Analysis of the Impact of the Popularization of Uber on Drunk Driving in Densely Populated Counties, US
Poster Number
20A
Format
Poster Presentation
Faculty Mentor Name
Michelle Amaral
Faculty Mentor Department
Economics
Abstract/Artist Statement
The introduction of the rideshare service Uber in 2009 revolutionized the way consumers travel locally, inviting opportunities for study of its effects on society. While some of these effects may serve to bring positive changes to the number and nature of accidents that occur on the road, there lacks a comprehensive analysis of advancements that Uber potentially brings to these issues. This gives rise to a need for investigation into Uber’s influence on road conditions, and in particular, drunk driving. To analyze the potential impact of Uber on drunk driving, we utilize accident data from Fatality Analysis Reporting System (FARS) by county from 2007-2017. Using the introduction of Uber into any of the one hundred most densely populated counties in the US, we determine the number of accidents involving inebriated drivers per one thousand population during this time period. Using this as a dependent variable, we exploit variation across counties and over time in the introduction of Uber in densely populated counties to identify the relationship between the introduction of Uber in a county and the amount of drunk driving accidents. We run a series of additional model specifications to test the robustness of the model. This analysis is practically significant because it can be utilized in further studies of regional impacts of similar rideshare services as well as campaigning for methods to reduce drunk driving fatalities in the future. It is possible that these results may become more significant in magnitude in the future as the rideshare service becomes more prevalent in less densely populated areas across the country if its popularity continues to increase. This study is limited in that it does not account for Uber’s pricing or for counties that have restrictions on the ridesharing service’s use. Future work should incorporate these limitations.
Location
DeRosa University Center Ballroom
Start Date
27-4-2018 12:30 PM
End Date
27-4-2018 2:30 PM
Analysis of the Impact of the Popularization of Uber on Drunk Driving in Densely Populated Counties, US
DeRosa University Center Ballroom
The introduction of the rideshare service Uber in 2009 revolutionized the way consumers travel locally, inviting opportunities for study of its effects on society. While some of these effects may serve to bring positive changes to the number and nature of accidents that occur on the road, there lacks a comprehensive analysis of advancements that Uber potentially brings to these issues. This gives rise to a need for investigation into Uber’s influence on road conditions, and in particular, drunk driving. To analyze the potential impact of Uber on drunk driving, we utilize accident data from Fatality Analysis Reporting System (FARS) by county from 2007-2017. Using the introduction of Uber into any of the one hundred most densely populated counties in the US, we determine the number of accidents involving inebriated drivers per one thousand population during this time period. Using this as a dependent variable, we exploit variation across counties and over time in the introduction of Uber in densely populated counties to identify the relationship between the introduction of Uber in a county and the amount of drunk driving accidents. We run a series of additional model specifications to test the robustness of the model. This analysis is practically significant because it can be utilized in further studies of regional impacts of similar rideshare services as well as campaigning for methods to reduce drunk driving fatalities in the future. It is possible that these results may become more significant in magnitude in the future as the rideshare service becomes more prevalent in less densely populated areas across the country if its popularity continues to increase. This study is limited in that it does not account for Uber’s pricing or for counties that have restrictions on the ridesharing service’s use. Future work should incorporate these limitations.