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

Author

Listed:
  • Zhaochen He

    (Christopher Newport University)

  • John Camobreco

    (Christopher Newport University)

  • Keith Perkins

    (Christopher Newport University)

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.

Suggested Citation

  • Zhaochen He & John Camobreco & Keith Perkins, 2022. "How he won: Using machine learning to understand Trump’s 2016 victory," Journal of Computational Social Science, Springer, vol. 5(1), pages 905-947, May.
    • Handle: RePEc:spr:jcsosc:v:5:y:2022:i:1:d:10.1007_s42001-021-00147-3
    DOI: 10.1007/s42001-021-00147-3
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    References listed on IDEAS

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