Monthly Archives: April 2022

Research brain drain from the global south

The Director of the School of Oriental and African Studies (SOAS) in London, Dr Adam Habib, recently argued that universities in the global north are taking the best and the brightest from the global south and failing to return them.

360info asked me to reflect on this for a special issue on the education brain drain, and write about it from the perspective of research in the global south. What I wrote builds on previous ideas I’ve published and blogged about around the idea of “trickle down science” and decolonising research. This is an edited version of the 360info article.

The indigenous Bajau Laut of southeast Asia live a nomadic existence at sea. They have lived on houseboats for more than 1,000 years, free-diving for marine resources to sustain themselves. Research on the human genetic changes that allowed the Bajau Laut to adapt to this life at sea was published in 2019 in Cell. All but one of the article’s authors came from developed economies. The one Indonesian researcher had no relevant disciplinary background and appeared to be logistical support. The Indonesian government saw the study as exploitative and legislated to restrict overseas researchers from fly-in, fly-out, “grab the data and run” research. 

It’s an example of a common problem: the world’s poorest economies suffer health and development deficits that require research, but they are least likely to do research. When they do research with developed economy collaborators, it is often not the most relevant research to the developed economy.

The highest-income economies graduate the most PhDs per capita — the principal qualification for researchers — whilst the poorest economies graduate the least. The current stop-gap solution, critiqued by Dr Habib, is for developing economies to send their best and brightest students away to overseas PhD programs, often in developed economies. But the PhD experience in developed economies is usually geared towards research training involving sophisticated techniques and equipment unavailable at home. The student cannot replicate the research environment when they return to their home institutions and fall into an intellectual suzerainty. 

A supplementary approach to improving research capacity is through research collaborations. Many developed economy researchers enjoy the opportunity to collaborate with developing economy researchers. The developed economy researchers offer much-needed injections of capital and equipment; they can also provide experience using the latest collection techniques or analytic methods. Through the collaborations, developing economy researchers grow their skills and their networks. They are also much more likely to become authors of well-cited journal articles, which improves their international standing. 

However, significant concerns have been raised recently about the nature of the research collaborations between developed and developing economies. The concerns pivot on whether the relationship is exploitative. Are the collaborators from developing economies equal partners in the research, or are they logistical support, as in the case of the Bajau Laut study? Improving research capacity in developing economies needs to be realistic about the challenges and the structural deficits. There needs to be mutual respect. And it needs to be resilient to foreseeable and unforeseeable shocks. 

Around 10-years ago, the Wellcome Trust funded a project to establish a virtual institute for interdisciplinary research of infectious diseases of poverty in four countries (five institutions) in West Africa. Two developed economy institutions provided support. Nigeria and Mali had Boko Haram insurgencies during the project, and Côte d’Ivoire had a coup. Unfortunately, these external shocks are not atypical examples of the challenges of research capacity strengthening.

Political upheaval notwithstanding, the North-South-South (NSS) approach taken in developing the virtual institute was promising. The project networked developing economy institutions with some developed economy institutions, and it focused on the institutes, not on individual researcher capacity—which is easily lost. It is more holistic and looks to the development of infrastructure, governance, and human capital. Because the approach is based on a multilateral partnership, there are opportunities for mutual support within and between institutions and individual researchers. Governance developments in one institution can be replicated and adapted in another. Depending on the nature of the research, infrastructure can also be shared, such as cloud computing and gene sequencers.

The Norwegian government uses this approach, as does the World Health Organization, albeit in a slightly different form. The NSS approach also stands in marked contrast to supporting one-off projects or funding individual research degrees. The NSS PhD training is based in the developing economy institutions with support from the developed economy institutions in the network, including support from supervisors in the developing economies institutions. The approach simultaneously builds the developing economies’ supervisory capacity and decreases the likelihood of brain drain. The research is also driven by the relevance of the research to the developing economies and utilises technology that is available. 

It is not possible to mandate mutual respect. Developed economy institutions that have been successful over the past half-century in the traditional engagement models — “send your brightest and we will train them”, or “here’s some money, send the data” — may find changes in the status quo unappealing. However, there is no doubt that the NSS approach requires a different mindset, particularly in the institutions of the global north. The research capacity needs of the global south are enormous. The traditional approaches can not meet the needs because they do not scale. New global north institutional players will be needed, and they won’t have the baggage of past practice to weigh them down.

The original article was published under Creative Commons by 360info™. This is an edited version.

Local causation and implementation science

If you want to move a successful intervention from here (where it was first identified) to there (a plurality of new settings), spend your time understanding the context of the intervention. Understand the context of success. Implementation Science—the science of moving successful interventions from here to there—assumes a real (in the world effect) that can be generalised to new settings. In our latest (open access) article, recently published in Social Science and Medicine, we re-imagine that presumption.

As researchers and development specialists, we are taught to focus on causes as singular things: A causes B. Intervention A reduces infant mortality (B1), increases crop yields (B2), keeps girls in school longer (B3), or…. When we discover the new intervention that will improve the lives of the many, we naturally get excited. We want to implement it everywhere. And yet, the new intervention so often fails in new settings. It isn’t as effective as advertised and/or it’s more expensive. The intervention simply does not scale-up and potentially results in harm. Effort and resources are diverted from those things that already work better there to implement the new intervention, which showed so much promise in the original setting, here.

The intervention does not fail in new settings because the cause-effect never existed. It fails in new settings because causes are local. The effect that was observed here was not caused by A alone. The intervention was not a singular cause. A causes B within a context that allows the relationship between cause and effect to be manifest. The original research in which A was identified had social, economic, cultural, political, environmental, and physical properties. Some of those properties are required for the realisation of the cause-effect. This means that generalisation is really about re-engineeering context. We need to make sure the target settings have the the right contextual factors in place for the intervention to work. We are re-creating local contexts. The implementation problem is one of understanding the re-engineering that is required.