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).

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.

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.

India IN: Poverty Headcount Ratio at $3.65 a Day: 2017 PPP: % of Population data was reported at 44.000 % in 2021. This records a decrease from the previous number of 48.200 % for 2020. India IN: Poverty Headcount Ratio at $3.65 a Day: 2017 PPP: % of Population data is updated yearly, averaging 62.000 % from Dec 1977 (Median) to 2021, with 14 observations. The data reached an all-time high of 89.100 % in 1977 and a record low of 44.000 % in 2021. India IN: Poverty Headcount Ratio at $3.65 a Day: 2017 PPP: % of Population 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. Poverty headcount ratio at $3.65 a day is the percentage of the population living on less than $3.65 a day at 2017 international prices.;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).

India Poverty Headcount Ratio at Societal Poverty Lines: % of Population data was reported at 33.100 % in 2021. This records a decrease from the previous number of 34.800 % for 2020. India Poverty Headcount Ratio at Societal Poverty Lines: % of Population data is updated yearly, averaging 38.450 % from Dec 1977 (Median) to 2021, with 14 observations. The data reached an all-time high of 63.500 % in 1977 and a record low of 32.400 % in 2018. India Poverty Headcount Ratio at Societal Poverty Lines: % of Population 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 poverty headcount ratio at societal poverty line is the percentage of a population living in poverty according to the World Bank's Societal Poverty Line. The Societal Poverty Line is expressed in purchasing power adjusted 2017 U.S. dollars and defined as max($2.15, $1.15 + 0.5*Median). This means that when the national median is sufficiently low, the Societal Poverty line is equivalent to the extreme poverty line, $2.15. For countries with a sufficiently high national median, the Societal Poverty Line grows as countries’ median income grows.;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).

In 2022, the total male population living in poverty in India was about ** million. By contrast, the number of females in poverty during the same time period was around ** million.

India Proportion of Population Pushed Below the 60% Median Consumption Poverty Line By Out-of-Pocket Health Expenditure: % data was reported at 2.570 % in 2017. This records a decrease from the previous number of 3.400 % for 2011. India Proportion of Population Pushed Below the 60% Median Consumption Poverty Line By Out-of-Pocket Health Expenditure: % data is updated yearly, averaging 1.930 % from Dec 1995 (Median) to 2017, with 9 observations. The data reached an all-time high of 3.400 % in 2011 and a record low of 1.290 % in 2001. India Proportion of Population Pushed Below the 60% Median Consumption Poverty Line By Out-of-Pocket Health Expenditure: % 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. This indicator shows the fraction of a country’s population experiencing out-of-pocket health impoverishing expenditures, defined as expenditures without which the household they live in would have been above the 60% median consumption but because of the expenditures is below the poverty line. Out-of-pocket health expenditure is defined as any spending incurred by a household when any member uses a health good or service to receive any type of care (preventive, curative, rehabilitative, long-term or palliative care); provided by any type of provider; for any type of disease, illness or health condition; in any type of setting (outpatient, inpatient, at home).;Global Health Observatory. Geneva: World Health Organization; 2023. (https://www.who.int/data/gho/data/themes/topics/financial-protection);Weighted average;This indicator is related to Sustainable Development Goal 3.8.2 [https://unstats.un.org/sdgs/metadata/].

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.

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.

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.

The Bureau of the Census has released Census 2000 Summary File 1 (SF1) 100-Percent data. The file includes the following population items: sex, age, race, Hispanic or Latino origin, household relationship, and household and family characteristics. Housing items include occupancy status and tenure (whether the unit is owner or renter occupied). SF1 does not include information on incomes, poverty status, overcrowded housing or age of housing. These topics will be covered in Summary File 3. Data are available for states, counties, county subdivisions, places, census tracts, block groups, and, where applicable, American Indian and Alaskan Native Areas and Hawaiian Home Lands. The SF1 data are available on the Bureau's web site and may be retrieved from American FactFinder as tables, lists, or maps. Users may also download a set of compressed ASCII files for each state via the Bureau's FTP server. There are over 8000 data items available for each geographic area. The full listing of these data items is available here as a downloadable compressed data base file named TABLES.ZIP. The uncompressed is in FoxPro data base file (dbf) format and may be imported to ACCESS, EXCEL, and other software formats. While all of this information is useful, the Office of Community Planning and Development has downloaded selected information for all states and areas and is making this information available on the CPD web pages. The tables and data items selected are those items used in the CDBG and HOME allocation formulas plus topics most pertinent to the Comprehensive Housing Affordability Strategy (CHAS), the Consolidated Plan, and similar overall economic and community development plans. The information is contained in five compressed (zipped) dbf tables for each state. When uncompressed the tables are ready for use with FoxPro and they can be imported into ACCESS, EXCEL, and other spreadsheet, GIS and database software. The data are at the block group summary level. The first two characters of the file name are the state abbreviation. The next two letters are BG for block group. Each record is labeled with the code and name of the city and county in which it is located so that the data can be summarized to higher-level geography. The last part of the file name describes the contents . The GEO file contains standard Census Bureau geographic identifiers for each block group, such as the metropolitan area code and congressional district code. The only data included in this table is total population and total housing units. POP1 and POP2 contain selected population variables and selected housing items are in the HU file. The MA05 table data is only for use by State CDBG grantees for the reporting of the racial composition of beneficiaries of Area Benefit activities. The complete package for a state consists of the dictionary file named TABLES, and the five data files for the state. The logical record number (LOGRECNO) links the records across tables.

Abstract

Financial inclusion is critical in reducing poverty and achieving inclusive economic growth. When people can participate in the financial system, they are better able to start and expand businesses, invest in their children’s education, and absorb financial shocks. Yet prior to 2011, little was known about the extent of financial inclusion and the degree to which such groups as the poor, women, and rural residents were excluded from formal financial systems.

By collecting detailed indicators about how adults around the world manage their day-to-day finances, the Global Findex allows policy makers, researchers, businesses, and development practitioners to track how the use of financial services has changed over time. The database can also be used to identify gaps in access to the formal financial system and design policies to expand financial inclusion.

Geographic coverage

National Coverage. Sample excludes Northeast states and remote islands. In addition, some districts in Assam, Bihar, Jammu and Kashmir, Jharkhand, and Uttar Pradesh were replaced because of security concerns. The excluded areas represent less than 10% of the population.

Analysis unit

Individual

Universe

The target population is the civilian, non-institutionalized population 15 years and above.

Kind of data

Sample survey data [ssd]

Frequency of data collection

Triennial

Sampling procedure

As in the first edition, the indicators in the 2014 Global Findex are drawn from survey data covering almost 150,000 people in more than 140 economies-representing more than 97 percent of the world's population. The survey was carried out over the 2014 calendar year by Gallup, Inc. as part of its Gallup World Poll, which since 2005 has continually conducted surveys of approximately 1,000 people in each of more than 160 economies and in over 140 languages, using randomly selected, nationally representative samples. The target population is the entire civilian, noninstitutionalized population age 15 and above. The set of indicators will be collected again in 2017.

Surveys are conducted face to face in economies where telephone coverage represents less than 80 percent of the population or is the customary methodology. In most economies the fieldwork is completed in two to four weeks. In economies where face-to-face surveys are conducted, the first stage of sampling is the identification of primary sampling units. These units are stratified by population size, geography, or both, and clustering is achieved through one or more stages of sampling. Where population information is available, sample selection is based on probabilities proportional to population size; otherwise, simple random sampling is used. Random route procedures are used to select sampled households. Unless an outright refusal occurs, interviewers make up to three attempts to survey the sampled household. To increase the probability of contact and completion, attempts are made at different times of the day and, where possible, on different days. If an interview cannot be obtained at the initial sampled household, a simple substitution method is used. Respondents are randomly selected within the selected households by means of the Kish grid. In economies where cultural restrictions dictate gender matching, respondents are randomly selected through the Kish grid from among all eligible adults of the interviewer's gender.

In economies where telephone interviewing is employed, random digit dialing or a nationally representative list of phone numbers is used. In most economies where cell phone penetration is high, a dual sampling frame is used. Random selection of respondents is achieved by using either the latest birthday or Kish grid method. At least three attempts are made to reach a person in each household, spread over different days and times of day.

The sample size in India was 3,000 individuals.

Mode of data collection

Computer Assisted Personal Interview [capi]

Research instrument

The questionnaire was designed by the World Bank, in conjunction with a Technical Advisory Board composed of leading academics, practitioners, and policy makers in the field of financial inclusion. The Bill and Melinda Gates Foundation and Gallup Inc. also provided valuable input. The questionnaire was piloted in multiple countries, using focus groups, cognitive interviews, and field testing. The questionnaire is available in 142 languages upon request.

Questions on cash withdrawals, saving using an informal savings club or person outside the family, domestic remittances, school fees, and agricultural payments are only asked in developing economies and few other selected countries. The question on mobile money accounts was only asked in economies that were part of the Mobile Money for the Unbanked (MMU) database of the GSMA at the time the interviews were being held.

Sampling error estimates

Estimates of standard errors (which account for sampling error) vary by country and indicator. For country-specific margins of error, please refer to the Methodology section and corresponding table in Asli Demirguc-Kunt, Leora Klapper, Dorothe Singer, and Peter Van Oudheusden, “The Global Findex Database 2014: Measuring Financial Inclusion around the World.” Policy Research Working Paper 7255, World Bank, Washington, D.C.

Socioeconomic differentials in households falling poverty line (poverty headcount ratio) and average deficit from the poverty line (poverty gap ratio) due to out-of-pocket health payments.

This dataset provides a detailed view of South Asian countries' socio-economic, environmental, and governance metrics from 2000 to 2023. It compiles key indicators like GDP, unemployment, literacy rates, energy use, governance measures, and more to facilitate a comprehensive analysis of each country’s growth, stability, and development trends over the years. The data covers Bangladesh, Bhutan, India, Pakistan, Nepal, Sri Lanka, Afghanistan, and Maldives.

Key Indicators Economic Metrics: Includes GDP (both total and per capita in USD), annual GDP growth rates, inflation, and foreign direct investment. These metrics offer insight into economic health, growth rate, and international investment trends across the region. Employment and Trade: Tracks unemployment rates as a percentage of the labor force and trade (as a percentage of GDP), helping assess workforce stability and international commerce engagement. Income and Poverty: Features the Gini index (for income inequality) and poverty headcount ratio at $2.15/day, showing income distribution and poverty levels. These indicators reveal disparities and poverty within each country. Population Statistics: Includes total population, annual population growth, and urban population percentage, capturing demographic trends and urbanization rates. Social Indicators: Covers literacy rates, school enrollment in primary education, life expectancy at birth, infant mortality rates, and access to electricity, basic water, and sanitation services. These data points help measure the population’s health, education levels, and access to essential services. Environmental and Energy Metrics: Tracks CO2 emissions, PM2.5 air pollution, renewable energy consumption, and forest area. This environmental data is crucial for analyzing air quality, sustainable energy use, and forest coverage trends. Governance Indicators: Includes metrics such as control of corruption, political stability, regulatory quality, rule of law, and voice and accountability. These indicators reflect each country’s governance quality and institutional stability. Digital and Technological Growth: Measures internet usage rates, research and development spending, and high-technology exports. These statistics indicate digital access, innovation, and technological progress. This dataset, sourced from the World Bank DataBank, provides a robust foundation for studying South Asia's socio-economic, environmental, and governance progress. By analyzing these diverse indicators, researchers and policymakers can gain a deeper understanding of the region’s development path and identify areas that need improvement.

Abstract

Financial inclusion is critical in reducing poverty and achieving inclusive economic growth. When people can participate in the financial system, they are better able to start and expand businesses, invest in their children’s education, and absorb financial shocks. Yet prior to 2011, little was known about the extent of financial inclusion and the degree to which such groups as the poor, women, and rural residents were excluded from formal financial systems.

By collecting detailed indicators about how adults around the world manage their day-to-day finances, the Global Findex allows policy makers, researchers, businesses, and development practitioners to track how the use of financial services has changed over time. The database can also be used to identify gaps in access to the formal financial system and design policies to expand financial inclusion.

Geographic coverage

Sample excludes Northeast states and remote islands, representing less than 10% of the population.

Analysis unit

Individuals

Universe

The target population is the civilian, non-institutionalized population 15 years and above.

Kind of data

Observation data/ratings [obs]

Sampling procedure

The indicators in the 2017 Global Findex database are drawn from survey data covering almost 150,000 people in 144 economies-representing more than 97 percent of the world’s population (see table A.1 of the Global Findex Database 2017 Report for a list of the economies included). The survey was carried out over the 2017 calendar year by Gallup, Inc., as part of its Gallup World Poll, which since 2005 has annually conducted surveys of approximately 1,000 people in each of more than 160 economies and in over 150 languages, using randomly selected, nationally representative samples. The target population is the entire civilian, noninstitutionalized population age 15 and above. Interview procedure Surveys are conducted face to face in economies where telephone coverage represents less than 80 percent of the population or where this is the customary methodology. In most economies the fieldwork is completed in two to four weeks.

In economies where face-to-face surveys are conducted, the first stage of sampling is the identification of primary sampling units. These units are stratified by population size, geography, or both, and clustering is achieved through one or more stages of sampling. Where population information is available, sample selection is based on probabilities proportional to population size; otherwise, simple random sampling is used. Random route procedures are used to select sampled households. Unless an outright refusal occurs, interviewers make up to three attempts to survey the sampled household. To increase the probability of contact and completion, attempts are made at different times of the day and, where possible, on different days. If an interview cannot be obtained at the initial sampled household, a simple substitution method is used.

Respondents are randomly selected within the selected households. Each eligible household member is listed and the handheld survey device randomly selects the household member to be interviewed. For paper surveys, the Kish grid method is used to select the respondent. In economies where cultural restrictions dictate gender matching, respondents are randomly selected from among all eligible adults of the interviewer’s gender.

In economies where telephone interviewing is employed, random digit dialing or a nationally representative list of phone numbers is used. In most economies where cell phone penetration is high, a dual sampling frame is used. Random selection of respondents is achieved by using either the latest birthday or household enumeration method. At least three attempts are made to reach a person in each household, spread over different days and times of day.

The sample size was 3000.

Mode of data collection

Computer Assisted Personal Interview [capi]

Research instrument

The questionnaire was designed by the World Bank, in conjunction with a Technical Advisory Board composed of leading academics, practitioners, and policy makers in the field of financial inclusion. The Bill and Melinda Gates Foundation and Gallup Inc. also provided valuable input. The questionnaire was piloted in multiple countries, using focus groups, cognitive interviews, and field testing. The questionnaire is available in more than 140 languages upon request.

Questions on cash on delivery, saving using an informal savings club or person outside the family, domestic remittances, and agricultural payments are only asked in developing economies and few other selected countries. The question on mobile money accounts was only asked in economies that were part of the Mobile Money for the Unbanked (MMU) database of the GSMA at the time the interviews were being held.

Sampling error estimates

Estimates of standard errors (which account for sampling error) vary by country and indicator. For country-specific margins of error, please refer to the Methodology section and corresponding table in Demirgüç-Kunt, Asli, Leora Klapper, Dorothe Singer, Saniya Ansar, and Jake Hess. 2018. The Global Findex Database 2017: Measuring Financial Inclusion and the Fintech Revolution. Washington, DC: World Bank

A Retrospective Impact Evaluation of the Tamil Nadu Empowerment and Poverty Alleviation (Pudhu Vaazhvu) Project This is a one period survey with retrospective questions of changes on changes over time, collected to do a "quick" evaluation of PVP Phase 1 project in Tamil Nadu. Collaboration: World Bank Social Observatory Team, with Govt of Tamil Nadu's Pudhu Vaazhvu project. Community based livelihood interventions, which focus directly on increasing income and employment, have become an increasingly important component of large-scale poverty reduction programmes. We evaluate the impact of a participatory livelihoods intervention- the Tamil Nadu Empowerment and Poverty Reduction (Pudhu Vaazhvu) Project (PVP) using propensity score matching methods. The paper explores the impact of PVP on its core goals of empowering women and the rural poor, improving their economic welfare, and facilitating public action. We find significant effects of PVP on reducing the incidence of high cost debt and diversifying livelihoods. We also find evidence of women’s empowerment, and increased political participation. Our data come from a survey implemented by PVP, in collaboration with the World Bank during the period December 2012-March 2013. This survey covered ten districts, out of the 16 total districts where PVP had implemented its interventions over the period 2006-2010. The sample districts were chosen to ensure representation from different geographic regions of PVP’s operationv. Since this survey was designed and implemented after this evaluation was designed, our data was collected from households in matched project and non-project block pairs in these districts. Within each district, the survey covered the matched block pair, and matched VPs within these blocks. As mentioned earlier, 12 to16 VPs that had the closest match on propensity scores were sampled. The lower bound of this range was defined at the number of VPs at which our sample would in effect have picked a census of VPs within the block, that is, we saturate the treatment VPs within a block. In each VP, we sample two villages, at random. In the case of VPs with only one village, our sample covers that single village. In each village, a household questionnaire was administered to a sample of 12 households; and to the elected president of the VP. In order to measure the impact of the project, which targets the disadvantaged poor, the household sample was drawn using stratified random sampling. Stratification was, therefore, used to oversample SC/ST households; and this was based on their population proportions within the village. With this oversampling, SC/ST households comprise a third of the sample. In all, we administered the household questionnaire to 3,692 households, drawn from 268 VPs. The household questionnaire had two components: (i) a general household module that included an LSMSvi type consumption module; and detailed information on the livelihoods portfolio and debt profile of the household, and (ii) a woman’s module that was administered to an adult married woman in the household, and measured different metrics of women’s empowerment. These measures included questions on decision-making within the household, and on women’s participation in local government and civic action. At the household level, we also collected retrospective data on assets and housing quality. Retrospective data on other outcomes, such as mobility, intra-household decision-making and public action- were not collected due to a higher likelihood of recall error on these measures. In addition to this household module, two other modules were administered. A village focus group discussion collected information on key infrastructure facilities in the village, and public good preferences. A VP president survey collected information on his/her political backgrounds and preferences. In PVP areas, we also collected data on the key activities of VPRC. We use data on 3,678 households, almost equally split between PVP and non-PVP areas in our final analysis. The caste composition of the sample is similar in PVP and non-PVP areas (see Table 4, Appendix B). Women headed households are 14.46 per cent of the sample. SHG membership is high across both project and non-project areas, reflecting the long history of SHG movement in the state. 51.69 per cent of the sample households in projects areas are members of SHGs, while this proportion is 44.41 per cent in non-project areas. This research is an output of the Social Observatory Team of the World Bank and Pudhu Vaazhvu Project (PVP). Discussions with the PVP project team, led by the Additional Project Director RV Shajeevana, were critical to the design of this evaluation. Support from all Project Directors of PVP; and from Kevin Crockford, Samik Das and Makiko Watanabe from the World Bank... Visit https://dataone.org/datasets/sha256%3A29f480573bb5d79b73e3007634f50bea40813a2a016239b8e2608b70c2f1efc2 for complete metadata about this dataset.

Percentage of people deprived in each indicator of the MPI by social groups in India.

BackgroundPeople with disabilities are vulnerable because of the many challenges they face attitudinal, physical, and financial. The National Policy for Persons with Disabilities (2006) recognizes that Persons with Disabilities are valuable human resources for the country and seeks to create an environment that provides equal opportunities, and protection of their rights, and full. There are limited studies on health care burden due to disabilities of various types.AimThe present study examines the socioeconomic and state-wise differences in the prevalence of disabilities and related household financial burden in India.MethodsData for this study was obtained from the National Sample Survey (NSS), 76th round Persons with Disabilities in India Survey 2018. The survey covered a sample of 1,18,152 households, 5,76,569 individuals, of which 1,06,894 of had any disability. This study performed descriptive statistics, and bivariate estimates.ResultsThe finding of the analysis showed that prevalence of disability of any kind was 22 persons per 1000. Around, one-fifth (20.32%) of the household’s monthly consumption expenditure was spent on out-of-pocket expenditure for disability. More than half (57.1%) of the households were pushed to catastrophic health expenditure due to one of the members being disabled. Almost one-fifth (19.1%) of the households who were above the poverty line before one of members was treated for disability were pushed below the poverty line after the expenditure of the treatment and average percentage shortfall in income from the poverty line was 11.0 percent due to disability treatment care expenditure.ConclusionThe study provides an insight on the socioeconomic differentials in out-of-pocket expenditure, catastrophic expenditure for treatment of any kind of disability. To attain SDG goal 3 that advocates healthy life and promote well-being for all at all ages, there is a need to recognize the disadvantaged and due to disability.

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

Socioeconomic differentials in average out of pocket expenditure, household consumption expenditure, and share of out-of-pocket expenditure on total household expenditure for treatment of any disability in India.