The effect of currency exchange rates on foreign direct investment

mergers and acquisitions:

Evidence for the Eurozone

Tilburg School of Economics and Management

Department of Finance

Master Thesis

Author: J.P. van Doorn BSc.

ANR: 648713

Student number: U1236506

Supervisor: dr. M.R.R. van Bremen

Chairperson: dr. D.A. Hollanders

J.P. van Doorn 2

Abstract

Previous research on the exchange rate affecting foreign direct investment (FDI),

generated mixed results. This study examines the link between real exchange rate

and FDI in the Eurozone, using quarterly data from top investor countries

between 1999 and the second quarter of 2016, subdivided in industry

specifications. In addition, other relevant determinants are investigated to explain

short-run mergers and acquisitions (M&A) flows. Empirical results show no

significant effect of the exchange rate in investor country combined analysis.

However, country-specific results highlight the significant positive correlation

between a depreciating Euro and Canadian high R&D acquisitions in the

Eurozone. Combining empirical evidence and recent literature to date, the firm-

specific asset theory for Canadian M&A in the Eurozone can be confirmed.

J.P. van Doorn 3

Table of contents

Table of contents ........................................................................................................................................... 3

1. Introduction ............................................................................................................................................... 5

1.1. Research aim and relevance ............................................................................................................... 5

1.2. Problem statement .............................................................................................................................. 7

1.3. Research questions ............................................................................................................................. 7

1.4. Outline................................................................................................................................................ 8

2. Literature review ....................................................................................................................................... 9

2.1. Foreign direct investment................................................................................................................... 9

2.2. The link between currency exchange rate and FDI ............................................................................ 9

2.3. Domestic acquisitions by other Eurozone firms affecting FDI inflows ........................................... 14

2.4. The effect of the real GDP growth rate on FDI ............................................................................... 15

2.5. Growth of stock market price index affecting FDI .......................................................................... 15

2.6. Omitted factors ................................................................................................................................. 16

2.7. Research hypotheses ........................................................................................................................ 17

3. Research methodology ............................................................................................................................ 20

3.1. Empirical model ............................................................................................................................... 20

3.1.1. Empirical specification ............................................................................................................. 20

3.1.2. Variable definition .................................................................................................................... 20

3.1.3. Expectations .............................................................................................................................. 23

3.2. Data and variable construction ......................................................................................................... 23

3.2.1. Number of FDI M&As into the Eurozone ................................................................................ 23

3.2.2. Real exchange rate .................................................................................................................... 24

3.2.3. Number of domestic Eurozone M&As ..................................................................................... 24

3.2.4. Real GDP growth ...................................................................................................................... 25

3.2.5. Stock market price index growth .............................................................................................. 25

3.2.6. Descriptive statistics ................................................................................................................. 27

3.3. Statistical procedures and analysis ................................................................................................... 27

4. Empirical results ..................................................................................................................................... 31

4.1. First impression ................................................................................................................................ 32

4.2. Statistical soundness ........................................................................................................................ 32

4.3. Panel regression model .................................................................................................................... 33

4.4. Determinants of foreign direct investment into the Eurozone ......................................................... 34

4.5. Investor country-specific determinants of FDI into the Eurozone ................................................... 36

J.P. van Doorn 4

5. Discussion ............................................................................................................................................... 40

5.1. Hypotheses testing and interpretation .............................................................................................. 40

5.2. Implications...................................................................................................................................... 42

5.3. Limitations ....................................................................................................................................... 44

6. Conclusion .............................................................................................................................................. 45

Appendix ..................................................................................................................................................... 46

References ................................................................................................................................................... 57

J.P. van Doorn 5

1. Introduction

1.1. Research aim and relevance

In recent years, more and more listed- and non-listed European firms fall into foreign hands.

Through mergers and acquisitions (M&A, hereafter) control of these companies is relinquished to foreign

say. According to UNCTAD’s1 World Investment Report 2016, foreign direct investment (FDI hereafter)

flows to Europe show a large increase during the year 2015. FDI went up almost 65 percent in comparison

to the year before, to an inflow of 504 billion Dollar. The expansion of FDI flows to developed countries is

primarily driven by a surge in cross-border M&A activity during the year, since deal making in Europe

went up with 36 percent. Next to the largest target country in Europe, the United Kingdom, Ireland has

become runner-up with M&A sales of 48 billion Dollar and France reached a historical 44 billion Dollar in

value in 2015. Main acquirers of the European assets were multinational enterprises (MNE hereafter) from

developed countries, the majority of which are located in North America, and China.

Multiple studies try to explain causes of swings in M&A flows. Traditional trade theories argue

that FDI is a result of comparative costs when international trade did not equalize factor prices. This theory

dates out of a time where the bulk of MNEs were located in the U.S. and a large portion of their investments

was located in less developed countries with lower costs, according to Blonigen (1997). He argues that

traditional theories concerning FDI are possibly explaining long run flows of FDI, but offer no clear

explanation of short-run movements. In addition, these theories do not explain the recent movements into

Europe, since Europe is not generally seen as a less developed region.

Martynova and Renneboog (2008) observe recurring surges and downfalls in M&As and examine

these different waves and underlying motives. They state that takeovers occur as a result of external

economic, technological, financial, regulatory, and political shocks. The waves usually occur in periods of

economic recovery (following a market crash and economic depression) and accompany rapid credit

expansion, which in turn results from external capital markets that prosper, and stock market booms. These

waves can take length of up to multiple decades and might offer little evidence for short-run fluctuations in

M&A activity in recent years.

1 United Nations Conference on Trade and Development. UNCTAD’s Division on Investment and Enterprise is

specialized in all matters related to foreign direct investment and multinational enterprises in the United Nations

System. It performs research and policy analysis on investment and enterprise development, fosters intergovernmental

consensus-building, and provides technical assistance to over 150 countries.

J.P. van Doorn 6

While reports point out an increase in European M&A inflows, the value of the Euro compared to

several currencies has declined in recent periods. Figure 1 shows the number of Eurozone acquisitions by

large foreign investor countries and the corresponding bilateral real exchange rates (amount of Euro per

unit of foreign currency). The graph shows short-run movements in Eurozone inward FDI which are

unlikely explained by sudden adjusted comparative costs or transaction costs between countries in the

Eurozone and developed investor countries. However, the fluctuations in the bilateral exchange rate next

to the FDI inflows might suggest some valuable explanatory power.

The declining value of the Euro in recent years and the increasing amount of FDI of foreign firms

in Europe could raise concerns that Eurozone firms can be acquired at bargain prices. Froot and Stein (1991)

explain with their relative wealth effects theory, that depreciation of target companies’ currency, can

0

50

100

150

200

250

40,0

60,0

80,0

100,0

120,0

140,0

160,0

180,0 Number of

FDI M&As

into the

Eurozone

Real

exchange

rate index

1999Q1=100

Time

EUR/AUD EUR/CAD EUR/CNY EUR/JPY EUR/GBP EUR/USD M&A FDI

Figure 1

Total number of foreign direct investment M&As into the Eurozone and the real exchange rates

Figure 1 shows the number of foreign direct investment M&As into the Eurozone for all industries, by Australian,

Canadian, Chinese, Japanese, United Kingdom-based and United States-based firms and the indexed quarterly real

exchange rate. Base quarter for the indexed real exchange rate is 1999Q1. The figure is constructed by using

quarterly data from 1999Q1 to 2016Q2

Note: The indexed quarterly real exchange rates are constructed using the nominal exchange rate offer prices

(EUR/X) and the consumer price indexes. The FDI M&A figures, contain total FDI inflow from the investor

countries into the initial 14 Eurozone countries, with a minimum of 10% ownership stake after transaction closing.

Sources: Exchange rates are retrieved from WM/Reuters closing spot rates and CPI is obtained from the OECD.

FDI data is acquired from the International Mergers Database of SDC Platinum

J.P. van Doorn 7

provide foreign firms an advantage. A low Euro could increase relative wealth of non-Eurozone companies,

making it easier to invest in Eurozone countries. Blonigen (1997) on the other hand focusses not on the

price of the asset but states that the rate of return of an asset should be relevant when acquiring a firm-

specific asset. These assets can generate returns in several markets and currencies at the same time without

any foreign currency transactions are needed, by enhancing plant efficiency of the acquiring firm for

example.

While a large amount of research tried to supply evidence for the effect of exchange rate variation

or level on FDI, not all studies find an unambiguous significant relationship. This study aims to provide

new information and evidence on the effect of exchange rate level on the amount of cross-border M&A

FDI in the Eurozone, while previous studies focused primarily on U.S. data and to a lesser extent, Japanese

data. Furthermore, time periods in which previous empirical tests have been done are quite outdated. The

effect and coefficients in this relationship found may be specific to the period of time considered. This study

will test if the effect of exchange rate level will be applicable for data concerning countries in the Eurozone

to possibly help explain cross-border M&A flows.

1.2. Problem statement

In order to examine the effect of exchange rate level on FDI, the main research question this study

will try to answer is:

Does the currency exchange rate level affect foreign direct investment mergers and acquisitions into the

Eurozone?

1.3. Research questions

Sub-questions to help answer the main problem statement are:

What are foreign direct investments?

What are the determinants of FDI M&As?

What is the effect of the exchange rate on FDI M&A?

What is the effect of other influencing factors on FDI M&A?

Is the effect of a depreciating Euro on FDI M&A more positive in manufacturing than non-

manufacturing industries?

Is the effect of a depreciating Euro on FDI M&A more positive in high R&D than low R&D

manufacturing industries?

J.P. van Doorn 8

1.4. Outline

The second chapter contains the theoretical framework. This chapter will elaborate on the

dependent variable and will discuss the factors considered in this study. The aim of this chapter is to explain

the research questions and form hypotheses to answer the main problem statement. The third chapter serves

as a pivot where the theoretical section is connected to the empirical research methodology. Furthermore,

research data used to conduct this research, accompanied by the descriptive statistics, are being presented.

Chapter four will consist of the empirical results of the conducted tests. Chapter five will discuss the derived

insights of the research, test the hypotheses formed in chapter two and will analyze these results in contrast

with previous work discussed in the literature review. In addition, the implications and limitations of this

study will be discussed. Finally, this research concludes with chapter six, containing a brief summary and

conclusion.

J.P. van Doorn 9

2. Literature review

2.1. Foreign direct investment

The dependent variable of interest in this study is the number of foreign direct investment mergers

and acquisitions into the Eurozone. FDI is an investment by an individual or firm from one country, in

business assets located in another country. FDI has two primary components: the form of greenfield

investment2 in a foreign country or by acquiring business assets to gain lasting controlling interest or

ownership of the foreign firm. A cross-border M&A is a component of the latter category, as an existing

target firm is obtained by a foreign acquirer.

Where foreign portfolio investment contains solely purchasing equities of foreign-based firms, the

crucial characteristic of foreign direct investment is that the investment made in a company establishes

effective control or substantial influence over the foreign firm.

FDIs are generally categorized as horizontal, vertical or conglomerate direct investments.

Horizontal direct investments indicate that the company conducts the same type of business operation in

the target country as it is engaged in in its home country. In the case of vertical direct investment, the

operation is different than the acquiring firm’s main business but is still related to it. Acquiring and

integrating a foreign supplier of components used in a manufacturing process is an example of foreign

vertical direct investment. In the case of a conglomerate type of FDI however, the investment in a business

is unrelated to the existing main business of the investor and involves entering an unfamiliar industry.

2.2. The link between currency exchange rate and FDI

There have been many theoretical and empirical studies investigating the link between the exchange

rate and FDI. Traditional belief is that the currency exchange rate levels should not affect the incentive to

invest. McCulloch (1991 p. 179) states: “if a U.S. asset is seen as a claim to a fixed stream of future Dollar-

denominated profits, and if those profits will be converted back into the domestic currency of the investor

at the same exchange rate, the level of the exchange rate does not affect the present discounted value of the

investment.” So If exchange rates are considered a random walk and the price to acquire an asset and the

return of that asset are expressed in the same currency, the relative valuation of the asset will stay unaffected

for either domestic or foreign buyers. However, McCulloch (1991) does add to this argument that if profits

are generated by activities that require imported inputs or by exporting outputs to other markets, the value

of the profits in the domestic currency may not typically be independent of exchange rate fluctuations.

2 A greenfield investment is a form of FDI where a parent company builds its operation in a foreign market from the

ground up. Greenfield investments are distinctive as the operation construction is done to its own specifications and

employees are trained focused on the standard of the investing company, so that the fabrication process can be highly

controlled.

J.P. van Doorn 10

At the same time, several empirical studies do reveal a possible correlation between a depreciating

currency and increasing FDI inflows, especially acquisition FDI. The observed correlation tends to confirm

the belief that assets and technology can be acquired at bargain prices by foreign firms when the domestic

currency is weak. Among others, Froot and Stein (1991), Klein and Rosengren (1994), Swenson (1994),

Blonigen (1997) and Georgopoulos (2008), tested and found a link between currency exchange rate level

and FDI inflow. Other studies such as Ray (1989) and Stevens (1998) have included exchange rate as a

factor in their empirical model and found a weaker or no significant effect on movements in FDI.

While the research mentioned above focuses on the effect of exchange rate level, early papers by

Cushman (1985 and 1988) investigates the impacts of long-run exchange rate volatility on FDI. Cushman

(1985) constructs a theoretical firm-level model of international investment where four different forms of

company regimes are examined, distinctive in exchange rate expectations, trade linkages and financing

options. Greater uncertainty or risk due to exchange rate fluctuations would encourage FDI as a substitute

for exports. The empirical research of Cushman (1985) finds a positive relationship of exchange rate

volatility on FDI outflow data from the United States to Canada, France, Germany, Japan and the United

Kingdom. Cushman (1988) analyzes FDI flow into the U.S. from these countries and also finds a positive

impact of exchange rate volatility.

Froot and Stein (1991) were one of the first offering an interpretation for the correlation found

between the exchange rate level and FDI. Their research and reasoning imply a distinct relationship when

capital markets are imperfect. They state (p. 1191): “as the dollar declines relative to its long-run

equilibrium value, the returns on all dollar assets will fall as well, and hence the prices of these assets will

rise. There are no “steals” to be had by foreigners.” We live in a world with high (and even more increasing)

mobility of capital and if a foreign firm has an advantage acquiring a domestic firm with his own currency,

why is it not possible for another domestic investor to borrow the complete amount in the foreign currency

and acquire the firm? Froot and Stein (1991) build their relative wealth effects theory on information

asymmetry and lenders tend to charge a premium for monitoring costs. Because of this asymmetry, it will

be very costly or even impossible for investors to finance an asset entirely with external capital. They state

(p. 1194): “the more net wealth an entrepreneur can bring to such an “information-intensive” investment,

the lower will be his total cost of capital.” Froot and Stein (1991) emphasize the link between an investors’

wealth position and investments, which extends in the relationship between the exchange rate and FDI

acquisitions. Foreign firms hold a majority of their wealth in their own currency denominated form and an

appreciation of this currency compared to for example the Dollar, boosts the relative wealth position of the

foreign firm. This will lower the relative cost of capital of this firm because of the contribution of its own

capital and provides more financial capacity to outbid U.S. domestic firms.

J.P. van Doorn 11

This effect is illustrated by an adjusted example of the one Froot and Stein (1991) use. Both a Dutch

and a U.S. investor take part in the auction of a Dutch windmill park. The turbines will provide revenues

of 100 EURm in the following year. The Dutch and U.S. investor can both get a loan on the same terms,

but the bank will only lend up to 90 percent of the total acquiring price. The Dutch firm holds 7 EURm of

own funds and the U.S. investor has 5 USDm of cash available. With an exchange rate of 1 USD/EUR, the

U.S. investor can bid as high as 50 EURm, while the Dutch Investor can bid up to 70 EURm and will win

the auction. In the case the Euro depreciates and the current exchange rate is 0.5 USD/EUR, the U.S.

investor’s wealth increases to 10 EURm, he can bid up to 100 EURm and will win the bidding. The

depreciation of the Euro has increased the relative wealth of the U.S. investor.

In the example above capital is mobile. The U.S. bidder can access the same external capital as the

domestic Dutch investor. The imperfection lies in the information about the assets that are acquired. This

will not be equal for every type of asset or investment. Passive investment portfolios with bonds and stocks

are not that information sensitive according to Froot and Stein (1991). These investments could practically

exclusively be financed with external capital and therefore, it is expected that portfolio flows are not highly

correlated with exchange rates. In the case of FDI acquisitions however, it is very likely to encounter

information asymmetry. In their empirical section, the exchange rate is regressed on inflows of FDI into

the U.S., for the period 1973 to 1988 in thirteen different industries. For all thirteen separate industries,

coefficients of exchange rate level show negative signs. Five of these coefficients are statistically significant

and the strongest effect is seen in the manufacturing industries, indicating that a depreciating U.S. Dollar

leads to greater FDI inflow.

Not everyone agrees with the statement of Froot and Stein (1991). Stevens (1998) for example,

mimics and criticizes their research. He states that their findings are not robust for subsamples within their

original chosen sample period and when the period is extended to 1991, the exchange rate variable is

insignificant in this relationship. Blonigen (1997) indicates that Froot and Stein (1991) provide an important

first step in linking exchange rate fluctuations to FDI movements, but leave certain important matters

unanswered. He states (p. 450): “First, while their wealth effect need not distinguish the effect of exchange

rate movements on different types of FDI, their empirical findings present evidence that various forms of

FDI respond differently to the exchange rate changes. Second, it may be empirically difficult to distinguish

relative wealth gains from currency movements compared to other factors affecting firm wealth.” Blonigen

(1997) cites the Japanese FDI flow in the U.S. in the late 1980’s. Did Japanese firms experience wealth

gains from the fluctuations in exchange rates or from the speculative bubble in Japanese real estate markets

and stock market? This potential influence will be addressed later on.

The relative wealth theory of Froot and Stein (1991) assumes that the price of the acquired asset is

relevant. Blonigen (1997) presents in his paper theoretical and empirical evidence, showing that not the

J.P. van Doorn 12

price of an asset, but the rate of return of that asset should matter when a foreign firm locates abroad.

Traditional arguments essentially assume that assets acquired in foreign FDI are similar to bonds or

comparable assets where the price and nominal return are both in the same currency. Blonigen (1997) states

that this is not necessarily the case when observing mergers and acquisition FDI, because the target’s assets

are often in the form of firm-specific assets, which can generate returns in several markets and currencies

at the same time, without any foreign currency transactions. The model of Blonigen (1997) leans on the

assumption that the firm-specific assets, such as innovations or technology for example, are easily

transferable within the acquiring company and are able to generate returns in not just the currency the assets

are acquired with. A depreciation of the target country’s currency lowers the bidding price of the asset for

foreign investors, but this depreciation does not affect the returns. Therefore, should the depreciation of the

target firm’s currency, increase FDI inflows into the target firm’s country. While Froot and Stein (1991)

find that imperfections on the capital market cause a link between currency exchange rate movements and

FDI, Blonigen’s theory (1997) assumes the imperfection of goods markets. A domestic target firm has

limited or no access to the foreign market relative to the foreign firm to sell its output. For example, suppose

both a Dutch and a U.S. company have an equal opportunity to acquire a Dutch firm with a valuable

transferable innovation. Depreciation of the Euro compared to the Dollar will increase the net present value

of the firm for the U.S. company, since the cost of purchasing the Dutch firm will decrease. In the case of

imperfection of the goods market, the net present value of the asset for the Dutch bidding firm does not

change, because it has no (or less) access to the U.S. market compared to the bidding U.S. firm.

Blonigen (1997) empirically tests his hypothesis using data on Japanese acquisitions in the United

States across multiple industries from 1975 to 1992. Previous research has provided evidence that Japanese

M&A FDI into the U.S. is strongly driven by technology-related and firm-specific asset acquisition motive.

These useful and valuable technology is more common in high research and development (R&D hereafter)

manufacturing industries, therefore it is likely that Blonigen’s (1997) theory is supported in these industries.

The data on the Japanese acquisitions in the United States in the sample time period confirm the theory,

displaying a strong correlation between periods of a weaker U.S. Dollar and a higher amount of M&A FDI

in the U.S., for industries which more likely involve firm-specific assets. This effect however, is not found

in greenfield FDI. One critique on the analysis of Blonigen (1997) that he mentions in his empirical results

section (p458 – 459): “R&D expenditures may be a proxy for other characteristics of an industry that would

make a Japanese firm more likely to acquire in that industry when the dollar depreciates. Perhaps high R&D

industries are also the ones that are relatively more capital intensive.” This remark connects his theory and

findings with the capital market approach of Froot and Stein (1991), which suggests that industries that are

capital-intensive gain more benefit from depreciation of the exchange rate.

J.P. van Doorn 13

Other researchers re-examine Blonigen’s (1997) theory. Georgopoulos (2008) tests the hypothesis

by linking cross-border FDI acquisition data between U.S. and Canada to the exchange rate level. This

research’ empirical results are consistent with the asset acquisition hypothesis of Blonigen (1997) for high

R&D intensive industries. While Georgopoulos focusses on single country inbound FDI activity, Lee (2013)

examines the proposed link between exchange rate and FDI using M&A FDI data from multiple country

sources that are inbound for various countries in the period 1989 to 2007. The result of Lee’s (2013) study

provides evidence that Blonigen’s (1997) suggested link between the exchange rate level and asset-seeking

acquisition FDI can be confirmed for U.S. inbound acquisition, but not for inbound M&A FDI in other

various developed countries. The analysis of Lee (2013) finds that Blonigen’s (1997) evidence is mainly

driven by U.S. inbound M&A data. When he excludes the U.S. data and only inbound acquisition FDI into

other foreign countries are considered, support for the hypothesis could not be found. This may sound

feasible, since the United States spend the most on R&D in the world (2016 Global R&D Funding Forecast3)

and can be considered the largest marketplace for technology, and may attract asset-seeking cross-border

acquisitions. Lee (2013) states that another explanation might be that the U.S. market is more open to

foreign direct investments in comparison with other countries such as Japan for example.

This study examines the influence of the exchange rate level of the Euro on the amount of M&A

FDI inflows into the Eurozone to see if Blonigen’s (1997) asset acquisition hypothesis will hold for this

sample, as no study did this before. The effect of the currency exchange rate level will be examined instead

of volatility, since interest lies in investments to enrich firms by acquiring valuable firm-specific assets for

a lower price and not acquisitions to mainly diminish exchange rate risk. Blonigen’s (1997) theory depends

on the imperfect goods market assumption as an explanation why domestic firms cannot generate equal

returns in the foreign country where the currency has appreciated. The effect of the exchange rate risk shall

be reduced when assuming that firm-specific assets produce higher profits without the need of additional

exchange rate exposure. However, with a low stable value of target’s currency, firm-specific assets are still

very attractive as it may improve plant efficiency of the acquirer for example.

Previous studies have mostly focused on U.S. inbound FDI or on U.S. outbound FDI. Therefore,

literature primarily tested the exchange rate effects almost exclusively with U.S. data and to a lesser extent,

with Japanese data. Furthermore, the time periods in which previous empirical tests have been done are

quite outdated. Blonigen (1997) for example, tested his theory ranging from 1975 – 1992, Georgopoulus

(2008) 1985 – 2001 and Lee (2013) for the period 1989 – 2007. The effect and coefficients in this

relationship found may be specific to the period of time they considered.

3 2016 global R&D funding forecast is drafted by the Industrial Research Institute (IRI), Research-Technology

Management (RTM) and R&D magazine. Source for expenditures on R&D in this article: IRI, R&D Magazine,

International Monetary Fund, World Bank, CIA Fact Book, OECD.

J.P. van Doorn 14

This study will test if the effect of exchange rate level will be applicable for data concerning

countries in the Eurozone. Europe contains multiple well-developed countries, which contribute over 20

percent of global R&D spending (2016 Global R&D Funding Forecast). Therefore, manufacturing and non-

manufacturing industries will be distinguished. Furthermore, manufacturing industries will be divided into

high and low R&D industries to test the firm-specific asset acquisition theory on Eurozone firms.

2.3. Domestic acquisitions by other Eurozone firms affecting FDI inflows

Literature states that even in an increasing international economy, domestic acquisitions are still

the largest portion of mergers and acquisitions. Cross-border M&A inside the Eurozone is likely to take

place due to reduced distance and a similar currency for example. These transactions are not directly

influenced by a depreciation or appreciation of the Euro and can contain other relevant influences on the

FDI inflows from non-Eurozone firms that help to explain the main question of this research.

Without supply, a transaction cannot be accomplished and to capture the industry firm supply

component, Blonigen (1997) includes a variable capturing the amount of domestic acquisitions by other

U.S. firms. This variable should control for the overall domestic M&A activity and target firm supply. If

the of quantity of M&As is large internally, chances are that also internationally the number of incoming

deals will be higher. He expects a positive correlation between the supply variable and number of foreign

acquisitions in an industry. His expectation is confirmed by his empirical results, where the domestic

acquisition coefficients show a positive sign and are statistically significant. Lee (2013) also includes a

domestic acquisition factor in his model, similarly constructed as the main dependent variable: FDI M&A.

The coefficients in his result section also show a (small) positive but strong significant statistical effect.

Dewenter (1995) approaches the potential influence of domestic acquisition activity in another way.

In her research examining the relationship between the value of the dollar on cross-border acquisitions into

the U.S., she tests her variables on both the absolute level of FDI flow and the FDI controlled for overall

level of investment activity. Her relative FDI independent variable is constructed as foreign investment

relative to domestic acquisitions.

This research will take into account the domestic M&A activity in the model since this is an

appropriate control variable. This factor captures a favorable acquisition environment, unrelated to

exchange rate fluctuations and contributes to a stronger explaining model. Internal Eurozone M&A is not

explained by the main explanatory variable, the currency exchange rate. However, by adding this

investment supply variable, the effect of internal cross-border Eurozone M&A activity on the inflow of FDI

out of the Eurozone is taken into account.

J.P. van Doorn 15

2.4. The effect of the real GDP growth rate on FDI

Froot and Stein’s (1991) theory suggests that even appreciating foreign wealth independent of

exchange rate, will generate an increase in FDI. Gross Domestic Product4 (GDP hereafter) is commonly

used as an indicator of the economic health, country’s standard of living, or productivity. When GDP

growth is adjusted for inflation you get the real GDP growth, which can be used to measure nation’s

economic growth (or decline) and is positively related to wealth.

Di Giovanni (2005) estimates the effect of multiple macroeconomic, financial and institutional

factors, to explain cross-border M&A flows. He includes various factors, including real GDP for both the

acquiring and target country to measure a countries economic size. His regression output shows a positive

significant effect for both variables.

While Blonigen’s (1997) uses U.S. domestic acquisitions as a control variable to measure supply

characteristics, his research inter alia controls for Japanese demand for U.S. target firms by including a

variable that captures annual real growth of Japanese GDP. His empirical results show a significant positive

link between the real GDP growth and Japanese acquisitions in the United States and, when looking to the

full sample period, consistent with the expected positive correlation. However, when a distinction is made

between high and low R&D manufacturing firms, the coefficient on GDP real growth rate is only significant

and positive for high R&D manufacturing companies.

In imitation of Blonigen (1997), Lee (2013) includes the real GDP growth rate in his empirical

specification to control for the demand side factor. He expects that appetite for M&A will be higher for

foreign countries with economies that grew over the years. The real GDP growth rate variable in his results

has a positive coefficient and are statistically significant, meaning that higher GDP growth increases the

demand for FDI M&A in his data sample.

According to previous literature, coefficient signs of real GDP growth of the acquirer’s nation on

dependent variable cross-border FDI, are not all equal and significant but seem to be a potential good control

variable complementing the empirical model of this research.

2.5. Growth of stock market price index affecting FDI

Earlier in this study, Blonigen’s (1997) possible alternative explanation for the movements in

Japanese acquisition FDI, especially the large spikes in the late 1980’s and early 1990’s, was cited. The

boom of U.S. firms acquired by Japanese investors could be explained as consequence of the speculative

“bubble” economy during those years, as Blonigen (1997) defines it. The speculative bubble is potentially

4 GDP is the monetary value of all the finished goods and services produced within borders of a particular country in

a specific time period. It includes the private and public consumption, government expenditure, investments and net

exports (the value of total exports minus total imports).

J.P. van Doorn 16

highly correlated with exchange rate fluctuations in the late 1980’s and early 1990’s and therefore, is a

potentially relevant factor. Froot and Stein (1991) mention this development as additional support to their

theory that shocks to wealth, other than exchange rate driven, can help explain U.S. FDI inflows. “Between

the end of 1987 and early 1991, the real value of the yen did not change importantly; yet the rate of Japanese

net FDI inflows increased until the end of 1989 (nearly doubling during those two years), and then fell.”

(Froot & Stein, 1991, p. 1215). Their imperfect capital markets approach explains why internal cost of

capital is lower than borrowing from external capital resources. If a foreign stock market grows, wealth of

the foreign firm will grow and provide more capital to acquire a certain asset.

Klein and Rosengren (1994) measure the relative wealth by creating a factor that represents the

value of the U.S. stock market to an index of the value of the stock market of a foreign country. They find

a significant negative influence of the relative wealth variable on inward U.S. acquisitions when the trend

factor is added in the regression. Their use of time trend allows controlling for increasing presence of

foreign ownership in the U.S.

Di Giovanni (2005) expects that cash is key in financing cross-border transactions, but a rebounding

equity market could increase the use of equity in deal financing. In addition, improving equity prices could

boost confidence among firms’ board to pursue acquisition tactics.

To take this possible relationship into account, Blonigen (1997) includes the annual growth in the

Tokyo Stock Price Index as a regressor, with an expected positive coefficient. When examining the

empirical results, a significant positive sign is found in the “all industries” column, less or no significance

for the (non-)manufacturing subdivision. Lee (2013) mimics the assumption above and constructs a stock

market variable to capture wealth effects from the stock market. The variable contains annual growth rates

of each of the foreign country’s representative stock market price index. Lee (2013) however, finds no

statistically significant effect in his empirical model of the stock market factor on FDI.

Multiple researchers added the foreign stock market index variable to capture wealth effects of

foreign investors from stock market fluctuations. Martynova and Renneboog (2008) examined the large

M&A waves, and state that these usually occur after a market crash and periods of economic depression.

The time period used in this research includes among others a portion of the speculative Dot-com bubble

and its burst and the global financial crisis plus both recovery periods. Adding the stock market factor could

therefore possibly increase the explanatory power of the empirical model.

2.6. Omitted factors

This study focusses primarily on the exchange rate affecting FDI, although certain variables that

are likely to influence FDI due to their economic effects and previous literature, are added. There are many

J.P. van Doorn 17

researchers that try to extend models and add many more different variables in the model that are not

feasible or essential relevant for this study.

Blonigen (1997) tries to control for alternative explanations that account for fluctuations in

acquisition FDI in the United States. He includes an industry-specific U.S. protection variable in his model

to account for tariff-jumping5, because this explanation for incoming FDI may be relevant for Japan in his

sample period. Japan could invest in the U.S. during this period, to avoid the newly established protection

mechanism. But when his empirical estimations are examined, the U.S. protection variable has no

significant effect and seems no explanatory variable. Blonigen (1997) explains this insignificant coefficient

may occur since, in his model, the effect of protection is tested over time, while FDI fluctuations may be

affected by the protection variable only around the time such mechanisms are put into place. Secondly, he

addresses that the U.S. protection may be higher in industries where alternative variables diminish FDI

activity.

Another frequent variable of interest concerning FDI is taxation. There is a large amount of research

that focus primarily on the effect of taxation on FDI and Blonigen (2005) discusses a number of them in

his review of empirical literature on FDI determinants. In summary, there are many varying conclusions

about this relationship. A MNE faces tax rates at a variety of levels and possibly in both the target and

parent country. In addition, there are many tax treaties, which negotiate tax reductions in countries’

withholding rates (Blonigen, 2005), and the empirical approaches and data samples differ considerably in

the literature, stating no clear one minded effect.

The tax variable and import tariff or domestic protection variable are omitted from this research.

The protection variable turned out to have no explicit and significant effect in previous studies, making it

unlikely that this is a primary explanatory variable. Furthermore, it is unavailable and almost impossible to

display all sectoral import mechanisms between the Eurozone countries and the largest investor countries

in the data sample. The same argument applies to the taxes factor, since most tax treaties and their content

are not generally known and, at the level of disaggregation of the data, are unavailable.

2.7. Research hypotheses

In order to answer the research questions in this study, several hypotheses are presented. The

hypotheses have the purpose to mainly clarify the connection between the exchange rate and M&A FDI for

the Eurozone. In addition, the relationship of the control variables is tested on the independent variable: the

number of FDI M&A into the Eurozone.

5 Tariff is a tax imposed on imported goods and services. They are often used to regulate trade by increasing the price

of imported goods. Tariffs can be described as a government tool to shape trade policies and are often used to protect

domestic industries from foreign competition.

J.P. van Doorn 18

Many theoretical and empirical studies have failed to ensure a unanimous opinion on exchange rate

affecting FDI. While traditional theory states that currency fluctuations should not affect the incentive to

invest, Cushman (1985) argues and finds that greater uncertainty due to exchange rate fluctuations would

encourage FDI as a substitute for exports. Froot and Stein (1991) construct their relative wealth effects

theory to offer interpretation for the correlation found between exchange rate level and FDI. Multiple

researchers found mixed results on the relationship of interest. These studies primarily tested the effects

almost exclusively with U.S. data and to a lesser extent, with Japanese data. Furthermore, the effect and

coefficients in this relationship found may be specific to the period of time they considered. Therefore, the

effect of a depreciating Euro on FDI inflows in a more current time period will be tested by the first

hypothesis.

Hypothesis 1: A depreciation (appreciation) of the Euro, increases (decreases) the number of incoming

FDI acquisitions into the Eurozone

M&As inside the Eurozone are not directly influenced by a depreciation or appreciation of the Euro

and may contain other relevant influences on the FDI inflows from non-Eurozone firms that help to explain

the main question of this research. To account for supply of target firms inside the Eurozone and measure

the effect of domestic M&A activity on FDI inflows in the Eurozone of foreign countries, hypothesis 2 is

constructed.

Hypothesis 2: Higher (lower) number of domestic acquisitions, increases (decreases) the number of

incoming FDI acquisitions into the Eurozone

Froot and Stein’s (1991) theory suggests that even appreciating foreign wealth independent of the

exchange rate, will generate an increase in FDI. GDP is commonly used as an indicator of the economic

health, country’s standard of living, or productivity. When GDP growth is adjusted for inflation you get

real GDP growth, which can be used to measure nation’s economic growth and should affect the wealth of

the investor country. When affecting relative wealth of an investor, exchange rate level should have a larger

influence and increase FDI. To test this effect, hypothesis 3 is configured.

Hypothesis 3: Higher (lower) foreign GDP growth rates, increases (decreases) the number of incoming

FDI acquisitions into the Eurozone

J.P. van Doorn 19

Multiple researchers added the foreign stock market index variable to capture wealth effects of

foreign investors from stock market fluctuations. Di Giovanni (2005) argues that a rebounding equity

market could increase the use of equity in deal financing. In addition, improving equity prices could boost

confidence among firms’ board to pursue acquisition tactics. Blonigen (1997) adds the variable to his model

because the boom of U.S. firms acquired by Japanese investors could be explained as consequence of the

speculative “bubble” during the years covered in his sample. The sample period covered in this study

contains the recession following the Dot-com bubble and the global financial crisis. Therefore, hypothesis

4 examines whether stock market shocks affect FDI.

Hypothesis 4: Higher (lower) growth rate of the acquirer’s stock market price index increases

(decreases) the number of incoming FDI acquisitions into the Eurozone

Blonigen’s (1997) firm-specific asset acquisition hypothesis argues that with a low value of the

target’s currency firm-specific assets such as technology, are very attractive. Easily transferable technology

is acquired at a discount due to a depreciation of the target country’s currency and can provide more

efficiency or return for the acquiring firm. The multiple well-developed countries in the Eurozone,

contribute to a large part of global R&D spending. To test if depreciation of the Euro, increases the foreign

incentive to buy technology more “cheaply”, this study will distinguish manufacturing and non-

manufacturing industries and, in manufacturing industries, distinguish high and low R&D industries. To

test the firm-specific asset acquisition theory on Eurozone firms, hypotheses 5 and 6 are constructed.

Hypothesis 5: The effect of a depreciating (appreciating) Euro on the number of incoming FDI

acquisitions, is more positive (negative) for manufacturing industries than non-manufacturing industries

Hypothesis 6: The effect of a depreciating (appreciating) Euro on the number of incoming FDI

acquisitions, is more positive (negative) for high R&D manufacturing industries than low R&D

manufacturing industries

J.P. van Doorn 20

3. Research methodology

3.1. Empirical model

To test the hypotheses mentioned in the previous chapter and be able to answer the main research

question of this study, the determinants of the dependent variable are specified in a relationship estimation

equation. The basis of this study’s model relies on the equation relationship projected in equation 1.

3.1.1. Empirical specification

𝐹𝐷𝐼𝑀&𝐴𝑖𝑗𝑘𝑡 = 𝑓(𝑙𝑛𝑅𝐸𝑅𝑖𝑗𝑡 , 𝐷𝑜𝑚𝑀&𝐴𝑖𝑘𝑡, 𝑅𝐺𝐷𝑃𝐺𝑗𝑡 , 𝑆𝑀𝑃𝐼𝐺𝑗𝑡) (1)

Element Description

𝐹𝐷𝐼𝑀&𝐴𝑖𝑗𝑘𝑡 The number of foreign direct investments mergers and acquisitions into country 𝑖 from country

𝑗 in industry 𝑘 at time 𝑡

𝑙𝑛𝑅𝐸𝑅𝑖𝑗𝑡

Logged real exchange rate expressed in the currency of target country 𝑖 to one unit of investor

country 𝑗’s currency at time 𝑡

𝐷𝑜𝑚𝑀&𝐴𝑖𝑘𝑡

The number of domestic acquisitions inside the Eurozone by country 𝑖 in industry 𝑘 at time 𝑡

𝑅𝐺𝐷𝑃𝐺𝑗𝑡 Real GDP growth rate in country 𝑗 at time 𝑡

𝑆𝑀𝑃𝐼𝐺𝑗𝑡 Growth of the leading stock market price index in country 𝑗 at time 𝑡

3.1.2. Variable definition

3.1.2.1. The number of FDI M&As into the Eurozone

Number of FDI M&As into the Eurozone is the variable of interest in this research. The number of

acquisitions is chosen rather than the value because deal value is often not necessarily disclosed. The

dependent variable in this study is measured as FDI acquisitions of firms in the initial Eurozone6 countries,

from the time stock prices are indicated in terms of the Euro. Therefore, period of interest in this research

is 4 January 1999 up to 30 June 2016. The top investor countries considered in the analysis are Australia,

6 The countries which acceded to the Eurozone at establishment in 1999 are: Austria, Belgium, Finland, France,

Germany, Ireland, Italy, Luxembourg, Monaco, the Netherlands, Portugal, San Marino and Spain. Vatican City has

been omitted due to deficiency of data available.

Table 1

Variable overview

Table 1 presents an overview of the variables specified in the model of this study.

J.P. van Doorn 21

Canada, China, Japan, the United Kingdom and the United States, as they represent the economies with

highest values of net purchases of cross-border M&As, that are not included in the Eurozone.7 This study

includes deals in the dataset when the investing firm owns at least 10 percent in the Eurozone based

company after de purchase. According to the Organisation for Economic Co-operation and Development

(OECD), FDI is an investment that reflects the objective of establishing a lasting interest from a firm in one

economy in a firm that is located in another economy. The investment implies a long-term interest of the

investor in the acquired assets and a significant degree of influence on the management. The ownership of

10% or more of the voting power of an enterprise is the threshold for establishing a controlling interest,

according to OECD guidelines8.

To see if Blonigen’s (1997) firm-specific asset theory is applicable to the Eurozone and current

time period, the model will be tested on different industry data samples. Aside from total all industry FDI

M&As, a distinction between non-manufacturing and manufacturing industries is made. Furthermore, high

and low R&D manufacturing industries are disaggregated to test if the theory of Blonigen (1997) will hold.

Greenfield investments are disregarded from this study because the interest is in (non-)tangible transferable

technological or licensable assets. In addition to acquiring proprietary assets, acquiring a domestic firm

might accelerate the gain of market presence for the foreign company, which is not the case in greenfield

investments.

3.1.2.2. Real exchange rate

The main independent explanatory variable is the quarterly logged real exchange rate level

expressed as the domestic currency of the Eurozone, the Euro, per unit of foreign currency. The real

exchange rate is explicitly used instead of nominal exchange rate because price levels could also affect

investment decisions. The real exchange rate is corrected for differences in price level between the two

countries, while the nominal exchange rate is not. In imitation of Lee (2013), this study uses logarithms of

the quarterly individual real exchange rates, allowing for the percentage changes of different exchange rates

to be comparable, interpretable and less influenced by potential outliers.

The Eurozone is seen as a developed economic area with a relatively high amount of high

technology firms. According to the firm-specific asset theory, a foreign firm could acquire valuable assets

at a lower price when the domestic currency devaluates. The real exchange rate variable, denominated in

Euro per foreign currency, is therefore expected to have a positive effect.

7 Value of cross-border M&As, by region/economy of seller/purchaser, 2009−2015, World Investment Report 2016

published by UNCTAD. 8 OECD Benchmark Definition of Foreign Direct Investment fourth edition 2008

J.P. van Doorn 22

3.1.2.3. Domestic M&As inside the Eurozone

To capture industry and country supply characteristics, the number of domestic acquisitions inside

the Eurozone by Eurozone countries is implemented in the model. This variable represents overall M&A

activity in the Euro area in an industry and includes the aspects of a convenient acquisition environment,

which are not related to the real exchange rate.

An area, with high M&A activity and a sufficient supply of targets, is likely to attract foreign

investors. The relationship between domestic M&A and incoming FDI in the Eurozone is expected to be

positive.

3.1.2.4. Real GDP growth

Real GDP is an inflation-adjusted measure of the value of all goods and services produces in an

economy during a specific period. Unlike nominal GDP, real GDP is corrected for changes in price level

of a country and therefore provides a more accurate measurement of economic growth. The real GDP

growth is used in the model so that growth rates of the different countries are easier to compare.

The real GDP growth variable is used to measure demand for FDI. Higher economic growth should

increase the demand for investments and M&A. Therefore, a positive relationship between the real GDP

growth of the investor country and FDI M&A into the Eurozone is expected.

3.1.2.5. Stock market price index growth

Growth of the investor country’s stock market price index is added to the model to capture demand

for FDI, which is not necessarily generated by real exchange rate changes. This independent variable

measures the wealth effect of stock price changes of the leading stock market index of every country and

also controls for possible effects of stock market bubbles and crashes that might affect the FDI acquisition

decisions of firms located in one of the investor countries. Stock market price index growth is used in the

model to compare growth rates of the different leading stock markets of each country.

The stock market growth variable is included to control for its influence on demand and is

expected to have a positive relationship with the dependent variable of this research.

J.P. van Doorn 23

3.1.3. Expectations

Variable Coefficient Expectation

𝑙𝑛𝑅𝐸𝑅𝑖𝑗𝑡 +

𝐷𝑜𝑚𝑀&𝐴𝑖𝑘𝑡 +

𝑅𝐺𝐷𝑃𝐺𝑗𝑡 +

𝑆𝑀𝑃𝐼𝐺𝑗𝑡 +

3.2. Data and variable construction

3.2.1. Number of FDI M&As into the Eurozone

Number of FDI M&As in the Eurozone by foreign firms is constructed from data retrieved from

SDC Platinum. This database provides extensive information on deals such as target and acquirer nation,

allowing to narrow the deals down to Eurozone target nations and the large investing countries as acquiring

nationality. In this way, the effects can be measured over the full time period used in this model. The time

period 4 January 1999 to 30 June 2016, contains 119,549 M&A deals with Eurozone target nations. As

foreign out-of-Eurozone acquiring firms, companies located in the six large investor countries are used.

When filtering out other acquiring nations than the six mentioned earlier, 14,563 deals remain.

Furthermore, the database yields information of the percent of shares owned after the transaction.

Accounting for the OECD threshold of 10% mentioned before, results in a remaining 10,969 FDI take-

overs in the Eurozone.

In addition, SDC Platinum also specifies the target firm’s SIC9 code for each deal enabling to break

down the deals into four-digit SIC level industries and distinguish 3,936 manufacturing industry deals and

6,909 non-manufacturing industry deals10. Within the manufacturing industries, subdivision gives 2,240

high R&D industry acquisitions and 1,696 low R&D acquisitions.11

9 Standard Industrial Classification (SIC) codes are four-digit numerical codes assigned by the U.S. government to

business establishments to identify the primary business of the establishment. The first two digits of the code identify

the major industry group, the third digit identifies the industry group and the fourth digit identifies the industry. 10 Manufacturing and non-manufacturing deals do not add up to total deals, because not all SIC codes in the dataset

are compliant. 11 Following Blonigen (1997), SIC codes between 2000 and 3999 are manufacturing industries, which can be

subdivided into high and low R&D industries. High R&D industries are SIC 2810-2899, 3510-3599, 3650-3679, 3710-

3729, 3760-3769 and 3810-3899. Low R&D industries are all other manufacturing industries. High R&D industries

are determined as those where expenditures on R&D as percentage of sales, are at or above average of the

manufacturing industry.

Table 2

Variable coefficient expectation

Table 2 presents the expected signs (positive/negative) of the coefficients in the model specified in this study.

J.P. van Doorn 24

When categorizing the deals into the quarters of each year, the dependent variable data at industry

subdivision level and country pair for each quarter in the relevant time period of this research is constructed.

3.2.2. Real exchange rate

Main independent explanatory variable in this research, the real exchange rate, is constructed using

the quarterly nominal exchange rate of the foreign currencies to one Euro. Share prices on the stock

exchanges of initial Eurozone countries are displayed in the Euro since 4 January 1999. Therefore, the first

quarter of 1999 is the starting period of this study. The exchange rates used are the WM/Reuters closing

spot rates, retrieved from Thomson Reuters Datastream. To adjust them to Euro per unit of foreign currency,

1 is divided by the extracted nominal exchange rates. The interest is in the logged real exchange rate and is

calculated with formula 2.

𝑙𝑛𝑅𝐸𝑅𝑖𝑗𝑡 = 𝐿𝑛 (𝑒𝑡 ∗ 𝑃𝑗𝑡

𝑃𝑖𝑡) (2)

Where:

𝑙𝑛𝑅𝐸𝑅𝑖𝑗𝑡 = Logged real exchange rate of the currency of country 𝑖 for country j at time 𝑡

𝑒𝑡 = Nominal exchange rate at time 𝑡

𝑃𝑗𝑡 = Consumer price index of country 𝑗 at time 𝑡

𝑃𝑖𝑡 = Consumer price index of the Eurozone at time 𝑡

The quarterly consumer price index (CPI, hereafter) with base year 2010, for every investor country

and the Eurozone, are obtained from OECD Data.12

3.2.3. Number of domestic Eurozone M&As

The number of domestic M&As in the Eurozone is constructed with a similar approach as the

dependent variable of this study, the number of FDI M&As into the Eurozone. As mentioned before, SDC

Platinum provides 119,551 deals with a Eurozone company as target firm. 90,060 of these deals are closed

between two initial Eurozone parties. Correcting for the FDI threshold of at least 10 percent owners stake

leaves 64,609 of total domestic Eurozone M&As in the relevant time period.

Using the industry SIC codes to split the deals, 43,374 Non-manufacturing industry acquisitions

during the sample period are found and manufacturing industry deals sum up to a total of 20,549.

Subdivision of the manufacturing industry counts 7,700 high R&D and 12,849 low R&D take-overs. When

categorizing the deals into the quarters of each year, the independent variable data at industry subdivision

level and country pair for each of the 70 quarters between 1999 and 2nd quarter of 2016 is constructed.

12 OECD Data: https://data.oecd.org/price/inflation-cpi.htm.

J.P. van Doorn 25

3.2.4. Real GDP growth

Investor countries’ quarterly real GDP are retrieved from Thomson Reuters Datastream for the

period 4th quarter 1998 to 2nd quarter 2016. The GDP data seasonally adjusted prices with constant prices

are selected to obtain the real GDP growth, unaffected by price changes. To adjust the data to growth of

real GDP, formula 3 is used:

𝑅𝐺𝐷𝑃𝐺𝑗𝑡 =𝑅𝑒𝑎𝑙 𝐺𝐷𝑃𝑗𝑡−𝑅𝑒𝑎𝑙 𝐺𝐷𝑃𝑗𝑡−1

𝑅𝑒𝑎𝑙 𝐺𝐷𝑃𝑗𝑡−1

(3)

Where:

𝑅𝐺𝐷𝑃𝐺𝑗𝑡 = Real GDP growth of country 𝑗 at time 𝑡

𝑅𝑒𝑎𝑙 𝐺𝐷𝑃𝑗𝑡 = Real GDP of country 𝑗 at time 𝑡

𝑅𝑒𝑎𝑙 𝐺𝐷𝑃𝑗𝑡−1 = real GDP of country 𝑗 at time 𝑡 − 1

3.2.5. Stock market price index growth

Quarterly leading stock market price indices of the relevant investor countries are retrieved from

Thomson Reuters Datastream from the 4th quarter of 1998 to 2nd quarter of 2016. For Australia, data of the

All-ordinaries Stock Index, a stock index comprised of common shares from the Australian Stock Exchange,

is used. The All-Ordinaries Index is the most quoted benchmark for Australian equities. S&P/TSX

Composite Index is used to observe the stock market in Canada. This index contains stocks of the largest

companies on the Toronto Stock Exchange. China’s stock market is measured by the Shanghai Stock

Exchange Composite, made up of all the A-shares and B-shares listed on the largest stock exchange in

mainland China, to get a broad overview of the performance. Tokyo Price Index (TOPIX) is used as a

metric for stocks on the Tokyo Stock exchange. The TOPIX provides an appropriate representation of the

Japanese stock markets. For United Kingdom’s stock market data, the Financial Times Stock Exchange

(FTSE) All Share price index is included. This index is used as data for broad measuring the stock market

fluctuations on the London Stock Exchange. Finally, representing the United States stock market, the S&P

500 Composite price index is included. This index is seen as a leading indicator of the performance of

United States equities. To adjust the stock market indices to growth numbers, formula 4 is used.

𝑆𝑀𝑃𝐼𝐺𝑗𝑡 =𝑆𝑡𝑜𝑐𝑘 𝑀𝑎𝑟𝑘𝑒𝑡 𝑃𝐼𝑗𝑡− 𝑆𝑡𝑜𝑐𝑘 𝑀𝑎𝑟𝑘𝑒𝑡 𝑃𝐼𝑗𝑡−1

𝑆𝑡𝑜𝑐𝑘 𝑀𝑎𝑟𝑘𝑒𝑡 𝑃𝐼𝑗𝑡−1

(4)

Where:

𝑆𝑀𝑃𝐼𝐺𝑗𝑡 = Stock market growth of country 𝑗 at time 𝑡

𝑆𝑡𝑜𝑐𝑘 𝑀𝑎𝑟𝑘𝑒𝑡 𝑃𝐼𝑗𝑡 = Stock market price index of country 𝑗 at time 𝑡

𝑆𝑡𝑜𝑐𝑘 𝑀𝑎𝑟𝑘𝑒𝑡 𝑃𝐼𝑗𝑡−1 = Stock market price index of country 𝑗 at time 𝑡 − 1

J.P. van Doorn 26

Variable Mean Std. Dev. Min Max

Total FDI M&As into the Eurozone overall 26.117 32.176 0 135

between 33.298

within 10.447

Non-manufacturing industries overall 16.450 21.093 0 80

between 21.346

within 8.021

Manufacturing industries overall 9.371 11.824 0 55

between 12.101

within 0.141

High R&D manufacturing industries overall 5.333 7.092 0 34

between 7.243

within 2.546

Low R&D manufacturing industries overall 4.038 5.257 0 23

between 5.026

within 2.557

Logged real exchange rate overall -1.286 1.748 -5.076 0.514

between 1.906 -4.779 0.303

within 0.141 -1.583 -0.850

Total Eurozone domestic M&As overall 922.700 165.153 585 1,267

between 0.000

within 165.153

Non-manufacturing industries overall 619.629 124.612 372 902

between 0.000

within 124.612

Manufacturing industries overall 293.557 45.821 208 382

between 0.000

within 45.821

High R&D manufacturing industries overall 110.000 20.632 70 155

between 0.000

within 20.632

Low R&D manufacturing industries overall 183.557 28.968 122 238

between 0.000

within 28.968

Real GDP growth overall 0.008 0.010 -0.041 0.043

between 0.007 0.002 0.022

within 0.007 -0.035 0.032

Stock market price index growth overall 0.012 0.096 -0.274 0.339

between 0.006 0.007 0.023

within 0.096 -0.284 0.328

Table 3

Descriptive statistics

Table 3 displays the descriptive statistics of the main variables used in this study. All variables are constructed by

using quarterly data from 1999Q1 to 2016Q2 for the six investor countries. N = 420, n = 6 and T = 70. All estimates

are rounded to 3 decimals.

Note: SIC codes between 2000 and 3999 are manufacturing industries, all other are non-manufacturing industries.

High R&D industries are SIC 2810-2899, 3510-3599, 3650-3679, 3710-3729, 3760-3769 and 3810-3899. Low

R&D industries are all other manufacturing industries.

J.P. van Doorn 27

3.2.6. Descriptive statistics

Table 3 presents the summary statistics of the datasets, which contain the 6 investor countries with

70 quarters of data. The summary statistics show that the overall standard deviation of the FDI M&As into

the Eurozone is larger than its mean in all industries. However, most of the variation in the dependent

variable is caused by deviation between investor countries rather than within a country. The table shows

that on average more non-manufacturing firms are acquired rather than manufacturing firms. Furthermore,

on average more high R&D firms are targeted compared to low R&D firms, which can probably be

explained by the relatively technological well developed Eurozone.

The logged real exchange rate deviation is larger between countries, which is expected since the

price level of the multiple currencies are at different heights compared to each other. (e.g. Japanese Yen

per Euro is considerably higher than American Dollar per Euro)

The standard deviation of the Eurozone domestic M&As variable is equal to zero between the

investor countries, because the panel variable investor country does not influence the number of acquisitions

in the Eurozone.

3.3. Statistical procedures and analysis

The variables constructed with the data mentioned in the previous section are merged in a strongly

balanced panel dataset. The panel variable in this model is country, representing the investor country, and

time variable is expressed in quarters, from 1999 to the second quarter of 2016. The panel nature of the

dataset allows for the distinguishing of the variables on country level. This empirical study starts with the

construction of a scatter plot of the dependent variable and the main explanatory variable.

Before the effects of the independent variables on number of FDI M&As are examined, the

statistical soundness of this study’s regression model is investigated. The error terms of the empirical model

are assumed to be identically and independently distributed (I.I.D.). To test if this assumption holds, the

presence of different common statistical problems in regression analyses are investigated and controlled

for.

The first problem examined is serial correlation or autocorrelation, which shows smaller estimated

standard errors of the coefficients than they actually are. Autocorrelation occurs when the error terms in a

panel data model are correlated with each other and thus are not independently distributed. This study

examines different investor countries over multiple quarters and it is most likely that different observations

of an investor country are correlated. To test for serial correlation, a Wooldridge test for autocorrelation in

panel data (Wooldridge, 2002) is performed.

If error terms do not have a constant variance and are non-identically distributed, heteroskedasticity

occurs. Heteroskedasticity does not result in biased coefficient estimates, but causes biased standard errors.

J.P. van Doorn 28

This affects the test statistics and confidence interval for interpretation of the measured effects. To test for

heteroskedasticity for the fixed effects model a modified Wald test is conducted, for the random effects

model a Likelihood-ratio test for heteroskedasticity, based on Breusch and Pagan (1979) is performed.

In addition to the potential statistical complications mentioned above, a too strong correlation

between two or more predictor variables can be problematic. Multicollinearity can be the cause of skewed

biased results regarding the coefficients of the independent variables that explain the dependent variable.

Presence of multicollinearity can increase standard errors of the correlated independent variables, which

can cause problems when interpreting the results. First, a correlation matrix is produced to monitor the

dependence between the multiple variables considered in this model. For properly testing if predictor

variables in the model are highly correlated to one another, a Variance Inflation Factor (VIF, hereafter)

matrix is constructed. “a VIF of 10 indicates that (all other things being equal) the variance of the 𝑖th

regression coefficient is 10 times greater than it would have been if the 𝑖th independent variable had been

linearly independent of the other independent variable in the analysis.” (O’brien, 2007 p. 684). So, the VIF

measures how much the variance of the regression coefficients estimated in the model, are inflated because

of the linear dependence with other independent variables.

To analyze the panel data, as starting point an ordinary least squares model (OLS, hereafter) is

assumed to measure the effect of the independent variables on the dependent variable as Wooldridge (2002)

suggests. Table A.1. in the Appendix displays the yearly FDI acquisitions into the Eurozone per investor

country. With the exception of the year 1999, when the Dot-com bubble did not yet burst and there was

substantial market overconfidence, the early years in the period of interest show a smaller amount of

acquisitions than the more recent years. Especially Chinese investments grew considerably during this

study’s observed time period. In this research time trend is added to the model to control for movements in

the number of FDI M&As into the Eurozone developed over time that are not explained by the independent

variables in the model. This could be due to technological development, of the internet for example, which

increases the information availability or the increased open economy. Furthermore, monitoring costs

decreased and could influence foreign investing decisions. However, this is hard to measure and the time

trend captures this assumption.

Furthermore, time dummies for each year are included to absorb year-specific effects that are not

explained by the regressors. As mentioned before, this study’s data period includes multiple crises which

may or may not affect investor confidence. Additionally, certain unobserved tariff agreements or

implementation of tax regulations for example, can influence FDI M&A. Therefore, time dummies are

implemented in this model.

J.P. van Doorn 29

The empirical specification of the pooled OLS model is shown in formula 5.

𝐹𝐷𝐼𝑀&𝐴𝑖𝑗𝑘𝑡 = 𝛼𝑖𝑘 + 𝛽𝑙𝑛𝑅𝐸𝑅 𝑙𝑛𝑅𝐸𝑅𝑖𝑗𝑡 + 𝛽𝐷𝑜𝑚𝑀&𝐴 𝐷𝑜𝑚𝑀&𝐴𝑖𝑘𝑡 + 𝛽𝑅𝐺𝐷𝑃𝐺 𝑅𝐺𝐷𝑃𝐺𝑗𝑡 +

𝛽𝑆𝑀𝑃𝐼𝐺 𝑆𝑀𝑃𝐼𝐺𝑗𝑡 + 𝜆𝑡 + ∑ 𝜏𝑙 𝑇𝑙𝑇−1𝑙=1 + 𝜀𝑖𝑗𝑘𝑡 (5)

Where:

𝛼𝑖𝑘 = The intercept in Eurozone 𝑖 in industry 𝑘

𝛽𝑥 = The coefficient for independent variable 𝑥

𝜆 = The coefficient for the time trend 𝑡, increasing with equal steps

𝜏𝑙 = The coefficients for time dummies 𝑇𝑙

𝑇𝑙 = Time dummy, equal to 1 for the year evaluated 𝑙, zero elsewhere

𝜀𝑖𝑗𝑘𝑡 = The error term

If individual country or time specific effects do not exist, pooled OLS conducts consistent

coefficient estimates. However, if individual effects are not zero, specific characteristics that are not

captured in the regressors of the model may influence the core assumptions of OLS. This results in the

conclusion that the model is no longer the best unbiased linear estimator. Disturbances may have different

variances across different countries (heteroskedasticity) and are related to each other (autocorrelation).

Different panel data models examine these unobserved effects across countries and account for individual

heterogeneity. Wooldridge (2002) labels two different estimation methods, random effects estimation and

fixed effects estimation, to deal with these unobserved effects.

The fixed effects model controls for individual specific effects that are time invariant and

considered as part of the intercept. This model assumes the same slopes, constant variance across countries

and allows the fixed effect to be correlated with other regressors in the model. The fixed effects model is

designed to study causes of changes within an investor country and the use such a model could help to

determine the net effect of the regressors, since the estimated coefficients of the model are not biased by

omitted time-invariant characteristics. The fixed effect model is displayed in formula 6.

𝐹𝐷𝐼𝑀&𝐴𝑖𝑗𝑘𝑡 = (𝛼𝑖𝑘 + 𝑢𝑖𝑗𝑘) + 𝛽𝑙𝑛𝑅𝐸𝑅 𝑙𝑛𝑅𝐸𝑅𝑖𝑗𝑡 + 𝛽𝐷𝑜𝑚𝑀&𝐴 𝐷𝑜𝑚𝑀&𝐴𝑖𝑘𝑡 +

𝛽𝑅𝐺𝐷𝑃𝐺 𝑅𝐺𝐷𝑃𝐺𝑗𝑡 + 𝛽𝑆𝑀𝑃𝐼𝐺 𝑆𝑀𝑃𝐼𝐺𝑗𝑡 + 𝜆𝑡 + ∑ 𝜏𝑙 𝑇𝑙𝑇−1𝑙=1 + 𝜀𝑖𝑗𝑘𝑡 (6)

Where:

𝑢𝑖𝑗𝑘 = is a fixed effect specific to a country 𝑗 and industry 𝑘, not included in the other variables

𝜀𝑖𝑗𝑘𝑡 = The error term

A random effects model considers that the unobserved effects are not correlated with the regressors

in the model and estimates the error variance as country specific. Thus, the random effects model assumes

that differences among individuals arises from its individual specific error and not in their intercepts. The

random effects model has a composite error term, which contains the conventional error term and a random

intercept. Random variable 𝑢𝑖𝑗𝑘 measures the random deviation of each entity’s intercept term from the

‘global’ intercept term 𝛼𝑖𝑘 (Brooks, 2014, p. 536).

J.P. van Doorn 30

The random fixed effect model is displayed in formula 7.

𝐹𝐷𝐼𝑀&𝐴𝑖𝑗𝑘𝑡 = 𝛼𝑖𝑘 + 𝛽𝑙𝑛𝑅𝐸𝑅 𝑙𝑛𝑅𝐸𝑅𝑖𝑗𝑡 + 𝛽𝐷𝑜𝑚𝑀&𝐴 𝐷𝑜𝑚𝑀&𝐴𝑖𝑘𝑡 + 𝛽𝑅𝐺𝐷𝑃𝐺 𝑅𝐺𝐷𝑃𝐺𝑗𝑡 +

𝛽𝑆𝑀𝑃𝐼𝐺 𝑆𝑀𝑃𝐼𝐺𝑗𝑡 + 𝜆𝑡 + ∑ 𝜏𝑙 𝑇𝑙𝑇−1𝑙=1 + (𝑢𝑖𝑗𝑘 + 𝜀𝑖𝑗𝑘𝑡) (7)

Where:

𝑢𝑖𝑗𝑘 = is a random effect uncorrelated with the predictors and not included in the other variables

To test if fixed and/or random effects exist in the data and dismiss the OLS as unbiased linear

estimator, the assumption that individual effects are zero has to be rejected. An F-test is conducted to see if

fixed effects improve the goodness of fit of the model relative to the pooled OLS. Random effects are

examined by the Breusch-Pagan Lagrange Multiplier test (Breusch and Pagan, 1980). If in both tests, the

null hypothesis is not rejected, the pooled OLS is preferred. In the case of rejection of the hypotheses of

both tests, to decide whether to use fixed or random effects, a Hausman specification test (Hausman, 1978)

is conducted. This tests whether the unique 𝑢𝑖 errors, or between entity errors, are correlated with the

regressors in the model. The random effects assumes that the unobserved effects are uncorrelated with the

independent variables, while the fixed effects does not. If this assumption is wrong the random effects

model will be inconsistent and fixed effects model is the best fit. However, if there is no significant

difference between the two estimates, the random effects estimator is more efficient and thus the appropriate

choice.

Most of the comparable studies previously done, used annual frequency of data. Because of the

quarterly data available and limited observations when using annual data, this research measures the effects

of the variables at quarterly frequency. The model regression is done separately for the total dataset,

subdivision of non-manufacturing and manufacturing and for high and low R&D industries, to test if

Blonigen’s (1997) firm-specific asset theory holds for FDI into the Eurozone. To see the effects of the

independent variables on the amount of FDI M&As in the subdivided industries for the different investor

country specific, individual regressions are conducted.

J.P. van Doorn 31

4. Empirical results

Figure 2

Scatter plot of total number of FDI M&As into the Eurozone and the logged real exchange rate

Figure 2 shows scatter plots of the dependent variable all industry FDI M&As into the Eurozone and the logged

real exchange rate, subdivided into investor county for the sample period 1991Q1 to 2016Q2. The red solid line

indicates the line of best fit or trend line of the scatter plot.

Note: The FDI M&A figures, contain total FDI inflow from the investor countries into the initial 14 Eurozone

countries, with a minimum of 10% ownership stake after transaction closing. The real exchange rates are

constructed using the nominal exchange rate offer prices (EUR/X) and the consumer price indexes.

Sources: Exchange rates are retrieved from WM/Reuters closing spot rates and CPI is obtained from the OECD.

FDI data is acquired from the International Mergers Database of SDC Platinum

J.P. van Doorn 32

4.1. First impression

To visually investigate the effect of a descending Euro value on the number of FDI acquisitions

into the Eurozone, a plot of the main independent and the dependent variable of this study is created. Figure

2 shows the scatter plots of the dependent variable, number of FDI M&As into the Eurozone, and the logged

real exchange rate, for every investor country of interest in this study. The red trend line or line of best fit

in the subfigures, shows an initial upward slope for the Australian (panel A), Canadian (panel B), Chinese

(panel C) and United Kingdom-based (panel E) investors. This can be interpreted as a potential positive

relationship between a higher logged exchange rate, which means more Euro per foreign currency, and the

amount of incoming FDI into the Eurozone from these foreign investors. However, subfigures for Japan

(panel D) and the United States (panel F) show a downward line, so no clear unanimous relationship is

examined. In addition, the scatterplots show some considerable deviation from the line of best fit, which

might be problematic for the significance in the analysis of this study. Comprehensive statistical analysis

could provide more evidence on the relationship, taking into consideration the independent explanatory and

control variables.

4.2. Statistical soundness

As mentioned in the previous chapter, the statistical soundness of the model has to be investigated

before turning to the regression model. The potential problems will be discussed in this section.

To investigate the presence of autocorrelation: the interdependence between the error terms, a

Wooldridge test for autocorrelation in panel data (Wooldridge, 2002) is performed on the data of this study.

Table A.2. in the Appendix presents the results of this test for each industry subdivision. The null hypothesis

of the test of no autocorrelation is rejected on at least a 0.05 significance level for all parts and for all

industries, manufacturing and high R&D industries even at 0.01 significance level. Therefore, it can be

concluded that there is a presence of autocorrelation among the error terms in the dataset.

The second statistical problem to be investigated is heteroskedasticity. Table A.3. of the Appendix

contains the results of the Likelihood-ratio test for heteroskedasticity for each industry subdivision, where

the null hypothesis of the test is homoskedasticity of the error terms.13 The null hypothesis is strongly

rejected for all parts, indicating that there is indeed heteroskedasticity and errors are not identically

distributed for all industries.

13 The modified Wald test for heteroskedasticity in fixed effects model is not included in this research because a

random effects estimation model is used in this study and the Wald test cannot be used in the case of random effects

model estimation. The Hausman specification test (Hausman, 1978) presented later on in this chapter will support this

choice.

J.P. van Doorn 33

The first two statistical problems discussed, the presence of autocorrelation and heteroskedasticity,

influence the standard errors of the model. To ensure the unbiasedness of the test statistics and standard

error estimates are robust to disturbances of both problems, Rogers clustered robust standard errors are used

as explained by Hoechle (2007, p.4). The panel identifier “investor country” is the cluster variable that

makes the Rogers standard errors heteroskedasticity and autocorrelation consistent.

The potential existence of multicollinearity is first monitored by looking at the correlation matrices

of Table A.4. in the Appendix. These matrices show per part, the correlation between the five main variables

in this study for each industry. The positive significant correlation between the real exchange rate and FDI

M&As in each part gently confirms the expected positive relationship between the two variables. However,

the correlation coefficient is not extremely high. Surprisingly the real GDP growth variable and FDI show

a significant negative correlation coefficient. In addition, the real GDP growth variable is significantly and

positively correlated with the stock market price index growth, which is in line with expectations. The other

independent variables however, show no fierce and/or significant correlation with each other and it is

therefore unlikely that a large multicollinearity problem will occur.

To formally investigate the presence of multicollinearity, Table A.5. presents the VIFs of the

independent explanatory variables in the regression model. The VIF measures how much the variance of

the regression coefficients estimated in the model, are inflated because of linear dependence with other

independent variables. O’brien (2007) discusses the VIF examines the rules of thumb with respect to the

cut-off values, which vary significantly between studies. A common but strict rule of thumb is a cut-off

value of 4. Analyzing the results of the VIF matrices, show that there are no major differences in VIFs for

the variables among the different industry parts of Table A.5. The Eurozone domestic acquisitions variable

has the highest VIF, which takes values between 2.02 in non-manufacturing industries (part B) and 2.05 in

manufacturing (part C) and High R&D (part D) industries. The values for the remaining explanatory

variables fluctuate between 1.08 and 1.67, indicating no excessive multicollinearity problem in the study’s

model and no variables have to be excluded.

4.3. Panel regression model

The next step is to determine the appropriate regression methodology for this research. As

mentioned in the previous chapter, starting position shall be the pooled OLS. Because unobserved effects

are likely to influence the number of FDI M&As in this study, tests are conducted to see if the fixed effects

or random effects regression give a better estimation of the relationship between the variables.

Table A.6. contains the F-test for each industry subdivision, which tests the null hypothesis that the

unobserved fixed effects are equal to zero. For every industry the null hypothesis is strongly rejected,

indicating that the fixed effects model is a better estimator than the simple pooled OLS. Table A.7. presents

J.P. van Doorn 34

the results of the Breusch-Pagan Lagrange Multiplier test. The null hypothesis of the LM test is that that

the variances across entities are equal to zero. Since that hypothesis is also strongly rejected for all industries

and it can be concluded that variances across entities are significantly non-zero, it is clear that the random

effects model is preferred relative to the pooled OLS model.

Previous findings suggest that both the fixed effects and random effects model are a better

estimation model for this study’s dataset compared to the pooled OLS. To formally test which estimation

method fits best, a Hausman specification test is conducted. The null hypothesis is that individual effects

are not correlated with other regressors in the model. If this hypothesis is rejected, a fixed effect model is

recommended instead of the random effects. If this hypothesis is not rejected, the random effects model is

the consistent and efficient estimation method. Table A.8. presents the results of the Hausman specification

test for each industry. The null hypothesis cannot be rejected in any of the industries, so the random effects

estimation is the appropriate choice for this research.

4.4. Determinants of foreign direct investment into the Eurozone

This section analyzes the regression results of the independent variables mentioned in the previous

chapters, on FDI M&As into the Eurozone of all considered investor countries in the time period 1999Q1

to 2016Q2. Table 4 presents the regression results and shows the estimates of the determinants of for each

specification, where industries are subdivided. The results obtained from the estimation model and effects

of the independent explanatory variables are briefly considered.

The main explanatory variable and regressor of interest in this study, the real exchange rate, does

not have a statistically significant effect in either of the specifications. Even though, the expected positive

sign of the coefficient is displayed, because of high standard errors no interpretation of the relationship can

be made.

Domestic acquisitions in the Eurozone show a positive and weakly significant effect in the all

industry, non-manufacturing and low R&D specifications. While significant, the economic magnitude is

limited; if the variable Eurozone domestic acquisitions increases with one standard deviation (165.153) in

the all industries specification, the number of FDI M&As increase with 2.312.

In the manufacturing industry specification, investor real GDP growth has a positive and weak

significant coefficient. One standard deviation increase in the Real GDP growth variable (0.010), results in

an increase of 0.506 FDI acquisitions into the Eurozone.

J.P. van Doorn 35

Dependent variable: Foreign direct investment M&A into the Eurozone, 1999Q1 - 2016Q2

All industries Non-manufacturing Manufacturing

Variables Total High R&D Low R&D

Logged real exchange rate 8.779 4.573 5.415 1.712 3.116

(10.359) (6.636) (4.824) (1.929) (2.605)

Eurozone domestic acquisitions 0.014* 0.013* 0.011 0.000 0.008*

(0.008) (0.007) (0.008) (0.006) (0.005)

Real GDP growth 133.859 95.654 50.596* 20.103 28.610

(90.615) (64.245) (30.167) (18.738) (22.758)

Stock market price index growth -8.937* -7.515* -1.308 -0.426 -0.623

(4.940) (4.264) (1.582) (0.591) (1.465)

Time trend 0.050 -0.057 0.075 0.106* -0.047

(0.071) (0.069) (0.055) (0.055) (0.035)

Constant 27.905* 17.345* 14.156*** 7.096** 8.208**

(14.293) (10.343) (5.359) (2.881) (3.576)

Year dummies Yes Yes Yes Yes Yes

Random effects Yes Yes Yes Yes Yes

sigma_e 9.2776 7.1548 4.0151 2.4944 5.9415

sigma_u 37.6530 21.5340 15.9906 10.1161 2.4609

rho (fraction of var due to u_i) 0.9428 0.9006 0.9407 0.9427 0.8536

theta (θ) 0.9706 0.9603 0.9700 0.9705 0.9506

N 420 420 420 420 420

In both the all industry and non-manufacturing specification, the Stock market price index growth

variable shows a negative coefficient at the 0.1 significance level. The economic magnitude of this

coefficient shows that if stock market price index grows one standard deviation (0.096), the dependent

variable of this study decreases with 0.721 for non-manufacturing industries.

The time trend coefficient in this regression is only significant for the high R&D specification in

this regression, while the constant is significant for all the industries subdivisions.

Table 4

Regression Results – Determinants of FDI M&A into the Eurozone

Table 4 presents the regression results of the explanatory and control variables on the number of FDI M&As into

the Eurozone with the random effects model (7) defined in chapter 3. The table contains the estimations for the

separate defined subdivided industries. All variables are constructed by using quarterly data from 1999Q1 to

2016Q2. All regressions are obtained with the xtreg command with random effects, using Stata. Rogers clustered

standard errors are included in parentheses. ***, ** and * show the level of significance of 0.01, 0.05 and 0.1

respectively. Estimates are rounded to 3 decimals.

Note: SIC codes between 2000 and 3999 are manufacturing industries, all other are non-manufacturing industries.

High R&D industries are SIC 2810-2899, 3510-3599, 3650-3679, 3710-3729, 3760-3769 and 3810-3899. Low

R&D industries are all other manufacturing industries

J.P. van Doorn 36

Sigma_e represents the standard deviation of the overall error term 𝜀𝑖, while Sigma_u shows the

standard deviation of the residuals between the panels 𝑢𝑖. For every specification except low R&D, the

standard deviation of 𝑢𝑖 is higher. Rho shows the ratio of variance due to individual specific error variance

to the complete composite error variance in the model and may be interpreted as a goodness-of-fit indicator.

The high rho in all the specifications indicates that individual specific errors accounts for a large proportion

of the composite error variance.

The random effects estimator used in this study is a matrix weighted average of the estimates

produced by the between and within estimators. None of the R² estimates shown in the output, corresponds

directly to the relevant R². Therefore, Theta (𝜃) is added as an additional measure of goodness-of-fit. “Theta

is a function of 𝜎𝑢2 and 𝜎𝜀

2 . If 𝜎𝑢2 = 0, meaning that 𝑢𝑖 is always 0, 𝜃 = 0 and the relationship can be

estimated by OLS directly. Alternatively, if 𝜎𝜀2 = 0, meaning that 𝜀𝑖 is 0, 𝜃 = 1 and the within estimator

returns all the information available”. (StataCorp LP, 2015, p. 403) The high theta in all specifications,

indicates that there is indeed a considerable amount of variation in the model because of variation in 𝑢𝑖.

4.5. Investor country-specific determinants of FDI into the Eurozone

Previous section discussed the effect of the independent variables on the number of FDI

acquisitions into the Eurozone for all top investor countries. The observed overall relationship might

however be blurred by investor countries that show deviating or abnormal values. As mentioned in chapter

2, Lee (2013) finds that the suggested link between exchange rate level and the asset-seeking acquisition

FDI can only be confirmed for U.S. inbound acquisitions. When U.S. data is excluded, no support for the

hypothesis could not be found. To test the regressors on the investor country specific level, regressions are

run for each individual top investor country. Since the analysis is no longer on a panel dataset, OLS

regressions are performed. This section discusses the regression results of the country specific data in each

industry. In order to better interpret the outcome of the regressions, the mean and standard deviation of the

variables on investor country level are presented in table A.9. in the Appendix. Since not all investor country

regressions show admissible significant results, the less relevant output is located in the appendix. Table 5

presents the regression results of the investor countries; Canada, Japan and the United States and are briefly

considered.

The real exchange rate has a positive and significant effect on the number of Canadian FDI

acquisitions in the manufacturing (0.05 significance level) and high R&D (0.01 significance level)

industries. Because of the logarithmic modification of the independent variable, the interpretation is

different than other variables. If the variable logged real exchange rate increases with one percent, the

number of Canadian FDI M&As in Eurozone increases with 0.11949 for high R&D industries.

J.P. van Doorn 37

While the real exchange rate in the Canadian panel shows a positive relationship, the Japanese and

United States panel show significant negative signs in some specifications. In the non-manufacturing

industries, one percent increase of the logged real exchange rate decreases the number of Japanese FDI with

0.083 Japan and US acquisitions with 0.805.

The variable Eurozone domestic acquisitions shows a significant negative sign in the Canadian

high R&D panel, while positive in the Japanese manufacturing and all industry specification of the U.S.

panel. However, the economic magnitude is not large; if the all industry Eurozone domestic acquisitions

variable increases with one standard deviation (166.148), the number of U.S. FDI into the Eurozone

increases with 5.815.

Panel A - Dependent variable: Canadian FDI M&A into the Eurozone, 1999Q1 - 2016Q2

All industries Non-manufacturing Manufacturing

Variables Total High R&D Low R&D

Logged real exchange rate 2.442 -11.785 12.384** 11.949*** 3.059

(9.306) (8.587) (5.050) (3.560) (3.851)

Eurozone domestic acquisitions 0.002 0.008 -0.010 -0.024** 0.006

(0.005) (0.006) (0.007) (0.011) (0.008)

Real GDP growth 84.952 36.143 39.010 15.737 47.653

(69.659) (64.663) (37.540) (27.105) (28.546)

Stock market price index growth -2.825 -6.764 3.703 1.643 1.908

(4.955) (4.592) (2.649) (1.887) (1.974)

Time trend 0.008 -0.022 -0.045 0.057 -0.048

(0.310) (0.289) (0.168) (0.122) (0.124)

Constant 7.081 -5.387 12.269*** 9.651*** 1.927

(6.425) (5.863) (3.231) (2.140) (2.241)

Year dummies Yes Yes Yes Yes Yes

F-test 2.42*** 2.41*** 1.32 1.55 0.75

sigma_e 2.7313 2.5326 1.4665 1.0501 1.0897

Within R² 53.08% 52.96% 38.17% 42.06% 31.08%

N 70 70 70 70 70

Table 5

Regression results – Investor country FDI acquisitions into the Eurozone

Table 5 presents the regression results of the explanatory and control variables on the number of investor country

specific FDI M&As into the Eurozone with the OLS model. The table contains the estimations for the separate

defined subdivided industries. All variables are constructed by using quarterly data from 1999Q1 to 2016Q2. All

regressions are obtained with the xtreg command, which in this case gives the regular OLS estimators, using Stata.

Robust standard errors are included in parentheses. ***, ** and * show the level of significance of 0.01, 0.05 and

0.1 respectively. Estimates are rounded to 3 decimals.

Note: SIC codes between 2000 and 3999 are manufacturing industries, all other are non-manufacturing industries.

High R&D industries are SIC 2810-2899, 3510-3599, 3650-3679, 3710-3729, 3760-3769 and 3810-3899. Low

R&D industries are all other manufacturing industries

J.P. van Doorn 38

Panel B - Dependent variable: Japanese FDI M&A into the Eurozone, 1999Q1 - 2016Q2

All industries Non-manufacturing Manufacturing

Variables Total High R&D Low R&D

Logged real exchange rate -6.175 -8.284* 1.822 4.886 -3.341

(6.788) (4.370) (4.511) (3.799) (3.869)

Eurozone domestic acquisitions 0.003 0.002 0.013* 0.011 0.003

(0.004) (0.004) (0.008) (0.013) (0.009)

Real GDP growth -7.260 -8.569 15.849 1.837 8.797

(28.750) (18.150) (19.651) (16.161) (17.045)

Stock market price index growth -4.426 0.412 -5.871** -1.855 -3.320

(3.614) (2.255) (2.439) (2.007) (2.080)

Time trend 0.169 0.100 0.179 0.175 -0.022

(0.242) (0.155) (0.165) (0.138) (0.140)

Constant -27.231 -37.390* 6.903 22.293 -14.615

(31.755) (20.560) (20.653) (17.429) (17.681)

Year dummies Yes Yes Yes Yes Yes

F-test 3.84*** 1.82** 3.91*** 2.52*** 1.89**

sigma_e 2.2190 1.4086 1.4838 1.2512 1.2717

Within R² 64.24% 46.00% 64.68% 54.10% 46.88%

N 70 70 70 70 70

Panel C - Dependent variable: United States FDI M&A into the Eurozone, 1999Q1 - 2016Q2

All industries Non-manufacturing Manufacturing

Variables Total High R&D Low R&D

Logged real exchange rate -94.563*** -80.517*** -15.012 -25.604* 9.927

(34.638) (24.458) (22.559) (15.053) (11.237)

Eurozone domestic acquisitions 0.035* 0.031 0.026 0.002 0.029

(0.021) (0.020) (0.351) (0.051) (0.025)

Real GDP growth 183.866 108.966 104.335 -50.104 167.872*

(311.218) (223.150) (195.413) (136.507) (97.309)

Stock market price index growth -14.923 -13.808 -1.110 3.629 -4.935

(21.302) (15.143) (13.528) (9.218) (6.760)

Time trend 0.329 0.120 0.061 0.195 -0.179

(1.247) (0.892) (0.807) (0.553) (0.397)

Constant 50.373** 29.350** 26.675** 16.735** 12.069**

(20.095) (13.329) (11.673) (5.087) (5.270)

Year dummies Yes Yes Yes Yes Yes

F-test 5.76*** 6.55*** 1.16 0.99 1.82**

sigma_e 11.2661 8.0300 7.1337 4.8940 3.5357

Within R² 72.93% 75.42% 35.10% 31.65% 45.95%

N 70 70 70 70 70

J.P. van Doorn 39

The United States real GDP growth appears to have a (weak) significant positive effect on the

amount of low R&D industry acquisitions in the Eurozone by bidders with a registered office in the U.S.

When the real GDP growth variable increases with one standard deviation (0.006), the number of United

States’ FDI M&As into the Eurozone increases with 1.007.

In the manufacturing industry specification, the stock market price index growth variable appears

to have a statistically significant negative effect on incoming Japanese FDI in the Eurozone. One standard

deviation (0.103) increase of the independent variable, depreciates the number of acquisitions with 0.605.

Time trend is not significant in any of the specifications of the investor countries. The constant on

the other hand appears to be significant and positive for the United States’ panel, but shows no unanimous

outcomes across the Canadian and Japanese regression results. The F-test in the regression output tests

whether the coefficients on the regressors in the model are all jointly non-zero. F-tests of the estimators in

the Japanese panel are all significant and indicate adequate models. Finally, also the within R² is presented

below the coefficient section. While the regressions on the complete panel dataset, none of the R² give the

appropriate measure, in this case the within estimator gives the correct estimation. The subdivision of the

panel data into investor countries neutralizes the panel nature of the date, without random effects, allowing

the within estimator to present an interpretable R². The R² shows the portion variable variation that is

explained by the linear model in all specifications. While in some specifications the estimator takes

considerable large values, there are specifications across countries where the model shows moderate

explanation which might be caused by the small amount of observations available.

J.P. van Doorn 40

5. Discussion

5.1. Hypotheses testing and interpretation

The empirical results presented in chapter 4 of this study, are used to test the hypotheses formulated

in chapter 2. Each hypothesis is discussed in detail, the findings are interpreted and compared with previous

research and the current state of literature.

Hypothesis 1: A depreciation (appreciation) of the Euro, increases (decreases) the number of incoming

FDI acquisitions into the Eurozone

While scatter plots for multiple investor countries would suggest a positive relationship, Table 4

presents the coefficient of the logged real exchange rate which seems to have no significant effect in any

of the specifications in the complete investor countries’ regression. Coefficients show a positive sign, but

because of high standard errors no interpretation of the relationship can be made. In addition, Table 5 panel

B shows analyses for the Eurozone acquisitions of the U.S. firms, where the coefficient in multiple industry

specifications has a significantly negative sign. These results are line with Lee (2013), who finds that the

enhancing effect of a devaluating domestic currency on the amount of FDI, is mainly driven by U.S.

inbound acquisitions. The United States market is probably more open to foreign firms and is most likely

the leading marketplace for innovation and technology in the world. Even though the Eurozone contains

multiple well-developed countries, the U.S. is considered to be the vanguard in technological development.

The significant negative coefficients in the U.S. panel (C) seem to indicate that a devaluating Euro only

negatively affects Euro denominated profits for the U.S. firms, while no real high-value technological assets

can be acquired that provide value without the need of additional exchange rate exposure. Hypothesis 1 is

therefore rejected for U.S. acquisitions in the Eurozone.

Table 5 panel A however, displays a positive significant effect of the real exchange on the number

of Canadian FDI acquisitions in the manufacturing and high R&D industries. Although not for every

industry subdivision the coefficient is significant, this result seems consistent with an extensive number of

studies on this relationship (e.g. Blonigen, 1997; Frood & Stein, 1991). Hence the hypothesis cannot be

rejected for the Canadian data, meaning that Canadian incentive for FDI acquisitions in the Eurozone is

positively influenced by the decreasing value of the Euro in the manufacturing and high R&D industries.

Hypothesis 2: Higher (lower) number of domestic acquisitions, increases (decreases) the number of

incoming FDI acquisitions into the Eurozone

The total FDI M&A into the Eurozone analysis of Table 4, reveals a positive effect of number of

domestic acquisitions on the amount of foreign incoming acquisitions for all specifications except

manufacturing and high R&D industries. In addition, the Japanese manufacturing and U.S.’ all industry

J.P. van Doorn 41

specification show a positive and significant coefficient in panel B and panel A of Table 5 respectively.

Coefficients derived from this research, confirm previous findings of Blonigen (1997) and Georgopoulos

(2008) of domestic acquisitions as proxy accounting for M&A supply. It should be noted however, that

Canadian analysis shows a negative sign for its high R&D specification.

Although the economic magnitude seems limited, an active domestic M&A environment boosts

the number of FDI acquisitions for the overall, non-manufacturing and low R&D industries and for

Japanese acquirers, in the manufacturing industry. Conclusively, the hypothesis cannot be rejected, for the

mentioned industry and investor country specifications.

Hypothesis 3: Higher (lower) foreign GDP growth rates, increases (decreases) the number of incoming

FDI acquisitions into the Eurozone

The manufacturing specification of Table 4 shows a positive relationship between investor real

GDP growth and the amount of FDI into the Eurozone. In addition, the United States real GDP growth

appears to confirm the positive relationship between these factors in the low R&D industry specification of

Table 5 panel C. Di Giovanni (2005) and Blonigen (1997) findings are confirmed in this research for these

specifications. The supply control variable accounts for economic growth of the investor country, which

should positively influence the demand of foreign investors for FDI in the Eurozone. Although not

unanimously for all specifications, the positive effect found is significant for the manufacturing industry

for all investors and in low R&D industries for the U.S. Therefore, the hypothesis cannot be rejected for

the mentioned specifications.

Hypothesis 4: Higher (lower) growth rate of the acquirer’s stock market price index increases

(decreases) the number of incoming FDI acquisitions into the Eurozone

Table 4 presents in both the all industries and non-manufacturing specification, a negative effect of

the acquirer’s main stock market price index growth and amount of FDI M&As into the Eurozone. In

addition, Japanese incoming FDI is also negatively influenced by an increase in stock market price index

growth for the manufacturing specification. Interestingly, findings of this study contradict research findings

and assumptions of Frood and Stein (1991) and Blonigen (1997) for certain specifications. They expect a

flourishing stock market to increase wealth of the potential acquirer and enhance demand for FDI. Instead

of the positive relationship, a significant negative effect is found in this study. Therefore, the hypothesis is

rejected for the all industry, and non-manufacturing specification of all investors analysis and for Japanese

acquisitions in the manufacturing industry. Since the significant negative effect is especially seen in the all

industry and non-manufacturing markets, a possible explanation might be that foreign investors prefer to

invest their wealth in a bullish domestic market instead of acquiring assets abroad.

J.P. van Doorn 42

Hypothesis 5: The effect of a depreciating (appreciating) Euro on the number of incoming FDI

acquisitions, is more positive (negative) for manufacturing industries than non-manufacturing industries

As mentioned before, the real exchange rate shows no significant effect in the complete all investor

regression. Table 5 panel A however, does only show a significant relationship between the real exchange

rate and FDI for the manufacturing industry specification. This means that the real exchange rate does not

influence the number of FDI M&A in non-manufacturing industries and therefore it seems confirmed that

a depreciating Euro has a more positive effect on FDI in manufacturing than non-manufacturing industries.

Thus, hypothesis 5 cannot be rejected for Canadian acquisitions in the Eurozone. This provides support for

among others, Froot and Stein’s (1991) and Blonigen’s (1997) findings, that acquiring manufacturing firms

is beneficial when the Euro devaluates.

However, analyses on Japanese and United States’ data show no significant effect in the

manufacturing specifications, while a significantly negative coefficient for the non-manufacturing

industries. Because in the mentioned specifications, the relationship is negative for non-manufacturing

industries while there is no significant influence on manufacturing acquisitions, here too the hypothesis

cannot be rejected.

Hypothesis 6: The effect of a depreciating (appreciating) Euro on the number of incoming FDI

acquisitions, is more positive (negative) for high R&D manufacturing industries than low R&D

manufacturing industries

To test if Blonigen’s (1997) firm-specific asset theory holds, effects in high and low R&D

manufacturing industries are compared. Analyses of determinants of Canadian FDI in the Eurozone show

an insignificant coefficient in low R&D industries while a highly significant positive relationship for high

R&D industries. This is consistent with the findings of Georgopoulos’ (2008) and Lee’s (2013) concerning

the firm-specific asset theory. The hypothesis can therefore not be rejected for the Canadian acquisitions.

Thus, Blonigen’s (1997) theory on acquiring high technology firms seems to be confirmed for the Canadian

FDI into the Eurozone.

However, it should be noted that the complete manufacturing specification shows a larger positive

(but less significant) coefficient than for high R&D industries, which slightly contradicts the theory behind

acquiring high technology assets more cheaply.

5.2. Implications

The previous section interpreted the results of this study and briefly compared the outcome with

the current state of literature on the subject. This section discusses the potential useful implications of this

research for different relevant practitioners. To determine the academic, theoretical and practical

J.P. van Doorn 43

implication of this study, the main results of both the multiple investors analysis and investor country

specific are compared and examined with previous findings in existing literature.

The empirical section of this research examines effects of determinants of foreign direct

investments M&A into the Eurozone. Main determinant of interest, the real exchange rate, seems to have

no significant effect in the specification of all investor countries combined. This is similar to the findings

of Lee (2013), who argues that the overall effect found by Blonigen (1997) is likely more U.S. data bound.

In addition, the number of domestic acquisitions seems to demonstrate a positive M&A environment,

attracting FDI as the literature prescribed. However, foreign stock market growth seems to have a negative

effect on the number of incoming FDI in the all industry and non-manufacturing specification. This might

be explained by that foreign investors prefer to invest their wealth in a bullish domestic market instead of

acquiring foreign non-manufacturing assets that do not have any potential technological value. One

interesting additional observation is that there seems to exist a time trend in the overall high R&D industries,

which seems to indicate that more and more high tech companies in the Eurozone are acquired by foreign

firms.

Subsequently, the determinants are analyzed on investor country-specific level to determine

specific investor country effects on the determinants. The main variable of interest, the real exchange rate

shows a significant and positive effect in the manufacturing and high R&D specifications for Canadian FDI.

The positive effect on high R&D industry acquisitions, and no significant effect in non-manufacturing and

low R&D industries, imply that Blonigen’s (1997) firm-specific asset theory can be confirmed for Canadian

investment in the Eurozone. However, the opposite effect is found for the U.S. investors. Depreciation of

the Euro has a negative effect on the U.S. acquisitions in the total industries, non-manufacturing and high

R&D industries. Again, this seems to confirm Lee’s (2013) claim of U.S. inbound specific.

Altogether, empirical results found in this study provide support for interesting areas of future

research. Even though not one determinant had a significant effect in all specifications across all countries,

it seems that the real exchange rate does influence the number of FDI and even confirms the asset acquiring

theory for Canada. Furthermore, an interesting area of future research could be, describing the negative

effect of the investor country’s stock market on the amount of FDI found. In addition, the analysis of

Chinese data did not give great significant results, while the scatter plot showed an upward sloping line and

the Chinese Yuan increased in value compared to the Euro in recent years. Absence of empirical evidence

on this expected relationship, while the amount of incoming Chinese acquisitions increased significantly,

could be explained by their recent investment appetite compared to a low amount of deals in the early period

of date sample investigated in this study (Table A.1 of the Appendix). Future research could investigate the

effect on a shifted time period.

J.P. van Doorn 44

As stated above and in chapter 4 of this study, evidence for the firm-specific asset theory for

Canadian investors is provided. Finding a positive effect of the depreciating Euro on FDI incentive for

certain countries, alongside domestic acquisitions and GDP growth for some industries, could be valuable

information for business owners and M&A advisors. Findings help professionals understand differences in

M&A incentive across investing countries and help in the search of a potential foreign buyer when

analyzing the determinants applicable to specific countries. In addition, the depreciating Euro might yield

a higher bidding price in negotiations with a foreign bidder compared to a domestic one because of the

higher relative wealth, or high-tech firm-specific assets which are more valuable to the foreign firm. Finally,

the results help understand what can drive short-term mergers and acquisitions fluctuations and predict

future flows.

5.3. Limitations

Several limitations of research have to be acknowledged. This empirical study relies on the

availability of data. The first limitation is that only the occurrence of a deal is included because deal value

is often not public information and omitted. In addition, this data dependence also means that undisclosed

deals due to unavailability or measurement error can occur because of for example secret data from certain

less open economies. Secondly, M&A is more common in the United States market, which provides more

data available compared to European countries. This makes it harder to find effects of the determinants.

Thirdly, in the case of a merger, there is no real acquiring firm. This problem is not dealt with in this

research since the dataset always provides a target and an acquiring firm. Di Giovanni (2005) notes the

same problem but states that what often is announced as a merger, afterward turns out to be an acquisition.

Fourth issue is the cut-off value of 10% ownership stake after transaction to count as true FDI. This research

uses the threshold established by the OECD, but there are cases where less than 10% of company’s voting

shares established effective ownership. This assumption limits the number of (FDI) M&As taken into the

M&A variables of this study. Fifth, the choice of the specific investor countries also influenced the study,

just like the assumption to use the first initial Eurozone countries. The Eurozone countries examined are a

diverse mix of multiple countries, which are in principle considerably well developed and seem open

economies because of the Euro, but may differ significantly in technological advancement and business

operation. The final limitation is briefly discussed in section 2.6; omitted factors. Introduction of new taxes,

tariffs or other economical protection methods for example, may influence FDI decisions but are not

available on this level of data disaggregation. But also elections, the Brexit or determinants that are not

investigated, might have an impact. Further investigation could reveal new effects of certain new elements

of explanation.

J.P. van Doorn 45

6. Conclusion

The increasing amount of Eurozone firms acquired by foreign firms in recent year, accompanied

by a decreasing Euro, attracted attention. Dissension in empirical findings of previous literature on the link

between currency exchange rate and cross-border FDI, which is mainly U.S. bound, is the rationale for new

research on this relationship. In search for an explanation that clarifies short-run M&A fluctuations, this

study examines the link between the real exchange rate and FDI M&A into the Eurozone by using quarterly

data between the introduction of the euro on 4 January 1999 and 30 June 2016. To investigate if Blonigen’s

(1997) firm-specific asset acquisition theory may apply to the Eurozone, a subdivision between

(non-)manufacturing and high R&D and low R&D industries is made in the empirical analysis. In addition,

other potential determinants mentioned in relevant literature are included in the model to account for supply

and demand of cross-border M&As.

To investigate if a decreasing Euro value positively affects the number of FDI acquisitions into the

Eurozone, this study first analyzes the top 6 investing countries outside of the Eurozone. Results of this

analysis provide no clear evidence for a positive effect of the real exchange rate when examining all investor

countries combined. However, the output shows that Eurozone domestic acquisitions and investor country’s

real GDP growth, positively affect the number of incoming FDI in multiple industries. In contrast to the

predicted relationship by previous literature, investor stock market price index growth seems to have a

significant negative effect on FDI acquisitions. This might indicate that in a bullish stock market, investor

firms prefer to put their wealth in domestic investments rather than acquiring foreign assets.

These results confirm previous findings of Lee (2013), who states that the effects found of a

devaluating domestic currency on the amount of FDI, is mainly driven by U.S. inbound M&A data. When

he excludes the U.S. data and only inbound acquisition FDI into other foreign countries are considered,

support for the hypothesis could not be found. This might sound feasible since the U.S. spend the most on

R&D and are considered the birth nation of many high-tech innovations. Another explanation might be that

the U.S. market is more open to FDI investments.

To shed a light on if the relationship might be investor country specific, results per investor country

are presented. Although, for some countries no real significant effect of any determinants can be found,

Canadian, Japanese and U.S. data show interesting results. A depreciating Euro has a strong significant

positive effect on the number of Canadian acquisitions in the Eurozone in the high R&D industry, while no

significant effect in the low R&D specification. Therefore the Canadian data seems to confirm Blonigen’s

(1997) theory of high-tech assets that are acquired more cheaply and generate returns that are not necessarily

denominated in the target currency, but for example increase efficiency of the acquirer’s plant. However,

the U.S. panel shows a (weak) significant negative relationship in the high R&D specification, so the effect

indeed appears to be country specific.

J.P. van Doorn 46

Appendix

Year Total number of FDI M&As into the Eurozone

Australia Canada China Japan

United

Kingdom

United

States

1999 11 36 2 16 290 392

2000 10 19 1 16 347 334

2001 12 24 2 21 240 264

2002 8 24 4 15 166 222

2003 7 22 0 7 182 250

2004 13 26 6 9 216 328

2005 19 29 7 28 272 354

2006 27 31 3 22 295 352

2007 48 26 4 18 333 347

2008 10 25 8 29 222 305

2009 8 15 11 17 150 208

2010 7 29 9 19 197 277

2011 15 44 23 31 200 318

2012 11 27 24 35 157 281

2013 9 33 29 33 140 317

2014 12 47 45 42 226 405

2015 18 47 38 36 267 412

Table A.1.

Yearly number of FDI acquisitions in the Eurozone in all industries

Table A.1. contains the yearly total number of FDI M&As into the 14 initial Eurozone countries, for all industries,

from the six investor countries studied in this research. Time period considered in this table is 1991 – 2015.

Note: The FDI M&A figures, contain total FDI inflow from the investor countries into the initial 14 Eurozone

countries, with a minimum of 10% ownership stake after transaction closing.

Sources: FDI data is acquired from the International Mergers Database of SDC Platinum

J.P. van Doorn 47

Part A - Wooldridge test for autocorrelation in panel data (All industries)

H0: no first-order autocorrelation F( 1, 5) = 31.915

Prob > F = 0.0024

Part B - Wooldridge test for autocorrelation in panel data (Non-manufacturing)

H0: no first-order autocorrelation F( 1, 5) = 15.063

Prob > F = 0.0116

Part C - Wooldridge test for autocorrelation in panel data (Manufacturing)

H0: no first-order autocorrelation F( 1, 5) = 45.701

Prob > F = 0.0011

Part D - Wooldridge test for autocorrelation in panel data (High R&D)

H0: no first-order autocorrelation F( 1, 5) = 44.247

Prob > F = 0.0012

Part E - Wooldridge test for autocorrelation in panel data (Low R&D)

H0: no first-order autocorrelation F( 1, 5) = 9.834

Prob > F = 0.0258

Table A.2.

Wooldridge test for autocorrelation in panel data

Table A.2. presents the results of the Wooldridge test for autocorrelation in panel data performed per industry.

Each part contains the test in a different industry. The dependent variable is the number of FDI M&A into the

Eurozone in each industry and the independent variables are the logged real exchange rate, Eurozone domestic

acquisitions in each industry, investor country real GDP growth and investor country stock market growth. All

variables are constructed by using quarterly data from 1999Q1 to 2016Q2. Estimates are rounded to 4 decimals.

Note: SIC codes between 2000 and 3999 are manufacturing industries, all other are non-manufacturing industries.

High R&D industries are SIC 2810-2899, 3510-3599, 3650-3679, 3710-3729, 3760-3769 and 3810-3899. Low

R&D industries are all other manufacturing industries

J.P. van Doorn 48

Part A - Likelihood-ratio test for heteroskedasticity (All industries)

(Assumption: . nested in hetero) LR chi2(5) = 998.26

Prob > chi2 = 0.0000

Part B - Likelihood-ratio test for heteroskedasticity (Non-manufacturing)

(Assumption: . nested in hetero) LR chi2(5) = 971.77

Prob > chi2 = 0.0000

Part C - Likelihood-ratio test for heteroskedasticity (Manufacturing)

(Assumption: . nested in hetero) LR chi2(5) = 803.38

Prob > chi2 = 0.0000

Part D - Likelihood-ratio test for heteroskedasticity (High R&D)

(Assumption: . nested in hetero) LR chi2(5) = 774.83

Prob > chi2 = 0.0000

Part E - Likelihood-ratio test for heteroskedasticity (Low R&D)

(Assumption: . nested in hetero) LR chi2(5) = 612.97

Prob > chi2 = 0.0000

Table A.3.

Likelihood-ratio test for heteroskedasticity

Table A.3. presents the results of the Likelihood-ratio test for heteroskedasticity performed per industry. Each part

contains the test in a different industry. The dependent variable is the number of FDI M&A into the Eurozone in

each industry and the independent variables are the logged real exchange rate, Eurozone domestic acquisitions in

each industry, investor country real GDP growth and investor country stock market growth. All variables are

constructed by using quarterly data from 1999Q1 to 2016Q2. Estimates are rounded to 4 decimals.

Note: SIC codes between 2000 and 3999 are manufacturing industries, all other are non-manufacturing industries.

High R&D industries are SIC 2810-2899, 3510-3599, 3650-3679, 3710-3729, 3760-3769 and 3810-3899. Low

R&D industries are all other manufacturing industries

J.P. van Doorn 49

Part A - Correlation matrix (All industries)

𝐹𝐷𝐼𝑀&𝐴 𝑙𝑛𝑅𝐸𝑅 𝐷𝑜𝑚𝑀&𝐴 𝑅𝐺𝐷𝑃𝐺 𝑆𝑀𝑃𝐼𝐺

𝐹𝐷𝐼𝑀&𝐴 1.0000

𝑙𝑛𝑅𝐸𝑅 0.4883*** 1.0000

𝐷𝑜𝑚𝑀&𝐴 0.1021** -0.0028 1.0000

𝑅𝐺𝐷𝑃𝐺 -0.1910*** -0.0289 -0.0874* 1.0000

𝑆𝑀𝑃𝐼𝐺 -0.0261 -0.0055 -0.0285 0.2347*** 1.0000

Part B - Correlation matrix (Non-manufacturing)

𝐹𝐷𝐼𝑀&𝐴 𝑙𝑛𝑅𝐸𝑅 𝐷𝑜𝑚𝑀&𝐴 𝑅𝐺𝐷𝑃𝐺 𝑆𝑀𝑃𝐼𝐺

𝐹𝐷𝐼𝑀&𝐴 1.0000

𝑙𝑛𝑅𝐸𝑅 0.5171*** 1.0000

𝐷𝑜𝑚𝑀&𝐴 0.1287*** 0.0003 1.0000

𝑅𝐺𝐷𝑃𝐺 -0.1921*** -0.0289 -0.0705 1.0000

𝑆𝑀𝑃𝐼𝐺 -0.2890 -0.0055 -0.0445 0.2347*** 1.0000

Part C - Correlation matrix (Manufacturing)

𝐹𝐷𝐼𝑀&𝐴 𝑙𝑛𝑅𝐸𝑅 𝐷𝑜𝑚𝑀&𝐴 𝑅𝐺𝐷𝑃𝐺 𝑆𝑀𝑃𝐼𝐺

𝐹𝐷𝐼𝑀&𝐴 1.0000

𝑙𝑛𝑅𝐸𝑅 0.3937*** 1.0000

𝐷𝑜𝑚𝑀&𝐴 0.0430 -0.0090 1.0000

𝑅𝐺𝐷𝑃𝐺 -0.1649*** -0.0289 -0.0978** 1.0000

𝑆𝑀𝑃𝐼𝐺 -0.0187 -0.0055 0.0202 0.2347*** 1.0000

Part D - Correlation matrix (High R&D)

𝐹𝐷𝐼𝑀&𝐴 𝑙𝑛𝑅𝐸𝑅 𝐷𝑜𝑚𝑀&𝐴 𝑅𝐺𝐷𝑃𝐺 𝑆𝑀𝑃𝐼𝐺

𝐹𝐷𝐼𝑀&𝐴 1.0000

𝑙𝑛𝑅𝐸𝑅 0.3466*** 1.0000

𝐷𝑜𝑚𝑀&𝐴 0.0170 -0.0132 1.0000

𝑅𝐺𝐷𝑃𝐺 -0.1601*** -0.0289 -0.0418 1.0000

𝑆𝑀𝑃𝐼𝐺 -0.0151 -0.0055 0.0189 0.2347*** 1.0000

Part E - Correlation matrix (Low R&D)

𝐹𝐷𝐼𝑀&𝐴 𝑙𝑛𝑅𝐸𝑅 𝐷𝑜𝑚𝑀&𝐴 𝑅𝐺𝐷𝑃𝐺 𝑆𝑀𝑃𝐼𝐺

𝐹𝐷𝐼𝑀&𝐴 1.0000

𝑙𝑛𝑅𝐸𝑅 0.4178*** 1.0000

𝐷𝑜𝑚𝑀&𝐴 0.0562 -0.0048 1.0000

𝑅𝐺𝐷𝑃𝐺 -0.1553*** -0.0289 -0.1249** 1.0000

𝑆𝑀𝑃𝐼𝐺 -0.0207 -0.0055 0.0185 0.2347*** 1.0000

Table A.4.

Correlation matrix

Table A.4. presents pairwise correlation coefficients of the variables in this study per industry. The dependent

variable is the number of FDI M&A into the Eurozone in each industry and the independent variables are the

logged real exchange rate, Eurozone domestic acquisitions in each industry, investor country real GDP growth

and investor country stock market growth. All variables are constructed by using quarterly data from 1999Q1 to

2016Q2. ***, ** and * show the level of significance of 0.01, 0.05 and 0.1 respectively. Estimates are rounded to

4 decimals.

Note: SIC codes between 2000 and 3999 are manufacturing industries, all other are non-manufacturing industries.

High R&D industries are SIC 2810-2899, 3510-3599, 3650-3679, 3710-3729, 3760-3769 and 3810-3899. Low

R&D industries are all other manufacturing industries

J.P. van Doorn 50

Part A - Variance Inflation Factor (All industries) Part B - Variance Inflation Factor (Non-manufacturing)

Variable VIF 1/VIF Variable VIF 1/VIF

𝐷𝑜𝑚𝑀&𝐴 2.03 0.4929 𝐷𝑜𝑚𝑀&𝐴 2.02 0.4962

𝑅𝐺𝐷𝑃𝐺 1.66 0.6011 𝑅𝐺𝐷𝑃𝐺 1.66 0.6014

𝑙𝑛𝑅𝐸𝑅 1.52 0.6565 𝑙𝑛𝑅𝐸𝑅 1.52 0.6595

𝑆𝑀𝑃𝐼𝐺 1.08 0.9292 𝑆𝑀𝑃𝐼𝐺 1.08 0.9288

Mean VIF 1.57 Mean VIF 1.57

Part C - Variance Inflation Factor (Manufacturing) Part D - Variance Inflation Factor (High R&D)

Variable VIF 1/VIF Variable VIF 1/VIF

𝐷𝑜𝑚𝑀&𝐴 2.05 0.4887 𝐷𝑜𝑚𝑀&𝐴 2.05 0.4882

𝑅𝐺𝐷𝑃𝐺 1.67 0.6002 𝑅𝐺𝐷𝑃𝐺 1.67 0.5972

𝑙𝑛𝑅𝐸𝑅 1.53 0.6535 𝑙𝑛𝑅𝐸𝑅 1.52 0.6561

𝑆𝑀𝑃𝐼𝐺 1.08 0.9299 𝑆𝑀𝑃𝐼𝐺 1.08 0.9299

Mean VIF 1.58 Mean VIF 1.58

Part E - Variance Inflation Factor (Low R&D)

Variable VIF 1/VIF

𝐷𝑜𝑚𝑀&𝐴 2.03 0.4925

𝑅𝐺𝐷𝑃𝐺 1.66 0.6041

𝑙𝑛𝑅𝐸𝑅 1.53 0.6540

𝑆𝑀𝑃𝐼𝐺 1.08 0.9300

Mean VIF 1.57

Table A.5.

Variance Inflation Factor

Table A.5. presents the Variance Inflation Factor matrices of the independent explanatory variables in the

regression to formally test for multicollinearity. Variance Inflation Factors measure how much the variance of the

regression coefficients estimated in the model, are inflated because of the linear dependence with other

independent variables. Each part describes the VIFs in a different industry. The dependent variable is the number

of FDI M&A into the Eurozone in each industry and the independent variables are the logged real exchange rate,

Eurozone domestic acquisitions in each industry, investor country real GDP growth and investor country stock

market growth. All variables are constructed by using quarterly data from 1999Q1 to 2016Q2. Estimates are

rounded to 4 decimals.

Note: SIC codes between 2000 and 3999 are manufacturing industries, all other are non-manufacturing industries.

High R&D industries are SIC 2810-2899, 3510-3599, 3650-3679, 3710-3729, 3760-3769 and 3810-3899. Low

R&D industries are all other manufacturing industries

J.P. van Doorn 51

Part A - F-test for fixed effects (All industries)

Test: H0: all u_i = 0 F(5, 392) = 625.49

Prob > F = 0.0000

Part B - F-test for fixed effects (Non-manufacturing)

Test: H0: all u_i = 0 F(5, 392) = 401.50

Prob > F = 0.0000

Part C - F-test for fixed effects (Manufacturing)

Test: H0: all u_i = 0 F(5, 392) = 505.75

Prob > F = 0.0000

Part D - F-test for fixed effects (High R&D)

Test: H0: all u_i = 0 F(5, 392) = 491.81

Prob > F = 0.0000

Part E - F-test for fixed effects (Low R&D)

Test: H0: all u_i = 0 F(5, 392) = 219.12

Prob > F = 0.0000

Table A.6.

F-test for fixed effects in panel data

Table A.6. presents the F-test test for fixed effects in the model of this study. Each part contains the test in a

different industry. The dependent variable is the number of FDI M&A into the Eurozone in each industry and the

independent variables are the logged real exchange rate, Eurozone domestic acquisitions in each industry, investor

country real GDP growth and investor country stock market growth. All variables are constructed by using

quarterly data from 1999Q1 to 2016Q2. Estimates are rounded to 4 decimals.

Note: SIC codes between 2000 and 3999 are manufacturing industries, all other are non-manufacturing industries.

High R&D industries are SIC 2810-2899, 3510-3599, 3650-3679, 3710-3729, 3760-3769 and 3810-3899. Low

R&D industries are all other manufacturing industries

J.P. van Doorn 52

Part A - Breusch-Pagan LM test (All industries) Part B - Breusch-Pagan LM test (Non-manufacturing)

Estimated results: Estimated results:

Var sd = sqrt(Var) Var sd = sqrt(Var)

FDI 1035.3160 32.1763 FDI 444.9354 21.0935

e 86.0739 9.2776 e 51.1913 7.1548

u 1417.7450 37.6530 u 463.7138 21.5340

Test: Var(u) = 0 chibar2(01) = 10,477.98 Test: Var(u) = 0 chibar2(01) = 9,117.99

Prob > chibar2 = 0.0000 Prob > chibar2 = 0.0000

Part C - Breusch-Pagan LM test (Manufacturing) Part D - Breusch-Pagan LM test (High R&D)

Estimated results: Estimated results:

Var sd = sqrt(Var) Var sd = sqrt(Var)

FDI 139.8140 11.8243 FDI 50.2991 7.0922

e 16.1206 4.0151 e 6.2220 2.4944

u 255.7005 15.9906 u 102.3362 10.1161

Test: Var(u) = 0 chibar2(01) = 10,157.93 Test: Var(u) = 0 chibar2(01) = 10,233.76

Prob > chibar2 = 0.0000 Prob > chibar2 = 0.0000

Part E - Breusch-Pagan LM test (Low R&D)

Estimated results:

Var sd = sqrt(Var)

FDI 27.6401 5.2574

e 6.0558 2.4609

u 35.3018 5.9415

Test: Var(u) = 0 chibar2(01) = 7,109.98

Prob > chibar2 = 0.0000

Table A.7.

Breusch-Pagan Lagrangian multiplier test for random effects

Table A.7. presents the Breusch-Pagan Lagrangian multiplier test for random effects in the model of this study.

Each part contains the test in a different industry. The dependent variable is the number of FDI M&A into the

Eurozone in each industry and the independent variables are the logged real exchange rate, Eurozone domestic

acquisitions in each industry, investor country real GDP growth and investor country stock market growth. All

variables are constructed by using quarterly data from 1999Q1 to 2016Q2. All estimates are rounded to 4 decimals.

Note: SIC codes between 2000 and 3999 are manufacturing industries, all other are non-manufacturing

industries. High R&D industries are SIC 2810-2899, 3510-3599, 3650-3679, 3710-3729, 3760-3769 and 3810-

3899. Low R&D industries are all other manufacturing industries

J.P. van Doorn 53

Part A - Hausman specification test (All industries)

Test: H0: difference in coefficients not systematic chi2(21) = 0.07

Prob>chi2 = 1.0000

Part B - Hausman specification test (Non-manufacturing)

Test: H0: difference in coefficients not systematic chi2(21) = 0.35

Prob>chi2 = 1.0000

Part C - Hausman specification test (Manufacturing)

Test: H0: difference in coefficients not systematic chi2(21) = 0.69

Prob>chi2 = 1.0000

Part D - Hausman specification test (High R&D)

Test: H0: difference in coefficients not systematic chi2(21) = 0.04

Prob>chi2 = 1.0000

Part E - Hausman specification test (Low R&D)

Test: H0: difference in coefficients not systematic chi2(21) = 3.94

Prob>chi2 = 1.0000

Table A.8.

Hausman specification test

Table A.8. presents the results of the Hausman specification test for the fixed effects and random effects estimation

regressions. Each part contains the test in a different industry. The dependent variable is the number of FDI M&A

into the Eurozone in each industry and the independent variables are the logged real exchange rate, Eurozone

domestic acquisitions in each industry, investor country real GDP growth and investor country stock market

growth. All variables are constructed by using quarterly data from 1999Q1 to 2016Q2. Estimates are rounded to

4 decimals.

Note: SIC codes between 2000 and 3999 are manufacturing industries, all other are non-manufacturing industries.

High R&D industries are SIC 2810-2899, 3510-3599, 3650-3679, 3710-3729, 3760-3769 and 3810-3899. Low

R&D industries are all other manufacturing industries

J.P. van Doorn 54

Panel A - Australia Panel B - Canada Panel C - China

Variable Mean Std. Dev Mean Std. Dev Mean Std. Dev

Total FDI M&As into the Eurozone 3.600 2.985 7.543 3.291 3.471 4.099

Non-manufacturing industries 2.700 2.601 4.957 3.048 1.043 1.637

Manufacturing industries 0.829 0.963 2.486 1.539 2.343 2.823

High R&D manufacturing industries 0.557 0.773 1.471 1.139 1.414 1.781

Low R&D manufacturing industries 0.271 0.509 1.014 1.080 0.929 1.386

Logged real exchange rate -0.488 0.143 -0.378 0.077 -2.180 0.160

Total Eurozone domestic M&As 922.700 166.148 922.700 166.148 922.700 166.148

Non-manufacturing industries 619.629 125.362 619.629 125.362 619.629 125.362

Manufacturing industries 293.557 46.097 293.557 46.097 293.557 46.097

High R&D manufacturing industries 110.000 20.756 110.000 20.756 110.000 20.756

Low R&D manufacturing industries 183.557 29.143 183.557 29.143 183.557 29.143

Investor country real GDP growth 0.007 0.004 0.005 0.007 0.022 0.007

Investor country stock market growth 0.012 0.072 0.014 0.081 0.023 0.145

Panel D - Japan Panel E - UK Panel F - US

Variable Mean Std. Dev Mean Std. Dev Mean Std. Dev

Total FDI M&As into the Eurozone 5.800 3.063 57.329 17.244 78.957 17.873

Non-manufacturing industries 2.300 1.582 40.857 13.812 46.843 13.337

Manufacturing industries 3.414 2.061 15.729 6.105 31.429 7.308

High R&D manufacturing industries 2.100 1.524 7.143 2.845 19.314 4.886

Low R&D manufacturing industries 1.314 1.440 8.586 4.302 12.114 3.969

Logged real exchange rate -4.779 0.187 0.303 0.119 -0.197 0.142

Total Eurozone domestic M&As 922.700 166.148 922.700 166.148 922.700 166.148

Non-manufacturing industries 619.629 125.362 619.629 125.362 619.629 125.362

Manufacturing industries 293.557 46.097 293.557 46.097 293.557 46.097

High R&D manufacturing industries 110.000 20.756 110.000 20.756 110.000 20.756

Low R&D manufacturing industries 183.557 29.143 183.557 29.143 183.557 29.143

Investor country real GDP growth 0.002 0.011 0.005 0.006 0.005 0.006

Investor country stock market growth 0.007 0.103 0.007 0.075 0.011 0.082

Table A.9.

Investor country descriptive statistics

Table A.9. presents the mean and standard deviation of the main variables used in this study per investor country.

All variables are constructed by using quarterly data from 1999Q1 to 2016Q2. Each panel contains information

per investor country with T = 70. All estimates are rounded to 3 decimals.

Note: SIC codes between 2000 and 3999 are manufacturing industries, all other are non-manufacturing industries.

High R&D industries are SIC 2810-2899, 3510-3599, 3650-3679, 3710-3729, 3760-3769 and 3810-3899. Low

R&D industries are all other manufacturing industries.

J.P. van Doorn 55

Panel A - Dependent variable: Australian FDI M&A into the Eurozone, 1999Q1 - 2016Q2

All industries Non-manufacturing Manufacturing

Variables Total High R&D Low R&D

Logged real exchange rate 9.574 5.397 3.452 2.242 2.464

(6.073) (5.229) (2.970) (2.197) (1.668)

Eurozone domestic acquisitions -0.002 -0.003 -0.002 -0.008 0.006

(0.004) (0.005) (0.005) (0.008) (0.004)

Real GDP growth -19.566 -19.737 4.168 9.045 -6.645

(64.604) (56.985) (29.675) (22.171) (15.959)

Stock market price index growth -0.500 -2.577 1.800 0.107 1.396

(4.461) (3.842) (2.179) (1.616) (1.180)

Time trend 0.130 0.152 -0.065 0.007 -0.071

(0.225) (0.196) (0.111) (0.084) (0.059)

Constant 10.686** 6.838 3.828 2.567 1.391

(5.157) (4.435) (2.322) (1.831) (1.157)

Year dummies Yes Yes Yes Yes Yes

F-test 4.66*** 4.74*** 0.89 1.33 0.75

sigma_e 2.0282 1.7560 0.9805 0.7353 0.5299

Within R² 68.55% 68.95% 29.33% 38.43% 26.03%

N 70 70 70 70 70

Table A.10.

Regression results – Investor country FDI acquisitions into the Eurozone

Table A.10. presents the remaining regression results of the explanatory and control variables on the number of

investor country specific FDI M&As into the Eurozone with the OLS model, that are not included in the main text

of the study. The table contains the estimations for the separate defined subdivided industries. All variables are

constructed by using quarterly data from 1999Q1 to 2016Q2. All regressions are obtained with the xtreg command,

which in this case gives the regular OLS estimators, using Stata. Robust standard errors are included in

parentheses. ***, ** and * show the level of significance of 0.01, 0.05 and 0.1 respectively. Estimates are rounded

to 3 decimals.

Note: SIC codes between 2000 and 3999 are manufacturing industries, all other are non-manufacturing industries.

High R&D industries are SIC 2810-2899, 3510-3599, 3650-3679, 3710-3729, 3760-3769 and 3810-3899. Low

R&D industries are all other manufacturing industries

J.P. van Doorn 56

Panel B - Dependent variable: Chinese FDI M&A into the Eurozone, 1999Q1 - 2016Q2

All industries Non-manufacturing Manufacturing

Variables Total High R&D Low R&D

Logged real exchange rate -8.977 -2.890 -7.050 -7.506* 0.388

(5.941) (2.668) (5.525) (3.980) (3.123)

Eurozone domestic acquisitions 0.003 0.004* 0.000 0.000 -0.001

(0.003) (0.002) (0.008) (0.011) (0.007)

Real GDP growth 34.300 -1.235 36.356 21.791 14.295

(50.221) (22.721) (46.221) (33.431) (26.189)

Stock market price index growth -1.296 -0.655 0.311 1.889 -1.546

(2.554) (1.146) (2.354) (1.695) (1.334)

Time trend -0.006 -0.017 0.029 -0.215 0.049

(0.214) (0.098) (0.200) (0.145) (0.113)

Constant -22.454* -8.066 -16.112 -16.866* 0.815

(13.260) (5.993) (12.097) (8.754) (6.845)

Year dummies Yes Yes Yes Yes Yes

F-test 12.48*** 9.36*** 6.02*** 4.06*** 4.00***

sigma_e 1.8989 0.8550 1.7507 1.2674 0.9915

Within R² 85.38% 81.42% 73.80% 65.53% 65.17%

N 70 70 70 70 70

Panel C - Dependent variable: United Kingdom FDI M&A into the Eurozone, 1999Q1 - 2016Q2

All industries Non-manufacturing Manufacturing

Variables Total High R&D Low R&D

Logged real exchange rate 26.369 8.366 20.848 -6.320 24.418

(44.658) (35.470) (22.074) (10.772) (17.105)

Eurozone domestic acquisitions 0.030* 0.028 0.024 0.001 0.006

(0.016) (0.018) (0.022) (0.023) (0.025)

Real GDP growth 403.083 303.266 80.783 114.352 -45.628

(292.719) (234.408) (144.102) (70.859) (111.378)

Stock market price index growth -5.188 -9.742 6.349 -1.894 9.951

(18.605) (14.715) (9.265) (4.477) (7.154)

Time trend 0.188 0.051 0.066 0.325 -0.363

(1.049) (0.841) (0.527) (0.259) (0.398)

Constant 28.509 25.748 4.871 9.125* 3.278

(24.538) (18.271) (12.311) (5.087) (9.524)

Year dummies Yes Yes Yes Yes Yes

F-test 8.80*** 8.93*** 3.58*** 2.95*** 2.75***

sigma_e 9.2349 7.3516 4.5203 2.2331 3.4451

Within R² 80.46% 80.70% 62.66% 58.04% 56.32%

N 70 70 70 70 70

J.P. van Doorn 57

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