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.

United States US: Income Share Held by Highest 20% data was reported at 46.900 % in 2016. This records an increase from the previous number of 46.400 % for 2013. United States US: Income Share Held by Highest 20% data is updated yearly, averaging 46.000 % from Dec 1979 (Median) to 2016, with 11 observations. The data reached an all-time high of 46.900 % in 2016 and a record low of 41.200 % in 1979. United States US: Income Share Held by Highest 20% data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s United States – Table US.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.

VITAL SIGNS INDICATOR Jobs by Wage Level (EQ1)

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

LAST UPDATED January 2019

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

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

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

CONTACT INFORMATION vitalsigns.info@bayareametro.gov

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

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

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

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

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

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.

United States US: Income Share Held by Highest 10% data was reported at 30.600 % in 2016. This records an increase from the previous number of 30.100 % for 2013. United States US: Income Share Held by Highest 10% data is updated yearly, averaging 30.100 % from Dec 1979 (Median) to 2016, with 11 observations. The data reached an all-time high of 30.600 % in 2016 and a record low of 25.300 % in 1979. United States US: Income Share Held by Highest 10% data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s United States – Table US.World Bank.WDI: Poverty. Percentage share of income or consumption is the share that accrues to subgroups of population indicated by deciles or quintiles.; ; 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.

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).

The formation and stability of social hierarchies is a question of general relevance. Here, we propose a simple generalized theoretical model for establishing social hierarchy via pair-wise interactions between individuals and investigate its stability. In each interaction or fight, the probability of “winning” depends solely on the relative societal status of the participants, and the winner has a gain of status whereas there is an equal loss to the loser. The interactions are characterized by two parameters. The first parameter represents how much can be lost, and the second parameter represents the degree to which even a small difference of status can guarantee a win for the higher-status individual. Depending on the parameters, the resulting status distributions reach either a continuous unimodal form or lead to a totalitarian end state with one high-status individual and all other individuals having status approaching zero. However, we find that in the latter case long-lived intermediary distributions often exist, which can give the illusion of a stable society. As we show, our model allows us to make predictions consistent with animal interaction data and their evolution over a number of years. Moreover, by implementing a simple, but realistic rule that restricts interactions to sufficiently similar-status individuals, the stable or long-lived distributions acquire high-status structure corresponding to a distinct high-status class. Using household income as a proxy for societal status in human societies, we find agreement over their entire range from the low-to-middle-status parts to the characteristic high-status “tail”. We discuss how the model provides a conceptual framework for understanding the origin of social hierarchy and the factors which lead to the preservation or deterioration of the societal structure.

In 2022, less than eight percent of the population in Latin America had either a high or upper-middle income level. Slightly over a fifth of the population fell in the non-poor with low incomes' stratum.

Graph and download economic data for Real Median Personal Income in the United States (MEPAINUSA672N) from 1974 to 2023 about personal income, personal, median, income, real, and USA.

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.

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)

BackgroundOlder people in low- and middle-income countries are more susceptible to the impact of childhood experiences. This study comprehensively examines how childhood socioeconomic status (SES) and adult SES collectively influence late-life healthy longevity from a life course perspective, providing insights for shaping health-related policies.MethodsThis study analyzed data from the Chinese Longitudinal Healthy Longevity Survey (1998–2018) with 37,264 individuals aged 65 and above. Using R software, we applied continuous-time multi-state models incorporating the Rockwood frailty index with 38 indicators to assess participants’ health. Childhood SES or life course SES trajectories were core explanatory variables, while age and gender were controlled. Multinomial regression estimated annual transition probabilities between different states, and the multi-state life table method calculated total and frailty-specific life expectancy (LE).Results(1) Social mobility among older people in China showed an upward trend from childhood to adulthood. (2) Transition probabilities for robust-frailty, robust-dead, and frailty-dead increased with age, while frailty-robust decreased. Transition probabilities and LE varied across different childhood SES (low, medium, high) or life-course SES trajectory categories (low-low, low-medium, low-high, medium-low, medium-medium, medium-high, high-low, high-medium, high-high), with probabilities of robust-frailty, robust-dead, and frailty-dead decreasing sequentially across different categories, and frailty-robust increasing sequentially across different categories. Total LE, robust LE, and robust LE proportion increased sequentially across different categories, while frailty LE decreased sequentially across different categories. (3) Women had higher total LE and frailty incidence, but lower recovery rate, mortality risk, robust LE, and robust LE proportion compared to men.ConclusionFavorable childhood SES and lifelong accumulation of SES advantages protect against frailty morbidity, improve recovery rate, reduce mortality risk, and increase total LE, robust LE, and robust LE proportion. High childhood SES has a stronger protective effect than high adult SES, indicating the lasting impact of childhood conditions on healthy longevity. Systematic interventions in education, food supply, and medical accessibility for children from impoverished families are crucial.

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.

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.

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.

We predict the social class will affect the individuals’ account-choice in the pool-choice dilemma: Low social classes are more likely to choose the large account whereas high social classes are more likely to choose the small account. There may be two competing mechanisms: 1.depth of thinking (low social classes think more deeply than high social classes). Therefore, low social classes have more thinking-K3 and less thinking-K2 than high social classes. 2.overconfidence (high social classes are more overconfident than low social classes). Therefore, high social classes have more thinking-K2 and less thinking-K1 than low social classes.

Graph and download economic data for Share of Net Worth Held by the Top 1% (99th to 100th Wealth Percentiles) (WFRBST01134) from Q3 1989 to Q3 2024 about net worth, wealth, percentile, Net, and USA.