College aantekeningen
Lectures Competition in Health Care Markets
This document summarizes all lectures, including the pre-recorded lectures, of the course Competition in Health Care Markets for the academic year .
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Voorbeeld van de inhoud
Lecture 1 (07-02-2022) – The Industrial Organization of Health Care MarketsIn the health care sector, we need a combination of market incentives and government regulation.
Competition in itself is never a good policy instrument to solve problems in health care, but neither is
government regulation. In industrial organizations, which are also found in the health care sector, it is the
combination of competition and government regulation that might solve the problems faced by these
organizations. If markets do not function properly, they can affect the three public goals in health care
(accessibility, quality, and affordability of health care).
An example of a non-horizontal (vertical) merger is the acquisition of providers of care by an insurer.
Another example is hospitals and physicians who integrate.
In a market with perfect competition, the outcome is exactly as a society wants it to be. In other words,
there is no social loss. Theoretical requirement for perfect competition are transparency (perfect
information), free entry and exit, a large number of buyers and sellers (such that none of them has any
market power), and homogenous goods and services. The reasons why these requirements are not
fulfilled in the health care sector are as follows:
• Standard (homogenous) products; there are a lot of different treatments and different locations.
• Price-taking behavior; there is not a market-driven price that cannot be influenced by either
buyers or sellers.
• Free entry and exit; there are entry barriers to prevent that everyone (without the right
qualifications) can act as a doctor.
• Perfect information; there is asymmetric information because some parties are better informed
than others.
The fact that the four requirements for perfect competition are not fulfilled in health care means that we
need government regulation. Governments, however, are also far from perfect, for instance because they
are influenced by lobbyists and they focus more on short-term (rather than long-term) issues for political
reasons. Improving the functioning of health care markets is all about the question how to navigate
between market failure and government failure.
Gaynor, Ho, and Town (2015) introduce a multistage model. They discuss the empirical and theoretical
literature on industrial organizations in health care, structured around five stages. Each stage has an
impact on the equilibrium outcome and welfare. Stages are also related to each other; optimal choices in
one stage are functions of expectations regarding the rest. The five stages are as follows:
• (1) Quality determination in provider markets.
• (2) Price and network determination in provider markets.
• (3) Premium determination in insurance markets.
• (4) Consumer choice in insurance markets.
• (5) Incentives and provider referral decisions/consumer utilization.
In this lecture we focus on the first stage; quality determination in provider markets. From a theoretical
perspective, the question is how hospital competition could affect quality. It is very unlikely that hospitals
strategically choose for lower quality (in order to be able to ask lower prices). It is more likely that health
care providers put lower effort in improving quality in more concentrated hospital markets (with fewer
providers who have a larger market share, such that there will be less competition in this market) than in
,less concentrated hospital markets. Quality is increasing in price, the elasticity of demand with respect to
quality, and the firm’s total demand. Quality is decreasing in the marginal costs of quantity or quality. In
markets with regulated prices, if the regulated price is high enough and patients respond to quality
differences, then providers have an incentive to improve on quality and more competition will result in
higher quality. In markets where hospitals set both prices and quality, it is unclear what happens to quality
if competition increases because it depends on the price elasticity of demand and the quality elasticity of
demand. Quality will increase if the quality elasticity of demand increases or the price elasticity of demand
declines (and vice versa). Quality will also increase if price increases relative to the marginal cost of quality
(and fall if the opposite happens). For these kind of markets, research should reveal what happens. There
are two approaches here:
• Structure-Conduct-Performance (SCP); you use an indicator of market structure, such as the
Herfindahl-Hirschmann Index (HHI) for the degree of market concentration. The HHI is the sum of
the squared market shares; the closer it is to 10,000, the more concentrated and less competitive
the market is.
• Event study; you take a closer look at the difference between periods t-1 and t+1, where there is
an event in between. You identify an event and control group which you expect to be differently
affected by the policy change.
When looking at empirical literature on markets with regulated prices, some researchers have found that
competition is associated with lower mortality. Others found that mortality is lower in less-concentrated
markets. When looking at empirical literature on markets where hospitals set both price and quality,
researchers have found that differences in mortality for hospitals in areas with competitors and those
with no competitors were higher during periods when competition was promoted compared to periods
when competition was discouraged. It appeared that hospitals in competitive markets reduced
unmeasured and unobserved quality in order to improve measured and observed waiting times.
Roos et al. (2020) conclude that, overall, their results lend provisional support for the conclusion that
permitting price competition among Dutch hospitals did not negatively impact quality. Three potential
explanations for this ‘null effect’ are the following:
• The focus on readmissions misses effects on other types of quality. An example of another quality
indicators that could have been used is patient-reported outcome measures (patient satisfaction).
• The empirical strategy delivers a lower bound on the quality effect.
• Dutch hospitals, which are all not-for-profit, were not prepared to grasp a competitive advantage
obtainable by cutting prices if this required skimping on quality. Dutch hospitals were allowed to
make a profit, but they could not distribute it to shareholders. In other words, hospitals were not
forced by shareholders to increase profits. Without a strong financial incentive, why would you
want to strive towards lower prices if this means skimping on quality?
Lecture 2 (08-02-2022) – Network Formation and Price Negotiations
In the previous lecture, we described the five stages that are being discussed in the paper by Gaynor, Ho,
and Town (2015). In this lecture, we focus on the second stage; price determination in insurance markets.
,There are four key distinguishing features of healthcare that affect bilateral negotiations (which are
negotiations between two parties):
• Set of available hospitals; insurers and hospitals negotiate about inclusion in a health insurance
plan. Patients can only go to contracted hospitals (or they must pay for non-contracted hospitals
by themselves).
• Prices for patients; insured patients do not pay the full price of a treatment.
• Timing of choice; patients choose a health insurance product before they get sick.
• Different incentives; health insurers want to charge the lowest premiums, while hospitals want to
provide high-quality care.
The papers that will be discussed in this lecture treat quality as given. If we are looking at network
formation, the papers also treat price as given. If we look at price negotiations, they usually treat network
formation as given. This is because it is difficult to incorporate the dynamic effects into models.
The paper by Ho (2006) is about a situation in which there are initially four contracted hospitals, but then
insurers leave out one of them. The key question of the paper is: “What is the effect of restricted hospital
choice on consumer welfare?” The author follows three steps:
• (1) Demand for individual hospitals given hospital characteristics. To which hospitals is a patient
going? The author knows the characteristics of the hospitals, such as distance to patient. With
this information, she can assess what patients value when choosing a hospital.
• (2) Utility for networks (combination of individual hospitals). In this step, she calculates the
demand for networks.
• (3) Demand for health plans given plan characteristics (including the value of consumers over
network). Here, the left-hand side of the equation is demand for a particular health plan, while
the right-hand side of the equation is plan characteristics.
Utility is usually measured by means of willingness-to-pay (which is here defined in terms of premiums);
how much are patients willing to pay less or more for the ex- or inclusion of a certain hospital in the
network? Ho (2006) finds that consumers care about hospital networks; a plan’s market share would be
predicted to decrease if it decided to exclude hospitals. The welfare effect (which is the difference
between what consumers and producers get from a move from a restricted network to an unrestricted
network) is positive. This results in the question why insurers would not offer an unrestricted hospital
choice. One reason could be that the author keeps prices (premiums) fixed, while this is not a realistic
assumption.
The paper by Ho (2009) is concerned with two key questions: How do plans and hospitals choose their
networks? And what determines the division of profits generated by each contract? She models the
procedure in five stages:
• (1) Hospitals make price offers to plans.
• (2) Plans choose their hospital networks.
• (3) Plans set premiums.
• (4) Consumers and employers jointly choose plans.
• (5) Sick consumers visit hospitals; plans pay hospitals per service provided (if the hospital is
included in the network).
, What determines demand for a certain hospital (to be included in the plan)? One could think of hospital
costs and the demand for a certain hospital by patients. The author distinguished three types of hospitals
that are even more likely to be included in a health insurance plan: hospitals with a ‘star’ status (those
hospitals for which patients have a certain preference), capacity constrained hospitals (hospital that, by
nature or choice, are constrained in their capacity; if they know that they are valued by patients, then
they can use the capacity constraints as a bargaining tool), and system hospitals (hospitals that are merged
into systems, for instance two hospitals have been merged; they can threaten insurers that they do not
get a contract with any of the hospitals if they do not want to contract all of them). The author finds that
hospitals in systems take a larger fraction of the surplus and also penalize plans that do not contract with
all members. Hospitals that are very attractive to consumers (star hospitals) also capture high markups
and hospitals with higher costs per patient receive lower markups per patient than other providers.
Providers would also benefit by limiting their bed numbers in order to become capacity constrained. She
concludes by arguing that the major causes of the reduced leverage of health insurers suggested by a
number of recent papers are a rising consumer demand for choice and an extensive consolidation of
hospitals, resulting in increased market power. Capacity constraints are also mentioned as a source of
hospital leverage.
As mentioned earlier, many papers treat quality, price, or network formation as given. What happens if
we relax (some of) these assumptions? Ho and Lee (2019) argue that it is critical to accurately account for
premium adjustments in response to quality adjustments by insurers. They find that if premiums were
instead fixed and not allowed to adjusted when networks changed, consumers would always be harmed
by any exclusion.
Ho (2009) argued that star hospitals are known as centers of advanced medical treatment and research
but, partly as a result, also quite expensive. Shepard (2016) also looks at star hospitals and finds that by
excluding them, insurers limit access to top provides but by doing so reduce costs by steering patients to
cheaper hospitals. However, insurers’ incentives to balance this cost-quality trade-off can also be
influenced by selection. Typically, economists assume that selection occurs because generous plans
attract people with high medical risks. But in addition to medical risk, some consumers have high costs
because, for a given illness, they tend to use more expensive providers. The author finds that plans
covering star hospitals face adverse selection on both of these cost dimensions: they attract both the sick
and people who tend to use star hospitals for their care. Both of these selection channels discourage
insurers from covering star hospitals and can lead to inefficiently low access to them.
An alternative to narrow networks is tiered networks. Tiered networks are networks with price
discrimination. For instance, in tiered networks, insured people pay higher copayments or higher
deductibles for more expensive hospitals that are included in the network.
Gowrisankaran, Nevo, and Town (2015) first set out a negotiation game. The key questions are: To what
extent do bargaining and patient coinsurance influence pries? And what is the impact of hospital mergers
and policy remedies on prices? If two health providers want to merge, which might harm competition,
then regulators can set remedies to combat this anticompetitive behavior. The hospital-insurer bargaining
is modelled as a two-stage game. In the first stage, hospitals and insurers negotiate terms. In the second
stage, sick patients choose hospitals for their treatment. In merger analysis, you estimate the change in
the willingness-to-pay resulting from the merged entity being part of the network as compared to a
situation where each hospital is in the network separately. The model then attempts to predict the change
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