scholarly journals Macroeconomic Dynamics of the Total Factor Productivity of the Russian Economy

2021 ◽  
Vol 17 (3) ◽  
pp. 799-813
Author(s):  
Sergey A. Mitsek
Keyword(s):  
Total Factor Productivity ◽  
Public Investment ◽  
Labour Productivity ◽  
Aggregate Demand ◽  
Factor Productivity ◽  
Export Prices ◽  
Russian Economy ◽  
Economic Trends ◽  
Total Productivity ◽  
Economic Growth Rates

The growth rate of Russia’s total productivity has been slowing down significantly since 2008. The majority of relevant publications either describe an economic methodology or specifically focus on labour productivity. However, economic growth rates, as well as community welfare, largely depend on total factor productivity. The paper aims to determine the reasons for the slowdown in the growth of total factor productivity after 2008. This negative dynamics was assessed using a macroeconomic econometric model and estimates for Russian regions and types of economic activity. Elasticity of dependent variables was calculated based on econometric equations as well as multipliers of exogenous variables presented in the model. Ordinary and rank correlations between the variables were also examined. The calculations revealed that the stagnation of total factor productivity was caused by the misallocation of resources across industries and regions, de crease in aggregate demand, increase in capital goods prices (primarily due to rouble devaluation) and a slowdown in digital economy development. In turn, these trends were influenced by a decline in public investment and export prices, as well as a slowdown in population growth and liquidity. Simultaneously, growth of the world economy contributed to the demand for Russian export goods, preventing a decrease in productivity. The findings can be used for forecasting Russian economic trends and developing relevant policy measures. Further research will examine the role of human capital, energy intensity, climate and institutional factors in increasing the total productivity.

Two sides, but not of the same coin: digitalization, productivity and unemployment

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Oxana Krutova ◽  
Pertti Koistinen ◽  
Tuuli Turja ◽  
Harri Melin ◽  
Tuomo Särkikoski
Keyword(s):  
Total Factor Productivity ◽  
Job Loss ◽  
Labour Productivity ◽  
Research Problem ◽  
Quality Of Work Life ◽  
Macro Level ◽  
Factor Productivity ◽  
Content Type ◽  
Technological Unemployment ◽  
Factors Of Production

PurposeThis paper aims to examine how input from the digital restructuring of the workplace and productivity affects the risk of job loss and unemployment.Design/methodology/approachRelying on the concepts of technological unemployment and the productivity paradox as well as the theory of skills-biased technological change, the analysis incorporated micro-level individual determinants of job loss, macro-level economic determinants of input and the contribution from traditional (machinery and equipment) vs innovative (ICT) factors of production. The model has been also controlled for “traditional” indicators of “outsiderness” in the labour market. The Quality of Work Life Survey, which is a broad-based national interview survey produced by Statistics Finland, for 2018, the latest year available (N = 4,110) has been used in the analysis. Binomial logistic regression has been applied in order to estimate the effects of individual- and macro-level factors on the risk of job loss.FindingsThe results support arguments for the divergence between effects from labour- vs total-factor productivity on the risks of job loss, as well as the divergence between effects for temporary (layoff) vs permanent job loss (dismissal or unemployment). While the contribution from “traditional” factors of production to labour productivity potentially decreases the risk of permanent job loss, input from “innovative” factors of production on total-factor productivity potentially causes adverse effects (e.g. growing risks of permanent job loss).Originality/valueThe paper contributes to the theoretical discussion about technological unemployment and productivity by means of including two different concepts into a single econometric model, thus enabling examination of the research problem in an innovative way.

scholarly journals TOTAL FACTOR PRODUCTIVITY AND FRONTIER PRODUCTION FUNCTION = PRODUKTIVITAS FAKTOR PRODUKSI TOTAL DAN FUNGSI PERODUKSI FRONTIER

Agro Ekonomi ◽  
2016 ◽  
Vol 24 (2) ◽  
pp. 2
Author(s):  
Sri Widodo
Keyword(s):  
Total Factor Productivity ◽  
Production Function ◽  
Productivity Growth ◽  
Factor Productivity ◽  
Single Factor ◽  
Cross Sectional ◽  
Potential Output ◽  
Total Productivity ◽  
Cross Sectional Studies ◽  
Frontier Production Function

The total factor productivity became an interesting concept in the measurement of productivity growth. Productivity is a ratio of output to input. The most common measurement of productivity is single factor productivity or partial productivity such as of land, labor, or capital.A total (factor) productivity is a productivity of all factors of production where the factors are aggregated. In cross-sectional studies this total productivity is a ratio of actual to potential output where the potential output is estimated from ther frontier production function. One of the methods to estimate this frontier function is by using linear programming technique.The total productivity does not always coincide with a single factor productivity of land (yield), that in the study area the larger farms tend to have higher total productivity than yield

scholarly journals DOES ICT CAPITAL AFFECT ECONOMIC GROWTH IN THE EU-15 AND EU-12 COUNTRIES?

2014 ◽  
Vol 16 (2) ◽  
pp. 387-406 ◽  
Author(s):  
Jana Hanclova ◽  
Petr Doucek ◽  
Jakub Fischer ◽  
Kristyna Vltavska
Keyword(s):  
Economic Growth ◽  
Total Factor Productivity ◽  
Least Squares Method ◽  
Labour Productivity ◽  
The European Union ◽  
Factor Productivity ◽  
Average Growth ◽  
Capital Growth ◽  
Capital Services ◽  
The Eu

The paper examines economic growth in old and new member countries of the European Union (EU-15 and EU-12) during the years of 1994–2000 and 2001–2008 mainly due to changes in information and communication technology (ICT) capital development. The first group EU-15 is presented by old EU countries and the second group EU-12 is presented by new member countries that joined the EU in 2004–2007. The threefactor Cobb-Douglas production function is estimated through the panel general least squares method. The input factors that might influence the economic growth are labour, ICT capital services and non-ICT capital services. Since ICT capital growth data are not available for all selected economies, the groups of countries were reduced to EU-14 and EU-7. The estimated panel production functions confirmed that the average growth of GDP in the EU-7 countries was supported by the stable growth of labour quantity and ICT-capital and increasing total factor productivity. A short-term drop in non-ICT capital growth with follow-up stagnation was caused rather by lower labour productivity. The research discovered that the drop in GDP growth in the EU-14 countries was a result of the slower growth of non-ICT capital and total factor productivity and the stagnated growth of ICT capital with low elasticity, and showed that even the compensation of growth in labour quality did not prevent a decrease in total factor productivity and economic growth.

Productivity performance of the Canadian pulp and paper industry

1990 ◽  
Vol 20 (6) ◽  
pp. 825-836 ◽  
Author(s):  
David L. Frank ◽  
Asghedom Ghebremichael ◽  
Tae H. Oum ◽  
Michael W. Tretheway
Keyword(s):  
Total Factor Productivity ◽  
Pollution Control ◽  
Capital Investment ◽  
Paper Industry ◽  
Labour Productivity ◽  
Pulp And Paper Industry ◽  
Pulp And Paper ◽  
Point Estimate ◽  
Factor Productivity ◽  
The Impact

This paper analyzes the productivity performance of the Canadian pulp and paper industry for the 1963–1984 period. The industry's productivity is first measured, then its sources are analyzed. Total factor productivity is used to measure industry productivity, and statistical estimation of neoclassical cost functions are used to determine sources of the productivity changes. In addition to decomposing the productivity changes into technical changes and changing the scale of the industry's output and capacity utilization, an attempt is also made to assess the impact of pollution control expenditures. The paper finds that although labour productivity grew at 2.5% per year (modest in comparison to other industries), the gross total factor productivity grew only by 1.2% per year. This is largely due to the fact that capital investment raises labour productivity but retards overall productivity. Of the 1.2% growth in total factor productivity, 0.88% was due to the increased scale of the industry output and 0.32% to technical change. Although there is no statistically conclusive evidence, the point estimate indicates that pollution control expenditures may have retarded productivity growth. However, this subject requires further investigation.

scholarly journals Dynamics of Changes in the Level of Competitiveness of Manufacturing in Poland

2020 ◽  
Vol 34 ◽  
Author(s):  
Elżbieta Roszko-Wójtowicz
Keyword(s):  
Economic Policy ◽  
Total Factor Productivity ◽  
Labour Productivity ◽  
Factor Productivity ◽  
Manufacturing Enterprises ◽  
Central Statistical ◽  
Central Statistical Office ◽  
Level Of Aggregation ◽  
The Subject ◽  
Business Entities

The development of modern economies is inseparably connected with the phenomenon of competition and competitiveness of business entities. Issues in the field of competitiveness of economies, sectors and enterprises have become the subject of intensive analysis worldwide. In Poland, there is also an urgent need to conduct research on various aspects of competitiveness which should thoroughly diagnose the situation in this respect and indicate the development of adequate instruments of economic policy stimulating the growth of competitiveness. This article is a response to this need. The main aim of the study is to assess and compare selected aspects of competitiveness of enterprises from manufacturing divisions. Therefore, the analysis covered manufacturing enterprises (Section C) at the two-digit level of aggregation, i.e. at the level of divisions in this Section. To assess competitiveness in the years 2010-2016, the following measures were used: export/import ratio, intra-industrial trade index (IIT), sold production, labour productivity, and total factor productivity (TFP). The research proceedings were based on data published by the Central Statistical Office (Statistics Poland).

scholarly journals Total Factor Productivity and Environmental Efficiency of the Most Important Cereals Crops in Egypt

2020 ◽  
pp. 1-17
Author(s):  
Ahmed A. El-Rasoul ◽  
Alaa M. Ramadan ◽  
E. El-Seify ◽  
Sameh M. Shehab
Keyword(s):  
Technological Change ◽  
Co2 Emissions ◽  
Total Factor Productivity ◽  
Rice Crop ◽  
Wheat Crop ◽  
Factor Productivity ◽  
Summer Maize ◽  
Maize Crop ◽  
Average Change ◽  
Total Productivity

Purpose of the Study: Egyptian agriculture suffers from many problems related to the use of available economic resources, the most important of which is lack of optimal utilization of resources, wasteful use of agricultural production inputs, reduced efficiency of irrigation water use, and the fertility of agricultural lands are deteriorating, in addition to increasing rates of encroachments on agricultural lands and shifting it from agricultural use to other non-agricultural uses, which limits the agricultural sector ability to achieve high growth rates, especially with the increasing global production of biofuels from crops that individuals consume as food, including wheat and corn, which constitutes an explicit threat to Egyptian food and national security. Objectives: The research aimed to: Estimate the changes in the sources and components of the total productivity of the factors for the main cereal crops in Egypt in the presence and absence of carbon dioxide emissions, Environmental impact assessment of changes in the productivity of these crops. Methods: The study applied analytical approaches to measure changes in productivity, as parameter analysis methods are used as methods of the aggregate production function, and non-parameterized methods of estimation, in addition to (Malmquist, 1953) which is one of the most important indicators of measurement changes in productivity and relies on a Data Envelopment Analysis (DEA) to measure efficiency and changes in TFP productivity and identify the sources of changes in productivity through changes in technical competence and technological change, as the two most important components of the change in total productivity. Results: Wheat Crop: Wheat crop by estimating the change in the different efficiencies of the wheat crop with co2 emissions, it was clear that a decrease in technological change (TC) during the study period, and thus a decrease in the average change in the total factor productivity (TFP), while without co2 emissions effect, the average change in the total factor productivity of (TFPc) indicates an increase in the actual wheat efficiency which is low due to the environmental impact of the emissions. Rice Crop: Rice crop by estimating the change in the different efficiencies of the rice crop with co2 emissions, it became clear that a decrease in the average technological change (TC), thus increasing the average change in the total factor productivity of the (TFP), whereas, without co2 emissions, it was found that the average change in the total factor productivity of the (TFPc) for the study areas was higher. Summer Maize Crop: It was clear that the average technological change (TC) for the summer maize crop with co2 emissions, decreased during the study period, and therefore a decrease in the average change in the total factor productivity of the (TFP), but without co2, an increase in the annual average of the change in technical efficiency (TEC), and a decrease in the average technological change (TC), i.e. in the use of technology, and an increase in the average change in the total factor productivity (TFPc).

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