Concept of Offshore Financial Centers: In Search of an Operational Definition
Author: Ahmed Zoromé

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This paper proposes a new definition of Offshore Financial Centers (OFCs) and develops a statistical method to differentiate between OFCs and non-OFCs using data from the Coordinated Portfolio Investment Survey (CPIS), the International Investment Position (IIP), and the balance of payments. The suggested methodology identifies more than 80 percent of the OFCs in the study sample that also appear in the a priori list used by the IMF to conduct its OFC assessment program. The methodology distinguishes OFCs based strictly on their macroeconomic features and avoids subjective presumptions on their activities or regulatory frameworks. The study also identifies three new countries meeting OFC criteria.


This paper proposes a new definition of Offshore Financial Centers (OFCs) and develops a statistical method to differentiate between OFCs and non-OFCs using data from the Coordinated Portfolio Investment Survey (CPIS), the International Investment Position (IIP), and the balance of payments. The suggested methodology identifies more than 80 percent of the OFCs in the study sample that also appear in the a priori list used by the IMF to conduct its OFC assessment program. The methodology distinguishes OFCs based strictly on their macroeconomic features and avoids subjective presumptions on their activities or regulatory frameworks. The study also identifies three new countries meeting OFC criteria.

I. Introduction

In recent years, there has been an increasing recognition of the need to improve the understanding of the activities of offshore financial centers (OFCs) because these centers have captured a significant proportion of global financial flows. A clear definition of what constitutes an OFC would be useful to the IMF in the context of its assessment program of the centers. Indeed, following concerns expressed by the Financial Stability Forum (FSF) in 2000, the Executive Board mandated the IMF to enhance OFCs’ adherence to internationally accepted prudential and supervisory standards.

Concerns regarding potential risks posed by OFCs to the international financial system have resulted in a number of global initiatives to improve oversight.2 The IMF, in the context of its remits, is concerned with OFCs in many respects. First, OFCs provide financial services predominantly to nonresidents. In conducting its surveillance of economic and financial policies, the IMF is interested in the impact on the national economies of its member countries of the operations undertaken in OFCs. Second, there are specific vulnerabilities to financial system stability in countries operating OFCs. Although the scope for regulatory arbitrage is being minimized through various multilateral initiatives, anonymity, opacity of financial operations and legal protection in some OFCs have heightened the potential for financial abuses. Third, because OFCs depend on their ability to attract global financial business, competition is strong, and incentives for compliance with international standards are significantly different in OFCs compared to primarily domestic markets. There is a greater risk that profitability is achieved at the expense of regulatory and supervisory standards.

Motivated by its mandate to promote financial stability, the IMF, in 2000, embarked on an assessment program aimed at determining the extent to which OFCs met the standards advocated by international standard setters3 in banking, insurance, securities and anti-money-laundering regimes; and at helping to strengthen their financial supervision.

Notwithstanding this focus, there is no unanimity on what constitutes an OFC. While authors have proposed various lists of what they consider to be OFCs, an empirical framework for uniform classification does not exist.

This paper (i) proposes a framework for a uniform classification; (ii) develops operational and measurable indicators of the OFC status of any given economy; and (iii) derives a list of countries/jurisdictions4 that could be classified as OFCs.

Following this introduction, sections II and III surveys and assesses the literature, respectively. Section IV proposes a definition, as well as empirical indicators, and section V concludes the paper.

II. Definitions of an OFC: A Survey of the Literature

There is no consensus among scholars and practitioners on what constitutes an Offshore Financial Center, even though various attempts have been made to define OFCs, since they started to have an impact on international financial markets in the early 1970s.

Many variants of the term have been used, including International Financial Center (IFC), International Banking Center (IBC), International Banking Facilities (IBFs), and Offshore Banking Center. All these terms broadly refer to the same concept of offshore financial center.

The survey identifies two groups of definitions: the conceptual definitions (mostly proposed by academics) and the operational definitions intended for practical applications (mostly proposed by the IMF).

Table 1 summarizes the characteristics used in the conceptual definitions. Three distinctive and recurrent characteristics of OFCs have emerged from these definitions: (i) the primary orientation of business toward nonresidents; (ii) the favorable regulatory environment (low supervisory requirements and minimal information disclosure) and; (iii) the low-or zero-taxation schemes.

Table 1.

OFC Definitions

(Quote of each definition is reported in Appendix II)

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In the early 1990s, when the IMF used to publish data on cross-border positions of OFCs in the International Financial Statistics (IFS) some operational definitions were proposed. OFCs were alternatively defined (IMF, 1995) as countries where “the banking system, acting as financial entrepôt, acquires substantial external accounts beyond those associated with economic activity in the country concerned,” or countries where the ratio of deposit banks’ external assets to exports of goods and services is significantly higher than the world average. “Significantly larger than the world average” here means at least three times the world average (Landell-Mills, 1986).5 More recently, the IMF’s Statistics Department (IMF, 2002), in an effort to define the perimeter of its data collection, called OFC “a jurisdiction in which international investment position assets, including as resident all entities that have legal domicile in that jurisdiction, are close to or more than 50 percent of GDP and in absolute terms more than $1 billion.”6

III. Limitations of the Definitions Proposed So Far

All the definitions examined above tend to equate OFCs with a regulatory and taxation phenomenon and do not differentiate OFCs based on distinctive (preferably measurable) macroeconomic features they have developed as a result of the cross-border nature of their financial intermediation.

The issue of an objective definition is of crucial importance to the work of the IMF. Indeed, the IMF has been carrying out a program of OFC assessments with an initial list of OFCs based on an FSF list that includes IMF member countries and non-member OFCs (Table 10). Since the OFC assessment program is voluntary and relies on a cooperative effort to enhance the supervisory capacity of the assessed jurisdictions, enshrining the eligibility in the program through objective and mutually acceptable criteria will go a long way toward promoting participation in, and ownership of, the assessment programs and the ensuing reforms.

IV. Definition of OFCs and Empirical Indicators

A. Proposed Definition

As indicated above, the current definitions of OFCs do not adequately capture the intrinsic feature of the OFC phenomenon, which is its raison d’être—the provision of financial services to nonresidents, namely, exports of financial services. Although one could argue that any given economy, to some extent, provides financial services, the peculiarity of OFCs is that they have specialized in the supply of financial services on a scale far exceeding the needs and the size of their economies. The following definition attempts to capture that feature so characteristic of OFCs.

An OFC is a country or jurisdiction that provides financial services7 to nonresidents on a scale that is incommensurate with the size and the financing of its domestic economy.

Regardless of the motivations for nonresident financial dealings with OFCs (local savoir faire, zero taxation, lax regulation, etc.) and the nature of the activities undertaken (banking, insurance, special purpose vehicles (SPVs), or otherwise), the setting up of an OFC usually results from a conscious effort to specialize the economy in the export of financial services, in order to generate revenues that often constitute a critical proportion of the national income.8

The receipts of these exports typically consist of:

  • financial services billed to nonresidents by entities domiciled offshore (bank fees for advisory services and financial engineering; intermediary service fees, such as those related to lines of credit, financial leasing, and foreign exchange; commissions on funds administration, and on securities transactions, including brokerage, placements of issues, underwritings, arrangement of swaps, options, and other hedging instruments; services related to asset management; and security custody services, etc.); and

  • registration/renewal fees for licensed entities (offshore banks, insurance companies, collective investment vehicles, international business companies, trusts and estates, etc.).9

B. Indicator of OFC Status

Consistent with the proposed definition, an indicator of the OFC status of a country or jurisdiction would relate the level of its net exports of financial services to a measure of its national income or domestic financing needs. More specifically, it can be considered that the ratio of net financial services exports10 to GDP could be an indicator of the OFC status of a country or jurisdiction.

This ratio relates two flows and could, in principle, be computed from a relatively detailed current account of the balance of payments, prepared in accordance with the fifth edition of the Balance of Payments Manual (BPM5; IMF,1993).11 In practice however, the measurement of the ratio is hampered by omissions in the reporting of financial services’ entries in many countries’ balance of payments. Furthermore, many jurisdictions, especially OFCs, neither collect nor disseminate balance of payments statistics. To circumvent these limitations, the flow concept of the ratio can be supplemented by a stock concept, for which data are available.

OFCs: Why Should We Expect a Positive Correlation Between Flows of Financial Services and Stocks of Financial Assets?

The use of offshore vehicles by corporations and high-net-worth individuals generally obeys one fundamental principle: to capture higher return on investments, in exchange for services fees paid to the host jurisdictions. In this process, various vehicles are used, such as asset- holding vehicles, to park and isolate high-risk assets; collective investment and derivatives trading vehicles, to take advantage of tax incentives or undertake risky investments difficult to implement under onshore regulation; asset protection schemes, to circumvent inheritance taxes or potential expropriation; SPVs to levy financing (bond issuing and syndicated loans) while keeping the liabilities “off balance sheet”; and trade vehicles, to keep export receipts offshore. All these activities, which represent the major part of offshore business, engender a change in assets domiciliation. Therefore, in theory, and as confirmed by the empirical results (Section IV.E), a positive correlation exists between the exports of financial services and the accumulation of assets in offshore jurisdictions.

C. Proxy Indicators of OFC Status

These indicators are based on the premise that exports of financial services from OFCs are generally matched by underlying capital flows from partner countries, which, in turn, affects the assets and liabilities position of the OFC.

Based on accounting identity, every cross-border capital flow is matched by a change in the assets and liabilities positions of the countries involved. These positions (stocks) are the result of past external transactions measured at current market price. In tracking these positions for various countries, one would expect countries or jurisdictions with the biggest stocks of assets to have been the ones that registered the largest flows of financial services over time and, as a result, exported the most financial services to nonresidents for a given period.

As part of their daily cross-border transactions, banks, security dealers, collective investment schemes (mutual funds and hedge funds), insurance companies, pension funds, and nonfinancial corporations domiciled (resident) in OFCs execute orders on behalf of nonresidents and trade nonresident-issued securities. These securities comprise equities and debt securities. The assets positions resulting from these transactions are recorded as portfolio investment assets, that is, securities issued by nonresidents and held by residents.12 OFCs are characterized by a proportionally high level of portfolio investment assets because they are home (legal domicile) to a large number of primarily custodian entities, which hold and manage securities on behalf of clients residing outside the OFC. Shell companies, trusts, and SPVs, in addition to the entities cited above, often perform custody functions. Assets are booked offshore, while in most cases the management is located elsewhere, often onshore.

For the stock analysis, the Coordinated Portfolio Investment Survey (CPIS) and the International Investment Position (IIP) statistics published by the IMF were used to devise two measurements, called CPIS Assets and filtered IIP assets, respectively.

Proxy indicator based on CPIS (CPIS Assets)

The CPIS collects data on cross-border holdings of portfolio investment assets (broken down into equities, long-term debt securities, and short-term debt securities) by residence of the issuer.13 The strength of the CPIS is that the data are reported in accordance with the residence principle recommended by the BPM5 (IMF,1993). Thus, countries with offshore entities have agreed to include in their CPIS all banks, insurance companies, and mutual funds deemed to be legally domiciled in their jurisdictions, even if treated as nonresident for the purpose of compiling balance of payments and national income accounts statistics. Using the CPIS database, we compile the ratio of CPIS Portfolio Assets to GDP (in percent).

Proxy indicator based on international investment position data (Filtered IIP)

Although the CPIS coverage is quite large (about 70 economies provided data on their portfolio holdings in 2003), data from the IMF’s International Financial Statistics (IFS) were used to construct a second sample of countries, to which a second proxy indicator derived from the IIP statistics was applied.14

Starting from total IIP assets,15 this paper, in order to arrive at a measurement analogous to CPIS assets, introduced the concept of filtered IIP assets, defined as total IIP assets less the components not pertaining to portfolio capital transactions.16

Although financial derivatives are not reported in CPIS because they are not classified as securities, this paper includes them in filtered IIP assets. Indeed, not only are derivatives an essential component of OFC service providing, but their valuation is also difficult to separate from the underlying assets.

However, the paper excludes general government assets, reserve assets, and assets under the control of the monetary authorities (monetary gold, SDRs, etc.) from filtered IIP assets because it is focusing on private-sector-driven OFC business.

Thus, the second proxy indicator can be defined as the ratio of filtered IIP assets to GDP.

D. Data Description and Issues

Data description

Not all countries provide the same type and quality of data to the IMF: one group submits only balance of payments’ financial services, another only IIP data, and yet another provides only CPIS data. However, the simultaneous use of the three different measures improves substantially the coverage in the present study from 77 to more than a hundred countries and jurisdictions (104), including some jurisdictions that are not IMF members.

The sample of economies examined comprises countries and jurisdictions whose financial systems are at various stages of development and therefore could not be treated uniformly. Thus, following the 2003 World Bank analytical classification of income published in the World Development Indicators, the countries were classified in two categories17 based on their income level (low and middle income; and high income) in order to compute financial services ratios, CPIS Assets, filtered IIP, and other relevant indicators for each group.

A detailed description, as well as treatment of the data used, is provided in Appendix III.

Data issues

Table 2. summarizes the main findings in terms of data issues. However, a narrative and an expanded discussion on data deficiencies related to the three measures are provided in Appendix III.

Table 2

Summary of Data Issues Related to the Measures of OFC Status

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Table 3.

High income countries, Max (CPIS, IIP), Equation 1.

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Table 4.

High income countries, “filtered IIP”, Equation 2.

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Table 5.

Low and middle income countries, Max (CPIS, IIP), Equation 1.

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Table 6.

Low and middle income countries, “filtered IIP”, Equation 2.

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Table 7

- Summary

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Table 8.

High Income Countries - Balance of Payments Financial Services Data, CPIS and Investment Position Data - 2003

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Source: IMF’s EDSS and CPIS databases.

Excluding Luxembourg

Table 9.

Low and Middle Income Countries - Balance of Payments Financial Services Data, CPIS and Investment Position Data - 2003

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Source: IMF’s EDSS and CPIS databases.1/ Mauritius ratio of net exports estimated, since the balance of payments do not record offshore activities.
Table 10.

Lists of Offshore Financial Centers According to the IMF and the Financial Stability Forum (FSF)

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Sources: IMF; and Financial Stability Forum (2000).

E. Econometric Estimations

The ratio of net exports of financial services to GDP was estimated for countries and jurisdictions that did not provide one, to construct a homogeneous series of ratios of net exports of financial services to GDP for all countries in each group of income (regardless of the type of data they provided).

For countries/jurisdictions providing CPIS and international investment position data, the series Max (CPIS, IIP) which comprises the higher of both CPIS total assets and portfolio investment asset position (IIP) of the balance of payments, was first assembled (see Section A in Appendix III for construction and rationale). Then, an ordinary least squares (OLS) regression was run on the sample of countries for which both series (net exports of financial services and Max (CPIS, IIP)) were available, using the following equation:
Ratio of net financial services exports=α+β(Ratio of Max(CPIS IIP))+μ(1)(1)

where α and β are parameters, and μ the error term.

This allowed the computation of estimated ratios of net exports of financial services for countries that provided at least one component of Max (CPIS, IIP).

For countries and jurisdictions for which filtered IIP and financial services net exports were available, an OLS regression of the following equation was run:
Ratio of net financial services exports=γ+δ(Ratio of filtered IIP)+ε(2)

where γ and δ are parameters, and ε the error term.

This formulation enabled the estimation of ratios of net financial services exports for countries that initially provided only data pertaining to their international investment assets positions.

The summary results of the regressions are as follows:

The results of the regressions show that, for both groups of income, the equations using the ratio of filtered IIP as independent variable (eq. 2) are more robust than those using Max (CPIS, IIP) as independent variable (eq. 1). In addition, the regressions for the group of high-income countries present a better fit than those of the group of low-and middle-income countries, presumably reflecting the better quality of data from the high-income country group.

To construct our final series of ratios of net exports of financial services for each group of income, we combined (i) the observed value of the financial services exports variable, (ii) the estimated value of this variable generated by the regression using filtered IIP (eq. 2); and (iii) the estimated value of the same variable using Max (CPIS, IIP) (eq. 1), when an estimation out of filtered IIP was not available (see Table 8 for high-income countries and Table 9 for low-and middle-income countries).

Although the results of the regressions display a statistically significant relation between the independent variables and the observed ratios of financial services exports, these regression equations are not intended to be behavioral equations per se. They reflect and confirm the assumption of a positive correlation between flows and stocks made in section IV.C. In this context, Max (CPIS, IIP) and filtered IIP can be interpreted as “instrumental variables” used to construct a comprehensive financial services series.

In the high-income group, 29 values were observed, and 11 estimated, of which 4 were estimated using equation (2) (filtered IIP) and 7 using equation (1) (Max (CPIS, IIP)). As for the low-and middle-income group, 51 values were observed, and 13 estimated, of which 9 through equation (2) and 4 through equation (1).

F. Empirical Results

After computing the ratio of net exports of financial services to GDP for each of the two income groups, the mean and the standard deviation for each group were also calculated.

The standard deviation was used as the threshold above which a country or a jurisdiction is considered an OFC. Although it is classically interpreted by statisticians as a measure of the degree of dispersion of the data from the mean value, we can also, based on its very construction, state that the standard deviation is an “average” or “expected” variation around an average. It indicates how far a typical member of a sample is from the mean value of that sample. Therefore, ratios above the standard deviation were considered as atypical (i.e., beyond the expected variation around the average ratio) and indicative of OFCs.

Results for high-income countries and jurisdictions

One jurisdiction in this group (Luxembourg) qualifies as an outlier18 (five standard deviations above the mean) with respect to the CPIS data and, as such, was excluded from the regression of equation (1) and the statistical moments. Some other studies, including Khorana et al. (2005), and Bertaut and Kole (2004), adopted a similar approach to deal with this type of jurisdictions.

Metadata for Aruba shows that the offshore activity of the jurisdiction was totally or partially excluded from both CPIS and balance of payments records, making the data irrelevant for the selection criteria.

Of the some 40 countries belonging to this category of income (Figure 1 and Table 8), 11 emerged as OFCs: The Bahamas, Bahrain, Bermuda, the Cayman Islands, Cyprus, Hong Kong, Guernsey, Ireland, Isle of Man, Jersey, Luxembourg, Malta, Netherlands Antilles, Singapore, Switzerland, and the United Kingdom. In this group, if the threshold of two standard deviations, which is an even more stringent indicator of the OFC status, is considered, the certainty that Bahrain, Bermuda, the Cayman Islands, Hong Kong, Guernsey, Isle of Man, Jersey, Luxembourg, Netherlands Antilles, Singapore, and Switzerland are OFCs is even greater (see Figure 1). These findings confirm the generally accepted attributes of these places as OFCs or major international financial centers. Save for the United Kingdom, all these centers are already on the list of OFCs established by the IMF (Table 10) in the framework of its assessment of standards and codes under the OFC program (IMF, July 2003).

Figure 1.
Figure 1.

Ratio of Net Financial Services Exports to GDP - High Income Countries

(in percentage of GDP)

Citation: IMF Working Papers 2007, 087; 10.5089/9781451866513.001.A001

Results for low-and middle-income countries and jurisdictions

Of the more than 60 members of this category, 6 qualified as OFCs (Figure 2, Table 9): Barbados, Latvia, Mauritius, Panama, Uruguay, and Vanuatu. All the countries and jurisdictions of this short list are well-established OFCs that participate in the IMF OFC program, except Latvia and Uruguay. Latvia is known to host numerous offshore banks and companies serving mainly nonresident CIS19 clients, with offshore investment coming from Eastern Europe and Russia. Indeed, more than half of bank total deposits in Latvia are of nonresident origin (IMF, 2005). As for Uruguay, its OFC status is demonstrated by the operations of some 12 offshore banks and about half a dozen offshore mutual fund companies. Uruguay is already under consideration for participation in the IMF OFC program.

Figure 2.
Figure 2.

Ratio of Net Financial Services Exports to GDP - Low and Middle Income Countries

(in percentage of GDP)

Citation: IMF Working Papers 2007, 087; 10.5089/9781451866513.001.A001

Data for Mauritius required individual treatment. First, although Mauritius provided actual data for financial services, the metadata on Mauritius, as reported in the IMF General Data Dissemination System (GDDS),20 shows that “offshore financial transactions with the rest of the world are not presently covered in the balance of payments.” Therefore, the ratio of net exports of financial services to GDP for Mauritius was inaccurate, since the report of cross- border financial services is considerably underestimated. Second, with respect to CPIS assets data, and the regression of equation (1), Mauritius, at 6 standard deviations above the mean and a ratio of CPIS assets to GDP of over 470 percent (compared with an average of 7 percent in its group), was treated as an outlier.

Overall, as one could expect (Figures 1 and 2), a majority of low-and middle-income countries and jurisdictions are net importers of financial services, while the majority of countries and jurisdictions in the high-income group are net exporters. Twenty-two countries and jurisdictions have been identified by this study as offshore or international financial centers, of which 16 belong to the group of high-income countries (Table 7).

V. Conclusion

This paper has (i) proposed an alternative definition of OFCs based on the nature of their trade and, (ii) developed a statistical method to distinguish between OFCs and non-OFCs. The approach was purposely made as simple and accessible as possible to minimize potential divergences about the conclusions of the study stemming from technicalities. Application of our definition and methodology to the initial list of OFCs used in the IMF OFC program (Table 10) yielded a few interesting results. First, the study identified 80 percent of the OFCs in the study sample that also appear in the a priori list used by the IMF to conduct its OFC program—which constitutes a broad ex post confirmation of the empirical list. Second, it differentiated OFCs strictly based on distinctive macroeconomic features, thus avoiding subjective presumptions about the activities of OFCs. Third, in terms of specific findings, the study identified Latvia, the United Kingdom, and Uruguay as OFCs—a suggestion that is corroborated by the facts. Indeed, offshore financial entities are present in these countries and they cater to an extensive nonresident clientele.

Looking ahead, many avenues could be explored to refine the findings of the study. For instance, it would be interesting to see if sectoral proxies could also be constructed to examine OFC activity from the perspective of one sector at a time (banking, insurance, securities, etc.). One could also take advantage of the Bank for International Settlements (BIS) locational statistics, which provide detailed data on the banking sector, to supplement the CPIS and IIP statistics in order to come with an extended sample and identify minor centers, which, generally, have an important offshore banking component.

Concept of Offshore Financial Centers: In Search of an Operational Definition
Author: Ahmed Zoromé