Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
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 Hustler. The dataset can be utilized to gain insights into gender-based income distribution within the Hustler population, aiding in data analysis and decision-making..
Key observations
When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.
Income brackets:
Variables / Data Columns
Employment type classifications include:
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.
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/.
This dataset is a part of the main dataset for Hustler median household income by race. You can refer the same here
Apache License, v2.0https://www.apache.org/licenses/LICENSE-2.0
License information was derived automatically
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 | Description |
---|---|
Company | Name of the organization where the individual is employed |
Job Title | Designation of the employee (e.g., Software Engineer, Product Manager) |
Industry | Sector of employment (e.g., Technology, Finance, Healthcare) |
Location | City and/or country of the job or the headquarters |
Employment Type | Full-time, Part-time, Contract, or Internship |
Experience Level | Job seniority: Entry, Mid, Senior, or Lead |
Remote Flexibility | Indicates whether the job is Remote, Hybrid, or Onsite |
Salary (Annual) | Annual gross salary before tax |
Currency | Currency in which the salary is paid (e.g., USD, EUR, INR) |
Years of Experience | Total years of professional experience the employee has |
https://brightdata.com/licensehttps://brightdata.com/license
The LinkedIn Jobs Listing dataset emerges as a comprehensive resource for individuals navigating the contemporary job market. With a focus on critical employment details, the dataset encapsulates key facets of job listings, including titles, company names, locations, and employment specifics such as seniority levels and functions. This wealth of information is instrumental for job seekers looking to align their skills and aspirations with the right opportunities. The inclusion of direct application links and real-time application numbers enhances the dataset's utility, offering users a streamlined approach to engaging with potential employers. Beyond aiding job seekers, the dataset serves as a valuable tool for analysts and researchers, providing nuanced insights into industry trends and the evolving demands of the job market. The temporal aspect, captured through job posting timestamps, allows for the observation of job trends over time. Moreover, the dataset's integration of company details, including unique identifiers and LinkedIn profile links, enables a deeper exploration of hiring organizations. Whether for job seekers or analysts, the LinkedIn Jobs Listing dataset emerges as a versatile and informative repository, empowering users with the knowledge to make informed decisions in their professional pursuits.
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
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 Hustler. 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 Hustler, the median income for all workers aged 15 years and older, regardless of work hours, was $61,250 for males and $30,000 for females.
These income figures highlight a substantial gender-based income gap in Hustler. Women, regardless of work hours, earn 49 cents for each dollar earned by men. This significant gender pay gap, approximately 51%, underscores concerning gender-based income inequality in the village of Hustler.
- Full-time workers, aged 15 years and older: In Hustler, among full-time, year-round workers aged 15 years and older, males earned a median income of $73,906, while females earned $71,250, resulting in a 4% gender pay gap among full-time workers. This illustrates that women earn 96 cents for each dollar earned by men in full-time positions. While this gap shows a trend where women are inching closer to wage parity with men, it also exhibits a noticeable income difference for women working full-time in the village of Hustler.Interestingly, when analyzing income across all roles, including non-full-time employment, the gender pay gap percentage was higher for women compared to men. It appears that full-time employment presents a more favorable income scenario for women compared to other employment patterns in Hustler.
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:
Employment type classifications include:
Variables / Data Columns
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.
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/.
This dataset is a part of the main dataset for Hustler median household income by race. You can refer the same here
Looking for a job as Data Analyst? Maybe this dataset can help you.
Amidst the pandemic many people lost their jobs, with this dataset it is possible to hone the job search so that more people in need can find employment. This dataset was created by picklesueat and contains more than 2000 job listing for data analyst positions, with features such as: - Salary Estimate - Location - Company Rating - Job Description - and more.
- Find the best jobs by salary and company rating
- Explore skills required in job descriptions
- Predict salary based on industry, location, company revenue
- Your kernel can be featured here!
- Data Engineer Jobs
- Business Analyst Jobs
- Data Scientist Jobs
- More Datasets
If you use this dataset, please support the author.
License
License was not specified at the source
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Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
Unemployment Rate in the United States decreased to 4.10 percent in June from 4.20 percent in May of 2025. This dataset provides the latest reported value for - United States Unemployment Rate - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.
This dataset contains the owner information for all the accounts listed in the Business License Dataset, and is sorted by Account Number. To identify the owner of a business, you will need the account number or legal name, which may be obtained from theBusiness Licenses dataset: https://data.cityofchicago.org/dataset/Business-Licenses/r5kz-chrr. Data Owner: Business Affairs & Consumer Protection. Time Period: 2002 to present. Frequency: Data is updated daily.
Attribution-NonCommercial-ShareAlike 4.0 (CC BY-NC-SA 4.0)https://creativecommons.org/licenses/by-nc-sa/4.0/
License information was derived automatically
# Title
Interview-Based Stress Assessment Dataset
# Overview
The dataset includes stress evaluations (6 grades) assessed by interviews of 50 Japanese workers (49 completed follow-up), as well as self-reported stress and attribute information and personality information measured at the pre and one-month follow-up.
# Data Source
Interviews were conducted between December 2022 and January 2023. The average follow-up period was 34.2 days.
The main variables were interview-based stress evaluation, with self-reported stress (stress load, mental symptoms and physical symptoms from the Brief Job Stress Questionnaire), well-being (life satisfaction and happiness), and burnout were measured pre and 1 month later. Interview-based stress evaluations were conducted by two occupational health professionals in addition to an evaluation by the interviewer, a psychologist.
# Data Description
## main variables are total (time 1 self-reported stress), burnout, wellbeing, meanStressEv (mean overall stress ratings of interviewer and two evaluators), T2_loadAll, T2_mental, T2_physical, T2_burnout, and T2_wellbeing
no: Record number or identifier.
age: Age of the individual in years.
gender: Gender of the individual. Possible values include 'male', 'female', etc.
height_cm: Height of the individual in centimeters.
weight_kg: Weight of the individual in kilograms.
BMI: Body Mass Index, calculated based on height and weight.
drinking_freq: Frequency of alcohol consumption. Example values might be 'daily', 'weekly', 'monthly', etc.
smoking_habits: Smoking habits of the individual. Possible values include 'smoker', 'non-smoker', etc.
money_spending_hobby: Attitude towards spending money on hobbies. Describes how much an individual spends on their hobbies.
employment_status: Current employment status. Possible values include 'employed', 'unemployed', 'self-employed', etc.
full_time: employment_status
part_time: employment_status
discretionary: employment_status
side_job: This variable likely indicates whether the individual has a side job in addition to their primary employment. The values could be binary (yes/no) or provide more detail about the nature of the side job.
work_type: This variable probably categorizes the type of work the individual is engaged in. It could include categories such as 'full-time', 'part-time', 'contract', 'freelance', etc.
fixedHours: This variable might indicate whether the individual's work schedule has fixed hours. It could be a binary variable (yes/no) indicating the presence or absence of a fixed work schedule.
rotationalShifts: This variable likely denotes whether the individual works in rotational shifts. It could be a binary (yes/no) variable or provide details on the shift rotation pattern.
flexibleShifts: This variable possibly reflects if the individual has flexible shift options in their work. This could involve varying start and end times or the ability to switch shifts.
flexTime: This variable might indicate the presence of 'flextime' in the individual's work arrangement, allowing them to choose their working hours within certain limits.
adjustableWorkHours: This variable probably denotes whether the individual has the ability to adjust their work hours, suggesting a degree of flexibility in their work schedule.
discretionaryWork: This variable could indicate whether the individual's work involves a degree of discretion or autonomy in decision-making or task execution.
nightShift: This variable likely indicates if the individual works night shifts. It could be a simple binary (yes/no) or provide details about the frequency or regularity of night shifts.
remote_work_freq: This variable probably measures the frequency of remote work. It could include categories like 'never', 'sometimes', 'often', or 'always'.
primary_job_industry: This variable likely categorizes the industry sector of the individual's primary job. It could include sectors like 'technology', 'healthcare', 'education', 'finance', etc.
ind: industry
ind.manu–ind.gove: binary coding of industry
primary_job_role: This variable likely represents the specific role or position held by the individual in their primary job. It could include titles like 'manager', 'engineer', 'teacher', etc.
job: job
job.admi–job.carClPa: binary coding of job
job_duration_years: This variable probably indicates the duration of the individual's current job in years. It typically measures the length of time an individual has been in their current job role.
years: Without additional context, this variable could represent various time-related aspects, such as years of experience in a particular field, age in years, or years in a specific role. It generally signifies a duration or period in years.
months: Similar to 'years', this variable could refer to a duration in months. It might represent age in months (for younger individuals), months of experience, or months spent in a current role or activity.
job_duration_months: This variable is likely to indicate the total duration of the individual's current job in months. It's a more precise measure compared to 'job_duration_years', especially for shorter employment periods.
working_days_per_week: This variable probably denotes the number of days the individual works in a typical week. It helps to understand the work pattern, whether it's a standard five-day workweek or otherwise.
work_hours_per_day: This variable likely measures the average number of hours the individual works each day. It can be used to assess work-life balance and overall workload.
job_workload: This variable might represent the overall workload associated with the individual's job. This could be subjective (based on the individual's perception) or objective (based on quantifiable measures like hours worked or tasks completed).
job_qualitative_load: This variable likely assesses the qualitative aspects of the job's workload, such as the level of mental or emotional stress, complexity of tasks, or level of responsibility.
job_control: This variable probably measures the degree of control or autonomy the individual has in their job. It could assess how much freedom they have in making decisions, planning their work, or the flexibility in how they perform their duties.
hirou_1–hirou_7: Working Conditions of Fatigue Accumulation Checklist
hirou_kinmu: Sum of Working Conditions of Fatigue Accumulation Checklist
WH_1–WH_2: Items related to workaholic
workaholic: Sum of items related to workaholic
WE_1–WE_3: Items related to work engagement
engagement: Sum of items related to work engagement
relationship_stress: This variable likely measures stress stemming from personal relationships, possibly including family, romantic partners, or friends.
future_uncertainty_stress: This variable probably captures stress related to uncertainties about the future, such as career prospects, financial stability, or life goals.
discrimination_stress: This variable indicates stress experienced due to discrimination, possibly based on factors like race, gender, age, or other personal characteristics.
financial_stress: This variable measures stress related to financial matters, such as income, expenses, debt, or overall financial security.
health_stress: This variable likely assesses stress concerning personal health or the health of loved ones.
commuting_stress: This variable measures stress associated with daily commuting, such as traffic, travel time, or transportation issues.
irregular_lifestyle: This variable probably indicates the presence of an irregular lifestyle, potentially including erratic sleep patterns, eating habits, or work schedules.
living_env_stress: This variable likely measures stress related to the living environment, which could include housing conditions, neighborhood safety, or noise levels.
unrewarded_efforts: This variable probably assesses feelings of stress or dissatisfaction due to efforts that are perceived as unrewarded or unacknowledged.
other_stressors: This variable might capture additional stress factors not covered by other specific variables.
coping: This variable likely assesses the individual's coping mechanisms or strategies in response to stress.
support: This variable measures the level of support the individual perceives or receives, possibly from friends, family, or professional services.
weekday_bedtime: This variable likely indicates the typical bedtime of the individual on weekdays.
weekday_wakeup: This variable represents the typical time the individual wakes up on weekdays.
holiday_bedtime: This variable indicates the typical bedtime of the individual on holidays or non-workdays.
holiday_wakeup: This variable measures the typical wake-up time of the individual on holidays or non-workdays.
avg_sleep_duration: This variable likely represents the average duration of sleep the individual gets, possibly averaged over a certain period.
weekday_bedtime_posix: This variable might represent the weekday bedtime in POSIX time format.
weekday_wakeup_posix: Similar to bedtime, this represents the weekday wakeup time in POSIX time format.
holiday_bedtime_posix: This variable likely indicates the holiday bedtime in POSIX time format.
holiday_wakeup_posix: This represents the holiday wakeup time in POSIX time format.
weekday_bedtime_posix_hms: This variable could be the weekday bedtime in POSIX time format, specifically in hours, minutes, and seconds.
weekday_wakeup_posix_hms: This variable might represent the weekday wakeup time in POSIX time format in hours, minutes, and seconds.
holiday_bedtime_posix_hms: The holiday bedtime in POSIX time format, detailed to hours, minutes, and seconds.
holiday_wakeup_posix_hms: The holiday wakeup time in POSIX time format, in hours, minutes, and
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
Analysis of ‘Job Classification Dataset’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/HRAnalyticRepository/job-classification-dataset on 30 September 2021.
--- Dataset description provided by original source is as follows ---
This is a dataset containing some fictional job class specs information. Typically job class specs have information which characterize the job class- its features, and a label- in this case a pay grade - something to predict that the features are related to.
The data is a static snapshot. The contents are ID column - a sequential number Job Family ID Job Family Description Job Class ID Job Class Description PayGrade- numeric Education Level Experience Organizational Impact Problem Solving Supervision Contact Level Financial Budget PG- Alpha label for PayGrade
This data is purely fictional
The intent is to use machine learning classification algorithms to predict PG from Educational level through to Financial budget information.
Typically job classification in HR is time consuming and cumbersome as a manual activity. The intent is to show how machine learning and People Analytics can be brought to bear on this task.
--- Original source retains full ownership of the source dataset ---
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
Employment Rate in the United States remained unchanged at 59.70 percent in June. This dataset provides - United States Employment Rate- actual values, historical data, forecast, chart, statistics, economic calendar and news.
Transform Your Business with Our Comprehensive B2B Marketing Data Our B2B Marketing Data is designed to be a cornerstone for data-driven professionals looking to optimize their business strategies. With an unwavering commitment to data integrity and quality, our dataset empowers you to make informed decisions, enhance your outreach efforts, and drive business growth.
Why Choose Our B2B Marketing Data? Unmatched Data Integrity and Quality Our data is meticulously sourced and validated through rigorous processes to ensure its accuracy, relevance, and reliability. This commitment to excellence guarantees that you are equipped with the most up-to-date information, empowering your business to thrive in a competitive landscape.
Versatile and Strategic Applications This versatile dataset caters to a wide range of business needs, including:
Lead Generation: Identify and connect with potential clients who align with your business goals. Market Segmentation: Tailor your marketing efforts by segmenting your audience based on industry, company size, or geographical location. Personalized Marketing Campaigns: Craft personalized outreach strategies that resonate with your target audience, increasing engagement and conversion rates. B2B Communication Strategies: Enhance your communication efforts with direct access to decision-makers, ensuring your message reaches the right people. Comprehensive Data Attributes Our B2B Marketing Data offers more than just basic contact information. With over 20+ attributes, you gain in-depth insights into:
Decision-Maker Roles: Understand the responsibilities and influence of key figures within an organization, such as CEOs, executives, and other senior management. Industry Affiliations: Analyze industry-specific data to tailor your approach to the unique dynamics of each sector. Contact Information: Direct email addresses and phone numbers streamline communication, enabling you to engage with your audience effectively and efficiently. Expansive Global Coverage Our dataset spans a wide array of countries, providing a truly global perspective for your business initiatives. Whether you're looking to expand into new markets or strengthen your presence in existing ones, our data ensures comprehensive coverage across the following regions:
North America: United States, Canada, Mexico Europe: United Kingdom, Germany, France, Italy, Spain, Netherlands, Sweden, and more Asia: China, Japan, India, South Korea, Singapore, Malaysia, and more South America: Brazil, Argentina, Chile, Colombia, and more Africa: South Africa, Nigeria, Kenya, Egypt, and more Australia and Oceania: Australia, New Zealand Middle East: United Arab Emirates, Saudi Arabia, Israel, Qatar, and more Industry-Wide Reach Our B2B Marketing Data covers an extensive range of industries, ensuring that no matter your focus, you have access to the insights you need:
Finance and Banking Technology Healthcare Manufacturing Retail Education Energy Real Estate Telecommunications Hospitality Transportation and Logistics Government and Public Sector Non-Profit Organizations And many more… Comprehensive Employee and Revenue Size Information Our dataset includes detailed records on company size and revenue, offering you the ability to:
Employee Size: From small businesses with a handful of employees to large multinational corporations, we provide data across all scales. Revenue Size: Analyze companies based on their revenue brackets, allowing for precise market segmentation and targeted marketing efforts. Seamless Integration with Broader Data Offerings Our B2B Marketing Data is not just a standalone product; it integrates seamlessly with our broader suite of premium datasets. This integration enables you to create a holistic and customized approach to your data-driven initiatives, ensuring that every aspect of your business strategy is informed by the most accurate and comprehensive data available.
Elevate Your Business with Data-Driven Precision Optimize your marketing strategies with our high-quality, reliable, and scalable B2B Marketing Data. Identify new opportunities, understand market dynamics, and connect with key decision-makers to drive your business forward. With our dataset, you’ll stay ahead of the competition and foster meaningful business relationships that lead to sustained growth.
Unlock the full potential of your business with our B2B Marketing Data – the ultimate resource for growth, reliability, and scalability.
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
Analysis of ‘HR Analytics: Job Change of Data Scientists’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/arashnic/hr-analytics-job-change-of-data-scientists on 28 January 2022.
--- Dataset description provided by original source is as follows ---
A company which is active in Big Data and Data Science wants to hire data scientists among people who successfully pass some courses which conduct by the company. Many people signup for their training. Company wants to know which of these candidates are really wants to work for the company after training or looking for a new employment because it helps to reduce the cost and time as well as the quality of training or planning the courses and categorization of candidates. Information related to demographics, education, experience are in hands from candidates signup and enrollment.
This dataset designed to understand the factors that lead a person to leave current job for HR researches too. By model(s) that uses the current credentials,demographics,experience data you will predict the probability of a candidate to look for a new job or will work for the company, as well as interpreting affected factors on employee decision.
The whole data divided to train and test . Target isn't included in test but the test target values data file is in hands for related tasks. A sample submission correspond to enrollee_id of test set provided too with columns : enrollee _id , target
Note: - The dataset is imbalanced. - Most features are categorical (Nominal, Ordinal, Binary), some with high cardinality. - Missing imputation can be a part of your pipeline as well.
#
Features
#
- enrollee_id : Unique ID for candidate
city: City code
city_ development _index : Developement index of the city (scaled)
gender: Gender of candidate
relevent_experience: Relevant experience of candidate
enrolled_university: Type of University course enrolled if any
education_level: Education level of candidate
major_discipline :Education major discipline of candidate
experience: Candidate total experience in years
company_size: No of employees in current employer's company
company_type : Type of current employer
last_new_job: Difference in years between previous job and current job
training_hours: training hours completed
target: 0 – Not looking for job change, 1 – Looking for a job change
--- Original source retains full ownership of the source dataset ---
These data include the individual responses for the City of Tempe Annual Business Survey conducted by ETC Institute. These data help determine priorities for the community as part of the City's on-going strategic planning process. Averaged Business Survey results are used as indicators for city performance measures. The performance measures with indicators from the Business Survey include the following (as of 2023):1. Financial Stability and Vitality5.01 Quality of Business ServicesThe location data in this dataset is generalized to the block level to protect privacy. This means that only the first two digits of an address are used to map the location. When they data are shared with the city only the latitude/longitude of the block level address points are provided. This results in points that overlap. In order to better visualize the data, overlapping points were randomly dispersed to remove overlap. The result of these two adjustments ensure that they are not related to a specific address, but are still close enough to allow insights about service delivery in different areas of the city.Additional InformationSource: Business SurveyContact (author): Adam SamuelsContact E-Mail (author): Adam_Samuels@tempe.govContact (maintainer): Contact E-Mail (maintainer): Data Source Type: Excel tablePreparation Method: Data received from vendor after report is completedPublish Frequency: AnnualPublish Method: ManualData DictionaryMethods:The survey is mailed to a random sample of businesses in the City of Tempe. Follow up emails and texts are also sent to encourage participation. A link to the survey is provided with each communication. To prevent people who do not live in Tempe or who were not selected as part of the random sample from completing the survey, everyone who completed the survey was required to provide their address. These addresses were then matched to those used for the random representative sample. If the respondent’s address did not match, the response was not used.To better understand how services are being delivered across the city, individual results were mapped to determine overall distribution across the city.Processing and Limitations:The location data in this dataset is generalized to the block level to protect privacy. This means that only the first two digits of an address are used to map the location. When they data are shared with the city only the latitude/longitude of the block level address points are provided. This results in points that overlap. In order to better visualize the data, overlapping points were randomly dispersed to remove overlap. The result of these two adjustments ensure that they are not related to a specific address, but are still close enough to allow insights about service delivery in different areas of the city.The data are used by the ETC Institute in the final published PDF report.
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
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 Hustler. 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 2021
Based on our analysis ACS 2017-2021 5-Year Estimates, we present the following observations: - All workers, aged 15 years and older: In Hustler, the median income for all workers aged 15 years and older, regardless of work hours, was $47,440 for males and $31,076 for females.
These income figures highlight a substantial gender-based income gap in Hustler. Women, regardless of work hours, earn 66 cents for each dollar earned by men. This significant gender pay gap, approximately 34%, underscores concerning gender-based income inequality in the village of Hustler.
- Full-time workers, aged 15 years and older: In Hustler, among full-time, year-round workers aged 15 years and older, males earned a median income of $52,919, while females earned $60,126Surprisingly, within the subset of full-time workers, women earn a higher income than men, earning 1.14 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.
https://i.neilsberg.com/ch/hustler-wi-income-by-gender.jpeg" alt="Hustler, WI gender based income disparity">
When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2017-2021 5-Year Estimates. All incomes have been adjusting for inflation and are presented in 2022-inflation-adjusted dollars.
Gender classifications include:
Employment type classifications include:
Variables / Data Columns
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.
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/.
This dataset is a part of the main dataset for Hustler median household income by gender. You can refer the same here
CC0 1.0 Universal Public Domain Dedicationhttps://creativecommons.org/publicdomain/zero/1.0/
License information was derived automatically
City of LA job applicants by the job they applied for and demographic information.
We are currently undergoing a data inventory to improve usability on the site. We're aware that this dataset is out of date but wanted to err on the side of making incomplete data available. Thank you for your patience, please contact the dataset owner or mayor.opendata@lacity.org with questions or ideas.
https://creativecommons.org/publicdomain/zero/1.0/https://creativecommons.org/publicdomain/zero/1.0/
This dataset was created by PromptCloud and Datastock. This dataset has 30K record counts in the file and has been taken of Dice.com USA of all Job Data.
You can download the full dataset here.
This file contains: - uniq_id, - crawl_timestamp, - url, job_title, - company_name, - city, state, - country, - post_date, - job_description, - job_type, - company_description, - contact_person, - job_board, - geo, - site_name, - domain, - postdate_yyyymmdd, - postdate_in_indexname_format, - inferred_city, - inferred_state, - inferred_country, - fitness_score
We wouldn't be here without the help of our in house web scraping team at PromptCloud and Datastock. We owe it to them.
This dataset was created for the people who want to know more about job data and for various purposes.
Forager.ai's Small Business Contact Data set is a comprehensive collection of over 695M professional profiles. With an unmatched 2x/month refresh rate, we ensure the most current and dynamic data in the industry today. We deliver this data via JSONL flat-files or PostgreSQL database delivery, capturing publicly available information on each profile.
| Volume and Stats |
Every single record refreshed 2x per month, setting industry standards. First-party data curation powering some of the most renowned sales and recruitment platforms. Delivery frequency is hourly (fastest in the industry today). Additional datapoints and linkages available. Delivery formats: JSONL, PostgreSQL, CSV. | Datapoints |
Over 150+ unique datapoints available! Key fields like Current Title, Current Company, Work History, Educational Background, Location, Address, and more. Unique linkage data to other social networks or contact data available. | Use Cases |
Sales Platforms, ABM Vendors, Intent Data Companies, AdTech and more:
Deliver the best end-customer experience with our people feed powering your solution! Be the first to know when someone changes jobs and share that with end-customers. Industry-leading data accuracy. Connect our professional records to your existing database, find new connections to other social networks, and contact data. Hashed records also available for advertising use-cases. Venture Capital and Private Equity:
Track every company and employee with a publicly available profile. Keep track of your portfolio's founders, employees and ex-employees, and be the first to know when they move or start up. Keep an eye on the pulse by following the most influential people in the industries and segments you care about. Provide your portfolio companies with the best data for recruitment and talent sourcing. Review departmental headcount growth of private companies and benchmark their strength against competitors. HR Tech, ATS Platforms, Recruitment Solutions, as well as Executive Search Agencies:
Build products for industry-specific and industry-agnostic candidate recruiting platforms. Track person job changes and immediately refresh profiles to avoid stale data. Identify ideal candidates through work experience and education history. Keep ATS systems and candidate profiles constantly updated. Link data from this dataset into GitHub, LinkedIn, and other social networks. | Delivery Options |
Flat files via S3 or GCP PostgreSQL Shared Database PostgreSQL Managed Database REST API Other options available at request, depending on scale required | Other key features |
Over 120M US Professional Profiles. 150+ Data Fields (available upon request) Free data samples, and evaluation. Tags: Professionals Data, People Data, Work Experience History, Education Data, Employee Data, Workforce Intelligence, Identity Resolution, Talent, Candidate Database, Sales Database, Contact Data, Account Based Marketing, Intent Data.
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
Job Offers in the United States increased to 7769 Thousand in May from 7395 Thousand in April of 2025. This dataset provides the latest reported value for - United States Job Openings - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.
MIT Licensehttps://opensource.org/licenses/MIT
License information was derived automatically
In March 2020, Mayor Carter announced the Saint Paul Bridge Fund to provide emergency relief for families and small businesses most vulnerable to the economic impacts of the COVID-19 pandemic. The program was funded through $3.25 million dollars from the Saint Paul Housing and Redevelopment Authority along with contributions from philanthropic, corporate and individual donors. Through these additional contributions, the fund provided $4.1 million to families and small businesses in Saint Paul.Data previously shared in this space included only the 380 recipients funded through "Phase 1". This dataset includes all three phases that were ultimately rolled out through the Bridge Fund for Small Business program.Nearly 2,000 unique applications applied for a small business grant of $7,50036% were from ACP50 areas (Areas of Concentrated Poverty where 50% or more of the residents are people of color)The applications were reviewed in order of a random number assigned at application close. Of these applications:633 small businesses were awarded a $7,500 grant36% of applications in the city were from ACP50 areas86% of applicants in the city cited they were ordered closed under one of the Governor’s Executive OrdersThis is a dataset of the small businesses that applied for the Bridge Fund and includes:Self-reported survey responsesAward informationGeographic information Additional information about the Saint Paul Bridge Fund may be found at stpaul.gov/bridge-fund.
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
The O*NET Database contains hundreds of standardized and occupation-specific descriptors on almost 1,000 occupations covering the entire U.S. economy. The database, which is available to the public at no cost, is continually updated by a multi-method data collection program. Sources of data include: job incumbents, occupational experts, occupational analysts, employer job postings, and customer/professional association input.
Data content areas include:
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
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 Hustler. The dataset can be utilized to gain insights into gender-based income distribution within the Hustler population, aiding in data analysis and decision-making..
Key observations
When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.
Income brackets:
Variables / Data Columns
Employment type classifications include:
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.
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/.
This dataset is a part of the main dataset for Hustler median household income by race. You can refer the same here