scholarly journals Evaluation of Vegetable Circulation Efficiency and Analysis of Influencing Factors in Henan Province

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Xueqiang Guo ◽  
Bingjun Li
Keyword(s):  
Panel Data ◽  
Dynamic Analysis ◽  
Static Analysis ◽  
Total Factor Productivity ◽  
Evaluation Model ◽  
Henan Province ◽  
Factor Productivity ◽  
Factors Affecting ◽  
Specific Factors ◽  
Dea Method

In order to explore the deep-seated reasons affecting the development of vegetable circulation in Henan Province, combined with the panel data of Henan Province from 2014 to 2019, this paper first makes a static analysis on the vegetable circulation efficiency in Henan Province by using DEA method. Second, the Malmquist method is used to establish the total factor productivity evaluation model of vegetable circulation in Henan Province, and the dynamic analysis is carried out. The analysis results show that the main problem in the development of vegetable circulation in Henan Province is the low level of management and technology. Then, GM(1, N) model is established to further analyze the specific factors affecting the vegetable circulation efficiency in Henan Province. Finally, some reasonable suggestions are put forward for the development of vegetable circulation in Henan Province.

Firm Characteristics and Total Factor Productivity: Evidence from Indian Manufacturing Firms

2017 ◽  
Vol 11 (1) ◽  
pp. 77-98 ◽  
Author(s):  
Lopamudra D. Satpathy ◽  
Bani Chatterjee ◽  
Jitendra Mahakud
Keyword(s):  
Total Factor Productivity ◽  
Ordinary Least Squares ◽  
Jel Classification ◽  
Raw Material ◽  
Firm Characteristics ◽  
Manufacturing Companies ◽  
Factor Productivity ◽  
Size Of Firms ◽  
Specific Factors

Measurement of the productivity of firms is an important research issue in productivity literature. Over the years, various methods have been developed to measure firm productivity across the globe. But there is no unanimity on the use of methods, and research on the identification of factors which determine productivity has been neglected. In view of these gaps, this study aims to measure total factor productivity (TFP) and tries to identify firm-specific factors which determine productivity of Indian manufacturing companies. The study is based on data of 616 firms from 1998–99 to 2012–13. To measure TFP, the Levinsohn–Petrin (L-P) method has been employed, and the fully modified ordinary least squares (FMOLS) method has been used to identify factors that affect TFP. The results reveal that embodied and disembodied technology plays a crucial role in the determination of productivity overall in manufacturing and other sub-industries. Similarly, the size of firms and intensity of raw material imports are also important for the determination of productivity across the sub-industries. JEL Classification: C14, C33, D24, L60

Determinants of total factor productivity in Visegrad Group Nuts-2 regions

2018 ◽  
Vol 68 (1) ◽  
pp. 31-50 ◽  
Author(s):  
Barbara Danska-Borsiak
Keyword(s):  
Panel Data ◽  
Total Factor Productivity ◽  
Latent Variables ◽  
Fixed Effects ◽  
Orthogonal Transformation ◽  
Common Factors ◽  
Dynamic Panel Data ◽  
Factor Productivity ◽  
The Common ◽  
Visegrad Group

This article attempts to estimate the total factor productivity (TFP) for 35 NUTS-2 regions of the Visegrad Group countries and to identify its determinants. The TFP values are estimated on the basis of the Cobb-Douglas production function, with the assumption of regional differences in productivity. The parameters of the productivity function were analysed with panel data, using a fixed effects model. There are many economic variables that influence the TFP level. Some of them are highly correlated, and therefore the factor analysis was applied to extract the common factors – the latent variables that capture the common variance among those observed variables that have similar patterns of responses. This statistical procedure uses an orthogonal transformation to convert a set of observations of possibly correlated variables into a set of values of linearly uncorrelated variables called principal components. Each component is interpreted using the contributions of variables to the respective component. I estimated a dynamic panel data model describing TFP formation by regions. An attempt was made to incorporate the common factors among the model’s explanatory variables. One of them, representing the effects of research activity, proved to be significant.

Agricultural Growth Accounting and Total Factor Productivity in Jordan

2020 ◽  
pp. 1709-1723
Author(s):  
Samia Nadeem Akroush ◽  
Boubaker Dhehibi ◽  
Aden Aw-Hassan
Keyword(s):  
Total Factor Productivity ◽  
Productivity Growth ◽  
Agricultural Research ◽  
Agricultural Productivity ◽  
Primary Source ◽  
Factor Productivity ◽  
Factors Affecting ◽  
Tfp Growth ◽  
Food And Agriculture ◽  
Food And Agriculture Organization

This article develops new estimates of historical agricultural productivity growth in Jordan. It investigates how public policies such as agricultural research, investment in irrigation capital, and water pricing have contributed to agricultural productivity growth. The Food and Agriculture Organization (FAO) annual time series from 1961 to 2011 of all crops and livestock productions are the primary source for agricultural outputs and inputs used to construct the Törnqvist Index for the case of Jordan. The log-linear form of regression equation was used to examine the relationship between Total Factor Productivity (TFP) growth and different factors affecting TFP growth. The results showed that human capital has positive and direct significant impact on TFP implying that people with longer life expectancy has a significant impact on TFP growth. This article concludes that despite some recent improvement, agricultural productivity growth in Jordan continues to lag behind just about every other region of the world.

scholarly journals The Relationship between Environmental Regulation and Green Total Factor Productivity in China: An Empirical Study Based on the Panel Data of 177 Cities

2020 ◽  
Vol 17 (15) ◽  
pp. 5287 ◽  
Author(s):  
Mingliang Zhao ◽  
Fangyi Liu ◽  
Wei Sun ◽  
Xin Tao
Keyword(s):  
Economic Development ◽  
Panel Data ◽  
Environmental Regulation ◽  
Total Factor Productivity ◽  
Inflection Point ◽  
Factor Productivity ◽  
Positive Effects ◽  
Development Level ◽  
The Mean ◽  
The Relationship

Promoting the coordinated development of industrialization and the environment is a goal pursued by all of the countries of the world. Strengthening environmental regulation (ER) and improving green total factor productivity (GTFP) are important means to achieving this goal. However, the relationship between ER and GTFP has been debated in the academic circles, which reflects the complexity of this issue. This paper empirically tested the relationship between ER and GTFP in China by using panel data and a systematic Gaussian Mixed Model (GMM) of 177 cities at the prefecture level. The research shows that the relationship between ER and GTFP is complex, which is reflected in the differences and nonlinearity between cities with different monitoring levels and different economic development levels. (1) The relationship between ER and GTFP is linear and non-linear in different urban groups. A positive linear relationship was found in the urban group with high economic development level, while a U-shaped nonlinear relationship was found in other urban groups. (2) There are differences in the inflection point value and the variable mean of ER in different urban groups, which have different promoting effects on GTFP. In key monitoring cities and low economic development level cities, the mean value of ER had not passed the inflection point, and ER was negatively correlated with GTFP. The mean values of ER variables in the whole sample, the non-key monitoring and the middle economic development level cities had all passed the inflection point, which gradually promoted the improvement of GTFP. (3) Among the control variables of the different city groups, science and technology input and the financial development level mainly had positive effects on GTFP, while foreign direct investment (FDI) and fixed asset investment variables mainly had negative effects.

New Evidence on the Relationship Between Economic Freedom and Growth: A Panel Cointegration Analysis for The Case of OECD

2012 ◽  
Vol 12 (3) ◽  
pp. 1850263 ◽  
Author(s):  
Ekrem Erdem ◽  
Can Tansel Tugcu
Keyword(s):  
Economic Growth ◽  
Panel Data ◽  
Total Factor Productivity ◽  
Economic Freedom ◽  
Global Financial Crisis ◽  
Oecd Countries ◽  
Cointegration Analysis ◽  
Factor Productivity ◽  
The Impact ◽  
The Relationship

The aim of this paper is to find a new answer to an old question “Is economic freedom good or not for economies?” which was refreshed after the Global Financial Crisis of 2008. For this purpose, the relationship between economic freedom and economic growth, and the relationship between economic freedom and total factor productivity in OECD countries were investigated by using panel data for the period of 1995-2009. Study employed the recently developed cointegration test by Westerlund (2007) and the estimation technique by Bai and Kao (2006) which account for cross-sectional dependence that is an important problem in the panel data studies. Although no significant relationship found between economic freedom and total factor productivity, cointegration analysis revealed that economic freedom matters for economic growth in OECD countries in the long-run, and estimation results showed that direction of the impact is negative.

The Impact of Total Factor Productivity on Economic Growth for Developed and Emerging Countries: A Second-generation Panel Data Analysis

2017 ◽  
Vol 11 (4) ◽  
pp. 404-417 ◽  
Author(s):  
Ömer Yalçınkaya ◽  
İbrahim Hüseyni ◽  
Ali Kemal Çelik
Keyword(s):  
Economic Growth ◽  
Panel Data ◽  
Total Factor Productivity ◽  
Second Generation ◽  
Capital Formation ◽  
Emerging Countries ◽  
Fixed Capital ◽  
Factor Productivity ◽  
Fixed Capital Formation ◽  
The Impact

This article investigates the determinants of economic growth and also seeks to determine whether or not the impact of total factor productivity (TFP) changes with respect to the level of development for selected countries. In this manner, the present study examines the impact of gross fixed capital formation, employed labour and the TFP of G-7, G-12 and G-20 countries on real GDP per capita using second-generation panel data analyses over the period 1992–2014. The results reveal that TFP has a greater impact on economic growth than fixed capital formation and employed labour for all country groups. Furthermore, the impact of TFP on economic growth was found to be greater for developed countries than for emerging countries. JEL Classification: C21, C22, C23

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