技术进步水平与城市学习

经济增长理论认为技术进步是长期经济增长的动力。针对我国地级城市经济增长出现的巨大差异,从全要素生产率的视角,采用Hall和Jone(1999)基于水平增长的人均产出分解,对我国188个地级城市2000年的全要素生产率进行计算,发现全要素生产率较好地解释了我国地级城市人均产出的差异,相关系数达到0.839;然后从我国城市的技术进步主要来自于城市的模仿与学习机会的角度,分析了地级城市全要素生产率差异的原因,提出通过加大吸引外商直接投资力度,发挥城市集聚规模效益,建设学习型城市,促进城市经济的持续增长的建议。     

Spatial analysis of change trend and influencing factors of total factor productivity in China’s regional construction industry

Total factor productivity (TFP) is a measure of long-term economic growth and a comprehensive industry-level productivity measure. There are large gaps in China’s regional construction industry development due to unbalanced regional economy. Based on TFP measurement, this article puts forward a two-hierarchical analysis framework with coefficient of variation, Moran scatterplot and coefficient of convergence to analyse change trend of the construction industry TFP in three major regions in terms of spatial diversity, correlation and convergence. Then, the geographically weighted regression model is utilized to explore the influencing mechanism on the TFP. The results indicate the differences of the regional construction industry TFP are enlarging. There is obvious spatial correlation and heterogeneity in the regional TFP without a relatively stable space pattern. The TFP also exhibits convergence effects among three major regions. The construction industry productivity in all regions is significantly affected by economic environment, industrial organization structure and technological level. Industrial organization structure exerts the various influences on the productivity in different regions.     

Be productive or face decline. On the sources and determinants of output growth in Italian manufacturing firms

This article investigates the sources and determinants of output growth of Italian manufacturing firms. Applying stochastic frontier techniques, we decompose output growth into factor accumulation and TFP growth for the period 1998–2003. TFP growth is further decomposed into technological change, efficiency change and scale effects. Two key results emerge from the analysis. After confirming that both input accumulation and TFP growth are important in explaining output growth, we show that efficiency change (technological catch-up) is the most significant component of TFP growth in explaining output growth distribution. Furthermore, using a specific model of the asymmetric error component, we find that R&D spillovers, banking efficiency and public infrastructures have statistically significant and economically relevant effects on technological catch-up.     

金融发展与全要素生产率增长* ——基于中国省际面板数据的实证分析

本文以2000-2012年中国省际平衡面板数据为样本,从金融规模发展和金融效率发展两个维度衡量金融发展水平,实证检验金融发展与全要素生产率增长的联系以及中间渠道。研究结果发现,2000-2012年中国全要素生产率出现正增长且主要是由技术进步推动的;无论是从金融规模发展角度还是从金融效率发展角度去分析,都证实了金融发展显著地促进了全要素生产率的增长,金融效率发展的全要素生产率增长效应显著地高于金融发展规模的全要素生产率增长效应,且金融发展促进全要素生产率增长的中间渠道是技术进步效应而非技术效率增长效应;通过引入衡量金融发展缺陷程度指标（民间金融发展规模）,证实了中国金融发展体系效率低下。民间金融发展具有TFP增长效应,且民间金融发展TFP增长效应要显著地大于正规金融发展的TFP增长效应。     

Computerization in France: An Evaluation Based on Individual Company Data

In this article we evaluate the contribution of information and communication technologies (ICT) to the growth of value added during the past 15 years in France. Following North American studies, we use traditional growth accounting methods to assess the relative size of two types of contribution: on the one hand the effect of the use of information technologies (IT) on growth due to the accumulation of IT capital taking place within all industries; on the other hand the contribution of the production of ICT to growth due to the strong total factor productivity (TFP) gains achieved in the industries producing ICT. We use individual company data aggregated by industry, which provide us with a measure of the firm's computer stock and makes a detailed investigation possible.     

Parametric Approaches to Productivity Measurement: A Comparison among Alternative Models

This paper deals with modeling total factor productivity (TFP) growth in a flexible manner using panel data. Several competing parametric models are used to explore whether there are any similarities in the estimates of TFP growth and technical change among these models. Using a primal approach, we decompose TFP growth into different components. The models are then used to measure productivity and technical change in the Swedish cement industry. In general, the results are found to be model dependent and often conflicting, although much less so for returns to scale and overall productivity growth.
JEL classification: O 30; C 33     

How fast can China grow? The Middle Kingdom's prospects to 2030

Given its size and importance for global commodity markets, the question of how fast China can grow over the medium term is an important one. Using a Cobb–Douglas production function, we decompose the growth of trend GDP into those of the capital stock, labour, human capital and total factor productivity (TFP) and then forecast trend output growth out to 2030 using a bottom‐up approach based on forecasts that we build for each one of these factors. Our paper distinguishes itself from existing work in that we construct a forecast of Chinese TFP growth based on the aggregation of forecasts of its key determinants. In addition, our analysis is based on a carefully constructed estimate of the Chinese productive capital stock and a measure of human capital (based on Chinese wage survey data) that better reflects the returns to education in China. Our results suggest that Chinese GDP growth will slow from around 7% currently to approximately 5% by 2030, consistent with a gradual rebalancing of the Chinese economy characterized by a decline in the investment rate. Moreover, our findings underscore the growing importance of TFP growth as a driver of Chinese growth.     

Business cycles and productivity growth: Are temporary downturns productive or wasteful?

This paper investigates the relationship between the structure of the business cycle and total factor productivity (TFP) growth in U.S. manufacturing. Previous empirical studies on cycle-growth interactions have been conducted at the aggregate level, and have produced mixed results. In contrast, we examine the dynamic linkages between temporary employment shocks and TFP at the industry level, using the NBER Productivity Database. Given the substantial differences in factor intensities, costs and the nature of innovation and productivity-enhancing activities across industries, there are good reasons why cycle-growth interactions may be more readily captured at the sectoral level. We construct an exactly identified Vector Autoregressive model for employment and TFP growth, and find strong support for the “opportunity-cost” view of business cycle-productivity growth interaction. This result suggests that recessions can lead to TFP growth through reorganization and restructuring effects. On the other hand, we find little support for the notion that temporary booms increase TFP through “learning-by-doing” effects. The responsiveness of TFP to employment shocks also seems to be related to the degree of job reallocation within individual industries and the capital intensity of the industry in question. This is further evidence in favour of the view that employment shocks can trigger reorganization effects. Finally, we suggest possible future avenues for this research, including the adjustment of TFP measures to allow for variable factor utilization over the cycle.     

On modeling the determinants of TFP growth

We investigate the determinants of TFP growth of Italian manufacturing firms. Using stochastic frontier techniques, we consider three approaches for taking into account the influence of external factors, i.e., the determinants or drivers of growth. First, in our novel approach external factors may influence the technological progress, that is the shift of the frontier. To model this possible unexplored effect, we extend the standard time trend model to make it a function of the external factors. Then, following more standard approaches, we model external factors as either influencing the distance from the frontier, i.e., inefficiency, or the shape of the technology. Using a sample of manufacturing firms in 1998–2003, we find that technological investments and spillovers, human capital and regional banking inefficiency all have a significant effect on TFP growth.     

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。     

The Magnitude of the Task Ahead: Macro Implications of Heterogeneous Technology

The empirical growth literature is dominated by accounting and regression methods that assume common production technology across countries. Our empirical model relaxes this assumption and further allows unobservable determinants of output (total factor productivity, TFP) to differ across countries and time, while accounting for endogeneity and cross-section correlation arising from global shocks. Using manufacturing sector data for 48 economies, we show that the assumption of common technology is rejected in our regressions. We illustrate that the erroneous choice of homogeneous technology has a substantial impact on the patterns and magnitudes of the resulting TFP estimates.     

What determines the productivity growth? Evidence from Chinese renewable energy firms by using Bayesian stochastic frontier approach

This paper examines the determinants of the total factor productivity (TFP) growth of Chinese renewable energy firms from 2011 to 2016, using a Bayesian stochastic frontier approach. Employing the “Bauer–Kumbhakar” decomposition method, the TFP growth is decomposed into the technology part and the market part. The empirical results reveal that the TFP improvement of Chinese renewable energy firms is mainly due to technical progress, followed by technical efficiency change. With regard to the market part, the misallocation of production factors has hindered the TFP growth. Our findings also indicate that only for large firms, the TFP growth can benefit from the scale economy effect. Compared with non‐state‐owned firms, state‐owned firms suffer much lower allocative efficiency.     

Determinants of TFP growth: A close look at industries driving the EU–US TFP gap

This paper explores the determinants of the EU–US TFP growth gap using EU KLEMS. As found in previous analyses, TFP growth appears to be driven by catching-up phenomena associated with the gradual adoption of new technologies. TFP growth is also significantly driven by developments at the “technological frontier”, especially since the mid-1990s. Industries with higher R&D expenditures and higher adoption rates for ICT-intensive technologies appear to exhibit higher TFP growth rates, whilst human capital has mostly a significant effect across countries. Regarding determinants in industries relevant for the different TFP performance of the EU versus the US, ICT-producing industries appear to benefit from R&D in terms of stronger spillovers from TFP gains at the frontier, network utilities are strongly affected by product market regulations, whilst the retail and wholesale trade industry is significantly influenced by consumption dynamics which permit a better exploitation of scale economies.     

中国内外资企业TFP增长及其影响因素的对比研究——基于35个工业行业面板数据的实证分析

本文运用DEA-Malmquist生产率指数法测算了2000—2007年我国内外资企业35个行业的全要素生产率（TFP）及其分解指标,在对比分析的基础上,分别对内外资企业各项效率指标的影响因素进行了实证检验。结果发现,在资源和劳动密集型行业中,内资企业的各项效率指标均明显高于外资企业,在资本和技术密集型行业中,二者则相差无几;2004年后,技术效率对内资企业的TFP增长平均贡献达到44．5％,外资企业的TFP增长中技术进步的贡献一直在80％左右。回归结果显示,各因素对内外资企业TFP增长的影响方向和程度表现出显著差异。最后,本文给出了提高内资企业生产率增长的政策建议。     

中国农业全要素生产率的变动趋势及区域差异——基于1978～2008年曼奎斯特指数分析

使用1978～2008年省域面板数据,运用非参数曼奎斯特(Malmquist)生产率指数模型测算了中国农业全要素生产率的变动指数,并分析了全要素生产率(TFP)的时空差异。结果表明:全国、三大地区及绝大多数省份农业TFP的增长主要属于技术进步型的增长;全国及三大地区农业TFP增长呈现出明显的波动特征;农业TFP增长呈现出显著的区域间和区域内省际间的不平衡;农业技术效率的下滑制约全国及三大地区农业TFP的增长,而农业技术效率下滑是纯技术效率损失和规模效率下降共同作用的结果。     

人力资本对我国全要素生产率增长作用的空间计量研究

通过构建距离衰减的空间权重矩阵,利用空间Benhabib-Spiegel模型探讨人力资本空间溢出对全要素生产率增长的影响,结果表明:人力资本对全要素生产率增长的作用取决于考察省区人力资本水平、邻近省区人力资本水平,以及考虑地理距离的考察省区技术追赶效应;人力资本平均水平对全要素生产率增长起到积极的促进作用,邻近省区人力资本对考察地区TFP增长产生正向空间溢出效应。各省区若要充分发挥人力资本的空间溢出效应,就必须选择均衡适度的人力资本配置结构。     

Productivity growth in passenger-bus transportation: A heteroskedastic error component model with unbalanced panel data

This paper considers an econometric approach to measure total factor productivity (TFP) growth and technical change (TC) for 31 publicly-owned passenger-bus companies in India during 1983–1987. A translog variable cost function is used to represent the production technology. Firm heterogeneity is incorporated in the cost function using an error component model with firm-specific variances. TFP growth is decomposed into TC and economies of scale components. The TC component is further decomposed into pure, non-neutral, scale, and quasi-fixed factors/network components. An ownership group-wise comparison reveals that the public undertakings exhibit the highest rate of productivity growth, followed by the units operated by the state and local governments. The main source of TFP growth for the public undertakings and government-operated units is economies of scale, while the main source of falling TFP growth for corporations is technological regress.     

AN ATTEMPT TO EXPLAIN DIFFERENCES IN ECONOMIC GROWTH: A STOCHASTIC FRONTIER APPROACH

Total factor productivity (TFP), factor accumulation, and growth are analysed for a panel of 40 countries in 2001–11. TFP growth and technical inefficiency are estimated using a stochastic frontier model. Environmental variables are found to have an important role in explaining differences in inefficiency across countries. Over 2001–11, the general improvement in technical efficiency of countries is almost outweighed by technological regress. Results indicate that differences in factor accumulation between OECD and emerging economies are more important than differences in TFP change to explain differences in economic growth. Results also indicate negative and significant random shocks for the OECD countries.     

Labor productivity growth: disentangling technology and capital accumulation

How much of the convergence in labor productivity that we observe in manufacturing is due to convergence in technology versus convergence in capital-labor ratios? To shed light on this question, we introduce a nonparametric counterfactual decomposition of labor productivity growth into growth of the capital-labor ratio (K/L), technological productivity (TEP) and total factor productivity (TFP). Our nonparametric specification enables us to model technology allowing for heterogeneity across all relevant dimensions (i.e. countries, sectors and time). Using data spanning from the 1960s to the 2000s, covering 42 OECD and non OECD countries across 11 manufacturing sectors, we find TEP and TFP to account for roughly 46 and ?6% of labor productivity growth respectively, on average. While technological growth at the world level is driven primarily by the US and a handful of other OECD countries, we find strong evidence of convergence in both technology and capital-labor ratios. Interestingly, very few of the usual growth determinants are found to enhance the process of technological catching-up.     

Barriers to Capital Accumulation and Aggregate Total Factor Productivity*

I develop a growth model where a single good can be produced with a traditional and a modern technology. The traditional technology features low total factor productivity (TFP) and a low share of reproducible capital. In this framework, barriers to capital accumulation affect technology use and therefore aggregate TFP. The theory thus connects recent models of factor accumulation and of TFP. The model is calibrated by interpreting traditional production as agriculture and nonreproducible capital as land. The theory implies that barriers are associated with large agricultural shares, as supported by cross‐country and time‐series evidence. The required TFP differences needed in the model to account for a given income disparity are reduced by 1/2 relative to the standard model that abstracts from technology choice.