Adverse Selection

Adverse Selection and Moral Hazard

What is Adverse Selection?

  • Definition: Adverse selection arises when there is hidden information before a transaction occurs.

  • Market Dynamics:

    • One side of the market possesses more information about their own “type” (riskiness, quality, health, etc.) than the other.

    • In insurance markets, adverse selection refers to individuals with higher risk being more likely to buy insurance compared to lower-risk individuals.

  • Consequences:

    • Can lead to a death spiral:

    • Death Spiral: A situation where rising premiums, due to the high-risk composition of insured individuals, drive low-risk customers out of the market, resulting in a higher average risk and subsequent premium hikes.

Definition & Mechanism of the Death Spiral

  • Death Spiral Definition: A death spiral occurs when increasing premiums drive lower-risk individuals out of the market, causing average risk (and premiums) to rise further.

  • Outcome of a Death Spiral:

    • This iterative process continues until only the highest-risk and most expensive participants remain.

    • Often results in market collapse or extremely high insurance costs.

How a Death Spiral Unfolds

  1. Initial Premium Set:

    • Insurers set premiums based on the expected average risk of the overall pool.

    • Individuals with higher risk are more motivated to buy coverage.

  2. High-Risk Composition Increases:

    • The insured pool contains a disproportionately large share of high-risk individuals.

    • Claims increase above what the original premium was designed to cover.

  3. Premiums Rise:

    • Insurers raise premiums for everyone in the pool.

    • Low-risk individuals tend to drop coverage due to these increased costs.

  4. Risk Pool Worsens Further:

    • More low-risk consumers leave the pool, causing the average risk to increase again.

    • Insurers face higher claim costs per remaining policyholder.

  5. Repeating Cycle:

    • Insurers raise premiums again to compensate for the riskier pool.

    • Moderate-risk consumers may exit or choose less comprehensive plans.

    • This cycle continues until only the very highest-risk individuals are left.

Example: Health Insurance

  • Initial Situation:

    • Insurer expecting an average of $4,000 in claims per person sets a premium at approximately $4,500 (to cover overhead + profit).

    • Healthy individuals, with expected claims of $1,000, feel they are “overpaying” and may opt not to purchase insurance.

  • First Premium Hike:

    • With fewer healthy individuals, average claims rise (say to $5,000 per person).

    • Insurer increases premiums, potentially to $5,500.

    • Moderately healthy individuals (expected claims between $2,500 and $3,000) feel overcharged and may leave as well.

Classic Example: The Market for Lemons (Expanded)

  • Setup:

    • Two types of used cars for sale: Lemon (low quality) and Peach (high quality).

    • Sellers know which type they have, while buyers cannot directly observe the quality of the car.

  • Numbers in Our Example:

    • Assumption: 50% of cars are lemons, and 50% are peaches.

    • Value to buyers:

    • Lemon: $2,000

    • Peach: $6,000

    • Buyers initially assume cars are of average quality due to the lack of information.

  • Expected Value & First Price:

    • Expected maximum price buyers are willing to pay:

    • 0.5 imes 2000 + 0.5 imes 6000 = 4000

Example 1: Used Smartphones

  • Setup:

    • Two types of used smartphones: Defective (low quality, e.g., battery issues) and Like-New (high quality, well maintained).

    • Sellers know the exact condition while buyers cannot easily tell by a brief listing.

  • Numbers in Our Example:

    • 40% of the phones are defective; 60% are like-new.

    • Value to buyers is:

    • Defective: $100

    • Like-New: $300

  • Expected Value & First Price:

    • Initial buyer willingness to pay if 40% chance of defective and 60% chance of like-new:

    • 0.4 imes 100 + 0.6 imes 300 = 220

    • Outcome: Owners of like-new phones may not sell at $220, leading to more defective phones remaining.

Example 2: Designer Handbags

  • Setup:

    • Two types of bags: Counterfeits (low-quality fakes) and Authentic (high-quality originals).

    • Sellers know their item's condition; buyers may not always tell authenticity from images.

  • Numbers in Our Example:

    • 30% of listings are counterfeits and 70% are authentic.

    • Value to buyers:

    • Counterfeit: $50

    • Authentic: $400

  • Consequences:

    • Owners of genuine bags may find $295 too low for their $400 item and exit the market, increasing the fraction of fakes.

    • This contributes to reinforcing the lemons problem even further.

Example 3: Private Tutoring Services

  • Setup:

    • Two types of tutors: Skilled and Experienced vs. Inexperienced and Unqualified.

    • Tutors know their qualifications; students cannot directly observe skill.

  • Numbers in Our Example:

    • 50% of tutors are highly skilled; 50% are inexperienced.

    • Skilled tutor value: $50/hour; Inexperienced: $10/hour.

  • Outcome:

    • Skilled tutors may refuse to work for $30/hour, prompting high-quality tutors to leave the market, thus decreasing average quality.

    • Resulting behavior diminishes the potential pay for students as market dynamics shift downward.

Adverse Selection in Insurance

  • Health Insurance:

    • Individuals aware of their poor health are more likely to purchase generous insurance coverage.

    • Result: Insurers must raise premiums to cover high expected costs, driving healthier people away.

  • Auto Insurance:

    • Risky drivers tend to seek comprehensive coverage; safe drivers may only opt for minimal coverage if premiums escalate.

    • Potential outcome could lead to overpriced insurance or market unraveling, where only the highest-risk individuals remain insured.

Strategies to Mitigate Adverse Selection

  1. Screening:

    • Definition: The less informed party gathers information actively.

    • Examples:

      • Auto insurance companies check driving history.

      • Health insurers require medical exams.

      • Employers conduct background checks.

    • Purpose: Helps reduce unknowns by allowing insurers to set premiums based on actual risk and deter high-risk types from entering the market.

  2. Signaling:

    • Definition: The informed party reveals private information through observable actions.

    • Examples:

      • Job applicants obtaining degrees to signal competence.

      • Safe drivers sharing telematics data.

    • Characteristics of a Credible Signal:

      • Costly for low-quality types to imitate.

      • Observable by the other party to establish trust.

  3. Mandates / Pooling:

    • Mandates: Policies requiring everyone to purchase insurance (e.g., individual mandates in health insurance).

    • Pooling: Regulated community rating or risk pooling mixes high and low-risk individuals, stabilizing costs across the population.

    • Rationale: Prevents market collapse, promotes equity, stabilizes costs for high-risk individuals.

What is Moral Hazard?

  • Definition: Moral hazard occurs when there is hidden action following a transaction, leading individuals to change behavior in ways that cannot be observed by the other party.

  • Examples:

    • After securing full auto coverage, a driver may adopt riskier driving behaviors.

    • A homeowner may neglect security once their home is insured.

Examples of Moral Hazard

  1. Auto Insurance Scenario:

    • A fully insured driver may drive faster or park in riskier places.

    • The insurer cannot monitor every decision made by the driver, leading to increased accident probabilities.

  2. Employment Contract Scenario:

    • Employees may reduce effort levels after being hired under fixed compensation, knowing their work cannot be perfectly observed by the employer.

  3. Banking Scenario:

    • Large banks may engage in riskier investments if they believe they’ll be bailed out by the government, reducing their incentive to avoid risk.

  4. All-You-Can-Eat Buffet Scenario:

    • A diner may overindulge at a buffet since the cost of additional food is effectively zero, leading to waste and excessive consumption.

Reducing Moral Hazard in Insurance

  1. Deductibles and Copayments:

    • Definition: A deductible is the fixed amount paid by the insured before coverage kicks in. A copayment is a small charge for each service.

    • Incentive: These mechanisms ensure shared costs, discouraging frivolous claims and excessive service use.

  2. Coinsurance:

    • Definition: The insured pays a percentage of each claim, similar to copay but expressed as a fraction of total costs.

    • Incentive Effect: Encourages careful decision-making regarding claims as individuals face a portion of financial responsibility.

  3. Monitoring / Telematics:

    • Definition: Insurers collect data on insured party behaviors post-policy issuance.

    • Trade-Off: Helps reduce hidden actions, but raises privacy concerns among policyholders.

  4. No-Claims Bonus:

    • Definition: Discounts for maintaining a claim-free status over periods.

    • Incentive Effect: Encourages careful behavior to avoid claims and maintain lower premiums.

  5. Experience Rating:

    • Definition: Premiums adjusted based on the insured's claims history.

    • Behavioral Change: Awareness that claims drive premium increases encourages ongoing caution.

  6. Coverage Limits & Exclusions:

    • Definition: Certain high-risk items may be capped or excluded in coverage.

    • Rationale: Prevents the potential for financial loss from high-risk claims and limits moral hazard incentives.