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.

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

Regional

Analysis unit

Households

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]

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.

Abstract

The Land and Livestock Holdings Survey (LLHS) of National Sample Survey Organization (NSSO) is one of the main sources of information on livestock and poultry held by the household sector of the economy. It also provides estimates of two basic distributions of land holdings, which are; distribution of land owned by households and that of agriculturally operated land. The survey of Land and Livestock Holdings carried out in the 59th round (January-December 2003) of the NSSO is the sixth in the series of similar surveys conducted so far by the NSSO. The objective of these surveys has been to generate basic quantitative information on the agrarian structure of the country, which is relevant to land policy. In the 59th round, information on various aspects of ownership and operational holdings was collected for both rural and urban areas. Each sample household was visited twice during the period of survey with a gap of four to eight months. Two different schedules of enquiry were canvassed in the two visits. The first visit was made during January to August 2003 and the second, during September to December 2003. The survey was conducted in both rural and urban areas. The information present here is for the first visit.

Geographic coverage

National Coverage

Analysis unit

Households

Kind of data

Sample survey data [ssd]

Sampling procedure

A stratified multi-stage design was adopted for the 70th round survey. The First Stage Units (FSUs) are the 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 (USUs) are households in both sectors. In case of large FSUs, there is an intermediate stage of sampling in which two Hamlet Groups (HGs)/ sub-blocks (sbs) from each rural/ urban FSU. For the rural sector, the list of 2001 census villages updated by excluding the villages urbanised and including the towns de-urbanised after 2001 census (henceforth the term 'village' would mean Panchayat wards for Kerala) constitutes the sampling frame. For the urban sector, the latest updated list of UFS blocks (2007-12) is considered as the sampling frame.

The stratification procedure is as follows: (a)Stratum was formed at district level. Within each district of a State/ UT, generally speaking, two basic strata were formed: i) rural stratum comprising of all rural areas of the district and (ii) urban stratum comprising all the urban areas of the district. However, within the urban areas of a district, if there were one or more towns with population 10 lakhs (1 million) or more as per population census 2011 in a district, each of them formed a separate basic stratum and the remaining urban areas of the district was considered as another basic stratum.

(b)However, a special stratum in the rural sector was formed at State/UT level before district- strata were formed in case of each of the following 20 States/UTs: Andaman & Nicobar Islands, Andhra Pradesh, Assam, Bihar, Chhattisgarh, Delhi, Goa, Gujarat, Haryana, Jharkhand, Karnataka, Lakshadweep, Madhya Pradesh, Maharashtra, Odisha, Punjab, Rajasthan, Tamil Nadu, Uttar Pradesh and West Bengal. This stratum will comprise all the villages of the State with population less than 50 as per 2001 census.

(c)In case of rural sectors/areas in Nagaland, one special stratum has been formed within the State consisting of all the interior and inaccessible villages. Similarly, for Andaman & Nicobar Islands, one more special stratum has been formed within the UT consisting of all inaccessible villages. Thus for Andaman & Nicobar Islands, two special strata have been formed at the UT level: (i)special stratum 1 comprising all the interior and inaccessible villages (ii)special stratum 2 containing all the villages, other than those in special stratum 1, having population less than 50 as per 2001 census.

Sub-stratification was also done for the different sectors/ areas. They include: 1. Rural sector: Different sub-stratifications are done for 'hilly' States and other States. Ten (10) States are considered as hilly States: Jammu & Kashmir, Himachal Pradesh, Uttarakhand, Sikkim, Meghalaya, Tripura, Mizoram, Manipur, Nagaland and Arunachal Pradesh. The different sub-stratifications include:

(a) sub-stratification for hilly States: If 'r' be the sample size allocated for a rural stratum, the number of sub-strata formed was 'r/2'. The villages within a district as per frame have been first arranged in ascending order of population. Then sub-strata 1 to 'r/2' 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. (b) sub-stratification for other States (non-hilly States except Kerala): The villages within a district as per frame were first arranged in ascending order of proportion of irrigated area in the cultivated area of the village. Then sub-strata 1 to 'r/2' 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 cultivated area. The information on irrigated area and cultivated area was obtained from the village directory of census 2001. (c) sub-stratification for Kerala: Although Kerala is a non-hilly State but because of non-availability of information on irrigation at FSU (Panchayat Ward) level, sub-stratification by proportion of irrigated area was not possible. Hence the procedure for sub-stratification was same as that of hilly States in case of Kerala.

1. Urban sector: There was no sub-stratification for the strata of cities with > one million in population. For other strata, each district was divided into 2 sub-strata as follows: sub-stratum 1: all towns of the district with population less than 50000 as per census 2011 sub-stratum 2: remaining non-million plus towns of the district

Total sample size (FSUs): 8042 FSUs have been allocated for the central sample at all-India level. For the state sample, there are 8998 FSUs allocated for all-India.

Allocation of total sample to States and UTs: The total number of sample FSUs have been allocated to the States and UTs in proportion to population as per census 2011 subject to a minimum sample allocation to each State/ UT. While doing so, the resource availability in terms of number of field investigators as well as comparability with previous round of survey on the same subjects 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 2011 with double weightage to urban sector subject to the restriction that urban sample size for bigger states like Maharashtra, Tamil Nadu etc. should not exceed the rural sample size. A minimum of 16 FSUs (minimum 8 each for rural and urban sector separately) is allocated to each state/ UT.

Allocation to strata: Within each sector of a State/ UT, the respective sample size has been allocated to the different strata in proportion to the population as per census 2011. Allocations at stratum level are adjusted to multiples of 2 with a minimum sample size of 2.

For special stratum formed in the rural areas of 20 States/UTs, 2 FSUs were allocated to each.

For special stratum 1 in the rural areas of Nagaland and Andaman & Nicobar Islands, 4 and 2 FSUs were allocated respectively.

Allocation to sub-strata: Rural: Allocation is 2 for each sub-stratum in rural. Urban: Stratum allocations have been distributed among the two sub-strata in proportion to the number of FSUs in the sub-strata. Minimum allocation for each sub-stratum is 2

Sampling deviation

There was no deviation from the original sampling plan.

Mode of data collection

Face-to-face paper [f2f]

Response rate

No. of First Stage Units (FSUs) is 4469 and No. of Second Stage Units (SSUs) is 35,604.

The estimated per capita income across Sikkim was the highest among Indian states at around 588 thousand Indian rupees in the financial year 2024. Meanwhile, it was the lowest in the northern state of Bihar at over 60 thousand rupees. India’s youngest state, Telangana stood in the fifth place. The country's average per capita income that year was an estimated 184 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 40 percent of the total wealth in 2022.