Abstract：By using the stochastic frontier translog production function to calculate the agricultural technical efficiency of 31 provinces and cities from 2002 to 2017, the results show that the agricultural technical efficiency of each province has a certain degree of loss, and the average agricultural technical efficiency of western China is the lowest. Further combining with the national and regional financial support to agriculture panel data, by constructing panel data econometric model, the results show that the effect of financial support to agriculture on the agricultural technical efficiency has obvious regional differences. The relationship between finance support to agriculture and agricultural technical efficiency is linear in the eastern, western and northeastern regions, and inverted N-shaped in the central region.
王丹亚, 高齐圣. 财政支农对农业技术效率的影响研究[J]. 复杂系统与复杂性科学, 2021, 18(2): 81-88.
WANG Danya, GAO Qisheng. The Influence of Financial Support to Agriculture on Agricultural Technical Efficiency. Complex Systems and Complexity Science, 2021, 18(2): 81-88.
[1]刘佳, 余国新. 地方财政支农支出对农业技术效率影响分析——基于随机前沿分析方法[J]. 中国农业资源与区划, 2014, 35(5): 129-134. Liu Jia, Yu Guoxin. Provincial variance analysis of effect of local finance for supporting agriculture on the agricultural technical efficiency[J].Chinese Journal of Agricultural Resources and Regional Planning, 2014, 35(5): 129-134. [2]王亚飞, 张毅, 廖甍. 外商直接投资对农业全要素生产率的影响:作用机理与经验证据[J]. 当代经济研究, 2019(6): 74-86,113. Wang Yafei, Zhang Yi,Liao Meng. The impact of foreign direct investment on agricultural total factor productivity: mechanism of action and empirical evidence[J]. Contemporary Economic Research, 2019(6): 74-86,113. [3]Farrell M J. The measurement of productive efficiency[J]. Journal of the Royal Statal Society, 1957, 120(3): 253-290. [4]Kumbhaker S C, Tsionas M G. On the estimation of technical and allocative efficiency in a panel stochastic production frontier system model: some new formulations and generalizations[J]. European Journal of Operational Research, 2020, 287(2): 762-775. [5]Lai H P, Kumbhakar S C. Technical and allocative efficiency in a panel stochastic production frontier system model[J]. European Journal of Operational Research, 2019, 278(1):255-265. [6]张海鑫, 杨钢桥. 耕地细碎化及其对粮食生产技术效率的影响——基于超越对数随机前沿生产函数与农户微观数据[J]. 资源科学, 2012, 34(5): 903-910. Zhang Haixin, Yang Gangqiao. The effects of land fragmentation on technical efficiency of food production: an empirical analysis based on stochastic frontier production and micro-data of households[J]. Resources Science, 2012, 34(5): 903-910. [7]唐建, Jose Vila. 粮食生产技术效率及影响因素研究——来自1990—2013年中国31个省份面板数据[J].农业技术经济, 2016(9): 72-83. Tang Jian, Jose Vila. Research on technical efficiency of grain production and its influencing factors[J]. Journal of Agrotechnical Economics, 2016(9): 72-83. [8]Boubacar S B, Hajime K, Makoto N, et al. An analysis of technical efficiency of mangrove rice production in the Guinean Coastal Area[J]. Journal of Agricultural Science, 2014, 6(8): 179-196 [9]周鹏飞, 谢黎, 王亚飞. 我国农业全要素生产率的变动轨迹及驱动因素分析——基于DEA—Malmquist指数法与两步系统GMM模型的实证考察[J]. 兰州学刊, 2019(12): 170-186. Zhou Pengfei, Xie Li, Wang Yafei. Analysis on the changing track and driving factors of agricultural total factor productivity in China[J]. Lanzhou Academic Journal, 2019(12): 170-186. [10] 李谷成, 冯中朝, 占绍文. 家庭禀赋对农户家庭经营技术效率的影响冲击——基于湖北省农户的随机前沿生产函数实证[J]. 统计研究, 2008(1): 35-42. Li Gucheng, Feng Zhongchao, Zhan Shaowen. An empirical analysis about the effect of household endowments on the technical efficiency of farmer's household management[J]. Statistical Research, 2008(1): 35-42. [11] 李双杰, 范超. 随机前沿分析与数据包络分析方法的评析与比较[J]. 统计与决策, 2009(7): 25-28. Li Shuangjie, Fan Chao. Evaluation and comparison of stochastic frontier analysis and data envelopment analysis methods[J]. Statistics & Decision, 2009(7): 25-28. [12] 陈真玲, 赵伟刚, 李金铠.中国制造业能源拥挤效应研究:基于RAM-DEA模型的分析[J]. 系统工程理论与实践, 2019, 39(7): 1831-1844. Chen Zhenling, Zhao Weigang,Li Jinkai. Research on energy congestion effects in China's manufacturing sector: an analysis based on RAM-DEA[J]. System Engineering Theory and Practice, 2019, 39(7): 1831-1844. [13] Battese G E, Coelli T J. Frontier production functions, technical efficiency and panel data: with application to paddy farmers in India[J]. Journal of Productivity Analysis, 1992, 3(1/2):153-169. [14] Kumbhakar S C, Lovell C A K. Stochastic Frontier Analysis[M]. Cambridge: Cambridge University Press, 2000: 263-276. [15] 邹薇, 杨胜寒. 超大城市对我国经济的影响有多大——基于劳动投入,TFP和工资差异的分析[J]. 系统工程理论与实践, 2019,39(8): 1991-2004. Zou Wei, Yang Shenghan. How do megacities matter in Chinese economy: analysis based on labor force, TFP and wage differences[J]. System Engineering Theory and Practice, 2019,39(8): 1991-2004. [16] 吴方卫. 我国农业资本存量的估计[J]. 农业技术经济, 1999(6): 3-5. Wu Fangwei. Estimation of China′s agricultural capital stock[J]. Journal of Agrotechnical Economics, 1999(6): 3-5. [17] 张军, 吴桂英, 张吉鹏. 中国省际物质资本存量估算: 1952—2000[J]. 经济研究, 2004(10): 35-44. Zhang Jun, Wu Guiying, Zhang Jipeng. The estimation of China's provincial capital stock: 1952-2000[J]. Economic Research Journal, 2004(10): 35-44. [18] 朱万里, 胡瑜杰. 财政支农、农业技术创新对农业经济增长的影响效应研究[J]. 科学与管理, 2019, 39(4): 73-80. Zhu Wanli, Hu Yujie. Financial support to agriculture, agriculture technology innovation and agricultural economic growth[J]. Science and Management, 2019,39(4): 73-80. [19] 白俊红, 江可申, 李婧. 应用随机前沿模型评测中国区域研发创新效率[J]. 管理世界, 2009(10): 51-61. Bai Junhong, Jiang Keshen, Li Jing. Evaluate the R&D and innovation efficiency in China based on the stochastic frontier model[J]. Management World, 2009(10): 51-61. [20] 高齐圣, 路兰. 中国水资源长期需求预测及地区差异性分析[J]. 干旱区资源与环境, 2016, 30(1): 90-94. Gao Qisheng, LU Lan. China's long-term water demand forecast and regional variance analysis[J]. Journal of Arid Land Resources and Environment, 2016, 30(1): 90-94.
