Summary: The number of distinct locations of immigrant-serving organizations per 10,000 non-citizen immigrants.

Data Source(s): GuideStar Pro Database; U.S. Census Bureau, 2019 American Community Survey 5-year Summary File.
Universe: All 501(c)(3) Form 990 and 990EZ organizations in California identified as serving immigrants (numerator) and all non-citizen immigrants (denominator).

Methods: The methodology we implemented in 2022 departs from our original (2019) methodology due to two major reasons. First, the data product that we used in the original methodology (GuideStar Research Fundamentals PLUS) was discontinued. For the 2022 update, we relied on the GuideStar Pro Plus database which has a significantly different data structure. Second, we updated our methodology to incorporate a broader definition of immigrant-serving organization that is more in line with current scholarly thinking.

Our original approach implemented in 2019 was top-down and involved filtering down to immigrant-serving organizations in California from a complete 2016 dataset of all nonprofit organizations in the U.S. that filed Form 990. Due to changes in GuideStar’s data product, our new bottom-up approach uses keywords and search criteria to build up a dataset of immigrant-serving organizations in California. One important difference to note is that the GuideStar Pro Database allows users to search for Form 990-EZ nonprofits (annual gross receipts between $50,000 and $200,000) in addition to Form 990 nonprofits (annual gross receipts more than $200,000). As a result, we were able to capture significantly more small-to-mid size nonprofit organizations during the update.

To build up our dataset, we used a series of search criteria to find potential immigrant serving organizations. Our basic search setting limited the search geography to California and excluded nonprofit organizations with revoked status in 2021. For the initial step, we used two keywords, refugee and immigrant, and found 1,806 organizations. Next, we added 662 organizations with their subject area labeled as immigrant service to the dataset. Current scholarly research on immigrant-serving organizations uses an expansive understanding of immigrant serving. Even though an organization might not necessarily describe or advertise itself as immigrant serving (e.g., a nonprofit hospital), an organization can still be considered as immigrant serving if it has a substantial number of immigrant clients. To adapt this idea into our methodology, we added organizations that served ethnic groups to our dataset. We included all ethnic groups (Middle Eastern, Latin American, European, Asian, African, and multiracial) except for Indigenous groups (Native American, Native Hawaiian, Alaska Natives, etc.). This added 1,633 organization to the dataset. After removing duplicate records that appeared during the searches, we built a dataset containing 2,716 potential immigrant-serving organizations in California. 

We then qualitatively reviewed and evaluated the organizations to ensure that all organizations in the final dataset were actually immigrant serving. Our qualitative criteria included mission statement, program description, service offered, language offered, and scope of work. For mission statement, program description, and service offered, we looked for indications that an organization provided services (e.g., legal service) to immigrants. Additionally, following the expansive understanding of immigrant serving, we also included organization that performed arts, culture, and advocacy-based work related to immigrants. Lastly, we included local, faith-based organizations to highlight the important role that religion plays in immigrant communities. For scope of work, we excluded all organizations that focused on international work with the one exception of organizations that worked in the Tijuana, Mexico region. These organizations work to serve encamped migrants and refugees looking to enter the U.S. As noted in our original methodology, one major shortcoming of the GuideStar data is the one-to-one organization-location match even if it an organization has multiple offices. To account for the shortcoming, we conducted internet searches on each Form 990 organization (as they tend to be larger and more likely to operate in multiple locations) and recorded the number of different locations along with their respective addresses. In the end, our final dataset contained 2,675 records and 1,633 unique immigrant-serving nonprofit organizations. 

To aggregate the data to the various levels of geography for which data are reported, we first geocoded all addresses using Google API and a Google Apps script. Then, we used ArcGIS Pro and spatially joined the geocoded locations into CIDP geographies (state, county, sub-county, and city or place). We then matched in data from the 2019 American Community Survey 5-year Summary File on the number of non-citizen immigrants to calculate the number of immigrant-serving organizations per 10,000 non-citizen immigrants. Lastly, we set the measure to missing for geographies with fewer than 1,500 non-citizen immigrants and fewer than 2 organizations. This filter reduces large spikes in the data caused by a small denominator that can lead to inconsistent geographic comparisons.

Summary: The share of English-learner, non-English-learner and all high school graduates earning a Biliteracy Seal (the state’s official recognition of a student’s proficiency in a language other than English).

Data Source(s): California Department of Education (CDE), California Longitudinal Pupil Achievement Data System (CALPADS), Adjusted Cohort Graduation Rate (ACGR) and Outcome Data, https://www.cde.ca.gov/ds/ad/filesacgr.asp  

Universe: All high school graduates in the ACGR cohort in schools with at least 11 graduating students who are English Learners.

Methods: Biliteracy Seal data was sourced from the CDE’s CALPADS database, available starting school year 2016-17. The dataset came pre-aggregated for State, County, District, and School level geographies. For State and County geographies in this indicator, the pre-aggregated data was used as-is. For City and CPUMA level geographies, we aggregated up the School level data by geocoding the schools and spatially joining them to City and CPUMA shape-files. For all geographies and years, we restricted the universe to only include schools with valid English-Learner data available (those with at least 11 graduating English Learners). We further restricted to geography-years in which at least 80 percent of the total pre-restricted student cohort population is represented by those schools with valid English-Learner data available. Similarly, the only geography-years shown are those in which there is less than a 5 percentage point difference in biliteracy seal attainment rates (for all graduates) between the pre-restricted universe and the restricted universe used in this indicator, in order to limit any large bias in our restrictions. See the methodology page for other relevant notes.

Notes: 

• Years represent the year of graduation for a given school year (e.g. 2017 for the 2016-17 school year).
• No data reported for cohorts with less than 11 students.
   

Summary: The number of immigration court deportation cases by year in which they were initially filed and nationality of the defendant, and the composition of cases by legal representation and outcome as of February 2022.

Data Source(s): Transactional Records Access Clearinghouse, Syracuse University, trac.syr.edu, State and County Details on Deportation Proceedings in Immigration Court, https://trac.syr.edu/phptools/immigration/nta/.Universe: All deportation proceedings initiated by the Department of Homeland Security and its predecessor, the Immigration and Naturalization Service, for immigrants residing in California.

Methods: The source data is based on analyses done by the Transactional Records Access Clearinghouse (TRAC) at Syracuse University of court records obtained from the Executive Office for Immigration Review (EOIR) using the Freedom of Information Act. Data for the California Immigrant Data Portal was collected from publicly accessible data made available through the online TRAC data tool. The number and percentage of deportation proceedings for immigrants residing in California were calculated by representation, outcome, immigrant county of residence and country of origin for each year and geography. Many countries of origin were aggregated into broader regional categories. See the methodology page for other relevant notes.

Notes: 

• Detailed information about the source data can be found here: https://trac.syr.edu/phptools/immigration/nta/about_data.html.
• No data available prior to 2001.
• Data only available for counties and statewide.
• The years reflected in the data are based on the fiscal year (October 1 to September 30) in which the deportation case was initiated, not when the case outcome was decided.
• Data measured by outcome are based on current filing status when the data was downloaded from Syracuse University’s Transactional Records Access Clearinghouse website (currently February 2022). 
• Geography is based on the immigrant’s residential address recorded in court records.
• Totals may differ due to rounding errors in the source data caused by how the data was crosswalked between zip codes and counties using an immigrant’s residential address.
• The full list of nationalities available on Syracuse University’s Transactional Records Access Clearinghouse data tool for court deportations are listed below. For the purposes of the California Immigrant Data Portal, the full list of nationalities were aggregated into broader groups. The table below shows how each nationality in the TRAC data was aggregated into detailed and broad groupings for the California Immigrant Data Portal (CIDP).

Summary: The number of hate crimes per 100,000 residents and composition by bias type, as reported by law enforcement agencies to the California Department of Justice.

Data Source(s): California Department of Justice, OpenJustice, Hate Crimes, Criminal Justice Statistics Center (CJSC) Hate Crime database (HATE), openjustice.doj.ca.gov; U.S. Census Bureau, Intercensal Population Estimates and Vintage Population Estimates.

Universe: All hate crimes reported by law enforcement agencies to the California Department of Justice (numerator), estimated annual population from the U.S. Census Bureau (denominator).

Methods: Hate crimes reported are submitted to the Department of Justice (DOJ) on a monthly basis by law enforcement agencies (LEAs) throughout the state of California. A hate crime is defined as an event that involved one or more criminal offenses, committed against one or more victims, by one or more suspects or perpetrators where there is a reasonable cause to believe that the crime was motivated by the victim’s race, ethnicity, religion, gender, sexual orientation, or physical or mental disability. If victims have more than one offense committed against them in a given incident, this data provides information on the most serious of the offenses committed. 

It is important to note that due to various factors associated with underreporting, data presented here does not represent the totality of hate crime incidents that occur throughout the state of California. Some hate crimes are misidentified as hate incidents while others are unreported. As a result, there is missing data for certain geographies in certain years.

While the database contains data from 2001 onward, data presented here only include 2002 onward due to changes in data collection. In 2002, the DOJ began to count each offense in a hate crime event, whether it was one offense or multiple offenses (Hate Crimes Report 2013). 

For the purposes of this indicator, hate crime records were aggregated by year, most serious bias type, and geography. The most serious bias type variable reflects the motivation of the most serious offense committed during a given hate crime incident. Most serious bias type categories include race/ethnicity/ancestry, religion, sexual orientation, disability, gender, and gender nonconformity.

Geographic aggregation was based on the LEA and County reporting the hate crime. Data on population was merged from the U.S. Census Intercensal and Vintage Population Estimates to derive the rate of hate crimes reported per 100,000 people. It is important to note that the number of hate crimes reported per 100,000 people reflect the total number of hate crimes reported for each year or period of years. For a period of multiple years, rates are calculated based on the average yearly population for a given geography. For more information on how the data is collected, see Hate Crime Context. See the methodology page for other relevant notes.

Notes:

• This data does not represent the totality of hate crime incidents throughout the state of California.
• For city/place level geographies, only the hate crimes reported by agencies that operate entirely within city/place boundaries (i.e. not county-wide entities) are shown.
• No data available for geographies with zero hate crimes reported in a given time period.
• No data available for sub-counties (CPUMAs).

Summary: The number and percentage of people ages 5 or older who speak a language other than English at home by race, nativity, and immigration status; and the top non-English languages spoken at home. Data for 2019 represent a 2015-2019 average. Immigration status is estimated based on an approach developed by the Equity Research Institute.

Data Source(s): Integrated Public Use Microdata Series, IPUMS USA, University of Minnesota, www.ipums.org, 2000 5% sample, 2010 and 2019 American Community Survey 5-year samples.

Universe: All people ages five or older.

Methods: The number and percentage of people age five or older that speak various languages at home and all of those speaking a language other than English at home were calculated by race, nativity, and immigration status for each geography. The categories for immigration status include immigrants, undocumented immigrants, lawful residents, and naturalized U.S. citizens. See the methodology page for other relevant notes.

Notes:

• Latinos include people of Hispanic origin of any race and all other groups exclude people of Hispanic origin.
• Data for 2019 represent a 2015-2019 average.
• Languages listed in the “top languages” breakdown are limited to the 50 most common languages other than English spoken at home (plus ties) in each geography for the overall population age five or older.  
• Immigration status is estimated using a probability model (not self-reported). See here for details.
• Immigration status is limited to all, U.S.-born, immigrant, and naturalized U.S. citizen in years prior to 2019 due to lack of more detailed immigration status estimates in those years. 
• For the “top languages” breakdown, no estimates of the number of people speaking a given language are reported if they are based on fewer than 30 individual survey respondents (i.e., unweighted).
• Language or language family names with an asterisk (*) are shortened and/or abbreviated for display purpose. See the table below for language name abbreviations and the corresponding full name from IPUMS USA.