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地理科学    2017, Vol. 37 Issue (2): 161-171     DOI: 10.13249/j.cnki.sgs.2017.02.001
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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
韩增林1,2(),孙嘉泽1(),刘天宝2,彭飞1,钟敬秋1,2
1.辽宁师范大学海洋经济与可持续发展研究中心,辽宁 大连 116029
2.辽宁师范大学城市与环境学院,辽宁 大连 116029
The Spatiotemporal Characteristics and Development Trend Forecast of Innovative TFP Growth in China’s Three Northeastern Provinces
Zenglin Han1,2(),Jiaze Sun1(),Tianbao Liu2,Fei Peng1,Jingqiu Zhong1,2
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
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摘要 

使用DEA-Malmquist方法在对比1994~2013年间中国东北三省、东部、中部和西部地区之间创新全要素生产率(TFP)时空差异的基础上,分析了东北三省内部创新TFP的时空演变特征,并运用PVAR模型对东北三省创新TFP的发展趋势进行了预测。研究表明:从空间格局上看东北三省创新TFP增速略高于西部地区,但低于中部和东部地区;东北三省创新TFP增长的主要动力来源于技术进步,在东北三省内部吉林省创新TFP的增长最快,其增长动力主要源于纯效率变化,其次为技术进步;而辽宁省和黑龙江省创新TFP的动力主要是技术进步;预测显示东北三省创新TFP增长幅度未来会逐渐放缓,其增长的主要动力将会由技术进步转变为规模效率的改进。

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韩增林
孙嘉泽
刘天宝
彭飞
钟敬秋
关键词 创新全要素生产率(TFP)Malmquist指数PVAR模型东北三省 
Abstract

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.

Key wordsinnovation total factor productivity    Malmquist index    PVAR model    Northeast China
收稿日期: 2016-03-14      出版日期: 2017-04-18
基金资助:国家自然基金项目(41571122,41601114);辽宁省社科联项目(2016lSLJDWT-28);大连市社科联重点课题(2015DLSKZD078)项目资助
引用本文:   
韩增林, 孙嘉泽, 刘天宝等 . 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.
Zenglin Han, Jiaze Sun, 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.
链接本文:  
http://geoscien.neigae.ac.cn/CN/10.13249/j.cnki.sgs.2017.02.001      或      http://geoscien.neigae.ac.cn/CN/Y2017/V37/I2/161
指数 Malmquist PEC PTC SEC STC
东北三省 1.091 1.003 1.108 1.041 1.005
东部 1.097 1.007 1.129 1.028 1.024
中部 1.123 1.016 1.084 1.048 1.040
西部 1.080 1.019 1.083 1.056 1.009
Table 1  全国各地区创新TFP及其分解(1995~2013)
Fig. 1  1995~2013年全国四大地区创新TFP时空分布
指数 Malmquist PEC PTC SEC STC
1995~1996 1.300 1.048 1.254 1.044 0.947
1996~1997 0.910 0.933 0.979 1.135 0.877
1997~1998 1.158 1.061 1.084 0.683 1.475
1998~1999 1.059 1.073 1.001 1.197 0.824
1999~2000 1.527 1.095 1.409 0.982 1.008
2000~2001 0.728 0.966 1.011 0.857 0.871
2001~2002 1.087 0.951 1.227 1.443 0.645
2002~2003 0.808 0.522 1.775 0.658 1.327
2003~2004 1.204 1.558 0.698 1.224 0.905
2004~2005 1.342 0.994 1.297 1.272 0.819
2005~2006 0.914 0.923 0.882 1.100 1.019
2006~2007 1.532 0.920 1.382 0.975 1.237
2007~2008 0.697 0.887 0.980 1.095 0.732
2008~2009 1.563 1.443 1.025 0.737 1.434
2009~2010 0.819 0.622 0.998 1.300 1.013
2010~2011 1.260 1.043 1.234 0.992 0.987
2011~2012 0.874 1.089 0.800 1.016 0.987
2012~2013 0.850 0.932 0.911 1.025 0.976
1995~2013 1.091 1.003 1.108 1.041 1.004
Table 2  1995~2013年东北三省创新TFP及其分解结果
Fig. 2  1995~2013年东北三省创新TFP时空分布
时期 地区 Malmquist PEC PTC SEC STC
黑龙江 1.075 1.011 1.072 1.065 0.991
1995~2002年 吉林 1.244 1.103 1.111 1.019 1.016
辽宁 1.120 1.017 1.139 1.062 0.972
黑龙江 1.104 1.097 1.200 1.003 1.043
2002~2008年 吉林 1.654 1.547 1.351 1.025 1.055
辽宁 1.051 0.950 1.203 1.039 0.999
黑龙江 1.018 1.008 0.978 0.952 1.105
2008~2013年 吉林 1.156 1.248 0.940 0.947 1.093
辽宁 1.111 1.036 1.054 1.001 1.027
黑龙江 1.069 1.039 1.088 1.013 1.040
1995~2013年 吉林 1.356 1.291 1.144 1.001 1.050
辽宁 1.094 1.000 1.136 1.037 0.996
Table 3  东北三省创新TFP及其分解(1995~2013)
变量 LLC Breitung IPS
Malmquist -6.0505*** -4.0174*** -3.6022***
PEC -7.8016*** -4.1239*** -4.0515***
SEC 13.7514 -5.2212*** -4.7774***
PTC -5.6219*** -4.3472*** -3.4753***
STC -1.7345** -2.6164*** -4.2480***
Table 4  平稳性检验结果
Fig. 3  Malmquist响应的脉冲响应
VARIABLES Malmquist PTC PEC STC SEC
L.Malmquist -1.833*** -0.0998 -2.327*** -0.182*** 0.150***
(0.181) (0.106) (0.355) (0.0291) (0.0354)
L.PTC 1.682*** -0.305 2.820*** 0.319*** -0.222***
(0.235) (0.191) (0.483) (0.0494) (0.0566)
L.PEC 1.453*** 0.409*** 1.268*** 0.162*** -0.103***
(0.114) (0.0852) (0.196) (0.0215) (0.0298)
L.STC 4.903*** 1.532*** 5.388*** -0.371*** 0.318**
(0.584) (0.315) (0.732) (0.120) (0.146)
L.SEC 5.832*** 2.479*** 5.730*** 0.307** -0.287*
(0.631) (0.300) (0.805) (0.120) (0.163)
Observations 48 48 48 48 48
Table 5  PVAR 模型 GMM 估计结果
时期 模型变量 PTC PEC STC SEC Malmquist
2 Malmquist 0.0005 0.2737 0.0347 0.2800 0.4111
5 Malmquist 0.0974 0.2326 0.0476 0.2581 0.3643
10 Malmquist 0.1011 0.2257 0.0641 0.2502 0.3589
15 Malmquist 0.1017 0.2254 0.0644 0.2499 0.3586
Table 6  创新TFP的方差分解
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