| 摘要: |
| 目的 通过分析农业全要素生产率增长的动态变化和长期收敛性，为促进农业经济增长和缩小地区农业发展差距提供理论基础。方法 文章基于中国29个省（市、区）1978—2017年的面板数据，利用随机前沿生产函数模型测算农业全要素生产率的增长。在此基础上，运用基于Log（t）回归的PS收敛检验分析累积农业TFP增长的收敛性。结果 农业TFP的动态分析表明，农业经济的增长主要依靠农业全要素生产率的支撑，由于农业科技创新能力不足，农业TFP增长的速度放缓。收敛性检验结果表明，1986—2017年中国农业累积TFP增长在全国省际层面和东、中、西、东北四大区域层面都发散，但存在6组俱乐部收敛和4个不收敛地区。从收敛速度来看，俱乐部1的收敛速度最快，是唯一的绝对收敛，俱乐部2的收敛速度最慢；根据累积农业TFP增长的差异，可以将收敛俱乐部划分为“高速增长”“平稳增长”和“低速增长”3种类型。“高速增长”俱乐部（俱乐部1、2）与其他俱乐部（俱乐部3~6）的增长差距主要来源于技术进步，技术效率是其他俱乐部之间增长差距的重要影响因素。结论 为推动农业高质量发展，各地区应加强农业先进技术的交流，提升农业科技创新能力，增强资源禀赋与农业科技创新的耦合关系，实现有效创新，提高农业科技成果转化率。 |
| 关键词: 农业全要素生产率 地区差异 随机前沿模型 俱乐部收敛 Log（t）回归 |
| DOI：10.7621/cjarrp.1005-9121.20220106 |
| 分类号:F323.5 |
| 基金项目:国家自然科学基金面上项目“补偿到期后贫困地区退耕农户行为追踪、驱动因素与成果巩固长效机制研究”（71873017）;中央高校基本科研业务费专项项目“生态文明背景下林业经济与生态协调发展机制及主要模式研究”（2015ZCQ-JG-03） |
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| DYNAMIC ANALYSIS OF AGRICULTURAL TOTAL FACTOR PRODUCTIVITY IN CHINABASED ON SFA MODEL AND LOG(T) REGRESSION METHOD |
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Liu Xiating, Li Qiang, Wu Chao, Ma Jinyi
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Economics and Management College, Beijing Forestry University, Beijing 100083, China
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| Abstract: |
| This paper analyzes the dynamics and long-term convergence of agricultural total factor productivity growth in China, and provides a theoretical basis for promoting agricultural economic growth and narrowing the agricultural development gaps between regions. Based on panel data from 1978 to 2017 from twenty-nine regions in China, we first employed a stochastic frontier production function model to measure the growth of agricultural total factor productivity. Then we analyzed the convergence of cumulative agricultural TFP growth using the PS convergence test that was based on Log（t） regression. The dynamic analysis of agricultural TFP showed that the growth of agricultural economy was mainly supported by the increase in agricultural total factor productivity. However, due to lack of innovation capabilities in agricultural technologies, the growth rate of agricultural TFP had slowed down. The results from the convergence test showed that China's cumulative agricultural TFP growth from 1986 to 2017 diverged at the provincial level and in four major regions (east, central, west, and northeast). We aslo found six groups of club convergence and four non-convergence regions. In terms of convergence speed, Club 1, which was the only absolute convergence, had the fastest convergence rate, and Club 2 had the slowest convergence rate. According to the differences in the cumulative agricultural TFP growth, the convergence clubs were divided into three types including "fast-growth" club, "steady growth" club, and "slow-growth" club. The gap between "fast-growth" clubs (club 1,2) and other clubs (club 3-6) was mainly due to technological progress, while technical efficiency was an important factor affecting the gap among other clubs. So, in order to promote the high-quality development of agriculture, all regions should strengthen the transfer of advanced agricultural technology, enhance the innovation capacity of agricultural technology, improve the coupling relationship between resource endowments and agricultural technology innovation to achieve effective innovation, and increase the commercialization of agricultural technology innovation. |
| Key words: agricultural total factor productivity regional differences stochastic frontier model club convergence Log（t） regression |
