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

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  • Giampiero Marra
  • Matteo Fasiolo
  • Rosalba Radice
  • Rainer Winkelmann

Abstract

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.

Suggested Citation

  • Giampiero Marra & Matteo Fasiolo & Rosalba Radice & Rainer Winkelmann, 2022. "A flexible copula regression model with Bernoulli and Tweedie margins for estimating the effect of spending on mental health," ECON - Working Papers 413, Department of Economics - University of Zurich.
    • Handle: RePEc:zur:econwp:413
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    More about this item

    Keywords

    Binary response; co-payment; copula; health expenditures; penalized regression spline; Rand experiment; simultaneous estimation; Tweedie distribution;
    All these keywords.

    JEL classification:

    • I13 - Health, Education, and Welfare - - Health - - - Health Insurance, Public and Private
    • C31 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models; Quantile Regressions; Social Interaction Models

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