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Root for the Tubers : Extended-Harvest Crop Production and Productivity Measurement in Surveys

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  • Kilic,Talip
  • Moylan,Heather G.
  • Ilukor,John
  • Mtengula,Clement
  • Pangapanga-Phiri,Innocent

Abstract

To document the relative accuracy of methods for microdata collection on root and tuber crop production, an experiment was implemented in Malawi over a 12-month period, randomly assigning cassava-producing households to one of four approaches: daily diary-keeping, with semi-weekly supervision visits; daily diary-keeping, with semi-weekly supervisory phone calls; two six-month recall interviews, with six months in between; and a single 12-month recall interview. Lapses in diary-keeping can underestimate true production, albeit to a lesser degree compared to recall. And the comparisons between the diary variants and the variation in underestimation by recall period are unclear ex ante. The analysis reveals that compared to traditional diary-keeping, the household-level annual cassava production is 295 kilograms higher, on average, (and assumed as closer to the truth) under diary-keeping with phone calls. This effect corresponds to 28 percent of the average traditional diary-keeping production estimate. Although the difference between the estimates based on six-month recall and traditional diary-keeping is statistically insignificant, 12-month recall underestimates annual production, on average, by 516 kilograms and 221 kilograms, respectively, compared to diary-keeping with phone calls and traditional diary-keeping. While the recall-based approaches both underestimate true production, six-month recall does so to a lesser extent. The evidence additionally demonstrates likely gross overestimation in international and ministerial statistics on cassava yields in Malawi. For improved microdata on root and tuber crop production, the adoption of (i) diary-keeping with phone calls (particularly if deployed in a broader mobile phone?based survey) or (ii) six-month recall, as a second-best alternative, is recommended.

Suggested Citation

  • Kilic,Talip & Moylan,Heather G. & Ilukor,John & Mtengula,Clement & Pangapanga-Phiri,Innocent, 2018. "Root for the Tubers : Extended-Harvest Crop Production and Productivity Measurement in Surveys," Policy Research Working Paper Series 8618, The World Bank.
  • Handle: RePEc:wbk:wbrwps:8618
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    Cited by:

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    2. Maxwell Mkondiwa & Jeffrey Apland, 2022. "Inter-district food flows in Malawi," Food Security: The Science, Sociology and Economics of Food Production and Access to Food, Springer;The International Society for Plant Pathology, vol. 14(6), pages 1553-1568, December.
    3. André, Pierre & Delesalle, Esther & Dumas, Christelle, 2021. "Returns to farm child labor in Tanzania," World Development, Elsevier, vol. 138(C).
    4. Michler, Jeffrey D. & Josephson, Anna & Kilic, Talip & Murray, Siobhan, 2022. "Privacy protection, measurement error, and the integration of remote sensing and socioeconomic survey data," Journal of Development Economics, Elsevier, vol. 158(C).
    5. Anderson, Ellen & Lybbert, Travis J. & Shenoy, Ashish & Singh, Rupika & Stein, Daniel, 2024. "Does survey mode matter? Comparing in-person and phone agricultural surveys in India," Journal of Development Economics, Elsevier, vol. 166(C).
    6. 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.
    7. Dang, Hai-Anh H & Kilic, Talip & Hlasny, Vladimir & Abanokova, Kseniya & Carletto, Calogero, 2024. "Using Survey-to-Survey Imputation to Fill Poverty Data Gaps at a Low Cost: Evidence from a Randomized Survey Experiment," IZA Discussion Papers 16792, Institute of Labor Economics (IZA).
    8. 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.
    9. Gourlay, Sydney & Kilic, Talip & Martuscelli, Antonio & Wollburg, Philip & Zezza, Alberto, 2021. "Viewpoint: High-frequency phone surveys on COVID-19: Good practices, open questions," Food Policy, Elsevier, vol. 105(C).
    10. Wollburg, Philip & Tiberti, Marco & Zezza, Alberto, 2021. "Recall length and measurement error in agricultural surveys," Food Policy, Elsevier, vol. 100(C).
    11. Anna Josephson & Jeffrey D. Michler & Talip Kilic & Siobhan Murray, 2024. "The Mismeasure of Weather: Using Remotely Sensed Earth Observation Data in Economic Context," Papers 2409.07506, arXiv.org.
    12. Jeffrey D. Michler & Dewan Abdullah Al Rafi & Jonathan Giezendanner & Anna Josephson & Valerien O. Pede & Elizabeth Tellman, 2024. "Impact Evaluations in Data Poor Settings: The Case of Stress-Tolerant Rice Varieties in Bangladesh," Papers 2409.02201, arXiv.org.
    13. Yacoubou Djima, Ismael & Kilic, Talip, 2024. "Attenuating measurement errors in agricultural productivity analysis by combining objective and self-reported survey data," Journal of Development Economics, Elsevier, vol. 168(C).
    14. Zezza,Alberto & Mcgee,Kevin Robert & Wollburg,Philip Randolph & Assefa,Thomas Woldu & Gourlay,Sydney, 2022. "From Necessity to Opportunity : Lessons for Integrating Phone and In-Person Data Collectionfor Agricultural Statistics in a Post-Pandemic World," Policy Research Working Paper Series 10168, The World Bank.

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    More about this item

    Keywords

    Crops and Crop Management Systems; Climate Change and Agriculture; Food Security; Primary Metals; Telecommunications Infrastructure; Nutrition;
    All these keywords.

    JEL classification:

    • C83 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Survey Methods; Sampling Methods
    • 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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