Download fdi between eu member states: gravity model and taxes

FDI BETWEEN EU MEMBER STATES:
GRAVITY MODEL AND TAXES1
Paweł Folfas M.A.2
Warsaw School of Economics
Institute of International Economics
Abstract
Gravity models utilize the attractive force concept as an analogy to explain volume of
trade, capital flows and migration among the countries of the world. A fundamental goal of
my paper is to prove that direct investment flows between EU Member States are determined
by standard variables included in gravity models such as: gross domestic products (total and
per capita) of home and host economy and distance between them.
In my gravity model of FDI I include not only geographic distance but also variables
illustrating economic distance, namely: membership in euro area, cultural similarity, duration
of membership in the EU (EC). Additionally, my model encompasses variables linked with
tax policy of EU Member States. I prove that differences in corporate tax burdens determine
FDI flows. States with simpler and lower taxes allure more direct capital. Especially, model
confirms that European offshore financial centers: Cyprus, Luxembourg and Malta play a
significant role in intra-EU direct capital flows.
On one hand, my model includes time invariant variables, so the model with fixed
effects is not proper. On the other hand, as individual effects might be correlated with some
independent variables, model with random effects can be also not adequate. Consequently, I
use Hausman-Taylor estimator. My FDI gravity model is based mainly on OECD and WDI
databases concerning two last decades.
Keywords: FDI, gravity model, the European Union, corporate taxes
JEL codes: F14, F15, F21
1
Participation in ETSG 2011 Conference was financed from doctoral scholarship of National Science Centre
(No. of agreement 3953/B/H03/2011/40). Text includes partial results of PhD research.
2
[email protected]
1. Background studies on gravity models of foreign direct investments
Gravity models appear as an adaptation of the law of universal gravitation3 for
socioeconomic phenomena. For the first time the law of gravitation was applied to social
science by Carey [1858, pp. 42, 88–90, 371–373, 465]. According to Carey phenomena in
physics and in social science are determined by similar laws and the gravitational attraction is
also present as a “molecular gravity in a society”. He claimed that more people in some area
means stronger “social attractive force” which is directly proportional to the “mass of people”
and inversely proportional to the distance (not only geographic, but also social or
psychological distance) between persons. Carey launched studies based on gravity models
which focused on determinants of masses’ behaviour, especially interdependence between
size of population, distance and attractive force between people. Gravity models became tools
in analyzing: size of migrations and trade, number of phone calls and number of passengers
and goods in transport. These studies concerned the attractive force between masses of people
living in different regions of single country4.
In 1960s gravity models were applied to analyzing international trade flows. Pioneers
in these studies were: Linemann [1963], Pöyhönen [1963], Pullainen [1963] and Tinbergen
[1962]. They were conducting independent and simultaneous studies which brought similar
results. However the most known is study proposed by Tinbergen who was announced as a
discoverer of gravity law (gravity equation) in international economics (see equation (1)).
(1)
X ij  C
Yi a Y jb
Dijd
where notation is defined as follows:
Xij – international flow from reporter i (origin country) to partner j (destination country) or the
sum of the flows in both directions (for example: export, import or sum of export and import
values; size of migration, foreign direct investment, tourism),
Yi(j) – economic sizes of the two locations (for example: GDP, population, endowment of
labour, land or capital),
Dij – distance between the locations (usually between their economic centres),
3
In 1687, Newton proposed the law of universal gravitations which states states that every point mass in the
universe attracts every other point mass with a force that is directly proportional to the product of their masses
and inversely proportional to the square of the distance between them.
4
See for example: Ravenstein [1885], Reilly [1929], Stewart [1948], Converse [1949], Zipf [1949], Dodd
[1950], Hammer and Ikle [1957].
2
G – gravitational constant,
a, b and d – elasticity of Xij to change in Yi, Yj and Dij.
Since Tinbergen’s study the gravity equation has been one of the most popular
empirical equations that has been successfully used to analyze the wide spectrum of
interactions in international economics. The gravity equation postulates that the amount of
flow between two locations increases in their economic sizes and decreases in the cost of
transportation between them as measured by the distance between economic centres of
locations.
However, there are many variables which can embody economic masses of locations.
In studies of bilateral trade and direct investment flows the economic size of the exporting and
importing countries are usually measured by gross domestic product. However economic size
can be also measured by: gross national product, gross domestic product and population, gross
domestic product per capita or endowment of production factors (absolute value or per
capita). Therefore, there are a number methods of measuring gross domestic products: in
current prices, in constant prices or in purchasing power parity. It is debatable which measure
is the most adequate for gravity models. In my opinion, as international flows of trade or
capital are measured in current prices, gross domestic product in current prices is the most
proper. Additionally, gross domestic products in PPP distort the difference between countries,
as their values are higher for developing countries and lower for developed economies
[Czarny, Folfas 2011, pp. 5– 6].
Distance in the law of universal gravitation illustrates resistance to attractive force
between point masses. In international economics resistance to gravity force between
locations is embodied by all factors which hamper international flows of goods, services,
capital, labour and technology. These factors can be divided in two groups: economic and
noneconomic. The first group encompasses: geographic distance between economic centres of
locations, adjacency, characteristics of geographic location (island, landlocked country),
cultural similarity (common languages, colonial links), volatility of exchange rates (common
currency, exchange rate regime), tariffs and nontariffs trade barriers, membership in
international organizations (free trade areas, custom unions, common markets, economic and
monetary unions) and the quality of infrastructure. The second group includes: regulations,
political conflicts and environmental barriers [Head 2000, pp. 5–10].
Moreover, there are a few candidates for dependent variable in gravity models.
Bilateral international flows can be illustrated by: export, import or sum of both of them. In
3
the case of international trade, exports value is not distorted by tariffs, nontariffs barriers and
cost of transport and insurance. However data concerning imports are more precise thanks to
the registration linked with customs duties (data concerning exports are usually based on tax
returns). Taking into consideration a sum of export and import is the most complete method,
however it meets difficulties with gaps in data. In the case of foreign direct investments and
other capital flows data concerning inflows are more precise than data concerning outflows,
which usually are riddled with gaps [Czarny, Folfas 2011, p. 7].
Popularity of gravity models in empirical studies concerning international trade in
goods and services and international movements of production factors has encouraged
economist to create theoretical foundations of gravity law, especially gravity model of
international trade. To the most important belong studies conducted by: Anderson [1979],
Bergstrand [1985, 1989], Helpman [1987], Deardoff [1998], Anderson and van Wincoop
[2003], Feenstra [2004], Haveman and Hummels [2004], Debaere [2005], Helpman, Melitz
and Rubinstein [2008]. The majority of mentioned authors conducted empirical studies of
bilateral trade based on different version of gravity models5.
Generally, gravity model is a kind of short-hand representation of supply and demand
forces. If Xij represents exports and if the country i is the origin, then Yi represents the amount
it is willing to supply. Meanwhile Yj represents the amount destination j demands, in other
words the amount of income country j spends on all goods from any source i. Big economies
with high income usually spend a lot of money on import. They are also able to allure quite
big share of other economies’ expenditures (so they are able to export a lot of goods) because
they produce goods in wide variety. Consequently, the amount of export is higher due to big
economic size at least of one partner [Head 2000, p. 3; Krugman, Obstfeld 2007, pp. 25–29].
Gravity model has become one of the most popular and successful analytical tool in
international economics, especially due to its high explanatory power and easily available data
in studies concerning international trade of goods. However, recently gravity model has
become very popular analytical tool also in explaining bilateral flows of capital, especially in
explaining determinants of foreign direct investments (FDI). Unfortunately construction of
FDI gravity models meets a number of additional obstacles. Firstly, the values of bilateral FDI
flows are available only for selected countries, mostly for developed countries (OECD, UE)
or countries from one region. Consequently, construction of FDI gravity models for all (even
the majority) countries of the world seems to be not possible. Secondly, FDI flows are
5
Studies based on gravity model of trade conducted by Polish economist – see for example Cieślik [2009],
Cieślik, Michałek, Mycielski [2009] and Czarny, Śledziewska [2009].
4
vulnerable to single events (for example large cross-border mergers and acquisitions) what
may lead to irregularities disturbing gravity law. Instead of FDI flows, FDI stocks can be
used. However in my opinion FDI stocks as cumulative data are not adequate for independent
variables in model which describe values of GDPs or endowments of production factors only
in one year. Thirdly, there are negative values of FDI (disinvestments) which can also disturb
the regularity of gravity law. In my opinion these negative cannot be excluded from the
sample because it may lead to falsifying the results of empirical studies (disinvestments
account for 10–20% of all observations).
Despite of all mentioned difficulties, in the last ten years gravity models of FDI have
become very popular6. Apart from standards variables describing GDPs of host and home
country and geographic distance between economic centres of two locations, independent
variables in FDI gravity models illustrates issues such as:

endowment of physical and human capital (see Egger, Pffaffermayr [2004a]),

endowment pf labour force (see Bevan, Estrin [2004], Egger, Pffaffermayr [2004a, b],
Milner et al. [2004]),

quality of legal system (see Milner et al. [2004], Lada, Tchorek [2008]),

infrastructure – transport networks, number of phone calls (see Eggger, Pffaffermayr
[2004b], Roberto [2004], Portes, Rey [2005]),

investment risk and barriers, protection of foreign investors (see Bevan, Estrin
[2004]),

cultural similarity – common language, similarity in legal systems (see Buch et al.
[2003]),

taxation (see Egger, Pffaffermayr [2004b], Milner et al. [2004]),

bilateral trade, openness of economies (see Stone, Jeon [1999], Szczepkowska,
Wojciechowski [2002], Kumar, Zajc [2003], Bevan, Estrin [2004], Portes, Rey [2005],
Lada, Tchorek [2008]),

unemployment rate (see Roberto [2004], Szczepkowska, Wojciechowski [2002]),
6
Apart from gravity model bilateral FDI relations has been also scrutinized in other contexts. Desbordes, Vicard
[2009] investigates the effect of implementation of bilateral investment treaties on bilateral stocks of foreign
direct investment. They show that the effect of entry into force of bilateral treaty crucially depends on the quality
of political relations between signatory countries; it increases FDI more between countries with tense
relationships than between friendly countries. Therefore, Benassy-Quere, Coupet and Mayer [2007] investigate
institutional determinants of FDI and find that the similarity of institutions between the host and home country
raises bilateral FDI flows. Additionally, Choi [2004] examines the role of FDI in convergence of income level
and growth. Panel analysis brings conclusion that income level and growth gaps between source and host
countries decreases as bilateral FDI increases. Finally, UNCTAD [2007, pp. 19–22] includes studies concerning
bilateral FDI relationships based on FDI intensity index – see also my paper from previous edition of ETSG
[Folfas 2010b].
5

membership in international organizations (see Brenton et al. [1999], Buch et al.
[2003], Kumar, Zajc [2003], Lada, Tchorek [2008]),

education of labour force (see Kajmar, Zajc [2003], Eggger, Pffaffermayr [2004b],
Lada, Tchorek [2008]),

unit labour costs, average wages (see Szczepkowska, Wojciechowski [2002], Bevan,
Estrin [2004], Lada, Tchorek [2008]),

interests rates (see Bevan, Estrin [2004]),

development of financial markets (see Portes, Rey [2004], Lada, Tchorek [2008]),

bilateral investments treaties (see Egger, Pffaffermayr [2004b]),

exchange rates, common currencies (see Szczepkowska, Wojciechowski [2002], Lada,
Tchorek [2008])

expenditures on R&D (see Lada, Tchorek [2008]),

inflation rate, GDP deflator (see Szczepkowska, Wojciechowski [2002], Lada,
Tchorek [2008]).
The wide spectrum of independent variables appears as a symptom of high potential of
FDI gravity models and the perspective of their further development. As my empirical studies
focuses on FDI bilateral flows between EU–27 Member States, I pay attention to two groups
of determinants of FDI: investment creation effect (due to processes of integration in the old
continent) and corporate taxation. Firstly, investment creation effect means intensification of
FDI flows between countries participating in regional integration (see for example Baldwin,
Forslid and Haaland [1996], Motta, Norman [1996]). It is possible that membership in the EU
and in the euro area intensify flows of direct capital between EU Member States. Secondly,
there are significant disparities in corporate taxation between EU Member States. Differences
in taxes especially in corporate tax rates allure transnational corporations to use transfer
pricing in order to minimize total tax payments. Transfer pricing is directly linked with flows
of direct capital, especially to and from offshore financial centres7 (tax havens) such as:
7
Traditionally, tax haven is a country or territory where certain taxes (especially direct taxes such as: income
taxes or inheritance taxes) are levied at a low rate or not at all. Tax havens allow non-residents to escape higher
taxes in their country of residence. Particularly, liberal tax jurisidictions allure to establish foreign affiliates of
transnational corporations (TNCs) originating from developed countries, where corporate taxes are much higher.
According to the traditional approach tax havens can be divided into two groups: no-tax havens and low-tax
havens. The first group encompasses: Bermuda, British Virgin Islands, Cayman Islands, Montserrat, Nauru and
Turks and Caicos Islands. These tax havens impose nil taxes generally or impose taxes only on domestic
incomes (the rule of no-tax-on-foreign-income). Low tax havens include: Andorra, Anguilla, Antigua and
Barbuda, Aruba, Bahamas, Bahrain, Barbados, Belize, Cook Islands, Dominica, Gibraltar, Grenada, Guersney,
Hong Kong (SAR of China), Isle of Man, Jersey, Liberia, Liechtenstein, Macau (SAR of China), Maldives,
Marshall Islands, Mauritius, Monaco, Netherland Antilles, Niue, Panama, Samoa, Saint Lucia, Saint Kitts and
Nevis, Saint Vincent and the Grenadines, Samoa, Seychelles, Tonga, US Virgin Islands, Vanuatu. However,
6
Cyprus, Luxembourg and Malta. Consequently, it is possible that differences in corporate tax
rates also intensify direct capital flows between EU Member States.
2. Data and methodology
In my study, I use bilateral direct investments flows between EU–27 Member States
based on data concerning FDI inflows into host economy (i) from home economy (j). The
sample covers the period 1990–20098. My gravity model is estimated in terms of natural
logarithms (ln) – see equation (2).
(2)
ln FDI ijt   0   1 ln GDPit   2 ln GDPjt   3 ln Dij  Z ijt  cij   ijt
where notation is defined as follows:
i, j, t – indexes respectively for: host economy, home economy and year,
FDIijt – FDI inflow into host economy coming from home economy in year t,
GDPi(j)t – Gross Domestic Product of host economy (home economy) in year t,
Dij – geographic distance between economic centres of host and home economy (constant for
EU Member States during 1990–2009),
Zijt – vector of other variables which determines bilateral FDI flow;
cij – individual location-pair specific effect,
 ijt – error term.
Including in my specification individual location-pair specific effect suggests
application one of the typical panel data based estimators, namely fixed or random effects
approach. However, the fixed effects approach is not adequate for models including time
invariant variables – for example distance, which is one of the fundamental variable. On the
contrary, random effects approach is available also for models with time invariant variables.
Additionally, this approach needs zero correlation between the individual effects and the
traditional approach of tax haven is not enough. Contemporary name of tax havens is offshore financial centres
(OFCs) and mean jurisdictions that make their living mainly by attracting foreign financial capital. They offer
foreign business and well-heeled individuals not only low or no taxes, but also: economic and political stability,
business- friendly regulation and laws, well-developed sector of services (especially in finance, banking and
insurance), lack of constraints in capital flows and above all discretion. OFCs includes traditional tax havens but
also countries (territorias) such as: Costa Rica, Cyprus, Malta, Dublin (Ireland), Labuan (Malaysia), Lebanon,
Luxembourg, Singapore, Switzerland which has not been perceived as traditional tax havens. Geographically,
OFCs can be divided into three groups: European OFCs, American OFCs and Asian (with Oceania) OFCs – see
more The Economist [2007], Folfas [2008], Folfas [2010a].
8
Before 1990s new EU–12 Member States had been cut off from substantial FDI inflows, consequently the first
included year is 1990. Therefore, data for 2010–2011 has not been available yet (apart from some partial data).
7
independent variables in the model. Unfortunately, in my specification this assumption does
not hold. My model encompasses independent variables which characterize pair of host and
home economy (for example difference in corporate tax rates or simultaneous membership in
euro area). These kinds of variables are potentially correlated with individual effect,
consequently approach based on random effects is also not proper. In this situation there is
still one solution to be applied – Hausman-Taylor [1981] estimation method. It allows using
of both time-varying and time invariant variables and some of them can be endogenous in the
sense of correlation with individual effects, but remain exogenous with respect to error term
[Czarny et al. 2010, pp. 10–12].
Additional variables in my specification encompass variables linked with European
integration and corporate taxation in EU–27 Member States (a few of them are dummy
variables) – see equation (3).
(3)
ln FDI
ijt
    ln GDP   ln GDP   ln D   ln gdpit  gdp jt   EU   EA 
0
1
it
2
jt
3
ij
1
2
ijt
3 ijt
  Accession   Culture   ln Taxit Tax jt   OFC  c  
4
ij
5
ij
6
7
ij
ij
ijt
where notation is defined as follows:
Abbreviation
FDI
GDP
D
gdp
EU
EA
Accession
Culture
Tax
OFC
Description
Foreign direct investments inflows (current prices,
USD)
Gross Domestic Product (current prices, USD)
Geographic distance between capitals (km)
Gross Domestic Product per capita (current prices,
USD)
Dummy variable: 1 if both states belong to the EU and
0 otherwise (this variable incorporates EU
enlargements)
Dummy variable: 1 if both states use euro as legal
tender and 0 otherwise
Dummy variable: 1 if both states join the EU in the
same year and 0 otherwise
Dummy variable: 1 if both states belong to the same
group and 0 otherwise
Corporate tax rate (part of profit; if progressive tax
average rate)
Dummy variable: 1 if host or home economy is
offshore financial centre (CY, LU, MT)
8
Data source
OECD and Eurostat
The World Bank, WDI
CEPII
The World Bank, WDI
Eurostat
Eurostat
Eurostat
Own
classification
including
groups of EU Member States: (1)
Celtic (IE, UK); (2) Mediterranean
(CY, EL, IT, MT, PT); (3)
Francophone and Benelux (BE,
FR, LU, NL); (4) German (AT,
DE, HU); (5) Slavonic (CZ, PL, SI,
SK); (6) Baltic (EE, LT, LV); (7)
Scandinavian (DK, FI, SE); (8)
Other (BG, RO)
Eurostat
OECD and The Economist [2007]
(list of tax havens/offshore
financial centres)
Therefore I conduct empirical analysis for smaller sample which covers the period
2005–2009, however with additional independent variables linked with taxation9 - see
equation (4).
(4)
ln FDI
ijt
    ln GDP   ln GDP   ln D   ln gdpit  gdp jt   EU   EA 
0
1
it
2
jt
3
ij
1
2
ijt
3 ijt
  Accession   Culture   ln Tax it Tax jt   OFC 
4
ij
5
ij
6
7
ij
  ln TaxTime  TaxTime   ln TaxPayments  TaxPayments   ln TaxTotal  TaxTotal  c  
8
it
jt
9
it
jt
10
it
jt
ij
ijt
where notation is defined as follows:
Abbreviation
TaxTime
TaxPayments
TaxTotal
Description
Total time required to fulfill all tax payments (hours)
Number of tax payments
Total costs due to tax payments (part of profit)
Data source
The World Bank, WDI
The World Bank, WDI
The World Bank, WDI
3. Estimation results
The estimated parameters on variables derived from basic version of gravity equation
are statistically significant and have expected signs (see table 1).
Table 1. Estimation results (part 1)
Period: 1990–2009
Equation (3)
Number of pairs (host and home economy): 696
Number of observations: 8404
Significant codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘^’ 0.1
Variable
Coefficient
Standard Error
ln GDPit
0.161112
0.053612
ln GDPjt
0.623921
0.054455
ln Dij
-1.000338
0.143937
ln |gdpit-gdpjt|
-0.142701
0.071172
EUijt
0.036161
0.114326
EAijt
-0.107184
0.152513
Accessionij
-0.122758
0.261631
Cultureij
0.519821
0.299310
ln|Taxit-Taxjt|
0.121970
0.052579
OFCij
1.673478
0.252150
Constant
0.399593
1.628515
t-value
3.0052
11.4575
-6.9499
-2.0050
0.3163
-0.7028
-0.4692
1.7367
2.3198
6.6368
0.2454
Source: Own study based on computations in R-package
9
Data for these variables have been available since 2005.
9
P>|t|
0.002654 **
<2.2e-16 ***
3.657e-12 ***
0.044961 *
0.751776
0.482188
0.638925
0.082434 ^
0.020354 *
3.205e-11 ***
0.806168
The positive values of the estimated parameters on the GDP of both host and home
economy show that FDI flows are bigger between larger economies. However the estimated
coefficients are lower than in typical gravity model of international trade (values between 0.7
and 1.1). It can be caused by disinvestments and fluctuations in FDI inflows caused by single
events (for example large M&A). Therefore, the value of estimated parameter on the GDP of
home economy is much higher than value on the GDP of host economy. It suggests the higher
magnitude of home economy in bilateral FDI flows. As expected distance has negative impact
on bilateral FDI flows and also the impact of difference in GDP per capita is negative. It
proves that between similar economies FDI flows are more intensive.
Figure 1. FDI inflows into the EU–27 Member States vs. global FDI inflows (1990–2009, Mio. USD)
2500000
2000000
1500000
1000000
500000
0
1990
1991
1992
1993
1994
1995
1996
Into EU-27 from World
1997
1998
1999
2000
2001
2002
Global FDI inflows
2003
2004
2005
2006
2007
2008
2009
Into EU-27 from EU-27
Source: Own study based on UNCTAD’s and Eurostat’s data
All three variables linked with the European integration processes are not statistically
significant. There is no positive impact on FDI inflows when both host and home economy
belong to the EU, euro zone or both joined the EU (EC) in the same year. Firstly, it may
suggest that individual characteristics of host and home economies are more important for
bilateral FDI flows than participation in regional integration (weak investment creation
effect). Secondly, dummy variable might not be enough adequate for illustrating the results of
integration processes which is continuous, not discrete. Consequently, investment creation
effected linked with the EU-enlargement in 2004 and 2007 could have occurred earlier – for
example when new EU Member States joined free trade area, started accession negotiations
and so on. The share of FDI inflows to the EU–27 Member States coming from EU–27
Member States increased in early 1990s – probably due to intensive FDI flows from EU–15
10
Member States to associated CEE countries which opened their markets for foreign direct
capital – and also increased in late 1990s during the finish of accession negotiations (see
figures 1 and 2). Thirdly, it is possible that since 2004 new EU Member States has allured
more foreign capital from economies outside of the EU–27. In consequence, investment
diversion effect can be stronger than investment creation effect. The insignificance of
variables describing the same year of accession suggests that “fresh”, flourishing markets can
allure more foreign capital than “old”, saturated markets.
Figure 2. The geographic structure of FDI inflows into the EU–27 Member States (1990– 2009)
100%
90%
80%
70%
60%
50%
40%
30%
20%
10%
0%
90 91 92 93 94 95 96 97 98 99 00 01 02 03 04 05 06 07 08 09
19 19 19 19 19 19 19 19 19 19 20 20 20 20 20 20 20 20 20 20
EU-27
non EU-27
Source: Own study based on Eurostat’s data
Moreover, the cultural similarity has positive impact on bilateral FDI inflows.
Similarity in business mentality, law in book, law in action and common language and
customs lead to stronger FDI relationships between host and home economy. Finally,
differences in corporate tax rates have also positive impact on flows of foreign direct capital.
It proves that tax avoidance including transfer pricing remains one of the most significant tool
in maximizing of total net profit. Transnational corporations use also offshore financial
centres, consequently FDI flows between Cyprus, Luxembourg, Malta and other EU–27
Member States are more intensive (the variable OFC is also statistically significant).
Homogeneous corporate tax rate in the EU, especially at relatively high level (for example
incumbent rate in Germany or France) may lead to decrease in FDI flows between EU
Member States and might intensify capital relationships with offshore financial centres (tax
havens) located outside of the EU. Corporate tax rate appears as an extremely significant
11
instrument not only in domestic, but also in foreign economic policy. Therefore, it seems to
be a symbol of economic sovereignty within the EU.
Table 2. Estimation results (part 2)
Period: 2005–2009
Equation (4)
Number of pairs (host and home economy): 643
Number of observations: 2706
Significant codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘^’ 0.1
Variable
Coefficient
Standard Error
ln GDPit
0.201054
0.112678
ln GDPjt
0.767659
0.110482
ln Dij
-0.643572
0.223238
ln |gdpit-gdpjt|
0.075379
0.250517
EUijt
-0.268919
0.433840
EAijt
-0.245080
0.621024
Accessionij
0.098786
0.543694
Cultureij
1.063568
0.490245
ln|Taxit-Taxjt|
0.200297
0.178924
OFCij
2.035610
0.637526
ln|TaxTimeit-0.064335
0.097594
TaxTimejt|
ln|TaxPaymentsit-0.088111
0.093801
TaxPaymentsjt|
ln|TotalTaxit-0.082067
0.226195
TotalTaxjt|
Constant
-6.393546
3.993103
t-value
1.7843
6.9483
-2.8829
0.3009
-0.6199
-0.3946
0.1817
2.1695
1.1195
3.1930
-0.6592
P>|t|
0.074369 ^
3.697e-12 ***
0.003940 **
0.763496
0.535351
0.693109
0.855823
0.030048 *
0.262946
0.001408 **
0.509763
-0.9393
0.347554
-0.3628
0.716742
-1.6011
0.109344
Source: Own study based on computations in R-package
According to the estimation of second model covering period 2005–2009 and
including additional variables linked with taxation, basic variables (GDPs and distance) are
statistically significant and the parameters display expected values (see table 2). Again
variables illustrating participation in regional integration are not statistically significant and
cultural similarity has positive impact on bilateral FDI flows. Therefore, in period 2005–2009
apart from transactions with OFC, all other taxation issues are not statistically significant for
flows of direct capital. These results confirms only up to a point the importance of tax
disparities and role of transfer pricing, however it is possible that the relatively small size of
sample has impact on estimation results. Additionally, disparities in corporate tax rates were
lower during period 2005–2009 than during last two decades.
12
4. Conclusions
Using gravity model I prove that direct investment flows between EU–27 Member
States are determined by variables such as: gross domestic products (total and per capita) of
home and host economy and distance between them. Therefore, other characteristics of
individual economies are more important for bilateral FDI flows than participation in the
regional integration. My estimation results confirm the sense of the EU motto In varietate
concordia10. In the case of FDI the variety appears to be more significant, especially
disparities in corporate tax rates intensify flows of direct capital. Moreover, cultural similarity
within groups of EU–27 Member States has also positive impact on FDI, what confirms the
magnitude of not strictly economic factors. To sum up, gravity model seems to be very useful
analytical tool and the further research based on it seems to be very lucrative.
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