This graph shows the unemployment rate forecasts following the outbreak of the coronavirus (COVID-19) in France from the first quarter of 2020 to the fourth quarter of 2025. OECD predictions estimated that unemployment will increase gradually in each quarter of 2022 and 2023, before a decrease in 2024.

After the outbreak of the coronavirus (COVID-19) in Denmark in March 2020, unemployment rates increased all over the country. In March 2020, the rate was highest in Northern Denmark. In July 2024, the unemployment rate was around three percent in all five regions.The first case of COVID-19 in Denmark was confirmed on February 27, 2020. For further information about the coronavirus (COVID-19) pandemic, please visit our dedicated Facts and Figures page.

The seasonally-adjusted national unemployment rate is measured on a monthly basis in the United States. In February 2025, the national unemployment rate was at 4.1 percent. Seasonal adjustment is a statistical method of removing the seasonal component of a time series that is used when analyzing non-seasonal trends. U.S. monthly unemployment rate According to the Bureau of Labor Statistics - the principle fact-finding agency for the U.S. Federal Government in labor economics and statistics - unemployment decreased dramatically between 2010 and 2019. This trend of decreasing unemployment followed after a high in 2010 resulting from the 2008 financial crisis. However, after a smaller financial crisis due to the COVID-19 pandemic, unemployment reached 8.1 percent in 2020. As the economy recovered, the unemployment rate fell to 5.3 in 2021, and fell even further in 2022. Additional statistics from the BLS paint an interesting picture of unemployment in the United States. In November 2023, the states with the highest (seasonally adjusted) unemployment rate were the Nevada and the District of Columbia. Unemployment was the lowest in Maryland, at 1.8 percent. Workers in the agricultural and related industries suffered the highest unemployment rate of any industry at seven percent in December 2023.

Unemployment in King County resulting from strategies to slow the spread of COVID-19

This data package includes the underlying data and files to replicate the calculations, charts, and tables presented in US unemployment insurance in the pandemic and beyond, PIIE Policy Brief 20-10. If you use the data, please cite as: Furman, Jason. (2020). US unemployment insurance in the pandemic and beyond. PIIE Policy Brief 20-10. Peterson Institute for International Economics.

United States Pandemic Unemployment Assistance (PUA): Continued Claims: US data was reported at 17.945 Person th in 28 Jan 2023. This records a decrease from the previous number of 20.107 Person th for 21 Jan 2023. United States Pandemic Unemployment Assistance (PUA): Continued Claims: US data is updated weekly, averaging 791.060 Person th from Mar 2020 (Median) to 28 Jan 2023, with 149 observations. The data reached an all-time high of 14,884.261 Person th in 22 Aug 2020 and a record low of 6.841 Person th in 19 Nov 2022. United States Pandemic Unemployment Assistance (PUA): Continued Claims: US data remains active status in CEIC and is reported by U.S. Department of Labor. The data is categorized under Global Database’s United States – Table US.G146: Unemployment Insurance: Weekly Pandemic Claims (Discontinued). [COVID-19-IMPACT]

United States Unemployment: sa: Female data was reported at 3,211.000 Person th in Dec 2024. This records a decrease from the previous number of 3,288.000 Person th for Nov 2024. United States Unemployment: sa: Female data is updated monthly, averaging 3,048.000 Person th from Jan 1948 (Median) to Dec 2024, with 924 observations. The data reached an all-time high of 11,864.000 Person th in Apr 2020 and a record low of 510.000 Person th in May 1953. United States Unemployment: sa: Female data remains active status in CEIC and is reported by U.S. Bureau of Labor Statistics. The data is categorized under Global Database’s United States – Table US.G036: Current Population Survey: Unemployment: Seasonally Adjusted. [COVID-19-IMPACT]

The COVID-19 pandemic caused unemployment and layoffs across the world, and hit youth particularly hard. While the deficit was more than eight percent among youth in 2020, it was less than half of that among adults.

Millennials and Gen Z are spreading coronavirus—but not because of parties and barshttps://www.nationalgeographic.com/science/2020/09/millennials-generation-z-coronavirus-scapegoating-beach-parties-bars-inequality-cvd/

Data from the Washington State Employment Security Department and converted to display as a table.

Graph and download economic data for Pandemic Unemployment Assistance Initial Claims in South Carolina (PUAICSC) from 2020-04-04 to 2022-11-05 about pandemic, assistance, initial claims, SC, unemployment, and USA.

The data set provides readers with data on the first half of the workforce for the years 2011 to 2020, per capita income for the first half of 2020 compared to 2019, and the unemployment rate in the working age. activities in the first half of the year from 2011 to 2020.

The employment and unemployment indicator shows several data points. The first figure is the number of people in the labor force, which includes the number of people who are either working or looking for work. The second two figures, the number of people who are employed and the number of people who are unemployed, are the two subcategories of the labor force. The unemployment rate is a calculation of the number of people who are in the labor force and unemployed as a percentage of the total number of people in the labor force.

The unemployment rate does not include people who are not employed and not in the labor force. This includes adults who are neither working nor looking for work. For example, full-time students may choose not to seek any employment during their college career, and are thus not considered in the unemployment rate. Stay-at-home parents and other caregivers are also considered outside of the labor force, and therefore outside the scope of the unemployment rate.

The unemployment rate is a key economic indicator, and is illustrative of economic conditions in the county at the individual scale.

There are additional considerations to the unemployment rate. Because it does not count those who are outside the labor force, it can exclude individuals who were looking for a job previously, but have since given up. The impact of this on the overall unemployment rate is difficult to quantify, but it is important to note because it shows that no statistic is perfect.

The unemployment rates for Champaign County, the City of Champaign, and the City of Urbana are extremely similar between 2000 and 2023.

All three areas saw a dramatic increase in the unemployment rate between 2006 and 2009. The unemployment rates for all three areas decreased overall between 2010 and 2019. However, the unemployment rate in all three areas rose sharply in 2020 due to the effects of the COVID-19 pandemic. The unemployment rate in all three areas dropped again in 2021 as pandemic restrictions were removed, and were almost back to 2019 rates in 2022. However, the unemployment rate in all three areas rose slightly from 2022 to 2023.

This data is sourced from the Illinois Department of Employment Security’s Local Area Unemployment Statistics (LAUS), and from the U.S. Bureau of Labor Statistics.

Sources: Illinois Department of Employment Security, Local Area Unemployment Statistics (LAUS); U.S. Bureau of Labor Statistics.

According to a survey in August 2020, people on middle incomes of between 50,000 and 100,000 U.S. dollars per year were less likely to support continuing increased unemployment benefits due to the COVID-19 pandemic. With the additional payments having expired on July 31, 2020, 24 percent of middle-income respondents believed that Congress should cease the payments, compared to 18 percent of respondents earning below 50,000 U.S. dollars, and 16 percent of respondents earning above 100,000 U.S. dollars.

Graph and download economic data for Pandemic Unemployment Assistance Continued Claims in Virginia (PUACCVA) from 2020-03-28 to 2022-10-22 about pandemic, assistance, continued claims, VA, unemployment, and USA.

Coronavirus-19 Cases (Hourly Update) vs. Median Household Income (ACS)See Detailed graphs and tables describing the COVID-19 crisis in New Mexico, updated daily (includes some county level data not found elsewhere) - https://sites.google.com/view/new-mexico-covid19-tracking/homeCDC's Description of the Social Vulnerability Index (takes into account 15 different selected indicators):https://svi.cdc.gov/

MUM02 - Covid-19 Adjusted Monthly Unemployment Estimates. Published by Central Statistics Office. Available under the license Creative Commons Attribution 4.0 (CC-BY-4.0).Covid-19 Adjusted Monthly Unemployment Estimates...

The global economy has been hard hit by the COVID-19 pandemic. Many countries are experiencing a severe and destructive recession. A significant number of firms and businesses have gone bankrupt or been scaled down, and many individuals have lost their jobs. The main goal of this study is to support policy- and decision-makers with additional and real-time information about the labor market flow using Twitter data. We leverage the data to trace and nowcast the unemployment rate of South Africa during the COVID-19 pandemic. First, we create a dataset of unemployment-related tweets using certain keywords. Principal Component Regression (PCR) is then applied to nowcast the unemployment rate using the gathered tweets and their sentiment scores. Numerical results indicate that the volume of the tweets has a positive correlation, and the sentiments of the tweets have a negative correlation with the unemployment rate during and before the COVID-19 pandemic. Moreover, the now-casted unemployment rate using PCR has an outstanding evaluation result with a low Root Mean Square Error (RMSE), Mean Absolute Percentage Error (MAPE), Symmetric MAPE (SMAPE) of 0.921, 0.018, 0.018, respectively and a high R2-score of 0.929.

According to a forecast from March 2024, the number of unemployed people in Denmark increased in 2023, and was forecast to grow slightly in the coming years. Unemployment numbers were at around 130,000 people in 2020 following the outbreak of COVID-19, but decreased in 2021. In 2024, the number of unemployed people in Denmark is forecast to be 90,000.

The survey charted Finnish opinions on various policy measures during the coronavirus epidemic. The respondents were with statements on the COVID-19 pandemic, social security, climate policy and possible ways to stimulate the economy and raise employment rates. The data were collected as part of the 'Tackling Biases and Bubbles in Participation' (BIBU) research project, which explores how structural changes in the economy impact policy-making. The BIBU project examines how economic restructuring changes citizens' and decision-makers' political capacities, interests and emotions. The research project is a collaboration between six universities and research institutes, led by Professor Anu Kantola from the University of Helsinki. First, the respondents' views on various economic policy measures were surveyed with a series of statements. These policy measures included, for example, changes to unemployment benefits, working hours, job alternation leave, terms of employment, and working life in general. In addition to policies related to employment, the survey included statements on education, social security, the COVID-19 pandemic and investment in the future. Views on climate policy were also investigated with a series of statements (e.g. Finland should take a leading role internationally in reducing greenhouse gas emissions, Finland's economic competitiveness is more important than addressing climate change). The respondents' political opinions were surveyed with questions on where they would place themselves on the left-right political scale and which party they would vote for if parliamentary elections were held now. Additionally, the respondents were asked how they would rate their social status in relation to other people, how they felt that their social status had changed during the past five years, how they anticipated their social status would change in the next five years, and what they considered the risk of being made redundant (e.g. due to automation, relocation of production, or financial difficulties of the company) to be in their current job. Background variables included, among others, the respondent's age, gender, municipality and region of residence, level of education, occupational status, economic activity, and gross personal monthly income.