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.

This graph shows the unemployment rate forecasts following the outbreak of the coronavirus (COVID-19) in France from the first quarter of 2020, when the unemployment rate stood at around ***** percent, 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.

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.

In 2023, it was estimated that over 161 million Americans were in some form of employment, while 3.64 percent of the total workforce was unemployed. This was the lowest unemployment rate since the 1950s, although these figures are expected to rise in 2023 and beyond. 1980s-2010s Since the 1980s, the total United States labor force has generally risen as the population has grown, however, the annual average unemployment rate has fluctuated significantly, usually increasing in times of crisis, before falling more slowly during periods of recovery and economic stability. For example, unemployment peaked at 9.7 percent during the early 1980s recession, which was largely caused by the ripple effects of the Iranian Revolution on global oil prices and inflation. Other notable spikes came during the early 1990s; again, largely due to inflation caused by another oil shock, and during the early 2000s recession. The Great Recession then saw the U.S. unemployment rate soar to 9.6 percent, following the collapse of the U.S. housing market and its impact on the banking sector, and it was not until 2016 that unemployment returned to pre-recession levels. 2020s 2019 had marked a decade-long low in unemployment, before the economic impact of the Covid-19 pandemic saw the sharpest year-on-year increase in unemployment since the Great Depression, and the total number of workers fell by almost 10 million people. Despite the continuation of the pandemic in the years that followed, alongside the associated supply-chain issues and onset of the inflation crisis, unemployment reached just 3.67 percent in 2022 - current projections are for this figure to rise in 2023 and the years that follow, although these forecasts are subject to change if recent years are anything to go by.

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.

Unemployment Rate in China decreased to 5 percent in May from 5.10 percent in April of 2025. This dataset provides - China Unemployment Rate - actual values, historical data, forecast, chart, statistics, economic calendar and news.

After reaching historic low levels of unemployment in the months before the coronavirus pandemic, the unemployment rate in the Netherlands is forecast to decraese due to the recovery from COVID-19. The unemployment rate is expected to decrease to 3.5 percent by 2023. The Netherlands was already implementing several employment protection measures, especially in the shape of worktime reduction benefits and income support for self-employed.

The Covid-19 crisis did not have the same impact by age and gender. In fact, in February 2020, the unemployment rate for the under ** year olds was at **** percent, three months later the rate had reached **** percent. This is the category that has seen the biggest increase. Women have also been more affected by the crisis with an increase of *** points between February and May 2020. The unemployment rate for men increased by *** points over the same period.

Russia Unemployment Rate: Urban data was reported at 1.800 % in Mar 2025. This records a decrease from the previous number of 2.100 % for Feb 2025. Russia Unemployment Rate: Urban data is updated monthly, averaging 4.300 % from Aug 2009 (Median) to Mar 2025, with 188 observations. The data reached an all-time high of 7.800 % in Jan 2010 and a record low of 1.800 % in Mar 2025. 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]

Context & Content

This dataset consists of the unemployment rate and education level of adults in the USA by county. That is, for each county in the USA, this dataset provides the count and percentage of unemployed adults as well as the count and percentage of adults of various educational backgrounds. Each county was been assigned one of four locale categories (City, Suburb, Town, Rural) according to its 2013 Urban Influence Code and their descriptions provided in UIC_codes.csv. From the descriptions of each of the codes and the descriptions of the locales "City", "Suburb", "Town", and "Rural" provided on page 2 of the locale user manual (locale_user_manual.pdf), each county was assigned one of four locales.

The unemployment rate data includes the count and percentage of unemployed adults for each county in the USA for each year from 2000-2020. The median household income for 2019 is also included. The education level data includes the count and percentage of adults with less than a high school diploma, a high school diploma only, some college, and a bachelor's degree/four years of college or more for the years 1970, 1980, 1990, 2000, and 2019. The Urban Influence Code data includes the UIC and locale description of each county in the USA and the locale user manual has been included as a PDF as strictly a reference file, to understand how each county was assigned a locale within the unemployment.csv and education.csv files.

Data Sources

Source for the unemployment rate and education level data by county: "County-level Data Sets." USDA Economic Research Service, US Department of Agriculture. Access date: Sept 8, 2021. URL: https://www.ers.usda.gov/data-products/county-level-data-sets/

Source for Urban Influence Codes by county: "Urban Influence Codes." USDA Economic Research Service, US Department of Agriculture. Access date: Sept 8, 2021. URL: https://www.ers.usda.gov/data-products/urban-influence-codes/#:~:text=The%202013%20Urban%20Influence%20Codes,to%20metro%20and%20micropolitan%20areas.&text=An%20update%20of%20the%20Urban,is%20planned%20for%20mid%2D2023.

Inspiration

This dataset was created to be used as an additional data source for the LearnPlatform COVID-19 Impact on Digital Learning Kaggle competition, but is suitable for other analyses related to unemployment rate and education level in the USA.

Canada LFS: Unemployment Rate: sa: Quebec data was reported at 5.300 % in Feb 2025. This records a decrease from the previous number of 5.400 % for Jan 2025. Canada LFS: Unemployment Rate: sa: Quebec data is updated monthly, averaging 9.000 % from Jan 1976 (Median) to Feb 2025, with 590 observations. The data reached an all-time high of 18.200 % in Apr 2020 and a record low of 3.800 % in Nov 2022. Canada LFS: Unemployment Rate: sa: Quebec data remains active status in CEIC and is reported by Statistics Canada. The data is categorized under Global Database’s Canada – Table CA.G021: Labour Force Survey: Unemployment. [COVID-19-IMPACT]

Data of Thailand National Statistics Office (NSO) showed that the novel coronavirus (COVID-19) pandemic has impacted on jobs. These datasets are derived from major findings from Labour Force Survey (http://statbbi.nso.go.th/staticreport/Page/sector/th/02.aspx).

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.

As of May 2022, the unemployment rate in India was recorded at nearly ***** percent, a decrease from the previous month. While the unemployment rate had significantly declined over the course of 2021 since having peaked in **********, 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 ** 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, *** 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.

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 state and 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 2nd, 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:

Unemployment rate gap changes (β) by Social Vulnerability Index (SVI), overall and by svi theme, among rapid riser counties† (N = 585) before and after¶ a rapid rise in COVID-19 incidence --- United States.

Italy Labour Market: Unemployment Rate data was reported at 6.500 % in Feb 2025. This records a decrease from the previous number of 6.900 % for Jan 2025. Italy Labour Market: Unemployment Rate data is updated monthly, averaging 8.766 % from Jan 2004 (Median) to Feb 2025, with 254 observations. The data reached an all-time high of 14.427 % in Nov 2014 and a record low of 5.200 % in Aug 2024. Italy Labour Market: Unemployment Rate data remains active status in CEIC and is reported by Italian National Institute of Statistics. The data is categorized under Global Database’s Italy – Table IT.G035: Activity, Employment and Unemployment. [COVID-19-IMPACT]

UnemploymentRate

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: April 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: June 24th, 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 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: