1.Center for Studies of Marine Economy and Sustainable Development, Liaoning Normal University, Dalian 116029,Liaoning,China 2.College of Urban and Environment, Liaoning Normal University, Dalian 116029, Liaoning, China
Among various different measures which can be used to make the promotion of regional economic development, the role of innovation cannot be ignored. Over the past 20 years, the proportion of the total number of researchers in the three northeastern provinces (including Heilongjiang, Jilin and Liaoning) of China has dropped 6.6 percent. Besides, the R & D / GDP of the three northeastern provinces increased by only 0.09%, which is far below the national average (1.51%). According to the current researches, it can be found that the economic growth achieved through the innovation capacity in the northeastern provinces accounts for 49.3%, which indicates that innovation plays an important role during the process of promoting the economic development.Innovation has turned into more and more important for regional development in China. On the basis of the panel data of China ranging from 1995 to 2013, Malmquist index model was used to make the analysis on the spatial and temporal differences of innovation TFP in the three provinces of Northeastearn China. The PVAR model was constructed on decomposed data. The change trend of TFP and its decomposition inthese three provinces of Northeast Chinaareforecasted. In accordance with the above process, three conclusions can be made as follows:1) The innovation TFP growth speed of Northeast China is slightly higher than that of Western China while is lower than Central China and Eastern China. 2) From the inside of the three northeastern provinces, the innovation TFP of Jilin Province, which is driven by the improvement of the management level and the adjustment of the system (PEC), grew fastest from 1995 to 2013. Secondly, the major driving force of innovation TFP of Liaoning Province came from technology. However, its technological progress has been limited by the scale of innovation. The growth rate of innovation TFP in Heilongjiang Province is the slowest, which is mainly due to the limited growth rate of endogenous power (SEC, PTC, STC, PEC). At the same time, the result shows that the promotion of the system and management level in these three northeastern provinces is far greater than the technological progress and the enlargement of the scale. 3) Based on the forecast results, the three provinces in the Northeast innovation TFP growth rate may continue to slow down in the near future. From the perspective of unit changes, STC and SEC unit changes play the dominant roles in improving TFP of the three northeastern provinces. In terms of the degree of influence, the innovation of TFP in Northeast China is mainly affected by the change of PEC and STC.
. 东北三省创新全要素生产率增长的时空特征及其发展趋势预测[J]. 地理科学,
2017, 37(2): 161-171.
Tianbao Liu et al
. The Spatiotemporal Characteristics and Development Trend Forecast of Innovative TFP Growth in China’s Three Northeastern Provinces[J]. SCIENTIA GEOGRAPHICA SINICA,
2017, 37(2): 161-171.
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<p>运用空间计量模型对1990~2011 年中国全要素生产率进行研究，发现：此间中国省域全要素生产率在大部分年份呈现了空间自相关性，表明这22 a 间中国省域全要素生产率并不是完全的随机状态，受其它区域的影响。进一步运用空间计量经济模型从空间维度探究了区域全要素生产率的影响因素，研究表明：经济的集聚水平越高，全要素生产率会得到显著改善；人力资本对经济增长与效率的提升有着显著地积极作用，并表现一定程度的溢出；政府干预和产业结构对全要素生产率的影响为负，说明政府部门要减少对经济的干预；同时表明了中国的产业结构可能存在不合理的地方；信息化水平、基础设施水平对全要素生产率的影响为正，但基础设施水平在统计学意义上并不显著；民营化所占比重的提升对全要素生产率的改进是显著的，因为私有化致使企业的权力下放有助于提高技术效率；经济开放水平显著提升了中国的区域全要素生产率；中国部分省份土地投入规模过大而出现规模不经济的问题。从全要素生产率在各个地区间溢出的证据出发，各个地方政府在统筹区域经济发展的过程中不仅需要考虑本地区经济发展的实际，需要打破目前行政区经济的界限，实现跨区域的协调与合作，实现共赢，最终实现所有地区全要素生产率的提高。</p>
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In two widely cited but unpublished working papers, Simar and Wilson (1998) and Zofío and Lovell (1998) proposed an alternative decomposition of the Malmquist Productivity Index, which retained what seemed to be the strongholds of previous proposals with regard to the contribution of technological and efficiency change to productivity change. Namely, a technical change term with regard to the best practice (VRS) technology which is to be found in Ray and Desli (1997) and a scale efficiency change term that illustrates a firm’s situation with regard to optimal scale (benchmark technology), F01re, Grosskopf, Norris and Zhang (1994). Attaining this objective required the introduction of an additional term in the Malmquist Productivity Index decomposition, which would reflect the scale bias of technical change. It is our objective to provide economic rationale for this term within a theory of production context, the existing decompositions and recent articles that further elaborate on this issue. The ideas are illustrated using productivity trends in 17 OECD countries
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Abstract<br/><p class="a-plus-plus">This paper evaluates to what extent policy-makers have been able to promote the creation and consolidation of comprehensive research groups that contribute to the implementation of a successful innovation system. Malmquist productivity indices are applied in the case of the Spanish Food Technology Program, finding that a large size and a comprehensive multi-dimensional research output are the key features of the leading groups exhibiting high efficiency and productivity levels. While identifying these groups as benchmarks, we conclude that the financial grants allocated by the program, typically aimed at small-sized and partially oriented research groups, have not succeeded in reorienting them in time so as to overcome their limitations. We suggest that this methodology offers relevant conclusions to policy evaluation methods, helping policy-makers to readapt and reorient policies and their associated means, most notably resource allocation (financial schemes), to better respond to the actual needs of research groups in their search for excellence (micro-level perspective), and to adapt future policy design to the achievement of medium-long term policy objectives (meso and macro-level).</p><br/>
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