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.

This dataset contains numbers of COVID-19 outbreaks and associated cases, categorized by setting, reported to CDPH since January 1, 2021.

AB 685 (Chapter 84, Statutes of 2020) and the Cal/OSHA COVID-19 Emergency Temporary Standards (Title 8, Subchapter 7, Sections 3205-3205.4) required non-healthcare employers in California to report workplace COVID-19 outbreaks to their local health department (LHD) between January 1, 2021 – December 31, 2022. Beginning January 1, 2023, non-healthcare employer reporting of COVID-19 outbreaks to local health departments is voluntary, unless a local order is in place. More recent data collected without mandated reporting may therefore be less representative of all outbreaks that have occurred, compared to earlier data collected during mandated reporting. Licensed health facilities continue to be mandated to report outbreaks to LHDs.

LHDs report confirmed outbreaks to the California Department of Public Health (CDPH) via the California Reportable Disease Information Exchange (CalREDIE), the California Connected (CalCONNECT) system, or other established processes. Data are compiled and categorized by setting by CDPH. Settings are categorized by U.S. Census industry codes. Total outbreaks and cases are included for individual industries as well as for broader industrial sectors.

The first dataset includes numbers of outbreaks in each setting by month of onset, for outbreaks reported to CDPH since January 1, 2021. This dataset includes some outbreaks with onset prior to January 1 that were reported to CDPH after January 1; these outbreaks are denoted with month of onset “Before Jan 2021.” The second dataset includes cumulative numbers of COVID-19 outbreaks with onset after January 1, 2021, categorized by setting. Due to reporting delays, the reported numbers may not reflect all outbreaks that have occurred as of the reporting date; additional outbreaks may have occurred that have not yet been reported to CDPH.

While many of these settings are workplaces, cases may have occurred among workers, other community members who visited the setting, or both. Accordingly, these data do not distinguish between outbreaks involving only workers, outbreaks involving only residents or patrons, or outbreaks involving both.

Several additional data limitations should be kept in mind:

• Outbreaks are classified as “Insufficient information” for outbreaks where not enough information was available for CDPH to assign an industry code.

• Some sectors, particularly congregate residential settings, may have increased testing and therefore increased likelihood of outbreak recognition and reporting. As a result, in congregate residential settings, the number of outbreak-associated cases may be more accurate.

• However, in most settings, outbreak and case counts are likely underestimates. For most cases, it is not possible to identify the source of exposure, as many cases have multiple possible exposures.

• Because some settings have been at times been closed or open with capacity restrictions, numbers of outbreak reports in those settings do not reflect COVID-19 transmission risk.

• The number of outbreaks in different settings will depend on the number of different workplaces in each setting. More outbreaks would be expected in settings with many workplaces compared to settings with few workplaces.

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.

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 57 thousand employees being laid off. By the second quarter, layoffs impacted more than 43 thousand tech employees. In the final quarter of the year around 12 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 167.6 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 263 thousand laid off employees in the global tech sector by trhe 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.

Full France data by activity (according to the aggregated classification of 88 NAF posts) relating to the carryovers of contributions of employers affiliated to the general scheme in the context of the COVID-19 health crisis (dues from 15 March to 15 December 2020). Situations at the end of March to December 2020.

In order to take into account the impact of the coronavirus outbreak on economic activity, the Urssaf network triggered exceptional measures to support companies with serious cash flow difficulties as of the deadline of 15 March. In the event of major difficulties, companies could postpone, first without prior request and then on request, all or part of the payment of employee and employer contributions.

This dataset describes all the amounts carried over, regardless of whether they are part of a mechanism allowing the carry-over or not. The amounts of carry-overs therefore correspond to the “rests to be recovered”.

The data are declined by payment deadline: the 5th or 15th of the month. Contributions must in principle be paid during the month following the period of paid employment:

• no later than the 5th of that month for employers with at least 50 employees whose pay is paid in the same month as the period of work;

• no later than the 15th of this month in other cases.

Source: ACOSS-Urssaf, extraction early May 2021

Indicators:

• Number of institutions at maturity (*)
• Amount of contributions due
• Number of establishments that have carried over (*)
• Amount of carryovers

(*) WARNING: information on the number of establishments should be interpreted with caution. Indeed, as institutions are counted at each maturity, the selection of a period covering more than one month leads to the same institutions being counted several times (an institution is likely to report each month). Thus, in order to have the total number of institutions without double accounts, it is necessary to select two maturities of the same month.

Methodological clarifications:

• these data take into account the social contribution exemption and payment aid schemes introduced by Article 65 of Law No 2020-935 of 30 July 2020 to support the companies most affected by the crisis. The contributions due are indeed amounts after application of the exemptions. And carry-overs are amounts after the payment aid has been charged, which reduces the amounts to be paid by the undertakings concerned.
• an institution is counted as deferred if the amount of contributions not paid at maturity exceeds EUR 44
• the sector of activity “nca not elsewhere” includes the agricultural sector (AZ) for the general scheme (most of the AZ sector falls under the agricultural scheme, out of field here), extraterritorial activities (UZ) and unknown activities.

DATAVIZ: putting in perspective

Access the Data HereWhat 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).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.

India has witnessed a dramatic shift in the movement of workers within the nation. There are more than 100 million migrant workers in different Industries according to official employment estimation. Migrant workers employed in informal sectors mostly engage in temporary, unskilled work, characterized by low wages, job insecurity, and economic vulnerability, which are peculiar characteristics of informal work environments (Atnafu, 2014)They are exposed to experience multiple psychological disorders like anxiety and depression due to cultural differences in migrated states, loss of social network, identity crises and lack of access to public health care facilities in addition to other problems like malnutrition and acute poverty. COVID 19 has worsened the problem of migrant workers with increased rates of anxiety, stress, and distress psychiatric conditions. With the lockdown and travel restrictions, many groups of stranded laborers were struggling to reach their hometowns. Migrant workers were identified as the most vulnerable group to the risk of catching the COVID- 19 infections. This has added to their mental health issues. With the focus on migrant workers as the subject of investigation, this data set is aimed at enhancing the knowledge of the users of the data on the impact of the pandemic on the mental health issues of the internal migrant workers. Our survey probes into the psychiatric issues that migrant works face and trauma that they underwent during the pandemic using Corona Virus Anxiety Scale (CAS) which was originally developed by Lee in the year 2019. The scale uses four dimensions namely Cognitive, Emotional, Behavioural, and Psychological. The data was constructed with the help interview schedule which was developed by employing Corona Virus Anxiety Scale (CAS). We have developed this data out of 1350 valid responses elicited through the telephonic interview. The data were collected from June to August 2020 which is considered to be the peak pandemic period. We consider the period was crucial since it was characterized by the uncertain atmosphere with job markets badly hit and workers having the fear of reintroducing travel restriction being imposed by the government. Thus the data provides insights and guidance for the interested researchers to carry our related studies on the mental health of vulnerable groups like migrant workers. The data would also enable researchers and academicians to investigate the impact of such pandemic and uncertain events on the mental health of vulnerable groups. This data would enable the researcher to understand the possible adverse impacts on the anxiety levels of the dispersed population of migrant workers.

The number of small and medium-sized enterprises in the United States was forecast to continuously decrease between 2024 and 2029 by in total 6.7 thousand enterprises (-2.24 percent). After the fourteenth consecutive decreasing year, the number is estimated to reach 291.94 thousand enterprises and therefore a new minimum in 2029. According to the OECD an enterprise is defined as the smallest combination of legal units, which is an organisational unit producing services or goods, that benefits from a degree of autonomy with regards to the allocation of resources and decision making. Shown here are small and medium-sized enterprises, which are defined as companies with 1-249 employees.The shown data are an excerpt of Statista's Key Market Indicators (KMI). The KMI are a collection of primary and secondary indicators on the macro-economic, demographic and technological environment in more than 150 countries and regions worldwide. All input data are sourced from international institutions, national statistical offices, and trade associations. All data has been are processed to generate comparable datasets (see supplementary notes under details for more information).

This dataset is part of the wider BARCOVID project, which studies the impact of the COVID-19 pandemic on collective bargaining in Europe. This file is an updated version for a selected number of countries of the WageIndicator Collective Agreements Database (Ceccon and Medas, 2022). The database, maintained by the WageIndicator Foundation (WIF), offers a comprehensive collection of annotated CBAs from around the globe. As of April 2023, it contains 2300+ CBAs in 27 different languages, originating from 67 countries. The database provides a unique opportunity to closely examine and compare agreements across countries, industries, and bargaining levelsFor the BARCOVID project, the CBA Database was expanded with new agreements for several countries. These agreements are (often) renewals of agreements that were already in the database, allowing, before-after comparisons during the COVID19 pandemic. The dataset contains information Austria, Czech Republic, Estonia, France, Hungary, Italy, Netherlands, Poland, Slovakia, Spain and Turkey.

For more detailed information on the coding scheme and variables used in the annotation process, the WageIndicator Collective Agreements Database Codebook (Ceccon and Medas, 2022) can be consulted as a reference - see related identifiers.

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).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.

The policy timeline was developed as a dataset for the Freelancers in the Dark Project by Ali FitzGibbon and Laura Harris with support from Alexandra Young. Using public statements, media coverage and online reporting, it marks dates of relevance to the experience of theatre freelancers between January 2020 (when certain early reporting of COVID-19 began to emerge) and March 2022, the 2 year anniversary of the UK outbreak and lockdowns and end of the research project. The timeline was published using the open-source tool developed by knight lab. An early version was published in June 2021 and contributions invited. The final version was completed on 28 June 2022. Events are labelled according to their place of relevance: UK, England, Wales, Scotland, Northern Ireland or Global. The raw dataset (in MS Excel) can be filtered to support searches for particular changes to guidance, key campaigns from freelancers, trade unions, etc.

COVID-19 threatens the performing arts; closures of theatres and outlawing of public gatherings have proven financially devastating to the industry across the United Kingdom and, indeed, the world. The pandemic has sparked a wide range of industry-led strategies designed to alleviate financial consequences and improve audience capture amidst social distancing. COVID-19 has affected all levels of the sector but poses an existential threat to freelancers--Independent Arts Workers (IAWs)--who make up 60% of industry workforce in the UK (EU Labour Force Survey 2017). The crisis has put a spotlight on the vulnerable working conditions, economic sustainability, mental wellbeing, and community support networks of IAWs. IAWs are often overlooked by the industry and researchers, however it is their very precarity that makes them pioneers of adaptability responsible for key innovation within the sector. IAWs may prove essential for the industry's regrowth post-COVID-19. An investigation is necessary into the impact of COVID-19 on IAWs and the wide-ranging creative solutions developing within the industry to overcome them.

There has been increasing pressure to gather 'robust, real-time data' to investigate the financial, cultural, and social potential long-term consequences of COVID-19 on the UK theatre industry. The impact of the pandemic on IAWs is particularly complex and wide-ranging. A TRG Arts survey stated that 60% of IAWs predict their income will 'more than halve in 2020' while 50% have had 100% of their work cancelled. Industry researchers from TRG Arts and Theatres Trust have launched investigations examining the financial impact of COVID-19 on commercial venues and National Portfolio Organisations, but there has been insufficient research into the consequences for IAWs (eg. actors, directors, producers, writers, theatre makers, technicians) and the smaller SMEs beyond income loss and project cancellation data. In May 2020, Vicky Featherstone of the Royal Court Theatre, stated the importance of support for the 'massive freelance and self-employed workforce' she believed has been 'taken for granted' by the industry. Our study fills this gap by capturing and analysing not only the economic impact, but the social and cultural transformations caused by COVID-19 by and for IAWs. We will compare regional responses across England, Wales, Northern Ireland and Scotland as well as variations across racial and socio-economic groups. Our aims are to document and investigate the impact of COVID-19 on IAWs, identify inequalities in the sector, investigate changes in the type of work produced post-COVID-19, and help develop strategies for how the sector can move forward from this crisis. We will investigate connections between the financial consequences of COVID-19 and creative strategies for industry survival including social support networks, communication initiatives between arts venues and IAWs, and the development of mixed-media work in the wake of the pandemic. Our study scrutinizes the economic, cultural, and social impact of COVID-19 on IAWs and the organisations that serve them with the aim of informing strategies for sector recovery.

The UK Data Service also holds a Secure Access version of the QLFS (see below); household datasets; two-quarter and five-quarter longitudinal datasets; LFS datasets compiled for Eurostat; and some additional annual Northern Ireland datasets.

LFS Documentation
The documentation available from the Archive to accompany LFS datasets largely consists of the latest version of each user guide volume alongside the appropriate questionnaire for the year concerned (the latest questionnaire available covers July-September 2022). Volumes are updated periodically, so users are advised to check the latest documents on the ONS Labour Force Survey - User Guidance pages before commencing analysis. This is especially important for users of older QLFS studies, where information and guidance in the user guide documents may have changed over time.

LFS response to COVID-19

From April 2020 to May 2022, additional non-calendar quarter LFS microdata were made available to cover the pandemic period. The first additional microdata to be released covered February to April 2020 and the final non-calendar dataset covered March-May 2022. Publication then returned to calendar quarters only. Within the additional non-calendar COVID-19 quarters, pseudonymised variables Casenop and Hserialp may contain a significant number of missing cases (set as -9). These variables may not be available in full for the additional COVID-19 datasets until the next standard calendar quarter is produced. The income weight variable, PIWT, is not available in the non-calendar quarters, although the person weight (PWT) is included. Please consult the documentation for full details.

Occupation data for 2021 and 2022 data files

The ONS has identified an issue with the collection of some occupational data in 2021 and 2022 data files in a number of their surveys. While they estimate any impacts will be small overall, this will affect the accuracy of the breakdowns of some detailed (four-digit Standard Occupational Classification (SOC)) occupations, and data derived from them. Further information can be found in the ONS article published on 11 July 2023: Revision of miscoded occupational data in the ONS Labour Force Survey, UK: January 2021 to September 2022.

2024 Reweighting

In February 2024, reweighted person-level data from July-September 2022 onwards were released. Up to July-September 2023, only the person weight was updated (PWT23); the income weight remains at 2022 (PIWT22). The 2023 income weight (PIWT23) was included from the October-December 2023 quarter. Users are encouraged to read the ONS methodological note of 5 February, Impact of reweighting on Labour Force Survey key indicators: 2024, which includes important information on the 2024 reweighting exercise.

End User Licence and Secure Access QLFS data

Two versions of the QLFS are available from UKDS. One is available under the standard End User Licence (EUL) agreement, and the other is a Secure Access version. The EUL version includes country and Government Office Region geography, 3-digit Standard Occupational Classification (SOC) and 3-digit industry group for main, second and last job (from July-September 2015, 4-digit industry class is available for main job only).

The Secure Access version contains more detailed variables relating to:

• age: single year of age, year and month of birth, age completed full-time education and age obtained highest qualification, age of oldest dependent child and age of youngest dependent child
• family unit and household: including a number of variables concerning the number of dependent children in the family according to their ages, relationship to head of household and relationship to head of family
• nationality and country of origin
• finer detail geography: including county, unitary/local authority, place of work, Nomenclature of Territorial Units for Statistics 2 (NUTS2) and NUTS3 regions, and whether lives and works in same local authority district, and other categories;
• health: including main health problem, and current and past health problems
• education and apprenticeship: including numbers and subjects of various qualifications and variables concerning apprenticeships
• industry: including industry, industry class and industry group for main, second and last job, and industry made redundant from
• occupation: including 5-digit industry subclass and 4-digit SOC for main, second and last job and job made redundant from
• system variables: including week number when interview took place and number of households at address
• other additional detailed variables may also be included.

The Secure Access datasets (SNs 6727 and 7674) have more restrictive access conditions than those made available under the standard EUL. Prospective users will need to gain ONS Accredited Researcher status, complete an extra application form and demonstrate to the data owners exactly why they need access to the additional variables. Users are strongly advised to first obtain the standard EUL version of the data to see if they are sufficient for their research requirements.

Latest edition information:

For the third edition (June 2022), 2022 weighting variable PWT22 was added to the study, and the 2020 weight removed.

The UK Data Service also holds a Secure Access version of the QLFS (see below); household datasets; two-quarter and five-quarter longitudinal datasets; LFS datasets compiled for Eurostat; and some additional annual Northern Ireland datasets.

LFS Documentation
The documentation available from the Archive to accompany LFS datasets largely consists of the latest version of each user guide volume alongside the appropriate questionnaire for the year concerned (the latest questionnaire available covers July-September 2022). Volumes are updated periodically, so users are advised to check the latest documents on the ONS Labour Force Survey - User Guidance pages before commencing analysis. This is especially important for users of older QLFS studies, where information and guidance in the user guide documents may have changed over time.

LFS response to COVID-19

From April 2020 to May 2022, additional non-calendar quarter LFS microdata were made available to cover the pandemic period. The first additional microdata to be released covered February to April 2020 and the final non-calendar dataset covered March-May 2022. Publication then returned to calendar quarters only. Within the additional non-calendar COVID-19 quarters, pseudonymised variables Casenop and Hserialp may contain a significant number of missing cases (set as -9). These variables may not be available in full for the additional COVID-19 datasets until the next standard calendar quarter is produced. The income weight variable, PIWT, is not available in the non-calendar quarters, although the person weight (PWT) is included. Please consult the documentation for full details.

Occupation data for 2021 and 2022 data files

The ONS has identified an issue with the collection of some occupational data in 2021 and 2022 data files in a number of their surveys. While they estimate any impacts will be small overall, this will affect the accuracy of the breakdowns of some detailed (four-digit Standard Occupational Classification (SOC)) occupations, and data derived from them. Further information can be found in the ONS article published on 11 July 2023: Revision of miscoded occupational data in the ONS Labour Force Survey, UK: January 2021 to September 2022.

2024 Reweighting

In February 2024, reweighted person-level data from July-September 2022 onwards were released. Up to July-September 2023, only the person weight was updated (PWT23); the income weight remains at 2022 (PIWT22). The 2023 income weight (PIWT23) was included from the October-December 2023 quarter. Users are encouraged to read the ONS methodological note of 5 February, Impact of reweighting on Labour Force Survey key indicators: 2024, which includes important information on the 2024 reweighting exercise.

End User Licence and Secure Access QLFS data

Two versions of the QLFS are available from UKDS. One is available under the standard End User Licence (EUL) agreement, and the other is a Secure Access version. The EUL version includes country and Government Office Region geography, 3-digit Standard Occupational Classification (SOC) and 3-digit industry group for main, second and last job (from July-September 2015, 4-digit industry class is available for main job only).

The Secure Access version contains more detailed variables relating to:

• age: single year of age, year and month of birth, age completed full-time education and age obtained highest qualification, age of oldest dependent child and age of youngest dependent child
• family unit and household: including a number of variables concerning the number of dependent children in the family according to their ages, relationship to head of household and relationship to head of family
• nationality and country of origin
• finer detail geography: including county, unitary/local authority, place of work, Nomenclature of Territorial Units for Statistics 2 (NUTS2) and NUTS3 regions, and whether lives and works in same local authority district, and other categories;
• health: including main health problem, and current and past health problems
• education and apprenticeship: including numbers and subjects of various qualifications and variables concerning apprenticeships
• industry: including industry, industry class and industry group for main, second and last job, and industry made redundant from
• occupation: including 5-digit industry subclass and 4-digit SOC for main, second and last job and job made redundant from
• system variables: including week number when interview took place and number of households at address
• other additional detailed variables may also be included.

The Secure Access datasets (SNs 6727 and 7674) have more restrictive access conditions than those made available under the standard EUL. Prospective users will need to gain ONS Accredited Researcher status, complete an extra application form and demonstrate to the data owners exactly why they need access to the additional variables. Users are strongly advised to first obtain the standard EUL version of the data to see if they are sufficient for their research requirements.

Latest edition information

For the eighth edition (January 2025), the 2022 person weight (PWT22) was replaced with the 2024 person weight (PWT24). Only the person weight has been replaced with a 2024 version; the 2022 income weight (PIWT22) remains.

Business Oregon was allocated American Rescue Plan Act (ARPA) Coronavirus State Fiscal Recovery Funds to assist various industries negatively affected by by the COVID-19 pandemic. Awards were executed in three phases that served different sectors impacted by the pandemic, the Small and Independent Community Movie Theaters Program, the Live Event Venue Operators and Producer/Presenters Program, and the Live Event Support Industries Program. This report provides data for Fiscal Year 2023 For more information, please visit https://www.oregon.gov/biz/programs/ARPA_live_events/Pages/default.aspx

An aggregated dataset of PPP (Paycheck Protection Program) SBA (Small Business Administration) loans involving 3 million businesses would be a comprehensive collection of financial information, aimed at analyzing the distribution and impact of these loans. This dataset would include key details such as the names of the businesses, loan amounts, loan disbursement dates, and the terms of the loans. Additionally, the dataset would contain information on board members of these businesses, providing insights into the governance structures and potential networks influencing the flow of SBA funds. This aspect of the dataset can be crucial for understanding the distribution patterns of PPP loans, identifying trends in funding allocation among different types of businesses, and examining any correlations between board composition and loan receipt. Such a dataset would be valuable for various analyses, including: Financial Analysis: Assessing the financial health and stability of businesses that received PPP loans, and understanding how these loans have impacted their operations during challenging economic times. Governance Analysis: Evaluating the role of board members in acquiring PPP loans, and whether certain types of governance structures were more successful in securing funds. Economic Impact Assessment: Measuring the broader economic impact of the PPP loans, such as job retention, business survival rates, and sector-wise distribution of funds. Network Analysis: Mapping the connections between different businesses and their board members to identify any potential networks or clusters that may have influenced the flow of funds. Policy Evaluation: Providing data-driven insights to policymakers for assessing the effectiveness of the PPP program and for planning future economic relief measures.

According to Cognitive Market Research, The Global EDA Market size will be USD 14.9 billion in 2023 and will grow at a compound annual growth rate (CAGR) of 10.50% from 2023 to 2030.

The demand for the EDA Market is rising due to the rise in outdoor and adventure activities.
Changing consumer lifestyle trends are higher in the EDA market.
The cat segment held the highest EDA Market revenue share in 2023.
North American EDA will continue to lead, whereas the European EDA Market will experience the most substantial growth until 2030.

Supply Chain and Risk Analysis to Provide Viable Market Output

The industry is facing supply chain and logistics disruptions. EDA tools have been instrumental in analyzing supply chain data, identifying vulnerabilities, predicting risks, and developing disruption mitigation strategies. Consumer behavior has undergone drastic changes due to blockages and restrictions. EDA helps companies analyze changing trends in buying behavior, online shopping preferences, and demand patterns, enabling organizations to adjust their marketing and sales strategies accordingly.

Health and Pharmaceutical Research to Propel Market Growth.

EDA tools have played a key role in analyzing large amounts of data related to vaccine development, drug trials, patient records and epidemiological studies. These tools have helped researchers process and interpret complex medical data, leading to advances in the development of treatments and vaccines. The pandemic has created challenges in data collection, especially in sectors affected by lockdowns or blackouts. Rapidly changing conditions and incomplete data sets make effective EDA difficult due to data quality issues. The economic uncertainty caused by the pandemic has led to budget cuts in some sectors, impacting investment in new technologies. Some organizations have limited budgets that limit their ability to adopt or update EDA tools.

Market Dynamics of the EDA

Privacy and Data Security Issues to Restrict Market Growth.

With the focus on data privacy regulations such as GDPR, CCPA, etc., organizations need to ensure compliance when handling sensitive data. These compliance requirements may limit the scope of the EDA by limiting the availability and use of certain data sets for information analysis. EDA often requires data analysts or data scientists who are skilled in statistical analysis and data visualization tools. A lack of professionals with these specialized skills can hinder an organization's ability to use EDA tools effectively, limiting adoption. Advanced EDA techniques can involve complex algorithms and statistical techniques that are difficult for non-technical users to understand. Interpreting results and deriving actionable insights from EDA results pose challenges that affect applicability to a wider audience.

Key Opportunity of market.

Growing miniaturization in various industries can be an opportunity.

With the age of highly advanced electronics, miniaturization has become a trend that enabled organizations across diverse sectors such as healthcare, consumer electronics, aerospace and defense, automotive and others to design miniature electronic devices. The devices incorporate miniaturized semiconductor components, e.g., surgical instruments and blood glucose meters in healthcare, fitness bands in wearable devices, automotive modules in the automotive sector, and intelligent baggage labels. Miniaturization has a number of advantages such as freeing space for other features and better batteries. The increased consciousness among consumers towards fitness is fueling the demand for smaller fitness devices such as smartwatches and fitness trackers. This is motivating companies to come up with innovative products with improved features, while researchers are concentrating on cost-effective and efficient product development through electronic design tools. Besides, use of portable equipment has gained immense popularity among media professionals because of the increasing demand for live reporting of different events like riots, accidents, sports, and political rallies. As a result of the inconvenience in the use of cumbersome TV production vans to access such events, demand for portable handheld equipment has risen. Such devices are simply portable and can be quickly moved to the event venue if carried in backpacks. Therefore, the need for compact devices across various indust...

This study investigated the relationship between demographic, healthcare, and socio-economic factors, and COVID-19 incidence rate per 100,000 population in Thailand at the province level between January 2020 and March 2022, using a five-phase approach by spatial analysis. OLS models were initially used with significant variables: household, hospital, and industry density, nighttime light index (NTLI). Spatial dependency led to spatial error (SEM) and spatial lag models (SLM), performing better with similar significant variables being applied. SEM explains 58, 65 and, 70 percent in Wave 1, 4 and 5 of COVID-19 variation. SLM explains 25 and 76 percent in Wave 2 and 3 of incidence rate. Positive associations were found between incidence and household density, hospital/medical establishments with beds, Nighttime Light Index (NTLI), and negative with population, hospital, and industry density. Wave 5 showed significant changes with negative for household, hospital, and industry density, urban population; positive for hospital/medical establishments with beds, internet access, NTLI. The study showed that significant predictors of COVID-19 incidence rate vary across waves. Population, household and hospital density, urbanization, access to medical facilities, industrialization, internet access, and NTLI all play a role. The study suggests SEM and SLM models are more appropriate, providing useful information for policymakers and health officials in managing pandemic in Thailand.