Moral Hazard: From Insurance Fraud to the 2008 Financial Meltdown

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A drop-proof phone case can make you more careless with your phone, and that small shift in behavior is the seed of one of the most debated ideas in economics. Moral hazard arises when someone takes on more risk because another party will bear the cost. This episode traces the term from 17th century insurance ledgers, where it meant outright fraud by a policyholder of bad character, to the 1960s, when economist Kenneth Arrow recast it as the rational, predictable result of shifting risk from one party to another.

From there the story scales up. Copays and deductibles turn out to be tools for keeping skin in the game, a response to the principal-agent problem and the hidden actions no insurer or employer can monitor. The 1998 rescue of Long-Term Capital Management, the so-called Greenspan put, and the private label mortgage machine of the 2000s show what happens when nobody in the chain holds the risk long enough to care. The episode also weighs the counterarguments: Lehman Brothers went bankrupt, executives lost fortunes, and flawed models may explain as much as bailout expectations.

• English insurers in the 19th century used the word moral literally, fearing the owner of a failing warehouse who buys fire coverage and then knocks over a lantern.
• Ex-ante moral hazard changes behavior before a loss, like smoking in bed with fire insurance, while ex-post moral hazard appears afterward, as when a fully covered patient demands the most expensive treatment.
• Long-Term Capital Management ran at 25 to 1 with contracts across Wall Street, and New York Fed head William J. McDonough organized its rescue over the fierce criticism of Paul Volcker.
• Agency securitizers such as Fannie Mae kept the default risk, but in private label deals brokers, lenders, and investment banks collected fees and passed the loans along.
• A 2017 Basel Committee report flagged fair value accounting under IFRS 9 and 13, which let banks value frozen assets with their own internal models, in effect grading their own homework.