Medicaid reform: programming solutions to the equity problem

This paper demonstrates the application of a mathematical programming model to a longstanding policy issue in the Medicaid reform debate: the redistribution of program funds necessary to achieve equity in eligibility and benefit coverage across states. The model is used to estimate the potential degree of equity achievable in the current Medicaid system given various budgetary and political constraints. Two model simulations, based on a 1979 data set for aged recipients of Supplementary Security Income, are presented. The results indicate that half or more of the interstate differences in spending for this population group are due to actuarial and efficiency factors rather than deviations from equity potential. The implications of eliminating the remaining differences are discussed.     

Choosing appropriate techniques for socio-political forecasting

After defining scientific forecasting, the crucial role of assumptions in such forecasts is explicated. This is followed by a discussion of the representations upon which forecasting systems are based. Six variables are then introduced to capture differences in socio-political forecasting circumstances: level of detail, accuracy, agreement on problem representation, robustness-brittleness, number of variables and interdependencies, and disturbance. A categorization of forecasting approaches - expert based, Bayesian, extremal statistical, and rule based - is offered. These forecasting approaches are then cross-referenced with the forecasting circumstances to produce recommendations for choosing an appropriate forecasting technique in a given policy circumstance. Most examples in the article are drawn from the realm of foreign policy and international politics, and the cross-referencing section concentrates on foreign energy policy examples.     

Determining an optimum percentage of defendants to release before trial: Applying inventory modeling concepts to legal policy problems

Some aspects of determining what proportion of defendants to release before trial are discussed using cost and other data obtained from judges, prosecutors, defense attorneys, and bail project directors in twenty-three cities. Various holding costs, releasing costs, and percentages of defendants held are used. Smooth curves are fitted to the data points to determine at what percentage of defendants held the total cost curve bottoms out. The effects of varying the costs and the cities, the causal implications of why the optimum percentage differs substantially from the actual percentage, and the policy implications of using the analysis in pretrial release policy and in reducing the total costs are shown. Ways in which the model can be expanded to obtain insights into the optimum level of various kinds of due process and law enforcement are also indicated.