Regional integration and the economic geography of Belarus

Abstract

This study presents novel research on the economic geography of Belarus. The 118 regions of Belarus are examined in relation to the Eurasian Customs Union (EACU) through the period 2005-2014. Our estimation methods specifically take spatial and dynamic processes into account. We observe that EACU membership has corresponded to a slowdown in the process of regional economic convergence in Belarus, and intensified economic competition with a geographical dimension among regions. Furthermore, we find some evidence that urban and industrial regions, and regional clusters of private business activity have benefited more from the EACU relative to less urbanized areas. Additionally, spatial clusters and outliers are identified and compared across the periods prior and after the establishment of the EACU. Our preferred estimation model results suggest half-lives of convergence of about 9.4 and 31.5 years for the pre-EACU and EACU periods, respectively.

Innovativeness, Work Flexibility, and Place Characteristics: A Spatial Econometric and Machine Learning Approach

“…The aforementioned spatial effects are particularly relevant within the context of innovation, growth, and development [66][67][68]. This relevance was theoretically formalised by Ertur and Koch [69], who showed that spatial externalities resulting from the local accumulation of knowledge result in interdependencies among countries.…”

Impact of Covid‐19 on the convergence of GDP per capita in OECD countries

“…Considering the diversity of realities within the European Union integration process (Furkova, 2020), the respective convergence/divergence trends have attracted the attention of several researchers (Olejnik, 2008), to assess the impacts on new member-states (Smetkowski, 2015). However, there are other contexts that have deserved the consideration of academics who have carried out studies related to spatial dynamics, such as those from the following countries: Russia (Balash et al, 2020), Belarus (Celbis et al, 2018), Mexico (German-Soto & Brock, 2015, Romania (Goschin, 2017), China (He et al, 2017), Great Britain (Henley, 2005), Tunisia (Labidi, 2019), the Iberian Peninsula , the United States (Rey & Montouri, 1999), Colombia (Royuela & Adolfo Garcia, 2015) and Brazil (Silveira-Neto & Azzoni, 2006). Spatial autocorrelation approaches have also been considered in other assessments, such as the following: personal insolvency (Bishop, 2013), ripple effect on housing values (de la Paz et al, 2017), technical efficiency (Ezcurra, Iraizoz, & Rapun, 2008), transport infrastructures (Gao et al, 2019), economic growth efficiency with low carbon (Ju & Zhang, 2020), pollutant emissions (Li et al, 2018), food inflation (Liontakis & Kremmydas, 2014), eco-efficiency , rental housing , transport efficiency (Ma, Wang, Sun, Liu, & Li, 2018), sulphur dioxide emissions (Nan et al, 2020), fertility rate (Salvati et al, 2020), homicides and personal damages (Santos-Marquez & Mendez, 2020), interregional migration (Sardadvar & Rocha-Akis, 2016), diabetes incidence/ prevalence (Shrestha et al, 2016), carbon emissions (Su, 2020), educational standards (Tselios, 2008) and energy efficiency (Zhang et al, 2017).…”