Results
Tables 3 and 4 present the results with and
without adjustment for sample selection bias,
i.e. pre and post-application of the Heckman
procedure. For each pay decomposition, the
reference group are European. The contributing
factors that are included in the analysis represent
four domains as mentioned earlier – individual
characteristics; educational attainment; region;
and job-related characteristics (encompassing
occupation, industry, permanent and part-time
status).
Table 3: Oaxaca decomposition without adjustment for sample selection bias
Variable
Māori
Pacific
Asian
Male
Female
Male
Female
Male
Female
19.03
11.71
24.27
14.76
13.90
8.19
92.33***
84.67***
46.41***
47.33***
-27.58***
-36.48**
Individual
27.81***
12.92***
16.02*
30.08***
-11.24
-68.71*
Education
18.93***
40.13***
33.77***
63.94***
104.53***
117.47***
Hourly pay difference (%)
Explained (% of difference)
Explained
Region
5.81*
7.03*
-27.15***
-55.77***
64.04***
104.46***
Job-related
42.05***
36.82***
79.17***
62.56***
-58.99***
-56.64***
Sample size
5,157
5,592
4,737
5,070
5,502
5,748
Note: Variable categories correspond to domains in Table 1. *, **, and *** denote significance at the 10%, 5%, and 1% levels
respectively.
There are a few patterns evident from Table 3.
First, for Māori, regardless of gender, much of
their pay gap with Europeans can be explained by
observable characteristics. In particular, individual
and job-related characteristics for males;
and educational attainment and job-related
characteristics for females. The role of individual
characteristics for pay differences between Māori
males and European males is likely due to the
younger age profile of the Māori population
relative to their European counterparts. Age is
also a proxy for employment experience. The
important role of job-related characteristics
emphasizes the occupational segregation present
in the labour market.
For Pacific peoples, the difference in job-related
characteristics with respect to the reference
provides a substantial contribution in explaining
the pay gap, for both males and females. Given
that occupational segregation is interrelated
with a higher likelihood of experiencing poverty,
understanding the drivers in this space are critical.
They include, but are not limited to, discriminatory
practices; barriers to upskilling; and the influence
of neighbourhood networks and residential
segregation.
The negative contributions for region for both
Pacific males and females means that the overall
wage difference would be even larger if Pacific
and European had a similar regional distribution.
Pacific peoples are disproportionately located in
Auckland, where wages are higher on average. If
they were not more concentrated in this region,
then the ethnic pay gap would increase. Note that,
as shown in Table 2, 72 percent (71 percent) of
the male (female) Pacific population in our sample
were living in the Auckland region; whereas the
corresponding proportions for European and
Māori were 27 percent (28 percent) and 19
percent (22 percent) respectively.
Empirical analysis of Pacific, Māori and ethnic pay gaps in New Zealand
9