Estimation of Total Factor Productivity Growth (TFPG) of Major Cereal Crops in Rajasthan

2021 ◽  
Keyword(s):  
Growth Rate ◽  
Total Factor Productivity ◽  
Production Function ◽  
Output Growth ◽  
Productivity Index ◽  
Cereal Crops ◽  
Total Output ◽  
Factor Productivity ◽  
Tfp Growth ◽  
Moderate Growth

Cereal crops provide essential nutrients and energy in the everyday human diet through direct human consumption and meat production since they comprise a major livestock feed. In the current study, the Tornqvist Theil Index was used to compute the total output index, total input index, and total factor productivity index. The Tornqvist Index is exact for the homogenous translog production function that can deliver a second-order approximation to an arbitrary twice differentiable homogenous production function. This study has indicated moderate TFP in wheat (1.45percent), and the contribution of TFP to output growth was high, about 87 percent for wheat in Rajasthan state. The annual compound growth rate of the TFP of barley increased at the rate of 1.65 percent per annum (moderate growth), and the contribution of TFP to output growth was average, at about 63.47. In comparison, the compound growth rate of TFP of annual maize crop increased at 1.80 percent per annum (moderate growth), while its TFP to output growth was about 73.09 percent. The annual compound growth rate of the TFP of bajra increased by 2.56 percent per year. The contribution of TFP to output growth was 61.29 percent for bajra in Rajasthan. The real cost of production of barley and maize increased by 0.88 and 1.59 percent, which decreased for wheat and bajra by -0.93 and -0.21 percent per annum, respectively. It was revealed that in the bajra crop, Rajasthan state showed good performance of TFP growth among the selected cereal crops. The technology, including agronomical practices, plant protection measures, and mechanization, helped to sustain TFP growth in the bajra crop.

​​Estimation of Total Factor Productivity Growth of Major Pulse Crops in Rajasthan, India

2021 ◽  
Author(s):  
Devendra Kumar Verma ◽  
Hari Singh ◽  
S.S. Burark ◽  
Jitendra Suman ◽  
Priyanka Lal
Keyword(s):  
Growth Rate ◽  
Total Factor Productivity ◽  
Production Function ◽  
Output Growth ◽  
The State ◽  
Relative Price ◽  
Green Gram ◽  
Black Gram ◽  
Factor Productivity ◽  
Pulse Crops

Background: Pulses, supplemented with cereals, provide a perfect mix of vegetarian protein of high biological value. The productivity of pulses in India is less than half of the productivity levels in the USA and Canada. Present investigation was aimed to Total Factor Productivity (TFP) growth in three pulse crops in the state of Rajasthan from 2000-01 to 2017-18. Methods: In the current study, the Tornqvist Theil Index was used to compute the total output index, total input index and total factor productivity index. The Tornqvist Index is exact for the homogenous translog production function that can deliver a second order approximation to an arbitrary twice differentiable homogenous production function. The translog function does not require perfect substitutes for inputs. If the relative price of input increases, the producer decreases its use (substituting other inputs) until all marginal productivities are proportional to the new prices. Result: The results of this study has indicates low TFP in Gram (0.98%) despite a 59.23 per cent share in the total pulse output of the state. The annual compound growth rate of TFP of black gram increased at the rate of 1.11 per cent per annum (moderate growth) and the contribution of TFP to output growth was low; at about 41.63. Whereas, the compound growth rate of TFP of annual green gram crop increased at the 2.38 per cent per annum (high growth) while its TFP to output growth was about 66.43 per cent. The real cost of production of gram, black gram and green gram crop increased by 0.77, 1.49 and 1.57 per cent per annum, respectively.

Total factor productivity of the Latin-American industry: large shipbuilding in Peru

2017 ◽  
Vol 66 (2) ◽  
pp. 231-250 ◽  
Author(s):  
Jorge Benzaquen
Keyword(s):  
Growth Rate ◽  
Total Factor Productivity ◽  
Latin American ◽  
Technical Progress ◽  
Economies Of Scale ◽  
Output Growth ◽  
Productivity Index ◽  
Factor Productivity ◽  
Shipbuilding Industry ◽  
Content Type

Purpose The purpose of this paper is to propose and analyze a model to obtain a total factor productivity of an industry through quantitative empirical analysis in order to determine the joint contribution of the production and technology function, and the change and technical progress. The case of the Peruvian large shipbuilding industry between the years 1969 and 1990 was considered for the analysis of the proposed model. The large shipbuilding in Peru finished in 1992 and has restarted in 2014. The importance of the study lies in the fact that the analysis is focused on an industry which is resurfacing, and in this regards, the study of the first production period will yield more and accurate information to make decisions regarding its future development. Design/methodology/approach One way of considering the several effects of technical progress, in line with Sato (1970) such as growth and bias, is to specify a production function maintaining the linear homogeneity property, such as: Y(t)=F [A(t)K(t), B(t)L(t)], where Y(t) is the aggregate product over a period of time (t); K(t) is the capital; L(t) is the labor; and A(t) and B(t) are the efficiencies or augmentations of K(t) and L(t), respectively. Based on the regression analysis data, the value of σ can be estimated to a residual growth rate (Kennedy and Thirlwall, 1972) that allows assessing the technical knowledge that is not attributable to the factors’ efficiency grains: TCTR = T ˙ / T − ( α ( A ˙ / A ) + β ( B ˙ / B ) ) . This last expression measures the residual technological growth rate (TCTR, by its Spanish acronym). Findings The results of the analysis of the large shipbuilding at SIMA-Callao during the given period (22 years of operation, between 1969 and 1990) show that the necessary installed capacity and the technological knowledge was available in order to develop a complex industrial process in the South Pacific region, thus, contributing to the sector’s growth in the country. The evolution of the shipbuilding activities coincides with the GDP expansion and decline periods in Peru. According to the results, the total factor productivity increased during 1969-1976, 1979-1982, and 1986-1987 periods and it has been confirmed that the contribution of the efficiencies of the production factors were inversely related to the economies of scale and output growth. Practical implications The analysis is based on the activities carried out throughout 22 years of operations in SIMA-Callao shipyards (1969-1990). The data regarding the product, labor, imported materials costs, local material costs, direct expenses, wages, and man-day costs was obtained from several sources within the shipyard. Direct expenses correspond to classification, inspections, administrative expenses (dock, quality control, equipment rental, etc.), drawings, technical data, insurance, and materials freight. Additionally, the sources of information are project construction contracts, annual expenses reports, and man-day cost quarterly reports of the shipbuilding area. The man-day cost includes salary, social benefits, and the company’s functional cost. Originality/value There are different ways to obtain productivity index. In this case, the authors used the stated model. In addition, based on this experience, this can be applied to other industries.

scholarly journals BIASED TECHNICAL CHANGE, INTERMEDIATE GOODS, AND TOTAL FACTOR PRODUCTIVITY

2011 ◽  
Vol 16 (2) ◽  
pp. 184-203 ◽  
Author(s):  
Alessio Moro
Keyword(s):  
Total Factor Productivity ◽  
Production Function ◽  
Technical Change ◽  
Value Added ◽  
Intermediate Goods ◽  
Factor Productivity ◽  
Aggregate Production ◽  
Tfp Growth ◽  
Aggregate Production Function ◽  
The Impact

In this paper I show that the intensity at which intermediate goods are used in the production process affects aggregate total factor productivity (TFP). To do this, I construct an input–output model economy in which firms produce gross output by means of a production function in capital, labor, and intermediate goods. This production function is subject, together with the standard neutral technical change, to intermediates-biased technical change. Positive (negative) intermediates-biased technical change implies a decline (increase) in the elasticity of gross output with respect to intermediate goods. In equilibrium, this elasticity appears as an explicit part of TFP in the value added aggregate production function. In particular, when the elasticity of gross output with respect to intermediates increases, aggregate TFP declines. I use the model to quantify the impact of intermediates-biased technical change for measured TFP growth in Italy. The exercise shows that intermediates-biased technical change can account for the productivity slowdown observed in Italy from 1994 to 2004.

scholarly journals Trends in Productivity Growth of Indian Pharmaceutical Industry: A Growth Accounting Analysis

2021 ◽  
pp. 437-446
Author(s):  
Ariful Hoque ◽  
Subhrabaran Das
Keyword(s):  
Pharmaceutical Industry ◽  
Total Factor Productivity ◽  
Decomposition Analysis ◽  
Output Growth ◽  
Growth Accounting ◽  
Series Data ◽  
Factor Productivity ◽  
Entire Study ◽  
Tfp Growth ◽  
Study Results

The pharmaceutical industry of India is one of the most rapidly expanding research-based industries of Indian manufacturing. This paper attempts to examine the trends in partial and total factor productivity (TFP) growth of India’s pharmaceutical industry using industry-level time series data covering a period of 25 years from 1993-94 to 2017-18, which is further divided into pre-product and post-product patent periods. Three alternative indices of growth accounting approach viz., Translog, Solow, and Kendrick have been used to measure the growth of total factor productivity with four input production framework. The study results indicate significant increasing trends in capital intensity as well as labour, energy and material productivity and a significant declining trend in capital productivity over the entire study period. This study also finds a positive turnaround in the TFP growth of Indian’s pharmaceutical industry during the post-product patent era. The decomposition analysis confirms that output growth in the pharmaceutical industry is input-driven rather than productivity-driven as TFP growth contributes only 8.5 percent to the observed output growth. From the policy standpoint, this paper also suggests greater emphasis on resource efficiency by improving the quality of factor inputs, particularly capital, through increased R&D activities and adoption of cutting-edge technology.

scholarly journals What Is the Focus of Structural Reform in China?—Comparison of the Factor Misallocation Degree within the Manufacturing Industry with a Unified Model

2018 ◽  
Vol 10 (11) ◽  
pp. 4051 ◽  
Author(s):  
Laiqun Jin ◽  
Changwei Mo ◽  
Bochao Zhang ◽  
Bing Yu
Keyword(s):  
Growth Rate ◽  
Total Factor Productivity ◽  
Manufacturing Industry ◽  
Severity Index ◽  
Unified Model ◽  
Structural Reform ◽  
Industrial Enterprise ◽  
Factor Productivity ◽  
Tfp Growth ◽  
Production Factors

The misallocation of production factors, with structural misallocation as an important aspect, is a key instigator of low total factor productivity (TFP) growth rate in China, but one important question is which structural misallocation of what factor is more serious in China. Using China’s manufacturing industrial enterprise data from 1998 to 2013, we calculated and compared the factors misallocation degree among industries, ownerships and regions. The results indicated that, the misallocation among industries was most serious, which led to a TFP loss of 8.12% annually. The misallocation among ownerships ranked second, which led to a TFP loss of 5.49%. The least degree of the misallocation recorded among provinces led to TFP loss of 3.05%. By using the relative severity index, the rank is the same. As to the capital, the misallocation among ownerships was most serious, which led to TFP loss of 4.62%. But as to the labor, the misallocation among industries was most serious, which led to TFP loss of 4.58%. Moreover, the misallocation among ownerships alleviated rapidly from 1998 to 2007, while alleviated slower among industries and regions. However, from 2008 to 2013, all three types of structural misallocation have become worse, especially in labor. These conclusions are important to identify the focus of structural reform in China.

Haze pollution, new-type urbanization and regional total factor productivity growth: based on a panel dataset involving all 31 provinces within the territory of China

Kybernetes ◽  
2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Ruihan Zhang ◽  
Bing Sun ◽  
Mingyao Liu ◽  
Jian Hou
Keyword(s):  
Total Factor Productivity ◽  
Critical Mass ◽  
Estimation Method ◽  
Productivity Index ◽  
Factor Productivity ◽  
Content Type ◽  
Tfp Growth ◽  
Haze Pollution ◽  
New Type ◽  
Spatiotemporal Heterogeneity

Purpose This paper aims to analyze the spatiotemporal heterogeneity of regional total factor productivity (TFP) growth and explores how haze pollution and different levels of new-type urbanization affect China’s economic growth. Design/methodology/approach This paper constructs an index for evaluating the TFP growth of China’s 31 provinces by integrating slack-based measures and the Global Malmquist (GM) productivity index. Meanwhile, the panel threshold estimation method is used to examine the complex relationships among haze pollution, new-type urbanization and TFP growth. Findings The results reflect conspicuous spatiotemporal heterogeneity in TFP growth in China. Interestingly, the influence of haze pollution on TFP growth is limited by the “critical mass” of new-type urbanization in China. When new-type urbanization does not cross the first threshold, haze pollution has a negative but non-significant effect on TFP growth. When new-type urbanization crosses the first threshold but not the second, haze pollution has a significant positive impact on TFP growth. When new-type urbanization crosses the second threshold, haze pollution significantly and positively affects TFP growth with the strongest positive effect. Originality/value This study innovates by combining haze pollution and TFP growth and proposing an integrated framework from the perspective of new-type urbanization, providing insight into how different degrees of new-type urbanization impact the mechanism between haze pollution and TFP growth. Using panel data in China and emphasizing green development, a sustainable economy and new-type urbanization, this study contributes to the current studies on haze pollution and economic development based on developed countries.

Productivity Analysis of Coal-fired Thermal Power Plants in India Using Malmquist Index Approach

2019 ◽  
Vol 21 (6) ◽  
pp. 1338-1353
Author(s):  
Amritpal Singh Dhillon ◽  
Hardik Vachharajani
Keyword(s):  
Total Factor Productivity ◽  
Power Plants ◽  
Thermal Power ◽  
Malmquist Productivity Index ◽  
Productivity Index ◽  
Scale Efficiency ◽  
Productivity Change ◽  
Factor Productivity ◽  
Efficiency Change ◽  
Tfp Growth

The sustainable socio-economic growth of any country depends on the availability of adequate and reliable power at reasonable rates. This is even true in case of a rapidly developing country like India where coal-based power plants account for the majority of electricity generation. Making use of data envelopment analysis (DEA) and Malmquist productivity index (MPI), this study analyses the productivity change of coal-fired power plants during 2002–2012. Productivity change is further decomposed into technical efficiency change (EFFCH), technological change (TECHCH), scale efficiency change (SECH), pure technical change (PECH) and total factor productivity change (TFPCH). The study revealed that 0.70 per cent of average annual total factor productivity (TFP) growth was witnessed from 2002–2003 to 2011–2012 indicating overall progress. The contribution of TECHCH in TFP growth is positive, that is, 1.3 per cent per annum. It demonstrates that expansion of the efficient frontier. However, there was a decrease in technical EFFCH of −0.6 per cent per year, indicating the adverse sign of progress. Plants in the central sector achieved maximum growth of 4.6 per cent annually. A total of 54.05 per cent of plants have recorded negative TFP growth. Power plants between 500 and 999 MW achieved the highest operational performances in all indices except SECH.

Total Factor Productivity and Convergence of Port Logistics with Tianjin Port

2014 ◽  
Vol 998-999 ◽  
pp. 1653-1656
Author(s):  
Li Ping Wang ◽  
Meng Meng Yin ◽  
Bi Xi Dong
Keyword(s):  
Total Factor Productivity ◽  
Structural Changes ◽  
Malmquist Productivity Index ◽  
Productivity Index ◽  
Factor Productivity ◽  
Logistics Industry ◽  
Tfp Growth ◽  
Port Logistics ◽  
The Impact ◽  
Productivity And Competitiveness

Port logistics productivity reflects the productivity and competitiveness of a country or region port logistics industry. The research took Malmquist Productivity Index as a method, and explored the reasons of Tianjin Port Logistics TFP changes by analyzing the TFP growth trends and its structural changes during 2002 to 2011. The results showed that the overall productivity is growing, especially in the period 2009-2010 that is increased by 27.4%. However, changes of productivity had some volatility, primarily due to the impact of technological change.

scholarly journals ECONOMIC PRODUCTIVITY: FACTOR APPROACHES

2021 ◽  
pp. 55-64
Author(s):  
Anton Filipenko
Keyword(s):  
Total Factor Productivity ◽  
Value Added ◽  
Output Growth ◽  
Total Output ◽  
Factor Productivity ◽  
Economic Productivity ◽  
Multifactor Productivity ◽  
Goods And Services ◽  
Capital Productivity ◽  
Residual Contribution

Abstract. The article focuses on economic productivity and have stressed the theory of marginal utility (productivity). In modern researches such aggregate indicators as a total factor productivity and a multifactor productivity prevail. Total Factor Productivity is measured by combining the effects of all there sources used in the production of goods and services (labour, capital, materials, energy, etc.) and dividing it into the output. Multifactor productivity (MFP) is the ratio of total output to a subset of inputs. A subset of inputs might consist of only labour and materials or it could include capital. MFP is the residual contribution to output growth of an industry or economy after calculating the contribution from all its factor inputs. The OECD methodology examines key single-factor (aggregated) indicators of labour and capital productivity, considering total output and costs and, most importantly, the value added, which reflects the real increase in the welfare of the nation.

scholarly journals Technology or Institutions: Which Is the Source of Green Economic Growth in Chinese Cities?

2021 ◽  
Vol 13 (19) ◽  
pp. 10934
Author(s):  
Jing Han ◽  
Xi Chen ◽  
Yawen Sun
Keyword(s):  
Economic Growth ◽  
Growth Rate ◽  
Total Factor Productivity ◽  
Technological Innovation ◽  
Institutional Innovation ◽  
Green Economy ◽  
Productivity Index ◽  
Factor Productivity ◽  
Urban Green ◽  
The Impact

To relax the increasingly tight resource and environmental constraints on development, China needs to follow a pattern of growth that comprehensively encompasses economic growth, environmental protection, and resource conservation, namely, green economic growth. The key to achieving green economic growth is to improve green total factor productivity, of which technological innovation and institutional innovation are the primary driving forces. Based on the panel data of 266 cities in China from 2004 to 2018, this paper first uses the Directional Distance Function and Global Malmquist–Luenberger productivity index to measure the urban green total factor productivity to represent urban green economic growth; then, the impact of technological innovation and institutional innovation on urban green economic growth is studied by using the panel Granger causality test and SYS-GMM dynamic panel model. The results are described as follows: China’s urban green total factor productivity shows an increasing trend from 2004 to 2018, and the average growth rate of green total factor productivity is 3.27%, which is far lower than the average GDP growth rate of 9.14%; both technological innovation and institutional innovation can significantly promote the growth of the urban green economy, but institutional innovation has a greater role in promoting the growth of the urban green economy than technological innovation. In addition, the relationship between institutional innovation and urban green economic growth is more stable.

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