The Linkage between Prices, Wages, and Labor Productivity: A Panel Study of Manufacturing Industries

2004 ◽  
Vol 70 (4) ◽  
pp. 920 ◽  
Author(s):  
Jack Strauss ◽  
Mark E. Wohar
Keyword(s):  
Labor Productivity ◽  
Panel Study ◽  
Manufacturing Industries

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.

CAUSALITY BETWEEN EXPORTS AND PRODUCTIVITY IN THE MALAYSIAN ECONOMY

2011 ◽  
Vol 56 (03) ◽  
pp. 377-395 ◽  
Author(s):  
NURHANI ABA IBRAHIM
Keyword(s):  
Error Correction ◽  
Empirical Evidence ◽  
Granger Causality ◽  
Labor Productivity ◽  
Productivity Growth ◽  
Capital Intensity ◽  
Manufacturing Industries ◽  
Error Correction Mechanism

Empirical evidence linking exports and productivity growth has been mixed and inconclusive. This study re-examines the direction of the causality between them for Malaysian industries by using the error-correction mechanism and Granger causality models. In a panel of 63 manufacturing industries, for the period of 1981 to 1999, it is found that these industries support the export-led growth and the growth-driven export hypotheses. A further look into the results indicates that there are possibilities of indirect causalities between productivity growth and export through size and capital intensity, as both exports and labor productivity have bidirectional causality with size and capital intensity.

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.

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