The World Religion Project (WRP) aims to provide detailed information about religious adherence worldwide since 1945. It contains data about the number of adherents by religion in each of the states in the international system. These numbers are given for every half-decade period (1945, 1950, etc., through 2010). Percentages of the states' populations that practice a given religion are also provided. (Note: These percentages are expressed as decimals, ranging from 0 to 1, where 0 indicates that 0 percent of the population practices a given religion and 1 indicates that 100 percent of the population practices that religion.) Some of the religions are divided into religious families. To the extent data are available, the breakdown of adherents within a given religion into religious families is also provided.

The project was developed in three stages. The first stage consisted of the formation of a religion tree. A religion tree is a systematic classification of major religions and of religious families within those major religions. To develop the religion tree we prepared a comprehensive literature review, the aim of which was (i) to define a religion, (ii) to find tangible indicators of a given religion of religious families within a major religion, and (iii) to identify existing efforts at classifying world religions. (Please see the original survey instrument to view the structure of the religion tree.) The second stage consisted of the identification of major data sources of religious adherence and the collection of data from these sources according to the religion tree classification. This created a dataset that included multiple records for some states for a given point in time. It also contained multiple missing data for specific states, specific time periods and specific religions. The third stage consisted of cleaning the data, reconciling discrepancies of information from different sources and imputing data for the missing cases.

The National Religion Dataset: The observation in this dataset is a state-five-year unit. This dataset provides information regarding the number of adherents by religions, as well as the percentage of the state's population practicing a given religion.

This dataset describes the world’s religious makeup in 2020 and 2010. We focus on seven categories: Christians, Muslims, Hindus, Buddhists, Jews, people who belong to other religions, and those who are religiously unaffiliated. This analysis is based on more than 2,700 sources of data, including national censuses, large-scale demographic surveys, general population surveys and population registers. For more information about this data, see the associated Pew Research Center report "How the Global Religious Landscape Changed From 2010 to 2020."

World Religions Across Regions

Analyzing Adherence Across Regions, States and the Global System

By Correlates of War Project [source]

About this dataset

The World Religion Project (WRP) is an ambitious endeavor to conduct a comprehensive analysis of religious adherence throughout the world from 1945 to 2010. This cutting-edge project offers unparalleled insight into the religious behavior of people in different countries, regions, and continents during this time period. Its datasets provide important information about the numbers and percentages of adherents across a multitude of different religions, religion families, and non-religious affiliations.

The WRP consists of three distinct datasets: the national religion dataset, regional religion dataset, and global religion dataset. Each is focused on understanding individually specific realms for varied analysis approaches - from individual states to global systems. The national dataset provides data on number of adherents by state as well as percentage population practicing a given faith group in five-year increments; focusing attention to how this number evolves from nation to nation over time. Similarly, regional data is provided at five year intervals highlighting individual region designations with one modification – Pacific Ocean states have been reclassified into their own Oceania category according to Country Code Number 900 or above). Finally at a global level – all states are aggregated in order that we may understand a snapshot view at any five-year interval between 1945‐2010 regarding relationships between religions or religio‐families within one location or transnationally.

This project was developed in three stages: firstly forming a religions tree (a systematic classification), secondly collecting data such as this provided by WRP according to that classification structure – lastly cleaning the data so discrepancies may be reconciled and imported where needed with gaps selected when unknown values were encountered during collection process . We would encourage anyone wishing details undergoing more detailed reading/analysis relating various use applications for these rich datasets - please contact Zeev Maoz (University California Davis) & Errol A Henderson _(Pennsylvania State University)

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How to use the dataset

The World Religions Project (WRP) dataset offers a comprehensive look at religious adherence around the world within a single dataset. With this dataset, you can track global religious trends over a period of 65 years and explore how they’ve changed during that time. By exploring the WRP data set, you’ll gain insight into cross-regional and cross-time patterns in religious affiliation around the world.

Research Ideas

• Analyzing historical patterns of religious growth and decline across different regions
• Creating visualizations to compare religious adherence in various states, countries, or globally
• Studying the impact of governmental policies on religious participation over time

Acknowledgements

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

License

License: Dataset copyright by authors - You are free to: - Share - copy and redistribute the material in any medium or format for any purpose, even commercially. - Adapt - remix, transform, and build upon the material for any purpose, even commercially. - You must: - Give appropriate credit - Provide a link to the license, and indicate if changes were made. - ShareAlike - You must distribute your contributions under the same license as the original. - Keep intact - all notices that refer to this license, including copyright notices.

Columns

File: WRP regional data.csv | Column name | Description | |:-----------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------| | Year | Reference year for data collection. (Integer) | | Region | World region according to Correlates Of War (COW) Regional Systemizations with one modification (Oceania category for COW country code ...

This dataset provide a count of Veteran by their religious affiliation and state of residence. The dataset set covers all 50 states, District of Columbia and other territories.

This table contains 21 series, with data for years 1871 - 1971 (not all combinations necessarily have data for all years). This table contains data described by the following dimensions (Not all combinations are available): Unit of measure (1 items: Persons ...) Geography (1 items: Canada ...) Religious denominations (21 items: Total religious denominations; Baptist; Congregationalist; Anglican ...).

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 census is undertaken by the Office for National Statistics every 10 years and gives us a picture of all the people and households in England and Wales. The most recent census took place in March of 2021.The census asks every household questions about the people who live there and the type of home they live in. In doing so, it helps to build a detailed snapshot of society. Information from the census helps the government and local authorities to plan and fund local services, such as education, doctors' surgeries and roads.Key census statistics for Leicester are published on the open data platform to make information accessible to local services, voluntary and community groups, and residents.Further information about the census and full datasets can be found on the ONS website - https://www.ons.gov.uk/census/aboutcensus/censusproductsReligionThis dataset provides Census 2021 estimates that classify usual residents in England and Wales by religion. The estimates are as at Census Day, 21 March 2021.Definition: The religion people connect or identify with (their religious affiliation), whether or not they practice or have belief in it.This question was voluntary and the variable includes people who answered the question, including 'No Religion', alongside those who chose not to answer this question.This variable classifies responses into the eight tick-box response options. Write-in responses are classified by their "parent" religious affiliation, including 'No Religion', where applicable.This dataset contains details for Leicester City and England overall. There is also a dashboard that has been produced to show a selection of Census statistics for the city of Leicester which can be viewed here: Census 21 - Leicester dashboard.

The annual Report to Congress on International Religious Freedom � the International Religious Freedom Report � describes the status of religious freedom in every country. The report covers government policies violating religious belief and practices of groups, religious denominations and individuals, and U.S. policies to promote religious freedom around the world. The U.S. Department of State submits the reports in accordance with the International Religious Freedom Act of 1998.

About this dataset

Context

The dataset tabulates the population of Faith by race. It includes the population of Faith across racial categories (excluding ethnicity) as identified by the Census Bureau. The dataset can be utilized to understand the population distribution of Faith across relevant racial categories.

Key observations

The percent distribution of Faith population by race (across all racial categories recognized by the U.S. Census Bureau): 97.04% are white and 2.96% are multiracial.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2018-2022 5-Year Estimates.

Racial categories include:

• White
• Black or African American
• American Indian and Alaska Native
• Asian
• Native Hawaiian and Other Pacific Islander
• Some other race
• Two or more races (multiracial)

Variables / Data Columns

• Race: This column displays the racial categories (excluding ethnicity) for the Faith
• Population: The population of the racial category (excluding ethnicity) in the Faith is shown in this column.
• % of Total Population: This column displays the percentage distribution of each race as a proportion of Faith total population. Please note that the sum of all percentages may not equal one due to rounding of values.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Faith Population by Race & Ethnicity. You can refer the same here

Contained within the 3rd Edition (1957) of the Atlas of Canada is a plate consisting of six condensed maps which show the principle religious denominations of the people living in Canada. The 1951 Census of Canada enumerated the religious denomination of which each person was either a member or to which he or she adhered or favoured. This plate shows the distribution of population on this basis of the six religious groups which were most numerous in 1951. These six groups are as follows: Roman Catholic, The United Church of Canada, The Anglican Church of Canada, Presbyterians, Baptists and Lutherans. Each map is accompanied by a pie chart showing the percentage distribution of each denomination by province and territory.

About this dataset

Context

The dataset presents the median household income across different racial categories in Faith. It portrays the median household income of the head of household across racial categories (excluding ethnicity) as identified by the Census Bureau. The dataset can be utilized to gain insights into economic disparities and trends and explore the variations in median houshold income for diverse racial categories.

Key observations

Based on our analysis of the distribution of Faith population by race & ethnicity, the population is predominantly White. This particular racial category constitutes the majority, accounting for 100% of the total residents in Faith. Notably, the median household income for White households is $40,873. Interestingly, White is both the largest group and the one with the highest median household income, which stands at $40,873.

https://i.neilsberg.com/ch/faith-sd-median-household-income-by-race.jpeg" alt="Faith median household income diversity across racial categories">

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2017-2021 5-Year Estimates.

Racial categories include:

• White
• Black or African American
• American Indian and Alaska Native
• Asian
• Native Hawaiian and Other Pacific Islander
• Some other race
• Two or more races (multiracial)

Variables / Data Columns

• Race of the head of household: This column presents the self-identified race of the household head, encompassing all relevant racial categories (excluding ethnicity) applicable in Faith.
• Median household income: Median household income, adjusting for inflation, presented in 2022-inflation-adjusted dollars

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Faith median household income by race. You can refer the same here

This study assessed the effects of male inmate religiosity on post-release community adjustment and investigated the circumstances under which these effects were most likely to take place. The researcher carried out this study by adding Federal Bureau of Investigation criminal history information to an existing database (Clear et al.) that studied the relationship between an inmate's religiousness and his adjustment to the correctional setting. Four types of information were used in this study. The first three types were obtained by the original research team and included an inmate values and religiousness instrument, a pre-release questionnaire, and a three-month post-release follow-up phone survey. The fourth type of information, official criminal history reports, was later added to the original dataset by the principal investigator for this study. The prisoner values survey collected information on what the respondent would do if a friend sold drugs from the cell or if inmates of his race attacked others. Respondents were also asked if they thought God was revealed in the scriptures, if they shared their faith with others, and if they took active part in religious services. Information collected from the pre-release questionnaire included whether the respondent attended group therapy, religious groups with whom he would live, types of treatment programs he would participate in after prison, employment plans, how often he would go to church, whether he would be angry more in prison or in the free world, and whether he would be more afraid of being attacked in prison or in the free world. Each inmate also described his criminal history and indicated whether he thought he was able to do things as well as most others, whether he was satisfied with himself on the whole or felt that he was a failure, whether religion was talked about in the home, how often he attended religious services, whether he had friends who were religious while growing up, whether he had friends who were religious while in prison, and how often he participated in religious inmate counseling, religious services, in-prison religious seminars, and community service projects. The three-month post-release follow-up phone survey collected information on whether the respondent was involved with a church group, if the respondent was working for pay, if the respondent and his household received public assistance, if he attended religious services since his release, with whom the respondent was living, and types of treatment programs attended. Official post-release criminal records include information on the offenses the respondent was arrested and incarcerated for, prior arrests and incarcerations, rearrests, outcomes of offenses of rearrests, follow-up period to first rearrest, prison adjustment indicator, self-esteem indicator, time served, and measurements of the respondent's level of religious belief and personal identity. Demographic variables include respondent's faith, race, marital status, education, age at first arrest and incarceration, and age at incarceration for rearrest.

This dataset contains regional-level information on religious typologies across various European territories. Each record is expected to classify regions into religious categories (e.g., Catholic, Protestant, Orthodox, Mixed, or other denominations), providing insight into Europe’s diverse religious landscape. Such data supports research in historical sociology, cultural geography, and studies exploring how religious composition correlates with political, economic, and social developments across different European regions.

Data on religion by gender and age for the population in private households in Canada, provinces and territories.

This dataset provides Census 2021 estimates that classify usual residents in Birmingham by ethnic group, by religion, and by age.

Ethnic Group: The ethnic group that the person completing the census feels they belong to. This could be based on their culture, family background, identity or physical appearance. Religion: The religion people connect or identify with (their religious affiliation), whether or not they practise or have belief in it. Age: A person's age on Census Day, 21 March 2021 in England and Wales.CoverageThis dataset is focused on the data for Birmingham at city level. About the 2021 CensusThe Census takes place every 10 years and gives us a picture of all the people and households in England and Wales.Protecting personal dataThe ONS sometimes need to make changes to data if it is possible to identify individuals. This is known as statistical disclosure control. In Census 2021, they:Swapped records (targeted record swapping), for example, if a household was likely to be identified in datasets because it has unusual characteristics, they swapped the record with a similar one from a nearby small area. Very unusual households could be swapped with one in a nearby local authority.Added small changes to some counts (cell key perturbation), for example, we might change a count of four to a three or a five. This might make small differences between tables depending on how the data are broken down when they applied perturbation.For more geographies, aggregations or topics see the link in the Reference below. Or, to create a custom dataset with multiple variables use the ONS Create a custom dataset tool.

This folder consists of files for a case study of the methods used by Pew Research Center to make direct and indirect estimates for our report on The Religious Composition of the World's Migrants. Two subfolders demonstrate the procedures of the algorithm using two statistical programs, which mirror one another.

This source hails from the earlier years of large-scale survey research in Britain, with the electronic data file created following scanning of and data capture from original survey returns. The data collection provides insight into the lifestyles and religiosity of urban young people, predominantly working-class, at the dawn of the affluent society. It comprises a stratified random sample survey of the religious, social and associational lives of young people aged 15-24 in urban England in 1957. It was designed and fielded by the Newman Demographic Survey, a private research institute directed by religious sociologist Tony Spencer, in collaboration with Young Christian Workers, a faith-based youth organisation. The investigators aimed to yield a sample of English urban youth which would include at least 1000 Catholic respondents, representing all English Catholic dioceses. 8196 was achieved, of which following some apparently random data loss 5834 were of sufficient quality for scanning and data capture in 2010. The survey instrument consisted primarily of closed-form items piloted in Gateshead, Highgate and Manchester, and was designed following correspondence with specialist survey experts: Len England (1901-1999), Director of Mass Observation; Leslie Austen, director of Social Surveys (Gallup Poll) Ltd; and W.L. Readman at the National Food Survey at the Ministry of Agriculture, Fisheries and Food. John Mandeville of the British Tabulating Machine Company, a British-based company operating under licence to IBM, also provided advice to the survey investigators. The electoral register was used as the sampling frame, using a version of the 'nth page' method. To prevent interviewer fatigue, about half of the respondents (70% of Anglicans) completed a short version of the questionnaire, covering items on leisure and religious belief, while the remainder completed a longer version including items on associational memberships, schooling, religious attendance and practice, marital status, and parental country and religion of origin. Some written-in responses (on leisure, religious affiliation, associational memberships and occupation) have been captured. Design and post-stratification weights have been calculated for users.

Metron is interested in taking a data science approach to gleaming deeper insights into matters of spirituality, religion and extranormal experience. Data scientists at all levels of experience are encouraged to participate in this analysis.

This data set contains several public files of religious and spiritual texts. Also included is a “wildcard” file on the subject of machine super intelligence. This file is licensed under a Creative Commons Attribution-Share Alike 2.5 Switzerland License. More information can be found at: https://creativecommons.org/licenses/by-sa/2.5/ch/deed.en

Metron is interested in various text analysis techniques that can further an understanding of concepts that assist in furthering development of a body of knowledge on the topic of comparative religion and spirituality. Also other interesting observations that will fuel further lines of inquiry and questions are highly desirable.

Suggested Analysis Types: Scatter plot analysis - Metron would like to see a scatter plot of frequently recurring words in the texts If possible, can this be taken to the level of deriving conceptual correlations? Example, we may be able to state that “unconditional love” was the primary concept conveyed from the dataset. Followed by “communion” etc.

Word cloud analysis - create a scatter plot that uses a horizontal position indicating most popular words and a vertical position that indicates (some other type to be defined of) popularity.

Topic modeling - identify common topics found in the set of documents. Drill down to most common words per topic.

Word & document frequencies (tf-idf) - word frequency measurement and comparison across all documents. Charts of the highest tf-idf words in each text within the corpus.

Word dendrogram cluster - graphical representation of hierarchical word clusters.

Pairwise correlation - Words most correlated with other words in chart format.

Sentiment analysis - the most common words in texts associated with sentiments. Example: sentiment = creation. Associated words: God, genesis, Ein Sof, Allah, primordial, etc.

Word Network using tf-idf as a metric to find characteristic words for each description field rather than using counts of words.

Note: the above techniques can be found in “Data Science from Scratch - First Principles with Python” and “Text Mining with R - A Tidy Approach” both books from O’Reilly publishing.

The files are:

St. Augustine City of God 108 Upanishads 7 Tablets of Creation vols 1 and 2 Advaita Vendanta Aryan sun myths Autobiography of a yogi The Book of Illumination”

Attributed to Rabbi Nehunia ben haKana Bhagavad Gita Bible KJV Collected Fruits of Occult Teaching by A.P. Sinnett Epistle to the Son of the Wolf

Hidden Nature-The Startling Insights of Viktor Schauberger HILDEGARD OF BINGEN: SELECTED WRITINGS History of Zoroastrianism by

Maneckji Nusservanji The Philosophy of the Kaivalya Upanishad Kitab-i-Iqan (Book of Certitude) Knowledge of the Higher Worlds Rudolf Steiner Kularnava Tantra The Life of Buddha Machine Super Intelligence The Planet Mars and its Inhabitants By Eros Urides (A Martian) THE NATURE OF THE GODS. M. Tullius Cicero (“nature-gods”) OCCULT THEOCRASY BY LADY QUEENBOROUGH Urantia Book The Book of the People: POPUL VUH THE DHAMMAPADA VEDIC HYMNS Vedic Hymns, Part II Secret Instructions of the Society of Jesus THE CHALDEAN ACCOUNT OF GENESIS THE KITAB-I-AQDAS THE PATH OF LIGHT The Buddha's Way of Virtue The Yoga Sutras of Patanjali: The Book of the Spiritual Man by Patañjali The Vedanta-Sutras with the Commentary by Ramanuja The Kybalion Buddhism, in Its Connexion with Brahmanism and Hinduism, and in Its Contrast

MP Religion & Assisted Dying Dashboard

This data powers a dashboard presenting insights into the religious affiliations and Assisted Dying voting patterns of UK Members of Parliament (MPs). It can be found here:

👉 https://davidjeffery.shinyapps.io/mp-religion/.

Please cite all uses of the data.

📊 What is this dashboard?

This dashboard presents insights into the religious affiliations and Assisted Dying voting patterns of UK Members of Parliament. It combines publicly available data to support transparency and understanding of Parliament’s composition.

📂 Where does the data come from?

The data is compiled from publicly available parliamentary records and voting data. You can download it directly from the link in the header or view it in the Raw Data tab of the dashboard.

🙏 How is religion determined?

There are three steps to determining religion. An MP is classified as having a religion based on the following criteria:

1. If the MP is a member of a religiously based group, they are classified as a member of that religion.

2. If a member has publicly spoken about their religion, they are classified as a member of that religion.

3. Finally, the text an MP swore in on is used to help infer their religion.

These sources are used in order of priority. For example, Tim Farron is a member of Christians in Parliament and has spoken about his religious views. However, he did not take the oath on the Bible, but made a solemn affirmation on no text. Regardless, he is still classed as Christian.

🧾 Variable Reference

What do those variable names mean?

• Member ID – member_id – A unique numeric identifier for each MP provided by Parliament.

• Name – display_as – The full display name of the MP.

• Gender – gender – The MP’s gender.

• Party – party – The full political party name.

• Party (Simplified) – party_simple – A shortened or cleaned version of the party name.

• Religion – mp_final_relig – The MP’s classified religion based on multiple criteria outlined above.

• AD: 2nd Reading Vote – ass_suicide_2nd – The MP’s vote (Yes, No, Abstain) on the Assisted Dying Bill 2nd Reading.

• AD: 3rd Reading Vote – ass_suicide_3rd – The MP’s vote (Yes, No, Abstain) on the Assisted Dying Bill 3rd Reading.

• LGBT Status – lgbt – Whether the MP is publicly identified as LGBT (LGBT.MP).

• Ethnic Minority – ethnic_mp – Whether the MP identifies as an ethnic minority.

• Religious Group: Christian – relig_christian – MP belongs to a Christian group (1 = Yes).

• Religious Group: Muslim – relig_muslim – MP belongs to a Muslim group (1 = Yes).

• Religious Group: Jewish – relig_jewish – MP belongs to a Jewish group (1 = Yes).

• Religious Group: Sikh – relig_sikh – MP belongs to a Sikh group (1 = Yes).

• Oath Taken – mp_swear – Whether the MP took the Oath or made an Affirmation.

• Oath Book – mp_swear_book – The specific religious text (e.g., Bible, Quran) used when swearing in.

• Inferred Religion – mp_inferred_relig – The religion inferred from the swearing-in text.

• Election Outcome – elected – Whether the MP was re-elected in the most recent election.

• Majority – majority – The MP’s vote share margin.

• Constituency Type – constituency_type – Type: Borough or County.

• Claimant Rate – cen_claimant – % of constituents claiming unemployment benefits.

• % White (Census) – cen_eth_white – Proportion of white ethnicity in the constituency.

• % Christian – cen_rel_christian – Constituency Christian population from the Census.

• % Buddhist – cen_rel_buddhist – Constituency Buddhist population.

• % Hindu – cen_rel_hindu – Constituency Hindu population.

• % Jewish – cen_rel_jewish – Constituency Jewish population.

• % Muslim – cen_rel_muslim – Constituency Muslim population.

• % Sikh – cen_rel_sikh – Constituency Sikh population.

• % No Religion – cen_rel_no religion – Constituents identifying as non-religious.

• % No Qualifications – cen_qual_none – Constituents with no formal qualifications.

• % Graduates – cen_qual_grad – Constituents with degree-level education.

• % Some Disability – cen_disab_some – Constituents reporting a form of disability.

✝️ Why are Catholics separate from Christians?

Don’t worry, I’m not suggesting we bring back the Test Acts. The logic here is that more granular data is better.

When swearing in, there are versions of the Bible specific to Catholics — typically the New Jerusalem Bible or the Douay–Rheims Bible — whereas if someone just asks for “the Bible”, they are given the King James Version and could be from any Christian denomination.

It would be a shame to lose that detail, so I provide the option to break out Catholic MPs separately.

📚 Where can I find more information about swearing in and the parliamentary oath?

The Parliament website has a great guide:

👉 https://www.parliament.uk/about/how/elections-and-voting/swearingin/

👤 Who created this dashboard?

This dashboard was created by Dr David Jeffery, University of Liverpool.

Follow me on Twitter/X or Bluesky.

❓ Why did you create this dashboard?

I needed to know MPs’ religion, and the text MPs used to swear in seemed like a valid proxy. This information was held by Humanists UK and when I asked for it, they said no.

So I did what any time-starved academic would do: I collected the data myself, by hand, and decided to make it public.

Anti-Jewish attacks were the most common form of anti-religious group hate crimes in the United States in 2023, with ***** cases. Anti-Islamic hate crimes were the second most common anti-religious hate crimes in that year, with *** incidents.