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Response bias in survey-based measures of household consumption

Author

Listed:
  • Winter, Joachim

Abstract

An important aspect of household surveys is the design of consumption questions. A controlled experiment shows that a single question on total monthly nondurables expenditure and a design with 35 disaggregated categories produce different results. These differences vary with household characteristics.

Suggested Citation

  • Winter, Joachim, 2003. "Response bias in survey-based measures of household consumption," Munich Reprints in Economics 19725, University of Munich, Department of Economics.
    • Handle: RePEc:lmu:muenar:19725
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    Citations

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    Cited by:

    1. David Comerford & Liam Delaney & Colm Harmon, 2009. "Experimental Tests of Survey Responses to Expenditure Questions," Fiscal Studies, Institute for Fiscal Studies, vol. 30(Special I), pages 419-433, December.
    2. Martin Browning & Thomas F. Crossley & Guglielmo Weber, 2003. "Asking consumption questions in general purpose surveys," Economic Journal, Royal Economic Society, vol. 113(491), pages 540-567, November.
    3. Giuseppe De Luca & Franco Peracchi, 2012. "Estimating Engel curves under unit and item nonresponse," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 27(7), pages 1076-1099, November.
    4. Marcin Hitczenko & Mingzhu Tai, 2014. "Measuring unfamiliar economic concepts: the case of prepaid card adoption," Working Papers 14-9, Federal Reserve Bank of Boston.
    5. Essig, Lothar, 2005. "Imputing total expenditures from a non-exhaustive list of items : an empirical assessment using the SAVE data set," Papers 05-21, Sonderforschungsbreich 504.
    6. Pia R. Pinger, 2017. "Thinking about Tomorrow? Predicting Experimental Choice Behavior and Life Outcomes from a Survey Measure of Present Bias," SOEPpapers on Multidisciplinary Panel Data Research 935, DIW Berlin, The German Socio-Economic Panel (SOEP).
    7. Lan My Le & Gabriela Flores & Tessa Tan-Torres Edejer & Toan Khanh Tran & Chuc Thi Kim Nguyen & Do Thanh Tran & Phuc Dang Ho & Isaiah Awintuen Agorinya & Fabrizio Tediosi & Amanda Ross, 2020. "Investigating the effect of recall period on estimates of inpatient out-of-pocket expenditure from household surveys in Vietnam," PLOS ONE, Public Library of Science, vol. 15(11), pages 1-14, November.
    8. Giuseppe De Luca & Franco Peracchi, 2007. "A sample selection model for unit and item nonresponse in cross-sectional surveys," CEIS Research Paper 95, Tor Vergata University, CEIS.
    9. Pia Rosina Pinger, 2017. "Predicting Experimental Choice Behavior and Life Outcomes from a Survey Measure of Present Bias," Economics Bulletin, AccessEcon, vol. 37(3), pages 2162-2172.
    10. Claire Greene & Shaun O'Brien & Scott Schuh, 2017. "U.S. Consumer Cash Use, 2012 and 2015 : An Introduction to the Diary of Consumer Payment Choice, Research Data Report No. 17-6," Consumer Payments Research Data Reports 2017-06, Federal Reserve Bank of Atlanta.
    11. Lothar Essig, 2005. "Imputing total expenditures from a non-exhaustive list of items: An empirical assessment using the SAVE data set," MEA discussion paper series 05081, Munich Center for the Economics of Aging (MEA) at the Max Planck Institute for Social Law and Social Policy.
    12. Dietrich, Alexander M., 2024. "Consumption categories, household attention, and inflation expectations: Implications for optimal monetary policy," Journal of Monetary Economics, Elsevier, vol. 147(C).
    13. Thomas F. Crossley & Joachim K. Winter, 2014. "Asking Households about Expenditures: What Have We Learned?," NBER Chapters, in: Improving the Measurement of Consumer Expenditures, pages 23-50, National Bureau of Economic Research, Inc.
    14. Essig, Lothar, 2005. "Measures for savings and saving rates in the German SAVE data set," Papers 05-20, Sonderforschungsbreich 504.
    15. Brian Bucks & Karen Pence, 2015. "Wealth, pensions, debt, and savings: Considerations for a panel survey," Journal of Economic and Social Measurement, IOS Press, issue 1-4, pages 151-175.
    16. Prydz, Espen Beer & Jolliffe, Dean & Serajuddin, Umar, 2021. "Mind the Gap," GLO Discussion Paper Series 944, Global Labor Organization (GLO).
    17. Daniel H. Cooper, 2010. "Imputing household spending in the Panel Study of Income Dynamics: a comparison of approaches," Working Papers 10-12, Federal Reserve Bank of Boston.
    18. Sweeney, Sedona & Mukora, Rachel & Candfield, Sophie & Guinness, Lorna & Grant, Alison D. & Vassall, Anna, 2018. "Measuring income for catastrophic cost estimates: Limitations and policy implications of current approaches," Social Science & Medicine, Elsevier, vol. 215(C), pages 7-15.
    19. Lucelly Carolina Meza-Ariza & Martha Torres-Barreto & Leda Paz Muñoz-Molina & Marianela Luzardo Briceño, 2019. "Competitive Advantage: An Approach From The Dynamic Capacities Of Learning, Absorption And Innovation [Ventaja Competitiva: Una Aproximación Desde Las Capacidades Dinámicas De Aprendizaje, Absorció," Working Papers hal-02102996, HAL.
    20. Prydz,Espen Beer,Jolliffe,Dean Mitchell,Serajuddin,Umar, 2021. "Mind the Gap : Disparities in Assessments of Living Standards Using National Accounts and Household Surveys," Policy Research Working Paper Series 9779, The World Bank.
    21. Espen Beer Prydz & Dean Jolliffe & Umar Serajuddin, 2022. "Disparities in Assessments of Living Standards Using National Accounts and Household Surveys," Review of Income and Wealth, International Association for Research in Income and Wealth, vol. 68(S2), pages 385-420, December.
    22. Claire Greene & Shaun O'Brien & Scott Schuh, 2017. "U. S. consumer cash use, 2012 and 2015: an introduction to the Diary of Consumer Payment Choice," Research Data Report 17-6, Federal Reserve Bank of Boston.
    23. Persichina, Marco & Kriström, Bengt, 2022. "Self-selected intervals in psycho-physic experiments and the measurement of willingness to pay," Journal of Behavioral and Experimental Economics (formerly The Journal of Socio-Economics), Elsevier, vol. 98(C).

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    JEL classification:

    • C8 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs

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