How we classified nearly $900 million in contributions from individuals, companies and PACs

This story was originally published by CalMatters . Sign up for their newsletters. Millions of campaign finance contributions poured in over the 2025-2026 election cycle to fund California candidates, proposition committees that support or oppose ballot measures, political action committees (PACs) and other political committees. Due to the scale of contributions and the fact that a large sum of this money is wrapped up in ambiguously named PACs, the raw data alone offers limited insight into what types of people and institutions are funding political campaigns. In order to uncover patterns behind who is bankrolling the candidates and propositions on the ballot this year, CalMatters classified and published financial contributions to the gubernatorial , insurance commissioner and statewide proposition official campaign committees (for example, Proposition 39 , which would have required identification for voting). We did this by determining the industry affiliations of the largest contributors to each of those campaigns during the 2025-2026 election cycle. All results are published on our Voter Guide . Our team classified nearly $900 million in contributions raised by these campaigns and proposition committees and processed tens of thousands of contribution records. We analyzed raw data on state-level campaign contributions maintained by the California Secretary of State, accessed by using its Power Search tool. Methodology Contributor Selection CalMatters focused on the largest contributors to the gubernatorial, insurance commissioner and statewide proposition official campaign committees. We assigned one of 20 classifications to contributors that gave at least $5,000 to each candidate or proposition across the 2025-2026 election cycle. Contributors who gave less than $5,000 in the same period were not categorized. (See: Entity resolution ) Industry Categories CalMatters designed a custom schema of industry categories to better serve our readers. The categories largely align with the North American Industry Classification System (NAICS) codes, which is the standard used by federal agencies. We consolidated NAICS codes where appropriate and based some of our categories on more granular NAICS subsectors. In several cases, we grouped certain contributors into a custom category based on shared interests not reflected well with NAICS: Major Donor category This category was a fallback for individuals who didn’t clearly provide an employer we could categorize. Individuals who listed their employer as retired, student, homemaker, unemployed, self employed, or some variation of none automatically fell into this category. When an individual listed employers from different industries across multiple contribution records, they were also placed in the “Major Donor” category if we could not determine that they were primarily aligned with a single industry. (See: Multiple possible classifications) Technology category Given reader interest with technology companies in the state, and that NAICS does not provide a distinct code, we created a custom category to represent the technology industry. We classified entities whose business centered around apps or websites to facilitate business as “Technology,” even though those entities could be categorized differently. This includes companies such as Uber, AirBnB, Amazon, Netflix, Meta and Google, as well as companies engaged in artificial intelligence, cryptocurrency and software. Additional custom categories Other custom categories not reflected in NAICS include “Self-Funding,” “Candidates/Political Parties” and “Small Donors.” See: Appendix: CalMatters campaign finance categories Classification Overview CalMatters assigned contributors to a category through a multi-step process. We tried to match contributors to NAICS codes or our unique categories, by using five different techniques. In order, we attempted to match contributors by: Comparing existing NAICS codes and similar categories from multiple external data sources Comparing contributors’ names, employers or occupation to a custom list of keywords that highly correlated with specific industry categories Using a CalMatters machine learning classifier to assign codes using the semantic meaning of a contributor’s name or employer (company) and occupation Using two large language models (LLMs) to assign a classification to each unique contributor Manually reviewing the data Throughout these steps, CalMatters code flagged for manual review any instance of doubt. For example, if classifiers disagreed on a contributor’s category or if a contributor was only able to be classified by an LLM, it was flagged for manual review. CalMatters also performed manual spot checks on contributors who had been successfully classified without manual review. When we could not reliably identify the industry of a contributor, that contributor was left uncategorized. We categorized organizations and businesses directly and we classified individuals by the employer listed in their contribution record with a few exceptions. For example, we grouped individuals who listed their employer or occupation as retired, student, homemaker, self employed or some variation of none into the “Major Donor” category. We also manually evaluated how to classify contributors that reported more than one employer across multiple contributions, or if they could be classified in multiple ways. (See: Major Donor category) Campaign finance tends to be a web of interconnected political committees that contribute to one another before using that money for independent expenditures or landing in the coffers of an official campaign committee. To account for this, we categorized contributions from a political committee based on the classifications of all contributors that gave it $5,000 or more and, in certain cases, smaller contributions when we could reasonably determine it came from employees of a single company, a union or an association’s membership. (See: Political Committees ) Data Sources H1B Employer Hub CalMatters collected data from the H1B Employer Data Hub , which provides a list of employers that have submitted petitions to hire foreign workers and the NAICS code associated with the employer. If the name of a contributing organization or a contributor’s employer was identical after basic data normalization (e.g. stripping out extra whitespace, converting names from uppercase) to an employer listed in this database, that contributor was assigned the corresponding NAICS code. EDD California’s Employment Development Department (EDD) also maintains a search tool to find California businesses and their associated NAICS codes. CalMatters developed a program to search each contributing entity’s name. If company names matched exactly after basic data normalization, the contributor was assigned the corresponding NAICS code. If we found a likely partial match, such as comparing “KAISER” to “KAISER PERMANENTE,” we assigned the company’s corresponding NAICS code. In cases where we found multiple partial matches, we matched with the company with the highest number of employees. Open Secrets OpenSecrets is a nonprofit organization that collects and analyses data on money in politics, including classifying campaign finance contributors according to its custom industry coding system . CalMatters looked for contributors in our data that also appeared in classified data we received from OpenSecrets. When a match was found, we translated the OpenSecrets category to the CalMatters schema. To match individuals to the OpenSecrets data, we built a record linkage algorithm to determine when individuals were the same across datasets based on their name, employer, occupation, city, state and zip code. Organizations were linked across the contribution data and OpenSecrets data using two methods: a fuzzy matching algorithm and name variant matching. For example, we creat