REGION https://openjournals.wu-wien.ac.at/ojs/index.php/region <p>REGION - the journal of ERSA, powered by WU, is a peer reviewed scientific journal for the global exchange of knowledge in Regional Science, Regional Economics, Economic Geography and related areas.</p> en-US <p>REGION is an open journal, and uses the standard Creative Commons license: <strong> Copyright</strong> We want authors to retain the maximum control over their work consistent with the first goal. For this reason, authors who publish in REGION will release their articles under the <a href="http://creativecommons.org/licenses/by/4.0/">Creative Commons Attribution</a> license. This license allows anyone to copy and distribute the article provided that appropriate attribution is given to REGION and the authors. For details of the rights authors grant users of their work, see the <a href="http://creativecommons.org/licenses/by/4.0/">"human-readable summary" of the license</a>, with a link to the full license. (Note that "you" refers to a user, not an author, in the summary.) Upon submission, the authors agree that the following three items are true: 1) The manuscript named above: a) represents valid work and neither it nor any other that I have written with substantially similar content has been published before in any form except as a preprint, b) is not concurrently submitted to another publication, and c) does not infringe anyone’s copyright. The Author(s) holds ERSA, WU, REGION, and the Editors of REGION harmless against all copyright claims. d) I have, or a coauthor has, had sufficient access to the data to verify the manuscript’s scientific integrity. 2) If asked, I will provide or fully cooperate in providing the data on which the manuscript is based so the editors or their assignees can examine it (where possible) 3) For papers with more than one author, I as the submitter have the permission of the coauthors to submit this work, and all authors agree that the corresponding author will be the main correspondent with the editorial office, and review the edited manuscript and proof. If there is only one author, I will be the corresponding author and agree to handle these responsibilities.</p> F.Rowe-Gonzalez@liverpool.ac.uk (Francisco Rowe) gunther.maier@wu.ac.at (Gunther Maier) Tue, 30 Jan 2024 14:47:51 +0000 OJS 3.3.0.8 http://blogs.law.harvard.edu/tech/rss 60 Labour Market Effects of Trade in a Small Open Economy https://openjournals.wu-wien.ac.at/ojs/index.php/region/article/view/466 <p>Austria is a small open economy that in the last decades underwent two different waves of increasing trade integration: one with Eastern Europe and one with China. Drawing on trade theory, this paper studies the effects of increases in trade with China and Eastern Europe on labour market dynamics in Austrian NUTS-4 regions for two ten-year periods between 1995 and 2015. Given the limited data available, the current analysis could not identify significant effects on aggregate labour dynamics neither for rising imports from Eastern Europe or China, nor for rising exports to Eastern Europe. However, there is weak evidence that exports to China have facilitated employment growth, especially in high quality segments. Overall, these results add a cautious perspective to the discussion of import competition.</p> Agnes Kügler, Klaus Friesenbichler, Cornelius Hirsch Copyright (c) 2024 Agnes Kügler, Klaus Friesenbichler, Cornelius Hirsch https://creativecommons.org/licenses/by/4.0 https://openjournals.wu-wien.ac.at/ojs/index.php/region/article/view/466 Wed, 31 Jan 2024 00:00:00 +0000 The influence of underlying conditions of countries on the COVID-19 lethality rate https://openjournals.wu-wien.ac.at/ojs/index.php/region/article/view/491 <p>The management of the COVID-19 pandemic not only depends on the stringency measures established by governments but also and more importantly on the underlying capacity of territories in economic and health and sanitary infrastructure. This study aims to identify how the underlying conditions of countries influence on their level of COVID-19 lethality rate. To do so, a classification of countries is first conducted by the means of the k-means partitioning method, using COVID-19-related variables such as the lethality rate, the contagion growth rate and the number of days with respect to China. Based on the resulting groups of countries of the first stage, Tobit and Ordinary Least Squares regressions are estimated to determine the effect of the underlying characteristics of countries on their COVID-19 lethality rate. Risks factors which increase the lethality rate in countries are the contagion growth rate, the trade flow with China, the age composition of the population and, to a lesser extent, the population density. Factors that help to reduce the lethality rate are the government effectiveness, the health infrastructure (hospital beds) and, to a lesser extent, the economic growth rate.</p> Grace Carolina Guevara Rosero, Eymy Coralia Illescas Navarrete Copyright (c) 2024 Grace Carolina Guevara Rosero, Eymy Coralia Illescas Navarrete https://creativecommons.org/licenses/by/4.0 https://openjournals.wu-wien.ac.at/ojs/index.php/region/article/view/491 Tue, 19 Mar 2024 00:00:00 +0000 Bioeconomy firms and where to find them https://openjournals.wu-wien.ac.at/ojs/index.php/region/article/view/523 <p>The bioeconomy represents a transformative approach to economic development and sustainability by harnessing biological resources and knowledge to produce goods, services, and energy while reducing dependence on non-renewable resources. In order to understand and support the bioeconomy, scholars and policymakers rely on an accurate measurement and monitoring of biobased economic activities. However, existing statistical frameworks and industry classifications often fall short in capturing the unique characteristics and complexities of the bioeconomy. This article addresses this challenge by developing a methodological approach for comprehensive measurement and mapping of biobased economic activities. We build a novel data set of bioeconomy firms in Germany using web-mining and machine learning techniques. This data set enables detailed analysis of biobased economic activities, providing valuable insights into the spatial organization of the bioeconomy. The paper demonstrates the applicability of the data set by testing several stylized facts about the bioeconomy. Our research contributes to a better understanding of the bioeconomy's regional impacts and offers a valuable resource for policymakers and researchers interested in understanding the geography of biobased economic activities. We make an aggregated version of the data set freely available online.</p> Lukas Kriesch, Sebastian Losacker Copyright (c) 2024 Lukas Kriesch, Sebastian Losacker https://creativecommons.org/licenses/by/4.0 https://openjournals.wu-wien.ac.at/ojs/index.php/region/article/view/523 Mon, 08 Apr 2024 00:00:00 +0000