In financial year 2023, Uttar Pradesh, India's most populated state had over ** percent people living under the poverty line of **** U.S. dollars per day. A decade ago the state had over ** percent of its population living under the threshold. The state of Bihar also witnessed a significant reduction in poverty rates from over ** percent in the financial year 2012 to over ** percent in the financial year 2023.

Looking back 45 years or so, progress against poverty in India has been highly uneven over time and space. It took 20 years for the national poverty rate to fall below—and stay below—its value in the early 1950s. And trend rates of poverty reduction have differed appreciably between states. This research project aimed to understand what influence economy-wide and sectoral factors have played in the evolution of poverty measures for India since the 1950s, and to draw lessons for the future. This database contains detailed statistics on a wide range of topics in India. The data are presented at the state level and at the all-India level separately. The database uses published information to construct comprehensive series in six subject blocks. Period coverage is roughly from 1950 to 1994. The database contains 30 spreadsheets and 89 text files (ASCII) that are grouped into the six subject blocks. The formats and sizes of the 30 spreadsheets vary considerably. The list of variables included: . Expenditures (distribution) . National Accounts . Prices Wages . Population . Rainfall

The dataset contains Year and State wise Poverty Rate-Number of Persons and Percentage

Lakdawala Methodology: An older method to measure poverty in India based on minimum calorie intake (2,400 rural / 2,100 urban). It used a 30-day recall for all expenses but did not include health and education costs.

Tendulkar Methodology:A revised method that considers actual spending on food, health, education, etc. It uses a mixed recall period and provides a more realistic estimate of poverty.

Mixed Recall Period: Combines two recall periods: 30 days for regular items and 365 days for infrequent ones. This helps reduce errors and gives a better picture of total household spending.

30-Day Recall Period: Collects data based on what households spent in the last 30 days for all items. It may miss big or occasional expenses and can underestimate actual consumption.

As of 2024, the Sustainable Development Goal (SDG) index score for reducing poverty (SDG 1) ranges between ** and ** for Indian states and union territories. Among the states, Tamil Nadu and Telangana were the front-runners with a score of ** and **. Among the union territories, Dadra & Nagar Haveli and Daman & Diu were the front-runner with a score of **.

The Multidimensional Poverty Index (MPI) is a comprehensive measure that assesses poverty beyond income, capturing individuals’ various deprivations in areas critical to human well-being. Unlike traditional poverty metrics, which primarily focus on monetary aspects, the MPI incorporates multiple dimensions, including health, education, and living standards. Each dimension is further broken down into indicators, such as child mortality, years of schooling, access to clean water, sanitation, and adequate housing.

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.

Looking back 45 years or so, progress against poverty in India has been highly uneven over time and space. It took 20 years for the national poverty rate to fall below—and stay below—its value in the early 1950s. And trend rates of poverty reduction have differed appreciably between states. This research project aimed to understand what influence economy-wide and sectoral factors have played in the evolution of poverty measures for India since the 1950s, and to draw lessons for the future.

This database contains detailed statistics on a wide range of topics in India. The data are presented at the state level and at the all-India level separately. The database uses published information to construct comprehensive series in six subject blocks. Period coverage is roughly from 1950 to 1994. The database contains 30 spreadsheets and 89 text files (ASCII) that are grouped into the six subject blocks. The formats and sizes of the 30 spreadsheets vary considerably. The list of variables included:

. Expenditures (distribution)
. National Accounts
. Prices Wages
. Population
. Rainfall

The statistic represents the regions with the highest rural poverty population across India in *******, distributed by states and union territories. With close to ** percent of its population living under the poverty line, the union territory of Dadra and Nagar Haveli had the highest rural poverty share, followed by the state of Chhattisgarh with over ** percent of its rural people living in poverty.

The rural poverty line across India in *******, broken down by states and union territories can be found here.

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.

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; child; family; poverty; FL; persons; and USA.

Percentage of abject poor households, moderate poor households and the percentage of population living below the poverty line (consumption poverty) in the states of India, 2005–06.

Agricultural Offtake: Bihar: Rice: TPDS: Below Poverty Line (BPL) data was reported at 0.102 Ton th in Jun 2014. This records a decrease from the previous number of 1.320 Ton th for May 2014. Agricultural Offtake: Bihar: Rice: TPDS: Below Poverty Line (BPL) data is updated monthly, averaging 87.533 Ton th from Oct 2011 (Median) to Jun 2014, with 33 observations. The data reached an all-time high of 144.946 Ton th in May 2013 and a record low of 0.102 Ton th in Jun 2014. Agricultural Offtake: Bihar: 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 .

The statistic shows the regions with the highest urban poverty across India in *******, distributed by states and union territories. With close to ** percent of its population living under the urban poverty line, the state of Manipur had the highest urban poverty that year, followed by the state of Bihar with over ** percent of its urban population living in poverty.

The urban poverty line across India in *******, broken down by states and union territories can be found here.

Agricultural Offtake: Lakshadweep: Rice: TPDS: Below Poverty Line (BPL) data was reported at 0.495 Ton th in Feb 2015. This records an increase from the previous number of 0.198 Ton th for Jan 2015. Agricultural Offtake: Lakshadweep: Rice: TPDS: Below Poverty Line (BPL) data is updated monthly, averaging 0.160 Ton th from Nov 2011 (Median) to Feb 2015, with 10 observations. The data reached an all-time high of 0.742 Ton th in Mar 2013 and a record low of 0.014 Ton th in Apr 2013. Agricultural Offtake: Lakshadweep: 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 .

Goal 1: End poverty in all its forms everywhereGlobally, the number of people living in extreme poverty has declined by more than half from 1.9 billion in 1990. However, 836 million people still live in extreme poverty. About one in five persons in developing regions lives on less than $1.25 per day.Southern Asia and sub-Saharan Africa are home to the overwhelming majority of people living in extreme poverty.High poverty rates are often found in small, fragile and conflict-affected countries.One in four children under age five in the world has inadequate height for his or her age.The all India Poverty Head Count Ratio (PHCR) has been brought down from 47% in 1990 to 21% in 2011-2012, nearly halved.Data source: https://niti.gov.in/sites/default/files/SDG-India-Index-2.0_27-Dec.pdfPlease find detailed metadata here.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.

The statistic represents the poverty line across urban India in *******, broken down by states and union territories. The state of Manipur had a poverty line of just over ***** rupees during that time period.

Agricultural Allotment: Kerala: Rice: TPDS: Below Poverty Line (BPL) data was reported at 26.566 Ton th in Oct 2016. This stayed constant from the previous number of 26.566 Ton th for Sep 2016. Agricultural Allotment: Kerala: Rice: TPDS: Below Poverty Line (BPL) data is updated monthly, averaging 26.566 Ton th from Oct 2011 (Median) to Oct 2016, with 61 observations. The data reached an all-time high of 26.566 Ton th in Oct 2016 and a record low of 26.566 Ton th in Oct 2016. Agricultural Allotment: Kerala: 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.RII013: Agricultural Allotment under Targeted Public Distribution System (TPDS): Rice: by States .

This layer shows Socio- Economic Profiles and Inter-State Comparison of Selected Major States of IndiaData source Url: https://www.indiabudget.gov.in/economicsurvey/doc/stat/tab89a.pdfSource: Office of Registrar General of India(RGI). Andhra Pradesh excludes Telangana for Sex ratio at birthNITI Aayog, Figures in parenthesis are rank amongst the selected 20 States Press Note on Poverty Estimates 2011-12, Government of India (2013)School Education in India, U-DISE + 2019-20 (Provisional)CSO, GSDP at constant prices and per capita income is at current prices ( Base Year 2011-12)National Statistical Office (NSO), Monthly per capita expenditure (MPCE) is based on mixed modified recall period,Periodic Labour Force Survey, 2019-20 (NSO); WPR (Worker Participation Rate) and Unemployment Rate are based on Usual Principal & Subsidiary Status (UPSS). 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.Note: Transition Rate: The number of new entrants admitted to the first grade of the next stage of school education in a given year, expressed as a percentage of number of pupils enrolled in the final grade of the current stage of school education in the previous year.

The statistic represents the rural poverty line across India in *******, broken down by states and union territories. The union territory of Puducherry had a poverty line of just over ***** rupees during that time period.

The project’s central research question was: to what extent do initiatives to make local governance more participatory enhance poor people's opportunities for political empowerment? Looking at four rural field sites in West Bengal and Kerala, India, it examines poor people's use of the formal opportunities they have for participation within the local state. This ‘invited participation’ is examined within the context of the social relations reproducing poverty and marginalisation, and informal structures of authority and power, both of which reshape governance reforms away from their intended practice. The data available for archiving comes from two distinct groups of research participants; those implementing participatory initiatives within the local state (including civil servants, political leaders and community activists); and marginalised communities themselves. Both were subjects of in-depth qualitative interviews (in Bengali/Malayalam) with a field team that was located within the research areas for a period of 8 months. Transcription and translation is of mixed quality, so the research team has largely worked with the original audio voice recordings in West Bengal. Copies of the original audio files of all interviews have been archived with both project partner institutions (Centre for Development Studies, Trivandrum and Centre for Studies in Social Sciences Calcutta). The materials remain a rich source for the research team, but detached from their proper context their value to third-party researchers is uncertain. In addition, a short questionnaire was conducted with every household in three wards of each of the four local councils (panchayats) of the study. This provides a micro-level snapshot of some basic poverty indicators within the four field sites, and was constructed to contextualise the qualitative field materials. This data could not be used to generalise about conditions at scales above the fieldsites themselves – for example, making comparisons at a District or State level about poverty, as this would have required a far larger stratified sampling procedure. Qualitative interviews: Kerala – 21 interviews with social/political leaders and 50 interviews with marginalised communities were conducted in the Palakkad fieldsite, and a further 19 interviews with social/political leaders and 50 interviews with marginalised communities were conducted in the Wayanad field site. Good translations of the interviews (conducted in Malayalam) have been provided in almost all cases, and two examples are attached as Word files. Within West Bengal the equivalent numbers were 23 leadership and 53 community interviews for the first field site (in Dubrajpur Block), and 24 leadership and 50 community interviews for the second (in Mayreswar I Block). Interview transcription and translation here was of mixed quality due to the skills available within West Bengal (one example is attached) – and in writing papers from these materials, the team has largely worked with the original (Bengali) audio voice recordings. Questionnaire Data: A copy of the questionnaire is attached (Houselisting Questionnaire 06-12-08.doc), and the data is provided in SPSS format split in to two SPSS files for each state (West Bengal Household; West Bengal Individual; Kerala Individual; Kerala Household). A short document with summary tables on giving a basic comparison between the four field sites is also attached (Tables of Voices of the Poor120810).Poor people’s lack of voice and influence are globally recurring themes their own accounts of their poverty, and are indicative of their wider political disempowerment. This project evaluates attempts to tackle this core element of poverty through local governance reform. Its central research question is: to what extent do participatory initiatives within local governance enhance poor people’s opportunities for political empowerment? Local governance reform has become a key site of development intervention, underpinned by an assumption that it will deliver positive feedback between popular participation, democratisation and poverty alleviation. The project critically analyses this assumption, focusing on two Indian States internationally recognised for innovations in local governance, West Bengal and Kerala. Primary data collection in each State centres on poor people’s own evaluations of participatory governance initiatives. It asks whether participatory initiatives create new public arenas where poor people do voice their concerns, whether they practically assist poor people in pressing their claims in these arenas and elsewhere, and whether participation actually challenges underlying political exclusion. The project has been designed in collaboration with Indian partner institutions (CDS, Trivandrum, and CSSSC, Kolkata), and will engage with potential users - from local research participants to policy makers - from the outset.