Why global migration data should recognise subgroups like the Teochew

In nations such as France and Norway, official censuses deliberately refrain from inquiring about race and ethnicity, guided by a commitment to national unity. These countries operate under the belief that categorising citizens by physical features risks fostering division and perpetuating prejudice. Conversely, in the United States, United Kingdom, Canada, and the Commonwealth, an opposing perspective prevails.

Data collection is perceived not as a divisive tool, but as a crucial instrument to identify discrimination, enhance social safety nets, and tackle health disparities. However, both approaches are ill-equipped to address the mounting global migration and fluid movement of people across borders. The current data frameworks, which amalgamate large, diverse populations into generic classifications like "Asian," "Chinese," or "Indian," fail to capture the intricate subgroup dynamics that shape commerce, culture, and community life.

The Teochew people, hailing from the coastal Chaoshan region of Guangdong province, serve as a prime example. In standard census forms across North America and Europe, a Teochew individual is typically classified as either "Chinese" or "Asian." Yet, many Teochews residing in Western countries identify as Vietnamese, Cambodian, Thai, or Singaporean diaspora migrants.

A singular checkbox fails to encapsulate a multifaceted identity, a profound historical migration, and an extensive economic network spanning the globe. The box-office triumph of Dear You, a film deeply rooted in Teochew language and heritage, resonated profoundly within the global diaspora. By depicting the history of qiaopi—the family letters and remittances sent by migrant workers to southern China— it illuminated a critical aspect of Teochew identity.

In technological innovation, Yang Zhilin, a Moonshot AI founder born in Shantou, the Chaoshan area, exemplifies the Teochew tradition of entrepreneurial spirit. Li Ka-shing, another Teochew immigrant, established one of Asia's most formidable business empires, capitalising on deep-rooted commercial networks from his home region.

In the consumer goods sector, David Tran, a Teochew-Vietnamese refugee who settled in California, revolutionised the culinary world with Sriracha sauce, transforming a regional flavour profile into an international household staple. When statistical systems aggregate diverse figures like those of Li, Tran, and Yang into a generic label, they overlook the cultural engines and connections that propel their journeys.

This predicament is not exclusive to Chinese communities. The Indian diaspora, for instance, encompasses a kaleidoscope of subgroups, each with distinct linguistic backgrounds, community networks, and economic circumstances. A Gujarati businessman operating in London, a Malayali nurse working in the Gulf, and a Telugu tech professional in Silicon Valley possess vastly different experiences, relying on unique support systems and encountering distinct economic challenges.

In the era of big data and artificial intelligence, relying on simplistic demographic categories represents a significant opportunity lost. For businesses, treating a vast, diverse group as a monolithic market leads to inauthentic advertising campaigns and squandered resources. A marketing strategy tailored for the "Asian consumer" is likely to misrepresent the cultural nuances, dietary preferences, and linguistic distinctions that differentiate a Teochew family from a Cantonese one.

In terms of public policy, the ramifications are equally significant. When governments measure average income or health using broad racial categories, wealthier subgroups can mask deep poverty or health concerns within less fortunate communities. If a minority group appears successful "on average," its struggling sub-communities risk being overlooked when allocating health resources, language assistance, or social aid.

This issue has been reiterated in Asian American studies, particularly through data disaggregation efforts aimed at spotlighting ethnicities such as Chinese and Indian. However, the argument here is to extend this approach further by emphasising subgroups within ethnicities. The solution does not entail ceasing the collection of population data or imposing rigid categorisation upon individuals.

Instead, data science must adapt to the evolving nature of modern migration. Survey designers, market researchers, and policy analysts should gather more voluntary, flexible subgroup data, encompassing factors such as the language spoken at home, regional origins, and migration history. Additionally, there is a need to integrate the rich insights and theoretical frameworks derived from decades of qualitative research on ethnic economies: the economic relationships and networks that emerge from cultural and linguistic ties.

While this area of academic inquiry has yielded valuable findings, its impact on economic and social policymaking remains limited. In a globally interconnected world, identity is multifaceted, locally rooted, and fluid. Until our data systems evolve to recognise subgroups like the Teochew, our understanding of global migration—and our capacity to cultivate more equitable societies—will remain incomplete.