In 2023, the largest share of employees who preferred to work-from-home in the United States were those between 26 and 41 years old and totaled 41 percent of those surveyed within this age group. The age group with the least desire to work from home were between 18 and 25 years old.

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 Brook 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 Brook township, the median income for all workers aged 15 years and older, regardless of work hours, was $40,625 for males and $24,000 for females.

These income figures highlight a substantial gender-based income gap in Home Brook township. Women, regardless of work hours, earn 59 cents for each dollar earned by men. This significant gender pay gap, approximately 41%, underscores concerning gender-based income inequality in the township of Home Brook township.

- Full-time workers, aged 15 years and older: In Home Brook township, among full-time, year-round workers aged 15 years and older, males earned a median income of $85,000, while females earned $44,375, 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 Brook 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 Brook township median household income by race. You can refer the same here

European Practising Health Care Assistants and Home-based Personal Care Workers Share by Country (Units (Persons)), 2023 Discover more data with ReportLinker!

The shuttering of society led companies, institutions, and education to move to remote work during the COVID-19 pandemic. With this has come favourable circumstances for women with childcare and eldercare responsibilities to be employed because of continued work at home opportunities. Since 2020 there has been an increase of women in the workforce with children under the age of five inclusive of married women, single women, women with a high school education and less, as well as newcomers and immigrants. While todays work at home jobs are mainly digital jobs in the professions of accounting, legal, finance, marketing, human resources, health, customer service, and cybersecurity, there is also opportunity in the field of apparel manufacturing. By enabling apparel workers to work remotely an often ignored and potential workforce presents itself in an industry that is increasingly challenged with finding sewing skill and expertise. This is mutually beneficial as the women have access to an economy that might otherwise not be available to them because of their caregiving responsibilities.

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 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 township, the median income for all workers aged 15 years and older, regardless of work hours, was $41,364 for males and $25,417 for females.

These income figures highlight a substantial gender-based income gap in Home township. Women, regardless of work hours, earn 61 cents for each dollar earned by men. This significant gender pay gap, approximately 39%, underscores concerning gender-based income inequality in the township of Home township.

- Full-time workers, aged 15 years and older: In Home township, among full-time, year-round workers aged 15 years and older, males earned a median income of $64,375, while females earned $49,375, leading to a 23% gender pay gap among full-time workers. This illustrates that women earn 77 cents for each dollar earned by men in full-time roles. This analysis indicates a widening gender pay gap, showing a substantial income disparity where women, despite working full-time, face a more significant wage discrepancy compared to men in the same roles.

Surprisingly, the gender pay gap percentage was higher across all roles, including non-full-time employment, for women compared to men. This suggests that full-time employment offers a more equitable income scenario for women compared to other employment patterns in Home township.

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 township median household income by race. You can refer the same here

Even before the Pandemic, some places had been actively trying to attract remote workers. For example, Montana was trying to attract remote workers rather than attract businesses/employers even as early as 2015 (NPR, Flathead Beacon). With remote work now here to stay, it can be helpful to see how this varies by geography. This map shows where remote workers (i.e. workers who work from home) live for states, counties, and tracts. Counts are depicted by size of symbol and percent is depicted by color of symbol. Map opens in Phoenix, AZ and has full nation-wide coverage.This map uses these hosted feature layers containing the most recent American Community Survey data. These layers are part of the ArcGIS Living Atlas, and are updated every year when the American Community Survey releases new estimates, so values in the map always reflect the newest data available.

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 Forest Home 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 Forest Home township, the median income for all workers aged 15 years and older, regardless of work hours, was $56,000 for males and $37,850 for females.

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

- Full-time workers, aged 15 years and older: In Forest Home township, among full-time, year-round workers aged 15 years and older, males earned a median income of $59,318, while females earned $53,125, resulting in a 10% gender pay gap among full-time workers. This illustrates that women earn 90 cents for each dollar earned by men in full-time positions. While this gap shows a trend where women are inching closer to wage parity with men, it also exhibits a noticeable income difference for women working full-time in the township of Forest Home township.

Interestingly, when analyzing income across all roles, including non-full-time employment, the gender pay gap percentage was higher for women compared to men. It appears that full-time employment presents a more favorable income scenario for women compared to other employment patterns in Forest Home township.

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 Forest Home township median household income by race. You can refer the same here

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 2021

Based on our analysis ACS 2017-2021 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 $67,287 for males and $19,254 for females.

These income figures highlight a substantial gender-based income gap in Home Lake township. Women, regardless of work hours, earn 29 cents for each dollar earned by men. This significant gender pay gap, approximately 71%, 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, for full-time, year-round workers aged 15 years and older, while the Census reported a median income of $81,068 for males, while data for females was unavailable due to an insufficient number of sample observations.

As there was no available median income data for females, conducting a comprehensive assessment of gender-based pay disparity in Home Lake township was not feasible.

https://i.neilsberg.com/ch/home-lake-township-mn-income-by-gender.jpeg" alt="Home Lake Township, Minnesota gender based income disparity">

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2017-2021 5-Year Estimates. All incomes have been adjusting for inflation and are presented in 2022-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 2022
• 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 gender. You can refer the same here

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 Brook 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 2021

Based on our analysis ACS 2017-2021 5-Year Estimates, we present the following observations: - All workers, aged 15 years and older: In Home Brook township, the median income for all workers aged 15 years and older, regardless of work hours, was $35,468 for males and $18,315 for females.

These income figures highlight a substantial gender-based income gap in Home Brook township. Women, regardless of work hours, earn 52 cents for each dollar earned by men. This significant gender pay gap, approximately 48%, underscores concerning gender-based income inequality in the township of Home Brook township.

- Full-time workers, aged 15 years and older: In Home Brook township, among full-time, year-round workers aged 15 years and older, males earned a median income of $61,342, while females earned $58,099, resulting in a 5% gender pay gap among full-time workers. This illustrates that women earn 95 cents for each dollar earned by men in full-time positions. While this gap shows a trend where women are inching closer to wage parity with men, it also exhibits a noticeable income difference for women working full-time in the township of Home Brook township.

Interestingly, when analyzing income across all roles, including non-full-time employment, the gender pay gap percentage was higher for women compared to men. It appears that full-time employment presents a more favorable income scenario for women compared to other employment patterns in Home Brook township.

https://i.neilsberg.com/ch/home-brook-township-mn-income-by-gender.jpeg" alt="Home Brook Township, Minnesota gender based income disparity">

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2017-2021 5-Year Estimates. All incomes have been adjusting for inflation and are presented in 2022-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 2022
• 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 Brook township median household income by gender. You can refer the same here

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 Mountain Home. 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 2021

Based on our analysis ACS 2017-2021 5-Year Estimates, we present the following observations: - All workers, aged 15 years and older: In Mountain Home, the median income for all workers aged 15 years and older, regardless of work hours, was $38,956 for males and $21,097 for females.

These income figures highlight a substantial gender-based income gap in Mountain Home. Women, regardless of work hours, earn 54 cents for each dollar earned by men. This significant gender pay gap, approximately 46%, underscores concerning gender-based income inequality in the city of Mountain Home.

- Full-time workers, aged 15 years and older: In Mountain Home, among full-time, year-round workers aged 15 years and older, males earned a median income of $47,892, while females earned $34,876, leading to a 27% gender pay gap among full-time workers. This illustrates that women earn 73 cents for each dollar earned by men in full-time roles. This analysis indicates a widening gender pay gap, showing a substantial income disparity where women, despite working full-time, face a more significant wage discrepancy compared to men in the same roles.

Surprisingly, the gender pay gap percentage was higher across all roles, including non-full-time employment, for women compared to men. This suggests that full-time employment offers a more equitable income scenario for women compared to other employment patterns in Mountain Home.

https://i.neilsberg.com/ch/mountain-home-id-income-by-gender.jpeg" alt="Mountain Home, ID gender based income disparity">

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2017-2021 5-Year Estimates. All incomes have been adjusting for inflation and are presented in 2022-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 2022
• 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 Mountain Home median household income by gender. You can refer the same here

In 2023, the national median was the main factor that companies based their pay scale on for their remote or distributed employees in the technology industry globally, with 27 percent of respondents so. A close second was employee's locations and using geo-differentials with a 26 percent share.

This layer shows workers' place of residence by mode of commute. This is shown by tract, county, and state boundaries. This service is updated annually to contain the most currently released American Community Survey (ACS) 5-year data, and contains estimates and margins of error. There are also additional calculated attributes related to this topic, which can be mapped or used within analysis. This layer is symbolized by the percentage of workers who drove alone. To see the full list of attributes available in this service, go to the "Data" tab, and choose "Fields" at the top right. Current Vintage: 2019-2023ACS Table(s): B08301 (Not all lines of this ACS table are available in this feature layer.)Data downloaded from: Census Bureau's API for American Community Survey Date of API call: December 12, 2024National Figures: data.census.govThe United States Census Bureau's American Community Survey (ACS):About the SurveyGeography & ACSTechnical DocumentationNews & UpdatesThis ready-to-use layer can be used within ArcGIS Pro, ArcGIS Online, its configurable apps, dashboards, Story Maps, custom apps, and mobile apps. Data can also be exported for offline workflows. For more information about ACS layers, visit the FAQ. Please cite the Census and ACS when using this data.Data Note from the Census: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 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.Data Processing Notes:This layer is updated automatically when the most current vintage of ACS data is released each year, usually in December. The layer always contains the latest available ACS 5-year estimates. It is updated annually within days of the Census Bureau's release schedule. Click here to learn more about ACS data releases.Boundaries come from the US Census TIGER geodatabases, specifically, the National Sub-State Geography Database (named tlgdb_(year)_a_us_substategeo.gdb). Boundaries are updated at the same time as the data updates (annually), and the boundary vintage appropriately matches the data vintage as specified by the Census. These are Census boundaries with water and/or coastlines erased for cartographic and mapping purposes. For census tracts, the water cutouts are derived from a subset of the 2020 Areal Hydrography boundaries offered by TIGER. Water bodies and rivers which are 50 million square meters or larger (mid to large sized water bodies) are erased from the tract level boundaries, as well as additional important features. For state and county boundaries, the water and coastlines are derived from the coastlines of the 2023 500k TIGER Cartographic Boundary Shapefiles. These are erased to more accurately portray the coastlines and Great Lakes. The original AWATER and ALAND fields are still available as attributes within the data table (units are square meters).The States layer contains 52 records - all US states, Washington D.C., and Puerto RicoCensus tracts with no population that occur in areas of water, such as oceans, are removed from this data service (Census Tracts beginning with 99).Percentages and derived counts, and associated margins of error, are calculated values (that can be identified by the "_calc_" stub in the field name), and abide by the specifications defined by the American Community Survey.Field alias names were created based on the Table Shells file available from the American Community Survey Summary File Documentation page.Negative values (e.g., -4444...) have been set to null, with the exception of -5555... which has been set to zero. These negative values exist in the raw API data to indicate the following situations: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.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.The median falls in the lowest interval of an open-ended distribution, or in the upper interval of an open-ended distribution. A statistical test is not appropriate.The estimate is controlled. A statistical test for sampling variability is not appropriate.The data for this geographic area cannot be displayed because the number of sample cases is too small.

This project adopts two main research instruments - two online questionnaires (2 surveys of circa 1400 UK ‘new’ homeworkers each, June-July 2020 & Dec-February 2021). The second instrument is a series of semi-structed interviews (4 x Interviews with 80 ‘new’ homeworkers across UK, May 2020 – July 2021).

The COVID-19 outbreak has forced companies to embrace home-based working (HBW) at such speed that they have had little opportunity to consider the impact on their workers. It can be argued that the crisis has led to the most significant, intensive social experiment of digital, HBW that has ever occurred. The current situation, which involves the whole household being based at home, is an unprecedented challenge which may be at least an intermittent fixture, for the next eighteen months (BBC Futures, 25/03/20).

The press have suggested that this revolution might also offer an opportunity for many companies to finally build a culture that allows long-overdue work flexibility ... many employees for companies who have sent all staff home are already starting to question why they had to go into the office in the first place (The Guardian, 13/02/20). These optimistic takes on the current patterns of work focus on HBW's emancipatory potential, offering flexibility, the lubrication of work and family responsibilities and the promise of increased productivity. Yet, this new world order, where the home becomes a multi-occupational, multi-person workplace and school, not only challenges boundaries but also conceptions of the domestic space.

The impact of homeworking is likely to present significant variation depending on organisational support, the worker's role, socio-economic status, employment status, as well as household composition and size of living space. There are significant concerns regarding intensified HBW, including poor work-life balance, enhanced domestic tensions and disproportionately negative impacts on those in lower socio-economic groupings. Moreover, HBW increases the proportion of time women (most often) spend on housework and childcare, reproducing and reinforcing gender roles within the new 'work-space'

We will examine in-depth this radical shift in working arrangements and how it impacts on the wellbeing and productivity of workers and their households. Using a combination of in-depth interviews with sixty participants, representing the spectrum of this novel group of homeworkers, as well as a large-scale survey, this project (Working@Home) will provide unrivalled insights into the experience of home-working for the UK population and will serve as a permanent record of the lives of citizens in this unprecedented time.

The research will be key in understanding the expectations that organisations have placed on workers, as well as the robustness of support systems that have been put in place, taking into account the rapid advancement of home working systems with almost no preparation and only limited existing support structures or expertise. The findings will provide a benchmark for the resilience of both individuals and businesses and demonstrate the potential for the robustness of the infrastructure in the return to a 'new normal' after the crisis.

In order to ensure that the findings from the project are accessible to all, we are developing a website (workingathome.org.uk) that will host up to date information on the progress of the project, details of the project team, guidance for participants as well as information regarding our webinar series. The project aims to produce guidance to individuals, organisations and policy makers on how to best manage the ongoing medical emergency from a home-working perspective as well as providing guidance for any future pandemic scenario.

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, for 2010, the 2010 Census provides the official counts of the population and housing units for the nation, states, counties, cities and towns..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 2000 data. Boundaries for urban areas have not been updated since Census 2000. As a result, data for urban and rural areas from the ACS do not necessarily reflect the results of ongoing urbanization..While the 2010 American Community Survey (ACS) data generally reflect the December 2009 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..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, 2010 American Community Survey

European Practising Nurses, Midewives, Health Care Assistants and Home-based Personal Care Workers Share by Country (Units (Persons)), 2023 Discover more data with ReportLinker!

The study surveyed the characteristics of Finnish employees' work and work-related use of information technology. Several questions dealt with the respondents' occupation, occupational status, size of workplace, working time and line of work. The characteristics of work were examined by asking whether the respondents' work demanded presenting ideas, whether they could influence their working pace, did the job involve management or producing information and could they influence the decisions at workplace on the basis of their status. Questions covering occupational health and safety looked into physical and mental stressfulness of work, changes at workplace during the last few years and recent emotions experienced by the respondents. Respondents were also asked whether they were on a tight schedule at work, whether they found it difficult to forget work at leisure time and would they be able to cope with their present job till retirement. Regarding the use of information technology, respondents were asked what kind of telecommunications or computer technology equipment they used at work, did they use e-mail or Internet and which activities the use of computers was connected to. There were also questions about how they had acquired their computer skills, the extent of computer training on the job, their assessment of the equipment provided and whether they worked at home (home-based work) with the help of computers. Several questions examined the respondents' education and employment history: educational background and field of education, correspondence of work to education, the need for further education. Additional questions surveyed the respondents' working experience in management, security of their job, experiences of temporary dismissals or unemployment and their methods in seeking work. Also examined was the process by which they got their present job, whether they were interested in self-employment (becoming entrepreneurs), possible stays abroad on account of study or work and which aspects of work were most important to them. Opinions on trade union membership, various social problems, and income disparity were also canvassed. Respondents were also asked whether they believed that there is work for those who want it, how would they react to reorganisation in their workplace, which social class did they belong to, and what was important for doing well in the Finnish society. The survey carried a set of statements relating to poverty and activities of the state. Respondents were asked who should take care of various services. Questions related to family life and leisure included marital status, number of children, spouse's occupation, leisure time activities, income of the respondent, family income and family's economic status. The study also surveyed what the respondents talked about with their friends and how important employment, family and leisure were to them. Final questions examined who supported the family in respondents' parental home, occupation of the mother and father and the occupational status of the family breadwinner.

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.

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.

Average percentage of employees based in a different province or territory as employer that are anticipated to telework exclusively, by North American Industry Classification System (NAICS), business employment size, type of business, business activity and majority ownership, fourth quarter of 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)