Use our Glassdoor dataset to find market trends and business information on companies as well as how current and past employees perceive and rate them. You may purchase the entire dataset or a customized subset depending on your needs. Popular use cases: competitive business intelligence, location-based marketing, geotargeting, B2B data enrichment, and more. The Glassdoor companies information dataset, one of the largest jobs and recruiting sites, offers a complete company overview with reviews and FAQs that provide insights about jobs and companies. The dataset includes all major data points: Location, Founding date, Revenue range, Size,Management, Company rating, CE outlook, Reviews, and FAQ as added by employees, Rating CEO approvalm and more.

Between 2023 and 2027, the majority of companies surveyed worldwide expect big data to have a more positive than negative impact on the global job market and employment, with ** percent of the companies reporting the technology will create jobs and * percent expecting the technology to displace jobs. Meanwhile, artificial intelligence (AI) is expected to result in more significant labor market disruptions, with ** percent of organizations expecting the technology to displace jobs and ** percent expecting AI to create jobs.

Enhance your workforce insights with comprehensive Employee Dataset, designed to help businesses improve recruitment strategies, track employment trends, and optimize workforce planning. This dataset provides structured and reliable employee data for HR professionals, recruiters, and analysts.

Dataset Features

Employee Profiles: Access detailed public employee data, including names, job titles, industries, locations, experience, and skills. Ideal for talent acquisition, workforce analytics, and competitive hiring strategies. Company Employment Data: Gain insights into company workforce distribution, employee tenure, hiring trends, and organizational structures. Useful for market research, HR benchmarking, and business intelligence. Job Listings & Open Positions: Track job postings, employment trends, and hiring patterns across industries. This data includes job titles, company names, locations, salary ranges, and job descriptions.

Customizable Subsets for Specific Needs Our Employee Dataset is fully customizable, allowing you to filter data based on industry, location, job role, or company size. Whether you need a broad dataset for market analysis or a focused subset for recruitment purposes, we tailor the dataset to your specific needs.

Popular Use Cases

Recruitment & Talent Sourcing: Identify top talent, analyze hiring trends, and enhance recruitment strategies with up-to-date employee data. HR Analytics & Workforce Planning: Optimize workforce management by tracking employee movement, industry hiring patterns, and job market trends. Competitive Intelligence: Monitor hiring activity, employee retention rates, and workforce distribution to gain insights into competitors’ strategies. Market Research & Business Expansion: Analyze employment trends to identify growth opportunities, emerging job markets, and industry shifts. AI & Predictive Analytics: Leverage structured employee data for AI-driven workforce predictions, job market forecasting, and HR automation.

Whether you're looking to improve recruitment, analyze workforce trends, or gain competitive insights, our Employee Dataset provides the structured data you need. Get started today and customize your dataset to fit your business objectives.

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 East New Market. 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 East New Market, the median income for all workers aged 15 years and older, regardless of work hours, was $37,500 for males and $24,271 for females.

These income figures highlight a substantial gender-based income gap in East New Market. 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 town of East New Market.

- Full-time workers, aged 15 years and older: In East New Market, among full-time, year-round workers aged 15 years and older, males earned a median income of $47,857, while females earned $77,083

Surprisingly, within the subset of full-time workers, women earn a higher income than men, earning 1.61 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 East New Market 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 New Market. It showcases annual income, providing insights into gender-specific income distributions and the disparities between full-time and part-time work. The dataset can be utilized to gain insights into gender-based pay disparity trends and explore the variations in income for male and female individuals.

Key observations: Insights from 2023

Based on our analysis ACS 2019-2023 5-Year Estimates, we present the following observations: - All workers, aged 15 years and older: In New Market, the median income for all workers aged 15 years and older, regardless of work hours, was $35,104 for males and $21,830 for females.

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

- Full-time workers, aged 15 years and older: In New Market, among full-time, year-round workers aged 15 years and older, males earned a median income of $39,740, while females earned $45,260

Surprisingly, 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.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates. All incomes have been adjusting for inflation and are presented in 2023-inflation-adjusted dollars.

Gender classifications include:

• Male
• Female

Employment type classifications include:

• Full-time, year-round: A full-time, year-round worker is a person who worked full time (35 or more hours per week) and 50 or more weeks during the previous calendar year.
• Part-time: A part-time worker is a person who worked less than 35 hours per week during the previous calendar year.

Variables / Data Columns

• Year: This column presents the data year. Expected values are 2010 to 2023
• Male Total Income: Annual median income, for males regardless of work hours
• Male FT Income: Annual median income, for males working full time, year-round
• Male PT Income: Annual median income, for males working part time
• Female Total Income: Annual median income, for females regardless of work hours
• Female FT Income: Annual median income, for females working full time, year-round
• Female PT Income: Annual median income, for females working part time

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for New Market median household income by race. You can refer the same here

This dataset is about books. It has 1 row and is filtered where the book is Flat broke in the free market : how globalization fleeced working people. It features 7 columns including author, publication date, language, and book publisher.

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.

Unlock the full potential of LinkedIn data with our extensive dataset that combines profiles, company information, and job listings into one powerful resource for business decision-making, strategic hiring, competitive analysis, and market trend insights. This all-encompassing dataset is ideal for professionals, recruiters, analysts, and marketers aiming to enhance their strategies and operations across various business functions. Dataset Features

Profiles: Dive into detailed public profiles featuring names, titles, positions, experience, education, skills, and more. Utilize this data for talent sourcing, lead generation, and investment signaling, with a refresh rate ensuring up to 30 million records per month. Companies: Access comprehensive company data including ID, country, industry, size, number of followers, website details, subsidiaries, and posts. Tailored subsets by industry or region provide invaluable insights for CRM enrichment, competitive intelligence, and understanding the startup ecosystem, updated monthly with up to 40 million records. Job Listings: Explore current job opportunities detailed with job titles, company names, locations, and employment specifics such as seniority levels and employment functions. This dataset includes direct application links and real-time application numbers, serving as a crucial tool for job seekers and analysts looking to understand industry trends and the job market dynamics.

Customizable Subsets for Specific Needs Our LinkedIn dataset offers the flexibility to tailor the dataset according to your specific business requirements. Whether you need comprehensive insights across all data points or are focused on specific segments like job listings, company profiles, or individual professional details, we can customize the dataset to match your needs. This modular approach ensures that you get only the data that is most relevant to your objectives, maximizing efficiency and relevance in your strategic applications. Popular Use Cases

Strategic Hiring and Recruiting: Track talent movement, identify growth opportunities, and enhance your recruiting efforts with targeted data. Market Analysis and Competitive Intelligence: Gain a competitive edge by analyzing company growth, industry trends, and strategic opportunities. Lead Generation and CRM Enrichment: Enrich your database with up-to-date company and professional data for targeted marketing and sales strategies. Job Market Insights and Trends: Leverage detailed job listings for a nuanced understanding of employment trends and opportunities, facilitating effective job matching and market analysis. AI-Driven Predictive Analytics: Utilize AI algorithms to analyze large datasets for predicting industry shifts, optimizing business operations, and enhancing decision-making processes based on actionable data insights.

Whether you are mapping out competitive landscapes, sourcing new talent, or analyzing job market trends, our LinkedIn dataset provides the tools you need to succeed. Customize your access to fit specific needs, ensuring that you have the most relevant and timely data at your fingertips.

Labor Force Participation Rate in the United States decreased to 62.30 percent in June from 62.40 percent in May of 2025. This dataset provides the latest reported value for - United States Labor Force Participation Rate - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

Background
The Labour Force Survey (LFS) is a unique source of information using international definitions of employment and unemployment and economic inactivity, together with a wide range of related topics such as occupation, training, hours of work and personal characteristics of household members aged 16 years and over. It is used to inform social, economic and employment policy. The LFS was first conducted biennially from 1973-1983. Between 1984 and 1991 the survey was carried out annually and consisted of a quarterly survey conducted throughout the year and a 'boost' survey in the spring quarter (data were then collected seasonally). From 1992 quarterly data were made available, with a quarterly sample size approximately equivalent to that of the previous annual data. The survey then became known as the Quarterly Labour Force Survey (QLFS). From December 1994, data gathering for Northern Ireland moved to a full quarterly cycle to match the rest of the country, so the QLFS then covered the whole of the UK (though some additional annual Northern Ireland LFS datasets are also held at the UK Data Archive). Further information on the background to the QLFS may be found in the documentation.

Longitudinal data
The LFS retains each sample household for five consecutive quarters, with a fifth of the sample replaced each quarter. The main survey was designed to produce cross-sectional data, but the data on each individual have now been linked together to provide longitudinal information. The longitudinal data comprise two types of linked datasets, created using the weighting method to adjust for non-response bias. The two-quarter datasets link data from two consecutive waves, while the five-quarter datasets link across a whole year (for example January 2010 to March 2011 inclusive) and contain data from all five waves. A full series of longitudinal data has been produced, going back to winter 1992. Linking together records to create a longitudinal dimension can, for example, provide information on gross flows over time between different labour force categories (employed, unemployed and economically inactive). This will provide detail about people who have moved between the categories. Also, longitudinal information is useful in monitoring the effects of government policies and can be used to follow the subsequent activities and circumstances of people affected by specific policy initiatives, and to compare them with other groups in the population. There are however methodological problems which could distort the data resulting from this longitudinal linking. The ONS continues to research these issues and advises that the presentation of results should be carefully considered, and warnings should be included with outputs where necessary.

New reweighting policy
Following the new reweighting policy ONS has reviewed the latest population estimates made available during 2019 and have decided not to carry out a 2019 LFS and APS reweighting exercise. Therefore, the next reweighting exercise will take place in 2020. These will incorporate the 2019 Sub-National Population Projection data (published in May 2020) and 2019 Mid-Year Estimates (published in June 2020). It is expected that reweighted Labour Market aggregates and microdata will be published towards the end of 2020/early 2021.

LFS Documentation
The documentation available from the Archive to accompany LFS datasets largely consists of the latest version of each user guide volume alongside the appropriate questionnaire for the year concerned. However, volumes are updated periodically by ONS, so users are advised to check the latest documents on the ONS Labour Force Survey - User Guidance pages before commencing analysis. This is especially important for users of older QLFS studies, where information and guidance in the user guide documents may have changed over time.

Additional data derived from the QLFS
The Archive also holds further QLFS series: End User Licence (EUL) quarterly data; Secure Access datasets; household datasets; quarterly, annual and ad hoc module datasets compiled for Eurostat; and some additional annual Northern Ireland datasets.

Variables DISEA and LNGLST
Dataset A08 (Labour market status of disabled people) which ONS suspended due to an apparent discontinuity between April to June 2017 and July to September 2017 is now available. As a result of this apparent discontinuity and the inconclusive investigations at this stage, comparisons should be made with caution between April to June 2017 and subsequent time periods. However users should note that the estimates are not seasonally adjusted, so some of the change between quarters could be due to seasonality. Further recommendations on historical comparisons of the estimates will be given in November 2018 when ONS are due to publish estimates for July to September 2018.

An article explaining the quality assurance investigations that have been conducted so far is available on the ONS Methodology webpage. For any queries about Dataset A08 please email Labour.Market@ons.gov.uk.

Occupation data for 2021 and 2022 data files

The ONS has identified an issue with the collection of some occupational data in 2021 and 2022 data files in a number of their surveys. While they estimate any impacts will be small overall, this will affect the accuracy of the breakdowns of some detailed (four-digit Standard Occupational Classification (SOC)) occupations, and data derived from them. Further information can be found in the ONS article published on 11 July 2023: https://www.ons.gov.uk/employmentandlabourmarket/peopleinwork/employmentandemployeetypes/articles/revisionofmiscodedoccupationaldataintheonslabourforcesurveyuk/january2021toseptember2022" style="background-color: rgb(255, 255, 255);">Revision of miscoded occupational data in the ONS Labour Force Survey, UK: January 2021 to September 2022.

2022 Weighting

The population totals used for the latest LFS estimates use projected growth rates from Real Time Information (RTI) data for UK, EU and non-EU populations based on 2021 patterns. The total population used for the LFS therefore does not take into account any changes in migration, birth rates, death rates, and so on since June 2021, and hence levels estimates may be under- or over-estimating the true values and should be used with caution. Estimates of rates will, however, be robust.

The data in this dataset relates to the status of persons employed in Wales, breaking the total down into those who are self-employed or employees and those who are in full time or part time. As the data come from a survey, the results are sample-based estimates and therefore subject to differing degrees of sampling variability, i.e. the true value for any measure lies in a differing range about the estimated value. This range or sampling variability increases as the detail in the data increases, for example individual local authority data are subject to higher variability than Wales data. LFS data is collected throughout the year, and is available from the ONS in a variety of ways. This dataset contains the latest annual results, as referred to in the second bullet below. Key data on the labour market is updated every month showing the position for the latest three months, for the UK and each of the UK countries and English regions. Note these data are seasonally adjusted and also that no sub-regional (i.e. local authority) data are published by the ONS to a monthly timetable. Annual results covering the periods described earlier are also available from the ONS, providing more detailed data from the LFS, including data for sub-Wales geographies. These annual datasets use results from the samples for the quarterly surveys used for the key series, together with results from additional persons sampled to provide a more robust (boosted) dataset, with estimates subject to much lower sampling variability. Quarterly results are also available, again providing more detailed data from the LFS than the key series, including data for sub-Wales geographies. However, although these data are available earlier than the data taken from the annual datasets, data for sub-Wales geographies taken from the quarterly datasets are no longer included on StatsWales as the results are far less robust than those which come from the annual datasets. Note that as data are taken from the ANNUAL Labour Force Survey datasets they do NOT exactly match annual averages derived from the 4 QUARTERLY datasets in the relevant 12 month period covered due to differences in the sampling structure.

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.

Overview: Each quarter, the Temporary Foreign Worker Program (TFWP) publishes Labour Market Impact Assessment (LMIA) statistics on Open Government Data Portal, including quarterly and annual LMIA data related to, but not limited to, requested and approved TFW positions, employment location, employment occupations, sectors, TFWP stream and temporary foreign workers by country of origin. The TFWP does not collect data on the number of TFWs who are hired by an employer and have arrived in Canada. The decision to issue a work permit rests with Immigration, Refugees and Citizenship Canada (IRCC) and not all positions on a positive LMIA result in a work permit. For these reasons, data provided in the LMIA statistics cannot be used to calculate the number of TFWs that have entered or will enter Canada. IRCC publishes annual statistics on the number of foreign workers who are issued a work permit: https://open.canada.ca/data/en/dataset/360024f2-17e9-4558-bfc1-3616485d65b9. Please note that all quarterly tables have been updated to NOC 2021 (5 digit and training, education, experience and responsibilities (TEER) based). As such, Table 5, 8, 17, and 24 will no longer be updated but will remain as archived tables. Frequency of Publication: Quarterly LMIA statistics cover data for the four quarters of the previous calendar year and the quarter(s) of the current calendar year. Quarterly data is released within two to three months of the most recent quarter. The release dates for quarterly data are as follows: Q1 (January to March) will be published by early June of the current year; Q2 (April to June) will be published by early September of the current year; Q3 (July to September) will be published by early December of the current year; and Q4 (October to December) will be published by early March of the next year. Annual statistics cover eight consecutive years of LMIA data and are scheduled to be released in March of the next year. Published Data: As part of the quarterly release, the TFWP updates LMIA data for 28 tables broken down by: TFW positions: Tables 1 to 10, 12, 13, and 22 to 24; LMIA applications: Tables 14 to 18; Employers: Tables 11, and 19 to 21; and Seasonal Agricultural Worker Program (SAWP): Tables 25 to 28. In addition, the TFWP publishes 2 lists of employers who were issued a positive or negative LMIA: Employers who were issued a positive LMIA by Program Stream, NOC, and Business Location (https://open.canada.ca/data/en/dataset/90fed587-1364-4f33-a9ee-208181dc0b97/resource/b369ae20-0c7e-4d10-93ca-07c86c91e6fe); and Employers who were issued a negative LMIA by Program Stream, NOC, and Business Location (https://open.canada.ca/data/en/dataset/f82f66f2-a22b-4511-bccf-e1d74db39ae5/resource/94a0dbee-e9d9-4492-ab52-07f0f0fb255b). Things to Remember: 1. When data are presented on positive or negative LMIAs, the decision date is used to allocate which quarter the data falls into. However, when data are presented on when LMIAs are requested, it is based on the date when the LMIA is received by ESDC. 2. As of the publication of 2022Q1- 2023Q4 data (published in April 2024) and going forward, all LMIAs in support of 'Permanent Residence (PR) Only' are included in TFWP statistics, unless indicated otherwise. All quarterly data in this report includes PR Only LMIAs. Dual-intent LMIAs and corresponding positions are included under their respective TFWP stream (e.g., low-wage, high-wage, etc.) This may impact program reporting over time. 3. Attention should be given for data that are presented by ‘Unique Employers’ when it comes to manipulating the data within that specific table. One employer could be counted towards multiple groups if they have multiple positive LMIAs across categories such as program stream, province or territory, or economic region. For example, an employer could request TFWs for two different business locations, and this employer would be counted in the statistics of both economic regions. As such, the sum of the rows within these ‘Unique Employer’ tables will not add up to the aggregate total.

About this dataset

Context

The dataset tabulates the New Market household income by gender. The dataset can be utilized to understand the gender-based income distribution of New Market income.

Content

The dataset will have the following datasets when applicable

Please note: The 2020 1-Year ACS estimates data was not reported by the Census Bureau due to the impact on survey collection and analysis caused by COVID-19. Consequently, median household income data for 2020 is unavailable for large cities (population 65,000 and above).

• New Market, MD annual median income by work experience and sex dataset : Aged 15+, 2010-2022 (in 2022 inflation-adjusted dollars)
• New Market, MD annual income distribution by work experience and gender dataset (Number of individuals ages 15+ with income, 2021)

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/.

Interested in deeper insights and visual analysis?

Explore our comprehensive data analysis and visual representations for a deeper understanding of New Market income distribution by gender. You can refer the same here

Dataset

This dataset was created by Kajal Yadav

Released under Attribution 4.0 International (CC BY 4.0)

Contents

🌍 Global B2B Person Dataset | 755M+ LinkedIn Profiles | Verified & Bi-Weekly Updated Access the world’s most comprehensive professional dataset, enriched with over 755 million LinkedIn profiles. The Forager.ai Global B2B Person Dataset delivers work-verified professional contacts with 95%+ accuracy, refreshed every two weeks. Ideal for recruitment, sales, research, and talent mapping, it provides direct access to decision-makers, specialists, and executives across industries and geographies.

Dataset Features Full Name & Job Title: Up-to-date first/last name with current professional role.

Emails & Phone Numbers: AI-validated work and personal email addresses, plus mobile numbers.

Company Info: Current employer name, industry, and company size (employee count).

Career History: Detailed work history with job titles, durations, and role progressions.

Skills & Endorsements: Extracted from public LinkedIn profiles.

Education & Certifications: Universities, degrees, and professional certifications.

Location & LinkedIn URL: City, country, and direct link to public LinkedIn profile.

Distribution Data Volume: 755M+ total profiles, with 270M+ containing full contact information.

Formats Available: CSV, JSON via S3 or Snowflake; API for real-time access.

Access Methods: REST API, Enrichment API (lookup), full dataset delivery, or custom solutions.

Usage This dataset is ideal for a variety of applications:

Executive Recruitment: Source passive talent, build role-based maps, and assess mobility.

Sales Intelligence: Find decision-makers, personalize outreach, and trigger campaigns on job changes.

Market Research: Understand talent concentration by company, geography, and skill set.

Partnership Development: Identify key stakeholders in target firms for business development.

Talent Mapping & Strategic Hiring: Build full organizational charts and skill distribution heatmaps.

Coverage Geographic Coverage: Global – including North America, EMEA, LATAM, and APAC.

Time Range: Continuously updated; profiles refreshed bi-weekly.

Demographics: Cross-industry coverage of seniority levels from entry-level to C-suite, across all sectors.

License CUSTOM

Who Can Use It Recruiters & Staffing Firms: For building target lists and sourcing niche talent.

Sales & RevOps Teams: For targeting by department, title, or decision-making authority.

VCs & PE Firms: To assess leadership teams and monitor executive movement.

Data Scientists & Analysts: To train models for job mobility, hiring trends, or org structure prediction.

B2B Platforms: For enriching internal databases and powering account-based marketing (ABM).

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 Elko New Market. 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 Elko New Market, the median income for all workers aged 15 years and older, regardless of work hours, was $83,672 for males and $49,327 for females.

These income figures highlight a substantial gender-based income gap in Elko New Market. Women, regardless of work hours, earn 59 cents for each dollar earned by men. This significant gender pay gap, approximately 41%, underscores concerning gender-based income inequality in the city of Elko New Market.

- Full-time workers, aged 15 years and older: In Elko New Market, among full-time, year-round workers aged 15 years and older, males earned a median income of $92,135, while females earned $71,699, leading to a 22% gender pay gap among full-time workers. This illustrates that women earn 78 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 Elko New Market.

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 Elko New Market median household income by race. You can refer the same here

Salutary Data is a boutique, B2B contact and company data provider that's committed to delivering high quality data for sales intelligence, lead generation, marketing, recruiting / HR, identity resolution, and ML / AI. Our database currently consists of 148MM+ highly curated B2B Contacts ( US only), along with over 4M+ companies, and is updated regularly to ensure we have the most up-to-date information.

We can enrich your in-house data ( CRM Enrichment, Lead Enrichment, etc.) and provide you with a custom dataset ( such as a lead list) tailored to your target audience specifications and data use-case. We also support large-scale data licensing to software providers and agencies that intend to redistribute our data to their customers and end-users.

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About this dataset

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 Elko New Market. The dataset can be utilized to gain insights into gender-based income distribution within the Elko New Market population, aiding in data analysis and decision-making..

Key observations

• Employment patterns: Within Elko New Market, among individuals aged 15 years and older with income, there were 1,599 men and 1,767 women in the workforce. Among them, 1,206 men were engaged in full-time, year-round employment, while 895 women were in full-time, year-round roles.
• Annual income under $24,999: Of the male population working full-time, 1.24% fell within the income range of under $24,999, while 1.56% of the female population working full-time was represented in the same income bracket.
• Annual income above $100,000: 46.10% of men in full-time roles earned incomes exceeding $100,000, while 12.85% of women in full-time positions earned within this income bracket.
• Refer to the research insights for more key observations on more income brackets ( Annual income under $24,999, Annual income between $25,000 and $49,999, Annual income between $50,000 and $74,999, Annual income between $75,000 and $99,999 and Annual income above $100,000) and employment types (full-time year-round and part-time)

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Income brackets:

• $1 to $2,499 or loss
• $2,500 to $4,999
• $5,000 to $7,499
• $7,500 to $9,999
• $10,000 to $12,499
• $12,500 to $14,999
• $15,000 to $17,499
• $17,500 to $19,999
• $20,000 to $22,499
• $22,500 to $24,999
• $25,000 to $29,999
• $30,000 to $34,999
• $35,000 to $39,999
• $40,000 to $44,999
• $45,000 to $49,999
• $50,000 to $54,999
• $55,000 to $64,999
• $65,000 to $74,999
• $75,000 to $99,999
• $100,000 or more

Variables / Data Columns

• Income Bracket: This column showcases 20 income brackets ranging from $1 to $100,000+..
• Full-Time Males: The count of males employed full-time year-round and earning within a specified income bracket
• Part-Time Males: The count of males employed part-time and earning within a specified income bracket
• Full-Time Females: The count of females employed full-time year-round and earning within a specified income bracket
• Part-Time Females: The count of females employed part-time and earning within a specified income bracket

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.

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

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