What to Do at Low Doses: A Bounding Approach for Economic Analysis

To quantify the health benefits of environmental policies, economists generally require estimates of the reduced probability of illness or death. For policies that reduce exposure to carcinogenic substances, these estimates traditionally have been obtained through the linear extrapolation of experimental dose-response data to low-exposure scenarios as described in the U.S. Environmental Protection Agency's Guidelines for Carcinogen Risk Assessment (1986). In response to evolving scientific knowledge, EPA proposed revisions to the guidelines in 1996. Under the proposed revisions, dose-response relationships would not be estimated for carcinogens thought to exhibit nonlinear modes of action. Such a change in cancer-risk assessment methods and outputs will likely have serious consequences for how benefit-cost analyses of policies aimed at reducing cancer risks are conducted. Any tendency for reduced quantification of effects in environmental risk assessments, such as those contemplated in the revisions to EPA's cancer-risk assessment guidelines, impedes the ability of economic analysts to respond to increasing calls for benefit-cost analysis. This article examines the implications for benefit-cost analysis of carcinogenic exposures of the proposed changes to the 1986 Guidelines and proposes an approach for bounding dose-response relationships when no biologically based models are available. In spite of the more limited quantitative information provided in a carcinogen risk assessment under the proposed revisions to the guidelines, we argue that reasonable bounds on dose-response relationships can be estimated for low-level exposures to nonlinear carcinogens. This approach yields estimates of reduced illness for use in a benefit-cost analysis while incorporating evidence of nonlinearities in the dose-response relationship. As an illustration, the bounding approach is applied to the case of chloroform exposure.     

Optimal orthogonal-array-based latin hypercubes

The use of optimal orthogonal array latin hypercube designs is proposed. Orthogonal arrays were proposed for constructing latin hypercube designs by Tang (1993). Such designs generally have better space filling properties than random latin hypercube designs. Even so, these designs do not necessarily fill the space particularly well. As a result, we consider orthogonal-array-based latin hypercube designs that try to achieve optimality in some sense. Optimization is performed by adapting strategies found in Morris & Mitchell (1995) and Ye et al. (2000). The strategies here search only orthogonal-array-based latin hypercube designs and, as a result, optimal designs are found in a more efficient fashion. The designs found are in general agreement with existing optimal designs reported elsewhere.     

Benchmark Analysis: Shopping with Proper Confidence

We discuss the issue of using benchmark doses for quantifying (excess) risk associated with exposure to environmental hazards. The paradigm of low-dose risk estimation in dose-response modeling is used as the primary application scenario. Emphasis is placed on making simultaneous inferences on benchmark doses when data are in the form of proportions, although the concepts translate easily to other forms of outcome data.     

Testing How Management Matters in an Era of Government by Performance Management

Public administration finds itself in an era of government byperformance management, which is reflected in the widespreadassumption that management is a key determinant of performance,and that it is reasonable to expect managers to measurably improveorganizational effectiveness. This article joins a growing literaturein seeking to conceptualize and empirically test how externalenvironmental influences and internal management factors combineto create performance, relying on data from the 2002–2003National Administrative Studies Project (NASP-II) survey ofstate government health and human services officials. We categorizemanagerial efforts to facilitate organizational performanceas determined either through their interactions with the organizationalenvironment, or through employing workable levers to changeinternal organizational culture, structure, and technology.Among the external environmental variables we find that thesupport of elected officials and the influence of the publicand media have a positive impact on effectiveness. Among internalmanagement choices, the ability to create a developmental organizationalculture, establish a focus on results through goal clarity,and decentralize decision-making authority are all positivelyassociated with organizational effectiveness.     

Measuring the non-profit sector cross-nationally: a comparative methodology

As a result in part of the way non-profit institutions are defined and treated in the United Nations System of National Accounts (SNA), little is known about the size, scope, financial base and role of this set of institutions at the international level, even though their importance is increasingly being recognised throughout the world. To remedy this significant lack of systematic information on non-profit institutions cross-nationally, the Johns Hopkins Comparative Nonprofit Sector Project has undertaken a major analysis of the economic role of this sector in twelve countries throughout the world, utilising a more meaningful definition of the sector and an approach that is otherwise consistent with the overall thrust of the SNA system. This paper outlines the basis of the more inclusive definition of the non-profit sector embodied in this project, the classification system formulated to structure data-gathering on this more broadly-defined sector and the data assembly strategy developed to build up key estimates of the scale, structure and revenue sources of the non-profit sector in the project countries. The article concludes with a recommendation to incorporate a similar approach to the assembly of data on the non-profit sector into the ongoing SNA system on a regular basis.Professor Lester Salamon is Director of the Institute for Policy Studies and Director of the Comparative Nonprofit Sector Project at the Johns Hopkins University, Baltimore, Maryland, 21218.Helmut Anheier is Research Scientist at the Institute for Policy Studies at the Johns Hopkins University and Assistant Professor of Sociology at Rutgers University, New Brunswick, New Jersey, 08903. He is co-editor ofVoluntas.     

Gender and returns to promotion: The role of social and power relations

This study extends a theoretical framework of structural resources and vulnerabilities to an examination of individual promotion outcomes. Promotions of 454 individuals to administrative and professional staff positions in one higher education organization are analyzed. The findings confirm that both individual and structural resources are important for explaining the administrative level achieved by promotion in this setting. Although the returns to resouces do not vary by gender, women receive significantly less return to their promotions than men do to theirs. These findings underscore the importance of examining not only individual and structural resources but also the role of gender in the social and power relations that influence the promotion of women and men in organizations.     

MEANINGFUL REGRESSION AND ASSOCIATION MODELS FOR CLUSTERED ORDINAL DATA

Many proposed methods for analyzing clustered ordinal data focus on the regression model and consider the association structure within a cluster as a nuisance. However, the association structure is often of equal interest—for example, temporal association in longitudinal studies and association between responses to similar questions in a survey. We discuss the use, appropriateness, and interpretability of various latent variable and Markov models for the association structure and propose a new structure that exploits the ordinality of the response. The models are illustrated with a study concerning opinions regarding government spending and an analysis of stability and change in teenage marijuana use over time, where we reveal different behavioral patterns for boys and girls through a comprehensive investigation of individual response profiles.     

National survey evidence on disasters and relief: Risk beliefs, self-interest, and compassion

A nationally representative sample of respondents estimated their fatality risks from four types of natural disasters, and indicated whether they favored governmental disaster relief. For all hazards, including auto accident risks, most respondents assessed their risks as being below average, with one-third assessing them as average. Individuals from high-risk states, or with experience with disasters, estimate risks higher, though by less than reasonable calculations require. Four-fifths of our respondents favor government relief for disaster victims, but only one-third do for victims in high-risk areas. Individuals who perceive themselves at higher risk are more supportive of government assistance.