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Anchoring Bias and Covariate Nonresponse

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  • Rosalia Vazquez-Alvarez

Abstract

Non-random item nonreponse makes identification if parameters problematic. Such nonresponse can occur with respect to both dependent and conditioning variables. A method often used to reduce nonresponse is that of adding unfolding brackets as follow up to open-ended questions. With these, initial non-respondents can provide additional (incomplete) information on the missing values. However, recent studies suggest that responses to unfolding brackets can lead to a type of bias as a result of the Achoring Effect. In this paper, bounding intervals of the tzpe as presented in Horowitz and Manski (1998) are extended to incorporate information provided by bracket respondents while allowing for different types of anchoring, and, therefore, accounting for significant nonresponse in the conditioning set. The theoretical framework is illustrated with empirical evidence based on the 1996 wave of the Health and Retirement Study (HRS, 1996).

Suggested Citation

  • Rosalia Vazquez-Alvarez, 2003. "Anchoring Bias and Covariate Nonresponse," University of St. Gallen Department of Economics working paper series 2003 2003-19, Department of Economics, University of St. Gallen.
    • Handle: RePEc:usg:dp2003:2003-19
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    File URL: http://ux-tauri.unisg.ch/RePEc/usg/dp2003/dp19_rosalia_ganz.pdf
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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, vol. 80(2), pages 319-323, May.
    2. Green, Donald & Jacowitz, Karen E. & Kahneman, Daniel & McFadden, Daniel, 1998. "Referendum contingent valuation, anchoring, and willingness to pay for public goods," Resource and Energy Economics, Elsevier, vol. 20(2), pages 85-116, June.
    3. 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.
    4. Horowitz, Joel L & Manski, Charles F, 1995. "Identification and Robustness with Contaminated and Corrupted Data," Econometrica, Econometric Society, vol. 63(2), pages 281-302, March.
    5. Horowitz, Joel L. & Manski, Charles F., 1998. "Censoring of outcomes and regressors due to survey nonresponse: Identification and estimation using weights and imputations," Journal of Econometrics, Elsevier, vol. 84(1), pages 37-58, May.
    6. Francis Vella, 1998. "Estimating Models with Sample Selection Bias: A Survey," Journal of Human Resources, University of Wisconsin Press, vol. 33(1), pages 127-169.
    7. Matthew Rabin, 1998. "Psychology and Economics," Journal of Economic Literature, American Economic Association, vol. 36(1), pages 11-46, March.
    8. Charles F. Manski, 1989. "Anatomy of the Selection Problem," Journal of Human Resources, University of Wisconsin Press, vol. 24(3), pages 343-360.
    9. Heckman, James, 2013. "Sample selection bias as a specification error," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 31(3), pages 129-137.
    10. Vazquez-Alvarez, R. & Melenberg, B. & van Soest, A.H.O., 2001. "Nonparametric Bounds in the Presence of Item Nonresponse, Unfolding Brackets and Anchoring," Other publications TiSEM cb4befff-4232-459b-ab68-2, Tilburg University, School of Economics and Management.
    11. Michael D. Hurd & Daniel McFadden & Harish Chand & Li Gan & Angela Menill & Michael Roberts, 1998. "Consumption and Savings Balances of the Elderly: Experimental Evidence on Survey Response Bias," NBER Chapters, in: Frontiers in the Economics of Aging, pages 353-392, National Bureau of Economic Research, Inc.
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    2. Reza C. Daniels, 2012. "Questionnaire Design and Response Propensities for Employee Income Micro Data," SALDRU Working Papers 89, Southern Africa Labour and Development Research Unit, University of Cape Town.

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    More about this item

    Keywords

    Unfolding bracket design; anchoring effects; survey nonresponse; bounding intervals.;
    All these keywords.

    JEL classification:

    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • 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
    • D31 - Microeconomics - - Distribution - - - Personal Income and Wealth Distribution

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