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    Anticipatory ex ante moral hazard and the effect of medicare on prevention, by Laure B. de Preux (Health Economics)

    This paper extends the ex ante moral hazard model to allow healthy lifestyles to reduce the probability of illness in future periods, so that current preventive behaviour may be affected by anticipated changes in future insurance coverage. In the United States, Medicare is offered to almost all the population at the age of 65. We use nine waves of the US Health and Retirement Study to compare lifestyles before and after 65 of those insured and not insured pre 65. The double-robust approach, which combines propensity score and regression, is used to compare trends in lifestyle (physical activity, smoking, drinking) of the two groups before and after receiving Medicare, using both difference-in-differences and difference-in-differences-in-differences. There is no clear effect of the receipt of Medicare or its anticipation on alcohol consumption nor smoking behaviour, but the previously uninsured do reduce physical activity just before receiving Medicare.

    “Healthy, Wealthy and Wise?” Revisited: An Analysis of the Causal Pathways from Socio-economic Status to Health, by Till Stowasser, Florian Heiss, Daniel McFadden, Joachim Winter (NBER)

    Much has been said about the stylized fact that the economically successful are not only wealthier but also healthier than the less affluent. There is little doubt about the existence of this socio-economic gradient in health, but there remains a vivid debate about its source. In this paper, we review the methodological challenges involved in testing the causal relationships between socio-economic status and health. We describe the approach of testing for the absence of causal channels developed by Adams et al. (2003) that seeks identification without the need to isolate exogenous variation in economic variables, and we repeat their analysis using the full range of data that have become available in the Health and Retirement Study since, both in terms of observations years and age ranges covered. This analysis shows that causal inference critically depends on which time periods are used for estimation. Using the information of longer panels has the greatest effect on results. We find that SES causality cannot be ruled out for a larger number of health conditions than in the original study. An approach based on a reduced-form interpretation of causality thus is not very informative, at least as long as the confounding influence of hidden common factors is not fully controlled.

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