scholarly journals A Dual Challenge in China’s Sustainable Total Factor Productivity Growth

2020 ◽  
Vol 12 (13) ◽  
pp. 5342
Author(s):  
Shaohua Zhang ◽  
Tzu-Pu Chang ◽  
Li-Chuan Liao
Keyword(s):  
Total Factor Productivity ◽  
Productivity Growth ◽  
Industrial Structure ◽  
Critical Issue ◽  
Sustainable Growth ◽  
Total Factor Productivity Growth ◽  
Productivity Index ◽  
Productivity Change ◽  
Factor Productivity ◽  
The Government

Since total factor productivity growth plays an essential role in China’s economic growth, the source of this growth has been a critical issue over the past decades. Hence, this paper applies an input slack-based productivity (ISP) index to investigate the contributors (i.e., labor and capital inputs) to China’s total factor productivity growth. The ISP index, combining the features of the directional distance function and Luenberger productivity index, can calculate the productivity change of each input factor under the total factor framework. According to the decomposition analyses, we find that China is confronting a dual challenge in total factor productivity growth: first, capital productivity growth exhibits a remarkable slowdown after the mid-1990s; second, although labor productivity continually expands, the relative labor efficiency among provinces has deteriorated since the 2000s. The results imply that the government should not only advocate upgrading industrial structure, but also consider balanced regional development policies for China’s sustainable growth.

scholarly journals Total Factor Productivity Growth of Indian General Insurance Companies in the Recent Period: A Bootstrapped Approach

2019 ◽  
Vol 11 (1-2) ◽  
pp. 59-80
Author(s):  
Ram Pratap Sinha
Keyword(s):  
Private Sector ◽  
Total Factor Productivity ◽  
Productivity Growth ◽  
Malmquist Index ◽  
American Economic Review ◽  
Productivity Index ◽  
Insurance Companies ◽  
Productivity Change ◽  
Productivity Analysis ◽  
Factor Productivity

This study estimates Malmquist index of total factor productivity change of 14 major general insurers in India over the period 2009–10 to 2016–17 over 7 annual windows. The study decomposes total factor productivity index into its constituent components, using several approaches including Färe et al. (1989, Productivity Developments in Swedish Hospitals: A Malmquist Output Index Approach. Carbondale: Department of Economics, Southern Illinois University; 1992, Journal of Productivity Analysis 3(1): 85–101), Färe et al. (1994, American Economic Review 84(1): 66–83), Ray and Desli (1997, American Economic Review 87(5): 1033–39) and Wheelock and Wilson (1999, Journal of Money, Credit and Banking 31(2): 212–23). Furthermore, the study uses bootstrap data envelopment analysis (DEA) method to obtain bias-corrected point and interval estimates of Malmquist index and its components. Finally, the study makes a comparison of productivity performance between public and private sector insurers. The results indicate a modest growth in total factor productivity during the period contributed mainly by efficiency changes. The private sector insurers performed better than the public sector in terms of productivity growth. The variations in productivity performance indicate that insurer scale of activity can affect their performance. JEL Classification: G-23, C-61, D-21

scholarly journals Measuring Productivity Change in U.S. Agriculture

1975 ◽  
Vol 7 (2) ◽  
pp. 69-75 ◽  
Author(s):  
Yao-Chi Lu
Keyword(s):  
Public Policy ◽  
Total Factor Productivity ◽  
Productivity Growth ◽  
Agricultural Production ◽  
Functional Form ◽  
Productivity Index ◽  
Productivity Change ◽  
Factor Productivity ◽  
Index Numbers ◽  
Increase Productivity

To understand the sources of change in productivity, that appropriate public policy and programs can be developed to increase productivity growth, a reliable and updated measure is needed. The term “productivity” discussed here refers to total factor productivity, or the ratio of value of total agricultural output to that of all inputs used in agricultural production.The first comprehensive work on the measurement of productivity change in U.S. agriculture was done by Loomis and Barton in 1961. Since then, this index has been updated annually as an offical USDA agricultural productivity index. The weakness of using index numbers lies in the arithmetic formula used. It implies a specific functional form of the production function that may not accurately describe the data. Thus, a need arises to consider an alternative estimate of productivity.

scholarly journals Non-parametric decomposition of total factor productivity growth in yam production in North-Central Nigeria

2021 ◽  
pp. 91-98
Author(s):  
R.G. Isonguyo ◽  
M.A Ojo ◽  
A.J. Jirgi ◽  
E.S. Yisa
Keyword(s):  
Total Factor Productivity ◽  
Productivity Growth ◽  
Data Bank ◽  
Productivity Index ◽  
Productivity Change ◽  
Regression Result ◽  
Factor Productivity ◽  
Tobit Regression ◽  
North Central ◽  
Non Parametric

Abstract. Non-parametric analysis of total factor productivity change in yam production in North-Central Nigeria from 1992 to 2016 was carried out with the use of secondary data. The secondary production data of yam for that period were collected from Food and Agriculture Statistical (FAOSTAT) data bank. Malmquist Total Factor Productivity Index (MTFPI) based on Data Envelopment Analysis (DEA), was used to empirically analyse the total factor productivity of the yam, while Tobit regression was used to analyse the determinants of total factor productivity in the study area. The results of the MTFPI analysis reveal that yam contributed 1.4% of technical efficiency change to productivity growth over the period studied. The technological contributions to productivity growth regressed at 1.8%. The study revealed the productivity growth of yam to be 0.2%. Tobit regression result showed credit borrowed, government policy (Agricultural Transformation Agenda – ATA), capital, and labour to have significant and positive relationships with the productivity of the crop at either p≤0.05 or p≤0.001 level of probability, which implies that increase in them led to increase in the crop’s productivity. Capital-labour was statistically significant but negatively related to yam productivity at p≤0.01, which implied that utilization of labour in a greater proportion than capital led to reduction or regress in its productivity growth. The study recommends farmers’ training on farm practices and techniques to increase yam productivity. They should be encouraged to accept improved yam varieties from research institutes, properly allocate the production resources and adopt improved technology to achieve productivity growth in the study area.

Identifying sources and trends for productivity growth in a sample of Queensland broad-acre beef enterprises

2011 ◽  
Vol 51 (5) ◽  
pp. 443 ◽  
Author(s):  
Daniel Gregg ◽  
John Rolfe
Keyword(s):  
Technical Efficiency ◽  
Total Factor Productivity ◽  
Productivity Growth ◽  
Technological Progress ◽  
Positive Influence ◽  
Total Factor Productivity Growth ◽  
Productivity Change ◽  
Factor Productivity ◽  
Average Growth ◽  
Farm Business

The research reported in this paper considers the question of the possible sources of productivity change in the broad-acre beef sector in northern Australia over the last decade. Analysis is conducted over the components of total factor productivity growth for a subset of broad-acre beef production enterprises in Queensland. Specifically we consider the contributions of technological progress, scale changes (changes in the ‘size’ of an enterprise), and technical efficiency (how efficiently an enterprise combines their inputs to produce output) changes to total factor productivity growth using an index based on a decomposition of productivity change. The analysis employed a form for the production technology, which allowed for linear technological progress over time, accounted for rainfall and differences in land types and allowed for the testing of a range of sources of efficiency change. Results suggested that productivity growth within the sample was strong between 1999 and 2008 averaging 3.8% per year. The majority of this growth appeared to originate from technological progress (average growth of 2.7% per year) but there is the possibility that sample-leakage effects caused relatively low estimated contributions from technical efficiency growth (averaged 1.2% per year). Participation in a privately operated farm-business auditing program appeared to have a positive influence on enterprise technical efficiency.

scholarly journals Total Factor Productivity Growth, Technological Progress and Technical Efficiency Changes: Productivity Change Dimensions in Tunisia

2020 ◽  
Vol 4 (2) ◽  
pp. 1-1
Author(s):  
Maha Kalai ◽  
Kamel Helali
Keyword(s):  
Technical Efficiency ◽  
Total Factor Productivity ◽  
Productivity Growth ◽  
Technological Progress ◽  
Stochastic Frontier ◽  
Total Factor Productivity Growth ◽  
Productivity Change ◽  
Factor Productivity ◽  
Economic Sectors ◽  
Total Productivity

In this study, we use the stochastic frontier production approach to split the total productivity growth sources into technical progress and technical efficiency changes of the economic sectors in Tunisia between 1961 and 2014. Based on the sectors’ evolution, the analysis is centred on the technological progress trend, the technical efficiency change, and the role of productivity change in the economic growth. The empirical results show that the production factors have a significant effect on productivity. The review of the total factor productivity growth sources reveals that the contribution of technological progress is the main source of this growth.

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