2000-2013年中国城市群经济绩效动态实证分析——基于DEA和Malmquist生产率指数法

doi:10.18306/dlkxjz.2017.06.004

Abstract

Abstract:

With the enormous radiation range, great potential and vigor, urban agglomerations are deemed as the core growth pole both at present and in the future under the background of globalization and new-type urbanization in China. Behind the seemingly ever-growing urban agglomerations, however, massive input elements such as resource, labor, capital, and other factors are needed. It is of profound significance to evaluate the economic performance of urban agglomerations, which may vigorously promote the sustainable and healthy development of urbanization. How to objectively evaluate the efficiencies such as industry scale concentration, resource allocation, and technological change of urban agglomerations has become an important question. Therefore, in this study we applied the data envelopment analysis (DEA) model to quantitatively measure the input and output efficiency of typical urban agglomerations in China from 2000 to 2013, based on time series data including capital input, urban construction land increment, labor supply, scientific-technological investment, and economic output value. Moreover, we analyzed urban agglomerations' total factor productivity index (TFP) dynamically by means of Malmquist productivity index method and lucubrated their spatial and temporal differentiation patterns, mechanisms of change, influencing factors, and other related contents. The results show that the urban agglomerations' stationery cross-section input and output efficiency made significant strides but an unbalanced spatial distribution pattern remained. In 2000, the average comprehensive technological efficiency was only 73.1% of the optimal level with barely three large urban agglomerations achieved the optimal DEA efficiency. After years of steady development, the corresponding index reached 96.8% of the optimal level with eight urban agglomerations achieved the optimal DEA efficiency. However, from the perspective of dynamic time series analysis, urban agglomerations' TFP decreased by 6% from 2000 to 2013 mainly due to the poor performance of technology change index. Unlike the disappointing performance of technology change index, other indices such as comprehensive technological efficiency change index, pure technological efficiency change index, and scale efficiency change index all shaped up, indicating that the level of resource allocation and utilization efficiency rose steadily during this period. The lag of the contribution of technological progress to a certain extent offset the positive effects brought by the expansion of urban agglomeration and the optimization of resource allocation. Specifically, only the Yangtze River Delta, the Pearl River Delta, and Beijing-Tianjin-Hebei urban agglomerations gained ascending contribution of technological progress, which illustrates that the current development stages and dynamic mechanisms of urban agglomerations in China maintained diversified characteristics. To conclude, this article puts forward a series of specific suggestions to optimize the development of urban agglomerations in China, namely, moderately expanding the scale of urban agglomerations, placing emphasis on input and output efficiency leaning against technological changes and transforming the present unbalanced regional development situation.

Key words: urban agglomeration, economic performance, total factor productivity (TFP), data envelopment analysis (DEA), Malmquist productivity index method, China

Jinchuan HUANG, Haoxi LIN, Ming CHEN. The dynamics and empirical analysis of input and output efficiency of urban agglomerations in China, 2000-2013: Based on the DEA model and Malmquist index method[J].PROGRESS IN GEOGRAPHY, 2017, 36(6): 685-696.

Figures/Tables 9

Tab.1

Fig.1

Tab.2

Fig.2

Fig.3

Tab.3

Annual variation of urban agglomeration's overall total factor productivity change (TFPC) and its decomposition indices"

时段	$EC$	$TC$	$PTEC$	$SEC$	$TFPC$
2000-2001	1.088	0.939	1.001	1.087	1.022
2001-2002	0.997	0.903	1.018	0.979	0.900
2002-2003	0.997	0.883	0.998	0.999	0.880
2003-2004	1.101	0.934	1.075	1.024	1.028
2004-2005	1.020	0.870	0.999	1.021	0.887
2005-2006	0.925	1.003	0.965	0.959	0.928
2006-2007	1.047	0.944	1.005	1.042	0.988
2007-2008	1.002	0.977	1.022	0.981	0.979
2008-2009	1.026	0.876	1.001	1.025	0.899
2009-2010	1.023	0.906	1.015	1.007	0.927
2010-2011	1.059	0.970	1.045	1.014	1.027
2011-2012	0.984	0.889	0.982	1.002	0.875
2012-2013	0.985	0.914	0.991	0.994	0.900
平均值	1.020	0.924	1.009	1.010	0.942

Tab.3

Fig.4

Fig.5

Fig.6

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城市群	地域范围面积/万km2	2000年			2010年			2013年 GDP/万亿	经济增速
城市群	地域范围面积/万km2	GDP/万亿		总人口/万人	城市人口/万人	GDP/万亿	总人口/万人	城市人口/万人	2000-2010/%	2010-2013/%
长三角	104158	13401	7107	4415	69828	10240	7235	95900	17.9	11.2
珠三角	101270	6768	4158	2966	39706	6484	5337	60060	19.4	14.8
京津冀	37040	4912	2951	2088	27792	4299	3302	44839	18.9	17.3
山东半岛	73592	5527	3958	1934	26187	4414	2554	34373	16.8	9.5
辽中南	41854	3121	1978	1443	14194	2195	1750	22560	16.4	16.7
海西	66757	3213	2401	1078	11579	2862	1707	17732	13.7	15.3
武汉	86492	2373	2248	1374	8941	2402	1579	19623	14.2	30.0
中原	14101	1242	1211	620	6057	1473	906	10706	17.2	20.9
长株潭	23623	1228	1257	526	6903	1314	830	11664	18.8	19.1
北部湾	12650	407	464	267	2141	669	412	4817	18.1	31.0
关中	26919	965	1041	649	4552	1362	866	10094	16.8	30.4
重庆	26960	888	1175	653	6674	1754	1155	12657	22.3	23.8
成都	28893	1297	1180	714	6486	1587	1105	13339	17.5	27.2
合计	644309	45344	31131	18726	231040	41055	28741	358364	/	/

指标类型	具体指标	指标含义	数据来源及说明
投入指标	资本	全社会固定资产投资	源于《中国城市统计年鉴》和《中国县(市)社会经济统计年鉴》。其中2000-2013年精确至县级行政单位,统计口径为全社会固定资产投资;1995-2000年精确至地级市(或以上)行政单元,统计口径为固定资产投资
	土地	城市建成区面积	源于《中国城市统计年鉴》,精确到地级市(或以上)行政单元
	劳动	年末单位从业人员总数	源于《中国城市统计年鉴》和《中国县(市)社会经济统计年鉴》,精确到县级行政单位
	科技	科学事业支出经费	源于《中国城市统计年鉴》,精确到地级市(或以上)行政单元
产出指标	经济总量	地区生产总值	源于《中国城市统计年鉴》和《中国县(市)社会经济统计年鉴》,精确到县级行政单位

The dynamics and empirical analysis of input and output efficiency of urban agglomerations in China, 2000-2013: Based on the DEA model and Malmquist index method

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