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.

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

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).

According to a survey conducted in 2015 across India, over ** percent of the surveyed households had an average monthly income up to 10,000 Indian rupees. This percentage varied among the rural and urban areas, where over ** percent of the rural households and ** percent of the urban households earned up to 10,000 Indian rupees monthly. India had a high rate of rural to urban migration, as Indian cities provided better standards of living and employment opportunities.

Multiple income generators

For most of the population, income is earned in form of wages or salary, rent from residential or commercial property, interest from financial investments, and profits from family businesses. Most Indian households have multiple earning members to support consumption expenses on a day to day basis. During the surveyed year, around ** percent of the households had a single earner, mostly the head of the family, followed by about ** percent of households with two earning members.

Employment scenario

There are a lot of uncertainties in the job market in India. Non-availability of jobs matching education and skills was one of the main reasons for unemployment among Indian graduates. Underemployment was also a problem, and it was higher in urban areas than rural ones. Even though a majority of the population was self-employed, most jobs taken by workers had no written job contracts in both the salaried and casual employment sectors.

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

In the financial year 2021, the average annual expenditure of rich households in India was over * million Indian rupees, a stark contrast to destitute category which spent ** thousand Indian rupees. A rich household spent almost ** times that of a destiture household, * times that of an aspirer household, and almost * times that of a middle-class household.

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.

About this dataset

Context

The dataset presents the median household income across different racial categories in Indian Village. It portrays the median household income of the head of household across racial categories (excluding ethnicity) as identified by the Census Bureau. The dataset can be utilized to gain insights into economic disparities and trends and explore the variations in median houshold income for diverse racial categories.

Key observations

Based on our analysis of the distribution of Indian Village population by race & ethnicity, the population is predominantly White. This particular racial category constitutes the majority, accounting for 60% of the total residents in Indian Village. Notably, the median household income for White households is $59,450. Interestingly, despite the White population being the most populous, it is worth noting that Black or African American households actually reports the highest median household income, with a median income of $101,336. This reveals that, while Whites may be the most numerous in Indian Village, Black or African American households experience greater economic prosperity in terms of median household income.

https://i.neilsberg.com/ch/indian-village-in-median-household-income-by-race.jpeg" alt="Indian Village median household income diversity across racial categories">

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2017-2021 5-Year Estimates.

Racial categories include:

• White
• Black or African American
• American Indian and Alaska Native
• Asian
• Native Hawaiian and Other Pacific Islander
• Some other race
• Two or more races (multiracial)

Variables / Data Columns

• Race of the head of household: This column presents the self-identified race of the household head, encompassing all relevant racial categories (excluding ethnicity) applicable in Indian Village.
• Median household income: Median household income, adjusting for inflation, presented 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/.

Recommended for further research

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

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.

About this dataset

Context

The dataset presents median income data over a decade or more for males and females categorized by Total, Full-Time Year-Round (FT), and Part-Time (PT) employment in Indian Beach. It showcases annual income, providing insights into gender-specific income distributions and the disparities between full-time and part-time work. The dataset can be utilized to gain insights into gender-based pay disparity trends and explore the variations in income for male and female individuals.

Key observations: Insights from 2021

Based on our analysis ACS 2017-2021 5-Year Estimates, we present the following observations: - All workers, aged 15 years and older: In Indian Beach, the median income for all workers aged 15 years and older, regardless of work hours, was $54,046 for males and $24,321 for females.

These income figures highlight a substantial gender-based income gap in Indian Beach. Women, regardless of work hours, earn 45 cents for each dollar earned by men. This significant gender pay gap, approximately 55%, underscores concerning gender-based income inequality in the town of Indian Beach.

- Full-time workers, aged 15 years and older: In Indian Beach, for full-time, year-round workers aged 15 years and older, the Census Bureau did not report the median income for both males and females due to an insufficient number of sample observations.

As income data for both males and females was unavailable, conducting a comprehensive analysis of gender-based pay disparity in the town of Indian Beach was not possible.

https://i.neilsberg.com/ch/indian-beach-nc-income-by-gender.jpeg" alt="Indian Beach, NC gender based income disparity">

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2017-2021 5-Year Estimates. All incomes have been adjusting for inflation and are presented in 2022-inflation-adjusted dollars.

Gender classifications include:

• Male
• Female

Employment type classifications include:

• Full-time, year-round: A full-time, year-round worker is a person who worked full time (35 or more hours per week) and 50 or more weeks during the previous calendar year.
• Part-time: A part-time worker is a person who worked less than 35 hours per week during the previous calendar year.

Variables / Data Columns

• Year: This column presents the data year. Expected values are 2010 to 2022
• Male Total Income: Annual median income, for males regardless of work hours
• Male FT Income: Annual median income, for males working full time, year-round
• Male PT Income: Annual median income, for males working part time
• Female Total Income: Annual median income, for females regardless of work hours
• Female FT Income: Annual median income, for females working full time, year-round
• Female PT Income: Annual median income, for females working part time

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 Beach median household income by gender. You can refer the same here

In 2022, government or private services accounted for the highest source of income at *** thousand Indian rupees in rural households, while livestock rearing was valued at over **** thousand Indian rupees.

In the 3 years to March 2021, black households were most likely out of all ethnic groups to have a weekly income of under £600.

Key information about Russia Household Income per Capita

• Russia Annual Household Income per Capita reached 8,179.123 USD in Dec 2024, compared with the previous value of 7,549.747 USD in Dec 2023.
• Russia Annual Household Income per Capita data is updated yearly, available from Dec 1992 to Dec 2024, with an averaged value of 5,882.623 USD.
• The data reached an all-time high of 9,660.697 USD in Dec 2013 and a record low of 183.584 USD in Dec 1992.
• In the latest reports, Retail Sales of Russia grew 5.200 % YoY in Dec 2024.

CEIC calculates Annual Household Income per Capita from Monthly Average Household Income per Capita multiplied by 12 and converts it into USD. Federal State Statistics Service provides Average Household Income per Capita in local currency. The Bank of Russia average market exchange rate is used for currency conversions.

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.

The NABARD All India Rural Financial Inclusion Survey (NAFIS) provides data on the financial inclusion status of rural households in India. The data was collected in 2016-17 and includes information on bank account ownership, credit access, insurance coverage, financial literacy and household economic status. The NAFIS is a valuable resource for researchers, policy makers, and journalists who are interested in the financial inclusion status of rural households in India. The dataset can be used to track the progress of financial inclusion in rural India, to identify the challenges to financial inclusion, and to develop policies and programs to promote financial inclusion. This subset of the NAFIS dataset provides information on households in different states, including average monthly consumption expenditure, households reporting savings, average savings for saver households, incidence of indebtedness among households, households associated with microfinance institutions, average landholding size, average monthly household income, and average monthly agricultural household income.

In 2024, the average monthly salary was **** thousand Indian rupees in Mumbai city of India. The average monthly salary in the capital city of Delhi was around **** thousand Indian rupees. In comparison, the average monthly salary was over ** thousand Indian rupees in Madurai during the same year.

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

Abstract

The National Sample Survey Organisation (NSSO) has been carrying out All-India surveys quinquennially on consumer expenditure and employment - unemployment. The 50th round (July 1993 - June 1994) was the Fifth quinquennial survey on Consumer Expenditure and Employment - Unemployment. The previous four quinquennial surveys were the 27th (Oct. 1972 - Sept. 1973), the 32nd (Jul.1977 - Jun. 1978), the 38th ( Jan. - Dec. 1983) and 43rd (Jul. 1987 - Jun. 1988) rounds. In other rounds of NSS, also, a consumer expenditure inquiry on a limited scale was being carried out from the 42nd round (1986-87) onwards. From the 45th round onwards the subject coverage of this schedule has been expanded to include some important questions on employment so that an annual series of consumer expenditure and employment data is now available. While some of these smaller-scale surveys are spread over a full year and others over six months only, the quinquennial (full-scale) surveys have all been of a full year's duration. Household consumer expenditure is measured as the expenditure incurred by a household on domestic account during a specified period, called reference period. It includes the imputed values of goods and services, which are not purchased but procured otherwise for consumption. In other words, it is the sum total of monetary values of all the items (i.e. goods and services) consumed by the household on domestic account during the reference period. The imputed rent of owner-occupied houses is excluded from consumption expenditure. Any expenditure incurred towards the productive enterprises of the households is also excluded from household consumer expenditure. The household consumer expenditure schedule used for the survey collected information on quantity and value of household consumption with a reference period of "last 30 days" for some items of consumption and "last 365 days" for some less frequently purchased items. To minimise recall errors, a very detailed item classification was, as usual, adopted to collect information, including 148 items of food, 13 items of fuel, 28 items of clothing, bedding and footwear, 18 items of educational and medical expenses, 52 items of durable goods, and about 85 other items. The schedule also collected some other household particulars including age, sex and educational level etc. of each household member.

The schedule design for the survey was more or less similar to that adopted in the previous quinquennial round. The field work for the survey was conducted, as usual, by the Field Operations Division of the Organisation. The collected data were processed by the Data Processing Division of NSSO and tabulated by the Computer Centre of Department of Statistics. The reports have been prepared by Survey Design & Research Division (SDRD) of NSSO under the guidance of the Governing Council, NSSO.

Geographic coverage

The survey period of the 50th round was from July 1993 to June 1994. The geographical coverage of the survey was to be the whole of the Indian Union except Ladakh and Kargil districts of Jammu & Kashmir, 768 interior villages of Nagaland and 172 villages in Andaman & Nicobar Islands which remain inaccessible throughout the year. However, certain districts of Jammu & Kashmir viz., Doda, Anantnag, Pulwama, Srinagar, Badgam, Baramula and Kupwara, and Punjab's Amritsar district, had to be excluded from the survey due to unfavourable field conditions.

Analysis unit

Randomly selected households based on sampling procedure and members of the household

Universe

The survey used the interview method of data collection from a sample of randomly selected households and members of the household.

Kind of data

Sample survey data [ssd]

Sampling procedure

The sample design adopted for this round of survey was similar to that followed in the past surveys in its general aspects. The general scheme was a two stage stratified design with the first stage units being villages in the rural areas and urban frame survey blocks (UFS) in the urban areas. The second stage units were the households.

Sampling frame for first stage units: The latest available lists of census villages (which are mostly the 1981 census lists) constitute the sampling frame for the rural sector. For Nagaland, the villages located within 5kms of a bus route constitute the sampling frame whereas, for Andaman & Nicobar Islands, the list of accessible villages constituted the sampling frame. For the urban sector, the lists of NSSO Urban Frame Survey (UFS) blocks have been considered as the sampling frame. However, for some of the newly declared towns of 1991 census for which UFS frame has not been received, the lists of 1991 census EBs have been considered as the sampling frame.

Region formation and stratification: States were divided into regions by grouping contiguous districts similar in respect of population density and cropping pattern. In rural sector each district was treated a separate stratum if the population was below 2 million and where it exceeded 2 million, it was split into two or more strata. This cut off point of population was taken as 1.8 million ( in place of 2 million ) for the purpose of stratification for districts for which the 1981 census frame was used. In the urban sector, strata were formed, within each NSS region on the basis of population size class of towns. However, for towns with population of 4 lakhs or more the urban blocks were divided into two classes viz. one consisting of blocks inhabited by affluent section of the population and the other consisting of the remaining blocks.

Selection of first stage units : Selection of sample villages was done circular systematically with probability proportional to population and sample blocks circular systematically with equal probability. Both the sample villages and the sample blocks were selected in the form of two or more independent sub-samples. In Arunachal Pradesh the procedure of cluster sampling has been followed. Further large villages/blocks having present population of 1200 or more were divided into a suitable number of hamlet- groups/ sub-blocks having equal population content. Two hamlet- groups were selected from the larger villages while one sub-block was selected in urban sector for larger blocks.

Selection of households : While listing the households in the selected villages, certain relatively affluent households were identified and considered as second stage stratum 1 and the rest as second stage stratum 2.

A total of 10 households were surveyed from the selected village/hamlet-groups, 2 from the first category and remaining from the second.Further in the second stage stratum-2, the households were arranged according to the means of livelihood. The means of livelihood were identified on the basis of the major source of income as i) self-employed in non-agriculture, ii) rural labour and iii) others. The land possessed by the households was also ascertained and the frame for selection was arranged on the basis of this information. The households were selected circular systematically from both the second stage strata.

In the urban blocks a different method was used for arranging the households for selection. This involved the identification means of livelihood of households as any one of a) self-employed, b) regular salaried/wage earnings, c) casual labour, d) others. Further the average household monthly per capita consumer expenditure (mpce) was also ascertained. All households with MPCE of (i) Rs. 1200/- or more (in towns with population less than 10 lakhs or (ii) Rs. 1500/- or more (in towns with population 10 lakh or more) formed second-stage stratum 1 and the rest, second-stage stratum 2.The households of second-stage stratum 2 were arranged according to means of livelihood class and MPCE ranges before selection of sample households. A total of 10 households were selected from each sample block as follows (i) For affluent strata/classes : 4 households from second- stage stratum 1 and 6 households from second-stage stratum 2 (ii) For other strata/classes : 2 households from second-stage stratum 1 and 8 from second-stage stratum 2. Households were then selected circular systematically with a random start.

Shortfall in the required number of household in any second-stage stratum was made up by increasing the quota for the other second stage stratum.

A total of 7284 sample villages (Rural) and 4792 sample blocks (Urban) were allotted in central sample. 6983 sample villages and 470 sample blocks were successfully surveyed covering 356351 persons in sample villages and 208389 persons in sample blocks.

Sampling deviation

There was no deviation from the original sampling design.

Mode of data collection

Face-to-face [f2f]

India’s per capita net national income or NNI was around *** thousand rupees in financial year 2025. The annual growth rate was *** percent as compared to the previous year. National income indicators   While GNI (Gross National Income) and NNI are both indicators for a country’s economic performance and welfare, the GNI is related to the GDP plus the net receipts from abroad, including wages and salaries, property income, net taxes and subsidies receivable from abroad. On the other hand, the NNI of a country is equal to its GNI net of depreciation. In 2020, India ranked second amongst the Asia Pacific countries in terms of its gross national income. This has been possible due to a favorable GDP growth in India. Measuring wealth versus welfare   National income per person or per capita is often used as an indicator of people's standard of living and welfare. However, critics object to this by citing that since it is a mean value, it does not reflect the real income distribution. In other words, a small wealthy class of people in the country can skew the per capita income substantially, even though the average population has no change in income. This is exemplified by the fact that in India, the top one percent of people, control over 40 percent of the country’s wealth.

Key information about India Household Debt: % of GDP

• India household debt accounted for 17.1 % of the country's Nominal GDP in Mar 2024, compared with the ratio of 14.9 % in the previous year.
• India household debt to GDP ratio is updated yearly, available from Mar 1998 to Mar 2024.
• The data reached an all-time high of 17.1 % in Mar 2024 and a record low of 2.2 % in Mar 1999.

CEIC calculates annual Household Debt as % of Nominal GDP from annual Household Debt and annual Nominal GDP. The Reserve Bank of India provides Household Debt in local currency. Ministry of Statistics and Programme Implementation provides Nominal GDP in local currency based on SNA 2008, at 2011-2012 prices. Loans are used due to the lack of Flow of Funds statistics. Household Debt includes Consumer Durables, Housing and Rest of Personal Loans. Household Debt as % of Nominal GDP is in annual frequency, ending in March of each year.


Related information about India Household Debt: % of GDP

• In the latest reports, India Household Debt reached 617.3 USD bn in Mar 2024.
• Money Supply M2 in India increased 7.9 % YoY in Nov 2024.
• India Foreign Exchange Reserves was measured at 547.5 USD bn in Dec 2024.
• The Foreign Exchange Reserves equaled 9.2 Months of Import in May 2023.
• India Domestic Credit reached 3,168.1 USD bn in Dec 2024, representing an increased of 11.1 % YoY.
• The country's Non Performing Loans Ratio stood at 2.8 % in Mar 2024, compared with the ratio of 3.9 % in the previous year.