As of August 2024, there were about ******* employees in South Korea who worked from home or remotely, a decrease from the previous year. During the height of the COVID-19 pandemic, remote work became more commonplace in South Korea, though this has waned in the years since.

In January 2021, 24 percent of employees in Germany worked from home. The coronavirus (COVID-19) pandemic is ongoing. Since March 2020, many employees in the country have faced fluctuating waves of having to work from home, depending on current regulations introduced by the government and virus waves. The survey on which this graph is based also included those looking for employment.

As of December 2024, around *** million employees in China had used online services to work from home, accounting for around **** percent of the Chinese internet user base. After four years of on-and-off lockdowns due to the COVID-19 pandemic, the adoption of remote working in China largely sustains with the support of AI-powered software.

Between January and March 2021, the sector of *********************** was estimated to have the largest share of employees in home office in Italy. The highest peak was recorded in March and April 2020, when around ** percent of employees in this industry were working from home. Other sectors in which many employees had the chance to work remotely were energy, insurance and finance, real estate, and education. Unsurprisingly, employees working in manufacturing companies, in the health sector, as well as in the water supply, sewage system, waste management, redevelopment industry were among those recording the lowest share. Overall, data refers to companies which enabled people to work remotely.

Number of active employees, aggregating information from multiple data providers. This series is based on firm-level payroll data from Paychex and Intuit, worker-level data on employment and earnings from Earnin, and firm-level timesheet data from Kronos. This data is compiled by Opportunity Insights. Data notes from Opportunity Insights: Data Source: Paychex, Intuit, Earnin, Kronos Update Frequency: Weekly Date Range: January 15th 2020 until the most recent date available. The most recent date available for the full series depends on the combination of Paychex, Intuit and Earnin data. We extend the national trend of aggregate employment and employment by income quartile by using Kronos timecard data and Paychex data for workers paid on a weekly paycycle to forecast beyond the end of the Paychex, Intuit and Earnin data. Data Frequency: Daily, presented as a 7-day moving average Indexing Period: January 4th - January 31st Indexing Type: Change relative to the January 2020 index period, not seasonally adjusted. More detailed documentation on Opportunity Insights data can be found here: https://github.com/OpportunityInsights/EconomicTracker/blob/main/docs/oi_tracker_data_documentation.pdf

THE IMPACTS OF WORKING REMOTELY AND IN AN OFFICE

Abstract: Working from home nowadays, particularly after COVID-19 hit the world, became the preferable choice for many employees because it gives flexibility and saves more time, according to them. However, many studies revealed that working from home caused a negative effect on many employees’ mental and physical health, such as isolation and back pain. The careless and unplanned way of living while working remotely, such as lack of socialization and equipment for a healthy home office, is the cause for that negative effect. In this paper, we explore the reasons that lead to the negative impact of working remotely on mental and physical health and investigate whether employees are aware of the negative and the positive effects of working either from home or in an office. Our investigation involved a questionnaire handed to hundred employees and revealed that the majority of them were aware of the negative and the positive impacts of working remotely and in an office and suggest, therefore, a mixed-mode of working to obtain the best advantages of both modes.

Keywords: COVID-19; working from home; working in an office; questionnaire; advantages; disadvantages; negative impact; positive impact; mental health; physical health; work experience

1. Introduction

Who would not like to wake up late and avoid the traffic every morning? I always had dreamed of that, and I guess you too. Working from home, which provides these advantages, has become the preferred choice for many employees and employers for the sake of getting more flexibility, increasing productivity, and saving time and money (Ipsen et al., 2021). I have noticed, especially during the COVID-19 pandemic, that many people switched willingly to work from home, expecting their life would totally improve. On the other hand, many people do not have the office work option. For instance, people work in the human resources, marketing, and customer service sectors (Iacurci, 2021). They work remotely until a hundred percent effective covid vaccine is developed. However, many studies, such as "Survey reveals the mental and physical health impacts of home working during Covid-19" by RSPH (2021), revealed that people who work from home are likely to suffer from mental and physical disorders.

In fact, the reason for the negative impact is not the work from home. Rather, it is the unmanaged lifestyle that comes with working from home. Of course, many other jobs still need people to be physically present, such as working in hospitals and beauty centers. However, Iacurci (2021) suggests that people will work remotely even after the pandemic finishes and the economy reopens. While many people are switching to work from home, and many others hoping so, it might be an opportunity for them to know the negative impact of working remotely, such as isolation and back pain, due to lack of socialization and equipment for a healthy home office. I am not willing to tell people what they should do in order to work healthily from home because this is not my study field. However, because I have experienced that negative impact, I will only give hints about the consequences, which could happen if they did not take care of themselves when working from home. Thus, this research investigated hundred people who have already worked before, regardless of gender identity, whether they are aware of the negative and the positive impacts of working from home in order to take care of themselves.

2. Literature review

Reviewing Worker's and Employees' Opinions in Working from Home

Before the COVID-19 pandemic, people could choose between working from home and in an office. However, many people are forced or got the opportunity to work from home to reduce the number of new daily infections during the pandemic. Thus, it was an opportunity for researchers to do research on a large number of people to figure out how working from home experience affected them. Also, after the pandemic is over, what would they prefer if they could choose between working remotely or being physically in an office.

In the study, "Six key advantages and disadvantages of working from home in Europe during COVID-19," Ipsen et al. (2021) investigated employees who have experience with working from home during the pandemic in 29 European countries. They used first the six key advantages and disadvantages approach, which involves the employees' opinions in working from home. Although the employees mentioned 16 disadvantages and 11 advantages, its results indicate that "the majority (55%) of employees were mostly positive about WFH" (p. 11). However, they assumed that maybe there are other circumstances that make the employees prefer working remotely over in an office. Hence, Ipsen et al. (2021) used the six factors approach, which involved the employe...

From 2017 to 2018, about **** million full-time workers could work from home in the United States. In May 2020, there were ****** million full-time workers in the United States.

Creating a robust employee dataset for data analysis and visualization involves several key fields that capture different aspects of an employee's information. Here's a list of fields you might consider including: Employee ID: A unique identifier for each employee. Name: First name and last name of the employee. Gender: Male, female, non-binary, etc. Date of Birth: Birthdate of the employee. Email Address: Contact email of the employee. Phone Number: Contact number of the employee. Address: Home or work address of the employee. Department: The department the employee belongs to (e.g., HR, Marketing, Engineering, etc.). Job Title: The specific job title of the employee. Manager ID: ID of the employee's manager. Hire Date: Date when the employee was hired. Salary: Employee's salary or compensation. Employment Status: Full-time, part-time, contractor, etc. Employee Type: Regular, temporary, contract, etc. Education Level: Highest level of education attained by the employee. Certifications: Any relevant certifications the employee holds. Skills: Specific skills or expertise possessed by the employee. Performance Ratings: Ratings or evaluations of employee performance. Work Experience: Previous work experience of the employee. Benefits Enrollment: Information on benefits chosen by the employee (e.g., healthcare plan, retirement plan, etc.). Work Location: Physical location where the employee works. Work Hours: Regular working hours or shifts of the employee. Employee Status: Active, on leave, terminated, etc. Emergency Contact: Contact information of the employee's emergency contact person. Employee Satisfaction Survey Responses: Data from employee satisfaction surveys, if applicable.

Code Url: https://github.com/intellisenseCodez/faker-data-generator

Employee Attrition and Factors

Examining Performance, Financials, and Job Role for Impact on Retention

By [source]

About this dataset

This dataset offers a comprehensive and varied analysis of an organization's employees, focusing on areas such as employee attrition, personal and job-related factors, and financials. Included are numerous parameters such as Age, Gender, Marital Status, Business Travel Frequency, Daily Rate of Pay, Departmental Information such as Distance From Home Office or Education Level Obtained by the employee in question. Also included is a variant series of parameters related to the job being performed such as Job Involvement (level), Job Level (relative to similar roles within the same organization), Job Role specifically meant for that individual(function/task), total working hours in a week/month/year be it overtime or standard hours for a given role. Furthermore detailed aspects include Percent Salary Hike during their tenure with the company from promotion or otherwise , Performance Rating based on specific criteria established by leadership , Relationship Satisfaction among peers at workplace but also taking into account outside family members that can influence stress levels in varying capacities ,Monthly Income considered at its starting point once hired then compared against their monthly payrate with overtime hours included if applicable along with Number Companies Worked before if any. Lastly the Retirement Status commonly known as Attrition is highlighted; covering whether there was an intent to stay with one employer through retirement age or if attrition took place for reasons beyond ones control earlier than expected . Through this dataset you can get an insight into various major aspect regarding today's workforce management philosphies which have changed drastically over time due to advancements in technology

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How to use the dataset

• Understand the variables that make up this dataset. The dataset includes several personal and job-related variables such as Age, Gender, Marital Status, Business Travel, Daily Rate, Department, Distance From Home, Education, Education Field, Employee Count, Employee Number, Environment Satisfaction Hoursly Rate and so on. Knowing what each variable is individuallly will help when exploring employee attrition as a whole.
• Analyze the data for patterns as well as outliers or anomalies either at an individual level or across all of the data points together. Identifying these patterns or discrepancies can offer insight into factors that are related to employee attrition.
• Visualize the data using charts and graphs to allow for easy understanding of which relationships might be causing higher levels of employees leaving the organization over time dimensions like age or job role can be key factors in employee attrition rates visually displaying how they relate to one another can provide clarity into what needs to change within an organization in order to reduce attrition rates
• Explore relationships between pairs of variables through correlation analysis correlations are measures of how strongly two variables are related when looking at employment retention it’s important to analyze correlations at both an individual level and for all variables together showing which pairings have more influence than others when it comes to influencing employee decisions
  5 Use descriptive analytics methods such as scatter plots histograms boxplots etc with aggregated values from each field like average age average monthly income etc These analytics help gain a deeper understanding about where changes need to be made internally
  6 Utilize predictive analytics with more advanced techniques such as regressions clustering decision trees in order identify trendsfrom past data points then build models on those insights from different perspectives helping further prepare organizations against potential high levelsinvolving employees departing ?

Research Ideas

• Identifying performance profiles of employees at risk for attrition through predictive analytics and using this insight to create personalized development plans or retention strategies.
• Using the data to assess the impact of different financial incentives or variations in job role/structure on employee attitudes, satisfaction and ultimately attrition rates.
• Analyzing different age groups' responses to various perks or turnover patterns in order to understand how organizations can better engage different demographic segments

Acknowledgements

If you use this dataset in your research, pl...

‘This dataset provides information regarding the total approved actual expenses incurred by Montgomery County government employees traveling non-locally (over 75 miles from the County’s Executive Office Building at 101 Monroe St. Rockville, MD) for official business, beginning on or after August 12, 2015. The dataset includes the name of traveling employee; the employee’s home department; travel start and end dates; destination; purpose of travel; and actual total expenses funded by the County. Update Frequency: Monthly

A group showing the number of employees in the head office and branches

According to data provided by Competitive Data, the number of employees of companies producing/distributing home and office furniture in Italy slightly decreased from just under ***** people employed in 2019 to around ***** in 2020.

Number local units or enterprises shows the number of businesses by employment size band. A local units is a place of work factory, a shop, or a branch. An enterprise can be thought of as the overall business, made up of all the individual sites or workplaces (local units). It is defined as the smallest combination of legal units (generally based on VAT and/or PAYE records) that has a certain degree of autonomy within an enterprise group. SME data can be found in these table. An SME is any business with less than 250 employees. Micro-enterprises have up to 10 employees. Small enterprises have up to 50 employees. Medium-sized enterprises have up to 250 employees. Figures are provided for VAT and/or PAYE based enterprises and local units. Where an enterprise has several local units, the location of the enterprise is generally the main operating site or the head office. Since 2008 the publication has been enhanced to include enterprises based on PAYE employers that are not also registered for VAT, extending the scope from the previous VAT based enterprise publication. This is a major change to the scope of the publication. The increase in units is most noticeable in the VAT-exempt industries of finance (J), education (M), health (N) and public administration (L, O and Q). Analysis for VAT and/or PAYE based enterprises can be found on the ONS website in their reports titled UK Business: Activity, Size and Location using the link below. Where an enterprise has several local units, the location of the enterprise is generally the main operating site or the head office. Data on size of firms (micro-business, SME, large) for business and employees in London by industry can be found on the ONS website. Trend data by MSOA is also available.

Dataset Description

The uploaded dataset contains information about corporate employees, capturing details about their roles, salaries, contact information, and additional attributes. It consists of 673 rows and 12 columns.

Columns Description

employee_id: Unique identifier for each employee (no missing values). full_name: Full name of the employee (no missing values). job_title: The employee's role or title in the organization (19 missing values). department: The department in which the employee works (no missing values). salary: Annual salary of the employee in USD (no missing values). hire_date: The date the employee was hired (no missing values). email: Official email address of the employee (45 missing values). phone_number: Contact number of the employee (10 missing values). manager_id: ID of the manager supervising the employee (26 missing values). office_location: Location of the employee's office (no missing values). credit_card: Type of credit card used (38 missing values). car: Model of car owned (20 missing values).

File Info

Total Records: 1673 Total Columns: 12 Missing Data Summary: Email has the highest missing values (45). Several fields such as job_title, credit_card, and car also have some missing entries.