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Compliance with International Soft Law
Author(s) -
Michael D’Rosario,
John Zeleznikow
Publication year - 2018
Publication title -
international journal of strategic decision sciences
Language(s) - English
Resource type - Journals
eISSN - 1947-8569
pISSN - 1947-8577
DOI - 10.4018/ijsds.2018070101
Subject(s) - soft law , normative , corporate governance , extant taxon , estimation , government (linguistics) , set (abstract data type) , artificial neural network , computer science , compliance (psychology) , perceptron , law , international law , artificial intelligence , political science , econometrics , psychology , economics , social psychology , finance , linguistics , philosophy , management , evolutionary biology , biology , programming language
The present article considers the importance of legal system origin in compliance with ‘international soft law,' or normative provisions contained in non-binding texts. The study considers key economic and governance metrics on national acceptance an implementation of the first Basle accord. Employing a data set of 70 countries, the present study considers the role of market forces and bilateral and multi-lateral pressures on implementation of soft law. There is little known about the role of legal system structure-related variables as factors moderating the implementation of multi-lateral agreements and international soft law, such as the 1988 accord. The present study extends upon research within the extant literature by employing a novel estimation method, a neural network modelling technique, with multi-layer perceptron artificial neural network (MPANN). Consistent with earlier studies, the article identifies a significant and positive effect associated with democratic systems and the implementation of the Basle accord. However, extending upon traditional estimation techniques, the study identifies the significance of savings rates and government effectiveness in determining implementation. Notably, the method is able to achieve a superior goodness of fit and predictive accuracy in determining implementation.

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