Rural poverty reduction and its spatial spillover effects of Chinese urbanization: Based on the analysis of spatial econometric model with provincial panel data

Bosheng ZHANG; Zisheng YANG

doi:10.11821/dlyj020190775

Journals >Geographical Research >Volume 39 >Issue 7 >Page 1592 > Article

Geographical Research
Vol. 39, Issue 7, 1592 (2020)

Rural poverty reduction and its spatial spillover effects of Chinese urbanization: Based on the analysis of spatial econometric model with provincial panel data

Bosheng ZHANG¹ and Zisheng YANG^2、3、*

Author Affiliations

¹School of Economics, Yunnan University of Finance and Economics, Kunming 650221, China

²Institute of Land & Resources and Sustainable Development, Yunnan University of Finance and Economics, Kunming 650221, China

³Institute of Targeted Poverty Alleviation and Development, Yunnan University of Finance and Economics, Kunming 650221, China

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DOI: 10.11821/dlyj020190775 Cite this Article

Bosheng ZHANG, Zisheng YANG. Rural poverty reduction and its spatial spillover effects of Chinese urbanization: Based on the analysis of spatial econometric model with provincial panel data[J]. Geographical Research, 2020, 39(7): 1592 Copy Citation Text

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Fig. 1. Mechanism of rural labor productivity improvement driven by urbanization

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Fig. 2. Spatial clustering of rural poverty and urbanization in provinces of China from 2010 to 2017

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Fig. 3. Effects of rural poverty alleviation by population, land and economic urbanization respectively

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Fig. 4. Comparison of population urbanization and threshold value in provinces of China from 2010 to 2017

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Fig. 5. Comparison of economic urbanization and threshold value in provinces of China from 2010 to 2017

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变量类型	具体指标（变量）	指标含义（单位）
被解释变量	农村贫困发生率（pove）	农村贫困人口占农村总人口比例（%）
核心解释变量	人口城镇化（urb_p）	城镇常驻人口占总人口比例（%）
	土地城镇化（urb_l）	建成区面积占总面积比例（%）
	经济城镇化（urb_e）	第三产业产值占GDP的比例（%）
控制变量	经济增长（gdp_g）	GDP增长率（%）
	农村收入水平（inco）	农民人均纯收入（元）
	城乡收入差距（u_r_d）	城乡居民人均收入之比
	农村转移人口就业环境（empl）	城镇登记失业率（%）
	农村人力资本（huma）	农村人口平均受教育年限（年）
	农村市场化水平（mark）	农村人均社会消费品零售额（万元）
	农村资本投入（capi）	农村住户人均固定资产投资（万元）
	农村技术进步（tech）	农林牧渔业单位产值农用机械总动力（kW/万元）
	农村劳动生产率（prod）	农村单位劳动力农林牧渔业产出（万元）
	农村扶贫政策（poli）	农村低保人口与总人口之比（%）

Table 1. Variable description and explanation

变量	样本数	均值	方差	最小值	最大值
pove	216	10.1255	8.6124	0.0000	45.1000
urb_p	216	52.6840	8.3339	33.8100	69.8500
urb_l	216	1.0372	0.9466	0.0200	4.3100
urb_e	216	41.8125	5.8811	28.6000	56.1000
gdp_g	216	9.8121	2.8180	-2.5000	17.1000
u_r_d	216	2.7782	0.4432	2.0600	4.0700
empl	216	3.3942	0.5460	1.7300	4.4700
huma	216	8.3059	0.3220	7.4300	9.0700
mart	216	0.4719	0.3213	0.1100	2.0400
capi	216	0.1580	0.0542	0.0600	0.3700
tech	216	1.2882	0.5384	0.3900	2.7000
prod	216	81.3034	37.1621	17.6600	178.7300
poli	216	7.8815	4.9441	1.0900	23.5300

Table 2. Descriptive statistics

年份	pove		urb_p		urb_l		urb_e
年份	Moran′s I	P值	Moran′s I	P值	Moran′s I	P值	Moran′s I	P值
2010	0.476	<0.001	0.330	0.002	0.379	<0.001	0.202	0.027
2011	0.511	<0.001	0.307	0.003	0.381	<0.001	0.171	0.048
2012	0.501	<0.001	0.291	0.005	0.392	<0.001	0.142	0.078
2013	0.490	<0.001	0.293	0.005	0.400	<0.001	0.139	0.082
2014	0.496	<0.001	0.286	0.005	0.498	<0.001	0.016	0.334
2015	0.492	<0.001	0.293	0.005	0.409	<0.001	-0.155	0.182
2016	0.457	<0.001	0.301	0.004	0.396	<0.001	-0.184	0.128
2017	0.453	<0.001	0.302	0.004	0.406	<0.001	-0.045	0.481

Table 3. Global Moran's I of rural poverty and urbanization in provinces of China from 2010 to 2017

普通OLS模型检验统计量	数值	P值	空间面板模型检验统计量	数值	P值
F值	68.26	<0.001	Hausman	72.73	<0.001
Adj R²	0.82		Robust Hausman	210.99	<0.001
LM_Spatial error	26.86	<0.001	Wald_Spatial error	61.66	<0.001
Robust LM_Spatial error	0.24	0.623	LR_Spatial error	69.13	<0.001
LM_Spatial lag	62.57	<0.001	Wald_Spatial lag	55.91	<0.001
Robust LM_Spatial lag	35.96	<0.001	LR_Spatial lag	51.11	<0.001

Table 4. Results of model test

变量	SPDM			SPLM	SPEM	非空间固定效应	SPDM空间滞后项（W_X）
	空间固定	时间固定	双固定	空间固定	空间固定	非空间固定效应	空间固定	时间固定	双固定
	模型（1）	模型（2）	模型（3）	模型（4）	模型（5）	模型（6）	模型（1）	模型（2）	模型（3）
urb_p	-3.851^***	-3.279^***	-4.035^***	-3.758^***	-4.232^***	-4.524^***	-1.733	-5.248^***	-2.637^**
	(-8.63)	(-7.02)	(-9.02)	(-9.56)	(-10.05)	(-5.29)	(-1.52)	(-4.60)	(-2.27)
(urb_p)²	0.039^***	0.029^***	0.039^***	0.031^***	0.036^***	0.034^***	0.012	0.057^***	0.023^*
	(8.37)	(6.25)	(8.55)	(7.48)	(8.21)	(3.64)	(1.01)	(5.07)	(1.81)
urb_l	1.848	-7.626^***	3.845	5.068	2.326	11.285	9.828	-7.051^**	18.255^*
	(0.42)	(-5.69)	(0.87)	(1.26)	(0.53)	(1.45)	(1.04)	(-2.34)	(1.84)
(urb_l)²	0.149	1.670^***	-0.302	-0.475	-0.835	-1.314	-1.984	1.108	-1.629
	(0.26)	(5.71)	(-0.50)	(-0.89)	(-1.44)	(-1.45)	(-1.25)	(1.42)	(-1.00)
urb_e	1.100^***	1.319^***	0.773^*	0.397	-0.218	0.017	1.624^**	-0.420	1.247
	(2.93)	(3.07)	(1.85)	(1.21)	(-0.60)	(0.02)	(2.18)	(-0.44)	(1.26)
(urb_e)²	-0.013^***	-0.017^***	-0.010^**	-0.006^*	0.001	-0.003	-0.022^**	-0.011	-0.019^*
	(-3.16)	(-3.37)	(-1.98)	(-1.68)	(0.31)	(-0.31)	(-2.55)	(-0.95)	(-1.68)
gdp_g	-0.013	0.076	-0.119	0.063	0.006	0.226	0.222	-0.925^***	-0.436^*
	(-0.12)	(0.57)	(-1.14)	(0.64)	(0.05)	(1.23)	(1.08)	(-3.11)	(-1.67)
u_r_d	0.378	1.081	-0.049	-1.122	2.612^*	-0.236	-3.250	-6.638^***	-1.689
	(0.25)	(1.01)	(-0.03)	(-0.90)	(1.81)	(-0.08)	(-1.36)	(-2.99)	(-0.48)
empl	1.898^***	-2.014^***	1.787^***	2.093^***	2.422^***	3.340^***	3.418^**	1.915^*	1.815
	(3.18)	(-5.02)	(3.05)	(3.50)	(3.70)	(3.44)	(2.07)	(1.66)	(1.05)
huma	-1.572	3.118^***	-1.372	-3.739^***	-3.697^***	-5.409^**	1.814	-0.266	4.269
	(-1.24)	(2.85)	(-1.09)	(-2.79)	(-2.75)	(-2.62)	(0.69)	(-0.10)	(1.47)
mart	3.038	-5.176^***	3.179	2.186	3.259^**	4.071^*	-5.708	-0.170	-5.400
	(1.52)	(-3.89)	(1.63)	(1.46)	(2.02)	(1.75)	(-1.50)	(-0.05)	(-1.31)
capi	-25.592^***	-5.970	-27.078^***	-21.471^***	-26.196^***	-27.301	-13.473	-60.926^***	2.430
	(-4.02)	(-1.13)	(-4.20)	(-4.20)	(-4.94)	(-1.25)	(-1.01)	(-4.66)	(0.16)
tech	0.690	0.392	0.578	0.335	0.884	0.203	-0.843	1.815	-0.691
	(1.04)	(0.54)	(0.87)	(0.52)	(1.37)	(0.18)	(-0.62)	(1.25)	(-0.44)
prod	-0.039^**	-0.037^***	-0.041^**	-0.022	-0.039^**	-0.038	0.016	-0.100^***	0.021
	(-2.05)	(-2.99)	(-2.14)	(-1.38)	(-2.03)	(-1.68)	(0.49)	(-3.27)	(0.59)
poli	0.249^***	0.096	0.231^***	0.092^**	0.196^***	0.115^**	-0.178^**	0.214	-0.108
	(3.70)	(1.13)	(3.50)	(2.17)	(2.63)	(2.17)	(-2.30)	(1.49)	(-1.00)
ρ/λ	0.361^***	0.205^**	0.080	0.465^***	0.780^***
R²	0.936	0.829	0.321	0.914	0.836	0.889
Log L	-386.542	-475.152	-373.455	-415.473	-429.328	-449.930

Table 5. Estimation of spatial and non-spatial panel fixed effects model

变量	直接效应		间接效应		总效应
变量	系数	P值	系数	P值	系数	P值
urb_p	-4.148^***	<0.001	-4.669^***	0.004	-8.817^***	<0.001
(urb_p)²	0.041^***	<0.001	0.040^**	0.036	0.081^***	<0.001
urb_l	3.245	0.470	14.000	0.321	17.246	0.289
(urb_l)²	-0.083	0.886	-2.697	0.249	-2.780	0.286
urb_e	1.294^***	0.001	2.984^***	0.009	4.278^***	0.002
(urb_e)²	-0.016^***	<0.001	-0.039^***	0.003	-0.055^***	0.001
gdp_g	0.012	0.913	0.324	0.257	0.335	0.282
u_r_d	0.045	0.974	-4.602	0.186	-4.557	0.194
empl	2.354^***	<0.001	6.244^**	0.019	8.598^***	0.005
huma	-1.434	0.304	1.776	0.696	0.341	0.949
mart	2.545	0.260	-6.603	0.307	-4.059	0.616
capi	-27.285^***	<0.001	-32.802^*	0.095	-60.087^***	0.010
tech	0.633	0.353	-0.767	0.705	-0.134	0.954
prod	-0.040^**	0.037	0.006	0.902	-0.034	0.147
poli	0.244^***	<0.001	-0.127	0.159	0.117	0.538

Table 6. Decomposed spatial effects of SPDM with spatial fixed effects

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