In medicine, applied research and development responds strongly to profit signals, more basic research not so much
“No amount of real resources devoted to medical research would have helped European society in 1348 to solve the riddle of the Black Death.” - Joel Mokyr (1998)
To some degree, research and development of new products is just another things firms do, like hiring workers, setting prices, and building factories, and accordingly, a simple model of profit maximization suffices to explain R&D. To the extent such a model is true, if you want more R&D, you can just make R&D more profitable, for example by offering prizes or committing to purchase an innovation. But there are limits to this perspective, and I think the borders of those limits are well illustrated by the effect of market demand on medical innovation. On the one hand, the R&D performed by healthcare firms is well explained by standard models of profit maximization - when it becomes more profitable to prevent or treat a disease, firms invest in finding new ways to prevent or treat it. But on the other hand, the extent of this R&D seems curiously limited. Specifically, market incentives seem to have little impact on more fundamental research.
Let’s start by looking at some studies on vaccines. Finkelstein (2004) identifies three US policy changes that increased the profitability of vaccines for some diseases but not others. She then looks to see if firms respond by creating more vaccines for the affected diseases, relative to the unaffected diseases. Indeed, they do.
The three policies Finkelstein uses are (1) the 1991 CDC recommendation that all infants be vaccinated against Hepatitis B; (2) the 1993 decision for Medicare to fully cover the cost of influenza vaccination for Medicare recipients and; (3) the 1986 creation of the Vaccine Injury Compensation Fund which indemnified vaccine manufacturers from lawsuits relating to adverse effects for some specified vaccines. In each of these three cases, policy choices made vaccines for some diseases more profitable, but had no effect on other diseases.
As a control group, Finkelstein considers various sets of alternative diseases that were not affected by these policies, but which otherwise share some of the same characteristics as the affected diseases. All told, she has data on preclinical trials, clinical trials, and vaccine approvals for 6 affected diseases and control groups consisting of 7-26 other diseases, over 1983-1999.
Diseases where policy increased profitability saw an additional 1.2 clinical trials per year and an additional 0.3 new approved vaccines per year (but only 7 years after the policy took effect), as compared to controls. So the promise of more profit did pull in more vaccine development.
But the effect only travels so far up the research stream. When Finkelstein looks farther up the development pipeline, the effect disappears. Affected diseases had no more preclinical trials than the control group. This suggests firms responded to the increased profit opportunity by pulling vaccines already far along off the shelf and putting them into clinical trials. But if it stimulated more basic research, the effect was too small to be detected.
Another case study: in 2007, a coalition of governments and the Gates Foundation attempted to spur the development and deployment of a new vaccine by waving a big carrot in front of vaccine makers. The coalition pledged $1.5bn towards the production of 200 million annual doses of a pneumococcal conjugate vaccine for developing countries. If a manufacturer would supply the vaccine at a price of no more than $3.50 per dose, the advance market commitment would top up the rest with a share of the $1.5bn pledged. The program launched in 2009 and in 2010 GSK and Pfizer each committed to supply 30 million doses annually. This amount was increased over time, and a third supplier entered in 2019. Annual distribution exceeded 160 million doses annually by 2016.
Uptake of the pneumococcus vaccine was much faster than uptake for vaccines for a different virus without an advance market commitment (rotavirus). So the advance market commitment seems to have worked.
But there's an important caveat: very little R&D was required to develop the pneumococcal conjugate vaccine. When it was selected, vaccines for similar diseases in developed countries already existed, and vaccines covering the strains in developing countries were already in late-stage clinical trials. So in this case, the advance market commitment pushed firms to quickly build up manufacturing and distribution capacity, but it didn’t push them to do extensive R&D since none was needed.
There is also a rich vein of research on the extent to which general pharma R&D (not vaccines) respond to changes in the size of the market for different health products. Dubois, Mouson, Scott-Morton, and Seabright (2015) look at the link between potential profits and innovation in the context of global pharmaceutical innovation. They've got data on drug sales in 14 major countries, which they use to make estimates of the size of the market for different categories of therapeutic medicine. Their goal is to see how changes in the size of the market for a drug change the propensity to develop new drugs for the market. In this case, they're holding the measure of innovation to a relatively high bar: a newly approved drug, marketed in one of their 14 countries, that is also a new chemical entity (i.e., not a modification of an existing drug).
One challenge is that better drugs can, themselves, change the size of the market. Suppose for example, that new drugs just come along randomly as a result of serendipity. In that case, potential profit doesn't actually induce firms to develop new drugs. But if these new drugs find a market, and we're measuring the size of the market by looking at spending on drugs, then we'll create a misleading correlation between the "size" of the market and the number of new drugs. In this case, the number of drugs is "causing" the size of the market, rather than vice-versa. To avoid this, they use a statistical technique (instrumental variables) to pull out the parts of demand that vary due to demographics and overall GDP growth (neither of which should be affected by drug innovation over the 11-year period they work with).
When they do this, they find that bigger markets do indeed lead to more drugs. On average, when the market for a therapeutic category grows by 10%, there are 2.6% more new chemical entities approved over a given time period.
Further mitigating concerns that these results might be spurious somehow, Agarwal and Gaule (2022) largely confirm this result on a newer slice of data (2015-2019), and using different measures of each key variable. For R&D effort, they look at the number of clinical trials for 75 different diseases. To measure market size, they add up national disease mortality for each disease, for each country in the world, weighting each country by GDP per capita. Importantly, this method of measuring market size may be less susceptible to reverse causality (whereas innovation in a disease category might lead to more revenues in that category, it probably won’t lead to more deaths). Using this method, a 10% increase in the size of the market for a drug is associated with 3.6-4.3% more clinical trials.
But how scientifically novel are these new drugs? Suggestive evidence comes from Acemoglu and Linn (2004), who perform a similar exercise as Dubois, Mouson, Scott-Morton and Seabright (2015), but on US rather than global sales data. When the market for different diseases in the US changes due to shifting demographics, how does this change the flow of new drug approvals for those diseases? Acemoglu and Linn find the effect of a bigger market is much, much stronger for generic drugs than for new molecular entities.
More direct evidence comes from Dranove, Garthwaite, and Hermosilla (2020) and Byrski, Gaessler, and Higgins (2021), both of whom use the US Medicare Part D extension to see if the promise of higher profits changes the direction of more fundamental medical research. The basic idea is that Medicare Part D extended medicare to pay for enrollee's pharmaceutical drugs beginning in 2006. This created a big new market for drugs used by Medicare enrollees (US residents aged 65 and up), raising the profitability of research related to diseases that disproportionately affect this group.
Dranove, Garthwaite, and Hermosilla have data on worldwide pharmaceutical company drug trials, and they want to see if companies run more trials on scientifically novel drugs in response to the new opportunities created by Medicare part D. Byrski, Gaessler, and Higgins extend this back even further, to the period before drugs are tested. They have data on scientific publications, broken down by disease group, and look to see if scientists publish more basic scientific research related to diseases that have become more profitable to treat as a function of the Medicare extension. Let’s start with whether drug companies test more novel drugs.
To measure the scientific novelty of a drug, Dranove, Garthwaite, and Hermosilla count the number of times the specific "target-based action" of the drug has been explored in previous drug trials (of similar or stronger intensity). A target based action comprises the specific (targeted) biological entity and the mechanism used to modify its function: for example, a p38 MAP kinase inhibitor is a target-based action that targets the p38 mitogen-activated protein kinases and inhibits its function. If this target-based action has never before been used in a clinical trial, then a drug using it is considered maximally novel. The more often it has been previously used, the less novel.
With this measure in hand and data on 76,161 clinical trials on 36,002 molecules, Dranove, Garthwaite, and Hermosilla look to see if therapeutic areas with greater profit potential in the wake of Medicare Part D see more clinical trials for scientifically novel drugs. While they do find that more exposed therapeutic areas do see a small increase in trials for the most novel kinds of drugs, once again the effect is much stronger for the least novel drugs. Over 2012-2018 the number of trials for the most novel group of drugs increased 14%, while the number of trials for the least novel group increased 106%.
So firms don’t respond very much to profit opportunities by testing highly novel drugs. Byrski, Gaessler, and Higgins dig back even further, to the research that precedes drug trials. They have data on about half a million scientific publications published between 1997 and 2016, carved up into topics related to 129 different disease categories. Like the other studies mentioned above, they find that when the Medicare extension passed in 2003, it led to a significant uptick in drug development for drugs that had become more profitable, as indicated in the figure below (which compares the number of new chemical entity drugs in disease categories as a function of the disease prevalence among medicare enrollees).
When they dig into the impact on scientific publications, they see a similar effect for the publication output of corporate scientists. That is, scientists working for pharma companies do publish more scientific research related to diseases that Medicare made more profitable to treat.
But once you leave the corporate sector, things look decidedly less responsive. Looking at scientific publications overall (of which the corporate share is pretty small), the effects are never large enough to be statistically distinguishable from zero.
So far, this suggests the private sector and its scientists are pretty responsive to the profit motive, but the (much larger) non-corporate academic world is not. But it’s actually a bit worse than that. When Bryski, Gaessler, and Higgins dig into the kind of research corporate scientists publish, they find the increase is concentrated in applied work, not basic science. The increase in corporate science articles comes from articles that are either about new clinical trials or about pharmaceutical products. Even in the corporate sector, more basic science (defined in various ways) doesn’t budge.
Lastly, we can turn to the biggest shock to health in living memory: covid-19. In the relatively early days of the pandemic, Bryan, Lemus, and Marshall (2020) tracked the number of covid-19 therapies at any stage of development, as well as the number of academic publications related to covid-19, to produce this figure:
The black line that is shooting off to the top of the chart is the total number of therapies or publications related to covid-19, as measured against the number of days since the beginning of the pandemic/epidemic. The various dashed lines correspond to the number of therapies and publications for other diseases and/or pandemics (Ebola, Zika, H1N1, and breast cancer). Two things are immediately apparent.
First, covid-19 research was much higher than research related to other pandemic diseases. Second, the gap between covid-19 and other diseases widened as the magnitude of the covid-19 pandemic became clearer. It seems obvious these differences are entirely driven by the difference in demand for a covid-19 therapy, both relative to other drugs and over time, rather than some scientific breakthrough that made it suddenly easier to do covid-19 research. So the above figure is strong evidence that pharma companies respond to profit opportunities.
But dig into the data a bit deeper and the same sort of story emerges. An unusually large share of trials were for repurposed drugs, rather than novel therapies or vaccines. This difference grew over time, as the scope of the pandemic widened. This suggests the rising profitability of a covid-19 treatment pushed ever more firms to focus on therapies that are not necessarily the best treatment for the disease, but which are most likely to get to the market soon. Vaccines tend to be harder than drugs, and novel drugs tend to be harder than repurposing existing drugs. In the end, the pfizer, Moderna, and AstraZenca vaccines for covid-19 were able to advance as quickly as they did because the underlying basic research had been done long before covid-19. Famously, the vaccine was designed within days of the covid-19 genome being published; all the rest was the applied work of clinical trials.
But two caveats are worth noting, each of which emphasizes how extraordinary covid-19 was and one of which cuts against some of the claims in this article.
First, Agarwal and Gaule (2022) show the rise in covid-19 related clinical trials was extraordinary, even relative to the size of its “market”, defined as the mortality of the disease weighted by the GDP per capita where those deaths occurred. Using this measure, the number of new clinical trials was 7-20 times larger than their model would have predicted.
Second, even though clinical trials data does not reflect much in the way of fundamental research being done, it is notable that covid-19 did have an extraordinary impact on science as a whole. This is reflected in the figure at right, and also by Hill et al. (2021), which documents an extraordinary pivot, across the scientific ecosystem, into covid-19 related research: by May 2020 and through the rest of the year, about 1 in every 20-25 papers published was related to covid-19. Not just biomedical papers either; all papers! The article Building a New Research Field argues covid-19 may have had this unusually large impact on fundamental science because it was large enough to create an obvious focal point, around which a critical mass of new research could easily coordinate.
Finally, it may not be that medicine is unique in this regard. The article Pulling more fuel efficient cars into existence finds that “pull policies”, such as high fuel prices or fines on vehicles that fail to achieve certain emissions standards, are also effective in promoting clean energy innovation in vehicles. But as with medicine, the case for these policies seems strongest for incremental research.
To sum up: across vaccines and drugs, when it becomes more profitable to treat a disease (either because the affected population grows, or a change in policy means treatments are covered) firms swiftly respond by developing new treatments. But this effect is strongest for treatments that are already well understood and has only small (or even negligible) effects on treatments that still need a lot of R&D. That might be rational - research is unpredictable and rife with unintended spillovers, and it can take a very long time (especially from a firm’s perspective) to go from a scientific discovery to a technological application. That makes the expected profit from a more experimental treatment lower and so it might make sense to prioritize treatments that are further along. But it does mean we shouldn’t necessarily expect technological breakthroughs to be hastened along by the promise of strong profits.
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Bryan, Kevin, Jorge Lemus, and Guillermo Marshall. 2020. Crises and the Direction of Innovation. SSRN Working Paper. http://dx.doi.org/10.2139/ssrn.3587973
Hill, Ryan, Yian Yin, Carolyn Stein, Dashun Wang, and Benjamin F. Jones. 2021. Adaptability and the Pivot Penalty in Science. SSRN Working Paper. https://dx.doi.org/10.2139/ssrn.3886142