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The Relationship of Political Risk and Foreign Direct Investment
and the mediating effect of Exchange Rate Volatility
by Kelvin Kalt
University of Groningen Faculty of Economics and Business
Master Thesis for Msc IB&M (EBM719A20)
1 ABSTRACT
The purpose of this paper is to analyze the relationship between political risk and the inflows of foreign direct investment at a national level. In addition, I analyze whether the variable exchange rate volatility mediates this relationship. As it stands, contradicting results from the literature review have been found. This paper contributes to the discussion in relation to these constructs and provides practical implications. I hypothesize that political risk has a negative effect on the FDI inflows and that this relationship is mediated by exchange rate volatility. In order to test this relationship, a sample of 58 countries is empirically analyzed in the period between 2002 and 2015. Measuring the political risk is done by using the Worldwide Governance Indicators per country. The foreign direct investment inflows of a country are measured by using the data collected by the World Bank. The mediating effect of the exchange rate volatility is analyzed by using the historical exchange rates with the SDR as the base currency, provided by the International Monetary Fund. The results of this quantitative study are concluded through regression and correlation analyses. The main findings from these results indicate that political risk does influence the foreign direct investment inflows of a country negatively. In addition, no significant evidence has been found regarding the mediating effect of exchange rate volatility on this relationship. However, the negative effect of political risk on exchange rate volatility is statistically supported.
Key words: Exchange Rate Volatility, Foreign Direct Investment, Political Risk Research theme: International Business & Management
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TABLE OF CONTENT
1. INTRODUCTION ... 3
2. LITERATURE REVIEW ... 5
2.1 Political Risk ... 5
2.2 Foreign Direct Investment ... 5
2.3 Relationship Political Risk and FDI ... 6
2.4 Exchange Rate ... 9
2.5 Mediating effect Exchange Rate Volatility ... 9
2.6 Conceptual model ... 11
3. METHODOLOGY AND EMPERICAL ANALYSIS ... 12
3.1 Measures ... 12
3.1.1 Measurement of Political Risk ... 12
3.1.2 Measurement of FDI inflows ... 13
3.1.3 Measurement of Exchange Rate Volatility ... 14
3.1.4 Control variables ... 14
3.2 Findings ... 15
4. DISCUSSION ... 21
5. CONCLUSION AND LIMITATIONS ... 23
5.1 Conclusions ... 23
5.2 Limitations ... 24
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1. INTRODUCTION
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based on cost sensitivity and differing needs. This is not based on the factor of political instability, because it may be far above what is necessary for the MNEs. However, this evidence is based on developed economies. By further researching this relationship, a contribution to the discussion is created.
The possible mediating effect of exchange rate volatility on this relationship will also be analyzed. Various researchers have analyzed the effect of political risk on the exchange rate volatility. Since a stable political environment reflects a stable economy (Bachman, 1992) the currency is expected to show little volatility. When an event of political risk or instability occurs, the economy is also unstable resulting in a volatile home currency (Lobo and Tufte, 1998). However, some scientists find no significant evidence of this relationship. Ochieng (2012) only finds some significant effect for the US Dollar and almost no effect for the Euro. The other currencies analyzed do not show a relationship between political risk and exchange rate volatility.
Research has also been conducted regarding the relationship between exchange rate volatility and FDI inflows. Once again, the empirical evidence of various researchers are mixed. Cushman (1985) states that due to risk aversion and higher risk associated costs, exchange rate volatility is likely to reduce the FDI inflows of the affected country. With a similar conclusion, Deseatnicov and Akiba (2016) show that Japanese MNEs do not favor exchange rate volatility in their FDI activities. However, Goldberg and Kolstad (1995) find that US firms favor exchange rate volatility and choose to invest in these countries. This evidence is supported by Sayintha (2001), showing that EU firms also favor exchange rate volatility in their investment abroad.
Based on the literature the central research question of this paper is formulated as following: "To what extent does political instability affect the FDI inflows and is this relationship mediated by exchange rate volatility?"
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2. LITERATURE REVIEW
This section reviews the existing literature regarding the relationship of political risk and FDI inflows of a country. First the definitions and theoretical frameworks of the dependent and independent variables will be given. These variables are political risk, FDI inflows, and Exchange Rate Volatility. Then the constructs between the variables are analyzed, resulting in the development of the hypotheses. The literature review is concluded by illustrating the relationship between the independent variables and dependent variable with a conceptual model.
2.1 Political Risk
Political risk is an important indication for investment returns. When a country faces political changes or instability, the (economic) efficiency of a country is affected. Multiple papers have discussed the effect of political risk on economic growth. For example Alesina et al. (1996) find significant evidence that the economic growth of a country is affected when the political risk is high. In their paper they define political risk as the propensity of a government collapse. Unexpected changes in the legal regulations increases the political instability, reflected in the level of political risk. Or as how Butler and Joaquin (1998) defined it: "The risk that a sovereign host government will unexpectedly change the "rules of the game" under which businesses operate". Another more recent definition of political risk is given by Hayakawa et al. (2013). According to their paper, political risk is the risk an investment runs on its returns resulting from low institutional quality and political instability.
2.2 Foreign Direct Investment
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motivation of these foreign investors stems from the belief that their knowledge and technology is far more developed and will result in obtaining market share of the selected country (Denisia, 2010).
An eclectic theory regarding FDI is constructed by Dunning (1977), known as the "OLI Framework". The framework is a mix of three theories which are used to seek advantage in regards to investing abroad. The O stands for ownership, L for Location and I for Internalization. The ownership advantages refer to the intangible assets. There are three specific ownership advantages. The first advantages reflects the monopoly advantages. These advantages stem from the privileged access to markets. Secondly, the technology advantages through ownership. This covers the knowledge regarding the results of the innovation activities. Thirdly, the economy of large size advantages. These advantages are divided over access to learning, economies of scale & scope and more access to financial capital. Location also brings 3 specific advantages. The first advantage regards the economic benefits such as transportation and communication costs. The second advantage considers the factor politics. According to Dunning, government policies affect FDI flows and may bring strategic advantages. An example could be the taxing regulations in the Netherlands for MNEs. The last advantage of location refers to the social advantage. Location contributes to close the cultural gap between home and host countries, resulting in a reduction in the liability of foreignness. The last component of the OLI framework regards the internalization. This is defined by the willingness or capability of the MNE to exploit its powers between host and home country (Dunning, 1973, 1980, 1988).
2.3 Relationship Political Risk and FDI
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classified as developed or developing economies by looking at the OECD member ship. Another method is created by the World Bank, based on the country's income (Yu, Zhang, Southern and Joiner, 2004). Yu et al. (2004) show that MNEs are more likely to invest in a developed economy whether it is unstable or not, since it may be far above what is necessary for the MNE (Peng and Beamish 2008). However, different results are shown for developing countries. If the country is considered as a developing economy, MNEs do choose to invest in political stable environments (Peng and Beamish, 2008). Qian and Baek (2011) found that political risk influences FDI in both developed and developing economies. However, Peng and Beamish (2011) find that the FDI inflows of developing economies are only affected by political risk, based on cost sensitivity and differing needs. Another study finds the same evidence for Japanese MNEs. Political and exchange rate risks associated with the FDI activities of a MNE are tolerated less in developing countries (Deseatnicov and Akiba, 2016). For developed countries, more risk is tolerated by the MNE and their FDI activity if the level of political and exchange rate stability is enough for their fundamental need.
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environment; thirdly, war and political violence, including terrorist attacks. This can harm the foreign assets of the MNE directly as a cause of damage, or indirectly by depressing the productivity in the long run (Jensen, 2008). In order to increase FDI, governments choose to reduce the risk of nationalization. This is done by implementing policies which advocate strong property rights to reduce expropriation risk (Biglaiser and DeRouen, 2006). Habib and Zurawicki (2002) show that corruption impacts FDI negatively. This is due to the personal morals of the investors and the cost inefficiencies which arise from corruption. Bastiaens (2016) investigates the effect of authoritarian Regimes on FDI. She finds evidence that authoritarian regimes increase the political risk profile of the country. However, if the authoritarian countries sign an international investments treaty the risk can be minimized and attract FDI inflows. As a result an increase of FDI in these countries is evident. In the period from 1996 to 2016 the FDI inflows in authoritarian countries increased from 16% to 23% (World Bank 2011). Bastiaens (2016) also finds that openness to citizen input is an important factor to ensure political stability, which in turn will result in attracting FDI. More specifically, scientists find that democracy reduces political risk. This will result in MNEs favoring to invest in these countries (Jensen 2003; Busse, 2007). However, Li and Resnik (2003) argue that democratic rights lead to stronger property rights protection. This increase in stronger property rights protection is the true motive for MNEs to invest abroad. Biswas (2002) finds significant evidence that the strengthening of the property rights will result in an increase of FDI inflows. Berger, Busse, Nunnenkamp and Roy (2010) indicate that the host country will notice higher FDI inflows when they agree to treat foreign and national investors equally. This is done by removing the arbitrary treatment, thus lowering discrimination. Another study conducted by Khan and Akbar (2013) finds a negative and significant relationship between political risk and FDI. In their study, they analyze 94 countries are analyzed in the period from 1986 to 2006. The countries are categorized based on income levels. These are high, upper middle, lower middle and low income countries. The strongest negative relationship is found in countries classified as upper middle-income countries. These are countries with economies between $3,976-$12,275 GDP per capita, classified by the World Bank. Most scientists however, show that a political stable environment will promote FDI. Based on the literature discussed I hypothesize that a high perceived level or indication of political risk will negatively affect the FDI inflows of the host country.
9 2.4 Exchange Rate
The exchange rate reflects the price of the country's or nation's currency in terms of another currency (Dell’Ariccia, 1999). A currency can be exchanged at the spot rate, the current value of the currency, or forward rate, the spot rate adjusted by the costs to deliver the currency in the future. The exchange rate consists of two components. The first component reflects the domestic or home currency. The second component is the foreign currency. The valuation of the currency can be quoted either direct or indirect. With a direct quotation meaning the price of 1 unit foreign currency expressed in the home currency. Indirect quotation is the expression of 1 unit home currency in the foreign currency. If the home currency is not indicated in the exchange rate, it is known as a cross currency or cross rate. If the exchange rate increases of one currency against the other, it is known as an appreciation. Depreciation is the decrease of value of one currency against the other. Most important for this paper is the exchange rate volatility. This refers to a given period where the exchange rate's appreciation and depreciation is measured (Clark, Tamirisa and Wei, 2004).
2.5 Mediating effect Exchange Rate Volatility
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so high in Kenya during the 1990s. However, exchange rate volatility is a controversial topic in the theoretical and empirical research. Ochieng (2012) concludes that an increase in political risk should lead to the depreciation of the currency of the country that is experiencing the political risk, thus increasing the exchange rate volatility. Based on his findings the effect of local political risky events had a statistically significant effect on the USD and a relative minimal reaction for the Euro showed. However, for the other currencies analyzed no relationship was found between political risk and exchange rate volatility (Ochieng, 2012). Based on these findings, political risk is seen as an unstable event and these types of events have generally been concluded to impact major currencies as suggested. I therefore hypothesize that an increase in political risk will result in an increase of exchange rate volatility.
Hypothesis 2: Political risky countries will increase exchange rate volatility.
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firms. Also these results are supported by Sayintha (2001) for FDI within Europe. However, Sayintha combines the relationship of exchange rate volatility on FDI with contract enforceability. Finding mixed empirical results. In his model he shows that trade will be significant lower when exchange rate volatility and contract enforceability is high. However, the effect is reversed when the insecurity of contract enforceability is low, creating a stimulus for trade during high exchange rate volatility. Also, the effect disappears completely when contract enforceability is at the maximum. Based on these findings, I hypothesize that high levels of exchange rate volatility will affect the FDI inflows of the affected country negatively.
Hypothesis 3: Exchange rate volatility will lower the FDI inflows of the affected country. Since I hypothesized that the political risk has a positive relationship with the exchange rate volatility and a negative effect of exchange rate volatility on FDI inflows, a mediating effect may be evident. Thus, the relationship between political risk and FDI inflows may flow partly through the exchange rate volatility as based on the previous research (Goldberg and Koldstad, 1995; Lobo and Tufte, 1998).
Hypothesis 4: Exchange rate volatility mediates the relationship between political risk and FDI inflows.
2.6 Conceptual model
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3. METHODOLOGY AND EMPERICAL ANALYSIS
This section clarifies the methodology needed in order to measure the relationship between political risk and FDI inflows. To test the hypotheses derived from the literature review, quantitative research is used. This section further elaborates the population of the research, measurement of the variables, data collection and how the data will be analyzed. In the end the findings are given.
3.1 Measures
In order to test the hypotheses, data regarding the variables constructed earlier are analyzed. For this study, the dependent variable is the FDI inflow of a country. The independent variables include the political risk, and exchange rate volatility. Openness to trade is the control variable.
3.1.1 Measurement of Political Risk
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Voice and Accountability (VoAc) - Reflects perceptions of the extent to which a country's citizens are able to participate in selecting their government, as well as freedom of expression, freedom of association, and a free media.
Political Stability and Absence of Violence/Terrorism (PoVi) - Political Stability and Absence of Violence/Terrorism measures perceptions of the likelihood of political instability and/or politically-motivated violence, including terrorism.
Government Effectiveness (GoEf)- Reflects perceptions of the quality of public services, the quality of the civil service and the degree of its independence from political pressures, the quality of policy formulation and implementation, and the credibility of the government's commitment to such policies.
Regulatory Quality (ReQu) - Reflects perceptions of the ability of the government to formulate and implement sound policies and regulations that permit and promote private sector development.
Rule of Law (RuLa)- Reflects perceptions of the extent to which agents have confidence in and abide by the rules of society, and in particular the quality of contract enforcement, property rights, the police, and the courts, as well as the likelihood of crime and violence. Control of Corruption (CoCo) - Reflects perceptions of the extent to which public power is exercised for private gain, including both petty and grand forms of corruption, as well as "capture" of the state by elites and private interests.
3.1.2 Measurement of FDI inflows
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In order to measure the exchange rate volatility, the historical exchange rates against the SDR is used. IMF provides daily exchange rates of 1 SDR against the foreign currency. The reason why SDR is chosen as the base currency is due to its valuation. SDR is valued based on a basket of five major currencies. These are the US dollar, the Euro, the Chinese renminbi (RMB), the Japanese yen, and the British pound sterling. Since the basket of five major currencies are chosen as the base currency, the exchange rate will be precise and consistent to measure the volatility (Dell’Ariccia, 1999). The most widely used method to measure exchange rate volatility is done by using the standard deviation of the first difference of logarithms of the exchange rate (Clark et al., 2004). If the measure equals zero, the exchange rate is following the existing trend. However, if the measurement does not equal zero the exchange rate is considered as volatile. The higher this number is, the more volatile the exchange rate is. Most other studies compute the exchange rate over one month and use the end of month data. The standard deviation is calculated over a one year period, to measure the short-run volatility. The long run volatility is measured by capturing the 5 year period of the exchange rate. This type of practice in order to measure exchange rate volatility has been used by many researchers. For example the research done by Brodsky (1984), Kenen and Rodrick (1986), Frankel and Wei (1993), Dell’Ariccia (1999), Rose (2000), and Tenreyro (2007) used this type of methodology.
3.1.4 Control variables
15 3.2 Findings
This study attempts to measure the relationship of political risk on FDI inflows with the expected mediation effect of exchange rate volatility. FDI inflow is the dependent variable, whereas the 6 dimensions of political risk the independent variable. The control variable added to this relationship is the openness to trade. Theoretically it is expected that political risk will lower the FDI inflows of a given country, since investors perceive this as a threat to their investments. A regression analysis will be conducted in order to encapsulate this relationship. The correlation between the independent and dependent variables is analyzed by using a cross section analysis, including the descriptive statistics. For this research the regressions are retrieved from the ordinary least squares. In order to do so, the following theoretical function, similar to other studies (Qian and Baek, 2011; Khan and Akbar, 2013), is used:
FDIi= β + β1PRi + β2ERVi+ β3OTTi + ei
FDI stands for the Log of annual FDI inflows expressed in US Dollar. PR indicates the political risk. This variable consists of 6 dimensions of governance, namely: Voice and Accountability (VoCa), Political Stability and Absence of Violence (PoVi), Government
Table 1.Summary of the variables
Variables Definition Data
Source
VoAc - Voice and Accountability Annual country index of the 6 governance dimensions, scaling from 0 (weak governance) to 5 (strong governance)
World Bank
PoVi - Political Stability and Absence of Violence idem GoEf - Government Effectiveness idem
ReQu - Regulatory Quality idem
RuLa - Rule of Law idem
CoCo - Control of Corruption idem
FDI - Foreign Direct Investment Log of annual average FDI inflows of a country in USD
World Bank
ERV - Exchange Rate Volatility Standard deviation of the first difference of logarithms of the exchange rate measured in SDR
IMF
OTT - Openness to trade Control variable, log of sum of total annual imports and exports as percentage of GDP
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Effectiveness (GoEf), Regulatory Quality (ReQu), Rule of Law (RuLa) and Control of Corruption (CoCo). These 6 dimensions of governance are computed as the variable PR. This variable is derived from taking the average of these 6 variables. ERV is the Log of annual exchange rate volatility, with SDR as the base currency. OTT is the control variable openness to trade, expressed as the annual log of total imports and exports as a percentage of GDP in US dollars. Also, ei is the normally distributed error.
In order to measure the relationship of political risk on FDI inflows and the mediation effect of Exchange Rate Volatility, 3 regression models are constructed. Model 1 is used to encapsulate the mediating effect. This is done by using the computed variable PR as the independent variable. The dependent variable is the exchange rate volatility. Model 2 has as the independent variable PR and the control variable OTT, with the FDI inflows as the dependent variable. Model 3 represents the same model as model 2, however the mediating variable ERV is added. For all models, the period from 2002 to 2015 is analyzed across 58 countries. Each country has relevant data for all variables. This is the reason why the country pool of 200 countries went down to 58 countries. In table 2 the countries which are researched with their corresponding currency can be found.
Table 2. Country sample with corresponding currency
Country Currency Country Currency
Australia Australian Dollar Singapore Russian Ruble Bahrein Bahrain Dinar South Africa Saudi Arabian Riyal Botswana Botswana Pula Sri Lanka Singapore Dollar Brazil Brazilian Real Sweden (EU) South African Rand Brunei Brunei Dollar Switzerland (EU) Sri Lanka Rupee
Canada Canadian Dollar Thailand Swedish Krona
Chile Chilean Peso Trinidad & Tobago Swiss Franc China Chinese Yuan United Arab Emirates Thai Baht
Colombia Colombian Peso United Kingdom (EU) Trinidad And Tobago Dollar Czech Republic (EU) Czech Koruna United States U.A.E. Dirham
Denmark (EU) Danish Krone Austria U.K. Pound Sterling Hungary Hungarian Forint Belgium U.S. Dollar
Iceland (EU) Icelandic Krona Cyprus Euro
India Indian Rupee Estonia Euro
Indonesia Indonesian Rupiah Finland Euro
Iran Iranian Rial France Euro
Israel Israeli New Sheqel Germany Euro
Japan Japanese Yen Greece Euro
(South) Korea Republic Korean Won Ireland Euro
Kuwait Kuwaiti Dinar Italy Euro
Malaysia Malaysian Ringgit Latvia Euro
Nepal Mexican Peso Lithuania Euro
New Zealand Nepalese Rupee Luxembourg Euro Norway (EU) New Zealand Dollar Malta Euro
Oman Norwegian Krone Netherlands Euro
Pakistan Rial Omani Portugal Euro
Poland (EU) Pakistani Rupee Slovakia Euro
Qatar Polish Zloty Slovenia Euro
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Table 3 represents the cross section analysis, including the descriptive statistics. From Table 3 I find for almost all variables highly significant correlations. Only no significant correlation has been found for the variable ERV and FDI inflows. The relationship however, as expected is negative. ERV does show significant correlation with some of the governance dimensions. GoEf and RuLa are considered as significant at a p level of smaller than 0,05. ReQu is even significant at a p level of smaller than 0,01. Besides, ERV does show significant correlation with the control variable OTT at a p level of smaller than 0,01. Moreover, OTT shows a significant correlation of -0,073 with FDI inflows, at a p level of smaller than 0,01. Regarding the other correlations, they show highly significant correlations, at the lowest p level. On top of that, the 6 dimensions for governance show a high correlation between each other.
Table 4 also covers the descriptive statistics and correlation matrix, but with the computed variable political risk (PR). The computed variable PR is used for the regression analysis. From the table I find again significant correlations between PR and the other variables. PR has a negative correlation with the ERV, indicating that a higher score on PR will lower the ERV. Moreover, the control variable OTT also has a negative correlation with PR.
Table 4. Descriptive statistics and correlation matrix. Period 2002 to 2015. Correlations Variables N Mean SD 1 2 3 4 1. FDI inflows 812 22,49 2,02 1 2. ERV 812 0,02 0,02 -0,005 1 3. PR 812 3,26 0,79 0,318** -0,072* 1 4. OTT 812 4,43 0,58 -0,073* -0,115** 0,325** 1 Note: **p <0,01 and *p < 0,05 (2-tailed)
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Based on the OLS regression, the data must satisfy four assumptions. Firstly, there must be a linear relationship between the variables. In figures 2 to 4 the scatter plots of the variables are given. Figure 2 shows the scatter plot of the variables FDI inflow and PR, showing a positive linear relationship. Figure 3 shows the scatter plot of the variables FDI inflow and ERV, showing almost a straight line. Figure 4 does show a negative linear relationship between the variables ERV and PR. Based on these results the assumption is satisfied for figure 2 and 4.
Secondly, all the variables must be multivariate normal. Figure 5 to 7 covers the histograms testing for multivariate normal distribution. Figure 5 shows the histogram for the variable FDI inflows, satisfying the normality assumption. Figure 6 shows the histogram for the variable ERV, although the data is slightly skewed the normality assumption is met. The same results are shown in figure 7, thus satisfying the normality assumption.
Figure 2: Scatter plot FDI inflow and PR Figure 3: Scatter plot FDI inflow and ERV Figure 4: Scatter plot ERV and PR
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Thirdly, multicollinearity must not exist within the regression analysis. When analyzing the correlation analysis in Table 2, I find high correlation between multiple variables (>0,7). This indicates that the assumption is not met for OLS regression. However, these correlations occur when the 6 dimensions of governance are taken separately. For this research the composited variable PR is used in the regression analysis for analyzing political risk. When running a correlation matrix with this variable (Table 4), no high correlations are found between the variables (<0,7). Table 4 shows these results and satisfies the multicollinearity assumption.
Fourthly, no auto correlation in the data is allowed. Running a Durbin-Watson test on all three regression models (Table 5 column 1, Table 6 column 1 and 2) shows values of 1,882, 1,951 and 1,948 respectively. Values between 1,5 and 2,5 show no auto-correlation in the data, indicating that this assumption is also satisfied.
Three regression equations need to be tested in order to analyze the mediation effect (Baron and Kenny, 1986). Table 5 and 6 are the results from these OLS regression analysis. Table 5
shows the outcome from the OLS regression for the effect between the independent variable PR on the dependent variable ERV. Based on these results, I show that the variable PR has a significant effect on the variable ERV. The analysis of the mediating effect can be continued. The constant is significant at a p level of lower than 0,01. While the PR is significant at a p level lower than 0,05. The sign of the variable PR is negative. This indicates that the variable ERV will be lower when the variable PR increases.
Table 5. OLS Regressions for Exchange Rate Volatility 1.
Constant 0,027***
(9,42) Independent Variable (IV)
PR -0,072** (-2,06) N 812 F-statistic 4,25 R² 0,005 R² (adjusted) 0,004
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1. 2.
Constant 22,359*** 22,358***
(42,76) (41,36) Independent Variable (IV)
PR 0,383*** 0,383*** (11,08) (11,07) Mediator ERV 0,044 (0,01) Control Variable OTT -0,198*** -0,198*** (-5,73) (-5,70) N 812 812 F-statistic 63,89 42,54 R² 0,136 0,136 R² (adjusted) 0,134 0,133
Notes: Coefficients are standardized; t-statistics are in parentheses * P < 0,10; ** p < 0,05; *** p < 0,01.
Table 6 shows the results of the regression for the relationship between political risk and FDI inflows. The variable political risk has been composited by taking the arithmetic average of the 6 dimensions for governance. This variable is labeled as PR (Political Risk). Two columns can be found in Table 6. In the first column the independent variable is PR and the dependent variable FDI inflows. OTT is the control variable. Based on these results I show that both the variables PR and OTT are significant at a p level of lower than 0,01. The sign for the variable PR is positive, meaning that a better score on the 6 dimensions of governance will result in a higher FDI inflow of the country. OTT has a negative sign. This indicates that higher the OTT value is, the lower the FDI inflows are. When I look at the explanation of the analysis for this model, I find a relative low explanation (R² (adjusted) = 0,134). However due to a relative low R², the lack of more control variables may be an explanation.
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4. DISCUSSION
This section will further discuss the findings from the empirical analysis. All four hypothesizes will be reflected on these findings. Also the information gathered from the literature review is used for this discussion.
Hypothesis 1 expected a negative relationship between the independent variable political risk and the dependent variable FDI inflows. Based on the correlation analysis in Table 3, all 6 dimensions of governance are significant at the p level of 1%. Besides the significance of these correlations, the variables also show some correlation. Of these variables 4 are above the 0,3 mark, and indicate a correlative relationship of more than 30% on the dependent variable FDI inflows. This indicates that an increase of the measured index of 1 will result in an increase of FDI inflows of 30%. Since these indexes are ranged from 0 (weak governance performance) to 5 (strong governance performance), an increase of the index indicates a better political performing country. This implies that if a country would score 1 point less on this measurement, the FDI inflows will be lowered by roughly 30%. This is in favor of the first hypothesis, namely a negative relationship of the political risky countries on FDI inflows. When I look at the regression results from Table 6, I also find significant coefficients for the independent variable PR at a p level of <0,01. Furthermore, the sign of the coefficient is positive, indicating a positive relationship with FDI inflows. However, this positive relationship indicates good governance performance, thus lower political instability. This is in line with the findings from Busse and Hefeker (2007) and Hayakawa et al. (2013), advocating a negative relationship of FDI inflows and political risk. Therefore hypothesis 1 is supported, since a weak governance performance is associated with less FDI inflows.
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and significant coefficients from the regression and correlation analysis, hypothesis 2 is supported.
Hypothesis 3 regards the negative effect of exchange rate volatility on FDI inflows. From the results of the correlation analysis in Table 3, I do find the expected but insignificant negative sign (-0,005). The sign of the coefficient is in line with the research done by Cushman (1985), Sayintha (2001) and Deseatnicov and Akiba (2016). They argue that due to a higher perceived risk of the investor the FDI inflows will be lower. Based on the regression analysis I find a insignificant and positive coefficient (0,044), contradicting the hypothesis and previous results from the correlation matrix. This result is in favor of the findings from Goldberg and Kolstad (1995) and Mensah, Bokpin and Fosu-Hene (2017). They stated that the FDI inflows will increase when the volatility is high. Based on these mixed findings I cannot support hypothesis 3.
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5. CONCLUSION AND LIMITATIONS 5.1 Conclusions
The aim of this paper is to analyze the relationship between political risk and foreign direct investment inflows. In addition, I analyze whether the variable exchange rate volatility mediates this relationship. In order to answer these questions, a sample of 58 countries is empirically analyzed. Of these 58 countries I have collected information regarding the 6 dimensions of political instability, the corresponding exchange volatility, openness to trade and the FDI inflows. The information reflects the period of 2002 to 2015. The section will summarize the findings.
With respect to relationship between political risk and FDI inflows, I find that political instability does significantly affect the FDI inflows of a country. All 6 dimensions of political instability show significant correlation with the dependent variable FDI inflows. On top of that, the regression analysis shows that the composited variable PR is a significant determent of the FDI inflows. The sign of the coefficient is positive, hence supporting hypothesis 1. The variable PR is a composition of the 6 dimensions of political instability. The 6 dimensions are Voice and Accountability, Political Stability and Absence of Violence/Terrorism, Government Effectiveness, Regulatory Quality, Rule of Law and Control of Corruption. These variables are an index focused on the governmental performance of these dimension. The index ranges from weak performance (0) to strong performance (5). Therefore, in order to attract more FDI inflows into a country the policy makers should focus on improving the governance of these dimensions.
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coefficient values are shown when the mediator is added to the model. This indicates that the ERV does not mediate this relationship.
5.2 Limitations
The use of empirical research comes with its limitations. These limitations should be addressed in order to stimulate better future research. First, annual averages are used for the period of 2002 to 2015 per 58 countries. However, to gain more insight in the fluctuations during these annual periods, future research should narrow down the yearly average to monthly or even weekly data. This way more specific fluctuations can be analyzed. Moreover, future research can analyze distinct events and its effect on FDI. Since these events occur mostly over a short period, taking annual averages might even out the total effect on FDI inflows.
Secondly, the period of 2002 to 2015 could be expanded to a longer period in order to increase the validity of the research. On top of that more countries could be added with the same reasoning. At the moment 58 countries are used. It would be more favorable to analyze more countries. Furthermore, a distinction of the countries between developed and developing countries is advised in order to narrow down the relationships between the variables. This could be even further analyzed by looking at the dominant type of industry per country. Thirdly, after finding data for the variables of 58 countries some information was still lacking. In order to solve this problem, the average throughout the period 2002-2015 per country was taken to fill in the missing values. However, for future research the average should not be taken but a different and more reliable method should be used. This could be by looking at linear regression and fill in the missing values. Perhaps access to more comprehensive databases could also help further research.
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