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«Assessing the Effect of Commute Time on Poverty in the United States Introduction Quinn Majeski Since the Great Recession there has been a growing ...»

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0.013 and 0.012 respectively, a more appropriate interpretation of this effect would be that every ten feet of roadway per person reduces commute time by 7.9 seconds.

The results, detailed in Table 2 below, show that the instrument, state road miles per capita, is statistically significant and highly predictive of the endogenous hypothesis variable, commute time. The size of this effect includes any reduced effectiveness as a result of induced demand. These results support the theorized strength of using state road miles per capita as an instrumental variable for commute time.

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InstrumentalVariable Probit The results of the IV probit model indicate that there is a positive relationship between the hypothesis variable, commute time, and outcome variable, use of public assistance. The mean marginal effect of a one minute increase in commute time is a one percent decrease in the likelihood that an individual is receiving public assistance. This effect is statistically significant at the 0.01 alpha level.

The coefficient on commute time is not as large as the coefficients on the demographic control variables, but is comparable to the coefficients on the continuous control variable years of education. A full summary of results is shown in Table 3.

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Beyond commute time, the control variables included in the specification all produce the expected effects. Increases in age and years of education, which typically correlate with higher earning potential, are associated with a decrease in the probability that an individual receives public assistance. In contrast, demographic factors that are often associated with greater economic adversity have the opposite effect;

being female or non-white both raise the expected likelihood of an individual receiving public assistance, although being of Asian descent lowers it. All of these coefficients are statistically significant at the 0.01 alpha level.

Possessing limited English language proficiency also raises the expected likelihood of an individual receiving public assistance, though not to the same extent as demographic factors. Although not directly related to the focus of this research, VOL 6, SPRING 2016 17 there appears to be a notable difference in coefficients between ‘Good’ or ‘Very Good’ English language skills and the ‘Poor’ or ‘None’ levels, suggesting a threshold proficiency for lowering the likelihood of receiving public assistance. All degrees of proficiency were statistically significant, with only ‘Very good’ failing to reach the

0.01 alpha level.

Discussion These results appear to support my hypothesis that there is a positive relationship between commute time and poverty. An increase in commute time has a statistically significant effect on the likelihood that an individual is receiving public assistance benefits – the outcome variable that I use as an indicator of whether or not an individual is living in poverty.

These results cannot necessarily confirm the theory underpinning my hypothesis, however. A statistically significant relationship between commute time and likelihood of receiving public assistance does not prove that the opportunity cost of travel are hindering individuals efforts to advance professionally. There could be another explanation for the relationship between longer commutes and the likelihood an individual is in poverty.

Nonetheless, these findings have implications for elected officials and public managers to consider. Investments in transportation infrastructure and programs that reduce commute times can play a role in broader efforts to address poverty. As can efforts to encourage dense residential development near employment centers and fixed rapid transit lines. Greater coordination between transportation departments and social service programs could yield more productive programs and policy interventions.

In addressing poverty, efforts to reduce commute time may also be useful in alleviating the need for government spending in public assistance programs. The return on investment of transportation dollars may be greater than previously imagined when factoring in potential savings realized in in these other programs.

Conclusion

In this paper I have sought to test, on an individual level, whether commute time has any significant effect on whether or not an individual is living in poverty. Using an IV probit model with state road miles per capita as my instrument, I found that an increase in commute time results in a statistically significant increase in the likelihood that an individual receives public assistance benefits.

MAJESKI

These results appear to lend credence to previous research, which has shown that neighborhood and regional transportation access affect employment, asset building, and intergenerational economic mobility. Such findings have important implications for policymakers in many fields and levels of government. While traffic reduction can be politically advantageous in its own right, policy tools that reduce commute times can also play a role in efforts to reduce poverty and potentially lower public assistance program costs.

The intersection of transportation and poverty is an area that is ripe for additional study. This paper builds on earlier work and contribute to a greater understanding of the subject, but additional experimental and quasi-experimental research is needed to fully understand and map the relationship between these two areas of public policy.

Works Cited American Community Survey. “American Community Survey and Puerto Rico Community Survey 2013 Subject Definitions.” Retrieved 3/9/16. http://www2.census.gov/programs-surveys/acs/ tech_docs/subject_definitions/2013_ACSSubjectDefinitions.pdf American Community Survey. “PUMS Data.” U.S. Census Bureau. Retrieved 12/4/15. https:// www.census.gov/programs-surveys/acs/data/pums.html Bivens, Josh and Lawrence Mishel. “Understanding the Historic Divergence Between Productivity and a Typical Worker’s Pay.” September 2, 2015. http://www.epi.org/publication/understandingthe-historic-divergence-between-productivity-and-a-typical-workers-pay-why-it-matters-and-whyits-real/ Bouchard, Mikayla. “Transportation Emerges as Crucial to Escaping Poverty.” The New York Times.

May 7, 2015. http://www.nytimes.com/2015/05/07/upshot/transportation-emerges-as-crucial-to-escaping-poverty.html?_r=0 Chetty, R. et al. “Where is the Land of Opportunity? The Geography of Intergenerational Mobility in the United States.” Quarterly Journal of Economics 129(4), 2014: 1553-1623.

DeSilver, Drew. “Who’s poor in America? 50 years into the ‘War on Poverty,’ a data portrait.” Pew Research Center. January 13, 2014. http://www.pewresearch.org/fact-tank/2014/01/13/ whos-poor-in-america-50-years-into-the-war-on-poverty-a-data-portrait/

DeNavas-Walt, Carmen and Bernadette D. Proctor. “Income and Poverty in the United States:

2014.” U.S. Census Bureau. September 2015. https://www.census.gov/content/dam/Census/ library/publications/2015/demo/p60-252.pdf Duranton, Gilles and Matthew A. Turner. “The Fundamental Law of Road Congestion: Evidence from US Cities.” American Economic Review. October 2011.

“Funding Challenges in Highway and Transit: A federal-state-local analysis.” Pew Charitable VOL 6, SPRING 2016 19 Trust. February 24, 2015. http://www.pewtrusts.org/en/research-and-analysis/analysis/2015/02/24/funding-challenges-in-highway-and-transit-a-federal-state-local-analysis Ingraham, Christopher. “The astonishing human potential wasted on commutes.” The Washington Post. February 25, 2016. https://www.washingtonpost.com/news/wonk/wp/2016/02/25/ how-much-of-your-life-youre-wasting-on-your-commute/ Jaffe, Eric. “The One Chart That Explains All Your Traffic Woes.” CityLab. March 2, 2015.

http://www.citylab.com/commute/2015/03/the-one-chart-that-explains-all-your-trafficwoes/386594/ Kaufman, Sarah M. et al. “Mobility, Economic Opportunity and New York City Neighborhoods.” New York University Wagner School, Rudin Center for Transportation Policy & Management.

November 2015. http://wagner.nyu.edu/rudincenter/wp-content/uploads/2015/11/JobAccessNov2015.pdf Litman, Todd. “Generated Traffic and Induced Travel: Implications for Transport Planning.” Victoria Transport Institute. January 27, 2015. http://www.vtpi.org/gentraf.pdf Saez, Emmanuel. “Striking it Richer: The Evolution of Top Incomes in the United States.” University of California, Berkeley. September 3, 2013. http://eml.berkeley.edu//~saez/saez-UStopincomes-2012.pdf “Table 1-1: Public Road Length, Miles by Functional System: 2013.” USDOT Bureau of Transportation Statistics. June 2015. http://www.rita.dot.gov/bts/sites/rita.dot.gov.bts/files/publications/state_transportation_statistics/state_transportation_statistics_2015/chapter-1/table1_1 U.S. Census Bureau. “American Community Survey, 1-Year Estimates, 2013.” U.S. Department of Commerce. Retrieved 12/14/15. http://www.census.gov/acs/www/data/data-tables-andtools/subject-tables/



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