Scott William Hegerty

Understanding the determinants of trade flows between countries is of particular interest to policymakers, central bankers, business owners and investors. The study examines the long-run impact of the real exchange rate, exchange-rate volatility, and output on the trade balances of 10 SITC (Standard International Trade Classification) sectors between three Visegrad countries and Germany. Because the linear Autoregressive Distributed Lag (ARDL) approach shows little effect across countries and sectors, we decompose the impacts into positive and negative changes via the Nonlinear ARDL approach. The paper adds value in the following respects. The first is that while the overall macroeconomic determinants have a relatively weak connection to these trade balances, the strongest connections are in the primary-product-producing sectors. The second finding is that while most of these trade flows often depend on the country, the sector analyzed, and the method used, there are interesting, stylized results, including the region’s chemical sector and manufacturing in Hungary, for example. The third finding is that nonlinear models show cointegration between the real exchange rate and the trade balance in Visegrad-Germany trade for a higher number of industries, even though the long-run coefficients continue to be insignificant in many cases.

Clusters, defined as geographic concentrations of interconnected companies, and institutions, have evolved as crucial ecosystems supporting innovation. This work examines the impact of clusters on the innovation performance of companies, drawing insights from a benchmarking study of cluster initiatives in Poland. The study leverages data from a survey conducted by the Polish Agency for Enterprise Development (2022–2023), involving 41 clusters and 642 member firms. The analysis reveals that cluster participation significantly enhances firms’ innovation capabilities through mechanisms such as knowledge spillovers, access to specialized resources, collaborative projects, and pro-innovation services. However, challenges such as the risk of lock-in and uneven distribution of benefits are also highlighted. The originality of this article lies in its comprehensive, data-driven evaluation, utilizing a robust quantitative methodology, including principal component analysis and regression models, to explore how clusters foster innovation through specific mechanisms. The findings underscore the importance of supportive policies and continuous evaluation to maximize the innovation potential of clusters.