سیاست های بهینه پولشویی
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سیاست های بهینه پولشویی

عنوان فارسی مقاله: یک پایه اقتصاد خرد برای سیاست های بهینه پولشویی
عنوان انگلیسی مقاله: A microeconomic foundation for optimal money laundering policies
مجله/کنفرانس: بررسی بین المللی حقوق و اقتصاد – International Review of Law and Economics
رشته های تحصیلی مرتبط: اقتصاد
گرایش های تحصیلی مرتبط: اقتصاد مالی، اقتصاد پولی
کلمات کلیدی فارسی: پولشویی، شبکه های اجتماعی، سیاست ضد پولشویی
کلمات کلیدی انگلیسی: Money laundering، Social networks، Anti-money laundering policy
نوع نگارش مقاله: مقاله پژوهشی (Research Article)
نمایه: Scopus – Master Journals List – JCR
شناسه دیجیتال (DOI): https://doi.org/10.1016/j.irle.2019.105856
دانشگاه: Utrecht School of Economics, Utrecht University, the Netherlands
ناشر: الزویر - Elsevier
نوع ارائه مقاله: ژورنال
نوع مقاله: ISI
سال انتشار مقاله: 2019
ایمپکت فاکتور: 0.612 در سال 2019
شاخص H_index: 38 در سال 2020
شاخص SJR: 0.514 در سال 2019
شناسه ISSN: 0144-8188
شاخص Quartile (چارک): Q1 در سال 2019
فرمت مقاله انگلیسی: PDF
تعداد صفحات مقاله انگلیسی: 8
وضعیت ترجمه: ترجمه نشده است
قیمت مقاله انگلیسی: رایگان
آیا این مقاله بیس است: بله
آیا این مقاله مدل مفهومی دارد: ندارد
آیا این مقاله پرسشنامه دارد: ندارد
آیا این مقاله متغیر دارد: دارد
کد محصول: E14143
رفرنس: دارای رفرنس در داخل متن و انتهای مقاله
فهرست انگلیسی مطالب

Abstract


۱٫ Introduction


۲٫ The model


۳٫ Equilibrium


۴٫ The optimal budget-sharing rule


۵٫ Empirical application


۶٫ Conclusions


Appendix A. Supplementary data


References

نمونه متن انگلیسی مقاله

Abstract


In this paper, we present a game-theoretic analysis of social networks in the money laundering process. In our model, criminals compete against each other in a crime market, but collaborate with other criminals and “dishonest” workers in the attempt to launder their crime proceeds via covert money laundering ties. Our first result shows that in the equilibrium money laundering network, a core group of criminals spreads its total crime proceeds over as many money launderers as available, giving rise to a core-periphery network where the size of the core group depends on the relative profitability of crime versus the outside option wage. We then study the optimal decision of a law enforcement agency that aims to minimize the total criminal activity in this society. We derive an optimal sharing rule that shows how much of a given crime-fighting budget the agency should optimally spend on anti-crime and antimoney laundering policies, respectively. This budget-sharing rule can be quantified empirically using readily available estimates for the expected crime proceeds, outside option wages, and fines in a society. Our predictions for four European countries (Sweden, the Netherlands, Poland, and Spain) show that the optimal budget share spent on money laundering controls should be about 35%.


Introduction


In this paper, we present a game-theoretic analysis of social networks in the money laundering process. Our first objective is to develop a tractable model of a society of criminals and money launderers. In the model we propose, agents first choose to either enter the crime market or to pick up a legal job. Those who opt for a criminal career can subsequently form money laundering ties with every other active criminal and every “dishonest” worker, that is, every other agent who dropped out of the crime market. These ties help criminals to launder their anticipated crime proceeds, thereby reducing the probability that criminals are detected and prosecuted for a criminal act. Criminals, finally, determine their actual level of criminal activity, whereby they compete with other criminals for a booty of a fixed size. Our main finding on this game is that it has a unique subgame perfect Nash equilibrium when the costs of forming money laundering ties, which mainly consist ofthe risk of being detected and punished as a money launderer, are “not too high”. In this equilibrium, a core-periphery network emerges that connects all active criminals with every other criminal agent in the society. The equilibrium network thus shares some important features with real money laundering organizations in that the network is non-hierarchical and connects different criminals with each other (Kleemans, 2007).

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