Tackling illicit financial flows to unleash funds for development


SDG indicators
SDG target 16.4: By 2030, significantly reduce illicit financial and arms flows, strengthen the recovery and return of stolen assets and combat all forms of organized crime
SDG indicator 16.4.1: Total value of inward and outward illicit financial flows (in current United States dollars) (Tier III)

Countries lose substantial resources through IFFs. These flows pose a direct threat to sustainable and inclusive development by draining domestic resources that could be used for social spending and productive investment. They also weaken political and institutional legitimacy and taxpayer compliance, therefore affecting overall economic activity.

The ability to achieve the SDGs remains fragile when undermined by IFFs. Indeed, the 2030 Agenda underscores the need for an increased mobilization of financial resources dedicated to sustainable development, including through an improved capacity for revenue collection, and repeatedly calls for more resources dedicated to investment. This call could be jeopardized by IFFs. For this reason, SDG target 16.4 aims to “significantly reduce illicit financial flows and arms flow, strengthen the recovery and return of stolen assets and combat all forms of organised crime” by 2030. The Addis Ababa Action Agenda on financing for development also calls for a redoubling of efforts to substantially reduce IFFs, with a view to eventually eliminating them (United Nations, 2015).

IFFs differ across countries and regions, and may originate from several sources, such as criminal activities, tax practices, trade misinvoicing, and other activities. A broad categorization distinguishes between three types of IFFs, depending on the transactions at the source of the illicit flow: (1) IFFs from illegal activities, (2) IFFs from corruption, and (3) IFFs from tax and commercial practices. It is important to note that the transactions and illicit transfers behind the IFFs can be but need not necessarily be illegal under the jurisdictions involved.

We need to measure IFFs to understand and tackle the problem

Considering the potentially large illicit flows draining resources for development, it is particularly important to quantify the total value of IFFs. The Action Agenda invites the “appropriate international institutions and regional organizations to publish estimates of the volume and composition of illicit financial flows” (United Nations, 2015, paragraph 24). UNODC and UNCTAD have been jointly mandated to address this task.

SDG indicator 16.4.1, “total value of inward and outward illicit financial flows”, is currently categorized as a Tier III indicator, meaning that its concepts, definitions and methodology still need to be developed and agreed upon (United Nations Statistics Division, 2019a). A further complication relates to the very nature of IFFs: they are intended to be hidden, making their measurement extremely difficult.

There is still no universally accepted definition of IFFs. Different studies rely on definitions varying on scope, coverage and terminology. A working definition of IFFs currently used by the custodian agencies is “value illicitly generated, transferred or utilized that is moved from one country to another” (UNCTAD and UNODC, 2017). According to United Nations (2016), IFFs may include: profits from illegal activities; funds from legitimate sources that are transferred abroad in contravention of domestic laws; legitimate funds that are used for unlawful purposes; and funds that, through legal loopholes or other arrangements, circumvent the spirit of the law. In a similar way, there is no globally approved methodology for measuring IFFs in a comprehensive and consistent manner. During the development process for SDG indicator 16.4.1, the definition and measurement methodologies for IFFs will be debated and agreed upon by the membership of the United Nations Statistical Commission.

In spite of this, a range of aggregate estimates, as well as a number of country-specific case studies, are already available in the literature.1 However, there is little global agreement on an empirical methodology to measure IFFs and some of the methods applied in the recent literature have proven controversial. In addition, existing estimates only cover some of the sources of these flows and they lack the granularity required to closely and comprehensively monitor the problem.

The lack of statistical indicators on IFFs reduces clarity regarding the size of these flows, where they originate and their consequences for institutions and economic activity. The absence of reliable, objective information undermines the ability to tackle the problems caused by these flows. This gap in evidence can also weaken efforts to develop and implement interventions targeted at curbing IFFs and eventually freeing up resources for financing development.

IFFs are multi-dimensional, comprising on one hand flows originating from illegal activities and on the other hand tax- and trade-related illicit transactions. Reflecting this complexity, the indicator has two custodians: UNODC leading the work on crime-related IFFs and UNCTAD leading the development of methods to measure IFFs related to taxes and trade. To progress with the challenging measurement task, the co-custodians are undertaking a series of coordinated actions to develop and test a statistical methodology to measure IFFs. UNODC and UNCTAD have held several expert consultations, covering different types of IFFs, to take stock of current research findings and knowledge regarding different types of IFFs. They have also formed a technical task force comprised of national and international compilers of official statistics to discuss practical steps towards adopting a set of statistical concepts and implementing a set of measurement tools. In the coming months, these preliminary methodologies will be applied and evaluated in pilot tests in several developing countries across Latin America and Africa.2

Estimates show large illicit financial flows across countries

IFFs are markedly difficult to measure. In addition to the lack of clarity as to what should be measured, these flows are deliberately hidden so that only traces can be detected in traditional data sources available to statistical authorities. However, this is an active field in the literature and, as mentioned above (see Note 1), there are many recent studies attempting to quantify the volume of IFFs.

Research mostly focuses on a single type or source of IFF at a time. Studies rely on what data are available to researchers, with the result that analyses are usually restricted to only a few countries, typically developed countries, where more and better-quality statistics are available. To compute estimates of IFFs, researchers have relied on assumptions and interpolations that have been debated and even debunked by other authors. Nevertheless, in one way or another, these studies all shed light on the main trends and significance of illicit flows and, given their size, help assess their potential impact on affected economies.

This chapter presents three recent estimates available from this body of research. Their inclusion here does not suggest that UNCTAD endorses any particular methodology, but rather recognizes their contribution to studying the IFF puzzle while official statistical methodologies are being developed and agreed upon.

Trade misinvoicing is a significant channel for IFFs

Trade misinvoicing occurs when the value of an export or import transaction is different from the arm’s length value of such transaction. This can refer to transfer pricing within affiliated enterprises, but also between seemingly unrelated parties. This type of IFF can serve many purposes. For example, overpricing imports can lead to artificially deflated revenues and reduced profits, making it possible to shift undeclared profits out of the country. Also, underpricing imports can be used as a mechanism to evade import tariffs or currency controls.

One solution for estimating trade misinvoicing is to compare mirror statistics from trading partners. In other words, compare the exports from one country with the imports of another. For example, if country A reports exports to country B of a certain amount, but country B reports imports from A of a different amount, a potential case of trade misinvoicing is flagged. The comparison is usually done at the most detailed product level available in the merchandise trade statistics. Because this approach uses readily available data, it is one of the first and most exploited solutions for estimating IFFs.

Map 1 shows a recent estimate compiled by Global Financial Integrity (2019), using United Nations Comtrade data for export and import transactions between, on one hand, developing and transition economies, and, on the other hand, developed countries. The figures show potentially large amounts of misinvoicing, sometimes reaching one quarter of total trade or more, in some economies in Africa, Latin America, the Caucasus and Central Asia. Overall, the authors estimate that total misinvoicing from developing and transition countries reached US$940 billion in 2015, 18 per cent of their total trade (exports plus imports).

Map 1. IFFs from trade misinvoicing in developing and transition economies, 2015, estimates from Global Financial Integrity (2019)
(Percentage of total trade)
Economies

Source: Global Financial Integrity (2019).
Notes: Only developing and transition economies according to UNCTAD classification are represented here. For each country, the figure represents total estimates for trade over- and under-invoicing from exports and imports as a percentage of the country’s total trade (exports plus imports). Estimates calculated by comparing adjusted mirror statistics for exports and imports, as reported in Comtrade database. This figure includes data as reported in the original source, its reproduction in this report does not imply endorsement by UNCTAD or its partners.

This methodology has been questioned by many authors and statisticians, remarking that an asymmetry or discrepancy between reported values in bilateral trade statistics could be explained by a variety of other reasons beyond illicitly motivated flows (Hong and Pak, 2017; Nitsch, 2016; United Nations Statistics Division, 2019b). For instance:

  • CIF/FOB differences between reported exports and imports;
  • different country of allocation by exporter and importer;
  • reporting of transit or entrepôt trade;
  • products shipped for processing not accounted for by one country but reported by the partner;
  • use of different product classifications or different application of the same classification;
  • confidential trade, which could be included as unallocated trade by one partner;
  • reported values might include trade margins if the exporting party is an affiliate of an MNE group;
  • shipment time lags, as the date reported by partners may fall under different reporting periods; and
  • statistical errors and differences in measurement between countries.

While trade statistics are constantly improving3 and methodological development address some of their deficiencies, for example, by explicitly estimating CIF/FOB ratios or correcting for known cases of entrepôt trade, it remains a real challenge to isolate illicit misinvoicing from other statistical noise. Nevertheless, comparing asymmetries at the most disaggregated level can still be a useful approach for detecting irregular transactions and flagging them as potential cases of misinvoicing for further scrutiny (UNECA, 2019).

Large scale of MNEs’ profit shifting

It has become increasingly common for businesses to spread their value chains across countries. International cost differences, such as lower relative wage costs and lower trade and transport costs, improved logistics, less expensive and faster communication systems, differences in taxation, and improved intellectual property rights protection and contract enforcement have motivated the creation of these global value chains (United States International Trade Commission, 2011). According to UNECE (2015), global production arrangements within MNEs may be tax-driven rather than driven by the competitive advantages of countries. These global production arrangements pose many challenges for statisticians and national accountants, as not all MNE transactions reflect real economic activity. They also pose real challenges for policy makers as their activities could result in a redistribution of tax revenues between countries, thus influencing countries’ development capability.

MNEs may employ several schemes to shift profits from high-tax to low-tax jurisdictions: transfer pricing, merchanting, strategic stationing of intangible assets and intellectual property, and inter-company loans (or debt shifting), among others. Given this potential, many recent studies have focused on tax avoidance by MNEs.

Tørsløv et al. (2018) compare the profitability (measured by the profits-to-wage ratio; i.e., pre-tax profits divided by total wages paid) of foreign affiliates in different countries. They find out that affiliates of foreign firms are less profitable than local firms in high-tax jurisdictions, while the opposite is observed in low-tax countries. By assuming that profit shifting is behind this difference in profitability, they estimate that about 40 per cent of profits are shifted between countries by MNEs to minimize tax burden.

This type of analysis requires detailed data that are not available for all countries. The authors, therefore, report results only for OECD countries and a few developing countries. The first panel of figure 1 shows those economies that are most affected by profit misalignment. It highlights the estimated gap in tax revenues due to profits loosened out of the country. This MNE tax gap is measured as a percentage of corporate tax collected. The second panel shows matching figures for those countries that receive the profits that MNEs transferred out of higher-tax countries.

This methodology is only an indicator of tax planning and profit misalignment. It risks confounding profit shifting with other factors that could also explain differences in profitability between local and foreign firms. In addition, the heavy data requirements of this methodology make it unsuitable for many developing countries. However, this approach nevertheless makes a significant contribution to the literature on the measurement of profit shifting and a good indication of the type of methodologies that could be implemented to measure the size of these flows.

Figure 1. MNE profit shifting, 2015, estimates from Tørsløv et al. (2018)
(Percentage of corporate tax collected)

Source: Tørsløv et al. (2018).
Notes: Only selected jurisdictions with available data are reported by the authors. Estimates calculated through a comparison of profitability of domestic vs foreign affiliates. In panel (a), for readability, only the first 20 countries are shown. This figure includes data as reported in the original source, its reproduction in this report does not imply endorsement by UNCTAD or its partners.

Measuring FDI-related profit shifting

Debt shifting is an important channel by which MNEs move profits from one country to another. In this case, an affiliate of an MNE group located in a low-tax jurisdiction makes a loan at artificially high interest rates to a profitable affiliate of the same group located in a high-tax country. In this way, the profits of the affiliate receiving the loan are reduced, while those of the affiliate making the loan are inflated. Inter-company loans appear in official statistics as part of FDI. Because of this and other channels, FDI statistics could potentially be used to monitor profit shifting.

This approach was pioneered by UNCTAD (2015). A recent study by Janský and Palanský (2018) made available the first country-level estimates calculated with this methodology. The authors calculate the FDI rate of return (calculated as the share of FDI income over FDI stocks) and estimate its relationship with bilateral FDI stocks. They find a negative association between FDI from low-tax countries and the rate of return on investment. In other words, the data suggest that companies are using the FDI channel to reduce their profits in high-tax economies and transfer it to low-tax economies. According to this study, between US$67 and US$82 billion worth of tax revenues were lost through this mechanism.

The authors then used the results to calculate country-level estimates through this channel. They first include all countries, both developing and developed, in their panel, but data availability limits the sample. The 2015 results for the countries with sufficient data are presented in figure 2, which shows these estimate of tax losses as a share of GDP. Even if not all countries are covered, the authors find sufficient evidence to support the hypothesis that low income countries lose more tax revenue through this channel of profit shifting, in relative terms, than high income countries.

Figure 2. MNE profit shifting, 2015, estimates from Janský and Palanský (2018)
(Tax loss as a percentage of GDP)

Source: Janský and Palanský (2018).
Notes: Only selected jurisdictions with available data are reported by the authors. Estimates calculated through a regression model of the rate of return of foreign direct investment. For readability, only the first 50 countries are shown. This figure includes data as reported in the original source, its reproduction in this report does not imply endorsement by UNCTAD or its partners.

The figures reported in this study are modelled estimates, derived from FDI data, rather than an observed measurement. Similar to the other approaches illustrated above, these estimates can confound profit shifting with many other determinants that could also explain why rates of return on FDI differ from country to country. However, they present some interesting conclusions and highlight some cases that call for more in-depth study.

Conclusion

As shown in the three examples above, there are already several proposals to measure the different components or channels of IFFs. Each approach has advantages and disadvantages, and some require data at a level that is unavailable in most developing countries. Furthermore, if used simultaneously, there is a risk of double-counting, since these indicators in fact measure similar concepts using different methods and sources of information. UNCTAD and UNODC are working on a unified conceptualization of IFFs and a first set of statistical measurement methodologies to be tested in developing countries, where the effects of IFFs on resources for development are most damaging. The objective of this work is to provide the affected countries with evidence detailed enough to inform their policies to fight IFFs.

Notes

  1. For some recent volume estimates of IFFs, see African Union and UNECA (2015); Cobham and Janský (2015, 2018); UNCTAD (2015); Crivelli et al. (2016); Institute for Advanced Studies (2017); Johansson et al. (2017); Janský and Palanský (2018); Tørsløv et al. (2018); and Global Financial Integrity (2019).
  2. For more details on this work, see UNCTAD (2019).
  3. For instance, in 2016 the statistical authorities of Canada and China made a special effort to reconcile their trade statistics due to a large asymmetry of US$21.3 billion. There were many reasons for this difference, with indirect trade as the main contributor. After this exercise, the trade asymmetry between the two countries was reduced to only US$1.0 billion (Statistics Canada, 2018). As statisticians develop new methods to identify different treatments of trade flows between countries and correct asymmetries, the analysis of IFFs based on remaining asymmetries could become more reliable.

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