A dataset listing the richest zip codes in Virginia per the most current US Census data, including information on rank and average income.

This annual study provides selected income and tax items classified by State, ZIP Code, and the size of adjusted gross income. These data include the number of returns, which approximates the number of households; the number of personal exemptions, which approximates the population; adjusted gross income; wages and salaries; dividends before exclusion; and interest received. Data are based who reported on U.S. Individual Income Tax Returns (Forms 1040) filed with the IRS. SOI collects these data as part of its Individual Income Tax Return (Form 1040) Statistics program, Data by Geographic Areas, ZIP Code Data.

A dataset listing the richest zip codes in North Carolina per the most current US Census data, including information on rank and average income.

Explore Louisville's wealthiest ZIP Codes using Esri's latest 2016 wealth, demographic, and lifestyle characteristics.Click on this link to see Austin Business Journal's full story and map: http://www.bizjournals.com/austin/subscriber-only/2016/08/19/wealthiest-zip-codes-2016.html

A dataset listing the richest zip codes in New Jersey per the most current US Census data, including information on rank and average income.

The mission of the non-partisan Paul Simon Public Policy Institute polling is to provide citizens, policy-makers, and academic researchers with objective information about trends and issues facing society. The 2012 Simon Poll interviewed 1,261 registered voters across Illinois. For the entire sample, the statistical margin for error is plus or minus 2.77 percentage points at the 95 percent confidence level. Areas covered by the poll include: general outlook, Illinois 2012 general election, presidential race, legislative redistricting, financial disclosure in politics, special interest influence, corruption, political reform, wealth in the U.S., abortio n, and gay marriage. Demographic information is also included, covering age, race, gender, income, political party affiliation, political ideology, employment, household income, and religious activities. Respondents’ ZIP codes and telephone area codes are included.

This map shows per capita income (income per person) in the U.S. in 2022 in a multiscale map by country, state, county, ZIP Code, tract, and block group. ArcGIS Online subscription required. Per capita income is calculated by taking the sum of all incomes and dividing by the total population.The pop-up is configured to include the following information for each geography level:2022 Per capita incomeTotal population2027 projected per capita incomeThe data shown is from Esri's 2022 Updated Demographic estimates using Census 2020 geographies. The map adds increasing level of detail as you zoom in, from state, to county, to ZIP Code, to tract, to block group data. Esri's U.S. Updated Demographic (2022/2027) Data - Population, age, income, sex, race, home value, and marital status are among the variables included in the database. Each year, Esri's Data Development team employs its proven methodologies to update more than 2,000 demographic variables for a variety of U.S. geographies. Additional Esri Resources:Esri DemographicsU.S. 2022/2027 Esri Updated DemographicsEssential demographic vocabularyThis item is for visualization purposes only and cannot be exported or used in analysis.Permitted use of this data is covered in the DATA section of the Esri Master Agreement (E204CW) and these supplemental terms.

The mission of the non-partisan Paul Simon Public Policy Institute polling is to provide citizens, policy-makers, and academic researchers with objective information about trends and issues facing society. The 2012 Southern Illinois Poll interviewed 400 registered voters in 18 southern Illinois counties. The sample of voters came from the Southern I llinois counties of Alexander, Franklin, Gallatin, Hamilton, Hardin, Jackson, Jefferson, Johnson, Massac, Perry, Pope, Pulaski, Randolph, Saline, Union, Washington, White, and Williamson. For the entire sample, the statistical margin for error of plus or minus 4.9 percentage points at the 95 percent confidence level. Areas covered by the poll are: general outlook, Illinois 2012 primary election, quality of life issues, and Illinois budget. The data also include a series of questions regarding property rights and eminent domain. Another series investigates public disclosure of elected officials. Wealth and income inequality are also queried. Demographic information is also included, covering age, race, gender, income, political party affiliation, political ideology, employment, household income, and religious activities. ZIP codes and other geographic locators are included.

Our paper addresses the relationship between parental wealth and children’s post-secondary transitions. More specifically, we contrast the likelihood of children with an upper secondary degree to make a transition into further education or the labor market with their likelihood to stay inactive, i.e., to engage neither in further education nor in labor market activity (NEET) after leaving school for the first time. While previous research argues that there is a general positive association between parental wealth and children’s educational and occupational transitions, we argue that for children of wealthy parents, this association might be weaker or even negative. Our study focuses on Germany, where wealth has a weak correlation with the traditional measures of parental socio-economic background. For our empirical analyses, we apply data from the German Socio-Economic Panel Study (SOEP) and use binary logistic regression models for discrete-time event history analyses. Although not statistically significant, our results show that the relationship between parental wealth and children’s post-secondary transitions is not linear. Our study contributes to previous research by providing a detailed examination of the potential mechanisms underlying the relationship between parental wealth and children’s post-secondary transitions.

A dataset listing the richest zip codes in Missouri per the most current US Census data, including information on rank and average income.

Comprehensive demographic dataset for Pacific Palisades, Los Angeles, CA, US including population statistics, household income, housing units, education levels, employment data, and transportation with year-over-year changes.

A dataset listing the richest zip codes in South Dakota per the most current US Census data, including information on rank and average income.

A dataset listing the richest zip codes in Puerto Rico per the most current US Census data, including information on rank and average income.

Background Rural U.S. communities are at risk from COVID-19 due to advanced age and limited access to acute care. Recognizing this, the Vashon Medical Reserve Corps (VMRC) in King County, Washington, implemented an all-volunteer, community-based COVID-19 response program. This program integrated public engagement, SARS-CoV-2 testing, contact tracing, vaccination, and material community support, and was associated with the lowest cumulative COVID-19 case rate in King County. This study aimed to investigate the contributions of demographics, geography and public health interventions to Vashon’s low COVID-19 rates. Methods This observational cross-sectional study compares cumulative COVID-19 rates and success of public health interventions from February 2020 through November 2021 for Vashon Island with King County (including metropolitan Seattle) and Whidbey Island, located ~50 km north of Vashon. To evaluate the role of demography, we developed multiple linear regression models of COVID-19 rates using metrics of age, race/ethnicity, wealth and educational attainment across 77 King County zip codes. To investigate the role of remote geography we expanded the regression models to include North, Central and South Whidbey, similarly remote island communities with varying demographic features. To evaluate the effectiveness of VMRC’s community-based public health measures, we directly compared Vashon’s success of vaccination and contact tracing with that of King County and South Whidbey, the Whidbey community most similar to Vashon. Results Vashon’s cumulative COVID-19 case rate was 29% that of King County overall (22.2 vs 76.8 cases/K). A multiple linear regression model based on King County demographics found educational attainment to be a major correlate of COVID-19 rates, and Vashon’s cumulative case rate was just 38% of predicted (p<.05), so demographics alone do not explain Vashon’s low COVID-19 case rate. Inclusion of Whidbey communities in the model identified a major effect of remote geography (-49 cases/K, p<.001), such that observed COVID-19 rates for all remote communities fell within the model’s 95% prediction interval. VMRC’s vaccination effort was highly effective, reaching a vaccination rate of 1500 doses/K four months before South Whidbey and King County and maintaining a cumulative vaccination rate 200 doses/K higher throughout the latter half of 2021 (p<.001). Including vaccination rates in the model reduced the effect of remote geography to -41 cases/K (p<.001). VMRC case investigation was also highly effective, interviewing 96% of referred cases in an average of 1.7 days compared with 69% in 3.7 days for Washington Department of Health investigating South Whidbey cases and 80% in 3.4 days for Public Health–Seattle & King County (both p<0.001). VMRC’s public health interventions were associated with a 30% lower case rate (p<0.001) and 55% lower hospitalization rate (p=0.056) than South Whidbey. Conclusion While the overall magnitude of the pre-Omicron COVID-19 pandemic in rural and urban U.S. communities was similar, we show that island communities in the Puget Sound region were substantially protected from COVID-19 by their geography. We further show that a volunteer community-based COVID-19 response program was highly effective in the Vashon community, augmenting the protective effect of geography. We suggest that Medical Reserve Corps should be an important element of future pandemic planning. Methods The study period extended from the pandemic onset in February 2020 through November 2021. Daily COVID-19 cases, hospitalizations, deaths and test numbers for King County as a whole and by zip code were downloaded from the King County COVID-19 dashboard (Feb 22, 2022 update). Population data for King County and Vashon are from the April 2020 US Census. Zip code level population data are the average of two zip code tabulation area estimates from the WA Office of Financial Management and Cubit (a commercial data vendor providing access to US Census information). The Asset Limited, Income Constrained, and Employed (ALICE) metric, a measure of the working poor, was obtained from United Way.