There are currently more than 85,000 confirmed cases of COVID-19 in at least 60 countries. Yet, we know very little about this pathogen, except that everyone is worried. And, with the number of cases rising each day, intensifying concerns probably will lead many people to behave in ways that undermine economic activity. They will shy away from places where the virus can be transmitted. That means avoiding mass transit, schools, and workplaces.
Moreover, many people will stay away until they are confident that the disease is manageable. That confidence probably requires an effective treatment, a very low likelihood of infection, or both. Not surprisingly, many observers are reducing their projections for economic growth this year, while financial market participants anticipate easier monetary policy to cushion the shock.
The challenge of re-establishing public confidence that it is safe to venture out bears striking similarity to the one that authorities face in stemming a bank run. Our ability to identify and quarantine people infected with COVID-19 is analogous to our ability to recognize and isolate a bank bordering on insolvency. This and other similarities suggest that the means we use to control bank runs also may be useful in managing the economic consequences of an emerging pandemic like COVID-19….
The two biggest U.S. P2P lenders, Prosper and Lending Club, started operations in 2005 and 2007, respectively. Over the past decade, their business has grown so that they now originate more than $10 billion in loans per year. The public information provided by Lending Club gives us an opportunity to judge how they are doing. At first, P2P lending returns appear remarkably high (adjusted for volatility), but growing evidence of adverse selection highlights how difficult it will be to sustain growth.
When we last wrote about P2P lending, we suggested that profitability might be a consequence of the booming economy (see here and here). We concluded that one would need to see performance in a recession before judging P2P’s long-run potential. That is, when you are making consumer loans, it is relatively easy to make money as the unemployment rate falls from 10% to 3.5%. However, profitability over the course of an entire business cycle, including periods when joblessness is rising, is an entirely different story.
The term moral hazardoriginated in the insurance business. It was a reference to the need for insurers to assess the integrity of their customers. When modern economists got ahold of the term, the meaning changed. Instead of making judgments about a person’s character, the focus shifted to incentives. For example, a fire insurance policy might limit the motivation to install sprinklers while a generous automobile insurance policy might encourage reckless driving. Then there is Kenneth Arrow’s original example of moral hazard: health insurance fosters overtreatment by doctors. Employment arrangements suffer from moral hazard, too: will you shirk unpleasant tasks at work if you’re sure to receive your paycheck anyway?
Moral hazard arises when we cannot costlessly observe people’s actions and so cannot judge (without costly monitoring) whether a poor outcome reflects poor fortune or poor effort. Like its close relative, adverse selection, moral hazard arises because two parties to a transaction have different information. This information asymmetry manifests itself in two ways. Where adverse selection is about hidden attributes, affecting a transaction before it occurs, moral hazard is about hidden actions that have an impact after making an arrangement.
In this post, we provide a brief introduction to the concept of moral hazard, focusing on how various aspects of the financial system are designed to mitigate the challenges it causes....
Information is the basis for our economic and financial decisions. As buyers, we collect information about products before entering into a transaction. As investors, the same goes for information about firms seeking our funds. This is information that sellers and fund-seeking firms typically have. But, when it is too difficult or too costly to collect information, markets function poorly or not at all.
Economists use the term adverse selection to describe the problem of distinguishing a good feature from a bad feature when one party to a transaction has more information than the other party. The degree of adverse selection depends on how costly it is for the uninformed actor to observe the hidden attributes of a product or counterparty. When key characteristics are sufficiently expensive to discern, adverse selection can make an otherwise healthy market disappear.
In this primer, we examine three examples of adverse selection: (1) used cars; (2) health insurance; and (3) private finance. We use these examples to highlight mechanisms for addressing the problem....
