scholarly journals Firm heterogeneity and total factor productivity: New panel-data evidence from Vietnamese manufacturing firms

2020 ◽  
pp. 1505-1512 ◽  
Author(s):  
Quang Thanh Ngo ◽  
Quang Van Tran
Keyword(s):  
Panel Data ◽  
Total Factor Productivity ◽  
Firm Heterogeneity ◽  
Manufacturing Firms ◽  
Factor Productivity

Is There Really a Foreign Ownership Productivity Advantage? Evidence from Nigerian Manufacturing Firms

2020 ◽  
Vol 29 (5) ◽  
pp. 475-490
Author(s):  
Kilishi Adamu Alka
Keyword(s):  
Panel Data ◽  
Measurement Errors ◽  
Firm Heterogeneity ◽  
Labour Productivity ◽  
Policy Issue ◽  
Capital Intensity ◽  
Foreign Ownership ◽  
Manufacturing Firms ◽  
Factor Productivity

ABSTRACT The role of foreign ownership of an enterprise in impacting their performance is a vital policy issue for Africa. In this paper, panel data for manufacturing firms is used to investigate that role in Nigeria. After controlling for unobserved firm heterogeneity, inputs simultaneity, measurement errors and possible selection bias, I find that foreign ownership has a positive, but statistically insignificant, effect on total factor productivity (TFP). However, foreign-owned firms operate on a far larger scale than domestic ones, with much higher levels of employment, capital intensity and labour productivity. As it is labour productivity that determines the ability of firms to pay higher wages, the evidence presented in this paper suggests it is their ability to operate at higher scale, rather than having higher TFP, is what characterises foreign-owned firms in Nigeria.

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.

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.

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