estimates, measurement error in GPS-estimated area biases the Cobb-Douglas coefficient on land down by 39% (27% for self-reported) and labor up by 44% (47% for self-reported). I show that the results also have implications for measuring misallocation.

Suggested Citation

  • Alex Cohen, 2019. "Estimating Farm Production Parameters with Measurement Error in Land Area," Economic Development and Cultural Change, University of Chicago Press, vol. 68(1), pages 305-334.
  • Handle: RePEc:ucp:ecdecc:doi:10.1086/700557
    DOI: 10.1086/700557
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    Citations

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    Cited by:

    1. Mensah, Edouard R. & Kostandini, Genti, 2020. "The inverse farm size-productivity relationship under land size mis-measurement and in the presence of weather and price risks: Panel data evidence from Uganda," 2020 Annual Meeting, July 26-28, Kansas City, Missouri 304477, Agricultural and Applied Economics Association.
    2. 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.
    3. Dillon, Andrew & Mensah, Edouard, 2024. "Respondent biases in agricultural household surveys," Journal of Development Economics, Elsevier, vol. 166(C).
    4. 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.
    5. Carletto,Calogero & Dillon,Andrew S. & Zezza,Alberto, 2021. "Agricultural Data Collection to Minimize Measurement Error and Maximize Coverage," Policy Research Working Paper Series 9745, The World Bank.
    6. 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.
    7. C. S. C. Sekhar & Namrata Thapa, 2023. "Rural market imperfections in India: Revisiting old debates with new evidence," Development Policy Review, Overseas Development Institute, vol. 41(5), September.

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