Author
Listed:
- Yıldırım, Engin
- Sert, Mehmet Fatih
- Kartal, Burcu
- Çalış, Şuayyip
Abstract
The paper investigates all (971) non-executed pending leading cases of the European Court of Human Rights (ECtHR) between 2012 and 2020 through Machine Learning (ML) techniques. Drawing on the extant scholarship, our interest on compliance has centred on state level and case level variables. For the identification of important variables, four databases have been used. Each country party to the European Convention on Human Rights (ECHR) received 232 distinct factors for eight years. Since we aim to make a parameter estimation for a high-dimensional data set, Simulated Annealing (SA) is employed as feature selection method. In the state level analysis, Support Vector Regression (SVR) model has been applied yielding the coefficients of the variables, which have been found to be important in spelling out non-compliance with the ECtHR decisions. For the case level analysis, different cluster techniques have been utilized and the countries have been grouped into four different clusters. We have found that the states that have relatively high levels of equality before the law, protection of individual liberties, social class equality with regard to enjoying civil liberties, access to justice and free and autonomous election management arrangements, are less susceptible to non-compliance of the decisions of the ECtHR. For the case level analysis, type of violated rights, the existence of dissent in the decision and dissenting votes of national judges for their appointing states affect the compliance behaviour of the states. In addition, a notable result of the research is that if a national judge casts a dissenting vote against the violation judgment of the ECtHR involving the state that appointed him/her, the judgment is likely not to be executed by the respondent state.
Suggested Citation
Yıldırım, Engin & Sert, Mehmet Fatih & Kartal, Burcu & Çalış, Şuayyip, 2023.
"Non-compliance of the European Court of Human Rights decisions: A machine learning analysis,"
International Review of Law and Economics, Elsevier, vol. 76(C).
Handle:
RePEc:eee:irlaec:v:76:y:2023:i:c:s0144818823000455
DOI: 10.1016/j.irle.2023.106167
Download full text from publisher
As the access to this document is restricted, you may want to search for a different version of it.
Corrections
All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:irlaec:v:76:y:2023:i:c:s0144818823000455. See general information about how to correct material in RePEc.
If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.
We have no bibliographic references for this item. You can help adding them by using this form .
If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/irle .
Please note that corrections may take a couple of weeks to filter through
the various RePEc services.