variance than those calculated from the Census. In general, across both data sets, the larger changes are for the most highly educated groups who saw positive increases in the share of immigrants over time. The two groups with certificates (year 12 and no year 12) saw the largest decreases in immigrant share. Our data is somewhat shorter in time span and contains slightly fewer annual observations than typically found in the international literature. Borjas (2003, 2006) uses decennial census data from 1960 through 2000. Ottaviano and Peri (2012) use basically the same data as Borjas (2006). They also use annual CPS data for the period 1962-2006. Manacorda et al. (2012) use the annual UK Labour Force Survey and General Household Survey from mid 1970s to mid 2000s. UK and US data have larger populations to work with, so might be expected to be better placed to detect small effects than we are, all else being equal. 4. Empirical results We estimate models of the labour market outcomes of Australian-born workers (annual and weekly earnings, weekly hours worked, hourly wage rate, labour force participation, and unemployment rate) against the share of migrants with different specifications: (i) models that include only the time dummy variables, (ii) models controlling for all dummy variables including dummies for education groups, for experience groups, and dummies for time but without any interaction terms; (iii) models controlling for education, experience, time and the interactions between dummy variables that allow for changing skill premia over time. We present weighted regressions using the weights defined as the number of Australian-born in each education-experience cell for whom the relevant outcome variable is defined. That is, we weight labour force participation regressions by the native population, unemployment
High school dropout 0.108 0.096 0.354 0.346 0.564 Year 12 0.304 0.197 0.324 0.275 0.447 Certificate (w/o Year 12) 0.324 0.252 0.040 0.103 0.493 Certificate (w Year 12) 0.388 0.306 0.283 0.146 0.271 Degree 0.703 0.614 0.622 0.512 0.110 Numbers in table indicate the proportion of individuals who would have to change occupation to make the occupational distribution identical for two groups.
Log annual earnings Log weekly earnings Log of wage rate Weekly hours Participation rate Unemployment rate 2011 Full Sample (OLS estimation) Census and HILDA 0.253 (0.185) 0.210 (0.193) 0.264 (0.202) 0.044 (2.678) -0.023 (0.036) 0.028** (0.012) 2011 males (OLS estimation) Census and HILDA 0.065 (0.099) 0.058 (0.103) 0.041 (0.096) 0.495 (1.210) -0.015 (0.015) 0.010** (0.005) 2011 males (OLS estimation) Census and HILDA 0.545*** (0.174) 0.519*** (0.157) 0.441*** (0.156) 3.020 (3.404) 0.015 (0.037) 0.006 (0.012) Table 11: Effects of migrant share on Australian-born workers, identification through geographical variation Variable Unemployment rate (OLS) Participation rate (OLS) Unemployment rate (IV) Participation rate (IV) 2001 and 2006 full sample Census data only -.087 (.099) .008 (.104) -.0112 (.024) -.065 (.048) 2001 and 2006 males Census data only .049 (.134) -.100 (.102) 0.007 (0.032) -0.032 (0.047) 2001 and 2006 females Census data only -.238** (.098) .073 (.169) -0.027 (0.018) -0.059 (0.054) Table A: Selected changes to Australia’s migration policy