Electricity Markets and the Clean Power Plan/Consolidation in the Coal Sector

Students

2019
Wharton

Faculty

Assistant Professor of Business Economics and Public Policy

Project Summary

This summer I worked with Professor Mike Abito and his research assistant Jin Soo Hang in the Business Economics and Public Policy department. I assisted Professor Abito with his research projects on the effect of consolidation in the coal sector on negotiation of prices between coal and power plants, and the impact of the Clean Power Plan on power supplier’s energy investments. Both projects had a heavy emphasis on energy, which greatly appealed to me, considering the recent tumultuous events occurring in the industry due to the volatility of oil prices and climate change policy.

As both projects were still in the early phase of their completion, the focus of my work for the most part was data collection. I had to clean the project databases by cross-referencing the data entries with the data found on global data banks, like Bloomberg’s SDC Platinum. Apart from this, I would use online sources such as Factiva to help with preparing the data for analysis. For the first time, I found that a simple search on Google would not return the results I needed. I was exposed to searching for obscure data in novel ways. In particular, I learned about industry standard databases from which information can be procured. I was also briefly exposed to the data analysis phase of the project. I was shown how commands could be executed and how code could be organized on Stata, a software tool that would allow us to merge datasets and go on to carry out statistical analyses on them.

The content of the projects allowed me to observe the effectiveness of government policy in response to the adverse effects of global warming from the perspective of the market, which will be essential for any careers in the financial sector. Additionally, I briefly learned about how economic models are built, and the compromises that are made in order to prevent the models from being too mathematically complex. One such example was to observe data from a specific cross section of time, instead of across an indefinite time period.