Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Economics, 2012. / Cataloged from PDF version of thesis. / Includes bibliographical references (p. 135-138). / This thesis studies the impact of private information on the existence of insurance markets. In the first chapter, I study the case of insurance rejections. Across a wide set of non-group insurance markets, applicants are rejected based on observable, often high-risk, characteristics. I explore private information as a potential cause by developing and testing a model in which agents have private information about their risk. I derive a new no-trade result that can theoretically explain how private information could cause rejections. I use the no-trade condition to generate measures of the barrier to trade private information imposes. I develop a new empirical methodology to estimate these measures that uses subjective probability elicitations as noisy measures of agents' beliefs. I apply the approach to three non-group markets: long-term care (LTC), disability, and life insurance. Consistent with the predictions of the theory, in all three settings I find significant evidence of private information for those who would be rejected; I find that they have more private information than those who can purchase insurance; and I find that it is enough to cause a complete absence of trade. This presents the first empirical evidence that private information leads to a complete absence of trade. In the second chapter, I show that private information explains the absence of a private unemployment insurance market. I provide the empirical evidence that a private UI market would be afflicted by private information and suggest the amount of private information is sufficient to explain a complete absence of trade. I present evidence a private market would still not arise even if the government stopped providing unemployment benefits. Finally, in the third chapter I use the empirical and theoretical tools developed in the first chapter to explore the impact of an adjusted community rating policy that would force insurance companies to only price based on age. My results suggest such a policy would completely unravel the LTC insurance market. Not only would welfare not be improved for those who are currently rejected, but the regulation would prevent the healthy from being able to purchase long-term care insurance. / by Nathaniel Hendren. / Ph.D.
| Identifer | oai:union.ndltd.org:MIT/oai:dspace.mit.edu:1721.1/72830 |
| Date | January 2012 |
| Creators | Hendren, Nathaniel |
| Contributors | Daron Acemoglu and Amy Finkelstein., Massachusetts Institute of Technology. Dept. of Economics., Massachusetts Institute of Technology. Dept. of Economics. |
| Publisher | Massachusetts Institute of Technology |
| Source Sets | M.I.T. Theses and Dissertation |
| Language | English |
| Detected Language | English |
| Type | Thesis |
| Format | 138 p., application/pdf |
| Rights | M.I.T. theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. See provided URL for inquiries about permission., http://dspace.mit.edu/handle/1721.1/7582 |
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