scholarly journals Evaluation of Forestry Ecological Efficiency: A Spatiotemporal Empirical Study Based on China’s Provinces

Forests ◽  
2021 ◽  
Vol 12 (2) ◽  
pp. 142
Author(s):  
Shuai Chen ◽  
Shunbo Yao
Keyword(s):  
Total Factor Productivity ◽  
Carbon Dioxide Emissions ◽  
High Tech ◽  
Productivity Change ◽  
Eastern Region ◽  
Factor Productivity ◽  
Tobit Regression ◽  
Ecological Efficiency ◽  
Annual Increase ◽  
Average Annual Increase

Forests play a very important role in carbon dioxide emissions and climate change, and the development of China’s forestry is of great significance to our citizens. However, it is an arduous task for us to improve forestry output at a high and good level while taking environmental factors into account. In this paper, the non-expected super-efficiency SBM (slacks-based measure) model was used to measure the forestry ecological efficiency (FEE) of 31 provinces in China from 2004 to 2018, and the spatial and temporal evolution of FEE in different regions of China was analysed by using spatial econometric method. Tobit regression and random forest algorithm were selected to analyze the influence on FEE. The results showed that, firstly, the average annual increase of the national total factor productivity change of China’s forestry was 1.2%, and that the average annual increase of the national total factor productivity change in the eastern region was lower than that in the central and western regions. Secondly, the distribution of China’s FEE of the northeast and the south was higher, and FEE of China’s middle regions was relatively lower in 2004, but then the FEE in Northeast China has decreased, and the FEE has increased gradually from north to south in 2018. The agglomeration of high-tech industries in most regions of China had obvious positive spatial correlation characteristics in 2018. Thirdly, there was a negative correlation between forestry fixed assets investment and FEE, environmental regulation was an important factor affecting the ecological efficiency of forestry in China, and the level of economic development and industrial structure also had a certain impact on FEE.

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.

scholarly journals DETERMINANTS OF TECHNICAL EFFICIENCY AND TOTAL FACTOR PRODUCTIVITY CHANGE OF THE MANUFACTURING INDUSTRY IN EAST JAVA: EFFORTS TO INCREASE PERFORMANCE AND INDUSTRIAL COMPETITIVENESS

2018 ◽  
Vol 2 (1) ◽  
pp. 22-41
Author(s):  
Imroatul Amaliyah
Keyword(s):  
Technical Efficiency ◽  
Total Factor Productivity ◽  
Manufacturing Industry ◽  
Malmquist Index ◽  
Productivity Change ◽  
Data Envelopment ◽  
Factor Productivity ◽  
Tobit Regression ◽  
Capital Labor Ratio ◽  
Manufacture Industry

This research aims to calculate and analyze the level of technical efficiency and total factor productivity change of manufacture industry, and to examine the factors that influence the value of technical efficiency of manufacture industry in East Java. The method used for this research is Data Envelopment Analysis (DEA) and Malmquist Index with Bootstrapping approach, and Tobit regression. This research used micro data from Indonesian Large and Medium-Scale Industry Survey within the year of 2007 to 2013. The results of this research are: (1) the estimated result of DEA with bootstrapping approach using output-oriented variable return to scale (VRS) assumption shows that the level of technical efficiency of manufacture industry in East Java has been not good enough and overall, it still has the potential to increase its output to reach an efficient condition; (2) the estimated result of Tobitregression demonstrates that the level of technical efficiency of the company is influenced by the company’s size, HHI, capital labor ratio, export and types of company ownership; (3) the estimated result of Malmquist Index with Bootstrapping approach shows that theaverage of total factor productivity change (TFPCH) of manufacture industry from 2007 to 2013 hasexhibited a positive change. The main factor that affects TFPCH, in order, are technological change, efficiency change, and efficiency scale change.

scholarly journals The Impact of Participation in PPP Projects on Total Factor Productivity of Listed Companies in China

2021 ◽  
Vol 13 (14) ◽  
pp. 7603
Author(s):  
Xiangdong Liu ◽  
Guangxi Cao
Keyword(s):  
Total Factor Productivity ◽  
High Speed ◽  
Operation Mode ◽  
Listed Companies ◽  
Financing Constraints ◽  
Eastern Region ◽  
Time Varying ◽  
Factor Productivity ◽  
The Impact ◽  
China’S Economy

The key to transforming China’s economy from high-speed growth to high-quality development is to improve total factor productivity (TFP). Based on the panel data of China’s listed companies participating in PPP (Public–Private Partnerships) projects from 2010 to 2019, this paper constructs the time-varying DID method to test the impact of participation in PPP projects on the company’s TFP empirically, explore the mechanism of the effect of participation in PPP projects on the company’s TFP, and then conduct heterogeneous analysis from four perspectives: region, industry, ownership form, and operation mode. The empirical results show that participation in PPP projects can significantly promote the growth of the company’s TFP, which mainly comes from the promotion of the innovation level of listed companies and the alleviation of financing constraints by participating in PPP projects. In addition, participation in PPP projects has a significant impact on TFP of listed companies in the eastern region, listed companies in the secondary and tertiary industries, state-owned listed companies, and listed companies participating in PPP projects under the BOT mode.

scholarly journals Industry Efficiency and Total Factor Productivity Growth under Resources and Environmental Constraint in China

2012 ◽  
Vol 2012 ◽  
pp. 1-10 ◽  
Author(s):  
Feng Tao ◽  
Ling Li ◽  
X. H. Xia
Keyword(s):  
Energy Consumption ◽  
Total Factor Productivity ◽  
Environmental Efficiency ◽  
Pollutant Emissions ◽  
Productivity Index ◽  
Productivity Change ◽  
Factor Productivity ◽  
Energy And Environment ◽  
Capital Labor Ratio ◽  
Investment Capital

The growth of China's industry has been seriously depending on energy and environment. This paper attempts to apply the directional distance function and the Luenberger productivity index to measure the environmental efficiency, environmental total factor productivity, and its components at the level of subindustry in China over the period from 1999 to 2009 while considering energy consumption and emission of pollutants. This paper also empirically examines the determinants of efficiency and productivity change. The major findings are as follows. Firstly, the main sources of environmental inefficiency of China's industry are the inefficiency of gross industrial output value, the excessive energy consumption, and pollutant emissions. Secondly, the highest growth rate of environmental total factor productivity among the three industrial categories is manufacturing, followed by mining, and production and supply of electricity, gas, and water. Thirdly, foreign direct investment, capital-labor ratio, ownership structure, energy consumption structure, and environmental regulation have varying degrees of effects on the environmental efficiency and environmental total factor productivity.

scholarly journals KOMPARASI EFISIENSI & PRODUKTIVITAS PERUSAHAAN ASURANSI JIWA KONVENSIONAL DAN SYARIAH DI INDONESIA PADA TAHUN 2014 – 2017, DENGAN PENDEKATAN DEA & INDEKS MALMQUIST

2020 ◽  
Vol 6 (9) ◽  
pp. 1877
Author(s):  
Oky Suryoaji ◽  
Eko Fajar Cahyono
Keyword(s):  
Data Envelopment Analysis ◽  
Total Factor Productivity ◽  
Malmquist Index ◽  
Productivity Change ◽  
Data Envelopment ◽  
Factor Productivity ◽  
Constant Return

Tujuan penelitian ini adalah untuk mengetahui tingkat efisiensi dan produktivitas perusahaan asuransi jiwa antara konvensional dan syariah (baik Unit Usaha Syariah maupun Full Fledge) periode 2014 – 2017. Penelitian ini menggunakan pendekatan kuantitatif dengan metode non parametrik DEA (Data Envelopment Analysis) yang dilandaskan dengan asumsi CRS (Constant Return to Scale) dan VRS (Variable Return to Scale) dan Indeks Malmquist asumsi TFPC (Total Factor Productivity Change) dengan diolah menggunakan aplikasi DEAP Versi 2.1. Variabel yang digunakan meliputi Total Aset, Beban, Klaim, Premi/Dana Tabrru’, dan Pendapatan. Subjek yang digunakan dalam penelitian ini sebanyak 29 perusahaan asuransi jiwa syariah yang terdiri 10 perusahaan asuransi jiwa syariah dan 19 perusahaan asuransi jiwa konvensional. Hasil penelitian menunjukkan bahwa rata-rata perusahaan asuransi jiwa konvensional dan syariah belum mencapai efisien (CRS) dan rata-rata TFPC perusahaan asuransi jiwa konvensional sudah mencapai produktivitas sementara syariah belum mencapai produktivitas.Keywords:Asuransi Jiwa Syariah, Efisiensi, Produktivitas, Data Envelopment Analysis (DEA), Constant Return to Scale (CRS), Variable Return to Scale (VRS), Malmquist Index (MI), Total Factor Productivity Change (TFPC)

scholarly journals Productivity Change of Microfinance Institutions in Bosnia and Herzegovina

2019 ◽  
Vol 14 (2) ◽  
pp. 23-33
Author(s):  
Velid Efendić ◽  
Nejra Hadžiahmetović
Keyword(s):  
Total Factor Productivity ◽  
Bosnia And Herzegovina ◽  
Malmquist Productivity Index ◽  
Productivity Index ◽  
Productivity Change ◽  
Microfinance Institutions ◽  
Factor Productivity ◽  
Strategic Objectives ◽  
Average Decrease ◽  
Recent Financial Crisis

Abstract The main aim of this paper is to investigate the productivity changes of microfinance institutions (MFIs) in Bosnia and Herzegovina (BiH) during and after the recent financial crisis. The study covers the period starting from 2008 until 2015. Using the Malmquist Productivity Index (MPI) over the sample of 10 MFIs and a balanced panel dataset of 80 observations, this study explores technical and technological change as well as total factor productivity (TFP) change. The empirical findings indicate a decline in TFP in most of the analyzed periods with an average decrease of 2.5%. The study reveals an average technological decline in the industry of 1.7%, while technical efficiency change is recorded at the level of -0.8%. Overall, crisis efficiency recovery occurred during the period between 2009 and 2013. However, due to technological inefficiencies, average total factor productivity change remains negative. Hence, policy makers need to enhance the technological progress in order to meet their strategic objectives in BiH MFIs.

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

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