Originally published here

Summary

This chapter looks at ethnic minority progress at work. It considers the history of, and current state of, pay and employment, and the trends in social class mobility across generations.

The employment rates for the White British and Indian ethnic groups were 77% and 76% respectively in 2019. For some others it was significantly lower at 69% for Black people, and 56% for people in the combined Pakistani and Bangladeshi ethnic group (this last figure is the result of a much lower female participation rate).[footnote 1] Unemployment differences have been declining, though remain significantly higher for younger people.[footnote 2]

The pay gap, meaning the difference between the median hourly earnings of all ethnic minority (not including White minority) groups and White groups, is at its lowest level since 2012 at 2.3%. Employees from the White Irish, Indian and Chinese ethnic groups on average have higher hourly earnings than the White British ethnic group.[footnote 3]

Ethnic minorities have been making progress up the professional and occupational class ladder, though some more than others, and there remains under-representation at the very top. Employees from ethnic minority backgrounds are more likely than those from a White British background to say experiencing discrimination contributed to their failure in achieving their career expectations (20% versus 11%).[footnote 4]

Also, using data from previous reviews led by Baroness Ruby McGregor-Smith and Sir John Parker, evidence heard from a wide range of stakeholders, and an examination of new data, the Commission identified 4 areas of focus:

1. Ethnicity pay gap, evaluating trends in pay and considering the value of ethnicity pay reporting in promoting fair outcomes, using the NHS as a case study.

2. Fairness at work, challenging existing approaches and examining alternative ways to promote fairness for ethnic minorities that leads to better outcomes and achieves inclusivity.

3. Empowering the next generation of entrepreneurs, stimulating the entrepreneurial instincts of enterprising young people from all backgrounds.

4. Artificial intelligence, considering how to identify and mitigate bias in artificial intelligence, and use it as a tool to promote fairness.

The case studies highlighted in this chapter have been identified by the Commission, during its evidence gathering phase, as positive examples of what works and are used for illustrative purposes only in the context in which they arise. The Commission fully recognises that there will be many other examples of similar good practice in the respective fields and industries, so wishes to make clear that in highlighting them they are not particularly endorsed or being given preferential treatment.

To read the full report, click here.