This survey illustrates the differences in satisfaction of the upper, middle and lower class in the United States as of August 2012. 62 percent of upper class respondents stated they feel more financially secure now than they did ten years ago. 44 percent of middle class Americans and 29 percent of lower class Americans agree.

This statistic shows the median household income in the United States from 1970 to 2020, by income tier. In 2020, the median household income for the middle class stood at 90,131 U.S. dollars, which was approximately a 50 percent increase from 1970. However, the median income of upper income households in the U.S. increased by almost 70 percent compared to 1970.

About half of the Polish population belonged to the middle class in April 2019. Nearly a third were lower-class, and the minority were upper-class. When considering only income, a larger share of the population was upper- and middle-class, whereas when considering the only occupation, a larger share was lower class.

About this dataset

Context

The dataset presents the mean household income for each of the five quintiles in Middle Inlet, Wisconsin, 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 21,360, while the mean income for the highest quintile (20% of households with the highest income) is 162,915. This indicates that the top earners earn 8 times compared to the lowest earners.
• *Top 5%: * The mean household income for the wealthiest population (top 5%) is 282,509, which is 173.41% higher compared to the highest quintile, and 1322.61% higher compared to the lowest quintile.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2017-2021 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 2022 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 Middle Inlet town median household income. You can refer the same here

Disposable Income per Capita: Urban: Middle Income data was reported at 48,508.000 RMB in 2024. This records an increase from the previous number of 46,276.000 RMB for 2023. Disposable Income per Capita: Urban: Middle Income data is updated yearly, averaging 8,678.295 RMB from Dec 1985 (Median) to 2024, with 40 observations. The data reached an all-time high of 48,508.000 RMB in 2024 and a record low of 737.280 RMB in 1985. Disposable Income per Capita: Urban: Middle Income data remains active status in CEIC and is reported by National Bureau of Statistics. The data is categorized under China Premium Database’s Household Survey – Table CN.HD: Income by Income Level. Since 2013, All households in the sample are grouped, by per capita disposable income of the household, into groups of low income, lower middle income, middle income, upper middle income, and high income, each group consisting of 20%, 20%, 20%, 20%, and 20% of all households respectively.

This dataset contains replication files for "The Fading American Dream: Trends in Absolute Income Mobility Since 1940" by Raj Chetty, David Grusky, Maximilian Hell, Nathaniel Hendren, Robert Manduca, and Jimmy Narang. For more information, see https://opportunityinsights.org/paper/the-fading-american-dream/. A summary of the related publication follows. One of the defining features of the “American Dream” is the ideal that children have a higher standard of living than their parents. We assess whether the U.S. is living up to this ideal by estimating rates of “absolute income mobility” – the fraction of children who earn more than their parents – since 1940. We measure absolute mobility by comparing children’s household incomes at age 30 (adjusted for inflation using the Consumer Price Index) with their parents’ household incomes at age 30. We find that rates of absolute mobility have fallen from approximately 90% for children born in 1940 to 50% for children born in the 1980s. Absolute income mobility has fallen across the entire income distribution, with the largest declines for families in the middle class. These findings are unaffected by using alternative price indices to adjust for inflation, accounting for taxes and transfers, measuring income at later ages, and adjusting for changes in household size. Absolute mobility fell in all 50 states, although the rate of decline varied, with the largest declines concentrated in states in the industrial Midwest, such as Michigan and Illinois. The decline in absolute mobility is especially steep – from 95% for children born in 1940 to 41% for children born in 1984 – when we compare the sons’ earnings to their fathers’ earnings. Why have rates of upward income mobility fallen so sharply over the past half-century? There have been two important trends that have affected the incomes of children born in the 1980s relative to those born in the 1940s and 1950s: lower Gross Domestic Product (GDP) growth rates and greater inequality in the distribution of growth. We find that most of the decline in absolute mobility is driven by the more unequal distribution of economic growth rather than the slowdown in aggregate growth rates. When we simulate an economy that restores GDP growth to the levels experienced in the 1940s and 1950s but distributes that growth across income groups as it is distributed today, absolute mobility only increases to 62%. In contrast, maintaining GDP at its current level but distributing it more broadly across income groups – at it was distributed for children born in the 1940s – would increase absolute mobility to 80%, thereby reversing more than two-thirds of the decline in absolute mobility. These findings show that higher growth rates alone are insufficient to restore absolute mobility to the levels experienced in mid-century America. Under the current distribution of GDP, we would need real GDP growth rates above 6% per year to return to rates of absolute mobility in the 1940s. Intuitively, because a large fraction of GDP goes to a small fraction of high-income households today, higher GDP growth does not substantially increase the number of children who earn more than their parents. Of course, this does not mean that GDP growth does not matter: changing the distribution of growth naturally has smaller effects on absolute mobility when there is very little growth to be distributed. The key point is that increasing absolute mobility substantially would require more broad-based economic growth. We conclude that absolute mobility has declined sharply in America over the past half-century primarily because of the growth in inequality. If one wants to revive the “American Dream” of high rates of absolute mobility, one must have an interest in growth that is shared more broadly across the income distribution.

During a 2023 survey, around 35 percent of respondents interviewed in Brazil said they belonged to the middle class. Meanwhile, 24.3 percent of the interviewees defined their social class as "low" and 25.7 percent stated that they were part of the middle class.Furthermore, Brazil's Gini coefficient, an indicator that measures wealth distribution, shows Brazil is one of the most unequal countries in the Latin American region.

About this dataset

Context

The dataset presents the mean household income for each of the five quintiles in Deptford Township, New Jersey, 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 21,507, while the mean income for the highest quintile (20% of households with the highest income) is 257,345. This indicates that the top earners earn 12 times compared to the lowest earners.
• *Top 5%: * The mean household income for the wealthiest population (top 5%) is 384,780, which is 149.52% higher compared to the highest quintile, and 1789.09% 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 Deptford township median household income. You can refer the same here

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

About this dataset

Context

The dataset presents the mean household income for each of the five quintiles in Morton, IL, 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 22,974, while the mean income for the highest quintile (20% of households with the highest income) is 263,716. This indicates that the top earners earn 11 times compared to the lowest earners.
• *Top 5%: * The mean household income for the wealthiest population (top 5%) is 460,980, which is 174.80% higher compared to the highest quintile, and 2006.53% 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 Morton median household income. You can refer the same here

By 2030, the middle-class population in Asia-Pacific is expected to increase from 1.38 billion people in 2015 to 3.49 billion people. In comparison, the middle-class population of sub-Saharan Africa is expected to increase from 114 million in 2015 to 212 million in 2030.

Worldwide wealth

While the middle-class has been on the rise, there is still a huge disparity in global wealth and income. The United States had the highest number of individuals belonging to the top one percent of wealth holders, and the value of global wealth is only expected to increase over the coming years. Around 57 percent of the world’s population had assets valued at less than 10,000 U.S. dollars; while less than one percent had assets of more than million U.S. dollars. Asia had the highest percentage of investable assets in the world in 2018, whereas Oceania had the highest percent of non-investable assets.

The middle-class

The middle class is the group of people whose income falls in the middle of the scale. China accounted for over half of the global population for middle-class wealth in 2017. In the United States, the debate about the middle class “disappearing” has been a popular topic due to the increase in wealth to the top billionaires in the nation. Due to this, there have been arguments to increase taxes on the rich to help support the middle-class.

Graph and download economic data for Real Median Family Income in the United States (MEFAINUSA672N) from 1953 to 2023 about family, median, income, real, and USA.

Ghana GH: Income Share Held by Lowest 20% data was reported at 5.400 % in 2012. This records an increase from the previous number of 5.200 % for 2005. Ghana GH: Income Share Held by Lowest 20% data is updated yearly, averaging 6.200 % from Dec 1987 (Median) to 2012, with 6 observations. The data reached an all-time high of 7.000 % in 1988 and a record low of 5.200 % in 2005. Ghana GH: Income Share Held by Lowest 20% data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Ghana – Table GH.World Bank.WDI: Poverty. Percentage share of income or consumption is the share that accrues to subgroups of population indicated by deciles or quintiles. Percentage shares by quintile may not sum to 100 because of rounding.; ; World Bank, Development Research Group. Data are based on primary household survey data obtained from government statistical agencies and World Bank country departments. Data for high-income economies are from the Luxembourg Income Study database. For more information and methodology, please see PovcalNet (http://iresearch.worldbank.org/PovcalNet/index.htm).; ; The World Bank’s internationally comparable poverty monitoring database now draws on income or detailed consumption data from more than one thousand six hundred household surveys across 164 countries in six regions and 25 other high income countries (industrialized economies). While income distribution data are published for all countries with data available, poverty data are published for low- and middle-income countries and countries eligible to receive loans from the World Bank (such as Chile) and recently graduated countries (such as Estonia) only. See PovcalNet (http://iresearch.worldbank.org/PovcalNet/WhatIsNew.aspx) for definitions of geographical regions and industrialized countries.

Disposable Income per Capita: Middle Income data was reported at 33,925.000 RMB in 2024. This records an increase from the previous number of 32,195.000 RMB for 2023. Disposable Income per Capita: Middle Income data is updated yearly, averaging 24,111.810 RMB from Dec 2013 (Median) to 2024, with 12 observations. The data reached an all-time high of 33,925.000 RMB in 2024 and a record low of 15,697.999 RMB in 2013. Disposable Income per Capita: Middle Income data remains active status in CEIC and is reported by National Bureau of Statistics. The data is categorized under China Premium Database’s Household Survey – Table CN.HD: Income by Income Level.

The system of social indicators for the Federal Republic of Germany - developed in its original version as part of the SPES project under the direction of Wolfgang Zapf - provides quantitative information on levels, distributions and changes in quality of life, social progress and social change in Germany from 1950 to 2013, i.e. over a period of more than sixty years. With the approximately 400 objective and subjective indicators that the indicator system comprises in total, it claims to measure welfare and quality of life in Germany in a differentiated way across various areas of life and to observe them over time. In addition to the indicators for 13 areas of life, including income, education and health, a selection of cross-cutting global welfare measures were also included in the dashboard, i.e. general welfare indicators such as life satisfaction, social isolation or the Human Development Index. Based on available data from official statistics and survey data, time series were compiled for all indicators, ideally with annual values from 1950 to 2013. Around 90 of the indicators were marked as "key indicators" in order to highlight central dimensions of welfare and quality of life across the various areas of life. The further development and expansion, regular maintenance and updating as well as the provision of the data of the system of social indicators for the Federal Republic of Germany have been among the tasks of the Center for Social Indicator Research, which is based at GESIS, since 1987. For a detailed description of the system of social indicators for the Federal Republic of Germany, see the study description under "Other documents".
The data on the area of life “Socio Economic Classification and Social Stratification” is composed as follows:

Intergenerational mobility: employed people in the upper service class without intergenerational mobility, employed people in the lower service class without intergenerational mobility, employed skilled workers and technicians without intergenerational mobility, employed unskilled workers without intergenerational mobility, employed self-employed people without intergenerational mobility, employed people in agricultural professions without intergenerational mobility. Social mobility: proportion of class-homogeneous marriages among men and women in the upper service class, proportion of class-homogeneous marriages among men and women in the lower service class, proportion of class-homogeneous marriages among men and women - skilled workers and technicians, proportion of class-homogeneous marriages among men and women - unskilled workers, share of class-homogeneous marriages among men and women - self-employed, share of class-homogeneous marriages among men and women with agricultural professions. Socio-economic breakdown of the population: Number of private households according to participation in the working life of the reference person, share of private households according to participation in the working life of the reference person, number of private households according to the occupational status of the reference person, share of private households according to the occupational status of the reference person, share of the population earning a living through employment , share of the population earning a living through unemployment benefits and assistance, share of the population earning a living through pensions, share of the population earning a living from family members, share of self-employed people in all employed people, share of helping family members in all employed people, share of civil servants in all employed people, share of employees in all employed people , proportion of workers in all employed persons, employed people in the upper service class, employed people in the lower service class, employed people - skilled workers and technicians, employed people - unskilled workers, employed people - self-employed, employed people with agricultural professions. Subjective class classification: Population according to subjective class classification (working class, middle class, upper middle and upper class, none of these classes).

Compared to other Western democracies, in the U.S. fewer people subjectively identify as working class historically and many working class individuals think of themselves as middle class. This likely has important political implications. We argue, however, that union membership can strengthen identification with the working class, through communications from leaders and interactions among members. Using General Social Survey data from five decades, we develop an original multi-indicator IRT-based measure of objective class status and find that union membership makes it more likely that individuals will identify as working class, across all objective class groups. Panel data analysis shows that union membership predicts future working class identification but that the opposite is not true, suggesting that these associations are causal. Finally, we show that identifying with the working rather than middle or upper class is associated with more support for redistribution and the welfare state.

Economic inequality qualifies as a structural characteristic leading to political action, albeit this relationship manifests differently across socioeconomic classes. COVID-19 pandemic has amplified existing economic inequalities in ways that increased social tensions and political unrest around the world. This research investigates the effect of COVID-19 personal impacts on the relationship between perceived economic inequality and individuals' political participation. An online survey was administered to an Italian representative sample of 1,446 people (51% women, mean age of 42.42 years, SD = 12.87). The questionnaire assessed the perceived economic inequality, the personal impacts of COVID-19 (i.e., on finance, mental health, and ability to procure resources), and individuals' involvement in political participation. Moderation analyses were conducted separately for different socioeconomic classes (i.e., lower, middle, and upper classes). Results showed that individuals who perceive greater economic inequality, while controlling for perceived wage gap, are more likely to take action, but only if they belong to the higher class. For lower-class individuals, perceiving greater inequality erodes political action. Interaction effects occurred mainly in the middle class and with COVID-19 impacts on resources procurement, which inhibits political action.

This data set accompanies the following paper:Vasileios Lampos, Nikolaos Aletras, Gens Jeyti, Bin Zou and Ingemar J. Cox. Inferring the Socioeconomic Status of Social Media Users based on Behaviour and Language. Proceedings of the 38th European Conference on Information Retrieval (ECIR), 2016.Data description- Temporal resolution: February 1, 2014 to March 21, 2015- data_matrix.csv: Main input file. Each line represents a user (1342 users in total). See below for the interpretation of the dimensions (columns) related to textual content. Dimensions 1284 to 1287 contain the ratios of user replies, mentions (of other accounts), retweets (of tweets from other accounts) and unique mentions (of other accounts) over the total number of tweets of a particular user, respectively. Dimensions 1288 to 1291 contain the log-number of followers+1, followees+1, listings+1 and the impact score for a particular user. The definition of the impact score has been adopted from the following paper: V. Lampos, N. Aletras, D. Preotiuc-Pietro and T. Cohn. Predicting and Characterising User Impact on Twitter. Proceedings of the 14th Conference of the European Chapter of the Association for Computational Linguistics (EACL), pages 405–413, 2014.- sec_labels.txt: Socioeconomic status class labels for each user; 1,2 and 3 denote the upper, middle and lower socioeconomic classes respectively. Each line of sec_label.txt corresponds to a line of data_matrix.csv.- voc_1grams.txt: Vocabulary index of frequent 1-grams extracted from the users' tweets. Represents dimensions 1 to 560 from data_matrix.csv.- voc_bio_1grams.txt: Vocabulary index of 1-grams in the bio description of the users. Represents dimensions 561 to 786 from data_matrix.csv.- voc_bio_2grams.txt: Vocabulary index of 2-grams in the bio description of the users. Represents dimensions 787 to 1083 from data_matrix.csv.- voc_clusters.txt: Vocabulary index used in the formation of clusters.- voc_clusters_ids.csv: Each line contains the 1-gram ids (line numbers) from voc_clusters.txt that are members of a cluster. In total we have derived 200 clusters, represented by dimensions 1084 to 1283 in data_matrix.csv.

For detailed information, visit the Tucson Equity Priority Index StoryMap.Download the layer's data dictionaryNote: This layer is symbolized to display the percentile distribution of the Limited Resources Sub-Index. However, it includes all data for each indicator and sub-index within the citywide census tracts TEPI.What is the Tucson Equity Priority Index (TEPI)?The Tucson Equity Priority Index (TEPI) is a tool that describes the distribution of socially vulnerable demographics. It categorizes the dataset into 5 classes that represent the differing prioritization needs based on the presence of social vulnerability: Low (0-20), Low-Moderate (20-40), Moderate (40-60), Moderate-High (60-80) High (80-100). Each class represents 20% of the dataset’s features in order of their values. The features within the Low (0-20) classification represent the areas that, when compared to all other locations in the study area, have the lowest need for prioritization, as they tend to have less socially vulnerable demographics. The features that fall into the High (80-100) classification represent the 20% of locations in the dataset that have the greatest need for prioritization, as they tend to have the highest proportions of socially vulnerable demographics. How is social vulnerability measured?The Tucson Equity Priority Index (TEPI) examines the proportion of vulnerability per feature using 11 demographic indicators:Income Below Poverty: Households with income at or below the federal poverty level (FPL), which in 2023 was $14,500 for an individual and $30,000 for a family of fourUnemployment: Measured as the percentage of unemployed persons in the civilian labor forceHousing Cost Burdened: Homeowners who spend more than 30% of their income on housing expenses, including mortgage, maintenance, and taxesRenter Cost Burdened: Renters who spend more than 30% of their income on rentNo Health Insurance: Those without private health insurance, Medicare, Medicaid, or any other plan or programNo Vehicle Access: Households without automobile, van, or truck accessHigh School Education or Less: Those highest level of educational attainment is a High School diploma, equivalency, or lessLimited English Ability: Those whose ability to speak English is "Less Than Well."People of Color: Those who identify as anything other than Non-Hispanic White Disability: Households with one or more physical or cognitive disabilities Age: Groups that tend to have higher levels of vulnerability, including children (those below 18), and seniors (those 65 and older)An overall percentile value is calculated for each feature based on the total proportion of the above indicators in each area. How are the variables combined?These indicators are divided into two main categories that we call Thematic Indices: Economic and Personal Characteristics. The two thematic indices are further divided into five sub-indices called Tier-2 Sub-Indices. Each Tier-2 Sub-Index contains 2-3 indicators. Indicators are the datasets used to measure vulnerability within each sub-index. The variables for each feature are re-scaled using the percentile normalization method, which converts them to the same scale using values between 0 to 100. The variables are then combined first into each of the five Tier-2 Sub-Indices, then the Thematic Indices, then the overall TEPI using the mean aggregation method and equal weighting. The resulting dataset is then divided into the five classes, where:High Vulnerability (80-100%): Representing the top classification, this category includes the highest 20% of regions that are the most socially vulnerable. These areas require the most focused attention. Moderate-High Vulnerability (60-80%): This upper-middle classification includes areas with higher levels of vulnerability compared to the median. While not the highest, these areas are more vulnerable than a majority of the dataset and should be considered for targeted interventions. Moderate Vulnerability (40-60%): Representing the middle or median quintile, this category includes areas of average vulnerability. These areas may show a balanced mix of high and low vulnerability. Detailed examination of specific indicators is recommended to understand the nuanced needs of these areas. Low-Moderate Vulnerability (20-40%): Falling into the lower-middle classification, this range includes areas that are less vulnerable than most but may still exhibit certain vulnerable characteristics. These areas typically have a mix of lower and higher indicators, with the lower values predominating. Low Vulnerability (0-20%): This category represents the bottom classification, encompassing the lowest 20% of data points. Areas in this range are the least vulnerable, making them the most resilient compared to all other features in the dataset.

About this dataset

Context

The dataset presents the mean household income for each of the five quintiles in Winchester, VA, 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 14,125, while the mean income for the highest quintile (20% of households with the highest income) is 215,015. This indicates that the top earners earn 15 times compared to the lowest earners.
• *Top 5%: * The mean household income for the wealthiest population (top 5%) is 344,621, which is 160.28% higher compared to the highest quintile, and 2439.79% 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 Winchester median household income. You can refer the same here