摘要
依据黄淮海平原5省570个粮食类家庭农场的调研数据,运用选择实验法,在对家庭农场忽略的属性进行信息处理的基础上,借助混合Logit模型和潜在类别模型,分析家庭农场对不同扶持政策的偏好程度,以及不同偏好类型的家庭农场特征差异。结果发现:采用推断性属性非参与方法进行信息处理后的模型拟合效果最好;家庭农场更偏好农业补贴、信贷支持和技术支持,但其对政策传播渠道的偏好也不能忽视;面临相同的政策属性组合时,受教育水平高、经营规模合理和对扶持政策认知程度高的家庭农场利润率提升的可能性更大;家庭农场可划分为资金偏好型、知识与技术偏好型、土地流转偏好型和政策信息偏好型四种类型。应该基于偏好异质性制定家庭农场扶持政策,将扶持政策向家庭农场相对更为偏好的农业补贴、信贷支持和技术支持适当倾斜,并对农场主受教育水平高、经营规模合理和对扶持政策认知度高的家庭农场进行重点扶持。
Employing the choice experiment method in surveying 570 family farms in Huang-huaihai Plains,this paper processes the information on attributes neglected in grain family farms and analyzes preferences for different support policies and the characteristics of grain family farms with heterogeneity preferences with the assistance of mixed logit and latent class models.Results show that the model using inferred attribute non-attendance method has the best fit;policies comprising agricultural subsidy,credit support and technical support are preferred by grain family farms;the preference of grain family farms for policy communication channel cannot be ignored;grain family farms with high education level,reasonable land scale and high cognitive competence to supporting policies are expected to increase profit rate in front of the same policy;there are four types of grain family farms,namely the capital-preferred,the knowledge-and-technology-preferred,the land-transfer-preferred and the policy-preferred.Family farm supporting policy must be formulated on the basis of heterogeneity preferences,and the supporting policy should be appropriately inclined to the preferred agricultural subsidy,credit and technical support.Family farms with highly-educated farmers,reasonable land scale and high cognitive competence must be intensively supported.
引文
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(1)本文未将税收优惠和农业保险作为家庭农场扶持政策的属性变量,其原因分别在于:第一,从2006年1月1日起,我国废止《农业税条例》,对直接从事农业生产、有农业收入的单位和个人免征农业税。第二,我国虽然通过保费补贴、管理费补贴和再保险补贴等,降低保险公司经营风险和提高保险公司服务供给的积极性,但保险公司定损和赔付的手续繁琐、保额不能补偿家庭农场的基本物化成本、部分险种赔付标准过高、大部分县没有专门的查勘定损专家组。此外,种植业核定理赔要到作物收割时才能进行赔付,耽误了家庭农场种植其他作物。可见,农业保险政策更多偏向于保险公司。
(1)指行为个体报告的非参与信息可能与其他不可观测的因素存在相关性。
(1)分成2~7类时的BIC值依次为:3 517.423、3 462.210、3 225.083、3 364.759、3 497.655、3 633.787。