CoChat
Sign Up
Cited by 3Early Childhood Education and DevelopmentDevelopmental and Educational NeuropsychologyCognitive Abilities and Testing

The DOI matches its Crossref record; 12 of the 12 references it cites were found in a registry or an index.

Executive functions as mediators of early educational disparities by SES, gender and birth month

Open access
Rémeau, Margot, Schmitt, Philippe, Borst, Grégoire·2026·npj Science of Learning
DOI 10.1038/s41539-026-00422-wPMC PMC13265730
Open this paper in CoChat — free accountOpen PDF

Abstract

Educational inequalities emerge early, yet the combined influence of socioeconomic status (SES), gender, and birth month remains understudied in preschool. While SES-related disparities have been linked to executive functions (EFs)—cognitive processes supporting goal-directed behavior—their role in gender- and birth-month-related inequalities is unknown. In a sample of 2,618 preschoolers (mean age = 42.61 months, SD = 3.41; girls/boys = 1254/1364), SES, gender, and birth month each independently predicted pre-academic abilities. EFs significantly mediated these associations, explaining 23% of SES-related disparities, 39% of gender-related disparities, and 51% of birth-month-related disparities. These findings provide novel evidence that early educational inequalities may partly originate from EF differences, underscoring their central role in shaping school readiness.

Overview

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1038/s41539-026-00422-w

Cites

12 of 12 references verified0 citation errors · 0 not checkable

Statistics

Verification

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

  • In the French context, parental occupation and education are particularly common indicators of social background in educational statistics.

    “In the French context, parental occupation and education are particularly common indicators of social background in educational statistics (Rémeau et al., 2025, Rémeau, Raffy, & Borst 2026, Rémeau, Schmitt, & Borst 2026).”

    From Participation in extracurricular activities predicts academic growth and mediates socioeconomic inequalities in primary school (2026) · cites this paper as Rémeau, Schmitt, & Borst 2026

    The Methods (Statistical analysis; Materials and procedure, page unavailable) use parental education and occupation to measure family socioeconomic status in a French Ministry of Education dataset. This supports their use as indicators in a French educational study, but the paper does not establish that they are particularly common across French educational statistics. (LLM, conf=0.82)

    Experimental — accuracy not yet measured

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Citations

Cites

Flagged first, then by citations

Research works printed but not linked: 66

  1. OECD. (2017). Educational opportunity for all: Overcoming inequality throughout the life course. OECD Publishing. https://doi.org/10.1787/9789264287457-en. · DOI
  2. Hertzman, C. & Wiens, M. Child development and long-term outcomes: A population health perspective and summary of successful interventions. Soc. Sci. Med. 43, 1083–1095, https://doi.org/10.1016/0277-9536(96)00028-7 (1996). · DOI · Paper page
  3. Givord, P. (2020). How a student’s month of birth is linked to performance at school: New evidence from PISA. OECD Education Working Papers, 221. https://doi.org/10.1787/822ea6ce-en. · DOI
  4. Liu, J., Peng, P., Zhao, B. & Luo, L. Socioeconomic status and academic achievement in primary and secondary education: A meta-analytic review. Educ. Psychol. Rev. 34, 2867–2896 (2022). https://psycnet.apa.org/doi/10.1007/s10648-022-09689-y. · DOI · Paper page
  5. Coulangeon, P. The impact of participation in extracurricular activities on school achievement of French middle school students: Human capital and cultural capital revisited. Soc. Forces 97, 55–90, https://doi.org/10.1093/sf/soy016 (2018). · DOI · Paper page
  6. Felouzis, G. Inégalités scolaires et politiques éducatives: une introduction. Economie et. Statistique 528, 3–8 (2021).
  7. Caro, D. H., McDonald, J. T. & Willms, J. D. Socio-economic status and academic achievement trajectories from childhood to adolescence. Can. J. Educ./Rev. canadienne de. l'éducation 32, 558–590 (2009). https://journals.sfu.ca/cje/index.php/cje-rce/article/view/3052.
  8. Voyer, D. & Voyer, S. D. Gender differences in scholastic achievement: A meta-analysis. Psychological Bull. 140, 1174–1204, https://doi.org/10.1037/a0036620 (2014). · DOI · Paper page
  9. Brandlistuen, R. E., Flatø, M., Stoltenberg, C., Helland, S. S. & Wang, M. V. Gender gaps in preschool age: A study of behavior, neurodevelopment and pre-academic skills. Scand. J. Public Health 49, 503–510, https://doi.org/10.1177/1403494820944740 (2021). · DOI
  10. Else-Quest, N. M., Hyde, J. S. & Linn, M. C. Cross-national patterns of gender differences in mathematics: A meta-analysis. Psychological Bull. 136, 103–127, https://doi.org/10.1037/a0018053 (2010). · DOI · Paper page
  11. Eteve. Y., Garnero. M., Paillet. V. (2025). Évolution des écarts de performances entre filles et garçons en mathématiques, au fil du temps et de la scolarité, Note d’Information, n° 25-04, DEPP. https://doi.org/10.48464/ni-25-04. · DOI
  12. Martinot, P. et al. Rapid emergence of a maths gender gap in first grade. Nature https://doi.org/10.1038/s41586-025-09126-4 (2025). · DOI
  13. Guiso, L., Monte, F., Sapienza, P. & Zingales, L. Culture, gender, and math. Science 320, 1164–1165 (2008). · DOI
  14. Maeda, Y. & Yoon, S. Y. A Meta-Analysis on Gender Differences in Mental Rotation Ability Measured by the Purdue Spatial Visualization Tests: Visualization of Rotations (PSVT:R). Educ. Psychol. Rev. 25, 69–94, https://doi.org/10.1007/s10648-012-9215-x (2013). · DOI
  15. Breda, T. & Napp, C. Girls’ comparative advantage in reading can largely explain the gender gap in math-related fields. Proc. Natl. Acad. Sci. 116, 15435–15440 (2019). · DOI
  16. Robertson, E. The effects of quarter of birth on academic outcomes at the elementary school level. Econ. Educ. Rev. 30, 300–311, https://doi.org/10.1016/j.econedurev.2010.10.005 (2011). · DOI
  17. Urruticoechea, A. et al. The relative age effects in educational development: A systematic review. Int. J. Environ. Res. Public Health 18, 8966, https://doi.org/10.3390/ijerph18178966 (2021). · DOI · Paper page
  18. Elder, T. E. & Lubotsky, D. H. Kindergarten entrance age and children’s achievement: Impacts of state policies, family backgrounds, and peers. J. Hum. Resour. 44, 641–683 (2009).
  19. Valdés, M. T. The effect of the month of birth on academic achievement: Heterogeneity by social origin and gender. Eur. Societies 26, 1094–1120, https://doi.org/10.1080/14616696.2023.2289652 (2024). · DOI
  20. Bernardi, F. & Valdés, M. T. Sticky educational expectations: A cross-country comparison. Res. Soc. Stratification Mobil. 75, 100624. https://doi.org/10.1016/j.rssm.2021.100624 (2021). · DOI
  21. Ding, X., Li, S., Zhang, X. & Shi, J. The mediating role of executive function between socioeconomic status and academic achievement: A meta-analytic structural equation model. Learn. Individ. Differences 110, 102418. https://doi.org/10.1016/j.lindif.2024.102418 (2024). · DOI
  22. Best, J. R. & Miller, P. H. A developmental perspective on executive function. Child Dev. 81, 1641–1660, https://doi.org/10.1111/j.1467-8624.2010.01499.x (2010). · DOI · Paper page
  23. Zelazo, P. D. et al. The development of executive function in early childhood. Monogr. Soc. Res. Child Dev. 68, i–151, https://doi.org/10.1111/j.0037-976x.2003.00260.x (2003). · DOI
  24. Hendry, A., Jones, E. J. & Charman, T. Executive function in the first three years of life: Precursors, predictors and patterns. Developmental Rev. 42, 1–33, https://doi.org/10.1016/j.dr.2016.06.005 (2016). · DOI
  25. Ursache, A., Noble, K. G. & Pediatric Imaging, Neurocognition and Genetics Study Socioeconomic status, white matter, and executive function in children. Brain Behav. 6, e00531. https://doi.org/10.1002/brb3.531 (2016). · DOI · Paper page
  26. Murphy, Y. E., Zhang, X. & Gatzke-Kopp, L. Early executive and school functioning: Protective roles of home environment by income. J. Appl. Developmental Psychol. 78, 101369. https://doi.org/10.1016/j.appdev.2021.101369 (2022). · DOI
  27. Merz, E. C. et al. Socioeconomic disparities in chronic physiologic stress are associated with brain structure in children. Biol. Psychiatry 86, 921–929, https://doi.org/10.1016/j.biopsych.2019.05.024 (2019). · DOI
  28. Haft, S. L. & Hoeft, F. Poverty’s impact on children’s executive functions: Global considerations. N. Directions Child Adolesc. Dev. 2017, 69–79, https://doi.org/10.1002/cad.20220 (2017). · DOI
  29. Likhitweerawong, N. et al. Associated biological and environmental factors of impaired executive function in preschool-aged children: A population-based study. Infant Child Dev. 32, e2404. https://doi.org/10.1002/icd.2404 (2023). · DOI
  30. Palomino, C. I. & Brudvig, A. Examining the role of demographic characteristics, attachment, and language in preschool children’s executive function skills. Early Child Dev. Care 192, 1967–1981, https://doi.org/10.1080/03004430.2021.1958803 (2022). · DOI
  31. Grissom, N. M. & Reyes, T. M. Let’s call the whole thing off: Evaluating gender and sex differences in executive function. Neuropsychopharmacology 44, 86–96 (2019). https://psycnet.apa.org/doi/10.1038/s41386-018-0179-5. · DOI · Paper page
  32. Schirmbeck, K., Rao, N. & Maehler, C. Similarities and differences across countries in the development of executive functions in children: A systematic review. Infant Child Dev. 29, Article e2164, https://doi.org/10.1002/icd.2164 (2020). · DOI
  33. Yamamoto, N. & Imai-Matsumura, K. Gender differences in executive function and behavioural self-regulation in 5 years old kindergarteners from East Japan. Early Child Dev. Care 189, 56–67, https://doi.org/10.1080/03004430.2017.1299148 (2019). · DOI
  34. Holding, P. et al. Can we measure cognitive constructs consistently within and across cultures? Evidence from a test battery in Bangladesh, Ghana, and Tanzania. Appl. Neuropsychol. Child 7, 1–13, https://doi.org/10.1080/21622965.2016.1206823 (2018). · DOI
  35. Cortés Pascual, A., Moyano Muñoz, N. & Quílez Robres, A. The relationship between executive functions and academic performance in primary education: Review and meta-analysis. Front. Psychol. 10, 449759, https://doi.org/10.3389/fpsyg.2019.01582 (2019). · DOI · Paper page
  36. Davidse, N. J., de Jong, M. T. & Bus, A. G. Causal relations among executive functions and academic skills from preschool to end of first grade. Engl. Linguist. Res. 4, 49, https://doi.org/10.5430/elr.v4n1p49 (2015). · DOI
  37. Mulder, H., Verhagen, J., Van der Ven, S. H., Slot, P. L. & Leseman, P. P. Early executive function at age two predicts emergent mathematics and literacy at age five. Front. Psychol. 8, 274895, https://doi.org/10.3389/fpsyg.2017.01706 (2017). · DOI
  38. Beisly, A., Kwon, K. A. & Jeon, S. Executive function and learning behaviors: Associations with academic skills among preschoolers. Early Child Dev. Care 190, 2469–2483, https://doi.org/10.1080/03004430.2019.1585347 (2020). · DOI
  39. Schmitt, S. A., Geldhof, G. J., Purpura, D. J., Duncan, R. & McClelland, M. M. Examining the relations between executive function, math, and literacy during the transition to kindergarten: A multi-analytic approach. J. Educ. Psychol. 109, 1120–1140, https://doi.org/10.1037/edu0000193 (2017). · DOI
  40. Blair, C. & Razza, R. P. Relating Effortful Control, Executive Function, and False Belief Understanding to Emerging Math and Literacy Ability in Kindergarten. Child Dev. 78, 647–663, https://doi.org/10.1111/j.1467-8624.2007.01019.x (2007). · DOI · Paper page
  41. Willoughby, M. T., Wylie, A. C. & Little, M. H. Testing longitudinal associations between executive function and academic achievement. Developmental Psychol. 55, 767–779, https://doi.org/10.1037/dev000066 (2019). · DOI
  42. Ellis, A. et al. Reciprocal associations between executive function and academic achievement: A conceptual replication of Schmitt et al. (2017). J. Numer. Cognition 7, 453–472, https://doi.org/10.5964/jnc.7047 (2021). · DOI
  43. Muñez, D., Bull, R. & Lee, K. Socioeconomic status, home mathematics environment and math achievement in kindergarten: A mediation analysis. Developmental Sci. 24, e13135. https://doi.org/10.1111/desc.13135 (2021). · DOI
  44. Shen, Y. & Del Tufo, S. N. Parent-child shared book reading mediates the impact of socioeconomic status on heritage language learners’ emergent literacy. Early Child. Res. Q. 59, 254–264, https://doi.org/10.1016/j.ecresq.2021.12.003 (2022). · DOI
  45. Matthews, J. S., Ponitz, C. C. & Morrison, F. J. Early gender differences in self-regulation and academic achievement. J. Educ. Psychol. 101, 689–704, https://doi.org/10.1037/a0014240 (2009). · DOI
  46. Rinaldi, P., Pasqualetti, P., Volterra, V. & Caselli, M. C. Gender differences in early stages of language development. Some evidence and possible explanations. J. Neurosci. Res. 101, 643–653, https://doi.org/10.1002/jnr.24914 (2023). · DOI · Paper page
  47. Endendijk, J. J., Groeneveld, M. G. & Mesman, J. Gender-differentiated parenting revisited: Meta-analysis reveals very few differences in parental control of boys and girls. PLOS ONE 13, e0190193. https://doi.org/10.1371/journal.pone.0159193 (2018). · DOI
  48. Deer, L. K., Hastings, P. D. & Hostinar, C. E. The role of childhood executive function in explaining income disparities in long-term academic achievement. Child Dev. 91, e1046–e1063, https://doi.org/10.1111/cdev.13383 (2020). · DOI
  49. Scionti, N., Cavallero, M., Zogmaister, C. & Marzocchi, G. M. Is Cognitive Training Effective for Improving Executive Functions in Preschoolers? A Systematic Review and Meta-Analysis. Front. Psychol. 10, 2812, https://doi.org/10.3389/fpsyg.2019.02812 (2020). · DOI · Paper page
  50. Eberhart, J., Paes, T. M., Ellefson, M. R. & Marcovitch, S. Executive functions and play. Trends Neurosci. Educ. 30, 100198. https://doi.org/10.1016/j.tine.2023.100198 (2023). · DOI
  51. Kolić-Vehovec, S., Bajšanski, I., Zubković, B.R. Metacognition and Reading Comprehension: Age and Gender Differences. In Trends and Prospects in Metacognition Research (eds Efklides, A.& Misailidi, P.) https://doi.org/10.1007/978-1-4419-6546-2_15 (Springer, Boston, MA, 2010). · DOI
  52. Bembenutty, H. & Karabenick, S. A. Inherent association between academic delay of gratification, future time perspective, and self-regulated learning. Educ. Psychol. Rev. 16, 35–57, https://doi.org/10.1023/B:EDPR.0000012344.34008.5c (2004). · DOI
  53. Davidson, M. C., Amso, D., Anderson, L. C. & Diamond, A. Development of cognitive control and executive functions from 4 to 13 years: Evidence from manipulations of memory, inhibition, and task switching. Neuropsychologia 44, 2037–2078, https://doi.org/10.1016/j.neuropsychologia.2006.02.006 (2006). · DOI · Paper page
  54. Camerota, M., Willoughby, M. T. & Blair, C. B. Speed and accuracy on the Hearts and Flowers task interact to predict child outcomes. Psychological Assess. 31, 995 (2019). https://psycnet.apa.org/doi/10.1037/pas0000725. · DOI
  55. Fournier, M. & Albaret, J. M. Étalonnage des blocs de Corsi sur une population d’enfants scolarisés du CP à la 6 e. Développements n° 16-17, 76–82, https://doi.org/10.3917/devel.016.0076 (2013). · DOI
  56. Raudenbush, S. W. et al. Longitudinally adaptive assessment and instruction increase numerical skills of preschool children. Proc. Natl. Acad. Sci. 117, 27945–27953, https://doi.org/10.1073/pnas.2002883117 (2020). · DOI
  57. Vyas, S. & Kumaranayake, L. Constructing socio-economic status indices: how to use principal components analysis. Health policy Plan. 21, 459–468 (2006). · DOI · Paper page
  58. Linting, M., Meulman, J. J., Groenen, P. J. F. & van der Koojj, A. J. Nonlinear principal components analysis: Introduction and application. Psychological Methods 12, 336–358, https://doi.org/10.1037/1082-989X.12.3.336 (2007). · DOI
  59. Kolenikov, S. & Angeles, G. Socioeconomic status measurement with discrete proxy variables: Is principal component analysis a reliable answer? Rev. income wealth 55, 128–165, https://doi.org/10.1111/j.1475-4991.2008.00309.x (2009). · DOI
  60. Rosseel, Y. lavaan: An R package for structural equation modeling. J. Stat. Softw. 48, 1–36, https://doi.org/10.18637/jss.v048.i02 (2012). · DOI · Paper page
  61. Saris, W. E. & Satorra, A. Power evaluations in structural equation models. Sage Focus Ed. 154, 181–181 (1993).
  62. Van Buuren, S. & Groothuis-Oudshoorn, K. mice: Multivariate imputation by chained equations in R. J. Stat. Softw. 45, 1–67, https://doi.org/10.18637/jss.v045.i03 (2011). · DOI · Paper page
  63. Madley-Dowd, P., Hughes, R., Tilling, K. & Heron, J. The proportion of missing data should not be used to guide decisions on multiple imputation. J. Clin. Epidemiol. 110, 63–73, https://doi.org/10.1016/j.jclinepi.2019.02.016 (2019). · DOI
  64. Enders, C. K. & Bandalos, D. L. The relative performance of full information maximum likelihood estimation for missing data in structural equation models. Struct. Equ. Modeling 8, 430–457, https://doi.org/10.1207/S15328007SEM0803_5 (2001). · DOI · Paper page
  65. Asparouhov, T. & Muthén, B. Structural equation models and mixture models with continuous nonnormal skewed distributions. Struct. Equ. Modeling: A Multidiscip. J. 23, 1–19, https://doi.org/10.1080/10705511.2014.947375 (2016). · DOI
  66. Hu, L. T. & Bentler, P. M. Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Struct. Equ. Modeling: A Multidiscip. J. 6, 1–55, https://doi.org/10.1080/10705519909540118 (1999). · DOI · Paper page

Cited by

Sorted by citations

Metadata

Authors
Rémeau, M., Schmitt, P., & Borst, G.
Affiliations
  • Centre National de la Recherche Scientifique (FR)
  • Ministere de l'Education Nationale (FR)
Corresponding author
Rémeau, Margot
ORCID
Venue
npj Science of Learning
Publisher
Springer Science and Business Media LLC
Published
2026-04-16
Volume
11
Issue
1
ISSN
2056-7936 (ISSN-L)
DOI
10.1038/s41539-026-00422-w
PMC
PMC13265730
OpenAlex
W7154607471
Cited by
3
References
12
Citations by year
2026: 3
FWCI
15.50
Type
journal article
Language
en
Open access
Licence
Locations
  • npj Science of Learning (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
Funders
Agence Nationale de la Recherche (ANR-21-ESRE-0034)
Topics
  • Social Sciences › Education › Early Childhood Education and Development
  • Social Sciences › Psychology › Neuropsychology and Physiological Psychology › Developmental and Educational Neuropsychology
  • Social Sciences › Psychology › Experimental and Cognitive Psychology › Cognitive Abilities and Testing
Keywords
executive function, educational inequality, socioeconomic status, gender differences, birth month effects, preschool children, school readiness, mediation, early childhood education

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review