Using Polarization and Alignment to Identify Quick-Approval Law Propositions: An Open Linked Data Application


Since the return of democracy in 1990 until the end of 2020, Chile’s Congress has processed and approved 2404 laws, with an average processing time of 695 days from proposal to official publication. Recent political circumstances have given urgency to identifying those law propositions that might be shepherded to faster approval and those that will likely not be approved. This article proposes to classify law proposals, as well as parliamentarians and political parties, along two axes: polarization (lack of agreement on an issue) and (political) alignment (intra-party coincidence of a group’s members regarding specific opinion), yielding four quadrants: (a) “ideological stance” (high polarization, high alignment), (b) “personal interests” (high polarization, low alignment), (c) “thematic interest” (low polarization, low alignment), and (d) “technical consensus” (low polarization, high alignment). We used this scheme to analyze an existing open-linked dataset that records parliamentarians’ political parties and their voting on law proposals during 1990–2020. A simple visualization allows identifying a large set of propositions (1,643 = 68%) with technical consensus (i.e., low polarization and high alignment), which could have been quickly shepherded to approval, but instead took 687 days on average (i.e., essentially the same time as others). Wider adoption of this analysis may speed up legislative work and ultimately allow Congress to serve citizens more promptly.

In International Conference on Applied Informatics, ICAI 2023.