Presidential Elections in Red Counties in Deep Blue States

• In Event: Local-level Policy and Resource Variation: Political Origins and Consequences

Abstract

Politics determines “who gets what, when, and how.” Political activities, for instance voting, occasion distributive consequences in form of public policy. American federalism renders diverse policy outcomes across jurisdictions at various levels such as states, counties, and cities. Where you live decides what you get in terms of public goods and services. Political Economy scholars connect voting choice to what people receive in return for their contributions, i.e., taxes. Acknowledging such relationship, citizens “vote with feet” and “homevoters” engage in local politics to ensure good public schools and steady home values. Benefiting from such discourses in the fields of State Politics and Federalism, Elections and Voting, and Political Economy, this paper explains the relationship between electoral outcomes and public services. By examining where Trump won votes in 2016 across states and counties, this paper suggests that voters consider what they have received locally. Public services seem to matter significantly and interact with ideology. New York is labeled as a “blue” state.

Given the electoral system of Electoral College, except for swing states, regardless of Republican votes, Democratic candidates most likely win the assigned electoral votes to the states. Rational choice scholars would ask, then, why they bother to vote. Rational voters would refrain from voting since the cost of voting outweighs the benefit only if the election outcome is considered as the core benefit.

Counting popular votes by the county level within a state, a deep blue state is no longer considered to be blue. In the state of New York, voters in almost 81% of counties (50 out of 62 counties) supported Trump over Clinton in the 2016 Presidential Election. In only 12 counties, mostly metropolitan areas including New York City and adjacent suburbs, Clinton was the winner of popular votes.

Trump supporters were not necessarily “forgotten men and women” in the Rust Belt and farmlands. Nicholas Carnes and Noam Lupu (2017) present their analysis that it is a myth that working-class white people made up the bulk of Trump’s supporters. Most Trump voters were not mostly working-class people and from middle-and high-income households. Twenty percent of white Trump voters without a college degree had a house hold income over $100,000 (Washinton Post June 5, 2017).

This research seeks an answer to the
aforementioned question by connecting public goods that voters received from their local governments to their voting behavior in the Presidential Election. Federalism creates multiple layers of governments from the top national government to the bottom municipalities, the governments of villages, towns and cities. In significant elections such as presidential ones, voters might still do the cost-benefit analysis by calculating what they received from the government or what they did not in their own communities. Provided that most of public services and goods that residents receive are tied to their local communities and therefore local governments, voters might project their voting preference beyond the local boundary to the national election. Despite the fact that they are subject to many governments at the town, county, state and national levels, voters might vote consistently through levels of government.

Fischel (2001)’s Homevoter Hypothesis focuses on public school as the main public service that voters consider most. The Tiebout theory predicts voters move to districts with lower taxes. Fischel explains that residents incorporate the benefit of public goods and services such as good public schools into consideration when they decide to pay higher taxes. Higher taxes and additional pecuniary contributions to the community are duly capitalized into their housing prices, which become middle class households’ main asset. Therefore, it is a rational choice to move into a community with good public schools and expensive houses.

A preliminary regression analysis on NYS counties shows that Trump voters in a deep blue state are not necessarily irrational. Public services in form of government provision are measured primarily as the number of fire stations within a county and high school graduation rates. It shows a mixed result. Less fire stations are correlated with more Trump votes while the higher high school graduation is with more Trump supporters in the community. This seems due to the fact that most Trump supporters are high school graduates. Graduate or professional degrees are negatively correlated with Trump votes. As expected, racial composition of communities mattered. More white residents indicated higher Trump vote share while communities with more minorities voted against Trump.
I plan to expand this research by investigating more public provision variables and other socio-economic and demographic variables and by including more blue states beyond NYS.

Author

• Eunju Kang, SUNY Geneseo