NSDP Per Capita: Bihar data was reported at 60,180.373 INR in 2024. This records an increase from the previous number of 53,400.628 INR for 2023. NSDP Per Capita: Bihar data is updated yearly, averaging 36,849.782 INR from Mar 2012 (Median) to 2024, with 13 observations. The data reached an all-time high of 60,180.373 INR in 2024 and a record low of 21,749.854 INR in 2012. NSDP Per Capita: Bihar data remains active status in CEIC and is reported by Ministry of Statistics and Programme Implementation. The data is categorized under Global Database’s India – Table IN.GEI004: Memo Items: State Economy: Net State Domestic Product per Capita.

The estimated per capita income across the eastern state of Bihar in India stood at around ** thousand Indian rupees in the financial year 2024. Despite the consistent increase in the income per capita in the state since financial year 2012, Bihar had the lowest per capita in the last decade when compared to other states in the country.

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

NSDP Per Capita: 2004-05p: Bihar data was reported at 16,801.373 INR in 2015. This records an increase from the previous number of 15,506.408 INR for 2014. NSDP Per Capita: 2004-05p: Bihar data is updated yearly, averaging 10,635.227 INR from Mar 2005 (Median) to 2015, with 11 observations. The data reached an all-time high of 16,801.373 INR in 2015 and a record low of 7,588.466 INR in 2006. NSDP Per Capita: 2004-05p: Bihar data remains active status in CEIC and is reported by Ministry of Statistics and Programme Implementation. The data is categorized under Global Database’s India – Table IN.GEI004: Memo Items: State Economy: Net State Domestic Product per Capita. Rebased from 2004-2005 base to 2011-2012 base. Replacement series ID: 374619617

Structural Change is the phenomenon witnessed by almost all the developing and the developed states in their journey towards a higher sustained economic growth. The rise in per capita income and the structural change are complementary to each other. A rise in per capita income reinforces structural change and structural change leads to the rise in the productive efficiency stimulating rise in the per capita income of the people. The paper analyses this phenomenon in the state of Bihar which has, of late, started ta witness a higher growth trajectory in its economy. Sourcing the data from the archival records of the various government agencies, the study found that the state of Bihar has mirrored the structural change being experienced by the economy of India. The share of the primary sector in the state's GSDP has drastically declined and is replaced by the services sector. This phenomenon is particularly ascribed to the rising share of trade, communications and the government led public spending in the construction sector. However, the state is not experiencing a balanced growth pattern in its economy; some districts have become more developed relative to the other districts. It has also been seen that due to the low economic base of the state, we cannot expect that the state's socio-economic indicators will converge sooner to the_ more developed states of India.

Hungary GDP per Capita: Northern Great Plain: Hajdu Bihar data was reported at 2,526.000 HUF th in 2016. This records an increase from the previous number of 2,502.000 HUF th for 2015. Hungary GDP per Capita: Northern Great Plain: Hajdu Bihar data is updated yearly, averaging 1,922.000 HUF th from Dec 2000 (Median) to 2016, with 17 observations. The data reached an all-time high of 2,526.000 HUF th in 2016 and a record low of 985.000 HUF th in 2000. Hungary GDP per Capita: Northern Great Plain: Hajdu Bihar data remains active status in CEIC and is reported by Hungarian Central Statistical Office. The data is categorized under Global Database’s Hungary – Table HU.A018: ESA 2010: GDP per Capita: by Region.

Abstract

A two-part study of rural poverty was carried out in 1997-98 in south and eastern Uttar Pradesh and north and central Bihar. This study utilized both qualitative methods - rapid rural appraisal (RRA) & participatory rural appraisal (PRA) methodologies, and semi-structured interviews - as well as quantitative methods drawing on data collected from household and community surveys modelled after the World Bank's Living Standards Measurement Study (LSMS) surveys.

The data being distributed are from the quantitative component of the study, field work for which was carried out between December 1997 and March 1998. Data were collected through household and village-level questionnaires in 120 villages drawn from a sample of 25 districts in UP and Bihar states; a total of 2,250 households were interviewed during the course of the survey (more details on distribution of the sample are provided in the sampling section of this note). Of the sample of 120 villages where the household and village surveys were conducted, 30 had been visited in the earlier qualitative component of the study, while the remaining 90 were drawn at random from the sample districts.

Geographic coverage

The survey covered south and eastern Uttar Pradesh and north and central Bihar.

Analysis unit

• Households
• Individuals
• Community

Kind of data

Sample survey data [ssd]

Sampling procedure

Sampling Information:

Uttar Pradesh and Bihar, the two states selected for the study, are divided into 8 statistical regions: 5 in Uttar Pradesh (Himalayan, Western, Central, Eastern, and Southern) and 3 in Bihar(Southern, Northern, and Central).

Sampling Universe:

The universe for the study comprised 4 statistical regions: 2 in Uttar Pradesh (Eastern and Southern), and 2 in Bihar (Northern and Central). Altogether, there were 55 districts in the area covered by the study: 24 districts in the 2 statistical regions in Uttar Pradesh, and 31 districts in the 2 statistical regions covered in Bihar.

In the first phase of the project, qualitative field work was carried out in 30 villages: 3 villages each from 4 districts in Bihar (Mungher, Jehanabad, Saharsa, and Vaishali), and 6 villages each from 3 districts in Uttar Pradesh (Banda, Allahabad, and Gorakhpur).

Sampling Strategy:

The sampling strategy followed for the quantitative study basically involved dividing the sample population into four main strata: 1) districts that were covered in the qualitative study in Bihar (i.e. 4 districts) 2) districts that were covered in the qualitative study in Uttar Pradesh (i.e. 3 districts) 3) remaining districts in the 2 selected regions of Bihar (i.e. 27 districts) 4) remaining districts in the 2 selected regions of Uttar Pradesh (i.e. 21 districts)

All 12 villages in Stratum 1 that were covered in the qualitative study were included in the sample. Similarly, all 18 villages in Stratum 2 that were covered in the qualitative study were included in the sample. In each of these 30 villages, 30 households each were picked at random for the survey.

In stratums 3 and 4, 45 villages each were selected for the survey. A two-step procedure was used to select villages in these two strata: first, 9 districts were selected in each stratum using PPS. In each of the 9 districts, 5 villages were then selected at the second stage, again using PPS. In each of these 90 villages altogether, 15 households each were selected for the survey.

Mode of data collection

Face-to-face [f2f]

Research instrument

The household questionnaire comprised ten main sections, and collected information on:

SECTION 1. HOUSEHOLD INFORMATION This section has three main purposes. The first purpose is to identify all persons who are members of the household. The second is to provide basic demographic information (i.e. age, sex, marital status) for each person. The third is to collect information on the main sources of income for the household, as well as identify the main breadwinner in the household. Section 1 is divided into two parts: Part A: Household Roster and Part B: Sources of Livelihood.

SECTION 2. ACTIVITIES This section gathers information on economic activities undertaken by members of the household. Detailed questions are included on activities in the wage sectors -- casual wage employment, long-term wage employment in agriculture, and salaried employment. Additional information is also collected on non-farm self employment --activities in both large and very small business, trade, and manufacturing enterprises.

SECTION 3. HOUSING AND ACCESS TO FACILITIES This section collects information in three areas: the type of dwelling occupied by the household, access to basic services (water, sanitation, and electricity), and access to various facilities providing services

SECTION 4. EDUCATION This section collects information on the following: (i) use of child development (for example, anganwadi/balwadi) and early childhood education programs for children aged 0-6 years; (ii) formal schooling for children and young adults (6 to 19 years old) and expenses on education incurred during the past 12 months for those currently in school; Literacy of household members - i.e. which persons in the household can read and write - and educational attainment for each person - i.e. the highest level of schooling completed - are covered in the roster.

SECTION 5. HEALTH This section gathers information on the following: (a) Illness and injuries among all household members in the past year which have made it difficult for the person to perform their normal activities for at least a week (i.e. working, attending school, etc.) (b) Immunizations received since birth by children aged 5 years or less. (c) Incidence of diarrhea during the past 30 days for all children aged 5 years or less.

SECTION 6. MARRIAGE AND MATERNITY HISTORY This section collects information on: (a) maternity history from female household members aged 15 - 45 years, and (b) pre-natal and post-natal care from women who had a child in the last three years, and (c) perceptions amongst women about their role as members of the household and community.

SECTION 7. FOOD EXPENSES AND HOME PRODUCTION This section collects information on the household's total expenditure on food of various types, including an estimate of the value of home produced or home-grown food consumed by the household. It also is used to estimate food consumed that was received as payment in-kind, i.e. as remuneration for work done on someone else's farm, as gifts, or as presents from relatives and/or friends. In addition, respondents are asked about the ownership of household durable stocks.

SECTION 8. VULNERABILITY This section collects information on the following: (i) food availability throughout the year and use of the Public Distribution System (PDS - Fair price shops); (ii) loans outstanding or contracted over the past 12 months; (iii) receipts from safety net programs.

SECTION 9. FARMING AND LIVESTOCK The purpose of this section is to collect information on the household's agricultural assets as well as some data on farming practices followed by the household. Landholding: land owned, land operated, and irrigation Crop production and use of fertilizer Ownership of livestock Ownership of farming assets

SECTION 10. REMITTANCES AND TRANSFERS RECEIVED This section aims to capture the flow of remittances and transfers into the household.

The village questionnaire comprised seven sections covering (1) village characteristics, including size, caste composition and political structure, (2) access to facilities and services, (3)agriculture, irrigation and forestry, (4) employment and migration, (5) anti-poverty programs and organizations, and (6) changes over time, and (7) visits to facilities including the angawadi center, primary schools, health posts, and the PDS fair price shop.

Census: Population: Bihar: Rajgir data was reported at 41,587.000 Person in 03-01-2011. This records an increase from the previous number of 33,738.000 Person for 03-01-2001. Census: Population: Bihar: Rajgir data is updated decadal, averaging 18,034.000 Person from Mar 1951 (Median) to 03-01-2011, with 7 observations. The data reached an all-time high of 41,587.000 Person in 03-01-2011 and a record low of 3,870.000 Person in 03-01-1951. Census: Population: Bihar: Rajgir data remains active status in CEIC and is reported by Office of the Registrar General & Census Commissioner, India. The data is categorized under India Premium Database’s Demographic – Table IN.GAC005: Census: Population: By Towns and Urban Agglomerations: Bihar.

Census: Population: Bihar: Barh: Male data was reported at 32,823.000 Person in 03-01-2011. This records an increase from the previous number of 26,177.000 Person for 03-01-2001. Census: Population: Bihar: Barh: Male data is updated decadal, averaging 8,177.000 Person from Mar 1901 (Median) to 03-01-2011, with 12 observations. The data reached an all-time high of 32,823.000 Person in 03-01-2011 and a record low of 4,420.000 Person in 03-01-1921. Census: Population: Bihar: Barh: Male data remains active status in CEIC and is reported by Office of the Registrar General & Census Commissioner, India. The data is categorized under India Premium Database’s Demographic – Table IN.GAC005: Census: Population: By Towns and Urban Agglomerations: Bihar.

Census: Population: Bihar: Sasaram data was reported at 147,408.000 Person in 03-01-2011. This records an increase from the previous number of 131,172.000 Person for 03-01-2001. Census: Population: Bihar: Sasaram data is updated decadal, averaging 33,523.500 Person from Mar 1901 (Median) to 03-01-2011, with 12 observations. The data reached an all-time high of 147,408.000 Person in 03-01-2011 and a record low of 22,308.000 Person in 03-01-1921. Census: Population: Bihar: Sasaram data remains active status in CEIC and is reported by Office of the Registrar General & Census Commissioner, India. The data is categorized under India Premium Database’s Demographic – Table IN.GAC005: Census: Population: By Towns and Urban Agglomerations: Bihar.

Bihar: Hajipur: Total Voters data was reported at 1,006,817.000 Person in 03-01-2019. This records an increase from the previous number of 905,704.000 Person for 03-01-2014. Bihar: Hajipur: Total Voters data is updated quinquennially, averaging 749,558.000 Person from Mar 1971 (Median) to 03-01-2019, with 13 observations. The data reached an all-time high of 1,006,817.000 Person in 03-01-2019 and a record low of 401,687.000 Person in 03-01-1971. Bihar: Hajipur: Total Voters data remains active status in CEIC and is reported by Election Commission of India. The data is categorized under India Premium Database’s General Election – Table IN.GEA005: General Election: Loksabha: Election Outcome of Parliamentary Constituencies: Bihar.

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 2022, ****** was home to the highest number of millionaires, followed by India’s capital New Delhi, and the IT capital - Bengaluru. This comes as no surprise since all three cities have the largest share of high net worth households along with a booming economic outlook. Overall, India had around *** billionaires as of March 2023, and ranked third globally in terms of its ultra-net-worth individuals. A growing wealth gap Despite this, India also has a very high wealth inequality with millions of people living below the poverty line. In fact, according to the last census, the state of Maharashtra (with Mumbai as its capital city) had the highest number of slums across the country with over *** million households. Furthermore, according to a 2015 study on the geography of the super-rich, Bangalore was ranked first in terms of the inequality between its rich and poor, with the wealth of the city’s billionaires being ******* times that of the average per capita GDP in the city. Mumbai came second in this listing, while Delhi was ranked fifth. It's a rich man's world As of 2018, the richest ** percent of Indians owned **** percent of the country’s wealth. The Indian economy was also seen to be one of the fastest growing economies across the world. This indicates the level of unequal distribution of wealth in the country. This is a matter of grave concern and has several implications in terms of the country’s development and progress.

Bihar: Khagaria: Margin data was reported at 161,131.000 Unit in 03-01-2024. This records a decrease from the previous number of 248,570.000 Unit for 03-01-2019. Bihar: Khagaria: Margin data is updated quinquennially, averaging 109,854.500 Unit from Mar 1971 (Median) to 03-01-2024, with 14 observations. The data reached an all-time high of 248,570.000 Unit in 03-01-2019 and a record low of 548.000 Unit in 03-01-1971. Bihar: Khagaria: Margin data remains active status in CEIC and is reported by Election Commission of India. The data is categorized under India Premium Database’s General Election – Table IN.GEA005: General Election: Loksabha: Election Outcome of Parliamentary Constituencies: Bihar.