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Gender, education expansion and intergenerational educational mobility around the world

Nature Human Behaviour volume 7, pages 583–595 (2023)Cite this article

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Abstract

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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Fig. 1: Cohort changes in education composition, by region and society.
Fig. 2: Cohort changes in parents’ educational pairing patterns, by region and society.
Fig. 3: Coefficients for mothers’ and fathers’ educational positions from models predicting men’s and women’s educational positions, across 106 societies.
Fig. 4: Coefficients for mothers’ and fathers’ educational positions from models predicting men’s and women’s educational positions, across 1956–1990 birth cohorts.
Fig. 5: Average marginal effects of education expansion and parents’ educational pairing patterns on intergenerational educational persistence.
Fig. 6: Predicted coefficients for parents’ educational positions over the distributions of education expansion and parents’ educational pairing patterns.

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Data availability

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;

Brazil: https://microdata.worldbank.org/index.php/catalog/277;

Ghana: https://microdata.worldbank.org/index.php/catalog?sort_by=rank&sort_order=desc&sk=LSMS+ghana+;

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;

Colombia: https://microdata.worldbank.org/index.php/catalog/2012;

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/).

Code availability

The codes for data cleaning, harmonization, analysis and production of all graphs and tables reported in the main article and Supplementary Information (Section 1.5) are publicly available through the Open Science Framework at https://osf.io/3q75x/.

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Acknowledgements

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.

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Authors and Affiliations

  1. Department of Sociology, Lancaster University, Lancaster, UK

    Yang Hu

  2. Department of Sociology, University of British Columbia, Vancouver, British Columbia, Canada

    Yue Qian

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Contributions

Y.H. and Y.Q. conceptualized and designed the study. Y.H. curated the datasets and conducted the data analysis. Y.Q. conducted analysis replication and validation. Y.H. and Y.Q. wrote, reviewed, revised, edited and finalized the manuscript.

Corresponding authors

Correspondence to Yang Hu or Yue Qian.

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Nature Human Behaviour thanks Sarah Giroux, Jo Blanden and Moris Triventi for their contribution to the peer review of this work. Peer reviewer reports are available.

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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

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