Household composition and optimal commodity taxes

This paper investigates conditions under which demographic variables will have no impact on commodity taxes. We allow nonlinear and nonseparable preferences, a general demographic demand procedure, and a demogrant scheme linked to the number of children. Formulae for demographic revision of tax estimates are presented in a form that can be easily applied, and the only marginal data requirement is the number of children in the household. The paper extends an earlier exercise (Ray 1988) in avoiding the need for equivalence scales, and in using a demogrant scheme that is consistent with current practice in several European countries. The study confirms the robustness of the earlier discussion to the demogrant scheme adopted.I acknowledge, with thanks, the comments of two anonymous referees, but retain responsibility for errors that may remain.     

ESTIMATING TEMPORAL FARM INCOME DISTRIBUTIONS USING SPATIAL SMOOTHING TECHNIQUES

The Australian Bureau of Agricultural & Resource Economics (ABARE) has conducted the annual Australian Agricultural and Grazing Industries Survey, covering the broadacre sector, in a consistent format since 1978/79. In this period the incomes of farmers have fluctuated considerably. Interest has focused on temporal income variation because of its relationship to the inherent riskiness of farming. Part of these income fluctuations can be explained by changing commodity prices and by highly variable climatic conditions. This paper outlines a method of measuring the magnitude of this income variation and how it relates to other variables. Due to rotation within the sample of farms surveyed, data are limited for determining the income variability of any particular farm. This paper, using spatial locations of surveyed farms, shows how kernel smoothing techniques can be adapted to estimate the distribution of a farm's income. This analysis uses data collected from 1978/79 to 1991/92. Summary information from these distributions is cross-tabulated against several other variables. This shows that income variability is strongly related to the production mixture of farming businesses.     

STENO:an expert system for medical diagnosis based on graphical models and model search

Causal probabilistic models have been suggested for representing diagnostic knowledge in expert systems. This paper describes the theoretical basis for and the implementation of an expert system based on causal probabilistic networks. The system includes model search for building the knowledge base, a shell for making the knowledge base available for users in consultation sessions, and a user interface. The system contains facilities for storing knowledge and propagating new knowledge, and mechanisms for building the knowledge base by semi-automated analysis of a large sparse contingency table. The contingency table contains data acquired for patients in the same diagnostic category as the intended application area of the expert system. The knowledge base of the expert system is created by combining expert knowledge and a statistical model search in a model conversion scheme based on a theory developed by Lauritzen & Spiegelhalter and using exact tests as suggested by Kreiner. The system is implemented on a PC and has been used to simulate the diagnostic value of additional clinical information for coronary artery disease patients under consideration for being referred to coronary arteriography.     

A Discrete Time Parametric Model for the Analysis of Failure Time Data

This paper introduces a parametric discrete failure time model which allows a variety of smooth hazard function shapes, including shapes which are not readily available with continuous failure time models. The model is easy to fit, and statistical inference is simple. Further, it is readily extended to allow for differences between subjects while retaining the ease of fit and simplicity of statistical inference. The performance of the discrete time analysis is demonstrated by application to several data sets.     

Population and economics: A bioeconomic analysis

The notion of optimal population is recast as a problem of optimal scale (population times per capita resource use). Bioeconomic limits to scale are considered, drawing on concepts from A.J. Lotka and N. Georgescu-Roegen. The ethical choice between many people at low resource use per capita versus fewer people at higher resource per capita transcends bioeconomics, but is unavoidable and highlights the issue of sufficiency as well as efficiency. A policy of increasing resource severance taxes and lowering income taxes, especially on lower incomes, is suggested as a way of serving both efficiency and sufficiency.Presented at the symposium on Population and Scarcity: The Forgotten Dimensions.     

A neural' network applied to tlie calculation of lyapunov exponents

Chaotic deterministics systems are characterised by the instability of orbits on an attractor. The largest Lyapunov exponent measures on average the exponential growth rate of small deviations along an orbit and gives as such an indication whether or not the dynamic generating process is unstable. The direct method for calculation of the Lyapunov exponent, based on finite differences as formulated by the so-called Wolf-algorithm,fails on medium sized data sets. Alternatively, one can use a neural network with backpropagation to estimate a data generating function. This so-calletl indirect method enables us to recover the theoretical value of the largest Lyapunov exponent in several examples.