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A comparison of parametric and non-parametric adjustments using vignettes for self-reported data

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  • Andrew M. Jones; Nigel Rice, Silvana Robone;
  • Nigel Rice;
  • Silvana Robone:

Abstract

This paper compares the use of parametric and non-parametric approaches to adjust for heterogeneity in self-reported data. Despite the growing popularity of the HOPIT model to account for reporting heterogeneity when dealing with self-reported categorical data, recent evidence has questioned the validity of this heavily parametric approach. We compare the performance of the HOPIT model with the non-parametric estimators put forward by King et al. (2004) and King and Wand (2007). Using data relating to the health domains of mobility and memory from the Survey of Health, Ageing and Retirement in Europe (SHARE) we perform pairwise country comparisons of self-reported health, objective measures of health, and measures of health adjusted for the presence of reporting heterogeneity. Our study design focuses on comparisons of countries where there exist a discrepancy between the distribution of self-reported data and objective measures of health and assesses whether vignettes are able to reconcile this difference. Comparisons of distributions are based on first order stochastic dominance. In general, HOPIT and non-parametric estimation produce similar results in terms of first order stochastic dominance for the domains of both mobility and memory. Neither method consistently explains discrepancies across countries between self-reported and objective measures of health mobility and memory.

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Bibliographic Info

Paper provided by HEDG, c/o Department of Economics, University of York in its series Health, Econometrics and Data Group (HEDG) Working Papers with number 12/10.

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Date of creation: Jun 2012
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Handle: RePEc:yor:hectdg:12/10

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Postal: HEDG/HERC, Department of Economics and Related Studies, University of York, York, YO10 5DD, United Kingdom
Phone: (0)1904 323776
Fax: (0)1904 323759
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Web page: http://www.york.ac.uk/economics/postgrad/herc/hedg/
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Related research

Keywords: reporting heterogeneity; anchoring vignettes; Hierarchical Ordered Probit; stochastic dominance; cross-country comparisons; health; SHARE;

This paper has been announced in the following NEP Reports:

References

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  1. Arie Kapteyn & James P. Smith & Arthur van Soest, 2009. "Work Disability, Work, and Justification Bias in Europe and the U.S," Working Papers wp207, University of Michigan, Michigan Retirement Research Center.
  2. Nigel Rice & Silvana Robone & Peter Smith, 2009. "Vignettes and health systems responsiveness in crosscountry comparative analyses," Health, Econometrics and Data Group (HEDG) Working Papers 09/29, HEDG, c/o Department of Economics, University of York.
  3. Arnaud Chevalier & Antony Fielding, 2011. "An introduction to anchoring vignettes," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 174(3), pages 569-574, 07.
  4. Angelini, V.; & Cavapozzi, D.; & Corazzini L.; & Paccagnell O.;, 2011. "Do Danes and Italians Rate Life Satisfaction in the Same Way? Using Vignettes to Correct for Individual-Specific Scale Biases," Health, Econometrics and Data Group (HEDG) Working Papers 11/20, HEDG, c/o Department of Economics, University of York.
  5. Datta Gupta, Nabanita & Kristensen, Nicolai & Pozzoli, Dario, 2009. "External Validation of the Use of Vignettes in Cross-Country Health Studies," IZA Discussion Papers 3989, Institute for the Study of Labor (IZA).
  6. Franco Peracchi & Claudio Rossetti, 2010. "The heterogeneous thresholds ordered response model: Identification and inference," EIEF Working Papers Series 1012, Einaudi Institute for Economics and Finance (EIEF), revised Apr 2012.
  7. Bago d'Uva T & Lindeboom M & O'Donnell O & van Doorslaer E, 2009. "Slipping Anchor? Testing the Vignettes Approach to Identification and Correction of Reporting Heterogeneity," Health, Econometrics and Data Group (HEDG) Working Papers 09/30, HEDG, c/o Department of Economics, University of York.
  8. Franco Peracchi & Claudio Rossetti, 2009. "Gender and regional differences in self-rated health in Europe," CEIS Research Paper 142, Tor Vergata University, CEIS, revised 30 Sep 2009.
  9. Nigel Rice & Silvana Robone & Peter Smith, 2011. "Analysis of the validity of the vignette approach to correct for heterogeneity in reporting health system responsiveness," The European Journal of Health Economics, Springer, vol. 12(2), pages 141-162, April.
  10. Gaston Yalonetzky, 2013. "Stochastic Dominance with Ordinal Variables: Conditions and a Test," Econometric Reviews, Taylor & Francis Journals, vol. 32(1), pages 126-163, January.
  11. Pfarr, Christian & Schmid, Andreas & Schneider, Udo, 2011. "Reporting Heterogeneity in Self-Assessed Health among Elderly Europeans: The Impact of Mental and Physical Health Status," MPRA Paper 29900, University Library of Munich, Germany.
  12. Kristensen, Nicolai & Johansson, Edvard, 2008. "New evidence on cross-country differences in job satisfaction using anchoring vignettes," Labour Economics, Elsevier, vol. 15(1), pages 96-117, February.
  13. Viola Angelini & Danilo Cavapozzi & Omar Paccagnella, 2011. "Dynamics of reporting work disability in Europe," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 174(3), pages 621-638, 07.
  14. Nicolas Sirven & Brigitte Santos-Eggimann & Jacques Spagnoli, 2012. "Comparability of Health Care Responsiveness in Europe," Social Indicators Research, Springer, vol. 105(2), pages 255-271, January.
  15. Omar Paccagnella, 2011. "Anchoring vignettes with sample selection due to non‐response," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 174(3), pages 665-687, 07.
  16. Corrado, L. & Weeks, M., 2010. "Identification Strategies in Survey Response Using Vignettes," Cambridge Working Papers in Economics 1031, Faculty of Economics, University of Cambridge.
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Cited by:
  1. William H. Greene & Mark N. Harris & Bruce Hollingsworth, 2014. "Inflated Responses in Measures of Self-Assessed Health," Working Papers 14-12, New York University, Leonard N. Stern School of Business, Department of Economics.

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