Acta Universitatis Danubius. Œconomica, Vol 14, No 3 (2018)

Consequences of Money Laundering on Economic Growth – The Case of Kosovo and its Trade Partners

Alban Hetemi¹, Safet Merovci², Ozan Gulhan³

Abstract: The main purpose of this paper is to explore the impact of money laundering phenomenon on economic growth level, respectively focusing on Republic of Kosovo and its trade partners’ economic growth. In order to achieve this objective, the authors used a dynamic panel generalised methods of moments (GMM) technique. This paper provides results about measure of effect of money laundering on economic growth and the objective is to provide reasonable evidence that money laundering empirically impacts the macroeconomic indicators, respectively the economic growth of the country. When compare with past literature, similar results are found about negative effect of money laundering on economic growth. Through this paper, it is concluded that reductions in annual growth rates were associated with increases of variables related with money laundering. The key contribution of the paper is that it provides clear results about the effect of this phenomenon on economic growth which is very important for academics, researchers and universities. Moreover, the study is original and unique because puts Republic of Kosovo to the centre which is not studied in the past. As conclusion, through this paper is proved the hypothesis that money laundering has a significant effect on economic growth and this effect is negative.

Keywords: money laundering; crime; corruption; informal economy; economic growth.

JEL Classification: C39; C82; D73; E26; H26; O17

The views expressed in this study are those of authors and do not represent those of institutions where they are working in.

1. Introduction

The money laundering is a worldwide phenomenon which has started being treated since the beginning of deregulation of financial markets, where such a deregulation has made the countries more vulnerable to this subject. In particular, in today’s world the capital and fund movement takes place within seconds and with the usage of this high technology the money laundering process accelerates.

Nowadays, in the global market, free movement of capital between various countries enabled through the usage of a high technology (electronic banking) allow money launderers to move their funds (of a criminal origin) to different countries in order to conceal the origin of funds. Apart from the use of traditional ways of money laundering through the bank or other business activities, currently, due to the development of financial markets, various financial instruments (financial derivative instruments) are used in order to hide the origin of funds.

An interesting perspective is offered by Sterling Seagrave, a historian, in his book “The Lords of the Rim”, who explains how the merchants in China about 2000 years BC had hidden their wealth from the rulers who simply had taken away their wealth and had casted it out. Therefore, traders, besides hiding their wealth, have moved and invested it in businesses in distant provinces or even outside China. Thus, the so-called “offshore” industry term has originated, a term that refers to anything situated or located outside the national border. This includes doing business abroad in order to benefit from advantages of the countries having few or no rules at all.

The term “money laundering” refers to criminal proceeds and disguising illegal sources in order to use funds to perform legal or illegal activities. Put differently, we refer the money laundering as a process whereby making dirty money appears to be clean. Pietschmann, T. & Walker, J.R., (2011) has conducted a study to determine the size of illegal funds generated through drug trafficking and organized crime and to investigate to what extent these funds have been laundered. The report estimates that in 2009, criminal revenues amounted to 3.6% of global GDP, with 2.7% (or $ 1.6 trillion) laundered. This falls within the assessment cited extensively by the International Monetary Fund, which in 1998 stated that the total amount of money laundering in the world may be somewhere between 2 to 5% of GDP in the world, on annual basis.

The rule of law, regulatory quality and legal environment in general must be strengthened in order to fight and prevent money laundering and other financial crimes. Money laundering cases can be related to politics, suspicious businesses activities in construction and corruption and in such cases the relevant investigations must be conducted. The money laundering is related to other crimes as well, where generating of funds from these crimes is subject to money laundering and as aftermath these crimes must be treated and if possible prevented by the institutions of countries (specifically corruption and organized crime).

This paper addressed the issues of money laundering effect on economic growth of the country and provides scientific contribution to a number of relevant parties such as: Financial Intelligence Units, Central Banks, Police, Customs, Judicial and Prosecutorial Council etc., and certain groups of researchers of particular interest in this field.

The hypotheses tested through this paper provides information and recommendations to macroeconomic policy makers regarding how money laundering has impacted on economic growth and which steps shall be taken in order to mitigate risks of money laundering or prevent this phenomenon.

This paper is intended to provide a scientific input in taking additional measures from the country to reduce the negative effects of money laundering in the country, and thereby increase economic growth. The paper provides a further scientific contribution to literature in the field of money laundering as well as the importance of having in place a strong legal environment that combats this worldwide phenomenon with a particular focus in the Republic of Kosovo and other countries in scope. The paper examined if the money laundering influences the economic growth and this is tested using dynamic GMM estimation technique.

Paper continues as follows. Theoretical and empirical literature review is discussed in section 2. Methodology of the study, hypotheses and conceptual framework in section 3. The same section describes the variables used in the study. Section 4 performs various descriptive statistics, different statistics related to the dataset, dynamic GMM estimation and its tests. Section 5 concludes the study.

2. Literature Review

Past studies deals with different aspects of subject while using different scope of data and methodology.

Based on Bartlett (2002), money laundering has a significant impact on economic growth. Since through money laundering activities the funds have been re-directed from the sound and no risky projects to those of high risks, from productive to non-productive investments and crime and corruption are alleviated, then economic growth can be affected.

When a particular enterprise or industry is no longer attractive to money launderers, they simply tend to abandon it, potentially causing the fall of these sectors and serious damage to their respective economies. (McDowell, 2001)

Moreover, with its detrimental effect on financial institutions, which is crucial to economic growth and its impact on resource allocation, money laundering further reduces economic growth. (Tanzi, 1997)

Barro (1990) analysed the role of human capital in economic growth by using cross-section data of 98 countries for the period 1960-1985. He ran different regressions using different variables such as fertility rate, life expectancy, government expenditures, political instability, economic systems and market distortion. Growth rates were positively related to measures of political stability and inversely related to a proxy for market distortions. (Barro, 1991)

A study is conducted by Quirk (1996) which is performed for 18 developed countries for the period of 1983-90, and resulted in reductions in annual economic growth rates linked to increase of money laundering activities. He used a model developed initially by Barro (1991) for the effect of human capital on economic growth, but Quirk (1996) had included a variable for money laundering instead of human capital. Moreover Quirk (1996) expanded Barro’s model and replaced human capital with a money laundering variable, respectively the level of crime and found out that major differences between regressions was when he excluded government expenditure. Based on this study and results when he excluded government expenditure the effect became negative and significant and money laundering was closely and positively related with the level of government expenditure.

Based on Ferwerda and Bosma (2005), who measured the amount of money laundering in Netherlands, they have taken Quirk (1997) and Walker (1992-1999) models in order to issue the conclusion about the effect of money laundering on the economic growth of Netherlands. Based on their research and the above-mentioned variables, they have concluded that the overall effect of money laundering on growth is positive, while on the other hand the effect of the crime is negative, since the effect of the crime increases money laundering.

The danger of money laundering on the economy is not that directly it affects macroeconomic variables such as output, employment, or growth. The danger lies in the fact that money laundering increases crime and crime has negative effects on the economy. (Unger et al., 2006)

The FDI effect on economic growth was analysed by Barro (1990), Barrel & Pain (1997), Balasubramanyam et al., (1996), Borensztein et al., (1998) which was found positive. Grossman & Helpman (1993) emphasize that an increase in competition and innovation promote and has positive effect on economic growth in long run. Li and Liu (2005) found out positive effect of FDI on economic growth through its interaction with human capital in developing countries.

The effect of remittances on economic growth is analysed by Shera & Meyer (2016). Based on their study remittances have a positive impact on growth. Adams, Page & Acosta et al. (2005) argued that migrant remittances impact positively on the economic growth. Ratha (2005) concludes that remittances increase the consumption level of rural households, which might have substantial multiplier effects, because they are more likely to be spent on domestically produced goods and this finally impacts the economic growth. Giuliano & Ruiz-Arranz (2009) has found that remittances can enhance economic growth only in less financially developed countries. The importance of remittances inflow and its implication for economic growth is analysed by Qayyum & Javid et al, (2008) and they found out that the importance of remittance is crucial and positive on the social and economic conditions of the recipient country.

If referred to empirical literature, Devarajan et al, (1996) studied the effects of different expenditure component on growth and their study shows that public expenditure has positive impact on growth, while capital expenditure exerts negative impact on growth. Liu et al. (2008) analysed the relationship between GDP and public expenditure for United States of America and his study suggests that government expenditure causes growth of GDP, but, growth of GDP does not cause expansion of government expenditure. His conclusion is that public expenditure raises the US economic growth.

Pak Hung Mo (2001) found out that increase in the corruption level reduces the growth rate and as per his study the most important channel through which corruption affects economic growth is political instability.

Cabaravdic, A. & Nilsson, M. (2017) gives interesting results in their study where the relationship between economic growth and corruption was significant and positive. This implies that a higher level of corruption leads to a higher real gross domestic product per capita growth. As per their analysis they this is only short run effects and corruption may indeed grease the wheels of the economy in short-run.

The relationship between economic growth and corruption has been subjected to different studies. (Aidt, 2009; Levy, 2007; Khan, 2000) According to these studies and researchers corruption has a negative effect on economic growth which is increased substantially.

In this paper, as a starting point, it is considered the study of Quirk (1996) and as proxy of money laundering are used number of crimes, informal economy (as % of GDP) and corruption. In the model it is also included Foreign Direct Investment, Government Expenses and Remittances (as controlled variables) which impacts economic growth of the country.

3. Methodology Research, Hypotheses and Conceptual Framework

For the purposes of the paper, data ranging from 2008 to 2015 was used. The data was obtained from databases of reputable international institutions such as World Bank and United Nations Office on Drugs and Crimes. Moreover, we took into account also the study of Schneider & Medina (2017), Assessment on informal economy in Kosovo, EU Office and Business & Strategy of Europe 2014, Assessment on informal economy in Kosovo, EU Office and Business & Strategy of Europe 2017.

The paper included only countries mostly trading with the Republic of Kosovo. This list of countries is obtained from the Kosovo Agency of Statistics, based on Kosovo’s export and import with these countries. This list includes, Albania, Austria, Bosnia and Herzegovina, Bulgaria, China, Croatia, Czech Republic, France, Germany, Greece, India, Italy, Netherland, Poland, Romania, Slovenia, Switzerland, Turkey, United Kingdom and Kosovo as case study. Serbia and Macedonia are excluded because there was no available data about these countries.

The paper includes 8 years and 20 countries (with focus to Kosovo) and 6 variables. In total there are 160 observations.

The paper is based on studies of Quirk (1996) as well as Ferwerda & Bosma (2005). Moreover, we have consulted studies of Aninat et al. (2002), Bartlett (2002), Camdessus (1998), McDonell (1997), McDonell (2001), Tanzi (1997), Cabaravdic, A. and Nilsson, M. (2017).

Using the above variables listed, the main objective of our paper is to find out if the money laundering impacts/influences Kosovo and its trade partners’ economic growth. The hypotheses are listed as below:

H1: Money laundering has effect on the level of economic growth of the country.

H2: The effect of money laundering on economic growth is negative.

H3: Increase of crime, corruption and informal economy decrease the economic growth.

Since GDP is dependent variable, formula below applies:

GDP = f (FDI, R, G, IE, Cr, NC) (1)

In Table 1 the details about the variables used in the model is given.

Table 1. Variables used in the model and their expected effects

Source: Authors’ compilation

* Kosovo related data is obtained also from the Central Bank of Kosovo, Kosovo Agency of Statistics, National strategy of the Republic of Kosovo for the prevention of and fight against informal economy, money laundering, terrorist financing and financial crimes 2014-2018

The paper treats the impact of money laundering on economic growth in the country and this is estimated by using the equation below and dynamic GMM approach:

(2)

4. Data Structure and Results of the Model

In this chapter data structure will be presented which includes descriptive statistics, correlation matrix and information about the GMM technique will be presented and results of the model is discussed.

Table 2 shows the descriptive statistics of the dataset used in the analyses. According to results the average of dependent variable is 11.39 while maximum value is 13.00. Since most of the variables presented in natural logarithm descriptive statistics are close.

Table 2. Descriptive Statistics

Source: Authors’ compilation

Table 3 presents the correlation between variables. Results shows there are significant positive correlation between the dependent variable GDP and independent variables which are FDI, R, G CR and NC. On the other hand IE has a significant negative correlation with GDP. Correlation between other independent variables are significant but only exception is CR and R which the correlation is insignificant.

Table 3. Correlation Matrix

Source: Authors’ compilation

In order to define the consequences of money laundering on economic growth GMM method is used. The main idea of using GMM estimator is since the money laundering variables may change in periods, it gives more accurate results comparing to static panel. According to Baltagi (2008), dynamic relations characterised by if there is a delayed independent variable exists between regressions. When the lagged independent variable added to the model, constant or fixed effect models may present biased results because of autocorrelation. Instrumental variables are used for the variables which are correlated with error term.

The advantage of the GMM method is that it gives chance for estimation in case of autocorrelation, heteroscedasticity, multicollinearity and non-linear cases about parameters or variables. Standard and System GMM are two different forms of GMM estimator. System GMM (Arellano & Bover, 1995; Blundell & Bond, 1998) is the advanced version of standard GMM which is developed by Arellano ve Bond (1991). Standard GMM estimator (Arellano & Bover, 1995), uses the lagged levels of variables instrumental variables. In addition to that when the variables are close to random walk, lagged variables are weak to explain the concept. Because of this problem in System GMM method, lagged levels and also raw levels of the variables are present in the model. This method concludes with solving two equations. Moreover this method allows to use lagged levels of independent variables and also dependent variable as instrumental variables. Baltagi (2008) asserts that System GMM estimator gives more accurate and reasonable results.

Table 4 represents the results of GMM. According to the model control variables FDI, R and G have positive significant effect on economic growth. This conclusion is in line with the past literature. When the other three variables which are related to money laundering, IE and CR has a significant negative impact on GDP growth. On the other hand NC has a positive effect but the variable is statistically insignificant. Model results show that money laundering has a negative impact on economic growth.

Table 4. GMM Results

Source: Authors’ compilation

For the relevance of System GMM Arellano-Bond Serial Correlation Test is done. For the estimators to be relevant the probability of first level autocorrelation should be statistically significant and second level autocorrelation should be statistically insignificant. Table 5 shows the results of Arellano-Bond Serial Correlation Test, according to results we cannot reject the hypothesis that there is autocorrelation in first level however we can reject the hypothesis that there is autocorrelation in second level.

Table 5. Arellano-Bond Serial Correlation Test

Source: Authors’ compilation

5. Conclusion

The paper examines money laundering phenomenon, respectively the effect of money laundering on economic growth level of the country. The reader easily understands that relationship between money laundering and economic growth from now on is no longer a contestable matter in literature, but this matter has not received adequate attention. Therefore, with this paper we treat this relationship (money laundering – economic growth) using a dynamic panel GMM estimation technique. In our model we found out that money laundering has negative effect on the level of economic growth which is in line with the past literature. When money laundering was interacted with economic growth, the impact on economic growth was found negative and significant. As conclusion, through this paper is proved the hypothesis that money laundering has negative effect on economic growth.

The effect of control variables, foreign direct investments, remittances and government expenses, on economic growth was in line with theoretical predictions (was positive and significant). Informal economy and corruption which are two variables represents the money laundering has negative and significant effect on economic growth. On the other hand the effect of number of crimes is not as expected however it is not found statistically significant.

The main conclusion of this research is that countries should implement policies, laws, regulations and guidelines aimed at improving mechanism to combat and prevent money laundering in order to impact also economic growth.

In the future the studies can be expanded further by researching other effects of money laundering in the economy such as, impact on consumption and savings, changes in exports and imports, output/income and employment. Therefore, it is important to study the relationship between money laundering, money supply and demand.

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