Lebenszyklusverhalten angesichts großer Gesundheitsschocks
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Typically, life-cycle models of optimal health behaviour are based on an ex-ante view, i.e. a representative individual faces a depreciation of a health stock, a mortality risk, or is subject to the accumulation of health deficits. The optimal behaviour over the life-cycle is described in light of these average processes. However, in the real world health does not develop smoothly over the life-course, but is subject to smaller or greater shocks. Large shocks, such as accidents and/or the onset of (life-threatening and/or chronic) diseases, have the propensity to affect survival probability, income and well-being as well as their dynamics and, thus, the entire life-course. In consequence, individuals are prone to alter life-cycle behaviour in the face of health shocks. This includes both the (ex-post) adjustment of behaviour in response to a shock and anticipatory/preventive behaviour (ex-ante). In this project, we study the implications of large health shocks for the individual and its life- course. We compare different types of anticipation (full anticipation, myopic), and consider the effects of health/disability insurance and a longevity insurance through the annuity market. In order to analyze these questions we use optimal control theory with a random stopping time, where the stopping rate is determined by the probability of the health shock. In order to analyze and solve the model we have to extend and adapt existing mathematical methods. In addition to a theoretical investigation of the model, we are planning to calibrate and solve numerically the model for different types of shock (diseases) and different specifications of health insurance and longevity insurance. This enables us to understand the quantitative implications of the various shocks to health for health and saving behaviours. Both is important and necessary to understand the impact of policy-making and the relative efficiency of policies toward the design of the various types of insurance.
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