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Identification of structural models in the presence of measurement error due to rounding in survey responses

Author

Listed:
  • Stefan Hoderlein

    (Boston College)

  • Bettina Siflinger

    (University of Mannheim)

  • Joachim Winter

    (University of Munich)

Abstract

Distortions in the elicitation of economic variables arise frequently. A common problem in household surveys is that reported values exhibit a significant degree of rounding. We interpret rounding as a filter that allows limited information about the relationship of interest to pass. We argue that rounding is an active decision of the survey respondent, and propose a general structural model that helps to explain some of the typical distortions that arise out of this active decision. Specifically, we assume that there is insufficient ability of individuals to acquire, process and recall information, and that rational individuals aim at using the scarce resources they devote to a survey in an optimal fashion. This model implies selection and places some structure on the selection equation. We use the formal model to correct for some of the distorting effects of rounding on the relationship of interest, using all the data available. Finally, we show how the concepts developed in this paper can be applied in consumer demand analysis by exploiting a controlled survey experiment, and obtain plausible results.

Suggested Citation

  • Stefan Hoderlein & Bettina Siflinger & Joachim Winter, 2015. "Identification of structural models in the presence of measurement error due to rounding in survey responses," Boston College Working Papers in Economics 869, Boston College Department of Economics.
    • Handle: RePEc:boc:bocoec:869
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    References listed on IDEAS

    as
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    Cited by:

    1. Leonard Goff, 2022. "Causal identification with subjective outcomes," Papers 2212.14622, arXiv.org, revised Feb 2023.
    2. Brzozowski, Matthew & Crossley, Thomas F. & Winter, Joachim K., 2017. "A comparison of recall and diary food expenditure data," Food Policy, Elsevier, vol. 72(C), pages 53-61.
    3. Christoph Breunig & Stephan Martin, 2020. "Nonclassical Measurement Error in the Outcome Variable," Papers 2009.12665, arXiv.org, revised May 2021.

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    More about this item

    Keywords

    heaping; nonparametric; survey design; bounded rationality; identification;
    All these keywords.

    JEL classification:

    • C20 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - General
    • C26 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Instrumental Variables (IV) Estimation
    • D12 - Microeconomics - - Household Behavior - - - Consumer Economics: Empirical Analysis

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