In 2023, it was estimated that over 161 million Americans were in some form of employment, while 3.64 percent of the total workforce was unemployed. This was the lowest unemployment rate since the 1950s, although these figures are expected to rise in 2023 and beyond. 1980s-2010s Since the 1980s, the total United States labor force has generally risen as the population has grown, however, the annual average unemployment rate has fluctuated significantly, usually increasing in times of crisis, before falling more slowly during periods of recovery and economic stability. For example, unemployment peaked at 9.7 percent during the early 1980s recession, which was largely caused by the ripple effects of the Iranian Revolution on global oil prices and inflation. Other notable spikes came during the early 1990s; again, largely due to inflation caused by another oil shock, and during the early 2000s recession. The Great Recession then saw the U.S. unemployment rate soar to 9.6 percent, following the collapse of the U.S. housing market and its impact on the banking sector, and it was not until 2016 that unemployment returned to pre-recession levels. 2020s 2019 had marked a decade-long low in unemployment, before the economic impact of the Covid-19 pandemic saw the sharpest year-on-year increase in unemployment since the Great Depression, and the total number of workers fell by almost 10 million people. Despite the continuation of the pandemic in the years that followed, alongside the associated supply-chain issues and onset of the inflation crisis, unemployment reached just 3.67 percent in 2022 - current projections are for this figure to rise in 2023 and the years that follow, although these forecasts are subject to change if recent years are anything to go by.

In July 2024, 3.16 billion U.S. dollars were paid out in unemployment benefits in the United States. This is an increase from June 2024, when 2.62 billion U.S. dollars were paid in unemployment benefits. The large figures seen in 2020 are largely due to the impact of the coronavirus pandemic. Welfare in the U.S. Unemployment benefits first started in 1935 during the Great Depression as a part of President Franklin D. Roosevelt’s New Deal. The Social Security Act of 1935 ensured that Americans would not fall deeper into poverty. The United States was the only developed nation in the world at the time that did not offer any welfare benefits. This program created unemployment benefits, Medicare and Medicaid, and maternal and child welfare. The only major welfare program that the United States currently lacks is a paid maternity leave policy. Currently, the United States only offers 12 unpaid weeks of leave, under certain circumstances. However, the number of people without health insurance in the United States has greatly decreased since 2010. Unemployment benefits Current unemployment benefits in the United States vary from state to state due to unemployment being funded by both the state and the federal government. The average duration of people collecting unemployment benefits in the United States has fluctuated since January 2020, from as little as 4.55 weeks to as many as 50.32 weeks. The unemployment rate varies by ethnicity, gender, and education levels. For example, those aged 16 to 24 have faced the highest unemployment rates since 1990 during the pandemic. In February 2023, the Las Vegas-Henderson-Paradise, NV metropolitan area had the highest unemployment rate in the United States.

The employment and unemployment indicator shows several data points. The first figure is the number of people in the labor force, which includes the number of people who are either working or looking for work. The second two figures, the number of people who are employed and the number of people who are unemployed, are the two subcategories of the labor force. The unemployment rate is a calculation of the number of people who are in the labor force and unemployed as a percentage of the total number of people in the labor force.

The unemployment rate does not include people who are not employed and not in the labor force. This includes adults who are neither working nor looking for work. For example, full-time students may choose not to seek any employment during their college career, and are thus not considered in the unemployment rate. Stay-at-home parents and other caregivers are also considered outside of the labor force, and therefore outside the scope of the unemployment rate.

The unemployment rate is a key economic indicator, and is illustrative of economic conditions in the county at the individual scale.

There are additional considerations to the unemployment rate. Because it does not count those who are outside the labor force, it can exclude individuals who were looking for a job previously, but have since given up. The impact of this on the overall unemployment rate is difficult to quantify, but it is important to note because it shows that no statistic is perfect.

The unemployment rates for Champaign County, the City of Champaign, and the City of Urbana are extremely similar between 2000 and 2023.

All three areas saw a dramatic increase in the unemployment rate between 2006 and 2009. The unemployment rates for all three areas decreased overall between 2010 and 2019. However, the unemployment rate in all three areas rose sharply in 2020 due to the effects of the COVID-19 pandemic. The unemployment rate in all three areas dropped again in 2021 as pandemic restrictions were removed, and were almost back to 2019 rates in 2022. However, the unemployment rate in all three areas rose slightly from 2022 to 2023.

This data is sourced from the Illinois Department of Employment Security’s Local Area Unemployment Statistics (LAUS), and from the U.S. Bureau of Labor Statistics.

Sources: Illinois Department of Employment Security, Local Area Unemployment Statistics (LAUS); U.S. Bureau of Labor Statistics.

The seasonally-adjusted national unemployment rate is measured on a monthly basis in the United States. In February 2025, the national unemployment rate was at 4.1 percent. Seasonal adjustment is a statistical method of removing the seasonal component of a time series that is used when analyzing non-seasonal trends. U.S. monthly unemployment rate According to the Bureau of Labor Statistics - the principle fact-finding agency for the U.S. Federal Government in labor economics and statistics - unemployment decreased dramatically between 2010 and 2019. This trend of decreasing unemployment followed after a high in 2010 resulting from the 2008 financial crisis. However, after a smaller financial crisis due to the COVID-19 pandemic, unemployment reached 8.1 percent in 2020. As the economy recovered, the unemployment rate fell to 5.3 in 2021, and fell even further in 2022. Additional statistics from the BLS paint an interesting picture of unemployment in the United States. In November 2023, the states with the highest (seasonally adjusted) unemployment rate were the Nevada and the District of Columbia. Unemployment was the lowest in Maryland, at 1.8 percent. Workers in the agricultural and related industries suffered the highest unemployment rate of any industry at seven percent in December 2023.

The outbreak of coronavirus disease-2019 (COVID-19) ineluctably caused social distancing and unemployment, which may bring additional health risks for patients with cancer. To investigate the association of the pandemic-related impacts with the health-related quality of life (HRQoL) among patients with melanoma during the COVID-19 pandemic, we conducted a cross-sectional study among Chinese patients with melanoma. A self-administered online questionnaire was distributed to melanoma patients through social media. Demographic and clinical data, and pandemic-related impacts (unemployment and income loss) were collected. HRQoL was determined by the Functional Assessment of Cancer Therapy-General (FACT-G) and its disease-specific module (the melanoma subscale, MS). A total of 135 patients with melanoma completed the study. The mean age of the patients was 55.8 ± 14.2 years, 48.1% (65/135) were male, and 17.04% (34/135) were unemployed since the epidemic. Unemployment of the patients and their family members and income loss were significantly associated with a lower FACT-G score, while the MS score was associated with the unemployment of the patients' family members. Our findings suggested that unemployment is associated with impaired HRQoL in melanoma patients during the COVID-19 epidemic.

The clean energy sector in the United States has already lost a total of 594,347 jobs due to the coronavirus pandemic. As of April 2020, the sector most affected was energy efficiency, with 413,486 employees having been laid off. California was the most affected state.

Abstract

In the WAEMU countries, COVID-19 is expected to affect households in many ways. First, governments might reduce social transfers to households due to the decline in revenue arising from the potential COVID-19 economic recession. Second households deriving income from vulnerable sectors such as tourism and related activities will likely face risk of unemployment or loss of income. Third an increase in prices of imported goods can also negatively impact household welfare, as a direct consequence of the increase of these imported items or as indirect increase of prices of local good manufactured using imported inputs. In this context, there is a need to produce high frequency data to help policy makers in monitoring the channels by which the pandemic affects households and assessing its distributional impact. To do so, the sample of the longitudinal survey will be a sub-sample of the 2018/19 household survey in each country.

For Mali, the survey which is implemented by the National Statistical Office (INSTAT), is conducted using cell phone numbers of household members collected during the 2018/19 survey. This has the advantage of conducting cost effectively welfare analysis without collecting new consumption data. The 35 minutes questionnaires covered 10 modules (knowledge, behavior, access to services, food security, employment, safety nets, shocks, etc…). Data collection is planned for six months (six rounds) and the questionnaire is designed with core modules and rotating modules. Survey data collection started on May 11th, 2020 and households are expected to be called back every three to four weeks.

The main objectives of the survey are to: • Identify type of households directly or indirectly affected by the pandemic; • Identify the main channels by which the pandemic affects households; • Provide relevant data on income and socioeconomic indicators to assess the welfare impact of the pandemic.

Geographic coverage

National coverage including rural and urban

Analysis unit

• Households
• Individuals

Universe

The survey covered only households of the 2018/19 survey which excluded populations in prisons, hospitals, military barracks, and school dormitories.

Kind of data

Sample survey data [ssd]

Sampling procedure

The Mali COVID-19 impact monitoring survey is a high frequency Computer Assisted Telephone Interview (CATI). The survey’s sample was drawn from the population of the 2018/19 - Enquête Harmonisée des Conditions de Vie des Ménages (EHCVM) -, which was conducted between October 2018 and July 2019. EHCVM is itself a sample survey representative at national, regional and by urban/rural. For the 7,000 HHs in EHCVM, phone numbers were collected for about 90 percent of them. Each HH has between 1-4 phone numbers. The sampling, which was similar across WAEMU, aimed at having representative estimates by three zones: the capital city of Bamako, other urban areas and the rural area. The minimum sample size was 1,908 for which 1,766 were successfully interviewed, that is about 98 % of the expected minimal sample size at the national level. Given that Mali is conducting a phone survey for the first time, a total of 2,270 were drawn (25% increase) to take into account unknown non-response rates or presence of invalid numbers in the database.

The total number of completed interviews in round one is 1,766. The total number of completed interviews in round two is 1,935. The total number of completed interviews in round three is 1,901. The total number of completed interviews in round four is 1,797. The total number of completed interviews in round five is 1,766.

Mode of data collection

Computer Assisted Telephone Interview [cati]

Research instrument

All the interview materials were translated in french for the NSO. The questionnaire was administered in local languages with about varying length (30-35 minutes) and covered the following topics: 1- Household Roster 2- Knowledge of COVID-19 3- Behaviour and Social Distancing 4- Access to Basic Services 5- Employment and Income 6- Prices and Food Security 7- Other Impacts of COVID-19 8- Income Loss 9- Coping/Shocks 10- Social Safety Nets 11- Fragility 12- Governance and socio-political crisis

Cleaning operations

At the end of data collection, the raw dateset was cleaned by the NSO. This included formatting, and correcting results based on monitoring issues, enumerator feedback and survey changes.

Response rate

The minimum sample expected is 1,809 households (with 603 households per domain). This sample was therefore 99% covered for Bamako, about 100% for other urban areas and 91% for rural areas. Overall, the minimum sample is 98% covered. This level of coverage provides reliable data at national level and for each domain.

Round one response rate was 77.8%. Round two response rate was 85.2%. Round three response rate was 83.7%. Round four response rate was 79.2%. Round five response rate was 79.7%.

Abstract

To better understand the impact of the shock induced by the COVID-19 pandemic on Morocco and assess the policy responses in a rapidly changing context, reliable data is imperative, and the need to resort to a dynamic data collection tool at a time when countries in the region are in a state of flux cannot be overstated. The COVID-19 MENA Monitor Survey was led by the Economic Research Forum (ERF) to provide data for researchers and policy makers on the socio-economic and labor market impact of the global COVID-19 pandemic on households. The ERF COVID-19 MENA Monitor Survey is constructed using a series of short panel phone surveys that are conducted approximately every two months, covering topics such as demographic and household characteristics, education and children, labor market status, income, social safety net, employment and unemployment detection, employment characteristics, and social distancing. In addition to the survey's panel design, which will permit the study of various phenomena over time, the survey also takes into account the key demographic and socio-economic characteristics of each country in the questionnaires' design to understand the different distributional consequences of the impact of COVID-19 and responses to it. This design allows further study of the effect of the pandemic on different vulnerable groups including women, informal and irregular workers, low skilled workers, and youth. The ERF COVID-19 MENA Monitor Survey is a wide-ranging, nationally representative panel survey. The wave 3 of this dataset was collected in April 2021, harmonized by the Economic Research Forum (ERF) and is featured as data for Household/Individual. The survey is in the process of further expansion to include other waves.

The harmonization was designed to create comparable data that can facilitate cross-country and comparative research between other Arab countries (Egypt, Tunisia, Jordan, and Sudan). All the COVID-19 MENA Monitor surveys incorporate similar survey designs, with data on households and individuals within those households.

Geographic coverage

National

Analysis unit

Household and Individuals

Universe

The survey covered a national random sample of mobile phone users aged 18-64.

Kind of data

Sample survey data [ssd]

Sampling procedure

The sample universe for the household survey was mobile phone users aged 18-64. Random digit dialing (RDD), within the range of valid numbers, was used, with up to three attempts if a phone number was not picked up/answered, was disconnected or busy, or picked up but could not complete the interview at that time. Samples were stratified by country-specific market shares of mobile operators. The sample is designed to cover at least 2000 unique households and individuals. A question is included in the survey for the number of phone numbers within the household to weight appropriately. Further weighting of the household and individual samples was done to reflect the demographic composition of the population as obtained by the most recent publicly available data with individual phone ownership and relevant demographic and labour market characteristics. In the individual interview, respondents who are employers or self-employed were asked to respond to either the household enterprise or farmer modules. For follow-up waves, previous wave respondents were recontacted if they consented to follow-up in the previous wave. Up to three attempts were used, including contacting second and family/friend numbers, if provided in wave one, on the third call. If the individual could not be reached or refused, a refresher individual was added to the sample in their place, randomly selected as with base wave respondents. All the respondents who consented to follow up in the prior wave were contacted in order to include them in the subsequent wave. Households are be followed up every two months up to a total of four interviews. Interviews are conducted by experienced survey research or polling firms in each country using computer-assisted telephone interviewing (CATI) techniques.

Mode of data collection

Computer Assisted Telephone Interview [cati]

In 2020, global gross domestic product declined by 6.7 percent as a result of the coronavirus (COVID-19) pandemic outbreak. In Latin America, overall GDP loss amounted to 8.5 percent.

Unemployment Rate in Canada decreased to 6.90 percent in June from 7 percent in May of 2025. This dataset provides - Canada Unemployment Rate - actual values, historical data, forecast, chart, statistics, economic calendar and news.

As a result of the COVID-19 crisis, a total 495 billion U.S. dollars of labor income were lost in the first three quarters of 2020 in Latin America and the Caribbean. In September 2020, it was estimated that this income reduction represented around 19 percent of the region's total labor income and over 10 percent of its GDP. Up until that month, Latin America lost roughly 150 million jobs due to the pandemic.

Abstract

To better understand the impact of the shock induced by the COVID-19 pandemic on Tunisia and assess the policy responses in a rapidly changing context, reliable data is imperative, and the need to resort to a dynamic data collection tool at a time when countries in the region are in a state of flux cannot be overstated. The COVID-19 MENA Monitor Survey was led by the Economic Research Forum (ERF) to provide data for researchers and policy makers on the socio-economic and labor market impact of the global COVID-19 pandemic on households. The ERF COVID-19 MENA Monitor Survey is constructed using a series of short panel phone surveys that are conducted approximately every two months, covering topics such as demographic and household characteristics, education and children, labor market status, income, social safety net, employment and unemployment detection, employment characteristics, and social distancing. In addition to the survey's panel design, which will permit the study of various phenomena over time, the survey also takes into account the key demographic and socio-economic characteristics of each country in the questionnaires' design to understand the different distributional consequences of the impact of COVID-19 and responses to it. This design allows further study of the effect of the pandemic on different vulnerable groups including women, informal and irregular workers, low skilled workers, and youth. The ERF COVID-19 MENA Monitor Survey is a wide-ranging, nationally representative panel survey.The baseline wave of this dataset was collected in November 2020 and harmonized by the Economic Research Forum (ERF) and is featured as wave 1 for Household/Individual data.This dataset was collected in February 2021, harmonized by the Economic Research Forum (ERF) and is featured as the second wave for Egypt in the COVID-19 MENA Monitor Surveys The survey is in the process of further expansion to include other waves

The harmonization was designed to create comparable data that can facilitate cross-country and comparative research between other Arab countries (Egypt, Morocco, Jordan, and Sudan). All the COVID-19 MENA Monitor surveys incorporate similar survey designs, with data on households and individuals within those households.

Geographic coverage

National

Analysis unit

Household and Individuals

Universe

The survey covered a national random sample of mobile phone users aged 18-64.

Kind of data

Sample survey data [ssd]

Sampling procedure

The sample universe for the household survey was mobile phone users aged 18-64. Random digit dialing (RDD), within the range of valid numbers, was used, with up to three attempts if a phone number was not picked up/answered, was disconnected or busy, or picked up but could not complete the interview at that time. Samples were stratified by country-specific market shares of mobile operators. The sample is designed to cover at least 2000 unique households and individuals. A question is included in the survey for the number of phone numbers within the household to weight appropriately. Further weighting of the household and individual samples was done to reflect the demographic composition of the population as obtained by the most recent publicly available data with individual phone ownership and relevant demographic and labour market characteristics. In the individual interview, respondents who are employers or self-employed were asked to respond to either the household enterprise or farmer modules. For follow-up waves, previous wave respondents were recontacted if they consented to follow-up in the previous wave. Up to three attempts were used, including contacting second and family/friend numbers, if provided in wave one, on the third call. If the individual could not be reached or refused, a refresher individual was added to the sample in their place, randomly selected as with base wave respondents. All the respondents who consented to follow up in the prior wave were contacted in order to include them in the subsequent wave. The follow-up occurred for the second wave and 64.7% (1,294 of 2,000) Nov. 2020 respondents in Tunisia were successfully tracked on Feb. 2021.

Households are be followed up every two months up to a total of four interviews. Interviews are conducted by experienced survey research or polling firms in each country using computer-assisted telephone interviewing (CATI) techniques.

Mode of data collection

Computer Assisted Telephone Interview [cati]

Abstract

To better understand the impact of the shock induced by the COVID-19 pandemic on Jordan and assess the policy responses in a rapidly changing context, reliable data is imperative, and the need to resort to a dynamic data collection tool at a time when countries in the region are in a state of flux cannot be overstated. The COVID-19 MENA Monitor Survey was led by the Economic Research Forum (ERF) to provide data for researchers and policy makers on the socio-economic and labor market impact of the global COVID-19 pandemic on households. The ERF COVID-19 MENA Monitor Survey is constructed using a series of short panel phone surveys that are conducted approximately every two months covering topics such as demographic and household characteristics, education and children, labor market status, income, social safety net, employment and unemployment detection, employment characteristics, and social distancing. In addition to the survey's panel design, which will permit the study of various phenomena over time, the survey also takes into account the key demographic and socio-economic characteristics of each country in the questionnaires' design to understand the different distributional consequences of the impact of COVID-19 and responses to it. This design allows further study of the effect of the pandemic on different vulnerable groups including women, informal and irregular workers, low skilled workers, and youth. The ERF COVID-19 MENA Monitor Survey is a wide-ranging, nationally representative panel survey.The baseline wave of this dataset was collected in February 2021. This dataset was collected in August 2021, harmonized by the Economic Research Forum (ERF) and is featured as the third wave for Jordan in the COVID-19 MENA Monitor Surveys.

The harmonization was designed to create comparable data that can facilitate cross-country and comparative research between other Arab countries (Egypt, Tunisia, Morocco, and Sudan). All the COVID-19 MENA Monitor surveys incorporate similar survey designs, with data on households and individuals within those households.

Geographic coverage

National

Analysis unit

Household and Individuals

Universe

The survey covered a national random sample of mobile phone users aged 18-64.

Kind of data

Sample survey data [ssd]

Sampling procedure

The sample universe for the household survey was mobile phone users aged 18-64. Random digit dialing (RDD), within the range of valid numbers, was used, with up to three attempts if a phone number was not picked up/answered, was disconnected or busy, or picked up but could not complete the interview at that time. Samples were stratified by country-specific market shares of mobile operators. The sample will be designed to cover at least 2,500 unique households and individuals (2000 Jordanians, 500 Syrian Refugees). Attrition is addressed through the addition of refresher households in later waves to maintain that target. A question is included in the survey for the number of phone numbers within the household to weight appropriately. Further weighting of the household and individual samples was done to reflect the demographic composition of the population as obtained by the most recent publicly available data with individual phone ownership and relevant demographic and labour market characteristics. In the individual interview, respondents who are employers or self-employed were asked to respond to either the household enterprise or farmer modules.

Households were be followed up every two months up to a total of three interviews. Interviews are conducted by experienced survey research or polling firms in each country using computer-assisted telephone interviewing (CATI) techniques.

Mode of data collection

Computer Assisted Telephone Interview [cati]

Abstract

To better understand the impact of the shock induced by the COVID-19 pandemic on Sudan and assess the policy responses in a rapidly changing context, reliable data is imperative, and the need to resort to a dynamic data collection tool at a time when countries in the region are in a state of flux cannot be overstated.

The COVID-19 MENA Monitor Survey was led by the Economic Research Forum (ERF) to provide data for researchers and policy makers on the socio-economic and labor market impact of the global COVID-19 pandemic on households.

The ERF COVID-19 MENA Monitor Survey is constructed using a series of short panel phone surveys that are conducted approximately every two months, covering topics such as demographic and household characteristics, education and children, labor market status, income, social safety net, employment and unemployment detection, employment characteristics, and social distancing. In addition to the survey's panel design, which will permit the study of various phenomena over time, the survey also takes into account the key demographic and socio-economic characteristics of each country in the questionnaires' design to understand the different distributional consequences of the impact of COVID-19 and responses to it. This design allows further study of the effect of the pandemic on different vulnerable groups including women, informal and irregular workers, low skilled workers, and youth.

The ERF COVID-19 MENA Monitor Survey is a wide-ranging, nationally representative panel survey. The wave 1 of this dataset was collected in April 2021, harmonized by the Economic Research Forum (ERF) and is featured as data for Household/Individual. The survey is in the process of further expansion to include other waves.

The harmonization was designed to create comparable data that can facilitate cross-country and comparative research between other Arab countries (Egypt, Tunisia, Jordan, Morocco and Sudan). All the COVID-19 MENA Monitor surveys incorporate similar survey designs, with data on households and individuals within those households.

Geographic coverage

National.

Analysis unit

• Household
• Individuals

Universe

Mobile phone users aged 18-64.

Kind of data

Sample survey data [ssd]

Sampling procedure

The sample universe for the household survey was mobile phone users aged 18-64. Random digit dialing, within the range of valid numbers, was used, with up to three attempts if a phone number was not picked up/answered, was disconnected or busy, or picked up but could not complete the interview at that time. Samples were stratified by country-specific market shares of mobile operators.

For follow-up waves, previous wave respondents were recontacted if they consented to follow-up in the previous wave. Up to three attempts were used, including contacting second and family/friend numbers, if provided in the previous wave, on the third call. If the individual could not be reached or refused, a refresher individual was added to the sample in their place, randomly selected as with base wave respondents.

Mode of data collection

Computer Assisted Telephone Interview [cati]

Response rate

29% for April 2021

As of November 2021, the U.S. goverment dedicated ***** percent of the GDP to soften the effects of the coronavirus pandemic. This translates to stimulus packages worth **** trillion U.S. dollars Economic impact of the Coronavirus pandemic The impact of the COVID-19 pandemic was felt throughout the whole world. Lockdowns forced many industries to close completely for many months and restrictions were put on almost all economic activity. In 2020, the worldwide GDP loss due to Covid was *** percent. The global unemployment rate rocketed to **** percent in 2020 and confidence in governments’ ability to deal with the crisis diminished significantly. Governmental response In order to stimulate the economies and bring them out of recession, many countries have decided to release so called stimulus packages. These are fiscal and monetary policies used to support the recovery process. Through application of lower taxes and interest rates, direct financial aid, or facilitated access to funding, the governments aim to boost the employment, investment, and demand. Stimulus packages Until November 2021, Japan has dedicated the largest share of the GDP to stimulus packages among the G20 countries, with ***** percent (*** trillion Yen or **** trillion U.S. dollars). While the first help package aimed at maintaining employment and securing businesses, the second and third ones focused more on structural changes and positive developments in the country in the post-pandemic future.

Abstract

THE CBI PDM Household Survey was conducted in Tajikistan between February, to April, 2021. Tajikistan hosts the largest number of refugees in Central Asia, predominantly from neighbouring Afghanistan. While some progress has been achieved in areas such as access to health and education for refugees, livelihoods and self-reliance, though, continue to pose a challenge. As the result of Covid-19, refugees faced a myriad of challenges, including the loss of daily incomes and livelihoods to cover basic needs such as rent, food and health care. For refugees in Tajikistan, who largely rely on daily work, the impact of Covid 19 has been devastating as it has led to widespread unemployment. As a response measure, UNHCR jointly with its NGO partner provided Covid-19 cash assistance to 414 refugee households over the course of six months (July-December 2020). The results from this survey suggest that cash assistance provided as an immediate measure to support vulnerable refugee households during the Covid-19 pandemic has had a positive impact on the lives of the respondents. Cash assistance predominantly has been spent to cover food, medicines and rent costs.

UNHCR uses Post-Distribution Monitoring (PDM) as a mechanism to collect refugees' feedback on the quality, sufficiency, utilization and effectiveness of the assistance items they receive. The underlying principle behind the process is linked to accountability, as well as a commitment to improve the quality and relevance of support provided, and related services. UNHCR increasingly uses Cash-Based Interventions (CBIs) as a preferred modality for delivering assistance, offering greater dignity and choice to forcibly displaced and stateless persons in line with UNHCR's core protection mandate. In order to ensure that the cash assistance provided meets the intended programme objectives and that desired outcomes are achieved, UNHCR conducts regular post-distribution and outcome monitoring with a sample or all of refugee recipients.

Geographic coverage

The survey is conducted in Bokhtar, Dushanbe, J. Rasulov, Rudaki and Vahdat.

Analysis unit

Households

Universe

The sample universe includes all beneficiaries subject to the Cash-Based Intervention.

Kind of data

Sample survey data [ssd]

Sampling procedure

The survey's objective was to deliver representative data of all refugee households that were beneficiaries of Cash-Based Interventions implemented in 2021 as a response to Covid-19. The total number of households that received Cash-Based Interventions in 2021 was 419 households. For this survey, a simple random sample design was applied. The total sample size was 90 refugee households.

Mode of data collection

Computer Assisted Telephone Interview [cati]

As of October 2024, there were 133.89 million full-time employees in the United States. This is a slight decrease from the previous month, when there were 134.15 million full-time employees. The impact COVID-19 on employment In December 2019, the COVID-19 virus began its spread across the globe. Since being classified as a pandemic, the virus caused a global health crisis that has taken the lives of millions of people worldwide. The COVID-19 pandemic changed many facets of society, most significantly, the economy. In the first years, many businesses across all industries were forced to shut down, with large numbers of employees being laid off. The economy continued its recovery in 2022 with the nationwide unemployment rate returning to a more normal 3.4 percent as of April 2023. Unemployment benefits Because so many people in the United States lost their jobs, record numbers of individuals applied for unemployment insurance for the first time. As an early response to this nation-wide upheaval, the government issued relief checks and extended the benefits paid by unemployment insurance. In May 2020, the amount of unemployment insurance benefits paid rose to 23.73 billion U.S. dollars. As of December 2022, this value had declined to 2.24 billion U.S. dollars.

The economic landscape of the United Kingdom has been significantly shaped by the intertwined issues of Brexit, COVID-19, and their interconnected impacts. Despite the country’s robust and diverse economy, the disruptions caused by Brexit and the COVID-19 pandemic have created uncertainty and upheaval for both businesses and individuals. Recognizing the magnitude of these challenges, academic literature has directed its attention toward conducting immediate research in this crucial area. This study sets out to investigate key economic factors that have influenced various sectors of the UK economy and have broader economic implications within the context of Brexit and COVID-19. The factors under scrutiny include the unemployment rate, GDP index, earnings, and trade. To accomplish this, a range of data analysis tools and techniques were employed, including the Box-Jenkins method, neural network modeling, Google Trend analysis, and Twitter-sentiment analysis. The analysis encompassed different periods: pre-Brexit (2011-2016), Brexit (2016-2020), the COVID-19 period, and post-Brexit (2020-2021). The findings of the analysis offer intriguing insights spanning the past decade. For instance, the unemployment rate displayed a downward trend until 2020 but experienced a spike in 2021, persisting for a six-month period. Meanwhile, total earnings per week exhibited a gradual increase over time, and the GDP index demonstrated an upward trajectory until 2020 but declined during the COVID-19 period. Notably, trade experienced the most significant decline following both Brexit and the COVID-19 pandemic. Furthermore, the impact of these events exhibited variations across the UK’s four regions and twelve industries. Wales and Northern Ireland emerged as the regions most affected by Brexit and COVID-19, with industries such as accommodation, construction, and wholesale trade particularly impacted in terms of earnings and employment levels. Conversely, industries such as finance, science, and health demonstrated an increased contribution to the UK’s total GDP in the post-Brexit period, indicating some positive outcomes. It is worth highlighting that the impact of these economic factors was more pronounced on men than on women. Among all the variables analyzed, trade suffered the most severe consequences in the UK. By early 2021, the macroeconomic situation in the country was characterized by a simple dynamic: economic demand rebounded at a faster pace than supply, leading to shortages, bottlenecks, and inflation. The findings of this research carry significant value for the UK government and businesses, empowering them to adapt and innovate based on forecasts to navigate the challenges posed by Brexit and COVID-19. By doing so, they can promote long-term economic growth and effectively address the disruptions caused by these interrelated issues.

This is the dataset I used to figure out which sociodemographic factor including the current pandemic status of each state has the most significan impace on the result of the US Presidential election last year. I also included sentiment scores of tweets created from 2020-10-15 to 2020-11-02 as well, in order to figure out the effect of positive/negative emotion for each candidate - Donald Trump and Joe Biden - on the result of the election.

Details for each variable are as below: - state: name of each state in the United States, including District of Columbia - elec16, elec20: dummy variable indicating whether Trump gained the electoral votes of each state or not. If the electors casted their votes for Trump, the value is 1; otherwise the value is 0 - elecchange: dummy variable indicating whether each party flipped the result in 2020 compared to that of the 2016 - demvote16: the rate of votes that the Democrats, i.e. Hillary Clinton earned in the 2016 Presidential election - repvote16: the rate of votes that the Republicans , i.e. Donald Trump earned in the 2016 Presidential election - demvote20: the rate of votes that the Democrats, i.e. Joe Biden earned in the 2020 Presidential election - repvote20: the rate of votes that the Republicans , i.e. Donald Trump earned in the 2020 Presidential election - demvotedif: the difference between demvote20 and demvote16 - repvotedif: the difference between repvote20 and repvote16 - pop: the population of each state - cumulcases: the cumulative COVID-19 cases on the Election day - caseMar ~ caseOct: the cumulative COVID-19 cases during each month - Marper10k ~ Octper10k: the cumulative COVID-19 cases during each month per 10 thousands - unemp20: the unemployment rate of each state this year before the election - unempdif: the difference between the unemployment rate of the last year and that of this year - jan20unemp ~ oct20unemp: the unemployment rate of each month - cumulper10k: the cumulative COVID-19 cases on the Election day per 10 thousands - b_str_poscount_total: the total number of positive tweets on Biden measured by the SentiStrength - b_str_negcount_total: the total number of negative tweets on Biden measured by the SentiStrength - t_str_poscount_total: the total number of positive tweets on Trump measured by the SentiStrength - t_str_poscount_total: the total number of negative tweets on Trump measured by the SentiStrength - b_str_posprop_total: the proportion of positive tweets on Biden measured by the SentiStrength - b_str_negprop_total: the proportion of negative tweets on Biden measured by the SentiStrength - t_str_posprop_total: the proportion of positive tweets on Trump measured by the SentiStrength - t_str_negprop_total: the proportion of negative tweets on Trump measured by the SentiStrength - white: the proportion of white people - colored: the proportion of colored people - secondary: the proportion of people who has attained the secondary education - tertiary: the proportion of people who has attained the tertiary education - q3gdp20: GDP of the 3rd quarter 2020 - q3gdprate: the growth rate of the 3rd quarter 2020, compared to that of the same quarter last year - 3qsgdp20: GDP of 3 quarters 2020 - 3qsrate20: the growth rate of GDP compared to that of the 3 quarters last year - q3gdpdif: the difference in the level of GDP of the 3rd quarter compared to the last quarter - q3rate: the growth rate of the 3rd quarter compared to the last quarter - access: the proportion of households having the Internet access

In 2020, the surveyed micro-sized private enterprises in Vietnam laid off about ** percent of their total workforce due to the impacts of the COVID-19 pandemic. In comparison, the surveyed micro-sized FDI firms in the country reduced around ** percent of the total number of employees during the pandemic.