Data and descriptives Data The data used in this study is sourced from the June 2019 and June 2020 quarters of the Income Survey. The Income Survey is a supplemental survey to the Household Labour Force Survey (HLFS). While the HLFS is conducted quarterly, the Income Survey is only conducted in the June quarter of each year. The HLFS is the standard data source for analysing hourly earnings information in New Zealand. It provides earnings data for approximately 15,000 households per quarter (which equates to around 30,000 individuals). The survey asks for information on both pay and work hours and provides a comprehensive picture of the labour market with respect to a range of individual, household, and job characteristics (including data on an individual’s occupation and industry category). An alternative data source for earnings information is the Inland Revenue Department (IRD). IRD provides more frequent data (monthly) on earnings, and is population wide, but unfortunately does not include work hours information for our sample period. Therefore, we have primarily relied on the HLFS. Our key results are based on analysis using the June 2020 sample. However, because the Covid-19 pandemic hit in late March 2020, and New Zealand entered a lockdown period from then till mid-May, we also repeat our analysis with the June 2019 sample, in case any of the results from 2020 are Covid-affected. In the results section, for the sake of brevity, we only report the results from the 2020 sample. It is worth noting that in most cases, the 2019 and 2020 results are qualitatively very similar. 1 We limit our sample to the working age population (i.e. aged 16 to 64). We also trim the sample to remove the top and bottom 1 percent of hourly wage earnings, and exclude the self-employed.1 Ethnic groups Ethnic groups available in our data can be categorised as European, Māori, Pacific, Asian, Middle Eastern, Latin American and African (MELAA), and Other. Our focus in this empirical analysis is comparing the earnings outcomes (and factors that contribute to earnings gaps) for Māori, Pacific and Asian, relative to European. Due to their small sample size, we do not delve into the outcomes for MELAA or the ‘Other ethnicity’ category. Focussing on these four ethnic groups, we find that those who list only European as their ethnicity account for 58.5 percent of our sample; whereas the corresponding proportions that only list Māori, Pacific, and Asian are 6.7; 4.9; and 14.3 percent respectively. With respect to overlaps across ethnic groups, where an individual reports affiliation to more than one ethnic group, the largest overlap is between European and Māori – this accounts for 4.6 percent of our sample. European and Pacific are less than 1 percent; as are Māori and Pacific; European and Asian; and those who report the three ethnic affiliations of European, Māori and Pacific. For the purposes of our decomposition analysis, we use prioritised ethnicity classifications, so as to create mutually exclusive ethnic categories. The order of prioritisation is Māori, Pacific, Asian, MELAA, Other, and lastly, European. All imputed and proxy observations are included in our sample. Empirical analysis of Pacific, Māori and ethnic pay gaps in New Zealand 3