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Anchoring Bias and Covariate Nonresponse

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  • Rosalia Vazquez-Alvarez

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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).

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Bibliographic Info

Paper provided by Department of Economics, University of St. Gallen in its series University of St. Gallen Department of Economics working paper series 2003 with number 2003-19.

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Length: 48 pages
Date of creation: Dec 2003
Date of revision:
Handle: RePEc:usg:dp2003:2003-19

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Keywords: Unfolding bracket design; anchoring effects; survey nonresponse; bounding intervals.;

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  1. Donald Green & Karen Jacowitz & Daniel Kahneman & Daniel McFadden, 1995. "Referendum Contingent Valuation, Anchoring, and Willingness to Pay for Public Goods," Working Papers _010, University of California at Berkeley, Econometrics Laboratory Software Archive.
  2. Rabin, Matthew, 1997. "Psychology and Economics," Department of Economics, Working Paper Series qt8jd5z5j2, Department of Economics, Institute for Business and Economic Research, UC Berkeley.
  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. 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.
  5. 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.
  6. Heckman, James J, 1979. "Sample Selection Bias as a Specification Error," Econometrica, Econometric Society, vol. 47(1), pages 153-61, January.
  7. repec:att:wimass:8909 is not listed on IDEAS
  8. Manski, Charles F, 1990. "Nonparametric Bounds on Treatment Effects," American Economic Review, American Economic Association, vol. 80(2), pages 319-23, May.
  9. Charles F. Manski, 1989. "Anatomy of the Selection Problem," Journal of Human Resources, University of Wisconsin Press, vol. 24(3), pages 343-360.
  10. Vazquez-Alvarez, R. & Melenberg, B. & Soest, A.H.O. van, 2001. "Nonparametric Bounds in the Presence of Item Nonresponse, Unfolding Brackets and Anchoring," Discussion Paper 2001-67, Tilburg University, Center for Economic Research.
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Cited by:
  1. 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.
  2. Reza C. Daniels, 2012. "A Framework for Investigating Micro Data Quality, with Application to South African Labour Market Household Surveys," SALDRU Working Papers 90, Southern Africa Labour and Development Research Unit, University of Cape Town.

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