Data obtained from ILOSTAT website. Collated various datasets from covid monitoring section. Most of the estimates are from 2020.

Description about columns: 1. country - Name of Country 2. total_weekly_hours_worked(estimates_in_thousands) - Total weekly hours worked of employed persons 3. percentage_of_working_hrs_lost(%) - Percentage of hours lost compared to the baseline (4th quarter of 2019) 4. percent_hours_lost_40hrs_per_week(thousands) - Percentage of hours lost compared to the baseline (4th quarter of 2019) expressed in full-time equivalent employment losses. This measure is constructed by dividing the number of weekly hours lost due to COVID-19 and dividing them by 40. 5. percent_hours_lost_48hrs_per_week(thousands) - Percentage of hours lost compared to the baseline (4th quarter of 2019) expressed in full-time equivalent employment losses. This measure constructed by dividing the number of weekly hours lost due to COVID-19 and dividing them by 48. 6. labour_dependency_ratio - Ratio of dependants (persons aged 0 to 14 + persons aged 15 and above that are either outside the labour force or unemployed) to total employment. 7. employed_female_25+_2019(estimates in thousands) - Employed female in 2019 who, during a specified brief period, were in one of the following categories: a) paid employment (whether at work or with a job but not at work); or b) self-employment (whether at work or with an enterprise but not at work). 8. employed_male_25+_2019(estimates in thousands) - Employed male in 2019 who, during a specified brief period, were in one of the following categories: a) paid employment (whether at work or with a job but not at work); or b) self-employment (whether at work or with an enterprise but not at work). 9. ratio_of_weekly_hours_worked_by_population_age_15-64 - Ratio of total weekly hours worked to population aged 15-64.

Under the restrictions placed due to coronavirus (COVID-19), 2020 has experienced one of the largest historic job losses in the United States. Likewise, the clean energy industry experienced a significant drop with over ******* people losing their jobs in this industry by the end of 2020. California recorded the greatest number of job losses, at ******. This was followed by Texas, where ****** clean energy jobs were cut.

For further information about the coronavirus (COVID-19) pandemic, please visit our dedicated Fact and Figures page.

The temporary shutdown orders and social distancing measures taken to fight the COVID-19 outbreak have caused substantial job losses in the United States. Women, especially those without a college degree, have taken a bigger hit in the first wave of job losses. This imbalance could lead to prolonged damage to women’s employment and labor market attachment if job losses deepen and persist in the coming months.

As of April 7, 2020, it is estimated that roughly **** million people working in air travel related industries in the Asia Pacific region will lose their jobs due to the coronavirus outbreak. In the Middle East this number will be equivalent to under *********** unemployed people.

Due to the impact of the coronavirus (COVID-19) pandemic, it was estimated that the global travel and tourism market had lost roughly 63 million jobs in 2020. While this scenario improved significantly in 2022, the sector still reported around 39 million fewer jobs worldwide compared to 2019. Overall, the Asia-Pacific region recorded the most significant employment loss due to the COVID-19 pandemic, with approximately 28 million fewer travel and tourism jobs in 2022 compared to 2019.

Check our data versus labor surveys.

What is the COVID-19 Economic Vulnerability Index?The COVID-19 Vulnerability Index (CVI) is a measurement of the negative impact that the coronavirus (COVID-19) crisis can have on employment based upon a region's mix of industries. For example, accommodation and food services are projected to lose more jobs as a result of the coronavirus (in the neighborhood of 50%) compared with utilities and healthcare (with none or little expected job contraction).This updated dataset contains 116 jobs attributes including the 10 most likely jobs to be impacted for each county, the total employment and employment by sector. An attribute list is included below.An average Vulnerability Index score is 100, representing the average job loss expected in the United States. Higher scores indicate the degree to which job losses may be greater — an index score of 200, for example, means the rate of job loss can be twice as large as the national average. Conversely, an index score of 50 would mean a possible job loss of half the national average. Regions heavily dependent on tourism with relatively high concentrations of leisure and hospitality jobs, for example, are likely to have high index scores. The Vulnerability Index only measures the impact potential related to the mix of industry employment. The index does not take into account variation due to a region’s rate of virus infection, nor does it factor in local government's policies in reaction to the virus. For more detail, please see this description.MethodologyThe index is based on a model of potential job losses due to the COVID-19 outbreak in the United States. Expected employment losses at the subsector level are based upon inputs which include primary research on expert testimony; news reports for key industries such as hotels, restaurants, retail, and transportation; preliminary release of unemployment claims; and the latest job postings data from Chmura's RTI database. The forecast model, based on conditions as of March 23, 2020, assumes employment in industries in each county/region would change at a similar rate as employment in national industries. The projection estimates that the United States could lose 15.0 million jobs due to COVID-19, with over half of the jobs lost in hotels, food services, and entertainment industries. Contact Chmura for further details.Attribute ListFIPSCounty NameStateTotal JobsWhite Collar JobsBlue Collar JobsService JobsWhite Collar %Blue Collar %Service %Government JobsGovernment %Primarily Self-Employed JobsPrimarily Self-Employed %Job Change, Last Ten YearsIndustry 1 NameIndustry 1 EmplIndustry 1 %Industry 2 NameIndustry 2 EmplIndustry 2 %Industry 3 NameIndustry 3 EmplIndustry 3 %Industry 4 NameIndustry 4 EmplIndustry 4 %Industry 5 NameIndustry 5 EmplIndustry 5 %Industry 6 NameIndustry 6 EmplIndustry 6 %Industry 7 NameIndustry 7 EmplIndustry 7 %Industry 8 NameIndustry 8 EmplIndustry 8 %Industry 9 NameIndustry 9 EmplIndustry 9 %Industry 10 NameIndustry 10 EmplIndustry 10 %All Other IndustriesAll Other Industries EmplAll Other Industies %Agriculture, Food & Natural Resources EmplArchitecture and Construction EmplArts, A/V Technology & Communications EmplBusiness, Management & Administration EmplEducation & Training EmplFinance EmplGovernment & Public Administration EmplHealth Science EmplHospitality & Tourism EmplHuman Services EmplInformation Technology EmplLaw, Public Safety, Corrections & Security EmplManufacturing EmplMarketing, Sales & Service EmplScience, Technology, Engineering & Mathematics EmplTransportation, Distribution & Logistics EmplAgriculture, Food & Natural Resources %Architecture and Construction %Arts, A/V Technology & Communications %Business, Management & Administration %Education & Training %Finance %Government & Public Administration %Health Science %Hospitality & Tourism %Human Services %Information Technology %Law, Public Safety, Corrections & Security %Manufacturing %Marketing, Sales & Service %Science, Technology, Engineering & Mathematics %Transportation, Distribution & Logistics %COVID-19 Vulnerability IndexAverage Wages per WorkerAvg Wages Growth, Last Ten YearsUnemployment RateUnderemployment RatePrime-Age Labor Force Participation RateSkilled Career 1Skilled Career 1 EmplSkilled Career 1 Avg Ann WagesSkilled Career 2Skilled Career 2 EmplSkilled Career 2 Avg Ann WagesSkilled Career 3Skilled Career 3 EmplSkilled Career 3 Avg Ann WagesSkilled Career 4Skilled Career 4 EmplSkilled Career 4 Avg Ann WagesSkilled Career 5Skilled Career 5 EmplSkilled Career 5 Avg Ann WagesSkilled Career 6Skilled Career 6 EmplSkilled Career 6 Avg Ann WagesSkilled Career 7Skilled Career 7 EmplSkilled Career 7 Avg Ann WagesSkilled Career 8Skilled Career 8 EmplSkilled Career 8 Avg Ann WagesSkilled Career 9Skilled Career 9 EmplSkilled Career 9 Avg Ann WagesSkilled Career 10Skilled Career 10 EmplSkilled Career 10 Avg Ann Wages

In 2022, the total number of jobs generated, directly and indirectly, by travel and tourism worldwide remained below the figures reported before the impact of the coronavirus (COVID-19) pandemic. Overall, among the countries with the highest number of travel and tourism jobs worldwide in 2022, China recorded the sharpest drop in employment, with around 19 million fewer travel and tourism jobs compared to 2019.

The COVID-19 pandemic has triggered economic disruptions with heavy blow to the labour markets. Steep economic contraction due to lockdown has resulted in a huge amount of job loss in most emerging economies like India. This paper attempts to analyse the Indian job loss scenario from the dimensions of area of residence, gender, education and age. The conditions of the salaried workers have been analysed using CMIE data on employment over the time span of March 2020 to May 2020.A pronounced rural-urban divide has been found in the case of overall job loss scenario. Higher levels of education have been found to act as a shield against job losses. It is the rural youth who was found to be in a more challenging position than their urban counterpart. An increasing informalisation of the salaried workers have been noticed.

IntroductionCoronavirus disease (COVID-19) is an infectious disease caused by the SARS-CoV-2-virus. COVID-19 has officially been declared as the latest in the list of pandemics by WHO at the start of 2020. This study investigates the associations among decrease in economic activity, gender, age, and psychological distress during the COVID-19 pandemic considering the economic status and education level of countries using multinational surveys.MethodsOnline self-report questionnaires were administered in 15 countries which were spontaneously participate to 14,243 respondents in August 2020. Prevalence of decrease in economic activity and psychological distress was stratified by age, gender, education level, and Human Development Index (HDI). With 7,090 of female (49.8%), mean age 40.67, 5,734 (12.75%) lost their job and 5,734 (40.26%) suffered from psychological distress.ResultsAssociations among psychological distress and economic status, age, and gender was assessed using multivariate logistic regression, adjusted for country and education as random effects of the mixed model. We then measured the associations between HDI and age using multivariate logistic regression. Women had a higher prevalence of psychological distress than men with 1.067 Odds ratio, and younger age was significantly associated with decrease in economic activity for 0.998 for age increasing. Moreover, countries with lower HDI showed a higher prevalence of decrease in economic activity, especially at lower education levels.DiscussionPsychological distress due to COVID-19 revealed a significant association with decrease in economic activity, women, and younger age. While the proportion of decrease in economic activity population was different for each country, the degree of association of the individual factors was the same. Our findings are relevant, as women in high HDI countries and low education level in lower HDI countries are considered vulnerable. Policies and guidelines for both financial aid and psychological intervention are recommended.

This 6MB download is a zip file containing 5 pdf documents and 2 xlsx spreadsheets. Presentation on COVID-19 and the potential impacts on employment

May 2020Waka Kotahi wants to better understand the potential implications of the COVID-19 downturn on the land transport system, particularly the potential impacts on regional economies and communities.

To do this, in May 2020 Waka Kotahi commissioned Martin Jenkins and Infometrics to consider the potential impacts of COVID-19 on New Zealand’s economy and demographics, as these are two key drivers of transport demand. In addition to providing a scan of national and international COVID-19 trends, the research involved modelling the economic impacts of three of the Treasury’s COVID-19 scenarios, to a regional scale, to help us understand where the impacts might be greatest.

Waka Kotahi studied this modelling by comparing the percentage difference in employment forecasts from the Treasury’s three COVID-19 scenarios compared to the business as usual scenario.

The source tables from the modelling (Tables 1-40), and the percentage difference in employment forecasts (Tables 41-43), are available as spreadsheets.

Arataki - potential impacts of COVID-19 Final Report

Employment modelling - interactive dashboard

The modelling produced employment forecasts for each region and district over three time periods – 2021, 2025 and 2031. In May 2020, the forecasts for 2021 carried greater certainty as they reflected the impacts of current events, such as border restrictions, reduction in international visitors and students etc. The 2025 and 2031 forecasts were less certain because of the potential for significant shifts in the socio-economic situation over the intervening years. While these later forecasts were useful in helping to understand the relative scale and duration of potential COVID-19 related impacts around the country, they needed to be treated with care recognising the higher levels of uncertainty.

The May 2020 research suggested that the ‘slow recovery scenario’ (Treasury’s scenario 5) was the most likely due to continuing high levels of uncertainty regarding global efforts to manage the pandemic (and the duration and scale of the resulting economic downturn).

The updates to Arataki V2 were framed around the ‘Slower Recovery Scenario’, as that scenario remained the most closely aligned with the unfolding impacts of COVID-19 in New Zealand and globally at that time.

Find out more about Arataki, our 10-year plan for the land transport system

May 2021The May 2021 update to employment modelling used to inform Arataki Version 2 is now available. Employment modelling dashboard - updated 2021Arataki used the May 2020 information to compare how various regions and industries might be impacted by COVID-19. Almost a year later, it is clear that New Zealand fared better than forecast in May 2020.Waka Kotahi therefore commissioned an update to the projections through a high-level review of:the original projections for 2020/21 against performancethe implications of the most recent global (eg International monetary fund world economic Outlook) and national economic forecasts (eg Treasury half year economic and fiscal update)The treasury updated its scenarios in its December half year fiscal and economic update (HYEFU) and these new scenarios have been used for the revised projections.Considerable uncertainty remains about the potential scale and duration of the COVID-19 downturn, for example with regards to the duration of border restrictions, update of immunisation programmes. The updated analysis provides us with additional information regarding which sectors and parts of the country are likely to be most impacted. We continue to monitor the situation and keep up to date with other cross-Government scenario development and COVID-19 related work. The updated modelling has produced employment forecasts for each region and district over three time periods - 2022, 2025, 2031.The 2022 forecasts carry greater certainty as they reflect the impacts of current events. The 2025 and 2031 forecasts are less certain because of the potential for significant shifts over that time.

Data reuse caveats: as per license.

Additionally, please read / use this data in conjunction with the Infometrics and Martin Jenkins reports, to understand the uncertainties and assumptions involved in modelling the potential impacts of COVID-19.

COVID-19’s effect on industry and regional economic outcomes for NZ Transport Agency [PDF 620 KB]

Data quality statement: while the modelling undertaken is high quality, it represents two point-in-time analyses undertaken during a period of considerable uncertainty. This uncertainty comes from several factors relating to the COVID-19 pandemic, including:

a lack of clarity about the size of the global downturn and how quickly the international economy might recover differing views about the ability of the New Zealand economy to bounce back from the significant job losses that are occurring and how much of a structural change in the economy is required the possibility of a further wave of COVID-19 cases within New Zealand that might require a return to Alert Levels 3 or 4.

While high levels of uncertainty remain around the scale of impacts from the pandemic, particularly in coming years, the modelling is useful in indicating the direction of travel and the relative scale of impacts in different parts of the country.

Data quality caveats: as noted above, there is considerable uncertainty about the potential scale and duration of the COVID-19 downturn. Please treat the specific results of the modelling carefully, particularly in the forecasts to later years (2025, 2031), given the potential for significant shifts in New Zealand's socio-economic situation before then.

As such, please use the modelling results as a guide to the potential scale of the impacts of the downturn in different locations, rather than as a precise assessment of impacts over the coming decade.

Experience of employment or earnings loss related to COVID-19.

Context

In May 2020, the United States suffered one of the largest single-month job losses in its history as state and local government imposed public policy measures to slow the spread of the COVID-19 virus which, in many cases, forced businesses to close or significantly curtail business activity. But not all counties experienced job losses compared to the prior year. Instead, some supported job gains. Can location quotients, which measure the importance of jobs in specific industries, be efficient predictors of job losses? Are there certain businesses, or groups of businesses, that had an effect on job gains/losses?

Content

The file data.csv contains Quarterly Census of Employment and Wage data published by the U.S. Bureau of Labor Statistics (https://www.bls.gov/cew/). The data is combined data from 2019 and May 2020, for each county, or county-equivalent, in the U.S.

area_fips: FIPS codes for U.S. county and county-equivalent entities area_title: Name of county may2020_empl_yy_pc: Year-over-year percent change in county total employment in May 2020 may2020_empl: Count of total employment in May 2020 naics_1111 to naics_9999: Employment concentration/location quotient for each 4-digit NAICS sectors. A location quotient less than 1.0 indicates that the count's share of sector employment to total employment is lower than the same ratio in the U.S overall, while a location quotient greater than 1.0 means that the county's share of sector employment to total employment is higher than the U.S. ratio. A description of the NAICS 4-digit numeric codes can be found at https://www.bls.gov/cew/classifications/industry/industry-titles.htm.

Decisions to shutdown economic activities to control the spread of COVID-19 early in the pandemic remain controversial, with negative impacts including high rates of unemployment. Here we present a counterfactual scenario for the state of California in which the economy remained open and active during the pandemic’s first year. The exercise provides a baseline against which to compare actual levels of job losses. We developed an economic-epidemiological mathematical model to simulate outbreaks of COVID-19 in ten large Californian socio-economic areas. Results show that job losses are an unavoidable consequence of the pandemic, because even in an open economy, debilitating illness and death among workers drive economic downturns. Although job losses in the counterfactual scenario were predicted to be less than those actually experienced, the cost would have been the additional death or disablement of tens of thousands of workers. Furthermore, whereas an open economy would have favoured populous, services-oriented coastal areas in terms of employment, the opposite would have been true of smaller inland areas and those with relatively larger agricultural sectors. Thus, in addition to the greater cost in lives, the benefits of maintaining economic activity would have been unequally distributed, exacerbating other realized social inequities of the disease’s impact.

The "Historical IT Job Opportunities and Job Loss Percentage Dataset (2000-2023)" is a comprehensive collection of data that tracks the evolution of IT job opportunities and job loss percentages over a span of 24 years. This dataset is a valuable resource for understanding the dynamic nature of the IT job market and the influence of significant economic events on employment trends within the technology sector.

The dataset includes two key columns: "Date" and "Job Loss Percentage Due to Event." The "Date" column records the specific date for each data point, while the "Job Loss Percentage Due to Event" column quantifies the percentage of job losses attributed to various economic events.

Notably, the dataset provides a detailed analysis of the impact of the dotcom crash in the early 2000s, the repercussions of the global financial crisis, and the unprecedented challenges posed by the COVID-19 pandemic on IT job opportunities. It also includes random job loss percentage values between 20% and 60% for the years 2019, 2020, and 2021, offering insights into the variability of job market conditions during these critical years.

Researchers, analysts, and students can use this dataset to conduct in-depth investigations into the fluctuations of IT job opportunities over time and draw conclusions about the resilience and adaptability of the technology job sector in response to economic shifts.

This dataset is a valuable resource for academic research, economic analysis, and career planning within the IT industry.

Belgium Number of Job Losses: Manufacturing data was reported at 176.000 Unit in Mar 2023. This records an increase from the previous number of 163.000 Unit for Feb 2023. Belgium Number of Job Losses: Manufacturing data is updated monthly, averaging 176.000 Unit from Jan 2008 (Median) to Mar 2023, with 183 observations. The data reached an all-time high of 890.000 Unit in May 2012 and a record low of 12.000 Unit in May 2020. Belgium Number of Job Losses: Manufacturing data remains active status in CEIC and is reported by Directorate-General Statistics - Statistics Belgium. The data is categorized under Global Database’s Belgium – Table BE.G032: Number of Job Losses. [COVID-19-IMPACT]

Belgium Number of Job Losses: Human Health and Social Work Activities data was reported at 53.000 Unit in Mar 2023. This records an increase from the previous number of 28.000 Unit for Feb 2023. Belgium Number of Job Losses: Human Health and Social Work Activities data is updated monthly, averaging 21.000 Unit from Jan 2008 (Median) to Mar 2023, with 182 observations. The data reached an all-time high of 240.000 Unit in Feb 2020 and a record low of 0.000 Unit in Aug 2018. Belgium Number of Job Losses: Human Health and Social Work Activities data remains active status in CEIC and is reported by Directorate-General Statistics - Statistics Belgium. The data is categorized under Global Database’s Belgium – Table BE.G032: Number of Job Losses. [COVID-19-IMPACT]

An October 2021 report examined the number of job losses in the out-of-home leisure economy due to the coronavirus (COVID-19) pandemic in the United Kingdom in 2020. According to the study's estimates, the food-led subsector suffered the most from within the out-of-home leisure industry, having lost roughly *** thousand jobs in the first year of the pandemic.

Belgium Number of Job Losses: Other Service Activities data was reported at 144.000 Unit in Mar 2023. This records an increase from the previous number of 67.000 Unit for Feb 2023. Belgium Number of Job Losses: Other Service Activities data is updated monthly, averaging 45.000 Unit from Jan 2008 (Median) to Mar 2023, with 183 observations. The data reached an all-time high of 260.000 Unit in Oct 2012 and a record low of 6.000 Unit in Aug 2021. Belgium Number of Job Losses: Other Service Activities data remains active status in CEIC and is reported by Directorate-General Statistics - Statistics Belgium. The data is categorized under Global Database’s Belgium – Table BE.G032: Number of Job Losses. [COVID-19-IMPACT]

In an opinion poll conducted late March 2020, **** percent of New York City residents surveyed said they or someone in their household had lost their job as a result of the COVID-19 outbreak.