Sex Ratio at Birth: Female per 1000 Male: West Bengal: Rural data was reported at 941.000 NA in 2020. This records a decrease from the previous number of 948.000 NA for 2019. Sex Ratio at Birth: Female per 1000 Male: West Bengal: Rural data is updated yearly, averaging 940.000 NA from Dec 2006 (Median) to 2020, with 15 observations. The data reached an all-time high of 953.000 NA in 2015 and a record low of 932.000 NA in 2007. Sex Ratio at Birth: Female per 1000 Male: West Bengal: 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.GAJ001: Memo Items: Sex Ratio at Birth.

Sex Ratio at Birth: Female per 1000 Male: West Bengal data was reported at 936.000 NA in 2020. This records a decrease from the previous number of 944.000 NA for 2019. Sex Ratio at Birth: Female per 1000 Male: West Bengal data is updated yearly, averaging 941.000 NA from Dec 2006 (Median) to 2020, with 15 observations. The data reached an all-time high of 952.000 NA in 2014 and a record low of 931.000 NA in 2006. Sex Ratio at Birth: Female per 1000 Male: West Bengal 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.GAJ001: Memo Items: Sex Ratio at Birth.

Sex Ratio at Birth: Female per 1000 Male: West Bengal: Urban data was reported at 920.000 NA in 2020. This records a decrease from the previous number of 928.000 NA for 2019. Sex Ratio at Birth: Female per 1000 Male: West Bengal: Urban data is updated yearly, averaging 949.000 NA from Dec 2006 (Median) to 2020, with 15 observations. The data reached an all-time high of 964.000 NA in 2014 and a record low of 903.000 NA in 2006. Sex Ratio at Birth: Female per 1000 Male: West Bengal: 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.GAJ001: Memo Items: Sex Ratio at Birth.

The total gross enrollment ratio of students from the pre-primary to second grade across the state of West Bengal in India during financial year 2024 was around ** percent. The enrollment ratio of students from sixth grade to eighth grade was higher among female students compared to male students that year.

India General Election: West Bengal: Number of Voters: Male to Female Ratio data was reported at 1.088 % in 2014. This records a decrease from the previous number of 1.134 % for 2009. India General Election: West Bengal: Number of Voters: Male to Female Ratio data is updated yearly, averaging 1.223 % from Mar 1962 (Median) to 2014, with 14 observations. The data reached an all-time high of 1.701 % in 1962 and a record low of 1.088 % in 2014. India General Election: West Bengal: Number of Voters: Male to Female Ratio data remains active status in CEIC and is reported by CEIC Data. The data is categorized under India Premium Database’s General Election – Table IN.GEF036: General Election: Loksabha: Number of Voters: West Bengal.

According to the 76th round of the NSO survey conducted between July and December 2018, a higher percentage of men had disabilities compared to women in India. In the state of West Bengal, 2.5 percent of men had multiple disabilities, while this was at 1.8 percent among females. 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.

We examine intra-household gender-differentiated effects of property rights securitisation following West Bengal's tenancy registration program, using two independently gathered datasets. In both samples, higher program implementation increased male child survival rates in families without a firstborn son, but not in those that already have a firstborn male child. We argue this reflects intensified son preference as land rights improve, ostensibly to ensure a male heir to inherit land. Consistent with this, girls with firstborn brothers also experience increased survival, but not girls with firstborn sisters. The gender bias manifests both in infant mortality rates and the sex ratio at birth.

Sex Ratio at Birth: Female per 1000 Male: West Bengal在2020达936.000NA，相较于2019的944.000NA有所下降。Sex Ratio at Birth: Female per 1000 Male: West Bengal数据按每年更新，2006至2020期间平均值为941.000NA，共15份观测结果。该数据的历史最高值出现于2014，达952.000NA，而历史最低值则出现于2006，为931.000NA。CEIC提供的Sex Ratio at Birth: Female per 1000 Male: West Bengal数据处于定期更新的状态，数据来源于Office of the Registrar General & Census Commissioner, India，数据归类于India Premium Database的Demographic – Table IN.GAJ001: Memo Items: Sex Ratio at Birth。

Abstract

The National Family Health Surveys (NFHS) programme, initiated in the early 1990s, has emerged as a nationally important source of data on population, health, and nutrition for India and its states. The 2005-06 National Family Health Survey (NFHS-3), the third in the series of these national surveys, was preceded by NFHS-1 in 1992-93 and NFHS-2 in 1998-99. Like NFHS-1 and NFHS-2, NFHS-3 was designed to provide estimates of important indicators on family welfare, maternal and child health, and nutrition. In addition, NFHS-3 provides information on several new and emerging issues, including family life education, safe injections, perinatal mortality, adolescent reproductive health, high-risk sexual behaviour, tuberculosis, and malaria. Further, unlike the earlier surveys in which only ever-married women age 15-49 were eligible for individual interviews, NFHS-3 interviewed all women age 15-49 and all men age 15-54. Information on nutritional status, including the prevalence of anaemia, is provided in NFHS3 for women age 15-49, men age 15-54, and young children.

A special feature of NFHS-3 is the inclusion of testing of the adult population for HIV. NFHS-3 is the first nationwide community-based survey in India to provide an estimate of HIV prevalence in the general population. Specifically, NFHS-3 provides estimates of HIV prevalence among women age 15-49 and men age 15-54 for all of India, and separately for Uttar Pradesh and for Andhra Pradesh, Karnataka, Maharashtra, Manipur, and Tamil Nadu, five out of the six states classified by the National AIDS Control Organization (NACO) as high HIV prevalence states. No estimate of HIV prevalence is being provided for Nagaland, the sixth high HIV prevalence state, due to strong local opposition to the collection of blood samples.

NFHS-3 covered all 29 states in India, which comprise more than 99 percent of India's population. NFHS-3 is designed to provide estimates of key indicators for India as a whole and, with the exception of HIV prevalence, for all 29 states by urban-rural residence. Additionally, NFHS-3 provides estimates for the slum and non-slum populations of eight cities, namely Chennai, Delhi, Hyderabad, Indore, Kolkata, Meerut, Mumbai, and Nagpur. NFHS-3 was conducted under the stewardship of the Ministry of Health and Family Welfare (MOHFW), Government of India, and is the result of the collaborative efforts of a large number of organizations. The International Institute for Population Sciences (IIPS), Mumbai, was designated by MOHFW as the nodal agency for the project. Funding for NFHS-3 was provided by the United States Agency for International Development (USAID), DFID, the Bill and Melinda Gates Foundation, UNICEF, UNFPA, and MOHFW. Macro International, USA, provided technical assistance at all stages of the NFHS-3 project. NACO and the National AIDS Research Institute (NARI) provided technical assistance for the HIV component of NFHS-3. Eighteen Research Organizations, including six Population Research Centres, shouldered the responsibility of conducting the survey in the different states of India and producing electronic data files.

The survey used a uniform sample design, questionnaires (translated into 18 Indian languages), field procedures, and procedures for biomarker measurements throughout the country to facilitate comparability across the states and to ensure the highest possible data quality. The contents of the questionnaires were decided through an extensive collaborative process in early 2005. Based on provisional data, two national-level fact sheets and 29 state fact sheets that provide estimates of more than 50 key indicators of population, health, family welfare, and nutrition have already been released. The basic objective of releasing fact sheets within a very short period after the completion of data collection was to provide immediate feedback to planners and programme managers on key process indicators.

Geographic coverage

• National (29 states )
• Regional (for HIV Prevalence : Andhra Pradesh, Karnataka, Maharashtra, Manipur, and Tamil Nadu)
• Local (population and health indicators for slum and non-slum populations for eight cities, namely Chennai, Delhi, Hyderabad, Indore, Kolkata, Meerut, Mumbai, and Nagpur)

Analysis unit

• Household
• Women age 15-49
• Men age 15-59

Universe

The population covered by the 2005 DHS is defined as the universe of all ever-married women age 15-49, NFHS-3 included never married women age 15-49 and both ever-married and never married men age 15-54 as eligible respondents.

Kind of data

Sample survey data

Sampling procedure

SAMPLE SIZE

Since a large number of the key indicators to be estimated from NFHS-3 refer to ever-married women in the reproductive ages of 15-49, the target sample size for each state in NFHS-3 was estimated in terms of the number of ever-married women in the reproductive ages to be interviewed.

The initial target sample size was 4,000 completed interviews with ever-married women in states with a 2001 population of more than 30 million, 3,000 completed interviews with ever-married women in states with a 2001 population between 5 and 30 million, and 1,500 completed interviews with ever-married women in states with a population of less than 5 million. In addition, because of sample-size adjustments required to meet the need for HIV prevalence estimates for the high HIV prevalence states and Uttar Pradesh and for slum and non-slum estimates in eight selected cities, the sample size in some states was higher than that fixed by the above criteria. The target sample was increased for Andhra Pradesh, Karnataka, Maharashtra, Manipur, Nagaland, Tamil Nadu, and Uttar Pradesh to permit the calculation of reliable HIV prevalence estimates for each of these states. The sample size in Andhra Pradesh, Delhi, Maharashtra, Tamil Nadu, Madhya Pradesh, and West Bengal was increased to allow separate estimates for slum and non-slum populations in the cities of Chennai, Delhi, Hyderabad, Indore, Kolkata, Mumbai, Meerut, and Nagpur.

The target sample size for HIV tests was estimated on the basis of the assumed HIV prevalence rate, the design effect of the sample, and the acceptable level of precision. With an assumed level of HIV prevalence of 1.25 percent and a 15 percent relative standard error, the estimated sample size was 6,400 HIV tests each for men and women in each of the high HIV prevalence states. At the national level, the assumed level of HIV prevalence of less than 1 percent (0.92 percent) and less than a 5 percent relative standard error yielded a target of 125,000 HIV tests at the national level.

Blood was collected for HIV testing from all consenting ever-married and never married women age 15-49 and men age 15-54 in all sample households in Andhra Pradesh, Karnataka, Maharashtra, Manipur, Tamil Nadu, and Uttar Pradesh. All women age 15-49 and men age 15-54 in the sample households were eligible for interviewing in all of these states plus Nagaland. In the remaining 22 states, all ever-married and never married women age 15-49 in sample households were eligible to be interviewed. In those 22 states, men age 15-54 were eligible to be interviewed in only a subsample of households. HIV tests for women and men were carried out in only a subsample of the households that were selected for men's interviews in those 22 states. The reason for this sample design is that the required number of HIV tests is determined by the need to calculate HIV prevalence at the national level and for some states, whereas the number of individual interviews is determined by the need to provide state level estimates for attitudinal and behavioural indicators in every state. For statistical reasons, it is not possible to estimate HIV prevalence in every state from NFHS-3 as the number of tests required for estimating HIV prevalence reliably in low HIV prevalence states would have been very large.

SAMPLE DESIGN

The urban and rural samples within each state were drawn separately and, to the extent possible, unless oversampling was required to permit separate estimates for urban slum and non-slum areas, the sample within each state was allocated proportionally to the size of the state's urban and rural populations. A uniform sample design was adopted in all states. In each state, the rural sample was selected in two stages, with the selection of Primary Sampling Units (PSUs), which are villages, with probability proportional to population size (PPS) at the first stage, followed by the random selection of households within each PSU in the second stage. In urban areas, a three-stage procedure was followed. In the first stage, wards were selected with PPS sampling. In the next stage, one census enumeration block (CEB) was randomly selected from each sample ward. In the final stage, households were randomly selected within each selected CEB.

SAMPLE SELECTION IN RURAL AREAS

In rural areas, the 2001 Census list of villages served as the sampling frame. The list was stratified by a number of variables. The first level of stratification was geographic, with districts being subdivided into contiguous regions. Within each of these regions, villages were further stratified using selected variables from the following list: village size, percentage of males working in the nonagricultural sector, percentage of the population belonging to scheduled castes or scheduled tribes, and female literacy. In addition to these variables, an external estimate of HIV prevalence, i.e., 'High', 'Medium' or 'Low', as estimated for all the districts in high HIV prevalence states, was used for stratification in high HIV prevalence states. Female literacy was used for implicit stratification (i.e., villages were

General Election: West Bengal: Number of Electors: Male to Female Ratio在2014达1.084 %，相较于2009的1.106 %有所下降。General Election: West Bengal: Number of Electors: Male to Female Ratio数据按每年更新，1962至2014期间平均值为1.144 %，共13份观测结果。该数据的历史最高值出现于1962，达1.307 %，而历史最低值则出现于2014，为1.084 %。CEIC提供的General Election: West Bengal: Number of Electors: Male to Female Ratio数据处于定期更新的状态，数据来源于CEIC Data，数据归类于India Premium Database的General Election – Table IN.GEE036: General Election: Loksabha: Election Polling Stations, Constituencies and Electors: West Bengal。