A longitudinal study of domestic water conservation behavior

A 1988 study of a school-linked sample in a metropolitan and a regional urban area established baseline data for knowledge, attitudes, intentions, and behavior with regard to water management and conservation (Murphy, Watson, & Moore, 1991). This paper reports on a 1991 follow-up, utilising both longitudinal and cross-sectional samples of students, teachers and parents, which aimed at identifying changes within the community since the initial study. In addition, the study sought to identify factors influential in change and the extent to which the pattern of relationships between knowledge, attitudes, intentions and behaviors had remained stable over time. The results indicated that there was a move towards greater conservation as measured by the variables studied over the three year period, that media interventions and water costs were perceived as influential in this change, and that reported conserving behavior continued to be better predicted by stated intentions than by knowledge. Little difference in the pattern of intervariable relationships was observed across the time span studied.     

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.     

A timely measure of unemployment

Conventional unemployment statistics have two dimensions—size and date. Population-at-risk unemployment rates add a third dimension—time. They are unlikely to revolutionise our picture of the nature of unemployment, says Ray Thomas . But they do give a more accurate and comprehensive picture than existing measures, and provide new insights into old problems, such as indicating the importance of statistics of entrants in helping to explain geographical variation in unemployment.     

ROBUST ESTIMATION OF SMALL‐AREA MEANS AND QUANTILES

Small‐area estimation techniques have typically relied on plug‐in estimation based on models containing random area effects. More recently, regression M‐quantiles have been suggested for this purpose, thus avoiding conventional Gaussian assumptions, as well as problems associated with the specification of random effects. However, the plug‐in M‐quantile estimator for the small‐area mean can be shown to be the expected value of this mean with respect to a generally biased estimator of the small‐area cumulative distribution function of the characteristic of interest. To correct this problem, we propose a general framework for robust small‐area estimation, based on representing a small‐area estimator as a functional of a predictor of this small‐area cumulative distribution function. Key advantages of this framework are that it naturally leads to integrated estimation of small‐area means and quantiles and is not restricted to M‐quantile models. We also discuss mean squared error estimation for the resulting estimators, and demonstrate the advantages of our approach through model‐based and design‐based simulations, with the latter using economic data collected in an Australian farm survey.     

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.