Ryan Bookstein headshot

Ryan Bookstein

PhD Student • Department of Government and Politics • University of Maryland

I am a fourth-year PhD student in the Department of Government and Politics at the University of Maryland. My research centers on American political institutions, with a focus on how legislators allocate and distribute resources across their constituencies and how committees shape legislative behavior.

At UMD, I teach courses on quantitative methodology and data science.

My work has been published in Congress & the Presidency.

Publications

  • Party Defection in Senate Confirmations of Executive Nominees (with Kristina C. Miler). 2026. Congress & the Presidency.

    Legislative-executive relations are generally portrayed as partisan and combative. The conventional wisdom is that Senate confirmation votes follow party lines, but the reality is different. Based on an original dataset of nearly 500 presidential nominations to executive agencies from 2017–2024, we find that the vote follows the party line only 12% of the time, and the majority of the time, ten or more senators defect from their party’s position. We examine the factors that explain the extent of party defection, focusing on the role of electoral timing, the position to which the individual is nominated, and the nominees themselves. We find that party defection is more likely when nominees are not seen as being ideological or partisan, and when the position is of higher status or in an agency perceived as less ideological. These findings reveal overlooked bipartisan dynamics due to the expectation that all politics is partisan.

Working Papers

  • “The Geography of Senate Constituency Service: Field Office Siting, Accessibility, and Persistence” (with James G. Gimpel).
  • “Deference or Discipline?: Party Defection and Senator Behavior on Executive Nominations” (with Kristina C. Miler).
  • “Committees as Vehicles for Issue-Specific Lawmaking Effectiveness.”
  • “Representational Infrastructure: Local Resource Allocation in the Senate.”
  • “Measuring Localism in Congressional Communication.”

Teaching

  • GVPT 622: Quantitative Methods for Political Science (Fall 2026) — Content
  • Introduction to Data Science (Summer 2026) — Content