Bachelor Thesis in Economics and Business Economics

The impact of terrorism on FDI in affected emerging and frontier markets

Author: Supervisor:Maximilian Ast Caroline Witte381650

July 17th, 2016Academic Year 2015/2016

Abstract:This study investigates the impact of terrorism on foreign direct investment in emerging and frontier markets. Data on foreign direct investment, terrorism and a number of measures potentially influencing foreign direct investment were retrieved in order to conduct an empirical analysis ought to test the causal effect of terrorist acts of violence and the mere threat of terrorism on foreign direct investment as a whole or sector specific. Additionally, a real options model is laid out, which illustrates investors’ behavior in light of a threat of terrorism. Using a fixed effects model approach, the study found no evidence of an impact of terrorism on foreign direct investment in any way, irrespective of the sector.

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Table of Contents

1 Introduction 4

2 Theoretical Framework52.1

Terrorism....................................................................................................... 52.2 Foreign Direct

Investment.......................................................................... 62.3 Literature

Review.......................................................................................... 62.4 Real Options

Theory.................................................................................... 10

3 Methods and Material143.1

Data..................................................................................................................... 14

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3.1.1 Data Sources........................................................................................ 14

3.1.2 Definition Emerging Markets and Frontier Markets.......................... 143.1.3 Selection of cross-section

units............................................................ 153.1.4 Panel Data

........................................................................................... 153.1.5 Summary of

Variables.......................................................................... 163.2

Methodology........................................................................................................ 19

3.2.1 Approaches to Empirical Analysis of Panel Data................................ 19

3.2.2 Hausman Test...................................................................................... 20

3.2.3 The Fixed Effects Model....................................................................... 21

4 Results 214.1 Terrorist acts of violence and their impact on FDI in emerging

markets........... 214.2 The impact of terrorism on FDI in emerging markets varies by

sector.............. 224.3 The mere threat of terrorism and its impact....................................................... 24

5 Conclusion24

6 Appendix 28

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1. IntroductionAn underlying and vital condition for a country’s sustainable economic progress is its securing of political stability and safety (Busey, 1965). Sporadic, unexpected terrorist attacks have become a prominent threat to this very condition in the recent past.Parallel to this trend, Foreign Direct Investment (FDI) is becoming an increasingly important component of a successful investment strategy for companies that are seeking high returns in today’s rapidly evolving global economic integration. FDI is especially lucrative for investors when applying it in market economies that are considered to be emerging, i.e. the world expects to see these countries’ GDP to experience high growth in the near future (Angel, Eunni & Fouto, 2010). Next to emerging markets, it is also frontier markets that have attracted attention. Both classifications receive this attention due to their diversification opportunities. This is a result of their low correlation with markets in developed countries (FTSE Russell, 2014).Developing countries are considered emerging markets or frontier markets when these experience significant growth and reach a number of other economic benchmarks (MSCI 2015). A definition for when a market economy can be classified as emerging or frontier market is retrieved from the US company MSCI that created and annually updates the MSCI Emerging Markets Index. According to the Financial Times, MSCI checks for the number of quoted companies of a certain size and for the proportion of shares available for ordinary investors to buy (2016). Furthermore, the market’s openness to foreign ownership and capital is crucial in order to be classified as an emerging market (FTSE Russell, 2014).

Apart from being characterized by fast economic growth, emerging and frontier markets are often characterized by an immature political and institutional system making them vulnerable to war crime or, specifically, terrorism. Ideally, an emerging market’s institutional and political environment improves as its economy progresses, so that it has the power to defend itself against transnational terrorism or to put an end to domestic radical movements before they evolve to being a

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potential violent threat. Unfortunately, this is not always the case and the reality is that we see deeply embedded socioeconomic discrepancies and political instability in countries that are actually proving to have economic potential. Hence, it becomes a crucial task for an investor to assess a market’s socioeconomic and political issues before deciding on FDI in that country.

This thesis aims to research exactly this problem. By investigating 32 countries that are currently classified as emerging and frontier markets by the MSCI and FTSA, a possible relationship between the exposure to terrorist acts of violence and the volume of FDI in a given country will be studied. This leads to the following research question, which shall be answered in the course of this paper:

What are the consequences of exposure to terrorism for FDI in emerging and frontier markets?

In order to come to an answer to this question, panel data on terrorist acts of violence in all relevant emerging and frontier markets between 2002 and 2012 as well as panel data on FDI for the same time period and entities will be statistically analyzed in order to reveal possible evidence for a negative impact of terrorism on FDI in emerging and frontier markets. First, a possible negative impact of terrorist acts of violence on FDI will be examined by means of a fixed effects estimation model, regressing on the total amount of FDI in a given market. Following, we examine whether this negative impact may vary by sectors to which FDI is flowing in. Lastly, a real options model is tested, which investigates whether or not the mere threat of terrorism causes FDI to decrease.Before the statistical analysis begins, though, all relevant concepts and underlying economic theories will be defined and thoroughly explained in a theoretical framework, which makes the next part of this thesis.

2. Theoretical Framework2.1 TerrorismSo far, there has not been a universally accepted definition for terrorism. Nonetheless, for this thesis, I formulate a definition that is based on widely

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accepted rationales in understanding and talking about terrorism. For this, I refer to the U.S. Code’s official definition: “[…] terrorism involves violent acts or acts dangerous to human life that is against federal or state law”. Furthermore, these acts are motivated by one or more of the following three objectives: “(i) to intimidate or coerce a civilian population; (ii) to influence the policy of a government by intimidation or coercion; or (iii) to affect the conduct of a government by mass destruction, assassination, or kidnapping”. 1 Additionally, it is important to note that terrorism does not necessarily include an executed act of violence; the mere threat of use of violence is enough in order to be classified as terrorism (Oetzel & Oh, 2013). To illustrate, the creation of widespread intimidation or fear among civilians who are not directly involved in a conflict already puts pressure on political decision makers who are then often forced to concede to terrorists’ demands (Bueno de Mesquita 2005; Kydd & Walter 2002). Moreover, terrorism can stem from a range of different types of motivations such as religious, political, or ideological. For the purpose of this thesis, we consider any type of terrorism that has the potential to affect the decision of a multinational enterprise (MNE) to directly invest in a foreign emerging country. Lastly, one distinguishes between domestic and transnational terrorism. When a terrorist attack in a country involves targets, victims, institutions, or citizens of another country, we speak of transnational terrorism (Enders & Sandler, 1996). Since we deal with foreign direct investment, it is only foreign capital that can be affected by terrorism in the scope of this thesis. For terminology reasons, we can therefore conclude that only transnational terrorism will be taken into account in this study. (Enders & Sandler, 1996).

2.2 Foreign Direct InvestmentForeign Direct Investment (FDI) plays a crucial role in global economic integration by creating long-lasting international relationships between 1 https://www.fbi.gov/about-us/investigate/terrorism/terrorism-definition

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economic agents (Harrison, 1994). It represents an agreement in which both parties benefit: the investing company is able to introduce its products to new markets and the host may benefit from knowledge spill-overs and consequential economic growth. According to OECD, FDI is defined as “cross-border investment by a resident entity in one economy with the objective of obtaining a lasting interest in an enterprise resident in another country” (2016). An important criterion is that the direct investor holds an ownership of at least 10% of the voting power with which he exhibits significant influence on the management’s decision making, motivated by his long-term interest in the invested enterprise. Additionally, Net Foreign Direct Investment (NFDI) stands for the residual of all foreign capital in- and outflows.

2.3 Literature ReviewThere has been a variety of research conducted on FDI in emerging markets and whether the presence of terrorism can have an influence on it. In order to provide the reader with an overview of what previous studies have found on this topic and to provide a basis for the formulation of hypotheses, a number of relevant papers will be discussed. First, previous research on the effect of terrorism on FDI in specific regions will be examined. Secondly, the focus will be on papers that investigated the effects of terrorism on FDI in specific industries. Following, studies discussing the effects of a mere risk or threat of terrorism on FDI are outlined. To conclude, a real options model is laid out which illustrates investors’ behavior when confronted with the risk of terrorism.

Abadie and Gardeazabal (2003) investigated to what extent the Spanish terrorist ensemble ETA affected the Basque Country’s economy, the Spanish state which incurred the most severe terrorist attacks in the 20th century. The two authors aimed at distilling the magnitude of the terrorist attacks on the Basque economy by comparing the Basque economy’s development to that of other Spanish states where a similar economic

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development could be expected, while being spared from terrorism. In order to make a feasible comparison possible, it was necessary to construct a “synthetic” Basque Country, that is, a Spanish state that is very similar to the Basque state in terms of economic characteristics. This allowed the authors to assume that fluctuations in the Basque economy can be narrowed down to the effects related to terrorist attacks by ETA. Concluding, Abadie and Gardeazabal state that after the outbreak of terrorism in the late 1960’s, per capita GDP in the Basque Country declined by about 10 percentage points relative to a synthetic Spanish control region (2003).

Enders and Sandler (1996) also initiated an attempt to investigate whether or not terrorism influences foreign direct investment in specific regions. For their analysis, they focused on Greece and Spain – two countries that could have been considered as emerging markets at that time, making the article a very relevant reference for this thesis. Enders’ and Sandler’s primary purpose was to ascertain whether terrorist acts had a measurable and significant effect since the 1970’s on NFDI in Spain and Greece. To find this out, the authors aimed to quantify the influence of terrorism on NFDI by employing modern tools of time-series analysis. They concluded that in both countries, terrorism led to a persistent and significant negative influence on NFDI and on the stock of foreign-owned capital.

A. Zussmann, N. Zussmann, and Nielson (2006) affirm the results of Enders and Sandler (1996) and Abadie and Gardeazabal (2003). According to the three Cornell based professors, Israel-Palestine conflicts caused a significant decline in asset prices during their occurrences (Zussmann et al, 2006). Moreover, they found that if peace treaties were initiated, the trend goes into the opposite direction: Asset prices increased in Israel if active diplomatic steps were taken to resolve the violent conflicts. Eckstein and Tsiddon (2004) make clear, however, that during times Israel became victim of terror, the long run equilibrium is of lower output and welfare, despite

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the government’s efforts to offset terror by spending tax revenue on defense.

Given these findings of previous research on the effect of terrorism on FDI, the first hypothesis to be empirically tested is the following:

H1: Terrorist acts of violence have a negative impact on FDI in emerging markets

Five years after their first publication, Abadie and Gardeazabal (2008) conducted another study in which they approached the topic of terrorism from a broader perspective. They examined how the exposure to terrorist acts of violence affected the world economy on the whole, instead of focusing on one specific location (Abadie & Gardeazabal, 2008). Abadie and Gardeazabal outline which four main effects terrorism has from an economic standpoint. These effects include the reduction of a country’s capital stock (human and physical) and the induction of higher levels of uncertainty. Furthermore, an increase in counter-terrorism expenditures is to be expected, which draws resources from productive sectors (Abadie & Gardeazabal, 2008). Lastly, terrorism is known to negatively affect specific industries such as tourism (Abadie & Gardeazabal, 2008). Given this finding, it is worth questioning whether some industries are more inclined to be negatively affected by terrorism than others. Presumably, industries which are more capital-intensive are more vulnerable to terrorism. At least, this was the case during the terror in the Basque country, where several corporate buildings were destroyed by the terror group ETA (Abadie and Gardeazabal, 2003). Thus, FDI in the manufacturing sector, a capital-intensive industry that usually includes the construction and maintenance of large plants and machinery, is assumed to be more severely affected by a terrorist attack than FDI in the service sector, for instance, where, for example, office space does not even need to be purchased or built, but can simply be rented.

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In order to investigate whether the magnitude of the negative impact of terrorism on FDI in emerging markets varies among industries, the following hypothesis is going to be tested:

H2: The impact of terrorism on FDI in emerging markets varies by sectorWhile in the studies mentioned in the beginning terrorism caused

numerous casualties, Abadie and Dermisi (2008) analyze the case where merely the threat of terrorism was present. Specifically, they focus on Chicago, a city whose downtown central business district (CBD) represents a favorable target for terrorists given its high population density, economic significance, and the existence of government facilities and landmark buildings, such as the Sears Tower or the Hancock Center. In fact, it was exactly these characteristics that arguably made New York City the target of the 9/11 terrorist attacks. Given that Chicago may be regarded as a potential target of terrorists, Abadie and Dermisi investigated the economic impact of the increase in the perception of risk in Chicago’s CBD as a result of 9/11 by comparing the evolution of vacancy rates. More precisely, the authors observed the evolution of vacancy rates at office buildings around landmark buildings, such as the Sears Tower (highest building in the US), as well as vacancies within Chicago’s landmark buildings themselves. Their results indeed suggest that 9/11 influenced the location decision of many high-end office tenants, limiting the economic growth opportunities that come along with agglomeration effects which one normally expects in CBD’s like downtown Chicago.

Having outlined what effect the mere fear of terrorism can have, Abadie and Gardeazabal go on to constructing a theoretical model, which is based on the underlying hypothesis that in a sufficiently open world economy, changes in the intensity of terrorism can cause large movements of productive capital across countries (Abadie & Gardeazabal, 2008). They assert that this is because investors decide to withdraw investments from regions that are exposed to the negative economic effects of terrorism

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discussed above, such as overall uncertainty and the consequential reduction of an expected return on investment (Abadie & Gardeazabal, 2008). In accordance with the predictions of this model, the authors find that higher levels of terrorist risk are associated with lower levels of NFDI even when controlling for other types of country risks (Abadie & Gardeazabal, 2008).

Chen and Siems (2004) come to a similar conclusion. They find that global capital markets respond promptly to terrorist and military attacks. Thus, one can say that terrorist attacks and military invasions indeed have great potential to affect capital markets around the world within a short period of time (Chen & Siems, 2004).

In order to analyze an investor’s behavior towards possible market shocks, an imaginary scenario of an investors’ behavior is embedded into a theoretical model, which is further explained in the subsequent section.

2.4 Real Options TheoryGiven the mobility of productive capital illustrated by Abadie and Gardeazabal (2008) and given investors’ ability to quickly reallocate investments in the light of bad news, the question arises when investors actually opt for a reallocation of resources. For this, I would like to introduce and outline the real options theory. Real options are fundamentally similar to financial options, which are derivatives that give their holders the right to buy or sell a traded asset. A real option, on the other hand, refers to the right to make a particular capital budgeting decision, such as a company’s investment in research and development. It is said that the key difference between financial options and real options is that the latter’s underlying asset is not necessarily traded in competitive markets. For example, a company’s decision to incur expenses for an expensive training for its employees is an investment that cannot be traded (Berk & DeMarzo, 2014). To illustrate, if a manager decides to pay for the training and the first two training sessions turn out to be useless to his

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employees, he cannot simply resell this investment again. Nonetheless, there are certain cases in real options theory, where the underlying asset can actually be traded, such as the investment by a car manufacturer in a new plant abroad.

The three most common options to occur in capital budgeting are (Berk & DeMarzo, 2014):

1) The option to wait for the optimal time to invest2) The option to grow in the future3) The option to abandon a poorly performing project

All three types of options can be applied to the case of an investor contemplating about an investment in an emerging country where terrorism poses a threat to the invested amount. In order to provide a concrete example of how real options look in practice, a real options model is laid out which illustrates the decision process of an investor active in a terrorism-infected emerging market economy in form of a decision tree. Regarding the type of the real options model, I will focus on a model which includes a wait-and-see strategy, i.e. waiting for the optimal time to invest. In this example, the decision process will consist of several lags. Before each lag, the investor has the possibility to make a decision regarding his investment.Assume that imaginary company Exxagon has the opportunity to found a subsidiary in an emerging market. This would not only increase the company’s international reach and prestige, but would also be an investment with lucrative monetary returns. However, it is known that a terrorist group is located in the said emerging market. The terrorist group who has radical political motives has conducted a series of deadly attacks on foreign corporations’ employees and destructed a number of corporate buildings. Exxagon fears that their subsidiary might become victim to this group of terrorists so that the company would put its employees in danger and lose a lot of money with this investment. However, the said emerging market’s government assures foreign investors that they are working hard

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to eliminate the terrorist threat. In the past month, 5 out of 10 terrorists were either killed or arrested. It is assumed that the government will have the situation under control within the next month, so that the terrorist group would not impose a threat to foreign corporations anymore by the end of the month. Now let us picture two scenarios: In the first scenario Exxagon does not have the information about the government’s success in eliminating the terrorist group and the forecast about the end of the threat. In the second one Exxagon knows about the information and can act upon it. In form of a decision tree diagram, the first scenario would look like this:

Figure 1:

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In the above scenario, Exxagon would incur the initially invested amount of 800,000$ plus another estimated 100,000$ loss that may include costs such as voiding contracts of employees who were only employed for this specific venture, for example. The NPV for Exxagon would be 1,500,000$ 0.6 + 0.4 (-100,000$) – 800,000$ = 60,000$.However, if Exxagon is informed about the government’s progress, the decision tree diagram would look like this:

Pursue investment(-800,000$)

Terrorists attack Exxagon's property and

employees (40%)(-100,000$)

No terrorist attack was directed against Exxagon (60%)(1,500,000$)

Do not pursue investment (0$)

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Figure 2:

In this scenario, the NPV would be -15,000$ + 0.4 0 + 0.6 1,500,000$ - 800,000$ = 85,000$. The value of the real option is the difference of both NPV’s: 85,000$ - 60,000$.This example shows how the optimal time to invest adds value in the case of FDI in emerging countries. In order to see whether investors who invest in emerging markets apply the real options theory when an investment is threatened to default due to occurred terrorist attacks or the high probability of a terrorist attack to occur, the following hypothesis will be tested:

H3: The mere threat of a possible terrorist attack has a negative impact on FDI in emerging markets.

Invest reosurces in conducting research on the emerging market. Exxagon now knows

about the government's progress. (-15,000$)

Terrorists attack Exxagon's property

and employees (40%)

Pursue investment (-

100,000$)

Do not pursue investment (0$)

There were no terrorist attacks

aimed at Exxagon (60%)

Pursue investment

(1,500,000$)

Do not pursue investment (0$)

Exxagon cancels its plan of founding a

subsidiary due to the risk of terrorism (0$)

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Specifically, it will be tested whether the previously laid out model of the wait-and-see strategy is a phenomenon, which can be observed in real life. For this, a variable will be included in the analysis, which represents a hint towards an increased risk of terrorism in a given emerging market.

3. Methods and Material3.1 Data3.1.1 Data SourcesThe required data on greenfield FDI is retrieved from fDi Markets, a service

by theFinancial Times, which tracks cross-border greenfield FDI across all sectors

and countries worldwide (Financial Times, 2015). For data on terrorism, the Global

Terrorism Database(GTD) by the National Consortium for the Study of Terrorism and

Responses to Terrorism is used (START, 2015).

3.1.2 Definition Emerging Markets and Frontier MarketsFor a definition of emerging markets, I refer to Morgan Stanley Capital International (MSCI) and England based FTSE Russel, who both created and annually update the MSCI Emerging Markets Index and the FTSE Emerging Index respectively (MSCI, 2015) (FTSE Russell, 2015). According to the Financial Times (2016), MSCI checks for the number of quoted companies of a certain size and for the proportion of shares available for ordinary investors to buy. Furthermore, the market’s openness to foreign ownership and capital is crucial in order for a country to be classified as an emerging market, according to MSCI. To complete the definition, FTSE Russel’s country classification (2015) adds the existence of formal stock market regulatory authorities who actively monitor the market as one of the most important criteria to be classified as an emerging market.

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It is important to note that there is no generally accepted definition for what constitutes

to being classified as an emerging market or frontier market. Nonetheless, the merged

definitions mentioned before are a good representation of what characteristics financial

institutions do agree on when classifying markets as emerging or frontier markets.

To illustrate and expand the set of characteristics broached in the definitions by MSCI

and FTSE Russell, the following table summarizes generally accepted traits of emerging

and frontier markets once more:

Table 1: Emerging Markets and Frontier Markets Characteristics

Emerging Markets Frontier Markets

Existence of market exchange and regulatory

body

A type of emerging market (also called “pre-

emerging markets”)

Liquidity in local debt and equity markets Considered to have lower market capitalization and less liquidity than many

emerging markets

Physical financial infrastructure including banks, a stock exchange, and a unified currency (open to outside investors)

Investable stock markets that are less established than those in emerging

markets

Progresses towards being an advanced market economy

Progresses towards being an emerging market economy

Investopedia (2016), FTSE Russell (2014)

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As one can see from the table, the characteristics of both classifications are not strikingly different. In fact, the only major difference is that frontier markets’ stock exchanges are less established and less liquid. Therefore, and for convenience purposes, I will henceforth assume frontier markets and emerging markets being part of the same classification, given the similarities in their defining characteristics. Thus, when referring to emerging markets, both classifications shall be considered from now on.

3.1.3 Selection of cross-section unitsIn order to decide which countries currently qualify as emerging markets (incl. frontier markets) and should therefore be included in the empirical analysis, I refer to MSCI and FTSE Russell again, who both publish a regularly updated list of all emerging countries based on their respective benchmarks. Table 2 (see appendix) presents a merged list of both index providers’ lists. All countries included in this table are going to be the cross-section units of the upcoming empirical analysis.

3.1.4 Panel DataA panel data set (also known as cross-sectional time-series data set) is one which contains more than two observations on multiple units within a given time interval (Hsiao, 2003). For economic and social research, a panel data set has several advantages over time-series or cross-section data sets: Firstly, and most importantly, panel data allow for more accurate inference of the model’s estimators. This is due to the fact that panel data contain a larger amount of data compared to cross-section or time-series data, which increases the degrees of freedom and reduces the collinearity between independent variables (Hsiao, 2003). Furthermore, panel data present a solution to a common problem that empirical analyses face, namely that of omitted variables bias (Hsiao, 2003). In econometrics, the presence of omitted variables, which are correlated with independent variables, often cause misleading effects and consequentially lead to false conclusions.

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Panel data allow to control for these omitted variables by estimating a ‘fixed effects’ model (Plümper & Troeger, 2006). This will be presented more thoroughly in the subsequent methodology section. Unlike one-dimensional data sets, panel data do not only require a dependent variable and independent variables, but also a so-called panel variable and date variable.For this research, the panel variable is chosen to be the ISO (International Organization for Standardization) three-digit country code of all emerging markets included in this analysis. The date variable is made up of a yearly interval ranging from 2003 to 2012. Since there are no missing observations for any of the countries and since the countries’ observations are all within the same time period, one can conclude that the panel data is ‘strongly balanced’. This is important to mention because it allows to avoid expansive pre-analysis conditioning of the data, which is common practice when dealing with unbalanced panel data (Hsiao, 2003).

3.1.5 Summary of VariablesVariables which are included as explanatory variables in the empirical analysis are chosen based on their theoretical relevance. This step deserves special attention since an inclusion of theoretically irrelevant variables unnecessarily uses up degrees of freedom. Following, this can cause an increase in standard errors, leading to less precision of the valid predicting variables’ estimates (Kellogg, 2010). A list with all variables included in this analysis and their respective descriptive statistics can be seen in Table 3 (see appendix). Next to this, every single variable’s theoretical relevance is going to be explained:The corruption variable is retrieved from the International Country Risk Guide (ICRG) and it is based on a point score of 1 to 6, where 6 indicates the highest possible score of corruption. The variable refers to corruption within a political system, which would be a direct threat to foreign direct investment and could therefore have a relevant potential effect on the

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dependent variable FDI. A political system suffering from corruption is likely to intervene in markets through bribery, excessive patronage, and nepotism. All these are enough reason for investors to avoid countries governed by corrupt politicians.wgi_effect is a variable retrieved from the Worldwide Governance Indicators (WGI) project and stands for government effectiveness. It captures the perceptions of the quality of public services, the quality of the civil services and the degree of independence from political pressures, the quality of policy formulation and implementation, and the credibility of the government’s commitment to such policies. The inferred theoretical relevance of this variable is simply that it ensures government stability, which is an important factor in an investor’s assessment of a country. Further, government effectiveness influences both, terrorism and FDI, which makes it indispensable to include as a regressor.Just like the government effectiveness variable, wgi_rule is retrieved from the WGI. It stands for a country’s rule of law and captures to what extent agents have confidence in the rules of society and whether they abide to them. It includes the quality of contract enforcement, property rights, the police, and the courts, as well as the likelihood of crime and violence. Apart from having a stable government, an investor ought to know whether contracts have legal power, making his investments secure against any potential litigations. Thus, it is highly relevant to control for law and order when estimating a country’s FDI volume. Continuing, trade is included as a control variable. It was retrieved from The World Governance Indicators (2015) and is measured as a percentage of GDP of a given country. A country’s trade balance represents its openness to foreign countries in economic terms. A high trade volume signals an investor that the country’s market does not have liquidity issues and is a strong argument for an investment in this country. Additionally, the variable harmonizes with the emerging market’s definition which requires a market’s openness to trade.

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The presented variables so far are mostly of political or legal nature making them relevant to both, FDI and terrorism. A stable government is more likely to have the means to fight and prevent domestic terrorism. Likewise, a stable government is a positive signal for investors that their investments are protected from nationalization, for instance.Following, gdp, gdp_growth, and gdp_cap are added as explanatory variables. GDP (Gross Domestic Product) is a globally accepted measurement of an economy’s well-being in terms of output, consumption, investments, or its current account balance. It gives the investor an idea of how a country’s economy compares to that of other countries. GDP per capita indicates whether a country’s high GDP actually leads to economic well-being of the single citizen, which is a crucial point when judging a country’s economic well-being. Lastly, GDP growth can signal whether there is a potential for high returns on investment. However, historical data on growth might be not enough in order to reap the highest possible returns of emerging market economies.Regarding GDP, it is important to point out that it theoretically impacts both, terrorism and FDI: Low or even negative GDP growth goes along with poverty and economic depression, which may lead to violent revolts in form of terrorist attacks. At the same time, it can cause FDI to decrease since a weak economy cannot attract investment.Closely related to GDP, a variable for an economy’s inflation is included (wdi_infl). A modest amount of inflation stands for economic progress, and its value gives an investor a fundamental idea about a country’s economic potential in the short-term.Especially in emerging markets, populations tend to be large and grow at high rates. This entails market potential given that a big population signals a large market size. For this reason, a variable measuring a country’s population was decided to be included in the analysis (wdi_pop).Furthermore, a fatalities variable is included which serves as the measurement of the magnitude of terrorism in a given country. Since the

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causal relationship between terrorism and FDI is going to be investigated, the main focus is put on the fatalities variable.Concluding, a variable measuring a country’s overall military expenditures as a percentage of its GDP is added. The magnitude of a country’s military spending will serve as a rough proxy to estimate expenditures on counter terrorism initiatives by the government (Investopedia, 2016). This, in turn, serves as an indicator for the threat of terrorism – an increased amount of spending in counter terrorism undertakings implies an increase in the risk of terrorism in a given country.The dependent variables which measure FDI are investment_amount, inv_manufacturing, inv_nontradables, inv_services , and inv_natural (retrieved from fDi Markets). The first variable represents the entire volume of FDI flowing into a country while the other four variables stand for FDI in specific sectors. These sectors include the manufacturing sector, the sector of non-tradable goods, the services sector, and the sector of natural resources. Non-tradable goods are goods that are mostly of public nature such as a city’s electricity and water system, a railroad system, or similar. The service sector comprises typical tertiary sector goods such as consulting services or financial services. While the manufacturing sector includes the (mostly automatic) production of goods, the sector of natural resources includes commodities and raw materials.The dependent variables’ descriptive statistics are in line with expectable figures: manufacturing is the sector, which receives the largest amount of FDI (Table 3). This is because, emerging economies tend to be characterized by a large manufacturing sector given that labor is cheaper than in developed countries. Examples for this are China or Bangladesh, where industries such as electronics and textile are very large (Islam, Khan & Islam, 2013; Liu, Parker, Vaidya, & Wei, 2001).

3.2 Methodology3.2.1 Approaches to Empirical Analysis of Panel Data

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When dealing with panel data, the random effects model and the fixed effects model are probably the most common approaches. Both models have advantages and disadvantages and it highly depends on the specific case in order to judge which model is the right one to choose. A random effects model has three distinct benefits:

1) It will produce unbiased estimates of the coefficients given that strict assumptions regarding the error term hold

2) It will use all data available3) It produces very small standard errors that are often lower than those

of other models. However, these benefits come at a cost because the random effects model estimator is only valid, if and only if there are no omitted variables that are correlated with the model’s explanatory variables (Allison, 2009). Given that the likelihood of this condition is very low (Clark & Linzer, 2014), the model which is preferred in most cases is the fixed effects model. The fixed effects model makes it possible to control for omitted variables that are time-invariant, since the effect of these time-invariant omitted variables is assumed to be the same regardless of the point time in time they would be measured in. Hence, the effects of these omitted variables are considered to be ‘fixed’. The underlying condition for this to be true is that the omitted variables must have time-invariant values with time-invariant effects (Allison, 2009). The fixed effects model allows us to control for time-invariant variables even though they are not explicitly measured (Plümper & Troeger, 2006). One of the techniques the fixed effects model achieves this with is the so-called demeaning variables technique. In this method, the within-subjects means for each variable are subtracted from the actual observed values of the variables. For all time-invariant variables the ‘demeaned’ value will be zero, given that all observed values over time are the same. This will rid the model of the between-subjects variance so that one only needs to analyze the within-subjects variance (Plümper & Troeger, 2006).

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Given that we are only interested in the effect of terrorism on FDI, which is a time-varying variable, the fixed effects model will be used. Before this can be done, though, this choice of models needs to be statistically verified by conducting a Hausman test.

3.2.2 Hausman TestIn order to see whether the random effects model’s assumption of an explanatory variable being uncorrelated with the unit effect a i holds, a Hausman specification test is conducted (Clark & Linzer, 2014). A significant test result indicates that there exists a correlation between the explanatory variable X ¿ and a i, meaning that the random effects model will not yield reliable estimates (Clark & Linzer, 2014). Alas, if the Hausman test result is significant, we choose the fixed effects model for coefficient estimations. The Hausman test will be conducted before every single regression in order to make sure that the appropriate model is always used. Since we are interested in a time-varying explanatory variable and the fact that our panel data entities are countries so that omitted time-invariant variables are likely to be present (e.g. area or institutions), it is to be expected that the fixed effects model will be the model of choice throughout this thesis.

3.2.3 The Fixed Effects Model

Y ¿=α i+ β X¿+u¿ , i=1 , …,n , t=1 , …,T ,

where X ¿is a k1 vector of regressors and where (X¿ ,u¿) satisfy four assumptions, which assure that we are dealing with a heteroskedastic panel data model with conditionally serially uncorrelated errors (Stock & Watson, 2007). These four assumptions are:

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1. (X i 1 ,…, X ¿∧u i 1, …,u¿¿ are identical and independently distributed over i = 1,…, n , i.e. over all emerging countries included in this analysis

2. E (u¿|X i1 , …, X¿¿=0, meaning that we have a strict zero mean

independence assumption, or strict exogeneity of the error term3. The variance is nonsingular (no perfect multicollinearity)

4. E (u¿uis|X i 1 , …, X ¿¿=0 for t ≠ s (conditionally serially uncorrelated errors)

4. Results4.1 Terrorist acts of violence and their impact on FDI in emerging marketsIn order to verify whether the fixed effects model was valid to use, a random effects regression as well as a fixed effects regression are run so that a Hausman specification test can be conducted. When running the random effects regression, ‘fatalities’ is significant at the 10% significance level with a p-value of 0.075 (Table 4). Following, the Hausman test is run and results in a highly significant Dun-Hausman test statistic at the 1% significance level. Therefore, the hypothesis that there are no omitted variables correlated with any of our explanatory variables is rejected and the fixed effects model is the recommended model to use in this case. With a p-value of 0.568, the coefficient estimate for the ‘fatalities’ variable is not significant at any of the significance levels (Table 4). Thus, there is no evidence that the amount of deaths caused by terrorist acts of violence affects the volume of FDI in any emerging market economy when estimating the coefficient by means of a fixed effects model.Regarding the model’s control variables, one can see from Table 4 that the corruption coefficient estimate is significant at the 5% level with an estimate value of 3,616. Thus, one can infer that there is evidence for an increase in FDI by $3,616 million as the score of corruption increases by one unit, which certainly is an unexpected finding. Furthermore, the government effectiveness estimate is significant at 5% with a value of 10,452, meaning that an increase of the variable’s score by one unit results

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in an increase in FDI by $10,452 million (Table 4). This coefficient estimate, unlike the corruption coefficient’s estimate, is in line with the rationale of political stability leading to a higher FDI inflow.Following, the GDP coefficient estimate as well as the GDP per capita coefficient estimate are both found to be significant at the 5% level (Table 4). While GDP’s estimate is of value 12.28, the estimate for GDP per capita has a slightly smaller value of 1.197. Both values indicate that an increase in overall GDP and an increase in GDP per capita lead to an increase in FDI, where the former measure causes FDI to increase by up to $12.28 million (Table 4). Lastly, the population estimate was found to be significant at the 1% level with a value of 0.000188 (Table 4). This implies that a one-unit increase in a country’s population leads to an increase in FDI by 118$. This finding confirms the expectation that an increase in market size leads to an increase in FDI.Given that the model’s estimate for the number of casualties caused by terrorism is not significant, there is no evidence to support the first hypothesis. Thus, the first hypothesis, stating that terrorist acts of violence have a negative impact on FDI in emerging countries, is rejected.

4.2 The impact of terrorism on FDI in emerging markets varies by sectorIn order to test the second hypothesis, regressions are run, where the volume of FDI flowing into a specific sector is the dependent variable. The manufacturing sector is the first industry which is investigated: The Hausman test shows that the fixed effect model is the estimation approach which is recommended to be used, given that the test result is significant at 5%. With a p-value of 0.512, ‘fatalities’ is insignificant at all levels when using the fixed effects estimation model (Table 5). From this we can infer that there is no evidence that the amount of people killed by terrorists negatively affects the amount of foreign capital flowing into the manufacturing sector of an emerging market. Apart from government

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effectiveness, the control variables whose coefficient estimates are significant in this model are the same control variables whose coefficient estimates were significant in the previous model. Thus, similar to the previous model’s findings, there is evidence that an increase in corruption leads to more FDI in the manufacturing sector.For the industry of non-tradable goods, the picture looks similar to that of the manufacturing industry. FDI in non-tradable goods, which includes investments in public transportation, electricity, water, or real-estate (i.e. anything that is near to impossible to transport), turns out to be unaffected by the amount of killings caused by terrorists. Even though the Hausman test statistic is negative, which was likely caused by the fact that the sample size is quite small, it was decided to use the fixed effects estimation anyways due to comparability reasons. When estimating the ‘fatalities’ coefficient with the fixed effects model, the estimate is insignificant with a p-value of 0.295 (Table 6). At the same time, the estimate of the random effects model is insignificant as well with a p-value of 0.132 (Table 6).In order to investigate the service sector, a Hausman test statistic significant at the 10% level supported the decision to continue using the fixed effects estimation model (Table 7). The findings follow the trend: given an insignificant coefficient estimate for the ‘fatalities’ variable, no evidence could be found that would have proven a negative impact of an increase in the number of casualties caused by terrorism on FDI in a specific sector (Table 7). Again, the corruption coefficient estimate is significant at the 5% level, however the estimate’s value is lower than in the previous models (Table 7).Lastly, the naturals sector was examined. Similar to the non-tradable goods sector, the Hausman test statistic is insignificant (Table 8). For reasons of completeness, we will only consider the fixed effects model again. No coefficient estimate was found to be significant apart from GDP and GDP per capita (Table 8). It is important to note that this is the first model, in which the corruption coefficient estimate was not significant.

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Given these findings, we reject the second hypothesis which claimed that the impact of terrorism varies by sector. In order for this hypothesis to be accepted, we would have needed evidence stating that terrorism actually impacts FDI in any sector.

4.3 The mere threat of terrorism and its impactWhen investigating the effect of the risk that a terrorist attack might occur, we focus on the independent variable measuring military spending as a percentage of GDP. After conducting a Hausman test which resulted in an insignificant result, it becomes clear that the choice of the model does not matter per se – in both models the coefficient estimate of the military spending variable is insignificant (Table 9). For the sake of completeness, however, the fixed effects model is used in order to analyze the control variables’ coefficient estimates. One can observe that these are nearly identical to the estimates from the first model (Table 4, Table 9). Again, corruption, GDP, GDP per capita, government effectiveness, and population have significant coefficient estimates, where the corruption estimate is negative again (Table 9). Given the lack of evidence, we reject the third hypothesis which states that the mere threat of terrorism negatively impacts FDI in an emerging market.

5. ConclusionWith the evidence found in this study, one cannot conclude that terrorism negatively impacts FDI in emerging countries in any way. This holds regardless of the sector to which FDI is flowing in. Regarding the real options model laid out in this paper, one can conclude that it was not possible to evidentially support the theoretical inferences made in the model given that the military spendings coefficient estimate was insignificant.

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While the study’s focus was on terrorism and its possible negative impact, an interesting by-product was to find out that an increased level of corruption leads to an increase in FDI in emerging market economies. This certainly is a surprising finding and it serves as a good starting point for further research on this topic. From a technical point of view, the probably biggest drawback was that the sample size used in this study was rather small. This especially became evident when the Hausman test failed to calculate a test statistic for the regressions on FDI in non-tradable goods. This was likely caused by the issue of the sample size. Thus, for future research, it is advisable to draw from a larger sample.In order to answer the research question, one can conclude that there were no consequences for FDI in emerging market economies when exposed to terrorism between 2002 and 2012, irrespective of the sector to which FDI was directed to.

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Appendix:

Table 2: List of Emerging Markets to be analyzed:

Emerging Markets

ArgentinaBangladeshBotswana

BrazilBulgaria

ChinaColombia

GhanaIndia

IndonesiaIran, Islamic Rep.

Cote d’IvoireJordan

KazakhstanKenya

LebanonMalaysiaMauritius

MexicoMoroccoNigeriaPakistan

PeruPhilippinesRomania

South AfricaSri LankaTaiwan

ThailandTunisiaTurkeySerbia

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Table 3: Summary Descriptive Statistics

(1) (2) (3) (4) (5)N mean standard dev. min max

investment_amount 340 10,717 19,377 0 126,400inv_manufacturing 340 4,545 10,393 0 80,020inv_nontradables 340 1,336 3,252 0 22,561inv_services 340 2,486 4,922 0 32,700inv_natural 340 2,350 3,680 0 24,362wdi_infl 286 8.014 7.126 -5.992 80.75wdi_pop 288 1.330e+08 2.943e+08 1.223e+06 1.344e+09corruption 327 2.273 0.633 0.500 4trade 314 80.15 39.80 22 210gdp 320 3.794e+11 8.919e+11 5.610e+09 8.229e+12gdp_growth 320 4.748 3.425 -7 33gdp_cap 320 4,327 3,310 372 15,364wgi_effect 340 -0.0414 0.535 -1.340 1.250wgi_rule 340 -0.255 0.592 -1.520 1.060fatalities 340 83.86 272.0 0 2,775wdi_military 330 1.979 1.171 0.146 6.104

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Table 4: Total FDI

(1) (2) (3)investment_amount Fixed Effects Random Effects Hausman Test

corruption 3,616** 4,156** 4,156***(1,763) (1,703) (1,021)

wgi_effect 10,452** 6,631* 6,631**(4,424) (3,819) (2,911)

trade 68.86 70.97* 70.97***(46.74) (37.69) (21.47)

wgi_rule -108.2 -10,598** -10,598***(5,785) (4,818) (2,760)

gdp -5.03e-09** 3.44e-09 3.44e-09***(1.96e-09) (5.16e-09) (1.24e-09)

gdp_growth -204.6 -136.5 -136.5(133.5) (112.6) (164.3)

gdp_cap 1.197** 0.582 0.582**(0.517) (0.408) (0.262)

wdi_infl 72.32 134.2** 134.2*(61.11) (63.12) (80.49)

wdi_pop 0.000188*** 5.79e-05*** 5.79e-05***(4.39e-05) (2.08e-05) (3.98e-06)

fatalities -1.341 -7.487* -7.487***(2.322) (4.200) (2.822)

Constant -29,704*** -17,351** -17,351***(6,783) (6,980) (3,946)

Observations 266 266 266R-squared 0.214Number of iso3n 30 30 30Country FE YESWu-Hausman Statistic 28.37***

Robust standard errors in parentheses*** p<0.01, ** p<0.05, * p<0.1

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Table 5: FDI in Manufacturing Sector

(1) (2) (3)inv_manufacturing Fixed Effects Random Effects Hausman Test

corruption 1,329** 1,516*** 1,516***(551.1) (540.8) (520.2)

wgi_effect 2,343 3,336** 3,336**(1,904) (1,699) (1,336)

trade 4.218 35.77** 35.77***(21.83) (14.26) (9.207)

wgi_rule -130.7 -4,389** -4,389***(3,176) (2,191) (1,269)

gdp -2.43e-09** 4.44e-09 4.44e-09***(1.06e-09) (3.22e-09) (6.28e-10)

gdp_growth 25.93 -3.014 -3.014(70.76) (55.08) (89.48)

gdp_cap 0.553** 0.153 0.153(0.265) (0.190) (0.121)

wdi_infl -3.387 33.83 33.83(15.92) (21.53) (41.80)

wdi_pop 0.000141*** 2.56e-05** 2.56e-05***(3.46e-05) (1.07e-05) (1.84e-06)

fatalities -0.607 -4.013 -4.013***(0.915) (3.128) (1.373)

Constant -19,353*** -8,019*** -8,019***(5,693) (2,352) (1,979)

Observations 266 266 266R-squared 0.179Number of iso3n 30 30 30Country FE YESWu-Hausman Statistic 15.02**

Robust standard errors in parentheses*** p<0.01, ** p<0.05, * p<0.1

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Table 6: FDI in Nontradables Sector

(1) (2) (3)inv_nontradables Fixed Effects Random Effects Hausman Test

corruption 496.0 569.7 569.7*(521.2) (517.9) (308.7)

wgi_effect 4,400** 909.3 909.3(1,643) (926.4) (871.8)

trade 43.49** 24.18*** 24.18***(17.94) (9.275) (6.386)

wgi_rule -567.6 -1,496 -1,496*(1,044) (991.6) (826.5)

gdp -6.70e-10*** 2.28e-10 2.28e-10(1.21e-10) (4.93e-10) (3.74e-10)

gdp_growth -67.00 -23.41 -23.41(42.97) (37.78) (49.88)

gdp_cap 0.179** 0.0358 0.0358(0.0672) (0.0621) (0.0787)

wdi_infl 34.31 36.12 36.12(24.74) (24.87) (24.36)

wdi_pop -1.44e-05* 7.36e-06** 7.36e-06***(8.07e-06) (3.16e-06) (1.19e-06)

fatalities 0.603 -1.331 -1.331(0.566) (0.884) (0.850)

Constant -1,345 -3,506* -3,506***(1,379) (1,954) (1,190)

Observations 266 266 266R-squared 0.096Number of iso3n 30 30 30Country FE YESWu-Hausman Statistic -81.25

Robust standard errors in parentheses*** p<0.01, ** p<0.05, * p<0.1

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Table 7: FDI in Service Sector

(1) (2) (3)inv_services Fixed Effects Random Effects Hausman Test

corruption 478.6** 667.9** 667.9***(222.7) (262.6) (252.3)

wgi_effect 904.1 -177.1 -177.1(1,382) (1,346) (753.3)

trade 16.23 10.13 10.13*(10.68) (7.462) (5.773)

wgi_rule 1,912 -687.1 -687.1(1,902) (1,370) (715.4)

gdp -4.48e-10 1.12e-09 1.12e-09***(6.80e-10) (1.09e-09) (3.07e-10)

gdp_growth -32.87 -30.17 -30.17(27.82) (25.82) (39.63)

gdp_cap 0.476*** 0.285** 0.285***(0.166) (0.121) (0.0676)

wdi_infl -2.385 5.668 5.668(7.101) (6.521) (19.73)

wdi_pop 5.12e-05*** 1.42e-05*** 1.42e-05***(1.12e-05) (4.28e-06) (1.05e-06)

fatalities -0.539 -0.940 -0.940(0.672) (0.639) (0.711)

Constant -7,791*** -3,264** -3,264***(2,078) (1,357) (989.2)

Observations 266 266 266R-squared 0.267Number of iso3n 30 30 30Country FE YESWu-Hausman Statistic 15.13*

Robust standard errors in parentheses*** p<0.01, ** p<0.05, * p<0.1

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Table 8: FDI in Naturals Sector

(1) (2) (3)inv_natural Fixed Effects Random Effects Hausman Test

corruption 1,312 1,324* 1,324***(807.5) (734.9) (423.3)

wgi_effect 2,806 2,179** 2,179*(2,335) (912.4) (1,311)

trade 4.914 2.526 2.526(18.25) (9.556) (10.39)

wgi_rule -1,322 -3,403*** -3,403***(1,555) (1,237) (1,248)

gdp -1.49e-09*** -8.99e-10* -8.99e-10*(2.49e-10) (4.86e-10) (5.16e-10)

gdp_growth -130.7** -99.76* -99.76(61.07) (55.00) (65.43)

gdp_cap -0.0111 -0.000575 -0.000575(0.112) (0.0956) (0.117)

wdi_infl 43.78 60.18 60.18*(36.39) (38.81) (32.94)

wdi_pop 1.02e-05 7.75e-06*** 7.75e-06***(1.20e-05) (2.15e-06) (1.87e-06)

fatalities -0.798 -1.719** -1.719(0.768) (0.705) (1.211)

Constant -1,215 -1,899 -1,899(1,598) (2,299) (1,687)

Observations 266 266 266R-squared 0.086Number of iso3n 30 30 30Country FE YESWu-Hausman Statistic 8.29

Robust standard errors in parentheses*** p<0.01, ** p<0.05, * p<0.1

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Table 9: Effect of Mere Threat of Terrorism

(1) (2) (3)investment_amount Fixed Effects Random Effects Hausman Test

corruption 3,591** 4,249** 4,249***(1,737) (1,721) (1,037)

wgi_effect 10,423** 7,474* 7,474**(4,318) (4,292) (2,930)

trade 69.83 74.97* 74.97***(47.43) (39.53) (21.48)

wgi_rule -192.2 -10,914** -10,914***(5,814) (5,316) (2,857)

gdp -5.11e-09** 3.96e-09 3.96e-09***(1.92e-09) (5.19e-09) (1.25e-09)

gdp_growth -185.8 -52.12 -52.12(137.1) (120.2) (164.7)

gdp_cap 1.310** 0.577 0.577**(0.574) (0.418) (0.266)

wdi_infl 75.24 101.3 101.3(61.59) (63.50) (81.86)

wdi_pop 0.000184*** 5.48e-05*** 5.48e-05***(4.92e-05) (2.11e-05) (3.88e-06)

wdi_military 1,652 -560.3 -560.3(1,764) (433.4) (660.6)

Constant -33,328*** -17,282** -17,282***(8,252) (7,668) (4,302)

Observations 266 266 266R-squared 0.216Number of iso3n 30 30 30Country FE YESWu-Hausman Statistic 1.65

Robust standard errors in parentheses*** p<0.01, ** p<0.05, * p<0.1

41