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Table 2

Payoffs to Selected Characteristics from Analysis of Earnings, by Birthplace, Males Aged 20-64, 2001





Payoffs from Earnings Function

% Change



Variable



Standard

Controlling for 9 Occupations

Controlling for 44 Occupations


With 9 Occupations


With 44 Occupations

A. Australian Born

Educational Attainment


8.8


6.7


5.2


-23.9


-40.9

Experience

- 10 years

- 20 years


1.86

0.92


1.74

0.78


1.72

0.84


-6.5

-15.2


-7.5

-8.7

















B. Overseas Born, English-speaking Developed Countries


Educational Attainment


8.1


5.0


4.6


-38.3


-43.2

Pre-Immigration

Experience

- 10 years

- 20 years



1.88

0.86



1.58

0.66



1.66

0.72



-16.0

-23.3



-11.7

-16.3

Migrated

Before 1986


-8.5


-7.7


-6.9


9.4


18.8



















C. Overseas Born, non-English-speaking Countries

Educational Attainment


5.8


3.1


2.0


-46.6


-65.5

Pre-Immigration

Experience

- 10 years

- 20 years



0.5

0.2




0.66

0.22



0.86

0.32



32.0

10.0



72.0

60.0

Migrated

Before 1986


10.7


10.0


8.3


-6.5


-22.4

Speaks English

Well

Not Well

Not at All



-13.5

-22.6

-42.1



-8.6

-18.3

-39.7



-6.6

-15.2

-31.2



-36.3

-19.0

-5.7



-51.1

-32.7

-25.9

Source: Authors’ calculations based on Table 1.

There is minimal change to the payoff to labour market experience for those born in Australia, with the reduction ranging from 7 to 15 percent. This implies that labour market experience has only a modest impact on occupational status for those born in Australia.

The control for occupation has a slightly greater impact on the payoff to pre-immigration experience for immigrants from English-speaking countries. This ranges from 12 to 23 percent. Among immigrants from non-English-speaking countries, the pattern of effects is quite different, with the payoff to pre-immigration labour market experience rising once account is taken of occupation. Evaluated at 10 years, the payoff to pre-immigration labour market experience rises from 0.5 percent per year in the benchmark model, to 0.66 percent (a 32 percent increase) following control for Major Group occupation. It rises further to 0.86 percent (a 72 percent increase over the benchmark model) when dichotomous variables for the 44 Census occupations are included in the model.


The immigrant duration variables (i.e., post-migration experience) have opposite patterns for the two groups of immigrants. Immigrants from non-English-speaking countries who arrived before 1986 are shown to have significantly greater earnings than the most recent arrivals (1996-2001). This earnings advantage falls by between 7 and 22 percent once occupation is held constant. Immigrants from English-speaking countries, however, who arrived in the past five years are shown to have relatively high earnings, though their earnings position only differs significantly from the group who arrived before 1986(a).

Finally, it is seen that there are pronounced changes to the earnings effects associated with English proficiency following the incorporation of information on occupation into the earnings equation. The changes range from 6 to 36 percent when the information on Major Group occupation is used, and from 26 to 51 percent when the information on all 44 Census occupational categories is used.

These changes in the estimated effects as the earnings equation is augmented with information on occupation follow the pattern found for the US labour market by Chiswick and Miller (2007). Comparison of Table 2 for Australia and Table 3 for the US reveals that the changes in the effects of educational attainment on earnings following standardisation for occupation are broadly the same in Australia and the United States. While precise estimates of the differences in the relative magnitudes of the changes in Australia and the United States due to holding occupation constant are hard to assess, given the different definitions of the variables and the level of detail on occupation, it appears that inter-occupational earnings differences are greater in the payoff to education in Australia than in the US. This may follow from the more centralised system of wage determination, and perhaps greater union power, in Australia than in the US, and as a result the more egalitarian distribution of earnings within each occupation (see Miller, Mulvey and Martin, 1995).


The impact of taking account of occupation in the earnings function on the payoff to experience is also broadly the same for immigrants from non-English-speaking countries in Australia (Table 2) and for immigrants in the United States (Table 3).

Similar changes are associated with limited English skills in the two labour markets. There is a reduction in the earnings disadvantage associated with limited English skills when occupation is held constant, or equivalently, part of the earnings advantage associated with better English language skills comes about through these skills facilitating the workers’ access to higher paying occupations. The changes in the partial effects of English-language proficiency are greater in the Australian labour market than is the case in the US labour market for those who report they speak English very well, but smaller for those with a lower level of proficiency.

Using the standard formula for analysing omitted variables bias, the changes in the estimated coefficients summarised in Table 2 are due to two sets of factors. First, there is the independent effect that occupation has on earnings in the augmented equation. Second, there are the correlations between the other explanatory variables (such as educational attainment) and occupation. The differences in the effects that controlling for occupation has on the partial effects of educational attainment, duration, pre-immigration labour market experience and the English proficiency variables for the Australian born and immigrants must therefore be due to both of these factors. If either one is zero, the effect of the explanatory variable is the same in the benchmark and the augmented equation.


Table 3

Payoffs to Selected Characteristics from Analysis of Earnings, by Birthplace, Males Aged 25-64, 2000 US Census




Payoffs from Earnings Function

% Change



Variable



Standard

Controlling for 23 Occupations

Controlling for 509 Occupations


With 23

Occupations


With 509 Occupations

A. US Born

Educational Attainment


10.6


8.2


5.8


-23


-45

Experience

- 10 years

- 20 years


2.16

1.02


2.16

1.02


2.10

1.00


0

0


-3

-2



















B. Foreign Born

Educational Attainment


5.3


3.2


2.3


-40


-57


Pre-Immigration

Experience

- 10 years

- 20 years



0.88

0.56



1.24

0.68



1.30

0.70



+41

+21



+48

+25

Years Since

Migration

-10 years

-20 years



0.88

0.66



0.96

0.72



0.92

0.64



+9

+9



+5

-3

Speaks English

Very Well

Well

Not Well

Not at All



-8.0

-26.1

-37.3

-37.8



-7.1

-17.7

-26.9

-30.0



-5.7

-13.4

-21.7

-25.2



-11.3

-32.2

-27.9

-20.6



-28.8

-48.7

-41.8

-33.3

Note: Only 11 percent of the foreign born were from English-speaking developed countries.

Source: Chiswick and Miller (2007).

(ii) Occupation Fixed Effects

Differences in the impact of occupation on earnings across the three birthplace groups can be assessed informally by plotting the fixed effects from the respective earnings equations. Figures 1 and 2, respectively, present the plot of the estimated occupational “fixed effects” coefficients from the model for the Australian born against the “fixed effects” coefficients for immigrants from English-speaking countries and non-English-speaking countries. The straight line AA in these figures is the simple regression of the coefficients on occupation for the Australian born on the coefficients for immigrants from English-speaking countries and non-English-speaking countries, respectively.5



Figure 1

Occupational Fixed Effects in Natural Logarithmic Form for the Australian Born and for Immigrants from English-Speaking Countries, 2001

A

A



Note: The benchmark occupation in the analysis is Managers and Administrators n.f.d. It has a coefficient of zero for both birthplace groups in the Figure.

Source: Table 1, column (iii) specification.


Figure 2

Occupational Fixed Effects in Natural Logarithmic Form for the Australian Born and for Immigrants from Non-English-Speaking Countries, 2001

A

A



Note: The benchmark occupation in the analysis is Managers and Administrators n.f.d. It has a coefficient of zero for both birthplace groups in the Figure.

Source: Table 1, column (iii) specification.


It is clear from Figures 1 and 2 that relatively high-paying occupations for one birthplace group are generally also relatively high-paying occupations for the other birthplace groups. The correlation coefficient between the occupational fixed effects in Figure 1 is 0.716, while that for Figure 2 is 0.549.6 If the data points are weighted by the employment shares of the immigrant groups the correlation coefficients are 0.889 and 0.917, respectively. If the occupational employment shares of the Australian born are used then the correlation coefficients are somewhat lower, 0.796 and 0.840, respectively.

These comparisons of the occupational fixed effects suggest that they are so close that they are not likely to be the main contributor to the different pattern of results across birthplace groups in Table 2. Given these findings, the explanation for the differences in the estimates of the earnings equation between the Australian born and the foreign born when occupation is held constant needs to focus on the partial effects of the explanatory variables on occupational choice.



IV Occupational Attainment

This section presents estimates of a model of occupational attainment that assists in accounting for the pattern of effects reported in Table 2. A model in the tradition of the status attainment models of Nickell (1982) and Evans (1987) is employed. Hence, the analysis proposed is the estimation (using OLS) of a status attainment model:

(2)

where is the mean occupational earnings of the Census occupational category (i.e., mean earnings in each of the 44 Census occupations) in which individual i works, is a set of the individual’s attributes that influences this occupational outcome, and is a random error term. As a check on the robustness of the empirical findings, ordered probit models are also estimated using mean occupational earnings as the ranking instrument.7

Table 4 contains two sets of OLS estimates of the status attainment model for each birthplace group. Specification (i) is based on mean occupational earnings for the nine major group occupations, while specification (ii) is for mean occupational earnings for the 44 Census occupational categories.



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