Projections and forecasts in Australia: The context
Despite the considerable expansion of demographic projection activity in Australia over the past two decades, comparatively little of this work has found its way into the academic literature. This issue of the Journal attempts to address this deficiency by bringing together six distinctive contributions on various aspects of demographic projections and forecasting. The present introductory paper describes the context of this work and provides an overview of the papers which follow.
Household projections for Australia
Abstract In October 1999 the Australian Bureau of Statistics released household and family projections for Australia for the first time. It is argued that the methodology used to undertake these projections, although an improvement on previous methodologies, may be giving misleading results and further work may need to be undertaken in developing a suitable methodology for household projections.
The use of demographics to predict attitudes and product usage of utility customers -- an application of discriminant analysis.
Discriminant analysis was used to produce predictive models of attitudes and usage of utility customers based on the households usage and demographic characteristics. Once the models were developed they were applied to the utilitys entire customer base to produce estimates of the "attitude category" most likely for each customer. 1 of the models developed for Mississippi Power & Light Company (MPL MPL household level data base; 1980 census data; and 1985 aggregate list statistics. All of the discriminant function models appeared to be tapping the same basic dimensions i.e. all predictive variables were in some way related to or correlated with an age dimension and a socioeconomic dimension. In terms of attitude the age dimension seemed to discriminate utility residential customers with positive and neutral attitudes from customers with negative attitudes. Younger customers tended to hold negative attitudes toward their utility; older persons tended to hold neutral or positive attitudes. The older a utility customer the more likely he/she was to have a positive attitude. Utility customers of higher socioeconomic status tended to hold neutral attitudes and middle-class customers tended to have negative attitudes. Customers with lower socioeconomic status tended to hold a positive attitude. In sum demographics seem to be predictive of attitudes relating to utility companies. In general the various models could predict between 50-200% better than chance.