Until our data systems see past broad labels like Asian or Chinese, and recognise subgroups, our picture of global migration is incomplete
3-MIN READ3-MINCharles ChearCharles Chear is a clinical associate professor at the University of North Carolina Chapel Hill School of Social Work. Published: 9:30am, 4 Aug 2026In countries like France and Norway, the government census strictly avoids asking about race and ethnicity. Governed by a desire for national unity, these states operate on a firm premise: categorising citizens by skin colour risks creating divisions and reinforcing prejudice.
Across the Atlantic and in the Commonwealth, you find a different philosophy. The United States, United Kingdom and Canada measure race and ethnicity. In these societies, data collection isn’t seen as a wedge to divide people, but as an essential tool to spot discrimination, design better social safety nets and address health disparities.
Yet both models are falling short. As global migration accelerates and people move fluidly between countries, our basic data buckets are failing to keep up. By lumping vast, highly diverse populations into monolithic labels like “Asian”, “Chinese” or “Indian”, standard census forms miss the rich subgroup dynamics that drive business, culture and community life.
A single checkbox erases a complex identity, a deep history of migration and an economic network that spans the globe.
