Actual value and historical data chart for India Age Dependency Ratio Young Percent Of Working Age Population

In 2024, the estimated youth unemployment rate in India was at 16.03 percent. According to the source, the data are ILO estimates. For the past decade, India’s youth unemployment rate has been hovering around the 22 percent mark. What is the youth unemployment rate?The youth unemployment rate refers to those in the workforce who are aged 15 to 24 years and without a job, but actively seeking one. Generally, youth unemployment rates are higher than the adult unemployment rates, and India is no exception: youth unemployment in India is significantly higher than the national unemployment rate. The Indian workforce, young and oldIndia’s unemployment rate in general is not remarkably high when compared to those of other countries. Both India’s unemployment rate and youth unemployment rate are below their global equivalents. In a comparison of the Asia-Pacific region countries, India ranks somewhere in the middle, with Cambodia’s unemployment rate being estimated to be below one percent, and Afghanistan’s the highest at 8.8 percent.

Actual value and historical data chart for India Unemployment Youth Total Percent Of Total Labor Force Ages 15 24 National Estimate

This dataset, "Diabetes in Young Adults in India", contains 100,000 records of synthetic but realistic data reflecting the prevalence of diabetes and associated factors among young adults (ages 15-25) in India. The data is designed to capture genetic predispositions, lifestyle habits, and key health metrics that influence the onset of diabetes in this demographic.

The dataset includes columns for demographic details, genetic risk factors, lifestyle habits, health metrics, and diabetes outcomes. It offers opportunities for exploring trends, patterns, and correlations that contribute to diabetes onset in young populations.

Insights You Can Derive from This Dataset:

1) Risk Factors Analysis:

Evaluate how genetic predisposition (e.g., family history, parental diabetes) contributes to diabetes onset.

Analyze the impact of lifestyle factors (e.g., BMI, physical activity, dietary habits) on diabetes risk.

2) 3)3Diabetes Prediction:

Build predictive models for identifying individuals at high risk of prediabetes or Type 2 diabetes.

Health Metrics Correlation:

Examine correlations between fasting blood sugar, HbA1c levels, and cholesterol with diabetes types.

Investigate how stress levels, sleep hours, and screen time influence health outcomes.

3) Demographic Trends:

Identify regional variations in diabetes prevalence.

Study differences in diabetes onset between genders or income groups.

4) Behavioral Insights:

Analyze how fast food intake, smoking, and alcohol consumption relate to diabetes outcomes.

Determine the combined effects of physical activity and dietary habits on health metrics.

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.

Graph and download economic data for Youth Unemployment Rate for India (SLUEM1524ZSIND) from 1991 to 2024 about 15 to 24 years, India, unemployment, and rate.

Unemployment Rate: India's unemployment rate has been a significant concern, with fluctuations over the years. As of my last knowledge update in January 2022, the unemployment rate was around 6-7%.

Rural-Urban Disparities: Unemployment is often higher in rural areas compared to urban areas, where there are more employment opportunities.

Youth Unemployment: India has a significant issue of youth unemployment. A large portion of the population is under the age of 30, and providing employment opportunities for this demographic is a challenge.

Underemployment: Many individuals in India are also affected by underemployment, where they are employed in jobs that are below their skill levels and pay less than their qualifications.

Informal Sector: A substantial portion of India's workforce is engaged in the informal sector, which lacks job security and social benefits.

Gender Disparities: There are notable gender disparities in unemployment rates, with women often facing higher rates of unemployment compared to men.

Education and Unemployment: Higher education levels do not always guarantee employment in India, leading to a mismatch between skills and job opportunities.

Government Initiatives: The Indian government has launched various schemes and initiatives to address unemployment, such as the Mahatma Gandhi National Rural Employment Guarantee Act (MGNREGA) and the Skill India program.

COVID-19 Impact: The COVID-19 pandemic had a significant impact on employment, leading to job losses and economic challenges.

India Labour Force Participation Rate: Youth Adults: Aged 25+ data was reported at 62.994 % in 2024. This records a decrease from the previous number of 63.267 % for 2023. India Labour Force Participation Rate: Youth Adults: Aged 25+ data is updated yearly, averaging 61.872 % from Dec 1971 (Median) to 2024, with 16 observations. The data reached an all-time high of 65.656 % in 2005 and a record low of 54.732 % in 2018. India Labour Force Participation Rate: Youth Adults: Aged 25+ data remains active status in CEIC and is reported by International Labour Organization. The data is categorized under Global Database’s India – Table IN.ILO.LFS: Labour Force Participation Rate: By Sex and Age: Annual.

This statistic represents the population of children and young adults across India in 2016, broken down by age groups. The population for 16 to 17 year olds during the measured time period was approximately ** million, while young adults between 18 and 23 years old accounted for the largest numbers in this category.

The proportion of selected age groups of world population in 2016, broken down by region can be found here.

India Labour Force Participation Rate: Male: Youth Adults: Aged 15-64 data was reported at 81.482 % in 2024. This records an increase from the previous number of 80.490 % for 2023. India Labour Force Participation Rate: Male: Youth Adults: Aged 15-64 data is updated yearly, averaging 80.947 % from Dec 1991 (Median) to 2024, with 14 observations. The data reached an all-time high of 86.200 % in 1994 and a record low of 77.710 % in 2018. India Labour Force Participation Rate: Male: Youth Adults: Aged 15-64 data remains active status in CEIC and is reported by International Labour Organization. The data is categorized under Global Database’s India – Table IN.ILO.LFS: Labour Force Participation Rate: By Sex and Age: Annual.

India Labour Force Participation Rate: Male: Youth Adults: Aged 15+ data was reported at 77.516 % in 2024. This records an increase from the previous number of 76.409 % for 2023. India Labour Force Participation Rate: Male: Youth Adults: Aged 15+ data is updated yearly, averaging 78.863 % from Dec 1961 (Median) to 2024, with 17 observations. The data reached an all-time high of 90.220 % in 1961 and a record low of 73.857 % in 2018. India Labour Force Participation Rate: Male: Youth Adults: Aged 15+ data remains active status in CEIC and is reported by International Labour Organization. The data is categorized under Global Database’s India – Table IN.ILO.LFS: Labour Force Participation Rate: By Sex and Age: Annual.

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.

Overview

This dataset provides comprehensive data from the Youth Tobacco Survey conducted across various states and union territories in India. It captures tobacco use behaviors, exposure to secondhand smoke, awareness of tobacco-related policies, and cessation attempts among youth. The data is disaggregated by area (Total, Urban, Rural) for India as a whole and for individual states/territories, offering insights into regional and demographic variations in tobacco use and related attitudes.

The dataset is valuable for researchers, policymakers, and public health professionals studying tobacco consumption patterns, the effectiveness of anti-tobacco campaigns, and the impact of regulations like the Cigarettes and Other Tobacco Products Act (COTPA) in India. It can be used to analyze trends, identify high-risk areas, and inform targeted interventions to reduce tobacco use among youth.

Source

The data appears to be sourced from a national or regional youth tobacco survey, likely conducted by a public health authority or research organization in India. It includes detailed metrics on tobacco use, exposure, and awareness, reflecting a systematic effort to monitor tobacco-related behaviors among young populations.

Column Descriptions

Below is a detailed description of each column in the dataset:

State: The name of the Indian state or union territory (e.g., India, Andhra Pradesh, Bihar). "India" represents aggregated national data.

Area: The geographic area within the state (Total, Urban, Rural).

Ever_Tob_Use: Percentage of youth who have ever used any tobacco product.

Curr_Tob_Use: Percentage of youth currently using any tobacco product.

Ever_Smoke: Percentage of youth who have ever smoked any tobacco product.

Curr_Smoke: Percentage of youth currently smoking any tobacco product.

Ever_Cig: Percentage of youth who have ever smoked cigarettes.

Curr_Cig: Percentage of youth currently smoking cigarettes.

Ever_Bidi: Percentage of youth who have ever smoked bidis (a type of hand-rolled tobacco product).

Curr_Bidi: Percentage of youth currently smoking bidis.

Ever_SLT: Percentage of youth who have ever used smokeless tobacco (SLT) products (e.g., chewing tobacco, gutkha).

Curr_SLT: Percentage of youth currently using smokeless tobacco products.

Ever_PM_Tob: Percentage of youth who have ever used paan masala with tobacco.

Suscept_Cig: Percentage of youth susceptible to starting cigarette smoking.

Age_Init_Cig: Average age of initiation for cigarette smoking.

Age_Init_Bidi: Average age of initiation for bidi smoking.

Age_Init_SLT: Average age of initiation for smokeless tobacco use.

E_Cig_Aware: Percentage of youth aware of electronic cigarettes.

E_Cig_Ever: Percentage of youth who have ever used electronic cigarettes.

Quit_Smoke_12mo: Percentage of youth who attempted to quit smoking in the past 12 months.

TryQuit_Smoke_12mo: Percentage of youth who tried to quit smoking in the past 12 months.

WantQuit_Smoke: Percentage of youth who want to quit smoking.

Quit_SLT_12mo: Percentage of youth who attempted to quit smokeless tobacco in the past 12 months.

TryQuit_SLT_12mo: Percentage of youth who tried to quit smokeless tobacco in the past 12 months.

WantQuit_SLT: Percentage of youth who want to quit smokeless tobacco.

Smoke_Exposure: Percentage of youth exposed to secondhand smoke (general).

Smoke_Home: Percentage of youth exposed to secondhand smoke at home.

Smoke_Enclosed: Percentage of youth exposed to secondhand smoke in enclosed public places.

Smoke_Outdoor: Percentage of youth exposed to secondhand smoke in outdoor public places.

Seen_Smoke_School: Percentage of youth who observed smoking on school premises.

Source_Cig_Store: Percentage of youth who obtained cigarettes from a store.

Source_Cig_Paan: Percentage of youth who obtained cigarettes from a paan shop.

Source_Bidi_Store: Percentage of youth who obtained bidis from a store.

Source_Bidi_Paan: Percentage of youth who obtained bidis from a paan shop.

Source_SLT_Store: Percentage of youth who obtained smokeless tobacco from a store.

Source_SLT_Paan: Percentage of youth who obtained smokeless tobacco from a paan shop.

Bought_Cig_Loc: Percentage of youth who purchased cigarettes from a local source.

Bought_Bidi_Loc: Percentage of youth who purchased bidis from a local source.

Refused_Cig_Sale: Percentage of youth refused cigarette sales due to age restrictions.

Refused_Bidi_Sale: Percentage of youth refused bidi sales due to age restrictions.

Refused_SLT_Sale: Percentage of youth refused smokeless tobacco sales due to age restrictions.

Cig_Stick: Percentage of youth purchasing cigarettes as single sticks.

Bidi_Stick: Percentage of youth purchasing bidis as single sticks.

Seen_AT_Message: Percentage of youth who have seen anti-tobacc...

India Labour Force Participation Rate: Female: Youth Adults: Aged 15-64 data was reported at 36.770 % in 2024. This records a decrease from the previous number of 37.493 % for 2023. India Labour Force Participation Rate: Female: Youth Adults: Aged 15-64 data is updated yearly, averaging 31.211 % from Dec 1991 (Median) to 2024, with 14 observations. The data reached an all-time high of 38.940 % in 2001 and a record low of 22.039 % in 2018. India Labour Force Participation Rate: Female: Youth Adults: Aged 15-64 data remains active status in CEIC and is reported by International Labour Organization. The data is categorized under Global Database’s India – Table IN.ILO.LFS: Labour Force Participation Rate: By Sex and Age: Annual.

In 2022, the degree of literacy in India was about 97 percent among the youth between the ages of 15 to 24 years. An exponential increase in the literary rate was seen over the years from 1981 in the country.

The changing population age structure has a significant influence on the economy, society, and numerous other aspects of a country. This paper has innovatively applied the method of compositional data forecasting for the prediction of population age changes of the young (aged 0–14), the middle-aged (aged 15–64), and the elderly (aged older than 65) in China, India, and Vietnam by 2030 based on data from 1960 to 2016. To select the best-suited forecasting model, an array of data transformation approaches and forecasting models have been extensively employed, and a large number of comparisons have been made between the aforementioned methods. The best-suited model for each country is identified considering the root mean squared error and mean absolute percent error values from the compositional data. As noted in this study, first and foremost, it is predicted that by the year 2030, China will witness the disappearance of population dividend and get mired in an aging problem far more severe than that of India or Vietnam. Second, Vietnam’s trend of change in population age structure resembles that of China, but the country will sustain its good health as a whole. Finally, the working population of India demonstrates a strong rising trend, indicating that the age structure of the Indian population still remains relatively “young”. Meanwhile, the continuous rise in the proportion of elderly population and the gradual leveling off growth of the young population have nevertheless become serious problems in the world. The present paper attempts to offer crucial insights into the Asian population size, labor market and urbanization, and, moreover, provides suggestions for a sustainable global demographic development.

India Labour Force Participation Rate: Female: Youth Adults: Aged 15-24 data was reported at 16.901 % in 2024. This records a decrease from the previous number of 17.455 % for 2023. India Labour Force Participation Rate: Female: Youth Adults: Aged 15-24 data is updated yearly, averaging 17.444 % from Dec 1971 (Median) to 2024, with 16 observations. The data reached an all-time high of 33.810 % in 1981 and a record low of 9.968 % in 2018. India Labour Force Participation Rate: Female: Youth Adults: Aged 15-24 data remains active status in CEIC and is reported by International Labour Organization. The data is categorized under Global Database’s India – Table IN.ILO.LFS: Labour Force Participation Rate: By Sex and Age: Annual.

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

Location of Units of Observation: Cross-national; Subnational Population: Children aged approximately 1 year old and their households, and children aged 8 years old and their households, in Ethiopia, India (Andhra Pradesh), Peru and Vietnam, in 2002. 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

A key need for the study's objectives was to obtain data at different levels - the children, their households, the community in which they resided, as well as at regional and national levels. This need thus determined that children should be selected in geographic clusters rather than randomly selected across the country. There was, however, a much more important reason for recruiting children in clusters - the sites are also intended to provide suitable settings for a range of complementary thematic studies. For example, one or a few sites may be used for a qualitative study designed to achieve a deeper level of understanding of some social issues, either because they are important in that particular place, or because the sites are appropriate locales to investigate a more general concern. The quantitative panel study is seen as the foundation upon which a coherent and interesting range of linked studies can be set up.

Thus the design was decided, in each country, comprising 20 geographic clusters with 100 children sampled in each cluster.

For details on sample design, see the methodological document which is available in the documentation.

Sampling deviation

Ethiopia: 1,999 (1-year-olds), 1,000 (8-year-olds); India: 2,011 (1-year-olds), 1,008 (8-year-olds); Peru: 2,052 (1-year-olds), 714 (8-year-olds); Vietnam: 2,000 (1-year-olds), 1,000 (8-year-olds).

Mode of data collection

Face-to-face interview

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 6-17.9 month old household questionnaire • Section 1: Locating information • Section 2: Household composition • Section 3: Pregnancy, delivery and breastfeeding • Section 4: Child care • Section 5: Child health • Section 6: Caregiver background • Section 7: Livelihoods and time allocation • Section 8: Economic changes • Section 9: Socio-economic status • Section 10: Caregiver psychosocial well-being • Section 11: Social capital • Section 12: Tracking details • Section 13: Anthropometry

Core 7.5-8.5 year old household questionnaire • Section 1: Locating information • Section 2: Household composition • Section 3: Births and deaths • Section 4: Child school • Section 5: Child health • Section 6: Caregiver background • Section 7: Livelihoods and time allocation • Section 8: Economic changes • Section 9: Socio-economic status • Section 10: Child mental health • Section 11: Social capital • Section 12: Tracking details • Section 13: Anthropometry

The communnity questionnaire consists of the following sections: • Section 1: Physical environment • Section 2: Social environment • Section 3: Infrastructure and access • Section 4: Economy • Section 5: Health and education

Actual value and historical data chart for India Literacy Rate Youth Female Percent Of Females Ages 15 24

A survey conducted in January 2022 revealed that 18 to 25-year-olds constituted the largest share of social media users in India, with ** percent using six to nine social media platforms at a time. Contrariwise, nearly *********** of the respondents aged 56 and above claimed to not having used social media platforms. The source noticed that people residing in urban areas were using more social media platforms than those in rural areas.