The Role of Cancer Risk in the Regulation of Industrial Pollution

The extent of carcinogen regulation under existing U.S. environmental statutes is assessed by developing measures of the scope and stringency of regulation. While concern about cancer risk has played an important political role in obtaining support for pollution control programs, it has not provided the predominant rationale for most regulatory actions taken to date. Less than 20% of all standards established to limit concentrations of chemicals in various media address carcinogens. Restrictions on chemical use are more frequently based on concerns about noncancer human health or ecological effects. Of the chemicals in commercial use which have been identified as potential human carcinogens on the basis of rodent bioassays, only a small proportion are regulated. There is an inverse relationship between the scope of regulatory coverage and the stringency of regulatory requirements: the largest percentages of identified carcinogens are affected by the least stringent requirements, such as information disclosure. Standards based on de minimis cancer risk levels have been established for only 10% of identified carcinogens and are restricted to one medium: water. Complete bans on use have affected very few chemicals. The general role that carcinogenicity now plays in the regulatory process is not dramatically different from that of other adverse human health effects: if a substance is identified as a hazard, it may eventually be subject to economically achievable and technically feasible restrictions.     

Mining the Data: Analyzing the Economic Implications of Mining for Nonmetropolitan Regions

Extractive industries such as logging and mining are generally expected to bring significant economic benefits to rural regions, but a growing number of findings have now challenged that common expectation. Still, it is not clear whether the findings of less–than–desirable economic outcomes are isolated or representative. In this article, we assemble literally all of the relevant quantitative findings on mining that we have been able to identify in published and/or technical literature from the United States. In the interest of rigor, we limit the assessment to cases in which strictly nonmetropolitan mining regions are compared against other nonmetropolitan regions and/or against those regions’ own experiences over time. Overall, 301 findings meet the criteria for inclusion. Contrary to the long–established assumptions, but consistent with more recent critiques, roughly half of all published findings indicate negative economic outcomes in mining communities, with the remaining findings being split roughly evenly between favorable and neutral/indeterminate ones. Positive findings are more likely to be associated with incomes than with poverty or (especially) unemployment rates, and they are more likely to come from the western United States, where much of the mining involves relatively large, new coal strip mines. Over half of all positive findings come from the years prior to 1982. In virtually all other categories, the plurality or majority of findings have been negative. When the patterns of findings are subjected to one–sample means tests, the only way to produce a significantly positive outcome is by combining all neutral/indeterminate findings with the positive ones, while focusing exclusively on incomes; by contrast, in the case of poverty or unemployment rates—as well as for the overall body of findings—the results are consistently and significantly negative, whether the neutral/indeterminate findings are combined with negative ones or omitted from the equations altogether. Until or unless future studies produce dramatically different findings, there appears to be no scientific basis for accepting the widespread, “obvious” assumption that mining will lead to economic improvement.     

Review Essay: What Is Left of the Left?

Sociological Forum -     

A Comparison of Partially Adaptive and Reweighted Least Squares Estimation

The small sample performance of least median of squares, reweighted least squares, least squares, least absolute deviations, and three partially adaptive estimators are compared using Monte Carlo simulations. Two data problems are addressed in the paper: (1) data generated from non-normal error distributions and (2) contaminated data. Breakdown plots are used to investigate the sensitivity of partially adaptive estimators to data contamination relative to RLS. One partially adaptive estimator performs especially well when the errors are skewed, while another partially adaptive estimator and RLS perform particularly well when the errors are extremely leptokur-totic. In comparison with RLS, partially adaptive estimators are only moderately effective in resisting data contamination; however, they outperform least squares and least absolute deviation estimators.     

Sources of family violence

This paper suggests a direction for the exploration of the causes of family violence. Explanatory models of family violence were considered in this regard, with the recommendation that a multi-determined model should be considered to ensure the most accurate explanation. We suggest that family violence will be best understood and prevented or alleviated, if a model is used that considers the interaction of structural violence and the personality features of all the family members.