In 2024, the Gini coefficient of wealth in India stood at **. This was a slight decrease from previous years. The trend since 2005 shows rising inequalities among the Indian population. What is Gini coefficient of wealth? The Gini coefficient is a measure of wealth inequality. The coefficient of the Gini index ranges from 0 to 1 with 0 representing perfect equality and 1 representing perfect inequality. Wealth and income distribution and inequality can however vary greatly. In 2023, South Africa topped the list of the most unequal countries in the world in terms of income inequality. Why do economic inequalities persist in India? By the end of 2022, the richest citizens in the country owned more than ** percent of the country’s wealth. Asia’s two richest men Mukesh Ambani and Gautam Adani are Indians. The number of high-net-worth individuals has continuously increased over the last decades. While millions of people escaped poverty in the country in the last few years, the wealth distribution between rich and poor remains skewed. Crony capitalism and the accumulation of wealth through inheritance are some of the factors behind this widening gap.

Graph and download economic data for GINI Index for India (SIPOVGINIIND) from 1977 to 2022 about gini, India, and indexes.

In 2023, the Gini coefficient for income in India stood at ****. The Gini coefficient, or the Gini index, measures the inequality of income distribution, whereas a higher value closer to one (or 100 percent) represent greater inequality.

Historical dataset showing India income inequality - gini coefficient by year from N/A to N/A.

Gini Coefficient data was reported at 0.328 NA in 2021. This records a decrease from the previous number of 0.338 NA for 2020. Gini Coefficient data is updated yearly, averaging 0.339 NA from Dec 1977 (Median) to 2021, with 14 observations. The data reached an all-time high of 0.359 NA in 2017 and a record low of 0.316 NA in 1993. Gini Coefficient data remains active status in CEIC and is reported by Our World in Data. The data is categorized under Global Database’s India – Table IN.OWID.ESG: Social: Gini Coefficient: Annual.

In 2011, the Gini coefficient in rural India stood at ****, while urban India reached a higher score of **. The Gini coefficient, or the Gini index, measures the inequality of income distribution, whereas a higher value closer to one (or 100 percent) represent greater inequality.

In a survey conducted from August 2023 to July 2024, ********* recorded the lowest Gini coefficient, indicating reduced consumption inequality in rural areas of India. The Gini coefficient had decreased across states compared to the previous year.

In a survey conducted from August 2023 to July 2024, it was found tha******** recorded the lowest Gini coefficient, indicating reduced consumption inequality in urban areas of India. The Gini coefficient had decreased across states compared to the previous year.

Comparing the *** selected regions regarding the gini index , South Africa is leading the ranking (**** points) and is followed by Namibia with **** points. At the other end of the spectrum is Slovakia with **** points, indicating a difference of *** points to South Africa. The Gini coefficient here measures the degree of income inequality on a scale from * (=total equality of incomes) to *** (=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 *** 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).

South Africa had the highest inequality in income distribution in 2024, with a Gini score of **. Its South African neighbor, Namibia, followed in second. The Gini coefficient measures the deviation of income (or consumption) distribution among individuals or households within a country from a perfectly equal distribution. A value of 0 represents absolute equality, and a value of 100 represents absolute inequality. All the 20 most unequal countries in the world were either located in Africa or Latin America & The Caribbean.

Data and insights on Wealth Distribution in India - share of wealth, average wealth, HNIs, wealth inequality GINI, and comparison with global peers.

Goal 10: Reduce inequality within and among countriesOn average – and taking into account population size – income inequality increased by 11% in developing countries between 1990 and 2010.A significant majority of households in developing countries – more than 75% – are living today in societies where income is more unequally distributed than it was in the 1990s.Children in the poorest 20% of the population are still up to three times more likely to die before their fifth birthday than children in the richest quintiles.Social protection has been significantly extended globally, yet persons with disabilities are up to five times more likely than average to incur catastrophic health expenditures.Despite overall declines in maternal mortality in the majority of developing countries, women in rural areas are still up to three times more likely to die while giving birth than women living in urban centres.The Gini Coefficient of income inequality for India has risen from 33.4% in 2004 to 33.6% in 2011.This map layer is offered by Esri India, for ArcGIS Online subscribers, If you have any questions or comments, please let us know via content@esri.in.

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 AssetsThis 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. All surveyed households and their members are included in our estimates of Gini and Atkinson coefficients, percentile ratios, and poverty lines. Poverty lines are calculated based on the total population. Those lines are then used to calculate poverty rates among subgroups (children and the elderly). Thus, when calculating poverty rates, the subgroups vary, but the poverty lines remain constant within any given dataset. The data file includes the Gini coefficient calculated for different wealth welfare aggregates constructed for all LWS datasets in all waves (as of March 2022).

These datasets form the basis of an empirical inquiry into whether income inequality belongs in a macro model of voter turnout. Time series modeling suggests that the Gini coefficient enters nonlinearly in Canada and this finding is confirmed in a panel data model of Indian states.

ObjectiveTo assess the change in the level of educational inequality and the contribution of social factors and demographic factors.Data sourceThree rounds of National Sample Survey viz. 64th (2007–2008), 71st (2014), and 75th (2017-2018) have been used.MethodsEducation Gini is used to study the extent of educational inequality over the time period. Decomposition method is used for "within-group" and “between group” inequality. Tobit regression model is utilized to study factors influencing average years of schooling (AYS). Finally, regression-based Shapley decomposition method is used to identify factors contributing in educational inequality.ResultsThe level of AYS has improved over the period and reached to 7.7 years in 2018. Further, the level of educational inequality gone down between 2007 and 2018, but the Gini indices are still concentrated around 38%. Decomposition of the Gini and Shapley regression approach indicates that the within-group component and rural-urban division contribute the most to educational inequality. Tobit model signifies that digital exposure, household occupation, wealth quintile, and household size play a key role in determining educational attainment.ConclusionThe paper underscores the improvement of education in rural areas by focusing on school infrastructure, e-learning, educational quality, and parent involvement.

The data contains 5 different files, classified by topic. The file india_pov_c81_revise.dta contains variables on the number of dams in each district as well as information about the neighbouring districts. The data set also includes data on local poverty, such as a povertygap measure, the gini coefficient, mean per capita expenditure. The file india_ag_extend contains in addition, data on agricultural produc tion ( value, yield) for major crops and distinguishes between water-intensive and non-water-intensive crops. The file census.dta contains data on the population size and occupation. The file india_public_updown_doc.dta contains data on the availability of public goods such as water access, power facilities and road. The file malaria_code81.dta contains in addition a variable about malaria incidence.

基尼系数在12-01-2021达0.328NA，相较于12-01-2020的0.338NA有所下降。基尼系数数据按年更新，12-01-1977至12-01-2021期间平均值为0.339NA，共14份观测结果。该数据的历史最高值出现于12-01-2017，达0.359NA，而历史最低值则出现于12-01-1993，为0.316NA。CEIC提供的基尼系数数据处于定期更新的状态，数据来源于Our World in Data，数据归类于全球数据库的印度 – Table IN.OWID.ESG: Social: Gini Coefficient: Annual。

BackgroundSince the implementation of various maternal health programs, Maternal Mortality Ratio (MMR) has significantly declined in India through improvements in maternal health services. However, inequality persists at the regional and socio-economic levels. In light of this, the present study aims to assess the existing regional disparities in utilising various government initiatives for safe motherhood in India.MethodsNational-level datasets such as National Family and Health Surveys (NFHS-3 (2005–06); NFHS-4 (2015–16) and NFHS-5(2019–21); Health Management Information System (HMIS), 2019–20; Sample Registrar System (SRS), 2001–2018) were used in the study. In addition, composite Index and inequality measures (Range, Ratio, and Gini) were calculated to examine inequality. At the same time, the Pearson correlation was used to investigate the correlation between various components of maternal health services and Maternal Mortality Rate (MMR).ResultsThe composite index score (0.65) reflects that India is still far behind the targets of the utilisation of maternal health care services. Within the utilisation of services, the Gini coefficient reveals that the least inequality was recorded in skilled birth assistance deliveries (0.03) and institutional deliveries (0.04). In contrast, the highest inequality was recorded in receiving Iron and Folic Acid (IFA) Tablets for 100 days (0.19) and four Antenatal Care (ANC) visits (0.13) among selected states. Based on the composite score for maternal health utilisation, Kerala, Tamil Nadu, Andhra Pradesh, Odisha, and Delhi were amongst the best performers, whereas Bihar, Jharkhand, Uttar Pradesh, and Assam were amongst the worst performers.ConclusionThis indicates that the government’s single-minded focus on enhancing institutional deliveries and skilled health-assisted deliveries has detracted from other essential interventions related to maternal health. Therefore, the states with the utilisation of maternal services need to initiate immediate action to increase the ANC and Post-natal Care (PNC utilisation with more attention towards better implementation of existing ANC programmes by the government.

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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In fiscal year 2022, the total income of Gini Silk Mills was over *** million Indian rupees. This was a significant increase in income as compared to the previous financial year. The company is one of the leading cotton and polyester textile company with headquarters in Mumbai, India.