Chart and table of population level and growth rate for the Mumbai, India metro area from 1950 to 2025.

As of year 2024, the population of Mumbai, India was over **** million inhabitants. This was a **** percent growth from last year. The historical trends indicate that the population of Mumbai has been steadily increasing since 1960. The UN estimates that the population is expected to reach over ** million by the year 2030.

About this dataset

Context

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

Key observations

There is a slight majority of male population, with 51.67% of total population being male. 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 Bombay town is shown in this column.
• % of Total Population: This column displays the percentage distribution of each gender as a proportion of Bombay town 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 Bombay town Population by Race & Ethnicity. You can refer the same here

As of the year 2024, the population of the Indian city of Mumbai was over ** million people. This was a **** percent growth from the previous year. The historical trends show a fall in growth rate post-2000. However, the population growth rate has been on an upward trajectory since 2021. As per UN estimates, population growth is expected to slow down to **** percent in 2030.

About this dataset

Context

The dataset tabulates the Bombay town population by age cohorts (Children: Under 18 years; Working population: 18-64 years; Senior population: 65 years or more). It lists the population in each age cohort group along with its percentage relative to the total population of Bombay town. The dataset can be utilized to understand the population distribution across children, working population and senior population for dependency ratio, housing requirements, ageing, migration patterns etc.

Key observations

The largest age group was 18 to 64 years with a poulation of 807 (62.51% of the total population). 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.

Age cohorts:

• Under 18 years
• 18 to 64 years
• 65 years and over

Variables / Data Columns

• Age Group: This column displays the age cohort for the Bombay town population analysis. Total expected values are 3 groups ( Children, Working Population and Senior Population).
• Population: The population for the age cohort in Bombay town is shown in the following column.
• Percent of Total Population: The population as a percent of total population of the Bombay town is shown in the following column.

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 Bombay town Population by Age. You can refer the same here

Delhi was the largest city in terms of number of inhabitants in India in 2023.The capital city was estimated to house nearly 33 million people, with Mumbai ranking second that year. India's population estimate was 1.4 billion, ahead of China that same year.

About this dataset

Context

The dataset tabulates the Bombay town population over the last 20 plus years. It lists the population for each year, along with the year on year change in population, as well as the change in percentage terms for each year. The dataset can be utilized to understand the population change of Bombay town across the last two decades. For example, using this dataset, we can identify if the population is declining or increasing. If there is a change, when the population peaked, or if it is still growing and has not reached its peak. We can also compare the trend with the overall trend of United States population over the same period of time.

Key observations

In 2023, the population of Bombay town was 1,252, a 0.24% decrease year-by-year from 2022. Previously, in 2022, Bombay town population was 1,255, a decline of 0.55% compared to a population of 1,262 in 2021. Over the last 20 plus years, between 2000 and 2023, population of Bombay town increased by 53. In this period, the peak population was 1,356 in the year 2010. The numbers suggest that the population has already reached its peak and is showing a trend of decline. Source: U.S. Census Bureau Population Estimates Program (PEP).

Content

When available, the data consists of estimates from the U.S. Census Bureau Population Estimates Program (PEP).

Data Coverage:

• From 2000 to 2023

Variables / Data Columns

• Year: This column displays the data year (Measured annually and for years 2000 to 2023)
• Population: The population for the specific year for the Bombay town is shown in this column.
• Year on Year Change: This column displays the change in Bombay town population for each year compared to the previous year.
• Change in Percent: This column displays the year on year change as a percentage. 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 Bombay town Population by Year. You can refer the same here

About this dataset

Context

The dataset tabulates the data for the Bombay, New York population pyramid, which represents the Bombay town population distribution across age and gender, using estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates. It lists the male and female population for each age group, along with the total population for those age groups. Higher numbers at the bottom of the table suggest population growth, whereas higher numbers at the top indicate declining birth rates. Furthermore, the dataset can be utilized to understand the youth dependency ratio, old-age dependency ratio, total dependency ratio, and potential support ratio.

Key observations

• Youth dependency ratio, which is the number of children aged 0-14 per 100 persons aged 15-64, for Bombay, New York, is 29.3.
• Old-age dependency ratio, which is the number of persons aged 65 or over per 100 persons aged 15-64, for Bombay, New York, is 22.0.
• Total dependency ratio for Bombay, New York is 51.3.
• Potential support ratio, which is the number of youth (working age population) per elderly, for Bombay, New York is 4.5.

Content

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

Age groups:

• Under 5 years
• 5 to 9 years
• 10 to 14 years
• 15 to 19 years
• 20 to 24 years
• 25 to 29 years
• 30 to 34 years
• 35 to 39 years
• 40 to 44 years
• 45 to 49 years
• 50 to 54 years
• 55 to 59 years
• 60 to 64 years
• 65 to 69 years
• 70 to 74 years
• 75 to 79 years
• 80 to 84 years
• 85 years and over

Variables / Data Columns

• Age Group: This column displays the age group for the Bombay town population analysis. Total expected values are 18 and are define above in the age groups section.
• Population (Male): The male population in the Bombay town for the selected age group is shown in the following column.
• Population (Female): The female population in the Bombay town for the selected age group is shown in the following column.
• Total Population: The total population of the Bombay town for the selected age group is shown in the following column.

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 Bombay town Population by Age. You can refer the same here

As per the Census data dated 2011, the slum dwellers population in Mumbai was the highest among all other major metropolitan cities of India, at around ************. Hyderabad and Delhi followed it. A total of about ** million people were estimated to be living in slums across the country.

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

As of 2021, the share of multidimensional poor in the total population of Mumbai sank to **** percent during the NFHS round of 2021 as compared to **** percent between 2015 and 2016. The proportion of multidimensionally poor in the population is arrived at by dividing the number of multi-dimensionally poor persons by the total population.

This file contains National Sample Survey of India 52nd round (1995-96), 60th (2004-05) and 71st (2014-15) on social consumption on health in India. And this file also contains age-wise population of census (1991, 2001 and 2011) India.

According to the 2011 census, the population density in the Indian state of Maharashtra was 365 individuals per square kilometer. Located on the Deccan Plateau, it is the second-most populous state in the country. A steady increase in the population of the state can be attributed to growing urban districts such as Mumbai and Pune, with diverse employment opportunities in several sectors.

India's economic powerhouse

With a contribution of over 22 trillion Indian rupees in the financial year 2017, the state of Maharashtra had the highest gross state domestic product in the country. A per capita income of over 175 thousand Indian rupees was estimated across the state for the preceding year. Based on its economic model, the state was a highly preferred destination for domestic and foreign investments.

The most populous Indian state

Mumbai, the capital city of Maharashtra, was the most populous city after Delhi. As the country's economic core, it serves as the financial and commercial capital while providing numerous job opportunities. Many are attracted to this dream city in search of a lucrative career and to make it big in the world-famous Bollywood film industry.

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).

Abstract

The National Family Health Survey (NFHS) is a large-scale, multi-round survey conducted in a representative sample of households throughout India. Four rounds of the survey have been conducted in 1992-93, 1998-99, 2005-06, and 2015-16. The fifth round of the survey (2019-2020) is currently in the field. All of the surveys are part of the Demographic and Health Surveys (DHS) Program. The surveys provide information on population, health, and nutrition at the national and state level. Since 2015-16, the surveys have also provided information at the district level. Some of the major topics included in NFHS-4 (2015-16) are fertility, infant and child mortality, family planning, maternal and reproductive health, child vaccinations, prevalence and treatment of childhood diseases, nutrition, women’s empowerment, domestic violence, marriage, sexual activity, employment, anemia, anthropometry, HIV/AIDS knowledge and testing, tobacco and alcohol use, biomarker tests (anthropometry, anemia, HIV, blood pressure, and blood glucose), and water, sanitation, and hygiene. The primary objective of the NFHS surveys is to provide essential data on health and family welfare, as well as emerging issues in these areas. The information collected through the NFHS surveys is intended to assist policymakers and program managers in setting benchmarks and examining progress over time in India’s health sector. The Ministry of Health and Family Welfare (MOHFW), Government of India, designated the International Institute for Population Sciences (IIPS), Mumbai, as the agency responsible for providing coordination and technical guidance for all of the surveys. IIPS has collaborated with a large number of field agencies for survey implementation. The Demographic and Health Surveys Program has provided technical assistance for all of the surveys.

Documentation

You can access the data through the DHS website. Data files are available in the following five formats:

• Hierarchical CSPro file
• Flat files: ASCII data with syntax, Stata, SPSS, SAS

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All datasets are distributed in archived ZIP files that include the data file and its associated documentation. The DHS Program is authorized to distribute, at no cost, unrestricted survey data files for legitimate academic research. Registration is required to access the data.

Additional information about the surveys is available on the India page on the DHS Program website. This page provides a list of surveys and reports, plus Country Quickstats for India, and it is the gateway to accessing more information about the India surveys and datasets.

Methodology

2015-16 National Family Health Survey (NFHS-4): Fieldwork for NFHS-4 was conducted in two phases, from January 2015 to December 2016. The fieldwork was conducted by 14 field agencies, including three Population Research Centers. Laboratory testing for HIV was done by seven laboratories throughout India. NFHS-4 collected information from a nationally representative sample of 601,509 households, 699,686 women age 15-49, and 112,122 men age 15-54. The survey covered all 29 states, 7 Union Territories, and 640 districts in India.

Funding for the survey was provided by the Ministry of Health and Family Welfare, Government of India; the United States Agency for International Development (USAID); UKAID/DFID; the Bill & Melinda Gates Foundation; UNICEF; the United Nations Population Fund (UNFPA); and the MacArthur Foundation. Technical Assistance for NFHS-4 was provided by Macro International, Maryland, USA.

2005-06 National Family Health Survey (NFHS-3): Fieldwork for NFHS-3 was conducted in two phases, from November 2005 to August 2006. The fieldwork was conducted by 18 field agencies, including six Population Research Centers. Laboratory testing for HIV was done by the SRL Ranbaxy laboratory in Mumbai. NFHS-3 collected information from a nationally representative sample of 109,041 households, 124,385 women age 15-49, and 74,369 men age 15-54. The survey covered all 29 states. Only the Union Territories were not included.

Funding for the survey was provided by the United States Agency for International Development (USAID); United Kingdom Department for International Development (DFID); the Bill & Melinda Gates Foundation; UNICEF; the United Nations Population Fund (UNFPA); and the Government of India. Technical assistance for NFHS-3 was provided by Macro International, Maryland, USA.

1998-99 National Family Health Survey (NFHS-2): Fieldwork for NFHS-2 was conducted in two phases, from November 1998 to December 1999. The fieldwork was conducted by 13 field agencies, including five Population Research Centers. NFHS-2 collected information from a nationally representative sample of 91,196 households and 89,188 ever-married women age 15-49. Male interviews were not included in the survey. The survey cover

Abstract

The Enterprise Surveys of Micro firms (ESM) conducted by the World Bank Group's (WBG) Enterprise Analysis Unit (DECEA) in India. The survey covers nine cities: Hyderabad, Telangana; Jaipur, Rajasthan; Kochi, Kerala; Ludhiana, Punjab; Mumbai, Maharashtra; Sehore, Madhya Pradesh; Surat, Gujarat; Tezpur, Assam; and Varanasi, Uttar Pradesh.

The primary objectives of the ESM are to: i) understand demographics of the micro enterprises in the covered cities, ii) describe the environment within which these enterprises operate, and iii) enable data analysis based on the samples that are representative at each city level.

Geographic coverage

Nine cities in India: Hyderabad, Telangana; Jaipur, Rajasthan; Kochi, Kerala; Ludhiana, Punjab; Mumbai, Maharashtra; Sehore, Madhya Pradesh; Surat, Gujarat; Tezpur, Assam; and Varanasi, Uttar Pradesh.

Analysis unit

• Firms

Universe

The universe of ESM includes formally registered businesses in the sectors covered by the ES and with less than five employees. The definition of formal registration can vary by country. The universe table for each of the nine cities covered by ESM in India was obtained from the 6th Economic Census (EC) of India (conducted between January 2013 and April 2014), which has its own well-defined definition of registration. Generally, this entails registration with any central/government agency, under Shops & Establishment Act, Factories Act etc.

In terms of sectors, the survey covers all non-agricultural and non-extractive sectors. In particular, according to the group classification of ISIC Revision 4.0, it includes: all manufacturing sectors (group D), construction (group F), wholesale and retail trade (group G), transportation and storage (group H), accommodation and food service activities (group I), a subset of information and communications (group J), some administrative and support service activities (codes 79) and other service activities (codes 95). Notably, the ESM universe excludes the following sectors: financial and insurance activities (group K), real estate activities (group L), and all public or utilities-sectors.

Kind of data

Sample survey data [ssd]

Sampling procedure

The sample for Enterprise Survey of Micro firms in India 2022 was selected using stratified random sampling, following the methodology explained in the Sampling Note (https://www.enterprisesurveys.org/content/dam/enterprisesurveys/documents/methodology/Sampling_Note-Consolidated-2-16-22.pdf). Stratified random sampling was preferred over simple random sampling for several reasons, including: a. To obtain unbiased estimates for different subdivisions of the population with some known level of precision, along with the unbiased estimates for the whole population. b. To make sure that the final total sample includes establishments from all different sectors and that it is not concentrated in one or two of industries/sizes/regions. c. To exploit the benefits of stratified sampling where population estimates, in most cases, will be more precise than using a simple random sampling method (i.e., lower standard errors, other things being equal.) d. Stratification may produce a smaller bound on the error of estimation than would be produced by a simple random sample of the same size. This result is particularly true if measurements within strata are homogeneous. e. The cost per observation in the survey may be reduced by stratification of the population elements into convenient groupings.

Two levels of stratification were used in this survey: industry and region. For stratification by industry, two groups were used: Manufacturing (combining all the relevant activities in ISIC Rev. 4.0 codes 10-33) and Services (remainder of the universe, as outlined above). Regional stratification was done across nine cities included in the study, namely: Hyderabad, Jaipur, Kochi, Ludhiana, Mumbai, Sehore, Surat, Tezpur and Varanasi.

Mode of data collection

Face-to-face [f2f]

The number of registered vehicles across the financial capital of India was over ************* at the end of fiscal year 2020. Mumbai was the most car-congested city in the country in 2020. In recent years, the density of privately-owned vehicles increased by ** percent in the city. Despite the private car population being ********* of the nation’s capital Delhi, the lack of infrastructure has proved to be a significant shortcoming.

Automotive in India

India ranked ****** in the passenger car production sector worldwide in 2020, with over ************* units of passenger vehicles produced in fiscal year 2021. Although, in contrast, the domestic market has been dominated by the two-wheeler segment. This was probably due to the latter’s ability to navigate the narrow Indian roads. Sales volume for two-wheelers continued to increase over the past few years.

An e-future

The number of electric vehicle sales in the dominating two-wheeler segment across the country quintupled between 2016 and 2020, with help from government initiatives to enhance e-mobility. The number of electric two-wheelers was estimated to cross the ********** mark by 2030. With strict emission laws and reduced taxes on electric vehicles, the Indian government has been making efforts to make a radical yet streamlined switch to e-mobility.

This statistic depicts the age distribution of India from 2013 to 2023. In 2023, about 25.06 percent of the Indian population fell into the 0-14 year category, 68.02 percent into the 15-64 age group and 6.92 percent were over 65 years of age. Age distribution in India India is one of the largest countries in the world and its population is constantly increasing. India’s society is categorized into a hierarchically organized caste system, encompassing certain rights and values for each caste. Indians are born into a caste, and those belonging to a lower echelon often face discrimination and hardship. The median age (which means that one half of the population is younger and the other one is older) of India’s population has been increasing constantly after a slump in the 1970s, and is expected to increase further over the next few years. However, in international comparison, it is fairly low; in other countries the average inhabitant is about 20 years older. But India seems to be on the rise, not only is it a member of the BRIC states – an association of emerging economies, the other members being Brazil, Russia and China –, life expectancy of Indians has also increased significantly over the past decade, which is an indicator of access to better health care and nutrition. Gender equality is still non-existant in India, even though most Indians believe that the quality of life is about equal for men and women in their country. India is patriarchal and women still often face forced marriages, domestic violence, dowry killings or rape. As of late, India has come to be considered one of the least safe places for women worldwide. Additionally, infanticide and selective abortion of female fetuses attribute to the inequality of women in India. It is believed that this has led to the fact that the vast majority of Indian children aged 0 to 6 years are male.

Geographic coverage

The survey covers the whole of the Indian Union except (i) interior villages of Nagaland situated beyond five kilometres of the bus route and (ii) villages in Andaman and Nicobar Islands which remain inaccessible throughout the year.

Analysis unit

Enterprise

Kind of data

Sample survey data [ssd]

Sampling procedure

Outline of sample design:

A stratified multi-stage design has been adopted for the 67th round survey. The first stage units (FSU) is 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 (USU) is enterprises in both the sectors. In case of large FSUs, one intermediate stage of sampling will be the selection of three hamlet-groups (hgs)/ sub-blocks (sbs) from each large rural/ urban FSU.

Sampling frame to be used for selection of first stage units

Census 2001 list of villages is used as the sampling frame for rural areas. Auxiliary information such as number of enterprises, number of workers, type of enterprises, activities of enterprises, etc. available from EC-2005 frame is used for stratification, sub-stratification and selection of enterprises.

In Kerala, list of panchayat wards as per Census 2001 will be used as frame since list of such wards is not available as per EC 2005 frame.

In the urban sector, EC-2005 frame is used for 26 cities with population more than a million as per census 2001. Although Mumbai is a million plus city, EC-2005 frame is not used for Mumbai because of identification problem for IV unit/blocks in the EC for the city. For other cities/towns (including Mumbai), UFS frame (2002-07 phase or latest available phase prior to 2002-07 if it is not available) is used.

Stratification:

Each district is a basic stratum in both rural and urban areas. However, in case of urban, each city with population of 1 million or more as per Census 2001 forms a separate stratum and all other cities/towns of a district is grouped to form another stratum.

Sub-stratification:

(i) Rural: There is three sub-strata in the rural sector: (1) Villages with at least 5 establishments (NDE/DE) (see para 1.4.17 and 1.4.18 for definition of NDE/DE) under coverage in the manufacturing sector as per EC-2005 information; (2) Remaining villages having at least 5 NDE/DE under coverage in the services sector including trade as per EC-2005 information; (3) Remaining villages of the stratum.

For the State(s) where EC-2005 information cannot be used as auxiliary information for stratification/sub-stratification due to limitations of EC 2005 frame, each district is sub-stratified into 'r/4' sub-strata with a sample allocation of 4 per sub-stratum where 'r' is the sample allocation for the district/stratum. The sub-strata is formed by arranging the villages in terms of population so that total population of each sub-stratum is approximately the same.

(ii) Urban, Million plus cities (excluding Mumbai) : For each stratum / million plus city, 20 sub-strata will be formed as under: Sub-stratum 1: Blocks with one or more establishment in insurance & pension funding;
Sub-stratum 2: Remaining blocks with one or more establishment in storage & warehousing;
Sub-stratum 3: Remaining blocks with one or more establishment in accommodation;
Sub-strata 4-8: Remaining blocks with one or more establishment in broad activities of manufacturing (as per SSS formation discussed subsequently under para 1.3.10);
Sub-strata 9-12: Remaining blocks with one or more establishment in broad activities of trade (as per SSS formation in para 1.3.10);
Sub-strata 13-19: Remaining blocks with one or more establishment in broad activities of other services (as per SSS formation in para 1.3.10) excluding the activities covered under sub-strata 1-3.
Sub-strata 20: All remaining blocks of the stratum.

(iii) Urban, Other cities and towns (including Mumbai): Two sub-strata is formed: Sub-stratum 1: UFS block types: Bazaar area (BA)/ Industrial area (IA)/ Hospital area/ (HA)/ Slum area (SA) which are likely to contain relatively higher number of enterprises;
Sub-stratum 2: Remaining UFS blocks of the stratum.

If the number of FSUs in the frame of a rural or urban sub-stratum is found to be less than 8, then separate sub-stratum is formed and it is merged with the adjacent sub-stratum. There is only one town (Leh) in Leh district and one town (Kargil) in Kargil district of J & K. These two towns are out of UFS coverage. These are treated as sub-stratum 2 and the entire town is treated as one FSU.

Total Sample size (FSUs):

A sample of 16000 FSUs for central sample and 17176 FSUs for state sample have been allocated at all-India level.

Allocation of total sample FSUs:

(i) All-India allocation over States: All-India sample size (FSUs) have been allocated to different State/UTs taking into account the minimum allocations required for a State/UT and the proportion of non-agricultural workers as per EC-2005 in the State/UT.

(ii) State/UT allocation over rural/urban sectors: State/UT sample sizes is allocated to rural and urban sectors of the State/UT in proportion to number of non-agricultural workers as per EC-2005 with the constraint that urban allocation should not be too high compared to rural allocation and both rural and urban allocations is in multiples of 8.

(iii) State × sector allocation over strata: Stratum allocations of State/UT sample sizes for each sector is made in proportion to number of non-agricultural workers as per EC-2005. For the States/UTs where census 2001 frame was used in the rural sector, allocations to strata are made in proportion to population as per census.

(iv) Stratum allocation over sub-strata: Allocations to sub-strata are made: (a) In proportion to number of non-agricultural workers as per EC-2005 in rural sector as well as in million plus cities (after assuming the number as 1 for those villages/blocks where number of non-agricultural workers is 0); (b) In proportion to number of blocks with a double weight to sub-stratum 1 for other than million plus cities.

Minimum allocation for a sub-stratum is 4.

Selection of FSUs: (a) Rural & million plus cities: From each sub-stratum, required number of sample villages/blocks will be selected by probability proportional to size with replacement (PPSWR), size being the number of total non-agricultural workers under coverage in the village/block as per EC-2005. For the State(s) where EC-2005 information cannot be used as auxiliary information for selection of FSUs due to limitations of EC 2005 frame, size for PPSWR selection is the population of the village as per Census 2001.

(b) Urban (other than million plus cities): From each sub-stratum FSUs are selected by using Simple Random Sampling Without Replacement (SRSWOR). However, for Leh and Kargil towns, each town is selected 4 times, once in each sub-round. Both rural and urban samples is drawn in the form of two independent sub-samples and equal number of samples is allocated among the four sub rounds.

Formation of segment 9 and selection of hamlet-groups/ sub-blocks

Proper identification of the FSU boundaries: The first task of the field investigators is to ascertain the exact boundaries of the sample FSU as per its identification particulars given in the sample list. For urban samples, the boundaries of each FSU may be identified by referring to the map corresponding to the frame code specified in the sample list (even though map of the block for a latter period of the UFS might be available).

Formation of Segment 9: Having determined the boundaries of the sample FSU, all non-agricultural enterprises having 20 or more workers in the entire FSU and having operated at least one day during last 365 days preceding the day of survey (hereinafter to be called as 'big enterprises') are listed and all the eligible units under coverage are surveyed. All the listed big units (whether under coverage or not) constitute segment 9. All eligible enterprises under coverage were surveyed in segment 9.

Criterion for hamlet-group/ sub-block formation: Having constituted segment 9 as stated above, it is to be determined whether listing is done in the whole sample FSU or not. For this, approximate present population (P) and approximate total number of non-agricultural enterprises (E) for the whole FSU may be ascertained first from knowledgeable persons. Depending upon the values of 'P' and 'E', it is divided into a suitable number (say, D) of 'hamlet-groups' in the rural sector and 'sub-blocks' in the urban sector as stated below. Final value of 'D' is the higher of the two values 'P' and 'E' based on the dual criteria. While considering enterprise criteria, segment 9 enterprises, if any, may be excluded from the count of 'E', if possible.

For rural areas of Himachal Pradesh, Sikkim, Uttarakhand (except four districts Dehradun (P), Nainital (P), Hardwar and Udham Singh Nagar), Poonch, Rajouri, Udhampur, Doda, Leh (Ladakh), Kargil districts of Jammu and Kashmir and Idukki district of Kerala, the number of hamlet-groups is formed as follows: population (P) | no. of hgs/ sbs to be formed | no. of non-agricultural enterprises (E) | no. of hgs/ sbs to be formed
less than 600 | 1 | less than 120 | 1
600 - 799 | 4 | 120 - 159 | 4
800 - 899 | 5 | 160 - 199 | 5
1000 - 1199 | 6 | 200 - 239 | 6
and so on | … | and

The overall population of pet dogs in India was over 33 million in 2023. The population is likely to reach more than 51 million by 2028. The growth in the number of pet dogs has led to an increase in pet food sales across the country.