Evidence-based economic policy at the regional level

Abstract

This article revisits approaches to regional development by exploring both previously proposed and new policy opportunities for regions facing the greatest challenges in adapting to emerging geo-economic conditions. This revision is based on the methodology of comparative analysis of discrete structural (institutional) alternatives - an essential component for ensuring the necessary evidential level in selecting economic policy instruments, complementing other applied research tools. The Kaliningrad region is one of Russia's most complex due to its geographical isolation and historical background. The most comprehensive and consistent review of development options, or structural alternatives, for this area is found in the works of Gennady Fedorov, a professor at the Immanuel Kant Baltic Federal University. This study elucidates the need to draw on the ideas of regional and spatial economic development of the Kaliningrad region reflected in the works of Prof. Fedorov and his colleagues from 1991 to 2023, when developing scenarios for Russia's westernmost region. The main advantage of their findings is that they are presented through the lens of interdisciplinary discourse, utilising concepts from new institutional economic theory to provide an economic perspective. This study reveals the fundamental ideas behind the concept of the geo-demographic situation, the so-called 'Fedorov matrix' highlighting structural alternatives for the development of the Kaliningrad region and the spatially distributed clusters. The article examines the three main development strategies of the Kaliningrad region, as analysed by Fedorov, to trace the evolution of the region's economic activity regulation regime. A conclusion is drawn regarding the demand for industrial policy instruments for the development of the region's economy, while also emphasising their insufficient efficiency in application. The viability of Fedorov's forecasts, as outlined in his works, is assessed through the example of planning a spatially distributed tourism and recreation cluster.