scholarly journals Energy Use and Labor Productivity in Ethiopia: The Case of the Manufacturing Industry

Energies ◽  
2020 ◽  
Vol 13 (11) ◽  
pp. 2714 ◽  
Author(s):  
Selamawit G. Kebede ◽  
Almas Heshmati
Keyword(s):  
Panel Data ◽  
Labor Productivity ◽  
Alternative Model ◽  
Manufacturing Industry ◽  
Energy Use ◽  
Manufacturing Industries ◽  
Dynamic Panel ◽  
Control Variables ◽  
Theoretical Predictions ◽  
Dynamic Panel Estimator

This study investigates the effect of energy use on labor productivity in the Ethiopian manufacturing industry. It uses panel data for the manufacturing industry groups to estimate the coefficients using the dynamic panel estimator. The study’s results confirm that energy use increases manufacturing labor productivity. The coefficients for the control variables are in keeping with theoretical predictions. Capital positively augments productivity in the industries. Based on our results, technology induces manufacturing’s labor productivity. Likewise, more labor employment induces labor productivity due to the dominance of labor-intensive manufacturing industries in Ethiopia. Alternative model specifications provide evidence of a robust link between energy and labor productivity in the Ethiopian manufacturing industry. Our results imply that there needs to be more focus on the efficient use of energy, labor, capital, and technology to increase the manufacturing industry’s labor productivity and to overcome the premature deindustrialization patterns being seen in Ethiopia.

scholarly journals Labor Productivity Convergence in Indonesia: Spatially Dynamic Panel Data Analysis

2019 ◽  
Vol 14 (2) ◽  
Author(s):  
Ahmad Fajar Novianto ◽  
Waris Marsisno
Keyword(s):  
Panel Data ◽  
Labor Productivity ◽  
Panel Data Analysis ◽  
Estimation Method ◽  
Dynamic Panel Data ◽  
Tertiary Sector ◽  
Dynamic Panel ◽  
Productivity Convergence ◽  
Time Required ◽  
Dynamic Panel Data Analysis

The problem of labor productivity in Indonesia is a regional and sectoral inequality. To know the time required to remove inequality, can be measured by the level of convergence of labor productivity. The research would analyze the rate of sectoral labor productivity convergence among provinces in Indonesia spatially and identify the determinant factors of labor productivity. The analytical methods used is spatial dinamic panel data with Spatially Corrected Blundell-Bond (SCBB) estimation method. The results show that there are spatially sectoral labor productivity convergence. Primary sector takes the longest half-life convergence of 7-8 years, while secondary takes 1-2 years and tertiary sector takes 3-4 years. Furthermore, the Gross Capital Fixed Formation, Mean Years of Schooling, and real wage sectoral are significantly have positive affect to the labor productivity while Life Expectancy is significantly have negative affect to labor productivity.Keywords : convergence, spatial analysis, labor productivity

scholarly journals An Analysis of Energy Use Efficiency in China by Applying Stochastic Frontier Panel Data Models

Energies ◽  
2020 ◽  
Vol 13 (8) ◽  
pp. 1892 ◽  
Author(s):  
Xiaoyan Zheng ◽  
Almas Heshmati
Keyword(s):  
Energy Efficiency ◽  
Panel Data ◽  
Energy Policy ◽  
Energy Use ◽  
Stochastic Frontier ◽  
Control Variables ◽  
Energy Use Efficiency ◽  
Level Data ◽  
Use Efficiency ◽  
Province Level

This paper investigates energy use efficiency at the province level in China using the stochastic frontier panel data model approach. The stochastic frontier model is a parametric model which allows for the modeling of the relationship between energy use and its determinants using different control variables. The main control variables in this paper are energy policy and environmental and regulatory variables. This paper uses province level data from all provinces in China for the period 2010–2017. Three different models are estimated accounting for the panel nature of the data; province-specific heterogeneity and province-specific energy inefficiency effects are separated. The models differ because of their underlying assumptions, but they also complement each other. The paper also explains the degree of inefficiency in energy use by its possible determinants, including those related to the public energy policy and environmental regulations. This research supplements existing research from the perspective of energy policy and regional heterogeneity. The paper identifies potential areas for improving energy efficiency in the western and northeastern regions of China. Its findings provide new empirical evidence for estimating and evaluating China’s energy efficiency and a transition to cleaner energy sources and production.

scholarly journals The Contribution of Agricultural Manufacturing in the Egyptian Economic Growth: Kaldor's Hypotheses

2021 ◽  
pp. 156-166
Author(s):  
Ahmed Abou El-Yazid El-Rasoul ◽  
Mai Mustafa Hassan Morsi ◽  
Mohamed Ibrahim Younis
Keyword(s):  
Economic Growth ◽  
Growth Rate ◽  
Labor Productivity ◽  
Manufacturing Industry ◽  
Agricultural Sector ◽  
Manufacturing Sector ◽  
Least Square ◽  
Manufacturing Industries ◽  
The Real ◽  
Real Growth

This research uses a Kaldor’s hypotheses to estimate the contribution of the agricultural manufacturing sector to increase the economic growth of the Egyptian agricultural sector during the period 1997-2018. It based on the three "hypotheses" of growth. Kaldor model depends on three hypotheses related to the relationship between the growth of manufacturing sector and the economic growth. The study used the growth rate, dummy variable, Ordinary Least Square (OLS) test, and used CUSUM squares test and Chow breakpoint test. In addition to, testing the stability of time series depended on E-view 11.0. The food, beverage, tobacco industries and textiles industry are the largest two sectors in the Egyptian agricultural manufacturing industries, as they represent about 83.58% of the total value of the agricultural manufacturing industries output during the period 1997-2018. The results shows that the increase of real growth rates of food, beverage, tobacco industries and textile production lead to increasing in the real growth rate of agricultural output. According to CUSUM Sq test and Chow test, the year 2003 is considered as the switch point for the study variables. Also, if the real agricultural manufacturing production growth rate increases, the real agricultural manufacturing labor productivity growth rate will increase. And if the real growth rate of agricultural manufacturing production value increases, the real growth rate of agricultural non-manufacturing labor productivity will increase. The results of the research assist decision-makers in the field of manufacturing industry and agriculture in Egypt, especially in the stages of economic development.

scholarly journals Technological progress spillover effect in Lithuanian manufacturing industry

Equilibrium ◽  
2021 ◽  
Vol 16 (4) ◽  
pp. 783-806
Author(s):  
Mantas Markauskas ◽  
Asta Baliute
Keyword(s):  
Labor Productivity ◽  
Technological Progress ◽  
Manufacturing Industry ◽  
Vector Autoregression ◽  
Spillover Effect ◽  
Manufacturing Industries ◽  
Advantages And Disadvantages ◽  
Productivity Measure ◽  
Factors Influencing ◽  
Profit Value

Research background: Various methods for technological progress assessment and evaluation exist in the context of economic development. Each of the methods possesses distinct advantages and disadvantages in analysis of technological progress fluctuations. For most neoclassical growth theories, technological progress measures are included as exogenous variables, thus excluding evaluation of factors influencing technological progress variation throughout time. Purpose of the article: The aim of this article is to offer improvements on classical technological progress evaluation methodologies for manufacturing industries, separating effect of intersectoral technological progress spillover effect from internal factors influencing technological progress growth and perform analysis in the case of Lithuanian manufacturing industry. Methods: Earlier research papers used linear time series regression and vector autoregression methods to assess technological progress values and define equations explaining effect of different manufacturing level indicators on technological progress measure growth. This research paper uses results of previously mentioned methods and performs simulation analysis applying agent-based modelling framework. Findings & value added: The conducted vector autoregression analysis has showed that two variables which influence technological progress most significantly are labor productivity measure and gross profit value. Sensitivity analysis emphasizes that effect of these two variables on technological progress growth is substantially different. Increase in gross profit value affects technological progress growth for wider range of sectors from Lithuanian manufacturing industry (15 out of 18 analyzed sectors? technological progress measure values are affected by changes in gross profit, while changes in labor productivity influence technological progress values in the case of 9 sectors). Rising gross profit values also produce intersectoral technological progress spillover effect more significantly, while growth in labor productivity measure has stronger effect on technological progress fluctuations for sectors which are able to exploit this effect. Presented research suggests improved methodology for intersectoral technological progress spillover effect assessment in the context of manufacturing industries.

scholarly journals Determinan Produktivitas Tenaga Kerja Industri Manufaktur Besar dan Sedang di Pulau Jawa

2021 ◽  
Vol 21 (2) ◽  
pp. 185-203
Author(s):  
Rika Dwi Puspita Sari ◽  
Siskarossa Ika Oktora
Keyword(s):  
Panel Data ◽  
Labor Productivity ◽  
National Level ◽  
Fixed Effect Model ◽  
Fixed Effect ◽  
Manufacturing Industries ◽  
Real Wages ◽  
Effect Model ◽  
Panel Data Regression ◽  
Data Regression

Industrialization is one of the government’s focuses on development. Java is an area focused on the industry. However, the labor productivity of large and medium manufacturing industries in Java is lower than regions outside Java and national level of productivity. This study aims to analyze determinants of labor productivity in large and medium manufacturing industries in all provinces in Java from 2010 to 2015 using panel data regression. As the best model, fixed effect model showed that HDI, real wages, and vehicle PMTB has a positively significant effect on labor productivity. -------------------------------------- Industrialisasi merupakan salah satu fokus pemerintah dalam pembangunan. Pulau Jawa merupakan wilayah yang difokuskan untuk industri. Namun, produktivitas tenaga kerja Industri Besar dan Sedang (IBS) di Pulau Jawa lebih rendah dibandingkan daerah di luar Pulau Jawa dan tingkat produktivitas nasional. Penelitian ini bertujuan untuk menganalisis determinan produktivitas tenaga kerja IBS seluruh provinsi di Pulau Jawa periode 2010–2015 dengan menggunakan metode regresi data panel. Hasil analisis menunjukkan Fixed Effect Model merupakan model terbaik untuk penelitian ini, dengan Indeks Pembangunan Manusia (IPM), upah riil, dan Pembentukan Modal Tetap Bruto (PMTB) kendaraan berpengaruh secara signifikan positif terhadap produktivitas tenaga kerja.

scholarly journals Energy Intensity and Long- and Short-Term Efficiency in US Manufacturing Industry

Energies ◽  
2020 ◽  
Vol 13 (15) ◽  
pp. 3954
Author(s):  
Oleg Badunenko ◽  
Subal C. Kumbhakar
Keyword(s):  
Manufacturing Industry ◽  
Energy Use ◽  
Energy Intensity ◽  
Stochastic Frontier ◽  
Manufacturing Industries ◽  
Short Term ◽  
Energy Use Efficiency ◽  
Short And Long Term ◽  
Use Efficiency ◽  
High Level

We analyze energy use efficiency of manufacturing industries in US manufacturing over five decades from 1960 to 2011. We apply a 4-component stochastic frontier model, which allows disentangling efficiency into a short- and long-term efficiency as well as accounting for industry heterogeneity. The data come from NBER-CES Manufacturing Industry Database. We find that relative to decade-specific frontiers, the overall efficiency of manufacturing industries, which is a product of transient and persistent efficiencies has deteriorated greatly in the 1970s and rebounded only in the 2000s. The industries are very efficient in the short-term and this has not changed over five decades. The high level of overall inefficiency is almost completely due to the structural inefficiency which can be explained by what is referred to as the “energy paradox”. Finally, higher energy-intensive industries perform worse in terms of energy use efficiency than their low energy-intensity counterparts.

Study on the Regional Agglomeration of Manufacturing Industries

2013 ◽  
Vol 340 ◽  
pp. 222-225
Author(s):  
Xiao Juan Li
Keyword(s):  
Economic Growth ◽  
Economic Development ◽  
Panel Data ◽  
Manufacturing Industry ◽  
Geographic Location ◽  
Manufacturing Industries ◽  
Promoting Effect ◽  
First Mover Advantage ◽  
The Relationship ◽  
First Mover

Since 70's in last century, Shanghai is one of first-mover advantage and superior geographic location, walking in front of other provinces and cities in terms of economic development. It is an important part of the manufacturing industry as well as the engine of economic growth. According to the previous research shows that, the regional industrial aggregation has a promoting effect on the economic development of Shanghai. This paper made an empirical analysis on the relationship between aggregation and economic growth by using the panel data of Shanghai manufacturing industry.

scholarly journals Impact of Backward Linkages and Domestic Contents of Exports on Labor Productivity and Employment: Evidence from Japanese Industrial Data

2021 ◽  
Vol 36 (4) ◽  
pp. 607-625
Author(s):  
Mohamedou Nasser dine ◽  
Tengku Munawar Chalil
Keyword(s):  
Labor Productivity ◽  
Manufacturing Industry ◽  
Service Industry ◽  
Value Added ◽  
Dynamic Panel ◽  
Dynamic Panel Model ◽  
Panel Model ◽  
Key Driver ◽  
Using Data ◽  
Backward Linkages

This study examines how backward linkages (foreign value added [FVA] exports) and domestic value-added (DVA) exports impact industry-level labor productivity and employment in Japan by estimating a static and dynamic panel model using data drawn from the World Input-Output Dataset and Socio-Economic Accounts. We find that the domestic content of trade is a key driver of productivity and employment in Japan for all industries, while backward linkages lead to declining productivity and foster labor displacement. A sectoral analysis reveals that productivity benefits most of the backward linkages and domestic value-added exports in the manufacturing industry but weakens as the backward linkages increase in the service industry. We find that the DVA exports variable promotes employment, whereas the FVA variable displaces it.

scholarly journals A dynamic panel data analysis for relationship between technological innovation, entrepreneurship and Economic Growth

2017 ◽  
Vol 1 (1) ◽  
pp. 31-43
Author(s):  
Tarek Sadraoui ◽  
Anis Ammari
Keyword(s):  
Economic Growth ◽  
Panel Data ◽  
Technological Innovation ◽  
Panel Data Analysis ◽  
Growth Function ◽  
Dynamic Panel Data ◽  
Entrepreneurial Activity ◽  
Dynamic Panel ◽  
Theoretical Predictions ◽  
Two Measures

This manuscript aims to study elements of answers to the effect of adequate entrepreneurial activity that would have effects on economic growth. This paper analyzes the relationship between entrepreneurship and economic growth for a dynamic panel data of developing countries over the 2004–2017 periods. We used two measures of entrepreneurship: the new density and the potential of innovation. We estimated a growth function using the method of static and dynamic panel data. Our results show that the new density and growth are significantly and positively correlated. Our results also show that if the short-term impact of technological innovation on growth is negative, this effect is positive in the long term. This result confirms the theoretical predictions, namely the theory of spillage.

scholarly journals FAKTOR-FAKTOR YANG MEMPENGARUHI PRODUKSI INDUSTRI MANUFAKTUR DI INDONESIA

2019 ◽  
Vol 1 (2) ◽  
pp. 365
Author(s):  
Fatma Syara Arzia ◽  
Sri Ulfa Sentosa
Keyword(s):  
Panel Data ◽  
Manufacturing Industry ◽  
Labor Relations ◽  
Raw Materials ◽  
Random Effect ◽  
Random Effect Model ◽  
Secondary Data ◽  
Manufacturing Industries ◽  
Business Units ◽  
Effect Model

This study aims to determine: (1) the effect of labor on the production of manufacturing industries in Indonesia. (2) the influence of the number of business units on the production of manufacturing industries in Indonesia. (3) the influence of raw materials on the production of manufacturing industries in Indonesia. This study aims to determine and analyze the influence of labor relations, the number of business units and raw materials on the production of manufacturing industries in Indonesia. The data used are panel data from 33 Provinces in Indonesia during the period 2011 to 2015. The type of research used is descriptive and associative. The type of data used is secondary data. This study uses a Random Effect Model (REM) approach. The results of this study indicate that: (1) Labor has a negative and significant effect on the production of manufacturing industries in Indonesia, (2) The number of business units has a negative and significant effect on the production of manufacturing industries in Indonesia, (3) Raw materials have a positive and significant effect on production manufacturing industry in Indonesia.

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