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:

• Worker Characteristics (e.g., Abilities, Interests, Work Styles)
• Worker Requirements (e.g., Education, Knowledge, Skills)
• Experience Requirements (e.g., On-the-Job Training, Work Experience)
• Occupational Requirements (e.g., Detailed Work Activities, Work Context)
• Occupation-Specific Information (e.g., Job Titles, Tasks, Technology Skills)

Graph and download economic data for Employment Level - Office and Administrative Support Occupations (LNU02032207) from Jan 1983 to May 2025 about administrative, occupation, 16 years +, household survey, employment, and USA.

About This Dataset

This dataset provides a comprehensive record of Labor Condition Application (LCA) disclosures for H1B visa petitions filed with the U.S. Department of Labor (DOL) from 2020 to 2024. It has been cleaned and prepared for public analysis to offer valuable insights into employment trends, job categories, salaries, and geographic distribution of H1B workers.

What is H1B?

The H1B visa is a non-immigrant visa that allows U.S. companies to employ foreign workers in specialty occupations requiring theoretical or technical expertise. These roles typically include fields such as IT, engineering, finance, healthcare, and more. The H1B program is critical for addressing skill gaps in the U.S. workforce and supporting economic growth.

What is LCA?

The Labor Condition Application (LCA) is a prerequisite for filing an H1B visa petition. Employers submit the LCA to the DOL to ensure compliance with wage and working condition requirements. The LCA process protects both U.S. workers and foreign employees by enforcing: - Payment of prevailing wages. - Assurance that hiring foreign workers will not adversely affect local labor conditions.

Each LCA disclosure contains information about the employer, job title, job location, wages, and visa classification.

Why Analyze LCA Disclosure Data (2020-2024)?

The dataset spans a crucial period (2020-2024) characterized by: - Pandemic Impact: Changes in employment patterns and visa policies due to COVID-19. - Remote Work Trends: Shifts in work location dynamics for H1B visa holders. - Tech Layoffs and Restructuring: Evolving job roles and industry demands, especially in tech. - Economic Recovery: Insights into how industries and geographic regions rebounded post-pandemic.

Analyzing this data can provide: 1. Employment Trends: Discover trends in job roles, industries, and geographic locations hiring H1B workers. 2. Wage Comparisons: Compare wages across job titles, industries, and states. 3. Policy Insights: Assess the impact of government policies on foreign employment. 4. Geographic Distribution: Identify areas with the highest demand for H1B workers. 5. Industry Insights: Explore the reliance of various industries on foreign talent.

Key Features of This Dataset

• Years Covered: 2020 to 2024.
• Number of Records: Includes all LCAs filed during this period.
• Attributes: Comprehensive fields such as case number, job title, employer name, wage data, work location, and visa classification.
• Source: Data is based on public disclosures from the U.S. Department of Labor.

Who Can Use This Dataset?

• Researchers: To study the economic impact of H1B workers and labor trends.
• Policymakers: To analyze the effects of visa policies on labor markets.
• Job Seekers: To explore prevailing wages and in-demand job roles for H1B petitions.
• Businesses: To understand competitive trends in hiring foreign talent.

VITAL SIGNS INDICATOR Jobs (LU2)

FULL MEASURE NAME Employment estimates by place of work

LAST UPDATED March 2020

DESCRIPTION Jobs refers to the number of employees in a given area by place of work. These estimates do not include self-employed and private household employees.

DATA SOURCE California Employment Development Department: Current Employment Statistics 1990-2018 http://www.labormarketinfo.edd.ca.gov/

U.S. Census Bureau: LODES Data Longitudinal Employer-Household Dynamics Program (2005-2010) http://lehd.ces.census.gov/

U.S. Census Bureau: American Community Survey 5-Year Estimates, Tables S0804 (2010) and B08604 (2010-2017) https://factfinder.census.gov/

Bureau of Labor Statistics: Current Employment Statistics Table D-3: Employees on nonfarm payrolls (1990-2018) http://www.bls.gov/data/

METHODOLOGY NOTES (across all datasets for this indicator) The California Employment Development Department (EDD) provides estimates of employment, by place of employment, for California counties. The Bureau of Labor Statistics (BLS) provides estimates of employment for metropolitan areas outside of the Bay Area. Annual employment data are derived from monthly estimates and thus reflect “annual average employment.” Employment estimates outside of the Bay Area do not include farm employment. For the metropolitan area comparison, farm employment was removed from Bay Area employment totals. Both EDD and BLS data report only wage and salary jobs, not the self-employed.

For measuring jobs below the county level, Vital Signs assigns collections of incorporated cities and towns to sub-county areas. For example, the cities of East Palo Alto, Menlo Park, Portola Valley, Redwood City and Woodside are considered South San Mateo County. Because Bay Area counties differ in footprint, the number of sub-county city groupings varies from one (San Francisco and San Jose counties) to four (Santa Clara County). Estimates for sub-county areas are the sums of city-level estimates from the U.S. Census Bureau: American Community Survey (ACS) 2010-2017.

The following incorporated cities and towns are included in each sub-county area: North Alameda County – Alameda, Albany, Berkeley, Emeryville, Oakland, Piedmont East Alameda County - Dublin, Livermore, Pleasanton South Alameda County - Fremont, Hayward, Newark, San Leandro, Union City Central Contra Costa County - Clayton, Concord, Danville, Lafayette, Martinez, Moraga, Orinda, Pleasant Hill, San Ramon, Walnut Creek East Contra Costa County - Antioch, Brentwood, Oakley, Pittsburg West Contra Costa County - El Cerrito, Hercules, Pinole, Richmond, San Pablo Marin – all incorporated cities and towns Napa – all incorporated cities and towns San Francisco – San Francisco North San Mateo - Brisbane, Colma, Daly City, Millbrae, Pacifica, San Bruno, South San Francisco Central San Mateo - Belmont, Burlingame, Foster City, Half Moon Bay, Hillsborough, San Carlos, San Mateo South San Mateo - East Palo Alto, Menlo Park, Portola Valley, Redwood City, Woodside North Santa Clara - Los Altos, Los Altos Hills, Milpitas, Mountain View, Palo Alto, Santa Clara, Sunnyvale San Jose – San Jose Southwest Santa Clara - Campbell, Cupertino, Los Gatos, Monte Sereno, Saratoga South Santa Clara - Gilroy, Morgan Hill East Solano - Dixon, Fairfield, Rio Vista, Suisun City, Vacaville South Solano - Benicia, Vallejo North Sonoma - Cloverdale, Healdsburg, Windsor South Sonoma - Cotati, Petaluma, Rohnert Park, Santa Rosa, Sebastopol, Sonoma

The Online Recruitment Sites industry has boomed since the 2000s as job searches have moved online and the internet has become an indispensable part of daily life. The internet has become the primary medium for communicating and accessing information, the main driving force behind this industry's rise. Job seekers and employers have increasingly turned to online recruitment sites to look for new openings and find new talent pools. Revenue generated from online recruitment sites is expected to grow at a CAGR of 8.3% to $15.7 billion over the five years to 2023. While growth has been fueled by an extremely tight labor market following pandemic disruptions, revenue is forecast to contract 4.6% in 2023. Low costs associated with starting an online company have encouraged new companies to begin operations online. The largest online recruitment sites have increased market share through organic growth and via the acquisition of smaller players, which have targeted niche industries. Incumbents hold a competitive advantage in developing brand names, which has made it difficult for new sites to gain market share. Nonetheless, low barriers to entry and strength in demand for professional and technical recruiting have enabled some niche job boards to succeed within their respective markets. The growing advantages associated with using online recruitment sites and the scalability of online platforms enable sizable profit margins. Online hiring played an integral role across the economy during the COVID-19 pandemic, as employers have also to interact with customers and employees in new ways. Driven by the rapid development and adoption of big data analytics and mobile computing, online recruitments sites are expected to provide a broader range of services that go beyond standard job posting services and resume collection. These services will enable online recruiters to compete more effectively with traditional recruiting companies and in-house hiring departments. Meanwhile, a steady labor market will likely create new job openings even as interest rates rise, particularly in small- and medium-sized businesses. Revenue across online recruitment sites is forecast to grow at a CAGR of 4.4% to $19.5 billion over the five years to 2028.

VITAL SIGNS INDICATOR Jobs (LU2)

FULL MEASURE NAME Employment estimates by place of work

LAST UPDATED October 2019

DESCRIPTION Jobs refers to the number of employees in a given area by place of work. These estimates do not include self-employed and private household employees.

DATA SOURCE California Employment Development Department: Current Employment Statistics 1990-2018 http://www.labormarketinfo.edd.ca.gov/

U.S. Census Bureau: LODES Data Longitudinal Employer-Household Dynamics Program (2005-2010) http://lehd.ces.census.gov/

U.S. Census Bureau: American Community Survey 5-Year Estimates, Tables S0804 (2010) and B08604 (2010-2017) https://factfinder.census.gov/

Bureau of Labor Statistics: Current Employment Statistics Table D-3: Employees on nonfarm payrolls (1990-2018) http://www.bls.gov/data/

METHODOLOGY NOTES (across all datasets for this indicator) The California Employment Development Department (EDD) provides estimates of employment, by place of employment, for California counties. The Bureau of Labor Statistics (BLS) provides estimates of employment for metropolitan areas outside of the Bay Area. Annual employment data are derived from monthly estimates and thus reflect “annual average employment.” Employment estimates outside of the Bay Area do not include farm employment. For the metropolitan area comparison, farm employment was removed from Bay Area employment totals. Both EDD and BLS data report only wage and salary jobs, not the self-employed.

For measuring jobs below the county level, Vital Signs assigns collections of incorporated cities and towns to sub-county areas. For example, the cities of East Palo Alto, Menlo Park, Portola Valley, Redwood City and Woodside are considered South San Mateo County. Because Bay Area counties differ in footprint, the number of sub-county city groupings varies from one (San Francisco and San Jose counties) to four (Santa Clara County). Estimates for sub-county areas are the sums of city-level estimates from the U.S. Census Bureau: American Community Survey (ACS) 2010-2017.

The following incorporated cities and towns are included in each sub-county area: North Alameda County – Alameda, Albany, Berkeley, Emeryville, Oakland, Piedmont East Alameda County - Dublin, Livermore, Pleasanton South Alameda County - Fremont, Hayward, Newark, San Leandro, Union City Central Contra Costa County - Clayton, Concord, Danville, Lafayette, Martinez, Moraga, Orinda, Pleasant Hill, San Ramon, Walnut Creek East Contra Costa County - Antioch, Brentwood, Oakley, Pittsburg West Contra Costa County - El Cerrito, Hercules, Pinole, Richmond, San Pablo Marin – all incorporated cities and towns Napa – all incorporated cities and towns San Francisco – San Francisco North San Mateo - Brisbane, Colma, Daly City, Millbrae, Pacifica, San Bruno, South San Francisco Central San Mateo - Belmont, Burlingame, Foster City, Half Moon Bay, Hillsborough, San Carlos, San Mateo South San Mateo - East Palo Alto, Menlo Park, Portola Valley, Redwood City, Woodside North Santa Clara - Los Altos, Los Altos Hills, Milpitas, Mountain View, Palo Alto, Santa Clara, Sunnyvale San Jose – San Jose Southwest Santa Clara - Campbell, Cupertino, Los Gatos, Monte Sereno, Saratoga South Santa Clara - Gilroy, Morgan Hill East Solano - Dixon, Fairfield, Rio Vista, Suisun City, Vacaville South Solano - Benicia, Vallejo North Sonoma - Cloverdale, Healdsburg, Windsor South Sonoma - Cotati, Petaluma, Rohnert Park, Santa Rosa, Sebastopol, Sonoma

Key Table Information.Table Title.Mining: Location of Mining Establishments by Employment Size for the U.S., States, and Offshore Areas: 2022.Table ID.ECNLOCMINE2022.EC2221LOCMINE.Survey/Program.Economic Census.Year.2022.Dataset.ECN Sector Statistics Economic Census: Mining: Location of Mines by Employment Size for Subsectors and Industries for the U.S., States, and Offshore Areas.Source.U.S. Census Bureau, 2022 Economic Census, Sector Statistics.Release Date.2025-05-15.Release Schedule.The Economic Census occurs every five years, in years ending in 2 and 7.The data in this file come from the 2022 Economic Census data files released on a flow basis starting in January 2024 with First Look Statistics. Preliminary U.S. totals released in January 2024 are superseded with final data shown in the releases of later economic census statistics through March 2026.For more information about economic census planned data product releases, see 2022 Economic Census Release Schedule..Dataset Universe.The dataset universe consists of all establishments that are in operation for at least some part of 2022, are located in one of the 50 U.S. states, associated offshore areas, or the District of Columbia, have paid employees, and are classified in one of nineteen in-scope sectors defined by the 2022 North American Industry Classification System (NAICS)..Methodology.Data Items and Other Identifying Records.Employment size of establishmentsNumber of establishmentsDefinitions can be found by clicking on the column header in the table or by accessing the Economic Census Glossary..Unit(s) of Observation.The reporting units for the economic census are employer establishments. An establishment is generally a single physical location where business is conducted or where services or industrial operations are performed. A company or firm is comprised of one or more in-scope establishments that operate under the ownership or control of a single organization. For some industries, the reporting units are instead groups of all establishments in the same industry belonging to the same firm..Geography Coverage.The data are shown for the U.S., State, and Offshore Area levels that vary by industry. For information about economic census geographies, including changes for 2022, see Geographies..Industry Coverage.The data are shown at the 2- through 6-digit 2022 NAICS code levels for U.S., States, and Offshore Area. For information about NAICS, see Economic Census Code Lists..Sampling.The 2022 Economic Census sample includes all active operating establishments of multi-establishment firms and approximately 1.7 million single-establishment firms, stratified by industry and state. Establishments selected to the sample receive a questionnaire. For all data on this table, establishments not selected into the sample are represented with administrative data. For more information about the sample design, see 2022 Economic Census Methodology..Confidentiality.The Census Bureau has reviewed this data product to ensure appropriate access, use, and disclosure avoidance protection of the confidential source data (Project No. 7504609, Disclosure Review Board (DRB) approval number: CBDRB-FY23-099).To protect confidentiality, the U.S. Census Bureau suppresses cell values to minimize the risk of identifying a particular business’ data or identity.To comply with disclosure avoidance guidelines, data rows with fewer than three contributing firms or three contributing establishments are not presented. Additionally, establishment counts are suppressed when other select statistics in the same row are suppressed. More information on disclosure avoidance is available in the 2022 Economic Census Methodology..Technical Documentation/Methodology.For detailed information about the methods used to collect data and produce statistics, survey questionnaires, Primary Business Activity/NAICS codes, NAPCS codes, and more, see Economic Census Technical Documentation..Weights.No weighting applied as establishments not sampled are represented with administrative data..Table Information.FTP Download.https://www2.census.gov/programs-surveys/economic-census/data/2022/sector21/.API Information.Economic census data are housed in the Census Bureau Application Programming Interface (API)..Symbols.D - Withheld to avoid disclosing data for individual companies; data are included in higher level totalsN - Not available or not comparableS - Estimate does not meet publication standards because of high sampling variability, poor response quality, or other concerns about the estimate quality. Unpublished estimates derived from this table by subtraction are subject to these same limitations and should not be attributed to the U.S. Census Bureau. For a description of publication standards and the total quantity response rate, see link to program methodology page.X - Not applicableA - Relative standard error of 100% or morer - Reviseds - Relative standard error exceeds 40%For a complete list of sy...

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 Thedford. 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 Thedford, the median income for all workers aged 15 years and older, regardless of work hours, was $65,833 for males and $26,908 for females.

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

- Full-time workers, aged 15 years and older: In Thedford, among full-time, year-round workers aged 15 years and older, males earned a median income of $73,864, while females earned $46,250, 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.

Remarkably, across all roles, including non-full-time employment, women displayed a similar gender pay gap percentage. This indicates a consistent gender pay gap scenario across various employment types in Thedford, showcasing a consistent income pattern irrespective of employment status.

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 Thedford 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 Fremont. 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 Fremont, the median income for all workers aged 15 years and older, regardless of work hours, was $99,996 for males and $55,990 for females.

These income figures highlight a substantial gender-based income gap in Fremont. Women, regardless of work hours, earn 56 cents for each dollar earned by men. This significant gender pay gap, approximately 44%, underscores concerning gender-based income inequality in the city of Fremont.

- Full-time workers, aged 15 years and older: In Fremont, among full-time, year-round workers aged 15 years and older, males earned a median income of $136,764, while females earned $101,991, leading to a 25% gender pay gap among full-time workers. This illustrates that women earn 75 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 Fremont.

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 Fremont 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 Declo. 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 Declo, the median income for all workers aged 15 years and older, regardless of work hours, was $46,359 for males and $16,932 for females.

These income figures highlight a substantial gender-based income gap in Declo. Women, regardless of work hours, earn 37 cents for each dollar earned by men. This significant gender pay gap, approximately 63%, underscores concerning gender-based income inequality in the city of Declo.

- Full-time workers, aged 15 years and older: In Declo, among full-time, year-round workers aged 15 years and older, males earned a median income of $68,167, while females earned $46,250, leading to a 32% gender pay gap among full-time workers. This illustrates that women earn 68 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.

Remarkably, across all roles, including non-full-time employment, women displayed a similar gender pay gap percentage. This indicates a consistent gender pay gap scenario across various employment types in Declo, showcasing a consistent income pattern irrespective of employment status.

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

Although the American Community Survey (ACS) produces population, demographic and housing unit estimates, the decennial census is the official source of population totals for April 1st of each decennial year. In between censuses, the Census Bureau's Population Estimates Program produces and disseminates the official estimates of the population for the nation, states, counties, cities, and towns and estimates of housing units and the group quarters population for states and counties..Information about the American Community Survey (ACS) can be found on the ACS website. Supporting documentation including code lists, subject definitions, data accuracy, and statistical testing, and a full list of ACS tables and table shells (without estimates) can be found on the Technical Documentation section of the ACS website.Sample size and data quality measures (including coverage rates, allocation rates, and response rates) can be found on the American Community Survey website in the Methodology section..Source: U.S. Census Bureau, 2023 American Community Survey 1-Year Estimates.ACS data generally reflect the geographic boundaries of legal and statistical areas as of January 1 of the estimate year. For more information, see Geography Boundaries by Year..Data are based on a sample and are subject to sampling variability. The degree of uncertainty for an estimate arising from sampling variability is represented through the use of a margin of error. The value shown here is the 90 percent margin of error. The margin of error can be interpreted roughly as providing a 90 percent probability that the interval defined by the estimate minus the margin of error and the estimate plus the margin of error (the lower and upper confidence bounds) contains the true value. In addition to sampling variability, the ACS estimates are subject to nonsampling error (for a discussion of nonsampling variability, see ACS Technical Documentation). The effect of nonsampling error is not represented in these tables..Users must consider potential differences in geographic boundaries, questionnaire content or coding, or other methodological issues when comparing ACS data from different years. Statistically significant differences shown in ACS Comparison Profiles, or in data users' own analysis, may be the result of these differences and thus might not necessarily reflect changes to the social, economic, housing, or demographic characteristics being compared. For more information, see Comparing ACS Data..Employment and unemployment estimates may vary from the official labor force data released by the Bureau of Labor Statistics because of differences in survey design and data collection. For guidance on differences in employment and unemployment estimates from different sources go to Labor Force Guidance..Workers include members of the Armed Forces and civilians who were at work last week..Industry titles and their 4-digit codes are based on the 2022 North American Industry Classification System. The Industry categories adhere to the guidelines issued in Clarification Memorandum No. 2, "NAICS Alternate Aggregation Structure for Use By U.S. Statistical Agencies," issued by the Office of Management and Budget..Occupation titles and their 4-digit codes are based on the 2018 Standard Occupational Classification..The health insurance coverage category names were modified in 2010. See https://www.census.gov/topics/health/health-insurance/about/glossary.html#par_textimage_18 for a list of the insurance type definitions..Beginning in 2017, selected variable categories were updated, including age-categories, income-to-poverty ratio (IPR) categories, and the age universe for certain employment and education variables. See user note entitled "Health Insurance Table Updates" for further details..Estimates of urban and rural populations, housing units, and characteristics reflect boundaries of urban areas defined based on 2020 Census data. As a result, data for urban and rural areas from the ACS do not necessarily reflect the results of ongoing urbanization..Explanation of Symbols:- The estimate could not be computed because there were an insufficient number of sample observations. For a ratio of medians estimate, one or both of the median estimates falls in the lowest interval or highest interval of an open-ended distribution. For a 5-year median estimate, the margin of error associated with a median was larger than the median itself.N The estimate or margin of error cannot be displayed because there were an insufficient number of sample cases in the selected geographic area. (X) The estimate or margin of error is not applicable or not available.median- The median falls in the lowest interval of an open-ended distribution (for example "2,500-")median+ The median falls in the highest interval of an open-ended distribution (for example "250,000+").** The margin of error could not be computed because there were an insufficient number of sample observations.*** Th...

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 Englishtown. 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 Englishtown, the median income for all workers aged 15 years and older, regardless of work hours, was $70,729 for males and $44,514 for females.

These income figures highlight a substantial gender-based income gap in Englishtown. Women, regardless of work hours, earn 63 cents for each dollar earned by men. This significant gender pay gap, approximately 37%, underscores concerning gender-based income inequality in the borough of Englishtown.

- Full-time workers, aged 15 years and older: In Englishtown, among full-time, year-round workers aged 15 years and older, males earned a median income of $96,369, while females earned $61,711, leading to a 36% gender pay gap among full-time workers. This illustrates that women earn 64 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.

Remarkably, across all roles, including non-full-time employment, women displayed a similar gender pay gap percentage. This indicates a consistent gender pay gap scenario across various employment types in Englishtown, showcasing a consistent income pattern irrespective of employment status.

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 Englishtown 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 Rolette. 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 Rolette, the median income for all workers aged 15 years and older, regardless of work hours, was $46,563 for males and $24,375 for females.

These income figures highlight a substantial gender-based income gap in Rolette. Women, regardless of work hours, earn 52 cents for each dollar earned by men. This significant gender pay gap, approximately 48%, underscores concerning gender-based income inequality in the city of Rolette.

- Full-time workers, aged 15 years and older: In Rolette, among full-time, year-round workers aged 15 years and older, males earned a median income of $81,875, while females earned $45,417, leading to a 45% gender pay gap among full-time workers. This illustrates that women earn 55 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.

Remarkably, across all roles, including non-full-time employment, women displayed a similar gender pay gap percentage. This indicates a consistent gender pay gap scenario across various employment types in Rolette, showcasing a consistent income pattern irrespective of employment status.

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 Rolette 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 Joice. 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 Joice, the median income for all workers aged 15 years and older, regardless of work hours, was $49,219 for males and $27,212 for females.

These income figures highlight a substantial gender-based income gap in Joice. Women, regardless of work hours, earn 55 cents for each dollar earned by men. This significant gender pay gap, approximately 45%, underscores concerning gender-based income inequality in the city of Joice.

- Full-time workers, aged 15 years and older: In Joice, among full-time, year-round workers aged 15 years and older, males earned a median income of $55,625, while females earned $31,563, leading to a 43% gender pay gap among full-time workers. This illustrates that women earn 57 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.

Remarkably, across all roles, including non-full-time employment, women displayed a similar gender pay gap percentage. This indicates a consistent gender pay gap scenario across various employment types in Joice, showcasing a consistent income pattern irrespective of employment status.

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 Joice 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 Pierce. 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 Pierce, the median income for all workers aged 15 years and older, regardless of work hours, was $51,955 for males and $35,625 for females.

These income figures highlight a substantial gender-based income gap in Pierce. Women, regardless of work hours, earn 69 cents for each dollar earned by men. This significant gender pay gap, approximately 31%, underscores concerning gender-based income inequality in the town of Pierce.

- Full-time workers, aged 15 years and older: In Pierce, among full-time, year-round workers aged 15 years and older, males earned a median income of $72,574, while females earned $43,207, leading to a 40% gender pay gap among full-time workers. This illustrates that women earn 60 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.

Remarkably, across all roles, including non-full-time employment, women displayed a similar gender pay gap percentage. This indicates a consistent gender pay gap scenario across various employment types in Pierce, showcasing a consistent income pattern irrespective of employment status.

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 Pierce 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 Bow Mar. 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 Bow Mar, the median income for all workers aged 15 years and older, regardless of work hours, was $116,667 for males and $33,438 for females.

These income figures highlight a substantial gender-based income gap in Bow Mar. Women, regardless of work hours, earn 29 cents for each dollar earned by men. This significant gender pay gap, approximately 71%, underscores concerning gender-based income inequality in the town of Bow Mar.

- Full-time workers, aged 15 years and older: In Bow Mar, among full-time, year-round workers aged 15 years and older, males earned a median income of $217,917, while females earned $79,271, leading to a 64% gender pay gap among full-time workers. This illustrates that women earn 36 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.

Remarkably, across all roles, including non-full-time employment, women displayed a similar gender pay gap percentage. This indicates a consistent gender pay gap scenario across various employment types in Bow Mar, showcasing a consistent income pattern irrespective of employment status.

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 Bow Mar 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 Central. 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 Central, the median income for all workers aged 15 years and older, regardless of work hours, was $61,327 for males and $39,288 for females.

These income figures highlight a substantial gender-based income gap in Central. Women, regardless of work hours, earn 64 cents for each dollar earned by men. This significant gender pay gap, approximately 36%, underscores concerning gender-based income inequality in the city of Central.

- Full-time workers, aged 15 years and older: In Central, among full-time, year-round workers aged 15 years and older, males earned a median income of $80,791, while females earned $53,839, leading to a 33% gender pay gap among full-time workers. This illustrates that women earn 67 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.

Remarkably, across all roles, including non-full-time employment, women displayed a similar gender pay gap percentage. This indicates a consistent gender pay gap scenario across various employment types in Central, showcasing a consistent income pattern irrespective of employment status.

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 Central 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 Delphos. 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 Delphos, the median income for all workers aged 15 years and older, regardless of work hours, was $47,787 for males and $25,740 for females.

These income figures highlight a substantial gender-based income gap in Delphos. Women, regardless of work hours, earn 54 cents for each dollar earned by men. This significant gender pay gap, approximately 46%, underscores concerning gender-based income inequality in the city of Delphos.

- Full-time workers, aged 15 years and older: In Delphos, among full-time, year-round workers aged 15 years and older, males earned a median income of $60,906, while females earned $41,771, leading to a 31% gender pay gap among full-time workers. This illustrates that women earn 69 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.

Remarkably, across all roles, including non-full-time employment, women displayed a similar gender pay gap percentage. This indicates a consistent gender pay gap scenario across various employment types in Delphos, showcasing a consistent income pattern irrespective of employment status.

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 Delphos 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 Pottawattamie Park. 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 Pottawattamie Park, the median income for all workers aged 15 years and older, regardless of work hours, was $62,500 for males and $35,714 for females.

These income figures highlight a substantial gender-based income gap in Pottawattamie Park. Women, regardless of work hours, earn 57 cents for each dollar earned by men. This significant gender pay gap, approximately 43%, underscores concerning gender-based income inequality in the town of Pottawattamie Park.

- Full-time workers, aged 15 years and older: In Pottawattamie Park, among full-time, year-round workers aged 15 years and older, males earned a median income of $94,000, while females earned $41,607, leading to a 56% gender pay gap among full-time workers. This illustrates that women earn 44 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.

Remarkably, across all roles, including non-full-time employment, women displayed a similar gender pay gap percentage. This indicates a consistent gender pay gap scenario across various employment types in Pottawattamie Park, showcasing a consistent income pattern irrespective of employment status.

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 Pottawattamie Park 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 Doland. 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 Doland, the median income for all workers aged 15 years and older, regardless of work hours, was $46,250 for males and $21,563 for females.

These income figures highlight a substantial gender-based income gap in Doland. Women, regardless of work hours, earn 47 cents for each dollar earned by men. This significant gender pay gap, approximately 53%, underscores concerning gender-based income inequality in the city of Doland.

- Full-time workers, aged 15 years and older: In Doland, among full-time, year-round workers aged 15 years and older, males earned a median income of $57,321, while females earned $34,271, leading to a 40% gender pay gap among full-time workers. This illustrates that women earn 60 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.

Remarkably, across all roles, including non-full-time employment, women displayed a similar gender pay gap percentage. This indicates a consistent gender pay gap scenario across various employment types in Doland, showcasing a consistent income pattern irrespective of employment status.

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