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.

China Disposable Income per Capita: Urban: Upper Middle Income data was reported at 68,151.000 RMB in 2024. This records an increase from the previous number of 65,430.000 RMB for 2023. China Disposable Income per Capita: Urban: Upper Middle Income data is updated yearly, averaging 11,827.130 RMB from Dec 1985 (Median) to 2024, with 40 observations. The data reached an all-time high of 68,151.000 RMB in 2024 and a record low of 861.960 RMB in 1985. China Disposable Income per Capita: Urban: Upper 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 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.

Middle-income trap refers to the economic growth strategies that transition low-income countries into middle-income ones but fail to transition the middle-income countries into high-income countries. We observe the existence of a middle-income trap for upper-middle- and lower middle-income countries. We examine the reasons for the middle-income trap using the Bayesian model averaging (BMA) and generalized method of moments (GMM). We also explore the transformation of middle-income economies into high-income economies using logistic, probit and Limited Information Maximum Likelihood (LIML) regression analyses. Random forest analysis is also used to check the robustness of the findings. BMA analysis shows that education plays an enabling role in high-income countries in determining economic growth, whereas the full poten tial of education is not fully utilized in middle-income countries. GMM estimations show that the education coefficient is positive and significant for high-income and middle-income countries. This implies that education plays a decisive positive role in achieving economic growth and gives a path to escape from the middle-income trap. However, the education coefficient for middle-income countries is approximately half that of high-income countries. Therefore, the findings of this study call for additional investment and focused strategies relating to human capital endowments

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.

Data of lower middle-income countries between 1980 and 2018 to study whether indigenous or foreign innovation efforts are more important for the transition of lower middle-income economies to the upper middle-income rank. Data are designed for discrete-time hazard models.

About **** of the Polish population belonged to the middle class in April 2019. Nearly ******* 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.

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.

China Disposable Income per Capita: Urban: Lower Middle Income data was reported at 33,351.000 RMB in 2024. This records an increase from the previous number of 32,202.000 RMB for 2023. China Disposable Income per Capita: Urban: Lower Middle Income data is updated yearly, averaging 6,367.340 RMB from Dec 1985 (Median) to 2024, with 40 observations. The data reached an all-time high of 33,351.000 RMB in 2024 and a record low of 632.880 RMB in 1985. China Disposable Income per Capita: Urban: Lower 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.

Research Hypothesis

The research investigates the relationship between economic growth and income inequality, drawing on Kuznets' theory of an inverted U-shaped relationship. The central hypotheses are:

H0: Income inequality is not affected by GDP growth, indicating no relationship between economic growth and income inequality.
H1: GDP growth influences income inequality, which may increase or decrease depending on societal and economic contexts.
H2: GDP growth positively affects income inequality, widening income disparities.
H3: GDP growth negatively affects income inequality, reducing disparities and promoting equitable distribution.
H4: In lower-middle-income countries, GDP growth reduces income inequality.

Description of Data

The study utilizes data from the World Bank for 39 countries spanning the years 2004 to 2019. The dataset includes:

Gross Domestic Product (GDP): Measured in constant local currency units (LOGGDP), used as a proxy for economic growth.
Gini Index: A standardized measure of income inequality, ranging from 0 (perfect equality) to 100 (maximum inequality).
Income Categories: Countries are grouped into high, upper-middle, and lower-middle income categories based on the World Bank’s GNI per capita classification.

Methodology and Data Gathering

Selection Criteria: Countries were selected to represent diverse income groups, ensuring a balanced and comprehensive analysis of varying economic contexts.
Data Source: All data were sourced from the World Bank’s publicly available databases.
Data Analysis:
  Correlation analysis to explore the general relationship between GDP and inequality.
  Linear regression models to identify causal relationships across income categories.
  Group-specific analysis to investigate how GDP impacts inequality within high-, upper-middle-, and lower-middle-income countries.

Notable Findings

Overall Trends:
  Across all countries, a positive correlation was observed between GDP and the Gini index, indicating that GDP growth is generally associated with increasing income inequality.
  The regression model (GINI = 23.931 + 0.937 × LOGGDP) confirmed a statistically significant relationship, with an F-value (p < 0.05) supporting the model’s validity.

Income Group Analysis:
  High-Income Countries: No statistically significant relationship between GDP growth and inequality.
  Upper-Middle-Income Countries: A weak relationship was observed, but it lacked statistical significance.
  Lower-Middle-Income Countries: A significant negative relationship was identified (β = -22.291, p < 0.001), suggesting that in these countries, GDP growth reduces income inequality.

Interpretation and Use of Data: The findings can be interpreted in light of Kuznets' hypothesis, which posits that inequality first rises and then falls as economies develop.

China Household Survey: Number of Employee per Household: Urban: Upper Middle Income data was reported at 1.470 Person in 2012. This records a decrease from the previous number of 1.480 Person for 2011. China Household Survey: Number of Employee per Household: Urban: Upper Middle Income data is updated yearly, averaging 1.810 Person from Dec 1985 (Median) to 2012, with 28 observations. The data reached an all-time high of 2.300 Person in 1985 and a record low of 1.470 Person in 2012. China Household Survey: Number of Employee per Household: Urban: Upper 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.HC: No of Household Surveyed: Urban: By Income Level.

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

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.

China Disposable Income per Capita: Urban: Low Income data was reported at 18,003.000 RMB in 2024. This records an increase from the previous number of 17,478.000 RMB for 2023. China Disposable Income per Capita: Urban: Low Income data is updated yearly, averaging 3,829.760 RMB from Dec 1985 (Median) to 2024, with 40 observations. The data reached an all-time high of 18,003.000 RMB in 2024 and a record low of 546.720 RMB in 1985. China Disposable Income per Capita: Urban: Low 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.

China Disposable Income per Capita: Urban: High Income data was reported at 113,763.000 RMB in 2024. This records an increase from the previous number of 110,639.000 RMB for 2023. China Disposable Income per Capita: Urban: High Income data is updated yearly, averaging 21,501.935 RMB from Dec 1985 (Median) to 2024, with 40 observations. The data reached an all-time high of 113,763.000 RMB in 2024 and a record low of 1,012.320 RMB in 1985. China Disposable Income per Capita: Urban: High 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.

Explore The Human Capital Report dataset for insights into Human Capital Index, Development, and World Rankings. Find data on Probability of Survival to Age 5, Expected Years of School, Harmonized Test Scores, and more.

Low income, Upper middle income, Lower middle income, High income, Human Capital Index (Lower Bound), Human Capital Index, Human Capital Index (Upper Bound), Probability of Survival to Age 5, Expected Years of School, Harmonized Test Scores, Learning-Adjusted Years of School, Fraction of Children Under 5 Not Stunted, Adult Survival Rate, Development, Human Capital, World Rankings

Afghanistan, Albania, Algeria, Angola, Antigua and Barbuda, Argentina, Armenia, Australia, Austria, Azerbaijan, Bahrain, Bangladesh, Belarus, Belgium, Benin, Bhutan, Bosnia and Herzegovina, Botswana, Brazil, Brunei, Bulgaria, Burkina Faso, Burundi, Côte d'Ivoire, Cambodia, Cameroon, Canada, Central African Republic, Chad, Chile, China, Colombia, Comoros, Congo, Costa Rica, Croatia, Cyprus, Denmark, Dominica, Dominican Republic, Ecuador, Egypt, El Salvador, Estonia, Eswatini, Ethiopia, Fiji, Finland, France, Gabon, Gambia, Georgia, Germany, Ghana, Greece, Grenada, Guatemala, Guinea, Guyana, Haiti, Honduras, Hungary, Iceland, India, Indonesia, Iran, Iraq, Ireland, Israel, Italy, Jamaica, Japan, Jordan, Kazakhstan, Kenya, Kiribati, Kuwait, Latvia, Lebanon, Lesotho, Liberia, Lithuania, Luxembourg, Madagascar, Malawi, Malaysia, Mali, Malta, Marshall Islands, Mauritania, Mauritius, Mexico, Micronesia, Moldova, Mongolia, Montenegro, Morocco, Mozambique, Myanmar, Namibia, Nauru, Nepal, Netherlands, New Zealand, Nicaragua, Niger, Nigeria, North Macedonia, Norway, Oman, Pakistan, Palau, Panama, Papua New Guinea, Paraguay, Peru, Philippines, Poland, Portugal, Qatar, Romania, Russia, Rwanda, Samoa, Saudi Arabia, Senegal, Serbia, Seychelles, Sierra Leone, Singapore, Slovenia, Solomon Islands, South Africa, South Sudan, Spain, Sri Lanka, Sudan, Sweden, Switzerland, Tajikistan, Tanzania, Thailand, Timor-Leste, Togo, Tonga, Trinidad and Tobago, Tunisia, Turkey, Tuvalu, Uganda, Ukraine, United Arab Emirates, United Kingdom, Uruguay, Uzbekistan, Vanuatu, Vietnam, Yemen, Zambia, Zimbabwe, WORLD

Follow data.kapsarc.org for timely data to advance energy economics research.

Last year edition of the World Economic Forum Human Capital Report explored the factors contributing to the development of an educated, productive and healthy workforce. This year edition deepens the analysis by focusing on a number of key issues that can support better design of education policy and future workforce planning.

China Household Survey: Number of Permanent Resident per Household: Rural: Upper Middle Income data was reported at 3.614 Person in 2012. This records a decrease from the previous number of 3.627 Person for 2011. China Household Survey: Number of Permanent Resident per Household: Rural: Upper Middle Income data is updated yearly, averaging 3.810 Person from Dec 2002 (Median) to 2012, with 11 observations. The data reached an all-time high of 3.920 Person in 2002 and a record low of 3.614 Person in 2012. China Household Survey: Number of Permanent Resident per Household: Rural: Upper 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.HC: No of Household Surveyed: Rural: By Income Level.

Introduction Antibiotics are indispensable to maintaining human health; however, their overuse has resulted in resistant organisms, increasing morbidity, mortality and costs. Increasing antimicrobial resistance (AMR) is a major public health threat, resulting in multiple campaigns across countries to improve appropriate antimicrobial use. This includes addressing the overuse of antimicrobials for self-limiting infections, such as upper respiratory tract infections (URTIs), particularly in lower- and middle-income countries (LMICs) where there is the greatest inappropriate use and where antibiotic utilization has increased the most in recent years. Consequently, there is a need to document current practices and successful initiatives in LMICs to improve future antimicrobial use.MethodologyDocumentation of current epidemiology and management of URTIs, particularly in LMICs, as well as campaigns to improve future antimicrobial use and their influence where known.ResultsMuch concern remains regarding the prescribing and dispensing of antibiotics for URTIs among LMICs. This includes considerable self-purchasing, up to 100% of pharmacies in some LMICs. However, multiple activities are now ongoing to improve future use. These incorporate educational initiatives among all key stakeholder groups, as well as legislation and other activities to reduce self-purchasing as part of National Action Plans (NAPs). Further activities are still needed however. These include increased physician and pharmacist education, starting in medical and pharmacy schools; greater monitoring of prescribing and dispensing practices, including the development of pertinent quality indicators; and targeted patient information and health education campaigns. It is recognized that such activities are more challenging in LMICs given more limited resources and a lack of healthcare professionals.ConclusionInitiatives will grow across LMICs to reduce inappropriate prescribing and dispensing of antimicrobials for URTIs as part of NAPs and other activities, and these will be monitored.

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.

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).