森林保险保费补贴政策的林业产出规模效应实证分析——基于双重差分模型与事件研究模型

doi:10.3969/j.issn.1004-1524.2021.02.19

浙江农业学报 ›› 2021, Vol. 33 ›› Issue (2): 355-368.DOI: 10.3969/j.issn.1004-1524.2021.02.19

森林保险保费补贴政策的林业产出规模效应实证分析——基于双重差分模型与事件研究模型

富丽莎(), 秦涛, 潘焕学^*(), 邓晶

北京林业大学经济管理学院,北京 100083

收稿日期:2020-08-12 出版日期:2021-02-25 发布日期:2021-02-25
通讯作者: 潘焕学
作者简介:*潘焕学,E-mail: panhuanxue@126.com
富丽莎(1994—),女,甘肃天水人,博士研究生,研究方向为森林保险。E-mail: 18810934961@163.com
基金资助:
国家自然科学基金青年科学基金(71403022);教育部人文社会科学研究青年基金(20YJA790059);中央高校基本科研业务费专项资金(JGZKPY005);中央高校基本科研业务费专项资金(2015ZCQ-JG-01)

Empirical analysis on scale effect of forest output of forest insurance premium subsidy policy in China: based on difference-in-differences model and event study model

FU Lisha(), QIN Tao, PAN Huanxue^*(), DENG Jing

School of Economics & Management, Beijing Forestry University, Beijing 100083, China

Received:2020-08-12 Online:2021-02-25 Published:2021-02-25
Contact: PAN Huanxue

摘要/Abstract

摘要：

森林保险保费补贴政策在中国实施已超10 a,其能否和在多大程度上促进林业生产的命题亟待科学系统的实证考查。为此,特结合生产者行为理论探寻森林保险保费补贴政策的林业产出规模效应,剖析森林保险保费补贴政策对林业产出的作用机理。在此基础上,基于2001—2017年中国31个省区市的面板数据,以2009年森林保险保费补贴政策冲击为自然实验,综合运用双重差分模型与事件研究模型实证分析该补贴政策对林业产出的影响及其长期动态影响效果,并进行2项安慰剂实验以排除研究结论的2个潜在威胁。结果表明:森林保险保费补贴政策对林业第一产业涉林产值有显著(P<0.1)正向影响,且影响效果随政策实施时间的推进而不断增强。补贴政策实施后,试点省较非试点省区市的林业第一产业涉林产值平均增加了104万元。以2010年为政策冲击年进行稳健性检验,所得结论与以2009年为政策冲击年进行分析的结果一致。安慰剂实验的结果排除了本研究结论的2个潜在威胁。研究结果对中央财政补贴下森林保险的作用持肯定态度,建议应从补贴对象瞄准、补贴规模科学测算、补贴标准合理设定等方面优化森林保险保费补贴政策,以增强其产出效应。

关键词: 森林保险, 保费补贴政策, 产出效果, 双重差分模型, 事件研究模型

Abstract:

The forest insurance premium subsidy policy has been implemented for more than 10 years in China, and the proposition of whether and to what extent it can promote forestry production is in urgent need of scientific and systematic empirical test. Combined with producer behavior theory, we explored the scale effect of forest insurance premium subsidy policy on forest output, and analyzed the mechanism of forest insurance premium subsidy policy on forest output. On this basis, by using panel data of 31 provinces (autonomous regions, municipalities) in China from 2001 to 2017, and taking the impact of China’s forest insurance premium subsidy policy in 2009 as a natural experiment, we explored the impact of the subsidy policy on the forest-related output value of the primary forestry industry via difference-in-differences model and event study model. Two placebo tests were also carried out to rule out the potential threat of conclusions. The results showed that the forest insurance premium subsidy policy had a significant (P<0.1) positive effect on forestry output, and the effect would increase with the prolonged time of implementation. After the implementation of the subsidy policy, the forest-related output value of the primary forestry industry in pilot regions increased by 1.04 million yuan compared with that in non-pilot regions. Moreover, the robustness test was carried out in 2010 as the year of policy impact, and the conclusions were consistent. Additionally, two placebo trials also ruled out two potential threats to the results. The study held a positive attitude towards the role of forest insurance under the central financial subsidy, and it was suggested that the subsidy policy should be further optimized from the aspects of subsidy scale and subsidy standard to enhance its output effect.

Key words: forest insurance, premium subsidy policy, output effect, difference-in-difference model, event study model

中图分类号:

S-9
F840.66

富丽莎, 秦涛, 潘焕学, 邓晶. 森林保险保费补贴政策的林业产出规模效应实证分析——基于双重差分模型与事件研究模型[J]. 浙江农业学报, 2021, 33(2): 355-368.

FU Lisha, QIN Tao, PAN Huanxue, DENG Jing. Empirical analysis on scale effect of forest output of forest insurance premium subsidy policy in China: based on difference-in-differences model and event study model[J]. Acta Agriculturae Zhejiangensis, 2021, 33(2): 355-368.

图/表 8

参考文献 20

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变量 Variable	统计指标 Statistical indicator	符号 Symbol	平均值 Average	标准误差 Standard deviation	中位数 Median	最小值 Minimum	最大值 Maximum
林业产出 Forestry output	林业第一产业涉林产值 Forest-related output of the first forest product/(10⁸ yuan)	EY	297.84	362.36	142.39	0.95	2365.26
林业风险 Forestry risk	成灾面积 Disaster area/(10⁴ hm²)	Disa	8.60	10.33	6.14	0	73.05
林业产业重要性 Importance	地区林业产业产值占GDP的比重 Proportion of forestry industry output to GDP/%	Per	5.79	4.42	4.64	0	28.21
林业劳动力 Labor force	林业劳动力数量 Forestry labor force/10⁴	Emp	67.14	58.81	48.27	1.06	324.48
林地资源 Land resources	林业用地面积 Forestry land area/(10⁴ hm²)	Lan	954.43	889.31	761.83	2.25	4403.61
林业投资 Forestry investment	林业固定资产投资 Fixed investment/(10⁸ yuan)	Inv	29.94	96.67	11.21	0.07	931.00
地区经济发展水平 Economic	农村人均可支配收入 Rural per capita disposable income/yuan	Inc	6 772.53	4 681.66	5 410.00	1 404.00	27 825
development level	城镇化率 Urbanization rate/%	Urb	49.85	15.11	48.44	20.71	89.61

变量 Variable	统计指标 Statistical indicator	符号 Symbol	平均值 Average	标准误差 Standard deviation	中位数 Median	最小值 Minimum	最大值 Maximum
林业产出 Forestry output	林业第一产业涉林产值 Forest-related output of the first forest product/(10⁸ yuan)	EY	297.84	362.36	142.39	0.95	2365.26
林业风险 Forestry risk	成灾面积 Disaster area/(10⁴ hm²)	Disa	8.60	10.33	6.14	0	73.05
林业产业重要性 Importance	地区林业产业产值占GDP的比重 Proportion of forestry industry output to GDP/%	Per	5.79	4.42	4.64	0	28.21
林业劳动力 Labor force	林业劳动力数量 Forestry labor force/10⁴	Emp	67.14	58.81	48.27	1.06	324.48
林地资源 Land resources	林业用地面积 Forestry land area/(10⁴ hm²)	Lan	954.43	889.31	761.83	2.25	4403.61
林业投资 Forestry investment	林业固定资产投资 Fixed investment/(10⁸ yuan)	Inv	29.94	96.67	11.21	0.07	931.00
地区经济发展水平 Economic	农村人均可支配收入 Rural per capita disposable income/yuan	Inc	6 772.53	4 681.66	5 410.00	1 404.00	27 825
development level	城镇化率 Urbanization rate/%	Urb	49.85	15.11	48.44	20.71	89.61

变量 Variable	R1	R2	R3
T_i_,_td_i*_,_t	165.000^*** (58.400)	104.000^** (49.200)	74.800^* (44.200)
d_i_,_t	372.000^*** (27.100)	127.000^*** (27.300)	37.500 (28.800)
T_i_,_t	84.800^*** (20.700)	-117.000^*** (32.200)	-21.200 (22.400)
Inv	—	0.074 (0.132)	0.425^*** (0.106)
Urb	—	-367.000^*** (92.600)	-179.000^* (105.000)
Inc	—	0.032^*** (0.006)	0.061^*** (0.007)
Per	—	3 690.000^*** (490.000)	1 570.000^*** (375.000)
Lan	—	0.003 (0.010)	0.014 (0.012)
Emp	—	0.814^*** (0.169)	1.036^*** (0.166)
Disa	—	-1.771^*** (0.677)	-0.468 (0.772)
常数项 Constant	84.200^*** (6.375)	-23.600 (35.100)	-81.600^** (35.200)

变量 Variable	R1	R2	R3
T_i_,_td_i*_,_t	165.000^*** (58.400)	104.000^** (49.200)	74.800^* (44.200)
d_i_,_t	372.000^*** (27.100)	127.000^*** (27.300)	37.500 (28.800)
T_i_,_t	84.800^*** (20.700)	-117.000^*** (32.200)	-21.200 (22.400)
Inv	—	0.074 (0.132)	0.425^*** (0.106)
Urb	—	-367.000^*** (92.600)	-179.000^* (105.000)
Inc	—	0.032^*** (0.006)	0.061^*** (0.007)
Per	—	3 690.000^*** (490.000)	1 570.000^*** (375.000)
Lan	—	0.003 (0.010)	0.014 (0.012)
Emp	—	0.814^*** (0.169)	1.036^*** (0.166)
Disa	—	-1.771^*** (0.677)	-0.468 (0.772)
常数项 Constant	84.200^*** (6.375)	-23.600 (35.100)	-81.600^** (35.200)

变量Variable	R1	R2	R3
T_i_,_td_i*_,_t	196.000^*** (48.100)	169.000^*** (48.500)	85.500^* (51.100)
d_i_,_t	348.000^*** (32.000)	126.000^*** (30.200)	42.500 (35.400)
T_i_,_t	115.000^*** (21.000)	-13.400 (3.160)	9.036 (22.700)
Inv	—	0.076 (0.136)	0.450^*** (0.106)
Urb	—	-409.000^*** (115.000)	-191.000 (144.000)
Inc	—	0.030^*** (0.007)	0.059^*** (0.009)
Per	—	3 180.000^*** (487.000)	1 530.000^*** (374.000)
Lan	—	-0.013 (0.010)	-0.023^* (0.013)
Emp	—	0.919^*** (0.168)	1.291^*** (0.179)
Disa	—	1.900^** (0.773)	-0.014 (0.885)
常数项 Constant	104.000^*** (8.156)	39.800 (42.300)	-62.000 (46.900)

森林保险保费补贴政策的林业产出规模效应实证分析——基于双重差分模型与事件研究模型

Empirical analysis on scale effect of forest output of forest insurance premium subsidy policy in China: based on difference-in-differences model and event study model

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事件窗口 Event window	R1	R2	R3
政策实施前Before policy implementation
[-4,-3]	3.000	98.000	63.000
	(97.000)	(93.000)	(92.000)
[-2,-1]	47.000	64.000	47.000
	(97.000)	(93.000)	(92.000)
政策实施后After policy implementation
[1,3]	100.000	140.000^*	74.000
	(84.000)	(82.000)	(81.000)
[4,6]	180.000^**	180.000^**	85.000
	(84.000)	(82.000)	(81.000)
[7,9]	280.000^***	290.000^***	180.000^*
	(97.000)	(94.000)	(93.000)
Inv	—	0.768^***	0.784^***
		(0.126)	(0.124)
Urb	—	270.000^* (0.015)	580.000^*** (0.018)
Inc	—	0.529 (0.403)	0.607 (0.356)
Per	—	6800.000^*** (550.000)	5900.000^* (430.000)
Lan	—	-0.011	-0.411^**
		(0.169)	(0.173)
Emp	—	-1.000^**	-0.800
		(0.520)	(0.512)
Disa	—	-4.939^***	-4.475^***
		(1.358)	(1.338)
常数项	-240.000^***	-410.000^***	1 200.000^***
Constant	(57.000)	(120.000)	(450.000)

事件窗口 Event window	R1	R2	R3
政策实施前Before policy implementation
[-4,-3]	55.000	13.000	10.000
	(71.000)	(60.000)	(62.000)
[-2,-1]	110.000	67.000	53.000
	(71.000)	(61.000)	(62.000)
政策实施后After policy implementation
[1,3]	220.000^***	140.000^***	110.000^*
	(71.000)	(53.000)	(54.000)
[4,6]	240.000^***	140.000^**	87.000
	(71.000)	(54.000)	(55.000)
[7,9]	260.000^***	160.000^*	100.000
	(71.000)	(81.000)	(83.000)
Inv	—	-0.262^*	-0.078
		(0.137)	(0.143)
Urb	—	110.000	390.000^**
		(130.000)	(160.000)
Inc	—	-0.370 (0.194)	-0.648 (0.203)
Per	—	5 700.000^***	4 700.000^***
		(510.000)	(540.000)
Lan	—	0.062	-0.220
		(0.150)	(0.159)
Emp	—	-0.839 (0.487)	-0.691 (0.343)
Disa	—	-1.761	-2.042
		(1.226)	(1.239)
常数项	-220.000^***	-270.00^***	280.000
Constant	(57.000)	(100.000)	(400.000)

变量 Variable	第1组 Group 1	第2组 Group 2	第3组 Group 3	第4组 Group 4
T_i_,_td_i*_,_t	-2.240 (33.600)	-36.000 (44.100)	86.500 (54.000)	-156.000^*** (49.500)
d_i_,_t	155.000^*** (28.400)	160.000^*** (27.500)	154.000^*** (26.300)	302.000^*** (51.400)
T_i_,_t	33.600^* (20.000)	38.600^* (22.900)	-6.370 (29.520)	120.000^*** (23.500)
Inv	-0.046 (0.107)	-0.047 (0.107)	-0.014 (0.115)	-0.050 (0.115)
Urb	-219.000^** (93.700)	-237.000^** (92.000)	-253.000^*** (89.400)	-250.000^*** (91.400)
Inc	-0.030^*** (0.006)	0.030^*** (0.006)	0.029^*** (0.006)	0.030^*** (0.006)
Per	2 940.000^*** (365.000)	2 960.000^*** (363.000)	2 860.000^*** (384.000)	2 950.000^*** (397.000)
Lan	-0.016^* (0.009)	-0.016^* (0.009)	-0.016 (0.010)	-0.017^* (0.009)
Emp	1.091^*** (0.195)	1.034^*** (0.178)	1.023^*** (0.177)	1.069^*** (0.177)
Disa	1.532 (0.660)^**	1.552^** (0.662)	1.393^* (0.737)	1.476 (0.641)
常数项 Constant	-124.000^*** (38.400)	-113.000^*** (34.300)	-94.100^*** (34.600)	-218.000 (40.300)

变量 Variable	R1	R2	R3
T_i_,_td_i*_,_t	-68.825^*** (7.412)	-62.278^*** (8.964)	-75.636^*** (9.803)
d_i_,_t	80.899^*** (6.411)	70.190^*** (8.500)	85.800^*** (9.540)
T_i_,_t	-42.976^*** (2.716)	-43.485^*** (6.316)	-57.195^*** (7.051)
Inv	—	0.000^*** (0.000)	0.000^*** (0.000)
Urb	—	-102.048^*** (24.321)	-126.831^*** (32.618)
Inc	—	0.002 (0.001)	0.000 (0.002)
Per	—	224.073^** (92.827)	442.787^*** (111.757)
Lan	—	-0.008^** (0.003)	-0.005^* (0.003)
Emp	—	-0.281^*** (0.049)	-0.297^*** (0.056)
Disa	—	<0.001^*** (<0.001)	<0.001^*** (<0.001)
常数项 Constant	50.813^*** (2.390)	95.724^*** (11.090)	106.016^*** (13.488)