Many Connecticut residents, particularly those who identify as racial or ethnic minorities or who live in historically-disadvantaged neighborhoods, face astonishingly high unemployment rates.
In New Haven, unemployment ranges from 3 percent in high-income neighborhoods such as Westville and East Rock, to 20 percent in low- income neighborhoods such as Dixwell, Newhallville, and the Hill — and “underemployment” rates are often twice these figures. New mapping tools can allow us to visualize exactly what these barriers to job access might look like when plotted across a metropolitan region.
In a recent Living Cities article, WANTED: Job, Training, and a Bus Pass, Emily Garr Pacetti of the Fund for Our Economic Future proposes a “Growth and Opportunity Framework” to ensure that the benefits of economic growth are widely shared and sustained. The three-pronged approach (see Venn Diagram) asserts that “sustained growth can only come through cross-sector strategies that reinforce connections among workforce and training efforts, employer demand, and the spatial and social disconnect between jobs and workers.”
Simply put, this means that new jobs and training programs will have little impact on unemployment rates or local opportunity if workers cannot get to these jobs — either through systems change (e.g., reduced discrimination on the part of employers), or through more sustainable metropolitan development patterns and increased transportation options. Likewise, even if jobs are created and workers can access them via transportation, opportunity will be limited if our city’s education and training programs do not adequately prepare all residents.
Let’s take a hands-on approach to visualizing these barriers to opportunity by exploring an interactive Access to Jobs in the Tri-State Region map recently published by the Regional Plan Association. This map, in its simplest form (pictured here), shows you the number and location of jobs in the tri-state region. The cool part, though, it that the map also allows you to filter these jobs by mode of travel, maximum travel time, industry, and worker education, and to narrow or widen your scope as you wish.
Listed below are three example scenarios, intentionally simplified to show how the tool can tell us about job access in the Greater New Haven area. (Try it out for yourself and tell us about the most interesting trends you find in the comments section below)!
1. Better Buses Reach More Jobs
Suppose that a young manufacturing worker residing in the Hill neighborhood of New Haven recently got laid off. Given constraints of dependent care and limited financial assets, he is only able to make a one-hour commute to work each way using public transportation. Under these conditions, the RPA map shows that there are only 9,000 job opportunities available to him in the manufacturing industry (technically, this 9,000 figure represents total industry-wide jobs, so the actual number of opportunities within any given month is much smaller – perhaps 200-300 job openings, including both advertised and non-advertised positions).
What if, however, public transit systems were improved marginally such that commutes became 15 minutes faster? To gauge the potential effect of this improvement, let’s now slide the maximum travel time bar to 75 minutes (which would actually be 60 minutes in the new, hypothetical situation). As depicted in the screenshot below, this young man’s number of job opportunities has increased to 17,000 – an 89 percent increase – as he can now reach factories in areas such as Bridgeport and shoreline suburbs. This example makes clear the ability of better public transportation to connect workers to jobs – even before considering the potential benefits that entirely new bus lines might bring.
2) Graduate High School, Cut Your Commute Time
Next, let’s take a look at the impact of further education on job access. Take, for instance, a high school dropout in Milford who commutes to work by car. Given the high costs of commuting by car and facing rush hour traffic, he is only willing to spend one hour traveling each way – but would prefer to spend much less than that. Not having a high-school diploma, his options within one hour are currently limited to 38,000 total jobs within our study area (not including some jobs that may fall outside the study area included in this mapping tool).
Now, suppose he decides to earn a high school diploma and passes the GED. Stimulating this change by keeping the box next to “less than high school” checked, and checking the box next to “high school” in the “by education” filter, we see that his job options have increased to 122,000 jobs within this same area – a 221 percent increase!
From a health and wellbeing standpoint, this also effectively means that by earning a diploma, he can afford to look only at jobs within a more reasonable 25-30 minute commuting radius and still have access to the same number of jobs.
3) Improve Transportation Alternatives Within Neighborhoods
For this last case study, let’s focus on residents of the Dixwell neighborhood of New Haven who have a high school diploma, but no college or more advanced degree. Considering jobs within a 30 minute commute time for workers with this level of education, we can see there are 11,000 within walking distance, 20,000 within the reach of public transportation (pictured below), 26,000 within biking distance, and 33,000 reachable with a car.
This example demonstrates the power having access to a car gives to workers in lower-income city neighborhoods. However, the trouble that often occurs is that the workers who need the car the most are the ones who cannot afford a personal vehicle, or in many cases, particularly among youth and the long-term unemployed, who do not have the ability to register a vehicle (due to lack of driver’s license, accumulated fines, or other factors).
Another interesting point to make a note of is that the number of positions accessible by bicycle is greater than the number of positions accessible by public transportation. This indicates that it is also worthwhile to think about improving the current condition of bike routes and bike rack-equipped transit systems, so that both biking and public transit (or the two combined, as bikes can reduce workers’ time getting to or from bus stops) are more on par with private automobiles. Major barriers remain here: in DataHaven’s Greater New Haven Wellbeing Survey, only half of adults in the city center felt they had places to bicycle in their neighborhoods that were safe from traffic.
Aside from these three specific cases, the map also brings to life the implications of “spatial mismatch,” that is, the fact that employment opportunities for low-income people are increasingly located farther from the neighborhoods where they live. Poverty clusters in older urban centers and peripheral cities such as Waterbury and Meriden, but low-wage, low-skill job opportunities are becoming more concentrated in wealthier suburban districts. According to DataHaven, which is conducting a Jobs Access and Transit Study, the mismatch between where low-income residents work and where they live is clear in Greater New Haven, particularly for people of color:
There are 4,000 African-American and Hispanic workers earning less than $40,000/year who live in New Haven’s “Outer Ring suburbs,” a collection of 10 towns that surround our core municipalities of New Haven, East Haven, Hamden, and West Haven – that represents 4 percent of the total number of workers who live in those towns. At the same time, there are 16,000 African-American and Hispanic workers earning less than $40,000/year who hold jobs in those towns – that’s 15 percent of the total employment base in those towns.
As we showed in our Community Index, high-income workers living in our suburbs rely on New Haven for the majority of their high-paying jobs, but they also rely in large part on a substantially less well-paid, diverse workforce that commutes in from places like New Haven. If everyone had access to high-quality transportation whether or not they owned a vehicle, more of these suburban jobs – many of which fall within second shifts at retail centers – might be accessible to city residents.
We hope that these three case studies illustrate how cutting-edge data and online tools, when properly used, can help inform urban planning and policy development at a local level.
For more information on the topics of spatial mismatch and job accessibility, here are some additional starting points:
*The Metropolitan Policy Program at Brookings recently conducted a study on transit and job accessibility in the 100 largest metropolitan areas in the United States. These two reports, complete with clear graphics, nicely summarize their findings for the New Haven-Milford area.
*This thesis written at the Massachusetts Institute of Technology, Department of Urban Studies and Planning, looks at the spatial mismatch problem in greater detail, focusing specifically on low-skilled workers without cars.
*This paper written by professors at the University of California, Berkeley is interesting because it looks at the potential practical solutions to the job/transit accessibility problem. In particular, they evaluate the impact of schedule extensions, new fixed bus routes, shuttle services, user-side assistance, and car loan programs.
Deanna Song is an Dwight Hall Urban Fellow based at DataHaven in New Haven. Mark Abraham, Executive Director of DataHaven, helped edit this article.