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.

We analyze how median real incomes in the United States have changed since 1980 under a definition of the middle class that adjusts for changes in demographics. We find that failing to adjust for demographic shifts in the population relating to age, race, and education can indicate a more positive outlook than is truly the case. We also find that the real median incomes of today’s middle class are somewhat higher than they used to be, particularly for households headed by two adults. We find, as in prior research, that prices for housing, healthcare, and education have risen more than middle-class incomes, while prices for transportation, food, and recreation have risen less than middle-class incomes.

This statistic shows a ranking of metropolitan areas with the lowest shares of upper-income adults in the United States in 2014. In 2014, the Laredo metropolitan area in Texas was last in the list with * percent of the adult population living in the upper-income tier.

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 Amherst, New York, 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 18,852, while the mean income for the highest quintile (20% of households with the highest income) is 296,153. This indicates that the top earners earn 16 times compared to the lowest earners.
• *Top 5%: * The mean household income for the wealthiest population (top 5%) is 495,426, which is 167.29% higher compared to the highest quintile, and 2627.98% 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 Amherst town 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 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

ABSTRACT The article presents a panorama of socioeconomic hierarchies in late Nineteenth-century Brazil. Income analysis of social classes underpins these echelons. Within a theoretical and historical approach focused on social class, the article reckons that the Brazilian Empire was relatively egalitarian in terms of wages. A broad expressiveness of the lower classes, rather than a hypothetical robustness of the middle or the upper classes, explains this equality. The analysis of purchasing power and patterns of consumption made it possible to identify the degree of precariousness of the popular classes, as well as the existence of mainly urban middle classes. Lastly, salary data on the upper classes should not hide concentration of wealth, a main characteristic of the Empire’s decay, which was largely due to a polarized structure of slave property.

This Commentary investigates whether there has been a growing divergence in the consumption of luxury and necessity goods across income classes. The analysis shows that while necessities represent a majority of the consumption basket for lower and middle income quintiles, their consumption of necessities in inflation-adjusted dollars has been declining in the face of higher prices of such goods and stagnant income growth. Higher income quintiles have seen increases in their consumption of luxuries, simultaneous with a decline in their consumption of necessities.

In 2019, most of Italians assumed to belong to the middle class. More specifically, 52 percent of individuals defined their social status as middle class. Moreover, 37 percent of Italians stated to be part of the lower social class. Data for social class perception suggested that the occupation with the highest share of upper-class people was being a student. At the same time, freelance professional was most popular job position among middle class citizens, while the majority of unemployed people felt to belong to the lower class.

How much do Italians earn on average?

From 2006 to 2015, gross household disposable income per capita in Italy was fluctuating with no precise pattern. In the next three years, however, gross income per capita steadily increased until peaking above 31 thousand U.S. dollars in 2018. This figure put Italy at the 17th place in the ranking of OECD countries with the gross disposable income per household.

Income inequalities in Italy

National average figures can be quite misleading. In Italy, substantial economic differences across regions and also due to gender can be observed. Inhabitants of the South and the Islands earn on average around ten thousand euros less annually than Italians from the North East. Moreover, female households’ average net income in 2017 was eight thousand euros smaller than male households’ income.

The academic and public debate on social inequality has recently been fuelled by large disparities in income and wealth, profound changes in the labour market, and other emerging cleavages in post-industrial societies. This article contributes to the discussion by arguing that class divisions are theoretically based on four types of capital: people’s economic means, their social capital, their cultural resources, and the combination of their health and attractiveness (‘person capital’). From this premise, the social structure of the Netherlands is examined. A dedicated survey was linked to microdata from the national population register, tax authorities and benefit agencies. Using latent class analysis, we assess contingencies in the distribution of the different resources, and identify a structure consisting of six capital groups. The established upper echelon (15.5% of the adult population) has the most capital, followed by the privileged younger people (12.7%), the employed middle echelon (26.9%) and the comfortable retirees (16.6%). Total capital is lowest among the insecure workers (13.5%) and the precariat (14.8%). Each social class has a distinctive mix of the four types of capital, highlighting the need to look beyond economic differences in order to comprehend structural inequality. The results of this study also indicate that resource disparities between classes coincide with other forms of social hierarchy and contrasts by age. Moreover, the contemporary class structure is associated with divergent views and experiences among the Dutch. Classes with little capital tend to rate politics, society, and their own social position more negatively. In addition, they value self-enhancement and hedonism less than today’s upper classes and report lower levels of well-being.

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.

Content

The World Bank classifies the world's economies into four income groups — high, upper-middle, lower-middle, and low. We base this assignment on Gross National Income (GNI) per capita (current US$) calculated using the Atlas method. The classification is updated each year on July 1st.

The classification of countries is determined by two factors:

A country’s GNI per capita, which can change with economic growth, inflation, exchange rates, and population. Revisions to national accounts methods and data can also influence GNI per capita.
Classification threshold: The thresholds are adjusted for inflation annually using the SDR deflator.

Check this link for more: https://blogs.worldbank.org/opendata/new-country-classifications-income-level-2019-2020

Source

https://datahelpdesk.worldbank.org/knowledgebase/articles/906519-world-bank-country-and-lending-groups

The WorldBank health data set has been compiled by the authors using Health data from the World Bank DataBank (https://data.worldbank.org, accessed May, 2023.) Among others, the DataBank offers country data for a variety of health indicators. We have filtered all health indicators for the Health Nutrition and Population Statistics database, for the year 2021, for all countries. We have eliminated all indicators that have missing values for more than 75% of the countries. The rest of the missing values in the data were replaced with the average value of their Region. Each year, countries are classified by the World Bank into four income categories: High Income, Upper-middle Income, Lower-middle Income, Low Income, and Not classified. Based on this information, we proposed two classes: lower-income countries (46 countries) and upper-income classes (140 countries).

Visual representation of the relation between cognitive ability and demand for redistribution. Preferred redistribution defined as in text, ranging from no (0 percent) to full (100 percent) redistribution. Cognitive ability scaled to have mean 0 and sd 1 in the sample of all enlisters. We rank individuals according to cognitive ability and construct twelve equal-sized bins. The figures show mean redistribution against mean cognitive ability in each bin. N = 271. Panel A: Raw correlation. Panel B: Controlling for age, for whether subject continued from primary to secondary school, and for socio-economic status during childhood (answer to question “How would you classify yourself in terms of class when you grew up?” with alternatives “Working class”, “Lower middle class”, “Middle class”, “Upper middle class”, “Upper class”). To obtain this figure we first regress demand for redistribution on these control variables. We then add the mean of the demand for redistribution variable to the residuals obtained from that regression and plot this variable against cognitive ability.

Why do both economically advantaged and disadvantaged voters sometimes converge in their support for conservative parties? This study examines how subjective class consciousness mediates the relationship between economic inequality and political behavior in South Korea. Moving beyond conventional class-voting models based on income or occupation, it conceptualizes class as a relational and perceptual construct formed through social comparison. This study argues that rising inequality weakens identification with the subjective middle class, which is generally associated with progressive orientations, while reinforcing symbolic divisions between those who perceive themselves as upper or lower class. Using nationally representative survey data and district-level electoral returns from 2012 to 2022, the analysis finds that both subjective class identification and local inequality significantly predict conservative support. At the individual level, voters identifying as upper or lower class are more likely to support conservative parties than those identifying as middle class. At the district level, higher inequality corresponds with greater conservative vote shares. These findings suggest that inequality influences political behavior not only through material conditions but also through perceptions of social hierarchy.

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.

In Sweden, ************* people counts as upper class or above, earning at least the equivalent of the highest ** percent of global income earners as of 2022 in purchasing power parity (PPP) terms. Meanwhile, the share was highest in Norway, and lowest in Finland. The countries are among the ones with the highest gross domestic product (GDP) per capita worldwide.

For detailed information, visit the Tucson Equity Priority Index StoryMap.Download the Data DictionaryWhat 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.

Abstract Many Brazilian municipalities have specific legislation focused on the management of urban stormwater with the main purpose runoff control. Among the promoted measures are the mandatory implementation of different devices that operate storing or infiltrating the runoff, compensating its impact. This practice is known as compensatory technique (CT). Given the different possible CT that can be chosen, this article presents the main preferences and perceptions of the population in three different socioeconomic groups. The study was accomplished in the city of Santa Maria / RS, that as other municipalities of medium size, should also adopt some sort of public policy towards the stormwater management. A total of 518 households were surveyed in representative districts of the upper lower class, upper middle class and lower middle class, who have the same guideline for land use. The results revealed important aspects regarding the understanding of the public to the management of rainwater in urban areas, and showed that the preference between storage or infiltration may be influenced by socioeconomic status. In addition, the survey was able to identify that more than 90% of households have already knowledge about this type of public policy.