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What Does Variation in Survey Design Reveal About the Nature of Measurement Errors in Household Consumption?

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  • John Gibson

    ()
    (University of Waikato)

  • Kathleen Beegle

    (World Bank)

  • Joachim De Weerdt

    (EDI Tanzania)

  • Jed Friedman

    (World Bank)

Abstract

We use data from eight different consumption questionnaires randomly assigned to 4,000 households in Tanzania to obtain evidence on the nature of measurement errors in estimates of household consumption. While there are no validation data, the design of one questionnaire and the resources put into its implementation make it likely to be substantially more accurate than the others. Comparing regressions using data from this benchmark design with results from the other questionnaires shows that errors have a negative correlation with the true value of consumption, creating a non-classical measurement error problem for which conventional statistical corrections may be ineffective.

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File URL: ftp://mngt.waikato.ac.nz/RePEc/wai/econwp/1301.pdf
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Bibliographic Info

Paper provided by University of Waikato, Department of Economics in its series Working Papers in Economics with number 13/01.

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Length: 16 pages
Date of creation: 11 Feb 2013
Date of revision:
Handle: RePEc:wai:econwp:13/01

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Keywords: consumption; Engel curves; household surveys; measurement error; Tanzania;

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  1. Alderman, Harold & Hoogeveen, Hans & Rossi, Mariacristina, 2006. "Reducing child malnutrition in Tanzania: Combined effects of income growth and program interventions," Economics & Human Biology, Elsevier, vol. 4(1), pages 1-23, January.
  2. John Gibson & Bonggeun Kim, 2009. "Non-Classical Measurement Error in Long-Term Retrospective Recall Surveys," CIRJE F-Series CIRJE-F-658, CIRJE, Faculty of Economics, University of Tokyo.
  3. 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 341-59, September.
  4. Beegle, Kathleen & De Weerdt, Joachim & Friedman, Jed & Gibson, John, 2010. "Methods of household consumption measurement through surveys : experimental results from Tanzania," Policy Research Working Paper Series 5501, The World Bank.
  5. Angus Deaton & Christina Paxson, 1998. "Economies of Scale, Household Size, and the Demand for Food," Journal of Political Economy, University of Chicago Press, vol. 106(5), pages 897-930, October.
  6. John Bound & Alan B. Krueger, 1989. "The Extent of Measurement Error In Longitudinal Earnings Data: Do Two Wrongs Make A Right?," NBER Working Papers 2885, National Bureau of Economic Research, Inc.
  7. Khandker, Shahidur R., 2003. "Microfinance and poverty - evidence using panel data from Bangladesh," Policy Research Working Paper Series 2945, The World Bank.
  8. Naeem Ahmed & Matthew Brzozowski & Thomas Crossley, 2006. "Measurement errors in recall food consumption data," IFS Working Papers W06/21, Institute for Fiscal Studies.
  9. Andrew Chesher & Christian Schluter, 2002. "Welfare Measurement and Measurement Error," Review of Economic Studies, Oxford University Press, vol. 69(2), pages 357-378.
  10. Pischke, J.S., 1994. "Measurement Error and Earnings Dynamics: Some Estimates from the PSID Validation Study," Working papers 94-01, Massachusetts Institute of Technology (MIT), Department of Economics.
  11. 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.
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