Tuyara Gavrilyeva (p), Nikita Bochkarev , Yana Afanasyeva

Fernando Perobelli

The paper is devoted to the analysis of the main trends in the spatial distribution of various types of economic activity in Yakutia. The Yakutia (the largest region of Russia that area is 3.1 million sq. km) is extremely sparsely populated. There are dispersion spatial system, complicated transport logistic and high costs for life-support due to extreme climatic conditions. The main task of research is obtaining of statistically significant gravity models on various samples, whose characteristics can be used to obtain indicators of settlement's economic resilience in condition of a sparsely populated region. Database of research: open data of state and municipal statistics for 411 settlements of Yakutia for 2006-2017 period. A matrix of transport distances in winter period was developed. On its basis, panel data on the geographical potential were obtained, and the reverse indicator – agglomeration potential – was proposed. For the first time in Yakutia, the geographically weighted regression method was used to test hypotheses: on the relationship between the volume of agricultural production and the availability of markets, as well as the agricultural lands area; on the impact of people’s income and agglomeration potential on the small business; on the relationship between the volume of production of large and medium-sized enterprises, agglomeration potential and amount of investments. The direct relationship between the volume of agricultural production and lands area, as well as the availability of neighboring markets, is substantiated. In addition, agglomeration potential directly related with dynamics of production of large and medium-sized businesses, as well as small and micro enterprises.

Davide Fiaschi (p, Lisa Gianmoena, Angela Parenti

Tuyara Gavrilyeva

The most significant stylized fact emerging from the recent literature on European regions is that the cross-regional GDP per worker distribution is characterized by the presence of two peaks and displays spatial agglomeration (Fiaschi et al, 2017; Baumont et al., 2003; Le Gallo et al., 2003; Le Gallo and Ertur, 2003; Fischer and LeSage, 2015).
The presence of spatial clubs in European regions has direct implications for the design of European Cohesion Policy (see, e.g., Farole et al., 2011) and poses serious
concerns on the feasibility of a plan of higher fairness among the populations of EU state members as proposed by Juncker (2014), as well as on the long-run (in)stability of European institutions, which appears permanently mined by a pervasive tendency to regional disparities (De Grauwe, 2014).
Several, and potentially complementary, candidates for the observed inequality among the EU regions have been advanced in the literature: i) local technological spillovers (Erthur and Koch, 2007), ii) (spatial) heterogeneity in human capital (Glaser et al., 2004), iii) the quality of institutions or other unobserved or not-easily measurable variable with (spatial) heterogeneity as the ``culture'' advanced by Tabellini (2010).

This paper aims to analyse the sources of inequality in terms of GDP per worker looking at a large sample of European regions. To achieve this goal, we propose a simple model to estimate the drivers of spatial agglomeration which accounts for the role of interregional technological spillovers, physical and human capital accumulation and reallocation of factors across regions. The model is estimating using a spatial fixed panel in a sample of 254 NUTS2 European regions over the period 1991-2012 and it is then used to evaluate the contribution to EU regional inequality of interregional technological spillovers, regional heterogeneity in human capital, and unobserved regional heterogeneity. Finally, we attempt to explain the estimated unobserved regional heterogeneity in terms of ``culture'' (Tabellini, 2010).

We find that the contribution of interregional technological spillovers is crucial for explaining regional inequality and polarization, estimated unobserved heterogeneity (fixed effects) also explains an important share of inequality, while human capital has not distributional effect. Finally, our proxy for ``culture'' is positively correlated with the estimated fixed effects but only explained a very low part of the variability.

Fernando Perobelli (p), Randall Jackson, Amir Neto, Inacio Junior

Davide Fiaschi

The interest in this paper lies in the environmental costs of the European Union (EU). EU membership requires a series of economic and political changes that should impact the country's production and consumption structures and its trade relationships. These, in turn, will affect $CO_2$ emissions sources and levels. This is especially true for the former Soviet Union countries that recently joined the EU, given the difference in their levels of development and production structure. Using a structural decomposition analysis we are able to quantify the main drivers of changes in emissions differentiating six components, namely: emissions intensity, industrial structure and sourcing, consumer preferences, final demand sourcing and consumption level. Grouping the countries into five clubs, New European Union countries, Old European Union countries, the United States of America, China, and the Rest of the World, we measure trading pattern changes and their impact on CO_2 emission levels