The extent to which people’s social status is associated with their parents’ status has far-reaching implications for the openness of and stratification in society. Whereas most research focused on the father-child association in advanced economies, less is known about the role mothers play in intergenerational mobility, particularly in a global context. We assembled a dataset of 1.79 million individuals born in 1956–1990 across 106 societies to examine the global patterns of intergenerational educational mobility and how they vary with education expansion and changes in parents’ educational pairing. With education expansion, father-child associations in educational status become weaker and mother-child associations become stronger. With the prevalence of hypogamous parents (mother more educated), mother-child associations are stronger, but father-child associations are weaker. With the prevalence of hypergamous parents (father more educated), mother-daughter associations are weaker. Our global evidence calls for a gender-sensitive understanding of how education expansion matters for intergenerational mobility.
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Secondary data from multinational and national surveys and United Nations archival records were analysed in this study. As the datasets are proprietary and require access permission from the original data collectors/holders, we are unable to make the data publicly available. The datasets and links to apply for and download the data are as follows, with further information provided in Supplementary Information Section 1.1: Encuesta de Caracterización Socioeconómica Nacional (https://www.casen2022.gob.cl/); General Social Survey, Canada (https://www150.statcan.gc.ca/n1/en/catalogue/89F0115X); Chinese General Social Survey (http://cgss.ruc.edu.cn/English/Home.htm); Ecuador Living Conditions Survey (https://www.ecuadorencifras.gob.ec/documentos/web-inec/ECV/ECV_2015/); EDAM – Enquête Djiboutienne auprès des Ménages – Indicateurs Sociaux (https://microdata.worldbank.org/index.php/catalogue/3463); EMOVI – ESRU Social Mobility Survey in Mexico (https://www.icpsr.umich.edu/web/DSDR/studies/35333); Encuesta Nacional de Calidad de Vida (https://www.datos.gov.co/Estad-sticas-Nacionales/Encuesta-Nacional-de-Calidad-de-Vida-ECV-/mz9y-3x9k); European Social Survey (https://www.europeansocialsurvey.org/); European Values Survey (https://europeanvaluesstudy.eu/); Generations and Gender Programme (https://www.ggp-i.org/); General Household Survey, Nigeria (https://microdata.worldbank.org/index.php/catalog/3557); General Social Survey, USA (https://gss.norc.org/); Household Income and Expenditure Survey, Liberia (https://microdata.worldbank.org/index.php/catalog/2986); India Human Development Survey (https://ihds.umd.edu/); The Indonesian Family Life Survey (https://www.rand.org/well-being/social-and-behavioural-policy/data/FLS/IFLS.html); Integrated Household Survey (Gambia: https://microdata.worldbank.org/index.php/catalog/3323/related-materials; Malawi: https://microdata.worldbank.org/index.php/catalog/1003, https://microdata.worldbank.org/index.php/catalog/2936, https://microdata.worldbank.org/index.php/catalog/3818); International Social Survey Programme (https://issp.org/); Japanese General Social Survey (https://csrda.iss.u-tokyo.ac.jp/english/socialresearch/joint/); Korean General Social Survey (https://kossda.snu.ac.kr/handle/20.500.12236/21830); Kagera Health and Development Survey, Tanzania (https://microdata.worldbank.org/index.php/catalog/359, https://microdata.worldbank.org/index.php/catalog/79, https://microdata.worldbank.org/index.php/catalog/2251); Living Conditions Survey (Benin: https://microdata.worldbank.org/index.php/catalog/4291; Burkina Faso: https://microdata.worldbank.org/index.php/catalog/4290; Cote D’Ivoire: https://microdata.worldbank.org/index.php/catalog/2847; Guinea-Bissau: https://microdata.worldbank.org/index.php/catalog/4293; Mali: https://microdata.worldbank.org/index.php/catalog/4295; Niger: https://microdata.worldbank.org/index.php/catalog/4296; Senegal: https://microdata.worldbank.org/index.php/catalog/4297; Togo: https://microdata.worldbank.org/index.php/catalog/4298); Life in Transitions Survey (https://www.ebrd.com/what-we-do/economic-research-and-data/data/lits.html); Labour Market Panel Surveys (Egypt: http://www.erfdataportal.com/index.php/catalog/157; Jordan: http://www.erfdataportal.com/index.php/catalog/139; Tunisia: http://www.erfdataportal.com/index.php/catalog/105); Living Standard Measurement Survey (Albania: https://microdata.worldbank.org/index.php/catalog/64;
Nigeria: https://microdata.worldbank.org/index.php/catalog/1002); National Income Dynamics Study, South Africa (http://www.nids.uct.ac.za/); National Panel Survey, Uganda (https://microdata.worldbank.org/index.php/catalog/1001/; https://microdata.worldbank.org/index.php/catalog/2663); National Household Sample Survey, Brazil (https://www.ibge.gov.br/en/statistics/social/housing/20620-summary-of-indicators-pnad2.html?=&t=microdados); Socioeconomic Survey (Ethiopia: https://microdata.worldbank.org/index.php/catalog/3823; Ghana: https://microdata.worldbank.org/index.php/catalog/2534; Iraq: https://microdata.worldbank.org/index.php/catalog/2334); STEP Skills Measurement Household Survey (Armenia: https://microdata.worldbank.org/index.php/catalog/2010; Bolivia: https://microdata.worldbank.org/index.php/catalog/2011;
Georgia: https://microdata.worldbank.org/index.php/catalog/2013; Ghana: https://microdata.worldbank.org/index.php/catalog/2015; Kenya: https://microdata.worldbank.org/index.php/catalog/2226; Laos: https://microdata.worldbank.org/index.php/catalog/2016; Macedonia: https://microdata.worldbank.org/index.php/catalog/2568; the Philippines: https://microdata.worldbank.org/index.php/catalog/3182; Sri Lanka: https://microdata.worldbank.org/index.php/catalog/2017; Ukraine: https://microdata.worldbank.org/index.php/catalog/2572; Vietnam: https://microdata.worldbank.org/index.php/catalog/2018); Taiwan Social Change Survey (https://www2.ios.sinica.edu.tw/sc/en/home2.php); World Values Survey (https://www.worldvaluessurvey.org/wvs.jsp) and World Population Prospects (https://population.un.org/wpp/).
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We thank Y. T. Nip for research assistance with the preliminary screening of Canada’s General Social Survey data, and N. Guppy for helpful comments on the results of our initial analysis. The authors received no specific funding for this work.
The authors declare no competing interests.
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Supplementary Methods, Figs. 1–22, Tables 1–10, Results and References.
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Hu, Y., Qian, Y. Gender, education expansion and intergenerational educational mobility around the world. Nat Hum Behav 7, 583–595 (2023). https://doi.org/10.1038/s41562-023-01545-5