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