Labor market entry and earnings dynamics: Bayesian inference using mixtures‐of‐experts Markov chain clustering
利用大型行政数据，识别出年轻劳动力市场进入者收入状态转换的四种不同模式，并考察进入时劳动力市场条件对所属模式概率的影响，采用马尔可夫链聚类和贝叶斯方法进行估计。
SUMMARY This paper analyzes patterns in the earnings development of young labor market entrants over their life cycle. We identify four distinctly different types of transition patterns between discrete earnings states in a large administrative dataset. Further, we investigate the effects of labor market conditions at the time of entry on the probability of belonging to each transition type. To estimate our statistical model we use a model‐based clustering approach. The statistical challenge in our application comes from the difficulty in extending distance‐based clustering approaches to the problem of identifying groups of similar time series in a panel of discrete‐valued time series. We use Markov chain clustering, which is an approach for clustering discrete‐valued time series obtained by observing a categorical variable with several states. This method is based on finite mixtures of first‐order time‐homogeneous Markov chain models. In order to analyze group membership we present an extension to this approach by formulating a probabilistic model for the latent group indicators within the Bayesian classification rule using a multinomial logit model. Copyright © 2011 John Wiley & Sons, Ltd.