Tag Archives: Insurance

Risk Aversion and human Behavior

Say you are looking for a job. What kind of income would you like? A steady paycheck of say Rs 25,000 or income fluctuating between 5000 to 45000 which is expected to average to 30,000. Most of us would like the steady paycheck. Which explains why insurance jobs with commission-based income are not attractive to most of us. However, most companies would like to incorporate a variable incentive as a part of the pay package as a completely fixed salary may breed complacency and does not reward the big achievers.

Let’s go back to the restaurant example. While a standalone restaurant may have fluctuating business, a chain of restaurants would have a far steadier stream of income. While the income stream of each individual restaurant in the chain is uncertain, the variations tend to balance out in a chain.

The same effect can be seen in many situations. Infosys or TCS has a large number of clients with no client contributing more than say 10% of the overall business. However a Satyam (now Mahindra Satyam) has an inherently riskier business model with a large portion of its business coming from a single client, in this case British Telecom.

The President and the Vice President of the United States never fly together on one plane. Thus, the likelihood of both planes crashing is obviously much lower. However, it is to be noted that the like hood of any one of the two planes crashing (and hence any one dying) is more than that of both traveling on the same plane.

That is how an insurance company operates. Will I die tomorrow or over the next week or next month. I don’t know and neither does my insurance company. However they are still willing to insure me because they can estimate quite accurately how many 36-year-old Healthy Indian Males will die over the next year.

Lets take a simple example to see how that works. Lets say the insurance company XYZ Life insures 1,00,000 young healthy Males for a 1 L policy. Through a complicated formula they calculate that 0.5% or 500 people will die over the next year. So they total payout is Rs 500L. That amount plus a premium is charged equally as insurance premium. Say the operating expenses and desired profit for XYZ life is 100 L, then the premium for each individual is 600L/1L or Rs 600. Note the Probability based expected value is Rs 500 and the Rs 100 extra that you pay is to mitigate the risk.

Insurance companies are not the only examples of risk transfer.

Say that you are a book retailer. A publisher offers you books at a 20% commission. If you sell a book worth Rs 100, you make Rs 20. However any books that you are unable to sell will not be taken back. Now the risk of variable sale is yours. How much would you order? If you over estimate you run the risk of excess inventory i.e. a loss of Rs 100. If you underestimate, you run the risk of lost sales i.e. a loss of Rs 20 on every lost sale.

Given that most people are risk averse what would you expect to see? Most retailers would under order. My Author friends also complain that Publishers often under publish, but that is another story.

As a publisher, what would you do if retailers routinely under order. One option is take on the risk your self. You give the retailers an option of returning unsold books, perhaps reducing the commission to say 15%.

Now the retailers have no fear of underestimation. On the other hand, they may develop a tendency to over estimate, as they have no problem returning the books.

Similarly, a retailer who offers consumers a easy refund if they bring back the product will find sales increasing (as customers have lower risk) but runs the risk of having a high number of refund cases.

In all these cases, it is imperative to find a balance. How much of the risk different players in the distribution channel assume varies from industry to industry and depends of the objectives and relative bargaining power of the players.

Let us go back a few days to the beginning of the world cup. Now say the Mumbai Cricket Association is trying to decide on ticket prices for the world cup final. Now if India makes it to the final, they can sell the tickets at a premium and still expect to sell out all tickets. However, if they wait too late and India exits the cup, then the tickets would have to be sold cheap or may remain unsold. Please note that in the end ticket prices had reached a lakh plus per ticket.

Now MCA has to determine when to start the ticket sale (during league stage, after QF, after SF).

Or they could offer a ‘50% money back’ in case India does not reach the final. Thus they could still sell tickets early and also earn more in case India does reach the final.

Rahul Reddy


Vanguard Business School

(In the concluding part of this article we will look at Moral Hazard and Adverse selection about how high bonuses at Investment Banks may have been responsible for the great financial meltdown of 2008)