The Empirical Analysis of Money Laundering Trend in Iran (by Using PLS-SEM Method )

Maddah, Majid; Sinaeyan, Mahla

doi:10.29252/jemr.10.40.99

Volume 11, Issue 40 (6-2020) jemr 2020, 11(40): 99-122 | Back to browse issues page

‎ 10.29252/jemr.10.40.99

‎ 20.1001.1.22286454.1399.10.40.6.5

Mendeley

Zotero

RefWorks

Maddah M, Sinaeyan M. The Empirical Analysis of Money Laundering Trend in Iran (by Using PLS-SEM Method ). jemr 2020; 11 (40) :99-122
URL: http://jemr.khu.ac.ir/article-1-2046-en.html

The Empirical Analysis of Money Laundering Trend in Iran (by Using PLS-SEM Method )

Majid Maddah ^*

¹, Mahla Sinaeyan²

1- Semnan University , majid.maddah@semnan.ac.ir
2- Semnan University

Abstract: (4203 Views)

Money Laundering (ML) reduces the confidence of investors to the financial market, worsen political instability and deviates resources allocation to unproductive sectors by weakening of financial institutions credit. In the ML, illegal resources are entered into the legal economy secretly and outside formal control whereby it has hidden nature. The aim of this paper is to study the different sizes ML and its changes in Iranian economy in the context of latent variables literature using Partial Least Squares Structural Equation Modeling (PLS-SEM) over the period 1360 to 1396. According to the results from paper firstly, drug trafficking and theft crimes have a positive and significant effect on ML trend. Besides that, economic conditions influence an individual’s motivation to enter illegal activities. Secondly, ML growth is associated with decreasing economic growth and increasing the volume of cash that waste economic stability. Thirdly, ML has an upward trend which based on it can be anticipated that in spite of crimes growth, especially drug trafficking, the increasing trend of ML will continue.

Keywords: Money Laundering, Crime, Structural Equation Modeling, Iran

Full-Text [PDF 1871 kb] (1812 Downloads)

Type of Study: Applicable | Subject: سایر
Received: 2020/03/8 | Accepted: 2020/08/26 | Published: 2020/09/22

References

1. Aluko, A. & Bagheri, M. (2012). The impact of money laundering on economic and financial stability and on political development in developing countries. Journal of Money Laundering Control, 15(4), 442-457. [DOI:10.1108/13685201211266024]

2. Amroabadi Sadeghi, B., Googerdchian, A. & Shahbazi, N. (2012). Empirical analysis of money laundering shocks on economic growth, government expenditure and income inequality in IRAN. Strategic Research on Social Problems in Iran, 1(1), 97-111. (In Persian).

3. The Parliament of Islamic Republic of Iran, Anti-Money Laundering Amendment Act, 2019.1.23. (available at: https://rc.majlis.ir/fa/law/show/1107413). (In Persian).

4. Arab Mazar Yazdi, A. & Khodkari, L. (2007). Estimating dirty money in Iran. Journal of Economic Research, 27(7), 119-142. (In Persian).

5. Argentiero, A., Bagella, M. & Busato, F. (2008). Money laundering in a two-sector model: using theory for measurement. European Journal of Law and Economics, 26(3), 341-359. [DOI:10.1007/s10657-008-9074-6]

6. Blackburn, K., Neanidis, K. C. & Rana, M. P. (2017). A theory of organized crime, corruption and economic growth. Economic Theory Bulletin, 5(2), 227-245. [DOI:10.1007/s40505-017-0116-5]

7. Buchanan, B. (2004). Money laundering-a global obstacle. Research in International Business and Finance, 18(1), 115-127. [DOI:10.1016/j.ribaf.2004.02.001]

8. Buehn, A. & Schneider, F. (2013). A preliminary attempt to estimate the financial flows of transnational crime using the MIMIC method. In Research handbook on money laundering. Edward Elgar Publishing. [DOI:10.4337/9780857934000.00024]

9. Busato, F., Chiarini, B. & Di Maro, V. (2006). Using theory for measurement: an analysis of the underground economy. Aarhus University Department of Economics Working Paper. [DOI:10.2139/ssrn.1147615]

10. Chin, W., (1998).The partial least squares approach to structural equation modeling. In G.A. Marcoulides [Ed.]. Modern Methods for Business Research. Mahwah, NJ: Lawrence Erlbaum Associates, Publisher, 295-336.

11. Davari, A. & Rezazadeh. A. (2016). Structural equation modeling with PLS. Iranian Student Book Agency Press. (In Persian)

12. Dell'Anno, R. (2019). Corruption around the world: an analysis by partial least squares-structural equation modeling. Public Choice, 1-24.

13. Ebrahimi, S. & Ahangari. A. (2013). Estimated the index of crime in Iran using the fuzzy approach. Quarterly Journal of Quantitative Economics, 10(3), 139-163. (In Persian).

14. Feiz Bakhsh, R., Ghorbanzadeh, H. & Akbar Shahi, M. (2016). Money-laundering and its relation to organized crimes (drug trafficking). The Professional drug Studies, 8 (2829), 65-84. (In Persian).

15. Falahati, A., Nazari, S. & Poshtehkeshi, M. (2020). Institutional quality, natural resource rent, and shadow economy. Journal of Economic Modeling Research, 10 (39), 149-185 (In Persian).

16. Hair, J. F., Ringle, C. M. & Sarstedt, M. (2011). PLS-SEM: Indeed a Silver Bullet. Journal of Marketing Theory and Practice, 19(2), 139-151. [DOI:10.2753/MTP1069-6679190202]

17. Henseler, J. & Sarstedt, M. (2013). Goodness-of-fit indices for partial least squares path modeling. Computational Statistics, 28(2), 565-580. [DOI:10.1007/s00180-012-0317-1]

18. Khajavi, M., Rezaei, E. & Khodaveisi, H. (2010). Estimating dirty money and examining its consequences in the Iranian economy: an application of the Bounds test approach. Quarterly Journal of Quantitative Economics, 7(4), 81-99. (In Persian).

19. Kumar, V. A. (2012). Money laundering: concept, significance and its impact. European Journal of Business and Management, 4(2).

20. Loayza, N., Villa, E. & Misas, M. (2019). Illicit activity and money laundering from an economic growth perspective: a model and an application to Colombia. Journal of Economic Behavior & Organization, 159, 442-487. [DOI:10.1016/j.jebo.2017.10.002]

21. Maddah, M. & Noe Iran, F. (2013). Estimating the value of informal economy in Iran based on environmental variables: The Kalman Filter approach. Journal of Economic Modeling Research, 3 (10):1-19 (In Persian).

22. Maddah, M. & Farahati, M. (2019). The empirical analysis of the direct effect of unemployment on the shadow economy in Iran (money demand approach). Journal of Economic Research, 54(2), 419-441. (In Persian).

23. Mohammed, S. A. S. A. (2020). Money laundering in selected emerging economies: is there a role for banks?. Journal of Money Laundering Control.

24. Poursalimi, M., Keikha, M. & Salmani, K. (2016). Innovating a modern model for estimating the amount of money laundering in Iran (the application of numerical and inverse problem methods in economy). Financial Monetary Economics, 23(11), 215-238. (In Persian).

25. Quintano, C. & Mazzocchi, P. (2018). Behind the GDP: some remarks on the shadow economy in mediterranean countries. European Journal of Law and Economics, 45(1), 147-173. [DOI:10.1007/s10657-014-9434-3]

26. Quirk, P. J. (1996). Macroeconomic implications of money laundering. Washington, Fondo Monetario Internacional, WP, 96, 66. [DOI:10.5089/9781451962123.001]

27. Reganati, F. & Oliva, M. (2018). Determinants of money laundering: evidence from Italian regions. Journal of Money Laundering Control. [DOI:10.1108/JMLC-09-2017-0052]

28. Riccardi, M. & Levi, M. (2018). Cash, crime and anti-money laundering. In The Palgrave Handbook of Criminal and Terrorism Financing Law (pp. 135-163). Palgrave Macmillan, Cham. [DOI:10.1007/978-3-319-64498-1_7]

29. Sahraian, M. (2003). Parts of money laundering findings in Iran. Majlis & Rahbord, 37, 337-368. (In Persian).

30. Schneider, F. (2008). Money laundering and financial means of organized crime: some preliminary empirical findings. Paolo Baffi Centre Research Paper, (2008-17). [DOI:10.2139/ssrn.1136149]

31. Schneider, F. (2010). Turnover of organized crime and money laundering: some preliminary empirical findings. Public choice, 144(3-4), 473-486. [DOI:10.1007/s11127-010-9676-8]

32. Taremi, M. H. (2009). Money laundering and its relationship with bribery. Pegah Hozeh, 262. (In Persian).

33. Walker, J. (1999). How big is global money laundering? Journal of Money Laundering Control. [DOI:10.1108/eb027208]

34. Yearwood, D. L. & Koinis, G. (2011). Revisiting property crime and economic conditions: an exploratory study to identify predictive indicators beyond unemployment rates. The Social Science Journal, 48(1), 145-158. [DOI:10.1016/j.soscij.2010.07.015]