Foreign Direct Investment and Wage Inequality: Evidence from the People\u27s Republic of China

Based on theoretical analysis of effects of foreign direct investment (FDI) on the wage gap between foreign firms and domestic firms in the host country, we use data from Chinese Industrial Enterprises Database to measure these effects. Theoretical results show that the wage gap between foreign firms and domestic firms in the host country caused by the FDI labor transfer effect and technology spillover effect tends to increase then decrease, which implies an inverted U curve track. The empirical results show that the FDI has significant effects on the wage gap in the People’s Republic of China (PRC) during the observed time period. The contribution of the FDI to change of the wage gap is above 10%, which is in the second position among all observed factors. From the overall point of view, the contribution of the FDI tends to decrease. The reason is that the wage gap caused by the FDI has stepped into the decreasing stage. This means the wage gap between foreign firms and domestic firms currently has been on the latter part of the inverted U curve. The Chinese government should expand fields for FDI so as to decrease the wage gap between foreign firms and domestic firms. This policy implication should be helpful for the PRC to step over the “middle-income trap”

A Multi-Agent System for E-Business Processes Monitoring in a Web-Based Environment

In this paper, we present a multi-agent system MAGS for the e-business processes monitoring in a web-based environment. We classify the types of agents in MAGS by their monitoring capabilities. An algorithm is given to explain the mechanism of supervising and controlling the execution of business processes. An abstract model of alerts, which can give warnings of infringement on business policies, is proposed. Access control can also be realized by MAGS, which manifests in delivering different view of the business process to different roles participate in it. Being successfully adopted in a customer service management system, MAGS has been proven flexible and practical

Semi-Supervised Self-Taught Deep Learning for Finger Bones Segmentation

Segmentation stands at the forefront of many high-level vision tasks. In this study, we focus on segmenting finger bones within a newly introduced semi-supervised self-taught deep learning framework which consists of a student network and a stand-alone teacher module. The whole system is boosted in a life-long learning manner wherein each step the teacher module provides a refinement for the student network to learn with newly unlabeled data. Experimental results demonstrate the superiority of the proposed method over conventional supervised deep learning methods.Comment: IEEE BHI 2019 accepte