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The regression results yield several insights into the determinants of FDI in the MENA region. First, a country‘s oil and gas reserves are negatively associated with its FDI performance. This finding is in contrast to the hypothesis of Dunning (1980) that natural resources attract resource seeking FDI. The negative association between a country‘s endowments of energy reserves is significant for both the overall country sample and for non-OPEC countries in particular and the negative relationship has grown stronger over time. However, within the group of OPEC countries, differences in energy endowments no longer help to explain differences in FDI performance.

This important finding can be seen as a variant of the ‗Dutch Disease‘ or ‗resource curse‘. The strict ‗Dutch Disease‘ analogy does not apply here since the currency of most OPEC members in the MENA region is pegged to the dollar, so the presence of oil reserves has not resulted in a strong appreciation of the local currency. However, it is clearly the case that countries with higher oil and gas reserves have attracted relatively less FDI than countries without such natural resource

endowments, especially when controlling for the other explanatory variables in the model. Since foreign investors are unlikely to be deterred by the presence of oil and gas reserves in a country per se, the question that remains to be answered is how, or through what channel, energy endowments lead to relatively low levels of foreign investment. A potential explanation is to be found in the fact that countries with large reserves of oil and gas have enough financial resources and foreign currency available to finance their own economic development. Such countries may view that any expertise required to exploit the natural resources can be purchased through licensing and contractual arrangements, rather than by sharing ownership of the investments made to exploit natural resources. Since FDI is sometimes associated with a loss of economic sovereignty,

particularly in undiversified economies, oil rich countries have typically not actively encouraged FDI and have stipulated local ownership requirements in many, if not all, industry sectors (Lopez-Carlos

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In order to deal with the issues regarding heteroscedasticity, Model 1 has also been run as a Fixed Effects model. The results (not reported here) show that the same parameters are significant as in the pooled regression Model 1.

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& Schwab, 2005 and 2007). This potential explanation is difficult to test for the entire MENA region for the period under study (1987 – 2008), since data on FDI restrictions such as the OECD FDI Restrictiveness Index (OECD 2010).

The only dataset available data for a substantial group of MENA countries is from the World Economic Forum‘s Arab World Competitiveness Report (Lopez-Carlos & Schwab, 2005 and 2007), reported in Table 8 in Section 3.4. It is remarkable that among the top 5 countries in terms of perceived openness to foreign investors, only Oman (in shared fourth place) has energy reserves of any significance. The six OPEC members and the oil producing states Syria and Yemen occupy the bottom eight places on the list when ranked according to openness. Furthermore, the correlation coefficient between openness as reported by the World Economic Forum (Lopez-Carlos & Schwab, 2005 and 2007) and the average FDI performance for the period 2005 – 2007 is 0.674, indicating a strong correlation between openness to FDI and FDI performance. Although the data is not complete and only relates to recent years, this analysis does provide strong support for the notion that oil producing countries receive less FDI at least partly as a result of policy choices related to the openness of their economies to FDI.

Only recently have some OPEC members started to encourage FDI, notably Saudi Arabia and the UAE. These efforts have met with significant success, making these two countries the top two FDI recipients in the MENA region since 2006. The UAE is already a large recipient of FDI since 2001, although it is worth noting that a large part of FDI in the UAE goes towards Dubai, which now has very little oil revenues left. Saudi Arabia‘s FDI inflows have grown particularly strongly after its entry into the World Trade Organization in 2005. Other OPEC members in the region are still among the bottom performers in the region in terms of attracting FDI (for example Kuwait, Algeria, Iran) or have started to attract more investment only very recently (for example Qatar, Libya) after a long period of very low FDI inflows.

Considering the other dependent variables, the role of GDP per capita, as a proxy for market attractiveness, is not significant when looking at the full country sample, but is significant for the individual sub-samples of OPEC and non-OPEC countries. This finding is potentially a result of the fact that OPEC countries have a higher average GDP per capita than non-OPEC countries. Since OPEC countries also have higher energy reserves and a relatively poor FDI performance, GDP per capita is not found to be a determinant when comparing across OPEC and non-OPEC countries,

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but is a significant determinant when considering each of these two countries groups in isolation. GDP per capita is also significant in Models 5 and 6, which are based on the later years in the sample and contain in fact the vast majority of FDI that has taken place in the region since 1988. These findings provide further support for the role of market seeking as a motive for FDI in the MENA region.

The role of manufacturing exports is positive and significant in the total country sample and the OPEC country sub-sample. The question of causality remains; do manufacturing exports promote FDI inflows or vice versa? Jun & Singh (1995) arrived at a similarly robust result for export orientation and conducted a Granger causality test, concluding that for some countries in their sample exports preceded FDI, for one country FDI preceded exports and for other countries the results were inconclusive. This study does not further investigate the causality question regarding manufacturing exports and FDI and does not come to conclusions in this regard other than

demonstrating that the two variables are positively associated with each other. Given this statistically significant and positive association, manufacturing exports do at least contribute to the model as a control variable for the other parameters.

The lack of significance of environmental risk, as measured by either the ICRG Composite Risk or the Global Insight Overall Risk rating, is somewhat surprising in the context of the available

literature. There is even a significant negative association between risk and FDI performance for the later period (Model 5) and for risk as measured by Global Insight data (Model 6). Existing theory suggests that investors are attracted by government stability and a strong investment profile. However, the results in these models do not support this hypothesis. At this stage, there are three possible explanations. First, it is possible that investors are not greatly concerned about overall risk levels in a country, provided that they are adequately protected or are compensated for taking the risk. Secondly, investors may not be concerned about environmental risk as long as it is below a certain threshold level. Although environmental risk is generally high in the MENA region, it can be argued that once a company has decided to enter the MENA region at all, nearly all countries are potential markets to operate in. Thirdly, it is possible that investors are sensitive to particular aspects of environmental risk rather than the overall level. In this context, the results of Model 5 could be explained by a negative correlation between FDI and the scores of certain types of environmental risk. This topic will be investigated further in Chapter 6.

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The level of world oil prices is an important determinant of FDI performance throughout the region. There are two potential and complementary explanations for this finding. For oil producing countries (both OPEC and non-OPEC), higher oil prices make marginal investments in oil

exploration and extraction attractive and such investments may at least partly be realised through FDI. Secondly, higher oil prices directly affect the revenues of OPEC country governments. The additional revenues typically result in budget surpluses which are in turn partly reinvested in

neighboring countries (both OPEC and non-OPEC), for example through Sovereign Wealth Funds or other government controlled investment vehicles. This explanation is particularly relevant for Model 3, which shows the significant role played by the level of oil prices in determining FDI flows into non-OPEC countries.

In summary, the most robust results are provided by Models 5 and 6 which are based on ICRG and Global Insight risk data respectively. These models show that FDI in the MENA region is

determined by GDP per capita, openness to trade and oil prices. At the same time, both models show a negative relation between FDI and a country‘s energy reserves and overall environmental risk profile (i.e. countries with higher environmental risk attract more FDI).

5.5 Conclusions

The MENA region is an appropriate region for the research in the sense that it contains countries with many similarities, but great differences between countries in terms of energy endowments, environmental risk levels and GDP per capita. This, together with the absence of other significant natural resource endowments, makes the MENA region highly appropriate for testing the role of natural resource endowments as well as other more traditional factors in determining FDI inflows. As a result, the research has confirmed some of the traditional determinants of FDI flows found in the literature and has obtained new findings regarding the role of energy resources and energy prices in determining FDI.

Referring back to the hypotheses formulated in Chapter 4, hypothesis 1 on the role of GDP in determining FDI is supported through the analysis of the correlation coefficients. Hypothesis 1b on GDP per capita is also supported, but only if the OPEC and non-OPEC countries in the region are

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considered separately. These findings provide general support for the view that there is a strong market seeking component among the motivations for FDI into the MENA region.

Hypothesis 2 on the role manufacturing exports is also generally supported, specifically among OPEC countries and in the latter half of the sample period. Countries with a high share of

manufacturing exports in their economy attract more FDI, especially among OPEC countries where manufacturing industries are more likely to be in energy intensive industries that require and attract significant investment, such as petrochemicals and aluminium.

Surprisingly, there is no support for hypothesis 3 on the role of environmental risk. Further research is needed to determine whether other, more specific types of environmental risk play a role in determining FDI performance. This subject is addressed in the next chapter.

Hypothesis 4 on the negative relationship between energy endowments and FDI performance is supported. Based on the available evidence it seems that there is no complete analogy with the ‗Dutch disease‘ phenomenon, especially given that exchange rates are pegged to the dollar for the major oil exporters in the region. Rather, the historically low levels of FDI among countries rich in energy resources appears to be a result of policy choices related to national economic sovereignty and the wish among host nations to prevent foreign investors from controlling major stakes in strategic sectors. Only since 2005 have several of the largest energy exporters (UAE, Saudi Arabia, Qatar) started to encourage FDI actively, while other energy exporters (Iran, Algeria, Kuwait, Iran) still attract little FDI.

Finally, there is strong support for hypothesis 5. The role of the world oil price on FDI inflows is shown to be positive. A likely explanation is that as oil rich countries accumulate foreign exchange reserves, at least a part of these reserves find their way as FDI within the region.

As a result, this study has provided support for the role of market attractiveness and trade openness in determining FDI into the MENA region and has produced two new testable propositions for which evidence is provided. The first testable proposition is that large endowments of natural resources are associated with relatively low FDI inflows. The most plausible explanation for this is that countries with such endowments make policy decisions that discourage FDI. This proposition in particular warrants further research to see if the same phenomenon can be found among resource rich countries in other regions.

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The second proposition is that an increase in the world price for a commodity has a positive impact on FDI into countries that are in the geographic vicinity of countries that are rich in that particular resource. The propositions can be tested for energy resources as well as for other natural resources which may be highly significant in different geographies. Finally, the study has shown that FDI in the region is not determined by an overall measure of environmental risk.

The results of the econometric model have several limitations. First, by omitting variables relating to education, quality of the workforce and infrastructure, potentially significant variables are excluded from the model. As mentioned, measurement difficulties have precluded these variables from being included in the model. Secondly, the measure for environmental risk is at a high level of aggregation. Future research may look further into these potentially significant variables and into different types of environmental risk and how these affect FDI flows. This shortcoming will be addressed in Chapter 6. The study is at a macro (country) level, which by definition entails certain restrictions. Due to data availability issues, the study does not differentiate between different regions or sectors of the economy and does not address the country of origin of FDI flows. Also, as a macro level study, the actual factors that companies take into account when making location and operation mode decisions which lead to FDI flows, is not taken into account. This concern is addressed in Chapter 7. Finally, the R-squared of the regression model is relatively low. This is partly a result of choosing FDI/GDP as the dependent variable, which means that only the variance in FDI not already accounted for by the size of the country‘s economy is investigated. At the same time, it must be recognised that one of the findings of the econometric models is that there is a significant

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Chapter 6

The role of environmental risk factors 6.1 Introduction

This Chapter builds on the work shown in the previous chapter on the determinants of FDI flows into the MENA region. Specifically, the role of different types of environmental risk is assessed. Chapter 5 has shown that environmental risk, when defined at an aggregate level such as the ICRG Composite Risk or Global Insight Overall Risk rating score, is not significant or is even negatively associated with FDI flows into countries in the MENA region. One possible explanation for this finding is that the Composite Risk score is an amalgamation of different specific risk ratings, some of which may have a positive impact on FDI, some a negative impact and some that have no impact that is significant. The Composite Risk score may be a measure that is defined at too high a level of aggregation to accurately correspond to investors‘ concerns about risk in the MENA region. This argument is explored in detail in this chapter, by analysing the role of 26 risk scores in determining FDI into the MENA region, while controlling for other variables that have been demonstrated to be associated with FDI flows in the previous chapter.

6.2 The model

In order to test the role of specific environmental risks in determining FDI flows into the MENA region, this study once again takes the ICRG ratings as its basis for the measurement of risk, with Global Insight ratings used for robustness checks. As reported in Chapter 4, the ICRG ratings are produced not only for a country‘s overall environmental risk level (as measured by the Composite Risk score), but also for a series of specific Political, Economic and Financial risks which are available for each year from 1987 to 2008 for each country in the sample. In this chapter, the three levels of risk that together make up the Composite Risk score (i.e. Political, Economic and Financial risk) are referred to as risk categories. Each risk category is in turn made up of a number of specific risks, which are referred to as risk types. A detailed description of the ICRG risk rating definitions and methodology can be found on the PRS Group website (www.prsgroup.com).

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On the basis of the ICRG ratings for the 16 countries over 22 years, it can be investigated what specific types of environmental risk play a role in determining FDI flows. Just as for the risk category scores reported in Chapter 3, the countries in the MENA region display a wide variety of risk levels when considering the individual risk categories across the country sample and over time. As expected, there is a substantial level of correlation between the various risk measures, as

demonstrated by Table 13 which contains the correlation coefficients for the rating categories for all the countries and years in the sample. The correlations between the various individual risk types are not reported here, but there is also a significant correlation between several of these risk types, albeit generally at lower levels than the correlations between risk categories reported here (and in some case individual risk types are even negatively correlated). This high level of correlation between the risk measures makes it unfeasible to combine different risk measures in one multiple regression model without running into issues of multicollinearity. Instead, a total of 26 individual regressions are run to determine the role played by each of the individual environmental risk factors. In this way, the risk of multicollinearity between the different dependent variables is avoided.

Table 13: Bivariate Correlation Coefficient for ICRG risk measures

Composite risk Political risk Economic risk Financial risk Composite 1 Political .922** ₁ Economic .785** .547** 1 Financial .927** .777** .690** 1 Note:

** Correlation is significant at the 1% level (2-tailed). * Correlation is significant at the 5% level (2-tailed).

The model structure used is the same as the one in Chapter 5, with FDI as a share of GDP as the independent variable and GDP per capita, manufacturing exports as a share of GDP, energy reserves, oil prices and risk as the dependent variables. However, this time, the model is run separately for each of the 26 different risk types defined by ICRG. The basic specification remains:

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FDI/GDP = ƒ (GDP PC, OPENNESS, RISK, ENERGY, OIL PRICE)

In this model, all dependent variables except for the risk variable are simply included as control variables, since they have already been tested for in Chapter 5.