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Inferential and Behavioral Implications of Measurement Error in Agricultural Data

Author

Listed:
  • Kibrom A. Abay

    (International Food Policy Research Institute (IFPRI), Cairo, Egypt)

  • Tesfamicheal Wossen

    (International Institute of Tropical Agriculture (IITA), Nairobi, Kenya)

  • Gashaw T. Abate

    (International Food Policy Research Institute (IFPRI), Washington, DC, USA)

  • James R. Stevenson

    (CGIAR Standing Panel on Impact Assessment, Rome, Italy)

  • Hope Michelson

    (Department of Agricultural and Consumer Economics, University of Illinois at Urbana-Champaign, Urbana, Illinois, USA)

  • Christopher B. Barrett

    (Charles H. Dyson School of Applied Economics and Management, Cornell University, Ithaca, New York, USA)

Abstract

An evolving literature evaluates the inferential and behavioral implications of measurement error (ME) in agricultural data. We synthesize findings on the nature and sources of ME and potential remedies. We provide practical guidance for choosing among alternative approaches for detecting, obviating, or correcting for alternative sources of ME, as these have different behavioral and inferential implications. Some ME biases statistical inference and thus may require econometric correction. Other types of ME may affect (and shed light on) farmers’ decision-making processes even if farmers’ responses are objectively incorrect. Where feasible, collecting both self-reported and objectively measured data for the same variable may enrich understanding of policy-relevant agricultural and behavioral phenomena.

Suggested Citation

  • Kibrom A. Abay & Tesfamicheal Wossen & Gashaw T. Abate & James R. Stevenson & Hope Michelson & Christopher B. Barrett, 2023. "Inferential and Behavioral Implications of Measurement Error in Agricultural Data," Annual Review of Resource Economics, Annual Reviews, vol. 15(1), pages 63-83, October.
    • Handle: RePEc:anr:reseco:v:15:y:2023:p:63-83
    DOI: 10.1146/annurev-resource-101422-090049
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    Citations

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    Cited by:

    1. Markhof, Yannick Valentin & Wollburg, Philip Randolph & Palacios-Lopez, Amparo & Castaing, Pauline & Sagesaka, Akiko & Contreras, Ivette, 2026. "The Effect of Survey Mode on Data Quality : Experimental Evidence from Nigeria," Policy Research Working Paper Series 11302, The World Bank.
    2. Abay, Kibrom A. & Chamberlin, Jordan & Chivenge, Pauline & Spielman, David J., 2025. "Fertilizer, soil health, and economic shocks: A synthesis of recent evidence," Food Policy, Elsevier, vol. 133(C).
    3. Ray, Srabashi, 2025. "Joint agricultural input use and food security: evidence from Tanzania," Food Policy, Elsevier, vol. 136(C).
    4. Holden, Stein T. & Makate, Clifton & Tione, Sarah, 2025. "Missing Parcels and Farm Size Measurement Error: Do Nationally Representative Surveys Provide Reliable Estimates?," CLTS Working Papers 4/25, Norwegian University of Life Sciences, Centre for Land Tenure Studies.
    5. Abate, Gashaw T. & Abay, Kibrom A. & Chamberlin, Jordan & Sebsibie, Samuel, 2024. "Measuring land rental market participation in smallholder agriculture can survey design innovations improve land market participation statistics?," IFPRI discussion papers 2255, International Food Policy Research Institute (IFPRI).
    6. James R. Stevenson & Karen Macours & Douglas Gollin, 2023. "The Rigor Revolution: New Standards of Evidence for Impact Assessment of International Agricultural Research," Annual Review of Resource Economics, Annual Reviews, vol. 15(1), pages 495-515, October.
    7. Bohr, Nils & Deisemann, Tim & Gollin, Douglas & Kosmowski, Frédéric & Lybbert, Travis J., 2024. "The seeds of misallocation: Fertilizer use and maize varietal misidentification in Ethiopia," Journal of Development Economics, Elsevier, vol. 171(C).
    8. Melesse, Mequanint B. & Maredia, Mywish K. & Wossen, Tesfamicheal & Odeny, Damaris & Spielman, David J. & Michelson, Hope & Waza, Showkat Ahmad & Kamunye, Kelvin & Alene, Arega & Dar, Manzoor H. & Afa, 2025. "The role of genotyping in measuring improved variety adoption and impact: advances, challenges, and policy directions," Food Policy, Elsevier, vol. 137(C).
    9. Abay, Kibrom A. & Ayalew, Hailemariam & Terfa, Zelalem & Karugia, Joseph & Breisinger, Clemens, 2025. "How good are livestock statistics in Africa? Can nudging and direct counting improve the quality of livestock asset data?," Journal of Development Economics, Elsevier, vol. 176(C).
    10. Ivan Portoghese & Raffaella Matarrese & Laura Mirra & Giacomo Giannoccaro, 2025. "Assimilating Farmers’ Behaviour in the Development of an ET-Based Irrigation Water-Accounting Model," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 39(14), pages 7749-7774, November.

    More about this item

    Keywords

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    JEL classification:

    • C12 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Hypothesis Testing: General
    • C81 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Methodology for Collecting, Estimating, and Organizing Microeconomic Data; Data Access
    • C83 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Survey Methods; Sampling Methods
    • D13 - Microeconomics - - Household Behavior - - - Household Production and Intrahouse Allocation
    • 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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