Category Archives: The Real World

Insurance death spiral in the news…

It turns out that the so-called “insurance death spiral” which we modeled in class this past Tuesday (see page 21 of the Moral Hazard and Adverse Selection lecture note for a verbal description and the Dynamic Adverse Selection Spreadsheet for a numerical illustration of the problem) is very much in the news these days; e.g., see the editorial entitled Salvaging Private Health Insurance in today’s Wall Street Journal and yesterday’s page 1 WSJ article entitled In Start to Unwinding the Health Law, Trump to Ease Insurance Rules.  Quoting from today’s WSJ editorial,

“ObamaCare’s defenders are calling all of this “sabotage” and warning about “adverse selection,” in which a more robust individual market will siphon off the healthy customers that prop up ObamaCare’s exchanges. They predict a death spiral of higher premiums for the sick or elderly left on the exchanges.”

Sound familiar?  As we discussed in class last Tuesday, since the implementation of combined premium schemes effectively force good risks to pay too much whereas bad risks pay too little, the good risks opt out.  When this occurs,  expected cost of claims (and correspondingly higher premiums) are in store for those who remain in the risk pool.

Adverse Selection – a definition, some examples, and some solutions

During last Thursday’s Finance 4335 class meeting, I introduced the topic of adverse selection. Adverse selection is often referred to as the “hidden information” problem. This concept is particularly easy to understand in an insurance market setting; if you are an insurer, you have to be concerned that the worst possible risks are the ones that want to purchase insurance. However, it is important to note that adverse selection occurs in many market settings other than insurance markets. Adverse selection occurs whenever one party to a contract has superior information compared with his or her counter-party. When this occurs, often the party with the information advantage is tempted to take advantage of the uninformed party.

In an insurance setting, adverse selection is an issue whenever insurers know less about the actual risk characteristics of their policyholders than the policyholders themselves. In lending markets, banks have limited information about their clients’ willingness and ability to pay back on their loan commitments. In the used car market, the seller of a used car has more information about the car that is for sale than potential buyers. In the labor market, employers typically know less than the worker does about his or her abilities. In product markets, the product’s manufacturer often knows more about product failure rates than the consumer, and so forth…

The problem with adverse selection is that if left unchecked, it can undermine the ability of firms and consumers to enter into contractual relationships, and in extreme cases, may even give rise to so-called market failures. For example, in the used car market, since the seller has more information than the buyer about the condition of the vehicle, the buyer cannot help but be naturally suspicious concerning product quality. Consequently, he or she may not be willing to pay as much for the car as it is worth (assuming that it is not a lemon). Similarly, insurers may be reticent about selling policies to bad risks, banks may be worried about loaning money to poor credit risks, employers may be concerned about hiring poor quality workers, consumers may be worried about buying poor quality products, and so forth…

A number of different strategies exist for mitigating adverse selection. In financial services markets, risk classification represents an important strategy. The reason insurers and banks want to know your credit score is because consumers with bad credit not only often lack the willingness and ability to pay their debts, but they also tend to have more accidents than consumers with good credit. Signaling is used in various settings; for example, one solution to the “lemons” problem in the market for used cars is for the seller to “signal” by providing credible third party certification; e.g., by paying for Carfax reports or vehicle inspections by an independent third party. Students “signal” their quality by selecting a high-quality university (e.g., like Baylor! :-)). Here the university provides potential employers with credible third-party certification concerning the quality of human capital. In product markets, if a manufacturer provides a long-term warranty, this may indicate that quality is better than average.

Sometimes it’s not possible to fully mitigate adverse selection via the methods described above. Thus, insurers commonly employ pricing and contract design strategies which incentivize policyholders to reveal their actual risk characteristics according to their contract choices. Thus, we obtain a “separating” (AKA Rothschild-Stiglitz) equilibrium in which high-risk insureds select full coverage “high-risk” contracts whereas low-risk insureds select partial coverage “low risk” contracts:

Rothschild-Stiglitz

The Rothschild-Stiglitz equilibrium cleverly restricts the menu of available choices in such a way that the insurer induces self-selection. Here, the insurer offers contract L, which involves partial coverage at an actuarially fair price (based upon the loss probability of the low risk insured), and contract H, which provides full coverage at an actuarially fair price (based upon the loss probability of the high risk insured). The differences in the shapes of the indifference curves are due to the different accident probabilities, with a lower accident probability resulting in a more steeply sloped indifference curve. Here, the high-risk policyholder optimally chooses contract H and the low-risk policyholder optimally chooses contract L. The high-risk policyholder prefers H to L because L would represent a point of intersection with a marginally lower indifference curve (here, the Ih curve lies slightly above contract L, which implies that contract H provides the high-risk policyholder with higher expected utility than contract L). The low-risk policyholder will prefer L, but would prefer a full coverage contract at the point of intersection of APl line with the full insurance (45 degrees) line. However, such a contract is not offered since both the low and high-risk policyholders would choose it, and this would cause the insurer to lose money. Thus, one of the inefficiencies related to adverse selection is that insurance opportunities available to low-risk policyholders are limited compared with the world where there is no adverse selection.

There is a very practical implication of this model. If you are a good risk, you owe it to yourself to select high-deductible insurance. The problem with a low deductible is that you will unnecessarily bear adverse selection costs if you follow this strategy.

Case studies of how (poorly designed) insurance creates moral hazard

During last week’s class meetings, we discussed how contract designs and pricing strategies can “fix” the moral hazard that insurance might otherwise create. Insurance is “good” to the extent that it enables firms and individuals to manage the risks that they face. However, we also saw insurance has a potential “dark side.” The dark side is that too much insurance and/or incorrectly priced insurance can create moral hazard by insulating firms and individuals from the financial consequences of their decision-making. Thus, in real world insurance markets, we commonly observe partial rather than full insurance coverage. Partial insurance ensures that policyholders still have incentives to mitigate risk. Furthermore, real world insurance markets are characterized by pricing strategies such as loss-sensitive premiums (commonly referred to as “experience rated” premiums), as well as premiums that are contingent upon the extent to which policyholders invest in safety.

In competitively structured private insurance markets, we expect that the market price for insurance will (on average) be greater than or equal to its actuarially fair value. Under normal circumstances, one does not to observe negative premium loadings in the real world. Negative premium loadings are incompatible with the survival of a private insurance market since this would imply that insurers are not able to cover capital costs and would, therefore, have incentives not to supply such a market.

Which brings us to the National Flood Insurance Program (NFIP). The NFIP is a federal government insurance program managed by the Federal Emergency Management Agency (AKA “FEMA”). According to Cato senior fellow Doug Bandow’s blog posting entitled “Congress against Budget Reform: Voting to Hike Subsidies for People Who Build in Flood Plains”,

“…the federal government keeps insurance premiums low for people who choose to build where they otherwise wouldn’t. The Congressional Research Service figured that the government charges about one-third of the market rate for flood insurance. The second cost is environmental: Washington essentially pays participants to build on environmentally-fragile lands that tend to flood.”

Thus, the NFIP provides us with a fascinating case study concerning how subsidized flood insurance exacerbates moral hazard (i.e., makes moral hazard even worse) rather than mitigates moral hazard. It does this by encouraging property owners to take risks (in this case, building on environmentally fragile lands that tend to flood) that they otherwise might not take if they had to pay the full expected cost of these risks.

There are many other examples of moral hazard created by insurance subsidies. Consider the case of crop insurance provided to farmers by the U.S. Department of Agriculture. According to this Bloomberg article, the effective premium loading on federally provided crop insurance is more than -60%, thus putting crop insurance on a similar footing to flood insurance (in terms of its cost compared to its actuarially fair value). Once again, incorrect pricing encourages moral hazard. As the Bloomberg article notes,

“…subsidies give farmers an incentive to buy “Cadillac” policies that over-insure their holdings and drive up costs. Some policies protect as much as 85 percent of a farm’s average yield.”

Just as mis-priced flood insurance effectively encourages property owners to build in flood plains, mis-priced crop insurance incentivizes farmers to cultivate acreage that may or may not even be fertile.

I could go on (probably for several hundred more pages – there are innumerable egregious examples that I could cite), but I think I will stop for now…

Extra Credit opportunity: Cyber Day Panel Discussion

Here is a very worthwhile extra credit opportunity for Finance 4335. You may earn extra credit by attending and reporting on the Cyber Day Panel Discussion described below.  In order to receive extra credit for this presentation, you must submit (via email sent to risk@garven.com) a 1-2 page executive summary of what you learn from this panel discussion. The executive summary is due by no later than 5 p.m. on Monday, October 9th.  This extra credit will replace your lowest quiz grade in Finance 4335 (assuming the extra credit grade is higher).

Confirmation bias in the form of “information avoidance”

This article from the Wall Street Journal provides an interesting followup to yesterday’s behavioral finance discussion. “Information avoidance” represents a particularly strong (and potentially deadly) form of confirmation bias!

Getting past information avoidance to deal with health issues, financial difficulties and other worries.
wsj.com|By Elizabeth Bernstein

On the Determinants of Risk Aversion

In January 2014, The Economist published a particularly interesting article about the determinants of risk aversion, entitled “Risk off: Why some people are more cautious with their finances than others”. Here are some key takeaways from this article:

1. Economists have long known that people are risk-averse; yet the willingness to run risks varies enormously among individuals and over time.

2. Genetics explains a third of the difference in risk-taking; e.g., a Swedish study of twins finds that identical twins had “… a closer propensity to invest in shares” than fraternal ones.

3. Upbringing, environment and experience also matter; e.g., . “…the educated and the rich are more daring financially. So are men, but apparently not for genetic reasons”.

4. People’s financial history has a strong impact on their taste for risk; e.g., “… people who experienced high (low) returns on the stock market earlier in life were, years later, likelier to report a higher (lower) tolerance for risk, to own (not own) shares and to invest a bigger (smaller) slice of their assets in shares.”

5. “Exposure to economic turmoil appears to dampen people’s appetite for risk irrespective of their personal financial losses.” Furthermore, a low tolerance for risk is linked to past emotional trauma.

Interview With Meir Statman (extra credit opportunity)

Meir Statman has very important things to say about decision-making under risk and uncertainty; I introduced Professor Statman to you in my previous blog posting entitled “Your Tolerance for Investment Risk Is Probably Not What You Think.”  Here is an extra credit opportunity for Finance 4335 based upon a 1 hour, 25 minute podcast (recorded in July 2017) hosted by Barry Ritholtz’s Masters of Business podcast (link provided below) entitled “Interview with Meir Statman.”

You may earn extra credit by listening to and reporting on Mr. Ritholtz’s interview with Meir Statman about behavioral finance.   In order to receive extra credit for this assignment, you must submit (via email sent to risk@garven.com) a 1-2 page executive summary of what you learned from this podcast; it is due by no later than 5 p.m. on Monday, September 18.  This extra credit assignment will replace your lowest quiz grade in Finance 4335 (assuming the extra credit grade is higher).

Bloomberg View columnist Barry Ritholtz interviews Meir Statman, the Glenn Klimek Professor of Finance at Santa Clara University. His research focuses on behavioral finance. He attempts to understand how investors and managers make financial decisions and how these decisions are reflected in financial markets. His most recent book is “Finance for Normal People: How Investors and Markets Behave,” published by Oxford University Press. This commentary aired on Bloomberg Radio.

Your Tolerance for Investment Risk Is Probably Not What You Think

This WSJ article is authored by Professor Meir Statman,  the Glenn Klimek Professor of Finance at Santa Clara University.  Professor Statman’s research focuses on behavioral finance, which is a very important topic in decision theory that I hope to cover during tomorrow’s meeting of Finance 4335.
The questions financial advisers ask clients to get at the answer actually measure something completely different—often leading to misguided investment strategies.

How government policy exacerbates hurricanes like Harvey

Here’s the (very timely) cover story of the latest issue of The Economist. Quoting from the article, “Underpricing (of flood insurance) encourages the building of new houses and discourages existing owners from renovating or moving out. According to the Federal Emergency Management Agency, houses that repeatedly flood account for 1% of NFIP’s properties but 25-30% of its claims. Five states, Texas among them, have more than 10,000 such households and, nationwide, their number has been going up by around 5,000 each year. Insurance is meant to provide a signal about risk; in this case, it stifles it.”

As if global warming were not enough of a threat, poor planning and unwise subsidies make floods worse.

On the Economics of Price Gouging

This is an oldie (from 2007) but goody – on the economics of price gouging in the wake of a hurricane. The principles discussed are timeless and well worth pondering!

Mike Munger of Duke University recounts the harrowing (and fascinating) experience of being in the path of a hurricane and the economic forces that were set in motion as a result. One of the most important is the import of urgent supplies when thousands of people are without electricity. Should prices be allowed to rise freely or should the government restrict prices? Listen in as Munger and EconTalk host Russ Roberts discuss the human side of economics after a catastrophe.