One option for mitigating problems derived from asymmetric information is designing your contract carefully so that whoever buys into it has less to gain from being a lemon. There are many practical implications for this, of which the most clear is probably in the insurance market, where making those insured pay part of the pay out in case of a claim can not only reduce adverse selection, but also moral hazard (by only attracting the safer drivers and also making sure they park more carefully).

Another practical example can be found in the labour market, with variable salaries, commissions and bonuses. Million pound payouts to disgraced bankers may by now be infamous, but when hiring somebody they really help mitigate the risk of having hired somebody who slipped through the signalling net. Not only does it mean that if you do hire a lemon by mistake you’ll have to pay them less, but in theory, they will work harder (limiting moral hazard) and, curiously, you’ll attract the best. Supposing the average salary for a financial analyst is 50.000€ and you offer 20.000€ plus a 50.000€ variable bonus based on performance, the lemons will slide towards the more secure end of the job market, preferring to net their 50.000€. Those that are truly bright will go for the 20 + 50, because if their performance is measured fairly they will earn more than the market average. This deals with the adverse selection side, and, in fact, accepting this type of wage structure can be a form of signalling in itself.

By now, the idea should be clear. Incentives work either by mitigating the effects of:

• Moral hazard: contracts that have incentives built in to make us either work harder, drive safer, or stop smoking because by the way the contract is designed, we either stand to pay less or earn more by doing what is in the ‘weaker’ agents’ interests.
• Adverse selection: contracts that weed out the lemons by making them attractive only to those who are safer, healthier or cleverer by offering above average conditions for those who perform above market average but much worse conditions for those who perform below market average.

In their 1981 paper, “Credit Rationing in Markets with Imperfect Information”, Joseph E. Stiglitz and Andrew Weiss define a situation similar to the case of The Market for Lemons, an article by George Akerlof, except in the financial markets. In this case, it is the ‘seller’ of credit who pulls out of the market because of adverse selection.

Credit rationing implies that credit is not available for all those who want it, at any interest rate. If markets worked perfectly, supply and demand for credit would fix a level of interest rates at which lenders would be happy to assume the risk, and borrowers would be happy to accept it in order to fund their projects. However, accepting high interests can also be a signal: if we have a good credit rating and are not desperately in need of funds, we probably won’t accept excessively high interest rates, we will simply put off our projects or purchases. Really needing money is intrinsically a bad sign for lenders, which can mean that when interest rates rise above a certain level, financial agents can assume that you are a ‘lemon’, and that the risk of you defaulting on your credit is too high. The chances are you are either acquiring the loan in good will but in dire financial straits (which means that despite your best efforts you’ll probably default anyway) or that you simply don’t care about the interest rate you are charged because you have no intention of paying the loan back in the first place.

Supposing there are two types of borrowers, As and Bs, who both want to invest in similar projects with a different level of risk, and assuming that As invest in ‘safer’ projects than Bs although the mean return is the same, say 110. As have a range of possible ROIs, ranging between 90 and 120 for their investment, meaning that if they invest 100, then for interest rates over 20% their profit will be 0 even if their project is successful. However, Bs also have a mean return of 110 for their 100 invested, but with a range of between 70 and 180, with a distribution that skews their average ROI to 10%, just like the As. This means that, if they are really optimistic (as riskier investors tend to be), they can potentially accept interest rates of up to 80% and still break even.

Assuming both As and Bs default on their credit if their ROI is negative (below 100), which is an assumption that banks are fond of making, then they will have a natural cut off point, assuming that all those willing to accept rates of over 20% are therefore risky investors, Bs. When interest rates approach 20%, credit will simply dry up as all those willing to accept higher rates and still borrow will be singled out as probable defaulters.

Moral hazard is a case of asymmetric information. It occurs when both parties (usually an agent and a principal) assign or are subject to a different probability of a same (normally adverse) event occurring. The behaviour of the agent changes ex-post, after a contract is signed and as a consequence of their new, advantageous position. As economist Paul Krugman puts it, moral hazard refers to “any situation in which one person makes the decision about how much risk to take, while someone else bears the cost if things go badly”.

In order to clarify this concept, let’s take a quick look at an example: insurance premiums. The person taking out an insurance policy is at an advantage versus the insurer, who therefore charges a premium for the risk derived from their imperfect information, aside from any mark-up designed purely in order to generate benefits.

Lucy has never smoked in her life, because she has always been concerned about expensive hospital stays taking away disposable income that could be better spent on her butterfly-collecting hobby. However, her cautious friend who works for a large insurance company talks her into subscribing to a healthcare policy. After this, Lucy decides that as her health is now a fixed cost (she pays the same premium whether she becomes ill or not), she might as well start smoking, drinking, and driving like a lunatic. In fact, she swaps her butterfly collecting hobby for cliff jumping. Lucy’s advantage is ex-post.

Moral hazard also appears in other markets, such as in the credit market. In their article about credit rationing, economists Joseph E. Stiglitz and Andrew Weiss explain how raising interest rates will make indebted people take higher risks.

Signalling is similar to screening, except it is the agent with complete information who decides to move first to mark themselves out as a ‘good’ agent, as a cherry. The most cited example is generally in the job market. When we examine most qualified positions, we realise that those carrying out those jobs generally have some form of higher education. However, except in fields like science and medicine, the qualifications these employees hold are often unrelated to the work they are carrying out: for example, a financial analyst with a degree in philosophy. Until recently, it was assumed that a degree could be wholly unrelated because the point is that it offers some form of unspecified human capital that can be useful in any field, like a greater capacity for analysis which was derived from logical thinking and problem solving applied throughout their higher education.

Be this as it may, newer currents specify that a degree is simply a form of ‘signalling’: that is, a way of showing that you have certain qualities, be they innate or nurtured, that have allowed you first to reach that level of education and then to complete it. It is a question of ‘what came first’ – whether your degree has made you employable, or whether you already were but needed to prove it. In this scenario, a degree is merely a piece of paper proving you know a lot about philosophy but nothing about finance, but it does prove certain things: that you have a certain level of intelligence, that your background is probably at least lower middle class (which may be important to corporate culture) and that you have enough perseverance to have been able to stick out a three year course.

Most HR specialists will admit that hiring graduates for white collar jobs is at least a combination of both what your degree affords you and what the fact you have a degree says about you. The point is that most people didn’t (just) choose to do a degree based on their innate love for philosophy. Rather, they knew they ‘had’ to get a degree in something (based on family pressure, expectations, peer pressure and future employability, as well, of course, as an insatiable curiosity for knowledge) and then narrowed the field to something they hoped they could stick out for the duration. It is the same as the art of constructing a CV: you choose hobbies to detail that you know are acceptable and hopefully interesting (something reasonably risky like rock climbing balanced with something intellectual and cosy like poetry writing!) and you detail other, absolutely unrelated achievements. Being captain of the chess club or having published numerous articles on dog breeding won’t have prepared you for stock trading or designing bridges, but it does say a lot about who you are, marking you out as a cherry rather than a lemon: a ‘good’ agent, capable of advanced financial analysis, rather than a lemon, someone who will slack off from the first day and lead the company to bankruptcy.

Screening is one of the main strategies for combating adverse selection. It is often confused with signalling, but there is one main difference: in both, ‘good’ agents (the cherries of this world) are set apart from the ‘bad’ agents, or lemons, which are weeded out. In signalling, it is the uninformed agent (the victim of asymmetric information) who moves first, and comes up with a strategy to weed out the lemons. In signalling, however, it is the cherries, the informed agents, who make the first move to set themselves apart. The analysis of screening processes was put forward by Michael Spence in his article “Job Market Signaling”, 1973.

The best known theoretical explanation is that of competitive screening, put out by Rothschild and Stiglitz in 1977, in their article “Equilibrium in Competitive Insurance Markets”, which shows how insurance companies can get around the people taking advantage of adverse selection by offering different types of insurance options which will attract only the risk adverse. This is covered in more detail in insurance models, which are covered by the field analysing risk and uncertainty.

There are two basic types of screening: in the first, the ‘victim’ of asymmetric information simply sets about finding out as much as possible about the other agent. For example, carrying out a health check before offering health insurance, or running a background check before offering a job. This, aside from verging on the morally questionable, is often highly regulated in many countries. The second option is using game theory to set up the terms of a contract so that they only interest the cherries. Something as simple as copayment in case of a claim (for example, paying a small percentage of the claim amount in case a car is damaged) can help to weed out those who are not risk adverse.

“The Market for ‘Lemons’” is a key article written by George Akerlof in 1970, which aims to explain some of the market failures derived from imperfect information, in this case asymmetry. The paper itself is available on the bibliography and is characterised by its approachability and humour: as Akerlof himself stated, he lacked the mathematical dexterity to fully model the problem (although this has later been done by a host of economists, of which Hal Varian’s analysis is probably the most well known).

We are presented with the problem of someone who wants to buy a car, and decides to scout the used car market for a bargain. The market itself is composed of two types of cars: those that are being sold in good faith and those that are being sold off because they are known to be unreliable: these are the ‘lemons’ (in US slang). The seller, of course, knows how good the car is: they’ve had time to decide. The buyer, however, comes to the market blind: all they have to go on is the average quality of the used car market (which Akerlof defines as μ) and the price of the car, p. Obviously, all similar models of cars need to be sold for an identical price, p. If we sell below market price, be it a lemon or not, we are sending out signals that the car is worth less than the market price, leading buyers to assume it is a lemon. However, it is also logical to assume that, if we know the car we are selling is reliable, we will want more than the average market price, because that average includes a proportion of lemons.

Therefore, two prices are naturally set up: one for the lemons (say 1000$), and one for the good cars (sometimes called ‘cherries’ or ‘plums’), say 2500$. Below these prices, the seller will not be willing to let go of their vehicle and would derive greater utility from keeping it. Above this price, they would rather sell. The problem is, therefore, that competition will naturally drive down the price to one below which the honest seller are willing to go. Buyers will not be willing to pay 2500$ knowing that there is a probability that what they are buying is really a lemon: they need to reflect the premium related to the uncertainty derived from the asymmetrical information in a lower price. Lemon seller will be able to assume this drop in price, all the way down to 1000$, but honest sellers will not. Eventually, all that will be left in the market will be an orchard of lemons, but no cherries nor plums.

Analytically, if the quality of lemons is 0 and the quality of cherries is 1, and there are equal amounts of each, then

Buyers are willing to pay up to 3/2 q for a car with quality equal to q, and sellers are willing to sell for q (we are assuming p=q for simplicity’s sake, but the ratio could be any we decide to set). This means that, as

then

which means that no cars will be sold.