of the day. By comparing actual and free-flow speeds, TTI is able to estimate congestion-induced speed reductions for different hours of the day. It then scales up those speed reductions using traffic-volume data to compute the total amount of time lost to traffic congestion.
In recent years, travel-speed data have come from INRIX, a private company that monitors travel times on most major roads in the United States. (The institute has made considerable efforts to align earlier data with INRIX data.) Traffic-volume data come from the FHWA’s Highway Performance Monitoring System. The INRIX speed data are recorded in fifteen-minute intervals for every day of the year, allowing TTI to account for both daily and hourly variations in congestion levels.5 For the sample of California urban areas from 1982 to 2011, discussed later, annual delay per auto commuter ranged from two hours to eighty-nine hours, with a mean delay of thirty-four hours per year.
Economic-Performance Measures
County-level economic performance measures are used that include real GDP, wages, employment, and originating freight traffic transported by truck. Real GDP, wages, and employment data for the period 1982–2011 were provided by the Brookings Institution’s Metropolitan Policy Program, using data from Moody’s Analytics.6 Freight flows, measured as thousands of tons of commodities transported by truck across California counties, were obtained from the California Statewide Freight Forecasting Model, which combines 2007 data from the FHWA’s Freight Analysis Framework with demographic data to forecast flows for 2010.7 The forecast flows are not adjusted to account for any unanticipated changes in congestion.
GDP, wages, and employment are expressed in terms of annual growth rates as
where
Using Self-Help County Taxes as an Instrument for Highway Congestion
There are two fundamental challenges to estimating the effect of congestion on an economic performance measure (for example, employment): omitted variables and reverse causality. Omitted variables most likely arise because some variables that affect both congestion and an economic performance measure, such as certain types of weather (Sweet 2014), are omitted from the model because they are difficult to quantify. Reverse causality most likely occurs because an economic performance measure is closely related to the volume of passenger and freight traffic on the road and thus will affect congestion. The standard approach to minimizing the bias from omitted variables and reverse causality is to use an appropriate instrumental variable that is correlated with the explanatory variable of interest (in this case, highway congestion) but is not correlated with the dependent variable (for example, employment) or with omitted variables that affect the dependent variable.
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