Will Price Transparency Cure America's High Healthcare Costs?

Why more information is not always better

In healthcare, most patients are not going to know the price of the services they buy. It’s paid for by insurance, with the exact price being decided via negotiation between the big companies. You could not extract from the hospital an exact list of what every procedure will cost before you purchase it. In fact, medical providers will actively resist telling you this – good luck asking how much a test will cost, and whether you really need it!

Many people have suggested that this might be a cause of America’s high health care prices. Accordingly, there have been several initiatives to improve price transparency in healthcare markets. In particular, the CMS required that all hospitals in America provide a machine-readable file of all items and services beginning in 2021. Surely, this will reduce healthcare costs?

We cannot be so sure that this is the case, though. The relationship between information and market outcomes does not have a ready, reliable relationship, and could go either way depending on the circumstances. Suppose there are several sellers of a homogenous good. Under Bertrand competition – which is to say, prices are set first – the price will equal marginal cost. The only way for them to get positive profits is to collude on setting higher prices. Stigler (1964) argued that the great enemy of such collusive behavior are secret price cuts; Green and Porter (1984) showed that collusion is still possible even when you cannot observe prices and quantities, by treating every decrease in demand for your own product as a breakdown of the agreement, but it makes the agreement fragile and imperfect. What the colluders would really like is to know exactly how much every other firm produces and what price they sell it at, even though it’s not in their interest to unilaterally reveal it. Government interventions in favor of price transparency allow the firms to get around their collective action problem – and that’s bad for the rest of us.

There’s a cool case study of the ready-mix concrete industry in Denmark which is relevant here. Concrete – note, not cement – is an extraordinarily non-transportable good. Once you mix it together, it starts hardening, and it must be delivered to the job site within an hour. This means that each concrete mixing plant can serve only a small area, and while there are many plants, each market serves only a small area. In 1993, the Danish government decided that the status quo – rigid posted prices, but frequent secret discounts to bulk buyers – would not do, so they required that each quarter the concrete firms place their prices in the industry newsletter. Prices immediately rose by about 15-20%, and stabilized at a high level. What’s more, there was no more dispersion of prices, strongly suggesting that the firms were using the information to converge upon the same price. Not without some embarrassment, the Danish government repealed the law in 1996.

But what is strange is that it can actually go the other way. Consider the case of the gasoline market. In gasoline, above competitive prices are maintained not through direct collusion at a permanently high level, but through an Edgeworth cycle (1897), which was formalized by Maskin and Tirole (1988). In the simplest case, suppose that there are two firms who set their prices every other day, trading whose day it is back and forth. Suppose the good can be produced at no marginal cost with no cost of storage, and that the optimal price for their own welfare would be 1. Both start with a price of 0, and make no profit. It is therefore a matter of indifference to a firm if their price is at 0 or 1, so one firm chooses to raise their price and goes from 0 profit to 0 profit. The point is that the other firm will follow them by setting their price at just a hair under 1, so that you can undercut them the next day just a hair under what they set, and back and forth until you go down to 0 and the cycle resets. It looks like this:

This figure is from Byrne, de Roos, Lewis, Marx, and Wu (2025) with data from the Australian gasoline market. The gasoline companies received the prices of their competitors, updated every 15 minutes, from a company called Informed Sources. The Australian competition authority disliked this, as they thought that the detailed information was allowing the firms to converge upon supracompetitive prices, and began an investigation. One of the firms, Coles, was in a weaker position and chose to settle before the others, leading to them withdrawing from the platform. The others, which settled later, did not withdraw. Thus, Coles would

What happened is that when Coles raised its prices, enabling others to just undercut it, they would not be able to cut as aggressively as before. Prices were actually higher, and the cycle would last longer. You can see the red line of Coles above the others, and persistently so.

From their calculations, the uninformed firm actually profited, though not as much as the others did. This is in line with other work showing how, when having algorithms setting your price, knowing less about the demand for other products actually allows you to converge to supracompetitive prices when knowing more would lead to the competitive outcome. Cooper, Homem-de-Mello, and Kleywegt (2015) compare what happens when firms don’t know the demand for their own products, and try to discover it with random price experiments. Quantity demanded is dependent upon both one’s own prices, and also on the prices of competitors. Yet, including the prices of competitors into a simple regression model will be less profitable, because it leads to firms converging toward the competitive price, rather than the joint profit maximizing price which observing only your own prices would lead you too. The firm would prefer to be blinded to its opponents’ prices!

Providing information to one party, even in secret, need not necessarily be to their advantage. Zoe Cullen and Bobak Pakzad-Hurson have a paper examining the effects of “Right of Workers to Talk” laws, which establish a protected right of workers to discuss with their fellow workers at a firm what their wages are. It led to an immediate drop in wage by about two percent, concentrated among educated workers who are more likely to have meaningful negotiations about their wages. The channel was that now the firm could credibly say that they can’t pay you anything more, because if they did, they would have to pay everyone else that too. The workers would be better off if they couldn’t know what other people made at the firm, yet they all individually benefit from peeking.

The effects of knowing wages within a firm are not the same as knowing wages across firms, of course. Arnold, Quach, and Taska (2025) exploit legal changes in Colorado requiring that workers include salary information in online job postings. They have data from Lightcast (formerly BurningGlass), which is pretty transparently scraped data from Linkedin, and find that wages increased by 1.3-3.6%. Notably, pay in jobs which were already transparent increased afterwards, consistent with much of the effect being from enabling competition.

I bring these up to illustrate that the relationship between information and prices is incredibly context dependent, and does not imply that more information is always better. So, after the long detour, we return to our original example. Should we mandate price transparency in healthcare markets, or should we not?

On net, it appears that price transparency laws reduce healthcare prices. What is striking, however, is that it does not appear to be due to consumers shopping around, but entirely due to changing the bargaining power of insurers. When price transparency affects prices, it is through the channel of insurers being better informed about the deals that other insurers are getting, and pushing for tougher prices. We need not expect this to raise welfare – and indeed, we could even expect it to reduce it.

One key line of evidence comes from New Hampshire, who was the first state to mandate hospitals report prices for standard imaging tests like X-rays and CAT scans, and whose website for them has been consistently considered the best in the union since then. First Zach Brown (2019) and then Yujie Feng (2025) study the effects on prices, which did indeed fall. However, Feng shows that this is due to the insurers, not consumers. She can show this because only some insurer-procedure combinations get listed (their rollout is staggered), but all of them fall, whether listed on the website or not.

This corresponds with how infrequently the price transparency websites were actually used by the consumer. Given the dispersion of rates, and the fact that checking is free, consumers must be leaving a lot of money on the table by not checking. And yet, they consistently don’t check. Between 2011 and 2013, only 1% of state residents actually used the website. When the state advertised their service, it led to a 700% increase in the number of people using it, yet had no effect on people’s use of lower cost providers. It is worth noting that the results of Kwon and Zhang (2025), who use a similar website in Maine, and do attribute some of the decline to shopping around. It doesn’t fit the narrative, though, so let’s skip over it.

By contrast, when the prices being revealed are more relevant to the shopping consumer, it actually increases prices. Barnes, Glied, Handel, and Kim (2024) conducted an experiment in New York on price transparency in billed charges, which are not the same as the negotiated rates, and are much more relevant to out-of-network consumers who will be actually paying much more of their own bill. Here, they found that prices became less dispersed, and the average price paid actually went up, consistent with some of the providers learning that they were charging too little relative to their peers.

Supposing that price transparency does bring down prices through the insurance channel. How sure are we that this is a good thing? Consider a bilateral monopoly, where one insurance company negotiates with one healthcare provider over the optimal set of payments. The solution is given by the gains from trade, the outside options available, and the bargaining weights to each (which is a reduced form parameter, but you can think of it as the discount rate of each), which gives us a unique division of surplus. If price transparency changes the bargaining weight of the insurer, this has no effect on the price paid by the consumer. This remains the same monopoly price before and after.

Now I must not be too pessimistic. A good deal of the gains from this will be passed on to the consumer, although I do not know how much. I would not say that I am an unquestionable expert here, but I cannot find an estimate of the effect of a shock to bargaining weights alone on premiums. However, I can say with great confidence that it will not “cure” America’s high healthcare prices.