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

doi:10.12302/j.issn.1000-2006.202305026

JOURNAL OF NANJING FORESTRY UNIVERSITY ›› 2024, Vol. 48 ›› Issue (6): 229-238.doi: 10.12302/j.issn.1000-2006.202305026

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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
Contact: LYU Jiehua E-mail:1048116902@qq.com;Lvjiehua2004@126.com

Abstract

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

CLC Number:

F326.27

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, 2024, 48(6): 229-238.

Figures/Tables 8

Table 1

Fig. 1

Fig. 2

Table 2

Table 3

Table 4

Table 5

Regression results of spatial and temporal effects of forestry labor productivity in China"

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

Table 5

Table 6

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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	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)

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

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