Rodyti trumpą aprašą

dc.contributor.authorMačiulytė-Šniukienė, Alma
dc.contributor.authorButkus, Mindaugas
dc.contributor.authorMacaitienė, Renata
dc.contributor.authorDavidavičienė, Vida
dc.date.accessioned2023-09-18T16:26:28Z
dc.date.available2023-09-18T16:26:28Z
dc.date.issued2022
dc.identifier.issn2227-7390
dc.identifier.other(crossref_id)143454561
dc.identifier.urihttps://etalpykla.vilniustech.lt/handle/123456789/113991
dc.description.abstractOne of the priority areas of the EU is infrastructure development. Over 2021–2027, it is planned to allocate more than 116 billion EUR of support from EU structural funds to transport and ICT infrastructure. For investments to promote the growth of lagging regions and reduce regional disparities, investments must be efficiently allocated. Considering limitations of previous studies, this study aims to provide recommendations for policymakers regarding infrastructure investment allocation after assessing the non-linear relationships between transport and ICT infrastructure development and convergence of EU MS NUTS2 regions. The general specification for estimations is based on the neoclassical conditional beta-convergence model. Additionally, a non-linear specification with interactions is developed to estimate the effect of infrastructure development on convergence. We used Generalized Methods of Movement estimator for the robustness check to reduce possible endogeneity bias. Estimations indicated that a non-linear relationship between infrastructure development and convergence is present. We have found strong evidence of the diminishing marginal effect of infrastructure development on convergence and have identified a tipping point after which infrastructure development slows down convergence, i.e., convergence is still present but at a slower rate. The study results made it possible to present several essential recommendations to policymakers that would increase the effectiveness of investments in infrastructure. Investments should be distributed according to smaller regional units, i.e., NUTS 2 level. The optimal level of infrastructure development that ensures convergence of regions for each type of infrastructure has to be established to ensure that the investments are not too intense and to generate the maximum potential outcomes.eng
dc.formatPDF
dc.format.extentp. 1-23
dc.format.mediumtekstas / txt
dc.language.isoeng
dc.relation.isreferencedbyScience Citation Index Expanded (Web of Science)
dc.relation.isreferencedbyDOAJ
dc.relation.isreferencedbyRePec
dc.relation.isreferencedbyEconPapers
dc.relation.isreferencedbyScopus
dc.source.urihttps://www.mdpi.com/2227-7390/11/1/1
dc.titleInfrastructure and EU regional convergence: What policy implications does non-linearity bring?
dc.typeStraipsnis Web of Science DB / Article in Web of Science DB
dcterms.accessRightsThis article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/ 4.0/).
dcterms.licenseCreative Commons – Attribution – 4.0 International
dcterms.references50
dc.type.pubtypeS1 - Straipsnis Web of Science DB / Web of Science DB article
dc.contributor.institutionVilniaus Gedimino technikos universitetas
dc.contributor.institutionVilniaus universitetas
dc.contributor.facultyVerslo vadybos fakultetas / Faculty of Business Management
dc.subject.researchfieldS 004 - Ekonomika / Economics
dc.subject.researchfieldS 003 - Vadyba / Management
dc.subject.researchfieldN 001 - Matematika / Mathematics
dc.subject.studydirectionJ01 - Ekonomika / Economics
dc.subject.studydirectionL02 - Vadyba / Management studies
dc.subject.vgtuprioritizedfieldsEV03 - Dinamiškoji vadyba / Dynamic Management
dc.subject.ltspecializationsL103 - Įtrauki ir kūrybinga visuomenė / Inclusive and creative society
dc.subject.eninfrastructure
dc.subject.entransport infrastructure
dc.subject.enICT infrastructure
dc.subject.enconvergence
dc.subject.enNUTS2 regions
dc.subject.enpolicy implications
dcterms.sourcetitleMathematics: Special issue: Frontiers in economic analysis and statistics modeling with applications to social sciences
dc.description.issueiss. 1
dc.description.volumevol. 11
dc.publisher.nameMDPI
dc.publisher.cityBasel
dc.identifier.doi143454561
dc.identifier.doi000908881300001
dc.identifier.doi10.3390/math11010001
dc.identifier.elaba150372640


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