Every transaction involves a bet on incomplete information. When you buy a car, you do not know its full maintenance history. When an employer hires an employee, they cannot directly observe the candidate's true work ethic. When you buy health insurance, the insurer does not know how carefully you will drive or what genetic risks you carry.

These situations, in which one party to a transaction knows significantly more than the other, define asymmetric information — one of the most consequential concepts in modern economics. Understanding it explains why markets sometimes fail, why institutions exist in the forms they do, and why trust is one of the most economically valuable commodities in human society.

The concept is central to information economics, a branch of microeconomics developed primarily in the 1970s. Three economists — George Akerlof, Michael Spence, and Joseph Stiglitz — shared the 2001 Nobel Memorial Prize in Economic Sciences for their foundational work analyzing markets with asymmetric information. Their combined insights transformed economics from a discipline that often assumed information was freely available and symmetrically distributed to one that treats informational frictions as a central feature of market structure.

What Is Asymmetric Information?

Asymmetric information occurs when one party in an economic transaction has substantially more or better information than the other party about something material to the transaction. This informational imbalance distorts the behavior of both parties, can prevent mutually beneficial transactions from occurring, and in extreme cases can cause entire markets to collapse.

The information advantage can take several forms:

  • Hidden information (also called adverse selection): one party knows something about themselves or the good being transacted that the other party cannot observe before the deal is struck — for example, a borrower knows their own creditworthiness, a seller knows the condition of the item being sold
  • Hidden action (also called moral hazard): after a deal is struck, one party takes actions the other party cannot monitor — for example, an insured person drives recklessly, an employee shirks
  • Hidden type: one party has private information about their fundamental characteristics that affects how they will behave in the relationship

These are not edge cases. They describe the normal informational structure of most economically important transactions: insurance, employment, credit, healthcare, used goods markets, and corporate finance all involve profound information asymmetries.

Akerlof's Market for Lemons: How Asymmetry Destroys Markets

George Akerlof's 1970 paper "The Market for Lemons: Quality Uncertainty and the Market Mechanism," published in the Quarterly Journal of Economics, is one of the most influential and elegantly argued papers in the history of economics. It demonstrated that a small informational asymmetry can, under certain conditions, cause a market to unravel entirely.

The Used Car Market

Akerlof's illustration used the used car market. Suppose used cars come in two types: good cars ("peaches") and defective ones ("lemons"). Sellers know which they have. Buyers do not — they can only estimate the probability that any given car is a lemon based on general market data.

If good cars are worth $20,000 and lemons are worth $10,000, and buyers estimate 50% of cars are lemons, buyers will offer approximately $15,000 for any car (the expected value). Sellers of good cars, knowing their car is worth $20,000, will refuse $15,000 and withdraw from the market. Now the remaining cars are disproportionately lemons. Rational buyers, observing this, lower their offers further. More sellers of decent cars exit. The market spirals toward collapse: only the worst cars remain, and even those are difficult to trade.

This process is called adverse selection — the selection of the worst participants into a market, driven by informational asymmetry. The result is not that the market works imperfectly; it can be that the market does not work at all.

"The difficulty of distinguishing good quality from bad is inherent in many economic situations. The automobile market is just an illustrative example." — George Akerlof, "The Market for Lemons" (1970)

Akerlof's paper was initially rejected by three major economics journals before appearing in the Quarterly Journal of Economics. Reviewers found the argument "trivial" or alternatively "too important to be right." It won the Nobel Prize 31 years later. The paper's publication history is itself instructive: the idea that informational asymmetry alone could cause complete market failure was so counterintuitive to economists trained on efficient market theory that it seemed either obvious or impossible.

The paper was also remarkably prescient about where adverse selection appears. Akerlof himself pointed to health insurance markets, credit markets in developing countries, and discrimination in labor markets as parallel cases. Each has been extensively documented empirically in the decades since.

Where Adverse Selection Appears

Adverse selection is not limited to used cars. It is structurally present wherever sellers or participants have private information about quality:

Market Information Advantage Adverse Selection Effect
Health insurance Individuals know their own health risks Sicker people disproportionately buy insurance; premiums rise; healthy people exit
Life insurance Policyholders know their own risk behaviors High-risk individuals buy more coverage; actuarial tables become skewed
Credit markets Borrowers know their repayment intention High-risk borrowers are most eager for loans at any rate
Labor markets Workers know their own abilities and effort High-ability workers have options; low-ability workers apply broadly
Auction markets Sellers know true asset quality Objects sold at auction skew toward those sellers value least
Annuity markets Buyers know their expected lifespan Healthier (longer-lived) people buy more annuities; insurers must price for longer lives

The health insurance adverse selection problem is particularly consequential for policy. In an unregulated individual insurance market, the logic of Akerlof's lemons model predicts a death spiral: sick people buy insurance; premiums rise to reflect the sicker-than-average pool; healthy people drop out; premiums rise again; and so on until only the very sick remain, at unaffordable premiums. This is not merely theoretical — it was observed in individual insurance markets in several U.S. states before the Affordable Care Act's coverage mandates and subsidies addressed the selection problem.

Cutler and Reber (1998), in a detailed study of health insurance at Harvard University, documented Akerlof dynamics within a large employer's health plan. When Harvard introduced price competition between insurance plans, employees with high expected medical costs disproportionately selected into the more generous plans; the generous plans had to raise premiums to cover their sicker enrollees; healthier employees switched to cheaper plans; the generous plans' premiums increased further. Within four years, the most generous plan was forced to withdraw from the market.

Moral Hazard: Changing Behavior After the Transaction

Moral hazard is asymmetric information's temporal sibling. Where adverse selection operates before a transaction (affecting who enters a market), moral hazard operates after a transaction — a party changes their behavior because they are insulated from the full consequences of their actions.

The term has origins in the insurance industry. Insurers observed that people with insurance sometimes became less careful than they would be without it — a moral dimension that insurers found troubling enough to name.

Classical examples:

  • Homeowners with flood insurance are less likely to invest in flood prevention
  • Drivers with comprehensive insurance may take more risks behind the wheel
  • Banks that expect government bailouts ("too big to fail") may take on excessive risk
  • Employees who know job security is very high may exert less effort

Moral hazard does not require dishonesty or even conscious calculation. It is a rational behavioral response to changed incentive structures. If you are fully insulated from downside risk, you face different incentives than if you bear the full consequences of your decisions.

The evidence for moral hazard in insurance is mixed but real. Manning et al. (1987), in the landmark RAND Health Insurance Experiment — a large-scale randomized trial of different insurance arrangements — found that people with more comprehensive insurance used substantially more healthcare than those with higher cost-sharing. People with free care used approximately 40% more services than those with high cost-sharing. Importantly, the additional care taken by those with free insurance did not produce measurably better health outcomes on most measures, suggesting that the extra care was largely discretionary rather than medically necessary — classic moral hazard.

The Principal-Agent Problem

Moral hazard is formalized in principal-agent theory, which models the relationship between a principal (who hires or delegates) and an agent (who acts on the principal's behalf). The agent has private information about their own actions and effort, creating the classic moral hazard structure.

Principal-agent problems pervade modern economic life:

  • Shareholders (principals) and corporate managers (agents): managers may pursue empire-building, excessive compensation, or risk avoidance rather than shareholder value
  • Patients (principals) and physicians (agents): fee-for-service medicine may incentivize unnecessary procedures
  • Voters (principals) and politicians (agents): politicians may pursue personal or partisan interests over constituent welfare
  • Insurance companies (principals) and insured parties (agents): the insured may behave recklessly knowing losses are covered

The structure is always the same: the principal cannot perfectly observe the agent's actions or information, and the agent's incentives are not perfectly aligned with the principal's interests.

Optimal contract design tries to address principal-agent problems by structuring compensation to align incentives. Performance pay, stock options for executives, co-payments in insurance, and profit-sharing in employment contracts are all responses to moral hazard. The design of these contracts is a central topic in contract theory, for which Oliver Hart and Bengt Holmstrom were awarded the 2016 Nobel Prize in Economics.

"When contracts are incomplete — when not everything can be specified in advance — who has control rights matters enormously for economic outcomes." — Oliver Hart, Nobel Prize Lecture (2016)

The limits of contract solutions are important. In some settings — like healthcare or education — outcome-based pay creates perverse incentives. Doctors paid per procedure may order unnecessary tests. Teachers evaluated on test scores may teach to the test and neglect deeper learning. The imperfect alignment of measurable outcomes with true objectives means that even well-designed incentive contracts can create their own distortions.

Signaling: Communicating Quality Credibly

If buyers cannot observe quality directly, how can high-quality sellers communicate their advantage? Michael Spence's 1973 paper "Job Market Signaling" introduced signaling theory as the answer — and changed how economists think about education.

The Education Signal

Spence posed a puzzle: suppose education genuinely teaches workers nothing of productive value. Could rational employers still rationally use educational credentials as hiring criteria? His surprising answer was yes — under specific conditions.

The key condition is that the signal must be costly to produce, and costlier for low-ability types than high-ability types. If obtaining a university degree requires three years of sustained effort and the discipline to persist through difficult coursework, and this is harder for less capable workers than more capable ones, then obtaining the degree is a credible signal of ability — even if the degree content is irrelevant to the job.

Why? Because if obtaining the credential is harder for low types, low-type workers would not find it rational to mimic high-type workers by obtaining the credential. The signal separates types precisely because it is differentially costly.

Spence was not claiming education has no productive value (he explicitly noted it may have both). He was demonstrating that a signal can function purely as a sorting mechanism even without that value.

This distinction has practical implications for education policy. If the primary value of a college degree is signaling rather than human capital accumulation, then expanding access to university education may not increase aggregate productivity — it may simply shift the credential threshold upward without improving skills. Bryan Caplan's The Case Against Education (2018) develops this argument extensively, estimating that signaling accounts for at least 80% of the private return to education in the United States. Caplan's view is controversial but has forced a serious engagement with the distinction between productivity-enhancing and purely signaling functions of education.

What Makes an Effective Signal

Effective signals across markets share key properties:

Observable: The recipient must be able to verify the signal Costly: Cheap signals can be mimicked by low-quality types Differentially costly: The signal must be harder (more expensive in time, money, effort, or risk) for low-quality types to mimic Stable: The signal must be reliably correlated with the underlying quality over time

Beyond education, signals include:

  • Warranties and guarantees (costly for producers of defective goods, cheap for quality producers)
  • Advertising spend (high spending signals that a company expects repeat purchases and long-term brand value — a signal not worth making unless quality is real)
  • Probationary periods (workers willing to work initially at lower wages signal confidence in their ability)
  • Premium pricing on luxury goods (high prices signal exclusivity and quality by deterring price-sensitive, lower-income buyers who are more likely to resell)
  • Medical board certification (costly to obtain, credibly signals specialized competence to patients who cannot evaluate clinical skill directly)

The Problem of Signal Inflation

Over time, signals can lose effectiveness through credential inflation — as more people obtain a credential, its signaling value falls and employers demand higher credentials for the same jobs. In the United States, the share of jobs requiring a college degree has grown substantially faster than the actual skill requirements of those jobs would justify (Burning Glass Technologies, 2014). This inflation represents pure social waste if driven by signaling dynamics: more costly, time-consuming credentials providing no additional productivity signal.

Screening: When the Uninformed Party Elicits Information

Screening is the complement of signaling: instead of the informed party sending a signal, the uninformed party designs mechanisms to elicit or reveal information.

Insurers screen by offering a menu of contracts at different deductibles and premiums. Low-risk customers prefer high-deductible, low-premium contracts (they expect few claims). High-risk customers prefer low-deductible, high-premium contracts (they expect many claims). By observing which contract type customers choose, the insurer partially reveals their private risk information.

This approach is called mechanism design or incentive-compatible contracting — designing choices that induce parties to reveal their true types through self-selection. The revelation principle in mechanism design theory (developed by Roger Myerson, who won the Nobel Prize in 2007) formalized the insight that with clever contract design, parties can be induced to truthfully reveal their private information.

Employers screen through job interviews, work sample tests, background checks, reference calls, and probationary employment periods. Each mechanism is designed to reveal information about candidates that candidates have and employers do not. Work sample tests — asking a software developer to solve coding problems, asking a writer to produce sample content — are among the most effective screening tools because they directly measure the relevant capability rather than proxies.

Banks screen borrowers through credit scores, income verification, asset documentation, and requested loan-to-value ratios. The development of the FICO credit score in the 1980s was a major innovation in borrower screening — aggregating payment history, credit utilization, and other behavioral signals into a single number that substantially improved lenders' ability to predict default risk.

How Markets and Institutions Reduce Information Asymmetry

Markets have evolved a rich set of institutions and mechanisms to reduce the damage from information asymmetry:

Warranties and Guarantees

A seller who provides a strong warranty credibly signals quality because the warranty is costly to honor if the product is defective. A company that offers a 10-year guarantee on its product is making a costly commitment — one that would be financially disastrous if the product fails frequently. Consumers correctly interpret strong warranties as quality signals.

This is why extended warranties are sold as add-ons and why their pricing often exceeds their expected actuarial value: the seller's willingness to sell an extended warranty signals confidence in product quality, and consumers may pay a premium not just for the insurance value but for the signal.

Professional Licensing and Credentials

Occupational licensing — requiring doctors, lawyers, engineers, and electricians to hold verified credentials — is partly a response to the information asymmetry between service providers and clients. Clients typically cannot directly evaluate the technical competence of their physician or structural engineer. Licensing substitutes third-party verification for direct consumer evaluation.

Approximately 25% of U.S. workers now require a government license to work in their occupation, up from about 5% in the 1950s (Kleiner & Krueger, 2013). The proliferation of licensing requirements reflects both genuine quality concerns and rent-seeking — incumbent practitioners using licensing requirements to restrict competition and raise their own wages. Distinguishing between welfare-improving licensing (where information asymmetry is genuine and consequences of low quality are severe) and welfare-reducing licensing (where restrictions primarily serve incumbents) is an important policy question.

Reputation Systems

Online reputation systems (Amazon reviews, Airbnb ratings, Uber driver scores) are a technological solution to information asymmetry. They aggregate past transaction data to give new parties information about counterparty quality without requiring direct experience.

Luca (2016), in a study of Yelp ratings and restaurant demand, found that a one-star increase in Yelp rating caused a 5-9% increase in restaurant revenue — a large effect that demonstrates how much consumers value reputation information. However, the effectiveness of reputation systems depends on the reliability of the rating mechanism. Mayzlin, Dover, and Chevalier (2014) documented systematic fake review inflation on TripAdvisor, particularly by hotels near competitors, illustrating how the signal can be gamed.

Disclosure Requirements

Mandatory disclosure requirements in financial markets (prospectuses, earnings reports, material event announcements) are regulatory responses to information asymmetry between insiders and outside investors. Without disclosure requirements, rational investors would discount all assets because they could not distinguish informed insiders from uninformed outsiders.

The Sarbanes-Oxley Act (2002), passed in response to accounting fraud at Enron and WorldCom, mandated CEO and CFO certification of financial statements and strengthened auditor independence — regulatory interventions designed to reduce information asymmetry between corporate insiders and outside investors.

Intermediaries

Real estate agents, insurance brokers, and investment bankers exist partly because they have information that their clients lack. Intermediaries reduce but do not eliminate asymmetry — they introduce their own principal-agent problem, since the intermediary's incentives may not align perfectly with the client's interests. A real estate agent who is paid a percentage of the sale price has an incentive to close quickly at any price, not to maximize the client's price — a classic principal-agent conflict documented by Levitt and Syverson (2008), who found that homes owned by real estate agents themselves sold for about 3.7% more and stayed on the market 9.5 days longer than client-owned homes.

The Limits of Market Solutions

Not all markets can solve their information asymmetry problems through organic mechanisms. Several conditions lead to persistent failure:

One-time transactions: Reputation mechanisms require repeated interaction. Markets where buyers and sellers never transact more than once have no reputation equilibrium and are particularly vulnerable to asymmetry problems.

Credence goods: Some goods are characterized by unverifiable quality even after consumption. You may never know whether the car tune-up you received was actually necessary. Healthcare, legal services, and consulting all have significant credence good characteristics. Dulleck and Kerschbamer (2006) provide a theoretical framework for understanding credence good markets and the conditions under which they can function despite the fundamental information asymmetry.

Catastrophic downside risk: When the consequences of selecting a lemon are catastrophic (food safety, aircraft components, medical devices), market self-regulation through reputation may be insufficient. Regulatory inspection and certification become necessary because the cost of waiting for reputation mechanisms to work is too high.

Asymmetric Information and the 2008 Financial Crisis

The 2008 global financial crisis illustrated asymmetric information's ability to destabilize entire financial systems.

Mortgage-backed securities and collateralized debt obligations (CDOs) bundled thousands of individual mortgages into securities sold to investors worldwide. The originators of the underlying mortgages had detailed information about borrower quality that was not fully transmitted to investors who purchased the securities. Rating agencies, hired by the issuers (a principal-agent problem in itself), gave high ratings to instruments whose true risk was hidden in complex layered structures.

Ashcraft and Schuermann (2008), in an analysis of mortgage securitization pipelines, identified seven distinct points of information asymmetry between mortgage originators, securitizers, servicers, rating agencies, and final investors. At each point, the party with more information could exploit the information gap at the expense of the party with less — a chain of adverse selection and moral hazard.

When mortgage defaults began to exceed expectations, the asymmetric information became apparent: investors realized they could not reliably evaluate the quality of the assets they held. The result was classic Akerlof dynamics: trust collapsed, transactions stopped, and capital markets froze globally. The interbank lending market — through which banks lend to each other overnight — effectively ceased to function in September 2008 because banks could not evaluate each other's exposure to bad mortgage assets. Mutual suspicion of lemon balance sheets shut down the market.

The LIBOR-OIS spread, which measures the premium banks charge each other for short-term lending over the risk-free rate, jumped from about 10 basis points pre-crisis to over 350 basis points in October 2008 — a direct measure of the cost of asymmetric information in interbank markets.

"The failure of rating agencies was fundamentally an information problem. Complex CDOs were designed to be opaque. When the opacity resolved into visible losses, the entire rating system lost credibility — and without credible ratings, markets froze." — Gary Gorton, Slapped by the Invisible Hand (2010)

The policy responses — bank stress tests, enhanced disclosure requirements, the Dodd-Frank Act's Volcker Rule — were primarily attempts to reduce information asymmetries between financial institutions and their counterparties, regulators, and investors.

Asymmetric Information in the Digital Economy

The digital economy has created new forms of asymmetric information while also providing new tools to reduce it. Platforms like Uber, Airbnb, and Amazon have industrialized reputation systems, dramatically reducing some transaction costs associated with asymmetric information. A traveler booking an Airbnb in a foreign city has access to more credible quality signals than was possible in any pre-internet world.

However, the digital economy has also created new information asymmetries. Large technology platforms possess vast amounts of data about user behavior, preferences, and vulnerabilities that users themselves do not have access to. Advertisers, platforms, and data brokers exploit this asymmetry commercially and sometimes harmfully. The emergence of algorithmic pricing — in which sellers dynamically adjust prices based on inferred buyer characteristics and willingness to pay — exploits information asymmetry to extract consumer surplus.

Acquisti, Taylor, and Wagman (2016) provide a comprehensive survey of the economics of privacy, documenting how information asymmetries about consumer data affect market outcomes. Their analysis suggests that privacy regulation is partly a response to an information asymmetry problem: consumers cannot adequately evaluate how their data will be used and monetized, making the market for personal data systematically non-transparent.

Conclusion

Asymmetric information is not a market anomaly. It is the normal condition of economic life. Every market participant knows something the other side does not, and this reality shapes everything from insurance pricing to education credentialing to the structure of employment contracts and financial regulation.

The insight that information asymmetry can cause markets to fail — rather than simply function imperfectly — was genuinely transformative. It shifted economics from a naive view of markets as self-correcting toward a richer understanding of the specific conditions under which markets work and the specific mechanisms (signals, screening devices, disclosure requirements, credentials, warranties) that address their failures.

Understanding asymmetric information does not mean distrusting all transactions. It means knowing what questions to ask, what guarantees to demand, and what institutional structures exist to fill the knowledge gaps that individual parties cannot bridge on their own.

References

  • Akerlof, G. A. (1970). The Market for Lemons: Quality Uncertainty and the Market Mechanism. Quarterly Journal of Economics, 84(3), 488-500.
  • Spence, M. (1973). Job Market Signaling. Quarterly Journal of Economics, 87(3), 355-374.
  • Stiglitz, J. E., & Weiss, A. (1981). Credit Rationing in Markets with Imperfect Information. American Economic Review, 71(3), 393-410.
  • Manning, W. G., et al. (1987). Health Insurance and the Demand for Medical Care: Evidence from a Randomized Experiment. American Economic Review, 77(3), 251-277.
  • Myerson, R. B. (1979). Incentive Compatibility and the Bargaining Problem. Econometrica, 47(1), 61-73.
  • Cutler, D. M., & Reber, S. J. (1998). Paying for Health Insurance: The Trade-Off Between Competition and Adverse Selection. Quarterly Journal of Economics, 113(2), 433-466.
  • Kleiner, M. M., & Krueger, A. B. (2013). Analyzing the Extent and Influence of Occupational Licensing on the Labor Market. Journal of Labor Economics, 31(S1), S173-S202.
  • Levitt, S. D., & Syverson, C. (2008). Market Distortions When Agents Are Better Informed: The Value of Information in Real Estate Transactions. Review of Economics and Statistics, 90(4), 599-611.
  • Luca, M. (2016). Reviews, Reputation, and Revenue: The Case of Yelp.com. Harvard Business School Working Paper 12-016.
  • Mayzlin, D., Dover, Y., & Chevalier, J. (2014). Promotional Reviews: An Empirical Investigation of Online Review Manipulation. American Economic Review, 104(8), 2421-2455.
  • Dulleck, U., & Kerschbamer, R. (2006). On Doctors, Mechanics, and Computer Specialists: The Economics of Credence Goods. Journal of Economic Literature, 44(1), 5-42.
  • Ashcraft, A. B., & Schuermann, T. (2008). Understanding the Securitization of Subprime Mortgage Credit. Federal Reserve Bank of New York Staff Reports, No. 318.
  • Gorton, G. B. (2010). Slapped by the Invisible Hand: The Panic of 2007. Oxford University Press.
  • Caplan, B. (2018). The Case Against Education: Why the Education System Is a Waste of Time and Money. Princeton University Press.
  • Acquisti, A., Taylor, C., & Wagman, L. (2016). The Economics of Privacy. Journal of Economic Literature, 54(2), 442-492.
  • Hart, O. (2016). Incomplete Contracts and Control. Nobel Prize Lecture. Nobel Foundation.
  • Burning Glass Technologies. (2014). Moving the Goalposts: How Demand for a Bachelor's Degree Is Reshaping the Workforce. Boston, MA.

Tags

economics decision-making markets behavioral economics information

Frequently Asked Questions

What is asymmetric information?

Asymmetric information describes a situation in which one party to a transaction has substantially more or better information than the other party. This imbalance can distort decision-making, undermine market efficiency, and lead to outcomes that are worse for both parties than they would be with equal information. It is pervasive in real-world markets: sellers typically know more about a product's quality than buyers, employees know more about their own abilities than employers, and insured individuals know more about their own risk than insurers.

What is Akerlof's market for lemons?

George Akerlof's 1970 paper 'The Market for Lemons' demonstrated that asymmetric information can cause markets to collapse entirely. Using the used-car market as an example, Akerlof showed that sellers know whether their car is a 'lemon' (defective) but buyers do not. Buyers, aware they might receive a lemon, offer only average prices. Sellers of high-quality cars refuse these prices and exit the market. This leaves only lemons, buyers lower prices further, and the market unravels. Akerlof won the Nobel Prize in Economics in 2001 for this work.

What is the difference between adverse selection and moral hazard?

Adverse selection occurs before a transaction when the party with more information self-selects in ways that disadvantage the less-informed party — for example, people with high health risks disproportionately purchasing health insurance. Moral hazard occurs after a transaction when a party changes their behavior because they are protected from the consequences of their actions — for example, someone driving less carefully after buying comprehensive insurance. Both are forms of market failure caused by asymmetric information, but they operate at different points in the relationship.

What is signaling theory in economics?

Signaling theory, developed by Michael Spence in his 1973 paper 'Job Market Signaling,' explains how parties with superior private information credibly communicate that information to others. Spence showed that education can function as a signal of worker quality even if the education itself imparts no productive skills — because the cost of obtaining the signal (years of study, tuition) is lower for more able workers, education reliably sorts workers by ability. For a signal to work, it must be costly enough that lower-quality parties cannot afford to mimic it.

How do markets and institutions reduce asymmetric information?

Markets and institutions have developed several mechanisms: warranties and guarantees (sellers credibly commit to quality), professional licensing and credentials (third-party verification of competence), reputation systems and reviews (accumulated track records), screening (the uninformed party designs tests or contracts to reveal information), and regulation requiring disclosure (like prospectus requirements for securities). Insurance markets use underwriting to screen applicants, and employment markets use probationary periods to evaluate workers whose abilities were uncertain at hire.

Share this article