Hidden Treasure: The Impact of Automatic Exchange of Information on Cross-Border Tax Evasion
  • 1 0000000404811396https://isni.org/isni/0000000404811396International Monetary Fund
  • | 2 0000000404811396https://isni.org/isni/0000000404811396International Monetary Fund

Contributor Notes

Author’s E-Mail Address: sbeer@imf.org; mcoelho@imf.org; and sleduc@imf.org

We analyze the impact of exchange of information in tax matters in reducing international tax evasion between 1995 and 2018. Based on bilateral deposit data for 39 reporting countries and more than 200 counterparty jurisdictions, we find that recent automatic exchange of information frameworks reduced foreign-owned deposits in offshore jurisdictions by an average of 25 percent. This effect is statistically significant and, as expected, much larger than the effect of information exchange upon request, which is not significant. Furthermore, to test the sensitivity of our findings, we estimate countries’ offshore status and the impact of information exchange simultaneously using a finite mixture model. The results confirm that automatic (and not upon request) exchange of information impacts cross-border deposits in offshore jurisdictions, which are characterized by low income tax rates and strong financial secrecy.


We analyze the impact of exchange of information in tax matters in reducing international tax evasion between 1995 and 2018. Based on bilateral deposit data for 39 reporting countries and more than 200 counterparty jurisdictions, we find that recent automatic exchange of information frameworks reduced foreign-owned deposits in offshore jurisdictions by an average of 25 percent. This effect is statistically significant and, as expected, much larger than the effect of information exchange upon request, which is not significant. Furthermore, to test the sensitivity of our findings, we estimate countries’ offshore status and the impact of information exchange simultaneously using a finite mixture model. The results confirm that automatic (and not upon request) exchange of information impacts cross-border deposits in offshore jurisdictions, which are characterized by low income tax rates and strong financial secrecy.

I. Introduction1

International tax evasion raises several concerns from a public policy perspective as it reduces government revenues, impacts the fairness of the tax system, and ties in with other illegal activities such as money laundering, terrorist financing, bribery and corruption. One of the most important policy tools to fight international tax evasion and help countries2 enforce worldwide taxation of their residents is exchange of information (EOI) between national revenue authorities (Keen and Lighart, 2006). While each EOI framework varies in its scope and modalities, the underlying principle remains the same: EOI is meant to provide tax authorities access to information which they may not otherwise be able to obtain. In doing so, it supports tax authorities’ ability to detect international tax evasion through targeted risk and personal income tax assessments and to deter its occurrence, including through taxpayer awareness campaigns (EC, 2018).

Drawing on Bank of International Settlements (BIS) restricted locational banking statistics, we test the impact of four different EOI frameworks in addressing international tax evasion. The dataset records foreign-owned deposits in 39 countries held by residents of over 200 counterparty jurisdictions on a quarterly basis since 1995.3 Assuming EOI agreements pose a credible threat to tax evaders insofar as they increase the perceived risk of the domestic tax authority becoming aware of undeclared foreign income, one would expect to observe a behavioral response from tax evaders (Allingham and Sandmo,1972). For instance, some tax evaders may opt to bring undeclared foreign deposits onshore by unwinding round-tripping schemes or by taking advantage of voluntary disclosure initiatives to protect themselves against criminal prosecution. Others may decide to leave their undeclared deposits offshore but reallocate them to jurisdictions where the risk of being reported is perceived as being smaller. Based on a sample of over 230,000 observations, we find that deposits held in offshore jurisdictions4 decrease by between 8 and 12 percent following the conclusion of a bilateral agreements related to EOI upon request (EOIR), although this relationship has weak statistical significance. In contrast, automatic EOI (AEOI) agreements are significantly more effective, reducing deposits in offshore jurisdictions by around 25 percent.

We complement this analysis by quantifying the effect of EOI agreements on deposits in non-offshore centers held by (or through) residents from offshore centers. As the locational banking statistics indicate immediate rather than ultimate ownership, money flowing through entities residing in offshore centers may not be beneficially owned by residents of that offshore country, but rather by residents of the non-offshore bank holding the deposits or even by third-country residents. Earlier evidence of a home bias in portfolio choices (e.g., see French and Poterba (1991), Cooper and Kaplanis (1994), and Tesar and Werner (1995)) indeed suggests that some of the wealth hidden in offshore centers is likely reinvested in the tax evader’s country of residence, behavior which is also known as round-tripping. Testing the response in the deposits held by offshore center residents in non-offshore banks, our results also tend to confirm the effect of EOI in countering possible round-tripping schemes. However, we do not find evidence that the US automatic exchange initiative, the Foreign Account Tax Compliance Act (FATCA), has reduced round-tripping to the US. In contrast, we find that deposits from offshore centers into the US increased following the introduction of FATCA Intergovernmental Agreements (IGAs). This might be related to various factors, including the removal of a withholding tax threat, a lack of information exchange reciprocity by the US,5 and the non-imposition of tax on certain US sourced portfolio interest received by non-residents.

This paper contributes to the existing literature in three key areas. First, our work extends earlier evidence on the effects of EOI, in terms of both the scope of the agreements analyzed and the time-period covered. Prior work tends to focus on the effect of specific EOI agreements. For instance, Johannesen (2014) analyzes the effect of the EU’s Savings Directive, Johannesen and Zucman (2014), henceforth JZ2014, examine the effect of the Savings Directive and EOIR, De Simone, Lester and Markle (2018) investigate the effect of FATCA on round-tripping and Casi, Spengel, and Stage (2018), henceforth CSS2018, analyze the effect of the Common Reporting Standard (CRS).6 These studies use data covering between 3 and 13 years, and consistently find that EOI exerts some influence on foreign-owned deposits. However, empirical results from studies failing to control for all agreements simultaneously likely suffer from an omitted variable bias, since EOI agreements can affect both the deposits between any given country-pair and the probability that those countries will enter into additional EOI agreements. To overcome this gap, we analyze the effects of all existing bilateral EOI agreements over the last 20 years – which includes over 3,000 EOIR agreements, 85 FATCA agreements, EOI under the European Union Savings Directive and its related arrangements, and the CRS – rather than only a subset of them.

Second, we improve identification of the causal impact of EOI agreements by employing a triple-differenced estimator. Previous work has typically used a simpler difference-in-differences (DD) based approach where the progression of deposits in offshore jurisdictions without an EOI agreement serves as the counterfactual outcome for treated offshore jurisdictions. In other words, this approach exploits a narrow subset of all cross-border deposits, by using offshore jurisdictions as both the treatment and control group. However, the treatment effect identified as such potentially captures various factors, some of which might be unrelated to tax evasion. For instance, if double tax treaties increase tax certainty for foreign investors, foreign-owned deposits would likely increase as a result. However, double tax treaties also provide for EOI which should exert a negative effect on deposits in offshore jurisdictions. By focusing on offshore jurisdictions only, the DD based approach confounds positive and negative effects and thus biases results. The triple differenced estimator improves identification by estimating the effect on offshore and non-offshore jurisdictions separately and using the difference of these effects as an estimator of EOI’s impact on tax evasion.7 Our findings are economically more meaningful when using the triple-differenced approach, underscoring the importance of controlling for factors that are correlated with EOI but likely not related to tax evasion.

Third, recognizing the subjectivity involved in classifying countries as offshore jurisdictions, we test the sensitivity of our findings to the choice of offshore list. Various classifications have been proposed in the literature:8 for instance, Hines and Rice (1994) develop a list of 41 jurisdictions based on the coexistence of low business tax rates and the identification of the countries included by “multiple authoritative sources” (Dharmapala and Hines, 2009); the Organisation for Economic Co-operation and Development (OECD) published in 2000 a black-list (which was later complemented by a grey and white list) of 35 countries based on the level of taxation and a lack of either EOI, transparency or substance requirements (Avi-Yonah, 2009); JZ2014 use a list of 51 countries based on similar criteria. The overlap between these lists is less than perfect. Notwithstanding, the choice of which countries constitute offshore jurisdictions is an integral part of the identification approach and has a profound impact on the measured effect of EOI.

To address this challenge, we estimate the offshore-status of over 200 jurisdictions and the impact of EOI agreements simultaneously, using a finite mixture model.9 Intuitively, the algorithm identifies offshore centers by their distinct response to the introduction of information exchange. In an iterative procedure, we first obtain a probabilistic classification of countries into offshore centers and then re-estimate the response to information exchange. Consistent with our baseline regressions, we find that AEOI, but not EOIR, impacts cross-border deposits in offshore jurisdictions. We find that offshore centers are typically (though not exclusively) characterized by low income tax rates (both personal and corporate), high financial secrecy, English as an official language, large FDI stocks and small trade flows (both relative to GDP). In contrast to Dharmapala and Hines (2009), we do not find a statistically significant association between governance indicators and the offshore status of countries, but we do find that UN membership is a significant predictor of a country’s offshore status.

The remainder of this paper is structured as follows. Section 2 provides background on the different EOI frameworks. Section 3 discusses potential identification strategies and classifies existing work on the impact of EOI. Section 4 summarizes the data used in the empirical analysis whose results are presented in Section 5 and Section 6 concludes.

II. Background on Exchange of Information in Tax Matters

A. Different Frameworks for EOI Over Time

Cross-border deposits can exist for a gamut of reasons, several of which can be orthogonal to tax evasion. Savers may want to invest elsewhere for passive diversification of foreign exchange, exploiting interest rate differentials, cross-border trade settlement, or migration linkages (such as remittance flows) (see for example Fornari and Levy, 2000; Masciandaro, 2004; Karpowicz, 2006; and Lane and Milesi-Ferretti, 2008). Notwithstanding, there is empirical evidence relating non-bank international deposits to tax rates on interest income and standards for domestic bank interest reporting, which is suggestive of tax evasion motives (Huizinga and Nicodeme, 2004).

Income earned on offshore assets is particularly vulnerable to imperfect compliance due to the additional layer of informational asymmetry between a foreign tax authority and an individual’s resident authorities. In this context, EOI arrangements can support the enforcement of residence-based taxation, with positive effects on production efficiency (Keen and Lighart, 2006), by increasing the likelihood of detection of cross-border tax evasion or money laundering.

The traditional model consisted in undertaking EOIR, if and when domestic tax authorities needed information located in another jurisdiction to properly enforce worldwide taxation of its residents.10 This model suffered from a range of limitations. First, there was no obligation for a jurisdiction to provide the information requested by another jurisdiction if the first-mentioned jurisdiction did not have an interest in the information requested for its own domestic tax purposes. In 2005, the OECD amended its Model Tax Convention by adding a new paragraph 4 to Article 26 to deal with this issue.11 Second, EOIR required that the requesting jurisdiction present a sufficiently detailed request in which it needed to demonstrate that the information being requested is foreseeably relevant to the administration or enforcement of its domestic tax laws.12 The need to meet this somewhat subjective threshold significantly limited the potential benefits from EOIR as tax evasion schemes are, by their very design, intended to ensure that information is concealed from domestic tax authorities. Third, EOIR has historically relied on bilateral relationships under a double tax treaty (DTT) or a tax information exchange agreement (TIEA) as the legal basis for undertaking the exchange. Access to information thus necessitated the successful conclusion of one of those instruments, which could take years in the making. And, in the case of a DTT, require tax concessions which may outweigh the expected benefits of obtaining access to the information being sought. While the number of bilateral relationships providing for EOIR has been steadily growing over the last few years, it remained challenging to ensure that all possible offshore locations were effectively covered under a given jurisdiction’s DTT/TIEA network (see Figure 1 which differentiates DTTs and TIEAS with and without paragraph 4).

Figure 1.
Figure 1.

EOIR under DTTs and TIEAs

Citation: IMF Working Papers 2019, 286; 10.5089/9781513519975.001.A001

Data source: Exchange of information portal, OECD.

In light of these challenges, new models for undertaking EOI in tax matters emerged.13 These models focused on the automatic sharing of taxpayer information, which effectively amounts to an extraterritorial extension of the longstanding practice of using third-party reporting to support taxpayer compliance under self-assessment-based tax systems.

The first significant multilateral move towards a systematic framework for AEOI came from the European Union (EU). In June 2003, EU Member States reached an agreement to have banks and other financial institutions (“paying agents”) disclose information on interest income earned by non-resident individuals to the domestic tax authority, who would then automatically exchange that information with the tax authorities of the accountholder’s jurisdiction of residence.14 This landmark agreement, colloquially referred to as the EU Savings Directive (EUSD),15 came into effect on 1 July 2005. In order to help preserve a level playing field, similar arrangements providing for either reporting or withholding were concluded with select non-EU countries, as well as 10 dependent and associated territories.16 The EUSD was eventually repealed and superseded by parts of Council Directive 2014/107/EU which provides for the implementation of broader AEOI consistent with the “Common Reporting Standard” (discussed below) as of 1 January 2016.17

Amidst several banking scandals involving international tax evasion schemes—including with LGT Bank in Liechtenstein and, most notably, the Union Bank of Switzerland (UBS) and Crédit Suisse— and growing concerns that the qualified intermediary program that had been implemented in 2001 was being exploited (Byrnes and Munro, 2017), the US followed suit and made a much bolder move towards global application of AEOI. Enacted in 2010, the Hiring Incentives to Restore Employment (HIRE) Act included provisions—commonly referred to as FATCA—targeting non-compliance by US taxpayers (both US residents and citizens) using undeclared foreign accounts to conceal income. On the threat of a 30 percent withholding tax on US-source income, foreign financial institutions (FFIs) were either “encouraged” to directly enter into an FFI Agreement with the US Internal Revenue Service (IRS) to undertake certain due diligence, reporting and withholding requirements under FATCA or required to comply with the FATCA Intergovernmental Agreements (IGA) treated as in effect in their jurisdictions. FATCA requires FFIs to identify and report information about financial accounts held by US taxpayers, or by foreign entities in which US taxpayers hold a substantial ownership interest, such as the name, address, and taxpayer identification number of the account holder, the account number, account balance, and income (including interest and dividends), and sales proceeds. FATCA went into effect on July 1, 2014 and to date 113 jurisdictions have reached agreement with the US to undertake AEOI under FATCA IGAs.18

Recognizing the benefits of implementing an AEOI framework on a truly global (and multilateral) basis, and mindful of the need to standardize FATCA-style due diligence procedures so as to minimize implementation costs for the financial sector, other countries soon followed suit in moving towards a new AEOI regime. In September 2013 at the St. Petersburg Summit, G20 Leaders committed to AEOI as the “new global standard” and endorsed the OECD’s work to develop the Common Reporting Standard (CRS) which provides for multilateral exchange of financial account information in tax matters. In July 2014, the OECD released the final version of the CRS, which contains the reporting and due diligence procedures that underpin AEOI under the new standard. Much like FATCA from which it largely draws, the CRS has broad scope and covers many financial institutions such as custodial institutions, depository institutions, investment entities and specified insurance companies. It also requires financial institutions to look through passive entities to identify the relevant controlling persons, the “beneficial owners”. Aside from a few mostly technical details, the main difference between the CRS and FATCA resides in the fact that the CRS was designed for multilateral implementation with full reciprocity between participating jurisdictions.19 It marks an unprecedented international effort to combat international tax evasion; as of November 2018, 101 jurisdictions committed to start AEOI under the CRS framework by 2017 or 2018, and an additional seven countries committed to doing so in 2019 or 2020.20

B. Skepticism Over the Impact of AEOI Frameworks

While AEOI under the EUSD, FATCA and the CRS were all meaningful steps in the fight against international tax evasion, questions have emerged as to whether these frameworks have or would truly be effective in achieving their underlying objectives of detecting and deterring tax evasion.

For example, from the EUSD’s inception, there were persistent concerns that it would be far less successful at preventing tax evasion as first hoped as it could be circumvented in a number of ways. For instance, as the EUSD initially only focused on accountholders who are individuals, it could be avoided through the use of trusts, foundations, or other entities. Other sources of concerns pertained to the possible relocation of assets outside the EUSD-covered jurisdictions to avoid reporting or withholding, or their relocation to other investments not covered under the EUSD’s relatively narrow scope.

There is broad skepticism that FATCA has generated anything close to the amount of revenue initially anticipated. FATCA was projected to generate $8.7 billion in revenue between fiscal years 2010–2020 (Joint Committee on Tax), a yearly average of $792 million. The limited information that is publicly-available suggests that FATCA may not have lived up to its hype. For example, since the launch of the Offshore Voluntary Disclosure Program, a partial amnesty program that allows persons not under audit to disclose unreported offshore activities and benefit from reduced civil and criminal penalties, in 2009, more than 56,000 taxpayers paid a total of $11.1 billion in back taxes, interest and penalties.21 However, the bulk of these proceeds are believed to come from anti-money laundering penalties applied against individuals’ foreign assets for failure to file the Report of Foreign Bank and Financial Accounts (FBAR) form, instead of being based on non-compliance with tax obligations (Byrnes and Munro, 2017). Also, while the IRS has spent approximately $380 million to implement and administer the FATCA program, a recent report from the Treasury Inspector General for Tax Administration (TIGTA) concludes that the IRS is still not prepared to enforce compliance with FATCA.22

In the case of the CRS, participating jurisdictions may choose to allow its financial institutions to apply a threshold such that preexisting entity accounts below US$ 250,000 are not subject to review. Hence, tax evaders using sham entities in a jurisdiction availing themselves of this option may remain undetected by fragmenting their holdings into multiple entity accounts before the entry into force of the enabling domestic legislation. Another technique to conceal beneficial ownership (which rests on a 25 percent threshold, the international standard set by the Financial Action Task Force), would be to dilute interest in a given passive entity between related individuals (e.g., between spouses, children, grandchildren, etc.). For new individual accounts, due diligence procedures contemplate that FIs can rely on customers’ self-certification (and the FI’s confirmation of its reasonableness).23 A concern arises with the increasing number of countries offering “citizenship/residence by investments” schemes,24 which would seemingly allow these individuals to avoid reporting in their true residence jurisdiction by documenting nexus in the countries where citizenship was purchased (e.g., by providing a passport and a utility bill).25

These claims all appear to raise legitimate concerns about the different challenges that these AEOI frameworks may face in being effective in combatting offshore tax evasion. However, they are largely based on anecdotal evidence and a priori analysis of the underlying due diligence and reporting obligations. To more systematically analyze the impact of the EUSD, FATCA and CRS on tax evasion in offshore jurisdictions, we turn to empirical analysis.

III. Empirical Strategy

A. Measuring the Impact of EOI

Previous empirical work quantified the impact of EOI by estimating variants of the regression specification:


where, depositijt are foreign-owned deposits in country i held by residents of country j in time t, TiJt is an indicator variable taking the value one if an EOI agreement between i and j is effective in year t, λt is a time-specific fixed effect, μij is a pair-specific fixed effect and εijt is an idiosyncratic error. By including a set of time-specific fixed effects, the coefficient β can be interpreted a DD-based estimate of the treatment effect: it captures the relative change in the deposits of countries where an EOI agreement became effective that exceeds the relative change in the control group where a similar agreement is not in effect26

The BIS locational data provides information on the deposits held in 39 reporting countries (banks) owned by non-residents in over 200 counterparty jurisdictions (savers). By classifying banks and savers into offshore and non-offshore jurisdictions, the data can be seen to consist of four different subsets (Table 1). Previous studies have focused on different subsets to identify the treatment effect of EOI. For instance, JZ2014 use the subset of banks in offshore jurisdictions and savers from non-offshore jurisdictions (subset C in Table 1) to estimate equation (1).27 In this case, the implicit control is the progression of deposits held in offshore jurisdictions by non-offshore savers whose country has not concluded an EOI agreement. In contrast, given the focus on the treatment effect of EOI agreements on round-tripping, Menkhoff and Miethe (2017) use the subset of non-offshore banks and offshore savers (subset B). The implicit control group in this case are the deposits held in non-offshore jurisdictions by savers in offshore countries where there are no EOI agreements in place.

Table 1.

Subsets of Locational Data and Measured Effects

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CSS2018 use a slightly different approach and combine subsets A and C to look at deposits held in all banks, including offshore and non-offshore, by non-offshore savers. To identify the treatment effect of the CRS, they include an interaction between Tijt and an indicator which classifies banks into offshore and non-offshore jurisdictions as an additional regressor in equation (1).

Exploiting information of subsets A and C allows to control for factors that are unrelated to tax evasion but potentially correlated with EOI agreements. For instance, increased legal certainty provided by DTTs might attract additional FDI and thus increase foreign-owned deposits. Treatment effects identified on basis of subset C alone would confound this positive effect from increased legal certainty, in this example, with the potentially negative effect from an increased risk for tax evaders. In contrast, the DD approach based on the wider sample captures the positive effect from legal certainty, which also impacts deposits in subset A, and allows differentiating this effect from the negative effect from an increased risk for tax evaders, which only affects deposits in subset C. Notably, if a given EOI framework leaves deposits in the non-offshore sample unaffected, then the DD approach using the wider sample identifies the same treatment effect which could be obtained by using subset C alone.

We employ a generalized version of the methodology used by CSS2018 by estimating


on the sample of deposits held in all banks (offshore and non-offshore) by savers located in non-offshore jurisdictions (subsets A and C in Table 1).28 The indicator Offt takes the value of one if the bank is classified as an offshore jurisdiction according to JZ2014 in their online appendix.29 We use four indicator variables for the existence of a specific EOI agreement (EOI4, EUSD, FATCA, and CRS), and the indicator variable AEOI, which takes the value of one if any of the automatic exchange of information frameworks exists. Furthermore, we include two sets of time-specific fixed effects, one set for offshore banks λtOff and one for non-offshore banks λtNonoff,30 and a set of pair-specific fixed effects μij. We use robust standard errors (Arellano, 1987) in all estimations that account for clustering at the bank-saver level and allow for arbitrary heteroscedasticity.

By including two sets of time-specific fixed effects, the coefficient β2 can be interpreted as a triple differenced estimate of the treatment effect. It captures the relative effect of EOI agreements on offshore banks which exceeds the effect of EOI agreements on non-offshore banks. Note that the same estimator could be obtained by estimating equation (1) separately for subsets A and C, and then taking their difference. We expect the coefficient to take a negative value for effective EOI agreements.

In a second set of estimations, we apply the logic of equation (2) to test the effect of EOI agreements on round-tripping. Specifically, using the subset of deposits held by all savers (offshore and non-offshore) in non-offshore banks (subsets A and B), we re-estimate equation (2) where now Offj takes the value of one if saver j is an offshore jurisdiction. We again expect the coefficient to take a negative value for effective EOI agreements.

B. Identifying Offshore Banks

Effective EOI agreements are only expected to reduce deposits in offshore jurisdictions where income is hidden, but not in other countries. The classification of countries into offshore centers and non-offshore centers is thus critical for measuring EOI’s impact. While an excessively broad definition of offshore centers will result in a downward bias of EOI’s measured impact, too narrow a definition may result in either a downward or upward bias.31

Given the lack of a universally accepted list of offshore centers, we employ a data-driven stochastic classification procedure to test the sensitivity of our findings. The approach builds on the idea that EOI reduces foreign-owned deposits in offshore jurisdictions but not (or much less so) in non-offshore jurisdictions. Country-specific estimates on the sensitivity of cross-border deposits thus provide information on a country’s likely offshore status. We implement the idea using a finite mixture model (A.P. Dempster, N.M. Laird, and D.B. Rubin, 1977, and Quandt and J. Ramsey, 1978), where the impact of EOI and the probabilistic classification of countries are estimated jointly in an iterative procedure.32

To formalize the idea, assume that cross-border deposits take the following functional form:


where, βcb,cs captures the response to EOI agreements conditional on whether the bank is an offshore center (cb = off) or not (cb = on) and whether the saver is an offshore center (cs = off) or not (cs = on). Similarly, time fixed effects may differ, depending on the classification of banks and savers. As before, we include pair-specific fixed effects βij and βijt represents an idiosyncratic error.

Next, we denote by pi the probability that country i is an offshore center and assume this probability is a logistic function of a predictor vector zit = (z1it, z2it,...):


which includes both time-varying and time-constant variables. Following Hines and Dharmapala (2014), we include the WB’s governance indicators, measures of a country’s size (GDP, population), English language use, and UN membership as variables that potentially help explaining the offshore status of countries. In addition, we include a measure of trade-openness (the sum of imports and exports as a share of GDP), FDI stocks (as a share of GDP), tax rates (corporate and personal), a financial secrecy index (which is only available for one year), and dummy variables that indicate whether a country was including in an existing list of offshore centers.

We denote the probability that both bank i and saver j are offshore centers by qi,joff,off=pipj and the probability that these countries are both not offshore centers by qi,jon,on=(1pi)(1pj). Further, we denote by f(log(depositijt) |θcb,cs,xijt) the density of logarithmic cross-border deposit stocks conditional on the parameter vector θcb,cs = (βcb,cscb,cs) and explanatory variables xijt. While bilateral deposit stocks follow the same distributional form, a normal distribution, differences in the underlying parameters imply four different distributions of cross-border deposits. Combining the above, the density of bilateral deposit stocks between i and j is given by:


where, θ=(θoff,on,θoff,off,θon,off,θon,on)andq=(qijoff,off,qijoff,off,qijon,off,qijon,on).

We obtain the parameter vector θ and the probability vector p = {pit} by maximum likelihood estimation using an EM-algorithm.

Intuitively, the iterative procedure consists of three steps.

  • First, we estimate equation (3) with weighted least squares for each of the four subsets depicted in Table 1, where the weights reflect the prior probability of an observation belonging to any one of the four groups. To test whether the response to AEOI differs, we estimate these four equations in a SUR framework.

  • Second, we compute posterior probabilities that a country is an offshore center, based on last round’s parameter estimates. This step identifies outliers of the current classification by contrasting observed reactions with prototypical reactions. We restrict posterior probabilities to be constant across time.

  • Third, we use the computed posterior probabilities to estimate equation (4). This step allows refining measured posterior probabilities by explaining the measurements with a vector of control variables that should be related to the likelihood of being an offshore center. We then use these predicted probabilities as a prior for next round’s iteration.

The iterative process ends when posteriors do not change from one round to the next. In one of the specifications, we effectively skip the third step by including a vector of country-specific fixed effects in estimating equation (4). Predicted and computed posteriors are thus equivalent. Annex 1 provides further details.

IV. Data

Our primary source of data is the BIS’ restricted locational banking statistics, where we analyze bilateral deposit stocks held by non-resident non-banks. Our sample includes quarterly data for 39 reporting countries33, 19 of which have data as far back as Q1 1995, and 17 of which are classified as offshore jurisdictions according to JZ2014.34 The latest quarter of available data at the time of writing is Q2 2018.

Figure 2 illustrates the evolution of cross-border deposits as a share of the reporting countries’ aggregate GDP, both in aggregate and differentiating between offshore and non-offshore deposits (based on the JZ2014 classification). On average, aggregate cross-border deposits represent around 10 percent of GDP in any given quarter. Starting in 2003, aggregate foreign-owned deposits increased and reached 15 percent of GDP in 2008.35 However, they have moderately declined since, resembling the levels observed in the early 2000s. Figure 2 shows that the aggregate trend depicts two very distinct patterns for offshore and non-offshore jurisdictions. As could be expected, the cross-border deposits-to-GDP ratio of offshore centers has historically been at much higher levels; it has, however, shown considerable volatility and has embarked onto a pronounced downward trend since reaching a peak at around 60 percent in 2008. In comparison, the foreign deposits-to-GDP ratio in non-offshore jurisdictions has been much more stable and has even slightly increased in recent years. While the cross-border deposits database recorded by the BIS also captures financial flows which are unrelated to tax evasion36 and does not allow to differentiate between entity and individual accountholders, it arguably remains the richest source of information available to investigate how recent AEOI initiatives may have been effective in curtailing international tax evasion in offshore jurisdictions.37

Figure 2.
Figure 2.

Evolution of Cross-Border Bank Deposits (in % of Group-Specific GDP)

Citation: IMF Working Papers 2019, 286; 10.5089/9781513519975.001.A001

Source: BIS locational statistics, Offshore classification according to JZ2014

In addition, four key sets of indicator variables on the timing and nature of bilateral EOI relationships are used: first, we extend the dataset compiled by JZ2014 on EOIR using data from the OECD’s Exchange of Tax Information Portal. This dataset covers the universe of DTTs and Tax Information Exchange Agreements (TIEAs). It describes for over 6,500 bilateral relationships information such as the date when EOI upon request became effective, whether the legal basis is a DTT or a TIEA, and whether this basis includes a provision akin to paragraph 4 of Article 26 of the OEDC Model Tax Convention. Second, information on the Savings Directive is collected directly from the European Commission’s website. The EUSD covers over 1,300 bilateral relationships, including the agreements between the EU and third countries and overseas territories. Third, information on FATCA agreements is downloaded from the US Treasury’s website. At the time of writing, 88 FATCA relationships are in effect. Fourth, activated relationships under the CRS are retrieved from the OECD’s Automatic Exchange Portal.38 Based on the CRS, more than 4,600 country pairs exchange information currently.

Table 2 provides descriptive statistics for our baseline sample. Non-bank deposits held by nonresidents range between 1 USD and 750 billion USD. The EOI4 indicator takes the value of one among 11 percent of the baseline sample’s observations. The respective shares for the EUSD, FATCA and CRS are 8 percent, 0.1 percent, and 4 percent. When combining indicators (i.e. when letting the treatment indicator equal 1 whenever at least one type of AEOI arrangement is in place between the country-pair), 11 percent of the sample is governed by an AEOI relationship. As control variables, we use the logs of both GDP and foreign exchange rates, as provided by the IMF’s World Economic Outlook database, as well as the World Bank’s Governance indicators.

Table 2.

Descriptive Statistics

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Finally, in Section V.D, we use a number of variables as predictors of a country’s probability of being classified as an offshore jurisdiction. These overlap with several of the variables formerly used by Dharmapala and Hines (2009), and include: UN membership indicator as of 2019, use of English as an official language (based on the CEPII dataset), statutory corporate tax rate and top marginal personal income tax rate in force in each country-year (from the IBFD), an index of financial secrecy (from the Tax Justice Network), total trade as a ratio to GDP from WEO, imports and exports from the WB, and the FDI inbound stocks from UNCTAD.

V. Empirical Findings

A. Effect on Deposits in Offshore Jurisdictions

Table 3 summarizes our baseline results. The first four specifications examine the effect of EOI4 agreements, while columns 5 and 6 estimate the effect of all EOI agreements. All estimations include a set of pair-specific fixed effects and time-specific fixed effects. A more flexible time-fixed effects structure that allows for different trends among offshore banks and non-offshore banks which did not conclude an EOI agreement is used from specification 3 onward.

Table 3.

Baseline Results

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*, **, and *** indicate significance at the 10, 5, and 1 percent level. Fully robust standard errors in brackets.

Overall, the results confirm a strong and statistically significant effect of automatic exchange of information agreements in reducing cross-border deposits into offshore jurisdictions. The measured effect implies that such deposits decreased by between 35 percent (for the CRS) to 50 percent (for the EUSD) following the introduction of agreements to undertake AEOI. In contrast, EOIR reduced deposits in offshore jurisdictions by around 12 percent, although this effect is not statistically significant in most specifications.

The importance of controlling for factors that are unrelated to tax evasion is evidenced by the results in Columns 1 and 2. Following JZ2014, column 1 estimates the effect of EOI4 relationships using only the subset of offshore banks and non-offshore savers (subset C). This specification suggests that deposits in offshore jurisdictions increased by 8 percent following the introduction of an EOIR relationship. This finding is inconsistent with earlier evidence on a negative impact of EOIR (for instance, in the order of 12 percent in JZ2014). Column 2 adds the sample of non-offshore banks and identifies the effect on tax evasion by interacting the treatment variable with the offshore indicator. Using this broader sample, we find that EOIR increased deposits in non-offshore jurisdictions by around 33 percent. Relative to this benchmark, the effect in offshore jurisdictions is much lower (-44 percent) and statistically significant, suggesting that EOIR reduces income concealed in offshore jurisdictions that signed such an agreement. However, this second specification presumes that non-treated banks followed the same time trajectory.

The specification presented in Column 3 allows for more flexibility by introducing a separate set of time fixed effects for offshore and non-offshore banks. Using this specification, the estimated effect of EOIR in addressing tax evasion in offshore jurisdictions remains negative (-12 percent) but loses statistical significance.

Columns 4 and 5 estimate the effect of both EOIR and AEOI. As before, we use a triple differenced estimator to identify the treatment effect of EOI agreements on tax evasion. By expanding the set of explanatory variables, the measured effect of EOI4 remains relatively stable at around -12 percent but is also consistently not statistically significant. In contrast, the effects of AEOI range from -35 percent for the CRS percent to -51 percent for the EUSD, with both results being highly statistically significant. At -45 percent, the measured effect of FATCA is sizeable but not statistically significant. Based on a Wald test, we cannot rule out that the effects of FATCA, CRS, and the EUSD are identical and thus increase estimation precision in column 6 by estimating the effects of these agreements jointly. Using the combined AEOI indicator, the measured effect is also very large (-53 percent) and significant at the 1 percent level.

B. Robustness

Table 4 reports a series of robustness checks, using bank deposits between 2002 and 2018. By restricting the number of years, we lose around 18 percent of the baseline sample observations but potentially increase the accuracy of our estimated effects by reducing the total number of estimated coefficients. All specifications include two sets of time fixed effects as well as pair-specific (bank-saver) fixed effects. Throughout the specifications, we include additional explanatory variables to test the sensitivity of our findings. To improve readability, we do not show the first order terms (non-interacted EOI agreements) in the table even though these are included in the estimations.

Table 4.

Robustness Checks

article image
*, **, and *** indicate significance at the 10, 5, and 1 percent level. Fully robust standard errors in brackets. All specifications include separate time-fixed effects for offshore locations and non-offshore locations. The non-interacted EOI agreements are included in the estimations but not presented in this table for simplicity.

Columns 1 and 2 repeat the baseline regressions used in columns 4 and 5 in Table 3 (omitting coefficients on the non-interacted EOI agreements). The estimated effects remain largely unchanged using the restricted sample: EOIR exerts a small and statistically non-significant impact on offshore deposits while all AEOI frameworks exert a strong negative impact (still statistically insignificant for FATCA only). The second column again restricts the effects of AEOI agreements to be uniform, with the measured effect being only slightly smaller than in the baseline estimations (-46 percent).

Columns 3 to 5 allow for bank-specific time trends in addition to the two sets of time-specific fixed effects (our preferred specification). By allowing for more flexibility, the measured effect of AEOI agreements is substantially reduced to 25 percent. The associated standard error implies that, in 95 percent of the observed cases, the bank deposits held in offshore jurisdictions by non-offshore residents declined by between 13 and 37 percent. This measured effect remains unaffected by including GDP in the bank and saver locations, as well as the foreign exchange rate between the bank location and the US dollar, as explanatory variables in column 4, and by adding a governance indicator in column 5.

Overall, our results substantiate earlier evidence on the impact of AEOI but offers a nuanced view on the impact of EOIR. JZ2014 report that EOIR agreements reduce deposits in offshore jurisdictions by an average of 12 percent. While our analysis confirms the magnitude of these effects, the variability of the measured effect increases when using a broader timespan such that we cannot reject the assumption that the effect of EOIR in reducing deposits in offshore jurisdictions may be null. This finding supports the conclusion of Menkhoff and Miethe (2017) that the effects of EOIR may have weakened over time. As for AEOI, Johannesen (2014) shows reactions of Swiss deposits in the order of 30–40 percent in response to the introduction of the EUSD and, most recently, CSS2018 also find a statistically significant impact of the CRS on cross-border bank deposits in offshore jurisdictions (14 percent).39

C. Effect on Countering Round-Tripping

To investigate the effect of EOI relationships in countering round-tripping schemes, that is, indirect investments made by a resident in his home country through an offshore center, we next analyze the sample of deposits held in non-offshore jurisdictions by savers from offshore jurisdictions (subset B). 40 This analysis provides an indirect test of EOI’s overall impact, insofar as it builds on the assumption that concealed income from residents in non-offshore jurisdictions in routed through offshore jurisdictions and reinvested in the investor’s residence country.41

Table 5 presents the results. Overall, we find that the presence of an EOI relationship reduces deposits held in non-offshore locations by residents of offshore centers. This finding, which appears most statistically significant for EOIR, would suggest a reduction in round-tripping behavior. However, we find a reverse effect of FATCA agreements on the cross-border deposits from offshore centers into the US: following the conclusion of FATCA agreements with offshore jurisdictions, deposits in the US increased by roughly the same amount by which other non-offshore jurisdictions witnessed a decline; by 20 percent, possibly due to the non-reciprocal nature of FATCA compared to other types of AEOI arrangements, as explained further below.

Table 5.

Round-Tripping Results

article image
*, **, and *** indicate significance at the 10, 5, and 1 percent level. Fully robust standard errors in brackets. All specifications include separate time-fixed effects for offshore locations and non-offshore locations.

Column 1 estimates the treatment effect for each EOI framework separately. In contrast to the direct test of EOI’s effect shown in subsection A, the indirect test does not suggest that AEOI is more effective in reducing tax evasion than EOIR. The coefficients on EOI4, EUSD and CRS indicate that deposits decreased by between -14 and -20 percent. While a Wald test confirms that these point estimates do not differ statistically from each other, only EOIR is significantly different from zero at the 10 percent level. Nonetheless, we note that the reduction in deposits from offshore savers in non-offshore banks under EOIR is consistent with an attempt to mitigate triggering an EOI request by the non-offshore jurisdiction (an incentive that would not be present under AEOI). Surprisingly, however, the effect of FATCA is positive, implying that cross-border deposits in the US increased by 23 percent following the conclusion of FATCA agreements with offshore jurisdictions.

Column 2 estimates effects of the EUSD and CRS jointly by including a combined indicator variable NON_FATCA. The measured effect is now statistically significant at the 10 percent level and suggests a reduction in the order of 19 percent. Columns 3 to 5 add control variables, with little impact on the measured treatment effects: EOIR continues to exert a statistically significant negative impact on the deposits in non-offshore jurisdictions; the effect of the EUSD and CRS (again measured jointly) become slightly lower in magnitude and measured with less precision, which is likely due to less time variation compared to EOI4.

Our measured effect of EOIR agreements on deposits in non-offshore jurisdictions is smaller than the effect of -36 percent reported by Menkhoff and Miethe (2017), which is likely driven by differences in the classification of offshore jurisdictions as we further discuss below. CSS2018 find that the introduction of the CRS induced a reduction in cross-border deposits held in traditional offshore locations. However, the authors show that such wealth has not been repatriated but instead relocated mostly to the US, which they argue has emerged as a potentially attractive location for cross-border evasion. Our results are not inconsistent with this finding, as they point to the US FATCA being the only AEOI agreement which has not led to a reduction in deposits from offshore centers to a non-offshore country (here, the US) following the introduction of such agreements. One rationale for the observed response is that FATCA IGAs concluded by offshore centers provide certainty to evaders located in third countries that investing in the US through these centers avoids the application of FATCA withholding. At the same time, the existence of a FATCA IGA de facto preserves anonymity for the offshore saver, since the US does not reciprocate in terms of identification and reporting of beneficial owners. Finally, the US may also be an attractive jurisdiction in which to hold bank deposits in light of its offering of a zero percent withholding tax rate on related interest payments to nonresident aliens.42

D. Finite mixture model results

Our previous assessment of EOI’s impact in addressing international tax evasion was based on the JZ2014 classification of offshore jurisdictions. We next test the sensitivity of our findings by estimating the probability of being an offshore center and the sensitivity of cross-border deposits to the introduction of EOI agreements simultaneously, using a finite mixture model. While any given jurisdiction’s usefulness to serve as an offshore center likely depends on bilateral-specific characteristics, such as the existence of double tax treaties, we follow existing classification schemes by ruling out this possibility. That is, we restrict a country’s estimated posterior probability of being an offshore center to be constant across all counterparty jurisdictions. In contrast to existing classification schemes, however, we allow in some specifications for the countries’ probabilistic offshore status to change over time.

Table 6 provides the results. The upper part of the table depicts the relative effect of EOI agreements on offshore banks which exceeds the effect of EOI agreements on non-offshore banks (i.e. triple-differenced treatment effects). The lower part of the table presents estimation results on the drivers of the data-driven classification. We allow posterior probabilities to vary annually in the first two specifications (Columns 1 and 2) and restrict them to be constant across time in subsequent specifications.

Table 6.

Finite Mixture Model Results

article image
Notes: Upper table provides estimated treatment effects of EOI4 and AEOI. Lower table provides results of logit regression, explaining offshore jurisdiction status. *, **, and *** indicate significance at the 10, 5, and 1 percent level. Robust standard errors in parentheses.

Overall, the sensitivity analysis confirms the impact of AEOI. We find that cross-border deposits decrease by an excess of between 16 and 33 percent in offshore centers, as identified by the EM algorithm, following the introduction of an AEOI agreement. These effects are statistically significant at the one percent level. In contrast, the measured impact of EOIR on offshore centers does not differ statistically from the measured impact on non-offshore centers. Results presented in the first four columns of Table 6 show that non-sovereign, English-speaking countries, with low tax rates (both personal and corporate), large FDI stocks, small trade flows and high financial secrecy, are more likely to be categorized as an offshore center by the EM algorithm. While we do find that offshore centers do have higher governance indicators, as highlighted by Dharmapala and Hines (2009), we do not find a statistically significant relationship.

The first four columns estimate a country’s offshore status based on country-specific variables. Columns 1 and 2 allow posterior probabilities to vary on a yearly basis. Here, personal income tax rates are a better predictor for a country’s offshore status than corporate income tax rates. For instance, increasing the personal income tax rate by 10 percentage points reduces the odds of being an offshore center by almost 20 percent. Countries that use English as an official language are more likely to be offshore centers, as are non-sovereign states, which are not a UN member. Taken together, the estimations imply that non-sovereign English-speaking countries are more than 3.5 time more likely to be an offshore center than being not an offshore center. Notably, while Dharmapala and Hines (2009) also find a positive association between English language use and offshore center status, they do not find a statistically significant relationship between UN membership and offshore status. The second specification drops the weakest predictors of offshore center status, leaving the estimated effects of the remaining predictors largely unchanged. When restricting posterior probabilities to be constant across time (Columns 3 and 4), estimated effects on the predictor variables are larger in magnitude and tend to be more significant. As before, larger FDI stocks (as a share of GDP) signal offshore center status, as do smaller trade flows. The CIT rate is a better predictor for the offshore status in these specifications. When dropping the worst predictors of the third specification, we find that the secrecy score becomes a significant predictor of offshore center status.

As the choice of predictor variables impacts measured effects, we abstain from using country-specific macro variables in Columns (5) and (6). Specifically, we use the offshore center lists compiled by JZ2014, Hines and Rice (1994), Zoromé (2007), and CCS2018 to predict a country’s offshore status in column (5). While all lists are positively related to the endogenously determined posterior probability of being an offshore center, only the list compiled by Hines and Rice (1996) is a significant predictor. Finally, the estimation presented in column (6) uses a vector of country-specific fixed effects to estimate posterior probabilities of being an offshore center. Since posterior probabilities are, by construction, constant across time, this specification effectively skips the predictor stage and directly uses country-specific posterior probabilities as an input for next round’s iteration. The final specification identifies 20 countries as offshore centers. Even though this classification is much narrower than the classification used in our baseline analysis, measured effects of AEOI are very comparable.

VI. Conclusion

Considerable effort has been deployed since the early-2000s to rethink how exchange of information for tax purposes could be made more effective. While initial steps focused on tweaking the initial EOIR model based on bilateral relationships, new models emerged to provide for the systematic transmission of taxpayer information across national boundaries. After a regional multilateral approach under the EUSD, the US attempted a more global reach by concluding bilateral FATCA agreements with over 80 countries. Most recently, the leadership of the G20 and OECD led to the adoption of a new global and multilateral approach to AEOI, the CRS which will be implemented in over 100 jurisdictions worldwide. Though these were notable milestones in international cooperation in tax matters, many have expressed doubts over their ability to successfully detect and deter international tax evasion.

We empirically test the effect of these EOI frameworks in reducing cross-border deposits in offshore jurisdictions, which can be interpreted as assessing their impact in curtailing tax evasion in these jurisdictions. We don’t observe a consistent reduction in offshore jurisdiction deposits following the introduction of EOIR. This result is consistent with our a priori expectation and potentially explains why policymakers worldwide devoted considerable efforts into adopting new standards for information exchange. In contrast, we find strong evidence suggesting that AEOI has been effective in limiting the use of offshore jurisdictions to evade taxes, with deposits therein dropping by an average of 25 percent after the entering into force of any AEOI agreement. However, as reiterated at various points in this paper, the precise implications of this observation for global tax evasion are ambiguous, since a reduction in presumably tax evading deposits in one offshore jurisdiction could imply an increase in tax evading deposits elsewhere. Furthermore, not all cross-border deposits in offshore jurisdictions need to have been there for tax evasion purposes, and therefore should not be expected to decline.

Among the three AEOI agreements studied, the EUSD and the CRS appear to have been most effective with a strong statistical relationship, while FATCA’s impact is most often not statistically significant. Interestingly, while the EUSD and CRS are also found to reduce deposits held by residents of offshore centers in non-offshore banks—and effective in addressing round-tripping schemes—FATCA is not. What is more, the conclusion of a FATCA agreement with an offshore center seems to have increased deposits into the US, a result consistent with prior findings (such as CSS2018) that the US may have become a more attractive location in which to hold—and perhaps conceal—income.

Annex 1. Finite Mixture Model Estimations

From equation (5), it follows that the parameter vector θcb,csmaximizing the log-likelihood of the sample at hand needs to satisfy




is the posterior probability that bank i is classified as cb and that saver j is classified as cs.

We estimate the probabilities p = (p1, ...pN) and the parameters of the four density distributions in an iterative procedure using 3 steps.

Step 1 – Estimation of θ. Conditional on a vector of posterior probabilities, an explicit solution to equation (5) is given by a weighted least squares regression of yijton xijtwhere the vector of posterior probabilities is used as a weight To initiate the algorithm, we use the JZ2014 list to assign country-specific posteriors. We set the posterior to 0.85 for countries classified as offshore centers and to 0.15 for countries not classified as offshore centers.

Step 2 – Updating of country-specific probabilities p. We require that each country is assigned a unique probability of being an offshore center. Conditional on the parameter estimates and last round’s prior, Pi, the likelihood of country i being an offshore center is proportional to the product of observed densities over time and over counterparty jurisdictions multiplied by the prior probability:


Conversely, the likelihood of country i not being an offshore center is proportional to


It follows that the posterior probability of country i being an offshore center is given by


Step 3 – Predictor step. We estimate the expected value of country-specific probabilities conditional on a vector of explanatory variables z, based on the regression


Where k is a logistic function. Subsequently, we combine the vector of country-specific posterior probabilities p to obtain joint probabilities qcb,csof observing any country-pair combination, which we then use as a prior in step 1. The iterative procedure is stopped when posterior probabilities do not change from one round to the next

All estimations control for pair-specific fixed effects and take the JZ list to assign prior probabilities of country classification.43 To increase the accuracy of classification, we drop savers from the restricted dataset with fewer than 1400 observations, which results in a sample of 85 countries which are classified with the EM algorithm.


  • Allingham, Michael G. and Agnar Sandmo, 1972, Income Tax Evasion: A Theoretical Analysis, Journal of Public Economics, Vol. 1, Issue 3–4, pp. 323338.

    • Search Google Scholar
    • Export Citation
  • Alm, J., Deskins, J., and McKee, M., 2009. Do Individuals Comply on Income Not Reported by Their Employer? Public Finance Review, 37(2), 120141

    • Search Google Scholar
    • Export Citation
  • Arellano, Manuel, 1987, “Computing Robust Standard Errors for Within-Group Estimators.” Oxford Bulletin of Economics and Statistic, Vol. 49(4), pp. 431434.

    • Search Google Scholar
    • Export Citation
  • Avi-Yonah, Reuven S., 2009, The OECD Harmful Tax Competition Report: A Tenth Anniversary Retrospective (Paris, France).

  • Bank for International Settlements, 1995-2018, Locational Banking Statistics: Loans and Deposits by Non-Resident Non-Banks, https://www.bis.org/statistics/bankstats.htm.

    • Search Google Scholar
    • Export Citation
  • Byrnes, William, and Robert J. Munro, 2017, Background and Current Status of FATCA, Legal Studies Research Paper Series, Research paper No. 17–31.

    • Search Google Scholar
    • Export Citation
  • Casi, Elisa, Christoph Spengel, and Barbara Stage, 2018, Cross-Border Tax Evasion after the Common Reporting Standard: Game Over? ZEW – Centre for European Economic Research Discussion Paper No. 18–036.

    • Search Google Scholar
    • Export Citation
  • Cooper, Ian and Evi Kaplanis, 1994, What Explains the Home Bias in Portfolio Investment, Review of Financial Studies, Vol. 7, pp. 4560.

    • Search Google Scholar
    • Export Citation
  • De Simone, Lisa, Rebecca Lester, and Kevin Markle, 2018, Transparency and Tax Evasion: Evidence from the Foreign Account Tax Compliance Act (FATCA), Stanford University Graduate School of Business Research Paper No. 17–62.

    • Search Google Scholar
    • Export Citation
  • Dempster, Arthur P., Nan M. Laird, and Donald B. Rubin, Maximum Likelihood from Incomplete Data via the EM Algorithm, J. Roy. Statist. Soc. B 39 (1977), pp. 138.

    • Search Google Scholar
    • Export Citation
  • Dharmapala, Dhammika and James R. Hines, 2009, Which Countries Become Tax havens? Journal of Public Economics, Vol. 93 (9–10), pp.10568.

    • Search Google Scholar
    • Export Citation
  • Egger, Peter, Merlo, Valeria and Wamser, Georg. 2014. Unobserved Tax Avoidance and the Tax Elasticity of FDI, Journal of Economic Behavior & Organization, Vol. 108, Issue C, pp. 118

    • Search Google Scholar
    • Export Citation
  • European Commission, 2003, Council Directive 2003/48/EC

  • European Commission, 2011, Council Directive 2011/16/EU.

  • European Commission, 2014, Council Directive 2014/107/EU.

  • European Commission; 2018, Report from the Commission to the European Parliament and the Council: On Overview and Assessment of the Statistics and Information on the Automatic Exchanges in the Field of Direct Taxation.

    • Search Google Scholar
    • Export Citation
  • French, Kenneth R. and James M. Poterba, 1991, Investor Classification and International Equity Markets, American Economic Review, Papers and Proceedings, pp. 222226.

    • Search Google Scholar
    • Export Citation
  • Fornari, Fabio and Levy, Aviram, 2000, Global liquidity in the 1990s: geographical allocation and long-run determinants, BIS Conference on International Financial Markets and the Implications for Monetary and Financial Stability, BIS Conference Papers No. 8. Available at http://www.bis.org/publ/confer08a.pdf

    • Search Google Scholar
    • Export Citation
  • Global Forum on Transparency and Exchange of Information for Tax Purposes, 2018, AEOI: Status of Commitments.

  • Global Forum on Transparency and Exchange of Information for Tax Purposes, 1998–2018. “Exchange of Information Portal.” http://www.eoi-tax.org

    • Search Google Scholar
    • Export Citation
  • Hanlon, Michelle, Edward L. Maydew, and Jacob R. Thornock, 2015, Taking the Long Way Home: U.S. Tax Evasion and Offshore Investments in U.S. Equity and Debt.

    • Search Google Scholar
    • Export Citation
  • Heckemeyer, Jost and Hemmerich, Aaron, 2018, Information Exchange and Tax Haven Investment in OECD Securities Markets. Unpublished working paper.

    • Search Google Scholar
    • Export Citation
  • Hines, James R. and Eric M. Rice, 1994, Fiscal Paradise: Foreign Tax Havens and American Business, Quarterly Journal of Economics 109, Vol. (1), pp. 14982

    • Search Google Scholar
    • Export Citation
  • Huizinga, Harry and Nicodème, Gaëtan, 2004, Are international deposits tax-driven, Journal of Public Economics. 88: 10931118.

  • International Monetary Fund, 2000. Offshore Financial Centers. IMF Background Paper. https://www.imf.org/external/np/mae/oshore/2000/eng/back.htm

    • Search Google Scholar
    • Export Citation
  • Johannesen, Niels 2014. Tax Evasion and Swiss Bank Deposits. Journal of Public Economics, 111:4662.

  • Johannesen, Niels and Gabriel Zucman, 2014, The End of Bank Secrecy? An Evaluation of the G20 Tax Haven Crackdown, American Economic Journal: Economic Policy, Vol. 6 (1), pp. 6591.

    • Search Google Scholar
    • Export Citation
  • Karpowicz, I., 2006, Determinants of Emigrant Deposits in Cape Verde, IMF Working Paper WP/06/132

  • Lane, Philip, and Milesi-Ferretti, Gian Maria, 2008, The Drivers of Financial Globalization. The American Economic Review, 98 (2), 327332.

    • Search Google Scholar
    • Export Citation
  • Masciandaro, Donato, 2004. Global Financial Crime: Terrorism, Money Laundering, and Off Shore Centres, Book. Publisher: Ashgate Publishing, Limited, Aldershot, United Kingdom

    • Search Google Scholar
    • Export Citation
  • Menkhoff, Lukas and Jakob Miethe, 2017, Dirty Money Coming Home: Capital Flows into and out of Tax Havens, Discussion Paper No. 1711 (Berlin, Germany).

    • Search Google Scholar
    • Export Citation
  • Organisation for Economic Co-operation and Development, 2017, Model Tax Convention on Income and Capital, Committee on Fiscal Affairs, (Paris: OECD Publishing).

    • Search Google Scholar
    • Export Citation
  • Organisation for Economic Co-operation and Development, 2018, Standard for Automatic Exchange of Financial Account Information in Tax Matters, Implementation Handbook, Second Edition (Paris: OECD Publishing).

    • Search Google Scholar
    • Export Citation
  • Organisation for Economic Co-operation and Development, Towards Global Tax Co-operation, Progress in Identifying and Eliminating Harmful Tax Practices.

    • Search Google Scholar
    • Export Citation
  • Organisation for Economic Co-operation and Development, Automatic Exchange Portal – CRS by Jurisdiction, Available at: http://www.oecd.org/tax/automatic-exchange/crs-implementation-and-assistance/crs-by-jurisdiction/.

    • Search Google Scholar
    • Export Citation
  • Quandt, Richard and James Ramsey, 1978, Estimating Mixtures of Normal Distributions and Switching Regression, Journal of the American Statistical Association, Vol. 73, pp. 730738.

    • Search Google Scholar
    • Export Citation
  • Schjelderup, Guttorm, 2016, Secrecy Jurisdictions, International Tax and Public Finance, Springer; International Institute of Public Finance, Vol. 23(1), pp. 168189, February.

    • Search Google Scholar
    • Export Citation
  • Tesar, Linda and Ingrid M. Werner, 1995, Home Bias and High Turnover, Journal of International Money and Finance, Vol. 14, Issue 4, pp. 467492.

    • Search Google Scholar
    • Export Citation
  • US Department of the Treasury, Resource Center — Foreign Account Tax Compliance Act, https://www.treasury.gov/resource-center/tax-policy/treaties/pages/fatca.aspx.

    • Search Google Scholar
    • Export Citation
  • US Joint Committee on Tax, Estimated Revenue Effects Contained in Senate Amendment 3310, the “ Hiring Incentives to Restore Employment Act.”

    • Search Google Scholar
    • Export Citation
  • US Treasury Inspector General for Tax Administration, 2018, Final Report, Reference number 2018-30-040.

  • Zoromé, Ahmed, 2007, Concept of Offshore Financial Centers; In Search of an Operational Definition, IMF Working Papers 07/87, International Monetary Fund.

    • Search Google Scholar
    • Export Citation

The authors would like to thank Ruud De Mooij, Michael Keen, and participants of the FAD seminar for providing helpful comments.


Although the paper generally uses the term “country”, it should be acknowledged that that some jurisdictions discussed herein do not have legal status as countries.


As discussed throughout the paper, we recognize that not all cross-border deposits are driven solely for tax evasion purposes, and that furthermore, deposits present only a partial view of the equation, since wealth can be stored under other financial and non-financial assets. Notwithstanding, we highlight such limitations whenever important for interpretation and choose to use this dataset in continuity with existing literature on the subject.


We use the terminology “offshore” to designate countries or jurisdictions which have proven to be desirable locations for non-residents to hold financial assets. We primarily rely on the list of offshore jurisdictions provided by Johannesen and Zucman (2014), which does not imply endorsement of this or other lists by the IMF Executive Board or management.


Lack of reciprocity in this context refers to US financial institutions not having equivalent due diligence procedures and reporting obligations as those imposed on other jurisdictions.


The international standard underpinning the global effort to develop an automatic exchange of information regime by the OECD and G20, described in further detail in section Error! Reference source not found.


A related but distinct approach is chosen by CSS2018 who also use non-offshore jurisdictions as a control. However, due to the time structure included in their empirical model, the measured effect is not a triple differenced estimator. See section III.A for further details.


See Schjelderup (2016) for an overview.


See Zorome (2007) for another data-driven attempt to classify offshore jurisdictions.


Our analysis of EOIR focuses on bilateral relationships, and thus omits relationships established under the 1988 multilateral Convention on Mutual Administrative Assistance in Tax Matters, as amended by Protocol in 2010. The authors recognize that future research would gain from incorporating such relationships into the analysis.


At the same time, paragraph 5 was also added which does not permit the requested country to decline to supply information solely because the information is held by a bank, other financial institution, nominee or person acting in an agency or a fiduciary capacity or because it relates to ownership interests in a person.


Under EOIR, a requesting competent authority needs to identify a specific taxpayer and strictly prohibited so-called “fishing expeditions” (such as requesting all bank account information held in a foreign country by its residents).


Though AEOI per se is not completely new. For example, Canada and the US have had AEOI since 1942.


Three countries—namely Austria, Belgium, and Luxembourg—were permitted to apply a withholding tax in lieu of reporting. This means that the identity of the accountholder could be preserved, for a price. Withholding rates were set to gradually increase from 15 percent (2005–2007), to 20 percent (2008–2010), and then finally to 35 percent (2011 onwards), with a 75/25 revenue sharing scheme between the residence and withholding country respectively. Belgium applied withholding until 31 December 2009, Luxembourg until 31 December 2014, and Austria until 31 December 2016. The distinction between reporting and withholding is not taken to be a significant one for the purpose of our analysis, as both could be expected to disincentivize undeclared foreign bank accounts.


The official name is Council Directive 2003/48/EC on taxation of savings income in the form of interest payments.


It should also be noted that through Directive 2011/16/EU, EU cooperation in tax matters also extends to AEOI in respect of five non-financial sources of income: employment income, pensions, directors’ fees, life insurance products, and immovable property ownership and income related thereto.


Out of the 113 jurisdictions with reached agreement, 88 IGAs are in force, 13 are signed but not yet in force, and 12 are agreed in substance. https://www.treasury.gov/resource-center/tax-policy/treaties/pages/fatca.aspx. Three types of FATCA IGAs exist: Model 1 IGAs (both 1A and 1B) require the relevant competent authorities to collect information from their domestic FIs and submit this information to the IRS (a procedure aimed at circumventing domestic privacy legislation). The difference between Model 1A and Model 1B is that the former is partially reciprocal while the latter is not. Model 1A IGAs are not fully reciprocal as the US does not provide the balance or value of reportable accounts, gross proceeds, or beneficial ownership information, nor does the US require its FIs to look through entities, whereas FATCA partners are required to. A Model 2 type requires FFIs to register with the IRS and directly report to the IRS customer-specific information as required under the FFI Agreement with the US.


In addition, FATCA’s requirement to also identify and report on US citizens, not just US residents, is perhaps another notable difference.


Global Forum on Transparency and Exchange of Information for Tax Purposes: “AEOI: Status of Commitments,” November 2018.


Treasury Inspector General for Tax Administration, July 5, 2018 Final Report (reference number 2018–30-040).


Regarding non-resident holders of existing accounts, FIs need to conduct ‘Permanent residence test’ based already existing documentary evidence or residence-indicia search of electronically searchable data maintained in the FI.


Including countries such as Saint Lucia, Antigua and Barbuda, Dominica, St. Kitts and Nevis, Grenada, Comoros, Vanuatu, Malta, Cyprus, Bulgaria, and Austria. See Finance & Development, International Monetary Fund, December 2015 issue featured “A passport of Convenience” (http://www.imf.org/external/pubs/ft/fandd/2015/12/gold.htm).


The OECD is aware of the issue and initiated public consultation on abuse of residence by investment schemes to circumvent the CRS. See http://www.oecd.org/tax/oecd-releases-consultation-document-on-misuse-of-residence-by-investment-schemes-to-circumvent-the-common-reporting-standard.htm. More recently, the OECD has established criteria for schemes which are high risk for the CRS and published such a list. There is CRS guidance for additional expectations on a FI encountering an account holder claiming to be resident in such a country. See, e.g., http://www.oecd.org/tax/automatic-exchange/news/jurisdictions-take-action-to-address-the-potential-misuse-of-rbi-cbi-schemes-for-crs-circumvention-purposes.htm.


Time-specific fixed effects allow for variation in control group deposits over time while the common trend assumption of DD-based identification provides the necessary restriction to interpret differences in trends between the two groups, as captured by the coefficient β, as an estimated treatment effect.


Likewise, Menkhoff and Miethe (2017) used this subset in the analysis of outbound investments.


Notably, we do not use the entire sample of all banks and all savers in this first set of regressions, since neither subset B nor subset D could provide an adequate counterfactual to the effect of EOI agreements on foreign-owned deposits which are unrelated to tax evasion.


Section V.D then tests the sensitivity of our findings to the choice of offshore list.


CSS2018 include residence (saver) country time-specific fixed effects. Since the treatment of interest is understood to be at the bank/deposit receiving country level, our variant of time fixed effects captures differences in pre-treatment trends between offshore and non-offshore jurisdictions in our sample.


An upward bias would arise for instance where the deposits are relocated to another offshore jurisdiction in the control group that was mistakenly classified as not being offshore.


For as similar approach, see Egger, Merlo and Wamser (2014) who distinguish multinational firms that avoid taxes from non-avoiders when evaluating their investment decisions’ responsiveness to taxes.


These include Austria, Australia, Bahrain, Bahamas, Belgium, Bermuda, Brazil, Canada, Curacao, Cyprus, Chile, , Denmark, Finland, France, Greece, Guernsey, Hong Kong, India, Indonesia, Ireland, Isle of Man, Italy, Jersey, Luxembourg, Macau, Mexico, Netherlands, Panama, Philippines, Portugal, Singapore, South Africa, South Korea, Spain, Sweden, Switzerland, Taiwan, United Kingdom, United States.


These include Austria, Bahamas, Bahrain, Belgium, Bermuda, Chile, Curacao, Cyprus, Guernsey, Hong Kong SAR, Isle of Man, Jersey, Luxembourg, Macau SAR, Panama, Singapore and Switzerland.


Part of this trend could be due to an increase in the number of countries reporting to the BIS. In 2001–02, seven additional countries started reporting to the BIS (in a sensitivity check, we restrict our sample to post-2001 observations).


On the other hand, the data also offers a narrow look at tax evasion insofar as it is limited to cross-border bank deposits and does not include, for example, information on tax evasion through other financial or real assets such as bonds, financial securities, and real estate.


See JZ2014 for further discussion of this data source.


A country adhering to the CRS is not bound to exchange automatically with all other CRS-participating jurisdictions. Bilateral CRS relationships still require some form of administrative “activation” in order the make them effective.


However, only five countries are considered as offshore centers, which appear to influence their result. For example, Casi, Spengel and Stage (2018) find no statistically significant effect of the CRS on deposits when the more comprehensive offshore list used in JZ2014. Futher, Casi, Spengel and Stage (2018) only rely on four years of data (2014–2017).


For similar empirical strategies see Menkhoff and Miethe (2017) and De Simone, Lester and Markle (2018).


The present analysis shies away from an analysis of round-tripping investment via portfolio assets or direct investment in the resident country via shell structures in offshore jurisdictions, since our dataset only covers cash deposits. For an analysis of the same phenomenon conceptually using non-deposit investment vehicles, see Hanlon et al (2015) and Hemmerich and Heckemeyer (2018).


US nonresident aliens who receive interest income from deposits with a US bank, savings & loan institution, credit union, or insurance company, or who receive portfolio interest are exempt from taxation on such interest income as long as such interest income is not effectively connected with a United States trade or business. See: https://www.irs.gov/individuals/international-taxpayers/aliens-which-income-to-report.


In the initial estimation round, countries which are included in the JZ list are given a posterior probability of 75 percent of being an offshore jurisdiction.

Hidden Treasure: The Impact of Automatic Exchange of Information on Cross-Border Tax Evasion
Author: Sebastian Beer, Maria Delgado Coelho, and Sebastien Leduc