IDEAS home Printed from https://ideas.repec.org/p/wbk/wbrwps/7058.html
   My bibliography  Save this paper

Measuring agricultural knowledge and adoption

Author

Listed:
  • Kondylis,Florence
  • Mueller, Valerie
  • Zhu, Siyao Jessica

Abstract

Understanding the trade-offs in improving the precision of agricultural measures through survey design is crucial. Yet, standard indicators used to determine program effectiveness may be flawed and at a differential rate for men and women. The authors use a household survey from Mozambique to estimate the measurement error from male and female self-reports of their adoption and knowledge of three practices: intercropping, mulching, and strip tillage. Despite clear differences in human and physical capital, there are no obvious differences in the knowledge, adoption, and error in self-reporting between men and women. Having received training unanimously lowers knowledge misreports and increases adoption misreports. Other determinants of reporting error differ by gender. Misreporting is positively associated with a greater number of plots for men. Recall decay on measures of knowledge appears prominent among men but not women. Findings from regression and cost-effectiveness analyses always favor the collection of objective measures of knowledge. Given the lowest rate of accuracy for adoption was around 80 percent, costlier objective adoption measures are recommended for a subsample in regions with heterogeneous farm sizes.

Suggested Citation

  • Kondylis,Florence & Mueller, Valerie & Zhu, Siyao Jessica, 2014. "Measuring agricultural knowledge and adoption," Policy Research Working Paper Series 7058, The World Bank.
  • Handle: RePEc:wbk:wbrwps:7058
    as

    Download full text from publisher

    File URL: http://www-wds.worldbank.org/external/default/WDSContentServer/WDSP/IB/2014/10/08/000158349_20141008152903/Rendered/PDF/WPS7058.pdf
    Download Restriction: no
    ---><---

    Other versions of this item:

    References listed on IDEAS

    as
    1. Michael Baker & Mark Stabile & Catherine Deri, 2004. "What Do Self-Reported, Objective, Measures of Health Measure?," Journal of Human Resources, University of Wisconsin Press, vol. 39(4).
    2. 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.
    3. Johnston, David W. & Propper, Carol & Shields, Michael A., 2009. "Comparing subjective and objective measures of health: Evidence from hypertension for the income/health gradient," Journal of Health Economics, Elsevier, vol. 28(3), pages 540-552, May.
    4. Pender, John L. & Nkonya, Ephraim M. & Kato, Edward & Kaizzi, Crammer & Ssali, Henry, 2009. "Impacts of Cash Crop Production on Land Management and Land Degradation: The Case of Coffee and Cotton in Uganda," 2009 Conference, August 16-22, 2009, Beijing, China 50760, International Association of Agricultural Economists.
    5. Beegle, Kathleen & Carletto, Calogero & Himelein, Kristen, 2012. "Reliability of recall in agricultural data," Journal of Development Economics, Elsevier, vol. 98(1), pages 34-41.
    6. Kondylis, Florence & Mueller, Valerie & Zhu, Jessica, 2017. "Seeing is believing? Evidence from an extension network experiment," Journal of Development Economics, Elsevier, vol. 125(C), pages 1-20.
    7. de Nicola, Francesca & Giné, Xavier, 2014. "How accurate are recall data? Evidence from coastal India," Journal of Development Economics, Elsevier, vol. 106(C), pages 52-65.
    8. Ragasa, Catherine, 2012. "Gender and Institutional Dimensions of Agricultural Technology Adoption: A Review of Literature and Synthesis of 35 Case Studies," 2012 Conference, August 18-24, 2012, Foz do Iguacu, Brazil 126747, International Association of Agricultural Economists.
    9. F. Bailey Norwood & Jayson L. Lusk, 2011. "Social Desirability Bias in Real, Hypothetical, and Inferred Valuation Experiments," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 93(2), pages 528-534.
    10. Deininger, Klaus & Carletto, Calogero & Savastano, Sara & Muwonge, James, 2012. "Can diaries help in improving agricultural production statistics? Evidence from Uganda," Journal of Development Economics, Elsevier, vol. 98(1), pages 42-50.
    11. Andre Croppenstedt & Markus Goldstein & Nina Rosas, 2013. "Gender and Agriculture: Inefficiencies, Segregation, and Low Productivity Traps," The World Bank Research Observer, World Bank, vol. 28(1), pages 79-109, February.
    12. House, Lisa & Lusk, Jayson L. & Jaeger, Sara & Traill, W. Bruce & Moore, Melissa & Valli, Carlotta & Morrow, Bert & Yee, Wallace M.S., 2004. "Objective And Subjective Knowledge: Impacts On Consumer Demand For Genetically Modified Foods In The United States And The European Union," 2004 Annual meeting, August 1-4, Denver, CO 20125, American Agricultural Economics Association (New Name 2008: Agricultural and Applied Economics Association).
    13. Butler, J S, et al, 1987. "Measurement Error in Self-reported Health Variables," The Review of Economics and Statistics, MIT Press, vol. 69(4), pages 644-650, November.
    14. Uaiene, Rafael N. & Arndt, Channing, 2009. "Farm Household Efficiency In Mozambique," 2009 Conference, August 16-22, 2009, Beijing, China 51438, International Association of Agricultural Economists.
    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. Denise Hörner & Adrien Bouguen & Markus Frölich & Meike Wollni, 2019. "The Effects of Decentralized and Video-based Extension on the Adoption of Integrated Soil Fertility Management – Experimental Evidence from Ethiopia," NBER Working Papers 26052, National Bureau of Economic Research, Inc.
    2. Alix-Garcia, Jennifer M. & Sims, Katharine R.E. & Costica, Laura, 2021. "Better to be indirect? Testing the accuracy and cost-savings of indirect surveys," World Development, Elsevier, vol. 142(C).
    3. Khushbu Mishra & Abdoul G. Sam & Gracious M. Diiro & Mario J. Miranda, 2020. "Gender and the dynamics of technology adoption: Empirical evidence from a household‐level panel data," Agricultural Economics, International Association of Agricultural Economists, vol. 51(6), pages 857-870, November.
    4. Tewodaj Mogues & Valerie Mueller & Florence Kondylis, 2019. "Cost-effectiveness of community-based gendered advisory services to farmers: Analysis in Mozambique and Tanzania," PLOS ONE, Public Library of Science, vol. 14(3), pages 1-32, March.
    5. Kondylis, Florence & Mueller, Valerie & Zhu, Jessica, 2017. "Seeing is believing? Evidence from an extension network experiment," Journal of Development Economics, Elsevier, vol. 125(C), pages 1-20.
    6. Okello, Julius & Shikuku, Kelvin Mashisia & Lagerkvist, Carl Johan & Rommel, Jens & Jogo, Wellington & Ojwang, Sylvester & Namanda, Sam & Elungat, James, 2023. "Social incentives as nudges for agricultural knowledge diffusion and willingness to pay for certified seeds: Experimental evidence from Uganda," Food Policy, Elsevier, vol. 120(C).
    7. Rachid Laaja & Karen Macours, 2021. "Measuring Skills in Developing Countries," Journal of Human Resources, University of Wisconsin Press, vol. 56(4), pages 1254-1295.
    8. Shikuku, K.M., 2018. "Information exchange links, knowledge exposure, and adoption of agricultural technologies in Northern Uganda," 2018 Conference, July 28-August 2, 2018, Vancouver, British Columbia 275974, International Association of Agricultural Economists.
    9. Shikuku, Kelvin Mashisia & Okello, Julius Juma & Wambugu, Stella & Sindi, Kirimi & Low, Jan W. & McEwan, Margaret, 2019. "Nutrition and food security impacts of quality seeds of biofortified orange-fleshed sweetpotato: Quasi-experimental evidence from Tanzania," World Development, Elsevier, vol. 124(C), pages 1-1.
    10. 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.

    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. Chaoran Chen & Diego Restuccia & Raül Santaeulàlia-Llopis, 2023. "Land Misallocation and Productivity," American Economic Journal: Macroeconomics, American Economic Association, vol. 15(2), pages 441-465, April.
    2. 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.
    3. David Johnston & Carol Propper & Stephen Pudney & Michael Shields, 2014. "Child Mental Health And Educational Attainment: Multiple Observers And The Measurement Error Problem," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 29(6), pages 880-900, September.
    4. Wollburg, Philip & Tiberti, Marco & Zezza, Alberto, 2021. "Recall length and measurement error in agricultural surveys," Food Policy, Elsevier, vol. 100(C).
    5. Calogero Carletto & Dean Jolliffe & Raka Banerjee, 2015. "From Tragedy to Renaissance: Improving Agricultural Data for Better Policies," Journal of Development Studies, Taylor & Francis Journals, vol. 51(2), pages 133-148, February.
    6. Pudney, Stephen & Propper, Carol & W. Johnston, David & A. Shields, Michael, 2010. "Is there an income gradient in child health? It depends whom you ask," ISER Working Paper Series 2010-08, Institute for Social and Economic Research.
    7. Godlonton, Susan & Hernandez, Manuel A. & Paz, Cynthia, 2021. "Can survey design reduce anchoring bias in recall data? Evidence from Malawi," IFPRI discussion papers 2055, International Food Policy Research Institute (IFPRI).
    8. Federico Belotti & Joanna Kopinska & Alessandro Palma & Andrea Piano Mortari, 2022. "Health status and the Great Recession. Evidence from electronic health records," Health Economics, John Wiley & Sons, Ltd., vol. 31(8), pages 1770-1799, August.
    9. Yokoo, Hide-Fumi & Arimura, Toshi H. & Chattopadhyay, Mriduchhanda & Katayama, Hajime, 2023. "Subjective risk belief function in the field: Evidence from cooking fuel choices and health in India," Journal of Development Economics, Elsevier, vol. 161(C).
    10. van Ooijen, R. & Alessi, R. & Knoef, M., 2015. "Health status over the life cycle," Health, Econometrics and Data Group (HEDG) Working Papers 15/21, HEDG, c/o Department of Economics, University of York.
    11. Vaiknoras, Kate A. & Larochelle, Catherine & Alwang, Jeffrey, 2021. "How the adoption of drought-tolerant rice varieties impacts households in a non-drought year: Evidence from Nepal," 2021 Annual Meeting, August 1-3, Austin, Texas 313877, Agricultural and Applied Economics Association.
    12. Cinzia Di Novi & Anna Marenzi & Dino Rizzi, 2018. "Do healthcare tax credits help poor-health individuals on low incomes?," The European Journal of Health Economics, Springer;Deutsche Gesellschaft für Gesundheitsökonomie (DGGÖ), vol. 19(2), pages 293-307, March.
    13. Alistair Munro, 2020. "Using experimental manipulation of questionnaire design and a Kenyan panel to test for the reliability of reported perceptions of climate change and adaptation," Climatic Change, Springer, vol. 162(3), pages 1081-1105, October.
    14. Datta Gupta, Nabanita & Jürges, Hendrik, 2012. "Do workers underreport morbidity? The accuracy of self-reports of chronic conditions," Social Science & Medicine, Elsevier, vol. 75(9), pages 1589-1594.
    15. Ziebarth, Nicolas, 2010. "Measurement of health, health inequality, and reporting heterogeneity," Social Science & Medicine, Elsevier, vol. 71(1), pages 116-124, July.
    16. Roozbei Hosseini & Karen Kopecky & Kai Zhao, 2022. "The Evolution of Health over the Life Cycle," Review of Economic Dynamics, Elsevier for the Society for Economic Dynamics, vol. 45, pages 237-263, July.
    17. Kilic, Talip & Moylan, Heather & Ilukor, John & Mtengula, Clement & Pangapanga-Phiri, Innocent, 2021. "Root for the tubers: Extended-harvest crop production and productivity measurement in surveys," Food Policy, Elsevier, vol. 102(C).
    18. Johnston, David W. & Propper, Carol & Shields, Michael A., 2009. "Comparing subjective and objective measures of health: Evidence from hypertension for the income/health gradient," Journal of Health Economics, Elsevier, vol. 28(3), pages 540-552, May.
    19. Vaiknoras, Kate & Larochelle, Catherine & Alwang, Jeffrey, 2020. "IFAD RESERACH SERIES 64 - How the adoption of drought-tolerant rice varieties impacts households in a non-drought year: Evidence from Nepal," IFAD Research Series 308809, International Fund for Agricultural Development (IFAD).
    20. Roozbei Hosseini & Karen Kopecky & Kai Zhao, 2022. "The Evolution of Health over the Life Cycle," Review of Economic Dynamics, Elsevier for the Society for Economic Dynamics, vol. 45, pages 237-263, July.

    More about this item

    Keywords

    Agricultural Knowledge and Information Systems; Primary Education; Population Policies; Rural Development Knowledge&Information Systems; Crops and Crop Management Systems;
    All these keywords.

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    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:wbk:wbrwps:7058. 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: Roula I. Yazigi (email available below). General contact details of provider: https://edirc.repec.org/data/dvewbus.html .

    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.