Total Factor Productivity Growth in Chinese Industry: 1952–2005

Özyurt, Selin

doi:10.1080/13600810802660836

Cited by 27 publications

(15 citation statements)

References 27 publications

(36 reference statements)

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“…TFP is calculated as the difference between the log of output and factor of input (Schiff and Wang, 2003) from this production function. "A corresponds to TFP or the Solow residual which is in general, assimilated to technological change" (Ozyurt, 2009). There are several different approaches to calculate TFP including growth accounting, parametric, and non-parametric approaches.…”

Section: Econometric Modelmentioning

confidence: 99%

Cross-country technology gap in Latin America

Misra

Memili²,

Welsh³

et al. 2015

Cross Cultural Management

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2015),"Non-financial performance measures and the BSC of multinational companies with multi-If you would like to write for this, or any other Emerald publication, then please use our Emerald for Authors service information about how to choose which publication to write for and submission guidelines are available for all. Please visit www.emeraldinsight.com/authors for more information. About Emerald www.emeraldinsight.comEmerald is a global publisher linking research and practice to the benefit of society. The company manages a portfolio of more than 290 journals and over 2,350 books and book series volumes, as well as providing an extensive range of online products and additional customer resources and services.Emerald is both COUNTER 4 and TRANSFER compliant. The organization is a partner of the Committee on Publication Ethics (COPE) and also works with Portico and the LOCKSS initiative for digital archive preservation. AbstractPurpose -The purpose of this paper is to investigate the factors influencing the total factor productivity (TFP) gap between the USA and eight Latin American countries for the period of 1970-2000. Design/methodology/approach -The paper provides an explicit application of TFP estimation by employing a growth accounting approach (Solow Residual) in the presence of non-constant returns to scale and a non-parametric approach (DEA -Malmquist Index) while relaxing the scale-related constraint. A macro-based economic model of innovator and follower countries is employed to explore the linkage between technology gaps and innovations, labor productivity, trade openness, foreign direct investment, and adult workforce illiteracy rates. A pooled model and a fixed effects model are used to determine the factors of the technology gap between the innovator and the follower countries. Findings -The results show that the labor productivity gap, adult work force illiteracy rates, patent filing gap, and trade openness are significant determinants of the technology gap between innovator and follower country. Practical implications -Latin American countries would benefit from the technology diffusion from an innovator country; but a minimum threshold of human capital, such as adult workforce illiteracy rates and patent filing has to be met. The authors find government policies on trade openness also have large effects on technology limitations in foreign countries. Originality/value -This paper is of value to researchers, policy makers, and economic development specialists trying to improve the rate of technology adoption and innovation.

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Section: Econometric Modelmentioning

confidence: 99%

Cross-country technology gap in Latin America

Misra

Memili²,

Welsh³

et al. 2015

Cross Cultural Management

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“…For this reason, there is now a considerate research concerning the plotting and forecasting of changes in China's human capital formation and its interrelation with educational attainment as a proxy for a nation's economic growth e.g. [6,7,2].…”

Section: Introductionmentioning

confidence: 99%

“…With regard to time-series forecasting, there is a long research tradition that places considerable emphasis on judging the accuracy of the different models by comparing several measures of forecasting performance on a wide-range of data from sales, tourism demands, R&D expenditure, industrial trends, to human capital formation [10][11][12]7]. Among all the forecasting studies in economic trends, extrapolation of time-series data on the quality and quantity of human capital in different nations has become an emerging topic of interest, e.g.…”

Section: Introductionmentioning

confidence: 99%

On the fit and forecasting performance of grey prediction models for China’s labor formation

Yin

Tang

2013

Mathematical and Computer Modelling

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a b s t r a c tMotivated by the high probability of inconsistent and insufficient information from official statistics in China, this research paper conducted an out-of-sample forecasting competition between grey prediction and straightforward extrapolation for quantity and quality of laborers in China under the assumption that the performance of grey prediction is superior in dealing with uncertain and insufficient data inputs. In line with previous comparative studies on time-series forecasting techniques, the purpose of this study was to verify that the GM(1,1), GM(1,1) rolling and FGM(1,1) models derived from grey system theory would provide forecasts that are at least as accurate as the straightforward extrapolation approach for China's labor variables. The findings revealed that the forecasting efficiency of GM(1,1) and GM(1,1) rolling models were superior to straightforward extrapolation and FGM(1,1). The results can offer valuable insights and provide a basis for further research in model building for short-term estimation under the circumstances of data incompleteness or information inconsistency.

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“… Though not directly comparable, macroeconomic estimates include Chow [] and Ozyurt [] who find higher capital coefficients. These studies do not account for materials.…”

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confidence: 99%

Firm Productivity Differences From Factor Markets

Cheng

Morrow

2018

The J Industrial Economics

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We model firm adaptation to local factor markets in which firms care about both the price and availability of inputs. The model is estimated by combining firm and population census data, and quantifies the role of factor markets in input use, productivity and welfare. Considering China's diverse factor markets, we find that within an industry interquartile labor costs vary by 30-80%, leading to 3-12% interquartile differences in TFP. In general equilibrium, homogenization of labor markets would increase real income by 1.33%. Favorably endowed regions attract more economic activity, providing new insights into within-country comparative advantage and specialization. * We thank

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Total Factor Productivity Growth in Chinese Industry: 1952–2005

Cited by 27 publications

References 27 publications

Cross-country technology gap in Latin America

Cross-country technology gap in Latin America

On the fit and forecasting performance of grey prediction models for China’s labor formation

Firm Productivity Differences From Factor Markets

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