diploma graduates without year 12 education (persons who obtained a certificate or a diploma but did not complete year 12); diploma graduates after completing year 12 (persons who obtained a certificate or a diploma after having completed year 12); and university graduates (persons whose highest education was either a undergraduate or postgraduate degree, or a graduate diploma certificate, after having completed year 12). Individuals are also classified into eight experience groups based on the number of years that have elapsed since the person completed school. We assume that the age of entry into the labour market is: 17 for a typical high school dropout; 19 for a typical highschool graduate as well as for a typical diploma graduate without year 12 education; 21 for a diploma graduate after completing year 12; and 23 for a typical university graduate after completing year 12. The work experience is then given by the age of the individual minus the age at which the individual entered the labour market. We restrict our analysis to people who have between 1 and 40 years of experience and aggregate the data into eight experience groups with fiveyear experience intervals such as 1 to 5 years of experience, 6 to 10 years of experience, and so on. The individual data is aggregated into different educationexperience For each of these cells, the share of immigrants in the population is given by: where Mijt is the number of immigrants in cell (i, j, t), and Nijt is the number of Australia born individuals in cell (i, j, t). We estimate the following specification: (1) where: yijt is the mean value of a particular labour market outcome for Australiaborn workers in cell (i, j, t); si is a vector of dummy variables for education groups (i=1 to 5); xj is a vector of variables for experience groups (j=1 to 8); is vector of dummy variables for time (5 time periods for the SIH data and 3 time periods for the matched HILDA / census data); is a normally distributed random error.
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