A flexible copula regression model with Bernoulli and Tweedie margins for estimating the effect of spending on mental health - Giampiero Marra; Matteo Fasiolo; Rosalba Radice; Rainer Winkelmann (University of Zurich, Department of Economics, 2022)
Previous evidence shows that better insurance coverage increases medical expenditure. However, formal studies on the effect of spending on health outcomes, and especially mental health, are lacking. To fill this gap, we reanalyze data from the Rand Health Insurance Experiment and estimate a joint non-linear model of spending and mental health. We address the endogeneity of spending in a flexible copula regression model with Bernoulli and Tweedie margins and discuss its implementation in the freely available GJRM R package. Results confirm the importance of accounting for endogeneity: in the joint model, a 1000 spending in mental care is estimated to reduce the probability of low mental health by 1.3 percentage points, but this effect is not statistically significant. Ignoring endogeneity leads to a spurious (upwardly biased) estimate.
Keywords: I13; C31; 330; binary response; co-payment; copula; health expenditures; penalized regression spline; Rand experiment; simultaneous estimation; Tweedie distribution