中国林业劳动生产率的时空关联效应及其影响因素解析

doi:10.12302/j.issn.1000-2006.202305026

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南京林业大学学报（自然科学版） ›› 2024, Vol. 48 ›› Issue (6): 229-238.doi: 10.12302/j.issn.1000-2006.202305026

中国林业劳动生产率的时空关联效应及其影响因素解析

刘艳迪(), 张诚, 孙喆, 吕洁华^*()

东北林业大学经济管理学院,黑龙江哈尔滨 150040

收稿日期:2023-05-24 修回日期:2023-07-03 出版日期:2024-11-30 发布日期:2024-12-10
通讯作者: *吕洁华(Lvjiehua2004@126.com),教授。
作者简介:
刘艳迪(1048116902@qq.com),博士。
基金资助:
国家社会科学基金项目(21BGL166)

An analysis of the spatial and temporal correlation effects of forestry labor productivity and its influencing factors in China

LIU Yandi(), ZHANG Cheng, SUN Zhe, LYU Jiehua^*()

College of Economics and Management, Northeast Forestry University, Harbin 150040, China

Received:2023-05-24 Revised:2023-07-03 Online:2024-11-30 Published:2024-12-10

摘要/Abstract

摘要：

【目的】研究中国林业劳动生产率的时空关联特征,分析其影响因素和时空溢出效应,并提出促进中国林业劳动生产率增长的对策建议。【方法】利用2010—2020年中国大陆31个省(直辖市、自治区)的林业劳动生产率面板数据,通过单变量和双变量Moran指数检验区域林业劳动生产率的时空关联特征,在此基础上,分别建立无时空效应、时间效应、空间效应和时空效应4个模型并进行对比,最终选择建立动态空间面板模型分析林业劳动生产率的影响因素并进行时空效应分解。【结果】①中国区域林业劳动生产率存在循环累积时间效应和正向时空交互效应。②本地林地劳动比、产业集聚水平、产业结构优化、劳动者报酬、技术进步、纯技术效率和规模效率的提升对当地林业劳动生产率有促进作用,其中林地劳动比和劳动者报酬的贡献率最高。③资本劳动比和劳动者报酬的提升对邻近地区林业劳动生产率有负向溢出效应,而技术进步和规模效率的提升对邻近地区林业劳动生产率有正向溢出效应。④除资本劳动比的长期效应不显著外,其他因素的长期效应与短期效应方向一致,平均影响程度是短期效应的2.368倍。【结论】提高林业劳动生产率,应充分考虑各因素的本地效应和空间溢出效应,同时考虑政策措施的长期影响,权衡各种因素的作用,以做出合理决策。

关键词: 林业劳动生产率, 时空效应, Moran指数, 动态空间面板模型

Abstract:

【Objective】This study aims to investigate the spatio-temporal correlation patterns of forestry labor productivity in China, examine the factors influencing it and their spillover effects, and propose actionable measures to boost forestry labor productivity.【Method】Using the panel data of 31 provinces (including municipalities and autonomous regions) in mainland China from 2010 to 2020, the study employed univariate and bivariate Moran indices to test the spatio-temporal correlation characteristics of regional forestry labor productivity. Based on the findings, four models were established and compared: no spatio-temporal effect, temporal effect, spatial effect, and spatio-temporal effect. Ultimately, a dynamic spatial panel model was selected to analyze the factors driving labor productivity and decompose the spatio-temporal effects.【Result】(1) There were cumulative circular time effects and positive spatio-temporal interaction effects on regional forestry labor productivity in China. (2) The improvement of local forest land labor ratio, industrial agglomeration level, industrial structure optimization, labor remuneration, technological progress, pure technical efficiency and scale efficiency had contributed to local forestry labor productivity, among which the contribution rates of forest land labor ratio and labor remuneration were the highest. (3) The improvement of capital labor ratio and labor remuneration had negative spillover effects on neighboring forestry labor productivity, while the improvement of technological progress and scale efficiency had positive spillover effects. (4) Except for the capital labor ratio, which was insignificant, the long-term effects of other factors were in the same direction as their short-term effects, and their average degree of influence was 2.368 times that of the short-term effects.【Conclusion】To improve forestry labor productivity, policymakers should consider the local effects and spatial spillover effects of each factor and carefully weigh the effects of various factors while taking into account the long-term effects of policy measures, in order to make reasonable decisions.

Key words: forestry labor productivity, spatio-temporal effects, Moran index, dynamic spatial panel model

中图分类号:

F326.27

刘艳迪,张诚,孙喆,等. 中国林业劳动生产率的时空关联效应及其影响因素解析[J]. 南京林业大学学报（自然科学版）, 2024, 48(6): 229-238.

LIU Yandi, ZHANG Cheng, SUN Zhe, LYU Jiehua. An analysis of the spatial and temporal correlation effects of forestry labor productivity and its influencing factors in China[J].Journal of Nanjing Forestry University (Natural Science Edition), 2024, 48(6): 229-238.DOI: 10.12302/j.issn.1000-2006.202305026.

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变量 variable	样本量 sample	均值 mean	标准差 SD	最小值 min.	最大值 max.
lnY	341	5.867	1.335	2.032	8.636
lnC_LR	341	4.449	1.024	2.486	7.355
lnF_LR	341	5.679	0.868	3.621	9.371
A_GG	341	0.991	0.714	0.017	4.236
I_SO	341	1.657	0.294	1.009	2.355
lnR_EM	341	10.629	0.491	9.464	11.998
T_E	341	2.090	0.654	0.652	4.112
P_E	341	1.296	0.539	0.774	3.718
S_E	341	0.837	0.255	0.201	1.620

年份year	I_x	Z
2010	0.551^***	5.123 2
2011	0.576^***	5.360 5
2012	0.579^***	5.307 6
2013	0.586^***	5.356 7
2014	0.606^***	5.534 8
2015	0.609^***	5.569 1
2016	0.602^***	5.532 8
2017	0.620^***	5.728 9
2018	0.655^***	5.974 4
2019	0.704^***	6.404 7
2020	0.717^***	6.528 9

年份 year	外向溢出效应 outward spillover effect		内向溢出效应 inward spillover effect
年份 year	I_xy	Z	I_xy	Z
2010—2011	0.573^***	5.334 2	0.555^***	5.190 0
2011—2012	0.583^***	5.398 3	0.576^***	5.343 8
2012—2013	0.584^***	5.359 4	0.581^***	5.329 5
2013—2014	0.593^***	5.432 7	0.601^***	5.491 5
2014—2015	0.607^***	5.557 8	0.605^***	5.535 4
2015—2016	0.599^***	5.528 2	0.611^***	5.638 8
2016—2017	0.613^***	5.659 1	0.608^***	6.640 5
2017—2018	0.638^***	5.866 1	0.636^***	5.855 7
2018—2019	0.687^***	6.280 4	0.674^***	6.179 3
2019—2020	0.707^***	6.445 2	0.713^***	6.515 9

溢出方式 spillover mode	年份 year	高-高 high-high	低-高 low-high	低-低 low-low	高-低 high-low
外向溢出 outward spillover	2010—2011	安徽、福建、河南、湖北、湖南、江苏、江西、山东、浙江、海南、广东、广西、上海	北京、贵州	黑龙江、陕西、山西、甘肃、吉林、宁夏、青海、云南、新疆、西藏、内蒙古	重庆、天津、河北、辽宁、四川
	2019—2020	重庆、安徽、福建、贵州、河南、湖北、湖南、江苏、江西、山东、浙江、海南、广东、广西、上海	云南	北京、天津、黑龙江、辽宁、陕西、山西、甘肃、吉林、宁夏、青海、新疆、西藏、内蒙古	河北、四川
内向溢出 inward spillover	2010—2011	安徽、福建、河南、湖北、湖南、江苏、山东、浙江、海南、广东、广西、上海	北京、贵州、江西	黑龙江、陕西、山西、甘肃、吉林、宁夏、青海、云南、新疆、西藏、内蒙古	重庆、天津、河北、辽宁、四川
	2019—2020	重庆、安徽、福建、贵州、河南、湖北、湖南、江苏、江西、山东、浙江、海南、广东、广西、上海	云南	北京、天津、黑龙江、辽宁、陕西、山西、甘肃、吉林、宁夏、青海、新疆、西藏、内蒙古	河北、四川

变量 variable	无时空效应(模型1) no spatio-temporal effect (model 1)	时间效应(模型2) temporal effect (model 2)	空间效应(模型3) spatial effect (model 3)	时空效应(模型4) spatio-temporal effect (model 4)	时空效应(模型5) spatio-temporal effect (model 5)
L_lnY(τ)		0.290 4^***(10.00)		0.286 2^***(10.56)	0.286 5^***(10.59)
W×lnY(ρ)			0.460 9^***(4.14)	0.245 5(1.10)	0.179 3(1.28)
W×L_lnY(η)				0.359 7^***(3.04)	0.363 1^***(3.56)
lnC_LR(β₁)	0.022 3(0.58)	0.002 0(0.06)	-0.033 4(-0.97)	-0.035 2(-1.10)	-0.023 2(-0.75)
lnF_LR(β₂)	0.443 0^***(7.06)	0.430 3^***(8.02)	0.492 2^***(8.18)	0.544 1^***(10.24)	0.543 5^***(10.62)
A_GG(β₃)	0.242 2^***(5.98)	0.166 2^***(4.54)	0.289 5^***(8.49)	0.169 1^***(5.06)	0.170 1^***(5.12)
I_SO(β₄)	-0.041 9(-0.40)	0.103 0(1.17)	0.059 4(0.66)	0.137 7^*(1.82)	0.159 9^**(2.16)
lnR_EM(β₅)	0.188 7^***(4.69)	0.095 1^***(2.63)	0.133 2^***(2.78)	0.165 0^***(3.62)	0.156 4^***(3.49)
T_E(β₆)	0.503 7^***(19.85)	0.282 2^***(10.53)	0.380 6^***(14.08)	0.249 4^***(9.65)	0.237 0^***(10.29)
P_E(β₇)	0.310 5^***(10.10)	0.233 1^***(8.27)	0.286 0^***(9.87)	0.211 6^***(8.17)	0.205 6^***(8.40)
S_E(β₈)	1.029 9^***(17.24)	0.857 6^***(16.08)	1.033 3^***(19.90)	0.819 7^***(16.76)	0.810 7^***(17.04)
W×lnC_LR(θ₁)			-0.437 6^***(-2.60)	-0.298 9^*(-1.88)	-0.165 7^**(-2.22)
W×lnF_LR(θ₂)			0.175 9(0.81)	0.150 4(0.73)
W×A_GG(θ₃)			0.031 5(0.11)	-0.148 5(-0.50)
W×I_SO(θ₄)			-0.485 4^*(-1.67)	-0.266 4(-1.09)
W×lnR_EM(θ₅)			0.022 0(0.19)	-0.408 0^***(-3.53)	-0.446 6^***(-6.15)
W×T_E(θ₆)			0.118 9(1.09)	0.378 1^***(2.78)	0.321 1^***(3.38)
W×P_E(θ₇)			-0.384 6^***(-2.68)	0.022 4(0.12)
W×S_E(θ₈)			-0.475 4^*(-1.73)	0.994 3^**(2.46)	0.882 7^***(2.90)
R²	0.941 2	0.955 0	0.958 2	0.963 6	0.963 7
F	603.83^***	637.40^***
对数似然值 Log likelihood			317.65	356.35	355.26
Hausman检测 Hausman test	30.03^***	206.96^***	-13.55

中国林业劳动生产率的时空关联效应及其影响因素解析

An analysis of the spatial and temporal correlation effects of forestry labor productivity and its influencing factors in China

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变量 variable	短期效应short-term effect			长期效应long-term effect
变量 variable	直接效应 direct effect	间接效应 indirect effect	总效应 total effect	直接效应 direct effect	间接效应 indirect effect	总效应 total effect
lnC_LR	-0.019 0 (-0.65)	-0.143 0^** (-1.99)	-0.162 0^* (-1.95)	-0.033 7 (-0.79)	-0.3630 (-1.57)	-0.396 7 (-1.57)
lnF_LR	0.540 6^*** (11.39)	-0.074 7 (-1.32)	0.465 9^*** (6.46)	0.762 7^*** (11.18)	0.350 2 (1.02)	1.112 9^*** (3.01)
A_GG	0.169 1^*** (5.46)	-0.023 2 (-1.26)	0.145 9^*** (4.40)	0.238 6^*** (5.42)	0.110 6 (0.97)	0.349 2^*** (2.60)
I_SO	0.164 5^** (2.24)	-0.023 7 (-1.07)	0.140 8^** (2.18)	0.231 9^** (2.24)	0.100 8 (0.87)	0.332 7^* (1.82)
lnR_EM	0.159 8^*** (3.44)	-0.406 2^*** (-5.62)	-0.246 4^*** (-4.01)	0.208 1^*** (3.23)	-0.792 1^*** (-3.81)	-0.584 0^*** (-2.76)
T_E	0.233 3^*** (10.12)	0.236 3^*** (3.95)	0.469 6^*** (8.07)	0.340 6^*** (10.14)	0.758 4^*** (3.19)	1.099 0^*** (4.42)
P_E	0.208 0^*** (8.84)	-0.029 0 (-1.30)	0.179 0^*** (6.18)	0.293 4^*** (8.82)	0.133 4 (1.00)	0.426 8^*** (2.99)
S_E	0.805 2^*** (16.25)	0.627 6^*** (3.38)	1.432 8^*** (7.77)	1.166 9^*** (16.11)	2.174 7^*** (3.20)	3.341 6^*** (4.75)