A survey conducted in 2022 found that members of Generation Z were the least likely to say they were just as productive when working from home versus working in the office. In contrast, nearly ***** times the number of Baby Boomers said they were just as productive working from home versus the office.

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.

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

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

Analysis of how working from home has affected individuals’ spending and how this differs by characteristics, Great Britain.

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.

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).

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.

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).

Businesses or organizations anticipated to shrink office locations due to the workforce teleworking, by North American Industry Classification System (NAICS), business employment size, type of business, business activity and majority ownership, third quarter of 2021.

F7111 - Usual Resident population aged 15 years who work from home. Published by Central Statistics Office. Available under the license Creative Commons Attribution 4.0 (CC-BY-4.0).Usual Resident population aged 15 years who work from home...

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.

This is a data set that contains the impact of WFO (Work From Office), WFA (Work From Anywhere), and WFH (Work From Home). Other data are used to support companies that want to transition from working in the office to remote work.

F7137 - Population Aged 15 Years and Over at Work by Number of Days Working from Home. Published by Central Statistics Office. Available under the license Creative Commons Attribution 4.0 (CC-BY-4.0).Population Aged 15 Years and Over at Work by Number of Days Working from Home...

This dataset contains breakdowns of homeworkers by different characteristics using 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 Sweet 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 Sweet Home, the median income for all workers aged 15 years and older, regardless of work hours, was $35,745 for males and $22,144 for females.

These income figures highlight a substantial gender-based income gap in Sweet Home. Women, regardless of work hours, earn 62 cents for each dollar earned by men. This significant gender pay gap, approximately 38%, underscores concerning gender-based income inequality in the city of Sweet Home.

- Full-time workers, aged 15 years and older: In Sweet Home, among full-time, year-round workers aged 15 years and older, males earned a median income of $53,992, while females earned $43,309, leading to a 20% gender pay gap among full-time workers. This illustrates that women earn 80 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 Sweet Home.

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

The Travel Time to Work dataset was compiled using information from December 31, 2023 and updated December 12, 2024 from the Bureau of Transportation Statistics (BTS) and is part of the U.S. Department of Transportation (USDOT)/Bureau of Transportation Statistics (BTS) National Transportation Atlas Database (NTAD). The Travel Time to Work table from the 2023 American Community Survey (ACS) 5-year estimates was joined to 2023 tract-level geographies for all 50 States, District of Columbia and Puerto Rico provided by the Census Bureau. A new file was created that combines the demographic variables from the former with the cartographic boundaries of the latter. The national level census tract layer contains data on the number and percentage of commuters (workers 16 years and over who did not work from home) with a range of travel times to work.

Breakdowns of the prevalence of homeworking by industry, occupation, region, age, sex and ethnicity.

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