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Determinants of Foreign Direct Investments in Emerging Economies: Evidence from Europe, the Middle East and Africa (Dissertation Example)

Written by Emily S.

 

Abstract

This study employs panel data analysis to investigate whether the market size, availability of infrastructure, price stability, trade openness, political stability, absence of violence and terrorism, control of corruption and government effectiveness affect the Foreign Direct Investment (FDI) inflow to ten developing countries from Europe, Middle East and Africa using data for a period of twenty years (1996-2016). Unlike previous studies that focused on one specific region, this dissertation provides evidence for three different regions. Results of the panel data analysis show that the market size and inflation rate are statistically significant factors that influence FDI in the selected countries. However, the study has limitations such as issues with comparability of the World Bank Governance Indicators for different regions. It is recommended that developing countries should focus on economic growth and control of inflation to attract more FDI.

 

1. Introduction

Foreign direct investment (FDI) is “an investment involving a long-term relationship and reflecting a lasting interest and control by a resident entity in one economy (foreign direct investor or parent enterprise) in an enterprise resident in an economy other than that of the foreign direct investor (FDI enterprise or affiliate enterprise or foreign affiliate)” (UNCTAD, 2007, p 245.). According to Odozi (1995), FDI is an inflow of resources invested in enterprises operating outside of the investor’s home country. In recent decades, emerging markets began to attract investments from transnational corporations. Nevertheless, such markets need a constant inflow of FDI to accelerate economic growth and strengthen their position on the international arena. FDI allows the host country to develop the infrastructure, technology, reduce unemployment and improve the competitiveness of local industries. Moreover, FDI brings additional benefits such as marketing and management skills and competences.

FDI decisions are made by multinational corporations on the basis of the analysis of various factors. These factors include economic environment and non-economic factors such as institutional factors, barriers to entry, size of the market, competition, availability of resources, infrastructure, tax regime, quality of governance, protection of private property rights and the level of corruption. Previous studies (Akpan et al, 2014; Mukhtar et al., 2014; Erdogan and Unver, 2015; Thales Pacific et al., 2015; Kumari and Sharma, 2017; Rodriguez-Pose and Cols, 2017) were aimed at identifying the underlying determinants of FDI. Nevertheless, due to large discrepancies in the results, an attempt to create a model involving universal factors has not been fully developed. These studies established that the role of determinants of FDI was country specific and sensitive to fluctuations in the external and internal environment. For example, according to the findings of recent studies (Akpan et al, 2014; Kumari and Sharma, 2017; Rodriguez-Pose and Cols, 2017), the factors such as GDP indicating the host market size, openness of the market, labour costs, tax rates and the quality of political and economic institutions could produce both positive and negative effects on FDI inflows depending on the country. Thus, new research is required to assess the significance of these factors on a sample of several countries from different regions.

 

1.1. Aim and Objectives

The aim of this study is to identify the significance of key determinants of FDI in emerging economies. The objectives are:

  • To analyse to what extent macroeconomic variables determine the amount of net FDI inflows;
  • To assess whether institutional variables have a significant impact on FDI inflows.

The study uses panel data for a period of twenty years (1996-2016). Unlike previous studies that focused on one specific region, this study covers various unrelated regions. There are ten developing countries used in the analysis. The sample of countries is taken from Europe, the Middle East and Africa, and the final list of countries includes China, Czech Republic, Egypt, Greece, Hungary, Poland, Qatar, Russia, Turkey and United Arab Emirates. This work studies the influence of Market Size, Inflation Rate, Trade Openness, Political Stability and Absence of Violence/Terrorism, Control of Corruption and Government Effectiveness as potential economic and non-economic determinants of FDI. These factors are selected based on their relative importance in previous studies (Tintin, 2013; Singhania and Gupta, 2011; Kumari and Sharma, 2017; Rodriguez-Pose and Cols, 2017).

 

1.2. Outline of Dissertation

This paper is structured as follows. Chapter 2 discusses the main theories of FDI and past results of recent empirical studies on FDI and their determinant. Chapter 3 outlines the methodology of the research and data collection process. Chapter 4 summarises the analysis of empirical findings. Chapter 5 concludes on the whole work completed and makes recommendations to policymakers and future researchers, highlighting limitations of this study.

 

2. Literature Review

2.1. Theories of FDI

Many authors (Haddad and Harrison, 1993; Odozi, 1995; Perez, 1997; Kinoshita, 2001) see FDI as the supply of capital, technology, management and marketing expertise to a foreign economy. The aim of FDI is the acquisition of lasting interest and expansion of the market. There is evidence that FDI positively affect the host economy and contribute to its growth (Kinoshita, 2001). So, governments of developing countries try to maximise FDI inflows. At present, a variety of models and approaches are available to assist researchers with measurement of FDI determinants.

The models based on the exchange rate risk (Itagaki, 1981; Cushman, 1985) consider the influence of currency factors on FDI. Cushman (1985) performed an empirical analysis that showed that depreciation of the local currency in the host economy stimulated FDI in US dollars, while the strengthening of the local currency reduced FDI made in US dollars. Nevertheless, the foreign exchange rate theory cannot explain the simultaneous investments between two countries that have different national currencies.

The Internalisation Theory (Buckley and Casson, 1976) tries to explain the growth of transnational corporations and their commitment to increase foreign direct investments. This theory demonstrates the idea that such companies organise activities on the internal level to strengthen firm-specific advantages. Companies benefit from international transactions when the external market is imperfect or when the use of internal resources is associated with high costs. Hymer (1976) developed the idea of market imperfection. In his work, Hymer (1976) considered a significant difference in the cost of information for foreign and domestic firms, problems in cooperation with the authorities, and currency risk. The results showed that FDI was the preferred option only when the use of internal company benefits outweighed the associated costs of foreign operations. The Internalisation theory was also used by Dunning (1980) in the development of his Eclectic Theory. However, he argues that the Internalisation theory explains only a part of FDI flows.

The Eclectic Paradigm Theory (Dunning, 1980) shows that FDI is primarily determined by three categories of factors, namely: ownership, location and internalisation (OLI). This concept is also known as the OLI paradigm. Ownership advantages include technology and expertise in spheres, where the company outperforms its competitors. These advantages are necessary for a successful entry into a foreign market and lead to higher profit margins, which allows the firm to maintain its financial stability (Dunning, 1980).

Locational advantages are associated with the individualities of the host countries. There are three categories of locational advantages, namely: economic, political and social advantages (Dunning, 1988). The economic advantages include quantitative and qualitative factors such as the level of production, quality of transport and infrastructure, market size and GDP growth rate. The political advantages consist of specific government policies, quality of institutions, tax benefits and tax rates. The social advantages may include productive labour, cultural diversity and even attitudes towards foreigners.

Internalisation is associated with the creation and exploitation of the firm’s key competences. Competence advantages allow the company to assess different options for future cross-border agreements and sales development (Parry, 1985). The Eclectic Paradigm shows that objectives and strategies of the firm will depend on many factors. The final decision on investing in a foreign economy would be based on a weighted assessment of the economy, its political system and socio-cultural characteristics. Based on this conclusion, the Gravity Models were developed (Fontagné et al, 1997; Hejazi and Safarian, 1999). They take into account the geographic distance between the countries and international markets. Other models highlight the role of human capital (Lucas, 1988) and a variety of other factors that drive FDI. Nevertheless, researchers could not create a single theoretical model describing or predicting the movement of all FDI in the world.

 

2.2. Empirical Evidence

Academic and non-academic researchers identified a number of determinants of FDI in emerging economies. The results of empirical studies show the complex relationship between different factors and FDI (Akpan et al, 2014; Erdogan and Unver, 2015; Rodriguez-Pose and Cols, 2017). The key variables are reviewed in this section.

Empirical study conducted by Mukhtar et al. (2014)  in the context of India, Pakistan and Bangladesh illustrated that FDI inflows into emerging markets were significantly determined by GDP growth rate, price stability, local fiscal policy, trade openness, and the quality of infrastructure and governance.  An important finding is the influence of political risk, which also affects the flow of FDI.

Thales Pacific et al. (2015) came to a similar conclusion in their research. The research was based on a sample of 23 African countries covering the period from 2004 until 2012. The purpose of the study was to examine the factors that influenced FDI decisions. This study tested both standard economic indicators and governance indicators such as political stability and absence of violence. The results showed that an increase in investment inflows could be achieved by reducing corruption and increasing transparency, targeting the low level of inflation and improving infrastructure. Strong economic indicators also allow the market to attract long-term investments.

Pathan (2017) analysed the impact of different variables that could explain FDI movements based on the OLI paradigm.  This study was based on annual panel data for 196 countries during the period from 1970 until 2009. The results of the study coincide with the conclusions of earlier empirical findings about the significance of international trade openness in encouraging trade oriented FDI inflows. Similarly, the availability of natural and domestic credit also attracted multinational corporations. Another contribution of this study was that it emphasised the importance of the governance and legal factors in attracting FDI inflows. The quality of property rights, power of law and legal protection of investment were found to be significant determinants of FDI as higher level of investment inflows were identified in the countries with English and French legal systems.

Kakar and Khilji (2011) showed that trade openness positively affected FDI and economic growth in Pakistan and Malaysia. An important result of their findings is that trade openness had an impact on the long-run growth and attractiveness of the host country. Moreover, controversial results were obtained for estimating the impact of GDP growth on FDI. For example, in Malaysia, GDP growth was identified as a significant determinant of FDI inflows whereas in the context of Pakistan this factor did not show a statistically significant effect.

In another empirical study, Kumari and Sharma (2017) used unbalanced panel data for the years 1990-2012 to explore key factors of FDI inflows in twenty developing countries from the South-East Asian region. Their findings showed that trade openness, market size and human capital could positively affect FDI inflows. Likewise, the empirical results of their study confirmed the existence of a positive relationship between research and development, human capital and FDI inflows in developing countries. On the other hand, the results showed a negative correlation between infrastructure and FDI inflows. Despite the fact that the results of their work confirmed the conclusions of many other authors, several studies have shown a positive relationship between these variables.

Controversial findings were also obtained by Anyanwu and Yameogo (2015) in studying FDI in West Africa. Even though trade openness, availability of natural resources and monetary integration produced a significant effect on the increase of FDI inflows, the level of economic development represented by real GDP per capita and life expectancy showed a negative correlation with FDI, which is counterintuitive.

Hoa and Lin (2016) examined the influence of economic, institutional and political factors on FDI levels in Cambodia, Laos and Vietnam.  Their study was based on a sample of these three countries for the period of 16 years from 1996 until 2012. The empirical results showed that the market size, government effectiveness, rule of law and political stability had a positive correlation with incoming FDI. The empirical analysis was produced using panel unit root tests and random effect regressions to account for country-specific differences.

Another study conducted by Erdogan and Unver (2015) investigated three different determinants of FDI, defined as social security expenditures, health expenditures and corruption levels, which had been ignored in previous studies. The results showed that these variables had statistically significant effects on FDI. It was also discovered that previous FDI levels, and economic growth and development significantly affected FDI in future periods. Other important factors were the degree of financial openness, market size, private sector credits and labour force growth rates. Based on their results, the authors concluded that countries with low corruption levels attract more investments, and countries with high social security and health expenditures attracted fewer investments.

Empirical research conducted by Akpan et al (2014) united economic, institutional and political determinants of FDI in a more general model. Their sample was based on economic data for BRICS economies over a 10-year period from 2000 until 2009. General assumptions of their model were based on a previous study performed by Cuervo-Cazurra (2006). The results showed a significant influence of economic factors in comparison with institutional and political determinants of FDI. Most of the institutional and political variables were not statistically significant. Voice and accountability showed a negative effect supporting the conclusion of Cuervo-Cazurra (2006) that investors from countries with high corruption level selected similar countries where they could find applications of their competencies of doing business in corrupt environments.

Rodriguez-Pose and Cols (2017) proposed a model showing the relationship between FDI inflows, institutional factors and the quality of government. Their econometric analysis, applied to a sample of twenty-two Sub-Saharan African countries for the period between 1996 and 2015, identified positive effects of natural resources, macroeconomic stability and the level of human capital on the attractiveness of the host country to foreign investors. However, the size, internal wealth, and market openness were insignificant drivers of FDI. According to the authors, the most important finding of the paper was related to the role of the quality of governance. Their results demonstrated that a sound and effective legal system would encourage more FDI in the country.

The effect of political and financial risk factors on FDI were empirically investigated in the research carried out by Hayakawa, et al. (2013). The research was conducted based on a sample of 89 countries for the period from 1985 until 2007. The results showed that political risk had a reciprocal relationship with FDI inflows. When political risk increased, FDI declined. Moreover, lower financial risk did not help to bring a greater amount of FDI inflows when political risks were high.

 

2.3. Summary

In summary, the results of previous studies reviewed show mixed results and evidence that do not allow for a simple answer to the question what factors determine FDI. Moreover, these results were rarely in agreement. An important limitation of most of these studies is that they were carried out for short periods and for a limited number of countries. As a result, the problem of structural breaks due to changes in economic conditions or reforms was usually not taken into account in previous studies.

 

3. Methodology

3.1. Method

This study uses the panel data regression analysis because other methods such cross-sectional regressions and time-series analysis are unable to account for both longitudinal and cross-sectional properties of variables (Baltagi and Kao, 2000). The relationship between FDI inflows and their explanatory variables is modelled as follows:

Where, i is the time period, j is the country, FDI is net FDI inflows in the economy; MS is the Market Size proxied by GDP; INFRA is the level of infrastructure development; INFL is inflation rate that represents price stability; TRADE is Trade Openness measured by imports and exports scaled by GDP; PS is Political Stability and Absence of Violence/Terrorism; CC is Control of Corruption; GE is Government Effectiveness. As seen from the model, some of the variables are converted to natural logarithm assuming their long-normal distribution.

 

3.2. Data and Sample

This study is based on balanced panel data for a period of 1996-2016. Both economic and institutional data for ten emerging markets from Europe, the Middle East and Africa have been retrieved from World Bank (2016a; 2016b). The countries included in the sample are China, Czech Republic, Egypt, Greece, Hungary, Poland, Qatar, Russia, Turkey and the United Arab Emirates. The classification of these ten emerging markets is based on the MSCI (2016) Emerging Markets Index.

In this study, net FDI inflow (BOP in current USD, billion) is defined as the dependent variable. The list of independent or explanatory variables is chosen on the basis of recent studies that were discussed in Literary Review. The proposed model includes seven explanatory variables, namely: market size, availability of infrastructure, price stability, trade openness, political stability, control of corruption and government effectiveness.

Market size has been identified as a statistically significant factor by many authors (Tintin, 2013; Erdogan and Unver, 2015; Hoa and Lin, 2016). Previous studies made an assumption that total GDP or GDP per capita could reflect the purchasing power of the population in the host economy. Therefore, these indicators could be used as proxies for market size. In this study, market size is represented by the log of GDP measured in current US dollars as was proposed by Hoa and Lin (2016). 

The availability of infrastructure could positively affect the inflow of FDI based on previous research. In line with a number of previous studies, the infrastructure variable is represented by the electric power consumption measured in kWh per capita (Sahoo and Dash, 2009; Singhania and Gupta, 2011; Kumari and Sharma, 2017).

The annual growth rate of the consumer price index (CPI) is used as a measure of price stability or inflation. This is a traditional variable that shows changes in the general level of prices of a fixed basket of goods and services in the economy of host country over time (Blanchard, 2000). The inflation rate is commonly measured by calculating the percentage change in the consumer price index. Another common variable is trade openness.  Various authors (Asiedu and Lien, 2004; Tintin, 2013; Kumari and Sharma, 2017) suggested that a good proxy for this variable is the ratio of the total sum of exports and imports to GDP. This approach was also applied in this dissertation.

The institutional variables such as political stability, control of corruption and government effectiveness are included in the model based on evidence from previous research (Rodriguez-Pose and Cols, 2017; Hayakawa et al, 2013; Akpan et al, 2014; Erdogan and Unver, 2015). Political stability, control of corruption and government effectiveness are indices measured on a scale from -2.5 to 2.5 where higher values indicate more stable societies with lower level of corruption and effective system of governance. The data on these variables is collected from World Bank (2016b).

 

4. Analysis and Discussion

4.1. Summary Statistics

The descriptive summary statistics for economic and institutional variables for the chosen ten emerging markets from Europe, the Middle East and Africa are presented in Table 1.

The total number of panel observation is 140 as the sample has been adjusted for missing values. It can be observed from Table 1 that the natural logarithm of net FDI inflows to selected economies range from -3.38 to 5.67 with the average of 1.99 and the standard deviation of 1.62. Similarly, the maximum and minimum values of natural logarithm of the market size proxied by GDP are 2.88 and 9.03, respectively, while the average value is 5.77 and the standard deviation is 1.31. Thus, FDI are found to be more volatile than economic growth. Inflation rate fluctuates from -4.8% to 8.5% with the mean value of 7.9% and standard deviation of 12.26%. This is due to the heterogeneity of the economies included in the sample. For government effectiveness, the minimum and maximum values are -0.88 and 1.44, respectively, while the mean and standard deviation are 0.33 and 0.53, respectively. The boundaries of this index are from -2.5 to 2.5, where -2.5 means extremely low efficiency and 2.5 means extremely high efficiency. Thus, the data in the table above indicates that the sample includes countries with different levels of government efficiency. The same applies to the indices of political stability and control of corruption. These indices produced quite similar average values with varying standard deviations. Political stability is found to be more volatile compared to the control of corruption. The next chapter provides correlation analysis of these variables.

 

4.2. Correlation Analysis

Table 2 shows the result of the correlations analysis for the determinants of FDI inflows.

It is discovered that Political Stability, Control of Corruption and Government Effectiveness are highly correlated, which could potentially create a problem of multicollinearity in the regression analysis. In order to solve this problem of multicollinearity, only one of the highly correlated institutional variables was retained in the model. Other variables show medium or weak correlations, which are not considered a threat to the regression model.

 

4.3. Panel Regression Analysis

The empirical model outlined in the methodology chapter has been estimated using a panel data regression with random effects. Before estimating the regression, a test was run to choose between the fixed-effect and random-effect specifications. This test was developed by Hausman (1978) and it has a null hypothesis that random-effect specification produces consistent coefficients. The results of the test are outlined below.

The p-value of the chi-square statistic from the Hausman specification test is higher than 0.05, indicating that the null hypothesis should be accepted. The null hypothesis of the Hausman test means that the random effects model is more appropriate. Therefore, the panel data regression with random effects is preferred in this study.

The output of the regression with random effects is provided in Table 4.

The estimated coefficient of determination R-squared is equal 0.4899, which means that the model explains 49% of changes in FDI inflows in the selected economies. The other 51% is explained by random factors or determinants not included in the model. The F statistics is equal 25.74112, and the probability of the F statistics is 0.0000. Such probability shows the statistical significance of the results and allows for rejecting the null hypothesis that all slope coefficients are equal zero.

The results in the Table 4 show that the market size and inflation rate are statistically significant at the 95% confidence level as their probability values are less than 0.05. The estimated coefficient for the market size proxied by log of GDP is 0.916914. It shows that a 1 percent increase in GDP will result in a 0.9% growth of FDI inflows in the economy, other things being equal. The coefficient is positive, which means that growth of GDP tends to encourage FDI. Therefore, a positive and significant relationship between these variables is confirmed in this study. This conclusion agrees with previous findings (Avom and Ongo Nkoa, 2013; Mukhtar et al., 2014; Kumari, and Sharma, 2017). On the other hand, it contradicts with conclusion of Thales Pacific et al. (2015), whose studies of Francophone and Anglophone African economies showed little significance of GDP in explaining FDI movements. The discrepancy in results could be explained by differences in the period and sample composition.

The coefficient for inflation rate is -1.884444. The influence of inflation on net FDI inflow is found to be negative and statistically significant as inflation problem decreases the competitiveness of local businesses and purchasing power of customers. A 1% increase in inflation rate is expected to be associated with a 1.88% decline in FDI inflows in the economy. Usually, investors regard a low and constant rate of inflation as an indicator of economic and price stability. This conclusion is in accordance with those of previous studies conducted by Thales Pacific et al. (2015), Erdogan and Unver (2015) and Rodriguez-Pose and Cols (2017).

In contrast to expectations, trade openness was found to be statistically insignificant at the 95% level. However, the TRADE coefficient is 0.514606, which shows that there could be a positive relationship between FDI inflow and trade openness. The results of this study are very different from previous results made by Kakar and Khilji (2011), Mukhtar et al. (2014), Anyanwu, and Yameogo (2015). These authors advocate the importance of liberal trade policies in attracting foreign direct investment. The difference in the conclusions can be explained by the volatility of exports and imports, as well as the differences in the countries and period investigated.

The results of the analysis highlight that availability of infrastructure, expressed through the electric power consumption (in kWh per capita), does not make an important difference for FDI. The theory that foreign investors are aimed at countries with good infrastructure is supported by many authors (Sahoo and Dash, 2009; Mukhtar et al., 2014; Thales Pacific et al., 2015). The importance of infrastructure is caused by the fact that investors see it as an indicator of business effectiveness within the host country (Kinoshita and Campos, 2003). However, the findings of the recent studies performed by Liu et al. (2014) and Kumari, and Sharma (2017) found a negative relationship between infrastructure and FDI inflows. Therefore, the impact of infrastructure on net FDI inflow can vary from positive to negative. Although this study shows that the projects in electric power industry do not attract FDI donors, other metrics such as energy use of oil or gas per capita, length of telephone lines per 1,000 of population, rail or air transport freight (million tons per kilometer) or a proportion of paved road in the total road network could also be proposed.

The results of the analysis also show that institutional variables do not have a significant effect on net FDI inflows. This finding is not in line with Rodriguez-Pose and Cols (2017), Akpan et al (2014) and Cuervo-Cazurra (2006). According to these authors, countries with the low quality of governance are often preferred by investors for low compliance costs and the possibility of obtaining favorable contracts. An alternative explanation is that foreign direct investments flow to countries with weak governance because investors see potential for future growth.

 

5. Conclusion

5.1. Conclusions

This dissertation investigated whether the market size, availability of infrastructure, price stability, trade openness, and government effectiveness significantly affected FDI inflows in emerging economies. Overall, a sample of ten developing countries from Europe, the Middle East and Africa during the period 1996-2016 has been investigated.

The review of recent studies on determinants of FDI showed that researchers could not create a single model that allowed for capturing all factors of FDI inflows. Moreover, the results of their studies often contradicted each other. Nevertheless, the literature review revealed that many factors of FDI could be explained by the OLI paradigm. Based on previous empirical and theoretical work, this dissertation produced an empirical model for testing in the chosen specific context.

The first objective of this dissertation was to analyse the influence of macroeconomic variables on net FDI inflows. The results of panel data analysis showed that the market size and inflation rate were the most significant factors that influenced FDI inflows in the selected developing countries. According to the findings, there is a significant positive effect of market size of the host economy on FDI inflows but at the same time there is a significant negative effect of inflation on FDI.

The second objective was to assess the influence of institutional variables on FDI inflows. Correlation analysis showed that institutional variables such as political stability, control of corruption and government effectiveness were strongly correlated, which created an issue with multicollinearity in the regression analysis. Therefore, it was decided to exclude political stability and control of corruption from the model to improve estimation results and solve the problem of collinearity. Nevertheless, the level of government effectiveness is found to be statistically insignificant in attracting FDI.

The results of this study have implications for policymakers and future researchers. Based on the results obtained, policymakers would be able to tackle the right factors and formulate the right policies that would attract foreign investors.

 

5.2. Limitations

As all empirical work, this study faced some limitations. One of the limitations is the lack of data on the effectiveness of public institutions, which could be employed to enhance the regression model. Another limitation is that the data for earlier periods is either incomplete or unavailable, which prevented sample expansion. Furthermore, the World Bank (2016b) Governance Indicators have some limitations, as they are based on primary surveys that could be biased. Such surveys are very sensitive to the weights of expert assessments, which may lead to differences in the rating of different countries. Another limitation is the comparability of this data. Only several countries’ ratings are based on comparable sources according to World Bank (2016b).

 

5.3. Recommendations for Policy Makers and Future Researchers

Based on the results of this study, several recommendations can be made. Market size and price stability are able to affect FDI. Thus, in order to increase FDI inflows, developing countries need to establish a control over inflation as this factor has been found to negatively affect FDI. The results of this study show an insignificant relationship between the availability of infrastructure and the inflow of FDI. However, this does not mean that countries with poorly developed infrastructure should not invest in its improvement. The infrastructure was proxied by a single variable whereas in reality it is a complex term. Thus, future researchers can be recommended to use more than one proxy for infrastructure to investigate its effect on FDI. For example, the development of the transport system or the availability of telecommunications could be used as alternative proxies.

In addition, future studies are recommended to apply different institutional variables besides the Governance Indicators published by World Bank (2016b) in order to get a full picture of the relationship between institutions and FDI. It will also be interesting to compare the determinants of FDI in developed and developing countries in future research.

 

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