労働生産性と人間の移動性に関する確率過程模型と複雑系ネットワーク研究

目前的工作重点是进一步了解职业流动的决定因素。以前使用网络分析，观察了哪些职业吸引了最多的员工（例如，在美国使用PSID数据库的情况下，经理和行政人员），哪些职业交换了最多的劳动力等，简而言之，不同时期来自不同国家的职业流动网络的拓扑结构。目前，我们使用重力模型来估计职业流动，即在一定时间内在两个职业之间移动的人数，并创建人工职业流动网络（OMN）。进一步，我们比较了人工OMN与经验OMN的性质。我们假设在职业流动和重力之间有一个类比，因为从一个职业到另一个职业的流动的大小主要取决于两个职业的雇员数量（质量）和两个职业之间的距离，这两个职业被认为是运动的限制。将重力模型应用于高等教育毕业生数据库，结果表明，高校毕业生的职业流动与高校毕业生在各职业总就业中所占的比重呈负相关，与工资呈正相关。

The current work focused on further understanding the determinants of the occupational mobility. Previously employing the network analysis it was observed which occupations that attract the most employees (e.g. Managers and administrators in the case USA using PSID database), which occupations exchange the most labour force etc., in short the topological structure of the occupational mobility network from different countries in different time periods. Currently, using the gravity model we estimate the occupational flow, i.e. the number of people that moving between two occupations in a certain time frame and create artificial occupational mobility networks (OMN). Further we compare the properties of this artificial OMN with the empirical OMN. We assume that there is an analogy between the occupational flows and the force of gravity, as the magnitude of flows from one occupation to another is dependent mainly on the number of employees in both occupations (masses) and on a measure of distance between the two occupations regarded as constraints to movement. Apply the gravity model on the higher education graduates database the results show that the occupational flows of university graduates are negatively dependent of the share of university graduates in total employment for each occupation and positively dependent on wages.