The steady progress of Non-English-Speaking migrant women’s labour market participation in Australia

Abstract

This study deals with the labour force participation of Non-English-Speaking migrant women and draws a comparison with that of Australian-born women, using the 2016 Australian Confidentialised Unit Record File (CURF) Microdata based on a 1% sample from the Australian 2016 Census. A general probit model is used to estimate the probability of labour market participation of both groups, as well as for each of the groups separately, attributable to various factors. The results suggest that the participation rate of Non-English-Speaking migrant women (58.9%) is increasing over time, but is still much lower than for Australian-born women (69.5%), with the gap narrowing over time. Further, it also shows a reduced chance of participation in the labour market when they are old, married, and have children. However, Non-English-Speaking migrant women are more likely to participate than Australian-born women when they are old, married, and have children due to economic needs. Providing excellent English, education, recognition of overseas qualifications and experiences together with easy access to childcare might help Non-English-Speaking migrant women to further increase their chance of participation in the Australian labour market.

Keywords:

• Non-English-Speaking migrant women
• Australian born women
• Labour force participation
• General probit model
• Australia

Disclosure statement

No potential conflict of interest was reported by the authors.

Notes

1 The ABS 2016 Census of Population and Housing indicated that ∼94.48% of women in the 15–17 years range attended school, and 45.04% participated in the labour market as part-time or full-time employees. Of those who were 65 or more, only 4.15% were employees.

2 A woman is in the labour market if she is an employee, an own account worker, a contributing family worker or not employed but looking for full-time or part-time work.

3 NESBMW = 1 if a woman is of NESMW, 0 otherwise.

4 We interact each relevant variable with the NESBMW intercept dummy variable, for example, NESBMW × CHILD.

5 The coefficient of determination still shows high even after deleting the insignificant variables. The estimated power of the test is also very high.

6 The marginal effect of a binary variable indicates how the probability changes when that variable changes from 0 to 1, while holding all other independent variables constant. For continuous independent variables the marginal effect measures the rate of change for a one unit change in the independent variable ceteris paribus.

7 The effective ME for NESMW is 0.099 and 0.344 for ABW.

8 The effective ME for NESMW is 0.178 and 0.093 for ABW.

9 The effective ME for NESMW is −0.097 and −0.202 for ABW.

10 The effective ME for NESMW is 0.064 and 0.138 for ABW.

11 The effective ME for NESMW is 0.022 and 0.013 for ABW.

12 The effective ME for NESMW is −0.057 and −0.116 for ABW.

13 The effective ME for NESMW is −0.065 and −0.170 for ABW.

14 The effective ME for NESMW is −0.068 and −0.18 for ABW.

15 The effective ME for NESMW is −0.096 and −0.248 for ABW.

16 The effective ME for NESMW is −0.036 and −0.148 for ABW.

17 The effective ME for NESMW is −0.231 and −0.146 for ABW.

18 The effective ME for NESMW is −0.263 and −0.162 for ABW.

19 The effective ME for NESMW is −0.072 and −0.152 for ABW.