Economic data( Per capita GDP, GDP growth rate, Primary, secondary and tertiary industries to GDP, Gini index, Engel coefficient) of 34 key areas along the One Belt One Road are downscaled from coarse data. First, we collect the statistics of economic data( Per capita GDP, GDP growth rate, Primary, secondary and tertiary industries to GDP, Gini index, Engel coefficient) at the national or provincial scales, and use GIS spatial analysis methods to analyze the relationship between economic data and covariables (e.g.,night lighting NPP-VIIRS, road network density). Then, spatial regression analysis method is used to model relationship between the economic data and covariables, and economic data( Per capita GDP, GDP growth rate, Primary, secondary and tertiary industries to GDP, Gini index, Engel coefficient) at county level were downscaled and predicted. Based on statistical data and spatial analysis, the data of economic adult is finally integrated. The economic data( Per capita GDP, GDP growth rate, Primary, secondary and tertiary industries to GDP, Gini index, Engel coefficient) can provide important basic data for the development of social and economic research on key areas and regions along the Belt and Road.

In 2023, according to the Gini coefficient, household income distribution in the United States was 0.47. This figure was at 0.43 in 1990, which indicates an increase in income inequality in the U.S. over the past 30 years. What is the Gini coefficient? The Gini coefficient, or Gini index, is a statistical measure of economic inequality and wealth distribution among a population. A value of zero represents perfect economic equality, and a value of one represents perfect economic inequality. The Gini coefficient helps to visualize income inequality in a more digestible way. For example, according to the Gini coefficient, the District of Columbia and the state of New York have the greatest amount of income inequality in the U.S. with a score of 0.51, and Utah has the greatest income equality with a score of 0.43. The Gini coefficient around the world The Gini coefficient is also an effective measure to help picture income inequality around the world. For example, in 2018 income inequality was highest in South Africa, while income inequality was lowest in Slovenia.

Information about GDP per capita; GDP growth; total public expenditure as percent of GDP; percent public consumption; gini coefficient for equivalent disposable income; percent imports from developing countries; inflation; percent social security transfers; percent unionized; percent unemployed; female labor force participation rate; percent employed in industry; percent employed in agriculture; percent employed in service sector; percent of population aged 0-14 years; percent of population aged 15-64 years; percent of population aged 65 years or more for 16 industrialized countries during the period 1966-1994.

Purpose:

Find the reason to why income distribution change in industrialized countries.

Comparing the 130 selected regions regarding the gini index , South Africa is leading the ranking (0.63 points) and is followed by Namibia with 0.58 points. At the other end of the spectrum is Slovakia with 0.23 points, indicating a difference of 0.4 points to South Africa. The Gini coefficient here measures the degree of income inequality on a scale from 0 (=total equality of incomes) to one (=total inequality).The shown data are an excerpt of Statista's Key Market Indicators (KMI). The KMI are a collection of primary and secondary indicators on the macro-economic, demographic and technological environment in more than 150 countries and regions worldwide. All input data are sourced from international institutions, national statistical offices, and trade associations. All data has been are processed to generate comparable datasets (see supplementary notes under details for more information).

This statistic shows the inequality of income distribution in China from 2005 to 2023 based on the Gini Index. In 2023, China reached a score of ************ points. The Gini Index is a statistical measure that is used to represent unequal distributions, e.g. income distribution. It can take any value between 1 and 100 points (or 0 and 1). The closer the value is to 100 the greater is the inequality. 40 or 0.4 is the warning level set by the United Nations. The Gini Index for South Korea had ranged at about **** in 2022. Income distribution in China The Gini coefficient is used to measure the income inequality of a country. The United States, the World Bank, the US Central Intelligence Agency, and the Organization for Economic Co-operation and Development all provide their own measurement of the Gini coefficient, varying in data collection and survey methods. According to the United Nations Development Programme, countries with the largest income inequality based on the Gini index are mainly located in Africa and Latin America, with South Africa displaying the world's highest value in 2022. The world's most equal countries, on the contrary, are situated mostly in Europe. The United States' Gini for household income has increased by around ten percent since 1990, to **** in 2023. Development of inequality in China Growing inequality counts as one of the biggest social, economic, and political challenges to many countries, especially emerging markets. Over the last 20 years, China has become one of the world's largest economies. As parts of the society have become more and more affluent, the country's Gini coefficient has also grown sharply over the last decades. As shown by the graph at hand, China's Gini coefficient ranged at a level higher than the warning line for increasing risk of social unrest over the last decade. However, the situation has slightly improved since 2008, when the Gini coefficient had reached the highest value of recent times.

In 2024, the gross domestic product (GDP) of China amounted to around 18.7 trillion U.S. dollars. In comparison to the GDP of the other BRIC countries India, Russia and Brazil, China came first that year and second in the world GDP ranking. The stagnation of China's GDP in U.S. dollar terms in 2022 and 2023 was mainly due to the appreciation of the U.S. dollar. China's real GDP growth was 3.1 percent in 2022 and 5.4 percent in 2023. In 2024, per capita GDP in China reached around 13,300 U.S. dollars. Economic performance in China Gross domestic product (GDP) is a primary economic indicator. It measures the total value of all goods and services produced in an economy over a certain time period. China's economy used to grow quickly in the past, but the growth rate of China’s real GDP gradually slowed down in recent years, and year-on-year GDP growth is forecasted to range at only around four percent in the years after 2024. Since 2010, China has been the world’s second-largest economy, surpassing Japan.China’s emergence in the world’s economy has a lot to do with its status as the ‘world’s factory’. Since 2013, China is the largest export country in the world. Some argue that it is partly due to the undervalued Chinese currency. The Big Mac Index, a simplified and informal way to measure the purchasing power parity between different currencies, indicates that the Chinese currency yuan was roughly undervalued by 38 percent in 2024. GDP development Although the impressive economic development in China has led millions of people out of poverty, China is still not in the league of industrialized countries on the per capita basis. To name one example, the U.S. per capita economic output was more than six times as large as in China in 2024. Meanwhile, the Chinese society faces increased income disparities. The Gini coefficient of China, a widely used indicator of economic inequality, has been larger than 0.45 over the last decade, whereas 0.40 is the warning level for social unrest.

Explore gender statistics data focusing on academic staff, employment, fertility rates, GDP, poverty, and more in the GCC region. Access comprehensive information on key indicators for Bahrain, China, India, Kuwait, Oman, Qatar, and Saudi Arabia.

academic staff, Access to anti-retroviral drugs, Adjusted net enrollment rate, Administration and Law programmes, Age at first marriage, Age dependency ratio, Cause of death, Children out of school, Completeness of birth registration, consumer prices, Cost of business start-up procedures, Employers, Employment in agriculture, Employment in industry, Employment in services, employment or training, Engineering and Mathematics programmes, Female headed households, Female migrants, Fertility planning status: mistimed pregnancy, Fertility planning status: planned pregnancy, Fertility rate, Firms with female participation in ownership, Fisheries and Veterinary programmes, Forestry, GDP, GDP growth, GDP per capita, gender parity index, Gini index, GNI, GNI per capita, Government expenditure on education, Government expenditure per student, Gross graduation ratio, Households with water on the premises, Inflation, Informal employment, Labor force, Labor force with advanced education, Labor force with basic education, Labor force with intermediate education, Learning poverty, Length of paid maternity leave, Life expectancy at birth, Mandatory retirement age, Manufacturing and Construction programmes, Mathematics and Statistics programmes, Number of under-five deaths, Part time employment, Population, Poverty headcount ratio at national poverty lines, PPP, Primary completion rate, Retirement age with full benefits, Retirement age with partial benefits, Rural population, Sex ratio at birth, Unemployment, Unemployment with advanced education, Urban population

Bahrain, China, India, Kuwait, Oman, Qatar, Saudi Arabia

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Although China’s education development has made great progress, there are obvious regional differences in China’s educational development. A systematic investigation of the regional inequality in China’s educational development and its driving factors is of great significance for optimizing the allocation of educational resources and giving full play to the critical role of education in regional development. In addition, the research on the evolution and internal mechanism of educational development inequality in China can also provide experience and reference for the Global South. Therefore, we construct a comprehensive evaluation index system to measure the level of regional educational development, reveal the regional inequalities in China’s educational development, and employ spatial econometric model to dissect the factors influencing the regional inequalities. The results show that China’s educational development level continues to increasing from 2003 to 2020, but a significant decrease in its growth rate. In this process, regional differences in education inequality in China have gradually narrowed, which can be confirmed by changes in the Gini coefficient and Theil index. In terms of direct spillover effects, the per capita fiscal expenditure on education and urbanization rate have positive effects. In terms of indirect spillover effects, per capita GDP and per capita fiscal expenditure on education have negative effects, while population density and urbanization rate have positive effects. After replacing the weight matrix and removing the extreme values, the model also passes the robustness test. However, this mechanism is heterogeneous in different regions, therefore, we put forward the corresponding policies and measures according to the regional driving effects of influencing factors.

In 2024, the average annual per capita disposable income of households in China amounted to approximately 41,300 yuan. Annual per capita income in Chinese saw a significant rise over the last decades and is still rising at a high pace. During the last ten years, per capita disposable income roughly doubled in China. Income distribution in China As an emerging economy, China faces a large number of development challenges, one of the most pressing issues being income inequality. The income gap between rural and urban areas has been stirring social unrest in China and poses a serious threat to the dogma of a “harmonious society” proclaimed by the communist party. In contrast to the disposable income of urban households, which reached around 54,200 yuan in 2024, that of rural households only amounted to around 23,100 yuan. Coinciding with the urban-rural income gap, income disparities between coastal and western regions in China have become apparent. As of 2023, households in Shanghai and Beijing displayed the highest average annual income of around 84,800 and 81,900 yuan respectively, followed by Zhejiang province with 63,800 yuan. Gansu, a province located in the West of China, had the lowest average annual per capita household income in China with merely 25,000 yuan. Income inequality in China The Gini coefficient is the most commonly used measure of income inequality. For China, the official Gini coefficient also indicates the astonishing inequality of income distribution in the country. Although the Gini coefficient has dropped from its high in 2008 at 49.1 points, it still ranged at a score of 46.5 points in 2023. The United Nations have set an index value of 40 as a warning level for serious inequality in a society.

Although China’s education development has made great progress, there are obvious regional differences in China’s educational development. A systematic investigation of the regional inequality in China’s educational development and its driving factors is of great significance for optimizing the allocation of educational resources and giving full play to the critical role of education in regional development. In addition, the research on the evolution and internal mechanism of educational development inequality in China can also provide experience and reference for the Global South. Therefore, we construct a comprehensive evaluation index system to measure the level of regional educational development, reveal the regional inequalities in China’s educational development, and employ spatial econometric model to dissect the factors influencing the regional inequalities. The results show that China’s educational development level continues to increasing from 2003 to 2020, but a significant decrease in its growth rate. In this process, regional differences in education inequality in China have gradually narrowed, which can be confirmed by changes in the Gini coefficient and Theil index. In terms of direct spillover effects, the per capita fiscal expenditure on education and urbanization rate have positive effects. In terms of indirect spillover effects, per capita GDP and per capita fiscal expenditure on education have negative effects, while population density and urbanization rate have positive effects. After replacing the weight matrix and removing the extreme values, the model also passes the robustness test. However, this mechanism is heterogeneous in different regions, therefore, we put forward the corresponding policies and measures according to the regional driving effects of influencing factors.

This project aims to explore the relationship between the social conditions of different countries and the prevalence of mental disorders. By integrating multiple datasets, including mental health statistics, economic indicators, and demographic variables, we analyzed patterns and potential correlations to gain valuable insights.

To facilitate this analysis, we developed an interactive dashboard featuring various visualizations that compare different variables. The final dataset includes:

• Demographic & Geographic Data: Country ISO code, country name, age range, sex, rural population percentage.
• Health Indicators: AVD for mental disorders, suicide mortality rate (2019), life expectancy at birth.
• Economic & Social Indicators: HDI value, rank and classification, GDP, GDP per capita (PPP, constant 2021 dollars) and GDP classification, Gini Index (classified as high/not high), unemployment rate (ILO estimate, 2021).

By combining these diverse data sources, the project provides a comprehensive understanding of how social and economic conditions influence mental health outcomes globally.

This work was developed as part of a course in our degree program.

Theoretical links between nonpolitical volunteering and political participation and the outcome variables.

Individual-level effects of nonpolitical volunteering and political participation on eudaimonic well-being.

This Public Expenditure Review (PER) was prepared in response to a request from the Ministry of Finance (MoF) and is designed to inform Lesotho’s fiscal consolidation due to a narrowing of its fiscal space. Lesotho is facing a tough macro-fiscal outlook due to a sharp decline in Southern African Customs Union (SACU) revenues. This situation necessitates a significant adjustment in the current fiscal stance to ensure longer-term fiscal sustainability. However, the adjustment should be tailored to minimize any adverse growth and poverty impacts. Thus, this PER is intended to support the government’s efforts to adjust its policies to better address Lesotho’s current macro-fiscal circumstances. Lesotho is one of the poorest and most unequal countries in the world, despite a relatively good growth performance over the past 15 years. Lesotho’s per capita gross national income is about 1550 US dollars. Lesotho’s poverty rate is 59 percent (1.90 US dollars purchasing power parity [PPP] per day), its Gini coefficient is 0.541, and about 59 percent of the population now lives below the international poverty line of 1.90 dollar/day. Both poverty and extreme poverty disproportionately affect the rural population, and the bottom 40 percent of Lesotho’s population experienced a decline in consumption each year between 2002 and 2011. This compares to increases, albeit meager, for the remaining 60 percent of the population over the same period. Lesotho’s gross domestic product (GDP) grew at an annual average rate of 4 percent between 2000 and 2016, whereas its GDP per capita grew at an average rate of 2.8 percent during the same period. Despite the high level of government spending, Lesotho faces challenges in addressing inclusive growth and providing access to quality services for the poor, while also operating in a highly fragile environment. After political turmoil, the new government with a fragile coalition of 7 parties was established in June 2017. The government is facing a significant challenge to improving access to and the quality of public services. It is also seeking to invigorate the domestic private sector to diversify the growth sources of its economy. The level of unemployment is very high, with a low employment-to working-age population ratio, which limits prospects for social mobility and poverty reduction.

Individual-level effects of nonpolitical volunteering and political participation on social well-being.

In 2024, the average annual per capita disposable income of rural households in China was approximately ****** yuan, roughly ** percent of the income of urban households. Although living standards in China’s rural areas have improved significantly over the past 20 years, the income gap between rural and urban households is still large. Income increase of China’s households From 2000 to 2020, disposable income per capita in China increased by around *** percent. The fast-growing economy has inevitably led to the rapid income increase. Furthermore, inflation has been maintained at a lower rate in recent years compared to other countries. While the number of millionaires in China has increased, many of its population are still living in humble conditions. Consequently, the significant wealth gap between China’s rich and poor has become a social problem across the country. However, in recent years rural areas have been catching up and disposable income has been growing faster than in the cities. This development is also reflected in the Gini coefficient for China, which has decreased since 2008. Urbanization in China The urban population in China surpassed its rural population for the first time in 2011. In fact, the share of the population residing in urban areas is continuing to increase. This is not surprising considering remote, rural areas are among the poorest areas in China. Currently, poverty alleviation has been prioritized by the Chinese government. The measures that the government has taken are related to relocation and job placement. With the transformation and expansion of cities to accommodate the influx of city dwellers, neighboring rural areas are required for the development of infrastructure. Accordingly, land acquisition by the government has resulted in monetary gain by some rural households.

The module was administered as a post-election interview. The resulting data are provided along with voting, demographic, district and macro variables in a single dataset. CSES Variable List The list of variables is being provided on the CSES Website to help in understanding what content is available from CSES, and to compare the content available in each module. Themes: MICRO-LEVEL DATA: Identification and study administration variables: weighting factors; election type; date of election 1st and 2nd round; study timing (post-election study, pre-election and post-election study, between rounds of majoritarian election); mode of interview; gender of interviewer; date questionnaire administered; primary electoral district of respondent; number of days the interview was conducted after the election; language of questionnaire. Demography: year and month of birth; gender; education; marital status; union membership; union membership of others in household; business association membership, farmers´ association membership; professional association membership; current employment status; main occupation; socio economic status; employment type - public or private; industrial sector; current employment status, occupation, socio economic status, employment type - public or private, and industrial sector of spouse; household income; number of persons in household; number of children in household under the age of 18; number of children in household under the age of 6; attendance at religious services; religiosity; religious denomination; language usually spoken at home; region of residence; race; ethnicity; rural or urban residence; primary electoral district; country of birth; year arrived in current country. Survey variables: perception of public expenditure on health, education, unemployment benefits, defense, old-age pensions, business and industry, police and law enforcement, welfare benefits; perception of improving individual standard of living, state of economy, government's action on income inequality; respondent cast a ballot at the current and the previous election; vote choice (presidential, lower house and upper house elections) at the current and the previous election; respondent cast candidate preference vote at the current and the previous election; difference who is in power and who people vote for; sympathy scale for selected parties and political leaders; assessment of parties on the left-right-scale and/or an alternative scale; self-assessment on a left-right-scale and an optional scale; satisfaction with democracy; party identification; intensity of party identification, institutional and personal contact in the electoral campaigning, in person, by mail, phone, text message, email or social networks, institutional contact by whom; political information questions; expected development of household income in the next twelve month; ownership of residence, business or property or farm or livestock, stocks or bonds, savings; likelihood to find another job within the next twelve month; spouse likelihood to find another job within the next twelve month. DISTRICT-LEVEL DATA: number of seats contested in electoral district; number of candidates; number of party lists; percent vote of different parties; official voter turnout in electoral district. MACRO-LEVEL DATA: election outcomes by parties in current (lower house/upper house) legislative election; percent of seats in lower house received by parties in current lower house/upper house election; percent of seats in upper house received by parties in current lower house/upper house election; percent of votes received by presidential candidate of parties in current elections; electoral turnout; party of the president and the prime minister before and after the election; number of portfolios held by each party in cabinet, prior to and after the most recent election; size of the cabinet after the most recent election; number of parties participating in election; ideological families of parties; left-right position of parties assigned by experts and alternative dimensions; most salient factors in the election; fairness of the election; formal complaints against national level results; election irregularities reported; scheduled and held date of election; irregularities of election date; extent of election violence and post-election violence; geographic concentration of violence; post-election protest; electoral alliances permitted during the election campaign; existing electoral alliances; requirements for joint party lists; possibility of apparentement and types of apparentement agreements; multi-party endorsements on ballot; votes cast; voting procedure; voting rounds; party lists close, open, or flexible; transferable votes; cumulated votes if more than one can be cast; compulsory voting; party threshold; unit for the threshold; freedom house rating; democracy-autocracy polity IV rating; age of the current regime; regime: type of executive; number of months since last lower house and last presidential election; electoral formula for presidential elections; electoral formula in all electoral tiers (majoritarian, proportional or mixed); for lower and upper houses was coded: number of electoral segments; linked electoral segments; dependent formulae in mixed systems; subtypes of mixed electoral systems; district magnitude (number of members elected from each district); number of secondary and tertiary electoral districts; fused vote; size of the lower house; GDP growth (annual percent); GDP per capita; inflation, GDP Deflator (annual percent); Human development index; total population; total unemployment; TI corruption perception index; international migrant stock and net migration rate; general government final consumption expenditure; public spending on education; health expenditure; military expenditure; central government debt; Gini index; internet users per 100 inhabitants; mobile phone subscriptions per 100 inhabitants; fixed telephone lines per 100 inhabitants; daily newspapers; constitutional federal structure; number of legislative chambers; electoral results data available; effective number of electoral and parliamentary parties.

In 2023, the national gross income per capita in Brazil amounted to around 9,070 U.S. dollars, an increase from 8,240 dollars per person in the previous year. Gross national income (GNI) is the aggregated sum of the value added by residents in an economy, plus net taxes (minus subsidies) and net receipts of primary income from abroad. Excluding countries and territories in the Caribbean, Uruguay and Chile were the Latin American countries with the highest national income per capita. Demographic elements and income There are many factors that may influence the income level, such as gender, academic attainment, location, ethnicity, etc. The gender pay gap, for example, is significant in Brazil. As of 2023, the monthly income per capita of men was 3,271 Brazilian reals, while the figure was 2,588 reals in the case of women. Additionally, monthly per capita household income varies greatly from state to state; the figures registered in Distrito Federal and São Paulo more than double the income of federative units like Acre, Alagoas or Maranhão. A high degree of inequality The Gini coefficient measures the degree of income inequality on a scale from 0 (total equality of incomes) to 100 (total inequality). Between 2010 and 2022, Brazil's degree of inequality in wealth distribution based on the Gini coefficient reached 52.9. That year, Brazil was deemed one of the most unequal countries in Latin America. Although the latest result represented one of the worst values in recent years, the Gini index is projected to improve slightly in the near future.

New York was the state with the greatest gap between rich and poor, with a Gini coefficient score of 0.52 in 2023. Although not a state, District of Columbia was among the highest Gini coefficients in the United States that year.

Iran is a country locating in Middle east. Iran is located in a strategic region at the crossroads of Europe, Asia, and Africa. This has made it a major center of trade and commerce for centuries. Iran is also a member of the United Nations, the Non-Aligned Movement, and the Organization of Islamic Cooperation.

Despite its rich history, large population, and abundant economic potential, Iran is a lower-middle-income country (according to the World Bank). It has large reserves of raw materials, including oil, gas, and minerals, but unfortunately, it does not fully utilize these resources.

This dataset is all the data about Iran in the world bank website. Here is a summary:

Economic data(2022/23) - GDP (current US$): 463billion - GDPpercapita(currentUS): $5,211 - Inflation, GDP deflator (annual %): 31.5% - Oil rents (% of GDP): 25.6% - Gini index: 38.8 (2019)

Social data - Population, total: 88.5 million (2022) - Population growth (annual %): 1.1% (2022) - Net migration: 28,080 (2021) - Life expectancy at birth, total (years): 77 (2021) - Human Capital Index (HCI) (scale 0-1): 0.63 (2020)

Environmental data - CO2 emissions (metric tons per capita): 7.2 (2021) - Renewable energy consumption (% of total final energy consumption): 3.6% (2021) - Forest area (% of land area): 7.8% (2020)

You can access the data in this link. There is also lots of plots and other fun tools which you should try.

[World Bank notes] The World Bank systematically assesses the appropriateness of official exchange rates as conversion factors. In Iran, multiple or dual exchange rate activity exists and must be accounted for appropriately in underlying statistics. An alternative estimate (“alternative conversion factor” - PA.NUS.ATLS) is thus calculated as a weighted average of the different exchange rates in use in Iran. Doing so better reflects economic reality and leads to more accurate cross-country comparisons and country classifications by income level. For Iran, this applies to 1972-2022. Alternative conversion factors are used in the Atlas methodology and elsewhere in World Development Indicators as single-year conversion factors.

It is noted that the reporting period for national accounts data is designated as either calendar year basis (CY) or fiscal year basis (FY). For Iran, it is fiscal year based (fiscal year-end: March 20).