This statistic depicts the age distribution of India from 2013 to 2023. In 2023, about 25.06 percent of the Indian population fell into the 0-14 year category, 68.02 percent into the 15-64 age group and 6.92 percent were over 65 years of age. Age distribution in India India is one of the largest countries in the world and its population is constantly increasing. India’s society is categorized into a hierarchically organized caste system, encompassing certain rights and values for each caste. Indians are born into a caste, and those belonging to a lower echelon often face discrimination and hardship. The median age (which means that one half of the population is younger and the other one is older) of India’s population has been increasing constantly after a slump in the 1970s, and is expected to increase further over the next few years. However, in international comparison, it is fairly low; in other countries the average inhabitant is about 20 years older. But India seems to be on the rise, not only is it a member of the BRIC states – an association of emerging economies, the other members being Brazil, Russia and China –, life expectancy of Indians has also increased significantly over the past decade, which is an indicator of access to better health care and nutrition. Gender equality is still non-existant in India, even though most Indians believe that the quality of life is about equal for men and women in their country. India is patriarchal and women still often face forced marriages, domestic violence, dowry killings or rape. As of late, India has come to be considered one of the least safe places for women worldwide. Additionally, infanticide and selective abortion of female fetuses attribute to the inequality of women in India. It is believed that this has led to the fact that the vast majority of Indian children aged 0 to 6 years are male.

The growth in India's overall population is driven by its young population. Nearly 70 percent of the country's population was between the ages of 15 and 64 years old in 2020. With over 600 million people between 18 and 35 years old, India had the largest number of millennials and Gen Zs globally.

India Projection: Population: 10 Years: Age: 20-24 data was reported at 124,693,062.000 Person in 2031. This records an increase from the previous number of 119,917,327.000 Person for 2021. India Projection: Population: 10 Years: Age: 20-24 data is updated yearly, averaging 122,305,194.500 Person from Mar 2021 (Median) to 2031, with 2 observations. The data reached an all-time high of 124,693,062.000 Person in 2031 and a record low of 119,917,327.000 Person in 2021. India Projection: Population: 10 Years: Age: 20-24 data remains active status in CEIC and is reported by CEIC Data. The data is categorized under India Premium Database’s Demographic – Table IN.GAI002: Population Projection: 10 Years: by Age Group.

The median age in India was 27 years old in 2020, meaning half the population was older than that, half younger. This figure was lowest in 1970, at 18.1 years, and was projected to increase to 47.8 years old by 2100. Aging in India India has the second largest population in the world, after China. Because of the significant population growth of the past years, the age distribution remains skewed in favor of the younger age bracket. This tells a story of rapid population growth, but also of a lower life expectancy. Economic effects of a young population Many young people means that the Indian economy must support a large number of students, who demand education from the economy but cannot yet work. Educating the future workforce will be important, because the economy is growing as well and is one of the largest in the world. Failing to do this could lead to high youth unemployment and political consequences. However, a productive and young workforce could provide huge economic returns for India.

This dataset contains the All-India, gender and age-group wise distribution of population for each census year.

Population density per pixel at 100 metre resolution. WorldPop provides estimates of numbers of people residing in each 100x100m grid cell for every low and middle income country. Through ingegrating cencus, survey, satellite and GIS datasets in a flexible machine-learning framework, high resolution maps of population counts and densities for 2000-2020 are produced, along with accompanying metadata. DATASET: Alpha version 2010 and 2015 estimates of numbers of people per grid square, with national totals adjusted to match UN population division estimates (http://esa.un.org/wpp/) and remaining unadjusted. REGION: Africa SPATIAL RESOLUTION: 0.000833333 decimal degrees (approx 100m at the equator) PROJECTION: Geographic, WGS84 UNITS: Estimated persons per grid square MAPPING APPROACH: Land cover based, as described in: Linard, C., Gilbert, M., Snow, R.W., Noor, A.M. and Tatem, A.J., 2012, Population distribution, settlement patterns and accessibility across Africa in 2010, PLoS ONE, 7(2): e31743. FORMAT: Geotiff (zipped using 7-zip (open access tool): www.7-zip.org) FILENAMES: Example - AGO10adjv4.tif = Angola (AGO) population count map for 2010 (10) adjusted to match UN national estimates (adj), version 4 (v4). Population maps are updated to new versions when improved census or other input data become available. India data available from WorldPop here.

India Population: Census: Age: 15 to 19 Year data was reported at 120,526.449 Person th in 03-01-2011. This records an increase from the previous number of 100,216.000 Person th for 03-01-2001. India Population: Census: Age: 15 to 19 Year data is updated decadal, averaging 100,216.000 Person th from Mar 1991 (Median) to 03-01-2011, with 3 observations. The data reached an all-time high of 120,526.449 Person th in 03-01-2011 and a record low of 79,035.000 Person th in 03-01-1991. India Population: Census: Age: 15 to 19 Year data remains active status in CEIC and is reported by Office of the Registrar General & Census Commissioner, India. The data is categorized under India Premium Database’s Demographic – Table IN.GAD001: Census: Population: by Age Group.

Chart and table of India population from 1950 to 2025. United Nations projections are also included through the year 2100.

India Population: Census: Age: 30 to 34 Year data was reported at 88,594.951 Person th in 03-01-2011. This records an increase from the previous number of 74,274.000 Person th for 03-01-2001. India Population: Census: Age: 30 to 34 Year data is updated decadal, averaging 74,274.000 Person th from Mar 1991 (Median) to 03-01-2011, with 3 observations. The data reached an all-time high of 88,594.951 Person th in 03-01-2011 and a record low of 58,404.000 Person th in 03-01-1991. India Population: Census: Age: 30 to 34 Year data remains active status in CEIC and is reported by Office of the Registrar General & Census Commissioner, India. The data is categorized under India Premium Database’s Demographic – Table IN.GAD001: Census: Population: by Age Group.

White noise tests for error series of three age periods for China, India and Vietnam.

Population ages 0-14, total in India was reported at 360337993 Persons in 2023, according to the World Bank collection of development indicators, compiled from officially recognized sources. India - Population, ages 0-14, total - actual values, historical data, forecasts and projections were sourced from the World Bank on March of 2025.

This feature layers contain demographics about age, gender, education, employment, assets & amenities as reported by Office of the Registrar General & Census Commissioner, India in the Census 2011. These attributes cover topics such as male and female population counts by age, literacy, occupation, and household characteristics.Census of India counts every resident in India at village level. It is mandated by The Census Act 1948 of the Constitution and takes place every 10 years.Other demographics layers are also available:Country DemographicsDistrict DemographicsSub-district DemographicsVillage DemographicsCombined DemographicsEach layer contains the same set of demographic attributes. Each geography level has a viewing range optimal for the geography size, and the map has increasing detail as you zoom in to smaller areas.Data source: Explore Census DataAdmin boundary source (country, states, and districts): Survey of India, 2020For more information: 2011 Census Demographic ProfileFor feedback please contact: content@esri.inData Processing notes:Country, State and District boundaries are simplified representations offered from the Survey of India database.Sub-districts and village boundaries are developed based on the census provided maps.Field names and aliases are processed by Esri India as created for the ArcGIS Platform.For a list of fields and alias names, access the following excel document.Disclaimer:The boundaries may not be perfectly align with AGOL imagery. The Census PDF maps are georeferenced using Survey of India boundaries and notice alignment issues with AGOL Imagery/ Maps. 33k villages are marked as point location on Census PDFs either because of low scale maps where small villages could not have been drawn or digitization has not been completed. These villages are marked as 100m circular polygons in the data.This web 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.

India Population: Census: Age: 35 to 39 Year data was reported at 85,140.684 Person th in 03-01-2011. This records an increase from the previous number of 70,574.000 Person th for 03-01-2001. India Population: Census: Age: 35 to 39 Year data is updated decadal, averaging 70,574.000 Person th from Mar 1991 (Median) to 03-01-2011, with 3 observations. The data reached an all-time high of 85,140.684 Person th in 03-01-2011 and a record low of 52,399.000 Person th in 03-01-1991. India Population: Census: Age: 35 to 39 Year data remains active status in CEIC and is reported by Office of the Registrar General & Census Commissioner, India. The data is categorized under India Premium Database’s Demographic – Table IN.GAD001: Census: Population: by Age Group.

The statistic illustrates the old-age dependency ratio across India between 1961 and 2001, with a breakdown by gender. In 1991, the old-age dependency ratio was at 12.2 percent for both males and females.

About this dataset

Context

The dataset tabulates the population of Indian Village by gender, including both male and female populations. This dataset can be utilized to understand the population distribution of Indian Village across both sexes and to determine which sex constitutes the majority.

Key observations

There is a majority of female population, with 56.64% of total population being female. Source: U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Scope of gender :

Please note that American Community Survey asks a question about the respondents current sex, but not about gender, sexual orientation, or sex at birth. The question is intended to capture data for biological sex, not gender. Respondents are supposed to respond with the answer as either of Male or Female. Our research and this dataset mirrors the data reported as Male and Female for gender distribution analysis. No further analysis is done on the data reported from the Census Bureau.

Variables / Data Columns

• Gender: This column displays the Gender (Male / Female)
• Population: The population of the gender in the Indian Village is shown in this column.
• % of Total Population: This column displays the percentage distribution of each gender as a proportion of Indian Village total population. Please note that the sum of all percentages may not equal one due to rounding of values.

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 Population by Race & Ethnicity. You can refer the same here

The percentage distribution for population projections for the age groups 0 to 4 reflected a decrease in the year 2036 in comparison to 2011. This could be attributed to the projected declining fertility rates in the country. By contrast, the age groups from 40-44 to 80+ reflected an increase in the population projections in 2036 when compared with 2011. This projected increase in geriatric population within the country could be attributed to advancements made in the field of medical sciences, biotechnology and improved health care.

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

The North-Eastern region (NER) of India, comprising of Arunachal Pradesh, Assam, Manipur, Meghalaya, Mizoram, Nagaland and Tripura, is a hot spot for genetic diversity and the most probable origin of rice. North-east rice collections are known to possess various agronomically important traits like biotic and abiotic stress tolerance, unique grain and cooking quality. The genetic diversity and associated population structure of 6,984 rice accessions, originating from NER, were assessed using 36 genome wide unlinked single nucleotide polymorphism (SNP) markers distributed across the 12 rice chromosomes. All of the 36 SNP loci were polymorphic and bi-allelic, contained five types of base substitutions and together produced nine types of alleles. The polymorphic information content (PIC) ranged from 0.004 for Tripura to 0.375 for Manipur and major allele frequency ranged from 0.50 for Assam to 0.99 for Tripura. Heterozygosity ranged from 0.002 in Nagaland to 0.42 in Mizoram and gene diversity ranged from 0.006 in Arunachal Pradesh to 0.50 in Manipur. The genetic relatedness among the rice accessions was evaluated using an unrooted phylogenetic tree analysis, which grouped all accessions into three major clusters. For determining population structure, populations K = 1 to K = 20 were tested and population K = 3 was present in all the states, with the exception of Meghalaya and Manipur where, K = 5 and K = 4 populations were present, respectively. Principal Coordinate Analysis (PCoA) showed that accessions were distributed according to their population structure. AMOVA analysis showed that, maximum diversity was partitioned at the individual accession level (73% for Nagaland, 58% for Arunachal Pradesh and 57% for Tripura). Using POWERCORE software, a core set of 701 accessions was obtained, which accounted for approximately 10% of the total NE India collections, representing 99.9% of the allelic diversity. The rice core set developed will be a valuable resource for future genomic studies and crop improvement strategies.

Abstract

The National Family Health Survey (NFHS) was carried out as the principal activity of a collaborative project to strengthen the research capabilities of the Population Reasearch Centres (PRCs) in India, initiated by the Ministry of Health and Family Welfare (MOHFW), Government of India, and coordinated by the International Institute for Population Sciences (IIPS), Bombay. Interviews were conducted with a nationally representative sample of 89,777 ever-married women in the age group 13-49, from 24 states and the National Capital Territoty of Delhi. The main objective of the survey was to collect reliable and up-to-date information on fertility, family planning, mortality, and maternal and child health. Data collection was carried out in three phases from April 1992 to September 1993. THe NFHS is one of the most complete surveys of its kind ever conducted in India.

The households covered in the survey included 500,492 residents. The young age structure of the population highlights the momentum of the future population growth of the country; 38 percent of household residents are under age 15, with their reproductive years still in the future. Persons age 60 or older constitute 8 percent of the population. The population sex ratio of the de jure residents is 944 females per 1,000 males, which is slightly higher than sex ratio of 927 observed in the 1991 Census.

The primary objective of the NFHS is to provide national-level and state-level data on fertility, nuptiality, family size preferences, knowledge and practice of family planning, the potentiel demand for contraception, the level of unwanted fertility, utilization of antenatal services, breastfeeding and food supplemation practises, child nutrition and health, immunizations, and infant and child mortality. The NFHS is also designed to explore the demographic and socioeconomic determinants of fertility, family planning, and maternal and child health. This information is intended to assist policymakers, adminitrators and researchers in assessing and evaluating population and family welfare programmes and strategies. The NFHS used uniform questionnaires and uniform methods of sampling, data collection and analysis with the primary objective of providing a source of demographic and health data for interstate comparisons. The data collected in the NFHS are also comparable with those of the Demographic and Health Surveys (DHS) conducted in many other countries.

Geographic coverage

National

Analysis unit

• Household
• Data collected for women 13-49, indicators calculated for women 15-49

Universe

The population covered by the 1992-93 DHS is defined as the universe of all women age 13-49 who were either permanent residents of the households in the NDHS sample or visitors present in the households on the night before the survey were eligible to be interviewed.

Kind of data

Sample survey data

Sampling procedure

SAMPLE DESIGN

The sample design for the NFHS was discussed during a Sample Design Workshop held in Madurai in Octber, 1991. The workshop was attended by representative from the PRCs; the COs; the Office of the Registrar General, India; IIPS and the East-West Center/Macro International. A uniform sample design was adopted in all the NFHS states. The Sample design adopted in each state is a systematic, stratified sample of households, with two stages in rural areas and three stages in urban areas.

SAMPLE SIZE AND ALLOCATION

The sample size for each state was specified in terms of a target number of completed interviews with eligible women. The target sample size was set considering the size of the state, the time and ressources available for the survey and the need for separate estimates for urban and rural areas of the stat. The initial target sample size was 3,000 completed interviews with eligible women for states having a population of 25 million or less in 1991; 4,000 completed interviews for large states with more than 25 million population; 8,000 for Uttar Pradesh, the largest state; and 1,000 each for the six small northeastern states. In States with a substantial number of backward districts, the initial target samples were increased so as to allow separate estimates to be made for groups of backward districts.

The urban and rural samples within states were drawn separetly and , to the extent possible, sample allocation was proportional to the size of the urban-rural populations (to facilitate the selection of a self-weighting sample for each state). In states where the urban population was not sufficiently large to provide a sample of at least 1,000 completed interviews with eligible women, the urban areas were appropriately oversampled (except in the six small northeastern states).

THE RURAL SAMPLE: THE FRAME, STRATIFICATION AND SELECTION

A two-stage stratified sampling was adopted for the rural areas: selection of villages followed by selection of households. Because the 1991 Census data were not available at the time of sample selection in most states, the 1981 Census list of villages served as the sampling frame in all the states with the exception of Assam, Delhi and Punjab. In these three states the 1991 Census data were used as the sampling frame.

Villages were stratified prior to selection on the basis of a number of variables. The firts level of stratification in all the states was geographic, with districts subdivided into regions according to their geophysical characteristics. Within each of these regions, villages were further stratified using some of the following variables : village size, distance from the nearest town, proportion of nonagricultural workers, proportion of the population belonging to scheduled castes/scheduled tribes, and female literacy. However, not all variables were used in every state. Each state was examined individually and two or three variables were selected for stratification, with the aim of creating not more than 12 strata for small states and not more than 15 strata for large states. Females literacy was often used for implicit stratification (i.e., the villages were ordered prior to selection according to the proportion of females who were literate). Primary sampling Units (PSUs) were selected systematically, with probaility proportional to size (PPS). In some cases, adjacent villages with small population sizes were combined into a single PSU for the purpose of sample selection. On average, 30 households were selected for interviewing in each selected PSU.

In every state, all the households in the selected PSUs were listed about two weeks prior to the survey. This listing provided the necessary frame for selecting households at the second sampling stage. The household listing operation consisted of preparing up-to-date notional and layout sketch maps of each selected PSU, assigning numbers to structures, recording addresses (or locations) of these structures, identifying the residential structures, and listing the names of the heads of all the households in the residentiak structures in the selected PSU. Each household listing team consisted of a lister and a mapper. The listing operation was supervised by the senior field staff of the concerned CO and the PRC in each state. Special efforts were made not to miss any household in the selected PSU during the listing operation. In PSUs with fewer than 500 households, a complete household listing was done. In PSUs with 500 or more households, segmentation of the PSU was done on the basis of existing wards in the PSU, and two segments were selected using either systematic sampling or PPS sampling. The household listing in such PSUs was carried out in the selected segments. The households to be interviewed were selected from provided with the original household listing, layout sketch map and the household sample selected for each PSU. All the selected households were approached during the data collection, and no substitution of a household was allowed under any circumstances.

THE RURAL URBAN SAMPLE: THE FRAME, STRATIFICATION AND SELECTION

A three-stage sample design was adopted for the urban areas in each state: selection of cities/towns, followed by urban blocks, and finally households. Cities and towns were selected using the 1991 population figures while urban blocks were selected using the 1991 list of census enumeration blocks in all the states with the exception of the firts phase states. For the first phase states, the list of urban blocks provided by the National Sample Survey Organization (NSSSO) served as the sampling frame.

All cities and towns were subdivided into three strata: (1) self-selecting cities (i.e., cities with a population large enough to be selected with certainty), (2) towns that are district headquaters, and (3) other towns. Within each stratum, the cities/towns were arranged according to the same kind of geographic stratification used in the rural areas. In self-selecting cities, the sample was selected according to a two-stage sample design: selection of the required number of urban blocks, followed by selection of households in each of selected blocks. For district headquarters and other towns, a three stage sample design was used: selection of towns with PPS, followed by selection of two census blocks per selected town, followed by selection of households from each selected block. As in rural areas, a household listing was carried out in the selected blocks, and an average of 20 households per block was selected systematically.

Mode of data collection

Face-to-face

Research instrument

Three types of questionnaires were used in the NFHS: the Household Questionnaire, the Women's Questionnaire, and the Village Questionnaire. The overall content

This dataset contains the all-India and decade-wise population distribution by broad age groups. Three broad age groups are categorized here: 0-14; 15-59 and Above 60.