Comparing four estimation methods for uninsurance in Florida

Although the high percentage of the uninsured is an important public policy issue, discrepancies in both state and national estimates of the numbers of uninsured are reported. There is a critical need to address the methodological problem of the estimation. This study compares four advanced estimation methods for uninsurance by using Florida Health Insurance Survey data as an example. The four predictive models are decision tree, neural network, general logistic regression and two-stage logistic regression. The two-stage logistic regression model is found to be the best model for imputing missing data on health insurance. Risk factors to uninsurance are identified. Corresponding policy implications are discussed.