The statistic shows the life expectancy at birth in India from 2013 to 2023. The average life expectancy at birth in India in 2023 was 72 years. Standard of living in India India is one of the so-called BRIC countries, an acronym which stands for Brazil, Russia, India and China, the four states considered the major emerging market countries. They are all in a similar advanced economic state and are expected to advance even further. India is also among the twenty leading countries with the largest gross domestic product / GDP, and the twenty countries with the largest proportion of global gross domestic product / GDP based on Purchasing Power Parity (PPP). Its unemployment rate has been stable over the past few years; India is also among the leading import and export countries worldwide. This alone should put India in a relatively comfortable position economically speaking, however, parts of the population of India are struggling with poverty and health problems. When looking at a comparison of the median age of the population in selected countries – i.e. one half of the population is older and the other half is younger –, it can be seen that the median age of the Indian population is about twenty years less than that of the Germans or Japanese. In fact, the median age in India is significantly lower than the median age of the population of the other emerging BRIC countries – Russia, China and Brazil. Additionally, the total population of India has been steadily increasing. Regarding life expectancy, India is neither among the countries with the highest, nor among those with the lowest life expectancy at birth. The majority of the Indian population is aged between 15 and 64 years, with only about 5 percent being older than 64.

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.

The life expectancy of men at birth in India was 70.52 years in 2023. Between 1960 and 2023, the life expectancy rose by 24.22 years, though the increase followed an uneven trajectory rather than a consistent upward trend.

Graph and download economic data for Estimate, Median Age by Sex, Total Population (5-year estimate) in Indian River County, FL (B01002001E012061) from 2009 to 2023 about Indian River County, FL; Sebastian; age; FL; 5-year; median; and USA.

Actual value and historical data chart for India Life Expectancy At Birth Female Years

The life expectancy of women at birth in India was 73.6 years in 2023. Between 1960 and 2023, the life expectancy rose by 28.72 years, though the increase followed an uneven trajectory rather than a consistent upward trend.

This line chart displays median age (year) by date using the aggregation average, weighted by population in India. The data is about countries per year.

India Census Dataset

This dataset provides comprehensive census data at the district level for India. It includes detailed demographic, religious, educational, and workforce-related attributes, making it a rich resource for socio-economic analysis.

1. Columns and Descriptions

A. Basic Information

District_code: A unique numeric code for each district. State_name: Name of the state to which the district belongs. District_name: Name of the district.

B. Population Data

Population: Total population of the district. Male: Total male population in the district. Female: Total female population in the district.

C. Literacy Data

Literate: Total number of literate individuals in the district.

D. Workforce Data

Workers: Total number of workers in the district. Male_Workers: Total number of male workers in the district. Female_Workers: Total number of female workers in the district. Cultivator_Workers: Number of workers engaged as cultivators. Agricultural_Workers: Number of workers engaged in agricultural labor. Household_Workers: Number of workers engaged in household industries.

E. Religion Data

Hindus: Total number of Hindus in the district. Muslims: Total number of Muslims in the district. Christians: Total number of Christians in the district. Sikhs: Total number of Sikhs in the district. Buddhists: Total number of Buddhists in the district. Jains: Total number of Jains in the district.

F. Education Data

Secondary_Education: Number of individuals with secondary education. Higher_Education: Number of individuals with higher education qualifications. Graduate_Education: Number of individuals with graduate-level education.

G. Age Group Data

Age_Group_0_29: Population in the age group 0–29 years. Age_Group_30_49: Population in the age group 30–49 years. Age_Group_50: Population aged 50 years and above.

2. Key Highlights

Number of Districts: 640 Number of Columns: 25 Non-null Values: All columns are complete with no missing data. Detailed breakdown of population by gender, age group, literacy levels, and workforce distribution. Religious composition and education statistics are also included for each district.

3. Potential Use Cases

Data Analysis and Visualization:

Explore patterns in population distribution, literacy rates, workforce composition, and religious demographics. Machine Learning Applications:

Build predictive models to classify districts or forecast demographic trends. Social Research:

Investigate correlations between education levels, workforce participation, and religion. Policy Planning:

Help policymakers target specific demographics or regions for intervention. Educational Insights:

Analyze the impact of education levels on workforce participation or literacy.

4. Dataset Overview

Total Rows: 640 Total Columns: 25 This dataset provides a unique opportunity to understand India's socio-economic and demographic composition at a granular district level.

About this dataset

Context

The dataset tabulates the Indian Trail population distribution across 18 age groups. It lists the population in each age group along with the percentage population relative of the total population for Indian Trail. The dataset can be utilized to understand the population distribution of Indian Trail by age. For example, using this dataset, we can identify the largest age group in Indian Trail.

Key observations

The largest age group in Indian Trail, NC was for the group of age 15 to 19 years years with a population of 3,919 (9.52%), according to the ACS 2019-2023 5-Year Estimates. At the same time, the smallest age group in Indian Trail, NC was the 85 years and over years with a population of 236 (0.57%). 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

Age groups:

• Under 5 years
• 5 to 9 years
• 10 to 14 years
• 15 to 19 years
• 20 to 24 years
• 25 to 29 years
• 30 to 34 years
• 35 to 39 years
• 40 to 44 years
• 45 to 49 years
• 50 to 54 years
• 55 to 59 years
• 60 to 64 years
• 65 to 69 years
• 70 to 74 years
• 75 to 79 years
• 80 to 84 years
• 85 years and over

Variables / Data Columns

• Age Group: This column displays the age group in consideration
• Population: The population for the specific age group in the Indian Trail is shown in this column.
• % of Total Population: This column displays the population of each age group as a proportion of Indian Trail 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 Trail Population by Age. 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

India IN: Probability of Dying at Age 20-24 Years: per 1000 data was reported at 6.000 Ratio in 2019. This records a decrease from the previous number of 6.100 Ratio for 2018. India IN: Probability of Dying at Age 20-24 Years: per 1000 data is updated yearly, averaging 10.350 Ratio from Dec 1990 (Median) to 2019, with 30 observations. The data reached an all-time high of 14.000 Ratio in 1990 and a record low of 6.000 Ratio in 2019. India IN: Probability of Dying at Age 20-24 Years: per 1000 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: Health Statistics. Probability of dying between age 20-24 years of age expressed per 1,000 youths age 20, if subject to age-specific mortality rates of the specified year.; ; Estimates developed by the UN Inter-agency Group for Child Mortality Estimation (UNICEF, WHO, World Bank, UN DESA Population Division) at www.childmortality.org.; Weighted average; Aggregate data for LIC, UMC, LMC, HIC are computed based on the groupings for the World Bank fiscal year in which the data was released by the UN Inter-agency Group for Child Mortality Estimation.

About this dataset

Context

The dataset tabulates the Indian Head population distribution across 18 age groups. It lists the population in each age group along with the percentage population relative of the total population for Indian Head. The dataset can be utilized to understand the population distribution of Indian Head by age. For example, using this dataset, we can identify the largest age group in Indian Head.

Key observations

The largest age group in Indian Head, MD was for the group of age 50-54 years with a population of 530 (13.56%), according to the 2021 American Community Survey. At the same time, the smallest age group in Indian Head, MD was the 85+ years with a population of 0 (0.00%). Source: U.S. Census Bureau American Community Survey (ACS) 2017-2021 5-Year Estimates.

Content

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

Age groups:

• Under 5 years
• 5 to 9 years
• 10 to 14 years
• 15 to 19 years
• 20 to 24 years
• 25 to 29 years
• 30 to 34 years
• 35 to 39 years
• 40 to 44 years
• 45 to 49 years
• 50 to 54 years
• 55 to 59 years
• 60 to 64 years
• 65 to 69 years
• 70 to 74 years
• 75 to 79 years
• 80 to 84 years
• 85 years and over

Variables / Data Columns

• Age Group: This column displays the age group in consideration
• Population: The population for the specific age group in the Indian Head is shown in this column.
• % of Total Population: This column displays the population of each age group as a proportion of Indian Head 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 Head Population by Age. You can refer the same here

This scatter chart displays median age (year) against urban population (people) in India. The data is filtered where the date is 2023. The data is about countries per year.

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.

India Projection: Population: 10 Years: Age: 30-34 data was reported at 119,390,699.000 Person in 2031. This records an increase from the previous number of 110,920,915.000 Person for 2021. India Projection: Population: 10 Years: Age: 30-34 data is updated yearly, averaging 115,155,807.000 Person from Mar 2021 (Median) to 2031, with 2 observations. The data reached an all-time high of 119,390,699.000 Person in 2031 and a record low of 110,920,915.000 Person in 2021. India Projection: Population: 10 Years: Age: 30-34 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.

Ages chart illustrates the age and gender trends across all age and gender groupings. A chart where the the covered area is primarily on the right describes a very young population while a chart where the the covered area is primarily on the left illustrates an aging population.

This horizontal bar chart displays median age (year) by ISO 3 country code using the aggregation average, weighted by population in India. The data is filtered where the date is 2023. The data is about countries per year.

Supporting documentation on code lists, subject definitions, data accuracy, and statistical testing can be found on the American Community Survey website in the Data and Documentation section...Sample size and data quality measures (including coverage rates, allocation rates, and response rates) can be found on the American Community Survey website in the Methodology section..Tell us what you think. Provide feedback to help make American Community Survey data more useful for you..Although the American Community Survey (ACS) produces population, demographic and housing unit estimates, it is the Census Bureau''s Population Estimates Program that produces and disseminates the official estimates of the population for the nation, states, counties, cities and towns and estimates of housing units for states and counties..Explanation of Symbols:An ''**'' entry in the margin of error column indicates that either no sample observations or too few sample observations were available to compute a standard error and thus the margin of error. A statistical test is not appropriate..An ''-'' entry in the estimate column indicates that either no sample observations or too few sample observations were available to compute an estimate, or a ratio of medians cannot be calculated because one or both of the median estimates falls in the lowest interval or upper interval of an open-ended distribution..An ''-'' following a median estimate means the median falls in the lowest interval of an open-ended distribution..An ''+'' following a median estimate means the median falls in the upper interval of an open-ended distribution..An ''***'' entry in the margin of error column indicates that the median falls in the lowest interval or upper interval of an open-ended distribution. A statistical test is not appropriate..An ''*****'' entry in the margin of error column indicates that the estimate is controlled. A statistical test for sampling variability is not appropriate. .An ''N'' entry in the estimate and margin of error columns indicates that data for this geographic area cannot be displayed because the number of sample cases is too small..An ''(X)'' means that the estimate is not applicable or not available..Estimates of urban and rural population, housing units, and characteristics reflect boundaries of urban areas defined based on Census 2010 data. As a result, data for urban and rural areas from the ACS do not necessarily reflect the results of ongoing urbanization..While the 2011-2015 American Community Survey (ACS) data generally reflect the February 2013 Office of Management and Budget (OMB) definitions of metropolitan and micropolitan statistical areas; in certain instances the names, codes, and boundaries of the principal cities shown in ACS tables may differ from the OMB definitions due to differences in the effective dates of the geographic entities..Data are based on a sample and are subject to sampling variability. The degree of uncertainty for an estimate arising from sampling variability is represented through the use of a margin of error. The value shown here is the 90 percent margin of error. The margin of error can be interpreted roughly as providing a 90 percent probability that the interval defined by the estimate minus the margin of error and the estimate plus the margin of error (the lower and upper confidence bounds) contains the true value. In addition to sampling variability, the ACS estimates are subject to nonsampling error (for a discussion of nonsampling variability, see Accuracy of the Data). The effect of nonsampling error is not represented in these tables..Source: U.S. Census Bureau, 2011-2015 American Community Survey 5-Year Estimates

Comprehensive demographic dataset for Indian Springs, , TX, US including population statistics, household income, housing units, education levels, employment data, and transportation with year-over-year changes.

India Population: Census: Age: 25 to 29 year data was reported at 101,413.965 Person th in 03-01-2011. This records an increase from the previous number of 83,422.000 Person th for 03-01-2001. India Population: Census: Age: 25 to 29 year data is updated decadal, averaging 83,422.000 Person th from Mar 1991 (Median) to 03-01-2011, with 3 observations. The data reached an all-time high of 101,413.965 Person th in 03-01-2011 and a record low of 69,239.000 Person th in 03-01-1991. India Population: Census: Age: 25 to 29 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.