Graph and download economic data for Pandemic Unemployment Assistance Continued Claims in Massachusetts (PUACCMA) from 2020-03-28 to 2022-10-22 about pandemic, assistance, continued claims, MA, unemployment, and USA.

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Graph and download economic data for Pandemic Emergency Unemployment Compensation Continued Claims in Massachusetts (PEUCCCMA) from 2020-03-28 to 2022-10-22 about emergency, pandemic, continued claims, MA, compensation, unemployment, and USA.

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.

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.

Conventional politics approaches, emphasizing party ideology, electoral dynamics, committee member-ship, campaign donations and industry clout, exercise a powerful hold over assessments of public policies and their distributional effects. Emerging from pluralist and business power perspectives, such accounts see “who gets what and why” as the result of how politics and power shape policies, their implementation, and distributional outcomes. This pervades our understanding of the Paycheck Protection Program (PPP), the US government’s effort to avert mass unemployment during the pandemic by lending $786 billion to small businesses to keep employees on payroll. Yet contrary to prior studies of the PPP, we find that such factors were strikingly uncorrelated with distributional outcomes, revealing limits to such approaches to this case. Instead, we find that an institutional politics or politics-in-time (IP-PIT) approach better explains the program and its trajectories. IP-PIT revises the causal sequence by empha-sizing how institutions and policies generate politics, distributional outcomes and feedback loops. We engage both approaches via a mixed-methods analysis of the PPP and two new datasets. We anchor our study in a qualitative process-tracing of temporal variation in policy architectures, politics, policy revi-sions, and shifting access to loans within and across the program’s three periods before presenting quan-titative analyses of loan flows across congressional districts and periods using data on the entire corpus of PPP loans. We use one of the largest economic interventions since the New Deal as a case to advance research and debate over the dynamics and outcomes of US policy making during crises and the Ameri-can political economy in general. Ours is the first study of the PPP to conduct a mixed-methods analysis of loans across congressional districts or to use conventional and institutional approaches to address its politics, policy and outcomes. We document varieties of critical junctures, contribute arguments about what might shape policy or institutional innovation in those moments, and use the PPP to identify conditions under which systems are “their own grave diggers,” fueling negative-transformative rather than positive-reproductive feedback.

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.

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.

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.

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: Local Authority Tenants (Module 186): this module was asked on behalf of the Department of Environment, Transport and the Regions (DETR), and only applied to those respondents renting from local authorities in England and Wales. It combined a repeat of the 'Tenant's Charter' module run in 1991/2 and 1992/3 with questions based on ones from the 1994 module 'Information for LA Tenants' and the Survey of English Housing. Withheld Deposits (Module 193): this module was asked on behalf of the DETR and would have been included in the Survey of English Housing, but no space was available. It was asked in England only, to help the DETR compile a sample of respondents who had at some time in the past three years had a deposit that they had paid prior to moving into privately rented accommodation withheld when they left. Second Homes (Module 4): this module was asked on behalf of the 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. Attitudes to Disability Benefits (Module 191): this module was asked on behalf of the Department of Social Security. The questions focus on three different sorts of benefit claimants, the disabled, who can claim Income Replacement Benefit, people injured at work and carers. Alcohol brought into the United Kingdom (UK) from European Union (EU) countries (Module 164): this module was asked on behalf of Customs and Excise, and aimed to assess the extent of cross-border shopping since the Single European Market was introduced. It is only concerned with alcohol bought in other EU countries in outlets other than duty-free shops. Attitudes to in-work subsidies and unemployment benefit (Module 194): this module was asked on behalf of the DSS and focuses on attitudes to top-up benefits for low-paid workers with jobs, attitudes to unemployment benefit, and attitudes to unemployed couples with and without children. Lone Mothers (Module 184): this module was asked on behalf of the DSS. 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. Contraception (Module 170): the Special Licence version of this module is held under SN 6476. 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. Vulnerable consumers of financial products (Module 195): this module was asked on behalf of the Office for Fair Trading, who were conducting an enquiry into vulnerable consumers of financial services such as banking, savings and investments, credit and insurance. Multi-stage stratified random sample Face-to-face interview 1998 AGE ALCOHOL USE ALCOHOLIC DRINKS ATTITUDES BANK ACCOUNTS BEREAVEMENT CARE OF DEPENDANTS CHILD BENEFITS CHILD CARE CHILD DAY CARE CHRONIC ILLNESS CLEANING COHABITATION COMPREHENSION COSTS CREDIT CREDIT CARD USE Consumption and con... DAMAGE DEBILITATIVE ILLNESS DISABLED PERSONS DISEASES ECONOMIC ACTIVITY ECONOMIC VALUE EDUCATIONAL BACKGROUND EDUCATIONAL COURSES EMPLOYEES EMPLOYMENT EMPLOYMENT HISTORY EMPLOYMENT PROGRAMMES ENDOWMENT ASSURANCE ETHNIC GROUPS EUROPEAN UNION EXPORTS AND IMPORTS FAMILY MEMBERS FINANCIAL SERVICES FINANCIAL SUPPORT FULL TIME EMPLOYMENT FURNISHED ACCOMMODA... Family life and mar... GENDER GOVERNMENT DEPARTMENTS GRANTS HEADS OF HOUSEHOLD HEALTH CONSULTATIONS HOLIDAYS HOME CONTENTS INSUR... HOME OWNERSHIP HOME SELLING HOUSEHOLD BUDGETS HOUSEHOLDS HOUSING FINANCE HOUSING POLICY HOUSING TENURE HUMAN SETTLEMENT Health behaviour Housing INCOME INCOME TAX INDUSTRIES INFORMATION INFORMATION MATERIALS INFORMATION SOURCES INHERITANCE INSURANCE INTEREST FINANCE INVESTMENT Income JOB HUNTING JUDGMENTS LAW LANDLORDS LARGE SHOPS LEGAL PROCEDURE LOANS LOCAL GOVERNMENT LOCAL GOVERNMENT SE... MANAGERS MARITAL STATUS MARRIAGE DISSOLUTION MASS MEDIA MEDICAL CENTRES MEDICAL PRESCRIPTIONS MONEY MORTGAGES MOTHERS MOTOR VEHICLES ONE PARENT FAMILIES PARENTS PART TIME EMPLOYMENT PAYMENTS PENSIONS PERFORMANCE PLACE OF RESIDENCE PRESCHOOL CHILDREN PRIVATE PERSONAL PE... PUBLIC HOUSES PUBLIC INFORMATION PUBLIC OPINION PUBLIC SERVICES PURCHASING QUALIFICATIONS REDUNDANCY RENTED ACCOMMODATION RENTS REPORTS RESIDENTIAL MOBILITY RESTAURANTS RETAIL TRADE RETIREMENT RIGHTS AND PRIVILEGES SAVINGS SCHOOLCHILDREN SECOND HOMES SELF EMPLOYED SHOPS SMALL CLAIMS PROCEDURE SOCIAL HOUSING SOCIAL SECURITY BEN... SPOUSES STANDARDS STATE AID SUPERVISORS Social behaviour an... Specific social ser... TERMINATION OF SERVICE TIED HOUSING TRAINING TRANSPORT TRAVEL UNEMPLOYMENT UNEMPLOYMENT BENEFITS UNFURNISHED ACCOMMO... UNMARRIED MOTHERS UNWAGED WORKERS VOCATIONAL EDUCATIO... WAGES WORKING MOTHERS property and invest...