Accessibility of Services

Summary: The number of distinct locations of immigrant-serving organizations per 100,000 non-citizen immigrants. 

Data Source(s): Guidestar, Research Fundamentals PLUS Data Set of 501(c)(3) Public Charities; U.S. Census Bureau, 2018 American Community Survey 5-year Summary File.

Universe: All 501(c)(3) organizations in California identified as serving immigrants (numerator) and all non-U.S. citizen immigrants (denominator).

Methods: To create this measure, we used data on all nonprofits in California from Guidestar for fiscal year 2016 (the Research Fundamentals PLUS Data Set of 501(c)(3) Public Charities dataset). We initially selected all organizations with the terms "IMMIG" or "REFUGEE" in their name or mission statement, or that had the National Taxonomy of Exempt Entities (NTEE) code P84 for ethnic/immigrant centers. 

Because the P84 code also includes organizations that are not accurately characterized as immigrant-serving organizations (e.g. organizations focused on the Native American population), we examined the list of organizations and dropped those for which immigrant services did not seem to be a particular focus. To be sure we were not missing organizations that use the term "migrant" instead of "immigrant" in the data collected by GuideStar, we did another key word search, among organizations not already included in our list, for those with the term "MIGR" in their name or mission statement. Many of those found were not immigrant serving (for example, there were several organizations focused on migratory birds) but a handful were and we added them to the list. 

Finally, to exclude organizations focused on international work (e.g. religion-based missions organizations), we dropped all organizations reporting foreign activities. In the end, we identified 179 "immigrant-serving" organizations across the state based on this approach. However, one major shortcoming of Guidestar data is that it only includes one record (with one location/address) for each organization even if it has multiple offices in different locations. To account for this, we conducted internet searches on each organization and tabulated the number of different locations along with their respective addresses. We then geocoded all the addresses, did a spatial join with each of the portal geographies using an R package and summed location/address count. Finally, we matched in data from the 2018 American Community Survey 5-year summary file on the number of non-citizen immigrants to calculate the number of distinct locations of immigrant-serving organizations per 100,000 non-citizen immigrants. We then set the measure to missing for geographies with fewer than 1,000 non-citizen immigrants; in such geographies, even having one immigrant serving organization resulted in a very high rate per 100,000 non-citizen immigrants, due to the small denominator, making for inconsistent geographic comparisons. See the methodology page for other relevant notes.

Notes: 

  • Guidestar data augmented to include one observation per location (rather than per organization).
  • Non-citizen immigrants chosen as the denominator as this population is more likely to use immigrant services provided by non-profit organizations.
  • No data reported for geographies with fewer than 1,000 non-citizen immigrants.

Biliteracy Seal

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, www.cde.ca.gov/ds/sd/sd/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.
     

Court Deportation Proceedings

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 July 2020.

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 not only available for counties and statewide.
  • The years reflected in the data are based on the fiscal year 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 July 2020). 
  • 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 the each nationality in the TRAC data was aggregated into detailed and broad groupings for the California Immigrant Data Portal (CIDP).

Hate Crimes

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:

  • No data is available for Sierra County, California.
  • 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 sub-counties (CPUMAs).