ESSAY PRACTICE Applied
Economics - Healthcare
1. How do you detect adverse selection in the health insurance
market? Provide an example from the literature.
Adverse selection in the health insurance market means that before
insurance, people may have different risk types in terms of health risk.
With this in mind, adverse selection may cause people with greater
hidden health problems to buy health insurance whereas generally
healthy individuals are less likely to buy health insurance. As a result,
those who are buying insurance are more likely to get sick and claim
insurance. For insurers, this can become quite expensive and cause
problems in the health insurance market. With adverse selection,
insurers cannot observe who is less healthy, this information is assumed
to be private to each patient. If we detect adverse selection in the
insurance market then this proves the need for policy/ government
intervention. In order to detect adverse selection we can use the
positive correlation test.
2. What is meant by a positive correlation test? What does this
aim to show? To what extent is this method reliable? What
issues have been displayed in the literature?
The positive correlation test is used to detect adverse selection.
Adverse selection implies that the average cost of insured individuals is
higher than the average cost of the uninsured. We can detect this by
testing whether the Marginal Cost (MC) curve is downward sloping,
comparing the expected cost of those with insurance to those without or
to those with less insurance. If there is a positive correlation between
insurance coverage and expected costs then there is adverse selection.
In the literature there have been mixed results. There is evidence
consistent with adverse selection but there is also evidence that
discovers that there is advantageous selection instead and some
studies couldn’t even reject the null hypothesis that there is no
difference in average costs between those insured and those uninsured.
A finding of no correlation could mean that there exists moral hazard
and advantageous selection, offsetting each other, it is difficult to know
what is happening in reality. As a result of this, the positive correlation
, test isn’t proving to be very consistent with the hypothesis that there is
adverse selection in insurance markets.
3. What is the difference between moral hazard and adverse
selection? Why are they difficult to distinguish empirically?
Using the positive correlation test and comparing expected costs across
individuals with and without insurance may confound adverse selection
and moral hazard. Both adverse selection and moral hazard can
generate a positive correlation between insurance coverage and claims
but both types of asymmetric information have very different
implications for public policy. Moral hazard predicts that all individuals
are homogenous in their risk aversion and risk type before getting
insured. However, once people are insured to cover their spending on
healthcare, they are less likely to be cautious and watch their spending
on healthcare. As a consequence, there is an increase in demand for
healthcare and a rise in costs to insurers. We see this effect on the NHS
in the UK with the NHS becoming very overwhelmed with demand for
healthcare. People can also become less thoughtful about adopting
healthy behaviours due to the fact that they no longer have to face the
healthcare costs if they do not look after themselves. Coinsurance
policies can usually reduce this pressure.
In the Oregon experiment (Baicker et al, DATE) found that participants
who paid a share of their health care used fewer health services than
the group of participants who received free care. Cost sharing reduced
the use of both highly effective and less effective services in roughly
equal proportions but it didn’t significantly affect the quality of care
received by patients. They also found that free care led to
improvements in health where cost-sharing did not, and these
improvements were concentrated among the sickest and the poorest
patients. Thereby, it is debated whether coinsurance can actually solve
the moral hazard market failure. Policy makers have much difficulty
solving this problem in comparison to adverse selection.
We can test for moral hazard using the following diagram:
INSERT FIGURE 6
Figure 6 shows a graphical representation of an insurance market with
moral hazard but no selection. The lack of selection is captured by the
flat MC curves. Moral hazard is measured by drawing a separate MC
Economics - Healthcare
1. How do you detect adverse selection in the health insurance
market? Provide an example from the literature.
Adverse selection in the health insurance market means that before
insurance, people may have different risk types in terms of health risk.
With this in mind, adverse selection may cause people with greater
hidden health problems to buy health insurance whereas generally
healthy individuals are less likely to buy health insurance. As a result,
those who are buying insurance are more likely to get sick and claim
insurance. For insurers, this can become quite expensive and cause
problems in the health insurance market. With adverse selection,
insurers cannot observe who is less healthy, this information is assumed
to be private to each patient. If we detect adverse selection in the
insurance market then this proves the need for policy/ government
intervention. In order to detect adverse selection we can use the
positive correlation test.
2. What is meant by a positive correlation test? What does this
aim to show? To what extent is this method reliable? What
issues have been displayed in the literature?
The positive correlation test is used to detect adverse selection.
Adverse selection implies that the average cost of insured individuals is
higher than the average cost of the uninsured. We can detect this by
testing whether the Marginal Cost (MC) curve is downward sloping,
comparing the expected cost of those with insurance to those without or
to those with less insurance. If there is a positive correlation between
insurance coverage and expected costs then there is adverse selection.
In the literature there have been mixed results. There is evidence
consistent with adverse selection but there is also evidence that
discovers that there is advantageous selection instead and some
studies couldn’t even reject the null hypothesis that there is no
difference in average costs between those insured and those uninsured.
A finding of no correlation could mean that there exists moral hazard
and advantageous selection, offsetting each other, it is difficult to know
what is happening in reality. As a result of this, the positive correlation
, test isn’t proving to be very consistent with the hypothesis that there is
adverse selection in insurance markets.
3. What is the difference between moral hazard and adverse
selection? Why are they difficult to distinguish empirically?
Using the positive correlation test and comparing expected costs across
individuals with and without insurance may confound adverse selection
and moral hazard. Both adverse selection and moral hazard can
generate a positive correlation between insurance coverage and claims
but both types of asymmetric information have very different
implications for public policy. Moral hazard predicts that all individuals
are homogenous in their risk aversion and risk type before getting
insured. However, once people are insured to cover their spending on
healthcare, they are less likely to be cautious and watch their spending
on healthcare. As a consequence, there is an increase in demand for
healthcare and a rise in costs to insurers. We see this effect on the NHS
in the UK with the NHS becoming very overwhelmed with demand for
healthcare. People can also become less thoughtful about adopting
healthy behaviours due to the fact that they no longer have to face the
healthcare costs if they do not look after themselves. Coinsurance
policies can usually reduce this pressure.
In the Oregon experiment (Baicker et al, DATE) found that participants
who paid a share of their health care used fewer health services than
the group of participants who received free care. Cost sharing reduced
the use of both highly effective and less effective services in roughly
equal proportions but it didn’t significantly affect the quality of care
received by patients. They also found that free care led to
improvements in health where cost-sharing did not, and these
improvements were concentrated among the sickest and the poorest
patients. Thereby, it is debated whether coinsurance can actually solve
the moral hazard market failure. Policy makers have much difficulty
solving this problem in comparison to adverse selection.
We can test for moral hazard using the following diagram:
INSERT FIGURE 6
Figure 6 shows a graphical representation of an insurance market with
moral hazard but no selection. The lack of selection is captured by the
flat MC curves. Moral hazard is measured by drawing a separate MC
