More than ****** mass layoffs were reported in the first quarter of 2025 in Poland, making it the highest number since the 2020 COVID-19 pandemic.

Compared to February 2020, roughly 24.4 thousand people have become unemployed and 39.2 thousand people temporarily laid off mainly because of the coronavirus (COVID-19) pandemic in Finland. As of November 2020, the highest spike in the numbers of unemployed jobseekers and temporary layoffs during 2020 was recorded between March 30 and April 5 (week 14).

COVID-19 impact on unemployment Although the full-blown consequences of the coronavirus pandemic remain uncertain, the monthly unemployment rate spiked in Finland in May 2020. While many people have lost their jobs, even a larger group of people have been temporarily laid off. In order to avoid mass layoffs in companies, the Finnish government reduced the period of notice before layoff until 31 December 2020. However, it remains to be seen, to what extent temporary coronavirus layoffs turn permanent in the long run. Nonetheless, based on a forecast, the unemployment is expected to stay at a higher level in the upcoming years than before the COVID-19 outbreak.

Uneven prospects As of April 2020, the majority of Finnish people were still not particularly worried about the risk of losing a job or income because of the coronavirus pandemic. However, especially students are at risk of losing their income, as seasonal work has become scarce due to restrictions and business closures. This can potentially lead to long-term negative consequences for the income and career development of young people.

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.

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.

Background and Objectives: In order to curb the spread of coronavirus disease 2019 (COVID-19), the countries took preventive measures such as lockdown and restrictions of movements. This can lead to effects on mental health of the population. We studied the impact of COVID-19 on psychological well-being and associated factors among the Pakistani general population.Methods: An online cross-sectional survey was conducted between 26th April and 15th May and included participants from all over the Pakistan. Attitudes and worriedness about COVID-19 pandemic were assessed using a structured questionnaire. A validated English and Urdu version of the World Health Organization Well-Being Index (WHO-5) was used to assess the well-being. Factor analysis was done to extract the attitude item domains. Logistic regression was used to assess the factors associated with poor well-being.Results: A total of 1,756 people participated in the survey. Almost half 50% of the participants were male, and a similar proportion was employed. About 41% of the participants were dependent on financial sources other than salary. News was considered a source of fear as 72% assumed that avoiding such news may reduce the fear. About 68% of the population was worried about contracting the disease. The most common coping strategies used during lockdown were spending quality time with family, eating healthy food, adequate sleep, and talking to friends on phone. Prevalence of poor well-being was found to be 41.2%. Female gender, being unemployed, living in Sindh and Islamabad Capital Territory (ICT), fear of COVID-19, and having chronic illness were significantly associated with poor well-being. Similarly, coping strategies during lockdown (doing exercise; spending time with family; eating healthy food; having good sleep; contributing in social welfare work and spending time on hobbies) were also significantly associated with mental well-being.Conclusion: We found a high prevalence 41.2% of poor well-being among the Pakistani general population. We also investigated risk factors of poor well-being which included female gender, unemployment, being resident of ICT and Sindh, fear, chronic illness, and absence of coping strategies. This calls for immediate action at population level in the form of targeted mass psychological support programs to improve the mental health of population during the COVID-19 crises.

In January 2021, approximately **** million jobs in Europe's three largest economies were being supported by temporary employment schemes, with the UK's job retention scheme supporting approximately **** million jobs, France's Chômage partiel scheme *** million, while *** million workers were on Germany's Kurzarbeit system. Although some of these partial employment mechanisms were already in place before the COVID-19 pandemic, their usage accelerated considerably after the first Coronavirus lockdowns in Spring 2020. How much will this cost European governments? Early on in the pandemic, European governments moved swiftly to limit the damage that the Coronavirus pandemic would cause to the labor market. The spectre of mass unemployment, which would put a huge strain on European benefit systems anyway, was enough to encourage significant government spending and intervention. To this end, the European Union made 100 billion Euros of loans available through it's unemployment support fund (SURE). As of March 2021, Italy had received ***** billion Euros in loans from the SURE mechanism, and is set to be loaned **** billion Euros overall. Spain and Poland will receive the second and third highest amount from the plan, at **** billion, and ***** billion Euros respectively. What about the UK? The United Kingdom is not involved in the European Union's SURE scheme, but has also paid substantial amounts of money to keep unemployment at bay. As of January 31, 2021, there had been more than **** million jobs furloughed on the UK's job retention scheme. By this date, the expenditure of this measure had reached **** billion British pounds, with this figure expected to increase further, following the extension of the scheme to September 2021.

Abstract copyright UK Data Service and data collection copyright owner.The Opinions and Lifestyle Survey (formerly known as the ONS Opinions Survey or Omnibus) is an omnibus survey that began in 1990, collecting data on a range of subjects commissioned by both the ONS internally and external clients (limited to other government departments, charities, non-profit organisations and academia).Data are collected from one individual aged 16 or over, selected from each sampled private household. Personal data include data on the individual, their family, address, household, income and education, plus responses and opinions on a variety of subjects within commissioned modules. The questionnaire collects timely data for research and policy analysis evaluation on the social impacts of recent topics of national importance, such as the coronavirus (COVID-19) pandemic and the cost of living, on individuals and households in Great Britain. From April 2018 to November 2019, the design of the OPN changed from face-to-face to a mixed-mode design (online first with telephone interviewing where necessary). Mixed-mode collection allows respondents to complete the survey more flexibly and provides a more cost-effective service for customers. In March 2020, the OPN was adapted to become a weekly survey used to collect data on the social impacts of the coronavirus (COVID-19) pandemic on the lives of people of Great Britain. These data are held in the Secure Access study, SN 8635, ONS Opinions and Lifestyle Survey, Covid-19 Module, 2020-2022: Secure Access. From August 2021, as coronavirus (COVID-19) restrictions were lifting across Great Britain, the OPN moved to fortnightly data collection, sampling around 5,000 households in each survey wave to ensure the survey remains sustainable. The OPN has since expanded to include questions on other topics of national importance, such as health and the cost of living. For more information about the survey and its methodology, see the ONS OPN Quality and Methodology Information webpage.Secure Access Opinions and Lifestyle Survey dataOther Secure Access OPN data cover modules run at various points from 1997-2019, on Census religion (SN 8078), cervical cancer screening (SN 8080), contact after separation (SN 8089), contraception (SN 8095), disability (SNs 8680 and 8096), general lifestyle (SN 8092), illness and activity (SN 8094), and non-resident parental contact (SN 8093). See Opinions and Lifestyle Survey: Secure Access for details. Main Topics:Each month's questionnaire consists of two elements: core questions, covering demographic information, are asked each month together with non-core questions that vary from month to month. The non-core questions for this month were: Televisions (Module 177): this module was asked on behalf of the Department of National Heritage, to ascertain how many households have a television that did not work at the time and did not have another TV set that did work, and whether they intended to get the broken television set repaired in the next seven days after the interview took place. ACAS awareness (Module 187): this module was asked on behalf of ACAS, the Advisory, Conciliation and Arbitration Service, who wished to know how many people had heard of them and how many had a realistic idea of what sort of organisation they are and what they do. The module was asked of all respondents in paid employment. Second homes (Module 4): this module was asked on behalf of the Department of Environment, Transport and the Regions (DETR). It has appeared in previous Omnibus surveys in a slightly different form. The module queried respondents on ownership of a second home by any member of the household and reasons for having the second home. Expectation of house price changes (Module 137): this module asks respondents' views on changes to house prices in the next year and next five years. Fire safety (Module 33): this module covers fire safety and was asked in connection with Fire Safety Week. Questions assess awareness of fire risks and fire safety measures the respondent has taken. Lone mothers (Module 184): this module was asked on behalf of the Department of Social Security. The questions were taken from a British attitudes survey and compare attitudes towards mothers living in couples with children of varying ages with attitudes towards lone mothers. Smoking (Module 130): this module assesses people's smoking habits, past and present, attitudes to smoking in different scenarios, and awareness of cigarette advertising. Unemployment risk (Module 183): this module was asked on behalf of the Centre for Research in Social Policy at Loughborough University. The questions were designed to investigate respondents' assessment of the risks of being unemployed, their attitude towards unemployment insurance and their recent experience of unemployment. Contraception (Module 170): the Special Licence version of this module is held under SN 6475. PEPs and TESSAs (Module 185): this module was asked on behalf of the Inland Revenue, to gain more information about the distribution of PEPs and TESSAs and in particular the extent to which the two groups overlap. Multi-stage stratified random sample Face-to-face interview 1997 ACCIDENTS ADULTS ADVERTISING ADVICE AGE ARBITRATION ASTHMA ATTITUDES BANK ACCOUNTS CANCER CARDIOVASCULAR DISE... CAUSES OF DEATH CHILD BENEFITS CHILD CARE CHILD DAY CARE CHILDREN CINEMA COHABITATION COLOUR TELEVISION R... COMPANIES CONFLICT RESOLUTION COOKING EQUIPMENT COSTS COT DEATHS COURTS CREDIT CARD USE CULTURAL EVENTS Consumption and con... DIABETES DISEASES ECONOMIC ACTIVITY ECONOMIC VALUE EDUCATIONAL BACKGROUND ELECTRICAL EQUIPMENT EMPLOYEES EMPLOYMENT EMPLOYMENT CONTRACTS EMPLOYMENT HISTORY EMPLOYMENT PROGRAMMES ETHNIC GROUPS EXPENDITURE Economic conditions... FAMILY MEMBERS FINANCIAL SERVICES FIRE PROTECTION EQU... FULL TIME EMPLOYMENT FURNISHED ACCOMMODA... Family life and mar... GENDER GENERAL PRACTITIONERS GRANTS HEADS OF HOUSEHOLD HEALTH HEALTH CONSULTATIONS HEALTH PROFESSIONALS HEARING HEATING SYSTEMS HOLIDAYS HOME CONTENTS INSUR... HOME OWNERSHIP HOME SELLING HOSPITAL SERVICES HOURS OF WORK HOUSEHOLDS HOUSES HOUSING TENURE HUMAN SETTLEMENT Health behaviour Housing ILL HEALTH INCOME INCOME TAX INDUSTRIES INFLATION INFORMATION MATERIALS INFORMATION SOURCES INHERITANCE INSURANCE INTEREST FINANCE INVESTMENT Income JOB HUNTING JUDGMENTS LAW LABOUR RELATIONS LANDLORDS Labour relations co... MANAGERS MARITAL STATUS MARRIAGE DISSOLUTION MASS MEDIA MEDICAL CENTRES MEDICAL INSURANCE MEDICAL PRESCRIPTIONS MORTGAGES MOTHERS MOTOR VEHICLES ONE PARENT FAMILIES ORGANIZATIONS PARENTS PART TIME EMPLOYMENT PASSIVE SMOKING PENSIONS PERSONNEL PLACE OF RESIDENCE PRESCHOOL CHILDREN PRICES PRIVATE SECTOR PUBLIC HOUSES PUBLIC INFORMATION PUBLIC SERVICE BUIL... RADIO RECRUITMENT RENTED ACCOMMODATION RESPIRATORY TRACT D... RESTAURANTS RETIREMENT SAVINGS SCHOOLCHILDREN SCHOOLS SECOND HOMES SELF EMPLOYED SHOPS SICK LEAVE SMOKING SMOKING CESSATION SMOKING RESTRICTIONS SOCIAL HOUSING SOCIAL SECURITY BEN... SPORTING EVENTS SPOUSE S ECONOMIC A... SPOUSE S EMPLOYMENT SPOUSES STATE AID SUPERVISORS Social behaviour an... TELEPHONE HELP LINES TELEVISION ADVERTISING TELEVISION RECEIVERS TERMINATION OF SERVICE TIED HOUSING TOBACCO TRAINING TRAVEL UNEMPLOYMENT UNFURNISHED ACCOMMO... UNMARRIED MOTHERS UNWAGED WORKERS Unemployment VOCATIONAL EDUCATIO... WAGES WORKERS RIGHTS WORKING MOTHERS WORKPLACE property and invest...