Productivity growth, technological progress, and efficiency change in chinese agriculture after rural economic reforms: A DEA approach

This study applies a data envelopment analysis (DEA) approach to analyze total factor productivity, technology, and efficiency changes in Chinese agricultural production from 1984 to 1993. Twenty-nine provinces in China were classified into advanced-technology and low-technology categories. The Malmquist (1953) productivity measures were decomposed into two components: technical change index and efficiency change index. The results show that total factor productivity has risen in most provinces for both technology categories during the 1984–1993 period. Technical progress was mostly attributed to Chinese agricultural productivity growth after the rural economic reforms. The deterioration in technical efficiency in many provinces indicates China has great potential to increase productivity through improved technical efficiency. Enhancing rural education and research and development (R&D) in agriculture may also help farmers to improve technical efficiency and productivity in agricultural production.     

FINANCIAL DEVELOPMENT,ECONOMIC EFFICIENCY,AND PRODUCTIVITY GROWTH: EVIDENCE FROM CHINA

Financial development might lead to productivity improvement in developing countries. In the present study, based on the Data Envelopment Analysis approach, we use the Malmquist index to measure China's total factor productivity change and its two components (i.e., efficiency change and technical progress). We find that China has recorded an increase in total factor productivity from 1993 to 2001, and that productivity growth was mostly attributed to technical progress, rather than to improvement in efficiency. Moreover, using panel dataset covering 29 Chinese provinces over the period from 1993 to 2001 and applying the Generalized Method of Moment system estimation, we investigate the impact of financial development on productivity growth in China. Empirical results show that, during this period, financial development has significantly contributed to China's productivity growth, mainly through its favorable effect on efficiency.     

Productivity Growth in Different Plant‐size Groups in the Malaysian Manufacturing Sector*

In the present study, we develop a stochastic frontier production model that allows for different groups of firms to have different patterns of technical efficiency over time. We apply our model to the Malaysian manufacturing sector to decompose total factor productivity growth into technical efficiency change and technical progress for different plant size groups (e.g. large and small) in seven industries during 2000–2004. Our empirical results indicate that technical efficiency has worsened across all industries and plant‐size groups. In contrast, we find evidence of substantial technical progress in all industries. In fact, technical progress has been larger than technical efficiency deterioration in most industries and plant‐size groups, leading to total factor productivity growth. Our analysis identifies the industries and plant‐size groups that lag the most in terms of productivity, and thus have the greatest scope for policies that facilitate productivity growth.     

1993-2005年广东农业生产率的变动与分解——基于Malmquist生产率指数的实证分析

本文使用非参数的Malmquist生产率指数方法,对广东省1993—2005年间农业全要素生产率（TFP）的变动趋势进行了考察,并把TFP的增长构成分解为技术进步、纯技术效率变化、规模效率变化三个部分。结果表明：13年间广东全省农业TFP的增长主要是由技术进步推动的,纯技术效率和规模效率的下降对TFP的增长造成了不利影响;农业TFP增长的地区水平差异显著,而且增长的结构也有所不同。最后是相应的政策建议。     

电网企业技术进步与技术效率对生产率的影响测算

为了分析电网行业的生产率受技术进步和技术效率的影响程度,使用基于DEA的Malmquist生产率指数方法,以9家省电力公司为样本,对电网企业的技术和生产效率的动态变动情况进行了研究。结果表明,2005-2010年间样本电网企业的全要素生产率年平均增长率为3.2%,技术进步的贡献大于技术效率的贡献。技术效率指数表现不优是由于多数样本企业的实际规模与最佳规模之间的偏差较大。因此为了提高我国电网企业生产率,关键是提升效率特别是规模效率。     

Factor Determinants of Total Factor Productivity Growth in Malaysian Manufacturing Industries: a decomposition analysis

To decompose total factor productivity growth into technical progress, technical efficiency change, allocative efficiency change, and scale efficiency change, a stochastic frontier approach was applied to Malaysian manufacturing data covering the period 2000 to 2004. The results show that total factor productivity was driven mainly by technical progress but was hurt by deteriorating technical efficiency. Scale efficiency and allocative efficiency also exerted significant influences on total factor productivity. The skill and quality of workers were the most important determinants of technical efficiency, whereas foreign ownership, imports, and employee quality underpinned technical progress. The impact of firm size on scale economies differed across industries.     

Productivity change and the effects of policy reform in China's agriculture since 1979

During China's rural reforms, policies were frequently adjusted. Most policies favoured the continuation and deepening of reform; but some were contradictory or even led to regression in the reform process. How have the rural reforms affected China's agricultural production over the past three decades; and what lessons can be learned to aid the future course of reform? To answer these questions, this study estimates productivity change in China's agriculture and evaluates the effects of policy on agricultural output during the reform period. Aggregated provincial‐level data for the 1979–2008 period are used in a translog production frontier model to estimate indices of total factor productivity (TFP) change and its three components—technical change, technical efficiency change, and a scale effect—with a focus on explaining the variation in technical efficiency. The estimation results show that the impressive improvement of TFP change is dominated by the technical change component. However, technical efficiency change and scale effects have worked against the improvement in TFP change in most periods. To improve technical efficiency, social welfare policies designed to eliminate the rural–urban divide, and reform polices focusing on factor market reforms, such as reform of the household registration system (hukou) and reform of land rights, seem to hold some potential.     

Trends in Agricultural Production Efficiency and their Implications for Food Security in Sub‐Saharan African Countries

The main objective of this paper is to estimate the agricultural sector production efficiency using data envelopment analysis based on a cross‐section of 49 African countries over the 1995–2012 period in order to analyze the determinants of annual efficiency scores to assess the impact of agricultural efficiency on food security. We find that agriculture aid, agricultural sector infrastructure, sanitation, and good governance serve the main drivers of agriculture efficiency and its growth. Our findings also suggest that a large portion of Africa's agricultural sector growth can be attributed to technical progress as opposed to efficiency changes.     

中国装备制造业全要素生产率增长的分解:1998-2009——基于省际面板数据的研究

本文采用超越对数的随机前沿生产函数和1998-2009年的省际面板数据,测算了中国各地区装备制造业的全要素生产率(TFP)增长率,并从技术进步、技术效率变化、规模效率变化、配置效率变化四个方面对中国装备制造业全要素生产率变化的特点进行了分析。经验结果表明,样本期间内,全国以及东、中、西部地区装备制造业的TFP都有所增长,其增速皆呈现出先增后减再增的趋势;TFP增长对全国装备制造业产出增长的贡献率相对较低,仅为0.117。对TFP增长的分解发现,技术进步已成为中国装备制造业TFP增长的主要源泉,配置效率和规模效率恶化则严重阻碍了中国装备制造业TFP水平的提高。分地区来看,中部地区的TFP增长率最高,东部次之,西部最低,这主要是由于西部地区装备制造业要素配置效率的下降最为严重,东部地区规模效率的下降最为严重。     

服务业全要素生产率测度及其省际差异

运用2005～2009年中国31省份服务业面板数据,采用非参数Malmquist指数方法,对中国服务业全要素生产率的变动原因、空间差异与变动趋势进行考察,并将全要素生产率进一步分解为技术效率和技术进步。研究结果表明,中国服务业整体仍表现为粗放型的增长方式,全要素生产率呈现负增长,但增长速度有所放缓;服务业发展水平的空间差异明显,东部地区服务业发展水平高于中西部地区,其中西部和东北地区服务业发展水平较落后,且技术进步和技术效率对中国及各区域服务业的全要素生产率在时间和空间上有不同的影响。     

农业全要素生产率时间演变及区域差异研究——基于江苏省的实证分析

基于江苏省13个地级市的农业投入产出面板数据,运用DEA-Malmquist方法,对江苏农业全要素生产率进行了实证分析。结果表明,2001-2014年间江苏省农业技术进步增长幅度较大,而技术效率变动呈现递减态势,农业TFP增长主要是靠技术进步推动的,且江苏农业TFP增长率及其构成存在显著的地区差异,苏南农业TFP增速明显高于苏中和苏北地区。     

广东农业科技创新效率研究 ——基于超效率DEA模型和M almquist指数法

基于2010—2016年广东省各市的面板数据,运用超效率DEA模型和Malmquist指数法对广东农业科技创新效率和全要素生产率进行了实证分析。结果表明:广东省农业科技创新水平较高,但区域发展不平衡问题突出,粤北与珠三角、粤西、粤东有较大差距;广东全要素生产率指数平均值大于1,农业科技创新效率呈上升趋势,技术进步是全要素生产率增长的动力所在。因此,农业科技创新效率的提高关键在于提升农业技术水平,同时还需注意技术效率,科学调整农业科技资源配置,提升农业科技资源经营和管理水平。     

基于技能偏向性技术进步的经济增长动能分解

在传统Solow增长核算框架基础上引入异质性劳动投入以及技能偏向性技术进步,从而将经济增长分解为固定资本投入增长、劳动投入增长、技能结构深化、技能偏向性技术进步、中性技术进步及效率改进等五种动能。研究发现：第一,中国经济增长主要由固定资本投入以及全要素生产率增长共同驱动,其中资本投入对经济增长年均贡献率为61.64%,全要素生产率增长的年均贡献率则为35.66%,中国区别于其他中低收入国家的发展奇迹背后正是较快的全要素生产率增长;第二,即使对于高收入国家而言,资本投入仍然是主要的经济增长动能;第三,技能结构深化以及技能偏向性技术进步是全要素生产率的主要来源,二者对中国经济增长的年均贡献率分别为32.04%和21.27%;第四,资源配置效率变化是引起全要素生产率波动的主要因素,资源配置效率下降是导致2008年后中国以及大部分高收入国家全要素生产率增速放缓的主要原因。     

基于我国东部地区的服务业全要素生产率研究

采用非参数Malmquist指数方法实证分析了2001—2008年我国东部地区10个省份中国服务业全要素生产率的增长状况,并将其分解为技术效率和技术进步。通过研究发现我国东部地区服务业经济增长效率在这段时间是逐步提升的,技术进步是主要的推动力,而技术效率贡献微乎其微,要提高我国服务业全要素生产率水平,需要在注重技术进步的同时也要注重技术效率的提升。     

Agricultural productivity evolution in China: A generalized decomposition of the Luenberger-Hicks-Moorsteen productivity indicator

China has undergone a series of agricultural policy reforms since 1978. The measurement of the productivity gains and identification of the underlying drivers thereof are important facets of policy analysis. The commonly used Total Factor Productivity (TFP) measures often lack such desirable properties as completeness or independence of the direction of the optimization (orientation). In this paper, we take a top down approach by beginning with a TFP measure and then decomposing it into three mutually exclusive, exhaustive elements. In particular, we begin with the additively complete Luenberger-Hicks-Moorsteen (LHM) TFP indicator that takes into account both input and output changes when measuring productivity and then additively decompose it into measures of technological progress, technical efficiency change, and scale efficiency change. We develop a generalized decomposition of the LHM TFP indicator which encompasses both input-oriented and output-oriented changes over time. We illustrate this additively complete LHM TFP indicator using agricultural data from 31 Chinese provinces over the period 1997–2015. Our empirical results show that Chinese agricultural productivity growth (3.05% per annum) was mainly driven by technological progress (2.35% p.a.), with relatively small contributions from scale efficiency change (0.65% p.a.) and technical efficiency change (0.04% p.a.). We also found that productivity change and the relative importance of its components varied across both time and provinces.     

中国粮食生产的技术效率与全要素生产率增长：1978-2008

本文利用1978—2008年间我国30个省市区的面板数据,对改革开放以来我国粮食生产进行了随机前沿分析。以此为基础,对我国粮食生产技术效率进行了影响因素分析,并对粮食生产全要素生产率增长进行分解和趋势分析。结果表明,农业基础设施建设和制度因素是影响我国粮食生产技术效率的关键因素;我国粮食产量增长主要是由投入要素的增长拉动,31年间全要素生产率的平均增长率为1．17％,TFP对我国粮食产量的贡献很小,其原因在于技术进步与技术效率变化呈现相反趋势。2004年以来,技术效率提高的速度持续下降,技术进步逐渐成为TFP增长的主要动力。     

环境约束下中国区域工业技术效率与生产率及其影响因素实证研究

本文运用方向性距离函数和曼奎斯特-卢恩伯格指数方法测算了环境约束下中国1998--2007年各地区工业技术效率、全要素生产率指数和环境规制成本,并对影响技术效率和全要素生产率增长的因素进行了实证分析。结果显示：环境技术效率呈现东、中、西依次递减,并且技术效率越高的地区,环境管制成本越低;考虑环境因素后,中国各地区工业全要素生产率指数降低,主要是由技术进步推动;环境约束下工业全要素生产率东部最高,西部次之,中部最低;人均GRP、FDI、工业结构、能源结构、人口密度对技术效率和全要素生产率有不同程度的影响。     

中国新能源上市公司全要素生产率动态变化实证研究

文章从投入产出角度,运用DEA-Malmquist指数法对中国新能源上市公司的全要素生产率动态变化及其分解进行了测算。研究表明2006－2011年间中国新能源上市公司的全要素生产率增长水平较低,平均增长率仅为1.5％。增长的源泉主要是技术进步,技术效率的下降在一定程度上抵消了技术进步带来的效果。最后从不同类型新能源公司视角出发,分析了不同类型新能源公司全要素生产率变化、技术效率变化、技术进步之间的差异性。     

Anti‐dumping Shock and the Productivity Response of Chinese Industries

This paper provides a comprehensive analysis of the productivity effects of antidumping (AD) measures on Chinese industries. Industry‐year panel data and generalized method of moments estimators are used in the empirical analysis. Productivity indicators are calculated using data envelope analysis. The empirical results show that China's industrial total factor productivity has improved under the pressure of AD measures taken by developed countries, and the mechanism inspires technological progress but hurts technological efficiency. Developing countries' AD measures have no significant productivity effects on China's targeted industries, except for slightly positive effects on technological efficiency. These results indicate that China should pay more attention to technological innovation and take different counter‐measures for different cases of AD measures.     

Shift from Input‐based Growth to Productivity‐based Growth in Korean Manufacturing Industry

The Korean economy has entered a period of relatively sluggish growth after experiencing very dynamic and high economic growth since the 1970s. The recent growth rate has been around 3 percent, which is similar to that in many advanced countries. According to growth accounting, economic growth is possible because of input increases and productivity increases. Productivity growth is much more important than input increases for long‐term economic growth. We investigated whether there was a structural break in the Korean manufacturing industry and whether the main factor contributing to economic growth shifted from input to productivity. Using various econometric methods, we tested this question and found some interesting results. First, there was an important change in the 1990s, and the productivity trend seemed to show a big structural break at that time. Second, an input increase was the main factor contributing to economic growth before the break and productivity became more important after the break. Third, there was also a major change in the relationship between international trade and productivity.