本文選題：產(chǎn)業(yè)集聚 + 全要素生產(chǎn)率��； 參考：《中國海洋大學(xué)》2014年碩士論文

【摘要】：改革開放后，伴隨著我國經(jīng)濟(jì)的快速增長，經(jīng)濟(jì)發(fā)展過程中的一些問題也開始不斷顯現(xiàn)。這其中，各地區(qū)經(jīng)濟(jì)發(fā)展的不平衡和產(chǎn)業(yè)生產(chǎn)中高投入低效率的問題成為最需要迫切解決的兩個(gè)問題。而如何進(jìn)一步提升我國的全要素生產(chǎn)率水平則成為解決這些問題的關(guān)鍵所在。 從影響經(jīng)濟(jì)增長和生產(chǎn)率提高的因素看，受制于傳統(tǒng)的新古典經(jīng)濟(jì)理論在解釋地區(qū)生產(chǎn)率差異上的局限性，新經(jīng)濟(jì)地理學(xué)開始在規(guī)模報(bào)酬遞增、不完全競爭和冰山貿(mào)易成本的假設(shè)前提下，強(qiáng)調(diào)產(chǎn)業(yè)集聚在促進(jìn)生產(chǎn)率提高過程中的重要作用，認(rèn)為產(chǎn)業(yè)集聚水平的提高可以通過知識(shí)和信息外溢、共享基礎(chǔ)設(shè)施、共享資本勞動(dòng)力等要素資源、拓展產(chǎn)業(yè)前后向聯(lián)系等多種途徑來促進(jìn)全要素生產(chǎn)率水平的提高。然而，在將這些理論分析應(yīng)用于實(shí)證檢驗(yàn)時(shí)，支持與不支持產(chǎn)業(yè)集聚促進(jìn)生產(chǎn)率提高的結(jié)論卻都大量存在。這說明產(chǎn)業(yè)空間集聚水平的提高并不必然會(huì)促進(jìn)生產(chǎn)率的提高，產(chǎn)業(yè)集聚與生產(chǎn)率之間可能會(huì)存在非線性關(guān)系。在這樣的背景下，本文嘗試在非線性框架下對(duì)產(chǎn)業(yè)集聚促進(jìn)全要素生產(chǎn)率提高的內(nèi)在機(jī)制進(jìn)行探討。 本文首先對(duì)國內(nèi)外的相關(guān)文獻(xiàn)進(jìn)行了梳理。在此基礎(chǔ)上，對(duì)我國制造業(yè)空間集聚水平以及各地區(qū)全要素生產(chǎn)率水平進(jìn)行了測算和分析，并對(duì)二者之間的關(guān)系進(jìn)行了簡單的分析。結(jié)果發(fā)現(xiàn)：我國東、中、西部三個(gè)地區(qū)制造業(yè)空間集聚水平差距較大，其中東部地區(qū)集聚水平明顯高于中西部地區(qū)；我國各地區(qū)全要素生產(chǎn)率增長存在差異，其中東部地區(qū)明顯高于中西部地區(qū)。東部地區(qū)全要素生產(chǎn)率處于增長狀態(tài)，而中西部地區(qū)則相反；將制造業(yè)空間集聚水平與全要素生產(chǎn)率增長聯(lián)系起來觀察，雖然集聚水平較高的地區(qū)全要素生產(chǎn)率增長較快，，但二者之間并沒有明顯的線性關(guān)系。 然后，本文對(duì)產(chǎn)業(yè)空間集聚與全要素生產(chǎn)率之間的關(guān)系分別在線性和非線性框架下進(jìn)行了實(shí)證研究，結(jié)果發(fā)現(xiàn)，制造業(yè)空間集聚對(duì)全要素生產(chǎn)率的增長具有顯著促進(jìn)作用，這個(gè)過程主要是通過技術(shù)效率的改善體現(xiàn)出來的，集聚對(duì)技術(shù)效率的影響為正，而對(duì)技術(shù)進(jìn)步的影響卻為負(fù)。出現(xiàn)這種情況可能是因?yàn)榧鬯皆谝欢ㄏ薅葍?nèi)的提高有利于增加產(chǎn)業(yè)之間的關(guān)聯(lián)度，這樣有利于節(jié)省企業(yè)各方面成本，從而提高資源配置效率；集聚與全要素生產(chǎn)率增長之間存在著一種非線性關(guān)系，當(dāng)集聚水平低于某一臨界值時(shí)，集聚水平的提高會(huì)促進(jìn)全要素生產(chǎn)率的增長；而當(dāng)高于這一臨界值時(shí)便會(huì)阻礙全要素生產(chǎn)率的增長。 最后，本文在前面理論分析和實(shí)證檢驗(yàn)的基礎(chǔ)上，對(duì)今后我國如何合理調(diào)整產(chǎn)業(yè)布局、協(xié)調(diào)區(qū)域經(jīng)濟(jì)發(fā)展提出了相應(yīng)的對(duì)策和建議。
[Abstract]:After the reform and opening up, with the rapid economic growth in China, some problems in the process of economic development began to appear. Among them, the imbalance of regional economic development and the problems of high input and low efficiency in industrial production are the two most urgent problems to be solved. How to further improve the level of total factor productivity is the key to solve these problems. In view of the factors affecting economic growth and productivity improvement, the new economic geography began to increase returns on a scale due to the limitations of the traditional neoclassical economic theory in explaining regional productivity differences. Under the assumption of incomplete competition and iceberg trade cost, this paper emphasizes the important role of industrial agglomeration in the process of promoting productivity, and holds that the improvement of industrial agglomeration level can share infrastructure through knowledge and information spillover. Share capital labor force and other essential resources and expand industry forward and backward connection to promote the level of total factor productivity. However, the conclusions of supporting and not supporting industrial agglomeration to improve productivity exist in large numbers when these theoretical analyses are applied to empirical tests. This indicates that the improvement of industrial spatial agglomeration level does not necessarily promote the improvement of productivity, and there may be a nonlinear relationship between industrial agglomeration and productivity. In this context, this paper attempts to explore the internal mechanism of industrial agglomeration to promote the total factor productivity under the non-linear framework. In this paper, the literature at home and abroad is first combed. On this basis, the spatial agglomeration level of Chinese manufacturing industry and the level of total factor productivity in various regions are measured and analyzed, and the relationship between them is analyzed simply. The results show that the gap of manufacturing spatial agglomeration level in the east, middle and west regions of China is large, and the concentration level in the eastern region is obviously higher than that in the central and western regions, and there are differences in the growth of total factor productivity in different regions of China. The eastern region is obviously higher than the central and western regions. In the eastern region, the total factor productivity (TFP) is in a state of growth, while in the central and western regions, the level of spatial agglomeration of manufacturing industry is related to the growth of TFP, although the higher the level of agglomeration, the faster the growth of TFP. But there is no obvious linear relationship between the two. Then, the relationship between industrial spatial agglomeration and total factor productivity (TFP) is studied in the linear and nonlinear frame respectively. The results show that the spatial agglomeration of manufacturing industry has a significant role in promoting the growth of TFP. The effect of agglomeration on technological efficiency is positive but negative on technological progress. This situation may be because the increase of agglomeration level within a certain limit is conducive to increase the correlation between industries, which is conducive to saving the cost of all aspects of the enterprise, thereby improving the efficiency of resource allocation; There is a nonlinear relationship between agglomeration and total factor productivity growth. When the agglomeration level is below a certain critical value, the increase of agglomeration level will promote the growth of total factor productivity. When above this threshold, total factor productivity growth will be hindered. Finally, on the basis of theoretical analysis and empirical test, this paper puts forward corresponding countermeasures and suggestions on how to adjust the industrial layout and coordinate the development of regional economy in China in the future.
【學(xué)位授予單位】：中國海洋大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2014
【分類號(hào)】：F124;F224

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