Gender gaps in early educational achievement


This paper analyzes the source of the gender gap in third-grade numeracy and reading. We adopt an Oaxaca-Blinder approach and decompose the gender gap in educational achievement into endowment and response components. Our estimation relies on unusually rich panel data from the Longitudinal Survey of Australian Children in which information on child development reported by parents and teachers is linked to each child’s results on a national, standardized achievement test. We find that girls in low- and middle-socio-economic-status (SES) families have an advantage in reading, while boys in high-SES families have an advantage in numeracy. Girls score higher on their third-grade reading tests in large part because they were more ready for school at age 4 and had better teacher-assessed literacy skills in kindergarten. Boys’ advantage in numeracy occurs because they achieve higher numeracy test scores than girls with the same education-related characteristics.

This is a preview of subscription content, access via your institution.

Fig. 1
Fig. 2
Fig. 3
Fig. 4


  1. 1.

    In the gender wage gap literature, these are often referred to as the characteristics and returns components, respectively.

  2. 2.

    We are aware of only two exceptions. Jacob (2002) conducts an OB decomposition of the gap in men’s and women’s university attendance, while Fortin et al. (2015) adopt an extension of the OB method to decompose the gender gap in high academic achievement.

  3. 3.

    Recent exceptions include Husain and Millimet (2009), DiPrete and Jennings (2012), Cornwell et al. (2013), and Fortin et al. (2015).

  4. 4.

    The results, available upon request, are similar if we use an income- or education-based measure of SES.

  5. 5.

    The correlation in the WAI and WISC scores is 0.25.

  6. 6.

    This is based on a version of Goodman’s (1997) Strength and Difficulty Questionnaire (SDQ) that has been adapted for toddlers.

  7. 7.

    To facilitate interpretation, we normalize WAI and WISC scores, parental involvement, teacher-assessed absolute and relative achievement, and the SDQ measure to all have a mean of 0 and a standard deviation of 1.

  8. 8.

    Specifically, 17 % of cohort B children drop out of the survey before wave 4 (prior to third grade); 5 % did not consent to the data linkage; NAPLAN test scores could not be retrieved for 9 % of cases; and reading or numeracy test scores are missing for 1 % of cases.

  9. 9.

    The recoding indicator takes the value of 1 if information is missingin the case of dummy variables and takes the value of 1 if information is available in the case of continuous variables.

  10. 10.

    We compared the mean characteristics of our estimation sample to the full sample of respondents in cohort B. There are significant differences (at 5 %) in means for eight (out of 43) characteristics. Most of these differences are small and unlikely to be economically meaningful. On average, respondents in the estimation sample appear slightly more advantaged (they have a higher birth weight, more educated parents, more often live with two biological parents, and are less often indigenous) but at the same time less often get help with homework every day from the secondary parent. Results are available upon request.

  11. 11.

    On average, boys also have lower achievement in writing (24 points), spelling (19 points), and grammar (21 points).

  12. 12.

    Confidence intervals are boot strapped with 100 replications.

  13. 13.

    Simultaneous estimation across different values of τ allows the variance-covariance matrix of the different \(\alpha _{1}^{j\tau }\)to be obtained and the significance of the gender gap in test scores at points of the achievement distribution to be tested. The equality of \(\hat {\alpha }_{1}^{j\tau }\)at all values of τ was tested and rejected using an F test.

  14. 14.

    Analyses of grammar, spelling, and writing achievement scores result in conclusions similar to those based on reading. These additional results are available upon request.

  15. 15.

    Following Jann (2008), we include a gender indicator variable in the pooled regression.

  16. 16.

    Fortin et al. (2015) use an extension of the OB method to decompose mean differences in the propensity to get high and low grades. Their reweighting approach has certain advantages in providing more precise estimates if the conditional mean function is not linear. As we have no reason to believe that in our case, the conditional mean function is particularly nonlinear and there is debate in the literature about how sensitive the OB decomposition really is to deviations in linearity even if they exist (see Fortin et al. 2011), we have chosen to implement the standard OB method.

  17. 17.

    All models are estimated with STATA 13 using the “Oaxaca” command without the “categorical” option.

  18. 18.

    The OLS coefficients underpinning the decomposition analysis are presented in Appendices 3 and 4. Analysis of the other domains of literacy including writing, spelling, and grammar resulted in similar conclusions. These results are available upon request.

  19. 19.

    Differential expectations for boys’ and girls’ educational attainment may have long-term consequences. Fortin et al. (2015), for example, provide evidence that gender differences in students’ own post-secondary expectations are one of the most important factors underlying the relative advantage that girls now have in the grades they receive in high school.

  20. 20.

    LSAC allows us to control for parenting style using the consistent and warmth parenting scales. In contrast, the ECLS-K does not include the scale for consistent parenting. See Appendix Appendix2 for the details of these additional variables.


  1. Almond D, Currie J (2011) Human capital development before age five. In: Ashenfelter O, Card D (eds) Handbook of labor economics, vol 4B. Elsevier, New York, pp 1315–1486

  2. Amato PR, Rivera F (1999) Paternal involvement and children’s behavior problems. J Marriage Fam 61(2):375–384

    Article  Google Scholar 

  3. Apps P, Mendolia S, Walker I (2013) The impact of pre-school on adolescents’ outcomes: evidence from a recent English cohort. Econ Educ Rev 37:183–199

    Article  Google Scholar 

  4. Autor D, Figlio D, Karbownik K, Roth J, Wasserman M (2016) Family disadvantage and the gender gap in behavioral and educational outcomes. Center for Economic Studies and Ifo Institute (CESifo) Working Paper No. 5925

  5. Baker M, Milligan K (2016) Boy-girl differences in parental investments: evidence from three countries. J Hum Capital 10(4):399–441

    Article  Google Scholar 

  6. Bertrand M, Pan J (2013) The trouble with boys: social influences and the gender gap in disruptive behavior. Am Econ J Appl Econ 5(1):32–64

    Article  Google Scholar 

  7. Blinder A (1973) Wage discrimination: reduced form and structural estimates. J Hum Resour 8(4):436–455

    Article  Google Scholar 

  8. Brent D, May DC, Kundert DK (1996) The incidence of delayed school entry: a twelve-year review. Early Edu Dev 7(2):121–135

    Article  Google Scholar 

  9. Buchman C, DiPrete TA, McDanile A (2008) Gender inequalities in education. Am J Sociol 34:319– 337

    Google Scholar 

  10. Chatterji P, Kim D, Lahiri K (2014) Birth weight and academic achievement in childhood. Health Ecom 23(9):1013–1035

    Article  Google Scholar 

  11. Clark D, Martorell P, Rockoff J (2009) School principals and school performance. Working paper No. 38. National Center for Analysis of Longitudinal Data in Education Research

  12. Corak M (2006) Do poor children become poor adults? Lessons from a cross country comparison of generational earnings mobility. Res Econ Inequal 13(1):143–188

    Article  Google Scholar 

  13. Cornwell C, Mustard DB, van Parys J (2013) Noncognitive skills and the gender disparities in test scores and teacher assessments: evidence from primary school. J Hum Resour 48(1):236–264

  14. Currie J, Moretti E (2007) Biology as destiny? Short-and long-run determinants of intergenerational transmission of birth weight. J Labor Econ 25(2):231–264

    Article  Google Scholar 

  15. Currie J (2009) Healthy, wealthy, and wise: socioeconomic status, poor health in childhood, and human capital development. J Econ Lit 47(1):87–122

    Article  Google Scholar 

  16. Dee TS (2007) Teachers and the gender gaps in student achievement. J Hum Resour 17(3):528–554

    Article  Google Scholar 

  17. DiPrete TA, Jennings JL (2012) Social and behavioral skills and the gender gap in early educational achievement. Soc Sci Res 41(1):1–15

    Article  Google Scholar 

  18. Elsner B, Isphording IE (2015) A big fish in a small pond: ability rank and human capital investment. IZA Discussion Papers No. 9121

  19. Entwisle DR, Alexander KL, Olson LS (2007) Early schooling: the handicap of being poor and male. Sociol Educ 80(2):114–138

    Article  Google Scholar 

  20. Fan X, Fang H, Markussen S (2015) Mothers’ employment and children’s educational gender gap. NBER working paper No. 21183

  21. Fitzpatrick MD (2008) Starting school at four: the effect of universal pre-kindergarten on children’s academic achievement. B E J Econom Anal Policy 8 (1):46

    Google Scholar 

  22. Fortin N, Lemieux T, Firpo S (2011) Decomposition methods in economics. In: Ashenfelter O, Card D (eds) Handbooks in economics: labor economics, vol 4A. Elsevier, Amsterdam, pp 1–102

  23. Fortin N, Oreopoulos P, Phipps S (2015) Leaving boys behind: gender disparities in high academic achievement. J Hum Resour 50(3):549–579

    Article  Google Scholar 

  24. Fryer Jr, RG, Levitt SD (2010) An empirical analysis of the gender gap in mathematics. Am Econ J Appl Econ 2(2):210–240

  25. Gibbons S, Chevalier A (2008) Assessment and age 16 + education participation. Res Pap Educ 23(2):113–123

    Article  Google Scholar 

  26. Goodman R (1997) The strengths and difficulties questionnaire: a research note. J Child Psychol Psyc 38(5):581–586

    Article  Google Scholar 

  27. Guiso L, Monte F, Sapienza P, Zinaales L (2008) Culture, gender, and math. Science 320(30):1164–1165

    Article  Google Scholar 

  28. Heckman JJ (2011) The economics of inequality: the value of early childhood education. Am Educ 35(1):31–35

    Google Scholar 

  29. Hill MA, O’Neill J (1994) Family endowments and the achievement of young children with special reference to the underclass. J Hum Resour 29(4):1064–1100

    Article  Google Scholar 

  30. Holmlund H, Sund K (2008) Is the gender gap in school performance affected by the sex of the teacher? Labour Econ 15(1):37–53

    Article  Google Scholar 

  31. Husain M, Millimet DL (2009) The mythical ‘boy crisis’? Econ Educ Rev 28(1):38–48

    Article  Google Scholar 

  32. Jacob BA (2002) Where the boys aren’t: non-cognitive skills, returns to school, and the gender gap in higher education. Econ Educ Rev 21(6):589–598

    Article  Google Scholar 

  33. Jann B (2008) The blinder-oaxaca decomposition for linear regression models. Stata J 8(4):453–479

    Google Scholar 

  34. Johnston DW, Nicholls ME, Shah M, Shields MA (2009) Nature’s experiment? handedness and early childhood development. Demography 46(2):281–301

    Article  Google Scholar 

  35. Jones FL (1983) On decomposing the wage gap: a critical comment on blinder’s method. J Hum Resour 18(1):126–130

    Article  Google Scholar 

  36. Jones FL, McMillan J (2001) Scoring occupational categories for social research: a review of current practice, with Australian examples. Work Employ Soc 15(3):539–563

    Article  Google Scholar 

  37. Kautz T, Heckman JJ, Diris R, Ter Weel B, Borghans L (2014) Fostering and measuring skills: improving cognitive and non-cognitive skills to promote lifetime success. OECD Education Working Papers No 110. OECD Publishing, Paris

    Google Scholar 

  38. Lavy V, Sand E (2015) On the origins of gender human capital gaps: short and long term consequences of teachers stereotypical biases. NBER Working Paper No. 20909

  39. Legewie J, DiPrete TA (2012) School context and the gender gap in educational achievement. Am Sociol Rev 77(3):463–485

    Article  Google Scholar 

  40. Leibowitz A (1977) Parental inputs and children’s achievement. J Hum Resour 12(2):242–251

    Article  Google Scholar 

  41. Leigh A, Gong X (2009) Estimating cognitive gaps between Indigenous and non-Indigenous Australians. Edu Econ 17(2):239–261

    Article  Google Scholar 

  42. de Lemos MM, Doig B (1999) Who am i?: Developmental assessment Australian Council for Educational Research, Melbourne, Australia

  43. Levine SC, Vasilyeva M, Lourenco SF, Newcombe NS, Huttenlocher J (2005) Socio-economic status modifies the sex difference in spatial skill. Psychol Sci 16(11):841–845

    Article  Google Scholar 

  44. Neumark D (1988) Employers’ discriminatory behavior and the estimation of wage discrimination. J Hum Resour 23(3):279–295

    Article  Google Scholar 

  45. Nollenberger N, Rodrígues-planas N, Sevilla A (2016) The math gender gap: the role of culture. Am Econ Rev 106(5):257–261

    Article  Google Scholar 

  46. Oaxaca R (1973) Male-female wage differentials in urban labor markets. Int Econ Rev 14(3):693– 709

    Article  Google Scholar 

  47. Oaxaca RL, Ransom MR (1999) Identification in detailed wage decompositions. Rev Econ Stat 81(1):154–157

    Article  Google Scholar 

  48. OECD (2015) The ABC of gender equality in education: Aptitude, behaviour confidence, PISA, OECD Publishing. doi:10.1787/9789264229945-en

  49. Penner AM, Paret M (2008) Gender differences in mathematics achievement: exploring the early grades and the extremes. Soc Sci Res 37(1):239–253

    Article  Google Scholar 

  50. Pope DG, Sydnor JR (2010) Geographic variation in the gender differences in test scores. J Econ Perspect 24(2):95–108

    Article  Google Scholar 

  51. Riegle-Crumb C (2006) The path through math: course sequences and academic performance at the intersection of race-ethnicity and gender. Am J Educ 113 (1):101–122

    Article  Google Scholar 

  52. Rivkin SE, Hanushek E, Kain J (2005) Teachers, schools, and academic achievement. Econometrica 73(2):417–458

    Article  Google Scholar 

  53. Robinson JP, Lubienski ST (2011) The development of gender achievement gaps in mathematics and reading during elementary and middle school examining direct cognitive assessments and teacher ratings. Am Educ Res J 48(2):268–302

    Article  Google Scholar 

  54. Samson JF, Lesaux NK (2008) Language-minority learners in special education: rates and predictors of identification for services. J Learn Disabil 42(2):148–162

    Article  Google Scholar 

  55. Soloff C, Lawrence D, Johnstone R (2005) Sample design LSAC Technical Paper 1. Australian Institute of Family Studies, Melbourne

    Google Scholar 

  56. Terrier C (2016) Boys lag behind: how teachers’ gender biases affect student achievement. IZA Discussion Paper No. 10343

  57. Wake M, Nicholson JM, Hardy P, Smith K (2007) Preschooler obesity and parenting styles of mothers and fathers: Australian national population study. Paediatrics 120(6):1520–1527

    Article  Google Scholar 

  58. Wechsler D (2003) Wechsler Intelligence Scale for Children—fourth edition Psychological Corp, San Antonio, TX

  59. Willms JD, Shields M (1996) A Measure of Socioeconomic Status for the National Longitudinal Study of Children. Report prepared for Statistics Canada

  60. Wößmann L (2003) Schooling resources, educational institutions and student performance: the international evidence. Oxford B Econ Stat 65(2):117–170

    Article  Google Scholar 

Download references


The authors would like to thank the anonymous referees for helpful comments and suggestions. This paper uses unit record data from Growing Up in Australia: the Longitudinal Study of Australian Children, conducted in partnership between the Australian Government Department of Social Services (DSS), the Australian Institute of Family Studies (AIFS), and the Australian Bureau of Statistics (ABS). This research was supported by the Australian Research Council (ARC) Centre of Excellence for Children and Families over the Life Course (project number CE140100027). The Centre is administered by the Institute for Social Science Research at The University of Queensland, with nodes at The University of Western Australia, The University of Melbourne, and The University of Sydney. The findings and views reported in this paper are those of the authors and should not be attributed to DSS, AIFS, ABS, or ARC.

Author information



Corresponding author

Correspondence to Julie Moschion.

Additional information

Responsible editor: Erdal Tekin


Appendix: 1

Table 5 provides descriptions of the variables included in the Oaxaca decomposition. The variables in bold are shown individually in the results, while the rest are grouped in the “other” category.

Table 5 Variables included in the Oaxaca decomposition

Appendix: 2

Table 6 describes the variables that are included in robustness checks but not in the main model. All variables are measured at 6 years old unless specified otherwise.

Table 6 Variables included in the robustness checks

Appendix 3

Table 7 OLS coefficients for reading test scores by SES

Appendix 4

Table 8 OLS coefficients for numeracy test scores by SES

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Cobb-Clark, D.A., Moschion, J. Gender gaps in early educational achievement. J Popul Econ 30, 1093–1134 (2017).

Download citation


  • Gender gaps
  • Educational achievement
  • Education
  • Australia

JEL Classification

  • J16
  • I21
  • I24