Low-income households allocate a disproportionately higher share of their disposable income to energy expenses than other households do. According to the Energy Information Administration, American households with an annual income below $20,000 spent over 7.9% of their income on energy. This number is below 0.3% for households earning more than $120,000 per year. As a result, policies that aim to internalize negative externalities in the energy sector are notoriously regressive (Grainger and Kolstad, 2010). Currently, this regressivity in the energy sector is addressed with two types of policies: increasing block pricing and means-tested subsidized tariffs. In practice, the redistribution gains from these policies are modest (Borenstein, 2012). Every year, thousands of low-income households are simply not able to afford energy services, fall into arrears, and end up at risk of being disconnected.
Across the country, institutions such as the Fuel Fund of Maryland and the Dollar Energy Fund help families struggling with their utility bills. Their mission is to provide small grants to low-income households that have received a disconnection notice from their utility. Though bill assistance plays an important role, it is a last resort solution to maintain energy services and does not address the long-term issue of how to help low-income households cope with high and often increasing energy bills. Policies that help families reduce their energy demand can thus potentially play an important role in this context. One such policy is energy education.
For decades, analysts have argued that there are opportunities to lower energy demand through low-cost behavior change (Diezt et al. 2009). Though the barriers to such opportunities are still hotly debated (Allcott and Greenstone, 2012), recent evidence suggests that providing energy information can lead to short-term and long-term reduction in energy usage (Allcott, 2011, Allcott and Rogers 2014, Houde et al. 2013). Yet important research questions still exist about the effectiveness of different technologies for providing information to customers and about specific impacts on low-income households. For instance, energy education that aims to engage households in active learning about low-cost behavior change has been called a promising solution (Wilson and Dowlatabadi, 2007), but its effectiveness is still mostly unknown.
Using a randomized experiment, we investigate the role of energy education provided to low-income households that face the risk of disconnection from their utility services. Our first goal is to evaluate whether energy education can lead to a decrease in energy demand. Our second goal is to investigate the effectiveness of different educational technologies, particularly comparing online education to in-person programs.