Download An Economic Perspective on Double Tax Treaties with(in)

Julia Braun* and Martin Zagler**
An Economic Perspective on Double Tax Treaties
with(in) Developing Countries
There 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.
Contents
1.Introduction
243
2.Literature
244
2.1. Motivations to sign DTTs244
2.2. Choice of partner for DTTs246
3. Who Has Double Tax Treaties?
248
3.1.Methodology248
3.2.Results248
4. With Whom Do Countries Have Double Tax Treaties?
252
4.1. Data and Methodology252
4.2.Determinants of DTT conclusion between developing countries and OECD
member countries253
4.3. Determinants of DTT conclusion between two developing countries 257
4.4.Summary260
5. Advantages and Disadvantages for DTTs: Four Case Studies
260
5.1. South Africa261
5.2.Brazil262
5.3.Colombia263
5.4.Uruguay264
5.5. Comparative analysis and summary265
6.Conclusion
266
7.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 studies276
*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 referees 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.
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1. Introduction
There are about 2,600 double tax treaties worldwide.1 Such a double tax treaty (DTT henceforth) is a bilateral agreement between two governments to assign taxing rights of cross-border 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 drivers 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 beginning, 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 expansion of the treaty network be explained?
This article applies econometric methods to analyse the data on DTTs in a structured manner. 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 countries, 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.
2.
3.
4.
5.
6.
7
IBFD Tax Research Platform, available at http://www.ibfd.org.
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.
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”.
A. Easson, Do We Still Need Tax Treaties?, 54 Bull. Intl. Fiscal Docn. 12, pp. 619-625 (2000), Journals IBFD.
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 industrialized 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.
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.
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.
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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 industrialized 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 incentives 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 underlying 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 industrialized 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 limitation 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 encouraging the international expansion of domestic companies may arguably be more relevant for
8.
9.
10.
11.
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).
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.
For information on the OECD BEPS project, see http://www.oecd.org/tax/beps.htm.
P. Baker (2012), see supra n. 7.
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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 internationally accepted tax standards may be more relevant for developing countries.13
How can DTTs contribute to foster international economic activity?14 They may provide certainty 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 taxation 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
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 imposition 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 bargaining: 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.
12.
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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.
23.
24.
25.
26.
27.
28.
29.
Id., p. 446.
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).
See Pickering supra n. 14, at p. 5.
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).
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.
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 österreichische 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 interpretation, 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).
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.
A. Lejour, (2014), The Foreign Investment Effects of Tax Treaties, CPB Discussion Paper 265, Netherlands
Bureau for Economic Policy Analysis.
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also host government expenditure as a percentage of GDP significantly increase the likelihood 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 focusing 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 “developing 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 “increased 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.
31.
32.
33.
34.
35.
36.
37.
J. Ligthart, M. Vlachaki & J. Voget (2012), see supra n. 19.
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.
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.
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.
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.
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.
E. Neumayer & T. Plümper (2010), Spatial Effects in Dyadic Data, International Organization 64(1),
pp. 145-166, at p. 145.
D. Swenson, (2005), Why do developing countries sign BITs? Journal of International Law and Policy 12(1),
pp. 131-154.
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This article empirically analyses the decision of entering into DTTs accounting for economic, geographic, and political factors. It focuses on the relationships of developing countries 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 openness, 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 binomial 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 completely 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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DTTs with other developing countries
0
20
40
60
An Economic Perspective on Double Tax Treaties with(in) Developing Countries
Graph 1a: GDP
0
5
ln (GDP)
Asia and Oceania
Non-OECD Europe
Sub-Saharan Africa
10
15
Middle East - North Africa
Cental and South America
Negative binomial regression
Data source: Penn World Tables, IBFD Database.
0
DTT with OECD countries
10
20
30
40
Graph 1b: GDP
0
5
ln (GDP)
Asia and Oceania
Non-OECD Europe
Sub-Saharan Africa
10
15
Middle East - North Africa
Cental and South America
Negative binomial regression
Data source: Penn World Tables, IBFD Database.
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
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approximately one more DTT.39 The regression curve above indicates that the average marginal 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.
DTTs with other developing countries
0
20
40
60
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
Graph 2a: Openness
0
1
Openness
Asia and Oceania
Non-OECD Europe
Sub-Saharan Africa
2
3
Middle East - North Africa
Cental and South America
Negative binomial regression
Data source: Penn World Tables, IBFD Database.
39.
40.
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.
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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0
DTT with OECD countries
10
20
30
40
Graph 2b: Foreign direct investment
0
5
ln(FDI)
Asia and Oceania
Non-OECD Europe
Sub-Saharan Africa
10
15
Middle East - North Africa
Cental and South America
Negative binomial regression
Data source: Penn World Tables, IBFD Database.
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 correlated 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 member 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.
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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 voluntarily 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 industrialized 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 gravity model, which explains the economic activity between two countries, such as bilateral
41.
42.
See D. Paolini et al. (forthcoming), see supra n. 9.
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.
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trade or investment, with the size of the countries and the distance between the two countries. 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 asymmetric 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 countries 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.
44.
45.
46.
The variables are explained in more detail in the following subsections. The sources of the data are given in
Table 1 in the Annex.
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.
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).
The graphs in this subsection correspond to our preferred regression specification, which is shown in Table
10 column 3 in the Annex.
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Graph 3b: Source contagion
Graph 3c: Target contagion
5.00 6.00 7.00 8.00 9.0010.00
Distance (ln)
0.00
.2
0.50
Source contagion
1.00
0
0
0
.2
.2
.4
.4
Pr(Dtt)
.4
Pr(Dtt)
Pr(Dtt)
.6
.6
.6
.8
.8
1
1
.8
Graph 3a: Distance
0.00
0.50
Target contagion
1.00
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
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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 substitutes 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 significant, indicating that both exist concurrently. Graphs 3b and 3c show their respective marginal 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 interdependence 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.
48.
49.
50.
F. Barthel & E. Neumayer (2012), see supra n. 32, at p. 646f.
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.
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.
A. Shleifer (1985), A Theory of Yardstick Competition, The RAND Journal of Economics 16(3), pp. 319-327.
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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 statistically 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.
Graph 4c: Official development
assistance
.15
.15
Graph 4b: Agreement at UN
.05
Pr(Dtt)
Pr(Dtt)
-30.00 -20.00 -10.00 0.00 10.00
Foreign direct investment (ln)
0.00
0.50
Agreement at UN
1.00
0
0
.05
.1
.05
Pr(Dtt)
.15
.1
.1
.2
.25
Graph 4a: Foreign direct investment
-30.00 -20.00 -10.00 0.00 10.00
Official development assistance (ln)
Data sources: CEP II, IBFD Database, OECD, UN.
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.
52.
53.
54.
55.
See Table 9 in the Annex.
See Tables 10 to 12 in the Annex.
See M. Elsayyad (2012), Bargaining over Tax Information Exchange. Max Planck Institute for Tax Law and
Public Finance, Working Paper 2012-02. February 2012.
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).
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.
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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 industrialized 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 payments 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 (statistically) 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 neigh56.
57.
58.
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.
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).
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.
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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).
Graph 5c: Target contagion
.15
Pr(Dtt)
.1
.05
0
4.50 5.50 6.50 7.50 8.50 9.50
Distance (ln)
0.00
.3
0.50
Source contagion
1.00
0
.1
Pr(Dtt)
.2
.3
Pr(Dtt)
.2
.1
.2
Graph 5b: Source contagion
0
.4
Graph 5a: Distance
0.00
0.50
Target contagion
1.00
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 (measured 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 6b: Joint GDP per capita
0
8.00
10.00
12.00
14.00
Joint GDP (ln)
16.00
-10.00
0.00
10.00
Bilateral trade (ln)
-3.50
Pr(Dtt)
.01 .02 .03 .04 .05
Pr(Dtt)
.01
.02
0
-20.00
-6.50
-5.50
-4.50
Joint GDP per capita (ln)
Graph 6d: Joint corruption index
.03
Graph 6c: Bilateral trade
-7.50
20.00
30.00
0
0
.05
.005
Pr(Dtt)
.1
.15
Pr(Dtt)
.01 .015
.2
.02
Graph 6a: Joint GDP
20.00
40.00
60.00
80.00 100.00 120.00
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 formation 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 suggesting 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 outcome does not change substantially, thus confirming the baseline results.
60.
61.
The freedom from corruption index ranges from 0 to 100, with higher numbers reflecting lower levels of
corruption.
As of 2010, Brazil has 31 effective DTTs in place, China 93, India 83 and Russia 74.
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4.4. Summary
This section has analysed the impact of a range of geographical, economic, and political factors on the formation of DTTs. The pseudo R² measuring the fit of the two regressions presented 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 contributes 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 capitalexporting 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 determining, 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).
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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 passive 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).
Graph 7: South Africa
.5
2010
2009
2007
2000
2001
.1
1994 1995 1996
0
20
1997
2003
2002
2008
1998
40
60
Number of DTTs
2001
2000
2002
2005
1996
1995
2006
1997
1998
2004
2008
2007
2003 2011
2010
1999
-.2
Inward FDI stocks as % of GDP
.2
.3
.4
2006
2005
1999
2011
2004
.3
Graph 7b: Annual change in the share of
corporate tax revenue in GDP
Change in the share of CIT Revenue in GDP
0
-.1
.1
.2
Graph 7a: Inward FDI stocks
as % of GDP
80
2009
0
20
40
60
Number of DTTs
80
Data sources: OECD, UNCTAD, South African Revenue Service.
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.
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Julia Braun and Martin Zagler
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).
Graph 8: Brazil
Graph 8b: Annual change in the share of
corporate tax revenue in GDP
1
Graph 8a: Inward FDI stocks
as % of GDP
.05
Change in the share of CIT Revenue in GDP
.5
0
.1
Inward FDI stocks as % of GDP
.15
.2
.25
2011
2003
2001
2000
2002
2004
2005
2009
2007
2006
2008
1999
1993
1992
1994
1998
1991
1990
1997
1995 1996
20
25
30
Number of DTTs
35
-.5
.3
2010
1992
2002
1994
1996
2000
1995
1999
1997
2001
1998
1993
20
1991
2005
2008 2011
2007
2004
2009
2003 2006
2010
25
30
Number of DTTs
35
Data sources: OECD, PWT, UNCTAD.
Changing politics in the mid-1990s encouraged economic growth. After years of hyperinflation, 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 competitiveness 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.
65.
South Africa, inter alia, changed from taxation on a territorial basis to worldwide basis as of 2001.
For an overview of Brazil’s treaty network see Table 16 in the Annex.
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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 significantly afterwards exceeding 32% in 2009, with a slight decline below 30% in the following
two years (see Graph 9a).
Graph 9: Colombia
Graph 9b: Annual change in the share of
corporate tax revenue in GDP
Graph 9a: Inward FDI stocks as % of
GDP
2002
.1
2001
1997
1998
1999
2000
1996
.05
1995 1991
1990 1992
1993
1994
0
1.5
1
.5
2004
1991
1997
2003
1999
1994 1993
1996 1995
2000 2002
1992
1998
5
10
Number of DTTs
15
0
2003
2011
-.5
2005
2006
2008 2010
2007
Change in the share of CIT Revenue in GDP
.3
Inward FDI stocks as % of GDP
.15
.2
.25
2001
2
2009
0
2004
2005
2007
2006
2011
2009
2008
2010
5
10
Number of DTTs
15
Data sources: OECD, PWT, UNCTAD.
66.
For an overview of Colombia’s treaty network see Table 17 in the Annex.
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Julia Braun and Martin Zagler
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, including, amongst others, the levying of a special surcharge from 2003 to 2006, a tax rate reduction 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 signature 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 indication 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 internationally 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.
68.
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).
For an overview of Uruguay’s treaty network see Table 18 in the Annex.
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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 developed 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 signature (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.
Graph 10: Uruguay
.6
Graph 10b: Annual change in the share of
corporate tax revenue in GDP
.4
Graph 10a: Inward FDI stocks as % of
GDP
2011
2007
2008
2006
2005
2003 2004
2001
1999
1997
1995
1993
0
2002
2000
1990
1991
1994
1996
1998
1992
5
10
Number of DTTs
15
-.2
Inward FDI stocks as % of GDP
.1
.2
.3
0
2010
Change in the share of CIT Revenue in GDP
0
.2
.4
2004
2009
1993 1992
2008
2005 1996
1998
1995 2006
1994
1997
1999 2000
2001 2002
1991
2003
2009
2010
2011
2007
0
5
10
Number of DTTs
15
Data sources: OECD, UNCTAD.
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 determine 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
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Julia Braun and Martin Zagler
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 corporate tax revenues might have been impacted negatively.
Data on revenues collected from withholding taxes on passive income paid to foreign entities would give additional insight into the effects of DTTs, but unfortunately there is no systematic 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 developing 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.
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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 treaties 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 halfagreement 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 WORLD TAX JOURNAL OCTOBER 2014
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Julia Braun and Martin Zagler
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.
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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)
Population
(1)
(2)
*** 2.216
(0.475)
-0.2174
(0.599)
GDP (ln)
*** 3.362
(0.843)
GDP per
capita
(3)
(4)
(5)
*** 3.144
(0.605)
*** 3.325
(0.649)
*** 1.708
(0.546)
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.
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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)
Foreign direct
investment (ln FDI)
* 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)
0.310
(0.239)
* 0.304
(0.229)
Corruption index
(heritage
foundation)
0.038
(0.059)
Official development
assistance (ln)
0.509
(0.630)
Official development
assistance/GDP (ln)
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)
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 (chi 2) is the goodness
of fit test for the corresponding Poisson regression, which is rejected in all cases. All observations from the year 2010.
Table 6: Number of DTTs of developing countries (142) with other developing countries
(13)
(14)
(15)
*** 5.030
(0.889)
*** 2.051
(1.027)
*** 5.921
(0.896)
Openness
2.518
(2.723)
*** 10.058
(3.345)
Foreign direct
investment (ln FDI)
0.628
(1.075)
Population
(11)
(12)
*** 4.325
(0.966)
** -2.338
(1.003)
GDP (ln)
*** 7.368
(1.322)
GDP per capita
** 2.337
(1.003)
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)
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 (chi 2) is the goodness
of fit test for the corresponding Poisson regression, which is rejected in all cases. All observations from the year 2010.
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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
0.53
Common 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
Notes: All observations from the year 2010.
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
Notes: All observations from the year 2010.
Table 9: OECD and developing countries as partners, part 1
Source contagion
Target contagion
(1)
(2)
(3)
(4)
***-0.076
***-0.125
***-0.061
***-0.058
(0.015)
(0.013)
(0.012)
(0.011)
***0.455
***0.518
***0.523
(0.038)
(0.025)
(0.023)
***0.916
***0.977
***0.556
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Table 9: OECD and developing countries as partners, part 1
(1)
Joint GDP (ln)
Difference in GDP (ln)
Bilateral FDI (ln)
Bilateral trade volume (ln)
(2)
(3)
(4)
(0.067)
(0.124)
(0.086)
0.028
***0.105
0.003
(0.018)
(0.017)
(0.012)
0.012
***-0.064
0.007
(0.009)
(0.011)
(0.007)
***0.003
***0.005
***0.003
***0.003
(0.0004)
(0.0005)
(0.0004)
(0.0004)
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.
Table 10: OECD and developing countries as partners, part 2
Distance (ln)
Source contagion
Target contagion
Joint GDP (ln)
Difference in GDP per
capita (ln)
Bilateral FDI (ln)
Common language
Colony
(1)
(2)
(3)
(4)
***-0.059
***-0.058
***-0.050
***-0.049
(0.011)
(0.012)
(0.011)
(0.011)
***0.526
***0.530
***0.519
***0.524
(0.021)
(0.022)
(0.021)
(0.021)
***0.834
***0.855
***0.791
***0.793
(0.111)
(0.111)
(0.103)
(0.103)
0.006
0.011
0.006
0.006
(0.008)
(0.008)
(0.007)
(0.007)
0.012
*0.014
0.007
0.007
(0.008)
(0.008)
(0.007)
(0.007)
***0.002
***0.002
***0.002
***0.002
(0.0004)
(0.0003)
(0.0003)
(0.0003)
***0.075
***0.081
***0.077
***0.076
(0.016)
(0.017)
(0.017)
(0.017)
***0.087
***0.087
***0.084
***0.084
(0.023)
(0.024)
(0.025)
(0.025)
**0.116
**0.122
**0.115
(0.051)
(0.050)
(0.050)
Agreement at UN (19902010)
ODA (ln) 2006-2010
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Table 10: OECD and developing countries as partners, part 2
(1)
(2)
(3)
(4)
(0.001)
ODA per capita (ln) 20062010
**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)
Distance (ln)
Source contagion
Target contagion
Joint GDP (ln)
Difference in GDP
per capita (ln)
Bilateral FDI (ln)
Common language
Colony
Agreement at UN
(1990-2010)
(2)
(4)
(5)
***-0.051
***-0.050
***-0.051
***-0.049
***-0.051
(0.011)
(0.010)
(0.011)
(0.011)
(0.011)
***0.520
***0.520
***0.482
***0.518
***0.527
(0.021)
(0.021)
(0.030)
(0.021)
(0.021)
***0.802
***0.785
***0.792
***0.814
(0.104)
(0.097)
(0.103)
(0.113)
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)
***0.002
***0.002
***0.003
***0.002
***0.002
(0.0003)
(0.0003)
(0.0004)
(0.0003)
(0.0003)
***0.077
***0.074
***0.068
***0.077
***0.077
(0.017)
(0.016)
(0.023)
(0.017)
(0.017)
***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)
ODA (ln) 2006-2010
ODA (ln) 2008-2010
(3)
(0.096)
(0.051)
(0.053)
***0.004
**0.002
**0.002
(0.001)
(0.001)
(0.001)
***0.002
(0.0008)
ODA (ln) 2010
***0.002
(0.0006)
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Table 11: OECD and developing countries as partners, part 3 (robustness 1)
(1)
(2)
(3)
Target contagion 2
(4)
(5)
***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.
Table 12: OECD and developing countries as partners, part 4 (robustness 2)
Distance (ln)
Source contagion
Target contagion
Joint GDP (ln)
Difference in GDP per
capita (ln)
Bilateral FDI (ln)
Common language
Colony
Agreement at UN (19902010)
ODA (ln) 2006-2010
Observations
(1)
(2)
(3)
(4)
***-0.046
***-0.050
***-0.046
***-0.042
(0.011)
(0.011)
(0.011)
(0.011)
***0.520
***0.518
***0.539
***0.530
(0.021)
(0.021)
(0.019)
(0.019)
***0.817
***0.791
***0.863
***0.868
(0.105)
(0.103)
(0.113)
(0.121)
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)
***0.002
***0.002
***0.002
***0.002
(0.0003)
(0.0003)
(0.0003)
(0.0003)
***0.077
***0.077
***0.063
***0.057
(0.018)
(0.017)
(0.015)
(0.014)
***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)
**0.002
**0.002
**0.002
*0.002
(0.0009)
(0.0009)
(0.0009)
(0.0009)
4062
4158
3884
3728
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Table 12: OECD and developing countries as partners, part 4 (robustness 2)
(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
Distance (ln)
Source contagion
Target contagion
Joint GDP (ln)
(1)
(2)
(3)
(4)
(5)
***-0.031
***-0.032
***-0.026
***-0.029
***-0.029
(0.003)
(0.003)
(0.003)
(0.004)
(0.004)
***0.104
***0.105
***0.108
***0.112
***0.112
(0.008)
(0.008)
(0.008)
(0.008)
(0.008)
***0.133
***0.132
***0.133
***0.135
***0.135
(0.009)
(0.009)
(0.009)
(0.009)
(0.009)
***0.019
***0.018
***0.020
***0.021
***0.021
(0.002)
(0.002)
(0.002)
(0.002)
(0.002)
0.006
***0.013
**0.009
**0.009
(0.004)
(0.004)
(0.005)
(0.005)
***0.002
***0.002
***0.001
***0.001
***0.001
(0.0002)
(0.0002)
(0.0002)
(0.0002)
(0.0002)
***0.031
***0.025
***0.025
(0.007)
(0.008)
(0.008)
***0.030
***0.030
***0.030
(0.008)
(0.008)
(0.008)
***0.001
***0.001
(0.0002)
(0.0002)
Joint GDP per capita (ln)
Difference in GDP per
capita (ln)
-0.001
(0.002)
Bilateral trade volume (ln)
Common official language
Common colonial past
Corruption index
Agreement at UN (19902010)
-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.
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Table 14: Developing countries as partners, part 2 (Robustness: without BRIC countries)
Distance (ln)
Source contagion
Target contagion
Joint GDP (ln)
(1)
(2)
(3)
(4)
(5)
***-0.027
***-0.027
***-0.022
***-0.023
***-0.024
(0.003)
(0.003)
(0.003)
(0.003)
(0.003)
***0.088
***0.088
***0.091
***0.095
***0.094
(0.008)
(0.008)
(0.008)
(0.008)
(0.008)
***0.118
***0.117
***0.118
***0.120
***0.119
(0.008)
(0.008)
(0.008)
(0.009)
(0.009)
***0.018
***0.016
***0.019
***0.021
***0.021
(0.003)
(0.002)
(0.003)
(0.003)
(0.003)
*0.007
***0.011
*0.008
**0.007
(0.004)
(0.004)
(0.005)
(0.005)
***0.002
***0.002
***0.001
***0.001
***0.001
(0.0002)
(0.0002)
(0.0002)
(0.0002)
(0.0002)
***0.029
***0.021
***0.023
(0.006)
(0.007)
(0.007)
***0.027
***0.026
***0.027
(0.007)
(0.008)
(0.008)
***0.001
***0.001
(0.0002)
(0.0002)
Joint GDP per capita (ln)
Difference in GDP per
capita (ln)
-0.001
(0.002)
Bilateral trade volume (ln)
Common official language
Common colonial past
Corruption index
Agreement at UN (19902010)
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
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Table 15: South Africa – International tax treaties
Treaty partner
Date signed
Date into force
Type of treaty
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
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Table 15: South Africa – International tax treaties
Treaty partner
Date signed
Date into force
Type of treaty
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
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Table 15: South Africa – International tax treaties
Treaty partner
Date signed
Date into force
Type of treaty
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
Treaty partner
Date signed
Date into force
Type of treaty
Argentina
1980
1982
DTT
Austria
1975
1976
DTT
Belgium
1972
1973
DTT
Bermuda
2012
Not in force yet
TIEA
Canada
1984
1985
DTT
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
Finland
1996
1997
DTT
France
1971
1972
DTT
Guernsey
2013
Not in force yet
TIEA
Hungary
1986
1990
DTT
India
1988
1992
DTT
Israel
2002
2005
DTT
Italy
1978
1981
DTT
Japan
1967
1967
DTT
Jersey
2013
Not in force yet
TIEA
Korea, Republic of
1989
1991
DTT
Luxembourg
1978
1980
DTT
Mexico
2003
2006
DTT
Netherlands
1990
1991
DTT
Norway
1980
1981
DTT
Peru
2006
2009
DTT
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Table 16: Brazil – International tax treaties
Treaty partner
Date signed
Date into force
Type of treaty
Philippines
1983
1991
DTT
Portugal
2000
2001
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
Data sources: OECD Exchange of Tax Information Portal and IBFD Tax Research Platform.
Table 17: Colombia – International tax treaties
Treaty partner
Date signed
Date into force
Type of treaty
Bolivia
2004
2005
DTT
Canada
2008
2012
DTT
Chile
2007
2009
DTT
Curaçao
2013
Not in force yet
TIEA
Czech Republic
2012
Not in force yet
DTT
Ecuador
2004
2005
DTT
India
2011
Not in force yet
DTT
Korea, Republic of
2010
2014
DTT
Mexico
2009
2013
DTT
Peru
2004
2005
DTT
Portugal
2010
Not in force yet
DTT
Spain
2005
2008
DTT
Switzerland
2007
2011
DTT
United States
2001
2014
TIEA
Data sources: OECD Exchange of Tax Information Portal and IBFD Tax Research Platform.
Table 18: Uruguay – International Tax Treaties
Treaty partner
Date signed
Date into force
Type of treaty
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
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An Economic Perspective on Double Tax Treaties with(in) Developing Countries
Table 18: Uruguay – International Tax Treaties
Treaty partner
Date signed
Date into force
Type of treaty
Denmark
2011
Not in force yet
TIEA
Ecuador
2011
2012
DTT
Faroe Islands
2011
Not in force yet
TIEA
Finland
2011
2013
DTT
France
2010
2010
TIEA
Germany
2010
2011
DTT
Greenland
2011
Not in force yet
TIEA
Guernsey
2014
Not in force yet
TIEA
Hungary
1988
1994
DTT
Iceland
2011
2012
TIEA
India
2011
2013
DTT
Korea, Republic of
2011
2013
DTT
Liechtenstein
2010
2012
DTT
Malta
2011
2012
DTT
Mexico
2009
2010
DTT
Netherlands
2012
Not in force yet
TIEA
Norway
2011
2014
TIEA
Portugal
2009
2012
DTT
Romania
2012
Not in force yet
DTT
Spain
2009
2011
DTT
Sweden
2011
Not in force yet
TIEA
Switzerland
2010
2011
DTT
United Kingdom
2013
Not in force yet
TIEA
Vietnam
2013
Not in force yet
DTT
Data sources: OECD Exchange of Tax Information Portal and IBFD Tax Research Platform.
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