农业保险和农村振兴 - - 一项基于中国省级面板数据的经验分析
Chao Zhou1, Jia Liu2, Shenwei Wan3
1Research Center of the Economic and Social Development of Henan East Provincial Joint, Shangqiu Normal University, Shangqiu, China.
Frontiers in public health
|December 19, 2023
概括
农业保险对于中国农村振兴至关重要,大大促进了经济和社会发展. 然而,其影响因地区而异,观察到显著的双值效应,需要量身定制的投资和运营战略.
科学领域:
- 农业经济学 农业经济学
- 农村发展研究 农村发展研究
- 保险政策分析 保险政策分析
背景情况:
- 农业保险为生产者提供对损失的经济保护.
- 农村振兴旨在加强农村的经济,社会和文化发展.
- 农业保险在稳定农民收入和支持农村经济方面发挥着关键作用.
研究的目的:
- 从理论上分析农业保险对农村振兴的机制.
- 实证研究农业保险对中国农村振兴的影响.
- 检查农业保险对农村振兴的值影响.
主要方法:
- 专家小组对中国各省 (2011-2020年) 的数据进行分析.
- 计算农村振兴水平的TOPSIS重法.
- 系统通用时刻方法 (GMM) 和值模型.
主要成果:
- 农村振兴水平显示整体增长缓慢,省级差异很大.
- 农业保险发展增加1个单位,农村振兴增加0.1633个单位.
- 农业保险对农村振兴的四个维度产生重大影响,不包括社会礼仪和礼貌;其影响在东部省份是显著的,但在中部/西部地区没有,表现出双值效应.
结论:
- 农业保险是中国农村振兴的重要推动力.
- 区域差异和值效应需要定制的农业保险策略.
- 建议包括增加资本投资,创新的运营模式,以及农业保险的适应性发展战略.
相关概念视频
Multiple Regression
3.0K
Multiple regression assesses a linear relationship between one response or dependent variable and two or more independent variables. It has many practical applications.
Farmers can use multiple regression to determine the crop yield based on more than one factor, such as water availability, fertilizer, soil properties, etc. Here, the crop yield is the response or dependent variable as it depends on the other independent variables. The analysis requires the construction of a scatter plot...
Farmers can use multiple regression to determine the crop yield based on more than one factor, such as water availability, fertilizer, soil properties, etc. Here, the crop yield is the response or dependent variable as it depends on the other independent variables. The analysis requires the construction of a scatter plot...
3.0K
Statistical Methods for Analyzing Epidemiological Data
371
Epidemiological data primarily involves information on specific populations' occurrence, distribution, and determinants of health and diseases. This data is crucial for understanding disease patterns and impacts, aiding public health decision-making and disease prevention strategies. The analysis of epidemiological data employs various statistical methods to interpret health-related data effectively. Here are some commonly used methods:
371


