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Reconsidering the Income-Illness Relationship Using Distributional Regression: An Application to Germany

Author

Listed:
  • Alexander Silbersdorff
  • Julia Lynch
  • Stephan Klasen
  • Thomas Kneib

Abstract

In this paper we reconsider the relationship between income on health, taking a distributional perspective rather than one centered on conditional expectation. Using Structured Additive Distributional Regression, we find that the association between income on health is larger than generally estimated because aspects of the conditional health distribution that go beyond the expectation imply worse outcomes for those with lower incomes. Looking at German data from the Socio Economic Panel, we find that the risk of very bad health is roughly halved when doubling the net equivalent income from 15,000 Euro to 30,000 Euro, which is more than tenfold of the magnitude of change found when considering expected health measures. This paper therefore argues that when studying health outcomes, a distributional perspective that considers stochastic variation among observationally equivalent individuals is warranted.

Suggested Citation

  • Alexander Silbersdorff & Julia Lynch & Stephan Klasen & Thomas Kneib, 2017. "Reconsidering the Income-Illness Relationship Using Distributional Regression: An Application to Germany," SOEPpapers on Multidisciplinary Panel Data Research 931, DIW Berlin, The German Socio-Economic Panel (SOEP).
  • Handle: RePEc:diw:diwsop:diw_sp931
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    References listed on IDEAS

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    Blog mentions

    As found by EconAcademics.org, the blog aggregator for Economics research:
    1. Sam Watson’s journal round-up for 30th April 2018
      by Sam Watson in The Academic Health Economists' Blog on 2018-04-30 15:30:48

    More about this item

    JEL classification:

    • I14 - Health, Education, and Welfare - - Health - - - Health and Inequality
    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models

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