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This folder contains data behind the story Marriage Isn’t Dead — Yet.
Source for all data is Decennial Census (years 1960 to 2000) and American Community Survey (years 2001-2012), via IPUMS USA.
Except in the divorce file, figures represent share of the relevant population that has never been married (MARST == 6 in the IPUMS data). Note that in the story, charts generally show the share that have ever been married, which is simply 1 - n. In the divorce file, figures are share of the relevant population that is currently divorced, conditional on having ever been married.
Variable names are as follows. Number in variable names are age ranges, so all_2534 is the marriage rate for everyone ages 25 to 34.
| Header | Description |
|---|---|
all | Total (or all men/women in sex-specific files) |
HS | High school graduate or less (EDUCD < 65) |
SC | Some college (EDUCD >= 65 & <= 100) |
BAp | Bachelor's degree or more (EDUCD > 100) |
BAo | Bachelor's degree, no graduate degre (EDUCD > 100 & <= 113) |
GD | Graduate degree (EDUCD > 113) |
White | Non-Hispanic white |
Black | Black or African-American |
Hisp | Hispanic of any race |
NE | New England (REGION == 11) |
MA | Mid-Atlantic (REGION == 12) |
Midwest | Midwest (REGION == 21-23) |
South | South (REGION == 31-34) |
Mountain | Mountain West (REGION == 41) |
Pacific | Pacific (REGION == 42) |
poor | Family income in lowest 25% |
mid | Family income in middle 50% |
rich | Family income in top 25% |
work | Employed 50+ weeks prior year |
nowork | Not employed at least 50 weeks prior year |
nokids_all | No own children living at home |
kids_all | At least one own child living at home |
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!
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.
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TwitterMean 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.
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TwitterAnnual population estimates by marital status or legal marital status, age and sex, Canada, provinces and territories.
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TwitterNumber of persons who divorced in a given year and age-specific divorce rates per 1,000 legally married persons, by sex or gender and place of occurrence, 1970 to most recent year.
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TwitterAttribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
This table includes data on contracted marriages and registered partnerships among the Dutch population.
The following information is available: - Marriages by sex of the partners. - Marrying persons by sex and marital status before marriage. - Average age at marriage by sex and marital status. - Average number of contracted marriages by sex. - Partnership registrations by sex of the partners.
Data available from: 1950
Status of the figures: All figures in this publication are final data.
Changes per 13 June 2025: Final figures of 2024 have been added.
When will new figures be published? Figures of 2025 will be added in the 2nd quarter of 2026.
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TwitterNumber and percentage of marriages, by type of marriage (opposite-sex, same-sex), month of marriage, and place of occurrence, 2000 to 2004.
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The Demographic and Health Surveys (DHS) Program exists to advance the global understanding of health and population trends in developing countries.
The UN describes violence against women and girls (VAWG) as: “One of the most widespread, persistent, and devastating human rights violations in our world today. It remains largely unreported due to the impunity, silence, stigma, and shame surrounding it.”
In general terms, it manifests itself in physical, sexual, and psychological forms, encompassing: • intimate partner violence (battering, psychological abuse, marital rape, femicide) • sexual violence and harassment (rape, forced sexual acts, unwanted sexual advances, child sexual abuse, forced marriage, street harassment, stalking, cyber-harassment), human trafficking (slavery, sexual exploitation) • female genital mutilation • child marriage
The data was taken from a survey of men and women in African, Asian, and South American countries, exploring the attitudes and perceived justifications given for committing acts of violence against women. The data also explores different sociodemographic groups that the respondents belong to, including: Education Level, Marital status, Employment, and Age group.
It is, therefore, critical that the countries where these views are widespread, prioritize public awareness campaigns, and access to education for women and girls, to communicate that violence against women and girls is never acceptable or justifiable.
| Field | Definition |
|---|---|
| Record ID | Numeric value unique to each question by country |
| Country | Country in which the survey was conducted |
| Gender | Whether the respondents were Male or Female |
| Demographics Question | Refers to the different types of demographic groupings used to segment respondents – marital status, education level, employment status, residence type, or age |
| Demographics Response | Refers to demographic segment into which the respondent falls (e.g. the age groupings are split into 15-24, 25-34, and 35-49) |
| Survey Year | Year in which the Demographic and Health Survey (DHS) took place. “DHS surveys are nationally-representative household surveys that provide data for a wide range of monitoring and impact evaluation indicators in the areas of population, health and nutrition. Standard DHS Surveys have large sample sizes (usually between 5,000 and 30,000 households) and typically are conducted around every 5 years, to allow comparisons over time.” |
| Value | % of people surveyed in the relevant group who agree with the question (e.g. the percentage of women aged 15-24 in Afghanistan who agree that a husband is justified in hitting or beating his wife if she burns the food) |
Question | Respondents were asked if they agreed with the following statements: - A husband is justified in hitting or beating his wife if she burns the food - A husband is justified in hitting or beating his wife if she argues with him - A husband is justified in hitting or beating his wife if she goes out without telling him - A husband is justified in hitting or beating his wife if she neglects the children - A husband is justified in hitting or beating his wife if she refuses to have sex with him - A husband is justified in hitting or beating his wife for at least one specific reason
More - Find More Exciting🙀 Datasets Here - An Upvote👍 A Dayᕙ(`▿´)ᕗ , Keeps Aman Hurray Hurray..... ٩(˘◡˘)۶Haha
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TwitterPopulation (15 Years & Above) by Sex, Age Groups and Marital Status
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TwitterOpen Government Licence 3.0http://www.nationalarchives.gov.uk/doc/open-government-licence/version/3/
License information was derived automatically
Live births and stillbirths annual summary statistics, by sex, age of mother, whether within marriage or civil partnership, percentage of non-UK-born mothers, birth rates and births by month and mothers' area of usual residence.
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TwitterNumber and percentage of live births, by marital status of mother, 1991 to most recent year.
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Twitterhttps://creativecommons.org/publicdomain/zero/1.0/https://creativecommons.org/publicdomain/zero/1.0/
This folder contains data behind the story Marriage Isn’t Dead — Yet.
Source for all data is Decennial Census (years 1960 to 2000) and American Community Survey (years 2001-2012), via IPUMS USA.
Except in the divorce file, figures represent share of the relevant population that has never been married (MARST == 6 in the IPUMS data). Note that in the story, charts generally show the share that have ever been married, which is simply 1 - n. In the divorce file, figures are share of the relevant population that is currently divorced, conditional on having ever been married.
Variable names are as follows. Number in variable names are age ranges, so all_2534 is the marriage rate for everyone ages 25 to 34.
| Header | Description |
|---|---|
all | Total (or all men/women in sex-specific files) |
HS | High school graduate or less (EDUCD < 65) |
SC | Some college (EDUCD >= 65 & <= 100) |
BAp | Bachelor's degree or more (EDUCD > 100) |
BAo | Bachelor's degree, no graduate degre (EDUCD > 100 & <= 113) |
GD | Graduate degree (EDUCD > 113) |
White | Non-Hispanic white |
Black | Black or African-American |
Hisp | Hispanic of any race |
NE | New England (REGION == 11) |
MA | Mid-Atlantic (REGION == 12) |
Midwest | Midwest (REGION == 21-23) |
South | South (REGION == 31-34) |
Mountain | Mountain West (REGION == 41) |
Pacific | Pacific (REGION == 42) |
poor | Family income in lowest 25% |
mid | Family income in middle 50% |
rich | Family income in top 25% |
work | Employed 50+ weeks prior year |
nowork | Not employed at least 50 weeks prior year |
nokids_all | No own children living at home |
kids_all | At least one own child living at home |
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!
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.