Category Archives: The Real World

How Cash-Strapped Chicago Snagged a Triple-A Rating for Its New Bonds

Since the city of Chicago is apparently no longer considered to be insurable by the bond insurance industry (cf. https://www.wsj.com/articles/for-some-bond-investors-chicago-isnt-their-kind-of-town-1427926688), it has had to turn to other means for making its debt attractive to investors. The latest scheme involves issuing new bonds through a new (and separate) legal entity called the Sales Tax Securitization Corporation (STSC). The bonds offered by STSC are collateralized by a dedicated first claim to the city’s sales-tax revenue. Apparently similar strategies have been employed two years ago by the city of Detroit, throughout the past decade by Puerto Rico and 40-some years ago by New York City.

Interestingly, the bond rating agencies are somewhat split about the extent to which a so-called “dedicated first claim” to Chicago’s sales-tax revenues would obtain in the event of default; this divergence of opinion is reflected by the ratings given on these bonds; e.g., Fitch and Kroll gave STSC a AAA rating, whereas S&P scores it two grades lower.

Chicago has created a new company to sell the debt, offering a tempting pledge to investors: a dedicated first claim to the city’s sales taxes.

Is Da Vinci’s ‘Salvator Mundi’ Worth $450 Million or $454,680?

Fascinating geometric mean return calculation for Da Vinci’s ‘Salvator Mundi’; at its recent $450 million sales price, the annual rate of return for this artwork over the course of five centuries comes out to around 1.35%.

“Salvator Mundi” sold at Christie’s for more than $450 million. If we were to regard this work of art as an investment, has the Leonardo generated a good return since the master painted it?

A Federal Guarantee that is Sure to Go Broke

See the (November 2014) Wall Street Journal article entitled “A Federal Guarantee Is Sure to Go Broke” and related article from November 2015 entitled “Moody’s Predicts PBGC Premiums Will Become Unaffordable“.

Think of PBGC as essentially the FDIC of private pensions. Thus, the analysis the flowchart shown at the bottom of my “On the economics of financial guarantees” blog post concerning how FDIC guarantees bank deposits applies here; in the diagram from that posting, simply replace “FDIC” in the diagram with “PBGC”, and in place of “Bank” and “Depositors”, substitute “Company offering private pension to Workers” and “Workers”.

Quoting from the above referenced WSJ article:

How is the PBGC insurance program doing on its 40th anniversary? Well, it is dead broke. Its net worth is negative $62 billion as of the end of September. That is even more broke than it was a year ago, when its net worth was negative $36 billion… The PBGC has total assets of $90 billion but total liabilities of $152 billion. So its assets are a mere 59% of its liabilities. Put another way, its capital-to-asset ratio is negative 69%.

Why does the government have such a pathetic record at guaranteeing other people’s debts? It isn’t that Washington wasn’t warned. “My son, if you have become surety for your neighbor, have given your pledge for a stranger, you are snared in the utterance of your lips,” reads Proverbs 6: 1-2.

Federal Financial Guarantees: Problems and Solutions

Besides insuring bank and thrift deposits, the federal government guarantees a number of other financial transactions, including farm credits, home mortgages, student loans, small business loans, pensions, and export credits (to name a few).

In order to better understand the problems faced by federal financial guarantee programs, consider the conditions which give rise to a well-functioning private insurance market. In private markets, insurers segregate policyholders with similar exposures to risk into separate risk classifications, or pools. As long as the risks of the policyholders are not significantly correlated (that is, all policyholders do not suffer a loss at the same time), pooling reduces the risk of the average loss through the operation of a statistical principle known as the “law of large numbers”. Consequently, an insurer can cover its costs by charging a premium that is roughly proportional to the average loss. Such a premium is said to be actuarially fair.

By limiting membership in a risk pool to policyholders with similar risk exposures, the tendency of higher risk individuals to seek membership in the pool (commonly referred to as adverse selection) is controlled. This makes participation in a risk pool financially attractive to its members. Although an individual with a high chance of loss must consequently pay a higher premium than someone with a low chance of loss, both will insure if they are averse to risk and premiums are actuarially fair. By charging risk-sensitive premiums and limiting coverage through policy provisions such as deductibles, the tendency of individuals to seek greater exposure to risk once they have become insured (commonly referred to as moral hazard) is also controlled.

In contrast, federal financial guarantees often exaggerate the problems of adverse selection and moral hazard. Premiums are typically based upon the average loss of a risk pool whose members’ risk exposures may vary greatly. This makes participation financially unattractive for low risk members who end up subsidizing high risk members if they remain in the pool. In order to prevent low risk members from leaving, the government’s typical response has been to make participation mandatory. However, various avenues exist by which low risk members can leave “mandatory” risk pools. For example, prior to the reorganization of the Federal Savings and Loan Insurance Corporation (FSLIC) as part of the Federal Deposit Insurance Corporation (FDIC) during the savings and loan crisis of the 1980s and 1990s, a number of low risk thrifts became commercial banks. This change in corporate structure enabled these firms to switch insurance coverage to the FDIC, which at the time charged substantially lower premiums than did the FSLIC. Similarly, terminations of overfunded defined benefit pension plans enable firms to redeploy excess pension assets as well as drop out of the pension insurance pool operated by the Pension Benefit Guarantee Corporation (PBGC).

Although financial restructuring makes it possible to leave mandatory insurance pools, the costs of leaving may be sufficiently high for some low risk firms that they will remain. Unfortunately, the only way risk-insensitive insurance can possibly become a “good deal” for remaining members is by increasing exposure to risk; for example, by increasing the riskiness of investments or financial leverage. Furthermore, this problem is even more severe for high risk members of the pool, especially if they are financially distressed. The owners of these firms are entitled to all of the benefits of risky activities, while the insurance mechanism (in conjunction with limited liability if the firm is incorporated) minimizes the extent to which they must bear costs. Consequently, it is tempting to “go for broke” by making very risky investments which have substantial downside risk as well as potential for upside gain. The costs of this largely insurance-induced moral hazard problem can be staggering, both for the firm and the economy as a whole.

Ultimately, the key to restoring the financial viability of deposit insurance and other similarly troubled federal financial guarantee programs is to institute reforms which engender lower adverse selection and moral hazard costs. Policymakers would do well to consider how private insurers, who cannot rely upon taxpayer-financed bailouts, resolve these problems. The most common private market solution typically involves some combination of risk-sensitive premiums and economically meaningful limits on coverage. Federal financial guarantee programs should be similarly designed so that excessively risky behavior is penalized rather than rewarded.

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