Author: Vivek Sharan

Imagine buying something you hope you’ll never actually need to use. Then, if you do need to use it, you’re not even sure if it worked properly. Welcome to insurance – a product that can leave you scratching your head before, during, and after you buy it.

When you’re shopping for insurance, you’re basically taking a shot in the dark. Do you really need rental car coverage? Should you pay extra for that flood rider on your homeowner’s policy? Most of us don’t have the faintest idea until disaster strikes – and then it’s too late.

Even after you’ve been paying premiums for years, you still don’t know what you’ve bought. That policy sitting in your drawer is full of legal jargon and exclusions that might as well be written in ancient Greek. The true test only comes when you file a claim, and even then, it’s murky waters.

Let’s say your basement floods and the insurance company offers you $5,000 for repairs. Is that fair? Should it be $8,000? Most of us just don’t know enough about construction costs or policy interpretation to judge whether we got a square deal.

The whole system runs on a massive knowledge gap. Insurance companies employ armies of actuaries and risk analysts who can tell you the statistical likelihood of your house burning down based on your zip code, the age of your wiring, and probably what you had for breakfast. Meanwhile, the average customer is just hoping they’re not overpaying or underinsured.

This trust-based relationship explains why insurance companies spend billions on ads featuring friendly mascots and slogans about being “good neighbors” or having “good hands.” They’re trying to build trust in a market where customers often can’t evaluate what they’re buying.

That said, insurance isn’t quite as mysterious as some other purchases. There are rules of the road – state regulators watch over insurance companies, policy documents spell out what’s covered (even if nobody reads them), and you can always check a company’s complaint record before buying.

So while insurance might not be as baffling as figuring out if that expensive dental procedure was really necessary, it still leaves most of us playing a guessing game with our wallets. And that’s why choosing insurance often comes down to crossing your fingers and hoping the company with the catchiest jingle won’t let you down when disaster strikes.

When your car makes a weird noise and the mechanic says you need a $1,200 repair, you’re stuck in a tough spot. Do you need that expensive part? Could a simpler fix work? Is the price fair? Most of us have no clue – we just have to trust the person in the grease-stained uniform.

Welcome to the world of credence goods – things we buy where we’re flying blind, even after we’ve paid for them.

Economists Darby and Karni coined this term back in the ’70s, but we’ve all experienced it. Think about the last time you visited a doctor, hired a lawyer, or called a plumber. You handed over your money, hoping they’d solve your problem, but you couldn’t judge if they did the right thing or took you for a ride.

This is different from other purchases. When you buy a sweater, you can feel the fabric before buying (a search for good quality). When you try a new restaurant, you might not know if it’s good until you eat there, but afterwards, you can say if the meal was worth it (an experience good).

But with credence goods? You’re often clueless before, during, and after. Your tooth stops hurting after that expensive procedure, but did you need a root canal, or would a filling have worked? Your financial advisor’s investment choices might take decades to prove wise or foolish.

The problem is that one side knows way more than the other. The expert has knowledge and training, while you’re at their mercy. This creates a perfect setup for potential exploitation – mechanics recommending unnecessary repairs, doctors ordering excessive tests, or consultants stretching out billable hours.

That’s why we develop workarounds: seeking second opinions, looking for certifications, reading reviews, or establishing relationships with trusted professionals. Government regulations and professional ethics codes also try to bridge this knowledge gap.

But at the end of the day, for many important services we buy, we’re still largely taking someone’s word that we got what we paid for.

Insurance companies and their customers don’t always want the same things. This fundamental tension shapes how insurance works.

When you apply for coverage, you know far more about your actual risks than the insurer does. Maybe you’re a cautious driver applying for auto insurance, or perhaps you have a family history of heart disease that you didn’t mention on your health insurance application. This knowledge gap creates problems.

High-risk individuals naturally gravitate toward insurance, while the healthiest and safest among us might skip it altogether. Without safeguards, insurers end up with customer pools that are much riskier than expected. To counter this, insurance companies deploy several tactics: they ask detailed questions, examine medical records, use statistical models to set different prices for different risk levels, and design policies with various deductibles and coverage limits that essentially force customers to reveal their true risk tolerance.

Once you’re insured, another problem emerges. With protection in place, you might take more risks or be less careful than before. Why shovel your icy sidewalk if you’re covered for slip-and-fall lawsuits? Why bother with that expensive security system when your homeowner’s policy will replace stolen items?

Insurers fight this behavior by making you share the pain. Deductibles ensure you feel some financial sting from every claim. Coverage limits cap their exposure to truly catastrophic losses. Claims investigations weed out fraud. And if you file too many claims, your premiums will rise, creating a direct financial incentive to be careful.

The whole system gets even more complicated when insurance agents enter the picture. These intermediaries serve two masters – the insurer who pays their commission and the customer who relies on their advice. Their compensation structure sometimes rewards selling expensive policies rather than finding the perfect fit for the customer’s needs.

Understanding these inherent conflicts helps explain why insurance policies are so complex, why premiums vary dramatically between individuals, and why the market functions the way it does. The system isn’t perfect, but its features evolved precisely to manage these conflicting interests and information imbalances.

Prospect Theory flipped the script on how economists think about risk. While traditional models saw people as coldly rational calculators, Kahneman and Tversky noticed something more human: we feel losses much more sharply than equivalent gains, and we judge everything relative to where we stand now, not in absolute terms.

This insight revolutionised economics by explaining all sorts of real-world behaviours that made no sense under old models. Why do investors hang onto losing stocks too long? Why do consumers respond differently to identical offers framed differently? Prospect Theory offered compelling answers.

The insurance industry provides a perfect testing ground for these ideas. Here, Prospect Theory reveals fascinating contradictions in how we approach risk. We’re simultaneously willing to pay premiums to avoid unlikely catastrophes (overweighting small probabilities) while sometimes viewing those very premiums as painful, certain losses to avoid.

Looking at insurance through this lens helps crack the stubborn “underinsurance puzzle” – why people often skimp on coverage despite clear financial benefits. When premiums feel like guaranteed losses while disasters seem comfortably remote, many choose to roll the dice.

Real-world research in developing markets shows these effects in action. A study of rainfall insurance in India found that farmers who received payouts became significantly more likely to renew their policies. The experience shifted their reference point, making future premiums feel less like losses and more like smart investments.

The reach of these psychological insights extends far beyond economics, from marketing strategies to political decisions to workplace behaviour. By recognising that humans aren’t purely rational actors but instead complex decision-makers influenced by psychological biases, Prospect Theory gave us a more nuanced and accurate picture of economic behaviour.

What makes this framework so powerful is its ability to predict precisely how and when we’ll deviate from strictly rational choices, showing that our “irrational” behaviours often follow predictable patterns tied to how our minds process risks, losses, and uncertainty.

This paper digs into how a company with monopoly power handles the challenge of not knowing its customers’ true characteristics—whether that’s risk levels for insurance or how much they’re willing to pay for products.

Stiglitz shows that smart monopolists rarely use simple flat pricing. Instead, they create menus of options with different combinations of price, quantity, and quality. This isn’t just random variety—it’s a calculated strategy to get customers to reveal information about themselves through their choices.

The genius of this approach is that different customer types voluntarily pick different options, essentially sorting themselves into profitable categories without the company needing to directly identify who’s who. A customer choosing the high-deductible insurance plan or the economy package essentially declares “this is my type” without saying a word.

What makes this work particularly interesting is the deliberate inefficiencies built into these pricing schemes. The monopolist intentionally makes some options worse than they could be, especially those aimed at “lower value” customers, specifically to prevent “higher value” customers from choosing them instead of the pricier options designed to extract their maximum willingness to pay.

These strategic inefficiencies represent a direct tradeoff: the monopolist sacrifices some potential market efficiency to gain the ability to price discriminate more effectively.

The paper extends well beyond abstract theory, showing how this plays out in real-world insurance markets where companies offer varied combinations of premiums and deductibles that cleverly sort customers by risk level.

By connecting monopoly pricing with information problems, Stiglitz created a framework that explains why we see such complex pricing structures in markets ranging from wireless plans to airline tickets to insurance products.

Stiglitz’s 1976 paper on insurance markets cut through a puzzling problem: what happens when you know more about your own health risks than the company insuring you?

The answer isn’t pretty. When customers understand their personal risk better than insurers do, the whole market gets wobbly.

Insurance companies naturally want to charge everyone a price based on average risk. But this creates a dilemma – healthy people find these average rates too expensive and walk away, while high-risk folks eagerly sign up for what they see as a bargain. As healthier people drop out, the customer pool gets riskier, forcing prices up further, driving away even more low-risk customers.

What emerges instead are split markets where insurers offer different packages – high premiums with comprehensive coverage versus cheaper policies with hefty deductibles. The clever part? These options naturally sort customers. High-risk people gravitate toward fuller coverage despite the cost, while healthier customers accept higher deductibles to save money.

Through this process, customers essentially reveal their hidden risk levels through their choices. Insurers learn what they couldn’t otherwise know.

The trouble is, this sorting mechanism comes with hidden costs. Low-risk individuals often end up with less protection than they’d actually prefer, just to prove they’re low-risk. The market reaches stability, but at the expense of efficient outcomes.

Stiglitz showed that even fierce competition can’t eliminate these distortions when information is unevenly distributed. This insight fundamentally changed how economists think about markets – perfect competition doesn’t guarantee perfect outcomes when information problems exist.

Michael Spence’s 1973 paper “Job Market Signalling” tackled a thorny problem: how do employers figure out which job candidates are good when they can’t directly observe their abilities?

His breakthrough was showing how education works as more than just skill-building—it’s a way for talented people to prove themselves. The logic is elegant: getting a degree is harder for less capable people, so those who complete more education are sending a credible message about their abilities.

What makes this work is that the signal must be expensive—in time, money, or effort—and crucially, it must be more expensive for less productive people. When high-ability workers find it easier to get degrees than low-ability workers, education becomes a reliable sorting mechanism.

This creates what Spence called a “separating equilibrium” where different types of workers make different choices about education, allowing employers to make better hiring decisions based on credential levels.

The irony Spence pointed out is that education might work perfectly as a signal even if it taught nothing useful for the job. If it just helps employers identify talent, society might spend enormous resources on credentials that don’t actually boost productivity.

For a signal to work properly, it needs three things: it must cost something significant, it must cost less for truly qualified people, and employers must be able to easily verify it.

While Spence focused on education and jobs, his insight revolutionised how economists think about information problems across many markets—from warranties signalling product quality to companies using dividend policies to signal financial health.

This elegant theory helped explain puzzling real-world patterns and became a cornerstone of the economics of information.

The research paper “Credit Rationing in Markets with Imperfect Information” by Joseph Stiglitz and Andrew Weiss (1981) revolutionized the understanding of credit markets by demonstrating how credit rationing – the phenomenon where some borrowers are denied loans even at the prevailing interest rate – can be a rational outcome in markets characterized by imperfect information, rather than just a sign of market inefficiency or irrationality.

Key arguments:

• Information Asymmetries: The core of the paper lies in the recognition that lenders typically have less information about borrowers than the borrowers themselves. This information asymmetry creates two fundamental problems:
  • Adverse Selection: At higher interest rates, the pool of loan applicants tends to worsen, as safer borrowers are less willing to pay the higher cost of borrowing, while riskier borrowers, who have a higher probability of default, are more willing to do so.
  • Moral Hazard: Once a loan is granted, borrowers have an incentive to undertake riskier projects than they initially disclosed, as they capture the upside while the lender bears a significant portion of the downside risk in case of failure. Higher interest rates can exacerbate this by incentivizing borrowers to take on even riskier ventures to be able to repay the larger debt.
• Non-Monotonic Relationship between Interest Rates and Lender Returns: Due to adverse selection and moral hazard, Stiglitz and Weiss show that the expected return to lenders may not continuously increase with the interest rate. Beyond a certain point, raising the interest rate can actually decrease the lender’s expected profit by attracting riskier borrowers (adverse selection) or inducing borrowers to take on riskier projects (moral hazard), leading to a higher probability of default.
• Existence of a Profit-Maximizing Interest Rate: This non-monotonic relationship implies that there exists a profit-maximizing interest rate for lenders. At this rate, the lender may find it optimal to ration credit, meaning they will refuse to lend to some borrowers even if those borrowers are willing to pay the prevailing interest rate. This rationing occurs because lending to these marginal borrowers at a higher rate would actually reduce the lender’s overall expected return due to the adverse selection and moral hazard effects.
• Implications for Market Equilibrium: The paper demonstrates that a credit market equilibrium under imperfect information may involve credit rationing. The interest rate will settle at a level that maximizes the lender’s expected profit, and at this rate, there will be unsatisfied borrowers. This contrasts sharply with the perfectly competitive model where all willing and able borrowers at the equilibrium interest rate would receive loans.
• Policy Implications: The findings have significant policy implications, suggesting that interventions aimed at alleviating information asymmetries (e.g., through credit rating agencies or government guarantees) can potentially improve the efficiency of credit markets and reduce rationing.

In essence, Stiglitz and Weiss’s seminal paper provides a powerful framework for understanding credit rationing as a natural consequence of imperfect information in financial markets. It highlights how lenders’ rational responses to adverse selection and moral hazard can lead to the exclusion of some borrowers, even when they are willing to pay the going interest rate, thereby challenging the traditional view of credit rationing as solely a sign of market failure.

George Akerlof’s 1970 paper “The Market for ‘Lemons’” introduced a groundbreaking economic concept that explains how information asymmetry affects markets. Here’s a detailed summary:

Core Concept

Akerlof demonstrates how markets can fail when buyers and sellers have asymmetric information about product quality. His primary example is the used car market, where sellers know much more about their cars’ quality than buyers do.

The Used Car Market Example

• Sellers know whether their used cars are good quality or “lemons” (defective cars)
• Buyers cannot distinguish quality before purchase
• This information asymmetry leads rational buyers to assume that any given car might be a lemon
• Buyers therefore only offer a price reflecting the average expected quality
• This price is too low for sellers of good cars, who withdraw from the market
• Only sellers of lemons remain willing to sell at the lower price
• This further reduces average quality, pushing prices lower
• The cycle continues until the market potentially collapses entirely

Mathematical Model

Akerlof formalized this with a model where:

• Cars have quality q uniformly distributed from 0 to 2
• Sellers value their car at q
• Buyers value the same car at 1.5q
• Even though every transaction could create value (since buyers value cars more than sellers), information asymmetry prevents many beneficial trades

Broader Applications

Akerlof extends this analysis to multiple markets:

1. Insurance markets: Why elderly struggle to get health insurance
2. Labor markets in developing countries: How discrimination and statistical discrimination function
3. Credit markets in developing nations: Why lenders charge high interest rates
4. Business in underdeveloped countries: Why entrepreneurship struggles

Counteracting Institutions

Akerlof identifies institutions that emerge to combat information asymmetry:

• Guarantees/warranties: Signals of product quality
• Brand names: Reputation mechanisms that signal consistent quality
• Licensing/certification: Third-party verification of quality
• Chain establishments: Standardization that reduces uncertainty

Significance

The paper demonstrates that information problems can cause entire markets to function poorly or fail completely. This challenged traditional economic assumptions of perfect information and helped create the field of information economics.

This work was revolutionary and eventually contributed to Akerlof receiving the Nobel Prize in Economics in 2001, shared with Michael Spence and Joseph Stiglitz for their work on markets with asymmetric information.

Unlike adverse selection, moral hazard emerges after the contract is signed, when people change how they act once they have insurance coverage. Since the insured doesn’t shoulder all the financial fallout from a loss, their motivation to prevent or reduce losses often weakens.

When we talk about ex-ante moral hazard, we’re looking at reduced caution before anything goes wrong. In shipping, this might mean cutting corners on safety drills, putting off repairs that should happen now, taking more dangerous routes, or navigating with less care because insurance softens the financial blow if something goes wrong.

Ex-post moral hazard happens after damage occurs. Ship operators might inflate how bad the damage is, try to claim for things their policy doesn’t quite cover, or opt for pricier repairs than they would if paying entirely out of pocket. The big problem? More frequent or costlier insurance claims, driving up expenses across the whole system and forcing insurers to charge higher premiums. This happens because insurers simply can’t watch everything the insured does or verify every detail of every claim.

P&I liability coverage adds extra complications. There’s “claimant hazard” – third parties more eagerly sue or inflate claims when they know insurance exists. Then there’s “underwriting hazard” – insurers might relax their standards for long-term risks since the consequences won’t show up for years. The murky nature of maritime incidents and the human factors involved make classifying risks and monitoring behavior especially tough, potentially making moral hazard worse.

Economic theory offers some countermeasures to information asymmetry. Signalling happens when the knowledgeable party takes costly steps to prove their status, like when a shipowner who rarely has accidents voluntarily picks a policy with a high deductible. Screening occurs when insurers design various contract options that cause different risk types to reveal themselves through their choices, such as offering policies with different deductible/premium combinations. These concepts help explain why insurance contracts include features like deductibles, coverage limits, and exclusions, which serve screening or signalling purposes beyond just controlling moral hazard.