At the end of 2022, the total household wealth in India stood at over ** trillion U.S. dollars, up from over ** trillion in 2021. Except a decline during pandemic, the household wealth has been on the rise.

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

In the financial year 2021, a majority of Indian households fell under the aspirers category, earning between ******* and ******* Indian rupees a year. On the other hand, about ***** percent of households that same year, accounted for the rich, earning over * million rupees annually. The middle class more than doubled that year compared to ** percent in financial year 2005. Middle-class income group and the COVID-19 pandemic During the COVID-19 pandemic specifically during the lockdown in March 2020, loss of incomes hit the entire household income spectrum. However, research showed the severest affected groups were the upper middle- and middle-class income brackets. In addition, unemployment rates were rampant nationwide that further lead to a dismally low GDP. Despite job recoveries over the last few months, improvement in incomes were insignificant. Economic inequality While India maybe one of the fastest growing economies in the world, it is also one of the most vulnerable and severely afflicted economies in terms of economic inequality. The vast discrepancy between the rich and poor has been prominent since the last ***** decades. The rich continue to grow richer at a faster pace while the impoverished struggle more than ever before to earn a minimum wage. The widening gaps in the economic structure affect women and children the most. This is a call for reinforcement in in the country’s social structure that emphasizes access to quality education and universal healthcare services.

In the post-Covid financial year of 2021, the poorest ** percent of households witnessed income levels shrink by ** percent from levels in financial year 2016. The pandemic resulted in the gap between the richest and the poorest ** percent from *** times in financial year 2016 to ** times in financial year 2021. In the financial year 2023, the gap narrowed down to ***** times.

India Households: Gross Disposable Income data was reported at 131,525,002.483 INR mn in 2018. This records an increase from the previous number of 119,566,177.097 INR mn for 2017. India Households: Gross Disposable Income data is updated yearly, averaging 98,430,689.082 INR mn from Mar 2012 (Median) to 2018, with 7 observations. The data reached an all-time high of 131,525,002.483 INR mn in 2018 and a record low of 70,347,611.519 INR mn in 2012. India Households: Gross Disposable Income data remains active status in CEIC and is reported by Central Statistics Office. The data is categorized under Global Database’s India – Table IN.AI002: NAS 2011-2012: National and Personal Disposable Income.

In 2022, the majority of Indian adults had a wealth of 10,000 U.S. dollars or less. On the other hand, about *** percent were worth more than *********** dollars that year. India The Republic of India is one of the world’s largest and most economically powerful states. India gained independence from Great Britain on August 15, 1947, after having been under their power for 200 years. With a population of about *** billion people, it was the second most populous country in the world. Of that *** billion, about **** million lived in New Delhi, the capital. Wealth inequality India suffers from extreme income inequality. It is estimated that the top 10 percent of the population holds ** percent of the national wealth. Billionaire fortune has increase sporadically in the last years whereas minimum wages have remain stunted.

The dataset contains corrected versions of the All India Debt and Investment Survey (AIDIS) corresponding to NSS 70th round (AIDIS 2012) and NSS 77th round (AIDIS 2018). All monetary values are in INR.

About this dataset

Context

The dataset tabulates the median household income in Indian Village. It can be utilized to understand the trend in median household income and to analyze the income distribution in Indian Village by household type, size, and across various income brackets.

Content

The dataset will have the following datasets when applicable

Please note: The 2020 1-Year ACS estimates data was not reported by the Census Bureau due to the impact on survey collection and analysis caused by COVID-19. Consequently, median household income data for 2020 is unavailable for large cities (population 65,000 and above).

• Indian Village, IN Median Household Income Trends (2010-2021, in 2022 inflation-adjusted dollars)
• Median Household Income Variation by Family Size in Indian Village, IN: Comparative analysis across 7 household sizes
• Income Distribution by Quintile: Mean Household Income in Indian Village, IN
• Indian Village, IN households by income brackets: family, non-family, and total, in 2022 inflation-adjusted dollars

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Interested in deeper insights and visual analysis?

Explore our comprehensive data analysis and visual representations for a deeper understanding of Indian Village median household income. You can refer the same here

About this dataset

Context

The dataset presents the the household distribution across 16 income brackets among four distinct age groups in Indian Trail: Under 25 years, 25-44 years, 45-64 years, and over 65 years. The dataset highlights the variation in household income, offering valuable insights into economic trends and disparities within different age categories, aiding in data analysis and decision-making..

Key observations

• Upon closer examination of the distribution of households among age brackets, it reveals that there are 184(1.41%) households where the householder is under 25 years old, 4,951(37.86%) households with a householder aged between 25 and 44 years, 5,924(45.30%) households with a householder aged between 45 and 64 years, and 2,019(15.44%) households where the householder is over 65 years old.
• The age group of 25 to 44 years exhibits the highest median household income, while the largest number of households falls within the 45 to 64 years bracket. This distribution hints at economic disparities within the town of Indian Trail, showcasing varying income levels among different age demographics.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2018-2022 5-Year Estimates.

Income brackets:

• Less than $10,000
• $10,000 to $14,999
• $15,000 to $19,999
• $20,000 to $24,999
• $25,000 to $29,999
• $30,000 to $34,999
• $35,000 to $39,999
• $40,000 to $44,999
• $45,000 to $49,999
• $50,000 to $59,999
• $60,000 to $74,999
• $75,000 to $99,999
• $100,000 to $124,999
• $125,000 to $149,999
• $150,000 to $199,999
• $200,000 or more

Variables / Data Columns

• Household Income: This column showcases 16 income brackets ranging from Under $10,000 to $200,000+ ( As mentioned above).
• Under 25 years: The count of households led by a head of household under 25 years old with income within a specified income bracket.
• 25 to 44 years: The count of households led by a head of household 25 to 44 years old with income within a specified income bracket.
• 45 to 64 years: The count of households led by a head of household 45 to 64 years old with income within a specified income bracket.
• 65 years and over: The count of households led by a head of household 65 years and over old with income within a specified income bracket.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Indian Trail median household income by age. You can refer the same here

A: Results of differences in mean z-scores of nutritional indicators of Bengali children between Bangladesh and India. B: Results of differences in the proportions of undernutrition between under-five Bengali children in India and Bangladesh.

India Proportion of People Living Below 50 Percent Of Median Income: % data was reported at 9.800 % in 2021. This records a decrease from the previous number of 10.000 % for 2020. India Proportion of People Living Below 50 Percent Of Median Income: % data is updated yearly, averaging 6.200 % from Dec 1977 (Median) to 2021, with 14 observations. The data reached an all-time high of 10.300 % in 2019 and a record low of 5.100 % in 2004. India Proportion of People Living Below 50 Percent Of Median Income: % data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s India – Table IN.World Bank.WDI: Social: Poverty and Inequality. The percentage of people in the population who live in households whose per capita income or consumption is below half of the median income or consumption per capita. The median is measured at 2017 Purchasing Power Parity (PPP) using the Poverty and Inequality Platform (http://www.pip.worldbank.org). For some countries, medians are not reported due to grouped and/or confidential data. The reference year is the year in which the underlying household survey data was collected. In cases for which the data collection period bridged two calendar years, the first year in which data were collected is reported.;World Bank, Poverty and Inequality Platform. Data are based on primary household survey data obtained from government statistical agencies and World Bank country departments. Data for high-income economies are mostly from the Luxembourg Income Study database. For more information and methodology, please see http://pip.worldbank.org.;;The World Bank’s internationally comparable poverty monitoring database now draws on income or detailed consumption data from more than 2000 household surveys across 169 countries. See the Poverty and Inequality Platform (PIP) for details (www.pip.worldbank.org).

BackgroundCardiovascular disease (CVD) is the leading cause of mortality in India. Yet, evidence on the CVD risk of India’s population is limited. To inform health system planning and effective targeting of interventions, this study aimed to determine how CVD risk—and the factors that determine risk—varies among states in India, by rural–urban location, and by individual-level sociodemographic characteristics.Methods and findingsWe used 2 large household surveys carried out between 2012 and 2014, which included a sample of 797,540 adults aged 30 to 74 years across India. The main outcome variable was the predicted 10-year risk of a CVD event as calculated with the Framingham risk score. The Harvard–NHANES, Globorisk, and WHO–ISH scores were used in secondary analyses. CVD risk and the prevalence of CVD risk factors were examined by state, rural–urban residence, age, sex, household wealth, and education. Mean CVD risk varied from 13.2% (95% CI: 12.7%–13.6%) in Jharkhand to 19.5% (95% CI: 19.1%–19.9%) in Kerala. CVD risk tended to be highest in North, Northeast, and South India. District-level wealth quintile (based on median household wealth in a district) and urbanization were both positively associated with CVD risk. Similarly, household wealth quintile and living in an urban area were positively associated with CVD risk among both sexes, but the associations were stronger among women than men. Smoking was more prevalent in poorer household wealth quintiles and in rural areas, whereas body mass index, high blood glucose, and systolic blood pressure were positively associated with household wealth and urban location. Men had a substantially higher (age-standardized) smoking prevalence (26.2% [95% CI: 25.7%–26.7%] versus 1.8% [95% CI: 1.7%–1.9%]) and mean systolic blood pressure (126.9 mm Hg [95% CI: 126.7–127.1] versus 124.3 mm Hg [95% CI: 124.1–124.5]) than women. Important limitations of this analysis are the high proportion of missing values (27.1%) in the main outcome variable, assessment of diabetes through a 1-time capillary blood glucose measurement, and the inability to exclude participants with a current or previous CVD event.ConclusionsThis study identified substantial variation in CVD risk among states and sociodemographic groups in India—findings that can facilitate effective targeting of CVD programs to those most at risk and most in need. While the CVD risk scores used have not been validated in South Asian populations, the patterns of variation in CVD risk among the Indian population were similar across all 4 risk scoring systems.

This statistic describes the results of a survey among urban households across India about the wealth index in *******. For instance, some ** percent households in the urban area accounted for the highest category of the wealth index during the survey period.

Disposable Personal Income in India increased to 296383300 INR Million in 2023 from 273364818.90 INR Million in 2022. This dataset provides - India Total Disposable Personal Income - actual values, historical data, forecast, chart, statistics, economic calendar and news.

List of variables used for constructing wealth index for urban India, NFHS 1992 & 2006. (DOC)

The result of binary logistic regression of child’s nutrition indicators on the explanatory factors among under-five Bengali children separately for Bangladesh and India.

In the financial year 2021, the number of super-rich households earning more than ** million Indian rupees went up to **** million from **** million in the financial year 2016. This was an annual growth of **** percent. The number is expected to grow to over **** million in the financial year 2031 and ** million households in the financial year 2047. This will be the fastest growth across all income categories. On the other hand, destitute classified Indian households with earnings of less than *** thousand annually decreased only marginally to ***** million in financial year 2021 from **** million in 2016. However, it is estimated that the number of destitute households will fall to just *** million by the financial year 2047.

India GDCF: Gross Domestic Saving: Household: Financial Saving data was reported at 3,142,610.000 INR mn in 2004. This records an increase from the previous number of 2,544,390.000 INR mn for 2003. India GDCF: Gross Domestic Saving: Household: Financial Saving data is updated yearly, averaging 1,803,460.000 INR mn from Mar 1994 (Median) to 2004, with 11 observations. The data reached an all-time high of 3,142,610.000 INR mn in 2004 and a record low of 947,380.000 INR mn in 1994. India GDCF: Gross Domestic Saving: Household: Financial Saving data remains active status in CEIC and is reported by Central Statistics Office. The data is categorized under Global Database’s India – Table IN.AA017: NAS 1993-1994: Gross Domestic Product: by Expenditure and Income: Current Price.

Abstract

Young Lives: An International Study of Childhood Poverty is a collaborative project investigating the changing nature of childhood poverty in selected developing countries. The UK’s Department for International Development (DFID) is funding the first three-year phase of the project.

Young Lives involves collaboration between Non Governmental Organisations (NGOs) and the academic sector. In the UK, the project is being run by Save the Children-UK together with an academic consortium that comprises the University of Reading, London School of Hygiene and Tropical Medicine, South Bank University, the Institute of Development Studies at Sussex University and the South African Medical Research Council.

The study is being conducted in Ethiopia, India (in Andhra Pradesh), Peru and Vietnam. These countries were selected because they reflect a range of cultural, geographical and social contexts and experience differing issues facing the developing world; high debt burden, emergence from conflict, and vulnerability to environmental conditions such as drought and flood.

Objectives of the study The Young Lives study has three broad objectives: • producing good quality panel data about the changing nature of the lives of children in poverty. • trace linkages between key policy changes and child poverty • informing and responding to the needs of policy makers, planners and other stakeholders There will also be a strong education and media element, both in the countries where the project takes place, and in the UK.

The study takes a broad approach to child poverty, exploring not only household economic indicators such as assets and wealth, but also child centred poverty measures such as the child’s physical and mental health, growth, development and education. These child centred measures are age specific so the information collected by the study will change as the children get older.

Further information about the survey, including publications, can be downloaded from the Young Lives website.

Geographic coverage

Young Lives is an international study of childhood poverty, involving 12,000 children in 4 countries. - Ethiopia (20 communities in Addis Ababa, Amhara, Oromia, and Southern National, Nationalities and People's Regions) - India (20 sites across Andhra Pradesh and Telangana) - Peru (74 communities across Peru) - Vietnam (20 communities in the communes of Lao Cai in the north-west, Hung Yen province in the Red River Delta, the city of Danang on the coast, Phu Yen province from the South Central Coast and Ben Tre province on the Mekong River Delta)

Analysis unit

Individuals; Families/households

Universe

Cross-national; Subnational

Children aged approximately 5 years old and their households, and children aged 12 years old and their households, in Ethiopia, India (Andhra Pradesh), Peru and Vietnam, in 2006-2007. These children were originally interviewed in Round 1 of the study. See documentation for details of the exact regions covered in each country.

Kind of data

Sample survey data [ssd]

Sampling procedure

Purposive selection/case studies

Sampling deviation

Ethiopia: 1,886 (8-year-olds), 974 (15-year-olds); India: 1,930 (8-year-olds), 977 (15-year-olds); Peru: 1,946 (8-year-olds), 678 (15-year-olds); Vietnam: 1,963 (8-year-olds), 972 (15-year-olds)

Mode of data collection

Face-to-face interview; Self-completion

Research instrument

Every questionnaire used in the study consists of a 'core' element and a country-specific element, which focuses on issues important for that country.

The core element of the questionnaires consists of the following sections: Core 5 & 12 year old household questionnaire • Section 1: Parental background • Section 2: Household education • Section 3: Livelihoods and asset framework • Section 3a: Land & crops • Section 3b: Time allocation • Section 3c: Productive assets • Section 3d: Non-agricultural earnings • Section 3e: Transfers • Section 4: Consumption/Expenditure • Section 4a: Food consumption/expenditure • Section 4b: Non-food consumption/expenditure • Section 5: Social capital • Section 5a: Support networks • Section 5b: Family, group and political capital • Section 5c: Collective action and exclusion • Section 5d: Information networks • Section 6: Economic changes and recent life history • Section 7: Socio-economic status • Section 8: Child care, education & activities (blank in 12yr old household) • Section 9: Child health • Section 10: Child development (blank in 12yr old household) • Section 11: Anthropometry • Section 12: Caregiver perceptions & attitudes

Core 12 year old child questionnaire • Section 1: School and activities • Section 2: Child health • Section 3: Social networks, social skills and social support • Section 4: Feelings and attitudes • Section 5: Parents and household issues • Section 6: Perceptions of household wealth and future • Section 7: Child Development

The community questionnaire used in Ethiopia consists of the following sections: - MODULE 1 General Module • Section 1 General Community Characteristics • Section 2 Social Environment • Section 3 Access to Services • Section 4 Economy • Section 5 Local Prices - MODULE 2 Child-Specific Modules • Section 1 Educational Service (General) • Section 2 NOT INCLUDED IN ETHIOPIA CONTEXT INSTRUMENT • Section 3 Educational Services (Preschool, Primary, Secondary) • Section 4 Health Services • Section 5 Child Protection Services - MODULE 3 Country specific community level questions • Section 1 Conversion factors • Section 2 Migration • Section 3 Social protection program • Section 4 Equity and budget management in education and health

The community questionnaire used in India consists of the following sections: - MODULE 1 General Module • Section 1: General Community Characteristics • Section 2: Social Environment • Section 3: Access to Services • Section 4: Economy • Section 5; Local Prices - MODULE 2 Child-Specific Modules • Section 1: Educational Services (General) • Section 2: Child day care Services • Section 3: Educational Services (Preschool, Primary, Secondary) • Section 4: Health Services • Section 5: Child Protection Services

The community questionnaire used in Peru consists of the following sections: - MODULE 1 General Module • Section 1: General Community Characteristics • Section 2: Social Environment • Section 3: Access to Services • Section 4: Economy • Section 5: Local Prices - MODULE 2 Child-Specific Modules • Section 1: Educational Services (General) • Section 2: Child day care Services • Section 3: Educational Services (Preschool, Primary, Secondary) • Section 4: Health Services • Section 5: Child Protection Services

The community questionnaire used in Vietnam consists of the following sections: - MODULE 1 General Module • Section 1: General Community Characteristics • Section 2: Social Environment • Section 3: Access to Services • Section 4: Economy • Section 5: Local Prices • Section 6: Poverty Alleviation and Infrastructure Initiatives - MODULE 2 Child-Specific Module • Section 1: Educational Services (General and Country Specific) • Section 2: Child day care Services • Section 3: Educational Services (Preschool, Primary, Secondary) • Section 4: Health Services • Section 5: Child Protection Services

India IN: Survey Mean Consumption or Income per Capita: Bottom 40% of Population: 2017 PPP per day data was reported at 2.010 Intl $/Day in 2011. This records an increase from the previous number of 1.610 Intl $/Day for 2004. India IN: Survey Mean Consumption or Income per Capita: Bottom 40% of Population: 2017 PPP per day data is updated yearly, averaging 1.810 Intl $/Day from Dec 2004 (Median) to 2011, with 2 observations. The data reached an all-time high of 2.010 Intl $/Day in 2011 and a record low of 1.610 Intl $/Day in 2004. India IN: Survey Mean Consumption or Income per Capita: Bottom 40% of Population: 2017 PPP per day data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s India – Table IN.World Bank.WDI: Social: Poverty and Inequality. Mean consumption or income per capita (2017 PPP $ per day) of the bottom 40%, used in calculating the growth rate in the welfare aggregate of the bottom 40% of the population in the income distribution in a country.;World Bank, Global Database of Shared Prosperity (GDSP) (http://www.worldbank.org/en/topic/poverty/brief/global-database-of-shared-prosperity).;;The choice of consumption or income for a country is made according to which welfare aggregate is used to estimate extreme poverty in the Poverty and Inequality Platform (PIP). The practice adopted by the World Bank for estimating global and regional poverty is, in principle, to use per capita consumption expenditure as the welfare measure wherever available; and to use income as the welfare measure for countries for which consumption is unavailable. However, in some cases data on consumption may be available but are outdated or not shared with the World Bank for recent survey years. In these cases, if data on income are available, income is used. Whether data are for consumption or income per capita is noted in the footnotes. Because household surveys are infrequent in most countries and are not aligned across countries, comparisons across countries or over time should be made with a high degree of caution.