A recent paper in PLoS one [1] suggests that less than 1% of the globally active scientific community accounts for about 42% of all research output (measure = scientific publications; observation period = 1996-2011). If only high-impact papers are considered, a core group of scientists accounts for 87% of all publications. Through mining of appropriate data, the paper unravels a strong concentration of uninterrupted productivity in a very small group of scientists. In real terms, out of a 15 million strong force only about 150.000 continuously publish once a year. If one considers more than one publication per year, then the group of highly productive scientists decreases rapidly. The paper makes for some interesting food for thought, including the points highlighted below:

  1. Global science appears to be organised in an oligopolistic manner (displaying characteristics such as high barriers to entrance and homogenous product offering) where a ‘relatively’ small group of players dominates the market. Owing to the peer-review system (where scientists assess the quality of other scientists’ work through review of papers or funding proposals), the focus of ‘scientific production’ essentially converges to the dominant opinion [2] and cements the status quo (by way of forcing players to work in trending scientific areas or through the control of financial resources provided by public funders who make decisions based on assessment done predominantly by a core group of scientists).
  2. The consequence of this is that 99% of the research community play a catching-up-game while otherwise not contributing significantly to scientific output. In other words, it is difficult for new entrants to get into a market dominated by incumbent players because of the time and cost it requires to build the resources and, crucially, reputation needed to play successfully in the game.
  3. What does this mean for emerging market scientists such as on the African continent?
    1. Research agendas are set by international players and may not align with local or regional needs. This may put local scientists into a conflict of interest position (follow the money vs. follow the need) and dependence on external support (in the form of knowledge, funding and other resources);
    2. Researchers in Africa are forced to collaborate with international peers to increase their reputational capital and gain access to relevant financial resources. Considering the dominance of incumbent players, collaborations will most likely be brokered rather than cohesive [3]. This, in turn, poses challenges since the diffusion of knowledge or IP is less efficient in brokered collaborations because it depends more heavily on the willingness or ability of a central player to share;
    3. In view of the above, local funders will have a propensity to question the ability of local scientists to produce value; demand unrealistic returns; or limit funding in view of resource scarcity and conflicting needs (e.g. short-term poverty reduction vs long-term impact through scientific discovery). This in turn can create a vicious cycle of under-resourcing and -performance, essentially further limiting emerging market countries’ abilities to develop economically meaningful capacity.
  4. What does this mean for emerging market governments?
    1. One possible response is ‘Why bother’? As long access to scientific knowledge is essentially free for all, it could make sense to allocate a large share of resources to a small group of highly productive players. However, this rests on the premise that access to scientific knowledge will remain free indefinitely or at least for the foreseeable future. The current system of intense scientific productivity emanated from a time when the US was (and currently is) the dominating producer of scientific outputs. However, this position is challenged by emerging players, in particular China[4]. In view of the latter, there is a risk that the governments investing into science could in future limit access to knowledge created with their tax payers’ money. Even in a scenario with continuously free access to global knowledge, lack of local productivity would cement a system of dependence on external supply of resources (knowledge, IP, goods and services) and therefore limit the development of a local knowledge economy.
    2. Another response is to facilitate collaboration with others. While this will allow local players access knowledge and resources and, therefore, increase their reputational and financial capital to a certain extent, it potentially poses a number of challenges. Firstly, if collaborations are brokered, i.e. centred on a knowledge hub (i.e. an external scientist or group of scientists), access to and use of information, and collaborative outputs, will most likely be controlled by the centre. Secondly, even if the centre is willing and able to share knowledge and outputs, an effective transfer will be hampered by the concentration of tacit knowledge in the centre and the relative the lack of it on the periphery [5].
    3. Another possible response is to concentrate local resources on selected areas of scientific activity. While intuitively sense making, this will require paying closer attention to how scientific productivity is organised (as opposed to relying on the effectiveness of the global science system). Scientific discovery, in particular where some kind of breakthrough is to be achieved, is a function of new combinations of potential solutions that are being tried in tackling a problem. Therefore, a system needs to be created so that a very high number of permutations can be achieved. Such a system ought to make sure that the relative value of outputs is gravitating towards an above average level (e.g. by way of adding high-quality inputs, i.e. researchers, and through providing the requisite know-how for prioritizing outputs). In fact, if local breakthroughs are the desired outcome, then productivity ought to be organised so that a consistently high and diverse number of ‘shots’ are created on one, preferably on more than one target [6]. However, such an approach will be difficult to realise unless the players in the system are comfortable with the very high rate of failure it entails and the time it takes to yield an impact.

The paper’s findings and conclusions also suggest a variety of further investigations, such as:

  • A potential correlation between the concentration of output and the concentration of input (e.g. funding in USD);
  • The geographical distribution of the core group of scientists;
  • A potential correlation between economic activity and the concentration of output/input;
  • A potential correlation between resource allocation and breakthrough discoveries.

Overall, the study is an important contribution to the question of how invention and innovation (although not explicitly subject of the study) ought to be organised in order to yield significant returns. This question is increasingly burning for decision makers in resource-constrained emerging economies who want to balance long-term knowledge economy endeavors with short-term poverty reduction efforts. However, it is also relevant for developed nations whose economic output has contracted after 2008 (e.g. Spain, Portugal, Greece) and for companies whose very survival hinges on scientific break-through discoveries (i.e. the pharmaceutical industry).


[1] Ioannidis, J. P., Boyack, K. W., & Klavans, R. (2014). Estimates of the Continuously Publishing Core in the Scientific Workforce. PloS one, 9(7), e101698.

[2] Nicholson, J. M., & Ioannidis, J. P. (2012). Research grants: conform and be funded. Nature492(7427), 34-36.

[3] For a discussion of brokered vs cohesive collaboration, see Loh, P. (2008). Achieving breakthroughs in Innovation. Knowledgeworks Consultants.

[4] https://www.cpgr.org.za/blog/snapshot-global-trends-challenges-drivers-success-research-development-rd/

[5] For a discussion of the challenges inherent to collaborative research see Fleming, L. (2012). Breakthroughs and the “long tail” of innovation. MIT Sloan Management Review. v10.

[6] Fleming, L. (2012). Breakthroughs and the “long tail” of innovation. MIT Sloan Management Review. v10.