Industrial Production Growth Research Articles

ВИДОБОРА В. В. ЕКОНОМІЧНЕ ЗРОСТАННЯ В НЕОКЛАСИЧНОМУ НАПРЯМІ ЕКОНОМІЧНОЇ ТЕОРІЇ

The purpose of the article is to analyze the theoretical and methodological provisions of economic growth in the light of neoclassical economic theory and the possibility of applying its provisions in modern economic conditions. Methodology of research. The solution of the tasks set in the article is carried out with the help of the following general and special research methods: analysis and synthesis, systematization and generalization, reflection of economic thought, historical and genetic method. Findings. It is found that neoclassical theory focuses on elucidating the factors of economic growth, regardless of whether it will be growth in the long or short term. The main problem, solved by the neoclassicists, is the rational use of resources, which became limited under the influence of population growth and intensification of industrial production. It is found that this scientific direction is based on two main ideas of the theory of production: the value of products is created by factors of production, first of all labor and capital, each of which contributes to its creation; production function is a form of expression of the relationship between the product and its factors with all categories of functional analysis of this relationship of the marginal product, the elasticity of production, the elasticity of substitution of factors, etc. The neoclassical concept of economic growth adheres to the idea of the optimality of the market system as a self-regulating organism, which determines the best use of all production factors (both individual economic entities and the economy as a whole). Accordingly, there is no need for state intervention in the process of establishing the optimal ratio between the factors, as it is determined by free competition and market laws. Practical value. The application of the idea of neoclassical theory of growth in the practice of forming state growth policy in modern economic conditions allows considering economic growth in in dynamics and predicting its possibilities, taking into account both resource factors of growth and factors of social nature.

Decomposing productivity growth in the Indian sugar industry

We measure the growth in the total factor productivity (TFP) of the Indian sugar industry from 2002–03 to 2017–18 using the stochastic frontier production approach. The TFP grew at −10% per annum on average during the study period. The growth was negative because the allocative change and scale effect declined. To arrest the negative growth, the technical change must be improved urgently, modern processing technology must be adopted on a large scale, and the pricing policy of inputs, especially production factors, must be rationalized.

Poverty in the Russian Arctic: The Case of the Republic of Sakha (Yakutia)

The distinctive feature of absolute poverty in the Republic of Sakha (Yakutia), as a region of the North and the Arctic, is its persistence over a long time (2003–2019). The growth of industrial production resulting from the extensive development of natural resources does not affect the income of Yakutia’s population. Using a database containing measurements of social well-being and living standards in 1990–2019, linear regression models of poverty in Russia and Yakutia were built. Based on quantitative and comparative analyses, endogenous poverty factors were identified in Yakutia. These are high cost of living and demographic patterns: high birth rate and dependency ratio and a significant share of the rural population. Ineffective social policy, when a large proportion of social support is provided to people who do not need it, also contributes to the sustainability of poverty. Both the federal government and the government of Yakutia have sufficient resources to solve the problem of absolute poverty. This requires a new social model, a more equitable redistribution of income, and the alleviation of inequality not only among social groups, but also from a regional perspective.

Productivity Growth and Local Content Requirement of the Manufacturing Industry in Banten Province

Productivity growth needs to be optimized not only to produce quality products, increase exports, and reduce dependence on imports but also to support the performance of the manufacturing sector. This study aims to determine the manufacturing industry map in Banten Province, measure manufacturing industry productivity growth, and analyze the effect of Local Content Requirement (LCR) on manufacturing industry productivity growth from 2005 to 2017 by employing the use of K-Means cluster and panel data regression analysis. For this purpose, Total Factor Productivity (TFP) is the indicator used to measure productivity growth. The LCR is known as the percentage of local raw and auxiliary materials used in the production process. According to K-Means cluster analysis results, industrial companies are grouped into three clusters with different characteristics. The result of panel data regression analysis shows that the TFP of all manufacturing industry sub-sectors has increased and was influenced by LCR, total production, domestic investment, import, and provincial minimum wage. As a result, the government should integrate the LCR and other policies, such as the Indonesian National Standard, to increase productivity growth.

The elusive impacts of tariff reduction on firm selection

AbstractThe multi‐sector Melitz model predicts that unilateral trade liberalization leads to stronger selection effect and hence more pronounced industrial productivity growth in the non‐liberalized sectors, which is contrary to the empirical evidence that industrial productivity increases more in the liberalized sectors. This phenomenon is referred to as the ‘Melitz‐Trefler puzzle’. In this paper, we exploit the unique episode of China's entry into WTO and unpack the sources of the relative productivity growth in the liberalized sectors. More specifically, we show that the aggregate industrial productivity growth can be driven by either the ‘selection effect’ or the ‘shift effect’. Our empirical results suggest that the aggregate industrial productivity growth following tariff reduction is mainly driven by the ‘shift effect’ rather than the ‘selection effect’; further, the impacts of tariff reduction on the firm selection exhibit sharp industry heterogeneity, with the effect positive for industries far away from the technology frontier, and those with low technology and low R&D intensity, while negative for otherwise.

Analisis indikator makro ekonomi terhadap Jakarta Islamic Index

Stock price fluctuations in various sectors continue to show a decline because of the Covid-19 pandemic. This is because of its nature, which is strongly influenced by external changes or fundamental information. Here, the ability of investors to understand will influence investors' decisions to invest. This study aims to determine the effect of macroeconomic indicators on the Jakarta Islamic Index, the observation period 2015-2021, using monthly time series data. Macro indicators used in analyzing the stock of the Jakarta Islamic Index are inflation, interest rates, industrial production growth, crude oil, exchange rates, and the industry average Dow Jones as global index variables. The analysis technique used is multiple linear regression. Simultaneous test results show that the macro variable indicators and DJIA have a significant effect on JII. The results of the partial test show that inflation and DJIA have a significant positive effect on JII, while the exchange rate and crude oil have a significant positive effect on JII, for the variables of industrial production growth and interest rates have no significant effect on JII.

Developing two dynamic Malmquist-Luenberger productivity indices: An illustrated application for assessing productivity performance of Indian banks

In the presence of undesirable output(s) in the production process, the Malmquist-Luenberger productivity index (MLPI) has emerged as a standard tool to measure total factor productivity change and its distinct components, namely efficiency change and technical change. Notwithstanding, the standard version of MLPI has certain genuine downsides, particularly the commonness of infeasibility while computing cross-period directional distance functions with negative data values and the black-box representation of the production technology. For overcoming these downsides, this paper advances two extensions of the standard MLPI. Specifically, we propose dynamic Malmquist-Luenberger and dynamic sequential Malmquist-Luenberger productivity indices to measure the productivity change in a system that can be represented by a dynamic network production structure. The proposed indices take a holistic view of production technology that connects several divisions internally by intermediate processes and use the carryovers flow over time to add a temporal dimension to it. To exhibit the capability of the proposed extensions of MLPI, we model a three-stage dynamic network structure for Indian banks and compute the indices of productivity change, efficiency change, and technical change for forty-two banks for the period from 2010 to 2017. The empirical findings elucidate that the observed productivity growth in the Indian banking industry is primarily driven by technical progress.

“Marco Polo Economics” and Narrative in the NBER Soviet Growth Study

In his 1962 NBER volume, Growth of Industrial Production in the Soviet Union, Warren Nutter writes about how the study of the Soviet economy was hamstrung by official secrecy and misinformation. Western economists were forced to rely on what Nutter called “Marco Polo economics” or “travelers’ tales” to replace or interpret quantitative data. Nutter’s book is littered with these tales of Soviet economic activity from Soviet emigres, foreign visitors, and reports of Soviet citizens. The tales provide a narrative grounding for Growth of Industrial Production in the Soviet Union by outlining the flow of data through the Soviet statistical bureaucracy, establishing the reliability of Soviet data, and aiding in the interpretation of difficult to measure concepts like product quality or military production.

Economic Policy Uncertainty Index Meets Ensemble Learning.

We utilize a battery of ensemble learning techniques [ensemble linear regression (LM), random forest], as well as two gradient boosting techniques [Gradient Boosting Decision Tree and Extreme Gradient Boosting (XGBoost)] to scrutinize the possibilities of enhancing the predictive accuracy of Economic Policy Uncertainty (EPU) index. Applied to a data-rich environment of the Newsbank media database, our LM and XGBoost assessments mostly outperform the other two ensemble learning procedures, as well as the original EPU index. Our LM and XGBoost estimates bring EPU closer to the stylized facts of uncertainty than other uncertainty estimates. LM and XGBoost indicators are more countercyclical and have more pronounced leading properties. We find that EPU is more strongly correlated to financial volatility measures than to consumers’ assessments of uncertainty. This corroborates that the media place a much higher weight on the financial sector than on the economic issues of consumers. Further on, we considerably widen the scope of search terms included in the calculation of EPU index. Using ensemble learning techniques on such a rich set of keywords, we mostly manage to outperform the standard EPU in terms of correlation with standard uncertainty proxies. We also find that the predictive accuracy of EPU index can be considerably increased using a more diversified set of uncertainty-related terms than the original EPU framework. Our estimates perform much better in a monthly setting (targeting the industrial production growth) than targeting quarterly GDP growth. This speaks in favor of uncertainty as a purely short-term phenomenon.

Deep Learning Automated System for Thermal Defectometry of Multilayer Materials

Currently, along with growth in industrial production, the requirements for product quality testing are also increasing. In the tasks of defectoscopy and defectometry of multilayer materials, the use of thermal nondestructive testing method is promising. At the same time, interpretation of thermal testing data is complicated by a number of factors, which makes the use of traditional methods of data processing ineffective. Therefore, an urgent task is to search for new methods of thermal testing that will automate the diagnostic process and increase information content of obtained results. The purpose of article is to use the advances in deep learning for processing results of active thermal testing of products made of multilayer materials and development of an automated system for thermal defectoscopy and defectometry of such products. The proposed system consists of a heating source, an infrared camera for recording sequences of thermograms and a digital information processing unit. Three neural network modules are used for automated data processing, each of which performs one of the tasks: defects detection and classification, determination of the defect depth and thickness. The software algorithms and user interface for interacting with system are programmed in the NI LabVIEW development environment.Experimental studies on samples made of multilayer fiberglass have shown a significant advantage of the developed system over using traditional methods for analyzing thermal testing data. The defect classification (determining the type) error on the test dataset was 15.7 %. Developed system ensured determination of defect depth with a relative error of 3.2 %, as well as the defect thickness with a relative error of 3.5 %.

Implementation of a Degassing System at the MSW Landfill

Aitolo-Akarnania prefecture, western Greece, is an area with strong earthquakes and large active fault systems. The most, the second half of the 20th century was characterized for the world community by the aggravation of the environmental problem. Anthropogenic pollution of the environment with the growth of industrial and agricultural production, the growth of cities, the size of the population, the volume of their consumption clearly indicates that the world community is on the brink of an abyss. The destruction of forests, pollution of water bodies, degradation of soil, flora and fauna, the emergence of new diseases clearly shows that if urgent and drastic measures are not taken to save the environment, the life of future generations is problematic. In Russia, as in other countries of the world, the amount of solid household waste has been sharply increasing lately. Therefore, their processing and disposal is becoming an increasingly urgent problem that requires the adoption of complex solutions. At the same time, overcrowded and smoking landfills, as well as formed unauthorized landfills are the main sources of environmental pollution. Landfills of solid municipal waste not only cause an epidemiological hazard, but due to the anaerobic decomposition of organic waste, causing the formation of explosive biogas, become a powerful source of biological pollution. Biogas generated at MSW landfills in the process of decomposition has a toxic effect on living organisms, contributes to the outbreak of fires, and is a source of unpleasant odors. This problem must be solved by introducing a degassing system at municipal solid waste landfills already at the stage of their operation. The proposed degassing system at the MSW landfill is aimed at reducing the negative impact of biogas on the environment. Doi: 10.28991/cej-2021-03091706 Full Text: PDF

Economic and Political Reconstruction and Development of FRG in the Period after World War II

In the first post-war years, the policy of the Western occupying powers towards Germany was aimed at preventing the economic revival of their former formidable competitor. As a result of these efforts, West Germany rebuilt its economy to the pre-war level later than Great Britain or France. The undoubted shift in the economic development of West Germany began in mid-1948. The impetus for the rapid growth of industrial production was the monetary reform carried out by the Western occupying powers, as well as the inflow of funds under the Marshall Plan. The monetary reform carried out in June 1948 favoured the strengthening of the financial market and was an incentive to invest. The influx of capital under the Marshall Plan had a similar impact on the West Germany’s economy during this period. The western zones of Germany played a special role in this plan. The United States, striving to strengthen its position in these zones as much as possible and use them as a strategic base (aimed, inter alia, against the communist bloc), provided West Germany with a sum of loans and subsidies significantly exceeding the amount of aid provided to other Western European countries. An extremely serious burden for the Western occupation zones was the influx of refugees from neighbouring areas (a total of about 10 million people) and the need to maintain the occupation troops, which directly led to a huge deficit in food resources. Agricultural production fell and ranged only from 66% to 75% of the pre-war production level.

The adsorption performance of harmful gas on Cu doped WS2: A first-principle study

With the development of modern industry, the production of harmful gases brought about by the growth of industrial production will increase, seriously endanger human health, and will also cause pollution to the environment. The development of high-performance sensing materials to detect harmful gases has become an urgent problem to be solved in today's society. Based on the first-principles study, this paper analyzed the most stable doping points of Cu atom on the WS2 surface, and then calculates and compares the adsorption energy, charge transfer, adsorption distance and other parameters. At the microscopic level, we analyzed in detail the interaction between Cu doping WS2 (Cu-WS2) and four harmful gases (CO, CO2, NO, NO2). In addition, the sensing behavior of each gas molecule to the four gases was further evaluated by estimating the time required to remove each gas molecule from the surface of Cu-WS2. The results show that Cu atom tend to be located on top of the W atom and lead to a strong n-type doping, which will lead to an increase in the conductivity of WS2. Under certain conditions, high response and reasonable recovery time indicate that Cu-WS2 has ideal sensing behavior for CO and NO gases, while the very short recovery time indicates that Cu-WS2 was not suitable for detecting CO2 in actual engineering, and for NO2 gas, larger adsorption energy makes Cu-WS2 more suitable for gas scavenger. This work shows that Cu-WS2 is expected to become a new gas warning material or adsorption material for the detection and removal of harmful gases.

Industrial policy intensity, technological change, and productivity growth: Evidence from China

China has employed various industrial policies and science & technology (S&T) policies in its effort of catching up with the world technology frontier. This paper evaluates the effect of China's industrial policies and S&T policies with a newly constructed measurement of policy intensity and a national database of firm surveys. We argue that whether China's industrial policies and S&T policies contribute to productivity growth in an industry is conditioned by the relative development stage of that industry to that of the world frontier. Specifically, we argue that China's industrial policies and S&T policies contribute to greater productivity growth in globally emerging high-tech industries than in domestically catching-up and domestically mature industries. We then provide empirical evidence for our hypotheses. Our study identifies a new driver behind China's economic success in the past decades.

An empirical investigation of nuclear energy and environmental pollution nexus in India: fresh evidence using NARDL approach.

Environmental pollution caused by carbon emissions is an emerging issue to study among researchers. The nexus between environmental pollution and carbon emissions has motivated researchers to evaluate the impact of cleaner energy on carbon emissions. This study further contributes to the research by examining the above relationship by studying the asymmetric influence of nuclear energy (NE), industrial productivity (IP), and economic growth rate (GDP) on carbon emissions (CO2) in India from 1975 to 2018 using the asymmetric autoregressive distribution lag approach. The results of the bound test and the Wald test show that in the long run, there is an asymmetric relationship between CO2, NE, GDP, and IP, and in the short run, there is a symmetric relationship between CO2, GDP, and IP. The result implies that increased usage of nuclear energy over fossil fuels and improvement in industrial productivity, in the long run, reduces CO2 emission in India. Furthermore, the study concludes that the government should consider the asymmetric relationship between the variables and devise appropriate policies to increased nuclear energy and industrial productivity in India for environmental sustainability.

USE OF PROCESS FLOW MODEL OF STRATEGIC DEVELOPMENT IN THE CONTEXT OF STRATEGIC MANAGEMENT OF AN INDUSTRIAL ENTERPRISE

Currently, one of the key problems in the Russian Federation is the lack of intensive growth in industrial production, which is not only an economic and political, but also an acute social problem. The industry is the leading branch of the national economy, which produces more than half of the GDP and national income of the country's population.

The impact of environmental policy stringency on industrial productivity growth: A semi-parametric study of OECD countries

This paper employs a semi-parametric varying coefficient system approach to investigating the impact of environmental policy stringency on a nation's productivity growth using data for a panel of OECD countries over a period of two decades. A new cross-country proxy of environmental policy stringency is employed. Our results show that while stricter environmental policies might shift a country's total cost in production upward, for countries which have already adopted relatively more stringent environmental policies, further increasing their policy stringency seems to enhance these countries' productivity in the long run. We also find that more stringent environmental policies seem to render a country's use of intermediate inputs more inelastic to their own prices and decrease the substitutability between labor and intermediate inputs in the long run. We argue that more stringent environmental policies would exert tighter control over the use of several intermediate inputs such as energy, raw materials, pollution-intensive services etc., leading to the use of these inputs being less sensitive to changes in their market prices. Tighter control over the use of these intermediate inputs would also render them less of a substitute for labor input.

Changes in the Trade Paradigm and Agency Dilemma in Korea’s Core Export-Leading Industries

Purpose This study investigates the impact of firm dynamics and consequent business durations of incumbents on the productivity growth of vertical integration with capital share (VI) and non-vertical integration with capital share (NVI) structures in Korea’s core export-leading industries. Design/Methodology/Approach A stochastic frontier production function model was applied to unbalanced firm-level panel data for Korea’s IT manufacturing and automobile industries over 2006 2017. Findings Empirical results verified the agency dilemma between stable supply chain and innovation incentive as a catalyst of firm dynamics, and the consequent productivity gain or loss triggered by market exogenous shocks, structural reform, and market selection forces. Firms shifting from a VI to an NVI structure had a negative pre-exit impact on the productivity growth of the VI structure, while they had a significant positive impact on the productivity growth of the NVI structure they entered. All new entrants raised productivity growth in the IT manufacturing industry, while only new NVI entrants promoted productivity growth in the automobile industry. All exiting firms decreased the productivity growth in both industries. All persevering IT manufacturing firms reduced the productivity growth of their structures, whereas only persevering NVI automobile firms lowered the productivity growth of their NVI structure. Research Implications In terms of productivity growth from firms’ entry, exit, and business duration, IT manufacturing firms retained greater competitiveness and resilience than automobile firms under the adverse global value chain conditions of 2012, relative to those before 2012. However, deteriorating technical efficiency caused TFP growth to plunge in both VI and NVI structures in these industries since 2012.

Зарубежные аналитики о состоянии промышленности СССР в конце 1970-х — первой половине 1980-х гг.

The article considers the industry estimates of the USSR of the late socialist period contained in the works of foreign economists. Particular attention is paid to the sovietological interpretations of the state of the USSR industrial sector as well as the economic policy of Soviet leadership in generating growth of industrial production, development of the Eastern regions of the country, innovation and modernization of fixed assets. The author concludes that the studies conducted by foreign scientists make it possible to clarify the assessment of the industrial sector of the Soviet Union before Perestroika and to adjust the estimates of economic development of the USSR of the “Brezhnev Era”.

Macroeconomic variables for predicting bear stock markets of Taiwan and China

PurposeThis research examines how macroeconomic variables can precisely predict bull/bear stock markets in China and Taiwan.Design/methodology/approachThis paper adopts a two-state Markov switching model to characterize the bull and bear markets spanning from 1994 to 2019 and then conduct a bear stock market predictability test by running regressions between the filtered probabilities of bear markets and a series of macroeconomic variables in turn at different horizons of 1, 3, 6, 12 and 24 months.FindingsThis paper shows that inflation rates, changes in real exchange rates, and foreign currency reserve growth are key predictors of bear markets in China, while term spreads, unemployment rates and foreign reserve growth are major factors that can predict bear markets in Taiwan. Remarkably, industrial production growth does not have predictive power for bear markets, which may suggest emerging markets are driven by fund flows rather than real economic activities. Besides, the impact directions of foreign currency reserve growth are opposite, which may be due to different proportions of the financial accounts in their balance of payments.Practical implicationsIn practical respect, this paper provides market participants the usefulness, impact direction and implications of bear market predictors when building their market-timing strategies in China and Taiwan stock markets. The government institutions may also thereby make appropriate policies to prevent huge stock market downturns and serious drawbacks.Originality/valueIt highlights the “fund-driven market hypothesis” and “foreign currency reserve effects” that commonly dominate Taiwan and China stock markets since both are highly affected by international funds.