Household preferences for residential electricity contracts

In Australia, a trilemma has emerged among the three stated objectives of energy policy, namely maintaining high system reliability, providing affordable energy and achieving a drastic reduction in greenhouse gas emissions. These three objectives cannot be simultaneously achieved in the short to medium term. This suggests there are choices for society and potential trade-offs that can be explored in the short to medium term. This dissertation utilises two methods to investigate and simulate consumer preferences for aspects of these trade-offs as well as the potential for switching behaviours for different residential electricity contract features. Since households are impacted by the cost of these energy policies, it is important to understand the policies they prefer. Consumer preferences are explored using a Discrete Choice Experiment (DCE) through the design of an online, multi-treatment survey of respondents from the states of New South Wales and Victoria. Each treatment involved choice tasks with the following shared contract features: the proportion of electricity sourced from renewable energy generation, investments in battery storage, information provision through the installation of smart electricity meters, and the imposition of consumption restrictions. Additional information was collected to explore potential sources of preference heterogeneity including how the status quo contract (or Business-as-Usual) is described, risk preferences, and financial literacy. Finally, an Agent-Based Model (ABM) is combined with a DCE to demonstrate a decision support tool that simultaneously simulates the likelihood of switching electricity contracts as well as the selection of a specific contract based on the DCE. Combining both models provide insights into consumer behaviours relevant to energy policy that otherwise would not have been observed if treated in isolation. The first paper compares two treatments that presented different versions of the status quo contract. In the first treatment, respondents could select a status quo contract with no additional costs being imposed, though the contract did involve the imposition of consumption restrictions during the evening. The second treatment describes the status quo as the most expensive contract with the highest levels for each of the attributes, including no imposed consumption restrictions. The reported results for the first treatment are interpreted as the Willingness to Pay (WTP) to remove consumption restrictions as well as increases in the levels of the other contract features presented. In the second treatment the alternative contracts offered involved lower levels for all features including cost, therefore the reported results are defined as the Willingness to Accept Compensation in the form of Lower Cost Increases (WTA-LCI). When comparing both treatments the WTA-LCI estimates are statistically larger for most of the features when compared to the equivalent WTP estimates. This result is consistent with past studies analysing differences between WTP and WTA. Both sets of the results provide unique insights regarding two contrasting policy stances of whether the financial costs of these policies should be imposed on households or not. The second paper analyses whether a respondent’s preference for risk explains differences in the WTA-LCI observed between respondents. After completing the choice tasks respondents completed a risk preference elicitation exercise. Two groups of respondents were identified, the first being those who were highly risk-averse and the second representing all other respondents. The results of the DCE suggest that the highly risk-averse group requires more compensation for reductions in contract features relative to the other group. It may be the case that this difference in WTA-LCI is due to the uncertainty respondents perceive with reductions in contract features. Addressing these uncertainties would work towards fostering public acceptance for policies that delay investments in infrastructure and that lead to the implementation of demand-side management policies. The role of financial literacy is explored in the third paper with a range of electricity contracts. Each respondent completed a financial literacy quiz that assessed the respondent’s knowledge of financial investments. The number of correctly answered questions on the quiz is used as a proxy measure of their financial literacy. A hybrid scaled mixed logit model was estimated to identify links between a respondent’s socio-demographic characteristics, their score on the financial literacy quiz, and the choices they made in the DCE. Several socio-demographic characteristics including age, gender and education were identified as being correlated with the number of questions correctly answered in the quiz. Financial literacy was found to be positively correlated with how consistent respondents were in evaluating different combinations of electricity contracts and their stated WTP for contract features. The results suggest that respondents who scored high on the quiz treated the features as investments, with costs incurred today leading to benefits being realised over time. The fourth paper extends the DCE research by combining an Agent-Based Model (ABM) with a DCE to simultaneously model the decision to switch from an existing contract as well as the selection of electricity contracts with specific contract features. Agents in the ABM represent households with unique characteristics with respect to their propensity to switch, their average bill size, and the size of their social network. The DCE included three contracts that differed with respect to their cost and the number of consumption restrictions imposed. Feedback effects were modelled in the ABM with the contract selected in the DCE affecting the likelihood of neighbouring households also switching from their existing contract. Several simulations modelled alternative scenarios reflecting changes in the size of annual price changes, the variance in electricity bills, and the size of social networks. The results suggest that low-income households are the most likely group to switch from their existing electricity contract. The price-elasticity of contract switching was estimated as being inelastic, consistent with related studies looking at price-elasticities with respect to electricity consumption. The findings of these papers highlight that there is preference heterogeneity with respect to contract features. Identifying what role status quo descriptions, risk preferences, and financial literacy plays in explaining preference heterogeneity is a novel element in this dissertation. The combination of an ABM and DCE demonstrate how the two methods can be utilised to simulate potential scenarios relevant to evaluating alternative energy policies, as well as fill in a gap with respect to the combination of these two methods in the resource economics literature