Bachelor THESIS RSM P.J. Heeremans

Bachelor Thesis FDI on GDP per capita across regions

“This document is written by Patrick Heeremans and Anton Meeusen who declare that each individual takes responsibility

for the full contents of the whole document. We declare that the text and the work presented in this document is original and that no sources other than mentioned in the text and its references have been used in

creating it. RSM is only responsible for supervision of completion of the work but not for the contents.”

Team number: 8 Students: 1. Patrick Heeremans 373594

2. Anton Meeusen 347216 Date of submission: 29/05/2013 Institution: Rotterdam School of Management – Erasmus University

Erasmus University: Rotterdam School of Management 2

Table of contents Table of contents ............................................................................................................................... 2

Abstract ............................................................................................................................................ 3

1. Introduction .............................................................................................................................. 4

2. Literature review ....................................................................................................................... 6

3. Meta-analysis ............................................................................................................................ 9

3.1 Forest plot ............................................................................................................................... 9

3.2 Effect size parameter ............................................................................................................. 10

3.3 Vote counting ........................................................................................................................ 10

4. Methods .................................................................................................................................. 11

4.1 Research design ..................................................................................................................... 11

4.1.1 Time lag coefficient of FDI and GDP ................................................................................. 12

4.2 Defining the sample ............................................................................................................... 12

4.3 Measurement protocol .......................................................................................................... 13

4.3.1 Data collection and content analysis protocol ................................................................. 14

4.3.2 The World Databank, World development Indicators ...................................................... 14

4.3.3 The UNCTAD ................................................................................................................... 14

4.3.4 The OECD ........................................................................................................................ 15

4.4 Data Matrices and selected sample ........................................................................................ 16

4.5 Possible research biases ......................................................................................................... 21

5. Results ..................................................................................................................................... 22

6. Analyzing the results ................................................................................................................ 27

7. Discussion and future research ................................................................................................ 29

8. About the authors.................................................................................................................... 31

9. Summary form ......................................................................................................................... 32

10. References ........................................................................................................................... 34


Abstract This study tries to further uncover the relationship between Foreign Direct Investment (FDI) on GDP per capita (GDPPC). Our aims is to find out whether there is a positive or negative relationship between FDI and GDP. A lot of previous research claims a significant positive relationship. However, there are many differences in these studies about why and how this relationship is positive (table 2). We also haven’t found any study which tries to explain differences in the relationship of FDI and GDPPC across regions. Even though many academics have researched this topic, there isn’t any true consensus about the relationship. The topic remains important enough to research more in-depth. For host countries receiving FDI it is important to know to what extent they are able to transform FDI into an increase in GDPPC. The general premise is that an increase in wealth helps develop host countries and their inhabitants. We found a significant positive relationship between FDI and GDP. The overall regression coefficient we acquired via our analysis is 0.7305. This effect size is much higher than previous studies (see 3.1 Forest plot). Most previous studies already claimed a positive impact and we found an even stronger positive relationship. The overall regression coefficient effect size of the literature meta-analysis including our study is 0.3211. The meta-analysis without our study has an overall effect size of 0.2246. This shows that our study increases the effect size of the aggregated view of several previous researches that FDI stimulates GDPPC. The Middle-Eastern countries have profited the most out of their FDI. Eastern-Europe and Asia follow closely and are also relatively efficient with their FDI. Bolivia, Paraguay, Venezuela and to some extend Kenya, Serbia and the Philippines are especially bad at transforming FDI in to an increase of GDPPC. We hope this study contributes to the general knowledge of this topic. Our findings strengthened the general view of the majority of other researchers (see Table 2. Literary overview). We found a high enough overall effect size from which we can conclude that there is a strong positive relationship between both FDI and GDPPC. That is why we think all nations and especially developing countries and regions as a whole, should use FDI to increase their citizens welfare and level of development. For future research we recommend studying the different results we found in our sub-group analyses and to also study the poor performing countries more in-depth. We also weren’t able to identify the optimal time-lag for the dependent variable GDPPC. We assumed a time lag of 3 years. The optimal time lag coefficient will increase the reliability of future research. We also found several possible research problems that weren’t addressed by previous academics and we also weren’t able to take into account e.g. civil unrest, inflation rates and currency fluctuations on the impact of FDI on GDPPC. These moderating variables haven’t yet been addressed and future research could uncover there impact on FDI and GDPPC.


1. Introduction Our hypothesis for this research is as follows:

Foreign Direct Investment has a positive impact on the GDP per capita of

developing countries.

Based on this hypothesis we’re going to investigate whether the level of foreign direct investment (FDI) has a positive influence on the economic welfare within a certain country measured in GDP per capita (GDPPC). In general, it is assumed that an increasing amount of FDI to a specific country leads to a higher amount of GDP per capita in US Dollars Kalemli-Ozcan &Sayek (2000), Kumar & Pradhan (2002), Hermes and Lensink (2003), Nath (2005) Hansen & Rand (2005), Adams (2009) and Gohou & Soumaré (2012). It is interesting to know if this theorem is true, because this might urge developing countries with more closed systems and entry barriers to open up to FDI. If the relationship turns out to be positive there is a possibility for both corporations and host countries to achieve growth. It is difficult to say whether or not an effect is relevant for managerial or business practices. One might argue that something becomes relevant the moment its impact becomes significantly high enough to either pose a threat or an opportunity to existing business or new business. This study aims to find out if effect really is positive and exists based on previous studies from different researchers on different developing countries. Our pre research opinion is that FDI can be a way for increasing a host countries’ overall economic growth. However, the implicit reason for us to research this question is to find out if a country gets more developed. An increase in GDP per capita of a country doesn’t answer this question and it remains unanswered if everybody within that host country is able to profit, or that just a small population drives up the average GDP per capita. We also think FDI could have a different impact on different countries due to moderating variables. That is why we do not only concentrate on the hypothesis as mentioned earlier, but we will also try to find out if there are regional, continental and individual differences. We are going to try to identify which regions are using their FDI more effectively. If we can identify these regions, future studies can do more in-depth analysis on these specific countries and research why those specific regions perform better than others. Above all, this research hopefully results in a better understanding of the phenomenon and helps the developing countries to establish a healthier economic system. There has been a lot of academic interest in this topic. The current opinion within the research community is that FDI has a positive effect on GDPPC in a host country. Within the research community there is a broad division of two groups on this particular subject. There is a group which is skeptical with regard to the effect of FDI on the growth of GDPPC and a group that thinks there is a positive relationship between FDI and GDPPC. The more skeptical group claims FDI has no, a limited or a negative effect on the increase of GDPPC. FDI would have negative effects on developing countries, because the local companies are not able to compete with the investing companies and the local workforce isn’t capable of capturing the spillover effects caused by FDI Carkovic and Levine (2005), Carkovic & Levine (2002), Mansfield and Romeo (1980) and Amin (1974).


On the other side there is a group of academics who claim FDI is very important for economic growth. They see FDI as a resource for creating GDPPC growth and it has an important relationship in creating welfare. They think this is the way developing countries can increase economic welfare and that FDI should be utilized Kalemli-Ozcan & Sayek, (2000), Kumar & Pradhan (2002), Hermes and Lensink (2003), Nath (2005), Hansen & Rand (2005), Adams (2009) and Gohou & Soumaré (2012). There is also a third group of academics which is trying to investigate the causal relationship between FDI and GDP. They are trying to find out if an increase in GDPPC is the product of FDI, or if FDI is the product of an increased GDPPC Zhang (2001), Chowdhury and Mavrotas (2003), Toda and Yamamoto (1995) and De Mello (1999). We noticed that the subject has been gaining academic interest in the past 10 years. The gross of the studies conducted to this subject date between 2004 and the present. This is an interesting observation, because stimulating economic growth of developing countries has been for some time on the agenda of numerous countries and institutions. This might be because data for these studies are more readily available due to increased development of monitoring agencies. This together with the disagreement amongst researches could be the reason for all the interest in the subject.


2. Literature review The first thing we noticed from the literature is that studies before 2004 show mixed results and the 8 studies after 2004 all show a significant positive relationship between FDI and GDP per capita. This is an important observation and could be the result of increased reliability of data related to the host countries. In recent years the amount and reliability of data of developing countries has increased substantially due to the development of host countries and the institutions monitoring them. Another explanation could be that the scientific community is refining their research designs and methodology and thus finding more similar results. On the other hand it could just be a coincidence that the last 8 studies all show positive results. There is an increasing interest in the Granger Causality study design which tries to identify the causal relationship of FDI on GDPPC and GDPPC on FDI. Examples of these kinds of studies are Zhang (2001), Chowdhury and Mavrotas (2003), Toda and Yamamoto (1995), De Mello (1999), Hansen & Rand (2005) and Carkovic & Levine (2002). This is interesting because the issue of causality between FDI and GDPPC is one of the reasons researchers don’t agree with each other. Increasing GDPPC may cause more corporations to invest in countries, thus leading to an increase in FDI. FDI may also be the reason why GDPPC increases. These studies provided evidence that the causal relationship goes both ways. If this theory is true then more FDI leads to more GDPPC and more GDPPC leads to more FDI. This would be a positive investment spiral that enhances economic growth substantially. What we noticed is that most researchers argue for a precondition for having a positive relationship between FDI and GDP per capita. Some researchers argue that poorer countries or transitional countries perform better with certain amounts of FDI inflow Gohou & Soumaré (2012) and Apergis et al., 2008). Other research argues for the precondition that the country has to be wealthier on order for FDI to lead to GDP per capita Blomstrom, Lipsey & Zejan (1994). Others highlight the importance of a developed infrastructure in a host country Calvo & Sanches-Robles (2002) and Kumar & Pradhan (2002). Developing countries should consider these preconditions and whether or not they apply to them before they open up their FDI policy. Different researchers also found different results when analyzing the long term effect of FDI on GDPPC. There is the possibility that FDI won’t directly affect GDPPC but through a time lag. The possibility of the time lag dependant variable GDPPC has also been studied by some researchers with mix results. Hansen and Rand (2006) claim to have found long term significant positive results as opposed to Carkovic and Levine (2002) now claim that there is no significant result. Generally, every researcher uses a panel study which helps to contribute to the uniformity of the research. There are a couple of single country cases but these don’t contribute to the general knowledge due to external validity. The independent variable is often GDPPC in $ or FDI as a percentage of GDP (growth). The dependent variables chosen by the researchers are more diverse: GDP, GDP per capita, HDI, Real GDP or GDP as a percentage of GDP per capita. These variables will eventually measure the same thing: FDI and economic growth.


We expected that the different studies would rely on the same data sets. We didn’t expect that different institutions and government agencies would keep record for FDI per country for an entire continent. This improves the reliability of both our meta-analysis as our general research and eventually on our findings. We constructed a literature table summarizing the most important information from previous studies (see table 2). Almost every study was conducted on a panel basis and in order to conduct a comparable study ourselves it is important to also use a panel study. This will enable us to compare our study with previous ones. The research findings amongst different authors differ extensively and we had to begin with determining the overall effect size from where a result can be considered to be significantly relevant. In the next chapter we have therefore conducted a meta-analysis of previous studies and calculated an observed effect size (based on the standardized regression coefficient) from where our study can be considered to be significantly positive. An important precondition for this is that the studies used for this meta-analyses share our domain, focal unit, and results based on the standardized regression coefficient. Our study is based on the ‘new statistics’ pioneered by Cummings. The old inferential statistics has a couple of downsides: the research findings are heavily influenced by the number of observations and random chance plays a big factor, the results of multiple studies aren’t easily unified and authors are tempted to call their findings ‘significant’. Since this topic is already heavily studied with mixed findings we want to aggregate these results with the findings of our own study. We hope to thereby create a solid effect size measure that can unambiguously tell us something about the true nature of the causal relationship between FDI and GDPPC. Cummings new statistics suites this need perfectly and we will therefore use the ‘overall observed effect size parameter’ instead of p-values. P-values aren’t indicators for an empirical effect (Cumming, 2012). Using p-values can mislead us in trusting conclusions according to Cummings (2012). The new statistics also deals with the problem of missing values (Cumming, 2012). This is an important matter, because our sample will consist of countries with missing data values due to several reasons mentioned in 4.4 Data Matrices and selected sample. Author Significant/non-

significant/argue result between FDI and GDP (per capita)

Pre-condition for significant result/reason for conclusion

Type of research

1 Gohou & Soumaré (2012)

Significant positive result

Poorer countries show more result from FDI Panel study

2 Hermes & Lensink (2010)


Panel study

3 Adams (2009) Significant positive result

Only when recipient country has an overall incentive and capacity to capture spillover effects.

Panel study

4 Apergis et al. (2008)


Only transitional countries in data set Panel study

5 Eller, Haiss, and Steiner (2006)


Dataset is contaminated with developed European countries.

Panel study

6 XIAOYING & XIAMING (2005)


Only after 1985. Not prior of 1985. Panel study


7 Hansen & Rand (2005)


Panel study

8 Nath (2005) Significant positive result

Only in the presence of developed infrastructure

Panel study

9 Carkovic and Levine (2005)

No significant result found

Panel study

10 Alfaro et al. (2004)

Argue positive relationship

Only when sufficient developed financial markets are available to draw in FDI

Panel study

11 Hermes and Lensink (2003)


Only for financially developed countries Panel study

12 Calvo & Sanchez-Robles (2002)

Argue that there is a positive relationship

Only in the presence of a developed infrastructure

Panel study

13 Kumar & Pradhan (2002)


Only in the presence of a developed infrastructure

Panel study

14 Carkovic & Levine (2002)


Panel study and cross-sectional study as well

15 Kalemli-Ozcan &Sayek (2000)

Significantly positive result

Only when country has developed financial markets

Panel study

16 Borensztein, De Gregorio, and Lee (1998)

Argue for a positive relationship

Only when there is an educated workforce to capture spillover effects

Panel study

17 Balasubramanyam, & Dapsoford (1996)

Argue that trade openness is important to obtain positive relation

Panel study

18 Blomstrom, Lipsey, Zejan (1994)

Argue for positive relationship

Only when a country is sufficiently rich Panel study

19 Bornschier & Chase-Dunn (1985)

Argue negative effect

Foreign controlled corporations undermine organic growth of the economy

Panel study

20 Mansfield and Romeo (1980)


Panel study

21 Amin (1974) Argue negative effect

Foreign controlled corporations undermine organic growth of the economy

Panel study

Green: Authors show a significant positive result. Red: Authors show a significant negative result or no relationship at all. Yellow: Authors who argue for certain results but don’t have significant data. Table 2. Literature overview


3. Meta-analysis We decided to only use regression coefficient based empirical studies for our meta-analyses. We chose this type of coefficient, because there was an abundance of empirical studies that used the regression coefficient and this would make our meta-analysis more reliable. Our initial impression of the literature (based on our literature table) was that FDI has a positive influence on GDP per capita. Our meta-analysis tells us that there is a positive effect size, but this is a small positive effect.

3.1 Forest plot

From the forest plot we can derive that the average regression coefficient of all studies in relation to the hypothesis is 0.2246. This is a poor explanatory indication. This means that all FDI variables are not strongly explanatory for the degree of GDP growth in a certain country. On average the studies showed a positive result, but the effect size parameter of 3 out of the 7 studies ranged also into the negative. This indicates that there is a possibility that the relationship is actually negative. Overall on the basis of the average regression coefficient of determination, we can say there is a positive effect applicable to the dependent variable. However, this coefficient is lower than we expected with regard to our pre research opinion. The forest-plot based meta-analysis also showed us that the effect could actually be negative.

Figure 1: Meta- analysis forest plot of previous studies

Xiaoying & Xiaming (2005) Adams (2009) Ram & Zang (2002) Gohou & Soumaré (2012) Hermes & Lensink (2010) Balasubramanyam et all. (1996) Borenztein & Dapsoford (1997)


3.2 Effect size parameter

Our meta-analysis only consisted of regression coefficient based studies. The meta-analysis based forest plot shows that many past studies have an average effect size parameter between 0.0961 and 0.3456. The forest plot uses a 95% confidence interval in order to define the relevant effect size parameter, which is calculated to be 0.2246. There are only two studies which have a mean effect size of above 0.5. The first study in the forest plot of Xiaoying & Xiaming (2005) states there is a positive result with a mean of 0.1. Adams (2009) also states there is a significant positive result with a mean of 0.24. The study of Gouhou and Soumaré (2012) also claims significant positive results with a mean of 0.15. Hermes & Lensink (2010) also claim significant positive results with a mean of 0.6 in the forest plot. Balasubramanyam & Dapsoford (1996) argue a positive result, but also say they cannot prove this. They have a mean of above 0 and below 0.1 in the forest plot. Borenztein et al. (1997) are in the same position. The interpretation of these results indicate that there is some evidence for a causal relationship. The studies of Ram & Zang (2002), Adams (2009) and Borenztein et all. (1997) also measured negative results as is shown by the left bound intervals of their results. Adams (2009) states that these negative results are caused by the FDI cost, which co-exist with benefits of FDI. The managerial relevance of an effect was the criteria that something either has to poses a threat to existing businesses or provides an opportunity. Whether or not the effect size found here is relevant for managerial purposes is a question which still is not fully answered. The slightly positive meta-analysis regression coefficient together with the preconditions mentioned by previous studies in 2. Literary review does give us the impression that there could be an opportunity for countries and corporations to stimulate commerce and economic welfare. The potential relationship could present an opportunity and thus could be relevant for business practice.

3.3 Vote counting

In 2. Literature review we constructed a table of all the relevant studies and its findings. 11 of the 21 studies showed significant positive results. Three studies showed no significant results and 6 studies argued for a positive relationship but weren’t able to provide empirical evidence. Our interpretation of the current literature based on this table is that there probably is a positive relationship between FDI and GDPPC.


4. Methods The chosen hypothesis for this study is: Foreign Direct Investment has a positive impact on the GDP per capita of developing countries. Every hypothesis consists of three different components: focal unit, the domain and the relation. These will be specified for our hypothesis. (A) Focal Unit Our research will focus on the assumed relation that a higher extent of foreign direct investment will lead to a higher economic growth in GDP per capita for a particular host country. The focal unit will therefore be at a country level. (B) Domain The domains on which the research will concentrate are developing countries. We are going to limit this domain by focusing only on developing countries. The wealthier countries will be excluded from the research, because we want to find out if developing countries can enhance their economic welfare by FDI. After all, it is much more difficult to see the effect of FDI on GDPPC in relatively developed countries. Our opinion is that it is more relevant for developing countries to enhance their economic growth than for already developed countries. Within the domain our research findings should be applicable to all developing countries. (C) Relation The relation of the hypothesis concerns an assumed positive causal relation. An increase in the extent of the independent variable will result in an increase of the dependent variable.

4.1 Research design

We will conduct our research on a panel study basis with a longitudinal research approach in order to find differences in the amounts of FDI and the outcomes in economic growth (GDP per capita) of a host country. We will also try to uncover different effect sizes for different regions. By doing this we hope to identify regions that manage their FDI more efficiently and propose future research that can uncover the reason for their efficiency. By investigating the effect size in general and for specific regions we hope to contribute to the existing knowledge. An experiment has the highest validity but we aren’t able to conduct one. For obvious reasons the stakes are too high and a countries would never let us experiment with their economics. Nor will it be ethically correct to deprive the control county of economic welfare. This does not make it possible to test our hypothesis in a controlled environment, nor can we influence the independent variable (FDI) in order to compare results on GDPPC per capita. These are all pre-condition for conducting an experimental research. That is why we are conducting a panel research, which is a form of longitudinal research. In this research design we are going to investigate whether the impact of FDI on GDPPC changes over time. The effect of FDI on GDPPC can only be measured over a longer time period. By looking at the same cases multiple times this research is stronger in making conclusions about our hypothesis than a cross-sectional design alone would be. That is why we chose the panel approach over the cross-sectional design.


4.1.1 Time lag coefficient of FDI and GDP

One other crucial item to take into concern is whether or not there is a time lag effect between FDI and GDPPC. GDPPC could be a lagged dependent variable. FDI could influence GDP but this effect could only be significant after a number of years. This would result in the long run effect of FDI on GDP. This is also a crucial question to be addressed before analyzing the data. We have to ask ourselves which data years of FDI we must compare with which data years of GDP. Previous researchers have addressed this issue. Hansen & Rand (2006) for instance claim to have found a positive long term relationship between FDI and GDP. They incorporated a lagged dependent variable of GDP t-1, t-2, t-3. This could be of major influence in creating the most reliable result. We chose a time lag of three years for our research, because we believe the impact on FDI on GDP per capita cannot be measured in a shorter time span. This is however a guess of the optimal time lag coefficient. The years of time lag were the accumulated regression analysis yields the highest number and with the lowest confidence interval width should be used to run every regression. This is an area where future research can be beneficial for both the literature as well as the host country receiving FDI.

4.2 Defining the sample

While our intuition is quite clear on the definition of developing countries, segmenting countries into different categories is quite difficult. One has to ask themselves when a country is developed and what the criteria are to evaluate each country. There is also the added difficulty of identifying countries that became developed at some point in time and the identification of countries that were developed and became undeveloped. This could happen due to war, civil unrest or any other type of social-economic or political problem. IMF UNDP World Bank Name of 'developed countries'

Advanced Countries

Developed countries High-income countries

Name of 'developing countries'

Emerging and developing countries

Developing countries Low- and middle-income countries

Development threshold Not explicit 75 percentile in the HDI distribution

US$6,000 GNI per capita in 1987-prices

Type of development threshold

Most likely Absolute

Relative Absolute

Share of countries 'developed' in 1990

13 % 25 % 16 %

Share of countries 'developed' in 2010

17 % 25 % 26 %

Subcategories of 'developing countries'

(1) Low-income developing countries and (2) Emerging and other developing countries

(1) Low human development countries, (2) Medium human development countries and (3) High human development countries

(1) Low-income countries and (2) Middle-income countries

Table 3. Classifications of Countries Based on Their Level of Development: How it is Done and How it Could be Done (Nielsen, 2011).


When we look at the previous table it is obvious that there isn’t a clear-cut definition of developing countries. We therefore decided to only use a single database and use the definition given by that databank. Thereby we’re avoiding the difficulty of the plural definition.

4.3 Measurement protocol

First of all it is important to only pick countries which are in the focal unit of our study. The focal unit is developing countries from all over the world. Previous studies that investigated FDI and GDP and used both developing as well as developed countries are not useful for this research. Their findings have been compromised by polluted data. We use data in which the time periods are the same and in which we can differentiate between counties in the same region. We already specified this in 4.3 Data Matrices and sample selection. This simplifies our research and increases our reliability. The following three phenomena are the building blocks of our research and therefore clearly specified. Foreign direct investment is a direct investment of a foreign company in a host country. FDI is usually in the form of mergers & acquisitions, joint ventures, licensing agreement or other types of investments with controlling interest. Portfolio investments such as stocks and bonds, or other types of indirect investment which can be liquidated on short term basis and in which the foreign company doesn’t have a controlling interest of 10% aren’t considered to be FDI. The old classic definition of FDI only entailed the investments in buildings, but in recent years it also entails the management interest of at least 10%. This new definition also contains licensing of intellectual property and other forms of non-physical investment. According to the Stablecurrencybenchmark.com measurement of this phenomenon is usually in US dollars. To measure a country’s current economic wealth we are going to use the Gross Domestic Product Per Capita (GDPPC) in US $. This is also the most common way to measure a countries wealth per citizen and almost every researcher uses this indicator or a derivative of this indicator. Both variables, GDP and FDI, are measured in US Dollars. The US Dollar is a common currency to measure monetary values because it is a widely recognized metric with a relative large volume flow compared with other currencies. The US $ is standard used in the majority of databanks and studies related to economics. This contributes to the reliability and comparability of data between different monitoring agencies. Another benefit of the US dollar is that it is a relative stable currency and is ranked the 4th most stable currency worldwide by EWI Stable Currency Index at the moment. Another thing to notice is that any currency is of the ratio type. The plural definitions of FDI and the multiple definitions of developing countries together with the relative currency fluctuations and the possibility of different inflation rates can cause problems with our research. Our data is collected from monitoring agencies which should have a uniform definition of FDI. Nevertheless, it is important to assess how each database classifies which kind of investments as FDI and what is not classified as FDI. We


don’t know whether or not the currency fluctuations of the US $ is important in our research. We haven’t found any study which has addressed this possible issue. Nor have we found any study addressing the issue of different inflation rates.

4.3.1 Data collection and content analysis protocol

As mentioned before our literary review is crucial to identify where we can contribute to the existing knowledge and assess what kind of impact our results will have on the overall literature view. Our extensive literature review also enables us to asses where the different researchers got there data from. In order to get the most reliable and valid results we need to asses each database on the criteria of reliability and validity. It is also important to acquire information about how each database classifies its data and what is considered to be FDI and what is not. The studies of our meta-analysis used various databases: World Bank database Zang (2002), World Investment Directory by the UN Xiaoing & Xiaming (2005), International Financial Statistics by IMF Borensztein & De Gregorio (1998), World Economic output by IMF Carkovic & Levine (2002), Geographical Distribution of Financial Flows to developing countries by OECD, Transnational Corporation World Development Balasubramanyam & Salisu & Dapsford (1996). In short, a lot of different databases were used. Many of these databases are readily available and don’t require any special access. This relieves our research with the difficulty of finding data and presents us the opportunity to select and cross reference the most suitable databases. In term this will increases the reliability of our results but challenges us to scrutinize the database collection methods.

4.3.2 The World Databank, World development Indicators

The World Bank gathers FDI data through multiple monitoring agencies and can be considered to be a meta databank. Their definition of FDI includes the minimum investment of 10% holding stock in management interest. They also include equity capital, reinvestment of earnings, other long-term capital and short-term capital from the balance of payments. Data is represented in current US Dollars. Their data is supplemented by the International Monetary Fund, Balance of Payments database, the United Nations Conference on Trade and Development and official national sources. The information gathered by the World Bank is freely accessible through online downloading.

4.3.3 The UNCTAD

The United Nations Conference on Trade and Development (UNCTAD) is an organization which dedicates itself to helping developing countries to develop policies that help promote sustainable economic growth. Their data is also freely accessible through internet downloading and classifies not only countries, but also continents, regions and multiple categories of developing countries. They don’t explicitly state what their definition of FDI encompasses, but we’re going to assume it’s the same as the general description of the phenomenon. They don’t state explicitly where which monitoring or governmental agencies provide these data.


4.3.4 The OECD

The Organization for Economic Cooperation and Development also has an online database which is freely accessible. Their database is probably the most sophisticated with the most options to view different regions, types of FDI, categories etc. However, their website is slow and complicated to work with and not user friendly. We also need to find out which monitoring agencies provided the data to OECD. These databanks allow us to select individual countries and this helps us to prevent data pollution and sample corruption. It would not make sense using data from regions that are relatively developed since our hypothesis refers to developing host countries of FDI. Our domain consists of developing countries. By preventing data pollution we preserve our validity. We constructed a data collection checklist below: 1 2 3 4 5 6 7 8 9 Name of Source Eastern-

Europe Africa South-

America Central-America

Asia Middle-East

No Polluted data

Uses US Dollars

Data on separate countries

World Databank x X x x x x x x x UNCTAD X x x x x x x x OECD x X x x x x x

Table 4. Data collection checklist – Mark the relevant boxes. If a source marks boxes 7, 8 and 9 data of this source will contribute to the reliability of the research. If not, this source is excluded from this study. For creating an overview on different regions boxes 1 to 6 are incorporated in this table. Boxes 1 to 6 can be taped when a databank has information in a region on both FDI and GDP.

This tool allows us to evaluate and cross reverence the best possible candidate for our data source. All three databanks provide data on different continents, regions and countries; which makes it possible to compare the impact of FDI on GDPPC across continents. As can be seen UNCTAD has lesser value compared to World Databank because this databank doesn’t have information on Eastern-Europe. The OECD also doesn’t score that well. It doesn’t have data on South-American countries and the Middle-East. All three databanks provide information on FDI and GDPPC. We decided to use the World Databank as our main source of data. The World Databank encompasses every region we want to investigate in this paper and is the most users friendly. We decided to base the definitions of FDI and the definition of developing country on the data sources description of these entities, as described in 4.3 Measurement protocol. The definitions of FDI and the definition of developing country will be based upon the World Databanks definition.


4.4 Data Matrices and selected sample

For this research we are going to use a standardized regression coefficient just as we did in the meta-analysis. By using the same effect size measure we can incorporate our research in the meta-analysis of 3.1 Forest plot and contribute to knowledge in this topic area. According to the claims of different researchers, whose studies we have incorporated in our meta-analysis, the minimum effect size measure for having a significant positive result is 0.1. In order to measure the effect size we constructed data matrices as shown below. The columns show GDPPC in US$ and FDI in Us$ during certain time period. As can be seen in the data matrices we chose different countries from all over the world. These countries are grouped into regions in order to be able to compare results across continents and regions. For our data set we chose a resent sample which ranges from 1991 up until 2011. We didn’t include years prior to 1991, because data wasn’t available for all the countries on every year. In 1990 the Iron curtain lifted and former Soviet nations were able to receive and register FDI data. We want to include these former Soviet nations into our analysis and that’s why we chose data from 1991 onwards. We chose to exclude some countries, because their data is not available for every year between 1970 and 2009. Some countries only very recently became independent and don’t have data before their independence e.g. Curacao, Russia, Bermuda, Bhutan etc. (World Databank, 2013). We did use some countries with minor missing data values. We chose to make a worst case and average case scenario analysis for the countries with missing values only. By taking the average of the data set available we are able to make an average scenario and by filling in 0 for every missing value we are able to create a worst case scenario. Al ‘bold’ figures in the tables 5, 6, 7, 8 and 9 are the averages for GDP or FDI. We have used these averages to substitute for missing data. We have run a regression with these results and also made a worst case scenario by filling in worst possible figures for the missing data (see 5. Results). The bold sections of table 10 represent the worst average scenario. We are convinced that a sample of 20 observations per country is sufficient to uncover a possible relationship and we think that years prior of this data are not fully relevant for the current relationship between FDI and GDP. We argue that different levels of moderating variables in the past such as corruption, infrastructure, war, etc. could have a significant effect on the current and true effect size. Our data set shows that these countries qualify for the definition of being a developing country, according to the World Databank. The chosen countries are the countries per region/continent with the most available data (during the chosen time-period). The only country that doesn’t have the same amount of data as the rest of our sample is Serbia, which only has 13 observed years of FDI and GDI. We are still going to include this country in our analysis, because Serbia is a good candidate to investigate post-Soviet regimes countries’ response to FDI.


We are comparing results across continents, because our literature review consists of studies on many different countries, regions and continents. We chose for five countries per region for which data was readily available. In order to balance our results we specifically picked five countries for every region. By only selecting 5 countries per region we simplified our research and minimized our selection bias. By doing this we were not forced to make difficult tradeoff decisions on sample selection. By only selecting 5 countries per region we were also able to select the countries with the most reliable data and thus enhancing our reliability. The data matrices of every region separately can be found on the following five pages.

Congo Dem. Rep. Kenya Liberia Malawi Niger

1991 240 12390000

336,28105 18830977 166,3993 8410000 229,9255 -2,9E+07 289,6833 15157374

1992 208 -730000 327,80978 6363133 108,8026 -1,1E+07 185,8686 -7100000 282,534 56390111

1993 261 6870000 222,53693 1,46E+08 78,99178 -5,4E+07 212,994 8000000 187,2791 -3,4E+07

1994 136 -1500000 268,29482 7432413 64,8064 17380000 120,9355 24992618 176,2044 -1,1E+07

1995 128 -22350000 329,84805 42289248 64,35624 4600000 141,3948 5643046 204,9117 7188243

1996 127 24790000 427,28567 1,09E+08 72,32932 -1,3E+08 226,5047 15797661 209,2424 9873866

1997 131 -44350000 453,13056 62096810 125,3542 2,14E+08 258,0877 14868714 187,6381 18171246

1998 131 61330000 474,60278 26548246 141,5283 1,9E+08 164,9573 12104230 203,8859 -1023813

1999 97 11160000 423,34533 51953456 162,9679 2,56E+08 162,5963 58528206 191,33 276108,9

2000 87 72000000 406,52306 1,11E+08 185,8134 20800000 155,2715 25999996 164,6489 8437079

2001 92 80300000 404,84837 5302623 175,212 8300000 148,8812 19299991 172,0287 22895114

2002 106 141100000 399,28909 27618447 178,9003 2800000 225,2291 5899999 185,4133 2404632

2003 105 391300000 440,89251 81738243 134,6541 3,72E+08 199,6433 83151293 225,3957 14912242

2004 117 409000000 463,81304 46063931 150,9997 75351732 210,4682 1,3E+08 243,3181 26326708

2005 125 166600000 526,12996 21211685 170,3043 82802111 214,8549 1,4E+08 262,0568 49733809

2006 149 237700000 615,86076 50674725 182,2726 1,08E+08 236,204 35561532 270,809 40274237

2007 165 1793700000 726,59894 7,29E+08 212,5275 1,32E+08 268,431 1,24E+08 307,6591 98942805

2008 187 1672700000 792,2288 95585680 232,5295 2,84E+08 305,372 1,95E+08 371,62 2,82E+08

2009 175 -278000000 774,92834 1,16E+08 301,1386 1,28E+08 348,327 49130855 350,9428 6,31E+08

2010 199 2728800000 794,76721 1,78E+08 323,6497 4,52E+08 362,3028 97010028 348,796 9,4E+08

2011 231 1596024304 808,00057 3,35E+08 374,3332 1,31E+09 365,4536 92407704 374,4454 1,01E+09

GDP FDI GDP FDI GDP FDI GDP FDI GDP

Table 5. FDI and GDPPC in Africa


Colombia Bolivia Peru Venezuela Paraguay

1991 1218,303891 456900000 784,17811 52000000 1561,54

7000000 2561,487 1,92E+09 1435,075 86100000

1992 1428,504788 728700000 809,25273 93100000 1599,945 -7,9E+07 2830,261 6,29E+08 1443,635 1,18E+08

1993 1587,725226 959100000 803,38185 1,24E+08 1515,857 7,61E+08 2751,174 3,72E+08 1502,647 75000000

1994 2282,350312 1446497261 818,87057 1,3E+08 1918,86 3,29E+09 2619,187 8,13E+08 1481,369 1,37E+08

1995 2537,690248 968368273,6 898,83976 3,93E+08 2252,643 2,56E+09 3397,393 9,85E+08 1682,002 1,03E+08

1996 2617,980325 3111676590 968,48889 4,74E+08 2304,295 3,47E+09 3033,445 2,18E+09 1782,7 1,49E+08

1997 2823,543494 5562216362 1015,5556 7,31E+08 2400,86 2,14E+09 3738,725 6,2E+09 1769,235 2,36E+08

1998 2561,031495 2828826262 1066,0202 9,49E+08 2262,904 1,64E+09 3901,189 4,99E+09 1544,703 3,42E+08

1999 2204,099977 1507907130 1017,9707 1,01E+09 2021,679 1,94E+09 4105,004 2,89E+09 1393,308 94500000

2000 2511,974665 2436459923 1010,9074 7,36E+08 2060,576 8,1E+08 4818,708 4,7E+09 1323,482 1,04E+08

2001 2429,421963 2541942612 960,45685 7,06E+08 2056,398 1,14E+09 4963,042 3,68E+09 1181,83 84200000

2002 2384,075859 2133698124 914,28205 6,77E+08 2135,965 2,16E+09 3683,172 7,82E+08 915,0102 10000000

2003 2268,877065 1720493455 916,79651 1,97E+08 2279,163 1,34E+09 3257,077 2,04E+09 978,2064 27400000

2004 2762,130444 3015635874 976,68182 65430000 2559,464 1,6E+09 4304,03 1,48E+09 1200,875 37679000

2005 3404,23412 10251967315 1044,0097 -2,4E+08 2880,574 2,58E+09 5475,166 2,71E+09 1267,11 25300000

2006 3725,091367 6655995311 1230,4412 2,81E+08 3312,371 3,47E+09 6787,671 1,98E+08 1543,783 2,47E+08

2007 4678,901045 9486747731 1386,4344 3,66E+08 3817,12 5,49E+09 8382,055 2,59E+09 1997,235 1,09E+08

2008 5423,266868 10158358827 1733,6461 5,12E+08 4457,928 6,92E+09 11297,66 4,09E+08 2710,566 3,31E+08

2009 5133,391256 7137173746 1774,1952 4,23E+08 4412,387 6,43E+09 11605,8 -3E+09 2254,241 1,86E+08

2010 6186,024868 6745695605 1978,8543 6,22E+08 5283,225 8,45E+09 13657,75 7,77E+08 2840,35 4,75E+08

2011 7104,034988 13388306333 2373,9507 8,59E+08 6017,906 8,23E+09 10809,56 5,23E+09 3629,068 4,12E+08

Table 6. FDI and GDPPC in South-America


Iran Iraq Oman Sudan Syrian

1991 2452,919439 22590000 1487,644316 -3000000 5824,981 1,35E+08 418,9715 -620000 1022,982 54000000

1992 2452,919439 8500000 1487,644316 7780000 6129,968 1,04E+08 252,3483 90000 1014,555 57000000

1993 1037,070228 207550000 1487,644316 810000 5911,384 1,42E+08 310,4633 -160000 1019,136 1,09E+08

1994 1141,46831 2000000 1487,644316 -30000 5918,97 76462940 435,6511 99180000 732,9831 2,51E+08

1995 1519,977942 17000000 1487,644316 2400000 6183,911 46293889 458,8535 12000000 804,22 1E+08

1996 1818,173014 26000000 1487,644316 -3899260 6763,873 60600000 291,5798 400000 949,77 89000000

1997 1699,580022 53000000 455,44409 1120000 6987,72 64500000 368,0996 97900000 976,3584 80000000

1998 1626,120038 24000000 457,39281 7110000 6224,289 1,02E+08 345,6361 3,71E+08 1000,034 82000000

1999 1628,170452 35000000 760,59295 -6900000 6953,245 40100000 320,1214 3,71E+08 1019,331 2,63E+08

2000 1550,090608 39000000 1063,4815 -3140000 8774,934 82000000 358,5292 3,92E+08 1208,735 2,7E+08

2001 1740,796285 1084475000 759,33521 -6450000 8752,869 5201560 376,6295 5,74E+08 1282,281 1,1E+08

2002 1732,121661 3657067000 741,64176 -1590000 8706,258 1,09E+08 413,3175 7,13E+08 1272,319 1,15E+08

2003 1989,513801 2697865000 1487,644316 -20000 9221,989 24967490 481,5671 1,35E+09 1248,028 1,6E+08

2004 2369,265585 2863388000 957,16192 3E+08 10374,31 1,11E+08 572,1547 1,51E+09 1393,347 2,75E+08

2005 2753,612704 3135585000 1134,7369 5,15E+08 12720,7 1,54E+09 690,5694 2,3E+09 1561,284 5E+08

2006 3157,749312 1646568000 1585,4896 3,83E+08 14776,9 1,6E+09 893,0757 3,53E+09 1767,295 6,59E+08

2007 4004,42275 2005100000 1945,5396 9,72E+08 16360,06 3,33E+09 1125,895 2,43E+09 2099,459 1,24E+09

2008 4678,248802 1909200000 2867,2951 1,86E+09 22968,46 2,95E+09 1294,721 2,6E+09 2677,59 1,47E+09

2009 4525,948608 3047600000 2065,9297 1,6E+09 17280,1 1,51E+09 1239,352 1,82E+09 2691,598 2,57E+09

2010 2452,919439 3647500000 2532,3237 1,4E+09 20790,84 1,14E+09 1487,688 2,06E+09 2892,755 1,47E+09

2011 2452,919439 4150000000 3500,6557 1,4E+09 25220,62 7,88E+08 1435,131 1,94E+09 1552,77531 1,06E+09

GDP FDI GDP FDI GDP FDI GDP FDI GDP FDI

Table 7. FDI and GDPPC in the Middle-East – The ‘bold’ values in this table represent an average number filled in because data for this entry was missing.


Pakistan Philippines Sri lanka Vietnam Thailand

1991 395,3372263

258414487 719,23598 5,44E+08 521,2465 48349174 142,9659 3,75E+08 1702,168 2,01E+09

1992 412,1270858 336479857,1 819,3164 2,28E+08 556,8123 1,23E+08 144,1487 4,74E+08 1914,142 2,11E+09

1993 425,3357904 348556957,8 821,5959 1,24E+09 585,8937 1,94E+08 189,2605 9,26E+08 2130,7 1,8E+09

1994 418,0956814 421024638,5 946,55323 1,59E+09 654,9441 1,66E+08 229,9548 1,94E+09 2440,659 1,37E+09

1995 476,149484 722631560,7 1070,2398 1,48E+09 718,4438 55995588 288,0203 1,78E+09 2816,733 2,07E+09

1996 484,3313054 921976182,5 1169,6532 1,52E+09 757,9482 1,2E+08 337,0501 2,4E+09 3019,471 2,34E+09

1997 465,0321981 716253125,4 1136,9272 1,22E+09 812,7925 4,3E+08 361,2545 2,22E+09 2476,32 3,89E+09

1998 451,2935071 506000000 975,23238 2,29E+09 840,8738 1,93E+08 360,6008 1,67E+09 1814,125 7,31E+09

1999 445,7976721 532000000 1096,8098 1,25E+09 821,5965 1,76E+08 374,4764 1,41E+09 1964,948 6,1E+09

2000 511,7025555 308000000 1048,0705 2,24E+09 854,9267 1,73E+08 401,5478 1,3E+09 1943,238 3,37E+09

2001 490,0431322 383000000 965,7771 1,95E+08 837,6988 1,72E+08 415,7312 1,3E+09 1808,113 5,07E+09

2002 480,7402851 823000000 1009,0194 1,54E+09 903,8964 1,97E+08 440,7693 1,4E+09 1962,735 3,34E+09

2003 543,5866408 534000000 1019,6152 4,91E+08 984,8102 2,29E+08 491,5285 1,45E+09 2182,033 5,23E+09

2004 628,6264899 1118000000 1088,5732 6,88E+08 1063,161 2,33E+08 557,8234 1,61E+09 2442,308 5,86E+09

2005 690,8486252 2201000000 1204,7958 1,66E+09 1242,404 2,72E+08 642,2509 1,95E+09 2644,017 8,06E+09

2006 789,4085568 4273000000 1402,846 2,71E+09 1423,477 4,8E+08 731,1406 2,4E+09 3078,18 9,45E+09

2007 870,6294734 5590000000 1684,7771 3,25E+09 1614,411 6,03E+08 843,2043 6,7E+09 3642,917 1,13E+10

2008 978,7952813 5438000000 1925,2134 1,44E+09 2013,912 7,52E+08 1070,155 9,58E+09 3992,762 8,54E+09

2009 949,1165849 2338000000 1835,6365 2,71E+09 2057,113 4,04E+08 1129,675 7,6E+09 3838,25 4,85E+09

2010 1016,614375 2018000000 2140,1216 1,64E+09 2400,016 4,78E+08 1224,315 8E+09 4613,681 9,1E+09

2011 1189,373193 1308770000 2369,5179 1,87E+09 2835,408 9,56E+08 1407,105 7,43E+09 4972,374 7,78E+09


Table 8. FDI and GDPPC in Asia


Moldova Ukraine Slovenia Romania Serbia

1991

835,0154102 177190588,2 1489,6797 3564764706 6338,552 640122214,3 1254,148 40000000 3571,393813 979131348

1992 625,3015386 17000000 1417,8699 2E+08 6272,251 1,11E+08 1100,728 77000000 3571,393813 1,26E+08

1993 640,1654315 14000000 1258,1363 2E+08 6362,779 1,13E+08 1158,056 94000000 3571,393813 96107350

1994 460,8322558 11568000 1012,1052 1,59E+08 7230,921 1,17E+08 1323,024 3,41E+08 3571,393813 62584782

1995 476,992678 25910000 935,9681 2,67E+08 10523,72 1,5E+08 1563,95 4,19E+08 3571,393813 44985354

1996 462,172015 23740000 872,70919 5,21E+08 10635,43 1,73E+08 1562,124 2,63E+08 3571,393813 1000

1997 527,3916381 78740000 991,2301 6,23E+08 10282,32 3,35E+08 1564,508 1,22E+09 2795,031 7,4E+08

1998 448,83657 75500000 835,2603 7,43E+08 10974,49 2,16E+08 1871,189 2,03E+09 2141,214 1,13E+08

1999 321,0269725 37890000 635,76624 4,96E+08 11250,22 1,07E+08 1583,85 1,04E+09 2338,431 1,12E+08

2000 354,001668 127540000 635,70897 5,95E+08 10045,36 1,36E+08 1650,968 1,04E+09 809,2751 51887000

2001 407,7304677 54540000 780,73803 7,92E+08 10290,32 5,03E+08 1815,507 1,16E+09 1518,034 1,77E+08

2002 458,6781966 84050000 879,47505 6,93E+08 11599,9 1,66E+09 2101,741 1,14E+09 2013,667 5,67E+08

2003 548,2904455 73750000 1048,5225 1,42E+09 14607,2 3,02E+08 2736,975 1,84E+09 2613,534 1,41E+09

2004 720,9431325 87690000 1367,3524 1,72E+09 16944,19 8,31E+08 3481,2 6,44E+09 3168,881 1,03E+09

2005 831,1602652 190700000 1828,7176 7,81E+09 17854,64 9,71E+08 4572,048 6,87E+09 3391,371 979131348

2006 950,6164233 258680000 2303,0188 5,6E+09 19405,93 6,9E+08 5681,092 1,15E+10 3942,631 979131348

2007 1230,811876 536020000 3068,609 1,02E+10 23441 1,88E+09 7856,476 1,03E+10 5276,932 3,43E+09

2008 1695,973286 726610000 3891,0378 1,07E+10 27015,08 1,82E+09 9299,739 1,38E+10 6497,843 3E+09

2009 1525,526547 135150000 2545,4803 4,77E+09 24051,04 -3,5E+08 7500,34 4,93E+09 5484,054 1,94E+09

2010 1631,533195 201500000 2973,9817 6,45E+09 22897,94 6,33E+08 7539,357 3,2E+09 5269,644 1,34E+09

2011 1966,936147 294230000 3615,382 7,21E+09 24141,94 8,18E+08 8405,494 2,56E+09 6310,365 2,7E+09


Table 9. FDI and GDPPC in Eastern Europe - The ‘bold’ values in this table represent an average number filled in because data for this entry was missing.

4.5 Possible research biases

The plural definition of FDI, relative currency fluctuations and the possibility of different inflation rates can cause problems with our research. Also our time lag coefficient of 3 years is based on a guess and another time lag coefficient could result in a stronger relationship. Our data is collected from a monitoring agency which has a uniform definition of FDI (World Databank). Nevertheless, it remains important to assess how each database classifies which kind of investments as FDI and what is not classified as FDI. We don’t know whether or not the currency fluctuations of the US $ is important for our research. We haven’t found any study which has addressed this possible issue. Nor have we found any study addressing the issue of different inflation rates. The selection bias is also a big issue. We are faced with the difficulty of removing countries from our analysis because of missing data. This reduces the number of countries in our sample and also reduces our ability to claim something for the entire population of developing countries.


5. Results As we mentioned in 4.4 Data Matrices and selected sample we are going to run multiple regressions for each country and compare them with each other. Table 5 shows the regression data we obtained when analyzing the data from each country individually. We tried to make this data more graphically visible and easier to understand by making a forest plot out of table 5. Due to missing data and reasons mentioned in 4.4 Data Matrices and selected sample we started the time frame from 1991. We also incorporated a 3 year time lag in the dependent variable (GDP per capita) resulting in a maximum of 18 observations per country. We decided to use the “worst case scenario” to construct figure 2 and base the majority of our conclusions on. We did so, because we feel it is important to use the worst case scenario to make trustworthy conclusions. By using the worst case scenario we can be more confident in making conclusions. It will also help us in identifying host countries which are good and countries that are poor in handling their FDI. As mentioned before the countries Iran, Iraq, Syrian, Moldova, Ukraine, Slovenia and Serbia have missing data values. We are going to make a separate regression analysis for these countries with average values to create an average scenario. The missing values where replaced with average number (see 4.4 Data Matrices and selected sample). This average scenario helps us to asses in what manner the worst case deviates from an average case. However, we want our conclusions to be based on the worst case scenario to increase reliability and external validity. The average case is only going to be used for comparison. The results we obtained with this paper will be incorporated in the Forest plot of the meta analysis. This forest plot will become the completed version of figure 1 and must represent our impact on the general literature (see 3. Meta-analysis). By running a separate regression for each county we are able to compare effect sizes between countries. Next to this, we grouped the countries per region in order to compare the regions with each other. Al regressions where run in the statistical analysis program SPSS, which stands for Statistical Package for the Social Sciences. First, we transferred all the data from the World Databank into the SPSS program and ran each regression separately. Then we copied the R, R2, R adjusted, β, Beta, Std. Error, T-value, P-value and N values in Microsoft Excel to create the table on the next page.


Country R R2 Radjusted Β Beta Std. Error

T-value Sig, P-value n

Congo 0,788 0,62 0,597 122,408 0,788 6,659 18,381 0,000 18

Kenya 0,39 0,152 0,099 491,654 0,39 45,125 10,895 0,110 18

Liberia 0,526 0,277 0,232 150,722 0,526 21,344 7,062 0,025 18

Malawi 0,8 0,64 0,617 180,464 0,8 14,384 12,546 0,000 18

Niger 0,637 0,406 0,369 224,332 0,637 15,337 14,627 0,004 18

Pakistan 0,846 0,716 0,698 496,145 0,846 40,711 12,187 0,000 18

Philippines 0,377 0,142 0,088 1049,427 0,377 204,347 5,136 0,124 18

Sri Lanka 0,857 0,734 0,718 511,963 0,857 139,949 3,658 0,000 18

Vietnam 0,759 0,575 0,549 354,895 0,759 81,658 4,346 0,000 18

Thailand 0,684 0,468 0,435 1765,569 0,684 341,232 5,174 0,002 18

Colombia 0,843 0,728 0,711 2055,535 0,853 293,567 7,002 0,000 18

Bolivia 0,24 0,058 -0,001 1351,005 -0,024 163,126 8,282 0,338 18

Peru 0,751 0,564 0,537 1822,486 0,751 331,26 5,502 0,000 18

Venezuela 0,17 0,029 -0,032 6844,711 -0,17 1361,179 5,029 0,500 18

Paraguay 0,242 0,059 0 1525,391 0,242 280,419 5,44 0,333 18

Iran 0,814 0,663 0,639 1712,939 0,814 210,789 8,126 0,000 16

Iran 0,796 0,633 0,610 1685,752 0,796 204,476 8,244 0,000 18

Iraq Iraq

0,872 0,844

0,761 0,712

0,741 0,694

1040,443 1161,853

0,872 0,844

149,048 120,823

6,981 9,616

0,000 0,000

15 18

Oman 0,823 0,677 0,657 9102,742 0,823 985,615 9,236 0,000 18

Sudan 0,941 0,886 0,879 372,308 0,941 45,464 8,189 0,000 18

Syrian Syrian

0,828 0,606

0,685 0,367

0,664 0.328

1014,983 1183,526

0,828 0,606

129,165 167,088

7,858 7,083

0,000 0,008

17 18

Moldova Moldova

0,824 0,804

0,68 0,646

0,685 0,624

533,979 511,216

0,824 0,804

95,927 97,824

5,567 5,226

0,000 0,000

17 18

Ukraine Ukraine

0,836 0,814

0,699 0,662

0,679 0,641

1073,421 1030,943

0,836 0,814

188,118 193,054

5,706 5,340

0,000 0,000

17 18

Slovenia Slovenia

0,733 0,684

0,537 0,468

0,506 0,435

11990,849 11510,194

0,733 0,684

1456,439 1585,481

8,233 7,260

0,001 0,002

17 18

Romania 0,865 0,748 0,733 2143,057 0,865 443,256 4,835 0,000 18

Serbia Serbia

0,487 0,661

0,237 0,436

0,179 0,401

3003,816 2737,804

0,487 0,661

506,406 380,229

5,932 7,200

0,066 0,003

15 18

Table 10. FDI on GDPPC in selected developing countries with a time lag of 3 years for the dependent variable GDPPC – N equals the number of observations – The bold sections are the average case scenarios for the missing data of seven different countries.


We constructed a forest plot, out of table 10, which visually represents each individual regression analysis. This table enables us to compare and aggregate the observed effect sizes. The brackets shown on the plot show each individual confidence interval and the aggregate confidence interval is shown by the dotted lines (β = 0,6462 & 0,7972). The blue rhombuses represent the average observed effect sizes of each single regression. The red line in the forest plot with a β value of 0,7305 shows the overall effect size and enables a visual comparison with each individual country. The green line represents the effect size from where a result is considered to be positive based on our meta-analysis of previous studies (see 3.1 Forest plot).

Africa Asia South-America Middle-East Balkan

Study ß n t-value SE

1 Congo 0,788 18 18,381 6,6590

2 Kenya 0,39 18 10,895 45,1250

3 Liberia 0,526 18 7,062 21,3440

4 Malawi 0,8 18 12,546 14,3840

5 Niger 0,637 18 14,627 15,3370

6 Pakistan 0,846 18 12,187 40,7110

7 Philippines 0,377 18 5,136 204,3470

8 Sri Lanka 0,857 18 3,658 139,9490

9 Vietnam 0,759 18 4,346 81,6580

10 Thailand 0,684 18 5,174 341,2320

11 Colombia 0,843 18 7,002 293,5670

12 Bolivia 0,24 18 8,282 163,1260

13 Peru 0,751 18 5,502 331,2600

14 Venezuela 0,17 18 5,029 1361,1790

15 Paraguay 0,242 18 5,44 280,4190

16 Iran 0,814 16 8,126 210,7890

17 Iraq 0,872 15 6,981 149,0480

18 Oman 0,823 18 9,236 985,6150

19 Sudan 0,941 18 8,189 45,4640

20 Syrian 0,828 17 7,858 129,1650

21 Moldova 0,824 18 5,567 95,9270

22 Ukraine 0,836 18 5,706 188,1180

23 Slovenia 0,733 18 8,233 1456,4390

24 Romania 0,865 18 4,835 443,2560

25 Serbia 0,487 15 5,932 506,4060

Overall effect size: BETA LL UL LLbar ULbar

0,7305 0,6462 0,7972 0,0843 0,0667

Figure 2. Regression analysis forest plot


The forest plots below represent the worst case scenarios for the countries Iraq, Iran, Syrian, Moldova, Ukraine, Serbia and Slovenia. As mentioned before in 1. Introduction we wanted to complete an analysis of the differences in regions. In order to make this analysis we chopped the forest plot (figure 2) in 5 parts. Every part represents one of the five regions. After this we compared the overall effect sizes and the 95% confidence intervals (see 6. Analyzing the results).

Table 11. Sub-Group analysis Figure 3. Forest plots of different regions

Regions Left bound interval

Right bound interval

Confidence Interval width

Observed effect size

Africa 0,4782 0,7799 0,3017 0,6543

Asia 0,5616 0,8525 0,2909 0,7399

South-America

0,1413 0,7647 0,6234 0,5183

Middle-East

0,7950 0,9149 0,1199 0,8670

Eastern-Europe

0,6578 0,8637 0,2059

0,7812


For the seven countries with missing data we have also constructed an average case scenario. The figures used for this analysis are represented by the bold figures in table 10. We produced the forest plots below by using the sub-group analysis forest plots and replacing the worst case figures by the averages. After this, we compared the outcomes of both regions, where we had countries with missing data. Worst case analysis Average case analysis

Table 12. Average figures analysis Figure 4. Forest plots of worst and average case analysis

Regions Left bound interval

Right bound interval

Confidence Interval width

Observed effect size

Middle-

East

0,7950 0,9149 0,1199 0,8670

Eastern-

Europe

0,6578 0,8637 0,2059 0,7812

Middle-East

0,6960 0,9079 0,2119

0,8298

Eastern-Europe

0,6708 0,8524 0,1816

0,7773

0

5

10

15

20

25

30

0,0000 0,2000 0,4000 0,6000 0,8000 1,0000

Forest plot Middle-East

0

5

10

15

20

25

30

0,0000 0,2000 0,4000 0,6000 0,8000 1,0000

Forest plot Eastern-Europe


6. Analyzing the results An effect size of above 0.1 is considered significantly positive as we stated earlier in this report (see 4.3 Measurement protocol). All observed effect sizes and confidence intervals where above 0.1, are significant positive and of managerial relevance. The overall effect size measured in our study is marked by the red square in the forest plot (figure 2) and is 0.7305. The confidence interval width is β 0.1510 and ranges from β 0,7972 till β 0,6462. The high effect size makes our research findings strong and significantly positive. The narrow confidence interval width makes allows us to make reliable predictions. There are no studies that found an effect size as strong as ours. Our research findings come closest to the observed effect size of the study of Hermes & Lensink (2010), which measured a mean of 0.6. Our study and the study of Hermes & Lensink (2010) have a few things in common which might explain the relationship of both studies. Both studies have similar hypotheses and believe in a positive impact of FDI on GDPPC. The samples used for both studies are very similar, which is the most important reason for having a closely related observed effect size. Both studies have looked at the African countries Kenya and Niger; the Asian countries Pakistan, the Philippines, Thailand and Sri Lanka; the Middle-Eastern countries Syrian and Sudan; and have exactly the same sample of South-America consisting of Bolivia, Venezuela, Colombia, Peru and Paraguay. Up to 13 out of 25 countries in our sample are represented in the study of Hermes & Lensink (2010). This could explain our related research findings. Our findings from the meta-analysis of 3.1 Forest plot showed that most studies measure an effect of around 0.2246 and claim this to be positively significant. The observed confidence interval of this study has a left bound of 0.6462 and a right bound of 0.7972 (see blue dashed lines in figure 2). This creates a confidence interval width of 0.1510. 9 out of 25 means are within or in front of the left bound. However, most means are within or above the right bound. The left bound of the forest plot of previous studies (figure 1) is 0.0961 and the right bound is 0.3456. The fact that the confidence interval from figure 1 is lower than that of figure 2 is due to the more positive outcomes of our study. From all regions the Middle-East has the highest observed effect size (0,8670) followed by Eastern-Europe (0,7812). These regions are therefore most capable of transforming FDI into an increase of GDP per capita. Both the Middle-East and Eastern-Europe are regions with a very narrow confidence interval width, 0,1199 and 0,2059 respectively. This enables us to make accurate predictions on the effect of FDI on GDP per capita for these regions. Even though the Eastern-European countries suffered from closed borders shortly after WWII this region is fairly efficient with its FDI. A possible explanation is the fact that Eastern-Europe and other post communist countries were heavily supported by the EU, NATO and USA during the transition from communism to capitalism. It could also be because Eastern-Europe is a relatively ‘developed developing region’ compared to Africa. South-America has the lowest overall effect size (0,5183) and showed the highest β confidence interval width (0,6234). This number together with the observed forest plot of South-America shows us that these countries vary widely with each other. Bolivia, Paraguay and Venezuela showed the lowest observed effect sizes of all the countries in our sample. These countries are the reason for the large β confidence interval width as well as the low overall effect size parameter of South-America. These countries performed worst in being

Opmerking [C1]: What does your study have in common with theirs? And how is it different from other studies?


able to transform FDI in an increase in GDPPC. Paraguay and Bolivia are both inland countries boxed in by the Andes Mountains and with no access to the sea. This together with dictatorial regimes and political instability could explain their lack of economic growth due to FDI. Venezuela does have access to the sea but is also plagued by political instability, takeovers and dictatorial regimes. Colombia on the other hand is also plagued by civil unrest of drugs cartels, guerilla parties and corruption, but shows the highest correlation of the entire region. A possible explanation for the observed difference is we found here isn’t apparent. The observed effect sizes of Africa and Asia don’t vary as much as South-America, but are still noticeably volatile. The Philippines and Serbia have much lower observed effect sizes compared to other countries in their regions. A possible explanation for Serbia’s low effect size is the fact that this country suffered from the Yugoslavian war and this country was under the leadership of Slobodan Milosevic. This war deprived the country of FDI inflows and reduced the GDP per capita tremendously. Also GDPPC data up until 1996 and FDI data from the year 2004 and 2005 are missing. We don’t know why these data are missing. The FDI data of 2006 of Serbia also seems unrealistic with a value of $1000. This will have an influence on the regression analysis of Serbia but we don’t know how much. When we compare the average case scenarios with the worst case scenarios of the Middle-East and Eastern-Europe (figure 4) we don’t see allot of differences. In fact the average case isn’t that much better than the worst case scenario. This indicates to us that our results are reliable and missing data values aren’t that influential to the result. Table 10 does show us that Serbia gets a more positive overall effect size with a smaller confidence interval width in the average case scenario (0.661) compared to the worst case scenario (0.487). Considering the research strategy we took we can make a preliminary conclusion that FDI has a strong and positive influence on GDP per capita. The Middle-Eastern countries have profited the most out of their FDI. Eastern-Europe and Asia follow closely and are also relatively efficient with their FDI. Bolivia, Paraguay, Venezuela and to some extend Kenya, Serbia and the Philippines are especially bad at transforming FDI in to an increase of GDPPC. All our effect sizes are much higher than in previous studies (see 3.1 Forest plot and Figure 4) and we are going to make the careful conclusion that FDI has a positive impact on GDPPC and that our results strong enough and minimally influenced by chance. FDI has helped increasing people’s average income for the last two decennia for almost all the countries in our data sample. Our research findings suggest that nations and maybe even regions as a whole should use FDI as a means of increasing economic welfare of its citizens. We hope that our findings are able to contribute to the debate surrounding the effects of Foreign Direct Investment.


7. Discussion and future research We have constructed a meta-analysis forest plot including our study, which can be found below (figure 5). We have used the forest plot of 3. Meta-analysis and included our study’s overall result in it. When we incorporate our results in the meta-analysis of 3.1 Forest plot the confidence interval shifts to the right, creating a higher and more positive observed effect size. In other words the overall effect size of our literature meta-analyses becomes higher and changes from 0.2246 into 0.3211. Our findings supports the claim of a significant positive causal relationship between FDI and GDPPC and are in line with the conclusions of Kalemli-Ozcan &Sayek (2000), Kumar & Pradhan (2002), Hermes and Lensink (2003), Nath (2005) Hansen & Rand (2005), Adams (2009) and Gohou & Soumaré (2012).

Figure 5. Meta-analysis forest plot including our study


The left bound interval of figure 5 is 0.0400 and the right bound interval is 0.5552 (width 0.5152). We have used a 95% confidence interval represented by the blue dotted lines. Our study is marked by a green circle and lays far outside the 95% confidence interval. This tells us that our study found a strong positive result with minimal influence by random chance. The confidence interval width of figure 5 is 0.5152 and the width of the meta-analyses without our study is 0.2495. The meta-analysis including our study results in a wider confidence interval due to our very distinctive overall effect size. The conclusion that can be drawn from figure 5 is that our study makes the aggregate effect of the all the studies more positive. In 1. Introduction we wrote about the aim of this research. We wanted to uncover different effect sizes across regions with regard to our hypothesis so future research could investigate these regions more in depth. Our study shows there are differences between countries and regions. We identified the countries that excelled at transforming FDI to GDP and we identified the countries that didn’t excel. Why did countries such as Venezuela, Bolivia and Paraguay achieve so little profit from their FDI compared with the Middle-Eastern countries of Iraq, Iran, Sudan, Oman and Syrian? Future research into these countries might identify the factors that enable a country to convert investment more efficiently. Lessons learned from this future research could help countries achieve more economic growth. The impact of FDI on GDPPC is influenced by moderating variables which are extensively studied by previous researchers (see 2. Literary review table 2: Literary overview). Some credit these variables to be significant and others say there is no relationship. We found no studies which have investigated the moderating effects of civil unrest, political instability, war, domestic war, guerilla warfare, narcotic issues, etcetera, on the relationship of FDI and GDPPC. These variables are obviously hard to quantify and process statistically. However, companies’ decisions about investing or divesting in certain countries depend largely on these moderating variables. When the ruling authority in a specific host country is overthrown, every single company will reevaluate their business portfolio and their relationship with that county. Earlier on in this study we also mentioned possible biases (see 4.5 Possible research biases). These where the different inflation rates over time and the different exchange rates between US Dollars and the host countries currencies. We haven’t found any study that tried to incorporate these moderating variables in their study. Research on these problems would enhance the reliability of the results and improve the overall conclusions. We also stated that we guessed the appropriate time lag coefficient for the dependant variable GDPPC (See 4.1.1 Time lag coefficient of FDI and GDP). Not allot of research has been done to this topic. It would definitely be valuable to research the lag between FDI and its effect on GDPPC. With this knowledge countries could anticipate, monitor and evaluate FDI better.


8. About the authors Patrick Heeremans a pre-master student and Anton Meeusen a bachelor student, are

responsible for writing this paper. We both are both students at the Rotterdam School of

Management at the Erasmus University. We are not funded by any organization and we

don’t have a financial or non-financial interest in the conclusions made by this research

paper. By stating this we want to make clear that there is no conflict of interest related to

the outcome of this study. We feel that it is important for researchers to state how their

research is funded and if they could possibly benefit, either direct or indirect, from the

result they publish. Conflicts of interest in academic research can be devastating for science

and for the people that are researched.


9. Summary form

Name instructor

Camila Assis Gomide

Team number

8

Name student 1

Anton Meeusen

Name student 2

Patrick Heeremans

Hypothesis

Foreign Direct Investment has a positive impact on the GDP per capita of developing countries.

Focal unit

Host countries (of FDI) (from all over the world)

Theoretical domain

Developing countries (from all over the world)

Independent variable

Foreign Direct Investment (FDI) - in US Dollars

Dependent variable

GDP per capita - in US Dollars

Type of relation (Causal / Not causal)

Positive causal relation

Conclusions of the meta-analysis

A significant positive result for the standardized regression coefficient is above 0.1 (see Forest plot). Most previous studies show positive significant results.

Research strategy

Panel study

Population that was studied

The following developing countries: Congo, Kenya, Liberia, Malawi, Niger, Pakistan, Philippines, Sri Lanka, Vietnam, Thailand, Colombia, Bolivia, Peru, Venezuela, Paraguay, Iran, Iraq, Oman, Sudan, Syrian, Moldova, Ukraine, Slovenia, Romania and Serbia

Effect size parameter Regression coefficient and a value of above 0.1 in the forest plot counts as significant positive.

Observed effect size The observed effect size of this study is 0.7305 and therefore significantly positive.


The study’s contribution to what is known about the hypothesis

FDI has an impact on GDP per capita. This relationship in a positive one. Our research contributes to knowledge about the hypothesis, because we found this relationship is even more positive than previous research showed.

Most important recommendation for future research

Conduct more in-depth research to possible biases and difference between regions (Middle-East, Asia, Eastern-Europe, Africa and South-America).


10. References Articles

Adams S. (2009) Can foreign direct investment (FDI) help to promote growth in

Africa? African Journal of business magazine;3:178-183

Adams, S. (2009) Foreign Direct investment, domestic investment, and economic

growth in Sub-Saharan Africa. Journal of Policy Modeling;31:939-949

Alfaro L, Chanda A, Kalemli-Ozcan S, Sayek S. (2004) FDI and economic growth: the

role of local financial markets. Journal of International Economics. 2004;64:89-112.

Alfaro L. (2003) FDI Spillovers, Financial Markets and Economic Development: Laura

Alfaro ... [Et Al.]. Vol 3/186. Washington, DC: International monetary fund (IMF);

2003.

Apergis N, Lyroudi K, Vamvakidis A. (2008) The relationship between foreign direct

investment and economic growth: evidence from transition countries. Transition

Studies Review. 2008;15:37-51.

Balasubramanyam VN, Salisu M, Sapsford D. (1996) Foreign Direct Investment and

Growth in EP and is Countries. The Economic Journal;106:92-105.

Bengoa Calvo M, Sanchez-Robles Rute B. (2001) Crecimiento economico y

desigualdad en los paises latinoamericanos. (Economic Growth and Inequality in

Latin American Countries. With English summary.). Informacion Comercial Espanola

Revista de Economia:63-74.

Blomstr m, Robert E. Lipsey, Mario Zejan (1992) What Explains Developing Country

Growth? Vol no. 4132. Cambridge, MA: National Bureau of Economic Research.

Borensztein E, De Gregorio J, Lee J. (1998) How does foreign direct investment affect

economic growth? JOURNAL OF INTERNATIONAL ECONOMICS;45:115-135.

Carkovic M, Levine R. (2005) Does Foreign Direct Investment Accelerate Economic

Growth? .

Eller M, Haiss P, Steiner K. (2006) Foreign direct investment in the financial sector

and economic growth in Central and Eastern Europe: The crucial role of the

efficiency channel. Emerging Markets Review: pp. 300-319.

Freckleton, M. Wright, A. Craigwell, R (2012): Economic growth, foreign direct

investment and corruption in developed and developing countries. Journal of

Economic Studies, pp. 639-652.

Gohou, E and Soumaré, I. (2012) Does Foreign Direct Investment Reduce Poverty in

Africa and are There Regional Differences? Elsevier World Development (Vol. 40. No.

1), pp. 75-95

Hansen H, Rand J.(2006) On the Causal Links Between FDI and Growth in Developing

Countries. World Economy pp. 21-41.

Hermes N, Lensink R. (2003) Foreign direct investment, financial development and

economic growth. Journal of development studies. (vol 40) pp 40


Herzer, D. (2009) How Does Foreign Direct Investment Really Affect Developing

Countries' Growth? Review of International Economics; 20:396-414.

Li X, Liu X. (2005) Foreign Direct Investment and Economic Growth: An Increasingly

Endogenous Relationship. World Development; 33:393-407.

Mansfield E, Romeo A. (1980) Technology Transfer to Overseas Subsidiaries by U.S.-

Based Firms. The Quarterly Journal of Economics; 95:737-750.

Nath HK. Trade, (2009) Foreign Direct Investment, and Growth: Evidence from

Transition Economies. Comparative Economic Studies; 51:20-50.

RAM, R. & ZANG, K.H. (2002) Foreign direct investment and economic growth,

Economic Development and Cultural Change, 51: 205-225. Reiter, S.L. and Steensma, H.K. (2010) Human Development and Foreign Direct

Investment in Developing Countries: The Influence of FDI Policy and Corruption.

Elsevier World Development (Vol. 38. No. 12), p.1678-1691

Sharma, B. Abekah, J. (2008) Foreign Direct Investment and Economic Growth of

Africa. Atlantic Economic Journal.;36:117-118.

Sharma, B., & Gani, A. (2004) The effects of foreign direct investment on human

development. Global economy journal, p. 4.

Wijeweera, A. and Villano, R. Dollery, B. (2010) Economic Growth and FDI Inflows: A

Stochastic Frontier Analysis. The Journal of Developing Areas; pp.143-158.

Books

Cumming, G. (2012) Understanding the new statistics, Hove: Routledge

Websites

The World Databank data on FDI:

http://data.worldbank.org/indicator/BX.KLT.DINV.CD.WD, 2013

UNCTAD statistical data on FDI:

http://unctadstat.unctad.org/TableViewer/tableView.aspx?ReportId=88, 2013

OECD statistical database:

http://stats.oecd.org/index.aspx, 2013

SCB on US Dollars:

http://www.stablecurrencybenchmark.com/, 2013

Guide who is.com on the definitions of FDI:

http://guidewhois.com/2011/01/what-is-foreign-direct-investment-horizontal-and-

vertical/, 2013

Stable Currency Benchmark on the measurement of GDP:

http://www.stablecurrencybenchmark.com/, 2013

GuideWhois on different kind of FDI definitions:

http://guidewhois.com/2011/01/what-is-foreign-direct-investment-horizontal-and-

vertical/, 2013

http://data.worldbank.org/indicator/BX.KLT.DINV.CD.WD

http://unctadstat.unctad.org/TableViewer/tableView.aspx?ReportId=88

http://stats.oecd.org/index.aspx

http://www.stablecurrencybenchmark.com/

http://guidewhois.com/2011/01/what-is-foreign-direct-investment-horizontal-and-vertical/


http://www.stablecurrencybenchmark.com/



Bachelor THESIS RSM P.J. Heeremans

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