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 46.5 (0.465) 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 0.32 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 0.47 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.

Hong Kong SAR (China) Gini Coefficient: MHI: Post-Tax & Post-Social Transfer :EAH data was reported at 0.422 Unit in 2016. This records a decrease from the previous number of 0.430 Unit for 2011. Hong Kong SAR (China) Gini Coefficient: MHI: Post-Tax & Post-Social Transfer :EAH data is updated yearly, averaging 0.430 Unit from Dec 2006 (Median) to 2016, with 3 observations. The data reached an all-time high of 0.436 Unit in 2006 and a record low of 0.422 Unit in 2016. Hong Kong SAR (China) Gini Coefficient: MHI: Post-Tax & Post-Social Transfer :EAH data remains active status in CEIC and is reported by Census and Statistics Department. The data is categorized under Global Database’s Hong Kong SAR (China) – Table HK.G130: Gini Coefficient.

This statistic shows the Gini's concentration coefficient in Taiwan from 1980 to 2023. In 2023, the Gini index in Taiwan was 33.9 points, ranging at roughly the same level as in 2010. In the countries having relative equality in their distributions of income, the value of the Gini coefficient usually ranges between the scores of 20 and 35. In comparison, the Gini index in China ranged at around 46.7 points in 2022.

Hong Kong SAR (China) Gini Coefficient: MHI: Original: Per Capita data was reported at 0.499 Unit in 2016. This records a decrease from the previous number of 0.507 Unit for 2011. Hong Kong SAR (China) Gini Coefficient: MHI: Original: Per Capita data is updated yearly, averaging 0.499 Unit from Dec 1996 (Median) to 2016, with 5 observations. The data reached an all-time high of 0.507 Unit in 2011 and a record low of 0.491 Unit in 2001. Hong Kong SAR (China) Gini Coefficient: MHI: Original: Per Capita data remains active status in CEIC and is reported by Census and Statistics Department. The data is categorized under Global Database’s Hong Kong SAR (China) – Table HK.G130: Gini Coefficient.

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.

Hong Kong SAR (China) Gini Coefficient: MHI: Post-Tax data was reported at 0.524 Unit in 2016. This records an increase from the previous number of 0.521 Unit for 2011. Hong Kong SAR (China) Gini Coefficient: MHI: Post-Tax data is updated yearly, averaging 0.521 Unit from Dec 1996 (Median) to 2016, with 5 observations. The data reached an all-time high of 0.524 Unit in 2016 and a record low of 0.508 Unit in 1996. Hong Kong SAR (China) Gini Coefficient: MHI: Post-Tax data remains active status in CEIC and is reported by Census and Statistics Department. The data is categorized under Global Database’s Hong Kong SAR (China) – Table HK.G130: Gini Coefficient.

Hong Kong SAR (China) Gini Coefficient: MHI: Post-Tax & Post-Social Trfr: Per Capita: EAH data was reported at 0.376 Unit in 2021. This records a decrease from the previous number of 0.402 Unit for 2016. Hong Kong SAR (China) Gini Coefficient: MHI: Post-Tax & Post-Social Trfr: Per Capita: EAH data is updated yearly, averaging 0.401 Unit from Dec 2006 (Median) to 2021, with 4 observations. The data reached an all-time high of 0.412 Unit in 2006 and a record low of 0.376 Unit in 2021. Hong Kong SAR (China) Gini Coefficient: MHI: Post-Tax & Post-Social Trfr: Per Capita: EAH data remains active status in CEIC and is reported by Census and Statistics Department. The data is categorized under Global Database’s Hong Kong SAR (China) – Table HK.G130: Gini Coefficient. Data prior to 2011 excludes Government’s one-off relief measures.

Food security is one of the important issues in the current world development process. The article takes 31 provinces (districts and cities) in China as the research object and constructs a multidimensional food security level evaluation index system from four dimensions: quantitative security, nutritional security, ecological security, and capacity security. Using the entropy method, China’s food security index was calculated for the ten-year period from 2013 to 2022. Overall, China’s food security level showed an upward trend during the decade, with the provinces of Shandong, Heilongjiang, and Henan having the highest level of security. The distribution dynamics of food security and its spatiotemporal evolution in the seven regions were examined using the Dagum Gini coefficient and its decomposition, and the absolute and conditional convergence of food security in the different areas was verified. The results of the study show that the provinces within East China have the largest gap in food security levels between them, and there is absolute β-convergence. Looking at China as a whole, the development of its food security level is characterized by significant convergence, which means that provinces with a low level of food security will have a faster rate of growth than those with a high level of food security, resulting in a gradual narrowing of the gap in food security levels between provinces.

In China hat der Gini-Index im Jahr 2023 rund 46,5 Punkte betragen. Die Statistik zeigt die Entwicklung der Einkommensungleichheit anhand des Gini-Index in den Jahren 2013 bis 2023. Was ist der Gini-Index? Der Gini-Index oder Gini-Koeffizient ist ein statistisches Maß, das zur Darstellung von Ungleichverteilungen verwendet wird. Er kann einen beliebigen Wert zwischen 0 und 100 Punkten annehmen. Der Gini-Index zeigt die Abweichung der Verteilung des verfügbaren Einkommens auf Personen oder Haushalte innerhalb eines Landes von einer vollkommen gleichen Verteilung. Ein Wert von 0 bedeutet absolute Gleichheit, ein Wert von 100 absolute Ungleichheit.

This dataset is about book series and is filtered where the books is The trend of the Gini coefficient of China, featuring 4 columns: authors, book series, books, and publication dates. The preview is ordered by number of books (descending).

In 2024, the average annual per capita disposable income of rural households in China was approximately 23,119 yuan, roughly 43 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 700 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.

This data file includes the Gini coefficient calculated for different wealth welfare aggregates constructed for all Luxembourg Wealth Study (LWS) datasets in all waves (as of March 2022). It includes Gini coefficients calculated on: • Disposable Net Worth • Value of Principal residence • Financial Assets

This project sought to renew the ESRC's invaluable financial support to LIS (formerly the Luxembourg Income Study) for a period of five more years. LIS is an independent, non-profit cross-national data archive and research institute located in Luxembourg. LIS relies on financial contributions from national science foundations, other research institutions and consortia, data-providing agencies, and supranational organisations to support data harmonisation and enable free and unlimited data access to researchers in the participating countries and to students world-wide. LIS' primary activity is to make harmonised household microdata available to researchers, thus enabling cross-national, interdisciplinary primary research into socio-economic outcomes and their determinants. Users of the Luxembourg Income Study Database and Luxembourg Wealth Study Database come from countries around the globe, including the UK. LIS has four goals: 1) to harmonise microdatasets from high- and middle-income countries that include data on income, wealth, employment, and demography; 2) to provide a secure method for researchers to query data that would otherwise be unavailable due to country-specific privacy restrictions; 3) to create and maintain a remote-execution system that sends research query results quickly back to users at off-site locations; and 4) to enable, facilitate, promote and conduct crossnational comparative research on the social and economic wellbeing of populations across countries. LIS contains the Luxembourg Income Study (LIS) Database, which includes income data, and the Luxembourg Wealth Study (LWS) Database, which focuses on wealth data. LIS currently includes microdata from 46 countries in Europe, the Americas, Africa, Asia and Australasia. LIS contains over 250 datasets, organised into eight time "waves," spanning the years 1968 to 2011. Since 2007, seventeen more countries have been added to LIS, including the BRICS countries (Brazil, Russia, India, China, South Africa), Japan, South Korea and a number of other Latin American countries. LWS contains 20 wealth datasets from 12 countries, including the UK, and covers the period 1994 to 2007. All told, LIS and LWS datasets together cover 86% of world GDP and 64% of world population. Users submit statistical queries to the microdatabases using a Java-based job submission interface or standard email. The databases are especially valuable for primary research in that they offer access to cross-national data at the micro-level - at the level of households and persons. Users are economists, sociologists, political scientists, and policy analysts, among others, and they employ a range of statistical approaches and methods. LIS also provides extensive documentation - metadata - for both LIS and LWS, concerning technical aspects of the survey data, the harmonisation process, and the social institutions of income and wealth provision in participating countries. In the next five years, for which support is sought, LIS will: - expand LIS, adding Waves IX (2013) and X (2016), and add new middle-income countries; - develop LWS, adding another wave of datasets to existing countries; acquire new wealth datasets for 14 more countries in cooperation with the European Central Bank (based on the Household Finance and Consumption Survey); - create a state-of-the-art metadata search and storage system; - maintain international standards in data security and data infrastructure systems; - provide high-quality harmonised household microdata to researchers around the world; - enable interdisciplinary cross-national social science research covering 45+ countries, including the UK; - aim to broaden its reach and impact in academic and non-academic circles through focused communications strategies and collaborations.

In 2023, the gross domestic product (GDP) of China amounted to around 17.8 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 three percent in 2022 and 5.2 percent in 2023. In 2023, per capita GDP in China reached around 12,600 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 2023. 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 31 percent in 2023. 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 2023. 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.

In 2023, approximately 127.1 million people lived in Guangdong province in China. That same year, only about 3.65 million people lived in the sparsely populated highlands of Tibet. Regional differences in China China is the world’s most populous country, with an exceptional economic growth momentum. The country can be roughly divided into three regions: Western, Eastern, and Central China. Western China covers the most remote regions from the sea. It also has the highest proportion of minority population and the lowest levels of economic output. Eastern China, on the other hand, enjoys a high level of economic development and international corporations. Central China lags behind in comparison to the booming coastal regions. In order to accelerate the economic development of Western and Central Chinese regions, the PRC government has ramped up several incentive plans such as ‘Rise of Central China’ and ‘China Western Development’. Economic power of different provinces When observed individually, some provinces could stand an international comparison. Jiangxi province, for example, a medium-sized Chinese province, had a population size comparable to Argentina or Spain in 2023. That year, the GDP of Zhejiang, an eastern coastal province, even exceeded the economic output of the Netherlands. In terms of per capita annual income, the municipality of Shanghai reached a level close to that of the Czech Republik. Nevertheless, as shown by the Gini Index, China’s economic spur leaves millions of people in dust. Among the various kinds of economic inequality in China, regional or the so-called coast-inland disparity is one of the most significant. Posing as evidence for the rather large income gap in China, the poorest province Heilongjiang had a per capita income similar to that of Sri Lanka that year.

Food security is one of the important issues in the current world development process. The article takes 31 provinces (districts and cities) in China as the research object and constructs a multidimensional food security level evaluation index system from four dimensions: quantitative security, nutritional security, ecological security, and capacity security. Using the entropy method, China’s food security index was calculated for the ten-year period from 2013 to 2022. Overall, China’s food security level showed an upward trend during the decade, with the provinces of Shandong, Heilongjiang, and Henan having the highest level of security. The distribution dynamics of food security and its spatiotemporal evolution in the seven regions were examined using the Dagum Gini coefficient and its decomposition, and the absolute and conditional convergence of food security in the different areas was verified. The results of the study show that the provinces within East China have the largest gap in food security levels between them, and there is absolute β-convergence. Looking at China as a whole, the development of its food security level is characterized by significant convergence, which means that provinces with a low level of food security will have a faster rate of growth than those with a high level of food security, resulting in a gradual narrowing of the gap in food security levels between provinces.

The Gini coefficients of TCM health resources from 2012 to 2020.

Hong Kong SAR (China) Gini Coefficient: MHI: Original: Economically Active Household (EAH) data was reported at 0.482 Unit in 2016. This records a decrease from the previous number of 0.489 Unit for 2011. Hong Kong SAR (China) Gini Coefficient: MHI: Original: Economically Active Household (EAH) data is updated yearly, averaging 0.489 Unit from Dec 2006 (Median) to 2016, with 3 observations. The data reached an all-time high of 0.490 Unit in 2006 and a record low of 0.482 Unit in 2016. Hong Kong SAR (China) Gini Coefficient: MHI: Original: Economically Active Household (EAH) data remains active status in CEIC and is reported by Census and Statistics Department. The data is categorized under Global Database’s Hong Kong SAR (China) – Table HK.G130: Gini Coefficient.

We analyzed the formation mechanism of digital economic gap (DEG), measured the DEGs at four levels (the gaps in information and communication technology accessibility, application skill, digital economic outcome, and efficiency), and explored its spatiotemporal evolution in China by using DEA–Malmquist index method, Gini Coefficent method, Kernel density, and Geodetector. Data from 263 cities in China between 2011 and 2019 were collected. The results demonstrated that (1) The four levels of DEGs showed different trends. The first-, second- and third- level DEGs showed ceiling effects, and the fourth-level DEG oscillated upward. (2) The distribution location of the four levels of DEGs varied. The first- and second-level DEGs shifted at a stable low degree. The third-level DEG increased steadily and polarized. The fourth-level DEG increased steadily and formed a multi-polarization tre...

Description and measurement of variables and data sources.

Grey water footprint (GWF) efficiency is a reflection of both water pollution and the economy. The assessment of GWF and its efficiency is conducive to improving water environment quality and achieving sustainable development. This study introduces a comprehensive approach to assessing and analyzing the GWF efficiency. Based on the measurement of the GWF efficiency, the kernel density estimation and the Dagum Gini coefficient method are introduced to investigate the spatial and temporal variation of the GWF efficiency. The Geodetector method is also innovatively used to investigate the internal and external driving forces of GWF efficiency, not only revealing the effects of individual factors, but also probing the interaction between different drivers. For demonstrating this assessment approach, nine provinces in China’s Yellow River Basin from 2005 to 2020 are chosen for the study. The results show that: (1) the GWF efficiency of the basin increases from 23.92 yuan/m3 in 2005 to 164.87 yuan/m3 in 2020, showing a distribution pattern of "low in the western and high in the eastern". Agricultural GWF is the main contributor to the GWF. (2) The temporal variation of the GWF efficiency shows a rising trend, and the kernel density curve has noticeable left trailing and polarization characteristics. The spatial variation of the GWF efficiency fluctuates upwards, accompanied by a rise in the overall Gini coefficient from 0.25 to 0.28. Inter-regional variation of the GWF efficiency is the primary source of spatial variation, with an average contribution of 73.39%. (3) For internal driving forces, economic development is the main driver of the GWF efficiency, and the interaction of any two internal factors enhances the explanatory power. For external driving forces, capital stock reflects the greatest impact. The interaction combinations with the highest q statistics for upstream, midstream and downstream are capital stock and population density, technological innovation and population density, and industrial structure and population density, respectively.