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

doi:10.3969/j.issn.1004-1524.2021.02.19

Acta Agriculturae Zhejiangensis ›› 2021, Vol. 33 ›› Issue (2): 355-368.DOI: 10.3969/j.issn.1004-1524.2021.02.19

• Agricultural Economy and Development • Previous Articles Next Articles

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

Abstract

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

CLC Number:

S-9
F840.66

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.

Figures/Tables 8

References 20

[1]	秦涛, 田治威, 潘焕学. 我国森林保险保费补贴政策执行效果、存在的主要问题与建议[J]. 经济纵横, 2017(1):105-110.
	QIN T, TIAN Z W, PAN H X. Analysis on the implementation effect and main issue of forest insurance premium subsidies policy of China[J]. Economic Review, 2017(1):105-110.(in Chinese with English abstract)
[2]	富丽莎, 秦涛, 潘焕学. 森林保险制度体系重塑与运行机制优化[J]. 浙江农业学报, 2020,32(6):1112-1122.
	FU L S, QIN T, PAN H X. Remolding of system and optimizing of operational mechanism for forest insurance[J]. Acta Agriculturae Zhejiangensis, 2020,32(6):1112-1122.(in Chinese with English abstract)
[3]	QIN T, DENG J, PAN H X, et al. The effect of coverage level and premium subsidy on farmers’ participation in forest insurance: an empirical analysis of forest owners in Hunan Province of China[J]. Journal of Sustainable Forestry, 2016,35(3):191-204. DOI URL
[4]	SIHEM E. Economic and socio-cultural determinants of agricultural insurance demand across countries[J]. Journal of the Saudi Society of Agricultural Sciences, 2019,18(2):177-187.
[5]	邓晶, 陈启博. 基于DEA模型的我国森林保险保费补贴效率研究[J]. 林业经济, 2018,40(10):88-95.
	DENG J, CHEN Q B. Study on the efficiency of forest insurance premium subsidies in China based on DEA model[J]. Forestry Economics, 2018,40(10):88-95.(in Chinese with English abstract)
[6]	何玥. 中国森林保险制度效率及影响因素研究[D]. 北京: 北京林业大学, 2015.
	HE Y. Study on the efficiency and influencing factors of China’s forest insurance system[D]. Beijing: Beijing Forestry University, 2015. (in Chinese with English abstract)
[7]	郑彬, 高岚. 森林保险保费补贴效率测评: 基于SE-DEA模型与Malmquist指数[J]. 资源开发与市场, 2019,35(1):7-12.
	ZHENG B, GAO L. Evaluation of efficiency of forest insurance premium subsidies: based on SE-DEA model and malmquist index[J]. Resource Development & Market, 2019,35(1):7-12.(in Chinese with English abstract)
[8]	顾雪松, 谢妍, 秦涛. 森林保险保费补贴的“倒U型”产出效应: 基于我国省际非平衡面板数据的实证研究[J]. 农村经济, 2016(6):95-100.
	GU X S, XIE Y, QIN T. “Inverted U-shaped” output effect of forest insurance premium subsidy: an empirical study based on China’s inter-provincial non-equilibrium panel data[J]. Rural Economy, 2016(6):95-100. (in Chinese)
[9]	MÂRZA B, ANGELESCU C, TINDECHE C. Agricultural insurances and food security: the new climate change challenges[J]. Procedia Economics and Finance, 2015,27:594-599.
[10]	LUSK J L. Distributional effects of crop insurance subsidies[J]. Applied Economic Perspectives and Policy, 2017,39(1):1-15.
[11]	肖攀, 刘春晖, 苏静. 粮食安全视角下农业保险财政补贴政策效果评估[J]. 统计与决策, 2019(23):157-160.
	XIAO P, LIU C H, SU J. Effect assessment of agricultural insurance financial subsidy policy from the perspective of food security[J]. Statistics & Decision, 2019(23):157-160. (in Chinese)
[12]	张伟, 易沛, 徐静, 等. 政策性农业保险对粮食产出的激励效应[J]. 保险研究, 2019(1):32-44.
	ZHANG W, YI P, XU J, et al. Incentivizing effect of policy agricultural insurance on grain output[J]. Insurance Studies, 2019(1):32-44. (in Chinese with English abstract)
[13]	MÜLLER B, JOHNSON L, KREUER D. Maladaptive outcomes of climate insurance in agriculture[J]. Global Environmental Change, 2017,46:23-33.
[14]	张哲晰, 穆月英, 侯玲玲. 参加农业保险能优化要素配置吗?: 农户投保行为内生化的生产效应分析[J]. 中国农村经济, 2018(10):53-70.
	ZHANG Z X, MU Y Y, HOU L L. Does participation in agricultural insurance optimize factor allocation?: an analysis of endogenous farmers’ insurance decision-making and its effect on production[J]. Chinese Rural Economy, 2018(10):53-70.(in Chinese with English abstract)
[15]	袁辉, 谭迪. 政策性农业保险对农业产出的影响效应分析: 以湖北省为例[J]. 农村经济, 2017(9):94-100.
	YUAN H, TAN D. Analysis of the impact of policy-based agricultural insurance on agricultural output: a case study of Hubei Province[J]. Rural Economy, 2017(9):94-100. (in Chinese)
[16]	张卓, 李秉坤, 尹航. 我国政策性农业保险对农业产出规模的挤出效应: 基于干预-控制框架DID模型的分析[J]. 商业研究, 2019(8):110-117.
	ZHANG Z, LI B K, YIN H. The crowding out effect of policy agricultural insurance on agricultural output scale in China: an analysis based on DID model of intervention-control framework[J]. Commercial Research, 2019(8):110-117.(in Chinese with English abstract)
[17]	余洋, 董志华. 政治均衡视阈中的农业保险保费补贴政策: 比较优势与险种选择[J]. 宏观经济研究, 2016(4):12-20.
	YU Y, DONG Z H. Agricultural insurance premium subsidy policy from the perspective of political equilibrium: comparative advantage and insurance type selection[J]. Scientific Management Research, 2016 (4):12-20. (in Chinese)
[18]	邵全权, 郭梦莹. 发展农业保险能促进农业经济增长吗?[J]. 经济学动态, 2020(2):90-102.
	SHAO Q Q, GUO M Y. Can agricultural insurance promote agricultural economic growth?[J]. Economic Perspectives, 2020(2):90-102.(in Chinese with English abstract)
[19]	陈林, 伍海军. 国内双重差分法的研究现状与潜在问题[J]. 数量经济技术经济研究, 2015,32(7):133-148.
	CHEN L, WU H J. Research status and potential problems of differences-in-differences method in China[J]. The Journal of Quantitative & Technical Economics, 2015,32(7):133-148.(in Chinese with English abstract)
[20]	马九杰, 崔恒瑜, 吴本健. 政策性农业保险推广对农民收入的增进效应与作用路径解析: 对渐进性试点的准自然实验研究[J]. 保险研究, 2020(2):3-18.
	MA J J, CUI H Y, WU B J. Analysis of the effect and mechanism of policy-oriented agricultural insurance’s promotion on farmers’ income: a quasi-natural experimental research on progressive pilots[J]. Insurance Studies, 2020(2):3-18. (in Chinese with English abstract)

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)

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)