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

Listed author(s):
  • Andrew M. Jones; Nigel Rice, Silvana Robone;
  • Nigel Rice;
  • Silvana Robone:

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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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
Handle: RePEc:yor:hectdg:12/10
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HEDG/HERC, Department of Economics and Related Studies, University of York, York, YO10 5DD, United Kingdom

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  9. 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.
  10. 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;Deutsche Gesellschaft für Gesundheitsökonomie (DGGÖ), vol. 12(2), pages 141-162, April.
  11. Nicolas Sirven & Brigitte Santos-Eggimann & Jacques Spagnoli, 2012. "Comparability of Health Care Responsiveness in Europe," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 105(2), pages 255-271, January.
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  13. C. Dominguez-Pery, 2011. "Introduction," Post-Print halshs-00740570, HAL.
  14. Gaston Yalonetzky, 2013. "Stochastic Dominance with Ordinal Variables: Conditions and a Test," Econometric Reviews, Taylor & Francis Journals, vol. 32(1), pages 126-163, January.
  15. Viola Angelini & Danilo CAVAPOZZI & Luca CORAZZINI & Omar PACCAGNELLA, 2009. "Do Danes and Italians Rate Life Satisfaction in the Same Way? Using Vignettes to Correct for Individual-Specific Scale Biases," "Marco Fanno" Working Papers 0090, Dipartimento di Scienze Economiche "Marco Fanno".
  16. Ali M. Kutan, 2011. "Introduction," Emerging Markets Finance and Trade, Taylor & Francis Journals, vol. 47(1), pages 3-4, January.
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  18. 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.
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  23. 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.
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