This statistic shows the median household income in the United States from 1990 to 2023 in 2023 U.S. dollars. The median household income was 80,610 U.S. dollars in 2023, an increase from the previous year. Household incomeThe median household income depicts the income of households, including the income of the householder and all other individuals aged 15 years or over living in the household. Income includes wages and salaries, unemployment insurance, disability payments, child support payments received, regular rental receipts, as well as any personal business, investment, or other kinds of income received routinely. The median household income in the United States varies from state to state. In 2020, the median household income was 86,725 U.S. dollars in Massachusetts, while the median household income in Mississippi was approximately 44,966 U.S. dollars at that time. Household income is also used to determine the poverty line in the United States. In 2021, about 11.6 percent of the U.S. population was living in poverty. The child poverty rate, which represents people under the age of 18 living in poverty, has been growing steadily over the first decade since the turn of the century, from 16.2 percent of the children living below the poverty line in year 2000 to 22 percent in 2010. In 2021, it had lowered to 15.3 percent. The state with the widest gap between the rich and the poor was New York, with a Gini coefficient score of 0.51 in 2019. The Gini coefficient is calculated by looking at average income rates. A score of zero would reflect perfect income equality and a score of one indicates a society where one person would have all the money and all other people have nothing.

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

Key observations

• Employment patterns: Within Pelham town, among individuals aged 15 years and older with income, there were 4,368 men and 4,386 women in the workforce. Among them, 2,774 men were engaged in full-time, year-round employment, while 2,110 women were in full-time, year-round roles.
• Annual income under $24,999: Of the male population working full-time, 0.68% fell within the income range of under $24,999, while 1% of the female population working full-time was represented in the same income bracket.
• Annual income above $100,000: 73.72% of men in full-time roles earned incomes exceeding $100,000, while 62.94% 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 Pelham town median household income by race. You can refer the same here

About this dataset

Context

The dataset presents the the household distribution across 16 income brackets among four distinct age groups in Milford town: Under 25 years, 25-44 years, 45-64 years, and over 65 years. The dataset highlights the variation in household income, offering valuable insights into economic trends and disparities within different age categories, aiding in data analysis and decision-making..

Key observations

• Upon closer examination of the distribution of households among age brackets, it reveals that there are 11(1%) households where the householder is under 25 years old, 282(25.75%) households with a householder aged between 25 and 44 years, 345(31.51%) households with a householder aged between 45 and 64 years, and 457(41.74%) households where the householder is over 65 years old.
• The age group of 25 to 44 years exhibits the highest median household income, while the largest number of households falls within the 65 years and over bracket. This distribution hints at economic disparities within the town of Milford town, showcasing varying income levels among different age demographics.

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:

• Less than $10,000
• $10,000 to $14,999
• $15,000 to $19,999
• $20,000 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 $59,999
• $60,000 to $74,999
• $75,000 to $99,999
• $100,000 to $124,999
• $125,000 to $149,999
• $150,000 to $199,999
• $200,000 or more

Variables / Data Columns

• Household Income: This column showcases 16 income brackets ranging from Under $10,000 to $200,000+ ( As mentioned above).
• Under 25 years: The count of households led by a head of household under 25 years old with income within a specified income bracket.
• 25 to 44 years: The count of households led by a head of household 25 to 44 years old with income within a specified income bracket.
• 45 to 64 years: The count of households led by a head of household 45 to 64 years old with income within a specified income bracket.
• 65 years and over: The count of households led by a head of household 65 years and over old with income within a specified income bracket.

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 Milford town median household income by age. You can refer the same here

About 50.4 percent of the household income of private households in the U.S. were earned by the highest quintile in 2023, which are the upper 20 percent of the workers. In contrast to that, in the same year, only 3.5 percent of the household income was earned by the lowest quintile. This relation between the quintiles is indicative of the level of income inequality in the United States. Income inequalityIncome inequality is a big topic for public discussion in the United States. About 65 percent of U.S. Americans think that the gap between the rich and the poor has gotten larger in the past ten years. This impression is backed up by U.S. census data showing that the Gini-coefficient for income distribution in the United States has been increasing constantly over the past decades for individuals and households. The Gini coefficient for individual earnings of full-time, year round workers has increased between 1990 and 2020 from 0.36 to 0.42, for example. This indicates an increase in concentration of income. In general, the Gini coefficient is calculated by looking at average income rates. A score of zero would reflect perfect income equality and a score of one indicates a society where one person would have all the money and all other people have nothing. Income distribution is also affected by region. The state of New York had the widest gap between rich and poor people in the United States, with a Gini coefficient of 0.51, as of 2019. In global comparison, South Africa led the ranking of the 20 countries with the biggest inequality in income distribution in 2018. South Africa had a score of 63 points, based on the Gini coefficient. On the other hand, the Gini coefficient stood at 16.6 in Azerbaijan, indicating that income is widely spread among the population and not concentrated on a few rich individuals or families. Slovenia led the ranking of the 20 countries with the greatest income distribution equality in 2018.

About this dataset

Context

The dataset presents the the household distribution across 16 income brackets among four distinct age groups in Callicoon town: Under 25 years, 25-44 years, 45-64 years, and over 65 years. The dataset highlights the variation in household income, offering valuable insights into economic trends and disparities within different age categories, aiding in data analysis and decision-making..

Key observations

• Upon closer examination of the distribution of households among age brackets, it reveals that there are 13(1%) households where the householder is under 25 years old, 373(28.83%) households with a householder aged between 25 and 44 years, 485(37.48%) households with a householder aged between 45 and 64 years, and 423(32.69%) households where the householder is over 65 years old.
• The age group of 25 to 44 years exhibits the highest median household income, while the largest number of households falls within the 45 to 64 years bracket. This distribution hints at economic disparities within the town of Callicoon town, showcasing varying income levels among different age demographics.

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:

• Less than $10,000
• $10,000 to $14,999
• $15,000 to $19,999
• $20,000 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 $59,999
• $60,000 to $74,999
• $75,000 to $99,999
• $100,000 to $124,999
• $125,000 to $149,999
• $150,000 to $199,999
• $200,000 or more

Variables / Data Columns

• Household Income: This column showcases 16 income brackets ranging from Under $10,000 to $200,000+ ( As mentioned above).
• Under 25 years: The count of households led by a head of household under 25 years old with income within a specified income bracket.
• 25 to 44 years: The count of households led by a head of household 25 to 44 years old with income within a specified income bracket.
• 45 to 64 years: The count of households led by a head of household 45 to 64 years old with income within a specified income bracket.
• 65 years and over: The count of households led by a head of household 65 years and over old with income within a specified income bracket.

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 Callicoon town median household income by age. You can refer the same here

IMPORTANT! PLEASE READ DISCLAIMER BEFORE USING DATA. To reduce the energy burden on income-qualified households within New York State, NYSERDA offers the EmPower New York (EmPower) program, a retrofit program that provides cost-effective electric reduction measures (i.e., primarily lighting and refrigerator replacements), and cost-effective home performance measures (i.e., insulation air sealing, heating system repair and replacments, and health and safety measures) to income qualified homeowners and renters. Home assessments and implementation services are provided by Building Performance Institute (BPI) Goldstar contractors to reduce energy use for low income households. This data set includes energy efficiency projects completed since January 2018 for households with income up to 60% area (county) median income. D I S C L A I M E R: Estimated Annual kWh Savings, Estimated Annual MMBtu Savings, and First Year Energy Savings $ Estimate represent contractor reported savings derived from energy modeling software calculations and not actual realized energy savings. The accuracy of the Estimated Annual kWh Savings and Estimated Annual MMBtu Savings for projects has been evaluated by an independent third party. The results of the impact analysis indicate that, on average, actual savings amount to 54 percent of the Estimated Annual kWh Savings and 70 percent of the Estimated Annual MMBtu Savings. The analysis did not evaluate every single project, but rather a sample of projects from 2007 and 2008, so the results are applicable to the population on average but not necessarily to any individual project which could have over or under achieved in comparison to the evaluated savings. The results from the impact analysis will be updated when more recent information is available. Some reasons individual households may realize savings different from those projected include, but are not limited to, changes in the number or needs of household members, changes in occupancy schedules, changes in energy usage behaviors, changes to appliances and electronics installed in the home, and beginning or ending a home business. For more information, please refer to the Evaluation Report published on NYSERDA’s website at: https://www.nyserda.ny.gov/-/media/Files/Publications/PPSER/Program-Evaluation/2012ContractorReports/2012-EmPower-New-York-Impact-Report.pdf. This dataset includes the following data points for projects completed after January 1, 2018: Reporting Period, Project ID, Project County, Project City, Project ZIP, Gas Utility, Electric Utility, Project Completion Date, Total Project Cost (USD), Pre-Retrofit Home Heating Fuel Type, Year Home Built, Size of Home, Number of Units, Job Type, Type of Dwelling, Measure Type, Estimated Annual kWh Savings, Estimated Annual MMBtu Savings, First Year Modeled Energy Savings $ Estimate (USD). How does your organization use this dataset? What other NYSERDA or energy-related datasets would you like to see on Open NY? Let us know by emailing OpenNY@nyserda.ny.gov.

About this dataset

Context

The dataset presents the the household distribution across 16 income brackets among four distinct age groups in Hume town: Under 25 years, 25-44 years, 45-64 years, and over 65 years. The dataset highlights the variation in household income, offering valuable insights into economic trends and disparities within different age categories, aiding in data analysis and decision-making..

Key observations

• Upon closer examination of the distribution of households among age brackets, it reveals that there are 7(1%) households where the householder is under 25 years old, 151(21.51%) households with a householder aged between 25 and 44 years, 288(41.03%) households with a householder aged between 45 and 64 years, and 256(36.47%) households where the householder is over 65 years old.
• The age group of 25 to 44 years exhibits the highest median household income, while the largest number of households falls within the 45 to 64 years bracket. This distribution hints at economic disparities within the town of Hume town, showcasing varying income levels among different age demographics.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2018-2022 5-Year Estimates.

Income brackets:

• Less than $10,000
• $10,000 to $14,999
• $15,000 to $19,999
• $20,000 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 $59,999
• $60,000 to $74,999
• $75,000 to $99,999
• $100,000 to $124,999
• $125,000 to $149,999
• $150,000 to $199,999
• $200,000 or more

Variables / Data Columns

• Household Income: This column showcases 16 income brackets ranging from Under $10,000 to $200,000+ ( As mentioned above).
• Under 25 years: The count of households led by a head of household under 25 years old with income within a specified income bracket.
• 25 to 44 years: The count of households led by a head of household 25 to 44 years old with income within a specified income bracket.
• 45 to 64 years: The count of households led by a head of household 45 to 64 years old with income within a specified income bracket.
• 65 years and over: The count of households led by a head of household 65 years and over old with income within a specified income bracket.

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 Hume town median household income by age. You can refer the same here

About this dataset

Context

The dataset presents the the household distribution across 16 income brackets among four distinct age groups in Annsville town: Under 25 years, 25-44 years, 45-64 years, and over 65 years. The dataset highlights the variation in household income, offering valuable insights into economic trends and disparities within different age categories, aiding in data analysis and decision-making..

Key observations

• Upon closer examination of the distribution of households among age brackets, it reveals that there are 10(1%) households where the householder is under 25 years old, 288(28.77%) households with a householder aged between 25 and 44 years, 339(33.87%) households with a householder aged between 45 and 64 years, and 364(36.36%) households where the householder is over 65 years old.
• The age group of 25 to 44 years exhibits the highest median household income, while the largest number of households falls within the 65 years and over bracket. This distribution hints at economic disparities within the town of Annsville town, showcasing varying income levels among different age demographics.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2018-2022 5-Year Estimates.

Income brackets:

• Less than $10,000
• $10,000 to $14,999
• $15,000 to $19,999
• $20,000 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 $59,999
• $60,000 to $74,999
• $75,000 to $99,999
• $100,000 to $124,999
• $125,000 to $149,999
• $150,000 to $199,999
• $200,000 or more

Variables / Data Columns

• Household Income: This column showcases 16 income brackets ranging from Under $10,000 to $200,000+ ( As mentioned above).
• Under 25 years: The count of households led by a head of household under 25 years old with income within a specified income bracket.
• 25 to 44 years: The count of households led by a head of household 25 to 44 years old with income within a specified income bracket.
• 45 to 64 years: The count of households led by a head of household 45 to 64 years old with income within a specified income bracket.
• 65 years and over: The count of households led by a head of household 65 years and over old with income within a specified income bracket.

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 Annsville town median household income by age. You can refer the same here

About this dataset

Context

The dataset presents the the household distribution across 16 income brackets among four distinct age groups in Montezuma town: Under 25 years, 25-44 years, 45-64 years, and over 65 years. The dataset highlights the variation in household income, offering valuable insights into economic trends and disparities within different age categories, aiding in data analysis and decision-making..

Key observations

• Upon closer examination of the distribution of households among age brackets, it reveals that there are 5(1%) households where the householder is under 25 years old, 142(28.51%) households with a householder aged between 25 and 44 years, 185(37.15%) households with a householder aged between 45 and 64 years, and 166(33.33%) households where the householder is over 65 years old.
• The age group of 25 to 44 years exhibits the highest median household income, while the largest number of households falls within the 45 to 64 years bracket. This distribution hints at economic disparities within the town of Montezuma town, showcasing varying income levels among different age demographics.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2018-2022 5-Year Estimates.

Income brackets:

• Less than $10,000
• $10,000 to $14,999
• $15,000 to $19,999
• $20,000 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 $59,999
• $60,000 to $74,999
• $75,000 to $99,999
• $100,000 to $124,999
• $125,000 to $149,999
• $150,000 to $199,999
• $200,000 or more

Variables / Data Columns

• Household Income: This column showcases 16 income brackets ranging from Under $10,000 to $200,000+ ( As mentioned above).
• Under 25 years: The count of households led by a head of household under 25 years old with income within a specified income bracket.
• 25 to 44 years: The count of households led by a head of household 25 to 44 years old with income within a specified income bracket.
• 45 to 64 years: The count of households led by a head of household 45 to 64 years old with income within a specified income bracket.
• 65 years and over: The count of households led by a head of household 65 years and over old with income within a specified income bracket.

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 Montezuma town median household income by age. You can refer the same here

In 2021, the per capita income in San Francisco city was at 80,383 U.S. dollars. San Francisco was followed in this regard by Seattle and Washington, D.C. The most populated cities in the U.S. are ranked by per capita income in this statistic. While New York, New York had the highest population, San Francisco had the highest per capita income in 2021. The median household income in San Francisco in 2020 was 119,136 dollars, the highest among the most populated cities in the United States.

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 Gilbertsville. 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 Gilbertsville, the median income for all workers aged 15 years and older, regardless of work hours, was $36,964 for males and $36,458 for females.

Based on these incomes, we observe a gender gap percentage of approximately 1%, indicating a significant disparity between the median incomes of males and females in Gilbertsville. Women, regardless of work hours, still earn 99 cents to each dollar earned by men, highlighting an ongoing gender-based wage gap.

As there was no available median income data for males, conducting a comprehensive assessment of gender-based pay disparity in Gilbertsville was not feasible.

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

In 2023, roughly 1.49 billion adults worldwide had a net worth of less than 10,000 U.S. dollars. By comparison, 58 million adults had a net worth of more than one million U.S. dollars in the same year. Wealth distribution The distribution of wealth is an indicator of economic inequality. The United Nations says that wealth includes the sum of natural, human, and physical assets. Wealth is not synonymous with income, however, because having a large income can be depleted if one has significant expenses. In 2023, nearly 1,700 billionaires had a total wealth between one to two billion U.S. dollars. Wealth worldwide China had the highest number of billionaires in 2023, with the United States following behind. That same year, New York had the most billionaires worldwide.

IMPORTANT! PLEASE READ DISCLAIMER BEFORE USING DATA. The Residential Existing Homes Program is a market transformation program that uses Building Performance Institute (BPI) Goldstar contractors to install comprehensive energy-efficient improvements. The program is designed to use building science and a whole-house approach to reduce energy use in the State’s existing one-to-four family and low-rise multifamily residential buildings and capture heating fuel and electricity-related savings. The Program provides income-based incentives, including an assisted subsidy for households with income up to 80% of the State or Median County Income, whichever is higher to install eligible energy efficiency improvements including building shell measures, high efficiency heating and cooling measures, ENERGY STAR appliances and lighting. D I S C L A I M E R: Estimated Annual kWh Savings, Estimated Annual MMBtu Savings, and First Year Energy Savings $ Estimate represent contractor reported savings derived from energy modeling software calculations and not actual realized energy savings. The accuracy of the Estimated Annual kWh Savings and Estimated Annual MMBtu Savings for projects has been evaluated by an independent third party. The results of the impact analysis indicate that, on average, actual savings amount to 35 percent of the Estimated Annual kWh Savings and 65 percent of the Estimated Annual MMBtu Savings. The analysis did not evaluate every single project, but rather a sample of projects from 2007 and 2008, so the results are applicable to the population on average but not necessarily to any individual project which could have over or under achieved in comparison to the evaluated savings. The results from the impact analysis will be updated when more recent information is available. Many factors influence the degree to which estimated savings are realized, including proper calibration of the savings model and the savings algorithms used in the modeling software. Some reasons individual households may realize savings different from those projected include, but are not limited to, changes in the number or needs of household members, changes in occupancy schedules, changes in energy usage behaviors, changes to appliances and electronics installed in the home, and beginning or ending a home business. Beginning November 2017, the Program requires the use of HPXML-compliant modeling software tools and data quality protocols have been implemented to more accurately project savings. For more information, please refer to the Evaluation Report published on NYSERDA’s website at: http://www.nyserda.ny.gov/-/media/Files/Publications/PPSER/Program-Evaluation/2012ContractorReports/2012-HPwES-Impact-Report-with-Appendices.pdf. The New York Residential Existing Homes (One to Four Units) dataset includes the following data points for projects completed during Green Jobs Green-NY, beginning November 15, 2010: Home Performance Project ID, Home Performance Site ID, Project County, Project City, Project Zip, Gas Utility, Electric Utility, Project Completion Date, Customer Type, Low-Rise or Home Performance Indicator, Total Project Cost (USD), Total Incentives (USD), Type of Program Financing, Amount Financed Through Program (USD), Pre-Retrofit Home Heating Fuel Type, Year Home Built, Size of Home, Volume of Home, Number of Units, Measure Type, Estimated Annual kWh Savings, Estimated Annual MMBtu Savings, First Year Energy Savings $ Estimate (USD), Homeowner Received Green Jobs-Green NY Free/Reduced Cost Audit (Y/N). How does your organization use this dataset? What other NYSERDA or energy-related datasets would you like to see on Open NY? Let us know by emailing OpenNY@nyserda.ny.gov.

Analysis of ‘Residential Existing Homes (One-to-Four Units) Energy Efficiency Projects for Households with Income up to 60% State Median Income: Beginning January 2018’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://catalog.data.gov/dataset/361fed56-140f-4b31-8092-22e8d4c807a5 on 12 February 2022.

--- Dataset description provided by original source is as follows ---

IMPORTANT! PLEASE READ DISCLAIMER BEFORE USING DATA. To reduce the energy burden on income-qualified households within New York State, NYSERDA offers the EmPower New York (EmPower) program, a retrofit program that provides cost-effective electric reduction measures (i.e., primarily lighting and refrigerator replacements), and cost-effective home performance measures (i.e., insulation air sealing, heating system repair and replacments, and health and safety measures) to income qualified homeowners and renters. Home assessments and implementation services are provided by Building Performance Institute (BPI) Goldstar contractors to reduce energy use for low income households. This data set includes energy efficiency projects completed since January 2018 for households with income up to 60% area (county) median income.

D I S C L A I M E R: Estimated Annual kWh Savings, Estimated Annual MMBtu Savings, and First Year Energy Savings $ Estimate represent contractor reported savings derived from energy modeling software calculations and not actual realized energy savings. The accuracy of the Estimated Annual kWh Savings and Estimated Annual MMBtu Savings for projects has been evaluated by an independent third party. The results of the impact analysis indicate that, on average, actual savings amount to 54 percent of the Estimated Annual kWh Savings and 70 percent of the Estimated Annual MMBtu Savings. The analysis did not evaluate every single project, but rather a sample of projects from 2007 and 2008, so the results are applicable to the population on average but not necessarily to any individual project which could have over or under achieved in comparison to the evaluated savings. The results from the impact analysis will be updated when more recent information is available. Some reasons individual households may realize savings different from those projected include, but are not limited to, changes in the number or needs of household members, changes in occupancy schedules, changes in energy usage behaviors, changes to appliances and electronics installed in the home, and beginning or ending a home business. For more information, please refer to the Evaluation Report published on NYSERDA’s website at: https://www.nyserda.ny.gov/-/media/Files/Publications/PPSER/Program-Evaluation/2012ContractorReports/2012-EmPower-New-York-Impact-Report.pdf.

This dataset includes the following data points for projects completed after January 1, 2018: Reporting Period, Project ID, Project County, Project City, Project ZIP, Gas Utility, Electric Utility, Project Completion Date, Total Project Cost (USD), Pre-Retrofit Home Heating Fuel Type, Year Home Built, Size of Home, Number of Units, Job Type, Type of Dwelling, Measure Type, Estimated Annual kWh Savings, Estimated Annual MMBtu Savings, First Year Modeled Energy Savings $ Estimate (USD).

How does your organization use this dataset? What other NYSERDA or energy-related datasets would you like to see on Open NY? Let us know by emailing OpenNY@nyserda.ny.gov.

--- Original source retains full ownership of the source dataset ---

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..Respondents could report more than one major for their bachelor's degree. This table only counts the first major that was reported and does not necessarily reflect the first degree earned..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.*** The margin of error could not be computed because the median falls in the lowest interval or highest interval of an open-ended distribution.***** A margin of error is not appropriate because the corresponding estimate is controlled to an independent population or housing estimate. Effectively, the corresponding estimate has no sampling error and the margin of error may be treated as zero.

In 2023, according to the Gini coefficient, household income distribution in the United States was 0.47. This figure was at 0.43 in 1990, which indicates an increase in income inequality in the U.S. over the past 30 years. What is the Gini coefficient? The Gini coefficient, or Gini index, is a statistical measure of economic inequality and wealth distribution among a population. A value of zero represents perfect economic equality, and a value of one represents perfect economic inequality. The Gini coefficient helps to visualize income inequality in a more digestible way. For example, according to the Gini coefficient, the District of Columbia and the state of New York have the greatest amount of income inequality in the U.S. with a score of 0.51, and Utah has the greatest income equality with a score of 0.43. The Gini coefficient around the world The Gini coefficient is also an effective measure to help picture income inequality around the world. For example, in 2018 income inequality was highest in South Africa, while income inequality was lowest in Slovenia.

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 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, 2022 American Community Survey 1-Year Estimates.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..The 2022 American Community Survey (ACS) data generally reflect the March 2020 Office of Management and Budget (OMB) delineations of metropolitan and micropolitan statistical areas. In certain instances the names, codes, and boundaries of the principal cities shown in ACS tables may differ from the OMB delineations due to differences in the effective dates of the geographic entities..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.*** The margin of error could not be computed because the median falls in the lowest interval or highest interval of an open-ended distribution.***** A margin of error is not appropriate because the corresponding estimate is controlled to an independent population or housing estimate. Effectively, the corresponding estimate has no sampling error and the margin of error may be treated as zero.

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, 2019-2023 American Community Survey 5-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..The Hispanic origin and race codes were updated in 2020. For more information on the Hispanic origin and race code changes, please visit the American Community Survey Technical Documentation website..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.*** The margin of error could not be computed because the median falls in the lowest interval or highest interval of an open-ended distribution.***** A margin of error is not appropriate because the corresponding estimate is controlled to an independent population or housing estimate. Effectively, the corresponding estimate has no sampling error and the margin of error may be treated as zero.

Abstract

The Consumer Expenditure Survey (CE) program provides a continuous and comprehensive flow of data on the buying habits of American consumers. These data are used widely in economic research and analysis, and in support of revisions of the Consumer Price Index. To meet the needs of users, the Bureau of Labor Statistics (BLS) produces population estimates (for consumer units or CUs) of average expenditures in news releases, reports, and articles in the Monthly Labor Review. Tabulated CE data are also available on the Internet and by facsimile transmission (see Section XVI. Appendix 5). These microdata files present detailed expenditure and income data for the Diary component of the CE for 2005. They include weekly expenditure (EXPD), annual income (DTBD) files, and imputed income files (DTID). The data in EXPD, DTBD, and DTID files are categorized by a Universal Classification Code (UCC). The advantage of the EXPD and DTBD files is that with the data classified in a standardized format, the user may perform comparative expenditure (income) analysis with relative ease. The FMLD and MEMD files present data on the characteristics and demographics of CUs and CU members. The summary level expenditure and income information on the FMLD files permits the data user to link consumer spending, by general expenditure category, and household characteristics and demographics on one set of files. Estimates of average expenditures in 2005 from the Diary survey, integrated with data from the Interview survey, are published in Consumer Expenditures in 2005. A list of recent publications containing data from the CE appears at the end of this documentation.

The microdata files are in the public domain and, with appropriate credit, may be reproduced without permission. A suggested citation is: “U.S. Department of Labor, Bureau of Labor Statistics, Consumer Expenditure Survey, Diary Survey, 2005”.

State Identifier Since the CE is not designed to produce state-level estimates, summing the consumer unit weights by state will not yield state population totals. A CU's basic weight reflects its probability of selection among a group of primary sampling units of similar characteristics. For example, sample units in an urban nonmetropolitan area in California may represent similar areas in Wyoming and Nevada. Among other adjustments, CUs are post-stratified nationally by sex-age-race. For example, the weights of consumer units containing a black male, age 16-24 in Alabama, Colorado, or New York, are all adjusted equivalently. Therefore, weighted population state totals will not match population totals calculated from other surveys that are designed to represent state data.

To summarize, the CE sample was not designed to produce precise estimates for individual states. Although state-level estimates that are unbiased in a repeated sampling sense can be calculated for various statistical measures, such as means and aggregates, their estimates will generally be subject to large variances. Additionally, a particular state-population estimate from the CE sample may be far from the true state-population estimate.

Interpreting the data Several factors should be considered when interpreting the expenditure data. The average expenditure for an item may be considerably lower than the expenditure by those CUs that purchased the item. The less frequently an item is purchased, the greater the difference between the average for all consumer units and the average of those purchasing. (See Section V.B. for ESTIMATION OF TOTAL AND MEAN EXPENDITURES). Also, an individual CU may spend more or less than the average, depending on its particular characteristics. Factors such as income, age of family Members, geographic location, taste and personal preference also influence expenditures. Furthermore, even within groups with similar characteristics, the distribution of expenditures varies substantially.

Expenditures reported are the direct out-of-pocket expenditures. Indirect expenditures, which may be significant, may be reflected elsewhere. For example, rental contracts often include utilities. Renters with such contracts would record no direct expense for utilities, and therefore, appear to have no utility expenses. Employers or insurance companies frequently pay other costs.CUs with Members whose employers pay for all or part of their health insurance or life insurance would have lower direct expenses for these items than those who pay the entire amount themselves. These points should be considered when relating reported averages to individual circumstances.

The Diary survey PUMD are organized into five major data files for each quarter: 1. FMLD - a file with characteristics, income, and summary level expenditures for the household 2. MEMD - a file with characteristics and income for each member in the household
3. EXPD - a detailed weekly expenditure file categorized by UCC 4. DTBD - a detailed annual income file categorized by UCC
5. DTID - a household imputed income file categorized by UCC

Analysis unit

Consumer Unit

Kind of data

Sample survey data [ssd]

Sampling procedure

A. SURVEY SAMPLE DESIGN

Samples for the CE are national probability samples of households designed to be representative of the total U. S. civilian population. Eligible population includes all civilian noninstitutional persons.

The first step in sampling is the selection of primary sampling units (PSUs), which consist of counties (or parts thereof) or groups of counties. The set of sample PSUs used for the 2005 sample is composed of 102 areas. The design classifies the PSUs into four categories:

• 28 "A" certainty PSUs are Metropolitan Statistical Areas (MSA's) with a population greater than 1.5 million. • 42 "B" PSUs, are medium-sized MSAs. • 16 "C" PSUs are nonmetropolitan areas that are included in the CPI. • 16 "D" PSUs are nonmetropolitan areas where only the urban population data will be included in the CPI.

The sampling frame (that is, the list from which housing units were chosen) for the 2005 survey is generated from the 2000 Population Census file. The sampling frame is augmented by new construction permits and by techniques used to eliminate recognized deficiencies in census coverage. All Enumeration Districts (EDs) from the Census that fail to meet the criterion for good addresses for new construction, and all EDs in nonpermit-issuing areas are grouped into the area segment frame.

To the extent possible, an unclustered sample of units is selected within each PSU. This lack of clustering is desirable because the sample size of the Diary Survey is small relative to other surveys, while the intraclass correlations for expenditure characteristics are relatively large. This suggests that any clustering of the sample units could result in an unacceptable increase in the within-PSU variance and, as a result, the total variance.

Each selected sample unit is requested to keep two 1-week diaries of expenditures over consecutive weeks. The earliest possible day for placing a diary with a household is predesignated with each day of the week having an equal chance to be the first of the reference week. The diaries are evenly spaced throughout the year.

B. COOPERATION LEVELS

The annual target sample size at the United States level for the Diary Survey is 7,800 participating sample units. To achieve this target the total estimated work load is 11,275 sample units. This allows for refusals, vacancies, or nonexistent sample unit addresses.

Each participating sample unit selected is asked to keep two 1-week diaries. Each diary is treated independently, so response rates are based on twice the number of housing units sampled.

Mode of data collection

Computer Assisted Personal Interview [capi]

Response rate

The response rate for the 2005 Diary Survey is 68.9%. This response rate refers to all diaries in the year.

Major League Baseball (MLB) teams are owned by a variety of individuals and groups. The Toronto Blue Jays are owned by Rogers Communications, led by CEO Edward S. Rogers III, with a net worth of 11.5 billion U.S. dollars. Individual owners of MLB franchises include wealthy individuals such as Mark Attanasio (Milwaukee Brewers), John Henry (Boston Red Sox), and Arte Moreno (Los Angeles Angels), who also have other business interests outside of sports ownership. There are also multiple ownership groups that own MLB franchises, made up of a mix of businesspeople, investors, and wealthy individuals who come together specifically to purchase and operate the teams. In rare occurrences, franchises have also been owned by the league, such as the Montreal Expos and the Texas Rangers in the past. The ownership process often includes the approval of the other MLB owners and requires significant financial resources.

Steven A. Cohen Steven A. Cohen's purchase of the New York Mets for 2.4 billion U.S. dollars in 2020 constituted a significant event in the sport. The purchase made him the wealthiest franchise owner in the league, with a personal wealth of around 16 billion U.S. dollars. Cohen stated that he planned to invest in the team to help bring it success on the field, as well as in its business operations. One of his main goals with the purchase was to renovate Citi Field, the team's stadium, and to invest in the team's player development facilities. He also announced plans to enhance the fan experience through technology and fan engagement, aiming to bring the team closer to its fan base. Billionaire sports club ownership The growing trend of wealthy individuals buying sports teams can have both positive and negative impacts. On one hand, wealthy owners bring significant financial resources to teams, which can improve the team's performance and overall competitiveness. However, there are also concerns such as concentration of wealth and power among a small number of teams and owners, a less competitive league, and owners prioritizing financial returns over competitive balance. Additionally, the high price of ownership can make it difficult for new ownership groups to enter the market and lead to lack of diversity among ownership group of sports teams. This can also lead to high-priced tickets and merchandise, making it harder for low-income fans to support their team.