California Policy Lab

This report uses state tax and safety-net enrollment data from tax year (TY) 2019 to simulate the impact of the 2021 Child Tax Credit (CTC), expanded under the American Rescue Plan Act (ARPA), on children enrolled in safety-net programs in California. We find the number of children eligible for the CTC in the safetynet caseload rose 67% under the ARPA. Put differently: we estimate that one quarter of all children enrolled in the Supplemental Nutrition Assistance Program (SNAP) or Temporary Assistance for Needy Families (TANF) in California (about 610,000 children) became newly eligible for the CTC under the ARPA. As a result, children enrolled in safety-net programs in California became eligible for $3.6 billion in credit payments through the ARPA, over and above the credit payments they were eligible for under 2020 — and current — law. Overall, we find that 76% of eligible California children (about 1.2 million) who were enrolled in SNAP or TANF have likely received the 2021 credit, totaling $3.8 billion in credits. The ARPA CTC had wide reach into California’s most vulnerable communities, including those in California’s poorest regions and across all racial and ethnic groups. Among all families who receive safety-net benefits and who had annual wage earnings of more than $5,000 in 2019, we estimate that, in aggregate, they received 90-95% of the total ARPA CTC payments allocated for them. To ensure equitable distribution of the CTC, future efforts should focus on increasing access for children residing in households with little to no income, Spanish-speaking households, children living in rural communities, and children living in mixed-status immigration households. However, across demographic groups, the biggest hurdle to comprehensive CTC access remains low tax-filing rates among households at the lowest earnings levels. We estimate that the low tax filing rate among families with earnings less than $10,000 annually leaves $790 million in unclaimed ARPA CTC. That amount represents 85% of the estimated $928 million in ARPA Child Tax Credits left unclaimed by California families enrolled in SNAP or TANF.

This work has been supported, in part, by the University of California Multicampus Research Programs and Initiatives grants MRP-19-600774 and M21PR3278.

People with multiple, complex health and housing needs frequently receive fragmented care because the providing systems operate independently. Typically, individuals who come into frequent contact with the emergency medical system (e.g., emergency departments; emergency medical services) also interact with other health services and public systems such as psychiatric facilities, substance use treatment centers, shelters, and jails. Cross-sector care coordination is limited, in part, because data systems are not linked across physical health, behavioral health (mental health and substance use), housing, and criminal legal systems. To help San Francisco better serve this high need population, the California Policy Lab at UC Berkeley and the UCSF Benioff Homelessness and Housing Initiative worked with our partners in San Francisco’s public health and criminal legal systems to link together ten years of data from the physical health, behavioral health, housing, and criminal legal sectors. Using these linked data, we identify individuals with high utilization of the criminal legal system and the medical and behavioral health systems in a single year. High criminal legal utilization is defined as at least three jail bookings in a year, while high healthcare utilization is seven or more urgent/emergent healthcare contacts in a year. To understand trends before and after a year of high utilization, we analyze two cohorts. The 2011 cohort includes 211 people with high utilization of both systems in fiscal year 2011, while the 2020 cohort includes 161 individuals with high utilization of both systems in fiscal year 2020. This allows us to observe patterns of system use before and after years of high utilization. We find: • Almost all the individuals in both cohorts experienced homelessness (98–99%) • High utilization is linked to premature death: more than one quarter of the 2011 cohort is deceased within 10 years • Between 80–90% of individuals in both cohorts have substance use disorders, and many also have co-occurring mental health and physical health disorders • More than 90% of the individuals in both cohorts have been booked into jail for a felony and a misdemeanor • Many of the individuals in the 2020 cohort were in San Francisco and had contact with at least one of these systems in the prior 10 years. For example, 30% of the 2020 cohort was booked into jail in 2011. These findings highlight the need for coordinated, evidence-based interventions to address these individuals’ complex needs, stabilize housing, and prevent poor health outcomes including untimely death. 

This work has been supported, in part, by the University of California Multicampus Research Programs and Initiatives grants MRP-19-600774 and M21PR3278.

Criminal sentences resulting in admission to a California state prison are determined by both the nature of the criminal incident as well as the criminal history of the person convicted of the offense. Cases with convictions for multiple offenses may lead to multiple sentences that are either served concurrently or consecutively. Characteristics of the offense (such as the use of a f irearm) or aspects of the person’s criminal history (such as a prior conviction for a serious or violent offense) may add to the length of the base sentence through what are commonly referred to as offense or case enhancements, respectively. California’s Three-Strikes law presents a unique form of sentence enhancement that lengthens sentences based on an individual’s criminal history. Consider an individual with one prior serious or violent felony conviction (one “strike”) who is subsequently convicted of another felony. Under Three Strikes, the sentence for the subsequent felony will be double the length specified for the crime regardless of whether the new conviction is for a serious or violent offense. For an individual with two prior violent or serious felony convictions, a third conviction for a serious or violent felony would receive an indeterminate prison term of at least 25 years to life, with the exact date of release determined by the Parole Board. 

This work has been supported, in part, by the University of California Multicampus Research Programs and Initiatives grants MRP-19-600774 and M21PR3278.

Every year, over a million Californians receive workforce support and training from state and federally funded programs. In an effort to learn about the benefits of these programs, an inter-agency partnership led by the California Workforce Development Board (CWDB) created Cross-System Analytics and Assessment for Learning and Skills Attainment (CAAL-Skills). The CAAL-Skills partnership facilitates data-sharing across seven California state agencies that deliver thirteen workforce programs. Bringing this data together significantly improves the state’s ability to observe who is enrolled in these programs and makes it possible, for the first time, to measure the impacts these programs have on participants’ employment and earnings.

This report highlights findings from the first causal study to estimate the impacts of ten California workforce training programs that share data with CAAL-Skills. The causal impact measures the effect of receiving training on participants’ employment and earnings, relative to what those same workers would have experienced without training.

This work has been supported, in part, by the University of California Multicampus Research Programs and Initiatives grants MRP-19-600774 and M21PR3278.

The COVID-19 pandemic had an immense impact on the labor market in California and the U.S., with unemployment reaching highs not seen since the Great Depression. The regular Unemployment Insurance (UI) system, as well as federal legislation that created supplemental UI programs and benefit extensions, played a fundamental role in the country’s economic and public health response. Individual-level data on who applied for and received UI benefits can provide crucial insights into understanding how the crisis evolved, how well the government’s response worked, and what the implications are for future crises. The pandemic also brought to the forefront more fundamental issues with our nation’s UI system, such as pervasive inequities in which workers actually receive benefits and large differences in benefit amounts and durations. While some of these issues have persisted for decades, a lack of access to individual-level UI data has prevented a deeper understanding of the extent of the inequities. The crisis also revealed that even the most fundamental statistics that policymakers rely on have important flaws, and basic information on which workers benefit from some of the core UI programs is completely missing, despite the data being collected every day by UI agencies as they pay benefits. The California Policy Lab (CPL) partnered with the California Employment Development Department (EDD), which manages unemployment insurance in California, to help bring greater clarity to policymakers about the impact of the crisis in California. CPL’s first analysis was published in April 2020, and through this unique relationship, CPL was able to use anonymized claims data to track the labor market crisis in close to real-time and to provide in-depth, detailed insights on the federal government’s response. Through a series of 19 reports, CPL generated new findings about a range of issues in the California UI system. CPL’s research also shed light on which demographic groups and types of workers benefited the most from the different program extensions during the pandemic and which workers fell through the cracks and were unable to access vital UI benefits. By working directly with complicated claims data for more than two years, CPL developed new, more accurate and timely measures of how many people were relying on the UI system as compared to the measures that policymakers and the media typically have relied on. This work directly demonstrated for state and federal policymakers how the measures they had historically relied on provided a distorted view of the labor market during the crisis. For example, a 2020 GAO report cited CPL’s research, finding that published claims data were likely inflated (Government Accountability Office, 2020). This report highlights ten key insights about the UI system and the labor market that are based on CPL’s unique partnership with EDD and the unique data access this partnership provided. The report focuses on six insights on equity and disparities in access to unemployment insurance benefits during the pandemic. It then documents four insights on measurement issues in the publicly available data and how access to California’s administrative records allows CPL to overcome these issues. We also share how these improved measurements can inform policy choices to improve equity. 

This work has been supported, in part, by the University of California Multicampus Research Programs and Initiatives grants MRP-19-600774 and M21PR3278

Unemployment Insurance (UI) benefits provided a lifeline to workers who lost their jobs during the pandemic. However, access to these benefits has been uneven across communities and states (Edwards, 2020). Identifying and documenting these disparities is an important step to addressing them and to rendering the UI system more equitable. Utilizing a conceptual framework of unemployment claims, we developed three metrics to measure access to UI benefits across the claim lifecycle. We then analyzed these measures to provide insight into differential access to UI benefits across U.S. states and across counties within California.   

The first measure of access is the First Payment Rate and corresponds to the earliest part of the claim lifecycle. It measures the share of people who file their first claim and who subsequently receive a UI payment. After the First Payment Rate, the primary measure of access in the report is the Recipiency Rate. The recipiency rate measures the share of unemployed or underemployed workers who are actually receiving UI benefits. This is the traditional measure (Wittenberg et al., 1999) of UI access, and reflects access in the middle of the claim lifecycle. The final measure of access is the Exhaustion Rate, which corresponds to the final part of the claim lifecycle. It measures the share of claimants who have exhausted eligibility for both regular and extended UI benefits.   

We calculated these metrics in each state by using publicly available data from the U.S. Department of Labor reports and by county in California using tabulations based on individuallevel claims data from the California Employment Development Department. The additional information available in the California claims data allows us to improve and further segment our measures of access, allowing us to identify new facts and patterns from the data. We generated these metrics for the year 2020 and focused our analysis from the beginning of the pandemic in March through December 2020, just prior to the initial rollout of COVID-19 vaccines. In addition, we compared these to the corresponding values in December 2019 as a pre-pandemic benchmark.   

We use these measures to analyze disparities in access to UI benefits during and before the pandemic and identify community attributes and policy choices that are associated with differential access. This analysis cannot identify causal relationships, however, across metrics, there is a pattern of correlations showing that workers in states with more generous labor and UI policies have greater access to benefits, potentially indicating the importance of policy choices in shaping UI access. The correlations also show a pattern by which less affluent areas and areas with a higher share of disadvantaged social groups are associated with lower access to UI benefits. Additional research is needed to identify the causal mechanisms between policies and UI access.

Along with research on the effects of rules of the UI and other state and federal programs, we conclude the report by providing further recommendations on future data collection and research funding priorities.

This work has been supported, in part, by the University of California Multicampus Research Programs and Initiatives grants MRP-19-600774 and M21PR3278

In Los Angeles County, Black people represent 9% of the general population yet comprise 40% of the homeless population. In its 2018 groundbreaking report, the Los Angeles Homeless Services Authority Ad Hoc Committee on Black People with Lived Experience of Homelessness concluded that homelessness is a by-product of racism in the United States. The Committee also found racial inequities in outcomes for Black residents of homeless services, particularly Permanent Supportive Housing (PSH).

This report, in partnership with LAHSA and community-based service providers, further examines why there are racial inequities in returns to homelessness or interim housing for Black PSH residents. To estimate the racial inequity in returns to homelessness, we used administrative data from the Homelessness Management Information System (HMIS). To identify potential factors that contribute to Black residents falling out of PSH and returning to homelessness, we conducted interviews and focus groups with PSH program managers, case managers, and Black residents.

This work has been supported, in part, by the University of California Multicampus Research Programs and Initiatives grants MRP-19-600774 and M21PR3278.

In order to better understand the role that race or ethnicity may play in who is stopped by their officers, the California Highway Patrol (CHP) provided the California Policy Lab (CPL) with a data set of 2,141,817 enforcement stops made by the CHP from January to December of 2019. The data was collected pursuant to California’s Racial and Identity Profiling Act of 2015 (RIPA). In order to extend the statistical analysis presented in the 2021 Annual RIPA Board Report, we evaluated enforcement stops in combination with non-enforcement stops using two generally accepted approaches to measure racially disparate policing: benchmarking and a hit rate analysis.

This work has been supported, in part, by the University of California Multicampus Research Programs and Initiatives grants MRP-19-600774.

The Public Safety Assessment (PSA) is an empirically-based risk assessment tool that is used to inform pretrial release decisions across the country. The tool measures the risk of a person failing to appear at a court hearing, being arrested for new criminal activity while on pretrial release, or being arrested for new violent criminal activity while on pretrial release. San Francisco adopted the PSA in May 2016. In addition to the tool, criminal justice stakeholders in the county, including the courts, Sheriff, and District Attorney, developed a local policy document called the Decision-Making Framework (DMF) which translates the PSA score into a recommendation to the judge. The San Francisco DMF includes overrides to the tool for certain charges that increase the supervision level recommend by the PSA or generate a recommendation not to release.

Under SB 36, (passed in October 2019), California requires all counties to validate their pretrial risk assessments by July 1, 2021 and every three years thereafter. This validation study examines the accuracy and reliability of the PSA in predicting failures to appear, new arrests, and new arrests for violent offenses for persons released pretrial in San Francisco. The study also investigates whether there is any disparate effect or bias in the tool’s scoring based on sex, race, or ethnicity.

This work has been supported, in part, by the University of California Multicampus Research Programs and Initiatives grants MRP-19-600774 and M21PR3278.

As described in A Rising Tide, student participation in dual enrollment has been growing steadily over the last four years. Yet, participation varies across racial/ethnic subgroups and special populations of students. This online appendix reports the variation that also exists across California’s 58 counties. For each county, we provide the rate of dual enrollment participation overall in the last four years and depict the differences in participation rates for the four largest racial/ethnic subgroups (Asian, Black, Latinx and White) and for subgroups of socioeconomically disadvantaged students, English learners, students with disabilities, foster youth, and homeless students. In some county-level graphs, student subgroups are omitted due to cell size restrictions for reporting on subgroups with few students.

This work has been supported, in part, by the University of California Multicampus Research Programs and Initiatives grants MRP-19-600774.