About this dataset

Context

The dataset presents median income data over a decade or more for males and females categorized by Total, Full-Time Year-Round (FT), and Part-Time (PT) employment in Indian Beach. It showcases annual income, providing insights into gender-specific income distributions and the disparities between full-time and part-time work. The dataset can be utilized to gain insights into gender-based pay disparity trends and explore the variations in income for male and female individuals.

Key observations: Insights from 2021

Based on our analysis ACS 2017-2021 5-Year Estimates, we present the following observations: - All workers, aged 15 years and older: In Indian Beach, the median income for all workers aged 15 years and older, regardless of work hours, was $54,046 for males and $24,321 for females.

These income figures highlight a substantial gender-based income gap in Indian Beach. Women, regardless of work hours, earn 45 cents for each dollar earned by men. This significant gender pay gap, approximately 55%, underscores concerning gender-based income inequality in the town of Indian Beach.

- Full-time workers, aged 15 years and older: In Indian Beach, for full-time, year-round workers aged 15 years and older, the Census Bureau did not report the median income for both males and females due to an insufficient number of sample observations.

As income data for both males and females was unavailable, conducting a comprehensive analysis of gender-based pay disparity in the town of Indian Beach was not possible.

https://i.neilsberg.com/ch/indian-beach-nc-income-by-gender.jpeg" alt="Indian Beach, NC gender based income disparity">

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2017-2021 5-Year Estimates. All incomes have been adjusting for inflation and are presented in 2022-inflation-adjusted dollars.

Gender classifications include:

• Male
• Female

Employment type classifications include:

• Full-time, year-round: A full-time, year-round worker is a person who worked full time (35 or more hours per week) and 50 or more weeks during the previous calendar year.
• Part-time: A part-time worker is a person who worked less than 35 hours per week during the previous calendar year.

Variables / Data Columns

• Year: This column presents the data year. Expected values are 2010 to 2022
• Male Total Income: Annual median income, for males regardless of work hours
• Male FT Income: Annual median income, for males working full time, year-round
• Male PT Income: Annual median income, for males working part time
• Female Total Income: Annual median income, for females regardless of work hours
• Female FT Income: Annual median income, for females working full time, year-round
• Female PT Income: Annual median income, for females working part time

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Indian Beach median household income by gender. You can refer the same here

Context

Government of India(GoI) does Census of entire country every ten years, last census was done in 2011 and next will be done in 2021. Purpose of census is to get good understanding of the country population and other associated things, these data helps GoI to create and enhance the the policy and new reforms.

Content

The attached CSV file has data related to Literacy in India according to India Census 2011. - First Column has simple serial number - Second column has the District name - Third column has State name corresponding to the district from second column. - Last column has the Literacy data corresponding to the district from second column.

Acknowledgements

All thanks to GoI and volunteers who help in collecting dataset.

Inspiration

This can be used to get insight about the education, as well as it can used along with other datasets as per need.

About this dataset

Context

The dataset tabulates the population of Indian Village by gender, including both male and female populations. This dataset can be utilized to understand the population distribution of Indian Village across both sexes and to determine which sex constitutes the majority.

Key observations

There is a majority of female population, with 56.64% of total population being female. Source: U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Scope of gender :

Please note that American Community Survey asks a question about the respondents current sex, but not about gender, sexual orientation, or sex at birth. The question is intended to capture data for biological sex, not gender. Respondents are supposed to respond with the answer as either of Male or Female. Our research and this dataset mirrors the data reported as Male and Female for gender distribution analysis. No further analysis is done on the data reported from the Census Bureau.

Variables / Data Columns

• Gender: This column displays the Gender (Male / Female)
• Population: The population of the gender in the Indian Village is shown in this column.
• % of Total Population: This column displays the percentage distribution of each gender as a proportion of Indian Village total population. Please note that the sum of all percentages may not equal one due to rounding of values.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Indian Village Population by Race & Ethnicity. You can refer the same here

Abstract

ABSTRACT OF ECONOMIC CENSUS IN INDIA

1. GENESIS

A reliable and robust database is the foundation of organized and proper planning. TheCentral Statistics Office (CSO), since its inception, has been instrumental in creation of database forvarious sectors of the economy and its periodic updation so as to meet the requirements of the plannersfor sound and systematic planning both at the macro as well as micro levels. While data requirementsmay be enormous in various sectors, the judicious collection and maintenance of data for varioussectors within the available resource is a challenge. Our economy can broadly be classified into twosectors, namely, Agricultural and Non-Agricultural sectors. Fairly reasonable database exists forAgricultural Sector whereas such data base for Non-Agricultural sector is much desired. Keeping inview the importance of the non-agricultural sector in the economy and non-availability of basic framefor adoption in various sampling techniques for collection of data and estimation of various parameters,conducting Economic Census was felt necessary. With this background, the CSO started EconomicCensus for preparing frame of establishments, particularly the ‘area frame’ which could be used forvarious surveys for collection of detailed data, mainly on non-agricultural sector of the economy.

1. EARLIER ATTEMPTS

Broadly the entire planning period may be divided into two: prior to conduct of the FirstEconomic Census i.e. prior to 1977 and thereafter i.e. after the economic census was carried outperiodically. Efforts to fill up the data gaps for the non-agricultural sector were made right from thebeginning of the First Five Year Plan. The first National Sample Survey (NSS) round (1950-51)covered non-agricultural household establishments as one of its subject themes. Such establishmentswere covered regularly up to the tenth NSS round (1955-56). Subsequently, selected activities weretaken up for survey intermittently in different rounds (14th, 23 rd & 29th rounds). Establishmentschedules were canvassed in 1971 population census. The census of unorganized industrial units wascarried out during 1971 -73. Census of the units falling within the purview of Development Commissioner, Small Scale Industries, was carried out during 1973-74 and a survey on distributivetrade was conducted by some of the States during the Fourth Five-Year Plan period (1969-74). Allsuch efforts made prior to 1977 to collect data on non-agricultural establishments have been partial andsporadic. Area sampling with probability proportional to population were mostly used even to captureestablishments. For a survey of establishments such sample design is not only inefficient but alsoresults in under coverage of desired number of establishments and low reliability of the estimatesderived. The prolonged efforts of statisticians and planners in finding a way out for collection ofinformation on amorphous areas of activity resulted in a decisive breakthrough with the advent ofconduct of Economic Census.

1. ECONOMIC CENSUSES CONDUCTED IN THE PAST

The Economic Enquiry Committee set up in 1925 under the Chairmanship of Dr.Visweswarayya and more importantly the Bowley-Robertson Committee set up later in 1934, were mainly responsible for the government’s decision to set up an Inter-Departmental Committee with theEconomic Adviser to the Government of India as the chairman. The Inter-Departmental Committeerecommended the formation of a Central Statistical Office for coordination, institution of a statisticalcadre, establishment of State Bureaus at State Head Quarters and maintenance of important statisticsfor the entire country. Bowley and Robertson Committee also commissioned a study to explore thepossibility of conducting economic censuses in India. The first coordinated approach was made by theerstwhile Central Statistical Organisation (CSO), Government of India, by launching a plan scheme'Economic Census and Surveys' in 1976. The scheme envisaged organising countrywide census of alleconomic activities (excluding those engaged in crop production and plantation) followed by detailedsample surveys of unorganised segments of different sectors of non-agricultural economy in a phasedmanner during the intervening period of two successive economic censuses.The basic purpose of conducting the economic census (EC) was to prepare a frame for followup surveys intended to collect more detailed sector specific information between two economiccensuses. In view of the rapid changes that occur in the unorganised sectors of non-agriculturaleconomy due to high mobility or morbidity of smaller units and also on account of births of new units,the scheme envisaged conducting the economic census periodically in order to update the frame fromtime to time.

1. First Economic Census (EC -1977) and Follow Up Surveys

The First Economic Census was conducted throughout the country, except Lakshadweep,during 1977 in collaboration with the Directorate of Economics & Statistics (DES) in the States/UnionTerritories (UT). The coverage was restricted to only non-agricultural establishments employing atleast one hired worker on a fairly regular basis. Data on items such as description of activity, number ofpersons usually working, type of ownership, etc. were collected.Reports based on the data of EC-1977 at State/UT level and at all India level were published.Tables giving the activity group-wise distribution of establishments with selected characteristics andwith rural and urban break up were generated. State-wise details for major activities and size-class ofemployment in different establishments, inter-alia, were also presented in tables.Based on the frame provided by the First Economic Census, detailed sample surveys werecarried out during 1978-79 and 1979-80 covering the establishments engaged in manufacturing, trade,hotels & restaurants, transport, storage & warehousing and services. While the smaller establishments(employing less than six workers) and own account establishments were covered by National SampleSurvey Organisation (NSSO) as a part of its 33rd and 34th rounds, the larger establishments were covered through separate surveys by the CSO. Detailed information on employment, emoluments,capital structure, quantity & value of input, output, etc. were collected and reports giving all importantcharacteristics on each of the concerned subjects were published.

1. Second Economic Census (EC-1980) and Follow Up Surveys

The Second Economic Census was conducted in 1980 along with the house-listing operations ofPopulation Census 1981. This was done with a view to economizing resources, manpower, time andmoney. The scope and coverage were enlarged. This time all establishments engaged in economicactivities - both agricultural and non-agricultural whether employing any hired worker or not werecovered, except those engaged in crop production and plantation. All States/UTs were covered withthe sole exception of Assam, where Population Census 1981 was not conducted.The information on location of establishment, description of economic activity carried out,nature of operation, type of ownership, social group of owner, use of power/fuel, total number ofworkers usually engaged with its hired component and break-up of male and female workers werecollected. The items on which information were collected in Second Economic Census were more orless the same as those collected in the First Economic Census. However, based on experience gained inthe First Economic Census certain items viz. years of operation, value of annualoutput/turnover/receipt, mixed activity or not, registered/ licensed/recognised and act or authority, ifregistered were dropped.The field work was done by the field staff consisting of enumerators and supervisors employedin the Directorate of Census Operations of each State/UT. The State Directorates of Economics &Statistics (DES) were also associated in the supervision of fieldwork. Data processing and preparationof State level reports of economic census and their publication were carried out by the DES.Based on the frame thrown up by EC-1980, three follow-up surveys were carried out, one in1983-84 on hotels & restaurants, transport, storage & warehousing and services, second in 1984-85 onunorganised manufacturing and third in 1985- 86 on wholesale and retail trade.The economic census scheduled for 1986 could not be carried out due to resource constraints.However, the EC- 1980 frame was updated during 1987-88 in 64 cities (12 cities having more than 10lakh population and 52 other class-I cities) which had problems of identification of enumerationblocks and changes due to rapid urbanization. On the basis of the updated frame, four follow-upsurveys were conducted during 1988-89, 1989-90, 1990-91 and 1991-92 covering the subjects ofhotels & restaurants and transport, unorganized manufacturing, wholesale & retail trade and medical,educational, cultural & other services respectively.

1. Third Economic Census (EC-1990) and Follow Up Surveys

The Third Economic Census was synchronized with the house listing operations of the Population Census 1991 on the same pattern as EC- 1980. The coverage was similar to that of EC-1980. All States/UTs except Jammu & Kashmir, where Population Census 1991 was not undertaken,were covered.Based on the frame thrown up by EC-1990 four follow up surveys were carried out:(i) Establishment Survey covering sectors of mining & quarrying, storage & warehousingin 1992-93;(ii) Establishment Survey covering sectors of hotels & restaurants and transport in 1993-94;(iii) NSS 51 st round covering directory, non-directory and own account establishments inunregistered manufacturing sector in 1994-95; and(iv) Directory Trade Establishments Survey in 1996-97. NSS 53 rd round covered theresidual part of the unorganised trade sector in 1997.

1. Fourth Economic Census (EC-1998) and Follow up Surveys

The dataset contains the details of farm holdings in India by Size Class. The Size class include - Below 0.5 hectares 0.5-01. hectares 1-2 hectares 2-3 hectares 3-4 hectares 4-5 hectares 5-7.5 hectares 7.5-10 hectares 10-20 hectares Above 20 hectares The categorization is also done on social grouping with further categorization of gender wise holdings and categorization by Individual or joint holding

Abstract

The National Family Health Survey 2019-21 (NFHS-5), the fifth in the NFHS series, provides information on population, health, and nutrition for India, each state/union territory (UT), and for 707 districts.

The primary objective of the 2019-21 round of National Family Health Surveys is to provide essential data on health and family welfare, as well as data on emerging issues in these areas, such as levels of fertility, infant and child mortality, maternal and child health, and other health and family welfare indicators by background characteristics at the national and state levels. Similar to NFHS-4, NFHS-5 also provides information on several emerging issues including perinatal mortality, high-risk sexual behaviour, safe injections, tuberculosis, noncommunicable diseases, and the use of emergency contraception.

The information collected through NFHS-5 is intended to assist policymakers and programme managers in setting benchmarks and examining progress over time in India’s health sector. Besides providing evidence on the effectiveness of ongoing programmes, NFHS-5 data will help to identify the need for new programmes in specific health areas.

The clinical, anthropometric, and biochemical (CAB) component of NFHS-5 is designed to provide vital estimates of the prevalence of malnutrition, anaemia, hypertension, high blood glucose levels, and waist and hip circumference, Vitamin D3, HbA1c, and malaria parasites through a series of biomarker tests and measurements.

Geographic coverage

National coverage

Analysis unit

• Household
• Individual
• Children age 0-5
• Woman age 15-49
• Man age 15 to 54

Universe

The survey covered all de jure household members (usual residents), all women aged 15-49, all men age 15-54, and all children aged 0-5 resident in the household.

Kind of data

Sample survey data [ssd]

Sampling procedure

A uniform sample design, which is representative at the national, state/union territory, and district level, was adopted in each round of the survey. Each district is stratified into urban and rural areas. Each rural stratum is sub-stratified into smaller substrata which are created considering the village population and the percentage of the population belonging to scheduled castes and scheduled tribes (SC/ST). Within each explicit rural sampling stratum, a sample of villages was selected as Primary Sampling Units (PSUs); before the PSU selection, PSUs were sorted according to the literacy rate of women age 6+ years. Within each urban sampling stratum, a sample of Census Enumeration Blocks (CEBs) was selected as PSUs. Before the PSU selection, PSUs were sorted according to the percentage of SC/ST population. In the second stage of selection, a fixed number of 22 households per cluster was selected with an equal probability systematic selection from a newly created list of households in the selected PSUs. The list of households was created as a result of the mapping and household listing operation conducted in each selected PSU before the household selection in the second stage. In all, 30,456 Primary Sampling Units (PSUs) were selected across the country in NFHS-5 drawn from 707 districts as on March 31st 2017, of which fieldwork was completed in 30,198 PSUs.

For further details on sample design, see Section 1.2 of the final report.

Mode of data collection

Computer Assisted Personal Interview [capi]

Research instrument

Four survey schedules/questionnaires: Household, Woman, Man, and Biomarker were canvassed in 18 local languages using Computer Assisted Personal Interviewing (CAPI).

Cleaning operations

Electronic data collected in the 2019-21 National Family Health Survey were received on a daily basis via the SyncCloud system at the International Institute for Population Sciences, where the data were stored on a password-protected computer. Secondary editing of the data, which required resolution of computer-identified inconsistencies and coding of open-ended questions, was conducted in the field by the Field Agencies and at the Field Agencies central office, and IIPS checked the secondary edits before the dataset was finalized.

Field-check tables were produced by IIPS and the Field Agencies on a regular basis to identify certain types of errors that might have occurred in eliciting information and recording question responses. Information from the field-check tables on the performance of each fieldwork team and individual investigator was promptly shared with the Field Agencies during the fieldwork so that the performance of the teams could be improved, if required.

Response rate

A total of 664,972 households were selected for the sample, of which 653,144 were occupied. Among the occupied households, 636,699 were successfully interviewed, for a response rate of 98 percent.

In the interviewed households, 747,176 eligible women age 15-49 were identified for individual women’s interviews. Interviews were completed with 724,115 women, for a response rate of 97 percent. In all, there were 111,179 eligible men age 15-54 in households selected for the state module. Interviews were completed with 101,839 men, for a response rate of 92 percent.

The dataset contains Decade and State wise Urban, Rural, Total Population and Decadal Growth Rate

Note: 1. The Population figures exclude population of areas under unlawful occupation of Pakistan and China, where Census could not be taken. 2. In Arunachal Pradesh, the census was conducted for the first time in 1961. 3. Population data of Assam include Union Territory of Mizoram, which was carved out of Assam after the 1971. 4. The 1981 Census could not be held in Assam. Total Population for 1981 has been worked out by Interpolation. 5. The 1991 Census could not be held in Jammu & Kashmir. Total Population for 1991 has been worked out by Interpolation. 6. India and Manipur figures include estimated Population for those of the three sub-divisions viz., Mao Maram,Paomata and Purul of Senapati district of Manipur as census result of 2001 in these three sub-divisions were cancelled due to technical and administrative reasons

Census: Population: Uttar Pradesh data was reported at 199,812,341.000 Person in 03-01-2011. This records an increase from the previous number of 166,197,921.000 Person for 03-01-2001. Census: Population: Uttar Pradesh data is updated decadal, averaging 65,208,689.000 Person from Mar 1901 (Median) to 03-01-2011, with 12 observations. The data reached an all-time high of 199,812,341.000 Person in 03-01-2011 and a record low of 44,556,427.000 Person in 03-01-1921. Census: Population: Uttar Pradesh 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.

Census: Population: Uttar Pradesh: Mathura data was reported at 454,937.000 Person in 03-01-2011. This records an increase from the previous number of 323,315.000 Person for 03-01-2001. Census: Population: Uttar Pradesh: Mathura data is updated decadal, averaging 115,515.500 Person from Mar 1901 (Median) to 03-01-2011, with 12 observations. The data reached an all-time high of 454,937.000 Person in 03-01-2011 and a record low of 52,840.000 Person in 03-01-1921. Census: Population: Uttar Pradesh: Mathura 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.GAC035: Census: Population: By Towns and Urban Agglomerations: Uttar Pradesh.

Abstract

The current India Agriculture Census with reference year 2010-11 is ninth in the series.

The Department of Agriculture and Cooperation, Ministry of Agriculture, Government of India conducts Agriculture Census, quinquennially, to collect data on operational holdings in the country. The reference period for Agriculture Census is the Agricultural year (July-June). Being the ultimate unit for taking agriculture-related decisions, operational holding has been taken as statistical unit at micro-level for data collection.

The Agriculture Census was conducted in three distinct Phases. The provisional results for first Phase of the current Census were released at State and all India level in October, 2012. After, scrutinizing the results at District/Tehsil level, this database has now been finalized and is being published in the form of an All India Report on number and area of operational holdings.

The main objectives of the Agriculture Census are: i) To describe structure and characteristics of agriculture by providing statistical data on operational holdings, including land utilization, irrigation, source of irrigation, irrigated and unirrigated area under different crops, live-stock, agricultural machinery and implements, use of fertilizers, seeds, agricultural credit etc. ii) To provide benchmark data needed for formulating new agricultural development programmes and for evaluating their progress. iii) To provide basic frame of operational holdings for carrying out future agricultural surveys and, iv) To lay a basis for developing an integrated programme for current agricultural statistics.

Geographic coverage

National

Analysis unit

Agricultural household, individual

Kind of data

Census/enumeration data [cen]

Sampling procedure

The Agriculture Census data is collected following two broad approaches; in States where comprehensive land records exist (Land Record States), for Phase-I of the Census, the data on primary characteristics of operational holdings are collected and compiled on complete enumeration basis through re-tabulation of information available in the Village Land Records. For other States (Non-Land record States), this data is collected on sample basis following household enquiry.

In land record States,data on Agriculture Census is pooled for all the parcels of an operational holding irrespective of its location. However, for operational convenience, the outer limit for pooling is restricted to taluka. This pooling is done for each operational holder in the village of his residence. In the non-land record States, the data is collected through sample survey in 20 per cent of villages in each block. These villages are selected through simple random sampling method and all the operational holdings in the selected villagesare enumerated following household enquiry approach.

In smaller UTs, like Lakshadweep, Daman & Diu etc., no sampling is done. i.e. all holdings in all the villages are surveyed for collection of data.

Mode of data collection

Face-to-face [f2f]

The Socio Economic Caste Census (SECC) is a comprehensive exercise undertaken by the Government of India to gather detailed information about the socio-economic status and caste demographics of Indian households. Conducted in 2011, this census was distinct from the traditional decennial population census and aimed to provide a holistic understanding of the living conditions and deprivation levels of people across the country. The SECC data encompasses various parameters, including income, occupation, land ownership, and educational status. Additionally, it marked a significant effort to collect caste-wise population data, a feat not attempted since the pre-independence census of 1931. The findings from the SECC play a pivotal role in shaping targeted policy interventions and welfare schemes for the marginalized and underprivileged sections of society.

The statistic shows the total population of India from 2019 to 2029. In 2023, the estimated total population in India amounted to approximately 1.43 billion people.

Total population in India

India currently has the second-largest population in the world and is projected to overtake top-ranking China within forty years. Its residents comprise more than one-seventh of the entire world’s population, and despite a slowly decreasing fertility rate (which still exceeds the replacement rate and keeps the median age of the population relatively low), an increasing life expectancy adds to an expanding population. In comparison with other countries whose populations are decreasing, such as Japan, India has a relatively small share of aged population, which indicates the probability of lower death rates and higher retention of the existing population.

With a land mass of less than half that of the United States and a population almost four times greater, India has recognized potential problems of its growing population. Government attempts to implement family planning programs have achieved varying degrees of success. Initiatives such as sterilization programs in the 1970s have been blamed for creating general antipathy to family planning, but the combined efforts of various family planning and contraception programs have helped halve fertility rates since the 1960s. The population growth rate has correspondingly shrunk as well, but has not yet reached less than one percent growth per year.

As home to thousands of ethnic groups, hundreds of languages, and numerous religions, a cohesive and broadly-supported effort to reduce population growth is difficult to create. Despite that, India is one country to watch in coming years. It is also a growing economic power; among other measures, its GDP per capita was expected to triple between 2003 and 2013 and was listed as the third-ranked country for its share of the global gross domestic product.

The Livestock Census started in the country in the year 1919. So far, 20 livestock censuses have been conducted. Livestock Census is a complete count of the livestock and poultry at pre-defined reference point of time. Similar to population census, primary workers are engaged to undertake house to house enumeration and ascertain the number, age, sex, etc., of livestock/poultry possessed by every household/household enterprise/non-household/non-household enterprises and institutions in rural & urban areas of the country. For the first time tablet computers were used for conduct of 20th livestock census.This layer contains state-wise count of animals from 20th livestock census. Following animal's count are captured based on their sex:BuffaloSheepGoatPigThis map layer is offered by Esri India, for ArcGIS Online subscribers. If you have any question or comments, please let us know via content@esri.in.

Abstract

The 2015-16 National Family Health Survey (NFHS-4), the fourth in the NFHS series, provides information on population, health, and nutrition for India and each state and union territory. For the first time, NFHS-4 provides district-level estimates for many important indicators. All four NFHS surveys have been conducted under the stewardship of the Ministry of Health and Family Welfare (MoHFW), Government of India. MoHFW designated the International Institute for Population Sciences (IIPS), Mumbai, as the nodal agency for the surveys. Funding for NFHS-4 was provided by the United States Agency for International Development (USAID), the United Kingdom Department for International Development (DFID), the Bill and Melinda Gates Foundation (BMGF), UNICEF, UNFPA, the MacArthur Foundation, and the Government of India. Technical assistance for NFHS-4 was provided by ICF, Maryland, USA. Assistance for the HIV component of the survey was provided by the National AIDS Control Organization (NACO) and the National AIDS Research Institute (NARI), Pune.

Geographic coverage

National coverage

Analysis unit

• Household
• Individual
• Children age 0-5
• Woman age 15-49
• Man age 15-54

Kind of data

Sample survey data [ssd]

Sampling procedure

The NFHS-4 sample was designed to provide estimates of all key indicators at the national and state levels, as well as estimates for most key indicators at the district level (for all 640 districts in India, as of the 2011 Census). The total sample size of approximately 572,000 households for India was based on the size needed to produce reliable indicator estimates for each district and for urban and rural areas in districts in which the urban population accounted for 30-70 percent of the total district population. The rural sample was selected through a two-stage sample design with villages as the Primary Sampling Units (PSUs) at the first stage (selected with probability proportional to size), followed by a random selection of 22 households in each PSU at the second stage. In urban areas, there was also a two-stage sample design with Census Enumeration Blocks (CEB) selected at the first stage and a random selection of 22 households in each CEB at the second stage. At the second stage in both urban and rural areas, households were selected after conducting a complete mapping and household listing operation in the selected first-stage units.

The figures of NFHS-4 and that of earlier rounds may not be strictly comparable due to differences in sample size and NFHS-4 will be a benchmark for future surveys. NFHS-4 fieldwork for Bihar was conducted in all 38 districts of the state from 16 March to 8 August 2015 by the Academic Management Studies (AMS) and collected information from 36,772 households, 45,812 women age 15-49 (including 7,464 women interviewed in PSUs in the state module), and 5,872 men age 15-54.

Mode of data collection

Computer Assisted Personal Interview [capi]

Research instrument

Four questionnaires - household, woman's, man's, and biomarker, were used to collect information in 19 languages using Computer Assisted Personal Interviewing (CAPI).

Data from tree census conducted in August 2019. Includes details of every tree counted in the cenus for a total of 4.09 million trees. Details include geo-location, type of tree, state of tree, ward number and management.

PerCapita_CO2_Footprint_InDioceses_FULLBurhans, Molly A., Cheney, David M., Gerlt, R.. . “PerCapita_CO2_Footprint_InDioceses_FULL”. Scale not given. Version 1.0. MO and CT, USA: GoodLands Inc., Environmental Systems Research Institute, Inc., 2019.MethodologyThis is the first global Carbon footprint of the Catholic population. We will continue to improve and develop these data with our research partners over the coming years. While it is helpful, it should also be viewed and used as a "beta" prototype that we and our research partners will build from and improve. The years of carbon data are (2010) and (2015 - SHOWN). The year of Catholic data is 2018. The year of population data is 2016. Care should be taken during future developments to harmonize the years used for catholic, population, and CO2 data.1. Zonal Statistics: Esri Population Data and Dioceses --> Population per dioceses, non Vatican based numbers2. Zonal Statistics: FFDAS and Dioceses and Population dataset --> Mean CO2 per Diocese3. Field Calculation: Population per Diocese and Mean CO2 per diocese --> CO2 per Capita4. Field Calculation: CO2 per Capita * Catholic Population --> Catholic Carbon FootprintAssumption: PerCapita CO2Deriving per-capita CO2 from mean CO2 in a geography assumes that people's footprint accounts for their personal lifestyle and involvement in local business and industries that are contribute CO2. Catholic CO2Assumes that Catholics and non-Catholic have similar CO2 footprints from their lifestyles.Derived from:A multiyear, global gridded fossil fuel CO2 emission data product: Evaluation and analysis of resultshttp://ffdas.rc.nau.edu/About.htmlRayner et al., JGR, 2010 - The is the first FFDAS paper describing the version 1.0 methods and results published in the Journal of Geophysical Research.Asefi et al., 2014 - This is the paper describing the methods and results of the FFDAS version 2.0 published in the Journal of Geophysical Research.Readme version 2.2 - A simple readme file to assist in using the 10 km x 10 km, hourly gridded Vulcan version 2.2 results.Liu et al., 2017 - A paper exploring the carbon cycle response to the 2015-2016 El Nino through the use of carbon cycle data assimilation with FFDAS as the boundary condition for FFCO2."S. Asefi‐Najafabady P. J. Rayner K. R. Gurney A. McRobert Y. Song K. Coltin J. Huang C. Elvidge K. BaughFirst published: 10 September 2014 https://doi.org/10.1002/2013JD021296 Cited by: 30Link to FFDAS data retrieval and visualization: http://hpcg.purdue.edu/FFDAS/index.phpAbstractHigh‐resolution, global quantification of fossil fuel CO2 emissions is emerging as a critical need in carbon cycle science and climate policy. We build upon a previously developed fossil fuel data assimilation system (FFDAS) for estimating global high‐resolution fossil fuel CO2 emissions. We have improved the underlying observationally based data sources, expanded the approach through treatment of separate emitting sectors including a new pointwise database of global power plants, and extended the results to cover a 1997 to 2010 time series at a spatial resolution of 0.1°. Long‐term trend analysis of the resulting global emissions shows subnational spatial structure in large active economies such as the United States, China, and India. These three countries, in particular, show different long‐term trends and exploration of the trends in nighttime lights, and population reveal a decoupling of population and emissions at the subnational level. Analysis of shorter‐term variations reveals the impact of the 2008–2009 global financial crisis with widespread negative emission anomalies across the U.S. and Europe. We have used a center of mass (CM) calculation as a compact metric to express the time evolution of spatial patterns in fossil fuel CO2 emissions. The global emission CM has moved toward the east and somewhat south between 1997 and 2010, driven by the increase in emissions in China and South Asia over this time period. Analysis at the level of individual countries reveals per capita CO2 emission migration in both Russia and India. The per capita emission CM holds potential as a way to succinctly analyze subnational shifts in carbon intensity over time. Uncertainties are generally lower than the previous version of FFDAS due mainly to an improved nightlight data set."Global Diocesan Boundaries:Burhans, M., Bell, J., Burhans, D., Carmichael, R., Cheney, D., Deaton, M., Emge, T. Gerlt, B., Grayson, J., Herries, J., Keegan, H., Skinner, A., Smith, M., Sousa, C., Trubetskoy, S. “Diocesean Boundaries of the Catholic Church” [Feature Layer]. Scale not given. Version 1.2. Redlands, CA, USA: GoodLands Inc., Environmental Systems Research Institute, Inc., 2016.Using: ArcGIS. 10.4. Version 10.0. Redlands, CA: Environmental Systems Research Institute, Inc., 2016.Boundary ProvenanceStatistics and Leadership DataCheney, D.M. “Catholic Hierarchy of the World” [Database]. Date Updated: August 2019. Catholic Hierarchy. Using: Paradox. Retrieved from Original Source.Catholic HierarchyAnnuario Pontificio per l’Anno .. Città del Vaticano :Tipografia Poliglotta Vaticana, Multiple Years.The data for these maps was extracted from the gold standard of Church data, the Annuario Pontificio, published yearly by the Vatican. The collection and data development of the Vatican Statistics Office are unknown. GoodLands is not responsible for errors within this data. We encourage people to document and report errant information to us at data@good-lands.org or directly to the Vatican.Additional information about regular changes in bishops and sees comes from a variety of public diocesan and news announcements.GoodLands’ polygon data layers, version 2.0 for global ecclesiastical boundaries of the Roman Catholic Church:Although care has been taken to ensure the accuracy, completeness and reliability of the information provided, due to this being the first developed dataset of global ecclesiastical boundaries curated from many sources it may have a higher margin of error than established geopolitical administrative boundary maps. Boundaries need to be verified with appropriate Ecclesiastical Leadership. The current information is subject to change without notice. No parties involved with the creation of this data are liable for indirect, special or incidental damage resulting from, arising out of or in connection with the use of the information. We referenced 1960 sources to build our global datasets of ecclesiastical jurisdictions. Often, they were isolated images of dioceses, historical documents and information about parishes that were cross checked. These sources can be viewed here:https://docs.google.com/spreadsheets/d/11ANlH1S_aYJOyz4TtG0HHgz0OLxnOvXLHMt4FVOS85Q/edit#gid=0To learn more or contact us please visit: https://good-lands.org/Esri Gridded Population Data 2016DescriptionThis layer is a global estimate of human population for 2016. Esri created this estimate by modeling a footprint of where people live as a dasymetric settlement likelihood surface, and then assigned 2016 population estimates stored on polygons of the finest level of geography available onto the settlement surface. Where people live means where their homes are, as in where people sleep most of the time, and this is opposed to where they work. Another way to think of this estimate is a night-time estimate, as opposed to a day-time estimate.Knowledge of population distribution helps us understand how humans affect the natural world and how natural events such as storms and earthquakes, and other phenomena affect humans. This layer represents the footprint of where people live, and how many people live there.Dataset SummaryEach cell in this layer has an integer value with the estimated number of people likely to live in the geographic region represented by that cell. Esri additionally produced several additional layers World Population Estimate Confidence 2016: the confidence level (1-5) per cell for the probability of people being located and estimated correctly. World Population Density Estimate 2016: this layer is represented as population density in units of persons per square kilometer.World Settlement Score 2016: the dasymetric likelihood surface used to create this layer by apportioning population from census polygons to the settlement score raster.To use this layer in analysis, there are several properties or geoprocessing environment settings that should be used:Coordinate system: WGS_1984. This service and its underlying data are WGS_1984. We do this because projecting population count data actually will change the populations due to resampling and either collapsing or splitting cells to fit into another coordinate system. Cell Size: 0.0013474728 degrees (approximately 150-meters) at the equator. No Data: -1Bit Depth: 32-bit signedThis layer has query, identify, pixel, and export image functions enabled, and is restricted to a maximum analysis size of 30,000 x 30,000 pixels - an area about the size of Africa.Frye, C. et al., (2018). Using Classified and Unclassified Land Cover Data to Estimate the Footprint of Human Settlement. Data Science Journal. 17, p.20. DOI: http://doi.org/10.5334/dsj-2018-020.What can you do with this layer?This layer is unsuitable for mapping or cartographic use, and thus it does not include a convenient legend. Instead, this layer is useful for analysis, particularly for estimating counts of people living within watersheds, coastal areas, and other areas that do not have standard boundaries. Esri recommends using the Zonal Statistics tool or the Zonal Statistics to Table tool where you provide input zones as either polygons, or raster data, and the tool will summarize the count of population within those zones. https://www.esri.com/arcgis-blog/products/arcgis-living-atlas/data-management/2016-world-population-estimate-services-are-now-available/

Census: Population: Chhattisgarh: Durg-Bhilai Nagar data was reported at 1,064,077.000 Person in 03-01-2011. This records an increase from the previous number of 927,864.000 Person for 03-01-2001. Census: Population: Chhattisgarh: Durg-Bhilai Nagar data is updated decadal, averaging 133,230.000 Person from Mar 1911 (Median) to 03-01-2011, with 11 observations. The data reached an all-time high of 1,064,077.000 Person in 03-01-2011 and a record low of 7,048.000 Person in 03-01-1911. Census: Population: Chhattisgarh: Durg-Bhilai Nagar 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.GAC007: Census: Population: By Towns and Urban Agglomerations: Chhattisgarh.

The Livestock Census started in the country in the year 1919. So far, 20 livestock censuses have been conducted. Livestock Census is a complete count of the livestock and poultry at pre-defined reference point of time. Similar to population census, primary workers are engaged to undertake house to house enumeration and ascertain the number, age, sex, etc., of livestock/poultry possessed by every household/household enterprise/non-household/non-household enterprises and institutions in rural & urban areas of the country. For the first time tablet computers were used for conduct of 20th livestock census.This layer contains district-wise count of animals from 20th livestock census. Following animal's count are captured based on their sex:BuffaloSheepGoatPigThis map layer is offered by Esri India, for ArcGIS Online subscribers. If you have any question or comments, please let us know via content@esri.in.

This data was scraped from: http://164.100.129.6/netnrega/secc_list.aspx See the GitHub repo: SECC for additional details. There are two types of files in this dataverse: 1. Final deduped data is uploaded in 2 files with the word deduped in the filename. See the Github repo. for what was done to produce these files. 2. Rest of the files are original files downloaded from the site. They have the word 'clean' in them to reflect minor known deduping.

Poverty and empowerment impacts of the Bihar Rural Livelihoods Project: Evidence from a Mixed-Methods Cluster-Randomized Trial Jeevika is a World Bank assisted project focussed (now under the umbrella of the NRLM) on building networks of women's self-help credit and savings groups,and then using them as a base of other "vertical" interventions. This houshold and village survey data was collected over two rounds to conduct an impact evaluation of Phase 2 of the project with random assignment of the project over a two year period. Collaboration: World Bank Social Observatory team with Government of Bihar. Evaluation design, methods and implementation In order to evaluate the impacts of Jeevika, 180 panchayats were randomly selected from within 16 blocks in seven districts where scale-up of the project was planned but had not yet occurred. Some of these blocks were in districts relatively far from Patna, which had not yet been entered by the project (Madhepura, Saharsa, Supaul), while others were within the larger districts within which Jeevika was already operating (Gaya, Nalanda, Madhubani, Muzaffarpur). The project had already entered these districts in Phase 1, but had not yet expanded to all blocks due to (project) capacity constraints. Within each of the study villages, hamlets (tolas) in which the majority of the population belonged to a scheduled caste or scheduled tribe were identified. This was the same procedure as used by Jeevika to identify the target population (of poor women) for mobilization into the project. Tolas were identified through a focus group discussion held in each village, along with the population of target castes (SC/STs) within each. In Bihar, tola boundaries are easily distinguishable. Field teams would enter the tola at a random point, determine the skip pattern based on the population size and target sample size, and select households through a random walk. Survey staff aimed to include 70% SC/ST households, and 30% households from other castes in each village, in order to ensure variation in socio-economic status within the sample. If the households in selected tolas included fewer SC/ST households than this, households from nearby non-SC/ST majority tolas were also included in the sample. Interviews for the quantitative study were conducted using a structured paper survey form. Baseline and follow up surveys included detailed questions on debt, asset holdings, consumption expenditures, livelihood activities, and women’s mobility, role in household decisions, and aspirations. In addition, in each village, a focus group discussion was conducted, through which data were collected on village level attributes such as local sources of credit, interest rates from each source, local wage rates, and the presence of or distance to markets and other institutions and amenities. Respondents were not compensated for their time. If a respondent was unavailable during initial field visit, the supervisor recorded contact details and returned with interviewers at a later date. As long as the survey team was in that district, repeat visits were undertaken, keeping attrition to a minimum. If a household could not be re-surveyed at endline, it was replaced with another household in the same village. Short re-surveys containing a subset of questions from the main survey were conducted by supervisors for 10% of the sample. Staff from the project also conducted occasional visits after the survey was completed in a village to confirm that all modules had been covered by survey staff. Data was entered in duplicate using CSPro and any discrepancies were corrected based on the paper form. Following the baseline survey, panchayats were stratified on the 16 administrative blocks in the sample and the panchayat-level mean of outstanding high cost (monthly interest rate of 4% or higher) debt held by households at baseline. They were then randomly assigned to an early rollout group or a late rollout group using the random number generator within the Stata statistical analysis software package. The baseline survey was administered to 8988 households across 333 villages in 179 panchayats. The target number of households per panchayat was 50, but there was some variation around this in reality. The lowest number of households in a given panchayat was 49 (9 panchayats), and the largest number was 53 households (3 panchayats). To ensure that control panchayats were not entered by the project, Jeevika held a quarterly ""evaluation panchayat"" meeting, which block project managers of the 16 blocks were required to attend. At these meetings the project M&E team checked whether any village in a control panchayat had been entered, and received an update on progress in treatment panchayats. This procedure was successful in maintaining adherence to randomized tr... Visit https://dataone.org/datasets/sha256%3A33337f03a8c2dabc0a718655e958c47678381b39ee277e0c820aeca2b66a6db8 for complete metadata about this dataset.