Purpose

It is widely argued that money laundering (ML) is not a new phenomenon and the pervasiveness of ML is associated with some severe economic, social and political costs. Due to the lack of studies on the ML’s issue in the UAE, this study aims to examine the determinants of ML in the country between 1975 and 2020.

Design/methodology/approach

The autoregressive distributed lag bounds testing results demonstrate the presence of long-run relationship between ML and the selected macroeconomics variables. The analysis is validated by the dynamic ordinary least squares, the fully modified ordinary least squares and the canonical co-integration regression estimators.

Findings

The estimation result reveals that while the real estate market, outflow of money, arms procurement and size of the underground economy influences the size of ML positively, gold trade, the level of financial development and the size of economic activities are negatively associated with ML, both in the short- and long-run.

Originality/value

Up to date from a country-level analysis, no study has been devoted to the ML in UAE, except for Aljassmi et al. (2023). To the best of the authors’ knowledge, this study is the first to investigate the determinants of laundered money in the UAE economy. Based on these outcomes, strategies and measures which will deter the laundering of illicit funds through the real estate and gold market, remittance system, financial system and arms procurement contracts in the UAE are recommended.

中文翻译：

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阿拉伯联合酋长国（UAE）洗钱的决定因素分析

目的

人们普遍认为，洗钱 (ML) 并不是一种新现象，洗钱的普遍存在与一些严重的经济、社会和政治成本有关。由于缺乏对阿联酋洗钱问题的研究，本研究旨在探讨 1975 年至 2020 年间该国洗钱的决定因素。

设计/方法论/途径

自回归分布式滞后边界测试结果证明了机器学习与所选宏观经济变量之间存在长期关系。该分析通过动态普通最小二乘法、完全修改的普通最小二乘法和典型协整回归估计器进行验证。

发现

估算结果表明，房地产市场、资金外流、武器采购、地下经济规模对ML规模有正向影响，而黄金贸易、金融发展水平、经济活动规模则与ML负相关。无论是短期还是长期。

原创性/价值

迄今为止，从国家层面的分析来看，除了 Aljassmi等人之外，还没有专门针对阿联酋 ML 的研究。（2023）。据作者所知，这项研究是第一个调查阿联酋经济中洗钱的决定因素的研究。根据这些结果，建议采取战略和措施，阻止通过阿联酋的房地产和黄金市场、汇款系统、金融系统和武器采购合同洗钱非法资金。