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  4. Forecasting Candidate States’ Compliance with EU Accession Rules, 2017–2050
 
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Forecasting Candidate States’ Compliance with EU Accession Rules, 2017–2050

Journal
Journal of European Public Policy
ISSN
1350-1763
ISSN-Digital
1466-4429
Type
journal article
Date Issued
2018-09-17
Author(s)
Böhmelt, Tobias
Freyburg, Tina  
DOI
10.1080/13501763.2017.1348385
Abstract
The European Union (EU) is said to be tired of enlargement – but how likely is it that a candidate would be ready to join within 10, 15 or more years? This research forecasts how prospective members are likely able to perform in implementing EU law until 2050. Using compliance data of all states from the 2004, 2007 and 2013 accession rounds, as well as of five current/potential candidates, we construct an empirical model on candidates’ ability to comply with the acquis communautaire. We employ in-sample and out-of-sample techniques to ensure high model prediction accuracy and, ultimately, forecast the five candidates’ potential compliance levels in 2017–2050. Our research shows that only one candidate might sufficiently be able to comply with the accession criteria until 2023, while many are unlikely to be ready before the mid-2030s. Focusing on prediction and forecasting, our contribution is given by the research’s policy relevance and its methodological innovation.
Language
English
HSG Classification
contribution to scientific community
HSG Profile Area
SEPS - Global Democratic Governance
Refereed
Yes
Publisher
Taylor & Francis
Publisher place
London
Volume
25
Number
11
Start page
1667
End page
1685
Official URL
https://www.tandfonline.com/doi/pdf/10.1080/13501763.2017.1348385?needAccess=true
URL
https://www.alexandria.unisg.ch/handle/20.500.14171/100024
Subject(s)

economics

political science

social sciences

Division(s)

SEPS - School of Econ...

IPW - Institute for P...

Eprints ID
254042
File(s)
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open.access

Name

XX_Forecasting candidate states compliance with EU accession rules 2017 2050.pdf

Size

1.54 MB

Format

Adobe PDF

Checksum (MD5)

a38a0f1e19746a3ade21c471b9708c7b

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