交通基础设施、空间溢出与农村减贫——基于面板数据的实证研究

doi:10.3969/j.issn.1004-1524.2021.03.21

浙江农业学报 ›› 2021, Vol. 33 ›› Issue (3): 553-564.DOI: 10.3969/j.issn.1004-1524.2021.03.21

• 农业经济与发展 • 上一篇

交通基础设施、空间溢出与农村减贫——基于面板数据的实证研究

张莎莎, 郑循刚^*(), 张必忠

四川农业大学管理学院,四川成都 611130

收稿日期:2020-07-23 出版日期:2021-04-02 发布日期:2021-03-25
通讯作者: 郑循刚
作者简介:*, 郑循刚,E-mail: zxg9@163.com
张莎莎(1983—),女,河北保定人,博士研究生,主要从事农业经济理论与政策研究。E-mail: shasha20072008@163.com
基金资助:
四川省科技厅软科学项目(2019JDR0178)

Transportation infrastructure, spatial spillover and rural poverty reduction:an empirical study based on panel data

ZHANG Shasha, ZHENG Xungang^*(), ZHANG Bizhong

School of Management, Sichuan Agricultural University, Chengdu 611130, China

Received:2020-07-23 Online:2021-04-02 Published:2021-03-25
Contact: ZHENG Xungang

摘要/Abstract

摘要：

交通基础设施建设是农村扶贫开发的一项重要工作。利用2008—2017年中国除香港、澳门、台湾、西藏外的30个省级行政区的面板数据,构建邻接矩阵和反地理距离矩阵,在进行空间相关检验的基础上选择相应空间计量模型,研究交通基础设施密度对农村减贫的直接效应和空间溢出效应。结果显示:省级层面上,农村贫困水平表现出显著(P<0.01)的空间集聚特征,省际分布呈现出高-高集聚或低-低集聚的特点,且随时间发展局部空间集聚特征逐渐增强。整体来看,公路密度和铁路密度的增大不仅对本地的农村减贫具有显著(P<0.01)的促进作用,对邻近地区的农村减贫也具有显著(P<0.05)的空间溢出效应,且在这2种效应上都以铁路的效果更好。从区域层面来看,公路密度的增大对本地的农村减贫仅在西部地区有显著(P<0.01)的直接效应,对邻近地区的农村减贫仅在东部地区产生显著的(P<0.01)空间溢出效应。铁路密度的增大在东部和中部地区对本地和邻近地区的农村减贫都具有显著(P<0.05)的促进作用,且这2种效应在中部地区的效果都较东部地区更好。据此建议,东部和中部地区应该优化铁路网络结构,提高路网质量,增大铁路在农村地区的覆盖范围和通达程度;西部地区应该积极发展公路交通,同时补齐铁路短板,通过促进当地资源开发和物资输出,发挥带动就业和减贫的作用。

关键词: 交通基础设施, 农村减贫, 空间计量模型, 溢出效应, 实证研究

Abstract:

The construction of transportation infrastructure is an important work for rural poverty reduction. In this paper, the panel data of 30 provincial administrative regions in China (Hong Kong, Macau, Taiwan, Tiebet not included) from 2008 to 2017 were used to construct adjacency matrix and inverse geographical distance matrix. Based on the spatial correlation test, the corresponding spatial econometric model was selected to study the direct effect and spatial spillover effect of transportation infrastructure density on rural poverty reduction. The results showed that, the rural poverty level exhibited a significant (P<0.01) spatial clustering feature on the whole, while the inter-provincial distribution exhibited the characteristics of high-high or low-low agglomeration, and this characteristic gradually increased with time. From a national perspective,the increase of road density and railway density not only significantly (P<0.01) promoted the local rural poverty reduction, but also had a significant (P<0.05) spatial spillover effect on the rural poverty reduction in neighboring areas, and the railway had a better poverty reduction effect in both of the two effects. From a regional perspective, the increase of road density not only significantly (P<0.01) promoted the local rural poverty reduction in the western region, but also had a significant (P<0.01) spatial spillover effect on the rural poverty reduction in neighboring areas in the eastern region. The increase of railway density had a significant (P<0.05) role in promoting rural poverty reduction in the local and neighboring areas in both the eastern and central regions, and the effect on poverty reduction in the central region was better than that in the eastern region in both of the two effects. Therefore, it was suggested that the government in the eastern and central regions should optimize the railway network structure, improve the quality of the road network, and increase the coverage and accessibility of railways in rural areas. In the western region, the government should actively develop road traffic, and at the same time make up for the shortcomings of railways. By promoting local resource development and material export, it would play a role in promoting employment and realizing poverty reduction.

Key words: transportation infrastructure, rural poverty, spatial econometric model, spillover effect, empirical study

中图分类号:

S-9
F328

张莎莎, 郑循刚, 张必忠. 交通基础设施、空间溢出与农村减贫——基于面板数据的实证研究[J]. 浙江农业学报, 2021, 33(3): 553-564.

ZHANG Shasha, ZHENG Xungang, ZHANG Bizhong. Transportation infrastructure, spatial spillover and rural poverty reduction:an empirical study based on panel data[J]. Acta Agriculturae Zhejiangensis, 2021, 33(3): 553-564.

图/表 9

参考文献 27

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变量 Variable	样本数量 Sample number	平均数 Mean	标准差 Standard deviation	最小值 Minimum	最大值 Maximum
Y	300	0.079	0.046	0.010	0.235
R_oad/(km·km^-2)	300	0.896	0.482	0.079	2.109
R_ail/(km·km^-2)	300	0.024	0.019	0.002	0.095
U_rban	300	0.547	0.132	0.291	0.896
H_uman	300	0.028	0.018	0.006	0.141
F_inance	300	0.111	0.030	0.030	0.190
S_ecurity	300	0.018	0.003	0.013	0.076

变量 Variable	样本数量 Sample number	平均数 Mean	标准差 Standard deviation	最小值 Minimum	最大值 Maximum
Y	300	0.079	0.046	0.010	0.235
R_oad/(km·km^-2)	300	0.896	0.482	0.079	2.109
R_ail/(km·km^-2)	300	0.024	0.019	0.002	0.095
U_rban	300	0.547	0.132	0.291	0.896
H_uman	300	0.028	0.018	0.006	0.141
F_inance	300	0.111	0.030	0.030	0.190
S_ecurity	300	0.018	0.003	0.013	0.076

年份Year	I₁	Z	P
2008	0.528	4.902	<0.001
2009	0.638	5.579	<0.001
2010	0.468	4.393	<0.001
2011	0.497	4.590	<0.001
2012	0.485	4.435	<0.001
2013	0.496	4.466	<0.001
2014	0.502	4.505	<0.001
2015	0.500	4.536	<0.001
2016	0.575	5.083	<0.001
2017	0.606	5.337	<0.001

年份Year	I₁	Z	P
2008	0.528	4.902	<0.001
2009	0.638	5.579	<0.001
2010	0.468	4.393	<0.001
2011	0.497	4.590	<0.001
2012	0.485	4.435	<0.001
2013	0.496	4.466	<0.001
2014	0.502	4.505	<0.001
2015	0.500	4.536	<0.001
2016	0.575	5.083	<0.001
2017	0.606	5.337	<0.001

检验 Test	统计量 Statistics	P
豪斯曼检验Hausman	23.350	0.015
空间滞后模型拉格朗日乘子检验 Spatial lag LM	5.752	0.016
空间误差模型拉格朗日乘子检验 Spatial error LM	1.340	0.247
空间滞后模型稳健性拉格朗日乘子检验 Spatial lag robust LM	4.493	0.034
空间误差模型稳健性拉格朗日乘子检验 Spatial error robust LM	0.080	0.777
空间固定效应似然比检验 Spatial fixed effects LR	32.085	0.364
时间固定效应似然比检验 Time fixed effects LR	93.256	<0.001
空间滞后模型瓦尔德检验 Spatial lag Wald	10.382	0.110
空间误差模型瓦尔德检验 Spatial error Wald	14.970	0.021

交通基础设施、空间溢出与农村减贫——基于面板数据的实证研究

Transportation infrastructure, spatial spillover and rural poverty reduction:an empirical study based on panel data

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变量 Variable	普通最小二乘法OLS		空间滞后模型SAR		空间杜宾模型SDM
变量 Variable	系数Coefficient	t统计量t-statistics	系数Coefficient	t统计量t-statistics	系数Coefficient	t统计量t-statistics
R_oad	-0.018^***	-7.41	-0.014^***	-4.74	-0.010^***	-2.72
R_ail	-0.543^***	-7.86	-0.365^***	-4.37	-0.315^***	-2.89
U_rban	-0.052^***	-5.54	-0.040^***	-3.35	-0.050^***	-3.51
H_uman	-0.198^***	-2.86	-0.029^*	-1.87	-0.036^**	-2.21
F_inance	0.069^**	2.10	0.106^*	1.82	0.110^*	1.81
S_ecurity	2.475^***	24.20	2.537^***	28.91	2.518^***	28.10
W₁*R_oad	—	—	—	—	-0.015^**	-2.19
W₁*R_ail	—	—	—	—	0.085	0.51
W₁*U_rban	—	—	—	—	-0.025	-0.95
W₁*H_uman	—	—	—	—	-0.031	-1.03
W₁*F_inance	—	—	—	—	-0.244^**	-2.19
W₁*S_ecurity	—	—	—	—	-0.170	-0.66
ρ	—	—	0.121^***	3.22	0.118	1.44
R²	0.872	—	0.877	—	0.875	—

变量 Variable	直接效应 Direct effect	t统计量 t-statistics	间接效应 Indirect effect	t统计量 t-statistics	总效应 Total effect	t统计量 t-statistic
R_oad	-0.015^***	-5.72	-0.002^**	-2.57	-0.017^***	-6.08
R_ail	-0.463^***	-5.97	-0.056^**	-2.56	-0.519^***	-6.26
U_rban	-0.052^***	-5.35	-0.006^**	-2.21	-0.058^***	-5.12
H_uman	-0.201^***	-2.93	-0.024^*	-1.92	-0.225^***	2.94
F_inance	0.063^*	1.75	0.008	1.33	0.071^*	1.74
S_ecurity	2.407^***	29.77	0.297^**	2.57	2.704^***	22.10

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变量 Variable	东部Eastern(n=110)		中部Central(n=80)		西部Western(n=110)
变量 Variable	系数Coefficient	t统计量t-statistics	系数Coefficient	t统计量t-statistics	系数Coefficient	t统计量t-statistics
R_oad	-0.005	-1.47	-0.007	-0.89	-0.030^***	-3.61
R_ail	-0.528^***	-6.11	-1.816^***	-3.80	0.203	0.62
U_rban	0.011	1.21	0.027	1.42	-0.110^**	-2.00
H_uman	0.085^***	2.59	0.033	0.88	0.147^**	2.05
F_inance	0.028	0.13	1.573^***	4.33	2.218^***	12.16
S_ecurity	-0.040	-0.60	0.076	0.39	-0.018	-0.06
W₂*R_oad	-0.041^***	-5.48	-0.001	-0.11	0.040	-1.61
W₂*R_ail	-0.203^**	-1.98	-2.161^***	-3.81	1.551	0.83
W₂*U_rban	0.106^***	3.95	0.033	0.95	-0.052	-0.46
W₂*H_uman	0.128^*	1.76	-0.003	-0.05	0.268	1.45
W₂*F_inance	0.985^***	3.46	3.518^***	4.75	0.528	0.94
W₂*S_ecurity	0.152	1.42	0.100	0.24	-2.254	-2.89
ρ	0.340^***	2.93	0.198^**	2.11	0.162^*	1.70
R²	0.596	—	0.303	—	0.578	—

变量 Variable	东部Eastern(n=110)		中部Central(n=80)		西部Western(n=110)
变量 Variable	系数Coefficient	t统计量t-statistics	系数Coefficient	t统计量t-statistics	系数Coefficient	t统计量t-statistics
R_oad	-0.005	-1.47	-0.007	-0.89	-0.030^***	-3.61
R_ail	-0.528^***	-6.11	-1.816^***	-3.80	0.203	0.62
U_rban	0.011	1.21	0.027	1.42	-0.110^**	-2.00
H_uman	0.085^***	2.59	0.033	0.88	0.147^**	2.05
F_inance	0.028	0.13	1.573^***	4.33	2.218^***	12.16
S_ecurity	-0.040	-0.60	0.076	0.39	-0.018	-0.06
W₂*R_oad	-0.041^***	-5.48	-0.001	-0.11	0.040	-1.61
W₂*R_ail	-0.203^**	-1.98	-2.161^***	-3.81	1.551	0.83
W₂*U_rban	0.106^***	3.95	0.033	0.95	-0.052	-0.46
W₂*H_uman	0.128^*	1.76	-0.003	-0.05	0.268	1.45
W₂*F_inance	0.985^***	3.46	3.518^***	4.75	0.528	0.94
W₂*S_ecurity	0.152	1.42	0.100	0.24	-2.254	-2.89
ρ	0.340^***	2.93	0.198^**	2.11	0.162^*	1.70
R²	0.596	—	0.303	—	0.578	—