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.

Population in largest city in India was reported at 33807403 in 2024, according to the World Bank collection of development indicators, compiled from officially recognized sources. India - Population in largest city - actual values, historical data, forecasts and projections were sourced from the World Bank on June of 2025.

As of 2024, Mumbai had a gross domestic product of *** billion U.S. dollars, the highest among other major cities in India. It was followed by Delhi with a GDP of around *** billion U.S. dollars. India’s megacities also boast the highest GDP among other cities in the country. What drives the GDP of India’s megacities? Mumbai is the financial capital of the country, and its GDP growth is primarily fueled by the financial services sector, port-based trade, and the Hindi film industry or Bollywood. Delhi in addition to being the political hub hosts a significant services sector. The satellite cities of Noida and Gurugram amplify the city's economic status. The southern cities of Bengaluru and Chennai have emerged as IT and manufacturing hubs respectively. Hyderabad is a significant player in the pharma and IT industries. Lastly, the western city of Ahmedabad, in addition to its strategic location and ports, is powered by the textile, chemicals, and machinery sectors. Does GDP equal to quality of life? Cities propelling economic growth and generating a major share of GDP is a global phenomenon, as in the case of Tokyo, Shanghai, New York, and others. However, the GDP, which measures the market value of all final goods and services produced in a region, does not always translate to a rise in quality of life. Five of India’s megacities featured in the Global Livability Index, with low ranks among global peers. The Index was based on indicators such as healthcare, political stability, environment and culture, infrastructure, and others.

Japan’s largest city, greater Tokyo, had a staggering 37.19 million inhabitants in 2023, making it the most populous city across the Asia-Pacific region. India had the second largest city after Japan with a population consisting of approximately 33 million inhabitants. Contrastingly, approximately 410 thousand inhabitants populated Papua New Guinea's largest city in 2023. A megacity regionNot only did Japan and India have the largest cities throughout the Asia-Pacific region but they were among the three most populated cities worldwide in 2023. Interestingly, over half on the world’s megacities were situated in the Asia-Pacific region. However, being home to more than half of the world’s population, it does not seem surprising that by 2025 it is expected that more than two thirds of the megacities across the globe will be located in the Asia Pacific region. Other megacities are also expected to emerge within the Asia-Pacific region throughout the next decade. There have even been suggestions that Indonesia’s Jakarta and its conurbation will overtake Greater Tokyo in terms of population size by 2030. Increasing populationsIncreased populations in megacities can be down to increased economic activity. As more countries across the Asia-Pacific region have made the transition from agriculture to industry, the population has adjusted accordingly. Thus, more regions have experienced higher shares of urban populations. However, as many cities such as Beijing, Shanghai, and Seoul have an aging population, this may have an impact on their future population sizes, with these Asian regions estimated to have significant shares of the population being over 65 years old by 2035.

In the first half of 2024, the existing stock for warehousing in the Mumbai in India accounted for around **** million square meters. It ranks the top among major Indian cities. The Indian warehousing stock was at **** million square meters during the same period.

This statistic illustrates the consumption expenditure per capita across the largest cities in India in 2015. The nation capital region, Delhi, had a per capita consumer expenditure of approximately 138,000 Indian rupees. Bangalore had the highest per capita consumption expenditure during the measured time period.

The global per capita expenditure on apparel in 2015 and 2025, broken down by region, can be found here.

Image Cover

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F1842206%2Fc0393227e715926755b2bcd49da447dc%2Fganges1.png?generation=1730219826318502&alt=media" alt="">

Context

Hourly and Daily Weather Dataset of Top 50 Most populous Indian cities. Weather data from https://open-meteo.com/ from January 01, 1980 to December 31, 1989.

Field documentation go here

• https://open-meteo.com/en/docs/historical-weather-api#start_date=1980-01-01&end_date=1989-12-31
• Datetime is in GMT

Visually verify coordinates

• Replace the placeholders with the latitude and longitude values of the cities https://www.latlong.net/c/?lat=(latitude)&long=(longitude)
• Another way to visualize the coordinates https://www.ncei.noaa.gov/maps/radar/

Citations and Acknowledgements

• Zippenfenig, P. (2023). Open-Meteo.com Weather API [Computer software]. Zenodo. https://doi.org/10.5281/ZENODO.7970649
• Hersbach, H., Bell, B., Berrisford, P., Biavati, G., Horányi, A., Muñoz Sabater, J., Nicolas, J., Peubey, C., Radu, R., Rozum, I., Schepers, D., Simmons, A., Soci, C., Dee, D., Thépaut, J-N. (2023). ERA5 hourly data on single levels from 1940 to present [Data set]. ECMWF. https://doi.org/10.24381/cds.adbb2d47
• Muñoz Sabater, J. (2019). ERA5-Land hourly data from 2001 to present [Data set]. ECMWF. https://doi.org/10.24381/CDS.E2161BAC
• Schimanke S., Ridal M., Le Moigne P., Berggren L., Undén P., Randriamampianina R., Andrea U., Bazile E., Bertelsen A., Brousseau P., Dahlgren P., Edvinsson L., El Said A., Glinton M., Hopsch S., Isaksson L., Mladek R., Olsson E., Verrelle A., Wang Z.Q. (2021). CERRA sub-daily regional reanalysis data for Europe on single levels from 1984 to present [Data set]. ECMWF. https://doi.org/10.24381/CDS.622A565A

India's Largest Cities Weather Data Weather Datasets

• 🇮🇳 India's Largest Cities Weather Data 1980-1989
• 🇮🇳 India's Largest Cities Weather Data 1990-1999
• 🇮🇳 India's Largest Cities Weather Data 2000-2009
• 🇮🇳 India's Largest Cities Weather Data 2010-2019
• 🇮🇳 India's Largest Cities Weather Data 2020-YTD

Note

Image generated with Bing Image Generator

This statistic displays the total consumption expenditure across the largest cities in India in 2015. Bengaluru had the lowest consumer expenditure in this list during that time period with approximately 3,500 billion Indian rupees.

The household final consumption expenditure in 2014, by country in the Asia Pacific region can be found here.

The India Smart Cities Market size is valued at USD 8.13 billion in 2023, driven by market opportunities, strategic insights, and top players. Explore market segmentation, growth projection, and key trends.

This horizontal bar chart displays agricultural land (km²) by capital city using the aggregation sum in India. The data is about countries per year.

The datasets contains date- and state-wise historically compiled data on air quality (by pollution level) in rural and urban areas of India from the year 2015 , as measured by Central Pollution Board (CPCB) through its daily (24 hourly measurements, taken at 4 PM everyday) Air Quality Index (AQI) reports.

The CPCB measures air quality by continuous online monitoring of various pollutants such as Particulate Matter10 (PM10), Particulate Matter2.5 (PM2.5), Sulphur Dioxide (SO2), Nitrogen Oxide or Oxides of Nitrogen (NO2), Ozone (O3), Carbon Monoxide (CO), Ammonic (NH3) and Lead (Pb) and calculating their level of pollution in the ambient air. Based on the each pollutant load in the air and their associated health impacts, the CPCB calculates the overall Air Pollution in Air Quality Index (AQI) value and publishes the data. This AQI data is then used by CPCB to report the air quality status i.e good, satisfactory, moderate, poor, very poor and severe, etc. of a particular location and their related health impacts because of air pollution.

Smart City Market Outlook

The global smart city market size was estimated at $500 billion in 2023 and is projected to reach $3 trillion by 2032, growing at a compound annual growth rate (CAGR) of 23%. This remarkable growth is driven by rapid urbanization, technological advancements, and increasing government initiatives aimed at sustainable development. The convergence of IoT, AI, and data analytics is playing a pivotal role in transforming urban landscapes into interconnected, efficient ecosystems.

One of the primary growth factors of the smart city market is the accelerated pace of urbanization. With more than half of the world’s population now residing in urban areas, cities face increasing pressure to improve infrastructure and services. Smart city technologies offer solutions for efficient resource management, enhanced public safety, and improved quality of life. The need for effective urban planning and sustainable development is pushing governments to adopt smart city initiatives at an unprecedented rate.

Advancements in technology, particularly in IoT, AI, and big data, are significantly contributing to the smart city market's expansion. IoT sensors and devices facilitate real-time data collection, enabling cities to monitor and manage resources such as water, electricity, and waste more efficiently. AI and data analytics are used to interpret this data, providing actionable insights that help in optimizing urban operations, reducing costs, and enhancing citizen services. The integration of these technologies is creating a symbiotic relationship between the digital and physical worlds, driving the evolution of smart cities.

Government support and initiatives are also major catalysts for the growth of the smart city market. Various governments around the world are investing heavily in smart city projects to address urban challenges such as traffic congestion, pollution, and energy consumption. For instance, the European Union has earmarked substantial funding for smart city projects under its Horizon 2020 program. Similarly, countries like China and India have launched extensive smart city missions aimed at transforming urban areas into technologically advanced, sustainable habitats.

Regionally, North America and Europe are leading the smart city market, owing to their advanced technological infrastructure and significant government investments. However, Asia Pacific is expected to exhibit the highest growth rate during the forecast period. Rapid urbanization, coupled with increasing government initiatives in countries like China, India, and Japan, is driving the smart city market in this region. Latin America and the Middle East & Africa are also showing promising growth, supported by improving economic conditions and increasing focus on sustainable development.

Component Analysis

The smart city market is segmented into three primary components: hardware, software, and services. Each of these components plays a crucial role in enabling and enhancing the various functionalities of a smart city. Hardware components include sensors, smart meters, and communication devices, among others. These devices are essential for collecting real-time data from various urban environments, which is then used to monitor and manage city operations.

Software solutions are integral to the smart city market as they provide the platforms and applications needed to analyze and interpret the data collected by hardware devices. These software solutions enable various functions such as traffic management, energy management, and public safety. They also offer predictive analytics capabilities, which help city administrators anticipate and mitigate potential issues before they escalate. The increasing complexity and volume of data generated by smart cities necessitate robust software solutions to manage and analyze this data effectively.

Services are another critical component of the smart city market. These include consulting services, system integration, and managed services, which are essential for the successful implementation and operation of smart city projects. Consulting services help cities identify their specific needs and design customized smart city solutions. System integration services ensure that various hardware and software components work seamlessly together, while managed services provide ongoing support and maintenance to ensure the smooth functioning of smart city systems.

The hardware segment is expected to account for a significant share of the smart city market, driv

This horizontal bar chart displays urban population living in areas where elevation is below 5 meters (% of total population) by capital city using the aggregation average, weighted by population in India. The data is about countries per year.

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

The report covers India Intra-city Logistics Companies and it is segmented by Service (Transportation, Warehousing and Distribution, and Value-added Services) and by City (Delhi, Bangalore, Mumbai, Hyderabad, Chennai, and Others). The market size and forecasts for the India intra-city logistics market in value (USD billion) for all the above segments.

The demand for commercial real estate space in top seven cities in India stood at 38 million square feet as of 2023. It was the same as previous year.

The India intra-city logistics market is experiencing robust growth, fueled by the burgeoning e-commerce sector, rapid urbanization, and increasing demand for faster and more efficient delivery services. With a Compound Annual Growth Rate (CAGR) exceeding 5%, the market, valued at approximately $XX million in 2025 (estimated based on provided data and industry trends), is projected to reach a significant size by 2033. Key drivers include the expansion of online retail, the rise of quick commerce models (e.g., 10-minute delivery), and the growing adoption of technology-driven solutions such as route optimization software and real-time tracking systems. The market is segmented geographically, with major cities like Delhi, Mumbai, Bangalore, Hyderabad, and Chennai accounting for a substantial share of the market volume. Furthermore, service offerings encompass transportation, warehousing and distribution, and value-added services like last-mile delivery and reverse logistics. The competitive landscape is dynamic, with established players like DTDC and Ecom Express coexisting with innovative startups like CityXfer and Porter, highlighting the market's attractiveness to both established logistics firms and emerging technology-driven businesses. The market's expansion is also shaped by several trends, including the increasing adoption of electric vehicles for last-mile delivery to reduce carbon footprint and meet sustainable goals. The integration of advanced technologies like AI and machine learning for improved route planning and predictive analytics is streamlining operations and enhancing efficiency. However, challenges remain, such as infrastructure limitations in certain areas, regulatory hurdles, and fluctuating fuel prices, which can impact overall costs and operational efficiency. Despite these restraints, the long-term outlook for the India intra-city logistics market remains positive, driven by consistent growth in e-commerce and the ongoing modernization of the logistics infrastructure. The market's success will hinge on the ability of logistics providers to adapt to changing consumer demands and leverage technology to improve service quality, reduce costs and maintain a competitive edge. This comprehensive report provides a detailed analysis of the burgeoning India intra-city logistics market, offering invaluable insights for businesses, investors, and policymakers. The study period covers 2019-2033, with 2025 serving as the base and estimated year. We delve into market size, segmentation, key players, growth drivers, challenges, and future trends, focusing on crucial aspects like last-mile delivery, warehousing, and e-commerce logistics. The report utilizes data from the historical period (2019-2024) and projects the market's trajectory up to 2033. This analysis encompasses key cities like Delhi, Mumbai, Bangalore, Hyderabad, and Chennai, among others. Recent developments include: November 2022 - Mahindra Logistics acquired delivery services provider Whizzard. Mahindra Logistic's current last-mile delivery business and its electric vehicle-based delivery services would be enhanced by the acquisition., July 2022 - Bengaluru-based COGOS Technologies has acquired logistics startup Porter's FMCG modern trade business. This acquisition will strengthen COGOS' platform and allow it to meet the demand for municipal logistics.. Key drivers for this market are: Industrial Growth Supporting the Market, Global Trade Driving the Market. Potential restraints include: Compliance Challenges Affecting the Market, Limited Infrastructure Inhibiting the Market. Notable trends are: Growing Demand for Intra-city Logistics from Tier-2 and Tier- 3 Cities.

Abstract

The National Family Health Survey (NFHS) was carried out as the principal activity of a collaborative project to strengthen the research capabilities of the Population Reasearch Centres (PRCs) in India, initiated by the Ministry of Health and Family Welfare (MOHFW), Government of India, and coordinated by the International Institute for Population Sciences (IIPS), Bombay. Interviews were conducted with a nationally representative sample of 89,777 ever-married women in the age group 13-49, from 24 states and the National Capital Territoty of Delhi. The main objective of the survey was to collect reliable and up-to-date information on fertility, family planning, mortality, and maternal and child health. Data collection was carried out in three phases from April 1992 to September 1993. THe NFHS is one of the most complete surveys of its kind ever conducted in India.

The households covered in the survey included 500,492 residents. The young age structure of the population highlights the momentum of the future population growth of the country; 38 percent of household residents are under age 15, with their reproductive years still in the future. Persons age 60 or older constitute 8 percent of the population. The population sex ratio of the de jure residents is 944 females per 1,000 males, which is slightly higher than sex ratio of 927 observed in the 1991 Census.

The primary objective of the NFHS is to provide national-level and state-level data on fertility, nuptiality, family size preferences, knowledge and practice of family planning, the potentiel demand for contraception, the level of unwanted fertility, utilization of antenatal services, breastfeeding and food supplemation practises, child nutrition and health, immunizations, and infant and child mortality. The NFHS is also designed to explore the demographic and socioeconomic determinants of fertility, family planning, and maternal and child health. This information is intended to assist policymakers, adminitrators and researchers in assessing and evaluating population and family welfare programmes and strategies. The NFHS used uniform questionnaires and uniform methods of sampling, data collection and analysis with the primary objective of providing a source of demographic and health data for interstate comparisons. The data collected in the NFHS are also comparable with those of the Demographic and Health Surveys (DHS) conducted in many other countries.

Geographic coverage

National

Analysis unit

• Household
• Data collected for women 13-49, indicators calculated for women 15-49

Universe

The population covered by the 1992-93 DHS is defined as the universe of all women age 13-49 who were either permanent residents of the households in the NDHS sample or visitors present in the households on the night before the survey were eligible to be interviewed.

Kind of data

Sample survey data

Sampling procedure

SAMPLE DESIGN

The sample design for the NFHS was discussed during a Sample Design Workshop held in Madurai in Octber, 1991. The workshop was attended by representative from the PRCs; the COs; the Office of the Registrar General, India; IIPS and the East-West Center/Macro International. A uniform sample design was adopted in all the NFHS states. The Sample design adopted in each state is a systematic, stratified sample of households, with two stages in rural areas and three stages in urban areas.

SAMPLE SIZE AND ALLOCATION

The sample size for each state was specified in terms of a target number of completed interviews with eligible women. The target sample size was set considering the size of the state, the time and ressources available for the survey and the need for separate estimates for urban and rural areas of the stat. The initial target sample size was 3,000 completed interviews with eligible women for states having a population of 25 million or less in 1991; 4,000 completed interviews for large states with more than 25 million population; 8,000 for Uttar Pradesh, the largest state; and 1,000 each for the six small northeastern states. In States with a substantial number of backward districts, the initial target samples were increased so as to allow separate estimates to be made for groups of backward districts.

The urban and rural samples within states were drawn separetly and , to the extent possible, sample allocation was proportional to the size of the urban-rural populations (to facilitate the selection of a self-weighting sample for each state). In states where the urban population was not sufficiently large to provide a sample of at least 1,000 completed interviews with eligible women, the urban areas were appropriately oversampled (except in the six small northeastern states).

THE RURAL SAMPLE: THE FRAME, STRATIFICATION AND SELECTION

A two-stage stratified sampling was adopted for the rural areas: selection of villages followed by selection of households. Because the 1991 Census data were not available at the time of sample selection in most states, the 1981 Census list of villages served as the sampling frame in all the states with the exception of Assam, Delhi and Punjab. In these three states the 1991 Census data were used as the sampling frame.

Villages were stratified prior to selection on the basis of a number of variables. The firts level of stratification in all the states was geographic, with districts subdivided into regions according to their geophysical characteristics. Within each of these regions, villages were further stratified using some of the following variables : village size, distance from the nearest town, proportion of nonagricultural workers, proportion of the population belonging to scheduled castes/scheduled tribes, and female literacy. However, not all variables were used in every state. Each state was examined individually and two or three variables were selected for stratification, with the aim of creating not more than 12 strata for small states and not more than 15 strata for large states. Females literacy was often used for implicit stratification (i.e., the villages were ordered prior to selection according to the proportion of females who were literate). Primary sampling Units (PSUs) were selected systematically, with probaility proportional to size (PPS). In some cases, adjacent villages with small population sizes were combined into a single PSU for the purpose of sample selection. On average, 30 households were selected for interviewing in each selected PSU.

In every state, all the households in the selected PSUs were listed about two weeks prior to the survey. This listing provided the necessary frame for selecting households at the second sampling stage. The household listing operation consisted of preparing up-to-date notional and layout sketch maps of each selected PSU, assigning numbers to structures, recording addresses (or locations) of these structures, identifying the residential structures, and listing the names of the heads of all the households in the residentiak structures in the selected PSU. Each household listing team consisted of a lister and a mapper. The listing operation was supervised by the senior field staff of the concerned CO and the PRC in each state. Special efforts were made not to miss any household in the selected PSU during the listing operation. In PSUs with fewer than 500 households, a complete household listing was done. In PSUs with 500 or more households, segmentation of the PSU was done on the basis of existing wards in the PSU, and two segments were selected using either systematic sampling or PPS sampling. The household listing in such PSUs was carried out in the selected segments. The households to be interviewed were selected from provided with the original household listing, layout sketch map and the household sample selected for each PSU. All the selected households were approached during the data collection, and no substitution of a household was allowed under any circumstances.

THE RURAL URBAN SAMPLE: THE FRAME, STRATIFICATION AND SELECTION

A three-stage sample design was adopted for the urban areas in each state: selection of cities/towns, followed by urban blocks, and finally households. Cities and towns were selected using the 1991 population figures while urban blocks were selected using the 1991 list of census enumeration blocks in all the states with the exception of the firts phase states. For the first phase states, the list of urban blocks provided by the National Sample Survey Organization (NSSSO) served as the sampling frame.

All cities and towns were subdivided into three strata: (1) self-selecting cities (i.e., cities with a population large enough to be selected with certainty), (2) towns that are district headquaters, and (3) other towns. Within each stratum, the cities/towns were arranged according to the same kind of geographic stratification used in the rural areas. In self-selecting cities, the sample was selected according to a two-stage sample design: selection of the required number of urban blocks, followed by selection of households in each of selected blocks. For district headquarters and other towns, a three stage sample design was used: selection of towns with PPS, followed by selection of two census blocks per selected town, followed by selection of households from each selected block. As in rural areas, a household listing was carried out in the selected blocks, and an average of 20 households per block was selected systematically.

Mode of data collection

Face-to-face

Research instrument

Three types of questionnaires were used in the NFHS: the Household Questionnaire, the Women's Questionnaire, and the Village Questionnaire. The overall content

The Housing Price Index in India is a statistical measure designed to reflect the changes in housing prices across various regions. It is calculated by the Reserve Bank of India (RBI) using data from housing transactions, which include registration documents and mortgage data from banks and housing finance companies. The HPI is constructed using a base year, and the price levels of that base year are set at 100. Changes in the index from the base year reflect how housing prices have increased or decreased. The Reserve Bank compiles quarterly house price index (HPI) (base: 2010-11=100) for ten major cities, viz., Mumbai, Delhi, Chennai, Kolkata, Bengaluru, Lucknow, Ahmedabad, Jaipur, Kanpur and Kochi. Based on these city indices, the average house price index represents all of India's house price movements. The Housing Price Index (HPI) is a critical economic indicator that measures the changes in residential housing prices over time. In India, the HPI is an essential tool used by policymakers, economists, real estate developers, investors, and homebuyers to gauge the trends in the real estate market. The HPI helps track the inflation or deflation in the housing market, thus providing insights into the economy's overall health.

Abstract

The Employment and Unemployment surveys of National sample Survey (NSS) are primary sources of data on various indicators of labour force at National and State levels. These are used for planning, policy formulation, decision support and as input for further statistical exercises by various Government organizations, academicians, researchers and scholars. NSS surveys on employment and un-employment with large sample size of households have been conducted quinquennially from 27th. round(October'1972 - September'1973) onwards. Cotinuing in this series the fourth such all-india survey on the situation of employment and unemployment in India was carried out during the period july 1987 - june 1988 .

The working Group set up for planning of the entire scheme of the survey, among other things, examined also in detail some of the key results generated from the 38th round data and recommended some stream-lining of the 38th round schedule for the use in the 43rd round. Further, it felt no need for changing the engaging the easting conceptual frame work. However, some additional items were recommended to be included in the schedule to obtain the necessary and relevant information for generating results to see the effects on participation rates in view of the ILO suggestions.5.0.1. The NSSO Governing Council approved the recommendations of the working Group and also the schedule of enquiry in its 44th meeting held on 16 January, 1987. In this survey, a nation-wide enquiry was conducted to provide estimates on various characteristics pertaining to employment and unemployment in India and some characteristics associated with them at the national and state levels. Information on various facets of employment and unemployment in India was collected through a schedule of enquiry (schedule 10).

Geographic coverage

The survey covered the whole of Indian Union excepting i) Ladakh and Kargil districts of Jammu & Kashmir ii) Rural areas of Nagaland

Analysis unit

Randomly selected households based on sampling procedure and members of the household

Kind of data

Sample survey data [ssd]

Sampling procedure

It may be mentioned here that in order to net more households of the upper income bracket in the Sample , significant changes have been made in the sample design in this round (compares to the design of the 38th round).

SAMPLE DESIGN AND SAMPLE SIZE The survey had a two-stage stratified design. The first stage units (f.s.u.'s) are villages in the rural sector and urban blocks in the urban sector. The second stage units are households in both the sectors. Sampling frame for f.s.u.'s : The lists of 1981 census villages constituted the sampling frame for rural sector in most districts. But the 1981 census frame could not be used for a few districts because, either the 1981 census was not held there or the list of 1981 census villages could not be obtained or the lists obtained from the census authorities were found to be grossly incomplete. In such cases 1971 census frame were used. In the urban sector , the Urban Frame Survey (U.F.S.) blocks constituted the sampling frame. STRATIFICATION : States were first divided into agro-economic regions which are groups of contiguous districts , similar with respect to population density and crop pattern. In Gujarat, however , some districts have been split for the purpose of region formation In consideration of the location of dry areas and the distribution of the tribal population in the state. The composition of the regions is given in the Appendix. RURAL SECTOR: In the rural sector, within each region, each district with 1981Census rural population less 1.8 million formed a single stratum. Districts with larger population were divided into two or more strata, depending on population, by grouping contiguous tehsils similar, as for as possible, in respect of rural population Density and crop pattern. (In Gujarat, however , in the case of districts extending over more than one region, even if the rural population was less than 1.8 million, the portion of a district falling in each region constituted a separate stratum. Further ,in Assam the old "basic strata" formed on the basis of 1971 census rural population exactly in the above manner, but with cut-off population as 1.5 million have been retained as the strata for rural sampling.) URBAN SECTOR : In the urban sector , strata were formed , again within NSS region , on the basis of the population size class of towns . Each city with population 10 lakhs or more is self-representative , as in the earlier rounds . For the purpose of stratification, in towns with '81 census population 4 lakhs or more , the blocks have been divided into two categories , viz . : One consisting of blocks in areas inhabited by the relatively affluent section of the population and the other consisting of the remaining blocks. The strata within each region were constituted as follows :

Table (1.2) : Composition of urban strata

Stratum population class of town

number

(1) (2)

1 all towns with population less than 50,000 2 -do- 50,000 - 199,999 3 -do- 200,000 - 399,999 4 -do- 400,000 - 999,999 ( affluent area) 5 (other area) 6 a single city with population 1 million and above (affluent area) 7 " (other area) 8 another city with population 1 million and above

9 " (other area)

Note : There is no region with more than one city with population 1 million and above. The stratum number have been retained as above even if in some regions some of the strata are empty. Allocation for first stage units : The total all-India sample size was allocated to the states /U.T.'s proportionate to the strength of central field staff. This was allocated to the rural and urban sectors considering the relative size of the rural and urban population. Now the rural samples were allocated to the rural strata in proportion to rural population. The urban samples were allocated to the urban strata in proportion to urban population with double weight age given to those strata of towns with population 4 lakhs or more which lie in area inhabited by the relatively affluent section. All allocations have been adjusted such that the sample size for stratum was at least a multiple of 4 (preferably multiple of 8) and the total sample size of a region is a multiple of 8 for the rural and urban sectors separately.
Selection of f.s.u.'s : The sample villages have been selected circular systematically with probability proportional to population in the form of two independent interpenetrating sub-samples (IPNS) . The sample blocks have been selected circular systematically with equal probability , also in the form of two IPNS' s. As regards the rural areas of Arunachal Pradesh, the procedure of 'cluster sampling' was:- The field staff will be supplied with a list of the nucleus villages of each cluster and they selected the remaining villages of the cluster according to the procedure described in Section Two. The nucleus villages were selected circular systematically with equal probability, in the form of two IPNS 's. Hamlet-group and sub-blocks : Large villages and blocks were sub- divided into a suitable number of hamlet-groups and sub-blocks respectively having equal population convent and one them was selected at random for surveys. Hamlet-group and sub-blocks : Large villages and blocks were sub- divided into a suitable number of hamlet-groups and sub-blocks respectively having equal population convent and one them was selected at random for surveys. Selection of households : rural : In order to have adequate number of sample households from the affluent section of the society, some new procedures were introduced for selection of sample households, both in the rural and urban sectors. In the rural sector , while listing households, the investigator identified the households in village/ selected hamlet- group which may be considered to be relatively more affluent than the rest. This was done largely on the basis of his own judgment but while exercising his judgment considered factors generally associated with rich people in the localitysuch as : living in large pucca house in well-maintained state, ownership/possession of cultivated/irrigated land in excess of certain norms. ( e.g.20 acres of cultivated land or 10 acres of irrigated land), ownership of motor vehicles and costly consumer durables like T.V. , VCR, VCP AND refrigerator, ownership of large business establishment , etc. Now these "rich" households will form sub-stratum 1. (If the total number of households listed is 80 or more , 10 relatively most affluent households will form sub-stratum 1. If it is below 80, 8 such households will form sub-stratum 1. The remaining households will 'constitute sub-stratum 2. At the time of listing, information relating to each household' s major sources of income will be collected, on the basis of which its means of livelihood will be identified as one of the following : "self-employed in non-agriculture " "rural labour" and "others" (see section Two for definition of these terms) . Also the area of land possessed as on date of survey will be ascertained from all households while listing. Now the households of sub-stratum 2 will be arranged in the order : (1)self-employed in non-agriculture, (2) rural labour, other households, with land possessed (acres) : (3) less than 1.00 (4) 1.00-2.49,(5)2.50-4.99, (6)