Vital Statistics: Birth Rate: per 1000 Population: Bihar data was reported at 25.500 NA in 2020. This records a decrease from the previous number of 25.800 NA for 2019. Vital Statistics: Birth Rate: per 1000 Population: Bihar data is updated yearly, averaging 28.500 NA from Dec 1997 (Median) to 2020, with 23 observations. The data reached an all-time high of 31.900 NA in 2000 and a record low of 25.500 NA in 2020. Vital Statistics: Birth Rate: per 1000 Population: Bihar 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.GAH002: Vital Statistics: Birth Rate: by States.

Population: Bihar data was reported at 127.855 Person mn in 2024. This records an increase from the previous number of 125.991 Person mn for 2023. Population: Bihar data is updated yearly, averaging 94.474 Person mn from Mar 1994 (Median) to 2024, with 31 observations. The data reached an all-time high of 127.855 Person mn in 2024 and a record low of 68.433 Person mn in 1994. Population: 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.GBG001: Population. [COVID-19-IMPACT]

Census: Population: Bihar data was reported at 104,099,452.000 Person in 03-01-2011. This records an increase from the previous number of 82,998,509.000 Person for 03-01-2001. Census: Population: Bihar data is updated decadal, averaging 31,962,992.500 Person from Mar 1901 (Median) to 03-01-2011, with 12 observations. The data reached an all-time high of 104,099,452.000 Person in 03-01-2011 and a record low of 21,243,632.000 Person in 03-01-1901. Census: Population: Bihar 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.GAB002: Census: Population: by States.

In Bihar, the share of males with multiple disabilities was 1.9 percent and females at 1.3 percent. According to the 76th round of the NSO survey conducted between July and December 2018, a higher percentage of disabled men than disabled women were present in India, reflected in the northern state. The National Statistical Office (NSO) is the statistical wing of the Ministry of Statistics and Programme Implementation (MOSPI), mainly responsible for laying down standards for statistical analysis, data collection, and implementation.

Vital Statistics: Birth Rate: per 1000 Population: Bihar: Rural data was reported at 26.200 NA in 2020. This records a decrease from the previous number of 26.500 NA for 2019. Vital Statistics: Birth Rate: per 1000 Population: Bihar: Rural data is updated yearly, averaging 29.300 NA from Dec 1997 (Median) to 2020, with 23 observations. The data reached an all-time high of 32.800 NA in 2000 and a record low of 26.200 NA in 2020. Vital Statistics: Birth Rate: per 1000 Population: Bihar: Rural 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.GAH002: Vital Statistics: Birth Rate: by States.

The statistic illustrates the literacy rate in the state of Bihar in India between 1991 and 2011, broken down by gender. In 2001, 60 percent of the male population living in Bihar knew how to read or write. India's literacy rate from 1981 through 2011 can be found here.

In 2022, the union territory of Delhi had the highest urban population density of over 18 thousand persons per square kilometer. While the rural population density was highest in union territory of Puducherry, followed by the state of Bihar.

This polygon dataset shows village boundaries with socio-demographic and economic Census data for 1991 for the State of Bihar, India linked to the 1991 Census. Includes village socio-demographic and economic Census attribute data such as total population, population by sex, household, literacy and illiteracy rates, and employment by industry. This layer is part of the VillageMap dataset which includes socio-demographic and economic Census data for 1991 at the village level for all the states of India. This data layer is sourced from secondary government sources, chiefly Survey of India, Census of India, Election Commission, etc.

Abstract

What do we know about incentives and norms in health bureaucracies and service delivery points at various levels of a state in India? For example, the logic of economic theory suggests that governments should be direct providers of services when there is a role for attracting intrinsically motivated agents (Francois, 2000), but we have no empirical evidence on integrity and public service motivation among state personnel across different cadres of service delivery. The available research has focused on documenting evidence of weak incentives and low accountability for service delivery in the public sector, and thence on evaluating interventions targeted at strengthening incentives, such as making some part of pay conditional on performance indicators (for example, Singh and Masters, 2017). But what is available is barely scratching the surface of knowledge needed to help reform leaders think about how to structure government bureaucracies and assign tasks to leverage intrinsic motivation and to reduce reliance on high-powered incentives. Even when increasing the power of incentives has been shown to “work”, the authors of those findings concede that implementing optimal incentive contracts at scale can place significant demands on state capacity (Muralidharan and Sundararaman, 2011). There is even less evidence available about the incentives and motivation of mid-level bureaucrats within the health system, compared to a growing body of research on frontline providers such as doctors and community health workers. Finally, the logic of economic theory, and growing international evidence in support of it, further suggests that politics casts a long shadow on culture in the bureaucracy, but we have no rigorous evidence for this claim for India.

To address these knowledge gaps we designed and implemented a complex survey of multiple types of respondents across districts, blocks (administrative sub-units within districts) and village governments (Gram Panchayats or GPs) in Bihar, one of the poorest states of India and with some of the worst statistics of child malnourishment.

Geographic coverage

16 study districts, from among the 38 of Bihar, selected to represent the 9 administrative divisions of Bihar: Patna, Tirhut, Darbhanga, Kosi, Purnia, Saran, Bhagalpur, Munger, Magadh

Analysis unit

Households Health Staff Politicians Bureaucrats

Universe

Citizens, Within the category of citizens, the survey additionally targeted office-bearing members of women’s Self Help Groups (SHG) under a rural livelihoods program in Bihar known as Jeevika. Politicians Bureaucrats Public Providers of Health Services

Kind of data

Sample survey data [ssd]

Sampling procedure

Budget and implementation constraints required us to select a sample of districts rather than covering all 38 districts of Bihar. At the same time, we needed a large sample to be representative of the diversity within the state, and allow us to capture some variation across district-level institutional characteristics. These constraints led us to determine 16 as the number of districts in which to undertake the survey. The purposive selection of which 16 study districts, from among the 38 of Bihar, was made using the following criteria:

• represent the 9 administrative divisions of Bihar: Patna, Tirhut, Darbhanga, Kosi, Purnia, Saran, Bhagalpur, Munger, Magadh • represent both border and interior districts • select "old" and "new" districts (those which were created after 1991) because district age might matter in interesting ways for their capacity to deliver (to be discussed further) • select districts which might vary in historical institutions that shape norms.

We first explored an established literature in India which finds that there are persistent effects on current service delivery of the long-gone historical institution of the Zamindari system of land revenue (Pandey, 2010; Banerjee and Iyer, 2005). However, since all of the districts of Bihar are classified as belonging to the Zamindari system, we could not use this established measure of historical institutions in selecting the study districts. We then turned to a newer literature which examines the early construction of railway lines in the late 1800s in the United States and India as a potential source of institutional variation (Donaldson, 2018; Donaldson and Hornbeck, 2016; Atack, Haines and Margo, various). The 16 districts in our study include those through which passed the first railway lines in Bihar, and those that received railway lines a decade or so later.

Within each of the 16 districts, 4 blocks were selected using a random number generator,after stratifying by proximity to the main railway line. Within each block, 4 Gram Panchayats (GPs) were selected using a random number generator. However, in one block each in the districts of Lakhisarai and Buxar, 3 GPs instead of 4 were selected because the sampling protocol required a sufficient number of replacement respondents to be available, and these districts only had 3 GPs fulfilling the replacement requirement (more details in section on Respondents below). This yields a sample of respondents drawn from 16 districts, 64 blocks from within those districts, and 254 Gram Panchayats (GPs) from within those blocks.

Citizen Survey: The citizen survey was aimed at respondents from 16 households residing in each GP area. The survey firm was provided with a list of respondents (with replacements) drawn randomly from the electoral rolls available of all voting-age adults in Bihar's population. The target sample size is thus 4064 citizens (16 each from 254 GPs). Within the category of citizens, the survey additionally targeted office-bearing members of women's Self Help Groups (SHG) under a rural livelihoods program in Bihar known as Jeevika. However, we had no lists available with names of SHG leaders of the village-level organziations across GPs. In the absence of these lists, we relied on the survey firm to ensure that enumerator teams would identify SHG leaders during their field-work. The data from SHG leaders that has been provided to us is thus subject to a greater than usual caveat: the risk of whether the enumerator teams accuratelyidentified and obtained interviews with the targeted SHG respondents. The instructions provided to the survey teams was to ask the GP Mukhiya and other GPlevel respondents (such as the ANM, ASHA and AWW) about the GP-level federated organzation of all the SHGs across the GP's communities to identify its President,Secretary and Treasurer. That is, 3 SHG leaders were targeted for each GP, for a total sample of 762 (3 each from 254 GPs) SHG leaders.

Politician Survey: Lists were provided to the survey teams of all incumbent Mukhiyas to be interveiwed, and a random selection (with replacement) of 3 Ward members and 3 candidates from among those who contested the previous GP elections of 2016. The targeted sample size of GP politicians is thus 1778 (7 each from 254 GPs)

Bureaucrats: The survey firm was responsible for identifying and interviewing the respondents holding these positions. The final data submitted by the survey firm contains 293 respondents in supervisory or management positions, including: 13 Civil Surgeons,11 Chief Medical Officers (including 4 who were in Acting capacity), 23 Superintendents (including 13 in Deputy or Acting capacity), 9 District Programme Officers- NHM, 4 District RCH and Immunization In-charge, 7 District Community Mobilizers, 58 MOICs, 58 Acting Facility Incharge, 43 Block Program Managers-NHM, 29 Block RCH Programme officers, and 35 Block Community Mobilizers.

Public Providers of Health Services: The survey team was provided a list (with replacements) of 3 AWW workers to interveiw per GP, for a targeted sample of 762 AWW respondents. The survey team was provided with a list of randomly selected candidates for the categories of respondents for all the PHCs and higher-level health facilities (such as District Hospitals) across the 64 blocks of the study area.

Sampling deviation

Block Level: The survey firm was responsible for identifying the block-level politicians targeted to be interviewed. The targeted sample size of Block-Panchayat (Panchayat Samiti) elected members’ is 128 respondents (2 each from 64 blocks). The 57 MLAs across the 64 blocks of the study area were also identified by the survey firm. However, because of problems of reaching politicians at a time that was close to the 2019 elections in India, the survey firm was able to complete interviews with only 39 MLAs (of the targeted 57) , and with 119 Panchayat Samiti members (of the targeted 128).

District Level: The survey firm was responsible for identifying the MPs from constituencies within the 16 study districts, and the 32 respondents of the District-Panchayat (Zilla Parishad). Again, because of problems reaching political leaders at election time, the survey firm was able to interviewonly 9 MPs, and 28 Zilla Parishad members.

Public Providers of Health Care Services: The survey team was provided with a list of randomly selected candidates for the categories of respondents for all the PHCs and higher-level health facilities (such as District Hospitals) across the 64 blocks of the study area. However, the survey team reports substantial difficulty in adhering to this list because the personnel were not found at the health facilities. The survey team was not able to reach a random sample of providers appointed at these positions.

Vital Statistics: Natural Growth Rate: per 1000 Population: Bihar: Urban data was reported at 15.700 NA in 2020. This records a decrease from the previous number of 16.000 NA for 2019. Vital Statistics: Natural Growth Rate: per 1000 Population: Bihar: Urban data is updated yearly, averaging 16.500 NA from Dec 1997 (Median) to 2020, with 23 observations. The data reached an all-time high of 18.500 NA in 2000 and a record low of 14.700 NA in 2014. Vital Statistics: Natural Growth Rate: per 1000 Population: Bihar: Urban 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.GAH004: Vital Statistics: Natural Growth Rate: by States.

The projected crude birth rate in India, at national level, was expected to decrease to about ** births per thousand people by 2031 to 2035 as opposed to the national crude birth rate from 2011 to 2015 which stood at more than ** births per thousand people. At state level, Bihar reflected the highest crude birth rate from 2011 to 2015 as well as the highest projected crude birth rate from 2031-2035. By contrast, the states with the lowest projected crude birth rates were Punjab, Tamil Nadu, and Andhra Pradesh during the same time period.

Poverty affects many people, but the ramifications and impacts affect all aspects of society. Information about the incidence of poverty is therefore an important parameter of the population for policy analysis and decision making. In order to provide specific, targeted solutions when addressing poverty disadvantage small area statistics are needed. Surveys are typically designed and planned to produce reliable estimates of population characteristics of interest mainly at higher geographic area such as national and state level. Sample sizes are usually not large enough to provide reliable estimates for disaggregated analysis. In many instances estimates are required for areas of the population for which the survey providing the data was unplanned. Then, for areas with small sample sizes, direct survey estimation of population characteristics based only on the data available from the particular area tends to be unreliable. This paper describes an application of small area estimation (SAE) approach to improve the precision of estimates of poverty incidence at district level in the State of Bihar in India by linking data from the Household Consumer Expenditure Survey 2011–12 of NSSO and the Population Census 2011. The results show that the district level estimates generated by SAE method are more precise and representative. In contrast, the direct survey estimates based on survey data alone are less stable.

The Northern Indian state of Bihar had over three million people per high court judge as of December 2022. By contrast, the eastern state of Sikkim had over 227.67 thousand people per judge that year.

The Indian state of Goa had *** registered motor vehicles per 1,000 population in fiscal year 2015. The least was in the state of Bihar with only ** motor vehicles per 1,000 population during the measured time period.

During fiscal year 2021, with over *********** fishermen the eastern state of Bihar had the highest fishermen population in India. This was followed by Uttar Pradesh with about ************ fishermen. There were over ********** fishermen across the country in the same period.

The number of workers across the eastern state of Bihar in India during financial year 2023 was nearly *** thousand workers. This was an increase from the previous year. The south Asian country of India had over ** million workers in financial year 2023.

In 2011, the Indian state of Uttar Pradesh had the highest number of rural inhabitants. Bihar stood second in line with approximately 92 million people living in the rural areas during the same time period.

In 2011, out of all India's states and union territories, Uttar Pradesh and Maharashtra had the largest share population with disability, accounting for respectively **** percent and ***** percent of Indian disabled population. Following behind were Bihar and Andhra Pradesh at less than **** percent each. According to the source, the share of the disabled population in the remaining ** States and Union Territories constituted less than *** percent of the total disabled population.

Vital Statistics: Death Rate: per 1000 Population: Bihar data was reported at 5.400 NA in 2020. This records a decrease from the previous number of 5.500 NA for 2019. Vital Statistics: Death Rate: per 1000 Population: Bihar data is updated yearly, averaging 7.000 NA from Dec 1997 (Median) to 2020, with 23 observations. The data reached an all-time high of 9.400 NA in 1998 and a record low of 5.400 NA in 2020. Vital Statistics: Death Rate: per 1000 Population: Bihar 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.GAH003: Vital Statistics: Death Rate: by States.

In 2020, the state of Bihar had the highest rural birth rate of 26.2 births per 1,000 inhabitants. It was followed by Uttar Pradesh and Madhya Pradesh. Among the larger states and union territories, the southern state of Kerala had the lowest birth rate in the rural areas that year.