Wealth, Power, and the Internal Revenue Service: Changing IRS Audit Policy through Litigation

Objectives . Although scholars have recognized that the wealth and power of litigants has substantial influence on litigation outcomes, there has been less recognition of the ability of the wealthy to sway agency policy through litigation. I argue that the wealthy, through lawsuits and the outcomes from the litigation, influence Internal Revenue Service (IRS) audits of the wealthy and the less affluent. Methods . I examine IRS state audit rates and use ordinary least squares regression with panel corrected standard errors. Results . I show that wealthy litigants can influence the IRS to decrease audits of the wealthy and increase audits of the less affluent. Conclusions . The wealthy appear to have the ability to influence IRS policy through lawsuits. Litigation can influence agency policy.     

A Social Psychology of National and International Group Relations

This concluding article provides a framework for a social psychological analysis of intergroup conflict and conflict resolution. The framework highlights the individual and intergroup factors that shape the nature of perceptions of intergroup relations and group representations, and describes how these perceptions lead to cognitive, affective, and behavioral responses toward groups. Included in the framework are the metatheoretical, theoretical, and practical contributions of the articles in this issue toward understanding intergroup relations. The potential and responsibility of social psychologists to move beyond the laboratory to applied national and international issues is also discussed.     

Statistical Inference Based on Pooled Data: A Moment-Based Estimating Equation Approach

We consider statistical inference on parameters of a distribution when only pooled data are observed. A moment-based estimating equation approach is proposed to deal with situations where likelihood functions based on pooled data are difficult to work with. We outline the method to obtain estimates and test statistics of the parameters of interest in the general setting. We demonstrate the approach on the family of distributions generated by the Box-Cox transformation model, and, in the process, construct tests for goodness of fit based on the pooled data.     

Methodological Approaches to Assessing Risk Perceptions Associated with Food-Related Hazards

The psychometric approach developed by Slovic and his co-workers has been effectively used to assess risk perceptions associated with different food-related hazards. However, further examination (using questionnaire data and partial correlation techniques) has indicated that technological hazards are highly differentiated from lifestyle hazards, in terms of both hazard control and knowledge about the hazard. Optimistic bias was also seen to vary between hazards. Further research has focused on a particular hazard, genetic engineering. Risk perceptions associated with genetic engineering are underpinned by ethical concern and questions relating to perceived need for the technology, as well as perceptions of risk or harm. However, increasing the specificity of hazard stimuli was found to alter the factor structure of underlying risk perceptions. The utility of preference mapping procedures in determining individual differences in trust in risk regulators is also discussed.     

A characteristic function approach to the biased sampling model,with application to robust logistic regression

Many sampling problems from multiple populations can be considered under the semiparametric framework of the biased, or weighted, sampling model. Included under this framework is logistic regression under case–control sampling. For any model, atypical observations can greatly influence the maximum likelihood estimate of the parameters. Several robust alternatives have been proposed for the special case of logistic regression. However, some current techniques can exhibit poor behavior in many common situations. In this paper a new family of procedures are constructed to estimate the parameters in the semiparametric biased sampling model. The procedures incorporate a minimum distance approach, but are instead based on characteristic functions. The estimators can also be represented as the minimizers of quadratic forms in simple residuals, thus yielding straightforward computation. For the case of logistic regression, the resulting estimators are shown to be competitive with the existing robust approaches in terms of both robustness and efficiency, while maintaining affine equivariance. The approach is developed under the case–control sampling scheme, yet is shown to be applicable under prospective sampling logistic regression as well.