Does Building Highways Reduce Traﬃc Congestion?
68 Pages Posted: 1 Oct 2020
Date Written: August 10, 2020
In a seminal study, Duranton and Turner (2011) ﬁnds evidence that points to the existence of the fundamental law of highway congestion in the US. They build a causal model using an instrumental variable (IV) approach that yields an estimate of 1.03 for the elasticity of vehicle miles traveled (VMT) to the stock of interstate highways in US metropolitan areas. The result means that government eﬀorts to alleviate traﬃc congestion by expanding capacity are likely to fail — any increase in the stock of highways is accompanied by a commensurate increase in VMT, leaving travel times unaﬀected. In this article, we explore the impact of unobserved heterogeneity on the fundamental law. We begin by using a simple partial equilibrium model to demonstrate how metropolitan statistical areas (MSAs) that are identical in most respects but have diﬀerent initial congestion levels respond diﬀerently to added capacity due to individual diﬀerences. These diﬀerences in MSAs gives rise to heterogeneity in the elasticity of VMT to capacity. We derive conditions under which the elasticity decreases with the initial congestion level. We then revisit the empirical analysis in Duranton and Turner (2011) using the instrumental variable quantile regression (IV-QR) model due to Chernozhukov and Hansen (2005, 2006, 2008). The IV-QR model allows us to incorporate variation in the elasticity due to the presence of unobserved diﬀerences across MSAs. Moreover, it allows us to evaluate the impact of changes in the stock of interstate highways on the entire conditional distribution of VMT, not just the impact on the conditional mean as in Duranton and Turner (2011). The IV-QR estimates show that as predicted by the simple partial equilibrium model, the elasticity declines as one goes up the quantile ladder, being more than one at the lower quantiles and less than one at the higher quantiles. The median IV-QR estimate being close to one. The IV-QR model implies that among observationally identical cities, expanding road capacity can lower the number of cities experiencing severe congestion, although the mean or median congestion levels are likely to remain constant. We also estimate the impact of increased road capacity on the unconditional distribution of VMT using the generalized quantile regression (GQR) model due to Powell (Forthcoming). The GQR estimates mirror the IV-QR estimates, but their conclusions are starker at the upper quantiles: building highways have no statistically signiﬁcant impact on VMT at the highest quantiles. The GQR results imply that building roads can lower the total number of cities having diﬀerent observed characteristics experiencing severe congestion levels. We further explore the mechanisms that drive the empirical ﬁndings by running simulations using a spatial general equilibrium model with an extensive road network calibrated to the Greater Los Angeles (LA) Region. In the general equilibrium model, besides commute trips, consumers have to travel to diﬀerent zones to acquire consumption goods. The model considers route choice in the network and mode choice. Building roads aﬀect road traﬃc through three channels: the total amount of consumption, mode choice, and the substitutions among goods sold at diﬀerent locations. We ﬁnd that the elasticity of VMT to capacity in LA is 0.321, and the elasticity decreases consistently with the initial congestion level. We report the welfare eﬀect and the changes in other travel-related variables. Our results have important policy implications in that they show that while building roads is unlikely to change the mean or median congestion level, it can reduce the number of cities experiencing high congestion levels.
Keywords: congestion, vehicle-kilometers-traveled, instrumental variable quantile regression, heterogeneity, the fundamental law of highway congestion
JEL Classification: R00, R41, C21, C26, C63
Suggested Citation: Suggested Citation