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Keywords:

  • bivariate probit;
  • binary endogenous regressor;
  • Frank copula;
  • Clayton copula

ABSTRACT

The bivariate probit model is frequently used for estimating the effect of an endogenous binary regressor (the ‘treatment’) on a binary health outcome variable. This paper discusses simple modifications that maintain the probit assumption for the marginal distributions while introducing non-normal dependence using copulas. In an application of the copula bivariate probit model to the effect of insurance status on the absence of ambulatory health care expenditure, a model based on the Frank copula outperforms the standard bivariate probit model. Copyright © 2011 John Wiley & Sons, Ltd.