The Current Population Survey (CPS) is a monthly survey of households conducted by the Bureau of Census for the Bureau of Labor Statistics. The earnings data are collected from one-fourth of the CPS total sample of approximately 60,000 households. Data measures usual hourly and weekly earnings of wage and salary workers. All self-employed persons are excluded, regardless of whether their businesses are incorporated. Data represent earnings before taxes and other deductions and include any overtime pay, commissions, or tips usually received. Earnings data are available for all workers, by age, race, Hispanic or Latino ethnicity, sex, occupation, usual full- or part-time status, educational attainment, and other characteristics. Data are published quarterly. More information and details about the data provided can be found at http://www.bls.gov/cps/earnings.htm

This dataset is now updated annually here.

Context

This dataset contains the salary, pay rate, and total compensation of every New York City employee. In this dataset this information is provided for the 2014, 2015, 2016, and 2017 fiscal years, and provides a transparent lens into who gets paid how much and for what.

Note that fiscal years in the New York City budget cycle start on July 1st and end on June 30th (see here). That means that this dataset contains, in its sum, compensation information for all City of New York employees for the period July 1, 2014 to June 30, 2017.

Content

This dataset provides columns for fiscal year, employee name, the city department they work for, their job title, and various fields describing their compensation. The most important of these fields is "Regular Gross Pay", which provides that employee's total compensation.

Acknowledgements

This information was published as-is by the City of New York.

Inspiration

• How many people do the various city agencies employ, and how much does each department spend on salary in total?
• What are the most numerous job titles in civic government employment?
• Where does overtime pay seem to be especially common? How much of it is there?
• How do New York City employee salaries compare against salaries of city employees in Chicago? Is the difference more or less than the difference in cost of living between the two cities?

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 Ontario. 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 Ontario, the median income for all workers aged 15 years and older, regardless of work hours, was $32,277 for males and $25,837 for females.

These income figures indicate a substantial gender-based pay disparity, showcasing a gap of approximately 20% between the median incomes of males and females in Ontario. With women, regardless of work hours, earning 80 cents to each dollar earned by men, this income disparity reveals a concerning trend toward wage inequality that demands attention in thecity of Ontario.

- Full-time workers, aged 15 years and older: In Ontario, among full-time, year-round workers aged 15 years and older, males earned a median income of $46,816, while females earned $42,018, resulting in a 10% gender pay gap among full-time workers. This illustrates that women earn 90 cents for each dollar earned by men in full-time positions. While this gap shows a trend where women are inching closer to wage parity with men, it also exhibits a noticeable income difference for women working full-time in the city of Ontario.

Interestingly, when analyzing income across all roles, including non-full-time employment, the gender pay gap percentage was higher for women compared to men. It appears that full-time employment presents a more favorable income scenario for women compared to other employment patterns in Ontario.

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

Context

[INSEE][1] is the official french institute gathering data of many types around France. It can be demographic (Births, Deaths, Population Density...), Economic (Salary, Firms by activity / size...) and more.
It can be a great help to observe and measure inequality in the french population.

Content

Four files are in the dataset :

• base_etablissement_par_tranche_effectif : give information on the number of firms in every french town, categorized by size , come from [INSEE][2].
  • CODGEO : geographique code for the town (can be joined with code_insee column from "name_geographic_information.csv')
  • LIBGEO : name of the town (in french)
  • REG : region number
  • DEP : depatment number
  • E14TST : total number of firms in the town
  • E14TS0ND : number of unknown or null size firms in the town
  • E14TS1 : number of firms with 1 to 5 employees in the town
  • E14TS6 : number of firms with 6 to 9 employees in the town
  • E14TS10 : number of firms with 10 to 19 employees in the town
  • E14TS20 : number of firms with 20 to 49 employees in the town
  • E14TS50 : number of firms with 50 to 99 employees in the town
  • E14TS100 : number of firms with 100 to 199 employees in the town
  • E14TS200 : number of firms with 200 to 499 employees in the town
  • E14TS500 : number of firms with more than 500 employees in the town

• name_geographic_information : give geographic data on french town (mainly latitude and longitude, but also region / department codes and names )

  • EU_circo : name of the European Union Circonscription
  • code_région : code of the region attached to the town
  • nom_région : name of the region attached to the town
  • chef.lieu_région : name the administrative center around the town
  • numéro_département : code of the department attached to the town
  • nom_département : name of the department attached to the town
  • préfecture : name of the local administrative division around the town
  • numéro_circonscription : number of the circumpscription
  • nom_commune : name of the town
  • codes_postaux : post-codes relative to the town
  • code_insee : unique code for the town
  • latitude : GPS latitude
  • longitude : GPS longitude
  • éloignement : i couldn't manage to figure out what was the meaning of this number

• net_salary_per_town_per_category : salaries around french town per job categories, age and sex

  • CODGEO : unique code of the town
  • LIBGEO : name of the town
  • SNHM14 : mean net salary
  • SNHMC14 : mean net salary per hour for executive
  • SNHMP14 : mean net salary per hour for middle manager
  • SNHME14 : mean net salary per hour for employee
  • SNHMO14 : mean net salary per hour for worker
  • SNHMF14 : mean net salary for women
  • SNHMFC14 : mean net salary per hour for feminin executive
  • SNHMFP14 : mean net salary per hour for feminin middle manager
  • SNHMFE14 : mean net salary per hour for feminin employee
  • SNHMFO14 : mean net salary per hour for feminin worker
  • SNHMH14 : mean net salary for man
  • SNHMHC14 : mean net salary per hour for masculin executive
  • SNHMHP14 : mean net salary per hour for masculin middle manager
  • SNHMHE14 : mean net salary per hour for masculin employee
  • SNHMHO14 : mean net salary per hour for masculin worker
  • SNHM1814 : mean net salary per hour for 18-25 years old
  • SNHM2614 : mean net salary per hour for 26-50 years old
  • SNHM5014 : mean net salary per hour for >50 years old
  • SNHMF1814 : mean net salary per hour for women between 18-25 years old
  • SNHMF2614 : mean net salary per hour for women between 26-50 years old
  • SNHMF5014 : mean net salary per hour for women >50 years old
  • SNHMH1814 : mean net salary per hour for men between 18-25 years old
  • SNHMH2614 : mean net salary per hour for men between 26-50 years old
  • SNHMH5014 : mean net salary per hour for men >50 years old

• population : [demographic][3] information in France per town, age, sex and living mode

  • NIVGEO : geographic level (arrondissement, communes...)
  • CODGEO : unique code for the town
  • LIBGEO : name of the town (might contain some utf-8 errors, this information has better quality name_geographic_information)
  • MOCO : cohabitation mode : [list and meaning available in Data description]
  • AGE80_17 : age category (slice of 5 years) | ex : 0 -> people between 0 and 4 years old
  • SEXE : sex, 1 for men | 2 for women
  • NB : Number of people in the category

• departments.geojson : contains the borders of french departments. From [Gregoire David (github)][4]

These datasets can be merged by : CODGEO = code_insee

Acknowledgements

The entire dataset has been created (and actualized) by INSEE, I just uploaded it on Kaggle after doing some jobs and checks ...

This dataset includes annual salary, regular pay, incentive pay, and gross pay for employees under the County Executive and independently elected County officials for the years 2016 to the present, and is updated twice per year. For December files, Annual Salary is the employee's annual salary or annualized wage as of the last pay period for the year, and the pay data fields (Regular Pay, Incentive Pay and Gross Pay) are payments made to the employee through the last pay period of the year. The June file contains the Annual Salary, and the pay data as of the last pay period in June. Note that the June file is replaced by the December file each year. Union contracted salaries and wages which were not settled during the calendar year reflect the wage as of the end of the year. Regular Pay for these positions includes salaries and wages for the year it was paid, not the year it was earned. In addition to salary or wages for days worked and retroactively settled contract payments, Regular Pay also includes pay for days such as holidays, sick days, and vacation days. Overtime Pay includes pay for extra work typically at a wage rate different from regular wages as set forth in a collective bargaining agreement. Incentive Pay includes such things as a wellness incentive and longevity pay. Employee names are included in the dataset with the following exceptions permitted by the Pennsylvania Right to Know Law: The names of individuals who were active sworn law enforcement officers during the year; and Information that would disclose individually identifiable health information. Additionally, records related to Court of Common Pleas employees would need to be requested from the Courts. In March 2022, the salary data files prior to 2021 were updated so that all columns matched for consistent presentation.

Each year, the City of Boston publishes payroll data for employees. This dataset contains employee names, job details, and earnings information including base salary, overtime, and total compensation for employees of the City.

See the "Payroll Categories" document below for an explanation of what types of earnings are included in each category.

Estimates of the number and proportion of UK employee jobs with hourly pay below the living wage, by region, work geography, local authority and Parliamentary constituency, as defined by the Living Wage Foundation.

This dataset contains Iowa civilian employed population estimate for individuals 16 years or older by by sex and class of worker for State of Iowa, individual Iowa counties, Iowa places and census tracts within Iowa. Data is from the American Community Survey, Five Year Estimates, Table B24080. Sex includes the following: Both, Male, and Female. Class of Worker includes the following: All Classes; Private-for-Profit Wage and Salary Workers; Private-for-Profit Wage and Salary Workers, Employee; Private-for-Profit Wage and Salary Workers, Self-Employed in Own INC; Private Not-for-Profit Wage and Salary Workers; Local Government Workers; State Government Workers; Federal Government Workers; Self-Employed; and Unpaid Family Workers.

Each year, the City of Boston publishes payroll data for employees. This dataset contains employee names, job details, and earnings information including base salary, overtime, and total compensation for employees of the City.

See the "Payroll Categories" document below for an explanation of what types of earnings are included in each category.

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 Pillsbury township. 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 Pillsbury township, while the Census reported a median income of $54,750 for all male workers aged 15 years and older, data for females in the same category was unavailable due to an insufficient number of sample observations.

Given the absence of income data for females from the Census Bureau, conducting a thorough analysis of gender-based pay disparity in the township of Pillsbury township was not possible.

- Full-time workers, aged 15 years and older: In Pillsbury township, among full-time, year-round workers aged 15 years and older, males earned a median income of $56,875, while females earned $53,889, resulting in a 5% gender pay gap among full-time workers. This illustrates that women earn 95 cents for each dollar earned by men in full-time positions. While this gap shows a trend where women are inching closer to wage parity with men, it also exhibits a noticeable income difference for women working full-time in the township of Pillsbury township.

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

Context

There are two commonly understood ways in which a company is considered public: first, the company’s securities trade on public markets; and second, the company discloses certain business and financial information regularly to the public.

With the COVID-19 pandemic affecting many aspects of the business, a large portion of companies have proceeded with executive compensation adjustments as a response. Corporate boards running America’s largest public firms are giving top executives outsize compensation packages that have grown much faster than the stock market and the pay of typical workers, college graduates, and even the top 0.1%.
Excessive CEO pay is a major contributor to rising inequality that we could safely do away with. CEOs are getting more because of their power to set pay and because so much of their pay (more than 80%) is stock-related, not because they are increasing their productivity or possess specific, high-demand skills. This escalation of CEO compensation, and of executive compensation more generally, has fueled the growth of top 1.0% and top 0.1% incomes, leaving less of the fruits of economic growth for ordinary workers and widening the gap between very high earners and the bottom 90%. The economy would suffer no harm if CEOs were paid less (or were taxed more).

Content

CEO_compensation_top50_2020.csv contains data about the top 50 paid CEOs in 2020. Parameters include: - Total Granted Compensation (TGC) - Total Realized Compensation (TRC) - Total Shareholder Return (TSR) in % - TSR 1YR growth in % - TGC 1YR growth in % - TRC 1YR growth in %

CEO_largestrevenue_highestpaid_2020-21.csv contains data about the CEO & Employee pay at the largest companies by revenue in 2020/2021, as well as the New York Times published 200 highest-paid CEOs in 2020. Parameters include: - CEO Total Compensation - Median Employee Pay - Pay change over previous year - Fiscal Year Revenue - Revenue change over previous year - CEO to Employee Pay Ratio

Acknowledgements

Source: CGLytics and Equilar

If you enjoyed this dataset, here's some similar datasets you may like 😎

Median pay and raw disability pay gap estimates across different characteristic breakdowns, using Annual Population Survey (APS) data.

The wages on the Job Bank website are specific to an occupation and provide information on the earnings of workers at the regional level. Wages for most occupations are also provided at the national and provincial level. In Canada, all jobs are associated with one specific occupational grouping which is determined by the National Occupational Classification. For most occupations, a minimum, median and maximum wage estimates are displayed. They are update annually. If you have comments or questions regarding the wage information, please contact the Labour Market Information Division at: NC-LMI-IMT-GD@hrsdc-rhdcc.gc.ca

The DSS Payment Demographic data set is made up of:

Selected DSS payment data by

• Geography: state/territory, electorate, postcode, LGA and SA2 (for 2015 onwards)

• Demographic: age, sex and Indigenous/non-Indigenous

• Duration on Payment (Working Age & Pensions)

• Duration on Income Support (Working Age, Carer payment & Disability Support Pension)

• Rate (Working Age & Pensions)

• Earnings (Working Age & Pensions)

• Age Pension assets data

• JobSeeker Payment and Youth Allowance (other) Principal Carers

• Activity Tested Recipients by Partial Capacity to Work (NSA,PPS & YAO)

• Exits within 3, 6 and 12 months (Newstart Allowance/JobSeeker Payment, Parenting Payment, Sickness Allowance & Youth Allowance)

• Disability Support Pension by medical condition

• Care Receiver by medical conditions

• Commonwealth Rent Assistance by Payment type and Income Unit type have been added from March 2017. For further information about Commonwealth Rent Assistance and Income Units see the Data Descriptions and Glossary included in the dataset.

From December 2022, the "DSS Expanded Benefit and Payment Recipient Demographics – quarterly data" publication has introduced expanded reporting populations for income support recipients. As a result, the reporting population for Jobseeker Payment and Special Benefit has changed to include recipients who are current but on zero rate of payment and those who are suspended from payment. The reporting population for ABSTUDY, Austudy, Parenting Payment and Youth Allowance has changed to include those who are suspended from payment. The expanded report will replace the standard report after June 2023.

Additional data for DSS Expanded Benefit and Payment Recipient Demographics – quarterly data includes:

• A new contents page to assist users locate the information within the spreadsheet

• Additional data for the ‘Suspended’ population in the ‘Payment by Rate’ tab to enable users to calculate the old reporting rules.

• Additional information on the Employment Earning by ‘Income Free Area’ tab.

From December 2022, Services Australia have implemented a change in the Centrelink payment system to recognise gender other than the sex assigned at birth or during infancy, or as a gender which is not exclusively male or female. To protect the privacy of individuals and comply with confidentialisation policy, persons identifying as ‘non-binary’ will initially be grouped with ‘females’ in the period immediately following implementation of this change. The Department will monitor the implications of this change and will publish the ‘non-binary’ gender category as soon as privacy and confidentialisation considerations allow.

Local Government Area has been updated to reflect the Australian Statistical Geography Standard (ASGS) 2022 boundaries from June 2023.

Commonwealth Electorate Division has been updated to reflect the Australian Statistical Geography Standard (ASGS) 2021 boundaries from June 2023.

SA2 has been updated to reflect the Australian Statistical Geography Standard (ASGS) 2021 boundaries from June 2023.

From December 2021, the following are included in the report:

• selected payments by work capacity, by various demographic breakdowns

• rental type and homeownership

• Family Tax Benefit recipients and children by payment type

• Commonwealth Rent Assistance by proportion eligible for the maximum rate

• an age breakdown for Age Pension recipients

For further information, please see the Glossary.

From June 2021, data on the Paid Parental Leave Scheme is included yearly in June releases. This includes both Parental Leave Pay and Dad and Partner Pay, across multiple breakdowns. Please see Glossary for further information.

From March 2017 the DSS demographic dataset will include top 25 countries of birth. For further information see the glossary.

From March 2016 machine readable files containing the three geographic breakdowns have also been published for use in National Map, links to these datasets are below:

• Statistical Area 2 - SA2

• Commonwealth Electoral Division - CED

• Local Government Area - LGA

Pre June 2014 Quarter Data contains:

Selected DSS payment data by

• Geography: state/territory; electorate; postcode and LGA

• Demographic: age, sex and Indigenous/non-Indigenous

Note: JobSeeker Payment replaced Newstart Allowance and other working age payments from 20 March 2020, for further details see: https://www.dss.gov.au/benefits-payments/jobseeker-payment

For data on DSS payment demographics as at June 2013 or earlier, the department has published data which was produced annually. Data is provided by payment type containing timeseries’, state, gender, age range, and various other demographics. Links to these publications are below:

• Statistical Paper series

Concession card data in the March and June 2020 quarters have been re-stated to address an over-count in reported cardholder numbers.

28/06/2024 – The March 2024 and December 2023 reports were republished with updated data in the ‘Carer Receivers by Med Condition’ section, updates are exclusive to the ‘Care Receivers of Carer Payment recipients’ table, under ‘Intellectual / Learning’ and ‘Circulatory System’ conditions only.

This table is part of a series of tables that present a portrait of Canada based on the various census topics. The tables range in complexity and levels of geography. Content varies from a simple overview of the country to complex cross-tabulations; the tables may also cover several censuses.

U.S. citizens with a professional degree had the highest median household income in 2023, at 172,100 U.S. dollars. In comparison, those with less than a 9th grade education made significantly less money, at 35,690 U.S. dollars. Household income The median household income in the United States has fluctuated since 1990, but rose to around 70,000 U.S. dollars in 2021. Maryland had the highest median household income in the United States in 2021. Maryland’s high levels of wealth is due to several reasons, and includes the state's proximity to the nation's capital. Household income and ethnicity The median income of white non-Hispanic households in the United States had been on the rise since 1990, but declining since 2019. While income has also been on the rise, the median income of Hispanic households was much lower than those of white, non-Hispanic private households. However, the median income of Black households is even lower than Hispanic households. Income inequality is a problem without an easy solution in the United States, especially since ethnicity is a contributing factor. Systemic racism contributes to the non-White population suffering from income inequality, which causes the opportunity for growth to stagnate.

Earnings and employment statistics from Pay As You Earn (PAYE) Real Time Information (RTI), UK, NUTS 1, 2 and 3 areas and local authorities, monthly, seasonally adjusted. These are official statistics in development.

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 Stillwater. It showcases annual income, providing insights into gender-specific income distributions and the disparities between full-time and part-time work. The dataset can be utilized to gain insights into gender-based pay disparity trends and explore the variations in income for male and female individuals.

Key observations: Insights from 2021

Based on our analysis ACS 2017-2021 5-Year Estimates, we present the following observations: - All workers, aged 15 years and older: In Stillwater, the median income for all workers aged 15 years and older, regardless of work hours, was $56,601 for males and $40,465 for females.

These income figures indicate a substantial gender-based pay disparity, showcasing a gap of approximately 29% between the median incomes of males and females in Stillwater. With women, regardless of work hours, earning 71 cents to each dollar earned by men, this income disparity reveals a concerning trend toward wage inequality that demands attention in thecity of Stillwater.

- Full-time workers, aged 15 years and older: In Stillwater, among full-time, year-round workers aged 15 years and older, males earned a median income of $80,000, while females earned $74,709, resulting in a 7% gender pay gap among full-time workers. This illustrates that women earn 93 cents for each dollar earned by men in full-time positions. While this gap shows a trend where women are inching closer to wage parity with men, it also exhibits a noticeable income difference for women working full-time in the city of Stillwater.

Interestingly, when analyzing income across all roles, including non-full-time employment, the gender pay gap percentage was higher for women compared to men. It appears that full-time employment presents a more favorable income scenario for women compared to other employment patterns in Stillwater.

https://i.neilsberg.com/ch/stillwater-mn-income-by-gender.jpeg" alt="Stillwater, MN gender based income disparity">

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2017-2021 5-Year Estimates. All incomes have been adjusting for inflation and are presented in 2022-inflation-adjusted dollars.

Gender classifications include:

• 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 2022
• 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 Stillwater median household income by gender. You can refer the same here

The dataset has been created by using the open-source code released by LNDS (Luxembourg National Data Service). It is meant to be an example of the dataset structure anyone can generate and personalize in terms of some fixed parameter, including the sample size. The file format is .csv, and the data are organized by individual profiles on the rows and their personal features on the columns. The information in the dataset has been generated based on the statistical information about the age-structure distribution, the number of populations over municipalities, the number of different nationalities present in Luxembourg, and salary statistics per municipality. The STATEC platform, the statistics portal of Luxembourg, is the public source we used to gather the real information that we ingested into our synthetic generation model. Other features like Date of birth, Social matricule, First name, Surname, Ethnicity, and physical attributes have been obtained by a logical relationship between variables without exploiting any additional real information. We are in compliance with the law in putting close to zero the risk of identifying a real person completely by chance.

These figures show the median gross annual pay for full-time workers on a workplace basis for the area, who are on adults rates of pay, and whose pay was not affected by absence. Figures are for GB pounds per annum. Full-time workers are defined as those who work more than 30 paid hours per week or those in teaching professions working 25 paid hours or more per week. Figures for earnings come from the Annual Survey of Hours and Earnings (ASHE) which is based on a 1 per cent sample of employees, information on whose earnings and hours is obtained from employers. The survey does not cover people who are self-employed, nor does it cover employees not paid during the reference period. Information relates to a pay period in April. The earnings information collected relates to gross pay before tax, national insurance or other deductions, and excludes payments in kind (i.e. payment made in the form of goods and services rather than cash). It is restricted to earnings relating to the survey pay period and so excludes payments of arrears from another period made during the survey period; any payments due as a result of a pay settlement but not yet paid at the time of the survey will also be excluded. Estimates for 2011 and subsequent years use a weighting scheme based on occupations which have been coded according to Standard Occupational Classification (SOC) 2010 that replaced SOC 2000. Therefore care should be taken when making comparisons with earlier years. Where the estimate is assessed with a coefficient of variation (CV) of over 20 per cent, these figures have been suppressed, as they are considered by the ONS as unreliable.Data is Powered by LG Inform Plus and automatically checked for new data on the 3rd of each month.