“ Dropping Out of Social Security ” Kent

The liability facing a pay-as-you-go social security system can be calculated in several ways. The exact liability measure chosen can significantly affect the conversion of a public pay-as-you-go system to a system based on individually funded accounts. Most conversions, including that which took place in Chile, as well as in many plans to convert the US system, assume the largest measure, known as the “shutdown liability.” That measure pays many workers who have contributed to the public system more money than the public system is actually worth to them, thereby placing a larger burden on future generations. Other liability measures, though, are hard to implement due to an information asymmetry between the government and individuals about an individual’s skill level. This paper demonstrates that a very simple reform plan –– simply letting people drop out of social security –– generates a truthful revelation equilibrium in which agents reveal private information about their skill level. The new assumed liability measure can be as little as half of the shutdown liability as the new measure more accurately assigns a liability for each individual based on their true value of remaining in social security. A smaller liability, therefore, is passed to future generations which also generates quicker transition paths. Moreover, interestingly, the drop out method also does a better job of protecting the welfare of the initial elderly when general revenue is used to pay for the transition. Simulation evidence is provided using a large-scale lifecycle simulation model that allows for heterogeneous skill levels. The evidence demonstrates the importance of the dropping out approach relative to the traditional conversion method that assumes the shutdown liability. Authors’ Acknowledgment The views expressed in the paper do not necessarily reflect the views of the International Monetary Fund. Helpful comments were received from seminar participants at The University of Virginia, Oxford University and The Wharton School. Very helpful comments were received from Laurence Kotlikoff and Olivia Mitchell. Kent Smetters participation was sponsored by a grant from the Social Security Administration (MRRC-UM01-01).