UNIVERSITAT SIEGEN

WIRTSCHAFTSWISSENSCHAFTEN, WIRTSCHAFTSINFORMATIK UND

WIRTSCHAFTSRECHT FAKULTAT

DEPARTMENT VOLKSWIRTSCHAFTSLEHRE

MASTERARBEIT

Name: Zhang Yi

Matr.-Nr.: 1117941

Thema: The Determinants of Foreign Direct Investment:

A study based on country-level panel data

Betreuer: Univ.-Prof. Dr. Carsten Hefeker

Siegen, 21. Januar 2016

Versicherung uber die selbststandige Anfertigung

Ich versichere, dass ich die schriftliche Ausarbeitung selbstandig angefertigt und keine

anderen als die angegebenen Hilfsmittel benutzt habe. Alle Stellen, die dem Wortlaut

oder dem Sinn nach (inkl. Ubersetzungen) anderen Werken entnommen sind, habe ich

in jedem einzelnen Fall unter genauer Angabe der Quelle (einschließlich des World

Wide Web sowie anderer elektronischer Datensammlungen) deutlich als Entlehnung

kenntlich gemacht. Dies gilt auch fur angefugte Zeichnungen, bildliche Darstellungen,

Skizzen und dergleichen. Ich nehme zur Kenntnis, dass die nachgewiesene

Unterlassung der Herkunftsangabe als versuchte Tauschung gewertet wird.

Ort, Datum Name

1

Acknowledgements

I would like to express my gratitude to all those who helped me during the writing of

this thesis. I gratefully acknowledge the help of my supervisor, Univ.-Prof. Dr. Carsten

Hefeker, who has offered me valuable suggestions in the academic studies. In the

preparation of the thesis, he has spent much time reading through each draft and

provided me with inspiring advice. Without his patient instruction, insightful criticism and

expert guidance, the completion of this thesis would not have been possible.

I also owe a special debt of gratitude to all the professors in Department of

economics, from whose devoted teaching and enlightening lectures I have benefited a

lot and academically prepared for the thesis.

Last my thanks would go to my beloved family for their loving considerations and great

confidence in me. I also owe my sincere gratitude to my boyfriend, who gave me his help

and time in helping me workout my problems during the difficult course of the thesis.

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Contents

1 Introduction 6

2 Literature review 8

2.1 Scientific research level . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8

2.2 Infrastructure . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9

2.3 Trade protection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10

2.4 Trade effects . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11

2.5 Domestic market . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13

2.6 Labor market . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13

2.7 Institution . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14

2.8 Exchange rate . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15

2.9 Portfolio Equity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17

3 Description of the Variables and the Data 18

3.1 Description of the Variables . . . . . . . . . . . . . . . . . . . . . . . . . . . 18

3.1.1 Description of dependent variables . . . . . . . . . . . . . . . . . . . 18

3.1.2 Description of independent variables . . . . . . . . . . . . . . . . . . 19

3.2 Description of the data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23

3.2.1 Statistics Database . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23

3.2.2 Countries . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24

3.2.3 Summary statistics for the full sample . . . . . . . . . . . . . . . . . 24

3.3 Data preparation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24

3.3.1 Correlation between independent variables . . . . . . . . . . . . . . 24

3.3.2 Regional dummy variables . . . . . . . . . . . . . . . . . . . . . . . 25

4 Empirical Results 26

5 Conclusion 37

References 39

A Appendix Code 43

B Appendix Tables 52

3

List of Symbols

FDI Foreign direct investment

PHONE Fixed telephone subscriptions

GDP Gross Domestic Production

LF Labor force

GDPc GDP per capita

GDPg GDP growth rate

RD Research and Development intensity

PGN Number of patents grants

TAR Tariff rate

IM Total Import amount

TRADE Trade share in GDP

LQ Labor quality

LC Labor cost

GEC Government expenditure consumption

PS Political stability

Ex Exchange rate

ExD Exchange rate deviation

PEI Portfolio equity inflow

IO Membership in international organization

4

Abstract

Foreign Direct Investment has become one of the major challenges for the coun-

tries who participated in international economy. Since there are several potential

determinants could affect the behavior of Foreign Direct Investment (FDI) in one host

country. This paper explores all of them and analyze how these variables affect FDI

distribution in countries across the world by using OLS statistics estimation. The re-

sults indicate that the variation in FDI is determined by complicated reasons in one

country. In addition, where the host countries located also has impact on FDI behav-

ior by adding regional dummy variables into the regression model.

Keywords: Foreign Direct Investment, Regression, Ordinary Least Square, Re-

gional dummy variables

5

1 Introduction

There was a dramatic increases of Foreign Direct Investment (FDI) happened dur-

ing the last few decades. FDI has grown at a relatively faster rate and became into

one of the most popular and major international transactions among multinational enter-

prises (MNE). At the same time, the growth of MNE activities interested more and more

economists to devote themselves into investigating the factors that drive FDI behavior.

FIGURE 1: FDI INWARD FLOWS, ANNUAL, 1996-2013, US DOLLAR IN MILLIONS

The above figure.1 show us the annual FDI inwards flows during the period from 1996

to 2013 in Germany, China and the United States. From the figure, it is obvious to find

out that there is a dramatic increases in FDI inflows happened after 2000 in Germany

and the United States. That means the 21th century is the beginning of the development

of FDI. The entire amount of inward FDI in the United States is larger than China as well

as Germany. Besides that, FDI inflows steadily increase in the United States from 2002

to 2008, but dramatically decrease after 2008. So, the economic crisis may influence on

the FDI behavior. Although the entire amount of inward FDI in China is not that much,

it is forging up on its own. In addition, there is only one peak development of inward

FDI in Germany at about 2000, but the FDI inflows decreased dramatically after that

and keep always oscillations since then. From the three countries’ cases, it appears that

there are several determinants may affect FDI behavior because of the different situations

happened in different countries.

6

One major objective of this paper is to find the determinants of FDI and how these

factors affect FDI behavior. Specially, I use cross-sectional data on 57 countries to answer

the following questions: (a) What factors cause the variation in FDI? (b) How and why

these factors affect FDI? (c) is there any differences of FDI distributions between countries

from different regions?

Regarding questions (a), there are several former literatures have worked on finding

out the possible determinants of FDI, they help me decide the potential factors which will

be analyzed later. Such as in Blonigen (2005), who surveyed literatures that empirically

examine the FDI decisions of multinational enterprises and finally resulted in the aggre-

gate location of FDI across the world. In addition, some previous literatures paid much

attention on the FDI distributions across countries all over the world or countries i some

certain organizations; Others turned to cities or provinces which are located in one single

countries. For example, as follows:

Lucas (1993) have analyzed the determinants of FDI to countries which are located in

east and south east of Asia. While, Asiedu (2002) concentrated on the determinants of

FDI to developing countries in Sub-Saharan Africa. Jadhav (2012) explored the role of

some factors in attracting FDI in BRICS 1 countries. While, Boubacar (2015) worked on

the determinants of the U.S. FDI in OECD 2 countries. Sun, Tong and Yu (2002) as well

as Liang (2015) paid much attention on the FDI behavior based on province-level and

city-level panel data in China

In order to deal with the question (b), I am going to apply ordinary least square esti-

mations to examine the correlations between each explanatory variables with FDI. Since

there are too many potential explanatory variables for FDI, the regression estimation has

to be divided into several models so as to figure out the specific effects of each factor on

FDI. Furthermore, because of the large sample, which contains many countries across

the world, I have to include the regional dummy variables into the regression model which

is aim at figuring out the different FDI behavior in different continent, hence solving the

question (c).1BRICS countries include Brazil, Russia, India and China2OECD refers to Organisation for Economic Co-operation and Development

7

The results can be summarized as follows: (i) infrastructure quality, domestic market

demand, domestic labor productivity and openness level have positive impacts on FDI

inflows to host countries; (ii) expenditure on research and development, total government

expenditure, depreciation of host countries currencies and the exchange rate volatility

have negative impacts on FDI inflows to host countries; (iii) where the host countries

located do has impact on FDI distribution by adding regional dummy variables into the

regression model. Countries of Africa and America have the relatively more advantage

of attracting FDI. On the contrary, countries which are located in Asia and Europe do not

attract that much FDI.

The remainder of the paper is organized as follows: Section 2 reviews the previous

literatures on the determinants of Foreign Direct Investment. Section 3 describes all po-

tential explanatory variables and data. Section 4 presents and discusses the empirical

results. Finally, section 5 concludes.

2 Literature review

There is an increasing number of literatures have paid much attention on the determi-

nants of FDI. The table.4, which is reported in the appendix B, presents the attitudes of

different authors about the effects of several variables on FDI. Obviously, the results of

different authors are conflicting.

The main purpose of this part is to review some former literatures and sum up all

possible determinants of FDI. Since there are several studies have worked on this field,

so I classify all possible determinants into nine categories and discuss each of them

separately as follows.

2.1 Scientific research level

RD intensity (the share of research and development expenditures in GDP) is used as

an important proxy for the presence of technology-related intangible assets.

Lots of literatures suggested that RD intensity plays a positive role in multi-nationality,

thus higher RD intensity will attract more FDI inflows. As what have been proved in

8

Morck and Yeung (1992), they found that only when firms processing intangible assets,

the foreign acquisition will increase the value of firms because of the positive correlation

between RD spending with abnormal returns. Abnormal returns increase with manage-

ment ownership. Firms whose manager have significant power, in which the managers

are more prefer to give high priority to increasing firm value. That will lead to the enhanc-

ing value of foreign acquisition.

However, there are also several authors hold the different idea with Morck and Yeung

(1992), they think RD intensity may be the proxy for other factors, which may not influence

on FDI inflows significantly. For example, Kogut and Chang (1991) analyzed Japanese

entries into the U.S. industries, they provided the evidence that there is no relative tech-

nological advantage for Japanese direct investment in industries inside the United States

with high RD expenditure. In addition, Blonigen (1997) also analyzed the same FDI

process as what Kogut and Chang (1991) have done. He made the regression model

in higher RD manufacturing industries as well as in non-manufacturing industries, then

found out that Japanese acquisition FDI in the U.S. industries is not necessarily involved

with their own firm-specific assets, namely the RD intensity.

Number of patents, which is same with RD intensity, is anther proxy for the level

of scientific research. From Sun, Tong and Yu (2002), they expected that higher level

of scientific research attract more inward FDI. Unfortunately, they finally excluded this

variable because of its relatively high correlation with other explanatory variables.

2.2 Infrastructure

Infrastructure quality (fixed telephone subscriptions per 100 people) refers to con-

centration of economic activities. Well infrastructure quality leads to positive externalities

and increasing scale of economies. Therefore, all these clues indicate that infrastructure

quality plays a significant role in FDI distribution.

Sun, Tong and Yu (2002) also analyzed the influence of infrastructure quality on FDI

distribution across China. The result of their study came to the conclusion that the im-

portance of the FDI determinants changes through time. However, infrastructure quality

always be proved to have positive relationship with FDI inflows. Meanwhile, Cheng and

Kwan (2000) also proved that good infrastructure quality positively related with inward

FDI by using the Chinese experience as well.

9

At the same time, Asiedu (2002) worked on the determinants of FDI to developing

countries, especially to Sub-saharan Africa countries. He has figured out that the reason

why Sub-saharan African countries unsuccessfully attract more FDI inflows. Regards to

infrastructure quality, the results indicated that infrastructure quality has no effect on FDI

to Sub-saharan Africa countries. However, it is indeed an essential factor which could

promote inward FDI to non Sub-saharan Africa countries.

In addition, an early study from Wheeler and Mody (1992) analyzed the investment

location decisions internationally as well, and proved that the U.S. investors paid much

attention on the infrastructure level and prefer to investing in countries with good infras-

tructure quality, especially for developing countries.

2.3 Trade protection

The relationship between trade protection and FDI inflows is not hard to expect. It is

obvious that higher trade protection should make investors more likely to invest in the

substitutions of export in order to avoiding the high costs of trade protection. So the

result of it leads to tariff-jumping FDI. However, evidences from several authors show that

it is hard to reach a clear result about the relationship between trade protection and FDI

inflows.

Anti-dumping policy is a main part of trade protection activities by the host coun-

tries, it is measured by firm-specific anti-dumping duties. Belderbos (1997) compared the

anti-dumping as well as other trade policies in the European Union versus in the United

States, and how these policies affect the manufacturing investments from Japanese elec-

tronic firms. The empirical results indicated that there is a positive correlation between

anti-dumping policies with FDI from Japanese investors. Especially, the EU anti-dumping

activities contribute more incentives to tariff-jumping FDI than the US. Furthermore, Bloni-

gen (2000) concentrated on the U.S. anti-dumping activities. He confirmed the state-

ment that there is a positive relationship between anti-dumping and inward FDI. However,

his results suggested that this kind of relationship only happened for multinational firms

based in industrialized countries. His statement also explained that the reason why de-

veloping countries are more concerned about applying to anti-dumping protection in the

World Trade Organization than countries with relatively advanced industrialism.

10

Due to the difficulty to quantify non-tariff forms of protection, authors always turn to

using industry-level measures. Grubert and Mutti (1991) used the weighted average tariff

rate on manufactures to measure the trade protection level of host countries and analyzed

the multinational activities of the U.S. investors, the empirical results of them shown that

tariffs has a strong impact on the U.S. multinational corporation activities. Tariffs influence

on the distribution of real capita, so the real investment from the U.S. firms changes so

as to responds to the changes of effective tariff rate of the host country. On the other

hand, Kogut and Chang (1996) analyzed the investment from Japanese electronic firms

in the United States. He proved that the initial entry is extremely important for investors

from home countries, because that it has a robust effect on the subsequent entry of the

further investments. In Blonigen (1997), he also worked on how the correlation between

the U.S. trade protection with the Japanese acquisition FDI. But all his empirical results

failed to prove the positive correlation between them significantly.

2.4 Trade effects

As it has been mentioned in the last section, FDI acts as a substitute for export to host

countries. The export (import of the host country) may not only have substitution effect

on FDI inflows to the host countries. It may also affect the openness level of the host

country positively.

For example, Lipsey and Weiss (1981) compared how the U.S. manufacturing invest-

ments abroad influence on the U.S. export to less-developed countries and on the export

from other foreign countries to those countries. They came to the conclusion that the

U.S. manufacturing investments are only positively related with its own export to the host

countries, but negatively related with the export to host countries by other foreign coun-

tries, and vice versa. In addition, in the later article of them [Lipsey and Weiss (1984)],

they continually analyzed and proved that the positive relationship between the output of

the U.S. firms with the firm’s export of not only intermediate goods but also final goods to

the host countries.

Openness (the sum of exports and imports of goods and services measured as a

share of GDP) is a proxy for the level of that how much a host country engage in interna-

tional events. It plays a critical role in the behavior of inward FDI in that country.

11

It is obvious to image that if a host country is more open than others, it may also

become more attractive for foreign investments. Openness is not only benefit to more

international trade but also benefit to any kinds of multinational activities.

Several authors have reached to a same statement that openness positively related

with FDI inflows. For example in Hausmann and Fernndez-Arias (2000). At the same

time, Gastanaga et al. (1998) clarified that the overall index (openness) has a positive

effect on inward FDI significantly by using the data from less-developed countries.

Membership (whether or not a country is a member of international organizations in

a given year) also may have influence on FDI. Dreher, Mikosch and Voigt (2010) con-

structed the binary dummy variable which indicate that for every individual organization

whether or not a country is a member, and then added the dummy variables to all orga-

nizations. So, they got the country-specific membership index. All these organizations3

share the equal weight, and the final indicator is normalized to a range from zero to one.

Results of his regression model shown that membership in international organizations

has significantly positive effect on inward FDI. That is because of the direct impact of

membership is to restrict a country from pursuing policies that damage the profit of for-

eign investors. For example, it could reduce political risk or multinational cooperation

costs.

In the later literature of them, Dreher, Mikosch and Voigt (2015) continually analyzed

this field and paid much attention on the membership in investment-related international

organizations. They confirmed their former statement. Besides that, they also found fur-

ther purposes of joining in an international organization, such as the desire to internalizing

border-crossing externalities and encouraging cooperation.

There is also an another literature from Buthe and Milner (2008), which was aim at

the effects of international agreements on the FDI distribution, especially in developing

countries. They discussed the international agreements (WTO and GATT) as well as

the preferential agreements (PTAs). The empirical results of them indicated that these

trade agreements could change the attitudes of foreign investors about their treatment of3The international organizations they used in their research, such as: (1) the General Agreement on Tar-

iffs and Trade and its successor, the World Trade Organization, (2) the International Finance Corporation, (3)the International Center for the Settlement of Investment Disputes, (4) the Multilateral Investment GuaranteeAgency, (5) the World Intellectual Property Organization and (6) the New York Convention.

12

assets. Hence, the foreign investments might increase because of more foreign investors

are interested in.

2.5 Domestic market

The domestic market of host countries is also an important factor on attracting inward

FDI. Authors of several literatures used the current GDP as a proxy for domestic market

size, the real GDP per captia and GDP growth rate as proxies for domestic market

demand. Evidences show that all of them are positively related with FDI inflows.[Asiedu

(2002)]

For example, in the early article of Kravis and Lipsey (1982), they drew the conclusion

that larger domestic market could attract more foreign production. Because that there are

economies of scale in production in these industries, that lead to more cheaper output

by However, once the domestic market has reached a certain level, the further increases

would not contribute to the increases of foreign investments as much as before. That

because economies of scale began to decline with the result that the peak rate of increase

in the share of exports came at a market size below the largest.

On the other hand, Blomstrom and Lipsey (1991) continually analyzed whether the

firm size could affect a firm’s multinational behavior by comparing the U.S. and Swedish

cases. The result shows that firm size is the prerequisite for firms when they are going to

engaging in foreign investment.

Inflation (the annual growth rate of the GDP implicit deflator) shows the price level

change in one host country. Only a few author clarified that the inflation rate has influence

on FDI inflows. For example in Asiedu (2002), he considered the inflation rate as the

proxy for the stability of the entire economy and made the regression model based on

Sub-Saharan African data. He expected there is a negative correlation between inflation

rate with FDI inflows. However, his results finally did not prove his expectation, hence

there is no significant correlation between inflation rate and FDI inflows.

2.6 Labor market

The domestic labor market of the host country consisted of three parts: labor force,

labor quality (the ratio of Labor force with tertiary education to total) and labor cost (the

13

adjusted labor share in GDP).

Firstly, it is obvious that more labor force of the host countries, which leads to more

productivity, positively effect on inward FDI. However, things are going to be more com-

plicated when taking into account of the analysis of labor quality and labor cost.

The ratio of Labor force with tertiary education to total is not the only proxy to measure

the labor quality level. As the regression model in Braunerhjelm and Svensson (1996),

they used the number of research scientists, engineers and technicians per 1000 of the

population to measure the level of labor quality and made the statement that host coun-

tries with higher labor quality level could attract more FDI, especially for RD-intensive

companies.

In the same way, many author also used wage to measure the level of labor cost.

Branstetter and Feenstra (2002) used the wage premium (the wages paid by multina-

tionals minus that in urban collectives, divided by that in multinationals), their conclusion

suggested that there is a positive correlation between labor cost with inward FDI in China.

Because higher wage refer to higher labor quality as well as higher labor productivity.

Another article also analyzed the determinants on FDI in China and used the average

wage to measure the labor cost level.[Sun, Tong and Yu (2002)] The labor costs might

be one of the things to consider for foreign investor whether to entry in a country or not,

investors might prefer countries with lower labor costs because of the cost minimization.

So, they came to an opposite conclusion as what Branstetter and Feenstra (2002) did

before.

2.7 Institution

The quality of institutions is also an essential factor which effect on FDI behavior,

especially for less-developed countries. Because poor quality of institutions could not

only increases the risk and costs of doing multinational business but also indicates poor

infrastructure, which would further limit the participation in multinational activities.

There are several terms could be used as the proxies for institutions quality. For ex-

ample, in the early study of Schneider and Frey (1985),they used political instability and

14

the host governments ideology position (right or left wing) to made the political regres-

sion model of FDI. The results proved that political instability significantly reduce inward

FDI. While, whether the host government’s ideology is right-wing or left-wing did not show

significant influences on FDI distribution.

Furthermore, a paper from Wei (2000) also studied the effect of corruption of host

countries on FDI inflows. The author provided the statement that corruption plays a

significantly negative role in inward FDI. So did Wheeler and Mody (1992), who also

used corruption to measure the political instability, but they did not find such significant

evidence in their research as Wei do.

So did the statement in Asiedu (2002), he used two variables to measure the quality of

institution, one is political instability (the probability of a change of government) and the

other is political violence (the sum of the frequency of political assassinations, violent

riots and politically motivated strikes). Results of his regression model indicated that

political instability has significantly negative correlation with FDI inflows but the political

violence does not have significant effect on FDI behavior.

In addition, Asiedu (2002) also analyzed the effect of government expenditure con-

sumption share in GDP, which is used as the proxy for government size, on inward

FDI. He has expected there is a negative relationship between government expenditure

consumption with inward FDI. Unfortunately, his regression result did not confirmed his

expectation and shown no significant relation between them.

2.8 Exchange rate

Official exchange rate (local currency units relative to the U.S. dollar) refers to the

exchange rate determined in the legally sanctioned exchange market and by national

authorities. Several authors analyzed the effects of exchange rate on FDI distribution by

using the annual average which is based on monthly averages.

The effects of exchange rate behavior on FDI could be divided by two parts, the one is

the changes in bilateral level of the exchange rate between countries:

Due to the bilateral relationship of exchange rate between countries, the appreciation

of host country’s currency implies the depreciation of home country’s currency. There

15

are many different ideas of several authors about how the appreciation of host country’s

currency affect inward FDI. Campa (1993) considered that a foreign firm would need more

information about the future exchange rate behavior so as to decide whether to entry in

a new market or not. Results of his analysis revealed that there is a positive effects of

exchange rate level on FDI inflows, namely, the appreciation of host country’s currency

foster the increases of inward FDI.

However, several authors have the opposite ideas. Blonigen (1997) analyzed the rela-

tionship between exchange rate (JP yen per US dollar) movement with Japanese acqui-

sition FDI in the United States. Especially, he also found that the acquisitions is involve

with firm-specific assets and goods-market imperfections, which prevent investors from

having equal access to all markets. He came to the conclusion that the depreciation of

U.S. dollar foster the Japanese acquisition FDI for industries which are more involved with

firm-specific assets. So did Cushmann (1985), he clarified that the real foreign currency

appreciation leads to lower foreign capital costs, thus simulating FDI inflows.

While the other part is the volatility of exchange rate. The annual volatility of ex-

change rate refers to the percentage gain or loss in the value of one country’s currency

against another country’s currency. It is calculated by the standard deviation of every

monthly average exchange rate. When it comes to the volatility of exchange rate, how

the exchange rate uncertainty effect on FDI distribution become more and more important

for the recent FDI behavior analysis.

Campa (1993) paid much attention on the influences of exchange rate uncertainty on

inward FDI. He found out that exchange rate uncertainty could not only effect on the

entry level in a new market but also effect on the way how a foreign firm entry in it. The

empirical results of him confirmed that exchange rate uncertainty negatively affect the

entry of foreign firms. Especially, his analysis was directed at the risk neutral firms. This

negative relationship indicated that firms would rather wait until they get more information

about the future exchange rate behavior and then decide whether to entry or not.

In addition, the later article of Chakrabarti and Scholnick (2002) also expected the

negative correlation between exchange rate uncertainty with FDI inflows. However, his

estimate results indicated that the correlation is not significant enough. They considered

the reason why FDI flows have low elasticity with exchange rate movement is because

16

that investors make their expectation about future exchange rate level based on the their

adjustment for small changes in exchange rate, but their treatment to relatively larger

shocks are expected to be totally different. So when investors face with large shocks,

they might expect the movement of exchange rate in the opposite direction in the future.

In addition, they also expected that the FDI flows might be more elastic with exchange

rate movement for developing countries than for developed countries because of the mo-

tivation of FDI flows in developing countries mainly is the costs minimization. But the

constraints of data prevent them from estimating and proving the hypothesis at that time.

Nevertheless, several authors also hold the opposite idea with Campa (1993) and

Chakrabarti and Scholnick (2002), Cushmann (1985) clarified that the uncertainties of

exchange rates are inflation rates and random fluctuations in the real exchange rate could

cause plenty of risks and significantly effects on direct investments. Furthermore, his re-

sult shown that the increases in risks could raise FDI inflows. Because that the increasing

risk is also involved with foreign and domestic market, thus trade flows would lead to the

changes of firm’s expectation. Then how firm deal with increasing risks would be further

changed.

Nevertheless, the estimated results of Campa and Goldberg (1995) indicated that there

is a relatively weak and insignificant correlation between exchange rate variability with the

inward FDI in U.S. manufacturing sector. Because they did not pay much attention on the

competitive structure of the industry which will influence on the endogenous response

of firms. Since different exchange rate patterns would lead to different inter-industry

competition as well as different investment strategies.

2.9 Portfolio Equity

Portfolio equity (including shares, stocks, depository receipts, and direct purchases

of shares in local stock markets by foreign investors) and direct investments are the two

parts of net inflows from equity securities.

Sun, Tong and Yu (2002) expected foreign equity inflow can partly substitute the FDI

inflows by using the data from different provinces in China. They considered that if a

province could tap the foreign capital market and invest in local industry by using that

money, the province would not need that much FDI. Nevertheless, if we consider the cor-

relation between portfolio equity inflows with FDI inflows in different countries, things are

17

going to be changed. For the host country, the partly substitute effect could be vanished

because the desire of foreign portfolio equity and foreign direct investment would really

much more than the desire in single province. Since that, the increases in foreign portfolio

equity and increases in foreign direct investment could be happened simultaneously.

Furthermore, there is one paper which discussed the effects of foreign direct invest-

ment and equity foreign portfolio investment (EFPI) on economic growth from Durham

(2004). In order to achieve this goal, he has analyzed several regression models by

considering many alternative variables, such as stock market capitalization as well as the

business regulation, property rights, corruption indexes and so on. Although his analysis

did not proved the positive effects of FDI and EFPI on economic growth which he has ex-

pected before. But I can get the clue from his research that there is a correlation between

FDI with EFPI, which is certainly worth discussing later.

3 Description of the Variables and the Data

3.1 Description of the Variables

3.1.1 Description of dependent variables

Foreign Direct Investment

Not only involving a long-term relationship, but foreign direct investment 4 is also

defined as an investment reflecting a lasting interest in and control by a resident entity in

home countries of an enterprise resident in a host countries. Such investment involves

not only the initial transaction between the two entities but also all subsequent transac-

tions between them and among foreign affiliates.

There are three components of foreign direct investment: equity capital, reinvested

earnings and intra-company loans.

- Equity capital is the foreign direct investor’s purchase of the shares of an affiliate enter-

prise in a country other than its own country.

- Reinvested earnings comprise the direct investor’s share of earnings not distributed as4A direct investment enterprise is defined as an incorporated or unincorporated enterprise in which the

direct investor, resident in another economy, owns 10 percent or more of the ordinary shares or voting power(or the equivalent).

18

dividends by affiliates or earnings not remitted to the direct investor. So, such retained

profits by affiliates are reinvested earnings of the direct investor.

- Intra-company loans refers to short-term or long-term borrowing and lending of funds

between direct investors and affiliate enterprises.

FDI inflows refers to capital provided by a foreign direct investor to a FDI enterprise,

while FDI outflows refers to capital received by a foreign direct investor from a FDI enter-

prise. In this paper, I use the data of inward FDI flows are presented on net bases.5 So,

the data of FDI flows with a negative sign indicate reverse investment that at least one of

the three components of FDI is negative and not offset by positive amounts of the other

components.

3.1.2 Description of independent variables

All the possible determinants of FDI have been summarised and shown in the table.5

of the appendix B.

i Scientific research level

Scientific research level could be measured by the share of RD expenditures in

GDP and the number of patents 6 in the host country. A higher scientific research level

whether increases FDI inflows or not is depend on which field the investments entry into.

If there is a technology-oriented FDI, higher level of scientific research will indeed lead to

higher FDI inflows. Because, the purpose of this kind of FDI will be fullfilled in a country

with higher level of scientific research. Otherwise, if there is another kind of FDI such

as market-seeking FDI, it could be expected that there is a negative correlation between

scientific research level and inward FDI, because higher RD expenditure might refer to

under-developed market, thus will further preventing inward FDI.

Based the previous statements what have been discussed in the last part, it is

hard to expect what exactly the relationship is between firm-specific factors (scientific

research level) with FDI inflows. Because different data, which from different countries,

lead to different estimated results. In this paper, most countries, which I am focus on, are5Net decreases in assets or net increases in liabilities are recorded as credits (with a positive sign), while

net increases in assets or net decreases in liabilities are recorded as debits (with a negative sign).6Total patent grants (direct and PCT national phase entries)

19

less-developed or developing countries, so I could consider that large part of FDI inflows

would be used in seeking new and promising market. The hypothesis of the correlations

between scientific research factors with FDI inflows are negative.

ii Infrastructure quality

For the host country, good infrastructure quality is an essential prerequisite for stim-

ulating FDI inflows. However, infrastructure quality is a qualitative concept. Taking into

account of both the availability and the reliability of all possible infrastructure variables, I

use the fixed telephone subscriptions 7 per 100 people to measure the level of infrastruc-

ture quality of the host country.

Although different authors have used different terms to measure the level of infras-

tructure quality, they simultaneously reached to the same statement that higher infras-

tructure quality stimulates the inward FDI. This statement implies that, for foreign firms,

the infrastructure quality of the host countries could be considered as the fundamental

condition that for deciding whether to entry in or not. Based on the previous statements, I

also expect that there is a positive relationship between infrastructure quality and inward

FDI.

iii International trade

International trade and FDI are considered to be the two essential ways that could

help a country open to the outside world and participate in the global events. I use the

trade term (the ratio of the sum of export and import to GDP) to measure the openness

level of a host country. As what have been mentioned in the last part, several authors

drew the same conclusion that more open countries will attract more investments be-

cause FDI reaches that country easily. So, it is obvious that the expected correlation

between openness and FDI inflows is positive. In addition, not only the trade term, the

import level of the host country could also be used as the proxy for openness level. I

consider both the two variables into the following regression models and firstly analyze

which one contribute better to the models.7Fixed telephone subscriptions refers to the sum of active number of analogue fixed telephone lines,

voice-over-IP (VoIP) subscriptions, fixed wireless local loop (WLL) subscriptions, ISDN voice-channel equiv-alents and fixed public payphones

20

On the other hand, due to the difficulty to get the data, which are used to measure

the level of anti-dumping policy or any other trade protection policies, applied weighted

mean tariff rate is used as the only one variable to measure the trade protection level.

From the previous studies, the relationship between trade protection and inward FDI is

delicate, it will dependent on the way of protection. Here I expect that there is a negative

correlation between tariff rate with inward FDI. Because tariff rate could not only influence

on the trade behavior directly, but also deter the openness level of the host country. Since

that, higher tariff rate is harmful to the host country open their market, thus further prevent

this country from attracting more inward FDI.

iv Domestic market

The domestic market environment is also an important determinant for FDI, which

could influence the expected revenue of FDI directly. Actually, one major goal of FDI is

to find new market. So FDI inflows will be stimulated because better domestic market

condition increases the productivity of investments. In this paper, real total GDP, real

GDP per capita and annual GDP growth rate are used to capture the market size, market

demand and attractiveness of the host country domestic market. Real total GDP as the

core variable has to be considered into the following basic regression model.

Several previous studied clarified that larger market size and higher market demand

contribute to more productivity of investments through FDI. In addition, the faster GDP

growth rate, the more attractiveness for inward FDI. So I can make the same expectation

as those former studies that well domestic market conditions, namely larger marker size,

more market demand and faster market development attracts more FDI inflows.

v Labor market

There are three important factors related to labor market for FDI consideration,

namely labor force, labor quality and labor cost. Labor force is used to measure the size

of labor market in the host country. Furthermore, there is one suitable proxy of labor

quality is measured by the ratio of Labor force with tertiary education to total. Due to the

lack of average wage data for several countries, I use the adjusted labor income share in

GDP 8 to measure the labor cost variable.8The adjusted labor share in GDP is the total compensation of all workers given as a percent of gross

domestic product (a measure of total output), both provided in nominal terms

21

Based on the former statement, I can image that larger labor market size and higher

labor quality attract more FDI inflows because the domestic labor market become more

efficiently productive. So, here I expected that both labor force and labor quality would

positively foster more inward FDI. Therefore, it is difficult to image whether labor cost

effects on FDI inflows positively or negatively. Some authors suggested that higher labor

cost could also reflect higher labor quality, thus more productive labor market will be. So

I can expect that labor cost would also positively stimulate FDI inflows.

vi Financial market

The financial market closely related with FDI behavior, one major influences on FDI

inflows is the behavior of exchange rate, matter both the level of exchange rate and its

volatility. The level of exchange rate is defined by the proportion of the U.S. dollar to the

host countries’ currency. So the increasing level of exchange rate means the appreciation

of currency in host country. At the same time, when comes to the exchange rate volatility,

I calculate it by making the annual standard deviation of the monthly exchange rate of

every host country. The calculation code is shown in the appendix A.

Based on the previous studies and the discussion in the last part, I expected that

appreciation of host country currency foster the inward FDI. Because, In rough terms,

an appreciation of host country currency would lead to an increased expected returns

through FDI, leaving other things identical. Higher investment returns further attracts

more FDI inflows when host country currency appreciate. Besides that, the exchange rate

volatility could reflect the stability of the entire financial market of one host country. Also

by considering the former literature, the hypothesis is that more exchange rate volatility

leads to higher uncertainty and risk of domestic market therefore inward FDI goes down.

vii Political risk

Political stability also plays a significant role in FDI distribution. More stable coun-

tries attract more FDI inflows, while higher political risk may prevent foreign investors from

entering in. Based on several former studies, there are plenty of ways to measure the sta-

bility of government in one host country. Such as: the direct index which are calculated by

some trustful organizations; the real number of political assassinations and revolutions;

and so on.

22

In this paper, there are two aspects to be used as the proxies for political risk. The

first one is the government size, it is measured by the share of government expenditures

to GDP. I expected that smaller government size benefits to more FDI inflows. The second

one is the index of political stability9, countries who recorded as lower score, which means

they do not have a stable domestic political condition, hence they are expected to be less

attractive for foreign investments.

viii Other determinants

There are also several other variables could affect FDI significantly. Firstly, portfolio

equity 10 other than direct investment, which includes net inflows from equity securities

by foreign investors, also has the ability to influence on the movement of FDI. Since that,

I expect that larger portfolio equity net inflow would betoken higher FDI inflows. Some

authors have take into account of its effect when analysing the determinants of FDI, there

may exist a synergy between the two variables.

Secondly, the membership in international organizations should be considered into

the regression models as well. I believe that the benefits of joining in international or-

ganisations or engaging in international trade agreements are not only foster the trade

performance in a country, but also influence on several other aspects which related with

multinational affairs. In this paper, I choose eight international organisations11 and con-

struct the index of membership by the way which has been used in Dreher, Mikosch and

Voigt (2010). The former studies have reached the same point and clarified the positive

relationship between membership with FDI, hence I make the same expectation.

3.2 Description of the data

3.2.1 Statistics Database

All data are obtained from trustful websites, such as World Development Indicators

(2015), Worldwide Governance Indicators (2014), UNCTADstat (2014), WIPO IP Statis-

tics (2015), ILO Statistics (2015) and OANDA (2015). All variables and their sources are9Political Stability and Absence of Violence/Terrorism: Estimate

Terrorism measures perceptions of the likelihood of political instability and/or politically-motivated.10Portfolio equity includes net inflows from equity securities other than those recorded as direct investment

and including shares, stocks, depository receipts (American or global), and direct purchases of shares inlocal stock markets by foreign investors. Data are in current U.S. dollars.

11(1)WTO and GATT; (2)IFC; (3)ICSID; (4)NYC; (5)ICCPR; (6)ICESCR; (7)OP to ICCPR; (8)CAT

23

presented in the table.6 of appendix B.

3.2.2 Countries

In this paper, I obtain data from 57 countries during the period from 1996 to 2013. The

57 countries located in four different regions around the world, namely Asia and Pacific,

Europe, Africa and America. All these countries are shown in the table.8 of appendix B.

3.2.3 Summary statistics for the full sample

In order to achieve suitable estimated results, I have to modify the measure unit of each

variable. The summary statistics of variables are reported in the table.7 of appendix B.

3.3 Data preparation

3.3.1 Correlation between independent variables

I There are totally 18 independent variables which we are going to analysis later. Then

comes a problem that is the possible high correlation between them. If there is high

correlation between variables that will lead to serious multicollinearity. So I calculate the

correlation matrix between all the independent variables in order to ascertain the degree

of multicollinearity. The result are shown in the table.9 of the appendix B. Moreover, the

calculation code of correlation matix is also shown in the appendix A.

I consider that the correlation between every two variables is too high if the correlation

coefficient between them is above 0.7. Then I have to exclude those variables who have

high correlation with others. From the table.9, I found out that only a few correlation

coefficients are above 0.7. The results indicate that every independent variables I used

here does not have higher correlation with any others. Since that, all variables could be

considered into the following regression models.

II There is also a new way has been proposed recently, which is the collinearity diagnos-

tics. It is a kind of matlab code for diagnosing collinearity in a regression design matrix.

[Lau (2014)] This code is used to determining the degree and nature of collinearity among

explanatory variables in a regression matrix.

24

By applying the strategy12, which is made by Belsley, Kuh and Welsch (1980), people

can get the results as follows: (i) The number of large condition indexes identifies the

number of near dependencies among the columns of the sample matrix. (ii) Large vari-

ance decomposition proportions identify covariates that are implicated the corresponding

near dependency, and (iii) the measure of these proportions, in conjunction with the con-

dition index, provides a measure of the degree to which the corresponding regression

estimate has been downgraded by the presence of collinearity.

If the condition index is between 5 to 10, that shows that this variable has weak collinear-

ity with others; while the condition index is between 30 to 100, that indicates that the

collinearity between this variable with others are increases from moderate to strong; fi-

nally when the condition index is over 100, that means there is a severe collinearity be-

tween this variable with others. Besides that, where a pair (or more) of variance decom-

position factors is larger than 0.5 warrant inspection.

The calculation code of collintest is also shown in the appendix A. Meanwhile, the

variance Decomposition is shown in the table.10 in the appendix B. From the table.10,

we can figure out that the collinearity between variables are weak because of the lower

condition index. So, all these variables could be considered in the following regression

models.

3.3.2 Regional dummy variables

Since there are 57 countries are including in this analysis, and they are located in four

different regions around the world. So I can use the regional dummy variables into the

regression models in order to figuring out the specific situation of FDI behavior in each

region.

Regional dummy variable is a numerical variable used in regression analysis to repre-

sent subgroups of the sample and often used to distinguish different treatment groups.

Here I use four regional dummy variables to represent four different regions of the world,

namely, Asia and Pacific dummy, Europe dummy, Africa dummy and America dummy. So

as to discuss the specific regression models in each region.

121) A singular value judged with a large condition index, and which is associated with; 2) Large variancedecomposition proportions for two or more covariates

25

4 Empirical Results

As what has been mentioned in the last part, I begin the analysis by determining vari-

ables that effect the variation of net inflows of FDI (current US$ in millions). For all esti-

mations, I use the ordinary least square(OLS).

The method of least squares is a standard approach in regression analysis to the ap-

proximate solution of estimators. It also leads to minimizing the sum of the squares of

the errors, which is the errors between estimated value and the real value of estimators.

There are also several categories of least square problem, for example, the ordinary least

square (OLS) as well as the general least square (GLS). When people applies OLS, Swe

should have assumed the variance of error term is keep unchanged. Otherwise, if we

apply OLS to a sample which exhibits heteroskedasticity, the estimated variance is the

biased standard error of the real variance; but the estimated value are unbiased. Hence

that, people turn to apply GLS in order to keep the variances of regression equation are

same by adding weighted value, thus leading to unbiased estimations.

Many authors have tried different way to solve regression problems, such as Sun, Tong

and Yu (2002) who applied both OLS and GLS to their sample analysis. Because the

model they used contains many provincial specific characteristics, which may give rise

to cross-sectional heteroskedasticity. To cater for this, they not only tried OLS estimation

with correction for heteroskedasticity, but also used GLS which allows for heteroskedas-

ticity. From what they have done, the estimated value of each variable, which is estimated

by these two methods, have the same direction. Additionally, since what I have mentioned

before, the estimated value is always unbiased by no matter what method has been used.

So, I use OLS for the following estimations in this paper. All the regression codes are

shown in the appendix A. While, the following tables present the results of all different

regression models. Since there are many explaining variables I used in the regression

models, the results have to be reported in several models separately.

The columns (1)-(11) of next table report the regression results with different variables

over 1996 to 2013 of countries across the world. Then the columns (12)-(15) present the

regression results with regional dummy variables in order to show the regional effects of

different regions over the world.

26

Model (1) (2) (3) (4) (5)

Independent variables:

Intercept -10.076 -2.7494 -5.0235 -4.952 -9.5598

(-5.7155)*** (-1.8101)* (-2.8862)*** (-2.4475)** (-3.781)***

PHONE 0.25956 0.18819 0.020387 0.37077 0.2627

(5.9295)*** (4.3152)*** (3.7188)*** (4.9345)*** (4.587)***

GDP 0.012031 0.0031891 0.0032066 0.010286 0.011954

(28.252)*** (3.6289)*** (3.6327)*** (7.2856)*** (24.69)***

LF 0.044824 0.020224 0.016712 0.035369 0.053431

(6.5721)*** (2.9225)*** (2.3539)** (4.5227)*** (6.1375)***

GDPc 2.7993

(0.049549)

GDPg 0.46044

(2.6374)***

RD -3.5782

(-2.8928)***

PGN -0.35957

(-7.2563)***

TAR -0.19279

(-2.1462)**

IM 0.062738 0.063493 0.053079

(10.321)*** (10.182)*** (7.2288)***

TRADE 0.085049 0.092868

(7.3689)*** (6.3844)***

R-square 0.581 0.601 0.604 0.643 0.57

Number of observation 1011 1026 1026 733 791

Table 1: OLS estimation

* significant at 10% level** significant at 5% level*** significant at 1% level

27

i Basic models

The first column and second column show that a small number of factors could

explain a large share of the variation in FDI. Therefore, the only difference between them

is that by which variables are used to measure the level of openness. The first one uses

the trade variable, while the second uses import variable. It is obvious that the second

regression model with import variable explains more than 60% of the variation in FDI, but

the first one with trade variable does not.

After the comparison, the second one could be set as the basic model which indi-

cates that higher infrastructure level, higher GDP, more domestic labor force and larger

degree of openness to international trade foster the inward FDI. The results are just con-

sistent with previous studies. When a host country has a larger and more domestic

market, this country has the ability to attract more inward FDI than others. So does the

infrastructure quality, higher quality of domestic infrastructure level in one host country

means it is more convenient for multinational firms doing business in that country, then

they would attract more foreign investments. In addition, if there are more labor force

in the host country, there will be higher productivity of the domestic labor market. It is

obvious that more labor force domestically is benefit to attracting FDI inflows.

Furthermore, I make the regression models by adding variables on the basic model

separately.

ii Domestic market

Not only the GDP level is used as the domestic market variables, there are also

other domestic market variables that may affect the FDI distribution. The third column

pays much attention on the effects of domestic market variables. We can see not only

GDP, there is also a positive relationship between GDP growth rate with FDI. However,

GDP per capita does not significantly influence on FDI.

Just as what have been discussed in the second part of this paper, higher GDP

growth rate not only indicates the faster development of the host country, but also reflects

several aspects, such as higher productivity, more openness, larger domestic market and

28

higher infrastructure quality. That is why high GDP growth rate positively affects inward

FDI, since foreign investments always search for countries which are developing faster.

On the other hand, GDP per capita, which is used to measure the domestic market

demand, does not have significant effects on FDI. In my opinion, the sample I used here

contains a large amount of countries from different regions of the world. Some countries

may have larger inward FDI, but with lower GDP per capita level because of the large

population, for example China. So that it is hard to get significant effects of GDP per

capita on FDI distribution.

iii Science research level

The fourth column concentrates on Science research level, both the RD intensity

and number of patents affect FDI inflows negatively. Nevertheless, R-square of this re-

gression model increases significantly by adding these two variables. That indicates that

the Science research level plays a significant role in affecting inward FDI.

Based on the previous studies, authors holds different opinions. Whether the level

of research quality effects on FDI positively or negatively will depends on the type of for-

eign investments. On the one hand, some authors claimed that higher level of research

quality leads to more inward FDI, because they considered the firm-specific foreign in-

vestments which is aim at searching for high-technology market. On the other hand,

some authors considered the market-seeking foreign investments, which is aim at enter-

ing in more market, has significantly negative correlation with the Science research level.

Same with what I did in my model, a large amount of countries in my model have a huge

potential to open their domestic market in order to attracting more market-seeking FDI

inflows. That is why it came up to a negative correlation in this model.

iv International trade

As what have been shown in the first and second columns, import variable plays

more important role in affecting FDI distribution than trade variables. So besides import

variables, I analysis how other international trade variables influence on FDI behavior in

this sub model. Results are shown in the fifth column. This model contains trade variable

and tariff rate variable. Just as what I have expected before, higher tariff rate is harmful to

29

attracting inward FDI, while higher trade level is good for attracting inward FDI. However,

this model result in relatively lower R-square, that means trade variables do not explaining

FDI behavior well.

Be consistent with the former literatures, the regression results shows that if a host

country is willing to open and engage in international events, it could attract more foreign

investment inflows, because FDI could access the gate and reach it easily. By contrast,

the trade protection, which is used to prevent countries from opening to the world, will

indeed deter the host country from participating in international affairs. If a country does

not have close connection with international affairs, it will become less appealing to for-

eign firms, which is willing to export investments. That is why higher trade protection

negatively affects inward FDI.

v Labor market

there are several variables that are related with labor market other than labor force

variable, such as: labor quality and labor cost. The next column (6) reports the results of

the regression model with those variables. However, the R-square of this model decrease

by more than 10% compared to former models. The low R-square of this sub model

indicates that variables of labor market could not explain the variation in FDI well. At the

same time, Labor cost variable does not significantly effects on FDI, just same as the

opinion of Zadia Feliciano and Robert E. Lipsey (1999) . While, Labor quality variable

shows positive relation with FDI as my expectation before.

Apart from the labor force variable, this sub model only contains the labor quality

variable and labor cost variable. For labor quality, it is obvious that there is a positive

correlation between it with inward FDI. Because higher labor quality refers to higher pro-

ductivity of the domestic labor market. Same amounts of labor with and without tertiary

education would generate different wealth, because they would participate in different

works. Labor with tertiary education are going to generate huge wealth, hence attracting

more foreign investments.

However, labor cost, which is also an essential variable in explaining FDI distribu-

tion, does not show significant effect on FDI in this sub model. It is expected to have

positive effect on inward FDI, and I am going to reconsider it in later regression model in

30

Model (6) (7) (8) (9) (10)

Independent variables:

Intercept -6.3099 8.7906 -3.8199 -4.5235 -2.6231

(-1.1717) (2.6012)** (-2.4905)** (-2.4394)** (-1.715)*

PHONE 0.14583 0.15378 0.1619 0.18629

(1.8525)* (3.495)*** (3.491)*** (4.2628)***

GDP 0.004837 0.0091519 0.002945 0.0031786

(3.4261)*** (7.2122)*** (3.3645)*** (3.6156 )***

LF 0.020875 0.022293 0.015949 0.02007

(2.6458)*** (3.3311)*** (2.2158)** (2.8982)***

GDPg 0.48226

(2.5976)***

PGN -0.36455

(-8.8178)***

IM 0.068328 0.060211 0.063878 0.076829 0.062832

(8.3851)*** (9.1348)*** (10.564)*** (24.558)*** (10.332)***

LQ 0.184

(1.7881)*

LC 0.00025554

(0.0017779)

GEC -0.38873

(-2.0145)**

PS 2.3135

(2.1988)**

Ex 5.3709

(4.2853)***

ExD -25.229

(-2.4937)**

PEI 0.14395

(4.9198)***

IO -0.0018247

(-0.71659)

R-square 0.473 0.631 0.608 0.613 0.601

Number of observation 661 782 1026 942 1026

Table 2: OLS estimation continued

31

order to figure out its effect. Because labor with higher education level would also have

higher wages. So if a country have higher level of labor quality, it may also bear higher

labor cost. That pursued me to consider its effects on FDI distribution in the following

model again.

vi Institution

Several previous studies paid much attention on the effects of institution variables

on FDI behavior, results in column (7) shows the sub model with institution variables,

namely government expenditure and political stability. Same with the conclusion of former

studies, more government expenditure leads to less inward FDI. Therefore, the effect of

Political stability on FDI is significantly positive. It is also consistent with the opinion of

Wei (2000) and Asiedu (2002).

It is obvious that higher government expenditure, which indicates a relatively larger

government size, would definitely deter this country from attracting inward FDI. Because

larger government size will increase the difficulty for the foreign investments entering

and operating in the host countries. Encumbered by larger government size with compli-

cated structure, the economy could not develop well. Especially, less-developed economy

would never attract more foreign investments.

Data for political stability level I used here has a narrow range of data, which is from

−2.5 to 2.5. Positive value refers to more stable government, while negative value refers

to less political stability. As what the regression results indicated that the more stable

government of a country, the more foreign investments would entry in this country. It is

obvious that FDI always find a stable destination, which would improve the chances of

profit. In addition, the improving R-square of this model indicates that the more significant

influence of institution variables on the variation of FDI.

vii Exchange rate

Many authors have concentrated on analyzing the effects of exchange rate and its

volatility on FDI behavior. My regression results are presented in the eighth column. In

this sub model, the exchange rate is calculated by the U.S. dollar divided by the host

32

countries currency.(increase of exchange rate refers to host country currency apprecia-

tion) These results from this model are consistent with the previous studies that appre-

ciation of host countries currency leads to more inward FDI, while more exchange rate

volatility is harmful to attracting FDI.

Since there are many authors have analyzed the relationship between exchange

rate with FDI, the different sample they used lead to different results. In this sub model, I

get the data from more than fifty countries across the world and over the 18− year period

from 1996 to 2013. The foreign investors would be stimulated by the appreciation of a host

country currency, which indicates the increasing purchasing power of this currency, and

expect to gain profit by investing in this country in the future. That is why the regression

result shows a positive relationship between exchange rate with inward FDI.

In addition, the exchange rate volatility, which refers to the economic instability of

a host country, plays an important role in the variation of FDI as well. In this sub model,

the exchange rate volatility is the percentage gain or loss in the value of the U.S. dollar

against the host countries currency, it is calculated by the standard deviation of monthly

exchange rate in one year. Due to the risk aversion, foreign investors prefer to investing

in a country with relatively stable economy rather than in a country which is in stormy

economic waters. So, more exchange rate volatility would prevent a host country from

attracting more foreign investment.

viii Portfolio equity inflow

As what have been discussed before, portfolio equity inflows as well as foreign

direct investments are the two ways of foreign investments. The regression results of

these two variables are shown in the next ninth column. Just same with my expectation,

portfolio equity inflows are positively related with FDI inflows. Furthermore, this sub model

explains the variation in FDI well because of the increasing R-square of it.

It is obvious to arrive the conclusion that if a host country could be able to attracting

more foreign investment, not only FDI inflows but also portfolio equity inflows would be

increased together. So, there is a significantly positive correlation between them.

33

ix Membership

Membership of international organizations seems to be an essential factor for anal-

ysis FDI determinants. Countries in a same organization tend to cooperate with each

other due to the less cost and the closer links between them. However, in this sub model,

membership of international organizations does not show significant effects on FDI dis-

tribution. Results of this sun model is shown in the column (10).

The result is opposite with my hypothesis and with some former statements, which

indicate the indeed positive relationship between membership with FDI inflows. I think the

reason why it leads to insignificant effect due to the small sample I used here, in which

countries are nearly in same organizations. So the differences of this membership index

between countries are not range wildly, hence it fail to cause significant effect on FDI

behavior.

x Whole regression model

Finally, the column (11) presents the results of the whole regression model which

contains factors from several different aspects. Expect for labor cost, all other variables

keep the same effects on FDI as what have been shown in former models. It becomes

significantly positive related with FDI, it is also consistent with my expectation. The final

R-square value reaches to 0.662, which means this model could explain more than 66%

of the variation in FDI. Then the whole regression model is shown in the next equation.

FDIit =αi + β1PHONEit + β2GDPit + β3LFit + β4RDit + β5PGNit + β6IMit

+ β7LCit + β8GECit + β9Exit + β10PEIit + εit,

(i = 1, 2, ..., 57 t = 1, 2, ...18)

(1)

All in all, I come to the conclusion from the whole model that host countries, which

has value added currency, relatively higher infrastructure quality, larger domestic market,

more labor cost, much more portfolio equity inflows and relatively less expenditure on

research as well as government consumption, are considered to be the most appealing

host countries to multinational investors. It is consistent with several previous studies and

just prove my expectation before.

34

Model (11) (12) (13) (14) (15)

Independent variables:

Intercept -7.0127 -6.0804 -10.641 -9.3304 -11.013

(-1.3526) (-1.1258) (-1.9837)** (-1.7261)* (-2.0336)**

PHONE 0.24979 0.24357 0.31214 0.28727 0.24903

(2.8856)*** (2.7937)*** (3.4777)*** (3.191)*** (2.887)***

GDP 0.0086596 0.0084013 0.0084057 0.0085407 0.0074329

(5.7518)*** (5.3756)*** (5.5912)*** (5.67)*** (4.7023)***

LF 0.02854 0.029603 0.028025 0.03003 0.030534

(3.4945)*** (3.5457)*** (3.443)*** (3.6532)*** (3.7335)***

RD -2.539 -2.4564 -3.5108 -3.2962 -2.3719

(-1.7845)* (-1.7182)* (-2.3891)** (-2.1851)** (-1.6711)*

PGN -0.35414 -0.34296 -0.37054 -0.34901 -0.3327

(-6.9444)*** (-6.3377)*** (-7.2334)*** (-6.8346)*** (-6.4532)***

IM 0.056116 0.056322 0.05877 0.056774 0.059226

(7.5333)*** (7.5502)*** (7.8397)*** (7.6152)*** (7.8661)***

LC 0.357 0.38432 0.31493 0.38855 0.38343

(2.4121)** (2.4875)** (2.1221)** (2.6014)*** (2.5931)***

GEC -0.71181 -0.80638 -0.291 -0.71573 -0,61655

(-2.5231)** (-2.5128)** (-0.88899) (-2.5391)** (-2.1727)**

Ex 2.004 2.037 2.1899 2.1835 2.1328

(1.7352)* (1.7611)* (1.8992)* (1.8822)* (1.8514)*

PEI 0.10613 0.10586 0.11103 0.1053 0.11172

(3.1116)*** (3.1022)*** (3.2619)*** (3.0895)*** (3.2798)***

Asia Dummy -1.5768

(-0.61926)

Europe Dummy -5.7129

(-2.5083)**

Africa Dummy 5.2925

(1.5)

America Dummy 6.6029

(2.4629)**

R-square 0.664 0.664 0.667 0.665 0.666

Number of observation 722 722 722 722 722

Table 3: OLS estimation continued

35

xi Regional dummy variables

Basis on the whole regression model, I also include dummy variables for further

analysis, namely Asia and Pacific dummy, Europe dummy, Africa dummy and America

dummy in order to test whether countries in some specific regions receive different FDI

inflows compared with countries in other region. These results are presented in column

(12)-(15).

From the table.3, we can see all the regional effects are statistically significant on

FDI distribution. Firstly, column (12) presents the result of Asia and Pacific dummy vari-

able, which shows that FDI net inflows for countries in Asia and Pacific is about 1.5768%

less than countries outside this region, but the effect of Asia and Pacific dummy is not

significant; Secondly, the result in column (13) indicates that regional effects cause the

FDI net inflows decreases by about 5.7129% in European countries compared with coun-

tries outside Europe; Thirdly, the 14th column refers to that FDI net inflows for countries

in Africa increases about 5.2925% by regional effects than countries outsides Africa, but

the t− test of Africa dummy is not big enough to show its significant effect on FDI; Finally,

from the last column of this table, that FDI net inflow for countries in America is about

6.6029% more than countries outside America. Except for Asia and Pacific dummy, the R-

square of all other models with regional dummy variables improve because the regional

effects have play a significant role in FDI distribution in countries which are located in

Europe, Africa and America.

When other things are equal, whether a country could attract more foreign invest-

ment or not might also depends on where the country located.

Countries in Asia attract relatively less foreign direct investment maybe because

that most of them are located inland, geographical condition may partly limit their open-

ness to outsides the world.

For countries in Europe, their FDI inflow are relatively lower than others either. Part

of countries in Europe also located inland the Eurasian plate, the geographical condition

might also influence on their ability to attracting FDI. Another part of countries in Europe

are the members of the EU, they tend to cooperate with each other rather than connect

with countries far away from them.

36

However, countries in Africa attract more inward FDI, because that the countries I

analyze here are relatively well-developed countries in Africa, they have plenty of nature

resources and well-functioned port. Besides that, most of them are located next to the

great shipping line and Canal. The closer connection with international affairs encourage

them to attract more foreign investment.

So do the countries in America, abundant resources, wide stretch of land and well-

developed transportation indeed guarantee these countries have much more FDI inflows.

5 Conclusion

The growing number of theoretical and empirical studies exploring the determinants of

the variation in FDI highlights the importance of this research area. This paper reviews

several previous literatures and estimates the influences of all potential determinants on

FDI. This analysis covers 57 countries across the world and spans a period from 1996-

2013. Furthermore, regression models in this paper also contain the regional dummy

variables which is aim at determining the regional effects of the host countries location.

Some of the results in this paper are consistent with the statements which are con-

cluded in former literatures. However, some of them are contrary to former statements.

The regression analysis in this paper is divided into many sub models in order to esti-

mate the specific effects of factors in each category. This research has found that the

huge potential market size and fast growing economy are the most significant factor for

FDI inflow, which is in line with both theory and previous studies. Openness level and

trade protection policies are another important reason; other key factors include govern-

ment expenditure level and the scientific research level. In addition that much labor force,

coupled with high labor quality as well as labor cost, are an unbeatable combination for

foreign firms. One of the other important findings from this research is that the exchange

rate as well as its volatility are also one of the key factors for some foreign firms investing

in a host country. So does the location, where the country located generally affects its

competitive power for attracting FDI.

This indicates that FDI is one of the very important multinational activities and the

attractiveness for foreign investments is part of the economic strength in one country. In

37

conclusion, not only by any single factor, foreign investments are also importantly driven

by a whole myriad of conflicting and competing reasons. For any country who is willing to

attracting much more foreign direct investment, taking advantage of any single dominant

factor is not enough for more inward FDI, the improvement of entire economic strength is

crucial to foreign investment attractiveness success.

Like all research, the findings need to be interpreted cautiously given the relatively

small sample size I used. Since that, the membership variable is estimated that to have

insignificantly relationship with FDI. Nevertheless, it is positive related with foreign in-

vestment it seems in several former literatures. Following what has been said, a further

research should start with including more appropriate measurements for the membership

variables. Besides that, the exploration of regional determinants of foreign direct invest-

ment could be expand into a large sample size with plenty of countries across the world,

even that these countries should be subdivided into any typical regions.

38

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42

A Appendix Code

All self generated Figure, Tables and Calculations were programmed with the software

Matlab, version R2015b from Mathworks. FDI inward flows plot code, correlation Matrix

code and collinearity test code, Exchange rate deviation code, Statistics summary code

and Regression estimations codes are as follows:

Code 1: FDI inward flows plot

1 c lose a l l , c l ea r a l l , c l c

%−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−

3 % READ DATA

%−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−

5 Y1=x ls read ( ’ z FDI . x l sx ’ ,1 , ’M1:M18 ’ ) ;

Y2=x ls read ( ’ z FDI . x l sx ’ ,1 , ’AN1: AN18 ’ ) ;

7 Y3=x ls read ( ’ z FDI . x l sx ’ ,1 , ’BE1 : BE18 ’ ) ;

%−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−

9 % PLOT

%−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−

11

f i g u r e 1 = f i g u r e ;

13 hold on

15 h1= p l o t (Y1 , ’ r ’ ) ;

h2= p l o t (Y2 , ’ c ’ ) ;

17 h3= p l o t (Y3 , ’ b ’ ) ;

19 t i t l e ( ’ FDI inward f lows , annual , 1996−2013, US Do l l a r s i n m i l l i o n s ’ ) ;

x l a b e l ( ’YEAR ’ ) ;

21 y l a b e l ( ’ FDI inward f lows ’ ) ;

23 set ( gca , ’ XTickLabel ’ ,1996:2:2014) ;

legend ( ’Germany ’ , ’ China ’ , ’ the United States ’ ) ;

25

27 hold o f f ;

Plot.m

43

Code 2: Correlation Matrix code and collinearity test

c lose a l l , c l ea r a l l , c l c

2 %−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−

% READ DATA

4 %−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−

FDI r=x ls read ( ’ z FDI . x l sx ’ ,1 , ’A1 : A1026 ’ ) ;

6 RD=x ls read ( ’ a RD. x l sx ’ ,1 , ’A1 : A1026 ’ ) ;

PGN r=x ls read ( ’ b PGN. x l sx ’ ,1 , ’A1 : A1026 ’ ) ;

8 TAR=x ls read ( ’ d TAR. x l sx ’ ,1 , ’A1 : A1026 ’ ) ;

IM r=x ls read ( ’ e IM . x l sx ’ ,1 , ’A1 : A1026 ’ ) ;

10 GDP r=x ls read ( ’ g GDP. x l sx ’ ,1 , ’A1 : A1026 ’ ) ;

GDPg=x ls read ( ’ h GDP growth . x l sx ’ ,1 , ’A1 : A1026 ’ ) ;

12 LQ=x ls read ( ’ i LT . x l sx ’ ,1 , ’A1 : A1026 ’ ) ;

LC=x ls read ( ’ j LC . x l sx ’ ,1 , ’A1 : A1026 ’ ) ;

14 LF r=x ls read ( ’ j LF . x l sx ’ ,1 , ’A1 : A1026 ’ ) ;

GEC=x ls read ( ’ k GEC. x l sx ’ ,1 , ’A1 : A1026 ’ ) ;

16 PS=x ls read ( ’ l PS. x l sx ’ ,1 , ’A1 : A1026 ’ ) ;

Ex=x ls read ( ’ o Ex . x l sx ’ ,1 , ’BH1: BH1026 ’ ) ;

18 ExD=x ls read ( ’ p ExD . x l sx ’ ,1 , ’A1 : A1026 ’ ) ;

PHONE=x ls read ( ’ c PHONE. x l sx ’ ,1 , ’A1 : A1026 ’ ) ;

20 TRADE=x ls read ( ’ f TRADE. x l sx ’ ,1 , ’A1 : A1026 ’ ) ;

GDPc r=x ls read ( ’ n GDP per C. x l sx ’ ,1 , ’A1 : A1026 ’ ) ;

22 PEI r=x ls read ( ’ q PEI . x l sx ’ ,1 , ’A1 : A1026 ’ ) ;

IO=x ls read ( ’ r IO . x l sx ’ ,1 , ’A1 : A1026 ’ ) ;

24 %−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−

% DATA PREPARATION

26 %−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−

FDI = FDI r ∗0.001;

28 IM = IM r ∗0.001;

GDP = GDP r∗0.001;

30 GDPc = GDPc r∗0.000001;

PEI = PEI r ∗0.000000001;

32 LF = LF r∗0.000001;

PGN = PGN r∗0.001;

34 %−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−

%CORRELATION

36 %−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−

format sho r t

38 i npu t = [PHONE GDP LF GDPc GDPg RD PGN TAR IM TRADE LQ LC GEC PS Ex ExD PEI IO ]

output = ones (18)

44

40 i = 1

wh i le i <=18

42 j = i +1

wh i le j <=18

44 R = cor rcoe f ( i npu t ( : , i ) , i npu t ( : , j ) )

ou tput ( j , i ) = R(1 ,2 )

46 output ( i , j ) = R(1 ,2 )

j = j +1

48 end

i = i +1

50 end

csvwr i t e ( ’ C o r r e l a t i o n c o e f f i c i e n t . csv ’ , ou tput )

52 %−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−

%COLLINTEST

54 %−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−

X = [PHONE GDP LF GDPc GDPg RD PGN TAR IM TRADE LQ LC GEC PS Ex ExD PEI IO ]

56 c o l l i n t e s t (X)

Correlation.m

45

Code 3: Exchange rate deviation

c lose a l l , c l ea r a l l , c l c

2 %−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−

% READ DATA

4 %−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−

ExM=x ls read ( ’ p ExM. x l sx ’ ,1 , ’B3 : BE218 ’ ) ;

6 %−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−

%DEVIATION

8 %−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−

ExDoutput = [ ] ;

10 i =1

row=1

12 tempVector = [ 0 ; 0 ; 0 ; 0 ; 0 ; 0 ; 0 ; 0 ; 0 ; 0 ; 0 ; 0 ]

%s t a r t w i th row

14 whi le i <=18

%s t a r t w i th co l

16 j =1

co l =1

18 whi le j <=56

%copy data to temp

20 tempcount = 1

whi le tempcount <=12

22 tempVector ( tempcount , 1 ) = ExM( row+tempcount−1, co l )

tempcount = tempcount+1

24 end

ExDoutput ( i , j ) = s td ( tempVector ) ;

26 j = j +1

co l=co l +1

28 end

i = i +1

30 row=row+12

end

32 csvwr i t e ( ’ p ExD . csv ’ , ExDoutput )

Deviation.m

46

Code 4: Statistics summary

1 c lose a l l , c l ea r a l l , c l c

%−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−

3 % READ DATA

%−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−

5 FDI r=x ls read ( ’ z FDI . x l sx ’ ,1 , ’A1 : A1026 ’ ) ;

RD=x ls read ( ’ a RD. x l sx ’ ,1 , ’A1 : A1026 ’ ) ;

7 PGN r=x ls read ( ’ b PGN. x l sx ’ ,1 , ’A1 : A1026 ’ ) ;

TAR=x ls read ( ’ d TAR. x l sx ’ ,1 , ’A1 : A1026 ’ ) ;

9 IM r=x ls read ( ’ e IM . x l sx ’ ,1 , ’A1 : A1026 ’ ) ;

GDP r=x ls read ( ’ g GDP. x l sx ’ ,1 , ’A1 : A1026 ’ ) ;

11 GDPg=x ls read ( ’ h GDP growth . x l sx ’ ,1 , ’A1 : A1026 ’ ) ;

LQ=x ls read ( ’ i LT . x l sx ’ ,1 , ’A1 : A1026 ’ ) ;

13 LC=x ls read ( ’ j LC . x l sx ’ ,1 , ’A1 : A1026 ’ ) ;

LF r=x ls read ( ’ j LF . x l sx ’ ,1 , ’A1 : A1026 ’ ) ;

15 GEC=x ls read ( ’ k GEC. x l sx ’ ,1 , ’A1 : A1026 ’ ) ;

PS=x ls read ( ’ l PS. x l sx ’ ,1 , ’A1 : A1026 ’ ) ;

17 Ex=x ls read ( ’ o Ex . x l sx ’ ,1 , ’BH1: BH1026 ’ ) ;

ExD=x ls read ( ’ p ExD . x l sx ’ ,1 , ’A1 : A1026 ’ ) ;

19 PHONE=x ls read ( ’ c PHONE. x l sx ’ ,1 , ’A1 : A1026 ’ ) ;

TRADE=x ls read ( ’ f TRADE. x l sx ’ ,1 , ’A1 : A1026 ’ ) ;

21 GDPc r=x ls read ( ’ n GDP per C. x l sx ’ ,1 , ’A1 : A1026 ’ ) ;

PEI r=x ls read ( ’ q PEI . x l sx ’ ,1 , ’A1 : A1026 ’ ) ;

23 IO=x ls read ( ’ r IO . x l sx ’ ,1 , ’A1 : A1026 ’ ) ;

%−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−

25 % DATA PREPARATION

%−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−

27 FDI = FDI r ∗0.001;

IM = IM r ∗0.001;

29 GDP = GDP r∗0.001;

GDPc = GDPc r∗0.000001;

31 PEI = PEI r ∗0.000000001;

LF = LF r∗0.000001;

33 PGN = PGN r∗0.001;

%−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−

35 % STATISTICS

%−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−

37 output = [ ] ;

i npu t = [ FDI PHONE GDP LF GDPc GDPg RD PGN TAR IM TRADE LQ LC GEC PS Ex ExD PEI

IO ] ;

47

39 i = 1

wh i le i <= 19

41 output ( i , 1 ) = mean( i npu t ( : , i ) ) ;

ou tput ( i , 2 ) = s td ( i npu t ( i , : ) ) ;

43 output ( i , 3 ) = min ( i npu t ( i , : ) ) ;

ou tput ( i , 4 ) = max( i npu t ( i , : ) ) ;

45 i = i +1

end

47 csvwr i t e ( ’Summary . csv ’ , ou tput )

Statistics.m

48

Code 5: Regression estimations

1 c lose a l l , c l ea r a l l , c l c

%−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−

3 % READ DATA

%−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−

5 FDI r=x ls read ( ’ z FDI . x l sx ’ ,1 , ’A1 : A1026 ’ ) ;

RD=x ls read ( ’ a RD. x l sx ’ ,1 , ’A1 : A1026 ’ ) ;

7 PGN r=x ls read ( ’ b PGN. x l sx ’ ,1 , ’A1 : A1026 ’ ) ;

TAR=x ls read ( ’ d TAR. x l sx ’ ,1 , ’A1 : A1026 ’ ) ;

9 IM r=x ls read ( ’ e IM . x l sx ’ ,1 , ’A1 : A1026 ’ ) ;

GDP r=x ls read ( ’ g GDP. x l sx ’ ,1 , ’A1 : A1026 ’ ) ;

11 GDPg=x ls read ( ’ h GDP growth . x l sx ’ ,1 , ’A1 : A1026 ’ ) ;

LQ=x ls read ( ’ i LT . x l sx ’ ,1 , ’A1 : A1026 ’ ) ;

13 LC=x ls read ( ’ j LC . x l sx ’ ,1 , ’A1 : A1026 ’ ) ;

LF r=x ls read ( ’ j LF . x l sx ’ ,1 , ’A1 : A1026 ’ ) ;

15 GEC=x ls read ( ’ k GEC. x l sx ’ ,1 , ’A1 : A1026 ’ ) ;

PS=x ls read ( ’ l PS. x l sx ’ ,1 , ’A1 : A1026 ’ ) ;

17 Ex=x ls read ( ’ o Ex . x l sx ’ ,1 , ’BH1: BH1026 ’ ) ;

ExD=x ls read ( ’ p ExD . x l sx ’ ,1 , ’A1 : A1026 ’ ) ;

19 PHONE=x ls read ( ’ c PHONE. x l sx ’ ,1 , ’A1 : A1026 ’ ) ;

TRADE=x ls read ( ’ f TRADE. x l sx ’ ,1 , ’A1 : A1026 ’ ) ;

21 GDPc r=x ls read ( ’ n GDP per C. x l sx ’ ,1 , ’A1 : A1026 ’ ) ;

PEI r=x ls read ( ’ q PEI . x l sx ’ ,1 , ’A1 : A1026 ’ ) ;

23 IO=x ls read ( ’ r IO . x l sx ’ ,1 , ’A1 : A1026 ’ ) ;

D=x ls read ( ’ y D ’ ,1 , ’A1 : D1026 ’ ) ;

25 %−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−

% DATA PREPARATION

27 %−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−

FDI = FDI r ∗0.001;

29 IM = IM r ∗0.001;

GDP = GDP r∗0.001;

31 GDPc = GDPc r∗0.000001;

PEI = PEI r ∗0.000000001;

33 LF = LF r∗0.000001;

PGN = PGN r∗0.001;

35 %−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−

% REGRESSION ESTIMATIONS

37 %−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−

%REGRESSION( 1 )−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−

39 X1=[PHONE GDP LF TRADE ] ;

49

41 [ b , b in t , r , r i n t , s t a t s ] = regress ( FDI , [ ones ( leng th ( FDI ) ,1 ) , X1 ] , 0 . 0 1 ) ;

43 mdl FDI = f i t l m (X1 , FDI , ’ l i n e a r ’ , ’ ResponseVar ’ , ’ on ’ )

%REGRESSION( 2 )−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−

45 X2=[PHONE GDP LF IM ] ;

47 [ b , b in t , r , r i n t , s t a t s ] = regress ( FDI , [ ones ( leng th ( FDI ) ,1 ) , X2 ] , 0 . 0 1 ) ;

49 mdl FDI = f i t l m (X2 , FDI , ’ l i n e a r ’ , ’ ResponseVar ’ , ’ on ’ )

%REGRESSION( 3 )−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−

51 X3=[PHONE GDP LF GDPc GDPg IM ] ;

53 [ b , b in t , r , r i n t , s t a t s ] = regress ( FDI , [ ones ( leng th ( FDI ) ,1 ) , X3 ] , 0 . 0 1 ) ;

55 mdl FDI = f i t l m (X3 , FDI , ’ l i n e a r ’ , ’ ResponseVar ’ , ’ on ’ )

%REGRESSION( 4 )−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−

57 X4=[PHONE GDP LF RD PGN IM ] ;

59 [ b , b in t , r , r i n t , s t a t s ] = regress ( FDI , [ ones ( leng th ( FDI ) ,1 ) , X4 ] , 0 . 0 1 ) ;

61 mdl FDI = f i t l m (X4 , FDI , ’ l i n e a r ’ , ’ ResponseVar ’ , ’ on ’ )

%REGRESSION( 5 )−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−

63 X5=[PHONE GDP LF TAR TRADE ] ;

65 [ b , b in t , r , r i n t , s t a t s ] = regress ( FDI , [ ones ( leng th ( FDI ) ,1 ) , X5 ] , 0 . 0 1 ) ;

67 mdl FDI = f i t l m (X5 , FDI , ’ l i n e a r ’ , ’ ResponseVar ’ , ’ on ’ )

%REGRESSION( 6 )−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−

69 X6=[PHONE GDP IM LQ LC ] ;

71 [ b , b in t , r , r i n t , s t a t s ] = regress ( FDI , [ ones ( leng th ( FDI ) ,1 ) , X6 ] , 0 . 0 1 ) ;

73 mdl FDI = f i t l m (X6 , FDI , ’ l i n e a r ’ , ’ ResponseVar ’ , ’ on ’ )

%REGRESSION( 7 )−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−

75 X7=[GDP LF IM PGN GEC PS ] ;

77 [ b , b in t , r , r i n t , s t a t s ] = regress ( FDI , [ ones ( leng th ( FDI ) ,1 ) , X7 ] , 0 . 0 1 ) ;

79 mdl FDI = f i t l m (X7 , FDI , ’ l i n e a r ’ , ’ ResponseVar ’ , ’ on ’ )

%REGRESSION( 8 )−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−

81 X8=[PHONE GDP LF IM Ex ExD ] ;

50

83 [ b , b in t , r , r i n t , s t a t s ] = regress ( FDI , [ ones ( leng th ( FDI ) ,1 ) , X8 ] , 0 . 0 1 ) ;

85 mdl FDI = f i t l m (X8 , FDI , ’ l i n e a r ’ , ’ ResponseVar ’ , ’ on ’ )

%REGRESSION( 9 )−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−

87 X9=[PHONE LF GDPg IM PEI ] ;

89 [ b , b in t , r , r i n t , s t a t s ] = regress ( FDI , [ ones ( leng th ( FDI ) ,1 ) , X9 ] , 0 . 0 1 ) ;

91 mdl FDI = f i t l m (X9 , FDI , ’ l i n e a r ’ , ’ ResponseVar ’ , ’ on ’ )

%REGRESSION(10)−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−

93 X10=[PHONE GDP LF IM IO ] ;

95 [ b , b in t , r , r i n t , s t a t s ] = regress ( FDI , [ ones ( leng th ( FDI ) ,1 ) , X10 ] , 0 . 0 1 ) ;

97 mdl FDI = f i t l m (X10 , FDI , ’ l i n e a r ’ , ’ ResponseVar ’ , ’ on ’ )

%REGRESSION(11)−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−

99 X11=[PHONE GDP LF RD PGN IM LC GEC Ex PEI ] ;

101 [ b , b in t , r , r i n t , s t a t s ] = regress ( FDI , [ ones ( leng th ( FDI ) ,1 ) , X11 ] , 0 . 0 1 ) ;

103 mdl FDI = f i t l m (X11 , FDI , ’ l i n e a r ’ , ’ ResponseVar ’ , ’ on ’ )

%REGRESSION(12)−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−

105 X12=[PHONE GDP LF RD PGN IM LC GEC Ex PEI D( : , 1 ) ] ;

107 [ b , b in t , r , r i n t , s t a t s ] = regress ( FDI , [ ones ( leng th ( FDI ) ,1 ) , X12 ] , 0 . 0 1 ) ;

109 mdl FDI = f i t l m (X12 , FDI , ’ l i n e a r ’ , ’ ResponseVar ’ , ’ on ’ )

%REGRESSION(13)−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−

111 X13=[PHONE GDP LF RD PGN IM LC GEC Ex PEI D( : , 2 ) ] ;

113 [ b , b in t , r , r i n t , s t a t s ] = regress ( FDI , [ ones ( leng th ( FDI ) ,1 ) , X13 ] , 0 . 0 1 ) ;

115 mdl FDI = f i t l m (X13 , FDI , ’ l i n e a r ’ , ’ ResponseVar ’ , ’ on ’ )

%REGRESSION(14)−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−

117 X14=[PHONE GDP LF RD PGN IM LC GEC Ex PEI D( : , 3 ) ] ;

119 [ b , b in t , r , r i n t , s t a t s ] = regress ( FDI , [ ones ( leng th ( FDI ) ,1 ) , X14 ] , 0 . 0 1 ) ;

121 mdl FDI = f i t l m (X14 , FDI , ’ l i n e a r ’ , ’ ResponseVar ’ , ’ on ’ )

%REGRESSION(15)−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−

123 X15=[PHONE GDP LF RD PGN IM LC GEC Ex PEI D( : , 4 ) ] ;

51

125 [ b , b in t , r , r i n t , s t a t s ] = regress ( FDI , [ ones ( leng th ( FDI ) ,1 ) , X15 ] , 0 . 0 1 ) ;

127 mdl FDI = f i t l m (X15 , FDI , ’ l i n e a r ’ , ’ ResponseVar ’ , ’ on ’ )

Regressions.m

B Appendix Tables

52

Det

erm

inan

tsof

FDI

Posi

tive

Neg

ativ

eIn

sign

ifica

nt

RD

inte

nsity

Mor

ckan

dYe

ung

(199

2)K

ogut

and

Cha

ng(1

991)

Blo

nige

n(1

997)

Num

bero

fPat

ents

Sun

,Ton

gan

dYu

(200

2)

Infra

stru

ctur

e

Sun

,Ton

gan

dYu

(200

2)

Whe

eler

and

Mod

y(1

992)

Che

ngan

dK

wan

(200

0)

Asi

edu

(200

2)

Trad

eP

rote

ctio

n

Gru

bert

and

Mut

ti(1

991)

Bel

derb

os(1

997)

Blo

nige

n(2

000)

Blo

nige

n(1

997)

Exp

ort

Gas

tana

gaet

al.(

1998

)Li

psey

and

Wei

ss(1

981)

Lips

eyan

dW

eiss

(198

4)

Ope

nnes

sH

ausm

ann

and

Fern

ndez

-Aria

s(2

000)

Mar

kets

ize

and

dem

and

Blo

mst

rom

and

Lips

ey(1

991)

Kra

vis

and

Lips

ey(1

982)

Asi

edu

(200

2)

Labo

rqua

lity

Bra

uner

hjel

man

dS

vens

son

(199

6)

Labo

rcos

tB

rans

tette

rand

Feen

stra

(200

2)S

un,T

ong

and

Yu(2

002)

Gov

ernm

ente

xpen

ditu

reA

sied

u(2

002)

Polit

ical

inst

abili

tyS

chne

ider

and

Frey

(198

5)

Wei

(200

0)W

heel

eran

dM

ody

(199

2)

Infla

tion

Asi

edu

(200

2)

Exc

hang

era

teC

ampa

(199

3)C

ushm

ann

(198

5)

Blo

nige

n(1

997)

Exc

hang

era

teun

cert

aint

yC

ampa

and

Gol

dber

g(1

995)

Cus

hman

n(1

985)

Cha

krab

arti

and

Sch

olni

ck(2

002)

Cam

pa(1

993)

Port

folio

equi

tyin

flow

Dur

ham

(200

4)S

un,T

ong

and

Yu(2

002)

Mem

bers

hip

But

hean

dM

ilner

(200

8)

Dre

her,

Mik

osch

and

Voig

t(20

10)

Dre

her,

Mik

osch

and

Voig

t(20

15)

Tabl

e4:

Effe

cts

ofse

lect

edva

riabl

eson

FDI

53

Category Proxy

i Scientific research levelRD expenditures

Number of Patents

ii Infrastructure quality Number of telephone users

iii International trade

Trade protection

Trade share in GDP

Total import amount

Tariff rate

iv Domestic market

GDP

GDP per capita

GDP growth rate

v Labor market

Labor force

Labor quality

Labor cost

Total labor force

The ratio of labor force with tertiary education to total

Adjusted labor income share in GDP

vi Financial marketOfficial exchange rate

Exchange rate deviation

vii InstitutionPolitical stability

Government expenditure

viii Other determinants

Portfolio equity inflow

Inflation rate

Membership in international organization

Table 5: The possible determinants of FDI

54

Variables Definition Sources

Dependent variables FDIit Foreign direct investment, net inflow (current US$ in mil-

lions)

World Development In-

dicators (2015)

Independent variables PHONEit Fixed telephone subscriptions (per 100 people) World Development In-

dicators (2015)

GDPit GDP (current US$ in millions) UNCTADstat (2014)

LFit Labor force, total (absolute value in thousand) UNCTADstat (2014)

GDPcit GDP per capita (current US$) UNCTADstat (2014)

GDPgit GDP growth (annual %) UNCTADstat (2014)

RDit Research and development expenditure (% of GDP) World Development In-

dicators (2015)

PGNit Total patent grants, direct and PCT national phase en-

tries (absolute value)

WIPO IP Statistics

(2015)

TARit Tariff rate, applied, weighted mean, all products (%) World Development In-

dicators (2015)

IMit Imports of goods and services (current US$ in millions) UNCTADstat (2014)

TRADEit Sum of export and import (% of GDP) World Development In-

dicators (2015)

LQit Labor force with tertiary education (% of total) World Development In-

dicators (2015)

LCit The adjusted labor share in GDP (%) ILO Statistics (2015)

GECit General government final consumption expenditure (% of

GDP)

World Development In-

dicators (2015)

PSit Political stability and absence of violence Worldwide Governance

Indicators (2014)

Exit Official exchange rate (LCU per US$, period average) World Development In-

dicators (2015)

ExDit Monthly exchange rate (US$ per LCU, period average) OANDA (2015), Calcu-

lation of author

PEIit Portfolio equity, net inflows (BoP, current US$) World Development In-

dicators (2015)

IOit Membership in International Organizations Calculation of author

Asia and

Pacific

Dummy

Dummy variable taking the value one if the country is

located in Asia

Calculation of author

Europe

Dummy

Dummy variable taking the value one if the country is

located in Europe

Calculation of author

Africa

Dummy

Dummy variable taking the value one if the country is

located in Africa

Calculation of author

America

Dummy

Dummy variable taking the value one if the country is

located in America

Calculation of author

Table 6: Name and sources of the variables used

55

Variables Mean Standard Deviation Minimum Maximum Expected Sign

FDI in billions 16.547 118.73 -0.91241 389.17

Phone subscriptions per 100 people 32.742 125.23 -0.91241 422.57 +

GDP in billions 751.85 126.71 -1.1424 425.27 +

Labor Force in billions 36.297 132.18 -1.1424 453.38 +

GDP per capita in billions 0.017951 135.62 -0.99123 467.79 +

GDP growth rate(%) 3.702 139.84 -0.99123 482.97 +

RD intensity 1.2141 145.24 -1.2444 504.95 -

Number of Patents in thousand 10.255 161.82 -1.5269 591.33 -

Tariff rate(%) 5.1382 186.41 -1.2191 715.46 -

Import in billions 159.45 211.93 -0.99096 837.5 +

Trade ratio of GDP(%) 88.695 234.53 -1.0584 947.91 +

Labor Quality ratio(%) 23.442 288.59 -1.1469 1206.1 +

Labor cost share in GDP(%) 44.054 310.61 -15.03 1294.1 +

Government consumption share in GDP(%) 17.151 317.59 -1.3283 1338.2 -

Political Stability 0.32596 399.05 -1.2332 1704.8 -

Official exchange rate(%) 0.52755 452.62 -4.0483 1930.5 +

Standard deviation of monthly exchange rate 0.022872 445.2 -1.252 1892.6 -

Portfolio equity inflows in billions 7.247 454.44 -1.1826 1937.8 +

Membership level 36.008 20.252 -1.8042 78.874 +

Table 7: Summary statistics for the full sample (57 countries) and the hypothesized signs

of all independent variables

56

Asia and Pacific Europe Africa America

India

Sri Lanka

Australia

China

Japan

Mongolia

New Zealand

Singapore

Armenia

Azerbaijian

Belarus

Turkey

Ukraine

Kazakhstan

Russian Federation

Austria

Belgium

Bulgaria

Czech Republic

Denmark

Finland

France

Germany

Hungary

Iceland

Ireland

Italy

Latia

Lithuania

Netherlands

Norway

Poland

Portugal

Romania

Slovak Republic

Slovenia

Spain

Sweden

the United Kingdom

Egypt

Iran

Israel

Morocco

Tunisia

Mauritius

South Africa

Argentina

Brazil

Colombia

Costa Rica

Mexico

Panama

Trinidad and Tobago

Urguay

Canada

the United States

Table 8: Countries grouped by regions

57

PH

ON

EG

DP

LFG

DP

cG

DP

gR

DP

GN

TAR

IMTR

AD

ELQ

LCG

EC

PS

Ex

ExD

PE

IIO

PH

ON

E1

0.25

605

-0.1

808

0.70

45-0

.245

80.

7206

0.19

955

-0.2

7779

0.32

229

0.16

383

0.41

793

0.63

586

0.48

077

0.61

850.

1723

80.

0319

540.

2411

9-0

.054

639

GD

P1

0.35

268

0.30

337

-0.0

9636

20.

3542

20.

8618

5-0

.018

633

0.91

517

-0.2

424

0.23

432

0.26

492

0.01

1971

0.03

934

0.08

7773

-0.0

208

0.57

945

-0.0

1941

7

LF1

-0.1

3878

0.18

875

0.02

3663

0.35

022

0.13

342

0.40

204

-0.2

0651

-0.0

5917

10.

0437

31-0

.171

54-0

.258

14-0

.000

2420

4-0

.046

468

0.14

135

-0.0

1536

7

GD

Pc

1-0

.304

490.

7227

40.

2182

1-0

.231

930.

3889

50.

1322

0.46

004

0.52

012

0.47

569

0.56

231

0.19

674

0.03

475

0.26

439

-0.0

8343

9

GD

Pg

1-0

.262

64-0

.055

453

0.07

9698

-0.1

0634

0.08

3953

-0.1

0291

-0.2

7027

-0.3

395

-0.1

9234

0.00

8777

1-0

.060

503

-0.0

5521

0.01

721

RD

10.

3457

1-0

.166

50.

3921

9-0

.012

974

0.44

970.

6126

70.

6337

70.

4052

10.

1471

70.

0093

547

0.23

875

-0.0

8046

4

PG

N1

0.09

4567

0.74

909

-0.2

0256

0.25

746

0.23

226

-0.0

2486

30.

0493

190.

0102

39-0

.052

928

0.49

393

-0.0

3553

5

TAR

1-0

.104

75-0

.185

56-0

.186

47-0

.225

14-0

.186

18-0

.231

42-0

.097

592

-0.0

3151

8-0

.027

408

-0.0

2985

8

IM1

-0.0

7592

10.

2559

50.

3036

40.

0473

680.

0851

270.

0956

870.

0018

133

0.49

769

-0.0

1738

4

TRA

DE

1-0

.004

2404

0.06

6656

-0.1

7417

0.29

995

0.00

8535

3-0

.036

271

-0.0

5057

9-0

.082

403

LQ1

0.27

825

0.27

355

0.14

894

0.23

652

-0.0

6446

0.17

784

-0.1

3506

LC1

0.60

116

0.44

451

0.15

970.

0506

880.

1759

7-0

.076

291

GE

C1

0.36

937

0.07

2649

-0.0

0121

450.

0005

0733

-0.1

4299

PS

10.

0764

42-0

.025

442

0.12

018

-0.2

0678

Ex

10.

6336

60.

1010

90.

2743

7

ExD

1-0

.007

884

0.40

349

PE

I1

-0.0

2813

3

IO1

Tabl

e9:

Cor

rela

tion

Mat

rixfo

rall

pote

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Tabl

e10

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posi

tion

59