DETECTING FOR CONVERGENCE TRENDS AMONG CHINESE UNIVERSITIES IN TERMS OF ACADEMIC PERFORMANCE

Maria ADAMAKOU

PhD Candidate, Department of Planning and Regional Development, University of Thessaly

madamakou@uth.gr

Dimitris KALLIORAS

Professor, Department of Planning and Regional Development, University of Thessaly

dkallior@uth.gr

Abstract

Under the conditions of the rapid market liberalization process that China has been experiencing, questions of spatial cohesion – and thus of convergence and divergence – become increasingly salient. This is so as the elimination of spatial imbalances is both a pre-condition and a core objective of the reforms aiming at market liberalization. The paper aims at detecting trends of convergence among Chinese universities in terms of academic performance. Taking into consideration that within the knowledge-based economy universities are emerging growth determinants, the topic of the paper is extremely important. This is so as the possible prevalence of divergence trends may indicate that the growth impact of Chinese universities is not space neutral. The empirical analysis of the paper covers the period 2018/19-2022/23, utilizes data obtained from the URAP database, and employs the methodological approach of gaps convergence clubs. The findings of the paper provide valuable insight into both theory and policy.

Keywords: Chinese universities, academic performance, gaps convergence clubs

JEL classification: C21, O43, O47

 pp. 25-31

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MACROECONOMIC EFFECTS OF TELEWORKING IN EU27: STOCHASTIC FRONTIER APPROACH

Lena MALEŠEVIĆ PEROVIĆ

Full professor, University of Split, Faculty of Economics, Business and Tourism, Split, Croatia and CERGE-EI Teaching Fellow

lena@efst.hr

Abstract

The main aim of this paper is to investigate macroeconomic effects of teleworking during the COVID-19 pandemic, using an atypical approach. We apply stochastic frontier analysis to a Cobb-Douglas production function broadened with teleworkability variable, and analyse the level of (in)efficiency of EU27 countries in producing their GDPs. We find that increasing the percentage of jobs that can be done at home by 1 percentage point reduces the level of technical inefficiency by 3.5%. Additionally, we use a unique e-survey conducted in April and May of 2020, which provides the data on the share of people who started working from home as a results of a COVID-19 situation, and combine it with the teleworkability variable. Overall, our findings suggest that more developed EU countries have a higher share of teleworkable jobs, which in turn reduces their inefficiencies, and furthermore results in more people beginning to work from home in the pandemic. 

Keywords: teleworking, production function, stochastic frontier analysis, EU, COVID-19

JEL classification: C21, O4, O33, O52

 pp. 33-42

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DETECTION OF FIRMS´ CLUSTERING BY LOCAL SCALING

Ilona BERKOVÁ

University of South Bohemia in České Budějovice, Faculty of Economics

berkova@ef.jcu.cz

Tomáš MRKVIČKA

University of South Bohemia in České Budějovice, Faculty of Economics

mrkvicka@ef.jcu.cz

Renata KLUFOVÁ

University of South Bohemia in České Budějovice, Faculty of Economics

klufova@ef.jcu.cz

Radim REMEŠ

University of South Bohemia in České Budějovice, Faculty of Economics

remes@ef.jcu.cz

Abstract

The paper analyses locations of headquarters of companies and their interactions by inhomogeneous point process, especially local scaling principles, because companies choose their locations according to the number of the local population. Relationships of the companies within economic sectors are studied using the analysis of locally scaled L function. The inhomogeneity was modelled using the local population, then the company’s size was included. Lastly the level of clustering in each sector was computed. The companies are located in three regions in the Czech Republic. It was found out that the companies tend to cluster when the population or the companies’ size is taken into account.

Keywords: Inhomogeneous point process, L-function, Global envelope test, Spatial clusters, Agglomeration

JEL classification: C21, L60, O18, R12

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