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How does heterogeneity shape the socioeconomic gradient in health satisfaction?

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  • Andrew M. Jones
  • Stefanie Schurer
Abstract
Individual heterogeneity plays a key role in explaining variation in self-reported well-being and, in particular, health satisfaction. It is hypothesised that the inuence of this heterogeneity varies over levels of health and increases over the life-cycle. These hypotheses are tested with data on health satisfaction from 22 waves of the German Socioeconomic Panel (GSOEP). Nonlinear xed eects methods that allow for unobserved heterogeneity are not readily available for categorical measures of well-being. One common solution is to revert to conditional fixed effects methods, at the price of a high degree of information loss. Another common solution is to ignore the association between unobserved heterogeneity and socio-economic status by using pooled or random eects models, at the price of potential bias. We use a generalization of the conditional xed eects logit, that allows for individual-specic reporting bias, heterogeneity in health endowments, and heterogeneity in the impact of income on health satisfaction. Adjusting for unobserved heterogeneity accounts for the relationship between income and very good health, but not between income and poorer health states. The income gradient for older age-groups is more strongly aected by controlling for unobserved heterogeneity: revealing an increasing inuence of heterogeneity on health satisfaction over the life-span.
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  • Andrew M. Jones & Stefanie Schurer, 2011. "How does heterogeneity shape the socioeconomic gradient in health satisfaction?," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 26(4), pages 549-579, June.
    • Handle: RePEc:wly:japmet:v:26:y:2011:i:4:p:549-579
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    More about this item

    JEL classification:

    • I12 - Health, Education, and Welfare - - Health - - - Health Behavior
    • C23 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Models with Panel Data; Spatio-temporal Models

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