The dataset contains the number of marriages categorized by the age groups of both the bride and the groom. Each record represents a combination of age groups for the bride and groom and the corresponding number of marriages for that combination. A marriage is the act, ceremony or process by which the legal relationship between two persons is formed. The legality of the union may be established by civil, religious or other means as recognised by the laws of the country.

Number of marriages that took place in England and Wales by age, sex, previous partnership status and civil or religious ceremony.

Abstract

The do-file marital_spouselinks.do combines all data on people's marital statuses and reported spouses to create the following datasets: 1. all_marital_reports - a listing of all the times an individual has reported their current marital status with the id numbers of the reported spouse(s); this listing is as reported so may include discrepancies (i.e. a 'Never married' status following a 'Married' one) 2. all_spouse_pairs_full - a listing of each time each spouse pair has been reported plus summary information on co-residency for each pair 3. all_spouse_pairs_clean_summarised - this summarises the data from all_spouse_pairs_full to give start and end dates of unions 4. marital_status_episodes - this combines data from all the sources to create episodes of marital status, each has a start and end date and a marital status, and if currently married, the spouse ids of the current spouse(s) if reported. There are several variables to indicate where each piece of information is coming from.

The first 2 datasets are made available in case people need the 'raw' data for any reason (i.e. if they only want data from one study) or if they wish to summarise the data in a different way to what is done for the last 2 datasets.

The do-file is quite complicated with many sources of data going through multiple processes to create variables in the datasets so it is not always straightforward to explain where each variable come from on the documentation. The 4 datasets build on each other and the do-file is documented throughout so anyone wanting to understand in great detail may be better off examining that. However, below is a brief description of how the datasets are created:

Marital status data are stored in the tables of the study they were collected in: AHS Adult Health Study [ahs_ahs1] CEN Census (initial CRS census) [cen_individ] CENM In-migration (CRS migration form) [crs_cenm] GP General form (filled for various reasons) [gp_gpform] SEI Socio-economic individual (annual survey from 2007 onwards) [css_sei] TBH TB household (study of household contacts of TB patients) [tb_tbh] TBO TB controls (matched controls for TB patients) [tb_tbo & tb_tboto2007] TBX TB cases (TB patients) [tb_tbx & tb_tbxto2007] In many of the above surveys as well as their current marital status, people were asked to report their current and past spouses along with (sometimes) some information about the marriage (start/end year etc.). These data are stored all together on the table gen_spouse, with variables indicating which study the data came from. Further evidence of spousal relationships is taken from gen_identity (if a couple appear as co-parents to a CRS member) and from crs_residency_episodes_clean_poly, a combined dataset (if they are living in the same household at the same time). Note that co-parent couples who are not reported in gen_spouse are only retained in the datasets if they have co-resident episodes.

The marital status data are appended together and the spouse id data merged in. Minimal data editing/cleaning is carried out. As the spouse data are in long format, this dataset is reshaped wide to have one line per marital status report (polygamy in the area allows for men to have multiple spouses at one time): this dataset is saved as all_marital_reports.

The list of reported spouses on gen_spouse is appended to a list of co-parents (from gen_identity) and this list is cleaned to try to identify and remove obvious id errors (incestuous links, same sex [these are not reported in this culture] and large age difference). Data reported by men and women are compared and variables created to show whether one or both of the couple report the union. Many records have information on start and end year of marriage, and all have the date the union was reported. This listing is compared to data from residency episodes to add dates that couples were living together (not all have start/end dates so this is to try to supplement this), in addition the dates that each member of the couple was last known to be alive or first known to be dead are added (from the residency data as well). This dataset with all the records available for each spouse pair is saved as all_spouse_pairs_full.

The date data from all_spouse_pairs_full are then summarised to get one line per couple with earliest and latest known married date for all, and, if available, marriage and separation date. For each date there are also variables created to indicate the source of the data.
As culture only allows for women having one spouse at a time, records for women with 'overlapping' husbands are cleaned. This dataset is then saved as all_spouse_pairs_clean_summarised.

Both the cleaned spouse pairs and the cleaned marital status datasets are converted into episodes: the spouse listing using the marriage or first known married date as the beginning and the last known married plus a year or separation date as the end, the marital status data records collapsed into periods of the same status being reported (following some cleaning to remove impossible reports) and the start date being the first of these reports, the end date being the last of the reports plus a year. These episodes are appended together and a series of processes run several times to remove overalapping episodes. To be able to assign specific spouse ids to each married episode, some episodes need to be 'split' into more than one (i.e. if a man is married to one woman from 2005 to 2017 and then marries another woman in 2008 and remains married to her till 2017 his intial married episode would be from 2005 to 2017, but this would need to be split into one from 2005 to 2008 which would just have 1 idspouse attached and another from 2008 to 2017, which would have 2 idspouse attached). After this splitting process the spouse ids are merged in.
The final episode dataset is saved as marital_status_episodes.

Analysis unit

Individual

Mode of data collection

Face-to-face [f2f]

Content

Cousin Marriage

This folder contains data behind the story Dear Mona: How Many Americans Are Married To Their Cousins?

Source: cosang.net

Context

This is a dataset from FiveThirtyEight hosted on their GitHub. Explore FiveThirtyEight data using Kaggle and all of the data sources available through the FiveThirtyEight organization page!

• Update Frequency: This dataset is updated daily.

Acknowledgements

This dataset is maintained using GitHub's API and Kaggle's API.

This dataset is distributed under the Attribution 4.0 International (CC BY 4.0) license.

Cover photo by Seth Doyle on Unsplash
Unsplash Images are distributed under a unique Unsplash License.

Number of persons who married in a given year and age-specific marriage rate per 1,000 unmarried persons, by legal marital status, gender (when available) and place of occurrence, 1991 to most recent year.

Dataset replaced by: http://data.europa.eu/euodp/data/dataset/kYUpewWd81KHgTddinyQ The crude marriage rate is the ratio of the number of marriages during the year to the average population in that year. The value is expressed per 1000 persons.

Abstract

The Marriages and Divorces (MD) dataset is one of three primary sources of of marriage and divorce statistics in South Africa. Unlike the other two sources (population censuses and household sample surveys), the MD dataset is compiled from administrative data and based on continuous recording (i.e. from civil registration systems and administrative records). Statistics South Africa (Stats SA) regularly publishes a series of data on marriages and divorces, with the first dataset in the series begining in 2006. The most recent dataset in the series is MD 2020.

Marriage data: Data on marriages for citizens and permanent residents are obtained from registered marriage records that are collected through the civil registration systems of the Department of Home Affairs (DHA). South Africa recognises three types of marriages by law: civil marriages, customary marriages and civil unions. Before 2008, marriage data only covered civil marriages. The registration of customary marriages and civil unions began in 2003 and 2007 respectively. However from 2008 onwards, Stats SA began publishing available data on customary marriages and civil unions.

Divorce data: Data on divorces are obtained from various regional courts that deal with divorce matters. The data are based on successful divorce cases that have been issued with a decree of divorce by the Department of Justice and Constitutional Development (DoJCD). Divorce cases come from marriages that were registered in different years as well as divorce cases that were filed in different years but whose divorce decrees were granted in the relevant year of collection.

NOTE: although both the data on marriages and divorces are collected in the same year, the data sets are not linked to each other.

Geographic coverage

National coverage

Analysis unit

Individuals

Universe

The data covers all civil marriages that were recoreded by the Department of Home Affairs and all divorce applications that were granted by the Department of Justice and Constitutional Development in 2021 in South Africa.

Kind of data

Administrative records

Mode of data collection

Other

Data appraisal

Geography is problematic in this dataset as not all the data files have geographic data. The Civil Marriages and Civil Unions data files include a Province of Registration variable but the Customary Marriages data file does not. There is also no geographical data in the Divorces file. As this data file includes divorce data from only a subset of divorce courts, this lack of geographical information compromises its usability.

This dataset shows all marriages in Basel-Stadt by date of marriage. All marriages in which the husband was resident in Basel-Stadt at the time of the wedding shall be taken into account. For methodological reasons, the values published here may differ from those in public statistics: In the latter, subsequently reported marriages are counted in the last year not yet concluded. In this record, they are subsequently counted in the year of the wedding date.

Yearly registered marriages – breakdown by Month

Registered marriage officiants in Ontario. The dataset includes: * first name, last name and location of the marriage officiant * affiliation * a date stamp at the end indicating the date when the changes were made by the operations team To search: Use the Control+F buttons to find a specific city or person. Note: Registrations and cancellations of marriage officiants are generally updated within 4 weeks of notification. This data is related to: * Getting married: How to get the government documents you need if you plan to marry in Ontario. * How to get a copy of an Ontario marriage certificate online * Law: Marriage Act

Abstract

The Marriages and Divorces (MD) dataset is one of three primary sources of of marriage and divorce statistics in South Africa. Unlike the other two sources (population censuses and household sample surveys), the MD dataset is compiled from administrative data and based on continuous recording (i.e. from civil registration systems and administrative records). Statistics South Africa (Stats SA) regularly publishes a series of data on marriages and divorces, with the first dataset in the series begining in 2006. The most recent dataset in the series is MD 2019.

Marriage data: Data on marriages for citizens and permanent residents are obtained from registered marriage records that are collected through the civil registration systems of the Department of Home Affairs (DHA). South Africa recognises three types of marriages by law: civil marriages, customary marriages and civil unions. Before 2008, marriage data only covered civil marriages. The registration of customary marriages and civil unions began in 2003 and 2007 respectively. However from 2008 onwards, Stats SA began publishing available data on customary marriages and civil unions.

Divorce data: Data on divorces are obtained from various regional courts that deal with divorce matters. The data are based on successful divorce cases that have been issued with a decree of divorce by the Department of Justice and Constitutional Development (DoJCD). Divorce cases come from marriages that were registered in different years as well as divorce cases that were filed in different years but whose divorce decrees were granted in the relevant year of collection.

NOTE: although both the data on marriages and divorces are collected in the same year, the data sets are not linked to each other.

Geographic coverage

The data has national coverage.

Analysis unit

Individuals

Universe

The data covers all civil marriages that were recoreded by the Department of Home Affairs and all divorce applications that were granted by the Department of Justice and Constitutional Development in 2019 in South Africa.

Kind of data

Administrative records

Mode of data collection

Other

Data appraisal

Geography is problematic in this dataset as not all the data files have geographic data. The Civil Marriages and Civil Unions data files include a Province of Registration variable but the Customary Marriages data file does not. There is also no geographical data in the Divorces file. As this data file includes divorce data from only a subset of divorce courts, this lack of geographical information compromises its usability.

A data set of first marriages (including marriage location and ages of spouses at marriage) and lifespans of spouses (including year and location of births and deaths, where known), for marriages conducted between 1600 and 1899 in the Netherlands, Belgium and Germany. Also included is a binary indicator for whether the marriage, birth or death locations were urban or rural between 1600 and 1800, according to the coding system used by Bosker et al. 2013 [Rev. Econ. Stat., 95(4), 1418-1437 doi:10.1162/REST_a_00284]. The data set is derived from a genealogical database, which was constructed from family tree (GEDCOM) files contributed by users of www.genealogieonline.nl. The genealogical data from contributed files was error-checked before being combined into a single database using the TreeChecker application. From the initial pool of >1600 f iles contributed by the users of www.genealogieonline.nl, 924 files were included in the database after an assessment of the percentage of errors in each file, hence the database is known as the GO 924 set.Duplicate marriages were identified by the year of marriage and the first 7 letters of each spouse surname, whereupon a random duplicate was selected for inclusion in the data set. Note that names of individuals and exact dates of marria ges, births and deaths are excluded from this data set to prevent identification of individuals, as the genealogical data was provided to our research group on the basis that it would only be published in an aggregated or anonymised format. Access to the un-anonymised data may be granted subject to confidentiality agreements, please contact the authors for further information. Marriages were only included where the place of marriage had been checked and geocoded with latitude and longitude coordinates, where (as far as we could ascertain) it was the first marriage of the spouse, age at marriage was > 13 for both spouses, all lifespans were < 111 and no estimated dates were used to calculate spouse lifespan.The dataset is in a long format, in which there is a separate record for each spouse. It can be determined whether the spouse is the husba nd or wife (and conversely whether the other is the wife or husband) by the spouse_sex variable. A description of each variable is included in the text file accompanying the csv data file.

Annual population estimates by marital status or legal marital status, age and sex, Canada, provinces and territories.

A data set based on marriages conducted between 1600 and 1999 in the Netherlands. Includes information on lifespans and marriage ages of spouses (focal spouse and other), also location data for births, marriages and deaths. Includes information on lifespans and death locations of parents of the spouses. Includes information on births, deaths and lifespans of the children of the spouse/couple. Includes information on whether the marriage, birth and death locations were urban or rural between 1600 and 1800, based on whether a place contained 5000 or more inhabitants (using a database developed by Bosker et al. 2013 [Rev. Econ. Stat., 95(4), 1418-1437 doi:10.1162/REST_a_00284]). The data set is derived from a genealogical database, which was constructed from family tree (GEDCOM) files contributed by users of www.genealogieonline.nl, via a program in which genealogists who were registered with the website were invited to contribute their family trees for scientific research purposes. The genealogical data from contributed files was error-checked before being combined into a single database using the TreeChecker application (http://www.treechecker.net). From the initial pool of >1600 contributed files, 924 files were included in the database after an assessment of the percentage of errors in each file, hence the database is known as the GO 924 set. Duplicate marriages were identified by the year of marriage and the first 7 letters of each spouse surname, whereupon a random duplicate was selected for inclusion in the data set. Note that names of individuals and exact dates of marriages, births and deaths are excluded from this data set to prevent identification of individuals, as the genealogical data was provided to our research group on the basis that it would only be published in an aggregated or anonymised format. Access to the un-anonymised data may be granted subject to confidentiality agreements, please contact the authors for further information. Marriages were only included where the place of marriage was in the Netherlands and marriage age of the spouse was > 13. The dataset is in a long format, in which there is a separate record for each spouse. It can be determined whether the spouse is the husband or wife (and conversely whether the other is the wife or husband) by the spouse_sex variable. A description of each variable is included in the accompanying text file: GO924_married_in_NL_inc_children_VARIABLES.txt.

This dataset compares birth, death and marriage registrations completed by the Office of the Registrar General, beginning in 1925, to the most current published annual report (2022). Data released for 2024 is preliminary and may not match counts from other sources. The data represents counts in the reference calendar quarters, which are collated approximately 90 days after the end of the quarter. Previously released counts for 2024 are updated to reflect vital event registrations completed after the release of the initial report. Each subsequent quarterly report is the cumulative total of the preceding quarterly reports. ServiceOntario’s ability to provide timely information depends on receiving vital event registration information from a variety of sources. The preliminary data presented may not represent all the events that occurred in the reporting period. This is particularly true for events that occurred near the end of the reporting period as they may not have been received by ServiceOntario by the time the data is collated. Final counts for the reporting year will be released with the publication of the Office of the Registrar General Annual Report. The Vital Statistics Act requires that after the end of each calendar year, the Registrar General publish a report that includes the number of births, marriages, deaths, still-births, adoptions and changes of name registered during the calendar year preceding the one that has ended.

Total first marriage rates and age-specific first marriage rates per 1,000 males, all marriages, by place of occurrence, 2000 to 2004.

Mean age and median age at divorce and at marriage, for persons who divorced in a given year, by sex or gender and place of occurrence, 1970 to most recent year.

Number of divorces and various divorce indicators (crude divorce rate, divorce rate for married persons, age-standardized divorce rate, total divorce rate, mean and median duration of marriage, median duration of divorce proceedings, percentage of joint divorce applications), by place of occurrence, 1970 to most recent year.

Abstract

How Couples Meet and Stay Together (HCMST) is a study of how Americans meet their spouses and romantic partners.

• The study is a nationally representative study of American adults.
• 4,002 adults responded to the survey, 3,009 of those had a spouse or main
  romantic partner.
• The study oversamples self-identified gay, lesbian, and bisexual adults
• Follow-up surveys were implemented one and two years after the main survey, to study couple dissolution rates. Version 3.0 of the dataset includes two follow- up surveys, waves 2 and 3.
• Waves 4 and 5 are provided as separate data files that can be linked back to the main file via variable caseid_new.

The study will provide answers to the following research questions:

1. Do traditional couples and nontraditional couples meet in the same way? What kinds of couples are more likely to have met online?
2. Have the most recent marriage cohorts (especially the traditional heterosexual same-race married couples) met in the same way their parents and grandparents did?
3. Does meeting online lead to greater or less couple stability?
4. How do the couple dissolution rates of nontraditional couples compare to the couple dissolution rates of more traditional same-race heterosexual couples?
5. How does the availability of civil union, domestic partnership or same-sex marriage rights affect couple stability for same-sex couples? This study will provide the first nationally representative data on the couple dissolution rates of same-sex couples.

Methodology

Universe:

The universe for the HCMST survey is English literate adults in the U.S.

**Unit of Analysis: **

Individual

**Type of data collection: **

Survey Data

**Time of data collection: **

Wave I, the main survey, was fielded between February 21 and April 2, 2009. Wave 2 was fielded March 12, 2010 to June 8, 2010. Wave 3 was fielded March 22, 2011 to August 29, 2011. Wave 4 was fielded between March and November of 2013. Wave 5 was fielded between November, 2014 and March, 2015. Dates for the background demographic surveys are described in the User's Guide, under documentation below.

Geographic coverage:

United States of America

Smallest geographic unit:

US region

**Sample description: **

The survey was carried out by survey firm Knowledge Networks (now called GfK). The survey respondents were recruited from an ongoing panel. Panelists are recruited via random digit dial phone survey. Survey questions were mostly answered online; some follow-up surveys were conducted by phone. Panelists who did not have internet access at home were given an internet access device (WebTV). For further information about how the Knowledge Networks hybrid phone-internet survey compares to other survey methodology, see attached documentation.

The dataset contains variables that are derived from several sources. There are variables from the Main Survey Instrument, there are variables generated from the investigators which were created after the Main Survey, and there are demographic background variables from Knowledge Networks which pre-date the Main Survey. Dates for main survey and for the prior background surveys are included in the dataset for each respondent. The source for each variable is identified in the codebook, and in notes appended within the dataset itself (notes may only be available for the Stata version of the dataset).

Respondents who had no spouse or main romantic partner were dropped from the Main Survey. Unpartnered respondents remain in the dataset, and demographic background variables are available for them.

**Sample response rate: **

Response to the main survey in 2009 from subjects, all of whom were already in the Knowledge Networks panel, was 71%. If we include the the prior initial Random Digit Dialing phone contact and agreement to join the Knowledge Networks panel (participation rate 32.6%), and the respondents’ completion of the initial demographic survey (56.8% completion), the composite overall response rate is a much lower .326*.568*.71= 13%. For further information on the calculation of response rates, and relevant citations, see the Note on Response Rates in the documentation. Response rates for the subsequent waves of the HCMST survey are simpler, using the denominator of people who completed wave 1 and who were eligible for follow-up. Response to wave 2 was 84.5%. Response rate to wave 3 was 72.9%. Response rate to wave 4 was 60.0%. Response rate to wave 5 was 46%. Response to wave 6 was 91.3%. Wave 6 was Internet only, so people who had left the GfK KnowledgePanel were not contacted.

**Weights: **

See "Notes on the Weights" in the Documentation section.

Usage

When you use the data, you agree to the following conditions:

1. I will not use the data to identify individuals.
2. I will not charge a fee for the data if I distribute it to others.
3. I will inform the contact person abo

Abstract Tina and Rick get married in Kosovo and head back to Italy, where they can start their happy life as a married couple. Or almost. Some problems with the chocolate factory are waiting for them back at home. While Ivan is ready to do anything to help his son and his friends, he also needs to deal with his complicated love life. Details Rick and Tina, with the help of their parents and their friends, start to plan the wedding. They planned a big party with 300 guests, including Rick’s mother, who flew from San Diego to Kosovo as soon as she received the invitation. She meets Tina for the first time while dressing up in her wedding gown, and although she is very happy for her son, she worries about them being unable to make it on their own. Surprisingly, Ivan reassures her this time, showing how this rescue trip changed his perspective. The celebration takes place smoothly with the help of their families, friends, and neighbours. The only person missing is Miriam, who, in the meantime, is fighting for the survival of the packaging team at the chocolate factory. When the time comes for the Abrate family to vote on merging with the French company, Miriam’s brother votes for it, betraying Miriam against everyone’s expectations. His wife, worried about the family’s finances, threatened to leave him if he did not vote against Miriam. The return journey to Italy is planned for the day following the wedding after Rick and Tina spent their first night together as a married couple. However, when they get to Italy, Miriam gives them the bad news that she is leaving the firm because the family decided to sell; they are out of a job. Miriam, who is deeply sad, refuses even to talk to Ivan. When he tries to call her and offers to meet with her, she turns him down again. The series fasts forward to three months later. The situation at the Abrate Factory has stayed the same, and Rick is missing his job and friends. Tired of Rick’s sadness, Ivan decides to take the matter into his own hands. He goes looking for Rick’s friends one by one and turns his home into a place where they can all hang out every day. Ready to do anything to find employment for his son, he shows up at Miriam’s house and convinces her to get into business together and turn her grandfather’s old Café into a classy restaurant where the packaging team could work. After weeks of training, the opening night finally comes. Many people show up, but it is far from being a success since the demanding tasks overwhelm Rick and his friends. After some time in which the situation at the restaurant has yet to progress, Miriam, Ivan, and the team start losing faith. Just as they are about to give up, one of them, Marione, starts hugging people walking down the street and invites them to come in and eat. As he does that, the place fills up, and just like that, Ivan realizes that they need a new business model that can benefit from the uniqueness of their team instead of considering their disability as a flaw. Therefore, they decided for the restaurant not to be a classy and pricy place but rather a simpler one, where people feel at home and loved. Meanwhile, Alessia reveals to Ivan that she still has feelings for him and has broken up with her partner. This comes unexpectedly for Ivan, who initially turns her down when she suggests giving their marriage another chance. He is still in love with Miriam, even though she still does not want to be in any relationship. One day, after Alessia helps him comfort Tina, who is feeling homesick, Ivan realizes that Alessia’s presence is positive and accepts her proposal to go back to living together. However, this only lasts for a while since Ivan cannot forget Miriam. In the meantime, working with Ivan makes her realize she is in love with him, but she is escaping from it because of the sense of guilt that her daughter’s death provoked in her. A karaoke night at the restaurant is the occasion that finally brings Miriam and Ivan together. When she invites him to sing on stage, he passionately kisses her, just like Rick had kissed Tina on their first karaoke night.