In 1800, the population of the region of present-day India was approximately 169 million. The population would grow gradually throughout the 19th century, rising to over 240 million by 1900. Population growth would begin to increase in the 1920s, as a result of falling mortality rates, due to improvements in health, sanitation and infrastructure. However, the population of India would see it’s largest rate of growth in the years following the country’s independence from the British Empire in 1948, where the population would rise from 358 million to over one billion by the turn of the century, making India the second country to pass the billion person milestone. While the rate of growth has slowed somewhat as India begins a demographics shift, the country’s population has continued to grow dramatically throughout the 21st century, and in 2020, India is estimated to have a population of just under 1.4 billion, well over a billion more people than one century previously. Today, approximately 18% of the Earth’s population lives in India, and it is estimated that India will overtake China to become the most populous country in the world within the next five years.

This dataset provides a comprehensive historical record of the population of India from the year of its independence in 1947 to 2011. The dataset encompasses six decades of demographic information, capturing key trends and changes in India's population over time.

The dataset includes both national and state-level population figures, allowing researchers and data enthusiasts to explore and analyze the population dynamics across different regions of India. It comprises accurate and reliable data sourced from official government reports, census records, and reputable statistical sources.

The median age in India was 27 years old in 2020, meaning half the population was older than that, half younger. This figure was lowest in 1970, at 18.1 years, and was projected to increase to 47.8 years old by 2100. Aging in India India has the second largest population in the world, after China. Because of the significant population growth of the past years, the age distribution remains skewed in favor of the younger age bracket. This tells a story of rapid population growth, but also of a lower life expectancy. Economic effects of a young population Many young people means that the Indian economy must support a large number of students, who demand education from the economy but cannot yet work. Educating the future workforce will be important, because the economy is growing as well and is one of the largest in the world. Failing to do this could lead to high youth unemployment and political consequences. However, a productive and young workforce could provide huge economic returns for India.

India is the most populous country in the world with one-sixth of the world's population. According to official estimates in 2022, India's population stood at over 1.42 billion.

This dataset contains the population distribution by state, gender, sex & region.

The file is in .csv format thus it is accessible everywhere.

According to India's last census in 2011, about **** percent of the total population identified as Muslims. This was an increase from about ten percent in 1951. Overall, India has been a religiously pluralistic and multiethnic democracy with people of several faiths.

This paper focuses to study the status of population in India and to analyze the status of healthcare indicators in India. The study found that after the independence, Indian population is in increasingtrend Population increases from 36.11 crores to 121.02crores in the year 2011. Average AnnualExponential Growth Rate is in increasing rate from 1.25 (1951) to 2.22 in the year 1981 and its goes ondeclining trend with present 1.64 percent in the year 2011. Sex ratio in India has since shown someimprovement as it has increased from 927 (1971) to 944 in the year 2011

In 2022, the majority of Indian adults had a wealth of 10,000 U.S. dollars or less. On the other hand, about *** percent were worth more than *********** dollars that year. India The Republic of India is one of the world’s largest and most economically powerful states. India gained independence from Great Britain on August 15, 1947, after having been under their power for 200 years. With a population of about *** billion people, it was the second most populous country in the world. Of that *** billion, about **** million lived in New Delhi, the capital. Wealth inequality India suffers from extreme income inequality. It is estimated that the top 10 percent of the population holds ** percent of the national wealth. Billionaire fortune has increase sporadically in the last years whereas minimum wages have remain stunted.

In 1800, the population of the area of modern-day Bangladesh was estimated to be just over 19 million, a figure which would rise steadily throughout the 19th century, reaching over 26 million by 1900. At the time, Bangladesh was the eastern part of the Bengal region in the British Raj, and had the most-concentrated Muslim population in the subcontinent's east. At the turn of the 20th century, the British colonial administration believed that east Bengal was economically lagging behind the west, and Bengal was partitioned in 1905 as a means of improving the region's development. East Bengal then became the only Muslim-majority state in the eastern Raj, which led to socioeconomic tensions between the Hindu upper classes and the general population. Bengal Famine During the Second World War, over 2.5 million men from across the British Raj enlisted in the British Army and their involvement was fundamental to the war effort. The war, however, had devastating consequences for the Bengal region, as the famine of 1943-1944 resulted in the deaths of up to three million people (with over two thirds thought to have been in the east) due to starvation and malnutrition-related disease. As the population boomed in the 1930s, East Bengal's mismanaged and underdeveloped agricultural sector could not sustain this growth; by 1942, food shortages spread across the region, millions began migrating in search of food and work, and colonial mismanagement exacerbated this further. On the brink of famine in early-1943, authorities in India called for aid and permission to redirect their own resources from the war effort to combat the famine, however these were mostly rejected by authorities in London. While the exact extent of each of these factors on causing the famine remains a topic of debate, the general consensus is that the British War Cabinet's refusal to send food or aid was the most decisive. Food shortages did not dissipate until late 1943, however famine deaths persisted for another year. Partition to independence Following the war, the movement for Indian independence reached its final stages as the process of British decolonization began. Unrest between the Raj's Muslim and Hindu populations led to the creation of two separate states in1947; the Muslim-majority regions became East Pakistan (now Bangladesh) and West Pakistan (now Pakistan), separated by the Hindu-majority India. Although East Pakistan's population was larger, power lay with the military in the west, and authorities grew increasingly suppressive and neglectful of the eastern province in the following years. This reached a tipping point when authorities failed to respond adequately to the Bhola cyclone in 1970, which claimed over half a million lives in the Bengal region, and again when they failed to respect the results of the 1970 election, in which the Bengal party Awami League won the majority of seats. Bangladeshi independence was claimed the following March, leading to a brutal war between East and West Pakistan that claimed between 1.5 and three million deaths in just nine months. The war also saw over half of the country displaced, widespread atrocities, and the systematic rape of hundreds of thousands of women. As the war spilled over into India, their forces joined on the side of Bangladesh, and Pakistan was defeated two weeks later. An additional famine in 1974 claimed the lives of several hundred thousand people, meaning that the early 1970s was one of the most devastating periods in the country's history. Independent Bangladesh In the first decades of independence, Bangladesh's political hierarchy was particularly unstable and two of its presidents were assassinated in military coups. Since transitioning to parliamentary democracy in the 1990s, things have become comparatively stable, although political turmoil, violence, and corruption are persistent challenges. As Bangladesh continues to modernize and industrialize, living standards have increased and individual wealth has risen. Service industries have emerged to facilitate the demands of Bangladesh's developing economy, while manufacturing industries, particularly textiles, remain strong. Declining fertility rates have seen natural population growth fall in recent years, although the influx of Myanmar's Rohingya population due to the displacement crisis has seen upwards of one million refugees arrive in the country since 2017. In 2020, it is estimated that Bangladesh has a population of approximately 165 million people.

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)

Type 2 diabetes (T2D) is a syndrome of multiple metabolic disorders and is genetically heterogeneous. India comprises one of the largest global populations with highest number of reported type 2 diabetes cases. However, limited information about T2D associated loci is available for Indian populations. It is, therefore, pertinent to evaluate the previously associated candidates as well as identify novel genetic variations in Indian populations to understand the extent of genetic heterogeneity. We chose to do a cost effective high-throughput mass-array genotyping and studied the candidate gene variations associated with T2D in literature. In this case-control candidate genes association study, 91 SNPs from 55 candidate genes have been analyzed in three geographically independent population groups from India. We report the genetic variants in five candidate genes: TCF7L2, HHEX, ENPP1, IDE and FTO, are significantly associated (after Bonferroni correction, p

In 1800, the population of the area of modern-day Pakistan was estimated to be just over 13 million. Population growth in the 19th century would be gradual in the region, rising to just 19 million at the turn of the century. In the early 1800s, the British Empire slowly consolidated power in the region, eventually controlling the region of Pakistan from the mid-19th century onwards, as part of the British Raj. From the 1930s on, the population's growth rate would increase as improvements in healthcare (particularly vaccination) and sanitation would lead to lower infant mortality rates and higher life expectancy. Independence In 1947, the Muslim-majority country of Pakistan gained independence from Britain, and split from the Hindu-majority country of India. In the next few years, upwards of ten million people migrated between the two nations, during a period that was blemished by widespread atrocities on both sides. Throughout this time, the region of Bangladesh was also a part Pakistan (as it also had a Muslim majority), known as East Pakistan; internal disputes between the two regions were persistent for over two decades, until 1971, when a short but bloody civil war resulted in Bangladesh's independence. Political disputes between Pakistan and India also created tension in the first few decades of independence, even boiling over into some relatively small-scale conflicts, although there was some economic progress and improvements in quality of life for Pakistan's citizens. The late 20th century was also characterized by several attempts to become democratic, but with intermittent periods of military rule. Between independence and the end of the century, Pakistan's population had grown more than four times in total. Pakistan today Since 2008, Pakistan has been a functioning democracy, with an emerging economy and increasing international prominence. Despite the emergence of a successful middle-class, this is prosperity is not reflected in all areas of the population as almost a quarter still live in poverty, and Pakistan ranks in the bottom 20% of countries according to the Human Development Index. In 2020, Pakistan is thought to have a total population of over 220 million people, making it the fifth-most populous country in the world.

The Socio Economic Caste Census (SECC) is a comprehensive exercise undertaken by the Government of India to gather detailed information about the socio-economic status and caste demographics of Indian households. Conducted in 2011, this census was distinct from the traditional decennial population census and aimed to provide a holistic understanding of the living conditions and deprivation levels of people across the country. The SECC data encompasses various parameters, including income, occupation, land ownership, and educational status. Additionally, it marked a significant effort to collect caste-wise population data, a feat not attempted since the pre-independence census of 1931. The findings from the SECC play a pivotal role in shaping targeted policy interventions and welfare schemes for the marginalized and underprivileged sections of society.

Supplementary information files for article A susceptibility putative haplotype within NLRP3 inflammasome gene influences ischaemic stroke risk in the population of Punjab, India

Despite strong genetic implications of NLRP3 inflammasome, its examination as genetic determinant of ischaemic stroke (IS) remains to be done in Punjab, which has been investigated in this study. In this case control study, 400 subjects (200 IS patients, 200 stroke free controls) were included. Contributions of 5 single nucleotide polymorphisms (SNPs) including a functional SNP within NLRP3 gene (rs10754558, rs4612666, rs2027432, rs3738488 and rs1539019) for the risk of IS were investigated through genetic models after correcting the effect of significant variables. Plasma levels of three pro-inflammatory markers, that is, C-reactive protein (CRP), interleukin-1beta (IL-1β) and interleukin-18 (IL-18) were measured by enzyme-linked immunosorbent assays (ELISA). Minor alleles of 3 out of 5 SNPs (rs10754558, rs4612666 and rs1539019) exhibited association with IS risk in additive, recessive and multiplicative models. Multivariable regression analysis confirmed that higher levels of systolic blood pressure (β ± SE: 1.42 ± 0.57, p = .013), CRP (β ± SE: 1.22 ± 0.41, p = .003), IL-1β (β ± SE: 1.78 ± 0.88, p = .043) and IL-18 (β ± SE: 1.13 ± 0.49, p = .021) were independent risk predictors for IS. Haplotype analysis revealed a susceptibility putative haplotype GTGTA, which approximately doubled the IS risk (OR: 1.98, 95% CI: 1.12–3.78, p = .04) in dominant mode after adjusting the effect with confounding variables. This susceptibility putative haplotype GTGTA was significantly associated with increased concentrations of CRP (β = 1.21, p = .014) and IL-1β (β = 1.53, p = .034) in dose-dependent manner (less in carriers of 1 copy than those who had 2 copies of GTGTA). The present study has revealed a susceptibility putative haplotype GTGTA within NLRP3 gene, carriers of which have double the risk of IS by having increased plasma levels of CRP and IL-1β in a dose-dependent manner.

The TIGER/Line shapefiles and related database files (.dbf) are an extract of selected geographic and cartographic information from the U.S. Census Bureau's Master Address File / Topologically Integrated Geographic Encoding and Referencing (MAF/TIGER) Database (MTDB). The MTDB represents a seamless national file with no overlaps or gaps between parts, however, each TIGER/Line shapefile is designed to stand alone as an independent data set, or they can be combined to cover the entire nation. Census tracts are small, relatively permanent statistical subdivisions of a county or equivalent entity, and were defined by local participants as part of the 2020 Census Participant Statistical Areas Program. The Census Bureau delineated the census tracts in situations where no local participant existed or where all the potential participants declined to participate. The primary purpose of census tracts is to provide a stable set of geographic units for the presentation of census data and comparison back to previous decennial censuses. Census tracts generally have a population size between 1,200 and 8,000 people, with an optimum size of 4,000 people. When first delineated, census tracts were designed to be homogeneous with respect to population characteristics, economic status, and living conditions. The spatial size of census tracts varies widely depending on the density of settlement. Physical changes in street patterns caused by highway construction, new development, and so forth, may require boundary revisions. In addition, census tracts occasionally are split due to population growth, or combined as a result of substantial population decline. Census tract boundaries generally follow visible and identifiable features. They may follow legal boundaries such as minor civil division (MCD) or incorporated place boundaries in some States and situations to allow for census tract-to-governmental unit relationships where the governmental boundaries tend to remain unchanged between censuses. State and county boundaries always are census tract boundaries in the standard census geographic hierarchy. In a few rare instances, a census tract may consist of noncontiguous areas. These noncontiguous areas may occur where the census tracts are coextensive with all or parts of legal entities that are themselves noncontiguous. For the 2010 Census and beyond, the census tract code range of 9400 through 9499 was enforced for census tracts that include a majority American Indian population according to Census 2000 data and/or their area was primarily covered by federally recognized American Indian reservations and/or off-reservation trust lands; the code range 9800 through 9899 was enforced for those census tracts that contained little or no population and represented a relatively large special land use area such as a National Park, military installation, or a business/industrial park; and the code range 9900 through 9998 was enforced for those census tracts that contained only water area, no land area.

Abstract

An all-India survey on unorganized manufacturing enterprises was carried out by the National Sample Survey Organization (NSSO) as a part of the 62nd round of National Sample Survey (NSS) during July 2005 - June 2006. Other subjects of inquiry were household consumer expenditure, employment and unemployment. Past surveys provided information on various operational characteristics of enterprises like location of enterprise, nature of operation, maintenance of accounts etc. in detail, as well as detailed estimates of employment, assets & borrowings. The 62nd survey round provides information on input, output & value added of unorganized manufacturing enterprises at all India level for different industry groups and at the level of States / UTs for all the industry groups taken together.

The manufacturing sector is one of the important sectors of industry in the Indian economy. As per the latest available National Accounts Statistics, during 2006-07, the manufacturing sector had a share of about 16% in the GDP at factor cost. For the purpose of data collection, the manufacturing sector has been broadly sub-divided into two categories i.e. organized and unorganized. While data for organized manufacturing sector are collected through Annual Survey of Industries (ASI), the same for unorganized manufacturing sector are collected periodically through sample surveys as follow-up surveys of Economic Censuses (EC). The unorganized manufacturing sector has roughly about one-third share in the total contribution by the manufacturing sector in the GDP.

Recognizing the importance of the unorganized manufacturing sector in terms of its share in GDP as well as in total employment, NSS has taken up this subject in many of its rounds. That way collection of data on unorganized manufacture has a long history in the NSS. In fact, the very first round of NSS had small-scale manufacturing and handicrafts as one of its subjects of enquiry. Thereafter, data on small-scale manufacture were collected also in the NSS rounds 3-10, 14, 23 and 29. These surveys used the list of villages from Population Census and list of census enumeration blocks, or lists of Urban Frame Survey (UFS) blocks of NSSO subject to their availability, as the sampling frame for selection of villages / urban blocks.

A review of the surveys conducted by NSSO in the initial rounds mentioned above indicated that a better sampling frame was necessary to generate more accurate statistics of the unorganized sector. The need for auxiliary information on areas of concentration of enterprises for stratification purpose was strongly felt for developing more efficient sampling designs. This demand ultimately culminated in the conduct of periodic Economic Censuses (EC), which provided the frame for the follow-up surveys on non-agricultural enterprises including those engaged in unorganized manufacturing.

With the launching of the EC in 1977 (five ECs have been conducted so far), the follow-up surveys of EC on unorganized manufacturing generally used the village and block level information on number of enterprises/workers as per the EC for selection of villages and urban blocks in the follow-up surveys. The approach of data collection from enterprises was also changed from the 'household approach' used earlier (i.e. prior to the launching of EC) to the 'site approach' whenever such sites existed. So far NSS has conducted six follow-up surveys of EC through rounds 33rd (1978-79), 40th (1984-85), 45th (1989-90), 51st (1994-95), 56th (2000-01), and 62nd (2005-06) with unorganized manufacture as the main subject of enquiry. In the 62nd round of NSS, area frame thrown up by the latest EC (1998) was however used only partially because the frame was considered to be old. However, for 27 cities having a population of one million or more (as per Census 2001) which are likely to have a substantial share in the total number of unorganized manufacturing enterprises in the country, a decision was taken to make use of the list of urban blocks giving count of number of enterprises/workers at the block level as per EC 1998 as the sampling frame for stratification and selection of urban blocks. For the remaining towns/cities, latest lists of UFS blocks were used as the sampling frame2. In case of rural areas, list of villages (or panchayat wards in case of Kerala) of Census 2001 served as the sampling frame for selection of villages as the first stage units (FSUs).

Geographic coverage

The survey covered the whole of the Indian Union except (i) Leh and Kargil districts of Jammu & Kashmir, (ii) interior village of Nagaland situated beyond five kilometers of bus route and (iii) villages of Andaman and Nicobar Islands which remain inaccessible throughout the year. All the sample FSUs of the districts Poonch and Rajouri of the state of Jammu and Kashmir became casualty. Thus, the estimates for Jammu and Kashmir as well as for all-India do not include these areas.

Analysis unit

• enterprise

Universe

Unorganized manufacturing enterprises not covered by ASI, under the two-digit codes 15 to 37 (Section 'D') of NIC-2004 and enterprises under cotton ginning, cleaning and baling (NIC-2004, code 01405). All government and public sector undertakings were outside the coverage of the survey. It is to be noted that only those enterprises, which operated for at least 30 days (15 days for seasonal enterprises) during the last 365 days preceding the date of survey, were eligible for survey.

Kind of data

Sample survey data [ssd]

Sampling procedure

One salient feature of the sample design adopted during the 62nd round was the use of list frame, in addition to the usual area frame, which was done to capture sufficient number of relatively 'bigger' enterprises with a view to improving the overall estimate of gross value added per worker, total number of workers, total input, total output, etc. A list of 8,000 big non-ASI manufacturing enterprises2 for the urban sector only was prepared as per the data of the census of manufacturing enterprises conducted by Development Commissioner of Small Scale Industries (DCSSI) in 2003. This list served as the list frame. All these units in the list frame were considered for survey without resorting to any sampling. For the coverage of all other enterprises in the universe, the usual area frame approach was followed for sampling of enterprises in stages. It is important to mention that this dual frame approach was experimented for the first time in the 62nd round. The effectiveness of using the list frame has been discussed under Chapter four.

In the area frame approach, the list of all the villages (panchayat wards in case of Kerala) / urban blocks of the country served as the sampling frame of first stage units (FSUs). Thus, the FSUs were villages (panchayat wards in case of Kerala) in the rural sector and urban blocks in the urban sector. The ultimate stage units were enterprises in both the sectors. However, in case of large FSUs requiring hamlet-group (hg) / sub-block (sb) formation, one intermediate stage in the sampling involved the selection of two hg's / sb's from each FSU out of a minimum of three hg's/sb's formed in the FSU. Of these two selected hg's/sb's, one was selected with probability '1' (termed as segment 1) and another one (termed as segment 2) was selected from among the remaining hg's/sb's of the FSU at random. The hg/sb selected with certainty (i.e. segment 1) was the hg/sb having maximum number of directory manufacturing establishments (DMEs) (or with maximum number of non-directory manufacturing establishments (NDMEs) if there was no DME, or with maximum number of own account manufacturing enterprises (OAMEs) if there was no DME/NDME, or with maximum population if there was no DME/NDME/OAME3 in the entire FSU). Smaller FSUs without any hg/sb formation were identified/categorized as segment 1 for the purpose of survey (segment 2 does not exist for such FSUs). As regards the first stage stratification, two basic strata were formed within each district of a State/UT: rural stratum comprising all rural areas of the district and urban stratum consisting of all urban areas of the district. However, each city with a population of one million or more as per Census 2001 was invariably treated as a separate stratum by itself. For details of stratification, sub-stratification and selection of sample FSUs, reference may be made to Appendix-B of of the final report no.526.

For each of segments 1 and 2 for the selected sample FSUs, a frame of eligible enterprises was prepared by the field investigators by visiting each and every house/household within the selected geographical area. While doing so, if any enterprise of the list frame was encountered, care was taken not to list it again within segment 1 or 2 as a part of the area sample / area frame to guard against duplication of enterprises between the two types of frames. Listing and sampling of enterprises in the area frame was independent for each of segments 1 and 2. In this context, it may be mentioned that for each selected FSU of rural sub-strata 1 and 2 only (see Appendix B for composition of these two sub-strata), segment 9 was also carved out within the FSU, which comprised top 10 big non-ASI registered SSI enterprises (identified by jointly considering the number of workers in the enterprise and gross value of output of the enterprise) located within the boundaries of the entire FSU. The list of such units for selected FSUs was made available to the field investigators in order to facilitate formation of segment 9. Respective frames of segments 1 and 2 in these FSUs excluded the units listed under segment 9. The effectiveness of the formation of segment 9 has been discussed under Chapter

BackgroundThe importance of maternal sanitation behaviour during pregnancy for birth outcomes remains unclear. Poor sanitation practices can promote infection and induce stress during pregnancy and may contribute to adverse pregnancy outcomes (APOs). We aimed to assess whether poor sanitation practices were associated with increased risk of APOs such as preterm birth and low birth weight in a population-based study in rural India.Methods and FindingsA prospective cohort of pregnant women (n = 670) in their first trimester of pregnancy was enrolled and followed until birth. Socio-demographic, clinical, and anthropometric factors, along with access to toilets and sanitation practices, were recorded at enrolment (12th week of gestation). A trained community health volunteer conducted home visits to ensure retention in the study and learn about study outcomes during the course of pregnancy. Unadjusted odds ratios (ORs) and adjusted odds ratios (AORs) and 95% confidence intervals for APOs were estimated by logistic regression models. Of the 667 women who were retained at the end of the study, 58.2% practiced open defecation and 25.7% experienced APOs, including 130 (19.4%) preterm births, 95 (14.2%) births with low birth weight, 11 (1.7%) spontaneous abortions, and six (0.9%) stillbirths. Unadjusted ORs for APOs (OR: 2.53; 95% CI: 1.72–3.71), preterm birth (OR: 2.36; 95% CI: 1.54–3.62), and low birth weight (OR: 2.00; 95% CI: 1.24–3.23) were found to be significantly associated with open defecation practices. After adjustment for potential confounders such as maternal socio-demographic and clinical factors, open defecation was still significantly associated with increased odds of APOs (AOR: 2.38; 95% CI: 1.49–3.80) and preterm birth (AOR: 2.22; 95% CI: 1.29–3.79) but not low birth weight (AOR: 1.61; 95% CI: 0.94–2.73). The association between APOs and open defecation was independent of poverty and caste. Even though we accounted for several key confounding factors in our estimates, the possibility of residual confounding should not be ruled out. We did not identify specific exposure pathways that led to the outcomes.ConclusionsThis study provides the first evidence, to our knowledge, that poor sanitation is associated with a higher risk of APOs. Additional studies are required to elucidate the socio-behavioural and/or biological basis of this association so that appropriate targeted interventions might be designed to support improved birth outcomes in vulnerable populations. While it is intuitive to expect that caste and poverty are associated with poor sanitation practice driving APOs, and we cannot rule out additional confounders, our results demonstrate that the association of poor sanitation practices (open defecation) with these outcomes is independent of poverty. Our results support the need to assess the mechanisms, both biological and behavioural, by which limited access to improved sanitation leads to APOs.

ObjectiveThe purpose of this study was to test the diagnostic accuracy of the 10g monofilament to screen for diabetic peripheral neuropathy (DPN) in India. The study further assessed the effect of physical activity, footwear use, and occupation on the outcome.MethodsNon-probabilistic purposive sampling was used to recruit patients with T2DM to assess the diagnostic utility of the 10 g monofilament. 160 participants were recruited divided into 4 groups. Each group consisted of 40 participants with 20 under each category described as “Physical Worker Vs Non- physical worker” (n = 40), “Barefoot Vs Footwear” (n = 40), “Use of Slipper at Home Vs No-slippers use at home” (n = 40), “Agriculture Vs Non- agriculture” (n = 40). 10 g monofilament was used to detect the presence of protective sensation towards screening of DPN against biothesiometer (Vibration Pressure Threshold).ResultsThe area under the ROC (receiver operating characteristic) curve was 0.6 for identifying DPN using the 10 g monofilament. Physical work (p = 0.04), footwear (p = 0.04), slipper use at home (p = 0.02) and occupation (p = 0.02) impacted on the diagnostic utility of the 10g monofilament.ConclusionsThis study shows that the 10 g monofilament has limited accuracy for detecting DPN in the Indian population and this is further affected by occupation, socioeconomic and religious practice.

In 2011, about 2.34 percent of the population in India identified Christianity as their religion, an increase from two percent in 1951. Overall, India has been a religiously pluralistic and multiethnic democracy with people of several faiths.

Abstract

The National Sample Survey Organisation (NSSO) has been set up by the Government of India in 1950 to collect socio-economic data employing scientific sampling methods. The NSSO conducts regular consumer expenditure surveys as part of its "rounds", each round being normally of a year's duration and covering more than one subject of study. The surveys are conducted through household interviews, using a random sample of households covering practically the entire geographical area of the country. Surveys on consumer expenditure are being conducted quinquennially on a large sample of households from the 27th round (October 1972 - September 1973) onwards. The fourth quinquennial survey on household consumer expenditure was carried out during July 1987 - June 1988. The three previous surveys of this series were carries out in the 27th (October-September 1973) , the 32nd (July 1977 to June 1978) and the 38th (January to December , 1983) rounds of the NSSO. The present survey like the previous one, covered the entire population. Expenditure incurred by the sample household for the purpose of domestic consumption were collected for the 30 days preceding the date of survey. No account has, however, been taken of any expenditure incurred towards the productive enterprises of the household. It may be mentioned here that in order to get more households of the upper income bracket in the Sample , significant changes have been made in the sample design in this round (compared to the design of the 38th round). The survey covered the whole of Indian Union excepting: i) Ladakh and Kargil districts of Jammu & Kashmir ii) Rural areas of Nagaland

The field work for the survey was conducted, as usual, by the Field Operations Division of the Organisation. The collected data were processed by the Data Processing Division of NSSO and tabulated by the Computer Centre of Department of Statistics. The reports have been prepared by Survey Design & Research Division (SDRD) of NSSO under the guidance of the Governing Council, NSSO.

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

Universe

The survey used the interview method of data collection from a sample of randomly selected households and members of the household.

Kind of data

Sample survey data [ssd]

Sampling procedure

The survey will have a two-stage stratified design. The first stage units (f.s.u.s) or 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 constitute 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 have been used. In the urban sector , the Urban Frame Survey (U.F.S.) blocks constitute the sampling frame.

Stratification: States are 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.

RURAL SECTOR: In the rural sector, within each region, each district with 1981 Census rural population less 1.8 million forms 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 are 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 1981 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.

Allocation for first stage units: The total all-India sample size has been 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.

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.

Sample size (central sample): The all India sample in respect of the central sample consists of 8518 villages and 4648 blocks.

Sample size (state sample): All the states and Union Territories except Andaman & Nicobar Islands, Chandigarh, Dadra & Nagar Haveli and Lakshadweep are participating in this round at least on an equal matching basis.

Sampling deviation

There was no deviation from the original sampling design.

Mode of data collection

Face-to-face [f2f]

Research instrument

The NSSO surveys on consumer expenditure aim to measure the household consumer expenditure in quantitative terms disaggregated by various household characteristics.

The data for this survey is collected in the NSS Schedule 1.0 used for household consumer expenditure. For this round, the schedule had 11 blocks.

Blocks 1 and 2 - are similar to the ones used in usual NSS rounds. These are used to record identification of sample households and particulars of field operations.

Block-3: Household characteristics like, household size, principal industry-occupation, social group, land possessed and cultivated, type of dwelling etc. are recorded in this block.

Block-4: In this block the detailed demographic particulars including age, sex, educational level, marital status, number of meals usually taken in a day etc. are recorded.

Block-5: In this block cash purchase and consumption of food, pan, tobacco, intoxicants and fuel & light during the last 30 days are recorded.

Block-6: Consumption of clothing during the last 30 and 365 days is recorded in this block.

Block-7: Consumption of footwear during the last 30 and 365 days is recorded in this block.

Block-8 : Expenditure on miscellaneous goods and services and rents and taxes during the last 30 days has been recorded in this block.

Block-9 : Expenditure for purchase and construction (including repairs) of durable goods for domestic use is recorded here.

Block-10 : Particulars of dwelling units are recorded in this block.

Block-11 : Summary of consumer expenditure during last 30 days is recorded in this block.

Abstract

The schedule on morbidity and health care (Schedule 25.0) framed for the 60th round consists of 13 blocks. The different blocks of the schedule are: Block 0: descriptive identification of sample household Block 1: identification of sample household Block 2: particulars of field operation Block 3: household characteristics Block 4: demographic particulars of household members Block 5: particulars of earstwhile household members who died during last 365 days Block 6: particulars of economic independence and ailments for persons aged 60 years and above Block 7: particulars of medical treatment received as inpatient of a hospital during last 365 days Block 8: expenses incurred for treatment of members treated as impatient of hospital during last 365 days and source of finance Block 9: particulars of spells of ailment of household members during last 15 days (including hospitalisation) Block 10: expenses incurred during last 15 days for treatment of members (not as an inpatient of hospital) and source of finance Block 11: particulars of immunisation of children (0 - 4 yrs.), pre-natal and post-natal care for ever married women of age below 50 years during last 365 days Block 12: remarks by investigator Block 13: comments by supervisory officer(s)

Geographic coverage

The survey will cover the whole of the Indian Union except (i) Leh (Ladakh) and Kargil districts of Jammu & Kashmir, (ii) interior villages of Nagaland situated beyond five kilometres of the bus route and (iii) 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 60th round survey. The first stage units (FSU) will be the 1991 census villages in the rural sector and Urban Frame Survey (UFS) blocks in the urban sector. The ultimate stage units (USU) will be households in both the sectors. In case of large villages/blocks requiring hamlet-group (hg)/sub-block (sb) formation, one intermediate stage will be the selection of two hgs/sbs from each FSU.

Sampling Frame for First Stage Units

For the rural sector, the list of Census 1991 villages (panchayat wards for Kerala) and Census 1981 villages for J & K will constitute the sampling frame. For the urban sector, the list of latest available Urban Frame Survey (UFS) blocks will be considered as the sampling frame.

Stratification

Rural sector: Two special strata will be formed at the State/ UT level, viz. Stratum 1: all FSUs with population between 0 to 50 and Stratum 2: FSUs with population more than 15,000.

Special stratum 1 will be formed if at least 50 such FSUs are found in a State/UT. Similarly, special stratum 2 will be formed if at least 4 such FSUs are found in a State/UT. Otherwise, such FSUs will be merged with the general strata. From FSUs other than those covered under special strata 1 and 2, general strata will be formed and its numbering will start from 3. Each district of a State/UT will normally be treated as a separate stratum. However, if the census rural population of the district is greater than or equal to 2.5 million as per population census 2001 or 2 million as per population census 1991, the district will be split into two or more strata, by grouping contiguous tehsils to form strata. However, in Gujarat, some districts are not wholly included in an NSS region. In such cases, the part of the district falling in an NSS region will constitute a separate stratum.

Urban sector: In the urban sector, strata will be formed within each NSS region on the basis of size class of towns as per Population Census 2001. The stratum numbers and their composition (within each region) are given below. stratum 1 : all towns with population less than 50,000
stratum 2 : all towns with population 50,000 or more but less than 2 lakhs
stratum 3 : all towns with population 2 lakhs or more but less than 10 lakhs
stratum 4, 5, 6,...: each town with population 10 lakhs or more

The stratum numbers will remain as above even if, in some regions, some of the strata are not formed.

Total sample size (FSUs)

7612 FSUs have been allocated at all-India level on the basis of investigator strength in different States/UTs for central sample and 8260 for state sample.

Allocation of total sample to States and UTs

The total number of sample FSUs is allocated to the States and UTs in proportion to provisional population as per Census 2001 subject to the availability of investigators ensuring more or less uniform work-load.

Allocation of State/UT level sample to rural and urban sectors

State/UT level sample is allocated between two sectors in proportion to provisional population as per Census 2001 with 1.5 weightage to urban sector subject to the restriction that urban sample size for bigger states like Maharashtra, Tamil Nadu etc. should not exceed the rural sample size. Earlier practice of giving double weightage to urban sector has been modified considering the fact that there has been considerable growth in urban population. A minimum of 8 FSUs will be allocated to each state/UT separately for rural and urban areas.

Allocation to strata:

Within each sector of a State/UT, the respective sample size will be allocated to the different strata in proportion to the stratum population as per census 2001. Allocations at stratum level will be adjusted to a multiple of 4 with a minimum sample size of 4.

Selection of FSUs

FSUs will be selected with Probability Proportional to Size With Replacement (PPSWR), size being the population as per Population Census 1991 in all the strata for rural sector except for stratum 1. In stratum 1 of rural sector and in all the strata of urban sector, selection will be done using Simple Random Sampling Without Replacement (SRSWOR). Within each stratum, samples will be drawn in the form of two independent sub-samples in both the rural and urban sectors.

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

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

Criterion for hamlet-group/sub-block formation: After identification of the FSU, it is to be determined whether listing will be done in the whole sample FSU or not. In case the population of the selected village or block is found to be 1200 or more, it will be divided into a suitable number (say, D) of „hamlet-groups? in the rural sector and „sub-blocks? in the urban sector as stated below. less than 1200 (no hamlet-groups/sub-blocks) 1
1200 to 1799 3
1800 to 2399 4
2400 to 2999 5
3000 to 3599 6
…………..and so on

For rural areas of Himachal Pradesh, Sikkim, Nagaland and Poonch, Rajouri, Udhampur, Doda districts of Jammu and Kashmir and Idukki district of Kerala, the number of hamlet-groups will be formed as follows. approximate present population of the sample village no. of hgs to be formed
less than 600 (no hamlet-groups) 1
600 to 899 3
900 to 1199 4
1200 to 1499 5
.………..and so on

Two hamlet-groups/sub-blocks will be selected from a large village/UFS block wherever hamlet-groups/sub-blocks have been formed, by SRSWOR. Listing and selection of the households will be done independently in the two selected hamlet-groups/sub-blocks.

Formation of hamlet-groups/sub-blocks: In case hamlet-groups/sub-blocks are to be formed in the sample FSU, the same should be done by more or less equalizing population (details are in Chapter Two). Note that while doing so, it is to be ensured that the hamlet-groups/sub-blocks formed are clearly identifiable in terms of physical landmarks.

Listing of households: Having determined the hamlet-groups/sub-blocks, i.e. area(s) to be considered for listing, the next step is to list all the households (including those found to be temporarily locked after ascertaining the temporariness of locking of households through local enquiry). The hamlet-group/sub-block with sample hg/sb number 1 will be listed first and that with sample hg/sb number 2 will be listed next.

Formation of Second Stage Strata and allocation of households for Schedule 25.0

In each selected village/block/hamlet-group/sub-block, four second stage strata (SSS) will be formed as given below. SSS 1: households with at least one member hospitalised during last 365 days
SSS 2: from the remaining households, households having at least one child of age below 5 years
SSS 3: from the remaining households, households with at least one member of age 60 years or above
SSS 4: other households

Selection of households for Schedules 1.0, 10 and 25.0

From each SSS the sample households for all the schedules will be selected by SRSWOR. If a household is selected for more than one schedule only one schedule will be canvassed in that household in the priority order of Schedule 1.0, Schedule 10 and Schedule 25.0 and in that case the household will be replaced for the other schedule. If a household is selected for Schedule 1.0 it will not be selected for Schedule 10 or Schedule 25.0. Similarly, if a household is not selected for Schedule 1.0 but selected for Schedule 10 it will not be selected for Schedule 25.0. However, for the household, selected from SSS1 of Schedule 25.0, the Schedule 25.0 will be canvassed even if the household is selected for other schedules.

Sampling