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
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
outcomes_v3_wp.docx