When we the sample by sex (table A.9) we again find mixed results. For males we find a positive association between recent migration and the participation rate but also a positive association with the unemployment rate. The wage and hours effects from the pooled sample are concentrated amongst female workers for men the effects are smaller — and not statistically different from zero. Table A.9 Estimated values of from equation (1): HILDA and Census (incumbents compared to recent immigrants); Broad experience groups and education categories 3 education categories and 4 experience categories Models include full set of time dummies, education and experience fixed effects and full set of interactions Note: *,**,*** indicate statistical significance at the 10%, 5%, and 1% significance level respectively. from Census; otherwise calculated from HILDA. †Calculated It is important to note that the negative wage effect in the pooled sample is also very fragile and driven by one skill group: degree holders with 110 years of experience. If we add a dummy variable for that group (or drop them from the analysis), the coefficient on immigrant share in the wage regression becomes positive, 0.5376, but insignificant. Between 2001 and 2011, this group of individuals had lower wage growth than expected but this could plausibly be for other reasons, such as differential effects of the Global Financial Crisis or the mining boom across educationexperience groupings. A priori, it is difficult to say whether the more narrow skill groups or the broader skill groups provide better estimates. Comparing table A.2 to table A.9, we can see that the standard errors are two to three times larger when we use the broader groups and the
When we the sample by sex (table A.9) we again find mixed results. For males we find a positive association between recent migration and the participation rate but also a positive association with the unemployment rate. The wage and hours effects from the pooled sample are concentrated amongst female workers — for men the effects are smaller and not statistically different from zero. Table A.9 Estimated values of from equation (1): HILDA and Census (incumbents compared to recent immigrants); Broad experience groups and education categories 3 education categories and 4 experience categories Models include full set of time dummies, education and experience fixed effects and full set of interactions Note: *,**,*** indicate statistical significance at the 10%, 5%, and 1% significance level respectively. †Calculated from Census; otherwise calculated from HILDA. It is important to note that the negative wage effect in the pooled sample is also very fragile and driven by one skill group: degree holders with 110 years of experience. If we add a dummy variable for that group (or drop them from the analysis), the coefficient on immigrant share in the wage regression becomes positive, 0.5376, but insignificant. Between 2001 and 2011, this group of individuals had lower wage growth than expected but this could plausibly be for other reasons, such as differential effects of the Global Financial Crisis or the mining boom across educationexperience groupings. A priori, it is difficult to say whether the more narrow skill groups or the broader skill groups provide better estimates. Comparing table A.2 to table A.9, we can see that the standard errors are two to three times larger when we use the broader groups and the
migrant-intake-supplementa.docx