Transportation and Commuting Patterns: A View from the Fifth District

Analysis of data from the ACS reveals a few interesting facts about commuting in the United States. (See table.) Commuters using public transportation tend to be younger than those who rely on cars, trucks, or vans and drive alone. Roughly equal proportions of female and male commuters use public transportation; however, since 47 percent of all commuters are female, this implies that female workers tend to use public transportation more than male workers. Blacks or African Americans tend to rely more on public transportation than other groups. Public transportation tends to be used more by median-income workers; on average, car, truck, or van drivers who drive alone have higher incomes and those who carpool have lower incomes.

On average, Americans' travel time to work is approximately 26 minutes. It varies across modes of transportation, with public transit the slowest (almost 50-minute long commutes). The national averages, however, hide large regional variations. Locations face different geographic challenges and rely on different transport technologies associated with different travel speeds and capacity. Mean commuting times vary as a result, from 17 minutes in South and North Dakota to 33 minutes in New York. (The difference between the highest and lowest times amounts to 128 hours per year for a typical full-time worker, or 16 workdays.) In the Fifth District, mean commuting times are about 32 minutes in Maryland, 30 minutes in D.C., 28 in Virginia, almost 26 in West Virginia, and 24 in North and South Carolina.

One reason transportation systems are a complex issue is that some commuters travel outside their location of residence. This behavior introduces several challenges regarding the organization, design, and financing of the transportation system. On average, in the United States, almost 28 percent of workers commute to a different county. Workers who commute outside their county of residence rely more on public transportation than those who work in the county of residence.

Commuting in Fifth District Cities

How are the commuting patterns observed in the Fifth District different from the national averages? Focusing on three of the District's largest cities — Richmond, Va., Baltimore, Md., and Charlotte, N.C. — the commuting behavior observed in these cities differs from the national data in several ways. First, the proportion of commuters who rely on public transportation is remarkably high in Baltimore (18 percent) compared to the national average (5.1 percent) and to the other two cities (5.2 percent in Richmond and 3.5 percent in Charlotte). Second, in all three cities, public transportation is used mostly by lower-income workers, in contrast with the national pattern in which it is used more commonly by median-income workers. Third, the percentage of workers walking to work in Baltimore and Richmond is higher than the national average (about 6 percent of workers choose this alternative in the two cities, while the national average is 2.7) and lower in Charlotte (2 percent). Fourth, average commuting time is markedly higher than the national average in Baltimore (30 minutes vs. 26 minutes) and markedly lower in Richmond (22 minutes). It's about the same as the national average in Charlotte (25 minutes).

The LODES data allow us to obtain information on distance traveled by commuters for these cities. The data indicate that most Baltimore residents and most workers commuting to Baltimore travel fewer miles than the same groups of commuters in the other two cities. Commuters to and from Charlotte travel the longest distances. (See table above.) This pattern suggests that the variability of commuting times across the three cities is not driven simply by miles traveled. While commuters travel shorter distances and experience longer commuting times in Baltimore, the opposite is observed in Charlotte. Other factors, such as geographic constraints, reliance on public transportation, available transportation infrastructure, and traffic definitely play an important role in explaining such differences.

Commuting times may not depend only on decisions made by local transportation planners since commuting flows, as shown earlier at the national level, take place across local jurisdictions. Where to work and where to reside are of course mutually dependent decisions that depend not only on individuals’ preferences, but also on the availability of jobs and transportation costs. Uncovering the extent to which mobility extends beyond local political borders is crucial, therefore, to coordinate transportation planning and investment efforts.

The LODES data, which contain detailed information on inflows and outflows of workers by city, allow us to quantify this phenomenon for the three cities under consideration. (See table.) The data reveal an interesting observation. The three cities clearly constitute large employment centers. In fact, the number of jobs in those cities largely exceed the number of local residents (the ratio of city residents to jobs is 72.6 in Baltimore, 64.2 in Charlotte, and 62.9 in Richmond). However, local jobs are not entirely filled by local residents (the percentage of local jobs filled by city residents is 33.3 in Baltimore, 41.8 in Charlotte, and 23.2 in Richmond). In fact, a large share of residents commute outside of the city (about 63 percent of the residents in Richmond commute outside of the city, 55 percent in Baltimore, and 35 percent in Charlotte).

The table also shows earnings obtained by city residents in jobs in and outside of the city and earnings obtained by workers commuting into the city. In all three cities, outside workers tend to work in higher-paying jobs than city residents (the percentage of workers receiving $3,333 per month or more in jobs available in the city is higher for outside workers than for city residents). Also, in all three cities, the proportion of city residents who work outside of the city in lower-income jobs (earning less than $1,250) is higher than the proportion of city residents who work in low-income jobs within the city.

Where do residents of the cities commute to work? LODES data show some interesting distinctive behavior across cities. (See table.) First, commuting flows in and out of the three cities are very dispersed. In other words, there are several origin and destination locations, each one explaining only a minor part of the overall commuting flows in and out of the three cities. Second, the data also reveal some amount of cross-commuting among certain locations. In the case of Richmond, large commuting flows take place both to and from Mechanicsville and Tuckahoe. A similar pattern is observed in Baltimore (commuting flows from and to Towson and Columbia) and in Charlotte (commuting flows from and to Concord, Raleigh, and Huntersville).

How Urban Economists Think About Transportation

Location decisions by firms and individuals, and as a result, the use of land, involve the consideration of several factors. Accessibility, determined by the transportation system in place, is one of them. But accessibility depends, at the same time, on where firms and individuals locate. In other words, transportation and land use interact and influence one another: Changes in transportation investment affect local accessibility levels; the latter affects location choices by firms and residents, which eventually affect accessibility, and so on. As a result, when economists think about transportation, they do not consider it in isolation but as one of the components of a more general and interrelated system that includes cities and regions.

Two basic principles characterize the role of transportation in the context of cities. First, one of the main reasons for the existence of cities is that there are special advantages, usually referred to as "economies of agglomeration," to carrying out economic activities in close proximity. In other words, costs are lower when cer­tain types of activities locate close to each other. Transportation is therefore critical: Anything that reduces transportation costs would allow a higher concentration of production, resulting in larger benefits from agglomeration.

Second, local wages and housing prices adjust at every location so that households and firms do not have an incentive to move; that is, wages and land prices should adjust until households and firms are indifferent between locations. When choosing where to live, individuals consider several factors, such as job opportunities, housing options, social networks, and commuting costs. Some people might choose to live far away from jobs, possibly accepting a costlier commute, because they would be compensated, in effect, by other factors such as lower housing costs.

A very specific trade-off between commuting costs and land prices emerges as a result: At locations near employment centers, commuting costs are low and land prices are high; at more distant locations, commuting costs are higher and land prices are lower. The different levels of accessibility are explained, in part, by the quality of the local transportation system.

Economic Importance of Transportation

Research in urban transportation has mainly focused on the effects of transportation on job accessibility and local economic conditions. Estimating those effects is challenging, however, precisely because of the interdependence between transportation and land use explained earlier.

In a 2011 American Economic Review article, Gilles Duranton of the University of Pennsylvania and Matthew Turner of Brown University explore the relationship between transportation infrastructure and traffic congestion. Specifically, they estimate the effect of increasing highway capacity on congestion. Their main finding is that people actually drive more when the stock of roads in their city increases. In fact, they find a one-for-one relationship between the two. It follows, then, that an increase in the provision of highways would not alleviate congestion. Their explanation of this outcome is that cities with better roads attract more people. The use of the roads would therefore increase until traffic congestion reaches its pre-existing levels. In a different article, published in 2012 in the Review of Economic Studies, the same authors examine the effect of increasing highway miles on employment growth in American cities; they find that a 10 percent increase in a city's initial stock of highways caused about a 1.5 percent increase in its employment over a 20-year period.

Between 1950 and 1990, the aggregate population of central cities in the United States declined by 17 percent, despite the fact that population increased by 72 percent in metropolitan statistical areas (MSAs). This process is generally known as suburbanization. Work by Nathaniel Baum-Snow of the University of Toronto, published in the Quarterly Journal of Economics in 2007, investigates the extent to which this phenomenon is attributable to the construction of highways, which tend to lower commuting costs. The paper finds a positive relationship between roads and suburbanization. The results indicate that one additional "ray," or segment, of interstate highway originating from the city center leads to about a 9 percent decline in the central city population. It should be noted, however, that other factors occurring at that time were inducing residents to move out of downtown areas: increases in income, a flight from blight due to crime, the degradation of housing stock, and changes in the school system.

U.S. cities show differing patterns of residential sorting by income. In most U.S. MSAs, the suburbs are of higher income status and the central cities are relatively poor. There are important exceptions, such as Chicago, Philadelphia, and others. The literature suggests different mechanisms that could explain this kind of spatial sorting of households. One such explanation focuses on transportation mode choices. In a 2008 article published in the Journal of Urban Economics, Edward Glaeser of Harvard University, Matthew Kahn of Johns Hopkins University, and Jordan Rappaport of the Kansas City Fed state that transport modes are key for explaining the central location of the poor. The reasons are twofold: First, the larger financial costs associated with owning a car may cause lower-income families to rely on other modes of transportation, such as public transit; and second, public transit is more accessible in central cities than in suburbs.

A different line of research that also focuses on job accessibility is related to the spatial mismatch hypothesis. The spatial mismatch hypothesis pioneered in 1968 by John Kain, then an economist at Harvard University, attempted to explain an apparent spatial disconnection between jobs and workers' locations. The shift of jobs predominantly toward the suburbs observed during the 1960s and 1970s hurt the labor market prospects of minorities. For different reasons, African-American populations, largely concentrated in central urban areas, were unable to relocate closer to the jobs. They experienced, as a result, either excessive commuting costs or higher and persistent unemployment levels. In Kain's view, the inability of minorities to move and follow the jobs to the suburbs was mainly due to racial discrimination in the suburban housing market.

The spatial mismatch hypothesis motivated a large body of research on job accessibility and transportation. This literature has mainly focused on determining how the lack of connection to job opportunities affects an individual's prospects in the labor market, especially low-skilled workers and minorities.

Research generally confirms the hypothesis. The main findings can be summarized as follows. First, the effect of spatial mismatch is stronger in large central urban areas, where low-skilled minorities tend to live. Jobs are generally located far away from central areas, and minorities face geographical barriers that prevent them from finding and keeping jobs. Second, the research indicates that better job accessibility significantly decreases the duration of joblessness among lower-paid displaced workers, the result being strongest for non-Hispanic, African-Americans, females, and older workers.

A corollary of these findings is that improving spatial access to jobs would lead to better labor market outcomes. Investing in transportation infrastructure and improving transportation services (increasing frequency, capacity, and so on) would increase connectivity between high-unemployment neighborhoods and locations with an abundance of jobs and help alleviate the negative consequences of the spatial mismatch.

Transportation Policies: Challenges and Opportunities