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Poverty Solutions awarded six new graduate research projects, ranging from assessing Detroit’s “blight flight” to measuring economic well-being in Uganda, to PhD students across the university. The second annual Graduate Research Assistant (GSRA) Awards are granted for creative, action-based research that can inform existing policies, practices and interventions or inform future policy directions.

Doctoral students will pursue research projects in collaboration with a faculty mentor that seek to contribute to efforts aimed at preventing and alleviating poverty in Michigan, the nation and the world.

“This program gives PhD students an opportunity to engage in academic research that emphasizes real-world solutions and responds to the needs of communities, service providers, and policymakers,” said Poverty Solutions associate faculty director Kristin Seefeldt. “We’re thrilled to support work that will both build expertise and resources for the university community while also informing real policy and programs.”

The six GSRA awardees include:

Assessing Detroit’s “Blight Flight”: Residential Perceptions and Neighborhood Attachment in Response Demolition Programs
Lydia Wileden, PhD Candidate, Public Policy and Sociology

Demolition is a critical policy lever for cities looking to eliminate neighborhood blight. Detroit’s demolition program has been allocated nearly half a billion dollars since 2014 to address the city’s glut of vacant homes. While policymakers and researchers generally believe that blight removal is necessary to rebuild distressed neighborhoods, most existing research focuses on the impacts of blight removal for neighborhood outcomes like crime. Little research has examined how demolitions influence residents’ neighborhood perceptions and the exodus of residents known as “blight flight.” This project uses administrative data and longitudinal survey data from the Detroit Metro Area Communities Study to examine how local demolition activity impacts resident perceptions and mobility. Through descriptive and causal analysis, it seeks to understand how local blight reduction shapes neighborhood attachment. A goal of this research is to make policy recommendations to inform ongoing blight removal efforts in Detroit and across other depopulated communities.

Predicting Poverty: Advancing Measurement of Economic Well-being in Uganda through the Combined Use of Publicly Available Satellite Imagery, Representative Surveys, and Government Infrastructure Records
Peter Carroll, PhD Candidate, Department of Political Science

Infrastructure deficiencies are among the main contributors to poverty in low-income countries. The effects of poverty mount when poor households lack nearby access to basic services like improved water and sanitation, electricity, transportation networks, and health clinics and schools. Governments can play a significant role in improving human development by addressing infrastructure needs, yet administrators charged with improving public goods access often lack sufficient information about which areas are in the greatest need. Recent approaches to address this information gap have used satellite imagery and other big data sources, in combination with household well-being surveys, to train machine learning models to estimate poverty. This project will employ a novel technique to improve these predictive models through the incorporation of government-collected, spatial infrastructure data, with an application in Uganda. By subsequently sharing these improved poverty estimates with government officials, the approach aims to influence the ability of administrators to better target resources to those in need.

Establishing the Relationship between Wealth Inequality, Income Inequality, and Poverty: Applying Counterfactual Historical Simulation to IRS Administrative Tax Data
Asher Dvir-Djerassi, PhD Student, Department of Sociology and Ford School of Public Policy

Over the last 30 years, increasing wealth stratification has been a significant component of increasing economic inequality and stagnant poverty in the US. Unlike the dominant explanations of stagnant poverty and rising inequality, this account centers on the role of capital income over labor income. Using IRS administrative tax records and Zillow real-estate estimates, my research team will construct the first-ever intergenerationally linked population level data of the joint distribution of income and wealth. Via the application of counterfactual historical simulations, estimates will be made of the effects of a multiplicity of forces – e.g., changes in tax regressivity and wealth exportation – on changes in wealth accumulation, capital income, and, consequently, economic inequality and poverty. Additionally, the potential effects on economic inequality and poverty of proposed policy solutions – e.g. baby bonds, an expanded child tax credit, and negative income taxation – will be simulated. These simulations will directly inform the policy discourse.

The Gendered Consequences of Crises in Eastern Kentucky
Lanora Johnson, PhD Candidate, Department of Sociology

Working-class people in Eastern Kentucky have weathered major disruptions over the past forty years, including the decline of coal and volatility of demand for rural men’s labor, the opioid epidemic, increased white, working-class mortality, and now the coronavirus pandemic. These disruptions have magnified existing strains on gendered work and family relationships. How does gender shape how working-class people navigate local and national crises in their day-to-day lives, and how does gender shape how people in economically disadvantaged, rural communities think about what is possible and likely in their futures? This project will include interviews with at least 60 working-class men and women living in Eastern Kentucky. Insight into individuals’ gendered strategies for survival in the midst of socioeconomic disruption can yield insight into the gaps in their local safety net, and how policy might better serve their needs.

Measuring the Association Between The COVID-19 Pandemic, Material Hardship, and Work Hours and Earnings in Recreation Industry Dependent Counties
Shoshana Shapiro, PhD Candidate, Public Policy and Sociology

The goal through this research is to understand the effects of the COVID-19 pandemic on recreation industry dependent counties in the United States. The two primary research questions of this study are: 1) Is the COVID-19 pandemic associated with elevated rates of material hardship in recreation industry dependent counties as compared to the nation as a whole? and 2) Is the COVID-19 pandemic associated with depressed hours and earnings in recreation dependent counties as compared to the nation as a whole? This research will give policymakers a better understanding of the place-based impacts of the COVID recession on recreation industry dependent counties and inform place-based policymaking to support communities that were disproportionately economically impacted by the pandemic.

Intergenerational Cycles of Poverty
Davis Daumler, PhD Candidate, Department of Sociology; Predoctoral Trainee, Population Studies Center, Institute for Social Research

This project will include two papers on intergenerational poverty. The first paper examines why early-childhood poverty generates the largest gaps in adult outcomes. The findings suggest that cumulative disadvantage processes, rather than age-specific explanations, offer a competing theory for the outsize consequences of early-life poverty. Therefore, reducing children’s cumulative rates of poverty is critical for breaking the intergenerational cycle of poverty. The second paper, coauthored with professor Fabian Pfeffer, examines the multigenerational associations of poverty across three generations of families. This study will be the first to establish the rate of “third-generation poverty.” By identifying the sources of multigenerational poverty, the paper has significant merit for policymakers and poverty scholars, especially for reducing poverty—and targeting people at the highest risk of experiencing multigenerational poverty.

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