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Why the Linear Utility Function is a Risky Choice in Discrete-Choice Experiments

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  • Michele Sennhauser

    () (Department of Economics, University of Zurich)

Abstract

This article assesses how the form of the utility function in discrete-choice experiments (DCEs) affects estimates of willingness-to-pay (WTP). The utility function is usually assumed to be linear in its attributes. Non-linearities, in the guise of interactions and higher-order terms, are applied only rather ad hoc. This paper sheds some light on this issue by showing that the linear utility function can be a risky choice in DCEs. For this purpose, a DCE conducted in Switzerland to assess preferences for statutory social health insurance is estimated in two ways: first, using a linear utility function; and second, using a non-linear utility function specified according to model specification rules from the econometrics and statistics literature. The results show that not only does the non-linear function outperform the linear specification with regard to goodness-of-fit, but it also generates significantly different WTP. Hence, the functional form of the utility function may have significant impact on estimated WTP. In order to produce unbiased estimates of preferences and to make adequate decisions based on DCEs, the form of the utility function should become more prominent in future experiments.

Suggested Citation

  • Michele Sennhauser, 2009. "Why the Linear Utility Function is a Risky Choice in Discrete-Choice Experiments," SOI - Working Papers 1014, Socioeconomic Institute - University of Zurich.
  • Handle: RePEc:soz:wpaper:1014
    as

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    File URL: http://www.econ.uzh.ch/static/wp_soi/wp1014.pdf
    File Function: first version, 2010
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    References listed on IDEAS

    as
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    More about this item

    Keywords

    Discrete-Choice Experiment; Preference Measurement; Health Insurance; Model Specification;

    JEL classification:

    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
    • C9 - Mathematical and Quantitative Methods - - Design of Experiments
    • I11 - Health, Education, and Welfare - - Health - - - Analysis of Health Care Markets
    • I18 - Health, Education, and Welfare - - Health - - - Government Policy; Regulation; Public Health

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