Unveiling the Essentials: Understanding Risk, Peril, and Hazard in Everyday Life

 

1.  Meaning of Risk:

The term "risk" encompasses various interpretations, with economists, behavioral scientists, risk theorists, statisticians, and actuaries offering diverse perspectives. Despite this, a common thread in defining risk is its association with uncertainty. In essence, risk is characterized as the uncertainty surrounding the potential occurrence of a loss. For instance, the risk of a fatal car accident exists due to the inherent uncertainty in such situations. Smokers face the risk of developing lung cancer because of the uncertainty regarding health outcomes. Similarly, the risk of failing a college course is rooted in the uncertainty inherent in academic performance.

It's noteworthy that in the insurance industry, employees often use the term "risk" to refer to the property or life being insured. In this context, phrases like "that driver is a poor risk" or "that building is an unacceptable risk" are commonplace.

It's important to acknowledge that, when risk is defined as uncertainty, some authors draw a careful distinction between objective risk and subjective risk. This highlights the nuanced nature of risk and the various ways in which it is conceptualized in different fields and industries.

·    Objective Risk:

Objective risk, also known as the degree of risk, is defined as the relative variation between actual and expected losses. To illustrate, consider a property insurer with 10,000 houses insured annually, experiencing an average of 1 percent, or 100 houses, burning each year. However, the exact number of houses burning deviates from this average. In some years, as few as 90 houses may burn, while in others, as many as 110 houses may burn. This variability of 10 houses from the expected 100, or a 10 percent variation, constitutes objective risk.

The level of objective risk diminishes as the number of exposures increases. Specifically, objective risk exhibits an inverse relationship with the square root of the number of cases observed. In the previous example with 10,000 insured houses, the objective risk was 10/100, or 10 percent. Now, imagine insuring 1 million houses; the expected number of burning houses remains at 10,000, but the actual loss variation is only 100. Consequently, objective risk decreases to 100/10,000, or 1 percent. As the number of cases increases from 100 to 1,000 (tenfold), objective risk reduces to one-tenth of its original level.

Statistical measures, such as standard deviation or coefficient of variation, can be employed to calculate objective risk. This quantifiable aspect makes objective risk a valuable concept for insurers and corporate risk managers. With an increasing number of exposures, insurers can more accurately predict future loss experiences, leveraging the law of large numbers. This principle asserts that as exposure units rise, the actual loss experience converges more closely to the expected loss experience. For instance, with an expanding pool of homes under observation, the accuracy of predicting the proportion of homes at risk of burning improves significantly.

·     Subjective Risk:

Subjective risk is characterized as uncertainty stemming from an individual's mental state or psychological condition. For instance, consider a scenario where a customer, after heavy drinking in a bar, decides to drive home. In this situation, the driver may harbor uncertainty about arriving home safely and avoiding arrest for drunk driving, constituting what is termed as subjective risk.

The impact of subjective risk is subjective itself, varying from person to person. Two individuals facing identical circumstances may perceive the risk differently, influencing their behavior accordingly. When an individual experiences heightened mental uncertainty regarding potential losses, it often prompts conservative and prudent behavior. Conversely, lower levels of subjective risk may lead to less cautious behavior.

To illustrate, suppose a motorist, previously arrested for drunk driving, acknowledges having consumed excessive alcohol. Faced with the mental uncertainty of potential consequences, this driver may take steps to mitigate the risk, such as arranging for someone else to drive or opting for a cab. In contrast, another driver in a similar situation may perceive the risk of arrest as minimal. Consequently, this second driver might adopt a more careless and reckless driving approach, reflecting the influence of lower subjective risk on less conservative behavior.

2.   Chance of Loss:

The likelihood of loss, closely intertwined with the concept of risk, is referred to as the chance of loss. This term encompasses the probability of an event taking place. Similar to risk, the notion of "probability" encompasses both objective and subjective dimensions.

·      Objective Probability:

Objective probability pertains to the long-term relative frequency of an event, assuming an infinite number of observations and no alterations in the underlying conditions. There are two methods for determining objective probabilities. First, deductive reasoning can be employed, leading to what is known as a priori probabilities. For instance, the probability of obtaining a head in a coin toss with a perfectly balanced coin is 1/2, given the two sides, where only one is a head. Similarly, the likelihood of rolling a 6 on a single die is 1/6, as there are six sides, and only one displays six dots.

Secondly, objective probabilities can be ascertained through inductive reasoning, which involves analyzing past experiences rather than deducing outcomes logically. For instance, predicting the probability of a 21-year-old person's death before the age of 26 cannot be logically deduced. However, through a meticulous examination of historical mortality data, life insurers can estimate the likelihood of death and offer a five-year term life insurance policy issued at age 21.

·      Subjective Probability:

Subjective probability reflects an individual's personal estimate of the likelihood of a loss occurring, and it may not align with objective probability. For instance, individuals purchasing a lottery ticket on their birthday might perceive it as a lucky day, leading them to overestimate the relatively small chance of winning. Various factors, such as age, gender, intelligence, education, and alcohol consumption, can influence subjective probability.

Moreover, a person's estimation of loss may diverge from objective probability due to ambiguity in perception. Consider a scenario where a slot machine in a casino requires three lemons for a win. The player might subjectively perceive a high probability of winning, unaware that there are 10 symbols on each reel and only one is a lemon. The objective probability of hitting the jackpot with three lemons is notably low, calculated as the product of the individual probabilities of each reel (1/10 x 1/10 x 1/10 = 1/1000). Casino owners leverage this knowledge, recognizing that many gamblers lack statistical training and are prone to overestimating the objective probabilities of winning.

·      Chance of Loss Distinguished from Risk:

The chance of loss and objective risk are distinct concepts. The chance of loss refers to the probability of an event causing a loss occurring, while objective risk pertains to the relative variation between actual and expected losses. It's possible for the chance of loss to be identical in two different groups, yet the objective risk may differ significantly.

For instance, consider a property insurer with homes insured in Los Angeles and 10,000 homes insured in Philadelphia, where the chance of a fire in each city is 1 percent. This implies that, on average, 100 homes should experience a fire annually in each city. However, if the annual variation in losses ranges from 75 to 125 in Philadelphia and only from 90 to 110 in Los Angeles, the objective risk is greater in Philadelphia. This discrepancy arises even though the chance of loss is the same in both cities.

 

3.   Peril and Hazard:

Peril: Peril is characterized as the underlying cause of loss. For instance, if a house sustains damage due to a fire, the peril, or cause of loss, is attributed to the fire itself. Similarly, in the event of a car being damaged in a collision with another vehicle, the peril is identified as the collision. Numerous perils can lead to property damage, including but not limited to fire, lightning, windstorm, hail, tornadoes, earthquakes, theft, and burglary.

Hazard: A hazard is a circumstance that either generates or amplifies the likelihood of loss. There exist four primary categories of hazards:

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Physical Hazard: A physical hazard refers to a tangible condition that elevates the likelihood of experiencing a loss. Instances of physical hazards encompass elements such as icy roads, which heighten the risk of auto accidents, defective wiring within a building, amplifying the risk of fire, and faulty locks on doors, which increase the likelihood of theft.

Moral Hazard: Moral hazard pertains to instances of dishonesty or character defects in an individual that contribute to an increased frequency or severity of losses. Examples of moral hazard encompass activities like fabricating accidents to make insurance claims, submitting fraudulent claims, inflating the amount of a claim, and deliberately causing damage to unsold insured merchandise. Another significant example is the act of causing harm to the insured party with the intent to collect life insurance proceeds

This aspect of moral hazard is prevalent across all types of insurance and poses challenges for effective control. Dishonest individuals may rationalize their actions by assuming that the insurer has abundant resources. However, this perspective is flawed, as insurers can only settle claims by collecting premiums from other insured individuals. The presence of moral hazard often results in higher premiums for all policyholders.

Insurers employ various strategies to manage moral hazard, including rigorous underwriting procedures for insurance applicants and the implementation of policy provisions such as deductibles, waiting periods, exclusions, and riders. These measures are aimed at mitigating the impact of moral hazard and maintaining a balance in the insurance system.

Morale Hazard: Certain insurance experts make a nuanced differentiation between moral hazard and morale hazard. Moral hazard involves dishonest behavior by an insured that amplifies the likelihood or severity of a loss. On the other hand, morale hazard refers to carelessness or indifference toward a potential loss due to the presence of insurance.

Morale hazard becomes evident when insured individuals exhibit negligence or lack of concern regarding potential losses because they are covered by insurance. Examples of morale hazard include leaving car keys in an unlocked vehicle, thereby increasing the risk of theft; neglecting to lock doors, enabling burglars to enter easily; and abruptly changing lanes on a congested interstate highway without signaling. Such careless actions elevate the probability of experiencing a loss and are attributed to the presence of insurance coverage.

Legal Hazard: Legal hazard pertains to features within the legal system or regulatory framework that contribute to an increased frequency or severity of losses. Instances of legal hazard encompass adverse jury verdicts or substantial damage awards in liability lawsuits, legal statutes mandating insurers to incorporate specific benefits, such as coverage for alcoholism, into health insurance plans, and regulatory interventions by state insurance departments limiting insurers' capacity to withdraw from a state due to unfavorable underwriting outcomes.