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.     

In Whose Backyard?: Concern About Siting a Nuclear Waste Facility*

Proponents of hazardous and nuclear waste depositories label opponents to local siting of such facilities “NIMBYs” (Not In My Backyard). This study assesses the extent to which the NIMBY label and the strategies of industry proponents to reduce opposition function on a reasonable set of assumptions. Using survey data and multiple regression techniques, the levels of concern of residents living in the county selected as the site of a low level radioactive waste disposal facility (imminent threat condition) are compared with a statewide sample (hypothetical threat condition). Consistent with proponents' theoretical assumptions, the levels of concern are greater for respondents under conditions of imminent threat than of hypothetical threat. However, within the host county, levels of concern are lowest, albeit most polarized, in the community closest to the proposed site. A conflict theory approach enhances an understanding of these findings by suggesting that within the most proximate community levels of concern are lowest for citizens who stand to gain the most economic benefits from the facility but highest for those citizens who are least likely to derive tangible gains.     

The Boston Area Diary Study and the Moral Citizenship of Care

This paper describes the theoretical foundations, empirical findings, and practical and philosophical implications of the Boston Area Diary Study (BADS), a study of the caring behavior of 44 participants over one calendar year. In particular, the paper presents an identification theory of care and discusses how it shaped the conceptualization, collection, and analysis of the data in a year-long diary study of daily voluntary assistance. The findings from the BADS (1) theoretically confirm the identification theory of care; (2) methodologically capture how individuals perceive and carry out caring behavior as a unity; and (3rpar; empirically document the existence of a moral citizenship in America that is substantially more vigorous than is implied by the usual indicators of civic and political citizenship.     

Enabling a Powerful Marine and Offshore Decision-Support Solution Through Bayesian Network Technique

A powerful practical solution is by far the most desired output when making decisions under the realm of uncertainty on any safety-critical marine or offshore units and their systems. With data and information typically being obtained incrementally, adopting Bayesian network (BN) is shown to realistically deal with the random uncertainties while at the same time making risk assessments easier to build and to check. A well-matched methodology is proposed to formalize the reasoning in which the focal mechanism of inference processing relies on the sound Bayes's rule/theorem that permits the logic. Expanding one or more influencing nodal parameters with decision and utility node(s) also yields an influence diagram (ID). BN and ID feasibility is shown in a marine evacuation scenario and that of authorized vessels to floating, production, storage, and offloading collision, developed via a commercial computer tool. Sensitivity analysis and validation of the produced results are also presented.     

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.     

Testing regression lack of fit without replication: a tutorial around Minitab's XLOF procedures

The tutorial is concerned with two types of test for the general lack of fit of a linear regression model, as found in the Minitab software package, and which do not require replicated observations. They aim to identify non-specified curvature and interaction in predictors, by comparing fits over the predictor region divided into two parts. Minitab's regression subcommand XLOF which gives the tests is only briefly documented in the manual and, unlike virtually all other statistical procedures in the software, it is not standard and cannot be readily found in textbooks. The two types of test are described here; they concern the predictors one-at-a-time and the predictors all-at-once. An example of their use is given. A suite of macros is available which reproduces the results of the XLOF tests in much more detail than is given by the XLOF subcommand.     

AN ALGORITHM FOR THE CONSTRUCTION OF OPTIMAL OR NEAR-OPTIMAL CHANGE-OVER DESIGNS

This paper presents an algorithm for the construction of optimal or near optimal change-over designs for arbitrary numbers of treatments, periods and units. Previous research on optimality has been either theoretical or has resulted in limited tabulations of small optimal designs. The algorithm consists of a number of steps:first find an optimal direct treatment effects design, ignoring residual effects, and then optimise this class of designs with respect to residual effects. Poor designs are avoided by judicious application of the (M, S)-optimality criterion, and modifications of it, to appropriate matrices. The performance of the algorithm is illustrated by examples.