Above: Morgan McKinney, 28th Class Emerson Fellow.
I had prior research experience before becoming an Emerson Fellow, and when it came time for the matching process for policy placements, I found myself longing to do research again. My desire for research led to my placement with the Brookings Institution, where much of my work centers on workforce development policies that impact the economic mobility of people from low-income backgrounds. I spend most days tracking federal and state policy changes, investigating local spending efforts, or analyzing metrics to assess how well governments are supporting low-income youth. I work to identify successful frameworks and models that can be scaled and search for leverage points to advance anti-poverty programs. At times I have become disenchanted with research—after all, we hardly need more data to prove inequity, poverty, and hunger are major (and preventable) problems in this country. At Brookings I have found that collecting and analyzing data remain essential tools for designing programs and policies, but without applying an equity lens, they miss the point.
Throughout the Emerson Fellowship fellows participate in a series of racial equity trainings which have moved me beyond an understanding of the structures that uphold racial inequity toward a set of concrete principles and actions I can take to center equity into my work. Part of this training included the importance of breaking down data by race in research. At Brookings I attended a presentation by the U.S. Department of Labor’s Deputy Secretary, Julie Su, to discuss their new data strategy. I learned that the Department of Labor is redesigning their data collection process to systematize data across agencies, improve the user experience for data reporters, and ensure that key measures are being collected to inform policymaking. Their strategy includes a commitment to further disaggregate racial and ethnic data with emphasis on the rich diversity within the broad racial and ethnic categories that tend to be reported on. Disaggregating these subgroups is a vital step toward creating more equitable policy, and I was eager to see movement toward building these systems. Without such disaggregation, disparate outcomes get blurred into averages, thereby hiding injustices and homogenizing diverse experiences.
Deputy Secretary Su illustrated the need to do better on a national level to collect and analyze disaggregated data. During the pandemic unemployment levels rose for all groups, but at different rates. Initially the Department of Labor had not collected enough data to report on unemployment for Native Americans. Once they had obtained that data, the graphs they were using to visualize unemployment had to be rescaled because unemployment rates were drastically higher for Native Americans in comparison to other racial and ethnic groups. Their analysis didn’t stop there. The Department of Labor took their analysis one step further and found that the negative employment impact for Native Americans could be traced primarily to five states. These data serve as a clear starting point for how decision makers should effectively target resources to the most adversely impacted communities, another pillar of racial equity.
Systematic data sharing is another central component of evidence-driven problem solving. While important considerations and precautions should be taken to maintain privacy, data sharing is necessary to develop evidence-based solutions. To take an example from school re-engagement work, community-based organizations cannot effectively recruit youth who have dropped out of school to reconnect them to education and work without knowing who those youth are. Schools typically have access to these data, often referred to as “leaver lists,” but there are many barriers for reengagement service providers to access these lists. The barriers often include a lack of established relationships with school districts and disincentives for schools to share dropout data to protect their limited enrollment and attendance-based funding. Establishing a common set of metrics and protocols for data sharing across school districts is a promising practice that could significantly advance reengagement efforts. Without such systematic approaches, many youth will continue to fall through the cracks and not receive the support they need to break the cycles and structures that keep people in poverty.
In a research project I am working on called Diverging employment pathways, we found that factors such as educational attainment continue to be significantly correlated with the economic trajectories of adults who experienced disadvantage in adolescence. These findings are bleak but not shocking, and point to specific areas for policy change. If the data comprehensively show earning a high school diploma and post-secondary degree is meaningfully associated with better economic outcomes, then policymakers should look to solutions that include meaningful investments to ensure all youth have appropriate opportunities and supports to earn their diploma. Findings also show that educational attainment rates are lower for Black and Latine adults in comparison to White adults. Still, other researchers find that the payoff for higher education does not result in racially equitable outcomes, reaffirming that effective solutions must tailor to the specific needs of BIPOC communities.
My time as an Emerson Fellow has rebuilt my faith in the research process, yet I still uphold that research must be coupled with urgent action in the anti-poverty space. We cannot wait for intensive, time-consuming research to act, but we can commit to developing rigorous, collaborative evaluations alongside action and adapt when new evidence arises. Through the fellowship I have been given the tools and training needed to do research that advances impactful, equitable programs without stalling action. Research frameworks like “human-centered design” and “design thinking” are incredibly useful, but researchers should go further to co-create with the communities being studied to avoid recreating historically racist research structures rife with power imbalance and abuse. Collecting and analyzing data are important tools, but they aren’t the only valid ones. Policymakers can make the best decisions when they have evidence-driven solutions at hand that are created and informed by people who have lived experience with poverty. After all, the knowledge of those who have experienced poverty is just as much expertise as the data science skills of PhD-level researchers.