中国旅游全要素生产率及其分解研究

doi:10.18402/resci.2018.12.13

摘要/Abstract

摘要：

为更为有效测算旅游全要素生产率,弥补传统效率测算方法的局限,本文将超效率数据包络构造思路引入到双前沿面下的DEA-Malmquist指数计算之中,构建了基于双前沿面的超效率数据包络分析模型,并补充规模报酬可变的条件约束,构建出更具可信性的基于双前沿面的中国旅游全要素生产率测度及分解模型,进而对2000—2015年中国30个省（直辖市、自治区）旅行社、星级酒店与旅游景区三种类型企业的全要素生产率进行比较与分析,结论显示：三类旅游企业的全要素生产率整体呈现增长趋势,但其演化态势与增长的决定因素亦存在较大差异;同类旅游企业全要素生产率增长的决定因素存在区域异质性;同区域旅游企业全要素生产率增长的决定因素存在行业异质性。其可能的原因是现代信息技术对旅游企业资源配置的影响存在行业差异,制度供给对旅游细分行业发展的影响存在差异,以及三类旅游企业自身发展过程中演化形成不同的行业特质。

关键词: 全要素生产率, 双前沿面数据包络分析, 旅游企业, 区域差异, 行业异质性, 中国

Abstract:

The current study introduces the mindset of super-efficiency data envelope into the DEA-Malmquist index calculation under the double frontier. We further construct a super-efficient data envelop analysis model based on double frontiers and relaxes the constraint of constant returns to scale. This model can not only make an effective distinction between multiple decision making units at the same frontier, but also give a consideration to the impact of scale agglomeration on total factor productivity. The current study monitored the total factor productivity of travel agencies, star hotels, and tourist attractions of 30 provinces (municipalities and autonomous regions) in China from 2000 to 2015. The heterogeneity of the productivity of the three types of tourism enterprises and the differences in their dominant mechanisms were further characterized based on the complexity of the structure, nature and multiple objectives of the industry. The main conclusions are as the following: the total factor productivity of the three types of tourism enterprises exhibited an overall growth trend, but the trend of change and the source of growth were also different. There are regional differences in the total factor productivity growth source of tourism enterprises of the same kind. The source of total factor productivity growth of tourism enterprises in the same region has industrial heterogeneity. The possible internal mechanism is that the modern information technology has heterogeneous influence on the resource allocation of different tourism enterprises, and the influence of the system supply on the development of tourism sub-sectors is different, as well as the different industry characteristics in the process of evolution of the three types of tourism enterprises.

Key words: total factor productivity, double frontier data envelop analysis, tourism enterprises, regional differences, industry heterogeneity, China

查建平, 钱醒豹, 赵倩倩, 舒皓羽. 中国旅游全要素生产率及其分解研究[J]. 资源科学, 2018, 40(12): 2461-2474.

Jianping ZHA, Xingbao QIAN, Qianqian ZHAO, Haoyu SHU. Estimation and decomposition of total factors productivity of China’s tourism[J]. Resources Science, 2018, 40(12): 2461-2474.

图/表 4

表1

图1

表2

2000—2015年中国三类旅游企业全要素生产率指数及其分解"

地区	景区				星级酒店				旅行社
地区	RMMI	RPTE	RSE	RTP	RMMI	RPTE	RSE	RTP	RMMI	RPTE	RSE	RTP
北京	0.996	1.026	0.961	1.010	1.009	1.012	0.978	1.020	1.095	1.004	1.064	1.024
天津	0.946	0.981	0.938	1.029	1.036	1.024	1.004	1.008	1.130	1.033	1.038	1.054
河北	0.939	0.975	0.953	1.009	0.951	0.949	0.989	1.013	1.039	0.959	1.023	1.058
山东	1.047	1.036	0.998	1.013	1.040	0.997	1.025	1.018	1.066	0.973	1.036	1.057
福建	1.062	1.045	1.009	1.008	1.040	1.019	1.020	1.001	1.113	1.008	1.055	1.047
上海	0.925	1.027	0.889	1.013	1.050	1.034	1.003	1.013	1.112	1.022	1.061	1.025
辽宁	1.022	1.054	0.963	1.008	1.009	0.990	1.004	1.016	1.155	1.020	1.055	1.073
江苏	1.037	1.039	0.986	1.013	1.023	1.010	0.993	1.020	1.055	0.978	1.066	1.012
浙江	0.990	0.945	1.034	1.014	1.019	1.015	0.995	1.008	1.107	0.986	1.055	1.064
广东	0.809	1.053	0.989	0.777	0.992	0.983	0.991	1.018	1.146	1.002	1.067	1.071
海南	1.045	1.023	1.015	1.006	1.058	1.022	1.019	1.016	1.141	1.026	1.035	1.076
东部均值	0.981	1.018	0.975	0.988	1.020	1.005	1.002	1.014	1.105	1.001	1.050	1.051
吉林	0.961	1.008	0.944	1.010	1.072	1.059	0.995	1.017	1.070	0.984	1.003	1.084
黑龙江	1.034	0.995	1.031	1.009	1.007	1.016	0.979	1.013	1.080	1.002	1.012	1.066
山西	1.044	1.085	0.952	1.011	0.986	0.970	1.007	1.009	1.096	0.991	1.019	1.086
内蒙古	1.065	1.007	1.050	1.008	0.993	0.981	1.001	1.010	1.057	0.981	0.999	1.078
安徽	1.204	1.091	1.100	1.004	1.039	1.025	1.004	1.009	1.070	0.977	1.023	1.071
江西	1.000	1.024	0.967	1.010	1.027	0.980	1.036	1.012	1.101	0.985	1.025	1.090
河南	1.152	1.048	1.086	1.012	0.999	0.989	1.000	1.010	1.064	0.982	1.014	1.068
湖北	1.029	1.034	0.982	1.014	1.022	1.015	0.996	1.012	1.088	1.030	1.036	1.020
湖南	1.251	1.180	1.057	1.003	1.003	0.989	1.004	1.010	1.148	1.002	1.046	1.095
广西	1.038	1.059	0.971	1.010	1.073	1.057	1.004	1.012	1.109	1.004	1.038	1.065
中部均值	1.075	1.052	1.012	1.009	1.022	1.008	1.002	1.011	1.088	0.994	1.022	1.072
重庆	0.921	0.901	1.011	1.011	1.032	1.004	1.015	1.012	1.172	1.044	1.064	1.054
四川	1.015	1.029	0.976	1.010	1.010	1.006	0.991	1.013	1.105	1.016	1.012	1.074
贵州	0.992	1.035	0.945	1.014	1.028	0.993	1.024	1.010	1.115	1.021	1.023	1.067
云南	1.022	0.965	1.047	1.011	0.999	0.995	0.991	1.013	1.155	1.047	1.049	1.052
陕西	1.151	1.072	1.063	1.010	1.050	1.022	1.016	1.011	1.055	0.975	1.007	1.074
甘肃	0.965	0.966	0.987	1.011	1.007	0.989	1.007	1.011	1.063	1.014	0.989	1.061
青海	0.881	0.940	0.925	1.014	1.158	1.167	0.984	1.009	1.093	1.039	0.969	1.085
宁夏	0.999	1.018	0.969	1.013	1.017	0.993	1.012	1.012	1.112	1.007	1.016	1.087
新疆	1.082	1.076	0.993	1.013	1.010	0.994	1.001	1.015	1.061	0.997	1.005	1.059
西部均值	1.000	0.999	0.990	1.012	1.034	1.017	1.004	1.012	1.103	1.018	1.014	1.068
全国均值	1.017	1.023	0.992	1.002	1.025	1.009	1.003	1.012	1.099	1.003	1.030	1.063

表2

图2

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