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Survey instruments and the reports of consumption expenditures: evidence from the consumer expenditure surveys

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  • Erich Battistin
  • Mario Padula

Abstract

type="main" xml:id="rssa12127-abs-0001"> The paper uses data from the consumer expenditure surveys to demonstrate that the mode of collection is important for the analysis of consumption data. We first show that population figures obtained with diaries markedly differ from figures obtained by using recall questions. We then exploit multiple measurements of food expenditure to identify the effects of the mode of collection on the distribution of reported consumption. Finally, we show how to combine information from multiple reports to obtain a single measure of total expenditure in consumer expenditure surveys. The paper concludes by offering guidelines for empirical analyses based on these data, and by providing an application of the methods proposed to the measurement of inequality and wellbeing.

Suggested Citation

  • Erich Battistin & Mario Padula, 2016. "Survey instruments and the reports of consumption expenditures: evidence from the consumer expenditure surveys," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 179(2), pages 559-581, February.
    • Handle: RePEc:bla:jorssa:v:179:y:2016:i:2:p:559-581
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    Cited by:

    1. Marcin Hitczenko, 2013. "Optimal recall period length in consumer payment surveys," Working Papers 13-16, Federal Reserve Bank of Boston.
    2. Olivier Coibion & Yuriy Gorodnichenko & Dmitri Koustas, 2021. "Consumption Inequality and the Frequency of Purchases," American Economic Journal: Macroeconomics, American Economic Association, vol. 13(4), pages 449-482, October.
    3. Li‐Chun Zhang, 2021. "Proxy expenditure weights for Consumer Price Index: Audit sampling inference for big‐data statistics," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 184(2), pages 571-588, April.
    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. Campos, Rodolfo G. & Reggio, Iliana, 2014. "Measurement error in imputation procedures," Economics Letters, Elsevier, vol. 122(2), pages 197-202.
    6. Giacomo De Giorgi & Luca Gambetti, 2017. "Business Cycle Fluctuations and the Distribution of Consumption," Review of Economic Dynamics, Elsevier for the Society for Economic Dynamics, vol. 23, pages 19-41, January.
    7. Giacomo De Giorgi & Luca Gambetti, 2012. "Consumption Heterogeneity over the Business Cycle," Working Papers 646, Barcelona School of Economics.
    8. Campos, Rodolfo G., 2013. "Measurement error and imputation of consumption in survey data," UC3M Working papers. Economics we1219, Universidad Carlos III de Madrid. Departamento de Economía.
    9. Giacomo De Giorgi & Luca Gambetti, 2012. "The Effects of Government Spending on the Distribution of Consumption," Working Papers 645, Barcelona School of Economics.
    10. Scrimgeour, Dean & Gorry, James, 2015. "Using Engel Curves to Estimate CPI Bias for the Elderly," Working Papers 2015-03, Department of Economics, Colgate University, revised 08 Jun 2015.
    11. Olga Gorbachev, 2011. "Did Household Consumption Become More Volatile?," American Economic Review, American Economic Association, vol. 101(5), pages 2248-2270, August.
    12. Rodolfo G. Campos & Iliana Reggio & Dionisio Garc𫑐, 2013. "Micro versus macro consumption data: the cyclical properties of the consumer expenditure survey," Applied Economics, Taylor & Francis Journals, vol. 45(26), pages 3778-3785, September.
    13. Campos, Rodolfo G. & García-Píriz, Dionisio, 2012. "Micro vs. macro consumption data : the cyclical properties of the consumer expenditure survey," UC3M Working papers. Economics we1220, Universidad Carlos III de Madrid. Departamento de Economía.
    14. Justine Hastings & Jesse M. Shapiro, 2018. "How Are SNAP Benefits Spent? Evidence from a Retail Panel," American Economic Review, American Economic Association, vol. 108(12), pages 3493-3540, December.
    15. Li-Chun Zhang, 2019. "Proxy expenditure weights for Consumer Price Index: Audit sampling inference for big data statistics," Papers 1906.11208, arXiv.org.

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    More about this item

    JEL classification:

    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models
    • D12 - Microeconomics - - Household Behavior - - - Consumer Economics: Empirical Analysis

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