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International Tax and Public Finance

Erschienen in:

01.04.2014

With which countries do tax havens share information?

verfasst von: Katarzyna Bilicka, Clemens Fuest

Erschienen in: International Tax and Public Finance | Ausgabe 2/2014

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Abstract

In recent years tax havens and offshore financial centres have come under increasing political pressure to cooperate with other countries in matters of taxation and efforts to crowd back tax evasion and avoidance. As a result many tax havens have signed tax information exchange agreements (TIEAs). In order to comply with OECD standards tax havens are obliged to sign at least 12 TIEAs with other countries. This paper investigates how tax havens have chosen their partner countries. We ask whether they have signed TIEAs with countries to which they have strong economic links or whether they have systematically avoided doing this, so that information exchange remains ineffective. We analyse 565 TIEAs signed by tax havens in the years 2008–2011 and find that on average tax havens have signed more TIEAs with countries to which they have stronger economic links. Our analysis thus suggests that tax havens do not systematically undermine tax information exchange by signing TIEAs with irrelevant countries. However, this does not mean that they exchange information with all important partner countries.

Nächster Artikel Social welfare effects of tax-benefit reform under endogenous participation and unemployment: an ordinal approach

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The EU has also launched an initiative against tax practises which are seen as harmful, which has led to a ‘code of conduct’ in business taxation; see Council of the European Union (1999). The focus of this code of conduct is on special tax regimes offered to multinational firms. For a critique of this approach see Keen (2001).

Along similar lines the EU has taken initiative for more effective enforcement of taxes on capital income by introducing automatic information exchange for tax purposes and source taxes within the European Union through its directive for the taxation of savings (Council Directive 2003/48/EC). For assessments of the impact of the directive see Hemmelgarn and Nicodème (2009), Johannesen (2010) and Klautke and Weichenrieder (2010).

There are at least two additional important factors driving the move towards more information exchange. Firstly, after the terrorist attacks on the United States on September 11, 2001, the US started to take action against offshore financial centres in order to make sure that these centres implement effective measures against terrorism financing and money laundering. Secondly, various spectacular cases of tax evasion were detected because employees of banks stole data from their employers and passed it on to tax authorities in various countries. This has drawn the attention of the public to the issue of bank secrecy and tax evasion.

When we refer to ‘tax havens’ in this paper we do so based on the list of countries classified as tax havens by the OECD, to which we add Switzerland, Luxembourg, Hong Kong and Singapore. The list we use is provided in the Appendix (Table 5).

These included Andorra, Liechtenstein, Liberia, the Republic of the Marshall Islands, Monaco, the Republic of Nauru and the Republic of Vanuatu.

This information is based on data on TIEA provided by the OECD. More information on our dataset is provided in Sect. 4 of this paper.

The mean for FDI is $US 2.58 billion, while for trade it is $US 0.96 billion. The average predicted probability of a tax haven signing a TIEA is 52.6 %.

One should note that the available portfolio investment data does not report portfolio investment of residents of one country in another country. It reports holdings of securities by residents of one country which are issued in another country. As we will discuss further below that it is not clear how relevant this type of economic link is to our analysis.

For this and other reasons our regression analysis does not capture dynamic effects.

Keen and Ligthart (2006a) offer an introduction to the key issues in the debate on information exchange.

As they note, the empirical approach is similar to Baier and Bergstrand (2004) and Egger and Larch (2008). These papers investigate why a pair of countries enters into a bilateral preferential trade agreement. The empirical method Ligthart et al. (2011) use is a dynamic probit approach which controls for unobserved regional heterogeneity. The data used span a long time period. TIEAs, in contrast, are a recent development. This is why we do not use the same dynamic panel approach in our paper.

Clearly, if tax evaders were perfectly mobile individual TIEAs would not matter as long as at least one tax haven remains outside the network of information exchange agreements.

It is important to distinguish between ordinary DTAs and the ones that OECD lists as meeting the standards of information exchange. Here we only include DTAs that meet the standards set by the OECD.

These are all OECD member countries because our FDI data is restricted to the OECD. Since the patterns for FPI and trade are similar, we do not report them here. They are available from the authors on request.

It would be natural to ask whether tax havens focus on signing agreements with countries to which they have no or negligible economic links. Here we find that no tax haven has signed TIEAs with more than three out of five bottom trade partners. More detailed results are available from the authors on request.

As discussed in the introduction, the theoretical literature suggests numerous country specific determinants of tax information exchange treaties formation. The inclusion of country fixed effects means that we do not need to control for country specific determinants of treaty formation. The use of control variables instead of country fixed effects is explored as a robustness check. However, the criticism can be that the choice of control variables is spurious. Data for tax havens are very limited; hence the control variables are likely to exclude important determinants of treaty formation as suggested by the theoretical literature.

The variables included are: distance, common colonizer, both countries member of WTO, double tax agreement between the two, regional trade agreement between the two, common language, common currency, and common legal origin.

In the robustness checks in Sect. 5.3 we also analyse imports of OECD countries from tax havens.

Clearly, all this implies that we drop important tax havens from our sample. In the robustness checks we address this problem and obtain similar results.

A marginal effect at mean is a point estimate of the marginal effect at a chosen value. In contrast an average marginal effect is a mean marginal effect for a population. Specifically, in the average marginal effect case a marginal effect is computed for each value and the all the effects are averages. Hence, the average marginal effect represents better the impact of the variable on the likelihood of the TIEAs.

The average number of non-OECD TIEAs that a tax haven has is 3.7, with standard deviation equal to 3.4. The values range from zero for some countries to maximum of 12.

This means that a 10 % increase in shares of investment in tax haven increases the likelihood of TIEA formation by 4.5 %; the standard deviation is 17 %.

Columns 1–5 match Table 1 columns, while columns 1′ to 5′ match Table 2 columns.

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Titel: With which countries do tax havens share information?
verfasst von: Katarzyna Bilicka
Clemens Fuest
Publikationsdatum: 01.04.2014
Verlag: Springer US
Erschienen in: International Tax and Public Finance / Ausgabe 2/2014
Print ISSN: 0927-5940
Elektronische ISSN: 1573-6970
DOI: https://doi.org/10.1007/s10797-013-9267-y

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