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.     

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.     

Exposure to occupational violence and the buffering effects of intra-oranizational support

This paper presents a conceptually distinct analysis of the data first reported by Leather et al. Specifically, it investigates the effects of exposure to a variety of forms of work-related violence upon work attitudes and general well-being within a sample of UK public house licensees—individuals who manage public houses and hold the licence permitting the sale of alcoholic drinks on the premises. In addition, it examines the role of social support in moderating such effects. Based upon a sample of 242 licensees, it reports a consistent interaction between exposure to such violence and the availability of perceived intra-organizational support in determining the size of any negative effects upon individual well-being, job satisfaction and organizational commitment. It is argued that exposure to all forms of work-related violence, including intimidation, verbal abuse and threat, should be seen as a potential stressor within the work environment, the negative effects of which are buffered by perceived support from within the organization, but not by that perceived to be available from informal sources such as family and friends.     

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.