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Heaping and leaping: survey response behaviour and the dynamics of self-reported consumption expenditure

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  • Pudney, Stephen

Abstract

Survey respondents often use simple strategies to answer retrospective questions about their level of consumption expenditure, resulting in the heaping of data at certain round numbers. In the panel context, wave-to-wave ‘leaping’ from one ‘heap’ to another can distort the sample pattern of consumption movements over time. Using BHPS energy expenditure data, we show that respondents use a variety of different response strategies, often switching between those strategies. We estimate a joint model of the dynamics of consumption and response behaviour and find that neglect of response behaviour can lead to serious biases in empirical studies of consumption dynamics.

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  • Pudney, Stephen, 2008. "Heaping and leaping: survey response behaviour and the dynamics of self-reported consumption expenditure," ISER Working Paper Series 2008-09, Institute for Social and Economic Research.
    • Handle: RePEc:ese:iserwp:2008-09
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    3. Giulia Cifaldi & Andrea Neri, 2013. "Asking income and consumption questions in the same survey: what are the risks?," Temi di discussione (Economic working papers) 908, Bank of Italy, Economic Research and International Relations Area.
    4. 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.
    5. Sonja C. Kassenboehmer & Stefanie Schurer, 2018. "Survey item-response behavior as an imperfect proxy for unobserved ability: Theory and application," Working Papers 2018-035, Human Capital and Economic Opportunity Working Group.
    6. Waddams Price, Catherine & Brazier, Karl & Wang, Wenjia, 2012. "Objective and subjective measures of fuel poverty," Energy Policy, Elsevier, vol. 49(C), pages 33-39.
    7. Jorge Valero-Gil & Magali Valero, 2013. "Nutritional Intake and Poverty in Mexico: 1984--2010," Journal of Development Studies, Taylor & Francis Journals, vol. 49(10), pages 1375-1396, October.
    8. Binder, Carola C., 2017. "Measuring uncertainty based on rounding: New method and application to inflation expectations," Journal of Monetary Economics, Elsevier, vol. 90(C), pages 1-12.
    9. Andrea Neri & Roberta Zizza, 2010. "Income reporting behaviour in sample surveys," Temi di discussione (Economic working papers) 777, Bank of Italy, Economic Research and International Relations Area.
    10. Eid, Nourhan & Maltby, Josephine & Talavera, Oleksandr, 2016. "Income Rounding and Loan Performance in the Peer-to-Peer Market," MPRA Paper 72852, University Library of Munich, Germany.
    11. Jorge N. Valero‐Gil & Magali Valero, 2008. "The effects of rising food prices on poverty in Mexico," Agricultural Economics, International Association of Agricultural Economists, vol. 39(s1), pages 485-496, November.
    12. Zachary H. Seeskin, 2016. "Evaluating the Use of Commercial Data to Improve Survey Estimates of Property Taxes," CARRA Working Papers 2016-06, Center for Economic Studies, U.S. Census Bureau.
    13. Byung-hill Jun & Hosin Song, 2019. "Tests for Detecting Probability Mass Points," Korean Economic Review, Korean Economic Association, vol. 35, pages 205-248.
    14. Helena Meier, Tooraj Jamasb, and Luis Orea, 2013. "Necessity or Luxury Good? Household Energy Spending and Income in Britain 1991-2007," The Energy Journal, International Association for Energy Economics, vol. 0(Number 4).
    15. Atella, Vincenzo & Brugiavini, Agar & Pace, Noemi, 2015. "The health care system reform in China: Effects on out-of-pocket expenses and saving," China Economic Review, Elsevier, vol. 34(C), pages 182-195.
    16. Scott R. Baker & Lorenz Kueng & Steffen Meyer & Michaela Pagel, 2018. "Measurement Error in Imputed Consumption," NBER Working Papers 25078, National Bureau of Economic Research, Inc.
    17. Groß, Marcus & Rendtel, Ulrich, 2015. "Kernel density estimation for heaped data," Discussion Papers 2015/27, Free University Berlin, School of Business & Economics.
    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. Marcus Groß & Ulrich Rendtel & Timo Schmid & Sebastian Schmon & Nikos Tzavidis, 2017. "Estimating the density of ethnic minorities and aged people in Berlin: multivariate kernel density estimation applied to sensitive georeferenced administrative data protected via measurement error," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 180(1), pages 161-183, January.
    20. Superti, Luiz Henrique, 2019. "Effects on Fertility of The Brazilian Cash Transfer Program: Evidence from a Regression Discontinuity Approach," MPRA Paper 104627, University Library of Munich, Germany, revised Oct 2020.

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