Unemployment Benefits and Labor Market Transitions: A Multinomial Logit Model with Errors in Classification
利用当前人口调查中的验证数据，将标准多项Logit模型推广到允许分类误差导致的虚假事件，并研究失业救济金对失业到就业转换及退出劳动力市场的影响，发现纠正分类误差后失业救济金延长失业期的效应更强。
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.