Data obtained from ILOSTAT website. Collated various datasets from covid monitoring section. Most of the estimates are from 2020.

Description about columns: 1. country - Name of Country 2. total_weekly_hours_worked(estimates_in_thousands) - Total weekly hours worked of employed persons 3. percentage_of_working_hrs_lost(%) - Percentage of hours lost compared to the baseline (4th quarter of 2019) 4. percent_hours_lost_40hrs_per_week(thousands) - Percentage of hours lost compared to the baseline (4th quarter of 2019) expressed in full-time equivalent employment losses. This measure is constructed by dividing the number of weekly hours lost due to COVID-19 and dividing them by 40. 5. percent_hours_lost_48hrs_per_week(thousands) - Percentage of hours lost compared to the baseline (4th quarter of 2019) expressed in full-time equivalent employment losses. This measure constructed by dividing the number of weekly hours lost due to COVID-19 and dividing them by 48. 6. labour_dependency_ratio - Ratio of dependants (persons aged 0 to 14 + persons aged 15 and above that are either outside the labour force or unemployed) to total employment. 7. employed_female_25+_2019(estimates in thousands) - Employed female in 2019 who, during a specified brief period, were in one of the following categories: a) paid employment (whether at work or with a job but not at work); or b) self-employment (whether at work or with an enterprise but not at work). 8. employed_male_25+_2019(estimates in thousands) - Employed male in 2019 who, during a specified brief period, were in one of the following categories: a) paid employment (whether at work or with a job but not at work); or b) self-employment (whether at work or with an enterprise but not at work). 9. ratio_of_weekly_hours_worked_by_population_age_15-64 - Ratio of total weekly hours worked to population aged 15-64.

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 2026. 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 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.

This paper presents a flow-based methodology for real-time unemployment rate projections and shows that this approach performed considerably better at the onset of the COVID-19 recession in spring 2020 in predicting the peak unemployment rate as well as its rapid decline over the year. It presents an alternative scenario analysis for 2021 based on this methodology and argues that the unemployment rate is likely to end slightly below 5 percent by the end of 2021. The predictive power of the methodology comes from its combined use of real-time data with the flow approach.

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

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 underemployment rate, the percent of employed people who are working part-time but prefer to be working full-time, moves closely with the unemployment rate, rising during recessions and falling during expansions. Following the Great Recession, the underemployment rate had stayed persistently elevated when compared to the unemployment rate, that is, until the COVID-19 recession. Since then, it has been consistent with its pre-2008 levels. We find that changes in relative industry size account for essentially none of the underemployment rate increase after the Great Recession nor the underemployment rate decrease after the COVID-19 recession. Based on this finding, we do not expect the underemployment rate to revert to its pre-COVID-19 levels if industry composition reverts to its pre-COVID-19 structure.

The open unemployment rate in Mexico amounted to *** percent in June 2020, a *** percentage point increase compared to a month earlier. Open unemployment refers to the economically active population that do not have a job but are looking for and available to work. In March 2020, Mexico's unemployment rate stood at *** percent. On February 29, 2020 the first two cases of coronavirus (COVID-19) were reported in Mexico, leading authorities to take containment measures in the following weeks to halt the spread of the virus.

Germany Labour Force: Unemployment Rate: Total data was reported at 3.500 % in Oct 2025. This records a decrease from the previous number of 4.000 % for Sep 2025. Germany Labour Force: Unemployment Rate: Total data is updated monthly, averaging 4.000 % from Jan 2007 (Median) to Oct 2025, with 226 observations. The data reached an all-time high of 9.600 % in Feb 2007 and a record low of 2.800 % in Oct 2019. 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.G: Labour Force: ILO Concept. [COVID-19-IMPACT]

United States Unemployment data was reported at 6,580.000 Person th in Apr 2025. This records a decrease from the previous number of 7,242.000 Person th for Mar 2025. United States Unemployment data is updated monthly, averaging 6,579.500 Person th from Jan 1948 (Median) to Apr 2025, with 928 observations. The data reached an all-time high of 22,504.000 Person th in Apr 2020 and a record low of 1,480.000 Person th in Oct 1952. United States Unemployment 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.G: Current Population Survey: Unemployment. [COVID-19-IMPACT]

ObjectiveTo quantify the effect of the unemployment created by COVID-19 on access to (sales of) statin drugs in the United States population.MethodsApproximately half a billion transactions for statin drugs in the United States between January 2018 and September 2020 are analyzed. We studied the potential causal relation between abnormal levels of unemployment during the first wave of COVID-19 in the U.S. and abnormal levels of sales of statin products (both variables defined at the state/week level). Variables are analyzed using the Two-Stage Least Squares (2SLS) method, which exploits comparisons of statin sales between states where, given the occupational distribution of their workforce, unemployment was more structurally vulnerable to mobility restrictions derived from COVID-19 against states where it was less structurally vulnerable.ResultsWhile we do not find unemployment effects on statin sales on most of the population, our estimates link COVID-fueled unemployment with a sharp sales reduction among Medicaid-insured populations, particularly those in working age. For the period between March and August of 2020, these estimates imply a 31% drop of statin sales among this population.DiscussionCOVID-fueled unemployment may have had a negative and significant effect on access to statin populations among Medicaid-insured populations.

Abstract This paper analyzes the economic impact of the COVID-19 pandemic on a small tourism dependent open economy. The lockdown affected both the demand side and the supply side of the economy, as production of goods and services dramatically dropped due to firms’ shutdowns, broken supply chains, or bankruptcies, and aggregate demand diminished due to lower consumer confidence and investment cutbacks, accompanied by a dramatic fall in international tourism demand, in particular due to travel restrictions. We look on these supply and demand changes through the lens of a macroeconomic model of a small open economy, comprising an industrial and a tourism sector. For this purpose, we modify Schubert’s (2013) model by introducing a multiple shock which reflects (i) reduced sectoral productivities due to, e.g., broken supply chains, (ii) a drop in employment due to firms’ lockdowns, and (iii) a sharp decline in international tourism demand. We find that the multiple shock leads to an immediate drop in GDP and a boost of the short-run unemployment rate, followed by a gradual transition back to steady state. The adverse effects on the tourism sector are the more severe the slower international tourism demand reverts to pre-crisis levels, but they do not strongly spill over to the industrial sector. Furthermore, even if international tourism demand recovers quickly, the effects on the industrial sector barely change. The length of the industrial sector’s recovery basically depends on the speed of restoring its sectoral productivity rather than on international tourism demand. The reason for this result can be found in the absorbing effect of the relative price of tourism services in terms of the industrial good.

Check our data versus labor surveys.

📊 Dataset Description:

This dataset is sourced from the U.S. Bureau of Labor Statistics (BLS) Employment Situation – Table A-42. It provides detailed insights into employment and unemployment trends by educational attainment for individuals aged 25 years and older in the United States.

🌐 Source:

~**Publisher:** U.S. Bureau of Labor Statistics (BLS)

~**Table Reference:** CPS Table A-42 – Unemployed persons by duration of unemployment

~**URL:** https://www.bls.gov/web/empsit/cpseea42.htm

~**Update Frequency:** Monthly (as part of the Employment Situation release)

📑 Dataset Overview:

---The dataset includes:

~Year and Month of observation

~Employment and Unemployment counts

~Unemployment rates categorized by educational attainment, such as:

~Less than a high school diploma

~High school graduates, no college

~Some college or associate degree

~Bachelor’s degree and higher

🎯 Potential Uses:

~Analyze how education level impacts unemployment rates.

~Study long-term labor market trends across different demographics.

~Build forecasting models for employment/unemployment rates.

~Perform policy analysis to understand the role of education in job security.

⚠️ Notes:

~All values are based on the Current Population Survey (CPS) conducted by the U.S. Census Bureau for the BLS.

~The dataset may contain seasonally adjusted and non-adjusted values.

~Numbers represent civilian noninstitutional population, 25 years and older.

United States Unemployment: sa data was reported at 7,165.000 Person th in Apr 2025. This records an increase from the previous number of 7,083.000 Person th for Mar 2025. United States Unemployment: sa data is updated monthly, averaging 6,602.500 Person th from Jan 1948 (Median) to Apr 2025, with 928 observations. The data reached an all-time high of 23,084.000 Person th in Apr 2020 and a record low of 1,596.000 Person th in May 1953. United States Unemployment: sa 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.G: Current Population Survey: Unemployment: Seasonally Adjusted. [COVID-19-IMPACT]

Unemployment numbers and rates for those aged 16 or over. The unemployed population consists of those people out of work, who are actively looking for work and are available to start immediately.

Unemployed numbers and rates also shown for equalities groups, by age, sex, ethnic group, and disability.

The data are taken from the Labour Force Survey and Annual Population Survey, produced by the Office for National Statistics.

The data are produced monthly on a rolling quarterly basis. The month shown is the month the quarter ends on.

The International Labour Organization defines unemployed people as: without a job, want a job, have actively sought work in the last 4 weeks and are available to start work in the next 2 weeks, or, out of work, have found a job and are waiting to start it in the next 2 weeks.

The figures in this dataset are adjusted to compensate for seasonal variations in employment (seasonally adjusted).

Data by equalities groups has a longer time lag and is only available quarterly from the Annual Population Survey, which is not seasonally adjusted.

Useful links

Click here for Regional labour market statistics from the Office for National Statistics.

Click here for Labour market statistics from the Office for National Statistics.

See here for GLA Economics' Labour Market Analysis.

See here for Economic Inactivity statistics.

See here for Employment rates.

This dataset is one of the Greater London Authority's measures of Economic Fairness. Click here to find out more.

The experts feared that social distancing as the foremost measure to prevent the further expansion of the coronavirus (COVID-19) pandemic, would lead to job cuts in Russia in 2020. Thus, the most feasible employment reduction rate of between 10 and 15 percent could leave from five to eight million people without a job countrywide.

Coronavirus impact on the Russian economy 

The coronavirus-induced crisis, which was enhanced by the drop of crude oil prices had a drastic impact on the Russian economy, the whole effect of which is yet to be seen in the coming months and years. The expected GDP loss for 2020 in Russia was estimated at around four percent, considering that the critical phase of the crisis and the negative manifestations would affect only 2020. For scenarios with a longer period of COVID-19 impact, the forecast was less optimistic. Shopping malls were the most affected businesses in the Russian capital during the lockdown.

 Coronavirus (COVID-19) outbreak in Russia   

While there were some cases of coronavirus reported in January 2020 in the Russian territory, outbreak of the disease in the country started a bit later, in March 2020. Up to date, there were roughly 4.4 million cases of coronavirus confirmed countrywide, roughly three fourths of which has already recovered, and over 27 thousand died as a result of COVID-19. The city of Moscow has been accounting for the highest number of reported cases in the country since the beginning of the pandemic.

For further information about the coronavirus (COVID-19) pandemic, please visit our dedicated Facts and Figures page.

Reason for Unemployment Data

  Current population Survey results showing unemployment data detailing the reason for unemployment. Presented in thousands; seasonally adjusted, quarterly from 2017 to present. About the BLS Unemployment Data including Current Population Survey Demographic Breakdowns: Links to several different datasets, including Current Population Survey results showing seasonally adjusted unemployment data broken out by ethnicity and age, reason for unemployment, and duration of employment prior to unemployment for years including 2017-2019.  Other datasets show over-the-year percent change in the third month's employment level and taxable wages by industry for a given quarter at the County, State, and MSA level yearly from 1990 - present.
  Geography Level: NationalItem Vintage: Not Available
  Update Frequency: N/AAgency: BLSAvailable File Type: Excel 

  Return to Other Federal Agency Datasets Page

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]