Estimating Illicit Financial Flows

Front matter

ALEX COBHAM and PETR JANSKY

A CRITICAL GUIDE TO THE DATA, METHODOLOGIES, AND FINDINGS

ESTIMATING ILLICIT FINANCIAL FLOWS

Preface

The last decade has seen growing concern over ‘illicit financial flows’ as a threat to human progress. This culminated in 2015 in the global agreement to set a target in the UN Sustainable Development Goals to reduce illicit flows. What was lacking then, and remains lacking as we write in 2019, is consensus on the indicators and measurement for this target.

In this book, we survey the landscape, and aim to offer a critical review of the many methodologies and estimates for various aspects of illicit flows. We have had the privilege of being actively involved in this research agenda as it has developed, and also of participating in many of the international policy processes. By sharing the draft chapters online as we wrote them, we were able to obtain inputs from across the generous community of expert scholars.

We have benefited from discussions, comments and collaboration from more people than we can name, including Charles Abugre, Dereje Alemayehu, Annette Alstadsæter, Owen Barder, Stephanie Blankenburg, David Bradbury, James Boyce, Peter Chowla, Michael Clemens, David Cobham, William Davis, Alex Erskine, Valpy FitzGerald, Maya Forstater, Daniel Haberly, Gamal Ibrahim, Charles Kenny, Mushtaq Khan, Alice Lépissier, Steve MacFeely, Jan Mare’, Mick Moore, Alvin Mosioma, Vera Mshana, Savior Mwambwa, Léonce Ndikumana, Miroslav Palansky, Sol Picciotto, Rakesh Rajani, Pooja Rangaprasad, Peter Reuter, Thomas Torslov, George Turner, Ludwig Wier, Harry Wood, Gabriel Zucman, as well as seminar and conference participants from Addis to Oslo and Lima to Jakarta by way of Beirut and New York, and the many expert colleagues at the Tax Justice Network, Global Alliance for Tax Justice, Financial Transparency Coalition, COFFERS project, Open Data for Tax Justice and the Independent Commission for the Reform of International Corporate Taxation (ICRICT).

In addition, we are grateful to a number of organisations that have provided financial support for elements of the underpinning research, as well as spaces and platforms for engagement and discussion. We would especially like to thank the United Nations Conference on Trade and Development (UNCTAD), the UN Economic Commission for Africa (ECA), the African Union/ECA High Level Panel on Illicit Financial Flows from Africa, the viii PREFACE

Tana High-Level Forum on Security in Africa, the Joffe Trust, Norad, the European Commission and the Ford Foundation.

Research on illicit financial flows is now developing apace. This is a very welcome change after a long period of undue neglect; but it does have the disadvantage of making this book an attempt to hit a moving target. We have sought to ensure full coverage of current work, while recognising (and hoping!) that new data and new methodologies will continue to emerge.

The common features of illicit financial flows are that they are deliberately hidden, made opaque to obscure their true role; and that they drain resources from states and/or make states less responsive to their peoples’ needs. As things stand, too much remains hidden for it to be possible even to judge whether any progress is being made on the SDG target to curtail illicit flows. In this book, we identify a set of the most promising channels to improve methodologies and data.

We believe the very act of measuring illicit flows more accurately will contribute to reduce their level—just as failure to measure has allowed them to balloon. The failure thus far to translate the UN target into a measurable framework with global and national accountabilities is deeply disappointing. It is our hope that with a continuing international policy focus on reducing illicit flows, this book may contribute to more rapid progress. This should include substantial shifts in the international policy architecture, to ensure that countries at all income levels are fully included in the benefits of new standards of transparency and cooperation. We hope that academic and policy researchers in the field may also benefit from this work, as we have benefited from theirs, and continue the process of strengthening understanding and analysis in this critical area.

Part 2 Estimates Of Iff Scale

2.1. Country-level Trade Estimates: Mirror trade statistics 2.1.1. Overview 2.1.2. Data 2.1.3. Methodology 2.1.4. Results 2.1.5. Conclusions

2.2. Commodity-level Trade Estimates: Abnormal prices 2.2.1. Overview 2.2.2. Data 2.2.3. Methodology 2.2.4. Results 2.2.5. Conclusions

2.3. Transaction-level Trade Estimates: Research frontier 2.3.1. Overview 2.3.2. Data 2.3.3. Methodology 2.3.4. Results 2.3.5. Conclusions

2.4. Conclusions on Trade Estimates

X CONTENTS

3.1. Capital Flight: Ndikumana and Boyce 3.1.1. Overview 3.1.2. Data 3.1.3. Methodology 3.1.4. Results 3.1.5. Conclusions

3.2. Capital Account Anomalies: Global Financial Integrity (GFI) 3.2.1. Overview 3.2.2. Data 3.2.3. Methodology 3.2.4. Results 3.2.5. Conclusions

3.3. Offshore Capital and Wealth: Henry’s Estimates 3.3.1. Overview 3.3.2. Data 3.3.3. Methodology 3.3.4. Results 3.3.5. Conclusions

3.4. Wealth in Tax Havens: Zucman’s, and Alstadsaeter, Johannesen, & Zucman’s Estimates 3.4.1. Overview 3.4.2. Data 3.4.3. Methodology 3.4.4. Results 3.4.5. Conclusions

3.5. Conclusions on Capital and Offshore Wealth

4.1. Empirical Findings on International Corporate Tax Avoidance 4.1.1. Overview 4.1.2. Data 4.1.3. Methodology 4.1.4. Results 4.1.5. United States 4.1.6. Europe

4.2. Estimates for the World, and Low- and Middle-income Countries in Particular

4.3. IMF’s Crivelli et al. (2016) 4.3.1. Overview 4.3.2. Data 4.3.3. Methodology 4.3.4. Results 4.3.5. Conclusions

73 73 74 75 77 78 79

81 84 84 84 86 88 90 91

4.4.

4.5.

4.6.

4.7.

4.8.

4.9.

4.10.

Overview Data Methodology 4.4.4. Results

Overview Data Methodology Results Conclusions

CONTENTS

Profit Shifting of US Multinationals Worldwide (Clausing, 2016)

4.6.1. 4.6.2. 4.6.3.

Data

Misalignment of Profits and Economic Activity of US Multinationals Worldwide (Cobham & Janský, 2019) 4.7.1. 4.7.2. 4.7.3.

Data

Corporate Income Tax Efficiency Estimates (IMF (2014) EPRS (2015))

4.8.1. 4.8.2. 4.8.3.

Data

Data

Conclusions on International Corporate Tax Avoidance xi

112 112 113 113 114 115

Part 3 Proposals For Iff Monitoring

5. Beyond Scale: Risk- and Policy-based Indicators 5.1. Policy Measures 5.2. Financial Secrecy Literature 5.3. Bilateral Financial Secrecy Index 5.4. IFF vulnerability measures 5.5. Conclusions

6. New Proposals for IFF Indicators in the Sustainable Development Goals 6.1. Profit Shifting: SDG 16.4.1a 6.1.1. Overview 6.1.2. Data 6.1.3. Methodology 6.1.4. Conclusions 6.2. Undeclared Offshore Assets: SDG 16.4.1b 6.2.1. Overview 6.2.2. Data 6.2.3. Methodology 6.2.4. Results 6.2.5. Conclusions 6.3. Combining the Two Components

7. Conclusion: Estimating illicit financial flows 7.1. Definition of Illicit Financial Flows 7.2. Estimates of IFF 7.2.1. Trade estimates 7.2.2. Capital and offshore wealth 7.2.3. International corporate tax avoidance 7.3. IFF Indicators 7.3.1. Non-scale IFF indicators 7.3.2. SDG proposals 7.4. Conclusions

References Index

131 131 135 136 139 143

183 197

5:2; 5:3;

5.4

6.4.

List of Figures

Main IFF types by nature of capital and transaction Multinational tax behaviour and IFFs

Overview of IFF and human security linkages

The vicious cycle of negative insecurity and illegal capital IFF The vicious cycle of positive insecurity and legal capital IFF

Share of BFSI covered by currently activated AIE relationships vs. the number of activated AIE relationships

IFF risk exposure, commodity trade

IFF risk exposure, direct investment

. IFF risk exposure, portfolio investment 6.1. 6.2. 6.3.

Approaches to sharing country-by-country (CbC) data Major jurisdictions in Vodafone's profit misalignment

Relative misalignment between profits and employees and turnover, respectively (% of gross profits), 2013-2017 mean for countries with at least 1000 million euro in profits reported in at least one of the years

Proposed aggregate CRS statistics

14 18 19 20 22

139 141 142 143 155 159

160 166

4.2

List of Tables

A typology of illicit financial flows and immediate impacts

A simpler outline of illicit financial flows

Broad classification of trade data-based estimates of illicit financial flows Types of trade mis-invoicing

The main profit shifting channels of MNEs

. Summary of estimates of global profit shifting and associated tax revenue losses

OECD (2015b) estimates of the revenue loss due to BEPS Overview of Key Financial Secrecy Indicators

Financial Secrecy Index 2018, top ten

Comparison of data fields in CBCR standards

A simple outline of illicit financial flows, excluding multinational profit shifting

12 17 29 31 83

96 108 136 136 150

Introduction

By 2030, significantly reduce illicit financial and arms flows, strengthen the recovery and return of stolen assets and combat all forms of organized crime

UN Sustainable Development Goals Target 16.4

Global agreement in 2015 on the Sustainable Development Goals framework, to guide worldwide progress in the period to 2030, includes for the first time a target to reduce illicit financial flows (IFF). But the challenge is this: without agreement on methodologies to measure the scale of IFF, how can the target be met? How, indeed, can it even be attempted, or monitored?

‘Illicit financial flows’ is an umbrella term, which came to policy prominence during the period of the Millennium Development Goals (2000-2015). Its success, in part, was due to its breadth. The term allowed a shared agenda among, for example, people who variously saw either kleptocratic leaders or rapacious multinational companies as a critical obstacle to development success. The common elements, of hidden behaviours that strip resources and weaken governance, were sufficiently clear that an irresistible momentum developed to ensure prioritisation within the Sustainable Development Goals (SDGs).

There are, however, important differences in the political sentiments underpinning support to tackle the various elements under the illicit flow umbrella. Consensus at the headline level does not necessarily imply consensus on each element.

At the technical level, there is equally great variation in the channels used for illicit flows and in the approaches to estimation and measurement that have been applied. And so while many SDG targets left open the question of definition and measurement, this is perhaps especially true in the case of 16.4. Which flows fall under the umbrella term? How will progress in reducing them be measured? How will that progress be achieved? Who should ultimately be held accountable for progress, and can the choice of indicators ensure they are?

Estimating illicit financial flows: A critical guide to the data, methodologies and findings. Alex Cobham and Petr Jansky, Oxford University Press (2020). © Alex Cobham and Petr Jansky. DOI: 10.1093/0s0/9780198854418.001.0001

In this book we provide a critical survey of the evidence base to answer these questions, and present our own proposals. The book is laid out in three parts, as follows.

In Part 1, we discuss the rise of ‘illicit financial flows. We chart the political rise of the term, and lay out the ideological differences that have to some extent been obscured. We present a typology showing the breadth of the underlying phenomena, and also highlight the important common elements and impacts. We distinguish between tax-abusive and market-abusive IFF, which tend to involve illicit transactions with capital of legal origin; and IFF related to theft and criminal markets, where the capital itself is illegal.

Overall, we confirm the substantive importance for development of illicit financial flows, and the tendency to undermine both the level of resources available to states, and the likelihood of states using their resources efficiently and for inclusive benefit. But we also confirm the inherent difficulties of tracking progress against phenomena that are, by definition, deliberately hidden from view.

For this reason, Part 2 provides a survey of the extensive literature on estimation and measurement of illicit flows. As well as underpinning the subsequent proposals, this is intended to provide a standing resource for researchers, students, policymakers and activists on the relative merits and reliability of different approaches to key elements of IFF. Each chapter in Part 2 follows a consistent format to allow easy use. For each methodology, a separate treatment is given for the quality of the data and the robustness of the methodology. These include assessments of the scope for improvement and/or the likelihood of access to better data. Each section, and each chapter, also include overview and conclusion sub-sections with key findings which can be read as standalone summaries.

First, chapter 2 evaluates the literature on trade-based IFF. Separate sections address the literature using national-level data; that using commodity-level data; and that using transaction-level data. Chapter 3 focuses on estimates based on capital accounts anomalies, with separate sections addressing the approaches of Global Financial Integrity; of Ndikumana and Boyce (e.g. 2000; 2008); of James Henry (2012; 2016); and assessing the estimates that combine trade-based and capital account components. Chapter 3 also evaluates leading estimates of undeclared wealth held ‘offshore’ specifically, the approaches associated with James Henry and with Gabriel Zucman (2013, 2015), respectively. Chapter 4 addresses the much larger and more varied literature on the extent of multinational profit shifting—from international organisations including UNCTAD, the IMF, and OECD, and key individual authors such as Kim

INTRODUCTION 3

Clausing (e.g. 2016) and Gabriel Zucman and co-authors (e.g. Torslov, Wier, & Zucman, 2018).

The intention in this volume is not to provide a comprehensive overview of estimates of every IFF component, nor of every IFF channel. We focus on the main areas of the literature—academic and beyond—in which rigorous, replicable methodologies have been developed. In general, we give priority to those approaches generating global estimates based on country-level findings. We also prioritise those estimates that have been most salient in policy discussions, and those which we find to be most robust—although these are, sadly, not always consistent categories.

As a result of this approach, since estimates of illicit flows associated with illegal markets are generally made at the national level, and tend not to be widely replicated across countries, this literature is largely excluded from consideration. Estimates of undeclared assets held offshore are included, however, and these include much of the ultimate proceeds of illegal market IFF. We also do not address the literature on national tax gaps. This is in part for the same reasons of global comparability and policy salience, and in part because tax gaps may be purely domestic rather than necessarily reflecting the cross-border transactions that characterise IFF.

We reach a number of conclusions from Part 2. The most extensive, rapidly developing literature, with relatively robust results, relates to multinationals’ profit shifting. Of the many approaches here, those which provide most confidence are based not on regression analyses but on direct measures of the gap between where economic activity takes place, and where the resulting profits are declared. This insight also provides an indication, exploited later, of how scale measures could be constructed for other IFF.

In Part 3, we look beyond the current estimates and set out various alternatives, including our proposed new, direct measures of scale for use as SDG indicators. In chapter 5, we consider two main types of non-scale alternative IFF indicators. First, we present a set of policy-based indicators that were originally proposed before the SDG process settled on a scale measure. These offer the scope to track global progress on transparency measures that are key to curtailing illicit financial flows, and also offer the jurisdiction-level disaggregation to support accountability for policies that maintain opacity.

Chapter 5 also includes two related sets of IFF risk measure, which follow the same logic but combine the policy-created opacity of jurisdictions with bilateral data on economic and financial transactions between jurisdictions, in order to evaluate the exposure of each country to IFF-facilitating secrecy elsewhere. Both approaches build on the global ranking constructed by the

Tax Justice Network, the Financial Secrecy Index. The first, pioneered by the High Level Panel on Illicit Financial Flows out of Africa, emphasises granular analysis of each countries’ vulnerabilities to IFF, to support policy prioritisation. The second, operating in a similar way, is a Bilateral Financial Secrecy Index, replicating the global approach at the national level.

In chapter 6, we present our proposals for SDG 16.4, which were developed as part of the UN process. These proposals build on the findings in relation to scale estimates, and on insights from the non-scale approaches. We propose two indicators. Both are direct measures, rather than estimates, in keeping with the conclusions from Part 2 on the robustness issues of many of the estimates.

The first proposed indicator relates to tax avoidance by multinationals. The proposal follows the stronger of the approaches surveyed in chapter 4 in preferring a measure of the broader phenomenon of profit misalignment, to a less certain estimate of the somewhat narrower phenomenon of profit shifting. It is proposed to construct the measure on the basis of newly available data following the introduction of a country-by-country reporting requirement for multinationals. Alternative ‘workaround’ approaches are also discussed, should data access prove problematic.

The second proposed indicator takes a similar approach, constructing a direct measure which also works on the basis of newly available data—in this case, data resulting from the introduction of multilateral, automatic exchange of tax information between jurisdictions. Following the insights of Henry (2012) and others surveyed in chapters 2 and 3, we propose an overarching measure for a common result of most illicit flows other than multinationals’ profit shifting: the volume of undeclared offshore assets.

The two proposed indicators share a valuable feature of the policy- and risk-based measures set out in chapter 5. This is that they can be fully disaggregated to the jurisdiction level in a way that will support accountability both for jurisdictions that benefit from provoking illicit outflows elsewhere, and for effective policy responses from those that suffer.

Finally, chapter 7 presents the overall conclusions of the book and makes recommendations for the most promising directions for future research, and the most pressing and realistic priorities for data collation and data access. We highlight the most robust estimates currently available, and draw out the implications of our analysis for target 16.4 of the Sustainable Development Goals. Recognising the growing momentum for wider UN measures, we also identify related proposals to improve the data infrastructure and policy instruments.

PART 1 ILLICIT FINANCIAL FLOWS l

1.1. Context and Motivation

The emergence of a global ‘tax justice’ movement, following the formal establishment of the Tax Justice Network in 2003, has had a powerful impact on international policymaking.’ By 2013, a range of innovative policy proposals had risen onto the agendas of the G8, G20 and OECD groups of countries. And by 2015, the UN Sustainable Development Goals (SDGs) themselves had come to embody that shift also.

Most obviously, tax appears as the first ‘means of implementation’ in the SDGs (target 17.1). This stands in stark contrast to the predecessor framework, the Millennium Development Goals, which contained no single reference to tax as a source of finance for development. In addition, the closely related issue of illicit financial flows has also gained major policy traction.

The illicit flows agenda emerged in a fair degree as an opposition to a view which saw corruption as a problem largely, or even exclusively, of lower-income countries. Raymond Baker, the US businessman who worked for decades in sub-Saharan Africa before setting up the NGO Global Financial Integrity, popularised the term ‘illicit financial flows’ in his 2005 book, Capitalism’ Achilles Heel. The key selling point of the book was Baker’s ballpark estimates of the scale of flows, with ‘commercial tax evasion’ many times larger than flows linked to the bribery of, and theft by, public officials.

Baker's first chapter is starkly titled “Global capitalism: Savior or predator?, and the emphasis is clear from the first paragraph (p.11):

Tm not trying to make a profit? This rocks me back on my heels. It’s 1962, and I have recently taken over management of an enterprise in Nigeria. The director of the John Holt Trading Company, a British-owned firm active since the 1800s, is enlightening me about how his company does business in Africa. When I ask

1 This chapter draws from our earlier papers (Cobham & Janský, 2017b, 2017c), and from Cobham (2014, 2018).

Estimating illicit financial flows: A critical guide to the data, methodologies and findings. Alex Cobham and Petr Jansky, Oxford University Press (2020). © Alex Cobham and Petr Jansky. DOI: 10.1093/0s0/9780198854418.001.0001 how he prices his imported cars, building materials and consumer goods, he adds, ‘Pricing’ not a problem. I'm just trying to generate high turnover.

Baker goes on to lay out powerfully how the abusive behaviour of multinationals of the period led to massive trade mispricing, and stripped lower-income host countries of their taxing rights—despite the often desperate need for revenues to support public spending on health, education and infrastructure. For the same reason, a key plank of the Tax Justice Network’s policy platform is the proposal for public, country-by-country reporting by multinationals (Murphy, 2003) to lay bare the discrepancies between where economic activity takes place and where taxable profits are declared.

Illicit financial flows encompass much more than multinational tax abuses, however. The opacity of corporate accounts that hides profit shifting finds a parallel in the financial secrecy offered by ‘tax haver jurisdictions— and this, too, is a critical driver of illicit flows.

In 2007, the Tax Justice Network began the process to create the Financial Secrecy Index, which identifies major financial jurisdictions like Switzerland— which typically does very well in international perceptions of corruption—as central to the problem of producing and promoting corrupt flows elsewhere (see Cobham, Jansky, & Meinzer, 2015). A narrative that sees corruption in lower-income countries only will miss this central driver of the problem— and so an important element of the illicit flows agenda is that it recognises the centrality of financial secrecy in particular, often high-income jurisdictions, to the undermining of revenues, the undermining of good governance in countries all around the world. Rather than saying “Why is your country corrupt?; it asks, “What are the drivers of corruption—and where?’

Underpinning most major cases of corruption around the world, and many major cases of tax abuse, can be found anonymously owned companies, from the British Virgin Islands to Delaware; opaque corporate accounting, typically in the biggest stock markets in the world, that cover the degree of profit-shifting and tax avoidance; and deliberate failures to exchange financial information that protect, even now, banking secrecy.

As such, international cooperation is needed—at least as much as domestically focused efforts. The global agreement on a target (16.4) in the SDGs committed to the reduction of illicit financial flows (IFF) is therefore particularly significant. Politically, the target can be traced back to the work of the High Level Panel on IFF from Africa, chaired by former South African president Thabo Mbeki, which worked with the UN Economic Commission for Africa to build the case for urgent action both on the continent and globally, and obtained unanimous African Union backing. It was natural that the subsequent report of the Secretary-General’s High Level Panel of Eminent Persons on the Post-2015 Development Agenda, co-chaired by President Susilo Bambang Yudhoyono of Indonesia, President Ellen Johnson Sirleaf of Liberia, and Prime Minister David Cameron of the United Kingdom, also clearly identified IFF as an issue to be included in the new framework.

Despite this broad backing, however, the IFF target has proven to be one of the most difficult to pin down. Even now in 2019, there is no specific indicator or group of indicators finalised as the basis to track progress. Worse, there has been a concerted effort to subvert the target by removing multinational companies from the scope, despite the consistent emphasis on their tax avoidance practices in the academic and policy literature and in the reports of the two high level panels that set the basis for global agreement on the target in 2015.

With UNCTAD and the UNODC now leading a technical expert process to identify and agree proposals, there is the potential—but not yet the certainty—of ensuring the target has indicators which both reflect the original policy intention, and also create appropriate accountability mechanisms to support genuine progress.

The current indicator title is this:

16.4.1 Total value of inward and outward illicit financial flows (in current USD)

Setting aside whether such an indicator is most suitable to support progress and accountability, or sufficient on its own, the process to identify a methodology for this indicator is severely complicated by the absence of agreement on how to measure the scale of illicit financial flows. A specific aim of this book is to provide a basis for rigorous comparison of current approaches to estimating IFF, that can support national and international policymaking and global accountability for progress towards SDG 16.4.

Our more general motivation is to provide a reference tool for scholars, students, activists and journalists. ‘Illicit financial flows’ is an umbrella term for a broad group of cross-border economic and financial transactions, each of which have different motivations and a range of varying impacts. For activists and journalists, for example, this makes it important to distinguish when estimates refer to one IFF component or another, for example, as well as to have a robust basis for preferring one estimate over another. For researchers and experts in one area of IFF, who will not necessarily be as familiar with issues related to another component, an up-to-date guide to methodological and data questions should have clear, practical value.

10 ESTIMATING ILLICIT FINANCIAL FLOWS 1.2. Definitions

There is no single, agreed definition of illicit financial flows (IFF). This is, in large part, due to the breadth of the term ‘illicit. The (Oxford) dictionary definition is: ‘forbidden by law, rules or custom? The first three words alone would define ‘illegal, and this highlights an important feature of any definition: illicit financial flows are not necessarily illegal. Flows forbidden by ‘rules or custom’ may encompass those which are socially and/or morally unacceptable, and not necessarily legally so.

This is in line with developments in criminology, which has seen a growing zemiological critique (e.g. Hillyard (2004) and Dorling et al. (2008); zemiology being the study of social harms). The critique emphasises a range of shortcomings in the crime-led approach, among them that crime is a social construct based on value judgements and so varies across time and geography—thereby undermining it as a consistent basis of comparison; and that crime as a category excludes many serious harms (e.g. poverty or pollution). A related point, first raised by Blankenburg & Khan (2012) and further developed by Khan, Roy, & Andreoni (2019), is that a legally-based definition requires a legitimate state actor. Cross-border flows could be declared illegal by an illegitimate state (a military dictatorship, say). But would they therefore be illicit? As such, working on the basis of harm done (or risk thereof) can provide a more consistent basis for the definition.

To take a specific example, commercial tax evasion affecting a low-income country where the tax and authorities have limited administrative capacity is much less likely to be either uncovered or successfully challenged in a court of law, than would be the same exact behaviour in a high-income country with relatively empowered authorities. A strictly legal definition of IFF is therefore likely to result in systematically—and wrongly—understating the scale of the problem in lower-income, lower-capacity states. In contrast, a zemiological approach would clearly support the inclusion of multinational profit shifting since the revenue impacts and related harms in the grey area of ‘possibly legal but untested’ avoidance are indistinguishable from those which are firmly in the ‘unlawful category.

For these reasons, a narrow, legalistic definition of IFF is rejected. The phenomenon with which we are concerned is one of hidden, cross-border flows, where either the illicit origin of capital or the illicit nature of transactions undertaken is deliberately obscured.

The most well-known classification stems from Baker (2005). In Baker’s assessment, there were three components: grand corruption accounted for

HISTORY AND OVERVIEW OF ‘IFF ll just a few per cent of illicit flows; laundering of the proceeds of crime between a quarter and a third; and the largest component by far was ‘commercial tax evasion, through the manipulation of trade prices, accounting for around two thirds of the problem.

A somewhat extended classification, from Cobham (2014), identifies four components of IFF, distinguished by motivation: 1—market/regulatory abuse, 2—tax abuse, 3—abuse of power, including the theft of state funds and assets, and 4—proceeds of crime. The third and fourth components map onto two of Baker’s. The tax abuse category makes explicit an issue that is sometimes obscured in presentation of Baker’s categorisation, namely that tax-motivated IFF include not only the actions of multinational companies but also those of individuals. The first category, of market/regulatory abuse, is largely additional to Baker’s categorisation. These IFF reflect cross-border flows in which ownership is hidden, for example to circumvent sanctions or anti-trust laws. Circumvention of (legal or social) limitations on political conflicts of interest may fall here or under abuse of power.

This categorisation allows in turn the identification of the major actors in IFF:

e private actors (individuals, domestic businesses and multinational company groups committing tax and regulatory abuse, and the related professional advisers—tax, legal and accounting)—these are the leading actors in IFF types 1, 2 and 3;

e public officeholders (both elected and employed)—these are important actors in IFF types 3 and 4, and may be involved in type 1; and

e criminal groups (a term used here to indicate both those motivated primarily by the proceeds of crime, and those using crime to fund political and social agenda)—the leading actors in IFF type 4.

Table 1.1 provides an overview of the underlying transaction types. It is unlikely to be comprehensive because there is potential to engineer an illicit flow in any transaction, and the range of potential illicit motivations is wide indeed; but nonetheless demonstrates the breadth of IFF phenomena. As the final two columns indicate, all four IFF types are likely to result in reductions in both state funds and institutional strength—that is, both in the funds available for public spending and in the likely quality of that spending.

There is substantial overlap in the mechanisms used for IFF, regardless of motivation. The opportunity to hide, where it exists, is likely to be exploited for multiple purposes. For example then, the legal use by a multinational of highly secretive jurisdictions may both provide cover for illegal use of the

Table 1.1. A typology of illicit financial flows and immediate impacts

Flow

Manipulation

Illicit motivation

Impact Impact on state onstate effectiveness funds

Exports

Imports

Inward investment

Outward investment

Over-pricing

Under-pricing

Under-pricing

Over-pricing

Under-pricing

Over-pricing Anonymity Anonymity

Under-pricing

Over-pricing

Exploit subsidy regime (Re)patriate undeclared capital

Shift undeclared (licit) income/ profit

Shift criminal proceeds out Evade capital controls (including on profit repatriation) Evade tariffs (Re)patriate undeclared capital