HSBC Asset Management
Like most, we initiated our use of diversity data with a focus on gender. Figures were accessible across markets globally, using existing data stored and readily available in the HR system. Through analysis of the data we were able to better understand disparities across levels of seniority, teams and geographies. This allowed us to set gender balance benchmarks for which we now regularly monitor progress against, inclusive of tracking leavers and joiners.
Amidst conversations and awareness sparked by 2020 social unrest and the Black Lives Matter movement, we began steps to tackle collection and measurement of ethnicity statistics as the next step in our use of diversity data. There was no existing ethnicity data held via the HR system, thus requiring us to reach out to staff directly in commencing a voluntary data collection process in markets where it was legally permissible.
As a group subsidiary, challenges in accessing the information submitted by staff became apparent. Without direct access to the HR system where the data was collected, there was an inability to view and evaluate figures specific to the asset management business. Awaiting clarifications regarding privacy rights and appropriate aggregating of the data led to significant delays in gaining the needed visibility. Furthermore, inability to see staff response rates prevented follow-ups or an active campaign to encourage self-submission, as there was no clarity on existing submission levels.
Once visibility was gained it became clear that submissions were uneven across markets. Use of employee resource groups and a more active campaign appear to have been effective in driving higher response rates in a particular market. Additional challenges of a group structure also became apparent. Given various central functions provide support to multiple group businesses, it is necessary to address how individual support staff are counted and allocated to the asset management business in order to generate the most accurate diversity statistics. We are now integrating learnings to build a more robust approach to analysis into both ethnicity and gender data moving forward.
There have been several additional learnings more broadly. First, we have realised that to enable progress in the use of diversity data, we must break the agenda up into digestible components to prevent the overall journey from stagnating. Second, and specific to ethnicity data, clear differences in representation and seniority within different ethnic minority groups means a blanket approach to address inequalities will be ineffective. Finally, and related to avoiding a blanket approach, regional differences in identification of ethnic minorities require more thought and a nuanced approach. Widely accepted government census categories make identification relatively straightforward for western markets. It is a more significant challenge in Asian markets where definitions of ethnic minorities are different and less consistent.