Unemployment Benefits and Labor Market Transitions: A Multinomial Logit Model with Errors in Classification
This paper utilizes validation data on survey response error in the Current Population Survey to generalize the standard multinomial logit model to allow for spurious events that result from classification error. The authors' basic approach could be used with other stochastic models of discrete events as well. They illustrate their algorithm by studying the effect of unemployment insurance on transitions from unemployment to employment and on labor-force withdrawal. Their results confirm earlier work suggesting that unemployment insurance lengthens unemployment spells and show that correcting for classification error strengthens the apparent effect of unemployment insurance on spell durations. Copyright 1995 by MIT Press.
Volume (Year): 77 (1995)
Issue (Month): 2 (May)
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