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Reliability of recall in agricultural data

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Listed:
  • Beegle,Kathleen G.
  • Carletto,Calogero
  • Kastelic,Kristen Himelein
  • Beegle,Kathleen G.
  • Carletto,Calogero
  • Kastelic,Kristen Himelein

Abstract

Despite the importance of agriculture to economic development, and a vast accompanying literature on the subject, little research has been done on the quality of the underlying data. Due to survey logistics, agricultural data are usually collected by asking respondents to recall the details of events occurring during past agricultural seasons that took place a number of months prior to the interview. This gap can lead to recall bias in reported data on agricultural activities. The problem is further complicated when interviews are conducted over the course of several months, thus leading to recall of variable length. To test for such recall bias, the length of time between harvest and interview is examined for three African countries with respect to several common agricultural input and harvest measures. The analysis shows little evidence of recall bias impacting data quality. There is some indication that more salient events are less subject to recall decay. Overall, the results allay some concerns about the quality of some types of agricultural data collected through recall over lengthy periods.

Suggested Citation

  • Beegle,Kathleen G. & Carletto,Calogero & Kastelic,Kristen Himelein & Beegle,Kathleen G. & Carletto,Calogero & Kastelic,Kristen Himelein, 2011. "Reliability of recall in agricultural data," Policy Research Working Paper Series 5671, The World Bank.
  • Handle: RePEc:wbk:wbrwps:5671
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    References listed on IDEAS

    as
    1. Margaret Grosh & Paul Glewwe, 2000. "Designing Household Survey Questionnaires for Developing Countries," World Bank Publications - Books, The World Bank Group, number 25338.
    2. Davis, Benjamin & Winters, Paul & Carletto, Gero & Covarrubias, Katia & Quiñones, Esteban J. & Zezza, Alberto & Stamoulis, Kostas & Azzarri, Carlo & DiGiuseppe, Stefania, 2010. "A Cross-Country Comparison of Rural Income Generating Activities," World Development, Elsevier, vol. 38(1), pages 48-63, January.
    3. Jeffrey M Wooldridge, 2010. "Econometric Analysis of Cross Section and Panel Data," MIT Press Books, The MIT Press, edition 2, volume 1, number 0262232588, April.
    4. 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.
    5. John Gibson, 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.
    6. George Judge & Laura Schechter, 2009. "Detecting Problems in Survey Data Using Benford’s Law," Journal of Human Resources, University of Wisconsin Press, vol. 44(1).
    7. James P. Smith & Duncan Thomas, 2003. "Remembrances of things past: test–retest reliability of retrospective migration histories," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 166(1), pages 23-49, February.
    8. Fermont, Anneke & Benson, Todd, 2011. "Estimating yield of food crops grown by smallholder farmers: A review in the Uganda context," IFPRI discussion papers 1097, International Food Policy Research Institute (IFPRI).
    9. repec:bla:obuest:v:64:y:2002:i:4:p:341-59 is not listed on IDEAS
    10. John Gibson & Bonggeun Kim, 2007. "Measurement Error in Recall Surveys and the Relationship between Household Size and Food Demand," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 89(2), pages 473-489.
    Full references (including those not matched with items on IDEAS)

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    More about this item

    Keywords

    Climate Change and Agriculture; Crops and Crop Management Systems; Educational Sciences; Food Security; Economics and Gender; Gender and Economic Policy; Gender and Poverty; Gender and Economics; Labor&Employment Law;
    All these keywords.

    JEL classification:

    • Q12 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Agriculture - - - Micro Analysis of Farm Firms, Farm Households, and Farm Input Markets
    • O12 - Economic Development, Innovation, Technological Change, and Growth - - Economic Development - - - Microeconomic Analyses of Economic Development

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