Insurance II

Last time we looked at insurance, we ended with several questions unanswered: How does regulation drive up costs? And lower benefits to insurance subscribers?

Back in the original post, we looked at ships' insurance. If we had a thousand ships, and each ship paid a thousand dollars for insurance, how much money would the insurance company have? One times a thousand is, a thousand. A thousand times a thousand is a million. A million times a thousand is a billion. Remember this scaling when you have any conversations about fiscal matters. How did we reach a point where being a billionaire is a big thing, but our government spending "trillions" of dollars isnt'? How many billions does it take to make a trillion?

To answer the question, then, a thousand thousand is a million dollars. Let's take for example, one of the world's most beautiful ships, the Cutty Sark. She was built for a cost of  £16,500. If an actuary were to assess the potential loss facing the owners of the Cutty, they would assess the owner of the ship a fraction of the ship's cost, to cover the potential loss of the ship due to certain circumstances, among which would be pirates, foul-weather, and acts of war. Given the conditions of loss, there might be certain limits of coverage for the loss of the vessel. Additionally, there might be coverage for the value of cargoes carried by the ships, and they, in turn, might be limited in loss coverage due to the circumstances of the loss.

The key to successful marketing of insurance coverage wasn't the promise of safety from loss. If you or I were unprepared to provide another with an hedge against loss, you or I might ask for a bond in the full amount of value of the asset. Give me the full value of your article, and if there's a loss, I'll return the value of the loss to you.

Not really a great hedge, is it?

If you could insure against loss, for a fraction of the value of the item insured, would you then be interested in purchasing an hedge that would provide making you whole, in the face of loss? And what, in the word's of President Obama, would be a reasonable betting line against loss? Let's not dawdle over the understanding of the world's smartest man. The simple explanation is, there are guys out there, in the world today, who do a thing called math. I know it isn't sexy, and for certain sectors of our population, the mere mention of mathematics is a game changer...don't go there.

If a thousand ships set sail, and 999 return after a year's sailing, then the loss of a ship is how likely?

If a thousand ships set sail, and 999 return after ten years of sailing, then the loss of a ship each year is how likely?

If we take a thousand dollars for a thousand ships, for ships that cost $25-thousand dollars, what is the apparent liability for the insurer, when in a year a thousand ships sail, and 999 return? (I'm doing a math thing with the Cutty at £16,500 being equivalent to $25-thousand bucks.)

It shouldn't take a math wizard to note that an insurance company with these defined rates against these defined risks, would be doing very well! Very well, indeed!

So, a market like Lloyd's offers insurance. The market is profitable, and if fact, extremely profitable (under the conditions cited.) But Lloyd's isn't an insurance company, it is a market. Certainly, a market with stringent requirements, but with Lloyd's is a group of insurance underwriters who represent investors in insurance and reinsurance firms. Proposing a request for insurance coverage to Lloyd's elicits a response from the Members, who on their own, judge the potential costs against possible claims arising from the queried insurance proposal. The ships owners of the Cutty Sark would have asked the market for insurance, and then had received various quotes from members, from amongst which would have been chosen the most favourable of terms and conditions for the sum of protection against loss being offered.

Within the very dynamic of Lloyd's was the competition amongst the members to gain access to a contract to provide insurance coverage. (Lloyd's was the first insurance exchange.)

All of these market forces were at work, amongst competing members of the exchange, to provide a guarantee of coverage, at the best terms, and at the lowest possible prices. .

Enter the conditions above; regulation. What are the effects of regulation? The market of Lloyd's is illustrative of the temptations toward regulation. A study of Lloyd's would be a starting point for any legislator who wished to impose regulation upon any industry.

Ship owners were required by law (regulation) to provide for the loss of sailors to their families. Ship owners were amongst the first employers who were required to provide payment for loss of life to survivors. In fact, many, if not most, conscriptees into the United States military were surprised to find that their lives were being insured on their behalf by Uncle Sam. Insurance against loss of life is a fairly new invention. An hundred years ago, people died. That was it. Their heirs inherited, life moved on.

Enter the Death Tax. In 1916, America introduced the Inheritance Tax. Then, further regulation introduced another tax, the Gift Tax. Simply dying wasn't enough. Progressive reform meant that the wealthy, even in death, had more than that which those who hadn't, deemed to be excessive. Life insurance was instituted to cover the shortfall in the value of an estate, and the penalty of death under the newly established estate taxes. And, only the wealthy opted to provide themselves with coverage. When young military conscriptees were introduced to their first insurance policies, many of them couldn't understand the value of the policies being provided. They were all coming home, after all.

From the farm, where people simply died, whether from getting kicked by a horse, getting mauled by a cougar, lost in the woods, broken leg while plowing, to a moment where loss could be indemnified generally was a stretch. It was an expense that couldn't be afforded, since most people were dealing with subsistence issues; enough money to eat, to clothe themselves and their families, provide housing. The pinch in life insurance was prompted by a change in tax laws for the rich. Most Americans weren't affected by inheritance taxes.

But the supposed theme of this post is, when regulation changes affect changes in costs for insurance. The regulation that imposed costs for the wealthy, increased the costs of insurance for the wealthy. The wealthy looked at inheritance taxes, and immediately looked for ways to indemnify themselves from loss. What any successful hedge would attempt to do. For most people, the cost of insurance wasn't a consideration; when they died, their estates were passed onto their progeny. Life insurance was a rich man's tool. Government payments for the loss of life during the wars introduced the idea that a man's life could be viewed as an asset, against which the loss of that life could be insured against.

For me, the introduction of insurance of any kind occurred in Junior High, when I signed up for extracurricular sports. As a part of playing football, I received a card explaining that I was covered for certain losses: an eye for $200, or $500 for both, that kind of thing. The card listed my coverage for one finger, one toe, an arm. Being a kid, I totaled all the potential losses and decided that one arm, one leg and an eye was the most I'd be willing to lose.

I, like millions of other kids who played sports, never lost anything. (Drat!) We never cashed in on the loss of limb insurance provided us. But, again, most ship owners never lost all their ships, all their crews, all their cargoes. (Drat!)

Let's leave this line of thinking before the idea of insurance fraud pops up.

Let's simply talk about actuarial tables. Maybe, you've never heard the term. Actuarial tables are really cool, and simply take all the available data on loss, whether its shipping loss, loss of arms playing football in junior high, or probability of death of old rich guys, and susses out where you, as an individual against the backdrop of all these statistics, stand. Insurance companies have gazillioins of statistics. (Yes, I looked briefly at a career as an actuary. The numbers are truly cool. And, my second oldest sister had a career at one point, working for the actuaries of Standard Insurance, a Portland company. Her job was cool, at least to me.)

Simple coverage for intramural athletics is easy to figure out. Put out ten thousand policies, and see how many claims there are. Put out fifty thousand policies, and see how many claims there are. Put out a million policies, and see how many claims there are. With a million policies at ten dollars each--prepaid--how many claims for an eye could be claimed before the insurer lost money? That's like 50-thousand one-eyed kids! Per year! If there were a sport claiming that many eyes, how long would that sport last?

America loses Fifty Thousand Eyes! Or, worse, American Youths Lose 50-Thousand Eyes!

When an activity becomes too risky, normally, the activity declines in popularity. Not always. But, that's for a later post.

Actuarial tables determine costs for insurance. The likelihood of an occurrence. My old stat prof used to tell the joke of the prof who was awarded an honor, but who refused to fly to New York to accept the award. The story goes, that the likelihood of the plane being a victim of a hidden bomb was too great, although slight, for the professor of statistics to fly. On the day the members of the Statistics Department were making their way onto the flight to take them all to New York, they turned and saw the reluctant professor waving his arms, asking them to hold the plane. As he boarded he was asked, "what changed your mind?" The professor said, "the chances of a single bomb on the plane was too great, but the chances of their being two bombs, was insignificant." And, at that point, pulled up his jacket and revealed another bomb.

That isn't the way statistics work, nor how statistical inference works.

Insurance, rather than projecting the worse case scenario, often invokes the best case scenario, in fact, without the best case scenario, insurance couldn't survive. Given leave to itself, insurance would do exactly what it is intended to do; give the purchaser an hedge against unforeseen outcomes. But actuary tables can't predict changes in regulations. Regulations come from legislatures, and the causes for legislation don't often, or at all, reflect the realities of those who wish to manage their exposure to risk.

Let's take a look at the Exxon Valdez. And changes to maritime regulation.

Next.