Graph and download economic data for Layoffs and Discharges: Total Nonfarm (JTSLDL) from Dec 2000 to Jun 2025 about discharges, layoffs, nonfarm, and USA.

Job Layoffs and Discharges in the United States decreased to 1604 Thousand in June from 1611 Thousand in May of 2025. This dataset includes a chart with historical data for the United States Job Layoffs And Discharges.

The tech industry had a rough start to 2024. Technology companies worldwide saw a significant reduction in their workforce in the first quarter of 2024, with over ** thousand employees being laid off. By the second quarter, layoffs impacted more than ** thousand tech employees. In the final quarter of the year around ** thousand employees were laid off. Layoffs impacting all global tech giants Layoffs in the global market escalated dramatically in the first quarter of 2023, when the sector saw a staggering record high of ***** thousand employees losing their jobs. Major tech giants such as Google, Microsoft, Meta, and IBM all contributed to this figure during this quarter. Amazon, in particular, conducted the most rounds of layoffs with the highest number of employees laid off among global tech giants. Industries most affected include the consumer, hardware, food, and healthcare sectors. Notable companies that have laid off a significant number of staff include Flink, Booking.com, Uber, PayPal, LinkedIn, and Peloton, among others. Overhiring led the trend, but will AI keep it going? Layoffs in the technology sector started following an overhiring spree during the COVID-19 pandemic. Initially, companies expanded their workforce to meet increased demand for digital services during lockdowns. However, as lockdowns ended, economic uncertainties persisted and companies reevaluated their strategies, layoffs became inevitable, resulting in a record number of *** thousand laid off employees in the global tech sector by the end of 2022. Moreover, it is still unclear how advancements in artificial intelligence (AI) will impact layoff trends in the tech sector. AI-driven automation can replace manual tasks leading to workforce redundancies. Whether through chatbots handling customer inquiries or predictive algorithms optimizing supply chains, the pursuit of efficiency and cost savings may result in more tech industry layoffs in the future.

Graph and download economic data for Layoffs and Discharges: Federal (JTU9100LDR) from Dec 2000 to Jun 2025 about discharges, layoffs, federal, and USA.

As of January 2024, the tech startup with the most layoffs was Amazon, with over 27 thousand layoffs, across five separate rounds of layoffs. It was followed by Meta and Google with around 21 thousand and 12 thousand job cuts announced respectively.

Layoffs in in the technology industry

Overall, layoffs across all industries began in 2020 due to the outbreak of the coronavirus (COVID-19) pandemic, with tech layoffs increasing in 2022. In the first quarter of 2023 alone, more than 167 thousand employees had been fired worldwide, a record number of job cuts in a single quarter and more than all of the layoffs announced in 2022 combined, marking a harsh start to of 2023 for the tech sector. From retail to finance and education, all sectors are suffering from this widespread downsizing. However, retail tech startups were hit the most, with almost 29 thousand layoffs announced as of September 2023. Most job losses happened in the United States, where tech giants like Amazon, Meta, and Google are based.

Reasons behind increasing tech layoffs

Layoffs in the technology sector started with the COVID-19 pandemic in 2020 when entire cities were in lockdown and mobility was restricted. Although restrictions loosened up in 2021, events such as the Russia-Ukraine war, the downturn in Chinese production, and rising inflation had a significant impact on the tech industry and continue to represent major concerns for tech companies. As a consequence, companies across the world have yet to overcome all economic challenges, examples of which are rising material and labor costs, as well as decreasing profit margins. To address such difficulties, tech companies have appointed business plans. For instance, in the United States, tech firms planned to focus more on consumer retention, automating software, and cutting operating expenses.

Challenger Job Cuts in the United States increased to 62075 Persons in July from 47999 Persons in June of 2025. This dataset provides the latest reported value for - United States Challenger Job Cuts - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

United States JOLTS: Separations Rates: Layoffs and Discharges (LD): NF data was reported at 1.000 % in May 2018. This records a decrease from the previous number of 1.100 % for Apr 2018. United States JOLTS: Separations Rates: Layoffs and Discharges (LD): NF data is updated monthly, averaging 1.300 % from Dec 2000 (Median) to May 2018, with 210 observations. The data reached an all-time high of 2.400 % in Jan 2009 and a record low of 0.800 % in Feb 2018. United States JOLTS: Separations Rates: Layoffs and Discharges (LD): NF data remains active status in CEIC and is reported by Bureau of Labor Statistics. The data is categorized under Global Database’s USA – Table US.G054: Job Openings and Labor Turnover Survey: Separation Rate.

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.

In February 2025, the number of job losers and persons who completed temporary jobs in the United States stood at about 3.3 million and is used when analyzing non-seasonal trends. The monthly unemployment rate can be found here.

In 2024, the gaming sector experienced a significant number of layoffs because of post-COVID industry contraction which has led to studio consolidation and ultimately, an estimated 14,800 video gaming employees losing their jobs. Additionally, 2023 had also not been kind to the industry, as already 10,500 game developers lost their jobs during industry layoffs during the year.

The Job Openings and Labor Turnover Survey (JOLTS) program provides national estimates of rates and levels for job openings, hires, and total separations. Total separations are further broken out into quits, layoffs and discharges, and other separations. Unadjusted counts and rates of all data elements are published by supersector and select sector based on the North American Industry Classification System (NAICS). The number of unfilled jobs—used to calculate the job openings rate—is an important measure of the unmet demand for labor. With that statistic, it is possible to paint a more complete picture of the U.S. labor market than by looking solely at the unemployment rate, a measure of the excess supply of labor. Information on labor turnover is valuable in the proper analysis and interpretation of labor market developments and as a complement to the unemployment rate. For more information and data visit: https://www.bls.gov/jlt/

In May 2025, the hiring rate in the United States was at 3.4 percent for total nonfarm industries. The seasonally adjusted total separations rate was at 3.3 percent. The data are seasonally adjusted. The separations figure includes voluntary quits, involuntary layoffs and discharges, and other separations, including retirements. Total separations also refer to as turnover.

Graph and download economic data for Unemployment Level - Job Losers on Layoff (LNS13023653) from Jan 1967 to Jul 2025 about job losers, layoffs, 16 years +, household survey, unemployment, and USA.

The average unemployment rate was six percent in Germany in 2024. Since 2005, the rate of unemployment has generally been declining, though a slight increase was evident in recent years. Unemployment in Germany and comparison with other countries Germany has a comparatively low unemployment rate compared to its European neighbors, and they are expected to stay at around three percent over the next few years. This is a result of the damage the economy suffered during the COVID-19 pandemic. During the lockdown, most businesses were closed, and many companies lost revenue meaning employees were let go. It is also possible that higher unemployment figures will continue into later years because of inflation and rising energy prices. There is also a slightly higher unemployment rate among men than there is among women. Social support Social support is money paid out to those who are unable to work for some reason, its purpose is to protect those who are most vulnerable. The status of being unemployed is defined as when an employed person is laid off, fired, or quits his work and is still looking for a job, this is what qualifies someone to receive a citizens allowance (Bürgergeld) in Germany. The payments are only made if you are unemployed and worked for the last 12 months. Otherwise, benefits are received in the form of Arbeitslosengeld II, also called Hartz IV, which distributes social payments to people without an income who cannot work to make a living. Since January 2023 though, Arbeitlosengeld has been replaced by Bürgergeld, since this is a new transition, it is still possible that people will still refer to the benefits as Arbeitlosengeld or Hartz IV.

Percentage of workforce laid off to adapt to the COVID-19 pandemic, by North American Industry Classification System (NAICS), business employment size, type of business, business activity and majority ownership.

To ensure respondent confidentiality, estimates below a certain threshold are suppressed. For Canada, Quebec, Ontario, Alberta and British Columbia suppression is applied to all data below 1,500. The threshold level for Newfoundland and Labrador, Nova Scotia, New Brunswick, Manitoba and Saskatchewan is 500, while in Prince Edward Island, estimates under 200 are suppressed. For census metropolitan areas (CMAs) and economic regions (ERs), use their respective provincial suppression levels mentioned above. Estimates are based on smaller sample sizes the more detailed the table becomes, which could result in lower data quality. Fluctuations in economic time series are caused by seasonal, cyclical and irregular movements. A seasonally adjusted series is one from which seasonal movements have been eliminated. Seasonal movements are defined as those which are caused by regular annual events such as climate, holidays, vacation periods and cycles related to crops, production and retail sales associated with Christmas and Easter. It should be noted that the seasonally adjusted series contain irregular as well as longer-term cyclical fluctuations. The seasonal adjustment program is a complicated computer program which differentiates between these seasonal, cyclical and irregular movements in a series over a number of years and, on the basis of past movements, estimates appropriate seasonal factors for current data. On an annual basis, the historic series of seasonally adjusted data are revised in light of the most recent information on changes in seasonality. Number of civilian, non-institutionalized persons 15 years of age and over who, during the reference week, were employed or unemployed. Estimates in thousands, rounded to the nearest hundred. Number of persons who, during the reference week, worked for pay or profit, or performed unpaid family work or had a job but were not at work due to own illness or disability, personal or family responsibilities, labour dispute, vacation, or other reason. Those persons on layoff and persons without work but who had a job to start at a definite date in the future are not considered employed. Estimates in thousands, rounded to the nearest hundred. Number of persons who, during the reference week, were without work, had looked for work in the past four weeks, and were available for work. Those persons on layoff or who had a new job to start in four weeks or less are considered unemployed. Estimates in thousands, rounded to the nearest hundred. The unemployment rate is the number of unemployed persons expressed as a percentage of the labour force. The unemployment rate for a particular group (age, gender, marital status, etc.) is the number unemployed in that group expressed as a percentage of the labour force for that group. Estimates are percentages, rounded to the nearest tenth. Industry refers to the general nature of the business carried out by the employer for whom the respondent works (main job only). Industry estimates in this table are based on the 2022 North American Industry Classification System (NAICS). Formerly Management of companies and administrative and other support services"." This combines the North American Industry Classification System (NAICS) codes 11 to 91. This combines the North American Industry Classification System (NAICS) codes 11 to 33. This combines the North American Industry Classification System (NAICS) codes 41 to 91. Unemployed persons who have never worked before, and those unemployed persons who last worked more than 1 year ago. For more information on seasonal adjustment see Seasonally adjusted data - Frequently asked questions." Labour Force Survey (LFS) North American Industry Classification System (NAICS) code exception: add group 1100 - Farming - not elsewhere classified (nec). When the type of farm activity cannot be distinguished between crop and livestock, (for example: mixed farming). Labour Force Survey (LFS) North American Industry Classification System (NAICS) code exception: add group 2100 - Mining - not elsewhere classified (nec). Whenever the type of mining activity cannot be distinguished. Also referred to as Natural resources. The standard error (SE) of an estimate is an indicator of the variability associated with this estimate, as the estimate is based on a sample rather than the entire population. The SE can be used to construct confidence intervals and calculate coefficients of variation (CVs). The confidence interval can be built by adding the SE to an estimate in order to determine the upper limit of this interval, and by subtracting the same amount from the estimate to determine the lower limit. The CV can be calculated by dividing the SE by the estimate. See Section 7 of the Guide to the Labour Force Survey (opens new window) for more information. The standard errors presented in this table are the average of the standard errors for 12 previous months The standard error (SE) for the month-to-month change is an indicator of the variability associated with the estimate of the change between two consecutive months, because each monthly estimate is based on a sample rather than the entire population. To construct confidence intervals, the SE is added to an estimate in order to determine the upper limit of this interval, and then subtracted from the estimate to determine the lower limit. Using this method, the true value will fall within one SE of the estimate approximately 68% of the time, and within two standard errors approximately 95% of the time. For example, if the estimated employment level increases by 20,000 from one month to another and the associated SE is 29,000, the true value of the employment change has a 68% chance of falling between -9,000 and +49,000. Because such a confidence interval includes zero, the 20,000 change would not be considered statistically significant. However, if the increase is 30,000, the confidence interval would be +1,000 to +59,000, and the 30,000 increase would be considered statistically significant. (Note that 30,000 is above the SE of 29,000, and that the confidence interval does not include zero.) Similarly, if the estimated employment declines by 30,000, then the true value of the decline would fall between -59,000 and -1,000. See Section 7 of the Guide to the Labour Force Survey (opens new window) for more information. The standard errors presented in this table are the average of standard errors for 12 previous months. They are updated twice a year The standard error (SE) for the year-over-year change is an indicator of the variability associated with the estimate of the change between a given month in a given year and the same month of the previous year, because each month's estimate is based on a sample rather than the entire population. The SE can be used to construct confidence intervals: it can be added to an estimate in order to determine the upper limit of this interval, and then subtracted from the same estimate to determine the lower limit. Using this method, the true value will fall within one SE of the estimate, approximately 68% of the time, and within two standard errors, approximately 95% of the time. For example, if the estimated employment level increases by 160,000 over 12 months and the associated SE is 55,000, the true value of the change in employment has approximately a 68% chance of falling between +105,000 and +215,000. This change would be considered statistically significant at the 68% level as the confidence interval excludes zero. However, if the increase is 40,000, the interval would be -15,000 to +95,000, and this increase would not be considered statistically significant since the interval includes zero. See Section 7 of the Guide to the Labour Force Survey (opens new window) for more information. The standard errors presented in this table are the average of standard errors for 12 previous months and are updated twice a year Excluding the territories. Starting in 2006, enhancements to the Labour Force Survey data processing system may have introduced a level shift in some estimates, particularly for less common labour force characteristics. Use caution when comparing estimates before and after 2006. For more information, contact statcan.labour-travail.statcan@statcan.gc.ca

Venezuela Unemployment: Female: Lay Off Rate data was reported at 6.958 % in Apr 2016. This records an increase from the previous number of 6.772 % for Mar 2016. Venezuela Unemployment: Female: Lay Off Rate data is updated monthly, averaging 8.827 % from Jan 1999 (Median) to Apr 2016, with 206 observations. The data reached an all-time high of 21.569 % in Jan 2004 and a record low of 5.280 % in Dec 2014. Venezuela Unemployment: Female: Lay Off Rate data remains active status in CEIC and is reported by National Institute of Statistics. The data is categorized under Global Database’s Venezuela – Table VE.G004: Unemployment.

The unemployment rate in Sweden decreased steadily from 2010 after the financial crisis the previous years. However, the employment rate increased since 2018, reaching nearly nine percent in 2021 after the outbreak of COVID-19 in 2020. In 2023, it stood at 7.7 percent. The unemployment rate among women was slightly higher than among men in 2022. Unemployment benefits As unemployed in Sweden, there is a possibility to receive unemployment benefits (A-kassa). To receive these benefits, the unemployed person needs to be registered at the Swedish Public Employment Service, needs to be ready to take on a job at any time, and needs to have had a job for at least six months during the last year. In 2022, nearly 228,000 individuals in Sweden received these benefits. The COVID-19 pandemic As the novel coronavirus (COVID-19) started spreading in Sweden in February 2020, the country's health authorities chose a milder way than most other European countries, allowing most stores, including cafés and restaurants to remain open. Ultimately, the government's handling of the pandemic was criticized as the country registered an unusually high number of deaths during the first weeks of the pandemic. Moreover, the country's economy was hit hard, with economic decline and layoffs.

Graph and download economic data for Monthly Transition Rate of All U.S. Workers From Employment to Non-Employment Due to a Layoff (EMELASA) from Jan 1978 to Jun 2025 about flow, labor force, labor, unemployment, employment, and USA.

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.

Analyzing the Potential Effects of Layoffs on Verizon Stock

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