Multinational Enterprise Locations: A European Study
July 2012
Erasmus University Rotterdam
Erasmus School of Economics
Author: Albena Gradeva
Student Number: 329851
Supervisor: Dr. Yvonne Adema
E-mail address: ag03@student.eur.nl
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ABSTRACT
This paper analyses the complex location decision for multinational firms who situated
outside of their parent country. Besides the extensively researched effects of economic
factors, this study also explores the effects of some social variables on the number of foreign
controlled enterprises (FAT) across 21 European host countries, over the period 2006-2009.
As theoretically predicted, the tax rate has a negative effect on the number of FATs per
country. The size of the country in GDP, low wages, a skilled labour force, and the
importance of university education in the hosting country has a positive effect on the number
of FATs. Inconclusive results are obtained on the effects of the number of foreign languages
spoken per tertiary educated person, as the data shows a surprising negative effect of this
variable on the number of FATs. The results regarding the effect of the percentage of the
population with a tertiary education also display an astounding negative relationship.
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Contents
1.
Introduction ......................................................................................................................... 4
2. Theoretical Background ......................................................................................................... 6
i) Tax effects ........................................................................................................................... 6
ii) Agglomeration effects ...................................................................................................... 11
3. Methodology......................................................................................................................... 16
i)
Data ............................................................................................................................... 16
ii) Empirical Specification ................................................................................................. 18
4. Estimation Results ................................................................................................................ 20
i) Description of the data ...................................................................................................... 20
ii)
Binary Logistic Regression Results and Assumption Tests ...................................... 22
ii)
OLS results and Assumption Tests............................................................................ 26
5. Conclusion ............................................................................................................................ 28
6. References ............................................................................................................................ 30
7. Appendix .............................................................................................................................. 33
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1. Introduction
As more globalization takes place it seems that international borders have become a
problem of the past. Governments worldwide are adopting more lenient migration policies,
the most astounding of which is that of the European Union (EU). In its Council Directive of
29 April 2004, the EU declared free migration and residence of member citizens in all its
territories a right. Since many multinational enterprises, MNEs, operate in many places
outside their parent countries, it only seems reasonable as to why these MNEs would choose
Europe for their host location, with its rather unrestrictive migration policy. In an article in the
Fortune Magazine of 2007, out of the top ten cities with the most headquarters, six of the ten
are European including Paris, London and Munich among others.1 Furthermore, Van Dijk et
al. (2000) studied how firms choose to relocate within the Netherlands. About one third of
total firm demographics are concerned with firm relocations, whether of MNEs or local
businesses. This proves the inevitable importance of the firm’s location decision.
The location choice for multinationals is a complex decision that depends on many
different determinants. This study seeks to find which different economic and social factors
affect the probability of hosting foreign affiliates, FATs, of multinational enterprises in
different countries. The effects of these variables on the number of FATs are also analyzed.
Over the past two decades much relocation of large MNEs has occurred from the US
and Europe to other European countries. Especially Western EU countries, with their welldeveloped and maintained infrastructures, are more than reasonable choices when deciding
where to situate. According to Bruinsma and Rietveld (1993), those cities among the top ten
regarding accessibility by air, rail and road are mainly located in the UK, France, Germany
the Netherlands and Belgium. Although Western Europe could seem more attractive for
MNEs due to these findings, some MNEs also situate in Eastern Europe. Evidence on this is
provided in Faggio (2001). After MNEs have chosen to locate in Eastern Europe, they
calculate the probability of choosing one of three Eastern EU members to locate to.
Many European countries have been trying to compete for foreign direct investments,
FDIs, a part of which consists of foreign affiliates, FATs. Many nations have taken different
approaches to attract large foreign firms. A major factor on which European states are
competing for FDIs is through applying different tax policies. A vast amount of the literature
is on the location of multinationals and the effects of taxes, especially on the US case.
1
Fortune global 500 2007 http://money.cnn.com/magazines/fortune/global500/2007/cities/
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Slemrod (1990), Hartman (1984) and Swenson (1994), among others, have studied the tax
effects on MNEs located in the US. As taxes seem to be of vital importance to the location
decision, they have also been included in this study.
Although tax is an important factor to consider, it is not the only factor on which
multinationals base their location. Another economic factor which has been extensively
researched is that on agglomeration economies and their effects on the location choice for
MNEs studied by Crozet et al (2003), and Brülhart et al (2012). As suggested by Davis and
Henderson (2003), the assortment of services available as well as the presence of other firm
headquarters attracts MNEs to situate at these sites, which weakens the tax effect on this
location decision.
Some research has been done by Basile et al (2008) and Hajkova et al.(2006) on social
factors affecting the locations of subsidiaries, but less research has been done compared to
taxes. In the study by Hajkova et al. (2006), she states that when border and labour policies
are considered along with taxes, the size of the tax effect is also tremendously reduced. This is
one of the main reasons why social factors are a main focus of this study. A major concern for
US investors is also the level of education. In a study on location choices of R&D MNEs
locating in Europe by Siedschlag et al (2009), they find that when education levels are
included, the amount of taxes charged becomes a less important factor when deciding where
to locate. The educational attainment per country is included to examine whether the tax
effects on the probability of MNE location is changed. Another rather neglected but
potentially important aspect for MNE location is the ease of communication as suggested by
Welch et al. (2005). When people speak the same language, it is much easier to get the
desired message across to employees. Perhaps the number of languages spoken by the
residents of a country makes it easier for different cultures to integrate into the host society.
Factors regarding the labour market also seem to suppress the effects of taxes regarding the
site choice for subsidiaries, as discussed in Faggio (2001).
Plenty of research exists on the country level, where MNEs need to decide in which
region of a country to locate, after already having picked the country. However, less literature
exists to explain how the country location is chosen. In this study the focus is on 21 European
countries, including countries from the North and South, as well as the East and the West.
The structure of this thesis is as follows: in the following section, some of the related
literature on the subject is discussed. Section 3 describes the data and methodology used. The
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results are presented in Section 4, and finally the conclusions and recommendations for future
research can be found in Section 5.
2. Theoretical Background
In this section, an overview of the main literature related to the subject studied on
multinational enterprise locations is provided. Much of the literature has focused on the
effects of taxes on different types of foreign direct investment. This is discussed in the first
subsection. The second subsection shows that when other factors are included, especially
agglomeration effects, the effects of taxes on the location decision of foreign investment are
weakened.
i) Tax effects
In recent years, quite some research has been done on MNEs and their location choice.
In a study by the OECD (2007), “Taxation and Foreign Direct Investments”, the OECD
provides an overview of the existing literature on taxes up until 2007. An analysis is given on
the first major studies carried out on time series, cross sectional, panel and discrete choice
models. One of the first studies done in the field of FDI´s locations was on taxation by
Hartman (1984). A basic time series specification is built to test whether tax rates in the US
have any explanatory power for the sites FDIs choose to situate at. He took this data on US
MNEs for the period 1965-1979. In his methodology, he regresses the FDI on variables in
logarithmic forms, such as the rate of return on inbound FDI and a variable measuring the
after tax rate of return on US capital. In this specification, only FDI financed by new equity is
used, instead of FDI which is financed by retained earnings. Due to this, there are no mixed
effects between completely new FDI entering the country for the first time and already
existing FDI. His findings empirically confirmed that a higher after-tax rate of return
stimulates higher FDIs. Also, the higher the amount of retained earnings, the more attractive a
location becomes for FDI. Another interesting finding is that the personal income tax rate as a
percentage of the corporate tax rate has a negative effect on the amount of FDI. The downside
to Hartman’s research is that it only uses data on the aggregate level for all FDI in the US.
FDI may consist of different investments, i.e. new foreign affiliates, mergers and acquisitions
and plant expansions. Special rules may apply to each specific type of investment, so
conclusions drawn about the effects of taxes should not be drawn for both physical and capital
FDIs.
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The findings for the times series studies seem to be in line with those on a crosssectional basis. The cross sectional studies also find evidence to support the assumption of
distortive tax effects on the amount of FDI. Slemrod (1990) uses data from 1960-1987
studying the US tax rate on inbound FDI, which adds to the literature by including
investments made by countries from all over the world. Two groups are compared. The first
group consists of Canada, the Netherlands, France and the former West-Germany, and the
other group includes Italy, the UK and Japan. This study focuses on the repatriation of
foreign sourced income, where a distinction is made between countries who exempt FDI from
taxes and those who use a credit tax system. Three consecutive effective marginal tax rates,
EMTRs, for the host country, the US, are included in this study. The EMTR is defined as the
marginal tax rate applied on a single investment, unlike the effective average tax rate, which
takes an average value for a number of different projects. Also specified is the difference
between home and host country taxes on corporate and personal income. He finds that the
EMTR has a negative effect on FDI. Tax exemption in the home country is an insignificant
predictor for the amount of FDI in the hosting US. However, a larger host (US) tax rate,
results in fewer FDI funds being sent to the host country. In sum, the amount of FDI from
each of the countries is negatively affected by the US, host country’s tax rate. Interestingly
enough, inbound FDI financed by retained earnings are not affected by the host tax rate.
Grubert & Mutti (1991) also specify a model studying the effects of taxes on property, plant
and equipment, where physical rather than capital investments are analyzed. The data covers
US MNEs from the manufacturing sector investing in 33 host countries. They find that
outbound investment is negatively affected by the host country tax rate. However, time series
analyses have their drawbacks.
The highest number of significant tax elasticities has been found on the panel basis,
amounting to two thirds of all studies on panels in the paper of the OECD (2007). Swenson’s
(1994) study shows the effects of US tax reforms on the foreign and native investor. A total of
18 industries over the period 1990-1991 have been examined, with data distinguishing
between physical and capital investments. The investors studied have been split into two
groups. The first group accounts for investors using international laws for their financial
reporting. The other group consists of investors who use national accounting rules. Swenson
has used logarithmically transformed variables to model FDI on the average tax rate (ATR),
the exchange rate, industry dummies and time fixed effects. Remarkably, a larger ATR has
been linked with a larger amount of FDI, which is in line with the prediction by Scholes and
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Wolfson (1990). This result may be due to the fact that non-tax predictors have been added to
the equation. The sign of the effect of tax rates on FDI may change when one or all of these
variables is accounted for.
Furthermore, Pain and Young (1996) have studied German and UK outbound FDI
between the periods 1977-1992 into other, mainly European countries. Among other
logarithmic variables, a first order lag of previous FDI has been regressed to determine the
effects of independent tax rates on the FDI. Only for the UK, and not for Germany, it is found
that the larger the home country tax the more sensitive an MNE is to the host country tax rate.
Finally, discrete choice models are considered, where the decision to locate is
mutually exclusive. As in the current estimations that follow in section 4, the studies in the
OECD Tax Policy Studies examine the effect of host country taxes on the probability of a
foreign affiliate choosing a location. Swenson (2001) studies the different types of FDI; new
plants, plant growths, mergers and acquisitions, joint ventures and the FDI due to the increase
in equity of firms. Instead of studying dollar terms of FDI, count data on the number of firms
per location is used. The effects of statutory tax rates, which are the legally allocated tax rates
in a country, are examined. A total of 46 countries investing into 50 different US states have
been sampled. It is found that the FDI in its physical form is also negatively affected by state
tax rates, especially for the new plants and growth of plants. Stöwhase (2003) uses a sample
from 1991-1998 to study how outbound German FDI is affect by the taxes in 8 European
countries. A number of fixed effects are included, namely the size of the country, labour costs
and the amount of government expenditure on investment in the sectors of production and
services. The average tax rate has a negative effect on new production plants being set up in a
specific country. For the FDI in the service sector, the statutory taxes also have a negative
effect on the investments made in new plants opened under the service sector. In conclusion,
the OECD study finds that not only the amount of capital invested in a country, but also the
location specifically chosen for a subsidiary is negatively impacted by tax rates
Other studies besides those reviewed in the OECD provide diverse results. Devereux
et al. (2003) find similar results. They analyze data for the US from 1979-1999 on
investments on simple plant and machinery in European countries like France, the UK and
Germany.
Like Slemrod (1990), in their methodology they use a weighted average of
effective average tax rates (EATR), the effective marginal tax rate (EMTR) and the applicable
statutory tax rate. They specify a range for the possible taxes due on the different earnings of
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projects unlike specifying only the EMTR, which is limited to the analysis of a single project
only. The UK seems to be the most favoured location, especially for highly profitable
investments due to high tax credits offered on repatriation of foreign income.
Hines et al (1996) examined what effect different corporate tax rates among 50 US
states had on the FDI location of MNEs. Evidence is found supporting the hypothesis that
foreign MNEs are sensitive to the foreign tax rates of the countries hosting them.
Voget (2011) studied the probability of parent companies to relocate given an increase
in the tax rate. Data is available on an international level, so a large country-comparison can
be made. The data included in this study extends over the period 1997-2007 for a worldwide
database of 33 countries. His main finding was that an additional tax on income from abroad,
after paying taxes for income produced within the home country, would make countries less
attractive to locate headquarters at. In this study, a comparison is made between two groups of
MNEs, mainly those that were taxed on worldwide profits and those exempted from paying
these taxes. An interesting methodology is used. The probability of relocating or not, a binary
outcome variable, is predicted using a logistic regression. A number of explanatory variables
are used in different specifications, including a ratio of MNE tax payments on their income,
dummies denoting whether foreign tax credits are provided and whether more than 95% of
dividend repatriations are exempted from taxation. This method is very similar to the one
which will be used in this paper’s empirical specification in section 4. The MNE may decide
to relocate either to avoid controlled foreign company legislation (CFC laws) such as taxes on
royalties or dividends, or to avoid restrictive rules which do not allow tax deferral until the
repatriation of foreign income. A dependent variable reports the value 1 if the headquarter has
relocated in the recent past, and 0 otherwise. The independent variables included are the
EATR and the presence of CFC legislation in the specific year of interest. The market value
of the MNE, leverage ratio, earnings over total assets as well as whether or not a firm is
positioned in the high tech sector are the variables included to control for any firm specific
effects. The results show that CFCs increase the chances of an MNE’s headquarter to relocate.
A foreign country with a lower foreign tax rate would have a higher chance to attract the
headquarters of an MNE to relocate to it.
Barrios et al. (2009) use European level data, for both parent and subsidiary plants
from 1999-2003. Out of the 33 countries considered, a total of 906 parent and 3,094
subsidiaries companies are included. In this sample, the UK, France and the Netherlands have
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the most parent companies with 144, 116 and 89 respectively. They study the effects of host
and home country taxation on the location decisions of MNEs. Both types of taxes have a
negative effect on MNE locations. A higher host tax rate would make a place less desirable to
locate to. Regarding taxation on parent firms in the home country, taxation has a highly
discriminatory nature. An important finding of this paper is that particularly double parentcountry taxation on MNE income has a negative impact on the attractiveness of a site to a
firm. In their final conclusion, they find that countries with lower taxes on foreign-source
income are more appealing to locate to. This is the case especially for parent firms, as
worldwide profits from subsidiaries are repatriated here.
Only a limited amount of studies are available which not only include more factors
which possibly influence where MNEs locate their subsidiaries, but that also include a crosscountry evaluation. One such study is done by Hajkova et al. (2006). It includes both different
plausible factors influencing this choice, as well as a rather large country-level data set
comprising of 28 OECD countries. Both policy and non-policy related issues are taken into
consideration when evaluating the attractiveness of a specific country for an MNE. In
accordance with previous research in this field, it is concluded that taxes have a significant
explanatory power on how desirable a site is to position at. However, when accounting for
policies regarding the labour market (especially concerning labour costs), as well as border
and product market restrictions, tax elasticities have a significant but smaller impact on the
selected location. On a cross-sectional level, where the attractiveness of different US states for
FDIs is inspected, it is still found that higher taxes repel foreign firms from locating at these
sites. This paper is further discussed in the following section.
For obvious reasons, cross-sectional analyses have their limitations too; hence
Swenson (1994) decided to approach the field by carrying out a panel study. Swenson
proposed that changes in the US Tax Reform Act of 1986 could have different effects on
investors from within and outside America. This reform largely impacted the tax rates due by
home investors, but less so for foreign ones. Foreign investors would be taxed on their foreign
source income in their country of origin, regardless of the one in the US. It is also found in
this study that depreciation of the US dollar has a positive effect on foreign investments in the
United States. This research added to this field greatly as it compares the difference between
foreign and local investors, and also between different time periods, before and after the
change in tax policies had been made.
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When choosing a location, some have proposed that locating in an urban area is more
attractive, especially for large corporations who need the developed facilities of agglomerated
areas. In a study by Laamanen et al. (2012) the reason why urban areas are chosen is
examined: mainly due to a highly developed, broad set of different services. The research has
been able to include multi-country data on a European level. They limit their research to
headquarter locations only. Having other headquarters in the surrounding improves the
likelihood of choosing a certain location. It can undoubtedly be assumed that their location
decisions depend on many more factors than for subsidiaries, as the headquarters’ success is
key to the firm’s survival and overall success in the business world. Still, the importance of
taxes is highly significant when a location is considered. International variation is also
examined in the present research, not only since different taxes are studied, but cultural
differences are also included in the analysis.
ii) Agglomeration effects
Many studies have been done on the effects agglomerations have on the location
choice of MNEs. Basile et al. (2003) use a nested logit model to study the location choice of
5,761 foreign subsidiaries from 1991-1999 in 55 regions of 8 European countries. It is found
that firms perceive differences on an international scale larger than differences across country
regions. Nations rather than regions compete for MNEs. The Cohesion policy, which
transferred extra funds to laggard regions, mitigates agglomeration effects of network
economies. US firms are attracted to areas with highly skilled labour whereas European firms
seek to take advantage of the large labour supply available in highly unemployed areas.
A major limitation of the studies mentioned previously is that they tend to overlook
other policies besides taxes, and therefore overstate their importance for inward and outward
FDI. Hajkova et al. (2006) empirically test whether this is indeed true on a sample of 28
OECD countries from 1991-2000. They also apply the EATR as it applies to investments that
earn profit and don’t simply break even, as Devereux et al (2003) have done. However, they
use both the EATR and EMTR in their estimations for robustness reasons. Besides the tax
policy applied per country, they include other policies such as trade barriers, as well as
regulations on the goods and labour markets. They consider some social factors in the analysis
such as the GDP representing the size of the market and the average education level of the
population. A semi-parametric model is used to account for unobservable effects such as the
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historical and cultural association between countries. In their estimations, they find that an
increase in the EMTR causes a decrease in the amount of FDI stock. An even larger, negative
effect for the EATR measure is found. Besides taxes, other policies seem to have a significant
effect too. Membership of more free trade areas seems to positively affect the amount of FDI
found per country. The results show that strict labour regulation and tough anti-trust laws
have a negative effect on FDI. Regarding the non-policy factors, a positive relationship
between the market size and FDI is found. Overall, the study finds that bilateral taxes are
highly overstated, and their effects are mitigated once other policy and non-policy factors are
included.
In a more recent study, Basile et al. (2008), a rather large dataset of 5,509 foreign
subsidiaries is used to study how firms located across 50 regions within a total of 8 European
host countries during 1991-1999. They estimate the probability of these subsidiaries to locate
in one of these territories using a mixed logit model. The models specified in my paper uses a
similar methodology to the one used by Basile et al. Such a model specifies a discrete choice
set, as investments are usually mutually exclusive, and it does not rely on the assumption of a
normal distribution, which is often violated with such data. This method is preferred
compared to a nested logit framework, because the mixed logit can make more clear
distinctions between different regions. Their specification includes the number of residents
per country, how far the subsidiaries’ headquarter is from the country and whether or not the
MNE has previous business experience in the region. They find that agglomeration effects
play an important role for the location decision. Also, European MNEs locate to low income
areas with higher unemployment and larger market potential. In contrast, non-Europeans
rather locate to high income areas with lower taxes on the production factors of capital and
labour. In addition, this study finds that Cohesion funds allocated to laggard regions have
managed to attract foreign investors to smaller scale regions, which weaken the effects of
agglomeration economies.
Devereux et al. (2007) studied how MNEs decide between different British sites when
making the decisions on where to situate new offices. Their approach is quite different, as
they focus mainly on the effects of policies implemented by the British government in
attracting FDIs. Specifically, they found that money granted to foreign companies is
important in determining which region they choose within Britain. However, when
agglomeration effects are simultaneously accounted for, grants have a positive but lower
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effect on choosing a location. Clearly, the existence of network economies makes territories
more attractive.
For the French case, Crozet et al. (2003) also reach a similar conclusion to
Devereux’s findings. Crozet et al. (2003) find that for inward FDI in France, an area’s
attractiveness increases along with the number of competitor’s in the area. A suggested
explanation for this result is that spill-over, learning effects are present. Foreign firms learn
from the pre-existing MNEs in the location. From experience, they have more information on
the best available sites; hence knowledge of other MNEs can also be transferred more easily
to new MNEs when they are located close to pre-existing ones. These results have been
estimated using both conditional and nested logit models. It seems that when hosting
international firms instead of local, French ones, a site becomes more attractive for MNEs. It
is also found that over time, being located in an agglomeration becomes less important, and
being closer to the market for the final product is more important.
In Faggio (2001), a conditional logit model is used to study micro-data from 19941997.Here the probability to locate to Romania, Poland or Bulgaria is determined, given that
the MNE has decided to locate in Eastern Europe. They take a rather unorthodox approach in
their empirical specification.. The nationality of the parent firms and agglomeration factors
played a role when deciding between these three developing countries. They control for social
effects like the culture and region of the potential host location as well as tax effects and how
close the location is to financial institutes. Their specification includes information on the size
of the hosting country, the wage paid to the labour force, the rigidness of the labour market
policies, the market concentration of other MNEs and the level of development of the R&D
sector. Less strict employment conditions seem to be favourable, as not only entrance but exit
costs for the firm are considered when locating. Once agglomerations are accounted for,
American firms weigh the amount of demand for their product or service heavily. The larger
the demand in the country, the more likely they are to locate to it. The German MNEs rely
largely on low wage locations.
In a similar study by Konings et al. (2004), a sample for European parent MNEs from
1993-1998 is used. They find that higher labour costs in Northern-European affiliates have a
significant negative effect on employment by the parent company in these countries. Perhaps
this is due to the fact that the labour force is more skilled in these countries, and hence one
person from Northern-Europe is relatively more productive than a worker from outside the
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Northern-European boundary. With such a result, one can assume that employment in the
Northern-European countries (with higher labour costs) is substituted by higher employment
in the other countries. Although intuitive, no concrete evidence to prove this has been found.
For the Southern, Central and Eastern part of Europe, no labour substitution took place due to
lower wages compared to Northern-Europe.
For Swiss municipalities a similar effect of agglomerations is found in Brülhart et al.
(2012).A dataset of Swiss municipalities from 1999-2002 is used. Brülhart et al. study how
the count data on the number of new firms established per municipality and sector are affected
by different region specific factors, including the tax rate. Although taxes are thought to have
a major influence on the location decision, it has been proven by this paper that their
importance has been highly overestimated. Here, agglomeration effects are included alongside
the corporate tax rates to re-estimate their importance on location decisions. Evidence is
found to support the hypothesis that the inclusion of agglomeration effects mitigates the
importance of the tax levels at a specific site. It is shown that other possible explanatory
factors should not be neglected. Though many similar studies have been done on the national
level, this is rather limiting when it comes to the robustness of conclusions on an international
level.
Davis et al. (2003) also seem to find agglomeration effects for headquarters a
significant determinant. It is preferred to locate to a place abundant with other headquarters. A
large variety of available services is also an attractive quality for headquarters choosing
between different locations.
Guimarães et al (2000) use data on all newly created affiliates in Portugal between
1985 and 1992. They also found that agglomeration economies with a large number of preexisting MNEs make a location attractive when deciding where to locate within Portugal.
Labour costs do not seem to be a major concern but the level of development of the firm’s
specific industry and urbanization spill-over effects as well as proximity to large urban areas
is of importance. In the paper by Guimarães et al. (2000), agglomeration effects are decisive
factors for MNEs considering different Portuguese regions. Especially with regard to the level
of development of the service sector within a district, agglomeration effects play an important
role on where they decide to situate their premises. However, as shown in Van Dijk (2000)
the opposite is found to be true. The relocation of firms within the Netherlands is more
14
affected by the internal environment of the firm itself, rather than the site specific
characteristics in each Dutch region.
Siedschlag et al. (2009) researched the effects of agglomerations on the R&D sector
within different European countries, with data used from 1999-2006. This study makes use of
a nested logit model which controls for the correlation between the different location choices
called ‘nests’ where comparable regions are grouped to allow for some dependence among
alternatives. This model allows for the assumption of Independence of Irrelevant Alternatives
(IIA) within each nest to hold. Unlike the conditional logit model, the nested logit controls for
the fact that the error terms for specific alternatives can be correlated. The outcome variable is
represented by 1 if a subsidiary locates in an area due to its higher expected profit relative to
alternative areas. MNEs are more likely to locate to markets with high demand for their
products, agglomerated networks, and low costs of manufacturing and less strict labour
policies. A site with a plentiful and skilled workforce, as well as a highly developed infrastructure and technological advancement makes locations more attractive. Moreover, it is
found that North American affiliates are more sensitive to agglomeration economies than
European ones.
Lastly, in a study by Mariotti et al (1995) it is researched how investors from abroad
chose between different regions within Italy. They conclude that agglomeration economies
are one of the major deciding factors when choosing where to locate. Pre-existing MNEs are a
signal that the location is a good option, which decrease the asymmetry costs faced by the
foreign MNEs. Also, they state that the distance from the home country has a negative effect
on the location of FDIs. Although intuitive, providing government grants has no effect on the
location decision of MNEs locating to Italy.
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3. Methodology
i)
Data
In this paper, we account for MNEs as a part of FDI. A total of 21 European countries
acting as hosts for the FATs are included. The countries analyzed are Austria, Belgium,
Bulgaria, the Czech Republic, Denmark, Estonia, Finland, France, Germany, Ireland, Italy,
Lithuania, the Netherlands, Poland, Portugal, Romania, Slovakia, Slovenia, Spain, Sweden
and the UK.
The data available for the multinational enterprises which are present in the host
countries have been obtained from Eurostat. This data can be found in Table 1 in the
Appendix. All the countries for which data was available for on the site have been included in
the analysis. This site provides official statistical data for potential and present members of
the European Union. The data from this site can be used for inter-country comparison. The
number of inward FATs, or foreign affiliates, has been extracted from this database. FATs are
‘enterprises or branches’ under the control of foreign divisions resident outside of the country
in which the FAT is located. According to Eurostat, an enterprise is defined as a unit which is
autonomous, in at least some respect, in the decision making process, especially with respect
to the use of available resources. In the Eurostat metadata, further explanation is given on the
definition of control; i.e. the controlling unit has more than half of the shareholder’s voting
rights or company shares, and has the power to appoint the board of directors.
The FAT data available per country stretches over the period 2001-2009. Before
2006, this data was sent on a voluntary basis only from some European Member states. Data
accumulation has only been harmonized from 2007 onwards, in agreement with the European
Parliament and the Council on Community statistics on the structure and activity of foreign
affiliates. However, as is shown in Table 1, there are only a few more missing data points for
the year 2006, compared to 2007. For 2006, 5 data points are missing, whereas for 2007, 3
points are missing. Also, as shown in Table 2, the specifications with beginning date 2006
shows a better model fit compared to that starting from 2007. The data specification including
the year prior to harmonization, 2006, shows a larger Chi-Square for the Omnibus test, a
smaller Hosmer & Lemeshow and a larger Cox & Snell R-square.
Also, upon visual
inspection of the data from 2006 compared to that for 2007, there seem to be no major
differences in the minimum and maximums which appear between these two years. As a
larger data set with more points is preferable to a smaller one, and due to all the above
mentioned reasons, the data range used for this study ranges from 2006-2009
16
Two other variables have been obtained from the Eurostat database. For the first, the
number of foreign languages known is expressed as a percentage of the population who has
obtained tertiary education and speaks at least one foreign language, up to a maximum of
three. This data is available only for the year 2007. However, as stated in Mejer et al (2010),
the number of foreign languages that has been studied in upper secondary schools has not
significantly changed between the periods 2000 and 2008.Therefore, it is reasonable to
assume that the self-reported number of languages known did not change over the period
studied. As an example of how this data is reported, if 35.4% of tertiary education graduates
speak 1 language, 36.6% speak 2 languages and 12.9% speak 3 languages, the sum of these
percentages is taken. This means that 85.9% of the tertiary education graduates speak at least
one foreign language. A dummy variable has been created to show whether the country’s
percentage of the population is below or above the EU27 average of 91.2%. However, as
these figures are self-reported, it could very well be that people over or understate their
abilities and the languages they consider themselves to know proficiently. Data for this
variable is not available for Ireland, the Netherlands and the United Kingdom, hence when
interpreting any results from this variable; this should be taken into consideration. The other
variable reports the persons with tertiary education as a percentage of the total population
between the ages of 15 and 65.
Some more independent variables have been obtained from the IMD World
Competitiveness database. Firstly, some economic factors have been extracted. Data on the
corporate tax rate before profit has been taken as a percentage of total profit. As a fixed effect,
to control for the size of the different countries under analysis, the GDP in US$ billions has
also been included.
Some socio-economic factors have also been considered. The productivity of labour
has been measured by the amount of GDP one worker can produce in an hour, measured in
US $. The availability of skilled labour in each country has been provided on the scale from 0
to 10, with 10 scoring the highest for this ranking. The next factor measures whether having a
university degree improves the competitiveness of the country’s economy at hand, i.e.
university education importance, on the same scale from 0, being the lowest score, to 10,
being the highest. The variable language skills are meeting the needs of enterprises measures
whether or not the language skills of the inhabitant people meet the needs of enterprises also
on a 1-10 scale. A final variable has been included to estimate the total salary that workers
obtain, including wages and other benefits. This variable is coded as compensation level
(hourly) and represents the total income per hour in US$.
17
ii)
Empirical Specification
As many MNEs are of US origin, much research has been done mainly on the United
States. In this paper, the probability of a country having a low or high number of FATs is
studied for a number of European countries, when certain exogenous variables are accounted
for. The study by Faggio (2001) focuses on the probability of locating a subsidiary in one of 3
Eastern-European regions, given that the multinational has decided to locate in the Eastern
Europe region, but the exact country has not been chosen yet. The current study takes a
different approach. It studies what effects certain factors have had on the location decision.
The firms studied have already located in these countries, i.e. the location decision has already
been made. Hence, unlike the study by Faggio, which uses a conditional logit model, here a
binary logistic regression is specified.
Most studies, like those by Slemrod (1990) and Swenson (1994), have specified linear
regressions. In accordance with this previous research, an Ordinary Least Squares regression
like the one in equation (1) has been specified. Fewer papers, however, have used the logistic
methodology to study the effects of certain factors on FATs; hence, like in the study by Voget
(2011), a logistic regression is estimated to further add to the literature. The methodology on
the binary logistic regression used in this paper is taken from the book by A. Field (2009). A
binary logistic regression studies the probability of a certain categorical variable being chosen
out of a set of 2 alternatives. The binary logistic regression has been used as only two possible
outcomes are studied, having a low or high number of FATs, which would be easier to
interpret and base policy recommendations on. A binary logistic regression is similar to a
simple multiple regression analysis like the one below:
(1)
𝑌𝑖𝑡 = 𝑏0 + 𝑏1 𝑋1𝑖𝑡 + 𝑏2 𝑋2𝑖𝑡 +. . . 𝑏𝑛 𝑋𝑛𝑖𝑡 + 𝜀𝑖 t
where the simple multiple regression studies the size effect of each factor 𝑥𝑛 on the outcome
variable 𝑌𝑖𝑡 , with constant term 𝑏0 and error term 𝜀𝑖𝑡 during each time period 𝑡 . Here n
represents the number of exogenous factors used in the model, and i represents the number of
possible outcome variables, which in this case are 2. The logistic regression predicts the
probability of 𝑌𝑖 occurring, given a number of n independent predictor variables:
(2)
P(Yit) =
1
1+𝑒 −(𝑏0+𝑏1𝑋1𝑖𝑡+𝑏2 𝑋2𝑖 + … 𝑏𝑛 𝑋𝑛𝑖𝑡 + 𝜀𝑖𝑡)
18
where P(Yi) represents the probability of Y occurring, e is the base of the natural logarithm to
the power of a multiple linear regression equation, of the form described in equation (1).
Although similarities exist between the linear and logistic regression, there is one major
difference. The outcome variable for a logistic regression is categorical. This variable is
defined by a name rather than a number, and therefore can be placed into different groups.
Due to this categorical nature of the outcome variable, the assumption of linearity for the
normal, linear regression is violated. However, the logistic model corrects for this violation by
transforming this variable into logarithmic form. Another major difference between linear and
logistic regression is on the interpretation of the independent variables. Where linear
regressions predict the size effect as well as the direction in which it affects the dependent
variable, the logistic regression is limited to only predicting the direction in which the
independent variables affect the outcome.
In this case, the outcome variable is whether a low or a high number of FATs is
present in a country. To create a categorical variable for the outcome variable, the number of
foreign controlled enterprises has been grouped into two clusters. To do this, percentiles have
been used to divide the data into appropriate ranges. They have been divided as follows:
Country Label
Low:
High:
Range
0 < y ≤ 5,895
5,895 ≤ y ≤ 25,835
where a country is labelled low if the number of foreign controlled enterprises is between 0
and 5,895. Here, 0 is not included as there are ten years for which certain countries have no
reported data. Hence, the value 0 is used as a proxy for missing data when doing the statistical
computation. All other countries lie within the top half of the percentile distribution.
Specifically for this study, a binary logistic regression has been chosen as there are
two categories into which the outcome variable can fall into. The outcome variable for this
regression can be interpreted as the probability of either having a low or a high number of
FATs in a country. The probability of having a high or low number of FATs is calculated in
the following way:
(3) P (event occurring) =
1
1+e−(b0 +b1(Economic_Factors)1it +b2 (Social Factors)2it + εit )
19
where economic and social factors are studied and their effects on the odds of the number of
foreign affiliates being high or low for a country. Four economic factors are studied, i.e. (i)
the effect of the corporate tax rate (in percentage), (ii) the GDP in US$ Billions, (iii) the
labour compensation level (in US$), (iv) and the labour productivity (in GDP per capita). The
social factors which have been analyzed include: (i) the availability of skilled labour, (ii) the
persons with tertiary education, (iii) university education importance, (iv) the number of
foreign languages known, and (v) whether language skills meet the needs of enterprises.
Finally, the odds of the outcome variable occurring are calculated as follows, as is
done by A. Field (2009):
P(event occurring)
Odds = (1−P(event occurring))
(3)
where the odds ratio is:
Odds after a unit change in predictor
Δ Odds = Odds before a unit change in predictor
If the odds ratio is greater than 1, then as the predictor increases, so do the odds of the
outcome occurring. However, if the odds ratio is smaller than 1, the odds of the outcome
occurring decrease as the predictor increases.
The results for this binary regression as well as the linear regression follow, where the
assumptions for both models have been tested.
4. Estimation Results
i) Description of the data
A sample of 21 European countries across 4 years, ranging from 2006-2009, has been
used. The descriptive statistics of the data can be found in Table 3. The number of statistics is
excluding the number of missing values for each variable. For the FATs (foreign controlled
enterprises) 73 cases are present, with a mean of 8,800; standard deviation of 5,895; a
minimum of 753 and a maximum of 25,835 (in units of enterprises). Simply by visually
inspecting the data, it can be seen that all of the variables are skewed in some way. As can be
expected the FAT and GDP present larger means compared to their medians. This means that
these distributions are skewed to the right, with a few, large values for the number of FATs as
20
well as GDP. Among the countries with the largest GDP, are France, the UK, Spain and Italy.
This is a reasonable result as these countries are relatively large by size. Also, as can be
expected, the variables skilled labour is readily available and university education meets the
needs of a competitive economy are also skewed to the right. Few countries have reached a
high level of development which also score very well compared to other, laggard regions. As
all these values display larger means compared to their medians, it can be expected that a high
number of FATs is related to the GDP level, the skilled labour availability and the university
educational quality. An example of one of the more developed economies studied is France.
For the year 2008, it portrayed the largest GDP of the whole data series of 3,632 US $ Billion,
which is larger than the mean value of 760.08. Also, a rather high compensation level of
26.66 is found, which is also above the average of 16.12, as well as one of the largest labour
productivities of 53, compared to the sample mean of 35.77. Skilled labour is more readily
available than in other countries, with a score of 6 compared to the mean of 5.37. University
education importance as well as the population with a tertiary education level is above the
mean, with a score of 6.25 and 21.18 respectively.
All the other variables are skewed to the left. Bulgaria had the tenth lowest GDP for
the whole sample in the year 2006, with a score of 33.21. As a laggard region, it scored 15 for
the tax rate, an hourly wage of $1.15 was paid, and the labour was also not so productive with
a score of $12 for its product per person. The labour force seems to be under-educated with a
score of 18.2, which is lower than the mean. The language skills seem not to be meeting the
needs of a competitive environment with a score of 5.33, which is below the average of 5.84.
As both the predictors and the number of FATs have skewed distributions, evidence in
support of the notion that the data is not normal is provided. This is one reason why
specifying an Ordinary Least Squares regression for the sample would be rather limiting.
21
ii)
Binary Logistic Regression Results and Assumption Tests
When performing logistic analysis, several methods are available. The Backward
Likelihood Ratio stepwise method has been used here. Initially a full model is estimated,
including all the variables in the regression. At each step, one by one, it excludes the variables
which are insignificant from the model. It tests which variables to remove by inspecting
whether the removal of one of the variables increases or decreases the explanatory power of
the model. The first explanatory variable which will be removed is the one which has the
smallest effect on the model fitting the data. This method is preferred over other methods
which start the model with only the constant, and test each variable and its explanatory power
for the model individually. It is preferred because of so called ‘suppressor effects’, which
means that variables may only be significant once other factors are kept constant. Therefore,
the Backward L.R. method is better as it is less likely to make a Type II error of rejecting a
variable from the model as insignificant when indeed it is significant. Each step differs from
the previous by one degree of freedom, as one variable is excluded at each step. A total of 4
steps have been regressed.
The high number of FATs range is taken as the reference category, so the regressions
are used to explain the low number of FATs. The results from the first stepwise regression
are presented in Table 4. The Wald statistic tests whether or not the independent variables
used in the regression significantly add to the model. From the 9 predictor variables used in
the regression, only 3 are significant; the corporate tax rate (p=0.020), number of foreign
languages (p=0.016) and the GDP level (p=0.036). The corporate tax rate has a positive
B-coefficient of 0.452. This means that the higher the corporate tax rate charged on income,
the higher the probability a low number of FATs in a country is present, as is found in Voget
(2011). This seems sensible, as FATs would be discouraged to locate in countries where their
income is highly taxed. Furthermore, the amount of GDP a country produces has a Bcoefficient of -0.002. If a country has a large economy and produces a large amount of
domestic product, then the probability of having a low number of FATs in a country is low. A
strong economy can attract FATs as it can help drive more revenue. Usually, a large economy
has an efficient infrastructure and a large number of efficient enterprises, so these FATs can
gain from agglomeration effects of other firms as well as other market efficiencies as is found
in Crozet et al (2003). The variable number of foreign languages known is also significant (p=
0.028), with a B-coefficient of 2.543, which suggests that the more foreign languages that the
society knows, the higher the probability of having a low number of FATs located in a
22
country. The data for this variable is only on people who have a tertiary education, which may
bias the results. There is no specific rule that FATs only hire people with a university
education. Also, the number of foreign languages is measured to be at least one or more
foreign languages. However, FATs will most probably base their hiring decision on the
proficiency in the language most used by the FAT. As long as the person can speak the
language which the FAT uses, this may be sufficient to hire that person. These results should
be interpreted with caution as there may also be a problem with suppressor effects. The
constant of the model is insignificant, but interpreting it would be unreasonable as many
variables with different measurement levels have been included.
The results from the 4th model can also be found in Table 4, after most of the
insignificant variables have been removed. A total of 6 predictors are significant. The factors
corporate tax rate (p=0.003), number of foreign languages (p=0.029) and the GDP level
(p=0.011) are significant once again, and their effects can be interpreted the same way as in
for Model 1.
What is interesting of this model is that the Compensation Level US$ (p=0.088),
Productivity of Labour US$(p=0.027) and University Education Importance (p=0.025), at the
10% significance level, are also significant once the Availability of Skilled Labour and
Persons with Tertiary Education % are excluded from the model. The B-coefficient for the
Compensation Level US$ is 0.194 which means that as the wage increases so does the
probability of having a low number of FATs. This result is quite intuitive, as the high wages
would cause the returns on the invested amount on the new subsidiary to be relatively low.
This is in line with previous research done by Faggio (2001), where it is found that German
MNEs consider locations with lower wages more attractive. It seems the variables Availability
of Skilled Labour and Persons with Tertiary Education %, which are excluded from the
analysis, have suppressing effects on the wage level, especially since they are also social
factors which influence the work force. The B-coefficient for the Productivity of Labour US$
is -0.258 and University Education Importance has a B-coefficient of -0.901, which means
that an increase in these variables decreases the probability of having a low number of FATs.
This finding is in line with those of Siedschlag et al. (2009), who find that education is a
major concern for R&D multinationals. It is reasonable to assume that the factors excluded
have an effect on these newly significant variables. As these variables were not significant
while the other social factors were included, the new findings of Model 4 should be
interpreted with caution.
23
The odds ratio, Exp(B) can also be found in Table4. They serve the same purpose as
the B-coefficient, only that their size effects are interpreted differently. The Exp(B) for the
significant variables in Model 4, corporate tax rate (1.704), compensation level US$ (1.214)
and number of foreign languages (8.440) ,are greater than 1. This indicates that as these
variables increase, the odds of having a low number of FATs increase. Further research is
required to make any assumptions regarding these outcomes, especially for the number of
languages spoken. FATs do not necessarily have to make their hiring decision on the number
of foreign languages a person speaks. As long as the candidate is proficient in the language
most widely used by the company, the candidate may stand a chance to be hired by the FAT.
For the GDP (0.998), Productivity of Labour US$ (0.772) and University Education
Importance (0.406), the Exp(B) coefficients are smaller than 1. This means that as these
variables increase the chances of having a low number of FATS decreases. It could be
assumed that a larger marketplace with a larger GDP may attract more FATs. This may lead
to agglomeration effects as found in Crozet et al. (2003) and Brülhart et al (2012). Also, if the
labour is more productive and can produce more per hour than other countries, FATs may
choose to locate in these countries over others. Furthermore, if firms in a country consider a
university diploma to be of high value in order to make an economy strong, then the
government may invest more in educational policies in order to make university education
free or relatively cheap. University education may then be made available for all, or a
relatively large portion of the population. Due to these reasons, FATs may wish to situate
themselves in regions which apply similar policies as the population may be better educated.
With regards to which model better explains the dependent number of FATs per
country, Model 1 is preferred over 4. The tests for goodness of fit can be found in Table 5.
The Omnibus Chi-Square statistic supports this preference, as Model 1 has a statistic of
32.435 (p=0.000) which is greater than that of Model 4 with 30.547 (p=0.000). Similarly, the
Hosmer & Lemeshow test also show evidence in favour of Model 1. This test favours models
with a higher significance level than 0.05 or a lower Chi-square statistic. For Model 1, a
smaller statistic of 5.158 (p=0.741) is found compared to 8.631(p=0.576) which shows
evidence for this choice over Model 4. In Table 5, R Squared information for each model has
been provided to seek further evidence for this choice. The -2 Log Likelihood for Model 1 is
lower than that of Model 4 with a statistic of 42.300 compared to 44.239, respectively,
suggesting that Model 1 is better at predicting the outcome variable. The Cox &Snell and
Nagelkerke R Square statistics also show proof that Model 1 is more powerful in predicting
24
the outcome. The Cox & Snell R Square shows a larger value for Model 1 of 0.452 compared
to 0.432 for Model 4. This means that Model 1 can explain 45.2% of the resulted low or high
number of FATs, which is greater than that of Model 4 with 43.3%. Likewise, the Nagelkerke
R Square shows that Model 1 can explain 60.3% of the outcome variable, whereas Model
4only explains 57.6%. This surprising result may simply be due to the fact that Model 1
includes more variables, and therefore explains the data set in a better way.
For a logistic regression to hold, a number of possible problems need to be tested for.
Firstly, the assumption of linearity should hold. To test this, interaction terms are specified
where the continuous variables are multiplied by their corresponding logarithms. If the
interaction effects of the variables are significant, these variables are not linear. The variables
population with tertiary education and language skills meet the needs of the enterprise are not
linear as their significance levels are smaller than the critical value of 0.05. These results can
be found in Table 6 in the Appendix. As these variables have been insignificant in the models
specified above, overall, the assumption of linearity holds.
Another assumption which should hold is that of multi-collinearity. To test whether
this holds, a linear regression with the same predictor variables has been regressed. The
results in Table 7 show that multi-collinearity is not a major problem. In accordance with the
suggestion made in A. Field (2009) that correlation larger than 0.8 is a cause for concern,
none of the variables seem to exceed this value. The Eigenvalues and Condition Indices have
also been inspected to test for multi-collinearity. The results for these estimates are in Table 8.
For Model 1, the last Eigenvalue is 0.005 which is smaller than 0.01 so this may be a cause
for concern. For the same 10th dimension, the Condition Index shows a value of 42.326 which
is great than 30. This shows that multi-collinearity may be a problem.
Furthermore, in Table 9, the Tolerance and Variance Inflation Factors (VIFs) have
been inspected. The VIF indicates whether a variable has a linear relationship with any of the
other variables. The Tolerance statistic is simply the reciprocal of the VIF. The VIF values
higher than 10 show multi-collinearity. For Model 1, except for the Productivity of Labour
(US$), with a VIF of 11.873, none of the other variables seem to suffer greatly from
collinearity. Also, no tolerance value for any of the other variables, except Productivity of
Labour US$ with a value of 0.084, are lower than 0.1. It has been found that the higher the
labour productivity the higher the probability of having a high number of FATs in a country.
This variable only became significant once other social factors on the labour force were
25
removed. The VIF and Tolerance values also provide evidence that labour productivity is
correlated to other explanatory variables in the model. Although it seems logical that a
productive work force increases the probability of having a high number of FATs in a
country, policy implications should be concluded from this result with caution.
ii)
OLS results and Assumption Tests
The OLS results can be found in Table 9. A similar backward stepwise model has been
used as with the binary logistic regression, where the variable which adds the least to the
explanatory power of the model is excluded at each step.
Table 9 shows the three variables which were significant in the first model of the
binary logistic regression which are also significant in Model 1 of the OLS estimation. The
corporate tax rate (p=0.057) and the number of foreign languages known (p=0.000) have a
negative effect on the number of FATs. An increase in either the tax rate or the number of
foreign languages known decreases the number of FATs that are present in a country. This
result seems rather intuitive for the tax rate. The results for the number of foreign languages
spoken is rather surprising, just like for the binary logistic regression. Increasing the number
of foreign languages spoken decreases the number of FATs in a country. This result should
carefully be considered. It is possible that such a surprising result may be due to the fact that
this data is self-reported. It may also be the case that firms are only interested in the skills of
the specific language which the company uses. As long as the level of knowledge of this
particular language is sufficient, the communication between employer and employee should
be satisfactory. The GDP level (p=0.011), displays a positive effect on the number of foreign
enterprises in a country, in line with the previous results from the binary logistic regression.
In Model 1, two social factors are rendered significant in the OLS model, which were
previously insignificant in the logistic regression. The Availability of Skilled Labour
(p=0.020) is found to have a positive effect on the number of FATs present. The skill level
should not be interpreted as synonymous with the level of education. A skilled labour force
may be due to the gain in experience rather than the education achieved. It seems that firms
are attracted to a skilled labour force. Perhaps firms who locate in Europe are headquarters of
the company and would require highly skilled workers to make decisions concerning the
entire company. Also, these firms may be involved in the service sector, which requires
highly skilled workers, unlike in the manufacturing sector for example. The Persons with
Tertiary Education % (p=0.010) factor has a significantly negative effect on the number of
26
FATs in a country. However, firms may hire people with a lower educational achievement,
such as the upper or lower secondary. The factors which are insignificant are Compensation
Level US$(p=0.762), Productivity of Labour US$(p=0.979), Language Skills are Meeting the
Needs of Enterprises (p=0.897) and University Education Importance (p=0.897). It seems the
wages paid, how much a worker produces and whether the language skills of the labour force
are sufficient, are not important for FATs when they decide on where to locate to. Also,
whether tertiary education is considered important or not in a country, and whether or not
policies make it accessible to all, is unimportant for the FATs when they choose where to
locate. This may be due to the fact that firms hire workers who are not university graduates,
but rather those who graduated from an upper secondary education.
In the fourth model specified for the OLS regression, the three variables with the
highest p-values, the Compensation Level US$, Productivity of Labour US$ and Language
Skills are Meeting the Needs of Enterprises, have been removed. All the variables which were
significant in the first OLS model are significant once again, with the same signs of the
coefficients affecting the number of FATs per region. Once these variables are excluded the
variable University Education Importance (p-value=0.001) becomes significant. Its effect on
the number of FATs present is positive. It seems that the variables removed in Model 4
suppressed the effects of this variable on the MNE location decision. This may be the reason
why this variable was insignificant in the first model.
In order to make any generalizations beyond this data, a number of assumptions have
to be checked for the data. Firstly the normality assumption is tested, the results of which can
be found in Table 10a. The Kolmogorov-Smirnov test statistic has been used to do this, where
a normal distribution exists if the null hypothesis of normality is not rejected, and the
significance level is greater than 0.05. According to this test, this assumption is violated for
the dependent number of FAT (p=0.004). Nearly none of the explanatory variables comply
with the normal distribution. Except for the Availability of Skilled Labour (p-value=0.200),
University Education Importance (p-value=0.088) and the variable Language Skills are
Meeting the Needs of Enterprises (p-value=0.200), all other variables are not normally
distributed. To try and correct for this, the logarithms of the explanatory variables are
computed. The results can be found in Table 10b. This specification only seems to correct for
the non-normal distribution of the number of FATs (p-value=0.200). The other two variables
which were previously normal still appear normal except the Number of Foreign Languages
27
Known %.. It is excluded from this analysis as it is a dummy variable, and taking the
logarithm of it would create a constant variable.
The Durbin-Watson statistic in Table 11 test checks whether the data display
independent errors. A statistic smaller than 1 is considered to be troublesome, and proves that
this assumption is violated. However, with the statistic of 1.081 displayed in Table 11,
evidence in support of this assumption is provided.
Three different methods have been used to check whether the dataset suffers from
heteroskedasticity. In Figure 1 in the Appendix, a scatter-plot of the standardized residuals
versus the standardized predicted variables has been made. This graph seems to display no
problem of heteroskedasticity. Next, the assumption of homoskedasticity is explored with the
Levene’s test Statistic. For this assumption to hold the p-values obtained should be greater
than the critical value of 0.05. Except for the dependent variable FAT (p-value=0.000) and the
two variables on the language factors, Language Skills are Meeting the Needs of Enterprises
(p-value=0.030) and Number of Foreign Languages Known (p-value=0.000), all factors seem
to comply with the assumption of homoskedasticity. However, this method is an ad hoc,
proxy method which actually tests for homogeneity of the variances. If it is violated, it is
likely that a problem of heteroskedasticity may exist. Due to this, the more formal White test
is also specified, which tests the null hypothesis of homoskedasticity and can be found in
Table 12. With the probability of 0.008 the null hypothesis is rejected. The different methods
seem to display contrasting results on whether homoskedasticity is a problem for the dataset
used. Hence this implies that the conclusions made beyond this dataset could possibly be
weaker, and therefore general conclusions should be drawn with caution.
As tested for in the binary logistic regression, it is found that multi-collinearity is also
a problem in the model, but only for the variable on labour productivity. Hence, the results
estimated for the linear regression as well as for the binary regression need to be interpreted
with caution.
5. Conclusion
In line with findings from previous literature by Hartman (1984) and Slemrod (1990),
it seems that the corporate tax rates affect the number of FATs in a country negatively.
Therefore, MNEs do consider this factor when choosing where to locate subsidiaries.
Furthermore, the higher the GDP level the higher the chance that many FATs locate to a
28
certain country, as is found in the study by Hajkova et al. (2006). The effects on wages are
similar to the findings from previous literature, such as that by Faggio (2001). Low wages
seem to attract MNEs to locate to certain countries, as they will have lower costs, and
therefore higher overall profit levels. As is found by Siedschlag et al (2009), a skilled labour
force also attracts MNEs. As the study is on a European level, perhaps the firms who choose
to situate in this continent are involved in the provision of services. Hence they may decide
between European countries, after already having chosen to locate in Europe. The result on
the university education importance is also similar to that of Siedschlag et al (2009), where it
has a positive effect in attracting FATs.
With regards to the finding that a higher number of foreign languages spoken leads to
fewer FATs, more research is required to make any policy implications. This may be because
MNEs only require the workers to speak the language which the firm uses in its daily
activities. This result may also be due to the fact that this data is available only for tertiary
education graduates. Perhaps firms also hire graduates from the lower and higher levels of
education.
Furthermore, although the intuitive result obtained that more productive labour
attracts more FATs is rather intuitive, further research is required to make any general
assumptions, as this variable suffers from collinearity to the other variables. The remarkable
finding that a higher educated workforce leads to a lower number of FATs also requires
further research. Although theoretically predicted, no evidence is found in favour of the
statement that a nation with a high tertiary education level would attract more MNEs. Instead,
a highly educated population seems to have a repulsive effect on the location choice of
MNEs.
As shown in Table 11, the independent variables used in this study help explain about
60% of the number of FATs. This means that there are many unobserved and possibly
spurious factors which are not accounted for. Future research should focus on not only
economic but social factors, as this study has shown that both may have some explanatory
power for the MNE and its location decision.
29
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32
7. Appendix
Table 1: Country FAT Statistics
Year
Country
Austria
Belgium
Bulgaria
Czech Republic
Denmark
Estonia
Finland
France
Ireland
Italy
Lithuania
Netherlands
Poland
Romania
Slovakia
Slovenia
Spain
Sweden
United Kingdom
2006
5,433
17,017
3,292
753
2,360
19,726
13,310
2,291
4,535
2,625
1,868
5,212
9,674
17,140
2007
8,510
1,459
11,648
16,191
3,166
826
2,536
19,223
13,429
5,320
5,352
5,418
3,004
2,032
10,311
19,330
2008
8,653
1,500
13,297
16,777
3,503
863
2,722
17,260
3,205
13,413
2,619
5,810
5,980
11,396
3,624
2,174
7,497
11,194
22,407
2009
8,773
13,915
22,871
3,296
800
2,805
16,715
3,451
13,136
5,989
6,058
25,835
3,483
2,171
8,712
11,772
21,516
Table 2:Model fit for data beginning from 2006
and 2007
Omnibus Cox & Hosmer and
ChiSnell R Lemeshow
square
Square Test
Data from
2006
Data from
2007
34.621
.338
6.582
23.183
.308
12.178
33
Table 3: Descriptive Statistics
Mean
8,800
24.76
760.08
16.12
35.77
Median
5,895
25.75
311.27
18.29
40.82
Std.
Deviation
6,959
7.05
981.57
11.17
11.84
Minimum
753
10.00
16.81
1.15
11.99
Maximum
25,835
39.63
3623.69
49.53
53.39
FAT
Corporate tax rate
GDP
Compensation level (hourly)
Labour productivity
N
73
73
73
73
73
Skilled labour is readily
available
73 5.37
5.31
1.16
2.25
7.92
University education
importance
73 5.52
5.38
1.40
2.50
8.13
Persons with tertiary
education
73 21.18
21.85
6.73
9.60
31.60
Number of foreign languages
known (self-reported)
73 0.67
1.00
0.47
0.00
1.00
Language skills are meeting
the needs of enterprises
5.96
1.79
2.45
8.72
73 5.84
34
Table 4: Results of Binary Logistic Regression
95% C.I.for
EXP(B)
Model 1
Model 2
Model 3
Model 4
Corporate tax rate on profit
B
.454
S.E.
.196
Wald
5.389
df
1
Sig.
.020
Exp(B)
1.575
Lower
1.073
Upper
2.310
GDP US$ (Billions)
-.002
.001
4.393
1
.036
.998
.996
1.000
Compensation Level US$
.133
.104
1.620
1
.203
1.142
.931
1.401
Productivity of Labour US$
-.202
.137
2.192
1
.139
.817
.625
1.068
Availability of Skilled
Labour
University Education
Importance
Persons with Tertiary
Education %
Number of Foreign
Languages Known %
-.374
.581
.413
1
.520
.688
.220
2.150
-1.018
.636
2.562
1
.109
.361
.104
1.257
.112
.097
1.338
1
.247
1.118
.925
1.352
2.543
1.057
5.790
1
.016
12.723
1.603
100.9
Language Skills are
Meeting the Needs of
Enterprises
Constant
.169
.522
.105
1
.746
1.184
.426
3.291
-2.399
2.594
.855
1
.355
.091
Corporate tax rate on profit
.458
.191
5.726
1
.017
1.580
1.086
2.299
GDP US$ (Billions)
-.002
.001
6.825
1
.009
.998
.996
.999
Compensation Level US$
.126
.101
1.566
1
.211
1.134
.931
1.381
Productivity of Labour US$
-.201
.135
2.215
1
.137
.818
.628
1.066
Availability of Skilled
Labour
University Education
Importance
Persons with Tertiary
Education %
Number of Foreign
Languages Known %
-.329
.566
.339
1
.560
.719
.237
2.180
-.870
.421
4.281
1
.039
.419
.184
.955
.111
.096
1.324
1
.250
1.117
.925
1.349
2.501
1.054
5.634
1
.018
12.192
1.546
96.139
Constant
-2.324
2.603
.797
1
.372
.098
Corporate tax rate on profit
.493
.187
6.949
1
.008
1.637
1.135
2.362
GDP US$ (Billions)
-.002
.001
6.816
1
.009
.998
.996
.999
Compensation Level US$
.132
.099
1.782
1
.182
1.142
.940
1.387
Productivity of Labour US$
-.243
.118
4.259
1
.039
.784
.623
.988
University Education
Importance
Persons with Tertiary
Education %
Number of Foreign
Languages Known %
-.927
.417
4.949
1
.026
.396
.175
.896
.113
.095
1.400
1
.237
1.119
.929
1.349
2.350
.995
5.580
1
.018
10.487
1.492
73.711
Constant
-3.107
2.222
1.956
1
.162
.045
Corporate tax rate on profit
.533
.181
8.637
1
.003
1.704
1.194
2.431
GDP US$ (Billions)
-.002
.001
6.533
1
.011
.998
.996
.999
Compensation Level US$
.194
.114
2.911
1
.088
1.214
.972
1.516
Productivity of Labour US$
-.258
.117
4.862
1
.027
.772
.614
.972
University Education
Importance
Number of Foreign
Languages Known %
-.901
.402
5.022
1
.025
.406
.185
.893
2.133
.978
4.754
1
.029
8.440
1.241
57.408
Constant
-2.181
2.184
.997
1
.318
.113
35
Table 5 :Goodness of Fit Tests
Omnibus Tests of Model Coefficients
-2 Log likelihood
Cox & Snell R Square
Nagelkerke R Square
Hosmer and Lemeshow Test
Model 1
Model 4
32.435
30.547
42.300
44.239
0.452
0.432
0.603
0.576
5.158
(0.741)
8.631
(0.576)
Note: The figures in brackets represent significance values
Table 6 : Linearity Test
B
-2.129
Sig.
.129
.006
.149
1.243
.285
Productivity of Labour
US$ by Log
.717
.525
Availability of Skilled
Labour by Log
.534
.931
University Education
Importance by Log
-5.624
.389
Persons with Tertiary
Education % by Log
2.699
.038
-17.855
.006
-46.612
.073
Corporate tax rate on
profit % by Log
GDP US$ (Billions) by Log
Compensation Level US$
by Log
Language Skills are
Meeting the Needs of
Enterprises by Log
Constant
36
Table 7 :Correlation Matrix
Compensation
Level US$
Productivity
of Labour
US$
Availability
of Skilled
Labour
University
Education
Importance
Persons
with
Tertiary
Education
%
Number of
Foreign
Languages
Known %
Language
Skills are
Meeting
the Needs
of
Enterprises
Constant
-.379
.193
.219
.170
-.458
.168
-.333
-.227
-.114
-.379
1.000
-.584
.431
-.740
.259
-.424
-.024
.237
-.024
Compensation
Level US$
.193
-.584
1.000
-.012
.172
-.095
.050
-.155
-.286
.490
Productivity of
Labour US$
.219
.431
-.012
1.000
-.681
.024
-.264
-.364
.055
.207
Availability of
Skilled Labour
.170
-.740
.172
-.681
1.000
-.461
.268
-.044
.006
-.059
University
Education
Importance
-.458
.259
-.095
.024
-.461
1.000
.048
.005
-.305
-.237
Persons with
Tertiary
Education %
.168
-.424
.050
-.264
.268
.048
1.000
-.164
-.389
-.742
Number of
Foreign
Languages
Known %
-.333
-.024
-.155
-.364
-.044
.005
-.164
1.000
.257
.070
Language
Skills are
Meeting the
Needs of
Enterprises
Constant
-.227
.237
-.286
.055
.006
-.305
-.389
.257
1.000
.162
-.114
-.024
.490
.207
-.059
-.237
-.742
.070
.162
1.000
Corporate
tax rate
on profit
%
GDP
US$
(Billions)
Corporate tax
rate on profit
%
1.000
GDP US$
(Billions)
37
Table 8: Collinearity Diagnostics
Variance Proportions
Model1
Model2
Model3
Model4
1
2
3
4
5
6
7
8
9
10
1
2
3
4
5
6
7
8
9
1
2
3
4
5
6
7
8
1
2
3
4
5
6
7
Eigenvalue
Condition
Index
Corporate
tax rate
on profit
Maximum
tax rate,
calculated
on profit
before tax
8.578
0.705
0.341
0.213
0.056
0.05
0.023
0.016
0.013
0.005
7.61
0.695
0.335
0.213
0.055
0.047
0.018
0.015
0.012
6.711
0.649
0.334
0.187
0.05
0.039
0.018
0.012
5.971
0.643
0.251
0.052
0.041
0.026
0.015
1
3.487
5.017
6.342
12.379
13.112
19.516
23.14
25.549
42.326
1
3.309
4.768
5.984
11.717
12.742
20.511
22.41
24.717
1
3.216
4.482
5.994
11.639
13.05
19.276
23.202
1
3.047
4.876
10.678
12.095
15.221
19.718
0
0
0
0
0
0.01
0.29
0.05
0
0.65
0
0
0
0
0
0.02
0.8
0.03
0.14
0
0
0
0
0.01
0.03
0.81
0.14
0
0
0
0
0.07
0.18
0.75
Gross
Domestic
Product
(GDP)
US$
billions
0
0.19
0.11
0
0.04
0.01
0.6
0.01
0.02
0.02
0
0.21
0.1
0
0.06
0
0.37
0.12
0.14
0
0.27
0.11
0.03
0.03
0
0.4
0.15
0
0.29
0.12
0.02
0.01
0.06
0.5
University
education
meets the
needs of a
competitive
economy
Persons
with
tertiary
education
attainment
and sex
(%) 1565yr olds
Number of
foreign
languages
known (selfreported) by
the highest
level of
education
attained (%)
0
0
0
0
0.06
0.04
0
0.7
0.09
0.1
0
0
0
0
0.08
0.02
0.12
0.77
0
0
0
0
0.01
0.05
0.59
0.32
0.02
0
0
0
0.1
0.44
0.25
0.21
0
0
0
0
0.43
0.1
0.06
0.08
0.24
0.08
0
0
0.01
0
0.36
0.18
0.31
0.01
0.12
0
0
0.01
0.01
0.54
0.02
0.29
0.13
0
0
0.03
0.82
0
0.1
0.04
0
0.06
0.19
0.54
0.05
0
0.06
0
0.01
0.09
0
0.06
0.21
0.55
0.05
0.01
0.1
0.02
0
0
0.09
0.24
0.48
0.06
0.04
0.09
0
0.01
0.08
0.66
0.13
0.04
0.03
0.05
Language
skills are
meeting the
needs of
enterprises
0
0
0
0.01
0.06
0.06
0.08
0.44
0.08
0.27
0
0
0
0.01
0.09
0.03
0.01
0.85
0
38
Table 9: OLS Coefficients
Model
1
Coefficients
Collinearity Statistics
B
11559.178
Std. Error
4720.322
T
2.449
Sig.
.019
Tolerance
VIF
-464.674
236.958
-1.961
.057
.176
5.689
3.218
1.206
2.668
.011
.309
3.235
-40.612
133.138
-.305
.762
.193
5.181
Productivity of Labour
US$
5.198
199.986
.026
.979
.084
11.873
Availability of Skilled
Labour
2408.170
997.373
2.415
.020
.360
2.780
University Education
Importance
1344.606
825.898
1.628
.111
.340
2.943
Persons with Tertiary
Education %
-428.226
158.242
-2.706
.010
.454
2.203
Number of Foreign
Languages Known %
-7155.293
1674.016
-4.274
.000
.737
1.358
93.487
719.964
.130
.897
.310
3.229
11558.976
4663.821
2.478
.017
.015
.294
3.406
-460.772
181.148
-2.544
3.222
1.180
2.730
.009
.315
3.173
.687
.376
2.663
.004
.558
1.793
.093
.366
2.733
.007
.477
2.098
.000
.767
1.303
.897
.354
2.829
(Constant)
Corporate tax rate on
profit %
GDP US$ (Billions)
Compensation Level US$
Language Skills are
Meeting the Needs of
Enterprises
2
(Constant)
Corporate tax rate on
profit %
GDP US$ (Billions)
Compensation Level US$
-38.200
94.306
-.405
Availability of Skilled
Labour
2423.616
791.439
3.062
University Education
Importance
1350.341
786.346
1.717
Persons with Tertiary
Education %
-429.123
152.579
-2.812
Number of Foreign
Languages Known %
-7164.018
1620.389
-4.421
86.899
665.798
.131
Language Skills are
Meeting the Needs of
Enterprises
39
Table 9 Continued: OLS Coefficients
Model
3
Coefficients
Collinearity Statistics
B
11619.740
Std. Error
4587.179
t
2.533
Sig.
.015
Tolerance
VIF
-454.918
173.490
-2.622
.012
.313
3.197
3.156
1.053
2.996
.005
.387
2.587
-41.497
89.815
-.462
.646
.405
2.472
Availability of Skilled
Labour
2432.928
779.154
3.123
.003
.562
1.779
University Education
Importance
1417.467
588.002
2.411
.020
.639
1.564
Persons with Tertiary
Education %
-429.259
150.821
-2.846
.007
.477
2.098
Number of Foreign
Languages Known %
-7172.924
1600.341
-4.482
.000
.769
1.301
(Constant)
12696.763
3915.258
3.243
.002
Corporate tax rate on
profit %
GDP US$ (Billions)
-448.717
171.417
-2.618
.012
.315
3.178
3.143
1.044
3.012
.004
.387
2.585
Availability of Skilled
Labour
2233.675
643.093
3.473
.001
.811
1.234
University Education
Importance
1404.554
582.065
2.413
.020
.641
1.560
Persons with Tertiary
Education %
-467.862
124.436
-3.760
.000
.688
1.454
Number of Foreign
Languages Known %
-7191.432
1585.476
-4.536
.000
.769
1.300
(Constant)
Corporate tax rate on
profit %
GDP US$ (Billions)
Compensation Level US$
4
40
Table 10a: OLS Assumption Tests
Levene
Statistic
df1
Sig.
KolmogorovSmirnov
df
Sig.
FATs
24.753
1
.000*
.154
51
.004*
Corporate tax rate on profit %
GDP US$ (Billions)
Compensation Level US$
Productivity of Labour US$
Availability of Skilled Labour
2.697
1
.107
.124
51
.048*
3.778
1
.058
.350
51
.000*
2.928
1
.093
.170
51
.001*
2.590
1
.114
.195
51
.000*
1.117
1
.296
.066
51
.200*
University Education
Importance
3.261
1
.077
.115
51
.088
1
.337
.136
51
.020*
1
.000*
.425
51
.000*
1
.030*
Language Skills are Meeting the 4.967
Needs of Enterprises
Note: * represents significance at a critical value of 5%
.105
51
.200*
Persons with Tertiary Education .939
%
Number of Foreign Languages
Known %
32.394
Table 10b:Normality Test on Logarithmically Transformed Data
Kolmogorov-Smirnov
logFAT
Statistic
.102
df
43
Sig.
.200*
logTAx
.167
43
.004*
logGDP
.135
43
.049*
logCompensationLevel
.165
43
.005*
logLaborProductivity
.222
43
.000*
logSkilledLabourAvailability
.127
43
.081
logUniImportance
.119
43
.135
logEducationLevel
.166
43
.004*
logLanguageSkills
.134
43
.050
Table 11: Model Summary
Adjusted R Square
.510
Change Statistics
F
R Square Change Change
.598
6.780
Model
1
R
.773
R
Square
.598
2
.773
.598
.522
.000
.001
3
.773
.598
.533
.000
.017
4
.772
.596
.541
-.002
.213
DurbinWatson
1.081
41
Table 12: Heteroskedasticity Test
White's test for heteroskedasticity
P-value
36.59825
0.008904
Figure 1 : Scatterplot
42