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Measuring Total Household Spending in a Monthly Internet Survey Evidence from the American Life Panel

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  • Michael D. Hurd
  • Susann Rohwedder

Abstract

Beginning in May 2009 the authors fielded a monthly Internet survey designed to measure total household spending as the aggregate of about 40 spending components. This paper reports on a number of outcomes from 30 waves of data collection. These outcomes include sample attrition, indicators of data quality such as item nonresponse and the variance in total spending, and substantive results such as the trajectory of total spending and the trajectories of some components of spending. They conclude that high-frequency surveying for total spending is feasible and that the resulting data show expected patterns of levels and change.

Suggested Citation

  • Michael D. Hurd & Susann Rohwedder, 2012. "Measuring Total Household Spending in a Monthly Internet Survey Evidence from the American Life Panel," Working Papers WR-939, RAND Corporation.
    • Handle: RePEc:ran:wpaper:wr-939
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    References listed on IDEAS

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    1. Michael Hurd & Susann Rohwedder, 2009. "Methodological Innovations in Collecting Spending Data: The HRS Consumption and Activities Mail Survey," Fiscal Studies, Institute for Fiscal Studies, vol. 30(Special I), pages 435-459, December.
    2. Matthew Brzozowski & Thomas F. Crossley, 2011. "Viewpoint: Measuring the well‐being of the poor with income or consumption: a Canadian perspective," Canadian Journal of Economics/Revue canadienne d'économique, John Wiley & Sons, vol. 44(1), pages 88-106, February.
    3. Michael Hurd & Susann Rohwedder, 2009. "Methodological Innovations in Collecting Spending Data: The HRS Consumption and Activities Mail Survey," Fiscal Studies, Institute for Fiscal Studies, pages 435-459.
    4. Melvin Stephens, 2004. "Job Loss Expectations, Realizations, and Household Consumption Behavior," The Review of Economics and Statistics, MIT Press, vol. 86(1), pages 253-269, February.
    5. Gruber, Jonathan, 1997. "The Consumption Smoothing Benefits of Unemployment Insurance," American Economic Review, American Economic Association, vol. 87(1), pages 192-205, March.
    6. Martin Browning & Thomas F. Crossley, 2009. "Shocks, Stocks, and Socks: Smoothing Consumption Over a Temporary Income Loss," Journal of the European Economic Association, MIT Press, vol. 7(6), pages 1169-1192, December.
    7. Michael D. Hurd & Susann Rohwedder, 2013. "Wealth Dynamics and Active Saving at Older Ages," NBER Chapters, in: Improving the Measurement of Consumer Expenditures, pages 388-413, National Bureau of Economic Research, Inc.
    Full references (including those not matched with items on IDEAS)

    Citations

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    Cited by:

    1. Francesco D’Acunto & Daniel Hoang & Michael Weber, 2022. "Managing Households’ Expectations with Unconventional Policies," The Review of Financial Studies, Society for Financial Studies, vol. 35(4), pages 1597-1642.
    2. Francesco D'Acunto & Daniel Hoang & Michael Weber & Michael Weber, 2016. "Unconventional Fiscal Policy, Inflation Expectations, and Consumption Expenditure," CESifo Working Paper Series 5793, CESifo.
    3. Axel H. Börsch-Supan & Tabea Bucher-Koenen & Michael D. Hurd & Susann Rohwedder, 2018. "Saving Regret," NBER Working Papers 25238, National Bureau of Economic Research, Inc.
    4. Francesco D’Acunto & Daniel Hoang & Michael Weber, 2017. "The Effect of Unconventional Fiscal Policy on Consumption Expenditure," ifo DICE Report, ifo Institute - Leibniz Institute for Economic Research at the University of Munich, vol. 15(01), pages 09-11, April.
    5. Francesco D'Acunto & Daniel Hoang & Michael Weber & Michael Weber, 2019. "Managing Households' Expectations with Salient Economic Policies," CESifo Working Paper Series 7793, CESifo.
    6. Adam Bee & Bruce D. Meyer & James X. Sullivan, 2013. "The Validity of Consumption Data: Are the Consumer Expenditure Interview and Diary Surveys Informative?," NBER Chapters, in: Improving the Measurement of Consumer Expenditures, pages 204-240, National Bureau of Economic Research, Inc.
    7. Mary A. Burke & Ali Ozdagli, 2023. "Household Inflation Expectations and Consumer Spending: Evidence from Panel Data," The Review of Economics and Statistics, MIT Press, vol. 105(4), pages 948-961, July.
    8. Martin Browning & Thomas F. Crossley & Joachim Winter, 2014. "The Measurement of Household Consumption Expenditures," Annual Review of Economics, Annual Reviews, vol. 6(1), pages 475-501, August.
    9. Jonathan A. Parker & Nicholas S. Souleles & Christopher D. Carroll, 2014. "The Benefits of Panel Data in Consumer Expenditure Surveys," NBER Chapters, in: Improving the Measurement of Consumer Expenditures, pages 75-99, National Bureau of Economic Research, Inc.
    10. Luc Bissonnette & Arthur van Soest, 2015. "The Financial Crisis and Consumers' Income and Pension Expectations," Cahiers de recherche 1502, Chaire de recherche Industrielle Alliance sur les enjeux économiques des changements démographiques.
    11. Federica Teppa, 2014. "Consumption behaviour and financial crisis in the Netherlands," DNB Working Papers 453, Netherlands Central Bank, Research Department.
    12. Michael Weber & Daniel Hoang & Francesco D'Acunto, 2015. "Inflation Expectations and Consumption Expenditure," 2015 Meeting Papers 1266, Society for Economic Dynamics.
    13. Bańnkowska, Katarzyna & Borlescu, Ana Maria & Charalambakis, Evangelos & Da Silva, António Dias & Di Laurea, Davide & Dossche, Maarten & Georgarakos, Dimitris & Honkkila, Juha & Kennedy, Neale & Kenny, 2021. "ECB Consumer Expectations Survey: an overview and first evaluation," Occasional Paper Series 287, European Central Bank.

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    More about this item

    Keywords

    consumption measurement; high-frequency interviewing; Internet surveys; American Life Panel;
    All these keywords.

    JEL classification:

    • D12 - Microeconomics - - Household Behavior - - - Consumer Economics: Empirical Analysis
    • C81 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Methodology for Collecting, Estimating, and Organizing Microeconomic Data; Data Access
    • C83 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Survey Methods; Sampling Methods

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