Labour Force Participation and Employment of Humanitarian Migrants: Evidence from the Building a New Life in Australia Longitudinal Data

Abstract

This study uses the longitudinal data from the Building a New Life in Australia survey to examine the relationships between human capital and labour market participation and employment status among recently arrived/approved humanitarian migrants. We find that the likelihood of participating in the labour force is higher for those who had pre-immigration paid job experience, completed study/job training and have better job searching knowledge/skills in Australia and possess higher proficiency in spoken English. We find that the chance of getting a paid job is negatively related to having better pre-immigration education, but it is positively related to having unpaid work experience and job searching skills in Australia, and better health. We also explore the ethical implications of the findings.

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Fig. 1

Notes

  1. 1.

    As a robustness check, in accordance with some existing studies, we use the generalised estimated equations (GEE) to estimate a population averaged model that pools observations from Waves 1 and 2 (Aydemir 2011; Correa-Velez et al. 2013). The population averaged model estimates the population averaged effect across time. Both random effects and population averaged models take advantage of the panel structure of the data.

  2. 2.

    For the list of variables used to calculate the IRSD, see ABS Catalogue No. 2033.0.55.001 on http://www.abs.gov.au/ausstats/abs@.nsf/Lookup/2033.0.55.001main+features100052011.

  3. 3.

    We conduct variance inflation factor (VIF) analysis for the specifications on labour force participation and employment status. All VIF values are well below ten. As a rule of thumb, a VIF of ten or greater is a cause for concern.

  4. 4.

    Recognising this issue, some organisations offer free service that provides professional business attire to humanitarian migrants who are unable to afford quality clothing. This helps them to make a good first impression at job interviews for refugees, and thus improve their likelihood to get appointed. See, for example, the Dress for Work program (see www.dressforwork.org.au).

  5. 5.

    Although financial hardship is the independent variable and probability of employment is the dependent variable, the regression results may only show association and not necessarily causality. It is possible that higher probability of not being employed could also be causing financial hardship.

  6. 6.

    We test whether the estimated coefficients are significantly different between the two waves. In the labour force participation models, none of the coefficients are significantly different between the two waves. In the employment status models, the coefficients on knowing how to find a job and the length of stay in Australia are significantly different between the two waves.

  7. 7.

    In the BNLA, pre-immigration occupations were analysed and coded under 8 major occupations or 43 sub-major occupations according to the 2009 Australian and New Zealand Standard Classification of Occupations (ANZSCO) (ABS Catalogue No. 1220.0). Neither major nor sub-major occupations are statistically significant in the estimates.

  8. 8.

    In this study, we do not intend to address potential endogeneity of other independent variables due to limited variations between the two waves which were collected within 18 months. Some studies in labour economics use lags and/or leads of independent variable as instrumental variables to identify causal relations (Wang et al. 2017). However, we cannot find valid instrumental variables in the BNLA data. Future research can reconsider this when more waves are released.

  9. 9.

    Again, note that employment status of females cannot be estimated in Table 4 due to a small sub-sample.

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Acknowledgements

We thank the two reviewers and Professor R. Edward Freeman (editor-in-chief) for their very useful comments and suggestions on earlier versions of the paper. All remaining errors are our own responsibility. We thank the Department of Social Services (DSS) and the Australian Institute of Family Studies (AIFS) for providing us with access to the data. Information about the Building a New Life in Australia survey data can be found at www.aifs.gov.au/bnla. Zhiming Cheng acknowledges the support from the Scientia Fellowship (Grant Number: PS45957) at the University of New South Wales (UNSW). The findings and views reported in this paper, however, are those of the authors and should not be attributed to the DSS, AIFS or UNSW.

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Correspondence to Zhiming Cheng.

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This study uses secondary data collected by the Department of Social Services and the Australian Institute of Family Studies. Information about the Building a New Life in Australia survey data and relevant ethical approval can be found at www.aifs.gov.au/bnla.

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Appendix

Appendix

See Tables 6, 7 and Fig. 1.

Table 6 Definitions of variables
Table 7 Descriptive statistics of control variables

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Cheng, Z., Wang, B.Z. & Taksa, L. Labour Force Participation and Employment of Humanitarian Migrants: Evidence from the Building a New Life in Australia Longitudinal Data. J Bus Ethics 168, 697–720 (2021). https://doi.org/10.1007/s10551-019-04179-8

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Keywords

  • Australia
  • Humanitarian migrant
  • Human capital
  • Labour force participation
  • Employment status
  • Settlement