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.

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.

Percentage of workforce teleworking or working remotely prior to February 1, 2020, on March 31, 2020, and percentage of workforce able to carry out a majority of their duties during the COVID-19 pandemic, by North American Industry Classification System (NAICS) code, business employment size, type of business and majority ownership.

Replication code for "Cullen, Z.; Pakzad-Hurson, B. and Perez-Truglia, R. (2025). Home Sweet Home: How Much Do Employees Value Remote Work?" at American Economic Association Papers & Proceedings

In these uncertain and difficult times caused by the spread of COVID-19, many of us have been forced to work from home and are required to complete job tasks on the same level of quality (as office). Several researchers argued that in any workplace, employees face positive and negative stressors.

The questionnaire is focused on finding out if employees are facing the same stressors while working from home and to what extent are they aware of these stressors. The research is divided into two stages:

1) This questionnaire

2) A phone interview

The questionnaire takes approximately 5 minutes to complete. The responses will be used exclusively for the purpose of this study.

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.

Based on novel survey data, we document a persistent rise in work from home (WFH) over the course of the COVID-19 pandemic. Using theory and direct survey evidence, we argue that three quarters of this increase reflects adoption of new work arrangements that will likely be permanent for many workers. A quantitative model matched to survey data predicts that twice as many workers will WFH full-time post-pandemic compared to pre-pandemic, and that one in every five instead of seven workdays will be WFH. These model predictions are consistent with survey evidence on workers' own expectations about WFH in the future.

In 2020, approximately *** million people worked mainly from home in the United Kingdom, an increase of around **** million people when compared with 1998, when just *** million workers mainly worked from home. As a share of all workers in the United Kingdom, this was the equivalent of **** percent of the UK workforce, compared with **** percent in 1998. Rise of the hybrid workforce More recent figures on working location trends in Great Britain, indicate that as of June 2025, around ** percent of workers had worked from home exclusively in the last seven days, with a further ** percent only travelling to work. Just over a ******* of British workers, however, had both worked from home and traveled to work in the last seven days. Although less common than only travelling to work, hybrid working has generally been more popular than only working at home since around Spring 2022 and is possibly one of the most enduring impacts that COVID-19 had on the labor market. Demographics of homeworkers While advancements in internet connectivity and communication software have enabled more people to work from home than ever before, there are still obvious disparities in the share of homeworkers by industry. Over **** of the UK’s agriculture workforce in 2020 regularly worked from home, compared with just *** percent of those that worked in accommodation or food service. In the same year, the region with the highest share of people working from home was South West England at **** percent, while Northern Ireland had the lowest at just *** percent.

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).

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

About this dataset

Context

The dataset presents the detailed breakdown of the count of individuals within distinct income brackets, categorizing them by gender (men and women) and employment type - full-time (FT) and part-time (PT), offering valuable insights into the diverse income landscapes within Home Lake township. The dataset can be utilized to gain insights into gender-based income distribution within the Home Lake township population, aiding in data analysis and decision-making..

Key observations

• Employment patterns: Within Home Lake township, among individuals aged 15 years and older with income, there were 39 men and 31 women in the workforce. Among them, 20 men were engaged in full-time, year-round employment, while 15 women were in full-time, year-round roles.
• Annual income under $24,999: Of the male population working full-time, 5% fell within the income range of under $24,999, while 33.33% of the female population working full-time was represented in the same income bracket.
• Annual income above $100,000: 30% of men in full-time roles earned incomes exceeding $100,000, while none of women in full-time positions earned within this income bracket.
• Refer to the research insights for more key observations on more income brackets ( Annual income under $24,999, Annual income between $25,000 and $49,999, Annual income between $50,000 and $74,999, Annual income between $75,000 and $99,999 and Annual income above $100,000) and employment types (full-time year-round and part-time)

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Income brackets:

• $1 to $2,499 or loss
• $2,500 to $4,999
• $5,000 to $7,499
• $7,500 to $9,999
• $10,000 to $12,499
• $12,500 to $14,999
• $15,000 to $17,499
• $17,500 to $19,999
• $20,000 to $22,499
• $22,500 to $24,999
• $25,000 to $29,999
• $30,000 to $34,999
• $35,000 to $39,999
• $40,000 to $44,999
• $45,000 to $49,999
• $50,000 to $54,999
• $55,000 to $64,999
• $65,000 to $74,999
• $75,000 to $99,999
• $100,000 or more

Variables / Data Columns

• Income Bracket: This column showcases 20 income brackets ranging from $1 to $100,000+..
• Full-Time Males: The count of males employed full-time year-round and earning within a specified income bracket
• Part-Time Males: The count of males employed part-time and earning within a specified income bracket
• Full-Time Females: The count of females employed full-time year-round and earning within a specified income bracket
• Part-Time Females: The count of females employed part-time and earning within a specified income bracket

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.

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

This table contains data on the percent of residents aged 16 years and older mode of transportation to work for California, its regions, counties, cities/towns, and census tracts. Data is from the U.S. Census Bureau, Decennial Census and American Community Survey. The table is part of a series of indicators in the Healthy Communities Data and Indicators Project of the Office of Health Equity. Commute trips to work represent 19% of travel miles in the United States. The predominant mode – the automobile - offers extraordinary personal mobility and independence, but it is also associated with health hazards, such as air pollution, motor vehicle crashes, pedestrian injuries and fatalities, and sedentary lifestyles. Automobile commuting has been linked to stress-related health problems. Active modes of transport – bicycling and walking alone and in combination with public transit – offer opportunities for physical activity, which is associated with lowering rates of heart disease and stroke, diabetes, colon and breast cancer, dementia and depression. Risk of injury and death in collisions are higher in urban areas with more concentrated vehicle and pedestrian activity. Bus and rail passengers have a lower risk of injury in collisions than motorcyclists, pedestrians, and bicyclists. Minority communities bear a disproportionate share of pedestrian-car fatalities; Native American male pedestrians experience four times the death rate Whites or Asian pedestrians, and African-Americans and Latinos experience twice the rate as Whites or Asians. More information about the data table and a data dictionary can be found in the About/Attachments section.

The number of employed persons in The United States decreased to 163106 Thousand in July of 2025 from 163366 Thousand in June of 2025. This dataset provides - United States Employed Persons - actual values, historical data, forecast, chart, statistics, economic calendar and news.

This spreadsheet replicates selected data tables from the ACT & Queanbeyan Household Travel Survey dashboard. Please refer to the attached spreadsheet on this page.

About the Work from Home theme For the 2022 survey, anyone with a work activity was also asked if they had the opportunity to work from home.

Survey participants provided information on which days they worked from home in the previous week. No further clarification was made about the amount of time that they work from home. Outcomes are summarised on the "WFH days" tab.

In addition, on the travel diary itself, people were asked whether they worked from home for at least two hours on their travel day. This is summarised in the "WFH behaviour" tab, and is a slightly different question to the one asked above. For completeness, both are shown. The overall potential to work from home (regardless of whether someone actually did work from home) is also reported here.

Note that the tables provided represent a small subset of data available. Use of the dashboard or raw survey datasets allow more complex descriptions of travel to be developed.

Source data The data shown is not a Census of travel, but a large survey of several thousand households from across the ACT and Queanbeyan. As with any survey there will be some variability in the accuracy of the results, and how well they reflect the movement of the entire population. For instance, if the survey were to be completed on another day, or with a different subset of households, the results would be slightly different. Interpretations of the data should keep this variability in mind: these are estimates of the broad shape of travel only. Even for the same person, travel behaviour will vary according to many factors: day of week, month of year, season, weather, school holidays, illness, family responsibilities, work from home opportunities, etc. Again, by summarising the travel of many different people, the data provides a view of average weekday patterns.

In interpreting the data, it is worth noting the following points: - A zero cell does not necessarily mean the travel is never made, but rather that the survey participants did not make this travel on their particular survey day. - Values are rounded, and may not sum to the totals shown.

The survey is described on the Transport Canberra and City Services' website: [Household Travel Survey homepage]

Cell annotations and notes Some cells have annotations added to them, as follows: * : Statistically significant difference across survey years (at the 95% confidence level). Confidence intervals indicate where the true measure would typically fall if the survey were repeated multiple times (i.e., 95 times out of 100), recognising that each survey iteration may produce slightly different outcomes. ~ : Unreliable estimate (small sample or wide confidence interval)

Additional information Analysis by Sift Research, March 2025. Contact research@sift.group for further information. Enclosed data tables shared under a 'CC BY' Creative Commons licence. This enables users to distribute, remix, adapt, and build upon the material in any medium or format, so long as attribution is given to the creator. The license allows for commercial use. [>More information about CC BY]

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 New 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 2023

Based on our analysis ACS 2019-2023 5-Year Estimates, we present the following observations: - All workers, aged 15 years and older: In New Home, the median income for all workers aged 15 years and older, regardless of work hours, was $45,938 for males and $24,583 for females.

These income figures highlight a substantial gender-based income gap in New 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 New Home.

- Full-time workers, aged 15 years and older: In New Home, among full-time, year-round workers aged 15 years and older, males earned a median income of $54,375, while females earned $26,944, leading to a 50% gender pay gap among full-time workers. This illustrates that women earn 50 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 New Home, 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 New Home median household income by race. You can refer the same here

Dataset shows an individual’s statistical area 2 (SA2) of usual residence and the SA2 of their workplace address, for the employed census usually resident population count aged 15 years and over, by main means of travel to work from the 2018 and 2023 Censuses.

The main means of travel to work categories are:

• Work at home
• Drive a private car, truck, or van
• Drive a company car, truck, or van
• Passenger in a car, truck, van, or company bus
• Public bus
• Train
• Bicycle
• Walk or jog
• Ferry
• Other.

​

Main means of travel to work is the usual method which an employed person aged 15 years and over used to travel the longest distance to their place of work.

​

Workplace address refers to where someone usually works in their main job, that is the job in which they worked the most hours. For people who work at home, this is the same address as their usual residence address. For people who do not work at home, this could be the address of the business they work for or another address, such as a building site.

Workplace address is coded to the most detailed geography possible from the available information. This dataset only includes travel to work information for individuals whose workplace address is available at SA2 level. The sum of the counts for each region in this dataset may not equal the total employed census usually resident population count aged 15 years and over for that region. Workplace address – 2023 Census: Information by concept has more information.

​

This dataset can be used in conjunction with the following spatial files by joining on the SA2 code values:

• Statistical area 2 2023 (generalised)
• Statistical area 2 2023 (Centroid Inside)

​

Download data table using the instructions in the Koordinates help guide.

​

Footnotes

​

Geographical boundaries

Statistical standard for geographic areas 2023 (updated December 2023) has information about geographic boundaries as of 1 January 2023. Address data from 2013 and 2018 Censuses was updated to be consistent with the 2023 areas. Due to the changes in area boundaries and coding methodologies, 2013 and 2018 counts published in 2023 may be slightly different to those published in 2013 or 2018.

​

Subnational census usually resident population

The census usually resident population count of an area (subnational count) is a count of all people who usually live in that area and were present in New Zealand on census night. It excludes visitors from overseas, visitors from elsewhere in New Zealand, and residents temporarily overseas on census night. For example, a person who usually lives in Christchurch city and is visiting Wellington city on census night will be included in the census usually resident population count of Christchurch city. 

​

Population counts

Stats NZ publishes a number of different population counts, each using a different definition and methodology. Population statistics – user guide has more information about different counts. 

​

Caution using time series

Time series data should be interpreted with care due to changes in census methodology and differences in response rates between censuses. The 2023 and 2018 Censuses used a combined census methodology (using census responses and administrative data).

​

Workplace address time series

Workplace address time series data should be interpreted with care at lower geographic levels, such as statistical area 2 (SA2). Methodological improvements in 2023 Census resulted in greater data accuracy, including a greater proportion of people being counted at lower geographic areas compared to the 2018 Census. Workplace address – 2023 Census: Information by concept has more information.

​

Working at home

In the census, working at home captures both remote work, and people whose business is at their home address (e.g. farmers or small business owners operating from their home). The census asks respondents whether they ‘mostly’ work at home or away from home. It does not capture whether someone does both, or how frequently they do one or the other.

​

Rows excluded from the dataset

Rows show SA2 of usual residence by SA2 of workplace address. Rows with a total population count of less than six have been removed to reduce the size of the dataset, given only a small proportion of SA2-SA2 combinations have commuter flows.

​

About the 2023 Census dataset

For information on the 2023 dataset see Using a combined census model for the 2023 Census. We combined data from the census forms with administrative data to create the 2023 Census dataset, which meets Stats NZ's quality criteria for population structure information. We added real data about real people to the dataset where we were confident the people who hadn’t completed a census form (which is known as admin enumeration) will be counted. We also used data from the 2018 and 2013 Censuses, administrative data sources, and statistical imputation methods to fill in some missing characteristics of people and dwellings.

​

Data quality

The quality of data in the 2023 Census is assessed using the quality rating scale and the quality assurance framework to determine whether data is fit for purpose and suitable for release. Data quality assurance in the 2023 Census has more information.

​

Quality rating of a variable

The quality rating of a variable provides an overall evaluation of data quality for that variable, usually at the highest levels of classification. The quality ratings shown are for the 2023 Census unless stated. There is variability in the quality of data at smaller geographies. Data quality may also vary between censuses, for subpopulations, or when cross tabulated with other variables or at lower levels of the classification. Data quality ratings for 2023 Census variables has more information on quality ratings by variable.

​

Main means of travel to work quality rating

Main means of travel to work is rated as moderate quality.

Main means of travel to work – 2023 Census: Information by concept has more information, for example, definitions and data quality.

​

Workplace address quality rating

Workplace address is rated as moderate quality.

Workplace address – 2023 Census: Information by concept has more information, for example, definitions and data quality.

​

Using data for good

Stats NZ expects that, when working with census data, it is done so with a positive purpose, as outlined in the Māori Data Governance Model (Data Iwi Leaders Group, 2023). This model states that "data should support transformative outcomes and should uplift and strengthen our relationships with each other and with our environments. The avoidance of harm is the minimum expectation for data use. Māori data should also contribute to iwi and hapū tino rangatiratanga”.

​

Confidentiality

The 2023 Census confidentiality rules have been applied to 2013, 2018, and 2023 data. These rules protect the confidentiality of individuals, families, households, dwellings, and undertakings in 2023 Census data. Counts are calculated using fixed random rounding to base 3 (FRR3) and suppression of ‘sensitive’ counts less than six, where tables report multiple geographic variables and/or small populations. Individual figures may not always sum to stated totals. Applying confidentiality rules to 2023 Census data and summary of changes since 2018 and 2013 Censuses has more information about 2023 Census confidentiality rules.

​

Percentages

To calculate percentages, divide the figure for the category of interest by the figure for ‘Total stated’ where this applies.

​

Symbol

-999 Confidential

​

Inconsistencies in definitions

Please note that there may be differences in definitions between census classifications and those used for other data collections.

Abstract copyright UK Data Service and data collection copyright owner. This study examined the ways in which home is conceptualised, experienced and evaluated by homeworkers, a hard-to-reach group. It explored the impact of homeworking on the experience of home and family life and sought to identify ways in which working from home may challenge the traditional stereotypical view of home. A pilot study suggested that the homeworking experience may be differentiated by economic, spatial and gender factors revealing potential tensions and inequalities among the broad range of people who work from home. The research sought to: explore the existence of supports, inequalities and tensions in the homeworking experience; and establish the particular qualities of home that are enhanced with working from home, as well as those that are limited by this activity. The study used mixed methods, including face-to-face qualitative interviews with individuals and focus groups, and a semi-structured questionnaire, from which a quantitative data file was complied. The qualitative sample consisted of 60 men and women who worked from home (45 individual interviewees, and fifteen other respondents comprising three focus groups), in varied types of work including professional, semi-skilled and unskilled. Four population areas in Northern England and Wales were targeted for this study. The quantitative data set included 62 questionnaires from a separate sample of national homeworkers. Key findings suggest that homeworking is differentiated by gender role, type of work and expectations. Furthermore there are both positive and negative aspects to the homeworking experience for all homeworkers. For example, flexibility is desired and enjoyed but this brings longer working hours. Finally, home is enhanced for some and invaded for others, but most make a conscious effort to make working from home viable. Main Topics: The qualitative interviews cover paid work done at home, how the interviewee began homeworking, location in the home used for work, how family/partner copes with interviewee's home work, enjoyment of homeworking, choice of other jobs outside the home, positive and negative aspects of homeworking, perception of how treated in comparison to other workers, structure of typical working day, separation of work and home life, leisure time activities, breaks from work, local neighbourhood, perceptions of home and changes brought on by working from home. Variables in the quantitative data file include interviewee number (the 45 interviews may be linked by number to the respondent information in the data file), age, gender, rural/urban location, type of work, employment status, employment details and sector, hours of work, employment and homeworking history, living arrangements and household, children and childcare, computer use, community access, ways of working from home, comparison with other ways of working and work locus of control. Standard Measures (questionnaire - quantitative data file): General Health Questionnaire short (12) form (data included on the file but may not be listed in the questionnaire); The Work Locus of Control Scale (Spector, 1988). Quota sample Purposive selection/case studies Volunteer sample Face-to-face interview Self-completion Psychological measurements

About this dataset

Context

The dataset presents the detailed breakdown of the count of individuals within distinct income brackets, categorizing them by gender (men and women) and employment type - full-time (FT) and part-time (PT), offering valuable insights into the diverse income landscapes within Home township. The dataset can be utilized to gain insights into gender-based income distribution within the Home township population, aiding in data analysis and decision-making..

Key observations

• Employment patterns: Within Home township, among individuals aged 15 years and older with income, there were 1,039 men and 828 women in the workforce. Among them, 501 men were engaged in full-time, year-round employment, while 230 women were in full-time, year-round roles.
• Annual income under $24,999: Of the male population working full-time, 17.56% fell within the income range of under $24,999, while 16.52% of the female population working full-time was represented in the same income bracket.
• Annual income above $100,000: 4.19% of men in full-time roles earned incomes exceeding $100,000, while 6.09% of women in full-time positions earned within this income bracket.
• Refer to the research insights for more key observations on more income brackets ( Annual income under $24,999, Annual income between $25,000 and $49,999, Annual income between $50,000 and $74,999, Annual income between $75,000 and $99,999 and Annual income above $100,000) and employment types (full-time year-round and part-time)

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Income brackets:

• $1 to $2,499 or loss
• $2,500 to $4,999
• $5,000 to $7,499
• $7,500 to $9,999
• $10,000 to $12,499
• $12,500 to $14,999
• $15,000 to $17,499
• $17,500 to $19,999
• $20,000 to $22,499
• $22,500 to $24,999
• $25,000 to $29,999
• $30,000 to $34,999
• $35,000 to $39,999
• $40,000 to $44,999
• $45,000 to $49,999
• $50,000 to $54,999
• $55,000 to $64,999
• $65,000 to $74,999
• $75,000 to $99,999
• $100,000 or more

Variables / Data Columns

• Income Bracket: This column showcases 20 income brackets ranging from $1 to $100,000+..
• Full-Time Males: The count of males employed full-time year-round and earning within a specified income bracket
• Part-Time Males: The count of males employed part-time and earning within a specified income bracket
• Full-Time Females: The count of females employed full-time year-round and earning within a specified income bracket
• Part-Time Females: The count of females employed part-time and earning within a specified income bracket

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.

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

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.

This dataset provides Census 2021 estimates that classify usual residents in England and Wales by method used to travel to work (2001 specification) and by distance travelled to work. The estimates are as at Census Day, 21 March 2021.

_As Census 2021 was during a unique period of rapid change, take care when using this data for planning purposes. Due to methodological changes the ‘mainly work at or from home: any workplace type’ category has a population of zero. Please use the transport_to_workplace_12a classification instead. Read more about this quality notice._

As Census 2021 was during a unique period of rapid change, take care when using this data for planning purposes. Read more about this quality notice.

Area type

Census 2021 statistics are published for a number of different geographies. These can be large, for example the whole of England, or small, for example an output area (OA), the lowest level of geography for which statistics are produced.

For higher levels of geography, more detailed statistics can be produced. When a lower level of geography is used, such as output areas (which have a minimum of 100 persons), the statistics produced have less detail. This is to protect the confidentiality of people and ensure that individuals or their characteristics cannot be identified.

Lower tier local authorities

Lower tier local authorities provide a range of local services. There are 309 lower tier local authorities in England made up of 181 non-metropolitan districts, 59 unitary authorities, 36 metropolitan districts and 33 London boroughs (including City of London). In Wales there are 22 local authorities made up of 22 unitary authorities.

Coverage

Census 2021 statistics are published for the whole of England and Wales. However, you can choose to filter areas by:

• country - for example, Wales
• region - for example, London
• local authority - for example, Cornwall
• health area – for example, Clinical Commissioning Group
• statistical area - for example, MSOA or LSOA

Method used to travel to workplace

A person's place of work and their method of travel to work. This is the 2001 method of producing travel to work variables.

"Work mainly from home" applies to someone who indicated their place of work as their home address and travelled to work by driving a car or van, for example visiting clients.

Distance travelled to work

The distance, in kilometres, between a person's residential postcode and their workplace postcode measured in a straight line. A distance travelled of 0.1km indicates that the workplace postcode is the same as the residential postcode. Distances over 1200km are treated as invalid, and an imputed or estimated value is added.

“Work mainly at or from home” is made up of those that ticked either the "Mainly work at or from home" box for the address of workplace question, or the “Work mainly at or from home” box for the method of travel to work question.

Distance is calculated as the straight line distance between the enumeration postcode and the workplace postcode.

Combine this variable with “Economic activity status” to identify those in employment at the time of the census.