THE FUNDAMENTAL LAW OF ROAD CONGESTION: EVIDENCE FROM US CITIES

G. Duranton & M.A. Turner

NBER Working Paper 15376

Principal Research Question and Key Result Does increasing the size of the interstate highway system relieve congestion? The key finding is that the elasticity of vehicle kilometres traveled to highway lane kilometres is almost 1 across all specifications which indicates that the amount of traffic increases proportionately with the size of the highway network. In other words, building roads is not a good means of reducing congestion.

 

Theory When deciding whether to enter the road system a driver assesses the marginal benefit of driving an extra kilometer (which is assumed to be a decreasing function), and the marginal cost (including his time, fuel etc.) of driving that kilometer. He drives until the marginal benefit equals marginal cost. However, the marginal social cost of him driving that kilometer is higher than the marginal personal cost as he imposes an externality on other drivers by being on the road (i.e. his presence on the road adds to congestion in general). Thus the social optimum equilibrium of the amount he drives will be lower than the private equilibrium. Transport policy can intervene in order to better equate the social and private costs such that the social optimum is reached.

This can occur in a variety of ways. Fuel tax could be used for example. However, this has been shown to affect largely leisure trips and not the travel to work trips (which are presumably less elastic with respect to price) that are the main cause of congestion. Another option would be to charge per metre of road used, with different pricing mechanism for the time of day and the amount of traffic. This option would be hard to implement and also it would be hard for an individual to respond rationally to a complex and changing charging mechanism. Congestion charging is a limited form of this, a point that will be returned to in later summaries.

The option under examination here is to build more roads. As this will increase capacity on the road network it should reduce the amount of negative externality the individual driver imposes on others, thus moving the marginal social cost nearer to the marginal private cost, and bringing congestion nearer to the socially optimum level. However, maybe more roads simply attract more drivers in which case all equilibiria are simply shifted outwards, and the result will be no nearer to the social optimum than the previous equilibrium.

 

Motivation The cost of congestion is huge. Between 1995 and 2001 the time spent on household travel increased 10% whilst distances remained constant, which is equivalent to billions of dollars’ worth of lost time.

 

Data Using Metropolitan Statistical Areas (MSA) they use official highway data to generate variables that detail the lane kilometres, vehicle kilometers traveled (VKT), and the average annual daily traffic (AADT).  They then do the same for other major roads. The summary stats show that the AADT increased from 4,832 vehicles per kilometer lane of highway in 1983, to 9,361 per lane kilometer in 2003. They have three cross sections of data.

 

Strategy There are a variety of strategies. Firstly they pool the cross sections and do a simple OLS regression VKT on the left and lane kilometres on the left with geographic, climactic, socio-economic and population controls.

Using the panel format they then control for fixed effects, and time fixed effects by differencing the data.

They recognize that there could be endogeneity issues. Specifically if VKT is correlated with some unobserved demand for driving, and planners respond with road building policies to that demand for driving by building roads then the coefficients will be overestimated as the increase in vehicle kilometres traveled will be due to demand for driving, not a consequence of the road building. Thus they have an IV strategy.

IV1: Planned highway kilometres from the 1947 Highway plan. This was a plan to connect the major population centres as directly as possible. Clearly this will be very relevant, and they argue it is exogenous as the plan was drawn up to connect population centres in the 1950s, without a thought for future traffic demand. This instrument is conditional on population. (don’t know what this means).

IV2: Rail network in 1898. Railroad travel connected a lot of cities and towns in the 19th century, and as the importance of railways waned, roads were built that followed their routes as substitutes. Given that the economy was very different when the railroads were constructed, and that they were done so primarily by private companies who were concerned with relatively short term gain, it is unlikely that they were made with future traffic flows in mind, and this they argue adds credibility to the exogeneity argument. They claim the instrument only need be exogenous conditional on the controls, so controlling for historical populations and geographic variables is sufficient to guarantee exogeneity. (check this)

IV3: Expedition routes between 1835 and 1850. Again they control for historical populations and geography and say it is hard to imagine how the explorers were selecting routes with travel between future cities in mind [I don’t see how that is the point particularly].

They then instrument for VKT using all instruments (though they do test them separately). As the F-stat in the first stage is less than 10, they do a LIML estimation as well which is supposedly more robust to weak instrument problems.

Results They have a coefficient of around 1 in all specifications (adding controls one at a time). This is the case when instruments are used one at a time, and also when amalgamated into a single first stage.

It appears then that new road capacity is met with a proportional increase in driving, thus confirming what Downes called the fundamental law of road congestion.

 

Robustness That the coefficients are robust to a wide variety of specifications is fairly good evidence that the results are not being driven by the nature of the model. (a more pessimistic interpretation would be that all specifications are affected equally by endogeneity).

They use data on availability of public transport and find that increasing public transport does not affect congestion. This is because public transport may take some people off the road, but as that effectively increases road capacity in a similar way as building new roads, the VKT demand response is the same.

Using data on what type of vehicles are using the road network over time they try to decompose VKT to understand where the extra demand is coming from. They find that commercial traffic accounts for between 10-20% of the increased VKT. Individuals account for around 11-45% of the increase. Population is thought to increase due to new highways as economic activity is increased. They find that a 10% increase in the road network causes a 1.3% increase in population in a MSA over 10 years and this accounts for around 5-15% of the extra VKT. Another mechanism could be diversion from other roads to the highways, but when they test this they find only very small results suggesting that traffic creation, not diversion is the problem (the mechanism for testing is regressing the VKT for highways on non-highway lane measures).

 

Problems Bad controls. – including socioeconomic controls could be dangerous as they are direct outcomes of the independent variable (kms of highway). Introducing outcome measures as additional controls biases estimates in indeterminable ways. Some comfort is taken from the fact that the results do not change significantly.

IV – exogeneity concerns remain particularly for the 1947 highway plan. Comfort is taken from the fact that the results are broadly the same across all specifications.

Weak Instruments – the instruments are weak, and it is not totally clear that the LIML estimation solves this problem. Again, it is comforting that all estimates are broadly similar across specifications.

 

Implications It appears then that new road capacity is met with a proportional increase in driving, thus confirming what Downes called the fundamental law of road congestion.

Public transport probably will not affect congestion levels. They do back of the envelope welfare calculations and find that the time saved by new highways is probably not worth the cost, whereas improvements in public transport are most likely to be welfare improving.

 

 

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