The trend of working remotely has been slowly increasing globally since 2015, with a *** to ***** percent annual increase rate. However, the COVID-19 pandemic in 2020 upended the world economy and global markets. Employment trends were no exception to this, with the share of employees working remotely increasing to some ** percent in 2022 from just ** percent two years prior. The industry with the highest share of remote workers globally in 2023 was by far the technology sector, with over ** percent of tech employees worldwide working fully or mostly remotely. How are employers dealing with remote work? Many employers around the world have already adopted some remote work policies. According to IT industry leaders, reasons for remote work adoption ranged from a desire to broaden a company’s talent pool, increase productivity, and reduce costs from office equipment or real estate investments. Nonetheless, employers worldwide grappled with various concerns related to hybrid work. Among tech leaders, leading concerns included enabling effective collaboration and preserving organizational culture in hybrid work environments. Consequently, it’s unsurprising that maintaining organizational culture, fostering collaboration, and real estate investments emerged as key drivers for return-to-office mandates globally. However, these efforts were not without challenges. Notably, ** percent of employers faced employee resistance to returning to the office, prompting a review of their remote work policies.

Experimental estimates from the Annual Population Survey for homeworking in the UK, including breakdowns by sex, full-time or part-time, ethnicity, occupation, industry, qualifications, hours worked, pay and sickness absence among others. Includes regression outputs on the different outcomes for homeworkers.

This dataset contains synthetic data on productivity, working hours, and well-being indicators for remote and in-office workers. It aims to help analyze the impact of work environment on various productivity and well-being metrics.

Before the coronavirus (COVID-19) pandemic, 17 percent of U.S. employees worked from home 5 days or more per week, a share that increased to 44 percent during the pandemic. The outbreak of the COVID-19 pandemic accelerated the remote working trend, as quarantines and lockdowns made commuting and working in an office close to impossible for millions around the world. Remote work, also called telework or working from home (WFH), provided a solution, with employees performing their roles away from the office supported by specialized technology, eliminating the commute to an office to remain connected with colleagues and clients. What enables working from home?

To enable remote work, employees rely on a remote work arrangements that enable hybrid work and make it safe during the COVID-19 pandemic. Technology supporting remote work including laptops saw a surge in demand, video conferencing companies such as Zoom jumped in value, and employers had to consider new communication techniques and resources. Is remote work the future of work?

The response to COVID-19 has demonstrated that hybrid work models are not necessarily an impediment to productivity. For this reason, there is a general consensus that different remote work models will persist post-COVID-19. Many employers see benefits to flexible working arrangements, including positive results on employee wellness surveys, and potentially reducing office space. Many employees also plan on working from home more often, with 25 percent of respondents to a recent survey expecting remote work as a benefit of employment. As a result, it is of utmost importance to acknowledge any issues that may arise in this context to empower a hybrid workforce and ensure a smooth transition to more flexible work models.

Employed persons working from home as a percentage of the total employment, by sex, age and professional status (%)

As of 2023, 8.9 percent of employed people in the European Union usually worked from home. This share of home-office workers varied widely between European countries, with a 21 percent of finish workers usually working from home, compared to only one percent of Romanian workers. It was in general more common for women to work from home usually than men, however, this was notably reversed in some countries, such as Ireland where almost 23 percent of men regularly worked from home.

This dataset reflects the volume and percentage of MCG employees by home and work location. Data is determined by information currently in employee personnel file (Data includes full-time and part-time regular employees only). Update Frequency : Annually

Data on working patterns and location of work of adults in Great Britain, including costs and benefits of homeworking and future expectations. Survey data from the Opinions and Lifestyle Survey (OPN).

“Telework”, “home office” and “work from home” have recently become very prominent working concepts due to social distancing regulations during the COVID-19 pandemic. According to a study by Kohlrausch and Zucco (2020), in Germany the share of people who regularly work from home has increased from about 4% before the pandemic to approx. 20% during the first wave of the pandemic. Furthermore, the share of workers who alternate between business and home office also increased. In this development, telework was not equally distributed across all occupational and social groups. With the project “Household structures and economic risks in East and West Germany during the COVID-19 pandemic: compensation or accumulation? (KOMPAKK)” we define economic risks that people were exposed to due to the COVID-19 pandemic. We therefore calculate several risk factors based on survey data from 2017 and 2018. As some occupations might be well executed from home while others are not, we created an index which reflects the possibility of working from home.

Remote Work Statistics: The future is here we say, as technology made sure to let employees spread around the globe to work remotely. Just before the pandemic people commuting to offices daily shifted to completely mobile work opportunities. Market reports of distance work state that the future of remote work will be adopted by many companies soon as employees focus on such job opportunities only. These Remote Work Statistics are written from various aspects that need to be taken into consideration while setting policies for mobile work. Editor’s Choice Mobile workers with communicative employers are 5X more productive and 3X less feel burned out. 25% of remote employees are planning to change their locations for a better lifestyle. Around 55% of Americans believe their work can be performed remotely in their industry. Remote work statistics say that, in May 2021, remote work job postings on LinkedIn increased by 350%. Remote work Statistics state that in the year 2022, the remote workplace market was valued at $20.1 billion, and it is projected to reach 58.5 billion by the year 2027 at a CAGR of 23.8%. 59% of distance employees said, their office is functional in 2 to 5 various times zones. For every mobile work employee companies save around $22K every month, on the other hand, employees save on average $4000 every year due to a reduction in commute. In the upcoming years, employers are planning to spend more on remote work tools as well as virtual manager training. 16% of people say that they are worried about their company not allowing mobile work once the pandemic ends. On average, women are more like to work remotely than men as stated by Remote Work Statistics.

This dataset comes from the biennial City of Tempe Employee Survey question about feeling safe in the physical work environment (building). The Employee Survey question relating to this performance measure: “Please rate your level of agreement: My physical work environment (building) is safe, clean & maintained in good operating order.” Survey respondents are asked to rate their agreement level on a scale of 5 to 1, where 5 means “Strongly Agree” and 1 means “Strongly Disagree” (without “don’t know” responses included).The survey was voluntary, and employees were allowed to complete the survey during work hours or at home. The survey allowed employees to respond anonymously and has a 95% confidence level. This page provides data about the Feeling Safe in City Facilities performance measure. The performance measure dashboard is available at 1.11 Feeling Safe in City FacilitiesAdditional InformationSource: Employee SurveyContact: Wydale HolmesContact E-Mail: Wydale_Holmes@tempe.govData Source Type: CSVPreparation Method: Data received from vendor and entered in CSVPublish Frequency: BiennialPublish Method: ManualData Dictionary (update pending)

WageIndicator is interviewing people around the world to discover what makes the Coronavirus lockdown easier (or tougher), and what is the COVID-19 effect on our jobs, lives and mood. WageIndicator shows coronavirus-induced changes in living and working conditions in over 110 countries on the basis of answers on the following questions among others in the Corona survey: Is your work affected by the corona crisis? Are precautionary measures taken at the workplace? Do you have to work from home? Has your workload increased/decreased? Have you lost your job/work/assignments? The survey contains questions about the home situation of respondents as well as about the possible manifestation of the corona disease in members of the household. Also the effect of having a pet in the house in corona-crisis times is included.

Ever wondered what REALLY drives employee turnover, performance, and retention? This power-packed dataset of 50,000 records uncovers the hidden patterns behind workforce dynamics, helping you decode the true story of hiring, leadership influence, and workplace engagement.

🔍 What’s Inside? 📅 Time-Based Analysis: Track hiring, promotions, and attrition over time. 👥 Leadership Influence: Identify which Senior Leaders drive success or struggle with retention. 📊 Performance & Productivity: Measure engagement, stress levels, job satisfaction, and training effectiveness. 💰 Hiring & Cost Efficiency: Evaluate recruitment costs, time to fill positions, and internal promotions. 🏡 Work-Life Balance: Analyze work-from-home trends, overtime, and stress levels across departments. 🎯 Retention & Risk Factors: Discover who is most at risk of leaving and why with retention risk analytics.

🔥 What Can You Do With It? ✅ Build Stunning Power BI Dashboards – Transform raw data into interactive insights. ✅ Solve Real-World HR Challenges – Use analytics to predict attrition, optimize hiring, and improve retention. ✅ Uncover Leadership Trends – Identify which leaders foster growth vs. those driving attrition. ✅ Analyze Workplace Culture – Understand how job satisfaction, training, and diversity impact engagement.

🔹 Problem 1: Attrition Analysis - Who is Leaving and Why? Scenario: Your company is experiencing a high turnover rate, and leadership wants to understand who is leaving and why.

Problem 2: Leadership Impact - Who is Retaining vs. Losing Talent? Scenario: Your company’s leadership wants to assess the effectiveness of senior leaders in retaining talent and managing high-performing teams.

Problem 3: Hiring Effectiveness - Which Sources Work Best? Scenario: HR wants to optimize the hiring process by identifying the most effective recruitment sources.

Problem 4: Workforce Diversity - Is the Organization Inclusive? Scenario: The leadership wants to understand diversity trends and whether they need to improve inclusivity in hiring.

Problem 5: Work-Life Balance - Who is Overworked? Scenario: There are concerns that some employees are working too many hours, leading to burnout and lower engagement.

Problem 6: Performance & Compensation - Are High Performers Paid Well? Scenario: The HR department suspects that high performers are not being fairly compensated.

Problem 7: Training Effectiveness - Does Training Improve Performance? Scenario: HR wants to assess whether training programs are improving employee performance and retention.

Background: Due to the Covid-19 pandemic, in 2020, many employees requested to work from home (WFH). During this WFH period, some employees encountered health issues related to sprains and neck or back pain owing to poor working conditions at home. As the WFH trend may continue over a prolonged period, the actual causes and solutions to ergonomic issues must be addressed to reduce injuries.Purpose: This study aims to identify the ergonomic issues encountered when working from home and suggest several solutions to minimise these issues.Methods: A qualitative ethnographic methodology was adopted. This study included a focus group discussion among experts from the fields of higher education, healthcare, human resource (HR), and ergonomics. The most common ergonomic issues identified were based on diagnoses and observations in previous studies.Results: The panellists agreed on ergonomics issues—comprising the use of unergonomic chairs, incorrect sitting postures, irregular arrangement of key objects, improper reach distances of the laptop/keyboard/mouse, poor desk designs, footrest absence, distortion/noise, poor lighting, and poor work environment. In the long run, WFH ergonomics issues may lead to burnout, carpal tunnel syndrome or other cumulative trauma disorders, high blood pressure, and stress on the cervical spine and neck. The proposed solutions include a complete WFH ergonomics and wellness checklist for employees and employers, webinar sessions on WFH ergonomics, meet-up sessions with ergonomics or HR experts, workspace rentals for co-workers, implementation of the 20-20-20 rule and job-sharing practices, and the involvement of employers or the government in procuring ergonomic equipment for WFH employees.Conclusions: This is a preliminary study and the researchers are exploring the root causes of WFH ergonomics issues and proposed solutions. While previous studies have examined workplace ergonomics, this study only focuses on WFH ergonomic issues and solutions during the ongoing pandemic.

The study on the future of work was conducted by Kantar Public on behalf of the Press and Information Office of the Federal Government. During the survey period from 13 to 22 June 2023, German-speaking people aged 16 to 67 in Germany, excluding pensioners, were surveyed in online interviews (CAWI) on the following topics: current life and work situation, future expectations, the use of AI and the digitalization of the world of work as well as attitudes towards demographic change and the shortage of skilled workers. The respondents were selected using a quota sample from an online access panel. Future: general life satisfaction; satisfaction with selected aspects of life (working conditions, education, qualifications, health situation, professional remuneration, family situation, financial situation); expectations for the future: rather confident vs. rather worried about the private and professional future; rather confident vs. rather worried about the professional future of younger people or the next generation; rather confident vs. rather worried about the future of Germany; confidence vs. concern regarding the competitiveness of the German economy in various areas (digitalization and automation of the working world, climate protection goals of industry, effects of the Ukraine war on the German economy, access to important raw materials such as rare earths or metals, reliable supply of energy, number of qualified specialists, general price development, development of wages and salaries, development of pensions); probability of various future scenarios for Germany in 2030 (Germany is once again the world export champion, unemployment is at an all-time low - full employment prevails in Germany, the energy transition has already created hundreds of thousands of new jobs in German industry, Germany has emerged the strongest in the EU from the crises of the last 15 years, the price crisis has led to the fact The price crisis has meant that politics and business have successfully set the course for the future, citizens can deal with all official matters digitally from home, German industry is much faster than expected in terms of climate targets and is already almost climate-neutral, Germany is the most popular country of immigration for foreign university graduates, the nursing shortage in Germany has been overcome thanks to the immigration of skilled workers). 2. Importance of work: importance of different areas of life (ranking); work to earn money vs. as a vocation; importance of different work characteristics (e.g. job security, adequate income, development prospects and career opportunities, etc.). 3. Professional situation: satisfaction with various aspects of work (job security, pay/income, development/career opportunities, interesting work, sufficient contact with other people, compatibility of family/private life and work. Work climate/ working atmosphere, further training opportunities, social recognition, meaningful and useful work); job satisfaction; expected development of working conditions in own professional field; recognition for own work from the company/ employer, from colleagues, from other people from the work context, from the personal private environment, from society in general and from politics; unemployed people were asked: currently looking for a new job; assessment of chances of finding a new job; pupils, students and trainees were asked: assessment of future career opportunities; reasons for assessing career opportunities as poor (open). 4. AI: use of artificial intelligence (AI) in the world of work rather as an opportunity or rather as a danger; expected effects of AI on working conditions in their own professional field (improvement, deterioration, no effects); opportunities and dangers of digitization, AI and automation based on comparisons (all in all, digitization leads to a greater burden on the environment, as computers, tablets, smartphones and data centers are major power guzzlers vs. All in all, digitalization protects the environment through less mobility and more efficient management, artificial intelligence and digitalization help to reduce the workload and relieve employees of repetitive and monotonous tasks vs. artificial intelligence and digitalization overburden many employees through further work intensification. Stress and burnouts will increasingly be the result, artificial intelligence and digitalization will primarily lead to job losses vs. artificial intelligence and digitalization will create more new, future-proof jobs than old ones will be lost, our economy will benefit greatly from global networking through speed and efficiency gains vs. our economy is threatened by global networking by becoming more susceptible to cyberattacks and hacker attacks, digitalization will lead to new, more flexible working time models and a better work-life balance vs. digitalization will lead to a blurring of boundaries between work and leisure time and thus, above all, to more self-exploitation by employees). 5. Home office: local focus of own work currently, before the corona pandemic and during the corona pandemic (exclusively/ predominantly in the company or from home, at changing work locations (company, at home, mobile from on the road); Agreement with various statements on the topic of working from home (wherever possible, employers should give their employees the opportunity to work from home, working from home leads to a loss of cohesion in the company, working from home enables a better work-life balance, digital communication makes coordination processes more complicated, home office makes an important contribution to climate protection due to fewer journeys to work, home office leads to a mixture of work and leisure time and thus to a greater workload, home office leads to greater job satisfaction and thus to higher productivity, since many professions cannot be carried out in the home office, it would be fairer if everyone had to work outside the home); attitude towards a general 4-day working week (A four-day week for everyone would increase the shortage of skilled workers vs. a four-day week for everyone would increase motivation and therefore productivity). 6. Demographic change: knowledge of the meaning of the term demographic change; expected impact of demographic change on the future of Germany; opinion on the future in Germany based on alternative future scenarios (in the future, poverty in old age will increase noticeably vs. the future generation of pensioners will be wealthier than ever before, in the future, politics and elections will be increasingly determined by older people vs. the influence of the younger generation on politics will become much more important, our social security systems will continue to ensure intergenerational fairness and equalization in the future vs. the distribution conflicts between the younger and older generations will increase noticeably, future generations will have to work longer due to the shortage of skilled workers vs. people will have to work less in the future due to digitalization and automation and will be able to retire earlier). 7. Shortage of skilled workers: shortage of skilled workers in own company; additional personal burden due to shortage of skilled workers; company is doing enough to counteract the shortage of skilled workers; use of artificial intelligence (AI) in the company could compensate for the shortage of skilled workers; evaluation of various measures taken by the federal government to combat the shortage of skilled workers (improvement of training and further education opportunities, increasing the participation of women in the labor market (e.g. by expanding childcare services, more flexible working hours, offers for older skilled workers to stay in work longer, facilitating the immigration of foreign skilled workers); evaluation of the work of the federal government to combat the shortage of skilled workers; attractiveness (reputation in society) of various professions with a shortage of skilled workers (e.g. social pedagogues/educators); evaluation of the work of the federal government to combat the shortage of skilled workers. B. social pedagogue, nursery school teacher, etc.); job recommendation for younger people; own activity in one of the professions mentioned with a shortage of skilled workers. Demography: sex; age; age in age groups; employment; federal state; region west/east; school education; vocational training; self-placement social class; employment status; occupation differentiated workers, employees, civil servants; industry; household size; number of children under 18 in the household; net household income (grouped); location size; party sympathy; migration background (respondent, one parent or both parents). Additionally coded were: consecutive interview number; school education head group (low, medium, high); weighting factor.

ObjectiveThe study aimed to investigate perceptions and determinants of the overall impact on life and work domains among a community of knowledge workers after 18 months of forced work from home due to the pandemic.MethodsA cross-sectional study with a retrospective assessment was conducted early in 2022 at the National Research Council of Italy. Five single-item questions explored the perceived impact on life domain while a 7-item scale the impact on the work domain. Bivariate analyses and multivariate regressions were used to evaluate the associations between impacts and some key factors defined by 29 ad hoc closed questions.ResultsMore than 95% of the 748 respondents reported a perceived change in at least one item of the life domain. For each of these items, although a large group of subjects has reported that working from home had no impact (from 27 to 55%), in the rest of the sample the positive evaluation (from 30 to 60%) clearly prevailed over the negative one. Overall, most of the subjects (64%) rated the impact on the work experience positively. Relationship with colleagues and participation in the work context were the items where the greatest number of negative rates was concentrated (27 and 25%, respectively). On the other hand, positive perceptions prevailed over both negative perceptions and lack of impact perceptions on the subjects of organizational flexibility and quality of work. The frequency of work-room sharing, home-work commute time and changes in sedentary lifestyle, have been identified as common explanatory factors of perceived impacts on both domains.ConclusionOverall, respondents reported positive rather than negative perceived impacts of forced work from home in both their lives and work. The obtained results suggest that policies to promote the physical and mental health of employees, strengthen inclusion and maintain a sense of community are necessary to improve workers' health and prevent the effects of perceived isolation on research activities.

As the UK went into the first lockdown of the COVID-19 pandemic, the team behind the biggest social survey in the UK, Understanding Society (UKHLS), developed a way to capture these experiences. From April 2020, participants from this Study were asked to take part in the Understanding Society COVID-19 survey, henceforth referred to as the COVID-19 survey or the COVID-19 study.The COVID-19 survey regularly asked people about their situation and experiences. The resulting data gives a unique insight into the impact of the pandemic on individuals, families, and communities. The COVID-19 Teaching Dataset contains data from the main COVID-19 survey in a simplified form. It covers topics such as Socio-demographics Whether working at home and home-schooling COVID symptoms Health and well-being Social contact and neighbourhood cohesion Volunteering The resource contains two data files: Cross-sectional: contains data collected in Wave 4 in July 2020 (with some additional variables from other waves); Longitudinal: Contains mainly data from Waves 1, 4 and 9 with key variables measured at three time points. Key features of the dataset Missing values: in the web survey, participants clicking "Next" but not answering a question were given further options such as "Don't know" and "Prefer not to say". Missing observations like these are recorded using negative values such as -1 for "Don't know". In many instances, users of the data will need to set these values as missing. The User Guide includes Stata and SPSS code for setting negative missing values to system missing.

The Longitudinal file is a balanced panel and is in wide format. A balanced panel means it only includes participants that took part in every wave. In wide format, each participant has one row of information, and each measurement of the same variable is a different variable.

Weights: both the cross-sectional and longitudinal files include survey weights that adjust the sample to represent the UK adult population. The cross-sectional weight (betaindin_xw) adjusts for unequal selection probabilities in the sample design and for non-response. The longitudinal weight (ci_betaindin_lw) adjusts for the sample design and also for the fact that not all those invited to participate in the survey, do participate in all waves.

Both the cross-sectional and longitudinal datasets include the survey design variables (psu and strata). A full list of variables in both files can be found in the User Guide appendix.Who is in the sample?All adults (16 years old and over as of April 2020), in households who had participated in at least one of the last two waves of the main study Understanding Society, were invited to participate in this survey. From the September 2020 (Wave 5) survey onwards, only sample members who had completed at least one partial interview in any of the first four web surveys were invited to participate. From the November 2020 (Wave 6) survey onwards, those who had only completed the initial survey in April 2020 and none since, were no longer invited to participate The User guide accompanying the data adds to the information here and includes a full variable list with details of measurement levels and links to the relevant questionnaire.

Socio-demographics; Whether working at home and home-schooling; COVID symptoms; Health and well-being; Social contact and neighbourhood cohesion; Volunteering.

The increase in the extent of working-from-home determined by the COVID-19 health crisis has led to a substantial shift of economic activity across geographical areas; which we refer to as a Zoomshock. When a person works from home rather than at the office, their work-related consumption of goods and services provided by the locally consumed service industries will take place where they live, not where they work. Much of the clientèle of restaurants, coffee bars, pubs, hair stylists, health clubs, taxi providers and the like located near workplaces is transferred to establishment located near where people live. These data are our calculations of the Zoomshock at the MSOA level. They reflect estimats of the change in the number of people working in UK neighbourhoods due to home-working.The COVID-19 shutdown is not affecting all parts of the UK equally. Economic activity in local consumer service industries (LCSI), such as retail outlets, restaurants, hairdressers, or gardeners has all but stopped; other industries are less affected. These differences among industries and their varying importance across local economies means recovery will be sensitive to local economic conditions and will not be geographically uniform: some neighbourhoods face a higher recovery risk of not being able to return to pre-shutdown levels of economic activity. This recovery risk is the product of two variables. The first is the shock, the effect of the shutdown on local household incomes. The second is the multiplier, the effect on LCSI economic activity following a negative shock to household incomes. In neighbourhoods where many households rely on the LCSI sector as a primary source of income the multiplier may be particularly large, and these neighbourhoods are vulnerable to a vicious circle of reduced spending and reduced incomes. This project will produce data measuring the shock, the multiplier, and the COVID-19 shutdown recovery risk for UK neighbourhoods. These variables will be estimated using individual and firm level information from national surveys and administrative data. The dataset, and corresponding policy report, will be made public and proactively disseminated to guide local and national policy design. Recovery inequality is likely to be substantial: absent intervention, existing regional inequalities may be exacerbated. This research will provide a timely and necessary input into designing appropriate recovery policy. These data reflect derived variables based on the methodology described in De Fraja, Matheson and Rockey (2021) (https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3752977). Variables are derived from 2011 Census data provided through the ONS Nomis website.

Due to changes in the collection and availability of data on COVID-19, this website will no longer be updated. The webpage will no longer be available as of 11 May 2023. On-going, reliable sources of data for COVID-19 are available via the COVID-19 dashboard and the UKHSA GLA Covid-19 Mobility Report Since March 2020, London has seen many different levels of restrictions - including three separate lockdowns and many other tiers/levels of restrictions, as well as easing of restrictions and even measures to actively encourage people to go to work, their high streets and local restaurants. This reports gathers data from a number of sources, including google, apple, citymapper, purple wifi and opentable to assess the extent to which these levels of restrictions have translated to a reductions in Londoners' movements. The data behind the charts below come from different sources. None of these data represent a direct measure of how well people are adhering to the lockdown rules - nor do they provide an exhaustive data set. Rather, they are measures of different aspects of mobility, which together, offer an overall impression of how people Londoners are moving around the capital. The information is broken down by use of public transport, pedestrian activity, retail and leisure, and homeworking. Public Transport For the transport measures, we have included data from google, Apple, CityMapper and Transport for London. They measure different aspects of public transport usage - depending on the data source. Each of the lines in the chart below represents a percentage of a pre-pandemic baseline. activity Source Latest Baseline Min value in Lockdown 1 Min value in Lockdown 2 Min value in Lockdown 3 Citymapper Citymapper mobility index 2021-09-05 Compares trips planned and trips taken within its app to a baseline of the four weeks from 6 Jan 2020 7.9% 28% 19% Google Google Mobility Report 2022-10-15 Location data shared by users of Android smartphones, compared time and duration of visits to locations to the median values on the same day of the week in the five weeks from 3 Jan 2020 20.4% 40% 27% TfL Bus Transport for London 2022-10-30 Bus journey ‘taps' on the TfL network compared to same day of the week in four weeks starting 13 Jan 2020 - 34% 24% TfL Tube Transport for London 2022-10-30 Tube journey ‘taps' on the TfL network compared to same day of the week in four weeks starting 13 Jan 2020 - 30% 21% Pedestrian activity With the data we currently have it's harder to estimate pedestrian activity and high street busyness. A few indicators can give us information on how people are making trips out of the house: activity Source Latest Baseline Min value in Lockdown 1 Min value in Lockdown 2 Min value in Lockdown 3 Walking Apple Mobility Index 2021-11-09 estimates the frequency of trips made on foot compared to baselie of 13 Jan '20 22% 47% 36% Parks Google Mobility Report 2022-10-15 Frequency of trips to parks. Changes in the weather mean this varies a lot. Compared to baseline of 5 weeks from 3 Jan '20 30% 55% 41% Retail & Rec Google Mobility Report 2022-10-15 Estimates frequency of trips to shops/leisure locations. Compared to baseline of 5 weeks from 3 Jan '20 30% 55% 41% Retail and recreation In this section, we focus on estimated footfall to shops, restaurants, cafes, shopping centres and so on. activity Source Latest Baseline Min value in Lockdown 1 Min value in Lockdown 2 Min value in Lockdown 3 Grocery/pharmacy Google Mobility Report 2022-10-15 Estimates frequency of trips to grovery shops and pharmacies. Compared to baseline of 5 weeks from 3 Jan '20 32% 55.00% 45.000% Retail/rec Google Mobility Report 2022-10-15 Estimates frequency of trips to shops/leisure locations. Compared to baseline of 5 weeks from 3 Jan '20 32% 55.00% 45.000% Restaurants OpenTable State of the Industry 2022-02-19 London restaurant bookings made through OpenTable 0% 0.17% 0.024% Home Working The Google Mobility Report estimates changes in how many people are staying at home and going to places of work compared to normal. It's difficult to translate this into exact percentages of the population, but changes back towards ‘normal' can be seen to start before any lockdown restrictions were lifted. This value gives a seven day rolling (mean) average to avoid it being distorted by weekends and bank holidays. name Source Latest Baseline Min/max value in Lockdown 1 Min/max value in Lockdown 2 Min/max value in Lockdown 3 Residential Google Mobility Report 2022-10-15 Estimates changes in how many people are staying at home for work. Compared to baseline of 5 weeks from 3 Jan '20 131% 119% 125% Workplaces Google Mobility Report 2022-10-15 Estimates changes in how many people are going to places of work. Compared to baseline of 5 weeks from 3 Jan '20 24% 54% 40% Restriction Date end_date Average Citymapper Average homeworking Work from home advised 17 Mar '20 21 Mar '20 57% 118% Schools, pubs closed 21 Mar '20 24 Mar '20 34% 119% UK enters first lockdown 24 Mar '20 10 May '20 10% 130% Some workers encouraged to return to work 10 May '20 01 Jun '20 15% 125% Schools open, small groups outside 01 Jun '20 15 Jun '20 19% 122% Non-essential businesses re-open 15 Jun '20 04 Jul '20 24% 120% Hospitality reopens 04 Jul '20 03 Aug '20 34% 115% Eat out to help out scheme begins 03 Aug '20 08 Sep '20 44% 113% Rule of 6 08 Sep '20 24 Sep '20 53% 111% 10pm Curfew 24 Sep '20 15 Oct '20 51% 112% Tier 2 (High alert) 15 Oct '20 05 Nov '20 49% 113% Second Lockdown 05 Nov '20 02 Dec '20 31% 118% Tier 2 (High alert) 02 Dec '20 19 Dec '20 45% 115% Tier 4 (Stay at home advised) 19 Dec '20 05 Jan '21 22% 124% Third Lockdown 05 Jan '21 08 Mar '21 22% 122% Roadmap 1 08 Mar '21 29 Mar '21 29% 118% Roadmap 2 29 Mar '21 12 Apr '21 36% 117% Roadmap 3 12 Apr '21 17 May '21 51% 113% Roadmap out of lockdown: Step 3 17 May '21 19 Jul '21 65% 109% Roadmap out of lockdown: Step 4 19 Jul '21 07 Nov '22 68% 107%

Why do employees experience work from home (WFH) differently? We draw on boundary theory to explain how WFH influences employees’ work–home interface. WFH intensity increases negative spillovers (i.e., work-to-home conflict and home-to-work conflict) and positive spillovers (i.e., work-to-home enrichment and home-to-work enrichment) between the work and home domains. Negative spillovers can be mitigated through high-quality work equipment and beneficial spatial conditions at home. Domain centrality predicts who can benefit from increased WFH intensity. We test our theory with a sample of 545 employees, obtained through a two-step random sampling procedure in the city of Munich/Germany during the COVID-19 pandemic. We find that WFH intensity increases work-to-home conflict and home-to-work enrichment, affecting employees’ relationship satisfaction and job satisfaction. High-quality work equipment mitigates the detrimental effects of WFH. Employees with a high family centrality can reap benefits of more WFH because they experience more home-to-work enrichment. The simultaneous desirable and detrimental effects of WFH intensity can partly explain why studies have found heterogenous WFH experiences among employees.