A model-based clustering approach for analyzing energy-related financial literacy and its determinants

Recent research highlights the role of consumer's energy-related financial literacy in adoption of energy efficient household appliances in order to reduce the energy-efficiency gap within the household sector. The computation of an indicator for such a literacy measure has followed a somewhat less refined approach though. This paper demonstrates the use of a model-based clustering strategy in order to differentiate the population based on the level of energy-related financial literacy. Using a Swiss data with 6, 722 respondents, we are able to identify three latent groups that represent low, mid and high levels of literacy. We use this new measure within an ordered logit setting with the goal of explaining the determinants of the level of energy-related financial literacy and compare empirical results using classical indicators and approaches. The empirical findings suggest a significant gender-gap among the Swiss population, i.e. females, even those with university education, are less likely to possess a high level of energy-related financial literacy. Individuals who display strong concern for free-riding on their own energy reduction behavior, are also found to have higher odds of belonging to the low literacy group. The results show that it is possible to identify latent classes that have a general and intuitive meaning and provides support to the model-based clustering approach as a sophisticated alternative. This could be a useful approach when empirical researchers are interested in (attribute-based) latent groups of consumers. The identification of latent classes also provides a possibility to target consumers belonging to these classes with specific policy measures in order to increase their level of literacy.