WHEN TRADE OPENNESS MEETS WEAK INSTITUTIONS: EVIDENCE ON TRADE-BASED MONEY LAUNDERING VULNERABILITY

Authors

  • Rishu Singh Research Scholar, School of Management Studies, National Forensic Sciences University, Gandhinagar- 382007.
  • Sangeeta Research Scholar, Institute of Management Studies, Kurukshetra University, Kurukshetra- 136118
  • Dr. Harishchandra Singh Rathod Professor, School of Commerce and Management, Sanjivani University (Maharashtra)

DOI:

https://doi.org/10.69980/n42vj366

Keywords:

Trade-based money laundering, illicit financial flows, institutional quality, trade openness, panel data, composite index, AML regulation, Forensic

Abstract

Trade-based money laundering (TBML) exploits the scale, documentary complexity, and jurisdictional fragmentation of international trade to disguise the proceeds of crime, yet the phenomenon remains poorly measured relative to its estimated economic significance. Drawing on principal-agent theory and the economics of informational asymmetry, this study develops and empirically tests a Composite TBML Vulnerability Index (CTVI) that integrates trade-price discrepancy, institutional quality, and trade-finance opacity into a single, reproducible diagnostic. Using an unbalanced panel of 40 economies spanning all World Bank income groups over 2013-2022, the study estimates a two-way fixed-effects model in which CTVI is regressed on trade openness, regulatory quality, financial depth, shadow-economy size, and the interaction between trade openness and regulatory quality. The results show that trade openness is positively associated with TBML vulnerability, but that this relationship is substantially moderated by institutional quality: economies in the lowest decile of regulatory quality experience amplification of TBML risk from trade liberalisation that is roughly twice the sample average. Financial depth and shadow-economy size are independently and positively associated with vulnerability. The findings imply that trade liberalisation and anti-money-laundering (AML) capacity-building should be pursued jointly, and they support specific revisions to the FATF Recommendation 14 framework and to cross-agency data-sharing architecture.

References

1. Akerlof, G. A. (1970). The Market for “Lemons”: Quality Uncertainty and the Market Mechanism. The Quarterly Journal of Economics, 84(3), 488. https://doi.org/10.2307/1879431

2. Brambor, T., Clark, W. R., & Golder, M. (2006). Understanding Interaction Models: Improving Empirical Analyses. Political Analysis, 14(1), 63–82. https://doi.org/10.1093/pan/mpi014

3. Buehn, A., & Farzanegan, M. R. (2012). Smuggling around the world: Evidence from a structural equation model. Applied Economics, 44(23), 3047–3064. https://doi.org/10.1080/00036846.2011.570715

4. Canhoto, A. I. (2021). Leveraging machine learning in the global fight against money laundering and terrorism financing: An affordances perspective. Journal of Business Research, 131, 441–452. https://doi.org/10.1016/j.jbusres.2020.10.012

5. Chaikin, D., & Sharman, J. C. (2009). Corruption and Money Laundering: A Symbiotic Relationship. Palgrave Macmillan US. https://doi.org/10.1057/9780230622456

6. CNBC. (2014). After port fraud, China’s vast warehouse sector under scrutiny. https://www.cnbc.com/2014/06/22/after-port-fraud-chinas-vast-warehouse-sector-under-scrutiny.html

7. Cobham, A., & Janský, P. (2020). Estimating Illicit Financial Flows: A Critical Guide to the Data, Methodologies, and Findings. Oxford University Press.

8. Emeka, E. T., & Asongu, S. (2026). Impact of illicit financial flows related to extractive commodity trade on Africa’s productive capacity: The moderating role of governance. Mineral Economics, 39(2), 697–721. https://doi.org/10.1007/s13563-025-00536-4

9. FATF. (2006). Trade-Based Money Laundering. The Financial Action Task Force. https://www.fatf-gafi.org/en/publications/Methodsandtrends/Trade-basedmoneylaundering.html

10. FATF. (2020). FATF/Egmont Trade-based Money Laundering: Trends and Developments. FINANCIAL ACTION TASK FORCE. https://www.fatf-gafi.org/en/publications/Methodsandtrends/Trade-based-money-laundering-trends-and-developments.html

11. FATF. (2021). Trade-Based Money Laundering: Risk Indicators. The Financial Action Task Force. https://www.fatf-gafi.org/content/dam/fatf-gafi/reports/Trade-Based-Money-Laundering-Risk-Indicators.pdf

12. Ferwerda, J., Kattenberg, M., Chang, H.-H., Unger, B., Groot, L., & Bikker, J. A. (2013). Gravity models of trade-based money laundering. Applied Economics, 45(22), 3170–3182. https://doi.org/10.1080/00036846.2012.699190

13. Foley, S., Karlsen, J. R., & Putniņš, T. J. (2019). Sex, Drugs, and Bitcoin: How Much Illegal Activity Is Financed through Cryptocurrencies? The Review of Financial Studies, 32(5), 1798–1853. https://doi.org/10.1093/rfs/hhz015

14. Gauld, R. (2022). Principal-Agent Theory of Organizations. In A. Farazmand (Ed.), Global Encyclopedia of Public Administration, Public Policy, and Governance (pp. 10081–10085). Springer International Publishing. https://doi.org/10.1007/978-3-030-66252-3_72

15. Im, K. S., Pesaran, M. H., & Shin, Y. (2003). Testing for unit roots in heterogeneous panels. Journal of Econometrics, 115(1), 53–74. https://doi.org/10.1016/S0304-4076(03)00092-7

16. IMF. (2018). Straight Talk: Cleaning Up. F&D Magazine. https://www.imf.org/en/publications/fandd/issues/2018/12/imf-anti-money-laundering-and-economic-stability-straight

17. Junejo, S., & Haidari, A. (2026). The Financial Crime Landscape: Scope, Impact, and Evolution. In L. Vardari & A. Rakaj (Eds.), Corruption and Crime in Finance (1st ed., pp. 1–26). Emerald Publishing Limited. https://doi.org/10.1108/978-1-83708-170-720261003

18. Kellenberg, D., & Levinson, A. (2019). Misreporting trade: Tariff evasion, corruption, and auditing standards. Review of International Economics, 27(1), 106–129. https://doi.org/10.1111/roie.12363

19. Lawal, O., Okolie, A., & Obunadike, C. (2025). AN EXPLAINABLE GRAPH NEURAL NETWORK FRAMEWORK FOR ANTI–MONEY LAUNDERING IN CRYPTOCURRENCY TRANSACTIONS USING THE ELLIPTIC DATASET. International Journal of Network Security & Its Applications, 17(6), 27–39. https://doi.org/10.5121/ijnsa.2025.17602

20. Lokanan, M. E. (2025). Enhancing AML compliance: A machine learning approach to suspicious activity detection through routine activity theory. Journal of Money Laundering Control, 28(4–5), 680–698. https://doi.org/10.1108/JMLC-07-2024-0114

21. Medina, L., & Schneider, F. G. (2019). Shedding Light on the Shadow Economy: A Global Database and the Interaction with the Official One. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.3502028

22. Mousavian, S., & Miah, S. J. (2025). Review of artificial intelligence-based applications for money laundering detection. Intelligent Systems with Applications, 27, 200572. https://doi.org/10.1016/j.iswa.2025.200572

23. Pedroni, P. (2004). PANEL COINTEGRATION: ASYMPTOTIC AND FINITE SAMPLE PROPERTIES OF POOLED TIME SERIES TESTS WITH AN APPLICATION TO THE PPP HYPOTHESIS. Econometric Theory, 20(03). https://doi.org/10.1017/S0266466604203073

24. Reuter, P. (Ed.). (2012). Draining development?: Controlling flows of illicit funds from developing countries. The World Bank. https://doi.org/10.1596/978-0-8213-8869-3

25. Singh, R., & Barot, H. B. (2026). External commercial borrowings and trade-based money laundering: A forensic risk analysis with evidence from India. Journal of Money Laundering Control, 29(1), 53–72. https://doi.org/10.1108/JMLC-10-2025-0179

26. Singh, R., Dr. Ashwini Pandit, & Dr. Haresh Barot. (2025). TRADE-BASED MONEY LAUNDERING: UNVEILING THE ECONOMIC CANCER IN INDIA - A FORENSIC APPROACH STUDY. https://doi.org/10.5281/ZENODO.14877623

27. Singh, R., Ranjan, N., Thakkar, H., Barot, H., & Dabhade, S. (2026). Institutional Governance and Capital Mobility: Evidence from India’s Trends in FDI and ODI. Journal of Risk and Financial Management, 19(4), 290. https://doi.org/10.3390/jrfm19040290

28. Spanjers, J., & Kar, D. (2015). Illicit Financial Flows from Developing Countries: 2004-2013. Global Financial Integrity. https://gfintegrity.org/report/illicit-financial-flows-from-developing-countries-2004-2013/

29. Tiwari, M., Ferrill, J., & Allan, D. M. C. (2025). Trade-based money laundering: A systematic literature review. Journal of Accounting Literature, 47(5), 1–26. https://doi.org/10.1108/JAL-11-2022-0111

30. Walker, J., & Unger, B. (2009). Measuring Global Money Laundering: “The Walker Gravity Model.” Review of Law & Economics, 5(2), 821–853. https://doi.org/10.2202/1555-5879.1418

31. World Bank. (2016). De-risking in the Financial Sector. World Bank Group. https://www.worldbank.org/en/topic/financialsector/brief/de-risking-in-the-financial-sector

32. WTO. (2026). Global trade statistics. The World Trade Organization. https://www.wto.org/english/res_e/statis_e/statis_e.htm

33. Zdanowicz, J. S. (2009). Trade-Based Money Laundering and Terrorist Financing. Review of Law & Economics, 5(2), 855–878. https://doi.org/10.2202/1555-5879.1419

Downloads

Published

2026-08-22