Project Description: 

During the second decade of the 21st century, many low-carbon electricity generation technologies (e.g. solar, wind) became economically feasible for the first time. Yet, only some countries chose to take advantage of this opportunity by incorporating these technologies into their electricity supply. What explains this variation? This project extends the emerging field of climate and energy politics to developing countries, which have heretofore been understudied. The objective is to understand the conditions under which political incumbents have incentive to incorporate low-carbon energy technologies in power generation buildouts. It is hypothesized that incentives depend on whether the incumbent is able to control who benefits from the supply chains that feed into each power generation technology. Therefore, the project aims to collect data on incumbent control over power generation supply chains, including rates of state ownership, the autonomy of state-owned enterprises, and the independence of regulatory agencies and line ministries which set procurement policy and allocate state contracts. Research assistants may also be asked to code information about other incumbent characteristics, such as the identity of energy and environmental cabinet ministers.

Undergraduate's Role: 

We are seeking to hire several students. The students would primarily collect qualitative data on institutional and market variables relating to power sector governance, drawing on research articles, newspaper databases, industry journals, government documents and websites etc. This would also involve using some existing databases on power sector governance and analyzing various reports. The students may also be asked to scrape/clean quantitative data found from a number of sources online.

Undergraduate's Qualifications: 

Some level of prior training in social science methodology or data science is desirable but not required. Some familiarity with power sector technologies, institutions, and/or economics is ideal. Please highlight coursework related to these fields in your application. Additionally, familiarity with R and Python is a plus, as is experience working with economic (especially-firm level) and institutional data.

3-6 hours