Money Laundering and its Regulation

1. Money Laundering and its Regulation Alberto Chong Florencio Lopez-de-Silanes Federal Reserve Bank and IADB Conference Washington, DC, September 2005 © Florencio Lopez-de-Silanes

2. Motivation • The recent wave of international terrorism and the increased concerns about drug activities have led to an increased focus on money laundering and its regulation. • Money laundering is not a recent phenomenon and has occupied the minds of policymakers and regulators for many centuries. • Forms vary, but feeder activities (legal and illegal) to money laundering have always searched for processes to turn their proceeds into usable assets. • We ask questions at two levels: • Do anti-money laundering laws matter for the size of money laundering demand? If so, • Which features of regulation are important and which ones are not?

3. Methodology and Results • We investigate empirically the determinants of ML (money laundering) and its regulation in over 80 countries. • Methodology: • Construct cross-country dataset for ML and feeder activities. • Construct ML regulation indices based on information on laws and their mechanisms of enforcement. • Measure impact of regulation on ML proxies • Interpret relationships in light of the theories of institutions and the relevance of historical factors in explaining the variation of regulation across countries (control for endogeneity). • Results: • The quality of enforcement matters • Tougher anti-money laundering regulation also matters • Endogeneity does not explain the results • Preliminary: criminalization and confiscation matter most.

4. Outline • Views on Anti-Money Laundering Regulation • Methodology and Data • Money Laundering Proxies • Anti-ML Regulation • Does Anti-ML Regulation matter? • Robustness check and Endogeneity • Which Anti-ML regulation matter most?

5. Two Views on the Role of Anti-ML Regulation • There are 2 views on the importance of Anti-ML regulation: • Anti-ML Regulation is either irrelevant or counterproductive (Coase [1960], Stigler [1964], Masciandro [1998], Rahn [2001]) • It targets the wrong area: ML is only the outcome of illegal activities. • Feeder activities should be the object of criminal legislation and enforcement • Reputational concerns of financial institutions complement framework. • It raises costs, and could negatively impact the efficiency of banks • It is ineffective dealing with drug trafficking, may have fostered the criminal industry (i.e., kidnapping), as people cannot hide their assets and as “police create demand for their services inventing new crimes.” • Anti-ML Regulation is an important supporting institution (Becker [1968], Landis [1938], Djankov et al [2002]). • Basic legal framework controlling crime is insufficient • Incentives to ML are too high for “long run” benefits of honesty to matter • Litigation may be too unpredictable and expensive to serve as a deterrent

6. Views on Anti-ML Regulation (2) • Two alternative hypotheses hold that Anti-ML laws matter. Based on: • Enforcement is costly and unpredictable. • Money launderers are rational profit-maximizers, as other criminals, so deterrence is essential to curtail their behavior (Becker 1968). • Both general crime laws and reputations are insufficient to keep ML under control as the payoffs from cheating is too high and criminal litigation is too infrequent and unpredictable to serve as a deterrent. • To reduce the enforcement costs and opportunistic behavior, the governments can introduce a series of Anti-ML laws that solidifies the regulatory framework and raises the costs for participants. • There are at least two sets of hypotheses that emphasize different kinds of government intervention in this framework.

7. Views on Anti-ML Regulation (2) • Preventive measures, disclosure and disincentives to participants: • Standardized disclosures and clearer (tougher) liability rules that create incentives for all participants increase the probability of detection. • Large role of financial intermediaries (bank and non-banks) in the laundering process. • Without standardized disclosures, there are large costs involved in detection of criminal activities. • Additionally Anti-ML regulation can also explicitly describe the obligations of various parties and burdens of proof. • Tougher liabilities and burdens of proof on intermediaries could turn them into more effective monitors, thereby reducing the costs of government monitoring. • Similar perspective to the use of underwriters in the regulation of securities markets (Landis 1938).

8. Views on Anti-ML Regulation (3) • Stronger Enforcement Powers: • Powerful enforcement can be essential to curtail laundering as it allows the collection of information, setting rules that facilitate enforcement and sanction misconduct. • Criminalization of money laundering and feeder activities raises the stakes for the criminal and could also facilitate the work of courts. • Confiscation: if criminals and their organizations are able to keep their gains, convictions and prison sentences may not be enough to deter such crimes. A powerful system of confiscation would also be cost effective • Potential benefits of a powerful agency specialized in pursuing launderers with broad powers. • International cooperation and joint action is an important element in the fight against money laundering.

9. Outline • Views on Anti-Money Laundering Regulation • Methodology and Data • Money Laundering Proxies • Anti-ML Regulation • Does Anti-ML Regulation matter? • Robustness check and Endogeneity • Which Anti-ML regulation matter most?

10. Methodology (1) • ML Proxies: • We construct a large cross-country dataset of money laundering proxies in the 1990s spanning over 80 jurisdictions. • ML proxies based on the fact that it is a process by which “dirty” money is turned into “clean” money. • Anti-ML Regulation: • We construct a data set of rules and regulations on money laundering from two different sources. • Test for the impact of Anti-ML regulation on the extent of ML, controlling for enforcement and country characteristics  All variables derived are defined in Table I

11. The Data: Legal components of the system for the protection of workers (2) • Industrial (collective) relations laws • Collective bargaining • Worker participation in management • Collective disputes • Social security laws • Old age, disability and death benefits • Sickness and health benefits • Unemployment benefits • Sample: 85 countries in 1997 across continents and levels of development.

12. The Data: Measures of Money Laundering • Sources/Feeder activities that need laundering: • Indirectly measure the extent of underground economy and tax evasion. • Reuter and Truman (2004) point main caveats of “macro” measures • We calculate different proxies using different methodologies measuring the underground economy as the discrepancy between the official value of a macro series and it’s actual (or estimated). • Currency Demand (Cagan ’58, Gutman ’77, Tanzi ’80 & ’83) • Electricity differences (Kaufman-Kaliberda ’96) • Shadow Economy (DYMIMIC) (Schneider-Klinglmair ’04) • multiple causes for the growth of the shadow economy and multiple effects of the shadow economy over time. • Subjective/Survey data: • Tax evasion (GCR 2001) 5-6. Prevalence of ML through banks and non-banks (GCR 2003).

13. Money Laundering measures, by continents

14. Money Laundering measures, by continents

15. Money Laundering measures, by continents

16. Money Laundering measures, by size of the economies

17. Money Laundering measures, by size of the economies

18. Money Laundering measures, by size of the economies

19. Correlations among ML Measures

20. The Data: Anti-ML Regulation measures (1) • ML Regulation (U.S. State Department) 1996-2004: • Annual data for 12 measures of regulation for + 180 countries • International Narcotics Control Strategy Report • Review includes an assessment of: • The conformance of laws and policies to international standards • Steps taken to address financial crime and money laundering, • Jurisdiction’s vulnerability to money laundering, • Effectiveness of government actions and political will • 12 binary (0,1) indicators to assess the compliance each years ’96-04 • Split in 3 areas: Financial System Regulation, Criminalization, International Cooperation

21. The Data: Anti-ML Regulation measures (2) • ML Regulation Convergence (FATF) 2004: • 7 measures for 83 countries • EstandardsForum assesses, based on laws, the efforts to converge to international standards and codes in 12 areas, including anti-money laundering. • Reports available legislation on anti-ML in 7 areas that reflect the convergence of the country FAFT. • Gradation of extent of the coverage or the stage: • 0.00 = no compliance • 0.25 = intent declared • 0.50 = enacted legislation • 0.75 = compliance in progress • 1.00 = full compliance • Split in 4 areas: Financial System Regulation, Criminalization, International Cooperation, Administrative Authorities

22. The Data: Anti-ML Regulation Sub-indices • Financial System Regulation and Disclosure: • Banks are required to maintain records of large transactions • Banking requirements to maintain records over time • Obligations to report suspicious transactions to the authorities. • Legislation requires non-banks to meet the same bank ID standards. • Criminalization and Confiscation • Laws criminalizing ML related to drug trafficking. • Anti-ML statutes to include non-drug related money laundering. • Authorities engage in the confiscation of laundered money. • International Cooperation: • Asset sharing arrangements by law; • Laws for banks to cooperate w/ international law enforcement; • Laws requiring the control of currency flows across borders; • Laws providing for mutual legal assistance; • Rules for disclosure protection safe harbor for financial institutions; • Part to the 1988 UN drug convention.

23. Anti-ML Regulations(U.S. State Dept.)

24. Anti-ML Regulations(U.S. State Dept.)

25. Anti-ML Regulations(FATF / Estandards)

26. Anti-ML Regulations(FATF / Estandards)

27. Correlations among Anti-ML indices

28. Outline • Views on Anti-Money Laundering Regulation • Methodology and Data • Money Laundering Proxies • Anti-ML Regulation • Does Anti-ML Regulation matter? • Robustness check and Endogeneity • Which Anti-ML regulation matter most?

29. Methodology (2) • Anti-ML Regulation should be associated with both a lower level of feeder activities and lower tax evasion (feeder activities), as well as lower levels of prevalence of ML according to investors:  ML by Currency Demand (Tanzi)  ML by Electricity Differences (KK)  Shadow Economy (DYMIMIC)  Tax Evasion  ML via banks  ML via non-banks • Anti-ML Reg & • Efficient enforcement

30. Methodology (3) • We construct various indices of Anti-ML regulation. Indices range from 0 to 1 with higher values indicating stronger laws. • For each of our six ML measures, we run the following regressions: • Controls includes variables that are likely to influence the extent of ML (e.g., judicial enforcement, size of country, geography, etc) • We lack direct measures of the enforcement of ML regulations. • GNP, geography (wealth), court efficiency. • We also address potential endogeneity problems (historically predetermined factors (latitude, fractionalization, legal origin, religious composition).

31. The impact of Anti-ML Regulation(OLS regressions)

32. The impact of Anti-ML Regulation(OLS regressions)

33. Outline • Views on Anti-Money Laundering Regulation • Methodology and Data • Money Laundering Proxies • Anti-ML Regulation • Does Anti-ML Regulation matter? • Robustness check and Endogeneity • Which Anti-ML regulation matter most?

34. Robustness Checks and Endogeneity • Robustness Checks: • Time periods (pre vs. post 2001) • Multiple enforcement indices • Various other control variables (geography, etc..) • Tobits • Endogeneity: • Do countries where ML problems have been solved adopt better Anti-ML regulations? • Instrumental Variables  Deeper political, cultural, legal variables: • Latitude • Ethno-linguistic Fractionalization • Legal Origin • Religious Composition

35. 2SLS Regressions for money laundering measures, controlling by Log (Duration check collection)

36. 2SLS Regressions for money laundering measures, controlling by Log (Duration check collection)

37. 2SLS Regressions for money laundering measures, controlling by Log (Duration check collection)

38. 2SLS Regressions for money laundering measures, controlling by Log (Duration check collection)

39. Outline • Views on Anti-Money Laundering Regulation • Methodology and Data • Money Laundering Proxies • Anti-ML Regulation • Does Anti-ML Regulation matter? • Robustness check and Endogeneity • Which Anti-ML regulation matter most?

40. OLS regressions for each Sub-Index(US State Dept 1996-2004)

41. OLS regressions for each Sub-Index(FATF Convergence)

42. Horse race among the sub-indices- OLS regressions

43. Horse race among the sub-indices- OLS regressions

44. Conclusions • Produce quantitative measures of proxies for ML and Anti-ML regulation that capture the theoretical questions raised above. • Examine in some detail the relationship between specific legal arrangements and proxies for money laundering. • Results: • Anti-ML regulations indeed matter, beyond enforcement. • Robust to potential endogeneity of money laundering regulation. • Strong support for measures of criminalization and confiscation • Relevance of historical factors in explaining the variation of money laundering regulation across countries sheds light on the theories of institutions and provides room for further action.