100+ datasets found
  1. Educational attainment worldwide 2020, by gender and level

    • statista.com
    Updated Jan 23, 2025
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    Statista (2025). Educational attainment worldwide 2020, by gender and level [Dataset]. https://www.statista.com/statistics/1212278/education-gender-gap-worldwide-by-level/
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    Dataset updated
    Jan 23, 2025
    Dataset authored and provided by
    Statistahttp://statista.com/
    Time period covered
    2020
    Area covered
    Worldwide
    Description

    According to the Global Gender Gap Report 2020, 88 percent of females worldwide had primary education, compared to 91 percent of males. By comparison, more females than males had attained tertiary education. The Global Gender Index benchmarks national gender gaps on economic, political, education, and health-based criteria. In 2020, the leading country was Iceland with a score of 0.87.

  2. Completion rates among bachelor students 2020, by gender

    • statista.com
    Updated Jun 23, 2025
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    Statista (2025). Completion rates among bachelor students 2020, by gender [Dataset]. https://www.statista.com/statistics/1346409/completion-rate-higher-education-oecd-gender/
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    Dataset updated
    Jun 23, 2025
    Dataset authored and provided by
    Statistahttp://statista.com/
    Time period covered
    2020
    Area covered
    Worldwide, OECD
    Description

    In all 25 OECD countries listed here, completion rates from bachelor's programs were higher among women than among men. The share of women who completed their bachelor's degree within the theoretical time frame varied from ** percent in Colombia to ** percent in the United Kingdom. Among men, students in the United Kingdom had the highest completion rates.

  3. Percentage of the U.S. population with a college degree, by gender 1940-2022...

    • statista.com
    Updated Sep 5, 2024
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    Statista (2024). Percentage of the U.S. population with a college degree, by gender 1940-2022 [Dataset]. https://www.statista.com/statistics/184272/educational-attainment-of-college-diploma-or-higher-by-gender/
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    Dataset updated
    Sep 5, 2024
    Dataset authored and provided by
    Statistahttp://statista.com/
    Area covered
    United States
    Description

    In an impressive increase from years past, 39 percent of women in the United States had completed four years or more of college in 2022. This figure is up from 3.8 percent of women in 1940. A significant increase can also be seen in males, with 36.2 percent of the U.S. male population having completed four years or more of college in 2022, up from 5.5 percent in 1940.

    4- and 2-year colleges

    In the United States, college students are able to choose between attending a 2-year postsecondary program and a 4-year postsecondary program. Generally, attending a 2-year program results in an Associate’s Degree, and 4-year programs result in a Bachelor’s Degree.

    Many 2-year programs are designed so that attendees can transfer to a college or university offering a 4-year program upon completing their Associate’s. Completion of a 4-year program is the generally accepted standard for entry-level positions when looking for a job.

    Earnings after college

    Factors such as gender, degree achieved, and the level of postsecondary education can have an impact on employment and earnings later in life. Some Bachelor’s degrees continue to attract more male students than female, particularly in STEM fields, while liberal arts degrees such as education, languages and literatures, and communication tend to see higher female attendance.

    All of these factors have an impact on earnings after college, and despite nearly the same rate of attendance within the American population between males and females, men with a Bachelor’s Degree continue to have higher weekly earnings on average than their female counterparts.

  4. Widening participation in higher education: 2021

    • gov.uk
    Updated Oct 14, 2021
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    Department for Education (2021). Widening participation in higher education: 2021 [Dataset]. https://www.gov.uk/government/statistics/widening-participation-in-higher-education-2021
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    Dataset updated
    Oct 14, 2021
    Dataset provided by
    GOV.UKhttp://gov.uk/
    Authors
    Department for Education
    Description

    Statistics providing information on measures of widening participation in higher education.

    These include estimates of progression to higher education (HE) by age 19 for state-funded pupils by personal characteristics, including:

    • eligibility for free school meals (FSM)
    • gender
    • ethnicity
    • special educational need (SEN) status
    • first language
    • looked after children
    • children in need

    The publication includes geographic breakdowns to enable comparisons of HE progression rates between local authorities and regions by personal characteristics.

    Figures are also provided showing estimated percentages of A level and equivalent students, by school or college type, who progressed to HE by age 19 with breakdowns for high tariff higher education providers.

    Further breakdowns include progression by POLAR disadvantage and Teaching Excellence and Student Outcomes Framework rating.

    The latest data relates to HE entry in 2019 to 2020, so the figures presented will be unaffected by the impact of the coronavirus (COVID-19) pandemic.

  5. B

    Belarus BY: Gender Parity Index (GPI): Tertiary School Enrollment: Gross

    • ceicdata.com
    Updated Mar 12, 2018
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    CEICdata.com (2018). Belarus BY: Gender Parity Index (GPI): Tertiary School Enrollment: Gross [Dataset]. https://www.ceicdata.com/en/belarus/social-education-statistics
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    Dataset updated
    Mar 12, 2018
    Dataset provided by
    CEICdata.com
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Time period covered
    Dec 1, 2011 - Dec 1, 2022
    Area covered
    Belarus
    Variables measured
    Education Statistics
    Description

    BY: Gender Parity Index (GPI): Tertiary School Enrollment: Gross data was reported at 1.120 Ratio in 2023. This records an increase from the previous number of 1.110 Ratio for 2022. BY: Gender Parity Index (GPI): Tertiary School Enrollment: Gross data is updated yearly, averaging 1.233 Ratio from Dec 1991 (Median) to 2023, with 29 observations. The data reached an all-time high of 1.313 Ratio in 2008 and a record low of 1.108 Ratio in 2020. BY: Gender Parity Index (GPI): Tertiary School Enrollment: Gross data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Belarus – Table BY.World Bank.WDI: Social: Education Statistics. Gender parity index for gross enrollment ratio in tertiary education is the ratio of women to men enrolled at tertiary level in public and private schools.;UNESCO Institute for Statistics (UIS). UIS.Stat Bulk Data Download Service. Accessed April 5, 2025. https://apiportal.uis.unesco.org/bdds.;Weighted average;

  6. Chile: enrollment in higher education, by gender 2013-2020

    • statista.com
    Updated Jul 9, 2025
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    Statista (2025). Chile: enrollment in higher education, by gender 2013-2020 [Dataset]. https://www.statista.com/statistics/779999/chile-higher-education-enrollment-by-gender/
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    Dataset updated
    Jul 9, 2025
    Dataset authored and provided by
    Statistahttp://statista.com/
    Area covered
    Chile
    Description

    During the period between 2013 and 2019, the enrollment of female students in higher education programs in Chile increased from approximately *** thousand students in 2013 to nearly *** thousand in 2019.

  7. Z

    Data and codes: Changing the Academic Gender Narrative through Open Access

    • data.niaid.nih.gov
    • zenodo.org
    Updated Nov 24, 2022
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    Lucy Montgomery (2022). Data and codes: Changing the Academic Gender Narrative through Open Access [Dataset]. https://data.niaid.nih.gov/resources?id=zenodo_6500293
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    Dataset updated
    Nov 24, 2022
    Dataset provided by
    Rebecca N Handcock
    Aniek Roelofs
    Lucy Montgomery
    Cameron Neylon
    Alkim Ozaygen
    Katie Wilson
    Chun-Kai (Karl) Huang
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    This Zenodo entry includes data and R codes used to generate the figures included in the manuscript "Changing the Academic Gender Narrative through Open Access", authored by members of the Curtin Open Knowledge Initiative (COKI). These include data that are either publicly available or derived through the COKI data infrastructure.

    The R file includes codes used to generate Figures 1, 2, 3, 4, 1A, 2A and 3A. It uses data contained in the files "au_data_all.csv", "au_groupings.csv", "uk_data_all.csv" and "uk_groupings.csv".

    This entry also includes the full data files (.csv and .xlsx) for Figures 5 and 6 included in the manuscript:

    Figure 5: ‘Percentages of women academic staff (headcount) compared to the total number of academics in the institution for 43 Australian universities by grouping, 2020’. The analysis is of publicly available data sourced from the Australian Department of Education, Skills and Employment.

    Figure 6: ‘Percentages of women academic staff (headcount) compared to the total number of academics in the institution for a subset of 165 United Kingdom higher education institutions by grouping, 2020’. The analysis is of publicly available data sourced from the United Kingdom Higher Education Statistics Agency (HESA).

  8. C

    Chad TD: Gender Parity Index (GPI): Tertiary School Enrollment: Gross

    • ceicdata.com
    Updated Oct 9, 2023
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    CEICdata.com (2023). Chad TD: Gender Parity Index (GPI): Tertiary School Enrollment: Gross [Dataset]. https://www.ceicdata.com/en/chad/social-education-statistics/td-gender-parity-index-gpi-tertiary-school-enrollment-gross
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    Dataset updated
    Oct 9, 2023
    Dataset provided by
    CEICdata.com
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Time period covered
    Dec 1, 2000 - Dec 1, 2020
    Area covered
    Chad
    Variables measured
    Education Statistics
    Description

    Chad TD: Gender Parity Index (GPI): Tertiary School Enrollment: Gross data was reported at 0.394 Ratio in 2020. This records an increase from the previous number of 0.293 Ratio for 2015. Chad TD: Gender Parity Index (GPI): Tertiary School Enrollment: Gross data is updated yearly, averaging 0.149 Ratio from Dec 1972 (Median) to 2020, with 22 observations. The data reached an all-time high of 0.394 Ratio in 2020 and a record low of 0.000 Ratio in 1972. Chad TD: Gender Parity Index (GPI): Tertiary School Enrollment: Gross data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Chad – Table TD.World Bank.WDI: Social: Education Statistics. Gender parity index for gross enrollment ratio in tertiary education is the ratio of women to men enrolled at tertiary level in public and private schools.;UNESCO Institute for Statistics (UIS). UIS.Stat Bulk Data Download Service. Accessed April 5, 2025. https://apiportal.uis.unesco.org/bdds.;Weighted average;

  9. Number of students in tertiary education in Tunisia 2014-2020, by gender

    • statista.com
    • thefarmdosupply.com
    Updated Jun 30, 2024
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    Statista (2024). Number of students in tertiary education in Tunisia 2014-2020, by gender [Dataset]. https://www.statista.com/statistics/1180673/number-of-students-enrolled-in-tertiary-education-in-tunisia-by-gender/
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    Dataset updated
    Jun 30, 2024
    Dataset authored and provided by
    Statistahttp://statista.com/
    Area covered
    Tunisia
    Description

    In 2020, almost 270,000 students were enrolled in tertiary education in Tunisia. Around 170,000 of these were women, making up roughly 63 percent of the students. From 2014 onwards, the number of students in tertiary education generally decreased, with male students always representing the minority of the enrollees.

    Higher education in Tunisia

    After obtaining the Baccalauréat at the end of secondary school, students in Tunisia can access higher education. Most students enrolled in university pursue bachelor’s degrees. In the 2017/2018 academic year, there were around 152,000 undergraduate students in the country, compared to approximately 37,000 postgraduates. Some foreign students also attend Tunisia's higher education institutions. Out of the total number of enrollees in tertiary education in the country, around 2.8 percent were foreign students as of 2019. In general, Tunisians mostly enroll in STEM disciplines (science, technology, engineering, and mathematics). Commercial and administrative affairs, engineering, and computer science are the most popular fields of study in tertiary education.

    Largest universities

    As of the 2018/2019 academic year, there were around 277 tertiary education institutions in Tunisia. The vast majority, amounting to 203, were public establishments. Despite a constant annual increment in number, private universities remain less prevalent in the country and attract a lower number of students compared to public institutes. The public universities of Sfax, Carthage, and Tunis El Manar lead the ranking of Tunisian universities by number of enrollees. In recent years, enrollment in tertiary education has been generally decreasing in Tunisia.

  10. Proportion of male and female postsecondary graduates, by field of study and...

    • www150.statcan.gc.ca
    Updated Nov 20, 2024
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    Government of Canada, Statistics Canada (2024). Proportion of male and female postsecondary graduates, by field of study and International Standard Classification of Education [Dataset]. http://doi.org/10.25318/3710013501-eng
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    Dataset updated
    Nov 20, 2024
    Dataset provided by
    Statistics Canadahttps://statcan.gc.ca/en
    Area covered
    Canada
    Description

    The proportion of male and female postsecondary graduates, by Classification of Instructional Programs, Primary groupings (CIP_PG), International Standard Classification of Education (ISCED) and age group.

  11. I

    India Gender Parity Index: Tertiary Education: Himachal Pradesh

    • ceicdata.com
    Updated Jun 8, 2017
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    CEICdata.com, India Gender Parity Index: Tertiary Education: Himachal Pradesh [Dataset]. https://www.ceicdata.com/en/india/gender-parity-index-tertiary-education/gender-parity-index-tertiary-education-himachal-pradesh
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    Dataset updated
    Jun 8, 2017
    Dataset provided by
    CEICdata.com
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Time period covered
    Sep 1, 2010 - Sep 1, 2021
    Area covered
    India
    Variables measured
    Education Statistics
    Description

    Gender Parity Index: Tertiary Education: Himachal Pradesh data was reported at 1.330 % in 2021. This stayed constant from the previous number of 1.330 % for 2020. Gender Parity Index: Tertiary Education: Himachal Pradesh data is updated yearly, averaging 1.236 % from Sep 2010 (Median) to 2021, with 12 observations. The data reached an all-time high of 10.400 % in 2012 and a record low of 0.966 % in 2013. Gender Parity Index: Tertiary Education: Himachal Pradesh data remains active status in CEIC and is reported by Ministry of Education. The data is categorized under India Premium Database’s Education Sector – Table IN.EDD006: Gender Parity Index: Tertiary Education.

  12. a

    Decoding Home Values: The Power of Education vs. Race, Ethnicity, and Gender...

    • chi-phi-nmcdc.opendata.arcgis.com
    Updated Jul 25, 2023
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    New Mexico Community Data Collaborative (2023). Decoding Home Values: The Power of Education vs. Race, Ethnicity, and Gender [Dataset]. https://chi-phi-nmcdc.opendata.arcgis.com/datasets/decoding-home-values-the-power-of-education-vs-race-ethnicity-and-gender
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    Dataset updated
    Jul 25, 2023
    Dataset authored and provided by
    New Mexico Community Data Collaborative
    Description

    A detailed explanation of how this dataset was put together, including data sources and methodologies, follows below.Please see the "Terms of Use" section below for the Data DictionaryDATA ACQUISITION AND CLEANING PROCESSThis dataset was built from 5 separate datasets queried during the months of April and May 2023 from the Census Microdata System (link below):https://data.census.gov/mdat/#/All datasets include information on Property Value (VALP) by: Educational Attainment (SCHL), Gender (SEX), a specified race or ethnicity (RAC or HISP), and are grouped by Public Use Microdata Areas (PUMAS). PUMAS are geographic areas created by the Census bureau; they are weighted by land area and population to facilitate data analysis. Data also Included totals for the state of New Mexico, so 19 total geographies are represented. Datasets were downloaded separately by race and ethnicity because this was the only way to obtain the VALP, SCHL, and SEX variables intersectionally with race or ethnicity data. Datasets were downloaded separately by race and ethnicity because this was the only way to obtain the VALP, SCHL, and SEX variables intersectionally with race or ethnicity data. Cleaning each dataset started with recoding the SCHL and HISP variables - details on recoding can be found below.After recoding, each dataset was transposed so that PUMAS were rows and SCHL, VALP, SEX, and Race or Ethnicity variables were the columns.Median values were calculated in every case that recoding was necessary. As a result, all Property Values in this dataset reflect median values.At times the ACS data downloaded with zeros instead of the 'null' values in initial query results. The VALP variable also included a "-1" variable to reflect N/A values (details in variable notes). Both zeros and "-1" values were removed before calculating median values, both to keep the data true to the original query and to generate accurate median values.Recoding the SCHL variable resulted in 5 rows for each PUMA, reflecting the different levels of educational attainment in each region. Columns grouped variables by race or ethnicity and gender. Cell values were property values.All 5 datasets were joined after recoding and cleaning the data. Original datasets all include 95 rows with 5 separate Educational Attainment variables for each PUMA, including New Mexico State totals.Because 1 row was needed for each PUMA in order to map this data, the data was split by Educational Attainment (SCHL), resulting in 110 columns reflecting median property values for each race or ethnicity by gender and level of educational attainment.A short, unique 2 to 5 letter alias was created for each PUMA area in anticipation of needing a unique identifier to join the data with. GIS AND MAPPING PROCESSA PUMA shapefile was downloaded from the ACS site. The Shapefile can be downloaded here: https://tigerweb.geo.census.gov/arcgis/rest/services/TIGERweb/PUMA_TAD_TAZ_UGA_ZCTA/MapServerThe DBF from the PUMA shapefile was exported to Excel; this shapefile data included needed geographic information for mapping such as: GEOID, PUMACE. The UIDs created for each PUMA were added to the shapefile data; the PUMA shapfile data and ACS data were then joined on UID in JMP.The data table was joined to the shapefile in ARC GiIS, based on PUMA region (specifically GEOID text).The resulting shapefile was exported as a GDB (geodatabase) in order to keep 'Null' values in the data. GDBs are capable of including a rule allowing null values where shapefiles are not. This GDB was uploaded to NMCDCs Arc Gis platform. SYSTEMS USEDMS Excel was used for data cleaning, recoding, and deriving values. Recoding was done directly in the Microdata system when possible - but because the system is was in beta at the time of use some features were not functional at times.JMP was used to transpose, join, and split data. ARC GIS Desktop was used to create the shapefile uploaded to NMCDC's online platform. VARIABLE AND RECODING NOTESTIMEFRAME: Data was queried for the 5 year period of 2015 to 2019 because ACS changed its definiton for and methods of collecting data on race and ethinicity in 2020. The change resulted in greater aggregation and les granular data on variables from 2020 onward.Note: All Race Data reflects that respondants identified as the specified race alone or in combination with one or more other races.VARIABLE:ACS VARIABLE DEFINITIONACS VARIABLE NOTESDETAILS OR URL FOR RAW DATA DOWNLOADRACBLKBlack or African American ACS Query: RACBLK, SCHL, SEX, VALP 2019 5yrRACAIANAmerican Indian and Alaska Native ACS Query: RACAIAN, SCHL, SEX, VALP 2019 5yrRACASNAsian ACS Query: RACASN, SCHL, SEX, VALP 2019 5yrRACWHTWhite ACS Query: RACWHT, SCHL, SEX, VALP 2019 5yrHISPHispanic Origin ACS Query: HISP ORG, SCHL, SEX, VALP 2019 5yrHISP RECODE: 24 original separate variablesThe Hispanic Origin (HISP) variable originally included 24 subcategories reflecting Mexican, Central American, South American, and Caribbean Latino, and Spanish identities from each Latin American counry. 7 recoded VariablesThese 24 variables were recoded (grouped) into 7 simpler categories for data analysis: Not Spanish/Hispanic/Latino, Mexican, Caribbean Latino, Central American, South American, Spaniard, All other Spanish/Hispanic/Latino Female. Not Spanish/Hispanic/Latino was not really used in the final dataset as the race datasets provided that information.SCHLEducational Attainment25 original separate variablesThe Educational Attainment (SCHL) variable originally included 25 subcategories reflecting the education levels of adults (over 18) surveyed by the ACS. These include: Kindergarten, Grades 1 through 12 separately, 12th grade with no diploma, Highschool Diploma, GED or credential, less than 1 year of college, more than 1 year of college with no degree, Associate's Degree, Bachelor's Degree, Master's Degree, Professional Degree, and Doctorate Degree.SCHL RECODE: 5 recoded variablesThese 25 variables were recoded (grouped) into 5 simpler categories for data analysis: No High School Diploma, High School Diploma or GED, Some College, Bachelor's Degree, and Advanced or Professional DegreeSEXGender2 variables1 - Male, 2 - FemaleVALPProperty Value1 variableValues were rounded and top-coded by ACS for anonymity. The "-1" variable is defined as N/A (GQ/ Vacant lots except 'for sale only' and 'sold, not occupied' / not owned or being bought.) This variable reflects the median value of property owned by individuals of each race, ethnicity, gender, and educational attainment category.PUMAPublic Use Microdata Area18 PUMAsPUMAs in New Mexico can be viewed here:https://nmcdc.maps.arcgis.com/apps/mapviewer/index.html?webmap=d9fed35f558948ea9051efe9aa529eafData includes 19 total regions: 18 Pumas and NM State TotalsNOTES AND RESOURCESThe following resources and documentation were used to navigate the ACS PUMS system and to answer questions about variables:Census Microdata API User Guide:https://www.census.gov/data/developers/guidance/microdata-api-user-guide.Additional_Concepts.html#list-tab-1433961450Accessing PUMS Data:https://www.census.gov/programs-surveys/acs/microdata/access.htmlHow to use PUMS on data.census.govhttps://www.census.gov/programs-surveys/acs/microdata/mdat.html2019 PUMS Documentation:https://www.census.gov/programs-surveys/acs/microdata/documentation.2019.html#list-tab-13709392012014 to 2018 ACS PUMS Data Dictionary:https://www2.census.gov/programs-surveys/acs/tech_docs/pums/data_dict/PUMS_Data_Dictionary_2014-2018.pdf2019 PUMS Tiger/Line Shapefileshttps://www.census.gov/cgi-bin/geo/shapefiles/index.php?year=2019&layergroup=Public+Use+Microdata+Areas Note 1: NMCDC attemepted to contact analysts with the ACS system to clarify questions about variables, but did not receive a timely response. Documentation was then consulted.Note 2: All relevant documentation was reviewed and seems to imply that all survey questions were answered by adults, age 18 or over. Youth who have inherited property could potentially be reflected in this data.Dataset and feature service created in May 2023 by Renee Haley, Data Specialist, NMCDC.

  13. Enrollment rate at higher education level in Malaysia 2020-2023, by gender

    • statista.com
    Updated Sep 15, 2025
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    Statista (2025). Enrollment rate at higher education level in Malaysia 2020-2023, by gender [Dataset]. https://www.statista.com/statistics/1458896/malaysia-university-enrollment-rate-by-gender/
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    Dataset updated
    Sep 15, 2025
    Dataset authored and provided by
    Statistahttp://statista.com/
    Area covered
    Malaysia
    Description

    In 2023, female students in Malaysia had a higher gross enrollment rate compared to male students at the higher education level, with around a ** percent rate. Although there were more women with university education in Malaysia, labor participation of women was lower compared to men.

  14. A

    Australia AU: Gender Parity Index (GPI): Secondary School Enrollment: Gross

    • ceicdata.com
    + more versions
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    CEICdata.com, Australia AU: Gender Parity Index (GPI): Secondary School Enrollment: Gross [Dataset]. https://www.ceicdata.com/en/australia/social-education-statistics/au-gender-parity-index-gpi-secondary-school-enrollment-gross
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    Dataset provided by
    CEICdata.com
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Time period covered
    Dec 1, 1993 - Dec 1, 2020
    Area covered
    Australia
    Variables measured
    Education Statistics
    Description

    Australia Gender Parity Index (GPI): Secondary School Enrollment: Gross data was reported at 0.962 Ratio in 2020. This records an increase from the previous number of 0.934 Ratio for 2019. Australia Gender Parity Index (GPI): Secondary School Enrollment: Gross data is updated yearly, averaging 0.962 Ratio from Dec 1993 (Median) to 2020, with 11 observations. The data reached an all-time high of 1.032 Ratio in 1997 and a record low of 0.867 Ratio in 2016. Australia Gender Parity Index (GPI): Secondary School Enrollment: Gross data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Australia – Table AU.World Bank.WDI: Social: Education Statistics. Gender parity index for gross enrollment ratio in secondary education is the ratio of girls to boys enrolled at secondary level in public and private schools.;UNESCO Institute for Statistics (UIS). UIS.Stat Bulk Data Download Service. Accessed October 24, 2022. https://apiportal.uis.unesco.org/bdds.;Weighted average;

  15. F

    Unemployment Level - College Graduates - Master's Degree, 18 to 19 years,...

    • fred.stlouisfed.org
    json
    Updated Dec 4, 2020
    + more versions
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    (2020). Unemployment Level - College Graduates - Master's Degree, 18 to 19 years, Women [Dataset]. https://fred.stlouisfed.org/series/CGMDUW1819
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    jsonAvailable download formats
    Dataset updated
    Dec 4, 2020
    License

    https://fred.stlouisfed.org/legal/#copyright-public-domainhttps://fred.stlouisfed.org/legal/#copyright-public-domain

    Description

    Graph and download economic data for Unemployment Level - College Graduates - Master's Degree, 18 to 19 years, Women (CGMDUW1819) from Oct 2007 to Nov 2020 about 18 to 19 years, master's degree, females, tertiary schooling, education, household survey, unemployment, and USA.

  16. Z

    UK HESA 2020 Academic women: Changing the Academic Gender Narrative through...

    • data.niaid.nih.gov
    Updated Jun 9, 2022
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    Roelofs, Aniek (2022). UK HESA 2020 Academic women: Changing the Academic Gender Narrative through Open Access [Dataset]. https://data.niaid.nih.gov/resources?id=zenodo_6510471
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    Dataset updated
    Jun 9, 2022
    Dataset provided by
    Handcock, Rebecca N.
    Wilson, Katie
    Roelofs, Aniek
    Ozaygen, Alkim
    Huang, Chun-Kai (Karl)
    Neylon, Cameron
    Montgomery, Lucy
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Area covered
    United Kingdom
    Description

    This Zenodo entry includes the full data files (.csv and .xlsx) for Figure 6: 'Percentages of women academic staff (headcount) in a subset of 165 United Kingdom universities by grouping, 2020', included in the manuscript "Changing the Academic Gender Narrative through Open Access", authored by members of the Curtin Open Knowledge Initiative (COKI). The analysis is of publicly available data sourced from the United Kingdom Higher Education Statistics Agency (HESA).

  17. C

    Colombia CO: Gender Parity Index (GPI): Primary School Enrollment: Gross

    • ceicdata.com
    Updated Feb 27, 2018
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    CEICdata.com (2018). Colombia CO: Gender Parity Index (GPI): Primary School Enrollment: Gross [Dataset]. https://www.ceicdata.com/en/colombia/social-education-statistics
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    Dataset updated
    Feb 27, 2018
    Dataset provided by
    CEICdata.com
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Time period covered
    Dec 1, 2009 - Dec 1, 2020
    Area covered
    Colombia
    Variables measured
    Education Statistics
    Description

    CO: Gender Parity Index (GPI): Primary School Enrollment: Gross data was reported at 0.966 Ratio in 2020. This records a decrease from the previous number of 0.967 Ratio for 2019. CO: Gender Parity Index (GPI): Primary School Enrollment: Gross data is updated yearly, averaging 1.002 Ratio from Dec 1970 (Median) to 2020, with 49 observations. The data reached an all-time high of 1.147 Ratio in 1990 and a record low of 0.966 Ratio in 2020. CO: Gender Parity Index (GPI): Primary School Enrollment: Gross data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Colombia – Table CO.World Bank.WDI: Social: Education Statistics. Gender parity index for gross enrollment ratio in primary education is the ratio of girls to boys enrolled at primary level in public and private schools.;UNESCO Institute for Statistics (UIS). UIS.Stat Bulk Data Download Service. Accessed October 24, 2022. https://apiportal.uis.unesco.org/bdds.;Weighted average;

  18. Data from: Longitudinal Cohort Study of Interpersonal Violence Among...

    • catalog.data.gov
    • datasets.ai
    • +1more
    Updated Mar 12, 2025
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    National Institute of Justice (2025). Longitudinal Cohort Study of Interpersonal Violence Among College-Aged Men and Women, United States, 2019-2020 [Dataset]. https://catalog.data.gov/dataset/longitudinal-cohort-study-of-interpersonal-violence-among-college-aged-men-and-women-2019--45f80
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    Dataset updated
    Mar 12, 2025
    Dataset provided by
    National Institute of Justicehttp://nij.ojp.gov/
    Area covered
    United States
    Description

    The extent and consequences of various forms of interpersonal violence (IV) among college-aged persons has been well-documented. This study sought to examine how IV might differ between young adults who go to college compared to those that do not go to college. To better understand the risks for, experiences with, and consequences of IV among young adults, in fiscal year 2016, the National Institute for Justice (NIJ) made an award to Westat to fund the planning phase of a longitudinal study to research the victimization and violence experienced by college-aged individuals. The planning phase was designed to produce a comprehensive plan to conduct a generalizable, longitudinal study examining long-term trajectories of risk for, experiences with, and recovery after experiencing violence and victimization among college-aged individuals. This pilot study was the result of this planning phase. The major variables in this study contained information regarding sexual assault and rape, dating violence, stalking, violence committed by peers, and violence committed by strangers, as well as demographic variables such as participant age, gender, and race.

  19. f

    Tertiary Education - Domestic and international students by ethnic group,...

    • figure.nz
    csv
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    Figure.NZ, Tertiary Education - Domestic and international students by ethnic group, age group and gender 2020 [Dataset]. https://figure.nz/table/kvokOeHtNbCV73to
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    csvAvailable download formats
    Dataset provided by
    Figure.NZ
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Area covered
    New Zealand
    Description

    These statistics relate to students enrolled in tertiary education providers by a range of demographic and other characteristics.

  20. Gross annual salary of long-term higher education graduates in France 2023,...

    • tokrwards.com
    • statista.com
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    Statista Research Department, Gross annual salary of long-term higher education graduates in France 2023, by gender [Dataset]. https://tokrwards.com/?_=%2Fstudy%2F136849%2Finequality-in-france%2F%23D%2FIbH0PhabzN99vNwgDeng71Gw4euCn%2B
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    Dataset provided by
    Statistahttp://statista.com/
    Authors
    Statista Research Department
    Area covered
    France
    Description

    This chart shows the amount of the declared median gross annual salary of French long-term tertiary education graduates in from 2020 to 2023, by gender, in euros. It can thus be seen that women with a long-term higher education qualification who graduated in 2023 had a median gross annual salary of 28,500 euros, compared with 34,000 for their male counterparts.

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Statista (2025). Educational attainment worldwide 2020, by gender and level [Dataset]. https://www.statista.com/statistics/1212278/education-gender-gap-worldwide-by-level/
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Educational attainment worldwide 2020, by gender and level

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12 scholarly articles cite this dataset (View in Google Scholar)
Dataset updated
Jan 23, 2025
Dataset authored and provided by
Statistahttp://statista.com/
Time period covered
2020
Area covered
Worldwide
Description

According to the Global Gender Gap Report 2020, 88 percent of females worldwide had primary education, compared to 91 percent of males. By comparison, more females than males had attained tertiary education. The Global Gender Index benchmarks national gender gaps on economic, political, education, and health-based criteria. In 2020, the leading country was Iceland with a score of 0.87.

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