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Economically Disconnected Area clusters in the CMAP region

Jan 25, 2018

Economically Disconnected Area clusters in the CMAP region

This Policy Update is the first in a series of three examining the region’s Economically Disconnected Areas (EDAs) and their transportation and commute challenges.  This Update groups EDAs that are geographically, demographically, and economically similar and discusses trends across them.   The second Update explores commute patterns and travel trends for workers residing in EDAs, identifying the clusters where commute disparities are highest.  The third illustrates several case studies to highlight the role transportation and land use play in linking residents of EDAs and economically connected areas to jobs. 

Inclusive economic growth is key to promoting the region’s competitiveness and increasing prosperity for all residents.  New research illustrates a relationship between economic inequality and regional economic prosperity.  Decreasing economic inequality produces longer and stronger periods of regional economic growth.  Regions are more likely to succeed when they engage all residents and provide opportunities for them to engage with and contribute to the regional economy.  Planning and other policy decisions made by local and regional actors influence whether and how residents are connected to the economy.  

The ON TO 2050 Inclusive Growth strategy paper identified EDAs in the Chicago region.  These areas have higher than regional average concentrations of low-income households and minority or limited English proficiency (LEP) populations.  While EDAs often have strong geographic proximity, they are not homogenous; local communities differ from each other in terms of their context, strengths, and needs.  Identifying regional EDA clusters allows for targeted research and policy implementation across multiple issue areas including regional economy, land use, and transportation.  

This analysis indicates disparate outcomes between EDA clusters in various measures, including educational attainment, income, unemployment, housing vacancy, homeownership, and commutes.  Measures such as educational attainment have some bearing on economic outcomes, but English proficiency as well as race and ethnicity appear to have a stronger relationship.  Majority non-white EDAs generally hold more unfavorable outcomes relative to majority white EDAs, with the least favorable outcomes seen in majority black EDA clusters. 

Identifying EDA clusters

Grouping EDAs that are economically and demographically similar to one another reveals distinct patterns within the region.  These clusters were identified via a spatially based cluster analysis tool that generated seven distinct EDA clusters located across the region.  Three of the seven clusters are located in the city of Chicago while the remaining clusters are in the suburbs or collar counties.  Two of the suburban clusters are spatially diffuse, demonstrating the difficulty of addressing the complex transportation, housing, education, and economic issues affecting economic opportunity.  These clusters contain approximately 2.8 million residents, roughly one-third of the region’s total population.  The map below depicts the EDAs by cluster. 

EDA clusters map Cluster profiles

An examination into the sociodemographic makeup of these clusters highlights differences across all qualifying measures: income, demographic makeup, and LEP population.  The following table depicts the underlying socioeconomic and demographic characteristics of each cluster.

EDA cluster profiles chart 

A predominately white cluster located outside the city of Chicago—Cluster 2—has the highest average median income relative to other EDA clusters.  Cluster 2 lies in the southwest suburbs, consisting of all or parts of several municipalities including Bedford Park, Bridgeview, and Chicago Ridge.  This cluster meets two EDA thresholds.  EDAs in this cluster have five percent or more of households with incomes at least 60 percent below the Chicago metropolitan statistical area median income.  The LEP rate is also above the regional average of 12.1 percent.  

EDAs in the Waukegan area (Cluster 1) and in southwest Chicago (Cluster 4) are majority Hispanic and have lower average median incomes than the majority white cluster.  These clusters also have higher levels of LEP.  Similarly, 48 percent of residents in Cluster 7—covering some of northwest Chicago and parts of the collar counties including Aurora, Round Lake, and Harvard—are Hispanic.  Another 33 percent are white.  EDAs in this cluster have higher median incomes relative to other non-white and non-Hispanic clusters and on average meet the LEP threshold.  

The remaining three clusters—Clusters 3, 5, and 6, located in south Chicago, west Chicago, and south Cook, respectively—are majority black with relatively lower incomes.  For example, 92 percent of residents in EDAs in south Chicago are black, and the average median income is $29,000.  

Economic outcomes across the clusters

The EDA clusters differ from one another across various economic-related outcomes, such as income.  A roughly $20,000 gap exists between the highest- and lowest-earning clusters.  The data illustrates a relationship between race and ethnicity, and income. Specifically, average median income decreases as the percent of a cluster’s population that is black increases.  A relationship also exists between LEP rate and average median income: as a cluster’s LEP rate increases, average median income also increases.

Chart showing share of cluster population that is black and AMI in EDA clusters

Similar disparities exist among EDA clusters in educational attainment.  EDAs, including those in the Waukegan area and southwest Chicago clusters, have relatively low educational attainment rates.  Residents in EDA clusters in the southwest suburbs and south Chicago have higher educational attainment rates. 

Though a relationship exists between educational attainment and income, data also suggests that the benefits of education may be tempered by an existing relationship between race and ethnicity, and income.  EDA clusters in the southwest suburbs and south Chicago, for example, share almost equal levels of educational attainment but also share a $20,000 gap in average median income.

Educational attainment and AMI by EDA cluster

Differences in income reflect existing disparities in unemployment and labor force participation rates among EDA clusters.  Predominately white EDAs enjoy higher rates of labor force participation than their majority black or Hispanic counterparts.  People of color are more likely to be unemployed than white residents in EDAs.  Twenty-seven percent of residents in the majority black south Chicago cluster are unemployed.  This is 16 percentage points higher than the unemployment rate in the nearby southwest suburbs whose residents are majority white.  

Generally, the unemployment rate within an EDA increases as the percent of residents that are black rises.  This is unlike the relationship between LEP and unemployment: as LEP rates increase, unemployment rates decrease. 

Chart showing share of cluster population that is black and unemployment rate in EDA clusters

 These disparities in labor force participation and unemployment are not necessarily related to educational attainment.  Residents in the Waukegan area cluster, for example, have relative low educational attainment rates but have the second highest labor force participation rate among the EDA clusters. 

Existing disparities across EDA clusters—particularly those related to race and ethnicity—suggest the influence of geographic and built-in factors.  To understand the potential effects of the built in environment on economic outcomes, CMAP staff analyzed housing vacancy rates.  Vacancy rate is a good measure for a community’s health and helps illustrate economic opportunity in an area.  Areas with higher vacancy rates suggest insufficient economic opportunity exists for residents to remain living there.  

Consistent with the economic outcomes discussed above, housing vacancy rates differ across clusters.  Predominately white EDAs, such as in the southwest suburbs cluster, have lower vacancy rates relative to clusters that are majority non-white.  Majority non-white EDA clusters have relatively high vacancy rates.  This is particularly true for majority black clusters. 

Housing vacancy and homeownership rates in EDA clusters 

EDA clusters also have lower than regional homeownership rates.  Majority black clusters in the city of Chicago have the lowest homeownership rates, while EDAs in the south suburbs and the Joliet area have rates similar to EDAs in the majority white southwest suburbs. 

Commute times in EDA clusters

Some EDAs have commutes that are comparable to or less than regional averages, while a few stand out for particularly long commutes. Average commute times for EDA clusters can be as high as 39.0 minutes and as low as 24.8 minutes, highlighting a strong disparity even among EDAs.  The longest commutes occur for EDAs within the city of Chicago, with shorter commutes, some below the regional average commute time, found in suburban areas. 


Looking ahead

Understanding economic outcomes across EDA clusters presents opportunities to improve planning at the local and regional levels.  This analysis highlights the need for collaborative efforts on inclusive growth that promote economic opportunity across the region via workforce, economic development, infrastructure investment, community building, and other comprehensive efforts that address the broad nature of lack of economic opportunity.  Local economic development staff and workforce development programs must meet the needs of a changing and diversifying economy, and promote growth of and access to jobs with pathways for upward mobility.  The region’s stakeholders must capitalize on existing efforts and identify new strategies to promote reinvestment in communities with a long-term loss of population and jobs, creating employment opportunities and building stronger communities where residents of EDA residents live. 

GO TO 2040 and initial development of ON TO 2050 emphasize the need for regional stakeholders to work collaboratively and comprehensively across issues of regional importance including regional economy, land use and transportation.  Policy and programming must be targeted, addressing specific barriers communities face.  The second and third Policy Updates in this series will consider transportation trends and commute patterns within EDA clusters, highlighting the role transportation and land use patterns play in promoting economic opportunity and positive outcomes.

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