Gender and Ethnicity Predictions for California City Council Members and School Board Members, 2010-2023

To conduct this study, I sourced demographic data from 2010 to 2023 from the California Elections Data Archive (CEDA) for city council members and school board members. The CEDA data provide a full list of candidate names and the number of votes a given candidate received for every city council and school board election. I assigned the gender to each candidate based on the lists of popular male and female names provided by the Social Security Administration. Since the average age of city council members is 46 years old according to the Bureau of Labor Statistics, I compiled a list of popular male and female given names for babies born in the 1960s, 1970s, and 1980s. Then, I automated the gender classification as follows: for example, as “Lisa” is identified as a popular female given name by the Social Security Administration, every candidate whose first name is “Lisa” was assigned “female” in our dataset. For a gender-neutral name that appeared on the lists for both male and female given names, which included “Alex” and “Casey,” I used the following keywords “[first name] [last name] [office type (either “city council” or “school board”)] [name of the city or the school district]” to search for more information about the official’s gender online. My search returned either a picture to help clearly identify the official’s gender and/or an article that refers to the official with gendered pronouns.

To identify the ethnicity of each elected official, I used the 2010 Census data and the 23AndMe Surname Discovery Tool. The 2010 Census lists surnames occurring at least 100 times, and it includes self-reported ethnicity data for individuals with a given surname. Similarly, the 23AndMe Surname Discovery Tool gives the percentage of individuals with the given surname who identify as each of four different ethnicity groups: Hispanic, White, Asian/Pacific Islander, and Black based on the 2010 US Census data. For surnames that did not appear on either the 2010 Census data or the 23AndMe Surname Discovery Tool, I used Python’s Ethnicolr library, which bases its prediction of ethnicity using either both first and last name or just the last name on the US census data (2000 and 2010), the Florida voting registration data, and the Wikipedia data.