Skip to main content

Ethnicity differentials in academic achievements: the role of time investments


In most English-speaking countries, the children of Asian immigrants have better academic outcomes than other children, yet the underlying causes of their advantages are unclear. Using decade-long time use diaries on two cohorts of children, we present new evidence that children of Asian immigrants spend more time than their peers on educational activities beginning at school entry and that the ethnicity gap in the time allocated to educational activities increases as children age. We can attribute the academic advantage of children of Asian immigrants mainly to their allocating more time to educational activities or their favorable initial cognitive abilities, not to socio-demographics or so-called “tiger parenting” styles. Furthermore, our results show substantial heterogeneity in the contributions of initial cognitive abilities and time allocations by test subjects, children’s ages, and points of the test score distribution.

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

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5


  1. This paper focuses on academic performance of second-generation immigrants, identified as those who were born in the country of review with at least one immigrant parent. In Section 5, we examine relative academic performance of third-generation immigrants who are classified as native-born children of two Australian-born parents where at least one grandparent is foreign-born. The Asian immigrant children’s academic advantages have been documented for Australia (Choi et al. 2015; Le and Nguyen 2018), Canada (Hansen and Kuera 2003; Aydemir and Sweetman 2007), the UK (Algan et al. 2010; Dustmann et al. 2012), the USA (Chiswick and DebBurman 2004; Fryer and Levitt 2006; Clotfelter et al. 2009; Choi et al. 2015; Gibbs et al. 2017; Figlio and Özek 2019), and New Zealand (May et al. 2016). However, such a phenomenon has not been reported in studies using data from other countries, including Ireland, probably due to the small number of second-generation immigrants with an Asian background in these countries (Gang and Zimmermann 2000). For purposes of focus, this paper only concentrates on studies examining the relative academic performance of Asian immigrant children. Reviews of the literature on academic performance by ethnicity/nativity can be found in Kao and Thompson (2003), Dustmann and Glitz (2011), Sweetman and van Ours (2015), or Duncan and Trejo (2018). Following the literature, we use the terms ethnicity and nativity interchangeably in this paper.

  2. They include in the test score regressions a comprehensive list of variables, including student’s study effort (measured by the number of hours doing homework and time watching TV), parental assistance (in terms of assistance in schoolwork and discussion about school), parents’ educational expectations for their children, and students’ participation in additional lessons and schooling activities. Like most studies in this literature (Hsin and Xie 2014; Gibbs et al. 2017), Peng and Wright (1994) employ a regression-based approach where the factor of interest is included as an explanatory variable in test score equations to quantify its contribution to the overall ethnicity test score gap. As will be shown in Sections 4, a decomposition approach employed in our study offers a more direct way to do so (Fortin et al. 2011).

  3. The study by Todd and Wolpin (2007) is an exception as it also uses an AVA model to examine the racial gap in test scores in the USA. However, that study focuses on the sources of test score gaps between Black, White, and Hispanic children and does not investigate the role of children’s time allocation in explaining the ethnicity test score gap like the current paper does.

  4. Possibly due to data availability, US studies usually rely on subjective measures of race/ethnic self-identification to clarify the ethnicity of second-generation immigrants (Chiswick and DebBurman 2004; Choi et al. 2015). As demonstrated by Duncan and Trejo (2011, 2017), using parents’ countries of birth, like the current paper does, would provide arguably more objective measures of the child’s ethnicity.

  5. Appendix Table B1 in Nguyen et al. (2020) also provides some suggestive evidence of a compounding effect where both parents were born in Asia. For example, children of two Asian immigrant parents lag behind children of other parents in the language-related ability of PPVT at ages 4/5 years old. By contrast, they are better at writing, spelling, grammar, and numeracy at grade 9.

  6. While all B cohort children were born in Australia, about 3.5% of K cohort children were born overseas. We experimented including students’ migration status in their test score equations and found their impact statistically insignificant. This finding is consistent with evidence that migrant children arriving in the host country at young ages have similar academic development as native ones (Figlio and Özek 2019). We also experimented excluding children born overseas from all regressions and found similar results. Therefore, all K cohort children are considered as “being born in Australia” in this study. We do not disaggregate the child’s ethnicity further (e.g., by major source countries such as China or India) to keep the sample size of each ethnicity group reasonably large to obtain reliable estimates and to keep the results, especially decomposition ones, manageable. Nevertheless, Appendix Table B2 in Nguyen et al. (2020) reports regression results where we separate Asian immigrant mothers and fathers from three largest countries of origin (i.e., China, India, and Viet Nam) and pool all remaining ones into a residual group. The results suggest a little evidence of heterogeneity in the performance of children across these more disaggregated Asian countries since the estimates are largely the same for them. While we did not find evidence of heterogeneity, we note that some caution is warranted in the interpretation of these results because we do not have a large number of observations from each country in the LSAC and this could affect our power to detect such differences, where they exist. Section 5.1 presents results using alternative ethnicity classifications.

  7. English-speaking countries include Australia, UK, Ireland, Canada, New Zealand, South Africa, and USA.

  8. Similar patterns have been documented in other Australian studies. In particular, immigrants usually have higher qualifications than natives, mainly because Australia maintains a skilled immigrant selection policy (Antecol et al. 2006). Furthermore, despite having higher qualifications, Australian female immigrants who are often secondary migrants in skilled-visa streams struggle to join the workforce (Nguyen and Duncan 2017).

  9. The available empirical evidence suggests that (healthy) babies of mothers with Chinese or South Asian heritage in the USA do tend, on average, to be lighter and have smaller head circumference than other children. For this reason, the application of (population-based) low birth-weight thresholds risks misclassifying some children and has led some authors to call for ethnically-specific birth-weight charts and thresholds. See Hanley and Janssen (2013) for a discussion and empirical results obtained for the state of Washington. Our low birthweight classification may be subject to the same criticism in respect of the birthweight of babies of Asian immigrants in Australia.

  10. Motivated by the idea that some Asian countries have son-preference cultures and that culture may influence academic outcomes of sons and daughters differently (Kaushal and Muchomba 2018), we experimented including an interaction term between ethnicity (as previously defined) and the child’s gender to test for whether there is any statistical significant difference in test scores by sons and daughters of Asian immigrants in Australia. Because we found no such evidence, we do not include that interaction term in the final regressions. For a similar reason, we do not analyze the nativity gaps in test scores and time allocation by gender. For brevity, the regression results for other covariates are not reported, but are available upon request. We explore the role of covariates further in Section 5.

  11. Fryer and Levitt (2006) use a US dataset which is quite similar to ours. Particularly, they use data from Early Childhood Longitudinal Study Kindergarten Cohort (ECLS-K), a nationally representative survey of over 20,000 children entering kindergarten in 1998.

  12. 95% CIs are obtained using 500 bootstrap repetitions. Visually, 95% CIs which do not include zero indicate a statistically significant (at the 5% level) estimate.

  13. Due to children “multi-tasking” (about 20% of time use diaries included a secondary activity, most commonly eating/drinking), the sum of the differences across all exhaustive activities reported in Table 1 does not add up to zero.

  14. Specifically, this approach so does not require any functional assumption about the relationship between ages and time allocation. We introduce the child’s ages in 2-year increment to accommodate the biennial survey design. We experimented including the child’ ages as separate indicator variables in every one-year increment and found estimates for the interaction term (β3) of some age groups imprecise, probably due to the small number of children in those ages surveyed in our sample.

  15. The differences in time allocated to educational activities between children of natives and children of Asian immigrants are in line with evidence on the differences in time uses between children of NESB immigrants and children of natives as documented in an Australian study by Nguyen et al. (2019b). Using time use diaries of children in the USA, Hofferth and Sandberg (2001) also report that Asian children spend significantly more time on reading than other children. Likewise, studies using data from various countries often document that children living in Asian countries spend much more time in school and studying than children living in other countries (Fuligni and Stevenson 1995; Varkey Foundation 2018). Existing studies only look at the static aspects of the nativity gap in time allocation of children and have not explored temporal dimensions of the gap as we do here.

  16. Estimates of other covariates (reported in Appendix Table A2 in Nguyen et al. (2020)) are usually as expected and largely similar to those described in the work by Nguyen et al. (2019b).

  17. Our approach to merge LSAC data with NAPLAN test scores in such a way that survey dates pre-date the NAPLAN test dates also helps mitigate the reverse causality issue.

  18. Notwithstanding, some studies use cross-equation covariance restrictions to achieve identification for time allocation variables (Del Boca et al. 2014; Lee and Seshadri 2019). The value-added model has been increasingly employed to deal with the possible endogeneity of some inputs of the cognitive production process such as parental investments (Pavan 2016; Lehmann et al. 2018), school choices (Elder and Jepsen 2014; Nghiem et al. 2015), or parenting styles (Cobb-Clark et al. 2019). Fiorini and Keane (2014) note that they choose an AVA model over an alternative instrumental variables model because it is “not feasible” to find a large set of valid instruments for multiple endogenous time use variables. The same reasoning applies to our model choice.

  19. Nevertheless, Tables 5, 6, 7, and 8 report the decomposition results of the return part for five groups of variables, either at the mean or at selected percentiles of the test score. The results suggest that the differences in return to each of the grouped variables, including previous cognitive skills and time allocations, do not contribute to explain the nativity test score gap because the estimates are typically statistically insignificant.

  20. In particular, from wave 1 to wave 3, families were given two TUDs to complete each wave so each child had up to two TUDs. However, from wave 4 to wave 6, each child was given one TUD to complete each wave. Furthermore, B cohort children are not asked to fill in TUD in waves 4 and 5.

  21. Consistent with a finding in the study by Fiorini and Keane (2014), regression results at means (reported in Appendix Table A4 in Nguyen et al. (2020)) suggest that time spent on educational activities is the most productive input for academic achievement in children because estimates for educational time variables (current and lagged) are more statistically significant and usually greater in magnitude than that of other time allocation variables. It should be noted that Fiorini and Keane (2014) do not examine NAPLAN test scores which were not available then. Appendix Table A4 in Nguyen et al. (2020) also reports estimates of other explanatory variables.

  22. The result on education is particularly important because Australia has a skilled migration program and there is evidence, in our dataset, that children of Asian-immigrants tend to have more highly educated mothers. The result thus provides some confidence that the results are not driven by higher average levels of parental education.

  23. We reach this finding by estimating a regression similar to model (1) for a pooled sample of test scores available at all ages/grades. To test a hypothesis of increasing returns to initial cognitive endowments, we include an interaction term between lagged scores and survey wave/test grade (as proxy for children’s ages) and test for its statistical significance. We found strong evidence supporting such a hypothesis in all test subjects, except writing (see Appendix Table A7 in Nguyen et al. (2020) for detail).

  24. Applying a slightly different classification of third-generation Asian immigrants as those who were born in Australia by two Australian-born parents with at least two Australian-born grandparents, we found similar results.

  25. For brevity, this section only presents results on nativity test score gaps at means. Other results, including nativity test score gaps along the distribution, nativity gaps in time allocations, and decomposition results, are available upon requests.

  26. Our main conclusions about the development of ethnicity differences in test scores and time allocation largely hold when we introduce individual fixed effects in models (1) and (2) to investigate within person effects of age/grade on educational outcomes (see Appendix Table B11 in Nguyen et al. (2020) for regression results) and time investments (Appendix Table B12 in Nguyen et al. (2020)).

  27. Following this line of research, a recent work by Nguyen et al. (2019a) exclusively examines the sources of ethnicity differences in socio-behavioral development in children and adolescents. One of their main findings is that ethnicity differences in children’s time allocations, mostly to active activities, reduce the non-cognitive skill advantage observed for Asian immigrant children. This result, when viewed with the current paper’s evidence that ethnic disparities in time allocations positively explain the Asian-Native gap in cognitive skills, highlights the opposite roles that the ethnic differences in children’s time allocations may contribute to the nativity gaps in cognitive and non-cognitive skills.


  • ACARA (2014) National Assessment Program – literacy and numeracy 2013: technical report. Australian Curriculum, Assessment and Reporting Authority (ACARA), Sydney

  • Algan Y, Dustmann C, Glitz A, Manning A (2010) The economic situation of first and second-generation immigrants in France, Germany and the United Kingdom. Econ J 120:F4–F30

    Google Scholar 

  • Antecol H, Kuhn P, Trejo SJ (2006) Assimilation via prices or quantities? Sources of immigrant earnings growth in Australia, Canada, and the United States. J Hum Resour 41:821–840

    Google Scholar 

  • Aydemir A, Sweetman A (2007) First-and second-generation immigrant educational attainment and labor market outcomes: a comparison of the United States and Canada. In: Chiswick BR (ed.) Immigration (Research in Labor Economics, Volume 27) Emerald Group Publishing Limited, 215-270

  • Baxter J (2007) Children’s time use in the longitudinal study of Australian children: data quality and analytical issues in the 4-year cohort. The Australian Institute of Family Studies Technical Paper No. 4

  • Betts JR (2011) In: Hanushek EA, Machin S, Woessmann L (eds) Chapter 7 - the economics of tracking in education. Elsevier, Handbook of the Economics of Education, pp 341–381

    Google Scholar 

  • Bleakley H, Chin A (2008) What holds Back the second generation?: the intergenerational transmission of language human capital among immigrants. J Hum Resour 43:267–298

    Google Scholar 

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

    Google Scholar 

  • Borjas GJ (1992) Ethnic capital and intergenerational mobility. Q J Econ 107:123–150

    Google Scholar 

  • Borjas GJ (1994) Long-run convergence of ethnic skill differentials: the children and grandchildren of the great migration. ILR Rev 47:553–573

    Google Scholar 

  • Cameron SV, Heckman JJ (2001) The dynamics of educational attainment for Black, Hispanic, and White males. J Polit Econ 109:455–499

    Google Scholar 

  • Carneiro PM, Garcia I, Salvanes KG, Tominey E (2015) Intergenerational mobility and the timing of parental income. Norwegian School of Economics Department of Economics Discussion Paper No. 23/2015

  • Chiswick BR, DebBurman N (2004) Educational attainment: analysis by immigrant generation. Econ Educ Rev 23:361–379

    Google Scholar 

  • Choi KH, Hsin A, McLanahan SS (2015) Asian children’s verbal development: a comparison of the United States and Australia. Soc Sci Res 52:389–407

    Google Scholar 

  • Chua A (2011) Battle hymn of the tiger mother. Bloomsbury Publishing

  • Clotfelter CT, Ladd HF, Vigdor JL (2009) The academic achievement gap in grades 3 to 8. Rev Econ Stat 91:398–419

    Google Scholar 

  • Cobb-Clark DA, Nguyen T-H (2012) Educational attainment across generations: the role of immigration background. Economic Record 88:554–575

    Google Scholar 

  • Cobb-Clark DA, Salamanca N, Zhu A (2019) Parenting style as an investment in human development. J Popul Econ 32:1315–1352

    Google Scholar 

  • Corey J, Gallagher J, Davis E, Marquardt M (2014) The times of their lives: collecting time use data from children in the longitudinal study of Australian children (LSAC). In: LSAC Technical Paper No. 13

  • Cunha F, Heckman JJ, Schennach SM (2010) Estimating the technology of cognitive and noncognitive skill formation. Econometrica 78:883–931

    Google Scholar 

  • Daraganova G, Edwards B, Sipthorp M (2013) Using National Assessment Program—literacy and numeracy (NAPLAN) data in the longitudinal study of Australian children (LSAC). In: LSAC Technical Paper No. 8, Australian Institute of Family Studies

  • Del Boca D, Flinn C, Wiswall M (2014) Household choices and child development. Rev Econ Stud 81:137–185

    Google Scholar 

  • Del Boca D, Monfardini C, Nicoletti C (2017) Parental and child time investments and the cognitive development of adolescents. J Labor Econ 35:565–608

    Google Scholar 

  • Duncan B, Trejo SJ (2011) Tracking intergenerational progress for immigrant groups: the problem of ethnic attrition. Am Econ Rev Pap Proc 101:603–608

    Google Scholar 

  • Duncan B, Trejo SJ (2017) The complexity of immigrant generations: implications for assessing the socioeconomic integration of Hispanics and Asians. ILR Rev 70:1146–1175

    Google Scholar 

  • Duncan B, Trejo SJ (2018) Socioeconomic integration of US immigrant groups over the long term: the second generation and beyond. In: Pozo S (ed) The human and economic implications of twenty-first century immigration policy. W.E. Upjohn Institute for Employment Research, Kalamazoo, pp 33–62

    Google Scholar 

  • Dunn LM, Dunn LM (1997) Examiner’s manual for the PPVT-III peabody picture vocabulary test: form IIIA and form IIIB. AGS

  • Dustmann C, Frattini T, Lanzara G (2012) Educational achievement of second-generation immigrants: an international comparison. Econ Policy 27:143–185

    Google Scholar 

  • Dustmann C, Glitz A (2011) Migration and education. In: Hanushek EA, Machin S, Woessmann L (eds) Handbook of the Economics of Education. Elsevier, pp 327–439

  • Eisenhauer P, Heckman JJ, Mosso S (2015) Estimation of dynamic discrete choice models by maximum likelihood and the simulated method of moments. Int Econ Rev 56:331–357

    Google Scholar 

  • Elder T, Jepsen C (2014) Are Catholic primary schools more effective than public primary schools? J Urban Econ 80:28–38

    Google Scholar 

  • Figlio D, Özek U (2019) Cross-generational differences in educational outcomes in the second great wave of immigration. Education Finance and Policy forthcoming

  • Fiorini M, Keane MP (2014) How the allocation of children’s time affects cognitive and non-cognitive development. J Labor Econ 3:787–836

    Google Scholar 

  • Firpo S (2007) Efficient semiparametric estimation of quantile treatment effects. Econometrica 75:259–276

    Google Scholar 

  • Firpo S, Fortin NM, Lemieux T (2009) Unconditional quantile regressions. Econometrica 77:953–973

    Google Scholar 

  • Fortin N, Lemieux T, Firpo S (2011) Chapter 1 - decomposition methods in economics. In: Orley A, David C (eds) Handbook of labor economics. Elsevier, Amsterdam, pp 1–102

    Google Scholar 

  • Fryer RG, Levitt SD (2006) The black-white test score gap through third grade. Am Law Econ Rev 8:249–281

    Google Scholar 

  • Fuligni AJ, Stevenson HW (1995) Time use and mathematics achievement among American, Chinese, and Japanese high school students. Child Dev 66:830–842

    Google Scholar 

  • Gang IN, Zimmermann KF (2000) Is child like parent? Educational attainment and ethnic origin. J Hum Resour 35:550–569

    Google Scholar 

  • Gayle G-L, Golan L, Soytas M (2015) What accounts for the racial gap in time allocation and intergenerational transmission of human capital? Federal Reserve Bank of St. Louis working paper no 015-019A

  • Gibbs BG, Shah PG, Downey DB, Jarvis JA (2017) The Asian American advantage in math among young children: the complex role of parenting. Sociol Perspect 60:315–337

    Google Scholar 

  • Goss P, Hunter J (2015) Targeted teaching: how better use of data can improve student learning. Grattan Institute

  • Hanley GE, Janssen PA (2013) Ethnicity-specific birthweight distributions improve identification of term newborns at risk for short-term morbidity. Am J Obstet Gynecol 209:428. e1–428. e6

    Google Scholar 

  • Hansen J, Kuera M (2003) The educational attainment of second generation immigrants in Canada: evidence from SLID. Statistics Canada

  • Heckman JJ, Mosso S (2014) The economics of human development and social mobility. Annu Rev Econ 6:689–733

    Google Scholar 

  • Hofferth SL, Sandberg JF (2001) How American children spend their time. J Marriage Fam 63:295–308

    Google Scholar 

  • Hsin A, Xie Y (2014) Explaining Asian Americans’ academic advantage over whites. Proc Natl Acad Sci 111:8416–8421

    Google Scholar 

  • Huang GHC, Gove M (2015) Asian parenting styles and academic achievement: views from Eastern and Western perspectives. Education 135:389–397

    Google Scholar 

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

    Google Scholar 

  • Jones FL, Kelley J (1984) Decomposing differences between groups: a cautionary note on measuring discrimination. Sociol Methods Res 12:323–343

    Google Scholar 

  • Kao G, Thompson JS (2003) Racial and ethnic stratification in educational achievement and attainment. Annu Rev Sociol 29:417–442

    Google Scholar 

  • Kao G, Tienda M (1998) Educational aspirations of minority youth. Am J Educ 106:349–384

    Google Scholar 

  • Kaushal N, Muchomba FM (2018) Missing time with parents: son preference among Asians in the USA. J Popul Econ 31:397–427

    Google Scholar 

  • Koenker R, Bassett G (1978) Regression quantiles. Econometrica 46:33–50

    Google Scholar 

  • Konstantopoulos S (2009) The mean is not enough: using quantile regression to examine trends in Asian-White differences across the entire achievement distribution. Teach Coll Rec 111:1274–1295

    Google Scholar 

  • Le HT, Nguyen HT (2018) The evolution of the gender test score gap through seventh grade: new insights from Australia using unconditional quantile regression and decomposition. IZA J Labor Econ 7

  • Lee SY, Seshadri A (2019) On the intergenerational transmission of economic status. J Polit Econ 127:855–921

    Google Scholar 

  • Lehmann J-YK, Nuevo-Chiquero A, Vidal-Fernandez M (2018) The early origins of birth order differences in children’s outcomes and parental behavior. J Hum Resour 53:123–156

    Google Scholar 

  • Lemos Md, Doig B (1999) Who am I? Developmental assessment manual. ACER Press, Melbourne

    Google Scholar 

  • Liu A, Xie Y (2016) Why do Asian Americans academically outperform Whites?–the cultural explanation revisited. Soc Sci Res 58:210–226

    Google Scholar 

  • Lundberg S (2015) Tiger parenting and American inequality: an essay on Chua and Rubenfeld’s the triple package: how three unlikely traits explain the rise and fall of cultural groups in America. J Econ Lit 53:945–960

    Google Scholar 

  • May S, Flockton J, Kirkham S (2016) PISA 2015: New Zealand summary report. Comparative Education Research Unit, Ministry of Education

  • Nghiem HS, Nguyen HT, Khanam R, Connelly LB (2015) Does school type affect cognitive and non-cognitive development in children? Evidence from Australian primary schools. Labour Econ 33:55–65

    Google Scholar 

  • Nguyen HT, Connelly L, Le HT, Mitrou F, Taylor C, Zubrick S (2019a) Sources of ethnicity differences in non-cognitive development in children and adolescents. Life course centre working paper series, 2019–21. Institute for Social Science Research, The University of Queensland

  • Nguyen HT, Connelly L, Le HT, Mitrou F, Taylor C, Zubrick S (2020) Ethnicity differentials in academic achievements: the role of time investments. Global labor organization (GLO) discussion paper no. 481

  • Nguyen HT, Duncan A (2017) Exchange rate fluctuations and immigrants’ labour market outcomes: new evidence from Australian household panel data. J Int Econ 105:174–186

    Google Scholar 

  • Nguyen, H.T., Le, H.T., Connelly, L.B., 2019b. Weather and children’s time allocation. Life course centre working paper no 2019-13

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

    Google Scholar 

  • Pavan R (2016) On the production of skills and the birth-order effect. J Hum Resour 51:699–726

    Google Scholar 

  • Peng SS, Wright D (1994) Explanation of academic achievement of Asian American students. J Educ Res 87:346–352

    Google Scholar 

  • Sweetman A, van Ours JC (2015) Chapter 21 - immigration: what about the children and grandchildren? In: Chiswick BR & Miller PW (eds.) Handbook of the economics of international migration. North-Holland, 1141-1193

  • Taylor CL, Christensen D, Lawrence D, Mitrou F, Zubrick SR (2013) Risk factors for children's receptive vocabulary development from four to eight years in the longitudinal study of Australian children. PLoS One 8:e73046

    Google Scholar 

  • Todd PE, Wolpin KI (2007) The production of cognitive achievement in children: home, school, and racial test score gaps. J Hum Cap 1:91–136

    Google Scholar 

  • Varkey Foundation, 2018. Global parents’ survey

  • Watkins M, Ho C, Butler R (2017) Asian migration and education cultures in the Anglo-sphere. J Ethn Migr Stud 43:2283–2299

    Google Scholar 

  • Zimmermann KF (2007) The economics of migrant ethnicity. J Popul Econ 20:487–494

    Google Scholar 

Download references


We would like to thank the Editor-in-Chief Klaus F. Zimmermann, anonymous reviewers of this journal, seminar participants at Curtin University, Monash University, the University of Adelaide, and the University of Queensland and conference participants at EALE 2019 for useful comments and suggestions. This paper uses unit record data from Growing Up in Australia, the Longitudinal Study of Australian Children. The study is conducted in partnership between the Department of Social Services (DSS), the Australian Institute of Family Studies (AIFS) and the Australian Bureau of Statistics (ABS). The findings and views reported in this paper are those of the author and should not be attributed to the DSS, the AIFS, the ARC, or the ABS.


This research is partly funded by the Australian Research Council (ARC) Centre of Excellence for Children and Families over the Life Course (CE140100027).

Author information

Authors and Affiliations


Corresponding author

Correspondence to Ha Trong Nguyen.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Additional information

Responsible editor: Klaus F. Zimmermann

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Nguyen, H.T., Connelly, L.B., Le, H.T. et al. Ethnicity differentials in academic achievements: the role of time investments. J Popul Econ 33, 1381–1418 (2020).

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI:


  • Migration
  • Education
  • Test score gap
  • Time use diary
  • Quantile regression
  • Second-generation immigrants
  • Australia

JEL classification

  • C21
  • I20
  • J13
  • J15
  • J22