The Effect of Geographical, Cultural and Political Distance on Foreign Direct Investment:

(1)

The Effect of Geographical, Cultural and Political

Distance on Foreign Direct Investment:

The Case of Norway

Abstract

This thesis examines FDI inflows to Norway using data from 35 investing countries. The study aims to analyze the effect of geographical, cultural and political distance between host and home country. The overall results show that distance country affects FDI flow to Norway negatively. Cultural and geographical distance and distance in rule of law has the most consistent negative effect. Furthermore, the results show that the effect of geographical distance is stronger than the effect of cultural distance.

Author: Karina Tytlandsvik Student Number: s2760258

Supervisor: Raquel Ortega Argiles

(2)

2

1. INTRODUCTION

Foreign direct investment (FDI) has increased substantially over the last decades and is linked to the rising global integration. Developed economies have been subject for a large part of total world FDI, with the United States as the largest investing country. In new growth literature, FDI has been linked to productivity gains and technology transfers (Ghosh & Wang, 2009). This is confirmed by the Norwegian Minister of Trade and Industry who stated that “For Norway,

foreign direct investment will mean more jobs, increased expertise and better access to foreign markets. The government wants to facilitate a comprehensive handling of inquiries from overseas companies seeking to establish in Norway”1. The development in FDI between 1996- 2013 in Norway confirms an increase in capital inflows over the past decade, confirming the global trend2. However, the geographical breakdown shows that FDI inflows to Norway are dominated by the EU countries3. The fact that such a large share of FDI in Norway originates from the EU countries, implies that distance is an important factor in explaining FDI inflows.

The purpose of this thesis is to examine three different distance concepts and test their effect on FDI inflows to Norway. Geographical, cultural and political distance between host and home country is analyzed based on theory and previous literature. For geographical distance I analyze how increased physical distance between Norway and a partner country affect FDI inflows. For political and cultural differences the distance between Norway and a partner country is calculated using secondary data.

Shapiro and Globerman (2003) expect that FDI will be attracted to countries characterized by a high quality of governance, all other things being equal. According to the World Bank’s

governance index Norway has a high institutional quality. This implies that Norway should attract FDI on the basis of its high institutional quality. Geographically and culturally, Norway is placed more in the European periphery. This can be a potential barrier to investors, especially if

1

Meld. St. 22 (2011-2012) Verktøy for vekst – om Innovasjon Norge og SIVA SF (Ministry of Trade and Industry, 2012).

2_{See appendix 5 for a graphical presentation of FDI inflows to Norway.} 3

(4)

4 access to markets is a main aim. On the one hand distance can be thought to deter investments, as the distance represents unfamiliarity for the investor. On the other hand, distance can be

attractive. Some investors might be motivated to invest in a country because of the differences from their home country - especially if another country can provide a more stable business environment. This leads to the specification of the research question:

How does the geographical, cultural and political distance affect FDI inflows to Norway?

The motivation behind this study is a scarcity of literature explicitly focused on developed country experiences with inward FDI, the United States being an exception. To my knowledge, cultural and political determinants have not previously been used to examine FDI flows to Norway. This research aims to bridge this gap. Knowledge on how distances affect FDI will increase awareness on the relationship between Norway and its partner countries. The results can have policy implications that imply how the Norwegian government should direct attention to attract more FDI.

1.1 Chapter Outline

This thesis is divided into six main chapters. It starts with an introduction of Hymer’s theory on the costs of doing business abroad. Further, Dunning’s Eclectic paradigm and the gravity model of trade are presented. As an extension of the gravity model the concepts of cultural and political distance are given. In chapter 3 findings in previous literature are reviewed, with a specification of hypotheses after each distance concept. Specifically, I test four hypotheses about the

relationship between inward investments and the different distance measurements.Chapter 4 gives an overview of the data used in this research. This chapter includes information about the sample, the dependent and independent variables. Chapter 5 introduces the methodology and the empirical method used. Chapter 6 presents the results and analyzes the outcome. It also includes a paragraph on robustness checks. Chapter 7 concludes the thesis and connects theories to the analysis. This chapter ends with policy implications derived from this research.

2. THEORETICAL BACKGROUND

(5)

5 combination of capital, technology and market access (Dunning, 1998). As such, FDI can be viewed as an investment of long-term interest in firms and production facilities that operate outside the economy of the investor4. The countries that make the investments to Norway are referred to as partner or home countries.

2.1 Costs of Doing Business Abroad

The theoretical concept of the costs of doing business abroad (CDBA) was first developed by Steven Hymer in 1960. CDBA is a well-known concept in the international business literature, measuring the “disadvantages or added costs borne by MNEs that are not borne by local firms in a host country” (Eden & Miller, 2004, p. 2). These costs arise from “unfamiliarity, relational and discriminatory hazards” that foreign firms encounter in a host country (Eden & Miller, 2004, p. 31). The argument is that MNEs face a liability of foreignness in host countries. MNEs suffer costs associated with long-distance communication as well as the costs associated with

unfamiliarity such as consumer tastes, legal and institutional frameworks of business, and local business customs (Eden & Miller, 2004). Hymer’s theory on the disadvantages with unfamiliarity is closely related to the concept of distance. As cultural distance and/ or distance in political institutions increase – unfamiliarity and expected costs increase.

2.2 Dunning’s Eclectic Paradigm

The first “all- inclusive” theoretical structure of FDI flows was introduced by John Dunning (1977). The theory is called the eclectic paradigm, but is often referred to as the OLI approach (Helpman, 2011). Dunning’s theory identifies three broad conditions that are necessary for a firm to engage in direct investments abroad, and illustrate the “who”, “where” and “why” of FDI activities (Brenton, et al., 1998).

First, the MNE must have ownership specific advantages (the “O” in OLI). This means that the firm must have competitive advantages in its home market that can be relocated abroad. This makes the foreign firm capable of competing with host country firms, despite the drawbacks of being a foreigner. Examples of ownership- specific advantages are economies of scale,

organizational expertise, product diversification or the ability to acquire access to foreign

4_{OECD defines FDI as: “a cross-border investment by a resident entity in one economy with the objective of}

(6)

6 markets. The second condition is location advantages (the “L” in OLI). There need to be

advantages of locating production to a foreign area, the so-called location-specific advantages. These location specific advantages attract foreign firms (Kvinge & Narula, 2001). Examples of location- specific advantages include national resources, labor productivity, physical distance, taxation and tariff barriers. The third condition is internalization advantages (the “I” in OLI). The inclination of firms to internalize their ownership and location advantages can explain why

international firms prefer FDI over alternatives that do not include ownership control of the foreign activity. Dunning’s OLI theory represents a mix of “three partial theories of FDI, but have not lost its relevance or authenticity today” (Khachoo & Khan, 2012, p. 4). The OLI framework is a good starting point for explaining why an MNE decides to establish abroad, however the theory also provides answers to the geographical distribution of FDI by analyzing the localization factors (Dunning, 1998, 2000).

2.3 The Gravity Model

The core idea behind the gravity model of trade is the assumption that trade is determined by the economic size of the countries involved as well as the geographical distance between them . The difference between trade and FDI can be hard to distinguish. Porter divides international trade into three sub-groups where one of them can be defined as FDI5. Studies of FDI flows are considerably parallel to trade literature (Blonigen, 2005). As such, the gravity model has also been used to explain the determinants of FDI. The gravity model specifies trade flows between countries as a function of the GDP of each country and the distance between the countries (Blonigen, 2005). The model describes the flow from origin to destination in terms of “supply factors in the origin (income and population), demand factors in the destination and factors relating to the specific flow, such as distance” (Brenton, et al., 1998, pp. 6-7). The potential trade flow is reduced by the distance between home and host country (Anderson, 2011).The gravity model has proved to be empirically successful in explaining bilateral trade flows (Guerin, 2006). A limitation to the gravity model is the symmetric approach to geographic distance. The

assumption being that the distance from country A to B is the same as the distance from B to A. The standard empirical gravity model assumes quasi-symmetry. This essentially means that the distance between two places has an equal effect regardless of the direction of the move. However,

(7)

7 the effect might be sensitive to distance in one direction more than in another (Bavaud, 2002). More recently, researchers have started to extend the scope of the gravity framework by studying the role of political and cultural differences (Heuchemer, et al., 2009).

2.3.1 Measure of cultural distance

Following the OLI framework cultural differences can be considered as possible explanations and barriers to capital flows. Cultural distance is a widely used concept in international business literature. It has been identified as a key factor explaining foreign market attractiveness and FDI expansion patterns (Shenkar, 2001). The most widely cited author in the field of cross-cultural research is Geert Hofstede (Kirkman, et al., 2006). Hofstede defines culture as “a kind of collective programming of the mind that distinguishes the members of one human group from another (Benito & Gripsrud, 1992, p. 467). Based on the result of a broad questioning in more than 50 countries, Hofstede (1980) conducted a factor analysis to identify variables that are used to calculate cultural distance. As a result of Hofstede’s research, each country has been

characterized by a score on each of the four dimensions. Despite the empirical study behind the index was conducted in the 1980’s it is still used by researchers and considered as the most comprehensive framework for cultural distance (Kirkman, et al., 2006). Hofstede’s four cultural dimensions are listed below.

The power distance index (PDI) is “the extent to which the less powerful members of

organizations and institutions (like the family) accept and expect that power is distributed unequally”. In a society that exhibit a high degree of power distance individuals accept

hierarchies. A low score means that power is decentralized and hierarchy exists for convenience (Hofstede, 1994, pp. 2-3).

Individualism (IDV) is “the degree of interdependence a society maintains among its members”.

Individualism stands as an opposite to collectivism; that is the degree to which individuals are integrated into groups (Hofstede, 1994, pp. 2-3).

Masculinity (MAS) is measured versus its opposite, femininity. It refers to the distribution of

(8)

8

Uncertainty avoidance (UAI) A society’s tolerance for unknown situations and ambiguity. A

high level of uncertainty avoidance implies a rule-oriented society to reduce uncertainty and risk (Hofstede, 1994, pp. 2-3).

In order to arrive at a measure of cultural distance among countries, Kogut and Singh (1988) constructed a composite index using Hofstede's dimensions. Their index is based on the deviation along each of the four cultural dimensions from the score of a given home country for each country. The deviations are corrected for differences in the variance of each dimension and then arithmetically averaged (Benito & Gripsrud, 1992). Algebraically, the Kogut-Singh index for cultural distance CDj is given as:

Eq. (1) 𝐶𝐷_𝑗 = ∑4_𝑖=𝑗{(𝐼_𝑖𝑗 − 𝐼_𝑖𝑠)2/𝑉_𝑖} /4 Iij = Index value for cultural dimension i of country j

Vi = Variance of the index for dimensions i

S = Norway

Criticism of the Cultural Distance Index

Hofstede’s research has been criticized for the sample being drawn from one MNE alone.

Hofstede based his empirical research on employees of IBM6 to represent the entire country. This can be assumed to reduce the importance of a generalizing index as the Kogut and Singh (1988) measure. Shenkar (2001) sums up the critique in three main points. First “The illusion of

symmetry” implicitly assumes that the cultural distance between country A and B is similar to the distance between country B and country A. However, it may be easier for a Chinese firm to invest in Norway, then vice versa. “The illusion of stability”: As cultural distance is measured at a single point in time, it is assumed to be constant over time. But cultures change, and firms may gain experience, so the role of cultural distance changes over time (Kirkman, et al., 2006, p. 298). “The Illusion of Linearity”: The assumption of linear impact on investment, entry mode and performance (Shenkar, 2001).

(9)

9

2.3.2 Measure of political distance

Governance infrastructure - including the nature of the legal system - is an important determinant of FDI inflows. Improvements in governance are likely to be more important for developing and transition economies than for all countries on average (Globerman & Shapiro, 2003). Following Globerman and Shapiro (2002, 2003) governance represents the qualities of legislation,

regulation, and legal systems that condition freedom of transacting, security of property rights, and transparency of government and legal processes.

The measure of political distance in this research is based on The World Governance Indicators (WGI) created by the World Bank7. Political distance is in this research a measure of differences in governance and as such differences in institutions between host and home country. The WGI index range from -2.5 to 2.5 where higher values correspond to higher quality of governance. The research permits governance quality to be compared between countries and over time (Kaufmann, et al., 2010). The following six political categories are defined: Voice and accountability,

political stability and absence of violence/terrorism, government effectiveness, regulatory quality, rule of law and control of corruption. This research is most concerned with the indicators that affect the investment decisions of MNEs, and chose to include four of the six indicators in the measurement of political distance. The four indicators are described below:

Political Stability and Absence of Violence/Terrorism

Captures “perceptions of the likelihood that the government will be destabilized or overthrown by unconstitutional or violent means, including politically-motivated violence and terrorism”

(Kaufmann, et al., 2010, p. 3). Investments in a foreign country will always be associated with a certain political risk, and investments will be less likely if the political environment is unstable.

Regulatory Quality

Captures “perceptions of the ability of the government to formulate and implement sound policies and regulations that permit and promote private sector development” (Kaufmann, et al., 2010, p. 3).

7_{The World Bank defines governance as “the traditions and institutions by which authority in a country is exercised.}

(10)

10

Rule of law

Captures “perceptions of the extent to which agents have confidence in and abide by the rules of society, and in particular the quality of contract enforcement, property rights, the police, and the courts, as well as the likelihood of crime and violence” (Kaufmann, et al., 2010, p. 3).

Control of Corruption

Captures “perceptions of the extent to which public power is exercised for private gain, including both petty and grand forms of corruption, as well as "capture" of the state by elites and private interests” (Kaufmann, et al., 2010, p. 3). This indicator can be seen as a measure for how well the institutions work in a country. A low score in this index implies a high level of corruption.

In order to arrive at a measure of political distance among countries I calculate the political distance between host and home countries using the four political indicators described. Before calculating the distance, the index is recoded from -2.5 -2.5 to 0-5, by adding 2.5 for each value. Political distance, PDij, is calculated as:

Eq. (2) 𝑃𝐷_𝑖𝑗 = 𝐼_𝑖𝑠− 𝐼_𝑖𝑗

Iij = Index value for political dimension i of country j

S = Norway

Critique of governance indicators

(11)

11

3. LITERATURE REVIEW

This section will present results from previous literature. After the presentation of the literature relevant for each distance measure, the hypotheses to be tested in this thesis are presented.

3.1 Geographic Distance

After the introduction of the gravity model, there has been a growth in studies on the role of geography on bilateral trade flows. Grosse and Trevino (1996) showed that geographic distance was negatively correlated with the amount of FDI going into the United States. In a more recent study Shenkar (2001) explains that geographic proximity lowers the costs of control and

coordination. And that this reduces the costs of monitoring (Shenkar, 2001). Wei and Lui (2001) similarly find that greater geographic distances between home and host country increases the costs of obtaining information, and that this leads to less inward FDI in the host country (Wei & Lui, 2001). This follows Coval and Moskowitz who state that foreign investors have easier access to information about companies located near them, preferring them over distant ones on which they have less information (Coval & Moskowitz, 1999).

Reviewing previous literature, there seems to be a general acceptance for that geographical distance discourages FDI inflows. Shatz and Venables (1999) even claim that the geographical distance variables in the “gravity” model explain as much as two-thirds of the trade patterns. This leads to the first hypothesis:

H1a: Geographical distance has a significant and negative effect on FDI inflows to Norway.

Shatz and Venables (1999) review the empirical studies on the determinants of FDI according to different country regions. Stating that “each region has presented its own set of patterns” (Shatz & Venables, 1999, p. 10). This makes me question whether the effect of geographical distance will be constant for different sets of country groups from the total country sample. Two macro-economic sub-samples are created, to analyze the patterns of these regions. The macro-macro-economic regions are presented in Chapter 4.3. Following previous literature, it is widely stated that

geographical distance has a negative effect on FDI inflows. Assuming this holds, it is reasonable to argue that the effect of geographical distance has a stronger effect when countries further away from the host country are included in the sample, as distances increase. I specify a second

(12)

12

H1b: The negative effect of geographical distance is stronger for the total country sample than

for the Europe sub-sample, as distance between host and home country is larger.

3.2 Political Distance

Previous literature linking political institutions and FDI have often focused on developing

countries, and the potential difficulties associated with low quality institutions. Using a sample of developing countries Singh and Jun (1995) find that political risk is an important determinant for attracting FDI. Busse and Hefeker (2005) find that government stability, the absence of internal conflict, basic democratic rights and ensuring law and order are highly significant determinants of foreign investment inflows. However, looking at the US, only weak evidence was found that political risk had an effect on FDI into the US (Grosse & Trevino, 1996). This implies that the consequences of poor political institutions and high levels of political risk will deter investors from entering a market. However, following Grosse and Trevino (1996) this does not necessarily mean that low levels of political risk, will have an effect on attracting FDI to a developed

country.

Wei and Shleifer (2000) point to corruption as a significant barrier to inward FDI. They find that corruption may deter FDI by making a host country unattractive to foreign investors via the high costs of entry and uncertainty (Wei & Shleifer , 2000). While a strand of empirical literature supports negative effects of corruption on FDI others have failed to find a significant negative effect. Brada (2012) argue that corruption can “grease the wheels of commerce”, and that this implies that bribes can allow the MNE to avoid regulations and administrative costs (Brada, et al., 2012: Egger & Winner, 2005). A paper closely related to the research of political distance is Habib and Zurawicki (2002). They also examine the difference in the corruption level between the host and home country. Their results suggest that foreign investors generally avoid countries with high levels of corruption (Habib & Zurawicki, 2002, p. 291). As such Habib and

(13)

13 makes establishment and designing contracts easier for a foreign investor. This argument implies that MNEs might invest in countries with a rule of law similar to their own, as this increase familiarity.

High regulatory quality can indicate strong institutions and government stability, something that can make investments less risky for a foreign investor. On the other hand, difficulties

understanding an intricate bureaucratic system, which can have a negative effect on FDI flows. Some macroeconomic variables are related to regulatory quality and may discourage investors from allocating capital to Norway. Examples of macroeconomic variables are high tax rates and tariff barriers that discourage investment (Slemrod, 1990).

Shapiro and Globerman (2003) expect that FDI will be attracted to countries characterized by a high quality of governance, all other things being equal. A favorable governance infrastructure creates beneficial conditions for investment. Norway scores very high on the WGI index. The average rank for Norway is over 908 in all four categories. The percentile rank indicate the percentage of countries worldwide that rank lower than the indicated country, so that high values indicate better governance scores (World Bank Governance Indicators, 2014). This means that for all of our political distance indicators, most partner countries have a lower institutional quality than Norway. This suggests Norway should attract FDI due to the country’s high quality of governance. This leads to the specification two hypotheses on political distance:

H2a: Difference in political institutions has a positive and significanteffect of FDI stock between

host and home countries.

H2b: The effect of political distance is caused by the home countries with lower institutional

quality than the host country.

3.3 Cultural Distance

High cultural distance between host and home country is associated with uncertainty and unfamiliarity. Integration exists on many levels including that of organization of a foreign

8

(14)

14 workforce and local competition. Thus FDI levels might be higher between two countries of similar culture (Loree & Guisinger, 1995). Loree and Guisinger (1995) reported that “as cultural distance increased, the amount of US foreign direct investment decreased. Grosse and Trevino (1996) similarly find that cultural distance has a negative effect on inwards FDI to the US. In the FDI literature, cultural distance has had three primary areas of interest. The first has been to explain market location and the patterns of FDI investments by MNEs. The second to predict the choice of entry mode into foreign markets and the third to account for the failure of MNE

performance in international markets (Shenkar, 2001, p. 520).

Davidson (1980) suggests that cultural similarity encourage direct investment. This is due to information can be distributed more efficiently in a country with a similar language and culture. If the cultural distance is high uncertainty and unawareness of local conditions and customs can pose a more difficult integration process in a new market (Davidson, 1980). More specifically Davidson (1980) finds that firms taking their first steps into foreign investment were more likely to prefer culturally similar countries, than those in an advanced stage of internalization. Kogut and Singh (1988)’s own empirical estimations find strong support for cultural distance’s influence on MNE’s choice of entry mode. Their results are robust even when controlling for outlier countries with the highest cultural distance from the home country. However, others have found that cultural distance did not affect FDI decisions. With cultural distance is measured by the Kogut and Singh (1988) index, Benito and Gripsrud (1992) find no support is found for the notion that the first FDI takes place in culturally closer countries than later FDIs. Furthermore, as the trend towards a more global business environment expands, the effect of cultural distance is expected to diminish over time (Loree & Guisinger, 1995, p. 289). Despite previous literature has reached different conclusions, I hypothesize that cultural distance will have a negative effect on FDI inflows to Norway. This is based on Norway not sharing a common language with any other country, which is believed to reduce cultural distance. In 2013 the main investing countries in Norway were The Netherlands and Sweden9. These are the two countries with the lowest cultural distance from Norway. This point to the fact that culturally similar countries invest in

9

https://en.santandertrade.com/establish-overseas/norway/foreign

(15)

15 Norway. It is thus plausible that cultural distance explain a part of the patterns of FDI

investments in Norway. This leads to the specification of the third hypothesis:

H3: Cultural distance has a significant and negative effect on FDI inflows between Norway and its partner countries.

4. DATA AND VARIABLES EXPLAINED

The data set consist of yearly observations of FDI inflows to Norway for the period 1996-2013. It includes data from 35 of Norway’s partner countries. A detailed description of all variables included and the sources used can be found in Appendix 3.

4.1 The Dependent Variable

The data on the dependent variable used, FDI, is obtained from Statistics Norway (Statistics Norway, 2015). FDI is defined as investment flowing into Norway from each specific country, and the data on FDI stocks are used. FDI stocks measure the total level of direct investment at a given time and are measured in NOK (OECD, 2014). In line with other countries, Statistics Norway implemented new guidelines for FDI in 2013. The new guidelines are based on the OECD Benchmark definition of direct investment10.

Various measurements have been used to model FDI in previous studies. Using the FDI/GDP rate has been used because it shows the relative importance of FDI in the host country. However, there is uncertainty using this measure, as the dependent variable is then dependent on GDP, and might not capture the true effect of the political and cultural variables as GDP also is affected by the same variables (Neumayer & Spess, 2005).Other methods include focusing only on the positive observation as Busse and Hefekener (2007). Instead, I use the natural log of FDI, as this helps reduce the skewedness of its distribution. To avoid excluding negative observations the negative values are recoded to equal one NOK. The negative FDI flow values imply “instances of reverse investment or disinvestment” (UNCTAD, 2015).

10_{Further information on new guidelines is found on the statiscs Norway website. Follow link below:}

(16)

16

4.2 The Independent Variables

The selection of independent variables is in general common determinants of FDI.

Geographical distance

Data on the geographic distance has been obtained from the CEPII database. This measure is a weighted distance (pop-wt, km). Mayer and Zignago (2011) have computed these distances using city level data to assess the geographic distribution of population (in 2004) of each nation. The basic idea, inspired by Head and Mayer (2002), is to calculate distance between two countries based on bilateral distances between the biggest cities of those two countries, those inter-city distances being weighted by the share of the city in the overall country’s population (Mayer & Zignago, 2011). Geographical distance is measured using the natural log.

Macro- economic regional sub-samples

I divide the total sample into two macroeconomic sub- samples. This is to analyze if each region has its own set of patterns related to geographic distance, following Shatz and Venables (1999). Regressions are run including only the sub-sample countries. The total sample of countries is restricted to countries in Europe and countries with a membership in the OECD11. Europe is included to analyze the effect of geography for the European region. It is expected that geographical distance has a weaker effect for this region, as distance are relatively short. Membership in OECD is included as a second sub-sample. This macro region is expected to include more homogenous countries, as “they have access to common technologies and have intensive intra-trade” (Scarpetta & Bassanini, 2002).

Political Distance

I have calculated the political distance between Norway and the partner countries based on four of the World Governance Indicators12. This gives an index with positive and negative values, showing the political distance from Norway13. Negative distance means the partner country has a higher institutional level than Norway. Correspondingly, positive distance applies to countries

11

Organization for European Economic Cooperation, membership list in appendix 1.

12

There are missing data for three years in this dataset, as the period between 1996-2001 only has observations for every second year.

(17)

17 with lower institutional quality than Norway. The absolute value of the political distance is used to evaluate whether political distance between Norway and a partner country has an effect on FDI inflows. Additionally, the political distance variables are restricted only to include the partner countries with a lower quality of institutions (positive distance), as it is hypothesized that they drive the effects of political distance.

Cultural Distance

In this study, the Kogut-Singh (1988) index is used as a measure of the cultural distance between Norway and the countries in the sample. The actual values for this calculation have been taken from Hofstede (Hofstede, 2015). Two countries in the sample are not included in Hofstede’s research, and are thus left out14. As expected the other Nordic countries have a small cultural distance to Norway. However, it is more surprising that the Netherlands is the country that is culturally closest to Norway. The four most culturally distant countries in the sample are China, Hungary, Panama and Japan, which seems plausible15.

4.2.1 Control variables

To assess the link between international capital flows and the determinants of FDI, other variables that effect capital inflow are controlled for. There are several potential determinants, apart from geographical, cultural and political distance, that can influence FDI location decision. Five control variables are added, each with data for all the countries in the sample and Norway.

Trade Openness equals the sum of exports and imports as a share of GDP. School enrollment is

the total enrollment in primary education. Unemployment is the share of total labor force that is without work, but seeking employment. GPD growth is added as this has been found to have a high explanatory effect on FDI flows. GDP growth is a measure of market size (Grosse and Trevino, 1996: Wheeler and Moody, 1992). Host and home country size is generally recognized as a significant determinant of FDI flows, and Population is used as a proxy for the size of each country. The natural logarithm for the population variable is used. I also include the same control variables for Norway. However education is measured as average years of schooling. The values for unemployment, education, GDP growth and trade openness are lagged. Lagged values are utilized since the information an investor has when making an investment decision is based on

14

Countries not included in Hofstede’s research: Cyprus and Bermuda.

(18)

18 the past performance of Norway. I do not use a lagged value for population, as population is not subject to change much over a short time period.

Table 1) Summary Statistics

Table 1 shows the summary statistics of the observations for the included variables. The table gives an idea of the characteristics of the data used. There is a total of 35 countries, with

observations for the 1996-2013 time period. For FDI inflows 8 yearly observations are missing in the database, making the dataset slightly unbalanced. The average number of FDI observations per country is 17.7, instead of 18.The school enrollment variable exceed 100 percent due to the inclusion of over-aged and under-aged students because of early or late school entrance and grade repetition (World Bank, 2015).

4.2.2 Omitted variables

Originally a variable of common language was intended as an additional measure of cultural distance. A common language variable has been customary to use on studies on the United States

Variable Obs Mean Std. Dev. Min Max

FDI (inflow Norway) 622 5,78 3,57 0,00 11,60

Geographical distance 630 7,98 0,97 6,22 9,66

Culture Distance 594 3,22 1,71 0,13 7,68

Political Stability, distance (abs) 526 0,72 0,68 0,00 3,54

Regulatory Quality, distance (abs) 526 0.54 0,48 0,0007 2,12

Control of Corruption, distance (abs) 526 1,06 0,89 0,0002 3,30

Rule of Law, distance (abs) 526 0,87 0,78 0,0026 4,34

Trade Openness 621 102,28 84,03 14,93 458,33

Unemployment 590 7,24 4,67 0,70 27,20

School enrollment 539 103,41 7,53 87,51 154,05

GDP growth 647 3,00 3,31 -10,51 15,24

Population 630 16,76 1,99 11,00 21,03

L.Trade Openness NOR 612 71,83 2,79 67,73 76,27

L.Unemployment NOR 612 3,61 0,66 2,50 4,80

L.Education NOR 324 12,52 0,37 11,50 12,70

L.GDP Growth NOR 612 2,21 1,70 -1,63 5,39

Population NOR 648 15,35 0,04 15,29 15,44

(19)

19 and also for other English or Spanish speaking countries. However, as the Norwegian language is only spoken in Norway, a common language variable does not prove useful. The similarity between Norwegian, Swedish and Danish is to my knowledge not detected in any existing language index. As a result the language variable has been omitted. Furthermore GDP per capita (GDP/Capita) is expected to have an influence on the flow of FDI, as it considers both the size of the market and the size of the population. However, as GDP per capita is highly correlated with the main explanatory variables in the model16. GDP growth has instead been used as a measure, as this variable has a lower correlation.

5. METHODOLOGY

Before presenting the econometric model used, I will take a closer look at the political indicators. Globerman and Shapiro (2003) state that the WGI indices are highly correlated with each other and that it is very difficult to use them all in a single equation. Thecorrelation table confirm high correlation for the four political indicators17. Based on the results, I check for multicollinearity by performing a variance inflation factor (VIF) test. The result of the VIF test shows a high variance inflation factor for the political distance in Rule of Law and Control of Corruption18. This means that the variable can be considered as a linear combination of other independent variables. A further VIF test for the regressions including only one of the political indicators show no sign of multicollinearity. An assumption under Pooled Ordinary Least Squared (POLS) is that the variance is homoscedastic. If this assumption is violated, there is a risk of overestimating the effect the each variable has on the dependent variable. Results of the likelihood ratio test show evidence of heteroskedasticity (p-value is 0,000). Furthermore the results of the Wooldridge test for autocorrelation confirms that there is also evidence of autocorrelation in the panel (p-value is 0,002). This makes me specify all models with heteroskedastic-consistent standard errors.

The models have both time-varying effects (political distance) and time-invariant effects

(geographical and cultural distance). In order to decide which model best fit the dataset, I conduct a Hausman test. The results proved that the preferred model is Fixed Effects (F.E). The reported p-value is 0,000 for all specifications. Using a F.E model will omit the time-invariant

16_{Correlation table can be found in Appendix 2.} 17

Correlation table can be found in Appendix 2.

(20)

20 independent variables. As a result I conduct a Breusch Pagan Lagrangian multiplier test that confirms that a Random Effects (R.E) model is preferred over a pooled POLS model (p-value of 0,000). The R.E model can better capture the effects of both of the time-invariant, country-fixed variables as well as the effects of variables which may change over time” (Hill, et al., 2012).

5.1 Model

To answer the four hypotheses stated in chapter 3, three separate regression models are specified. The corresponding equations are given below in equations 4- 6. Equation 3 specifies the baseline R.E model:

Eq. (3) ln(FDI)it = β0 +β1Geographical Distanceit + β2Cultural Distanceit +

β3Political Distanceit + β4Xit +

ε

it + uit

ln(FDI) is the outcome variable, X is a vector of the control variables and include: population,

GDP growth, trade openness, education and unemployment for host and home countries. β is the estimated coefficients for each explanatory variable, i is the partner country, t is the year. The R.E model accounts for between-country errors in u and within-country errors in ε.

5.1.1 Regression 1

The first regression aims to analyze the hypotheses on geographical and cultural distance. This corresponds to hypotheses: H1a, H1b and H3. An R.E model is used since the variables of interest

are consistent over time. POLS results are included additionally. The model for cultural and geographical distance is:

Eq. (4) ln(FDIit) = β0 +β1 Geographical Distanceit + β2Cultural Distanceit + β3Xit +

ε

it +

uit

(21)

21

The second regression is specified for the effect of political distance. It corresponds to hypothesis H2a. Political distance is measured in absolute values 19. As political distance varies over time an

F.E model is specified. This is in line with the results from the Hausman test. To ensure consistency with regression 1, both the F.E and R.E model is included. The model for political distance is:

Eq. (5) ln(FDIit) = β0 + β1Distance PoliticalStabilityit + β2Xit +

ε

it +

u

it

For the additional regressions, Distance PoliticalStabilityis replaced by the following political distance variables: Regulatory quality, control of corruption and rule of law. The political indicators in included at the time. The result following this regression is shown in regression table 2.

The third regression is run with the same model specifications as in regression 2. Regression 3 aims to analyze the relationship in hypothesis H2b. Distance Lower indicates that political

distance is restricted to the effect partner countries with lower institutional quality than Norway. Regression 3 is specified as:

Eq. (6) ln(FDIit) = β0 + β1 Distance Lower PoliticalStabilityit + β2Xit +

ε

it + uit

Distance Lower PoliticalStabilityis replaced by regulatory quality, control of corruption and rule of law, in separate regressions. Due to close similarities between regression 2 and 3, the results of regression 3 regression can be found in Appendix 6.

(22)

22

Regression Table 1)

(1) (2) (3) (4) (5) (6)

VARIABLES POLS R.E POLS R.E POLS R.E

Geographical Distance -0.893*** -1.169*** -0.817*** -0.762*** -4.119*** -4.011*** (0.201) (0.414) (0.218) (0.290) (0.605) (0.923) Culture Distance -0.532*** -0.556** -0.495*** -0.488** -0.0923 -0.107 (0.102) (0.236) (0.0995) (0.190) (0.150) (0.298) Trade Openness -0.00131 -0.000616 0.00841* 0.00934 0.0103** 0.00709 (0.00283) (0.00454) (0.00437) (0.00842) (0.00485) (0.00797) Unemployment -0.182*** -0.158*** -0.276*** -0.160* -0.130** -0.122 (0.0364) (0.0594) (0.0501) (0.0831) (0.0575) (0.107) School Enrollment -0.0107 0.0413 -0.0562 0.0792 0.134*** 0.122* (0.0292) (0.0440) (0.0400) (0.0626) (0.0480) (0.0655) GDP Growth -0.212*** -0.0497 -0.0525 0.0273 -0.136 -0.00776 (0.0671) (0.0512) (0.0933) (0.0533) (0.103) (0.0625) Population 0.210 0.0631 0.810*** 0.761* 0.439* 0.250 (0.158) (0.330) (0.209) (0.422) (0.244) (0.432)

L.Trade Openness NOR -0.282** -0.0740 -0.0101 0.0635 -0.149 0.0262

(0.138) (0.0735) (0.149) (0.0489) (0.175) (0.0667) L.Unemployment NOR -0.176 -0.00303 -0.0449 0.0190 -0.0600 0.0920 (0.435) (0.226) (0.422) (0.208) (0.473) (0.261) L.Education NOR 1.983*** 1.576*** 1.449*** 1.499** 1.415** 1.191** (0.559) (0.511) (0.544) (0.592) (0.604) (0.498) L.GDP Growth NOR 0.0755 -0.0232 -0.120 -0.129* 0.0236 -0.0716 (0.163) (0.0715) (0.170) (0.0663) (0.197) (0.0969) Population NOR 2.717 19.53*** 20.00 23.58*** 5.598 19.34*** (11.96) (5.031) (12.55) (4.764) (14.61) (7.286) Constant -30.62 -301.1*** -315.9 -391.6*** -76.40 -294.3** (188.4) (78.75) (198.8) (77.99) (231.8) (117.1) Observations 225 225 181 181 150 150 Number of idcountry 32 32 23 23 19 19 R-squared (overall) 0.483 0.452 0.456 0.400 0.535 0.527 R-Squared (between) - 0,483 - 0,357 - 0,569 R-Squared (within) - 0,439 - 0,456 - 0,433 Adjusted R-Squared 0.453 - 0.418 - 0.495 -F-Statistics 16,48 - 11,76 - 13,15

-Standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1

Notes:

a) The dependent variable is FDI inflows to Norway. Time period is 1996-2013

Total Sample OECD Europe

(23)

23

6.1 Results from regression 1

Overall the results are similar between the OLS and R.E model.I therefore comment on the results as a whole. I have conducted a t-test to check if the size of the regression coefficients can be compared across the macroeconomic sub-regions20. The result of the test for the OLS model confirms that the OECD and European sub-samples are significantly different from the total sample. I assume this result applies for the R.E model as well. This justifies comparing the magnitude of the effects across the sections.

As expected, geographical distance between home and host countries has a significant and negative effect on FDI for all the estimations. The effect is significant and constantly negative for all model specifications. For the total sample a 1% increase in the geographical distance between host and home country is estimated to decrease FDI inflow to Norway. This confirms the

previous findings by Grosse and Trevino (1996) and Coval and Moskowitz (1999), who stated that geographical distance is negatively correlated with FDI inflows. The results show that also

cultural distance has a significant and negative effect on FDI inflows to Norway. The result is

significant at a 1% level for the total sample and the OECD sub- sample. This implies that if cultural distance between host and home country increase less FDI will be conducted from the partner country. The coefficient of culture distance has the same sign and magnitude in all the significant models. This confirms the research done by Loree and Guisinger (1995) and Grosse and Trevino (1996). However, it counters the findings of Benito and Gripsrud (1992) who found that cultural distance had no significant effect on FDI. Cultural distance is not significant for the Europe sub-sample. It can be assumed that cultural distance is lower between the countries in this region, and that marginal increases do not have an effect on FDI.

The effect of geographical distance is stronger than the effect of cultural distance. This result is in line with the results obtained in Grosse and Trevino (1996). In sum, these findings make me confirm hypotheses H1a and H2. It implies that, as expected, countries that are culturally and

geographically distant from to Norway invest less. As such, it seems as though similarity and proximity is an important factor for countries investments to Norway.

20

(24)

24 Hypothesis H1b state that the negative effect of geographical distance is stronger for the total

country sample than for the Europe sub-sample. This implies that the effect of geographical distance is not linear. In this study I find that the effect of geographical distance has a stronger effect when it is restricted to countries from Europe, compared to the total sample. This implies that an increase in distance within Europe has a stronger negative effect on FDI than for the total sample of countries. This finding contradicts the hypothesized relationship. Hypotheses H1b is

(25)

25

Regression Table 2)

(1) (2) (3) (4) (5) (6) (7) (8)

VARIABLES F.E R.E F.E R.E F.E R.E F.E R.E

Distance Political Stabilityb -0.403 -1.156** - - -

-(0.946) (0.566)

Distance Regulatory Qualityb - - 1.011 -0.873 - - -

-(0.711) (0.545)

Distance Control of Corruptionb - - - - 0.126 -1.538*** -

-(0.522) (0.371)

Distance Rule of Lawb - - - -2.639** -2.510***

(1.139) (0.384) Geographical Distance - -0.764** - -0.844** - -0.808*** - -0.601** (0.366) (0.392) (0.279) (0.264) Culture Distance - -0.665*** - -0.604*** - -0.394*** - -0.273* (0.193) (0.197) (0.142) (0.155) Trade Openness 0.0175 0.00282 0.0214 0.000230 0.0161 0.00103 0.0139 0.00159 (0.0152) (0.00449) (0.0136) (0.00410) (0.0151) (0.00364) (0.0147) (0.00404) Unemployment -0.116 -0.121** -0.124 -0.130** -0.121 -0.102** -0.105 -0.0946* (0.0894) (0.0602) (0.0834) (0.0637) (0.0941) (0.0499) (0.0918) (0.0547) School entrollment 0.126* 0.0736** 0.148** 0.0548 0.129* 0.0384 0.113* 0.0510 (0.0632) (0.0372) (0.0669) (0.0372) (0.0671) (0.0383) (0.0621) (0.0372) GDP growth -0.00122 -0.0511 0.00415 -0.0486 -0.00471 -0.0381 0.0110 -0.0188 (0.0539) (0.0521) (0.0514) (0.0511) (0.0517) (0.0498) (0.0494) (0.0474) Population -3.677 0.483 -0.584 0.256 -3.088 0.524** -4.937 0.581*** (8.700) (0.379) (9.044) (0.332) (8.773) (0.254) (7.849) (0.217) L.Trade Openness NOR -0.203* -0.0316 -0.148 -0.360 -0.215** -0.0311 -0.0881 0.0404 (0.111) (0.104) (0.0935) (0.239) (0.102) (0.0940) (0.105) (0.0962) L.Unemployment NOR -1.318** -0.000579 -1.097* -0.00257 -1.290** 0.114 -1.040* -0.00292 (0.608) (0.258) (0.577) (0.256) (0.607) (0.266) (0.585) (0.246) L.Education NOR 1.318 -2.153 0 30.89 2.598*** -1.133** -6.662 -7.211*** (2.550) (1.541) (0) (21.70) (0.854) (0.551) (4.079) (1.181) L.GDP Growth NOR 0.315 -0.0427 0.262 -0.0676 0.304 -0.143 0.346* 0.0249 (0.218) (0.0756) (0.218) (0.0742) (0.216) (0.0733) (0.192) (0.0685) Population NOR - 15.32*** - 50.85** - 20.86*** - 9.960* (5.154) (24.97) (5.852) (5.812)

Years yes yes yes yes

Constant 58.29 -205.2** 14.60 -1,140* 32.46 -300.6*** 174.9 -64.60 (161.1) (82.24) (157.8) (638.0) (150.6) (92.63) (142.5) (97.72) Observations 198 198 197 197 198 198 198 198 Number of idcountry 31 31 31 31 31 31 31 31 R-Squared (overall) 0,006 0.460 0,003 0.444 0,002 0.623 0,045 0.641 R-Squared (between) 0,012 0,495 0,027 0,509 0,006 0,708 0,069 0,733 R-Squared (within) 0,255 0,218 0,252 0,191 0,254 0,186 0,269 0,236

Robust standard errors in parentheses, *** p<0.01, ** p<0.05, * p<0.1 Notes:

a) The dependent variable is FDI inflows to Norway. Time period is 1996-2013 b) Political distance is measured in absolute values.

c) Hausman test p-value is 0,000. F.E model most robust.

Political Stability Regulatory Quality Control of Corruption

(26)

26

6.2 Results for regression 2

The four political distance variables are a measure of how different a partner country is from Norway. Increased political distance means that the difference in institutional quality between host and home country increase.

Three out of four of the political distance variables are significant and negative. The effect of distance in rule of law and control of corruption is significant at a 1% level for. The effect of distance in political stability is significant at a 5% level. “Rule of law” measures most

importantly the quality of contract enforcement and property rights. Increased difference between Norway and a partner country in the rule of law index is estimated to decrease FDI inflow to Norway. This indicates that if distance in rule of law between host and home country increase, the partner country will invest less in Norway. The result for the rule of law indicator is the most robust, as the effect is significant and has the same magnitude in both the F.E and the R.E model. This confirms that rule of law is an important determinant of FDI inflows. This follows the findings by Bènassy- Quéré et al. (2005), who also find that rule of law has in important and significant effect on FDI. However, the results found here does not imply that high quality rule of law attracts FDI. “Control of corruption” measures the extent to which public power is exercised for private gain. The effect of control of corruption is negative and significant on a 1 % level. An increased difference between host and home country level of corruption is estimated to decrease FDI inflow to the host country. However, this result is only found in the R.E model, and the estimate is very low, considering the standard error. This implies that the larger the differences in corruption, the less a country invests in Norway. Distance in “Political stability” measure the likelihood that the government will be destabilized by unconstitutional or violent means. The effect of political stability is significant at a 5% level. Distance again has a negative sign. This confirms that increased distance between host and home country’s political stability level decreases the inflows of FDI to the host country.

(27)

27 political stability did have a significant effect on FDI flows. On the other hand, the measurement method used in Grosse and Trevino’s (1996) research did not measure the distance from host and home country, which can explain the different results.

Three out of four of the political distance indicators have a significant effect on FDI as expected. However the effect is negative and not positive. Hypotheses H2a state that increased political

distance will increase FDI inflows to Norway. At this point, I conclude that a large political distance between home and host country discourages investment flows to the host country – it does not encourage it. H2a is rejected as there is no indication that political distance increase FDI

inflows.

6.3 Results regression 3

Hypotheses H3b state that the effect of the political indicators is caused by the countries that have

a lower score than Norway on the World Governance Index. This followed the reasoning by Shapiro and Globerman (2003). They expected that FDI is attracted to countries with a high quality of governance. The question is if the negative effect on FDI found in regression 2, is caused by the countries with a lower institutional quality than Norway.

Overall the results obtained in regression 2 and 3 are very similar. The effect is negative, and has a comparable magnitude. This implies that the countries with a lower institutional quality than Norway are driving the effects seen in regression 2. For control of corruption and rule of law, the magnitude of the effect is very close to that in regression 2. For these to indicators, it is implied that the effect of political distance is caused by the countries with lower institutional quality than Norway. The effect of political stability is also similar, however the negative effect is stronger when only the lower institutional quality countries are included. For these three indicators hypothesis H3b is confirmed. This suggests that countries that have a lower institutional quality

than Norway invest less than the countries with a institutional quality to Norway. It is, however important to note that even though the effect is caused by the lower institutional quality countries as expected, the effect is in the opposite direction.

There is one difference to report between the two models. Regulatory quality is strongly

(28)

28 means that for the countries with lower institutional quality, regulatory quality has a negative effect on investments.

6.4 Role of control variables

For the partner countries most of the control variables are consistent in the regressions. The unemployment level is consistently significant and negative. This is expected as increased unemployment is normally linked to lower investments. School enrollment is significant and positive for the majority of the model specifications. Population size and degree of trade openness have proved to be important as a determinant of FDI in previous studies (Shatz & Venables, 1999). In this study, these controls are mainly not significant. The reason for a different result here is uncertain, but it can be linked to the composition of the country sample. For the host country, the education level and population size is constant and significantly positive throughout most of the regressions. Unemployment is negative as expected, and is significant in regression 2, but not in regression 1. GDP growth also has an inconsistent trend over the

regressions, both for the host and home countries.

6.5 Robustness checks

(29)

29 I have also tested if the results are sensitive to changes in the country sample. This has been tested by performing two restricted regressions. First, the US is excluded from the total country sample. The US is excluded because it is the largest investing country in the sample21.

Furthermore, I add a year dummy in the regression and restrict the sample to only include the years after 2003. I choose to separate the timeframe from 2004, following the trend showed in appendix 5. As FDI inflows increased significantly after 2004, I can test if the results correspond to that of the total time frame. If the results of the restricted regressions have similar results to the total sample, the results are not driven only by the effect of the US or only half the timeframe. The results from the robustness regression can be found in appendix 7.

Restricting the sample to only include the years after 2003 and excluding the US do not change the overall estimates. Compared to the regression for the total sample, the . Thus, the results obtained are not solely dependent on the influence of US investments or investments done before 2003. This increases confidence for the robustness for the entire country sample and timeframe.

7. CONCLUSION

Foreign direct investment (FDI) has increased substantially over the last decades and is linked to the rising global integration. MNEs generally engage in FDI because they can combine their ownership-specific advantages with location-specific advantages of the host country. The preference to internalize ownership and location advantages explains why firms that operate across borders prefer FDI to alternatives that do not include ownership control of the foreign activity. As explained through Hymer’s theory, there are also costs associated with doing

business abroad. These costs arise because firms might encounter different levels unfamiliarity in that foreign host country. The motive behind an MNE’s choice to invest in a foreign country depends on different factors. The political, cultural and geographic distance between home and host country are three aspects that affect this location decision.

The overall results show that the distance between host and home countries, whether measured in geographical, cultural or political terms is negative and significantly correlated with FDI into Norway. Countries that are culturally and politically different from Norway and/or

geographically distant tend to conduct less FDI to the host country. This supports previous

(30)

30 literature that the “gravity framework” is a sound explanation for FDI activity. The results for distance in “Rule of law”, geography and cultural distance are the most robust. The effects of these variables are negative and consistent in magnitude in all model specifications. The results for distance in political stability, control of corruption and regulatory quality, are also found significant. The outcome for regulatory quality is slightly more ambiguous as it shows a less consistent pattern.

The geographical distance has greater impact on the FDI location decision than the cultural distance. This suggests that MNEs investing in Norway find that the geographical distance is a larger barrier than the cultural differences. Furthermore, the role of geographical distance has a stronger effect for countries within Europe, than for the total country sample. This implies that the effect of geographical distance is not linear; and the effect has a stronger magnitude when distances are smaller.

The results can have policy implications for how the Norwegian government should work to attract more FDI. In 2013 Invest in Norway was established to facilitate communication between international firms that consider establishing in Norway22. “Invest in Norway is a function that act as the official Norwegian investment promotion agency” (Invest in Norway, 2015a). The policy implications from this thesis imply that creating an information channel such as Invest in

Norway is a step in the right direction.

Targeting countries that are similar to Norway might be of special interest as the results show that countries that it is countries similar to Norway that are most likely to invest. On the other hand, directing information specifically towards countries that are different from Norway can have a positive impact as well. Information might decrease the sense of unfamiliarity that potential investors associate with Norway. Targeted information about the Norwegian business culture might encourage new firms to enter a market that seems culturally distant. Information about the infrastructure in Norway and plans for potential infrastructural improvements should be informed of. Furthermore, investors might have an idea of Norway being located more in the periphery

22

(31)

(32)

32

Bibliography

Anderson, J. A., 2011. The Gravity Model. Annual Review of Economics, vol. 3, pp. 1-45.

Bavaud, F., 2002. The quasi-symmetric side of gravity modelling. Environment and Planning

Vol. 34, pp. 61-79.

Bénassy-Quéré, A., Coupet, M. & Mayer, T., 2005. Institutional Determinants of Foreign Direct Investment. The World Economy Vol. 30, No.5, p. 764–782.

Benito, G. R. & Gripsrud, G., 1992. The Expansion of Foreign Direct Investments: Discrete Rational Location Choices or a Cultural Learning Process?. Journal of International Business

Studies, Vol. 23, No. 3, pp. 461-476.

Blonigen, B. A., 2005. A Review of the Empirical Literature on FDI Determinants. Atlantic

Economic Journal Vol. 33, pp. 383-403.

Blonigen, B. A. & Wang, M., 2004. Inappropriate pooling of wealthy and poor countries in

empirical FDI studies. s.l.:NBER Working Paper Series.

Brada , J. C., Drabek, Z. & Perez, F. M., 2012. The effect of home country and host country corruption on foreign direct investment. Review of Development Economics Vol. 16, No. 4, pp. 640-663.

Brenton, P., DiMauro, F. & Lücke, M., 1998. Economic integration and FDI: an empirical

analysis of foreign direct investment in the EU and Central and Eastern Europe. Kiel Institute for

the World Economy (IfW): Kiel Working Papers, No. 890.

Busse, M. & Hefeker, C., 2005. Political Risk, Institutions and Foreign Direct Investment.

European Journal of Political Economy, Vol. 23, iss. 2, pp. 397-415.

Coval, J. D. & Moskowitz, T. J., 1999. Home Bias at Home: Local Equity Preference in Domestic Portfolios. Journal of Finance Vol. 54, iss. 6, LIV(6), pp. 2045-2073.

Davidson, W. H., 1980. The Location of Foreign Direct Investment Activity: Country

Characteristics and Experience Effects. Journal of International Business Studies, Vol. 11, No. 2, pp. 9-22.

Dunning, J. H., 1998. Location and the Multinational Enterprise: A Neglected Factor?. Journal of

International Business Studies, Vol. 91 No.1 , pp. 45-66.

(33)

33 Eden, L. & Miller, S. R., 2004. Distance matters: Liability of foreigness, institutional distance

and ownership strategies. Texas A&M University, College Station.: Bush School Working Paper

No. 404.

Egger, P. & Winner, H., 2005. Evidence on corruption as an incentive for foreign direct investment. European Journal of Political Economy Vol. 21, p. 932 – 952.

Ghosh, M. & Wang, W., 2009. Does FDI Accelerate Economic Growth? The OECD Experience Based on Panel Data Estimates for the Period 1980-2004. Global Economy Journal, Vol. 9 Iss. 4,

Art 1.

Globerman, S. & Shapiro, D. M., 2003. Governance Infrastructure and US Foreign Direct Investment. Journal of International Business Studies, Vol. 34, No. 1 , pp. 19-39.

Grosse, R. & Trevino, L. J., 1996. Foreign direct invesment in the United States: An analysis by country of origin. Journal of International Business Studies, pp. 193-155.

Guerin, S. S., 2006. The Role of Geography in Fiancial and Economic Integration: A

Comparative Analysis of Foreign Direct Investment, Trade and Portfolio Investment Flows. The

World Economy Vol. 29, No. 2, pp. 189-209.

Habib , M. & Zurawicki, L., 2002. Corruption and Foreign Direct Investment. Journal of

International Business Studies Vol. 33, No. 2, pp. 291-307.

Helpman, E., 2011. Understanding Global Trade. Cambridge and London: Harvard University Press, Belknap Press.

Heuchemer, S., Kleimeier, S. & Sander, H., 2009. The geography of European cross-border

banking: The impact of cultural and political factors. s.l.:Maastricht research school of

Economics of Technology and Organizations Discussion Paper RM/08/008.

Hill, R. C., Griffiths, W. E. & Lim, G. C., 2012. Principles of Econometrics. 4th Edition ed. s.l.:John Wiley & Sons Ltd.

Hofstede, G., 1994. The Business of International Business is Culture. International Business

Review Vol. 3, No. 1, pp. 1-14.

Hofstede, G., 2015. Homepage of Geert Hofstede. [Online] Available at: www. geert-hofstede.com

Invest in Norway, 2015a. invinor.no. [Online] Available at: http://www.invinor.no/

Invest in Norway, 2015b. invinor.no. [Online]

(34)

34 Kaufmann, D., Kraay, A. & Mastruzzi, M., 2010. The Worldwide Governance Indicators:

Methodology and Analytical Issues. The World Bank: Policy Research Working Paper Series:

5430.

Khachoo, A. Q. & Khan, M. I., 2012. Determinants of FDI inflows to developing countries: a

panel data analysis. Munich Personal RePEc Archive: MPRA Paper No. 37278.

Kirkman, B. L., Lowe, K. B. & Gibson, C. B., 2006. A Quarter Century of "Culture's Consequanses": A Review of Empirical Research Incorporating Hofstede's Cultural Values Framework. Journal of International Business Studies, Vol. 37, No. 3, pp. 285-320.

Kogut, B. & Singh, H., 1988. The Effect of National Culture on the Choice of Entry Mode.

Journal of International Business Studies, pp. 411- 432.

Kvinge, T. & Narula, R., 2001. FDI in Norway's manufacturing sector. Norwegain Research Council (NFR): TIK Working Paper No. 9.

Loree, D. W. & Guisinger, S. E., 1995. Policy and Non-Policy Determinants of U.S Equity Foreign Direct Investment. Journal of International Business Studies, Vol. 26, No. 2, pp. 281-299.

Lucas, R. E., 1993. On the Determinants of Direct Foreign Investment: Evidence from East and Southeast Asia. World Development, Vol. 21, No. 3, pp. 391-406.

Mayer, T. & Zignago, S., 2011. Notes on CEPII’s distances measures: The GeoDist database. CEPII: Working Paper No.25.

Ministry of Trade and Industry, 2012. government.no. [Online] Available at:

https://www.regjeringen.no/contentassets/e574980451b94330b833dc7da563f4f9/no/pdfs/stm201 120120022000dddpdfs.pdf

Neumayer, E. & Spess, L., 2005. Do Bilateral Investment Treaties Increase Foreign Direct Investment to Developing Countries?. World Development, Vol. 33, No. 10, pp. 1567-1585.

Nunnenkamp, P. & Spatz, J., 2003. Foreign direct investment and economic growth in

developing countries: How relevant are host-country and industry characteristics?. Institut fur

Weltwirtschaft, Kiel: Kieler Arbeitspapiere, No. 1176.

OECD, 2008. OECD Benchmark Definition of Foreign Direct Investment, s.l.: Organization for Economic Co-operation and Development.

OECD, 2013. oecd-ilibrary.org. [Online]

Available at:

(35)

35 OECD, 2014. data.oecd.org. [Online]

Available at: https://data.oecd.org/fdi/fdi-stocks.htm

Scarpetta, S. & Bassanini, A., 2002. Does human capital matter for growth in OECD countries? A Pooled mean-group approach. Economics Letters Vol. 74, pp. 399-405.

Shatz, H. J. & Venables, A. J., 1999. The Geography of International Investment. The World Bank: Policy Research Working Paper Series: 2338.

Shenkar, O., 2001. Cultural Distance Revisited: Towards a More Rigorous Conceptualization and Measurement of Cultural Differences. Journal of International Business Studies, Vol. 32, No. 3, pp. 519-535.

Slemrod, J., 1990. Taxation in the Global Economy. In: Tax Effects on Foreign Direct Investment

in the United States: Evidence from a Cross-Country Comparison. University of Chicago Press:

National Bureau of Economic Research, pp. 79-122.

Statistics Norway, 2013. Statistics Norway. [Online]

Available at: https://www.ssb.no/en/utenriksokonomi/statistikker/di/aar/2013-01-16

Statistics Norway, 2015. ssb.no. [Online]

Available at: https://www.ssb.no/en/utenriksokonomi/statistikker/di

UNCTAD, 2015. unctad.org. [Online] Available at:

http://unctad.org/en/Pages/DIAE/Investment%20and%20Enterprise/FDI_Flows.aspx

Wei, S.-J. & Shleifer , A., 2000. Local Corruption and Global Capital Flows. Brookings Papers

on Economic Activity, Vol. 2000, No. 2, pp. 303-354.

Wei, Y. & Lui, X., 2001. Foreign Direct Investment in China: Determinants and Impact. Cheltenham, U.K. and Northampton, Mass.: Elgar; distributed by American International Distribution Corporation, Williston, Vt..

World Bank Governance Indicators, 2014. Country Data Report for Norway, 1996-2013. [Online]

Available at: http://info.worldbank.org/governance/wgi/index.aspx#countryReports

World Bank Governance Indicators, 2014. info.worldbank.org. [Online]

Available at: http://info.worldbank.org/governance/wgi/index.aspx#countryReports

World Bank, 2015. data.worldbank.org. [Online]

(36)

36

Appendix

Appendix1) Country list and Cultural Distance Index

Source: Authors calculations based on Hofstede’s culure index

Number Country Cultural Distance Index OECD membership

1 Netherlands 0,13 Yes

2 Sweden 0,25 Yes

3 Finland 0,33 Yes

4 Denmark 0,56 Yes

5 Czech Republic 1,50 Yes

6 Canada 1,67 Yes 7 Luxemburg 1,74 Yes 8 Spain 2,21 Yes 9 France 2,50 Yes 10 Australia 2,53 Yes 11 Thailand 2,57 No 12 South Africa 2,65 No

13 Unites States 2,68 Yes

14 Germany 2,86 Yes

15 Ireland 3,04 Yes

16 United Kingdom 3,07 Yes

The Effect of Geographical, Cultural and Political Distance on Foreign Direct Investment: