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Reliability of recall in agricultural data

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  • Beegle, Kathleen
  • Carletto, Calogero
  • Himelein, Kristen

Abstract

Despite the importance of agriculture to economic development, and a vast accompanying literature on the subject, little research has been done on data quality. Due to survey logistics, agricultural data are usually collected by asking respondents to recall the details of events occurring during past agricultural seasons, potentially leading to recall bias. 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 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 large 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.

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Bibliographic Info

Article provided by Elsevier in its journal Journal of Development Economics.

Volume (Year): 98 (2012)
Issue (Month): 1 ()
Pages: 34-41

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Handle: RePEc:eee:deveco:v:98:y:2012:i:1:p:34-41

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Web page: http://www.elsevier.com/locate/devec

Related research

Keywords: Agriculture; Measurement error; Recall;

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References

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  1. 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).
  2. John Gibson, 2002. "Why Does the Engel Method Work? Food Demand, Economies of Size and Household Survey Methods," Working Papers in Economics 02/02, University of Waikato, Department of Economics.
  3. James P. Smith & Duncan Thomas, 2004. "Remembrances of Things Past: Test-Retest Reliability of Retrospective Migration Histories," Labor and Demography 0403026, EconWPA.
  4. 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.
  5. Jeffrey M. Wooldridge, 2001. "Econometric Analysis of Cross Section and Panel Data," MIT Press Books, The MIT Press, edition 1, volume 1, number 0262232197.
  6. 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.
  7. 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).
  8. 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.
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Citations

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Cited by:
  1. Francesca De Nicola & Xavier Gene, 2012. "How accurate are recall data? Evidence from coastal India," Working Papers id:5010, eSocialSciences.
  2. Zeng, Di & Alwang, Jeffrey & Norton, George & Shiferaw, Bekele & Jaleta, Moti & Yirga, Chilot, 2013. "Ex-Post Impacts of Improved Maize Varieties on Poverty in Rural Ethiopia," 2013 Annual Meeting, August 4-6, 2013, Washington, D.C. 149823, Agricultural and Applied Economics Association.
  3. Klasen, Stephan & Reimers, Malte, 2013. "Looking at Pro-Poor Growth from an Agricultural Perspective," 2013 Annual Meeting, August 4-6, 2013, Washington, D.C. 149745, Agricultural and Applied Economics Association.

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