中国农村劳动力转移对农业生态效率影响的空间溢出效应与门槛特征

doi:10.18402/resci.2018.12.14

摘要/Abstract

摘要：

农村劳动力的非农转移与农业产出的增长形成了鲜明对比,且农业生产不可避免地带来环境负外部性,为探讨农村劳动力转移对环境约束下农业生产的影响,基于1978—2016年中国省际面板数据,采用超效率SBM模型测算农业生态效率,在STIRPAT模型基础上,建立空间面板计量模型和面板门槛回归模型,探讨农村劳动力转移对农业生态效率影响的空间溢出效应与门槛特征。研究结果显示：① 1978—2016年间中国农业生态效率在波动中稳定上升,但整体仍处于较低水平,且地区间差异显著;② 农村劳动力转移对农业生态效率提升具有显著的空间溢出效应,且地区间的间接溢出要大于地区内的直接溢出效应,长期来看,能够有助于提升农业生态效率;③ 农村劳动力转移对农业生态效率的影响存在单一门槛效应,其门槛特征与空间溢出效应接近,在时间上大致表现出U型关系,通过门槛值的区间划分,各省农村劳动力转移呈现出显著的东中西部渐次递减的分布特征。最后,本文建议农业生产政策的制定必须考虑地区间的空间依赖性和异质性,结合本地区农村劳动力结构变化现状,加强地区间农业生产合作与交流,并与农业生态、粮食安全相协调。

关键词: 农村劳动力转移, 农业生态效率, 空间溢出效应, 门槛特征, 中国

Abstract:

Based on the panel data of provinces in China from 1978 to 2016, the super-efficient SBM model was used to measure the inter-provincial agricultural eco-efficiency (AEE). Coupled with the STIRPAT model, the spatial econometric model and panel threshold regression model were established to explore the spatial spillover effect and non-linear threshold characteristics of rural labor transfer on AEE. The results show that, the AEE in China presents a steady rise in fluctuation, but the overall level is still relatively low. The difference between regions is also significant, the rural labor transfer has a significant spatial spillover effect on the promotion of AEE, and the indirect spillover between regions is greater than the direct spillover effect in the region, which can help to improve the AEE in the long run. The influence of rural labor force transfer on AEE has a single threshold effect and its threshold character is close to the spatial spillover effect that generally shows a U-shaped relationship in terms of time. The rural labor transfer in each province also gradually evolves from a high-to-low incremental distribution characteristics through the interval division of threshold value by east-central-west regions. Therefore, the optimal allocation of rural labor transfer must consider the spatial dependence and heterogeneity between regions. The formulation of agricultural production policy also needs to combine with the change of rural labor structure in the region while coordinate with agricultural ecology and food security.

Key words: rural labor transfer, Agricultural Eco-Efficiency (AEE), spatial spillover effects, threshold characteristics, China

侯孟阳, 姚顺波. 中国农村劳动力转移对农业生态效率影响的空间溢出效应与门槛特征[J]. 资源科学, 2018, 40(12): 2475-2486.

Mengyang HOU, Shunbo YAO. Spatial spillover effects and threshold characteristics of rural labor transfer on agricultural eco-efficiency in China[J]. Resources Science, 2018, 40(12): 2475-2486.

图/表 7

表1

图1

表2

表3

表4

表5

农村劳动力转移对农业生态效率影响的面板门槛回归模型估计结果"

变量	估计值	t值	p值	变量	估计值	t值	p值
lnRLNE $<$ 3.805(RLNE $<$ 44.925%)	-0.152***	-6.20	0.000	lnMCI	-0.233***	-3.32	0.001
lnRLNE $≥$ 3.805(RLNE $≥$ 44.925%)	0.083***	3.15	0.002	lnCPS	-0.216***	-7.21	0.000
lnpDIRR	0.364***	18.24	0.000	lnFSA	0.010	0.46	0.643
lnMII	-0.414***	-3.02	0.005	lnADR	-0.061***	-4.15	0.000
F值	310.950***		0.000	R²	0.687

表5

表6

农村劳动力转移的区间划分及省份分布"

门槛值及区间	1978—2016年均值的省份分布	2016年的省份分布
低转移lnRLNE $<$ 3.805 （RLNE $<$ 44.925%）	河北、山西、内蒙古、辽宁、吉林、黑龙江、安徽、福建、江西、山东、河南、湖北、湖南、广东、广西、海南、重庆、四川、贵州、云南、陕西、甘肃、青海、宁夏、新疆	山西、内蒙古、吉林、黑龙江、山东、广西、海南、四川、贵州、云南、陕西、甘肃、青海、宁夏、新疆
高转移lnRLNE $≥$ 3.805 （RLNE $≥$ 44.925%）	北京、天津、上海、江苏、浙江	北京、天津、河北、辽宁、上海、江苏、浙江、安徽、福建、江西、河南、湖北、湖南、广东、重庆

表6

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	指标（变量）/单位	指标说明
被解释变量	农业生态效率（AEE）	由基于非期望产出的超效率SBM模型测算
关键解释变量	农村劳动力转移（RLNE）/%	（乡村从业人员-农林牧渔业从业人员）/乡村从业人员
其他解释变量	农村居民人均纯收入（pDIRR）/元	通过相关年鉴获取
	农业机械投入强度（MII）/（kW/hm²）	农业机械总动力/农作物总播种面积
	复种指数（MCI）/%	农作物总播种面积/耕地面积
	作物种植结构（CPS）/%	粮食作物种植面积/（农作物播种面积-粮食作物种植面积）
	财政支农水平（FSA）/（元/hm²）	财政农林水事务支出/农作物总播种面积
	农业受灾率（ADR）/%	农作物受灾面积/农作物总播种面积

变量	模型1-OLS	模型2-SLM		模型3-SDM
变量	固定效应	随机效应	固定效应	随机效应	固定效应
lnRLNE	-0.440***(-6.96)	-0.347***(-5.83)	-0.341***(-5.77)	-0.173**(-2.75)	-0.222***(-3.57)
(lnRLNE)²	0.063***(5.00)	0.055***(4.31)	0.054***(4.24)	0.020(1.47)	0.029**(2.28)
lnpDIRR	0.366***(16.76)	0.279***(12.89)	0.280***(12.94)	0.298***(12.77)	0.293***(12.75)
lnMII	-0.016(-0.38)	-0.069(-1.81)	-0.070(-1.82)	-0.267***(-6.62)	-0.284***(-7.14)
lnMCI	-0.335***(-4.36)	-0.339***(-4.89)	-0.342***(-4.79)	-0.405***(-5.82)	-0.427***(-6.09)
lnCPS	-0.211***(-6.81)	-0.147***(-5.00)	-0.155***(-5.31)	-0.247***(-8.13)	-0.223***(-7.34)
lnFSA	0.047(1.09)	-0.003(-0.06)	-0.002(-0.05)	0.012(0.55)	0.031(0.98)
lnADR	-0.073***(-4.86)	-0.062***(-4.39)	-0.061***(-4.36)	-0.050***(-3.58)	-0.052***(-3.79)
W×lnRLNE				-0.271**(-3.02)	-0.290***(-3.29)
W×(lnRLNE)²				0.054**(2.91)	0.059**(3.24)
W×lnpDIRR				-0.005(-0.16)	0.051(1.68)
W×lnMII				0.150**(2.61)	0.078(1.34)
W×lnMCI				0.325***(4.39)	0.885***(7.55)
W×lnCPS				-0.270***(-7.47)	-0.181***(-4.41)
W×lnFSA				0.156***(4.51)	0.177***(5.17)
W×lnADR				-0.025(-1.29)	-0.014(-0.75)
ρ		0.306***(10.98)	0.310***(11.05)	0.192***(5.39)	0.168***(4.69)
cons	-2.479***(-5.33)	-1.151*(-2.56)		-1.919***(-3.98)
LogL		100.697	-114.154	-108.979	-101.890
R²	0.672	0.706	0.707	0.745	0.751
Sigma²		0.071***(23.72)	0.069***(24.04)	0.061***(23.73)	0.058***(24.12)
AIC	356.969	425.393	248.308	257.958	139.779

变量	直接效应	间接效应	总效应
lnRLNE	-0.234***(-3.72)	-0.375***(-3.93)	-0.609***(-6.14)
(lnRLNE)²	0.031**(2.31)	0.049***(3.76)	0.080***(5.13)
lnpDIRR	0.299***(13.77)	0.115***(4.03)	0.415***(13.28)
lnMII	-0.285***(-7.74)	0.037(0.57)	-0.248***(-3.33)
lnMCI	0.394***(5.82)	-0.936***(-7.14)	-0.542***(-3.58)
lnCPS	-0.220***(7.32)	0.166***(3.63)	-0.054(-1.12)
lnFSA	0.028(1.30)	0.209***(5.94)	0.237***(5.58)
lnADR	-0.053***(-4.13)	-0.026(-1.26)	-0.079***(-3.47)

门槛变量	假设检验	F值（p值）	门槛值	95%置信区间
lnRLNE	H0：无门槛值;H1：有1个门槛值	94.12***(0.013)	3.795	[3.789, 3.799]
lnRLNE	H0：1个门槛值;H1：有2个门槛值	28.95**(0.257)	3.476;3.805	[3.432, 3.481];[3.792, 3.809]