IDEAS home Printed from https://ideas.repec.org/a/eee/deveco/v164y2023ics0304387823000913.html
   My bibliography  Save this article

Nonclassical measurement error and farmers’ response to information treatment

Author

Listed:
  • Abay, Kibrom A.
  • Barrett, Christopher B.
  • Kilic, Talip
  • Moylan, Heather
  • Ilukor, John
  • Vundru, Wilbert Drazi

Abstract

This paper reports on a randomized experiment conducted among Malawian agricultural households to study nonclassical measurement error (NCME) in self-reported plot area, and farmers' responses to new information — the objective plot area measure — subsequently provided to them. Farmers' pre-treatment self-reported plot areas exhibit considerable NCME. Most of the measurement error follows a regression-to-mean pattern with respect to plot area, and another 18 percent arises from asymmetric rounding to half acre increments. Randomized provision of GPS-based measures of true plot area generates four important findings. First, most treated farmers’ self-reports exhibit no reduction in NCME after the provision of true plot area measures. Second, farmers update asymmetrically in response to information, with upward corrections being far more common than downward ones even though most plot sizes were initially overestimated. Third, the magnitude of updating varies by true plot area, as well as the magnitude and direction of initial NCME. Fourth, the information treatment affects self-reported information about non-land inputs such as fertilizer and labor, indicating that the effects of measurement error and updating spillover across variables. NCME clearly carries implications for survey data collection methods, econometric inference, and the design of information-based interventions. It might also reflect behavioral anomalies that may matter for farm management practices, input allocation, agricultural productivity, and the design of effective interventions.

Suggested Citation

  • Abay, Kibrom A. & Barrett, Christopher B. & Kilic, Talip & Moylan, Heather & Ilukor, John & Vundru, Wilbert Drazi, 2023. "Nonclassical measurement error and farmers’ response to information treatment," Journal of Development Economics, Elsevier, vol. 164(C).
  • Handle: RePEc:eee:deveco:v:164:y:2023:i:c:s0304387823000913
    DOI: 10.1016/j.jdeveco.2023.103136
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0304387823000913
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.jdeveco.2023.103136?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Joshua Schwartzstein, 2014. "Selective Attention And Learning," Journal of the European Economic Association, European Economic Association, vol. 12(6), pages 1423-1452, December.
    2. Dercon, Stefan & Christiaensen, Luc, 2011. "Consumption risk, technology adoption and poverty traps: Evidence from Ethiopia," Journal of Development Economics, Elsevier, vol. 96(2), pages 159-173, November.
    3. Wollburg, Philip & Tiberti, Marco & Zezza, Alberto, 2021. "Recall length and measurement error in agricultural surveys," Food Policy, Elsevier, vol. 100(C).
    4. Alexander Wolitzky, 2018. "Learning from Others' Outcomes," American Economic Review, American Economic Association, vol. 108(10), pages 2763-2801, October.
    5. Benjamin R. Handel & Jonathan T. Kolstad, 2015. "Health Insurance for "Humans": Information Frictions, Plan Choice, and Consumer Welfare," American Economic Review, American Economic Association, vol. 105(8), pages 2449-2500, August.
    6. Bound, John & Brown, Charles & Mathiowetz, Nancy, 2001. "Measurement error in survey data," Handbook of Econometrics, in: J.J. Heckman & E.E. Leamer (ed.), Handbook of Econometrics, edition 1, volume 5, chapter 59, pages 3705-3843, Elsevier.
    7. Xavier Gabaix & David Laibson & Guillermo Moloche & Stephen Weinberg, 2006. "Costly Information Acquisition: Experimental Analysis of a Boundedly Rational Model," American Economic Review, American Economic Association, vol. 96(4), pages 1043-1068, September.
    8. Foster, Andrew D & Rosenzweig, Mark R, 1995. "Learning by Doing and Learning from Others: Human Capital and Technical Change in Agriculture," Journal of Political Economy, University of Chicago Press, vol. 103(6), pages 1176-1209, December.
    9. Hyslop, Dean R & Imbens, Guido W, 2001. "Bias from Classical and Other Forms of Measurement Error," Journal of Business & Economic Statistics, American Statistical Association, vol. 19(4), pages 475-481, October.
    10. Kilic, Talip & Zezza, Alberto & Carletto, Calogero & Savastano, Sara, 2017. "Missing(ness) in Action: Selectivity Bias in GPS-Based Land Area Measurements," World Development, Elsevier, vol. 92(C), pages 143-157.
    11. Carletto, Calogero & Savastano, Sara & Zezza, Alberto, 2013. "Fact or artifact: The impact of measurement errors on the farm size–productivity relationship," Journal of Development Economics, Elsevier, vol. 103(C), pages 254-261.
    12. 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.
    13. Binswanger-Mkhize, Hans P. & Savastano, Sara, 2017. "Agricultural intensification: The status in six African countries," Food Policy, Elsevier, vol. 67(C), pages 26-40.
    14. Berazneva, Julia & McBride, Linden & Sheahan, Megan & Güereña, David, 2018. "Empirical assessment of subjective and objective soil fertility metrics in east Africa: Implications for researchers and policy makers," World Development, Elsevier, vol. 105(C), pages 367-382.
    15. Michelson, Hope & Fairbairn, Anna & Ellison, Brenna & Maertens, Annemie & Manyong, Victor, 2021. "Misperceived quality: Fertilizer in Tanzania," Journal of Development Economics, Elsevier, vol. 148(C).
    16. Kibrom A. Abay, 2020. "Measurement errors in agricultural data and their implications on marginal returns to modern agricultural inputs," Agricultural Economics, International Association of Agricultural Economists, vol. 51(3), pages 323-341, May.
    17. Burke, William J. & Morgan, Stephen & Namonje, Thelma & Muyanga, Milu & Mason, Nicole M., 2019. "Beyond the "Inverse Relationship": Area Mismeasurement Affects Actual Productivity, Not Just How We Understand It," Feed the Future Innovation Lab for Food Security Policy Research Papers 303519, Michigan State University, Department of Agricultural, Food, and Resource Economics, Feed the Future Innovation Lab for Food Security (FSP).
    18. Annemie Maertens & Hope Michelson & Vesall Nourani, 2021. "How Do Farmers Learn from Extension Services? Evidence from Malawi," American Journal of Agricultural Economics, John Wiley & Sons, vol. 103(2), pages 569-595, March.
    19. Alexandre N. Kohlhas & Ansgar Walther, 2021. "Asymmetric Attention," American Economic Review, American Economic Association, vol. 111(9), pages 2879-2925, September.
    20. 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.
    21. Justine Hastings & Ali Hortaçsu & Chad Syverson, 2017. "Sales Force and Competition in Financial Product Markets: The Case of Mexico's Social Security Privatization," Econometrica, Econometric Society, vol. 85(6), pages 1723-1761, November.
    22. 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.
    23. Calogero Carletto & Sydney Gourlay & Paul Winters, 2015. "Editor's choice From Guesstimates to GPStimates: Land Area Measurement and Implications for Agricultural Analysis," Journal of African Economies, Centre for the Study of African Economies, vol. 24(5), pages 593-628.
    24. Andrew Dillon & Sydney Gourlay & Kevin McGee & Gbemisola Oseni, 2019. "Land Measurement Bias and Its Empirical Implications: Evidence from a Validation Exercise," Economic Development and Cultural Change, University of Chicago Press, vol. 67(3), pages 595-624.
    25. Ayala Wineman & Timothy Njagi & C. Leigh Anderson & Travis W. Reynolds & Didier Yélognissè Alia & Priscilla Wainaina & Eric Njue & Pierre Biscaye & Miltone W. Ayieko, 2020. "A Case of Mistaken Identity? Measuring Rates of Improved Seed Adoption in Tanzania Using DNA Fingerprinting," Journal of Agricultural Economics, Wiley Blackwell, vol. 71(3), pages 719-741, September.
    26. Gourlay, Sydney & Kilic, Talip & Lobell, David B., 2019. "A new spin on an old debate: Errors in farmer-reported production and their implications for inverse scale - Productivity relationship in Uganda," Journal of Development Economics, Elsevier, vol. 141(C).
    27. Wossen, Tesfamicheal & Abay, Kibrom A. & Abdoulaye, Tahirou, 2022. "Misperceiving and misreporting input quality: Implications for input use and productivity," Journal of Development Economics, Elsevier, vol. 157(C).
    28. Michelson, Hope & Gourlay, Sydney & Lybbert, Travis & Wollburg, Philip, 2023. "Review: Purchased agricultural input quality and small farms," Food Policy, Elsevier, vol. 116(C).
    29. Rema Hanna & Sendhil Mullainathan & Joshua Schwartzstein, 2014. "Learning Through Noticing: Theory and Evidence from a Field Experiment," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 129(3), pages 1311-1353.
    30. Saurabh Bhargava & George Loewenstein & Justin Sydnor, 2017. "Choose to Lose: Health Plan Choices from a Menu with Dominated Option," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 132(3), pages 1319-1372.
    31. Sims, Christopher A., 2010. "Rational Inattention and Monetary Economics," Handbook of Monetary Economics, in: Benjamin M. Friedman & Michael Woodford (ed.), Handbook of Monetary Economics, edition 1, volume 3, chapter 4, pages 155-181, Elsevier.
    32. Benjamin R. Handel, 2013. "Adverse Selection and Inertia in Health Insurance Markets: When Nudging Hurts," American Economic Review, American Economic Association, vol. 103(7), pages 2643-2682, December.
    33. Matthew Rabin & Joel L. Schrag, 1999. "First Impressions Matter: A Model of Confirmatory Bias," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 114(1), pages 37-82.
    34. Dan Lovallo & Colin Camerer, 1999. "Overconfidence and Excess Entry: An Experimental Approach," American Economic Review, American Economic Association, vol. 89(1), pages 306-318, March.
    35. Sendhil Mullainathan, 2002. "A Memory-Based Model of Bounded Rationality," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 117(3), pages 735-774.
    36. Maxwell, Nan L & Lopus, Jane S, 1994. "The Lake Wobegon Effect in Student Self-Reported Data," American Economic Review, American Economic Association, vol. 84(2), pages 201-205, May.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Deepti Sharma & Hema Swaminathan & Rahul Lahoti, 2024. "Does it matter who you ask for time-use data?," WIDER Working Paper Series wp-2024-1, World Institute for Development Economic Research (UNU-WIDER).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Abay,Kibrom A. & Barrett,Christopher B. & Kilic,Talip & Moylan,Heather G. & Ilukor,John & Vundru,Wilbert Drazi, 2022. "Nonclassical Measurement Error and Farmers’ Response to Information Reveal Behavioral Anomalies," Policy Research Working Paper Series 9908, The World Bank.
    2. Kibrom A. Abay & Tesfamicheal Wossen & Jordan Chamberlin, 2023. "Mismeasurement and efficiency estimates: Evidence from smallholder survey data in Africa," Journal of Agricultural Economics, Wiley Blackwell, vol. 74(2), pages 413-434, June.
    3. 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.
    4. Kibrom A. Abay, 2020. "Measurement errors in agricultural data and their implications on marginal returns to modern agricultural inputs," Agricultural Economics, International Association of Agricultural Economists, vol. 51(3), pages 323-341, May.
    5. Kosmowski, Frederic & Chamberlin, Jordan & Ayalew, Hailemariam & Sida, Tesfaye & Abay, Kibrom & Craufurd, Peter, 2021. "How accurate are yield estimates from crop cuts? Evidence from smallholder maize farms in Ethiopia," Food Policy, Elsevier, vol. 102(C).
    6. 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.
    7. 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.
    8. William J. Burke & Stephen N. Morgan & Thelma Namonje & Milu Muyanga & Nicole M. Mason, 2023. "Beyond the “inverse relationship”: Area mismeasurement may affect actual productivity, not just how we understand it," Agricultural Economics, International Association of Agricultural Economists, vol. 54(4), pages 557-569, July.
    9. 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).
    10. 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.
    11. Wollburg, Philip & Tiberti, Marco & Zezza, Alberto, 2021. "Recall length and measurement error in agricultural surveys," Food Policy, Elsevier, vol. 100(C).
    12. Paola Mallia, 2022. "You reap what (you think) you sow? Evidence on farmers’behavioral adjustments in the case of correct crop varietal identification," PSE Working Papers hal-03597332, HAL.
    13. Wossen, Tesfamicheal & Abay, Kibrom A. & Abdoulaye, Tahirou, 2022. "Misperceiving and misreporting input quality: Implications for input use and productivity," Journal of Development Economics, Elsevier, vol. 157(C).
    14. Dominik Naeher, 2022. "Technology Adoption Under Costly Information Processing," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 63(2), pages 699-753, May.
    15. Christopher B. Barrett & Asad Islam & Abdul Mohammad Malek & Debayan Pakrashi & Ummul Ruthbah, 2022. "Experimental Evidence on Adoption and Impact of the System of Rice Intensification," American Journal of Agricultural Economics, John Wiley & Sons, vol. 104(1), pages 4-32, January.
    16. Mbassi, Christophe Martial & Messono, Omang Ombolo, 2023. "Historical technology and current economic development: Reassessing the nature of the relationship," Technological Forecasting and Social Change, Elsevier, vol. 195(C).
    17. Fernando Aragon & Diego Restuccia & Juan Pablo Rud, 2022. "Assessing misallocation in agriculture: plots versus farms," Working Papers tecipa-718, University of Toronto, Department of Economics.
    18. Abate, Gashaw T. & de Brauw, Alan & Hirvonen, Kalle & Wolle, Abdulazize, 2023. "Measuring consumption over the phone: Evidence from a survey experiment in urban Ethiopia," Journal of Development Economics, Elsevier, vol. 161(C).
    19. Omotilewa, Oluwatoba J. & Jayne, T.S. & Muyanga, Milu & Aromolaran, Adebayo B. & Liverpool-Tasie, Lenis Saweda O. & Awokuse, Titus, 2021. "A revisit of farm size and productivity: Empirical evidence from a wide range of farm sizes in Nigeria," World Development, Elsevier, vol. 146(C).
    20. Markhof,Yannick Valentin & Ponzini,Giulia & Wollburg,Philip Randolph, 2022. "Measuring Disaster Crop Production Losses Using Survey Microdata : Evidence from Sub-Saharan Africa," Policy Research Working Paper Series 9968, The World Bank.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:deveco:v:164:y:2023:i:c:s0304387823000913. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/devec .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.