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On Inferring Demand for Health Care in the Presence of Anchoring, Acquiescence, and Selection Biases

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  • Jay Bhattacharya
  • Adam Isen

Abstract

In the contingent valuation literature, both anchoring and acquiescence biases pose problems when using an iterative bidding game to infer willingness to pay. Anchoring bias occurs when the willingness to pay estimate is sensitive to the initially presented starting value. Acquiescence bias occurs when survey respondents exhibit a tendency to answer 'yes' to questions, regardless of their true preferences. More generally, whenever a survey format is used and not all of those contacted participate, selection bias raises concerns about the representativeness of the sample. In this paper, we estimate students' willingness to pay for student health care at Stanford University while accounting for all of these biases. As there is no cost sharing for students, we assess willingness to pay by having a random sample of students play an online iterative bidding game. Our main results are that (1) demand for student health care is elastic by conventional standards; (2) ignoring anchoring bias would lead to a substantially biased measure of the demand elasticity; (3) there is evidence for acquiescence bias in student answers to the opening question of the iterative bidding game and failure to address this leads to the biased conclusion that demand is inelastic; and (4) standard selection correction methods indicate no bias from selective non-response and newer bounding methods support this conclusion of elastic demand.

Suggested Citation

  • Jay Bhattacharya & Adam Isen, 2008. "On Inferring Demand for Health Care in the Presence of Anchoring, Acquiescence, and Selection Biases," NBER Working Papers 13865, National Bureau of Economic Research, Inc.
  • Handle: RePEc:nbr:nberwo:13865 Note: HC HE
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    References listed on IDEAS

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    1. Manski, Charles F, 1990. "Nonparametric Bounds on Treatment Effects," American Economic Review, American Economic Association, pages 319-323.
    2. Ryan, Mandy & Scott, David A. & Donaldson, Cam, 2004. "Valuing health care using willingness to pay: a comparison of the payment card and dichotomous choice methods," Journal of Health Economics, Elsevier, vol. 23(2), pages 237-258, March.
    3. Philipson, Tomas, 2001. "Data Markets, Missing Data, and Incentive Pay," Econometrica, Econometric Society, vol. 69(4), pages 1099-1111, July.
    4. Herriges, Joseph A. & Shogren, Jason F., 1996. "Starting Point Bias in Dichotomous Choice Valuation with Follow-Up Questioning," Journal of Environmental Economics and Management, Elsevier, vol. 30(1), pages 112-131, January.
    5. van Soest, Arthur & Hurd, Michael, 2008. "A Test for Anchoring and Yea-Saying in Experimental Consumption Data," Journal of the American Statistical Association, American Statistical Association, pages 126-136.
    6. Charles F. Manski & John V. Pepper, 2000. "Monotone Instrumental Variables, with an Application to the Returns to Schooling," Econometrica, Econometric Society, vol. 68(4), pages 997-1012, July.
    7. Heckman, James, 2013. "Sample selection bias as a specification error," Applied Econometrics, Publishing House "SINERGIA PRESS", vol. 31(3), pages 129-137.
    8. Bhattacharya, Jay & Shaikh, Azeem M. & Vytlacil, Edward, 2012. "Treatment effect bounds: An application to Swan–Ganz catheterization," Journal of Econometrics, Elsevier, vol. 168(2), pages 223-243.
    9. Blumenschein, Karen & Johannesson, Magnus & Yokoyama, Krista K. & Freeman, Patricia R., 2001. "Hypothetical versus real willingness to pay in the health care sector: results from a field experiment," Journal of Health Economics, Elsevier, vol. 20(3), pages 441-457, May.
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    More about this item

    JEL classification:

    • C42 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Survey Methods
    • C81 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Methodology for Collecting, Estimating, and Organizing Microeconomic Data; Data Access
    • C9 - Mathematical and Quantitative Methods - - Design of Experiments
    • I1 - Health, Education, and Welfare - - Health

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