Average Weekly Hours in the United States remained unchanged at 34.20 Hours in September. This dataset provides - United States Average Weekly Hours - actual values, historical data, forecast, chart, statistics, economic calendar and news.

Number of average usual hours and average actual hours worked in a reference week by type of work (full- and part-time employment), job type (main or all jobs), gender, and age group, annual.

Corresponds to the number of hours the person normally works. Covers all hours including extra hours, both paid and unpaid. Excludes the travel time between the home and the place of work as well as the main meal breaks.

Average Weekly Hours in China decreased to 48.40 Hours in October from 48.60 Hours in September of 2025. This dataset includes a chart with historical data for China Average Weekly Hours.

Average actual weekly hours worked by industry, including by sex, UK, rolling three-monthly figures published quarterly. Labour Force Survey. These are official statistics in development.

This dataset provides values for AVERAGE WEEKLY HOURS reported in several countries. The data includes current values, previous releases, historical highs and record lows, release frequency, reported unit and currency.

The following dataset presents the average weekly number of hours worked by women in selected countries (Poland, Lithuania, Latvia, Estonia) in the years 1999 - 2016. The summary includes average weekly time worked in the main job, for women aged 15+. The estimates correspond to the declared amount. This includes part-time and full-time employment, as well as self-employment and caring for dependents. The data show that Poland is the country where women have the highest workload among the countries surveyed.

Average Weekly Hours in France decreased to 31.10 Hours in the third quarter of 2025 from 31.20 Hours in the second quarter of 2025. This dataset includes a chart with historical data for France Average Weekly Hours.

Number of employed persons by actual hours worked, class of worker, North American Industry Classification System (NAICS), and gender.

Actual weekly hours worked including by sex, full-time, part-time and second jobs, UK, rolling three-monthly figures published monthly, seasonally adjusted. Labour Force Survey. These are official statistics in development.

This dataset contains information related to individuals' lifestyle habits, health, and stress detection indicators. The dataset is structured with columns representing various factors that could influence or correlate with an individual's physical and mental well-being, particularly in relation to stress. Each row represents a unique individual with data points in several domains, such as sleep patterns, physical activity, diet, and health measurements.

Columns and their Description: Age: The age of the individual. Gender: Gender of the individual (Male/Female). Occupation: The profession or job title of the individual. Marital_Status: The marital status of the individual (e.g., Married, Single). Sleep_Duration: The total amount of sleep an individual gets on average (in hours). Sleep_Quality: Self-reported quality of sleep on a scale (e.g., 1-5). Wake_Up_Time: The time the individual wakes up on average. Bed_Time: The time the individual goes to bed on average. Physical_Activity: Frequency of physical activity (e.g., number of days/week or a score). Screen_Time: The average number of hours spent on electronic screens per day. Caffeine_Intake: The average amount of caffeine consumed (e.g., cups of coffee). Alcohol_Intake: The average amount of alcohol consumed (e.g., number of drinks per week). Smoking_Habit: Whether the individual smokes (Yes/No). Work_Hours: The number of hours worked per week. Travel_Time: The time spent commuting (e.g., in hours per day). Social_Interactions: The frequency of social interactions (e.g., number of social events attended). Meditation_Practice: Whether the individual practices meditation (Yes/No). Exercise_Type: The type of exercise the individual engages in (e.g., Yoga, Cardio, Strength Training). Blood_Pressure: The individual's blood pressure level (e.g., 120/80). Cholesterol_Level: The individual's cholesterol level (e.g., in mg/dL). Blood_Sugar_Level: The individual's blood sugar level (e.g., in mg/dL). Stress_Detection: A classification or indicator of the individual's stress level (e.g., High, Medium, Low).

Key Insights from the Data: The dataset includes information that can be used to explore correlations between lifestyle habits and physical/mental well-being, with a specific focus on stress levels. Factors like Sleep Duration, Physical Activity, and Alcohol Intake could be influential in determining an individual’s Stress Detection levels. Social Interactions and Meditation Practice are lifestyle factors that might positively affect stress levels. Health metrics like Blood Pressure, Cholesterol Levels, and Blood Sugar Levels provide insights into the physiological state that could influence or be influenced by stress.

Possible Applications: Stress Level Prediction: Using the features like lifestyle habits and health metrics, machine learning models could predict an individual's stress level (Stress_Detection). Health and Wellness Analysis: Analyzing the impact of various lifestyle factors (e.g., sleep, activity, screen time) on stress levels and overall health. Personalized Health Recommendations: The dataset could help in providing personalized recommendations on how to manage stress based on sleep, exercise, and dietary habits. This dataset could be particularly useful for health professionals, wellness apps, or even for academic research into the relationship between lifestyle habits and mental health, specifically in the context of stress detection.

This dataset represents the statewide average hours worked per week of Department of Rehabilitation’s total successful closures in State Fiscal Years 2014 through 2023 by county.

Average hours worked per week on cases where the individual was competitively employed after receiving services from Iowa Vocational Rehabilitation Services.

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 Hopkinton town. 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 Hopkinton town, for all workers aged 15 years and older, irrespective of full-time or part-time work, the median income was $36,290 for both males and females.

This indicates income parity between genders in Hopkinton town, where women and men, regardless of their work hours, earn an equal dollar amount for their efforts, reflecting a balanced income distribution across both sexes.

- Full-time workers, aged 15 years and older: In Hopkinton town, among full-time, year-round workers aged 15 years and older, males earned a median income of $47,308, while females earned $51,442

Surprisingly, within the subset of full-time workers, women earn a higher income than men, earning 1.09 dollars for every dollar earned by men. This suggests that within full-time roles, womens median incomes significantly surpass mens, contrary to broader workforce trends.

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

About this dataset

Context

The dataset presents median income data over a decade or more for males and females categorized by Total, Full-Time Year-Round (FT), and Part-Time (PT) employment in Garber. 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 Garber, for all workers aged 15 years and older, irrespective of full-time or part-time work, the median income was $41,161 for both males and females.

This indicates income parity between genders in Garber, where women and men, regardless of their work hours, earn an equal dollar amount for their efforts, reflecting a balanced income distribution across both sexes.

- Full-time workers, aged 15 years and older: In Garber, among full-time, year-round workers aged 15 years and older, males earned a median income of $70,208, while females earned $43,906, leading to a 37% gender pay gap among full-time workers. This illustrates that women earn 63 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.

Surprisingly, across all roles (including non-full-time employment), women had a higher median income compared to men in Garber. This might indicate a more advantageous income scenario for female workers across different employment patterns within the city of Garber, particularly in non-full-time positions.

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

Purpose: The dataset aims to facilitate the development and testing of hybrid optimization models for HRM, particularly those leveraging IoT devices, edge computing, and advanced machine learning techniques.

Data Sources:

IoT Devices: Simulated data from IoT sensors monitoring employee activity, attendance, and work environment metrics. Performance Records: Synthetic data representing employee task efficiency, workload, and satisfaction. Edge Computing Metrics: Simulated latency and bandwidth usage metrics to reflect edge server performance.

Employee Information:

employee_id: Unique identifier for employees. department: Department to which the employee belongs (e.g., HR, IT, Sales, Operations). role_level: Job level (Junior, Mid-level, Senior). Performance Metrics:

task_completion_rate: Percentage of completed tasks. hours_worked_per_week: Total hours worked in a week. overtime_hours: Hours worked beyond standard work hours. task_efficiency: Ratio of tasks completed to time taken. IoT Metrics:

activity_level: Physical activity level monitored by IoT devices. attendance_rate: Percentage of days attended. avg_desk_time: Average daily desk time (in hours). response_time: Average response time for work-related queries. motion_intensity: IoT-measured movement intensity. stress_level: Employee stress level derived from IoT data. Edge Computing Metrics:

latency: Average edge server response time (in ms). bandwidth_usage: Data usage by IoT devices (in MB). Resource Allocation Metrics:

allocated_tasks: Number of tasks assigned. task_allocation_cost: Cost incurred in task allocation (in $). resource_utilization: Ratio of time utilized to tasks allocated. Derived Metrics:

performance_index: Combined metric for task efficiency and task completion rate. satisfaction_score: Employee satisfaction level (scale of 1–10). optimization_score: Overall optimization score for resource allocation. Target Variable:

promotion_eligibility: Binary (0 or 1), indicating whether an employee is eligible for promotion.

Context

This dataset contains information about individuals' demographic and employment attributes to predict whether their income exceeds $50,000 per year. It originates from the 1994 U.S. Census database and has been widely used in classification problems, making it an excellent resource for machine learning, data analysis, and statistical modeling.

Content

The dataset includes various features related to personal and work-related attributes. The target variable is whether an individual's income exceeds $50,000 annually.

Key features include:

Age: Age of the individual.

Workclass: Employment type (e.g., private, government, self-employed).

Education: Highest level of education achieved.

Education-Num: Number corresponding to the level of education.

Marital Status: Marital status of the individual.

Occupation: Profession or job role.

Relationship: Family role (e.g., husband, wife, not in family).

Race: Race of the individual.

Sex: Gender of the individual.

Capital Gain: Income from investment sources other than salary.

Capital Loss: Losses from investment sources.

Hours Per Week: Average number of hours worked per week.

Native Country: Country of origin of the individual

Variables

Age: Continuous variable representing the age of the individual.

Workclass: Categorical variable indicating the type of employment (e.g., Private, Self-Employed, Government).

Education: Categorical variable showing the highest level of education achieved (e.g., Bachelors, Masters).

Education-Num: Numerical representation of the education level.

Marital Status: Categorical variable representing marital status (e.g., Married, Never-Married).

Occupation: Categorical variable indicating the job role or occupation

Relationship: Categorical variable describing the family relationship (e.g., Husband, Wife).

Race: Categorical variable showing the race of the individual.

Sex: Categorical variable indicating the gender of the individual.

Capital Gain: Continuous variable representing income from capital gains.

Capital Loss: Continuous variable representing losses from investments.

Hours Per Week: Continuous variable showing the average working hours per week.

Native Country: Categorical variable indicating the country of origin.

Income: Target variable (binary), indicating whether the individual earns more than $50,000 (>50K) or not (<=50K).

Acknowledgements

This dataset was derived from the 1994 U.S. Census database and has been made publicly available for research and educational purposes. It is not affiliated with any specific organization. Users are encouraged to comply with ethical data usage guidelines while working with this dataset.

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 Texas. 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 Texas, the median income for all workers aged 15 years and older, regardless of work hours, was $47,179 for males and $30,830 for females.

These income figures highlight a substantial gender-based income gap in Texas. Women, regardless of work hours, earn 65 cents for each dollar earned by men. This significant gender pay gap, approximately 35%, underscores concerning gender-based income inequality in the state of Texas.

- Full-time workers, aged 15 years and older: In Texas, among full-time, year-round workers aged 15 years and older, males earned a median income of $64,350, while females earned $51,470, 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 Texas.

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

Insights from City Supply and Demand Data This data project has been used as a take-home assignment in the recruitment process for the data science positions at Uber.

Assignment Using the provided dataset, answer the following questions:

1. Which date had the most completed trips during the two week period?
2. What was the highest number of completed trips within a 24 hour period?
3. Which hour of the day had the most requests during the two week period?
4. What percentages of all zeroes during the two week period occurred on weekend (Friday at 5 pm to Sunday at 3 am)? Tip: The local time value is the start of the hour (e.g. 15 is the hour from 3:00pm - 4:00pm)
5. What is the weighted average ratio of completed trips per driver during the two week period? Tip: "Weighted average" means your answer should account for the total trip volume in each hour to determine the most accurate number in whole period.
6. In drafting a driver schedule in terms of 8 hours shifts, when are the busiest 8 consecutive hours over the two week period in terms of unique requests? A new shift starts in every 8 hours. Assume that a driver will work same shift each day.
7. True or False: Driver supply always increases when demand increases during the two week period. Tip: Visualize the data to confirm your answer if needed.
8. In which 72 hour period is the ratio of Zeroes to Eyeballs the highest?
9. If you could add 5 drivers to any single hour of every day during the two week period, which hour should you add them to? Hint: Consider both rider eyeballs and driver supply when choosing
10. True or False: There is exactly two weeks of data in this analysis
11. Looking at the data from all two weeks, which time might make the most sense to consider a true "end day" instead of midnight? (i.e when are supply and demand at both their natural minimums) Tip: Visualize the data to confirm your answer if needed.

Data Description To answer the question, use the dataset from the file dataset_1.csv. For example, consider the row 11 from this dataset:

Date Time (Local) Eyeballs Zeroes Completed Trips Requests Unique Drivers

2012-09-10 16 11 2 3 4 6

This means that during the hour beginning at 4pm (hour 16), on September 10th, 2012, 11 people opened the Uber app (Eyeballs). 2 of them did not see any car (Zeroes) and 4 of them requested a car (Requests). Of the 4 requests, only 3 complete trips actually resulted (Completed Trips). During this time, there were a total of 6 drivers who logged in (Unique Drivers)

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 Brookston. 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 Brookston, while the Census reported a median income of $23,977 for all female workers aged 15 years and older, data for males in the same category was unavailable due to an insufficient number of sample observations.

Because income data for males was not available from the Census Bureau, conducting a comprehensive analysis of gender-based pay disparity in the city of Brookston was not possible.

- Full-time workers, aged 15 years and older: In Brookston, for full-time, year-round workers aged 15 years and older, while the Census reported a median income of $70,625 for males, while data for females was unavailable due to an insufficient number of sample observations.

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

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