Recent conceptual and methodological developments on measuring illicit financial flows for policy action

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 II)

As the world searches for the funds needed to recover from the COVID-19 pandemic and achieve the 2030 Agenda for Sustainable Development -—
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, potentially billions of dollars of IFFs slip through the cracks every year. IFFs stemming from organized crime, trade in illegal goods, corruption and illegal and illicit tax and commercial practices move across borders, often in the direction of financial havens.

These flows weaken state institutions by encouraging corruption and undermine the rule of law and the functioning of the criminal justice systems. They also divert resources that are needed for essential services. UNCTAD’s Economic Development in Africa Report -—
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found that some countries with high IFFs spend on average 25 per cent less on health and 58 per cent less on education compared with countries with low IFFs. By eroding the tax base and discouraging public and private investment, they hamper structural transformation, economic growth and sustainable development.

The ability to achieve the SDGs remains fragile when undermined by IFFs. The 2030 Agenda underscores the need for increased mobilization of financial resources for sustainable development, including through the improved capacity for revenue collection, and more resources dedicated to investment. The 2030 Agenda identifies the reduction of IFFs as a priority area, as reflected in target 16.4: “by 2030, significantly reduce illicit financial flows and arms flows, strengthen the recovery and return of stolen assets and combat all forms of organised crime”. The Addis Ababa Action Agenda -—
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on financing for development also calls for a redoubling of efforts to substantially reduce IFFs, with a view to eventually eliminating them.

In July 2017, the United Nations General Assembly adopted the indicator framework for the monitoring of progress towards SDGs -—
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. Indicator 16.4.1, “total value of inward and outward illicit financial flows”, was selected as one of two indicators to measure progress towards target 16.4. At the time, there was no universal agreement on the definition of IFFs, what should be included within their scope or how the component parts could be measured. Without reliable statistics on IFFs the high uncertainty about the size of these flows, their origins and impact on development hampers policy action to combat IFFs.

Conceptual development of SDG indicator 16.4.1

UNCTAD and UNODC, assigned by the General Assembly as custodians of indicator 16.4.1, lead the methodological work to develop statistical definitions and methods to measure IFFs. As a result of this work, UNCTAD and UNODC -—
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published a Conceptual Framework for the Statistical Measurement of Illicit Financial Flows, reflecting concepts and standards approved by the IAEG-SDGs and the United Nations Statistical Commission.1

In February 2021, these concepts were subsequently adopted by the FACTI Panel -—
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, noting that UNCTAD and UNODC together “developed the first statistical definition of the term to contribute to the development of SDG indicators”. This was followed by a reaffirmed agreement by the cluster on IFFs of the Financing for the Development in the Era of COVID-19 and Beyond Initiative on the Conceptual Framework and the United Nations definition of what constitutes IFFs for statistical purposes. Cluster 5 will contribute to the development and refinement of methodologies to measure IFFs.

Pioneering countries across continents are testing the Conceptual Framework and guidance by UNCTAD and UNODC to measure IFFs. Pilots are carried out in Africa, Asia and the Pacific, Europe and Latin America in coordination with Regional Commissions.

The agreed Conceptual Framework builds on a series of expert consultations and a stock taking of research, knowledge and experience with different types of IFFs, carried out from 2017 to 2019 -—
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. The expert consultations involved experts from national statistical offices, financial intelligence units, tax authorities, academia, non-governmental organisations, international organisations and other IFF experts.

The consultations highlighted an urgent need to agree on concepts and definitions and recommended further engagement with national statistical authorities in line with the General Assembly resolution -—
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2. To this end, UNCTAD and UNODC established a statistical Task Force in January 2019 to define concepts, assess data availability, develop statistical methods, and guide country-level activities. 3

The IAEG-SDG endorsed the resulting methodological proposal in October 2019 and reclassified indicator 16.4.1 from Tier III to Tier II, meaning that the indicator is conceptually clear and based on internationally established standards, while data are not yet available from countries.

Statistical definition of IFFs for SDG indicator 16.4.1

For the purpose of the SDG indicator, IFFs are defined as financial flows that are illicit in origin, transfer or use that reflect an exchange of value and cross country borders. This definition implies that IFFs have the following features:

  • Illicit in origin, transfer or use. A flow of value is considered illicit if it is illicitly generated (e.g. originates from criminal activities or tax evasion), illicitly transferred (e.g. violating currency controls) or illicitly used (e.g. for financing terrorism). The flow can be legally generated, transferred or used, but it must be illicit in at least one of these aspects. Some flows that are not strictly illegal may fall within the definition of IFFs, e.g. cross-border tax avoidance which erodes the tax base of a country where that income was generated.
  • Exchange of value, rather than money or purely financial transfers. Exchanges of value include not only currency exchanges, but also exchanges of goods and services, and financial and non-financial assets.
  • IFF measure a flow of value over a given time, as opposed to a stock measure, which would be the accumulation of value.
  • Flows that cross a border. This includes assets where the ownership changes from a resident of a country to a non-resident, even if the assets remain in the same jurisdiction.4

IFFs can be classified from many angles: sources, channels, impacts, actors involved, motives, etc. Figure 1 presents the main categories of activities that may generate IFFs.

Figure 1. Categories of activities that may generate IFFs
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According to this typology, four main categories of IFFs are distinguished:

  1. Illicit tax and commercial IFFs. This category includes illicit practices by legal entities as well as arrangements and individuals with the objective of concealing revenues, reducing tax burden, evading controls and regulations and other purposes. This category can be divided into two components:
    • IFFs from illegal commercial and tax practices. These include illegal practices such as tariff, duty and revenue offences, tax evasion, corporate offences, market manipulation and other selected practices. Some activities that are non-observed, hidden or part of the so-called shadow economy, the underground economy or the informal economy may also generate IFFs. Related activities included in the ICCS comprise tax evasion, tariff, duty and revenue offences, competition offences, import/export offences, acts against trade regulations, restrictions or embargoes and investment or stack/shares offences.
    • IFFs from aggressive tax avoidance. Illicit flows can also be generated from legal economic activities through what is sometimes called harmful or aggressive tax avoidance (see box 1 for more detail on the distinction between legal and illegal illicit flows). Aggressive tax avoidance can take place through a variety of forms, such as manipulation of transfer pricing, strategic location of debt and intellectual property, tax treaty shopping, and the use of hybrid instruments and entities. For the purposes of the measurement of the indicator, these flows need to be carefully considered, as they generally arise from licit business transactions and only the illicit part of the cross-border flows belongs to the scope of IFFs.
  2. IFFs from illegal markets. These include trade in illicit goods and services, when the money flows generated cross country borders. Such processes often involve a degree of criminal organisation aimed at creating profit. They include any type of illegal trafficking of goods, such as drugs and firearms, or services, such as smuggling of migrants. IFFs are generated by the flows related to international trade of illicit goods and services, as well as by cross-border flows from managing the illicit income from such activities.
  3. IFFs from corruption. The United Nations Convention against Corruption -—
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    defines acts considered as corruption, which are consistently defined in the ICCS. These include bribery, embezzlement, abuse of functions, trading in influence, illicit enrichment and other acts. When the economic returns from these acts directly or indirectly generate cross-border flows, they are considered IFFs.
  4. IFFs from exploitation-type activities and financing of crime and terrorism. Exploitation-type activities are illegal activities that entail a forced and/or involuntary transfer of economic resources between two actors. Examples include slavery and exploitation, extortion, trafficking in persons, and kidnapping. In addition, terrorism financing and financing of crime are illicit, voluntary transfers of funds between two actors with the purpose of funding criminal or terrorist actions. When the related financial flows cross country borders, they constitute IFFs.
Box 1: Aggressive tax avoidance and IFFs

A specific conceptual challenge is to specify what kinds of activities should be designated as illicit or licit. It is noteworthy that SDG target 16.4 refers to ‘illicit’ instead of ‘illegal’ financial flows. Aggressive tax avoidance, including by MNEs, although usually legal, can drain resources and be considered illicit.

The inclusion of tax avoidance in the definition of IFFs creates some challenges, as it blurs the line between legal and illegal activities. Noting that the boundary between legal and illegal tax practices may be unclear, the -—
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described the continuum of activities from legal tax planning to illegal tax evasion (see figure 2). In this context, aggressive tax planning is described as “taking advantage of the technicalities of a tax system or of mismatches between two or more tax systems for the purpose of reducing tax liability.”

Figure 2. Boundaries of aggressive tax planning
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.

IFFs stemming from aggressive tax avoidance are considered in detail in OECD -—
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, and can include BEPS through interest payments, strategic location of intangible assets, abuse of tax treaties, artificial avoidance of permanent establishment and transfer pricing manipulation. The BEPS package, released in 2015 by OECD and G20 countries, delivers guidance for governments to close gaps in existing international rules that allow corporate profits to be artificially shifted to low-tax jurisdictions where companies have little or no economic activity. Work to address outstanding BEPS issues by the Inclusive Framework is ongoing -—
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.

As part of the OECD Inclusive Framework on BEPS, progress has also been made in improving data availability to support the measurement of MNE tax avoidance. Country-by-country reporting statistics are released publicly in an aggregated and anonymised form and can be analysed at the microdata level by country authorities.

IFFs need to be classified using a discrete, exhaustive and mutually exclusive statistical classification aligned with existing statistical frameworks and principles -—
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. The ICCS -—
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is a good point of departure for defining some of the activities that could generate IFFs, such as exploitation-type activities and terrorism, illicit trafficking and corruption, as well as many activities related to tax and commercial malpractices. However, IFFs that are not part of illegal activities may not be covered and an extended classification is being developed. Table 1 provides examples of such activities and how to link the main categories of IFFs to activities that generate them.

Table 1. Examples of activities generating IFFs from crime, by ICCS categories
Categories of IFFsExamples
Tax and commercial practices08041 Tariff, taxation, duty and revenue offences
08042 Corporate offences including competition and import/export offences; acts against trade regulations
08045 Market manipulation or insider trading, price fixing
Exploitation-type activities and terrorism financing (parts of sections 02, 04, 09)020221 Kidnapping
0203 Slavery and exploitation
0204 Trafficking in persons
0302 Sexual exploitation
02051 Extortion
0401 Robbery
0501 Burglary
0502 Theft
09062 Financing of terrorism
Illegal marketsICCS includes a long list of activities, including for example drug trafficking (060132), firearm trafficking (090121), illegal mining (10043), smuggling of migrants (08051), smuggling of goods (08044), wildlife trafficking (100312)
Corruption (section 0703)07031 Bribery
07032 Embezzlement
07033 Abuse of functions
07034 Trading in influence
07035 Illicit enrichment
07039 Other acts of corruption
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Note: This list is only intended to provide some examples and it is not exhaustive.

Data availability and the selection of statistical methods are likely to depend on the type of activity generating IFFs.

Defining statistical methodologies to measure IFFs

IFFs are deliberately hidden and, as they take many forms and use varying channels, their measurement is challenging both conceptually and in practice. The challenges differ across countries, depending on main types of IFFs affecting the country, data availability, mandates of national institutions, statistical capacity and national policy priorities. This calls for space for country-specific solutions and the flexible application of methods in line with the common framework.

There is a relevant stream of literature that proposes methods to measure IFFs from illegal economic activities, and illicit tax and commercial practices. The methods proposed can be grouped in two general approaches:

  1. Top-down methods attempt to measure IFFs by interpreting or modelling inconsistencies in different types of aggregated data, such as currency demand, international trade, and capital account of BoP.
  2. Bottom-up approaches attempt to measure IFFs starting from the analysis of a given illicit activity, defining the set of flows that can be identified as IFFs and then producing estimates for each of them. Overall estimates are obtained by aggregating from a lower to a higher level.

Consistently with the statistical framework presented here, where different types of IFFs are defined in relation to the activity generating them, a bottom-up and direct measurement approach is preferred. 5 The -—
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Methodological Guidelines to Measure Tax and Commercial IFFs for pilot testing 6 identify main types of tax and commercial IFFs and methods for their pilot measurement (see table 2).

Table 2. Activities that may generate tax and commercial IFFs and types of flows
CategoriesActivities Flows
A. IFFs from illegal commercial and tax activitiesA1 Acts against public revenue provisions [08041]

A2 Acts against commercial or financial regulations [08042]

A3 Market manipulations or insider trading [08045]

A4 Acts of commercial fraud [07019]

A5 Other illegal commercial and tax acts [08049+]
F1 Transfer of wealth to evade taxes, i.e., flows related to undeclared offshore wealth
  • Outright undeclared (concealed e.g., in secrecy jurisdictions)
  • Undeclared via instruments (Phantom corporations or shell companies, tax havens)

F2 Misinvoicing
  • Under/over pricing
  • Multiple invoicing
  • Over/under reporting of quantities
  • Misclassification of tariff categories
B. IFFs from aggressive tax avoidanceB1 Acts departing from the arm’s length principle

B2 Acts related to strategic location of debt, assets, risks, or other corporate activities

B3 Other acts of aggressive tax avoidance
F3 Transfer mispricing

F4 Debt shifting
  • Intracompany loans
  • Interest payments

F5 Assets and intellectual property shifting
  • Strategic location of intellectual property
  • Strategic location of other assets
  • Cost-sharing agreements
  • Royalty payments
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Note: Activities in category A are based on level-3 categories of the ICCS (with corresponding codes in brackets).

The Methodological Guidelines are aimed at statistical and other national authorities with a mandate to collect and access relevant information. Microdata available to national authorities enable the compilation of more reliable estimates. However, simpler methods are proposed in parallel with more sophisticated methods to enable IFFs’ estimation also where less data are available. The UNCTAD guidelines provide a suite of methods for pilot testing the measurement of three main types (a-c) of tax and commercial IFFs:

  1. Trade misinvoicing by entities (flow F2, table 2)
    • Method #1 – Partner Country Method +
    • Method #2 – Price Filter Method +
  2. Aggressive tax avoidance or profit shifting by MNEs (flows F3-F5, table 2)
    • Method #3 – Global distribution of MNEs’ profits and corporate taxes
    • Method #4 – MNE vs comparable non-MNE profit shifting
  3. Transfer of wealth to evade taxes by individuals (flow F1, table 2)
    • Method #5 – Flows of undeclared offshore assets indicator
    • Method #6 – Flows of offshore financial wealth by country

The above methods are tier classified, allowing United Nations member States to exercise flexibility and select a feasible method reflecting on national capacity, existing data, feasible statistical methods, legal and regulatory frameworks, and other conditions. A three-tier classification is proposed, with tier 1 as the preferred method based on the soundness of methodology, data requirements, and expected quality of estimates. Tier 2 is proposed as a fallback option if tier 1 method cannot be applied. If neither are applicable, a tier 3 method could be used.

An important distinction is made to avoid double counting and link to the SNA between two different stages leading to IFFs: 7

  1. IFFs linked to income generation, as the set of cross-border transactions that are performed in the context of the production of illicit goods and services or the set of cross-border operations that directly generate illicit income for an actor during a non-productive illicit activity. Inward or outward IFFs occur when the operation in question is performed across a border.
  2. IFFs linked to income management, as the set of cross-border transactions finalised to use the (illicit) income for investment in (legal or illicit) financial and non-financial assets or for consuming (legal or illegal) goods and services. If spent abroad, the operation is an outward IFF. If stemming from illicit activity outside a jurisdiction but is spent in the domestic jurisdiction, an inward IFF is generated.

In sum, this approach considers the multi-dimensional nature of IFFs, comprising several different kinds of activities, including flows originating from illicit activities, illicit transactions to transfer funds that have a licit origin, and flows stemming from licit activity being used in an illicit way. It identifies the main types of IFFs to be measured and lays out a framework based on existing statistical definitions, classifications and methodologies, in line with the SNA and BoP.

Data requirements for measuring IFFs

National statistical systems already have some of the data needed for the measurement of IFFs, but these data are scattered across a range of authorities and statistical domains. For instance, existing national accounts and BoP statistics include estimates of illegal economic activities and the non-observed economy; they provide a good starting point for the measurement of IFFs.

Relevant data may be held by the police and ministries and councils of justice, financial intelligence units and other government agencies collecting information on seizures and criminal offences. In addition, tax authorities collect relevant data for assessing the tax gap, and they exchange country-by-country reporting data on multinational enterprises. Customs’ data and statistics on international trade in goods and services provide useful information on commercial IFFs.

Over 60 per cent of national statistical offices collate relevant data on underground, illegal and informal activities using surveys, administrative sources, mirror statistics, international studies and expert assessment -—
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. While these activities are largely domestic, many of them also generate cross-border flows. There are also systematic data collections on crime and related IFFs; UNODC, for instance, compiles statistics on drugs as reported directly by countries, including detailed data on demand, supply, prices, drug characteristics, seizure data, etc.

Compiling statistics on IFFs requires access to many data sources held by different authorities. Central banks, customs, tax authorities and national statistical offices often have the strongest mandate to collect and access such data. Several global databases also contain relevant data for the compilation of IFF estimates, for instance the OECD country-by-country reporting data, UNCTAD Global Transport Costs Dataset for International Trade, the United Nations Comtrade database and the locational banking statistics by the Bank of International Settlements.

Eventually, the many types of IFFs should be measured in one indicator. That will require close collaboration within the national statistical system and with administrative data providers. The compilation of SDG indicator 16.4.1 is a technical, statistical activity to be based on statistical considerations only in line with the Fundamental Principles of Official Statistics -—
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. National statistical offices, as the focal point for coordinating the compilation of SDG indicators, should lead and coordinate the work to bring the necessary stakeholders together to measure IFFs.

Country pilots to measure illicit financial flows

While some elements of IFFs are more readily measurable, others are highly challenging to estimate, including bribery, abuse of functions, illicit enrichment and illicit tax practices. Country pilots are central to building the capacity to measure IFFs and testing the feasibility of selected methodologies starting from types of IFFs for which data are available. Coverage of different IFFs will be improved gradually along with data improvements.

In 2021-2022, UNCTAD and UNODC, with partner organizations UNECA, ESCAP and ECLAC, will support countries in improving their statistical capacity to estimate IFFs. A series of pilot studies will provide critical information to refine the Conceptual Framework and guidance on statistical methods to measure IFFs.

The first pilots carried out in Latin America between 2018 and 2020, by UNODC, show the way forward for other countries. In the first pilots, Columbia, Peru, Ecuador, and Mexico measured IFFs from selected illegal markets, such as drugs trade and smuggling of migrants. 8 First estimates in Mexico, for instance, show that outward IFFs from smuggling of migrants increased from US$10 million in 2017 to almost US$14 million in 2018. A similar pilot in Afghanistan estimated illicit gross income of the opiate economy to be worth between US$1.2 and US$2.2 billion in 2018, a value corresponding from 6 to 11 per cent of the country’s GDP, and more than its officially recorded exports of goods and services, estimated at 4.3 per cent of GDP. Pilots also addressed the measurement of IFFs from illegal gold mining and trafficking in persons, even if data in such cases did not allow for a sufficiently robust estimate.

In 2021, interested African countries will pilot test the measurement of IFFs with UNCTAD and UNECA focusing on tax and commercial IFFs. UNCTAD and UNODC are also pilot testing IFFs’ measurement with ESCAP and six countries in Asia and the Pacific in 2021–2022. See map 1 for pilot countries, confirmed as of June 2021.

Map 1. IFF pilots carried out and in progress
Note: Situation reflected on the map as in June 2021.

Pilot testing starts by a review of national circumstances in the form of an IFF risk assessment, followed by a mapping of relevant national stakeholders, a review of data availability and data quality; and finally, the pilot calculation of IFF estimates with one or two selected methods. Challenges and opportunities encountered in the pilots will help refine the Methodological Guidelines and contribute to the reporting of progress towards SDG target 16.4 in the future.

UNCTAD and UNODC invite all interested countries to test the measurement of IFFs that affect their economies most. Estimating IFFs will not only provide clarity on the scope of IFFs, but also help improve the quality of key macroeconomic statistics, such as GDP.

The statistical Task Force will continue its work to support countries in the pilot testing of the measurement of IFFs with a view to developing a Statistical Framework for the Measurement of Illicit Financial Flows with practical and methodological guidance in line with the Conceptual Framework. This will include a classification of activities generating IFFs, linked to the SNA and BoP concepts, and recommended methods to measure different types of IFFs and aggregate them into SDG indicator 16.4.1.

Further work will also aim at developing nuanced measurement of IFFs to support policy action and at the same time developing methods to aggregate estimates of different types of IFFs into one SDG indicator, e.g., to adjust for double counting. In the future, the measurement of IFFs as a satellite account taking into consideration national accounts concepts and definitions could be worth exploring.

Notes

  1. This chapter is an abridged version of an UNCTAD and UNODC Conceptual Framework -—
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    with selected references to the Methodological Guidelines -—
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  2. The General Assembly resolution “stresses that official statistics and data from national statistical systems constitute the basis needed for the global indicator framework, recommends that national statistical systems explore ways to integrate new data sources into their systems to satisfy new data needs of the 2030 Agenda for Sustainable Development, as appropriate, and also stresses the role of national statistical offices as the coordinator of the national statistical system” -—
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  3. The Task Force is composed of statistical experts from Brazil, Finland, Ireland, Italy, Peru, South Africa and the United Kingdom, representing national statistical offices, central banks, customs or tax authorities. The Task Force also includes experts from international organisations with recognised expertise in this field. ECLAC, ESCAP, Eurostat, IMF, OECD, UNECA, UNSD, UNCTAD and UNODC are represented.
  4. The proposed bottom-up measurement approach considers domestic illicit financial flows as part of the illegal economy. These flows would not fall under the definition of IFFs for SDG indicator 16.4.1 but are of high relevance to understanding organised cross-border illicit flows.
  5. This approach is consistent with -—
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  6. The Methodological Guidelines suggest methods for pilot testing and will be refined during and after the pilot tests.
  7. This basic typology is coherent with the main concept of national accounts. Indeed, income generation refers to the set of operations that in national accounts relate to production account, and generation and distribution of income account, while income management refers to the set of operations that in national accounts refer to capital and use of income account.
  8. Preliminary estimates of IFFs from illegal activities resulting from pilot testing in Latin America were presented at a meeting in Latin America in March 2021 -—
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References

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    Donec tincidunt vel mauris a dignissim. Curabitur sodales nunc id vestibulum tempor. Nunc tortor orci, sodales nec eros eget.
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    Donec tincidunt vel mauris a dignissim. Curabitur sodales nunc id vestibulum tempor. Nunc tortor orci, sodales nec eros eget.

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