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.     

Analysis of child labour in Peru and Pakistan: A comparative study

This paper analyses child labour participation and its key determinants using data sets from Peru and Pakistan. The results include tests of the ‘Luxury’ and ‘Substitution’ hypotheses that play key roles in recent studies on child labour and child schooling. The results reject both hypotheses in the context of child labour in Pakistan and suggest that income and related variables do not have the expected negative effect on children's work input. Rising wages of adult female labour in Pakistan, and falling adult male wage in Peru lead to increased participation of children in the labour market. The results on the combined country data formally establish the presence of strong individual country effects in the estimated regressions. For example, ceteris paribus, a Peruvian child is more likely to experience schooling than a Pakistani child. However, both countries agree on the positive role that adult female education and infrastructure investment in basic amenities can play in discouraging child labour and encouraging child schooling. Received: 24 August 1998/Accepted: 10 March 1999     

M-quantile models with application to poverty mapping

Over the last decade there has been growing demand for estimates of population characteristics at small area level. Unfortunately, cost constraints in the design of sample surveys lead to small sample sizes within these areas and as a result direct estimation, using only the survey data, is inappropriate since it yields estimates with unacceptable levels of precision. Small area models are designed to tackle the small sample size problem. The most popular class of models for small area estimation is random effects models that include random area effects to account for between area variations. However, such models also depend on strong distributional assumptions, require a formal specification of the random part of the model and do not easily allow for outlier robust inference. An alternative approach to small area estimation that is based on the use of M-quantile models was recently proposed by Chambers and Tzavidis (Biometrika 93(2):255–268, 2006) and Tzavidis and Chambers (Robust prediction of small area means and distributions. Working paper, 2007). Unlike traditional random effects models, M-quantile models do not depend on strong distributional assumption and automatically provide outlier robust inference. In this paper we illustrate for the first time how M-quantile models can be practically employed for deriving small area estimates of poverty and inequality. The methodology we propose improves the traditional poverty mapping methods in the following ways: (a) it enables the estimation of the distribution function of the study variable within the small area of interest both under an M-quantile and a random effects model, (b) it provides analytical, instead of empirical, estimation of the mean squared error of the M-quantile small area mean estimates and (c) it employs a robust to outliers estimation method. The methodology is applied to data from the 2002 Living Standards Measurement Survey (LSMS) in Albania for estimating (a) district level estimates of the incidence of poverty in Albania, (b) district level inequality measures and (c) the distribution function of household per-capita consumption expenditure in each district. Small area estimates of poverty and inequality show that the poorest Albanian districts are in the mountainous regions (north and north east) with the wealthiest districts, which are also linked with high levels of inequality, in the coastal (south west) and southern part of country. We discuss the practical advantages of our methodology and note the consistency of our results with results from previous studies. We further demonstrate the usefulness of the M-quantile estimation framework through design-based simulations based on two realistic survey data sets containing small area information and show that the M-quantile approach may be preferable when the aim is to estimate the small area distribution function.     

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.     

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.