Peter Schmidt

E-Mail: EmailAddress: hidden: you can email any NBER-related person as first underscore last at nber dot org
Institutional Affiliation: Hochschule Bremen, Germany

NBER Working Papers and Publications

August 2001"Hall of Fame" Voting: The Econometric Society
with Daniel S. Hamermesh: w8435
We examine the determinants of election as Fellow of the Econometric Society, an example of voting within a group to confer honor on some members and perhaps achieve additional status for the entire group. Using data from annual elections from 1990-2000, we find that objective measures of quality help determine elections, as do attestations of quality by previous honorees. What one might view as ascriptive characteristics, such as candidates' subspecialty or institutional affiliation/location, also affect their electoral success.

Published: "The Determinants of Econometric Society Fellows Elections" Hamermesh, Daniel S.; Schmidt, Peter; Econometrica, January 2003, v. 71, iss. 1, pp. 399-407

December 1995Sampling Errors and Confidence Intervals for Order Statistics: Implementing the Family Support Act
with William C. Horrace, Ann Dryden Witte: w5387
The Family Support Act allows states to reimburse child care costs up to the 75th percentile of local market price for child care. States must carry out surveys to estimate these 75th percentiles. This estimation problem raises two major statistical issues: (1) picking a sample design that will allow one to estimate the percentiles cheaply, efficiently and equitably; and (2) assessing the sampling variability of the estimates obtained. For Massa- chusetts, we developed a sampling design that equalized the standard errors of the estimated percentiles across 65 distinct local markets. This design was chosen because state administrators felt public day care providers and child advocates would find it equitable, thus limiting costly appeals. Estimation of standard errors for the sample 75th...

Published: Journal of Economic and Social Measurement, Vol. 24 (1998): 181-207.

November 1987Predicting Criminal Recidivism Using "Split Population" Survival Time Models
with Ann Dryden Witte: w2445
In this paper we develop a survival time model in which the probability of eventual failure is less than one, and in which both the probability of eventual failure and the timing of failure depend (separately) on individual characteristics. We apply this model to data on the tiring of return to prison for a sample of prison releasees, and we use it to make predictions of whether or not individuals return to prison. Our predictions are more accurate than previous predictions of criminal recidivism. The model we develop has potential applications in economics: far example, it could tie used to model the probability of default and the timing of default on loans.

Published: Journal of Econometrics, Vol. 40, No. 1, (January 1989). citation courtesy of

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