The United States Code, the official collection of general and permanent federal laws arranged by subject, provides a unique opportunity to gauge a number of different aspects of congressional lawmaking activity through its annual updates of individual provisions of laws. In this paper, we discuss the U.S. Code as an expansive dataset for understanding congressional activities, performances, and priorities. We use Python to extract and parse sections of the U.S. Code spanning 1994 through 2016 from the Office of Law Revision Counsel’s (OLRC) XHTML data. Effectively, we have all provisions enacted into law and track their changes over time and across policy areas. We provide initial trends in the data that explain the breadth of lawmaking in various forms, as well as introduce new metrics that capture legislative performance. In using this new dataset, we can more rigorously analyze Congress’s governing capacity over time and issue areas.

About the Presenter

Scott Adler is Professor and incoming Chair of Political Science at the University of Colorado, Boulder. He is also Director of the American Politics Research Lab. His expertise is the US Congress, elections, political institutions, and policy making. Among his books are Why Congressional Reforms Fail: Reelection and the House Committee System (University of Chicago Press, 2002), The Macropolitics of Congress (co-edited with John Lapinski; Princeton University Press, 2006), and Congress and the Politics of Problem Solving (with John Wilkerson; Cambridge University Press, 2012). His current projects examine a variety of political institutions. One project, funded by the Hewlett Foundation's Madison Initiative, examines Congress’s ability to renew and update expiring programs and laws in the modern era. Other projects include research on presidential policy priorities, and an exploration of the behavior of cross-pressured lawmakers in Congress.