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ECONOMICS & POLITICS Volume 26 DOI: 10.1111/ecpo.12028 No. 1 March 2014 DOES SAYING ‘YES’ TO CAPITAL INFLOWS NECESSARILY MEAN GOOD BUSINESS? THE EFFECT OF ANTIMONEY LAUNDERING REGULATIONS IN THE LATIN AMERICAN AND THE CARIBBEAN ECONOMIES MARIA ELISA FARIAS* AND MONICA ARRUDA DE ALMEIDA** This study explores the level of compliance and the subsequent economic performance of states in the context of anti-money laundering (AML) regulations. Following Holmstrom and Tirole (1997) and Obstfeld and Rogoff (1998), we examine why countries admit illicit flows of money and the economic costs of these transactions. Analyzing 36 Latin American and Caribbean jurisdictions between 1960 and 2010, we find that poor institutional performance by a jurisdiction (AML ratings, blacklists with non-cooperator countries, and corruption indicators) affects negatively the investment ratio to GDP, the FDI ratio to GDP, and financial development (ratio of credit markets to GDP). These findings are novel in the literature, offering an important contribution to the debate on financial regulatory convergence. 1. INTRODUCTION Many developing countries benefited from capital account liberalization during the 1980s and the 1990s. In addition to adjusting external imbalances, inflows of foreign capital financed development projects, complementing domestic financial markets. Capital liberalization has been particularly beneficial for developing countries that have a historically poor export performance and difficulties in maintaining foreign reserves at sustainable levels, particularly to meet external payments. Despite the advantages of financial liberalization, it has been accompanied by an impressive surge in international flows of illicit capital. Money laundering, tax evasion, and the financing of terrorism are a few examples of illegitimate profit, concealment, and use of capital that are considered financial crimes. According to a United Nations report, money laundering alone reached up to US$ 2 trillion in 2010, or 5% of global GDP.1 Criminals and white-collar offenders alike have traditionally been attracted to offshore financial centers (OFCs) for their secrecy in banking transactions and the ease with which investors may transfer their money in and out of the jurisdictions.2 Lack of disclosure of information makes tracking either the account holder or the source of illegal money virtually impossible, especially after multiple rounds of electronic funds’ transfers (both within and across countries), a scheme known as “layering” in financial *Corresponding author: Marıa Elisa Farıas, Universidad Diego Portales, Manuel Rodrıguez Sur 415, Santiago, Chile. E-mail: [email protected] **Corresponding author: Monica Arruda De Almeida, 3700 O Street NW, ICC 484, Washington, DC 20057. E-mail: [email protected] 1 Bhaskar Menon, “India Cannot Deal With Black Money Unilaterally” (CounterCurrents.org, 8 July 2011). Notice that due to the concealed nature of money laundering, estimates about the scale of the practice are at best informed guesses. 2 We define jurisdiction as any territory with its own legal system, whether or not a sovereign state. (Tax Justice Network, 2007). © 2013 John Wiley & Sons Ltd 96 ILLICIT CAPITAL FLOWS IN THE LAC REGION 97 jargon. The difficulty of tracking illicit flows is exacerbated by the volume of global electronic transfers, which amount to over US$ 1.9 trillion per day.3 The Organization for Economic Cooperation and Development (OECD) and the Financial Action Task Force (FATF) have sponsored a set of financial practices to combat illicit capital flows. Whereas the OECD has focused on the more controversial fight against tax evasion and what it calls “harmful tax competition” in the OFCs, the FATF’s main mandate is to combat money laundering, as a means to curtail a variety of crimes, as well as to fight terrorism financing. This study examines a puzzling aspect of the global antimoney laundering (AML) and combating the financing of terrorism (CFT) effort, namely, the seemingly widespread adoption of a complex set of institutional reforms whose effectiveness has never been established (Helleiner, 2000; H€ ulsse and Kerwer, 2007; Sharman, 2006a). In fact, the FATF experience has been considered a successful case of international compliance. Since the initial publication of its 40 AML financial standards in 1990, FATF’s institutional affiliation has grown to over 140 jurisdictions, including the full membership of 34 countries and territories, and two regional organizations. More importantly, this apparent level of compliance has been achieved without the real use of “sticks,” such as sanctions, suggesting that the FATF’s strategy to “name and shame” non-compliant jurisdictions have persuaded them to adopt AML/CFT standards primarily due to reputational concerns (Helleiner, 2000; H€ ulsse and Kerwer, 2007; Sharman, 2006a; Simmons, 2000). Another important aspect about the FATF global effort is that jurisdictions that have committed to follow its regulatory practices have in essence opted to abide by a variety of financial regulations that are not only costly to implement and technically challenging, especially in economies that lack sophisticated financial sectors, but also lack any criteria for assessing their effectiveness (Reed, Quentin and Alessandra Fontana, 2011; Sharman, 2006a).4 This article studies the financial regulatory compliance experience of 36 members of two FATF-Style Regional Bodies (FSRBs): the Financial Action Task Force in South America (Gafisud) and the Caribbean Financial Action Task Force (CFATF). More specifically, we examine two of the FATF’s methods for evaluating compliance: Mutual Evaluation Reports (MERs), which rate jurisdictions according to their progress in implementing AML/CFT standards; and the list of Non-Cooperative Countries and Territories (NCCTs), also known as FATF’s blacklist. These lists alert international markets about jurisdictions that restrict the supervisory and investigative international authorities’ access to critical financial information.5 In addition to the above institutional variables, we include in our analysis a series of economic indicators from 1960 to 2010, which enable us to compare the jurisdictions’ economic performance before and after the FATF’s listings and compliance ratings. We find that the FATF’s strategy of maintaining blacklists and periodically publishing compliance ratings do affect the capacity of jurisdictions to attract and retain foreign capital. According to our analysis, jurisdictions can lose on average up to 3 Menon (2011). Reuter and Truman (2004) estimate that the total gross financial costs of the U.S. AML regime were around $7 billion in 2003, i.e., 0.06 percent of the country’s GDP that year. At the firm level, however, costs with AML seem more relevant. A survey conducted by the firm KPGM estimates that spending with AML controls among Latin American banks increased 73% from 2001 to 2004 (Valor Econ^ omico 21 June 2005). 5 2000 Report on Non-Cooperative Countries and Territories (www.fatfgafi.org). 4 © 2013 John Wiley & Sons Ltd 98 FARIAS AND ARRUDA DE ALMEIDA 20% of their FDI after being blacklisted.6 We also find that a low MER score reduces the ratio of capital to GDP in LAC countries and territories by at least 3 percent. Nevertheless, our results also highlight the real incentives that jurisdictions have for non-compliance. For example, until blacklisted by the FATF, the non-compliant jurisdictions enjoyed significant financial advantages over the non-listed ones. After controlling for the economic variables, the ratios of both portfolio and foreign direct investment over GDP in blacklisted jurisdictions were on average almost three times higher than those of not listed. We also observe that, with the same rate of variation in money supply (M2) across Mercosur (Southern Common Market) and Caricom (Caribbean Community) countries, domestic credit7 over GDP was on average 30 percent higher in blacklisted territories. It is important to note that when compared to the rest of the LAC region, blacklisted jurisdiction had lower rates of GDP and productivity growth, on average, and offered smaller gains in short-term investment.8 We prove, then, that even though the strategy followed by these jurisdictions may have short-term benefits, these benefits did not compensate the long-term loses, both in term of GDP growth and welfare. Therefore, this study offers two important contributions to the literature on AML compliance. First, our research shows that FATF’s negative evaluations and blacklisting have indeed penalized9 countries and territories that were target of the body’s critical reviews. Second, utilizing a group of macroeconomic indicators we can identify possible non-compliant jurisdictions. To our knowledge, never before has an econometric analysis been capable to either estimate the cost of non-compliance with antimoney laundering regulations or signal the presence of suspicious financial activity. The following is a brief background concerning the FATF initiative and the question of illicit capital flows more broadly. Later we introduce our model, where we combine the literature on macroeconomics and finance in the context of open economies. Following Holmstrom and Tirole (1997), and Obstfeld and Rogoff (1998), we develop a simple model that represents the dilemma faced by a small open economy that needs foreign capital but also is threatened by illicit flows in an imperfect information context. Next, we present the econometric methodology with which we combine three techniques of panel data (random effects, fixed effects, and generalized least squares) with the dynamic heterogeneous model of Pesaran and Shin (1999). We then proceed with the discussion of the results and conclusions. 2. THE CRIMINALIZATION OF MONEY LAUNDERING AND THE GLOBAL EFFORT FOR REGULATORY CONVERGENCE The global concern with money laundering and the international efforts to curtail this practice are recent phenomena. It was not until the passage of the 1986 Money Laundering Control Act by the U.S. Congress that money laundering became a crime in the United States. The American authorities’ strategy at that time was to criminalize 6 Because this 20% represents the average loss, there could be cases where a country loses more than 20%. and others, where the country loses less than this amount. 7 See the description of variables and data in section 4.3 for an explanation. 8 The gap between real interest rates in the United States and the Caricom region was negative, on average; it reached 4.9 percent in blacklisted territories, and 17 percent in the Mercosur area. 9 Financially and in real terms. © 2013 John Wiley & Sons Ltd ILLICIT CAPITAL FLOWS IN THE LAC REGION 99 money laundering to gain more enforcement tools in the fight against drug trafficking. The international community then followed suit. The 1988 Vienna Drug prohibition convention was inspired by the American antimoney laundering regime (Andreas and Nadelmann, 2006). A year later, the G7 countries, once again led by the United States, founded the FATF, an international policy-making body that is funded by member countries on a temporary basis. In 1990, the FATF announces its 40 initial financial practice recommendations that, although not binding on members, are enforced by sanctions that vary from warnings to outright expulsion from the organization (H€ ulsse and Kerwer, 2007). Following the 9/11 terrorist attack, the FATF revised its standards and added nine other special recommendations in subsequent years to address terrorism financing. One important aspect of the AML/CFT global effort is that it involves numerous international and regional organizations, including the United Nations Office on Drugs and Crime (UNODC), the World Bank, the IMF, and the Basel Committee on Banking Supervision (BCBS), which are all FATF observers.10 One feature of the FATF’s mutual evaluation reports is that they are standardized, enabling easy comparisons of compliance across jurisdictions. As the name implies, representatives of the jurisdictions themselves, who conduct on-site reviews of the country or territory under evaluation, compile the reports. A score is given for each of the FATF 40+9 recommendations based on a four-level scale (not applicable, noncompliant, partially compliant, and largely compliant). We compared jurisdictions’ FATF compliance ratings to their corresponding corruption indices using the Transparency International and the International Country Risk Guide (ICRG) database. As shown in Figure S1, there seems to be little correlation (r. 12) between levels of FATF compliance and of corruption.11 However, we find interesting discrepancies between the two rankings. For example, the Bahamas, Panama, and Peru have received very good FATF report ratings despite their relatively high levels of corruption. A partial explanation of this discrepancy may lie in the blacklisting of the Bahamas and Panama in June 2000 and their subsequent delisting a year later, which appears to have persuaded them to comply with FATF standards. Thus, by the time the Bahamas and Panama had their evaluations published in 2007 and 2006, respectively, the reports already reflected their having undertaken institutional reforms. Conversely, Figure S1 shows that places like Dominica, St. Lucia, St. Vincent and the Grenadines, and Uruguay have received much lower FATF evaluation scores than expected based on their lower levels of corruption. Judging by the relatively high quality of institutions in these jurisdictions, one possible interpretation is that they made an economic calculation not to fully adopt the FATF standards. Indeed, Uruguay has developed in recent years a reputation for being a tax haven among South American countries, although it is a full member of Mercosur. Brazil, in particular, has 10 Sharman explains that the IMF and World Bank were “recruited” by the G7 nations to offer financial expertise to members and surveillance of illegal financial activity. For that purpose, there has been a division of labor between these two institutions whereby the IMF works with developed countries and offshore centers and the World Bank with developing countries. The high number of institutions participating in the AML regime shows the priority given by the G7 countries to the issue, despite the challenges involving the coordination and overlapping of many initiatives (2006a). 11 Please notice that we set the corruption index so that “0” represents the highest level of corruption and “100” the lowest level, to make visual the comparison between the two indices easier. © 2013 John Wiley & Sons Ltd 100 FARIAS AND ARRUDA DE ALMEIDA complained about the lack of informational exchange between the two countries, making the tracking of money launders difficult to achieve.12 Despite the importance of the mutual evaluation reports in assessing international compliance with AML/CFT recommendations, the FATF’s blacklists, as our analysis shows, have proven to be effective in changing the behavior of uncooperative jurisdictions. However, the FATF is not the only entity that uses blacklists against countries and territories that are perceived as non-compliant with international financial regulatory standards. The OECD and the Financial Stability Forum (FSF)13 have published their own lists as well. Although the OECD’s list has gained greater international visibility and controversy, for its stance against low-tax jurisdictions, we focus in this study on the FATF’s list because it is the only one that aims solely at combating more serious international crimes, including terrorism financing, and therefore has greater relevance to international security. Nevertheless, despite the blacklists’ distinct policy objectives, the financial techniques used by white-collar offenders to evade taxes and by common criminals to launder money are strikingly similar. Thus, it should be no surprise that the OECD’s and the FATF’s lists overlap significantly, considering that of the 45 jurisdictions ever to be blacklisted by them, 20 were the target of both organizations (Kudrle, 2008). In this study, we examine the compliance performance of 36 countries and territories in the Latin American and Caribbean regions, which also represent two of the seven FATF-Style Regional Bodies (FSRBs): the Caribbean FATF and the FATF South America (Gafisud).14 The jurisdictions vary significantly in endowment, size, and level of economic development. As a group, they are also relevant to the financial markets in the Western Hemisphere (Ginsberg, 1991). In fact, a recent IMF report on small Caribbean states highlights the disproportional size of the financial sector in the region: total assets of the financial system averaged 320 percent of GDP, with 149 percent held by banks (International Monetary Fund, 2013).15 The significant disproportionality of the financial industry in these small states underscores the need for scholars and policy-makers to understand better the dynamics of those economies. As Table S1 in Online Appendix shows, these states are for most part import oriented and, particularly in the Caricom region, heavily dependent on capital inflows to balance their external accounts (the exceptions being Bermuda, Trinidad and Tobago, and Venezuela). Twenty-one of the 36 jurisdictions are recognized as OFCs, which provides us with a control group. Table S1 (in online Appendix) illustrates the 12 “Uruguai ainda e o paraıso fiscal mais temido pelo Brasil,” (Zero Hora, 21 July 2003). The Uruguayan Parliament has recently passed legislation that set a schedule for important regulatory reforms to address many of the concerns raised by the international community, including its lack of financial transparency and its unwillingness to exchange information. 13 The G7 Finance Ministers and the Central Bank Governors founded the FSF (succeeded by the Financial Stability Board in 2009) in 1999 “to coordinate at the international level the work of national financial authorities and international standard setting bodies and to develop and promote the implementation of effective regulatory, supervisory and other financial sector policies.” 14 Our original sample included 41 jurisdictions; however, we had to exclude five of them (the Cayman Islands, the British Virgin Islands, Curacßao, St. Marteen, and Turks and Caicos) due to unavailability of data. The other regional FATF associate members are The Asia/Pacific Group on Money Laundering (APG); the Eurasian Group (EAG); the Eastern and Southern Africa Anti-Money Laundering Group (ESAAMLG); the Inter Governmental Action Group against Money Laundering in West Africa (GIABA), and, finally, the Middle East and North Africa Financial Action Task Force (MENAFATF). 15 The small states examined in the IMF report include Antigua and Barbuda, The Bahamas, Barbados, Belize, Grenada, Guyana, St. Kitts and Nevis, St. Lucia, St. Vincent and the Grenadines, Suriname, and Trinidad and Tobago. © 2013 John Wiley & Sons Ltd ILLICIT CAPITAL FLOWS IN THE LAC REGION 101 economic and demographic diversity of the jurisdictions not only between non-OFCs and OFCs but also within the OFCs themselves. Some of them are high-income jurisdictions, such as Bermuda, the Cayman Islands, the British Virgin Islands, Aruba, and the Bahamas. On the other side of the spectrum, we find OFCs such as Belize, Guatemala, and Montserrat, considered low- to middle-income countries. Note that all the jurisdictions that have been blacklisted by the FATF in the LAC region were OFCs. Small OFCs stand to lose the most from the imposition of financial regulatory restrictions because they are less economically diverse. Perhaps that explains why many small OFCs reacted quickly to FATF’s blacklisting and started cooperating with FATF to have their name removed from the list (see Table S2 in online Appendix). There is no single definition of an OFC, or tax haven.16 Kudrle (2008) has grouped them into three different categories: (1) production havens, which offer low tax rates to attract legitimate businesses from abroad; (2) sham havens, whose financial markets serve mostly corporations that want to escape the tax reach of their shareholders’ countries; and (3) secrecy havens, which provide anonymity to investors. Reed, Quentin and Alessandra Fontana (2011) argue that the imprecision of the term “offshore” is unfortunate because it diverts attention from jurisdictions that facilitate illicit flows but are clearly onshore. Switzerland, the City of London, and the state of Delaware are just a few examples of places renowned for their financial secrecy.17 Of the 23 jurisdictions that the FATF has blacklisted around the world, only four of them have not been considered an OFC either by the IMF, the OECD, or the Financial Secrecy Index (FSI)18 (see Table S2 in online Appendix). In addition, there are blacklisted countries that have been labeled an OFC but they are onshore: Israel and Lebanon, for example. This suggests that FATF analysts were not targeting only small offshore economies and that they have made an effort to give the same treatment to jurisdictions everywhere; despite that complaints of double standards in favor of economically powerful states are abound (see, e.g., Mathers, 2004 and Sharman, 2006a). We do not distinguish the different kinds of OFCs in our econometric analysis because we are mostly interested in measuring the cost of non-compliance with FATF regulations. Future studies might benefit by including these categories in their analyses. In the next section, we develop our hypothesis and the econometric model. 3. DO ILLICIT FLOWS MEAN DOING GOOD BUSINESS? – MONEY LAUNDERING AND THE MACROECONOMY Most of the literature that analyzes illicit money focuses on how flows affect business and the whole economy, especially in developing countries. From the point of view of export countries, the literature stresses the damage these outflows cause in the economy by reducing its productive capacity and deteriorating its external accounts. From the point of view of import countries, the illicit inflows may be considered a gain if they increase the productive capacity in their economies, even if they tarnish the country’s reputation in the eyes of foreign investors. 16 In this study, we use the terms OFC and tax haven interchangeably although that is not necessarily the case. According to Tax Justice Network, “laws and other measures [that] can be used to evade or avoid tax laws or regulations of other jurisdictions mostly characterize a tax haven.” (Tax Justice Network, 2007). 17 Reed and Fontana (2011: 15). 18 The Tax Justice Network manages the FSI. © 2013 John Wiley & Sons Ltd FARIAS AND ARRUDA DE ALMEIDA 102 Therefore, the costs and benefits of the illicit money are determined in terms of the flows that a country loses or gains because of these activities (money laundering, tax evasion, etc.). Under this perspective, there is a dilemma for less developed and developing countries. Such countries require foreign capital to finance investment projects, but also need a good reputation with international community. Illicit capital flows can provide less developed countries with foreign capital, but at the cost of endangering their reputation. Given the uncertain origin of the illicit money, a country needs to devote resources to identify these flows, thereby incurring supervision costs. In addition, there is the opportunity cost of the forgone foreign flows when supervision successfully interdicts illicit capital. Traditional analyses have not considered this and other “hidden” costs that countries incur when they are involved in these activities. We adopt a different approach that combines the literature of finance and macroeconomics in a context of open economics and imperfect information. Following Holmstrom and Tirole (1997), and Obstfeld and Rogoff (1998), we develop a simple model that represents the dilemma that a small open economy faces when it needs foreign capitals to finance investment projects, but it is threatened by illicit flows. Flows of illicit money are linked to illegal activities such as corruption and crime, which divert productive resources of the economy toward unproductive activities. Consequently, both productivity and output levels can decrease, causing damage especially in less developed countries (Alesina and Perotti, 1994; Ibanez and Moya, 2010). As the rule of law does not apply to illegal activities, the returns on investments made with illicit capital are riskier, discouraging the most productive investment. Because these activities are more common in less developed countries than developed ones,19 social goals such as poverty reduction and economic development are affected by illicit outflows. In macroeconomic terms, flows of illicit money may affect external balances, causing financial instability. Linked to the phenomena of capital flights, outflows of illicit money would account for balance of payment problems in export countries. In the case of import countries, they can be more prone to suffer bubbles in financial markets, volatility, and balance of payment problems. The market for domestic credits is also affected by these activities, leading to incomplete markets in some cases; in others, the economy would be more vulnerable to financial distress (Obstfeld and Rogoff, 1998). The adoption of international standards such as FATF and GAFI may act as signals of country’s reputation in international markets. When compliance is effective, it can discourage illegal flows in both countries that export and import such funds. Thus, although adoption is costless, compliance might impose large costs in terms of institutional arrangements and capital losses. Therefore, despite the adverse effect of these inflows, a member country could be tempted to accept them to compensate current account deficits or ensure liquidity to finance domestic investment. A moral hazard problem, then, may arise between the country and the international community. Supervision and compliance are costly and imply the loss of a certain amount of flows; accordingly, the country can relax controls and obtain the short-term benefit of flaunting its agreement. 3.1 The Model There is a small open economy, populated by a continuum of households who act as entrepreneurs and live in two periods. At period 1, households make investment 19 In particular those countries with weak institutions (Kar and Cartwright-Smith, 2008; Karl and Curcio, 2011). © 2013 John Wiley & Sons Ltd ILLICIT CAPITAL FLOWS IN THE LAC REGION 103 decisions and consume. To finance these purchases, households dispose of their own resources and have the possibility to receive funds from foreign investors. There are a large number of foreign investors, who act competitively. At period 2, output is realized, households consume and foreign investors repatriate their funds. There is a single type of goods in the economy, which can be used for consumption or investment. For simplicity’s sake, we assume that the exchange rate in the domestic economy is = 1. Households. At period 1, households receive an endowment Y1 of goods that they can consume or use as capital to produce new goods in period 2. The stock of capital evolves according to K2 = (1 d)K1 + I1, where K1,2 is the stock of capital at periods 1 and 2, respectively, and I1 is the investment done at period 1. With the depreciation rate d being equal to 1, capital is fully depreciated at the end of period 2. Thus, at period 2, the stock of capital will be equal to the investment done at period 1, such that I1 = K2. Acting as entrepreneurs, households own the entire capital stock of the economy and possess the technology to produce goods. When the economy is closed, households are restricted to consume and invest from their own endowments; therefore, they have little room to absorb liquidity shocks. The household preferences are denoted by the expected utility function EU ¼ uðC1 Þ þ E½buðC2 Þ ð1Þ where u() is concave, continuous, twice differentiable, and C1 and C2 represent consumption at periods 1 and 2, respectively. The parameter b ∈ (0, 1) is the discount factor and E represents expectations based on period 1. Foreign Investors. There are a large number of foreign investors, who act competitively and invest in the domestic economy at period 1. Among them, there are legitimate investors, who provide licit flows, and illegitimate investors, who provide illicit flows. The investment financed by legitimate investors leads to secure levels of capital and output; the investment financed by illegitimate investors is risky. Let F be the amount of funds provided by foreign investors in units of the domestic good, the investment done by legitimate investors will be F ¼ Fm ð2Þ The investment done by illegitimate investors will be F ¼ Fm þ n ð3Þ where Fm is the amount of funds that lead to a secure investment, and ξ is a random variable (shock), which takes values in the interval (ξ,…, ξ +)20 and satisfies, E(ξ = 0) and ∑0,∞q(ξi) = 1 with q(ξi) = Prob(ξ = ξi). We assume that in a context of openness and financial integration, foreign resources that enter in the economy will be legitimate with probability c and illegitimate with probability (1 c). Therefore, the expected value of the external funds will be: 20 Assuming that the shock ξ can have positive or negative realizations, we consider that ξ is located in the lower bound of the real subset(∞, 0) and e+ is located in the upper bound(0,+ ∞). © 2013 John Wiley & Sons Ltd FARIAS AND ARRUDA DE ALMEIDA 104 EðFÞ ¼ cFm þ ð1 cÞ½Fm þ n ¼ Fa ð4Þ Fa in equation (4) is the average amount of funds that the domestic economy receives from foreign investors. Investment. Following Holmstrom and Tirole (1997), households use investment to produce capital and final goods for period 2. To finance this investment, households use their own savings and foreign resources. When they use domestic resources, households obtain a secure amount of capital and consequently, a certain output level at period 2. When they use foreign resources, the production of capital and final goods both become uncertain. Given that foreign resources come from legitimate and illegitimate investors, capital produced with these funds contains a risky component, which is represented by the random variable ξ and is unobservable when agents make decisions. Without any other source of uncertainty in the domestic economy, the level of output depends on the stock of capital and the technology of production. Both factors are known when agents make decisions. Nevertheless, the possibility of receiving risky capital from abroad introduces uncertainty in the outcome of production, such that the level of output will be Y2h if the shock ξ has a positive realization and equal to Y21 if ξ has a poor realization. Note that Y21 \E½Y2 \Y2h , and ξ = ξ+ when the shock is positive and equal to ξ = ξ-, when it is negative. To attenuate this uncertainty, domestic agents can supervise the quality of foreign investors, reducing the probability to receive illicit funds. Thus, output in period 2 is a function of the stock of capital at period 2 and the supervision efforts. Y2 ¼ FðK2 ; sÞ ð5Þ where F(•) in equation (5) is continuous and positive in the two factors, K and the level of supervision s, such that uncertainty decreases with s, oF(K2, s)/os > 0. Supervision is costly and imposes households to spend /(s) resources to guarantee a safe level of output at period 2, with /(s)’ > 0. Without supervision, the level of output will depend on the realizations of the shock ξ. In the case of poor realizations (ξ = ξ-), the level of output at period 2, Y2, will be lower than the secure level of output at this time, thus Y2 < E(Y2). However, when the shock ξ has positive realizations, the level of output will be larger than the secure level of output, and so Y2 > E(Y2). The Financial Contract. To receive foreign funds, households sign a contract with foreign investors at period 1, which sets the amount of funds F invested in the domestic economy and the rate of return R > 0, obtained in these transactions. The foreign funds are delivered to the domestic economy at period 1 and are fully repatriated at the end of period 2. To ensure this repatriation, households in the domestic economy must guarantee that they are committed to repay their debts. The reputation of the domestic economy then matters for foreign investors. There is a moral hazard problem between the domestic economy and the community of foreign investors. To distinguish the legitimate investors from the illegitimate ones, the economy must bear the supervision costs, which also implies it to renounce to the high levels of output in the “good times” (good realizations of shock ξ). For this reason, households can be tempted to reduce supervision and avoid these costs, taking the advantage of receiving illicit funds © 2013 John Wiley & Sons Ltd ILLICIT CAPITAL FLOWS IN THE LAC REGION 105 in the “good times”. Because of this possibility, the repatriation of foreign funds can be affected,21 adopting investment in the domestic economy a risky nature for foreign investors. Consequently, legitimate investors may prefer to participate in contracts with supervision than without supervision. Nevertheless, illegitimate investors need also to be sure the economy will deliver their funds at the end of period 2. 3.2 The Equilibrium Conditions The equilibrium conditions are found when domestic households solve their maximization problem in a two-period contract with foreign investors as follows Max uðC1 Þ þ bEuðC2 Þ Subject to (1) C1 þ I1 ¼ Y1 þ F1 (2) C2 þ ð1 þ R2 ÞF1 ¼ Y2 /ðsÞ (3) Y2 ¼ FðK2 ; sÞ (4) cil Yh2 Y2 /ðsÞ (5) ð1 þ R2 ÞF1 ð1 þ R2 ÞF1 Conditions (i) and (ii) are the households’ budget constraints at periods 1 and 2, respectively, and (iii) is the technology of production constraint. Condition (iv) is the incentive constraint for domestic agents, which indicates that the household should be at least indifferent between to choose the average level of output, achieved when it supervises foreign investors, and the highest level of output Y2h, obtained with probability cil = (1 – c) when it does not supervise these investors. Restriction (iv) is the participation constraint of foreign investors, where R2* is the foreign interest rate at period 2. By assuming that R is a risk-free rate and equal to R* in equilibrium, this constraint always binds with equality. Solving the first-order conditions of the household’s problem, we obtain the Euler equation: u0 ðC1 Þ ¼ bE½u0 ðC2 Þð1 þ R2 Þ ð6Þ where u’(C1) and u’(C2) are the marginal utilities of consumption at period 1 and period 2, respectively. On the other hand, constraints (i), (ii), and (iii) lead the marginal productivity of capital to equal the rate of return of the foreign funds Fk ¼ ð1 þ R2 Þ ð7Þ with Fk being equal to ∂F(K2, s)/∂K2 > 0, for K2 > 0 and K2 < ∞, if the Inada conditions holds. Similarly, from constraints (ii), (iii), and (iv), we conclude that the optimal choice for supervision implies that the marginal benefit of supervision should be equal to the marginal cost of this supervision 21 This would occur when an adverse shock hurts output in the domestic economy to a level where the foreign debt is impossible to pay. © 2013 John Wiley & Sons Ltd FARIAS AND ARRUDA DE ALMEIDA 106 Fs ¼ /0 ðsÞ ð8Þ where the marginal benefit of supervision Fs is equal to ∂F(K2, s)/∂s > 0, for s > 0. Finally, we have that in equilibrium; the incentive compatibility constraint leads to supervision costs proportional to the gains in output for the domestic economy, when it supervises /ðsÞ ¼ ½Y2 cil Yh2 ð9Þ However, equation (9) tells us that the incentives for supervision decrease with the probability of obtaining a good shock from illegitimate investors. For example, assuming that the supervision costs adopt a linear form such as /(s) = as, the expression in (9) stays as s ¼ ½Y2 cil Y2h =a, where 0 < a < 1 can be interpreted as an elasticity parameter (see Obstfeld and Rogoff, 1998). Thus, the effort that a country devotes in avoiding illicit flows of funds will be proportional to the ratio of illicit flows to total investment. The moral hazard problem arises because households in the domestic economy may refuse to exert controls on foreign investors and take advantage of the illicit inflows. On the other hand, if foreign investors have limited capability to enforce full repayment in the domestic economy, households could try to divert part of the foreign funds. In this case, the economy would be investment constrained, which may reduce the amount of funds it receives from abroad and/or raises the interest rate it pays for these funds, leading R to be larger than R*. Less worried about the return rate than legitimate investors, illegitimate investors would be willing to accept contracts with lower interest rates R, if capital repatriation is guaranteed. Moreover, if illegitimate investors face restrictions to channel their flows in other economies, they would accept contracts at interest rates R lower than R*. In this sense, reputation matters for open economies that need foreign investment, forcing them to adopt institutional arrangements such as those of FATF. The role of banks and other financial intermediaries is also the key, as they act as reputation guarantors in some cases and as supervisors in other cases. According to this simple model, countries with lower saving rates will be more dependent on foreign investment and would be more prone to accept illicit inflows of money or capitals, specially the small ones. On the other hand, the higher the ratio of these inflows to the whole economy, a higher frequency of deficits in current accounts would be expected in these countries, when they maintain flexible exchange rates, leading them to “perverse cycles” of current account deficits and exchange rate appreciations. When the exchange rates are fixed, we could expect high volatility in foreign reserves, transiting the economies of these countries from periods of booms to periods of slumps of reserves. This volatility threats the macroeconomic stability in these countries, becoming more vulnerable to external shocks. In fact, large amounts of fiscal debt and currency overvaluation, among other distortions, are commonly observed in most offshore centers in the Caribbean, even though they have pegged their currencies to the U.S. dollar (International Monetary Fund, 2013). In the next section, we conduct an econometric analysis to test the most important hypotheses of this section. 4. ECONOMETRIC ANALYSIS In this section, we test the most important conclusions obtained in the theoretical part. Specifically, we analyze the relationship between the main macrovariables, domestic © 2013 John Wiley & Sons Ltd ILLICIT CAPITAL FLOWS IN THE LAC REGION 107 investment, foreign investment and credits, and the FATF institutional variables. Even though we cannot identify the illicit flows of money directly from the data, indicators such as the FATF scores or Blacklist permit us to establish a relationship between those countries that follow the rules and those that do not. In such a case, there is a strong suspicious that the country has been involved or is currently involved in illegal transactions. In this way, a country with low FATF scores would be a good candidate for being listed in a blacklist. We can then estimate the effectiveness of the current regulation and the costs of the illicit inflows of money for the economies of Latin American and the Caribbean (LAC). We use a panel of data for this purpose built with data for 36 LAC countries between 1960 and 2010. The panel, which covers almost the whole region includes the largest LAC countries such as Brazil and Mexico. In addition, it includes 16 Caribbean countries, considered some of them the most important offshore centers, such as Bahamas and Bermuda. Although initially, we have considered several tax havens and offshore centers, such as the Cayman Islands and Virgin Islands, but we dropped them because we were unable to find enough information to obtain robust results. Because the sample of countries exhibited large heterogeneities in size, economic development, and cultural roots, we use two dummy variables to measure regional integration: Caricom and Mercosur. Whereas the former represents the main features of the Caribbean countries with European jurisdiction, the latter contains the largest countries of the Southern Cone. Our econometric models employ three panel data techniques (random effects, fixed effects with and without instrumental variables, and heteroskedastic generalized least squares). As robustness check, we use the Pooled Mean Group Estimator (PMGE) of Pesaran and Shin (1999). 4.1 Who is Willing to Accept Flows of Dirty Money? Main Hypothesis The theoretical framework of the previous section suggests that countries with low saving rates, fiscal deficits, and current account deficits will be more dependent on foreign capitals. On the one hand, these countries need capital inflows to compensate deficits in balance of payments. On the other hand, foreign capital permits them to finance investment projects. However, when the economy of these countries is not much productive, profits and returns fall, which discourages foreign investment. In a context of political instability and weak institutions, low returns may restrict the access of developing countries to international markets. Although any type of country would suffer this type of restriction, the most affected would be the smallest countries. Borrowing constrained, a country would become highly dependent on foreign capitals and would be willing to accept illicit inflows of money. Related to the magnitude of these inflows, a small country might accumulate large amounts of foreign reserves when the exchange rate is fixed, or its domestic currency would suffer large appreciations when this rate floated. Accordingly, a higher frequency of current account deficits would be common in this type of countries, passing through adverse cycles of inflows and deficits. Consequently, the ratio of foreign capital inflows to GDP would be larger in countries that receive illicit inflows, compared with others that show similar rates of return and receive only licit flows. The domestic financial market would become oversized as well. Considering the ratio of net credits provided by banks to GDP as a measure of financial development, this ratio would be larger in a country that receives illicit flows, given that a great part of these transactions is allocated through intermediaries. Thus, besides capital returns and productivity considerations (doing good business), the decisions of illegitimate investors will be based on market © 2013 John Wiley & Sons Ltd FARIAS AND ARRUDA DE ALMEIDA 108 access to the host economy and capital repatriation. Institutional factors play a key role. Sound institutions offer investors a guarantee of recovering their funds. Ratings such as those of the FATF may act as signals for both legitimate and illegitimate investors. A high rate of compliance would suggest the degree of the country’s seriousness for legitimate investors. At the same time, it would be a signal of market restrictions for the illegitimate ones. Other indicators, such as the corruption indexes, however, would be signals of the degree of institutional soundness for both types of investors. Based on the model of the previous section, then, the econometric analysis explores the relationship between the three macrovariables: domestic investment (I), foreign direct investment (fDI), and credits (credit); and three institutional indicators: the FATF rating (rating), which indicates the degree of compliance, Blacklist22 and the corruption indicator (corruption). Although portfolio investment is considered as one of the most common ways to channel illicit flows, we use foreign direct investment in this analysis because of its connection with domestic investment. Nevertheless, a larger inflow of portfolio investment than FDI would indicate a speculative behavior on the part of foreign investors, who look for short-term liquidity in the domestic economy. To analyze the effects of these interactions in the whole economy, we examine the relationship among these three variables and the institutional indicators with the dynamics of real GDP. We use the following equations for this purpose: Iit ¼ aoi þ a1 Rkit þ a2 FDIit þ a3 Ratingit þ a4 Blacklist þ a5 Corruption þ c0 CV þ -it FDIjt ¼ b0j þb1 Rkit þb2 Yjt þb3 DIERjt þb4 Ratingjt þb5 Blacklistjt þb6 Corruptionjt þf0 CVþtjt Creditkt ¼ v0k þ v1 RGapkt þ v2 Ykt þ v4 Ratingkt þ v5 Blacklistkt þv6 Corruptionkt þ u0 CV þ kt ð10Þ ð11Þ ð12Þ The constants a0, b0, and v0 in the three equations are country fixed effects.23 Iit in equation (10) represents domestic investment for country i at time t, Rk is the rate of return of capital at that time, FDI is the flow of foreign direct investment, and DIER represents nominal depreciation, for each country. Rating indicates the degree of country’s compliance with the FATF rules; blacklist is a dummy variable, taking a value equal to 1 when the country refuses to cooperate with the FATF rules and 0, otherwise. Corruption indicates the degree of soundness of governmental institutions, and CV is control variables. FDI in equation (11) is explained by the rate of return Rk, the level of per capita GDP (Y), which examines the effect of economic activity on FDI decisions, and variations in the nominal exchange rate (DIER). The gap between the domestic and the foreign interest rate (RGap) in equation (12) identifies how the 22 Elaborated also by the FATF and indicates that the country refuses to cooperate to control the entrance of illicit money. 23 To identify specific factors that affects the country’s performance, other than the explanatory variables (Wooldridge, 2002). © 2013 John Wiley & Sons Ltd ILLICIT CAPITAL FLOWS IN THE LAC REGION 109 domestic financial market (credit) responds to external financial conditions. The error terms ϖ, υ, and e are random variables with zero mean and constant variance; they are assumed to be identical and independently distributed. Finally, the ARDL (p, q) model in equation (13) analyzes how illicit activities affect the dynamics of output: Dyit ¼ Rj¼0;p1 dij Dyitj þ Rj¼0; q1 qij xitj þ p½yit1 ai b0 xit kt þ fit ð13Þ The dependent variable (y) in equation (13) is log of per capita GDP; x is a vector of explanatory variables, which includes domestic investment (I), FDI, Credit, and the three institutional variables (rating, blacklist, and corruption), and p and q are lags of the dependent variable and the set of explanatory variables, respectively. The expression in brackets represents the adjustment of variable y to its long-term trend, where the long-term equation is yit = ai + b’xit + kt + mit. The term k in this equation is a common variable for the entire group of countries that varies with time; p denotes the speed of adjustment of variable y to its long-term trend. The error terms z and v are random variables with zero mean and constant variance. 4.2 Data To estimate equations (10), (11), (12), and (13) we use a panel of data comprising 36 countries from 1960 to 2010. We estimate domestic investment (I ) by using annual data on fixed capital formation (FCF) adjusted by inflation, both in levels and in the ratio of FCF to GDP. We adopt a similar procedure to estimate foreign direct investment (FDI) and credits (credit), using annual flows and the ratios of these variables to GDP. The estimations of output dynamic in equation (13) were made with a constant U.S.-dollar series of per capita GDP. In the case of explanatory variables, we used money variation (D log of money) and productivity24 as proxies for the return of capital (Rk) because we could not obtain homogeneous data for this variable across the full sample of countries. Alternatively, as a measure of productivity, we use the variable schooling.25 Data of exchange rates correspond to the indexes of exchange rates for each country. We used the gap of interest rates (gap of interest rate) to represent the difference between the short-term interest rate for each country and the short-term interest rate of the United States. As control variables, we included the log of country’s population (population), the ratio of money to GDP (money/GDP), the ratio of credit to GDP (credit/GDP), and the ratio of foreign reserves to GDP (reserves/GDP). We also added two regional dummies for membership in Caricom and Mercosur, and one dummy variable for membership in the OECD. Among the institutional variables, we use the composed index rating, the dummy variable blacklist and the index corruption (see definition in 4.3). To compare the effect of these variables on credit markets with other institutional variables, we added the dummy variable leverage in the equations of credit. Note that although the regulation to prevent illicit flows of money would overlap some aspects of the banking regulation (included in the Basel rules), they are completely different in nature. Whereas the main concerns of Basel rules are financial stability and the soundness of financial 24 Measured as real output per worker. Educational attainment (see the details in the next section). Although the quality of education may differ across countries, the literature shows a positive relationship between schooling and labor productivity in developing countries. 25 © 2013 John Wiley & Sons Ltd FARIAS AND ARRUDA DE ALMEIDA 110 system, the FATF is focused on combating and preventing illegal transactions. Because these types of transactions operate through different economic sectors,26 the FATF rules involve different institutional aspects. For example, those rules related to the financial sector are mainly oriented to eliminate the clause of banking secrecy that protects the origin of financial transactions in the banking sector. 4.3 Variable Definitions and Sources Blacklist: Dummy variable that takes a value equal to 1 if the country has been pointed out as non-cooperating in year t, and 0 otherwise. Source: FATF, NonCooperative Countries and Territory Review (2007). Black Market Premium: Difference between a government’s official exchange rate and that sold in the black market. Source: World’s Currency Yearbook; Wood (1988). Caricom: Dummy variable that takes a value equal to 1 if the country was member of this group at time t, and 0 otherwise. Source: World Trade Organization (2010). Corruption: It takes values between 0 (no corruption) and 100 (full corruption). In the original index a “non-corrupt” country is graded with a high score (10), and a “corrupt” one with a low score (0). Source: ICRG, Transparency International (The PRS Group, 2010). Credit: Millions USD. Domestic credit provided by the banking sector includes all credit into the economy on a gross basis (it excludes credit to the central government). Source: IMF (2011); World Bank (2010b). Current Account: Net balances in millions USD, 1960–2010. Source: IMF (2011). Ex. Rate Index: Index of exchange rates, with 2000 as base year. Source: IMF (2011). FATF Ratings: Index of compliance based on 49 FATF scores of compliance. It take values between 100, for full compliance, and 0, for no compliance. Source: FATF, Mutual Evaluation Reports (various). Fixed Capital Formation: Annual flows in current USD, 1960–2010. Source: World Bank (2010b). Foreign Direct Investment: Net flows of FDI in millions USD, 1960–2010. Source: IMF (2011); World Bank (2010b). Gini coefficient: This index measures the extent to which the distribution of income among individuals or households within an economy deviates from a perfect equal distribution. A low score (0) represents perfect equality and a high score (100) implies perfect inequality. Source: World Bank (2010b). Inflation: As measured by consumer price indexes (CPI), it corresponds to the percentage of annual change in CPI for each country. Source: IMF (2011); World Bank (2010b). Interest rate gap: Ratio between the real interest rate for each country and the U.S. real interest rate at year t. Source: IMF (2011). Leverage: Dummy variable that takes values equal to 1 if the country includes in its banking regulation a simple leverage ratio which is required, and 0 otherwise. Source: Barth et al. (2006). Mercosur: Dummy variable that takes values equal to 1 if the country was member of this group at time t, and 0 otherwise. Source: World Trade Organization (2010). 26 Real estate, entertainment industry, professional services, and financial services, among others. © 2013 John Wiley & Sons Ltd ILLICIT CAPITAL FLOWS IN THE LAC REGION 111 Money: Money and quasimoney (M2). In local currencies, converted into U.S. dollars and adjusted by domestic inflation. Source: IMF (2011); World Bank (2010b). Per Capita GDP: Per capita GDP in constant (2000) U.S. dollars, 1960–2010. Source: World Bank (2010b). Population: Total country’s population in numbers of persons, 1960–2010. Source: The The Conference Board, Inc. (2010); World Bank (2010b). Portfolio Investment: Net portfolio investment in U.S. million dollars, 1968–2010. Source: IMF (2011). Productivity: Output contribution in real terms by employed person. Constant (1990) U.S. dollars, adjusted to constant (2000) U.S. dollars, 1960–2010. Source: The Conference Board, Inc. (2010). Real interest rate: (%) Real interest rate is the lending interest rate adjusted for inflation as measured by the GDP deflator, 1961–2009. Source: IMF (2011); World Bank (2010b). Reserves: Foreign reserves in millions USD, 1960–2010. Source: IMF (2011). Schooling: Educational attainment for population over 15 year old, average years of schooling. Source: Barro-Lee Educational Attainment Dataset (2010). Tax rate: Total tax rate as a percentage of commercial profits, 1980–2010. This rate represents the amount of taxes payable by businesses after accounting for allowable deductions and exemptions. It excludes personal income taxes and indirect taxes. Source: World Bank (2010b); International Tax Network (Cooper & Lybrand) (Loayza et al., 1998a). 5. RESULTS The results of the quantitative analysis are summarized in the tables of the Appendix. To contrast the econometric results, in addition of Tables 1 and 2 mentioned before, we include a summary of the main indicators in Tables S3, S4a, and S4b, and Figure S1 and S2 in online Appendix. Thus, Table 3 shows the relationship between per capita GDP and financial development by geographic zone, including the United States and other FATF members. Table S4a compares the indicators between LAC and the two regional agreements,27 and Table S4b compares the indicators between listed and not listed countries (Blacklist = 0). Figure S1 compares the FATF scores with the corruption index, and Figure S2 shows the correlation between per capita GDP and financial development. In the following pages, Tables 1–7 summarize the econometric results. Looking at Table S3 in online Appendix, we observe for the first group of countries a positive correlation between the relative size of the country (per capita GDP) and the level of financial development. We see the same trend in Figure S2. However, there are countries, such as Anguilla, Grenada, Dominica, and Panama, where the level of financial development exceeds the relative size of the country. This fact would be indicating that these countries receive money under special conditions. On the other hand, the indicators of Table S4a show similar rates of growth of per capita GDP (DGDPpc) across LAC and the two regional agreements, on average. The standard deviations of these rates of growth also show a similar behavior. However, we find large differences between listed and non-listed countries; the later have faster real GDP and productivity growth than the former. The same is observed comparing Mercosur to Caricom, which 27 Caricom and Mercosur. © 2013 John Wiley & Sons Ltd © 2013 John Wiley & Sons Ltd Yes No No Yes 949 92% 586 Yes – 0.05 (0.015)** 0.03 (0.005)*** 0.02 (0.007)* – Heterosk GLS Yes 949 22% – *p < 0.05; **p < 0.01; ***p < 0.001; – : No data. Control Variables: Yes Variables: population, schooling. Regional dummy var: Yes Regions: caricom, mercosur. Country fixed effects: No Observations R2 Chi-square 949 22% 71 – – Corruption Blacklist Rating Log of FDI 0.03 (0.021) 0.04 (0.007)*** 0.01 (0.008) – 0.03 (0.021) 0.04 (0.006)*** 0.01 (0.008) – Fixed Effects ΔLog of money Random Var. Model 1 Random Var. No Yes Yes 931 22% 79 (LOG OF Yes No Yes 931 22% – 0.04 (0.022) 0.04 (0.007)*** 0.02 (0.009)* 0.04 (0.014)* – Fixed Effects Model 2 CAPITAL FORMATION 0.04 (0.021) 0.04 (0.006)*** 0.02 (0.009)* 0.03 (0.014)* – TABLE 1. FIXED No Yes Yes 931 92% 562 0.04 (0.015)** 0.03 (0.005)*** 0.02 (0.007)** 0.00 (0.010) – Heterosk GLS FCF) No Yes Yes 0.07 (0.023)** 0.06 (0.009)*** 0.02 (0.009)* 0.00 (0.015) 0.18 (0.060)** 583 11% 65 Random Var. Yes No Yes 0.07 (0.023)** 0.06 (0.010)*** 0.02 (0.010) 0.01 (0.017) 0.21 (0.070)** 583 12% – Fixed Effects Model 3 No Yes Yes 0.02 (0.018) 0.03 (0.007)*** 0.02 (0.007)*** 0.01 (0.010) 0.18 (0.030)*** 583 80% 152 Heterosk GLS 112 FARIAS AND ARRUDA DE ALMEIDA © 2013 John Wiley & Sons Ltd Yes No No Yes 1038 41% 2339 Yes – 0.13 (0.086) 0.05 (0.012)*** 0.03 (0.034) – Heterosk GLS Yes 1038 18% – *p < 0.05; **p < 0.01; ***p < 0.001; – : No data. Control variables: Yes Variables: population, schooling. Regional dummy var: Yes Regions: caricom, mercosur. Country fixed effects: No Observations R2 Chi2 1038 44% 1675 – – Corruption Blacklist Rating FDI/GDP 0.06 (0.125) 0.05 (0.014)*** 0.10 (0.046)* – 0.08 (0.125) 0.05 (0.014)*** 0.09 (0.046)* – Fixed effects Model 1 Money/GDP Random var. No Yes Yes 1025 45% 1625 0.03 (0.129) 0.06 (0.014)*** 0.08 (0.050)* 0.01 (0.003) – Random var. Yes No Yes 1025 19% – 0.03 (0.129) 0.05 (0.014)*** 0.09 (0.049)* 0.00 (0.003) – Fixed effects Model 2 TABLE 2. CAPITAL RATIO (FCF/GDP) No Yes Yes 839 59% 1135 0.33 (0.081)*** 0.12 (0.014)*** 0.00 (0.003) 0.20 (0.022)*** – Heterosk GLS No Yes Yes 0.38 (0.123)** 0.04 (0.009)*** 0.10 (0.031)*** 0.01 (0.003)*** 0.24 (0.060)*** 567 31% 153 Random var. Yes No Yes 0.39 (0.123)** 0.03 (0.009)*** 0.11 (0.031)*** 0.01 (0.003)*** 0.21 (0.061)*** 567 12% – Fixed effects Model 3 No Yes Yes 0.30 (0.075)*** 0.06 (0.012)*** 0.00 (0.002)* 0.11 (0.022)*** 0.28 (0.023)*** 567 81% 656 Heterosk GLS ILLICIT CAPITAL FLOWS IN THE LAC REGION 113 © 2013 John Wiley & Sons Ltd *p < 0.05; **p < 0.01; ***p < 0.001; – : No data. No No No Yes Yes –706 76% 1098 Yes –726 64% 4323 1.08 (0.153)*** 3.35 (0.248)*** 0.60 (0.136) *** 0.11 (0.038)** 0.06 (0.064) – Random var. Yes –726 74% – – 0.85 (0.059)*** 2.61 (0.145)*** 0.73 (0.133)*** 0.17 (0.031)*** – Heterosk GLS Yes Control variables: Variables: population, log of Regional dummy var: Regions: caricom, mercosur. Country fixed effects: – 2.02 (0.233)*** 2.94 (0.445)*** 0.50 (0.133) 0.03 (0.035) – Fixed effects Yes Yes money to GDP. Yes No –726 76% 1165 – 1.11 (0.148)*** 3.38 (0.242)*** 0.61 (0.133***) 0.10 (0.035)** – Observations R2 chi2 Corruption Blacklist Rating Δ Ex rate index Schooling (log) Per capita GDP (log) Random var. Model 1 Yes No Yes –706 74% – 2.10 (0.247)*** 2.75 (0.468)*** 0.49 (0.135) *** 0.04 (0.039) 0.02 (0.068) – Fixed effects Model 2 TABLE 3. FOREIGN DIRECT INVESTMENT (LOG OF No Yes Yes –706 65% 4242 0.82 (0.061)*** 2.53 (0.151)*** 0.66 (0.127) *** 0.18 (0.034)*** 0.09 (0.035)* – Heterosk GLS FDI) No Yes Yes 0.83 (0.183)*** 4.33 (0.397)*** 0.64 (0.148)*** 0.03 (0.036) 0.12 (0.067) 0.03 (0.008)*** 517 74% 836 Random var. Yes No Yes –0.02 (0.008)** 517 74% – 2.16 (0.312)*** 4.05 (0.748)*** 0.40 (0.147)** 0.07 (0.037) (0.070) Fixed effects Model 3 No Yes Yes 0.69 (0.078)*** 1.94 (0.210)*** 0.76 (0.127)*** 0.17 (0.032)*** –0.20 (0.038)*** –0.03 (0.005)*** 517 76% 2921 Heterosk GLS 114 FARIAS AND ARRUDA DE ALMEIDA © 2013 John Wiley & Sons Ltd – 839 19% 175 0.07 (0.020)*** 0.04 (0.004)*** 0.01 (0.004)* 0.002 (0.001)* – – 839 15% – 0.07 (0.020)*** 0.04 (0.010)*** 0.01 (0.004)* 0.002 (0.001) – Fixed effects Model 1 *p < 0.05; **p < 0.01; ***p < 0.001; – : No data. Control Variables: Yes Yes Variables: population, ratio of money to GDP. Regional dummy var: Yes No Regions: caricom, mercosur. Country fixed effects: No Yes Observations R2 Chi2 Corruption Blacklist Rating Δ Ex rate index Per capita GDP (Δlog) Schooling (log) Random var. OF – Yes Yes No Yes No 796 20% 154 0.07 (0.020)*** 0.03 (0.004)*** 0.01 (0.004) 0.002 (0.001)* 0.001 (0.002) – Yes 839 28% 355 0.04 (0.013)** 0.02 (0.002)*** 0.01 (0.003)* 0.003 (0.001)*** – Random var. TO Yes No Yes 796 18% – No Yes Yes 796 31% 365 0.03 (0.013)** 0.02 (0.002)*** 0.00 (0.003) 0.004 (0.001)*** 0.003 (0.001)*** – Heterosk GLS GDP (FDI/GDP) 0.08 (0.020)*** 0.04 (0.012)*** 0.01 (0.004) 0.002 (0.001) 0.000 (0.002) – Fixed effects Model 2 FOREIGN DIRECT INVESTMENT Heterosk GLS TABLE 4. RATIO No Yes Yes 0.06 (0.025)* 0.06 (0.011)*** 0.01 (0.005) 0.001 (0.001) 0.003 (0.002) 0.04 (0.013)*** 515 20% 124 Random var. Yes No Yes 0.05 (0.024)* 0.09 (0.024)*** 0.00 (0.005) 0.001 (0.001) 0.000 (0.002) 0.06 (0.014)*** 515 9% – Fixed effects Model 3 No Yes Yes 0.02 (0.015) 0.03 (0.004)*** 0.01 (0.003)* 0.003 (0.001)*** 0.004 (0.001)*** 0.02 (0.006)*** 515 55% 240 Heterosk GLS ILLICIT CAPITAL FLOWS IN THE LAC REGION 115 © 2013 John Wiley & Sons Ltd 2.87 (1.435)* 530 33% 616 Leverage Yes Yes No No Yes 1.27 (0.133)*** 530 74% 1720 – 0.47 (0.422) 0.51 (0.113)*** 0.27 (0.057)*** – Heterosk GLS Yes *p < 0.05; **p < 0.01; ***p < 0.001; – : No data. Control variables: Yes Variables: population, Δ log of money. Regional dummy var: Yes Regions: caricom, mercosur. Country fixed effects: No Observations R2 Chi2 468 21% 1905 – – Corruption Blacklist Rating Per capita GDP (log) 1.00 (0.330)** 0.61 (0.436) 0.05 (0.059) – IV–Fixed effects 0.65 (0.294)* 1.27 (0.338)*** 0.23 (0.061)*** – Gap of interest rate Random var. Model 1 No Yes Yes 2.89 (1.477) 530 33% 632 0.65 (0.292)* 1.19 (0.339)*** 0.24 (0.061)*** 0.22 (0.118) – Random var. TABLE 5. LOG OF Yes No Yes 468 21% 1901 1.00 (0.330)** 0.62 (0.437) 0.05 (0.059) 0.04 (0.106) – IV–Fixed effects Model 2 CREDIT No Yes Yes 1.28 (0.139)*** 530 73% 1680 0.50 (0.437) 0.54 (0.138)*** 0.32 (0.068)*** 0.06 (0.084) – Heterosk GLS No Yes Yes 1.46 (0.344)*** 0.71 (0.466) 0.23 (0.064)*** 0.39 (0.132)** 1.92 (0.718)** 1.88 (1.423) 422 25% 482 Random var. Yes No Yes 393 15% 1975 0.98 (0.317)** 0.21 (0.533) 0.06 (0.057) 0.27 (0.118)* 0.92 (0.635)* – IV–Fixed effects Model 3 No Yes Yes 1.60 (0.529)** 0.70 (0.149)*** 0.23 (0.056)*** 0.15 (0.071)* 7.30 (0.725)*** 1.27 (0.194)*** 422 79% 671 Heterosk GLS 116 FARIAS AND ARRUDA DE ALMEIDA © 2013 John Wiley & Sons Ltd 0.22 (0.020)*** 501 75% 183 – 0.23 (0.080)** 501 18% 74 Leverage Yes Yes No Yes No Yes *p < 0.05; **p < 0.01; ***p < 0.001; –: No data. Control variables: Yes Variables: population, productivity. Regional dummy var: Yes Regions: OECD, Mercosur. Country fixed effects: No Observations R2 Chi2 501 13% – – – – Corruption Blacklist Rating ΔEx rate index 1.01 (0.262)*** 0.05 (0.033) 0.007 (0.008) – Heterosk GLS 1.28 (0.202)*** 0.03 (0.028) 0.000 (0.007) – Fixed effects Model 1 1.26 (0.205)*** 0.03 (0.027) 0.002 (0.007) – Reserves/GDP Random var. CREDIT Random var. OF No Yes Yes 0.19 (0.074)** 492 34% 122 1.06 (0.186)*** 0.06 (0.025)* 0.018 (0.006)** 0.08 (0.011)*** – TABLE 6. RATIO TO – Yes No Yes 492 6% – 1.09 (0.185)*** 0.05 (0.025)* 0.016 (0.006)** 0.07 (0.011)*** – Fixed effects Model 2 No Yes Yes 0.16 (0.018)*** 492 59% 436 0.80 (0.220)*** 0.09 (0.028)** 0.023 (0.007)** 0.14 (0.011)*** – Heterosk GLS GDP(CREDIT/GDP) No Yes Yes 0.92 (0.192)*** 0.01 (0.024) 0.016 (0.006)** 0.12 (0.014)*** 0.01 (0.047) 0.17 (0.092) 339 39% 131 Random var. Yes No Yes 339 24% – 0.91 (0.191)*** 0.00 (0.024) 0.018 (0.006)** 0.12 (0.014)*** 0.03 (0.050) – Fixed effects Model 3 No Yes Yes 0.83 (0.238)*** 0.04 (0.028) 0.003 (0.007) 0.14 (0.012)*** 0.25 (0.041)*** 0.15 (0.021)*** 339 68% 423 Heterosk GLS ILLICIT CAPITAL FLOWS IN THE LAC REGION 117 © 2013 John Wiley & Sons Ltd 0.01(0.002)*** 0.15(0.031)*** 0.03(0.005)*** – – 0.06(0.019)* 833 63% Credit/GDP 0.01(0.002)*** 0.08(0.016)*** 0.00(0.001)*** – – 0.07(0.023)* 833 64% Δlog of Money Δlog of Money 0.01(0.002)*** 0.09(0.019)*** 0.01(0.001)*** 0.01(0.002)*** – 0.05(0.021)* 821 64% 0.24(0.050)*** 0.22(0.045)*** 0.26(0.052)*** 1.35(0.675)* 0.06(0.006)*** 0.50(0.177)** 0.03(0.027) 0.05(0.022)*– 0.18(0.037)*** 1.18(0.611) Model 1 PER CAPITA GDP (YT) Credit/GDP 0.01(0.002)*** 0.09(0.018)*** 0.01(0.002)*** 0.01(0.002)*** – 0.06(0.019)** 821 63% 0.14(0.027)*** 0.06(0.006)*** 0.48(0.180)* 0.04(0.026) 0.06(0.021)*– 0.19(0.037)*** 0.72(0.638) Model 2 Pooled Mean Group Estimator (PMGE) OF 0.05(0.007)*** 0.81(0.195)** 0.14(0.027)*** – – 0.19(0.038)*** 0.06(0.006)*** 0.43(0.167)* 0.01(0.027) – – 0.19(0.037)*** 1.42(0.676)* *p < 0.05; **p < 0.01; ***p < 0.001; – : No data. Control variables: Short-term coefficients Capital ratio Log of FDI Schooling (log) Rating Blacklist Corruption Δ Log of PC GDP-1 Observations R2 Long term coefficients Capital ratio Log of FDI Schooling (log) Rating Blacklist Corruption Phi-coefficient TABLE 7. LOG Δlog of Money 0.01(0.002)*** 0.12(0.022)*** 0.02(0.004)*** 0.00(0.001)*** 0.02(0.004)*** 0.12(0.029)*** 549 15% 0.33(0.061)*** 0.06(0.008)*** 0.64(0.203)** 0.12(0.031)** 0.00(0.025)0.13(0.037)** 0.18(0.034)*** 1.82(0.705)* Model 3 Credit/GDP 0.01(0.002)*** 0.08(0.016)*** 0.00(0.001)*** 0.01(0.003)*** 0.03(0.006)*** 0.05(0.018)* 549 43% 0.18(0.038)*** 0.06(0.007)*** 0.42(0.184)* 0.02(0.026) 0.07(0.023)*0.15(0.051)* 0.18(0.037)*** 1.03(0.640) 118 FARIAS AND ARRUDA DE ALMEIDA ILLICIT CAPITAL FLOWS IN THE LAC REGION 119 contains most of the offshore centers. Turning to the current account deficits, Table S4a shows a higher ratio of deficit to GDP in Caricom than Mercosur and the LAC region on average. Comparing between listed and non-listed countries, the difference is more dramatic, reaching a ratio of 14.3 percent in listed countries, on average, and 5.2 percent in non-listed countries. Examining the ratio of portfolio investment to GDP and the ratio of FDI to GDP, we find similar ratio of portfolio investment in Caricom and LAC on average, but the ratio of FDI is higher in Caricom. Contrarily, Mercosur exhibits a higher ratio of portfolio investment than FDI, indicating that foreign investment would be more speculative in this country. Unsurprisingly, listed countries receive almost three times of both types of foreign investment than non-listed countries. Thus, whereas portfolio investment represented 6.5 percent of FDI in LAC between 1960 and 2010, and 5.5 percent in non-listed countries, it reached 36.9 percent in listed countries. Although Mercosur also shows a large ratio of portfolio investment, other indicators would suggest that listed countries have been receiving capital inflows for different reasons other than doing business. The same would be occurring in countries that are members of Caricom. If we look at each country’s gap between its interest rates and that of the United States, the gap, which reached 17 percent in that period, would explain large inflows of short-term capital in Mercosur. However, this gap was around 4.9 percent in listed countries and negative in Caricom, on average. Lower rates of GDP growth, slow productivity, and small gains in short-term investment cannot explain the huge investment ratios we observe in listed countries. Nor can tax rates explain the anomaly; the tax rates of listed countries are similar to other LAC on average. Moreover, although Caricom and Mercosur countries show a similar rate of money variation, the ratio of credit to GDP in Caricom was around 20 percent larger than Mercosur countries’ ratio on average. Comparing the ratios of listed and non-listed countries, the difference is almost 30 percent. Higher capital and credit ratios to GDP, unaccompanied by large gains in terms of productivity, GDP growth, or gaps of interest rates, suggest that foreign capital flows to these countries for motivations other than doing good business. 5.1 Investment Tables 1 and 2 of Appendix summarize the results of our estimates of equation (10) with data of fixed capital formation (FCF). In each table, Model 1 includes just one of the institutional variables (Rating), Model 2 includes two of them (rating and blacklist), and Model 3 includes all three of them (rating, blacklist, and corruption). In each case, we use three types of econometric methods to ensure robustness: random variables, fixed effects, and generalized least squares (GLS) with heteroskedastic disturbances. All models include population and schooling as control variables. The models estimated with random variables and GLS contain regional variables (Caricom and Mercosur) as control variables, and the models estimated with fixed effects, country fixed effects. In general, we found large correlation among the group of macro-explanatory variables. To avoid multicollinearity, we omitted some variables or used a variable scaled by each explanatory’s ratios to GDP instead the levels of the explanatory variables themselves. Thus, the estimations of Table 1 were obtained using the levels of the variables and those of Table 2, with the ratios. Among the macrovariables, the most significant was FDI, whether measured in levels (log of FDI) or as a ratio of FDI to GDP (FDI/GDP). D Log of money was significant only in the GLS estimations of © 2013 John Wiley & Sons Ltd FARIAS AND ARRUDA DE ALMEIDA 120 Models 1 and 2 of Table 1, although it was significant in the estimations of random variables and fixed effects of Model 3. Similarly, the ratio of money to GDP (money/ GDP) shows significance in the GLS estimation of Model 2. In Table 2, however, it was significant in the three estimations of Model 3. In addition to acquire significance, the coefficient value of Money/GDP increased as well in these estimations. The signs of the coefficients of money and FDI are consistent with the theory. As measures of market liquidity, D Log of Money and Money/GDP show a positive relationship with the level of FCF and the ratio to GDP, respectively. In addition, FDI (both log of FDI and FDI/GDP) shows a positive relationship with FCF as well, indicating that the former is a component of the latter. The behavior of the institutional variables is mixed. Rating was not much significant in the estimations of Model 1, in Table 1, but it acquired great significance in Models 2 and 3 of this table. When we use capital ratio instead of the level of FCF in Table 2, rating is more significant although the sign of the coefficient changes in the estimations of Model 3. Blacklist was barely significant in the level estimations of Table 1, but it acquired high significance in the ratio estimations of Table 2, specially, when we use GLS. Corruption was highly significant in the three types of estimations of Model 3, using both levels and capital ratio. Moreover, although the coefficient values of rating were small (around 0.02 on average), those of corruption were large enough (between 0.18 and 0.28 in absolute terms) to suggest substantively impact on the behavior of domestic investment; see Tables 1 and 2. The value of the coefficient of blacklist was also small, even though it was high when we used GLS in the estimations of Table 2. Thus, the results here suggest that domestic investment is highly sensitive to institutional aspects, especially those concerned with government behavior. Regarding the FATF indicators, the results show a positive relationship between investment and the FATF scores (rating), with some exceptions.28 Similarly, we find in general a negative relationship between blacklist and domestic investment. Indeed, according to the numbers of Table 2, we can say that a country would lose between 11 and 20 percent of domestic investment, when it is included in the blacklist.29 Nevertheless, the coefficients of the FATF indicators were not always significant, even in the GLS estimations, which are considered the most robust (in terms of levels of significance). An explanation of this result is that the FATF scores are more connected to the behavior of foreign flows of investment than that of domestic flows of investment. Beyond that, we need more evidence to draw conclusions about the FATF and the outcomes for the countries it targets. 5.2 Foreign Direct Investment To estimate equation (11), we use the log of schooling as a proxy for productivity or returns to capital and the log of per capita GDP to evaluate the effect of economic activity on foreign direct investment. Similar to the previous estimations, we use random variables, fixed effects, and GLS to estimate Models 1, 2, and 3. In the levels equations of Table 3 we include population and the D log of money as control variables. For the equations of Table 4, we use population and the ratio of money to GDP as control variables (similar to the use of levels and ratios in Tables 1 and 2 above). As we can see in Tables 3 and 4, per capita GDP and schooling were highly significant in 28 See Table 2. In terms of the ratio of fixed capital formation to GDP. 29 © 2013 John Wiley & Sons Ltd ILLICIT CAPITAL FLOWS IN THE LAC REGION 121 all the estimations. In addition, the coefficient values were always positive, even though the value was much larger in the level than the ratio equations. From these preliminary results, we may infer that foreign investment decisions are highly affected by the level of economic activity and the rates of returns in the host economy. Nevertheless, some reversal effect would be present in these findings as well, where faster GDP and productivity growth promote FDI. On the other hand, output growth and productivity are enhanced by FDI. Nominal devaluation (D ex rate index) was significant in the level equations of Table 3 but not much in the ratio equations of Table 4. In all the cases, the coefficient values were negative. The latter would suggest that FDI would have been allocated toward non-tradable activities more than tradable ones. To obtain more conclusive results, we would need additional information about the behavior of FDI, which is beyond the scope of this research. In the case of the institutional variables, we found greater significance and larger coefficient values for rating in the level equations of Table 3, than those found in the previous results. These results indicate that the FATF scores matter for foreign investment, more than for domestic investment. Some reversal effect would be present in these findings as well, where countries with large flows of foreign direct investment care more about compliance than those with low flows (see Barth et al., 2006). Beyond these facts, the results suggest that the FATF scores may act as signals of country’s seriousness for foreign investors. We find less significance and smaller coefficient values in the ratio equations of Table 4 for rating. Similar results were obtained for blacklist using both the level of FDI and FDI/GDP. Again, 0.20 was the larger coefficient value for this variable. The third institutional indicator, corruption, was always significant even though its coefficient values were much smaller than in the estimations of domestic investment of tables 1 and 2. We can infer from the above results that the FATF scores are more important for foreign investors than for domestic investors. By contrast, the rule of law (corruption) may be more important for domestic investors. In both case, the effect of being blacklisted would be similar. 5.3 Credits Using an approach similar to the previous exercises, we estimate equation (12) using the level of credits and its ratio to GDP. Table 5 displays the results of the level equations and Table 6 the results of the ratio equations. The results of Table 5 were obtained using the gap of interest rates (I rate gap) and per capita GDP as macroexplanatory variables. Here, I rate gap represents the short-term return and per capita GDP the long-term return on financial development. Given the strong relationship found in the literature between financial development and growth,30 we consider per capita GDP as an endogenous variable and use fixed effects with instrumental variables. However, due to problems of multicolinearity among the ratios of these variables, we replace them with Reserves/GDP and D Ex Rate Index in the ratio equations of Table 6. As control variables, we include population and D log of money in the level equations, and population and productivity in the ratio equations. Using D log of money, we can check the effect of economic policy on the depth of financial markets (Bernanke, 1992–3). Productivity looks for capturing the effect of long-term returns on credits. We include OECD to measure a group effect in the ratio equations of Table 6. 30 Levine and Zervos (1998); Rajan and Zingales (1998). © 2013 John Wiley & Sons Ltd 122 FARIAS AND ARRUDA DE ALMEIDA As we can see in Table 5, the estimate of the gap of interest rate was highly significant. Moreover, we obtained a large and positive coefficient value for this variable. We find similar results for per capita GDP, although the coefficient values are negative in the last equations of Model 3 (IV fixed effects and GLS). The ratio of reserves to GDP (reserves/GDP) was highly significant as well in the estimations of Table 6, where the value of its coefficient was large enough in all the estimations. These numbers suggest a strong connection between international reserves and domestic financial markets in the sample of countries. Nominal devaluation (D ex rate index) was not always significant, although the sign of the coefficients coincides with the theoretical predictions.31 Among the institutional variables, the behavior of rating improves compared to the estimations of Tables 1–4. In fact, rating reaches great significance when we use random variables and GLS to estimate the level of credits. The coefficient values surpass the previous estimations, reaching values between 0.23 and 0.27. However, we see less significance and smaller coefficient values in the ratio equations of Table 6. Beyond that, the results show a strong connection between the FATF scores and credits. The behavior of blacklist is more erratic in the level equations; in spite of this it shows high significance in the ratio equations of Table 6. Accordingly, a country could lose between 7 and 14 percent of financial development for appearing in the blacklist of FATF. Corruption also seems to be important; the effect is better captured in the level equations than the ratio equations of credit. The financial institutional variable, leverage, was also significant, reaching large coefficient values. Because leverage indicates the quality of financial regulation, this result would suggest that credits are enhanced in a sound institutional environment. However, as Barth et al. (2006) have pointed out, the strong correlation between credit and leverage we find here would be indicating a reversal effect more than causality. Indeed, it is reasonable to think that countries with large financial markets are more concerned about financial distress than countries with small financial markets. Then, the quality of financial regulation should improve with financial development. Thus, although there are some differences across the variety of equations we estimate, the econometric analysis shows a positive correlation between the amount of credits and compliance and a negative correlation between credits and blacklist. We also find a negative correlation between credits and corruption. We can infer then that institutional factors are important for domestic credit markets, acting these factors as signals of country seriousness for legitimate investors and as signals of market access for illegitimate investors. Indeed, Tables S3, S4a, and S4b (in online Appendix) show higher ratios of credits to GDP in Caricom and listed countries than in LAC, on average. Comparing with the United States and other FATF member countries, we see that the size of credit markets (financial development) in some cases exceeds the size of the country, especially in those countries that have been involved in illicit flows of money. Looking at the results obtained in the estimations of domestic investment, foreign direct investment, and credits, we can conclude that low institutional scores are meaningful for developing countries. In opposition to the results of Barth et al. (2006), we find a strong connection between these three macrovariables and the institutional variables. The three indicators, rating, blacklist, and corruption, were significant, and both the sign and the value of their coefficients indicate a negative relationship 31 At least in a context of fixed exchange rates, where nominal devaluations lead to more liquidity. © 2013 John Wiley & Sons Ltd ILLICIT CAPITAL FLOWS IN THE LAC REGION 123 between low scores and investor decisions. Even though other studies have found little evidence of such relationship, our results are robust in this respect. 5.4 Output Dynamics To evaluate the effect of institutional indicators in the whole economy, we estimate equation (13) using the pooled mean group estimator developed by Pesaran and Shin (1999). As Pesaran et al. (1999) pointed out; this method permits to estimate heterogeneous samples of data such as the group of LAC countries that share common characteristics (historical roots, language, etc.), in spite of their economies and current institutions differ. Thus, imposing the condition that the long-term coefficients must be identical, the PMGE allows the possibility that the short-run coefficients differ across the group of countries. If the group meets the condition, there is convergence; otherwise, the sample does not converge. In this research, the difference between the short run and the long term is given by the behavior of the macroeconomic variables. In the short run, these variables fluctuate with the business cycle. In the long term, they perform according to equilibrium conditions. In such a way, differences in the long term may be explained by structural factors. We use, then, the PMGE to examine how illicit activities, signaled by the FATF scores and corruption indicators, affect the dynamic of output through three channels: domestic investment, foreign direct investment, and credits. Given that these three variables were highly correlated, we use several proxies to obtain robust estimations. Table 7 summarizes the results. We use capital ratio as domestic investment, log of FDI and schooling as a measure of productivity. To examine the effect of credits on economic activity, we include D log of money and credit/GDP as control variables. Thus, the first column of each model contains the estimations that included D log of money as control variable and the second one, credit/GDP. In each case, the long-term coefficients are displayed at the top of Table 7 and the short-term coefficients (the average of the individual estimations), at the bottom. Because we find convergence in the estimations, we can infer that the countries have common long-run coefficients. The row between these subtables displays the phi coefficient that indicates the speed of the economy’s adjustment toward its long-term trend. Although the significance of the coefficients differ in the long term, all of them were significant in the short term. Among the economic variables, capital ratio and FDI were highly significant, even though the magnitude of the coefficients is irregular. Whereas the coefficients of capital ratio suggest a strong connection with output, both in the short term and in the long term, the coefficient of FDI was small in the short term, experiencing little rise in its value over the long term (see Table 7). Even though the value of the coefficients would be distorted by the correlation between these two variables, it is expected to find a stronger correlation between output and domestic investment. The coefficient values of schooling were larger, especially in the long-term equations. The latter would be capturing productivity changes that affect output in the long term and some reversal effect, where greater levels of output per capita would lead families to invest more in education. The role of the institutional variables differs between the short term and the long term. In the short term, the three indicators (rating, blacklist, and corruption) show great significance, although the coefficient values are small. In the long term, the significance varies with the estimations, and in some cases, the coefficient values are © 2013 John Wiley & Sons Ltd 124 FARIAS AND ARRUDA DE ALMEIDA larger than those obtained in the short-term equations. This is the case of rating in the estimations of Model 1 and Model 3, and corruption in Model 3. The significance in the long-term equations indicates persistence of the interaction between the institutional variables and the macrovariables. Greater long-run coefficients than short-run coefficients would be suggesting that the distortions caused by the illicit activities take time to become effective. In this regard, the results would indicate that although the effects of these activities are not perceptible in the short term, they could be affecting the economy in the long term. The behavior of blacklist can be explained by the fact that countries were listed during short periods (see Table S2 in online Appendix), although they may have been involved in illicit activities by several years. According to the results of Table 7, we obtain the best estimations using credit/ GDP as control variable. Thus, comparing the three models that use this control variable, we observe that the phi coefficient decreases when we include the three institutional variables (in Model 3). In this regard, in addition to distort the path of output directly, a bad institutional environment would reduce the adjustment capacity of the economy toward its long-term trend. To test this hypothesis, we estimated impulse response functions of per capita GDP for the group of blacklisted countries, using FDI as an exogenous variable. Given that, we do not have a series with the flows of illicit money, or the legal framework of foreign investment, FDI should be a good proxy of these flows when the country has been involved in illicit activities. Figures in Figures S3(a), 3(b), and 3(c) of online Appendix show the trends of these functions. They indicate irregular behavior of GDP when a shock on FDI threatens the economy in low compliance countries. This effect is more evident in low FATF rating countries, such as Aruba, Barbados, Grenada, and Panama (see Figures in the online Appendix). 6. DISCUSSION Our econometric analysis leads us to infer a strong correlation between domestic and foreign investment in LAC countries. Other factors, such as interest rates and productivity, can also intervene on investment decisions. However, poor institutions would carry these countries to a condition of large dependence on international capitals to finance projects. Paradoxically, even though illicit inflows may provide these funds, they can also increase dependence if licit capitals are deviated because of their activities. In a context of open markets, when productivity decreases, flows of FDI are also threatened, damaging the whole economy. Measuring the direct effects of low FATF scores, Table 2 tells us that the ratio of investment (fixed capital formation) can be reduced at least 2 percent (on average) when a country is poorly rated. Similarly, the country could lose investment at least in 5 percent (on average) when it has been included in the blacklist. Considering that the value of the coefficients indicate an average behavior across the group of countries, the magnitude of the costs can vary with the size of the country and other individual factors, not included in this analysis. There are many other factors explaining investment decisions in LAC, but illicit flows are meaningful for these economies, as well as reputation. With respect to credits markets, the econometric results show that poor institutions (rating, blacklist, and corruption) discourage financial development, although there is evidence that illicit flows increase the depth of financial markets. The evidence for the sample of countries is mixed. It includes cases such as Argentina, a medium-size © 2013 John Wiley & Sons Ltd ILLICIT CAPITAL FLOWS IN THE LAC REGION 125 country, with a FATF score equal to 29.7 percent in 2009 and a ratio of credits to GDP of 27 percent. On the other hand, Aruba, a small Caribbean country, has an FATF rating of 18.8 percent and a ratio of credits to GDP of 50.8 percent in the same year. Other factors may explain these differences, such as a history of macroeconomic imbalances and successive external defaults in countries with low levels of financial development as Argentina, or, on the other hand, close ties with the main financial centers of Europe and the United States, which has been the experience of many OFCs and members of Caricom. A deeper analysis would be required to incorporate these factors by exploring the microeconomics of financial markets and other key sectors within the offshore centers and countries with low scores. One potential weakness of our analysis is that we lack data on the effective flows of illicit money and data about the main offshore centers (Cayman Islands and Virgin Islands). Despite these difficulties, we find indirect mechanisms to identify countries potentially involved in illicit flows of money. We were capable to identify the economic costs of these transactions in terms of loses of productive capital and domestic credit for the recipient countries. Although the absolute costs can vary with the size of the country and the magnitude of these transactions, our results are robust, as we find loses in terms of productive investment and financial development both in the short term and in the long term. 7. CONCLUSIONS The global effort to combat money laundering and the financing of terrorism is fascinating, important, and daunting. The FATF’s policy mandate is very complex and difficult to achieve. It requires a great deal of coordination among international organizations, countries, and territories, as well as a deep understanding of the international financial markets and how they connect with domestic economies. It also calls for an understanding of macroeconomic fundamentals, accounting practices, international law, police enforcement, and intelligence. Considering these factors, the FATF has been around for more than two decades, how would we grade its performance? According to our analysis, the FATF is doing well at creating an environment that economically penalizes jurisdictions that are reluctant to adopt its 40 + 9 financial practice standards. Nevertheless, the FATF initiative does have shortcomings, where we find evidence that lower rated jurisdictions, in particular small ones, may still be receiving illicit flows. In this way, receiving a lower FATF report rating has not been enough to deter investors. Our econometric results are robust. We find evidence that the FATF’s blacklists and evaluation reports do have an effect on jurisdictions’ capacity to attract and retain foreign capital, especially the type of capital related to productive capacity. Indeed, our econometric models show that poor institutional performance by a jurisdiction (rating, blacklist, and corruption) affects negatively the investment ratio to GDP, the FDI ratio to GDP, and financial development (ratio of credit markets to GDP). We also find that although these effects can be imperceptible in the short term, they can become persistent, distorting the long-term equilibriums in the economies of these jurisdictions. Our main contributions in this regard can be summarized as follows; we demonstrate that FATF’s negative evaluations and blacklisting have indeed penalized countries and territories that were target of the body’s critical reviews. We were also able to measure the costs for a country of receiving illicit flows of money, both in the © 2013 John Wiley & Sons Ltd 126 FARIAS AND ARRUDA DE ALMEIDA short term and in the long term. Finally, the macroeconomic variables included in our theoretical and econometrical models can act as alert indicators to identify possible non-compliant jurisdictions. For this reason, we believe that our results offer an important contribution to the debate on financial regulatory convergence because of their academic and policy relevance. In closing, we argue that, as a whole, FATF’s strategy for international compliance is effective. Because the markets seem to be sensitive to reputational threats, perhaps the AML/CFT initiative would be even more successful if it named more often and shamed more loudly. MARIA ELISA FARIAS MONICA ARRUDA DE ALMEIDA Universidad Diego Portales Georgetown University REFERENCES Andreas, P. and Nadelmann, E., 2006, Policing the Globe. Criminalization and Crime Control in International Relations. (Oxford University Press, New York). Alesina, A. and Perotti, R., 1994, The political economy of growth: a critical of recent literature. 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(International Finance Corporation, World Bank). World Bank, 2010b, 2011, World Development Indicators, (Data Base). SUPPORTING INFORMATION Additional Supporting Information may be found in the online version of this article: Figure S1. FATF Score vs. Corruption Index. Figure S2. Financial Development and GDP. Figure S3(a). Impulse - Response Functions Countries Included in the Black List. Figure S3 (b). Impulse - Response Functions Countries Including in the Black List. Figure S3 (c). Impulse - Response Functions Countries Including in the Black List Table S1. Gafisud and CFATF Member Countries and Territories. Table S2. FATF’s Blacklisted Jurisdictions (2000–2006). Table S3. Financial Development by Zone. Table S4 (a). Main Indicators by Regional Agreement 1960–2010. Table S4 (b). Main Indicators by Compliance. © 2013 John Wiley & Sons Ltd