Wenxuan Yang (p)

Manaeva Inna

Nowadays, inequality in regional economic development is an essential issue in China's development process. China is one of the countries with the most severe regional inequality problems. With the help of the economic convergence framework, we can explore whether there is a trend towards a reduction in economic disparities in a region. In China, however, the lack of city-level data increases the difficulty of estimation. This study aims to use satellite lighting data to fill in the missing values in China's GDP data at the city level, then test whether there is convergence in the Chinese economy from 2012 to 2019. The originality of this study comes from three aspects:
1. This study develops a complete city-level dataset to measure development by combining existing GDP and lighting data.
2. This study incorporates the beta convergence framework with spatial factors and compares the results of classical convergence regressions with its spatial extension.
3. This study calculated local convergence rates using geographically weighted regressions and found significant regional differences in convergence rates.

To address the missing value issue, this study verifies the strong linear relationship between China's GDP per capita and nighttime lighting data (NTL data) to illustrate the applicability of NTL data in China. Then this paper combines satellite lighting data, provincial data, and city data to estimate GDP per capita for 344 prefecture-level cities in China, filling in the missing values.

This study calculates regional economic convergence in China, both overall and locally, through the lens of classical beta convergence, spatial modeling, and geographically weighted regressions. The regression results show that there is economic convergence in China in general and that China's economic development has significant spatial autocorrelation. Second, the LM test shows that spatial modeling is necessary for convergence framework, and it is more appropriate to use spatial error model. After introducing the spatial model, results suggest that the spatial factor would speed up the convergence process. Finally, the local regressions suggest significant regional differences in the speed of economic convergence in China. The fastest convergence rates were found in the less developed regions of the west and central China, while the convergence rates were slow in the eastern coastal areas. In conclusion, this study highlights the importance of incorporating spatial correlation and spatial heterogeneity into regional economic analysis.

György Kocziszky, Dora Szendi (p)

Wenxuan Yang

Various surveys show that the citizens of the Central and Eastern European countries, once part of the Soviet zone of interest, hoped for rapid catch-up (in terms of living standards, infrastructure, employment, social, environmental conditions) from the geopolitical changes that took place after 1989. This seems to have been only partially achieved. The post-socialist countries under study, despite their quantifiable achievements, are trapped in development, due to internal (economic history and values, imported institutional structures, lower productivity than the EU average) and external (geopolitical situation, vulnerability of their economies) reasons. It will be particularly interesting to see how the COVID waves started in 2019, followed by the Russian-Ukrainian war, will affect the development prospects of the six examined countries (Bulgaria, Czech Republic, Hungary, Poland, Romania, Slovakia).
After a brief theoretical overview of development and path dependency, the authors of the present study examine changes in the period of 1995-2020 using an aggregate development index based on forty indicators (economic, demographic situation, employment, human infrastructure, income and environmental conditions). They analyse the similarities and differences in development paths and then summarize the risks expected between 2021 and 2025 and their impact on future development paths.

Ade Paranata (p)

Dora Szendi

The purpose of this study is to analysis the extent to which the engagement of actors in Indonesia's foreign grant network. The data for this paper is obtained from Ministry of Finance of the Republic of Indonesia since semester 1 of 2011 to semester 1 of 2021. This paper examines the pattern of grants made by individual donors to recipients, either planned or unplanned channels. This paper employs a weighted matrix in conjunction with an undirected network. The institutions participating are referred to as nodes. The analysis utilized in this study includes several assessment criteria of network approach. Fruchterman Reingold is used to visualize networks. The undirected graph has 89 nodes. There are 957 foreign grant interactions occurred in Indonesia during this period. Two institutions namely Ministry of Public Works and Human Settlement and Ministry of National Development Planning play a central role. Most of the grants distributed by the two multilateral donors, namely the World Bank (WB) and the Asian Development Bank (ADB). There is difference in the composition of actors in betweenness and closeness centrality. The role of WB is prominent in betweenness centrality. Meanwhile, in closeness centrality, ADB takes an active role in the distribution of foreign grants in Indonesia. The obtained modularity value of 0.310 is acceptable. This number indicates that Indonesia's foreign grant network is well-structured. The data processing yielded a density value of 0.244. These findings show that the density of nodes is quite solid. The triangle approach generates 1431 paths. The clustering coefficient has an average value of 0.1516. Cooperation with foreign donors is a major hope for the regions to grow quickly and catch up, yet it has created its own set of difficulties for the regions.
Keywords: Donors, Recipients, Foreign grants, Network approach, Indonesia.

Inna Manaeva (p)

Ade Paranata

The "borrowed size" concept of the city, developed by Alonso in 1973. acquires special relevance in modern Russian conditions. According to this concept, small and medium-sized cities can "borrow" some of the advantages of the agglomeration from their neighbors, while avoiding the costs of agglomeration. A borrowed size city receives average size benefits typical of a larger city. Thus, size and proximity generate technological spillovers that raise the productivity of small towns to that of larger ones. The growth of small towns is explained by the introduction of geographic space (geographical proximity, not just the size of the urban industrial complex) as a source of externalities and growth.
The purpose of the study is to identify the presence of "borrowed dimensions" and determine their impact on the dynamic development of the region's urban system in modern Russian conditions.
Research stages:
Theoretical block: analysis and systematization of works on the "borrowed size" of the city;
Empirical block: in view of the wide differentiation of natural, geographical and climatic conditions on the territory of the Russian Federation, it is advisable to carry out the stated analysis separately for the cities of the central part of the country, the European North and Siberia. An analysis is planned for 2010 and 2019.
The assessment of the "borrowed amount" will be carried out according to the following formula:

〖borrowed size〗_c=∑_(j ≠1)^n▒〖pop〗_j/w_(geo c,i) ,∀ c≠j
where:
c and j are two cities;
Wgeo is a distance weight matrix that formalizes the spatial interdependence between cities;
Pop is the population of the city;
The results obtained by calculation will allow drawing conclusions and making recommendations.
The final block: conclusions will be presented, the theoretical and practical significance of the study will be determined.