differences between moral hazard,morale hazard and physical hazard

In short, Moral hazard is a hazard dealing with the difference between right and wrong while a moral hazards is a hazard dealing with people's attitudes. It also refer to the characters of individuals related to the property ( eg: the owners) that can increase the chance of loss.

example : conditions resulting from a weakness of human character (when someone should know the difference between right and wrong), such as embezzlement.

Morale hazard is a closely related to moral hazards it refer to the indifferent attitudes of individuals related to the property because of the pre sence of insurance policy.

examples : conditions resulting from a person's indifferent attitude toward a loss when a property of exposure is insured, such as failing to lock the doors or roll up the windows of your car or leaving valuables in plain sight in your car . . .especially during the holiday season.

Physical hazards refer to the physical features that can lead to an increase in the chance of loss from certain perils.

examples :
According to poor brakes and engine problem in heavy rains can nause road accidents.

Explain briefly the law of large number

In probability theory, the law of large numbers (LLN) is a theorem that describes the result of performing the same experiment a large number of times. According to the law, the average of the results obtained from a large number of trials should be close to the expected value, and will tend to become closer as more trials are performed.

For example, a single roll of a six-sided die produces one of the numbers 1, 2, 3, 4, 5, or 6, each with equal probability. Therefore, the expected value of a single die roll is

( 1+2+3+4+5+6) / (6) = 3.5.

According to the law of large numbers, if a large number of six-sided dice are rolled, the average of their values (sometimes called the sample mean) is likely to be close to 3.5, with the accuracy increasing as more dice are rolled.

It follows from the law of large numbers that the empirical probability of success in a series of Bernoulli trials will converge to the theoretical probability. For a Bernoulli random variable, the expected value is the theoretical probability of success, and the average of n such variables (assuming they are independent and identically distributed (i.i.d.)) is precisely the relative frequency.

For example, a fair coin toss is a Bernoulli trial. When a fair coin is flipped once, the theoretical probability that the outcome will be heads is equal to 1/2. Therefore, according to the law of large numbers, the proportion of heads in a "large" number of coin flips "should be" roughly 1/2. In particular, the proportion of heads after n flips will almost surely converge to 1/2 as n approaches infinity.

Though the proportion of heads (and tails) approaches 1/2, almost surely the absolute (nominal) difference in the number of heads and tails will become large as the number of flips becomes large. That is, the probability that the absolute difference is a small number, approaches zero as the number of flips becomes large. Also, almost surely the ratio of the absolute difference to the number of flips will approach zero. Intuitively, expected absolute difference grows, but at a slower rate than the number of flips, as the number of flips grows.

The LLN is important because it "guarantees" stable long-term results for random events. For example, while a casino may lose money in a single spin of the roulette wheel, its earnings will tend towards a predictable percentage over a large number of spins. Any winning streak by a player will eventually be overcome by the parameters of the game. It is important to remember that the LLN only applies (as the name indicates) when a large number of observations are considered. There is no principle that a small number of observations will converge to the expected value or that a streak of one value will immediately be "balanced" by the others.

List nad briefly explain the principles of risk classification
1. Protection of Program’s Financial Soundness

Main financial threat is adverse selection : 1. Possible in a market where buyers are free to select among different sellers, with a motivation to minimize the price. 2. Sellers have a limited ability to select buyers and have a basic need to maintain prices at an adequate level. 3. Adverse Selection is the movement of buyers to different sellers within an insurance market

ii. Risk Classification is one way to minimize the potential for adverse selection 1. Balances the economic forces governing buyer & seller actions. 2. Where participation is mandatory & choices are restricted, adverse selection is controlled by a restriction of the buyer’s freedom.

Enhanced Fairness
1. A system designed to reduce adverse selection tends to produce prices that are valid and equitable
2. Price differences should reflect differences in expected costs, with NO intended redistribution or subsidy among the classes

3. Prices & expected costs should also match within each class

4. An individual with a substantially higher (or lower) than average expected cost should be placed in a different class.

Economic Incentive

i. Companies that are most successful in serving customers will be rewarded with the majority of potential customers

ii. Insurers have incentives to expand their markets & to achieve high market penetration

iii. Insurers need a system that 1. will allow them to offer insurance to as many potential customers as possible 2. assures that the prices will be adequate to cover the customers’ financial uncertainty

iv. Competition for lower cost risks will be most intense

v. Insurers also want to sell to the higher cost risks

vi. Increased market penetration provides 1. Economies of scale in marketing or distribution system 2. Ability for insurer to provide better service

vii. To be more successful, and insurer bust refine its classification system and thus its pricing structure to more accurately reflect the differentials in expected costs among identifiable classes of risks.

viii. Risk Classification system must be efficient. Additional expense of obtaining more refinement should not be greater than the reduction in expected costs for the lower cost risk classification.

ix. Economic incentive operates over time to favor class plans that result in a price for each risk which most nearly equals the expected cost associated with the class to which that risk is assigned.

Basic Principles Needed to Achieve These:

1. Should reflect expected cost differences 2. Should distinguish among risks on the basis of relevant cost-related factors 3. Should be applied objectively 4. Should be practical and cost-effective 5. Should be acceptable to the public