Associated with manuscript titled: Fifty Muslim-majority countries have fewer COVID-19 cases and deaths than the 50 richest non-Muslim countriesThe objective of this research was to determine the difference in the total number of COVID-19 cases and deaths between Muslim-majority and non-Muslim countries, and investigate reasons for the disparities. Methods: The 50 Muslim-majority countries had more than 50.0% Muslims with an average of 87.5%. The non-Muslim country sample consisted of 50 countries with the highest GDP while omitting any Muslim-majority countries listed. The non-Muslim countries’ average percentage of Muslims was 4.7%. Data pulled on September 18, 2020 included the percentage of Muslim population per country by World Population Review15 and GDP per country, population count, and total number of COVID-19 cases and deaths by Worldometers.16 The data set was transferred via an Excel spreadsheet on September 23, 2020 and analyzed. To measure COVID-19’s incidence in the countries, three different Average Treatment Methods (ATE) were used to validate the results. Results published as a preprint at https://doi.org/10.31235/osf.io/84zq5(15) Muslim Majority Countries 2020 [Internet]. Walnut (CA): World Population Review. 2020- [Cited 2020 Sept 28]. Available from: http://worldpopulationreview.com/country-rankings/muslim-majority-countries (16) Worldometers.info. Worldometer. Dover (DE): Worldometer; 2020 [cited 2020 Sept 28]. Available from: http://worldometers.info

"Between October 2011 and November 2012, Pew Research Center, with generous funding from The Pew Charitable Trusts and the John Templeton Foundation, conducted a public opinion survey involving more than 30,000 face-to-face interviews in 26 countries in Africa, Asia, the Middle East and Europe. The survey asked people to describe their religious beliefs and practices, and sought to gauge respondents; knowledge of and attitudes toward other faiths. It aimed to assess levels of political and economic satisfaction, concerns about crime, corruption and extremism, positions on issues such as abortion and polygamy, and views of democracy, religious law and the place of women in society.

"Although the surveys were nationally representative in most countries, the primary goal of the survey was to gauge and compare beliefs and attitudes of Muslims. The findings for Muslim respondents are summarized in the Religion & Public Life Project's reports The World's Muslims: Unity and Diversity and The World's Muslims: Religion, Politics and Society, which are available at www.pewresearch.org. [...] This dataset only contains data for Muslim respondents in the countries surveyed. Please note that this codebook is meant as a guide to the dataset, and is not the survey questionnaire." (2012 Pew Religion Worlds Muslims Codebook)

Global Religious Demographics

Global religious demographics over time

By Throwback Thursday [source]

About this dataset

The dataset contains information on a wide range of religions, including Christianity, Judaism, Islam, Buddhism, Hinduism, Sikhism, Shintoism, Baha'i Faith, Taoism, Confucianism, Jainism, Zoroastrianism, Syncretic Religions (religious practices that blend elements from multiple faiths), Animism (belief in spiritual beings in nature), Non-Religious individuals or those without any religious affiliation.

For each religion and region/country combination recorded in the dataset we have the following information:

• Total population: The total population of the region or country.
• Religious affiliation percentages: The percentages of the population that identify with specific religious affiliations.
• Subgroup populations/percentages: The populations or percentages within specific denominations or sects of each religion.

The dataset also provides additional variables like Year and State Name (for regional data) for further analysis.

How to use the dataset

• Understanding the Columns

  The dataset contains several columns with different categories of information. Here's a brief explanation of some important columns:

  • Year: The year in which the data was recorded.
  • Total Population: The total population of a country or region.
  • State Name (StateNme): The name of the state or region.

  Each religion has specific columns associated with it, such as Christianity, Buddhism, Islam, Hinduism, Judaism, Taoism, Shintoism etc., representing its percentage and population for each category/denomination within that religion.

• Selecting Specific Data

  If you are interested in exploring data related to a particular religion or geographic location:

  • To filter data by Religion: Identify relevant columns associated with that religion such as 'Christianity', 'Buddhism', 'Islam', etc., and extract their respective percentage and population values for analysis.

    Example: If you want to analyze Christianity specifically, extract columns related to Christianity like 'Christianity (Percent)', 'Christianity (Population)', etc.

    Note: There might be multiple columns related to a specific religion indicating different categories or denominations within that religion.

  • To filter data by Geographic Location: Utilize the 'State Name' column ('StateNme') to segregate data corresponding to different states/regions.

    Example: If you want to analyze religious demographics for a particular state/region like California or India:

    i) Filter out rows where State Name is equal to California or India.

    ii) Extract relevant columns associated with your selected religion as mentioned above.

• Finding Trends and Insights

  Once you have selected the specific data you are interested in, examine patterns and trends over time or across different regions.

  • Plotting data using visualizations: Use graphical tools such as line charts, bar charts, or pie charts to visualize how religious demographics have changed over the years or vary across different regions.

  • Analyzing population proportions: By comparing the percentage values of different religions for a given region or over time, you can gather insights into changes in religious diversity.

• Comparing Religions

  If you wish to compare multiple religions:

Research Ideas

• Comparing religious affiliations across different countries or regions: With data on various religions such as Christianity, Islam, Buddhism, Judaism, Hinduism, etc., researchers can compare the religious affiliations of different countries or regions. This can help in understanding the cultural and religious diversity within different parts of the world.
• Exploring the growth or decline of specific religions: By examining population numbers for specific religions such as Jainism, Taoism, Zoroastrianism, etc., this dataset can be used to investigate the growth or decline of these religious groups over time. Researchers can analyze factors contributing to their popularity or decline in particular regions or countries

Acknowledgements

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

License

See the dataset description for more information.

Columns

File: ThrowbackDataThursday 201912 - Religion.csv | Column name...

This Religion and State-Minorities (RASM) dataset is supplemental to the Religion and State Round 2 (RAS2) dataset. It codes the RAS religious discrimination variable using the minority as the unit of analysis (RAS2 uses a country as the unit of analysis and, is a general measure of all discrimination in the country). RASM codes religious discrimination by governments against all 566 minorities in 175 countries which make a minimum population cut off. Any religious minority which is at least 0.25 percent of the population or has a population of at least 500,000 (in countries with populations of 200 million or more) are included. The dataset also includes all Christian minorities in Muslim countries and all Muslim minorities in Christian countries for a total of 597 minorities. The data cover 1990 to 2008 with yearly codings.

These religious discrimination variables are designed to examine restrictions the government places on the practice of religion by minority religious groups. It is important to clarify two points. First, these variables focus on restrictions on minority religions. Restrictions that apply to all religions are not coded in this set of variables. This is because the act of restricting or regulating the religious practices of minorities is qualitatively different from restricting or regulating all religions. Second, this set of variables focuses only on restrictions of the practice of religion itself or on religious institutions and does not include other types of restrictions on religious minorities. The reasoning behind this is that there is much more likely to be a religious motivation for restrictions on the practice of religion than there is for political, economic, or cultural restrictions on a religious minority. These secular types of restrictions, while potentially motivated by religion, also can be due to other reasons. That political, economic, and cultural restrictions are often placed on ethnic minorities who share the same religion and the majority group in their state is proof of this.

This set of variables is essentially a list of specific types of religious restrictions which a government may place on some or all minority religions. These variables are identical to those included in the RAS2 dataset, save that one is not included because it focuses on foreign missionaries and this set of variables focuses on minorities living in the country. Each of the items in this category is coded on the following scale:

0. The activity is not restricted or the government does not engage in this practice.
1. The activity is restricted slightly or sporadically or the government engages in a mild form of this practice or a severe form sporadically.
2. The activity is significantly restricted or the government engages in this activity often and on a large scale.

A composite version combining the variables to create a measure of religious discrimination against minority religions which ranges from 0 to 48 also is included.

ARDA Note: This file was revised on October 6, 2017. At the PIs request, we removed the variable reporting on the minority's percentage of a country's population after finding inconsistencies with the reported values. For detailed data on religious demographics, see the "/data-archive?fid=RCSREG2" Target="_blank">Religious Characteristics of States Dataset Project.

The Carnegie Middle East Governance and Islam Dataset was created by "https://lsa.umich.edu/polisci/people/faculty/tessler.html" Target="_blank">Mark Tessler at the "https://umich.edu/" Target="_blank">University of Michigan. The data set includes both individual-level and country-level variables. Data on individual-level variables are drawn from 35 surveys carried out in 12 Arab countries, Turkey and Iran. Most of the surveys were carried out either as the first wave of the "https://www.arabbarometer.org/" Target="_blank">Arab Barometer, the third, fourth and fifth waves of the "https://www.worldvaluessurvey.org/wvs.jsp" Target="_blank">World Values Survey, or a project on attitudes related to governance carried out by Mark Tessler with funding from the "https://www.nsf.gov/" Target="_blank">National Science Foundation.

This dataset is extracted from https://en.wikipedia.org/wiki/Islam_by_country. Context: There s a story behind every dataset and heres your opportunity to share yours.Content: What s inside is more than just rows and columns. Make it easy for others to get started by describing how you acquired the data and what time period it represents, too. Acknowledgements:We wouldn t be here without the help of others. If you owe any attributions or thanks, include them here along with any citations of past research.Inspiration: Your data will be in front of the world s largest data science community. What questions do you want to see answered?

Right-wing populist voices argue that Muslims do not belong in Western Europe because Islam opposes the “core Western value” of women's empowerment. Ironically, such hostilities could cause European Muslims to reject antagonistic natives and their “Western values,” potentially creating backlashes in Muslims' support for gender equality. Delving into this possibility, this study diverges from simple conceptualizations of one inherently patriarchal Islam to study the diversity among Muslims in the gendered meanings they attach to their religion in different contexts. Empirically, we use a uniquely pooled dataset covering over 9,000 European Muslims in 16 Western European countries between 2008 and 2019. Multilevel models show that while mosque attendance limits support for public-sphere gender equality, religious identifications only do so among men and individual prayer only among women. Additionally, our results tentatively indicate that in more hostile contexts, prayer's effects become more patriarchal while religious identification's connection to opposition to gender equality weakens. We conclude that Islamic religiosities shape Muslims' support for public-sphere gender equality in far more complex ways than any right-wing populist claim on one essential patriarchal Islam captures.

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 aim of the EURISLAM research project is to provide a systematic analysis of cross-national differences and similarities in countries’ approaches to the cultural integration of immigrants in general and Muslims in particular. The countries studied in the research project are Belgium, France, Germany, The Netherlands, Switzerland and the United Kingdom. The core research question can be formulated as follows: ‘How have different traditions of national identity, citizenship and church-state relations affected European immigration countries’ incorporation of Islam, and what are the consequences of these approaches for patterns of cultural distance and interaction between Muslim immigrants and their descendants, and the receiving society?’ In order to answer this question, policy differences are related to cross-national variation in cultural distance and interaction between Muslims and the receiving society population. Three more specific research questions have been designed which are the focus in 7 different Work packages of the EURISLAM research project. The different methodologies used in the Work packages are later combined in the research project, allowing for a triangulation of research findings and a combination of quantitative and qualitative insights.In Work package 3 of the EURISLAM project a survey questionnaire has been developed which enabled a study of the individual characteristics of Muslim immigrants. This survey is designed to answer one of the three specific research questions used in this project: ‘To what extent do we find differences across immigration countries in cultural distance and patterns of interaction between various Muslim immigrant groups and the receiving society population?’ On the one hand, we focussed on attitudes, norms, and values, particularly those relating to democratic norms, gender relations and family values, ethnic, religious, and receiving society identification, and attitudes towards relations across ethnic and religious boundaries. On the other hand, the study looked at cultural and religious resources and practices, such as language proficiency, adherence to various religious practices (e.g., attendance of religious services or wearing of a headscarf), interethnic and interreligious partnerships and marriages, the frequency and quality of interethnic and interreligious relationships with neighbours, friends, and colleagues, and memberships in social and political organisations of the own ethnic and religious group as well as of the receiving society. Both types of questions have been asked – of course where relevant in an adapted format – with regard to members of the dominant ethnic group of the receiving society, because, obviously, cultural distance and interactions are determined by the perceptions, attitude, and practices at both ends of the relationship. All these variables were gathered by way of a survey in each of the countries of a number of selected Muslim immigrant groups, as well as a sample of receiving society ethnics. The data of this survey is now published together with a Codebook.In the revised edition of the codebook new information is added on the religion group variables in Block 3. In retrospect ambiguity appeared in the survey questionnaire specifically in the religion questions which (may) imply missing values for respondents of the ‘Atheist/agnostic/Do not belong to any denomination’ religious faith denomination group. These missing values may lead to distortions when using variables of the religion group. More details on this issue can be found on page 16 (3.2 Information on religion variables) of the revised codebook.Specific information on the project duration has been added on page 8 (1.3 Project Duration) of the revised codebook.The EURISLAM Dataset Survey-data published on October 6, 2015 has not been revised.

Project Tycho datasets contain case counts for reported disease conditions for countries around the world. The Project Tycho data curation team extracts these case counts from various reputable sources, typically from national or international health authorities, such as the US Centers for Disease Control or the World Health Organization. These original data sources include both open- and restricted-access sources. For restricted-access sources, the Project Tycho team has obtained permission for redistribution from data contributors. All datasets contain case count data that are identical to counts published in the original source and no counts have been modified in any way by the Project Tycho team, except for aggregation of individual case count data into daily counts when that was the best data available for a disease and location. The Project Tycho team has pre-processed datasets by adding new variables, such as standard disease and location identifiers, that improve data interpretability. We also formatted the data into a standard data format. All geographic locations at the country and admin1 level have been represented at the same geographic level as in the data source, provided an ISO code or codes could be identified, unless the data source specifies that the location is listed at an inaccurate geographical level. For more information about decisions made by the curation team, recommended data processing steps, and the data sources used, please see the README that is included in the dataset download ZIP file.

Conventional banks are ‘indirectly’ allowed to take more risk under the shadow of sovereign guarantees. Banks commit moral hazards as any major banking crisis will be ‘cushioned’ by deposit insurance and bailed out using the taxpayer's money. This study offers an alternative explanation for the determinants of banks’ credit risk, particularly those from the Islamic regions. Although conventional banks and Islamic banks may share state and social cushioning systems, Islamic banks are strictly prohibited by moral and religious principles from gambling with depositors' funds, even if there is a cushion available to bail them out. However, banks belonging to collective societies, such as those in the MENA area, may be inclined to take more risks due to the perception of having a larger safety net to protect them in the event of failure. We analyse these theoretical intersections by utilising a dataset consisting of 320 banks from 20 countries, covering the time span from 2006 to 2021. Our analysis employs a combination of Ordinary Least Squares (OLS), Fixed Effects (FE), and 2-step System-GMM methodologies. Our analysis reveals that Islamic banks are less exposed to credit risk compared to conventional banks. We contend that the stricter ethical and moral ground and multi-layer monitoring system amid protracted geopolitical and post-pandemic crises impacting Islamic countries contribute to the lower credit risk. We examine the consequences for credit and liquidity management in Islamic banks and the risk management strategies employed by Islamic banks, which can serve as a valuable reference for other banks.

Time series data for the statistic Rank: Getting electricity (1=most business-friendly regulations) and country Iran, Islamic Rep.. Indicator Definition:

Conventional banks are ‘indirectly’ allowed to take more risk under the shadow of sovereign guarantees. Banks commit moral hazards as any major banking crisis will be ‘cushioned’ by deposit insurance and bailed out using the taxpayer’s money. This study offers an alternative explanation for the determinants of banks’ credit risk, particularly those from the Islamic regions. Although conventional banks and Islamic banks may share state and social cushioning systems, Islamic banks are strictly prohibited by moral and religious principles from gambling with depositors’ funds, even if there is a cushion available to bail them out. However, banks belonging to collective societies, such as those in the MENA area, may be inclined to take more risks due to the perception of having a larger safety net to protect them in the event of failure. We analyse these theoretical intersections by utilising a dataset consisting of 320 banks from 20 countries, covering the time span from 2006 to 2021. Our analysis employs a combination of Ordinary Least Squares (OLS), Fixed Effects (FE), and 2-step System-GMM methodologies. Our analysis reveals that Islamic banks are less exposed to credit risk compared to conventional banks. We contend that the stricter ethical and moral ground and multi-layer monitoring system amid protracted geopolitical and post-pandemic crises impacting Islamic countries contribute to the lower credit risk. We examine the consequences for credit and liquidity management in Islamic banks and the risk management strategies employed by Islamic banks, which can serve as a valuable reference for other banks.

The Religion and State (RAS) project is a university-based project located at Bar Ilan University in Ramat Gan, Israel. The general goal is to provide detailed codings on several aspects of separation of religion and state for 183 states on a yearly basis between 1990 and 2014. This constitutes all countries with populations of 250,000 or more, as well as a sampling of countries with lower populations.

This module recodes the governmental and societal discrimination variables used in the Religion and State, Round 3 except that it uses a minority group within a state as the unit of analysis. For example, in the UK, Buddhists, Hindus, Jews, Muslims, Orthodox Christians, and Sikhs are all coded separately. The dataset includes all minorities which are at least 0.2% of the population as well as the following categories of minorities regardless of their population size: (1) Christians in Muslim countries, (2) Muslims in Christian countries, and (3) Jews in Christian-majority and Muslim-majority countries, where present.

Context

The World Happiness Report Up to 2023 dataset offers a comprehensive and up-to-date examination of happiness metrics and the factors influencing well-being on a global scale. This dataset is designed to provide valuable insights for policymakers, researchers, and individuals interested in understanding the dynamics of happiness and well-being worldwide.

Content

There are 9 CSVs, each listing the same items. These datasets include key metrics related to global happiness and well-being, such as country names, regions, happiness scores, GDP per capita, social support, healthy life expectancy, freedom to make life choices, generosity, and perceptions of corruption. These metrics offer insights into the happiness and socio-economic conditions of various countries and regions, making it a valuable resource for analyzing and understanding well-being on a global scale.

Dataset

These datasets comprise various key indicators related to happiness, covering from 2015 up to 2023. It includes the following columns:

Acknowledgment

The primary dataset was retrieved from the World Happiness Report. I extend my sincere gratitude to the original authors and editors of the "World Happiness Report 2023 (11th ed.)" for providing the main dataset used in this compilation.

Editors: John Helliwell, Richard Layard, Jeffrey D. Sachs, Jan-Emmanuel De Neve, Lara B. Aknin, Shun Wang; and Sharon Paculor, Production Editor

©️ Citation: Helliwell, J. F., Layard, R., Sachs, J. D., Aknin, L. B., De Neve, J.-E., & Wang, S. (Eds.). (2023). World Happiness Report 2023 (11th ed.). Sustainable Development Solutions Network.

Image credit: Getty; The Atlantic