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.

As of 2023, Tokyo-Yokohama in Japan was the largest world urban agglomeration, with 37,8 million people living there. Jakarta ranked second with 34 million, with Delhi in third with 32 million inhabitants.

Chart and table of India population from 1950 to 2025. United Nations projections are also included through the year 2100.

Byrnihat was the most polluted city in India in 2024, with an average PM2.5 concentration of nearly 130 micrograms per cubic meter of air (μg/m³). This high level of pollution made the small industrial town on the Assam Meghalaya border the most polluted cities worldwide in 2024. Poor air quality across India India was the fifth-most polluted country in the world in 2024, with an average PM2.5 concentration of 50.6 μg/m³. That same year, the country's capital New Delhi was also the most polluted capital city in the world. Vehicle exhaust and wood burning are some of the main sources of particulate air pollution in India, together with soil, road and construction dust . Impacts of air pollution in India The severe air pollution in India can have detrimental health impacts on the country's population. Fine particle pollutants penetrate deeply in the lungs, causing respiratory problems and can even result in premature death. More than two million deaths are attibuted to air pollution in India every year.

Nigeria has the largest population in Africa. As of 2024, the country counted over 232.6 million individuals, whereas Ethiopia, which ranked second, has around 132 million inhabitants. Egypt registered the largest population in North Africa, reaching nearly 116 million people. In terms of inhabitants per square kilometer, Nigeria only ranks seventh, while Mauritius has the highest population density on the whole African continent. The fastest-growing world region Africa is the second most populous continent in the world, after Asia. Nevertheless, Africa records the highest growth rate worldwide, with figures rising by over two percent every year. In some countries, such as Niger, the Democratic Republic of Congo, and Chad, the population increase peaks at over three percent. With so many births, Africa is also the youngest continent in the world. However, this coincides with a low life expectancy. African cities on the rise The last decades have seen high urbanization rates in Asia, mainly in China and India. However, African cities are currently growing at larger rates. Indeed, most of the fastest-growing cities in the world are located in Sub-Saharan Africa. Gwagwalada, in Nigeria, and Kabinda, in the Democratic Republic of the Congo, ranked first worldwide. By 2035, instead, Africa's fastest-growing cities are forecast to be Bujumbura, in Burundi, and Zinder, Nigeria.

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 five million. Hyderabad and Delhi followed it. A total of about 65 million people were estimated to be living in slums across the country.

Monaco led the ranking for countries with the highest population density in 2024, with nearly 26,000 residents per square kilometer. The Special Administrative Region Macao came in second, followed by Singapore. The world’s second smallest country Monaco is the world’s second smallest country, with an area of about two square kilometers, and its population only numbers around 40,000. It is a constitutional monarchy located by the Mediterranean Sea, and while Monaco is not part of the European Union, it does participate in some EU policies. The country is perhaps most famous for the Monte Carlo casino and for hosting the Monaco Grand Prix, the world's most prestigious Formula One race. The global population Globally, the population density per square kilometer stands at about 60 inhabitants, and Asia is the most densely populated region in the world. The global population is increasing rapidly, so population density is only expected to increase as well. In 1950, for example, the global population stood at about 2.54 billion people, and it reached over eight billion during 2023.

Chart and table of population level and growth rate for the Hyderabad, India metro area from 1950 to 2025. United Nations population projections are also included through the year 2035.

Chart and table of population level and growth rate for the Indore, India metro area from 1950 to 2025. United Nations population projections are also included through the year 2035.

Japan’s largest city, greater Tokyo, had a staggering 37.27 million inhabitants in 2022, 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 32 million inhabitants. Contrastingly, approximately 400 thousand inhabitants populated Papua New Guinea's largest city in 2022.

A megacity region Not 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 populations Increased 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.

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

This dataset represents ethnic group (19 tick-box level) by highest level qualification, for England and Wales combined. The data are also broken down by age and by sex.

The ethnic group that the person completing the census feels they belong to. This could be based on their culture, family background, identity, or physical appearance. Respondents could choose one out of 19 tick-box response categories, including write-in response options.

Total counts for some population groups may not match between published tables. This is to protect the confidentiality of individuals' data. Population counts have been rounded to the nearest 5 and any counts below 10 are suppressed, this is signified by a 'c' in the data tables.

"Asian Welsh" and "Black Welsh" ethnic groups were included on the census questionnaire in Wales only, these categories were new for 2021.

This dataset provides Census 2021 estimates that classify usual residents in England and Wales by ethnic group. The estimates are as at Census Day, 21 March 2021. This dataset shows population counts for usual residents aged 16+ Some people aged 16 years old will not have completed key stage 4 yet on census day, and so did not have the opportunity to record any qualifications on the census.

These estimates are not comparable to Department of Education figures on highest level of attainment because they include qualifications obtained outside England and Wales.

For quality information in general, please read more from here.

Ethnic Group (19 tick-box level)

These are the 19 ethnic group used in this dataset:

• Asian, Asian British or Asian Welsh
  • Bangladeshi
  • Chinese
  • Indian
  • Pakistani
  • Other Asian
• Black, Black British, Black Welsh, Caribbean or African
  • African
  • Caribbean
  • Other Black
• Mixed or Multiple ethnic groups
  • White and Asian
  • White and Black African
  • White and Black Caribbean
  • Other Mixed or Multiple ethnic groups
• White
  • English, Welsh, Scottish, Northern Irish or British
  • Gypsy or Irish Traveller
  • Irish
  • Roma
  • Other White
• Other ethnic group
  • Arab
  • Any other ethnic group

No qualifications

No qualifications

Level 1

Level 1 and entry level qualifications: 1 to 4 GCSEs grade A* to C , Any GCSEs at other grades, O levels or CSEs (any grades), 1 AS level, NVQ level 1, Foundation GNVQ, Basic or Essential Skills

Level 2

5 or more GCSEs (A* to C or 9 to 4), O levels (passes), CSEs (grade 1), School Certification, 1 A level, 2 to 3 AS levels, VCEs, Intermediate or Higher Diploma, Welsh Baccalaureate Intermediate Diploma, NVQ level 2, Intermediate GNVQ, City and Guilds Craft, BTEC First or General Diploma, RSA Diploma

Apprenticeship

Apprenticeship

Level 3

2 or more A levels or VCEs, 4 or more AS levels, Higher School Certificate, Progression or Advanced Diploma, Welsh Baccalaureate Advance Diploma, NVQ level 3; Advanced GNVQ, City and Guilds Advanced Craft, ONC, OND, BTEC National, RSA Advanced Diploma

Level 4 +

Degree (BA, BSc), higher degree (MA, PhD, PGCE), NVQ level 4 to 5, HNC, HND, RSA Higher Diploma, BTEC Higher level, professional qualifications (for example, teaching, nursing, accountancy)

Other

Vocational or work-related qualifications, other qualifications achieved in England or Wales, qualifications achieved outside England or Wales (equivalent not stated or unknown)

As of 2024, Mumbai had a gross domestic product of 368 billion U.S. dollars, the highest among other major cities in India. It was followed by Delhi with a GDP of around 167 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.

As of the year 2024, the population of the capital city of India, Delhi was over 33 million people. This was a 2.63 percent growth from last year. The historical trends show that the population doubled between 1990 and 2010. However, the population growth rate in the last three years has been the lowest since the recorded period starting in 1960. The UN estimated that the population was expected to reach around 35 million by 2030. Reasons for population growth   As per the Delhi Economic Survey, migration added over 200 thousand people to Delhi’s population in 2022. The estimates showed relative stability in natural population growth for a long time before the pandemic. The numbers suggest a sharp decrease in birth rates from 2020 onwards and a corresponding increase in death rates in 2021 due to the Covid-19 pandemic. The net natural addition or the remaining growth is attributed to migration. These estimates are based on trends published by the Civil Registration System. National Capital Region (NCR) Usually, population estimates for Delhi represent the urban agglomeration of Delhi, which includes Delhi and some of its adjacent suburban areas. The National Capital Region or NCR is one of the largest urban agglomerations in the world. It is an example of inter-state regional planning and development, centred around the National Capital Territory of Delhi, and covering certain districts of neighbouring states Haryana, Uttar Pradesh, and Rajasthan. Noida, Gurugram, and Ghaziabad are some of the key cities of NCR. Over the past decade, NCR has emerged as a key economic centre in India.

Abstract

Objective of the consumer expenditure survey (CES): Firstly, as an indicator of level of living, monthly per capita expenditure (MPCE) is both simple and universally applicable. Average MPCE of any sub-population of the country (any region or population group) is a single number that summarises the level of living of that population. It is supplemented by the distribution of MPCE, which highlights the differences in level of living of the different parts of the population. More detailed analysis of the distribution of MPCE reveals the proportion and absolute numbers of the poor with respect to a given poverty line. A welfare state has to take note of these numbers in allocating its resources among sectors, regions, and socio-economic groups. The distribution of MPCE can also be used to measure the level of inequality, or the degree to which consumer expenditure is concentrated in a small proportion of households or persons, and this can be done without any predetermined poverty line or welfare norms.

If socialism was the ideal of the 1950's, the ideal of policy-makers during the last decade was "inclusive growth". Increasingly, inclusive growth is seen as the all-important target that we should aim at, at least for the immediate future. Not surprisingly, the NSS CES is being used by scholars as a searchlight focused on the country's development process that shows up just how inclusive the country's growth has been.

Since the data is collected not only on consumption level but also on the pattern of consumption, the CES has another important use. To work out consumer price indices (CPIs) which measure the general rise in consumer prices, one needs to know not only the price rise for each commodity group but also the budget shares of different commodity groups (used as weights). The budget shares as revealed by the NSS CES are being used for a long time to prepare what is called the weighing diagram for official compilation of CPIs. More extensive use of NSS CES data is planned to have a weighing diagram that uses a finer commodity classification, to prepare rural and urban CPIs separately for each State.

Apart from these major uses of the CES, the food (quantity) consumption data are used to study the level of nutrition of different regions, and disparities therein. Further, the budget shares of a commodity at different MPCE levels are used by economists and market researchers to determine the elasticity (responsiveness) of demand to income increases.

Two types of Schedule 1.0 viz. Schedule Type 1 and Schedule Type 2 was canvassed in this round. Schedule Type 1 and Type 2 are similar to those of NSS 66th round.

Reference period and schedule type: The reference period is the period of time to which the information collected relates. In NSS surveys, the reference period often varies from item to item. Data collected with different reference periods are known to exhibit certain systematic differences. Strictly speaking, therefore, comparisons should be made only among estimates based on data collected with identical reference period systems. In the 68th round - as in the 66th round -two schedule types have been drawn up. The two schedule types differonly in respect of reference period. Sample households were divided into two sets: Schedule Type 1 was canvassed in one set and Schedule Type 2 in the other.

Schedule Type 1 uses the same reference period system as Schedule Type 1 of NSS 66th round. Schedule Type 1 requires that for certain items (Clothing, bedding, footwear, education, medical (institutional), durable goods), the same household should report data for two reference periods - 'Last 30 days' and 'Last 365 days'. Schedule Type 2 has the same reference periods as Schedule Type 2 of NSS 66th round. For Group I items (Clothing, bedding, footwear, education, medical (institutional), durable goods), the reference period used in Schedule Type 2 is 'Last 365 days'.

As in the 66th round, items of food, pan, tobacco and intoxicants (Food-plus category) are split into 2 blocks - 5.1 and 5.2 - instead of being placed in a single block. • Block 5.1 consists of the item groups cereals, pulses, milk and milk products, sugar and salt. This block has a reference period of 30 days in both Schedule Type 1 and Schedule Type 2. • Block 5.2 consists of the other items of food, along with pan, tobacco and intoxicants. This block is assigned a reference period of 'Last 30 days' in Schedule Type 1 and a reference period of 'Last 7 days' in Schedule Type 2.

Thus Schedule Type 1, like Schedule 1.0 of NSS 66th round, uses the 'Last 30 days' reference period for all items of food, and for pan, tobacco and intoxicants.

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.

Kind of data

Sample survey data [ssd]

Sampling procedure

Sample design

Outline of sample design: A stratified multi-stage design has been adopted for the 68th round survey. The first stage units (FSU) are the 2001 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) are households in both the sectors. In case of large FSUs, one intermediate stage of sampling is the selection of two hamlet-groups (hgs)/ sub-blocks (sbs) from each rural/ urban FSU.

Sampling Frame for First Stage Units: For the rural sector, the list of 2001 census villages (henceforth the term 'village' would include also Panchayat wards for Kerala) constitutes the sampling frame. For the urban sector, the list of UFS blocks (2007-12) is considered as the sampling frame.

Stratification: Within each district of a State/ UT, generally speaking, two basic strata have been formed: i) rural stratum comprising of all rural areas of the district and (ii) urban stratum comprising of all the urban areas of the district. However, within the urban areas of a district, if there are one or more towns with population 10 lakhs or more as per population census 2001 in a district, each of them forms a separate basic stratum and the remaining urban areas of the district are considered as another basic stratum.

Sub-stratification: Rural sector r: If 'r' be the sample size allocated for a rural stratum, the number of sub-strata formed would be 'r/4'. The villages within a district as per frame were first arranged in ascending order of population. Then sub-strata 1 to 'r/4' have been demarcated in such a way that each sub-stratum comprised a group of villages of the arranged frame and have more or less equal population. Urban sector: If 'u' be the sample size for an urban stratum, 'u/4' number of sub-strata have been formed. In case u/4 is more than 1, implying formation of 2 or more sub-strata, this is done by first arranging the towns in ascending order of total number of households in the town as per UFS phase 2007-12 and then arranging the IV units of each town and blocks within each IV unit in ascending order of their numbers. From this arranged frame of UFS blocks of all the towns/million plus city of a stratum, 'u/4' number of sub- strata formed in such a way that each sub-stratum has more or less equal number of households as per UFS 2007-12.

Total sample size (FSUs): 12784 FSUs have been allocated for the central sample at all-India level and 14772 FSUs have been allocated for state sample.

Allocation of total sample to States and UTs: The total number of sample FSUs has allocated to the States and UTs in proportion to population as per census 2001 subject to a minimum sample allocation to each State/ UT. While doing so, the resource availability in terms of number of field investigators has been kept in view.

Allocation of State/ UT level sample to rural and urban sectors: State/ UT level sample size has been allocated between two sectors in proportion to population as per census 2001 with double weightage to urban sector. However, if such weighted allocation resulted in too high sample size for the urban sector, the allocation for bigger states like Maharashtra, Tamil Nadu, etc. was restricted to that of the rural sector. A minimum of 16 FSUs (minimum 8 each for rural and urban sector separately) is allocated to each state/ UT.

Allocation to strata/ sub-strata: Within each sector of a State/ UT, the respective sample size has been allocated to the different strata/ sub-strata in proportion to the population as per census 2001. Allocations at stratum level are adjusted to multiples of 4 with a minimum sample size of 4. Allocation for each sub-stratum is 4. Equal number of samples has been allocated among the four sub-rounds.

Selection of FSUs: For the rural sector, from each stratum/ sub-stratum, required number of sample villages has been selected by probability proportional to size with replacement (PPSWR), size being the population of the village as per Census 2001. For the urban sector, UFS 2007-12 phase has been used for all towns and cities and FSUs have been selected from each stratum/sub-stratum by using Simple Random Sampling Without Replacement (SRSWOR). Both rural and urban samples are to be drawn in the form of two independent sub-samples and equal number of samples have been allocated among the four sub rounds.

Selection of hamlet-groups/ sub-blocks - important steps

Criterion for hamlet-group/ sub-block formation: After identification of the boundaries of the FSU, it is first determined whether listing is to be done in the whole sample FSU or not. In case the population of the selected FSU is found to be 1200 or more, it has to be divided into a suitable number (say, D) of 'hamlet-groups' in the rural

In 2023, approximately a third of the total population in India lived in cities. The trend shows an increase of urbanization by more than 4 percent in the last decade, meaning people have moved away from rural areas to find work and make a living in the cities. Leaving the fieldOver the last decade, urbanization in India has increased by almost 4 percent, as more and more people leave the agricultural sector to find work in services. Agriculture plays a significant role in the Indian economy and it employs almost half of India’s workforce today, however, its contribution to India’s GDP has been decreasing while the services sector gained in importance. No rural exodus in sightWhile urbanization is increasing as more jobs in telecommunications and IT are created and the private sector gains in importance, India is not facing a shortage of agricultural workers or a mass exodus to the cities yet. India is a very densely populated country with vast areas of arable land – over 155 million hectares of land was cultivated land in India as of 2015, for example, and textiles, especially cotton, are still one of the major exports. So while a shift of the workforce focus is obviously taking place, India is not struggling to fulfill trade demands yet.

The Vatican City, often called the Holy See, has the smallest population worldwide, with only 496 inhabitants. It is also the smallest country in the world by size. The islands Niue, Tuvalu, and Nauru followed in the next three positions. On the other hand, India is the most populated country in the world, with over 1.4 billion inhabitants.

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.

As per the Global Liveability Index of 2024, five Indian cities figured on the list comprising 173 across the world. Indian megacities Delhi and Mumbai tied for 141st place with a score of 60.2 out of 100. They were followed by Chennai (59.9), Ahmedabad (58.9), and Bengaluru (58.7). What are indicators for livability The list was topped by Vienna for yet another year. The index measures cities on five broad indicators such as stability, healthcare, culture and environment, education, and infrastructure. As per the Economic Intelligence Unit’s suggestions, if a city’s livability score is between 50 to 60 then “livability is substantially constrained”. Less than 50 means most aspects of living are severely restricted. Least Liveable cities on the index The least liveable cities were in Sub-Saharan Africa and the Middle East and North Africa regions. Damascus and Tripoli ranked the lowest. Tel Aviv also witnessed significant drop due to war with Hamas.

The statistic shows the 30 largest countries in the world by area. Russia is the largest country by far, with a total area of about 17 million square kilometers.

Population of Russia

Despite its large area, Russia - nowadays the largest country in the world - has a relatively small total population. However, its population is still rather large in numbers in comparison to those of other countries. In mid-2014, it was ranked ninth on a list of countries with the largest population, a ranking led by China with a population of over 1.37 billion people. In 2015, the estimated total population of Russia amounted to around 146 million people. The aforementioned low population density in Russia is a result of its vast landmass; in 2014, there were only around 8.78 inhabitants per square kilometer living in the country. Most of the Russian population lives in the nation’s capital and largest city, Moscow: In 2015, over 12 million people lived in the metropolis.