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How robust is the renewable energy industry to political shocks? Evidence from the 2016 U.S. elections

Published online by Cambridge University Press:  27 September 2018

Michaël Aklin Open the ORCID record for Michaël Aklin [Opens in a new window]
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Abstract

Climate change mitigation relies increasingly on clean technologies such as renewable energy. Despite widespread success, further deployment of renewables has been met with resistance from voters and governments in several countries. How resilient is the renewable energy industry to adverse political events? I use the unexpected election of Donald Trump in the 2016 U.S. presidential race to study this question. As a vocal critic of renewables and a supporter of fossil fuels, his election is a plausible negative shock to the renewable energy sector. I examine stock market data to gauge the reaction of investors. I find that renewable energy stocks were adversely affected by the election. Overall, they experienced a cumulative abnormal loss in share values of about 6 percent on average over the twenty days that followed the election. However, I find that the negative effect is concentrated among non-U.S. firms. U.S. firms, on average, emerged unscathed. Non-U.S. companies, on the other hand, lost over 14 percent of their value in the aftermath of the election. This suggests that markets are more concerned by increasing obstacles to international business than a decrease of federal support for renewables.

Keywords

renewable energylock-inevent studystock markets
Type
Research Article
Information
Business and Politics , Volume 20 , Special Issue 4: Clean energy transitions in a global economy , December 2018 , pp. 523 - 552
Copyright
Copyright © V.K. Aggarwal 2018 and published under exclusive license to Cambridge University Press 

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Footnotes

I am grateful to Llewelyn Hughes, Jonas Meckling, two anonymous reviewers, and audience members at the University of Strathclyde for outstanding suggestions. Kelly Morrison and Maxfield Peterson provided excellent research assistance. Alice Kalinowski at Pitt's library kindly helped locate some of the data used in this analysis. The appendix and a replication package are available on Harvard Dataverse, https://doi.org/10.7910/DVN/9I106T.

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