The magnitude and consequences of money laundering
| Date | 11 January 2023 |
| Pages | 808-824 |
| DOI | https://doi.org/10.1108/JMLC-09-2022-0139 |
| Published date | 11 January 2023 |
| Author | Sadia Nazar,Abdul Raheman,Muhammad Anwar ul Haq |
The magnitude and consequences
of money laundering
Sadia Nazar,Abdul Raheman and Muhammad Anwar ul Haq
Department of Management Sciences, University of Gujrat –Hafiz Hayat Campus,
Gujrat, Pakistan
Abstract
Purpose –This study aims to estimate the amount of money laundering (ML) with multiple proxy
approaches and measure the effects of ML on various indicators of the economic and financial sectors.
Theoreticaljustifications are recruited from the parasite theoryof organised crime.
Design/methodology/approach –A quantitative research methodologywas used on abalanced panel
data set to test the study’s hypothesis through generalised method of moment (GMM). The studysample
consistedof 77 countries, and the data was collected for 15years (2005–2019).
Findings –A study has found that 1.23%of global gross domestic product is laundered yearly, and there is
no noticeable decline in ML activities. Further study has also found that ML has devastating effects on
countries, government revenue, foreign investment, economic development, political and peace conditions,
bank liquidity, interestrate volatility and exchange rate volatility. Thestudy has not witnessed the negative
consequenceof ML on countries’inflation rates.
Practical implications –Estimates of the study guide policymakersabout the volume of resources
fleeing and helps them to decide the level of response needed. Further findings help them prioritise the
responsesystem according to the area most affected.
Originality/value –This study is an originalcontribution by the authors and has studied the effectsof ML
by computingthe amount of ML by four different proxies.
Keywords Money laundering (ML), Multiple proxy, Consequences of ML,
GMM (generalised method of moment)
Paper type Research paper
1. Introduction
Crimes generate illegal funds, amounting to 3.6% of world gross domestic product (GDP),
and 2.7% is laundered (UNODC, 2003). This massive amount of money laundering (ML) is
almost equal to the GDP of the world’s third-largest economy UK (Moody, 2013). In recent
years, the dilemma of ML has become an area of concern for policymakers and academic
researchers due to its devastating effects on all economies, regardless of size and income
status. And further, due to globalisation and technological advancements consequences of
ML in societies, economies and financial systems are intensified (M. and Truman, 2014). In
short, ML has evolved into a parasite affecting societies, economies and financial
infrastructureworldwide (Quirk, 1997b).
ML is a straightforward concept; it is considered the procedure to obscure the source of
illegitimate funds and convert them into legitimate funds. FATF defines ML as “The
processing of criminal proceeds to disguise their illegal origin to legitimise the ill-gotten
gains of crime”(FATF, 2003). The area of ML should be studied at the institutional and
academic levels for several reasons. Firstly, ML is all about deception, and due to its
deceptive nature, the actualand exact amount of ML is unknown. And it is very rightly said
that “what does not get measured does not get done”.So to get rid of it, we need to quantify
it, which requires attention to this issue. Secondly, the far-reaching effects of ML on all
JMLC
27,5
808
Journalof Money Laundering
Control
Vol.27 No. 5, 2024
pp. 808-824
© Emerald Publishing Limited
1368-5201
DOI 10.1108/JMLC-09-2022-0139
The current issue and full text archive of this journal is available on Emerald Insight at:
https://www.emerald.com/insight/1368-5201.htm
economies and sectors indicateits severity. And the statement of the chairperson of the IMF
Interim Committee (highest decision-making authority), “money laundering as one of the
most serious issues facing the internationalfinancial community”, indicates the urgency and
seriousness of this issue. Thirdly to design and implement a practical policy framework
against it, its mechanism and size should be understood to discourage criminals by
recovering or confiscating lost assets. The problem is that due to the clandestine nature of
ML, there is no universal estimate of its amountavailable. Further, the magnitude and level
of effects on different economic and financial sector indicators are needed to have an
effective anti-ML framework. The purpose of the study is two-fold: to estimate the
magnitude of ML. And second is to assess the effects of ML on financial and economic
indicators.
The study has been designed to help policymakers and academicians in copious ways.
The estimates regardingthe amount of MLguide policymakersabout the volume of national
resources fleeing from the country and helps to decide the level of response needed.Further
study has identified the areas more affected by ML in the economic and financial sectors;
this can help policymakers prioritise the response system against ML.Moreover, it will
bring the attention of researchersor scholars to this negated or less explored.
2. Literature review
In practice, the issue of converting illicit money into legitimate form has been recognised
since 1950, and some serious steps against it have been taken in the past 20years. But in
academia, ML is still a less exploredor neglected field, particularly in finance and economics
(Brigitte Unger, 2009). The available literature on ML is mostly qualitative and follows
themes such as ML procedures and techniques or the effectiveness of anti-ML regulations
(Freckleton, 2019). A limited number of studies attempt to explore its determinants and
consequences empirically. This study explores this less explored dimension of ML: the
amount estimationand itsconsequences.
2.1 The amount of money laundering
“What does not get measured, does not get done”(Unger, 2009). ML takes place almost
everywhere in the world. Proper estimation regarding the size of this global ill is also
necessary to build a practicalpolicy framework against it. Various approaches are available
to estimate the magnitude of ML, but each method serves a differentpurpose and has area-
specific assumptions. Even though several techniques are available to estimate the volume
of ML, it is still challenging to generateundisputable estimates because of its hidden nature
(FATF, 2007). Given the clandestine nature of ML, measuring the amount of money being
laundered is difficult. For this reason, and aware of all the caveats, the study adopted the
approach of calculatingseveral different proxies using different methodologiesfor obtaining
estimates of the amount of ML. One should use various measure approaches to capture the
size of this mysterious phenomenon, also suggested by L. M. and F. Schneider (2018). To
measure the “amount of money laundering”(ML) for this study following methods have
been used:
World Bank residual model: This approach was first presented by (Colago et al.,
1985) in the World Development Bank Report 1985; it is based on cross-border
illegal financial flows. The recorded differences in a country’s sources (inflows) and
usage (outflows) of funds are considered illegal financial flow or ML.
IMF Shadow Economy (MIMIC approach): The shadow economy (SE) has been
labelled with many names, such as the informal or hidden economy (IMF, 2006). It is
Consequences
of money
laundering
809
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