Is there a problem?

Bloom, Jones, Van Reenen & Webb (2020) found in their multiple case studies, spanning Moore’s law, agricultural crop yields, mortality and life expectancy and research productivity in firm-level data, that each domain suffers from diminishing returns to investment. That means that a constant number of scientists would not keep coming up with a constant number of new ideas throughout the time in a given domain, but rather coming up with new ideas gets harder and harder and therefore we should expect them to come up with fewer and fewer new ideas and less progress.

Ideas in some domains might be getting more and more depleted, but new domains are arising and give opportunities for new and fresh ideas to be harvested. That is perhaps how science progress happens. The more surprising question comes when we try to generalize this to science as a whole, as Bloom et al. seem to be suggesting. Is it the case that it is getting harder and harder to find whole new domains of research? That our ability to find new ideas not in a specific domain, but anywhere, is hitting diminishing returns? Maybe our mechanism of knowledge creation and ability to understand the world is hitting diminishing returns as well?

I would say that evidence for this latter claim is much weaker than for diminishing returns in any given specific domain.
First, we can look at how economists measure the economic benefits of scientific progress. Bloom et al. model that by comparing inputs (investment into National Income and Product Accounts’ “intellectual property products”, a number that is primarily made up of research and development spending but also includes expenditures on creating other nonrival goods like computer software, music, books, and movies) and outputs (total factor productivity, TFP, which is a portion of the growth in output not explained by growth in traditionally measured inputs of labour and capital used in production). However, TFP does not seem to be an especially good measure of scientific progress – it does not track scientific progress per se, but rather all unmeasured factors contributing to productivity. This contains not only the economic benefits of new discoveries but also the diffusion of the existing knowledge, which seems to be qualitatively very different things. We have no way to tell how big proportion of TFP is caused by diffusion versus creation and how this varied in time. Further, some part of scientific progress works via enabling a greater supply of factors measured outside of the TFP (e.g. labor, capital, and land) or are embodied in concrete capital goods (which is perhaps how the benefits from most privately funded research are realised) and these will not be counted in TFP. The counterargument goes that if these biases are roughly constant over time, changes in TFP still would reflect changes in the rate of progress of science and technology (for more details and discussion see Cowen and Southwood, 2019, p.18). Anyway, I don’t think we have a strong reason to assume these biases are constant over time. TFP thus has the advantage of aggregating the science as a whole but seems not to be capturing scientific progress accurately enough to enable drawing strong conclusions about whether the science is slowing down or not. I think that if we wanted to make an argument using TFP, we could still say (although pretty vaguely), that since 1930s, TFP has been declining, whereas investments in research have been increasing, which suggests that at least some part of scientific discovery captured by TFP have been slowing down (or hitting diminishing returns).  

Further, we can take the notion of scientific progress as how much it does contribute to wellbeing measured in other ways than economic benefits (e.g. life satisfaction; chance of survival for the next thousand of years..). Is there any measure like that for scientific progress as a whole? Unfortunately, I’m not aware of any. 

Further yet, we may take a look at scientific progress from a perspective of how much it increases our understanding of the world. For example, measures that do not concern the economic benefits of science, but rather things like “how much a discovery changes our understanding of the world” and “how much does it enable us to make further discoveries”? Each of these interpretations would likely require its own measurement method.
One venture in this direction was done by Patrick Collison and Michael Nielson (2018), who asked top scientists to compare the importance of discoveries awarded by Nobel prizes in each decade. They found that scientists rate older discoveries as more important than the more recent ones. However, they focused again only on a few specific domains in which Nobel prizes are awarded (chemistry, physics, medical sciences), not on science as a whole, not to say newly developed domains of research. Further, the notion of “importance” might mean different things for different scientists –  some might weight economic application of that knowledge more, others might rather weight in how much it changed and formed our current understanding, etc.. Finally scientists might have a bias towards being correct, and it is safer to bet on older discoveries whose applications and importance have already been well demonstrated, rather than on more recent discoveries where applications are yet to be found. To conclude, while an interesting venture, this piece of research does not seem to be telling us much about whether science as a whole is slowing down.            

Diminishing returns to investments seem like a pretty natural way the things work when you want to exploit some resource (like gold mining). It also makes sense based on a heuristic that it’s easier to grow rapidly from small base than from large base (i.e. 10 % returns from investements in 1000 scientists are perhaps easier to achieve than 10 % returns from 1 000 000 scientists, because the latter benefit is just so much larger in absolute terms). Anyway, not all things in the world works this way – there are also things that work on the basis of constant returns or even increasing returns. An argument in this way could go like: the amount of pieces of knowledge is still increasing and by combining different pieces of knowledge, we can create yet another useful piece of knowledge. Therefore, the actual space of possible discoveries is getting larger and larger with each new discovery made, ther than smaller and smaller (as “ideas getting harder to find” hypothesis seems to suggest). However, it doesn’t say anything about (economic) returns to these discoveries – maybe ideas are increasingly easier to find, but the value of these ideas is lower and lower (or perhaps ideas with high economic value are getting harder and harder to find). One piece of evidence that could potentially point to the direction of increasing returns is the correlation evidence that firms and countries that invest more in R&D have higher returns to those investments (Hervas & Amoroso, 2016). However, this could also represent a result of comparative advantage over other countries/firms or there might be reversed causality (i.e. firms and countries that are doing economically better are using their additional resources to fund R&D, but R&D is not a causal mechanims for how they got economically better).

Anyway, let’s imagine science as a whole is really generating less and less benefit per dollar invested. Being true or not, this exercise is helpful in spotting things that might be suboptimal or might become problems in the future, something like writing a pre-mortem. So, why could that be?

Causes

1) New ideas are harder to find

As suggested by Bloom et al. and many others, it could be that just like in many other aspects of our lives, engaging in a given activity repeatedly brings less and less benefit. Good metaphor is the one with low-hanging fruit – we are gradually picking the low hanging fruit and now each new fruit is higher and thus harder and harder to pick. In that case, what kind of ability does this refer to specifically? It might mean that our ability to understand the world is hitting diminishing returns. Or it might be the method of knowledge creation that’s getting depleted. It might also mean that we just need more and more investment to get up to speed with our current level of knowledge. We can see this in several domains: PhD degrees in economics are taking longer to complete, co-authorship is increasing in mathematics (Odlyzko, 2000), economics (Brendel & Schweitzer, 2017) and research teams are growing larger in mathematics (Agrawal, Goldfarb & Teodoridis, 2016), suggesting one head can’t absorb all the required knowledge anymore. The age of the scientists at which they made a discovery for which they later received nobel prize also increased by about 10 years during the 20th century (Jones & Weinberg, 2011). However, note that all these pieces of evidence come from specific, very traditional disciplines, where the diminishing returns are most likely to be observed. They don’t assess science as a whole.  

Picking up the low hanging fruit seems like a natural principle – is it inevitable then? Can we do anything to reverse that or at least slow it down? I will propose some potential solutions in another post.

2) Science as an institution is getting significantly less effective in generating new benefits

This suggests that due to more bureaucracy researchers spend less time and attention on research itself. Or alternatively, with increasing numbers of researchers doing science, it’s getting harder and harder to coordinate and coordination costs cause large decreases in benefits generated.

One possible proxy for measuring the extent of bureaucratisation is looking at the proportion of time researchers spend actually doing research. One older study by Milem, Berger & Dey (2000) found that in the US between 1972 a 1992 the proportion of time devoted to research among academics at research universities actually increased. However, the time spent doing research is operationalized in a way that is likely also contains grant writing. Several more recent studies pointed to the large amount of time that gets wasted by researchers writing grant proposals and harshly competing for funding, with some researchers claiming they spent as much as 60 % of their time seeking funding (Fang & Casadevall, 2009) and overall costs of peer review grant selection process consuming as much as 20–35 per cent of the allocated budget for research (Gluckman, 2012) (for more discussion see e.g. this Nintil post). Moreover, this trend of wasted time seems to be increasing as success rate in grant competitions is getting lower over time (see e.g. this data from NIH or this data from CIHR, I will update with more data in the future to confirm whether this trend holds across domains and countries). Anyway, this trend would significantly contribute to decreasing returns only if the distribution of impact was more uniform, which might not be the case.
Another way to see whether administrative costs are increasing over time is to look whether there is increase in administrative positions (e.g. academic project managers) paid from the R&D budget (will need to find some more data about this).

However, as my friend Aleš suggested, reduced time researchers spent doing research might actually not be the most important form of bureaucratisation. The more important form of bureaucratisation might be expressed via hiring processes. If these processes become too stringent and inflexible, it might miss out some of the most extraordinary talents who won’t get the job and the processes might instead select for more average careerists, which might decrease the amount of innovation produced by academia by far in the long term.     

In the similar category of science institution getting less effective, the problem could also be insufficient research infrastructure. In the EU, the number of researchers grow approx 1,5 % per year, which means doubling once in 46 years (Baumberg, 2018, cited via Cowen a Southwood, 2019, other data since 1980s can be found e.g. via OECD, 2021). It is perhaps possible that institutions of science are finding it hard to keep up with such increases and, because the lack of coordination and infrastructure, the process of knowledge creation gets slower.
   

3) Privatization of research funding and benefits

Benefits from research are getting more and more privatized, oriented towards improving specific private goods rather than generating global public goods. This might explain why the TFP metric does not capture these benefits but also suggests a plausible hypothesis for how the overall value of research decreases. Indeed, the majority of research funding across G20 countries currently comes from business and private sources rather than government spending (1,26 % vs 0,65 % GDP on average) – but was it always like this or has it changed in the past decades?

During the second half of the 20th century, the profile of research funding changed greatly. In the 1960s United States, for example, public investment in research accounted for more than 2/3, while today it accounts for only 1/4 all investments in research. This privatization of research funding also applies to basic research – the proportion of publicly funded basic research has been declining since the 1960s, and in 2013 even more than half of all basic research spending in the US came from private sources (Mervis, 2017). This trend can also be seen in data from other OECD countries and Europe: the ratio of researchers paid from state/public sources vs. private sources is still declining (OECD, 2021).

If privately funded research brings smaller profits to the general public and greater profits to given private investors, then it is possible that this privatization of research funding ultimately reduces the overall economic benefits of investing in research. On the other hand, data from the US NSF show that the total amount of funding devoted to basic research increased 33 times between 1960 and 2000, and the ratio of basic research expenditure to total government expenditure (i.e. compared to applied research and development) increased from around 8% to around 27% over the same period (NSF, 2002). It is therefore true that overall research spending is being privatized, but spending on basic research is still rising – if (publicly funded) basic research is indeed more cost-effective in the long run because it allows knowledge to be more widely distributed and forms the basis for applications, then growth in investments in this type of research should ensure that the overall economic benefits of investing in research will grow, not fall.

Another pointer towards privatisation of research could be the Bayh-Dole Act, which might have encouraged universities to invest their resources to support of monetizable research and perhaps also decrease the disclosure and sharing of their research findings in order to monetize it later. Anyway, if “monetizable”=”more applied”, this should shown in some statistics (although perhaps not the NSF data, because that is strictly governmental funding, not university funding; also, it would be useful to compare how large proportion of research funding actually comes from universities vs governments to see whether that fraction is actually relevant).

Conclusion

To sum up, I think Bloom et al bring some evidence for decreasing economics returns to investments in any given domain, however, this not neccessary generalizes to science as whole. Because we don’t have especially good metric of economic benefits stemming from science as whole, perhaps the argument for diminishing returns for whole science is weak. Anyway, if it is true, there are number of reasons hypothesized – ranging from ideas getting harder to find to science as an institution getting less effective to privatisation of research funding and benefits. Perhaps all these effects are true at the same time and contribute to the observed decrease in benefits, but perhaps they vary in how much they contribute.