Presentation on theme: "15.3 Bearing and Eliminating Risk"— Presentation transcript:
1 15.3 Bearing and Eliminating Risk Why do people buy insurance?Why do people buy extended warranties?Why are extended warranties so expensive?What is a reasonable extended warranty?These questions are answered by:Actuarially Fair InsuranceRisk Premium
2 15.3 Actuarially Fair Insurance -insurance where the premium is equal to the expected value of the payout
3 Actuarially Fair Insurance Example Assume that you could buy fire insurance. You have a $100,000 job, and an 80% chance to lose $75,000 (house fire). Your utility is U=√I.Risky Income: p($100,000 )=0.2, p($25,000)=0.81) Calculate Actuarially Fair Insurance Premium
4 Actuarially Fair Insurance Example If you didn’t get insurance, your utility would be:U=√I Risky Income: p($100,000 )=0.2, p($25,000)=0.82) Utility without Insurance
5 Actuarially Fair Insurance Example With fair insurance, your utility would be:U=√I Risky Income: p($100,000 )=0.2, p($25,000)=0.8Insurance: $60,0002) Utility with Insurance
6 Actuarially Fair Insurance UtilityAFI gives you the expected income of a risky situationUUinsure•Uno insureD25K$40K=E(I)100KIncomeChapter Fifteen
7 15.3 Is Insurance ever Fair?Actual insurance premiums are rarely actuarially fair, partially due to a firm making profit, but also due to other factors:administrationmoral hazardadverse selection(which will be covered later)What is the maximum amount someone will pay above actuarially fair premiums?
8 15.3 Risk Premium Risk Premium -Maximum amount of money that a risk- averse person will pay to avoid taking a risk-Maximum amount a person will pay in premiums above actuarially fair premiumsNote: Even risk loving people consider themselves risk averse for large purchases.
9 Risk Premium • • E(U) Risk premium = horizontal distance ED E D UtilityRisk premium = horizontal distance EDUE••E(U)DIsE(I)IncomeChapter Fifteen
10 Calculating Risk Premium Calculate E(I) of risky choice.Calculate E(U) of risky choiceCalculate sure income Is of E(U)Risk Premium = E(I)- IsConclude
11 Risk Premium ExampleU=√I Risky Income: p($100,000 )=0.2, p($25,000)=0.81) Calculate E(I) of risky choice2) Calculate E(U) of risky choice
13 Risk Premium ExampleThis person would spend a maximum of $4,014 above actuarially fair insurance premiums to avoid the risk in his job.This person would accept a job paying at least $35,986 instead of taking the risky job.This person is willing to buy additional insurance against his risky job
15 15.3 Administration and Profit Providing insurance isn’t free, there are administration costs:Paying employeesOverheadLegal CostsEtcInsurance firms also desire profits. Many extended warranties carry 40%-80% profit margins.
16 15.3 Loading FeesLoading Fee = Actual Premium – Actuarially Fair Premium-Average loading ratio (actual premium/fair premium) for private US insurance companies is 1.2 (Phelps 2003)-(typical laptop service plan is $200 for 3 years, working out to a Loading Ratio of 4.0 – 10% failure rate in year 2 and 3 for $500 laptop)-keep in mind administration costs
17 15.3 Asymmetric Information Part of the additional costs of insurance, as well as items such as deductibles and mandatory insurance, arises from:ASYMMETRIC INFORMATION – when one party has information not available to another partyTypically, the person being insured has information the insurance company doesn’t:Hidden actions – Moral HazardHidden information – Adverse Selection
18 15.3 Moral HazardIf people have insurance, their actions may change in two ways:They are riskier (take laptop to beach, eat unhealthy – health insurance)They over consume insurance since it’s free (Send laptop to be fixed, ask for unneeded tests based on “House” – health insurance)This second effect can be shown through supply and demand:
19 This causes repair expenditures of area A. Moral HazardPWithout insurance, repairs cost P0 and Q0 repairs are made (where S=D).This causes repair expenditures of area A.S=MC (constant)P0ABP1QQ0Q1D=MBWith insurance, repairs cost P1 and Q1 are made (where new S=D). This causes repair expenditures of Area A +B (expenditures increase).
20 This overcomsumption causes deadweight loss where MC>MB: Moral HazardPThis overcomsumption causes deadweight loss where MC>MB:S=MC (constant)P0DWLP1QQ0Q1D=MBThe insurers are forced to cover waste, therefore insurance premiums increase.
21 15.3 Fighting Moral Hazard Moral Hazard can be decreased by: A) Including “reckless” situations that invalidate warranty ie: Casio Calculator Warranty:“The customer shall NOT have any claim under this warranty for repair or adjustment expenses if:”The problem is caused by improper, rough or careless treatment;The problem is caused by a fire or other natural calamity;
22 15.3 Fighting Moral Hazard3) The problem is caused by improper repair or adjustment made by anyone other than a CASIO Service Center;4) The problem is caused by battery leakage, bending of the unit, broken display or key;5) The battery is damaged or worn…7) The proof of purchase is not presented when requesting service-although it can be hard to prove that a customer has been “reckless”: “Of course I didn’t drop my ipad!”
23 15.3 Fighting Moral HazardB) Introducing a cost to claim the warranty/insurance.ie:DeductibleShipping CostsCost of timeLong repair timeHard-to-get-to repair location
24 15.3 Adverse SelectionInsurance can break down due to Adverse Selection – an increase in insurance premium increases the average risk of the insuredAssume there are 3 laptop purchasers:Bill has a laptop failure rate of 10% (he’s a computer technician)Charles has a laptop failure rate of 20% (he’s average)Denis has a laptop failure rate of 30% (he clicks on all the “you won” pop-ups)
25 15.3 Adverse SelectionRecall that actuarially fair insurance just charges enough to over expected repairsAFI = ($500xP(failure)):Bill: $50Charles $100Denis $150If you charge:$50 – Charles and Denis cause a loss$100 – Bill doesn’t want insurance and Denis causes a loss$150 – Charles and Bill don’t want insurance
26 15.3 Adverse SelectionIf insurance is optional, those with higher risk would buyThis leads to more expensive claimsThis leads to higher premiumsThis leads to more people not buying insuranceThe end result would be UNDERPROVISION of insurance
27 15.3 Adverse Selection5 Issues can keep Adverse Selection from killing a private insurance market:Risk AversionGroup InsuranceInsurance ChoiceRisk Categories/Risk ProfilingMandatory Insurance
28 i) Risk Aversion ii) Group Insurance Because people are risk averse, they are willing to pay a RISK PREMIUM above the actuarially fair premium.This may keep more people in the marketii) Group InsuranceLarger companies can offer group insurance plans that automatically cover everyone (high and low risk)This doesn’t help small firms or the self-employed
29 iii) Insurance ChoiceIf different levels of insurance at different costs are offered, people will self-sort themselves into different categories:Denis will pay $150+ for the full insurance (ie: Product Replacement Plan)Charles will pay $100+ for partial insurance (ie: Product Service Plan)Bill will pay $50+ for limited insurance (ie: manufacturer warranty included in price)
30 iv) Risk CategoriesAdverse selection occurs due to asymmetric info – inability to know a person’s riskHOWEVER, a company can charge premiums based on OBSERVABLE characteristics statistically linked to UNOBSERVABLE riskie: Male 20-year olds pay more for auto insurance because they are STATISTICALLY more likely to have an accident than Female 20-year Olds
31 iv) Risk Profiling?The Supreme Court of Canada ruled this does not violate the Canadian Charter of Rights and Freedoms because there is statistical evidence that 20-year-old males do have higher loss probabilitiesSome ask how long until we are charged based on:EthnicityReligionSexual Orientation (marital status already applies)If there is statistical evidence?
32 v) Manditory Insurance Public Health Insurance, Car insurance, etc is MANDATORY, and therefore Adverse selection is avoided since the low risk individuals can’t drop outPRO’s:Mid and High-risk individuals are covered at a reasonable rate ($100 in our example)Con’s:Low risk individuals would rather not be covered at a high rate (for them)
33 15.3 Diversification – Insurance Alternative Risk can also be managed through:Diversification – Reducing risk by allocating resources to a variety of activities whose outcomes are not closely relatedie:Stock Market – buying a variety of stocksSales – selling a variety of productsInsurance – buying a variety of good without the warranty.
34 15.3 Law of Large Numbers Diversification works because of: Law of Large Numbers – as the number of samples increases, the average of these samples is likely to reach the mean of the whole population (investopedia)ie: Stock has 50% fail rateFull fail chance of 1 stock = 50%Full fail chance of 2 stocks* = 25%Full fail chance of 8 stocks* = 0.39% *stocks must be unrelated-extreme outcomes reduce, expected outcome increases
35 15.3 Diversification – Extended Warranties Assume: You spend $5000 on electronics over 10 years, with an average FULL failure rate of 10%No Extended Warranty: You spend $500 on repairs and replacement E(repair)=cost * f(cost) E(repair)=$5000 * 0.10 = $500Extended Warranty: You spend $1000 on extended warranties (assume 50% profit margin)
36 15.4 Risk and Game TreesProbabilities can be combined with Game trees from chapter 14A player who MAKES decision is replaced by an outcome that is chosen by chanceThese game trees or decision trees can be FOLDED BACK in a method similar to backward induction to reduce the tree to the simple trees seen in chapter 14:
37 15.4 Risk and Game Trees Example 1 Circles represent CHANCE NODES (choices made by chance), while squares represent DECISION NODES (choices made by players).
38 15.4 Risk and Game Trees Example 1 Chance Nodes are FOLDED BACK by replacing them with the expected payoff:E(B)= Σ$f($)=0.5($50)+0.5($10)=$30
39 15.4 Risk and Game Trees Example 1 Now new best responses lead to an overall Equilibrium
40 15.4 Risk and Game Trees Example 2 Sometimes the process takes multiple steps
41 15.4 Risk and Game Trees Example 2 1)Backward induction of E and F2) Expected return of B and C
42 15.4 Risk and Game Trees Example 2 3) Expected Return of D
43 15.4 Risk and Game Trees Example 2 4) Final Backward Induction
44 15.4 Value of InformationThis previous example highlights the VALUE OF INFORMATIONThe firm expected return increases by $5 (million) if it is able to do a free testThe firm will pay up to $5 million for the testValue of Perfect Information – increase in a decision maker’s expected payoff when they can conduct a costless test to determine the outcome of a risky eventVPI = E($)with test- E($)without test
45 15.4 Value of Information Examples People pay money for information in a variety of ways:New house inspectionsCar inspectionsConsumer Report subscriptionsOnline dating sitesEtc.
46 Chapter 15 ConclusionsP(a) = Prob(a) = probability that event a will occurE($) = Σ$f($)E(U) = ΣUf(U)People can be risk averse, risk neutral, or risk loving depending upon their preferences between certain and uncertain incomes.Actuarially Fair Insurance=E(loss)Most people are willing to pay a RISK PREMIUM above Actuarially Fair Insurance
47 Chapter 15 Conclusions7) Insurance Premiums are increased by Asymmetric Information (Moral Hazard and Adverse Selection), which can be reduced but never eliminated.8) Diversification is an alternative to insurance9) Game trees including risky outcomes can be “Folded Back” using expected values and analyzed normally10) Information is valuable11) Unless you can’t sleep at night without one, say “no” to the extended warranty.