Household waste recycling behavior: a market segmentation model

The potential of social marketing has been recognized in the United Kingdom by the Department for Environment, Food and Rural Affairs (DEFRA) as a useful tool for behavioral change for environmental problems. The techniques of social marketing have been used successfully by health organizations to tackle current public health issues. This article describes a research project which explored the current barriers to recycling household waste and the development of a segmentation model which could be used at the local level by authorities charged with waste collection and disposal. The research makes a unique contribution to social marketing through the introduction of a competencies framework and market segmentation for recycling behaviors.

The analysis and prediction of the quantity and composition of household refuse

This thesis describes the development of a simple and accurate method for estimating the quantity and composition of household waste arisings. The method is based on the fundamental tenet that waste arisings can be predicted from information on the demographic and socio-economic characteristics of households, thus reducing the need for the direct measurement of waste arisings to that necessary for the calibration of a prediction model. The aim of the research is twofold: firstly to investigate the generation of waste arisings at the household level, and secondly to devise a method for supplying information on waste arisings to meet the needs of waste collection and disposal authorities, policy makers at both national and European level and the manufacturers of plant and equipment for waste sorting and treatment. The research was carried out in three phases: theoretical, empirical and analytical. In the theoretical phase specific testable hypotheses were formulated concerning the process of waste generation at the household level. The empirical phase of the research involved an initial questionnaire survey of 1277 households to obtain data on their socio-economic characteristics, and the subsequent sorting of waste arisings from each of the households surveyed. The analytical phase was divided between (a) the testing of the research hypotheses by matching each household's waste against its demographic/socioeconomic characteristics (b) the development of statistical models capable of predicting the waste arisings from an individual household and (c) the development of a practical method for obtaining area-based estimates of waste arisings using readily available data from the national census. The latter method was found to represent a substantial improvement over conventional methods of waste estimation in terms of both accuracy and spatial flexibility. The research therefore represents a substantial contribution both to scientific knowledge of the process of household waste generation, and to the practical management of waste arisings.

Estimating the SCAN*PRO model of store sales: HB, FM or just OLS?

In this paper we investigate whether consideration of store-level heterogeneity in marketing mix effects improves the accuracy of the marketing mix elasticities, fit, and forecasting accuracy of the widely-applied SCAN*PRO model of store sales. Models with continuous and discrete representations of heterogeneity, estimated using hierarchical Bayes (HB) and finite mixture (FM) techniques, respectively, are empirically compared to the original model, which does not account for store-level heterogeneity in marketing mix effects, and is estimated using ordinary least squares (OLS). The empirical comparisons are conducted in two contexts: Dutch store-level scanner data for the shampoo product category, and an extensive simulation experiment. The simulation investigates how between- and within-segment variance in marketing mix effects, error variance, the number of weeks of data, and the number of stores impact the accuracy of marketing mix elasticities, model fit, and forecasting accuracy. Contrary to expectations, accommodating store-level heterogeneity does not improve the accuracy of marketing mix elasticities relative to the homogeneous SCAN*PRO model, suggesting that little may be lost by employing the original homogeneous SCAN*PRO model estimated using ordinary least squares. Improvements in fit and forecasting accuracy are also fairly modest. We pursue an explanation for this result since research in other contexts has shown clear advantages from assuming some type of heterogeneity in market response models. In an Afterthought section, we comment on the controversial nature of our result, distinguishing factors inherent to household-level data and associated models vs. general store-level data and associated models vs. the unique SCAN*PRO model specification.

Breaks in the UK household sector money demand function

We use non-parametric procedures to identify breaks in the underlying series of UK household sector money demand functions. Money demand functions are estimated using cointegration techniques and by employing both the Simple Sum and Divisia measures of money. P-star models are also estimated for out-of-sample inflation forecasting. Our findings suggest that the presence of breaks affects both the estimation of cointegrated money demand functions and the inflation forecasts. P-star forecast models based on Divisia measures appear more accurate at longer horizons and the majority of models with fundamentals perform better than a random walk model.