Abstract
This study investigates the contribution of R&D spillover to firm performance in high-tech clusters, and attempts to explain the rapid growth of the Hsinchu Science Park (HSP). R&D spillover is distinguished into knowledge spillover and rent spillover. Knowledge spillover variables are constructed based on Jaffe (1986), while rent spillover variables are developed according to the method proposed by Terleckyj (1974). Total knowledge spillover contains local and external knowledge spillovers, which are constructed based on the clustering result. Additionally, nano knowledge spillover variables are developed using the distribution of nano patents. The construction of pseudo input-output table in firm level is the main contribution of this thesis. Input and output rent spillovers which from potential suppliers and customers and the R&D absorptive capacities are considered together to estimate their influence on HSP firm performance. For the analysis of knowledge spillover effects, the data set consists of 130 HSP high tech firms over the period 2003-2005. However, the data set for analyzing rent spillover effects is collected form 77 HSP IC and optoelectronics firms in 2005. This study adopted panel data models to test the impact of knowledge spillovers. Also, 2SLS is used to generate robust estimate. For the influence of rent spillovers, ordinary least squares is the method applied in this work. Empirical results of this study are summarized as follows. Total, local, external, and nano knowledge spillover stocks positively influence HSP firm performance. However, the significances of these knowledge spillover effects depend heavily on the adoption of econometric methods. Nevertheless, the rent spillover effects are obscure.