Identifying Friend or Foe

Given how often what’s said on the campaign trail bears so little resemblance to votes cast on the House or Senate floor, it’s become a full-time job holding our elected officials accountable. Stakeholder groups will spend hundreds of hours tracking bills and legislative voting records to generate candidate ratings every year, and some even pay professional lobbyists to provide those services. To what end? Simply to help identify friend or foe.

Voter Science has provided tools in the past to track bills of interest through the legislature, but we’ve found that the data entry required to tag bills that an organization supports or opposes can be a significant barrier to entry. Folks simply don’t have the time to maintain these lists because they’re too busy trying to work the halls of the state capitol and advocate for their positions with members one-on-one and in committee hearings. Fortunately, since 2014 we’ve had an online committee sign-in system for public hearings that’s a public record of positions that individuals and organizations have taken in support or opposition to every bill that’s been granted a hearing. In fact, at the beginning of the 2022 legislative session there were exactly 322,706 records from such public testimony. With this public data, we already know which bills that stakeholder groups have decided to support or oppose, so there’s really no need for any tedious data entry. Moreover, our state’s Legislative Web Services provide the public easy access to member voting records, so we can now automate the entire process of determining how those voting records correlate to each organization’s public policy position.

Today I’d like to introduce you to my latest pet project, codenamed Identify Friend or Foe (IFF) after the transponder system used by our military to identify combatants on the battlefield. You can access it from my WhipStat prototyping site here:

http://whipstat.com/Projects/Advocacy

The user interface is quite similar to my Partisan Leaderboard page, where I display a stack chart for all members by chamber and date range. However, here the main Organization drop-down lists over 1,500 lobbyist employers registered by the PDC that were referenced from hearing testimony records. When you select an organization, an aggregated list of “bills of interest” will be displayed beneath the chart. This table includes the bill number, title, total number of references from the selected organization, number willing to testify, and the percentage supporting the bill, with “Pro” counting as 1, “Con” as -1, and “Other as 0. Note that I’m using a fuzzy matching algorithm to match the organization name entered in committee sign-in to the official PDC records, so they may not be perfect…but we’re getting better every day. Use this list as a quick sanity check to ensure that your organization’s testimony records have been aggregated accurately.

The horizontal axis of the stack chart show shows the correlation between member voting records and organization position for each bill. Members who always vote the organization’s position will have 100% correlation and those who always take the opposite position will have -100% correlation. The dots for each member are color coded by party and if you hover over each you’ll see a tooltip with each members information and their actual correlation coefficient. To save a tab-delimited “leader list” of the member scores that can be imported into Excel, you can simply press the Download button.

Note that I’m currently collecting additional information that could potentially be used to further weight these scores (but that would make them less than a true Pearson correlation). Here are some examples:

  • A stakeholder’s willingness to testify or whether they’ve travelled from out of town
  • Committee votes made by members when advancing the bill to the floor
  • Committee leadership that could be positioned to advance or kill the bill

IFF is obviously a work in progress and we would welcome any feedback you have on our current user interface or algorithms. Since committee sign-in data now must be obtained by formal public records request, our plan is to update this tool at the end of every session, but if more frequent updates would be valuable to legislative advocacy groups we should encourage the Legislative Service Center (a.k.a. LegTech team) to incorporate the sign-in data into the Legislative Web Services, where it probably belongs.

We at Voter Science hope that IFF can usher in a new era of transparency for state government, freeing stakeholders and lobbyists from the tedious process of generating their own candidate rating systems and holding elected officials more accountable for their actual voting records when they inevitably come asking for campaign donations. It may seem obvious, but up until now it’s been surprisingly difficult to know who your friends in Olympia really are.

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