from reduced congestion by adjusting labor supply, production, and the like to reach new equilibrium levels of wages, employment, trade flows, and GDP growth.
Intuitively, it is well known that a given reduction in travel costs generally increases the value of a transportation network and the social gains that it generates by manyfold because the cost savings exponentially increase accessibility to more parts (nodes) of the network. Thus although our estimates may seem implausibly large, the significant improvements in mobility attributable to autonomous-vehicle use positively affect a considerable quantity of the nation’s inputs (labor and capital) and outputs that are transported on the extensive integrated network of local, state, and federal roads.2 Sensitivity analysis also indicates that even if very conservative assumptions are made about the effects of autonomous vehicles on reducing congestion, such as they are 50 percent lower for the United States as a whole than they are for California, then autonomous vehicles would still generate significant improvements in the nation’s annual rate of growth that approach 1 percentage point.
In sum, substantial reductions in congestion and improvements in travel time and travel-time reliability for automobiles and trucks have the potential to generate macroeconomic (supply-side) stimulative effects because more efficient transportation can facilitate favorable improvements in the labor, urban, trade, and industrial sectors that result in more people working, shopping, trading, and producing goods. The additional employment and better job-matching attributable to faster and more reliable commutes, the increased freight flows attributable to the reduction in transport costs, and the higher productivity attributable to reduced transit time for both capital and labor could combine to significantly increase the U.S. growth rate. Moreover, the findings we summarize from our simulations to preview those effects account only for the economic effects of self-driving cars through reductions in congestion, and so almost certainly understate the benefits of autonomous vehicles. As explained in chapter 7, there are a myriad of other ways in which autonomous vehicles could improve social welfare.
4
Estimating the Effects of Congestion on Economic-Performance Measures
Only fragmentary evidence exists of the effects of congestion on an economy. For example, Hymel (2009), Sweet (2014), and Angel and Blei (2015) find that highway congestion is associated with slower job growth in U.S. metropolitan areas, while Light (2007) uses the Bureau of Labor Statistics’ American Time-Use Survey to estimate reductions in workers’ productivity and income that are caused by traffic delays from highway congestion. This chapter provides a more comprehensive empirical picture of the effects of highway congestion on the U.S. economy as a basis for estimating the potential benefits from autonomous vehicles’ effect on congestion.
This picture reflects estimates of the causal effect of highway congestion on the growth rates of several different measures of economic performance. This empirical analysis focuses on California because the state has several highly congested urban areas, including eleven of the top sixteen highway bottlenecks in the nation (CPCS Transcom 2015), and its counties have had the option, since the early 1960s, to pass local sales taxes to fund spending for specific transportation projects that could reduce congestion.1
Such duly named “self-help” county taxes amount to a quasi-natural experiment because they have been enacted at various times by various counties primarily because public officials have successfully addressed various political issues, rather than seizing on economic factors relevant to economic growth. Keith Dunn, the executive director of California’s Self-Help Counties Coalition, notes that for the past twenty years the passage of any proposed self-help tax legislation has required the support of at least 67 percent of eligible voters. Building such support requires skillful political leaders who are willing to conduct sufficient outreach and can craft legislation that embodies a successful compromise among several competing interests, independent of economic conditions in the county.2 Accordingly, our identification strategy is to use the additional modest highway spending that is funded by self-help tax legislation as a valid instrument to determine the causal effect of highway congestion on measures of economic performance.
It could be argued that there must be some degree to which traffic conditions motivate efforts to raise a tax and motivate voters to support it. However, as discussed in detail below, self-help county taxes amount to a broad tax covering all travel modes and infrastructure, not just automobile transportation. In fact, roads get a modest share of the money—a share that must accumulate for decades before it generates reductions in congestion. Thus the causal path from road congestion to the passage of a self-help county tax that is expected to result in reasonably prompt reductions in congestion is far from clear a priori, and we find no evidence to suggest that such a path exists.
The Model
Traditional efficiency analyses of highway congestion measure the delay costs that motorists who travel during peak travel periods impose on other motorists.3 This study goes beyond the external costs to other motorists by using panel data to estimate the effect that highway congestion has on the economic performance of urban areas in California, as measured by their GDP, employment, labor-earnings, and trade-flow growth rates.4 The model begins with the demand for transportation, measured by traffic volume (V), and the supply of transportation, measured by infrastructure capacity (W). An equilibrium where transportation demand is sufficiently greater than transportation supply results in road congestion (C), so we adopt a formulation used by many authors, in which congestion rises as a power of the volume-capacity ratio:
where α is a constant that, for example, takes on a value of 2.5 for urban arterials and 4.0 for urban expressways (Small, Winston, and Evans 1989).
Given the preceding conceptual discussion and following previous work, our general model is a reduced form that relates congestion, which tends to grow over time because capacity cannot keep up with traffic volume, to economic growth and other controls. It can be described as
where Git is the growth rate of an economic performance variable in geographic unit i during year t, Cit is the level of congestion, Xit is an array of controls, and εit is a random error term.
In our empirical work, we use a log-linear specification, so our model can be summarized as
where γ is the causal effect of congestion level C on the growth rate, Xβ is an array of controls and coefficients, ϕt is the year dummy, ci is the urban-area dummy, and ε is the random error term.
Below, we summarize the available data to measure congestion and the growth-rate performance variables. We then describe and provide extensive justification for the instrument for congestion, discuss the data used to measure it, and summarize the final sample used for estimation.
Congestion
Congestion is measured using estimates of annual hours of delay per auto commuter from the Texas A&M Transportation Institute (TTI). Data are provided for the years 1982–2011 for all urban areas with more than 500,000 people. Auto commuters are defined as people who make trips by car during morning (6:00–10:00 a.m.) and evening (3:00–7:00 p.m.) peak periods. The numbers of auto commuters are estimated using data from the National Household Travel Survey, conducted by the Federal Highway Administration (FHWA). The Texas Transportation Institute adds measurements of peak-period delays to measurements of travel delay during nonpeak hours to estimate the total annual delay experienced by auto commuters.
To compute congestion-induced
