Government Hospital: Gujarat: Average Population Served per Hospital data was reported at 129,270.000 Person in 2016. This records a decrease from the previous number of 159,297.000 Person for 2014. Government Hospital: Gujarat: Average Population Served per Hospital data is updated yearly, averaging 135,694.000 Person from Dec 2005 (Median) to 2016, with 9 observations. The data reached an all-time high of 159,297.000 Person in 2014 and a record low of 32,598.000 Person in 2007. Government Hospital: Gujarat: Average Population Served per Hospital data remains active status in CEIC and is reported by Central Bureau of Health Intelligence. The data is categorized under India Premium Database’s Health Sector – Table IN.HLA001: Health Infrastructure: Government Hospitals.

Government Hospital: Gujarat: Average Population Served per Hospital Bed data was reported at 1,946.000 Person in 2016. This records a decrease from the previous number of 2,196.000 Person for 2014. Government Hospital: Gujarat: Average Population Served per Hospital Bed data is updated yearly, averaging 1,896.000 Person from Dec 2005 (Median) to 2016, with 9 observations. The data reached an all-time high of 2,196.000 Person in 2014 and a record low of 313.000 Person in 2011. Government Hospital: Gujarat: Average Population Served per Hospital Bed data remains active status in CEIC and is reported by Central Bureau of Health Intelligence. The data is categorized under India Premium Database’s Health Sector – Table IN.HLA002: Health Infrastructure: Government Hospital Beds.

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

POPULATION PROIECTIONS FOR INDIA AND STATES 2011 – 2036 (Downscaled to District, Sub-Districts and Villages/Towns by Esri India)REPORT OF THE TECHNICAL GROUP ON POPULATION PROIECTTONSJuly, 2020The projected population figures provided by the Registrar General of India forms the basis for planning and implementation of various health interventions including RMNCH+A, which are aimed at improving the overall health outcomes by ensuring quality service provision to all the health beneficiaries. These interventions focus on antenatal, intranatal and neonatal care aimed at reducing maternal and neonatal morbidity and mortality; improving coverage and quality of health care interventions and improving coverage for immunization against vaccine preventable diseases. Further, these estimates would also enable us to tackle the special health care needs of various population age groups, thus gearing the system for necessary preventive, promotive, curative, and rehabilitative services for the growing population to this report. PREETI SUDAN, IAS SecretaryThe Cohort Component Method is the universally accepted method of making population projections because of the fact that the growth of population is determined by fertility, mortality, and migration rates. In this exercise, 20 States and two UTs have been applied the Cohort Component method. These are Andhra Pradesh, Assam, Bihar, Gujarat, Haryana, Himachal Pradesh, Karnataka, Kerala, Madhya Pradesh, Maharashtra, Odisha, Punjab, Rajasthan, Tamil Nadu, Telangana, Uttar Pradesh, West Bengal, Jharkhand, Chhattisgarh, Uttarakhand, Jammu & Kashmir (UT) and NCT of Delhi. Based on the residual of the projected population of Jammu & Kashmir (State) and Jammu & Kashmir (UT), for which Cohort Component method has applied, projection of the Ladakh UT have been made. For the projections of Jammu & Kashmir (UT), SRS fertility and mortality estimates of Jammu & Kashmir (State) are used. The projection of the seven northeastern states (excluding Assam) has also been carried out as a whole using the Cohort Component Method. Separate projections for Andhra Pradesh and Telangana were done using the re-casted populations of these states. For the projections, for the years before 2014, combined SRS estimates of Andhra Pradesh and year 2014 onwards, separate SRS estimates of fertility and mortality of Andhra Pradesh and Telangana are used. For the remaining States and Union territories, Mathematical Method has been applied. The sources of data used are 2011 Census and Sample Registration System (SRS). SRS provides time series data of fertility and mortality, which has been used for predicting their future levelsEsri India Efforts:The Population Projections Report published by MoHFW contains output summary tables from series Table 8 to Table 14. Example: TABLE – 8: Projected total population by sex as on 1st March, 2011-2036: India, States and Union territories, TABLE – 9: Projected urban population by sex as on 1st March, 2011-2036: India, States and Union territories, etc. The parameters available with these census data tables are Census Year, Projected Total Persons with Gender categorization and Projected Urban Population from 2011 to 2036.By subtracting “Projected Urban Population” from “Projected Total Population”, a new data column has been added as “Projected Rural Population”. The data is available for all Union Territory and States for 25 years.A factor has been calculated by taking projected population and the base year population (2011). Subsequently, the factor is calculated for each year using the projected values provided by census of India. Projected Population by Sex as on 1st March - 2011 - 2036: India, States and Union Territories* ('000)YearGUJARAT GUJARAT URBANGUJARAT RURALPersonsMaleFemalePersonMaleFemalePersonMaleFemale2011 60,440 (A) 31,49128,94825,74513,69412,05134,69517,79716,8972012 61,383 (B)32,00729,37626,47214,08112,39134,91117,92616,985Factor has been applied below State level- Projected Population by Sex as on 1st March - 2011 - 2036: India, States and Union Territories* ('000)YearGUJARAT GUJARAT URBANGUJARAT RURALPersonsMaleFemalePersonMaleFemalePersonMaleFemale20121.01560225 (B/A)1.0163856341.0147851321.0282384931.0282605521.0282134261.0062256811.0072484131.005208025Esri India has access to SOI admin boundaries up-to district level and developed village, town and sub-district boundaries using census maps. The calculated factors have been applied to smallest geography at villages and towns and upscaled back to sub-district, district, state, and country. The derived values have been compared with the original values provided by census at state level and no deviation is confirmed.Data Variables: Year (2011-2036)Total Population MaleFemaleTotal Population UrbanMale UrbanFemale UrbanTotal Population RuralMale RuralFemale RuralData source: https://main.mohfw.gov.in/sites/default/files/Population Projection Report 2011-2036 - upload_compressed_0.pdfOther related contents are also available:India Population Projections 2011-2036Village Population Projections for India 2011-2036Sub-district Population Projections for India 2011-2036State Population Projections for India 2011-2036Country Population Projections for India 2011-2036This web layer is offered by Esri India, for ArcGIS Online subscribers. If you have any questions or comments, please let us know via content@esri.in.

POPULATION PROIECTIONS FOR INDIA AND STATES 2011 – 2036 (Downscaled to District, Sub-Districts and Villages/Towns by Esri India)REPORT OF THE TECHNICAL GROUP ON POPULATION PROIECTTONSJuly, 2020The projected population figures provided by the Registrar General of India forms the basis for planning and implementation of various health interventions including RMNCH+A, which are aimed at improving the overall health outcomes by ensuring quality service provision to all the health beneficiaries. These interventions focus on antenatal, intranatal and neonatal care aimed at reducing maternal and neonatal morbidity and mortality; improving coverage and quality of health care interventions and improving coverage for immunization against vaccine preventable diseases. Further, these estimates would also enable us to tackle the special health care needs of various population age groups, thus gearing the system for necessary preventive, promotive, curative, and rehabilitative services for the growing population to this report. PREETI SUDAN, IAS SecretaryThe Cohort Component Method is the universally accepted method of making population projections because of the fact that the growth of population is determined by fertility, mortality, and migration rates. In this exercise, 20 States and two UTs have been applied the Cohort Component method. These are Andhra Pradesh, Assam, Bihar, Gujarat, Haryana, Himachal Pradesh, Karnataka, Kerala, Madhya Pradesh, Maharashtra, Odisha, Punjab, Rajasthan, Tamil Nadu, Telangana, Uttar Pradesh, West Bengal, Jharkhand, Chhattisgarh, Uttarakhand, Jammu & Kashmir (UT) and NCT of Delhi. Based on the residual of the projected population of Jammu & Kashmir (State) and Jammu & Kashmir (UT), for which Cohort Component method has applied, projection of the Ladakh UT have been made. For the projections of Jammu & Kashmir (UT), SRS fertility and mortality estimates of Jammu & Kashmir (State) are used. The projection of the seven northeastern states (excluding Assam) has also been carried out as a whole using the Cohort Component Method. Separate projections for Andhra Pradesh and Telangana were done using the re-casted populations of these states. For the projections, for the years before 2014, combined SRS estimates of Andhra Pradesh and year 2014 onwards, separate SRS estimates of fertility and mortality of Andhra Pradesh and Telangana are used. For the remaining States and Union territories, Mathematical Method has been applied. The sources of data used are 2011 Census and Sample Registration System (SRS). SRS provides time series data of fertility and mortality, which has been used for predicting their future levelsEsri India Efforts:The Population Projections Report published by MoHFW contains output summary tables from series Table 8 to Table 14. Example: TABLE – 8: Projected total population by sex as on 1st March, 2011-2036: India, States and Union territories, TABLE – 9: Projected urban population by sex as on 1st March, 2011-2036: India, States and Union territories, etc. The parameters available with these census data tables are Census Year, Projected Total Persons with Gender categorization and Projected Urban Population from 2011 to 2036.By subtracting “Projected Urban Population” from “Projected Total Population”, a new data column has been added as “Projected Rural Population”. The data is available for all Union Territory and States for 25 years.A factor has been calculated by taking projected population and the base year population (2011). Subsequently, the factor is calculated for each year using the projected values provided by census of India. Projected Population by Sex as on 1st March - 2011 - 2036: India, States and Union Territories* ('000)YearGUJARAT GUJARAT URBANGUJARAT RURALPersonsMaleFemalePersonMaleFemalePersonMaleFemale2011 60,440 (A) 31,49128,94825,74513,69412,05134,69517,79716,8972012 61,383 (B)32,00729,37626,47214,08112,39134,91117,92616,985Factor has been applied below State level- Projected Population by Sex as on 1st March - 2011 - 2036: India, States and Union Territories* ('000)YearGUJARAT GUJARAT URBANGUJARAT RURALPersonsMaleFemalePersonMaleFemalePersonMaleFemale20121.01560225 (B/A)1.0163856341.0147851321.0282384931.0282605521.0282134261.0062256811.0072484131.005208025Esri India has access to SOI admin boundaries up-to district level and developed village, town and sub-district boundaries using census maps. The calculated factors have been applied to smallest geography at villages and towns and upscaled back to sub-district, district, state, and country. The derived values have been compared with the original values provided by census at state level and no deviation is confirmed.Data Variables: Year (2011-2036)Total Population MaleFemaleTotal Population UrbanMale UrbanFemale UrbanTotal Population RuralMale RuralFemale RuralData source: https://main.mohfw.gov.in/sites/default/files/Population Projection Report 2011-2036 - upload_compressed_0.pdfOther related contents are also available:Village Population Projections for India 2011-2036Sub-district Population Projections for India 2011-2036District Population Projections for India 2011-2036State Population Projections for India 2011-2036Country Population Projections for India 2011-2036This web layer is offered by Esri India, for ArcGIS Online subscribers. If you have any questions or comments, please let us know via content@esri.in.

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]

Government Hospital: Gujarat: Average Population Served per Hospital Bed在2016达1,946.000人口，相较于2014的2,196.000人口有所下降。Government Hospital: Gujarat: Average Population Served per Hospital Bed数据按每年更新，2005至2016期间平均值为1,896.000人口，共9份观测结果。该数据的历史最高值出现于2014，达2,196.000人口，而历史最低值则出现于2011，为313.000人口。CEIC提供的Government Hospital: Gujarat: Average Population Served per Hospital Bed数据处于定期更新的状态，数据来源于Central Bureau of Health Intelligence，数据归类于India Premium Database的Health Sector – Table IN.HLA002: Health Infrastructure: Government Hospital Beds。

Gujarat: Banaskantha: Total Voters data was reported at 1,102,081.000 Person in 03-01-2019. This records an increase from the previous number of 887,366.000 Person for 03-01-2014. Gujarat: Banaskantha: Total Voters data is updated quinquennially, averaging 573,929.000 Person from Mar 1971 (Median) to 03-01-2019, with 13 observations. The data reached an all-time high of 1,102,081.000 Person in 03-01-2019 and a record low of 233,765.000 Person in 03-01-1971. Gujarat: Banaskantha: Total Voters data remains active status in CEIC and is reported by Election Commission of India. The data is categorized under India Premium Database’s General Election – Table IN.GEA011: General Election: Loksabha: Election Outcome of Parliamentary Constituencies: Gujarat.

The estimated per capita income across Sikkim was the highest among Indian states at around *** thousand Indian rupees in the financial year 2024. Meanwhile, it was the lowest in the northern state of Bihar at over ** thousand rupees. India’s youngest state, Telangana stood in the fifth place. The country's average per capita income that year was an estimated *** thousand rupees. What is per capita income? Per capita income is a measure of the average income earned per person in a given area in a certain period. It is calculated by dividing the area's total income by its total population. If absolute numbers are noted, India’s per capita income doubled from the financial year 2015 to 2023. Wealth inequality However, as per economists, the increase in the per capita income of a country does not always reflect an increase in the income of the entire population. Wealth distribution in India remains highly skewed. The average income hides the disbursal and inequality in a society. Especially in a society like India where the top one percent owned over ** percent of the total wealth in 2022.