Yield-increasing effect of grain total factor productivity in China from 2008 to 2018 based on dynamic space Durbin model

doi:10.3969/j.issn.1004-1524.2021.11.22

Acta Agriculturae Zhejiangensis ›› 2021, Vol. 33 ›› Issue (11): 2195-2204.DOI: 10.3969/j.issn.1004-1524.2021.11.22

• Agricultural Economy and Development • Previous Articles Next Articles

Yield-increasing effect of grain total factor productivity in China from 2008 to 2018 based on dynamic space Durbin model

LUO Haiping^a^,^b(), HE Zhiwen^a^,^b^,^*(), LI Zhuoya^a^,^b

a. Research Center for Centr-China Economic Development, Nanchang University, Nanchang 330047, China
b. School of Economics & Management, Nanchang University, Nanchang 330047, China

Received:2020-10-06 Online:2021-11-25 Published:2021-11-26
Contact: HE Zhiwen

Abstract

Abstract:

In the present study, the yield-increasing effect of grain total factor productivity at provincial level in China (Hong Kong, Macao, Taiwan excluded) from 2008 to 2018 was empirically measured based on the dynamic spatial Durbin model. It was shown that the grain total factor productivity increased steadily from 1.024 in 2008 to 1.156 in 2018 in China, and the number of high-efficiency production regions increased from 0 in 2008 to 11 in 2018, but the “Matthew effect” emerged in the process of improvement. The rise in grain total factor productivity could significantly contribute to the increase of grain yield, and its effect was second to the sown area of grains. At the national level, the increase in grain total factor productivity in the neighboring region brought a significant positive spillover effect to the local region. From the spatial structure, the spillover effect of grain total factor productivity showed significant spatial heterogeneity, with a significant negative spillover effect in the central China, a positive spillover effect in west China, and no significant spillover effect in east China.

Key words: food security, spillover effect, dynamic spatial model

CLC Number:

S-9
F321.1

LUO Haiping, HE Zhiwen, LI Zhuoya. Yield-increasing effect of grain total factor productivity in China from 2008 to 2018 based on dynamic space Durbin model[J]. Acta Agriculturae Zhejiangensis, 2021, 33(11): 2195-2204.

Figures/Tables 5

References 33

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区域Region	2008	2009	2010	2011	2012	2013	2014	2015	2016	2017	2018
全国Nationwide	1.024	1.003	1.009	1.041	1.058	1.075	1.072	1.084	1.091	1.146	1.156
东部East China	1.016	1.001	1.003	1.05	1.056	1.068	1.042	1.071	1.091	1.129	1.139
中部Central China	1.051	1.011	1.041	1.084	1.102	1.123	1.13	1.134	1.126	1.236	1.222
西部West China	1.013	0.998	0.994	1.004	1.031	1.050	1.061	1.062	1.068	1.102	1.127
北京Beijing	1.077	1.004	1.004	1.127	1.137	1.171	1.029	1.161	1.190	1.191	1.189
天津Tianjin	1.009	1.025	1.025	1.043	1.004	1.052	1.021	1.041	1.103	1.212	1.210
河北Hebei	1.024	1.027	1.027	1.094	1.117	1.155	1.150	1.141	1.185	1.247	1.227
山西Shanxi	0.997	0.999	0.999	1.069	1.123	1.143	1.162	1.104	1.211	1.553	1.528
内蒙古Inner Mongolia	1.132	1.103	1.103	1.213	1.240	1.310	1.241	1.248	1.227	1.379	1.467
辽宁Liaoning	1.043	0.940	0.940	1.087	1.089	1.151	0.917	1.027	1.101	1.168	1.095
吉林Jilin	1.118	1.025	1.025	1.085	1.106	1.258	1.216	1.200	1.225	1.321	1.144
黑龙江Heilongjiang	1.175	1.245	1.245	1.366	1.339	1.347	1.355	1.338	1.270	1.476	1.477
上海Shanghai	1.040	1.047	1.047	1.040	1.036	1.077	1.084	1.099	1.125	1.191	1.266
江苏Jiangsu	1.006	1.026	1.026	1.043	1.059	1.070	1.088	1.100	1.069	1.095	1.123
浙江Zhejiang	1.025	1.015	1.015	1.047	1.034	0.984	1.005	0.990	1.007	0.998	1.032
安徽Anhui	1.029	1.038	1.038	1.056	1.108	1.104	1.150	1.190	1.147	1.248	1.249
福建Fujian	1.019	1.016	1.016	1.037	1.038	1.046	1.054	1.049	1.047	1.106	1.129
江西Jiangxi	1.015	0.989	0.989	1.025	1.035	1.048	1.056	1.053	1.056	1.111	1.146
山东Shandong	1.024	1.023	1.023	1.035	1.047	1.037	1.032	1.051	1.045	1.062	1.057
河南Henan	1.009	1.009	1.009	1.016	1.021	1.025	1.023	1.069	1.046	1.081	1.103
湖北Hubei	1.033	1.027	1.027	1.045	1.054	1.060	1.067	1.093	1.039	1.059	1.058
湖南Hunan	1.029	0.996	0.996	1.013	1.030	0.996	1.014	1.022	1.015	1.038	1.067
广东Guangdong	0.944	0.988	0.988	1.021	1.044	0.997	1.029	1.030	1.030	1.058	1.053
广西Guangxi	1.002	0.989	0.989	0.998	1.038	1.062	1.074	1.069	1.080	1.031	1.050
海南Hainan	0.969	0.920	0.920	0.974	1.015	1.010	1.056	1.092	1.101	1.091	1.149
重庆Chongqing	1.027	0.971	0.971	0.938	0.934	0.946	0.927	0.923	0.927	0.855	0.860
四川Sichuan	1.015	1.008	1.008	1.019	1.020	1.046	1.044	1.067	1.081	1.113	1.129
贵州Guizhou	0.945	0.825	0.825	0.615	0.739	0.785	0.828	0.821	0.823	0.895	0.824
云南Yunnan	1.018	0.991	0.991	1.053	1.072	1.112	1.132	1.146	1.165	1.213	1.221
西藏Tibet	1.001	0.949	0.949	0.966	0.957	0.898	0.942	0.913	0.921	0.984	0.990
陕西Shaanxi	1.031	1.073	1.073	1.109	1.159	1.158	1.151	1.180	1.183	1.169	1.205
甘肃Gansu	1.055	1.088	1.088	1.132	1.205	1.216	1.224	1.234	1.236	1.298	1.361
青海Qinghai	0.930	0.890	0.890	0.920	0.853	0.838	0.862	0.835	0.890	0.884	0.904
宁夏Ningxia	1.053	1.104	1.104	1.101	1.183	1.218	1.281	1.264	1.245	1.340	1.396
新疆Xinjiang	0.946	0.945	0.945	0.979	0.978	1.009	1.028	1.041	1.033	1.060	1.110

区域Region	2008	2009	2010	2011	2012	2013	2014	2015	2016	2017	2018
全国Nationwide	1.024	1.003	1.009	1.041	1.058	1.075	1.072	1.084	1.091	1.146	1.156
东部East China	1.016	1.001	1.003	1.05	1.056	1.068	1.042	1.071	1.091	1.129	1.139
中部Central China	1.051	1.011	1.041	1.084	1.102	1.123	1.13	1.134	1.126	1.236	1.222
西部West China	1.013	0.998	0.994	1.004	1.031	1.050	1.061	1.062	1.068	1.102	1.127
北京Beijing	1.077	1.004	1.004	1.127	1.137	1.171	1.029	1.161	1.190	1.191	1.189
天津Tianjin	1.009	1.025	1.025	1.043	1.004	1.052	1.021	1.041	1.103	1.212	1.210
河北Hebei	1.024	1.027	1.027	1.094	1.117	1.155	1.150	1.141	1.185	1.247	1.227
山西Shanxi	0.997	0.999	0.999	1.069	1.123	1.143	1.162	1.104	1.211	1.553	1.528
内蒙古Inner Mongolia	1.132	1.103	1.103	1.213	1.240	1.310	1.241	1.248	1.227	1.379	1.467
辽宁Liaoning	1.043	0.940	0.940	1.087	1.089	1.151	0.917	1.027	1.101	1.168	1.095
吉林Jilin	1.118	1.025	1.025	1.085	1.106	1.258	1.216	1.200	1.225	1.321	1.144
黑龙江Heilongjiang	1.175	1.245	1.245	1.366	1.339	1.347	1.355	1.338	1.270	1.476	1.477
上海Shanghai	1.040	1.047	1.047	1.040	1.036	1.077	1.084	1.099	1.125	1.191	1.266
江苏Jiangsu	1.006	1.026	1.026	1.043	1.059	1.070	1.088	1.100	1.069	1.095	1.123
浙江Zhejiang	1.025	1.015	1.015	1.047	1.034	0.984	1.005	0.990	1.007	0.998	1.032
安徽Anhui	1.029	1.038	1.038	1.056	1.108	1.104	1.150	1.190	1.147	1.248	1.249
福建Fujian	1.019	1.016	1.016	1.037	1.038	1.046	1.054	1.049	1.047	1.106	1.129
江西Jiangxi	1.015	0.989	0.989	1.025	1.035	1.048	1.056	1.053	1.056	1.111	1.146
山东Shandong	1.024	1.023	1.023	1.035	1.047	1.037	1.032	1.051	1.045	1.062	1.057
河南Henan	1.009	1.009	1.009	1.016	1.021	1.025	1.023	1.069	1.046	1.081	1.103
湖北Hubei	1.033	1.027	1.027	1.045	1.054	1.060	1.067	1.093	1.039	1.059	1.058
湖南Hunan	1.029	0.996	0.996	1.013	1.030	0.996	1.014	1.022	1.015	1.038	1.067
广东Guangdong	0.944	0.988	0.988	1.021	1.044	0.997	1.029	1.030	1.030	1.058	1.053
广西Guangxi	1.002	0.989	0.989	0.998	1.038	1.062	1.074	1.069	1.080	1.031	1.050
海南Hainan	0.969	0.920	0.920	0.974	1.015	1.010	1.056	1.092	1.101	1.091	1.149
重庆Chongqing	1.027	0.971	0.971	0.938	0.934	0.946	0.927	0.923	0.927	0.855	0.860
四川Sichuan	1.015	1.008	1.008	1.019	1.020	1.046	1.044	1.067	1.081	1.113	1.129
贵州Guizhou	0.945	0.825	0.825	0.615	0.739	0.785	0.828	0.821	0.823	0.895	0.824
云南Yunnan	1.018	0.991	0.991	1.053	1.072	1.112	1.132	1.146	1.165	1.213	1.221
西藏Tibet	1.001	0.949	0.949	0.966	0.957	0.898	0.942	0.913	0.921	0.984	0.990
陕西Shaanxi	1.031	1.073	1.073	1.109	1.159	1.158	1.151	1.180	1.183	1.169	1.205
甘肃Gansu	1.055	1.088	1.088	1.132	1.205	1.216	1.224	1.234	1.236	1.298	1.361
青海Qinghai	0.930	0.890	0.890	0.920	0.853	0.838	0.862	0.835	0.890	0.884	0.904
宁夏Ningxia	1.053	1.104	1.104	1.101	1.183	1.218	1.281	1.264	1.245	1.340	1.396
新疆Xinjiang	0.946	0.945	0.945	0.979	0.978	1.009	1.028	1.041	1.033	1.060	1.110

变量 Variable	模型Ⅰ Model Ⅰ	模型Ⅱ Model Ⅱ	模型Ⅲ Model Ⅲ	模型Ⅳ Model Ⅳ	模型Ⅴ Model Ⅴ
粮食产出时间滞后项Time lag term for grain output	—	—	0.652^***	—	0.090^***
粮食全要素生产率Grain total factor productivity	-0.058	0.667^***	0.185^***	0.694^***	0.691^***
化肥资源Fertilizer resources	0.117^***	0.065^***	0.034^**	0.022	0.010
水资源Water resources	0.186^***	0.055^***	0.085^***	0.078^***	0.044^**
农地Agricultural land	0.825^***	0.915^***	0.235^***	0.901^***	0.845^***
农业劳动力Agricultural labor	-0.095^***	-0.009	-0.019	-0.005	-0.007
财政支农Financial support for agriculture	0.058^***	0.030^***	0.006	0.025^*	0.024
产业结构Industry structure	-0.049^***	0.048^***	0.077^**	0.055^**	0.068^***
粮食全要素生产率空间滞后项	—	—	—	0.858^***	0.961^***
Spatial lag term of grain total factor productivity

变量 Variable	模型Ⅰ Model Ⅰ	模型Ⅱ Model Ⅱ	模型Ⅲ Model Ⅲ	模型Ⅳ Model Ⅳ	模型Ⅴ Model Ⅴ
粮食产出时间滞后项Time lag term for grain output	—	—	0.652^***	—	0.090^***
粮食全要素生产率Grain total factor productivity	-0.058	0.667^***	0.185^***	0.694^***	0.691^***
化肥资源Fertilizer resources	0.117^***	0.065^***	0.034^**	0.022	0.010
水资源Water resources	0.186^***	0.055^***	0.085^***	0.078^***	0.044^**
农地Agricultural land	0.825^***	0.915^***	0.235^***	0.901^***	0.845^***
农业劳动力Agricultural labor	-0.095^***	-0.009	-0.019	-0.005	-0.007
财政支农Financial support for agriculture	0.058^***	0.030^***	0.006	0.025^*	0.024
产业结构Industry structure	-0.049^***	0.048^***	0.077^**	0.055^**	0.068^***
粮食全要素生产率空间滞后项	—	—	—	0.858^***	0.961^***
Spatial lag term of grain total factor productivity

时期 Period	效应类型 Effect type	效应程度 Effect degree/%	P
短期 Short term	直接效应 Direct effect	0.680	<0.001
	溢出效应 Spillover effect	0.517	0.011
	总效应 Total effect	1.197	<0.001
长期 Long term	直接效应 Direct effect	0.748	<0.001
	溢出效应 Spillover effect	0.538	0.023
	总效应Total effect	1.286	<0.001

Yield-increasing effect of grain total factor productivity in China from 2008 to 2018 based on dynamic space Durbin model

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指标 Index	邻接空间权重矩阵 Adjacency space weight matrix		地理距离权重矩阵 Geographic distance weight matrix
指标 Index	静态面板 Static panel	动态面板 Dynamic panel	静态面板 Static panel	动态面板 Dynamic panel
粮食产出时间滞后项Time lag term for grain output	—	0.093^***	—	0.093^***
粮食全要素生产率Grain total factor productivity	0.655^***	0.625^***	0.666^***	0.638^***
粮食全要素生产率空间滞后项	0.127^*	0.175^**	0.328^***	0.411^***
Spatial lag term of grain total factor productivity

效应类型 Effect type	东部East China		中部Central China		西部West China
效应类型 Effect type	W₁	W₃	W₁	W₃	W₁	W₃
直接效应Direct effect	0.939^***	0.948^***	0.463^***	0.480^***	0.742^***	0.696^***
溢出效应Spillover effect	0.027	0.017	-0.177^*	-0.164^**	0.738^***	0.095
总效应Total effect	0.967^***	0.964^***	0.286^**	0.316^***	1.479^***	0.791^***

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