India: Poverty ratio, percent living on less than 5.50 USD a day: The latest value from 2021 is 81.8 percent, a decline from 83 percent in 2020. In comparison, the world average is 25.11 percent, based on data from 71 countries. Historically, the average for India from 1977 to 2021 is 89.86 percent. The minimum value, 80.7 percent, was reached in 2019 while the maximum of 97.8 percent was recorded in 1977.

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 13 million 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.

This dataset contains estimates of the poor and extreme poor rural population within each region (administrative level 1) of the CRPs countries. The poverty lines are defined using the thresholds of 3.10$/day and 1.90$/day respectively, expressed in 2011 PPP $. With the exception of India, all the other estimates are based on authors’ calculations using data from nationally representative household surveys.

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.

During 2019 to 2021, almost ** percent of the Indian population were reportedly multidimensionally poor. This reflected a much lower percentage of multidimensionally poor population in India. India has made significant progress in multidimensional poverty over the years. The share of multidimensional poor is expected to decline to around ** percent during 2022 to 2023.

Life expectancy is an estimate of how long a person would live, on average.

Life expectancy is affected by many factors such as: • Socioeconomic status, including employment, income, education and economic wellbeing. • The quality of the health system and the ability of people to access it; health behaviors such as tobacco and excessive alcohol consumption, poor nutrition and lack of exercise. • Social factors; genetic factors; and environmental factors including overcrowded housing, lack of clean drinking water and adequate sanitation, etc.

With the help of the above-mentioned factors, I tried to analyse t the data and come up with measurable solutions to improve the Life Expectancy.

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.

ObjectiveA focus on bacterial contamination has limited many studies of water service delivery in slums, with diarrheal illness being the presumed outcome of interest. We conducted a mixed methods study in a slum of 12,000 people in Mumbai, India to measure deficiencies in a broader array of water service delivery indicators and their adverse life impacts on the slum’s residents.MethodsSix focus group discussions and 40 individual qualitative interviews were conducted using purposeful sampling. Quantitative data on water indicators—quantity, access, price, reliability, and equity—were collected via a structured survey of 521 households selected using population-based random sampling.ResultsIn addition to negatively affecting health, the qualitative findings reveal that water service delivery failures have a constellation of other adverse life impacts—on household economy, employment, education, quality of life, social cohesion, and people’s sense of political inclusion. In a multivariate logistic regression analysis, price of water is the factor most strongly associated with use of inadequate water quantity (≤20 liters per capita per day). Water service delivery failures and their adverse impacts vary based on whether households fetch water or have informal water vendors deliver it to their homes.ConclusionsDeficiencies in water service delivery are associated with many non-health-related adverse impacts on slum households. Failure to evaluate non-health outcomes may underestimate the deprivation resulting from inadequate water service delivery. Based on these findings, we outline a multidimensional definition of household “water poverty” that encourages policymakers and researchers to look beyond evaluation of water quality and health. Use of multidimensional water metrics by governments, slum communities, and researchers may help to ensure that water supplies are designed to advance a broad array of health, economic, and social outcomes for the urban poor.

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.

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.

In many low- and middle-income countries, the quality of public goods available to the poor is inadequate. We report findings from a unique combination of a village-randomized controlled trial and a lab-in-the-field behavioral experiment. A village-randomized trial involving the establishment of financial “self-help” groups in one of the poorest districts in India shows that the presence of these groups improved villagers' access to and quality of certain critical local public goods, in particular, water. Our evidence suggests that the underlying mechanisms responsible were better information provision through the groups, stronger engagement by members in village governance, and lower coordination costs. Public goods games played in a subset of control and treatment villages four years following the start of the intervention, additionally, indicate that cooperative norms are stronger in villages where self-help groups were present. We find little evidence that membership leads to a convergence of tastes among group members. These results suggest that, in contrast to traditional participatory development programs, self-help groups can build durable social capital that can improve government performance in poor communities.

This paper examines the correlations between socioeconomic status, economic shocks, and depression, and how these vary by gender, in a sample of adults from India. Poverty and the exposure to negative shocks are both associated with depression. However, the frequency of negative shocks varies only slightly by socioeconomic status and gender. Instead, poor people and women appear to be more vulnerable to negative shocks. These patterns suggest that social protection programs may foster mental health for these groups and reduce mental health disparities.

India Railway Equipment Failure: Wagon: Poor Brake Power data was reported at 142.000 Unit in Apr 2018. This records an increase from the previous number of 2.000 Unit for Jan 2018. India Railway Equipment Failure: Wagon: Poor Brake Power data is updated monthly, averaging 1.000 Unit from Apr 2012 (Median) to Apr 2018, with 14 observations. The data reached an all-time high of 142.000 Unit in Apr 2018 and a record low of 1.000 Unit in Oct 2016. India Railway Equipment Failure: Wagon: Poor Brake Power data remains active status in CEIC and is reported by Ministry of Railways. The data is categorized under India Premium Database’s Transportation, Post and Telecom Sector – Table IN.TB007: Railway Statistics: Railway Equipment Performance.

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

Abstract

Poverty reduction via formation of community based organizations is a popular approach in regions of high socio-economic marginalization, especially in South Asia. The shortage of evidence on the impacts of such an approach is an outcome of the complexity of these projects, which almost always have a multi-sectoral design to achieve a comprehensive basket of aims. In the current research, we consider results from a rural livelihoods program in Bihar, one of India’s poorest states. Adopting a model prevalent in several Indian states, the Bihar Rural Livelihoods Project, known locally as JEEViKA, relies on mobilizing women from impoverished, socially marginalized households into Self Help Groups. Simultaneously, activities such as micro-finance and technical assistance for agricultural livelihoods are taken up by the project and routed to the beneficiaries via these institutions; these institutions also serve as a platform for women to come together and discuss a multitude of the socio-economic problems that they face. We use a retrospective survey instrument, coupled with PSM techniques to find that JEEViKA, has engendered some significant results in restructuring the debt portfolio of these households; additionally, JEEViKA has been instrumental in providing women with higher levels of empowerment, as measured by various dimensions.

In the current research, we consider a multi-sectoral approach which closely resembles the APDPIP design. We take a close look at the impacts of a rural poverty reduction program in Bihar, one of India’s poorest states. This program JEEViKA, focusses on building Self Help Groups (SHGs) of marginalized women; these groups are then federated into higher order institutions of such women at the village and local level. Cheap credit for a variety of purposes, technical assistance for various livelihood activities and encouraging awareness about various public services are the key agendas of this program. However, due to the very nature of JEEViKA’s target population, and given Bihar’s vicious income and gender inequality, the potential for impacts on women’s empowerment exists. A retrospective survey instrument, coupled with ‘Propensity Score Matching’ methods are used to estimate the impacts.

The results from the survey point out that JEEViKA has played an instrumental role in restructuring the debt portfolio of beneficiary households; households that have SHG members have a significantly lower high cost debt burden, are able to access smaller loans repeatedly and borrow more often for productive purposes, when compared to households without SHG members. Since JEEViKA works by mobilizing marginalized women into institutional platforms, such women demonstrate higher levels of empowerment, when empowerment is measured by mobility, decision making and collective action. Finally, we see some effects on the asset positions, food security and sanitation preferences of beneficiary households. It is worth pointing out here that the extent and significance of the results on debt portfolio and empowerment are robust to various matching modules and various specifications of the matched sample. The results on the other dimensions are subject to specifications or matching modules.

This brings out to the point about the timeline of these interventions and the materialization of impacts. In the context of such iterative, multi-sectoral poverty reduction approach, a well_x0002_designed research question must be able to identify the goals that a project should have achieved, given the time-line of that evaluation; the extent of such achievements are only a part of the evaluation agenda. The short review provided above provides some clues that a regular evaluation horizon of 2/3 years may be insufficient time to observe higher order effects, especially since actual benefits happen only after poor are mobilized into institutions and institutions are federated into higher-order institutions; indeed, the village-level institution, the Village Organization, which is made of 15 SHGs on an average, becomes functional 8-10 months after JEEViKA enters a village for the first time. The retrospective nature of the survey instrument also rules out any meaningful comparison of consumption or income levels between treatment and control areas.

Analysis unit

Household

Sampling procedure

The survey was administered to 10 randomly selected households from the target hamlets in all 200 project and 200 non-project villages; we can assume that had caste compositions changed significantly since 2001 in either the selected project or non-project villages, this should be reflected in the sample statistics. It is to be noted that the survey team did not have a beneficiary list for the treatment villages; thus the selection of interviewed HHs were truly random, and not a sample of beneficiary HHs only. The details on the questionnaire and selection of villages to survey are discussed at greater lengths in the Section 3 of the survey report - Data & Identification Strategy. The report is available for download under the Downloads section.

Mode of data collection

Face-to-face [f2f]

Research instrument

An identical survey instrument covering several broad areas on socio-economic indicators was administered to each of the 4000 households. The instrument had two broad modules; the general module was administered to a responsible adult (preferably HH head), and the women’s module was administered to an ever married adult woman. The general module collected economic information focused on asset ownership, debt portfolio, land holdings, savings habit and food security condition; social indicators attempting to capture changes in women’s empowerment focused on women’s mobility, decision making and networks were part of the women’s module. The demographic profile of each household was captured by an appropriate household roster and caste-religion profile; in addition, a livelihood roster was also administered. Given the retrospective nature of the study, questions on certain indicators were designed to capture the levels at end 2007, along with the current level. However for other indicators, like debt portfolio, questions for end 2007 levels were not asked since the chances for incorrect responses are considerable.

In 2021, over 12 percent of India's population was living on less than 2.15 U.S. dollars per day. When the 3.65 U.S. dollars per day threshold is considered, the share increased to over 44 percent.

Data and insights on Wealth Distribution in India - share of wealth, average wealth, HNIs, wealth inequality GINI, and comparison with global peers.

Objective: To assess the effect of experienced stigma on depth of multidimensional poverty of persons with severe mental illness (PSMI) in Delhi, India, controlling for gender, age and caste. Design: Matching case (hospital)–control (population) study. Setting: University Hospital (cases) and National Capital Region (controls), India. Participants: A case–control study was conducted from November 2011 to June 2012. 647 cases diagnosed with schizophrenia or affective disorders were recruited and 647 individuals of same age, sex and location of residence were matched as controls at a ratio of 1:2:1. Individuals who refused consent or provided incomplete interview were excluded. Main outcome measures: Higher risk of poverty due to stigma among PSMI. Results: 38.5% of PSMI compared with 22.2% of controls were found poor on six dimensions or more. The difference in multidimensional poverty index was 69% between groups with employment and income of the main contributors. Multidimensional poverty was strongly associated with stigma (OR 2.60, 95% CI 1.27 to 5.31), scheduled castes/scheduled tribes/other backward castes (2.39, 1.39 to 4.08), mental illness (2.07, 1.25 to 3.41) and female gender (1.87, 1.36 to 2.58). A significant interaction between stigma, mental illness and gender or caste indicates female PSMI or PSMI from ‘lower castes’ were more likely to be poor due to stigma than male controls (p<0.001) or controls from other castes (p<0.001). Conclusions: Public stigma and multidimensional poverty linked to SMI are pervasive and intertwined. In particular for low caste and women, it is a strong predictor of poverty. Exclusion from employment linked to negative attitudes and lack of income are the highest contributors to multidimensional poverty, increasing the burden for the family. Mental health professionals need to be aware of and address these issues.

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.