Using the Scorecard’s Local Data for Policy Advocacy

Among the primary findings in the 2017 Prosperity Now Scorecard main report was the deep malaise and uncertainty many families experience with respect to their financial and general well-being. Though the national economy has seen gradual but marked improvement over the past several years, whole communities are being left behind. While the state data in the Scorecard captures some of this dynamic, nuances are lost, particularly as they relate to underserved communities and intra-regional effects that these communities have fallen victim to. With this in mind, Prosperity Now has added data at the city, county and metro area levels for 26 measures to the 2017 Scorecard.

Through this more localized data, we’re better able to identify and dissect these intra-state and intra-regional dynamics that have always underpinned the state Scorecard estimates, and use the resultant findings to pinpoint policy solutions that put every family on the pathway to prosperity, no matter where they live.

City Data

The Scorecard’s city data is most illuminative in comparing urban and suburban outcomes. Disparities in outcomes by race in particular are more pronounced at the metro area level than in the center cities in a number of regions, suggesting that the founding principle of the American suburbs – residential segregation and the hoarding of the American dream by White households at the expense of households of color – still holds true. In Milwaukee Metro, for example, households of color are over seven times more likely to live in poverty (29.7%) and Black households more than eight times more likely (34.6%) than are White households (4.2%). In the city of Milwaukee, however, households of color are four times likelier than White households to live in poverty – an unacceptable disparity, to be sure, but far less stark than the metro area estimates.

Likewise, Black households own their homes at roughly the same rate in Milwaukee Metro as in the city proper (29%), while the white homeownership rate spikes by 13.5 percentage points as households move from the city of Milwaukee (56.1%) to the greater metro area (69.6%). This finding suggests that there is little opportunity for mobility and wealth building for Milwaukee’s Black residents even after they’ve moved to the suburbs, while White residents find greater economic security outside the city limits. Cleveland, Buffalo, New York City and a number of other regions perpetuate similar trends – relatively affluent, predominantly White suburban populations, surrounding highly segregated center cities that house a higher concentration of households of color.

County Data

Where the Scorecard’s city and metro area estimates illustrate the urban center/suburban divide, the county-level estimates provide insight into urban/rural and regional divisions. The state-level Scorecard estimates in states like New York and Illinois are often skewed by the massive population centers of New York City and Chicago, respectively. The Scorecard’s county-level estimates offer a more nuanced portrait of the intra-state dynamics that undergird those state-level estimates. In Illinois, for example, county estimates reveal pockets of deep deprivation in Southern Illinois that would otherwise be subsumed by analyses of the Chicagoland region. In Alexander County, households experience income poverty at a rate (27.5%) more than twice that experienced by Cook County residents (13.2%).

Likewise, in New York’s Yates County, 20.2% of residents are uninsured, compared to 15.5% of Queens residents, and 13.9% of New York City residents as a whole. Further, only four counties in the entire state of New York have an unbanked rate higher than the state’s 8% rate, with Montgomery County’s 8.8% rate the only rate higher than the state’s lying outside New York City’s borders. And in states like Mississippi and New Mexico, which often display abysmal outcomes in the Scorecard’s state data, county data allows more positive regional outcomes, like the (relatively) low income poverty rate in Greene County, MS (6.7%), or the 9.0% asset poverty rate in Catron County, New Mexico, to be readily identified.

Policy Implications

Much like our state data, these findings illustrate that policies matter. The effects of years of segregation and housing discrimination policy in Milwaukee, for example, continue to set back Black residents and other people of color as they strive to attain financial security. Milwaukee is unfortunately not alone in this plight; this story can be told in hundreds of cities and suburbs across the country.  State laws preventing landlords from discriminating against potential renters because of the source of their income is a Section 8 voucher is just one of the state laws that get at discriminatory practices, but affordable homeownership remains critical to supporting intergenerational financial security for families.

Unlike our state data, these findings illustrate interconnected outcomes between geographies – patterns in cities impact and reflect suburban outcomes and vice versa. This echoes many previous recommendations to better coordinate policy strategies, especially regional economic decisions like infrastructure, transportation and investments in affordable housing. As noted earlier, it also illustrates that states must recognize that urban/rural divides are real, and implementation of policies should account for these realities.

Using the Local Data

In our work with community organizations and municipal leaders, we have seen that local data transforms conversations to focus on targeted solutions. Data help change makers tell a story of what is so that they can fill in solutions to driving those issues or successes. Our partners have used the data to convey the depth of financial insecurity facing their communities, to develop programmatic and policy recommendations across sectors, and gain clarity about what financial prosperity looks like for all. This has resulted in new initiatives focused on financial empowerment, new framing, awareness, communication about poverty and new partnerships in cities.

Check out past profiles here. For a custom profile and partnership with Prosperity Now staff in your community, please contact And to create a custom profile yourself using Scorecard data, visit the Prosperity Now Scorecard’s profiles menu.

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