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Estimating the Productivity Impacts of Technology Adoption in the Presence of Misclassification

Author

Listed:
  • Tesfamicheal Wossen
  • Tahirou Abdoulaye
  • Arega Alene
  • Pierre Nguimkeu
  • Shiferaw Feleke
  • Ismail Y Rabbi
  • Mekbib G Haile
  • Victor Manyong

Abstract

This article examines the impact that misreporting adoption status has on the identification and estimation of causal effects on productivity. In particular, by comparing measurement error-ridden self-reported adoption data with measurement-error-free DNA-fingerprinted adoption data, we investigate the extent to which such errors bias the causal effects of adoption on productivity. Taking DNA-fingerprinted adoption data as a benchmark, we find 25% “false negatives” and 10% “false positives” in farmers’ responses. Our results show that misreporting of adoption status is not exogenous to household characteristics, and produces a bias of about 22 percentage points in the productivity impact of adoption. Ignoring inherent behavioral adjustments of farmers based on perceived adoption status has a bias of 13 percentage points. The results of this article underscore the crucial role that correct measurement of adoption plays in designing policy interventions that address constraints to technology adoption in agriculture.

Suggested Citation

  • Tesfamicheal Wossen & Tahirou Abdoulaye & Arega Alene & Pierre Nguimkeu & Shiferaw Feleke & Ismail Y Rabbi & Mekbib G Haile & Victor Manyong, 2019. "Estimating the Productivity Impacts of Technology Adoption in the Presence of Misclassification," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 101(1), pages 1-16.
    • Handle: RePEc:oup:ajagec:v:101:y:2019:i:1:p:1-16.
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    File URL: http://hdl.handle.net/10.1093/ajae/aay017
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    Citations

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    Cited by:

    1. 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.
    2. Tufa, Adane Hirpa & Alene, Arega D. & Manda, Julius & Akinwale, M.G. & Chikoye, David & Feleke, Shiferaw & Wossen, Tesfamicheal & Manyong, Victor, 2019. "The productivity and income effects of adoption of improved soybean varieties and agronomic practices in Malawi," World Development, Elsevier, vol. 124(C), pages 1-1.
    3. Tahirou Abdoulaye & Tesfamicheal Wossen & Bola Awotide, 2018. "Impacts of improved maize varieties in Nigeria: ex-post assessment of productivity and welfare outcomes," Food Security: The Science, Sociology and Economics of Food Production and Access to Food, Springer;The International Society for Plant Pathology, vol. 10(2), pages 369-379, April.
    4. Pepijn Schreinemachers & Teresa Sequeros & Saima Rani & Md. Abdur Rashid & Nithya Vishwanath Gowdru & Muhammad Shahrukh Rahman & Mohammed Razu Ahmed & Ramakrishnan Madhavan Nair, 2019. "Counting the beans: quantifying the adoption of improved mungbean varieties in South Asia and Myanmar," Food Security: The Science, Sociology and Economics of Food Production and Access to Food, Springer;The International Society for Plant Pathology, vol. 11(3), pages 623-634, June.
    5. 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.
    6. Michelson, Hope & Gourlay, Sydney & Lybbert, Travis & Wollburg, Philip, 2023. "Review: Purchased agricultural input quality and small farms," Food Policy, Elsevier, vol. 116(C).
    7. Abebe, Meseret Birhane & Endale, Kefyalew, 2023. "The Impact of Improved Seed Adoption on Nutrition Outcome: A Panel Endogenous Switching Regression Analysis," EfD Discussion Paper 23-1, Environment for Development, University of Gothenburg.
    8. Tsegaye Ginbo & Helena Hansson, 2023. "Intra-household risk perceptions and climate change adaptation in sub-Saharan Africa," European Review of Agricultural Economics, Oxford University Press and the European Agricultural and Applied Economics Publications Foundation, vol. 50(3), pages 1039-1063.
    9. Manda, Julius & Alene, Arega D. & Tufa, Adane H. & Abdoulaye, Tahirou & Wossen, Tesfamicheal & Chikoye, David & Manyong, Victor, 2019. "The poverty impacts of improved cowpea varieties in Nigeria: A counterfactual analysis," World Development, Elsevier, vol. 122(C), pages 261-271.
    10. 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.
    11. Jourdain C. Lokossou & Hippolyte D. Affognon & Alphonse Singbo & Michel B. Vabi & Ayoni Ogunbayo & Paul Tanzubil & Alcade C. Segnon & Geoffrey Muricho & Haile Desmae & Hakeem Ajeigbe, 2022. "Welfare impacts of improved groundnut varieties adoption and food security implications in the semi-arid areas of West Africa," Food Security: The Science, Sociology and Economics of Food Production and Access to Food, Springer;The International Society for Plant Pathology, vol. 14(3), pages 709-728, June.
    12. 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.
    13. Rosina Wanyama & Pepijn Schreinemachers & Justus Ochieng’ & Omary Bwambo & Roselyne Alphonce & Fekadu Fufa Dinssa & Ya-ping Lin & Roland Schafleitner, 2023. "Adoption and impact of improved amaranth cultivars in Tanzania using DNA fingerprinting," Food Security: The Science, Sociology and Economics of Food Production and Access to Food, Springer;The International Society for Plant Pathology, vol. 15(5), pages 1185-1196, October.
    14. Manda, Julius & Tufa, Adane & Alene, Arega & Swai, Elirehema & Muthoni, Francis & Hoeschle-Zeledon, Irmgard & Mateete, Bekunda, 2021. "The Average and Distributional Impacts of Soil and Water Conservation Technologies on the Welfare of Smallholder Farmers in Tanzania," 2021 Conference, August 17-31, 2021, Virtual 314992, International Association of Agricultural Economists.
    15. 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.
    16. 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.
    17. 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).
    18. 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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