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Revisiting the relationship between traffic congestion and the economy: a longitudinal examination of U.S. metropolitan areas

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Abstract

Conventional transportation practices typically focus on alleviating traffic congestion affecting motorists during peak travel periods. One of the underlying assumptions is that traffic congestion, particularly during these peak periods, is harmful to a region’s economy. This paper seeks to answer a seemingly straightforward question: is the fear of the negative economic effects of traffic congestion justified, or is congestion merely a nuisance with little economic impact? This research analyzed 30 years of data for 89 US metropolitan statistical areas (MSAs) to evaluate the economic impacts of traffic congestion at the regional level. Employing a two-stage, least squares panel regression model, we controlled for endogeneity using instrumental variables and assessed the association between traffic congestion and per capita gross domestic product (GDP) as well as between traffic congestion and job growth for an 11-year time period. We then investigated the relationship between traffic congestion and per capita income for those same 11 years as well as for the thirty-year time period (1982–2011) when traffic congestion data were available. Controlling for the key variables found to be significant in the existing literature, our results suggest that the potential negative impact of traffic congestion on the economy does not deserve the attention it receives. Economic productivity is not significantly negatively impacted by high levels of traffic congestion. In fact, the results suggest a positive association between traffic congestion and per capita GDP as well as between traffic congestion and job growth at the MSA level. There was a statistically insignificant effect on per capita income. There may be valid reasons to continue the fight against congestion, but the idea that congestion will stifle the economy does not appear to be one of them.

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Fig. 1

(Source: created using data collected from BEA and FHWA)

Fig. 2
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Notes

  1. The Durbin Wu–Hausman results for our traffic congestion variable are included in Table 2.

    With vehicle miles traveled variable: for Model 1, Durbin p = 0.5034 and Wu–Hausman p = 0.5096; for Model 2, Durbin p = 0.2259 and Wu–Hausman p = 0.2322; and for Model 3, Durbin p = 0.2769 and Wu–Hausman p = 0.2844.

    With the population density variable: for Model 1, Durbin p = 0.7809 and Wu–Hausman p = 0.7837; for Model 2, Durbin p = 0.4317 and Wu–Hausman p = 0.4375; and for Model 3, Durbin p = 0.2769 and Wu–Hausman p = 0.2844.

    With both the VMT and population density variables, these results tell us that instrumentation is not necessary.

  2. The results of the Durbin–Wu–Hausman tests of endogeneity are shown at the bottom of Table 2, and significant p values indicate that the variables in question are endogenous and need to be corrected.

References

  • Ahangari, H., Outlaw, J., Atkinson-Palombo, C., Garrick, N.W.: An Investigation into the Impact of Fluctuations in Gasoline Prices and Macroeconomic Conditions on Road safEty in Developed Countries. Transportation Reserach Board Annual Meeting, Washington DC (2014)

    Google Scholar 

  • Anas, A., Xu, R.: Congestion, land use, and job dispersion: A general equilibrium model. J. Urban Econ. 45, 451–473 (1999)

    Google Scholar 

  • Andolfatto, D., Ferrall, C., Gomme, P.: Human Capital Theory and the Life-Cycle Pattern of Learning and Earning, Income and Wealth. In: University, S.F. (ed.) Burnaby, BC, Canada (2000)

  • Arnott, R.: Congestion tolling with agglomeration externalities. J. Urban Econ. 62, 187–203 (2007)

    Google Scholar 

  • ASCE: Infrastructure Report Card. American Society of Civil Engineers, Washington DC (2017)

    Google Scholar 

  • Baum-Snow, N.: Did highways cause suburbanization? Quart. J. Econ. 122, 775–805 (2007)

    Google Scholar 

  • Bertini, R.: You Are the Traffic Jam: An Examination of Congestion Measures. Transportation Research Board, Washington DC (2006)

    Google Scholar 

  • Bilbao-Ubillos, J.: The costs of urban congestion: estimation of welfare losses arising from congestion on cross-town link roads. Transp. Res. A Policy Pract. 42, 1098–1108 (2008)

    Google Scholar 

  • BLS: CPI Inflation Calculator [Online]. Bureau of Labor Statistics, U.S. Department of Labor, Washington DC. http://www.bls.gov/data/inflation_calculator.htm (2017). Accessed 25 Sept 2017

  • Boarnet, M.G.: Infrastructure services and the productivity of public capital: The case of streets and highways. Natl. Tax J. 50, 39–57 (1997)

    Google Scholar 

  • Bobbitt-Zeher, D.: The gender income gap and the role of education. Sociol. Educ. 80, 1–22 (2007)

    Google Scholar 

  • C-SPAN: House Transportation and Infrastructure Committee [Online]. Cable-Satellite Public Affairs Network. www.c-span.org/organization/?2118&congress=114 (2016). Accessed 9 Aug 2016

  • Cervero, R.: Jobs-housing balance revisited—trends and impacts in the San Francisco Bay Area. J. Am. Plan. Assoc. 62, 492–511 (1996)

    Google Scholar 

  • Cervero, R.: Induced travel demand: research design, empirical evidence, and normative policies. J. Plan. Lit. 17, 3–20 (2002)

    Google Scholar 

  • Cervero, R., Duncan, M.: Which reduces vehicle travel more: jobs-housing balance or retail-housing mixing? J. Am. Plan. Assoc. 72, 475–490 (2006)

    Google Scholar 

  • Cervero, R., Hansen, M.: Induced travel demand and induced road investment—a simultaneous equation analysis. J. Transp. Econ. Policy 36, 469–490 (2002)

    Google Scholar 

  • Chatman, D.G., Noland, R.B.: Transit Service, Physical Agglomeration and Productivity in US Metropolitan Areas. Urban Studies 51, 917–937 (2014)

    Google Scholar 

  • Copeland, L.: Cities Afraid of Death by Congestion. USA Today, March 1 (2007)

  • Crane, R., Chatman, D.G.: Traffic and sprawl : evidence from U.S. commuting from 1985–1997. Plan. Mark. 6, 14–22 (2003)

    Google Scholar 

  • Detotto, C., Otranto, E.: Does crime affect economic growth? Kyklos 63, 330–345 (2010)

    Google Scholar 

  • DOJ & FBI: Uniform Crime Reporting Statistics [Online]. Clarksburg, WV: U.S. Department of Justice, Federal Bureau of Investigation. www.ucrdatatool.gov (2014)

  • Downs, A.: Stuck in Traffic. Brookings Institute, Washington DC (1992)

    Google Scholar 

  • Downs, A.: Can Traffic Congestion Be Cured? [Online]. Washington DC. http://www.brookings.edu/research/opinions/2006/06/30transportation-downs (2006)

  • Duranton, G., Turner, M.A.: The fundamental law of road congestion: evidence from US cities. Am. Econ. Rev. 101, 2616–2652 (2011)

    Google Scholar 

  • Dutzik, T., Baxandall, P.: A new direction: our changing relationship with driving and the implications for America’s future. U.S. PIRG Education Fund, Washington DC (2013)

    Google Scholar 

  • Ewing, R. & Cervero, R.: Travel and the built environment—A synthesis. Land Development and Public Involvement in Transportation, pp. 87–114 (2001)

  • Ewing, R., Pendall, R., Chen, D.: Measuring sprawl and its transportation impacts. Travel Demand Land Use 2003, 175–183 (2003)

    Google Scholar 

  • Federal Highway Administration.: Focus on Congestion Relief [Online]. U.S. Department of Transportation, Washington DC. http://www.fhwa.dot.gov/congestion/ (2013). Accessed 12 May 2014

  • Fernald, J.G.: Roads to prosperity? Assessing the link between public capital and productivity. Am. Econ. Rev. 89, 619–638 (1999)

    Google Scholar 

  • Frank, L.D., Pivo, G.: Impacts of mixed use and density on utilization of three modes of travel: single-occupant vehicle, transit, and walking. Transp. Res. Rec. 1466, 44–52 (1994)

    Google Scholar 

  • Glaeser, E., Kahn, M.E.: Working Paper No. 9733: Sprawl and Urban Growth. National Bureau of Economic Research, Cambridge (2003)

  • Glaeser, E.L.: Cities, information, and economic growth. Cityscape 1, 9–47 (1994)

    Google Scholar 

  • Glaeser, E.L.: Are cities dying? J. Econ. Perspect. 12, 139–160 (1998)

    Google Scholar 

  • Glaeser, E.L.: Triumph of the City: How Our Greatest Invention Makes Us Richer, Smarter, Greener, Healthier, and Happier. Penguin Press, New York (2011)

    Google Scholar 

  • Glaeser, E.L., Kallal, H.D., Scheinkman, J.A., Shleifer, A.: Growth in cities. J. Polit. Econ. 100, 1126–1152 (1992)

    Google Scholar 

  • Glaeser, E.L., Kohlhase, J.E.: Cities, regions and the decline of transport costs. Pap. Reg. Sci. 83, 197–228 (2004)

    Google Scholar 

  • Glaeser, E.L., Kolko, J., Saiz, A.: Consumer city. J. Econ. Geogr. 1, 27–50 (2001)

    Google Scholar 

  • Gordon, P., Kumar, A., Richardson, H.W.: Congestion, changing metropolitan structure, and city size in the United-States. Int. Reg. Sci. Rev. 12, 45–56 (1989)

    Google Scholar 

  • Graham, D.J.: Variable returns to agglomeration and the effect of road traffic congestion. J. Urban Econ. 62, 103–120 (2007)

    Google Scholar 

  • Grant-Muller, S., Laird, J.: International Literature Review of the Costs of Road Traffic Congestion with the Main Focus on the Different Methods Used to Measure the Costs of Congestion. Institute for Transport Studies, University of Leeds, Leeds, Scotland (2007)

    Google Scholar 

  • Greene, W.H.: Econometric Analysis. Prentice Hall, Boston (2012)

    Google Scholar 

  • Harris, T.F., Ioannides, Y.M.: Productivity and Metropolitan Density. Tufts University, Medford, MA (2000)

    Google Scholar 

  • Hartgen, D.T., Fields, M.G., Layzell, A.L., San Jose, E.: How Employers View Traffic Congestion: Results of National Survey. Transportation Research Record, pp. 56–66 (2012)

    Google Scholar 

  • Highway Research Board: Urban Traffic Congestion. National Academy of Science, National Research Council, Washington DC (1954)

    Google Scholar 

  • Hymel, K.: Does traffic congestion reduce employment growth? J. Urban Econ. 65, 127–135 (2009)

    Google Scholar 

  • Jorgensen, R.E.: Influence of expressways in diverting traffic from alternate routes and in generating new traffic. Highw. Res. Board Proc. 27, 322–330 (1947)

    Google Scholar 

  • Katchova, A.: Instrumental Variables [Online]. Econometrics Academy, Lexington, KY. https://sites.google.com/site/econometricsacademy/econometrics-models/instrumental-variables (2014). Accessed 25 Jan 2014

  • Katz, B., Bradley, J.: The Metropolitan Revolution: How Cities and Metros are Fixing Our Broken Politics and Fragile Economy. Brookings Institution Press, Washington DC (2013)

    Google Scholar 

  • Klaer, J., Northrup, B.: Effects of GDP on Violent Crime. Georgia Tech, Atlanta (2014)

    Google Scholar 

  • Kriger, D., Miller, C., Baker, M., Joubert, F.: Costs of urban congestion in Canada—A model-based approach. Transp. Res. Record, 94–100 (2007)

    Google Scholar 

  • Levinson, D., Marshall, W., Axhausen, K.: Elements of Access. Network Design Lab, Sydney (2017)

    Google Scholar 

  • Levinson, D., Wu, Y.: The rational locator reexamined: are travel times still stable? Transportation 32, 187–202 (2005)

    Google Scholar 

  • Levinson, D.M., Kumar, A.: The rational locator—why travel-times have remained stable. J. Am. Plan. Assoc. 60, 319–332 (1994)

    Google Scholar 

  • Lewis, J.B., Devine, B., Pitcher, L., Martis, K.C.: Digital Boundary Definitions of United States Congressional Districts, 1789–2012 [Online]. UCLA. http://cdmaps.polisci.ucla.edu (2013). Accessed 10 Aug 2016

  • Litman, T.: Congestion Costing Critique. Victoria Transport Policy Institute, Victoria (2014)

    Google Scholar 

  • Lomax, T., Turner, S., Shunk, G.: NCHRP Report 398: Quantifying Congestion. National Academy Press, Washington DC (1997)

    Google Scholar 

  • Manson, S., Schroeder, J., Riper, D.V., Ruggles, S.: PUMS National Historical Geographic Information System: Version 12.0. University of Minnesota, Minneapolis (2017)

  • Manville, M., King, D.A., Smart, M.J.: The driving downturn: a preliminary assessment. J. Am. Plan. Assoc. 83, 14p (2017)

    Google Scholar 

  • Marrocu, E., Paci, R., Usai, S.: Productivity growth in the old and new europe: the role of agglomeration externalities. J. Reg. Sci. 53, 418–442 (2013)

    Google Scholar 

  • Marshall, W.E., Garrick, N.W.: Effect of street network design on walking and biking. Transp. Res. Rec., 103–115 (2010)

  • Marshall, W.E., Garrick, N.W.: Does street network design affect traffic safety? Accid. Anal. Prev. 43, 769–781 (2011)

    Google Scholar 

  • Mathworks: Time Series Regression VIII: Lagged Variables and Estimator Bias [Online]. www.mathworks.com/help/econ/examples/time-series-regression-viii-lagged-variables-and-estimator-bias.html (2017)

  • McDonald, N.C.: Active transportation to school—trends among US schoolchildren, 1969–2001. Am. J. Prev. Med. 32, 509–516 (2007)

    Google Scholar 

  • Mondschein, A., Brumbaugh, S., Taylor, B.D.: Congestion and Accessibility: What’s the Relationship?. Institute of Transport Studies, Los Angeles (2010)

    Google Scholar 

  • Morris, E.: From horse power to horsepower. Access 30, 2–9 (2007)

    Google Scholar 

  • Murnane, R.J.: Educating urban children. National Bureau of Economic Research, Cambridge (2008)

    Google Scholar 

  • NCHRP: Synthesis 311: performance measures of operational effectiveness for highway segments and systems. Transportation Research Board, Washington DC (2003)

    Google Scholar 

  • Noland, R., Quddus, M.: A spatially disaggregate analysis of road casualties in England. Accid. Anal. Prev. 36, 973–984 (2004)

    Google Scholar 

  • Noland, R.B.: Relationships between highway capacity and induced vehicle travel. Transp. Res. A Policy Pract. 35, 47–72 (2001)

    Google Scholar 

  • Noland, R.B.: Transport planning and environmental assessment: implications of induced travel effects. Int. J. Sustain. Transp. 1, 1–28 (2007)

    Google Scholar 

  • Paci, R., Usai, S.: Externalities, Knowledge, Spillovers and the Spatial Distribution of Innovation. Cagliari, Solter (2000)

    Google Scholar 

  • Portes, A., Rumbaut, R.N.G.: Immigrant America: a portrait. University of California Press, Berkeley and Los Angeles, California (2014)

    Google Scholar 

  • Public Works Administration: National System of Interstate Highways. Public Works Administration, Washington DC (1947)

    Google Scholar 

  • Public Works Administration: Interstate and Defense Highways Map. Public Works Administration, Washington DC (1957)

    Google Scholar 

  • Rajamani, J., Bhat, C.R., Handy, S., Knaap, G., Song, Y.: Assessing impact of urban form measures on nonwork trip mode choice after controlling for demographic and level-of-service effects. Travel Demand Land Use 2003, 158–165 (2003)

    Google Scholar 

  • Rappaport, J.: Moving to nice weather. Reg. Sci. Urban Econ. 37, 375–398 (2007)

    Google Scholar 

  • Redman, A., Sai, A.: Opportunities in an Urbanizing World. Credit Suisse Research Institute, Zurich (2012)

    Google Scholar 

  • Ribeiro, A., Antunes, A.P., Paez, A.: Road accessibility and cohesion in lagging regions: empirical evidence from Portugal based on spatial econometric models. J. Transp. Geogr. 18, 125–132 (2010)

    Google Scholar 

  • Roads & Bridges.: Congestion: DC Tops TTI List of Most Congested Cities [Online]. Arlington Heights, IL. Available: www.roadsbridges.com/congestion-dc-tops-tti-list-most-congested-cities (2013). Accessed 25 Sept 2017

  • Roddy, H.J.: Complete geography, American Book Company, New York, Cincinnati etc (1902)

    Google Scholar 

  • Roman, J. The Puzzling Relationship Between Crime and the Economy. Citylab (2013)

  • Rose, H., Betts, J.R.: The effect of high school courses on earnings. Rev. Econ. Stat. 86, 497–513 (2004)

    Google Scholar 

  • Safirova, E., Gillingham, K., Houde, S.: Measuring marginal congestion costs of urban transportation: Do networks matter? Transp. Res. A Policy Pract. 41, 734–749 (2007)

    Google Scholar 

  • Schrank, D., Eisele, B., Lomax, T.: Urban Mobility Report. Texas A&M Transportation Institute, College Station (2012)

    Google Scholar 

  • Schrank, D., Eisele, B., Lomax, T., Bak, J.: Urban Mobility Scorecard. Texas A&M Transportation Institute, College Station (2015)

    Google Scholar 

  • Sciara, G.-C.: Planning for unplanned pork. J. Am. Plan. Assoc. 78, 17p (2012)

    Google Scholar 

  • Staley, S.: Traffic Congestion and the Economic Decline of Cities [Online]. Reason Foundation. http://reason.org/news/show/traffic-congestion-and-the-economic (2012). Accessed 6 April 2014

  • Sundquist, E., Mccahill, C.: For the first time in a decade, U.S. per capita highway travel ticks up. State Smart Transportation Initiative, Madison (2015)

  • Sweet, M.: Does traffic congestion slow the economy? J. Plan. Lit. 26, 391–404 (2011)

    Google Scholar 

  • Sweet, M.: Do firms flee traffic congestion? J. Transp. Geogr. 35, 10p (2014a)

    Google Scholar 

  • Sweet, M.: Traffic congestion’s economic impacts: evidence from US metropolitan regions. Urban Stud. 51, 2088–2110 (2014b)

    Google Scholar 

  • The Economist.: Falling together: The relationship between crime and GDP in America [Online]. London, England. www.economist.com/blogs/dailychart/2011/09/us-crime-and-gdp (2011). Accessed 25 Sept 2017

  • The Prism Climate Group: Climate Obsertations [Online]. Corvallis, OR: Oregon State University. http://prism.oregonstate.edu/normals (2014). Accessed 15 Feb 2014

  • Tsai, Y.H.: Quantifying urban form: compactness versus ‘Sprawl’. Urban Stud. 42, 141–161 (2005)

    Google Scholar 

  • U. S. Census: Geographic Areas Reference Manual. U.S. Department of Commerce, Washington DC (1994)

  • U.S. Census: 2010 Census Urban Area FAQs [Online]. Washington, DC. https://www.census.gov/geo/reference/ua/uafaq.html (2010). Accessed 7 March 2014

  • Weisbrod, G., Vary, D., Treyz, G.: NCHRP Report 463: Economic Implications of Congestion. National Academy Press, Washington DC (2001)

    Google Scholar 

  • Weisbrod, G., Vary, D., Treyz, G.: Measuring economic costs of urban traffic congestion to business. Transp. Finance Econ. Econ. Dev. 2003, 98–106 (2003)

    Google Scholar 

  • Wheaton, W.C.: Commuting, congestion, and employment dispersal in cities with mixed land use. J. Urban Econ. 55, 417–438 (2004)

    Google Scholar 

  • Woudsma, C., Jensen, J.F., Kanaroglou, P., Maoh, H.: Logistics land use and the city: a spatial-temporal modeling approach. Transp. Res. E-Logist. Transp. Rev. 44, 277–297 (2008)

    Google Scholar 

  • Wright, E.O.: Race, class, and income inequality. Am. J. Sociol. 83, 1368–1397 (1978)

    Google Scholar 

  • Zhu, P.Y., Brown, J.R.: Donor states and donee states: investigating geographic redistribution of the US federal-aid highway program 1974-2008. Transportation 40, 203–227 (2013)

    Google Scholar 

  • Zolnik, E.J.: The effects of sprawl on private-vehicle commuting distances and times. Environ. Plan. B-Plan. Des. 38, 1071–1084 (2011)

    Google Scholar 

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Acknowledgements

The authors would like to thank Ed Gaviria for his help during our initial discussions surrounding this topic as well as his preliminary data collection efforts. Although we ended up developing our own instrumental variables for this analysis, we appreciate Dr. Matthias Sweet of Ryerson University for generously sharing those used in his impressive papers. Lastly, we would like to thank the anonymous reviewers for their detailed and insightful recommendations.

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  1. Department of Civil Engineering, University of Colorado Denver, 1200 Larimer Street, Campus Box 113, Denver, CO, 80217, USA

    Wesley E. Marshall

  2. School of Urban and Regional Planning, Florida Atlantic University, 777 Glades Road, SO 284, Boca Raton, FL, 33431, USA

    Eric Dumbaugh

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Appendix

Appendix

See Tables 7, 8, 9, 10, 11, 12 and 13.

Table 7 OLS regression results for models included in paper
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Table 8 Unlogged dependent variable models
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Table 9 Model 1: Lagged dependent variable results
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Table 10 Model 2: Lagged dependent variable results
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Table 11 Model 3: Lagged dependent variable results
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Table 12 Model 4: Lagged dependent variable results
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Table 13 Two-stage least-squares panel model regression with fixed effects
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Marshall, W.E., Dumbaugh, E. Revisiting the relationship between traffic congestion and the economy: a longitudinal examination of U.S. metropolitan areas. Transportation 47, 275–314 (2020). https://doi.org/10.1007/s11116-018-9884-5

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