This dissertation contributes to literatures on the design and effects of two important public policies: Unemployment insurance and the minimum legal drinking age. I provide two unique perspectives on the design of unemployment insurance programs. First, by studying an oft-ignored policy parameter which influences benefit generosity, and second by analyzing an understudied margin along which these programs affect work incentives. I extend the literature on the minimum legal drinking age by empirically testing the hypothesis that increases in alcohol consumption at the drinking age have causal spillover effects on the alcohol consumption of younger peers.

In chapter 1, I analyze a previously unexamined policy parameter used in unemployment insurance (and other social insurance) programs. Since unemployment insurance replaces a percentage of prior earnings while a claimant is out of work, policymakers must define a base period from which prior earnings are measured. A base period structure common in the Unemployment Insurance (UI) program in the United States has two important implications. First, for claimants with volatile enough earnings, this base period structure creates ``benefit risk''---a job loss at the wrong time implies lower benefit amounts. Second, since base periods are determined by the claim filing date, claimants can partially avoid the negative effects of this risk by strategically timing their claims. Using several new sources of administrative data from California’s UI program, I provide four key results on this topic. First, I demonstrate that exposure to benefit risk is widespread. Of roughly 22 million claimants in my sample, over 8 million are exposed to some level of benefit risk. Second, using a bunching approach I demonstrate that roughly 3% of affected claimants strategically delay their claims after a job loss in order to receive higher benefits. Third, I provide evidence that information frictions are a key barrier preventing more widespread use of this strategic response. Finally, I use a dynamic model of job search and Unemployment Insurance to show that the private welfare costs of benefit risk are large. After accounting for claim-timing responses, the average claimant would trade 4% of their expected Unemployment Insurance benefits to eliminate exposure to benefit risk. This number rises substantially among young and especially low-income claimants.

In chapter 2, which is joint work with Lester Lusher and Rebecca L.C. Taylor, we provide causal evidence of an ex ante moral hazard effect of UI by matching plausibly exogenous changes in UI benefit duration across state-weeks during the Great Recession to high-frequency productivity measures from individual supermarket cashiers. Estimating models with day and cashier-register fixed effects, we identify a modest but statistically significant negative relationship between UI benefits and worker productivity. This effect is strongest for more experienced and less productive cashiers, for whom UI expansions are especially relevant. Additional analyses from the American Time Use Survey reveal a similar increase in shirking during periods with increased UI benefit durations.

Chapter 3, written with Eunju Lee, uses data on sibling pairs near the minimum legal drinking age to provide causal estimates of peer effects in alcohol consumption. Following prior work on other outcomes, we exploit the discontinuous increase in alcohol consumption of the older sibling at the legal drinking age in a regression discontinuity design. Our preferred point estimates imply that the number of binge drinking days reported by the younger sibling decreases by 27% of the mean at the cutoff. While our estimates are somewhat imprecise, we are able to consistently rule out leading positive estimates of peer effects in alcohol consumption. Our research design provides estimates which are interpretable as the causal effect of the peer's alcohol consumption. This is in contrast to most prior work which instead identifies the causal effect of exposure to the peer. We explain how this distinction matters for policy.

The California Policy Lab found that a majority (74%) of people who enrolled to receive homeless services in Los Angeles between 2010 and 2018 had worked in California before enrolling for services. Over one third (37%) worked in the two years prior to receiving homeless services, and about one in five (19%) of individuals were working in the same calendar quarter that they enrolled to receive services. This report provides an in-depth analysis of employment dynamics for people before, during, and after homelessness. The report includes an analysis of quarterly and annual employment rates, earnings, and differences among various groups, with a focus on “recent workers” who had worked within three or four years prior to enrolling for services and who tended to have higher employment rates and earnings than the rest of the sample.

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

Historically, the share of unemployed workers receiving regular UI benefits (recipiency rate) in California has been relatively low (as has also been the case in other states).This Data Point combines administrative data from California’s Employment Development Department (EDD) with monthly Current Population Survey (CPS) data to construct an improved recipiency rate to measure the extent to which unemployed and underemployed Californians are receiving regular UI benefits.

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

The Extended Benefits (EB) program automatically extends how long a person can claim Unemployment Insurance (UI) benefits when the share of workers claiming regular UI benefits reaches a certain level. However, a key measure of unemployment through which EB triggers on and off does not count individuals receiving benefits through extension programs, such as Pandemic Emergency Unemployment Compensation (PEUC) or EB. As a result, when a large share of unemployed workers transition from regular UI to extension programs, EB can mechanically trigger off, even if the total number of UI claimants remains unchanged or is increasing.

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 crisis has led to historically unprecedented increases in the level of initial Unemployment Insurance (UI) claims filed in California since the start of the crisis in mid-March. Through a partnership with the Labor Market Information Division of the California Employment Development Department, the California Policy Lab is analyzing daily initial UI claims to provide an in-depth and near real-time look at how the COVID-19 crisis is impacting various industries, regions, counties, and types of workers throughout California. 

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

The California Policy Lab partnered with the Employment Development Department (EDD) early in the crisis to analyze unemployment claims data. On April 29th, 2020, CPL released its first analysis, and it was clear, even then, that the crisis would disproportionately harm the state’s most vulnerable workers. The report is a snapshot of ten key trends from the unemployment crisis, based on 16 reports the research institute has released during the crisis, and suggests steps for how to translate this evidence into policy to reform the unemployment system.

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 crisis has led to historically unprecedented increases in the level of initial Unemployment Insurance (UI) claims filed in California since the start of the crisis in mid-March. Through a partnership with the Labor Market Information Division of the California Employment Development Department, the California Policy Lab is analyzing daily initial UI claims to provide an in-depth and near real-time look at how the COVID-19 crisis is impacting various industries, regions, counties, and types of workers throughout California. The Policy Briefs are updated on a monthly basis. Return to the main report page to see all of the policy briefs, data points, and media coverage.

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 crisis has led to historically unprecedented increases in the level of initial Unemployment Insurance (UI) claims filed in California since the start of the crisis in mid-March. Through a partnership with the Labor Market Information Division of the California Employment Development Department, the California Policy Lab is analyzing daily initial UI claims to provide an in-depth and near real-time look at how the COVID-19 crisis is impacting various industries, regions, counties, and types of workers throughout California. Return to the main report page to see all of the policy briefs, data points, and media coverage.

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