Labor Market Dynamics

宏观经济分析传统上侧重于对高度聚合的经济时间序列的规律性进行核算。例如，在劳动力市场的背景下，通常的做法是设法解释总就业或失业的变动。众所周知，高度汇总的数据掩盖了大量的经济活动；即使在总就业变化很小的时期，个别工人的就业状况和各个机构的就业分配也会发生很大的变化。标准的宏观经济模型从这些变化中抽象出来。在某些问题上，这种对潜在劳动力市场流动的抽象可能是无害的，但在其他情况下，它肯定是一个严重的缺点。对许多劳动力市场政策的分析显然最好是在考虑劳动力市场流动的汇总模型中进行。事实上，越来越多的实证和理论文献关注这些潜在的劳动力市场流动，这些模型已被用于开始解决各种政策的影响。然而，尽管取得了进展，仍有许多工作要做。本研究的目标是加强这一文献的理论和实证分支之间的联系，以便更好地理解哪些模型特征在解释数据中的关键规律时在数量上是重要的。这一努力也应该使经验和理论问题分别变得尖锐，因为面对理论与数据的过程将隔离那些经验关系，这些关系具有很大的能力来区分模型特征，因此值得更仔细的审查，以及隔离那些在说明经验规律方面最重要的模型特征，从而指导未来的理论扩展。粗略地说，人们可以确定两种产生劳动力市场流动的过程。一种是跨机构重新安排就业岗位，另一种是跨现有就业岗位重新安排工人。实证研究表明，这两个过程在计算劳动力市场流动方面都很重要。尽管前者已经被研究过，但对于哪些模型特征在定量上对匹配工作再分配的周期性特性很重要，我们仍然知之甚少。第二个过程很少受到分析研究。本项目将这一特征纳入了通常用于解决劳动力市场流动问题的匹配模型类别。欧洲和美国之间不同的劳动力市场法规可能会扭曲第二个过程，该项目还将调查限制工人工资差异的政策的分配和福利后果。

Macroeconomic analysis has traditionally focused on accounting for regularities in highly aggregated economic time series. In the context of the labor market, for example, it is common practice to seek to account for movements in aggregate employment or unemployment. It is well known that highly aggregated data mask a great deal of economic activity; even in periods when aggregate employment changes very little, there are very large changes in the employment status of individual workers and in the distribution of employment across individual establishments. Standard macroeconomic models abstract from these changes. For some issues, this abstraction from underlying labor market flows may be innocuous, but in other contexts it is surely a serious shortcoming. The analysis of many labor market policies are clearly best carried out in aggregate models which do account for labor market flows. Indeed, there are growing empirical and theoretical literatures that focus on these underlying labor market flows, and these models have been used to begin addressing the effects of various policies. Despite the advances that have been made, however, much remains to be done. The goal of this research is to sharpen the connection between the theoretical and empirical branches of this literature so as to better understand what model features are quantitatively important in accounting for the key regularities in the data. This effort should also sharpen both empirical and theoretical issues individually, since the process of confronting theory with data will isolate those empirical relationships which have a lot of power to discriminate between model features, and hence are worthy of closer scrutiny, as well as isolate those model features which are most significant in accounting for empirical regularities and hence guide future theoretical extensions. Loosely speaking, one may identify two processes that give rise to labor market flows. One is the rearranging of employment positions across establishments, and the other is the rearrangement of workers across existing employment positions. Empirical work has shown that both of these processes are important in accounting for labor market flows. Though the former has been studied, it remains true that not that much is known of what model features are important quantitatively to match the cyclical properties of job reallocation. The second process has been subject to little analytical study. This project brings this feature into the class of matching models commonly used to address labor market flows. Labor market regulations which differ between Europe and the US are likely to distort this second process and this project will also investigate the allocational and welfare consequences of policies which restrict wage differentials across workers.