A flexible copula regression model with Bernoulli and Tweedie margins for estimating the effect of spending on mental health

We develop a flexible two-equation copula model to address endogeneity of medical expenditures in a distribution regression for health. The expenditure margin uses the compound gamma distribution, a special case of the Tweedie family of distributions, to account for a spike at zero and a highly skewed continuous part. An efficient estimation algorithm offers flexible choices of copulae and link functions, including logit, probit and cloglog for the health margin. Our empirical application revisits data from the Rand Health Insurance Experiment. In the joint model, using random insurance plan assignment as instrument for spending, a $1000 increase is estimated to reduce the probability of a low post-program mental health index by 1.9 percentage points. The effect is not statistically significant. Ignoring endogeneity leads to a spurious positive effect estimate.

The effect of obesity on chronic diseases in USA: a flexible copula approach

We analyze the effect of obesity on the incidence of hypertension, hyperlipidemia and diabetes in USA using a health production theoretical framework along with a bivariate flexible semi-parametric recursive copula model that account for endogeneity. In this approach, the effects of control variables are flexibly determined using additive predictors that allow for a variety of effects. Our findings suggest that there exist a positive and significant effect of obesity on the prevalence of all chronic diseases examined. In particular, after endogeneity is accounted for, the probability of having hypertension, hyperlipidemia and diabetes for obese individuals are, respectively, 35%, 28% and 11% higher than those under the obesity threshold. These findings suggest that lowering obesity rates could lead to significant reductions in the morbidity and mortality associated with these diseases.