How he won: Using machine learning to understand Trump’s 2016 victory

Zhaochen He (), John Camobreco () and Keith Perkins ()
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Zhaochen He: Christopher Newport University
John Camobreco: Christopher Newport University
Keith Perkins: Christopher Newport University

Journal of Computational Social Science, 2022, vol. 5, issue 1, No 38, 905-947

Abstract: Abstract The meaning of Donald Trump’s 2016 victory has been widely debated. Some believe that Trump’s success stemmed from the decline of manufacturing and other macroeconomic changes. Others see a political strategy that exploited antagonism towards minorities and immigrants. We put both accounts to the test. Using data from the Quarterly Workforce Indicators (QWI) program, we construct a county-level metric of job decline and pair it with a large survey of political and social opinion. Using both logistic regression and random forest classification, we then estimate the impact of economics, race, and other factors on voter choice in 2016. We also perform a “what if” analysis, predicting how the election would have proceeded had voters experienced greater economic hardship, or harbored more progressive views towards race and immigration. Overall, our research indicates that attitudes towards race and immigration played a significantly larger role in the elections than economics. However, we do find evidence that deteriorating job conditions may have exacerbated the importance of racial views.

Keywords: Trump; 2016; Economics; Racism; Machine learning; Random forest (search for similar items in EconPapers)
Date: 2022
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DOI: 10.1007/s42001-021-00147-3

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