Theory of Asymmetric Information Definition & Challenges

What Is the Theory of Asymmetric Information?

The economic theory of asymmetric information was developed in the 1970s and 1980s as a plausible explanation for market failures. The theory proposes that an imbalance of information between buyers and sellers can lead to market failure.

Market failure, to economists, means an inefficient distribution of goods and services in a free market, in which prices are determined by the law of supply and demand.

Understanding Asymmetric Information Theory

Three economists were particularly influential in developing and writing about the theory of asymmetric information: George Akerlof, Michael Spence, and Joseph Stiglitz. The three shared the Nobel Prize in Economics in 2001 for their contributions.

Akerlof first argued about information asymmetry in a 1970 paper entitled "The Market for 'Lemons': Quality Uncertainty and the Market Mechanism." In this paper, Akerlof asserted that car buyers possess different information than car sellers, giving the sellers an incentive to sell goods of poor quality without lowering the price to compensate for the inferiority.

Akerlof uses the colloquial term lemons to refer to bad cars. He argues that buyers often do not have the information to distinguish a lemon from a good car. Thus, sellers of good cars cannot get better-than-average market prices for their products.

This argument is similar to Gresham's law about money circulation, which argues that poor-quality money triumphs over better money. That theory has faced considerable opposition.

The Hiring Gamble

Michael Spence added to the debate with a 1973 paper "Job Market Signaling." Spence maintains that new hires are uncertain investments for any company. That is, the employer cannot be certain of a candidate's productive capabilities. Spence compares the hiring process to a lottery.

In this case, Spence identifies the information asymmetries between employers and employees.

Insurance Markets

It was Stiglitz, however, who brought information asymmetry to mainstream acceptance. Using a theory of market screening, he authored or co-authored several papers, including significant work on asymmetry in the insurance markets.

Through Stiglitz's work, asymmetric information was placed into contained general equilibrium models to describe negative externalities that price out the bottom of markets. For instance, the health insurance premium needed to cover high-risk individuals causes all premiums to rise, forcing low-risk individuals away from their preferred insurance policies.

Other economists, such as Bryan Caplan at George Mason University, point out that not everyone is truly in the dark in real markets. Insurance companies, for example aggressively seek underwriting services. Caplan also suggests that models based on the ignorance of one party are flawed, given the availability of information from third parties such as Consumer Reports, Underwriters Laboratory, CARFAX, and the credit bureaus.

Economist Robert Murphy suggests that government intervention can prevent prices from accurately reflecting known information, which can cause market failure. For example, a car insurance company might be forced to raise all premiums equally if it cannot base its price decisions on an applicant's gender, age, or driving history.

Empirical Evidence and Challenges

Market research over the years has called into question the existence or the practical duration of asymmetric information causing market failure. Real-life analysis has been offered by economists including Erik Bond (for the truck market, in 1982), Cawley and Philipson (on life insurance, in 1999), Tabarrok (on dating and employment, in 1994), and Ibrahimo and Barros (on capital structure, in 2010).

Little positive correlation between insurance and risk occurrence has been observed in real markets, for instance. One possible explanation is that individuals do not usually have expert information about their own risk types, while insurance companies have actuarial life tables and significantly more experience in predicting risk.