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|Title: ||廠商成長、排名流動性與研發活動之研究;The Researches on firm growth, rank mobility and R&D activity|
|Authors: ||呂文正;Wen-Cheng Lu|
|Keywords: ||研發外溢效果;排名流動性;廠商成長;rank mobility;firm growth;R&D spillover effects|
|Issue Date: ||2009-09-22 15:05:13 (UTC+8)|
|Abstract: ||本文利用panel unit root檢定重新探討場商成長與其規模間的關係，在假設廠商間橫斷面相互獨立下，發現Gibrat’s law成立。而在考慮橫斷面相關下，利用Sarno與Taylor之MADF檢定與無母數分析方法，發現拒絕與接受虛無假設的結論不同。另外，本文利用SURADF檢定考慮廠商異質性與橫斷面相關的問題，並以電子產業為例，檢視何者接受Gibrat’s law. 第二章以台灣500大企業為研究對象，並從中區分成33個產業，以每個產業內廠商排名替換機率作為衡量產業流動性的基礎。我們考慮馬可夫鏈(Markov Chain)、吉伯特法則(Gibrat’s Law)與本文所提供的排名上升、不變與下降的三個狀態的馬可夫鏈，作為實際上廠商排名替換的比較基礎。分析的結果指出，三個狀態的馬可夫鏈較為接近實際的移轉機率。由流動性指標顯示，吉伯特法則所估計出的流動性最高。此外，Geroski and Toker (1996) 的方式並不適用於台灣中小企業為主的產業結構，計算流動性時，若僅考慮每一產業前五名的廠商，將高估產業的僵固性。 另外，本文利用上市製造業廠商的資料，探討台灣電子業廠商成長與研發活動的外溢效果之間的關係為何。本文將研發費用轉換為資本存量的觀念，再將研發的外溢效果(spillover effect)分成產業內與產業間的外溢效果，並探討不同的外溢效果對廠商成長的影響。Hall (1987) 認為傳統上研究廠商成長，並以兩期間廠商規模衡量成長與其決定因素間的議題，會受到廠商進入與退出的影響，若不考慮廠商進出的問題，則估計結果將產生存活偏誤。本文以Heckman兩階段估計法，考慮廠商退出與存活偏誤的問題並發現廠齡，整個產業研發支出、廣告和員工薪資對廠商存活的影響為正向。廠商規模、產業間的外溢效果、產業內的外溢效果、出口、整個產業的成長、資本勞動比對廠商成長的影響也為正向。 本文以一內生成長模型為基礎，將研發支出分成自行研發支出與向外購買並支付權利金兩部分，由廠商極大化自身利潤函數，從而推導出自行研發支出與向外購買支出的最適比例，該比例受到某廠商的國內與國外可學習的技術空間、國內與國外技術吸收能力與自行研發或向外購買所能達成的效率所影響。 In the early empirical studies, many economists focus on the determinant of firm growth but few papers to investigate the time series phenomenon of firm growth. We investigate the Gibrat’s law by using panel unit root test. Panel unit root can increase power in contrast to conventional individual ADF test. At first we use panel unit root test to testify Gibrat’s law under independent and identical distribution. The test results reject the null hypothesis of Gibrat’s law. Independent and identical distribution is not reasonable in real situation. Any firm in a given industry may have some correlation with other firms. The limiting distribution of Im, Pesaran, and Shin(IPS) statistic is invalid and it will produce large distortion. We apply Taylor and Sarno(1998) MADF test to deal with cross-sectional correlation problem and study the issue. We find that the conclusion is not the same. Next to the finding of chapter 2, we provide further evidence on Gibrat’s law from the panel unit root test of the Taiwan electronic industry. The test results of Gibrat’s law are not the same as previous empirical literature. We use the panel estimation of seemingly unrelated regressions for Augmented Dickey-Fuller tests (SURADF) to consider cross-sectional dependence and heteroscedasticity. The results show that four companies reject Gibrat’s law. Firm growth will make the change of firm rank. The movement of firm rank is an dynamic indicator for the rigidity of market structure. In Chapter 4, we adopt the top 500 firms as study object, and divides them into 33 industries. We use the probability of the turnover of firm’s ranking in each industry as the basis of measuring industry mobility. We consider Markov chain, Gibrat’s law and three states Markov chain proposed by this study as the comparison basis of the turnover of firms’ ranking. Analytical results indicate that the three states Markov chain has the transition probability that is most close to the ranking true probability. It can be shown that the mobility which is estimated by Gibrat’s law is highest from mobility indicator. In addition, Geroski and Toker (1996) is not suitable for studying Taiwan’s industry mobility by considering top 5 firms in a given industry, it will overestimate industry rigidity. Chapter 5 uses the Industry, Commerce and Services (ICS) sampling data to surdy the relationship between firm growth and R&D spillover effects considering firm exit behavior. There are a few papers to investigate this issue. We divide the R&D spillover effects into inter-industry and intra-industry spillover effects and apply the Heckman two-stage model to deal with empirical research. We find that firm age, total R&D expenditure, whole industry sales, advertising, and employee’s salary have positive contribution to firm survival. Firm size, inter-industry spillovers, intra-industry spillovers, export, industrial growth, and capital/labor ratio also have positive contribution to firm growth. The final Chapter is based on an endogenous growth model and we divide an innovation sourcing strategy into an internal source and an external source. With respect to the determinants of the decision by the innovative firm to produce technology itself or to source technology externally, the representative firm maximizes its profit function and the first-order condition can be derived. Based on our model, the optimal ratio of an internal technology-sourcing strategy and external-sourcing strategy can be found. The technology stock of the representative firm to total industry, the technology absorption capacity, and the internal and external R&D efficiencies will all influence the optimal ratio.|
|Appears in Collections:||[產業經濟研究所] 博碩士論文|
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