HKS Authors

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Abstract

Governments often require that products carry warnings to inform people about risks. The warnings approach, as opposed to the command and control approach to risk regulation, functions as a decentralized regulatory mechanism that empowers individuals to make decisions that take into account their own circumstances and preferences. Thus, individuals will be aware of the risks and the value of taking precautions, and they may avoid a product that others consume if they find the risk unacceptable. Ideally, warnings would allow individuals to assess both their personal level of risk and the benefits they will receive from another unit of consumption. Then those receiving positive expected benefits will consume more; those receiving negative net benefits will curtail their consumption. Only Pangloss would be happy with the current warning system. It fails miserably at distinguishing between large and small risks; that is to say between wolves and puppies. Such a system is of little value, since people quickly learn to ignore a warning, given that puppies, which pose little danger, are many times more plentiful than wolves. When a wolf is truly present, people all too often ignore the warning, having been conditioned to believe that such warnings rarely connote a serious threat. We illustrate the clumsy-discrimination issue with examples related to cigarette labeling, mercury in seafood, trans fat in food, and California’s Proposition 65. We argue that the decision to require a warning and the wording of the warning should be designed in a manner that will lead consumers to roughly assess their accurate risk level, or to at least distinguish between serious and mild risks. Empowering individuals to make appropriate risk decisions is a worthwhile goal. The present system fails to provide them with the requisite information.

Citation

Robinson, Lisa A., W. Kip Viscusi, and Richard Zeckhauser. "Efficient Warnings, Not “Wolf or Puppy” Warnings." HKS Faculty Research Working Paper Series RWP16-033, September 2016: Revised November 2016.