Six Decades of Total Factor Productivity Change and Sources of Growth in Bangladesh Agriculture (1948–2008)

This study applies the Färe–Primont index to calculate total factor productivity (TFP) indices for agriculture in 17 regions of Bangladesh covering a 61‐year period (1948–2008). It decomposes the TFP index into six finer components (technical change, technical‐, scale‐ and mix‐efficiency changes, residual scale‐ and residual mix‐efficiency changes). Results reveal that TFP grew at an average rate of 0.57% p.a. led by the Chittagong, Rajshahi, Rangpur, Dinajpur and Noakhali regions. TFP growth is largely powered by technological progress estimated at 0.74% p.a. Technical efficiency improvement is negligible (0.01% p.a.) due to stagnant efficiency in most of the regions. Decline in scale efficiency is also negligible (0.01% p.a.), but the decline in mix efficiency is high at 0.19% p.a. Decomposition of the components of TFP changes into finer measures of efficiency corrects the existing literature’s blame of a decline in technical efficiency as the main cause of poor TFP growth in Bangladesh. Among the sources, farm size, R&D investment, extension expenditure and crop specialisation positively influenced TFP growth, whereas the literacy rate had a negative influence on growth. Policy implications include encouraging investment in R&D and extension, land reform measures to increase average farm size, promotion of Green Revolution technology and crop diversification.     

Nonparametric estimates of productivity and efficiency change in Australian Broadacre Agriculture

This paper computes and decomposes Färe‐Primont indexes of total factor productivity of Australian broadacre agriculture by estimating distance functions. Using state‐level data from 1990 to 2011, the empirical results show that TFP grew at an average rate of 1.36 per cent per annum in the broadacre agriculture over the period 1990–2011. There are variations of total factor productivity (TFP) growth across states and fluctuations over time within each state and territory. However, overall, there is a clear movement towards slower TFP growth across the sample period. Further decomposition of TFP growth shows that it is declining growth in technical possibilities (technological progress) that is the main driver of the declining trend in productivity growth in broadacre agriculture in Australia.     

Measuring productivity growth when technological change is biased–a new index and an application to UK agriculture

Productivity growth is conventionally measured by indices representing discreet approximations of the Divisia TEP index under the assumption that technological change is Hicks-neutral. When this assumption is violated, these indices are no longer meaningful because they conflate the effects of factor accumulation and technological change. We propose a way of adjusting the conventional TFP index that solves this problem. The method adopts a latent variable approach to the measurement of technical change biases that provides a simple means of correcting product and factor shares in the standard Tornqvist-Theil TFP index. An application to UK agriculture over the period 1953–2000 demonstrates that technical progress is strongly biased. The implications of that bias for productivity measurement are shown to be very large, with the conventional TFP index severely underestimating productivity growth. The result is explained primarily by the fact that technological change has favoured the rapidly accumulating factors against labour, the factor leaving the sector.     

供给侧结构改革背景下的西南地区农业全要素生产率分析

［目的］探究在制度改革背景下的农业生产力发展水平和地区差异，有利于全面了解农业发展状况，为后期农业政策的制定、调整以及农业发展提供理论基础。［方法］文章利用DEA-Malmquist指数法对我国西南地区5个省（市）农业全要素生产率（TFP）水平变化发展趋势进行分析，并在此基础上对不同地区农业TFP差异变化和发展趋势进行预测。［结果］（1）Malmquist指数分析结果表明，我国西南地区农业TFP呈缓慢增长的趋势。通过对不同地区农业TFP结果进行比较，四川省农业TFP有先逐渐上升后下降的趋势，而技术进步是影响四川省农业TFP增长的原因；（2）重庆市农业TFP也是呈现波动式缓慢增加的趋势，年平均增长率为354%； 贵州省农业TFP近20年来增长速度最快，主要影响因素为技术进步； 云南和广西农业TFP也均呈缓慢增长的趋势，技术效率和技术进步在不同时期对农业TFP的影响有所差异。（3）收敛性分析结果表明，我国西南地区农业TFP差异有减小的趋势，并且不同地区农业TFP水平具有向各自稳定状态发展的可能性。［结论］我国西南地区农业TFP增长与技术进步具有较高的同步性，因此，制约我国西南地区农业全要素生产率增长的关键因素是农业科技进步。在供给侧结构性改革背景下，必须以市场需求为科研导向，加大科技创新投入力度，促进我国西南地区农业的可持续发展。     

Asian Agricultural Productivity and Convergence

This paper measures agricultural total factor productivity (TFP), for eighteen Asian countries, from 1965 to 1996. TFP is measured by calculating the Malmquist index with respect to the sequential frontier, which is appropriate when the cross section is relatively small. The results show that half the countries have experienced negative productivity growth, due to losses in technical efficiency combined with stagnation in technological progress. Both cross‐section and time series tests show that there is no evidence of convergence in agricultural productivity for these countries. The less productive countries are falling further behind, rather than catching up. Finally, comparisons with Africa show that although Asia has had faster TFP growth than Africa, three of the five African regions (East, Central and Southern) have grown faster than any of the Asian regions.     

Measuring and decomposing agricultural productivity and profitability change*

Profitability change can be decomposed into the product of a total factor productivity (TFP) index and an index measuring changes in relative prices. Many TFP indexes can be further decomposed into measures of technical change, technical efficiency change, scale efficiency change and mix efficiency change. The class of indexes that can be decomposed in this way includes the Fisher, Törnqvist and Hicks–Moorsteen TFP indexes but not the Malmquist TFP index of Caves, Christensen and Diewert (1982). This paper develops data envelopment analysis methodology for computing and decomposing the Hicks–Moorsteen index. The empirical feasibility of the methodology is demonstrated using country‐level agricultural data covering the period 1970–2001. The paper explains why relatively small countries tend to be the most productive, and why favourable movements in relative prices tend to simultaneously increase net returns and decrease productivity. Australia appears to have experienced this relative price effect since at least 1970. Thus, if Australia is a price‐taker in output and input markets, Australian agricultural policy‐makers should not be overly concerned about the estimated 15 per cent decline in agricultural productivity that has taken place over the last three decades.     

中国农业全要素生产率的动态演进及其影响因素分析

[目的]通过测算中国农业全要素生产率,揭示其动态演进趋势及影响因素,从而为推动我国农业供给侧结构性改革,实现农业现代化提供科学的决策依据。[方法]文章使用SBM-Global Malmqusit生产率指数法测算了中国农业全要素生产率,在此基础上利用Kernel核密度估计方法刻画了中国农业TFP增长的动态演进,并通过建立面板数据模型对影响中国农业TFP增长的诸多因素进行了分析。[结果]中国农业全要素生产率增长的主要源泉是技术进步,而技术效率的下降是阻碍其增长的主要原因;核密度估计表明各省份之间的农业相对全要素生产率水平差距不断扩大,技术效率普遍恶化,而技术进步增长速度显著;农业金融发展水平、工业化水平、农业科技水平对中国农业TFP都产生了显著的促进作用,而农业产业结构调整、对外开放水平对中国农业TFP则产生了阻碍作用,农村人力资本则对农业TFP产生的正效应不显著。[结论]提高农业全要素生产率,实现农业现代化.各省份应加强农业科技创新,深化农业科技体制改革,完善农村金融服务体系,推动工业化和农业现代化深度融合,培育新型职业农民等措施。     

Broadacre farm productivity and profitability in south‐western Australia

This paper examines broadacre farm performance in south‐western Australia. This region has experienced pronounced climate variability and volatile commodity prices since the late 1990s. Relationships between productivity and profitability are explored using panel data from 47 farms in the study region. The data are analysed using nonparametric methods. By applying the Fare‐Primont index method, components of farm productivity and profitability are measured over the period 1998–2008. Growth in productivity is found to be the main contributor of profitability. Gains in efficiency and technical change are identified as jointly and similarly important in their contribution to total factor productivity for the farm sample in the region from 1998 to 2008. However, across environments, efficiency gains play an increasingly important role in influencing productivity as growing season rainfall increases. We conclude that R,D&E that delivers further improvement in technical efficiency and technical change is needed to support the profitability of farms across the study region.     

Labor productivity measurement in Japanese agriculture, 1956–90

This paper investigates the factors responsible for a drastic decline in the growth rate of labor productivity of the agricultural sector for the 1956–90 period. This investigation is carried out by a newly devised procedure which decomposes the growth rate of labor productivity into (1) the total substitution effect which consists of the effects due to factor price changes and biased technological change, and (2) the TFP effect composed of the effects due to scale economies and technological progress. Based on empirical estimation of the translog cost function, it was found that the total substitution effect contributed to the growth of labor productivity much more than the TFP effect did for the period under question.     

基于碳排放的河南省粮食绿色全要素生产率分析及对策建议

［目的］为探索粮食绿色增产模式，推进农业供给侧结构性改革和绿色可持续发展。［方法］分别运用SBM-ML指数模型与DEA Malmquist指数模型对2004—2016年河南省粮食绿色全要素生产率和传统全要素生产率进行测算和对比分析，并采用ESDA法探讨了粮食绿色全要素生产率与粮食产出的空间关联性。［结果］河南省粮食绿色全要素生产率明显低于传统全要素生产率； 2004—2016年河南省粮食绿色全要素生产率的驱动因素经历了由技术进步驱动转变为技术效率驱动再转变为技术进步和技术效率共同驱动的过程，但技术进步的贡献率明显高于技术效率； 河南省粮食绿色全要素生产率与粮食产出的二元全局空间自相关大体呈“N”型变化，粮食绿色全要素生产率和粮食产出由不平衡发展逐渐趋向平衡发展。［结论］忽视碳排放的传统粮食全要素生产率测算与实际有较大偏差。建议通过转变发展模式，探索粮食绿色增产道路； 因地制宜，有效利用技术进步和技术效率双轮驱动； 不断推进河南省粮食绿色全要素生产率与粮食产出的平衡发展3项措施来实现粮食生产的综合提升。     

中国人参生产效率研究——基于HMB指数的评价

本研究利用1998~2005年中国人参生产投入产出面板数据(Panel Data),采用非参数HMB指数方法和数据包络分析(DEA)法对人参生产效率进行分析。结果表明:中国人参生产全要素生产率(TFP)变动主要受规模效率改善的影响;各样本区的全要素生产率存在一定差异,除规模效率差异的原因外,还有技术进步和技术效率方面的差异;混合效率的作用不显著;人参生产中的非效率是由于投入冗余、产出不足以及成本收益率低等原因导致的。因此,今后为进一步提高人参生产率,可以通过促进技术进步,提高投入产出比例等手段来实现。     

基于参数和非参数方法的玉米生产效率研究——以吉林省为例

本文以中国玉米主产区吉林省为例,基于非参数HMB指数方法和参数K-L随机前沿生产函数方法,采用1991-2005年投入产出的面板数据(Panel Data),对玉米生产的全要素生产率进行实证分析.结果表明,主产区的玉米全要素生产率(TFP)变动具有周期性的变化特点,每一周期技术进步、技术效率、规模效率和混合效率对玉米全要素生产率变动的作用不同;分区综合结果显示,黄金边缘区玉米生产率的增长幅度最高,黄金玉米主产区玉米生产率的增长幅度次之,非主产区玉米生产率的增长幅度最低.今后为促进主产区玉米全要素生产率增长应采取促进技术进步、挖掘主产区潜力等措施.     

我国水禽全要素生产率增长和贡献及区域差异分析

目的 通过测算水禽全要素生产率（TFP）来评估水禽的投入产出效率，同时分别对比各养殖区域之间TFP的增长率及贡献的差异。方法 文章依托全国22个省、（市、自治区）水禽投入产出数据，基于索洛残差法和DEA-Malmquist指数法分别从水禽TFP指数、TFP增长率和TFP贡献率3个角度展开分析。结果 （1）全国水禽TFP均值为1.352，大多数地区的水禽TFP指数均大于1，山东省的技术效率、纯技术效率、规模效率均为全国第一；（2）全国水禽TFP增长率平均值为0.19，水禽TFP贡献率平均值为120%，TFP贡献率和增长率较上一年都有所提升；（3）区域差异比较后发现，西南地区的水禽TFP指数、TFP增长率和水禽TFP贡献率的均值最大，而华中地区的3个指标均值均为全国最小，水禽的TFP区域特征呈现出“北向南移”趋势。结论 技术进步对水禽产业发展作出了很大的贡献，说明水禽产业需要进一步加强新技术投入；突发事件一定程度上刺激了水禽产量的增长，说明不仅要预防突发事件，而且要不断挖掘突发事件后的边际效应；水禽TFP区域差异显著，要求结合区域特征分析后因地制宜施策，最终提升水禽生产要素投入效率。     

Pricing Organic Nitrogen Under The Weak Disposability Assumption: An Application to the French Pig Sector

The nonparametric approach Data Envelopment Analysis is used in order to measure individual technical inefficiency. Different models are evaluated using a 1991 FADN data set of French pig farms. Organic nitrogen is considered as a byproduct of animal breeding. Used as plant nutrient, organic manure becomes an input in crop production which is pollutant when excessive amounts enter surface and groundwater. In the latter case, the usual hypothesis of strong disposability of organic nitrogen is refuted because the elimination of pollution is costly. In this paper, we introduce a hypothesis of weak disposability of organic nitrogen and compare the technical efficiency measures obtained with and without this assumption. Then, using the duality between the restricted cost function and the input-output distance function and following Färe and Primont (1995), we derive a shadow price of organic nitrogen which can be compared to the unit cost of different existing manure treatments.     

Dual Technological Development in Botswana Agriculture: A Stochastic Input Distance Function Approach

To improve the welfare of the rural poor and keep them in the countryside, the government of Botswana has been spending 40% of the value of agricultural GDP on agricultural support services. But can investment make smallholder agriculture prosperous in such adverse conditions? This paper derives an answer by applying a two‐output six‐input stochastic translog distance function, with inefficiency effects and biased technical change to panel data for the 18 districts and the commercial agricultural sector, from 1979 to 1996. This model demonstrates that herds are the most important input, followed by draft power, land and seeds. Multilateral indices for technical change, technical efficiency and total factor productivity (TFP) show that the technology level of the commercial agricultural sector is more than six times that of traditional agriculture and that the gap has been increasing, due to technological regression in traditional agriculture and modest progress in commercial agriculture. Since the levels of efficiency are similar, the same pattern is repeated by the TFP indices. This result highlights the policy dilemma of the trade‐off between efficiency and equity objectives.     

Publicly-funded UK agricultural R&D and 'social' total factor productivity

Most studies concerned with measuring the rate of return to publicly‐funded agricultural R&D investment have found high returns, suggesting under‐investment, and calls for increased expenditure have been common. However, the evaluation of returns tends to measure the effect of research expenditure against growth in total factor productivity (TFP), based on market inputs and outputs. When compared against growing public unease over the environmental effects of pursuing agricultural productivity growth, TFP indices become a misleading measure of growth. This paper integrates some non‐market components into the TFP index. The costs of two specific externalities of agricultural production, namely fertiliser and pesticide pollution, are integrated in a TFP index constructed for the period 1948–1995. This adjusted, or ‘social’, TFP index is measured against UK public R&D expenditures. The rates of return to agricultural R&D are reduced by using the ‘social’ as opposed to the traditional TFP index. Whilst both remain at justifiable levels, previous studies appear to have over‐estimated the effect of agricultural R&D expenditures. Furthermore, with changes in policy towards more socially acceptable but non‐productivity enhancing outcomes, such as animal welfare, rural diversification and organic farming, the future framework for analysing returns to agricultural R&D should not be so dependent on productivity growth as an indicator of research effectiveness.     

Evaluating research and education performance in Indian agricultural development

India's agricultural research spending has long been regionally uneven. Regional research intensity ratios, which account for size differences, indicate research spending has consistently favored southern and western states over northern, central, and eastern ones. Farm production patterns have, however, changed in recent years and regional output growth is being driven by new commodity mixes. In this article, we ask which region had the highest factor productivity growth and rate of return to investments in public agricultural research and higher education. Our analysis relies on a 1980–2008 Indian agricultural production and policy data set together with a dual heteroscedastic production frontier to decompose total factor productivity (TFP) growth into formal technical progress and efficiency elements. The model's regional flexibility affords region‐to‐region comparison of multiple research return rates, TFP growth, and the elements influencing them. Regardless of return‐rate measure or allocation scenario evaluated, the North has enjoyed the highest return to research spending, and the Central and East the lowest. Factor productivity growth has however been strongest in the South, primarily on account of efficiency gains. Overall, though, the technical progress and productivity growth easily attributable to government‐supported research has accounted for about one‐quarter of the sector's TFP growth.     

中国林业投入资金利用效率研究——基于Dea-Malmquist指数

以1998～2010年我国30个省面板数据为基础,对我国林业系统资金利用效率定量测算和分解。结果表明,全要素生产率(TFP)对我国林业系统产业总产值、在岗职工年均工资、造林面积等产出指标贡献度为负值,说明我国资金投入没有达到有效推进林业发展的目标。TFP分解后,技术进步落后是资金利用效率较低的重要诱因。就四大经济区而言,TFP指数与各区域技术进步水平一致,林业投入资金效率高低依次为东部地区、东北地区、西部地区、中部地区。     

Sources and Measurement of Agricultural Productivity and Efficiency in Canadian Provinces: Crops and Livestock

This paper measures and assesses the variation in total factor productivity (TFP) growth among Canadian provinces in crops and livestock production over the period 1940–2009. It also determines if agricultural productivity growth in Canada has recently slowed down as indicated by earlier studies. The paper uses the stochastic frontier approach that incorporates inefficiency to decompose TFP growth into technical change (TC), scale effect (SE), and technical efficiency change. The results indicate that productivity changes were mainly driven by TCs for crops, while the productivity changes in livestock was mainly driven by SEs and technical progress. Though change in technical efficiency is mainly positive (except for New Brunswick and Nova Scotia), its contribution to productivity growth was very little for the provinces. We also found that over the entire period, the productivity growth rates for the crop subsector are on average higher for the Prairie provinces than for the Eastern and Atlantic provinces. On the other hand, the productivity growth rates in the livestock subsector are on average higher in the Eastern and Atlantic provinces than in the Prairie region with the exception of Manitoba. Finally, we found that though there is some evidence of a recent decline in productivity growth for the crops subsector, there is no such evidence in the livestock subsector.     

Enhancing productivity on suburban dairy farms in China

Dairy farms in China's suburban areas have been playing an important role in providing urban markets with fresh milk. With the rising demand for fluid milk and dairy products in the cities, there is a perception that small and scattered dairy farms in China's provinces are gradually disappearing and more concentrated dairy cattle farming is being formed near suburban areas. This article uses farm‐level survey data and stochastic input distance functions to make estimates of total factor productivity (TFP) on suburban dairy farms, as well as for the entire dairy sector. The results show that over the past decade TFP growth has been positive on suburban dairy farms, and this rise in productivity has been driven mostly by technological change. However, at the same time we find that, on average, the same farms have been falling behind the advancing technical frontier. We also find one of the drivers of the suburban dairy sector is the relatively robust rate of technological change of these farms, which has been more rapid than on farms in the dairy sector as a whole. The results suggest that efforts to achieve greater adoption of new technologies and better advice on how to use the technologies and manage production and marketing within the suburban dairy sector will further advance productivity growth in the sector.