# Why do we buy Insurance? Expected Outcomes, Risk pooling and Risk preferences

In life a lot of events and outcomes are uncertain. Economic risk is the possibility of losing economic security to satisfy an individual’s needs/desires for food, shelter, clothes and other amenities, due to unforeseen events in future.

Thus uncertainty leads to risk. And insurance is just a means to manage this risk. As we will see later, insurance enables you to trade an uncertain outcome/risk (and potential loss) with a known amount (Premium). Behavioral economists have concluded that we humans, buy too less insurance. We don’t anticipate fringe (low probability) outcomes, which may eventually result in a huge payouts (loss).

Let’s take a simple example.

Consider a risk averse car owner has a chance of less than 20% of being in a single accident in a year. And no chance of being in more than one accident. 50% of Accidents are minor and involve repairs worth $500. 40% require repairs worth $5000. And 10% may require replacement of car, which is worth $20,000.

**Expected Loss and Standard Deviation**

Car Owner’s expected loss is the mean of the above loss distribution

E[X] = 80% * 0 + 20% * (50% * $500 + 40% * $5000 + 10% * $20000)

E[X] = $850

Thus, on an average, car owner spends $850 each year on repair but, the possibility of having to fork out $5000 or $20,000 in medium to severe accidents could create potential concern.

To measure the variability of outcomes, we should also look at the standard deviation of the loss distribution.

Var[X] = 80% * (0 – 850)^2 + 20% * (50% * (500 – 850) ^ 2+ 40% * (5000 – 850) ^ 2 + 10% * (20000 – 850)^2 )

Var[X] = $ 9,302,500

SD[X] = Sqrt (Var[X]) = $3050

CV = Coefficient of Variation of distribution = SD/Mean = 3.6

Thus the variance of outcome (dispersion of possible loss) is pretty high.

**Insurance Policies – Issuers and Holders**

Now let us see how insurance instruments can be used to reduce/manage individual risk. Insurance is an agreement where, for a stipulated payment (policy premium), one party (the policy issuer) agrees to pay the policy holder (car owner), a defined amount (policy claim) upon the occurrence of a specific loss (repairs from accident).

The issuer issues the policy to a pool of policy holders, assesses the Expected Total Loss and the Variation and charges a Policy Premium to cover all projected loss claims in the pool. The premium is thus the policy holder’s share of the total premium of the risk pool.

The idea is quite simple. Insurance is all about risk pooling. By subscribing to an insurance policy, the car owners can transfer the risk to the policy issuer. In a given year, not all car owners will be involved in accidents. The issuer pays these unfortunate few using the premium collected from

__all__the holders. Thus each policy holder exchanges their uncertain losses for a known premium. Also, as seen above, greater the standard deviation, greater the risk, and thus will require higher Premiums (will see again later).

The issuer could restrict the potential loss scenarios by defining or exempting certain “perils” in the contract. For instance, damages from natural disasters like Floods and Earthquakes could be exempted.

Now, extending the earlier example. Assuming, 100 car owners subscribe to an insurance policy which guarantees against all potential losses.

Expected Loss for the insurance pool now is,

E[nX] = n E[X] = 100 * 850 = $85,000

Variance = Var[nX] = n * Var[X]

SD = Sqrt (Var[nX] ) = Sqrt(n) * SD[X] = 10 * 3050 = 30,500

CV = SD/Mean = 30,500/85,000 = 0.36

Had, none of them subscribed to insurance, Expected loss for the group of 100 car owners would still have been nE[X] = $85,000

But, the Variance would have been the sum of their individual variances

n Var[X] = $305,000

Coefficient of Variation is an useful tool to measure variability between positive distributions of different mean. General form of the CV of the group is

CV = Sqrt(n) * SD[X] / n E[X] = SD[X]/ (E[X] * Sqrt (n)

Thus, as number of policy subscribers increases and becomes really large, CV tends to zero. In simple terms, it becomes easier for the issuer to predict expected losses.

**Pricing Risk Premium**

It should be clear that the existence of the insurance provider does not change the chances of the loss (Ignoring Moral Hazard for now). Since the issuer expects to pay out at least $85,000, the insurance premium would be priced above $85,000/1000 = $850. Generally, it would include some markups for administrative overheads, processing fees, some reserves for uncertainty and a small Profit. In a perfectly competitive market, the Profit margin is generally not that high. Assuming the issuer adds a 30% markup to cover all these, the policy holder ends up paying a premium of $1105 for the policy.

Net Premium = $850

Additional expenses and margins = $255

Gross Premium = $1105

Do note, some risk averse car owners are still willing to pay this amount, even though it is higher than their expected loss ($850) since they can exchange an unmanageable outcome (due to uncertainty) with a non-zero variance for a known amount (Premium) with zero variance. The number of eventual policy subscribers would depend on their risk preference. Had the Gross Premium per holder been $850 (equal to the Expected Loss), the insurance would have been actuarially fair. And all risk averse car owners would have immediately signed up for the insurance.

**Issues of Moral Hazard and Adverse Selection**

Earlier we mentioned that the insurance provider does not change the chances of the loss. However, studies have found that individuals seem to be more

*when they know they are insured. For instance, car owners might start driving more recklessly, increasing the chances of accidents and insurance pay outs. This is know as the problem of Moral Hazard.*

**risk-seeking**Adverse Selection occurs when buyers and sellers have asymmetric information. It describes a situation where an individual's demand for insurance (either the propensity to buy insurance, or the quantity purchased, or both) is positively correlated with the individual's risk of loss. Thus, more smokers opt in for health insurance, and more reckless drivers opt in for car insurance. To manage adverse selection, Insurance providers have a differential premium structure where high-risk users (identified from background checks and profile questionnaires) are charged higher Premium than the rest.

**Some interesting variations**

Modern insurance instruments are really complex. Two simple variations are Deductions and Benefit Limits (Ceilings). In Deductions, the issuer pays out after a minimum threshold. In our earlier example, if the minimum deduction is $500, and if the repairs are worth $5000, the issuer pays out only $4500. For any repair below $500, the owner has to pay from his own pocket. As expected, the probability of claim drops from 20% to 10% and so does the risk premium. Some stand-out advantages are –Lower Premiums, A check against Moral Hazard (since owners will be more careful due to the first $500 out-of-pocket payment) and Lesser overheads –Admin and Processing Fee (since small claims are not processed).

In Benefit Limits, the issuer sets an upper bound on the claim pay-out. Policy holders can subscribe to additional coverage by paying incremental Premium. It also provides a safety net for the issuer and reduces the risk burden. Though a thing to note is that the additional Premium per holder is generally not that high since, as seen earlier, the risk pool is shared among a large number of subscribers.

Well, that’s the basics. The model discussed above was between an issuer and a policy holder. Insurance portfolios are also traded by the issuer in the financial sector (Securitization). There is a lot of interesting literature out there on premium/insurance pricing. We briefly touched on the risk preference of the consumer. What we did not discuss was the optimal amount of insurance to buy. Intuitively, a consumer would want to maximize his Utility under uncertainty, that is, maximize his Expected utility -with and without the probability of loss, inclusive of his premium payouts and claims. Thus, the coverage or Premium that maximizes the solution will rely on individual utility and risk preferences. Let us leave it for a later post.

Src:

Risk and Insurance: Society of Actuaries - http://www.wsc.ma.edu/ecke/342/P-21-05.pdf

Premium Calculation and Insurance Pricing - http://www.econ.kuleuven.be/insurance/pdfs/premium3.pdf

Applications of Expected Utility Theory - http://econport.gsu.edu/econport/request?page=man_ru_applications_insurance

An Introduction to Risk-Aversion - http://econport.gsu.edu/econport/request?page=man_ru_basics4