An Economic Perspective on Double Tax Treaties with(in ... · WORLD TAX JOURNAL OCTOBER 2014 | 243 An Economic Perspective on Double Tax Treaties with(in) Developing Countries 1

WORLD TAX JOURNAL OCTOBER 2014 | 242

An Economic Perspective on Double Tax Treaties with(in) Developing CountriesThere are about 2,600 double tax treaties in the world, some 500 among industrialized economies, approximately 800 among developing economies and about 1,300 between industrialized and developing economies. Whereas the prior two categories are symmetric, the latter is asymmetric, as capital flows predominantly from industrialized to developing economies, and capital income the other way round. This article asks which developing countries have double tax treaties, whether with other developing countries or with industrialized countries. The results of the econometric analysis suggest that geography, size (GDP) and openness matter. Finally, political variables, such as colonial status, political similarity, and most strikingly development aid are correlated with the existence of a double tax treaty.

Contents1. Introduction 2432. Literature 244 2.1. Motivations to sign DTTs 244 2.2. Choice of partner for DTTs 2463. Who Has Double Tax Treaties? 248 3.1. Methodology 248 3.2. Results 2484. With Whom Do Countries Have Double Tax Treaties? 252 4.1. Data and Methodology 252 4.2. Determinants of DTT conclusion between developing countries and OECD

member countries 253 4.3. Determinants of DTT conclusion between two developing countries 257 4.4. Summary 2605. Advantages and Disadvantages for DTTs: Four Case Studies 260 5.1. South Africa 261 5.2. Brazil 262 5.3. Colombia 263 5.4. Uruguay 264 5.5. Comparative analysis and summary 2656. Conclusion 2667. Annex 267 7.1. Tables to section 3.: Who has DTT’s? 269 7.2. Tables to section 4.: Whith whom do countries have DTT’s? 271 7.3. Tables to section 5.: Case studies 276

Julia Braun* and Martin Zagler**

* Research Associate, DIBT (Doctoral Program of International Business Taxation). WU Vienna University of Economics and Business, Austria. Financial support from the Austrian Science Fund (FWF grant no. W 1235-G16) is gratefully acknowledged. The author can be contacted at [email protected].

** Professor of Economic Policy, UPO University of Eastern Piedmont, Italy, and Associate Professor of Economics, WU Vienna University of Economics and Business, Austria. Financial support from the Norwegian Science Fund (DeSTaT project) and OeNB Jubiläumsfonds No. 16017 is gratefully acknowledged. The author can be contacted at [email protected].

The authors wish to thank Sebastian Beer, Dale Boccabella, Pasquale Pistone, and the two anonymous refer-ees for their helpful comments, as well as the participants of the DeSTaT Workshop in Cape Town, the WU Department of Economics Research Seminar, and the Staff Seminar at the School of Taxation and Business Law at the University of New South Wales, Sydney. The authors would also like to thank Jennifer Roeleveld for her kind assistance with the data on South African Tax Revenues.


An Economic Perspective on Double Tax Treaties with(in) Developing Countries

1. Introduction

There are about 2,600 double tax treaties worldwide.1 Such a double tax treaty (DTT hence-forth) is a bilateral agreement between two governments to assign taxing rights of cross-bor-der transactions between the two signature states.2 Undoubtedly, every DTT has a particular reason why it came into existence. The main motivations may vary, but typically include encouraging cross-border economic activity, preventing international tax avoidance and evasion, and, more generally, strengthening political ties with the partner country.3 The aim of this article is to systematically shed some light on geographic, economic and political driv-ers that make a tax treaty between two countries more likely, using econometric techniques.

The first modern double tax treaty goes back to 1899 when Prussia and Austria-Hungary signed such a treaty.4 Since then, the number of treaties has been rising steadily; at the begin-ning, mostly industrialized countries5 entered into such treaties with each other. During the last two decades, developing economies6 have increasingly been integrated into the global treaty network. After 1990, the number of DTT signatures has been surging, so that around 60% of today’s DTTs have been signed in the last twenty years.7 How can this rapid expan-sion of the treaty network be explained?

This article applies econometric methods to analyse the data on DTTs in a structured man-ner. This will obviously abstract from the particular case, and we will therefore be able to explain only around 57% of the variation. Still, this form of analysis should give insights for the analysis of particular cases, too.

The focus of the analysis lies on developing economies. First, as opposed to DTTs between industrialized countries, DTTs between industrialized and developing countries and among developing countries have been studied much less rigorously and thus deserve our attention. Second, whilst approximately 80% of potential DTTs between industrialized countries have been forged, and therefore exhibit (econometrically too) little variation in the data, whereas only 30% of potential DTTs have been signed between industrialized and developing coun-tries, and even less among developing economies. Third, and most importantly, whereas DTTs between developing and between industrialized economies are symmetric, containing a similar amount of cross-border activity in each direction, a DTT between a developing and

1. IBFD Tax Research Platform, available at http://www.ibfd.org.2. The authors are aware that this is a rather crude explanation. For a more detailed introduction and precise

definition, see, for instance, M. Lang (2013), Introduction to the Law of Double Taxation Conventions, Linde Verlag, 2nd ed. Vienna.

3. Many double tax treaties have an official title similar to “Convention between (State A) and (State B) for the Avoidance of Double Taxation and the Prevention of Fiscal Evasion with Respect to Taxes on Income”.

4. A. Easson, Do We Still Need Tax Treaties?, 54 Bull. Intl. Fiscal Docn. 12, pp. 619-625 (2000), Journals IBFD. 5. The term “industrialized countries” is used instead of other options, such as “developed countries” to avoid

confusion. In the empirical analysis, all and only members of the OECD are included in the list of industrial-ized countries. As of 2014 the list includes the following 34 countries: Australia, Austria, Belgium, Canada, Chile, Czech Republic, Denmark, Estonia, Finland, France, Germany, Greece, Hungary, Iceland, Ireland, Israel, Italy, Japan, Korea, Luxembourg, Mexico, the Netherlands, New Zealand, Norway, Poland, Portugal, Slovak Republic, Slovenia, Spain, Sweden, Switzerland, Turkey, the United Kingdom and the United States.

6. For the purpose of this study, developing countries are defined as all countries not being OECD member countries and classified as low-income or middle-income countries by the World Bank (2013), New Country Classifications, available at http://data.worldbank.org/news/new-country-classifications.

7 P. Baker (2012), An Analysis of Double Tax Treaties and their Effect on Foreign Direct Investment, available at http://www2.warwick.ac.uk/fac/soc/economics/news_events/conferences/peuk12/paul_l__baker_dtts_on_fdi_23_may_2012.pdf, p. 2 referring to UNCTAD (2011), World Investment Report. Non-Equity Modes of International Production and Development, United Nations, New York and Geneva.


Julia Braun and Martin Zagler

an industrialized economy is most likely asymmetric, having a larger flow of capital towards the developing and a larger opposing flow of capital revenues towards the industrialized economy than the other way around. And this asymmetric characteristic of DTTs between developing and industrialized economies should lead to different motives to forge such a DTT.8 In particular, asymmetric tax treaties inevitably reduce the tax base of developing countries, so that we can and should expect some form of compensation from the industrial-ized to the developing economy in case a treaty comes into effect.9

This article proceeds as follows. After discussing related literature, in section 3. the authors investigate which countries have double tax treaties, depending on country characteristics. Section 4. then looks at single DTTs and inquires what makes such a treaty more likely. Section 5. looks at several country specific case studies to understand the economic incent-ives for developing economies to sign double tax treaties in the trade-off between attracting foreign direct investment and loosing tax base. Section 6. concludes.

2. Literature

This literature review focuses on two issues, namely the motivations of countries to sign DTTs and the way countries choose their partners to sign treaties with.

2.1. Motivations to sign DTTs

Originally, DTTs were signed to avoid double taxation, i.e. the taxation of the same underly-ing transaction by two governments. These days, reasons are more manifold, and include the mitigation of international tax avoidance and evasion and thus the protection the domestic tax base. This purpose has increasingly come into the focus of policy makers of both industri-alized and developing countries. The OECD BEPS (Base Erosion and Profit Shifting) project reflects the political importance of these issues.10 In various ways, DTTs can contribute to achieve these goals. They address cross-border transactions between associated enterprises (article 9 of the OECD Model Tax Convention on Income and on Capital (OECD Model)) and they provide for information-sharing between the contracting states (article 26 of the OECD Model). Furthermore, specific provisions and concepts are inserted such as the limi-tation of benefits provision or the beneficial ownership concept, which “restrict access to treaty benefits to resident of the contracting states.”11

Besides this, different motivations may be in the focus of industrialized and developing countries when signing tax treaties. Looking at the relation between industrialized and developing countries, we often observe an asymmetric investment position, industrialized countries being in the position of net capital exporters, and developing countries typically being net capital importers. Capital exporters and capital importers may pursue different goals when entering into tax treaties; fostering outbound investment and thus encourag-ing the international expansion of domestic companies may arguably be more relevant for

8. Albeit a number of emerging economies are increasingly also becoming capital exporters, inward FDI stocks exceeded outward FDI stocks in all developing countries in 2010 (UNCTAD FDI database, available at http://unctad.org/en/Pages/DIAE/FDI%20Statistics/Interactive-database.aspx).

9. D. Paolini, P. Pistone, G. Pulina & M. Zagler (forthcoming), Tax Treaties and the Allocation of Taxing Rights With Developing Countries, European Journal of Law and Economics.

10. For information on the OECD BEPS project, see http://www.oecd.org/tax/beps.htm.11. P. Baker (2012), see supra n. 7.



capital-exporting countries. For capital importers, encouraging inbound investment may be more in the focus, with policy makers wishing to attract foreign direct investment entailing the transfer of skills and technologies and thus fostering economic growth.12 Finally, the function of DTTs as a signaling device indicating that the signatory states play by the inter-nationally accepted tax standards may be more relevant for developing countries.13

How can DTTs contribute to foster international economic activity?14 They may provide cer-tainty in tax matters for international investors,15 prevent tax discrimination for investments in the other state, and avoid double taxation of income arising in cross-border transactions.16 The importance of double taxation relief for prospering international activity is stressed by the OECD:

Its [i.e. international juridical double taxation] harmful effects on the exchange of goods and services and movements of capital, technology and persons are so well known that it is scarcely necessary to stress the importance of removing the obstacles that double taxation presents to the development of economic relations between countries.17

DTTs mitigate double taxation by “harmonizing tax definitions, defining taxable bases, assigning taxation jurisdictions, and indicating the mechanisms to be used to remove double taxation when it arises”.18 Yet, many authors argue that double taxation can be – and by most countries is – prevented unilaterally.19

Some researchers hence argue that the main benefit of DTTs lies in the harmonization and the lowering of withholding tax rates on international capital income.20 Reduced source taxa-tion rates may contribute to making a country a more attractive investment location. This lowering of withholding tax rates, however, entails “distributional implications”.21 In the case of an asymmetric investment position, the lowering of withholding tax rates “involves

12. See, e.g., M. Lang & J. Owens (2014), The Role of Tax Treaties in Facilitating Development and Protecting the Tax Base, WU International Taxation Research Paper Series No. 2014 – 03, available at http://ssrn.com/abstract=2398438.

13. Dagan, The Tax Treaties Myth, N.Y.U. Journal of International Law and Politics 32, pp. 939-996.14. For a discussion see also A. Pickering (2013), Why Negotiate Tax Treaties?, Papers on Selected Topics in

Negotiation of Tax Treaties for Developing Countries, Paper No. 1-N. New York and Geneva: United Nations, p. 4 et seq.

15. See M. Zagler & C. Zanzottera (2012), Corporate Income Taxation Uncertainty and Foreign Direct Investment, WU International Taxation Research Paper Series No. 2012-07. Available at http://ssrn.com/abstract=2174928. The authors show how legal uncertainty negatively impacts foreign direct investment via tax rates in developing economies.

16. The OECD Commentary on the OECD Model defines international juridical double taxation as ”the imposi-tion of comparable taxes in two (or more) States on the same taxpayer in respect of the same subject matter and for identical periods” (2010, paragraph 1). The OECD Commentary on the OECD Model establishes that the relief of international juridical double taxation is the main purpose of double tax treaties (2010, paragraphs 1-3).

17. OECD, OECD Model Tax Convention on Income and on Capital – Condensed Version, (OECD 2010), para. 1, Models IBFD.

18. See Baker supra n. 7, p. 2.19. See Dagan supra n. 13.; J. Ligthart, M. Vlachaki & J. Voget (2012), The Determinants of Double Tax Treaty

Formation; J. Braun & D. Fuentes (2014), A Legal and Economic Analysis of the Austrian Tax Treaty Network. VIDC. Vienna; T. Rixen & P. Schwarz (2009), Bargaining over the Avoidance of Double Taxation: Evidence from German Tax Treaties, FinanzArchiv/Public Finance Analysis 65(4), pp. 442-471.

20. See, for instance, R. Davies (2003), Tax Treaties, Renegotiations, and Foreign Direct Investment, Economic Analysis & Policy 33(2), pp. 251-273; R. Chisik & R. Davies (2004), Asymmetric FDI and tax-treaty bargain-ing: theory and evidence, Journal of Public Economics 88(6), pp. 1119-1148; see Rixen & Schwarz supra n. 19, at p. 446.

21. See Rixen & Schwarz supra n. 19, at p. 446.



a revenue transfer from the net capital importer to the net capital exporter”.22 Thus, the benefits of DTTs are sometimes being described as more on the side of capital exporters.23

Typically having more economical strength and more bargaining power, capital exporters may have the power to pressure capital importers to enter into treaty negotiations.24

Paolini et al. (forthcoming) point out that DTTs may be perceived by capital-importing countries as means to partly regain their sovereignty with respect to the taxation of income, which non-residents generate on their territories.25 Due to the treaty, the allocation of taxing rights will be stated clearly and the taxes for business income paid in the capital-importing country will become final and thus relevant for firms. As a result, capital-importing countries will be in a position to use tax policy instruments in order to attract international investment flows.

2.2. Choice of partner for DTTs

How do countries select their partners with which they sign tax treaties? Double Tax Treaties (DTTs) and bilateral investment treaties (BITs) relate to international investment and/or taxation, and the methodology used in the empirical economic literature to analyse treaty formation in these cases is the same.26 Generally, we observe that countries have tax treaties in place with countries with which they have close economic ties.27 The economic literature empirically analysing how countries choose their partners to sign DTTs adds some further insight to this general wisdom.

Egger et al. (2006)28 and Lejour (2014)29 estimate the probability of OECD countries to sign double tax treaties with each other. Egger et al. (2006) find that the bilateral country size and

22. Id., p. 446.23. This is already pointed out by J.C. Dougherty (1978), Tax Credits under Tax Treaties with Developing

Countries, International Business Lawyer 6(i), pp. 28-46: “As long as Country A and Country B each get a fair share of the total volume of taxable income generated by their respective citizens in each other’s territories, the deal is a good one. If, on the other hand, the nature and volume of international trade and capital flow between the two countries is not relatively equal, the deal can be a very bad one for the country which has tax access to and revenue from the lesser trade volume. This is precisely the condition in which the developing countries (essentially the non-OECD countries) began to realize that they were” (p. 31).

24. See Pickering supra n. 14, at p. 5.25. A treaty establishing the credit method to prevent double taxation, only the tax rate in the residence state

but not in the source state is relevant for international investors. Thus, as investors will not react to tax incentives granted by the source state, the source state, often being a developing country, cannot use tax incentives/low tax rates to attract international investors See Paolini et al. (forthcoming) supra n. 9).

26. Similar methodology is also used to analyse the determinants of entering into bilateral preferential trade agreements by S. Baier & J. Bergstrand (2004), Economic determinants of free trade agreements, Journal of International Economics 64, pp. 29-63; and P. Egger & M. Larch (2008), Interdependent preferential trade agreement memberships: An empirical analysis, Journal of International Economics 76, pp. 384-399.

27. For an analysis of the Austrian tax treaty policy see, e.g., M. Lang (2012), Überlegungen zur österreichischen DBA-Politik, Steuer und Wirtschaft International, 22(3), pp. 108-127; or H. Jirousek, (2013), Die öster-reichische Position beim Abschluss von DBA, in Lang et al. (eds.), Die Österreichische DBA Politik, Vienna: Linde Verlag, pp. 15-29.; For a detailed analysis of the Australian DTT policy see, for instance, J. Taylor (2011), Some distinctive features of Australian tax treaty practice: An examination of their origins and inter-pretation, eJournal of Tax Research 9(3) (Special Edition: Double Tax Agreements in the Asia Pacific), pp. 294-338. He reports that the Australian Board of Taxation recommends Australia to enter into negotiations with countries that have the most important investment patterns with Australia (p. 337).

28. P. Egger, M. Larch, M. Pfaffermayer & H. Winner (2006), The Impact of Endogenous Tax Treaties on Foreign Direct Investment: Theory and Evidence, Canadian Journal of Economics, 39(3), pp. 901-931.

29. A. Lejour, (2014), The Foreign Investment Effects of Tax Treaties, CPB Discussion Paper 265, Netherlands Bureau for Economic Policy Analysis.



also host government expenditure as a percentage of GDP significantly increase the likeli-hood that a country-pair signs a DTT. Lejour (2014) finds that sharing a common colonial past and the same language have a significantly positive impact on the probability of two countries concluding a DTT, while distance is found to have a significantly negative effect.

Ligthart et al. (2012) empirically study the determinants of DTT formation for a large country sample covering both industrialized and developing countries.30 They conclude that countries sign DTTs rather to reduce double taxation than to allow for the exchange of information between tax authorities.31 Also using a global sample of OECD and non-OECD member countries, Barthel and Neumayer (2012) analyse DTT formation patterns focus-ing on spatial dependence. They find evidence that country-pairs (which they refer to as “dyads”) “are more likely to sign a DTT the more DTTs have previously been concluded by the regional peers of the dyad members as well as by other countries who compete with at least one of the dyad members in terms of export product structure”.32

Neumayer (2006) analyses with which developing countries industrialized countries sign bilateral investment treaties (BITs).33 He concludes that economic and political interests motivate industrialized countries when choosing their partners to sign BITs with. To a lesser extent, they also take into account the needs of developing countries. Good governance is not found to play a role. Also looking at BIT formation, Elkins et al. (2004)34 find that “develop-ing countries are more likely to sign BITs with developed countries if their competitors have done so already” and thus conclude that the spread of BITs can be explained by the “in-creased competition for FDI among developing countries”.35 Neumayer and Plümper (2010) find that “a capital-importing country is more likely to sign a BIT with a capital exporter only if other competing capital importers have signed BITs with this very same capital exporter. Similarly, other capital exporters’ BITs with a specific capital importer influence an exporter’s incentive to agree on a BIT with the very same capital importer”.36 Swenson (2005) concludes that BITs have a backward and a forward looking element. She finds evidence that developing countries enter into BITs to retain the existing FDI stock and also to attract new foreign investors.37

30. J. Ligthart, M. Vlachaki & J. Voget (2012), see supra n. 19.31. A similar topic is analysed by J. Ligthart & J. Voget (2009), The Determinants of Cross-Border Tax

Information Sharing: A Panel Data Analysis, mimeo, Tilburg University. The authors study the factors which determine with which national tax authorities the Netherlands exchange information. The authors find that there are more ‘cases’ of information exchange, the higher the domestic income tax rate, the higher the marginal cost of public funds, and the bigger the share of a country’s interest-bearing deposits held abroad are. Also, they conclude that exchange of tax-related information is predominantly reciprocal.

32. F. Barthel & E. Neumayer (2012), Competing for Scarce Foreign Capital: Spatial Dependence in the Diffusion of Double Tax Treaties, International Studies Quarterly 56, pp. 645-660, at p. 645.

33. E. Neumayer (2006), Self-Interest, Foreign Need, and Good Governance: Are Bilateral Investment Treaty Programs Similar to Aid Allocation?, Foreign Policy Analysis 2, pp. 245-267.

34. Z. Elkins, A. Guzman & B. Simmons (2004), Competing for Capital: The Diffusion of Bilateral Investment Treaties, pp. 1960-2000, Working Paper University of Illinois, University of California at Berkeley and Harvard University.

35. E. Neumayer (2006), Self-Interest, Foreign Need, and Good Governance: Are Bilateral Investment Treaty Programs Similar to Aid Allocation?, Foreign Policy Analysis 2, pp. 245-267, at p. 245.

36. E. Neumayer & T. Plümper (2010), Spatial Effects in Dyadic Data, International Organization 64(1), pp. 145-166, at p. 145.

37. D. Swenson, (2005), Why do developing countries sign BITs? Journal of International Law and Policy 12(1), pp. 131-154.



This article empirically analyses the decision of entering into DTTs accounting for eco-nomic, geographic, and political factors. It focuses on the relationships of developing coun-tries among each other and with OECD member countries, and inquires particularly into the political dynamics being involved in treaty negotiations.

3. Who Has Double Tax Treaties?

In this section, the authors try to correlate the number of double tax treaties that a country has signed with three sets of variables, namely measures of the size of the economy, its open-ness, and finally institutional variables.

3.1. Methodology

The number of double tax treaties of a single country, which is the dependent variable in the analysis of this section, is a count data. Conventional ordinary least square regressions (OLS) is feasible here, but suffers from a false underlying distribution and hence results in wrong estimators and standard errors. Count data have therefore be estimated either with Poisson regressions (which is assumes that the underlying distribution is Poisson) or negative bino-mial regressions (which is assumes that the underlying distribution is a negative binomial). The regression model is exponential, hence

yi = exp( + 1x1i + 2x2i + … + ui)

where yi is the number of DTTs of a single observation (country), and are coefficients, xji are explanatory variables,38 and ui is an i.i.d error term. This equation is estimated with maximum likelihood methods both for the Poisson and the negative binomial regression. We present two tests that indicate that the negative binomial regression is always preferable. In particular, the coefficient alpha is always statistically different from zero, and hence the distribution cannot be reduced to Poisson. Also on the grounds of the Goodness-of-fit 2 test of the Poisson regression, Poisson has always to be rejected. The analysis in section 3.2 is limited to a graphical exposition of the results, and the econometric analysis is deferred to Tables 4 to 6 in the Appendix.

3.2. Results

An obvious indicator for the size of a country is its population. The first column of Table 4 in the appendix shows that indeed countries with a bigger population tend to have more double tax treaties. However, once gross domestic product (GDP) is included, population is com-pletely dominated by GDP and ceases to exhibit any significance. GDP is therefore the vastly superior indicator for the size of the economy. Graphs 1a and 1b show the effect of GDP on the number of DTTs of a country. The left hand panel (Graph 1a) gives the result for DTTs among developing economies, whereas the right hand panel (Graph 1b) gives results for the number of DTTs between industrialized and developing economies. The dots all represent a single country (differentiated by colour according to world regions), whereas the central line gives the estimated regression relationship between the two variables.

38. To be discussed further in section 3.2.



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Asia and Oceania Middle East - North Africa Non-OECD Europe Cental and South America Sub-Saharan Africa Negative binomial regression

Data source: Penn World Tables, IBFD Database.

Graph 1a: GDP

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Graph 1b: GDP

Graph 1a shows a clear (and statistically significant) positive relationship between GDP and the number of DTTs a country has signed with other developing economies. The average marginal effect (i.e. holding all other variables constant at their sample mean) shows that a 6% increase in the level of GDP (relative to all other country averages) will coincide with



approximately one more DTT.39 The regression curve above indicates that the average mar-ginal effect is lower for countries with a lower than average GDP, and higher for countries with an above average GDP, indicating that DTTs kick off with increases in GDP. Graph 1b repeats the exercise for DTTs between industrialized and developing economies. Once again a statistically significant relationship between an increase in GDP and the number of DTTs is obtained. Given the relatively small number of OECD member countries (a total of 34), the average marginal effect is smaller, and it requires a 35% increase in GDP to reach the same additional DTT.

Alternatively, it might also be considered whether it is the size of an economy or richness, measured by GDP per capita. Neither for DTTs among developing economies nor for DTTs between industrialized and developing countries GDP per capita seems to matter. It is thus an issue of market size and not richness that matters. Now the openness of the economy will be discussed. Openness is measured by the sum of exports and imports divided by GDP. In addition, also the stock of outward foreign direct investment (FDI) is used as an additional indicator for the openness of an economy.40

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Graph 2a: Openness

39. Table 3 of the Appendix gives a mean of log GDP at 9.44. A one unit increase (as estimated by the average marginal effect) would increase log GDP to 10.44 or 10.6%. Table 5 in the appendix shows that this marginal effect is 1.835 for our preferred specification (10). In order to get the necessary increase of GDP to induce one additional DTT, we divide 10.5% by 1.835 to get 5.78%, which is rounded to 6% in the text above.

40. In addition to “real” bilateral FDI, we would expect that a country that has a large DTT network, may also be attractive as a conduit location and thus attract additional FDI being merely channelled through.



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Graph 2b: Foreign direct investment

Graph 2a shows that an increase in the degree of openness has a positive and statistically significant effect on the number of DTTs among developing economies. The magnitude however is contained. We can deduce this also from the average marginal effect, which shows that an 11% increase in the degree of openness is associated with only one additional DTT. Interestingly, DTTs between industrialized and developing economies are not corre-lated with the traditional measure of openness. However, there is a positive relation between FDI and DTTs in that case (shown in Graph 2b), something that by contrast is not present for DTTs among developing economies. Despite the fact that there are only 34 OECD mem-ber countries in the sample, as opposed to 142 developing economies, it only requires a 9% increase in FDI to stipulate one additional DTT.

Whereas in both cases GDP matters, the degree of openness is correlated with DTTs among developing economies, whereas DTTs between industrialized and developing economies depend on FDI. This permits a first stark conclusion. This result can be interpreted as a different strategy between industrialized and developing economies. Whereas developing economies seek new opportunities, and therefore tend to forge DTTs with open economies, industrialized countries by contrast seek investment opportunities, and thus propose DTTs with countries that have a lot of FDI.

Next, political variables are considered. The political system of a country may have an impact on the number of DTTs it can forge. As DTTs are both difficult to negotiate but also difficult to implement and prosecute, it is tested whether institutional variables matter for DTTs. Also, in view of DTTs also providing for the exchange of information, countries which are concerned about the secrecy of their citizens’ tax data may be less inclined to sign DTTs with states with high corruption levels. Both the overall institutional quality index and the corruption index from the Heritage Foundation are used as explanatory variables. However, none of these has an impact on the number of DTTs of a country.



Finally, asymmetric DTTs between industrialized and developing economies are scrutinized. Here, as has been demonstrated theoretically in Paolini et al. (forthcoming), and as will be shown anecdotically in section 5., countries undoubtedly lose tax base and hence tax revenue by signing a DTT that transfers part of the profits of foreign direct investors to the home country.41 Paolini et al. (forthcoming) postulate that asymmetric DTTs are signed volun-tarily only if compensation is paid. With very few exceptions, no such compensation has ever been heard of in DTTs. However, countries do execute transfers to others in many forms. The most notable here is official development assistance (ODA). ODA is paid by industrial-ized countries to developing economies,42 and thus coincides with the expected direction of compensation for asymmetric DTTs. Both ODA in absolute value and ODA as a share of GDP are tested as explanatory variables in the regression analysis. Whilst the level has no impact, a positive and statistically significant (at the 10% level) impact of ODA as a share of GDP on DTTs is found. The average marginal effect tells us that an additional DTT requires a 53% increase in ODA with respect to GDP. As the median of DTTs with industrialized countries per developing economy is only four, this implies that half of those may have been agreed upon with the help of ODA. This result amongst other confirms the theoretical result obtained by Paolini et al. (forthcoming).

This analysis has shown that the number of DTTs of a developing economy can be explained econometrically. However, the pseudo R2 is only 13% for DTTs with industrialized countries (OECD) and a meager 7% for DTTs with other developing countries, which means that much of the variation so far cannot be explained. Therefore, the next section looks at every DTT individually and estimates its probability based on economic, geographic and political variables.

4. With Whom Do Countries Have Double Tax Treaties?

The following section tries to establish determinants that explain which countries sign DTTs with each other.

4.1. Data and Methodology

Using a probit model approach, the probability of a country-pair to enter into a DTT is estimated. The regression model, which is estimated using the maximum likelihood method, looks as follows

probDTTij=pryij=1 |X= α+β1x1ij +β2x2ij+…+uij

The dependent variable yij is a binary variable taking the value of one if a country-pair (consisting of the countries i and j) has an effective DTT in place in the year 2010 and zero otherwise. X depicts the matrix of explanatory variables, α and βi are the coefficients, xji are the explanatory variables relating to each country-pair, and uij stands for the error term.

The choice of the explanatory variables is based on an extended version of the classical grav-ity model, which explains the economic activity between two countries, such as bilateral

41. See D. Paolini et al. (forthcoming), see supra n. 9.42. It is not novel to use ODA as an explanatory variable in economic and political analysis, see for instance I.

Kuziemko & E. Werker (2006), How much is a Seat on the Security Council Worth? Foreign Aid and Bribery at the United Nations, Journal of Political Economy, 114(5), pp. 905-930.



trade or investment, with the size of the countries and the distance between the two coun-tries. Besides such economic and geographical factors, also historical and political aspects are included in the analysis.43 As in section 3., a cross-section analysis for the year 2010 is conducted.

As explained above, the dynamics and motivations for signing DTTs may differ in asymmet-ric and symmetric situations. Thus, the DTT conclusion between OECD member countries and developing countries is studied first (section 4.2.) and subsequently DTTs concluded between developing countries are analysed (section 4.3.).

The dataset in subsection 4.2 consists of 34 OECD member countries and 131 developing countries. Due to data constraints in the explanatory variables, the number of observations varies between the regressions. The preferred specification, which corresponds to Table 10 column 3 and is depicted graphically in section 4.2., includes 29 OECD member countries and 120 developing economies.44 Thus, 4,158 unique country-pairs45 with a total of 903 DTTs are covered in this analysis. The descriptive statistics are depicted in Table 7 in the Annex.

The dataset used in subsection 4.3 consists of 5,253 unique country-pairs, each consisting of two developing countries. The graphs in that subsection depict the regression results presented in Table 13 column 5 in the Annex. In this specification, 4,753 country-pairs with 125 developing countries are included. The descriptive statistics of this regression are shown in Table 8 in the Annex.

4.2. Determinants of DTT conclusion between developing countries and OECD member countries

The analysis starts with the estimation of the likelihood of an OECD country to sign a DTT with a developing country. According to the gravity model, the distance between two coun-tries i and j is used as first explanatory variable. Graph 3a illustrates the marginal effect of the bilateral distance of two countries on the likelihood of them signing a DTT – with all other variables being kept at their respective mean values.46 The blue-shaded area illustrates the 95% confidence interval. As one would expect, the graph shows a downward sloping line, that is, the more distant two countries are, the more unlikely it is that they sign a DTT.

43. The variables are explained in more detail in the following subsections. The sources of the data are given in Table 1 in the Annex.

44. The developing economies covered in the analysis include: Afghanistan, Albania, Algeria, Angola, Argentina, Armenia, Azerbaijan, Bangladesh, Belarus, Belize, Benin, Bhutan, Bolivia, Bosnia and Herzegovina, Botswana, Bulgaria, Burkina Faso, Burundi, Cambodia, Cameroon, Cape Verde, Central African Republic, Chad, China, Colombia, Comoros, Congo (Republic of), Costa Rica, Cuba, Djibouti, Dominica, Dominican Republic, Ecuador, Egypt, El Salvador, Eritrea, Ethiopia, Fiji, Gabon, Gambia, Georgia, Ghana, Grenada, Guatemala, Guinea, Guinea-Bissau, Guyana, Haiti, Honduras, India, Indonesia, Iran, Iraq, Ivory Coast, Jamaica, Jordan, Kazakhstan, Kenya, Korea (North), Kyrgyzstan, Laos, Lebanon, Lesotho, Liberia, Libya, Macedonia, Madagascar, Malawi, Malaysia, Maldives, Mali, Marshall Islands, Mauritania, Mauritius, Moldova, Mongolia, Morocco, Mozambique, Myanmar, Namibia, Nepal, Nicaragua, Niger, Nigeria, Pakistan, Palau, Panama, Papua New Guinea, Paraguay, Peru, Philippines, Romania, Rwanda, Samoa, Sao Tome and Principe, Seychelles, Sierra Leone, Solomon Islands, Somalia, South Africa, Sri Lanka, St. Lucia, St. Vincent and the Grenadines, Sudan, Suriname, Swaziland, Syria, Tajikistan, Tanzania, Thailand, Togo, Tonga, Tunisia, Turkmenistan, Tuvalu, Uganda, Ukraine, Uzbekistan, Vanuatu, Venezuela, Vietnam, Yemen, Zambia, Zimbabwe.

45. That means that the dataset is constructed so that the country-pair Norway-Colombia is only once in the database (as Colombia-Norway obviously is not a distinct observation).

46. The graphs in this subsection correspond to our preferred regression specification, which is shown in Table 10 column 3 in the Annex.



0.2

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Graph 3a: Distance

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Graph 3b: Source contagion

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Graph 3c: Target contagion

Data sources: CEP II, IBFD Database.

Besides distance, two further geographical factors are accounted for. Barthel and Neumayer (2012) demonstrate that spatial interdependence plays an important role in the diffusion of DTTs. They show that the probability of a country-pair signing a treaty also depends on other countries’ treaty policies. In particular, two forms of spatial dependence are found to be particularly relevant for the diffusion of DTTs, the so-called “specific source contagion” and the “specific target contagion”. Both of them are thus included into the analysis.

First, we look at “specific source contagion”, that means, it is tested whether the probability of an OECD member country having a DTT with a specific developing country is affected by the fact that other m OECD member countries already have signed a DTT with the specific developing county. To illustrate, one would expect that the likelihood that, say, Norway signs a DTT with, say, Uruguay is higher the more other OECD member countries have already signed a DTT with Uruguay. Manifold reasons are conceivable for this interdependence. Norway may want to offer its residents an investment environment at least as attractive as other OECD member countries do. Besides, Norway may want to reduce the appeal of treaty shopping for its residents, i.e. prevent that they invest in Uruguay via another country in order to benefit from that country’s DTT when investing in Uruguay. Further, the fact that many other countries already have a treaty in place with Uruguay may indicate that this country offers attractive business opportunities to international investors.

Second, “specific target contagion” is accounted for, i.e. that a specific developing country may be more likely to sign a DTT with a specific OECD member country, if the developing country’s neighbouring countries have already entered into a DTT with that specific OECD member country. To illustrate, it is tested whether, say, Uruguay is more likely to sign a DTT



with, say, Norway if Uruguay’s neighbouring countries such as Brazil have already signed a DTT with Norway. For Norwegian firms, Uruguay and Brazil may represent close substi-tutes when making an investment in South America. Thus, Uruguay may be more ready to sign a treaty with Norway if Brazil already has a treaty in place, so not to be at a competitive disadvantage.

In the regression analysis, both types of spatial interdependence prove statistically signifi-cant, indicating that both exist concurrently. Graphs 3b and 3c show their respective mar-ginal effects. Looking at the average marginal effects as depicted in Table 10, column 3 in the Annex, one can clearly see that geographical interdependence has a strong effect on the probability of a DTT being signed or not.

According to Barthel and Neumayer (2012), the strong positive target contagion interdepen-dence can explain why developing countries sign DTTs with OECD member countries, even though the treaties “systematically favour a distribution of the taxes generated from MNCs [Multinational Corporations] to the advantage of the capital-exporting residence country”.47 The position developing countries find themselves in can be regarded as a classical prisoners’ dilemma:

Due to international competition for FDI, a country still can have an incentive to conclude such a tax treaty, because its own situation without a treaty deteriorates if other focal countries enter such treaties themselves and thereby gain a competitive edge […] Capital-importing countries are caught in a classical prisoners’ dilemma: Each country would be better off refusing to sign a DTT, but signing a DTT is the dominant strategy given that the highest payoff occurs if one signed a DTT, but the others do not.48

Countries are defined as more focal the closer they are to a specific developing country in the analysis,49 very similar to the theory of yardstick competition.50 When a multinational firm decides where to set up a subsidiary, it may have decided on the general region where to set up a subsidiary based on economic reasons, e.g. it might decide to establish an affiliate in Latin America. However, when it comes to deciding in which Latin American country to establish the subsidiary, tax implications, including the existence of a DTT, come into play. Thus, those countries are regarded as focal that developing countries compete with for foreign direct investment.

Additionally, in order to measure the size of the combined market of two economies, a single variable that aggregates the GDP of the two countries is included. This should measure the combined market. The hypothesis is that a larger combined market would increase the chance for a DTT. The variable indeed has a positive sign, indicating that the larger the joint country size, the more probable it is that two countries sign a DTT. However, this effect is statistically not significant (and is not shown graphically).

It is also tested whether other GDP-related variables may have an impact on the signing of a DTT. The regression controls for the influence of (i) the similarity of a country-pair in terms

47. F. Barthel & E. Neumayer (2012), see supra n. 32, at p. 646f.48. Id. 648. Also see E. Baistrocchi (2008). The Use and Interpretation of Tax Treaties in the Emerging World:

Theory and Implications. British Tax Review 4, pp. 352-391. He argues that developing countries trying to attract foreign investors find themselves in a prisoner’s dilemma situation and thus have an incentive to engage in harmful tax competition, e.g. in the form of double tax treaties that enable treaty shopping.

49. Alternatively, we define “focal countries” as neighbouring countries (see Target contagion 2 in Table 11 column 3 in the Annex). The results remain the same.

50. A. Shleifer (1985), A Theory of Yardstick Competition, The RAND Journal of Economics 16(3), pp. 319-327.



of size as measured by the difference in GDP,51 and (ii) the difference in GDP per capita between the two potential signatory states.52 In the context of DTTs, the latter variable has also been used to reflect the relative bargaining power of the two jurisdictions.53 However, these two variables are not found to have a consistent and statistically significant influence on DTT formation.54

Rather, close economic ties, measured as the amount of bilateral foreign direct investment (FDI) stocks, are found to significantly increase the likelihood of treaty formation (see Graph 4a). Bilateral trade, on the other hand, as a measure for economic ties, does not prove sta-tistically significant.55 This confirms the findings in section 3., namely that industrialized economies, seeking good investment opportunities, have DTTs with developing countries with a lot of inward FDI.

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Graph 4b: Agreement at UN

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Graph 4c: Official development assistance

Data sources: CEP II, IBFD Database, OECD, UN.

Graph 4a: Foreign direct investment

Next, we turn to the importance of historical, cultural and political determinants. One would expect that a common language simplifies communication between tax administrations and thus makes a DTT more probable. Indeed, countries that share an official language are found

51. See Table 9 in the Annex.52. See Tables 10 to 12 in the Annex.53. See M. Elsayyad (2012), Bargaining over Tax Information Exchange. Max Planck Institute for Tax Law and

Public Finance, Working Paper 2012-02. February 2012.54. It was also tested whether corporate income tax rates would play a role. However, they are found not to be

statistically significant either (see Table 11, column 5 in the Annex).55. For reasons of robustness, bilateral trade is measured in two different ways: the bilateral sum of imports

reported (Table 9 in the Annex) and the sum of exports reported (Table 11, column 4). Both lead to the same results.



to be more likely to sign a DTT (not shown graphically).56 Also colonial ties are found to play a role. The results show that if a developing country has ever been a colony of an industrial-ized country, the probability of these two countries signing a DTT is higher.

Moreover, a country-pair’s political agreement in international policy issues is taken into account. For this purpose, a voting similarity index is included, which ranges from zero to one, indicating how frequently a country-pair votes in the UN General Assembly in the same way. The closer the index is to one, the more often the two countries’ votes correspond with each other. Of this index, which is available for each year since 1946, an average of the period 1990 to 2010 is taken. The regression results suggest that political agreement increases the likelihood that a developing and an OECD member country sign a DTT (see Graph 4b).

Finally, the amount of bilateral official development assistance (ODA) an OECD member country gives to a developing country is taken into account, measured as the bilateral ODA given during the five years from 2006 to 2010.57 The results suggest that the more ODA a developing country receives from a donor, the more likely it is that the two have a DTT in place (see Graph 4c). This result persists also when controlling for the difference in GDP per capita (in order to control for the fact that poorer countries are also more likely to receive more ODA). That means, holding the GDP per capita difference constant, higher ODA pay-ments increase the probability of a country-pair to enter into a DTT significantly.

The robustness of this result is tested by changing the sample of the analysis. The United States are excluded from the analysis, which is the largest donor of foreign aid (Table 12, column 2), and the regressions are run excluding the largest ODA-recipients of each country (Table 12, columns 3 and 4). These regressions, too, bring about the same result of a positive correlation between the ODA a developing country receives from an industrialized country and the probability that these two jurisdictions have a DTT in place. The next subsection investigates which developing countries sign DTTs with each other.

4.3. Determinants of DTT conclusion between two developing countries

Again, the results of our preferred specification are shown graphically (for the regression results please see Table 13 column 5 in the Annex). As expected, distance has a (statisti-cally) significant negative effect on the likelihood of two developing countries to sign a DTT (see Graph 5a).58 With regard to geographical interdependence, again special source and target contagion for every country-pair are accounted for, with focal countries simply being defined as neighbouring countries of the potential treaty partner. The upward sloping lines of these two Graphs 5b and 5c suggest that when a country has signed a treaty with the neigh-

56. For studies that find that language matters in this context, see, e.g., J. Ligthart & J. Voget, (2009), see supra n. 31; J. Ligthart, M. Vlachaki & J. Voget (2012), see supra n. 19; V. Tanzi & H. Zee (2001), Modern Issues in the Lax of International Taxation, in K. Andersson, P. Melz & C. Silfverberg (eds.), Can Information Exchange be Effective in Taxing Cross-Border Income Flows?, Kluwer International.

57. In order to test the robustness of this result, three different measures are used: the total bilateral ODA given during three years from 2008 to 2010 (see Table 11, column 1 in the Annex), the bilateral ODA given in 2010 (see Table 11, column 2 in the Annex), and the total bilateral ODA during the five years from 2006 to 2010 (see Tables 10, 11, and 12 in the Annex). In addition to the total ODA flowing to a country, also ODA per capita (using the population of the recipient country) was used as explanatory variable (see Table 10 column 4).

58. The graphs in this subsection correspond to the regression presented in Table 13, column 5 in the Annex. The total sample consists of 4,753 unique country-pairs. In this specification, 125 developing countries are included. The descriptive statistics of this regression are shown in Table 8 in the Annex.



bours of another country, the likelihood increases that also with this country a treaty will be signed. As above, this geographical interdependence proves to be an important determinant of DTT formation (as indicated by the magnitude of the marginal effects depicted in Table 13, column 5 in the Annex).

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Graph 5b: Source contagion

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Graph 5c: Target contagion

Data sources: CEP II, IBFD Database, OECD, Penn World Tables, UN.

Unlike in the relationship between OECD member countries and developing countries, the likelihood of treaty formation increases with the joint country size of a country-pair (mea-sured by joint GDP) and with an increase in the joint GDP per capita (see Graphs 6a and 6b). The bilateral trade volume impacts the probability of a DTT positively, too (Graph 6c).59 This confirms the results of section 3., namely that developing countries tend to conclude treaties with more open economies – searching for new opportunities.

59. Due to data constraints, we cannot test for the impact of bilateral FDI.



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Graph 6a: Joint GDP

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Graph 6c: Bilateral trade

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Graph 6d: Joint corruption index

Data sources: CEP II, IBFD Database, OECD, Penn World Tables, UN.

Furthermore, historical, cultural, and political factors are found to play a role. Between developing countries, a common official language and colonial ties both make treaty forma-tion more likely (not shown graphically). In this case, two countries are defined as having colonial ties if they used to be part of the same colonial empire (after 1945).

As in section 3., also the impact of corruption levels on treaty formation is tested. Whereas in section 3., no impact of the quality of institutions on the number of DTTs per country was found, the joint quality of institutions, measured as the sum of the indices of freedom from corruption of both countries, is found to impact treaty formation. The lower the levels of corruption in two countries, the more likely they are to enter into a DTT (see Graph 6d).60 Unlike in the relationship between OECD member countries and developing countries, the effect of agreement in the UN General Assembly is statistically insignificant, possibly sug-gesting that political considerations are not as important as in the above case when OECD member countries were involved as treaty partners.

Finally, the results might be unduly influenced by the emerging economies, especially the BRICs (Brazil, Russia, India and China), which all have large economic power compared to the other developing countries and also a relatively large number of DTTs.61 Thus, the same specifications are run excluding the BRIC countries (see Table 14 in the Annex). The out-come does not change substantially, thus confirming the baseline results.

60. The freedom from corruption index ranges from 0 to 100, with higher numbers reflecting lower levels of corruption.

61. As of 2010, Brazil has 31 effective DTTs in place, China 93, India 83 and Russia 74.



4.4. Summary

This section has analysed the impact of a range of geographical, economic, and political fac-tors on the formation of DTTs. The pseudo R² measuring the fit of the two regressions pre-sented here is considerably higher than in the regressions in section 3. In the two preferred specifications, 57% and 51% of the variation in the data respectively can be explained (see pseudo R2 in the regressions in Table 10, column 3 and Table 13, column 5 in the Annex).

As expected, greater distance between two jurisdictions is found to make them less likely to enter into a DTT. Geographical interdependence has proven to be an important factor explaining the patterns of DTTs. The special form of target contagion interdependence con-tributes to explain why capital-importing countries are willing to sign DTTs, even though they are generally considered to benefit less or not at all from the treaty than their capital-exporting counterparts.

Closer economic relationships increase the likelihood of a DTT being signed. Between OECD and developing countries, economic ties in the form of bilateral FDI stocks are deter-mining, whereas a higher bilateral trade volume positively affect treaty formation in the case of two developing countries. In this case, also joint market size (common GDP), joint GDP per capita, as well as institutional quality impact on the likelihood of DTT formation. Moreover, sharing a common language and/or a common colonial past makes two countries more likely to sign a DTT. Besides, it seems that political considerations matter only when it comes to treaty negotiations between OECD and developing countries. Only in these analyses, political consent in international policy matters is found to impact the probability of treaty formation.

Additionally, evidence is found that some sort of revenue sharing may take place between industrialized and developing countries when striking a DTT. The likelihood of such an asymmetric treaty rises significantly, the more ODA the industrialized economy pays to the developing country. While with the cross-section method employed the causality does not become entirely clear, it becomes apparent that developing countries face a conflict between losing tax revenues and hoping for increased FDI when signing DTTs. The next section tries to shed more light on this dilemma by illustrating how selected developing countries are affected by the signature of DTTs.

5. Advantages and Disadvantages for DTTs: Four Case Studies

When evaluating the potential benefits and downsides of DTTs for developing countries, the core issues comprise of whether DTTs can contribute to attract foreign investment and how they affect domestic tax revenues. This section tries to shed some light on these issues by presenting four country case studies.62 For each country two graphs will be discussed. The first shows the relation between the number of DTTs with the amount of inward FDI stocks. Here a positive relation is expected, if the treaties fulfil their expectation. Then, a graph that gives the relation between DTTs and the change in the corporate income tax share (CIT/

62. The countries we focus on are the respective home countries to the project partners of the DeSTaT Project, that is, South Africa, Brazil, Colombia, and Uruguay. South Africa, Brazil, and Colombia are upper-middle income countries (gross national income per capita between 4,126 and 12,745 USD), Uruguay counts as a high-income country (gross national income per capita 12,746 USD or more) (see World Bank, 2013 wtj_2014_03_int_4.docx).



GDP) is presented. If DTTs really alter tax revenues, one should at least expect a slowdown in the growth rate of the CIT share, and thus observe a negative relationship.

Within these case studies, we will take a bird’s eyes view, and not go into details, which are undeniably important, too. Other factors that influence FDI, such as economic growth or liberalizations, will largely be neglected. Also other taxes, such as withholding taxes on pas-sive income or dividends will be ignored, as structured data are not available.

5.1. South Africa

South Africa has a large tax treaty network encompassing 78 DTTs and 17 TIEAs as of August 2014.63 While seven of the treaties were signed during the Apartheid regime, the large majority has been signed afterwards. Essentially, also FDI started to flow into the country after the international isolation associated with the Apartheid period had ended in 1994. The share of FDI stocks in GDP started to rise considerably since 1999 (see Graph 7a).

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Graph 7b: Annual change in the share ofcorporate tax revenue in GDP

Data sources: OECD, UNCTAD, South African Revenue Service.

Graph 7: South Africa

During the 1990s, revenues from corporate income tax were rather low and only started to rise substantially after 2002 to an all-time high of USD 21.1 billion in 2011. A major tax reform in 2001 aiming at tax rate cut cum base broadening has surely contributed to this

63. Whereas in the analysis in the previous sections, the authors only took into account DTTs that are already in force, the graphs in this section show the number of DTTs signed rather than those in force, because international investors may already react to the signature of a DTT rather than to wait until it enters into force. For an overview of South Africa’s treaty network see Table 15 in the Annex.



considerable rise in revenues.64 However, one can identify a declining growth rates from 1994 to 1999, a jump in the growth rate from 1999 to 2000, and then again a decline from 2000 to 2011. The tax reform of 2001 and anticipation effects may have caused this structural break in the data, which otherwise gives evidence for a reduction in the growth rate of the CIT share (see Graph 7b).

5.2. Brazil

Brazil entered into its first DTT in 1967 (with Japan), and since has signed 40 international tax treaties. By 1990, Brazil had 20 DTTs in place.65 Nonetheless, FDI stocks were at rather low levels, amounting to 11.3% of GDP (see Graph 8a).

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Graph 8a: Inward FDI stocksas % of GDP

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Data sources: OECD, PWT, UNCTAD.

Graph 8: Brazil

Changing politics in the mid-1990s encouraged economic growth. After years of hyperinfla-tion, the “Plano Real”, introduced in 1994, succeeded in stabilizing the Brazilian economy. The economy went through a process of liberalization, also including the privatization of numerous state-owned companies. These policies proved conducive to boosting the com-petitiveness of the private sector and attracting high inflows of foreign direct investment. Consequently, only in the mid-1990s, FDI inflows went up and the share of inward FDI stocks started to increase significantly in the 2000s, exceeding 28.1% of GDP in 2011 (see Graph 8a).

64. South Africa, inter alia, changed from taxation on a territorial basis to worldwide basis as of 2001.65. For an overview of Brazil’s treaty network see Table 16 in the Annex.



Turning to domestic resource mobilization in Brazil, corporate income tax revenues have been gradually increasing from 1990 to 2005 followed by an accelerated surge until 2011. The share of corporate tax revenues in GDP remained stable, with declines and increases during the observation period (see Graph 8b). Whilst the two outliers (1992 and 2002), give the impression of a negative trend, this cannot be confirmed, as we must consider these two years as outliers.

5.3. Colombia

Unlike Brazil, Colombia started rather late to sign tax treaties. Only in 2001, a TIEA was signed with the United States, which however did not enter into force until April 2014. The first DTTs were signed in 2004 with other Latin American states. Since then, Colombia has signed twelve DTTs (of which nine are in force of as August 2014), and a second TIEA (with Curaçao, not in force yet).66

Since 1990, inward FDI stocks have quintupled from 6.2% of GDP in 1990 to more than 30% in 2009. While the share of inward FDI in GDP amounted to 21.2% in 2004, it increased sig-nificantly afterwards exceeding 32% in 2009, with a slight decline below 30% in the following two years (see Graph 9a).

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Data sources: OECD, PWT, UNCTAD.

Graph 9: Colombia

66. For an overview of Colombia’s treaty network see Table 17 in the Annex.



Corporate tax revenues in absolute terms have been rising during the entire period from 1990 to 2011, and reached an all-time high of USD 11.3 billion USD in 2011. The share of corporate tax revenues in GDP has increased only slightly until 2007. It has been declining since, confirming the hypothesis of decreasing tax revenues due to DTTs (see Graph 9b). Since 1990, the Colombian corporate income tax has been subject to various reforms, includ-ing, amongst others, the levying of a special surcharge from 2003 to 2006, a tax rate reduc-tion to 33% in 2008, and changing rules regarding investment allowances. These reforms evidently have also impacted the volume of revenues collected from the corporate income tax, making it difficult to isolate the effect of DTTs.

As Colombia started signing DTTs during the period we analyse, we can make a before-after comparison with regard to tax revenues. Eight years of data are available after the first sig-nature of a DTT in 2004. During the eight years before that (1996-2003), the average annual growth rate of the share of corporate income tax revenues in GDP amounted to 18.4%. In the subsequent eight years (2004-2011), the rate was only 7.2%.67 This may be seen as an indica-tion that corporate tax revenues haven been impacted negatively. Still, about a third of the DTTs that Colombia has signed have not entered into force yet, so it remains to be seen how they will affect the Colombian economy once they will be applicable.

5.4. Uruguay

Apart from two DTTs, which Uruguay signed with Germany and Hungary in the 1980s, Uruguay has not had any DTTs in force until recently. This lack of DTTs contributed to Uruguay being blacklisted by the OECD as a jurisdiction that has not committed to the inter-nationally agreed tax standards. In view of such international pressure Uruguay started to enter into further DTTs in 2009. During the last five years, Uruguay has been very active in treaty negotiations and, as of August 2014, its treaty network encompasses 15 DTTs signed (of which 12 are in force) as well as 14 TIEAs (of which 4 are in force).68 On account of the country’s numerous signatures of tax treaties, which all comply with the OECD standards, as well as supplemental changes in its domestic laws, Uruguay has since 2011 been classified as compliant with the international standards of taxation and has passed Phase 1 of the OECD peer review process.

How has this profound change in international tax policy impacted the Uruguayan inward FDI and tax revenues? Inward FDI stocks have been increasing from 7.3% of GDP in 1990 to 35% of GDP in 2009 and have remained above 30% since then (see Graph 10a).

Hand in hand with high annual average GDP growth rates of 6.1% in the period between 2004 and 2011, also corporate tax revenues have been soaring. Between 2004 and 2011, they rose by USD one billion, reaching USD 1.2 billion in 2011. This trend did not cease after the first DTTs were signed in 2009. The share of corporate tax revenues in GDP ranged between

67. We also looked at time windows of five and eight years. Also in these cases, the average annual growth in the share of corporate income tax revenues in GDP before the country started signing DTTs is higher than the growth rate during the same time window after the signature of the first DTTs. For the five-year window, the annual average growth rates are 31.3%, before (1999-2003) and 9.0% after the signature (2004 and 2008). For a three-year window, the annual average growth rates are 63.0% before (2001-2003) and 20.5% after the signature (2004-2006).

68. For an overview of Uruguay’s treaty network see Table 18 in the Annex.



one and two percent between 1990 and 2004. Since 2004, this share increased and has been fluctuating around 2.7% of GDP during the last years (see Graph 10b).

As Uruguay practically started entering into DTTs during the period we analyse, we can additionally investigate how the share of corporate income tax revenues in GDP has devel-oped in the periods before and after the first signature of a DTT. During the three years before Uruguay started signing DTTs (i.e. from 2006 to 2008), the share of corporate income tax revenues in GDP rose on average by 3% per year. In the three years after the first sig-nature (2009-2011), this share annually decreased by 3% on average. Admittedly, a meagre three years of FDI and tax revenue data after the signing of the first Uruguayan DTTs being available (and only four of them being in force by the beginning of 2011), make it still early to assess their impact.

199019911992199319941995 199619971998

1999 20002001

2002

2003 20042005

2006

20072008

2009

2010 2011

0.1

.2.3

.4In

war

d FD

I sto

cks

as %

of G

DP


Graph 10a: Inward FDI stocks as % ofGDP

1991

19921993

19941995

1996

1997

1998

1999 2000

2001 20022003

2004

2005

2006

2007

2008

2009 2010

2011

-.20

.2.4

.6C

hang

e in

the

shar

e of

CIT

Rev

enue

in G

DP



Data sources: OECD, UNCTAD.

Graph 10: Uruguay

5.5. Comparative analysis and summary

Which conclusions can be drawn from a comparative analysis from these four case studies regarding a potential trade-off between increased FDI and lower tax revenues? One, over the 20 year-period of the analysis, increasing inward FDI can be observed in all four countries. In how far this trend can be attributed to the signature of DTTs, is however difficult to deter-mine with certainty.

With respect to the effect of DTTs on tax revenues, the evidence is less conclusive. In the cases of South Africa and Brazil, the data are difficult to interpret and do not lend themselves to any clear conclusions. For Colombia and Uruguay the analysis suggests that in the years after the countries started signing DTTs the share of corporate income tax revenues in GDP



grew more slowly, or even declined in the case of Colombia. Albeit that this slow down need not necessarily be caused by the signature of the DTTs, it might be an indication that corpo-rate tax revenues might have been impacted negatively.

Data on revenues collected from withholding taxes on passive income paid to foreign entit-ies would give additional insight into the effects of DTTs, but unfortunately there is no sys-tematic database of these specific tax revenues. One can only inquire into the tax rate effect, i.e. compare the domestic rates, which would apply in the absence of a treaty, with the rates stipulated in the DTTs.

With respect to tax rates on dividend payments the evidence is mixed. Compared to the domestic rates on dividends derived from portfolio investments, the average rates stipulated in DTTs are higher in the DTTs of South Africa, Brazil, and Colombia. With respect to tax rates on dividends paid to “qualifying companies”, the average rates stipulated in Brazilian and Uruguayan treaties are higher than the ones in their domestic laws. The average treaty rates in Colombia and South Africa, on the contrary, are lower than the domestic ones.

The average tax rates established for interest payments and royalties are lower in the DTTs than in the domestic laws in all four countries.69 If the treaties do not contribute to attract enough new investment (and thus tax base) to compensate for such reductions, the reduced treaty tax rates may induce tax revenue losses for the source country.

6. Conclusion

The ambition of this article was threefold. First, it showed determinants that increased the chance that countries would sign additional double tax treaties (DTTs). It was found that this was due to market size and market seeking (thru foreign direct investment) in the case of treaties between industrialized and developing economies. Market size and market potential (measured by openness) were found to matter in the case of DTTs between devel-oping economies. Most strikingly, evidence was gathered that industrialized countries may compensate developing countries for their loss of tax revenues due to a DTT thru official development assistance.

Second, individual double tax treaties were studied. Here also geography could be taken into account, and closeness was found to obviously increase the probability to sign a treaty. Also a contagion from treaties of neighbouring countries could be ascertained, both from the source as well as the destination country, which increases the probability for a treaty. It could also be shown that a common language and (colonial) history improve the chance for a DTT. Once again, evidence was found that industrialized countries will compensate developing countries for their loss thru official development assistance.

Third, the motives for four selected developing countries to sign DTTs were investigated. In particular, the trade-off between potential increases in FDI inflows and the potential loss in corporate income tax revenues was analysed. Whilst ample evidence for the prior could be demonstrated, evidence for a reduction in tax revenues is mixed. This may be due to the fact

69. Data on tax rates are taken from the withholding tax rate tables in the country analyses, which are provided by the IBFD Tax Research Platform. Where a treaty lays down several tax rates that apply to different situations, we always take the highest one for our calculations.



that other factors beyond DTTs may have had a positive impact on tax revenues during the same period that DTTs have been signed.

Summarizing, this article has demonstrated common factors that induce double tax trea-ties presenting a systematic analysis, and therefore contributes to our understanding of the emergence of DTTs.

7. Annex

Table 1: Explanation of the variables and data sources for empirical analysis

Variable Explanation Sources

Agreement at UN Voting similarity index (ranging from 0 to 1), computed using three category vote data (1 = “yes”, or approval for an issue; 2 = abstain (counts as half-agreement with a yes or a no vote), 3 = “no” or disapproval for an issue)

United Nations General Assembly Voting Data1

Bilateral sum of corporate tax rates

Sum of a country-pair’s corporate tax rates

J. Braun & A. Weichenrieder (forthcoming)2

Bilateral trade volume (ln)

Log of a country-pair’s bilateral trade volume (ln(importsij+ importsji)); as a robustness test also the bilateral sum of exports is used

United Nations International Trade Statistics (UN COMTRADE)3

Bilateral FDI (ln) Log of a country i’s outward foreign direct investment stocks in country j

OECD Foreign Direct Investment Statistics4

Colony Dummy variable (0 or 1) indicating whether a country i has ever been a colonizer of the other country j

CEP II5

Common colonial past

Dummy variable (0 or 1), indicating whether two countries have been part of the same colonial empire (after 1945)

CEP II

Common official language

Dummy variable (0 or 1), indicating whether two countries i and j share a common official language

CEP II

Corruption index Freedom from corruption index, ranging from 0 to 1, where lower numbers indicate higher corruption levels

Heritage Foundation6

Distance (ln) Log of bilateral distance between two countries of a country-pair (in km)

CEP II7

Effective DTT in place in 2010

Binary variable taking the value one or zero indicating whether a country-pair has a DTT in place in 2010

IBFD Tax Research Platform8

GDP (ln) Log of GDP (gross domestic product) (measured in current USD)

Penn World Tables9

GDP per capita (ln) A country-pair’s joint GDP per capita as well as the difference in GDP per capita are used (both measured in current USD)

Penn World Tables10

Institutions Overall index of economic freedom, ranging from 0 to 1 with lower number indicating more freedom

Heritage Foundation11



Table 1: Explanation of the variables and data sources for empirical analysis

Variable Explanation Sources

ODA (ln) 2006-2010 Log of the total bilateral Official development assistance (ODA) during the five years from 2006 to 2010 (measured in USD)

OECD International Development Statistics12

Openness A country’s openness to trade, measured as the log of its ((exports plus imports) divided by its GDP)

Penn World Tables13

Foreign direct investment (ln)

Log of a country’s outward foreign direct investment stocks

OECD Foreign Direct Investment Statistics and UNCTAD Foreign Direct Investment Statistics14

Population (ln) Log of a country’s population Penn World Tables15

Source contagion (in the regressions with developing and OECD countries)

Index ranging from zero to one (the higher the share of OECD countries already having a DTT with a specific developing country in place the higher this value)

Own calculation based on IBFD Tax Research Platform for information on DTTs,16 information on contiguity taken from CEPII;17 stata ado file from E. Neumayer & T. Plümper18

Source and target contagion (in the regressions with developing countries only)

Index ranging from zero to one (the higher the share of neighbouring countries of a developing country already having a DTT with a specific other developing country in place the higher this value)

See above.

Target contagion (in the regressions with developing and OECD countries)

Index ranging from zero to one (weighted average of the share of DTTs of the surrounding countries, with closer countries weighted higher)

See above.

Target contagion 2 (in the regressions with developing and OECD countries)

Index ranging from zero to one (the higher the share of neighbouring countries of a developing country already having a DTT with a specific OECD country in place the higher this value)

See above.

Tax revenues National tax revenues from corporate income tax

OECD tax database on Latin America19 and Annual Reports of the South African Revenue Service20

1. A. Strezhnev & E. Voeten (2013), United Nations General Assembly Voting Data, available at http://hdl.handle.net/1902.1/12379 UNF:5:s7mORKL1ZZ6/P3AR5Fokkw== Erik Voeten (Distributor).

2. J. Braun & A. Weichenrieder (forthcoming), Does exchange of information between tax authorities influence multinationals’ use of tax havens? Mimeo.

3. Available at: http://comtrade.un.org.4. Available at http://www.oecd.org/statistics.5. Available at http://www.cepii.fr/cepii/en/bdd_modele/bdd.asp.6. Id.7. Available at: http://www.cepii.fr/cepii/en/bdd_modele/bdd.asp.8. Available at: http://www.ibfd.org.9. R. Feenstra, R. Inklaar, M. Timmer (2013), The Next Generation of the Penn World Table, available at www.ggdc.net/pwt.10. Id.11. Available at http://www.heritage.org/index.12. Available at http://www.oecd.org/statistics.13. Id.14. Available at http://unctad.org/en/Pages/DIAE/FDI%20Statistics/Interactive-database.aspx.15. Id.16. Available at http://www.ibfd.org.17. Available at http://www.cepii.fr/cepii/en/bdd_modele/bdd.asp.18. Available at http://www.lse.ac.uk/geographyAndEnvironment/whosWho/profiles/neumayer/spspc.aspx.19. Id.20. South African Revenue Service, Annual Reports, various issues.



7.1. Tables to section 3.: Who has DTTs?

Table 2: Descriptive statistics: Developing countries with other developing countries (142)

Mean Std. deviation

Min Median Max

Double tax treaties 16.54 17.70 0 10 87

GDP (ln) 10.01 2.50 3.47 9.87 16.48

Openness 0.90 0.42 0.00 0.85 2.99

Notes: All observations from the year 2010.

Table 3: Descriptive statistics: Number of DTTs of developing countries (142) and industrialized countries (34), part 2

Mean Std. deviation

Min Median Max

Double tax treaties 9.01 9.49 0 4 33

GDP (ln) 9.44 2.23 3.47 9.39 15.60

Foreign direct investment

8.41 2.21 1.52 8.48 13.97

Official development assistance/GDP*

-6.21 3.21 -13.25 -5.93 1.96

Notes: All observations from the year 2010. *Official Development Assistance to GDP is in logs, which takes a negative value if the share is between 0 and 100%, which is the case in most cases.

Table 4: Number of DTTs of developing countries (142) and industrialized countries (34)

(1) (2) (3) (4) (5)

Population *** 2.216 (0.475)

-0.2174 (0.599)

GDP (ln) *** 3.362 (0.843)

*** 3.144 (0.605)

*** 3.325 (0.649)

*** 1.708 (0.546)

GDP per capita

0.217 (0.599)

-0.243 (0.660)

-0.475 (0.444)

Openness 2.858 (1.939)

Foreign direct investment (ln FDI)

** 1.043 (0.580)

Pseudo R2 3.96 6.50 6.50 6.98 11.56

Alpha 0.848 (0.113)

0.706 (0.099)

0.706 (0.099)

0.691 (0.098)

0.448 (0.089)

Poisson (chi2) 1020.6 (0.000)

859.3 (0.000)

859.3 (0.000)

849.3 (0.000)

376.4 (0.000)

Notes: The numbers indicate the average marginal effect of a negative binomial regression with the number of DTTs per country as the dependent variable. Standard errors are indicated below in parenthesis. Stars denote statistical significance levels: * p<0.1, ** p<0.05, *** p<0.01. The null hypothesis that the estimate of the coefficient alpha of the negative binomial distribution is equal to zero indicates if we should reject the negative binomial distribution in favour of a Poisson distribution. Poisson (chi2) is the goodness of fit test for the corresponding Poisson regression, which is rejected in all cases. All observations from the year 2010.



Table 5: Number of DTTs of developing countries (142) and industrialized countries (34), part 2

(6) (7) (8) (9) (10)

GDP (ln) *** 1.948 (0.710)

*** 2.100 (0.768)

*** 1.410 (0.738)

*** 1.816 (0.741)

*** 1.835 (0.712)

GDP per capita -0.275 (0.546)

-0.653 (0.724)

0.396 (0.683)

-0.061 (0.667)


* 1.187 (0.734)

1.056 (0.761)

1.236 (0.810)

* 1.355 (0.788)

** 1.326 (0.717)

Institutional quality (heritage foundation)

0.015 (0.014)

Corruption index (heritage foundation)

0.038 (0.059)

Official development assistance (ln)

0.509 (0.630)

Official development assistance/GDP (ln)

0.310 (0.239)

* 0.304 (0.229)

Pseudo R2 11.39 11.20 12.85 13.07 13.06

Alpha 0.437 (0.091)

0.443 (0.092)

0.405 (0.093)

0.400 (0.092)

.401 (0.092)

Poisson (chi2) 345.9 (0.000)

347.2 (0.000)

285.1 (0.000)

282.7 (0.000)

286.1 (0.000)


Table 6: Number of DTTs of developing countries (142) with other developing countries

(11) (12) (13) (14) (15)

Population *** 4.325 (0.966)

** -2.338 (1.003)

GDP (ln) *** 7.368 (1.322)

*** 5.030 (0.889)

*** 2.051 (1.027)

*** 5.921 (0.896)

GDP per capita ** 2.337 (1.003)

Openness 2.518 (2.723)

*** 10.058 (3.345)


0.628 (1.075)

Pseudo R2 2.54 6.98 6.98 4.45 7.39

Alpha 1.168 (0.138)

0.767 (0.103)

0.767 (0.106)

0.971 (0.166)

0.734 (0.010)

Poisson (chi2) 2446.6 (0.000)

1483.8 (0.000)

1483.8 (0.000)

806.6 (0.000)

1509.9 (0.000)




7.2. Tables to section 4.: With whom do countries have DTTs?

Table 7: Descriptive Statistics: Developing countries with OECD economies, Table 10, column 3

Mean Std. Deviation

Min Median Max

DTT 0.22 0.41 0.00 0.00 1.00

Distance (ln) 8.80 0.65 4.71 8.92 9.88

Joint GDP (ln) 13.17 1.37 9.44 13.07 16.83

Joint GDP per capita (ln) -3.63 0.80 -9.55 -3.40 -2.25

Bilateral FDI (ln) -21.80 14.60 -29.93 -29.93 11.24

Source contagion 0.22 0.27 0.00 0.06 1.00

Target contagion 0.29 0.10 0.08 0.30 0.53Common language 0.11 0.31 0.00 0.00 1.00

Colony 0.03 0.17 0.00 0.00 1.00

Agreement at UN 0.72 0.13 0.16 0.72 0.99

Official development assistance (ln)

-8.14 13.39 -29.93 -1.82 7.69


Table 8: Descriptive statistics: Developing countries with each other, Table 13, column 5

Mean Std. Deviation

Min Median Max

DTT 0.07 0.26 0.00 0.00 1.00

Distance (ln) 8.69 0.77 4.45 8.84 9.89

Joint GDP (ln) 11.25 1.55 7.48 11.03 15.91

Joint GDP per capita (ln) -5.31 0.79 -7.57 -5.21 -3.61

Bilateral trade (ln) -0.26 18.53 -23.03 10.39 24.99

Source contagion 0.09 0.22 0.00 0.00 1.00

Target contagion 0.09 0.21 0.00 0.00 1.00

Common language 0.14 0.35 0.00 0.00 1.00

Common colonizer 0.11 0.31 0.00 0.00 1.00

Joint corruption index 57.08 13.26 18.00 56.00 110.00

Agreement at UN 0.90 0.08 0.60 0.93 0.99


Table 9: OECD and developing countries as partners, part 1

(1) (2) (3) (4)

***-0.076 ***-0.125 ***-0.061 ***-0.058

(0.015) (0.013) (0.012) (0.011)

Source contagion ***0.455 ***0.518 ***0.523

(0.038) (0.025) (0.023)

Target contagion ***0.556 ***0.916 ***0.977




(1) (2) (3) (4)

(0.124) (0.086) (0.067)

Joint GDP (ln) 0.028 ***0.105 0.003

(0.018) (0.017) (0.012)

Difference in GDP (ln) 0.012 ***-0.064 0.007

(0.009) (0.011) (0.007)

Bilateral FDI (ln) ***0.003 ***0.005 ***0.003 ***0.003

(0.0004) (0.0005) (0.0004) (0.0004)

Bilateral trade volume (ln) 0.0002 ***0.002 0.0003

(0.0003) (0.0004) (0.0003)

Observations 4416 4416 4416 4416

Pseudo R² 0.47 0.31 0.54 0.54

Log-likelihood -1205.94 -1584.68 -1044.27 -1048.56

Notes: The numbers indicate the average marginal effect of a probit regression with a dummy variable indicating whether or not a country-pair has an effective DTT in place as the dependent variable. Robust standard errors are indicated in parentheses (clustered at OECD member country level). Stars denote statistical significance levels: * p<0.1, ** p<0.05, *** p<0.01. All observations from the year 2010.


(1) (2) (3) (4)

Distance (ln) ***-0.059 ***-0.058 ***-0.050 ***-0.049

(0.011) (0.012) (0.011) (0.011)

Source contagion ***0.526 ***0.530 ***0.519 ***0.524

(0.021) (0.022) (0.021) (0.021)

Target contagion ***0.834 ***0.855 ***0.791 ***0.793

(0.111) (0.111) (0.103) (0.103)

Joint GDP (ln) 0.006 0.011 0.006 0.006

(0.008) (0.008) (0.007) (0.007)

Difference in GDP per capita (ln)

0.012 *0.014 0.007 0.007

(0.008) (0.008) (0.007) (0.007)

Bilateral FDI (ln) ***0.002 ***0.002 ***0.002 ***0.002

(0.0004) (0.0003) (0.0003) (0.0003)

Common language ***0.075 ***0.081 ***0.077 ***0.076

(0.016) (0.017) (0.017) (0.017)

Colony ***0.087 ***0.087 ***0.084 ***0.084

(0.023) (0.024) (0.025) (0.025)

Agreement at UN (1990-2010)

**0.116 **0.122 **0.115

(0.051) (0.050) (0.050)

ODA (ln) 2006-2010 **0.002




(1) (2) (3) (4)

(0.001)

ODA per capita (ln) 2006-2010

**0.002

(0.001)

Observations 4416 4246 4158 4158

Pseudo R² 0.56 0.56 0.57 0.57

Log-likelihood -999.02 -979.85 -938.75 -939.84

Notes: The numbers indicate the average marginal effect of a negative binomial regression with a dummy variable indicating whether or not a country-pair has an effective DTT in place as the dependent variable. Robust standard errors are indicated in parentheses (clustered at OECD member country level). Stars denote statistical significance levels: * p<0.1, ** p<0.05, *** p<0.01. All observations from the year 2010. Due to the availability of some of the control variables, the sample sizes vary in the different regressions.

Table 11: OECD and developing countries as partners, part 3 (robustness 1)

(1) (2) (3) (4) (5)

Distance (ln) ***-0.051 ***-0.050 ***-0.051 ***-0.049 ***-0.051

(0.011) (0.010) (0.011) (0.011) (0.011)

Source contagion ***0.520 ***0.520 ***0.482 ***0.518 ***0.527

(0.021) (0.021) (0.030) (0.021) (0.021)


(0.104) (0.097) (0.103) (0.113)

Joint GDP (ln) 0.006 0.005 ***0.027 0.006 0.007

(0.007) (0.007) (0.010) (0.007) (0.008)


0.007 0.007 *0.021 0.007 0.006

(0.007) (0.007) (0.012) (0.007) (0.008)

Bilateral FDI (ln) ***0.002 ***0.002 ***0.003 ***0.002 ***0.002

(0.0003) (0.0003) (0.0004) (0.0003) (0.0003)

Common language ***0.077 ***0.074 ***0.068 ***0.077 ***0.077

(0.017) (0.016) (0.023) (0.017) (0.017)

Colony ***0.082 ***0.085 ***0.140 ***0.085 ***0.082

(0.024) (0.025) (0.043) (0.025) (0.025)


***0.123 ***0.124 0.145 **0.120 **0.127

(0.050) (0.048) (0.096) (0.051) (0.053)

ODA (ln) 2006-2010 ***0.004 **0.002 **0.002

(0.001) (0.001) (0.001)

ODA (ln) 2008-2010 ***0.002

(0.0008)

ODA (ln) 2010 ***0.002

(0.0006)




(1) (2) (3) (4) (5)

Target contagion 2 ***0.114

(0.019)

Bilateral trade volume (exports) (ln)

0.0002

(0.0001)

Bilateral sum of corporate tax rates

-0.009

(0.013)

Observations 4170 4165 3396 4158 3857

Pseudo R² 0.56 0.56 0.54 0.57 0.57

Log-likelihood -948.31 -943.57 -872.43 -938.65 -888.28

Notes: The numbers indicate the average marginal effect of a negative binomial regression with a dummy variable indicating whether or not a country-pair has an effective DTT in place as the dependent variable. Robust standard errors are indicated in parentheses (clustered at OECD member country level). Stars denote statistical significance levels: * p<0.1, ** p<0.05, *** p<0.01. All observations from the year 2010. Due to the availability of some of the control variables, the sample sizes vary in the different regressions.


(1) (2) (3) (4)

Distance (ln) ***-0.046 ***-0.050 ***-0.046 ***-0.042

(0.011) (0.011) (0.011) (0.011)

Source contagion ***0.520 ***0.518 ***0.539 ***0.530

(0.021) (0.021) (0.019) (0.019)


(0.105) (0.103) (0.113) (0.121)

Joint GDP (ln) 0.006 0.006 0.007 0.008

(0.008) (0.007) (0.008) (0.007)


0.006 0.007 0.008 0.010

(0.008) (0.007) (0.007) (0.007)

Bilateral FDI (ln) ***0.002 ***0.002 ***0.002 ***0.002

(0.0003) (0.0003) (0.0003) (0.0003)

Common language ***0.077 ***0.077 ***0.063 ***0.057

(0.018) (0.017) (0.015) (0.014)

Colony ***0.080 ***0.084 ***0.086 ***0.095

(0.024) (0.025) (0.026) (0.029)


**0.125 **0.122 **0.114 **0.104

(0.053) (0.050) (0.051) (0.053)

ODA (ln) 2006-2010 **0.002 **0.002 **0.002 *0.002

(0.0009) (0.0009) (0.0009) (0.0009)

Observations 4062 4158 3884 3728




(1) (2) (3) (4)

Pseudo R² 0.56 0.57 0.57 0.58

Log-likelihood -900.14 -938.75 -872.33 -813.99

Notes: The numbers indicate the average marginal effect of a negative binomial regression with a dummy variable indicating whether or not a country-pair has an effective DTT in place as the dependent variable. Robust standard errors are indicated in parentheses (clustered at OECD member country level). Stars denote statistical significance levels: * p<0.1, ** p<0.05, *** p<0.01. All observations from the year 2010. Due to the availability of some of the control variables, the sample sizes vary in the different regressions. Column (1) excludes the BRICs, (2) excludes the United States, (3) excludes the 1% biggest ODA recipients of each OECD member country, (4) excludes the 5% biggest ODA recipients of each OECD member country.

Table 13: Developing countries as partners, part 1

(1) (2) (3) (4) (5)

Distance (ln) ***-0.031 ***-0.032 ***-0.026 ***-0.029 ***-0.029

(0.003) (0.003) (0.003) (0.004) (0.004)


(0.008) (0.008) (0.008) (0.008) (0.008)

Target contagion ***0.133 ***0.132 ***0.133 ***0.135 ***0.135

(0.009) (0.009) (0.009) (0.009) (0.009)

Joint GDP (ln) ***0.019 ***0.018 ***0.020 ***0.021 ***0.021

(0.002) (0.002) (0.002) (0.002) (0.002)

Joint GDP per capita (ln) 0.006 ***0.013 **0.009 **0.009

(0.004) (0.004) (0.005) (0.005)


-0.001

(0.002)

Bilateral trade volume (ln) ***0.002 ***0.002 ***0.001 ***0.001 ***0.001

(0.0002) (0.0002) (0.0002) (0.0002) (0.0002)

Common official language ***0.031 ***0.025 ***0.025

(0.007) (0.008) (0.008)

Common colonial past ***0.030 ***0.030 ***0.030

(0.008) (0.008) (0.008)

Corruption index ***0.001 ***0.001

(0.0002) (0.0002)


-0.000002

(0.037)

Observations 5253 5253 5253 4753 4753

Pseudo R² 0.48 0.48 0.50 0.51 0.51

Log-likelihood -675.85 -674.95 -647.49 -602.55 -602.55

Notes: The numbers indicate the average marginal effect of a negative binomial regression with a dummy variable indicating whether or not a country-pair has an effective DTT in place as the dependent variable. Standard errors are indicated in parentheses. Stars denote statistical significance levels: * p<0.1, ** p<0.05, *** p<0.01. All observations from the year 2010. Due to the availability of some of the control variables, the sample sizes vary in the different regressions.



Table 14: Developing countries as partners, part 2 (Robustness: without BRIC countries)

(1) (2) (3) (4) (5)

Distance (ln) ***-0.027 ***-0.027 ***-0.022 ***-0.023 ***-0.024

(0.003) (0.003) (0.003) (0.003) (0.003)


(0.008) (0.008) (0.008) (0.008) (0.008)

Target contagion ***0.118 ***0.117 ***0.118 ***0.120 ***0.119

(0.008) (0.008) (0.008) (0.009) (0.009)

Joint GDP (ln) ***0.018 ***0.016 ***0.019 ***0.021 ***0.021

(0.003) (0.002) (0.003) (0.003) (0.003)

Joint GDP per capita (ln) *0.007 ***0.011 *0.008 **0.007

(0.004) (0.004) (0.005) (0.005)


-0.001

(0.002)

Bilateral trade volume (ln) ***0.002 ***0.002 ***0.001 ***0.001 ***0.001

(0.0002) (0.0002) (0.0002) (0.0002) (0.0002)

Common official language ***0.029 ***0.021 ***0.023

(0.006) (0.007) (0.007)

Common colonial past ***0.027 ***0.026 ***0.027

(0.007) (0.008) (0.008)

Corruption index ***0.001 ***0.001

(0.0002) (0.0002)


0.033

(0.036)

Observations 4950 4950 4950 4465 4465

Pseudo R² 0.48 0.48 0.50 0.51 0.51

Log-likelihood -563.03 -561.71 -536.80 -497.21 -497.64

Notes: The numbers indicate the average marginal effect of a negative binomial regression with a dummy variable indicating whether or not a country-pair has an effective DTT in place as the dependent variable. Standard errors are indicated in parentheses. Stars denote statistical significance levels: * p<0.1, ** p<0.05, *** p<0.01. All observations from the year 2010. Due to the availability of some of the control variables, the sample sizes vary in the different regressions.

7.3. Tables to section 5.: Case studies

Table 15: South Africa – International tax treaties

Treaty partner Date signed Date into force Type of treaty

Algeria 1998 2000 DTT

Argentina 2013 Not in force yet TIEA

Australia 1999 1999 DTT

Austria 1996 1997 DTT





Bahamas 2011 2012 TIEA

Barbados 2013 Not in force yet TIEA

Belarus 2002 2003 DTT

Belgium 1995 1998 DTT

Belize 2014 Not in force yet TIEA

Bermuda 2011 2012 TIEA

Botswana 2003 2004 DTT

Brazil 2003 2006 DTT

Bulgaria 2004 2004 DTT

Canada 1995 1997 DTT

Cayman Islands 2011 2012 TIEA

Chile 2012 Not in force yet DTT

China 2000 2001 DTT

Chinese Taipei 1994 1996 DTT

Congo, Democratic Republic of 2005 2012 DTT

Cook Islands 2013 Not in force yet TIEA

Costa Rica 2012 Not in force yet TIEA

Croatia 1996 1997 DTT

Cyprus 1997 1998 DTT

Czech Republic 1996 1997 DTT

Denmark 1995 1997 DTT

Dominica 2012 Not in force yet TIEA

Egypt 1997 1998 DTT

Ethiopia 2004 2006 DTT

Finland 1995 1995 DTT

France 1993 1995 DTT

Gabon 1995 Not in force yet DTT

Germany 1973 1975 DTT

Germany 2008 Not in force yet DTT

Ghana 2004 2007 DTT

Gibraltar 2012 2013 TIEA

Greece 1998 2003 DTT

Grenada 1960 1960 DTT

Guernsey 2011 2012 TIEA

Hungary 1994 1997 DTT

India 1996 1997 DTT

Indonesia 1997 1998 DTT

Iran 1997 1998 DTT

Ireland 1997 1997 DTT

Israel 1978 1980 DTT





Italy 1995 1999 DTT

Japan 1997 1997 DTT

Jersey 2011 2012 TIEA

Kenya 2010 Not in force yet DTT

Korea, Republic of 1995 1996 DTT

Kuwait 2004 2007 DTT

Lesotho 1996 1997 DTT

Liberia 2012 Not in force yet TIEA

Liechtenstein 2013 Not in force yet TIEA

Luxembourg 1998 2000 DTT

Malawi 1971 1971 DTT

Malaysia 2005 2006 DTT

Malta 1997 1997 DTT

Mauritius 1996 1997 DTT

Mexico 2009 2010 DTT

Monaco 2013 Not in force yet TIEA

Mozambique 2007 2009 DTT

Namibia 1998 1999 DTT

Netherlands 2005 2008 DTT

New Zealand 2002 2004 DTT

Nigeria 2000 2008 DTT

Norway 1996 1996 DTT

Oman 2002 2003 DTT

Pakistan 1998 1999 DTT

Poland 1993 1996 DTT

Portugal 2006 2008 DTT

Romania 1993 1995 DTT

Russian Federation 1995 2001 DTT

Rwanda 2002 2010 DTT

Samoa 2012 Not in force yet TIEA

San Marino 2011 2012 TIEA

Saudi Arabia 2007 2008 DTT

Seychelles 1998 2002 DTT

Sierra Leone 1968 1969 DTT

Singapore 1996 1997 DTT

Slovakia 1998 1999 DTT

Spain 2006 2007 DTT

Sudan 2007 Not in force yet DTT

Swaziland 2004 2005 DTT

Sweden 1995 1995 DTT





Switzerland 2007 2009 DTT

Tanzania 2005 2007 DTT

Thailand 1996 2007 DTT

Tunisia 1999 1999 DTT

Turkey 2005 2006 DTT

Uganda 1997 2001 DTT

Ukraine 2003 2004 DTT

United Kingdom 2002 2002 DTT

United States 1997 1997 DTT

Zambia 1956 1956 DTT

Zimbabwe 1965 1965 DTT

Data sources: OECD Exchange of Tax Information Portal and IBFD Tax Research Platform.

Table 16: Brazil – International tax treaties


Argentina 1980 1982 DTT

Austria 1975 1976 DTT

Belgium 1972 1973 DTT

Bermuda 2012 Not in force yet TIEA


Cayman Islands 2013 Not in force yet TIEA

Chile 2001 2003 DTT

China 1991 1993 DTT

Czech Republic 1986 1990 DTT

Denmark 1974 1974 DTT

Ecuador 1983 1987 DTT


France 1971 1972 DTT

Guernsey 2013 Not in force yet TIEA


India 1988 1992 DTT

Israel 2002 2005 DTT

Italy 1978 1981 DTT

Japan 1967 1967 DTT

Jersey 2013 Not in force yet TIEA


Luxembourg 1978 1980 DTT


Netherlands 1990 1991 DTT

Norway 1980 1981 DTT

Peru 2006 2009 DTT



Table 16: Brazil – International tax treaties


Philippines 1983 1991 DTT


Russian Federation 2004 2009 DTT

Slovakia 1986 1990 DTT

South Africa 2003 2006 DTT

Spain 1974 1975 DTT

Sweden 1975 1975 DTT

Trinidad and Tobago 2008 Not in force yet DTT

Turkey 2010 2012 DTT

Ukraine 2002 2006 DTT

United Kingdom 2012 Not in force yet TIEA

United States 2006 2013 TIEA

Uruguay 2012 Not in force yet TIEA

Venezuela 2005 Not in force yet DTT


Table 17: Colombia – International tax treaties


Bolivia 2004 2005 DTT


Chile 2007 2009 DTT

Curaçao 2013 Not in force yet TIEA

Czech Republic 2012 Not in force yet DTT


India 2011 Not in force yet DTT



Peru 2004 2005 DTT

Portugal 2010 Not in force yet DTT

Spain 2005 2008 DTT


United States 2001 2014 TIEA


Table 18: Uruguay – International Tax Treaties


Argentina 2012 2013 TIEA

Australia 2012 Not in force yet TIEA

Belgium 2013 Not in force yet DTT

Brazil 2012 Not in force yet TIEA

Canada 2013 Not in force yet TIEA



Table 18: Uruguay – International Tax Treaties


Denmark 2011 Not in force yet TIEA


Faroe Islands 2011 Not in force yet TIEA


France 2010 2010 TIEA

Germany 2010 2011 DTT

Greenland 2011 Not in force yet TIEA

Guernsey 2014 Not in force yet TIEA


Iceland 2011 2012 TIEA

India 2011 2013 DTT


Liechtenstein 2010 2012 DTT

Malta 2011 2012 DTT


Netherlands 2012 Not in force yet TIEA

Norway 2011 2014 TIEA


Romania 2012 Not in force yet DTT

Spain 2009 2011 DTT

Sweden 2011 Not in force yet TIEA


United Kingdom 2013 Not in force yet TIEA

Vietnam 2013 Not in force yet DTT


Documents

An Economic Perspective on Double Tax Treaties with(in ... · WORLD TAX JOURNAL OCTOBER 2014 | 243 An Economic Perspective on Double Tax Treaties with(in) Developing Countries 1