| dc.contributor.advisor | 毛維凌 | zh_TW |
| dc.contributor.advisor | Mao, Wei-Ling | en_US |
| dc.contributor.author (Authors) | 劉惠珍 | zh_TW |
| dc.contributor.author (Authors) | Liu, Hui-Chen | en_US |
| dc.creator (作者) | 劉惠珍 | zh_TW |
| dc.creator (作者) | Liu, Hui-Chen | en_US |
| dc.date (日期) | 2005 | en_US |
| dc.date.accessioned | 14-Sep-2009 12:46:13 (UTC+8) | - |
| dc.date.available | 14-Sep-2009 12:46:13 (UTC+8) | - |
| dc.date.issued (上傳時間) | 14-Sep-2009 12:46:13 (UTC+8) | - |
| dc.identifier (Other Identifiers) | G0929210471 | en_US |
| dc.identifier.uri (URI) | https://nccur.lib.nccu.edu.tw/handle/140.119/32030 | - |
| dc.description (描述) | 碩士 | zh_TW |
| dc.description (描述) | 國立政治大學 | zh_TW |
| dc.description (描述) | 行政管理碩士學程 | zh_TW |
| dc.description (描述) | 929210471 | zh_TW |
| dc.description (描述) | 94 | zh_TW |
| dc.description.abstract (摘要) | 本文透過追蹤資料 (panel data)模型，被解釋變數以平均廠商生產力當作衡量指標，利用台灣經濟研究院於2001-2003年對250家生物科技廠商追蹤調查報告，來實證群聚及研究發展 (R＆D)投入對台灣生物科技產業是否為該產業帶來生產力外溢效果 (spillover effect)之研究；藉此可探討群聚與研究發展對台灣生物科技產業產生之外溢效果，同時並可比較分析歐美日對生物科技產業推展之政策，本研究結果進一步在政府擬訂生物科技產業政策上或能提供些許參考。 生物科技產業依其產業範疇分成醫藥品、醫療器材、特用化學品與食品、農業生物技術、環保及生物技術服務業等5項次產業。本文建立追蹤資料模型的固定效果 (fixed effects) 和隨機效果 (random effects)群聚及研究發展外溢效果之實證模型，然後開始進行參數估計及假設檢定，並加以分析，實證結果顯示：一、生技產業不存在固定效果，存在的是隨機效果，表示隨機干擾項 與投入變數 不具相關性。二、群聚與研究發展對國內生物技術產業所引發的外溢效果確屬存在，惟群聚外溢效果對生產力影響不顯著，但研究發展外溢效果對生產力卻具正面影響而且顯著。三、生技產業隨機效果模型與移動平均動態調整Da Silva模型估計結果一致。四、研究發展投入存在有不穩定現象，可見研究發展投入並不是對所有生技產業均有正面貢獻，諸如：特化與食品、農業生技及環保生技服務業有利於自行從事研發活動；而醫藥品業及醫療器材業則可能以與其他生技廠商或研究機構建立策略聯盟關係，如合作研發、研發活動外包 (outsourcing)及購併方式取得技術較有利。五、研究發展外溢效果最高的是特化與食品，其次是環保生技服務業，最低的則是醫療器材業；群聚外溢效果最高的是醫藥品業，其次是醫療器材業，最低的是特化與食品。六、若加以控制產業內研究發展資本存量變數，則研究發展投入與產業間研究發展資本存量對廠商生產力影響，具顯著外溢效果，且該等變數估計結果顯示其對廠商生產力的影響更大。 關鍵字：外溢效果、群聚、研究發展 | zh_TW |
| dc.description.abstract (摘要) | This empirical study examines the spillover effect of biotech industry clusters and R&D in Taiwan between 2001 and 2003. A sample of 250 biotechnology firms in Taiwan is used for the analysis. The biotechnology industry is classified into pharmaceutical, medical devices, specialty chemical and food, agricultural biotechnology, environmental biotechnology and service industries. To tell the difference between fixed effects and random effects panel data model of clustering and R&D, this study employs several estimation methods and tests some useful hypotheses. The results of the study show that the biotechnology industry in Taiwan does exhibit random effects, but no fixed effects. This implies that regressors are not correlated with the effect. In addition, clustering and R&D variation can affect productivity of Taiwan’s biotechnology firms. The R&D influence on the productivity of biotechnology firms is positive and significant; however clustering does not have significant impact, a result similar to that between the Fuller-Battese estimation and the moving average Da Silva estimation. R&D investment influence on the productivity of biotechnology firms is not stable in Taiwan. The specialty chemical and food, agricultural biotechnology, environmental biotechnology and service industries, for example, exhibit positive results from independent R&D. The pharmaceutical and medical devices industries, on the other hand, could benefit from building strategic R&D alliances with firms or institutes for abroad as well as through R&D outsourcing and M&A (merger and acquisition). The specialty chemical and food industry has the highest R&D spillover effect, followed by the environmental biotechnology and service industry, and last is the medical devices industry. In terms of spillover effect from clustering, the pharmaceutical industry benefits most followed, in descending order by the medical devices industry, and the specialty chemical and food industry. This study illustrates that the estimator of R&D and between industries R&D are also much larger with Fuller-Battese estimation when the control for inter-industry R&D variable is excluded. Keywords: spillover effect, clusters, R&D | en_US |
| dc.description.tableofcontents | 第一章 緒論……………………………………………………………………………….1 第一節 前言………………………………………………………………………… 1 第二節 研究動機…………………………………………………………………….3 第三節 研究目的…………………………………………………………………… 3 第四節 研究架構…………………………………………………………………….5 第二章 文獻回顧………………………………………………………………………….7 群聚與研究發展外溢效果相關文獻………………………………………………….7 第三章 歐美日國家與台灣生物科技產業現況分析…………………………………….11 第四章 實證模型與資料來源…………………………………………………………….21 第一節 實證模型…………………………………………………………………….21 第二節 估計方法與檢定…………………………………………………………….30 第三節 資料來源…………………………………………………………………….31 第四節 實證結果…………………………………………………………………….35 第五章 結論與建議……………………………………………………………………….50 參考文獻…………………………………………………………………………………….53 圖次 圖1研究流程圖…………………………………………………………………………….6 圖2台灣生物科技產業群聚與研究發展外溢效果之研究架構………………………..6-1 表次 表1 主要國家的R＆D支出之比較(2002年)……………………………………………58表2 群聚與研究發展外溢效果主要模型之研究範疇…………………………………. 10 表3 2003年全球生技廠商現況………………………………………………………….59 表4 2003年我國生技產業現況…………………………………………………………..59 表5 台灣生技園區概況…………………………………………………………………..60 表6 各國生技產業策略比較……………………………………………………………..61表7 生技產業投入產出係數矩陣………………………………………………………..62 表8 追蹤資料模型中變數之定義與衡量方式…………………………………………..26 表9 生技產業分類範籌…………………………………………………………………..63 表10 生技產業生產力與主要要素投入之平均值………………………………………33 表11 生技產業廠商特性之平均值………………………………………………………33 表12 生技產業追蹤資料(非線型)估計結果(2001-2003年)……………………………41 表13 醫藥品業、醫療器材及特化與食品業追蹤資料(非線型)估計結果(2001-2003年)…………………………………………………………………………………..44 表14 農業生技及環保生技服務業追蹤資料(非線型)估計結果(2001-2003年)………45 表15 生技產業研究發展與群聚外溢效果實證結果……………………………………47 表16 生技產業分類產業研究發展與群聚外溢效果實證結果…………………………47 表17 生技產業分類產業研究發展外溢效果……………………………………………49 表18 生技產業分類產業群聚外溢效果…………………………………………………49 附錄： 生技產業SAS程式(非線型)……………………………………………………..64 | zh_TW |
| dc.language.iso | en_US | - |
| dc.source.uri (資料來源) | http://thesis.lib.nccu.edu.tw/record/#G0929210471 | en_US |
| dc.subject (關鍵詞) | 外溢效果 | zh_TW |
| dc.subject (關鍵詞) | 群聚 | zh_TW |
| dc.subject (關鍵詞) | 研究發展 | zh_TW |
| dc.subject (關鍵詞) | spillover | en_US |
| dc.subject (關鍵詞) | clusters | en_US |
| dc.subject (關鍵詞) | R&D | en_US |
| dc.title (題名) | 台灣生物科技產業群聚與研究發展之外溢效果 | zh_TW |
| dc.type (資料類型) | thesis | en |
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