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Understanding access to higher education amongst humanitarian migrants: an analysis of Australian longitudinal survey data

Abstract

Humanitarian migrants are amongst the most marginalised population groups in countries within the Global North, including Australia. An important channel for these migrants to successfully settle into the host society and improve their socio-economic outcomes is participation in the local education system, particularly in higher-education options. However, we know surprisingly little about the socio-demographic factors that structure inequalities in humanitarian migrants’ access to (higher) education, with evidence from robust quantitative studies being particularly scarce. The present study fills this important gap in knowledge by analysing Australian longitudinal survey data (Building a New Life in Australia; n = 2109 migrants and 8668 person-year observations) by means of random-effect panel regression models. Key results indicated that higher English-language proficiency and pre-arrival education levels are core factors fostering greater engagement with the Australian higher-education system amongst humanitarian migrants. Humanitarian-migrant women in our sample exhibited a greater adjusted likelihood of being a student than humanitarian-migrant men. Altogether, our findings confirmed inequalities in accessing the Australian higher-education system amongst humanitarian migrants, and that policy attention is required to redress this situation. However, they also stress that a ‘one size fits all’ policy strategy may be neither sufficient nor appropriate to boost their education prospects.

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Data availability

The data used in this study, Building a New Life in Australia: The Longitudinal Study of Humanitarian Migrants, can be obtained from the Australian Government Department of Social Services (for details, see https://dataverse.ada.edu.au/dataverse.xhtml?alias=bnla).

Code availability

Syntax code to replicate the analyses presented in this paper is available from the corresponding author upon request.

Notes

  1. While this is a broad age range, our results were generally robust to restricting our sample to individuals aged younger than 51 years or younger than 31 years (see Appendix 1 Table 5).

  2. The percentages for the different education categories do not add up to the overall percentage because a small number of respondents reported undertaking multiple courses falling into more than one of the categories (e.g. 5.3 + 5.0 + 1.7 ≠ 12.6). This also applies to other education variables.

  3. The results of an alternative model specification comparing individuals studying a degree to all other individuals, including non-students, were very similar (see Appendix 1 Table 6).

  4. Due to small cell sizes when disaggregating the sample by gender, it was not possible to undertake analyses of whether the qualifications that humanitarian migrants studied for or had attained were university degrees or other type of courses.

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Acknowledgements

This research was supported by the National Centre for Student Equity in Higher Education (NCSEHE) through its 2019 Research Grants Program and by the Australian Research Council Centre of Excellence for Children and Families over the Life Course (project number CE140100027). The analyses use data from Building a New Life in Australia: The Longitudinal Study of Humanitarian Migrants (BNLA). The BNLA project is a collaborative effort between the Australian Government Department of Social Services (DSS), the Australian Institute of Family Studies (AIFS) and Colmar Brunton Social Research.

Funding

This research was supported by the National Centre for Student Equity in Higher Education (NCSEHE) through its 2019 Research Grants Program and by the Australian Research Council Centre of Excellence for Children and Families over the Life Course (project number CE140100027).

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Correspondence to Francisco Perales.

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Appendices

Appendix 1: Additional tables

Tables 3, 4, 5, 6, and 7

Table 3 Sample means and standard deviations for analytic variables
Table 4 Sample means for analytic variables using different subsamples
Table 5 Odds ratios from random-effect logistic regression models, alternative age ranges
Table 6 Odds ratios from a random-effect logistic regression model of studying towards a university degree, assigning the value zero to non-students
Table 7 Odds ratios from a random-effect logistic regression models with gender interactions

Appendix 2: Formal model specification

Formally, the random-effect logistic models that we fit take the following form:

$$log\left(\frac{pr({E}_{it}=1)}{1-pr({E}_{it}=1)}\right)=\alpha +{\varvec{\beta}}{{\varvec{S}}}_{it}+{u}_{i}$$
(1)

where subscripts i and t stand for individual and time period, respectively; E is a binary measure capturing being enrolled in an education or HE course in Australia measured at time t; S represents the set of socio-demographic variables described before and measured at time t; α is a model intercept; β is the vector of coefficients of interest to be estimated; and u is a person-specific random intercept (i.e., a random effect). While an array of longitudinal approaches are available to analyse panel data of the sort available in BNLA, this random-effect approach is fit-for-purpose to achieve our research aim of identifying the socio-demographic correlates of HE access amongst humanitarian migrants. These random-effect models are more efficient and less data-demanding than other potential approaches. For example, random-effect models using lagged explanatory variables would reduce the number of waves available for analysis, substantially diminishing the sample size. Similarly, within-group fixed-effect models would preclude consideration of time-constant predictors (e.g. pre-arrival education, gender, and country of origin), and would be highly inefficient in estimating the coefficients on rarely changing variables (e.g. English-language proficiency, marital status, or general health).

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Perales, F., Xiang, N., Hartley, L. et al. Understanding access to higher education amongst humanitarian migrants: an analysis of Australian longitudinal survey data. High Educ 84, 373–397 (2022). https://doi.org/10.1007/s10734-021-00772-x

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Keywords

  • Australia
  • Education
  • Equity
  • Humanitarian migrants
  • Refugees
  • University