<p>In this paper, we investigate the relationship between Donald Trump’s coal-focused campaign during his race for the White House and the outcome of the 2016 presidential election. First, we document an increase in the Republican Party’s vote share in coal counties from 2016 onwards, relative to previous presidential elections. Subsequently, we employ a spatial Durbin Error model to estimate the relationship between coal production and the Republican vote share at the county level in the 2016 U.S. Presidential Election. To avoid biased estimates, we account for spillover effects and apply spatial clustering. Our results reveal a sudden increase in the Republican vote share in coal counties in 2016, associated with the campaign pledge. When considering both the size of coal production, which varies across coal counties, and spillover effects, we again find a significant positive relationship. This effect becomes even more pronounced when using the vote-share difference between Mitt Romney in 2012 and Donald Trump in 2016 as the dependent variable. The positive relationship between coal production and Republican vote share remains robust after accounting for nonlinearities and when coal production per worker and per hour worked are employed as the main explanatory variables.</p>

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Trump digs votes: the role of Trump’s coal campaign in the 2016 presidential election

  • Philipp Steinbrunner,
  • Marina Di Giacomo,
  • Wolfgang Nagl

摘要

In this paper, we investigate the relationship between Donald Trump’s coal-focused campaign during his race for the White House and the outcome of the 2016 presidential election. First, we document an increase in the Republican Party’s vote share in coal counties from 2016 onwards, relative to previous presidential elections. Subsequently, we employ a spatial Durbin Error model to estimate the relationship between coal production and the Republican vote share at the county level in the 2016 U.S. Presidential Election. To avoid biased estimates, we account for spillover effects and apply spatial clustering. Our results reveal a sudden increase in the Republican vote share in coal counties in 2016, associated with the campaign pledge. When considering both the size of coal production, which varies across coal counties, and spillover effects, we again find a significant positive relationship. This effect becomes even more pronounced when using the vote-share difference between Mitt Romney in 2012 and Donald Trump in 2016 as the dependent variable. The positive relationship between coal production and Republican vote share remains robust after accounting for nonlinearities and when coal production per worker and per hour worked are employed as the main explanatory variables.