During the week ending December 31, 2022, about 204,000 initial unemployment claims were made. This is a decrease from the week prior, when initial unemployment claims stood at 223,000. The number of unemployment claims tends to fluctuate rapidly in response to national or global events such as shortages, pandemics, and wars. Initial unemployment claims reached a record high during the COVID-19 pandemic, reaching nearly seven million unique initial claims by the end of March, 2020. The restaurant and retail industries in the United States were particularly impacted.

The COVID-19 pandemic caused regressive income declines, but also led to progressive policy responses. Using administrative U.S. tax data, which are a near-universal panel dataset that can track income changes over time, we consider the distribution of annual income declines during the COVID-19 pandemic relative to the Great Recession. We then show how the unprecedented policy response to the pandemic, through enhanced unemployment insurance benefits and stimulus checks, affected the distribution of these declines

As of October 2024, there were 133.89 million full-time employees in the United States. This is a slight decrease from the previous month, when there were 134.15 million full-time employees. The impact COVID-19 on employment In December 2019, the COVID-19 virus began its spread across the globe. Since being classified as a pandemic, the virus caused a global health crisis that has taken the lives of millions of people worldwide. The COVID-19 pandemic changed many facets of society, most significantly, the economy. In the first years, many businesses across all industries were forced to shut down, with large numbers of employees being laid off. The economy continued its recovery in 2022 with the nationwide unemployment rate returning to a more normal 3.4 percent as of April 2023. Unemployment benefits Because so many people in the United States lost their jobs, record numbers of individuals applied for unemployment insurance for the first time. As an early response to this nation-wide upheaval, the government issued relief checks and extended the benefits paid by unemployment insurance. In May 2020, the amount of unemployment insurance benefits paid rose to 23.73 billion U.S. dollars. As of December 2022, this value had declined to 2.24 billion U.S. dollars.

I show how, in the tradition of the dynamic labor supply literature, one can identify the moral hazard effects and liquidity effects of unemployment insurance (UI) using variations along the time profile of unemployment benefits. I use this strategy to investigate the anatomy of labor supply responses to UI. I identify the effect of benefit level and potential duration in the regression kink design using kinks in the schedule of benefits in the US. My results suggest that the response of search effort to UI benefits is driven as much by liquidity effects as by moral hazard effects. (JEL D82, J22, J65)

2020 data excluded because the U.S. Census Bureau did not release 2020 ACS 1-year estimates due to COVID-19. Some racial and ethnic categories are suppressed to avoid misleading estimates when the relative standard error exceeds 30%.

Data Source: American Community Survey (ACS) 1-Year Estimates

Why This Matters

Employment is the main source of income for most people. For many families and individuals, unemployment threatens access to basic needs, such as food, housing, transportation, health care, and education, among others.

Nationally, Black workers and workers of color, on average, experience persistently higher unemployment rates than white workers. Racist policies and practices, including segregation, employment discrimination, and inequities in the criminal justice system have undermined job security for workers of color.

The District's Response

Initiatives that support residents in career advancement and their efforts to secure sustainable employment through education and training support, such as Career MAP, Advanced Technical Centers (ATC), and the DC Infrastructure Academy, among other programs and services.

Administering federal and local safety net programs that provide temporary cash and health benefits to help residents experiencing unemployment and related economic hardship meet their basic needs, including unemployment insurance, Medicaid, TANF For District Families, SNAP, etc.

Programs to remove barriers employment for returning citizens, such as Pathways to Work and the Returning Citizens Access to Jobs Grant.

Access the Data HereWhat is the COVID-19 Economic Vulnerability Index?The COVID-19 Vulnerability Index (CVI) is a measurement of the negative impact that the coronavirus (COVID-19) crisis can have on employment based upon a region's mix of industries. For example, accommodation and food services are projected to lose more jobs as a result of the coronavirus (in the neighborhood of 50%) compared with utilities and healthcare (with none or little expected job contraction).An average Vulnerability Index score is 100, representing the average job loss expected in the United States. Higher scores indicate the degree to which job losses may be greater — an index score of 200, for example, means the rate of job loss can be twice as large as the national average. Conversely, an index score of 50 would mean a possible job loss of half the national average. Regions heavily dependent on tourism with relatively high concentrations of leisure and hospitality jobs, for example, are likely to have high index scores. The Vulnerability Index only measures the impact potential related to the mix of industry employment. The index does not take into account variation due to a region’s rate of virus infection, nor does it factor in local government's policies in reaction to the virus. For more detail, please see this description.MethodologyThe index is based on a model of potential job losses due to the COVID-19 outbreak in the United States. Expected employment losses at the subsector level are based upon inputs which include primary research on expert testimony; news reports for key industries such as hotels, restaurants, retail, and transportation; preliminary release of unemployment claims; and the latest job postings data from Chmura's RTI database. The forecast model, based on conditions as of March 23, 2020, assumes employment in industries in each county/region would change at a similar rate as employment in national industries. The projection estimates that the United States could lose 15.0 million jobs due to COVID-19, with over half of the jobs lost in hotels, food services, and entertainment industries. Contact Chmura for further details.

What is the COVID-19 Economic Vulnerability Index?The COVID-19 Vulnerability Index (CVI) is a measurement of the negative impact that the coronavirus (COVID-19) crisis can have on employment based upon a region's mix of industries. For example, accommodation and food services are projected to lose more jobs as a result of the coronavirus (in the neighborhood of 50%) compared with utilities and healthcare (with none or little expected job contraction).This updated dataset contains 116 jobs attributes including the 10 most likely jobs to be impacted for each county, the total employment and employment by sector. An attribute list is included below.An average Vulnerability Index score is 100, representing the average job loss expected in the United States. Higher scores indicate the degree to which job losses may be greater — an index score of 200, for example, means the rate of job loss can be twice as large as the national average. Conversely, an index score of 50 would mean a possible job loss of half the national average. Regions heavily dependent on tourism with relatively high concentrations of leisure and hospitality jobs, for example, are likely to have high index scores. The Vulnerability Index only measures the impact potential related to the mix of industry employment. The index does not take into account variation due to a region’s rate of virus infection, nor does it factor in local government's policies in reaction to the virus. For more detail, please see this description.MethodologyThe index is based on a model of potential job losses due to the COVID-19 outbreak in the United States. Expected employment losses at the subsector level are based upon inputs which include primary research on expert testimony; news reports for key industries such as hotels, restaurants, retail, and transportation; preliminary release of unemployment claims; and the latest job postings data from Chmura's RTI database. The forecast model, based on conditions as of March 23, 2020, assumes employment in industries in each county/region would change at a similar rate as employment in national industries. The projection estimates that the United States could lose 15.0 million jobs due to COVID-19, with over half of the jobs lost in hotels, food services, and entertainment industries. Contact Chmura for further details.Attribute ListFIPSCounty NameStateTotal JobsWhite Collar JobsBlue Collar JobsService JobsWhite Collar %Blue Collar %Service %Government JobsGovernment %Primarily Self-Employed JobsPrimarily Self-Employed %Job Change, Last Ten YearsIndustry 1 NameIndustry 1 EmplIndustry 1 %Industry 2 NameIndustry 2 EmplIndustry 2 %Industry 3 NameIndustry 3 EmplIndustry 3 %Industry 4 NameIndustry 4 EmplIndustry 4 %Industry 5 NameIndustry 5 EmplIndustry 5 %Industry 6 NameIndustry 6 EmplIndustry 6 %Industry 7 NameIndustry 7 EmplIndustry 7 %Industry 8 NameIndustry 8 EmplIndustry 8 %Industry 9 NameIndustry 9 EmplIndustry 9 %Industry 10 NameIndustry 10 EmplIndustry 10 %All Other IndustriesAll Other Industries EmplAll Other Industies %Agriculture, Food & Natural Resources EmplArchitecture and Construction EmplArts, A/V Technology & Communications EmplBusiness, Management & Administration EmplEducation & Training EmplFinance EmplGovernment & Public Administration EmplHealth Science EmplHospitality & Tourism EmplHuman Services EmplInformation Technology EmplLaw, Public Safety, Corrections & Security EmplManufacturing EmplMarketing, Sales & Service EmplScience, Technology, Engineering & Mathematics EmplTransportation, Distribution & Logistics EmplAgriculture, Food & Natural Resources %Architecture and Construction %Arts, A/V Technology & Communications %Business, Management & Administration %Education & Training %Finance %Government & Public Administration %Health Science %Hospitality & Tourism %Human Services %Information Technology %Law, Public Safety, Corrections & Security %Manufacturing %Marketing, Sales & Service %Science, Technology, Engineering & Mathematics %Transportation, Distribution & Logistics %COVID-19 Vulnerability IndexAverage Wages per WorkerAvg Wages Growth, Last Ten YearsUnemployment RateUnderemployment RatePrime-Age Labor Force Participation RateSkilled Career 1Skilled Career 1 EmplSkilled Career 1 Avg Ann WagesSkilled Career 2Skilled Career 2 EmplSkilled Career 2 Avg Ann WagesSkilled Career 3Skilled Career 3 EmplSkilled Career 3 Avg Ann WagesSkilled Career 4Skilled Career 4 EmplSkilled Career 4 Avg Ann WagesSkilled Career 5Skilled Career 5 EmplSkilled Career 5 Avg Ann WagesSkilled Career 6Skilled Career 6 EmplSkilled Career 6 Avg Ann WagesSkilled Career 7Skilled Career 7 EmplSkilled Career 7 Avg Ann WagesSkilled Career 8Skilled Career 8 EmplSkilled Career 8 Avg Ann WagesSkilled Career 9Skilled Career 9 EmplSkilled Career 9 Avg Ann WagesSkilled Career 10Skilled Career 10 EmplSkilled Career 10 Avg Ann Wages

The unemployment rate of the United Kingdom was 4.7 percent in May 2025, an increase from the previous month. Before the arrival of the COVID-19 pandemic, the UK had relatively low levels of unemployment, comparable with the mid-1970s. Between January 2000 and the most recent month, unemployment was highest in November 2011, when the unemployment rate hit 8.5 percent. Will unemployment continue to rise in 2025? Although low by historic standards, there has been a noticeable uptick in the UK's unemployment rate, with other labor market indicators also pointing to further loosening. In December 2024, the number of job vacancies in the UK fell to its lowest level since May 2021, while payrolled employment declined by 47,000 compared with November. Whether this is a continuation of a broader cooling of the labor market since 2022 or a reaction to more recent economic developments, such as upcoming tax rises for employers, remains to be seen. Forecasts made in late 2024 suggest that the unemployment rate will remain relatively stable in 2025, averaging out at 4.1 percent and falling again to four percent in 2026.
Demographics of the unemployed As of the third quarter of 2024, the unemployment rate for men was slightly higher than that of women, at 4.4 percent, compared to 4.1 percent. During the financial crisis at the end of the 2000s, the unemployment rate for women peaked at a quarterly rate of 7.7 percent, whereas for men, the rate was 9.1 percent. Unemployment is also heavily associated with age, and young people in general are far more vulnerable to unemployment than older age groups. In late 2011, for example, the unemployment rate for those aged between 16 and 24 reached 22.3 percent, compared with 8.2 percent for people aged 25 to 34, while older age groups had even lower peaks during this time.

Some 33 percent of respondents in the United States were satisfied with their fellow citizens’ response to the coronavirus pandemic on May 31, 2020. Are people satisfied with the media’s response to the coronavirus? As of March to May 2020, all over the world, people were watching news coverage more than usual due to the coronavirus outbreak. A lot of respondents seem to want to keep in-line with the latest trends of a pandemic that has effectively crippled many of the world’s biggest economies. Consumers of several age groups between 16 and 64 are watching news with a greater frequency which also means a greater scrutiny for the media outlets. In the U.S., where millions of people have filed for unemployment benefits since the corona outbreak, more than one third of respondents were satisfied with media’s response to COVID-19 as of May 3rd, 2020. However, a considerable share of respondents in the U.S. believe that media has overstated news related to coronavirus outbreak in the U.S. That goes for audiences from both democrat and republican-supporting groups as they believe that the media outlets have either slightly or greatly exaggerated news about COVID-19.

Reference Layer: Bureau of Labor Statistics Monthly Unemployment (latest 14 months)_This layer contains the latest 14 months of unemployment statistics from the U.S. Bureau of Labor Statistics (BLS). The data is offered at the nationwide, state, and county geography levels. Puerto Rico is included. These are not seasonally adjusted values.The layer is updated monthly with the newest unemployment statistics available from BLS. There are attributes in the layer that specify which month is associated to each statistic. Most current month: August 2022 (preliminary values at the county level)The attributes included for each month are:Unemployment rate (%)Count of unemployed populationCount of employed population in the labor forceCount of people in the labor forceData obtained from the U.S. Bureau of Labor Statistics. Data downloaded: October 21, 2022Local Area Unemployment Statistics table download: https://www.bls.gov/lau/#tablesLocal Area Unemployment FTP downloads:State and CountyNationData Notes:This layer is updated automatically when the BLS releases their most current monthly statistics. The layer always contains the most recent estimates. It is updated within days of the BLS's county release schedule. BLS releases their county statistics roughly 2 months after-the-fact. The data is joined to 2021 TIGER boundaries from the U.S. Census Bureau.Monthly values are subject to revision over time.For national values, employed plus unemployed may not sum to total labor force due to rounding.As of the January 2022 estimates released on March 18th, BLS is reporting new data for the two new census areas in Alaska - Copper River and Chugach - and historical data for the previous census area - Valdez Cordova.To better understand the different labor force statistics included in this map, see the diagram below from BLS:

Non Farm Payrolls in the United States increased by 147 thousand in June of 2025. This dataset provides the latest reported value for - United States Non Farm Payrolls - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

This study, integrating sentiment, emotion, discourse, and timeline analyses together, conducts a corpus-based sentiment analysis of the news articles on unemployment from the New York Times in 2020, to capture the emotional dynamics conveyed by the newspaper as the pandemic-induced unemployment developed in the US. The results reveal that positive sentiment in the news articles on unemployment is significantly higher than negative sentiment. In emotion analysis, “trust” and “anticipation”rank the first and second among the eight emotions, while “fear”and “sadness” top the negative emotions. Complemented with a discourse analysis approach, the study reveals that the change of the sentiments and emotions over time is linked with the evolution of the pandemic and unemployment, the policy response as well as the protests against ethnic inequalities. This study highlights the important role mainstream news media play in information dissemination and solution-focused reportage at the time of severe crisis.This dataset contains 14 documents for the data of 2 sentiments and 8 emotions, generated by Python. It includes NRC lexicon categories for the sentiments and emotions in the study (data1-10), top 10 high-frequency words associated to the sentiments and emotions in each of the 12 subcorpora (data11-12), and monthly values of the sentiments and emotions in 2020 (data 13-14).

This layer contains the latest 14 months of unemployment statistics from the U.S. Bureau of Labor Statistics (BLS). The data is offered at the nationwide, state, and county geography levels. Puerto Rico is included. These are not seasonally adjusted values. The layer is updated monthly with the newest unemployment statistics available from BLS. There are attributes in the layer that specify which month is associated to each statistic. Most current month: May 2025 (preliminary values at the county level) The attributes included for each month are:Unemployment rate (%)Count of unemployed populationCount of employed population in the labor forceCount of people in the labor forceData obtained from the U.S. Bureau of Labor Statistics. Data downloaded: July 18th, 2025Local Area Unemployment Statistics table download: https://www.bls.gov/lau/#tablesLocal Area Unemployment FTP downloads:State and County NationData Notes:This layer is updated automatically when the BLS releases their most current monthly statistics. The layer always contains the most recent estimates. It is updated within days of the BLS"s county release schedule. BLS releases their county statistics roughly 2 months after-the-fact. The data is joined to 2023 TIGER boundaries from the U.S. Census Bureau.Monthly values are subject to revision over time.For national values, employed plus unemployed may not sum to total labor force due to rounding.As of the January 2022 estimates released on March 18th, 2022, BLS is reporting new data for the two new census areas in Alaska - Copper River and Chugach - and historical data for the previous census area - Valdez Cordova. As of the March 17th, 2025 release, BLS now reports data for 9 planning regions in Connecticut rather than the 8 previous counties. To better understand the different labor force statistics included in this map, see the diagram below from BLS:

IntroductionThrough variations in reporting volume and discursive strategies, the media communicates risks to the public and shapes perceptions during crises.MethodsThis study conducted a corpus-based, quantitative analysis of topoi in American newspapers during the 2008 financial crisis and the 2020 COVID-19 pandemic, comparing media representations of unemployment.ResultsIt identifies four recurrent topoi common to both crises that characterize media coverage of unemployment. However, differences in other recurring topics reflect variations in the distinct causes of the crises, unemployment dynamics, and social policy responses.DiscussionThese findings highlight the media’s distinct influences on the evolution of each crisis and its portrayal of unemployment. By examining how media strategies shape discourse on unemployment, this study deepens our understanding of the interplay between media, discourse analysis, and crisis management during major economic disruptions.

This layer displays areas across the U.S. that are under severe weather conditions or other hazards and risks which have the potential to impact vulnerable populations or the functionality of critical infrastructure. About This LayerSevere weather conditions, hazards and risks are estimated based on live data feeds extracted from various authoritative sources and that are compared against predefined thresholds; an area is characterized as a potential risk if the conditions in this area exceed this threshold (see table below). The WoW layer is displayed alongside POST’s relative population exposure rank. The relative exposure of the population is calculated based on the US National Grid System (USNG) as the unit of analysis, in a spatial resolution of 1km2. To determine potential impacts on vulnerable populations, POST relies on socio-economic and demographic data made available by LandScan USA 2019 and collected by the American Community Survey (ACS) of the U.S. Census Bureau (e.g., number of elderly people, unemployment rate, number of people on public assistance or food stamps, number of mobile housing units). These data have been aggregated or disaggregated from the administrative block group or census tract division to the USNG division. POST then calculates a weighted Population Vulnerability Score (PVS) for each affected USNG cell (within each risk area as determined in the WoW layer), which in turn, are translated into a relative population exposure rank (values range between 1 and 5, where 1 is the highest exposure rank). The WOW and POST layers are updated every 4 hours.See WoW_Threats for a table with threats and thresholds used in WoW.

Professional employer organizations (PEOs) have fared with volatility in recent years. The labor market quickly recovered from the pandemic and the unemployment rate has hovered near record low levels, intensifying competition for talent. PEOs have been essential in a market where many companies struggled to staff up, while also helping businesses implement strategies to reduce attrition. At the same time, elevated interest rates in response to inflation have constrained growth among critical clients and tempered corporate profit, curtailing spending on PEO services. Overall, industry revenue is forecast to decline at a CAGR of 2.5% over the past five years to $215.9 billion, including growth of 1.7% in 2025. The importation of technology has already had a major impact on PEOs, with online job boards creating opportunities to match candidates with employers, while the adoption of automation technologies has improved the efficiency of matching candidates with job listings. The artificial intelligence (AI) revolution will streamline routine tasks like payroll processing, benefits administration and recruitment for PEOs, allowing companies to leverage new capabilities to improve strategic decision-making processes for clients. At the same time, the scale of change will encourage more companies to consolidate as larger companies are better able to invest in technology and spread administrative costs across a larger client base. Amid this changing landscape, profit margins will hold steady as growth opportunities will be counterbalanced by steep competitive pressures.PEOs will grow in the coming years, interest rates temper, strengthening corporate finances and better enabling companies to spend on external services. Economic expansion will encourage entrepreneurship, with a rise in the number of businesses creating new need for PEOs. As a result, industry revenue is forecast to increase at a CAGR of 1.6% over the next five years to 2030, although ongoing inflation fears alongside the imposition of tariffs could undermine these trends. PEOs will remain essential to helping companies navigate a changing economic landscape in the coming years, as the expansion of the gig economy reclassifies workers.

This study explains how the economy affects the foreign policy rhetoric used by American presidents. When economic conditions deteriorate, presidents criticize foreign nations to boost their approval ratings. Presidents use this "diversionary cheap talk" in response to the misery index of unemployment plus inflation, which poses a unique threat to their popularity. They target historical rivals, which make intergroup distinctions most salient. Diversionary cheap talk is most influential for and most frequently used by Democratic presidents, whose non-core constituents prefer hawkish foreign policy but already expect it from Republican presidents. I test the observable implications of the theory with the American Diplomacy Dataset, an original record of 50,000 American foreign policy events between 1851 and 2010 drawn from a corpus of 1.3 million New York Times articles.

NADAC data users should note that this data collection contains data on arts-related occupations. Please read the summary below for details. This data collection is comprised of responses from two sets of survey questionnaires, the basic Current Population Survey (CPS) and a survey on the topic of Contingent Employment in the United States, which was administered as a supplement to the February 2017 CPS. In addition to administering the basic CPS, interviewers asked the supplementary questions in three-fourths of the sample households. The CPS, administered monthly, is a labor force survey providing current estimates of the economic status and activities of the population of the United States, for the week prior to the survey. Specifically, the CPS provides estimates of total employment (both farm and nonfarm), nonfarm self- employed persons, domestics, and unpaid helpers in nonfarm family enterprises, wage and salaried employees, and estimates of total unemployment. The Contingent Work Supplement questions were asked of all applicable persons age 16 years and older. The supplement data is comprised of information on contingent or temporary work that a person did without expecting continuing employment from the particular employer they happened to be working for. Also included is information about each worker's expectation of continuing employment, satisfaction with their current employment arrangement, current job history, transition into the current employment arrangement, search for other employment, employee benefits, and earnings. The occupation and industry information variables in this data collection can help the data users identify individuals who worked in arts and culture related fields. The occupations are listed in categories like "Architecture and engineering occupations" and "Arts, Design, Entertainment, Sports, and Media Occupations," which include professions such as artists, architects designers, actors, musicians, and writers. Industries related to the arts and culture are in the "Arts, Entertainment, and Recreation" category. The supplement questions were not asked of unpaid family workers and persons not looking for work (this includes persons not in the labor force and unemployed persons on layoff who are not looking for work). Demographic variables include age, sex, race, Hispanic origin, marital status, veteran status, educational attainment, occupation, and income.