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)

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.

Contains data from the World Bank's data portal. There is also a consolidated country dataset on HDX.

Education is one of the most powerful instruments for reducing poverty and inequality and lays a foundation for sustained economic growth. The World Bank compiles data on education inputs, participation, efficiency, and outcomes. Data on education are compiled by the United Nations Educational, Scientific, and Cultural Organization (UNESCO) Institute for Statistics from official responses to surveys and from reports provided by education authorities in each country.

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.

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.

Iran is a country locating in Middle east. Iran is located in a strategic region at the crossroads of Europe, Asia, and Africa. This has made it a major center of trade and commerce for centuries. Iran is also a member of the United Nations, the Non-Aligned Movement, and the Organization of Islamic Cooperation.

Despite its rich history, large population, and abundant economic potential, Iran is a lower-middle-income country (according to the World Bank). It has large reserves of raw materials, including oil, gas, and minerals, but unfortunately, it does not fully utilize these resources.

This dataset is all the data about Iran in the world bank website. Here is a summary:

Economic data(2022/23) - GDP (current US$): 463billion - GDPpercapita(currentUS): $5,211 - Inflation, GDP deflator (annual %): 31.5% - Oil rents (% of GDP): 25.6% - Gini index: 38.8 (2019)

Social data - Population, total: 88.5 million (2022) - Population growth (annual %): 1.1% (2022) - Net migration: 28,080 (2021) - Life expectancy at birth, total (years): 77 (2021) - Human Capital Index (HCI) (scale 0-1): 0.63 (2020)

Environmental data - CO2 emissions (metric tons per capita): 7.2 (2021) - Renewable energy consumption (% of total final energy consumption): 3.6% (2021) - Forest area (% of land area): 7.8% (2020)

You can access the data in this link. There is also lots of plots and other fun tools which you should try.

[World Bank notes] The World Bank systematically assesses the appropriateness of official exchange rates as conversion factors. In Iran, multiple or dual exchange rate activity exists and must be accounted for appropriately in underlying statistics. An alternative estimate (“alternative conversion factor” - PA.NUS.ATLS) is thus calculated as a weighted average of the different exchange rates in use in Iran. Doing so better reflects economic reality and leads to more accurate cross-country comparisons and country classifications by income level. For Iran, this applies to 1972-2022. Alternative conversion factors are used in the Atlas methodology and elsewhere in World Development Indicators as single-year conversion factors.

It is noted that the reporting period for national accounts data is designated as either calendar year basis (CY) or fiscal year basis (FY). For Iran, it is fiscal year based (fiscal year-end: March 20).

IntroductionAlthough the number of people receiving antiretroviral therapy (ART) in Indonesia has increased in recent years, little is known about the specific characteristics affecting adherence in this population. Indonesia is different from most of its neighbors given that it is a geographically and culturally diverse country, with a large Muslim population. We aimed to identify the current rate of adherence and explore factors that influence ART adherence.MethodsData were collected from ART-prescribed outpatients on an HIV registry at a North Jakarta hospital in 2012. Socio-demographic and behavioral characteristics were explored as factors associated with adherence using logistics regression analyses. Chi squared test was used to compare the difference between proportions. Reasons for missing medication were analyzed descriptively.ResultsTwo hundred and sixty-one patients participated, of whom 77% reported ART adherence in the last 3 months. The level of social support experienced was independently associated with adherence where some social support (p = 0.018) and good social support (p = 0.039) improved adherence compared to poor social support. Frequently cited reasons for not taking ART medication included forgetting to take medication (67%), busy with something else (63%) and asleep at medication time (60%).DiscussionThis study identified that an increase in the level of social support experienced by ART-prescribed patients was positively associated with adherence. Social support may minimize the impact of stigma among ART prescribed patients. Based on these findings, if social support is not available, alternative support through community-based organizations is recommended to maximize treatment success.

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.

Negative binomial model of the relationship between ethnic religion interaction and male fertility (All MEN aged 15–59 years).

This article provides a statistical analysis of the determinants of Arab aid allocation using Heckman’s two-step estimator. It is found that poorer, Arab, Islamic and sub-Saharan African countries are more likely to receive some positive amount of Arab aid (gatekeeping stage). The same is true for countries not maintaining diplomatic relations with Israel as well as those with voting patterns in the United Nations General Assembly similar to Saudi Arabia. Arab and more populous countries also receive a higher share of the total aid allocated (level stage). The same is true for Islamic countries in the case of bilateral aid and countries with voting similarity in the case of multilateral aid. Donor interest, in particular Arab solidarity, plays a clear role at both stages, whereas recipient need as measured by a country’s level of income only affects the gate-keeping stage, not the level stage.

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.

Anaemia is a major public health concern in developing countries, particularly among children, adolescents, and women of reproductive age. The study aimed to assess the anaemia status among adolescent girls, pregnant, and lactating women with their contributing factors in the southern rural regions of Bangladesh. This cross-sectional study was conducted among 400 adolescent girls, 375 pregnant, and 375 lactating women using a multistage cluster-random sampling technique. Anaemia was measured through haemoglobin concentration in blood capillaries collected with a Hemocue 301 machine. Multinomial logistic regression was used to determine the factors associated with anaemia. The average age of pregnant and lactating women was 24 years and 15.2 years for girls. Overall, the prevalence of anaemia was 50% among pregnant women, 46% among lactating women, and 38% among adolescent girls. The risk of anaemia among adolescent girls was higher among non-Muslim (aOR = 2.13, 95%CI:1.05–4.31), belonged to families having >5 members (aOR = 2.24, 95%CI:1.16–4.31) while exposure to media reduced their risk (aOR = 0.33, 95%CI:0.15–0.74). Pregnant women who consumed a diversified diet, washed their hands after toilet, and received ≥4 ANC visits had a lower likelihood of developing anaemia. Lactating women who were employed, consumed a diversified diet, washed their hands before preparing food, and after toilet, had been exposed to media, received ≥4 ANC visits, and consumed ≥90 IFA, had a lower risk of developing anaemia. However, anaemia was more likely to be associated with lactating women who were non-Muslim (aOR = 3.75; 95%CI:1.26–11.22). The high prevalence of anaemia emphasizes the need to reconsider the existing strategy for the prevention and control of micronutrient deficiencies in Bangladesh.