In a June 2020 survey, participants that worked from home during the coronavirus pandemic were asked what they thought were the greatest sources of distraction. Among the respondents, **** percent said that their smartphones were affecting their productivity during the lockdown. Additionally, **** percent admitted that gaming was keeping them from their daily work responsibilities.

In the ********* year, there were about **** million employees who worked from home. Approximately *** million of those were ages 35 to 44 years old, while about **** were ages 15 years to 24 years old.

A 2022 survey found that ** percent of employed Americans have been offered full-time remote work. During the COVID-19 pandemic, many workers across the U.S. began working remotely for the first time. The popularity of remote work has continued as pandemic restrictions have relaxed.

Percentage and average percentage of workforce anticipated to work on-site or remotely over the next three months, by North American Industry Classification System (NAICS), business employment size, type of business, business activity and majority ownership, third quarter of 2025.

Percentage of workforce anticipated to work on-site or remotely over the next three months, by percentage ranges, North American Industry Classification System (NAICS), business employment size, type of business, business activity and majority ownership, third quarter of 2022.

Methods used to conduct sales or secure orders remotely prior to February 1, 2020, and on May 29, 2020, during the COVID-19 pandemic, by North American Industry Classification System (NAICS), business employment size, type of business, business activity and majority ownership.

According to a survey from 2021, ** percent of Swedish employees did not have the opportunity to work remotely. However, around ** percent of the respondents were only or partially working from home.

As of December 2024, around *** million employees in China had used online services to work from home, accounting for around **** percent of the Chinese internet user base. After four years of on-and-off lockdowns due to the COVID-19 pandemic, the adoption of remote working in China largely sustains with the support of AI-powered software.

Brazil Employed: Working Remotely as % Employed Population data was reported at 10.903 % in 22 Aug 2020. This records a decrease from the previous number of 11.071 % for 15 Aug 2020. Brazil Employed: Working Remotely as % Employed Population data is updated daily, averaging 12.456 % from May 2020 (Median) to 22 Aug 2020, with 15 observations. The data reached an all-time high of 13.445 % in 16 May 2020 and a record low of 10.903 % in 22 Aug 2020. Brazil Employed: Working Remotely as % Employed Population data remains active status in CEIC and is reported by Brazilian Institute of Geography and Statistics. The data is categorized under Brazil Premium Database’s Labour Market – Table BR.GBA001: Continuous National Household Sample Survey: Weekly.

This replication data repository contains all data and code required for our analysis, as well as a read-me file.

Before the COVID-19 pandemic in 2020 only **** percent of the respondents primarily worked from home (WFH), which is significantly less compared to the **** percent of respondents who WFH now in the midst of the pandemic. Interestingly enough, respondents answered that many would go back to their respective physical facility or the field after a vaccine is available.

Physical workplaces However, following the COVID-19 pandemic, the physical workplaces will change as the percentage of personnel that will primarily WFH is projected to be ***** percent more than prior to the worldwide pandemic. Other studies have suggested that post-pandemic, almost ** percent of enterprises worldwide are projected to have a distributed workforce, compared to around ** percent before the Covid-19 pandemic.

Distributed workforce A distributed workforce may become the new normal, as it is projected that by 2023, ** percent of the top Forbes Global 2000 companies will have commitments to a hybrid workforce design. It is worth noting that to work distributedly is different than only working remote, as to "work distributedly" assumes that there is not a single central location to work from. Instead, the company is distributed among various working environments. This allows personnel to engage either locally, remotely, in the field, or switching between locations as they please. This permits for a more fluid working environment, as many throughout the world either like or love WFH.

This dataset denotes Local Employment Dynamics (LED) data relative to Grantee areas for the Home Investment Partnership (HOME) Program. The LED Partnership is a voluntary federal-state enterprise created for the purpose of merging employee, and employer data to provide a set of enhanced labor market statistics known collectively as Quarterly Workforce Indicators (QWI). The QWI are a set of economic indicators including employment, job creation, earnings, and other measures of employment flows.

The Current Employment Statistics (CES) program is a Federal-State cooperative effort in which monthly surveys are conducted to provide estimates of employment, hours, and earnings based on payroll records of business establishments. The CES survey is based on approximately 119,000 businesses and government agencies representing approximately 629,000 individual worksites throughout the United States.

CES data reflect the number of nonfarm, payroll jobs. It includes the total number of persons on establishment payrolls, employed full- or part-time, who received pay (whether they worked or not) for any part of the pay period that includes the 12th day of the month. Temporary and intermittent employees are included, as are any employees who are on paid sick leave or on paid holiday. Persons on the payroll of more than one establishment are counted in each establishment. CES data excludes proprietors, self-employed, unpaid family or volunteer workers, farm workers, and household workers. Government employment covers only civilian employees; it excludes uniformed members of the armed services.

The Bureau of Labor Statistics (BLS) of the U.S. Department of Labor is responsible for the concepts, definitions, technical procedures, validation, and publication of the estimates that State workforce agencies prepare under agreement with BLS.

Co-working spaces have become an essential part of the digital economy but how will Covid-19 affect their growth in urban areas?

This Round 1 Innovation Fund project followed the experiences of several co-working projects through the pandemic to explore what role co-working spaces might play in new flexible, hybrid models of work.

Research questions How have co-working spaces responded to the COVID-19 crisis? How do co-working spaces stand to be incorporated into the economic recovery and urban regeneration efforts in the aftermath? Method Over 40 interviews were conducted in Brighton, Bristol and Manchester with representatives from a range of coworking spaces and of local and regional government.

Key findings The future of urban co-working spaces will be shaped by the wider dynamics of the urban property market and shifts in corporate demand for flexible workspace. These forces will likely prove more influential than anything specific to their founding organisation and social purpose. The pandemic underscored the ambivalent position of co-working spaces as hosts rather than employers and revealed the variable positions of different co-working space business models in the face of disrupted income streams. At the same time, co-working spaces have contributed to the recovery from the pandemic by providing places to work collaboratively or collectively alongside shifts towards more flexible work and working from home. In this respect their importance is likely to increase. Attention is shifting from the towering dominance of London to smaller urban hubs and especially commuting towns. Although local and national government are beginning to recognise the potential importance of co-working spaces, they have not begun to develop strategies to nurture them. This gap risks leaving co-working spaces and their users adrift in increasingly turbulent and competitive market conditions. This is especially important at a time where they stand to play a central role in providing sites for experimentation with, and adaptation to, new digitally-mediated working practices emerging from the pandemic, for a potentially much broader array of workers than spaces previously served.

Dataset

This dataset was created by Ricki Tomphson

Contents

Opinions and Lifestyle Survey (COVID-19 module), 3 to 14 November 2021

Source: Snapshot visualization of the estimated average number of individuals working from home by census block, disaggregated from ACS data.

Purpose: Tile layer utilized for visualization.

Contact Information: Charles Rudder (crudder@citiesthatwork.com)/ Alex Bell (abell@citiesthatwork.com)

In 2021, ** percent of employees in Germany considered having more free time due to not having to commute to work an advantage of working from home. The coronavirus (COVID-19) pandemic had a large part of the population switch to working from home as per government regulations to control the spread of the virus.

Supporting documentation on code lists, subject definitions, data accuracy, and statistical testing can be found on the American Community Survey website in the Data and Documentation section...Sample size and data quality measures (including coverage rates, allocation rates, and response rates) can be found on the American Community Survey website in the Methodology section..Although the American Community Survey (ACS) produces population, demographic and housing unit estimates, it is the Census Bureau''s Population Estimates Program that produces and disseminates the official estimates of the population for the nation, states, counties, cities and towns and estimates of housing units for states and counties..Explanation of Symbols:An ''**'' entry in the margin of error column indicates that either no sample observations or too few sample observations were available to compute a standard error and thus the margin of error. A statistical test is not appropriate..An ''-'' entry in the estimate column indicates that either no sample observations or too few sample observations were available to compute an estimate, or a ratio of medians cannot be calculated because one or both of the median estimates falls in the lowest interval or upper interval of an open-ended distribution..An ''-'' following a median estimate means the median falls in the lowest interval of an open-ended distribution..An ''+'' following a median estimate means the median falls in the upper interval of an open-ended distribution..An ''***'' entry in the margin of error column indicates that the median falls in the lowest interval or upper interval of an open-ended distribution. A statistical test is not appropriate..An ''*****'' entry in the margin of error column indicates that the estimate is controlled. A statistical test for sampling variability is not appropriate. .An ''N'' entry in the estimate and margin of error columns indicates that data for this geographic area cannot be displayed because the number of sample cases is too small..An ''(X)'' means that the estimate is not applicable or not available..Estimates of urban and rural population, housing units, and characteristics reflect boundaries of urban areas defined based on Census 2010 data. As a result, data for urban and rural areas from the ACS do not necessarily reflect the results of ongoing urbanization..While the 2013 American Community Survey (ACS) data generally reflect the February 2013 Office of Management and Budget (OMB) definitions of metropolitan and micropolitan statistical areas; in certain instances the names, codes, and boundaries of the principal cities shown in ACS tables may differ from the OMB definitions due to differences in the effective dates of the geographic entities..Workers include members of the Armed Forces and civilians who were at work last week..In data year 2013, there were a series of changes to data collection operations that could have affected some estimates. These changes include the addition of Internet as a mode of data collection, the end of the content portion of Failed Edit Follow-Up interviewing, and the loss of one monthly panel due to the Federal Government shut down in October 2013. For more information, see: User Notes.Data are based on a sample and are subject to sampling variability. The degree of uncertainty for an estimate arising from sampling variability is represented through the use of a margin of error. The value shown here is the 90 percent margin of error. The margin of error can be interpreted roughly as providing a 90 percent probability that the interval defined by the estimate minus the margin of error and the estimate plus the margin of error (the lower and upper confidence bounds) contains the true value. In addition to sampling variability, the ACS estimates are subject to nonsampling error (for a discussion of nonsampling variability, see Accuracy of the Data). The effect of nonsampling error is not represented in these tables..Source: U.S. Census Bureau, 2013 American Community Survey

Although the American Community Survey (ACS) produces population, demographic and housing unit estimates, it is the Census Bureau's Population Estimates Program that produces and disseminates the official estimates of the population for the nation, states, counties, cities, and towns and estimates of housing units for states and counties..Supporting documentation on code lists, subject definitions, data accuracy, and statistical testing can be found on the American Community Survey website in the .Technical Documentation.. section......Sample size and data quality measures (including coverage rates, allocation rates, and response rates) can be found on the American Community Survey website in the .Methodology.. section..Source: U.S. Census Bureau, 2014-2018 American Community Survey 5-Year Estimates.Data are based on a sample and are subject to sampling variability. The degree of uncertainty for an estimate arising from sampling variability is represented through the use of a margin of error. The value shown here is the 90 percent margin of error. The margin of error can be interpreted roughly as providing a 90 percent probability that the interval defined by the estimate minus the margin of error and the estimate plus the margin of error (the lower and upper confidence bounds) contains the true value. In addition to sampling variability, the ACS estimates are subject to nonsampling error (for a discussion of nonsampling variability, see .ACS Technical Documentation..). The effect of nonsampling error is not represented in these tables..Tables for Workplace Geography are only available for States; Counties; Places; County Subdivisions in selected states (CT, ME, MA, MI, MN, NH, NJ, NY, PA, RI, VT, WI); Combined Statistical Areas; Metropolitan and Micropolitan Statistical Areas, and their associated Metropolitan Divisions and Principal Cities; Combined New England City and Town Areas; New England City and Town Areas, and their associated Divisions and Principal Cities. Tables B08601, B08602, B08603, and B08604 are also available for Place parts and County Subdivision parts for the 5-year ACS datasets..These tabulations are produced to provide estimates of workers at the location of their workplace. Estimates of counts of workers at the workplace may differ from those of other programs because of variations in definitions, coverage, methods of collection, reference periods, and estimation procedures. The ACS is a household survey which provides data that pertains to individuals, families, and households..Workers include members of the Armed Forces and civilians who were at work last week..While the 2014-2018 American Community Survey (ACS) data generally reflect the February 2013 Office of Management and Budget (OMB) definitions of metropolitan and micropolitan statistical areas; in certain instances the names, codes, and boundaries of the principal cities shown in ACS tables may differ from the OMB definitions due to differences in the effective dates of the geographic entities..Estimates of urban and rural populations, housing units, and characteristics reflect boundaries of urban areas defined based on Census 2010 data. As a result, data for urban and rural areas from the ACS do not necessarily reflect the results of ongoing urbanization..Explanation of Symbols:..An "**" entry in the margin of error column indicates that either no sample observations or too few sample observations were available to compute a standard error and thus the margin of error. A statistical test is not appropriate..An "-" entry in the estimate column indicates that either no sample observations or too few sample observations were available to compute an estimate, or a ratio of medians cannot be calculated because one or both of the median estimates falls in the lowest interval or upper interval of an open-ended distribution, or the margin of error associated with a median was larger than the median itself..An "-" following a median estimate means the median falls in the lowest interval of an open-ended distribution..An "+" following a median estimate means the median falls in the upper interval of an open-ended distribution..An "***" entry in the margin of error column indicates that the median falls in the lowest interval or upper interval of an open-ended distribution. A statistical test is not appropriate..An "*****" entry in the margin of error column indicates that the estimate is controlled. A statistical test for sampling variability is not appropriate. .An "N" entry in the estimate and margin of error columns indicates that data for this geographic area cannot be displayed because the number of sample cases is too small..An "(X)" means that the estimate is not applicable or not available....