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.

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 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 forecast from May 2025, the unemployment rate in Italy could reach 5.9 percent by the end of the year, 3.5 percentage points less than in 2021, when the COVID-19 outbreak had a disastrous impact on the labor market. The rate is then expected to remain stable in 2026. 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.

Full edition for scientific use. The study "Causes and Consequences of Unemployment in the COVID-19 pandemic" addresses the financial and psychological consequences of unemployment for those affected in the second year of the Corona pandemic. The study is based on an Austria-wide standardised telephone survey of 1844 people aged 15 to 64. The interviews took place between 29 May and 11 July 2021. 1215 interviewees were unemployed at the time of the interview, 332 of them long-term unemployed, 629 interviewees were employed.

Germany Labour Force: Unemployment Rate: Total data was reported at 3.700 % in Mar 2025. This records an increase from the previous number of 3.600 % for Feb 2025. Germany Labour Force: Unemployment Rate: Total data is updated monthly, averaging 4.100 % from Jan 2007 (Median) to Mar 2025, with 219 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]

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: October 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 theU.S. Bureau of Labor Statistics.Data downloaded: December 20, 2024Local 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 2021TIGER boundariesfrom theU.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 belowfrom BLS:

This dataset constitutes a panel follow-up to the 2016/2017 Suriname Survey of Living Conditions. It measures welfare related variables before and after the onset of the COVID-19 pandemic including labor market outcomes, financial literacy, and food security. The survey was executed in August 2020. The Suriname COVID-19 Survey is a project of the Inter-American Development Bank (IDB). It collected data on critical socioeconomic topics in the context of the COVID-19 pandemic to support policymaking and help mitigate the crisis impacts on the populations welfare. The survey recontacted households interviewed in 2016/2017 by the Suriname Survey of Living Conditions (SSLC) and was conducted by phone due to the mobility restrictions and social distancing measures in place. It interviewed 1,016 households during August 2020 and gathered information about disease transmission, household finances, labor, income, remittances, spending, and social protection programs. Data and documentation of the 2016/2017 Suriname Survey of Living Conditions can be found at: https://publications.iadb.org/en/suriname-survey-living-conditions-2016-2017. The survey was designed and implemented by Sistemas Integrales. This publication describes the main methodological aspects, such as sample design, estimation procedures, topics covered by the questionnaire, field organization and quality control. It also presents the structure and codebook for the two resulting publicly available datasets.

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]

The data annex includes the Excel file "Ebbinghaus-Lehner-2022-CuiBono_Transfer_Replication_Data.xlsx" with the following sheets: Figures Fig-1: Figure 1. Business support and labour support logics. Fig-2: Figure 2. Unemployment rate in Europe and USA (OECD) 2020. Fig-3: Figure 3. Job retention rates (% dependent labour force) in Germany, France, Italy and United Kingdom, 2020–2021. Fig-4: Figure 4. Effectiveness of short-time work schemes varies between welfare states. Fig-5: Figure 5. Mapping business support and labour support logics to welfare state regimes. Table Table-1: Table 1. Characteristics of job retention schemes.

Unemployment Rate in China increased to 5.30 percent in August from 5.20 percent in July of 2025. This dataset provides - China Unemployment Rate - actual values, historical data, forecast, chart, statistics, economic calendar and news.

Analysis of ‘Unemployment in India’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/gokulrajkmv/unemployment-in-india on 30 September 2021.

--- Dataset description provided by original source is as follows ---

Context

The story behind this datasets is how lock-down affects employment opportunities and how the unemployment rate increases during the Covid-19.

Content

This dataset contains the unemployment rate of all the states in India

Region = states in India Date = date which the unemployment rate observed Frequency = measuring frequency (Monthly)
Estimated Unemployment Rate (%) = percentage of people unemployed in each States of India Estimated Employed = percentage of people employed Estimated Labour Participation Rate (%) = labour force participation rate by dividing the number of people actively participating in the labour force by the total number of people eligible to participate in the labor force force

Acknowledgements

I wouldn't be here without the help of my friends. I owe you thanks !!

Inspiration

questions? 1. How Covid-19 affects the employment 2. how far the unemployment rate will go

source of datasets https://unemploymentinindia.cmie.com/

--- Original source retains full ownership of the source dataset ---

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/

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.

This archive provides the code and data to replicate the analysis in "Unemployment Insurance Recipiency During the Covid-19 Pandemic" published in the National Tax Journal in 2023.

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.

Given the lack of potential vaccines and effective medications, non-pharmaceutical interventions are the major option to curtail the spread of COVID-19. An accurate estimate of the potential impact of different non-pharmaceutical measures on containing, and identify risk factors influencing the spread of COVID-19 is crucial for planning the most effective interventions to curb the spread of COVID-19 and to reduce the deaths. Additive model-based bivariate causal discovery for scalar factors and multivariate Granger causality tests for time series factors are applied to the surveillance data of lab-confirmed Covid-19 cases in the US, University of Maryland Data (UMD) data, and Google mobility data from March 5, 2020 to August 25, 2020 in order to evaluate the contributions of social-biological factors, economics, the Google mobility indexes, and the rate of the virus test to the number of the new cases and number of deaths from COVID-19. We found that active cases/1,000 people, workplaces, tests done/1,000 people, imported COVID-19 cases, unemployment rate and unemployment claims/1,000 people, mobility trends for places of residence (residential), retail and test capacity were the popular significant risk factor for the new cases of COVID-19, and that active cases/1,000 people, workplaces, residential, unemployment rate, imported COVID cases, unemployment claims/1,000 people, transit stations, mobility trends (transit), tests done/1,000 people, grocery, testing capacity, retail, percentage of change in consumption, percentage of working from home were the popular significant risk factor for the deaths of COVID-19. We observed that no metrics showed significant evidence in mitigating the COVID-19 epidemic in FL and only a few metrics showed evidence in reducing the number of new cases of COVID-19 in AZ, NY and TX. Our results showed that the majority of non-pharmaceutical interventions had a large effect on slowing the transmission and reducing deaths, and that health interventions were still needed to contain COVID-19.

In June 2025, 2.6 percent of the economically active population in Mexico was considered unemployed, down from 4.61 percent registered in the same month of 2021. In June 2020, just a few months after the COVID-19 outbreak, the unemployment rate reached its highest monthly value since the beginning of 2019, at 5.32 percent.