Unemployment Insurance Claims Research Articles

Why Are Unemployment Insurance Claims So Low?

The authors investigate reasons unemployment insurance (UI) claims have declined dramatically over the past 3 decades. The fall is concerning because it suggests a reduced countercyclical effectiveness of the UI program. Additionally, weekly initial UI claims are an important leading indicator of aggregate economic activity, so their meaning has changed. The authors identify the main factors that changed with the decline in claims. Their procedure suggests what the level of claims would have been later in the period, had values of variables or parameters of the system been at levels observed earlier in the period. The analysis of state-year data suggests that the decline in UI claims stems largely from changes in the industrial and occupational mix of employment interacting with changes in UI program features set by individual states. This decline could be offset by federal rules for states to improve benefit access, replacement rates, and potential UI durations.

Unemployment Insurance Claims During the covid-19 Pandemic: The Experiences of Temporary Migrant Workers Employed in South Africa

Abstract The Covid-19 pandemic was unprecedented and its impact on the overall welfare of people was felt all over the world. In particular, the pandemic exposed the vulnerability of migrant workers. As key role players providing critical services during the pandemic, migrant workers witnessed limited access to social protection. Data gathered through qualitative method using face-to-face interviews, WhatsApp interviews and content analysis of secondary data found limited access of foreign workers to Covid-19 related social protection in South Africa. Foreign workers who did not possess the South African Identity Document, but contributed to the Unemployment Insurance Fund (uif) experienced discrimination. They were not paid the Covid-19 Temporary Employer/Employee Relief (Covid-19 ters) for workers in the formal sector. The uif computer system recognised only South African identity numbers and not foreign passport numbers. Bureaucratic xenophobia was explored to understand the experiences of the foreign workers.

Using Longitudinal Surveillance of Unemployment Claims During Public Health Emergencies to Provide Timely and Granular Data on the Social Determinants of Health.

Employment is a well-documented social determinant of physical and mental health and can be used to determine who is disproportionately affected by public health emergencies. We examined trends in unemployment overall and by gender, by race or ethnic group, and by their interaction for 2 public health emergencies (the COVID-19 pandemic and the 2020 California wildfires). We obtained summary data files on the number of initial unemployment insurance (IUI) claims made in all 58 California counties from January 2018 through December 2021. We fit fixed-effects Poisson regression models to county data on weekly IUI claims cross-classified by gender and race or ethnic group. We used models to evaluate the overall effect of COVID-19, whether this effect changed over time increasing under compounding emergencies, and whether the overall and compounding effects of COVID-19 differed by gender and race or ethnic group. During the COVID-19 pandemic, weekly IUI claims rates increased to as much as 10 times their prepandemic level. The increase in IUI claims for COVID-19 weeks, compared with weeks from the same month in the 2 years prior, was greater for women than for men of all race or ethnic groups, except for Black women. The higher rates of IUI claims for most women during COVID-19 entailed a reversal of prepandemic gender differences in claims that persisted through 2021. Public health officials should consider using IUI claims for surveillance of social determinants of health, particularly in the context of emergencies, which we show can have a persisting effect on the social patterning of social determinants. Future research is needed to forecast these affects and inform public health and policy mitigation and prevention strategies.

Advance layoff notices and aggregate job loss

SummaryWe collect data from Worker Adjustment and Retraining Notification (WARN) Act notices and establish their usefulness as an indicator of aggregate job loss. The number of workers affected by WARN notices (“WARN layoffs”) leads state‐level initial unemployment insurance claims and unemployment rate (UR) and private employment changes. WARN layoffs comove with aggregate layoffs from Mass Layoff Statistics and the Job Openings and Labor Turnover Survey but are timelier and cover a longer sample. In a vector autoregression, changes in WARN layoffs lead UR changes and job separations. Finally, they improve pseudo real‐time forecasts of the UR and private employment changes.

Why Are Unemployment Insurance Claims So Low?

Why Are Unemployment Insurance Claims So Low?

Measuring Job Loss during the Pandemic Recession in Real Time with Twitter Data

We present an indicator of job loss derived from Twitter data, based on a fine-tuned neural network with transfer learning to classify if a tweet is job-loss related or not. We show that our Twitter-based measure of job loss is well-correlated with and predictive of other measures of unemployment available in the official statistics and with the added benefits of real-time availability and daily frequency. These findings are especially strong for the period of the Pandemic Recession, when our Twitter indicator continues to track job loss well but where other real-time measures like unemployment insurance claims provided an imperfect signal of job loss. Additionally, we find that our Twitter job loss indicator provides incremental information in predicting official unemployment flows in a given month beyond what weekly unemployment insurance claims offer.

Administrative Errors and Race: Can Technology Mitigate Inequitable Administrative Outcomes?

AbstractScholars have long recognized the role of race and ethnicity in shaping the development and design of policy institutions in the United States, including social welfare policy. Beyond influencing the design of policy institutions, administrative discretion can disadvantage marginalized clientele in policy implementation. Building on previous work on street-level bureaucracy, administrative discretion, and administrative burden, we offer a theory of racialized administrative errors and we examine whether automation mitigates the adverse administrative outcomes experienced by clientele of color. We build on recent work examining the role of technological and administrative complexity in shaping the incidence of administrative errors, and test our theory of racialized administrative errors with claim-level administrative data from 53 US unemployment insurance programs, from 2002 to 2018. Using logistic regression, we find evidence of systematic differences by claimant race and ethnicity in the odds of a state workforce agency making an error when processing unemployment insurance claims. Our analysis suggests that non-white claimants are more likely to be affected by agency errors that result in underpayment of benefits than white claimants. We also find that automated state–client interactions reduce the likelihood of administrative errors for all groups compared to face-to-face interactions, including black and Hispanic clientele, but some disparities persist.

Unemployment claims during COVID-19 and economic support measures in the U.S.

Governments want to know how effective COVID-19 anti-contagion policies and implemented economic stimulus measures have been to plan their short-run interventions. We condition on the state of the pandemic to assess the impact of non-pharmaceutical interventions and economic stimulus policies on the excess unemployment insurance claims in the United States. We focus on weekly data between February 2020 and January 2021 and motivate our analysis by the theoretical framework of the second-wave SIR-macro type models to build a panel Vector AutoRegressive (VAR) specification. Non-pharmaceutical interventions become effective immediately and impact the labor market negatively. Economic stimulus takes about a month to turn effective and only partially eases the economic welfare losses. Health-related restrictive measures are primarily driven by the state of the pandemic. Economic support policies depend predominantly on the reaction of the labor market rather than the severity of the pandemic itself.

Lessons from the 2020 Covid recession for understanding regional resilience

AbstractThe 2020 Covid‐19 recession differs from past recessions due to the immediate economic collapse, making it a unique setting in which to assess local economic resilience. Using county‐level data, we identify initial effects of the pandemic and accompanying stay‐at‐home orders on consumer spending, unemployment insurance claims, small businesses, and low‐income employment and earnings. We discuss heterogeneity related to pre‐pandemic county characteristics and find differences compared to past recessions. Counties with larger leisure services employment shares fared worse, while counties with less‐educated, younger workforces fared better. We find an ambiguous role of small businesses in contrast with favorable impacts during prior downturns.

Unemployment Effects of Stay-at-Home Orders: Evidence from High-Frequency Claims Data

Abstract We use the high-frequency, decentralized implementation of stay-at-home (SAH) orders in the United States to disentangle the labor market effects of SAH orders from the general economic disruption wrought by the COVID-19 pandemic. We find that each week of SAH exposure increased a state's weekly initial unemployment insurance (UI) claims by 1.9% of its employment level relative to other states. A back-of-the-envelope calculation implies that of the 17 million UI claims between March 14 and April 4, only 4 million were attributable to SAH orders. We present a currency union model to provide conditions for mapping this estimate to aggregate employment losses.

An Interpretable Model for Real-time Tracking of Economic Indicators Using Social Media Data

Measures of public opinion on economic matters are vital in creating accurate official statistics needed to design appropriate policy interventions and shape private investment decisions. This is traditionally done using public opinion polls and representative surveys. However, this process is time and money intensive and currently suffers from reduced public participation. As a result, official statistics are usually delayed and are not frequently available at the resolution needed for better policy interventions. Hence, researchers have looked into anonymized digital data to continuously sense information about public behaviors, especially those related to consumer sentiment index and unemployment insurance claims. However, past studies relied on linear models with simplistic assumptions and thus provided limited extrapolatory power and no insights as to why these predictive methods work. Worryingly, the strong correlations reported in these studies disappeared when the original models were tested with newer social media data. We propose a novel interpretable machine learning model, called Group Additive Gaussian Processes, to provide accurate and near real-time estimates of economic indicators about public behaviors using social media data, along with insights into the model behavior. Our model exploits the underlying structure in data and encodes interpretability in the modeling framework. It is based on Gaussian Process regression, which provides a robust non-parametric Bayesian learning framework that produces calibrated uncertainty measures along with its predictions. A key challenge in the learning task is learning from limited training data. We demonstrate how our model not only learns but also generalizes well in these scenarios. Through extensive evaluation we show how our model performs on two important indicators of economic health-consumer confidence index and unemployment insurance claims data. Further, we demonstrate how our model can reduce the need to conduct surveys by producing highly accurate and frequent estimates in between the surveying periods.

Applying for and receiving unemployment insurance benefits during the coronavirus pandemic

During the coronavirus disease 2019 pandemic in the United States, claims for unemployment insurance (UI) benefits rose sharply because of the substantial job loss and the expansion of UI programs. To improve upon UI administrative data, in this article, we use the Household Pulse Survey to estimate the number of people who applied for UI benefits, the number of people who received benefits, and the success rate of UI applicants (the share of applicants who received benefits) during the first 9 months of the pandemic. We examine differences by demographic group, educational attainment, and prepandemic household income. In addition, we relate state-level estimates to UI recipiency before the pandemic, job loss during the pandemic, and the differential spread of the coronavirus across states. Compared with individuals who applied for UI benefits but did not receive them, we find that individuals who received benefits had greater well-being in a variety of domains, including household finances, food security, and mental health.

Emergency unemployment benefits and health care spending during Covid.

ObjectiveTo estimate the impact of the $600 per week Federal Pandemic Unemployment Compensation (FPUC) payments on health care services spending during the Covid pandemic and to investigate if this impact varied by state Medicaid expansion status.Data SourcesThis study leverages novel, publicly available data from Opportunity Insights capturing consumer credit and debit card spending on health care services for January 18–August 15, 2020 as well as information on unemployment insurance claims, Covid cases, and state policy changes.Study DesignUsing triple‐differences estimation, we leverage two sources of variation—within‐state change in the unemployment insurance claims rate and the introduction of FPUC payments—to estimate the moderating effect of FPUC on health care spending losses as unemployment rises. Results are stratified by state Medicaid expansion status.Extraction MethodsNot applicable.Principal FindingsFor each percentage point increase in the unemployment insurance claims rate, health care spending declined by 1.0% (<0.05) in Medicaid expansion states and by 2.0% (<0.01) in nonexpansion states. However, FPUC partially mitigated this association, boosting spending by 0.8% (<0.001) and 1.3% (<0.05) in Medicaid expansion and nonexpansion states, respectively, for every percentage point increase in the unemployment insurance claims rate.ConclusionsWe find that FPUC bolstered health care spending during the Covid pandemic, but that both the negative consequences of unemployment and moderating effects of federal income supports were greatest in states that did not adopt Medicaid expansion. These results indicate that emergency federal spending helped to sustain health care spending during a period of rising unemployment. Yet, the effectiveness of this program also suggests possible unmet demand for health care services, particularly in states that did not adopt Medicaid expansion.

Why Have Initial Unemployment Claims Stayed So High for So Long?

&lt;ns3:p&gt;As the labor market recovers from the COVID-19 pandemic, claims for unemployment insurance (UI) have been surprisingly slow to return to conventional levels. As recently as 2021Q1, initial claims for regular UI benefits averaged nearly 800,000 per week (see Figure 1)—more than twice as many as were observed at a comparable point during the recovery from the Great Recession.&lt;/ns3:p&gt;

Forecasting unemployment insurance claims in realtime with Google Trends

Leveraging the increasing availability of ”big data” to inform forecasts of labor market activity is an active, yet challenging, area of research. Often, the primary difficulty is finding credible ways with which to consistently identify key elasticities necessary for prediction. To illustrate, we utilize a state-level event-study focused on the costliest hurricanes to hit the U.S. mainland since 2004 in order to estimate the elasticity of initial unemployment insurance (UI) claims with respect to search intensity, as measured by Google Trends. We show that our hurricane-driven Google Trends elasticity leads to superior real-time forecasts of initial UI claims relative to other commonly used models. Our approach is also amenable to forecasting both at the state and national levels, and is shown to be well-calibrated in its assessment of the level of uncertainty for its out-of-sample predictions during the Covid-19 pandemic.

Modeling COVID-19 spreading dynamics and unemployment rate evolution in rural and urban counties of Alabama and New York using fractional derivative models

The COVID-19 pandemic has been affecting the United States (U.S.) since the outbreak documented on 2/29/2020, and understanding its dynamics is critical for pandemic mitigation and economic recovery. This study proposed and applied novel time fractional derivative models (FDMs) to quantify the spatiotemporal dynamics of the COVID-19 pandemic spreading in the states of Alabama and New York, U.S., two states with quite different population compositions, urbanization, and industry structures. Model applications revealed that the pandemic evolving in the two states exhibited an overall similar time-dependent trend with subtle differences in propagation rates. Alabama may have more inter-county communications in rural areas than urban areas, while the opposite may be true for the New York State. Further analysis using the space FDM showed that the COVID-19 pandemic spread in rural/urban areas of the two states by following the tempered stable density distributions with different indexes, while the number of the state’s pandemic epicenters affected the pattern of the COVID-19 pandemic spreading in space. Finally, applications of a novel time FDM revealed that the evolution of the economy, represented by the weekly unemployment insurance claims in the two states, exhibited different spreading and recovery rates, most likely due to their different exposures and responses to the pandemic. Therefore, COVID-19 spreading dynamics exhibited strong and subtly different spatiotemporal memories in rural and urban areas in the Alabama and New York States, motivating the application of FDMs.

How Have FAFSA Submissions Differed During COVID-19?

We examine changes in California’s FAFSA (Free Application for Federal Student Aid) applications during the COVID-19 crisis. There was little change in applications for high school graduates due to an early deadline for state aid. After the deadline—from early March to mid-August—FAFSA applications of potential college freshmen declined 14%, relative to prior years. Although there were initial declines in applications among more experienced undergraduates and graduate students, these quickly rebounded and were 8% higher relative to prior years. FAFSA applications increased more in counties that had larger increases in unemployment insurance claims but declined more in zip codes that were lower income or were more heavily Black and Hispanic.

Nowcasting unemployment insurance claims in the time of COVID-19

Near-term forecasts, also called nowcasts, are most challenging but also most important when the economy experiences an abrupt change. In this paper, we explore the performance of models with different information sets and data structures in order to best nowcast US initial unemployment claims in spring of 2020 in the midst of the COVID-19 pandemic. We show that the best model, particularly near the structural break in claims, is a state-level panel model that includes dummy variables to capture the variation in timing of state-of-emergency declarations. Autoregressive models perform poorly at first but catch up relatively quickly. The state-level panel model, exploiting the variation in timing of state-of-emergency declarations, also performs better than models including Google Trends. Our results suggest that in times of structural change there is a bias–variance tradeoff. Early on, simple approaches to exploit relevant information in the cross sectional dimension improve forecasts, but in later periods the efficiency of autoregressive models dominates.

Potential COVID-19 Vulnerabilities in Employment and Healthcare Access by Sexual Orientation

The 2019 coronavirus (COVID-19) pandemic has led to more than 235,000 deaths and 9 million reported positive cases in the United States. Most of the country implemented stay-at-home orders to prevent the spread of COVID-19, which has led to job losses and more than 40 million claims for unemployment insurance. The objective of this study was to estimate the potential impact of COVID-19-related closures in employment and healthcare access by sexual orientation. We used data from the 2015 to 2018 National Health Interview Survey and employment industry codes to describe the working adult population who may be vulnerable to COVID-19 job losses by sexual orientation. Multivariable logistic regression models identified the risk factors for working in a COVID-19-sensitive industry and differences in healthcare access by sexual orientation. We estimate that there may be approximately 1.7 million sexual minorities and 36.6 million heterosexual adults employed in industries that make them vulnerable to unemployment, uninsurance, and limited access to care. Nearly two-thirds of working adults—regardless of sexual orientation—are at risk of severe illness should they become infected with COVID-19. Almost half of the sexual minority and heterosexual adults in industries sensitive to COVID-19 may remain or become uninsured during the pandemic. Approximately 60% of sexual minorities and 40% of heterosexual adults in COVID-19-sensitive industries are renting their home or apartment (rather than owning their home). Because sexual minority adults vulnerable to the COVID-19 pandemic are renting or within family income ranges for premium tax credits in the individual insurance marketplaces, policymakers should consider expanding protections for renters and broadening special enrollment periods for people seeking coverage in the federal and state-based marketplaces.

COBOLing Together UI Benefits: How Delays in Fiscal Stabilizers Impact Aggregate Consumption

The United States experienced an unprecedented increase in unemployment insurance (UI) claims starting in March 2020, mainly due to layoffs caused by COVID-19. State unemployment insurance systems were inadequately prepared to process these claims. Those states using an antiquated programming language, COBOL, to process UI claims experienced longer delays in benefit disbursement. Using daily card consumption data from Affinity Solutions, I employ a two-way fixed effects estimator to measure the causal impact of COBOL-induced delays in UI benefits on aggregate consumption. The delays caused a 4.4 percentage point relative decline in total card consumption in COBOL states relative to non-COBOL states. Performing a back-of-the-envelope calculation using 2019 data, I find that real GDP declined by $181 billion (in 2012 dollars).