We randomly assigned eight different consumption surveys to obtain evidence on the nature of measurement errors in estimates of household consumption. Regressions using data from more error-prone designs are compared with results from a ‘gold standard’ survey. Measurement errors appear to have a mean-reverting negative correlation with true consumption, especially for food and especially for rural households."> We randomly assigned eight different consumption surveys to obtain evidence on the nature of measurement errors in estimates of household consumption. Regressions using data from more error-prone designs are compared with results from a ‘gold standard’ survey. Measurement errors appear to have a mean-reverting negative correlation with true consumption, especially for food and especially for rural households."> We randomly assigned eight different consumption surveys to obtain evidence on the nature of measurement errors in estimates of household ">
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What does Variation in Survey Design Reveal about the Nature of Measurement Errors in Household Consumption?

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Listed:
  • John Gibson
  • Kathleen Beegle
  • Joachim De Weerdt
  • Jed Friedman
Abstract
type="main" xml:id="obes12066-abs-0001"> We randomly assigned eight different consumption surveys to obtain evidence on the nature of measurement errors in estimates of household consumption. Regressions using data from more error-prone designs are compared with results from a ‘gold standard’ survey. Measurement errors appear to have a mean-reverting negative correlation with true consumption, especially for food and especially for rural households.

Suggested Citation

  • John Gibson & Kathleen Beegle & Joachim De Weerdt & Jed Friedman, 2015. "What does Variation in Survey Design Reveal about the Nature of Measurement Errors in Household Consumption?," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 77(3), pages 466-474, June.
    • Handle: RePEc:bla:obuest:v:77:y:2015:i:3:p:466-474
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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. John Gibson & Bonggeun Kim, 2010. "Non‐Classical Measurement Error in Long‐Term Retrospective Recall Surveys," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 72(5), pages 687-695, October.
    3. Andrew Chesher & Christian Schluter, 2002. "Welfare Measurement and Measurement Error," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 69(2), pages 357-378.
    4. Bound, John & Krueger, Alan B, 1991. "The Extent of Measurement Error in Longitudinal Earnings Data: Do Two Wrongs Make a Right?," Journal of Labor Economics, University of Chicago Press, vol. 9(1), pages 1-24, January.
    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. Pischke, Jorn-Steffen, 1995. "Measurement Error and Earnings Dynamics: Some Estimates from the PSID Validation Study," Journal of Business & Economic Statistics, American Statistical Association, vol. 13(3), pages 305-314, July.
    7. Menno Pradhan, 2001. "Welfare Analysis with a Proxy Consumption Measure – Evidence from a Repeated Experiment in Indonesia," Tinbergen Institute Discussion Papers 01-092/2, Tinbergen Institute.
    8. Shahidur R. Khandker, 2005. "Microfinance and Poverty: Evidence Using Panel Data from Bangladesh," The World Bank Economic Review, World Bank, vol. 19(2), pages 263-286.
    9. 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.
    10. 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.
    11. repec:bla:obuest:v:64:y:2002:i:4:p:341-59 is not listed on IDEAS
    12. 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.
    13. 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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    More about this item

    JEL classification:

    • C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models
    • C81 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Methodology for Collecting, Estimating, and Organizing Microeconomic Data; Data Access
    • D12 - Microeconomics - - Household Behavior - - - Consumer Economics: Empirical Analysis

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