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.

Graph and download economic data for Pandemic Unemployment Assistance Initial Claims in Massachusetts (PUAICMA) from 2020-04-04 to 2022-11-05 about pandemic, assistance, initial claims, MA, unemployment, and USA.

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, compensation, MA, unemployment, and USA.

A new International Labor Organization report suggests that more than one in six young people worldwide has stopped working since the start of the Covid-19 pandemic, while the rest have seen their working hours cut by almost a quarter. In Southeast Asia, unemployment rates have spiked to almost unprecedented levels because of the pandemic, which has devastated economies to varying degrees. Until now, official unemployment rates in the region have been enviably low. Before 2020, Cambodia’s unemployment rates had barely risen above the 2% mark since the early 1990s, while Vietnam’s had also been consistently at less than 2%, according to data from the United Nations Development Program (UNDP). For the last decade, Thailand’s jobless rate has hovered around 0.6%.

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.

Unemployed benefit recipients are stigmatized and generally perceived negatively in terms of their personality characteristics and employability. The COVID19 economic shock led to rapid public policy responses across the globe to lessen the impact of mass unemployment, potentially shifting community perceptions of individuals who are out of work and rely on government income support. We used a repeated cross-sections design to study change in stigma tied to unemployment and benefit receipt in a pre-existing pre-COVID19 sample (n = 260) and a sample collected during COVID19 pandemic (n = 670) by using a vignette-based experiment. Participants rated attributes of characters who were described as being employed, working poor, unemployed or receiving unemployment benefits. The results show that compared to employed characters, unemployed characters were rated substantially less favorably at both time points on their employability and personality traits. The difference in perceptions of the employed and unemployed was, however, attenuated during COVID19 with benefit recipients perceived as more employable and more Conscientious than pre-pandemic. These results add to knowledge about the determinants of welfare stigma highlighting the impact of the global economic and health crisis on perception of others.

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.

As of October 2025, the tech startup with the largest wave of layoffs from March 2020 was Amazon, with 30,000 employees laid off on October 27, 2025. Furthermore, Intel announced a layoff of 22,000 employees on April 23, 2025. Layoffs 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 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.

Technology companies worldwide saw a significant reduction in their workforce in 2025. One of the most recent tech layoffs was by Amazon on October 27, 2025, with ****** employees being 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 ******* 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 ******* 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.

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.

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.