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.     

A measure of slope-rotatability for second order response surface experimental designs

In the design of experiments for estimating the slope of a response surface, slope-rotatability is a desirable property. In this paper, a measure is introduced that enables us to assess the degree of slope-rotatability for a given response surface design. The measure takes the value zero if and only if the design is slope-rotatable, and becomes larger as the design deviates from a slope-rotatable design. Examples of applying this measure to some response surface designs are also given.     

Analytical approximations for iterated bootstrap confidence intervals

Standard algorithms for the construction of iterated bootstrap confidence intervals are computationally very demanding, requiring nested levels of bootstrap resampling. We propose an alternative approach to constructing double bootstrap confidence intervals that involves replacing the inner level of resampling by an analytical approximation. This approximation is based on saddlepoint methods and a tail probability approximation of DiCiccio and Martin (1991). Our technique significantly reduces the computational expense of iterated bootstrap calculations. A formal algorithm for the construction of our approximate iterated bootstrap confidence intervals is presented, and some crucial practical issues arising in its implementation are discussed. Our procedure is illustrated in the case of constructing confidence intervals for ratios of means using both real and simulated data. We repeat an experiment of Schenker (1985) involving the construction of bootstrap confidence intervals for a variance and demonstrate that our technique makes feasible the construction of accurate bootstrap confidence intervals in that context. Finally, we investigate the use of our technique in a more complex setting, that of constructing confidence intervals for a correlation coefficient.     

Defining the linear portion of a sigmoid‐shaped curve: bend points

Formulae are provided that define the ‘bend points’, the beginning and end of the essentially linear dose–response region, for the four‐parameter logistic model. The formulae are expressed in both response and dose units. The derivation of the formulae is shown in order to illustrate the general nature of the methodology. Examples are given that describe how the formulae may be used while planning and conducting bioassays. Copyright © 2003 John Wiley & Sons, Ltd.