Summary of the Workshop on Issues in Risk Assessment: Quantitative Methods for Developmental Toxicology

This report summarizes the proceedings of a conference on quantitative methods for assessing the risks of developmental toxicants. The conference was planned by a subcommittee of the National Research Council's Committee on Risk Assessment Methodology ⁴ in conjunction with staff from several federal agencies, including the U.S. Environmental Protection Agency, U.S. Food and Drug Administration, U.S. Consumer Products Safety Commission, and Health and Welfare Canada. Issues discussed at the workshop included computerized techniques for hazard identification, use of human and animal data for defining risks in a clinical setting, relationships between end points in developmental toxicity testing, reference dose calculations for developmental toxicology, analysis of quantitative dose-response data, mechanisms of developmental toxicity, physiologically based pharmacokinetic models, and structure-activity relationships. Although a formal consensus was not sought, many participants favored the evolution of quantitative techniques for developmental toxicology risk assessment, including the replacement of lowest observed adverse effect levels (LOAELs) and no observed adverse effect levels (NOAELs) with the benchmark dose methodology.     

The nature of gender

I explain a biosocial model of women’s gendered behavior (behavior on which the sexes differ). This model integrates a macro sociological theory with a biological theory derived from primate behavior. The sociological model is designed to explain changes in the relationship between sex and behavior over time or between groups. The biological model is designed to explain individual within-sex variance and between-sex variance in gendered behavior in a cohort. Results from an original study are presented to demonstrate that within-sex variance in women’s gendered behavior is explained well by the primate model. I conclude that human nature is gendered. The implications of this conclusion are explored for demographic and other social science research.     

Reducing U.S. carbon dioxide emissions: an assessment of different instruments

The possibility that carbon dioxide emissions from fossil fuel use might lead to global warming has become a leading environmental concern. Many scientific and environmental organizations have called for immediate action to limit carbon dioxide production. For the most part, however, public debate has focused on a single policy instrument: a carbon tax applied to fossil fuels in proportion to their carbon content. We present a detailed model of the U.S. economy and use it to compare carbon taxes with two other instruments that could achieve the same reduction in carbon dioxide emissions: a tax on the energy content of fossil fuels (a BTU tax) and an ad valorem tax on fuel use. We find that carbon taxes can achieve a given reduction with the least overall effect on the economy, but with a large effect on coal mining. Energy taxes are fairly similar to carbon taxes but have slightly less impact on coal mining and slightly greater overall cost. In contrast, ad valorem taxes fall much more lightly on coal mining but have a much greater effect on the economy as a whole.     

Modelling map positional error to infer true feature location

The authors consider the issue of map positional error, or the difference between location as represented in a spatial database (i.e., a map) and the corresponding unobservable true location. They propose a fully model‐based approach that incorporates aspects of the map registration process commonly performed by users of geographic informations systems, including rubber‐sheeting. They explain how estimates of positional error can be obtained, hence estimates of true location. They show that with multiple maps of varying accuracy along with ground truthing data, suitable model averaging offers a strategy for using all of the maps to learn about true location.     

HOUSEHOLD CHANGE AND RURAL SCHOOL ENROLLMENT IN MALAWI AND KENYA

This study examines school enrollment in two sub-Saharan African nations, Malawi and Kenya. The article advances a refined family economy theoretical framework for understanding variations in school enrollment. It recasts family economy frameworks to consider not only how the household institution mediates a broad social change, but also how the family institution itself may be influenced by a macroinstitutional change. The findings suggest that household structural changes, as well as changes in parents' activities and perceptions, help explain enrollment practices in rural Malawi and Kenya.     

An alternative to farmer age as an indicator of life-cycle stage: The case for a farm family age index

In studies of farming, the age of the principal decision-maker (PDM) has been associated with numerous farm structural and managerial features and has been widely accepted as a good indicator of the influence of life-cycle factors on decision-making. As such, it has become an important aspect of many quantitative studies of agricultural change. However, contemporary studies of family farming demonstrate that the concept of a single PDM in family farms is becoming an anachronism as alternative enterprises, pluriactivity and the scale of family farms force more diffuse management/operating systems. This raises questions concerning whether the age of the PDM can still be taken as representative of farm structure, strategy or life-cycle stage? Using a study conducted in the Grampian Mountains region of Scotland in 2003 this note investigates the impact of using an alternative index—compiled by averaging the age of family members working on the farm. It suggests that PDM age is a relatively poor indicator of farm structural and managerial features compared to a family age index and calls for researchers to think about alternative approaches to measuring ‘age’ as an indicator.     

Optimal designs for generalized linear models

Summary This paper solves some D-optimal design problems for certain Generalized Linear Models where the mean depends on two parameters and two explanatory variables. In all of the cases considered the support point of the optimal designs are found to be independent of the unknown parameters. While in some cases the optimal design measures are given by two points with equal weights, in others the support is given by three point with weights depending on the unknown parameters, hence the designs are locally optimal in general. Empirical results on the efficiency of the locally optimal designs are also given. Some of the designs found can also be used for planning D-optimal experiments for the normal linear model, where the mean must be positive. This research was carried out in part at University College, London as an M.Sc. project. Thanks are due to Prof. I. Ford (University of Glasgow) and Prof. A. Giovagnoli (University of Perugia) for their valuable suggestions and critical observations.     

Liability in child welfare

Child welfare workers and agencies are vulnerable to lawsuits. Recent court rulings have more clearly defined the liability of workers. The distinguishing variable is custody. Children injured while in the custody of the state are entitled to sue for damages. Children injured by their parent, even if under the supervision of a child welfare agency, are not entitled to claim a violation of their 14th Amendment rights. These cases are reviewed and strategies designed to minimize vulnerability are offered.