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

doi:10.3969/j.issn.1004-1524.2021.03.21

Acta Agriculturae Zhejiangensis ›› 2021, Vol. 33 ›› Issue (3): 553-564.DOI: 10.3969/j.issn.1004-1524.2021.03.21

• Agricultural Economy and Development • Previous Articles

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

Abstract

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

CLC Number:

S-9
F328

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.

Figures/Tables 9

References 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

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	—