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.

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.

Publicly available data and code for "Working Remotely? Selection, Treatment and the Market for Remote Work"How does remote work affect productivity and how productive are workers who choose remote jobs? We decompose these effects in a Fortune 500 firm. Before Covid-19, remote workers answered 12% fewer calls per hour than on-site workers. After the offices closed, the productivity gap narrowed by 4%, and formerly on-site workers’ call quality and promotion rates also declined. Even with everyone remote, an 8% productivity gap persisted, indicating negative selection into remote jobs. A cost-benefit analysis indicates that the savings from remote work in reducing turnover and office rents could outweigh remote work's negative productivity impact but not the costs of attracting less productive workers.

🧠 About the Data

This dataset explores how remote work opportunities intersect with salaries, experience, and employment types across industries. It contains clean, structured records of 500 hypothetical employees in remote or hybrid job roles, suitable for salary modeling, HR analytics, or industry-based salary insights.

📌 Column Descriptions

📈 Potential Use Cases

• Predictive modeling for salary based on role, experience, and location
• Salary benchmarking per industry or employment type
• Visualizing remote vs onsite salary disparities
• Market research for HR and hiring trends
• Exploratory analysis on global employment models

A survey of 1,500 NSW workers during August and September 2020 (2020 Remote Working Survey) and March and April 2021 (2021 Remote Working Survey), commissioned to understand workers' experiences of and attitudes to remote and hybrid working. To be eligible, respondents had to be employed NSW residents with experience of remote working in their current job. After accounting for unemployed people and those whose jobs cannot be done remotely—for example, dentists, cashiers and cleaners—the sample represents around 59 per cent of NSW workers. Workers answered questions on: • their attitudes to remote working • the amount of time they spent working remotely • their employers’ policies, practices, and attitudes • how they spent their time when working remotely • how barriers to remote working have changed • the barriers they faced to hybrid working • their expectations for future remote working

Explore a comprehensive dataset on remote work’s impact on mental health, stress, and job satisfaction across various industries.

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.

With 44 percent, most of respondents state that collaborating on new projects works better than pre-COVID-19. This indicates that maintaining a hybrid workplace post-COVID-19 is a possibility for both employers and employees. However, this does not mean that remote work is always an appropriate alternative to being in the office. Over 30 percent of employees state that coaching and onboarding new hires remotely is worse that pre-COVID.

Profile of employees aged 18 years who availed of remote working in the previous 4 weeks

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

Profile of employees aged 18 years who availed of remote working in the previous 4 weeks by their blended working pattern

Mental Health in Tech Survey

Understanding Employee Mental Health Needs in the Tech Industry

By Stephen Myers [source]

About this dataset

This dataset contains survey responses from individuals in the tech industry about their mental health, including questions about treatment, workplace resources, and attitudes towards discussing mental health in the workplace. Mental health is an issue that affects all people of all ages, genders and walks of life. The prevalence of these issues within the tech industry–one that places hard demands on those who work in it–is no exception. By analyzing this dataset, we can better understand how prevalent mental health issues are among those who work in the tech sector.–and what kinds of resources they rely upon to find help–so that more can be done to create a healthier working environment for all.

This dataset tracks key measures such as age, gender and country to determine overall prevalence, along with responses surrounding employee access to care options; whether mental health or physical illness are being taken as seriously by employers; whether or not anonymity is protected with regards to seeking help; and how coworkers may perceive those struggling with mental illness issues such as depression or anxiety. With an ever-evolving landscape due to new technology advancing faster than ever before – these statistics have never been more important for us to analyze if we hope remain true promoters of a healthy world inside and outside our office walls

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How to use the dataset

In this dataset you will find data on age, gender, country, and state of survey respondents in addition to numerous questions that assess an individual's mental state including: self-employment status, family history of mental illness, treatment status and access or lack thereof; how their mental health condition affects their work; number of employees at the company they work for; remote work status; tech company status; benefit information from employers such as mental health benefits and wellness program availability; anonymity protection if seeking treatment resources for substance abuse or mental health issues ; ease (or difficulty) for medical leave for a mental health condition ; whether discussing physical or medical matters with employers have negative consequences. You will also find comments from survey participants.

To use this dataset effectively: - Clean the data by removing invalid responses/duplicates/missing values - you can do this with basic Pandas commands like .dropna() , .drop_duplicates(), .replace(). - Utilize descriptive statistics such as mean and median to draw general conclusions about patterns of responses - you can do this with Pandas tools such as .groupby() and .describe(). - Run various types analyses such as mean comparisons on different kinds of variables(age vs gender), correlations between different features etc using appropriate statistical methods - use commands like Statsmodels' OLS models (.smf) , calculate z-scores , run hypothesis tests etc depending on what analysis is needed. Make sure you are aware any underlying assumptions your analysis requires beforehand !
- Visualize your results with plotting libraries like Matplotlib/Seaborn to easily interpret these findings! Use boxplots/histograms/heatmaps where appropriate depending on your question !

Research Ideas

• Using the results of this survey, you could develop targeted outreach campaigns directed at underrepresented groups that answer “No” to questions about their employers providing resources for mental health or discussing it as part of wellness programs.
• Analyzing the employee characteristics (e.g., age and gender) of those who reported negative consequences from discussing their mental health in the workplace could inform employer policies to support individuals with mental health conditions and reduce stigma and discrimination in the workplace.
• Correlating responses to questions about remote work, leave policies, and anonymity with whether or not individuals have sought treatment for a mental health condition may provide insight into which types of workplace resources are most beneficial for supporting employees dealing with these issues

Acknowledgements

If you use this dataset in your research, please credit the original authors. Data Source

License

License: Dataset copyright by authors - You are free to: - Share - copy and redi...

This dataset contains 10,000 records of corporate employees across various departments, focusing on work hours, job satisfaction, and productivity performance. The dataset is designed for exploratory data analysis (EDA), performance benchmarking, and predictive modeling of productivity trends.

You can conduct EDA and investigate correlations between work hours, remote work, job satisfaction, and productivity. You can create new metrics like efficiency per hour or impact of meetings on productivity. Machine Learning Model: If you want a predictive task, you can use "Productivity_Score" as a regression target (predicting continuous performance scores). Or you can also create a classification problem (e.g., categorize employees into high, medium, or low productivity).

Percentage and average percentage of workforce anticipated to work on-site or remotely over the next three months, by North American Industry Classification System (NAICS), business employment size, type of business, business activity and majority ownership, fourth quarter of 2024.

Employees aged 18 years and over by whether they availed of remote working in their employment in the previous 12 months / pre COVID-19

Description The Post-Pandemic Remote Work Health Impact 2025 dataset presents a comprehensive, global snapshot of how remote, hybrid, and onsite work arrangements are influencing the mental and physical health of employees in the post-pandemic era. Collected in June 2025, this dataset aggregates responses from a diverse workforce spanning continents, industries, age groups, and job roles. It is designed to support research, data analysis, and policy-making around the evolving landscape of work and well-being.

This dataset enables in-depth exploration of:

• The prevalence of mental health conditions (e.g., anxiety, burnout, PTSD, depression) across different work setups.
• The relationship between work arrangements and physical health complaints (e.g., back pain, eye strain, neck pain).
• Variations in work-life balance, social isolation, and burnout levels segmented by demographic and occupational factors.
• Salary distributions and their correlation with health outcomes and job roles.

By providing granular, anonymized data on both subjective (self-reported) and objective (hours worked, salary range) factors, this resource empowers data scientists, health researchers, HR professionals, and business leaders to:

• Identify risk factors and protective factors for employee well-being. Benchmark health impacts across industries and regions.
• Inform organizational policy and future-of-work strategies.

| Column Name Description Example Values | | | Survey_Date Date when the survey response was submitted (YYYY-MM-DD) 2025-06-01 Age Age of the respondent (in years) 27, 52, 40 Gender Gender identity of the respondent Female, Male, Non-binary, Prefer not to say Region Geographical region of employment Asia, Europe, North America, Africa, Oceania Industry Industry sector of the respondent Technology, Manufacturing, Finance, Healthcare Job_Role Specific job title or function Data Analyst, HR Manager, Software Engineer Work_Arrangement Primary work mode Onsite, Remote, Hybrid Hours_Per_Week Average number of hours worked per week 36, 55, 64 Mental_Health_Status Primary self-reported mental health condition Anxiety, Burnout, Depression, None, PTSD Burnout_Level Self-assessed burnout (categorical: Low, Medium, High) High, Medium, Low Work_Life_Balance_Score Self-rated work-life balance on a scale of 1 (poor) to 5 (excellent) 1, 3, 5 Physical_Health_Issues Self-reported physical health complaints (semicolon-separated if multiple) Back Pain; Eye Strain; Neck Pain; None Social_Isolation_Score Self-rated social isolation on a scale of 1 (none) to 5 (severe) 1, 2, 5 Salary_Range Annual salary range in USD $40K-60K, $80K-100K, $120K+ | --- | | | |

The Synthetic Employee Attrition Dataset is a simulated dataset designed for the analysis and prediction of employee attrition. It contains detailed information about various aspects of an employee's profile, including demographics, job-related features, and personal circumstances.

The dataset comprises 74,610 samples, to facilitate model development and evaluation. Each record includes a unique Employee ID and features that influence employee attrition. The goal is to understand the factors contributing to attrition and develop predictive models to identify at-risk employees.

This dataset is ideal for HR analytics, machine learning model development, and demonstrating advanced data analysis techniques. It provides a comprehensive and realistic view of the factors affecting employee retention, making it a valuable resource for researchers and practitioners in the field of human resources and organizational development.

FEATURES:

Employee ID: A unique identifier assigned to each employee. Age: The age of the employee, ranging from 18 to 60 years. Gender: The gender of the employee Years at Company: The number of years the employee has been working at the company. Monthly Income: The monthly salary of the employee, in dollars. Job Role: The department or role the employee works in, encoded into categories such as Finance, Healthcare, Technology, Education, and Media. Work-Life Balance: The employee's perceived balance between work and personal life, (Poor, Below Average, Good, Excellent) Job Satisfaction: The employee's satisfaction with their job: (Very Low, Low, Medium, High) Performance Rating: The employee's performance rating: (Low, Below Average, Average, High) Number of Promotions: The total number of promotions the employee has received. Distance from Home: The distance between the employee's home and workplace, in miles. Education Level: The highest education level attained by the employee: (High School, Associate Degree, Bachelor’s Degree, Master’s Degree, PhD) Marital Status: The marital status of the employee: (Divorced, Married, Single) Job Level: The job level of the employee: (Entry, Mid, Senior) Company Size: The size of the company the employee works for: (Small,Medium,Large) Company Tenure: The total number of years the employee has been working in the industry. Remote Work: Whether the employee works remotely: (Yes or No) Leadership Opportunities: Whether the employee has leadership opportunities: (Yes or No) Innovation Opportunities: Whether the employee has opportunities for innovation: (Yes or No) Company Reputation: The employee's perception of the company's reputation: (Very Poor, Poor,Good, Excellent) Employee Recognition: The level of recognition the employee receives:(Very Low, Low, Medium, High) Attrition: Whether the employee has left the company, encoded as 0 (stayed) and 1 (Left).

PWLBRW11 - Employees aged 18 years and over that availed of remote working in their employment in the previous 12 months by their remote working pattern, where they worked in the previous 4 weeks. Published by Central Statistics Office. Available under the license Creative Commons Attribution 4.0 (CC-BY-4.0).Employees aged 18 years and over that availed of remote working in their employment in the previous 12 months by their remote working pattern, where they worked in the previous 4 weeks...

Data that is collected at the individual-level from mobile phones is typically aggregated to the population-level for privacy reasons. If we are interested in answering questions regarding the mean, or working with groups appropriately modeled by a continuum, then this data is immediately informative. However, coupling such data regarding a population to a model that requires information at the individual-level raises a number of complexities. This is the case if we aim to characterize human mobility and simulate the spatial and geographical spread of a disease by dealing in discrete, absolute numbers. In this work, we highlight the hurdles faced and outline how they can be overcome to effectively leverage the specific dataset: Google COVID-19 Aggregated Mobility Research Dataset (GAMRD). Using a case study of Western Australia, which has many sparsely populated regions with incomplete data, we firstly demonstrate how to overcome these challenges to approximate absolute flow of people around a transport network from the aggregated data. Overlaying this evolving mobility network with a compartmental model for disease that incorporated vaccination status we run simulations and draw meaningful conclusions about the spread of COVID-19 throughout the state without de-anonymizing the data. We can see that towns in the Pilbara region are highly vulnerable to an outbreak originating in Perth. Further, we show that regional restrictions on travel are not enough to stop the spread of the virus from reaching regional Western Australia. The methods explained in this paper can be therefore used to analyze disease outbreaks in similarly sparse populations. We demonstrate that using this data appropriately can be used to inform public health policies and have an impact in pandemic responses.

When asked whether remote work has been a success, 83 of employers agree, while only 73 percent of employees agree. This illustrates that the majority of respondents agree that remote work has been a success. Remote work refers to the practice of employees working from many different locations, relying on modern technologies to connect them to their coworkers.