Alberto Tidu (p), Stefano Usai

Maria Kyriakopoulou Deliorga

This paper aims to map the spatial distribution of economic activity in Italy and its evolution during the last decade. To attain this purpose, we rely on Marcon & Puech’s M – a distance measure that allows us to overcome the Modifiable Areal Unit Problem and the related biased featured by conventional measures based on pre-determined geographical units. Indeed, whereas early measures of localization dealt with the relative specialization of particular pre-defined geographical areas (typically administrative partitions of some larger territory of interest), distance measures escape those constraints and produce indices based on distances between pairs of firms or establishments.
We are able to fully exploit M's potential by working on an extremely detailed and accurate dataset: Frame SBS – Territoriale. This dataset is a part of a larger integrated system of registers on firms and establishments, covering the entire country. Indeed, Frame SBS Territoriale censes every establishment in Italy, providing information about geographical location, industry, added value, revenue, costs, employment and export propensity, amounting to almost five million establishments and over 17 million workers for each available year. Notably, this dataset is not limited to manufacturing activities, but also covers service industries which are often left out of the picture for lack of data.
We believe that a comprehensive quantitative study in such sense is lacking, and we are also deeply convinced that its development through distance-based methods provides much more meaningful results than more conventional measures would. The availability of a quantitative measurement of agglomeration for Italy is by and of itself a significant attainment, but an even more interesting results is the opportunity to understand how geographic patterns are changing and what is driving them. Indeed, our computations do not produce only figures and numbers, but also maps that graphically show how different territories are contributing to the industry overall level of spatial concentration. Finally, the availability of a multitude of other firm- and plant-related variables and multiple years allows us to explore how the spatial localization of an industry is associated with other characteristics, such as value added or its propensity to export and how these relationships change through time.

Maria Kyriakopoulou Deliorga (p), Panayiotis Manetos (p), Dimitris Kallioras

Julio Cesar Lopes

Airbnb constitute a significant urban phenomenon mostly seen in the center of big cities. Their advantages are numerous as owing to these many positions are created (receptionists, cleaners, administrators e.t.c.), many buildings are getting restored and the owners of Airbnb are being provided by an extra income. Consequently they apparently contribute to the development of tourism in a quite energetic way.
The aim of the present study is the special analysis of the variables that affect the revenue the hostess obtain. Effectively it is aimed the detection of those special factors that are used to interpret it and the understanding of their spatiotemporal variation in the Municipality of Athens. A calculated model was created for the needs of research processes and the method of Multiscale Geographically Weighted Regression (MGWR) was used as well. This research was carried out in the interface of GIS and MGWR software. Finally, it was determined the intertemporal comparison and evaluation of the outcomes through the studied years for the month of March.

Lorena Brandão, Julio Cesar Lopes (p), Daniela Freddo

György Vida

This research examines regional development from the perspective of economic complexity. Through spatial econometric exercises with panel data, the primary objective is to understand the effects of economic complexity and its spatial spillovers on the variation of GDP per capita and the socio-economic development of Brazilian microregions between 2007 and 2016.

In summary, the Economic Complexity Index (ECI) is related to the diversity and ubiquity of the products that a particular country is capable of exporting. Intuitively, a locality will be more complex depending on its degree of diversification and the ubiquity of its products, considering that less ubiquitous products are more complex than those that are more ubiquitous (Hidalgo, 2021).

Originally, the ECI is calculated using foreign trade data for countries; however, this measure of economic complexity can also be used to assess the productive capacity of subnational entities. In this study, a Regional Economic Complexity Index (ECI-r) is utilised, calculated from data on the formal labour market of Brazilian microregions. As a methodological strategy, spatial econometrics is employed, as traditional econometrics does not incorporate the socio-economic interaction between heterogeneous agents of a system, nor their characteristics in space (Almeida, 2012).

The results indicate that economic complexity in Brazilian states and microregions does not change significantly over the analysed period. Additionally, regional inequalities exist that, in general, are not alleviated by gains in economic complexity. The results of the Exploratory Spatial Data Analysis (ESDA) show evidence of positive spatial autocorrelation for measures of economic complexity, GDP per capita, and socio-economic development.

The spatial models suggest that the economic complexity of a given microregion may be impacted (positively associated) by the economic complexity of neighbouring microregions. The results also suggest that economic complexity has a positive association with GDP per capita growth and socio-economic development indicators related to employment and income. This research advances by analysing both global and local spatial effects. Furthermore, it is highlighted that the use of panel data aids in the generalisation, reliability, and robustness of the results.

Alberto Tidu

The current challenges of globalization, digitalization, and the energy transition cast a new light on regional competition and social polarization. These transformative processes influence economic structures and labor markets, often exacerbating existing disparities. One striking manifestation of social polarization is the worldwide increase in income inequality, which underscores the importance of examining both spatial and within-region income disparities to better understand their implications for economic and social cohesion.
In this presentation, we investigate the evolution of spatial and within-region income inequalities in Hungary over the period from 2010 to 2020, with particular attention to their complex interrelations. Our analysis employs weighted Gini coefficients and Coefficient of Variation to uncover inequalities manifesting in both territorial and social dimensions. In addition, the research utilizes a spatial econometric approach to examine the causal factors behind these differences, using the Eigenvector Spatial Filtering (ESF) method to correct for spatial autocorrelation, thereby providing a more reliable understanding of the determinants of income disparities.
Our findings emphasize that although income differences between regions in Hungary have diminished, within-region inequalities have persisted and, in some cases, have even intensified—particularly in certain urban areas and designated peripheral rural regions. This trend suggests that economic convergence at the regional level does not necessarily translate into more equitable income distribution within regions, highlighting the need for targeted policy interventions.
This research not only sheds light on post-socialist regional dynamics but also endeavors to propose policy recommendations aimed at reducing income disparities and promoting balanced, sustainable regional development. Strategies such as place-based economic support, improved access to education and digital infrastructure, and tailored labor market policies could play a crucial role in mitigating disparities and fostering long-term social and economic cohesion.