As per World Bank's thresholds, in 2022, over 23.9 percent of India's population was living on less than 3 U.S. dollars per day. When the 4.20 U.S. dollars per day threshold is considered, the share increased to over 5.3 percent. The poverty line of 4.20 per day is set by the World Bank to be representative of the definitions of poverty adopted in lower-middle-income countries.

As of 2022, over ********** children between the ages of 10 to 14 years were affected by poverty. In general, age groups from 0 to 19 years were most impacted by extreme poverty.

Historical dataset showing India poverty rate by year from 1977 to 2022.

During 2019 to 2021, ******* percent of the Indian population were reportedly multidimensionally poor. This reflected a much lower percentage of multidimensionally poor population in India as compared to 2016. India has made significant progress in multidimensional poverty over the years.

Graph and download economic data for Estimate of Related Children Age 5-17 in Families in Poverty for Indian River County, FL (PE5T17FL12061A647NCEN) from 1989 to 2023 about Indian River County, FL; Sebastian; 5 to 17 years; family; child; poverty; FL; persons; and USA.

BackgroundThough the concept of multidimensional poverty has been acknowledged cutting across the disciplines (among economists, public health professionals, development thinkers, social scientists, policy makers and international organizations) and included in the development agenda, its measurement and application are still limited. Objectives and MethodologyUsing unit data from the National Family and Health Survey 3, India, this paper measures poverty in multidimensional space and examine the linkages of multidimensional poverty with child survival. The multidimensional poverty is measured in the dimension of knowledge, health and wealth and the child survival is measured with respect to infant mortality and under-five mortality. Descriptive statistics, principal component analyses and the life table methods are used in the analyses. ResultsThe estimates of multidimensional poverty are robust and the inter-state differentials are large. While infant mortality rate and under-five mortality rate are disproportionately higher among the abject poor compared to the non-poor, there are no significant differences in child survival among educationally, economically and health poor at the national level. State pattern in child survival among the education, economical and health poor are mixed. ConclusionUse of multidimensional poverty measures help to identify abject poor who are unlikely to come out of poverty trap. The child survival is significantly lower among abject poor compared to moderate poor and non-poor. We urge to popularize the concept of multiple deprivations in research and program so as to reduce poverty and inequality in the population.

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).

Data on poverty & hunger in India - undernourishment, wasting and stunting in children, global hunger index, and more.

Agricultural Offtake: Andaman and Nicobar Islands: Rice: TPDS: Below Poverty Line (BPL) data was reported at 0.187 Ton th in Jan 2016. This records an increase from the previous number of 0.102 Ton th for Dec 2015. Agricultural Offtake: Andaman and Nicobar Islands: Rice: TPDS: Below Poverty Line (BPL) data is updated monthly, averaging 0.274 Ton th from Oct 2011 (Median) to Jan 2016, with 34 observations. The data reached an all-time high of 0.482 Ton th in Jan 2015 and a record low of 0.008 Ton th in Sep 2012. Agricultural Offtake: Andaman and Nicobar Islands: Rice: TPDS: Below Poverty Line (BPL) data remains active status in CEIC and is reported by Department of Food & Public Distribution. The data is categorized under India Premium Database’s Agriculture Sector – Table IN.RII015: Agricultural Offtake under Targeted Public Distribution System (TPDS): Rice: by States .

Additional file 1: Supplementary Figure 1. The cover page and the table of contents’ page in the flipbook for beneficiaries. The table of contents displays the name of the 7 topics covered in the flipbook. The flipbook is in Hindi (local language). The instructions of how to use the book are shown behind the covers page. The image depicted above was developed by us. Supplementary Figure 2. The cover page and the table of contents’ page in the module. The table of contents displays the name of the 10 sessions in the module. The module is in Hindi (local language). The image depicted above was developed by us. Supplementary Figure 3. Progress tracking chart of the intervention activities across three intervals of the project (first 15 months, middle 12 months and last 8 months).

According to data published by the Pew Research Center, India is estimated to have had a shrinking middle class as a result of the global recession brought on by the COVID-19 pandemic. It is estimated that the number of people in the middle income tier in India decreased from ** million to ** million following the COVID-19 global recession.

In 2024, the Gini coefficient of wealth in India stood at **. This was a slight decrease from previous years. The trend since 2005 shows rising inequalities among the Indian population. What is Gini coefficient of wealth? The Gini coefficient is a measure of wealth inequality. The coefficient of the Gini index ranges from 0 to 1 with 0 representing perfect equality and 1 representing perfect inequality. Wealth and income distribution and inequality can however vary greatly. In 2023, South Africa topped the list of the most unequal countries in the world in terms of income inequality. Why do economic inequalities persist in India? By the end of 2022, the richest citizens in the country owned more than ** percent of the country’s wealth. Asia’s two richest men Mukesh Ambani and Gautam Adani are Indians. The number of high-net-worth individuals has continuously increased over the last decades. While millions of people escaped poverty in the country in the last few years, the wealth distribution between rich and poor remains skewed. Crony capitalism and the accumulation of wealth through inheritance are some of the factors behind this widening gap.

Abstract

Objective of the consumer expenditure survey (CES): Firstly, as an indicator of level of living, monthly per capita expenditure (MPCE) is both simple and universally applicable. Average MPCE of any sub-population of the country (any region or population group) is a single number that summarises the level of living of that population. It is supplemented by the distribution of MPCE, which highlights the differences in level of living of the different parts of the population. More detailed analysis of the distribution of MPCE reveals the proportion and absolute numbers of the poor with respect to a given poverty line. A welfare state has to take note of these numbers in allocating its resources among sectors, regions, and socio-economic groups. The distribution of MPCE can also be used to measure the level of inequality, or the degree to which consumer expenditure is concentrated in a small proportion of households or persons, and this can be done without any predetermined poverty line or welfare norms.

If socialism was the ideal of the 1950's, the ideal of policy-makers during the last decade was "inclusive growth". Increasingly, inclusive growth is seen as the all-important target that we should aim at, at least for the immediate future. Not surprisingly, the NSS CES is being used by scholars as a searchlight focused on the country's development process that shows up just how inclusive the country's growth has been.

Since the data is collected not only on consumption level but also on the pattern of consumption, the CES has another important use. To work out consumer price indices (CPIs) which measure the general rise in consumer prices, one needs to know not only the price rise for each commodity group but also the budget shares of different commodity groups (used as weights). The budget shares as revealed by the NSS CES are being used for a long time to prepare what is called the weighing diagram for official compilation of CPIs. More extensive use of NSS CES data is planned to have a weighing diagram that uses a finer commodity classification, to prepare rural and urban CPIs separately for each State.

Apart from these major uses of the CES, the food (quantity) consumption data are used to study the level of nutrition of different regions, and disparities therein. Further, the budget shares of a commodity at different MPCE levels are used by economists and market researchers to determine the elasticity (responsiveness) of demand to income increases.

Two types of Schedule 1.0 viz. Schedule Type 1 and Schedule Type 2 was canvassed in this round. Schedule Type 1 and Type 2 are similar to those of NSS 66th round.

Reference period and schedule type: The reference period is the period of time to which the information collected relates. In NSS surveys, the reference period often varies from item to item. Data collected with different reference periods are known to exhibit certain systematic differences. Strictly speaking, therefore, comparisons should be made only among estimates based on data collected with identical reference period systems. In the 68th round - as in the 66th round -two schedule types have been drawn up. The two schedule types differonly in respect of reference period. Sample households were divided into two sets: Schedule Type 1 was canvassed in one set and Schedule Type 2 in the other.

Schedule Type 1 uses the same reference period system as Schedule Type 1 of NSS 66th round. Schedule Type 1 requires that for certain items (Clothing, bedding, footwear, education, medical (institutional), durable goods), the same household should report data for two reference periods - 'Last 30 days' and 'Last 365 days'. Schedule Type 2 has the same reference periods as Schedule Type 2 of NSS 66th round. For Group I items (Clothing, bedding, footwear, education, medical (institutional), durable goods), the reference period used in Schedule Type 2 is 'Last 365 days'.

As in the 66th round, items of food, pan, tobacco and intoxicants (Food-plus category) are split into 2 blocks - 5.1 and 5.2 - instead of being placed in a single block. • Block 5.1 consists of the item groups cereals, pulses, milk and milk products, sugar and salt. This block has a reference period of 30 days in both Schedule Type 1 and Schedule Type 2. • Block 5.2 consists of the other items of food, along with pan, tobacco and intoxicants. This block is assigned a reference period of 'Last 30 days' in Schedule Type 1 and a reference period of 'Last 7 days' in Schedule Type 2.

Thus Schedule Type 1, like Schedule 1.0 of NSS 66th round, uses the 'Last 30 days' reference period for all items of food, and for pan, tobacco and intoxicants.

Geographic coverage

The survey covers the whole of the Indian Union except (i) interior villages of Nagaland situated beyond five kilometres of the bus route and (ii) villages in Andaman and Nicobar Islands which remain inaccessible throughout the year.

Kind of data

Sample survey data [ssd]

Sampling procedure

Sample design

Outline of sample design: A stratified multi-stage design has been adopted for the 68th round survey. The first stage units (FSU) are the 2001 census villages (Panchayat wards in case of Kerala) in the rural sector and Urban Frame Survey (UFS) blocks in the urban sector. The ultimate stage units (USU) are households in both the sectors. In case of large FSUs, one intermediate stage of sampling is the selection of two hamlet-groups (hgs)/ sub-blocks (sbs) from each rural/ urban FSU.

Sampling Frame for First Stage Units: For the rural sector, the list of 2001 census villages (henceforth the term 'village' would include also Panchayat wards for Kerala) constitutes the sampling frame. For the urban sector, the list of UFS blocks (2007-12) is considered as the sampling frame.

Stratification: Within each district of a State/ UT, generally speaking, two basic strata have been formed: i) rural stratum comprising of all rural areas of the district and (ii) urban stratum comprising of all the urban areas of the district. However, within the urban areas of a district, if there are one or more towns with population 10 lakhs or more as per population census 2001 in a district, each of them forms a separate basic stratum and the remaining urban areas of the district are considered as another basic stratum.

Sub-stratification: Rural sector r: If 'r' be the sample size allocated for a rural stratum, the number of sub-strata formed would be 'r/4'. The villages within a district as per frame were first arranged in ascending order of population. Then sub-strata 1 to 'r/4' have been demarcated in such a way that each sub-stratum comprised a group of villages of the arranged frame and have more or less equal population. Urban sector: If 'u' be the sample size for an urban stratum, 'u/4' number of sub-strata have been formed. In case u/4 is more than 1, implying formation of 2 or more sub-strata, this is done by first arranging the towns in ascending order of total number of households in the town as per UFS phase 2007-12 and then arranging the IV units of each town and blocks within each IV unit in ascending order of their numbers. From this arranged frame of UFS blocks of all the towns/million plus city of a stratum, 'u/4' number of sub- strata formed in such a way that each sub-stratum has more or less equal number of households as per UFS 2007-12.

Total sample size (FSUs): 12784 FSUs have been allocated for the central sample at all-India level and 14772 FSUs have been allocated for state sample.

Allocation of total sample to States and UTs: The total number of sample FSUs has allocated to the States and UTs in proportion to population as per census 2001 subject to a minimum sample allocation to each State/ UT. While doing so, the resource availability in terms of number of field investigators has been kept in view.

Allocation of State/ UT level sample to rural and urban sectors: State/ UT level sample size has been allocated between two sectors in proportion to population as per census 2001 with double weightage to urban sector. However, if such weighted allocation resulted in too high sample size for the urban sector, the allocation for bigger states like Maharashtra, Tamil Nadu, etc. was restricted to that of the rural sector. A minimum of 16 FSUs (minimum 8 each for rural and urban sector separately) is allocated to each state/ UT.

Allocation to strata/ sub-strata: Within each sector of a State/ UT, the respective sample size has been allocated to the different strata/ sub-strata in proportion to the population as per census 2001. Allocations at stratum level are adjusted to multiples of 4 with a minimum sample size of 4. Allocation for each sub-stratum is 4. Equal number of samples has been allocated among the four sub-rounds.

Selection of FSUs: For the rural sector, from each stratum/ sub-stratum, required number of sample villages has been selected by probability proportional to size with replacement (PPSWR), size being the population of the village as per Census 2001. For the urban sector, UFS 2007-12 phase has been used for all towns and cities and FSUs have been selected from each stratum/sub-stratum by using Simple Random Sampling Without Replacement (SRSWOR). Both rural and urban samples are to be drawn in the form of two independent sub-samples and equal number of samples have been allocated among the four sub rounds.

Selection of hamlet-groups/ sub-blocks - important steps

Criterion for hamlet-group/ sub-block formation: After identification of the boundaries of the FSU, it is first determined whether listing is to be done in the whole sample FSU or not. In case the population of the selected FSU is found to be 1200 or more, it has to be divided into a suitable number (say, D) of 'hamlet-groups' in the rural

Literacy in India has been increasing as more and more people receive a better education, but it is still far from all-encompassing. In 2023, the degree of literacy in India was about 77 percent, with the majority of literate Indians being men. It is estimated that the global literacy rate for people aged 15 and above is about 86 percent. How to read a literacy rateIn order to identify potential for intellectual and educational progress, the literacy rate of a country covers the level of education and skills acquired by a country’s inhabitants. Literacy is an important indicator of a country’s economic progress and the standard of living – it shows how many people have access to education. However, the standards to measure literacy cannot be universally applied. Measures to identify and define illiterate and literate inhabitants vary from country to country: In some, illiteracy is equated with no schooling at all, for example. Writings on the wallGlobally speaking, more men are able to read and write than women, and this disparity is also reflected in the literacy rate in India – with scarcity of schools and education in rural areas being one factor, and poverty another. Especially in rural areas, women and girls are often not given proper access to formal education, and even if they are, many drop out. Today, India is already being surpassed in this area by other emerging economies, like Brazil, China, and even by most other countries in the Asia-Pacific region. To catch up, India now has to offer more educational programs to its rural population, not only on how to read and write, but also on traditional gender roles and rights.

The estimated per capita income across Sikkim was the highest among Indian states at around *** thousand Indian rupees in the financial year 2024. Meanwhile, it was the lowest in the northern state of Bihar at over ** thousand rupees. India’s youngest state, Telangana stood in the fifth place. The country's average per capita income that year was an estimated *** thousand rupees. What is per capita income? Per capita income is a measure of the average income earned per person in a given area in a certain period. It is calculated by dividing the area's total income by its total population. If absolute numbers are noted, India’s per capita income doubled from the financial year 2015 to 2023. Wealth inequality However, as per economists, the increase in the per capita income of a country does not always reflect an increase in the income of the entire population. Wealth distribution in India remains highly skewed. The average income hides the disbursal and inequality in a society. Especially in a society like India where the top one percent owned over ** percent of the total wealth in 2022.

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.

In 2024, the gross domestic product (GDP) of China amounted to around 18.7 trillion U.S. dollars. In comparison to the GDP of the other BRIC countries India, Russia and Brazil, China came first that year and second in the world GDP ranking. The stagnation of China's GDP in U.S. dollar terms in 2022 and 2023 was mainly due to the appreciation of the U.S. dollar. China's real GDP growth was 5.4 percent in 2023 and 5.0 percent in 2024. In 2024, per capita GDP in China reached around 13,300 U.S. dollars. Economic performance in China Gross domestic product (GDP) is a primary economic indicator. It measures the total value of all goods and services produced in an economy over a certain time period. China's economy used to grow quickly in the past, but the growth rate of China’s real GDP gradually slowed down in recent years, and year-on-year GDP growth is forecasted to range at only around four percent in the years after 2024. Since 2010, China has been the world’s second-largest economy, surpassing Japan.China’s emergence in the world’s economy has a lot to do with its status as the ‘world’s factory’. Since 2013, China is the largest export country in the world. Some argue that it is partly due to the undervalued Chinese currency. The Big Mac Index, a simplified and informal way to measure the purchasing power parity between different currencies, indicates that the Chinese currency yuan was roughly undervalued by 38 percent in 2024. GDP development Although the impressive economic development in China has led millions of people out of poverty, China is still not in the league of industrialized countries on the per capita basis. To name one example, the U.S. per capita economic output was more than six times as large as in China in 2024. Meanwhile, the Chinese society faces increased income disparities. The Gini coefficient of China, a widely used indicator of economic inequality, has been larger than 0.45 over the last decade, whereas 0.40 is the warning level for social unrest.

In 2024, India’s real gross domestic product (GDP) growth rate was around **** percent, the highest in South Asia. In contrast, Nepal reported the lowest real GDP growth rate in the region at approximately **** percent that year, but it was forecasted to increase to **** percent in 2026.Economy in South Asia In general, South Asia encompasses Sri Lanka, Pakistan, Afghanistan, Bangladesh, Nepal, India and Bhutan. In 2020, India had a GDP of over *** trillion U.S. dollars, while Bangladesh and Sri Lanka followed. The Maldives and Bhutan were among the countries with the lowest GDP in the Asia-Pacific region. In South Asia, the main economic activities include the services sector as well as the industrial and manufacturing sectors.Society in South AsiaFrom the South Asian countries, Bangladesh had the highest share of people living below the poverty line. The Maldives and Sri Lanka exhibited the highest and second-highest GDP per capita among the South Asian countries in 2021.

As of 2024, the Sustainable Development Goal (SDG) index score for zero hunger ranges between ** and ** for Indian states and union territories. The union territory of Puducherry had the highest score of ** and among the states, Kerala was the front-runner with a score of **.

According to the Global Hunger Index 2024, which was adopted by the International Food Policy Research Institute, Somalia was the most affected by hunger and malnutrition, with an index of 44.1. Yemen and Chad followed behind. The World Hunger Index combines three indicators: undernourishment, child underweight, and child mortality. Sub-Saharan Africa most affected The index is dominated by countries in Sub-Saharan Africa. In the region, more than one fifth of the population is undernourished . In terms of individuals, however, South Asia has the highest number of undernourished people. Globally, there are 735 million people that are considered undernourished or starving. A lack of food is increasing in over 20 countries worldwide. Undernourishment worldwide The term malnutrition includes both undernutrition and overnutrition. Undernutrition occurs when an individual cannot maintain normal bodily functions such as growth, recovering from disease, and both learning and physical work. Some conditions such as diarrhea, malaria, and HIV/AIDS can all have a negative impact on undernutrition. Rural and agricultural communities can be especially susceptible to hunger during certain seasons. The annual hunger gap occurs when a family’s food supply may run out before the next season’s harvest is available and can result in malnutrition. Nevertheless, the prevalence of people worldwide that are undernourished has decreased over the last decades, from 18.7 percent in 1990-92 to 9.2 percent in 2022, but it has slightly increased since the outbreak of COVID-19. According to the Global Hunger Index, the reduction of global hunger has stagnated over the past decade.