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Can diaries help in improving agricultural production statistics? Evidence from Uganda

Author

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  • Deininger, Klaus
  • Carletto, Calogero
  • Savastano, Sara
  • Muwonge, James

Abstract

Although good and timely information on agricultural production is critical for policy-decisions, the quality of underlying data is often low and improving data quality could have high payoff. We use data from a production diary, administered concurrently with a standard household survey in Uganda to analyze the nature and incidence of responses, the magnitude of differences in reported outcomes, and factors that systematically affect these. Despite limited central supervision, diaries elicited a strong response, complemented standard surveys in a number of respects and were less affected by problems of respondent fatigue than expected. The diary-based estimates of output value consistently exceed that from the recall-based production survey, in line with reported disposition. Implications for policy and practical administration of surveys are drawn out.

Suggested Citation

  • 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.
  • Handle: RePEc:eee:deveco:v:98:y:2012:i:1:p:42-50 DOI: 10.1016/j.jdeveco.2011.05.007
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    References listed on IDEAS

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    1. Beegle, Kathleen & De Weerdt, Joachim & Friedman, Jed & Gibson, John, 2012. "Methods of household consumption measurement through surveys: Experimental results from Tanzania," Journal of Development Economics, Elsevier, vol. 98(1), pages 3-18.
    2. Gibson, John, 2002. " Why Does the Engel Method Work? Food Demand, Economies of Size and Household Survey Methods," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 64(4), pages 341-359, September.
    3. Ravallion, Martin & Chen, Shaohua, 2007. "China's (uneven) progress against poverty," Journal of Development Economics, Elsevier, pages 1-42.
    4. Naeem Ahmed & Matthew Brzozowski & Thomas Crossley, 2006. "Measurement errors in recall food consumption data," IFS Working Papers W06/21, Institute for Fiscal Studies.
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    Blog mentions

    As found by EconAcademics.org, the blog aggregator for Economics research:
    1. Presumed poorer until proven net-seller: measuring who wins and who loses from high food prices
      by Gero Carletto in Development Impact on 2012-09-19 15:09:20

    Citations

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    Cited by:

    1. Florence Kondylis & Valerie Mueller & S. Zhu, 2015. "Measuring agricultural knowledge and adoption," Agricultural Economics, International Association of Agricultural Economists, vol. 46(3), pages 449-462, May.
    2. Ugo Pica-Ciamarra & Derek Baker & Nancy Morgan & Alberto Zezza & Carlo Azzarri & Cheikh Ly & Longin Nsiima & Simplice Nouala & Patrick Okello & Joseph Sserugga, 2014. "Investing in the Livestock Sector : Why Good Numbers Matter, A Sourcebook for Decision Makers on How to Improve Livestock Data," World Bank Other Operational Studies 17830, The World Bank.
    3. 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.

    More about this item

    Keywords

    Survey design; Agriculture; Africa;

    JEL classification:

    • O13 - Economic Development, Innovation, Technological Change, and Growth - - Economic Development - - - Agriculture; Natural Resources; Environment; Other Primary Products
    • 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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