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Blacklists, Market Enforcement, and
the Global Regime to Combat Terrorist Financing
Julia C. Morse∗
Abstract
This paper highlights how international organizations can use Global Performance
Indicators (GPIs) to drive policy change through transnational market pressure. When
international organizations are credible assessors of state policy, and when monitored
countries compete for market resources, GPIs transmit information about country risk
and stabilize market expectations. Under these conditions banks and investors may re-
strict access to capital in non-compliant states and incentivize increased compliance. I
demonstrate this market-enforcement mechanism through an analysis of the Financial
Action Task Force (FATF), an intergovernmental body that issues non-binding recom-
mendations to combat money laundering and the financing of terrorism. The FATF’s
public listing of non-compliant jurisdictions has prompted international banks to move
resources away from listed states and raised the costs of continued non-compliance,
significantly increasing the number of states with laws criminalizing terrorist financ-
ing. This finding suggests a powerful pathway through which institutions influence
domestic policy and highlights the power of GPIs in an age where information is a
global currency.
∗I am grateful to numerous government, IO, and financial industry professionals for agreeing to be in-
terviewed and for sharing their expertise. I also thank Ryan Brutger, Christina Davis, Kosuke Imai, Jeffry
Frieden, Julia Gray, Roy Hwang, Judith Kelley, Amanda Kennard, Robert Keohane, Christoph Mikulaschek,
Helen Milner, Duane Morse, Tyler Pratt, Beth Simmons, the Imai Research Group, and participants in the
Global Assessment Power project conferences for valuable feedback and guidance on this project.
1
1 Introduction
Over the last twenty years, intergovernmental organizations (IGOs) have increasingly relied
on global performance indicators (GPIs) to disseminate information about state policies. In
this volume, Kelly and Simmons suggest GPIs influence policy outcomes in states through
three pathways: changes in domestic politics, shifts in elite preferences, and transnational
pressure.1Bisbee et al. show that the Millennium Development Goals encourage greater do-
mestic attention to evaluated policy objectives,2while Kelley, Simmons, and Doshi suggest
the World Bank’s Ease-of-Doing-Business index provokes domestic awareness and pressures
bureaucrats to change business regulation.3Honig and Weaver reveal how the Aid Trans-
parency Index alters the behavior of development aid donors by diffusing professional norms
and affecting organizational learning.4In contrast to these works, I focus on the third causal
pathway – transnational market pressure – and highlight how such forces led to deep and
widespread policy change on how states combat terrorist financing. Following the 9/11
terrorist attacks, several international institutions, most notably the UN Security Council,
adopted resolutions calling for the worldwide adoption of domestic laws criminalizing terror-
ist financing. A decade later, most countries had laws that were weak and ineffective. Since
2010, however, a non-binding regulatory institution has used a GPI – in this case, a public
non-complier list – to reverse this trend. Today, more than 100 countries have adopted com-
prehensive laws on terrorist financing, making it significantly more difficult for terrorists to
use the international financial system.
How did one small institution achieve such an effect? It harnessed the power of GPIs
to outsource enforcement to market actors. Existing scholarship has highlighted how civil
1Kelley and Simmons 2019.
2Bisbee et al. 2019.
3Kelley, Simmons and Doshi 2019.
4Honig and Weaver 2019.
2
society can pressure governments to comply with international agreements.5In such models,
domestic actors draw attention to instances of non-compliance in their own states. I argue
that GPIs can lead transnational market actors to serve as outside enforcers, punishing
foreign countries that fail to comply with multilateral rules. Every day, market actors make
decisions about how to allocate their capital under conditions of uncertainty. Banks decide
whether individuals from countries with a high risk of money laundering can open savings
accounts, while investment firms decide whether to buy debt from emerging economies. In
such cases, market actors must evaluate potential risks based on limited information.
IGOs can use GPIs to fill this informational gap and stabilize market expectations. Due
to their multilateral nature, many IGOs have high credibility as monitors, and are able to
leverage their bureaucratic authority, technical expertise, and access to government policy to
provide unique, detailed insight into policy issues in different countries. When GPIs provide
credible information about the domestic policies of states, they are more likely to influence
the actions of outside audiences. GPIs are particularly likely to lead to market enforcement
when they provide information about country risk and serve as heuristics. Under these
conditions, IGO-produced GPIs can be influential in determining how market actors invest,
loan money, or make purchasing decisions.
I illustrate this argument through an analysis of global policy change on combating
terrorist financing. I highlight the role of the Financial Action Task Force (FATF), an
intergovernmental body that makes recommendations about state policies to combat money
laundering and terrorist financing, and has been instrumental in driving global compliance on
this issue. The FATF is a relatively weak international institution; although it issues global
recommendations, it has no permanent charter and only 35 members. Lacking legally binding
authority, the FATF relies in part on a non-complier list to generate policy change in states.
This list has been remarkably effective: more than 90 percent of listed countries had adopted
5See, for example, Dai 2007; Simmons 2009; Johns 2012; Mansfield and Milner 2012.
3
FATF-compliant laws on terrorist financing as of 2015, compared to only about 50 percent
of non-listed countries. This is a stark reversal in trends from 2009, when not one soon-to-
be-listed country had a compliant law. Using a new dataset that compiles information about
the laws of 179 states, I employ a cox proportional hazards model to show that the FATF
non-complier list makes states significantly more likely to adopt FATF-compliant laws on
terrorist financing in a given period.
Additional tests highlight market enforcement as a core causal mechanism for this process.
Although the list drives policy change across states, it has the strongest effect on compliance
in countries that are highly integrated into the global economy. In an analysis of how the
FATF list affects cross-border bank-to-bank lending, I show that listed countries experience
on average a 16 percent decrease in lending, compared to when they are not listed. I illustrate
the underlying causal process through a case study of Thailand, where market actors played
an integral role in pushing for policy change following FATF listing in 2010. Finally, I
conclude by highlighting the implications of this analysis for how we understand the nature
of power in the modern era.
IGO-produced GPIs and Market Enforcement
IGOs are credible sources of information about government policy due to institutional ad-
vantages like bureaucratic authority, technical expertise, and access to monitored states.
The authority of a GPI’s creator is likely to boost the GPI’s perceived legitimacy and
salience.6IGOs tend to be particularly authoritative evaluators of policy success due to
their bureaucratic nature; as Barnett and Finnemore point out, bureaucracies “embody a
form of authority, rational-legal authority, that modernity views as particularly legitimate
and good.”7IGO bureaucracies are also often a source of significant technocratic expertise,
6Kelley and Simmons 2019.
7Barnett and Finnemore 1999, 707.
4
which bureaucrats can draw upon as they assemble GPIs and which may intensify the im-
pact of GPIs on monitored states through the diffusion of professional norms8and specific
standards.9Indeed, states often delegate monitoring responsibilities to international organi-
zations in order to develop technical expertise.10 When IGOs assess state compliance with
specific standards, this process can lead to extra scrutiny of specific policies and encourage
greater domestic political attention to specific criteria.11
Authoritative and technical monitoring can come from many sources, but IGOs may
have a third comparative advantage: access. While non-governmental organizations provide
policy information through on-the-ground informants, reporting on economic or security
issues often requires direct access to governments. IGOs have significant advantages in this
regard – as long-standing organizations, they can draw on established relationships to extract
information from the evaluated countries. Indeed, because monitoring is so common across
IGOs, governments may be less likely to resist IGO monitoring. Government bureaucrats are
used to responding to IGO requests for information, meeting with IGO officials, and receiving
IGO-provided technical assistance. Many IGOs rely on interactive evaluation systems, where
monitoring procedures include a combination of government reporting, direct evaluation, and
final written assessments. The Organization for Economic Cooperation and Development’s
(OECD) monitoring process, for example, includes a detailed country questionnaire, two
staff team visits to the country, and several draft reports, with a final report adopted in the
OECD plenary. The entire process “is motivated by peer review and peer pressure.”12 This
type of participatory approach not only enhances the legitimacy of the reporting, but may
also make GPIs more effective at driving policy change.13
8Honig and Weaver 2019.
9Kelley, Simmons and Doshi 2019.
10Hawkins et al. 2006.
11Bisbee et al. 2019.
12Sch¨afer 2006, 74.
13Parks and Masaki 2017.
5
GPIs and Markets
Kelley and Simmons 2019 suggest that GPIs influence the conduct of states through three
pathways: domestic politics, elite politics, and transnational politics. Although many IGO-
produced GPIs affect elite interests and mobilize domestic audiences, in this paper, I focus
on the third, less common but perhaps more powerful mechanism: transnational market
pressure. When IGO-produced GPIs are credible, they can lead to a process of “market
enforcement,” whereby financial actors reallocate resources away from poorly performing
countries.14 Market enforcement is most likely to occur when market actors are dividing finite
resources among states, leading to intergovernmental competition. Under this condition,
market actors are likely to seek out new information about foreign governments. GPIs
fill such informational gaps when they reduce uncertainty about country risk and serve as
heuristics that shape expectations about market sentiments.
GPIs are most likely to lead to market enforcement when market actors are dividing finite
resources among states, not among sub-state units. For these types of financial decisions,
governments, rather than firms, are in competition with each other. When a government
issues a new sovereign bond, for example, investors evaluate not just the specific government
and its likeliness of repaying the debt, but also the larger economic climate and alternative
options for investment.15 Similarly, when a bank decides whether to establish a cross-border
banking relationship with a bank in another country, it weighs the profits and costs of working
in a particular country against other possible relationships that might be established.16 In
such cases, governments have incentives to make their countries as attractive as possible to
foreign market actors, even by withholding negative information. Market actors, meanwhile,
have incentives to seek out the best possible information to reduce uncertainty about the
14For previous work on the link between market pressures and government policy, see Simmons 2000;
Mosley 2003; Elkins, Guzman and Simmons 2006; Buthe and Milner 2008 among others.
15Hilscher and Nosbusch 2010; Baldacci, Gupta and Mati 2011; Longstaff et al. 2011.
16Bank for International Settlements 2016.
6
profitability and risk of different opportunities.
GPIs are also more likely to lead to market enforcement when they reduce uncertainty
about the quality of a government or characteristics of an investment environment.17 As long
as IGOs are credible providers of information, GPIs by their very nature are likely to lead
to “uncertainty absorption.”18 Although an IGO may have assembled an index from a large
body of evidence, the GPI communicates only the IGO’s inferences, not the original data.
In financial markets, as Bruce Carruthers explains, “rather than founder on the fact that
probabilities are truly unknown, decision-makers instead try to gather more information,
estimate probabilities and simply proceed with estimates.”19 GPIs, particularly rankings or
blacklists, are ideal inputs into this process because they “rely on the magic of numbers” to
render evaluations more certain and objective.20
By absorbing uncertainty and coordinating market expectations, GPIs act as a type of
heuristic for market participants. Recent work in political science highlights the degree to
which market actors like investors may use cognitive shortcuts to make financial decisions.21
Ozturk suggests that credit rating agencies use the Worldwide Governance Indicators to
assess the credit worthiness of governments, despite known problems with the data.22 While
existing arguments focus on investors and sovereign debt, a similar logic applies to banks
and other financial institutions that make investment and business decisions based in part
on country risk.
Market actors may also use GPIs as heuristics for understanding how other banks or
17Lee and Matanock (2019) highlight the importance of this scope condition. In this volume, they show
that while Transparency International’s Corruption Perception Index attracts media attention, it does not
influence the allocation of foreign aid, perhaps because policymakers are already aware of the information
contained in the index.
18March and Simon 1958.
19Carruthers 2013, 526
20Merry 2011, S84.
21See Tomz 2007 on investor beliefs about a government’s “type,” Gray 2013 on investors using a country’s
IO membership as a heuristic, and Brooks, Cunha and Mosley 2014 on investors comparing countries to their
peers.
22Ozturk 2016.
7
investors will evaluate the risk of doing business with a country. In many types of market
activity, the profitability of an investment depends on how other market participants judge
its quality.23 Investors consider not only the true value of a commodity or bond, but also
conventional wisdom surrounding a particular acquisition.24 For banks, heuristics about
country risk may affect loan rates. Banks may charge high-risk countries higher loan rates
because they expect fewer competitors will offer good rates, or because they assume that
the high-risk label will lead other market actors to cut off access to capital (thus decreasing
the likelihood of repayment).
From Market Enforcement to Policy Change
When IO monitoring leads to market enforcement, it can create new advocates for compli-
ance. In particular, banks, investors, or companies that are hurt by the market enforcement
process are likely to push the government to change its policies. Countries will vary in how
responsive they are to these processes; domestic institutions and politics are likely to influ-
ence how leaders allocate resources among competing policy priorities.25 However, one of
the strengths of market enforcement is that international banks and investors typically have
direct access to the leader’s “winning coalition,” i.e. those people whose support is essential
to maintaining power.26 The domestic banking community and the central bank are part of
the winning coalition in many countries, which makes them persuasive advocates for policy
change. In countries where market integration is high, domestic banks are likely to be an
influential part of the economy and to have significant pull with the government. Market
integration thus intensifies reputational effects, and incentivizes compliance.
23See, for example, arguments by Amato, Morris and Shin 2002 about the Central Bank or Shiller 2015
about the role of the media in driving major market movements.
24Abdelal and Blyth 2015.
25Milner 1997; Moravcsik 1997; Rickard 2010.
26Bueno de Mesquita et al. 2003.
8
Regulating Global Finance
I examine how GPIs can lead to market enforcement through an analysis of international
cooperation on “financial integrity” – efforts to keep illicit money out of the financial sys-
tem. The organization at the heart of this endeavor is the Financial Action Task Force
(FATF). The FATF is an informal intergovernmental body that was created in 1989 to set
global standards and promote the implementation of policies to combat money laundering.27
Founding members included the G-7 countries, the European Commission, and eight other
European states.28 Over the last three decades, the FATF has broadened its mission to
include combating terrorist financing and proliferation financing, and expanded its reach.
Today, it has 37 members (35 countries and two regional organizations) and nine associated
regional bodies that assess compliance in more than 180 countries.29
FATF Rules and Monitoring
The FATF issues recommendations on how states should combat the problems of money
laundering and terrorist financing through legal and regulatory action. Recommendations
include legal changes, preventive measures on how banks evaluate customer risk and keep
records, and improved international cooperation. The FATF’s formulation of global stan-
dards has been crucial for fighting money laundering and terrorist financing. For many years,
legal differences across jurisdictions created significant problems for the bureaucratic offices
anti-money laundering enforcement. Rule conflicts also provided opportunities for jurisdic-
tional arbitrage, whereby criminals could take advantage of multiple rules and conflicting
agreements.30 By formulating global standards, the FATF has helped states coordinate def-
27The FATF does not have a standing charter; instead, member states periodically extend its mandate
(the current one runs through 2020).
28Australia, Austria, Belgium, Italy, Luxembourg, Netherlands, Spain, and Switzerland.
29A full list of FATF members and the nine regional bodies is available in Appendix A.
30Unger and Busuioc 2007.
9
initions of money laundering and terrorist financing, producing greater legal harmonization
and facilitating policy implementation.
The FATF also has an impact on state policy through its monitoring and evaluation
system, which evaluates compliance in more than 180 countries. Each FATF assessment is
conducted by a small team of evaluators made up of legal and financial experts from peer
countries, FATF Secretariat officials, and often bureaucrats from the IMF or the World Bank.
This team of assessors rates a country’s level of compliance on each recommendation based
on the following scale: compliant, largely compliant, partially compliant, non-compliant,
or not applicable.31 The evaluation process is lengthy, often taking more than a year, and
technical; each country is fully assessed approximately once per decade. In between full
evaluations, the FATF and its regional bodies conduct shorter, more targeted evaluations of
specific problem areas.
Final FATF reports are adopted in tri-annual plenary meetings. During these sessions,
evaluated countries may argue against portions of the draft report, advocating for rating
changes.32 Such rating upgrades are difficult to achieve because the FATF operates by
consensus decision-making: an evaluated country must convince all other member countries
to support a rating upgrade on a specific recommendation. This is a difficult task; reports
are usually adopted over some objections from the evaluated states.33 34 Even G-7 countries
like the United States, Japan, and Canada receive non-compliant ratings.35
31FATF-GAFI 2009c.
32Nakagawa 2011.
33Interview of FATF regional body official, 7 January 2015; Participant-Observation, September 2016 and
May 2017.
34I conducted more than twenty interviews over the course of this project but due to the sensitive nature
of this issue area, most people were unwilling to speak for direct attribution. Additional details on the
interview process and a list of interviews are available in Appendix C.
35In the third round of mutual evaluations, the USA was rated non-compliant on 4 recommendations
(FATF-GAFI, 2006), while Japan and Canada were rated non-compliant on 10 and 11 recommendations
respectively (FATF-GAFI, 2008c,b).
10
FATF Technical Expertise and Credibility
Market actors, governments, and even other IOs rely on the FATF for information about
the financial integrity policies of states because of the highly technical nature of this issue
area. Governments require significant legal and administrative expertise to implement the
FATF’s recommendations. Without FATF guidance and substantive expertise, many coun-
tries would struggle to meet these requirements.36 The FATF issues detailed working papers
that highlight best practices for governments and for private sector actors. It also connects
bureaucrats across countries, building a network of technical experts. The FATF draws on
this network when it evaluates countries; because the FATF Secretariat is small, evaluation
teams always include bureaucratic officials from peer countries. Chip Poncy, former head of
the US delegation to FATF, described this network structure as essential to the FATF’s suc-
cess. “For FATF, the shareholder countries are the managers and also do the work, together
with the Secretariat. The minute you hire more people into the Secretariat, there’s daylight
between what managers are deciding and what shareholders are implementing.”37
The FATF’s credibility as a monitor is also due to its reputation as a highly technocratic,
apolitical organization. FATF plenary meetings are staffed by government bureaucrats,
rather than high-level political actors. FATF monitoring reports are drafted primarily by
the evaluation team, with only a limited discussion in the plenary session. When coun-
tries discuss specific ratings for reports during the plenary session, they are encouraged to
provide technical justifications for their support or dissent.38 Former FATF President Anto-
nio Gustavo Rodrigues described the FATF by highlighting this technocratic nature, saying
“FATF is a unique organization. Of course, in any organization with human beings, you
36Indeed, even a long-standing FATF member like Germany received failing ratings on 20 of the FATF
recommendations (FATF-GAFI, 2010).
37Author interview, 7 February 2018.
38This statement is based on the author’s participant-observation experience at two FATF regional body
meetings.
11
have politics. But in the FATF, politics is a secondary aspect.”39
The Non-Complier List
A few years into the FATF’s third round of mutual evaluations, FATF and regional affiliate
member states began to call for more consistent procedures for dealing with non-compliant
countries 40 In June 2009, the FATF adopted new, systematic procedures whereby all coun-
tries that received failing scores on 10 or more of the FATF’s 16 most important recom-
mendations41 would be eligible for inclusion on a “non-complier list.”42 Under this process,
the FATF has publicly identified 57 countries since February 2010.43 To select countries
for the list, the FATF uses the results of mutual evaluation reports, reviewing all countries
that fail on 10 or more recommendations. Figure 1 shows the results of the FATF’s third
round of mutual evaluations, comparing the number of non-listed (light blue, bottom of
stacked bars) and listed (dark blue, top of stacked bars) countries by the number of failing
recommendations.44
Once countries are eligible for listing, the FATF gives governments up to a year to un-
dertake policy improvements before deciding whether to list them. Governments work with
the FATF to develop and implement action plans to address deficiencies; countries that are
slow to implement their action plans are more likely to be listed. In addition to considering
political will, the FATF makes listing decisions using a “risk-based approach,” whereby coun-
tries with larger financial sectors or greater risks of money laundering or terrorist financing
are more likely to be listed.45 Other considerations include a country’s legal framework, its
39Author interview, 29 March 2017.
40FATF-GAFI 2009a.
41See Appendix B.
42The FATF “non-complier list” is formally known as the International Cooperation Review Group (ICRG)
process. The FATF adopted new procedures for this process in June 2009 (FATF-GAFI, 2009b) and issued
its first announcement in February 2010.
43See Appendix D for countries listed through June 2016.
44Data is limited to countries included in subsequent empirical analyses.
45FATF-GAFI 2009b.
12
0
5
10
15
20
0 5 10 15
Number of Failing Recommendations
Number of Countries
Figure 1: The figure shows the number of non-listed countries (light blue, bottom of stacked bars)
and listed countries (dark blue, top of stacked bars) by the total number of failing ratings.
responses to requests for international cooperation, and whether it is involved in a follow-up
process.46
The FATF non-complier list is an important source of information about the financial
integrity policies of other countries. Prior to the list’s creation, an observer interested in
learning about a state’s anti-money laundering policies had to read through its most recent
mutual evaluation report. The report would likely be several years old and more than 200
pages in length, and would not include any comparable summary judgment of the country’s
policies. But the FATF non-complier list is based on new, up-to-date information and the
list consolidates risk into an easily interpretable metric. As a result, the non-complier list is
a straightforward way for observers to clearly identify the highest-risk countries.
46FATF-GAFI 2009b.
13
Listing and Market Enforcement
The FATF non-complier list is a powerful driver of policy change because market actors use
the list to allocate resources away from non-compliant states. The FATF itself is responsible
for some of this market behavior. One of the FATF’s most important recommendations
requires banks, corporate service providers, remittance services, and lawyers to maintain
“customer due diligence procedures,” taking measures to verify customer identities using a
risk-based approach. In effect, this recommendation requires market actors to assess the risk
of money laundering and terrorist financing emanating from different jurisdictions, creating
a perfect audience for the non-complier list. Although some FATF members, like the United
States and the United Kingdom, might have adopted such regulations independently, the
FATF has encouraged worldwide policy diffusion.47
Banks have clear regulatory incentives to reallocate resources on the basis of the non-
complier list. Because of the diffusion of customer due diligence regulations, most banks
have standardized procedures for determining whether customers are high risk for money
laundering and terrorist financing, based in part on countries of origin. Banks typically
subject customers from high-risk jurisdictions to longer screening and administrative proce-
dures. In some cases, banks might even opt to forgo all business with high-risk countries.48
In countries like the United States, regulation is followed up with government enforcement
ensuring that banks are adequately complying with the law. The financial penalties for such
a violation can be enormous. In 2012, for example, the US Government fined HSBC 1.256
billion US dollars for “failing to maintain an effective anti-money laundering program.”49
Banks also need information on money laundering and terrorist financing risk because
they are likely to suffer significant reputational damage if they are involved in a financial
integrity scandal. Reputational damage can lead to financial costs. The US government’s
47Sharman 2008.
48Collin, Cook and Soramaki 2016.
49US Department of Justice 2012.
14
discovery that Riggs Banks was helping several dictators launder money, resulted in relatively
small financial penalties, but led to the bank’s demise.50 As a compliance executive from one
of the biggest banks in the United States described, “no firm wants the reputational damage
of having been used as a vehicle for criminal activity, or worse, as a channel for financing
terrorism.”51 Indeed, damage to reputation is often used as a way to sell risk management
systems to financial institutions.52
For both regulatory and reputational reasons, banks face a herculean task: to evaluate
and assess customer risk under conditions of high uncertainty. The FATF non-complier list
offers an easy way for banks to quantify this risk. Prior to the list, banks were stuck trying
to interpret the results of 200-page monitoring reports and making independent judgments
about which types of non-compliance posed the biggest threats. Such information was often
several years old. Chip Poncy, former head of the US delegation to FATF, noted the challenge
for market actors of digesting the lengthy FATF monitoring reports. “When you publish
300-page mutual evaluation reports that no one in the market really understands how to
read and there are no cumulative ratings, markets don’t know how to react. There’s not
enough depth, understanding, or expertise in the market yet to understand and react to
these technical issues absent country lists for material noncompliance.”53 With the advent
of the non-complier list, the FATF now provides more recent information and points banks
toward the highest risk countries.
Reputation
This process of market enforcement interacts with and intensifies the reputational effects of
GPIs. The FATF list damages a country’s international reputation not just through a “peer
50Jamieson 2006.
51Author interview, 28 August 2015.
52Author interview with Jeff Soloman, Financial and Risk Sales Specialist, Thomson Reuters, 28 September
2015.
53Author interview, 7 February 2018.
15
effect”54 but through something like a “lowest-common-denominator effect,” where countries
are judged by the worst of the group. After Antigua and Barbuda was listed in February
2010, for example, the leading opposition leader criticized the ruling party for the fact that
Antigua and Barbuda was on a list with Nigeria, Sudan, Ukraine, and Myanmar. This
problem is compounded by media coverage, where news outlets often ignore the nuances of
listing.55
Enforcement by market actors intensifies the reputational effects of the FATF list. Daniel
Glaser, the former US Assistant Secretary for Terrorist Financing and Financial Crimes,
noted that part of the power of the FATF list is that “it creates dynamics that you don’t
fully control, where small actions have systemic resonance. Once FATF lists a country, FATF
does not control how the market responds.”56 The perception that listing leads to market
enforcement increases reputational costs. If a government fails to prevent its country from
being listed, this policy failure signals to outside observers that the listed government is
unable or unwilling to tackle money laundering and terrorist financing. In some countries,
reputational concerns may be more important than specific financial consequences. An
official from one formerly listed country reported, “As far as markets, I’m not saying we’re
unaware of the side effects but at least from my perspective, that’s not the main motivation.
We just wanted a clean reputation internationally.”57
The FATF Non-Complier List: Testable Hypotheses
My theory suggests that the FATF non-complier list stigmatizes states directly and that
market pressure intensifies this effect. As a result, being listed should incentivize improved
54Brooks, Cunha and Mosley 2014.
55The FATF non-complier list is composed of several different lists, colloquially referred to as the “grey
list,” the “dark grey” list, the “black” list, and the counter measures list; however, this differentiation is
often lost in media reporting. See, for example, Rubenfeld 2011.
56Author interview, 12 February 2018.
57Author interview, 9 February 2016.
16
FATF compliance. Gordon Hook, the Executive Secretary of the Asia/Pacific Group on
Money Laundering, described this impact. “The list has had a phenomenal effect on pol-
icymakers. If they are listed, they work extremely hard and fast to get off the list. At
the government level, we always saw high levels of commitment from the executive but
that would slow down once parliament was involved. Now countries move at a much faster
pace.”58
•H1. Reputation Hypothesis: Countries that are listed by the FATF should adopt
FATF-compliant laws on terrorist financing more quickly than non-listed countries.
The actual process of market enforcement may occur through several pathways. In some
cases, banks simply exercise enhanced due diligence, subjecting customers in listed countries
to greater scrutiny or longer waiting times. In other cases, banks have refused to allow any
transactions from listed countries. In May 2014, for example, banks in the United States,
Europe, Germany, and Turkey stopped dealing with certain Afghan commercial banks.59 By
June, the cost of money transfers had gone up 80 percent60
One likely moderator for the effect of the non-complier list on compliance is a coun-
try’s integration into international markets. Countries that are more open to transnational
financial flows should be particularly responsive to the non-complier list. I proxy market
integration with cross-border bank liabilities, which indicate the amount of money that do-
mestic banks in a particular country owe to international banks. Bank-to-bank financial
transactions are a key part of the global economy, and facilitate trade finance, short-term
borrowing, and foreign investment. If the FATF list leads banks to reallocate resources away
from non-compliant countries, countries with higher levels of bank-to-bank lending should
be particularly affected by the process.
58Author interview, 30 June 2016.
59Donati 2014.
60Carberry 2014.
17
•H2. Market Enforcement Hypothesis: Listed countries that are highly integrated into
global markets should adopt FATF-compliant laws on terrorist financing more quickly
than less-integrated listed countries.
Market enforcement by banks is likely to lead to significant financial consequences for
listed countries. Banks integrate the FATF non-complier list directly into their risk models;
these models, in turn, drive bank procedures for verifying customer identities and monitoring
potential anti-money laundering transactions. Individuals and companies in listed countries
may experience delays in transferring money or conducting business abroad. Over the last
five years, international banks have increasingly opted to pull out of high-risk financial
jurisdictions. Although bank enforcement against listed countries could take a variety of
forms, the consequences of such action are straightforward – banks and customers in listed
countries should find it harder to access international capital.
•H3. Bank-to-Bank Lending Hypothesis: Banks in developed economies should be less
willing to loan money to banks in listed countries, compared to when these countries
are not listed.
Empirical Approach
My primary analysis examines how the FATF non-complier list affects state behavior. I focus
on a key indicator of compliance with the FATF standards – the criminalization of terrorist
financing (FATF Special Recommendation II) – and analyze how being included on the non-
complier list has affected the length of time that it takes for a country to criminalize terrorist
financing in line with FATF standards. I begin the analysis in February 2010 because that
is start of the current non-complier list, and my data goes through December 2015. Data on
country listing status is collected from FATF non-complier list announcements (published
online in February, June, and October every year).
18
I test my theory using a Cox Proportional Hazards model, which analyzes how variables
affect the length of time in months it takes for a country to criminalize terrorist financing
in line with the FATF recommendation. This model is appropriate given the unidirectional
nature of the data – once a country has fully criminalized terrorist financing, it is unlikely to
repeal its law. Due to this approach, however, countries that criminalized terrorist financing
in line with FATF guidelines prior to February 2010 are excluded from the analysis. In
analyses where the proportional hazard assumption does not hold, I follow the advice of
Box-Steffensmeier and Zorn, who suggest including a log-time interaction for variables with
substantial evidence of non-proportionality.61
Selection into listing poses potential challenges for the empirical analysis. If the FATF is
more likely to list countries that are also more likely to criminalize terrorist financing, failing
to account for the selection process could inflate my findings. Conversely, if the FATF is more
likely to list the most reluctant compliers, failing to account for selection could attenuate the
results. I address these concerns through sample construction and the addition of covariates.
I construct a full sample of 132 countries that had not criminalized terrorism in line with
FATF standards as of February 2010. This sample includes 46 of the 57 countries listed as
part of the non-complier list. As I add covariates, the sample drops to 120 countries (37
listed) in Model 2, 96 countries (32 listed) in Model 3, and 87 countries (30 listed) in Model
4.62
Second, I establish a universe of potential listed countries through matching. Ho et
al. suggest pre-processing data through matching produces more accurate and less model-
61Box-Steffensmeier and Zorn 2001. I include a log-time interaction for the variable US Ally, although
results are robust to not including this interaction term. See Appendix G, which replicates Table 1 without
the log-time interaction term.
62This reduction in the sample is primarily due to the addition of the variable ”Risk of Terrorism,” which
comes from the International Country Risk Guide and is only available for a subset of countries. Appendix
E lists the countries that are included in each model. The results are robust to imputing missing data
(Appendix F).
19
dependent causal inferences.63 To compare countries with similar probabilities of being
listed, I use nearest neighbor matching to create a set of 12 listed and 12 non-listed countries
that are similar in terms of diffusion, alliance with the United States, private sector credit,
capacity, level of democracy, and risk of terrorism. More specifically, I subset the data to the
first period of the analysis (February 2010) and assemble a matched data set of 24 countries
based on variable values in this period. I then assemble panel data for this set of 24 countries
for the full time period (2010 to 2015).64 Matching improves the balance of the sample on
the majority of variables included in the model.65
Finally, I construct a dataset of all non-FATF member countries that were eligible for
listing based on FATF bureaucratic criteria as of February 2010. When FATF member
states set the new listing eligibility threshold of 10 failing recommendations in June 2009,
the FATF and its regional bodies had already completed close to 100 evaluations of members
of the FATF global network. Most of these countries were not members of FATF regional
bodies, rather than the FATF itself, and were therefore uninvolved in setting the new listing
threshold. Instead, these countries found themselves suddenly under consideration for a new
listing process, with no ability to change their listing eligibility. I examine how listing affects
compliance outcomes within this set of 68 countries, 15 of which were listed by the FATF.
My unit of observation is country-month. In the simplest model for the full sample, this
equates to 7308 observations and 72 events (instances where a country criminalizes terrorist
financing in line with FATF guidelines). In the simplest model in the matched sample, there
are 1104 observations and 17 events. Finally, in the simplest model in the sample of countries
eligible for listing, there are 3420 observations, and 36 events.
63Ho et al. 2007
64A standard matching approach would use the entire data set to assemble a matched sample; however,
because I run a hazard model, countries drop out of the sample as they criminalize terrorist financing in
line with FATF standards. For this reason, I assemble a group of comparable countries based only on 2010
values, and then expand the sample to include data on this select group of countries from the complete time
period.
65See Appendix J for more details.
20
Dependent Variable: Criminalization of Terrorist Financing
Although the FATF issues 40 recommendations,66 this paper focuses on one specific indicator
of compliance: the criminalization of terrorist financing. The FATF considers the criminal-
ization of terrorist financing to be a top priority. Compliance with this recommendation
is also a clear indication of policy change. The FATF did not adopt the criminalization of
terrorist financing as a recommendation until 2001, and prior to that time, only a handful
of states had laws criminalizing terrorist financing.67
The FATF requirement to criminalize terrorist financing is broad and far-reaching. States
must criminalize terrorist financing beyond what is required in the Terrorist Financing Con-
vention, extending the terrorist financing offense to any person who provides or collects funds
with the intention that they be used to carry out a terrorist act, by a terrorist organization,
or by an individual terrorist. Laws must define “funds” as including assets of any kind
from both legitimate and illegitimate sources. Moreover, per FATF guidelines, laws should
stipulate that funds provided to terrorists do not actually have to be linked to any specific
terrorist act.
I collected data on the month and year in which each country adopted legislation that
fulfilled all of the FATF requirements on criminalizing terrorist financing. I coded this
variable based on information contained in FATF mutual evaluation reports and follow-up
reports, non-complier list announcements, and the FATF’s Terrorist Financing Fact-Finding
Initiative. For a law to be considered FATF-compliant, it has to extend to any person who
willfully provides or collects funds with the intention or knowledge that they are to be used
66This study covers the FATF’s third round of mutual evaluations, during which the FATF actually issued
49 recommendations. For its fourth round of evaluations (currently ongoing), the FATF consolidated its
recommendations to 40.
67This variable is, at best, a partial measure of compliance, as demonstrated by Findley, Nielson and
Sharman 2014. Legal compliance and even policy implementation cannot prove that the institution has
reduced money laundering or terrorist financing. While these are important issues, they are outside the
scope of this study.
21
Trends in Criminalization of Terrorist Financing
Year
Percent of Countries
2001 2003 2005 2007 2009 2011 2013 2015
0 20 40 60 80 100
Non−Listed Countries
Listed Countries
Figure 2: The figure shows the percent of never-listed countries (solid black line) and listed post-
2009 countries (red dashed line) that have adopted FATF-compliant laws on terrorist financing.
The dotted vertical line indicates the 2009 announcement of the revamped FATF non-complier
process that issued its first non-complier list in February 2010.
to carry out a terrorist attack, by a terrorist organization, or by an individual terrorist.68
Figure 2 shows the distribution of this variable over time, separated by whether a country
is eventually listed (red dashed line) or is never listed (black solid line). As of late 2008,
most countries had not adopted FATF-compliant laws on terrorist financing. Instead, many
countries had partial laws that criminalized terrorist financing only when linked to a terrorist
act.69 Such gaps are quite meaningful – funds are fungible, and while terrorist organizations
need relatively little money to mount an attack, they require significant resources to sustain
recruitment, propaganda, and legitimation activities.70 Non-compliance may also arise when
countries adopt a too-narrow definition of terrorism.
Since the FATF adopted new non-complier list procedures in 2009, countries have been
68FATF Interpretive Note to Recommendation 5 (Terrorist Financing Offence); FATF Methodology, 2012.
69FATF-GAFI 2015.
70FATF-GAFI 2008a.
22
significantly more likely to adopt laws on terrorist financing that meet FATF standards. As
of 2015, close to 90 percent of listed and formerly listed countries had FATF-compliant laws,
whereas only about 50 percent of non-listed countries had similarly compliant laws. This
significant policy change by listed countries is the reason that the FATF has removed so
many countries from listing – as of 2016, 46 of 57 listed countries had “graduated” from the
non-complier list following major improvements in their laws.71
Explanatory Variables
The primary variable of interest is whether, at a given point in time, a country is on the
non-complier list. I create a dichotomous variable Listing, which indicates whether a country
is on the non-complier list at any time. In the largest version of the data, approximately 17
percent of observations are coded as 1’s.72
To test the market enforcement hypothesis, I include in my sample the variable Market
Integration. This variable is a continuous measure of a country’s aggregate cross-border
liabilities in 2008, and proxies for a country’s level of market integration prior to the FATF’s
new non-complier list procedures.73
Data on cross-border bank-to-bank liabilities comes from the Bank for International
Settlements (BIS) locational banking statistics. This data set provides information about
outstanding claims and liabilities as reported by internationally active banks that are located
in the 44 reporting countries. Because these banks report international cross-border flows,
the data covers banking relationships in more than 200 countries, capturing about 95 percent
of all cross-border interbank business. For the countries included in the data set, this variable
71See Appendix D for more information.
72I also create an ordinal variable List Level, which disaggregates listing into different levels. See Appendix
I.
73I use this early time period to indicate market integration prior to the creation of the non-complier list.
I estimate the conditional marginal effect of listing moderated by market enforcement through an interaction
term, following guidelines set forth in Brambor, Clark and Golder 2006 and Hainmueller, Mummolo and Xu
Forthcoming. Appendix H provides support for the linearity assumption.
23
ranges from an average quarterly liabilities of 7 million US dollars for Dominica to 1.7 trillion
US dollars for Germany. Because the data is highly skewed, I transform the variable by
logging it.
A country’s direct ties to the FATF may also affect how quickly it meets FATF standards
on the criminalization of terrorist financing. In the full sample, I include the variable FATF
Member to account for whether a country is a member of the FATF in a given year.74
Countries may also be influenced by the policies of neighbors or regional partners through
processes of policy diffusion.75 Jason Sharman argues that diffusion has affected the adoption
of anti-money laundering policies throughout the developing world.76 I include the variable
Diffusion, which ranges from 0 to 1 and for each country, represents the proportion of member
states in the country’s FATF regional affiliate that have adopted FATF-compliant laws on
terrorist financing.77
Government capacity is also likely to affect the time to policy change; following previous
studies,78 I control for Capacity using gross domestic product (GDP) per capita.79 Countries
that face a higher threat of terrorism might also be faster to comply with the FATF recom-
mendation on terrorist financing. I include the variable Terrorism Risk, which ranges from
0 (lowest risk) to 3 (highest risk).80 The literature also suggests that a country’s political
system may impact its ability or willingness to fulfill international commitments.81 I include
74This variable is excluded from the matched sample analysis and the eligible country analysis because
these samples include no FATF members.
75Gleditsch and Ward 2006; Elkins, Guzman and Simmons 2006; Simmons and Elkins 2004.
76Sharman 2008.
77For comparability across institutions, the variable is scaled by rounding to nearest 0.1 value in the
regression.
78Horn, Mavroidis and Nordstr¨om 1999; Guzman and Simmons 2005.
79This variable is drawn from the World Bank World Development Indicators, and is standardized in 2010
US dollars. Due to the skewed distribution and for ease of interpretability, I transform the variable by adding
1 and taking the log.
80Drawn from the International Country Risk Guide. Variable scales from 1 (highest risk) to 4 (lowest
risk). For ease of interpretability, I have inverted the variable and set the minimum value at 0.
81See Helfer and Slaughter 1997, Raustiala and Victor 1998, Martin 2000, or Mansfield, Milner and
Rosendorff 2002, among others.
24
Democracy, drawn from Polity IV data.82
Confounders
Per FATF guidelines, the FATF considers a country’s legislative history on terrorist financing
when making listing decisions. Prior to 2010, many countries had criminalized terrorist
financing, but most of these laws were weak and not in keeping with FATF standards. I
include the variable Previous Terrorist Fin Law, which indicates whether a country had some
type of non-FATF-compliant law on terrorist financing as of the end of 2009 (two months
before the start of the non-complier list). Of the 141 countries included in the full sample,
96 (68 percent) had adopted some type of non-FATF-compliant law on terrorist financing
by the end of 2009.
The FATF builds a pool of potential listed countries based on all countries that receive
failing scores on 10 or more of the 16 most important recommendations in their third-round
mutual evaluation reports. I include the variable Eligible for Listing, which is a dichotomous
indicator of whether a country receives 10 or more failing scores on the FATF’s key and core
recommendations. The FATF and its regional bodies only evaluate a country once per cycle,
so for most countries, the number of failing recommendations does not change across the
data set.83
Another important listing determinant is the size of a country’s financial sector.84 As a
proxy for this factor, I include Private Sector Credit, which indicates the amount of financial
resources provided to the private sector by financial corporations. Such resources may be
provided through loans, purchases of non-equity securities, trade credits, or other accounts
82Polity IV codes a country’s political system on a scale of -10 to 10, where higher values equate to more
democratic countries. This data is supplemented with data from Gleditsch 2013. Due to data availability
issues for smaller countries, I omit this variable from the matched sample and the eligible-for-listing sample
analyses.
83This variable is omitted from the analysis of the eligible-for-listing sample since sample only includes
countries that had 10 or more failing recommendations.
84FATF-GAFI 2009b.
25
receivable that establish a claim for repayment. This variable is drawn from the World Bank,
based on its “Domestic credit to private sector (% of GDP)” and is standardized in 2010 US
dollars.
Plausible Alternative: US Power
The most plausible alternative explanation is the possibility that the United States is directly
or indirectly responsible for policy change. The FATF’s regulatory agenda aligns closely
with US foreign policy objectives.85 Scholars have also argued that US economic power has
contributed to the diffusion of US regulatory standards in other areas of global finance.86 The
US government devotes significant resources to providing technical assistance that promotes
the worldwide adoption of financial integrity standards; it also monitors other countries’
policies.
The US may impact a country’s willingness or ability to criminalize terrorist financing
through measures of US influence and coercion. I include the variable US Ally, which is
drawn from the Correlates of War project and indicates whether a country has a defense
pact, entente, or neutrality agreement with the United States in a given year.87 To further
account for US influence, I also re-run my main analysis with four additional controls. I
account for US trade ties with the variable Trade with US, which is drawn from the IMF
and reflects a country’s total volume of trade with the United States as a percent of GDP.
In a second model, I include US Foreign Aid, which is drawn from USAID and indicates the
amount of foreign aid disbursed to a particular country in a given year.
A third model controls for the possibility that the United States might use economic sanc-
tions to pressure countries to change its policies. Since 2001, the US Secretary of Treasury
has had the authority to designate foreign jurisdictions and institutions as “primary money
85Jakobi 2013, 2018.
86Simmons 2001; Drezner 2007; Posner 2009.
87Gibler 2009.
26
laundering concerns” under Section 311 of the USA PATRIOT Act. US financial institutions
and agencies are required to take special measures against designated entities. As of June
2017, the US Treasury had listed 20 banks and 5 countries under this process. I include the
dichotomous variable 311 Sanctions List to indicate if a country’s financial institution or
the country itself was on the 311 Special Measures list in a given month. Approximately 2
percent of observations are coded as 1’s in the dataset.
A final model controls for US bilateral pressure. The US State Department could raise
FATF compliance during bilateral meetings, or encourage foreign partners to seek technical
assistance. I proxy US bilateral pressure with data from the State Department’s annual
International Narcotics Control Strategy Report (INCSR), which summarizes money laun-
dering and terrorist financing policies across most countries. It prioritizes countries using a
three-tier classification system, where “Jurisdictions of Primary Concern” are major money
laundering countries where financial institutions “engage in transactions involving significant
amounts of proceeds from all serious crimes” or where financial institutions are vulnerable
because of weak supervisory or enforcement regimes.88 I create an ordinal variable US -
State Dept List that indicates each country’s assigned INCSR tier, where 1 indicates a coun-
try is of low concern and 3 indicates a country is categorized as a “Jurisdiction of Primary
Concern” in a given year. In the data, approximately 32 percent of observations are coded
as 3’s.
88See: https://www.state.gov/j/inl/rls/nrcrpt/2016/vol2/253367.htm.
27
Findings: Listing Increases Compliance Through Mar-
kets
Hypotheses 1 and 2: Time to Criminalization
The results provide strong support for hypotheses 1 and 2. Countries on the FATF non-
complier list adopt FATF-compliant laws on terrorist financing more quickly than their non-
listed counterparts and market integration appears to intensify this effect. Table 1 shows
the effect of listing on the time it takes for a country to criminalize terrorist financing in
line with FATF standards for the full sample. Model 1 serves as a baseline for the effect
of listing without controlling for any financial considerations. Model 2 tests the effect of
listing and market integration, adding controls for private sector credit and capacity. Model
3 adds a control for terrorism, while Model 4 adds a control for democracy. Across all four
models, listing has a positive and statistically significant effect on compliance. In Model
4, listed countries are 8 times as likely to criminalize terrorist financing in a given period.
Policy diffusion also has a strong effect, suggesting that as more states within an organization
criminalize terrorist financing, other states are increasingly likely to adopt new laws in line
with FATF standards.
Market integration appears to intensify the effect of listing in a consistently positive and
significant manner. In Model 4, a 50 percent increase in cross-border liabilities is associated
with an 11 percent increase in the probability of criminalizing terrorist financing.89 While
a 50 percent increase in a country’s cross-border liabilities may seem like a large change,
consider that between 2002 and 2009, at least 7 countries in Europe had increases larger
than this amount.90
89Data on cross-border liabilities is logged and therefore the coefficient cannot be interpreted directly. To
calculate this estimate, I use the following formula: e(ln(1.3)∗ln(1.5) .
90These countries are the United Kingdom, Denmark, the Netherlands, Norway, Sweden, Finland, and
Ireland Allen et al. 2011.
28
Dependent variable: Time to Criminalization
(1) (2) (3) (4)
Listing 9.029∗∗∗ 8.156∗∗∗ 5.849∗∗∗ 8.429∗∗∗
(0.336) (0.455) (0.526) (0.567)
Market Integration 1.006 1.003 1.026
(0.067) (0.096) (0.106)
Listing * Market Integration 1.207∗∗ 1.314∗∗ 1.345∗∗
(0.108) (0.142) (0.149)
FATF Member 1.013 0.623 0.672 0.929
(0.409) (0.612) (0.629) (0.737)
Previous Terrorist Fin Law 1.370 1.067 0.934 0.877
(0.286) (0.357) (0.402) (0.448)
Diffusion 1.059∗∗∗ 1.061∗∗∗ 1.073∗∗∗ 1.079∗∗∗
(0.013) (0.017) (0.020) (0.021)
Eligible for Listing 0.827 1.147 1.103 0.950
(0.380) (0.557) (0.611) (0.615)
US Ally 3.825 1.831 1.942 1.960
(1.390) (1.719) (1.753) (1.825)
Private Sector Credit 1.020 0.935 1.130
(0.183) (0.237) (0.260)
Capacity 1.144 1.174 0.822
(0.296) (0.321) (0.375)
Terrorism 1.071 1.435
(0.256) (0.314)
Democracy 0.945
(0.039)
Observations 7,308 5,850 4,635 4,114
Countries 132 120 96 87
Events 72 52 43 39
Note: ∗p<0.1; ∗∗p<0.05; ∗∗∗ p<0.01
Table 1: Listing, Market Enforcement, and Criminalization: Cox Proportional Hazards Models for Full
Sample - Hazards ratios for cox proportional hazards models. Values over 1 indicate a positive effect; values
below 1 indicate a negative effect. Standard errors are clustered by country and shown in parentheses. All
models include log-time interaction for US ally.
29
0 10 20 30 40
0.0
0.2
0.4
0.6
0.8
1.0
Effect of Market Integration and Listing on Criminalization
Cumulative Probability
Months
Figure 3: The figure shows the change in the cumulative probability of criminalizing terrorist
financing in line with FATF guidelines, comparing listed countries with high market integration
(one standard deviation above the mean) with listed countries with low market integration (one
standard deviation below the mean). First difference calculations estimated using the results of a
cox proportional hazards model (Model 4 in Table 1). Dotted lines show the 95 percent confidence
interval, calculated using a Monte Carlo simulation sampling over 500 iterations.
Figure 3 shows the effect of market integration on the cumulative probability that a listed
country criminalizes terrorist financing. The plot suggests that market integration has its
largest effect on criminalization in the first year of listing.
I replicate my main analyses on the matched sample and the sample of countries eligible
for listing. Within these samples, listing has an even stronger effect on compliance, and
market integration continues to moderate this effect.91 Table 2 shows these results. In the
matched sample, listed countries are 13.3 times more likely to criminalize terrorist financing
in a given period, while in the sample of eligible-for-listing countries, the estimated effect
is even stronger (18.5 in the full model). The sizable increase in the coefficients for listing
reflects separation in the data – almost all countries that change their laws are listed by the
91Although the interaction term in Model 4 of Table 2 is not significant, it has a p-value of 0.11.
30
FATF. In Model 1, for example, 15 of the 17 countries that comply with FATF standards
are listed countries. In Model 3, 31 of the 36 countries that eventually comply with FATF
standards are listed countries. Such skewed results suggest that the estimates in the full
sample are attenuated, and underestimate the full effect of listing. An additional possibility
is that the FATF listing process is driving countries toward the extremes of compliance –
listed countries become more likely than before to change their policies, while non-listed
countries become less likely (since they know that they’ve avoided the list).
Hypotheses 1 and 2: Robustness
US pressure is the most plausible alternative explanation for why countries criminalize ter-
rorist financing in line with FATF standards. I replicate Model 4 in Table 1, adding different
indicators to proxy for US power or coercion. Table 3 displays these results. Model 1 includes
a control for a country’s level of trade dependence on the United States. Model 2 adds a
control for annual US foreign aid to each country. Model 3 controls for whether a country is
on the US Department of Treasury’s 311 Sanctions List, which pertains specifically to high-
risk money laundering countries. Model 4 controls for US bilateral pressure, proxied with
an ordinal indicator of whether the United States State Department considers the country
to be a high-risk money laundering jurisdictions in a given year.
FATF listing continues to have a strong, positive effect on compliance, while variables
proxying for US pressure have insignificant or negative effects. Both trade dependence and
US foreign aid have weak, insignificant effects, while US government listing has a negative
effect. Countries included on either the US Department of Treasury’s 311 sanctions list or
the US Department of State’s list of high-risk money laundering jurisdictions are less likely
to comply with FATF standards in a given period. The most likely explanation for this
finding is the difference in the type of countries listed. The US government uses the 311
list only against the most reluctant compliers because listing requires market actors to stop
31
Dependent variable: Time to Criminalization
Matched Sample Eligible-for-Listing Sample
(1) (2) (3) (4)
Listing 11.467∗∗∗ 13.250∗17.067∗∗∗ 18.491∗∗∗
(0.891) (1.093) (0.501) (0.879)
Market Integration 1.251 1.011
(0.281) (0.218)
Listing * Market Integration 1.608∗1.589
(0.396) (0.287)
Previous Terrorist Fin Law 2.035 2.020 1.292 1.048
(0.791) (1.040) (0.363) (0.583)
Diffusion 2.083 3.824∗1.633∗∗∗ 1.754∗∗∗
(0.412) (0.601) (0.173) (0.275)
Eligible for Listing 0.932 1.138
(0.943) (1.267)
US Ally 0.457 1.010 5.090 0.955
(4.576) (5.688) (1.689) (2.979)
Private Sector Credit 0.390∗1.109
(0.716) (0.328)
Capacity 2.713 0.867
(0.924) (0.528)
Polity IV 0.893 0.933
(0.078) (0.056)
Terrorism Risk 0.531 2.091
(1.698) (0.517)
Weights 0.001∗∗∗ 0.00000∗∗
(3.072) (9.160)
Observations 1,104 1,018 3,420 1,675
Countries 24 24 66 39
Events 17 16 36 20
Note: ∗p<0.1; ∗∗p<0.05; ∗∗∗ p<0.01
Table 2: Listing, Market Enforcement, and Criminalization: Cox Proportional Hazards Models for Matched
Sample and Eligible-for-Listing Sample- Hazards ratios for cox proportional hazards models. Values over 1
indicate a positive effect; values below 1 indicate a negative effect. Standard errors are clustered by country
and shown in parentheses. All models include log-time interaction for US ally.
32
Dependent variable: Time to Criminalization
(1) (2) (3) (4)
Listing 8.372∗∗∗ 8.230∗∗∗ 8.618∗∗∗ 8.462∗∗∗
(0.568) (0.559) (0.564) (0.555)
Market Integration 1.028 1.022 1.050 0.969
(0.107) (0.106) (0.108) (0.110)
Listing * Market Integration 1.339∗∗ 1.364∗∗∗ 1.340∗∗ 1.306∗∗
(0.149) (0.149) (0.149) (0.151)
FATF Member 0.872 1.137 0.851 1.248
(0.779) (0.758) (0.736) (0.761)
Previous Terrorist Fin Law 0.889 0.877 0.888 0.809
(0.452) (0.448) (0.446) (0.456)
Diffusion 2.121∗∗∗ 2.058∗∗∗ 2.245∗∗∗ 2.046∗∗∗
(0.212) (0.211) (0.214) (0.210)
Eligible for Listing 0.981 1.018 0.966 0.879
(0.630) (0.611) (0.607) (0.600)
US Ally 1.990 1.773 2.204 1.742
(1.828) (1.831) (1.830) (1.819)
Private Sector Credit 1.145 1.179 1.236 1.194
(0.268) (0.259) (0.271) (0.259)
Capacity 0.821 0.908 0.673 0.755
(0.378) (0.385) (0.414) (0.377)
Democracy 0.946 0.936 0.942 0.926∗
(0.039) (0.040) (0.039) (0.041)
Terrorism Risk 1.431 1.514 1.453 1.516
(0.315) (0.319) (0.317) (0.314)
Trade with US (Percent GDP) 0.956
(0.196)
US Foreign Aid 1.128
(0.125)
US - 311 Sanctions List 0.301∗∗
(1.136)
US - AML List 0.612∗
(0.312)
Observations 4,103 4,114 4,114 4,090
Countries 88 88 88 88
Events 41 41 41 41
Note: ∗p<0.1; ∗∗p<0.05; ∗∗∗ p<0.01
Table 3: Listing, Market Enforcement, and Criminalization: Cox Proportional Hazards Models for US
Power Alternatives- Hazards ratios for cox proportional hazards models. Values over 1 indicate a positive
effect; values below 1 indicate a negative effect. Standard errors are clustered by country and shown in
parentheses. All models include log-time interaction for US ally.
33
all business with a listed country or bank. The State Department list, on the other hand,
focuses on countries with high volumes of money laundering, and therefore includes most
large financial centers (including the United States). As a result, the list is unlikely to lead
to any significant material consequences for identified countries.
An additional way to probe the robustness of these results might be to conduct a placebo
test for the period prior to the creation of the FATF non-complier list. Between 2005 and
2007, the FATF evaluated more than 60 countries, 56 of which had not criminalized terrorist
financing in line with FATF standards. Many of these countries would subsequently be
eligible for listing after the FATF created its new listing procedures in 2009. If the FATF non-
complier list is really driving policy change, then we should see no significant improvements
in compliance in 2008 and 2009 for countries evaluated during this earlier period. Descriptive
statistics confirm this trend. Of the 56 non-compliant countries evaluated between 2005 and
2007, only two countries adopted FATF-compliant laws on terrorist financing prior to 2010.92
Hypothesis 3: Bank Lending and Market Enforcement
If international market actors like multinational banks allocate resources differently based
on FATF non-complier list announcements, then international banks should be less willing
to do business with banks, companies, and individuals in listed countries. I test this causal
mechanism by examining how listing affects cross-border liabilities, i.e. the money that
banks in a given country owe international banks. Data on cross-border liabilities comes
from BIS and is available on a quarterly basis. Cross-border liabilities for the period of 2010
to 2015 range from 0 to 3.9 trillion. Because the distribution is highly skewed, I add 1 to all
values and take the log.93
To analyze the effect of listing on cross-border liabilities, I build an economic model that
92Liechtenstein and Georgia.
93Following Herrmann and Mihaljek 2010, my dependent variable does not consider changes in liabilities
but rather reveals changes in lending and borrowing.
34
takes into account a country’s underlying economic structure and macroeconomic fluctua-
tions that are likely to affect bank-to-bank flows. Core economic factors that are likely to
influence banking relationships include GDP Growth and Inflation. The rate of economic
growth in a country could affect its demand for bank-to-bank lending, while higher inflation
might limit the supply of credit. GDP data comes from the World Bank, while inflation
data comes from the IMF’s International Financial Statistics (IFS) database. I also include
the Real Exchange Rate, which comes from IFS and is computed using nominal exchange
rate data and the ratio of the US Consumer Price Index (CPI) to the local CPI in a given
year. Bruno and Shin link bank leverage and monetary policy, finding that a contradictory
shock to US monetary policy policy leads to a decrease in the cross-border capital flows of
the banking sector.94
A country’s level of debt (private sector and government) is also likely to affect the
willingness of banks to do business with a jurisdiction. Specifically, the local banking sector’s
leverage ratio, i.e. the relationship between its core capital and total assets, is likely to affect
bank-to-bank transfers across borders. Following Bruno and Shin, I include the variable
Credit-to-GDP Ratio, which proxies for the leverage of local banks using the ratio of bank
assets to capital from the World Bank WDI dataset.95 I also include Debt-to-GDP Ratio,
drawn from IFS. Debt-to-GDP ratio is a commonly used measure of a country’s economic
health, particularly for emerging economies. Higher levels of external debt should make
borrowers more vulnerable, which may reduce an international bank’s willingness to lend
money.96
I include Interest Spread to account for the difference between the local lending rate
and the US Fed Fund rate, which may affect the price determinants of local demand for
94Bruno and Shin 2015b. The results are robust to including trade balance in the model instead of the
real exchange rate. See Appendix K.
95Bruno and Shin 2015a.
96Tak´ats and Avdjiev 2014.
35
cross-border credit. Interest rate data comes from the World Bank WDI dataset, but is
only available for a subset of countries. As a result, I include this variable in two of the
four models. I also include the variable Money Supply (from the World Bank WDI) in the
latter two models. Local borrowers may borrow in US dollars and then deposit the local
currency proceeds into the domestic banking system, which would lead banking inflows to
be associated with increases in M2.97
I evaluate the effect of listing on cross-border liabilities between March 2010 and Decem-
ber 2015, where the unit of observation is country-quarter (producing four observations per
year). I use an ordinary least squares regression with country-fixed effects; as a result, the
unit of comparison is within country over time. In all models, I lag explanatory variables by
one year and cluster standard errors. Models 2 and 4 also include a time polynomial. In the
simplest model, I analyze the effect of listing on cross-border liabilities for 50 countries, 12
of which were listed by the FATF. As I add variables, the sample drops to 39 countries, 10
of which were listed by the FATF.98
Table 4 shows the estimated effect of listing on cross-border liabilities. In line with the
theory, listing leads to a statistically significant and substantively large decrease in cross-
border liabilities across all four models. In Model 4, listing leads to a 16 percent decrease
in liabilities. To provide context for this number, consider a country like the Philippines,
which was listed from 2010 to 2013. In 2010, the Philippines’ average cross-border liabilities
per quarter were 11.5 billion US dollars. Based on the estimate in this model, listing should
lead to a decline of 1.84 billion US dollars in cross-border liabilities. And indeed, by 2012,
the average quarterly cross-border liabilities in the Philippines had declined significantly to
8.7 billion, and rebounded to pre-listing levels only in 2014.
97For a more detailed explanation of this link, see Bruno and Shin 2015a, 21.
98I restrict my analysis to the 141 countries that were included in the previous regression analysis of how
listing affects the probability of criminalization. Economic data is not available for many of the smaller
countries included in that analysis, which is why the sample size decreases significantly in this test.
36
Dependent variable: Cross-border Liabilities (log)
(1) (2) (3) (4)
Listing −0.158∗∗∗ −0.149∗∗∗ −0.167∗∗∗ −0.157∗∗∗
(0.045) (0.045) (0.045) (0.045)
Inflation 0.008∗∗∗ 0.009∗∗∗ 0.008∗∗∗ 0.010∗∗∗
(0.002) (0.002) (0.002) (0.003)
GDP Growth (Percent Change) −0.002 0.0001 −0.002 0.002
(0.005) (0.005) (0.005) (0.005)
Real Exchange Rate −0.00001 −0.00001 0.00001 −0.00001
(0.00002) (0.00002) (0.00002) (0.00002)
Credit-to-GDP Ratio −0.005∗∗∗ −0.005∗∗∗ −0.005∗∗∗ −0.004∗∗∗
(0.002) (0.002) (0.002) (0.002)
Debt-to-GDP Ratio −0.005∗∗∗ −0.006∗∗∗ −0.005∗∗∗ −0.008∗∗∗
(0.002) (0.002) (0.002) (0.002)
Money Supply 0.001 0.001
(0.001) (0.001)
Interest Rate Spread −0.014∗∗ −0.006
(0.006) (0.006)
Observations 828 828 656 656
Countries 50 50 39 39
Country Fixed Effects Y Y Y Y
Time Polynomial N Y N Y
Note: ∗p<0.1; ∗∗p<0.05; ∗∗∗ p<0.01
Table 4: The Effect of Listing on Cross-Border Liabilities - Dependent variable is logged
cross-border liabilities. OLS regression with country-fixed effects, with robust clustered
standard errors shown in parentheses. Quarterly observations for 2010 to 2015.
37
Hypothesis 3: Robustness
I probe the robustness of these results with a placebo test that analyzes the effect of post-
2009 listing on cross-border liabilities in an earlier period. If the FATF non-complier list
is truly driving the change in cross-border liabilities, then being listed in subsequent years
(2010 and on) should have no effect on cross-border liabilities in previous years. In contrast,
if listing is proxying for an underlying state-specific characteristic, then this omitted variable
could have an effect on outcomes even in the period prior to the creation of the non-complier
list. I replicate the previous analysis for the period of 2006 to 2008, matching each country’s
listing status in the years 2010 to 2012.99 In the placebo time period, listing has no effect
on cross-border liabilities. These results are available in Appendix L.
Case Study of Thailand
Thailand’s experience with the non-complier list shows how the reputational consequences of
listing combine with market enforcement to generate policy change. When the FATF listed
Thailand in February 2010, the country was in compliance with very few FATF recommenda-
tions. The Thai government viewed anti-money laundering and combating terrorist financing
as low priorities. The FATF non-complier list’s impact on markets, however, reoriented the
government interests, as the banking community and private sector actors began to advocate
for compliance. Over the course of a few years, Thailand significantly improved its policies
and was subsequently removed from the list.
99For example, if a country was listed in 2010 but not 2011 or 2012, it will be listed in the placebo test in
2006 but not 2007 or 2008.
38
Thailand and the Non-Complier List
Although Thailand is highly integrated into the global economy, and also susceptible to
money laundering and terrorist financing, the Thai government did not prioritize compliance
with the FATF recommendations in the early 2000s.100 The Asia/Pacific Group on Money
Laundering’s 2007 mutual evaluation report rated Thailand fully compliant with only 2 of
the FATF’s 49 recommendations. At the time, the FATF had very few tools in place to
deal with non-compliant jurisdictions. The repercussions of non-compliance were minimal;
Thailand had only to submit follow-up reports. In 2009, however, the FATF revitalized
its process for dealing with non-compliant jurisdictions. When the FATF issued its first
non-complier list in February 2010, Thailand was one of 20 countries listed at the lowest
level. In its first statement, the FATF called on Thailand to criminalize terrorist financing,
establish and implement procedures to freeze terrorist assets, and strengthen its supervision
of relevant laws.
Thailand’s Anti-Money Laundering Office (AMLO) responded immediately to listing,
but its actions had little effect on the Thai parliament’s willingness to adopt reforms.101 The
AMLO launched a big public information campaign to convince the Thai National Assem-
bly to adopt new laws.102 Yet the response from the rest of the government was sluggish.
According to a senior Thai banking official, while the AMLO recognized the significance of
listing and the possible financial repercussions, the National Assembly was slow to under-
stand the possible consequences.103 As a result, by the end of 2010, Thailand had approved
a national anti-money laundering and combating the financing of terrorism strategy and
drafted proposed legislation,104 but made no other improvements.
100IMF 2007.
101The AMLO is a Thai government bureaucratic unit that supervises how the financial sector implements
financial integrity policy.
102Author interview with Thai government official, 14 February 2016.
103Author interview, 9 March 2017.
104US Department of State 2011.
39
In this first year, the market response was negligible. Since the list was so new, it received
very little media attention; indeed, The Wall Street Journal did not publish a single article
about the FATF list in the six months following its creation.105 Additionally, because the
non-complier list included several different lists, it was difficult for outside observers like
banks, investors, or even other countries to know how to interpret the meaning of a country’s
inclusion on one of the lists. For this reason, market actors were slow to integrate the list
into decision-making practices.
By February 2011, however, the FATF had issued three listing announcements, each
of which described ongoing compliance problems in listed countries. As such, third-party
observers like banks and investors began to realize that many countries would require signif-
icant legal change before the FATF would remove them from the list. Market actors began
to adjust risk appraisals accordingly. For Thailand, a country with an economy heavily de-
pendent on trade and investment, the response from markets was crucial for driving policy
change. It became more difficult for Thai banks to do business abroad, as foreign banks
began to ask more questions about anti-money laundering rules and regulations.106 Despite
these costs, Thailand failed to make significant changes to its legal framework in 2011, due
partly to domestic political unrest.107 In October 2011 the FATF placed Thailand on a list
of countries not making enough progress. In February 2012, Thailand was bumped up to
the so-called “blacklist.”
The higher listing level intensified the costs to Thailand’s financial sector and increased
pressure on the government to change its laws. In an interview, a Thai government of-
ficial reported that “the impact (of the blacklist) was considerably more acute...Financial
105This lack of media attention is notable compared to 2014 and 2015, when The Wall Street Journal
published approximately 5-6 articles per year mentioning the FATF non-complier list.
106Author interview with Thai banking official, 9 March 2017.
107According to a State Department assessment “Political and civil unrest in Thailand in mid-2010, followed
by catastrophic flooding, the dissolution of Parliament and subsequent general election in July 2011, have
impeded Thailand’s implementation of its AML/CFT action plan” US Department of State 2012, 171.
40
institutions reported unexpected difficulties in obtaining permits to open branches in EU
countries. A bank in (the) EU even contemplated scrapping a deal to lend money to Thai
banks.”108 The AMLO suddenly had new allies, as the Board of Trade, the Federation
of Thai Industries, the Thai Bankers Association, and the Federation of Capital Markets
Association began joint action with the AMLO and the Attorney General’s office to push
for new laws on money laundering and terrorist financing.109 Less than a month later, in
May 2012, Deputy Prime Minister Kittiratt Na-Ranong promised Thailand would amend
its Anti-Money Laundering Act by the end of the year. In a public statement, Na-Ranong
linked anticipated policy change directly to the FATF list.110 The Thai government followed
through on its promises, passing new laws on money laundering and terrorist financing weeks
before the February 2013 FATF plenary. Following an on-site visit to confirm progress, the
FATF removed Thailand from the non-complier list in June 2013.
Financial Costs of Listing
Quantifying the full impact of the non-complier list on Thailand’s economy is difficult due
to the diverse ways in which the list affected financial flows. There is, however, at least
correlational evidence that the non-complier list affected cross-border liabilities. Figure 4
shows cross-border liabilities (money that Thai banks owe to international banks) between
2009 and 2015. When the FATF listed Thailand in February 2010, cross-border liabilities
stayed relatively stable; however, after the FATF bumped Thailand up to a higher listing
level in February 2012, cross-border liabilities declined significantly. Specifically, cross-border
liabilities declined from 1.7 billion US dollars to 1.4 billion US dollars, a decrease of 17.6
percent. This number is remarkably close to the estimate obtained from the regression
analysis of the effect of listing on cross-border liabilities (Table 4, which suggested that listed
108Author interview, 14 February 2016.
109Private sector pressures for solution on FATF blacklist - The Nation 2012.
110Fernquest 2012.
41
Year
Cross−Border Liabilities
(US$ − Billions)
2009 2010 2011 2012 2013 2014 2015
1.4 1.6 1.8 2
Figure 4: The figure shows cross-border liabilities (billions of US dollars) from 2009 to 2015. The
FATF listed Thailand in February 2010 and in February 2012, placed Thailand on its “black” list
for failing to improve its laws in a timely fashion. Following significant legal changes, the FATF
removed Thailand from its monitoring process in June 2013.
countries experience a 16 percent decline in liabilities). In the case of Thailand, however,
the country almost immediately began to modify its policies, and cross-border flows began
to increase.
Since Thailand was removed from the FATF list in June 2013, the Thai government has
continued to improve its compliance with FATF standards, albeit at a slower pace. The
AMLO has taken a much more active role in regulating the banking sector, clarifying bank
reporting obligations and promoting information sharing on this issue.111 The FATF’s 2017
evaluation of Thailand notes that there is “strong political support for recent AML/CFT
reforms” and highlights how “institutional arrangements have developed significantly since
the 2007 mutual evaluation report.”112
111Author interview with Thai banking official, 9 March 2017.
112FATF-GAFI 2017, 3.
42
GPIs in a Globalized World
In today’s globalized world, institutionalized cooperation is essential for addressing transna-
tional threats. While most international institutions continue to lack formal enforcement
power, this gap should not suggest that such institutions are weak or ineffective. Instead,
the same processes of interdependence that generate new threats also expand opportuni-
ties for institutions to drive policy change. IGOs can use GPIs to harness institutional
advantages like credibility and technical expertise into informational power. The FATF case
suggests that GPIs are particularly effective drivers of policy change when they are used by
market actors like banks and investors to shift resources away from non-compliant states.
Markets are natural audiences for financial GPIs because such measures convert uncertainty
into risk. By stabilizing market expectations, GPIs can engender market pressure to create
new incentives for policy change.
43
Appendix A FATF Members and Associate Members
Members Associate Members: FATF-Style Regional Bodies
Argentina Asia/Pacific Group on Money Laundering (APG)
Australia Caribbean Financial Action Task Force (CFATF)
Austria MONEYVAL (Council of Europe)
Belgium Eurasian Group (EAG)
Canada Eastern and Southern Africa Anti-Money Laundering Group (ESAAMLG)
China Financial Action Task Force of Latin America (GAFILAT)
Denmark Inter Governmental Action Group against Money Laundering in West Africa (GIABA)
European Commission Middle East and North Africa Financial Action Task Force (MENAFATF)
Finland Task Force on Money Laundering in Central Africa (GABAC)
France
Germany
Greece
Gulf Cooperation Council
Hong Kong, China
Iceland
India
Ireland
Italy
Japan
Korea
Luxembourg
Malaysia
Mexico
Netherlands
New Zealand
Norway
Portugal
Russia
Singapore
South Africa
Spain
Sweden
Switzerland
Turkey
United Kingdom
United States
Table A1: The table shows FATF members and associate members. Italicized members are regional
organizations. Most member states belonging to FATF-style regional bodies are not FATF members.
44
Appendix B FATF 16 Key & Core Recommendations
The FATF has identified 16 of its “40+9” recommendations on combating money laun-
dering and terrorist financing as being the highest priority recommendations for states. In an
interview, a FATF regional body official described the core recommendations as the “build-
ing blocks of the AML/CFT regime, without which anything else would be pointless,” while
the key recommendations are ”extremely important, but to a lesser extent” (Interview, 27
January 2015). The general topics covered by these 16 key and core recommendations are
given below.
Core Recommendations
•Criminalization of money laundering and terrorist financing (Recommendation 1,
Special Recommendation II)
•Customer identification/record-keeping requirements (Recommendations 5 and 10)
•Suspicious transaction reports reporting (Recommendation 13,
Special Recommendation IV)
Key Recommendations
•International cooperation and mutual legal assistance (Recommendations 35, 36, 40,
Special Recommendations I and V)
•Freezing and confiscation (Recommendation 3, Special Recommendation III)
•Financial secrecy (Recommendation 4)
•Adequate regulation and supervision (Recommendation 23)
•Functional financial intelligence unit (Recommendation 26)
45
Appendix C Interviews by Author
I conducted numerous interviews with officials from listed governments, IOs, and the bank-
ing sector. Most of these people declined to be interviewed “on the record” due to the
sensitivities of this issue area and, in some cases, specific bureaucratic guidelines that do
not allow them to make statements for publication. Where possible, I have relied on quotes
from individuals who agreed to be interviewed on the record, or have used direct quotes from
interviews without specific attribution. A list of all interviews, both cited and un-cited, is
provided below.
Cited:
•Interview with UNODC official, 8 May 2014
•Interview with official from a FATF regional body, 27 January 2015
•Interview with compliance executive, top-five US bank, 28 August 2015
•Interview with Jeff Soloman, Financial and Risk Sales Specialist, Thomson Reuters,
28 September 2015
•Interview with official from Thomson-Reuters Country-Check, 29 September 2015
•Interview with official from formerly listed country, 9 February 2016
•Interview with official from a private bank in Ethiopia, 11 February 2016
•Interview with Thai government official, 14 February 2016
•Interview with official from FATF-style regional body, 30 June 2016
•Interview with Gordon Hook, Executive Director of the Asia/Pacific Group on Money
Laundering, 30 June 2016
•Participant Observation of Asia-Pacific Group Plenary, 6-8 September 2016
•Interview with Thai banking official, 9 March 2017
•Interview with former FATF President Antonio Gustavo Rodrigues, 29 March 2017
•Participant Observation of MONEYVAL Plenary, 30 May - 1 June 2017
•Interview with Chip Poncy, Head of US government delegation to FATF (2011 - 2013),
Senior delegation member (2002 - 2011), 7 February 2018
•Interview with Daniel Glaser, Assistant Secretary for Terrorist Financing (2011 - 2017),
Deputy Assistant Secretary for Terrorist Financing (2004 - 2011), US Government, 12
February 2018
46
Un-cited:
•Interview with UNODC official, 7 May 2014
•Interview with UNODC official, 8 May 2014
•Interview with Executive Director of a FATF regional body, 10 December 2014
•Interview with Gordon Hook, Executive Direct of the Asia/Pacific Group on Money
Laundering, 16 February 2015
•Interview with official from compliance company, 22 September 2015
•Interview with official from compliance company, 24 September 2015
•Interview with MSCI official, 25 September 2015
•Interview with Credit Agricole CIB official, 25 September 2015
•Interview with investment firm official, 8 February 2016
•Interview with official from an international development bank, 7 April 2016
•Interview with Gordon Hook, Executive Director of the Asia/Pacific Group on Money
Laundering, 28 January 2018
47
Appendix D FATF Non-Complier List Countries
Country Listed Graduated
Afghanistan 2012 –
Albania 2012 2015
Algeria 2011 2016
Angola 2010 2016
Antigua and Barbuda 2010 2014
Argentina 2011 2014
Azerbaijan 2010 2010
Bangladesh 2010 2014
Bolivia 2010 2013
Bosnia-Herzegovina 2015 –
Brunei Darussalam 2011 2013
Cambodia 2011 2015
Cuba 2011 2014
DPRK 2007 –
Ecuador 2010 2015
Ethiopia 2010 2014
Ghana 2010 2013
Greece 2010 2011
Guyana 2014 –
Honduras 2010 2012
Indonesia 2010 2015
Iran 2007 –
Iraq 2013 –
Kenya 2010 2014
Kuwait 2012 2015
Kyrgyzstan 2011 2014
Lao PDR 2013 –
Mongolia 2011 2014
Morocco 2010 2013
Myanmar 2010 2016
Namibia 2011 2015
Nepal 2010 2014
Nicaragua 2011 2015
Nigeria 2010 2013
Pakistan 2010 2015
Panama 2014 2016
Papua New Guinea 2014 2016
Paraguay 2010 2012
Philippines 2010 2013
Qatar 2010 2010
Sao Tome and Principe 2010 2013
Sri Lanka 2010 2013
Sudan 2010 2015
Syria 2010 –
Tajikistan 2011 2014
Tanzania 2010 2014
Thailand 2010 2013
Trinidad and Tobago 2010 2012
Turkey 2010 2014
Turkmenistan 2010 2012
Uganda 2014 –
Ukraine 2010 2011
Vanuatu 2016 –
Venezuela 2010 2013
Vietnam 2010 2014
Yemen 2010 –
Zimbabwe 2011 2015
Total 57 46
Table D2: Countries listed by the FATF (Feb 2010 - June 2016) - Table shows the countries included on the non-complier
list, the year of listing, and the year of graduation (where relevant). Countries that graduate are removed from FATF monitoring
due to significant policy change (with the exception of Sao Tome and Principe, which the FATF decided was a low threat and
no longer needed monitoring).
48
Appendix E Countries Included in Survival Analysis
Countries Included in All Models
Algeria Argentina Austria Bahrain Bangladesh
Belarus Belgium Bolivia Botswana Brazil
Bulgaria Burkina Faso Chile Cote d’Ivoire Croatia
Cyprus Czech Republic Dominican Republic Ecuador Egypt
El Salvador Estonia Finland Gambia Germany
Ghana Greece Guatemala Guyana Haiti
Honduras Hungary India Indonesia Iraq
Ireland Japan Jordan Kazakhstan Kenya
Korea Kuwait Latvia Lebanon Lithuania
Mali Mexico Mongolia Morocco Mozambique
Namibia Netherlands Nicaragua Niger Nigeria
Norway Oman Pakistan Panama Paraguay
Peru Philippines Poland Portugal Qatar
Romania Saudi Arabia Senegal Sierra Leone Slovenia
Spain Sri Lanka Sudan Suriname Sweden
Switzerland Tanzania Thailand Togo Tunisia
Turkey Uganda United Arab Emirates Uruguay Venezuela
Yemen Zambia
Countries Included Only in Models 1, 2, and 3
Bahamas Brunei Darussalam Guinea Guinea-Bissau Iceland
Liberia Malta Serbia Vietnam
Countries Included Only in Models 1 and 2
Afghanistan Belize Benin Cambodia Cabo Verde
Comoros Dominica Fiji Grenada Kyrgyzstan
Lao PDR Lesotho Macedonia Maldives Mauritania
Mauritius Nepal Samoa Seychelles St. Lucia
St. Vincent Swaziland Tajikistan Tonga
Countries Included Only in Model 1
Angola Antigua and Barbuda Barbados East Timor Moldova
Myanmar Nauru Sao Tome and Principe Syria Turkmenistan
Vanuatu Zimbabwe
Table E3: Countries Included in Survival Analysis
49
Appendix F Criminalization of Terrorist Financing:
Imputed Data
Dependent variable: Criminalization of Terrorist Financing
(1) (2) (3) (4)
Listing 9.050∗∗∗ 9.833∗∗∗ 9.712∗∗∗ 9.639∗∗∗
(0.327) (0.379) (0.389) (0.391)
Market Integration 1.037 1.034 1.034
(0.057) (0.061) (0.061)
Listing * Market Integration 1.184∗∗ 1.180∗∗ 1.178∗∗
(0.091) (0.093) (0.093)
FATF Member 1.137 0.797 0.788 0.757
(0.409) (0.491) (0.497) (0.521)
Previous Terrorist Fin Law 1.302 1.247 1.240 1.212
(0.280) (0.285) (0.288) (0.303)
Diffusion 1.056∗∗∗ 1.058∗∗∗ 1.058∗∗∗ 1.059∗∗∗
(0.012) (0.013) (0.013) (0.013)
Eligible for Listing 0.916 1.000 1.005 1.008
(0.378) (0.450) (0.451) (0.453)
US Ally 3.988 3.530 3.504 3.478
(1.390) (1.409) (1.410) (1.409)
Private Sector Credit 1.044 1.054 1.047
(0.164) (0.178) (0.181)
Capacity 1.062 1.059 1.073
(0.269) (0.270) (0.275)
Terrorism 0.976 0.961
(0.180) (0.190)
Democracy 1.007
(0.027)
Observations 7,617 7,617 7,617 7,617
Countries 137 137 137 137
Events 74 74 74 74
Note: ∗p<0.1; ∗∗p<0.05; ∗∗∗ p<0.01
Table F4: Listing, Market Enforcement, and Criminalization: Cox Proportional Hazards Models for Full
Sample with imputed data - Hazards ratios for cox proportional hazards models, replicates models for Table
1. Values over 1 indicate a positive effect; values below 1 indicate a negative effect. Standard errors are
clustered by country and shown in parentheses. All models include a log-time interaction for US ally.
50
Appendix G Results without Log-Time Interaction
Dependent variable: Criminalization of Terrorist Financing
(1) (2) (3) (4)
Listing 8.770∗∗∗ 8.122∗∗∗ 5.816∗∗∗ 8.338∗∗∗
(0.331) (0.454) (0.523) (0.565)
Market Integration 1.005 1.000 1.020
(0.067) (0.097) (0.106)
Listing * Market Integration 1.209∗∗ 1.319∗∗ 1.352∗∗
(0.108) (0.144) (0.150)
FATF Member 1.021 0.638 0.696 0.987
(0.412) (0.613) (0.632) (0.734)
Previous Terrorist Fin Law 1.370 1.056 0.915 0.865
(0.285) (0.355) (0.398) (0.446)
Diffusion 1.061∗∗∗ 1.061∗∗∗ 1.074∗∗∗ 1.080∗∗∗
(0.013) (0.017) (0.020) (0.021)
Eligible for Listing 0.864 1.155 1.104 0.963
(0.375) (0.554) (0.606) (0.611)
US Ally 1.046 0.934 0.664 0.642
(0.294) (0.343) (0.382) (0.428)
Private Sector Credit 1.009 0.919 1.106
(0.181) (0.236) (0.257)
Capacity 1.162 1.206 0.850
(0.293) (0.319) (0.372)
Terrorism 1.063 1.430
(0.255) (0.314)
Democracy 0.943
(0.039)
Observations 7,262 5,828 4,613 4,114
Countries 132 120 96 87
Events 72 52 43 39
Note: ∗p<0.1; ∗∗p<0.05; ∗∗∗ p<0.01
Table G5: Listing, Market Enforcement, and Criminalization: Cox Proportional Hazards Models for Full
Sample without log-time interaction for US ally - Hazards ratios for cox proportional hazards models,
replicates models for Table 1. Values over 1 indicate a positive effect; values below 1 indicate a negative
effect. Standard errors are clustered by country and shown in parentheses.
51
Appendix H Conditional Marginal Effect:
Additional Tests
Hainmueller et al. (Forthcoming) recommend two estimation strategies to estimate the conditional marginal
effect of a variable (in this case, the FATF non-complier list) on the outcome (compliance) across values
of the moderator (market integration). The first approach involves breaking a continuous moderator into
several bins, represented by dummy variables, and interacting these dummy variables with the treatment.
The results of this test are presented in figure H1. The second approach is a kernel smoothing estimator
of the marginal effect; the results of this test are presented in figure H2. Both tests support he linearity
assumption imposed by a standard multiplicative interaction model, although the estimated effect at high
levels of market integration in H1 is imprecise due to the small sample size for listing at this value.
L M H
−0.1
0.0
0.1
0.2
0.3
0.4
−5 0 5 10
Moderator: Market Integration
Marginal Effect of List on Crim TF
Figure H1: The figure shows the results of binning the variable ”Market Integration” into three
tercicles – low, medium, and high – and estimating the conditional marginal effect at the median
value of each tercicle.
−0.05
0.00
0.05
0.10
−5 0 5 10
Moderator: Market Integration
Marginal Effect of List on Crim TF
Figure H2: The figure shows the results of a kernel smoothing estimator of the marginal effect of
listing on compliance, moderated by market integration.
52
Appendix I Listing as an Ordinal Variable
The FATF’s non-complier list is actually composed of four separate lists. Most states are only listed at
the lowest level (the “grey” list), which identifies countries that have strategic deficiencies but have made
a “written high-level commitment” to improve relevant laws. Subsequent levels include a warning list that
identifies jurisdictions not making enough progress (the “dark grey” list) and an enhanced due diligence list
that identifies countries failing to make progress or failing to commit to an FATF action plan (the unofficial
“black” list). The highest level is the FATF’s counter-measures list, which is excluded from this analysis
because it has only ever included two states: Iran and North Korea.
To analyze whether the effect of listing depends on the strength of listing, I create an ordered categorical
variable which ranges from “no listing” to a country being on the “blacklist.” I replicate Models 1 and 4
from Table 1 with this variable. Table I6 shows the results of this analysis.
53
Dependent variable: Criminalization of Terrorist Financing
(1) (2)
List Level - Linear 6.469∗∗∗ 11.050∗∗∗
(0.316) (0.535)
List Level - Quadratic 0.467∗∗ 0.737
(0.384) (0.563)
List Level - Cubic 1.579 1.552
(0.447) (0.663)
Market Integration 1.025
(0.139)
FATF 0.955 1.467
(0.408) (0.801)
Previous Terrorist Fin Law 1.530 1.088
(0.297) (0.478)
Diffusion 1.059∗∗∗ 1.082∗∗∗
(0.013) (0.022)
Eligible for Listing 0.840 0.911
(0.378) (0.644)
US Ally 3.469 1.144
(1.390) (1.898)
Private Sector Credit 1.209
(0.288)
Capacity 0.738
(0.410)
Terrorism Risk 1.516
(0.336)
Democracy 0.932
(0.042)
List Level - Linear * Market Integration 0.784
(0.214)
List Level - Quadratic * Market Integration 0.621∗∗
(0.237)
List Level - Cubic * Market Integration 1.125
(0.213)
Observations 7,308 4,114
Countries 132 87
Events 72 39
Note: ∗p<0.1; ∗∗p<0.05; ∗∗∗ p<0.01
Table I6: List Level, Market Enforcement, and Criminalization: Cox Proportional Hazards Models for Full
Sample - Hazards ratios for cox proportional hazards models, replicates Models 1 and 4 in Table 1. Values
over 1 indicate a positive effect; values below 1 indicate a negative effect. Standard errors are clustered by
country and shown in parentheses. All models include log-time interaction for US ally.
54
Appendix J Details on Matching
To assemble a matched sample, I subset the data to period 1 (February 2010), and assemble a dataset of
all countries with complete information for model variables. I then use the R package ‘MatchIt’ and use
nearest neighbor matching to build a matched sample based on six covariates that could affect a country’s
probability of being listed. This matched sample includes 12 listed countries and 12 non-listed countries. I
then expand the analysis to included data for this set of 24 countries for the full time period (2010 to 2015).
Table J7 shows the improvement in balance generated by this matched sample. Specifically, it provides
the mean value for all variables included in the matching model, comparing listed and non-listed countries
in the full sample and in the matched value. Averages are for the year 2010.
Pre-Processing Matched
Means List Means No List Diff Means List Means No List Diff
Variable
Distance 0.280 0.154 0.126 0.280 0.278 0.002
Diffusion -18.092 -17.854 -0.238 -18.092 -17.259 -0.833
US Ally -0.0598 -0.006 -0.054 -0.060 0.024 -0.083
Private Sector Credit -0.885 -0.335 -0.551 -0.885 -1.626 0.741
Capacity -0.369 -0.152 -0.218 -0.369 -0.910 0.541
Polity IV -2.385 -0.843 -1.542 -2.385 -2.302 -0.083
Risk of Terrorism -0.752 0.063 -0.815 -0.752 -0.711 -0.042
Countries 27 41 12 12
Table J7: Balance Comparison: Balance comparison of matched vs. unmatched (pre-processing)
sample. Each variable is centered around its mean value, such that positive values indicate values
above the mean and negative values indicate values below the mean.
55
Appendix K Listing and Cross-Border Liabilities
(Inclusion of Trade Balance)
Dependent variable: Cross-border Liabilities (log)
(1) (2) (3) (4)
Listing −0.287∗∗∗ −0.280∗∗∗ −0.288∗∗∗ −0.265∗∗∗
(0.055) (0.055) (0.056) (0.054)
Inflation 0.009∗∗∗ 0.010∗∗∗ 0.009∗∗∗ 0.009∗∗∗
(0.002) (0.002) (0.003) (0.002)
GDP Growth (Percent Change) −0.002 0.002 −0.006 0.002
(0.004) (0.004) (0.006) (0.006)
Balance of Trade −0.000∗∗ −0.000∗∗ −0.000∗−0.000∗∗
(0.000) (0.000) (0.000) (0.000)
Credit-to-GDP Ratio −0.007∗∗∗ −0.006∗∗∗ −0.007∗∗∗ −0.006∗∗∗
(0.001) (0.001) (0.002) (0.002)
Debt-to-GDP Ratio −0.010∗∗∗ −0.011∗∗∗ −0.007∗∗ −0.015∗∗∗
(0.001) (0.001) (0.003) (0.003)
Time 0.021∗∗∗ 0.057∗∗∗
(0.007) (0.009)
Money Supply 0.001 0.002∗
(0.001) (0.001)
Interest Rate Spread −0.010 −0.002
(0.006) (0.006)
Observations 836 836 500 500
Countries 57 57 33 35
Country Fixed Effects Y Y Y Y
Time Polynomial N Y N Y
Note: ∗p<0.1; ∗∗p<0.05; ∗∗∗ p<0.01
Table K8: The Effect of Listing on Cross-Border Liabilities - Dependent variable is logged
cross-border liabilities. OLS regression with country-fixed effects, with robust clustered
standard errors shown in parentheses. Quarterly observations for 2010 to 2015.
56
Appendix L Placebo Test
Dependent variable: Cross-border Liabilities (log)
(1) (2) (3) (4)
Placebo Listing 0.192 0.171 0.064 0.098
(0.150) (0.145) (0.202) (0.195)
Inflation −0.040∗∗∗ 0.015 −0.008 0.011
(0.008) (0.014) (0.037) (0.036)
GDP Growth (Percent Change) 0.016∗∗ 0.011 0.071∗∗∗ 0.036
(0.008) (0.007) (0.027) (0.029)
Real Exchange Rate 0.002 0.003∗∗ 0.008∗∗∗ 0.008∗∗∗
(0.002) (0.002) (0.002) (0.002)
Credit-to-GDP Ratio 0.006∗0.016∗∗∗ −0.010 −0.005
(0.003) (0.004) (0.009) (0.008)
Debt-to-GDP Ratio −0.005 0.001 −0.002 −0.001
(0.003) (0.003) (0.006) (0.005)
Money Supply −0.003 0.001
(0.006) (0.006)
Interest Rate Spread 0.016 0.083∗∗
(0.030) (0.037)
Observations 360 360 108 108
Countries 50 50 14 14
Country Fixed Effects Y Y Y Y
Time Polynomial N Y N Y
Note: ∗p<0.1; ∗∗p<0.05; ∗∗∗ p<0.01
Table L9: The Effect of Listing on Bank-to-Bank Lending (Placebo Test)- Dependent variable
is logged cross-border liabilities. OLS regression with country-fixed effects, with robust
clustered standard errors shown in parentheses. Quarterly observations for 2006 to 2008.
Listing data is from 2010 to 2012.
57
58
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