Glass House: California Legislator Tracker
About the California Legislator Tracker
We Californians elect 120 legislators to make important decisions about what life is like for all 40 million of us. How schools and colleges are run. What roads we need and what cars we drive. Whether our streets are safe and our environment protected. And how we care for the elderly, the poor and the homeless.
Most of us don’t know who these people are, but you should. It makes a difference in how they vote, especially when they have to balance the public interest versus special interests. This guide is your introduction, with information about each legislator’s personal background, their political profiles and their policy priorities. Let them know what you like and what you don’t like. It matters.
About the Data
The data included in the directory come from various vetted sources. See specific sources for each section below.
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 up all the “aye” and “no” votes from every assembly member and state senator from the 2021-2022 legislative session. That includes floor session votes, but also votes in committee. Even after excluding resolutions, which are just procedural or symbolic, we were still left with 18,616 separate roll calls to analyze. We then fed that long list of up and down votes into a piece of 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 are based on their voting behavior.
An example: San Francisco’s Phil Ting and San Diego’s Chris Ward, both liberal Democrats, voted together about 96% of the time in the last session. Contrast that with Fresno Republican Jim Patterson, whose votes overlapped Ting’s only 63% of the time. Feed those patterns through the software and Ting and Ward are ideological neighbors whereas Patterson lives on the farside of town from both of them..
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 1980’s. There are, of course, other ways to compare lawmakers’ political proclivities: comparing how they vote on certain legislation, how much money they raise from specific interest groups or what grades they’ve been assigned by advocacy organizations. So why do it this way?
All of those methods require some tricky judgement calls to be made.
What, for example, does a lawmaker’s C-grade from an anti-abortion group tell us about their ideology? What if that same lawmaker also got an A-rating from the Sierra Club? Boiling the analysis down to just the “yes” and “no” votes of different legislators does away with that subjectivity, leaving only numbers. And numbers don’t lie.
You can read more about how we’ve used this method in the past here.
For all of the major topics in California, activists and lawmakers know who the key leaders are within the Legislature. Key leaders are those with the most influence and activity on these issues, often because they are experts on the topic with the power to build the support needed for passage. Sometimes these leaders are chairs of the topic committees, but not always. Most often these days, they are also Democrats because Republican lawmakers have a tough time building a coalition while the Democratic Party holds a supermajority of seats. These topic leaders were identified by our CalMatters reporters, who work full time with all of the stakeholders on these issues.
Contact information comes from the official California state Senate and Assembly websites.
Social media information comes from CalMatters data collection.
Legislator gender, race/ethnicity, sexual orientation, birth dates, birth places and residence data came from CalMatters data collection from elected official offices, the California State Library and Political Data Inc.
Analyze the demographics of the legislature more in the interactive “How much does the Legislature look like California?” by John Osborn D'Agostino, Sameea Kamal and Ariel Gans.
Legislators background data come from official ballots on the California Secretary of State Prior Elections page.
Committee assignments come from the official California state Senate and Assembly sites. Only standing committee assignments and roles are specified.
Committee descriptions were adapted from the committee websites or written by the CalMatters editorial team.
Political campaigns in California have to disclose their contributors to the Secretary of State. The data contains some information about the donor, the date of the payment, and the amount of money. You know, the objective stuff.
But 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?
CalMatters uses categories identified by OpenSecrets, a national nonprofit dedicated to comprehensive, nonpartisan analysis of political donations to state and federal office holders. 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. In California, OpenSecrets gets updates at least twice a year from the Secretary of State, processes the data, and then gives it to CalMatters.
The categorization system divides the entire economy into 20 sectors. Each of those sectors are 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.
In the three cases above, Tesla is a “Transportation” company, as is “DoorDash.” Amazon is within the “General Business” sector.
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 CalMatters. We go contributor by contributor and do our best to accurately capture the main economic interest of that person, company, or organization.
Here are all of the sectors that we use:
- Candidate contributions
- Communications and electronics
- Energy and natural resources
- Finance, insurance and real estate
- General business
- Government agencies/education/other
- Ideology or single issue
- Lawyers and lobbyists
- Public subsidy
- Unitemized contributions
You can learn more about which industries make up each sector at OpenSecrets.
Want to see how we coded a specific contributor? Go ahead and look for yourself!
Special interest groups
Included special interest groups were chosen by the CalMatters editorial team to reflect a variety of areas and perspectives.
Group descriptions were written by the CalMatters editorial team.
Gifts and sponsored trips
Legislators are required to submit a statement of economic interests annually to the Fair Political Practices Commission on a form called Form 700. They are required to disclose stock, property, and business interests as well as any gifts they received or any trips they took at somebody else's expense.
In April 2023, CalMatters collected the submitted forms, extracted all of the data, and standardized the names of gift givers and trip sponsors. We released the dataset on GitHub as a series of CSV files along with some detailed documentation about how we standardize the names.
Voter registration data come from the California Secretary of State's Report of Registrations as of October 2022.
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.
District map boundaries data come from the Statewide Database.
How do you know if a district is a sure thing for Democrats, a Republican lock, a toss up or something in between? Elections are unpredictable. But taking a few data points on voter registration, past voter behavior and some demographic information about a district’s voters can give us an informed guess.
That’s what we did with our partisan lean score. Data nerds, eat your heart out: This part gets a little technical.
We started by looking at both Assembly and state Senate elections going back to 2012, the first election that made use of the current electoral map. Our main question: What determines — or, at least, predicts — whether a district will elect a Democrat or a Republican (1) in the next election?
There are a bounty of possible variables to choose from. Do more Democrats live in a district or Republicans? How many more? Which party won the last election? By how much? Has the district flipped from one party to the other recently? And what about the voters themselves — their income, race and education level?
After gathering all of those numbers, our next step was to figure out which of these data points are actually important. Why not just use them all? Trying to predict the next election based solely on how many Democrats live in a district isn’t likely to be very accurate. But it’s also possible to include too many variables. A kitchen sink approach — including, say, the incumbent’s birthday month or hair color — is a good way to construct a very precise description of the past that isn’t flexible enough to tell us anything about the future. Statisticians call that “overfitting” a predictive model.
To balance that trade-off, we used a selection method common among statisticians and data scientists that rewards variables that add useful new predictive power while also adding a small penalty to each new factor you consider.
With our new list of vetted variables, we plugged them into a logistic regression model — a statistical method that’s often used to make predictions about things with only two possible values. In this case, those two values are “Democratic” and “Not Democratic.” With a little tweaking, those predictions can be converted into probabilities, which allows us to say something like the following: “There’s a 18.5% chance that a Republican will win the next election in Senate District 14 in the Central Valley.”
We should take that number with a few heaping tablespoons of salt. With just a few variables — none of them based on current polling — there’s no way we can put the odds on a future legislative race with that kind of precession. So instead, in our last step, we took these probabilities and grouped them into five rough categories: “safe” Republican and Democratic seats, partisan districts that “lean” toward one party or another and “toss ups.”
(1) Because one member of the Assembly, Chad Mayes of Rancho Mirage, is a political independent and therefore neither, technically we wanted to predict whether a district will vote Democratic or not Democratic.
Election results data come from the California Secretary of State's Elections Statistics.
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Glass House: California Legislator Tracker is a team effort, made possible by the following:
Developers: Erica Yee, John Osborn D'Agostino, and Jeremia Kimelman
Product manager: Sapna Satagopan
Political reporters: Ben Christopher and Sameea Kamal
Reporters: Joe Hong (K-12 education), Mikhail Zinshteyn (higher education), Ana B. Ibarra and Kristen Hwang (health), Manuela Tobias (housing), Nadia Lopez (environment), Nigel Duara (justice), Alexei Koseff (state budget), Jeanne Kuang (poverty)
Product managers: Shyla Nott and Trevor Eischen
Product engineer: Kevin Marsden
Deputy managing editor: Foon Rhee
Editor: Dave Lesher
We thank the prior contributions of Nick Garcia, Anne Wernikoff, Ron Chambers, Kim Fox, Ariel Gans, and Margarita Noriega.