Text as Policy: Measuring Policy Similarity through Bill Text Reuse

The identification of substantively similar policy proposals in legislation is important to scholars of public policy and legislative politics. Manual approaches are prohibitively costly in constructing datasets that accurately represent policymaking across policy domains, jurisdictions, or time. We propose the use of an algorithm that identifies similar sequences of text (i.e., text reuse), applied to legislative text, to measure the similarity of the policy proposals advanced by two bills. We study bills from U.S. state legislatures. We present three ground truth tests, applied to a corpus of 500,000 bills. First, we show that bills introduced by ideologically similar sponsors exhibit a high degree of text reuse, that bills classified by the National Conference of State Legislatures as covering the same policies exhibit a high degree of text reuse, and that rates of text reuse between states correlate with policy diffusion network ties between states. In an empirical application of our similarity measure, we find that Republican state legislators introduce legislation that is more similar to legislation introduced by Republicans in other states, than is legislation introduced by Democratic state legislators to legislation introduced by Democrats in other states.