A SIMPLE MODEL OF OPTIMAL HATE CRIME LEGISLATION

We present a simple model of the effects of hate crime legislation. We show that even if the direct harm to victims of hate crime is the same as for other crimes, because of other differences in the effects it may still be optimal to exert a different level of law‐enforcement effort to deter or prevent hate crime. These differences also have previously unrecognized effects on the optimal level of effort by potential hate crime victims to avoid being victimized, thus affecting the efficiency of government policies that encourage or discourage such effort. In some cases, the optimal level of government effort may be lower for hate crimes than for other crimes. Our analysis suggests a role for policy tools that influence individual avoidance effort directly. We discuss the implications of our results for similar types of crime, including terrorism. (JEL K42, K14, D02)     

The Family Tribute in Charitable Bequest Giving

The propensity for charitable bequest giving is much lower than for current charitable giving. Some nonexperimental evidence suggests that this difference may be due to the relatively greater importance of family benefit considerations in charitable bequest giving than in current charitable giving. To test this concept experimentally, the effects of a family–charity conflict reduction message (that is, a reminder of family connections to a cause and an opportunity to make a tribute gift) are measured for both current and bequest giving. For those with family connections to a cause, the message generated increased interest in charitable bequest giving but not in current charitable giving. This result provides experimental support for previous suggestions regarding the special role of family considerations in charitable bequest giving. Managers may profitably consider the use of tribute messages to encourage bequest, but not necessarily current, charitable giving. Such messages were effective at increasing bequest intentions for all organization types and demographic groups tested, especially where the tribute was in honor of an ascendant such as a mother, grandmother, grandfather, or uncle.     

Stranger Danger: Redistricting,Incumbent Recognition,and Vote Choice*

Objectives. We take a step forward in examining the electoral effects of redistricting by: (1) demonstrating that voters with a new incumbent because of redistricting are less likely to recognize their representative, and (2) voters are less likely to vote for their representative if they fail to recognize him or her. Methods. Our data come from a survey of white respondents who resided in the redrawn Eighth District of Georgia for the 2006 U.S. House elections. We use probit regressions to first measure the effect of redistricting on incumbent recognition. Then, we assess the likelihood of voting for the incumbent depending on whether a respondent was redrawn or has the same incumbent after redistricting, and whether or not the respondent could recognize his or her representative. Results. Our analyses make it clear that redrawn voters were much less likely to recognize their incumbent and it is the inability to recognize one's incumbent, irrespective of whether the representative has changed due to redistricting, which accounts for a reduced likelihood of voting for the incumbent. Conclusions. Other scholars have examined the relationship between redistricting and incumbent recognition. Likewise, many have evaluated the effects of redistricting on vote choice. This article, however, is the first to merge these two relationships. We find that redrawn constituents are less likely to know who their representative is, and it is indeed a lack of familiarity that reduces an incumbent's vote share. Thus, we have shown empirically that the absence of a personal vote, which is exacerbated by redistricting, proves electorally harmful to the incumbent.     

Multivariate analysis for the two-period repeated measures crossover design with application to clinical trials

Multivariate analysis techniques are applied to the two-period repeated measures crossover design. The approach considered in this paper has the advantage over the univariate analysis approach proposed recently by Wallenstein and Fisher (1977) that the former does not require any specific structure on the variance-covariance matrix of the repeated measures factor. (It should be noted that sums and differences of observations over periods are used for all tests. Therefore, there are two matrices under consideration, one for sums and one for differences.) Tests of significance are derived using the Wilks? criterion, and the procedure is illustrated with a numerical example from the area of clinical trials.     

A nonparametric estimator of the number of classes based on a stratified random sample

Under stratified random sampling, we develop a k^th-order bootstrap bias-corrected estimator of the number of classes θ which exist in a study region. This research extends Smith and van Belle’s (1984) first-order bootstrap bias-corrected estimator under simple random sampling. Our estimator has applicability for many settings including: estimating the number of animals when there are stratified capture periods, estimating the number of species based on stratified random sampling of subunits (say, quadrats) from the region, and estimating the number of errors/defects in a product based on observations from two or more types of inspectors. When the differences between the strata are large, utilizing stratified random sampling and our estimator often results in superior performance versus the use of simple random sampling and its bootstrap or jackknife [Burnham and Overton (1978)] estimator. The superior performance is often associated with more observed classes, and we provide insights into optimal designation of the strata and optimal allocation of sample sectors to strata.     

Reanalysis of Dose-Response Data from the Iraqi Methylmercury Poisoning Episode

Applying a hockey stick parametric dose-response model to data on late or retarded development in Iraqi children exposed in utero to methylmercury, with mercury (Hg) exposure characterized by the peak Hg concentration in mothers'hair during pregnancy, Cox et al. calculated the "best statistical estimate" of the threshold for health effects as 10 ppm Hg in hair with a 95% range of uncertainty of between 0 and 13.6 ppm.⁽¹⁾A new application of the hockey stick model to the Iraqi data shows, however, that the statistical upper limit of the threshold based on the hockey stick model could be as high as 255 ppm. Furthermore, the maximum likelihood estimate of the threshold using a different parametric model is virtually zero. These and other analyses demonstrate that threshold estimates based on parametric models exhibit high statistical variability and model dependency, and are highly sensitive to the precise definition of an abnormal response. Consequently, they are not a reliable basis for setting a reference dose (RfD) for methylmercury. Benchmark analyses and statistical analyses useful for deriving NOAELs are also presented. We believe these latter analyses—particularly the benchmark analyses—generally form a sounder basis for determining RfDs than the type of hockey stick analysis presented by Cox et al. However, the acute nature of the exposures, as well as other limitations in the Iraqi data suggest that other data may be more appropriate for determining acceptable human exposures to methylmercury.     

The E-R-A Model: The Role of Feelings in Family Therapy Reconsidered: Implications for a Classification of Theories of Family Therapy

The importance of considering a continuum of Emotionality (E) separately from Rationality (R) or Activity (A) is emphasized. A model showing equally all three aspects is presented that allows a clearer classification of family therapy theorists than has been possible heretofore. Diagnostic, therapeutic, and research implications are hinted at, but will be part of future work.     

ADVERTISING AS A PRIVATELY SUPPLIED PUBLIC GOOD

In this paper we take a view of advertising and certain other nonprice competition as public goods. This leads to a new fundamental justification of the zero price distribution of advertising, coupled with a mark-up of the advertised good as a disguised form of price discrimination which approximates that required for efficient pricing of a public good. Further, we present a numerical counterexample wherein the commonly-observed zero price distribution of advertising is shown to lead to higher consumer and seller welfare than its separate sales at cost.