We investigate the association between age and medical spending in the U.S. using data from the Medical Expenditure Panel Survey (MEPS). We estimate a partial linear seminonparametric model and construct “pure ” life-cycle profiles of health spending simultaneously controlling for time effects (i.e. institutional changes and business cycles effects) and cohort effects (i.e. generation specific conditions). We find that time and cohort effects introduce a significant estimation bias into predictions of health expenditures per age group, especially for individuals older than 60 years. The estimation biases introduced by cohort effects increase monotonically with age while time effects are non-monotone. Overall, cohort effect biases dominate time effect biases in magnitude for high age groups
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