Exploiting an unemployment insurance (UI) reform in Brazil, we study incentive effects of UI in the presence of informal labor markets. We find that eligibility for UI benefits increases formal layoffs by eleven percent. Most of the additional layoffs are related to workers transitioning to informal employment. We further document formal layoff and recall patterns consistent with rent extraction from the UI system. Workers are laid off as they become eligible for UI benefits and recalled when benefits cease. These patterns are stronger for industries and municipalities with a high degree of labor market informality.

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 57 thousand employees being laid off. By the second quarter, layoffs impacted more than 43 thousand tech employees. In the final quarter of the year around 12 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 167.6 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 263 thousand laid off employees in the global tech sector by trhe 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 Monthly Share of Prime-Age U.S. Workers Who Leave the Labor Force After a Quit (EMSHRNQP) from Jan 1978 to May 2025 about flow, labor force, labor, unemployment, employment, and USA.

This paper quantifies how the local skill remoteness of a laid-off worker’s last job affects subsequent wages, employment, and mobility rates. Local skill remoteness captures the degree of dissimilarity between the skill profiles of the worker’s last job and all other jobs in a local labor market. I implement a measure of local skill remoteness at the occupation-city level and find that higher skill remoteness at layoff is associated with persistently lower earnings after layoff. Earnings differences between workers whose last job was above or below median skill remoteness amount to a loss of more than $10,000 over 4 years, and are mainly accounted for by lower wages upon re-employment (not lower hoursworked). Workers who lost a skill-remote job also have a higher probability of changing occupation, a lower probability of being re-employed at jobs with similar skill profiles, and a higher propensity to migrate to another city after layoff. Finally, I show that jobs destroyed in recessions are more skill-remote than those lost in booms. Taking all these facts together, I conclude that the local skill remoteness of jobs is an empirically relevant factor to understand the severity and cyclicality of displaced workers’ earnings losses and reallocation patterns.

Brazil Formal Employment: Year to Date: Laid Off data was reported at 5,215,622.000 Unit in Apr 2019. This records an increase from the previous number of 3,932,813.000 Unit for Mar 2019. Brazil Formal Employment: Year to Date: Laid Off data is updated monthly, averaging 7,583,751.000 Unit from Feb 2003 (Median) to Apr 2019, with 195 observations. The data reached an all-time high of 21,270,737.000 Unit in Dec 2014 and a record low of 750,092.000 Unit in Jan 2004. Brazil Formal Employment: Year to Date: Laid Off data remains active status in CEIC and is reported by Ministry of Labor and Social Security. The data is categorized under Brazil Premium Database’s Labour Market – Table BR.GBB041: Formal Employment: by Region and State: Laid Off: Year-to-Date. Notes: The data included adjustments of the data deliver after the legal deadline. The concepts used in CAGED refer to changes in employment regulated by CLT (Consolidation of Labor Laws), occurred in the establishment, informs the movement of wage employment Hired Under Employment Laws. Therefore describes a portion of all working people. It is considered as an admission every entry of worker in a company in the current month. And as layoffs, every output from person whose employment relationship ceased during the month for any reason (resignation, retirement, death), either by the employer or the employee. Balance (Absolute Change), indicates the difference between Admitted and Laid Off.

Dataset Summary: This dataset analyzes layoff trends globally from 1995 to 2024, highlighting the evolution of job sectors and the influence of AI technologies on workforce dynamics. It provides insights into layoffs, reasons behind workforce changes, industry-specific impacts, and future job trends, making it a valuable resource for workforce analytics, AI adoption studies, and economic impact modeling.

Sources and Methodology: This dataset is modeled based on historical events, industry analyses, and logical extrapolations. Key data sources include:

Historical Trends:

Events like the dot-com bubble, global financial crises, and COVID-19.

Reliable sources: U.S. Bureau of Labor Statistics, World Bank, IMF Economic Outlook.

AI Trends and Projections:

Reports from McKinsey & Company, World Economic Forum, and Gartner.

Data on AI job growth and adoption: LinkedIn Economic Graphs, Crunchbase Layoff Tracker.

Skills and Future Jobs:

Reports on emerging skills and workforce trends: Future of Jobs Report 2023, TechCrunch, and Business Insider.

Projections and Logical Assumptions:

Projections for AI adoption, job creation, and displacement are based on publicly available research and extrapolation of trends.

Modeled features like "Future_Job_Trends" and "AI_Job_Percentage" combine factual data with predictive insights.

Potential Use Cases:

Economic Analysis: Study the impact of global events and technological advancements on workforce trends.

AI Adoption Trends: Explore how AI is influencing job creation and displacement across industries.

Policy Planning: Inform government and organizational policies on workforce development and reskilling.

Industry Insights: Gain insights into which industries are most affected by layoffs and which are adopting AI technologies.

Future Workforce Development: Identify emerging skills and prepare for future job market demands.

Disclaimer: This dataset is a combination of historical data, trends, and reasonable projections for future job markets influenced by AI technologies. Projections and estimates should be treated as approximations and not definitive predictions. All efforts have been made to use reliable sources and logical assumptions to ensure accuracy and usefulness for analytical purposes.

Citations:

U.S. Bureau of Labor Statistics (bls.gov)

McKinsey & Company (mckinsey.com)

World Economic Forum (weforum.org)

Gartner Reports (gartner.com)

Crunchbase Layoff Tracker (crunchbase.com)

Future of Jobs Report 2023 (weforum.org/reports)

LinkedIn Economic Graph (economicgraph.linkedin.com)

The programs replicate tables and figures from "The Geography of Unemployment", by Adrien Bilal.

Brazil Formal Employment: Year to Date: Laid Off: Manufacturing: Textile and Clothing data was reported at 107,086.000 Unit in Apr 2019. This records an increase from the previous number of 79,178.000 Unit for Mar 2019. Brazil Formal Employment: Year to Date: Laid Off: Manufacturing: Textile and Clothing data is updated monthly, averaging 213,993.500 Unit from May 2003 (Median) to Apr 2019, with 192 observations. The data reached an all-time high of 539,132.000 Unit in Dec 2011 and a record low of 19,176.000 Unit in Jan 2004. Brazil Formal Employment: Year to Date: Laid Off: Manufacturing: Textile and Clothing data remains active status in CEIC and is reported by Ministry of Labor and Social Security. The data is categorized under Brazil Premium Database’s Labour Market – Table BR.GBB007: Formal Employment: by Industry: Laid Off: Year-to-Date. Notes: The data included adjustments of the data deliver after the legal deadline. The concepts used in CAGED refer to changes in employment regulated by CLT (Consolidation of Labor Laws), occurred in the establishment, informs the movement of wage employment Hired Under Employment Laws. Therefore describes a portion of all working people. It is considered as an admission every entry of worker in a company in the current month. And as layoffs, every output from person whose employment relationship ceased during the month for any reason (resignation, retirement, death), either by the employer or the employee. Balance (Absolute Change), indicates the difference between Admitted and Laid Off.

Challenger Job Cuts in the United States decreased to 93816 Persons in May from 105441 Persons in April 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.

Graph and download economic data for Monthly Share of All U.S. Workers Who Leave the Labor Force After a Layoff (EMSHRNLA) from Jan 1978 to Apr 2025 about flow, labor force, labor, unemployment, employment, and USA.

China Employment: Urban: Reemployed from Layoff: Year to Date data was reported at 5,150.000 Person th in Dec 2024. This records an increase from the previous number of 3,880.000 Person th for Sep 2024. China Employment: Urban: Reemployed from Layoff: Year to Date data is updated quarterly, averaging 3,920.000 Person th from Dec 2004 (Median) to Dec 2024, with 70 observations. The data reached an all-time high of 5,670.000 Person th in Dec 2015 and a record low of 780.000 Person th in Mar 2020. China Employment: Urban: Reemployed from Layoff: Year to Date data remains active status in CEIC and is reported by Ministry of Human Resources and Social Security. The data is categorized under China Premium Database’s Labour Market – Table CN.GB: Employment.

Workers displaced during the pandemic recession experienced better earnings and employment outcomes than workers displaced during previous recessions. A sharp recovery in aggregate labor market conditions after the pandemic recession accounts for these better outcomes. The industry and occupation composition of displaced workers, the prevalence of recalls, and increased take-up of unemployment insurance benefits are unlikely explanations.JEL Classification: E24 Employment; Unemployment; Wages; Intergenerational Income Distribution; Aggregate Human Capital; Aggregate Labor Productivity J63 Labor Turnover; Vacancies; Layoffs J64 Unemployment: Models, Duration, Incidence, and Job Search J65 Unemployment Insurance; Severance Pay; Plant Closings

Formal Employment: Laid Off: Manufacturing: Paper, Cardboard and Publishing data was reported at 8,350.000 Unit in Apr 2019. This records an increase from the previous number of 8,162.000 Unit for Mar 2019. Formal Employment: Laid Off: Manufacturing: Paper, Cardboard and Publishing data is updated monthly, averaging 9,699.000 Unit from May 2003 (Median) to Apr 2019, with 192 observations. The data reached an all-time high of 14,227.000 Unit in Mar 2012 and a record low of 5,780.000 Unit in Nov 2003. Formal Employment: Laid Off: Manufacturing: Paper, Cardboard and Publishing data remains active status in CEIC and is reported by Ministry of Labor and Social Security. The data is categorized under Brazil Premium Database’s Labour Market – Table BR.GBB004: Formal Employment: by Industry: Laid Off. The concepts used in CAGED refer to changes in employment regulated by CLT (Consolidation of Labor Laws), occurred in the establishment, informs the movement of wage employment Hired Under Employment Laws. Therefore describes a portion of all working people. It is considered as an admission every entry of worker in a company in the current month. And as layoffs, every output from person whose employment relationship ceased during the month for any reason (resignation, retirement, death), either by the employer or the employee. Balance (Absolute Change), indicates the difference between Admitted and Laid Off.

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 May 2025 about flow, labor force, labor, unemployment, employment, and USA.

The data and programs replicate tables and figures from "The Great Canadian Recovery: The Impact of COVID-19 on Canada’s Labour Market", by Jones, Lange, Riddell, and Warman. Please see the ReadMe file for additional details.

Formal Employment: Laid Off: Manufacturing: Transport Equipment data was reported at 8,273.000 Unit in Apr 2019. This records an increase from the previous number of 7,705.000 Unit for Mar 2019. Formal Employment: Laid Off: Manufacturing: Transport Equipment data is updated monthly, averaging 9,091.000 Unit from May 2003 (Median) to Apr 2019, with 192 observations. The data reached an all-time high of 17,822.000 Unit in Feb 2009 and a record low of 3,673.000 Unit in Nov 2003. Formal Employment: Laid Off: Manufacturing: Transport Equipment data remains active status in CEIC and is reported by Ministry of Labor and Social Security. The data is categorized under Brazil Premium Database’s Labour Market – Table BR.GBB004: Formal Employment: by Industry: Laid Off. The concepts used in CAGED refer to changes in employment regulated by CLT (Consolidation of Labor Laws), occurred in the establishment, informs the movement of wage employment Hired Under Employment Laws. Therefore describes a portion of all working people. It is considered as an admission every entry of worker in a company in the current month. And as layoffs, every output from person whose employment relationship ceased during the month for any reason (resignation, retirement, death), either by the employer or the employee. Balance (Absolute Change), indicates the difference between Admitted and Laid Off.

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

Formal Employment: ytd: Laid Off: North: Rondônia data was reported at 37,008.000 Unit in Apr 2019. This records an increase from the previous number of 28,335.000 Unit for Mar 2019. Formal Employment: ytd: Laid Off: North: Rondônia data is updated monthly, averaging 48,983.000 Unit from Feb 2003 (Median) to Apr 2019, with 195 observations. The data reached an all-time high of 165,843.000 Unit in Dec 2011 and a record low of 4,492.000 Unit in Jan 2004. Formal Employment: ytd: Laid Off: North: Rondônia data remains active status in CEIC and is reported by Ministry of Labor and Social Security. The data is categorized under Brazil Premium Database’s Labour Market – Table BR.GBB041: Formal Employment: by Region and State: Laid Off: Year-to-Date. Notes: The data included adjustments of the data deliver after the legal deadline. The concepts used in CAGED refer to changes in employment regulated by CLT (Consolidation of Labor Laws), occurred in the establishment, informs the movement of wage employment Hired Under Employment Laws. Therefore describes a portion of all working people. It is considered as an admission every entry of worker in a company in the current month. And as layoffs, every output from person whose employment relationship ceased during the month for any reason (resignation, retirement, death), either by the employer or the employee. Balance (Absolute Change), indicates the difference between Admitted and Laid Off.

Brazil Formal Employment: Last 12 Months Accumulated: Laid Off: Service: Transport and Communications data was reported at 660,196.000 Unit in Apr 2019. This records an increase from the previous number of 653,087.000 Unit for Mar 2019. Brazil Formal Employment: Last 12 Months Accumulated: Laid Off: Service: Transport and Communications data is updated monthly, averaging 644,735.000 Unit from May 2003 (Median) to Apr 2019, with 192 observations. The data reached an all-time high of 962,182.000 Unit in Nov 2014 and a record low of 422,660.000 Unit in Nov 2003. Brazil Formal Employment: Last 12 Months Accumulated: Laid Off: Service: Transport and Communications data remains active status in CEIC and is reported by Ministry of Labor and Social Security. The data is categorized under Brazil Premium Database’s Labour Market – Table BR.GBB010: Formal Employment: by Industry: Laid Off: Last 12 Months Accumulated. Notes: The data included adjustments of the data deliver after the legal deadline. The concepts used in CAGED refer to changes in employment regulated by CLT (Consolidation of Labor Laws), occurred in the establishment, informs the movement of wage employment Hired Under Employment Laws. Therefore describes a portion of all working people. It is considered as an admission every entry of worker in a company in the current month. And as layoffs, every output from person whose employment relationship ceased during the month for any reason (resignation, retirement, death), either by the employer or the employee. Balance (Absolute Change), indicates the difference between Admitted and Laid Off.

Brazil Formal Employment: Last 12 Months Accumulated: Laid Off: Service: Education data was reported at 457,065.000 Unit in Apr 2019. This records an increase from the previous number of 453,989.000 Unit for Mar 2019. Brazil Formal Employment: Last 12 Months Accumulated: Laid Off: Service: Education data is updated monthly, averaging 374,456.000 Unit from May 2003 (Median) to Apr 2019, with 192 observations. The data reached an all-time high of 493,575.000 Unit in Jul 2015 and a record low of 194,269.000 Unit in May 2003. Brazil Formal Employment: Last 12 Months Accumulated: Laid Off: Service: Education data remains active status in CEIC and is reported by Ministry of Labor and Social Security. The data is categorized under Brazil Premium Database’s Labour Market – Table BR.GBB010: Formal Employment: by Industry: Laid Off: Last 12 Months Accumulated. Notes: The data included adjustments of the data deliver after the legal deadline. The concepts used in CAGED refer to changes in employment regulated by CLT (Consolidation of Labor Laws), occurred in the establishment, informs the movement of wage employment Hired Under Employment Laws. Therefore describes a portion of all working people. It is considered as an admission every entry of worker in a company in the current month. And as layoffs, every output from person whose employment relationship ceased during the month for any reason (resignation, retirement, death), either by the employer or the employee. Balance (Absolute Change), indicates the difference between Admitted and Laid Off.