Unemployment Rate in the United States increased to 4.40 percent in September from 4.30 percent in August of 2025. This dataset provides the latest reported value for - United States Unemployment Rate - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

Creating datasets like this takes significant time and effort. If you found this dataset useful, a kind upvote would be greatly appreciated!!

This dataset provides a 30 year comprehensive view of global employment, unemployment, and GDP trends from 1991 to 2022. It includes data of approx 183 countries on employment distribution across agriculture, industry, and services sectors, alongside unemployment rates and GDP figures.

What You Can Do with This Dataset: This dataset opens up several possibilities for analysis and exploration. You can study long-term trends in employment, unemployment, and GDP across countries and regions, and visualize how labor distribution has shifted from agriculture to services over the years. It also allows you to examine the impact of major global events, such as the 2008 Financial Crisis and the 2020 COVID-19 pandemic, on economic and employment patterns. Furthermore, the dataset can be used for time-series forecasting and predictive modeling, helping to estimate future employment trends and GDP growth.

Description of columns:

Country Name – The name of the country.

Year – The year of observation (1991–2022).

Employment Sector: Agriculture – Percentage of total employment in agriculture.

Employment Sector: Industry – Percentage of total employment in industry.

Employment Sector: Services – Percentage of total employment in services.

Unemployment Rate – Percentage of the labor force that is unemployed.

GDP (in USD) – Gross Domestic Product of the country (in U.S. dollars).

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.

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.

Unemployment Rate in Canada decreased to 6.90 percent in October from 7.10 percent in September of 2025. This dataset provides - Canada Unemployment Rate - actual values, historical data, forecast, chart, statistics, economic calendar and news.

The number of Americans applying for help from the Pandemic Unemployment Assistance scheme, which covers workers that do not qualify for initial claims, decreased to 0.897 thousand in the week ending December 25th from 1.554 thousand in the prior week. This dataset provides - United States Pandemic Unemployment Assistance Claims- actual values, historical data, forecast, chart, statistics, economic calendar and news.

US Covid-19 Cases, Deaths and Mobility by State/County

Analyzing the Impact of the Pandemic on Low-Income Populations

By Liz Friedman [source]

About this dataset

Welcome to the Opportunity Insights Economic Tracker! Our goal is to provide a comprehensive, real-time look into how COVID-19 and stabilization policies are affecting the US economy. To do this, we have compiled a wide array of data points on spending and employment, gathered from several sources.

This dataset includes daily/weekly/monthly information at the state/county/city level for eight types of data: Google Mobility; Low-Income Employment and Earnings; UI Claims; Womply Merchants and Revenue; as well as weekly Math Learning from Zearn. Additionally, three files- Accounting for Geoids-State/County/City provide crosswalks between geographic areas that can be merged with other files having shared geographical levels.

Our goal here is to enable data users around the world to follow economic conditions in the US during this tumultuous period with maximum clarity and precision. We make all our datasets freely available so if you use them we kindly ask you attribute our work by linking or citing both our accompanying paper as well as this Economic Tracker at https://tracktherecoveryorg By doing so you are also agreeing to uphold our privacy & integrity standards which commit us both to individual & business confidentiality without compromising on independent nonpartisan research & policy analysis!

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How to use the dataset

This dataset provides US COVID-19 case and death data, as well as Google Community Mobility Reports, on the state/county level. Here is how to use this dataset:

• Understand the file structure: This dataset consists of three main files: 1) US Cases & Deaths by State/County, 2) Google Community Mobility Reports, and 3) Data from third-parties providing small business openings & revenue information and unemployment insurance claim data (Low Inc Earnings & Employment, UI Claims and Womply Merchants & Revenue).
• Select your Subset: If you are interested in particular types of data (e.g., mobility or employment), select the corresponding files from within each section based on your geographic area of interest – national, state or county level – as indicated in each filename.
• Review metadata variables: Become familiar with the provided variables so that you can select which ones you need to explore further in your analysis. For example, if analyzing mobility trends at a city level look for columns such as ‘Retailer_and_recreation_percent_change’ or ‘Transit Stations Percent Change’; if focusing on employment decline look for columns such pay or emp figures that align with industries of interest to you such as low-income earners (emp_{inclow},pay_{inclow}).
• Unify dateformatting across row values : Convert date formats into one common unit so that all entries have consistent formatting if necessary; for exampe some entries may display dates using YYYY/MM/DD notation while others may use MM//DD//YY format depending on their source datasets; make sure to review column labels carefully before converting units where needed..

• Merge datasets where applicable : Utilize GeoID crosswalks to combine multiple sets with same geographical coverageregionally covering ; example might be combining low income earnings figures with specific county settings by reference geo codes found in related documents like GeoIDs-County .
  6 . Visualise Data : Now that all the different measures have been reviewed can begin generating charts visualize findings . This process may include cleaning up raw figures normalizing across currency formats , mapping geospatial locations others ; once ready create bar graphs line charts maps other visual according aggregate output desired Insightful representations at this stage will help inform concrete policy decisions during outbreak recovery period..

  Remember to cite

Research Ideas

• Estimating the Impact of the COVID-19 Pandemic on Small Businesses - By comparing county-level Womply revenue and employment data with pre-COVID data, policymakers can gain an understanding of the economic impact that COVID has had on local small businesses.
• Analyzing Effects of Mobility Restrictions - The Google Mobility data provides insight into geographic areas where...

The unemployment rate of the United Kingdom was five percent in September 2025, up from 4.8 percent in the previous month, and the highest rate of unemployment since 2021. Before the arrival of the COVID-19 pandemic, the UK had relatively low levels of unemployment. Between January 2000 and the most recent month, unemployment was highest in November 2011, when the unemployment rate hit 8.5 percent. Will unemployment continue to rise in 2025? Although low by historic standards, there has been a noticeable uptick in the UK's unemployment rate, with other labor market indicators also pointing to further loosening. In December 2024, the number of job vacancies in the UK fell to its lowest level since May 2021, while payrolled employment declined by 47,000 compared with November. Whether this is a continuation of a broader cooling of the labor market since 2022 or a reaction to more recent economic developments, such as upcoming tax rises for employers, remains to be seen. Forecasts made in late 2024 suggest that the unemployment rate will remain relatively stable in 2025, averaging out at 4.1 percent and falling again to four percent in 2026.
Demographics of the unemployed As of the third quarter of 2024, the unemployment rate for men was slightly higher than that of women, at 4.4 percent, compared to 4.1 percent. During the financial crisis at the end of the 2000s, the unemployment rate for women peaked at a quarterly rate of 7.7 percent, whereas for men, the rate was 9.1 percent. Unemployment is also heavily associated with age, and young people in general are far more vulnerable to unemployment than older age groups. In late 2011, for example, the unemployment rate for those aged between 16 and 24 reached 22.3 percent, compared with 8.2 percent for people aged 25 to 34, while older age groups had even lower peaks during this time.

Unemployment Rate in South Africa decreased to 31.90 percent in the third quarter of 2025 from 33.20 percent in the second quarter of 2025. This dataset provides - South Africa Unemployment Rate - actual values, historical data, forecast, chart, statistics, economic calendar and news.

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.

The economic landscape of the United Kingdom has been significantly shaped by the intertwined issues of Brexit, COVID-19, and their interconnected impacts. Despite the country’s robust and diverse economy, the disruptions caused by Brexit and the COVID-19 pandemic have created uncertainty and upheaval for both businesses and individuals. Recognizing the magnitude of these challenges, academic literature has directed its attention toward conducting immediate research in this crucial area. This study sets out to investigate key economic factors that have influenced various sectors of the UK economy and have broader economic implications within the context of Brexit and COVID-19. The factors under scrutiny include the unemployment rate, GDP index, earnings, and trade. To accomplish this, a range of data analysis tools and techniques were employed, including the Box-Jenkins method, neural network modeling, Google Trend analysis, and Twitter-sentiment analysis. The analysis encompassed different periods: pre-Brexit (2011-2016), Brexit (2016-2020), the COVID-19 period, and post-Brexit (2020-2021). The findings of the analysis offer intriguing insights spanning the past decade. For instance, the unemployment rate displayed a downward trend until 2020 but experienced a spike in 2021, persisting for a six-month period. Meanwhile, total earnings per week exhibited a gradual increase over time, and the GDP index demonstrated an upward trajectory until 2020 but declined during the COVID-19 period. Notably, trade experienced the most significant decline following both Brexit and the COVID-19 pandemic. Furthermore, the impact of these events exhibited variations across the UK’s four regions and twelve industries. Wales and Northern Ireland emerged as the regions most affected by Brexit and COVID-19, with industries such as accommodation, construction, and wholesale trade particularly impacted in terms of earnings and employment levels. Conversely, industries such as finance, science, and health demonstrated an increased contribution to the UK’s total GDP in the post-Brexit period, indicating some positive outcomes. It is worth highlighting that the impact of these economic factors was more pronounced on men than on women. Among all the variables analyzed, trade suffered the most severe consequences in the UK. By early 2021, the macroeconomic situation in the country was characterized by a simple dynamic: economic demand rebounded at a faster pace than supply, leading to shortages, bottlenecks, and inflation. The findings of this research carry significant value for the UK government and businesses, empowering them to adapt and innovate based on forecasts to navigate the challenges posed by Brexit and COVID-19. By doing so, they can promote long-term economic growth and effectively address the disruptions caused by these interrelated issues.

Labour Force Survey summary data, including employment, unemployment and economic inactivity levels and rates, UK, rolling three-monthly figures published monthly, non-seasonally adjusted. These are official statistics in development.

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.

A new International Labor Organization report suggests that more than one in six young people worldwide has stopped working since the start of the Covid-19 pandemic, while the rest have seen their working hours cut by almost a quarter. In Southeast Asia, unemployment rates have spiked to almost unprecedented levels because of the pandemic, which has devastated economies to varying degrees. Until now, official unemployment rates in the region have been enviably low. Before 2020, Cambodia’s unemployment rates had barely risen above the 2% mark since the early 1990s, while Vietnam’s had also been consistently at less than 2%, according to data from the United Nations Development Program (UNDP). For the last decade, Thailand’s jobless rate has hovered around 0.6%.

The Covid-19 pandemic has hit every country, every economy in a bad way. Many people lost their friends and relatives, but far more of them has been pushed to near death situation due to loss of livelihood. Unemployment rate has increased drastically since the start of the pandemic. The dataset consists of data pertaining to the unemployment rates in rural and urban India during the pandemic. Thanks to Centre for Monitoring Indian Economy for providing the data.

Unemployment rate, participation rate, and employment rate by educational attainment, gender and age group, annual.

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.

https://static01.nyt.com/images/2020/11/18/nyregion/00nyblind1/merlin_179220645_b77f46ff-a503-40b6-bf2b-4922a676e61b-superJumbo.jpg" alt=""> This dataset offers a comprehensive insight into the economic trajectories of nine major economies from the onset of the COVID-19 pandemic through the beginning of 2024. It encompasses crucial economic indicators and financial market data, covering aspects such as manufacturing and services performance, consumer sentiment, monetary policies, inflation rates, unemployment rates, and overall economic output. Additionally, it includes price data for each economy, with values compared against the dollar for clarity. With data spanning this period, the dataset provides valuable insights for analysts, researchers, and stakeholders into the impact of the pandemic and other significant events on these economies, facilitating an assessment of their resilience, challenges, and opportunities.

Countries included : Australia / Canada / China / Europe / Japan / New Zealand / Switzerland / United Kingdom / United States

Column Descriptions:

• Country : The name of the country.
• Date : The date format (e.g., YYYY-MM-DD).
• Manufacturing PMI : Purchasing Managers' Index (PMI) for the manufacturing sector, indicating the economic health and activity level of the manufacturing industry.
• Services PMI : Purchasing Managers' Index (PMI) for the services sector, indicating the economic health and activity level of the services industry.
• Consumer Confidence : A measure of consumer sentiment or confidence in the economy, indicating consumers' optimism or pessimism about their financial situation and the overall state of the economy.
• Interest Rates : The prevailing interest rates set by the central bank or monetary authority, which influence borrowing costs and investment decisions.
• CPI YoY : Consumer Price Index (CPI) Year-over-Year change, indicating the percentage change in the average price level of a basket of consumer goods and services over the previous year.
• Core CPI : Core Consumer Price Index (CPI), which excludes volatile items such as food and energy prices, providing a measure of underlying inflation trends.
• Unemployment Rate : The percentage of the labor force that is unemployed and actively seeking employment, indicating the health of the labor market.
• GDP YoY : Gross Domestic Product (GDP) Year-over-Year change, indicating the percentage change in the total value of goods and services produced by a country's economy.
• Ticker: Ticker symbol for the corresponding financial asset or index.
• Open: The opening price of the financial asset or index on the specified date.
• High: The highest price of the financial asset or index during the specified date.
• Low: The lowest price of the financial asset or index during the specified date.
• Close: The closing price of the financial asset or index on the specified date.

Unemployment Rate: India's unemployment rate has been a significant concern, with fluctuations over the years. As of my last knowledge update in January 2022, the unemployment rate was around 6-7%.

Rural-Urban Disparities: Unemployment is often higher in rural areas compared to urban areas, where there are more employment opportunities.

Youth Unemployment: India has a significant issue of youth unemployment. A large portion of the population is under the age of 30, and providing employment opportunities for this demographic is a challenge.

Underemployment: Many individuals in India are also affected by underemployment, where they are employed in jobs that are below their skill levels and pay less than their qualifications.

Informal Sector: A substantial portion of India's workforce is engaged in the informal sector, which lacks job security and social benefits.

Gender Disparities: There are notable gender disparities in unemployment rates, with women often facing higher rates of unemployment compared to men.

Education and Unemployment: Higher education levels do not always guarantee employment in India, leading to a mismatch between skills and job opportunities.

Government Initiatives: The Indian government has launched various schemes and initiatives to address unemployment, such as the Mahatma Gandhi National Rural Employment Guarantee Act (MGNREGA) and the Skill India program.

COVID-19 Impact: The COVID-19 pandemic had a significant impact on employment, leading to job losses and economic challenges.

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).