Graph and download economic data for Real Median Household Income in New York (MEHOINUSNYA672N) from 1984 to 2023 about NY, households, median, income, real, and USA.

IntroductionOur study explores how New York City (NYC) communities of various socioeconomic strata were uniquely impacted by the COVID-19 pandemic.MethodsNew York City ZIP codes were stratified into three bins by median income: high-income, middle-income, and low-income. Case, hospitalization, and death rates obtained from NYCHealth were compared for the period between March 2020 and April 2022.ResultsCOVID-19 transmission rates among high-income populations during off-peak waves were higher than transmission rates among low-income populations. Hospitalization rates among low-income populations were higher during off-peak waves despite a lower transmission rate. Death rates during both off-peak and peak waves were higher for low-income ZIP codes.DiscussionThis study presents evidence that while high-income areas had higher transmission rates during off-peak periods, low-income areas suffered greater adverse outcomes in terms of hospitalization and death rates. The importance of this study is that it focuses on the social inequalities that were amplified by the pandemic.

About this dataset

Context

The dataset presents the mean household income for each of the five quintiles in Sands Point, NY, as reported by the U.S. Census Bureau. The dataset highlights the variation in mean household income across quintiles, offering valuable insights into income distribution and inequality.

Key observations

• Income disparities: The mean income of the lowest quintile (20% of households with the lowest income) is 62,342, while the mean income for the highest quintile (20% of households with the highest income) is 1,206,232. This indicates that the top earners earn 19 times compared to the lowest earners.
• *Top 5%: * The mean household income for the wealthiest population (top 5%) is 1,779,703, which is 147.54% higher compared to the highest quintile, and 2854.74% higher compared to the lowest quintile.

Content

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

Income Levels:

• Lowest Quintile
• Second Quintile
• Third Quintile
• Fourth Quintile
• Highest Quintile
• Top 5 Percent

Variables / Data Columns

• Income Level: This column showcases the income levels (As mentioned above).
• Mean Household Income: Mean household income, in 2023 inflation-adjusted dollars for the specific income level.

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 Sands Point median household income. You can refer the same here

This poll, fielded April 1-5, 2009, is a part of a continuing series of monthly surveys that solicit public opinion on the presidency and on a range of other political and social issues. Respondents were asked whether they approved of the way Barack Obama was handling the presidency and issues such as the economy and foreign policy. A series of questions addressed the Obama Administration's approach to solving economic problems and whether the administration's policies favored the rich, the middle class, or the poor. Respondents gave their opinions of First Lady Michelle Obama, the United States Congress, the Republican and Democratic parties, and whether President Obama or the Republicans in Congress were more likely to make the right decisions about the national economy and national security. Views were sought on President Obama's proposed budget plan, including changes in federal income taxes and government spending, and proposals to give financial assistance to the banking and automotive industries. A series of questions addressed the condition of the national economy, the most important economic problem facing the nation, the financial situation of the respondent's household, and how the recession was affecting their life. Respondents compared their current standard of living with that of their parents at the same age and gave their expectations about the standard of living of their children. Other questions asked respondents what the phrase "American dream" meant to them and whether they had achieved the "American dream" or expected to in their lifetime. Additional topics addressed the bonuses given to AIG insurance company executives, the wars in Iraq and Afghanistan, international trade, health insurance coverage, and government spending on cancer research. Demographic variables include sex, age, race, education level, marital status, household income, employment status, perceived social class, political party affiliation, political philosophy, voter registration status and participation history, religious preference, whether respondents had children under the age of 18 years, and whether respondents considered themselves to be a born-again Christian.

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.

New York City Department of Education 2015-16 Final Class Size Report School Middle School and High School Core Average Class Size General Education (Gen Ed), Integrated Co-Teaching (ICT), Accelerated (Acc), Self-Contained (SC)

The purpose of this study was to compare how members of the French and American upper-middle class define being a "worthy person," and to explain the important cross-national differences in these definitions by examining broad cultural and structural features of French and American society. Subjects were 160 college educated, white male professionals, managers, and businessmen who lived in and around Indianapolis, New York, Paris, and Clermont-Ferrand. Respondents were randomly chosen from the phone directories of middle- and upper-middle-class suburbs and neighborhood. Brief phone interviews were conducted to determine availability and eligibility. The final participants were matches as closely as possible by level of education and occupation. Data collection centered on 2-hour semi-directed interviews. Variables assessed include labels participants used to describe people whom they placed above and below themselves, description of people with whom participants chose to associate, those they felt superior and inferior to and those who invoked hostility, indifference, and sympathy. Negative and positive traits of coworkers, perceptions of cultural traits most valued in their workplace, and child rearing values were also assessed. Audio Data Availability Note: This study contains audio data that have been digitized. There are 452 audio files available.

VITAL SIGNS INDICATOR Jobs by Wage Level (EQ1)

FULL MEASURE NAME Distribution of jobs by low-, middle-, and high-wage occupations

LAST UPDATED January 2019

DESCRIPTION Jobs by wage level refers to the distribution of jobs by low-, middle- and high-wage occupations. In the San Francisco Bay Area, low-wage occupations have a median hourly wage of less than 80% of the regional median wage; median wages for middle-wage occupations range from 80% to 120% of the regional median wage, and high-wage occupations have a median hourly wage above 120% of the regional median wage.

DATA SOURCE California Employment Development Department OES (2001-2017) http://www.labormarketinfo.edd.ca.gov/data/oes-employment-and-wages.html

American Community Survey (2001-2017) http://api.census.gov

CONTACT INFORMATION vitalsigns.info@bayareametro.gov

METHODOLOGY NOTES (across all datasets for this indicator) Jobs are determined to be low-, middle-, or high-wage based on the median hourly wage of their occupational classification in the most recent year. Low-wage jobs are those that pay below 80% of the regional median wage. Middle-wage jobs are those that pay between 80% and 120% of the regional median wage. High-wage jobs are those that pay above 120% of the regional median wage. Regional median hourly wages are estimated from the American Community Survey and are published on the Vital Signs Income indicator page. For the national context analysis, occupation wage classifications are unique to each metro area. A low-wage job in New York, for instance, may be a middle-wage job in Miami. For the Bay Area in 2017, the median hourly wage for low-wage occupations was less than $20.86 per hour. For middle-wage jobs, the median ranged from $20.86 to $31.30 per hour; and for high-wage jobs, the median wage was above $31.30 per hour.

Occupational employment and wage information comes from the Occupational Employment Statistics (OES) program. Regional and subregional data is published by the California Employment Development Department. Metro data is published by the Bureau of Labor Statistics. The OES program collects data on wage and salary workers in nonfarm establishments to produce employment and wage estimates for some 800 occupations. Data from non-incorporated self-employed persons are not collected, and are not included in these estimates. Wage estimates represent a three-year rolling average.

Due to changes in reporting during the analysis period, subregion data from the EDD OES have been aggregated to produce geographies that can be compared over time. West Bay is San Mateo, San Francisco, and Marin counties. North Bay is Sonoma, Solano and Napa counties. East Bay is Alameda and Contra Costa counties. South Bay is Santa Clara County from 2001-2004 and Santa Clara and San Benito counties from 2005-2017.

Due to changes in occupation classifications during the analysis period, all occupations have been reassigned to 2010 SOC codes. For pre-2009 reporting years, all employment in occupations that were split into two or more 2010 SOC occupations are assigned to the first 2010 SOC occupation listed in the crosswalk table provided by the Census Bureau. This method assumes these occupations always fall in the same wage category, and sensitivity analysis of this reassignment method shows this is true in most cases.

In order to use OES data for time series analysis, several steps were taken to handle missing wage or employment data. For some occupations, such as airline pilots and flight attendants, no wage information was provided and these were removed from the analysis. Other occupations did not record a median hourly wage (mostly due to irregular work hours) but did record an annual average wage. Nearly all these occupations were in education (i.e. teachers). In this case, a 2080 hour-work year was assumed and [annual average wage/2080] was used as a proxy for median income. Most of these occupations were classified as high-wage, thus dispelling concern of underestimating a median wage for a teaching occupation that requires less than 2080 hours of work a year (equivalent to 12 months fulltime). Finally, the OES has missing employment data for occupations across the time series. To make the employment data comparable between years, gaps in employment data for occupations are ‘filled-in’ using linear interpolation if there are at least two years of employment data found in OES. Occupations with less than two years of employment data were dropped from the analysis. Over 80% of interpolated cells represent missing employment data for just one year in the time series. While this interpolating technique may impact year-over-year comparisons, the long-term trends represented in the analysis generally are accurate.

2017- 2018 Class Size Report District Middle And High School Class Size Distribution

Preliminary Class Size Report Middle School and High School Core Average Class Size Core courses identified as English, Math, Social Studies, and Science classes for grades 6-12, where available in the scheduling application. General Education and Integrated Co-Teaching (ICT) classes and course sections with more than 100 students or fewer than seven students are excluded from this report, as are self-contained courses with fewer than two students.

Data is collected because of public interest in how the City’s budget is being spent on salary and overtime pay for all municipal employees. Data is input into the City's Personnel Management System (“PMS”) by the respective user Agencies. Each record represents the following statistics for every city employee: Agency, Last Name, First Name, Middle Initial, Agency Start Date, Work Location Borough, Job Title Description, Leave Status as of the close of the FY (June 30th), Base Salary, Pay Basis, Regular Hours Paid, Regular Gross Paid, Overtime Hours worked, Total Overtime Paid, and Total Other Compensation (i.e. lump sum and/or retro payments). This data can be used to analyze how the City's financial resources are allocated and how much of the City's budget is being devoted to overtime. The reader of this data should be aware that increments of salary increases received over the course of any one fiscal year will not be reflected. All that is captured, is the employee's final base and gross salary at the end of the fiscal year. In very limited cases, a check replacement and subsequent refund may reflect both the original check as well as the re-issued check in employee pay totals.

NOTE 1: To further improve the visibility into the number of employee OT hours worked, beginning with the FY 2023 report, an updated methodology will be used which will eliminate redundant reporting of OT hours in some specific instances. In the previous calculation, hours associated with both overtime pay as well as an accompanying overtime “companion code” pay were included in the employee total even though they represented pay for the same period of time. With the updated methodology, the dollars shown on the Open Data site will continue to be inclusive of both types of overtime, but the OT hours will now reflect a singular block of time, which will result in a more representative total of employee OT hours worked. The updated methodology will primarily impact the OT hours associated with City employees in uniformed civil service titles. The updated methodology will be applied to the Open Data posting for Fiscal Year 2023 and cannot be applied to prior postings and, as a result, the reader of this data should not compare OT hours prior to the 2023 report against OT hours published starting Fiscal Year 2023. The reader of this data may continue to compare OT dollars across all published Fiscal Years on Open Data.
NOTE 2: As a part of FISA-OPA’s routine process for reviewing and releasing Citywide Payroll Data, data for some agencies (specifically NYC Police Department (NYPD) and the District Attorneys’ Offices (Manhattan, Kings, Queens, Richmond, Bronx, and Special Narcotics)) have been redacted since they are exempt from disclosure pursuant to the Freedom of Information Law, POL § 87(2)(f), on the ground that disclosure of the information could endanger the life and safety of the public servants listed thereon. They are further exempt from disclosure pursuant to POL § 87(2)(e)(iii), on the ground that any release of the information would identify confidential sources or disclose confidential information relating to a criminal investigation, and POL § 87(2)(e)(iv), on the ground that disclosure would reveal non-routine criminal investigative techniques or procedures. Some of these redactions will appear as XXX in the name columns.

For this special topic poll, opinion was solicited before and after President Bill Clinton's State of the Union speech delivered February 17, 1993. Prior to the speech, questions were posed regarding Clinton's handling of the presidency, his campaign promises, the national economy, respondents' personal financial situations, and strategies to reduce the federal budget deficit. Other items assessed the share of tax dollars being spent on defense, Social Security, and health care, issues regarding homosexuals, and Al Gore's and Hillary Clinton's influence on the President. Additional questions concerned improving health care, the likelihood that respondents would watch Clinton's State of the Union speech, whether women nominated to high office by the Clinton Administration were being held to stricter standards than men, and the hiring of illegal aliens. Respondents recontacted in the call-back survey subsequent to the President's speech were queried regarding Clinton's handling of the presidency, the economic plan outlined in his speech, and the federal budget deficit. Background information on respondents includes perception of the amount of income needed to be too rich to be considered middle class, whether the respondent had a gay or lesbian friend/family member, the importance of religion, chances of being out of work sometime in the next 12 months, military service, parental status, economic self-placement, 1992 presidential vote choice, voter registration status, political party, political orientation, religious preference, fundamentalist self-identification, education, age, race, preference for "African-American" or "Black" as a label, Hispanic origin, marital status, family income, sex, and past involvement in expressing opinions by writing to Congress, calling in to a radio or television talk show, calling or writing to a newspaper, and calling an 800 or 900 number.

Studies of classical gentrification typically focus on the embourgeoisement of neighborhoods and displacement of marginalized people. Recently, a new form of gentrification – super-gentrification – has emerged with the expansion of global finance capital, according to urban geographer Loretta Lees. Super-gentrification entails the further upscaling of already gentrified neighborhoods with the in-migration of upper-income residents and displacement of middle class residents, many of whom were among the initial gentrifiers. Despite the attention policy makers, urban planners, and the media are paying to the “middle class squeeze,” few quantitative studies of super-gentrification exist. Using data from the United States Decennial Census, American Community Survey, public residential sales transaction records, and real estate listings, this article sheds light on the landscape of super-gentrification and how to identify it with a quantitative analysis of changes in income, demographics, and housing affordability in the Park Slope neighborhood of Brooklyn, New York since 1970.

Class Size Distribution District report for middle and high school grades by program type, number of students, number of classes, and average class size.

2018-19 Preliminary Class Size Report Borough Middle and High School Class Size Distribution Core courses identified as English, Math, Social Studies, and Science classes for grades 6-12, where available General Education and Integrated Co-Teaching (ICT) classes and course sections with more than 100 students or fewer than seven students are excluded from this report, as are self-contained courses with fewer than two students

Background: The Investment Framework Enhanced (IFE) proposed in 2013 by the Joint United Nations Programme on HIV/AIDS (UNAIDS) explored how maximizing existing interventions and adding emerging prevention options, including a vaccine, could further reduce new HIV infections and AIDS-related deaths in low- and middle-income countries (LMICs). This article describes additional modeling which looks more closely at the potential health impact and cost-effectiveness of AIDS vaccination in LMICs as part of UNAIDS IFE. Methods: An epidemiological model was used to explore the potential impact of AIDS vaccination in LMICs in combination with other interventions through 2070. Assumptions were based on perspectives from research, vaccination and public health experts, as well as observations from other HIV/AIDS interventions and vaccination programs. Sensitivity analyses varied vaccine efficacy, duration of protection, coverage, and cost. Results: If UNAIDS IFE goals were fully achieved, new annual HIV infections in LMICs would decline from 2.0 million in 2014 to 550,000 in 2070. A 70% efficacious vaccine introduced in 2027 with three doses, strong uptake and five years of protection would reduce annual new infections by 44% over the first decade, by 65% the first 25 years and by 78% to 122,000 in 2070. Vaccine impact would be much greater if the assumptions in UNAIDS IFE were not fully achieved. An AIDS vaccine would be cost-effective within a wide range of scenarios. Interpretation: Even a modestly effective vaccine could contribute strongly to a sustainable response to HIV/AIDS and be cost-effective, even with optimistic assumptions about other interventions. Higher efficacy would provide even greater impact and cost-effectiveness, and would support broader access. Vaccine efficacy and cost per regimen are critical in achieving cost-effectiveness, with cost per regimen being particularly critical in low-income countries and at lower efficacy levels.

This file shows average class sizes and size of smallest and largest class for each school, broken out by grade and program type (General Education, Self-Contained Special Education, Collaborative Team Teaching (CTT)) for grades K-9 (where grade 9 is not reported by subject area), and for grades 5-9 (where available) and 9-12, aggregated by program type (General Education, CTT, and Self-Contained Special Education) and core course (e.g. English 9, Math A, US History, etc.).

Official class size data for grades K-9* is based on October 31, 2008 Audited Registers; Core course class size data for MS CORE and grades 9-12 is based on January 23, 2009 active registers. *Where ninth grade data is not reported by core course - For middle schools using MSPA (ATS) or HSST to program, average class size is reported by core course, as well as by official class.
- For high schools, sections with matching day, period, room and core subject, and combined enrollment less than 34 are assumed to be co-teaching situations. In the report, duplicated sections are subtracted as "MATCHED SECTIONS" and paired sections are added back as "ASSUMED TEAM TEACHING".