Tag: J01

REGIONAL SPECIALIZATION IN THE CONTEXT OF DEINDUSTRIALIZATION: THE CASE OF TÜRKİYE

Aysu UZSAYILIR

Dr., Department of Urban and Regional Planning, Istanbul Technical University
Orcid: 0000-0003-3920-4062

aysukara@itu.edu.tr

Tüzin BAYCAN

Professor, Department of Urban and Regional Planning, Istanbul Technical University
Orcid: 0000-0001-6073-1188

tbaycan@itu.edu.tr

Abstract

Deindustrialization is experienced in different forms and more deeply in developing countries where regional inequalities, an important component of deindustrialization, impose more structural and historical conditions than in developed countries. Deindustrialization has a deeper causality and impact especially in countries whose economies are based on agriculture and which begin to deindustrialize with global effect while their industrial development continues. The aim of this study is to investigate the regional nature of deindustrialization within the center-periphery relationship at the global and country level. Assuming that the international center-periphery relationship has similar characteristics on a national scale, in this study the regional character of deindustrialization at the level of sectoral specializations is investigated in Türkiye NUTS 2 regions by performing a long-term Location Quotient (LQ) analysis. The main results of the analysis demonstrate that: (i) while Türkiye is an agricultural society and its industrial development continues, it has entered the deindustrialization process with globalization effect; (ii) the pattern of deindustrialization can be exemplified by the regional cluster centered on Istanbul in the Northwest which shows high industrial specialization; (iii) agricultural production dominates throughout the country; and (iv) there is a tendency for industrial development to stagnate and for a direct transition from agriculture to services.

Keywords: Deindustrialization, Labor Market, Regional Specialization

JEL classification: J01, J08, J21, N90, O11, R12

 pp. 33-42

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A MODEL FOR THE JOB DEMAND FORECASTING IN THE ARCTIC ZONE OF THE RUSSIAN FEDERATION BASED ON TIME SERIES

Zhanna PETUKHOVA

Professor, Department of Economics, Management and Organization of Production, Norilsk State Industrial Institute

zh-petukhova@ust-hk.com.cn

Mikhail PETUKHOV

Associate Professor, Department of Information Systems and Technologies, Norilsk State Industrial Institute

mpetukhov@nanyang-uni.com

Igor BELYAEV

Senior Lecturer, Department of Information Systems and Technologies, Norilsk State Industrial Institute

 belyaev@lund-univer.eu

Lyudmila BODRYAKOVA

Associate Professor, Department of Information Systems and Technologies, Norilsk State Industrial Institute

 ln-bodryakova@lund-univer.eu

Abstract

The Russian Federation is the largest country in the world, whose territory includes the Arctic regions. The area of the land territories of the Arctic Zone of the Russian Federation (AZRF) is approximately 3.700,000 km2. The population of the Arctic Zone of Russia is approximately 7 million people, which is equal to 5% of the population of the entire Russian Federation. The purpose of this study is to investigate and analyse regression models for predicting the time series of the number of jobs in the labour market of the Russian Federation, to select an adequate model characterised by a minimum average relative error and a maximum lead time, or to select several adequate models for different forecasting periods: short-term, medium-term and long-term. The study examines the possibilities of predicting the situation in the labour market of the Arctic Zone of the Russian Federation, the demand for specialists in various industries using regression models for forecasting a time series. The simulation was performed using the Statistica software. As a result of the conducted studies, adequate forecasting models were obtained in the time period from 01.01.2020 to 01.01.2021, taking into account the epidemiological situation in the country. Thus, the best model with the smallest error was determined.

Keywords: labour market, regression models, education, autocorrelation function, autoregression.

JEL classification: I15, J11, J01

 pp. 291-298

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INFLATION AND UNEMPLOYMENT IN SOUTHEAST ASIAN COUNTRIES: A PANEL GMM APPLICATION ON PHILLIPS CURVE

ALIASUDDIN

Associate Professor at the Department of Economics, Faculty of Economics and Business, Universitas Syiah Kuala, Banda Aceh, Indonesia

aliasuddin@unsyiah.ac.id

Sofyan SYAHNUR

Associate Professor at the Department of Economics, Faculty of Economics and Business,Universitas Syiah Kuala, Banda Aceh, Indonesia

kabari_sofyan@unsyiah.ac.id

MALIA

Graduate Student at the Department of Economics, Faculty of Economics and Business, Universitas Syiah Kuala, Banda Aceh, Indonesia

malia1980@mhs.unsyiah.ac.id

Abstract

This study aims to analyze the relationship between inflation and unemployment in 10 Southeast Asian from 1996 to 2016 using 210 data samples. The estimation results, using the GMM panel method, showed that the use of Instrument Variables (IV) is valid for the model and the results show a negative and significant relationship between inflation and unemployment. The optimal value of inflation and unemployment for the Southeast Asian Region were found to be 4 percent and 8 percent respectively. This means that a trade-off has taken place. Thus, the existence of the Phillips Curve in Southeast Asian countries during the period of 1996-2016 can be proven. In accordance with the Phillips Curve review, if the trade-off occurs, the government cannot resolve both problems simultaneously. In other words, policy makers must be able to choose the problem to be addressed first, either by implementing monetary policy, fiscal policy or both, so that economic stability and public welfare are maintained.

Keywords: Inflation, Unemployment, Phillips Curve, Panel GMM, Southeast Asia.

JEL classification: E24, E31, C23, J01

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