Decomposing ethnic differences in university academic achievement in New Zealand


We use individual-level administrative data to examine the extent and potential explanations for the relatively poorer academic performance of three ethnic minority groups in their first year of study at a New Zealand university. Substantial differences in course completion rates and letter grades are found for Māori, Pasifika, and Asian students relative to their European counterparts. These large and significant gaps persist in the face of alternative definitions of ethnicity and sample restrictions. We use regression analysis and formal decomposition techniques to test whether differences in other personal characteristics, high school backgrounds, and university enrollment patterns might account for these ethnic disparities in early academic achievement. We estimate that no more than one quarter of the relatively poorer performance of Māori and Pasifika students would be eliminated if they had the same relevant observable factors of European students. Substantial unexplained ethnic differences in early academic performance at university raise concerns about appropriate policies to close ethnic gaps in academic achievement at university.

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  1. 1.

    The largest eight sources of Asian migrants to New Zealand in 2013 were (in descending order) Chinese, Indian, Korean, Filipino, Japanese, Sri Lankan, Cambodian, and Thai. Asians are over-represented in the city of Auckland, where the university used in this study is located, relative to the rest of New Zealand.

  2. 2.

    Based on a GPA system with four-point maximum.

  3. 3.

    For example, Juhong and Maloney used data from an older, more-established urban university in New Zealand. Only one in seven students in their study was Māori or Pasifika. The current study includes data on all degree programs, with more than one in four students being Māori or Pasifika.

  4. 4.

    These letter grades and their numerical equivalents are A+ = 9, A = 8, A− = 7, B+ = 6, B = 5, B− = 4, C+ = 3, C = 2, C− = 1, and D = 0 (or any failing grade). Of course, the GPA from this system can be converted to the four-point US scale by multiplying by four ninths.

  5. 5.

    This category also includes unreported ethnicities or non-respondents. We eliminate this residual ethnic group in our subsequent pairwise analyses of the three ethnic minorities relative to Europeans.

  6. 6.

    For more information on these school deciles, see school/running-a-school/resourcing/operational-funding/school-decile-ratings/.

  7. 7.

    For more information on the NCEA system, see qualifications/ncea/understanding-ncea.

  8. 8.

    These bachelor’s degree programs are Arts (BA), Business (BBus), Computer and Information Systems (BCIS), Communication Studies (BCS), Design (BDes), Education (BEdu), Engineering Technology (BEngTech), Health Sciences (BHS), International Hospitality Management (BIHM), Sports and Recreation (BSR), and a residual category of several smaller degree programs (others). Students must enroll in degree programs in their first year of study at this university.

  9. 9.

    These same results can be interpreted in terms of ethnic differences in course non-completion rates between Europeans (14.60%) and Māori (24.55%), Pasifika (35.96%), and Asian (20.51%) students, where these gaps are substantially larger in relative terms.

  10. 10.

    To reduce the volume of reported results, we do not show the regression and decomposition estimates for alternative ethnicity definitions. However, they are available from the authors by request. Many of the regression results were qualitatively similar to those reported in this section. We summarize some of the key differences in the decomposition findings later in this paper.

  11. 11.

    Because the estimated coefficients have no direct interpretation in this non-linear estimation, we report mean marginal effects. For a dummy independent variable like being female, this is the mean of the estimated marginal effects for this sample as this variable goes from zero to one, holding all other individual covariates constant.

  12. 12.

    The actual Rank Score is divided by ten to make the estimated effects easier to interpret.

  13. 13.

    See Fairlie (1999) for this original discussion in developing an alternative non-linear decomposition technique in explaining black-white differences in the USA in self-employment incidence.

  14. 14.

    Because sample sizes of the two comparison groups are generally different, Fairlie (2003) explained why different random samples must be drawn from the majority ethnic group and matched to the ethnic minority sample for this decomposition technique. The reported results in our analysis are based on 100 replications of this procedure. All estimates come from the Fairlie non-linear decomposition routine available in Stata.

  15. 15.

    Although such data were not available for this current study, some of these background measures do exist in the New Zealand Integrated Data Infrastructure (IDI) and could be linked to these internal administrative data. For more information on this national linked administrative data source, see browse_for_stats/snapshots-of-nz/integrated-data-infrastructure.aspx.


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Access to the data used in this study was provided by a public university in New Zealand for the agreed purposes of this research project. The interpretations of the results presented in this study are those of the authors and do not reflect the views of this anonymous university. We wish to thank two anonymous referees of this journal for the useful feedback on an earlier version of this paper.

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Correspondence to Tim Maloney.

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Cao, Z., Maloney, T. Decomposing ethnic differences in university academic achievement in New Zealand. High Educ 75, 565–587 (2018).

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  • Higher education
  • University academic achievement
  • Ethnic differences or disparities
  • Decomposition techniques
  • New Zealand

JEL classifications

  • I23
  • I24
  • I28 and J71