Artifactual Uncertainty in Risk Analysis

The field of comparative risk analysis of electrical energy alternatives has traditionally been plagued by highly uncertain estimates of risk rates, and consequently by conflicting judgements of relative risk. To the extent that this uncertainty arises from traditional sources–imperfect observations or actual variance in the data–it can be brought within a Bayesian statistical framework which allows policy conclusions to be formulted and tested at different levels of confidence. It is shown that there are important methodological or "artifactual" sources of uncertainty, however, that cannot be treated by statistical means; these require conceptual advances for their resolution. By identifying these sources of uncertainty in simple thought experiments and examples, it is shown in what ways the concept of attributable risk, which is the policy-maker's chief concern, must be sharpened and refined to have unambiguous meaning. The conventional "multilinear" formula for calculating risk indices is challenged as a measure of attributable risk, and directions for further research to improve the methodological foundations of comparative risk analysis are identified.     

THE STAGING OF EMOTION: A DRAMATURGICAL ANALYSIS*

Dramaturgical analysis is employed in a participant observation study of the emotional performances of fans, coaches and players at a college football game. The structure of the staging for emotional display is described, as is the phasing of people into sets of contextually appropriate performances. The phasing is seen to evolve from expectation for emotional experience, to diffuse emotional readiness, and finally to specific emotional displays. The staging and phasing are shown to be directed by cue-producing others and events which evoke rapidly shifting emotional expressions. The implications of the findings for contrived emotional settings other than footgall games are discussed.     

More on insurance as a Giffen good

In this article, we generalize the Hoy and Robson (1981) analysis and provide a necessary and sufficient condition for insurance not to be a Giffen good. The condition gives a bound for the variation of absolute risk aversion that permits the wealth effect to be always dominated by the substitution effect.     

A Note on Boston's Racial Problems

This article offers an explanation of the underlying factors concerning relations among the blacks, Italians and Irish in Boston. Two studies were examined involving the economic situation of the Italians and Irish in Boston in order to understand this city's racial problems. These three groups (Italians, Irish and blacks) have been over represented in the lower occupation level and in the poorer housing areas. It is shown that the racial problems between the white ethnics (Irish and Italians) and blacks are caused by a lack of job and housing opportunities.     

Caveats for Causal Interpretations of Linear Regression Coefficients for Fine Particulate (PM2.5) Air Pollution Health Effects

Recent linear regression analyses have concluded that decreasing levels of fine particulate matter (PM2.5) air pollution have increased life expectancy in the United States. These findings have left unresolved questions about the causal relation between reductions in PM2.5 levels and changes in cause‐specific (especially, cardiovascular disease, CVD) mortality risks. Their robustness (e.g., sensitivity to deletion of a single data point) has also been questioned. We investigate these issues in the National Mortality and Morbidity Air Pollution Study database. Comparing changes in PM2.5 levels and cause‐specific mortality rates for elderly people in 24 cities between two periods separated by a decade (1987–1989 and 1999–2000) shows that reductions in PM2.5 were significantly associated with increases in respiratory mortality rates and with decreases in CVD mortality rates. CVD and all‐cause mortality risks fell equally for all months of the year over this period, but average PM2.5 levels increased significantly for winter months. This casts doubts on the causal interpretation that declines in PM2.5 over the decade caused reduced short‐term mortality risks. Nonlinear regression suggests that reduced or negative marginal health benefits are associated with reductions of PM2.5 below 1999–2000 levels (about 15 μg/m³). Such nonlinear relations imply that risk communication statements that project a constant incremental reduction in mortality risks per unit reduction in PM2.5 do not adequately reflect the realistic possibility of nonlinear exposure‐response relations and diminishing returns to further exposure reductions.     

The Value of Organizational Ambivalence for Small and Medium Size Enterprises in an Uncertain World

Prior literature suggests two competing mechanisms for how organizations respond to environmental forces – absorption and reduction – but there is no consensus on which mechanism is more effective. We solve this puzzle by analysing one underlying dimension of these two mechanisms – organizational ambivalence – in a small and medium size enterprise context. Specifically, we believe that because of the difference in consuming resources, organizational ambivalence can be divided into latent and active, which exhibit different uses in dealing with environmental forces and improving firm performance. We conducted two studies in four countries to test our hypotheses. These two studies yield unanimous support for our hypotheses regarding the impact of organizational ambivalence on organizational performance and mixed support for our hypotheses concerning environmental impacts on organizational ambivalence.     

Regression Versus Causation,Revisited

I recently discussed pitfalls in attempted causal inference based on reduced‐form regression models. I used as motivation a real‐world example from a paper by Dr. Sneeringer, which interpreted a reduced‐form regression analysis as implying the startling causal conclusion that “doubling of [livestock] production leads to a 7.4% increase in infant mortality.” This conclusion is based on: (A) fitting a reduced‐form regression model to aggregate (e.g., county‐level) data; and (B) (mis)interpreting a regression coefficient in this model as a causal coefficient, without performing any formal statistical tests for potential causation (such as conditional independence, Granger‐Sims, or path analysis tests). Dr. Sneeringer now adds comments that confirm and augment these deficiencies, while advocating methodological errors that, I believe, risk analysts should avoid if they want to reach logically sound, empirically valid, conclusions about cause and effect. She explains that, in addition to (A) and (B) above, she also performed other steps such as (C) manually selecting specific models and variables and (D) assuming (again, without testing) that hand‐picked surrogate variables are valid (e.g., that log‐transformed income is an adequate surrogate for poverty). In her view, these added steps imply that “critiques of A and B are not applicable” to her analysis and that therefore “a causal argument can be made” for “such a strong, robust correlation” as she believes her regression coefficient indicates. However, multiple wrongs do not create a right. Steps (C) and (D) exacerbate the problem of unjustified causal interpretation of regression coefficients, without rendering irrelevant the fact that (A) and (B) do not provide evidence of causality. This reply focuses on whether any statistical techniques can produce the silk purse of a valid causal inference from the sow's ear of a reduced‐form regression analysis of ecological data. We conclude that Dr. Sneeringer's analysis provides no valid indication that air pollution from livestock operations causes any increase in infant mortality rates. More generally, reduced‐form regression modeling of aggregate population data—no matter how it is augmented by fitting multiple models and hand‐selecting variables and transformations—is not adequate for valid causal inference about health effects caused by specific, but unmeasured, exposures.