Exploring the Influencing Factors of FDI in China’s Internal Regions:Evidence From Taiwan

This study aims to explore the influencing factors of the location determinants of Taiwan’s foreign direct investments(FDIs)in China’s internal regions.This study proposes three models to examine the influencing factors of Taiwan’s FDIs in China by combining the location advantages of the host country,the economy and politics of the home country,and network relationship in bilateral and regional trade agreements:First,analyze the location distribution of Taiwan’s FDIs in China’s six regions;Second,analyze the location determinants of Taiwan’s main sectors’(manufacturing and service)FDIs in China;Third,analyze the location determinants of Taiwan’s high-tech industries’FDIs in China’s six regions.The estimated results show that bilateral trade agreement seems to have positive effects on Taiwan’s FDIs in China,while regional trade agreement seems to have negative effects.In Taiwan’s political form,the ruling party with characteristics of political defense against China seems to have negative effects on most of Taiwan’s industries(except manufacturing)and the overall FDIs in China.In terms of China’s relevant economic factors,the main determinants of Taiwan’s high-tech industries’FDIs in the major regions of Eastern and South Central of China are infrastructure and high-level human capital resources.China’s GDP growth and increase in unit labor cost have negative effects on FDIs in most regions of China.This implies that the advantage of low wages in China Factory may gradually decline.This study provides some references and implications for future research and policy makers for the analysis of relevant influencing factors of FDI.

Object Tracking and Tracing:Hidden Semi-Markov Model Based Probabilistic Location Determination

The enhancement of radio frequency identification(RFID) technology to track and trace objects has attracted a lot of attention from the healthcare and the supply chain industry.However,RFID systems do not always function reliably under complex and variable deployment environment.In many cases,RFID systems provide only probabilistic observations of object states.Thus,an approach to predict,record and track real world object states based upon probabilistic RFID observations is required.Hidden Markov model(HMM) has been used in the field of probabilistic location determination.But the inherent duration probability density of a state in HMM is exponential,which may be inappropriate for modeling of object location transitions.Hence,in this paper,we put forward a hidden semi-Markov model(HSMM) based approach for probabilistic location determination. We evaluated its performance comparing with that of the HMM-based approach.The results show that the HSMM-based approach provides a more accurate determination of real world object states based on observation data.