Double disadvantage? The slow progress of non-English-speaking migrant women in accessing good jobs in Australia

ABSTRACT

In this paper, primary and secondary sector employment corresponds broadly to 'good' and 'bad' jobs. Previous studies indicate that non-English-speaking  background (NESB) migrant women are under-represented in 'good jobs' but none of those studies evaluates their chance of finding 'good jobs' in Australia. This study estimates their probability of getting good jobs and compares this with that of Australian-born women. The probability of securing  good jobs for each of these groups is also estimated separately, based on a new general probit model, after classifying women into primary and secondary sector employment from their occupational categories and incomes using 2016 Australian Census data. It showed that NESB migrant women had significantly lesser probability of securing primary sector employment compared to Australian-born women. While this difference is narrowing over time, NESB migrant women's progress in accessing 'good jobs' has been slow. Improving English proficiency, education, recognition of overseas qualifications and experience can significantly increase their chances of attaining good jobs. This study provides an exact estimate of  the probability of securing good jobs for both groups and the relevance of different determinants for this difference so that proper actions can be taken to improve the employment situation of NESB migrant women.

KEYWORDS:

• Non-English-speaking background (NESB) migrant women
• Australian-born women
• primary sector employment
• general probit model

Disclosure statement

The authors declare that they have no conflict of interest.

Notes

1. This paper divides the labour market employment into primary and secondary sectors that correspond broadly to ‘good’ and ‘bad’ jobs.

2. Most countries are NESB countries except for Australia, New Zealand, UK, Ireland, USA, Canada, Jamaica, and Trinidad and Tobago whose native speaking language is English and which all have a population greater than one million. NESB people vastly differ in culture and language competency, yet they are treated as a single group on the grounds that there are not many NESB migrant women from individual cultural groups who worked in the primary sector and who can be compared with Australian-born women to find statistical significance of differences between the groups.

3. There are many overseas studies among which Schoeni (1998), Torezani et al. (2008), Foroutan (2011), Kok et a. (211), Toselli et al. (2017), and Apergis and Georgellis (2018), can be mentioned. These authors analysed the barriers and problems of employment conditions of NESB migrant women in various countries.

4. Figure 1 does not establish that there are large barriers to mobility from lower to higher status jobs.

5. There are other four categories such as inadequately described, not stated, not applicable and overseas visitor are excluded from this study.

6. Census codes are provided in parentheses () for respective ANU2 Occupational Scales.

7. Vaughan’s two papers were the part of a study exploring sex and birthplace differences in occupational attainment, which investigated factors affecting skill level occupational attainment of women compared to men and overseas-born groups compared to the Australian-born.

8. For example, a primary sector employer might give preference to a young applicant even though his/her employment might have been menial and/or part-time or casual.

9. English skills of NESB migrants would tend to improve as their length of residency increased, however we have attempted to measure English skills with a different set of variables.

10. McClendon (1976) argued age as a surrogate measure for length of time in the work force

11. Lipset (1955) argued that urban-reared youth have a greater acquaintance with the board spectrum of occupational possibilities that exist in metropolitan areas than does rural youth, and this stimulates urban youth to aspire to high-status occupations.

12. Sewell et al. (1980) used one dummy ‘rural’, who found rural origin did not significantly reduced the status of first job among women; Miller (1987) and Evans and Kelley (1991) used two dummies: ‘small urban’ and ‘rural’ and ‘major/large urban’ for their analyses.

13. NESBMW = 1 if a woman is of NESB, 0 otherwise

14. We interact each relevant variable with the NESBMW intercept dummy variable, for example DEGREE × NESBMW

15. We have estimated the power of the test which shows very high. Also, the goodness of fit based on the coefficient of determination was still high even after dropping these variables.

16. Similar approach has been applied to a logit model, and the results were also very close to this general probit model.

17. The ME of a binary independent variable shows how do predicted probability change as the binary independent variable changes from 0 to 1 holding all other independent variables at their means. For continuous independent variable, the ME measures the instantaneous rate of change for one unit change of an independent variable ceteris paribus.

18. The effective ME for NESB is 0.217 and 0.382 for ABW.

19. The effective ME for NESB is 0.097 and 0.162 for ABW.

20. The effective ME for NESB is 0.109 and 0.164 for ABW.

21. The effective ME for NESB is 0.039 and 0.014 for ABW.

22. The effective ME for NESB is −0.182 and −0.114 for ABW.

23. The effective ME for NESBB is −0.38 and −0.106 for ABW.

24. The effective ME for NESB is −0.284 and −0.166 for ABW.

25. The effective ME for NESB is −0.319 and ABW −0.237.

26. The effective ME for NESB is −0.153 and −0.118 for ABW.

27. The effective ME for NESB is −0.202 and −0.114 for ABW.

28. The effective ME for NESB is −0.227 and −0.142 for ABW.

29. The effective ME for NESB is −0.295 and −0.205 for ABW.

Additional information

Notes on contributors

Tariq H. Haque

Dr Tariq Haque has B. Com (Hons) , B. Sc., and Ph. D degrees in Actuarial Studies and Finance from the University of Melbourne, Australia. He is a lecturer in Finance in the Adelaide Business School at The University of Adelaide. He has worked as an actuarial analyst. He is doing research in finance, economics and tourism. He has published many research papers in  reputed academic journals.

M. Ohidul Haque

Professor M Ohidul Haque did B. Sc (Hons), M. Sc, M. Phil; and Ph. D in Applied Statistics and Econometrics from the University of Sydney, Sydney, Australia. He was a Fellow of the Royal Statistical Society, and a Senior Fellow of Economics at the University of Melbourne, Australia. He is a professor at IIBASS/UBD. He wrote many prestigious books, published many papers in reputed journals, and wrote many technical/policy papers, government reports for social and individual well being on income and consumption, living standards, inequalities, and safety and security areas.