Statistical Evidence in Employment Discrimination Cases

Statistical methods in general, and multiple regression analysis in particular, are being used increasingly to provide evidence in employment discrimination cases. While the technical issues involved in using statistical methods to detect discrimination are straightforward, the conceptual issues are much less clearly understood. This article provides a framework to help clarify the conditions under which an estimated effect can be properly attributed to discrimination. Several interrelated issues have caused particular confusion, including the distinction between disparate impact and disparate treatment, the definition of test bias, the use of “reverse regression,” proxy variables for true productivity, and measurement error. A simple mathematical model is developed to analyze the precise nature of these issues. It is concluded that although employment discrimination cases involve all the usual problems involved in causal inference from observational data, certain aspects of the legal context may facilitate the valid application of statistical techniques

Predicting insurance losses under cross- classification: A comparison of alternative approaches

Various mathematical and statistical models for estimation of automobile insurance pricing are reviewed. The methods are compared on their predictive ability based on two sets of automobile insurance data for two different states collected over two different periods. The issue of model complexity versus data availability is resolved through a comparison of the accuracy of prediction.The models reviewed range from the use of simple cell means to various multiplicative-additive schemes to the empirical-Bayes approach. The empirical-Bayes approach, with prediction based on both model-based and individual cell estimates, seems to yield the best forecast

Insurance Fraud: Issues and Challenges

This article is devoted to the phenomenon of insurance fraud. We start by surveying the various forms of insurance fraud as well as its extent and cost. We proceed to analyse the problem as the product of motivation and opportunity, and address the complexities of fraud control. Finally, we provide a high-level overview of current anti-fraud activity. The Geneva Papers on Risk and Insurance (2004) 29, 313–333. doi:10.1111/j.1468-0440.2004.00290.x