Dataset

This dataset was created by Ifeanyichukwu Nwobodo

Contents

COVID-19 in Muslim vs. Non-Muslim Countries

A Comparative Study of 50 Muslim-Majority and 50 Richest Non-Muslim Countries

About Dataset

This dataset contains information on COVID-19 cases and deaths in 50 Muslim-majority countries compared to the 50 richest non-Muslim countries. The aim of the dataset is to investigate the differences in COVID-19 incidence between these two groups and to explore potential reasons for these disparities. The Muslim-majority countries in the sample had more than 50.0% Muslims, while the non-Muslim countries were selected based on their GDP, excluding any Muslim-majority countries listed. The data was collected on September 18, 2020, and includes information on the percentage of Muslim population per country, GDP, population count, and total number of COVID-19 cases and deaths. The dataset was transferred via an Excel spreadsheet on September 23, 2020 and analyzed using three different Average Treatment Methods (ATE) to validate the results. The dataset was published as a preprint and is associated with a manuscript titled "Fifty Muslim-majority countries have fewer COVID-19 cases and deaths than the 50 richest non-Muslim countries". The manuscript can be accessed via the following Link The sources of the data are also provided in the manuscript. The percentage of Muslim population per country was obtained from World Population Review and can be accessed at Link The GDP per country, population count, and total number of COVID-19 cases and deaths were obtained from Worldometers and can be accessed at Link

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Columns

Research Ideas / Data Use

• Comparative analysis: Researchers can use this dataset to compare the COVID-19 cases and deaths between Muslim-majority and non-Muslim countries. This can help to identify any disparities or differences in the response to the pandemic.
• Trend analysis: Over time, this dataset can be used to track the changes in the COVID-19 cases and deaths in Muslim-majority and non-Muslim countries. This can help to identify trends and patterns that may inform future research.
• Geographical analysis: This dataset can be used to explore the geographical distribution of COVID-19 cases and deaths in Muslim-majority and non-Muslim countries. This can help to identify hotspots and areas that may require special attention.
• Demographic analysis: Researchers can use the data to explore the impact of demographic factors on the spread and severity of the pandemic in Muslim-majority and non-Muslim countries. This can help to identify any patterns or correlations that may inform future research and policy decisions.
• Economic analysis: The data can be used to explore the economic impact of the pandemic on Muslim-majority and non-Muslim countries. By comparing the GDP and other economic indicators in these countries, researchers can identify any patterns or trends that may inform economic policy decisions.

Acknowledgements

if this dataset was used in your work or studies, please credit the original source Please Credit ↑ ⠀

License

License: CC0 1.0 Universal (CC0 1.0) - Public Domain Dedication No Copyright - You can copy, modify, distribute and perform the work, even for commercial purposes, all without asking permission. More Information

"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)

Pew Research Center’s “World’s Muslims” dataset is based on a survey conducted between October 2011 and November 2012. The study explores the religious beliefs, practices, social attitudes, and political views of Muslims across multiple countries, providing insights into diversity within the global Muslim population.

Religious Populations Worldwide

Religious Populations Worldwide by Year and Category

By Throwback Thursday [source]

About this dataset

The dataset includes data on Christianity, Islam, Judaism, Buddhism, Hinduism, Sikhism, Shintoism, Baha'i Faith, Taoism, Confucianism, Jainism and various other syncretic and animist religions. For each religion or denomination category, it provides both the total population count and the percentage representation in relation to the overall population.

Additionally, - Columns labeled with Population provide numeric values representing the total number of individuals belonging to a particular religion or denomination. - Columns labeled with Percent represent numerical values indicating the percentage of individuals belonging to a specific religion or denomination within a given population. - Columns that begin with ** indicate primary categories (e.g., Christianity), while columns that do not have this prefix refer to subcategories (e.g., Christianity - Roman Catholics).

In addition to providing precise data about specific religions or denominations globally throughout multiple years,this dataset also records information about geographical locations by including state or country names under StateNme.

This comprehensive dataset is valuable for researchers seeking information on global religious trends and can be used for analysis in fields such as sociology, anthropology studies cultural studies among others

How to use the dataset

Introduction:

• Understanding the Columns:

• Year: Represents the year in which the data was recorded.

• StateNme: Represents the name of the state or country for which data is recorded.

• Population: Represents the total population of individuals.

• Total Religious: Represents the total percentage and population of individuals who identify as religious, regardless of specific religion.

• Non Religious: Represents the percentage and population of individuals who identify as non-religious or atheists.

• Identifying Specific Religions: The dataset includes columns for different religions such as Christianity, Judaism, Islam, Buddhism, Hinduism, etc. Each religion is further categorized into specific denominations or types within that religion (e.g., Roman Catholics within Christianity). You can find relevant information about these religions by focusing on specific columns related to each one.

• Analyzing Percentages vs. Population: Some columns provide percentages while others provide actual population numbers for each category. Depending on your analysis requirement, you can choose either column type for your calculations and comparisons.

• Accessing Historical Data: The dataset includes records from multiple years allowing you to analyze trends in religious populations over time. You can filter data based on specific years using Excel filters or programming languages like Python.

• Filtering Data by State/Country: If you are interested in understanding religious populations in a particular state or country, use filters to focus on that region's data only.

Example - Extracting Information:

Let's say you want to analyze Hinduism's growth globally from 2000 onwards:

• Identify Relevant Columns:
• Year: to filter data from 2000 onwards.

• Hindu - Total (Percent): to analyze the percentage of individuals identifying as Hindus globally.

• Filter Data:

• Set a filter on the Year column and select values greater than or equal to 2000.

• Look for rows where Hindu - Total (Percent) has values.

• Analyze Results: You can now visualize and calculate the growth of Hinduism worldwide after filtering out irrelevant data. Use statistical methods or graphical representations like line charts to understand trends over time.

Conclusion: This guide has provided you with an overview of how to use the Rel

Research Ideas

• Comparing religious populations across different countries: With data available for different states and countries, this dataset allows for comparisons of religious populations across regions. Researchers can analyze how different religions are distributed geographically and compare their percentages or total populations across various locations.
• Studying the impact of historical events on religious demographics: Since the dataset includes records categorized by year, it can be used to study how historical events such as wars, migration, or political changes have influenced religious demographics over time. By comparing population numbers before and after specific events, resea...

This dataset contains Food Prices data for Iran (Islamic Republic of), sourced from the World Food Programme Price Database. The World Food Programme Price Database covers foods such as maize, rice, beans, fish, and sugar for 98 countries and some 3000 markets. It is updated weekly but contains to a large extent monthly data. The data goes back as far as 1992 for a few countries, although many countries started reporting from 2003 or thereafter.

This dataset is about book subjects. It has 1 row and is filtered where the books is Another world : losing our children to Islamic State : based on verbatim interviews developed with Nicolas Kent from his original idea. It features 10 columns including number of authors, number of books, earliest publication date, and latest publication date.

WorldPop produces different types of gridded population count datasets, depending on the methods used and end application. Please make sure you have read our Mapping Populations overview page before choosing and downloading a dataset.

A description of the modelling methods used for age and gender structures can be found in "https://pophealthmetrics.biomedcentral.com/articles/10.1186/1478-7954-11-11" target="_blank"> Tatem et al and Pezzulo et al. Details of the input population count datasets used can be found here, and age/gender structure proportion datasets here.
Both top-down 'unconstrained' and 'constrained' versions of the datasets are available, and the differences between the two methods are outlined here. The datasets represent the outputs from a project focused on construction of consistent 100m resolution population count datasets for all countries of the World structured by male/female and 5-year age classes (plus a <1 year class). These efforts necessarily involved some shortcuts for consistency. The unconstrained datasets are available for each year from 2000 to 2020.
The constrained datasets are only available for 2020 at present, given the time periods represented by the building footprint and built settlement datasets used in the mapping.
Data for earlier dates is available directly from WorldPop.

WorldPop (www.worldpop.org - School of Geography and Environmental Science, University of Southampton; Department of Geography and Geosciences, University of Louisville; Departement de Geographie, Universite de Namur) and Center for International Earth Science Information Network (CIESIN), Columbia University (2018). Global High Resolution Population Denominators Project - Funded by The Bill and Melinda Gates Foundation (OPP1134076). https://dx.doi.org/10.5258/SOTON/WP00646

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.

The complex evolution and affiliations of the organization currently known as the Islamic State of Iraq and the Levant (ISIL) presents challenges for systematic, comprehensive analysis of terrorist attacks carried out by ISIL and related groups and individuals. In order to facilitate analysis of the Global Terrorism Database (GTD) with respect to ISIL-related terrorism, the GTD team conducted supplemental research to generate this auxiliary dataset that identifies ISIL related attacks worldwide from 2002 through 2016 and classifies them based on the type of relationship the perpetrators of each event had to ISIL.

Islam and Christianity form the two dominant religions in Nigeria. The basis of traditional religions was systematically exterminated in the religio-cultural life of the Nigerian people after their contact with colonialism. Approximately 90 percent of the Nigerian people have since preferred to be identified with either Islam or Christianity.Nigeria’s contact with Islam predated that of Christianity and European colonialism; its spread was facilitated into Sub-Saharan Africa through trade and commerce. The northern part of Nigeria is symbolic to the history of Islam, as it penetrated the area through the Kanem-Borno Empire in the 11th century before spreading to the other predominately Hausa states. Islam was then introduced into the traditional societies of the Yoruba-speaking people of south-west Nigeria through their established commercial relationship with people of the North, particularly the Nupe and Fulani.Christianity reached Nigeria in the 15th century with the visitation of the Roman and Catholic missionaries to the coastal areas of the Niger-Delta region, although there were few recorded converts and churches built during this period. Christianity soon recorded a boost in the southern region given its opposition to the slave trade and its promotion of Western education. In contrast to the smooth process Christian evangelization underwent in the South, its process in the North was difficult because Islam had already become well-established.Given the philosophy of Islam as a complete way of life for a Muslim, Islam has always been closely attached to politics in Nigeria. The emergence of particular Islamic groups was significantly influenced by international events, particularly the 1979 Iranian revolution and the corresponding disenchantment from the West. These developments shaped Nigerian national politics of the period as Muslims radically redefined their political interests in line with religion and began to clamor for the incorporation of the Sharia legal system into the country’s judicial system. Nigeria then tried to harness opportunities accruable from other Muslim countries by becoming a registered member with the Organization of Islamic Conference (OIC) in 1985. This inflamed Christians and nurtured the fear of domination by their Muslim counterparts and the possibility of a gradual extinction of their religio-political strength in the national political structure. The distinct religious separation has also instigated violence in present-day Nigeria, including the Sharia riot in Kaduna in 2000, ongoing ethno-religious violence in Jos since 2001, and the 2011 post-election violence that erupted in some northern states. Nigerians’ continued loyalty to religion compared to that of the country continues to sustain major political debate, conflict, and violent outbreaks between populations of the two faiths.

ISO3 - International Organization for Standardization 3-digit country code

AREA_AFF - Geographic area affected by disease

DT_START - Date health event started

DT_END - Date health event ended

TYPE - Type of disease group

DISEASE - Name of disease

NUM_DTH - Number of people reported dead from disease

NUM_AFF - Number of people affected from disease

SOURCE_DT - Source creation date

SOURCE - Primary source

Collection

This HGIS was created using information collected from several websites. EM-DAT, the World Health Organization, and news reports provided information about the outbreaks.

The data included herein have not been derived from a registered survey and should be considered approximate unless otherwise defined. While rigorous steps have been taken to ensure the quality of each dataset, DigitalGlobe Analytics is not responsible for the accuracy and completeness of data compiled from outside sources.

Sources (HGIS)

Egunganga, Vincent, Ami Sadiq, and Hir Joseph. All AfricaHIR JOSEPH, "Nigeria: Lassa Fever Returns Vicio." Last modified March 09, 2013. Accessed April 16, 2013. http://allafrica.com/.

EM DAT, "Country Database; Nigeria." Last modified March 2013. Accessed April 16, 2013. http://www.emdat.be/.

World Health Organization, "Global Health Observatory; Nigeria." Last modified 2012. Accessed April 16, 2013. http://www.who.int/en/.

Sources (Metadata)

Encyclopedia of the Nations, "Nigeria Country Specific Information." Last modified 2013. Accessed March 28, 2013. http://www.nationsencyclopedia.com.

Kates, Jennifer, and Alyssa Wilson Leggoe. The Henry J. Kaiser Family Foundation, "HIV/AIDS; The HIV/AIDS Epidemic in Nigeria." Last modified October 2005. Accessed April 16, 2013. http://www.kff.org/.

United States Embassy in Nigeria, "Nigeria Malaria Fact Sheet." Last modified December 2011. Accessed April 16, 2013. http://nigeria.usembassy.gov.

World Health Organization, "Global Task Force on Cholera Control." Last modified January 18, 2012. Accessed April 16, 2013. http://www.who.int/.

World Health Organization, "Meningococcal disease: situation in the African Meningitis Belt." Last modified 2012. Accessed March 14, 2013. http://www.who.int/csr/don/2012_05_24/en/index.html.

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).

In the aftermath of the attacks on September 11, 2001, and subsequent terrorist attacks elsewhere around the world, a key counterterrorism concern was the possible radicalization of Muslims living in the United States. The purpose of the study was to examine and identify characteristics and practices of four American Muslim communities that have experienced varying levels of radicalization. The communities were selected because they were home to Muslim-Americans that had experienced isolated instances of radicalization. They were located in four distinct regions of the United States, and they each had distinctive histories and patterns of ethnic diversity. This objective was mainly pursued through interviews of over 120 Muslims located within four different Muslim-American communities across the country (Buffalo, New York; Houston, Texas; Seattle, Washington; and Raleigh-Durham, North Carolina), a comprehensive review of studies an literature on Muslim-American communities, a review of websites and publications of Muslim-American organizations and a compilation of data on prosecutions of Muslim-Americans on violent terrorism-related offenses.

World Bank activity file for IRAN, ISLAMIC REPUBLIC OF

Overview

This is the data repository for the 2019 Novel Coronavirus Visual Dashboard operated by the Johns Hopkins University Center for Systems Science and Engineering (JHU CSSE). This database was created in response to the Coronavirus public health emergency to track reported cases in real-time. The data include the location and number of confirmed COVID-19 cases, deaths and recoveries for all affected countries, aggregated at the appropriate province or state. It was developed to enable researchers, public health authorities and the general public to track the outbreak as it unfolds. Additional information is available in the blog post, Mapping 2019-nCoV (https://systems.jhu.edu/research/public-health/ncov/), and included data sources are listed here: https://github.com/CSSEGISandData/COVID-19

Sample Query 1

How many confirmed COVID-19 cases were there in the US, by state? This query determines the total number of cases by province in February. A "province_state" can refer to any subset of the US in this particular dataset, including a county or state. SELECT province_state, confirmed AS feb_confirmed_cases, FROM bigquery-public-data.covid19_jhu_csse.summary WHERE country_region = "US" AND date = '2020-02-29' ORDER BY feb_confirmed_cases desc

Sample Query 2

Which countries with the highest number of confirmed cases have the most per capita? This query joins the Johns Hopkins dataset with the World Bank's global population data to determine which countries among those with the highest total number of confirmed cases have the most confirmed cases per capita.

with country_pop AS( SELECT IF(country = "United States","US",IF(country="Iran, Islamic Rep.","Iran",country)) AS country, year_2018 FROM bigquery-public-data.world_bank_global_population.population_by_country)

SELECT cases.date AS date, cases.country_region AS country_region, SUM(cases.confirmed) AS total_confirmed_cases, SUM(cases.confirmed)/AVG(country_pop.year_2018) * 100000 AS confirmed_cases_per_100000 FROM bigquery-public-data.covid19_jhu_csse.summary cases JOIN country_pop ON cases.country_region LIKE CONCAT('%',country_pop.country,'%') WHERE cases.country_region = "US" AND country_pop.country = "US" AND cases.date = DATE_SUB(current_date(),INTERVAL 1 day) GROUP BY country_region, date

UNION ALL

SELECT cases.date AS date, cases.country_region AS country_region, SUM(cases.confirmed) AS total_confirmed_cases, SUM(cases.confirmed)/AVG(country_pop.year_2018) * 100000 AS confirmed_cases_per_100000 FROM bigquery-public-data.covid19_jhu_csse.summary cases JOIN country_pop ON cases.country_region LIKE CONCAT('%',country_pop.country,'%') WHERE cases.country_region = "France" AND country_pop.country = "France" AND cases.date = DATE_SUB(current_date(),INTERVAL 1 day) GROUP BY country_region, date

UNION ALL

SELECT cases.date AS date, cases.country_region AS country_region, SUM(cases.confirmed) AS total_confirmed_cases, SUM(cases.confirmed)/AVG(country_pop.year_2018) * 100000 AS confirmed_cases_per_100000 FROM bigquery-public-data.covid19_jhu_csse.summary cases JOIN country_pop ON cases.country_region LIKE CONCAT('%',country_pop.country,'%') WHERE cases.country_region = "China" AND country_pop.country = "China" AND cases.date = DATE_SUB(current_date(),INTERVAL 1 day)

GROUP BY country_region, date

UNION ALL

SELECT cases.date AS date, cases.country_region AS country_region, cases.confirmed AS total_confirmed_cases, cases.confirmed/country_pop.year_2018 * 100000 AS confirmed_cases_per_100000 FROM bigquery-public-data.covid19_jhu_csse.summary cases JOIN country_pop ON cases.country_region LIKE CONCAT('%',country_pop.country,'%') WHERE cases.country_region IN ("Italy", "Spain", "Germany", "Iran") AND cases.date = DATE_SUB(current_date(),INTERVAL 1 day) ORDER BY confirmed_cases_per_100000 desc

Dataset source

JHU CSSE

Update frequency

Daily

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.

Lithuania LT: Foreign Direct Investment Income: Inward: USD: Total: Iran, Islamic Republic Of data was reported at 0.065 USD mn in 2023. This records an increase from the previous number of -0.074 USD mn for 2022. Lithuania LT: Foreign Direct Investment Income: Inward: USD: Total: Iran, Islamic Republic Of data is updated yearly, averaging 0.000 USD mn from Dec 2005 (Median) to 2023, with 17 observations. The data reached an all-time high of 0.189 USD mn in 2021 and a record low of -0.125 USD mn in 2020. Lithuania LT: Foreign Direct Investment Income: Inward: USD: Total: Iran, Islamic Republic Of data remains active status in CEIC and is reported by Organisation for Economic Co-operation and Development. The data is categorized under Global Database’s Lithuania – Table LT.OECD.FDI: Foreign Direct Investment Income: USD: by Region and Country: OECD Member: Annual. Reverse investment: Netting of reverse investment in equity (when a direct investment enterprise acquires less than 10% equity ownership in its parent) and reverse investment in debt (when a direct investment enterprise extends a loan to its parent) is applied in the recording of total inward and outward FDI transactions and positions. Treatment of debt FDI transactions and positions between fellow enterprises: directional basis according to the residency of the ultimate controlling parent (extended directional principle). FDI transactions and positions by partner country and/or by industry are available excluding and including resident Special Purpose Entities (SPEs). The dataset 'FDI statistics by parner country and by industry - Summary' contains series including resident SPEs only. Valuation method used for listed inward and outward equity positions: Market value. Valuation method used for unlisted inward and outward equity positions: Own funds at book value. Valuation method used for inward and outward debt positions: Market and Nominal values. .; FDI statistics are available by geographic allocation, vis-à-vis single partner countries worldwide and geographical and economic zones aggregates. Partner country allocation can be subject to confidentiality restrictions. Geographic allocation of inward and outward FDI transactions and positions is according to the immediate counterparty. Inward FDI positions according to the ultimate counterparty (the ultimate investing country) are also available and publishable. In the dataset 'FDI statistics by parner country and by industry - Summary', inward FDI positions are showed according to the UIC. Intercompany debt between related financial intermediaries, including permanent debt, are excluded from FDI transactions and positions. FDI statistics are available by industry sectors according to ISIC4 classification. Industry sector allocation can be subject to confidentiality restrictions. Inward FDI transactions and positions are allocated to the activity of the resident direct investment enterprise. Outward FDI transactions are allocated according to the activity of the non resident direct investment enterprise. Outward FDI positions are allocated according to the activity of the non resident direct investment enterprise. Statistical unit: Enterprise.

Lithuania LT: Foreign Direct Investment Position: Outward: Total: Iran, Islamic Republic Of data was reported at 0.000 EUR mn in 2023. This stayed constant from the previous number of 0.000 EUR mn for 2022. Lithuania LT: Foreign Direct Investment Position: Outward: Total: Iran, Islamic Republic Of data is updated yearly, averaging 0.000 EUR mn from Dec 2005 (Median) to 2023, with 19 observations. The data reached an all-time high of 0.000 EUR mn in 2023 and a record low of 0.000 EUR mn in 2023. Lithuania LT: Foreign Direct Investment Position: Outward: Total: Iran, Islamic Republic Of data remains active status in CEIC and is reported by Organisation for Economic Co-operation and Development. The data is categorized under Global Database’s Lithuania – Table LT.OECD.FDI: Foreign Direct Investment Position: by Region and Country: OECD Member: Annual. Reverse investment: Netting of reverse investment in equity (when a direct investment enterprise acquires less than 10% equity ownership in its parent) and reverse investment in debt (when a direct investment enterprise extends a loan to its parent) is applied in the recording of total inward and outward FDI transactions and positions. Treatment of debt FDI transactions and positions between fellow enterprises: directional basis according to the residency of the ultimate controlling parent (extended directional principle). FDI transactions and positions by partner country and/or by industry are available excluding and including resident Special Purpose Entities (SPEs). The dataset 'FDI statistics by parner country and by industry - Summary' contains series including resident SPEs only. Valuation method used for listed inward and outward equity positions: Market value. Valuation method used for unlisted inward and outward equity positions: Own funds at book value. Valuation method used for inward and outward debt positions: Market and Nominal values. .; FDI statistics are available by geographic allocation, vis-à-vis single partner countries worldwide and geographical and economic zones aggregates. Partner country allocation can be subject to confidentiality restrictions. Geographic allocation of inward and outward FDI transactions and positions is according to the immediate counterparty. Inward FDI positions according to the ultimate counterparty (the ultimate investing country) are also available and publishable. In the dataset 'FDI statistics by parner country and by industry - Summary', inward FDI positions are showed according to the UIC. Intercompany debt between related financial intermediaries, including permanent debt, are excluded from FDI transactions and positions. FDI statistics are available by industry sectors according to ISIC4 classification. Industry sector allocation can be subject to confidentiality restrictions. Inward FDI transactions and positions are allocated to the activity of the resident direct investment enterprise. Outward FDI transactions are allocated according to the activity of the non resident direct investment enterprise. Outward FDI positions are allocated according to the activity of the non resident direct investment enterprise. Statistical unit: Enterprise.

Kuwait Exports: Asia: Islamic Non Arab Countries data was reported at 123,177.287 KWD th in 2017. This records a decrease from the previous number of 136,732.495 KWD th for 2016. Kuwait Exports: Asia: Islamic Non Arab Countries data is updated yearly, averaging 123,037.754 KWD th from Dec 1995 (Median) to 2017, with 22 observations. The data reached an all-time high of 284,896.422 KWD th in 2012 and a record low of 10,417.000 KWD th in 1995. Kuwait Exports: Asia: Islamic Non Arab Countries data remains active status in CEIC and is reported by Central Statistical Bureau. The data is categorized under Global Database’s Kuwait – Table KW.JA007: Exports: by Country: Annual.

Czech Republic CZ: Foreign Direct Investment Financial Flows: Outward: Total: Iran, Islamic Republic Of data was reported at 0.000 CZK mn in 2023. This stayed constant from the previous number of 0.000 CZK mn for 2022. Czech Republic CZ: Foreign Direct Investment Financial Flows: Outward: Total: Iran, Islamic Republic Of data is updated yearly, averaging 0.000 CZK mn from Dec 2013 (Median) to 2023, with 11 observations. The data reached an all-time high of 0.000 CZK mn in 2023 and a record low of 0.000 CZK mn in 2023. Czech Republic CZ: Foreign Direct Investment Financial Flows: Outward: Total: Iran, Islamic Republic Of data remains active status in CEIC and is reported by Organisation for Economic Co-operation and Development. The data is categorized under Global Database’s Czech Republic – Table CZ.OECD.FDI: Foreign Direct Investment Financial Flows: by Region and Country: OECD Member: Annual. Reverse investment: Netting of reverse investment in equity (when a direct investment enterprise acquires less than 10% equity ownership in its parent) and reverse investment in debt (when a direct investment enterprise extends a loan to its parent) is not applied in the recording of total inward and outward FDi transactions and positions. Such cases have never been observed. Treatment of debt FDI transactions and positions between fellow enterprises: directional basis according to the residency of the direct investor. Resident Special Purpose Entities (SPEs) do not exist or are not significant and are recorded as zero in the FDI database. Valuation method used for listed inward and outward equity positions: Own funds at book value. Valuation method used for unlisted inward and outward equity positions: Own funds at book value. Valuation method used for inward and outward debt positions: Nominal value.; FDI statistics are available by geographic allocation, vis-à-vis single partner countries worldwide and geographical and economic zones aggregates. Partner country allocation can be subject to confidentiality restrictions. Geographic allocation of inward and outward FDI transactions and positions is according to the immediate counterparty. Inward FDI positions according to the ultimate counterparty (the ultimate investing country) are also available and publishable. In the dataset 'FDI statistics by parner country and by industry - Summary', inward FDI positions are showed according to the UIC. Intercompany debt between related financial intermediaries, including permanent debt, are excluded from FDI transactions and positions. Direct investment relationships are identified according to the criteria of the Framework for Direct Investment Relationships (FDIR) method. Debt between fellow enterprises are completely covered. Collective investment institutions are covered as direct investment enterprises. FDI statistics are available by industry sectors according to ISIC4 classification. Industry sector allocation can be subject to confidentiality restrictions. Inward FDI transactions and positions are allocated to the activity of the resident direct investment enterprise. Outward FDI transactions are allocated according to the activity of the non resident direct investment enterprise. Outward FDI positions are allocated according to the activity of the non resident direct investment enterprise. Statistical unit: Enterprise.