Health Insurance

The Boston Globe reports that “Overseers of Massachusetts’ trailblazing healthcare program made their first cuts yesterday, trimming $115 million, or 12 percent, from Commonwealth Care, which subsidizes premiums for needy residents and is the centerpiece of the 2006 law.”  The reduction in the Commonwealth Care was caused by the bad economy.  Not only does a bad economy mean fewer tax revenues as earnings are cut, but demand for government health insurance grows as laid off employees lose employer provided care.

Opponents of government health plan may use this as evidence that government-run health care can’t work.  This is not the case however.  In a bad economy with private insurance, workers lose coverage when they lose their jobs.  If they do decide to purchase a nongroup health insurance plan, they will likely choose a less expensive plan.  Thus a bad economy effects individuals similarly with and without government provided health insurance; with fewer resources to go around everyone must cut medical expenditures irrespective of whether there is a government-provided health plan.  

The difference between the less generous insurance benefits is who decides on the cuts.  In a free market plan, individuals decide for themselves how much insurance to buy.  However, for some individuals who lose their jobs, health insurance will be unaffordable.  On the other hand, bureaucrats determine what will be cut in a government health plan.  

Democrats will argue that mediocre insurance for all is better than great insurance for some and none for others.  Republicans will claim that a government-run healthcare system will necessarily lead to mediocre insurance coverage in any bad economy. Further, legal immigrants may not be eligible for Commonwealth Care in order to save money.  Thus, there will still be individuals without insurance.

Who perspective do you think is right?

• Step 1: Create revolutionary medicine.
• Step 2: Patent the medicine to collect in the profits.
• Step 3: When the patent expires, combine the drug with another in order to extend its patent life and keep profits high.

Most people believe that Step 1 and 2 are ok.  The companies who make revolutionary drugs should be compensated for their R&D expenditures.  But simply repackaging their products to extend patent life?  This seems extremely inefficient.

This is exactly what GlaxoSmithKline did with its  migraine pill Imitrex, whose U.S. patent ran out in February.  It combined Imitrex with naproxen (a non-prescription anti-inflammatory drug) to form a new patented medicine: Treximet.

This time, doctors and insurers may be wising up.  Bloomberg reports:

…doctors and insurers object to the cost, calling the pill no better than the two generic drugs it combines…The expense of those two generics together may soon fall to $5 a dose, said Time Heady, chief of the pharmaceutical solutions unit for UnitedHealth Group Inc., which refuses to pay for Treximet on most of its plans…”There are instances where drugs are being brought to market that really aren’t different or offering any real benefit from a clinical or cost perspective. In those instances, it makes sense not to cover the drug at all.”

No sense at all.

From the USA Today, here are the wait times to see a doctor in the following cities:

• Boston: 49.6
• Philadelphia: 27
• Los Angeles: 24.2
• Houston: 23.4
• Washington, D.C.: 22.6
• San Diego 20.2
• Minneapolis: 19.8
• Dallas: 19.2
• New York: 19.2
• Denver: 15.4 days
• Miami: 15.4 days

The first thing that jumps out from these numbers is that Boston has by far the longest wait to see a doctor.  Is this caused by the universal health coverage enacted in Massachusetts?  The answer is maybe.  Physician supply adjusts slowly (i.e., it takes a long time to finish med school).  On the other hand, Massachusetts decision to increase insurance coverage lead to a spike in the demand for medical services.  Thus, universal health care may have caused the run up in wait times, but this phenomenon may be short lived.  Physicians may migrate to Massachusetts as insurance coverage becomes more available.  

Do wait times reflect quality of care?  If Boston residents have very short waits to see nurse practitioners or physicians assistants, this could be a cost-effective substitute for services provided by physicians in the primary care setting.  Further, longer wait times for specialists could be a good thing.  While longer wait times would certainly hurt some patients–likely the most seriously ill patients–it would discourage other patients from waiting to see a specialist.  This patients could, instead, forego treatment if had a low marginal benefit to begin with or they could rely on their primary care provider.  

Let’s dig deeper into the numbers (see original report):

• Wait times for Boston cardiologists decreased from 37 days in 2004 to 21 days in 2009.  
• Wait times for Boston orthopedic surgery increased from 24 days in 2004 to 40 days in 2009.  
• Wait times for a Boston ObGyn increased from 45 to 70 days between 2004 and 2009 in Boston.
• Wait times for a Boston Family Practice physician was 63 days in 2009. 

We see that after the Massachusetts health reform was enacted, there was no uniform effect on specialist wait times, but there was a large increase in wait times for primary care providers.  This could be explained by a number of phenomenon:

• Those who gained health insurance after the Massachusetts health reform were a healthier population and used their new insurance coverage to increase the number of primary care visits, but not specialist visits.
• After the Massachusetts health reform, the increase in demand was homogenous across primary and specialty care.  However, physician supply adjusted.  Specialist may have been more attracted to practicing in Massachusetts, but primary care doctors were not.  Specialists may have moved to Massachusetts in larger numbers, particularly if New England health plans reimburse specialists at a much higher rate.  
• This could be a statistical anomaly.  Sample sizes in were less than 20 for five specialities in Boston.

Whatever the case, further study is needed to understand how health insurance expansions affect waiting times in both the short- and long-run.

Using data from the 2004 and 2006 Health and Retirement Survey (HRS), Levy and Weir (2009) analyze the take up of Medicare Part D after its enactment on Jan 1, 2006.  They find that in 2006 only 7% of seniors lacked drug coverage compared to 24% in 2004.  It seems that Medicare Part D caused this large increase in drug coverage.

Medicare Part D Eligibility

Medicare Part D eligibility can be defined as follows:

• Medicaid-covered Medicare beneficiaries (”dual eligibles”) were automatically enrolled in both Part D and a means-tested subsidy. 
• Individuals with other coverage–usually through their employer–were instructed to keep their coverage.
• Medicare Advantage (MA) plans had to offer drug coverage after Part D was implemented. Many of the MA plans, however, already had included drug benefits in their benefits package.
• Individuals with private, non group insurance or those without prescription drug insurance had to decide whether or not they wanted to enroll in a Part D plan.

Results

The evolution of senior drug coverage is shown in the following table.  After the implementation of Medicare Part D we see that  7% of seniors lacked drug coverage compared to 24% before part D.  Why didn’t these 7% take up Medicare Part D?  Are they uneducated?  Are not native English-speakers? It turns out that they just have low demand for prescription drugs.  ”Those with low levels of education or income were no less likely to enroll in Part D than were beneficiaries with more education or income.”  Also, the authors find that 41% of individuals who didn’t take up Medicare Part D said they didn’t need any medications. 

Crowd out

Did part D crowd out employer drug coverage?  We did see  employer drug coverage drops from 40% in 2002 and 2004 to 37% in 2006.  However, individuals who have employer-provided drug benefits were almost just as likely to retain these benefits in 2004 as in 2006.  The authors argue that “while this does not rule out the possibility that some individuals dropped employer drug coverage because of Part D, it suggests that most new Part D enrollees are coming from individuals who would have remained uninsured or purchased Medigap in the absence of Part D.”

• Levy, Helen and Weir, David (2009) “Take-Up of Medicare Part D: Results from the Health and Retirement Study” NBER WP #14692.

In February, I discussed a cross border insurance plan that covers doctors visits, but not catastrophic medical issues.  Now the No Insurance Club (NIC) offers similar coverage in the U.S.  According to the firm’s press release, “patients receive up to 12 office visits per year that also include immunizations, $4 or less in-office prescriptions, and additional services including blood tests” with no deductibles and no co-pays.  The site claims that there is no premium but the one-time “membership fee” of $480/year is in essence a premium.

Healthcare Economist’s Take

I am not sure why these plans would be popular.  Since NIC does not cover catastrophic medical care, the enrollees do not receive the main benefit of insurance–protecting individuals against high cost, low probability events.  Consumers may like these basic medical plans since they are in essence forced savings; individual must pay the annual fee and after that do not have to worry about paying for future’s doctor’s visits.

It is also possible that NIC negotiated lower rates with doctors.  Instead of significant administrative costs inherent in the insurance claims systems, doctors may take a lower fee to receive a “retainer” from NIC.  Thus, the plan may provide a significant cost advantages for the uninsured over just paying for doctors visits yourself.  A final question is whether or not these doctors are of high quality.

Regardless of the merits of this type of insurance, it is nice to see new products emerging to meet consumers needs.

The answer: sometimes.  A study by Dafny (2009) finds that in markets with ten or more insurers, markets do seem to be competitive.  However, with six or less insurers, health insurers do have some market power due to switching costs.

“Using data on ‘fully insured’ health plans offered to employees of 184 publicly-held firms in over 100 geographic markets in the United States for the years 1998 to 2005, she finds that increases in company profits are associated with increases in health insurance premiums, but only in geographic markets served by fewer than ten major insurance carriers. In the most concentrated markets — those with six or fewer carriers – a 10 percent increase in company profits is associated with a 1.2 percent increase in health insurance premiums..

Further analysis suggests that in order to get lower rates, employers must be willing to change health plans. A plan switch is a ‘tough sell’ in good times because employees must identify in-network providers, transfer medical records, and figure out the claims reimbursement system. The data reveal that employers are ‘especially reluctant to drop health plans when profitable, a finding that supports the hypothesis that profits act to raise employers’ switching costs.’“ 

I would predict that integrated health IT and more regulation of minimum benefit levels could reduce switching costs.  However, increased minimum benefit levels will also drive up premiums.  I predict that switching out of integrated health delivery systems such as Kaiser Permanente would involve much higher switching costs for employees, since most Kaiser doctors are employed directly by the insurance company.  Thus, the patients would have to switch primary care providers (PCP) if their employer changed coverage.  On the other hand, switching between PPOs or less integrated HMOs might involve less switching costs since a patient’s PCP likely would accept insurance from a variety of health plans. 

• NBER Digest, May 2009.

For individuals who have recently lost their job, Carolyn’s Blog advises them how to get health insurance coverage.

“Unless you have a pre-existing condition you should only stick with COBRA until you find a private health insurance plan.  Believe it or not, if you go with a High Deductible Health Care Plan (HDHP) for a middle aged guy of 35, private health insurance can be around $75 a month — even with such well known companies as Humana and Blue Cross Blue Sheild when you live in the city of Chicago (very expensive health insurance here!)”

Healthy people sort to the less generous HDHP, sick people choose to the more generous COBRA.

Using real world data is fraught with complexity.  Wouldn’t it be nice to randomly change government regulations and see how people react?  A paper by Stephen Rassenti and Carl Johnston use a laboratory experiment to do just that.

In the experiment, survey participants are in charge of running a firm.  The firm must decide if it will provide health insurance for its employees and if so which plan should it choose.  Participants are randomly assigned for firms varying across firms size, high or low margin businesses, and industry.  Providing health insurance for one’s employee 1) reduces the probability they will get sick and also 2) can be used to attract employees.  However, more generous health insurance plans have an adverse impact on the firms bottom line.

The paper examines what happens under the following reform scenarios.

• No mandates and no mandated employer contributions.
• Employer Mandate – employers must offer insurance, but employees need not take it up
• Employer Mandate + 50%.  Employers must offer health insurance and are mandated to pay for at least 50% of health insurance costs.
• Individual Mandate – All U.S. residents must buy insurance, but employer has no obligation to offer it
• Employer Mandate + Individual Mandate – Employers must offer insurance.  Individuals must buy insurance, but individuals need not buy insurance from their employer.
• Restricted Rating – Insurers cannot increase rates on individual firms based on medical costs unless it raises rates on all other firms.  It can discriminate premiums based on firms size.
• Individual Mandate with Ratings restriction.

In most situations, economists believe that mandates decrease societal welfare.  Limiting the choice set of employers and employees through mandates eliminates potentially optimal health insurance choices.  However, the insurance market is complex.  Mandating insurance coverage can spread risk across individuals which may increase societal equity.  Further employer mandates may improve labor market matching, since workers may be more likely to leave for a better job, if they are sure they will have health insurance in the new firm.  

How do these predictions play out in the experimental market set up by Rossenti and Johnston?

Results

• Individual mandates decrease worker earnings. This is not supririsng.  Forcing an individual to buy health insurance will of course decrease the percentage of people who are uninsured.  Most people who do not have insurance want health insurance, but they choose not to purchase because of the expense.  Forcing people to buy health insurance decrease the amount of fund individuals have left to pay for rent, food, and education for their children.
• Employer Mandates.  ”Employer mandates cut earnings of companies,  particularly those of small companies…and firms with low-margin businesses”
• Employer/Individual Mandate combination.  ”…the combination of employer-and individual-mandates with mandatory minimum employer contributions was associated with the lowest profit performance (and highest employee earnings) in the study. On the other hand, combining individual and employer mandates with no mandatory minimums was associated with higher company profit, higher profitability, and a substantial drop in the cost of substitutes for workers on sick leave.
• Mandates and health.  Mandates did increase in workplace attendance (an indicator of health) in the survey.
• Required Employer Contributions. Mandated minimum employer health insurance contributions increase employees net wage compared to an individual mandate without employer minimums, but decrease firm profitability, especially for small business and low margin businesses.  Further, mandated minimums increase firm bankruptcy risk.
• Large Companies like mandates.  Why would a company want to force itself to pay health insurance premiums?  Since most large companies already provide health insurance, this mandate would compel its smaller competitiers to also offer health insurance.  Since they  have economies of scale, large companies can provide it cheaper than small or medium sized companies.  This gives large companies a competitive advantage in attracting superior talent.  

Healthcare Economist’s take

The experimental setting provides an interesting laboratory for testing different types of health reforms. While the experimental setting has the benefit of eliminating endogeity problems, the validity of the results depend how realistic is the experimenter’s parameterization of outcomes.  

The results do shows that mandates can affect employee earnings.  However, these changes in employee earnings could represent a short-term phenomenon. Gruber (1994) finds that the cost of the mandated maternity benefit is fully reflected in lower wages.  Thus, the increased cost to the firm of an employer mandate to provide health insurance will be reflected in a proportional drop in wages for workers in the long run.

The most interesting part of this paper is the differential effect of mandates on small and large business.  Large business are effective pooling mechanisms and can provide health insurance in a cost effective manner.  Thus, mandates to provide health insurance will have a small impact on large firm profitability.  The authors, however, run the experiment in only a domestic setting.  Large firms may not like mandates once we take into account that their foreign competition may not have to provide health insurance for their workers.  Thus, even large firms could be at a cost disadvantage, but this aspect is not part of the study.

Compared to large firms, small businesses are not an effective risk pooling institution and further do not have the economies of scale to administer health insurance plans efficiently.  When small businesses are forced to provide health insurance, the authors find that this adversely affects their bottom line and increases their risk of bankruptcy.  Mandating the businesses pay a fixed share of health insurance premiums only exacerbates this problem.

If you get sick and have a non-group health insurance plan, your premiums will increase.  When you think about it, this really doesn’t make much sense.  The concept of ‘health insurance’ is that it is supposed to protect your assets in the case where your health deteriorates.

John Cochrane proposes one solution: the creation of health status insurance.  ”If a health shock causes your medical-insurance premiums to rise, it pays a lump-sum payment sufficient to pay the higher medical-insurance premiums. (To deter fraud, the payment goes into a special account that can only be used for medical insurance premiums.)”

Last month, I blogged about allowing a government-sponsored health plan to compete with private insurers.  Joe Paduda gives one argument in favor of a public health insurer that any economist would love: increased competition.  

“The reality today is that almost every market is already dominated by a very few health plans, so much so that in most markets, there really is very little market competition amongst health plans…In 96% of markets, at least one insurer has share higher than 30%; in almost two-thirds of the markets, at one insurer has share greater than 50%.”

Could a public health plan actually increase competition?

In the run-up of real estate and stock market prices, demand for labor in the construction, real estate, finance industry was high.  With the drastic drop in real estate and stock market prices, the demand for loan officers, construction workers and investment bankers has dropped.  Individuals who have been laid must find a new job.  Those who are currently in dead-end jobs need to find positions in growing industries and cities.  For instance, a construction worker who used to build McMansions in the suburbs should be looking to move to new area where jobs are available working on government infrastructure projects.

Nevertheless, many employees in dead-end jobs may decide to try to keep these jobs.  Why?  One reason workers keep jobs they do not like is that they do not want to lose health insurance coverage for their family.  Moving to a new city can mean a temporary lapse of health insurance.  Further, new employers often do provide health insurance for a few months.  

The phenomenon that workers remain at sub-optimal jobs to maintain their health insurance is known as “job lock.”  I wrote a brief literature review about job lock 3 years ago.

A recent Economist article has revisited the problem of job lock as well:  

“…most Americans still get their health insurance from their jobs.  This makes it hard for anyone with a sick child to quit and start a new firm. It also makes it harder to switch jobs, despite a law helping employees to stay in company plans for 18 months after they leave. Scott Adams of the University of Wisconsin-Milwaukee found that married men with no alternative source of insurance were 22% less likely to switch jobs than those who, for example, could get covered by their wife’s employer.

Tying health care to a job can tie people to jobs they hate. Gerry Stover, who now runs a doctors’ group in West Virginia, recalls a time when his wife was pregnant and he couldn’t get health insurance at a private firm. He became a prison guard. As a public employee, his family was covered. But the job was neither pleasant nor a good use of his talents.”

While employer-provided health insurance is a good place to pool individuals of different health risks, tying health insurance to your employer may impede labor mobility and slow economic growth.

A recent report from Cover the Insured.org reviews the how health insurance trends have evolved over the last ten to fifteen years.  The percent of uninsured individuals has increased from 16.0% of the population in 1995 to 17.5% of the population in 2006.  The increase is entirely due to the increase in uninsurance among working aged adults (i.e., aged 18-64).  Among children, uninsurance rates dropped from 13.7% of children in 1995 to 11.7% of of children in 2006.  SCHIP expansions likely played a large role in the increase in childhood insurance coverage.

Why are more and more working-aged adults left without insurance?  The reason is that health insurance costs are increasing faster than income.  Let us look at the following table:

• Table – “Covering the Uninsured: At the Brink”

Between 1996 and 2006, overall health insurance premiums for employer-provided plans increased by 4.87% for single coverage and 5.98% for family coverage.  These are average annual increases above secular inflation (i.e., CPI).  However, median real income increased by only 0.76% per year between 1994 and 2006.  Is there really a 5% gap in between income and health insurance premium growth?

The answer is yes and no.  There is still a large gap between income and health insurance premium growth, but the gap is not 5%.    Using salary as the only measure of workers compensation does not take into account the fact employer contributions towards health insurance plans has increased over time. While salary has increased only by 0.76%, employer health insurance contributions have increased by 4.62% (single) and 6.03% (family) per year.  After taking into account employer health plan contributions, we see that the true increase in real worker compensation ranges between 1.01% and 2.02% per year.

Even after taking into account the increased employer contributions, we still see that real health insurance premiums have outpaced real labor compensation by about 3.9%.  In order to decrease the number of uninsured, we need to bring down the cost of health insurance.  This means either increased cost-sharing or enacting more limitations on medical services provided.  If we do not want to increase cost-sharing or limit care coverage, premiums will continue to increase.  Whether these premiums are paid by individuals, the employer or the government, they represent a real cost to society that needs to be spent efficiently.

• At the Brink: Trends in America’s Uninsured, March 2009, RWJ Foundation.

In 2004, 29% of Medicare enrollees had Medigap coverage.  Are these policies priced efficiently?

An NBER paper by Maestas, Schroeder and Goldman (2009) argues that the answer is no.  

Prior to July 1992, Medigap was minimally regulated.  With the passage of the Omnibus Budget Reconciliation Act of 1990 (implemented in July 1992), Medigap plans were standardized.  Each plan fell into one of ten types–labelled plans A though J. Medigap plans are basically identical within each type.  Further, since Medicap is a reinsurer–Medicare provides primary coverage–medical care quality is identical across plans.  

Because of this homogeneity, one would expect to see small variations in Medigap plan prices.  Maestas, Schroeder and Goldman, however, find significant price variation persists.  One reason for this price variation is high search costs.  Average search costs in the Medigap market are $72.  Further, the authors conclude that:

“…the extensive (and perhaps overwhelming) array of unique options available, the elevated incidence of cognitive limitations among older individuals, and the high costs associated with fixing ‘wrong’ choices, all lead to a setting in which ‘choice overload’ is likely to prevail.  To compensate, individuals turn to others whom they perceive to be experts:  insurance agents.  As we show, agents sell the vast majority of policies in the market but do not necessarily steer buyers to the best policies.”

• Maestas, Schroeder and Goldman (2009) “Price Variation in Markets with Homogenous Goods: The Case of Medigap,” NBER WP #14679.

In the Netherlands, the Health Insurance Act of 2006 mandates that all individuals have health insurance.  Health insurance is provided by the private sector and these private health insurers can charge any premium they please.  The government does provide some risk-adjusted payment to the insurance companies.  This means that the state gives insurance company more money if they take on sicker individuals.  Further, the state subsidies the cost of health insurance premiums for poor individuals.   Insurers are driving down prices by setting up their own pharmacies and primary care centers, and beginning to create preferred provider organizations.

In an earlier post, I wrote that the Dutch system could be a viable health reform option in the U.S.  A recent editorial in Health Economics, however, argues that the Dutch health reform is not complete.   Although there individuals have free choice between insurer they choose, insurers ability to negotiate prices with providers is severely limited.  For instance,

• “in the hospital sector, most prices are still derived from a fixed global budget and are the same for all insurers.”
• Insurers are responsible for the cost of building new facilities.  Only after they receive government approval will they be reimbursed for construction costs.  This creates significant uncertainty as to the cost effectiveness of plan hospital facility expansions.
• Preferred-provider structures are still in a basic state because of much uncertainty as to provider quality.  However,there has been some progress in terms of quality measurement.  ”The Health Care Inspectorate (IGZ) started to develop a basic set of hospital performance indicators about quality (including structure, process and outcome indicators), safety, efficiency and accessibility, in cooperation with the hospitals and medical specialists…the Netherlands Health Insurers Association since 2006 annually publishes a guide with hospital performance indicators.”
• Another reason for the limited use of preferred-provider contracts is consumer and provider backlash.  Both groups are weary of limitations to patient choice of provider.
• Another impediment to reform is that most of the insurers hospital costs are eventually reimbursed by the government.  Although this protect insurers from catastrophic losses for a few very sick patients, insurers’ competitive advantage should be in measuring risk.  Further, if insurers are not on the hook for the downstream hospital costs, it gives them less of an incentive to improve the quality of care and reduce hospitalizations.

There are some positive developments.  The Dutch government began using Diagnosis Treatment Combinations (DTC) to pay insurers for patient hospitalizations.   This payment system–similar to the DRG system used in the U.S.–replaced per diem rates and gives an incentive to insurers to decrease the number of days an individual is hospitalized.

The Dutch health care reforms have made significant progress towards moving the country towards a “managed competition” model.  Despite the positive effects of many reforms, there remains significant room for improvement.

• Wynand P. M. M. Van de Ven, Frederik T. Schut (2009) “Managed competition in the Netherlands: still work-in-progress” Health Economics, v18(3):253-255.

“If you do not have insurance you can choose to enroll in the new public plan, which will offer benefits similar to what every federal employee and member of Congress gets. Or you can choose private plan options…” Barack Obama.com

Should the government develop a health plan that would compete with private health plans?  Thomas Rice thinks this is a good idea.  In his Health Economics editorial, Rice believes that the government’s market power and lower administrative expenses will lead to lower health insurance costs.  He claims that “Government should have a strong role in providing coverage to those who are currently uninsured or who have meager coverage, and a government-sponsored option that competes against the current array of private insurers is an excellent way to start.”

I am not opposed to having a public health insurance plan competing with the private health insurance plans.  However, there are some issues that need to be dealt with:

• Budget Constraints.  Although the government may be able to charge lower health care premiums than private health insurers could, why is this the case?  If this is because  public health insurance is more efficient due to economies of scale, than this is great.  If they charge lower premiums however because they do not have a firm budget constraint, then this is a problem.  If public health insurers can unfairly compete by running a deficit every year, then this will not only drive private health insurers out of business, it will also saddle future generations with a large tax burden.
• Public School Problem.  Individuals who want to send their children to private school face a stark disincentive to do so. If they sent their children to public school, the only cost is the taxes they must pay.  If they want to send their children to private school, they must not only pay the taxes for the public school, but most also pay private school tuition.  Similarly, if a public health insurance plan is made available, individuals should not get this for free.  If there is a subsidy for public health insurance (e.g., based on income or health risk) then private health insurers should also be able to receive this subsidy.  This will create a more level playing field.  
• Risk Adjustment.  How will the public health insurance plan price premiums?  If the public plan is community rated, but the private plans are rated on an individual basis, all the sickest individuals will gravitate to the public plan. This will drive up medical costs for the government insurer.
• History.  Rice notes that “the Federal Employee Health Benefits Program…provides access to private health insurance plans to over eight million federal employees and dependents. Although the program has provided good coverage at a reasonable cost, historically it has been plagued by the same issues as other consortia of private insurance: difficulties in controlling costs, and selection bias.”

As I mentioned, I am not opposed to having a government-run health insurance plan.  However, we must realize that this in and of itself is not a cure-all for the health care problems facing the U.S.

In her California Healthcare Foundation report, Katherine Wilson does a nice job describing the health insurance market in California.  A little over health of individuals received health care from their employer or themselves (56%), a quarter of individuals receive health insurance through public programs, and 19% of Californians are uninsured (see chart). 

The private health insurance market cannot be portrayed as a monopoly nor one that is truly competitive in the neoclassical sense.  A handful of insurance carriers dominate the market.  Kaiser, Blue Cross, HealthNet, Blue Shield, Pacificare, and Aetna control 77% of the market (see chart).  

Of the premiums insurers receive, the percentage of revenues going to medical care varies widely.  Kaiser spends 93% of revenue on medical expenses, but Blue Shield and Pacificare’s traditional insurance plans spend only 70% of revenues on medical expenses.  Administrative expense ratios are also much lower for Kaiser (3.6%) than for providers such as Blue Shield’s tradiational Insurance plan (22.7%). More details about load factors are available here (see chart).

The full CHCF report can be viewed here.

Many policy experts have been proponents of value-based cost sharing.  Under value-based cost sharing, medical care that is seen to provide a higher marginal benefit to the patient will have lower coinsurance rates than medical care with lower marginal benefits.  If value-based cost sharing would be implemented, preventive care should have low coinsurance rates because of the subsequent health benefit.

This seems like a very attractive way to design health insurance benefits, but a paper by Pauly and Blavin (JHE 2008) asks if value-based cost sharing is truly a novel concept.

First, under perfect information, there is no reason to have value-based cost sharing.  In this case, patients should internalize the marginal benefits–now and in the future.  ”When all agents have perfect knowledge of patient illness states and benefits of care, optimal coinsurance should be zero; insurance should take the form of a fixed dollar (indemnity) payment to cover the full cost of care when care is cost-effective, and should pay nothing in circumstances in which care has benefits that fall short of cost.”

Under asymmetric information, however, patients may make naive decisions.  For instance, patients may decide to forgo preventive care because the do not realize its long-term health benefits.  Coinsurance rates must still be designed to balance the twin goals of risk sharing and averting moral hazard.  Lower coninsurance rates will incentivize patients to get needed care.  However, designing coinsurance rates solely based on the marginal benefit of the procedure may not be optimal once we take into account that patient moral hazard may increase medical care above optimal levels.  

Pauly and Blavin also note that under asymmetric information, “it may now be the more price responsive service that should get the cost reduction, since lowering its cost sharing will have a larger effect in terms of moving it closer to the ideal level than would be the case for a less responsively demanded service.  That is, if patient ignorance resulting in underestimation of benefits from care in a given setting is severe enough, the direct relationship between price responsiveness and optimal cost sharing should be reversed.”

Yet just because value-based cost sharing can work does not mean that is the only solution.  Instead of spending money to reduce coinsurance rates, insurance companies or policymakers could spend money to inform consumers of the true benefits and costs of different types of medical care.  This is especially true of medical services that are not price responsive; where the only way to convince patients to undergo these potentially uncomfortable procedures is to increase information dissemination.  For instance, most people would prefer not to undergo a colonoscopy even at a price of 0.  The only way to convince patients that the procedure is needed is by information dissemination.

Value-based cost sharing is not a magic bullet, but may be a useful technique in the presence of asymmetric information.

• Pauly MV, Blavin FE “Moral hazard in insurance, value-based cost sharing, and the benefits of blissful ignorance.” Journal of Health Economics, v27: 1407-1417.

If economists decided to re-write the Ten Commandments, “Thou shalt love Competition” may make the list.  However, does competition always improve quality?  Even in the case of health care?

A paper by Scanlon et al. (2008) “…found no evidence of a strong and consistent relationship between HMO competition (measured either by the HHI or the number of HMOs) and plans’ scores on the CAHPS and HEDIS measures of health plan performance.”  The authors did find, however, that increased competition can lead to lower health premiums.  

Because price is easily observable and quality is not, it seems sensible that increased competition will push down prices, but may not improve quality.  Further, more competition means more fragmented medical care, which can increase the cost to provide quality health care services. 

• Scanlon DP, Swaminathan S, Lee W, Chernew M. (2008) “Does competition improve health care quality?” HSR, v46(3):1931-1951.

What percentage of your prescription drug costs should your insurance company cover?  You may say “100%, of course!”  However, if health insurance cover all pharmaceutical costs this will drive up premiums.  

One solution to this problem is reference pricing.  If generics are available for $10 and name brand drugs are available for $100, the insurance company only covers $10 for drugs in this category.  Why would anyone want a $100 drug when a $10 one is available?  The Health Care Blog gives 4 reasons why physicians don’t prescribe more generics:

1. Many drugs are better known by their often simpler brand names and so physicians routinely write the brand name on the prescription, even if they do not mean that the brand has to be filled.
2. Physicians do not have any idea what drugs actually cost their patients, because we are “too busy” and because prescription drug pricing transparency might wake us up.
3. Some physicians believe, against the evidence in double-blind trials, that generics are inferior or less pure than the brand name version.
4. Some patients are convinced, also against the evidence in double-blind trials, that they do better with the brand than with the generic version and request that their physician specify the brand.

Yet the Wall Street Journal reports that CMS may ban reference pricing.  Authors Dr. Rick Peters and Dr. Karl Luber claim that reference pricing does much good and should not be banned.

I tend to agree with them.  If low cost, safe generics are available, then insurance should only cover the cost of generics.  This will lower costs and convince more people to take generics.  

There is a down-side to reference pricing, however.  By giving less money to the pharmaceutical companies who manufacture the name brand drugs, this may stifle innovation of these drugs in the long run.  However, incentives to innovate could be generated through extending patent lengths or giving prizes to pharmaceutical companies who develop new drugs.  

Reference pricing is a fair way to steer patients and physicians towards more cost-effective use of pharmaceuticals.

Most physicians, public health officials and economists believe that most individuals do not receive sufficient levels of preventive care.  Only half of American adults receive all recommended screening and preventive care.

The Partnership for Prevention has a plan to increase preventive care utilization. The organization proposes introducing:

“..federally funded insurance programs [that] would provide highly cost-effective clinical preventive services with no deductibles or co-pays, while Congress would provide incentives to states, health care providers and employers to deliver such services. Meanwhile, a stand-alone revenue source would be established to fund state and local efforts to create healthy environments and promote healthy lifestyles, while a Public Health Advisory Commission would be created to recommend how that funding should be allocated.”

Is this a good idea?  There are benefits to this plan.  Individuals without health insurance would have access to some of these preventive measures.  The Vaccines for Children (VFC) program currently provides free vaccines to poor children, and this program has generally been seen as a success.

Overall, however, I do not endorse this plan.  Here is why:

• Cost effectiveness: The idea is being presented as a cost saving initiative.  While effective preventive care can increase longevity and improve the quality of life, it often increases health care costs.
• Carve-out problem.  Enacting a universal, government provided health care system may be a good or bad thing depending out your point of view.  However, a limited, carve-out program for preventive care will be…well…limited.  Let us say the prevention health plan covers mammograms.  If an uninsured individual receives a mammogram using the proposed program and finds a cancerous tumor what is the next step?  The prevention health program will not cover surgery so the uninsured individual will be left with bad news and, if they are poor, few options to treat the disease.  This will lead to…
• Coverage creep.  In the example above, I anticipate an outcry for individuals from uninsured individuals who have breast cancer.  They will lead to an expansion of coverage to treatments that are less-cost effective.  Physicians will lobby to have certain treatments included in this national prevention health plan.  Thus, what may start out as a health plan which only targets cost-effective treatments, will likely expand into other areas.  For instance, the $700 billion bailout was targeted for financial firms only.  Unsurprisingly, politically powerful sectors (e.g., the auto industry) have been lobbying for their share of the pie.
• Cost shifting.  Private insurance companies who currently offer preventive services will not be able to shift their costs to the public sector.  A profit-maximizing strategy is to shift as much cost to the public sector as possible.  Thus, insurance companies will try to categorize as much medical as possible under a “prevention-eligible” diagnostic code.
• Paternalism.  The plan will also pay for health care targeted to reduce tobacco and alcohol use, improve the patient’s diet and increase the patient’s physical inactivity.  Most people know that using less drugs, exercising more and eating less will improve longevity.  I personally do not think that it is the government’s job to tell you how to live your life.  If you want a shorter life filled with more cheesecake, that should be left up to the individual.

Marketplace reports that UnitedHealth Group has just reached a large settlement with the State of New York.  What did UnitedHealth Group do wrong?

According to the State of New York, it was overcharging patients who went out of the network.  The N.Y. Times gives a good example:  ”The patient might receive a doctor’s bill for $100, for example, and expect the insurer to pay at least $70. But if the insurance database says that doctor bill should have been only $72, based on local rates, the patient might get back less than $55.”

Your gut reaction to this is likely one of two things.  If you are a patient, you may be saying ”I can’t believe the insurance company would stick me with this bill!”  On the other hand, if you work in the insurance industry, you may realize that a provider who is out-of-network has an incentive to charge big bucks to the patient since their another insurance company will be paying the bill.

Having insurance companies pay the “customary” amount for medical care received outside of the network seems sensible.  The problem, however, was with the company setting the reimbursement schedule.  The “customary” payment amounts for UnitedHealth Group’s out-of-network reimbursements was calculated by a company that was owned by…UnitedHealth Group.  Thus, the company had an incentive to lower the “customary” payment and shift more of the cost to the consumer.

There is nothing wrong with having an independent company decide on customary payments for out-of-network care.  In fact, this will give patients an incentive to be more frugal with their care levels. UnitedHealth Group’s lack of transparancy with respect to how these fees were set and inherent conflict of interest from owning the fee-setting company, however, does cause concern. 

This is not the first time UnitedHealth Group has made it to the Healthcare Economist for unsavory behavior (see “Options Backdating” post).

A paper by Buchmueller, LoSasso and Wong (2008) recounts that “6 million children who are eligible for public insurance remain uninsured.”  One question that remains to be answered is whether or not the 6 million children eligible for SCHIP who did not take it up were disproportionately from immigrant groups.  If this was true, public health workers could focus their take-up efforts in areas with high immigrant populations and produce more information material in foreign languages.

However, the authors the authors find the following: “In contrast to research on the earlier Medicaid expansions, we find similar take-up rates for the two groups. This suggests that state outreach strategies were not only effective at increasing take-up overall, but were successful in reducing disparities in access to coverage.”

• Buchmueller, Thomas C.; Lo Sasso, Anthony T.; and Wong, Kathleen N. (2008) “How Did SCHIP Affect the Insurance Coverage of Immigrant Children?,” The B.E. Journal of Economic Analysis & Policy: Vol. 8 : Iss. 2 (Advances), Article 3.

The pharmaceutical industry is well known for taking doctors (and medical students) out to nice dinners, giving them presents, and doing whatever they can to entice physicians to prescribe their drug.  Now, health insurers are following the pharmaceutical companies lead.

The AP reports that in order to convince physicians to prescribe generic drugs, a Rochester area health insurer invited doctors to “enjoy a free dinner at an elegant Rochester, N.Y., area restaurant specializing in steaks, chops and top-shelf wines, and pocket $100 on the way out the door.”  If the insurance company can convince physicians to prescribe generic drugs, insurance companies figure to save significant amounts of money.  

Other insurers are use more direct measures: cash.  ”Independent Health, a Buffalo, N.Y.-based insurer, offered doctors who prescribe 70 percent or more generic prescriptions in a month a bonus of 50 cents per patient per month. A doctor seeing 500 patients per month who meets the 70 percent minimum can collect $3,000 a year.”

In the long run, this policy may save insurance companies money and drive down premiums.  In the short run, however, physicians may be overly aggressive in convincing patients to take-up generics.  If a patient is currently taking a name-brand drug, switching them to a generic could risk destabilizing their condition.  States such as Massachusetts, Michigan, and New Jersey are contemplating laws which would bar insurers from giving physician cash to prescribe generics.

This goes back to an old story: do we want more choice (name-brands and generics) or lower costs (generics only)?  There is always a tradeoff.

“Peter Orszag, director of the Congressional Budget Office, estimates that 5 percent of the nation’s gross domestic product-—$700 billion per year –goes to tests and procedures that do not actually improve health outcomes…The unreasonably high cost of health care in the United States is a deeply entrenched problem that must be attacked at its root.”

This quotation comes from a Progressive Policy Institute (PPI) report.  There is little doubt that much of health care is unnecessary or at least is not worthwhile in the cost-benefit sense.  However, how do we fix this problem?  PPI has some suggestions which the Healthcare Economist will scrutinize.

1. Prospective Payment.  Currently, a majority of physicians are paid on a fee-for-service basis.  This encourages physicians to work harder (they get paid more for doing more services), but also encourages them to recommend unnecessary treatments to patients.  My own research finds that when specialists are paid on a fee-for-service basis, surgery rates increase 78% compared to when they are paid on a capitation or salaried basis.  Using a prospective payment system would give physicians an incentive to under-provide services.  Further, insurance companies could collect rents by enrolling only healthier patients so that the cost of care would be less for these individuals.  Prospective payment could work for specific diagnoses (as in the DRG system), but one must worry about DRG creep.  Also, if there is a high variance in the cost of treating a specific disease, than a fee-for-service compensation may be superior.   While the prospective payment system does have appeal in cutting costs, it could reduce patient access to medical care as well.
2. Let individuals choose their own plan. This proposition has great appeal for those who favor consumer choice.  Everyone likes choice.  However, issues of adverse selection can negate any welfare gains from additional choice.  High risk individuals have a hard time getting insurance and if they do the price is often unaffordable.  PPI suggests setting a up a local area purchasing pool to counteract the issues of adverse selection. I am not exactly sure how the PPI proposal would be implemented (do insurance companies charge a fixed rate for all individuals enrolled? do they adjust premium by age or sex?)  Would enrollment in this pooling mechanism be mandatory?  If so, this could drive down costs and significantly reduces issues of adverse selection.  However, mandatory enrollment in the pool could also reduce consumer choice.
3. Create a “Health Fed”.  ”A Health Fed, as former Sen. Tom Daschle has proposed, would set national goals for health-care spending and patient outcomes based on the potential gains for integrated care.”  This I think is a horrible idea.  Having the federal government try to reduce costs and improve quality at such a high level is likely to be expensive and counter-productive.  Spending money on medical is not a bad thing; good health is one of the items individuals value most in life.   Thus, if the federal government set medical spending limits, this could lead to rationing.  What we want to happen is to reduce medical spending for unnecessary or wasteful medical procedures.  I don’t think a “Health Fed” would be very helpful in accomplishing this goal.

Many economists have noted that wage growth has not kept up with overall economic growth over the past few decades.  We observe widening wage inequality since the 1970s.  Are workers getting poorer relative to the owners of capital?  Is a communist revolution needed to equalize the playing field?

Economist Martin Feldstein thinks not.  

“Feldstein concludes that…measurement mistakes have led some analysts to conclude that the rise in labor income has not kept up with the growth in productivity. The first is a focus on wages rather than total compensation: because of the rise in fringe benefits and other non-cash payments [such as health insurance], wages have not risen as rapidly as total compensation. Feldstein feels it is important to compare the productivity rise with the increase in total compensation rather than the increase in the narrower measure of just wages and salaries.”

Since health insurance costs have been increasing more than inflation over time, overall employee compensation has risen at about historical rates.  Of the compensation workers receive, however, a larger and larger percentage is going towards health insurance.  This is especially true for low income workers. This is a point I made in a post in January 2007.

• Feldstein, Martin (2008) “Did Wages Reflect Growth in Productivity” NBER WP #13953.

Readers Digest has a nice piece on how universal health insurance is working out for people in Massachusetts.  ”Massachusetts put into practice the health care solution everyone is arguing about. Here’s how it works and what it means for the rest of us.”

The Los Angeles Times has a three part series looking at the health insurance market in the U.S.

• An eroding model for health insurance.
• Seeking distance from medical risk, insurers branch out into banking.
• Individual policies, often the only coverage available, are pricey and precarious.

What are the tax implications of John McCain’s health care proposal?  The key components are that health insurance will no longer be tax deductible but individuals will receive a $5000 credit of purchasing health insurance.  Let’s work out some simple math to see how this will impact the life of a typical American.

Example with Max, Rob and Rich

Currently, the deductibility of employer provided health insurance is highly regressive.  Let’s look at 3 individuals.  One is middle class and his name is Max; the other two are rich and their names are Rob and Rich.  Middle class Max has a 25% tax rate, while Rob and Rich pay a 40% income tax rate. Rob has the same $12,000 health insurance package as Max, but Rich has a more generous $16,000 plan.  Let’s see how this affects their tax bills.

The tax system as it currently stands is very regressive.  Max, Rich and Robert pay the same $0 taxes on their health care benefit regardless of their income and regardless of the size of their health insurance benefit. If health insurance was taxed, then middle class Max will pay less taxes on his health insurance than rich Rob and rich Rich because Max has a lower marginal tax rate.  On the other hand, individuals with more generous health insurance packages get a larger tax benefit when health insurance benefits are tax deductible.  Even though Rob and Rich are in the same tax bracket, Rich saves more money than Rob when health insurance is tax deductible, since Rich has a more generous health insurance plan.  Tax deductibility encourages people to buy more generous health insurance packages at the expense of the taxpayer.

The McCain plan.

Will the McCain plan lead to higher net taxes?  In my example, Max and Rob save money under the McCain plan. Only Rich owes more taxes since he is in a higher tax bracket and has a more generous health insurance plan.  Of course, health insurance costs will increase over time, so McCain may want to index his health insurance credit to inflation.

Individuals are also worried that if they pay for health insurance themselves, this is a pure transfer of cost.  If I support McCain, will my health insurance costs go up by $12,000?  or $7000?   In reality, if employers stop paying for health insurance and transfer the burden to employees, in a competitive market employers will increase wages to compensate for the loss of the health insurance benefit. Most economic research has found that the cost incidence of employer-provided health insurance appears almost 100% through lower employee wages.

We do have to worry that as individuals start to pay for individual health insurance plans, the problems of adverse selection may worsen.  Further, non-group plans are more expensive to administer than group plans.  Thus, the shift in the type of plans individuals select may affect the cost, but the direct tax effect of the McCain plan will lead to a reduction or small increase in tax liability from health insurance benefits.

Effect on Employers

Some individual believe that the McCain plan would increase taxes for business.  This is incorrect.  If health insurance businesses were taxed, individuals would pay the tax.  For businesses, health insurance still counts as a labor cost and would reduce their profit and thus tax liability.  If individuals would receive a $12,000 health insurance package from work, currently they do not owe any taxes on this benefit.  If individuals were taxed on  this benefit, then an individual in a 25% tax bracket would owe $3000 in additional taxes.  If you are in the 40% tax bracket, you will owe $4800.  This means on net, the McCain plan would decrease your taxes by $2000 for the 25% tax bracket and $200 in the 40% tax bracket.

Also, even if the employer paid for health insurance for a group, each individual would be taxed according to the average cost of the health insurance plan per worker (likely weighted by whether it was a family or single plan).

Health insurance require that all individuals buy health insurance.  Most voters views on an individual mandate depend on how you frame the question.  If you ask voters: “Should everyone buy health insurance?”  Most people will say yes.

If you ask “Should the government compel all ndividuals to buy health insurance regardless of the cost?”  Then the response is much less positive.

Michael Cannon of the Cato Institute is against individual health insurance mandates.  Whether you are for or against them, Mr. Cannon make some valid points concerning the drawbacks of health insurance mandates in his “Perspectives on an Individual Mandate” article.  Below are some of the highlights.

• An individual mandate ≠ universal coverage: Even if there is an individual mandate, many individuals will still forego insurance coverage. Currently, Massachusetts has an insurance mandate, but there are still uninsured individuals.
• Reduced Choice.  Let us say you are a typical middle class worker.  You 401(k) just took a nose-dive, and your job may be at risk as your company’s sales drop in the weakened economy.  Do you use your limited savings for rent, utilities, school books for your kids or health insurance?  While this is a tough choice, it is one that families would no longer be able to make on their own.  A mandate would compel them to buy insurance.

Hospital-acquired, or nosocomial, infections are often caused by poor hospital care.  Patients arrive to the hospital and often leave with infections caused by unsanitary hospital conditions.  Should Medicare pay for these hospital-induced health care costs?

A knee jerk reaction would be to say no.  If the hospital adversely influence patient health, Medicare or other payors should not be responsible for those costs.  

However, if Medicare decided to implement a policy where they did not pay for nosocomial infections, doctors would report nearly all infections as community-acquired rather than hospital-acquired. Thus, not paying for nosocomial infections will adversely affect the reporting of these types infections.  If the infections are not reported, it will be difficult to eridicate them.

Thus, we are in a catch-22.  Paying for medical care resulting from nosocomial infections, discourages the prevention of these infections.  Not paying for medical care decreases the incentive to report an infection as nosocomial.  

Damned if you do, damned if you don’t.

This is the question asked by Newhouse and Sinaiko in their 2008 paper in the Forum for Health Economics and Policy. The answer is yes.

Source OECD

From the table above, we see that when we exclude the U.S., many multi-payer systems have similar health care costs as single payer systems. Further, Newhouse and Sinaiko find that states in the lower quintile of health care spending spend similarly to the single-payer nations cited above.

Specifically applying a single payer system to the U.S. might not reduce health care costs as much as conventional wisdom thinks. The paper wisely notes that “the dominant American fee-for-service reimbursement method is likely to generate greater billing costs than hospitals on fixed budgets, as in Canada, but it is not necessarily the case that implementing a single-payer regime in the United States would change how hospitals are paid.”

So is a single payer system right for the U.S? This question is still up for debate. A single-payer system is likely a sufficient condition for having lower health care spending levels, but it is not a necessary condition for reducing health care costs.

• Joseph P. Newhouse and Anna Sinaiko (2008) “Can Multi-payer Financing Achieve Single-Payer Spending Levels?,” Forum for Health Economics & Policy: Vol. 10: Iss. 1 (Frontiers in Health Policy Research), Article 2.
  http://www.bepress.com/fhep/10/1/2

Yesterday I wrote about the problems with fragmented medical care in America.  Is a single payer system the only solution?  A Commonwealth Fund report shows that the single payer system is not the only path towards improved, more integrated care.

What we want

The report outlines six general improvements that need to be made to improve the quality of care in the U.S.:

1. Patients’ clinically relevant information is available to all providers at the point of care and to patients through electronic health record systems.
2. Patient care is coordinated among multiple providers, and transitions across care settings are actively managed.
3. Providers both within and across settings have accountability to each other, review each other’s work, and collaborate to reliably deliver high-quality, high-value care.
4. Patients have easy access to appropriate care and information, including after hours.
5. There is clear accountability for the total care of patients.
6. The system is continuously innovating and learning in order to improve the quality, value, and patients’ experiences of health care delivery.

These are high ideals that need to be translated into specific action-items for providers.  However, a variety of different organizational structures have been able to accomplish each of these six goals.

Models

There are four general types of structures and all can be successful in delivering high quality care.

• Integrated delivery systems such as Kaiser Permanente and Geisinger Health System.
• Large multi-speciality groups such as the Mayo Clinic and Partners Healthcare.  Both groups are nonprofits.  While the Mayo Clinic directly employs doctors on a salaried basis, Partners contracts with over 1000 PCPs and 3500 specialists to provide high quality care to patients.
• Private networks of independent providers, such as Hill Physicians Medical Group and Northland Health Alliance, generally receive a capiation payment from insurers for each practice but doctors are compensated on a FFS basis.  This is similar to RVU compenation.
• Government-facilitated networks of independent providers.  Here, the government does not directly provide care, but instead coordinates care between providers.  This has worked will for Medicaid patients in North Carolina. Denmark’s universal health care system coordinates physicians, who are paid via fee-for-service plus a fee for serving as the patients medical home.  Ninety-eight percent of physicians have paperless offices, prescriptions and lab tests.

Shih et al. (2008) “Organizing the U.S. Health Care Delivery System for High Performance“, Commonwealth Fund Report  no. 1155.

The U.S. healthcare system is one of the more fragmented systems in the world. Traditionally, economists believe that a splash of decentralized planning with a heap of free markets is a recipe for efficient outcomes. In the case of health care coordination, however, information sharing, and collaborative work are needed if quality is to improve and decentralization may not be the best option. Cebul et al. (2008) describe some of the problems with America’s fragmented system. For instance:

• Health insurance is a high turnover product. About one-fifth of health insurance policyholders cancel their plans in any given year. Most of these changes are due to i) employees switching jobs and ii) employers cancelling their group plans in favor of other plans. When insurers have short term relationships with their customers, it likely does not pay for them to invest in preventive care or chronic disease management programs.
• Having a fragmented insurance market can give insurers an incentive to lower quality. When adverse selection is present, offering high quality medical care will attract sicker individuals which will drive up insurance premiums. Thus, insurers often do not have an incentive to provide high quality care.
• The fragmented insurance system means that hospitals must spend more money paying administrators to collect claims. Woolhander et al. (2003) finds that hospitals in the U.S. spend $315 per capita on administration compared with $103 in Canada.
• The fact that physicians are rarely employed by the hospitals has lead to some perverse behavior by nurses. For instance at Stanford Hospital, “Nurses were harshly blamed by surgeons for instrumentation failures, but nurses who delivered clean instruments on time achieved ’star status’ among surgeons. In this setting, some operating room staff shared instruments between surgical suites. Some nurses kept critical instruments in their personal lockers. Some surgeons also took instruments with them when they left the hospital.”
• Further, physician heterogeneity hurts efficiency by making standard operating procedures nearly impossible to implement. Generally, hospitals allow doctor to gets what they want in order to attract physicians with large patient bases to their hospital. However, this creates an incredible amount of complexity and possibility for error in the health care system.
• When providers do consolidate, it is often not done in the best interest of the patient. While vertical integration could improve quality, consolidation is often done with the purpose of locking-in profitable referrals or increasing bargaining power.
• “[Medicare] patients with diabetes see a median of eight physicians in five distinct medical practices.”

In future posts, I will give some examples of organizations that have been able to overcome these problems, as well as policy prescriptions to improve the health of America’s medical system.

See also: Fragmented Medical Care II (The Models) and III (Policy Options).

Cebul RD, Reibitzer JB, Taylor LJ, Votruba M (2008) “Organizational Fragmentation and Care Quality in the U.S. Health Care System” NBER WP 14212.

A recent Robert Wood Johnson Report (see also press release) finds that uninsured children receive less needed medical care than individuals with health insurance.  The report finds that 91% of children who are insured have had a physician visit in the last year compared to only 69% of uninsured children.  Seventy seven percent of children with insurance had a well-child visit in the last year compared to 45% of uninsured children.

Does this mean that having health insurance increases the quantity of health care children receive?  Yes.  Holding health insurance decreases the marginal cost of a physician visit and this increases the probability a child will visit the doctor.

Do these statistics imply that giving health insurance to the uninsured will increase well-child visits to rates of insured children?  Likely no.  Children who have insurance are likely to be different than those without health insurance.  Those with private insurance are likely more educated, richer, and more likely to have an English-speaking household than those without health insurance.  These individuals have likely have higher demand for medical care than the average person.  Thus, giving all uninsured children insurance will likely increase well-child visit rates, but will not increase them to the level of the currently insured.  Conversely, if all insured people lost their insurance, well child visit rates would still be above individuals who are currently uninsured.

While health insurance coverage likely contributes to physician visit rates, it is not the only factor which determines a child’s frequency of physician visits.

According to the U.S. Census:

• Both the percentage and number of people without health insurance decreased in 2007. The percentage without health insurance was 15.3 percent in 2007, down from 15.8 percent in 2006, and the number of uninsured was 45.7 million, down from 47.0 million.
• The percentage of people covered by private health insurance was 67.5 percent, down from 67.9 percent in 2006.
• The percentage of people covered by government health insurance programs increased to 27.8 percent in 2007, from 27.0 percent in 2006.

See also a USA Today story concerning these Census numbers on health insurance as well as income and poverty.

• U.S. Census (2008) “Income, Poverty & Health Insurance Coverage in the United States, 2007” p60-235.

NPR’s Morning Edition reports on what happened when health economist Philip Musgrove brought a dying man to an emergency room.  The receptionist with whom Dr. Musgrove interacted would not treat a the man until his health insurance information was collected.

In the July/August 2008 edition of Health Affairs, health economist Mark Pauly discuss his opinions with respect to the evolution of health insurance in India and China. He notes that in both countries, rising incomes has lead to increased demand for medical care, especially in urban areas. Despite the increased demand for medical care, there has not been nearly as much an increase in health insurance coverage. Out-of-pocket payments as a portion of total health care spending are 80% in India and 60% in China.

This has lead to calls by many politicians to increase “access to care” by increasing health insurance coverage rates. Pauly, cautions that mandating generous health insurance coverage may not be ideal:

“The problem with insurance that ‘improves access’ to care is that such additional use of care will almost surely raise average spending on care and, therefore, the premium that an unsubsidized insurer would have to charge…using regulation to push access and equity that makes insurance seem like a bad buy to its middle-class customers will be undesirable.”

If legislating a more generous insurance benefit package will reduce demand for health insurance, one solution is to have the government provide health insurance for all its citizens. This will increase equity, but could lead to other undesirable outcomes such as rent-seeking behavior, and politically determined medical care decisions. Further, using taxes to fund the public health insurance system could increase “black market” activity. That is,

“Using taxes as a vehicle to make insurance compulsory runs the risk of driving measurable and taxable income underground for people who expect to pay more in taxes from public goods than they will get.”

Dr. Pauly reminds us, that there is no easy way to solve the health care needs facings the citizens of the world’s two largest countries: India and China.

• Pauly MV (2008) “The Evolution of Health Insurance in India and China” Health Affairs, 27(4): 1016-1019.

Economists often state that uninsured individuals do not “want” health insurance. Joe Paduda claims that this is not the case; most uninsured do want health insurance. Mr. Paduda cites a Washington Post, Kaiser Family Foundation and Harvard University survey which shows that “when asked why they don’t participate in their employer’s program, 1% of survey respondents said it was because didn’t think they needed insurance.” Most people decide to not to purchase health insurance–not because they do not want it–because they can not afford it.

This is where economic terminology can create confusion and also clarify the situation. Let me give you an example of what “want” means to an economist.

I want an Audi R8. However, the cost of this car starts at $112,500. Thus, I prefer to drive a 2003 Toyota Matrix and have some money left over to buy food, pay for rent, etc. Although I do “want” the sports car, I want more to not owe a huge amount of debt and instead be able to afford for other goods that I desire.

Similarly, for economists, if an individual is uninsured, it must be the case that this is because they prefer this situation. This may seem like a tautology, but what it means is that an individual who is uninsured would rather be uninsured than pay $12,100 and be insured. The $12,100 that would have gone to health insurance, can be used for food, rent, etc. Further, if you are young and healthy, the probability that you will become sick is probably fairly small compared to the average insured individual and thus you will be paying more for insurance than the expected value of your medical costs.

Those who argue that all individuals should have health insurance can argue this based on equity goals. However, in order to make health insurance more attractive, one must either 1) lower the price of health insurance, or 2) increase the after-tax incomes of low income workers. The first can be done with more flexible insurance arrangements, offering more basic health insurance coverage, improving the efficiency of the health care sector and by man other means. The second means to increasing insurance can be accomplished by either increased economic growth or a more redistributive tax policy.

Nevertheless, nothing in this world is free (especially health care). Everyone would want health insurance if it were free; but because it is so expensive, other wants come to be more important than health insurance and thus individuals become uninsured.

Barack Obama and John McCain both believe that they know how to improve the American health care system. A policy brief by Michael Tanner has nice summary of the two candidates policies. I will review some of this paper today.

Obama’s general health care policy

Obama goal is to expand government provided health care and create a form of “managed competition” originally developed by Alain Einthoven. Obama supports expanding SCHIP and Medicaid eligibility. Although Obama does not support a health insurance mandate for adults, he does support a mandate for children and young adults (any one 25 or under). Obama’s goal to increase health care access, he would support a “pay-or-play” mandate. All but the smallest employers would be required to provide health insurance; those who didn’t would be compelled to pay into a national fund covering these uninsured workers. The mandate would likely require a minimum benefits package. Overall, Obama is pushing towards more government provided health care and more regulation.
McCain’s general health care policy

Compared to Obama, McCain is generally against more government participation and regulation. Instead of moving the U.S. to larger risk pools (e.g., government insurance, employer insurance) that are more severely regulated, McCain want to move the U.S. towards more individually provided health insurance. McCain’s main policy initiative is a $2,500 health insurance refundable tax credit for individuals ($5000 for families). The goal is to make health insurance more affordable, but make individuals incur the full cost of “better” health insurance at the margin. McCain is also considering risk-rating these vouchers so that individuals with severe health problems will receive a larger voucher. McCain would also allow individuals to buy health insurance from any state.

Side-by-side comparison

Commentary

So whose health insurance plan is better? If you are in favor of more government involvement in health care, you should support Obama. In the Audacity of Hope, Obama states that “the market alone cannot solve our health care woes–in part because the market has proven incapable of creating large enough insurance pools to keep costs to individuals affordable, in part because health care is not like other products or services (when your child gets sick, you don’t go shopping for the best bargain).” While Obama’s proposals will decrease insurance choice, increase regulation, and increase public funding of healthcare, Obama’s proposals are likely more progressive than McCains and will create larger risk pools. Obama’s plan is likely much more expensive. Further, an employer mandate may lead to higher unemployment levels (see Baicker and Levy paper).
If you are in favor of less government involvement, McCain is your man. McCain rejects “coercion and the use of state power to mandate care, coverage or costs.” The voucher system is similar to the one proposed by Victor Fuchs, and fairly similar to the Swiss managed competition system. A shift to individual–rather than employer-provided–health insurance accompanied by a decrease in regulation should: 1) reduce health insurance costs, 2) increase employment relative to Obama’s plan, 3) give insurance companies the incentive to create innovative products, 4) give workers more choice of their health insurance plan, and 5) be more fiscally sound for the government.

On the other hand, McCain’s plan will be more regressive and can adversely affect the ability of individuals with pre-existing conditions to buy health insurance (unless risk rating the voucher payment occurs). The McCain plan can only be successful if risk pooling can occur on the individual level. This is happening in Switzerland, but in Switzerland there is a standard benefit package which makes shopping for insurance coverage easier.
Additional Comment

Both candidates have proposals with respect to improving how medical care is delivered. Increased preventive care, EMR, and P4P are all popular measures. However, the NEJM states “Our findings suggest that the broad generalizations made by many presidential candidates can be misleading. These statements convey the message that substantial resources can be saved through prevention. Although some preventive measures do save money, the vast majority reviewed in the health economics literature do not.” The ability of any President to directly affect the quality of medical care provided to the patient is likely small. P4P initiatives are good in theory, but since most of medical care involves unmeasurable outcomes, or outcomes which depend on multiple causal factors (e.g., the quality of medical care, baseline patient health, patient behaviors), it is very difficult to implement them on a large scale.

The Boston Globe reports that some of Massachusetts largest insurers are beginning to cover medical visits made at retail clinics at CVS and Walgreens drug stores.  Blue Cross/Blue Shield of Minnesota is waiving copays for visits to retail clinics.  The American Association of Family Physicians (AAFP) is not happy about this.

Carpe Diem says the AAFP’s resistance to accept retail clinics can be understood follows: “The family doc cartel is worried about increased competition.”

Recently, health insurers have been more likely to offer a tiered copayment structure to enrollees. Patients face low co-payments for generic drugs, higher copayments for “preferred” name-brand drugs, and the highest for name-brand drugs on a “nonpreferred” list.

Consumer driven health plans (CDHPs) however offer a simpler payment system. The consumer generally places funds into an account which can be used to pay for medical spending of any kind (including pharmaceuticals). After the funds in the account are exhausted, the individual must pay for medical treatment out of pocket until the deductible is met.

A paper by Parente, Feldman and Chen (2008) takes data from a large, self-insured employer that had a CDHP and a POS with a 3-tier copayment structure and analyzes how CDHP affect pharmaceutical spending and utilization. The authors use a standard two part model:

• P(Rx>0)=α₀ + α₁X + α₂C + α₃T + α₄TC
• Ln(covered expenditure|expenditure >0)=β₀ + β₁X + β₂C + β₃T + β₄TC

Here, X are enrollee covariates, C is the choice of insurance, T is equal to unity after the CDHP is introduced, and TC is the interaction term. Thus, we have a difference-in-difference estimation strategy using a two part model.

The results of the study are as follows:

“CDHP cost sharing does not favor generic drugs to the extent found in three-tier benefits, which provide a substantial price reduction for generic drugs. However, we note differences among the three-tier designs where the PPO cohort used more brand name drugs and fewer generic drugs than the POS cohort”

The authors also hypothesized CDHP enrollees would utilize more mail order drugs to save on costs.

“Our third hypothesis that CDHP enrollees would use more mail-order prescriptions than other cohorts was supported in all 3 years, but the results were not statistically different from the POS cohort.”

• Stephen T. Parente, Roger Feldman, and Jon B. Christianson (2008) “Effects of a Consumer-Driven Health Plan on Medical Care Expenditures and Utilization” HSR, Published Online 13 May 2008.

Most people do not know the answer to this question.

Of course, individuals like plans with lower health insurance premiums, but patient deductibles, copayment and coinsurance rates also will determine how much the consumer will end up paying for a given insurance plan. Even if costs are similar across insurance plans, benefits packages may not be. How can consumers compare health insurance cost and benefit characteristics across plans?

Currently, this is fairly difficult. The California HealthCare Foundation (CHCF) is calling for standardized labelling of health insurance products. For instance, credit card companies must disclose a standardized set of information regarding the card’s characteristics (see example) and mutual funds must disclose standardized prospectuses including fund expenses, holdings, and other key characteristics.

According to the CHCF, a standardized health insurance disclosure would reveal the following characteristics of the plans:

• Annual Premium
• Annual Deductible
• The Percent of Health Expenditures paid by the insurance company
• Total cost (Premium + OOP expenses) for a consumer with average health
• Out of pocket maximum
• Various copayments and coinsurance rates for doctor’s visits, emergency room visits, surgery, etc.
• Are maternity benefits covered

Even though it is impossible to disclose all pertinent information with respect to health insurance characteristics, having some sort of standardized disclosure form could aid consumers in shopping for the best deal.

At least this is what David Williams of the Health Business Blog has experienced in paying for his firm’s Blue Cross/Blue Shield plan.  “This year’s increase is 13.3 percent, on top of last year’s 26.3 percent increase and an 11 percent increase the year before. Thanks to the magic of compounding it means the premium has gone up about 60 percent in three years. Health insurance has become a serious burden for us.”

With these large increases, Williams sympathizes with Wal-mart’s aversion to providing health care for their workers.

“A worker making the US minimum wage of $6.55 per hour, working 40 hours per week, 50 weeks per year would make $13,100. By contrast our company’s premium is more than $15,000 per family. And of course that doesn’t count the out-of-pocket payments if someone actually wants to use their insurance.”

With rising gas and food prices, in addition to the near constant pace of insurance price increases, consumers buying power is definitely getting squeezed.

There is an interesting article a few weeks back in the Wall Street Journal (”Opting Out“) which describes the plight of Amish and Old Order Mennonites who refuse to buy health insurance. Further, since these groups also refuse to participate in Medicaid government assistance will not bail them out either.

Nevertheless, these societies do have one form of insurance: mutual aid. When one member of the community becomes ill, the rest will pitch in to help finance the cost of the needed medical care. “Thousands of Amish families rely on the age-old system of churches paying bills members can’t afford, through voluntary donations.”

Because they are very closed societies, however, many Amish and Old Order Mennonite individuals marry distant cousins which can lead to a handful of genetic diseases. With such a high rate of expensive-to-treat diseases, this mutual aid system is faltering.

Further, since the Amish and Mennonite are uninsured, they actually pay more for medical care than would someone with private or public health insurance. This phenomenon was documented in my “Uncompensated Care” post.

What is the solution?

The Amish hope to persuade their local hospital to lower medical costs, but it is unlikely that a hospital will negotiate a lower rate for uninsured Amish compared to the uninsured non-Amish. The local Lancaster General hospital “…has increased its discount for uninsured patients to 25% from 15%…uninsured patients now receive the same discount that commercial insurers do, though not as much as the government does.”

The moral of the story is that it is very difficult to receive medical care in America today without health insurance.

• H.T.: Chronic Positivity
• H.T.: Lancaster Online “Faith not paying bills“

A Side note: If everyone receives at least a 25% discount, isn’t that just the regular price?

Can technological change make people worse off? Most economists think technical improvements are always good. Producing more of the output with fewer input is considered a more efficient use of resources. But is this the case in the medical field? John Goddeeris shows that this may not always be the case in his 1984 paper.

The Model

Let us assume that individuals maximize utility of the for developed by Arrow (1976):

• V=Σ_i p_i uⁱ(x_i, h_i(m_i))

Here, i indexes the state of illness, where the probability that each stat of illness occurs is p_i. Individuals can spend their income on consumer goods, x_i, or medical care, m_i, where medical care is translated into health by the function h_i(m_i). A technological advancement is defined as h_i^a(m_i)≥h_i^b(m_i), for all m_i, and strict inequality for some m_i.

We can now introduce insurance into the model. Individuals who buy insurance pay a premium equal to π and a coinsurance rate z. The price of the medical premium must be equal to the expected value that the insurance company expects to pay out in medical benefits (less the copayments).

One would think that V*_a>V*_b, but this may not always be the case. For instance, let us assume that a person can either be healthy or sick (i.e., i=2). Further, assume the following utility functions:

• V=(1-p)u¹(x₁) + p u²(x₂,h₂)
• u¹(x₁) = -exp[-x₁]
• u²(x₂,h₂) = -exp[-(x₂+h₂)]

If individuals are endowed with income x₀, then:

• x₁ = x₀ – π,
• x₂ = x₀ – π – zm₂,
• π = p(1-z)m₂.

Assume p=.1, x₀=10 and the original technology is:

• h₂(m₂) = -10 if m₂ < 5
• h₂(m₂) = -4 if m₂ > 5

This means that if medical spending is above 5, health will be partially restored. Goddeeris finds that the optimal coinsurance rate to maximize utility is no coinsurance (i.e., z=0). With no coinsurance, sick individuals choose m₂=5. The utility level under the original technology (i.e., V*_b) equals -.000476. What happens when there is a positive technological changes as follows:

• h₂(m₂) = -10 if m₂ < 5
• h₂(m₂) = -4 if 5 ≤ m₂ <15
• h₂(m₂) = -3 if m₂ > 15

Again, the author finds that no coinsurance (i.e., z=0) is optimal. With no coinsurance, individuals of course choose m₂ = 15. However , tutility level under the new technology (i.e., V*_a) equals -.000592. How can this technological improvement have decreased utility?

In this example, the true cost of the innovation is so large relative to its benefits are so large, people only choose to use it since coinsurance is 0. A higher coinsurance rate would have induced individuals to choose m₂ = 5. According to Goddeeris, “the larger added expenditures in the ill state leads to an even greater reduction in expected utility. A ero co-insurance rate remains optimal after the innovation. Thus V*_a < V*_b, and the innovation –which clearly expands productive capabiities and is in fact adopted–is welfare reducing by our standard.”

The reason this occurs, is that individuals act ex post as if their expenditure decisions have no impact on insurance premiums. While no individual person’s actions will affect insurance rates, since all sick individuals act similarly, health insurance premiums increase much more after the technological innovation than before it.

Despite the finding that technology is welfare reducing in this particular case, technological improvement are of course welfare improving in other cases. One question that remains is how to operationally decide when a technology is welfare enhancing and when is it welfare reducing. In which category do MRI machines fall? What about CT scanners?

• Goddeeris, John H. (1984) “Medical Insurance, Technological Change and Welfare” Economic Inquiry, 22(1): 56-67.
• Goddeeris, John H. (1984) “Insurance and Incentives for Innovation in Medical Care” Southern Economic Journal, Vol. 51, No. 2, pp. 530-539.
• Arrow (1976) “Welfare Analysis of Changes in Health Coinsurance Rates” NBER.

According to an article on TheHill.com, Medicare denies more claims than commercial insurers.

“Medicare was the most likely to deny any part of a claim, with a 6.9 percent rate. Aetna was a close second at 6.8 percent while the others ranged from 2.7 percent to 4.6 percent.

Coventry Health had the fastest median turnaround between receiving a claim and responding, at four days, according to the AMA. Medicare and CIGNA took a median 14 days; Humana and Aetna, 13 days; Health Net, 11; United Healthcare, 10 and Anthem, seven.”

Why is this? It could be the case that commercial health insurers have more efficient claims processing centers. While economists generally believe that the private sector is more efficient, in the case of health insurance claims firms make more money when they deny more claims. Thus, I am not sure that the profit motive is leading to more private-sector claims approvals.

Competition between insurers may increase claims approvals. Most physicians and hospitals must take Medicare because it represents so large a share of the helathcare spending. On the other hand, physicians may only accept patients whose insurance companies have prompt payment with fewer denials. This leads to some incentive for insurance companies to decrease claims denials.

Another reason for the differential claims denial rates is the demographics of Medicare and commercial insurance enrollees. Almost all Medicare enrollees are over 65, while commercial insurers have enrollees who are of varying ages. Since older individuals are more likely to demand high cost medical procedures, if high cost medical procedures are the ones that are more likely to be denied then Medicare’s higher denial rate may simply be due to the composition of its enrollees.

Whatever the reason, the fact that Medicare denies more claims than commercial insurers should dispel the myth that the government is simply a benevolent entity, while commercial insurers are ruthless, profit-hungry wolves. The truth–as always–lies not in the black nor the white but in the gray.

As mentioned in previous posts, most health insurance in the France public health care system involves significant copayments. While this helps to reduce the moral hazard problem, it may prevent poor individuals from utilizing the care they need. In 2000, France introduced free complementary health insurance plan which covers most out-of-pocket payments for the poorest 10% of French residents. Did this policy change increase utilization?

This is the question analyzed by Grignon, Perronnin and Lavis (2008). The authors note that three groups are effected by this change. This first is the very poor who already paid very few copays due to the existing means tested program (Aide Médicale Générale). The second group of individuals who were eligible for the complementary insurance program previously had commercial insurance, which in France is often used to finance the copayments of the national health insurance system. For the first two groups, we would expect little change in medical utilization. The third group, however, had no commercial or means tested complementary insurance and thus becoming eligible for the new French program likely will have a significant impact on access to care.

Results

The authors do not find a strong positive effect of being eligible for the the free complementary insurance plan, but this is likely because 87% of the sample was previously eligible for means tested benefits. There was some evidence that the utilization of specialist care did increase for the population eligible for the free complementary insurance program. Individuals who enrolled voluntarily into the free plan had significantly higher probability of using all types of care.

The authors summarize their findings concerning the increased utilization of those previously not covered as follows:

“This impact of the free plan on health-care utilization of those previously not covered has three causes: (1) a true price elasticity of demand for health care among the poor: faced with a lower (indeed zero) price, individuals use more care, mostly specialist visits and drugs than when faced with a variety of co-payments averaging 23%; (2) pent-up demand: the change in utilization among those previously not covered reflects the slope of their demand as well as the stock of past unmet needs and can therefore overestimate the longer-run elasticity of demand; and (3) enrolment bias: those who voluntarily enroll may be those who expect to use health care more. “

• Michel Grignon, Marc Perronnin, John N. Lavis (2008) “Does free complementary health insurance help the poor to access health care? Evidence from France,” Health Economics, Volume 17, Issue 2 (p 203-219)

Many studies have attempted to determine how the manner in which physicians are compensated by health insurance companies affects the quantity of medical care provided. Today I will summarize some seminal studies in this field.

Epstein, Begg and McNeil (NEJM 1986)

In this study, the authors examine whether or not there is a difference in the rate of ambulatory testing between physicians in a large fee-for-service (FFS) group and a large prepaid group. The physicians were internists who were caring for patients with uncomplicated hypertension. The authors found that 50% more electrocadriograms (EKGs) were obtained in the FFS group and 40% more chest radiographs were obtained in the FFS group [compared to the prepaid group]. There did not seem to be any difference in testing rates between FFS and prepaid doctors for blood counts and urinalyses. This is not surprising, however, because the profit physicians earned from EKGs and chest radiographs was significantly higher than the profit they made from each blood count or urinalysis. Further, the cost of preforming EKGs and chest radiographs was much higher than the cost to preform blood counts and urinalyses.

Thus, this study concludes that patient testing rates are different between FFS and prepaid doctors, but this effect is only observed for high cost and/or high margin ambulatory tests.

Newhouse and Marquis (JHR 1978)

Physicians may treat patients differently based on how the patient’s insurance company pays them. However, the patient base of most physicians includes a wide variety of health plans and thus it may be difficult to discriminate care levels by insurance type. The “norms hypothesis” predicts that physicians treat patients in accordance with the average or modal insurance coverage in a given metropolitan area. In other words, “the level of a community’s insurance coverage determines physician norms.” If this were true, it would imply that there would be significant variation in medical care quantities across geographic regions but very little variations by patient for each physician.

The authors use data from the RAND Health Insurance Study in Dayton, Ohio. The authors test whether or not the patients own insurance rate will affect hospital admissions, the length of a hospital stay or the number of physician office visits. The authors find no evidence that community-level coinsurance rate impact hospital admissions, while the patient’s own coinsurance rate significantly affects hospital admission. In the 1963 data, neither the community insurance variable nor individual insurance variable had any affect on the length of a hospital stay or the number of physician visits, however in the 1970 data, individual coinsurance rates had a large impact on the length of a hospital stay or the number of physician visits, while the community level coinsurance rate had no impact on these dependent variables.

Hellerstein (RAND J Econ 1998)

Most health policy wonks believe we could significantly reduce health care costs without sacrificing quality by using more generic drugs. Many states have passed “permissive substitution laws” which allow pharmacists to substitute generic drugs in place of name-brand ones unless the physician explicitly instructs them not to do so. Other states use a “two-line” prescription method. With these prescription pads, doctors can sign their name in one of two spots: the first indicates that generic substitution is allowed and the other indicates that the brand name option is medically necessary.

Hellerstein uses data from the 1989 NAMCS, and finds that “30% of the unobserved (residual) variance in the prescription choice is physician-specific, rather than patient specific.” Does an individual’s health insurance influence the physician’s prescribing behavior? This paper finds that an individual’s health insurance does not influence prescribing decisions. However, “conditional on a patient’s insurance status, a patient who switches to a physician with a marginally greater fraction of HMO patients is 10.12% more likely to receive a generically written prescription.”

Summary

Why does the composition of the physician’s patient base seem to matter for Hellerstein (1998), but not for Marquis and Newhouse (1978) or Epstein, Begg and McNeil (NEJM 1986)? The main reason is likely that Hellerstein examines medical care in which physician do not receive any compensation. Physicians receive no more or less revenue from prescribing name brand compared to generic drugs and thus have no incentive to find out how they are being paid by each individual’s insurance company. On the other hand, Marquis and Newhouse found that hospital admissions, the length of the hospital stay, and the number of office visits is affected by an indvidiual’s health insurance variables since this type of medical care is high margin and high cost. Similarly, Epstein, Begg and McNeil (1986) found that how an individual’s insurance company compensates physicians matters for high margin, high cost EKGs and chest radiographs, but does not seem to matter for low margin, low cost blood counts and urinalyses.

• Epstein AM, Begg CB, McNeil BJ. (1986) “The use of ambulatory testing in prepaid and fee-for-service group practices: Relation to perceived profitability.” N Engl J Med. 314(17):1089-94.
• Newhouse JP, Marquis MS. (1978) “The norms hypothesis and the demand for medical care.” J Hum Resour. 13 Suppl:159-82.
• Judith K. Hellerstein (1998) “The Importance of the Physician in the Generic versus Trade-Name Prescription Decision,” The RAND Journal of Economics, Vol. 29, No. 1, pp. 108-136 .

At least Joe Paduda thinks so.  His post today gives an example from the employer GTE.  GTE was worried about ER and inpatient admissions rate for children with asthma.  Why?  Because employees who were single parents would miss work to take care of their children when they were sick.

Mr. Paduda’s argues convincingly that employers care about the health of their employees in order that they come to work and make profit for the company.   Of course, individuals also have an incentive to maintain their health for personal reasons.  Employers may be more worried about employees missing work if they are paid on a salaried basis; the cost of the lost time at work accrues mostly to the company.  On the other hand, if an employee paid on an hourly basis without sick days, then the cost of missing work may fall more upon the employees.

The argument that employers prefer insurance benefits which return individuals to work and do not encourage them to stay at home is a key point.  However, employers likely care more about maintaining healthy people’s health.  This way they won’t miss work and the firm can recoup the training costs they invested in the employee.  On the other hand, if an employee is stricken with a long-term, debilitating disease, the employer would prefer to get rid of the employee and not cover his medical costs whereas for the individual, the case of a drawn out, expensive, debilitating illness is exactly the reason they want insurance.

Thus, while there are many compelling reasons why insurance should be employment based (e.g., risk pooling, economies of scale, more choice than a single payer system), believing that employer’s can design a superior health insurance benefit schedule is not one of them.

On Friday I posted on Consumer Driven Health Care.  These consumer driven health plans (CDHPs) involve individuals having direct discretion about how health care dollars are spent.  If you are interested in CDHP, there may still be some confusion over which H?A you prefer.  Is a HRA (Health Reimbursement Account or Health Reimbursement Arrangement) or a HSA (Health Savings Account) better?  Scott Borden of OFM Benefits Consulting gives some simple explanations in his Kansas City Star article (”…Health Insurance for Workers“).

Consumer directed health plans (CDHP) seem like an attractive option for small businesses. CDHPs utilize high deductible health plans (HDHP) making patients pay more money out of pocket. Because of this, insurance premiums are lower. These HDHPs can be linked to Health Reimbursement Arrangements (HRAs) or Health Savings Accounts (HSAs). Since small businesses do not benefit from economies of scale with respect to the purchase of insurance, HDHPs may be especially attractive for this group.

A paper by Gates, Kapur and Karaca Mandic (2008) find this not to be the case, however. Firms employing 3-49 people are no less likely to offer high deductible health plans than are large firms–conditional on offering insurance. Midsize firms employing 200-499 workers are less likely to offer HDHPs than larger firms.

If the firm offers a HD health plan, will they offer an HSA? One may guess that small firms are less likely to offer HSAs if there are fixed costs to implementing an HSA. Small firms will have higher average costs to offering HSAs, if offering HSA is a true fixed cost and its cost to the employer is not proportional to the number of employees in the firm.

It turns out that small firms between 3-49 workers and firms with 200-499 workers are less likely to offer HSAs–conditional on offering HDHPs–than large firms with 500 or more workers. Middle sized firms with between 50 and 199 workers are just as likely to offer HSAs as large firms.

Other findings of the study include that HSAs are most popular in the Midwest and the South and, surprisingly, firms with a higher proportional of low-income workers are more likely to offer HSAs.

• Susan M. Gates, Kanika Kapur, and Pinar Karaca-Mandic (2008) “Consumer-Directed Health Plans and Health Savings Accounts: Have They Worked for Small Business?,” Forum for Health Economics & Policy: Vol. 11: Iss. 2 (Health Economics), Article 4.

John Tierney writes in The New York Times (”Appeasing the Gods…“) that “”We buy insurance not just for peace of mind or to protect ourselves financially, but because…we think buying health insurance will keep us from getting sick.”

A rational person would believe that buying insurance against an event will not alter the probability that it will occur–ignoring issues of moral hazard.  For instance, the act of buying health insurance should not make us less likely to be sick.  Using more preventive care which is cheaper due to insurance can prevent illness, but the act of buying health insurance should not effect the probability one gets sick holding constant the medical care levels.

A better example may be travel insurance.  “Last year, tens of millions of people bought life insurance for scheduled flights of airlines in the United States. Not one of those insured passengers died in a crash.”  Is this a waste of money?  Not if you are superstitious and believe that the act of buying life insurance affects the probability your plane will crash.

“So when we think about passing up flight insurance, we conjure up disaster just as easily as ancient Greeks imagined a thunderbolt from Olympus, and we too figure we can avert it through the equivalent of a bull sacrifice. Intuitively, we haven’t made great strides since Homer’s day. But at least our gods take credit cards.”

• Hat tip to Arnold Kling at EconLog.

Throughout its history, Medicaid provided health insurance for the nation’s poor. It did this by reimbursing providers on a fee-for-service basis. In the 1990s, however, California and other states decided to let private insurance companies bid for the right to provide services for Medicaid patients. These HMOs would receive a fixed per patient per month payment and the private insurer would be responsible for providing health care to Medicaid enrollees.

HMOs may be more efficient than the government since 1) they have an incentive to keep enrollees healthy to save cost, 2) they can negotiate lower input prices, and 3) competition may lead to higher quality, lower priced medical care. On the other hand, keeping the government run fee-for-service program may have been more efficient if 1) the government’s size and negotiating power could decrease input costs, 2) there may be increasing returns to scale, 3) the HMOs may include significant markups in their bids, and 4) HMOs may offer medical services which do not appeal to unhealthy enrollees (i.e., adverse selection).

A paper by Mark Duggan in the Journal of Public Economics in 2004 aims to see if contracting out Medicaid health care provision to private HMOs decreased costs. Duggan uses the fact that California enacted a mandate that all AFDC Medicaid enrollees must switch to a private HMO. For other individuals, such as those on SSI and those who were disabled, deaf or blind, the switch to the HMO was voluntary. This mandate was enacted between January 1993 and December 1999 depending on the county. The author uses variation in the county enactment date to find the effect of Medicaid HMOs on cost.

Background

The manner in which California instituted the transitioned individuals into private managed care plans can be categorized into 3 groupings:

1. Geographic Managed Care. “the state government contracts with several commercial HMOs to coordinate care for Medicaid recipients. Plans initially applied by submitting a menu of prices at which they would be willing to insure each type of Medicaid recipient. The government then awarded contracts to the plans most likely to deliver high quality medical care at a low price, though the weight placed on quality and spending was not specified.”
2. County Organized Health System (COHS). “Under this model, the not-for-profit, community-based HMO was reimbursed a fixed amount per recipient-month that varied by eligibility category.” Individuals did not have any plan choice and the state did not allow bids from for-profit firms.
3. “Two plan” counties. In these counties, the Medicaid enrollees would be able to choose between one private, commercial plan and one not-for profit plan. “…the state solicited bids from private companies and awarded a contract to just one of the plans.”

The following chart gives the type and date of managed care mandate by county.

Methods

Duggan uses the following equations to estimate spending.

• ManCare_jkt = α₁ + γ₁Mandate_kt + μ₁X_jkt + θ_1j + λ_1t + t*ρ_1k + ε_1jkt
• Spending_jkt = α₂ + γ₂Mandate_kt + μ₂X_jkt + θ_2j + λ_2t + t*ρ_2k + ε_2jkt

Subscripts j, k, and t index individuals, counties, and years respectively. The variable Mandate is equal to the fraction of individual j’s Medicaid eligible months in which a mandate was in effect. ManCare is equal to the fraction of the j’s eligible months in which he is actually enrolled in an HMO. Spending is equal to the Medicaid spending for person j at time t.

Results

Duggan finds that the managed care mandate increased Medicaid spending. Medicaid spending increased by between 17% and 23% for counties in which the mandate came into effect. These results, however, were less pronounced where there was competitive bidding between insurance companies (i.e., the Geographic Managed Care and “Two plan” counties).

Also, despite the increased spending, the author finds no evidence of increased quality in terms of better infant birth outcomes.

• Duggan, Mark (2004) “Does contracting out increase the efficiency of government programs? Evidence from Medicaid HMOs” Journal of Public Economics, v 88(12): 2549-2572/

Consumers are starting to pay a larger share for high priced drugs.  According to the N.Y. Times (”Co-payments“), insurance companies “…are charging patients a percentage of the cost of certain high-priced drugs, usually 20 to 33 percent, which can amount to thousands of dollars a month.”  Medicare’s drug plans have introduced new fee schedules where patients pay larger copayments for Tier 4 and Tier 5 drugs.  Private insurers now followed Medicare’s lead.

Should consumers bear a larger burden of their health care costs?  On the one hand, moving towards more out-of-pocket costs will reduce premiums.  Further, higher co-payments will reduce moral hazard (i.e., the use of unnecessary medical care simply because insurance pays for it).  Also, this moves us closer toward insurance as a policy to insure people against catastrophic risk and not as a mechanism to pay for all medical care.

Still, health economist James Robinson from UC-Berkeley states that “It is very unfortunate social policy.  The more the sick person pays, the less the healthy person pays.”

David Whelan chronicles the rise (and possibly future fall) of Medicare Advantage programs in his article “Unfilled Prescription” in Forbes.

Earlier laws privatizing Medicare, starting with a pilot program in 1985, were written to give insurance companies only 95% of the money otherwise spent per Medicare member. The insurers were supposed to figure out how to make up the difference. It was a blunt way to save the Treasury money, but few companies stepped up…

The 2003 law hiked the payments to lure more insurers into the market. In some counties minimum payments to these plans reached as much as 128% of the amount Medicare traditionally spends per patient. Insurers rushed in, and costs soared. In the most remunerative counties, two times as many old people are enrolled in Medicare Advantage as the national average. As a result, taxpayers now pay an average of 12% more per private-plan beneficiary, not 5% less.

Whenever we talk about cost we also need to talk about quality.  Are people who opt for Medicare Advantage plans getting higher quality care than in traditional Medicare?  Are they able to see doctors in a more timely manner?  Is care more coordinated?  If this is the case, then the extra costs may be worth the money.

Nevertheless, an economist would guess that Medicare Advantage plans should be cheaper.  Even though the private plans have higher administration and advertising costs, they likely are more efficient than the government plans.  Further, one would anticipate that healthier seniors would choose the Medicare Advantage plans and sicker senior would be more likely to choose traditional Medicare.  This selection problem should make Medicare Advantage cheaper.

I agree that the federal government should not pay more money for private plans than it does for traditional Medicare.  It should reimburse the plans the same (or less if there is adverse selection) as it costs for the government to administer traditional Medicare and if firms want to increase the price, than seniors can pay the difference.  If seniors do not want to pay the difference, they can always opt for traditional Medicare.

Gruber (1994) shows that the costs of employer-provided health insurance benefits are passed on to employees through lower wages. But do employees with higher expected medical expenses have their wages reduced by a higher amount to reflect the additional medical costs to employers? This may not be the case if employers can not observe the health of employees when they hire them. On the other hand, for obese individuals, the fact that they are obese is easily observable. Papers such as Finkelstein, Fiebelkorn and Wang (2003) show that annual medical expenditures for obese individuals are $732 more than those of normal weight. Is this additional cost passed on to obese employees through lower wages?

This is what an NBER working paper by Bhattacharya and Bundorf (2005) aims to investigate. The authors compare the wages of obese and non-obese individuals in companies without health insurance. Then they compare the differences in wages of obese and non-obese employees in companies with health insurance. Since no health insurance costs will be passed on employees if no insurance is offered, the difference between the two wage gaps may be able to identify if higher health insurance premiums are passed on to employees through lower wages. If there are differences in wages between obese and non-obese workers (i.e.: due to discrimination, lower productivity, etc.) these differences are likely constant across firms with and without health insurance.

The authors find that “the incidence of obesity on wages for workers insured through their employers is -$1.68.” After controlling for a variety of covariates, this estimate lowers to -$1.44. This difference is mostly due to the fact that wages of obese workers with health insurance grew slower than thinner workers with health insurance. This may be due to the increasing price of medical care, the increasing severity of obesity–the BMI of individuals at the 95th body weight percentile has increased over time–or the aging of the population in the panel.

As a falsification test, the authors use a similar difference-in-difference estimation strategy comparing the obese vs. non-obese wage gap between employers with and without other fringe benefits (e.g.: life insurance, dental insurance, retirement benefits, child care, maternity leave, etc.). If obesity does not affect the cost of these fringe benefits, than we should see no difference in the obese/non-obese wage gap between employers who do and do not offer these benefits. The authors find that this difference-in-difference estimator is not statistically different from zero.

There are a few problems with this analysis. First the authors admit that “those with relatively low productivity due to health consequences of obesity may consume more medical care and, as a result, self select into firms offering health insurance.” Also, the data the authors have only reveals whether or not the individual has health insurance, and does not give the insurance premium paid by either the employer or the employee. Thus, these estimates are likely to be very imprecise.

Nevertheless, if this study’s results are true, then it would imply the following:

“If there are no externalities in these decisions, then “twinkie” taxes will only distort already optimal decisions. But if employer-provided insurance pools the health risk of the obese and non-obese, it will create an externality that reduces incentives to maintain a normal weight. Our evidence on the incidence of the obesity wage premium suggests that pooling of the obese and non-obese does not occur in the employer-sponsored insurance market; hence the externalities caused by health insurance on decisions about body weight are small.

• Jay Bhattacharya and M. Kate Bundorf (2005) “The Incidence of Healthcare Costs of Obesity,” NBER WP #11303.
• Jay Bhattacharyaa and M. Kate Bundorf (2009) “The incidence of the healthcare costs of obesity” Journal of Health Economics, Volume 28, Issue 3, May 2009, Pages 649-658.
• Finkelstein, Fiebelkorn and Wang (2003) “National Medical Spending Attributable To Overweight And Obesity: How Much, And Who’s Paying?,” Health Affairs, Web Exclusive, May 14, 2003.

In this blog, I have written about the Swiss (part one, part two) and Dutch healthcare system extensively. Both systems have a “regulated competition” where insurance is mandatory and insurance companies are mandated to provide a specific insurance benefit package. In the Swiss system, 85% of medical expenditures are financed by insurance premiums and 15% are financed by user fees. In the Netherlands, 50% of expenditures are paid by income-related contributions, 45% are paid by insurance premiums and 5% are paid by user fees. In both systems, the government pays a risk equalization premium to insurance companies who have a higher percentage of sick people to help eliminate cream skimming. However, does this risk equalization system still function when voluntary deductibles are introduced?

This is the question which a paper by van Kleef, et al. (2008) attempt to answer. Currently, the Swiss only count net claims (medical claims paid by the insurance company, ignoring out-of-pocket payments by insurers). Is this a problem? Let us give one example:

Let us assume that Healthy Hank spends $1000 on health care per year and is insured by HealthNet and Sick Sally spends $2000 on health care per year and is insured by SickFund. In the Swiss system, insurance companies receive (pay) a risk equalization payment based on whether they have above (below) average medical expenditures. This would mean that insurance premiums would be $1500, the average of Hank and Sally’s expenditures. HealthNet would pay $500 into the risk equalization pool and SickFund would receive $500.

What happens in the presence of deductibles? Let us assume that HealthNet offers an insurance package with a $500 deductible and HealthNet offers an insurance package with no deductible. Healthy Hanks will sort into the HealthNet deductible package and Sick Sallys sort into the no deductible SickFund package. Now, we have that HealthNet will have $500 of net claims on average since Hank will pay $500 and the insurance company will pay $500. SickFund will still have $2000 of cost.

Now the insurance premium will be $1250 since the insurance premium is based on net claims [(2000+500)/2]. HealthNet will have risk equalization payment of $750 and SickFund will receive $750. The insurance premium for Healthy Harry will be $1250 ($500 + the $750 equalization payment) . The premium for SickFund will be $1250 as well ($2000-the $750 risk equalization payment). Thus, there will be no benefit to choosing the deductible since there is no premium benefit. Yet policy makers would like people to choose the deductible plan to reduce moral hazard. The paper gives a few other scenarios where the risk equalization scheme fails and cream skimming occurs.

In general, economist love choice. Yet in insurance markets, the more choice is given to consumers, the more incentive insurance companies have to cream skim. Despite policymakers best attempts to control cream skimming through risk equalization payments, no risk equalization scheme will be perfect. Like everything in life, there is a tradeoff. In this case, the tradeoff is between offering consumers more choice, and reducing cream-skimming.

• RC van Kleef, K Beck, WPMM van de Ven, RCJA van Vliet (2008) “Risk equalization and voluntary deductibles: A complex interaction,” Journal of Health Economics , 27(2): 427-443.
  

Devon M. Herrick writes an article (”Why rent…“) creating a clever analogy comparing HSAs to equity in a house. He likens traditional health insurance to renting a home, while having a Health Savings Account (HSA) is more like owning the home. Making contributions to HSAs in essence gives you “equity” towards future health care expenses. On the other hand, if you do not use any medical care with traditional insurance, you lose all of your rent annual health insurance premium.

Mr. Herrick claims that he could cut his health insurance premiums by half if he had an HSA. There are 3 main reasons why health insurance premiums are lower. First, in a mechanical sense, health insurance plans are combined with HSAs which have higher deductibles. This means the insurance company will not pay for the first $1000 or so of medical care. Secondly, since there are high deductibles, utilization will decrease because of a reduction in the moral hazard problem. Finally, healthier people sort into HSAs and thus if everyone was compelled to have HSAs, health insurance prices would not decrease as much because there would be less advantageous selection.

As I have mentioned before in this blog, HSAs are highly unequal, since the rich 1) are the ones most likely to benefit from this legislation and 2) they have higher marginal tax rates and thus will receive a larger tax break for every dollar contributed.

Nevertheless, shifting more costs to the consumers and forcing consumers to face the true cost of medical procedures will help to reduce costs and to ensure than only necessary medical procedures are conducted.

• DM Herrick (2005) “Health Insurance: Why Rent When You Can Own?” Health Care News, The Heartland Institute, May 1, 2005.

Many reform advocates have claimed that the federal government should mandate a package of insurance benefits that all private and public health insurers would be legally compelled to provide. Switzerland is one country in which the government defines a what the insurance benefit will be for all standard health insurers. The National Coalition on Health Care also proposes “…requiring insurers to establish explicitly separate premiums for the core benefit package.” Is having the federal or state government mandate a minimum benefit package a good idea?

Cons

Most neo-classical economists would say that having the government mandate an insurance package is a bad idea. Regulation restricts choice. If consumers would prefer an insurance company to cover mental illness and another person would prefer their insurance company have more generous coverage for cancer treatment, then it would be welfare destroying to eliminate the individual’s choice. Even if regulators were able to determine an ‘optimal’ benefit package–even a benefit package deemed optimal for society is unlikely to be optimal for each individual–this optimality could only be achieved in a static setting. When new medical technologies and procedures became available would they be adopted? Adopting unproven medical technologies may not increase quality of care, but would increase the cost of premiums. Adopting technologies too late will harm the sick patients who could benefit from these advances.

Another issue is who would be deciding which procedures are included. Whether it is Congress or a medical “Federal Reserve,” these groups would be influenced by lobbying from the AMA, pharmaceutical companies, and patient interest groups. Further, Congressmen will have their own favorite diseases that they will include in the basic coverage plan, even when funding coverage for these diseases may not be as beneficial as for other diseases.

Pros

One benefit of the standardized medical package is that people would better be able to comparison shop. Currently, it is nearly impossible to determine what your insurance company covers unless you are an expert. With a mandated core benefit package, insurance companies would only be able to compete on the dimensions of price, service, and reputation. They would be no competition with regards to which procedures were covered. Also, this would help to attenuate the problem of adverse selection. Many insurers currently do not offer generous coverage since they know by doing so, they will attract the sickest individuals and likely decrease their profits.

Further with a standard benefit package there should be lower legal costs for both the insurance companies and patients. With a clear core benefit package, litigation would not be eliminated but it would certainly be curtailed since much of the payment ambiguity would be cleared up.

Supplemental Insurance

Regardless of whether or not you prefer a minimum insurance benefit, the government should allow supplementary insurance markets to exist. In this way, those who prefer more generous coverage could purchase additional insurance. Further, it is likely that supplemental insurance would be the first-adopters of new technology and could provide a testing group as to whether or not a new medical treatment should eventually be included into the core benefit package.

The San Diego Union Tribune has an article (”Cross-border coverage“) profiling entreprenuer Jim Arriola and his low cost health insurance plan covering medical care in both the U.S. and Mexico.

His company, Sekure Healthcare, provides a limited-benefit insurance program through employers along with a discount health card program. Both can be used by Sekure members and family members to visit doctors and hospitals on either side of the U.S.-Mexico border.

The health plan is not as generous as typical employer-provided health insurance, but may be an attractive option for low wage workers who can not afford top-of-the-line coverage.  Sekure specifically targets low wage Mexican workers in California.

While the plan certainly fills a niche, this type of cross border plan likely will not gain broad appeal.  First, most people want to receive their medical care where they live.  Thus, the option to have treatment in Mexico will likely only be attractive to frequent migrants or those living near the border (i.e. San Diego).  Secondly, the Sekuye plan does not cover catastrophic medical costs.

“Sekure pays up to $50 for each doctor’s office visit and a maximum of $300 a year for the service. Beneficiaries can get up to $800 a day and a maximum of $3,000 a year for hospitalization. They pay out of their own pockets for any charges exceeding their benefits. “

The Sekure plan is the exact opposite of health plans advocated by Republicans.  Instead of having catastrophic health insurance with a high deductible, the Sekure plan provides a minimal benefit and does not cover catastrophic costs.

Nevertheless, some insurance is better than no insurance for many low wage workers.

Yesterday, I spoke about the Swiss health care system. One of the main attributes of this system is that patients are allowed to choose from any health care plan and the health insurers can not refuse to cover them. Further, since the insurance benefit is mandated by law, there is very little quality difference between plans. Only 8% of health plans restrict provider choice in any way.

An NBER working paper by Frank and Lamiraud analyzes how the number of firms offering health insurance in Switzerland has affected price dispersion and expected price.

Economists generally believe that when more firms/products enter the market, this will increase the probability that a superior health insurer will be available to the public. However, when more products enter the market, search costs increase. Thus, it is theoretically possible for more health insurance offerings to decrease utility.

A paper by Janssen and Moraga-Gonzales (2004) show that an the welfare impact of an increase in the number of sellers depends on the consumers’ search intensity. When consumers search with low intensity, having more firms will reduce search, will not affect expected price and will to greater price dispersion. One can think that inexpensive and/or infrequently purchased items would fall in to this category. When consumers search with high intensity, an increase in the number of firms will increase searching and decrease prices. In the 401(k) market, when employees are offered more than 10 choices, there are reductions in consumer responses (i.e.: less investment switching occurs).

Frank and Lamiraud aim to analyze how the number of firms affects insurance choice in Switzerland. They use a panel survey from the Federal Office for Social Insurance (OFAS). It contains a sample of 2152 individuals and asks about their insurance coverage between 1997 and 2000. Yearly premiums are available at the Federal Office for Public Health (OFSP) website. While there was consolidation in the health insurance market among firms, the average number of health plans offered per canton increased from 39 in 1998 to 52 in 2003. The number of people switching plans was 4.8% in 1997, 5.4% in 1998, 2.7% in 1999 and 2.1% in 2000.

How do people choose their plan?

…40% of people choose a health plan following their parents’ and friends’ choices, and what they see as tradition. Furthermore, as many as 25% individuals declare that they do not strive to pick the health insurance plan with the lowest premium. A substantial number of people explicitly report staying with their health plan based on habit (13.5%) or because they are satisfied with their arrangement (79%)

What do the authors conclude from the data?

First, we show that consumers that switch health plan pay 15% to 16% less in health insurance premiums per month holding ceteris paribus. Second, we show that among consumers expressing dissatisfaction with their health plans those in markets with fewer choices are more likely to express intent to switch. Finally, consumers that used an agent to help them purchase insurance consistently paid significantly lower premiums. This set of results suggests that “mistakes may have been made”.

Despite what orthodox economic theory states, no market is perfect. Understanding these market imperfection imparts important knowledge for economists and health policy makers.

• Richard Frank, Karine Lamiraud, 2008. “Choice, Price Competition and Complexity in Markets for Health Insurance,” NBER Working Paper #13817.

Doctors often complain that health insurers are squeezing their profit margins. These insurers offer the physicians access to patients as part of their network in exchange for discounted fees. Physicians can decide not to join the network and charge higher prices, but may be left with fewer patients. The bargaining power of the health insurer depends on how many patients they are able to channel towards these physicians.

In the U.S., most health insurers restrict provider choice ex ante by using either prohibiting patients from visiting providers outside the network or charging the patients significantly higher co-payment rates if go to a provider outside the network. In the Netherlands, almost all care is free to patients so insurer need to use ex post incentives (e.g.: bonuses, gift certificates, and extra services) in order to entice the patients to use the services of the preferred provider.

A paper by Boonen, Schut and Koolman in the most recent edition of Health Economics examines how well the ex post incentives function in the Netherlands’ pharmacy market. Since pharmacies are regulated and prescription drugs are a homogeneous commodity, quality differences between pharmacies are negligible. The authors use data from two health insurers who attempt to direct their enrollees to specific pharmacies.

Using a multinomial logit framework, the authors find that convenience (i.e.: distance to the pharmacy) has a large impact. The financial incentives offered by health insurer A and B cause many enrollees to use the preferred provider. Health insurer A, however, gave a 10 € for the patient’s first visit to the pharmacy and 5 € for their second visit to the pharmacy. Under this incentive structure, individuals were more likely to switch to the preferred provider and then return to their original pharmacy after the incentives had disappeared. Only 25% of those who switch to the preferred provider continue to use them after the financial incentives disappear.

Health insurer B offered a discounts on products offered at the preferred pharmacy and these incentives were made permanent. Unsurprisingly, enrollees also were more likely to go to the preferred provider after the financial incentive regime was enacted.

One interesting item of note is that Health insurer B’s preferred pharmacy was in the same building as a general practitioner (GP). Since GPs function as gatekeepers in the Dutch system (i.e.: one cannot a prescription without the GPs approval), having the GP in the same building as the pharmacy was a huge convenience. Further, the GP could influence the patient to use the preferred pharmacy.

In summary, it was shown in the Dutch setting that even small incentives can have a large effect on provider choice.

• Boonen LH, Schut FT, Koolman X. “Consumer channeling by health insurers: natural experiments with preferred providers in the Dutch pharmacy market.” 17(3):299-316.

What is the cost of the last article of clothing you bought. This is easy to determine, just check your credit card statement.

Which is cost of health insurance? This answer is more difficult to find. Sure, there is the price of the premium, but different insurance plans have different co-pay/co-insurance levels and different deductible amounts. How do these insurance product design parameters affect the demand for insurance?

This is the question tackled by Marquis et al. (2007). The authors use a nested logit model to examine plan characteristics within the individual insurance market. In their nested logit, the authors assume individuals first choose whether to be insured or not. Then, they must choose which type of insurance (PPO, POS, HMO, etc.). After they choose which type of plan they prefer, then a carrier is chosen. This methodology is based on the work of McFadden (1978).

The authors find an elasticity estimate of -2.0 for plan choice among purchaser. This means that those who are insurer are very price sensitive. However, Marquis and co-authors also find that once a particular company is selected, there are significant switching costs to changing companies. The elasticity of switching companies once a plan type (PPO, HMO etc.) is chosen is only -0.4.

The authors also find that:

…a 3 percent decrease in the actuarially adjusted price (or a 4 percent decrease in the nominal premium) would induce a healthy consumer to switch to a plan with a 50 percent higher deductible. For a riskier consumer, however, it would take a 4.5 percent decrease in the actuarially adjusted premium (or a 5.5 percent decrease in the nominal premium) to make the switch. This suggests that there is potential for selection in consumer-directed health plans—an outcome that concerns many critics of these new plans. In addition, the findings suggest that introducing new high-deductible products is unlikely to play a major role in reducing the number of uninsured.

Consumer education regarding the choice of different plans and help to expand coverage by introducing consumers to low-cost (actuarially) insurance options.

• Marquis MS, Buntin MB, Escarce JJ, Kapur K. (2007). “The Role of Product Design in Consumers’ Choices in the Individual Insurance Market” HSR 42(6) : 2194-2223.
• McFadden, D. 1978. “Modelling the Choice of Residential Location.” In Spatial Interaction Theory and Residential Location, edited by A. Karlquist, pp. 75–96.
  
  Amsterdam: North-Holland.

Typically, economists when economists look at the health insurance market, they focus on the insurance side of it. By this I mean to define insurance as the purchase of a product which will reimburse the buyer in the case of an adverse event. However, one must also look at the concept of protection. Protection is defined as expending a costly effort to reduce the probability of an adverse event. This costly effort, however, will not effect the amount of the loss, only the probability that it occurs.

A seminal paper by Ehrlich and Becker (JPE 1972) finds the optimal levels of self-protection and how optimal self-protection change when insurance markets are introduced. Let us assume that the probability of a loss is p(e) where e is the effort expended and p’<0. An expected utility maximizer optimizes the following function:

• max_e [1-p(e)]*U(I -e) + p(e)*U(I – L – e)

The first order condition is:

• -p’*[U(I -e)-U(I - L - e)]=(1-p)*U’(I -e) + p*U’(I – L – e)

Ehrlich and Becker note that “[t]he term on the left is the marginal gain from the reduction in p; that on the right, the decline in utility due to the decline in both incomes, is the marginal cost.”

When we introduce an insurance market, the expected utility maximizer faces a new objective function.

• max_e,s ,s [1-p(e)]*U(I-e-s*π(e)) + p(e)*U(I – L – e + s)

Here s is the insurance benefit and π(e) is price of the insurance; s*π(e) is the insurance premium. Let U(0)=U(I-L-e+s) and U(1)=U(I-e-s*π(e)). The first order conditions now become:

• -(1-p)U’(1) + p*U’(0)=0
• -p’*[U(1)-U(0)] – (1-p)*U’(1)*[1+s*π'] – p*U’(0)=1

How does self protection change when insurance markets are introduced? According to Ehrlich and Becker “On the one hand, self protection is discouraged because its marginal gain is reduced by the reduction of the difference between the incomes and thus the utilities in different states, on the other hand, it is encouraged if the price of market insurance is negatively related to the amount spent on protection through the effect of these expenditures on the probabilities.”

If insurance companies are actually able to measure self-protection and can price insurance accordingly, then individuals will have some incentive to increase prevention in order to lower their premiums. If insurance is priced in an actuarially fair manner (i.e., π=p(e)/[1-p(e)]) we can show that premiums will drop when self-protection increases:

• ∂π/∂e=p’/(1-p)²<0

However, if insurance companies are not able to observe self-protection efforts, than it is likely that moral hazard will occur–self protection will decrease. In the words of the authors, “Self-protection would then usually be discouraged by market insurance–moral hazard would exist–because the main effect of introducing market insurance would be to narrow the differences between incomes in different states.”

• Ehrlich I, Becker G. 1972. “Market Insurance, Self-Insurance, and Self-Protection.” The Journal of Political Economy, 80(4), pp. 623-648.

According to the San Diego Union Tribune, yesterday PacifiCare was fined $3.5 million and the California Department of Managed Health Care is seeking up to $1.3 billion in additional penalties for “130,000 alleged claims-processing violations…in California between July 1, 2005, and May 31, 2007.” PacifiCare is the second largest HMO in San Diego and the fourth largest health insurer in California.

These violations have prompted California Insurance Commissioner Steve Poizner begin an audit of the eight largest California health insurers to determine whether or not these companies have engaged in similar billing practice.

Joe Paduda of Managed Care Matters argues that the ruling is another piece of evidence which favors a  single-payer system.  Mr. Paduda states:

For those (including me) forever excoriating health systems and hospitals for their outrageous error rates, the debacle at Pacificare, the recently-acquired division of United Healthcare (one of my past employers) make the delivery sector look like a paragon of performance. I’m not overly surprised, as mergers involve systems conversions, the amalgamation of provider networks and contracts, and the shifting of work around to different call centers and processing locations. Duplicate staff positions are identified and people laid off, and when they walk out the door so does the expertise and understanding that enabled the operation to run smoothly.

The question remains, would a single-payer system perform better?  The government is not known as the paragon of efficiency.  With a single payer system, likely one of two things will happen:

• Government administrators will make claims processing errors just as health insurance administrators do now, or
• government administrators will deny less claims erroneously, but this will likely coincide with the acceptance of more unnecessary or false claims, thus increasing overall health care costs.

A single payer system may lead to improved claims processing.  However, for anyone to be convinced that a single payer system is the way to go, one must not only show that the present system is flawed, but that a single payer system is a significant improvement.

Adverse selection is often seen as a major impediment to the efficient functioning of insurance markets. Rothschild and Stiglitz (1976) create a model where high risk people buy full insurance while low risk individuals buy partial insurance. Yet empirically, one finds that in some insurance markets, low risk individuals purchase more insurance than high risk individuals.

An NBER working paper by Cutler, Finkelstein and McGarry (2008) claims that preference heterogeneity may explain this phenomenon. If low-risk individuals also have a stronger risk aversion preferences, than they may buy more insurance than a high-risk individual who has risk loving preferences. This work is an extension of the Finkelstein and McGarry (AER 2006) article discussed in one of my earlier blog posts.

Using data from the Health and Retirement Study (HRS), the authors measure risk tolerance using the following variables: smoking, having 3+ alcoholic drinks per day, job-based mortality risk, receipt of preventive health services, and seat belt usage. The authors find a negative relationship between individuals who engage in risky behavior (i.e., smoking, drinking, and those working in a high-risk occupation) and the percent who purchase various types of insurance, and a positive relationship between those engage in risk reducing behavior (i.e., preventive medical care, seat belt usage) and the purchase of insurance.

In the above chart, ‘+’ represents a positive statistically significant correlation, ‘-’ represents a negative statistically significant correlation, and o indicates that the relationship is not statistically significant.

The authors can also measure risk preferences based on respondents answers to income gamble questions. There is a weak relationship between risk preferences and risk behavior however.

The authors confirm that risk behavior lead to an increased probability of adverse events which would be covered by insurance. For instance, smoking increases mortality which would lead to an earlier life insurance payout. Increased preventive health activities decrease the probability of a nursing home stay.

Thus the authors conclude the following:

Our analysis yields two main findings. First, in all five markets, we find that individuals who engage in what are commonly thought of as risky behaviors (smoking, drinking, or prior employment in jobs with higher mortality rates) or who do not take measures to thought to reduce risk (preventive health activities or wearing of a seat belt) are systematically less likely to hold each of these insurance products. Second, we find that these same individuals tend to have higher expected claims for life insurance and long term care insurance, but lower expected claims for annuities; for Medigap and acute health insurance, there is no systematic relationship between the behavior measures and expected claims.

These results can help to explain the puzzle of insurance we started with: why is adverse selection not more common? In annuity markets, there is clear evidence of adverse selection: people who live longer are more likely to buy insurance. The standard adverse selection model is one explanation for this, but so is variation in risk tolerance; people who have less risky behaviors live longer and are more likely to buy annuities. In life insurance, our results suggest that differential risk tolerance can help explain why people with lower mortality rates have more insurance. Similarly, in the case of long-term care insurance, people who use more preventive care or are more likely to wear seat belts buy insurance more readily but also stay out of nursing homes.

• David Cutler, Amy Finkelstein, Kathleen McGarry. 2008. “Preference Heterogeneity and Insurance Markets: Explaining a Puzzle of Insurance,” NBER WP #13746.
• Finkelstein, Amy and Kathleen McGarry. 2006. “Private information and its effect on market equilibrium: new evidence from long-term care insurance.” American Economic Review 96(4): 938 – 958

Many of the Democratic candidates support having employers provide insurance for their employees with the threat of a fine or tax if an employer decides not to comply. This of course will increase the cost of an employee for firms. If employees truly value the health insurance, then the cost of insurance can be passed on to the employee through lower wages.

This cannot happen, however, if you are a low-wage worker whose wage is at or near the minimum wage. This is, of course, because employers can not pay wages below the minimum. Thus, a “Pay or Play” mandate may reduce employment for the lowest skilled workers. A paper by Baicker and Levy examines this issue.

The authors find the following:

“The authors calculate the average cost of a health insurance plan to be about $9,000 for family coverage during their sample period, or $3.66 per hour for a full-time worker. Assuming that a mandate required employers to provide coverage similar to the average plan and to pay 80 percent of premiums, wages would need to fall by $3 per hour to fully offset the cost of the mandate.

The authors estimate that one-third of all uninsured workers, or 5.5 million U.S. private sector workers, have earnings within $3 of the minimum wage.

…the authors estimate that the implied increase in compensation resulting from the mandate would cause 224,000 workers to lose their jobs. The affected workers would be disproportionately low education, minority, and female.”

From the NBER Fall 2007 Bulletin on Aging and Health.

According to the N.Y. Times (”…Benefit Cut at 65 in Retiree Plans“) in 2001it is estimated that one-third of large employers and fewer than one-tenth of small employers offered health benefits to retirees.  These numbers may trend towards zero in the near future after an Equal Employment Opportunity Commission (EEOC) ruling.

NPR’s Marketplace reports (”Employers let off one health-care hook“) the EEOC has ruled  “that companies can cut their retirees’ health-care benefits once they turn 65.”  This will lead to more government provided health care.  Is this a good thing?

Businesses will certainly benefit from not having to be in the business of planning for the health insurance of seniors.   According to the N.Y. Times, Dianna B. Johnston, a lawyer for the commission, said many employers and labor unions had told it that “if they had to provide identical benefits for retirees under 65 and over 65, they would just drop retiree health benefits altogether for both groups.â€?

Further, a paper by Gopi Shah Goda, John B. Shoven, Sita Nataraj Slavov (reviewed on 10 Oct 07) claims that having Medicare as a Secondary Payer (MSP) creates an implicit tax for elderly workers.  The authors find that the tax is 15-20 percent at age 65 and increases to 45-70 percent by age 80.  While the authors claims are based on how MSP effects seniors’ incentives to work, it does not comment on whether or not implicit contracts guaranteeing retirees right to private insurance should be abolished or not.

In essence, this ruling is a transfer from retirees to businesses.  Retirees who believed they would receive private health insurance from their employer now must rely on Medicare or pay for private insurance in the individual market.  Businesses benefit from being able to eliminate costs from insuring retirees.

This raises the larger question of who should be paying for health insurance.  The government could do it, but this may lead to a monopolistic system with little choice and a potential for corruption.  Individuals could buy their own insurance, but without a mechanism to pool risk, sick individuals will have to pay significantly higher premiums than healthy individuals. Insurance is supposed to insulate individuals from income shocks due to changes in their health status and an individual market will not be able to accomplish this goal.  A natural risk pooling institution is the employer, but employers do not want to be in the business of planning their employees (large) health insurance choices.  Who should pay for health care is at the crux of the health care debate and needs to be resolved before policy reforms are implemented.

Recently, there has been much controversy regarding whether or not the RAND Health Insurance Experiment (HIE) results are truly robust. Many blogs have been questioning the results (see here, here and here). One of the major conclusions of the HIE are that higher co-insurance rates lead to lower levels of medical utilization and lower medical cost, but do not have any adverse impact on health outcomes.

A paper by John Nyman (2007) in the Journal of Health Politics, Policy and Law calls these findings into question. He notes that there was differential attrition in the insurance plans with cost-sharing for the patients compared to those with no cost-sharing. If people who became sick in the cost sharing insurance plans elected to drop out of the experiment and seek care from their previous insurance plan, then an attrition bias would occur. This bias would incorrectly deflate the medical utilization of the cost sharing group and thus lead to the erroneous conclusion that more cost sharing causes lower medical utilization patterns.

Nyman’s paper also wisely notes that moral hazard is only a problem with individuals afflicted with a disease. For instance, no matter if one has insurance that would reduce the price of a mastectomy to zero, no one ever elect to have a mastectomy unless they had breast cancer. There are personal costs of the side effects of treatment and time costs which imply that moral hazard will generally only be a problem for those afflicted with a disease. There are exceptions. For cosmetic surgery procedures, individual do not need to be sick to suffer from the problem of moral hazard with insurance.

Nyman’s points are valid, but I believe that the RAND HIE results are robust. First, each RAND HIE participant receive a participation payment each month. Even if the individual had large medical expenses and had to pay a large deductible (capped at $1000, which $4000 in 2007 dollars), the monthly participation fees would add up to more than $1000 so it was in the individual’s best interest to stay in the experiment. Nyman argues that cash flow problems many have affected some participants. If I receive $100 per month for a year, but have $1000 in medical expenses today, I may decide to leave the experiment. Yet because of this participation fee, it seem that this would not be a major problem.

Also, the problem of attrition was recognized in the original RAND HIE. Nyman himself states on his website:”When the RAND data were re-analyzed to account for the differential utilization from attrition, the difference in hospitalizations between free care and all cost-sharing arms was about 19 percent, not 25 percent as has been reported (Manning, Duan, Keeler, 1993, p. 13).” While the magnitude of the cost sharing effect may be somewhat smaller once we take into account attrition, the general finding remains the same: more cost sharing leads to less utilization.

Finally, while RAND is the only large scale RCT that has been conducted, many other studies have shown that increased cost sharing leads to lower medical utilization. Newhouse states that “enormous number of observational studies over many years, in many settings, with many different methodologies, that find utilization of medical services responds to relatively modest cost sharing.” Nyman’s response is that:

“I do not dispute that cost sharing reduces utilization. I think it does and agree that the non-experimental studies that Newhouse et al. (2007) refer to are generally convincing. The issue that I am addressing, however, is whether cost-sharing in the RAND Experiment actually produced a 25 percent reduction in equally effective hospitalizations and whether such a reduction would actually have no effect on health.”

One question you may still have is how is it possible that lower medical utilization leads to the same health outcomes. One possibility is that the moral hazard may have resulted in only the excess use ineffective medicine. It could be the case that much of medicine has little impact in the overall health of the patient and too much treatment can actually harm the patient (see my post on Overtreated). It could be the case that health was measured poorly by the HIE, but Nyman concedes that a “broad spectrum of health status measures” where used in the RAND experiment.

Overall, I do believe Nyman brought up some reasonable points. No experiment is ever perfect…even the RAND HIE. Attrition was a problem and may affect the magnitude of the result. Nevertheless, the conclusions from the RAND HIE remain robust and I believe that the attrition issue was addressed previously.

Can we still conclude that more cost sharing reduces medical utilization? Is moral hazard a problem in health insurance markets? We can still confidently say: YES!

• Nyman, John A. “American Health Policy: Cracks in the Foundation,” Journal of Health Politics, Policy and Law vol. 32, no. 5, October 2007, pp. 759-783.
• Newhouse et al. (2007) “Attrition and Refusal in the RAND Health Insurance Experiment” (from Newhouse and the Insurance Experiment Group, Free for All: Lessons from the RAND Health Insurance Experiment, pages 17-26).
• Nyman, John A. (2007) Attrition in the RAND Health Insurance Experiment: A Response to Newhouse et al. (2007).
• Manning, Willard G., Joseph P. Newhouse, Naihua Duan, Emmett B. Keeler and Arleen Leibowitz. “Health Insurance and the Demand for Medical Care: Evidence from a Randomized Experiment.” American Economic Review, 1987, 77 (3), pp. 251-277.

When I pay for health insurance, is most of my monthly premium going for medical care?  Or does most of the money go towards administrative expenses and insurance company profit?

John Aloysius Cogan Jr. of the Regulating Health Insurance blog finds that most of the money spent on insurance premiums in fact goes towards providing medical care.  The post looks at two Northeastern insurance companies: Blue Cross & Blue Shield of Rhode Island and UnitedHealthcare of New England.  The results are as follows:

While the administrative and profit costs are much higher for these two firms than for Medicare, the higher admin cost percentage does not tell the whole story. As mention on my July 27, 2006 post,  Medicare does not take into account the deadweight loss from the taxation needed to raise money to fund Medicare.  Further, Medicare assumes a zero cost of capital and likely is less vigilant about pursuing unnecessary claims than a private insurance plan.  And finally, a lower administrative cost may simply be due to the fact that Medicare spends more money per person (i.e.: the denominator of the administrative expense percentage grows large).

In 2005, for small employers, BC/BS cost $399/month and United Healthcare cost $392/month. The Regulating Health Insurance blog notes the following:

“Even if Blue Cross and United had slashed their 2005 small employer market administrative costs and profits by one-third, their base rates for 2005 would have still been quite high. Blue Cross’ rate would have been $377.30/mo. and United’s would have been $361.35/mo.

Wal-mart recently released the details of its new health plan for 2008 (see company press release or the Houston Chronicle article).  Here are some highlights of the plan.

• Employees now have “50 ways of customizing their health care coverage options…a substantial increase from last year, when most associates had only nine choices.”  However, it seems that the employees will not be able to choose different coverage options (i.e.: what medical procedures are covered), but can only alter their insurance on the dimensions of the deductible/health care credit and thus their premium.  More choices may be good, but it also may create an adverse selection death spiral in which the more generous plans become more and more expensive in the future.
• All Wal-mart employees will have deductibles (up to $2000 in the case of the cheapest plan), but employees will also receive a health-care credit of $100, $250 or $500.  I believe that that the reason for the credit is so that employees don’t forgo, low cost preventative measures (e.g.: regular doctor check-ups, immunizations) simply because they would have to pay out of pocket.
• There is no maximum insurance coverage which is good.  Insurance is made alleviate the risk of large-scale medical problems.
• Their plan offers $4 co-pay for over 2,400 covered generic prescriptions.

The Houston Chronicle reports:

As of the start of this year, 47 percent of Wal-Mart’s 1.34 million U.S. employees were enrolled in company coverage, compared with 46 percent a year earlier and 43 percent at the start of 2005.

Wal-Mart has said most of the remainder are insured through other plans, such as a spouse’s or a second job. That justifies its contention that 90 percent of employees have health coverage.

I am not sure whether this 90% figure is reliable.  It may count spousal coverage, but may also include those on Medicare or Medicaid, which would mean that John Q. Public would be footing the bill for these employees and Wal-mart should not implicitly take credit for providing insurance to these employees.

Is this good?

Having Wal-mart offer health insurance is not an unequivocal good thing.  When Wal-mart offers affordable health insurance plans, that means that Wal-mart will be a more desirable place to work.  The supply of individuals willing to work for Wal-mart will increase and thus the wage of Wal-mart employees will decrease.  In a reality with sticky wages, wages will likely not decrease, but the rate of increase of future Wal-mart employee wages will be lower than the wages of similarly-skilled employees working at companies without health insurance.  Risk loving people will not appreciate that some of their wages are taken to pay for health insurance.  Risk averse individuals–which likely represent the majority of Americans–will fully value the increased health benefits and will not mind a slight wage decrease.  Further, because of Wal-mart’s buying power, they will receive less expensive, more comprehensive health care costs than they would have on the non-group market.

Despite much public rhetoric, why is preventative and chronic care so poor in the U.S.? The easy answer is that patients switch plans so frequently that insurance companies who invest in preventative care will incur the cost, but not reap the benefits. The harder question is why patients are switching health plans.

According to a working paper by Cebul, Herschman, Rebitzer, Taylor and Votruba featured on Slate, the answer may be “search frictions.” In the paper, turnover is generated from two sources: 1) from employees leaving the company for new jobs and 2) by having the employer switch to a new health plan. Data from the Community Track Study in 1996/7, 1998/9, 2001 and 2003 show that average annual insurance cancellations are about 21%. More than one third of the turnover is caused by employers switching health plans. Small employers were more likely to switch insurance plans than larger employers. Why don’t they just stay with one plan?

The search friction model is developed from a labor economics paper by Burdett and Mortensen (1998). The authors argue persuasively that extending the model to the case of health insurance makes perfect sense.

“The market for health insurance is a natural place to expect search frictions. Health insurance is a complex, multi-attribute product and this complexity makes it difficult for clients to meaningfully compare more than a handful of proposals. Informal discussions with insurers suggest that they offer customers hundreds if not thousands of different policies. This complexity also makes the marketing of insurance costly so that companies can make only a limited number of appeals to employer groups in a period.”

The authors explain how the price friction mechanism works. The price of the insurance policy is p, the marginal cost of the policy is c, and the firm’s reservation price for buying insurance is p^R.

Suppose all firms made the same price offers p=c and so earned zero profit. Then one maverick firm could clearly increase profits by charging some discretely higher price (less than or equal to the reservation price p^R). This high offer would be rejected more frequently than the going price because any potential client who fielded more than one offer in a period would obviously reject the high offer. But on occasion the contacted client would have no other offers, and a policy would be sold. This would produce positive profit for the firm. Similarly, in a candidate equilibrium in which all firms were charging the same price (a price such that c<p<p^R ), a maverick firm could always increase profit by undercutting slightly the price charged by competitors, thereby increasing the number of clients while reducing by profit per client by only a trivial amount. In short, an equilibrium must entail a distribution of price offers.

Once market friction reach a sufficient level, in equilibrium we will observe a churning of employers going through different insurance policies each year. Introducing the issues that come along with adverse selection is likely to only increase market frictions because insurance companies now will want to screen employees.

Possible Solutions

The authors offer arguments made that may be solutions to the problem.

• Patient-financed health investments. Health care investments (i.e.: preventative care) should be financed by the client. This way, the person reaping the rewards from preventative care will also incur the costs. If the patient switches insurance plans, this will not be a problem since they will be eligible for lower premiums because of their preventative care history. On the other hand, determining what type of care is an expense and what is an investment may be very difficult practically. Further, shifting more risk onto the patient is the antithesis of what insurance is supposed to do. Finally, when switching insurers, it will be very difficult to verify the actual amount of preventative care received without a nation-wide standard for electronic medical records.
• Long term contracts. Another simple solution is just for employers to purchase long term contracts from insurance companies. This way, insurers will be able to reap more of their rewards from earlier years’ health investments. On the other hand, “given constantly-evolving medical technology and treatment protocols, as well as hard to predict changes in governmental regulation and mandates, it is difficult to see how long-term contracts might be implemented.”
• Price Caps. Government set price caps are probably the worse option. As Slate states, “But where should the government set the ceiling? If it’s too low, the government could end up destroying insurance companies’ incentives to stay in business at all.”
• Legislate a basic insurance package. The authors conclude the paper with the following: “It follows from this that much of the distortions resulting from frictions could be mitigated if there were a simple, easily understood and reasonably priced alternative insurance policy that would be available to all market participants. In the context of our search models, we believe we can prove that by making this alternative insurance available on a voluntary basis to all purchasers the inefficiencies resulting from search frictions could be greatly reduced.” Another option would be to offer everyone the choice of a nationalized health plan (a la Medicaid). People who did not want Medicaid, could choose to have vouchers (see Healthcare Vouchers) used to pay towards a private insurance plan of their choice. Many of the basic private insurance plans will likely mimic the nationalized Medicaid, but some plans will offer alternatives which will be more flexible and easier to adapt to new technology advances.

In a blog post (”Sicko Sticko Shock“), Marc Cooper discusses his recent hospital bill for a heart procedure of “moderate complexity.” He finds that the amount billed was $116,749. However, the procedure was much cheaper for Mr. Cooper since he had Blue Cross insurance. “In a column lateral to the “amount billed” I then find the “amount allowed” i.e. the amount that Blue Cross is actually willing to pay the hospital. That amount: $4730, or less than 4% of the total charge.” Further the $4730 was paid for entirely by his insurance company. Mr. Cooper wonders how he could have paid the $100,000 if he wasn’t insured. He concludes that “the system is absurd, insulting and inhuman.”

The Coyote Blog states (”You better shop around“) that the conclusion shouldn’t be that the current health care system should be scrapped, but instead that “we should find a way to have individuals experience both the cost and benefits of care, because only they can make these tradeoffs for themselves and shop around for better options.” The Coyote Blog makes the following analogy:

Sure, this [hospital bill] looks like a rip-off. But if you went in to buy a car, concerned only with the quality of the car, and never asked the price and then got a bill for $100,000 a few weeks later, would you be surprised? Would anyone give you sympathy if you complained you paid $100,000 for the car but admitted you never asked what the price was? So this is a dead-obvious outcome from the health care system we have, where no one has the incentive to shop.

This is of course the moral hazard trade off with insurance. People buy insurance because they are risk averse and fear an extremely medical care…or care repairs…or home repairs…or in whatever other areas people buy insurance. When one buys insurance, however, there is the moral hazard problem that people will not shop around for a better deal, they may not take as many precautions to protect their health if they know their insurer will pay for treatment. However, insurance is economically optimal when people are risk averse. Finding the correct balance between insuring people against losses while minimizing moral hazard is a delicate and difficult matter to resolve empirically–especially in the health care setting with so much asymmetric information and uncertainty.

Should hospitals with long waiting times have higher or lower budget transfers? Offering hospitals who have low wait times more money will increase a hospital’s incentives to decrease wait times. On the other hand, thus policy may hurt the busier hospitals and may not alleviate the wait times of those who are waiting the longest. In the case of public school transfers, if the best schools are rewarded, this encourages achievement, but may punish the worst off kids (i.e.: those at poorer schools). Transfers to low-performing school may mute incentives to increase achievement.

The issue of hospital payment structure is analyzed by Luigi Siciliani in his article on optimal contracts B.E. Journal of Economic Analysis & Policy. As with any thesis which claims to give an optimum solution, this optimum is based on some assumptions. This paper uses four major assumptions.

1. Demand for treatment can be controlled by dumping some patients. Doctors can tell patients who wish to have medical treatment that they either a) don’t really need it or b) that they will not provide it
2. The purchaser (i.e.: NHS, an insurance company, Medicare, etc.) can not observe the number of people dumped.
3. Dumping is costly for the specialist. By dumping patients, the specialist receives more complaints about their service level. Thus, either the physician’s reputation is tarnished (a cost) or the physician must spent more time (another cost) convincing the patient that they do not need treatment.
4. Hospitals differ in potential demand for treatment, either due to the catchment area of the hospital or from having a better or worse reputation.

Another key assumption is that no co-payment charges can be issued. This assumption is plausible, because it basically represents the British NHS system. Thus, the optimal solution must be seen not as the ideal optimal, but as the optimal with a centralized payer and no co-payments.

The Model

Hospitals have parameter θ which describes the public hospital type. This parameter θ indicates potential demand in the absence of a rationing system.

For each treatment, patients differ in the value they would receive from treatment. For instance, healthy patients would not benefit from heart surgery, but individuals with coronary artery blockages likely would benefit from surgery. Thus the author assumes that individuals’ value from treatment is uniformly distributed between v₀ and v₁.

Patients have three options:

1. They can be treated at a public sector hospital after a wait of time w, [u^p(v,p)]={∫^T₀ v dt} -p=vT-p]
2. They can be treated in a private sector hospital with no wait, but pay a price of p, [u^NHS(v,w)]={∫^T_w v*g(w) dt} =vg(w)(T-w)]
3. or they can receive no treatment[u^none=0]

There are two costs to going to the public hospital. First, the individual has to wait w weeks longer, so they do not get to enjoy the benefit of the treatment for as extended a period of time. Secondly, since 0<g(w)<1, the treatment becomes less effective or less valued the longer the patient waits.

Thus, from the math above, we can see that a person will choose a public hospital if and only if:

• v<V(w)=p/{T-g(w)*(T-w)

The comparative statics show that longer wait times decrease the probability of using a public hospital, higher prices, p, decrease the probability of using a private hospital, and higher valuations, v, increase the probability of using a private hospital.

Demand for public hospital services is written as:

• D(θ,w,x)=θV(w)-x
• x is the number of patients who are dumped (i.e.: not added to the waiting list)

The number of treatment supplied by hospital θ is y(θ) and since supply must equal demand, we have:

• θV(w)-x=y(θ)

The authors claim that providers receive disutility from dumping patients. Also, hospitals receive more disutility when they dump patients who value the treatment more (i.e.: high v, this is more likely to be the sicker patients). Thus, we are lead to our first major conclusion.

• Conclusion 1: The patients who are dumped are the ones with the lower benefits from treatment. This means that hospitals dump the patients who don’t really need the treatment.

After some more math, the Dr. Siciliani states a second conclusion:

• Conclusion 2: A mix of explicit rationing (through dumping) and implicit rationing (through waiting) is therefore optimal. Siciliani explains that: “Rationing by waiting alone induces excessive disutility for patients. Rationing by dumping alone generates excessive disutility for the specialists.”

The author continues to conclude that a separating or pooling equilibrium may occur.

“Under symmetric information, the optimal contract is for the purchaser simply to over a transfer in exchange for the provision of the desired level of activity and waiting time, without leaving any rent to the provider…Under asymmetric information, we found that a separating equilibrium exists when it is optimal for the purchaser of health services to contract more activity and higher waiting times to hospitals with higher demand. In this case providers with low potential demand have an incentive to mimic hospitals with high potential demand. To induce hospitals to self-select, the purchaser needs to pay a rent to hospitals with lower potential demand. [But] if it is not optimal for the purchaser to contract more activity and higher waiting time to hospitals with higher demand, then a separating equilibrium may not exist.”

Problems

One main problem with the paper is that it assumes that patients with a high value, v, cost the same to treat as low value patients. If v is a proxy for sickness, this is likely not to be the case; sicker patients with a high v are more expensive to treat. If this were the case, then conclusion 1 would not hold. Public hospitals would instead treat patients with the lowest benefit and dump patients with intermediate benefits–the high benefit patients would still go to the private sector hospital.

Also, the paper does not take into account any strategic interaction between hospitals. “If hospitals with higher potential demand are contracted higher waiting times, then patients will switch from the hospitals with high potential demand to hospitals with low potential demand, increasing excessively the amount of dumping and consequent disutility for hospitals with low potential demand.”

• Luigi Siciliani (2007) “Optimal Contracts for Health Services in the Presence of Waiting Times and Asymmetric Information,” The B.E. Journal of Economic Analysis & Policy: Vol. 7 : Iss. 1 (Contributions), Article 40.

What is the optimal way to pay physicians? If there were a singular variable ‘health’ that could be easily measured, patients could pay physicians for each unit of health they receive. Of course, this is not how the physician-patient relationship operates in the real world. Physicians are paid either a base rate per person per month or receive a fixed fee for each service provided. In physician contracts with health plans, the physician effort level to gather information (diagnosis) is non-contractible and pay based on the patient’s health condition (physicians’ private information) is also non-contractible. In a setting with so much uncertainty, what is the optimal physician contract?

This is the question which Izabela Jelovac attempts to answer in her 2004 paper in Health Economics. In her model, patients who visit the doctor can have two types of illnesses, one mild and one severe. The doctor chooses an effort level ε, in order to diagnose the illness. The more effort the doctor puts forth, the more likely they will diagnose the disease correctly. In the next stage of the game, the doctor chooses whether to treat the patient with an expensive treatment which will cure the severe disease and or the inexpensive treatment which will cure the serious disease. Prescribing the expensive treatment when the patient has the mild disease causes a health loss. If the patient recovers, then the game ends, but if the patient does not recover then the patient returns to the physician and receives the alternative treatment.

So what does the physician do? Typically, we would guess that the doctor will prescribe the adequate treatment strategy of providing the expensive treatment for the severe disease and the inexpensive treatment for the mild disease. However, “…if a single visit is less profitable to the physician than two visits, the physician is better off providing the most inadequate treatment than the most adequate one in order to increase the likelihood of a second visit.” This is like a car mechanic who damages people’s cars when they come in for an oil change in order to earn more money during the second visit from fixing the problem which they caused in the first.

The author also found that a strategy of always prescribing the inexpensive (or expensive) treatment during the first visit can be optimal as well. For instance, if diagnosing the serious illness is very costly (numerous expensive tests and many physician labor hours are incurred) and if the serious illness isn’t too severe, then giving all patients the inexpensive treatment during the first visit may be optimal.

The authors go through some dense math, but end up concluding that some supply-side cost sharing by the physician is optimal since it induces the physician to provide the most adequate treatment. With the cost sharing, having the patient return for a second visit is never profit maximizing.

This paper does have a few problems. A key assumption in this model is that “the need for a second treatment is necessarily the physician’s fault.” In reality, this need not be the case. Also, the authors do not take into account issues of the physician’s reputation. If 100% of a physician’s patients had 2 visits, patients may decide to leave this physician and sign up with a doctor who choses the “adequate treatment” strategy.

Hillman (Ann Intern Med 1990) writes that “whereas most physicians will act in the patient’s best interest when the medical decision is clear-cut, the effect of financial incentives may be most important in cases where the correct decision is not obvious.”

• Jelovac, Izabela (2001) “Physicians’ payment contracts, treatment decisions and diagnosis accuracy” Health Economics vol 10, pp. 9-25.

One session of the European Science Days summer school involved a presentation on long-term care (LTC) from Volker Meier. The main question is why is there so little demand for LTC insurance in the United States as well as in other countries? Below are some explanations as to why the LTC insurance market is so small and why–when people do buy LTC insurance–do they make the purchase so late in life:

• Loading factors. If loading factors are too high, this may possibly justify government intervention. However a paper by Brown and Finkelstein (JPubE 2007) finds that loading factors are comparable to those of life annuities. Meier claims that if there are fixed loading costs, purchasing later in life can save on these costs. However, I believe that these most insurance contracts likely have a fixed and variable loading factor even if economists do not typically model it this way, and this fixed loading cost explanation is likely a poor one.
• Adverse Selection. It seems likely that individuals have some information regarding the probability they will need LTC in the near future. Most individuals purchase health insurance because there is a non-trivial probability that they will become sick each year. Most younger individuals in good health, however, will not need LTC in the next year. Even if one falls sick, it may be years before LTC is needed. Thus, if one can predict the need for LTC with near certainty two years in advance, no one would purchase LTC insurance.
• Medicaid. In the U.S., Medicaid pays for about 40% of nursing home stays (Forbes). Thus, poor people have no incentive to purchase LTC insurance since Medicaid will pay. The middle class can run down their assets in the case where they need LTC and have Medicaid foot the bill. Thus, only for the rich will purchase LTC insurance in the United States in order to protect their accumulated assets.
• Proposal: Matching life insurance with LTC insurance. Peter Zweifel proposed this idea which seems logical. Individuals who live past 65 can use the proceeds from their life insurance policy to pay for LTC insurance. However, there would seem to be significant problems with insurance companies pricing LTC years in advance. Further, healthy individuals who reach age 65 may simply prefer to use the life insurance proceeds to spend on other things. This is especially true if LTC needs are predictable a few years in advance.

Care in a nursing home or care at home

Much debate in Europe and the U.S. has wondered whether paying family members to care for the elderly can save taxpayers money and increase the quality of care. Dr. Meier creates a model with the following characteristics:

• Individuals fall sick and the cost to care for them is variable. Individuals who go to the nursing home have a cost of K_n, while individuals can also receive care at home for a cost of β_iK_h, where β_minK_h < K_n < β_maxK_n.
• β_i can depend on the dependent’s opportunity cost or the seriousness of the disease. Thus, we would predict that individuals with better paying jobs and individuals who’s parents have more serious diseases will prefer to put their parents in nursing homes compared to at-home long term care.
• Another problem with paying individuals to care for their parents at home is the issue of fraud. Individuals can claim to be helping their parents cope with some diseases in order to receive extra cash, even though the work they do may be minimal or non-existent.
• Meier finds that it is optimal to offer two types of LTC insurance contracts. The first is for full insurance for nursing home care. Another insurance contract will give partial insurance for care at home. The premium for the first contract will be more expensive than for the second. The partial insurance will help to discourage fraud. Meier claims that this is how the German public long-term care insurance operates.

Reading Recommendations:

• Brown and Finkelstein (2007) “Why is the Market for Long-Term Care Insurance So Small?” J Pub Econ forthcoming
• Pauly, M.V. (1990) “The rational nonpurchase of long-term-care insurance.” J Pub Econ 98, 153-168.

Dr. Richard N. Fogoros has a very interesting website named, the Grand Unification Theory of Healthcare, which relates his views about health care.   His analysis is systematic.  One is able to understand the health care system from the point of view of physicians, patients, health plans, the government, and employers. His “Pathway # 2 to Enlightenment” is very long, but merits a read.  While the author’s website certainly won’t win him any awards for modesty, it does offer some very insightful commentary.  Below I give a brief summary of some of the points which I find particularly, well, enlightening.

Rationing is unavoidable

When a resource is scarce, such as health care, it must be rationed in some way.  In a typical market, goods are rationed by a pricing mechanism.  Only those with a willingness to pay above $3/gallon can buy a gallon of gas.  Other rationing mechanisms include queuing and refusing to give individuals products or services based on some guidelines.  Dr. Fogoros’ comment that rationing is unavoidable is best understood in his mind as that third party rationing is inevitable.

Wonkonians vs. Gekkonians

Dr. Fogoros divides the world into two halves: the Wonkonian School and the Gekkonian school.  The Wonkonians consist of liberals, government regulators, politicians, and public health officials who want government to strictly regulate the health care industry.  Gekkonians, modeled after Gordon Gekko, include healthcare executives, many physicians, and most political conservatives.  This group believes in a free market system.  Where the Wonkonian School wants health care run by large government, the Gekkonians would prefer it to be run by large corporations.

Benefits of both world views

• Wonkonians: These wonks have pushed through legislation to help reduce physician fraud.
• Gekkonians: The large managed care companies have increased efficiency and standardized care (when possible).  The concept of critical pathways was introduced by the Gekkonian school.

 Drawbacks of both world views

• Wonkonians: The wonks attempt to regulate the healthcare market has created a morass of legislation.  Physicians have less freedom to use their professional judgment since if they do not abide by the standard of care and decide to bill Medicare, they may be prosecuted for fraud.  The government can turn an earnest billing mistake into a large fraud case.  For instance, the University of Pennsylvania had to pay a $30m fine in 1995 after a PATH (Physicians at Teaching Hospitals) investigation found that the university was billing Medicare for services where the attending physician was not present.  Yet having an attending physician present at every service provided is an inefficient use of physician time and also reduces the learning experience of the trainee.  Also, compliance with government regulation costs the health care industry millions (if not billions) of dollars per year.
• Gekkonians: Insurance companies make money by signing up more people for their plan.  They lose money, whenever they have to pay out money for medical costs.  Early on, the health plans realized to retain healthy patients and compel ill patients to drop their plan, they needed to “let the system bog down in red tape for the ill, while, at the same time, to work hard to keep the system squeaky clean for healthy subscribers.”  For example, “providers can strategically locate and number specific services to make them easy (e.g., primary care) or difficult (e.g., specialists) to utilize.”  Also, some health plans began to pay physicians on a capitation basis, which encourages them to withhold care, especially from the sickest patients.

Other interesting Points

Finally, I will mention a few other interesting points that Dr. Fogoros brings up.

• The Erosion of the fiduciary relationship between physicians and patients. Doctors now must abide by standards of care in order to now run afoul of the law, even if a non-standard form of care would be beneficial for the patient.  They must abide by the cost rationing of their managed care bosses in order to reduce cost.  Thus, the physician is longer the person who will advocate for the patient to get adequate care, but instead is constrained by government and corporate rules.  In Dr. Fogoros opinion: “the traditional doctor-patient relationship is vital to the professional survival of the physician, and to the physical survival of the patient.  If we lose this relationship, we lose everything.”
• Non-profit hospitals.  The article also discusses how non-profit community hospitals were bought up by private health care corporations in the late 1990s.
• Health plan customers.  Who are a health plan’s customers?  Most people would say that it is the patients.  However, most patients do not actually choose their health plan directly; a human resources employee or benefit manager from their company generally chooses the health plans which are offered to the patients.  Thus, health plans must try to market their services to these HR managers.  But don’t the HR managers want high quality medical care for their employees at a reasonable price?

“My own eyes were opened on this issue several years ago when I attended a retreat, sponsored by my hospital, that featured a panel discussion by a group of prominent local employers.  When asked how they go about assuring themselves that the health coverage they buy for their employees provides high-quality care, the captains of industry responded thusly: ‘We make widgets, we don’t assess healthcare quality.  We don’t know how, and we don’t want to know how. So we’ve got to be practical about it.  To us, quality means quiet.  As long as we don’t hear more than the average number of complaints from our employees, the health coverage we provide is, by definition, good enough.’”

Laurence Baker is a health economist at Stanford’s Center for Health Policy. Much of Mr. Baker’s work has dealt with how HMOs have affected care levels. Today I will briefly review three of Baker’s articles:

HMO Penetration and the Cost of Health Care (AER 1996)

In this paper, Baker and Corts look how HMO market penetration affected health care premiums. I think the article is most pertinent to the health care atmosphere in the late 80s and early 90s when the distinction between HMOs and other insurance plans was starker than it is now.

The authors argue that there are four reasons why increased HMO penetration may affect the cost of traditional insurance.

1. Patient Self-Selection: Healthier patients may sort into HMO leaving traditional insurers with more unhealthy patients.
2. Physician Selection: HMOs may attract physicians who prefer a more conservative style of medicine.
3. Promulgation of Conservative Practice Styles: A higher level of HMO penetration may influence the ’standard of care’ prevalent in a given metropolitan area.
4. Cost shifting. If HMOs are good negotiators, providers may simply accept low margins for HMO patients and charge even higher rates to traditional insurance plans. This argument, however, is illogical if providers are assumed to be profit maximizers.
5. Plan characteristics. Traditional insurers may make their plans less generous in order to compete with HMOs.

Using data from 1991, the authors do find that increased HMO market share decreases premiums when the HMOs first enter the market (i.e.: HMO market share is between 0-10%). Additional HMO market penetration (i.e.: HMO market share above 10%), however, is actually found to increase health insurance premium in a local area.

Effect of HMO Market Share on Cancer Screening

The authors posit that HMOs may be more likely to screen patients for cancer since these health plans are more cost-conscious and take a longer-term view of health care. Spillovers effects non-HMO providers may occur where 1) physician practice patterns change due to an increased HMO presence, 2) patients may be more exposed to information regarding cancer screening in areas with high HMO concentration, and 3) areas with a high HMO market share may attract providers who are more likely to screen patients.

The authors use the 1996 Medical Expenditure Panel Survey-Household Component (MEPS-HC) to test their hypothesis. HMO market concentration is measure by segmenting markets into highest, middle two and lowest quartiles, as well as by using a Hirschman-Herfindahl index (HHI). The authors find that an increase in HMO market share increases the probability of breast and cervical cancer screening, but does not affect the propensity for men to get a prostate exam.

Calculating HMO Market Shares

How do Baker and colleagues calculate HMO market share? A paper studying the factors association with mammography screening has an appendix which details how the HMO market shares were calculated. In the paper, the authors find that those who were more likely to be screened were younger, had smaller families, higher education and income, had a recent Pap smear; reported breast problems; lived in an area that had more mammography facilities with reminder systems, areas with higher HMO market shares and higher screen charges.

• Baker; Corts (1996) “HMO Penetration and the Cost of Health Care: Market Discipline or Market Segmentation?” The American Economic Review, Vol. 86, No. 2, (May, 1996), pp. 389-394.
• Baker; Phillips; Haas; Liang; Sonneborn (2004) “The Effect of Area HMO Market Share on Cancer Screening” Health Services Research 39 (6p1), 1751–1772.
• Phillips; Kerlikowske; Baker; Chang, Brown (1998) “Factors associated with women’s adherence to mammography screening guidelines.” HSR, 33:1, pp. 29-53.

Does health insurance increase utilization of medical services? Economic theory generally predicts that it will. Health insurance decreases the price individuals pay for medical care and thus the equilibrium quantity of medical care used will increase.

A paper by Buchmueller, Grumbach, Kronick and Kahn (“Effect of Health Insurance on Medical Care Utilization…â€?) examines this phenomenon in more detail. The paper is a nice blend reasoning based on both economic theory and empirical evidence.

Adverse Selection: For instance, economic theory predicts that if insurance companies do not have full information regarding the health level of its customers (which is likely) adverse selection will occur. This means that healthy individuals will purchase no (or less generous) health insurance while sicker individuals will purchase (or purchase more generous) health insurance. The authors claim that while this is generally true, it may not hold in the U.S. institutional environment. Most people under 65 receive their health insurance through full-time employment. Since it is less likely that sick people work, some studies (Buchmueller 1995; Stroupe, Kinney and Kniesner 2000; Blumberg and Nicholas 2001; Holahan 2001) find evidence that workers in poorer health are less likely to obtain employer-sponsored coverage.

The Uninsured: A paper by Dubay, Holahan and Cook (Health Affairs 2007) shows that there are 44.6 Americans under 65 [17.5% of this population] who are uninsured. Yet uninsurance does not imply a complete absence of care. “In areas where there is a well-functioning safety net, the lack of insurance will not mean a complete lack of access to care, so the expansion of coverage will result in smaller changes in utilization than in localities where the uninsured have fewer options. In contrast, in many low-income areas, even persons with Medicaid face access problems because many physicians are not willing to accept Medicaid patients (Fossett and Peterson 1989; Fossett et al. 1992). In such areas where care to publicly insured patients is effectively rationed, increases in utilization from expanded coverage may be limited.â€?

Type of Coverage: Due to data limitations, few studies have been able to look at the type of coverage offered to the uninsured. Different expansions of government provided health insurance may lead to different utilization results depending on how generous the insurance plan is. Is the insurance coverage more ‘managed?’ Are there gatekeepers? What specific procedures does the insurance cover? Few studies have answered this question in such a detailed manner.

Supply Side: If the government did decide to provide a more universal type of coverage what would be the impact? Most studies examine the patient demand side and deduce what the patient response would be if they became eligible for government-sponsored or government-subsidized insurance. It is likely, however, that providers will also respond to any changes in the manner in which they are compensated. According to a study of Quebec after Canada, “after universal coverage was established, physicians shifted away from telephone consultations, which were not reimbursed under the new system, toward office visits, which were reimbursed (Enterline et al. 1973, 1975).â€?

The paper goes on to review a number of studies which examine how expanding insurance coverage affected how various groups (i.e.: children and adults) utilize different medical care services (i.e.: outpatient visits, hospitalizations, etc.). One interesting theory which is tested is that health insurance will increase outpatient visits, but because outpatient visits can often catch a disease in its early stages, hospitalizations will decrease. The authors cite a paper by Dafny and Gruber (2000) which finds that “…Medicaid eligibility reduces the rate of avoidable hospitalizations by 3.4% while increasing the overall hospitalization rate.â€?

• Buchmueller, Thomas; Grumbach, Kevin; Kronick, Richard; Kahn, James; (2005) “The Effect of Health Insurance on Medical Care Utilization and Implications for Insurance Expansion: A Review of the Literature” Medical Care Research and Review, Vol. 62, No. 1, pp. 3-30.

The RAND health insurance experiment (HIE) demonstrated that increasing coinsurance rates decreases medical care utilization. The HIE also found that health outcomes did not vary between individuals with high, low and zero coinsurance rates.

A working paper by Chandra, Gruber and McKnight (”Patient Cost Sharing…“) re-examines whether or not this is the case using a more current dataset specifically focused on the elderly. The medical utilization data the authors use is for of all CalPERS retirees between January 2000 and September 2003. Almost all of the retirees are covered by Medicare, but since Medicare typically has a 20% coinsurance rate, CalPERS provides supplemental insurance to their retirees. The authors conduct a difference in difference estimation comparing copayment changes from the CalPERS decision to raise PPO copayment rates in February 2001 and then to raise HMO copayment rates beginning in January 2002.
The authors find that physician office visits and prescription drug utilization are very price sensitive. For office visits, the estimated price elasticity is between -1.38 and -1.90 and for pharmaceuticals the price elasticity is between -0.20 and -1.4. These findings are surprising since it is typically assumed that the demand for medical care is inelastic.

The authors also found that increased cost sharing led to a slight increase in hospitalizations. However, when the subpopulation of individuals with chronic health conditions is examined, large increases in hospitalization rates are found. This means that individuals with chronic health conditions forego office visits and drug purchases due to the increase in price, but this decision will worsen their health and thus increase the chance they are hospitalized.

Why would an insurance company want to increase the number of expensive hospitalizations? It turns out that the CalPERS insurance plans pay for the ‘first-dollar’ of office visit and pharmaceutical costs. Thus, by increasing copayments, office visits and drug use decrease. Since Medicare pays for the ‘last dollar’ of medical costs (i.e.: Medicare pays for expensive hospitalizations and surgical procedures), the CalPERS plans do not incur the cost of the increased hospitalizations. To summarize, CalPERS receives the majority of the cost savings from increased copayments whereas Medicare bears the cost of the increased hospitalizations when office visit and pharmaceutical demand decreases.

This papers shows that it is always important to take a more global, more systematic view whenever a researcher is investigating the medical field.

• Candra, Amitabh; Gruber, Jonathan; McKnight, Robin (2007) “Patient Cost-Sharing, Hospitalization Offsets, and the Design of Optimal Health Insurance for the Elderly,” NBER Working Paper #12972.

There is an interesting post at GoozNews (”Getting Doctors to Compete“) in which Merrill Goozner comments on Harvard Business School professor Michael Porter’s belief that competition and integrated care are the solutions to the nation’s health care woes.

“Where we need to go is an integrated practice model,” he said. His model entails patient-focused practice groups that knit together every specialty needed to treat an individual’s medical condition. It’s not that physicians will no longer specialize; it’s that they’re no longer going to practice in specialty silos divorced or only marginally connected to all the other people providing that particular patient’s care.

Competition enters this new system by giving individuals information about the relative performance of these integrated practices.

Health savings accounts (HSAs) have been a major point of contention for health care reformers. Supporters claim that HSAs can reduce health care costs by decreasing the moral hazard problem inherent when third parties—such as insurance companies or the government—pay for medical services. Opponents claims that HSAs will attract rich and healthy individuals, leaving only poor or sick individuals in the ‘regular’ insurance pool.

One interesting point made in Cardon and Showalter (JHE 2007) is the following:

“Both opponents and advocates of HSAs tend to argue that HSAs will lead to less reliance on insurance, either through higher coinsurance rates and deductibles, or through fewer purchases of policies. This line of reasoning ignores the fact that accumulated HSA balances are wealth, and health insurance protects this wealth. Even individuals with large HSA balances would typically value insurance to protect those balances for future use. HSAs will tend to reduce levels of insurance coverage, but the effect seems unlikely to be as large as some previous researchers suggest.”

The Cardon and Showalter article also gives a nice description of the five main types of tax-preferred health savings accounts.

1. “Archer medical savings accounts (MSAs): accounts in which an individual and/or an employer can contribute pre-tax dollars to pay for most health care services. The tax advantage is the same as for employer-provided health insurance premiums. Unused monies can accumulate over time. An experiment authorized under the Kassebaum-Kennedy bill (Health Insurance Portability and Accountability Act of 1996) allowed for restricted introduction of MSAs which included the requirement of purchasing a catastrophic, (high-deductible) health insurance policy (MSA/CHP).
2. Flexible spending accounts (FSAs): like HSAs, but with no link to insurance coverage. Funds not used by the end of the year revert to the employer.
3. Rollover FSAs: these would allow limited rollover of FSA monies without the restrictions on insurance choices that the current HSA rules require.
4. Health reimbursement arrangements (HRAs): tax-exempt individual accounts used to pay for medical expenditures. Accounts are funded by employers; employee contributions are not allowed. Ownership of the accounts remains with the employer, unlike HSAs and FSAs.
5. Medical IRAs. This proposal would allow consumers to make penalty-free early withdrawals from their retirement plans to pay for allowable medical expenditures.”

• Cardon and Showalter (2007) “Insurance choice and tax-preferred health savings accounts” J Health Econ, v. 26(2), pp. 373-399.

I have recently been receiving some comments suggesting that one way to cut health care costs would be to reduce CEO pay. Would cutting CEO pay be beneficial to society?

Ideologically, I believe that markets—and not the government—should determine wages of all workers. If the U.S. government were to put a cap on CEO pay, there would likely be a ‘brain drain.’ Top-tier managers would leave the U.S. for other OECD countries where salaries were not capped in order to maximize their annual salary. Further, if only selective industries were to have caps on CEO pay, how would the government decide which industries’ CEOs need to have their pay regulated? Would lobbyists be able to bribe politicians in order to keep an industry off the CEO cap list?

Some people argue that health care is unique and merits special consideration. The argument for special consideration is usually based on the fact that health care is a ‘need’ good or that since the government is financing much of healthcare spending in the U.S., CEOs should not be profiting off John Q. Public.

Despite my general aversion to government regulation of salaries, I decided to do some calculations to see how limiting CEO pay would affect healthcare costs. Healthcare Economist reported on UnitedHealth Group CEO William McGuire’s $125 million worth of compensation in 2005 (and his subsequent stepping down from the position). This figure should be seen as the right tail of the health insurance CEO compensation distribution. If the U.S. government was to limit health plan CEO salaries to $5 million dollars and if the health insurance company decided to pass the savings on to consumers (rather than investors), this would result in approximately a $120 million savings to consumers. On its website, UnitedHealth Group states that it “serves more than 50 million Americans.” Thus the amount of cost savings per person enrolled in UnitedHealth Group would be less than $3.

With this over-simplified analysis, I conclude that CEO compensation is not the main driver of costs in the health care sector and regulating CEO pay—while likely cathartic for those fed up with high health care costs—will not make medical care services any less expensive for the average consumer.

In recent years, the federal government has attempted to increase access to government provided health insurance. Between 1984 and 2004, the percentage of non-elderly individual with government provided health insurance rose from 13.5% to 17.5%. Over the same time period, however, the percentage of American without health insurance also rose from 13.7% to 17.8%.

In their 1996 paper, Cutler and Gruber claim that increasing access to public health insurance plans crowds out private health insurance. It is an important policy question to understand whether expanding public health insurance is reducing the amount of uninsured individuals or simply shifting Americans from private to public insurance rolls. In Cutler and Gruber (QJE 1996), the authors estimate that a 10% increase in Medicaid coverage reduced private health insurance rates by 5%; this represents a 50% crowd out level.

Subsequent studies have argued that this 50% crowd out figure is an overestimate. Card and Shore-Sheppard (2004) use SIPP data (instead of the CPS) and found no crowd out with the 1990 OBRA Medicaid expansion. A paper a year later by Ham and Shore-Sheppard (2005) in Industrial and Labor Relations Review claims that by adding state*year interaction terms to the Cutler Gruber (1996) econometric specification changes the crowd out estimate to zero. Other studies, such as LoSasso and Buchmueller (2004) and Dubay and Keeney, have found crowd out estimates on the magnitude of the Culter/Gruber paper.

To combat these critics, Gruber and Simon have released a 2007 NBER working paper to re-estimate crowd out figures using updated data. The data used are the 1996-2002 SIPP data. Despite the panel nature of the data, Gruber and Simon have decided to treat the data as if it were simply a pooled cross-section, thus losing the ability to fully control for individual or household characteristics. Their econometric estimation technique is as follows:

• INS_ijt = α + ELIG_ijt + ν_j + Ï?_t + ε_ijt

They authors also use an instrumental variables approach similar to the one employed in Currie and Gruber (1996). A random sample of 300 children of each age (and their families) is taken from each year of the SIPP. Eligibility rules for each state are applied to this sample for each of the 12 months of each of the years to calculate the fraction of the national sample eligible (in state j, time t) that is eligible for Medicaid. This effectively weights the rules in each state by their effects if applied nationally. Eligibility is instrumented by this ‘simulated percent eligible‘ variable. The authors also later include state*year interaction terms as well.

Using an individual level of observation, the authors find 20% to 40% crowd out, although the authors can not rule out that these estimates are statistically different from zero. Using family level estimates, crowd out is larger 60% to 80%, and these results are more statistically significant. One problem of using the family level estimates are that families where all household members are eligible for Medicaid of SCHIP and families where none of the household members are eligible for Medicaid are composed of vastly different income levels. SCHIP health insurance is available for all children part of a household with income below 133% of the federal poverty line and some children between 133% and 350% of the federal poverty line depending on the state, whereas adult generally need to be below the poverty line to qualify for Medicaid. Also, I find it unintuitive that the paper finds more crowd-out for individuals with employer-provided health insurance compared to non-group policies. Could these individuals be switching jobs to higher paying jobs without insurance and then taking up Medicaid coverage?

The authors also examine anti-crowd out measures such as mandatory waiting times between when private insurance is dropped and when Medicaid insurance is taken up. Gruber and Simon find that the waiting times are ineffective against preventing crowd out but these estimates are not precise.

• Gruber and Simon (2007) “Crowd-Out Ten Years Later: Have recent public insurance expansions crowded out private health insurance” NBER Working Paper #12858.

Most economists believe that increasing the price of an item will decrease demand for the item. Health care is no different from any other good. If you increase the copayment or coinsurance rate, people will consume fewer medical services. The famous RAND Health Insurance Experiment (HIE) demonstrated that higher coinsurance rates discourage medical care consumption. As I said, health care is no different from any other good…or is it?

Dana Goldman and Tomas Philipson argue in their 2007 NBER working paper (”Integrated insurance design in the presence of multiple medical technologies“) that the problem of moral hazard in the health insurance market is different from moral hazard under most other insurance markets. For most other types of insurance, only one good is insured (e.g.: a car, a house, etc.). Health insurance, however, covers a wide variety of different services. Thus the authors claim that increasing prescription drug copay costs can actually increase health care spending and make patients worse off. Let us assume that prescription drugs and medical services are substitutes. If the price of prescription drugs increases, it is likely that the individual will consume the more of the expensive medical services which are fully covered by insurance. They suggest that a zero or negative copay may be optimal for some prescription drugs.

The optimal copay is determined by the patients elasticity of demand and the degree to which other medical services are complements or substitutes to the original item in question. The authors give some empirical evidence from other studies to support their claim:

• Soumerai, Ross-Degnan, Avorn, McLaughlin and Choodnovsky (NEJM 1991) compare Medicaid patients in New Hampshire—who had a three-drug limit per patient—and Medicaid patients in New Jersey without the limit. The authors found a 35% reduction in drug use, but a doubling in nursing home admission rates.

• Soumerai, McLaughlin, Ross-Degnan, Casteris, and Bollini (NEJM 1994) look at individuals on psychotropic medications and find that a drug cap led to a 15%-49% reduction in the use of drugs but a 43%-57% increase in mental health visits and emergency mental health services.

• Horn, Sharkey, Tracey, Horn, James and Goodwin (Am. J Man Care 1996) find that formulary limitations in 6 HMOs were associated with increased ER visits and hospitalizations for otitis media, atraumatic arthritis, ulcers, hypertension, and asthma.

• Gaynor, Li, and Vogt (NBER 2005) find that higher drug co-payments in a given year lead to increased spending during the following year.

• On the other hand, studies such as Johnson, Goodman, Hornbrook and Eldredge (Med Care 1997) and Tamblyn, Reid, Mayo, McLeod, and Churchill-Smith (J Clin Epidemio. 2000) found that increased co-pays did not increase outpatient visits, hospitalizations or ER visits.

The authors conclude that “the preponderance of evidence suggests strong negative cross-price elasticities between drugs and other medical spending, at least for patients with chronic disease.â€?

In 2005, approximately 114 million visits were made by Americans to the hospital emergency departments. Of these, more than eighty percent concluded with a discharge and a recommendation for follow-up care. Receiving prompt and adequate post-ER care is imperative for the resolution of many illnesses and temporary disabilities. Is timely care available for these patients?

A study by Asplin, et al. (2005) and a subsequent paper by Neath and Carlin (2006) look at how easy it is to schedule an appointment after an ER visit. To collect the data, clinics were phoned by a graduate students posing as patients just released from a hospital emergency department. Callers had four (made-up) medical conditions: pneumonia, elevated blood pressure, vaginal bleeding in the first trimester, and symptoms of depression. The depression observations were excluded from the study because many primary care physicians do not feel qualified to treat depression.

In each call, the individual claimed to have either: 1) private insurance, 2) Medicaid insurance, 3) no insurance and could not pay, or 4) no insurance but would pay for the visit out-of-pocket. A call was deemed successful if an appointment was made within 7 days and the out-of-pocket payment for the appointment was $20 or less.

Results

Asplin, et al. preform a simple paired comparison in which the same clinics are compared where the only difference between the observations is the unit of insurance the phony patient had. Neath and Carlin directly incorporate other covariates – such as the medical condition, safety-net status of the clinic, city dummy variables, etc. The results are similar in both studies, but the table below gives Neath and Carlin’s findings.

We can see that the “overall success probabilities in Asplin et al. were distressingly low.” One also notices that it is much easier to get an appointment if one has private insurance, but these differences are less severe at “safety net” clinics. Finally, the authors note that the majority of clinics made no attempt to determine the severity of the caller’s condition. Having trained staff answering the phone calls and preforming triage is costly, but is likely worth the cost for patients needing immediate assistance. Put more concisely, Asplin states: “Financial screening is trumping medical triage.”

• Asplin, et al. (2005) “Insurance Status and Access to Urgent Ambulatory Care Follow-up Appointments” JAMA, vol 294, pp. 1248-1254.
• Neath and Carlin (2006) “Does Insurance Status Affect Access to ER Follow-up Care?” Chance, vol. 19, pp. 58-60.

Yesterday, President Bush gave the State of the Union Address. In this post, I 1) analyze Bush’s new health care plan, 2) review some commentary from various blogs on the net, and 3) give a excerpt from the speech which directly relates to health care.

Healthcare Economist’s Analysis
The heart of the Bush proposal is as follows:

• Families With Health Insurance Will Not Pay Income Or Payroll Taxes On The First $15,000 In Compensation And Singles Will Not Pay Income Or Payroll Taxes On The First $7,500.

On the positive side, since this is a fixed deduction regardless of the generosity of the health insurance, there is less incentive for individuals to purchase “too much” insurance. In the case where each dollar worth of health insurance decreases one’s tax liability, insurance only costs (1-τ)*A dollars per year with the tax deduction when the true cost to society is A. The proposal is also good in that it the tax break does not discriminate between employer-provided and individual-based insurance. This will help (somewhat) to reduce the phenomenon that individuals often choose their job based on the type of insurance offered rather than actual job characterisitcs or the wage offered. According to the President, the health insurance deduction will decrease the taxes for most available which will free up more disposal income for them to spend on other items.

On the negative side, the plan is very inequitable. Since this is a tax deduction, if you are poor and do not owe any taxes, you will not receive any financial help with the deduction. Since tax rates are progressive, the deduction is most valuable to individuals in the high tax bracket, the rich. Look at the example below for a single individual.

We can see that the value of the health insurance tax deduction is worth more than 2x the value for the individual making $10,000,000 as for the person making $10,000.

Other problems with the proposal is the possibility of ‘fake’ health insurance. It seems that the government does not establish a minimum level of health insurance. Thus, someone who wants the tax deduction, but does not want to buy health insurance could buy a policy for $1 which pays for all medical expenses over $1 trillion. Of course, the person will never be able to use this policy but since they technically have insurance, they will receive the deduction. If the person gets sick, however, they can still go to the emergency room and get free care, paid for by the American taxpayers. Thus, some level of minimum insurance should be established in order to qualify for the deduction.

Also, while treating the employer-provider and individual-based insurance groups equivalently may be more fair, it may exacerbate the problem of adverse selection in the individual markets. People with pre-existing conditions may find it even more difficult to afford insurance in the individual market under this reform.

Overall, I think the plan does little to help those who need insurance most. The dollars saved from eliminating the deductibility of employer-provided health insurance could be used in a much more productive fashion to provide health care to more Americans.

Around The Blog-o-sphere
Below I have tried to give a diverse review of some early feedback about the SOTU plan from around the blog-o-sphere:

• SameFacts.com “Maybe I’m missing something here, but this just seems laughable. The idea of a deduction for the uninsured is silly: the value of deductions increases with tax rate, and most of the uninsured either don’t pay income taxes or at the lowest bracket.”
• Cato-at-Liberty: “the president’s proposal mirrors the proposal for “large HSAsâ€? that I introduced.”
• Managed Care Matters: “But it won’t do anything to fix the underlying problem – people who need insurance can’t get it, and if they can, many can’t afford it, leaving the rest of us to pay for their health care.”
• Paul Krugman at Economist’s View: “…the actual plan is to penalize workers with relatively generous insurance coverage…”
• The Heritage Foundation: “It would treat all Americans equally by ending the tax discrimination against families who buy their own health insurance, either because they do not have insurance offered by employers or because they prefer other coverage.“

Transcript
Below is a transcript of the section of Bush’s State of the Union speech which refers to health care. The full transcript is available at the White House website and there is a section on the Bush health care policy initiative as well.

A future of hope and opportunity requires that all our citizens have affordable and available health care. (Applause.) When it comes to health care, government has an obligation to care for the elderly, the disabled, and poor children. And we will meet those responsibilities. For all other Americans, private health insurance is the best way to meet their needs. (Applause.) But many Americans cannot afford a health insurance policy.

And so tonight, I propose two new initiatives to help more Americans afford their own insurance. First, I propose a standard tax deduction for health insurance that will be like the standard tax deduction for dependents. Families with health insurance will pay no income on payroll tax — or payroll taxes on $15,000 of their income. Single Americans with health insurance will pay no income or payroll taxes on $7,500 of their income. With this reform, more than 100 million men, women, and children who are now covered by employer-provided insurance will benefit from lower tax bills. At the same time, this reform will level the playing field for those who do not get health insurance through their job. For Americans who now purchase health insurance on their own, this proposal would mean a substantial tax savings — $4,500 for a family of four making $60,000 a year. And for the millions of other Americans who have no health insurance at all, this deduction would help put a basic private health insurance plan within their reach. Changing the tax code is a vital and necessary step to making health care affordable for more Americans. (Applause.)

My second proposal is to help the states that are coming up with innovative ways to cover the uninsured. States that make basic private health insurance available to all their citizens should receive federal funds to help them provide this coverage to the poor and the sick. I have asked the Secretary of Health and Human Services to work with Congress to take existing federal funds and use them to create “Affordable Choices” grants. These grants would give our nation’s governors more money and more flexibility to get private health insurance to those most in need.

There are many other ways that Congress can help. We need to expand Health Savings Accounts. (Applause.) We need to help small businesses through Association Health Plans. (Applause.) We need to reduce costs and medical errors with better information technology. (Applause.) We will encourage price transparency. And to protect good doctors from junk lawsuits, we passing medical liability reform. (Applause.) In all we do, we must remember that the best health care decisions are made not by government and insurance companies, but by patients and their doctors. (Applause.)

Over the past week, I have discussed California’s proposal to extend health insurance to all individuals. Today, I will examine—in my mind—a superior plan developed by former Republican Rep. Curt Gielow. According to a concept paper from the Wisconsin Health Plan website, the reforms will have the following impact:

“All eligible Wisconsin residents receive a “Premium Credit,â€? which the participant uses to purchase health insurance from competing, qualifying health insurance plans. In addition, all adults (age 18-64) also receive a Health Savings Account (HSA), funded at $500 each year.”

Eligible residents include all individuals living in Wisconsin for six or more months except for those eligible for Medicaid (BadgerCare), government employees and those incarcerated. In comparison to the California proposal, this “The Wisconsin Health Plan” is truly a universal entitlement in that there is no means testing.

Having some form of health insurance is seen by many as an important goal for an equitable society. On the other hand, giving away free health care—which in essence defines full insurance—will lead to spiraling costs and overuse of medical services. I applaud the Wisconsin plan for attempting to use some market-based mechanisms to contain costs. The Tier 1 plans set a $1200 annual deductible, a 10%-20% coinsurance rate with an out-of-pocket maximum of $2000 per person or $3000 per family. The child benefit package is more generous with the same coinsurance rates but an annual deductible of $100 and a maximum out of pocket expense of $500. Tier 1 insurance is free to residents but those who wish to have more comprehensive insurance (which is subdivided into Tier 2 and Tier 3 coverage) can purchase this type of insurance at an increased premium level. It is wise to give consumers some choice in their insurance type; having the default coverage include higher deductibles and coinsurance rates will create more efficient medical care allocation.

The Milwaukee Journal Sentinel also reports (”Big, Bold Plan?“) regarding another of the plan’s purported cost saving innovations:

“The Wisconsin plan would wisely make use of market-based incentives to control rising health care costs and save money. This would be accomplished by putting everyone in a state health insurance purchasing pool, patterned after the successful pool that already exists for state employees…Due to the sheer number of people in the plan, the pool would seemingly have the purchasing clout to convince health care providers and insurers to bid to provide high-quality care at competitive prices.”

‘Wisely’ is not the word I would use. The single payer system will reduce prices in the short run, but without competition between plans, hospitals and doctors will face a monopsony. This will likely decrease prices in the short-run but reduce the quantity and/or quality of services provided in the long run. The true value of a service will be unknown in a single buyer system and thus over-purchasing of inessential services and under-purchasing of more valuable—especially newer, more innovative—medical services will occur.

Critics assail the plan’s high cost and the increased payroll taxes as major obstacles to the plan. A study by M. Scott Niederjohn and Mark C. Schug claim that “payroll taxes assessed on Wisconsin businesses and employees would need to be more than 17% instead of the proposed nearly 13%.” Also, the plan states that “Any insurer (for example, HMOs, PPOs, or indemnity carriers) licensed to sell health insurance in Wisconsin — and that meets specified financial, coverage area, and disclosure standards — is qualified to compete to provide insurance coverage.” If the state, and not the insurers, is the entity negotiating prices and services with providers, then insurer competition will become very minimal.

I applaud the Wisconsin Plan, especially for allowing some consumer choice, for forcing consumers to face some of the cost of their medical services through coinsurance and deductible payments, and by making the program universal. The regulatory tiers should not be necessary; a simple minimal level of coverage would suffice with households paying extra for any type of insurance they required. I do not believe that the single-payer system proposed by the state will be cost-saving in the long-run. While the Wisconsin Plan is not optimal, it is a major step forward towards providing health care for all residents in a (relatively) affordable and efficient manner.

Last December, Governor Arnold Schwarzenegger held a press conference detailing some of the problems in the California health care system. For instance, there are 6.5 million California residents without insurance; the governor claims that individuals insurance premiums are about $500 higher (or $1200 for a family or four) than they would be if these uninsured were not treated. Schwarzenegger says:

“Tackling the problem of the uninsured is not only about expanding coverage, it’s about addressing this hidden tax on healthcare that Californians can no longer afford.”

The New America Foundation has a complete report which expounds on the governor’s comments.

A cursory economic evaluation would show that having the government pay for these millions of uninsured would likely decrease an individual’s health insurance premiums by $500 but would increase the average resident’s tax burden by $500. In fact, the tax burden may increase even more than $500 if one is take into account the moral hazard phenomenon.

It may, however, be possible to finance insurance for the uninsured for less than the $500 cited. The New York Times reports in October (”Hospitals try free basic care for uninsured“) that some hospitals are offering free primary care to area residents in order to cut costs. By providing medical care up front—especially in the preventative and chronic care settings—hospitals can save costs from fewer expensive ER visits. I am doubtful that providing insurance to the uninsured will save money, but it is possible that costs will be lower than expected; especially since many of the uninsured are younger adults who require fewer medical services.

An interesting post from Joe Paduda of Managed Care Matters gives us an inside look of ‘What insurance people are really like.’

It has been shown in various studies and opinion polls that consumers generally believe that HMOs provide an inferior level of care than non-HMO plans. This is true even when more objective measures of medical service quality are taken into account. Why is HMO satisfaction so low?

A study by Reschovsky, et al. (2002) claims that people who are dissatisfied with their medical care are more likely to report that they have an HMO. The authors use the Community Tracking Study. The CTS asks survey respondents what type of insurance they have. Later, they contact the individual’s insurance company in order to glean the details (copayment and coinsurance rates, deductibles, referral requirements, insurance type, etc.) of the person’s true insurance coverage.

As an outcome variable, the authors looked at various patient satisfaction measures (e.g.: the level of trust they have with their current physician, their satisfaction with their last doctor’s visit). When they compare people who have an HMO and correctly report this, with those who have an HMO but report they have non-HMO coverage, the authors find that those with the correct reporting have lower satisfaction scores. On the other hand, comparing people with a non-HMO insurance who correctly report this with those who have a non-HMO but report having an HMO, they find the people incorrectly reporting that they have an HMO had lower satisfaction scores. Below is the results for the dependent variable of “percent dissatisfied with their health care in general.”

Thus, they conclude that reporting of HMO coverage may be negatively correlated with actual satisfaction and may not accurately reflect the survey respondant’s true coverage. As the title indicates: “It’s not whether you are in an HMO but whether you think you are”

Reschovsky; Hargraves; Smith (2002) “Consumer Beliefs and Health Plan Performance: It’s Not Whether You Are in an HMO But Whether You Think You Are” Journal of Health Politics, Policy and Law, Vol 27, No. 3 pp.353-377.

As insurance markets began to develop in the U.S., we observed two types of insurance emerging: indemnity plans and health maintenance organizations (HMOs).  Indemnity plans compensated providers on a fee-for-service basis and HMOs used a capitation scheme.  Typically, HMOs used gatekeepers to restrict services while indemnity plan restrictions were few and far between.  Typical analysis of managed care’s affect on patient utilization involved simply comparing the average medical service usage in the two groups–after controlling for patient covariates and adverse selection.

Nowadays, all insurance plans are in some way ‘managed.’  If this is the case, how can a health economist measure the affect of managed care on service utilization?  Grembowski, et al. (2003) use a three tiered system to create an index of ‘managedness.’ Their system is described below:

1. Plan level: The authors use an index to rank plans according to the following characteristics: gatekeeping and lock-in provisions, the plan’s referral preauthorization requirements, and whether the plan versus the provider was at financial risk (FFS vs. capitation).  They also included an two benefits indexes measuring the benefits covered by the plan as well as cost-sharing (copayments, coinsurance, deductibles) for providers both inside and outside of the plan’s network.  The first benefits index looks at only in-network information and the second benefits index examines out-of-network data.
2. Office managed care: This was measured by examining office use of: utilization management, financial incentives (the percentage of the office’s revenue from capitation), and whether or not the office uses referral guidelines. 
3. Physician managed care: This measure was developed by examining whether the physician was compensated via a capitation or FFS scheme, whether financial withholds for referrals were put into place, and the number of Agency for Health Care Policy and Research (AHCPR) guidelines read or employed by the physician.

With these continuous indexes in place, healthcare economist can now perform a more subtle analysis of how managed care affects utilization.

Grembowski; Martin, Dieher; Patrick; Williams; Novak; Deyo; Katon; Dickstein; Engelberg; Goldberg (2003) “Managed Care, Access to Specialists, and Outcomes among Primary Care Patients with Pain” Health Services Research, v38(1 Pt 1) pp. 1-19.

In modern medicine, doctors are agents for two distinct groups. The physician is an agent for the patient, but also an agent for insurance companies-especially in the managed care settings.  In balancing both relationships, the doctor must juggle the conflicting principal-agent problems of information asymmetry and third party payment. 

Ake Blomqvist develops an interesting theoretical model to explain this phenomenon in his 1991 Journal of Health Economics paper. An individual’s expected utility is based on the level of their consumption (c) and their health status (h), which is a function of a health state variable (’θ‘) and medical expenditures (’z‘).  Health insurance premiums are given by ‘m‘.

• E=[Σ_i {π_i*h(z_i-θ_i)}] + u(y-m)]

If we assume perfect information and that insurers must break even (m – Σ_i [π_i*z_i] = 0), the we have the following first order conditions:

• z_0=0
• h′(z_i-θ_i)+)=u′(y-m)

These conditions state that a health person (state i=0) will not spend any money on medical expenses, and that the marginal utility of consumption should be the same in each state.  This involves a contingent contract for each health state (θ_i). 

Blomqvist then imposes asymmetric information and institutes a copayment rate of ‘σ’.  Consumption now changes from ‘y-m‘ to ‘y-m-σz‘ and the break-even constraint is ‘m=(1-σ)’Σ_i {π_i*z_i(m,σ)‘.  Since contracts are now incomplete and patients can choose the level of services they desire, variable ‘z’ is now a function of the copayment rate (’σ‘) and the premium (’m‘).  The author derives the conclusion that the optimal ‘σ’ is always located between zero and one.

Managed Care model

In managed care, the insurer has an incentive to minimize services, but this desire is counterbalanced by the threat of a competitor offering more generous services and thus attracting their customers.  The new maximization problem and resulting first order condition are:

• E=π_0*H+[Σ_{1 to N} {π_i*h(z - θ_i)}] + u(y-m)]
• s.t.: m-(1-π_0)*z
• FOC: ∑_{1 to N} {π_i*h’} – (1-π_0)*u’ = 0

The managed care firm selects a level of ‘z’ for all states (except complete health where z_0=0; h=H).  The benefit of this new equilibrium is that the problem of moral hazard has been solved since care is now rationed.  On the other hand, there is still the problem of information asymmetry and care is not state-contingent as in the first best scheme. 

Stochastic performance guarantee

Blomqvist now improves the efficiency of his model even more. Since managed care has an incentive to under-provide care, the author proposes that the government fine HMOs if the resulting health of the individual is below the expected level agreed upon in the contract.  If HMOs provide fewer medical services, they will save money on direct expenses, but also increase the risk that they will be fined.  Let us look at this proposition more formally.

Blomqvist defines a term measuring the distance between predicted health–given health state (θ_i) and medical expenditures (z_i)–and the ex post observed health outcome (ξ).

• ε=h(z_i - θ_i) – ξ

Let us assume ε~f(ε).  An HMO is deemed to have broken its contract to provide a given level of care when the following occurs:

• h(z_i-Θ) – ξ > γ,

The variable Θ represents the health state reported by the physician and this variable need not equal the value of true health state, θ.  When the above equation holds true, the HMO pays a fine of ‘F’. 

Thus the total cost of providing medical services is:

• C=z_i + P*F

The variable ‘P’ represents the probability that an HMO is ‘falsely convicted’ [P(ε>γ)].  Proposition 3 of the paper stats that for any value of the conviction criteria ’γ’ there exist an F which will induce HMO physicians to tell the truth if f(ε) is decreasing in the non-conviction range.  Blomqvist demonstrates that this can be a first best solution.

Problems

The author notes that many issues may confound this theory in reality.  Load factors and costs to oversee the system of fines could make the stochastic performance guarantee sub-optimal.  Also, second opinions may be a more efficient means to find out the accurate value of a patient’s health state from the physician. 

Blomqvist, Ake (1991) “The doctor as a double agent: Information asymmetry, health insruance, and medical care,” Journal of Health Economics, vol 10, pp. 411-432. 

Health economists frequently examine the effect of physician payment method on the provision of medical services.  It is often found that patients whose doctors are compensated via capitation or salaried schemes receive fewer services than patients whose doctors are compensated through a fee for service mechanism.  This finding is robust to a variety of medical settings and holds even after controlling for the possibility of patient adverse selection in insurance plans. 

Fred Hellinger (1996) shows that there are other potential biases to worry about.  The first is physician selection.  It is possible that physician who practice a more conservative (i.e.: less input intensive) brand of medicine will decide to work under capitations or salaried schemes, whereas doctors who prefer a more liberal style (i.e.: a higher quantity of service provision) may choose a fee for service.  It is possible that physician preference and not financial incentives are the cause of the above findings.  One small trial (Hickson, et al 1987) randomly assigned eighteen physicians (residents) to either a capitation payment scheme ($20 per month per patient) or a fee for service scheme ($2 per patient visit).  The study found that residents reimbursed on a per-visit basis scheduled and attended 22% more visits per capital than residents on a per month capitation scheme. 

A second source of bias analyzed by Hellinger is that of unmeasured plan characteristics.  When conducting a regression analysis, using dummy variables such as ‘HMO’ or ‘fee-for-service’ is likely too crude a categorization.  Ideally, one would like to have information on 1) benefit structure (copayments, deductibles), 2) use of guidelines, 3) method of physician reimbursement, and 4) utilization review.  Without this information, a researcher’s evalution may not be fine enough to produce any revealing conclusions regarding the state of healthcare in this country. 

Hellinger, Fred (1996) “The Impact of financial incentives on physician behavior in managed care plans: A review of the Evidence,”  Medical Care Research and Review, vol 53(3), pp. 294-314. 

Hickson, G.B.; Altemeier, W.A.; Perrin, J.M. (1987) “Physician reimbursement by salary or fee-for-service: effect on physician practice behavior in a randomized prospective study,” Pediatrics, vol 80(3), pp. 344-350.

Years ago, when someone needed care from a doctor they visited the physician directly whether they were a general practitioner or a specialist.  Nowadays, it is rarer for patients to visit a specialist without a referral.  The typical referral comes from a primary care physician, but it is also common for a specialist to refer the patient to another specialist (cross-referral).  Even when a patient sees a specialist without consulting another physician, this is now called a self-referral. 

Forrest and Reid (1997) use 1989 to 1995 data from the National Ambulatory Medical Care Survey to give more detail on the nature of referrals.  The authors compare the amount of referrals which occur inside and outside of managed care.  One might think that referrals are less common in managed care since these organizations are known to restrict the supply of specialist services; on the other hand, managed care organizations often require more referrals (fewer self-referrals are allowed) so it is possible that the number of referrals is higher in managed care as well. 

The authors found that primary care patient visits in the HMO setting were 66% more likely to lead to referrals than such visits under an indemnity plans.  Patients in HMOs were less likely to be able obtain self-referrals, however.  Thirty one percent of specialists’ managed care patients were self referred compared to 49.5% of their patients in indemnity plans.  Cross referrals between specialists occurred at similar rates in the managed care and fee for service settings. 

Economists have focused on primary care physicians’ financial incentives to refer patients to specialists.  Stephen Shortell (1973) claims that a social exchange model may more accurately reflect how referrals are performed today.  Mr. Shortell’s article in the Journal of Health and Social Behavior claims that non-financial incentives largely influence to which specialist a patient is referred.  Shortell hypothesizes that 1) a specialist’s status in the field, 2) their friendship level with the primary care physician, 3) their office’s distance from the primary care physician’s office, and 4) whether or not they are on the same network as the primary care physician likely influence the primary care physician’s decision-making process. 

 Forrest, Christopher; and Reid, Robert; (1997) “Passing the baton: HMOs’ Influence on Referrals to Specialty Care,” Health Affairs, vol 16(6), pp. 157-162.

Shortell, Stephen M.; (1973) “Patterns of Referral Among Internists in Private Practice: A Social Exchange Model,” Journal of Health and Social Behavior, vol 14(4), pp. 335-348.

Introduction 

Much of health care today is paid for by managed care plans.  If the managed care plans are profit maximizers–which I assume them to be–then they face a tradeoff.  By offering a lower quality of care, they will make more money; but lowering the quality of care reduces the demand for their insurance product.  In their 2000 Journal of Health Economics article, Frank, Glazer and McGuire create a model which employs “shadow prices” to measure the managed care firm’s incentives to provide care.  The shadow price “character[izes] the incentives a plan has to distort services away from the efficient level.  The shadow price captures how tightly or loosely a profit maximizing plan should ration services in a particular category in its own self interest.”

Model

Let us assume there is a vector of medical services (‘m_i‘) for each individual ‘i‘, and each medical service is indexed by ‘s‘.  Utility for each person is equal to:

• u_i(m_i)=v_i(m_i) + μ_i
• u_i(m_i)=[SUM_s  {v_{is}(m_{is})}] + μ_i

The individual will choose a plan if ‘u_i>u_i‘ where u_i is the valuation the individual places on the next preferred plan.  Thus we have:

• μ_i> u_i-v_i(m_i)

The managed care plan does not know μ_i but does know the distribution of μ_i.  Given ‘u_i, m_i’, the probability individual i chooses the plan is:

• n_i(m_i)=1- Φ_i[u_i - v_i(m_i)]

The individual maximizes their utility so that:

• v’_{is}()=q_s

On the firm side, the managed care organization sets a shadow price (’q_s‘) for each service in order to maximize the following profit function:   

• π(q)=SUM_i{n_i(q) * [r_i - SUM_s{m_is(q_s)}]}

The first order condition becomes:

•  SUM_i{(dn_i/ dq_s) * π_i - n_i*m’_is}
• π_i = r_i – SUM_s{m_is(q_s)}

The authors eventually solve this system of equations for ‘q_s‘ and find:

• q_s = (Sum_i{n_i * m_is})/(SUM_i {Φ’_i * m_is * π_i})

What does all this math mean?  Frank et al. explain it well as follows:

“The use of a shadow price as a description of rationing in managed care permits a natural interpretation of the division of responsibility between the ‘management’ of a plan, presumably most interested in profits, and the ‘clinicians’ in a plan who face the patients. Cost-conscious management allocates a budget or a physical capacity for a service. Clinicians working in the service area do the best they can for patients given the budget by rationing care so that care goes to the patients that benefit most. In this environment, management is in effect setting a shadow price for a service through its budget allocation. It is evident in data that individuals with the same disease get different quantities of service. The constant shadow price assumption is consistent with managed care rationing but with more care being received by patients who ‘need’ it more.”

Now we can return to the dilemma faced by profit maximizing managed care firms. These firms choose the optimal q but face a tradeoff.  By increasing the shadow price of a certain medical service (’q_s‘) the firm can make more money (- n_i*m’_is) since their costs have decreased as less services will be provided.  On the other hand, firms face the problem that for given per-person profit level (’π_i‘), increasing the shadow price will decrease the probability that any individual would like to purchase the managed care plan in the first place (dn_i/ dq_s <0).  This model can explain the appearance of the following phenomenon:

“Under simple capitation payments that now exist, providers and plans face strong disincentives to excel in care for the sickest and most expensive patients.  Plans that develop a strong reputation for excellence in quality of care for the sickest will attract high-cost enrollees.” Miller and Luft (1997 p. 20).

It not uncommon to observe an HMO offering free gym memberships (which are a perfectly predictable cost) in order to attract new healthy members, but to provide poor services to very sick patients.

Frank, Richard; Glazer, Jacob; McGuire, Thomas; (2000)  “Measuring adverse selection in managed health care” Journal of Health Economics, Vol 19, pp. 829-854.

There is a dynamic relationship between generalists and specialists.  Currently, 4.5% of visits to PCPs result in a referral.  A RAND study and my own investigation of the 1998-1999 Community Tracking Survey show that about 10% of individuals are hospitalized at least once each year.  How should we model the decision patients face between generalist and specialist care.

Model with perfect information 

This is the problem tackled by Blomqvist and Léger in their 2005 paper in the Journal of Health Economics.  They have a simple model:

• max_{q,J} U(X,H); J=G or S
• X=I-Cj(q)-
• H=q-

Consumption is represented by ‘X‘, which depends on income ‘I’; the copay rate , the cost of the procedure Cj(q) and the insurance premium . Health (’H') is a function of the amount of medical services chosen (’q‘) and the underlying health of the person ().  This variable is unknown ex ante, but its distribution F() is known.  The cost of the procedure depends on whether the patient chooses a generalist (j=G) or a specialist (j=S).  For each provider type, we have the following first order condition:

• U_x*(-C’j(q)) + U_h=0

The authors go on to show that there is a critical value * above which the individual chooses a specialist and below which the individual chooses a generalist.  This critical value will increase when the coinsurance rate () increases, since the individual as the individual bears more and more of the cost, they will prefer less dear services.  In proposition 3, the authors claim that consumers will choose an inefficiently small * if they are insured.  In other words, they will demand too much specialist relative to generalist care.  The expected value of the utility function ex ante is:

• EU=Int_{} U[I-Cj(q())-,q()-]dF()

If the consumer increases the critical value ex post, we see that:

• dE/d* = V_S(*,)-V_G(*,)+(dE/d)(d/d*)
•            = 0 +(dE/d)[C_G(q)-C_s(q)](1-)>0

Since dE/d<0 (since increasing the insurance premium ceteris paribus will decrease utility) and [C_G(q)-C_s(q)]<0 (since it is assumed that specialist services are more expensive than generalists services, we can show that the equation above is positive as long as the copay rate is less than 100%.  This is the mathematical representation of moral hazard. 

Model with imperfect information

The new model Blomvqist and Léger derive assumes that patients know approximately how sick they are, but only the doctors can know  with certainty.  From the paper:

“…assume that the distribution F() from which illness severity is drawn is bounded by _o and _L and is subdivided into L intervals [_{l-1},_l] l=1,…L.  Although the patient does not observe the exact value of , we assume that he or she can distinguish between these classes of illnesses; that is, the patient knows in which interval his or her true  is located.”

A doctor must offer an amount of treatment so that for q(_{l-1})>q>q(_{l}).  If the doctor does not offer services within this range, the patient will know the doctor is defrauding them by under- or over-providing services.  Fee for service doctors still have an incentive to choose the maximum amount of services within the interval, q(_{l}), and capitation/salaried doctors still have an incentive to provide the minimum amount of services within the interval, q(_{l-1}).  The paper continues stating that a managed care contract which specifies different cost sharing parameters _l for each interval will yield a higher expected utility than the optimal conventional contract of the form  (,()).

Analysis

This paper gives very intuitive conclusions and has straight-forward models.  Like most health economics models, this one greatly simplifies how medical service provision works.  There is only on dimension upon which health can vary and the degree of physician specialization divided into only two discrete categories (i.e.: specialists and generalists).  The model claims that appropriately written managed care dominate traditional contracts, but the model does not take into account the cost of information collection needed to correctly establish the copay rate for each of the L subintervals.  While this paper will not solve the problems of the medical field, it does put another simple yet insightful model into the healthcare economist’s toolbox. 

Blomqvist, Åke; Léger, Pierre Thomas (2005) “Information asymmetry, insurance and the decision to hospitalize” Journal of Health Economics, Vol 24(4), pp. 775-793.

Moral hazard and adverse selection are ever-present problems in the health insurance market.  Identifying their existence and their magnitudes is difficult.  Most of the papers presented in this blog have used a reduced form approach [see Finkelstein, McGarry (2003) and Bhattacharya, Vogt (2006)].  Today we will look at a paper by Bajari, Hong and Khwaja where the authors use a structural approach.

Model

The authors set up a two period model.  In the first time period the individuals choose the level of insurance; in the second period they choose a level of medical expenditures ‘m‘.  The individuals are assumed to have a separable utility function as follows:

• U(c,m-q;g)=(1-g1)^{-1}*c^(1-g1) + g2(1-g3)^{-1}*(m-q)^{1-g3}
• c=y-p-z(m)

The variables g1, g2, and g3 are parameters characterizing the consumer’s utility function; c is a consumption of non-medical goods; q is the consumers latent health status; y is an exogenous income measure, p is the insurance premium, z(m) is the copayment schedule.  The first order condition is:

• g2(m-q)^{-g3}=c^{-g1} * [z'(m)]

This first order condition will be the cornerstone of all subsequent empirical analysis.

Methodology 

The authors use data from the 1996 (wave 3) Health and Retirement Study (HRS).  The HRS is a panel study which tracks individuals over age 50 for a period of 2 years.  The first step in the analysis is estimate the health insurance co-payment schedule (z(m))using data on out of pocket medical expenditures and insurance choices.  The reimbursement schedule is unique for each insurance group (eg: employer, Medicare, etc.), but not for each individual.  Since c, y, p, m are known, and z(m) is estimated, we can now try to recover the g’s and the q variable.  The authors assume ‘y’ is exogenous.  This is unlikely since those with a poor health (high q) are unlikely to earn much money if they are continually sick.  Nevertheless, if y were to be exogenous, we can solve the FOC for q so that q_i=r(m_i,p_i,y_i,z(m_i),g). 

The authors then use three instrumental variables (x_i) to formulate a method of moments estimator where the median latent health status u_q, as well as the parameters g are estimated.  The median moment condition below is wisely employed since it is more robust to censoring at the upper and lower tails of the conditional distribution of the observables. 

• (u_q,g)=argmin || n^{-1} SUM_i [f(x_i)*{1{r()}-0.5}] ||

The brackets “|| ||” represent the quadratic norm, ie: ||x||=x’Wx.   If the instruments are truly valid, then f(x_i) should be orthogonal to r() when r is evaluated at the true vetor g.  The instruments used are as follows:

1. State level housing price index
2. County level malpractice insurance component of the Geographic Practice Cost Index (GPCI)
3. Number of establishments in a county

In order for these to be good instruments, the variables chosen must provide variation in the price of providing medical goods but be uncorrelated with the latent health distribution.  Each of these three seems to fulfill this requirement, but there could be some problems.  While higher rental prices should increase the cost of living and thus the cost of medical services, it is also possible that only healthier people are able to make sufficient income to be able to afford houses in high rent areas.  Malpractice insurance certainly effects the price of medical services and likely does not significantly effect health status.  The author mentions that any defensive medicine caused by higher malpractice insurance rates should be reflected in the price consumers pay for health insurance.  The number of establishments in a county may effect prices, but the direction is unclear.  This is likely a weak instrument. 

Results

The authors find that the coefficient of relative risk aversion for aggregate consumption (g1) is 0.85, while the same parameter for consumption of health services (g3) is 1.52.  This shows that individuals are more risk averse with respect to health status than the aggregate consumption commodity.  The authors find that the utility weight on the consumption of health services relative to the composite good (g2) is 1.37.  Thus the medical services good is more highly valued than the composite good. The authors’ estimate of the median value of latent health status (u_q) is $4063.  For the 25th percentile, u_q has a value of $708 and for the 75th percentile the value is $11,653.  We see how the latent health variable has a significant effect on healthcare costs.

The authors can also estimate the medical expenditure elasticity at different expenditure percentiles.  The median elasticity is -0.21, but is -0.47 at the 25th percentile and -0.01 at the 75th percentile.  Bajari, et al. also subdivide the elasticities by insurance type.  They find no significant difference in elasticities between insurance groups, except for the self employed who have a higher elasticity, likely because as a group they are healthier than the other categories.  The variation in the elasticity of demand for health care occurs within plans, not between them.  

To estimate the moral hazard phenomenon, the authors examine the correlation between estimated elasticities and various individual characteristics.  The authors find that elasticity monotonically increases as self reported health status improves.  This implies that younger and healthier individuals have a more elastic response to price incentive. 

At first glance, it seems that adverse selection is a problem since we find that those who are uninsured have the best latent health status at the median.  The authors use a Kolmogorov-Smirnov test statistic to see if the uninsured are truly different than the insured.  The K-S test cannot reject the equality of the distributions across the insurance categories.  So there is no adverse selection?  The authors wisely note that this could simply be due to the fact that the categories chosen (ie: Medicare, VA/CHAMPUS, employer-provided, etc.) are too broad and that there is adverse selection present, but it is within each insurance category.  Further, since the HRS deals with individuals above age 50, it is likely that adverse selection is less of a problem since many of these individuals are eligible for public insurance (Medicare for those over 65).

• Patrick Bajari, Han Hong, Ahmed Khwaja (2006) “Moral hazard, adverse selection and health expenditures: a semiparametric analysis” NBER Working Paper No. 12445.

Jason has insurance and his brother Nosaj does not.  Jason utilizes more medical services than Nosaj.  Is this situation occuring because Jason is truly sicker than Nosaj (adverse selection), or is this because since Jason has insurance, medical services are cheaper for him than Nosaj (moral hazard)?  Disentangling the problems of moral hazard and adverse selection is what Susan Ettner sets out to do in her 1997 JHE paper “Adverse selection and the purchase of Medigap insurance by the elderly.” 

Ettner motivates her paper by dividing individuals into four groups:

• Group A: High propensity to use services; Employer does not offer Medigap coverage
• Group B: Low propensity to use services; Employer does not offer Medigap coverage
• Group C: High propensity to use services; Employer does offer Medigap coverage
• Group D: Low propensity to use services; Employer does offer Medigap coverage

Ettner assumes that groups A, C and D will purchase Medigap and group B will not.  If individuals choose employment based on criteria apart from the quality of the firm’s health plan offerings, then groups C and D combined will accurately represent the population as a whole.  We can calculate the amount of adverse selection by taking the difference between the average utilization of group A versus groups C and D combined.  Moral hazard can be calculated by comparing the average utilization of group B versus groups D; however this not possible since empirically it will be impossible to separate out groups C and D.  Ettner says that comparing group B versus groups C and D combined will lead to an overestimate of moral hazard, but this will still be less biased then an estimator comparing uninsured (B) vs the insured (A, C, and D).

Data and Methodolgy

Ettner uses data from the 1991 Medicare Current Beneficiary Survey (MCBS) and runs a multinomial logit regression comparing individuals with employer Medigap, individual Medigap and Medicaid only policies.  In addition to usual demographic and socio-economic variables, Ettner uses state-level variables such as SSI income standard, a cost-of-living index and the price of the most comprehensive Medigap policy in the state. One problem is that individuals with a high propensity to consume medical services may elect not to choose Medicaid coverage since they know that if they become sick, they can ex post sign up for Medicaid and be covered.

The author proceeds to estimate moral hazard and selection effects on resource use.  The expected value of using a service ‘Y’ can be written:

• E(Y)=P(Y>0)*E(Y|Y>0)

The probability term is estimated using a probit model and the conditional expected value term is estimated using OLS.  In this part of the paper, Ettner enriches the analysis by subdividing the regressions into basic Medigap and enhanced Medigap (eg: those with nursing care and/or prescription drug coverage). 

Results

Ettner finds that overall, adverse selection is not a significant factor in the purchase of Medigap insurance.  There is some evidence of adverse selection (those with cardiovascular or musculoskeletal problems are more likely to purchase Medigap), but there is also evidence of favorable selection (individuals who are smokers or who rate their health to be poorer are less likely to buy Medigap policies). 

Regarding the utilization results, Ettner finds that the enhanced Medigap comparisons showed stronger moral hazard effects than those with basic Medigap policies.  If adverse selection is not controlled for, the paper demonstrates that the moral hazard estimates are biased upwards.

Ettner, Susan; (1997) “Adverse selection and the purchase of Medigap insurance by the elderly” Journal of Health Economics, Vol 16, pp. 543-562.

Daniel Polsky and Sean Nicholson have two papers which aim to look at employer health insurance offerings.  The first [Polsky, Nicholson (2004)] tries to estimate the factors driving the cost differences between HMO plans and non-HMO (eg: PPO, indemnity) plans.  The authors deconstruct the cost differences into three factors:

1. Utilization Effect: this occurs if individuals enrolled in an HMO plan use fewer services than those in a non-HMO plan.
2. Risk Selection Effect: this phenomenon appears if HMO enrollees are generally healthier than non-HMO enrollees.
3. Reimbursement Effect: this is apparent if HMOs negotiate lower payments to providers–for instance by offering higher volume–than do non-HMO plans.

The authors have an ingenious way of estimating this.  They use the 1996/1997 Community Tracking Study (CTS) to estimate the following equation:

• M=b_1*H + b_2*X + v

The medical resource use (’M‘) is a function of whether or not the individual is in an HMO (’H‘) as well as observable characteristics (’X‘).  The error term ‘v’ is composed of the sum of a random element (’e‘) as well as the unobservable characteristics (b_3*Z)  It is possible to estimate b_1 and b_2 consistently only if Cov(H,Z)=0.  The authors accomplish this by estimating the above equation for individuals whose employer offers them no choice of health plan–thus risk selection should not be a problem.  In order to determine the reimbursement effect, the authors estimate prices for each service using the MEPS.  Below are the formulas for each effect.  The coefficients are all estimated from the subsample with no choice (C=0) for the employee.

1. Utilization Effect: b_1
2. Risk Selection Effect:
1. Observable: b_2*X{H=1,C=0} -b_2*X{H=0,C=0}
2. Unobservable: b_2*X{H=1,C=1} -b_2*X{H=0,C=1}
3. Reimbursement Effect: [p{H=1}S{H=1}-p{H=0}S{H=1}] – [p{H=0}S{H=1}-p{H=0}S{H=0}].  This is done for each service type and summed over all services. 

The results of the study are that HMO expenditures per person were $188 lower (9.3% lower) than non-HMO expenditures per person.  Utilization Rates and Risk Selection were similar for HMO and non-HMO plans.  The difference was made up from different reimbursement rates for providers.

This paper is very clever econometrically and the CTS gives a wide variety of useful variables for the study.  The key assumption to this paper is that employees do not choose their work based on the health insurance that it offers (which Bhattacharya and Vogt would disagree with).  The authors claim if unhealthy employees choose jobs with non-HMO health insurance, then the utilization effect would be biased downward and the unobserved risk selection estimate would be biased upward.  Thus, one cannot even sign the direction of this problem. 

A second paper by Polsky and Nicholson, et al. (2005) uses the CTS data set as well.  It examines the factors which determine whether a worker takes up the health insurance offered by their employer.  A worker can choose to take-up the insurance of their employer, choose an ‘alternative insurance’ (such as government provided insurance or go on a spouse’s health insurance plan), or elect to remain uninsured.  A multinomial logit regression finds that single individuals generally take up an HMO plan if it is the only one offered, but married individuals often refuse to take up the HMO plan if it is the only one offered.  Married individuals have a choice to be on their spouses insurance while single individuals are left with a choice of either state health insurance or expensive individual insurance. 

The authors also use the CTS employer survey to estimate the cost of the plans offered to employees.  They estimate the cost to the employer (premium) and the cost of the plan to the employee (net premium).  They find that a higher net premium reduces employee take-up but a higher premium does not affect take-up rates.  Unsurprisingly, younger, less educated and poorer families are more likely to forgo health insurance. 

Some problems with this paper are the authors assume that the employer does not offer health insurance strategically.  This is unlikely.  The authors showed that when a firm offers only a HMO plan, a married worker is likely to take-up insurance with their spouse.  Thus, offering parsimonious coverage will reduce a firms costs by causing many of its employees to move off their roles.  The authors also assume that employees do not select jobs according to the generosity of their health insurance, and that the net premium workers pay is also exogenous.  Both assumptions are unlikely to be proven true in reality. 

Polsky, Daniel; Nicholson, Sean; (2004) “Why are managed care plans less expensive: risk selection, utilization or reimbursement?” The Journal of Risk and Insurance, pp. 21-40.

Polsky; Stein; Nicholson; Bundorf ;(2005) “Employer health insurance offerings and employee enrollment decisions” Health Services Research, pp. 1260-1278

According to the Kaiser Family Foundation, 160 million Americans receive their health insurance from their employers.  That figure represents three out of five non-elderly individuals.  Many experts argue that using employer provided health insurance eliminates the problem of adverse selection by forming an insurance pool around a non-medical issue (employment).  Jayanta Bahattacharya and William Vogt are not sure this is the case.  In their 2006 NBER working paper, the authors aim to test whether or not healthy individuals are more likely to receive employer provided insurance at their jobs. 

Model

The model Bhattacharya and Vogt set up is a two period model.  In the first period i) employer set wage and benefit levels, ii) workers are informed if they are health or sick, iii) workers choose their employer and then iv) a health shock occurs and the employee consumes the needed medical care.   In the second period: i) the workers see a new health state, ii) there is an involuntary turnover rate ‘T‘ where these workers must seek new employment, iii) workers in the ‘(1-T)‘ group can elect to switch employers, and iv) a second health shock occurs and the employee consumes the needed medical care.

The individual’s utility function is:

• u(Y-m)+v[H+f(m,e)]

‘Y‘ is one’s income and ‘m‘ is out of pocket medical expenses.  The separable v function is utility gained from health capital.  ‘H‘ is the initial health capital level and f is an increased or decreased level of health depending on health spending ‘m‘ and a random shock ‘e‘.  The shock variable is distributed according to the cdf ‘F‘ which depends on whether the individual is sick or healthy. F_sick first order stochastically dominates F_well.  Because of this assumption, we can prove (mathematically) the following three statements:

• The cost of care for the sick is higher than the cost of care for the well
• The Utility of the insured who are sick is lower than the utility of the insured who are well.
• The Utility of the uninsured who are sick is lower than the utility of the uninsured who are well.

There are four possible indirect utility functions in the first period. 

• U_SU: E_{F_s} [U(Y-m,H+f(m,e))]
• U_SI: E_{F_s} [U(Y,H+f(m,e))]
• U_WU: E_{F_w} [U(Y-m,H+f(m,e))]
• U_WI: E_{F_w} [U(Y,H+f(m,e))]

In the second period, however, the author assumes that there is a switching cost of ‘c‘ utils if the employee decides to change jobs.  This can be justified as a psychic cost to the worker or the loss of job-specific human capital.  The author assumes U_WU(W-p)-c<U_WU(W) meaning that one will reduce overall utility if the person leaves a job and purchases private health insurance rather than staying at a job and using the employer provided insurance. 

Equilibrium

The authors seek a symmetric subgame perfect Nash equilibrium where 1) both sick and well workers choose an employer offerreing insurance in period one and 2) neither sick nor well workers voluntarily turn over to change insurance status in period two.  Working out the mathematics we find the following conclusions:

1. Among people who do not receive employer provided insurance, the sick benefit the most from purchasing individual insurance: U_SI(W-p)-U_SU(W) > U_WI(W-p)-U_WU(W).  Thus the only people left uninsured will be the healthy ones.
2. A pooling equilibrium is more likely if there are high switching costs ‘c‘. 
3. A pooling equilibrium is more likely with a low exogenous turnover rate ‘T‘. 
4. A pooling equilibrium is more likely if the probability that a well person will become sick is high and the probability a sick person will become well is also high.  Algebraically, the author say this means that ‘P_ww‘ is low.

Empirical Tests

The authors use data from the 1995-2005 March CPS as well as the Occupational Information Network and the Census Public Use Microdata Sample.  They aim to test conclusions 2-4 above as well as seeing if there is any evidence of adverse selection.  The data also show that industries with a high level of job specific human capital–which in this case the authors use as a proxy for ‘c‘ –have workers which are more likely to be covered by their employers.  The coefficient on ‘P_ww‘ is not significantly different from zero, but the ‘P_ww‘ variable is likely not measure with accuracy.  A high turnover rate ‘T‘ actually has the opposite effect than the one predicted by the authors.  The measure ‘T‘ however is problematic to measure in the data since turnover empirically is a mix of exogenous and self selected turnover (quits) and thus the authors disregard any results for the variable. 

Analysis

The model of this paper is elegant and produces sensible conclusions.  While the homogeneity of employers greatly simplifies the model, it makes the mathematical conclusions less robust.  The empirical work gives some suggestive evidence that adverse selection in employer choice of workers may be a problem.  Using such a large data set adds precision to the author’s estimates, but it may be difficult to decompose the countervailing forces within each industry without having more detailed knowledge of each sector.  Still, the combination of theory and empirical work is good; using other data sets to substantiate the authors claims would make their conclusions more robust.

Bhattacharya, Vogt (2006); “Employment and adverse selection in health insurance,” NBER Working Paper No. 12430.

The adverse selection death spiral has reared its ugly head again.  PacAdvantage, an insurance pooling company for 6000 small and medium sized businesses in California has closed its doors.  The Sacramento Business Journal reports (”Backer pulls plug on PacAdvantage health purchasing pool“) that the three remaining insurers underwriting the plan have pulled out.   Michael Holt of The Health Care Blog analyzes has some perceptive analysis of the situation:

“What happens to voluntary purchasing pools? Simple economics—they only get customers who can’t get a better deal in the underwritten insurance market and so they go into a death spiral where the people in them are too sick to be supported by the premiums they charge. Today PacAdvantage announced that it was closing down, throwing 110,000 people into the small group and individual market, where by definition, no insurer wants them (unless they’re like me—very lucky).

PacAdvantage is the type of organization that our friends in the ‘voluntary universal insurance’ world (Cato, Galen et al) think is going to solve all of our problems, with no need for pesky mandates to buy insurance, or for community rating, or standardized benefits packages.”

In my June 15th post, I mentioned Cutler and Zeckhauser’s 1997 paper which discussed this concept of an adverse selection death spiral in the context of Harvard’s employee health insurance plans. 

Many papers on health insurance worry about the problem of adverse selection.  Critics of HMOs claim that the fact that HMOs have lower costs is not due to more efficient provision of services nor the limitation of the provision of services, but instead largely caused by the fact that the people who choose to enroll in HMOs are healthier on average.  Shen and Ellis (2002) aim to test whether or not costs incurred from modeling an individual’s risk lead to higher profits.  A patient’s risk is measured in 4 different ways:

1. using age-sex categorization
2. using prior medical service utilization
3. using Adjusted Clinical Groups (ACG) [example]
4. using Diagnostic Cost Groups (DCG)

The data used in the paper come from the Mercer privately insured dataset for the years 1992 and 1993.  The authors use OLS estimation and justify this econometric methodology by claiming that when data sets are large, OLS is nearly as good an approximation as cell-based or non-linear models.  The authors find that categories 2, 3, and 4 provide significant information regarding the probability that an individual will incur future medical costs (R-squared between 0.079 and .106).  Category 1 also improves cost prediction over the pricing with no categorization, but the age-sex category is less accurate (R-squared of 0.019).  The authors find ACG generally has the highest gross profit rates for insurance companies regardless of the information which the payer has.  Gross profit ranges between 24% and 60%.  Overall profit increase ranges from $68 to $260 million depending on the risk selection system used and the information the payer has about their own propensity to use medical services.

In order to obtain these results, the authors used a simulation methodology and assumed that it was costless to drop any unprofitable payer.  The authors also looked only at one-year profit maximization schemes and did not look at any reputation effects.  Shen and Ellis conclude that this additional information does help increase an insurance company’s bottom line.  The major contribution of this paper is that they quantify how much additional profit can be gained from these risk selection mechanisms, even if this quantification is done in a static environment in which customers can be costlessly dropped from coverage.  The authors do not mention whether or not risk selection is good or bad for society since the utilty loss of those dropped form coverage is not modeled.  The only area of study is insurance company profits. 

Shen and Ellis (2002) “How profitable is risk selection? A comparison of four risk adjustment models,” Health Economics, Vol 11, pp. 165-174.

In the mid to late 1990s, there was a large backlash against HMOs due to the perception that the “HMO cares more about saving money than it does about them.” People feared that HMO would drop coverage of services which they valued.

In this month’s NBER working paper edition, Yu-Chu Shen (2006) looks answer two questions related to this topic:

1. Does the degree of HMO penetration in a MSA increase the likelihood that a hospital will drop some of its services?
2. Does the percentage of for-profit HMO enrollment increase the likelihood that a hospital will drop some of its services?

Shen divides hospital services into two groups. The first consist of safety net services such as having an emergency department, a trauma center, HIV/AIDS services, and substance abuse services. The second group of services are safety net services which are generally profitable. Examples of this group are maternity care, birthing rooms, child wellness services, women health centers and sports medicine.

Using a hazard function (specifically the Cox proportional hazard model), Shen tests his hypothesis using dummies for low, medium and high HMO penetration and then repeats the procedure for low, medium and high dummies for the percentage of HMO enrollment which have for-profit HMOs.

In his results, Shen does not find any consistent trend in the abandonment of services, with the exception that in the 2000-2003 period it seems that areas with high level of HMO penetration dropped profitable services less frequently than low HMO penetration areas. Social pressure may be the reason there were few major difference in the hazard rates of dropping unprofitable safety net services between Shen’s categories. The study did find that government ownership is associated with a lower hazard of shutting down unprofitable services. Large hospitals and teaching hospitals have a lower hazard rate of shutting down any service.

The Cavalcade of Risk #4 is posted.  From the C or R website:

The purpose of the C of R is to offer insights into the world of risk management; generally, this will be insurance-related, but that’s not a requirement. Our goal is to help folks understand what risk is, and how to manage it. It’s about business and finance, of course, but it’s also about risks in our everyday lives and personal relationships.

How does one design an optimal insurance policy where physicians and patients are compelled to tell the truth about the medical procedures that were completed?  This is the question of Ching-To Albert Ma and Thomas McGuire in their 1997 AER paper.  The paper is somewhat technical but I will briefly explain their setup and conclusions, along with my own analysis.

Setup

Individuals become sick with probability ‘p‘.  If this occurs and they can purchase health (medical care) which is a strictly concave function of the number of procedures done (’t‘) and the physician effort level (’e‘).  Thus:

• Health=f(t,e)

The physicians can report any procedure level ‘T‘ to the insurance company that they wish regardless of the actual number of procedure (’t‘) that they complete and which are recorded in the medical records.  Individuals get utility from income and health.  In this paper, health is measured in cash equivalent units.  Physicians are profit maximizers, but receive less utility the more effort they put forth.

Conclusions

1. Truth Telling: In order to induce truth telling (T=t) physicians must receive a positive payment for their services (not strict capitation) and patients must have a positive co-payment.  This way, physicians will wish to increase T, but patients will not allow this since increasing T increases the co-pay.  The patient are able to reveal the true ‘t‘ to the insurer and thus truth telling is the Nash equilibrium.
2. Effort: If the second derivative f_{t,e} is negative, effort and treatment are substitutes.  This means that more physician effort will reduce the demand for medical procedures since.  If this is the case, physicians will reduce ‘e‘ to the minimum level to maximize ‘t‘ (and thus their profits).  If the second derivative f_{t,e} is positive, effort and treatment are complements.  This means physicians can only increase ‘t’ when they increase their effort level.  In this case, physicians will put forth an high level of effort.
3. Ethics:  What if physicians have ethical notion of a minimal level of care, so a necessary condition is that ‘f(t,e)>F‘?  In this case, e may increase from its lower bound (in the substitute case).  In a general equilibrium setting, capitation payments, however, may need to increase in order to induce individuals to enter/remain in the medical profession.  Overall, however, having an ethical minimal level of care is Pareto improving for society.

Analysis

This paper is interesting theoretically, but greatly simplifies the market.  Competition within insurance plans as well as the variety of plans available does not appear in this paper.  Further, there is likely no explicit function f(t,e) in which medical procedures translate into health; there is a significant stochastic element to health even in the face of known treatment quantities.  The paper also abstracts from many of the informational problems (such as the fact that patients may not know/understand the procedure they undergo) and assumes that supplier induced demand is limited by the patients’ medical knowledge. 

Ma and McGuire (1997), “Optimal health insurance and provider payment,” American Economic Review, Vol. 87(4), pp. 685-704.

‘Adverse selection’ and ‘moral hazard’ are phenomenons which affect any analysis of the insurance market.  For instance, Cutler and Zeckhauser (1997) speak of an adverse selection ‘death spiral’ which made untenable the continued offering of a generous health insurance benefit at Harvard University.  In their NBER paper, Finkelstein and McGarry (2003) attempt to estimate the effect of private information in the long-term care insurance market. In 2000, the authors state that long-term care expenditures in the U.S. comprised 7.5% of all medical expenditures and 1% of GDP for a total of $100 billion.  Thus, analyzing this segment of the health insurance market is non-trivial and it will only increase in importance as baby boomers continue to retire in larger numbers.

The first item Finkelstein and McGarry investigate is whether there is a positive correlation between the amount of long-term care insurance purchased and the likelihood of using the insurance benefit.  If adverse selection is a problem, one would expect people who purchase large amounts of insurance will also be the ones who incur expensive long-term care costs for the insurance companies.  Surprisingly, they encounter no evidence of this.  Using a more specific test for adverse selection, however, the authors do find proof that adverse selection is a problem.  Examining a supplement to the Health and Retirement Study (HRS), they find that people who expect to enter a nursing home in the next five years are 1) more likely to enter a nursing home and 2) more likely to purchase long-term care insurance. 

So what explains the lack of correlation between the amount of long-term care insurance purchased and the likelihood of entering a nursing home if adverse selection is a problem?  It turns out that preferences explain the residual.  The authors posit that risk averse individuals are more likely to undertake preventative health care measures such as having a flu shot, cholesterol test, mammogram or prostate screen.  Using these measures as a proxy for risk aversion, Finkelstein, et al. find that risk adverse individuals are 1) more likely to buy long-term care insurance, but 2) less likely to need the benefit.  We see that the effects of adverse selection are offset by the fact that healthier, risk averse individuals also purchase excess amounts of insurance.  Thus, in aggregate–at least for the long-term care insurance–this market can reach a stable equilibrium. 

Finkelstein and McGarry (2003) “Private information and its effect on market equilibrium: new evidence from long-term care insurance,” NBER WP #9957.

According to the Kaiser Family Foundation’s (KFF) 2005 Employer Health Benefits Survey, the estimated number of firms who will offer high deductible health plans has increased to 20% in 2005.  This is up from 5% in 2003 and 10% in 2004.  Despite this increase, only 3.9% of workers–about 2.4 million in total–are covered either by a High Deductible Plan combined with a Health Reimbursement Arrangement (HDHP/HRA) or a Health Savings Account (HSA).

The move towards consumer driven health care is upon us.  Services such as HealthGrades have begun to give quality ratings to hospitals as well physicians and nursing homes.  The HealthGrades offers basic 1-5 star ratings for free, but more detailed reports are available if you wish to pay for them.  (I have not elected to purchase these reports and cannot ascertain their quality.  The information for the Distinguished Hospital awards comes from inpatient mortality and complication rates from the CMS’s MedPAR data.) 

Further, The New York Daily News recently wrote an article (”Cutting Medical Care Costs“) on the trend towards consumer bargain hunting.  For instance:

Private insurers like Aetna have started programs in parts of the country (Cincinnati is an early example) where they’ll publish online the exact prices they’ve negotiated with doctors in the area for hundreds of medical procedures and tests.

Also, the story gives the following anecdote about patient Lew Randall:

Lew Randall, 64, from Freeland, Wash., recently suffered a shoulder injury. His doctor originally told him an MRI, at $1,200, would be in order to assess the damage.

When the doctor heard Randall would be paying the bill himself, he recommended a $300 barium X-ray instead?

“Is it just as good?” Randall asked.

“If it were my shoulder, that’s what I’d have,” the doctor replied.

“So why recommend the MRI?” Randall continued.

“It’s newer technology,” the doctor shrugged. “That’s what patients want.”

Lew Randall, however, is a director at the libertarian Cato Institute so his story may not be representative. 

Forbes reported last week (”Millions…“) that millions of dollars are wasted each year due to unnecessary tests. Their findings are based on an article by Dr. Dan Merenstein and co-authors and is to appear in the June issue of the American Journal of Preventive Medicine.

What is the definition of an unnecessary test? The United States Preventive Services Task Force grades each test on a scale from A to D. For tests which receive ‘C’ grade, the panel has made no recommendation either for or against its use. For tests which receive a grade of a ‘D,’ the panel recommends against giving the test due to harmful side-effects or additional stress placed upon the patient.

The study examines three tests given a ‘D’ rating by the panel: EKG or electrocardiogram, urinalysis and chest X-ray. The authors looked at over four thousand routine exams for adults over age 21. Forbes reports:

At least one of the three “D” interventions was ordered 43 percent to 46 percent of the time, the researchers said.

Using extrapolation techniques, Merenstein and his colleagues determined that direct medical costs for the three “D” tests ranged from $47 million to $194 million. Adding in two other tests from the “C” category pushed the costs up by another $12 million to $63 million.

Merenstein concluded that:

“Doctors could do it [provide unnecessary tests] to appease patients or because the physicians themselves think they’re supposed to do them. And, if they owned a lab, some doctors did it for financial reasons,”

This is hardly surprising. In a fee-for-service environment, physicians have an incentive to over-treat. Since patients usually have insurance and thus do not feel the full cost of the service, they also have little incentive to restrict the physician from conducting tests. If patients did have to pay the full cost for the test, they would certainly be more hesitant to accept to pay for these unnecessary procedures.

Health insurance should not be a formed of forced savings where all services are covered. If society desires to reduce the cost of health insurance, choosing insurance plans which only cover serious medical problems–and not routine check-ups–is one of the more effective, free-market means.

“One of the quirks of the health care system is that health plans individually negotiate different prices with hospitals and doctors. The result is that two health plans can pay different prices for the same procedure at the same hospital. The contracts typically prevent a health plan from saying that it charges a certain amount for a procedure, though a health plan can show the discounted charges on patients’ bills.

Many economists contend that the practice contributes to the inefficiency of the health care system and lessens hospitals’ incentive to become more efficient. It also makes it impossible for consumers to shop for hospitals with the best prices.”

- Milwaukee Journal Sentinal, “Health plan lifts the veil on charges“) Feb 23, 2006

One major impediemnt to adequate functioning of medical markets is that consumers often do not know what prices each hospital charges them (or their insurance). HealthCare Direct LLC is trying to change this using the philsophy that “Sunlight is the best disinfectant.” The aim is to steer self-insured employers towards more cost efficient solutions, by compelling providers to release their pricing schedules.

Kellogg Business School at Northwestern University cites a Journal Sentinal article which states that:

“HealthCare Direct LLC has persuaded ProHealth Care and Columbia St. Mary’s, two health care systems in the Milwaukee area, to accept a flat rate for 26 common hospital procedures and to disclose the price of each.

The goal is to help make health care a bit more like other markets, in which buyers have an easier time determining which companies have the lowest prices and, theoretically, are the most efficient.”

The poor can not afford health care. Health care costs rise above inflation year after year. Serious errors committed by hospitals and physicians are reported by the news media on a daily basis. How can we fix these problems? Can we rely on Uncle Sam to do what’s needed?

Will Wilkinson doubts that government intervention can solve the health care industry’s problems. Wilkinson is an analyst for the libertarian Cato Institute and in his “Health Care Fantasia” post on his blog, he aims for radical reform. His arguments are provocative to say the least.

Wilkinson advocates abolishing the FDA and stripping the AMA of its monopoly to certify all doctors. While I think this is extreme, we should reduce the stringency of FDA regulation. The FDA adds to the cost of drug development and increases the time between innovation and provision to the sick. Regarding, the AMA one thing one must keep in mind is that this is not an altruistic organization and mostly acts in the best interests of doctors. Reducing the AMA’s power would lead to more cost efficient provision of medical services. For instance, more procedures should be done by nurses and physicians assistants who earn less than doctors.

As expected, Wilkinson is a big fan of HSAs. He advocates that the US create a negative income tax (which I generally support) and the government would deposit a portion of the money into an HSA. I am not sure how this would lead to consumer driven insurance. If a poor person does not have enough money in the HSA for a procedure, will society deny them care? Politically this is infeasible. If we decided to put enough money aside for the poor in their HSA that they will not have to pay any money out of pocket, then this amounts to full insurance.

For uninsurable patients, Wilkinson advocates that the government provide a low quality alternative to private insurance which includes significant rationing. This is similar to what we have now with Medicaid, but instead of having the poor as its target population, it would focus on those with privately uninsurable conditions (the ‘health poor’). Compared to John Kerry’s proposal during his 2004 campaign was for the government to insure all citizens for catastrophic illness, Wilkinson’s proposal is less expensive, but also less favorable in terms of horizontal equity. Both the Wilkinson and Kerry systems for people with severe illnesses would include rationing decisions.

Economists typically assume that the majority of additional costs employers incur from hiring a worker are reflected in a lower compensation package for the employee.  For instance, employees owe payroll taxes for Social Security and Medicare on 7.65% (Social Security taxes are limited to earnings below $94,000 in 2006, but Medicare taxes are applied on all earnings).  Employers also pay a tax of 7.65% of workers wages to the government.  Economists believe that the true tax to workers is 15.3% since firms pass on the added costs to employees.

Increasing health insurance costs is another arena where employers may pass the costs on to employees.  A study by Goldman, Sood and Leibowitz (2005) analyzes how wages and benefits change in response to rising health insurance costs at one specific benefits consulting firm with a ‘cafeteria plan’.  The firm pays workers a wage and gives them a credit towards the purchase of a variety of benefits.  The credit can be used towards acquiring health insurance, as well as other benefits such a pension, accident insurance, life insurance, long term disability, etc.  The credit the firm grants to workers is based on their salary and their tenure at the firm.  Workers who wish to receive more benefits above their credit allocation can pay for the difference out of their salary.

Using a fixed-effects model with data between 1989-91, the authors estimate how changes in the price of health insurance affected the workers choice of benefits.  They find that when the price of health insurance increases by $1, workers finance the increase with a 52 cent reduction health insurance expenditures, a 37 cent reduction in take home wages, and a 17 cent reduction in other benefits.  This result shows the demand for health insurance is inelastic since a price increase leads to increased expenditures–48 cents in this example–on the good.

A problem with the study is the narrowness of its scope.  Since it only deals with one white collar firm, this result may not be generalizable to other sectors or the economy as a whole.  Also, the authors claim that as employees reduce the amount of other benefits as health care prices rise, an employee may be more vulnerable to health, mortality, disability and other risks.  We do not know, however, whether or not the employees decided to increase their savings rate in the face of decreased benefits in order to ’self insure’ against future risks.  Thus the magnitude and sign of the change in an employee’s vulnerability to future risk is indeterminate.

Source: Dana P. Goldman, Neeraj Sood, and Arleen Leibowitz (2005) “Wage and Benefit Changes in Response to Rising Health Insurance”, Forum for Health Economics & Policy, Forum: Frontiers in Health Policy Research, Volume 8: Article 3. http://www.bepress.com/fhep/8/3

Malcolm Gladwell is the author of the best selling book The Tipping Point. In his August 2005 New Yorker article (”The Moral Hazard Myth“), he gives a wonderful description moral hazard, the major reason many economists oppose overly generous health insurance schemes. A few excerpts:

• “Moral hazard” is the term economists use to describe the fact that insurance can change the behavior of the person being insured…If you have a no-deductible fire-insurance policy, you may be a little less diligent in clearing the brush away from your house. The savings-and-loan crisis of the nineteen-eighties was created, in large part, by the fact that the federal government insured savings deposits of up to a hundred thousand dollars, and so the newly deregulated S. & L.s made far riskier investments than they would have otherwise. Insurance can have the paradoxical effect of producing risky and wasteful behavior. Economists spend a great deal of time thinking about such moral hazard for good reason. Insurance is an attempt to make human life safer and more secure. But, if those efforts can backfire and produce riskier behavior, providing insurance becomes a much more complicated and problematic endeavor.In 1968, the economist Mark Pauly argued that moral hazard played an enormous role in medicine, and, as John Nyman writes in his book “The Theory of the Demand for Health Insurance,” Pauly’s paper has become the “single most influential article in the health economics literature.” Nyman, an economist at the University of Minnesota, says that the fear of moral hazard lies behind the thicket of co-payments and deductibles and utilization reviews which characterizes the American health-insurance system. Fear of moral hazard, Nyman writes, also explains “the general lack of enthusiasm by U.S. health economists for the expansion of health insurance coverage (for example, national health insurance or expanded Medicare benefits) in the U.S.”
• The moral-hazard argument makes sense, however, only if we consume health care in the same way that we consume other consumer goods, and to economists like Nyman this assumption is plainly absurd. We go to the doctor grudgingly, only because we’re sick. “Moral hazard is overblown,” the Princeton economist Uwe Reinhardt says. “You always hear that the demand for health care is unlimited. This is just not true. People who are very well insured, who are very rich, do you see them check into the hospital because it’s free? Do people really like to go to the doctor? Do they check into the hospital instead of playing golf?”

On The Health Care Blog, Brian Klepper of the Center for Practical Health Reform weighs in on his opinion on HDHP (”Can Consumerism Save Healthcare?“).  A few of his major points:

• Imperfect Information still a problem: HDHP’s force consumers to absorb a portion of the health care cost and thus these individuals should shop for providers based on quality and price.  Klepper states that while the internet is helpful in terms of patient self diagnosis,  “…so far, even though inexpensive evaluation tools exist, consumers still can’t get much information on the pricing and performance of hospitals, doctors and drugs. It’s hard to be an effective shopper if you don’t know what things cost or how the vendors stack up.”
• Chronic and catastrophic illnesses fuel rising healthcare costs.  While HDHPs may lower healthcare costs somewhat, their impact may be small.  “In truth, patients’ diagnostic and treatment choices represent a tiny portion of larger healthcare cost. The real money is associated with chronic disease and catastrophes. In those cases, healthcare professionals, not patients, guide the purchasing decisions. “
• Fee for Service: another driver of healthcare costs.  Fee for service compensation encourages physicians to prescribe too many services.  “The real roots of our healthcare crisis reside in the ways suppliers and clinicians are rewarded to deliver goods and services that are inappropriate, unnecessary and wasteful. Most healthcare experts agree that half or more of healthcare cost is due to these factors.”
• Klepper’s bottom line: “When it’s more mature, healthcare consumerism will likely include the mechanisms that help patients become better buyers and impact cost. Until then, HSAs and HDHPs are less expensive, slimmed down, short-term solutions that can work well if you’re healthy or financially secure. But they’ll do little to address our rapidly collapsing healthcare system. And as a national solution, they’re inadequate and oversold.”
  

Sunday’s San Diego Union Tribune (”Doctors object to ultimatum on health care: Sharp wants every senior on one plan“) had an interesting article on Sharp’s decision to issue its physicians an ultimatum: either provides services exclusively for our patients or find work elsewhere.  Let us look at this from a variety of perspectives.

Sharp:

Sharp is a non-profit organization, who is the largest health services provider in San Diego county.  According to the Sharp website, the group has “four acute-care hospitals, five specialty hospitals and three medical groups plus a full spectrum of other facilities and services.”  It also has $1.4 billion in revenue.  The contracts Sharp was proposing for the doctors was a flat fee per patient regardless of the number of services provided.  If these doctors were allowed to serve fee for service (FFS) patients in addition to Sharp’s capitation patients, the physician would have an incentive to provide care to the FFS patients and ignore Sharp patients.  Care provided to the FFS patients increases a physicians profits while care provided to Sharp’s capitation patients only increases costs.

Physicians:

The physicians think this is a raw deal.  First, they will lose a large portion of their clientèle if they choose to remain with Sharp.  Secondly, if a large portion of their capitation patients  become sick, they are not able to increase FFS volume to to makeup for lost income.  Third, doctors are not actuaries and passing the financial risk for caring for patients to doctors is a recipe for disaster.  Fourth, Sharp has significant market power in the San Diego area.  Sharp’s Secure Horizons senior plan pays physicians only $44/patient per month, while Health Net Seniority Plus pays doctors $110/patient per month.
Patients:

In the short term, this will certainly cause problems.  Many elderly patients will need to change doctors and may have to commute longer distances (although Sharp did offer “20 free one way trips to Sharp providers” among those who have to switch plans).  In the long term, the industry consolidation may help to reduce price.  Sharp’s decision to create an integrated IT network should increase efficiency and quality.

Conclusion
I think what you are seeing is a reigning in of costs in order to keep premiums down.  Physician and patient choice may suffer, but the costs to elderly San Diegan residents should decrease.