My dissertation looks at how Congress uses public policy information goods produced by organizations outside of Congress. I present a theory of ideological and interest based think tank coalitions, and explore how Congress relies on legislative subsidies produced by these outside actors. Using a dataset of over 200,000 white-papers and 14,000 congressional committee reports, this project employes text reuse detection and latent network inference methods to infer a legislative subsidy transmission network from the outside organizations that produce these white papers to the committees that produce reports on bills as they are sent to the floor. I also use survey data and survey experiments to study the staffer decision-making involved in the subsidy uptake process, focusing on the role of motivated reasoning and confirmation bias in subsidy source selection among staffers.
Congressional Capacity Project
In the fall of 2017 I fielded the 2017 Congressional Capacity Surveyas part of a larger joint New America and R Street project on Congressional Capacity. This survey sought to find out more about the backgrounds, career paths, policy views, and job experiences of congressional staffers. I have ongoing projects using data from this survey investigating partisan and ideological selection in information usage and trust, ideological diversity among members' staffs and how staffer issue knowledge effects information use patterns.
(with Tim Lapira, Lee Drutman, Alex Hertel-Fernandez and Kevin Kosar)
Semantic Smith Waterman
This project introduces a new method for detecting text reuse and paraphrasing in political texts, a critical measurement task in leveraging new text data to observe patterns of coordination, knowledge uptake, and policy transmission and diffusion. The method proposed is an extension of the Smith-Waterman local alignment algorithm with semantically aware mismatch penalties. This modification enables detection of instances of text reuse in which words are changed to semantically similar alternatives to fit new contexts or disguise the source of the text. Our implementation allows for arbitrary user defined semantic spaces, as well as arbitrary weighting of semantic dimensions. This enables users to tailor the semantic weighting to meet their domain specific needs. We apply this technique to find instances of text reuse from Congressional Research Service (CRS) Reports in congressional Committee Reports. The discovery of these instances of reuse enable us to explore when and under what conditions Committees rely on analysis produced by CRS.
(with Benjamin Edwards)
Theories of interest group representation and influence make often-competing predictions about the alignment of policy preferences between interest groups and those they lobby. To date, however, these predictions have remained largely untestable, because there have been few broadly applicable measures of interest group policy preferences, particularly at the U.S. federal level. Instead, scholars have relied on rough proxies such as partisanship or industry categorizations. In this paper, we use MapLight interest group position-taking data to estimate a new set of ideal points, "MLscores,'' for over 2,600 interest groups active from the 109th-114th Congresses. We estimate these scores jointly with Congressional roll call data, placing interest groups on the same scale as over 950 members of Congress. After showing that the measures conform to preexisting qualitative and quantitative expectations about the behavior of particular organizations and industries, we discuss opportunities for future research using these new measures.
The current working paper is available here.
(with Jesse Crosson and Geoff Lorenz)