Download Does Saying Yes to Capital Inflows Necessarily Mean Good

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).
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ILLICIT CAPITAL FLOWS IN THE LAC REGION
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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
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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.
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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.
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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.
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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.
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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).
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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,+ ∞).
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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
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ILLICIT CAPITAL FLOWS IN THE LAC REGION
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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
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–
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)
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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
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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
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ILLICIT CAPITAL FLOWS IN THE LAC REGION
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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
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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.
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ILLICIT CAPITAL FLOWS IN THE LAC REGION
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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
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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
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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
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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
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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