ADDRESSING SPATIAL JUSTICE AT LOWER TERRITORIAL LEVELS. SOME INSIGHTS FROM THE CENTRAL AND EAST EUROPEAN COUNTRIES’ PERSPECTIVE

Daniela- Luminița CONSTANTIN

Professor at the Department of Administration and Public Management, The Bucharest University of Economic Studies; Bucharest; Romania

danielaconstantin_2005@yahoo.com

Abstract

The current approaches of territorial inequalities from the perspective of territorial cohesion in relation to the European Social Model bring into discussion the concept of spatial justice, which combines place-based with people-based prosperity and points to adequate social and spatial integration models.  It has been supported by the “Europe 2020” strategy and will get even stronger emphasis in the new programme period, 2021-2027. In this context, the analysis of territorial inequalities at deeper level of spatial disaggregation gets a special significance for the design of the future regional policies, which will incorporate an important spatial justice component. It will entail a growing need for data at NUTS3 and LAU levels as well as for microdata (usually obtained on the occasion of population censuses). At international level there is already a growing interest in doing research at these levels by both institutions that support cohesion policy and individual authors. Starting from these overall considerations this paper proposes an overarching review of selected relevant studies undertaken in Central and Eastern Europe in order to highlight significant aspects of deeper territorial inequalities, as useful hints for the prioritisation of the EU funds allocation to less developed areas and for laying good foundations for the regional policies in these countries. The paper brings about a twofold contribution, namely a discussion of the difficulties that have to be faced for the construction of appropriate databases and proper methodologies as well as the emphasis on those territorial inequalities that are better captured at deeper disaggregation levels.

Keywords: territorial cohesion, spatial justice, overarching review, territorial inequalities, disaggregation level,

JEL classification: R11, R12, R19

 pp. 315-326

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DEMOGRAPHIC, GEOGRAPHICAL, AND ECONOMIC ASPECTS AMONG THE GREEK JEWRY, 1919-2019

Nikola YOZGOF-ORBACH

Senior lecturer, Department of Behavioral Sciences, Zefat Academic College, Jerusalem st 11, Zefat, Israel 1320611. Tel: 972-4-692-7866

yozgofo@zefat.ac.il

Abstract

This article discusses demographic, spatial and economic aspects of Greek Jewry in Israel from 1919 to 2019, focusing on its spatial distribution and its demographic processes over the years. This study is based on historicist and interpretive content analysis and on processing and analysis of statistical reports by the Central Bureau of Statistics, as well as analysis of the findings of questionnaires from 2008 and 2019 transmitted among Greek immigrants and their descendants in Israel. The findings show that after the Holocaust, most of the Greek Jews lived in Israel. Many of them settled mainly in urban centers, near the Israeli coastal plain, in the center of the country (Tel Aviv, Bat Yam and Rishon Lezion) or in Haifa. The waves of immigration from Greece to Israel are continuing, but the number of immigrants has diminished greatly over the years. In recent decades, only a few dozen have emigrated to Israel. Demographically Greek Jewry in Israel is characterized by an education rate that is higher than the general average in the country; with a higher level of secularism than the national average; with a low fertility rate compared to other Jewish women in Israel and with a higher  income than the average in Israel. It is also found that among the first generation, only a few hundred are still alive today. It was also revealed that the total number of Greek Jewry today, is 58,238 people and not 10,300 people as shown in the CBS publications of 2018.

Keywords: Jewish Demography, Israel, Greek Jews , Greek immigrants, Jewish Greek Economy

JEL classification:

 pp. 299-313

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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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