Data Sources & Methodology

The database created by Digital Democracy is unprecedented. It combines a variety of information and data from throughout state government that is publicly available, but isolated and difficult to access. This database combines these related data sources so they can be compared to reveal relationships, patterns and aberrations in the Legislature and the policy-making process. 

The database includes four categories of information:

• Transcripts: Every word uttered in a public legislative hearing or floor session is captured in the database.
  • The archive of transcripts spans from January 2025 to present. 
• Bill information: The database includes the text of every bill, amendments, bill analysis, vote, supporters and opponents.
  • Digital Democracy includes this bill information from 2025 forward.
  • For determining bill authors in Hawaii, Digital Democracy uses the name of the first person listed on the bill. There are some exceptions to this however, such as “by request” bills which are not authored by the legislator who introduces them
• Financial information including campaign donations, independent expenditures, political party spending and gifts.
  • Data for campaign donations, independent expenditures and political party spending going back to 2015 with periodic updates.
  • Data for gifts to legislators and sponsored travel. Digital Democracy contains this data from 2016 to the most updated data available for the last session year.
• District data including election results and demographics.
  • Election results are updated after each election.
  • Demographic data is drawn from the most recent U.S. Census Bureau’s American Community Survey.

One of the greatest challenges in using data to create transparency in state government is that there is no standardized identification required when a person, organization or company testifies in a public hearing or donates money to a campaign. 

As a result, it’s difficult to know if the “John Kealoha” who testified in 2024 is the same “John Kealoha” who testified in 2025. Similarly, we may have information under different versions of the same entity like “ACLU” versus “American Civil Liberties Union,” or different subgroups. These entities are the same for our data analysis. Conversely, entities such as “U.S. Chamber of Commerce” and “Maui Chamber of Commerce” are not the same organization or have a parent/chapter relationship even if they sound similar. 

Digital Democracy uses technology and human judgment to discern how these data points should be recorded and linked to other records. For example, we use facial recognition technology and human review to help us understand if “John Kealoha” in 2024 is the same person as “John Kealoha” in 2025. Artificial intelligence combined with human review also helps us understand which entities are related and which are not when they have similar names.

Even with the technology and human review, the database is still not precise. For that reason, a name typed into a search bar may still return no results or too many similar results. As we develop Digital Democracy, we will continue to improve the quality of the database. The technology we use is also evolving rapidly and becoming more efficient. But without an identification system for donors or those who testify, the database will always be imprecise and the results of a search or analysis should be considered with that context.

The problems with identification are found throughout the Digital Democracy database and website. In the transcripts, the name of a speaker may not be captured accurately if it is mumbled by the speaker, misspelled by the transcription program or improperly captured due to human error. The inconsistency in names — for people, organizations or companies — also means that some financial transactions may not be captured correctly. 

The challenge is important for Digital Democracy because this unique database is designed to capture and compare all of the interactions of a person or organization that are recorded in a variety of isolated and separate sources. For example, a Digital Democracy search for Hawaiian Electric Co. should reveal information about lobbyists the company employs and donations the company has made to legislators; testimony by a company representative from the transcript of a hearing; data about gifts or travel involving a legislator, and positions on bills from the Hawai’i State Ethics Commission. That broad analysis requires an accurate match within the Digital Democracy database of dozens of Hawaiian Electric data points created over several years by separate offices.

Please help us make this data as accurate as possible by letting us know if you spot any errors or problems. Send a note to us at [email protected].

Hawai’i state Senate and state House of Representatives record public hearings and floor sessions and post the video online within 24 hours. Digital Democracy sends the video to an online transcription service that uses artificial intelligence to transcribe the audio within a few hours. Speech-to-text technology is far from perfect, however, even with the latest advances in AI. In many applications, a 10% or 20% error rate is common. 

To significantly improve the quality of the transcript, a team of Digital Democracy contractors reviews each transcript to correct errors. Speakers are also identified with facial recognition technology, but human editors confirm the identification and link speakers to related information about that person in the database. To increase accuracy, the names of legislators and registered lobbyists are hard-coded into the program. To a limited extent, we use internet searches to identify some other speakers. 

Videos are also cut into smaller segments corresponding to bill discussions. Each segment of video and the corresponding transcript are indexed so that a search can precisely identify bill discussions, speaker quotes or keywords.

The human editing process takes at least two hours of review for every hour of video. In all, when Digital Democracy launched in 2025 in Hawai’i, more than 890 hours of video were processed and stored in the database. 

The correct spelling of Hawaiian words using diacritical marks, including names and places, is also a challenge for the AI transcription and human editors. Please consult a Hawaiian dictionary for correct spelling.

The database captures information about the policymaking process from various Hawai’i government websites. That data includes the text of bills and resolutions, amendments, committee and floor votes, supporters and opponents who registered official positions, governor vetoes, a history of a bill’s progress and the current status of the bill.

“In progress” status includes bills that may have been deferred but are technically still alive until the end of the two-year Legislature.

The data can be seen in several places on the Digital Democracy website:

• Legislator pages: This is used to create a “bill activity” graphic that displays how many bills and resolutions a legislator has authored and how many have passed or failed. It is also used to display all of the bills authored by each legislator.
• Bill pages: There is a web page for every bill introduced in the Legislature including text, status, analysis, votes, supporters and opponents.
• Hearing pages: Web pages have been created for each public hearing include data about legislation considered in the hearing.
• Issue pages: These web pages focus on major state topics including data about all of the current bills related to that topic.
• Search directories: The data also appears on directory pages produced by search queries.

We consider two categories of financial information: money given to help a candidate win an election and financial transactions involving an incumbent legislator, which we describe as “influence.” 

Political campaigns in Hawai’i have to disclose their contributors to the Hawaiʻi Campaign Spending Commission. The data contains some information about the donor, the date of the payment, and the amount of money. However, categorizing political donations by economic sector can be difficult. What categories should be used? Is a company like Tesla a car company or a tech company? What about delivery services like DoorDash? Amazon?

Digital Democracy uses categories identified by OpenSecrets, a national nonprofit dedicated to comprehensive, nonpartisan analysis of political donations to state and federal officeholders. Open Secrets, previously known as Follow the Money, has been a trusted source of campaign finance data for decades and is widely cited by major media organizations.

The categorization system divides the entire economy into 20 sectors. Each of those sectors is divided into industries, which are further segmented into 438 total business categories. There is a catch-all sector called “Uncoded” which are contributions that have yet to be categorized.

Because the data can have nuances (such as different name spellings, the inclusion of middle initials, or a slightly different version of the company name) all of this categorization is done by a person, either at OpenSecrets or at Digital Democracy. We go contributor by contributor and do our best to accurately capture the main economic interest of that person, company, or organization.

We describe financial transactions with an incumbent legislator as influence. Influence is divided into two subcategories: gifts and sponsored travel. The data is obtained from annual financial and gift disclosures which legislators file with the Hawai’i State Ethics Commission. They are required to disclose stock, property, and business interests as well as any gifts they received or any trips they took at someone else’s expense.

We display data about influence money on the pages for each legislator including: 

• Personal gifts: Legislators in Hawai’i are not allowed to accept a gift if it could reasonably be inferred that the gift is intended to influence their official actions or act as a reward for such actions. Flower lei and inexpensive tokens of aloha or appreciation are generally considered acceptable. The disallowed gift categories include gifts from lobbyists (besides small items with no resale value like pens), gifts from employees of state agencies, and gifts from organizations that have/may have business before the Legislature. For gifts that are allowed, legislators must file an annual gifts disclosure statement with the Hawai’i State Ethics Commission if they or their spouse or dependent child receive from a single source one or multiple gifts and the whole aggregate value exceeds $200.
• Sponsored travel: Legislators are allowed to receive sponsored travel but it must provide a legitimate state benefit and clearly relate to the legislator’s official duties.

Is it really possible to depict a person’s political ideology in all of its nuance and complexity with a single number? Of course it isn’t. But by looking at how often certain legislators vote with one another, we’ve come up with a starting point to give readers a better sense of how lawmakers stack up.

To do it, we gathered all the “aye” and “no” votes from every state House of Representatives member and state senator from the last legislative session. That includes floor session votes, but also votes in committee. We then fed that long list of votes into software written by political scientists at UCLA, USC, the University of Georgia and Rice to come up with a measure of ideological “distance” — how close or far apart different lawmakers are to one another based on their voting behavior.

That “distance” is assigned a number between -1 and 1, but we converted it from 0 to 100, with all the liberals clustered around 0 and the conservatives at the top of the range.

Political scientists have been tinkering with some version of this method, called NOMINATE, since the early 1980s. You can read more about how we’ve used this method in the past here.

District demographics for race/ethnicity, median household income, median age, poverty rate and educational attainment come from the U.S. Census Bureau American Community Survey.

Election results data come from the Hawaiʻi Office of Elections.

District map boundaries data come from the State of Hawaiʻi’s Open Data portal.

District partisanship — whether a district is considered safe Democrat, safe Republican or competitive — is based on an analysis by Honolulu Civil Beat reporters looking at voter registration data, election results from the Office of Elections and other information.

Contact and biographical information comes from the official Hawai’i Senate and House of Representatives websites and other public sources.

Social media information comes from Honolulu Civil Beat data collection.

Legislator gender, race/ethnicity, residence, high school and other data came from elected officials themselves, Civil Beat research and other public sources.