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創新對企業生產績效之影響 -以中國高新技術產業為例 / Innovation and firm performance- firm level evidence from high-tech industries in China鄭林譽, Cheng, Lin Yu Unknown Date (has links)
研究目的-中國從1989年就開始實行火炬計畫並投資了大量的資金在高新技術產業,在過去的三十年間,中國的經濟發展也快速地成長,本研究旨在透過中國高新技術產業的統計資料來估計其創新對企業生產績效之影響。
研究方法-本研究使用來自中國國家統計局的全國企業專利資料與全國企業績效資料並結合兩者為一資料庫,運用Olley and Pakes 生產力模型來估計生產力,最後利用追蹤資料的迴歸模型來進行創新對企業生產績效的實證研究。
發現-本研究把企業不分類別的專利總數當成依變數與企業績效如產出、生產力、出口等自變數進行追蹤資料的迴歸分析後,其呈現正向且顯著的關係。進一步把專利總數分成發明專利數、實用新型專利數與設計專利數三類依變數並與企業績效如產出、生產力、出口等自變數進行追蹤資料的迴歸分析後,其均呈現正向且顯著的關係。此外,本研究亦發現企業進出市場行為會反向的影響專利數與企業績效之間的關係。
價值-過去有關於創新與企業績效的研究通常都是以整體製造業的規模來進行分析並提供一個總體的估計,因此本研究針對中國製造業中的高新技術產業進行分析以提供對於中國的火炬計畫成效更詳盡的評估,並對未來有關高新技術產業的研究提供初步的研究方向。 / Purpose – China has started a Torch program and invested huge amount of money in high-tech industries since 1989. During last three decades, China’s economy also rapidly grows. According to above conditions, this study aims to create a reliable estimation for confirming the relationship between innovation and firm performance from the evidence of China’s high-tech industries.
Design/Methodology/Approach – By building a sample composed of the patent stock and firm-level performance data from China’s National Bureau of Statistics (NBS), we apply Olley and Pakes method to estimate the productivity and use the regression model for panel data to do the empirical study.
Findings – Both the total patent stock and the patent stock of three different categories maintain a positive and significant relationship toward firm performance such as output, productivity and exports. On the other hand, the finding also implies a negative effect of firm dynamics on the relationship between patent stock and firm performance.
Value – The researches related to innovation and firm performance in the past are usually conducted with the sample of whole manufacturing industries’ data and report an overall estimation. However, this study focuses on high-tech industries to provide a more detailed evaluation for China’s innovative efforts of Torch program and carves out a direction for future research on high-tech industries.
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