Scientific Data. 2023.

Contraception. 2022.

Politics & Gender. 2022.

Journal of Health Politics, Policy, and Law. 2021.

(with Jeffrey Hemmeter, Pamela Herd, Sebastian Jilke, Donald Moynihan, and Elana Safran)

There is evidence that expanding social safety net programs improve population health. But as many as 40 percent of those eligible for many of these programs don’t actually enroll. Helping people to overcome administrative burden – particularly learning costs associated with understanding eligibility and potential benefits from such programs – has been shown to increase access to these programs. Subsequently, it may also affect important health outcomes through material gains, psychosocial stress reductions, as well as by facilitating access to other health-protective programs through program linkages. Here we present evidence about the downstream mortality consequences of reducing administrative burden for the Supplemental Security Income (SSI) program. On the basis of a prior randomized controlled trial among more than 4 million individuals (Hemmeter et al. 2020), we show that those who received a letter reducing learning costs and afterwards successfully enrolled in receiving SSI benefits have a substantial reduction in mortality at 52-months after receiving the letter. The results highlight that burden reduction efforts to social safety net programs can substantially reduce critical health outcomes by increasing take-up.

Presented at the 2022 Association for Policy Analysis and Management and the 2023 Russel Sage Colloquium on Reducing Administration Burdens. Find Pre-registered Analysis Plan here.

This study shows how the racial and economic diversity of a county affect Medicaid enrollment. Unlike previous studies which analyze state-level Medicaid enrollment trends or look at a subset of U.S. counties, I analyze nationwide, county-level variation in Medicaid enrollment. Counties with high proportions of marginalized denizens tend to have social welfare offices that are in high demand, but underfunded. I use multilevel modelling to analyze how state and county characteristics affect Medicaid enrollment for all U.S. counties. The distinct federalist structure of the policy in theory means states can design their own policy to best suit the needs of their denizens. Here, that would mean providing Medicaid coverage to people who need it most. However, I find that in counties where there is a high concentration of people with incomes below the federal poverty level, Medicaid enrollment is lower than in more mixed income counties. This negative effect is exacerbated (i.e. Medicaid enrollment is even lower) in counties that are both very poor and racially segregated.

Presented at the 2022 DC Area Public Management Research Meeting, the 2022 Southern Political Science Association Conference, and the 2021 American Political Science Conference

(with Scott LaCombe)

Is being a policy leader an intrinsic characteristic of a state, or is policy leadership dependent on the state's needs, expertise, and the policy at hand? This study adds to both our general understanding of U.S. states as a network and also to our understanding of why some states persistently fill the role of leaders in U.S. policy diffusion networks and others are persistent laggards. We use role analysis and measures of network centrality to identify what states play similar roles in the latent diffusion network and categorize states as leaders and laggards. We then use a variety of external and internal state characteristics and find that policy leadership is stable over time, with states being much more likely to remain leaders once they become one.

Presented at the 2023 Political Networks Online Colloquium and the 2022 American Political Science Conference. Check out a draft on APSA Pre-Prints.

(with Fred Boehmke and Jeff Harden)

The central hypothesis of policy typology research is that different types of policies will have distinct patterns of politics. It follows then that different types of policy may have distinct patterns of diffusion. Using inferred latent diffusion networks, we compare the diffusion of two types of policy thought to be particularly distinct: morality and non-morality policy. Most studies of morality policy to date only look at a single policy or a few policies. Focusing on individual policies makes it difficult to understand states’ general behavior around these issues. Therefore, we code policies from the State Policy Innovation and Diffusion data using three definitions of morality policy - broad, narrow, and prima facie. Then, we infer latent diffusion networks for each type of policy and use several network analysis methods as well as pooled event history analysis to adjudicate if morality policy has “different” patterns of diffusion. We find that the diffusion of morality policy may not be as different as literature suggests.

(with Fred Boehmke, Bruce Desmaris, and Jeff Harden)

The use of network analysis has dramatically expanded in Political Science since Lazer (2011) described a “dearth of articles.'' Applications abound in the study of International Relations, Comparative, and American politics. As the number of studies using network tools to better understand how networks describe and shape political choices and behaviors increases, this provides the opportunity to more explicitly compare network structures to determine whether they are similar or different. Other disciplines that study network structures also seek to make comparisons between them. Over time, a set of methods for explicitly making such comparisons has emerged. These methods include comparisons of networks based on their aggregate features, such as clustering or hierarchy, as well as measures of the distance between two networks. Political Science is often interested in comparing networks, but typically uses a small set of network-level features. Methods allowing for more explicit comparisons would often be helpful. In this research note, we review and discuss the value of such techniques for research in Political Science. We provide an overview of the various methods for comparing networks.

(with Johabed Olvera Esquival and Candis Smith)

Many states have considerably expanded eligibility for Medicaid under the Affordable Care Act (ACA) since 2014, but some expansions have passed as recently as March 2023. Still, we remain relatively unclear how Medicaid expansion affects health and mortality. There is especially limited evidence as to how these health and mortality effects vary at the substate level and by race and ethnicity. In this paper, we examine the impact of county-level healthcare and social service capacity and medicaid outlays on Black-white and Latinx-white mortality disparities. Using data from the U.S. Center for Disease Control, the Agency for Healthcare Research and Quality, the American Communities Survey, and the National Center for Charitable Statistics between 2000 and 2020, we employ a difference-in-difference approach to compare changes in mortality disparities in counties where the state government expanded Medicaid under the ACA before and after the expansion happened to changes in non-expansion counties.

(with Charles Shipan, Fred Boehmke, and Benjamin Goehring)

Legislatures influence executive agencies through bureaucratic oversight, but to what extent is this influence coming from individual legislators rather than the legislature as a whole? We examine whether the ideological preferences of legislators across the American states influence the outcome of oversight. Specifically, we explore if bureaucratic actions are a function of the preferences of individual legislators, the aggregate legislature, or both. Further, we assess whether the effect of individual legislators depends on the ideological distance between the individual and the chamber. We investigate these relationships in the context of state administrators’ annual nursing home inspections, merging these inspections with data on state legislative districts, the ideology of the districts’ representatives in the state legislature, and the ideology of state legislative chambers. We perform standard count model analyses to account for nursing home, district, legislator, and state effects. Our findings provide evidence of both individual legislator and aggregate legislative effects and also show that the influence of individual legislators is partially contingent on the ideological differences between legislators and their chambers.