Foreign direct investment in China : a genetic algorithm approach | |
| Author | Qi, Chun |
| Call Number | AIT Diss no.SM-02-08 |
| Subject(s) | Investments, Foreign--China Genetic algorithms |
| Note | A dissertation submitted in partial fulfillment of the requirement for the degree of Doctor of Philosophy, School of Management |
| Publisher | Asian Institute of Technology |
| Abstract | Inward FDI is not occurring in China or its regions in the same manner as other countries. Therefore, the central question emerges: what are the major determinants of FDI flows into different Chinese provinces? It is necessary to study the determinants of FDI inflow to China to assess which factors contribute to making certain provinces attractive. This dissertation proposes a genetic algorithm (GA) approach as an analytical tool, with a carefully defined fitness function as a variable selection algorithm, and the multiple discriminant analysis method as a parameter evaluation method used in determinant analysis of inward FDI. GA is a search procedure that uses random choice as a method for guiding a highly exploitative search through the coding of a parameter space. Genetic algorithms are based on the observation that the evolution of a natural species is very efficient at adapting to changing environments. The objectives of this study are two-fold: (1) to develop a GA model to classify FDI in China to successful or unsuccessful inflow groups; and (2) to highlight the implications of the study for policy makers and researcher on FDI in China. To apply the GA approach, the analysis of FDI in China was converted into a classification problem, and the dependent variable was classified into successful or unsuccessful inflow groups. The study proposed a new fitness function and considers the accuracy rate and the relationships between the independent variables and the dependent variable as classification criteria. In this study, the proposed GA method will be compared with multiple discriminant analysis. Sensitivity analysis was conducted to test the performance of the proposed GA method and to determine whether the sample size has an influence on the results. An industry-based analysis was also carried out. The results show that the GA approach is better than multiple discriminant analysis, and 14 determinants of inward FDI have been selected by applying the proposed fitness function. The proposed fitness function is found to be better than the traditional fitness function for classifying FDI into successful or unsuccessful inflow groups by providing a higher classification accuracy rate using less decision variables. In the subsample analyses, it was found that when the sample size is reduced, the number of decision variables increases accompanied by a reduction in accuracy rates. The study also found that in the proposed fitness function, when the weight factor c1 is equal to 0.3 and c2 is equal to 0.8, the selected subset of decision variables provided a higher accuracy rate. In addition, it used the least number of decision variables. One implication of the results found in this study is that for policy makers, given scarcity of resources and the need to promote FDI, the proposed GA method can provide a more efficient way of concentrating efforts on fewer variables that are found to be important determinants for determining "successful" FDI inflow. Another possible application of the group relationship among independent variables could be for the Chinese government to focus on building an industrial agglomeration to attract FDI inflows. Because industrial agglomeration can offset information and search costs, coordination cost and substantial risks e.g. legal risks or political risks, it would appear that existing MNEs at a location can help service communication, transport, and other needs that will be important factors for location consideration. |
| Year | 2002 |
| Type | Dissertation |
| School | School of Management |
| Department | Other Field of Studies (No Department) |
| Academic Program/FoS | Master of Business Administration (MBA) (Publication code=SM) |
| Chairperson(s) | Tang, J.C.S.; |
| Examination Committee(s) | Swierczek, F.W.S.;Voratas Kachitvichyanukul;Beng, Chew Soon ; |
| Scholarship Donor(s) | -; |
| Degree | Thesis (Ph.D.) - Asian Institute of Technology, 2002 |