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www.ssoar.info The Determinants of Economic Competitiveness Kluge, Jan; Lappöhn, Sarah; Plank, Kerstin Veröffentlichungsversion / Published Version Arbeitspapier / working paper Empfohlene Zitierung / Suggested Citation: Kluge, J., Lappöhn, S., & Plank, K. (2020). The Determinants of Economic Competitiveness. (IHS Working Paper, 24). Wien: Institut für Höhere Studien (IHS), Wien. https://nbn-resolving.org/urn:nbn:de:0168-ssoar-71205-9 Nutzungsbedingungen: Terms of use: Dieser Text wird unter einer CC BY Lizenz (Namensnennung) zur This document is made available under a CC BY Licence Verfügung gestellt. Nähere Auskünfte zu den CC-Lizenzen finden (Attribution). For more Information see: Sie hier: https://creativecommons.org/licenses/by/4.0 https://creativecommons.org/licenses/by/4.0/deed.de IHS Working Paper 24 October 2020 The Determinants of Economic Competitiveness Jan Kluge Sarah Lappöhn Kerstin Plank Author(s) Jan Kluge, Sarah Lappöhn, Kerstin Plank Editor(s) Robert M. Kunst Title The Determinants of Economic Competitiveness Institut für Höhere Studien - Institute for Advanced Studies (IHS) Josefstädter Straße 39, A-1080 Wien T +43 1 59991-0 F +43 1 59991-555 www.ihs.ac.at ZVR: 066207973 Funder(s) OeNB Anniversary Fund License „The Determinants of Economic Competitiveness“ by Jan Kluge, Sarah Lappöhn, Kerstin Plank is licensed under the Creative Commons: Attribution 4.0 License (http://creativecommons.org/licenses/by/4.0/) All contents are without guarantee. Any liability of the contributors of the IHS from the content of this work is excluded. All IHS Working Papers are available online: https://irihs.ihs.ac.at/view/ihs_series/ser=5Fihswps.html This paper is available for download without charge at: https://irihs.ihs.ac.at/id/eprint/5455/ The Determinants of Economic ∗ Competitiveness ∗∗ Jan Kluge Sarah Lappöhn Kerstin Plank October 14, 2020 This paper aims at identifying relevant indicators for TFP growth in EU coun- tries during the recovery phase following the 2008/09 economic crisis. We proceed in three steps: First, we estimate TFP growth by means of Stochastic Frontier Analysis (SFA). Second, we perform a TFP growth decomposition in order to get measures for changes in technical progress (CTP), technical efficiency (CTE), scale efficiency (CSC) and allocative efficiency (CAE). And third, we use BART – a non-parametric Bayesian technique from the realm of statistical learning – in order to identify relevant predictors of TFP and its components from the Global Competitiveness Reports. We find that only a few indicators prove to be stable predictors. In par- ticular, indicators that characterize technological readiness, such as broad- band internet access, are outstandingly important in order to push technical progress while issues that describe innovation seem only to speed up CTP in higher-income economies. The results presented in this paper can be guidelines to policymakers as they identify areas in which further action could be taken in order to increase economic growth. Concerning the bigger picture, it becomes obvious that advanced machine learning techniques might not be able to replace sound economic theory but they help separating the wheat from the chaff when it comes to selecting the most relevant indicators of economic competitiveness. Keywords: Competitiveness,TFPgrowth,StochasticFrontierAnalysis,BART JEL classification: C23, E24, O47 ∗ TheauthorswishtothanktheOesterreichischeNationalbank(OeNB)foritsgenerousfinancialsupport (Anniversary Fund, project no. 17686) ∗∗ Corresponding author. Institute for Advanced Studies, Josefstädter Straÿe 39, 1080 Vienna, Austria, kluge@ihs.ac.at
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