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Can survey participation alter household saving behaviour?

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  • Crossley, Thomas
  • de Bresser, Jochem

    (Tilburg University, School of Economics and Management)

  • Delaney, L.
  • Winter, Joachim

Abstract

Much empirical research in economics is based on data from household surveys. Panel surveys are particularly valuable for understanding dynamics and heterogeneity. A possible concern with panel surveys is that survey participation itself may alter subsequent behavior. We provide novel evidence of survey effects on a central life-cycle choice: household saving. We exploit randomized assignment to survey modules within the LISS Panel, an internet panel survey which is representative of the Dutch population. We find that households that respond to detailed questions on expenditures and needs in retirement reduced their non-housing saving rate by 3.5 percentage points, on average. This mean effect is driven by high-education households which have the highest pension and housing wealth. Our saving measure is based on linked administrative wealth data. Thus we can rule out the possibility that the effect is on reporting, rather than on the underlying saving behavior. One interpretation is that the survey acted as a salience shock, possibly with respect to reduced housing costs in retirement.
(This abstract was borrowed from another version of this item.)
(This abstract was borrowed from another version of this item.)

Suggested Citation

  • Crossley, Thomas & de Bresser, Jochem & Delaney, L. & Winter, Joachim, 2017. "Can survey participation alter household saving behaviour?," Other publications TiSEM 56e57f52-f6eb-4203-8b19-a, Tilburg University, School of Economics and Management.
    • Handle: RePEc:tiu:tiutis:56e57f52-f6eb-4203-8b19-a6a5bb225d0f
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    Cited by:

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    3. Bert Van Landeghem & Anneleen Vandeplas, 2016. "Lower in rank, but happier: the complex relationship between status and happiness," Working Papers of LICOS - Centre for Institutions and Economic Performance 556194, KU Leuven, Faculty of Economics and Business (FEB), LICOS - Centre for Institutions and Economic Performance.
    4. Landeghem, Bert Van & Cörvers, Frank & Grip, Andries de, 2017. "Is there a rationale to contact the unemployed right from the start? Evidence from a natural field experiment," Labour Economics, Elsevier, vol. 45(C), pages 158-168.
    5. 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.
    6. Bodo Herzog, 2022. "Do Post-Corona European Economic Policies Lift Growth Prospects? Exploring an ML-Methodology," JRFM, MDPI, vol. 15(3), pages 1-13, March.
    7. Yi Fan & Diana M. Weinhold, 2022. "Urban noise, sleep disruption and health," Applied Economics, Taylor & Francis Journals, vol. 54(50), pages 5782-5799, October.

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    More about this item

    JEL classification:

    • C83 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Survey Methods; Sampling Methods
    • C81 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Methodology for Collecting, Estimating, and Organizing Microeconomic Data; Data Access
    • D14 - Microeconomics - - Household Behavior - - - Household Saving; Personal Finance

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