The top reasons why people want to work remotely are saving money, the possibility to work from anywhere, and to spend more time with family. All three of these reasons where named by more than 40 percent of respondents in the survey.

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.

According to the Statista Consumer Insights, the share of workers in the United States who indicated that they did not commute to work peaked in 2022 at nine percent. For the period between October 2022 and September 2023, this figure stood at a slightly lower eight percent.

In 2018 InternetNZ conducted a consolidated research project that incorporated three historical research projects for both the former NZRS, and InternetNZ. This project covered business and consumer use and attitudes towards domain names, as well as public perceptions of the Internet in general. In 2019 they have replicated a section of this research project, to understand any changes in consumer perceptions of the Internet. The survey follows the same processes for 2018, however from 2019 onwards, the survey focused only on consumer use of and perceptions of the Internet.

The percentage of the working population that does not commute to work. Source: U.S. Census Bureau, American Community SurveyYears Available: 2018-2022, 2019-2023Please note: We do not recommend comparing overlapping years of data due to the nature of this dataset. For more information, please visit: https://www.census.gov/programs-surveys/acs/guidance/comparing-acs-data.html

According to the Statista Consumer Insights, the share of workers in Brazil who indicated that they did not commute to work peaked in 2021 at five percent. For the period between October 2022 and September 2023, this figure stood at four percent.

The percentage of the working population that does not commute to work. Source: U.S. Census Bureau, American Community Survey Years Available: 2018-2022, 2019-2023

This dataset contains the percentage of workers who report working from home for each county in the U.S. with a population of over 65,000 for the years 2010 to 2019. The data were taken from the U.S. Census Bureau's American Community Survey, 1-year Summary, Commuting Characteristics by Sex (S0801-C01-13).

In a 2019 survey, the majority of respondents in Mexico (88 percent) said they used their smartphones to work remotely or from home. Laptops were the second most preferred device for mobile work or home office, with 72 percent of respondents. Only 36 percent of the employees surveyed said they worked remotely using a tablet.

In 2018 InternetNZ conducted a consolidated research project that incorporated three historical research projects for both the former NZRS, and InternetNZ. This project covered business and consumer use and attitudes towards domain names, as well as public perceptions of the Internet in general. In 2019 they have replicated a section of this research project, to understand any changes in consumer perceptions of the Internet. The survey follows the same processes for 2018, however from 2019 onwards, the survey focused only on consumer use of and perceptions of the Internet.

Due to technological advances and rapidly changing trends in the work environment, working from home is coming to the fore. Although the possibility of working from home has existed for a long time, the coronavirus disease 2019 (COVID-19) has brought the topic into sharp focus in recent years. At the same time, employees' needs for psychological safety at work and meaningful work are increasing. The aim of this paper is to analyze what influence working from home has on the work-related factors meaningful work and psychological safety. The role of the quality of interpersonal relationships is also investigated. To answer these questions, a survey was conducted in a large company in Switzerland. The survey participants were 808 employees from different departments. The results show that the percentage of time spent working from home has no effect on the meaningfulness of work or on psychological safety. Furthermore, the quality of interpersonal relationships does not affect these relationships. Based on these results, it is concluded that how often someone works on site or from home does not impact negatively on these factors. It is significant to discover what online and on site measures and frameworks can ensure that the quality of interpersonal relationships remains at a sufficient level and is not detrimental to the meaningfulness of work or employees’ psychological safety.

Hybrid models of working are on the rise in the United States according to survey data covering worker habits between 2019 and 2024. In the second quarter of 2024, ** percent of U.S. workers reported working in a hybrid manner. The emergence of the COVID-19 pandemic saw a record number of people working remotely to help curb the spread of the virus. Since then, many workers have found a new shape to their home and working lives, finding that a hybrid model of working is more flexible than always being required to work on-site.

These figures are experimental estimates of online job adverts provided by Adzuna, an online job search engine. The number of job adverts over time is an indicator of the demand for labour. To identify these adverts we have applied text-matching to find job adverts which contain key phrases associated with homeworking such as “remote working”, “work from home”, “home-based” and “telework”. The data do not separately identify job adverts which exclusively offer homeworking from those which offer flexible homeworking, such as one day a week from home.

The paper uses data from the BIBB/BAuA Employment Survey of the Working Population on Qualification and Working Conditions in Germany 2018, doi: 10.7803/501.18.1.1.10. The Survey was conducted by the Federal Institute for Vocational Education and Training (BIBB), and the Federal Institute for Occupational Safety and Health (BAuA). For further details, see https://www.bibb.de/de/65740.php and the BIBB-FDZ Data and methodological Report at https://www.bibb.de/veroeffentlichungen/de/publication/show/16563.

The data access was provided via a Scientific-Use-File (called ZA7574_v1-0-0.dta) of the Data Research Centre at the Federal Institute for Vocational Training and Education (BIBB-FDZ). The data are confidential, but not exclusive. To apply for data access, please follow the instructions at https://www.bibb.de/de/120401.php.

To replicate the results reported in the paper, access to this data set must be obtained from the data provider.

The STATA do-file “ik_replication.do” (also available as txt-file “ik_replication.txt”) is replicating all results presented in the paper. It first makes use of the BIBB-BAuA source file “ZA7574_v1-0-0.dta” (see above) to generate and label all relevant variables, specifies the sample, and finally generates a working data set. In a second step, this working data is used to generate the results. Thereby, the analysis makes use of several auxiliary data sets, which can be merged to the working data. These auxiliary data sets have been obtained and constructed from alternative data sources (which we make available as part of the replication package).

A. Google mobility report https://www.gstatic.com/covid19/mobility/2020-03-29_DE_Mobility_Report_en.pdf Google prepared this report to provide information on the responses to social distancing guidance related to COVID-19. We use information for the first weeks of the shutdown on mobility trend changes for places of work on March 29, 2020, relative to a baseline value. The respective numbers are already included in the do-file to replicate the results in the paper and the pdf-file is part of the replication folder (see Source Files/2020-03-29_DE_Mobility_Report_en.pdf).

B. Unemployment across occupations – Data files ba_jul.dta / ba_jul.txt We use information from the report ”Arbeitsmarkt nach Berufen” from July 2020 provided by the Federal Employment Agency (BA) to obtain yearly changes in unemployment for occupations at the three digit level according to the occupation classification KldB 2010. The original file is part of the replication folder (see Source Files/berufe-heft-kldb2010-d-0-202007-xlsx). We use information from sheet 1.1 for number of unemployed persons in July 2020 and 2019 and the respective difference. This information is merged to the working data using the data file “ba_jul.dta” (or “ba_jul.txt”). It contains the following variables: - kldb2010_3d: 3-digit KldB 2010 occupation code (also available in working data) - jul_2020: number of unemployed persons in July 2020 - jul_2019: number of unemployed persons in July 2020 - delta_abs_jul: difference between 2020 and 2019   C. Fadinger and Schymik (2020) – Data files wfh_sch.dta / wfh_sch.txt To generate Figure A.9 in the Appendix, we rely on estimates provided in recent work by Fadinger and Schymik (2020) , who use an alternative measure for the WFH potential at the NUTS2 level. This information is merged to the working data using the data file “wfh_sch.dta” (or “wfh_sch.txt”). It contains the following variables: - GEO: Name of NUTS2 region - shr_homewk_pssb: Estimates on WFH share from Fadinger and Schymik (2020) - region: NUTS2 number (also available in working data)

D. Spatial Autocorrelation – Data files geo_data.dta / geo_data.txt To check for spatial autocorrelation across the 38 NUTS2 regions in Germany, we compute Moran’s I statistic which requires information on the longitude and latitude of NUTS2 regions. This information can be merged to the working data using the data file “geo_data.dta” (or “geo_data.txt”). It contains the following variables: - nuts_id: NUTS2 code - region: NUTS2 number (also available in working data) - longitude: Longitude position - latitude: Latitude position

This dataset contains the percentage of workers in Atlanta metro counties who report working from home for the years 2010 through 2019. The data come from the U.S. Census Bureau's American Community Survey 1-year Summary. The data is in a "wide" format to support creating visualizations with specific software.

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, 2019 American Community Survey 1-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..2019 ACS data products include updates to several categories of the existing means of transportation question. For more information, see: Change to Means of Transportation..The 2019 American Community Survey (ACS) data generally reflect the September 2018 Office of Management and Budget (OMB) delineations 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 delineations 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.

In 2019, the two leading concerns among managers regarding employees working remotely in the United States were reduced employee productivity and reduced employee focus. 82 percent of respondents were the most concerned about these issues.

US Census American Community Survey (ACS) 2019, 5-year estimates of the key economic characteristics of Census Tracts geographic level in Orange County, California. The data contains 397 fields for the variable groups E01: Employment status (universe: population 16 years and over, table X23, 7 fields); E02: Work status by age of worker (universe: population 16 years and over, table X23, 36 fields); E03: Commuting to work (universe: workers 16 years and over, table X8, 8 fields); E04: Travel time to work (universe: workers 16 years and over who did not work at home, table X8, 14 fields); E05: Number of vehicles available for workers (universe: workers 16 years and over in households, table X8, 8 fields); E06: Median age by means of transportation to work (universe: median age, workers 16 years and over, table X8, 7 fields); E07: Means of transportation to work by race (universe: workers 16 years and over, table X8, 64 fields); E08: Occupation (universe: civilian employed population 16 years and over, table X24, 53 fields); E09: Industry (universe: civilian employed population 16 years and over, table X24, 43 fields); E10: Class of worker (universe: civilian employed population 16 years and over, table X24, 19 fields); E11: Household income and earnings in the past 12 months (universe: total households, table X19, 37 fields); E12: Income and earnings in dollars (universe: inflation-adjusted dollars, tables X19-X20, 31 fields); E13: Family income in dollars (universe: total families, table X19, 17 fields); E14: Health insurance coverage (universe: total families, table X19, 17 fields); E15: Ratio of income to Poverty level (universe: total population for whom Poverty level is determined, table X17, 8 fields); E16: Poverty in population in the past 12 months (universe: total population for whom Poverty level is determined, table X17, 7 fields); E17: Poverty in households in the past 12 months (universe: total households, table X17, 9 fields); E18: Percentage of families and people whose income in the past 12 months is below the poverty level (universe: families, population, table X17, 8 fields), and; X19: Poverty and income deficit (dollars) in the past 12 months for families (universe: families with income below Poverty level in the past 12 months, table X17, 4 fields). The US Census geodemographic data are based on the 2019 TigerLines across multiple geographies. The spatial geographies were merged with ACS data tables. See full documentation at the OCACS project github page (https://github.com/ktalexan/OCACS-Geodemographics).

About this dataset

Context

The dataset presents median income data over a decade or more for males and females categorized by Total, Full-Time Year-Round (FT), and Part-Time (PT) employment in Home Lake township. It showcases annual income, providing insights into gender-specific income distributions and the disparities between full-time and part-time work. The dataset can be utilized to gain insights into gender-based pay disparity trends and explore the variations in income for male and female individuals.

Key observations: Insights from 2023

Based on our analysis ACS 2019-2023 5-Year Estimates, we present the following observations: - All workers, aged 15 years and older: In Home Lake township, the median income for all workers aged 15 years and older, regardless of work hours, was $76,250 for males and $24,375 for females.

These income figures highlight a substantial gender-based income gap in Home Lake township. Women, regardless of work hours, earn 32 cents for each dollar earned by men. This significant gender pay gap, approximately 68%, underscores concerning gender-based income inequality in the township of Home Lake township.

- Full-time workers, aged 15 years and older: In Home Lake township, among full-time, year-round workers aged 15 years and older, males earned a median income of $88,750, while females earned $45,938, leading to a 48% gender pay gap among full-time workers. This illustrates that women earn 52 cents for each dollar earned by men in full-time roles. This level of income gap emphasizes the urgency to address and rectify this ongoing disparity, where women, despite working full-time, face a more significant wage discrepancy compared to men in the same employment roles.

Remarkably, across all roles, including non-full-time employment, women displayed a similar gender pay gap percentage. This indicates a consistent gender pay gap scenario across various employment types in Home Lake township, showcasing a consistent income pattern irrespective of employment status.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates. All incomes have been adjusting for inflation and are presented in 2023-inflation-adjusted dollars.

Gender classifications include:

• Male
• Female

Employment type classifications include:

• Full-time, year-round: A full-time, year-round worker is a person who worked full time (35 or more hours per week) and 50 or more weeks during the previous calendar year.
• Part-time: A part-time worker is a person who worked less than 35 hours per week during the previous calendar year.

Variables / Data Columns

• Year: This column presents the data year. Expected values are 2010 to 2023
• Male Total Income: Annual median income, for males regardless of work hours
• Male FT Income: Annual median income, for males working full time, year-round
• Male PT Income: Annual median income, for males working part time
• Female Total Income: Annual median income, for females regardless of work hours
• Female FT Income: Annual median income, for females working full time, year-round
• Female PT Income: Annual median income, for females working part time

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Home Lake township median household income by race. You can refer the same here