规模化养殖对我国肉鸡生产效率的影响——基于随机前沿生产函数的实证分析

采用随机前沿方法,利用2004—2010年我国24个省份的肉鸡生产数据,实证分析了规模化养殖对我国肉鸡生产效率的影响,并对不同养殖规模的肉鸡生产效率进行了测度和比较。结果显示:规模化养殖很大程度上提高了我国肉鸡生产效率,中规模养殖使肉鸡生产效率提高了16.89%,大规模养殖使肉鸡生产效率提高了57.62%;我国肉鸡生产效率的地区差异较大,且南方地区不同养殖规模的肉鸡生产效率存在显著差异,而北方地区的这种差异较小;标准化规模养殖程度不高是南方地区的肉鸡生产效率总体偏低且不同养殖规模的肉鸡生产效率存在较大差异的主要原因。     

基于DEA模型的山东省生猪生产效率研究

利用《全国农产品成本收益资料汇编》中的统计数据,运用DEA方法,比较分析了2000—2011年山东省散户养殖、小规模养殖、中规模养殖和大规模养殖的生猪生产技术效率、规模效率、规模收益和投入冗余。研究结果表明:按照生猪生产技术效率均值和规模效率均值从大到小对山东省4种养殖规模进行排序,依次为大规模养殖、散户养殖、中规模养殖和小规模养殖;散户养殖、小规模养殖和中规模养殖的生猪生产规模收益呈递减趋势,大规模养殖的生猪生产规模收益呈递增趋势;4种养殖规模的生猪生产在仔猪进价、饲料费和人工成本投入3项均存在不同程度的冗余。     

中国生猪生产的区域效率差异及其适度规模选择

运用DEA方法,测算了我国散养、小规模、中规模和大规模四种生猪饲养模式的生产效率。结果表明：2002—2010年生猪生产效率的提高主要依靠要素投入规模的扩大,技术效率的贡献次之;不同省份间不同养殖规模下的生猪生产效率及最优规模存在差异。研究认为各地要因地制宜地选择适度的养殖规模。     

中国生猪养殖生产效率的实证分析——基于DEA-Malmquist指数法

运用DEA-Malmquist指数法,分别测算2005—2010年中国东北、东部、中部和西部地区不同规模生猪养殖的全要素生产率变化及其分解项。研究结果显示:近年来中国不同规模生猪养殖的全要素生产率均呈一定程度的小额负向增长,技术退步是其下降的根源;在国家生猪生产扶持政策的引导下,2008年中国生猪养殖的全要素生产率出现显著上升;不同地区生猪养殖的全要素生产率变化存在明显差异,即东部地区有所提高,西部地区呈下降趋势。最后提出,加快生猪生产的科技进步、加大政府的扶持力度是提高生猪养殖的生产效率的关键措施。     

基于三阶段DEA的中国地区高校知识生产效率研究

利用我国地区高校知识生产数据,应用三阶段DEA方法,分析我国地区高校的知识生产效率。研究发现,我国地区高校知识生产的技术效率、纯技术效率和规模效率均值分别为0.651、0.873和0.756,规模效率不高是制约我国高校知识生产效率整体提升的主要原因;政府资助、企业与高校的合作对高校知识生产效率有显著的正向影响,地区经济发展水平对RD经费投入冗余有显著的正向影响,而对RD人员投入有显著的负向影响;按照纯技术效率和规模效率,将我国地区高校知识生产划分为"高高""高低""低高"和"低低"四种类型,各地区可依据自身的效率特点及所处的类型,有针对性地进行效率改进。     

我国生猪养殖的资源价值流核算比较研究

从循环经济和环境会计的角度,构建生猪养殖资源流分析方法,将生猪养殖的内部资源成本和外部损害价值纳入到综合评价体系中,构建生猪养殖的内部资源成本—外部环境损害成本综合评价模型,比较我国不同区域不同生猪养殖规模的内部资源成本,并估算我国生猪养殖的外部环境损害成本。测算2004—2014年生猪养殖生产的全要素生产率(TFP),并运用Tobit模型来验证内部资源成本和外部环境成本中的主要成本要素对生猪生产效率的影响程度,以影响程度明显的成本要素为重点改进环节,对比改进前后的生产效率差异和内部资源成本—环境损害成本二维图的移动情况。研究表明,我国生猪养殖生产的全要素生产效率偏低,内部资源成本和外部环境损害成本对全要素生产效率存在着一定的抑制作用。Malmquist生产率指数法及其分解技术,测算结果表明:总体上来看,2004—2014年我国生猪养殖的生产效率(TFP)不高,且其主要原因是技术进步率(TECH)较低。内部资源成本中的仔畜进价、精饲料费、青粗饲料费和人工成本对生猪养殖的全要素生产率均呈现出显著的负效应,说明这些指标的增加对生猪养殖的生产效率是具有显著的抑制效应的,生猪养殖的外部环境成本对生产效率也存在着一定的抑制作用。经过内部资源成本减量化和外部环境损害成本控制的模拟改进,资源内部消耗—外部环境损害成本综合改善效果较好,不同区域和不同养殖规模的内部资源成本—外部环境损害成本改进达到了预期效果。     

中国区域公共服务供给效率评价与差异性分析

运用三阶段DEA模型对31个省份公共服务供给效率进行了实证研究。结果表明:利用SFA模型剥离管理因素和随机因素对效率的影响很有必要;全国总体技术效率尚有25.9%的改进空间,且多数省份处于规模报酬递增阶段;人均GDP、人口密度、居民教育年限、城镇化水平等对效率有显著影响,但公共服务财政支出作用有限;三大地区的技术效率有着较大差别,中部最高,东部次之,西部最低;各地区应结合自身效率的特征分别着重进行管理水平的提高或生产规模的扩大来改善效率。     

中国乡镇企业生产效率格局及重心轨迹演变

乡镇企业生产效率事关乡村发展及城乡融合进程。基于1990—2016年乡镇企业生产投入、产出相关数据,综合运用DEA模型、GIS空间分析及经济重心等研究方法,考察中国乡镇企业生产效率格局演变及其移动路径。研究显示:中国乡镇企业生产效率具有以下特征:①整个研究期内,3个效率指标均值基本呈波动下行发展态势;②综合技术效率偏低,纯技术效率水平略高,规模技术效率得分最高,且不同研究单元差异显著;③从三大空间效率均值排序来看,综合技术效率为东、中、西部,纯技术效率为东、西、中部,规模技术效率则较为随机;④综合技术效率和纯技术效率重心转移方向先后经历由东南向西南的演变,最终转移方向为东北。     

中国规模生猪养殖全要素生产率的时空分异及收敛性——基于环境约束的视角

基于2004—2013年中国29个省份的面板数据,分别使用SML指数、空间自相关和空间β收敛分析方法对环境约束下规模生猪养殖全要素生产率的时空分异趋势及收敛性进行分析。研究发现:中国规模生猪养殖全要素生产率主要受到技术进步的拉动在考察期内以年均5.29%的速度增长,技术效率在波动中无明显改善;随着技术进步度过快速空间外溢时期,地区间规模生猪养殖全要素生产率增长的正向空间溢出作用经历了先增后降的变化趋势;HH类型地区以中南区为基础逐渐向西北方向的重庆和陕西扩张,LL类型地区在北方以及华东区的连片分布范特征逐渐增强,HL类型和LH类型地区的连片特征趋于减弱;中国规模生猪养殖全要素生产率增长存在显著的绝对β收敛和条件β收敛特征,经济发展水平、畜禽养殖业发展水平和农村人力资本水平是影响收敛性的主要因素。     

中国战略性新兴产业技术效率及其影响因素

本文使用2004~2011年中国7大战略性新兴产业46个样本行业的面板数据,采用随机前沿分析方法并构建超越对数生产函数模型,实证测度中国战略性新兴产业技术效率,并进一步检验其影响因素。研究发现:样本期间,我国战略性新兴产业生产活动存在技术无效率;样本行业技术效率值差异很大,极不平衡;在控制行业异质性后,行业平均企业规模、对外开放度及行业盈利能力对生产技术效率产生显著的正影响;国有产权比重对生产技术效率产生显著的负影响。     

Human Capital,Gender and Organisation in Transition Agriculture: Measuring and Explaining the Technical Efficiency of Bulgarian and Hungarian Farms

Based on survey data on Bulgarian and Hungarian crop and dairy farms, a double-peaked distribution of technical efficiency is observed. Several factors explain differences in efficiency. Human capital matters not only through age and education but also through gender, as farms with a higher proportion of women are more efficient. Contracting with upstream processors increases efficiency through facilitating the adoption of technology and access to credits. The superiority of family farms over corporate farms is confirmed for crops but not for dairy produce.     

Assessing the impact of economic liberalization across countries: a comparison of dairy industry efficiency in Canada and the USA

This paper examines and compares the technical efficiency measures of Ontario and New York dairy producers for the period 1992 to 1998. A nonparametric stochastic frontier model is introduced to estimate technical efficiency. The backfitting algorithm of Breiman and Friedman is used to estimate the frontier. Empirical results indicate that during the period of study, New York dairy farmers produced milk more efficiently than Ontario dairy producers, but the magnitude of the difference was small. The estimated mean technical efficiency for the former group is 0.602 as compared to 0.532 for the latter. The results also indicated that over time, dairy farms in both regions improved their level of technical efficiency. Furthermore, no correlation was found between farm size and estimated technical efficiency.     

Measuring farm sustainability and explaining differences in sustainable efficiency

A major objective of European agricultural policy is to have a sustainable and efficient farming sector that is applying environmentally-friendly production methods. Policy makers aim to combine a strong economic performance and a sustainable use of natural resources. Therefore, it is important to measure and to assess farm sustainability. For a large dataset of Flemish dairy farms, a valuation method that is based on the concept of opportunity costs is used to calculate and analyze differences among the sample farms with respect to the creation of “sustainable value”. But more important than measuring the creation of sustainable value is to analyze differences in sustainable efficiency. Therefore, sustainable efficiency measures are calculated and differences in sustainable efficiency are explained. Using panel data, an effect model captures the determinants of sustainable efficiency of the studied farms. The empirical model shows that, in general, larger farms have a higher sustainable efficiency. Also farmer's age and dependency on support payments proved to be determining characteristics for observed differences in sustainable efficiency.     

Measuring technical efficiency in the Greek agricultural sector

This paper measures the degree of technical efficiency of Greek farms at discrete points in time. Stochastic frontier production functions are estimated from four annual Farm Accountancy Data Network (FADN) surveys of the 1992–1995 period. From the results, a measure of technical efficiency is calculated for each farm for each year. The four distributions of technical efficiency values are examined and compared. All four samples show a wide range of farm-specific technical efficiency but efficiency is improving over the period. The paper also presents frontier estimates for small and large farms classified according to economic size. In that case, technical efficiency measures are calculated and their distributions are examined and compared. The results show that large farms are more efficient than small farms. However, efficiency is improving in both size farms over the period. In general, the results of this study indicate that there is substantial scope for improving technical efficiency of Greek farms.     

农户生产无公害蔬菜的技术效率及其影响因素分析——基于山东寿光无公害黄瓜和西红柿生产的调查

根据2008年对山东寿光样本农户生产无公害黄瓜和西红柿的调研数据,运用数据包络分析法和随机前沿分析法测算了其生产的技术效率,分析了农户生产无公害蔬菜的技术效率的影响因素。结果表明:农户生产无公害蔬菜的平均技术效率低,主要原因是部分要素投入过剩和规模不经济;获取科技信息和建立稳定的销售渠道对提高农户生产无公害蔬菜的技术效率有显著的正效应;利用两种测算方法所得的结果具有一致性。     

Determinants of technical efficiency of crop and livestock farms in Poland

Poland, one of the candidate countries for European Union membership, is currently experiencing acute structural problems within its agriculture sector. This article analyses technical efficiency and its determinants for a panel of individual farms in Poland specialized in crop and livestock production in 2000. Technical efficiency is estimated with stochastic frontier analysis (SFA) and confidence intervals are constructed. Determinants of inefficiency are also evaluated. The SFA results are compared with results using Data Envelopment Analysis (DEA). On average, livestock farms are more technically efficient than crop farms. For both specializations, the size–efficiency relationship is positive, that is large farms are more efficient. The SFA findings are generally supported by the DEA results. Soil quality and the degree of integration with downstream markets are highly important determinants of efficiency. The use of factor markets (land and labour) is important for crop farms, while livestock farms can rely on family labour and own land. Also, education is a constraint to efficiency particularly for crop farms.     

不同规模农户农业生产效率差异及影响因素研究——基于DEA-Tobit模型的实证分析

文章基于小麦主产区4个省份946个不同规模农户的调查数据,利用DEA模型和Tobit回归模型分析了小农户和家庭农场农业生产效率的影响机制。结果表明:家庭农场的农业生产效率明显高于小农户的农业生产效率,家庭农场的生产效率与经营规模呈倒"U"型变化特征;在影响因素方面,土地肥沃程度与农业技术指导对小农户和家庭农场的农业生产效率均有显著正向影响;家庭农场更倾向于通过提高受教育程度和增加单块耕地面积来提升农业生产效率;而小农户的农业生产效率对农户年龄和农户从事农作物种植年限呈现出较强的依赖性。基于此,建议有针对性地加大农业技术指导,适度扩大农业生产经营规模,加强小农户与新型农业经营主体之间的合作与联合,促进农业生产向高质量发展。     

我国工业行业全要素生产率变动分析

基于2006—2009年我国工业行业的面板数据,采用非参数Malmquist指数分析法,对我国工业的全要素生产率变动情况进行了实证研究,并将其分解为技术进步、纯技术效率和规模效率。结果表明:2006—2009年我国工业全要素生产率年均增长率仅为4.3%,技术进步年均增长率为1.6%,技术效率年均增长率为2.7%,技术进步已失去其推动全要素生产率增长的主导性优势地位;我国工业发展需注重同时提高技术效率和技术进步水平,否则全要素生产率增长会受到限制。     

Technical efficiency of small scale farmers: An application of the stochastic frontier production function to rural and urban farmers in Ondo State,Nigeria

Abstract

The main objective of this study was to estimate empirically the technical efficiency of rural and urban small-scale farmers in Ondo State, Nigeria, using the stochastic frontier production function. With the rapid urbanization being currently experienced in Nigeria, the socio-economic and the farming environment in both the rural and urban centres are undergoing transformation. This study involved collection and analysis of data on 200 food crop farmers from rural and urban centres in Ondo state of Nigeria. The results of analysis show that there are wide differences in the socio-economic and production inputs of the farmers from rural and urban centres. While farmers from rural centres have relatively larger farms, the urban farmers have better access to production inputs, especially, fertilizer. However, rural farmers, with a mean technical efficiency of 0.66 are found to be more technically efficient than urban farmers, who have a mean technical efficiency of 0.57. There is a wide confidence interval around the point estimates of the technical efficiency scores. Level of education, farming experience and land are found to have negative effects on technical inefficiency of both rural and urban farms. The marginal effect of inefficiency variables is, however, found to be highest for education among both rural and urban farms.