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.

The present research investigated the effects of social class on interpersonal trust. In a series of experiments, we showed how the contextualist socio-cognitive tendencies of the lower class and the solipsistic tendencies of the upper class were reflected in their trusting attitudes and behaviors. In Study 1 (N = 491), upper class individuals expressed the same levels of trust towards all partners, while lower class individuals adjusted their trust choices to the affect-rich information about their interaction partner and trusted warm partners more than cold partners. The results of Study 2 (N = 210) showed that when threatened, lower class individuals had generally less trusting attitudes, while upper class members were equally trusting as in a neutral situation. Study 3 (N = 200) revealed that upper class individuals explained a betrayal of their trust with dispositional factors to a higher degree than lower class individuals. We discuss how these differences contribute to perpetuating the disadvantage of the lower class.

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.

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.

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.

The Ministry of Educations' - Basic Education Statistical Booklet captures national statistics for the Education Sector in totality.

This dataset explores the no of textbook found at public and private primary schools by the core subjects of learning (Maths, English, Kiswahili, Social Studies and Science).

Source data Table 53 ; Public Primary Lower Class Text Books (Class 1-3) Table 54 : Public Primary Lower Class Text Book Ratios (Class 1-3) Table 55: Private Primary Lower Class Text Books (Class 1-3) Table 56: Private Primary Lower Class Text Book Ratios (Class 1-3) Table 57: Public Primary Upper Class Text Books (Class 4-8) Table 58: Public Primary Upper Class Text Book Ratios (Class 4-8) Table 59: Private Primary Upper Class Text Books (Class 4-8) Table 60: Private Primary Upper Class Text Book Ratios (Class 4-8)

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.

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.

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.

In the Post-industrial Era there has been an apparent weakening of the relationship between class and voting in the U.S., with lower class voters becoming less likely to support the Democratic Party. We argue that this reflects that lower class status predicts liberal economic attitudes, but conservative views on cultural and racial issues, while the parties are consistently liberal or conservative, creating conflicts for many voters. How do voters settle such internal conflicts? We argue that the salience voters attach to these different types of issues determines how policy attitudes, and indirectly class, shapes voting. Using ANES and GSS data since the 1970s, we find that class consistently predicts economic and cultural/minority policy attitudes, and that lower class voters who place more salience on economic issues, and upper class voters for whom cultural issues are more salient, are more likely to support the Democratic Party in presidential elections.

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.

Previous studies have shown that economic inequality influences psychological processes. In this article, we argue that economic inequality also makes masculine attributes more prototypical. In Study 1 (N = 106), using an experimental design, we showed that individuals belonging to a society characterized by a higher level of economic inequality are perceived as more masculine than feminine. Study 2 (N = 75) shows, also experimentally, that the upper social class is perceived mostly in terms of masculine traits, and that this effect is greater when economic inequality is relatively high. Conversely, the lower social class is more clearly perceived in terms of feminine traits. These results inform our understanding of the impact of economic inequality on social perception.

In 2021, 20.1% of people from the Indian ethnic group were in higher managerial and professional occupations – the highest percentage out of all ethnic groups in this socioeconomic group.

This dissertation examines how economic segregation shapes the provision of local public goods. Past research finds that economic segregation affects political attitudes and participation. However, few studies examine how economic segregation shapes local policy outcomes, particularly outcomes concerning local public goods. Using data on local government spending, data on ballot measures on local taxes, and data on the geographic location of affordable housing units, I find that economic segregation shapes local public goods provision in important ways. The first chapter, Income Segregation and the Provision of Local Public Goods,'' shows that economic segregation correlates with an increase in city-level spending on certain policy areas usually preferred by middle- and upper-class residents. The second chapter,Economic Segregation and Support for Local Taxes: Evidence from Municipal Ballot Measures in California,'' finds that economic segregation relates to increased support for tax increases dedicated to specific goods and services voted on by residents. I argue that, in economically segregated cities, this increased support comes from residents' decreased trust in local government, particularly in how local governments spend money. Finally, the third chapter, ``Partisanship and Affordable Housing: How Democrats and Republicans Geographically Distribute the Low-Income Housing Tax Credit Program,'' asks whether partisanship structures the distribution of low-income housing units to economically segregated neighborhoods using administrative data from the Low-Income Housing Tax Credit Program. I find little evidence to support partisan differences in the distribution of low-income housing units to low-poverty or to high-poverty neighborhoods. However, I do find that Republican administrations allocate significantly fewer low-income housing units to a neighborhood as its poverty rate increases. This suggests that partisanship may not necessarily shape the provision and distribution of new housing development for lower-income residents. Together, these findings show that economic segregation has a nuanced but significant relationship with the provision of local public goods.

Supporting documentation on code lists, subject definitions, data accuracy, and statistical testing can be found on the American Community Survey website in the Data and Documentation section...Sample size and data quality measures (including coverage rates, allocation rates, and response rates) can be found on the American Community Survey website in the Methodology section..Although the American Community Survey (ACS) produces population, demographic and housing unit estimates, for 2010, the 2010 Census provides the official counts of the population and housing units for the nation, states, counties, cities and towns. For 2006 to 2009, the Population Estimates Program provides intercensal estimates of the population for the nation, states, and counties..Explanation of Symbols:.An ''**'' entry in the margin of error column indicates that either no sample observations or too few sample observations were available to compute a standard error and thus the margin of error. A statistical test is not appropriate..An ''-'' entry in the estimate column indicates that either no sample observations or too few sample observations were available to compute an estimate, or a ratio of medians cannot be calculated because one or both of the median estimates falls in the lowest interval or upper interval of an open-ended distribution..An ''-'' following a median estimate means the median falls in the lowest interval of an open-ended distribution..An ''+'' following a median estimate means the median falls in the upper interval of an open-ended distribution..An ''***'' entry in the margin of error column indicates that the median falls in the lowest interval or upper interval of an open-ended distribution. A statistical test is not appropriate..An ''*****'' entry in the margin of error column indicates that the estimate is controlled. A statistical test for sampling variability is not appropriate. .An ''N'' entry in the estimate and margin of error columns indicates that data for this geographic area cannot be displayed because the number of sample cases is too small..An ''(X)'' means that the estimate is not applicable or not available..Estimates of urban and rural population, housing units, and characteristics reflect boundaries of urban areas defined based on Census 2000 data. Boundaries for urban areas have not been updated since Census 2000. As a result, data for urban and rural areas from the ACS do not necessarily reflect the results of ongoing urbanization..While the 2006-2010 American Community Survey (ACS) data generally reflect the December 2009 Office of Management and Budget (OMB) definitions of metropolitan and micropolitan statistical areas; in certain instances the names, codes, and boundaries of the principal cities shown in ACS tables may differ from the OMB definitions due to differences in the effective dates of the geographic entities..The Class of Worker status "unpaid family workers" may have earnings. Earnings reflect any earnings from all jobs held during the 12 months prior to the ACS interview. The Class of Worker status reflects the job or business held the week prior to the ACS interview, or the last job held by the respondent..The methodology for calculating median income and median earnings changed between 2008 and 2009. Medians over $75,000 were most likely affected. The underlying income and earning distribution now uses $2,500 increments up to $250,000 for households, non-family households, families, and individuals and employs a linear interpolation method for median calculations. Before 2009 the highest income category was $200,000 for households, families and non-family households ($100,000 for individuals) and portions of the income and earnings distribution contained intervals wider than $2,500. Those cases used a Pareto Interpolation Method..Data are based on a sample and are subject to sampling variability. The degree of uncertainty for an estimate arising from sampling variability is represented through the use of a margin of error. The value shown here is the 90 percent margin of error. The margin of error can be interpreted roughly as providing a 90 percent probability that the interval defined by the estimate minus the margin of error and the estimate plus the margin of error (the lower and upper confidence bounds) contains the true value. In addition to sampling variability, the ACS estimates are subject to nonsampling error (for a discussion of nonsampling variability, see Accuracy of the Data). The effect of nonsampling error is not represented in these tables..Source: U.S. Census Bureau, 2006-2010 American Community Survey

During a 2018 survey, approximately 40.5 percent of respondents in Chile stated that they belonged to middle class. Meanwhile, 38.2 percent of the people surveyed said they would describe themselves as lower middle class and 17.1 percent claimed to be part of the low class.

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.

For detailed information, visit the Tucson Equity Priority Index StoryMap.Download the layer's 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.

According to a 2023 survey conducted in France, across different income demographics, at-home internet access was higher among users with a higher income. Among the lower income demographics, ** percent reported having home internet connections, whereas ** percent of people from the upper middle income accessed the internet at home.