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.

Since the beginning of the coronavirus (COVID-19) crisis in Norway, many people have lost their jobs. This was especially the case for employees in the tourism and transportation sector. Before the coronavirus outbreak, the unemployment rate in the sector amounted to 3.4 percent. After the outbreak of COVID-19, however, the rate increased to 13.6 percent. Compared to other significantly affected industries, such as industrial work, the unemployment rate in the tourism and transportation sector was more than twice as high.

Traveling and tourism

As of July 2020, many companies in the traveling and tourism industry had completed layoffs. In detail, 85 percent of travel agencies and 95 percent of hotels had laid off employees due to the coronavirus crisis. The extent of these layoffs unfolded slightly differently in the two sectors: While 65 percent of travel agencies dismissed between 76 and 100 percent of their employees, 81 percent of hotels had to do the same.

Unemployment

Despite the significant rise in unemployment levels in Norway since March 2020, the number of unemployed individuals gradually decreased as of April 2020 before increasing again. While over 300,000 people were unemployed by the end of March 2020, the number had nearly halved by June 2020. As of February 2021, roughly 124 people registered as unemployed.

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.

According to a forecast from May 2024, the unemployment rate in Italy could reach 7.5 percent by the end of the year, two percentage points less than 2021, when the COVID-19 outbreak had a disastrous impact on the labor market. The rate is then expected to drop to 7.3 percent in 2025. Weak employment situation Unemployment in Italy started increasing after the 2008 financial crisis and peaked at 12.7 percent in 2014. It mostly affected the young population. Similarly, the youth unemployment rate also increased significantly during the same period, reaching over 40 percent in 2014. Even if the figures decreased in the following years, in 2022 the rates were still particularly high in the southern regions. Indeed, the youth unemployment rate in the regions of Sicily and Campania stood at around 43 percent. COVID-19 impact on the economy The coronavirus (COVID-19) outbreak had a serious impact on Italy’s economy. In June 2020, most Italian respondents declared that the coronavirus pandemic had impacted or would impact their personal incomes in the future. In addition, the fear of losing the job due to the pandemic has been increasing in the country, with more than half of respondents worrying about this in July 2020.

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.

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.

United States Unemployment: Female data was reported at 3,298.000 Person th in Feb 2025. This records an increase from the previous number of 3,234.000 Person th for Jan 2025. United States Unemployment: Female data is updated monthly, averaging 3,077.000 Person th from Jan 1948 (Median) to Feb 2025, with 926 observations. The data reached an all-time high of 11,494.000 Person th in Apr 2020 and a record low of 502.000 Person th in May 1953. United States Unemployment: 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.G035: Current Population Survey: Unemployment. [COVID-19-IMPACT]

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.

The unemployment rate in Sweden increased significantly from March 2020 due to the outbreak of the coronavirus (COVID-19). It was at its highest in June 2020, when it reached 9.3 percent, and was around nine percent until May 2021, when it started to decrease. It then started to increase again from early 2023, reaching 8.5 percent in September 2024. This was one of the highest in Europe.

As of May 2022, the unemployment rate in India was recorded at nearly seven percent, a decrease from the previous month. While the unemployment rate had significantly declined over the course of 2021 since having peaked in April 2020, the breakout of new coronavirus variants coupled with recurring lockdowns resulted in a fluctuating trend of unemployment gripping the nation.

The trickle-down effect

Between February and April 2020, the share of households that experienced a fall in income shot up to nearly 46 percent. Inflation rates on goods and services including food products and fuel were expected to rise later this year. Social distancing resulted in job losses, specifically those within Indian society’s lower economic strata. Several households terminated domestic help services – essentially an unorganized monthly-paying job. Most Indians spent a large amount of time engaging in household chores themselves, making it the most widely practiced lockdown activity.

Aid from the Pradhan Mantri Garib Kalyan Yojana

The most devastating impact of the virus and the lockdown had been on the economically backward classes, with limited access to proper healthcare and other resources. As a result the government launched various programs and campaigns to help sustain such households. Under the Pradhan Mantri Garib Kalyan Yojana, 312 billion Indian rupees were accrued and provided to around 331 million beneficiaries that included women, construction workers, farmers, and senior citizens. More aid was announced in mid-May, to mainly support small businesses through the crisis.

Germany Labour Force: Unemployment Rate: Total data was reported at 3.700 % in Jan 2025. This records an increase from the previous number of 3.200 % for Dec 2024. Germany Labour Force: Unemployment Rate: Total data is updated monthly, averaging 4.100 % from Jan 2007 (Median) to Jan 2025, with 217 observations. The data reached an all-time high of 9.600 % in Feb 2007 and a record low of 2.800 % in May 2023. Germany Labour Force: Unemployment Rate: Total data remains active status in CEIC and is reported by Statistisches Bundesamt. The data is categorized under Global Database’s Germany – Table DE.G008: Labour Force: ILO Concept. [COVID-19-IMPACT]

In 2021, the unemployment rate among the United States workforce was approximately 5.35 percent - this was roughly 3.8 percent lower than the unemployment rate in 2020, during the first year of the Covid-19 pandemic, but was still around one percent higher than pre-pandemic levels.

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/

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: November 2024 (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: February 3, 2025Local 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, 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.To better understand the different labor force statistics included in this map, see the diagram below from BLS:

Unemployment Rate in China increased to 5.40 percent in February from 5.20 percent in January of 2025. This dataset provides - China Unemployment Rate - actual values, historical data, forecast, chart, statistics, economic calendar and news.

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.

Graph and download economic data for Unemployment Rate in Memphis, TN-MS-AR (MSA) (MPHURN) from Jan 1990 to Jan 2025 about Memphis, AR, TN, MS, unemployment, rate, and USA.

Russia Unemployment Rate: Urban data was reported at 1.900 % in Jan 2025. This records a decrease from the previous number of 2.100 % for Dec 2024. Russia Unemployment Rate: Urban data is updated monthly, averaging 4.300 % from Aug 2009 (Median) to Jan 2025, with 186 observations. The data reached an all-time high of 7.800 % in Jan 2010 and a record low of 1.800 % in Nov 2024. Russia Unemployment Rate: Urban data remains active status in CEIC and is reported by Federal State Statistics Service. The data is categorized under Global Database’s Russian Federation – Table RU.GB007: Unemployment Rate. Since January 2018, the data cover the population 15 aged and above, but before that: 15-72. [COVID-19-IMPACT]

Netherlands Unemployment Rate: 15 to 24 Years data was reported at 9.100 % in Jan 2025. This records an increase from the previous number of 7.900 % for Dec 2024. Netherlands Unemployment Rate: 15 to 24 Years data is updated monthly, averaging 11.200 % from Jan 2003 (Median) to Jan 2025, with 265 observations. The data reached an all-time high of 15.300 % in Feb 2014 and a record low of 6.800 % in Dec 2022. Netherlands Unemployment Rate: 15 to 24 Years data remains active status in CEIC and is reported by Statistics Netherlands. The data is categorized under Global Database’s Netherlands – Table NL.G015: Unemployment Rate: Labour Force Survey. [COVID-19-IMPACT]