According to a 2023 survey in South Korea, around 48 percent of unemployed elderly individuals reported earning income from work or business. Pensions and severance pay were also common sources of income, cited by approximately 36 percent.

If you’re a senior with low income, you may qualify for monthly Guaranteed Annual Income System payments. #Maximum payment and allowable private income amounts for the period from July 1, 2025 to June 30, 2026 are: * $90 monthly for single seniors (maximum monthly payment amount), your annual private income must be less than $4,320 * $180 monthly for senior couples (maximum monthly payment amount), your annual private income must be less than $8,640 The data is organized by private income levels. GAINS payments are provided on top of the Old Age Security (OAS) pension and the Guaranteed Income Supplement (GIS) payments you may receive from the federal government. Learn more about the Ontario Guaranteed Annual Income System This data is related to The Retirement Income System in Canada

This statistic shows the total median income of senior citizens in Canada from 2000 to 2020, distinguished by age group. In 2020, the total median income of Canadian senior citizens aged 65 years and over amounted to 32,020 Canadian dollars.

About this dataset

Context

The dataset presents the detailed breakdown of the count of individuals within distinct income brackets, categorizing them by gender (men and women) and employment type - full-time (FT) and part-time (PT), offering valuable insights into the diverse income landscapes within Elder township. The dataset can be utilized to gain insights into gender-based income distribution within the Elder township population, aiding in data analysis and decision-making..

Key observations

• Employment patterns: Within Elder township, among individuals aged 15 years and older with income, there were 410 men and 397 women in the workforce. Among them, 146 men were engaged in full-time, year-round employment, while 124 women were in full-time, year-round roles.
• Annual income under $24,999: Of the male population working full-time, 3.42% fell within the income range of under $24,999, while 25.81% of the female population working full-time was represented in the same income bracket.
• Annual income above $100,000: 7.53% of men in full-time roles earned incomes exceeding $100,000, while 3.23% of women in full-time positions earned within this income bracket.
• Refer to the research insights for more key observations on more income brackets ( Annual income under $24,999, Annual income between $25,000 and $49,999, Annual income between $50,000 and $74,999, Annual income between $75,000 and $99,999 and Annual income above $100,000) and employment types (full-time year-round and part-time)

Content

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

Income brackets:

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

Variables / Data Columns

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

Employment type classifications include:

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

Good to know

Margin of Error

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

Custom data

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

Inspiration

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

Recommended for further research

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

Family characteristics of seniors by total income statistics for Canada, provinces and territories, census metropolitan areas and census agglomerations. Includes age of seniors, housing indicators, tenure including presence of mortgage payments and subsidized housing, and structural type of dwelling.

Income-driven repayment plans lower required payments for student loan borrowers when their income decreases. This helps to reduce student loan defaults. Despite universal availability, only a minority of student loan borrowers in the U.S. are in an income-driven repayment plan. In this study, I test whether a student’s choice of repayment plan is related to their expectations of earning a low income. Using an information experiment in a web survey, I create two groups of college seniors with an exogenous difference in low-income expectations. I find that respondents who see the major specific income information believe they, on average, have a higher probability of earning a low income. However, those respondents are not any more likely to choose the income-driven repayment plan. I conclude that students’ repayment plan preferences are not strongly related to their expectations of earning a low income. This may be due to students caring about things other than minimizing monthly payments when choosing a repayment plan.

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 Elder 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 Elder township, the median income for all workers aged 15 years and older, regardless of work hours, was $36,071 for males and $26,938 for females.

These income figures indicate a substantial gender-based pay disparity, showcasing a gap of approximately 25% between the median incomes of males and females in Elder township. With women, regardless of work hours, earning 75 cents to each dollar earned by men, this income disparity reveals a concerning trend toward wage inequality that demands attention in thetownship of Elder township.

- Full-time workers, aged 15 years and older: In Elder township, among full-time, year-round workers aged 15 years and older, males earned a median income of $58,125, while females earned $38,333, leading to a 34% gender pay gap among full-time workers. This illustrates that women earn 66 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 lower gender pay gap percentage. This indicates that Elder township offers better opportunities for women in non-full-time positions.

Content

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

Gender classifications include:

• Male
• Female

Employment type classifications include:

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

Variables / Data Columns

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

Good to know

Margin of Error

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

Custom data

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

Inspiration

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

Recommended for further research

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

About this dataset

Context

The dataset presents the detailed breakdown of the count of individuals within distinct income brackets, categorizing them by gender (men and women) and employment type - full-time (FT) and part-time (PT), offering valuable insights into the diverse income landscapes within Box Elder County. The dataset can be utilized to gain insights into gender-based income distribution within the Box Elder County population, aiding in data analysis and decision-making..

Key observations

• Employment patterns: Within Box Elder County, among individuals aged 15 years and older with income, there were 20,781 men and 17,548 women in the workforce. Among them, 12,742 men were engaged in full-time, year-round employment, while 5,713 women were in full-time, year-round roles.
• Annual income under $24,999: Of the male population working full-time, 6.40% fell within the income range of under $24,999, while 15.09% of the female population working full-time was represented in the same income bracket.
• Annual income above $100,000: 21.30% of men in full-time roles earned incomes exceeding $100,000, while 7.96% of women in full-time positions earned within this income bracket.
• Refer to the research insights for more key observations on more income brackets ( Annual income under $24,999, Annual income between $25,000 and $49,999, Annual income between $50,000 and $74,999, Annual income between $75,000 and $99,999 and Annual income above $100,000) and employment types (full-time year-round and part-time)

Content

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

Income brackets:

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

Variables / Data Columns

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

Employment type classifications include:

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

Good to know

Margin of Error

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

Custom data

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

Inspiration

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

Recommended for further research

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

In 2019, the annual salary of senior financial analysts in South Korea ranged from 70 million to 80 million South Korean won. This was the same as in the previous year, and the annual salary range is expected to stay the same in 2020.

In 2019, ** percent of university seniors said they were somewhat likely to take a side job in the gig economy in order to supplement their main income. A job in the gig economy would involve temporary or freelance work.

Income of individuals by age group, sex and income source, Canada, provinces and selected census metropolitan areas, annual.

Symbols in bright yellow represent areas where more seniors with burdensome housing costs are renters, whereas symbols that are blue represent areas with more owners. Map has national coverage but opens in Milwaukee. Use the map's bookmarks or the search bar to view other cities. Bookmarks include what are generally thought of as "affordable" cities - Fresno, Salt Lake City, New Orleans, Albuquerque, El Paso, Tusla, Raleigh, Milwaukee - but yet there are many seniors whose housing costs are 30 percent or more of their income. "The burden of housing costs combined with climbing health care expenses can significantly reduce financial security at older ages" according to the Urban Institute. The number of senior households is projected to grow in the coming years, making the issue of economic security for seniors even more pressing.Housing costs are defined as burdensome if they exceed 30 percent of monthly income, a widely-used definition by HUD and others in affordable housing discussions. For owners, monthly housing costs include payments for mortgages and all other debts on the property; real estate taxes; fire, hazard, and flood insurance; utilities; fuels; and condominium or mobile home fees.For renters, monthly housing costs include contract rent plus the estimated average monthly cost of utilities (electricity, gas, and water and sewer) and fuels (oil, coal, kerosene, wood, etc.) if these are paid by the renter.Income is defined as the sum of wage/salary income; net self-employment income; interest/dividends/net rental/royalty income/income from estates & trusts; Social Security/Railroad Retirement income; Supplemental Security Income (SSI); public assistance/welfare payments; retirement/survivor/disability pensions; & all other income.Only households with a householder who is 65 and over are included in these maps. The householder is a person in whose name the home is owned, being bought, or rented, and how answers the questionnaire as person 1.This map is multi-scale, with data for states, counties, and tracts. This map uses these hosted feature layers containing the most recent American Community Survey data. These layers are part of the ArcGIS Living Atlas, and are updated every year when the American Community Survey releases new estimates, so values in the map always reflect the newest data available.

These data from the 1990 Census comprise a sample of households with at least one person 60 years and older, plus a sample of persons 60 years and older in group quarters. The data are grouped into housing variables and person variables. Housing variables include area type, state and area of residence, farm/nonfarm status, type of structure, year structure was built, vacancy and boarded-up status, number of rooms and bedrooms, presence or absence of a telephone, presence or absence of complete kitchen and plumbing facilities, type of sewage facilities, type of water source, type of heating fuel used, property value, tenure, year moved into house/apartment, type of household/family, type of group quarters, household language, number of persons in the household, number of persons and workers in the family, status of mortgage, second mortgage, and home equity loan, number of vehicles available, household income, sales of agricultural products, payments for rent, mortgage and property tax, condominium fees, mobile home costs, and cost of electricity, water, heating fuel, and flood/fire/hazard insurance. Person variables cover age, sex, relationship to householder, educational attainment, school enrollment, race, Hispanic origin, ancestry, language spoken at home, citizenship, place of birth, year of immigration, place of residence in 1985, marital status, number of children ever born, military service, mobility and personal care limitation, work limitation status, employment status, occupation, industry, class of worker, hours worked last week, weeks worked in 1989, usual hours worked per week, temporary absence from work, place of work, time of departure for work, travel time to work, means of transportation to work, total earnings, total income, wages and salary income, farm and nonfarm self-employment income, Social Security income, public assistance income, retirement income, and rent, dividends, and net rental income.

About this dataset

Context

The dataset presents the detailed breakdown of the count of individuals within distinct income brackets, categorizing them by gender (men and women) and employment type - full-time (FT) and part-time (PT), offering valuable insights into the diverse income landscapes within Box Elder. The dataset can be utilized to gain insights into gender-based income distribution within the Box Elder population, aiding in data analysis and decision-making..

Key observations

• Employment patterns: Within Box Elder, among individuals aged 15 years and older with income, there were 4,710 men and 3,613 women in the workforce. Among them, 3,505 men were engaged in full-time, year-round employment, while 1,752 women were in full-time, year-round roles.
• Annual income under $24,999: Of the male population working full-time, 12.47% fell within the income range of under $24,999, while 17.98% of the female population working full-time was represented in the same income bracket.
• Annual income above $100,000: 14.18% of men in full-time roles earned incomes exceeding $100,000, while 4.17% of women in full-time positions earned within this income bracket.
• Refer to the research insights for more key observations on more income brackets ( Annual income under $24,999, Annual income between $25,000 and $49,999, Annual income between $50,000 and $74,999, Annual income between $75,000 and $99,999 and Annual income above $100,000) and employment types (full-time year-round and part-time)

Content

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

Income brackets:

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

Variables / Data Columns

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

Employment type classifications include:

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

Good to know

Margin of Error

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

Custom data

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

Inspiration

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

Recommended for further research

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

United States Avg Hourly Earnings: sa: EH: Homes for Elderly data was reported at 16.190 USD in May 2018. This records an increase from the previous number of 16.150 USD for Apr 2018. United States Avg Hourly Earnings: sa: EH: Homes for Elderly data is updated monthly, averaging 13.740 USD from Mar 2006 (Median) to May 2018, with 147 observations. The data reached an all-time high of 16.190 USD in May 2018 and a record low of 12.520 USD in Jul 2006. United States Avg Hourly Earnings: sa: EH: Homes for Elderly data remains active status in CEIC and is reported by Bureau of Labor Statistics. The data is categorized under Global Database’s USA – Table US.G033: Current Employment Statistics Survey: Average Weekly and Hourly Earnings: Seasonally Adjusted.

Elders reported AUD33.49M in Net Income for its fiscal semester ending in September of 2024. Data for Elders | ELD - Net Income including historical, tables and charts were last updated by Trading Economics this last August in 2025.

This is a special extract of the 2000 Census 5-Percent Public Use Microdata Samples (PUMS) created by the National Archive of Computerized Data on Aging (NACDA). The file combines the individual 5-percent state files for all 50 states, the District of Columbia, and Puerto Rico as released by the United States Census Bureau into a single analysis file. The file contains information on all households that contain at least one person aged 65 years or more in residence as of the 2000 Census enumeration. The file contains individual records on all persons aged 65 and older living in households as well as individual records for all other members residing in each of these households. Consequently, this file can be used to examine both the characteristics of the elderly in the United States as well as the characteristics of individuals who co-reside with persons aged 65 and older as of the year 2000. All household variables from the household-specific "Household record" of the 2000 PUMS are appended to the end of each individual level record. This file is not a special product of the Census Bureau and is not a resample of the PUMS data specific to the elderly population. While it is comparable to the 1990 release CENSUS OF POPULATION AND HOUSING, 1990: UNITED STATES: PUBLIC USE MICRODATA SAMPLE: 3-PERCENT ELDERLY SAMPLE (ICPSR 6219), the sampling procedures and weights for the 2000 file reflect the methodology that applies to the 5-percent PUMS release CENSUS OF POPULATION AND HOUSING, 2000 UNITED STATES: PUBLIC USE MICRODATA SAMPLE: 5-PERCENT SAMPLE (ICPSR 13568). Person variables cover age, sex, relationship to householder, educational attainment, school enrollment, race, Hispanic origin, ancestry, language spoken at home, citizenship, place of birth, year of immigration, place of residence in 1985, marital status, number of children ever born, military service, mobility and personal care limitation, work limitation status, employment status, occupation, industry, class of worker, hours worked last week, weeks worked in 1989, usual hours worked per week, temporary absence from work, place of work, time of departure for work, travel time to work, means of transportation to work, total earnings, total income, wages and salary income, farm and nonfarm self-employment income, Social Security income, public assistance income, retirement income, and rent, dividends, and net rental income. Housing variables include area type, state and area of residence, farm/nonfarm status, type of structure, year structure was built, vacancy and boarded-up status, number of rooms and bedrooms, presence or absence of a telephone, presence or absence of complete kitchen and plumbing facilities, type of sewage facilities, type of water source, type of heating fuel used, property value, tenure, year moved into house/apartment, type of household/family, type of group quarters, household language, number of persons in the household, number of persons and workers in the family, status of mortgage, second mortgage, and home equity loan, number of vehicles available, household income, sales of agricultural products, payments for rent, mortgage and property tax, condominium fees, mobile home costs, and cost of electricity, water, heating fuel, and flood/fire/hazard insurance. (Source: downloaded from ICPSR 7/13/10)

Please Note: This dataset is part of the historical CISER Data Archive Collection and is also available at ICPSR at https://doi.org/10.3886/ICPSR04204.v2. We highly recommend using the ICPSR version as they may make this dataset available in multiple data formats in the future.

Graph and download economic data for All Employees: Education and Health Services: Continuing Care Retirement Communities and Assisted Living Facilities for the Elderly in Florida (SMU12000006562330001) from Jan 1990 to Jun 2025 about elderly, nursing homes, nursing, retirement, assistance, health, education, FL, services, employment, and USA.

This material is based upon work supported by the National Science Foundation under Grant No. 2049358. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author and do not necessarily reflect the views of the National Science Foundation.Income-driven repayment plans lower required payments for student loan borrowers when their income decreases. This helps to reduce student loan defaults. Despite universal availability, only a minority of student loan borrowers in the U.S. are in an income-driven repayment plan. In this study, I test whether a student’s choice of repayment plan is related to their expectations of earning a low income. Using an information experiment in a web survey, I create two groups of college seniors with an exogenous difference in low-income expectations. I find that respondents who see the major specific income information believe they, on average, have a higher probability of earning a low income. However, those respondents are not any more likely to choose the income-driven repayment plan. I conclude that students’ repayment plan preferences are not strongly related to their expectations of earning a low income. This may be due to students caring about things other than minimizing monthly payments when choosing a repayment plan.

Graph and download economic data for All Employees: Education and Health Services: Continuing Care Retirement Communities and Assisted Living Facilities for the Elderly in California (SMU06000006562330001SA) from Jan 1990 to Jun 2025 about elderly, nursing homes, nursing, retirement, assistance, health, education, CA, services, employment, and USA.