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Research Details

(UM01-03) - Stochastic Modeling of the Dynamics of OASDI: Evaluation of Policy Alternatives and Behavioral Responses
Ronald D. Lee and Shripad Tuljapurkar

Abstract from Paper One: Demographers have shown that there are regularities in mortality change over time, and have used these to forecast changes due to population aging. Such models leave out potential economic feedbacks that should be captured by dynamic models such as the general-equilibrium, overlapping-generations model first studied by Yaari and Blanchard. Previous analytical and simple numerical work by economists has focused on comparative statistics and used simplistic representations of mortality, such as the assumption of a constant age-independent death rate, or some parametric approximation to a survival curve. We show that it is straightforward to analyze equilibria in such models if we work with the probability distribution of the age at death. US and other data show that this distribution can be plausibly described by a normal distribution—for this case we obtain analytical results. For the general case we have numerical results. We show that a proper accounting for the uncertainty of when one dies has significant qualitative and quantitative effects on the equilibria of such economic models. There are, in turn, significant lessons to be drawn for models of future fiscal policy. Abstract from Paper Two: We completed a study of the effects of an alternative approach to stochastic population forecasts that has been advocated by various investigators, a random scenario method that uses expert opinion to set target levels of mortality and fertility and also the types of time trajectories followed by the rates over time. This method also assumes that there is a probability distribution over these trajectories, and samples from them to generate alternate futures. We completed a study that compares such random scenario methods to our stochastic model, in which time series methods are used to model the vital rates.



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