Access to electricity continues to be a popular subject in empirical studies. However, the choice of key factors related to electricity access in the literature to date has been ad hoc due to the lack of a theoretical framework. This paper adopts a Bayesian Model Averaging (BMA) approach to selects important factors related to electricity access from 26 socioeconomic indicators using a sample of 48 developing countries, and reveal their long-term relationship with electricity access. The BMA approach allows us to identify the optimal empirical model when a theoretical foundation is not available. Moreover, it allows us to address the relative importance of variables using posterior inclusion probabilities and thus has clear policy relevance. Our results show that access to finance, education, economic development, infrastructure, and industrialisation are positively related to electricity access in the long-run. Although the long-run relationship does not indicate causality, it shows that to maintain this relationship, policy adjustments against any deviations from the relationship are needed. Our study suggests that electrification needs not only economic, educational and infrastructural development, but also private sector participation, governments’ commitment and political will, and integration with poverty reduction and other development schemes.
Bibliographical noteWe thank the financial support from the Economic Research Institute for ASEAN and East Asia's FY2017–2018 Study on ‘Energy Poverty in the ASEAN Region’ and valuable comments from Prof Fukunari Kimura, Dr Phoumin Han, and other participants in the project's two working group meetings. Tong gratefully acknowledges the financial support of the Planning Projects of Humanities and Social Sciences Foundation of Ministry of Education of China (No. 15XJA790006 ). Xunpeng likewise acknowledges the financial support of the National Natural Science Foundation of China under Grant Nos. 71828401 and 71873029 , as does Dayong for the financial support from the National Natural Science Foundation of China under Grant No. 71573214 and the 111 Project under Grant No. B16040 .
- Bayesian model averaging
- Developing countries
- Electricity access
- Long-run relationship
- Rural electrification
- Social and economic development