Pew Research Center conducted face-to-face surveys among 29,999 adults (ages 18 and older) across 26 Indian states and three union territories in 17 languages. The sample includes interviews with 22,975 Hindus, 3,336 Muslims, 1,782 Sikhs, 1,011 Christians, 719 Buddhists and 109 Jains. An additional 67 respondents belong to other religions or are religiously unaffiliated. Six groups were targeted for oversampling as part of the survey design: Muslims, Christians, Sikhs, Buddhists, Jains and those living in the Northeast region. Interviews were conducted under the direction of RTI International from November 17, 2019, to March 23, 2020. Data collection used computer-assisted personal interviews (CAPI) after random selection of households.

This project was produced by Pew Research Center as part of the Pew-Templeton Global Religious Futures project, which analyzes religious change and its impact on societies around the world. Funding for the Global Religious Futures project comes from The Pew Charitable Trusts and the John Templeton Foundation.

Two reports focused on the findings from this data: •Religion in India: Tolerance and Segregation: https://www.pewresearch.org/religion/2021/06/29/religion-in-india-tolerance-and-segregation/ •How Indians View Gender Roles in Families and Society: https://www.pewresearch.org/religion/2022/03/02/how-indians-view-gender-roles-in-families-and-society/

This study is Pew Research Center's most comprehensive, in-depth exploration of India to date. For this report, Pew surveyed 29,999 Indian adults (including 22,975 who identify as Hindu, 3,336 who identify as Muslim, 1,782 who identify as Sikh, 1,011 who identify as Christian, 719 who identify as Buddhist, 109 who identify as Jain and 67 who identify as belonging to another religion or as religiously unaffiliated). Interviews for this nationally representative survey were conducted face-to-face under the direction of RTI International from November 17, 2019, to March 23, 2020. Respondents were surveyed about religious beliefs and practices, religious identity, nationalism, and tolerance in Indian society.

The religious dataset consisting of Hindu and Muslim hate comments from Bangladesh and India in the Bangla language is a collection of online comments that contain religious hate speech targeting either the Hindu or Muslim communities. These comments were gathered from various sources such as newspapers, social media platforms, and online forums. The purpose of collecting this data is to analyze the prevalence of religious intolerance, identify patterns in hate speech, and contribute to the development of tools for automatically detecting and mitigating such content.

Key Features of the Dataset: Source and Collection:

Comments were sourced from both Bangladesh and India, reflecting religious sentiments in these neighboring countries where tensions between religious groups have often been a social issue. Sources include Bangla-language social media, news articles, opinion pieces, and comments sections on websites.

Content: The dataset contains a mix of both Hindu-targeted hate speech and Muslim-targeted hate speech, with derogatory, offensive, and inflammatory remarks based on religion. Hate comments include stereotypical statements, incitement to violence, communal hatred, and discriminatory language directed at members of the opposing community.

Purpose and Use Cases: Hate Speech Detection: This dataset is useful for developing machine learning models that can automatically identify and flag harmful content on social media platforms. Social Science Research: Researchers can study the psychological and sociopolitical factors that drive such hate speech. Policy and Moderation Tools: Governments, social media platforms, and civil society organizations can use insights from this dataset to design anti-hate speech policies and create moderation systems that reduce online hate.

Challenges: Contextual Nuances: Understanding the cultural and religious context of Bangla comments is crucial for accurately identifying hate speech. A comment that might seem neutral in one context could be deeply offensive in another. Code-Switching: Some comments might mix Bangla with English or regional languages, complicating the classification and sentiment analysis process. Bias in Data: The dataset might reflect a certain level of social bias depending on the region from which it was collected, which needs to be addressed when training AI models.

Conclusion: This dataset offers valuable insights into the dynamics of religious hate speech in Bangladesh and India, two countries with diverse religious populations and a history of interfaith tension. It can help in the development of tools for mitigating online hate speech, while also fostering better understanding and tolerance across religious communities.

The dataset contain the primary census abstract categorised by religion in Kerala. The list contains different religions including Hindu, Buddhist, Christian, Muslim, Jain, Sikh etc.. along with the region specifying whether it is urban or rural. The data is of the 2011 census.

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.

The dataset contain the primary census abstract categorised by religion in Assam. The list contains different religions including Hindu, Buddhist, Christian, Muslim, Jain, Sikh etc.. along with the region specifying whether it is urban or rural. The data is of the 2011 census.

As of 2010, Christianity was the religion with the most followers worldwide, followed by Islam (Muslims) and Hinduism. In the forty years between 2010 and 2050, it is projected that the landscape of world religions will undergo some noticeable changes, with the number of Muslims almost catching up to Christians. The changes in population sizes of each religious group is largely dependent on demographic development, for example, the rise in the world's Christian population will largely be driven by population growth in Sub-Saharan Africa, while Muslim populations will rise across various regions of Africa and South Asia. As India's population is set to grow while China's goes into decline, this will be reflected in the fact that Hindus will outnumber the unaffiliated by 2050. In fact, India may be home to both the largest Hindu and Muslim populations in the world by the middle of this century.

This dataset provides the unemployment rates for major religious groups in India, based on usual status (ps+ss). For years before 2017-18, the data was obtained in different quinquennial rounds of NSSO conducted from 2004-05 (NSS 61st) to 2011-12 (NSS 68th round). From 2017-18 the data is sourced from the annual report of the Periodic Labour Force Survey (PLFS) conducted by the Ministry of Statistics and Programme Implementation. The data highlights unemployment trends within different religious communities.

The dataset contain the primary census abstract categorised by religion in Uttarakhand. The list contains different religions including Hindu, Buddhist, Christian, Muslim, Jain, Sikh etc.. along with the region specifying whether it is urban or rural. The data is of the 2011 census.

This dataset provides the labour force participation rates (LFPR), in percentage terms, for major religious groups in India, based on usual status (ps+ss). It is sourced from the PLFS reports conducted by the Ministry of Statistics and Programme Implementation. The years covered in the survey are from July to June. For instance, 2023-24 refers to the period July 2023 to June 2024 and likewise for other years.

We investigate how religion concordance influences the effectiveness of preventive health campaigns. Conducted during the early stages of the COVID-19 pandemic in two major Indian cities marked by Hindu--Muslim tensions, we randomly assigned a representative sample of slum residents to receive either a physician-delivered information campaign promoting health-related preventive practices, or uninformative control messages on their mobile phones. Messages, introduced by a local citizen (the sender), were cross-randomized to start with a greeting signaling either a Hindu or a Muslim identity, manipulating religion concordance between sender and receiver. We found that doctor messages increased compliance with recommended practices and beliefs in their efficacy. Our findings suggest that the campaign's impact is primarily driven by shared religion between sender and receiver, leading to increased message engagement and compliance with recommended practices. Additionally, we observe that religion concordance helps protect against misinformation.

Misinformation has emerged as a key threat worldwide, with scholars frequently highlighting the role of partisan motivated reasoning in misinformation belief. Yet the mechanisms enabling the endorsement of misinformation may differ in contexts where other identities are salient. This study explores whether religion drives the endorsement of misinformation in India. Using original data, we first show that individuals with high levels of religiosity and religious polarization endorse significantly higher levels of misinformation. Next, to understand the causal mechanisms through which religion operates, we field an experiment where corrections rely on religious messaging, and/or manipulate perceptions of religious ingroup identity. We find that corrections including religious frames (1) reduce the endorsement of misinformation; (2) are sometimes more effective than standard corrections; and (3) work beyond the specific story corrected. These findings highlight the religious roots of belief formation and provide hope that social identities can be marshaled to counter misinformation.

It was estimated that by 2050, India's Muslim population would grow by ** percent compared to 2010. For followers of the Hindu faith, this change stood at ** percent. According to this projection, the south Asian country would be home not just to the world's majority of Hindus, but also Muslims by this time period. Regardless, the latter would continue to remain a minority within the country at ** percent, with ** percent or *** billion Hindus at the forefront by 2050.

419 Global export shipment records of Religious Items with prices, volume & current Buyer's suppliers relationships based on actual Global export trade database.

Subscribers can find out export and import data of 23 countries by HS code or product’s name. This demo is helpful for market analysis.

Subscribers can find out export and import data of 23 countries by HS code or product’s name. This demo is helpful for market analysis.

This data set contains experimental data collected as part of the field experiments conducted in West Bengal. These experiments study the effect of religious identity and religious fragmentation on cooperation, rent-seeking and income distribution among Hindu and Muslim groups. We study the effect of religious identity among Hindu and Muslim groups by varying the way our subjects are matched with each other. We implement in-group/in-group treatments where Muslim subjects play with fellow Muslim subjects and Hindu subjects play with fellow Hindu subjects; we also implement in-group/out-group treatments where Hindu subjects play with Muslim subjects. Finally, we have a control treatment where the identity of a subject's match is uncertain. To study the effect of fragmentation, we resort to a quasi-experimental approach. We take religious composition of villages as fixed, based on the village-level survey on religious fragmentation by Das et al. (2011). We select villages in two districts in West Bengal which conform to one of three categories: Muslim-dominated, where over 90% of the population is Muslim; Hindu-dominated, where over 90% of the population is Hindu; and fragmented, where the Muslim and Hindu communities are roughly equal. Our experimental design combines identity treatments with village types to understand how social identity interacts with fragmentation. For more details on the analysis of the data, please see the link to the first working paper to have come out of this project, which can be found in the "Related Resources" section. Tackling increasing resource scarcity is one of the major challenges to policy-makers in developing countries. An important aspect of resource scarcity involves public goods. Lack of public goods, like health and education, can significantly reduce the welfare of individuals and households and often this affects the poorest the most. In India, these issues are amplified by the existence of a long-standing social structure based around caste and religion. Such social fragmentation can result in social exclusion and/or lower public good provision. This project investigates the behavioural foundations of inter-group discrimination on economic performance in rural West Bengal, India. It builds on existing household survey work on religious- and caste-based social exclusion in villages in West Bengal by conducting a series of field experiments. Field experiments study the decisions of agents who in their daily lives are affected by poverty, and help determine the extent to which their preferences regarding caste, ethnicity and religion determine their willingness to socially exclude others or themselves to be excluded. This project‘s findings will help policy-makers to the extent that they facilitate the identification of the right policy response to social exclusion and lower economic performance, which in turn are key determinants of poverty. The data collection method employed experimental economics. The description of the recruitment of participants and experimental procedures is taken from section 3.4 of Chakravarty, S., Fonseca, M.A., Ghosh, S. and Marjit, S. (2015) "Religious fragmentation, social identity and cooperation: Evidence from a artefactual field experiment in India", University of Exeter Economics Department Discussion Paper Series 15/01. which is the first paper based on this project. A link to this paper is provided in this submission. Please see this paper for more details on the experimental procedures. We employed a mixed-gender, mixed-religion team of local research assistants to recruit participants and conduct the sessions, so as to minimize any possible experimenter demand effect. A week ahead of a planned session, our research assistants travelled to the village where that session would take place. A set of neighborhoods were randomly selected, and within each neighborhood, recruitment was done on a door-by-door basis. On a given street, every two consecutive houses were skipped and the third house would be approached and those who agreed to participate would be signed up. Participants were reminded about the session the day before it took place. Participants did not know the purpose of the experiment: when approached, they were informed that the research team would be conducting decision- making sessions. We conducted one session per village. In the H-H and M-M sessions, all subjects in the room shared the same religion. In the H-M sessions, subjects of both religious were present; Hindu subjects played a Muslim counterpart in every game and vice versa. This was common knowledge. Finally, in the MIX sessions, Hindu and Muslim subjects were present in the session, but they did not know the religion of the person with whom they were playing. Sessions were split in three parts. In the first part, participants played three games: the Prisoners' Dilemma, the Stag-Hunt game and the Tullock contest (in that specific order). In the second part of the session, participants played a series of individual decision-making tasks. In the third part, participants individually responded to a survey in a separate room, got feedback on the decisions made in the experiment, and received their corresponding payment. An experimenter standing in the middle of the room read the instructions aloud, using visual aids to explain the incentive structure of each game (see Appendix for the experimental materials). We did not employ written instructions since about a third of our subjects was unable to read or write. As such, we denoted payoffs in INR and used images of Indian notes and coins to represent payoffs. See materials for details. Prior to the start of each session, an experimenter informed subjects that all decisions taken would be anonymous, there would be no identifying information collected as part of the experiment. Subjects were also told that they had the right to abandon the session; they also had the right to opt out of the study without detriment to their payment for participation. Again, this information was announced orally, as a large proportion of participants were not able to read or write.

The catalog contains data related to Non-Workers by Main Activity, Age, Sex and Religion, Census 2001 - India and States. It includes data on Non-worker, Main Activity, Household Duty, Dependent, Pensioner, Beggar, Vagrant.

DISCLAIMER

India is a vast and diverse country, each day in India we have some or other festival at one or another place. This is just a list of popular ones as available on Google Calendar. Also, the dates can change according to Tithi or Muhurat, and different people might have different dates for the same festival.

This dataset is for data purposes only and we do not intend to hurt the sentiments of any individual, community, sect o or religion.

The catalog contains data related to population by religious community (for each caste/tribe separately), census 2001 - India and States. It includes data on Population, Scheduled Caste Population, Scheduled Tribe Population, SC Population, ST Population, Male Population, Female Population, Religion, ST Name, SC Name, Scheduled Caste Name, Scheduled Tribe Name, Rural Population, Urban Population.