中国制造业全要素生产率时空演变及影响因素

doi:10.11821/dlxb202112013

摘要/Abstract

摘要：

提高制造业全要素生产率是实现中国制造业高质量发展的重要途径。运用超效率SBM模型、标准差椭圆、空间自相关、地理加权回归等方法实证分析了2002—2016年中国制造业全要素生产率的时空演变特征及影响因素。结果显示：① 2002—2016年中国制造业全要素生产率总体呈波动上升趋势,由2002年的0.58上升至2016年的0.80;② 中国制造业全要素生产率的重心整体呈“西南—东北—西北”的变化格局,移动距离和速度逐渐缩小。标准差椭圆主要位于中国中东部地区,转角呈“缩小—增大—再缩小”的趋势,由偏东北—偏西南方向向正北—正南方向不断偏移,在偏东北—偏西南方向的空间分布趋于分散,在偏东南—偏西北方向的空间分布趋于极化;③ 中国制造业全要素生产率的正向空间相关关系逐渐减弱,高—高集聚区主要集中于江苏、上海等华东地区,低—低集聚区主要集中于陕西、甘肃、四川等地区;④ 中国制造业全要素生产率影响因素存在显著的空间异质性特征,金融发展是制造业全要素生产率提高的主要因素,对外开放、产业集聚和交通基础设施是制造业全要素生产率提高的重要正向影响因素,信息化水平阻碍了制造业全要素生产率的提高。

关键词: 制造业, 全要素生产率, 时空演变, 影响因素, 中国

Abstract:

Improving total factor productivity (TFP) in the manufacturing industry is essential to implementing China's manufacturing power strategy. In this study, we employed the super-efficiency SBM model, standard deviational ellipse, spatial autocorrelation, and the geographic weighted regression (GWR) to analyze the spatio-temporal evolution and influencing factors of TFP in China's manufacturing industry from 2002 to 2016. The main conclusions can be drawn as follows: (1) During the study period, the overall trend of TFP in China's manufacturing industry showed an upward tendency of fluctuation, with an increase from 0.58 in 2002 to 0.80 in 2016. (2) The center of the gravity of TFP in China's manufacturing industry showed a "southwest-northeast-northwest" spatial evolution pattern, with its moving distance and speed gradually shrinking. The standard deviation ellipse was mainly located in central and eastern China with its distribution pattern shifting from northeast-southwest to true north-south while tending to scatter in the northeast-southwest direction and polarized in the southeast-northwest direction. The turning angle showed a trend of "shrinking-increasing-shrinking". (3) The positive spatial correlation of TFP in China's manufacturing industry gradually weakened during the study period. High-high agglomeration areas were mainly concentrated in coastal provinces and cities of eastern China such as Jiangsu and Shanghai, while low-low agglomeration areas were mainly in Shaanxi, Gansu, and Sichuan. (4) There are obvious spatial heterogeneities in the factors influencing the TFP of China's manufacturing industry. Financial development is the leading factor in the improvement of manufacturing TFP. Opening-up, industrial agglomeration, and transportation infrastructure have a positive effect on the TFP in the manufacturing industry, while the level of informatization has a restraining influence.

Key words: manufacturing industry, TFP, spatio-temporal evolution, influencing factor, China

王泽宇, 王焱熙, 赵莉, 赵璐. 中国制造业全要素生产率时空演变及影响因素[J]. 地理学报, 2021, 76(12): 3061-3075.

WANG Zeyu, WANG Yanxi, ZHAO Li, ZHAO Lu. Spatio-temporal evolution and influencing factors of total factor productivity in China's manufacturing industry[J]. Acta Geographica Sinica, 2021, 76(12): 3061-3075.

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省份	2002	2004	2006	2008	2010	2012	2014	2016
安徽	0.52	0.63	0.65	0.65	1.03	0.75	1.01	1.00
北京	1.00	1.08	1.15	1.05	1.08	1.11	1.10	1.30
福建	0.65	0.71	0.76	0.73	1.03	1.03	1.05	1.01
甘肃	0.65	0.61	0.58	0.56	0.60	0.57	0.58	0.56
广东	0.91	1.08	1.05	1.03	1.03	1.02	1.04	1.09
广西	0.31	0.54	0.57	0.55	0.60	0.59	0.65	0.69
贵州	0.39	0.60	0.58	0.55	0.59	0.52	0.57	0.58
海南	0.44	0.54	1.02	1.07	1.09	0.52	0.79	0.90
河北	0.64	0.62	0.63	0.61	0.65	0.64	0.62	1.01
河南	0.65	0.63	0.62	0.64	0.82	0.63	0.76	0.75
黑龙江	0.63	0.81	0.57	0.52	0.51	0.48	0.50	0.68
湖北	0.65	0.61	0.60	0.61	0.62	0.72	0.93	0.99
湖南	0.46	0.58	0.60	0.61	0.62	0.63	0.63	0.69
吉林	0.67	0.66	0.62	0.65	1.03	1.01	1.00	1.02
江苏	0.75	1.04	1.04	1.30	1.02	1.03	0.75	0.86
江西	0.42	0.60	0.60	0.61	0.64	0.63	0.66	0.73
辽宁	0.50	0.62	0.60	0.60	0.62	0.60	0.61	0.93
内蒙古	0.42	0.61	0.62	0.60	0.66	0.58	0.60	0.82
宁夏	0.57	0.58	0.56	0.53	0.57	0.57	0.57	0.58
青海	0.43	0.60	0.57	0.54	0.60	0.53	0.58	0.79
山东	0.63	1.00	1.03	1.01	1.01	0.71	0.96	0.11
山西	0.59	0.59	0.53	0.52	0.56	0.53	0.54	0.83
陕西	0.62	0.58	0.58	0.56	0.58	0.56	0.60	0.70
上海	0.75	1.06	1.02	1.00	1.14	0.70	0.69	0.61
四川	0.59	0.59	0.60	0.62	0.84	0.65	1.06	1.04
天津	0.81	1.04	1.02	1.03	1.03	1.17	1.25	1.22
新疆	0.34	0.57	0.57	0.51	0.51	0.35	0.36	0.33
云南	0.43	0.61	0.63	0.59	0.65	0.56	0.56	0.54
浙江	0.62	0.88	1.01	0.80	1.05	0.70	0.66	0.59
重庆	0.41	0.58	0.57	0.59	0.62	0.65	0.72	1.02
均值	0.58	0.71	0.72	0.71	0.78	0.69	0.75	0.80

年份	重心坐标	移动方向	移动距离(km)	东西向距离(km)	南北向距离(km)	移动速度(km/a)	东西向速度(km/a)	南北向速度(km/a)
2002	34.21°N, 113.22°E
2007	33.38°N, 113.09°E	西偏南	91.54	3.09	91.49	18.31	0.62	18.30
2012	33.75°N, 113.34°E	东偏北	47.16	19.47	42.95	9.43	3.89	8.59
2016	33.79°N, 112.86°E	西偏北	44.34	44.34	0.17	8.87	8.87	0.03

参数	2002	2007	2012	2016
转角(°)	27.07	20.54	25.21	23.8
沿x轴标准差(km)	925.64	966.75	899.88	877.21
沿y轴标准差(km)	1123.37	1149.68	1130.22	1172.59

年份	Moran's I	P值	年份	Moran's I	P值	年份	Moran's I	P值
2002	0.305	0.008	2007	0.475	0.001	2012	0.278	0.016
2003	0.376	0.008	2008	0.434	0.002	2013	0.312	0.009
2004	0.376	0.005	2009	0.276	0.018	2014	0.129	0.102
2005	0.454	0.001	2010	0.387	0.003	2015	0.225	0.008
2006	0.449	0.001	2011	0.215	0.042	2016	0.082	0.143

模型	2002年		2016年
参数	R²	调整R²	R²	调整R²
OLS	0.46	0.35	0.24	0.08
GWR	0.52	0.38	0.36	0.20