Welfare-related health inequality: does the choice of measure matter?

Abstract

Using representative microdata from the German Socio-Economic Panel Study (SOEP), we show that the welfare measure choice has a substantial impact on the degree of welfare-related health inequality. To assess the sensitivity of welfare-related health inequality measures, we combine a unique set of income and wealth measures with different subjective, cardinalized, and (quasi-)objective health measures. The influence of the welfare measure is more pronounced when using subjective health measures than when using (quasi-)objective health measures.

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Notes

  1. 1.

    However, all correction methods do not derive a correction within a regression framework for the standard errors.

  2. 2.

    Since we use the 2006 (quasi-)objective health measures together with the 2007 wealth measures, a shift in the health distribution between 2006 an 2007 might contaminate our results. The same could be true for systematic health-related panel attrition. The SF12 and grip strength are surveyed every other year in the SOEP and the wealth measures every 5 years. Hence, as a robustness check, we performed the same analysis with the 2007 income and wealth and the 2008 (quasi-)objective health measures. The results are not sensitive and very stable. They are available upon request from the corresponding author.

  3. 3.

    We apply the modified OECD equivalence scale, which assigns a value of 1 to the household head, 0.5 to other adults, as well as 0.3 to children up to 14 years of age.

  4. 4.

    We decided to condition on the height and weight of the respondents, since it has been shown that both factors are correlated strongly with grip strength and thus physical health [13, 26]. Since physical health is also part of the overall health measures and for reasons of consistency, we have incorporated these variables throughout the analysis. However, one referee correctly pointed out that height and weight itself are health measures and thus potential outcome variables. Moreover, they are measured with error [29]. Likewise, one could argue that correlations of our health measures with labor market characteristics, education, and other socio-economic background characteristics should not be netted out prior to the main analysis. For example, health may affect the decision to work and, at the same time, employment itself may affect health [27]. Hence, we carried out exactly the same analysis as discussed below, but disregarded these variables, i.e., we indirectly standardized the health measures, but only with regard to gender and age. The results are very similar and robust, but almost all inequality measures increase slightly in size, i.e., welfare-related health inequality slightly decreases. This systematic shift does not affect our findings or conclusion. The results are available upon request from the corresponding author.

  5. 5.

    For each row of Table 1, i.e., for each health measure, we performed tests according to Eq. 2. Results are available upon request.

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Acknowledgments

We thank the editor, two anonymous referees, Cristina Blanco, Andrew Jones, Martin Karlsson, Jenny Kragl, Tom van Ourti, and participants at seminars in Darmstadt at the “Health. Happiness. Inequality–Modelling the Pathways between Income Inequality and Health” conference, as well as in Rome at the Meeting of the Applied Econometrics Association and the “Econometrics of Healthy Human Resources.” Special thanks go to Adam Lederer for co-editing this paper.

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Correspondence to Nicolas R. Ziebarth.

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Joachim R. Frick: Deceased.

In December 2011, after having bravely fought for more than one year against an extremely virulent form of cancer, Joachim R. Frick—my co-author on this manuscript—passed away. Joachim was more than just a mentor and advisor to me. I first met Joachim in July 2004. At that time, I was a Master’s student at the Berlin Institute of Technology (TU Berlin). In collaboration with the SOEP department at DIW Berlin, Joachim offered a course on how to work with SOEP data and with Stata®. This was the first time that I actually worked with microdata and I realized that research was what I wanted to do in the future. Joachim shaped the course of my life ever since. Without taking this course and without Joachim’s encouragement to apply I would never earned my PhD from TU Berlin and DIW Berlin. As I reflect back, and as I find my place in the academy, I am sure that without Joachim I would not be where I am now and would not be doing what I do now. Thanks Joachim, I will never forget. Rest in peace.

Appendix

Appendix

See Table 4.

Table 4 The impact of the welfare concept used on the degree of welfare-related health inequality: health measures corrected for differences in means and bounds (Wagstaff-correction)

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Frick, J.R., Ziebarth, N.R. Welfare-related health inequality: does the choice of measure matter?. Eur J Health Econ 14, 431–442 (2013). https://doi.org/10.1007/s10198-012-0387-6

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Keywords

  • Welfare-related health inequality
  • Concentration index
  • Income measurement
  • Wealth
  • SOEP

JEL Classifiaction

  • D31
  • I10
  • I12