Unemployment Rate in the United States decreased to 4.10 percent in June from 4.20 percent in May 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.

In April 2025, the agriculture and related private wage and salary workers industry had the highest unemployment rate in the United States, at eight percent. In comparison, government workers had the lowest unemployment rate, at 1.8 percent. The average for all industries was 3.9 percent. U.S. unemployment There are several factors that impact unemployment, as it fluctuates with the state of the economy. Unfortunately, the forecasted unemployment rate in the United States is expected to increase as we head into the latter half of the decade. Those with a bachelor’s degree or higher saw the lowest unemployment rate from 1992 to 2022 in the United States, which is attributed to the fact that higher levels of education are seen as more desirable in the workforce. Nevada unemployment Nevada is one of the states with the highest unemployment rates in the country and Vermont typically has one of the lowest unemployment rates. These are seasonally adjusted rates, which means that seasonal factors such as holiday periods and weather events that influence employment periods are removed. Nevada's economy consists of industries that are currently suffering high unemployment rates such as tourism. As of May 2023, about 5.4 percent of Nevada's population was unemployed, possibly due to the lingering impact of the coronavirus pandemic.

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.

Unemployment Rate in Japan remained unchanged at 2.50 percent in May. This dataset provides the latest reported value for - Japan Unemployment Rate - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

The table shows the latest data for the unemployment rate available from the national authorities. Note that these numbers are updated daily. The table also shows the unemployment rate three months ago and one year ago. The unemployment rate is defined as the number of people who don't have a job and are actively look for one as percent of the labor force. The labor force includes the employed and the unemployed people. Note that these categories do not include the so-called discouraged workers. These are people who can work and want to work but are not actively looking as they believe that they cannot find a job. Hence, besides unemployment, it is also useful to look at employment in order to evaluate the health of the labor market. The unemployment rate includes three components: the structural unemployment that is generated as some sectors of the economy decline and others expand; the frictional unemployment that captures people who are between jobs for personal reasons; and the cyclical unemployment that is related to the business cycle. The more flexible the labor market in terms of hiring and firing and setting wages, the lower the first two components of the unemployment rate. If interested, you can read our brief article on unemployment.

Unemployment Rate in China remained unchanged at 5 percent in June. This dataset provides - China Unemployment Rate - actual values, historical data, forecast, chart, statistics, economic calendar and news.

Correlation between the unemployment rate and number of articles.

Unemployment Rate in Turkey decreased to 8.40 percent in May from 8.60 percent in April of 2025. This dataset provides the latest reported value for - Turkey Unemployment Rate - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

In 1990, the unemployment rate of the United States stood at 5.6 percent. Since then there have been many significant fluctuations to this number - the 2008 financial crisis left millions of people without work, as did the COVID-19 pandemic. By the end of 2022 and throughout 2023, the unemployment rate came to 3.6 percent, the lowest rate seen for decades. However, 2024 saw an increase up to four percent. For monthly updates on unemployment in the United States visit either the monthly national unemployment rate here, or the monthly state unemployment rate here. Both are seasonally adjusted. UnemploymentUnemployment is defined as a situation when an employed person is laid off, fired or quits his work and is still actively looking for a job. Unemployment can be found even in the healthiest economies, and many economists consider an unemployment rate at or below five percent to mean there is 'full employment' within an economy. If former employed persons go back to school or leave the job to take care of children they are no longer part of the active labor force and therefore not counted among the unemployed. Unemployment can also be the effect of events that are not part of the normal dynamics of an economy. Layoffs can be the result of technological progress, for example when robots replace workers in automobile production. Sometimes unemployment is caused by job outsourcing, due to the fact that employers often search for cheap labor around the globe and not only domestically. In 2022, the tech sector in the U.S. experienced significant lay-offs amid growing economic uncertainty. In the fourth quarter of 2022, more than 70,000 workers were laid off, despite low unemployment nationwide. The unemployment rate in the United States varies from state to state. In 2021, California had the highest number of unemployed persons with 1.38 million out of work.

Youth unemployment stood at 9.7 percent in February 2025. Seasonal adjustment is a statistical method for removing the seasonal component of a time series that is used when analyzing non-seasonal trends. The unemployment rate by state can be found here, and the annual national unemployment rate can be found here. Youth unemployment in the United States The United States Bureau of Labor Statistics track unemployment of persons between the ages of 16 and 24 years each month. In analyzing the data, the Bureau of Labor Statistics performed a seasonal adjustment—removing seasonal influences from the time series, such that one month’s rate of unemployment could be analyzed in comparison with another month’s rate of unemployment. During the period in question, youth unemployment ranged from a high of 9.9 percent in April 2021, to a low of 6.5 percent in April 2023. The national youth unemployment rate can be compared to the monthly national unemployment rate in the United States, although youth unemployment tends to be much higher due to higher rates of participation in education. In May 2023, U.S. unemployment was at 3.7 percent, compared with 7.4 percent amongst those 16 to 24 years old. Additionally, as of May 2023, Nevada had the highest state unemployment rate of all U.S. states, at 5.4 percent.

ABSTRACT This article aims to investigate the long-term relationships between unemployment and labor force participation in the metropolitan areas of Porto Alegre and Salvador from 1997 to 2014, using Johansen’s cointegration methodology. The research was conducted with data from the Employment and Unemployment Survey - the only in the country that allows working with quarterly time series for these two metropolitan areas throughout the study period. In the metropolitan area of Salvador, both aggregated and gender-segmented data showed long-term relationships between unemployment and labor force participation rate; whereas in the metropolitan area of Porto Alegre, such a relationship was found only for the female segment. Estimates of the cointegration vectors showed a direct relationship between unemployment and labor force participation in both metropolitan areas, denoting the occurrence of the added worker effect.

Socioeconomic indicators like the poverty rate, population change, unemployment rate, and education levels vary across the nation. ERS has compiled the latest data on these measures into a mapping and data display/download application that allows users to identify and compare States and counties on these indicators.This record was taken from the USDA Enterprise Data Inventory that feeds into the https://data.gov catalog. Data for this record includes the following resources: Poverty Population Unemployment Education Web page with links to Excel files For complete information, please visit https://data.gov.

This analysis presents a rigorous exploration of financial data, incorporating a diverse range of statistical features. By providing a robust foundation, it facilitates advanced research and innovative modeling techniques within the field of finance.

What is the relationship between unemployment and inflation?

Financial data:

• Historical daily stock prices (open, high, low, close, volume)

• Fundamental data (e.g., market capitalization, price to earnings P/E ratio, dividend yield, earnings per share EPS, price to earnings growth, debt-to-equity ratio, price-to-book ratio, current ratio, free cash flow, projected earnings growth, return on equity, dividend payout ratio, price to sales ratio, credit rating)

• Technical indicators (e.g., moving averages, RSI, MACD, average directional index, aroon oscillator, stochastic oscillator, on-balance volume, accumulation/distribution A/D line, parabolic SAR indicator, bollinger bands indicators, fibonacci, williams percent range, commodity channel index)

Machine learning features:

• Feature engineering based on financial data and technical indicators

• Sentiment analysis data from social media and news articles

• Macroeconomic data (e.g., GDP, unemployment rate, interest rates, consumer spending, building permits, consumer confidence, inflation, producer price index, money supply, home sales, retail sales, bond yields)

Potential Applications:

• Stock price prediction

• Portfolio optimization

• Algorithmic trading

• Market sentiment analysis

• Risk management

Use Cases:

• Researchers investigating the effectiveness of machine learning in stock market prediction

• Analysts developing quantitative trading Buy/Sell strategies

• Individuals interested in building their own stock market prediction models

• Students learning about machine learning and financial applications

Additional Notes:

• The dataset may include different levels of granularity (e.g., daily, hourly)

• Data cleaning and preprocessing are essential before model training

• Regular updates are recommended to maintain the accuracy and relevance of the data

Graph and download economic data for Unemployment Rate - 20 Yrs. & over (LNU04000024) from Jan 1948 to Jun 2025 about 20 years +, unemployment, rate, and USA.

The following datasets are associated to the paper the "Social Fingerprints of Unemployment" Llorente A, Garcia-Herranz M, Cebrian M, Moro E (2015) Social Media Fingerprints ofUnemployment. PLoS ONE 10(5): e0128692. doi: 10.1371/journal.pone.0128692 http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0128692

Description of the files in this folder This folder contains two (2) filesTij.csvtable_municipalities.csv

Tij.csvThis files contains the matrix Tij, i.e. the total number of trips between different municipalities in our database. The file has four columnsi -> the municipality origin id of the tripsj -> the municipality destination id of the tripsTij -> the number of trips between i and j (symmetric)dij -> the distance between municipalities table_municipalities.csvThis files contains the demographic, unemployment and Twitter variables for each of the municipalities considered. The file has 16 columnsid -> the official id of the municipalitypobTOT -> total population of the municipality (as of Jan 2013)pobACT -> total active (employable) population of the municipalitypobUNEMP -> total population unemployed in the municipalitypobACTYOUNG -> total active young (below 25 years) population of the municipalitypobUNEMPYOUNG -> total young (below 25 years) population unemployed in the municipalityntwsTOT -> total number of tweets geolocalized in the municipalityntwsWDAY -> total number of tweets from Monday to Friday (working days) in the municipalityS-geo -> Geographical Entropy of the trips from/to the municipalityS-social -> Social Entropy of the communications fromt/to the municipalitynusers -> Total number of users with "home" in the municipality (see paper text)morning -> Total number of tweets between 8-10am during working days in the municipalityafternoon -> Total number of tweets between 3-5pm during working days in the municipalitynight -> Total number of tweets between 12-3am during working days in the municipalitynmiss -> Total number of misspellers in the municipalityemp -> Total number of tweets mentioning "employment"

Id's of the municipalities correspond with the official id's athttp://www.ine.es/daco/daco42/codmun/codmun10/10codmunmapa.htm

The downloadable ZIP file contains datasets and documentation.

Unemployment Rate in Philippines decreased to 3.90 percent in May from 4.10 percent in April of 2025. This dataset provides - Philippines Unemployment Rate - actual values, historical data, forecast, chart, statistics, economic calendar and news.

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.

This study, integrating sentiment, emotion, discourse, and timeline analyses together, conducts a corpus-based sentiment analysis of the news articles on unemployment from the New York Times in 2020, to capture the emotional dynamics conveyed by the newspaper as the pandemic-induced unemployment developed in the US. The results reveal that positive sentiment in the news articles on unemployment is significantly higher than negative sentiment. In emotion analysis, “trust” and “anticipation”rank the first and second among the eight emotions, while “fear”and “sadness” top the negative emotions. Complemented with a discourse analysis approach, the study reveals that the change of the sentiments and emotions over time is linked with the evolution of the pandemic and unemployment, the policy response as well as the protests against ethnic inequalities. This study highlights the important role mainstream news media play in information dissemination and solution-focused reportage at the time of severe crisis.This dataset contains 14 documents for the data of 2 sentiments and 8 emotions, generated by Python. It includes NRC lexicon categories for the sentiments and emotions in the study (data1-10), top 10 high-frequency words associated to the sentiments and emotions in each of the 12 subcorpora (data11-12), and monthly values of the sentiments and emotions in 2020 (data 13-14).

The unemployment rate of the United Kingdom was 4.7 percent in May 2025, an increase from the previous month. Before the arrival of the COVID-19 pandemic, the UK had relatively low levels of unemployment, comparable with the mid-1970s. 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.