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

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
Fig. 3

Notes

  1. 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. 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.

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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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Appendix

Appendix

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

Table 7 OLS regression results for models included in paper
Table 8 Unlogged dependent variable models
Table 9 Model 1: Lagged dependent variable results
Table 10 Model 2: Lagged dependent variable results
Table 11 Model 3: Lagged dependent variable results
Table 12 Model 4: Lagged dependent variable results
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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Keywords

  • Traffic congestion
  • Economy
  • GDP
  • VMT
  • Cities