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How Does Public Transit Connectivity Affect Voter Turnout? The Case of US
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This paper utilizes three methodological approaches to explore the relationship between public transit connectivity and voter turnout. The first approach compares the cases of Los Angeles County, where public transit was free on the day of the 2020 presidential election, and Alameda County, where there was no such program. The second approach utilizes a multi-linear regression to quantify the relationship between public transit connectivity and voter turnout. The third approach makes use of Random Forest, a machine learning model, to make more accurate predictions of voter turnout based on a range of independent variables including public transit connectivity. Overall, this paper finds that there is a positive relationship between public transit connectivity and voter turnout. However, on average across the US, the effects of improvements in public transit connectivity on voter turnout are minute; this paper finds that an average nationwide improvement in public transit connectivity by approximately 40% resulted in a roughly 0.028% average increase in voter turnout across the country. However, in specific cases, the effects can be drastic; for example, in Jackson County, Missouri, there was a 1.65% increase in voter turnout for every 1% improvement in public transit. These results suggest that while public transit can be useful in improving voter turnout, policies that improve public transit connectivity in order to increase voter turnout should be implemented on a case-by-case basis.
Title: How Does Public Transit Connectivity Affect Voter Turnout? The Case of US
Description:
This paper utilizes three methodological approaches to explore the relationship between public transit connectivity and voter turnout.
The first approach compares the cases of Los Angeles County, where public transit was free on the day of the 2020 presidential election, and Alameda County, where there was no such program.
The second approach utilizes a multi-linear regression to quantify the relationship between public transit connectivity and voter turnout.
The third approach makes use of Random Forest, a machine learning model, to make more accurate predictions of voter turnout based on a range of independent variables including public transit connectivity.
Overall, this paper finds that there is a positive relationship between public transit connectivity and voter turnout.
However, on average across the US, the effects of improvements in public transit connectivity on voter turnout are minute; this paper finds that an average nationwide improvement in public transit connectivity by approximately 40% resulted in a roughly 0.
028% average increase in voter turnout across the country.
However, in specific cases, the effects can be drastic; for example, in Jackson County, Missouri, there was a 1.
65% increase in voter turnout for every 1% improvement in public transit.
These results suggest that while public transit can be useful in improving voter turnout, policies that improve public transit connectivity in order to increase voter turnout should be implemented on a case-by-case basis.
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