Prof. John Wilkerson on Legbranch site, "Machine learning improves our understanding of how laws are made and who deserves credit for them"

When it comes to studying how bills are passed to become laws in Congress,  scholars and journalists typically study effectiveness by equating the progress of bills with the progress of policy ideas. This is flawed because bills sometimes advance for reasons that have nothing to do with the sponsor and it overlooked the lawmaking contributions of other legislators.

One way to address these limitations is to use "text reuse" to study the progress of policy ideas. It detects how legislators borrow other legislators' ideas and it also reflects institutional realities and strategic considerations. This is important because stand-alone bills are hard to pass because of agenda space and strategic partisan considerations. Additionally, legislators who set the agenda (majority party members and committee leaders) tend to hoard these valuable credit-claiming opportunities.

Lastly, because of Constitutional constraints, Senate revenue bill has to “hitchhike” into a House bill. This means the House of Representatives has to incorporate parts of the Senate revenue bill into its own revenue bill for it to become a law.

For the full article please link here.