In this podcast, Ceren Canal Aruoba, Principal, Cornerstone Research, speaks to Darius Lakdawalla, Quintiles Chair in Pharmaceutical Development and Regulatory Innovation, University of Southern California, about the economics of pharmaceutical markets. The podcast discusses the impacts of COVID-19 from the manufacturer perspective including innovation and vaccine liability issues and explores how the manufacturers might interact with the other stakeholders in the pharmaceutical markets to address issues raised by COVID-19. In addition, Professor Lakdawalla talks about the use of economic analysis in pharmaceutical litigation and the challenges brought on by COVID-19 in this process. Sponsored by Cornerstone Research.
To learn more about AHLA and the educational resources available to the health law community, visit americanhealthlaw.org.
In this podcast, Ceren Canal Aruoba, Principal, Cornerstone Research, speaks to Darius Lakdawalla, Quintiles Chair in Pharmaceutical Development and Regulatory Innovation, University of Southern California, about the economics of pharmaceutical markets. The podcast discusses the impacts of COVID-19 from the manufacturer perspective including innovation and vaccine liability issues and explores how the manufacturers might interact with the other stakeholders in the pharmaceutical markets to address issues raised by COVID-19. In addition, Professor Lakdawalla talks about the use of economic analysis in pharmaceutical litigation and the challenges brought on by COVID-19 in this process. Sponsored by Cornerstone Research.
To learn more about AHLA and the educational resources available to the health law community, visit americanhealthlaw.org.
Support for A H L A comes from cornerstone research. Cornerstone research provides economic and financial consulting and expert testimony in all phases of complex litigation and regulatory proceedings. The firm works with an extensive network of prominent faculty and industry practitioners to identify the best qualified expert for each assignment. Cornerstone Research has earned a reputation for consistent high quality and effectiveness by delivering rigorous state-of-the-art analysis. For more than 30 years, the firm has over 700 staff and offices in the US and uk. For more information, go to www.cornerstone.com.
Speaker 2:Hello, everyone. My name is Jaren Jan Ava, and I'm a principal at Cornerstone Research, a firm that specializes in economic and financial analysis in commercial litigation and regulatory proceedings. Today, my guest is Professor Darai Lwa, the Quantiles chair in pharmaceutical development and regulatory innovation at the University of Southern California. Professor Lata is a widely published award-winning researcher and a leading authority in pharmaceutical markets as well as health economics and health policy. Today we'll be discussing the pharmaceutical markets, what makes them unique to study from an economics perspective, and what impact Covid 19 might have in pharmaceutical litigation.
Speaker 3:Professor. Um, you've been studying the economics of pharmaceutical markets for over 20 years now. Um, can you please explain some of the aspects of pharmaceutical, pharmaceutical markets that make them so unique and interesting to study?
Speaker 4:I, I've been interested in this subject for quite a while, and in particular, interested in how real world pharmaceutical markets depart from the very simplified world of a Blackboard economic model. And I've pursued this general theme in the con in various contexts, looking at how health insurers and pharmaceutical firms interact, um, how drug pricing should be efficiently set, how reimbursements should be set, um, how medical in innovation should be encouraged, uh, how pharmaceuticals should be marketed and what the effects of the, of that marketing will be, um, et cetera. And just as a starting point, a key feature of the pharmaceutical industry that everybody's aware of is that it's very research and development intensive. Um, r and d costs per employee are, uh, more than twice as high in the pharmaceutical industry, um, than basically any other industry. And it's also a heavily regulated industry. Uh, new drugs have to be reviewed and approved by the FDA in the US and even after drugs enter the market, the FDA monitors to ensure that the drug continues to be safe. Um, but many researchers who've looked at this combination of facts that, uh, there's a lot of research and development effort, there's a lot of regulation, draw what I think are the wrong conclusions, uh, as one, I think quite prominent example, many economists have emphasized that r and d and intensive industries, which very often also have a lot of patent protection. And certainly that's true in the pharmaceutical industry, that, um, such industries tend to restrain utilization by setting patent protected prices that are much, much higher than competitive levels. And then they conclude that this provides a basis for, uh, price regulation, um, in an attempt to protect consumers. And, uh, while certainly there are prices that are higher than competitive levels in, in a sense, this inference overlooks what's one of the unique features of the industry, which is the interaction between drug manufacturers and health insurers. That health insurers are kind of in the middle of drug manufacturers and the patients using those drugs. My prior research has shown that the presence of health insurance, um, enables drug companies to charge one price to the insurance company. And, um, uh, meanwhile, another copay or co-insurance is charged to an insured patient who might then have pretty efficient access to drugs. So patients pay modest copays while the innovators receiving a price commensurate with the value of their innovation. In a sense, you're kind of decoupling what patients pay from what innovators are receiving, and that's a really convenient feature because it allows us to provide rewards for innovation without, um, heavily restraining utilization through prices to consumers that are much higher than, um, competitive levels. Um, another relevant, uh, point that comes out of the role of insurers is it's far from clear that patients necessarily end up paying higher insurance premiums even as a result of high drug prices that health insurers and the pharmacy benefit managers that negotiate drug prices on their behalf possess a lot of negotiating power of their own. And so for that reason, they're often able to exact discounts on drugs or rebates in, in the parlance of the industry, um, that result in net prices paid to, um, drug companies that are often much lower than the list prices that we're often looking at the public list prices that are disclosed. And then we, uh, often don't observe the true, um, net price, meaning the list price net of the rebate that's actually being paid to the drug company. Another important issue, um, comes from, uh, products liability for, uh, drug companies, which is quite salient to today's discussion that regulators and courts and policy makers typically assume that when they expand the liability burden on innovators, it leads to the safer use of prescription drugs. And unfortunately, the opposite can sometimes happen because, uh, overlooked in this analysis as the role of physicians. Um, my prior research on this subject shows that when you expand, uh, liability products liability for drug companies, it often produces somewhat perverse results. And the, the, the issue is that the prescribing physician from a legal perspective is a learned inter intermediary that faces some liability, um, of our own. Uh, that is, if, if there's a drug prescribed and the drug results in some adverse consequences, the physician does have some liability for that due to the, uh, prescription being written. And for that reason, because the physician is a participant and, and alerted inter intermediary plaintiffs in many cases will, um, attempt to settle with prescribing physicians in exchange for testimony against the drug company. This is sometimes called a Mary Carter agreement after the original plaintiffs, um, uh, that sort of pioneered this, this kind of contract. Um, but the problem is that when drug companies face higher liability, the physician's negotiating physician relative to the plaintiff actually improves because the physician is now more valuable, um, in helping the patient go up against the drug company that's now facing greater liability. So for that reason, the physician ends up getting a better settlement deal when the drug company faces higher liability. And as a result, physicians end up facing fewer consequences for their prescribing decisions. And so this is a bit of a complicated mechanism, but the outcome, which is kind of perverse if you ask me, is that more liability for drug companies can lead to more, um, prescribing of the drug that is, uh, being kind of, that that's supposed to be restrained, um, via this liability rule because physicians actually face, uh, less liability, um, in the wake of shifting the burden, not the drug companies. These are some examples of how interactions among this very complicated market with lots and lots of different stakeholders often produce unexpected consequences. And it's important to understand the role of each of these stakeholders. In addition, I've talked about physicians, I've talked about health plans, um, and they, along with, with PBMs or, or pharmacy benefit managers play an important role in drug selection, too. They determine what drugs are gonna be covered under what conditions they're gonna be reimbursed. They also determine what share of the consumer's cost they're gonna pay, what share of the patient's gonna pay. Uh, the generosity of coverage depends on what the PBM thinks about the clinical benefit of the drug compared to its competitors. And also on the commercial rebate negotiation between PBMs and drug companies. Rebates are really important when drugs are, uh, fairly similar in similar classes. For instance, a classic example is the market for novel hepatitis C treatments where you had, uh, several years ago, both Gilead and AbbVie developed drugs in the first generation of what's called the, the NS five A inhibitor class that, uh, first cured hepatitis C in patients. Gilead launched their drug first. AbbVie launched about a year later. As soon as AbbVie launched, Gilead's price ended up falling net price, I should say, ended up falling about 50% because it had to pay, uh, higher rebates to PBMs just to maintain the generosity of coverage in the face of AbbVie's competition. That is Gilead was facing the problem of having to hold on the market share against a new entrant. And in order to do that, it offered bigger discounts. It's, it's a pretty sensible, uh, sequence of events, but it means that the, uh, PBM plays has, has an important role to play in setting prices and also setting access to, uh, new medications. Um, when new drugs enter PBMs often gain leverage in negotiating rebates with drug companies. Um, one, one stakeholder we haven't talked about yet is this pharmacy. And they also, uh, matter quite a bit, particularly when it comes to generic drugs, that my research has shown that pharmacies earn higher absolute dollars of profit on generic prescriptions. That's, that's kind of remarkable because generic drug prices on average are one or two orders of magnitude lower oftentimes than, uh, branded drug prices. But the pharmacy is earning more dollars of margin of profit margin on the generic drugs. So their percentage profit margin, therefore, is much, much higher. And for that reason, in part, pharmacies are trying to substitute generic versions of drugs for branded versions whenever generics are available. Um, and they're determining the circumstances under which to, um, make those substitutions. There is some regulation of this behavior as well that has to be taken into consideration. Um, and finally, the patient plays a role. The patients face a certain, uh, certain structure of copay and co-insurance liability, and they're gonna have to decide whether to fill their prescription. It's worth noting that, um, throughout this discussion, I've talked about price in kind of a cavalier way, but the reality is there are a whole bunch of different prices set by different market participants, and you gotta pay attention to that in analyzing this industry. The start with the public list prices sometimes, or kind of sticker prices set by drug companies, then there are rebates or discounts negotiated between drug companies and individual pharmacy benefit managers. Then there are the co-payments and co-insurance which determine the is faced by consumers, their dispensing fees paid to pharmacies, cash prices paid by uninsured patients, and on and on. So we have to carefully understand all of these pricing institutions as well.
Speaker 3:It's, it's clear from your explanation that there are many different stakeholders with which different incentives in, in, in, in pharmaceutical markets, which as you said, makes it relatively unique to study. Um, but then I wonder, um, you know, we, we can't pass the, you know, we could call it an opportunity or not what Covid brings, um, to a complex market like this. Clearly, COVID has posed extraordinary challenges for the pharmaceutical industry in many ways. Um, but I do wonder what impact can an economic shock like Covid 19, um, have on pharmaceutical industry?
Speaker 4:Yeah, that's a great question. Well, I, I think first of all, COVID has brought pharmaceutical markets and as well as other healthcare markets to the epicenter of our policy discussions in the first place. It, it emphasizes the role that drug companies play in conducting innovative life-saving research vaccine. If we ever get a vaccine, and hopefully we will, that's reasonably effective, that will provide the best chance we all have at a return to normalcy. And there are dozens of candidate vaccines and trials all over the world. Um, innovators in this area are of course, motivated in part by the possibility of selling their innovations and, and, um, earning revenue from them. Uh, and, and the revenue opportunity is significant because so many people need vaccination and also because regulators are likely to be motivated to review these vaccines quickly and expedite the, the, um, review process. But there's, I think there is also, uh, some non-financial motivation as well that there's gonna be a lot of public, um, prestige that's conferred upon the winners of this vaccine rate. And more generally in the eyes of the public. The standing of the pharmaceutical industry has fallen over the past decade. I mean, well, they've been recipient of a lot of criticism when it comes to, uh, pricing policy, for instance. Uh, but here we have a golden opportunity for one or more companies to, uh, restore some, uh, public trust and favor by getting back to what their, their social value is, in a sense that they're around to provide lifesaving innovations, um, and benefit society as a result. Now, this, this is kind of a unique, I mean, really this is a once in a lifetime, maybe even once in several lifetimes, confluence of health events. And it brings a number of important legal questions with it. One question that I've been wondering about and I think might become relevant is, who should bear the risk of what seems to be a rush to review and approve vaccines or potentially drugs? It seems likely to me that the fda, uh, might permit shorter or smaller clinical trials, uh, for what is sure to be a very widely used vaccine. But what happens if a rare but serious side effect emerges after the vaccine's launched? Who's gonna end up bearing that risk? Um, a number of economists have argued that, uh, if there's compliance with FDA regulatory guidelines, that behavior should mitigate liability risk for an innovator, um, especially, uh, I would think in a context for the FDA deliberately and intentionally, uh, reduces the evidentiary burden, given the urgency of getting a drug to market and the pursuit of a public health goal. But in practice, I think the likely outcome is that innovators are gonna bear some of this liability. Um, and what does that mean? I think for the, the incentives to develop, launch and eventually market covid 19 vaccines, I think that is a, an open question and an extremely important question for companies engaged in, um, this innovation raise. I think there's some other questions that arise relating to, um, project warp speed, or the government's part of which is the US government's commitment to pay for vaccines, uh, before their maybe have been approved. The logic, which I think is a, is sound, is the pre-purchase agreements provide powerful incentives for vaccine manufacturers to pursue risky discoveries and also ensure, I think importantly, ensure adequate supply of vaccine is available quickly. Cuz manufacturing time for vaccines, it's considerable and it's, it's, it's one thing to get a vaccine approved is another, to actually get people vaccinated. Um, I've made the argument before that these kinds of pre-purchase agreements make a lot of sense in a number of contexts for antibiotics, vaccines, and other types of innovations where society is willing to share the risk with an innovator. But you can think of a number of scenarios that are kind of problematic. I mean, what happens, for instance, if one or more of the vaccines that the US government has chosen, uh, prove inferior to another vaccine candidate that doesn't have a pre-purchase commitment, will the federal government end up making good on the commitment it made to the first vaccine? Will that manufacturer have to accept some reduction in payment that maybe the funds will be diverted to another manufacturer that ended up producing, uh, a better vaccine? One can easily imagine disputes arising from these kinds of scenarios. And I wonder about what's gonna happen there. It, I think it, it's prudent to be prepared for these kinds of issues.
Speaker 3:It, it's, it's interesting that, um, you know, every stakeholder and pharmaceutical companies are, are, are trying to develop vaccines, vaccines and, and many others. But you, you mentioned disputes across many other, I think, stakeholders including, uh, pharmaceutical companies. But, um, what are some of the issues you foresee coming down the road for the industry related to this, these disputes?
Speaker 4:Yeah, that's a good question. I, I think just following up on the Covid 19 thread for a moment, uh, it's possible that some of the, the, the innovations we're seeing here, I'm talking about contractual and financial innovations like risk sharing agreements. It's possible that Covid 19 will ease the path towards other types of risk sharing agreements for other vaccines, rescue antibiotics or other treatments that are valuable, but face risky revenue prospects. Just as an example, the issue with rescue antibiotics is relevant because oftentimes, very late line antibiotics are valuable because we wanna have some option for patients that fail all the existing therapies. But we hope that option isn't used very often because we hope that the existing therapies work. So in that sense, it's valuable to produce that innovation, but that's not much comfort to the innovator, uh, because you might not get a lot of revenue, um, when the earlier line antibiotics are working. And so the problem here is that that innovator is, is essentially providing an insurance policy to society, um, and they need, they ought to be compensated for that. And, and risk sharing agreement, just like a pre-purchase agreement for vaccines, has been proposed as a novel contractual strategy, but it hasn't gotten a lot of traction. And so one, one possible consequences that that covid will ease the way forward for these kinds of novel contracting approaches. Uh, along similar lines, there's a possibility that it can encourage greater collaboration among, uh, competitors when they're pursuing compelling public health goals. For example, we're already seeing this in response to Covid, uh, back in March, um, about 15, uh, biopharma companies agreed to share information from their own internal proprietary libraries information about molecular compounds, um, with, uh, existing, uh, covid 19 safety and efficacy data. And that agreement, uh, was struck, um, in conjunction with the Gates Foundation welcome and MasterCard's Covid 19 Therapeutics Accelerator Program, uh, in BA just one month after that, in April, the W H O launched its access to Covid 19 tools accelerator, which was a collaboration designed to promote the development, production and distribution of vaccines, diagnostics, and drugs for covid 19. So these kinds of initiatives can create a lot of opportunities for collaboration, but they could also give rise to some unintended consequences that could lead to allegations of collusion, price fixing, exclusionary conduct, or market allocations. It can also lead the IP disputes if the collaborations are kind of rushed and counterparties end up miscalculating the value that they stand to gain. And indeed, the value of medical innovation in general has been an important focus of my own research. It's a, it's a complex subject and it's an area of increasing importance within the US market where everyone is looking for a solution to align drug prices with value.
Speaker 3:Um, as, as you know, as you and I know, and and our listeners might now, um, you know, economic analysis, as you said, you know, research plays, plays a big role studying some of these questions related to, you know, value of medical innovation. You brought up role of government intervention and any policy implications. Um, but again, as we know, there might be, you know, as, as economic analysis applies to these issues, um, there might be some common al mistakes, um, that are made along the way. So what are some of the common empirical mistakes that you have seen in the economic analysis that you performed for litigation?
Speaker 4:Yeah, that's a, that's a great question. I, I think the first thing to point out here is that when used correctly, economic analysis can be an extremely powerful tool to explain complicated scientific issues that arise in pharmaceutical litigation. Um, there was, uh, a couple decades ago, there was a so-called credibility revolution in empirical economics that raised the standard for rigor in the empirical, in particular causal inferences that economists make from, uh, empirical observation. But of course, the practice of empirical economics doesn't always match the high standard set, uh, in, in theory, and one of the most common and problematic mistakes does relate to causal inferences drawn from statistical analysis. Uh, many researchers use regression analysis, which is a statistical method that estimates the relationships among us between a single outcome of interest and a set of explanatory variables in, in theory and under a very, uh, specific set of circumstances. You can use regression to figure out if one type of factor causes another, but regressions are ultimately based on correlations among mean values. And everyone's heard that correlation does not imply causation. Um, and the good news though is that there, there are a number of diagnostic tests that we can apply to a regression to increase our confidence that it's results aren't indeed causal. Here's, here's a simple example. It's no doubt the case that ice cream sales are well correlated with sunburns. Okay? So it's in this simple case, it's pretty obvious that ice cream doesn't cause people to get sunburned, but instead there's a third factor here. In this case, it's sunny weather that results both in ice cream sales and in sunburn. But suppose that third factor weren't so obvious, how would we figure out that this correlation is not causal? Well, a common approach is to identify what economists call natural experiments that affect the variable or variables that we think to be causal. For example, suppose we identified an area of the country, or particular time summer period in which milk prices were unusually high, and ice cream prices were unusually high too. So as a result of the higher prices ice cream sales would go down if there were truly a causal link from ice cream sales to sunburns, we au then see in that summer or in that area of the country to see fewer sunburns. Sunburns would fall along with ice cream sales. But if in response to this natural experiment of high milk prices, if ice cream sales fall and sunburns don't, then our ice cream sunburn regression is gonna fail. The causality test here, the, the su the sudden shortage of milk acts a little bit like an experiment that can help us test whether ice cream causes sunburns. And there are various in the pharmaceutical industry turning our attention to the subject of the day. There are various natural experiments that I've used to study, uh, causality. For example, you can use the passage of legislation like Medicare, part D'S prescription drug benefit for Medicare beneficiaries. You can use the sudden arrival of new and revolutionary treatments for a condition you can use changes to the way Medicare and Medicaid, um, price drugs, and so on. Of course, causal inference and pharmaceutical litigation is complicated by a variety of things. One example relates to the understanding of how the entry of a generic, uh, drug company affects the sales of a particular molecule. Now, here, unlike with sunburns and ice cream, there's a solid theory that says generic entrance can drive prices lower and therefore boost, uh, drug sales. But, uh, careless regression analysis is still gonna get you in trouble here. And that's because larger markets and faster growth in markets attract generic entry. So the problem here is gonna be that the total correlation between generic entry and sales is gonna overstate the true causal effect of the generic drug entering on sales. In, in other words, the problem here is that, uh, part of the issue is that, uh, large markets or fast growing markets attract generic entrants. So this is kind of a reverse causality problem. Causality is running in both directions, and you can get tripped up by this. The way to solve this is to identify, again, a natural experiment for entry. One example might be the end of the Hatch Waxman exclusivity period for the first generic entrant. If we see sales spiking right after that exclusivity period expires, then we can be more confident the spike is the result of generic entry rather than perversely the cause of generic entry. So one set of issues here is about causal inference, and you have to be careful about inferring causality from correlations. It can be done, but it has to be done carefully and deliberately in, in accordance with a set of rules. Another mistake that I see is in sampling errors for any statistical analysis to be employed correctly, the sample we've drawn has to be representative of the population we care about. This explains why it's so important to test for what we call representativeness. A famous example is in 1936, literary Digest Magazine, which is one of the most respected magazines at, at the time with a, with a strong track record of predicting the winners of presidential elections, predicted that LF Landon would beat Franklin Roosevelt from the 1936 election. They had a very big sample, 2.4 million people, and so it seemed credible and compelling. The problem was Literary Digest recruited their sample using phone directories, drivers registrations, and country club memberships, and those methods tended to reach more affluent voters who were more likely to favor, um, Landon and not the less affluent voters who allow, who propelled Roosevelt to victory. So as a result, the literary digest results were way off, even though they had a very big sample. Because of this problem of sampling bias that resulted in Republican voters being overrepresented and democratic voters being underrepresented in the context of pharmaceutical litigation, we could see this problem if, for instance, you wanted to assess the reasonable and necessary nature of drugs being prescribed and submitted for reimbursement, you might not have the resources to review and analyze every single claim submitted, in other words, the population of claims. But what you want do is analyze a subset of those claims. The key though is the subset of those claims has to be representative of the population at issue. So not just the country club members, but the people who are going to play, um, tennis at the, or rather, uh, play basketball at the Y M C A along with the country club members. So you want to vary the strategy for recruitment, um, in any setting like this. So economists and statisticians here would typically investigate whether measures of disease severity, treatment utilization and demographics are similar for the subset you chose as for the general population of interest. Um, a final example, which I think is quite relevant to covid is, is recruitment into clinical trials. So clinical trials specify particular criteria for eligibility. You have to be a certain age, you have to have a certain health status and so on. But the medical literature is shown as time, and again, the clinical trial participants are often not representative of real world patients. And that's true sometimes in observable ways, in the sense that trial participants are younger or exhibit fewer comorbid conditions, but more problematically. It's also true in unobservable ways. The trial participants are, are often healthier than their conditions would suggest, and they're, and they're also often more compliant with their doctor's instructions, which is important if you're trying to run a trial and you want the respondents to pay attention to the rules. So this raises important questions about how comfortable we are generalizing, uh, from the safety and efficacy data, um, present in a clinical trial. Do do those results generalize to the broader real world population that's gonna get treated. For instance, in this, in the pressing case of Covid 19, an economists can help here by trying to identify whether trial participants differ from real work, real world patients, and if so, how and how can we adjust for
Speaker 3:Them? Interesting. So maybe if you focus more on the now, the future looking, um, part of our discussion, um, what kind of implications, if any, um, do you think Covid 19 will have for the pharmaceutical industry in the, the near future and in the long term?
Speaker 4:Yeah, that, that is, uh, that's a quite an interesting issue. I think already we've seen covid 19 lead to more risk taking, and this is true in science generally. For example, scientific journals are more willing to publish data earlier, maybe before it's undergone as much scrutiny as we as previously would've been routine. There are certainly risks associated with that. And we've, we've seen that, that sometimes studies are retracted or later studies find, uh, different results. But regulators are also traveling down the same path. Regulators seem more willing to issue emergency authorizations for tests, and I think they'll ultimately be more willing to off to, uh, to, to, um, authorize the use of particular vaccines, um, because of the pressing public health emergency that they face. And I think that that, uh, pharmaceutical companies are following suit here be because they understand this is a more relaxed, evidentiary, and regulatory environment. And it is so for a, a good reason that there's a compelling public health goal of getting vaccines and treatments out quickly and urgently. And every extra week of review means there are lives lost. And so I, I would say it's, it's sensible that there's a relaxed evidentiary and regulatory standard, but as a result, pharmaceutical companies are spending billions of dollars to accelerate their development programs under the assumption that reviews will be rapid. And the fast-paced clinical trial timelines will not be a liability in the review process. And the same thing I think goes for physicians who are experimenting with off-label treatments for covid-19, not to mention the sick patients and their families who are willing to, um, to, uh, be part of the, of that real world experimentation. The government also is taking a lot of risk because they're, as I mentioned earlier, they're entering into these pre-purchase agreements with, uh, drug companies to encourage r and d and vaccines. What remains unclear again, is who's gonna end up bearing the consequences of risks that might end up causing harm? For example, who should be liable when unforeseen side effects related to the off-label use of treatments in covid 19 surface? Uh, what are the, what are the damages associated with that? How much liability should the drug company have? What about the physician who bears the cost of a stock price that declines when a vaccine fails or when there's a, an episode of products of of harm from product? Economists have studied risk and reward in pharmaceutical markets quite extensively, and we have tools to analyze these kinds of questions about expected returns. And many of these tools can be employed here to look at the expected behavior of drug companies, physicians, and governments, um, that could be helpful in addressing these sorts of litigation questions. Now, in, in litigation more specifically, there's more risk, and that brings along with it more uncertainty and more opportunities for dispute. In, in, in our academic research economists, we, we are frequently trying to assess the impact of different policies, different kinds of firm behavioral decisions, events, et cetera. And that requires that we understand the counterfactual or, but for world that would've attained without those policies or without that conduct. And I think that's one reason why economists are well equipped to think about, but four worlds in the context of litigation. So given the facts of the case, what would the world have looked like absent the challenged conduct? All this uncertainty, though is gonna make it more difficult to assess. But four consequence, uh, I, I think covid 19 is gonna create some issues with data we frequently use in our analysis. For one thing, it's created what looks to be a structural change in the data. So the trends and patterns we saw in the world before, covid cannot readily be used to predict what's happening here and now and possibly not. What's gonna happen after, uh, COVID 19 is hopefully one day over in, in that sense, the arrival of Covid will diminish our ability to use past data to predict future behavior or to characterize the but four world. There are analytical tools we can use to account for these types of issues, but it has to be carefully considered and overcome once we start to see litigation post Covid 19. I also think Covid will bring about organizational changes in the industry that might raise the hackles of regulators. The pharmaceutical supply chain is global, and COVID 19 has exposed the vulnerabilities associated with global supply chain. I think we, I think we're more likely to see more investment in US based resources and perhaps less outsourcing the India, China, Eastern Europe, or elsewhere. The government's already trying to provide some incentives for insourcing of this type. Back in March, the Senate introduced the bill to encourage the manufacturer of active pharmaceutical ingredients in the US It was called the Securing America's Medicine Cabinet Act, I think. But competition among drug companies for these resources could raise antitrust concerns as well from government agencies. And I think the other issue here is, given the amount of money the government is putting into Covid 19 research, it's fair to expect more scrutiny by the, from the government that potentially could result in more false claims act cases as well. I think in, in some, we're at a pivotal and historic moment in health, and the pharmaceutical industry is transforming in front of us right now in so many ways. We have probably more questions than answers, but the good news is that economic analysis has come a long way over the past several decades, and we can help get to the bottom of all these new questions.
Speaker 3:Thank you, professor Al. It's been very educational. It's not enlightening to hear your thoughts on pharmaceutical markets and what, what, what's next given Covid-19. Thank you so much.
Speaker 4:Thank you. Thanks for.