The population of India is divided into several groups based on social, educational, and financial statuses. The formation of these groups is a result of the historical social structure of the country. Between 2019 and 2021, Other Backward Class (OBC) constituted the largest part of Indian households accounting for about ** percent. On the other hand, Schedule Tribes formed about *** percent of households. How prosperous is India’s caste-based society? India suffers from extreme social and economic inequality. The combined share of Schedule Tribe and Schedule Caste in the affluent population of India was less than ** percent. Contrary to this, economically and socially stronger groups constituted the major part of the affluent population. Hence, indicating a strong relationship between caste and prosperity. India’s thoughts on caste-based reservation The constitution of India provides reservations to the weaker sections of the society for their upliftment and growth. However, the need for reservation has increased with time, making the whole situation even more complicated. People are divided over the existence of a system that provides preference to certain castes or sects. In a survey conducted in 2016 about providing employment reservation to young adults of Schedule Caste and Schedule Tribe, many people expressed opposition. More than ** percent of opposition came from upper Hindu caste. Minimum opposition was observed from the people belonging to Schedule Tribe and Schedule Caste.

The literacy rate of the total population in the country was about 73 percent in 2011, in comparison to about 66 percent among the scheduled caste population. In India, scheduled caste and scheduled tribe and other backward class are officially recognized by the constitution as groups of disadvantaged indigenous people. They are the primary beneficiaries of reservation policies under the constitution.

Population by Caste/Echnic Groups for Nepal, Population Census 2011, Central Bureau of Statistics

This dataset contains the State, Year, Caste, Gender and Age-group-wise Population Projection.

This statistic displays the results of a survey about the share of affluent population across India in fiscal year 2016, based on caste. During the measured time period, approximately ** percent of the Muslim population across the country were considered affluent.

The literacy rate of the female population in the country was about 65 percent in 2011, in comparison to about 57 percent among the females in the scheduled caste population.

This dataset contains the State, Year, Caste, Level and Gender-wise Gross Enrolment Ratio (GER) in India. The Gross Enrolment Ratio (GER) compares the enrolment in a specific level of education to the population of the age-group which is most age-appropriate for that level of educationThe data is given for all caste categories, Scheduled Castes and Scheduled Tribes.

India Census: Population: by Religion: Hindu: Male data was reported at 498,306,968.000 Person in 2011. This records an increase from the previous number of 428,678,554.000 Person for 2001. India Census: Population: by Religion: Hindu: Male data is updated yearly, averaging 463,492,761.000 Person from Mar 2001 (Median) to 2011, with 2 observations. The data reached an all-time high of 498,306,968.000 Person in 2011 and a record low of 428,678,554.000 Person in 2001. India Census: Population: by Religion: Hindu: Male data remains active status in CEIC and is reported by Census of India. The data is categorized under India Premium Database’s Demographic – Table IN.GAE001: Census: Population: by Religion.

India Census: Population: by Religion: Muslim: Urban data was reported at 68,740,419.000 Person in 2011. This records an increase from the previous number of 49,393,496.000 Person for 2001. India Census: Population: by Religion: Muslim: Urban data is updated yearly, averaging 59,066,957.500 Person from Mar 2001 (Median) to 2011, with 2 observations. The data reached an all-time high of 68,740,419.000 Person in 2011 and a record low of 49,393,496.000 Person in 2001. India Census: Population: by Religion: Muslim: Urban data remains active status in CEIC and is reported by Census of India. The data is categorized under India Premium Database’s Demographic – Table IN.GAE001: Census: Population: by Religion.

This household questionnaire collects comprehensive socioeconomic and demographic data from families in the Ahmednagar district. It includes details about family structure, such as the head of the family, religion, caste, and relationship of each family member to the head. Information about education, occupation, income, and marital status is recorded, alongside data on living conditions like monthly expenditures, number of rooms, and household amenities (e.g., electricity, mobile phones, vehicles). The survey also assesses assets like livestock ownership and income from animals, building materials used in the home, and the type of cooking fuel. Additionally, it gathers information on the legal ownership of the house and the type of ration card held, reflecting the household's financial status.

Prison Inmates in India

Demographics, Age, Education, Caste, Wages, Rehabilitation, Technical Info

By Rajanand Ilangovan [source]

About this dataset

This dataset provides a detailed view of prison inmates in India, including their age, caste, and educational background. It includes information on inmates from all states/union territories for the year 2019 such as the number of male and female inmates aged 16-18 years, 18-30 year old inmates and those above 50 years old. The data also covers total number of penalized prisoners sentenced to death sentence, life imprisonment or executed by the state authorities. Additionally, it provides information regarding the crimehead (type) committed by an inmate along with its grand total across different age groups. This dataset not only sheds light on India’s criminal justice system but also highlights prevelance of crimes in different states and union territories as well as providing insight into crime trends across Indian states over time

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How to use the dataset

This dataset provides a comprehensive look at the demographics, crimes and sentences of Indian prison inmates in 2019. The data is broken down by state/union territory, year, crime head, age groups and gender.

This dataset can be used to understand the demographic composition of the prison population in India as well as the types of crimes committed. It can also be used to gain insight into any changes or trends related to sentencing patterns in India over time. Furthermore, this data can provide valuable insight into potential correlations between different demographic factors (such as gender and caste) and specific types of crimes or length of sentences handed out.

To use this dataset effectively there are a few important things to keep in mind: •State/UT - This column refers to individual states or union territories in India where prisons are located •Year – This column indicates which year(s) the data relates to •Both genders - Female columns refer only to female prisoners while male columns refers only to male prisoners •Age Groups – 16-18 years old = 21-30 years old = 31-50 years old = 50+ years old •Crime Head – A broad definition for each type of crime that inmates have been convicted for •No Capital Punishment – The total number sentenced with capital punishment No Life Imprisonment – The total number sentenced with life imprisonment No Executed– The total number executed from death sentence Grand Total–The overall totals for each category

By using this information it is possible to answer questions regarding topics such as sentencing trends, types of crimes committed by different age groups or genders and state-by-state variation amongst other potential queries

Research Ideas

• Using the age and gender information to develop targeted outreach strategies for prisons in order to reduce recidivism rates.
• Creating an AI-based predictive model to predict crime trends by analyzing crime head data from a particular region/state and correlating it with population demographics, economic activity, etc.
• Analyzing the caste of inmates across different states in India in order to understand patterns of discrimination within the criminal justice system

Acknowledgements

If you use this dataset in your research, please credit the original authors. Data Source

License

License: Attribution-ShareAlike 4.0 International (CC BY-SA 4.0) - You are free to: - Share - copy and redistribute the material in any medium or format for any purpose, even commercially. - Adapt - remix, transform, and build upon the material for any purpose, even commercially. - You must: - Give appropriate credit - Provide a link to the license, and indicate if changes were made. - ShareAlike - You must distribute your contributions under the same license as the original.

Columns

File: SLL_Crime_headwise_distribution_of_inmates_who_convicted.csv | Column name | Description | |:--------------------------|:---------------------------------------------------------------------------------------------------| | STATE/UT | Name of the state or union territory where the jail is located. (String) | | YEAR | Year when the inmate population data was collected. (Integer) ...

🇮🇳 인도 English The catalog contains data related to Population by Religious Community for Scheduled Caste (For Each Caste/Tribe Separately), Census 2001 - India and States. It includes data on Scheduled Caste, Scheduled Tribe Population by Rural, Urban, Religious Community.

The data refers to State/UT-wise and caste-wise details of prison inmates at the end of the reference year. The prison inmates are categorised into male and female population. The age of inmates are grouped into 16-18 yrs, 18-30 yrs, 30-50 yrs and 50 & above yrs. Castes of jail inmates are further categorized as OBCs, SCs, STs & Others.

The catalog contains data related to population ages 5-19 attending educational institutions by economic activity status and sex, census 2001 - India and states. It includes data on Population Attending Educational Institutions, Population Ages 5-19 Attending Educational Institutions, Population Attending Educational Institutions by Economic Activity, Population Attending Educational Institutions by Sex, Main Workers Attending Educational Institution, Marginal Workers Attending Educational Institution, Non-Workers Attending Educational Institution, Main Workers Not Attending Educational Institution, Marginal Workers Not Attending Educational Institution, Non-workers Not Attending Educational Institution, Census 2001, Scheduled Caste (SC) and Scheduled Tribe (ST).

Caste distribution across wards in Lungri

Abstract

National population and housing census 2011 (NPHC2011), marks a hundred years of census taking in Nepal. Nepal has been conducting population censuses almost decennially and the census 2011 is the eleventh. The first population census was conducted in 1911 A.D. (1968 B.S.). However, the census conducted in 1952/54 is considered to be the first modern census of Nepal introducing internationally comparable concepts, definitions and classifications. Computer processing was introduced for the first time in 1971 census using IBM 1401. NPHC 2011 carries special features of having scientific questionnaires, detailed EA maps for urban and Village Development Committees (VDC) maps for rural areas, ever most inclusive field staff, extensive publicity, independent observance by civil society, most economic operation based on domestic resources and most reliable data processing.

Geographic coverage

National

Analysis unit

• Household;
• Individual.

Kind of data

Census/enumeration data [cen]

Mode of data collection

Face-to-face [f2f]

Research instrument

Census questionnaires (Listing form, Individual form-1, Individual form-2), enumeration and other manuals, EA maps were major tools used to conduct field enumeration. Complete list of households was prepared during May and June 2011, a month before the main census. Listing schedule contains questions on number of house, households, household members by sex, agricultural land operated and livestock owned by households and operation of small scale nonagricultural activities. Individual Form-1 contains questions on types of house, household facilities/assets, ownership of the house or land of female members of the household, mortality and its causes and details of absent members of households. Also, included in the form-1 are name, surname, relationship to head, sex, age, caste/ethnicity, marital status and age at first marriage, religion, mother tongue and second language, citizenship, type of disability, literacy and level of education. However, questions on migration, fertility, labour force, occupation, industry, employment status and living arrangement of children (under the age 16) were asked under form-2 to every eighth household selected systematically.

Cleaning operations

Keeping in mind the quality of the data processing operation and the capacity of CBS to undertake, it has been decided to source out the operation as it was done in 2001 census. Nevertheless, processing site and the computers were provided to the contractor so that both quality and confidentiality lie strictly under the control of CBS. Manuals of coding, editing and key entry operation were prepared, training and key entry operation were monitored and supervised by the core team of the CBS. Despite delay in awarding the contract, the whole operation was completed in six months from the date the contract was signed. Approximately, six hundred processing staff plus experts were engaged in coding, editing, key entry operation and verification.

CSPro, an integrated software developed for data entry, editing, verification and tabulation by US Bureau of Census was used for data processing (Key entry, editing and verification). However, CSPro, SPSS and STATA are used for tabulations. Range and consistency checks were done thoroughly during the processing operation. The captured data were further cleaned with the assistance of international experts. New classifications of occupation and industry were prepared based on ISOC and ISIC (Rev. 4). Similarly, classifications of caste/ethnicity, religion and language were prepared based on the recommendations of the academicians and subject matter specialists.

Background: Suboptimal quality of care and disparities in services by healthcare providers are often reported in Nepal. Experience and perceptions about quality of care may differ according to women's socio-cultural background, individual characteristics, their exposure and expectations. This study aimed to compare perceptions of the quality of maternal healthcare services between two groups that are consistently considered vulnerable, women with disabilities from both the non-Dalit population and Dalit population and their peers without disabilities from both non-Dalit and Dalit communities.

Methods: A cross-sectional survey was conducted among 343 total women that included women with disabilities, Dalits and non-Dalits. Women were recruited for interview, who were aged 15–49 years, had been pregnant within the last five years and who had used maternal care services in one of the public health facilities of Rupandehi district. A 20-item, Likert-type scale with four sub-scales or dimensions: 'Health Facility', 'Healthcare Delivery', 'Inter-personal' and 'Access to Care' was used to measure women's perceptions of quality of care. Chi-square test and t test were used to compare groups and to assess differences in perceptions; and linear regression was applied to assess confounding effects of socio-demographic factors. The mean score was compared for each item and separately for each dimension.

Results: All groups, women with disabilities and women without disabilities, Dalit and non-Dalit rated their perceptions and experiences of quality of care lowly in a number of items. While perceived quality of care between women with disabilities and without disabilities in the 'Health Facility' dimension and associated items, was found to differ (p<0.05), this difference was linked to disability status, but was not linked to caste differences. For example, differences in mean scores relating to 'Cleanliness and Facilities', 'Open and Friendliness' and 'Compassion and Kindness' were highly significant (p<0.001), with women with disabilities rating these as better than women without disabilities. On the other hand, women without disabilities rated the 'Availability of cash Incentives' more highly (p<0.01). No significant differences were found between Dalit and non-Dalit women in perceived quality of care, except in relation to 'Cleanliness and facilities', which Dalit women rated lower than non-Dalits (p<0.05).

Conclusions: Perceptions about the quality of care differed significantly by disability status but not by caste. All groups rated the quality of healthcare delivery, interpersonal and personal factors as well as access to services 'low.' Poor service user experiences and perceptions of quality of care undermine opportunities to translate increased healthcare coverage into improved access and outcomes. Greater attention is required by policy makers, health planners and providers to the improvement of quality of care in health facilities.

Caste composition of Lungri with a total population of 27495

U5MR (per 1000 live births) in five years preceding the survey for SC,ST and Non-SC/ST population in high focus states of India, 2015–16.

We deposit here the results of a household-based survey that was used to understand the situation of Dalits and Adivasis in five rural localities in Kerala, Tamil Nadu, Maharashtra, Telangana and Himachal Pradesh, India. It was part of a wider project based on participation observation - living with the Adivasi and Dalit communities in question for a year -that asked how and why Dalits and Adivasis remained at the bottom of the Indian economic and social hierarchy despite economic growth. This survey sought to document how people made a livelihood (occupation/employment) as well as levels of education by caste/tribe (and within that by gender).In recent decades India has experienced exceptionally high economic growth rates, becoming one of the world's fastest growing major economies. Yet, the redistribution of the fruits of economic growth - the trickle down effects of growth - have been negligible for vast swathes of India's population, most of whom live in the countryside. The demographics of the poor are starkly socially marked. Economists tell us that India's dalit and adivasi communities, who account for almost 25% of the country's population and were historically seen as 'untouchable' and 'savage', suffer from disproportionate levels of poverty, remaining worse off than other groups almost everywhere across the country. But econometric analysis is unable to tell us how and why this is the case. This project consists of the following three components: 1) primary anthropological research to understand the processes by which poverty is reproduced through agrarian relations and the shift from farm-based social and economic hierarchies towards new forms of power and exploitation off the farm which lead to the persistence of dalit and adivasi marginalisation across India. 2) analytical development in the study of poverty and its persistence which, informed by recent statistical research and policy shifts at the national and state levels, crafts a more critical and powerful alternative to poverty measurements by ethnographically exploring the relationship between political and economic transformations in rural-based dalit and adivasi lives, and the transformations taking place at the macro level. Crucially this involves analytically establishing a research field which structures ethnography in the framework of political economic theory and brings this combination to the centre of understandings of poverty. 3) some of the first historically situated ethnographic studies which are comparative, not only in their regional distribution, but also in their underlying theoretical and methodological bases. The three cases - from central and eastern India - will be integrated at the level of planning, midterm goals and findings. Methodologically, the project will thus establish the value of an underdeveloped systematic ethnographic approach to poverty which will foreground the comparison of the consequences of rural political and economic transformation on dalit and adivasi lives across a number of different scales and settings in the most under-researched parts of the subcontinent. Five field locations were selected in five Indian states: Kerala, Tamil Nadu, Telangana, Maharashtra and Himachal Pradesh in order to cover a set of different conditions of Dalit and Adivasis across India. In Tamil Nadu, Telangana and Himachal Pradesh, the 'location' was defined as a revenue village. In Kerala, the location was a Tea Plantation and in Maharashtra 3 interrelated villages were covered. In each location a postdoctoral research fellow undertook a household survey, covering employment and education of all adult household members of all Dalit castes and all Adivasi groups. Where relevant, other caste groups were also included. Sampling varied across sites. Where possible the entire village/site was surveyed - as in the case of Himachal Pradesh and the Kerala tea estate. In Tamil Nadu almost all the Adivasi and Dalit castes were surveyed but there was also data included on a smaller random sample of other castes just for illustration of difference. In Telangana, a 30% random sample across castes was conducted. In Maharashtra, where the focus was on three villages, all villages were surveyed and included 40% of all houses in the hill village (fully Bhil), 15% of all houses in the resettled village (fully Bhil) and 10% of all households in the plain villages (and which included Bhils and Gujars). The researcher carried out participant observation living for a year with the communities. As is common in many anthropological studies of this kind consent was oral and based on the longterm trust developed with informants. Information about survey was also delivered verbally, Detailed information is available in: Alpa Shah, Jens Lerche, Richard Axelby, Dalel Benbabaali, Brendan Donegan, Jayaseelan Raj and Vikramaditya Thakur, 2018: Ground Down by Growth. Tribe, caste, class and inequality in twenty-first-century India. Pluto Press and OUP India.