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        Blacklists, Market Enforcement, and the Global Regime to Combat Terrorist Financing
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        Blacklists, Market Enforcement, and the Global Regime to Combat Terrorist Financing
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        Blacklists, Market Enforcement, and the Global Regime to Combat Terrorist Financing
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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 restrict access to capital in noncompliant states and incentivize increased compliance. I demonstrate this market-enforcement mechanism by analyzing the Financial Action Task Force (FATF), an intergovernmental body that issues nonbinding recommendations to combat money laundering and the financing of terrorism. The FATF's public listing of noncompliant jurisdictions has prompted international banks to move resources away from listed states and raised the costs of continued noncompliance, significantly increasing the number of states with laws criminalizing terrorist financing. 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.

Over the last twenty years, intergovernmental organizations (IGOs) have increasingly relied on global performance indicators (GPIs) to disseminate information about state policies. Kelly and Simmons suggest GPIs influence policy outcomes in states through three pathways: changes in domestic politics, shifts in elite preferences, and transnational pressure.1 Bisbee and colleagues show that the Millennium Development Goals encourage greater domestic attention to evaluated policy objectives,2 while Kelley, Simmons, and Doshi suggest the World Bank's Ease-of-Doing-Business index provokes domestic awareness and pressures bureaucrats to change business regulation.3 Honig and Weaver reveal how the Aid Transparency Index alters the behavior of development aid donors by diffusing professional norms and affecting organizational learning.4 In 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 United Nations Security Council, adopted resolutions calling for the worldwide adoption of domestic laws criminalizing terrorist financing. A decade later, most countries had laws that were weak and ineffective. Since 2010, however, a nonbinding regulatory institution has used a GPI—in this case, a public noncomplier list–to reverse this trend. Today, more than 100 countries have adopted comprehensive laws on terrorist financing, making it significantly more difficult for terrorists to use the international financial system.

One small institution achieved such an effect by harnessing the power of GPIs to outsource enforcement to market actors. Existing scholarship has highlighted how civil society can pressure governments to comply with international agreements.5 In such models, domestic actors draw attention to instances of noncompliance 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. Because of 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 by analyzing global policy change on combating terrorist financing and highlighting 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 thirty-eight members. Lacking legally binding authority, the FATF relies in part on a noncomplier list to generate policy change in states. This list has been remarkably effective: more than 90 percent of listed countries had adopted FATF-compliant laws on terrorist financing as of 2015, compared to only about 50 percent of nonlisted 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 data set that compiles information about the laws of 179 states, I employ a Cox proportional hazards model to show that the FATF noncomplier 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.

IGO-produced GPIs and Market Enforcement

IGOs are credible sources of information about government policy thanks to institutional advantages 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.6 Because of their bureaucratic nature, IGOs tend to be particularly authoritative evaluators of policy success. As Barnett and Finnemore point out, bureaucracies “embody a form of authority, rational-legal authority, that modernity views as particularly legitimate and good.”7 IGO bureaucracies are also often a source of significant technocratic expertise, which bureaucrats can draw upon as they assemble GPIs and which may intensify the impact of GPIs on monitored states through the diffusion of professional norms8 and specific standards.9 Indeed, states often delegate monitoring responsibilities to international organizations 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 nongovernmental 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 Organisation 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

GPIs and Markets

Kelley and Simmons suggest that GPIs influence states’ conduct through three pathways: domestic, elite, and transnational politics.14 Although many IGO-produced GPIs affect elite interests and mobilize domestic audiences, I focus here 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.15 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 substate 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.16 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.17 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 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. 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 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 compliance. 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 influence 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,” that is, 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.

Regulating Global Finance

To examine how GPIs can lead to market enforcement, I analyze international cooperation on “financial integrity”—efforts to keep illicit money out of the financial system. 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 G7 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 thirty-eight members (thirty-six economies and two regional organizations) and nine associated regional bodies that assess compliance in more than 190 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 standards 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 jurisdictional arbitrage, whereby criminals could take advantage of multiple rules and conflicting agreements.30 By formulating global standards, the FATF has helped states coordinate definitions 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 190 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 International Monetary Fund (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, noncompliant, 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. 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 Even G7 countries like the United States, Japan, and Canada receive noncompliant ratings.34

FATF Technical Expertise and Credibility

Market actors, governments, and even other IOs rely on the FATF for information about states’ financial integrity policies 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 countries would struggle to meet these requirements.35 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 success. “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.”36

The FATF's credibility as a monitor is also a result of its reputation as a highly technocratic, apolitical organization. Rather than high-level political actors, government bureaucrats staff FATF plenary meetings. FATF monitoring reports are drafted primarily by the evaluation team, with only a limited discussion in the plenary session. When countries discuss specific ratings for reports during the plenary session, they are encouraged to provide technical justifications for their support or dissent.37 Former FATF president Antonio 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 have politics. But in the FATF, politics is a secondary aspect.”38

The Noncomplier 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 noncompliant countries.39 In June 2009, the FATF adopted new, systematic procedures whereby all countries that received failing scores on ten or more of the FATF's sixteen most important recommendations40 would be eligible for inclusion on a “noncomplier list.”41 Under this process, the FATF has publicly identified fifty-seven countries since February 2010.42 To select countries for the list, the FATF uses the results of mutual evaluation reports, reviewing all countries that fail on ten or more of the sixteen most important recommendations. Figure 1 shows the results of the FATF's third round of mutual evaluations, comparing the number of nonlisted (gray, bottom of stacked bars) and listed (black, top of stacked bars) countries by the number of failing recommendations.43

Figure 1. Summary of FATF monitoring reports on country compliance

Once countries are eligible for listing, the FATF gives governments up to a year to undertake 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 countries with larger financial sectors or greater risks of money laundering or terrorist financing are more likely to be listed.44 Other considerations include a country's legal framework, its responses to requests for international cooperation, and whether it is involved in a follow-up process.45

The FATF noncomplier 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 noncomplier list is based on new, up-to-date information and consolidates risk into an easily interpretable metric. It is therefore a straightforward way for observers to clearly identify the highest-risk countries.

Listing and Market Enforcement

The FATF noncomplier list is a powerful driver of policy change because market actors use the list to allocate resources away from noncompliant 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 noncomplier 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.46

Banks have clear regulatory incentives to reallocate resources on the basis of the noncomplier 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 procedures. In some cases, banks might even opt to forgo all business with high-risk countries.47 Countries like the United States follow up regulation 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 USD for “failing to maintain an effective anti-money laundering program.”48

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 discovery that Riggs Banks was helping several dictators launder money resulted in relatively small financial penalties, but led to the bank's demise.49 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.”50 In fact, damage to reputation is often used as a way to sell risk-management systems to financial institutions.51

For both regulatory and reputational reasons, banks face a Herculean task: to evaluate and assess customer risk under conditions of high uncertainty. The FATF noncomplier 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 noncompliance 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.”52 With the advent of the noncomplier 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 GPIs’ reputational effects. The FATF list damages a country's international reputation not just through a “peer effect”53 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 lowest-common-denominator effect is compounded by media coverage where news outlets often ignore the nuances of listing.54

Enforcement by market actors intensifies the FATF list's reputational effects. 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.”55 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.”56

The FATF Noncomplier List: Testable Hypotheses

My theory suggests that the FATF noncomplier list stigmatizes states directly and that market pressure intensifies this effect. As a result, being listed should incentivize improved 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 policymakers. 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.”57

H1

(Reputation Hypothesis): Countries that are listed by the FATF should adopt FATF-compliant laws on terrorist financing more quickly than nonlisted countries.

The actual process of market enforcement can occur along 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.58 By June, the cost of money transfers had gone up 80 percent.59

One likely moderator for the noncomplier list's effect on compliance is a country's integration into international markets. Countries that are more open to transnational financial flows should be particularly responsive to the noncomplier list. I proxy market integration with cross-border bank liabilities, which indicate the amount of money that domestic 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 noncompliant countries, countries with higher levels of bank-to-bank lending should be particularly affected by the process.

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 noncomplier 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 noncomplier 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 noncomplier 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 the start of the current noncomplier list, and my data go through December 2015. Data on country listing status are collected from FATF noncomplier list announcements (published online in February, June, and October every year).

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. As a result of 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 nonproportionality.60

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 forty-six of the fifty-seven countries listed as part of the noncomplier list. As I add covariates, the sample drops to 120 countries (37 listed) in model 2, ninety-six countries (32 listed) in model 3, and eighty-seven countries (30 listed) in model 4.61

Second, I establish a universe of potential listed countries through matching. Ho and colleagues suggest that preprocessing data through matching produces more accurate and less model-dependent causal inferences.62 To compare countries with similar probabilities of being listed, I use nearest-neighbor matching to create a set of twelve listed and twelve nonlisted 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 twenty-four countries based on variable values in this period. I then assemble panel data for this set of twenty-four countries for the full time period (2010 to 2015).63 Matching improves the balance of the sample on the majority of variables included in the model.64

Finally, I construct a data set 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 ten failing recommendations in June 2009, the FATF and its regional bodies had already completed close to 100 evaluations of the FATF global network's members. Most of these countries were 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 sixty-eight countries, fifteen 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 7,308 observations and seventy-two events (instances where a country criminalizes terrorist financing in line with FATF guidelines). In the simplest model in the matched sample, there are 1,104 observations and seventeen events. Finally, in the simplest model in the sample of countries eligible for listing, there are 3,420 observations, and thirty-six events.

Dependent Variable: Criminalization of Terrorist Financing

Although the FATF issues forty recommendations,65 I focus on one specific indicator of compliance: the criminalization of terrorist financing. The FATF considers the criminalization 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.66

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 Convention, 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. According to 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, announcements about the noncomplier list, 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 to carry out a terrorist attack, by a terrorist organization, or by an individual terrorist.67

Figure 2 shows the distribution of this variable over time, separated by whether a country is eventually listed (dashed line) or is never listed (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.68 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.69 Noncompliance may also arise when countries adopt a too-narrow definition of terrorism.

Figure 2. Trends in criminalization of terrorist financing

Since the FATF adopted new noncomplier list procedures in 2009, countries have been 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 nonlisted countries had similarly compliant laws. This significant policy change by listed countries is the reason that the FATF has removed so many countries from its listing—as of 2016, forty-six of fifty-seven listed countries had “graduated” from the noncomplier list following major improvements in their laws.70

Explanatory Variables

The primary variable of interest is whether, at a given point in time, a country is on the noncomplier list. I create a dichotomous variable listing that indicates whether a country is on the noncomplier list at any time. In the largest version of the data, approximately 17 percent of observations are coded as 1s.71

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 noncomplier list procedures.72

Data on cross-border bank-to-bank liabilities come 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 forty-four reporting countries. Because these banks report international cross-border flows, the data cover banking relationships in more than 200 economies, capturing about 95 percent of all cross-border interbank business. For the countries included in the data set, this variable ranges from an average quarterly liabilities of USD 7 million for Dominica to USD 1.7 trillion for Germany. Because the data are 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.73 Countries may also be influenced by the policies of neighbors or regional partners through processes of policy diffusion.74 Jason Sharman argues that diffusion has affected the adoption of anti-money-laundering policies throughout the developing world.75 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.76

Government capacity is also likely to affect the time to policy change; following previous studies,77 I control for capacity using gross domestic product (GDP) per capita.78 Countries that face a higher threat of terrorism might also be faster to comply with the FATF recommendation on terrorist financing. I include the variable terrorism risk, which ranges from 0 (lowest risk) to 3 (highest risk).79 The literature also suggests that a country's political system may affect its ability or willingness to fulfill international commitments.80 I include democracy, drawn from Polity IV data.81

Confounders

In the 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 noncomplier list). Of the 141 countries included in the full sample, ninety-six (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 ten or more of the sixteen 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 ten or more failing scores on the FATF's sixteen most important recommendations. The FATF and its regional bodies evaluate a country only once per cycle, so for most countries, the number of failing recommendations does not change across the data set.82

Another important listing determinant is the size of a country's financial sector.83 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 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.84 Scholars have also argued that US economic power has contributed to the diffusion of US regulatory standards in other areas of global finance.85 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 can affect a country's willingness or ability to criminalize terrorist financing through measures of 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.86 To further account for US influence, I also rerun 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 sanctions 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 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 twenty banks and five countries under this process. I include the dichotomous variable 311 sanctions list to indicate whether 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 1s in the data set.

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 laundering 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.87 I create an ordinal variable us state dept list that indicates each country's assigned INCSR tier, where 1 indicates a country 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 3s.

Findings: Listing Increases Compliance Through Markets

Hypotheses 1 and 2: Time to Criminalization

The results provide strong support for hypotheses 1 and 2. Countries on the FATF noncomplier list adopt FATF-compliant laws on terrorist financing more quickly than their nonlisted 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 eight 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.

Table 1. Listing, market enforcement, and criminalization: Cox proportional hazards models for full sample

Notes: 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. *p < .10; **p < .05; ***p < .01.

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.88 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 seven countries in Europe had increases larger than this amount.89

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.

Figure 3. Effect of market integration and listing on criminalization

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.90 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 FATF. In model 1, for example, fifteen of the seventeen countries that comply with FATF standards are listed countries. In model 3, thirty-one of the thirty-six 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 nonlisted countries become less likely (since they know that they've avoided the list).

Table 2. Listing, market enforcement, and criminalization: Cox proportional hazards models for matched sample and eligible-for-listing sample

Notes: 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. *p < .10; **p < .05; ***p < .01.

Hypotheses 1 and 2: Robustness

US pressure is the most plausible alternative explanation for why countries criminalize terrorist 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.

Table 3. Listing, market enforcement, and criminalization: Cox proportional hazards models for US power alternatives

Note: 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. *p < .10; **p < .05; ***p < .01.

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 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 noncomplier list. Between 2005 and 2007, the FATF evaluated more than sixty countries, fifty-six 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 noncomplier 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 fifty-six noncompliant countries evaluated between 2005 and 2007, only two adopted FATF-compliant laws on terrorist financing prior to 2010.91

Hypothesis 3: Bank Lending and Market Enforcement

If international market actors like multinational banks allocate resources differently based on FATF noncomplier 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—the money that banks in a given country owe international banks. Data on cross-border liabilities come from BIS and are available on a quarterly basis. Cross-border liabilities for the period of 2010 to 2015 range from 0 to 3.9 trillion USD. Because the distribution is highly skewed, I add 1 to all values and take the log.92

To analyze the effect of listing on cross-border liabilities, I build an economic model that takes into account a country's underlying economic structure and macroeconomic fluctuations 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 come from the World Bank, while inflation data come 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.93

A country's level of debt (private sector and government) is also likely to affect banks’ willingness to do business with a jurisdiction. Specifically, the local banking sector's leverage ratio, that is, 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 data set.94 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.95

I include interest rate 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 cross-border credit. Interest rate data come from the World Bank WDI data set, but are available for only 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 M2.96

I evaluate the effect of listing on cross-border liabilities between March 2010 and December 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 fifty countries, twelve of which were listed by the FATF. As I add variables, the sample drops to thirty-nine countries, ten of which were listed by the FATF.97

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 crossborder 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 USD 11.5 billion. Based on the estimate in this model, listing should lead to a decline of USD 1.84 billion 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 prelisting levels only in 2014.

Table 4. The effect of listing on cross-border liabilities

Notes: 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. *p < .10; **p < .05; ***p < .01.

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 noncomplier 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 noncomplier list. I replicate the previous analysis for the period of 2006 to 2008, matching each country's listing status in the years 2010 to 2012.98 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 noncomplier 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 recommendations. The Thai government viewed anti-money-laundering and combating terrorist financing as low priorities. The FATF noncomplier list's impact on markets, however, reoriented the government's 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.

Thailand and the Noncomplier 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.99 The Asia/Pacific Group on Money Laundering's 2007 mutual evaluation report rated Thailand fully compliant with only two of the FATF's forty-nine recommendations. At the time, the FATF had very few tools in place to deal with noncompliant jurisdictions. The repercussions of noncompliance were minimal: Thailand had to submit only follow-up reports. In 2009, however, the FATF revitalized its process for dealing with noncompliant jurisdictions. When the FATF issued its first noncomplier list in February 2010, Thailand was one of twenty 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.100 Although the AMLO launched a big public information campaign to convince the Thai National Assembly to adopt new laws, the response from the rest of the government was sluggish.101 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 understand the possible consequences.102 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,103 but made no other improvements.

In this first year, the market response was negligible. Since the list was so new, it received very little media attention. The Wall Street Journal did not publish a single article about the FATF list in the six months following its creation.104 Because the noncomplier 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 significant 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 dependent 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.105 Despite these costs, Thailand failed to make significant changes to its legal framework in 2011, partly because of domestic political unrest.106 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 official reported that “the impact [of the blacklist] was considerably more acute … Financial 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.”107 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.108 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.109 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 noncomplier list in June 2013.

Financial Costs of Listing

Quantifying the full impact of the noncomplier list on Thailand's economy is difficult because the list affected financial flows in diverse ways. There is, however, at least correlational evidence that the noncomplier 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 USD 1.7 billion to USD 1.4 billion, a decrease of 17.6 percent. This number is remarkably close to the estimate obtained from the regression analysis of listing's effect on cross-border liabilities (Table 4, which suggested that listed 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.

Figure 4. Cross-border liabilities for Thailand (billions of USD) from 2009 to 2015

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.110 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.”111

GPIs in a Globalized World

In today's globalized world, institutionalized cooperation is essential for addressing transnational 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 opportunities 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 noncompliant 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.

1. Kelley and Simmons 2019.

2. Bisbee et al. 2019.

3. Doshi, Kelley, and Simmons 2019.

4. Honig and Weaver 2019.

5. See, for example, Dai 2007; Johns 2012; Mansfield and Milner 2012; Simmons 2009.

6. Kelley and Simmons 2019.

7. Barnett and Finnemore 1999, 707.

8. Honig and Weaver 2019.

9. Doshi, Kelley, and Simmons 2019.

10. Hawkins et al. 2006.

11. Bisbee et al. 2019.

12. Schäfer 2006, 74.

13. Parks and Masaki 2017.

14. Kelley and Simmons 2019.

15. For previous work on the link between market pressures and government policy, see Buthe and Milner 2008; Elkins, Guzman, and Simmons 2006; Mosley 2003; and Simmons 2000 among others.

16. See Baldacci, Gupta, and Mati 2011; Hilscher and Nosbusch 2010; Longstaff 2011.

17. Bank for International Settlements 2016.

18. March and Simon 1958.

19. Carruthers 2013, 526

20. Merry 2011, S84.

21. See 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.

22. Ozturk 2016.

23. See, 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.

24. Abdelal and Blyth 2015.

25. See Milner 1997; Moravcsik 1997; Rickard 2010.

26. Bueno de Mesquita et al. 2003.

27. The FATF does not have a standing charter; instead, member states periodically extend its mandate (the current one runs through 2020).

28. Australia, Austria, Belgium, Italy, Luxembourg, Netherlands, Spain, and Switzerland.

29. A full list of FATF members and the nine regional bodies is available in online Appendix A.

30. Unger and Busuioc 2007.

31. FATF-GAFI 2009c.

32. Nakagawa 2011.

33. Interview of FATF regional body official, 7 January 2015; participant observation, September 2016 and May 2017. I conducted more than twenty interviews over the course of this project but given 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.

34. In the third round of mutual evaluations, the USA was rated noncompliant on four recommendations (FATF-GAFI 2006), while Japan and Canada were rated noncompliant on ten and eleven recommendations respectively. FATF-GAFI 2008c, b.

35. Indeed, even a long-standing FATF member like Germany received failing ratings on twenty of the FATF recommendations. FATF-GAFI 2010.

36. Author interview, 7 February 2018.

37. This statement is based on my participant-observation experience at two FATF regional body meetings.

38. Author interview, 29 March 2017.

39. FATF-GAFI 2009a.

40. See Appendix B.

41. The FATF “noncomplier 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.

42. See Appendix D for countries listed through June 2016.

43. Data are limited to countries included in subsequent empirical analyses.

44. FATF-GAFI 2009b.

45. Ibid.

46. Sharman 2008.

47. Collin, Cook, and Soramaki 2016.

48. “HSBC Holdings Plc. and HSBC Bank USA N.A. Admit to Anti-Money Laundering and Sanctions Violations, Forfeit $1.256 Billion in Deferred Prosecution Agreement,” US Department of Justice, Office of Public Affairs, 11 December 2012, retrieved from <https://www.justice.gov/opa/pr/hsbc-holdings-plc-and-hsbc-bank-usa-na-admit-anti-money-laundering-and-sanctions-violations>.

49. “Reputation Damage: The Price Riggs Paid,” World Check, 2006, retrieved from <https://www.world-check.com/media/d/content_whitepaper_reference/whitepaper-3.pdf>.

50. Author interview, 28 August 2015.

51. Author interview with Jeff Soloman, Financial and Risk Sales Specialist, Thomson Reuters, 28 September 2015.

52. Author interview, 7 February 2018.

53. Brooks, Cunha, and Mosley 2014.

54. The FATF noncomplier list is composed of several different lists, colloquially referred to as the “gray list,” the “dark gray” list, the “black” list, and the countermeasures list; however, this differentiation is often lost in media reporting. See, for example, Samuel Rubenfeld, “FATF Removes Ukraine From Blacklist, Updates on Argentina,” The Wall Street Journal, 11 November 2011, retrieved from <https://blogs.wsj.com/corruption-currents/2011/11/01/fatf-removes-ukraine-from-blacklist-updates-on-argentina/>.

55. Author interview, 12 February 2018.

56. Author interview, 9 February 2016.

57. Author interview, 30 June 2016.

58. Jessica Donati, “Exclusive: Afghanistan Suffers Trade Blow as China Halts Dollar Deals with Its Banks,” Reuters, 22 May 2014, retrieved from <https://www.reuters.com/article/us-afghanistan-banking/china-halts-dollar-transactions-with-most-afghan-banks-central-bank-idUSBREA4L0MZ20140522>.

59. Sean Carberry, “Afghans Must Pass Anti-Money Laundering Law or Face Blacklist,” National Public Radio, 5 June 2014, retrieved from <https://www.npr.org/2014/06/05/319030334/afghans-must-passanti-money-laundering-law-or-face-blacklist>.

60. Box-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.

61. This reduction in the sample is primarily a result of the addition of the variable “risk of terrorism,” which comes from the International Country Risk Guide and is available for only 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).

62. Ho et al. 2007

63. A 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.

64. See Appendix J for more details.

65. This study covers the FATF's third round of mutual evaluations, during which the FATF actually issued forty-nine recommendations. For its fourth round of evaluations (currently ongoing), the FATF consolidated its recommendations to forty.

66. This variable is, at best, a partial measure of compliance, as Findley, Nielson, and Sharman 2014 demonstrated. 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.

67. FATF Interpretive Note to Recommendation 5 (Terrorist Financing Offence); “Methodology for Assessing Compliance with the FATF Recommendations and the Effectiveness of AML/CFT Systems,” FATF-OECD, February 2013.

68. FATF-GAFI 2015.

69. FATF-GAFI 2008a.

70. See Appendix D for more information.

71. I also create an ordinal variable list level, which disaggregates listing into different levels. See Appendix I.

72. I use this early time period to indicate market integration prior to the creation of the noncomplier 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 2019. Appendix H provides support for the linearity assumption.

73. This variable is excluded from the matched sample analysis and the eligible country analysis because these samples include no FATF members.

74. See Elkins, Guzman, and Simmons 2006; Gleditsch and Ward 2006; Simmons and Elkins 2004.

75. Sharman 2008.

76. For comparability across institutions, the variable is scaled by rounding to nearest 0.1 value in the regression.

77. See Guzman and Simmons 2005; Horn, Mavroidis, and Nordström 1999.

78. This 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.

79. Drawn 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.

80. See Helfer and Slaughter 1997; Martin 2000; Mansfield, Milner, and Rosendorff 2002; Raustiala and Victor 1998, among others.

81. Polity IV codes a country's political system on a scale of -10 to 10, where higher values equate to more democratic countries. These data are supplemented with data from Gleditsch 2013. Given data availability issues for smaller countries, I omit this variable from the matched sample and the eligible-for-listing sample analyses.

82. This variable is omitted from the analysis of the eligible-for-listing sample since the sample includes only countries that had ten or more failing recommendations.

83. FATF-GAFI 2009b.

84. See Jakobi 2013, 2018.

85. See Drezner 2007; Posner 2009; Simmons 2001.

86. Gibler 2009.

87. See “Major Money Laundering Countries,” US Department of State, retrieved from <https://www.state.gov/2017-incsr-volume-ii-money-laundering-and-financial-crimes-as-submitted-to-congress/>.

88. Data on cross-border liabilities are 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).

89. These countries are the United Kingdom, Denmark, the Netherlands, Norway, Sweden, Finland, and Ireland. Allen et al. 2011.

90. Although the interaction term in model 4 of Table 2 is not significant, it has a p-value of 0.11.

91. Based on my original data set, these two countries are Liechtenstein and Georgia.

92. Following Herrmann and Mihaljek 2010, my dependent variable does not consider changes in liabilities but rather reveals changes in lending and borrowing.

93. Bruno and Shin 2015a. The results are robust to including trade balance in the model instead of the real exchange rate. See Appendix K.

94. Bruno and Shin 2015b.

95. Takáts and Avdjiev 2014.

96. For a more detailed explanation of this link, see Bruno and Shin 2015b, 21.

97. I 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 are not available for many of the smaller countries included in that analysis, which is why the sample size decreases significantly in this test.

98. For 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.

99. IMF 2007.

100. The AMLO is a Thai government bureaucratic unit that supervises how the financial sector implements financial integrity policy.

101. Author interview with Thai government official, 14 February 2016.

102. Author interview, 9 March 2017.

103. US Department of State 2010.

104. This lack of media attention is notable compared to 2014 and 2015, when The Wall Street Journal published approximately five to six articles per year mentioning the FATF noncomplier list.

105. Author interview with Thai banking official, 9 March 2017.

106. According 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.

107. Author interview, 14 February 2016.

108. “Private Sector Pressures for Solution on FATF Blacklist,” The Nation, Thailand Portal, 4 May 2012, retrieved from <http://www.nationmultimedia.com/news/business/aec/30181304>.

109. Jon Fernquest, “Anti-money Laundering Blacklist Spells Trouble,” Bangkok Post, 22 May 2012, retrieved from <http://www.bangkokpost.com/learning/learning-from-news/294602/anti-money-laundering-blacklist-spells-trouble>.

110. Interview with Thai banking official, 9 March 2017.

111. FATF-GAFI 2017, 3.

Supplementary Material

Supplementary material for this article is available at <https://doi.org/10.1017/S002081831900016X>.

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Acknowledgments

I am grateful to numerous government, IO, and financial industry professionals for agreeing to be interviewed 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, participants in the Global Assessment Power project conferences, members of the IO editorial board, and two anonymous reviewers for valuable feedback and guidance on this project.