Partnering to Better Understand Opportunity Youth in Detroit

Data Driven Detroit is excited to announce a partnership with Microsoft and The Skillman Foundation to test out a new approach to measure “Opportunity Youth” (OY). OY are typically defined as young adults 16-24 years old who are neither working nor enrolled in school or a vocational training program.

OY is a challenging group to pinpoint using current data sources making it nearly impossible to identify the magnitude of need in Detroit. The size of Detroit’s city limits often requires breaking data into smaller geographic units like census tracts to identify the geographic concentrations of highest need.  Current data sources are usually too unreliable to break into small geographies and often have high margins of error even at the city level.

The most common data source is Public Use Microdata Files from the American Community Survey, which allows for crosstabulations of Race, Age, Employment Status, and Enrollment. In exploring its use, D3 realized the sample size at the city-level is very small resulting in high margins of error.

The American Community Survey also publishes aggregated data tables, but the only available table only identifies individuals who are 16-19 years old by work and education enrollment status. This leaves out the older population of interest (individuals 20-24 years old) and excludes people enrolled in trade schools. Furthermore, this data is barely useful with margins of error almost as large as the estimates themselves.

Our approach is different. Instead of relying on outside data sources, we decided that we might be able to better understand OY by synthesizing information from the organizations that serve them such as Detroit’s Summer Youth Employment Program. Integrating the data collected by these organizations into a common data infrastructure could provide us the ability to provide closer to real time tracking of the number and locations of OY, allowing organizations to target their programs geographically for higher impact and better outcomes.

To better identify the location of OY, the first step will be identifying the organizations and programs that serve them. In the initial phase of the project, D3 will undertake an intensive landscape analysis of relevant organizations in the city and then create an ecosystem map that shows how these different programs and organizations are connected to each other.  One example of this approach is the map of the early childhood ecosystem created by Detroit Collaborative Design Center.

Through identifying these partners, we expect to find a handful of organizations who are interested in participating in the next phase, which will be data collection.  Using the infrastructure developed in our partnership with the Microsoft Cities Team to build the Metro Detroit Data Alliance, D3 will work with GDYT and the other new partners to integrate the appropriate data into the MDDA warehouse. This will help us better understand how many OY there are in Detroit and what neighborhoods they live in, while still preserving the confidentiality of individuals.

After obtaining the data, we will begin the data analysis phase.  The primary research question will be focused on the number and geographic distribution of OY, based on the data from partner organizations.  We will also be exploring other interesting demographic characteristics such as age breakdown and length of time disconnected, if the data we acquire is able to provide these insights.

It is our hope that this project will better position Detroit organizations to serve a vulnerable and difficult to count population by identifying locations in the city with higher concentrations of OY. This data-driven approach will help improve program targeting and hopefully improve outcomes and increase efficiency. From a broader perspective, we’re excited to be testing whether a shared data infrastructure can help solve some of our stickiest data questions, and we look forward to exploring additional partnerships with organizations who are trying to identify novel solutions to data challenges.

If you are interested in getting involved, please reach out through AskD3.  Stay tuned for more updates as our analysis progresses.