Using alignment algorithms to measure similarity of US state bills
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Measuring Policy Similarity through Bill Text Reuse


The identification of substantively similar policy proposals in both proposed and adopted legislation is important to scholars of public policy diffusion and legislative politics. Conventional, manual, approaches are prohibitively costly in constructing datasets that accurately represent policymaking across policy domains, jurisdictions, or time. We propose the use of text-sequencing algorithms, applied to legislative text, to identify bills that introduce similar policy proposals. We present three ground truth tests, applied to a corpus of 500,000 bills from US-state legislatures. First, we show that bills introduced by ideologically similar sponsors are more likely to exhibit a high degree of text reuse. Second, we show that bills classified by the National Conference of State Legislatures as covering the same policies exhibit a high degree of text re-use. Third, we show that rates of text reuse across state borders correlate with the diffusion networks recently introduced by Desmarais, Harden and Boehmke (2015).


See the makefile for concrete replication steps. The original data is available here: