Assessing the level of digitalization and robotization in the enterprises of the European Union Member States.

One of the main reasons for the dynamic global economic development observed in recent years is the process of digitalization, referred to as Industry 4.0. The significance of digitalization for this development is appreciated by the EU-27. In order for these actions to be effective, it is necessary to diagnose the current level of digitalization in the EU-27countries. The article presents the results of the assessment of the level of digitalization of enterprises in the EU-27 countries. An empirical analysis was conducted using 16 determinants which describe the digitalization in a sample of 27 EU countries. Based on the adopted criteria and the Technique for Order Preference by Similarity to an Ideal Solution method, these countries were divided into four classes in terms of the level of digitalization. The analysis looked at the size of enterprises and was performed independently for small, medium and large enterprises. The adopted indicators allowed for the analysis of similarity between the EU-27 countries in terms of digitalization, using the Kohonen's networks. The result of this research was the division of the EU-27 countries into groups, also taking into account the size of studied enterprises. Due to the immensely diverse EU-27 economy, such a huge undertaking as the digital transformation process requires building logical internal "digital coalitions". The designated assessment and similarity between countries creates such opportunities, also in terms of building an effective policy to support these processes by the EU. This increases the chances of success of joint ventures and building a sustainable European community based on the latest technologies.

Return and volatility transmission between oil price shocks and agricultural commodities.

This paper studies the connectedness between oil price shocks and agricultural commodities. Our sample period ranges from January 2002 to July 2020, covering the three global crises; Global Financial Crisis, the European sovereign debt crisis and Covid-19 pandemic crisis. We employ Granger causality tests, and the static and dynamic connectedness spillover index methodology. We find that the shocks in oil prices are Granger-caused mainly by price changes of grains, live cattle, and wheat, while supply shock granger causes variations mostly in grain prices. We find that, from the point of view of static connectedness, for both, price and volatility spillovers, the livestock is the largest transmitter, while the lean hogs are the major receiver. Our dynamic analysis evidences that connectedness increases during the financial crisis period. Our results are potentially useful for investors, portfolios managers and policy makers.