基于农户受偿意愿的休耕补偿标准探讨——以河北样本户为例

doi:10.18402/resci.2018.07.06

摘要/Abstract

摘要：

为探讨农户效用最大化条件下有效激励农户主动参与休耕的补偿标准,以效用理论为指导,依托河北省293户样本,构建农户受偿意愿的效用函数测定休耕补偿标准,并通过Binary Logistic回归模型检验农户受偿意愿的影响因素。研究表明：① 通过多角度的验证,农户意愿视角下测定的休耕补偿标准具有充分的理论依据和现实合理性;② 休耕补偿标准测算结果显示,参数估计结果为7677.60元/hm²,非参数估计结果为9962.40元/hm²;③ 回归分析表明在河北整村推进实施休耕的背景下,农户的个人特征和家庭特征对受偿意愿的影响被削弱,生产特征和农户对休耕补偿政策的认知成为主要影响因素,农户生产特征中投入产出比过高是农户选择休耕的根本动因。基于上述结论,建议适当提高现行休耕补偿标准,休耕政策的推行应尊重农户意愿,并积极探索农民增收渠道以降低农户对土地的依赖性。本研究从农户意愿的角度出发探讨休耕补偿标准,是耕地休耕理论研究的新尝试,以期为完善休耕经济补偿机制提供有效参考。

关键词: 耕地休耕, 效用理论, 受偿意愿, 补偿标准, 河北

Abstract:

The aim of this study was to explore compensation under the condition of farmer’s utility maximization for incentive fallowing. This work could provide an effective reference for a more better land fallow compensation mechanism in Hebei. We found that the critical compensation amount that would mobilize farmers to participate in fallow is 7677.60 CNY per acre. The average expected compensation amount is 9962.40 CNY per acre. Compared with the current compensation standard, opportunity cost, land rent and other factors, the critical compensation amount is scientific and reasonable. The range from 7677.60 CNY to 9962.40 CNY per acre is a better dynamic adjustment interval for the fallow compensation standard in Hebei. Various factors affect famer participation in fallow, including individual characteristics, family characteristics, production features and farmer cognition of fallow compensation policy. Factors of individual and family characteristics that impact farmer willingness to fallow are weakened by government forces. However, the most important factors affecting farmer willingness to fallow are production characteristics and awareness of fallow compensation policy. Compared with other factors, a high input-output ratio is the fundamental factor for farmer willingness to fallow. It is recommended that the current fallow compensation standard be raised appropriately to protect the interests of fallow farmers, and adjust the fallow compensation standards according to price changes. The implementation of fallow policy should respect farmer willingness. To reduce farmer dependence on land, local government should increase sources of income for farmers.

Key words: winter wheat fallow, utility theory, willingness to accept, compensation amount, Hebei

曾黎, 杨庆媛, 廖俊儒, 陈展图, 陈伊多, 杨人豪. 基于农户受偿意愿的休耕补偿标准探讨——以河北样本户为例[J]. 资源科学, 2018, 40(7): 1375-1386.

Li ZENG, Qingyuan YANG, Junru LIAO, Zhantu CHEN, Yiduo CHEN, Renhao YANG. Fallow compensation based on farmer willingness to accept in Hebei[J]. Resources Science, 2018, 40(7): 1375-1386.

图/表 6

图1

图2

表1

表2

解释变量赋值及预期作用方向"

	变量名称	变量代码	变量定义	均值	标准差	预期作用方向
农户个人特征（I）	性别	I_gender	农户农业决策者性别：女=0,男=1	0.94	0.24	+/-
	年龄	I_age	农户农业决策者年龄	57.81	11.30	-
	健康状况	I_health	农户农业决策者健康状况：差=1,中=2,良=3,优=4	1.90	0.78	-
	文化程度	I_edu	农户农业决策者文化程度：文盲=1,小学=2,初中=3,高（职）中=4,大专及以上=5	2.70	0.87	+
	是否为干部	I_cadre	家庭成员是否有村/镇干部：有=1,否=0	0.08	0.27	+
家庭特征（F）	人口	F_family	家庭人口实际数量	4.26	1.79	-
	有效劳动力比	F_labour	农户劳动力数量与总人口数量之比	0.70	0.28	-
	职业	F_prof	家庭成员职业：纯农户=0,兼业=1	0.68	0.47	+
	年均人均纯收入	F_inc	家庭人均年收入/（元/人）	8 282.57	8 487.23	+
生产特征（P）	种植收入占家庭收入比例	P_farm	作物种植收入占家庭总收入的比例	0.39	0.44	-
	作物种植投入产出比	P_roi	作物投入/作物产出（农户产出量按市场价价值显化）	0.40	0.39	+
	人均耕地面积	P_pcland	家庭人均耕地面积 /（hm²/人）	0.13	0.10	-
	耕地破碎度	P_frag	耕地块数/耕地总面积 /（块/hm²）	13.45	6.53	+
农户对补偿政策的认知（C）	耕地责任人	C_subj	农户认为耕地保护责任人是：谁种地谁保护=1,农户=2,农村集体经济组织=3,地方政府=4,中央政府=5	3.12	1.75	-
	休耕目的	C_purp	休耕的目的：知道=1,不知道=0	0.90	0.30	+
	休耕政策认同度	C_cogn	农户对休耕政策认同程度：没有必要=1,可有可无=2,有必要=3	2.91	0.31	+
	休耕补偿满意度	C_sati	农户对现行补偿标准的满意度：不满意=1,一般=2,满意=3	2.04	0.87	+
	投标金额	C_expect	农户对休耕补偿愿意接受的金额 /（元/hm²）	10 029.00	2 811.15	+

表2

表3

表4

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变量代码	模型1(B)	Exp (B)	模型2(B)	Exp (B)	模型3(B)	Exp (B)	模型4(B)	Exp (B)
Igender	-0.099 (0.885)	0.906	-0.618 (0.400)	0.539	-0.654 (0.384)	0.520	-0.702 (0.406)	0.495
Iage	0.048*** (0.002)	1.049	0.054*** (0.001)	1.056	0.061*** (0.001)	1.063	0.072*** (0.001)	1.075
Ihealth	-0.022 (0.923)	0.978	-0.029 (0.908)	0.971	-0.101 (0.705)	0.904	-0.229 (0.450)	0.795
Iedu	0.092 (0.655)	1.097	0.133 (0.527)	1.142	0.145 (0.513)	1.156	0.107 (0.667)	1.113
Icadre	1.645 (0.116)	5.183	1.492 (0.159)	4.445	1.328 (0.216)	3.775	0.602 (0.601)	1.826
Ffamily			-0.261** (0.031)	0.770	-0.246* (0.081)	0.782	-0.338** (0.049)	0.713
Flabour			0.838 (0.163)	2.312	0.945 (0.145)	2.572	1.479** (0.044)	4.389
Fprof			1.482** (0.013)	4.401	-1.420 (0.468)	0.242	-1.960 (0.380)	0.141
Finc			0.000 (0.295)	1.000	0.000 (0.843)	1.000	0.000 (0.591)	1.000
Pfarm					-3.810 (0.102)	0.022	-5.341** (0.050)	0.005
Proi					0.795*** (0.005)	2.214	0.739** (0.016)	2.093
Ppcland					-3.473 (0.189)	0.031	-4.228 (0.154)	0.015
Pfrag					0.031 (0.269)	1.031	0.022 (0.465)	1.022
Csubj							-0.104 (0.421)	0.901
Cpurp							-1.043 (0.168)	0.352
Ccogn							1.661*** (0.006)	5.265
Csati							1.500*** (0.000)	4.480
Cexpect							0.075* (0.071)	1.078
D	-1.135	0.321	-1.684	0.186	0.215	1.240	-6.570	0.001
-2LL	234.307		194.254		102.561		87.379
Nagelkerke R2	0.079		0.219		51.312		66.544
预测准确率(%)	88.640		89.213		90.019		91.901
卡方值	13.574		39.183		53.635		90.502
sig.	0.019		0.000		0.000		0.000

WTA/（元/hm²）	绝对频数/（人次）	相对频度/%
4 500	1	0.34
6 000	1	0.34
7 500	99	33.79
9 000	41	13.99
10 500	59	20.14
12 000	65	22.18
13 500	7	2.39
15 000	18	6.14
16 500	2	0.68
合计	293	100.00