重新评估我国的增长潜力——基于全要素生产率和数据分析视角的解释

我国物价水平可能被严重低估从而导致增长和要素投入数据失真,这是大多数研究结果出现重大差异的关键原因,增长潜力研究也因而严重背离现实。本文通过名义产出与税收之间关系推算了实际物价并调整了经济增速,同时重新估算了要素投入,增加了数据的可靠性。本文研究发现,改革以来我国经济实际增速仅为8.2%,TFP平均仅增长1.7%,对产出贡献约为21%。但是基于劳动投入估算的TFP结果为-1%,这点主要是受金融危机冲击导致要素拉动模式越来越重要,TFP结果被拉低,因而我国增长质量和可持续性堪忧。     

我国茶产业全要素生产率增长及其分解--基于随机前沿生产函数的分析

文章利用超越对数的随机前沿生产函数,测算了1996-2007年我国茶产业的全要素生产率。研究表明：研究期间我国茶产业TFP平均增长0.70%,平均技术进步率为1.42%,平均技术效率为72.05%,但都呈逐步下降趋势; TFP增长的主要来自技术进步,但技术效率的变化和规模报酬率出现一定的退步,阻碍TFP的增长;各省份TFP增长有差异,其中四川省的TFP增长速度最快,而湖南省和陕西省的TFP成负增长;我国茶叶的劳动产出弹性逐年增大,而资本产出弹性逐渐变小,且我国茶叶生产一直处于规模报酬递减状态。     

中国生产性服务业细分行业全要素生产率异质性与影响因素研究

笔者运用Malmquist-DEA指数法测算了中国生产性服务业细分行业TFP增长变动情况,并对行业异质性和影响因素进行了分析。结果表明:研究期内TFP均呈增长趋势,并表现较大的行业异质性,但各指数的差异程度没有明显的趋同或扩大趋势。1991年～2002年,TFP增长的主要动力源自技术效率和技术进步共同作用,2003年之后,则主要依靠技术进步提高。另外,除市场开放度对TFP增长起负面影响外,创新投入、消费需求、工业化程度等因素均具有显著的正面促进作用。     

我国西部地区经济增长中的技术因素分析

利用1978—2010年我国西部地区12省市的数据,测算了我国西部地区的资本、劳动力和TFP对经济增长的贡献率。结果显示:当期我国西部地区的经济增长主要源于资本投入;但是,由于资本对经济增长的贡献太大,因此TFP呈相对平稳下降趋势,TFP对经济增长的作用还不是很明显。指出:若2010—2020年西部地区TFP对经济增长的贡献率为37.1%,则西部地区能实现以技术进步带来可持续增长。最后提出西部地区实现经济可持续增长的政策建议。     

中国经济能够实现持续增长吗?

为了证实我国经济增长的可持续性,文章以内生增长理论中生产性产品多样性模型为基础进行经济核算,提出了衡量TFP增长率变动趋势的三个命题。结合这三个命题,文章分别用技术吸收能力、研发支出/人均产出、研发支出/人均资本、投入到研发部门的劳动力数量以及劳动力份额作为TFP增长率的衡量指标,对近年来我国的TFP增长率进行了实证分析。结果发现,近年来我国的TFP增长率呈现出逐年攀升的势头。这表明我国经济具有持续的动态改进的力量。     

创新主体对研发全要素生产率的影响实证研究

利用非参数的Malmquist生产率指数方法研究了1999-2008年我国研发全要素生产率(TFP)的变动及其分解,在对TFP变动的时序特征及地区差异进行比较分析的基础上,考察了公共和企业创新对研发TFP的影响。研究表明:我国研发TFP的增长主要是由技术效率推动的,技术进步改善的作用相对有限,研发技术进步与技术效率显著负相关,地区之间研发TFP增长率也存在显著差异;公共和企业研发对研发TFP的影响均为负,且企业研发对TFP增长的负面效应大于公共研发。     

全要素生产率与区域工业的和谐快速发展——基于1995～2004年28个省市大中型工业的非参数生产前沿分析

文章采用Malmquist指数和DEA技术,研究中国28个省市地区大中型工业增长的动力以及地区间的发展差距,分析发现在1995~2004年期间:(1)整体而言,全要素生产率(TFP)增长已经成为中国大中型工业快速增长的核心动力,特别是技术进步和规模效率的改善对产出增长的贡献日渐突出,相反,要素投入对产出增长的贡献逐渐减弱;(2)地区之间发展的差距依然显著,但无论是人均产出还是增长速度,地区之间的差距在逐渐缩小;(3)随着中国工业经济的快速增长,TFP对缩小地区间工业发展差距的作用越显突出,资本要素的作用并不明显;(4)全要素生产率快速增长的四大因素:竞争、全球化和外商投资、民营化改革,以及经济扩张期。因此,文章认为全要素生产率增长,已成为区域经济和谐快速发展的中坚力量。     

基于Malmquist指数的服务业分行业生产率测算——以秦皇岛市为例

文章利用2005—2009年面板数据,采用数据包络分析的非参数Malmquist方法,分析了秦皇岛市服务业及13个行业的全要素生产率增长的特点。TFP增长对秦皇岛服务业产出增长具有明显的贡献,服务业TFP增长源泉中,技术效率改善的"水平效应"明显,而技术进步的"增长效应"有限。服务业各行业TFP增长的源泉存在明显的差异性。     

考虑人力资本的中国生产性服务业的技术进步

本文运用考虑人力资本的随机前沿分析法,利用中国29省市1993—2004年的面板数据,测算了中国生产性服务业的全要素生产率增长。研究表明,中国生产性服务业TFP在1994—2004年间年均增长4.92%,其中技术进步率为3.21%,技术效率改善1.71%。技术进步对TFP增长起主要作用,技术效率变化对TFP增长起着补充作用。考虑人力资本后TFP增长和技术进步率下降,但技术效率、技术效率变化及资本和劳动的产出弹性均有所提高。区域TFP增长不均衡,但技术效率存在收敛性。     

价格扭曲、要素错配和效率损失:理论和应用

本文把关于资源错配和效率损失的讨论纳入到传统的增长核算框架中,提出了测度要素价格扭曲引起的资源错配对于TFP以及产出变动影响的方法。应用这个分析框架,本文分析了我国制造业资源错配的影响。我们发现目前中国制造业内部各子行业间的资源错配大约造成了实际产出和潜在产出之间15%的缺口,并且在这些年中,扭曲没有得到显著的纠正。     

Productivity Growth And Market Structure In Telecommunications

This study examines sources of telecommunications sector productivity growth. Total factor productivity (TFP) growth is calculated using the Malmquist productivity index for a sample of 74 countries for the period 1991 through 1995. An econometric model is estimated which relates TFP growth to output growth, network digitisation, telecommunications development, output-mix, the business cycle and market structure. Model estimates suggest that higher digitisation rates dampen TFP growth in the short run, and cross-subsidisation of services creates inefficiency. However, developing countries can increase TFP growth through catch up, and increased privatisation and competition are conducive to productivity growth.     

Singapore's Manufacturing Sector's TFP Growth: A Decomposition Analysis

Singapore has been criticized recently for experiencing insignificant total factor productivity (TFP) growth. This paper examines whether this criticism is valid in the context of the manufacturing sector of Singapore. Using new data and the stochastic production frontier approach, TFP growth is decomposed into technological progress and changes in technical efficiency. While the results could not reject the hypothesis that Singapore's output growth is mostly input-driven, they show that, despite technological progress, technical inefficiency is the cause for the low and declining TFP growth in the manufacturing sector.J. Comp. Econom., December 2000, 28(4), pp. 828–839. The University of Queensland; Research School of Pacific and Asian Studies, The Australian National University.     

Creative accounting or creative destruction? Firm-level productivity growth in Chinese manufacturing

We present the first comprehensive set of firm-level total factor productivity (TFP) estimates for China's manufacturing sector that spans China's entry into the WTO. For our preferred estimate, which adjusts for a number of potential sources of measurement error and bias, the weighted average annual productivity growth for incumbents is 2.85% for a gross output production function and 7.96% for a value added production function over the period 1998-2007. This is among the highest compared to other countries. Productivity growth at the industry level is even higher, reflecting the dynamic force of creative destruction. Over the entire period, net entry accounts for over two thirds of total TFP growth. In contrast to earlier studies looking at total non-agriculture including services, we find that TFP growth dominates input accumulation as a source of output growth.     

结构改革与中国工业增长

1978年以来,中国工业在持续的结构改革中经历了强劲的增长和生产率水平的不断提高。本文估算了工业分行业随机前沿生产函数,发现1992年后,TFP增长超过了要素投入增长,但是TFP对工业增长的贡献在2001年后出现了下降。进一步对TFP增长分解后发现,由工业结构改革引致的行业间要素重置显然对改革开放期间中国工业生产率的提高乃至工业增长起到了实际推动作用,即结构红利是显著存在的,而2001年后要素配置效率的下降也成为同期TFP增长贡献份额下降的主要原因。因素回归分析显示,中国要素市场的改革和工业行业的结构调整主导了要素配置效率变化的总体走势,并造成了不同行业要素配置效率的显著差异。     

Output Growth Decomposition in the Presence of Input Quality Effects: A Stochastic Frontier Approach

How do physical capital accumulation and total factor productivity (TFP) individually add to economic growth? We approach this question from the perspective of the quality of physical capital and labor, namely the age of physical capital and human capital. We build a unique dataset by explicitly calculating the age of physical capital for each country and each year of our time frame and estimate a stochastic frontier production function incorporating input quality in five regions of countries (Africa, East Asia, Latin America, South Asia and West). Physical capital accumulation generally proves much more important than either the improved quality of factors or TFP growth in explaining output growth. The age of capital decreases growth in all regions except in Africa, while human capital increases growth in all regions except in East Asia.     

Sources of efficiency,productivity and output growth in Botswana agriculture

The purpose of this study is to explore reasons for the decline in agricultural performance in Botswana that has occurred during the implementation of a variety of policy measures, as well as the introduction of new technologies, and to identify ways in which it might be reversed. Panel data from six regions in Botswana (period 1979–2012) is analyzed with a stochastic output distance function and inefficiency effects model. We decompose agricultural output growth into; total factor productivity (TFP ) and changes in input use (factors of production). TFP is further decomposed into scale effects, technical efficiency and technological change. The results show that over the study period agricultural output grew at a very low rate of 0.072 percent per year, which is largely due to a growth in factors of production at 0.071 percent per year rather than TFP growth (which declined at 0.003 percent per year). We found that the decline in productivity has been due to technological regress and low growth in technical efficiency and scale efficiency. Policy options aimed at improving agricultural productivity and output growth will require the strengthening of extension services; improving the agronomic and husbandry management skills of farmers through training; and by encouraging farmers to adopt and utilize technologies that have been provided under existing policy programs.     

Sources of output growth in Singapore's services sector

As services are an important engine of growth for Singapore, this paper attempts to empirically investigate the sources of output growth in this sector to shed light on the debate sparked off by Krugman (1994) on the miraculous or mythical growth of Singapore and the other newly industralizing Southeast Asian economies. This is done by using the stochastic production frontier model with panel data. Unlike existing studies which used the conventional growth accounting approach to decompose output growth into just input growth and total factor productivity (TFP) growth, this approach further decomposes TFP growth into technological progress and changes in technical efficiency.     

Growth accounting for the euro area

This article is concerned with the estimation of euro area potential output growth and its decomposition according to the sources of growth. The growth accounting exercise is based on a multivariate structural time series model which combines the decomposition of total output according to the production function approach with price and wage equations that embody Phillips-type relationships linking inflation and nominal wage dynamics to the output gap and cyclical unemployment, respectively. Assuming a Cobb?CDouglas technology with constant returns to scale, potential output results from the combination of the trend levels of total factor productivity (TFP) and factor inputs, capital and labour (hours worked), which is decomposed into labour intensity (average hours worked), the employment rate, the participation rate and population of working age. The nominal variables (prices and wages) play an essential role in defining the trend levels of the components of potential output, as the latter should pose no inflationary pressures on prices and wages. The structural model is further extended to allow for the estimation of potential output growth and the decomposition according to the sources of growth at different horizons (long run, medium run and short run); in particular, we propose and evaluate a model-based approach to the extraction of the low-pass component of potential output growth at different cut-off frequencies. The approach has two important advantages: the signal extraction filters have an automatic adaptation property at the boundaries of the sample period, so that the real-time estimates do not suffer from what is often referred to as the ??end-of-sample bias??. Secondly, it is possible to assess the uncertainty of potential output growth estimates with different degrees of smoothness.     

建筑业全要素生产率增长与区域经济增长的耦合效应分析

采用随机边界分析方法测算了1997—2009年省际建筑业全要素生产率(TFP)增长水平,并进行了区域差异化比较;然后借鉴物理学的耦合度理论,构建建筑业TFP增长与区域经济增长的耦合效应模型,并进行了实证检验;采用灰色关联分析方法遴选出二者耦合效应的主要影响因素,最后对建筑业经济增长方式和阶段进行了研判。研究表明:区域建筑业TFP增长存在较大差异,依据其增长率大小将31个地区划分为负增长、低增长、中增长和高增长四种类型;二者的耦合程度较高而耦合协调度较低;约束建筑业TFP增长的主要因素是地区的GDP水平,判断中国区域建筑业的经济增长仍属于粗放型增长方式和资本经济阶段。     

Declining output growth volatility: A sectoral decomposition

A decomposition of the U.S. aggregate output growth volatility using two-digit industry-level data shows that more than 60% of the post-1983 reduction in aggregate output growth volatility is attributed to the lowered comovement in total factor productivity (TFP) growth between industries. In contrast, stabilized input and TFP growths within an industry contribute little.