Health Insurance is a Big Union
How bargaining happens
Health insurance often pays for things which are both predictable and certain. Isn’t that strange? The nominal purpose of insurance is to guard ourselves against risk — we want to be able to pay for unexpected large expenses without having to hold an enormous buffer stock of savings. There is no reason to first pool our money together if we are certain to pull it out again on a day of our choosing.
And why are health insurance companies so intimately involved in every aspect of our care? If health insurance worked like any other form of insurance, you would receive a lump sum of money upon the realization that you have a health condition, sufficient to pay for whatever care you need. And yet health insurance companies restrict the doctors you can choose from, choose what procedures you’re allowed to do, and negotiate the prices that will be paid. If life insurance worked this way, you would have in-network morticians, you’d have pine coffins fully covered but oak coffins have a co-pay, and the whole thing is settled two months after the funeral.
The answer to both of these is that health insurance is substantially not about pooling risk. It is about collective bargaining. By putting our money together, we can hold down the cost of medical care. Restricting what people can buy with insurance money allows them to implement something closer to an efficient transfer.
To understand this function of health insurance markets, we must learn about the economics of bargaining. This is a new and exciting area of economics, and its applications in antitrust law has been one of the cleanest examples of new theoretical advances making their way into practical relevance. We will also learn everything we can about the provision of healthcare in America, and how well-intentioned laws have undermined the incentives for health insurance companies to fight.
Finally, we need to consider the effect on innovation. Medical innovation, largely pharmaceuticals, requires the outlay of an enormous fixed cost to discover new ideas. Pushing outcomes closer to what is efficient in the short run can be worse in the long run, as new ideas are left undiscovered.
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Health insurance is provided both privately and publicly, with the public insurance often being through the auspices of private companies, and with private insurance often being regulated so severely as to barely be a free market at all. The old, as well as people with disabilities and people on kidney dialysis (among others) are covered by Medicare; the poor are covered by Medicaid, often as a supplement to private insurance.
The rest buy privately, with over half of all people buying health insurance through their employer. Strikingly, for two thirds of these, the insurance is not actually financially liable for paying out claims or collecting premiums. This is handled by the employer. The employer contracts with the insurer in order to get access to the insurer’s network of hospitals and doctors, the set of negotiated rates for different procedures, and the administrative set-up for billing.
It used to be that insurance simply paid your bills, whatever they might happen to be, in a “fee-for-service” arrangement. We do this instead of paying out a lump sum because it is fundamentally infeasible to give people a sum of money, and only then have people search. This would be perfectly fine in perfectly competitive conditions, but is very different under imperfectly competitive ones. When one discovers that one has a condition, their elasticity of demand changes. Empirically, customers also do a terrible job shopping around – a few months ago I wrote an article on this, but basically requiring price transparency has an extremely limited effect on utilization and prices, and what effects there are come from the major companies learning about each other’s prices, not from the consumer choosing better options.
Paying people’s medical bills, regardless of what they were, was rather predictably disastrous. Prices exploded from the 60s to the 90s, going from 5% of GDP in 1960 to 13% by the beginning of the 1990s.
Doctors and hospitals were encouraged to hike their prices to match the generosity of insurance coverage. On the patient side, the marginal cost of getting more healthcare once you have the plan might be zero, thus leading to inefficiently high levels of healthcare. Even if the insurance company attempted to control things through cost sharing, where the patient must pay a portion of the costs of care, this would only cut out the most egregious waste.
Then, expenditures flatlined. For much of the 1990s, healthcare spending as a share of GDP was constant, and even in the years it grew, its growth rate was lessened considerably. The rise of managed care was responsible. Between 1988 and 1998, the percentage of employees in traditional fee for service plans fell from 71% to 14%.
They accomplished these cost reductions with the tools of modern health insurance companies. Rather than pay any medical bills at any doctor, they would instead restrict the network of doctors and hospitals you could see. If they charge too much, they’ll find themselves out of the network and getting no business at all. Insurance companies began to involve themselves in what treatments a patient should be prescribed, requiring “prior authorization” for treatments of great expense and heterogeneous effectiveness. The most extreme versions of Health Maintenance Organizations (HMOs) like Kaiser Permanente would be fully vertically integrated, with the doctors being employees of the insurance company on salary. Even if they are not directly employed, they can still be paid a flat per-patient fee, or capitation, regardless of what procedures are ordered or what work is done.
These cost saving provisions worked. Cutler, McClellan, and Newhouse (2000) compare the cost of treating heart attacks and ischemic heart disease between traditional insurance plans and managed care. The managed care plans provided the same treatment for 30-40% less. Narrowing the network helps force out inefficient hospitals. Gruber and McKnight (2016) found that giving even a small subsidy was enough for people to switch to a narrow network, whereupon spending fell by 40%. Brot, Burn, Layton, and Vabson (2026) find that prior authorization in Medicare Part D (which, while publicly funded, is privately administered) reduces costs by 4% of total spending by reducing the utilization of the restricted drugs, and relatedly, Maggie Shi (2024) finds that every dollar spent on auditing for fraud and waste in Medicare reduces government spending by between 24 and 29 dollars. Even better, this cost-cutting came without negatively affecting the patient at all.
Yet, managed care was not very popular. Nobody wants to be told that they can’t go to every doctor, or that they need permission to be prescribed a drug. Moreover, physicians and hospitals hated it, precisely because it controlled costs.
So, largely between 1995 and 2001, states passed a number of laws restricting the power of insurers to bargain, the most consequential being “any willing provider” laws. These laws made it so that insurance companies could not exclude providers who were willing to accept the network rate. Since what was holding down prices was the threat to exclude to begin with, though, this is not a stable arrangement.
It is difficult for me to make the case for why the managed care backlash happened without sneering at it, because it really was an alliance of the cynical and the foolish; the cynical, of course, being the doctors, and the foolish being the public, who could not see how requiring more medical care to be provided for would simply show up in their premiums. Alain Enthoven comments: “As I have listened to the debate over managed care, I have often thought, ‘we are hearing from people who do not believe they are going to have to pay for what they are demanding.’”
It says something about the rigor of legislating that they banned things which didn’t even exist – basically every state has a gag clause ban, which were allegedly provisions forbidding doctors from discussing non-covered options. The Government Accountability Office (1997) investigated at the behest of Congress, and had to somewhat sheepishly report back that they couldn’t find anything, and in any case physicians didn’t read the contracts they signed anyway.
And so, yes, the backlash raised costs. Maxim Pinkovskiy (2020) exploits state-by-state variation in the timing of when states passed their laws, as well as differences in HMO density county by county. When a major law was passed, counties with a high density of HMOs and thus lower healthcare costs became like their low density neighbors. There was no corresponding improvement in mortality or health outcomes.
So how do they do this bargaining? Time for some theory. Suppose that there are two people who can combine their efforts to produce one good. In a competitive market the payment to each is determined by their marginal contribution to production, but if we say that each person’s effort is indispensable then this breaks down. Each person’s marginal contribution is everything. We need some way of dividing up the surplus in order to make the surplus exist.
We presume that there is no hidden information, and all facts are known by all participants. Bargaining occurs by making offers back and forth to each other. If there is no cost to dragging out the negotiation, then the outcome is undefined – we’d just go back and forth forever. If we add in a cost to delaying the negotiation, everybody can play out what will happen eventually, and agree immediately to whatever would have happened.
More precisely, out of a set of feasible options S, and with each player receiving some utility d if the agreement does not happen, the option chosen will be that S which multiplies the joint product of utility gains over no agreement. Thus, (S-d) times (S-d), with one more pair of S and d for each additional player in the game. Differences in patience give us the bargaining weight that each player has, which is denoted by beta and exponentiates each (S-d) term.
It turns out that this non-cooperative game spits out the solution proposed in 1950 by John Nash, as the only solution which is Pareto efficient, symmetric, invariant to scaling each player’s utility, and which is independent of irrelevant alternatives. These mean (in turn) that there are no improvements that could make one player better off while leaving everyone else unchanged left unused, that each of the players can be identical (note that the bargaining weights break the symmetry assumption), that one does not need to make interpersonal comparisons of utility, and that is unaffected by deleting unchosen options.
This is a perfectly good solution for parties bargaining with each other in one big negotiation. However, negotiation occurs between many parties separately. What this messes with is our disagreement point d – we don’t know what would happen if they walk away from an agreement unless we know the outcome of all the other agreements, which is precisely what we’re interested in. The solution is to have a Nash equilibrium of Nash bargains, as Horn and Wolinsky (1988) do – hence the current common name, Nash-in-Nash.
Somewhat confusingly, Nash has two important concepts named after him – Nash equilibrium and Nash bargaining – and they’re rather different. Nash bargaining is just a solution concept for a game where everyone has full information and is cooperating. A Nash equilibrium is when people are engaged in a non-cooperative game, and no player can improve their situation by unilaterally taking an action. In the context of bargaining, it would mean that, holding all the agreements but one fixed, nobody wants to renegotiate that agreement.
Horn and Wolinsky’s case is a very simplified one, where two firms separately bargain over an input from a monopoly provider. If the two goods are substitutes for each other, then bargaining simultaneously increases the share going to the monopoly provider compared to bargaining jointly. Each of them knows that if they walk away from the bargaining table, the other firm will expand to take all of their business, which improves the disagreement point for the monopolist. If, conversely, the goods are complements, then walking away is especially powerful, which worsens it. Companies want to merge if they are substitutes, and actually want to remain split up if they are complements.
In the real world, we do not observe all of the information which firms have. We do not get neat equations which Horn and Wolinsky have to characterize what happens. Instead, we have to find the outcome by iteration – or more precisely, we need to work backward from the observed agreements and prices to find the bargaining weights, disagreement points, and feasible actions which imply the observed outcomes.
Nash-in-Nash bargaining took its great leap into empirical usability with Crawford and Yurukoglu (2012), which, despite having nothing at all to do with health insurance, is such a great paper that we’re going to spend some time on it. They are interested in the cable networks, which bundle together many channels into a few discrete choices. (You might be thinking to yourself, why bundle at all? A simple example. Suppose there are two channels, ESPN and HGTV, which offer sports and gardening respectively. One consumer values ESPN at $10 and HGTV at $1, and another consumer values ESPN at $1 and HGTV at $10. Selling each separately, they’d put each on sale at $10 and make $20. If they can bundle, they’d offer both for $11 in total, and make $22. Note that this is more efficient! There is no reason to assume, a priori, that bundling is always bad for the consumer. It must be profitable for the company, of course, but it could be beneficial to the consumer!)
What would happen to consumer welfare if you required that the bundles be split apart, and channels be sold a la carte? To answer this, we have channels, cable companies, and consumers work through a four part game: channels and cable companies bargain over how much they will have to pay each other, then the cable companies set prices and bundles, then households buy bundles, and then they watch TV. We will go backwards from the end of the game, and figure out what parameters justify the observed costs by iterating until we get a vector of parameters that return a stable outcome.
To estimate consumer demand, you have data on how much people view each channel, and on the sets of channels which are purchased. We don’t observe what people are willing to pay for each of the channels individually, but we can rescue things by making the assumption that if people watch a channel more, they also value it more. The bundle purchase itself is standard BLP logit, with random coefficients representing people’s demands for particular characteristics, and with demographics providing additional micromoments to match.
With demand estimates in hand, we can then observe what the cable companies chose for their bundles, and the prices of those bundles. We are assuming that cable companies are competing on price – hence, Nash-Bertrand pricing – and so there is exactly one marginal cost, corresponding to the payment to the channel, which can justify the price chosen. Given those prices, we can then search for the set of bargaining weights which gives us the outcomes we observe. They lack precise fees for the channels, but they do observe the average fees paid. This allows them to put bounds on how valuable the channels are. Likewise, we don’t observe the channels independently of bundles. Instead, we presume that both the inclusion and non-inclusion of the channels is optimal, and that that puts the value of the channel between two points.
To test a new scenario, you change some element of your model, but keep the old parameters, and then resolve. This last step is rather scary, because there is absolutely no guarantee of existence. You can, in fact, circle the drain forever, with no vector of prices satisfying the outcomes. In the empirical example, mandating a la carte price doesn’t actually do anything for the consumer, because all of the gains simply get eaten up by renegotiating. If you didn’t have the full model, you would have thought that it would have substantially benefited the consumer. Pretty important to get that right!
Back to health insurance, Ho and Lee (2017) take the basic framework of Crawford and Yurukoglu, and add an additional layer, as well as some improvements. In California, the health insurance plans of public employees are negotiated through CalPERS, so they have an additional layer of negotiation – CalPERS bargains with the three insurance networks, who in turn simultaneously bargain with the many hospitals across the state. This layer of bargaining replaces the Nash-Bertrand prices of Crawford and Yurukoglu (2012). Unlike Crawford and Yurukoglu, they can perfectly observe the transfers between providers and insurers, so they do not need to have bounds. Demand for hospitals is estimated by basically assuming away unobserved quality and sorting across market lines, and thus being able to use geographic variation.
After estimating the parameters, they can then simulate what happens when one of the insurance companies is removed. Blue Cross offers a broad network plan, Blue Shield offers a narrower but cheaper plan, and Kaiser Permanente is fully vertically integrated but will not pay for you to go to hospitals outside the network at all. While in most cases, less competition among insurers increases prices for the consumer, in some cases removing an insurer will actually decrease prices. Removing a small insurer changes the bargaining position of the other insurers, and they can drive a tougher bargain.
Ho and Lee (2017) skip over the formation of the network, and instead only deal with the payoffs between pairs of agreements as they exist. This is because the model doesn’t actually do a great job of explaining the exclusion of providers from the network – there must surely be a price which makes the agreement work, and so not just doing that is a mystery. In a follow up paper, Ho and Lee (2019) extend the model to include the formation of a network. The insurer excludes at least one hospital in order to be able to credibly threaten to replace any given hospital with the held out hospital. If the insurer could not credibly commit to doing this, then any hospital knows that the insurer will eventually settle for something, and prices are higher. They call this Nash-in-Nash-with-Threat-of-Replacement (NNTR). They use the same CalPERs data from their 2017 paper, with the parameters estimated from 2017, but then allow for Blue Shield to reform the network. (Why only Blue Shield? Because if you allow for Blue Cross and Kaiser Permanente to respond strategically, you have made it literally impossible to compute. So please, give us this assumption).
This last extension was independently developed by Eli Liebman (2022) and Soheil Ghili (2022). (Ghili writes in footnote 12 “I recently became aware that an independent paper by Ho and Lee is in the process of being written, which has a similar practical objective … and similar modeling contributions.”) The idea was certainly out there. However, NNTR has been barely used, unlike straight Nash-in-Nash bargaining. The basic problem, beyond the computational complexity, is that you have to observe not only the consummated bargains, but also the bargains which could have been formed but weren’t. Lee and Fong (2013) is the framework for estimating network formation as a dynamic game with continuation values and everything, and it’s just, completely impossible to implement.
Insurers definitely hold down prices by negotiation. How much of this is captured by the insurers? How much passes through to the consumer? It is frustrating to report that I do not have a very good answer for you. Cabral, Geruso, and Mahoney (2017) use changes in subsidies to Medicare Advantage providers to estimate passthrough, which can stand in for how much market power providers have. The answer, of course, varies sharply across markets, but only 54% of payment increases get passed on to consumers. However, we can’t divide that up between insurers and hospitals from the information in the paper. Ho and Lee’s bargaining weight coefficients suggest that hospitals get 70% of the gains from trade, the insurance company gets 15%, and the bargainer on behalf of the consumer, CalPERS, gets the remaining 15%. Individual purchasers are likely to get less.
This bargaining literature was extremely important for antitrust action. Starting in the 1990s and continuing to today, healthcare providers have been consolidating both horizontally and vertically. Between 1998 and 2017, there were 1,577 hospital mergers, out of some 6,000 hospitals in the whole country. Many of these mergers were cross-market, such that the services the hospitals offer were not direct substitutes for each other.
The results for insurers, and thus patients, were bad, whether within a market or across it. Cooper, Craig, Gaynor and Van Reenen (2019) found that markets with monopoly hospitals had prices 12% higher, and that mergers lead to increased prices when the hospitals were nearby. Dafny, Ho, and Lee (2018) found that mergers across markets within a state, but in different markets, increased prices by 7-10%, while mergers across state lines had no such effect. Brand, Garmon, and Rosenbaum (2023) cover the 558 mergers from 2009-2016, and estimate an average price increase of 5%. Brot, Cooper, Craig, Klarnet, Lurie, and Miller (2026) point out that price increases caused by mergers function as a sort of payroll tax, and thus decreases employment among lower- and middle-income workers.
Even with access to incredibly detailed data on the inner workings of the hospitals, we cannot discern any increase in efficiency. (Recall that a merger which does not improve the efficiency of the constituent parts necessarily makes the consumer worse off). Gaynor, Sacarny, Sadun, Syverson, and Venkatesh (2021), which appears to spring out of consulting for an anonymous hospital – a hospital which they then all but name by saying precisely how many locations the two had and when the merger occurred – finds that despite the intended reforms being implemented, there were not apparent improvements in cost.
Yet, the FTC and DOJ could not win a case in a hospital merger to save their life. They lost seven straight merger cases between 1994 and 2001, and eventually just gave up. From 2002 to 2020, out of over a thousand mergers, the FTC took action against 13. Brot, Cooper, Craig, and Klarnet (2024) argue that 200 of them should have been disallowed under the screening tools the FTC was using, and that those predictably anticompetitive mergers increased prices by at least 5%.
The basic problem is that the economics of the courts were out of date. Much hinged on the definition of the market, which need not make sense. In 1998, for instance, the courts ruled that a merger in Poplar Bluff between the only two hospitals in town could go ahead because some patients traveled for care in Cape Girardeau, 60 miles away. (Nevermind if they were just seeing specialists!) And it goes without saying that the FTC didn’t even bother challenging mergers across markets, even though those should have just as much an ability to increase prices as those within a market.
The FTC’s workhorse is the willingness-to-pay approach of Capps, Dranove, and Satterthwaite (2003), which simplifies the question they must answer to “how much does adding this hospital increase demand for an insurance plan?”. With a logit demand model, you can compute that in as quickly as it takes to type a few lines of code and look up the variable names. What Nash-in-Nash bargaining gave the FTC, in the form of Gowrisankaran, Nevo, and Town (2015), was the intellectual legitimacy they needed in order to actually win cases. When you estimate a full Nash-in-Nash bargaining model and use it to evaluate mergers, you get answers which are pretty similar to the simple screen. Christopher Garmon (2017) compared the predicted results with the actual results of 28 mergers, and found that Capps, Dranove, and Satterthwaite’s approach does a pretty good job predicting demand.
I am not an unconditional fan of antitrust action. It can often be misdirected, especially when companies are innovative, and a broad discretionary remit enables a corrupt government to harass political enemies. I am nevertheless confident that antitrust action in the healthcare sector should be more vigorous than it presently is.
We have also seen an increase in vertical consolidation between health insurers and healthcare providers. This modern trend has been due largely to government interference. While early vertically integrated insurers and providers like Kaiser Permanente did this to hold down costs, and hopefully give the customer more coordinated care, the modern trend has been to bilk the government and take advantage of regulations.
First, many elements of Medicare are now privately run. Medicare Advantage insures half of seniors now through a risk-adjustment model. The government pays a subsidy to each insurer equal to the risk score of the patient, in order to make the marginal cost of taking on a customer the same. Insurers thus want the government to think that patients are much sicker than they actually are. The insurance company could strongly encourage the companies they contract with to rate patients as sicker, but it’s a lot simpler to just employ the doctors and have them upcode the patients.
Geruso and Layton (2020) is chock full of smoking guns for this being a problem. Once someone turns 65 and is eligible for Medicare Advantage, their risk scores take a jump. In particular, they are more likely to be coded for chronic conditions where the rating is squishy, diabetes being the biggest one. (There is no objective test for the severity of diabetes, and indeed we shorthand the severity of diabetes to just how much one is being treated for it.)
Even more damningly, this jump in upcoding is bigger when the provider is owned by the insurer. Otherwise identical people are rated 6 to 16 percent sicker when the diagnoser is owned by the insurer.
Among other things, the Affordable Care Act of 2010 required that insurers pay out at least 80% of premiums paid in healthcare claims for individual plans, and 85% for large groups. If the insurance has a year with unusually low claims, they must write a check to all of their policy holders equal to the gap. This Medical Loss Ratio (MLR) completely flips around the incentives for them to hold down costs, as now they actually profit from healthcare costs becoming higher. Cicala, Lieber, and Marone (2019) show that claims simply rose about 1-to-1 with exposure to the new rule. Their source of identification is the persistent variation from year to year across plans in how much they pay out in claims. The plans which were below the threshold in one year tend to be below it the next, and they would surely have continued that way were it not for the change in regulations.
Xiaoxi Zhao (2021) is, to my knowledge, the only quantification of the effect of the medical loss ratio requirements in a structural paper. She starts out with a BLP style random coefficients discrete choice model, which allows her to flexibly estimate substitution, and then does the now standard Nash-in-Nash bargaining, with the difference being that we now check after each iteration what the implied MLR would be. This rules out some low prices as being infeasible, and thus the insurer is now okay ripping up the bargain.
It should be little surprise that this raises prices substantially, although her data is too stylized to be able to say much conclusive about the real world. The work with real data has to hold premiums and demand fixed, which is missing how it will pass through to costs. A hypothetical but realistic example with three insurers, given in Table 8, suggests that the imposition of medical loss ratio laws – which are, I remind you, supposed to benefit consumers – actually lead to prices increasing by 55%!
The two can go together, because owning a provider allows you to directly inflate the medical loss ratio and pass profits into the part you can keep them from. Kakani, Yde, Kanter, Frank, and Bond (2026) document that vertically integrated pharmacies increased prices by 9.5% after the passage of price controls in Medicare Part D, with the insurers more at risk of hitting the caps hiking prices more.
We have established, I hope conclusively, that insurers hold down the price of healthcare through negotiating. This is not always a good thing. High prices can actually be good. Inventing a new drug, for instance, requires an enormous outlay to discover it and get it approved, which must be paid back with higher prices during the life of the patent. If pharmaceutical companies expected for their prices to be reduced by bargaining, then they would not pay the fixed costs to discover drugs at all.
This is a live question. The Inflation Reduction Act of 2022 included provisions to use the bargaining power of Medicare in order to reduce the price of drugs. I note two caveats with that – first, the drugs chosen are very narrow, being a small number of drugs nearing the end of their patent life. These are also “negotiations” only loosely – unlike with doctors, who can simply opt out of seeing Medicare patients without penalty, if you refuse to sell at the price the government sets they will tax what is notionally 95% of the revenue of that drug. (Although in fact, they define the tax base in such a way that they include the post-tax price, so the real rate is 1,900%.) Such price controls will reduce the amount that we spend on drugs. Will it then reduce the innovations we get?
The literature is extremely clear on this subject. Investment into innovation, and thus innovation, is directly related to the expected profits. Acemoglu and Linn (2004) is an early test of this. They argue that the demographics of a particular country is completely unrelated to where the technological frontier happens to be. As a population gets older, they demand more drugs which cure the diseases of the old. It seems unlikely that it just happened to be that drugs for gout just happened to get easier to find when the population gets older and fatter! They estimate an elasticity of 4 – for every 1 percent increase in market size, you get a 4 percent increase in the number of new drugs.
This is across categories, which raises the concern that the inputs to pharmaceutical innovation are inelastic. Even still, there is a substantial response of innovation to market size reallocating resources from non-drug to drug uses. Dubois, de Mouzon, Scott-Morton, and Seabright (2015) have an elasticity of .23, implying .23% more drugs for every 1% increase in market size. Basically all estimates of the elasticity lie between these two.
Medical devices, like stents and pacemakers, have a similar response. Ji and Rogers (2024) take advantage of Medicare cutting funding for some categories of medical devices to link a 61% decrease in spending to a 75% decrease in patenting. Going in reverse, Parker Rogers (2025) finds that decreasing the cost of approval for new devices increases both the quantity and quality of new devices. It is simply not reasonably contested that innovation responds to expected profits.
The natural next question is whether these innovations are something worth paying for, and the answer is yes. The social value of pharmaceuticals is incredibly high. To take an example, statins, which protect against heart attacks and strokes, had a social value of 1.25 trillion over 20 years, saving 40,000 lives. Not all drugs are statins, but the average value of a drug is something around $7-17 billion. Murphy and Topel (2003) famously estimated longevity gains since 1970 due to drugs at $320-480 billion a year. This is not just for the benefit of the world, which is able to free ride off the United States, and is not dependent on the choice of an extremely low discount rate, although of course caring more about the future or about other people will increase the social value.
We really can’t get cute with it. An alternative proposal is to tie the rates that we in America pay to those overseas. Drugs are much more expensive in America than in the poor, developing world, but also in the rest of the developed world too. This, too, however, would not be a good idea. The practical upshot is not that they sell drugs for less in America, or even that they increase the prices overseas. The result is that they do not sell the drug overseas at any price. Dubois, Gandhi, and Vasserman (2022) work out the consequences of reference pricing with just Canada, and find that it will not meaningfully decrease prices in America, while greatly increasing them in Canada. It goes without saying that what might be affordable in Canada is prohibitive in Nigeria.
I am quite skeptical, however, that the same innovations happen with healthcare delivery. We can test this! Clemens and Gottlieb (2014) find that an increase in pay by 2% leads to an increase in service provision of 3%, but with no change in mortality or health outcomes. The response of medical procedures to changes in funding is derisory, and they mostly arise out of hobbyist iteration. (See Dranove, Garthwaite, Heard, and Wu (2021) for more on this point).
This suggests a division of responsibilities. Where high costs are rents, as is the case for doctors, companies should bargain aggressively, and the government should bargain hard too. If there is substantial room for innovation, however, companies and the government must be mindful of the long run, and not mortgage the future for the benefit of the present.
This essay was several things in one: a partial exposition of the American healthcare system, an introduction to the Nash-in-Nash bargaining literature, comments on antitrust policy, and a digression into the effect of funding on innovation. I confess to jamming in some things which I had wanted to talk about for a while, but for which I could not sustain a full article. Your takeaways are for you to decide, but to the extent I can influence them, I would like to give some parting comments.
Most people’s understanding of healthcare is upside down. To them, the insurers are the great villains. It is they, after all, who are responsible for taking your payments, and who turn down claims for treatment. They are not friendly doctors, but bureaucrats. Nevertheless, our impressions are often wrong, and they are wrong here. The insurance company is more like a union. You may not always like a union, and they do not benefit everyone, but on the whole they increase your wages. Health insurance companies bargain over who is in the payment network, and in doing so hold prices down. Restricting the quantity simply allows you, the consumer, to capture more of the gains by replicating a lump sum transfer.