旅游援疆效率的区域差异和影响因素——基于三阶段DEA和Tobit模型

doi:10.12118/j.issn.1000-6060.2022.422

Abstract

Abstract:

The in-depth implementation of aid policy for Xinjiang promotes the efficiency of tourism aid in the region and improves the high-quality development of its tourism economy. Based on the three-stage data envelopment analysis and Tobit model, this paper measured the factors influencing the efficiency of tourism aid in Xinjiang from 2015 to 2019 and regional differences. The results show that: (1) Environmental factors have a strong influence on tourism aid efficiency. Overall, there is a large margin for improvement in tourism aid efficiency, which is mainly affected by efficiencies of scale. The trend of efficiency value change is pure technical efficiency>scale efficiency>comprehensive efficiency, and a distribution pattern corresponding to high values in the north of the region and low values in the south is evident. (2) Regional dummy variables, investment level, per capita educational investment, and attraction of tourism resources significantly improved efficiency. (3) From the perspective of vertical and horizontal compatibility and internal and external suitability, the mechanism influencing the efficiency of tourism aid in Xinjiang is summarized as comprising three periods, three dimensions, and seven aspects, and the changes and evolutionary course of tourism aid in Xinjiang are identified. The results have theoretical value and a meaningful reference for the macro-adjustment of aid policy in Xinjiang and the optimal allocation of aid resources in the future.

Key words: efficiency of tourism aid for Xinjiang, regional differences, influencing factors, three-stage data envelopment analysis model, Tobit model

BAI Yang,HU Jingxuan,CHEN Chunyan,LU Wen. Regional differences and influencing factors of efficiency of tourism aid for Xinjiang: Based on three-stage DEA and Tobit model[J].Arid Land Geography, 2023, 46(8): 1366-1375.

Figures/Tables 7

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References 28

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指标类型	变量	指标说明	单位
投入变量	旅游援疆资金	旅游援疆资金	10⁴元
	旅游援疆项目	旅游援疆项目数	个
	旅游人力资本	住宿、餐饮业、文化、体育、娱乐业从业人员数量总和	人
产出变量	人均实际旅游收入	实际旅游总收入/年末人口数	元
产出变量	旅游接待人次	旅游接待人次	10⁴人次
环境变量	旅游接待设施	旅行社、星级宾馆数量（熵值赋权法合成）	家
	对外开放程度	进出口贸易总额/GDP	-
	人均地区生产总值	地区生产总值/年末人口数	元

年份	效率	吐鲁番市	哈密市	昌吉州	伊犁州直	塔城地区	阿勒泰地区	博州	巴州	阿克苏地区	克州	喀什地区	和田地区	均值
2015	TE	1.000	0.382	1.000	1.000	0.517	0.743	0.205	0.729	0.372	0.198	0.244	0.085	0.540
	PTE	1.000	0.487	1.000	1.000	0.796	0.819	0.448	0.740	0.943	1.000	0.389	0.723	0.779
	SE	1.000	0.786	1.000	1.000	0.649	0.907	0.458	0.985	0.394	0.198	0.628	0.117	0.677
	状态	-	irs	-	-	irs	irs	irs	drs	irs	irs	irs	irs
2016	TE	1.000	0.341	1.000	1.000	0.325	1.000	0.752	0.688	0.496	0.177	0.364	0.107	0.604
	PTE	1.000	0.659	1.000	1.000	0.961	1.000	1.000	0.876	1.000	1.000	0.542	0.816	0.905
	SE	1.000	0.518	1.000	1.000	0.338	1.000	0.752	0.785	0.496	0.177	0.672	0.132	0.656
	状态	-	irs	-	-	irs	-	irs	irs	irs	irs	irs	irs
2017	TE	1.000	0.399	1.000	1.000	0.238	0.763	0.816	0.667	0.262	0.290	0.174	0.117	0.561
	PTE	1.000	0.560	1.000	1.000	0.705	0.828	1.000	0.684	0.914	1.000	0.293	0.797	0.815
	SE	1.000	0.712	1.000	1.000	0.338	0.921	0.816	0.976	0.287	0.290	0.594	0.147	0.673
	状态	-	irs	-	-	irs	drs	irs	drs	irs	irs	irs	irs
2018	TE	1.000	0.420	0.772	1.000	0.233	0.871	0.536	0.682	0.275	0.196	0.230	0.167	0.532
	PTE	1.000	0.700	1.000	1.000	0.628	1.000	0.840	0.722	0.663	1.000	0.359	0.969	0.823
	SE	1.000	0.600	0.772	1.000	0.371	0.871	0.638	0.945	0.415	0.196	0.641	0.172	0.635
	状态	-	irs	drs	-	irs	drs	irs	irs	irs	irs	irs	irs
2019	TE	1.000	0.511	1.000	1.000	0.414	1.000	0.440	1.000	0.388	0.201	0.296	0.295	0.629
	PTE	1.000	0.822	1.000	1.000	0.867	1.000	0.870	1.000	0.698	1.000	0.454	1.000	0.893
	SE	1.000	0.621	1.000	1.000	0.477	1.000	0.506	1.000	0.555	0.201	0.653	0.295	0.692
	状态	-	irs	-	-	irs	-	irs	-	irs	irs	irs	irs

解释变量	系数	标准差	t统计量	显著性水平
人均教育投资	0.130	0.075	1.74	0.088^*
技术创新	0.001	0.003	0.46	0.648
投资水平	0.175	0.098	1.78	0.080^*
人口密度	0.008	0.003	2.34	0.023^**
旅游资源吸引力	0.119	0.064	1.86	0.068^*
地区虚拟变量	0.562	0.080	7.05	0.000^***
常数项	-1.483	0.600	-2.47	0.017

Regional differences and influencing factors of efficiency of tourism aid for Xinjiang: Based on three-stage DEA and Tobit model

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