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This article examines alternative econometric models for health-care demand estimation. The analysis compares the Rand Health Insurance Study two-part model with sample-selection model estimators in a Monte Carlo simulation experiment designed to approximate individual-level health-care demand conditions. The underlying variable distributions are taken from cross- sectional data for a Swiss 1981 population survey. Three sets of error distribution assumptions are examined—bivariate normal, normal logistic, and Cauchy. Despite theoretical concerns with the two-part model, it outperforms the sample-selection model in terms of mean squared error of parameter estimate.
Hay et al. (Thu,) studied this question.