This in part reflects compositional effects, as a higher proportion of non-UK born ethnic minorities have no qualifications compared to their white counterparts. 19  All speeches are available online at www.bankofengland.co.uk/speeches  19  Chart 16: Granular ethnicity gaps by country of origin, sample average Ethnicity groups Mixed/Multiple ethnic groups Indian Pakistani  Bangladeshi Chinese Any other asian background Black/African/Caribbean/Black British  Other ethnic group ‐50  ‐40  ‐30  ‐20  ‐10  0  10  20  30  40  Ethnicity pay gap, per cent Non‐UK born  UK born  Source: ONS Labour Force Survey and Bank of England calculations. Note: A positive ethnicity pay gap in this chart indicates that ethnic minorities earn less than their white counterparts, whereas a negative ethnicity pay gap indicates that ethnic minorities earn more than their white counterparts. On average, ethnic minority women earn more than white women, whereas ethnic minority men earn less than white men (Chart 17). This is also true in the latest data. Unconditionally, there is a clear and well-defined pay gap for females, a clear and well-defined pay gap for ethnicity but, somewhat surprisingly, no clear, well-defined pay gap for ethnic minority females. This is a third puzzle in the unconditional pay data.
Finally, a rich literature uses experiments to assess bias in hiring, promotion and bargaining between groups (Neumark (2018)). In this paper, we use the first two approaches – regression-based techniques and decompositions – to analyse gender and ethnicity pay gaps in the UK. In the next section, we describe the dataset, summarise key trends in the UK labour market over the past 25 years and discuss unconditional pay gaps by gender and ethnicity. In the following section, we provide an explanation of these pay gaps using factordecomposition and regression-based methods. Section 5 summarises our key findings and policy recommendations. Section 3: Trends in the UK Labour Market This paper uses quarterly returns from the UK Labour Force Survey (LFS) from 1994 Q1 to 2019 Q1. This gives us a sample of 1,154,759 observations of employed individuals, reduced to 563,353 observations when incorporating pay variables, as there are fewer observations for respondents on pay.6 There are two  6 Throughout our analysis we only use wave 1 respondents in order to ensure that we are not counting multiple responses from the same individual. In addition to this, we only include workers aged 25 and over in order to remove noise from the data. Most workers under 25 are earning in the minimum wage range which is subject to regulation, and may also particularly represent part-time employment.