Abstract
In 2018, tax cuts coupled with foreign trade sanctions raised concerns on economic insecurity and voter disenfranchisement in America’s dominantly red heartland. President Trump’s campaign rhetoric also negatively characterized the Latinx population, a group tied to agricultural labor. Our research analyzes economic insecurity and identity politics as connected to vote share changes at the state and local levels between the 2016 and 2018 elections. Using identity theory, we posit that perceived threats, either economically or racially, will impact vote share. We find little evidence of a tax/tariff effect on changes in Republican vote share. However, race and ethnicity tied to Hispanic income gains or White-Black inequality in education impacted Republican vote share. We interpret these results as support for the argument that group-positional grievances motivated White voting behavior. All told, we find support for arguments that group-based grievances and perceived threats continued to motivate Republican support in the 2018 election.
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Notes
- 1.
They also have the capacity to change the narrative and redefine group definitions such that dominant group members define racial inequality as unjust and in need of being addressed.
- 2.
This measure is constructed based on the overall percentage, not the two party split. We include this because measuring the change in two party split percentage potentially ignores the impact of a third party candidate in one election and would overstate the relative percent of both major parties in that race. For example, imagine a theoretical district of 100,000 voters that Republicans won with 70% of the 2016 election. If reaction to tariffs creates a backlash against Republicans leading to a right-wing third party that draws 20,000 votes from the Republicans, the Republican Party will receive 50% of the overall vote, but 62.5% of the Republican-Democrat split. This would represent either a 20 or a 7.5% point decline, depending on the measure used. Since the overall, interest is in electoral outcomes, we opt for the measure that more accurately represents the likelihood of winning the election, rather than a hypothetical two party contest.
- 3.
So, the 2017 five-year estimate includes data from 2013 through 2017, and the 2012 estimates includes data from 2008–2012.
- 4.
The Census Bureau also provides examples of weighted outcomes, which we used to ensure that our weighting routine was accurate.
- 5.
In several instances, this data incorrectly identifies a candidate’s party and/or district. When such incidences arise, we default to the FEC elections outcome data (2016) or to the Politico elections results (2018).
- 6.
Models that tested the impact of the level and change in the percentage of foreign-born, non-citizen, and Asian residents also showed no significant association between those demographic measures and the change in the percent voting for the Republican candidate.
- 7.
Measures for the percentage of the population and the change in percentage who are not citizens and who are foreign-born were also estimated, the effects for these measures were also non-significant.
- 8.
The results remain substantively unchanged when state fixed effects are used.
- 9.
While we could rely on the ACS one-year estimates to estimate changes between 2015 and 2017, we prefer to adhere to the ACS suggestions to rely on the more accurate estimates provided in the five-year estimates.
- 10.
In the models all three measures are significant at the 0.05 level (change in percent above 150% of the poverty line: p < 0.001; change in percent living in a non-English speaking home: p < 0.004, interaction: p < 0.041)
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Doran, K., Sisco, T.S. (2020). Tariffs, Race, and Voting: A District-Level Analysis of the Trump Effect on the Republican Vote Share. In: Sisco, T., Lucas, J., Galdieri, C. (eds) The Unforeseen Impacts of the 2018 US Midterms. Palgrave Pivot, Cham. https://doi.org/10.1007/978-3-030-37940-7_6
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