Evidence of Investors Preference of Taxes over Security

Work in progress please do not cite

Dragana Stanisic

CERGE-EI

Politickych veznu 7

111 21 Praha 1

Czech Republic

dragana.stanisic@cerge-ei.cz

CERGE-EI, Prague

This version: November 20th, 2012

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Abstract

The paper studies how institutional quality and security factors affect Foreign

Direct Investment (FDI) outflows. Using country pairs panel data and fixed effects

estimation of 23 most developed countries’ FDI outflows and 57 host countries over

21 years, I estimate the effect of hosts’ quality of institutions (i.e. tax policy and

security factors) on investment. I confirm findings from previous studies that tax

policies and control of corruption are important determinants of FDI. However, I

find that country-level terrorism indicators do not significantly affect investment,

despite the negative effect of realized terrorist incidents. I show that the puzzle

is a consequence of country level measures of security, as they do not capture the

country-pair specific security relationship that is influential for foreign investments.

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Contents

1 Motivation 4

2 Relationship to Literature 6

2.1 Measurements of institutional quality . . . . . . . . . . . . . . . . . . . . . 7

3 Methodology 8

4 Data 11

4.1 Dependent Variable . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11

4.2 Terrorism Variable . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12

4.3 Market Indicators . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13

4.3.1 Worldwide Governance Indicators (WGI) . . . . . . . . . . . . . . 14

4.3.2 IHS Global Insight (IHS) . . . . . . . . . . . . . . . . . . . . . . . 14

4.4 Other Control Variables . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15

5 Results 15

5.1 IHS Global Risks, WB Governance Indicators and FDI . . . . . . . . . . . 15

5.2 Terrorist Incidents and Security Indicators . . . . . . . . . . . . . . . . . 17

5.3 Terrorist Incidents and FDI . . . . . . . . . . . . . . . . . . . . . . . . . . 17

5.4 Terrorist Incidents, Security and Terrorism Indicators, and FDI . . . . . . 18

6 Conclusion 20

7 Appendix 26

7.1 Appendix 1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26

7.2 Appendix 2 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28

7.2.1 Appendix 2.1 Graphs . . . . . . . . . . . . . . . . . . . . . . . . . 28

7.2.2 Appendix 2.2 Tables . . . . . . . . . . . . . . . . . . . . . . . . . . 29

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1 Motivation

Foreign direct investment (FDI1) has an important role for recipient countries. For

example, growing interest of foreign investors for the host markets signals important im-

provements of host country institutional stability and market potential (Alfaro, Kalemli-

Ozcan, and Sayen, 2009). Not surprisingly there has been noticeable trend in literature

focusing on crucial factors that determine what attracts FDI: quality of institutions and

policies, corruption, economy size, liberalized trade policies, labor costs, and tax polices

(Alfaro et al, 2003; Abadie and Gardeazabal, 2008; Wei, 2000; Sin and Leung,2001;

Hartman, 1984; Hines, 1996; Janicki and Wunnava, 2006).

Recently, there is an increasing attention towards the terrorism risk and safety on

FDI. Global Business Policy Council Survey showed that terrorism risk is one of the most

significant factors for corporate foreign investment (Abadie and Gardeazabal, 2008).

Consequently, a number of papers have emerged. Busse and Hefeker (2007) showed

that ethnic tensions, internal or external conflict influence FDI. Terrorism risk, domestic

terrorism, and international terrorist attacks have been recognized to negatively affect

FDI (Abadie and Gardeazabal, 2008; Sandler, Enders, 1996; Abadie and Gardeazabal,

2008; Liusa and Tavares, 2010; Enders, Sachida and Sandler, 2006; Frey at el, 2004;

Chen and Siems, 2004, Hess and Orphanides, 2004; Eckstein and Tsiddon, 2003).

In most of the referred literature terrorism has been treated as a country level vari-

able, without identification of nationalities of perpetrators or targets.2 However, both

international terrorist attacks and FDI flows have clearly identified parties in the rela-

tionship, i.e senders and hosts. In this paper I examine this common dimension between

1Foreign direct investment (FDI) is defined as investment involving a long-term relationship, reflectinga lasting interest in and control (equal and more than 10 percent of ownership) by a resident entity inone economy (foreign direct investor or parent enterprise) of an enterprise in a different economy (FDIenterprise or affiliate enterprise or foreign affiliate). Such investment involves both the initial transactionbetween the two entities and all subsequent transactions between them and among foreign affiliateswww.unctadstat.unctad.org

2Recent papers do analyze the pair-wise specific conditions for the emergence of terrorism, i.e. Kruegerand Maleckova (2009); Maleckova and Stanisic (2011).

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terrorism and FDI needs to be explored, since it could suggest the channels through

which terrorism affects FDI. While total level of domestic or international terrorism of

the host country can deter local business conditions and market institutions affecting all

foreign investors equally, attacks from recipient country to investing country targets can

have an additional negative effect on investment by jeopardizing business environment

for that particular investor.

Based on these assumptions I examine following questions: Do terrorist attacks from

FDI host towards sender countries affect investments? How long does the effect remain?

How important are security or terrorism indicators relative to tax or legal risks?

Using the example of Columbia as an FDI host country and origin country of terror-

ism I illustrate the rationale for the study. The top graph on Figure 1 shows that after

the attacks against Canadian entities by Colombian perpetrators in 2003, Canadian out-

ward investment towards Colombia dropped. The middle graph shows no similar drop

in FDI outflow from rest of the investors in Colombia after the attacks against Canada.

The bottom graph in Fig 1 shows Irish FDI outflows in Colombia that have not been

affected by terrorist attacks against Canadian targets. This comparison suggests that

terrorist attacks against Canadian entities will increase investment risk for Canadian,

but not necessarily for Irish investors. Fig 2, uses similar comparisons between flows of

outward FDI towards Tunisia and investors from France, and Italy. After France suf-

fered terrorist attacks from Tunisia, their FDI outflow dropped, while Italian remained

unaffected.

Terrorism is of special interest to investors because terrorist attacks jeopardize hu-

man lives and destroy property which have consequences on investment both direct and

indirect. Direct costs of terrorism on FDI are destruction of facilities (tangible capital)

or jeopardizing safety of local employees that can deter workers from effectively per-

forming their tasks. Deteriorating market environment and its perception of safety for

businesses indirectly affect willingness to invest in the country. Therefore, the object of

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this study is to contribute to empirical literature that examines influential factors of the

investment risks.

2 Relationship to Literature

The total volume of FDI has been constantly increasing for the past three decades (Fig

3)3 The increase of FDI in volume is followed by increase in the number of FDI host

countries. Fig 4 shows that the number of FDI receivers almost doubled for the investors

coming from US, UK, Canada and Japan.4 As presence of FDI started to give positive

influence on the host economies (Alfaro, Kalemli-Ozcan, and Sayek, 2009; Pessoa, 2008;

Drifffield and Love, 2007) the attention in the literature turned to examining conditions

that will make countries more attractive for investments.5

Firstly, government size was recognized to affect FDI (Edwards, 1992). Chunlai

(1997) shows on the example of China that liberalizing FDI and trade policies increases

FDI inflow in the country. Following, corruption and tax polices were found to sig-

nificantly impact location of FDI (Wei, 2000). Further, Alfaro, Kalemli-Ozcan, and

Volosovych (2005) showed that institutional quality is an important determinant for

FDI inflow. Bellan et al (2008) showed that labor costs and flexibility of labor policies

are important for investors.

Besides the quality of institutions, in the last two decades the issues of security, espe-

cially international terrorism, came to literature focus. In the case study of Greece and

Spain, Enders and Sandler (1996) find that countries suffered 13.5% and 11.9% decrease

of net FDI respectively, as a consequence of terrorist attacks.6 Abadie and Gardeaza-

3To illustrate this point I use the FDI outflow information from 23 FDI sending countries, towards56 FDI host countries, from 1990-2010.

4Source: UNCTAD dataset on FDI outflow from 23 developed countries toward 56 receiving countries,from 1990-2010.

5It is worthwhile mentioning that discussion prevails which FDI are beneficial for host economies.For the purpose of this paper I assume that FDI has a positive impact in the host economies.

6In literature there are number of papers studying the negative effect of terrorism on economies aswell. For example, Abadie and Gardeazabal (2008) find that terrorism produces a 10-percent negative

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bal (2008) show that the risk of terrorism significantly lowers the expected returns to

investments. Therefore, as a consequence of decreased expected returns, investors will

avoid countries where terrorist risk is high, resulting in different desired optimal levels of

international investments across countries. Enders, Sachsida and Sandler (2006) analyze

the effect of international terrorism on U.S outgoing FDI. Using time series analysis they

find a short term effect of the 9/11 attacks on U.S. FDI. Using panel data, the authors

find a negative effect of terrorist attacks on U.S. targets (entities or persons) on the level

of U.S FDI abroad.

Despite the number of papers that examines the effect of terrorism on FDI, I find gaps

in literature that are important to address. First, I focus on investors and examine factors

that influence their investment decisions. Second, I test effectives of risk indicators in the

context of the investment decisions. There are number of indices that classify countries in

terms of security or probability of terrorism. In literature so far, there are no studies that

empirically test the prediction power of security indicators. I find this point important

because the security and terrorism risk (in context of international flow of capital) is

defined as a country level variable, rather than two countries’ relationship. And finally,

I examine the effect institutional in host economy and FDI.

2.1 Measurements of institutional quality

There are no precise measures of country’s institutional quality or terrorism risk. ‘Ter-

rorism risk is a number trying to describe a very complex phenomenon.’ (Abadie and

Gardeazabal, 2008:pp 13).Therefore, different methodologies are used in order to esti-

mate these indicators, researchers use estimated numbers to describe otherwise unmea-

surable variables. For example, the World Bank produces World Governance Indicators

difference between Basque per capita GDP and similar regions in Spain without terrorism. Eckstein andTsiddon (2003) look at the effect of terrorism on Israeli economy and find that even though the deathrate from terrorism is similar to death rate of car accidents in Israel, terrorism affects the economy in afar more severe way.

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(WGI)7 collecting country surveys to capture perception and using respondents’ answers

to estimate quality of rule of law, regulatory quality, corruption, etc. Others, like IHS

Global Insight Country Risks (IHS)8 use different techniques (not available to public)

based on which they measure similar dimensions of the country.9

In this study I decide to use both groups of indicators in order to capture any essential

differences in measures aiming to describe similar characteristics of the country. At the

same time, there are two major assumptions that I make in order to use these indicators.

Firstly, security and terrorism indices are proxies of available information that investors

have. And second, the indicators are available at the time when investment decisions

are made. Additional details and descriptions regarding used indices are provided in the

data section.10

3 Methodology

FDI and bilateral trade share many common factors: geography, culture, and history.

Bilateral trade as well as FDI can be used to measure the effect of terrorism on country

pair economic relationships. Therefore, it is not unusual to assume that similar method-

ology is valid in both cases. However, there are some crucial differences between FDI

and bilateral trade that make the case for a different methodology approach when esti-

mating factors that influence FDI. First is the existence of disinvestment in FDI data,

which is represented with a negative FDI value. Gravity models used in bilateral trade

literature do not recognize such observations, which in FDI studies are important to

include. Second, models estimating factors of bilateral trade exclude the possibility of

7http://info.worldbank.org/governance/wgi/index.asp8http://www.ihs.com9IHS does not disclose their methodology since their estimators are used for commercial purposes.

10Table 1 provides the basic descriptions of the variables. For more detail informationon sources and methodology please refer to Worldwide Governance Indicators Project fromhttp://info.worldbank.org/governance/wgi/index.asp; and IHS Global Insight Methodology fromwww.ihsglobalinsight.com

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non-existent trade between countries, since they disregard zeros, while FDI outflows can

have zero values describing no investment between the countries. Therefore, in the case

of FDI fixed effects is the method suitable for longitudinal data with year and country-

pair effects. The model estimating the affect of terrorism on FDI outflow is build up on

Abadie and Gardeazabal (2008), by expanding it (1) with country pairs as observation

units; (2) introducing time variation; and finally (3) controlling for two different types

of indicators of institutional quality.

The following two-way error component accounts for time specific effects in order to

avoid spurious regression biases (Filer and Stanisic, 2012):

uijt = µij + λt + νijt,

µij unobservable country-pair effect;

λt unobservable time effect (individual variant and any time specific trends: change of

oil prices, embargo effect, etc.)11;

νijt remainder stochastic disturbance term.

To estimate the parameters of the relationship between terrorism and FDI, I use the

following model:

FDIi,j,t = βaAttacksi,j,t−1 + βbMarket Indicatorsi,t

+ Xi,tδk + Xj,tδl + µij + λt + νij,t, (1)

where FDIj,i,t is the FDI outflow from country j (FDI sender) towards country i

11Bellany (2007) analyzes international terrorism data from RAND/MIPT dataset and finds no evi-dence of a correlation between the number of attacks in the present and past years for the period from1968 to 2005. The author argues that international terrorism is a random variable without trends RandCorporation and Memorial Institute for Prevention of Terrorism. Alomar and El-Sakka (2011) find thelack of evidence of panel unit roots of terrorism variables.

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(FDI host); Attacksi,j,t number of terrorist attacks carried out by nationalities of country

i towards country j targets ; SIi,t vector of market indicators (WGI and IHS) for country

i (FDI host); X is a vector FDI host country variables; Y is a vector of sender country

variables.

The main hypothesis is that coefficient βa describing relationship between terrorist

attacks and FDI outflow from equation (1) will be negative and significant. At the same

time I expect to find no significance of parameter βb that describes the country level

terrorism risks.

Common issue in literature studying the relationship between FDI and terrorist

attacks is reverse causality. Since presence of FDI can indirectly decrease the cost of ter-

rorist attacks presence of foreign investors will actually increase the number of attacks.

Authors have so far failed to find suitable instrument variable that will capture variation

in terrorism and at the same time be independent of FDI. Therefore, researchers turned

to directly estimating the impact of FDI on terrorism. Li and Schaub (2004) use the

same terrorism dataset as in this study (ITERATE) to test the hypothesis that ”glob-

alization” through international trade, FDI and portfolio investment decreases the costs

of international terrorism and increases the number of terrorist attacks. They find no

evidence of the effect of FDI on terrorist attacks. In addition, Abadie and Gardeazabal

(2007) argue that even if terrorist attacks happen more often due to the presence of

foreign capital in the country, this will result in a positive bias on the estimated coef-

ficients of terrorism risk. And since the estimated coefficient on the terrorism risk will

be negative, the bias will just make it larger, but not qualitatively different. Therefore,

using the results from previous studies that show no effect of FDI on the occurrence of

terrorism, I interpret the results with no concerns of reverse causality.

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4 Data

The panel was created by pairing the 23 most developed FDI sending with 56 FDI host

countries for the period 1990-2010. Sending countries were not paired with all host

countries, which makes the panel unbalanced.12 It has 17,829 observations divided in

849 pairs. FDI data is from United Nations Conference for Trade and Development

(UNCTAD), terrorism data is from ITERATE (Mickolus et al., 2011), market indicators

data are from the World Bank Governance Indicators and IHS Global Insights, and the

rest of country level data are from the World Bank. Table 1 contains listed sources of

data and descriptions of the variables while Table 2 shows descriptive statistics.

4.1 Dependent Variable

The dependent variable is FDI outflow from sending country towards host country within

a year.13 The value of FDI is in millions of current US dollars, and it has zero, positive

and negative values, the last one implying disinvestment. Half of the sample has FDI

equal to zero, and close to 5 percent of observations are disinvestments. UNCTAD

defines FDI outlows as net decreases in assets (net increases in assets would be with a

negative sign indicating the disinvestment).14

To create the dataset UNCTAD uses a comprehensive list of sources15 However,

there is still a concern regarding the interpretation of the blank cells in the FDI dataset

(Hallward-Driemeier, 2003). Part of that problem is solved by approximation of yearly

FDI flows in cases where some data were available in those economies for which data

were not available [...] or only partial data (quarterly or monthly) were available, esti-

mates were made by annualizing the data if they are only partially available (monthly or

quarterly) from either the IMF or national official sources; using data on cross-border

12The list of countries is provided in Appendix 2, Table A2.b13FDI includes corporate sector investments and state assistance and donations.14www.unctadstat.unctad.org15The detailed list of sources that UNCTAD uses to construct the website is provided in the Appendix

1, and can be found at the www.unctad.org

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mergers and acquisitions (M&As) and their growth rates; and using UNCTADs own es-

timates .16 For the cases where such approximation was not made, I rely on previously

used method in the literature and assign zeros to blank cells and assume that FDI flow

did not happen or (if it did) it was smaller relative to recorded flows (Hallward-Diemeir,

2003).

There are in total 17, 829 observations where in 8, 845 cases there were no invest-

ments, in 8, 045 there were, and in 939 observations there is a disinvestment. Out of

total number of attacks between the countries (644), 44 percent (279) is associated with

no investment, 52 percent (341) is associated with positive investments, while 4 percent

is associated with the cases of disinvestment.

4.2 Terrorism Variable

Terrorist incidents data from 1990 to 2010 is from ITERATE 17 that defines terrorism

as ‘[...] use, or threat of use, of anxiety-inducing, extra-normal violence for political

purposes by any individual or group, whether acting for or in opposition to established

governmental authority, when such action is intended to influence the attitudes and

behavior of a target group wider than the immediate victims and when, through the

nationality or foreign ties of its perpetrators, its location, the nature of its institutional

or human victims, or the mechanics of its resolution, its ramifications transcend national

boundaries”.18 (Mickolus et al., 2011) Using the available information on the nation-

alities of perpetrators and targets, I was able to identify terrorist incidents that were

carried out by nationalities of FDI hosts, and where targets were nationalities of FDI

senders from the UNCTAD dataset. Using this information set I measure terrorism be-

tween the countries. The list of the FDI host countries with the number of produced

16www.unctad.org17The terrorism incidents are recorded based on information from Associated Press, United Press

International, Reuters tickers, the Foreign Broadcast Information Service (FBIS) Daily Reports, andmajor US newspapers (e.g.,the Washington Post, New York Times).

18NCTC is also the dataset that has similar variables that enables to identify pairs of countries in theattacks, however this dataset is only available from 2004

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terrorist attacks, and the list of FDI source countries per number of terrorist attacks re-

ceived is in Appendix 2, Table A2. To capture any time trend in international terrorism

occurring the the host country I control for the total number of international terrorist

incidents. Besides international terrorism, domestic terrorism affects FDI and needs to

be accounted for.19 I use the Global Terrorism Database (GTD) (START, 2011) and

measure domestic terrorism by the number of attacks where perpetrators and targets

are of same nationalities.

4.3 Market Indicators

To control for the quality of institutional factors or market indicators in the host

economies I use two sources: Worldwide Governance Indicators (WGI) (Kaufmann,

Kraay, and Mastruzzi, 2009) and IHS Global Insights (IHS). These groups of indica-

tors are described in detail in Worldwide Governance Indicators Project 20 and IHS

Global Insight Methodology21 respectfully.

Table 1 shows that for both group of indicators the variation of terrorism indicators

(Political Stability and Absence of Terrorism(WGI)22, and Security Risk (IHS)) is higher

relative to the other indicators, which leads to the conclusion that FDI hosts differ on

the basis of terrorism risks. Tables 2a and 2b present correlation between host market

indicators and terrorist attacks between host and sender countries. Correlations show

that even if we suspect that indicators (within each group) describe similar environment

conditions, they address different factors. Also, tables show that the negative correlation

between the indicators and attacks exist, indicating that occurrence of attacks negatively

affects all institutions in the country. However, this correlation is not high and not

causing multicollinearity problem in further analysis.

19For more details regarding the important reasons of including both domestic and internationalterrorism in the analysis please check Enders and Sandler (2006)

20http://info.worldbank.org/governance/wgi/index.asp21www.ihsglobalinsight.com22The variable is labeled as Political Stability in future text and tables.

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4.3.1 Worldwide Governance Indicators (WGI)

WGI include: Rule of Law, Voice and Accountability; Regulatory Quality; Government

Effectiveness; Control of Corruption; Political Stability, available from 1996 to 2010.23

The names of most indicators are self-explanatory, more detailed description is in Ap-

pendix 2, Table A1. The value of the indicator is from -2.5 (weak) to 2.5 (strong). The

higher the indicator is, the better the performance in the particular dimension of the

country is. Hallward-Driemeier (2003), Abadie and Gardeazabal (2008), and Thomas

(2010) are just few of many authors that use WGI for purposes of describing the coun-

try’s market conditions. I rely on their framework and include the indicators as variables

in estimation equations. The indicator Political Stability describes politically motivated

violence and terrorism. The indicator is expected to capture the terrorism risks that

investors face on the FDI host markets. Table 2a shows correlation between the WGI

indicators and terrorist incidents.

4.3.2 IHS Global Insight (IHS)

Besides WGI Indicators, I use IHS risk indicators to describe market environment for

investors. IHS Country Risks are: Political Risk, Economic Risk; Legal Risk; Tax Risk;

Operational Risk; and Security risk. Respectfully, they compose 25%, 25%, 15%, 15%,

10% and 10% percent in the composition of the Current Overall Risk. The value of

the risks is estimated on the range from 1 (best) to 5 (worst). The risks are available

from 1999 to 2009 for all 56 countries in the panel dataset (Table 1). The indicator

Security Risk describes the environment in terms of [country] suffers from a sustained

terrorist threat [...] whether the active [terrorist] groups are likely to target or affect

businesses. Table 2a shows how security risk correlate with other estimated risks and

terrorist incidents.

23Years 1998, 1999, and 2001 were missing. I approximated missing indicators as the average ofprevious and following year for the missing value.

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4.4 Other Control Variables

Following Abadie and Gardeazabal (2008) and Hallward-Driemeir (2003), I control for

the host country’s GDP (log(GDP host)) and GDP per capita (log(GDP pc host)) is

from World Bank Development Indicators. I also control for the five year regional growth

rates to capture the investors preference over regions that experience higher growth.24

To control for the human capital as an important determinant of FDI, I include the share

of population with tertiary level of education from the same data source. FDI stock in

the host country (FDI Stocki,t−1) is from UNCTAD. Controlling for the already existing

stock of FDI addresses concerns of preconditioned factors for attractiveness of FDI.25

Finally, varaible log(GDP sen) controls for FDI senders’ economic cycles influences on

outward FDI. Descriptions of variables and detailed sources are in Appendix 2, Table

A1, summary statistics is provided in Table 1.

5 Results

In this section I present the panel data estimation results. First, I estimate the relation-

ship between terrorism indicators (IHS and WGI) and FDI, next I estimate the effect of

terrorism incidents, and examine the correlation between the terrorism indicators and

realized terrorist incidents. Finally I present the panel data estimation results where I

control for both terrorism indicators and terrorist incidents.

5.1 IHS Global Risks, WB Governance Indicators and FDI

Table 3 presents fixed effects estimation results from regressions that include FDIstock

in the previous period, log GDP of host and sending country, log GDP per capita of

host country, share of population with tertiary education, and 5 year regional GDP

2456 host countries were divided in six regions: Arab World, East Asia and Pacific, Europe and CentralAsia, Latin America and Caribbean, Middle East and North Africa, and Sub-Saharan Africa

25This variable includes stock of all investments in the country (investments besides 23 most developedincluded in this study).

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growth rates. Each specification in Table 3 contains country-pair and year fixed effects.

Columns (1), (2), and (3) include IHS Country Risks, while the rest of the columns

include WG indicators. Columns (1) and (4) include the whole sample, in columns (2)

and (5) dependent variable excludes negative values (disinvestments), and columns (3),

(6)-(11) have dropped observations falling outside of the 10-to-90 percentile range of

FDI.26

Different specifications in Table 3 have similar results.27 First two robust results

across different specifications is that FDI stock is an important determinant for the

future FDI, and second the indicators that refer to terrorism risks (SecurityRisk and

Political Stability) are irrelevant for investors. Moreover, in the IHS specifications, the

indicator is positively significant implying a puzzling result of attractiveness of countries

that have higher risk. This finding contradicts expectations that terrorism risk will

negatively effect FDI that led me to examine the relationship between terrorist indicators

and incidents, which is addressed in the following section.

Results in Table 3, columns (1)-(3) show that legal risk and tax risk have strong

inverse relationship with FDI, as tax or legal risk increase in the host country the send-

ing country’s FDI outflow towards the host decreases. This result is consistent with the

previous findings in literature that stresses the importance of tax system in the host

country (Zodrow, 2006).28 The same holds for specifications that use WG Indicators

(columns (6) and (7)) implying that corruption and regulatory quality (that addresses

the taxation system in the country) are important determinants of FDI host countries.

26Since the sample is created by pairing 23 sending and 56 host countries, I had to address the concernsregarding the sample selection issue. I firstly create panel of more than 130 countries and control forthe same variables that are specified in Table 3, in addition I include dummy variable equal to 1 if thecountry is is one of the countries in the study. I find no evidence of sample selection bias.

27I perform a Hausman test of the model specified in Table 3 (p value < 0.01) and continue using onlyfixed effects in further estimations.

28Global Insight country risks are significantly correlated but not as much as WGI (Table 2a and 2b).I preformed the estimations where each of the indicators were separately included in the specification inorder to address the concerns of estimators significance due to the mulitcolinearity problem. I found nodifference in the results relative to the Table 3.

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5.2 Terrorist Incidents and Security Indicators

In the previous section I show that FDI host country security indicators are not signif-

icant for the FDI outflows towards host countries. This puzzle suggest that either the

methodology or information used to estimate these indicators can not predict the real

terrorism risks that investors face. Fig 5 shows lack of correlation between FDI and

security indicators.

In the next step, I look at the correlation between indicators and terrorist attacks.

The left graph in Fig 6 shows correlation between Political Stability and terrorist attacks

(−0.49, p < 0.01) per FDI host country, averaged for the period from 1996 to 2010. The

right graph in Fig 6 shows correlation between IHS Security Risk and terrorist attacks

(0.43, p < 0.01) per FDI host country from 1999-2009. Descriptive statistics imply that

on average security indicators are correlated with occurrence of terrorism. However, the

correlation changes once the time or country-pair dimension is included in the correlation

analysis. The left graph in Fig 7 shows lack of correlation between Political Stability

and occurrence of international terrorist attacks in Colombia (−0.078, p = 0.778) from

1996-2010. Similarly, the right graph in Fig 7 shows lack of correlation between the

same indicator and terrorist attacks from Colombia against Ireland, suggesting that

the indicator had no information value throughout years for investors from Ireland in

Colombia.29

5.3 Terrorist Incidents and FDI

In this section I examine the relationship between the occurrence of terrorism and FDI

between countries. Table 4 shows the estimation results similar to those in Table 3, but

using actual terrorism incidents instead of the terrorism indicators. Column (1) shows

significant negative relationship between the occurrence of terrorism and FDI, the results

29There is a vast literature of causes of terrorism. However, there is no consensus in literature aboutthe causes of intentional terrorism. In this paper I will not discuss what causes international terrorismbut rather focus on the predictive power of the security indicators.

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from column (1) suggest an immediate reaction of investors once terrorism occurs (Filer

and Stanisic, 2012). Estimators in column (2) include the remaining control variables

from the earlier specifications (Table 2). In column (3) negative FDI outflows are re-

placed with zeros using specification from column (2). Results confirm previous findings

of significance of current terrorist attacks.30 In column (4) dependent variable excludes

all negative values of FDI, which does not change previous results. Finally in column

(5) I re-estimate the specifications from columns (2) and(3) but dropping observations

falling outside of the 10-to-90 percentile range of FDI. Based on the estimation results

from Table 4, terrorism incidents have strong negative influence on FDI outflow from

targets towards source countries. The effect fades in second year after the incidents

occurred.

5.4 Terrorist Incidents, Security and Terrorism Indicators, and FDI

Tables 5 and 6 show estimation results of the relationship between market indicators,

terrorism and FDI outflow for 23 FDI sending and 56 FDI host countries, in the period

of of 21 years.

Both tables include specifications from Table 3, in addition to terrorism variables

(current and lagged attacks from host towards sender, total domestic and total inter-

national attacks), each specification contains country-pair and year fixed effects. Table

5 shows estimation results where market conditions is described by IHS Country Risks

from 1999 to 2009. Column (1) includes all observations, specification from Table 3. In

column (2) the same specification as in (1) is estimated except that the dependent vari-

able in case of disinvestment is replaced with zero. Column (3) drops observations that

are outside of 10-to-90 percentile range, while column (4) includes only investments and

zero values, and finally in column (5) specification (2) is estimated by dropping outliers.

30Abadie and Gardeazabal (2008) discuss the short run versus long run effect of terrorism on economies.

18

Similarly as in Table 5, Table 6 shows estimation results where institutional quality

is describe by WG Indicators from 1996 to 2010. Specification from Table 3 is expended

by terrorism variables: in column (1) all observations are included, in column (2) FDI

disinvestments are replaced with zeros. From column (3) to (10) all estimations have

dropped FDI outflows that are outside of the 10-to-90 percentile range. Columns (6)-

(10) control for the other WG indicators rule of law, government effectiveness, control

of corruption, regulatory quality, and voice and accountability, respectfully.

In both tables I find some similar results. First, FDI stock in the host country is a

strong predictor of future FDI. This result can be an outcome of several different factors.

First, FDI stock can be a signaling instrument for the new investors. Investing in a coun-

try with already existing foreign investments can lower the risk of investment entering

the markets for the first time. Second, FDI stock is necessarily bigger in more developed

economies (which have bigger FDI stocks). Therefore, more developed countries will

attracted only larger investments.

Second common result in both tables is a significant role of the tax regulation system

in the host country of investment (Tung and Cho, 2000). Results from Table 5 column

(3) and Table 6 column (9) show evidence of strong inverse relationship between quality

of tax system and FDI. This result implies that investors from different countries are

affected with same tax risk in the host countries.

Third common result is a lack of evidence that host country terrorism indicators have

significant role for FDI (Table 5 and Table 6, columns (1)-(3)). This result, although

puzzling, is explained by lack of correlation between the terrorism indicators and actual

terrorist incidents, and by inability of indicators to capture the country pair specific

security conditions.

Fourth common result between two groups of specifications is that regional 5 year

future growth rates do not have a significant impact on the FDI outflow. Besides the

reported results on growth rates I also estimated the spill-over effect of terrorist attacks

19

between the investment sending countries, since I find no significant results I do not

report them.

And finally, from Tables 5 and 6, with IHS and WG indicators respectfully, I find the

fifth common results of the significant negative effect of terrorist incidents on FDI. I find

that only current terrorist incidents affect the investments. In magnitude it translates to

decrease of 16 million US dollars per investor (approximately 18 percent of average FDI

outflow) if FDI host country increases attacks by one standard deviation (2 attacks).

This effect stays robust including total level of international and domestic terrorism in

the host country. The result explains the puzzle mentioned previously that terrorist

attacks are have identified targets and perpetrators and therefore carry an additional

information to assessment of the investment risk.

6 Conclusion

Using the sample of 23 most developed countries were paired with 56 host countries

over 21 years, I examine how country indicators influence FDI outflows from senders

towards hosts. Using fixed effects estimation with country pair and fixed effects I find

empirical evidence that tax policy and corruption are important factors for investors.

More importantly, I find puzzling results of irrelevance of country terrorism indicators on

FDI, despite of the significant impact of realized terrorist incidents on FDI. In particular,

I find that the increase of terrorist incidents by one standard deviation is followed by 18

percent decrease of average FDI outflow. I explain this puzzle by inability of country

level indicators to capture the particular security conditions between countries. Since

there is no uniform terrorism risk for all investors in the host market, I argue that

terrorism risk has to be diversified across different investors.

Next step in research that builds up on results of this paper is examining if combina-

tion of investors in a host country change due to terrorist attacks. For example, if lost

20

Canadian investment in Colombia are substituted with Irish investments? Or, if terrorist

attacks affect the industry structure of FDI? Future studies should also focus on factors

that influence the security conditions between countries: foreign policies, historical re-

lationships or some territorial disputes. The results of this paper suggest that there is

an essential difference between general security conditions in a country and particular

relationships between FDI host and sender countries. e.g. Investors from Canada, US,

or Ireland will be affected by Colombia’s tax policy in the similar fashion, but they will

face different terrorism or security risks. The diversification in the security risks across

sender countries can be driven by different factors that deserve further investigation.

21

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25

7 Appendix

7.1 Appendix 1

UNCTAD regularly collects published and unpublished national official FDI data di-

rectly from central banks, statistical offices or national authorities on an aggregated and

disaggregated basis for its FDI/TNC database. These data constitute the main source

for the reported data on FDI flows. These data are further complemented by the data

obtained from other international organizations such as the International Monetary Fund

(IMF), the World Bank, the Organization for Economic Co-operation and Development

(OECD), the Economic Commission for Europe (ECE) and the Economic Commission

for Latin America and the Caribbean (ECLAC), as well as UNCTADs own estimates.

For the purpose of assembling balance-of-payments statistics for its member coun-

tries, IMF publishes data on FDI inflows and outflows in the Balance of Payments

Statistics Yearbook. The same data are also available in the International Financial

Statistics of IMF for certain countries. Data from IMF used here were obtained directly

from the CD-ROMs of IMF containing balance-of-payments statistics and international

financial statistics. For those economies for which data were not available from national

official sources or the IMF or for those for which available data do not cover the entire

period, data from the World Banks World Development Indicators CD-ROMs were used.

The World Bank report covers data on net FDI flows (FDI inflows less FDI outflows)

and FDI inward flows only. Consequently, data on FDI outflows, which we report as

World Bank data, are estimated by subtracting FDI inward flows from net FDI flows.

For those economies in Latin America and the Caribbean for which the data are not

available from one of the above-mentioned sources, data from ECLAC were utilized.

Data from ECE were also utilized for those economies in Central and Eastern Europe,

Central Asia and selected economies in Developing Europe for which data are not avail-

able from one of the above-mentioned sources. Furthermore, data on the FDI outflows

26

of the OECD, as presented in its publication, Geographical Distribution of Financial

Flows to Developing Countries, and as obtained from their web databank, are used as

proxy for FDI inflows. As these OECD data are based on FDI outflows to develop-

ing economies from the member countries of the Development Assistance Committee

(DAC) of OECD, inflows of FDI to developing economies may be underestimated. In

some economies, FDI data from large recipients and investors are also used as proxies.

(http://www.unctad.org/en/Pages/Statistics.aspx)

27

7.2 Appendix 2

7.2.1 Appendix 2.1 Graphs

1990 1995 2000 2005 2010

020

040

060

0

FDI and attacks between Colombia and Canada

FD

I fro

m C

anad

a (U

SD

M)

02

Atta

cks

on C

anad

ian

targ

ets

FDIAttacks

1990 1995 2000 2005 2010

020

0040

0060

00

FDI and attacks between Colombia and other countriesF

DI f

rom

oth

er c

ount

ries

(US

D M

)

01

23

5

Atta

cks

on o

ther

targ

ets

FDIAttacks

1990 1995 2000 2005 2010

010

2030

FDI from Ireland to Colombia

FD

I fro

m Ir

elan

d (U

SD

M)

Figure 1: FDI and terrorist attacks in Colombia from 1990-2010

1990 1995 2000 2005 2010

010

030

0

FDI and attacks between Tunisia and France

FD

I fro

m F

ranc

e (U

SD

M)

01

Atta

cks

on F

renc

h ta

rget

s

FDIAttacks

1990 1995 2000 2005 2010

050

015

00

FDI and attacks between Tunisia and other countries

FD

I fro

m o

ther

cou

ntrie

s (U

SD

M)

01

Atta

cks

on o

ther

targ

ets

FDIAttacks

1990 1995 2000 2005 2010

010

030

0

FDI from Italy to Tunisia

FD

I fro

m It

aly

(US

D M

)

Figure 2: FDI and terrorist attacks in Tunisia from 1990-2010

.

28

1990 1995 2000 2005 2010

050

100

150

200

Total FDI 1990−2010

Year

Tota

l FD

I

Figure 3: [FDI in millions of USD (current) from 28 FDI sending countries from 1990-2010, UNCTAD data.]

US UK Canada Japan Russia

Number of FDI receivers by investor

010

2030

199019992009

Figure 4: [Number of FDI receiving countries has been increasing from 1990-2010, UNC-TAD data.]

7.2.2 Appendix 2.2 Tables

29

Figure 5: [Left: Correlation between WGI indicator of terrorism and FDI. Right:IHSSecurity Risk and FDI, in 57 FDI receiving countries. Source: UNCTAD data.]

Figure 6: [Left:Correlation between WGI indicator of terrorism and number of attacks.Right: IHS Security Risk and number of attacks, in 57 FDI receiving countries. Source:ITERATE data.]

Figure 7: [Left:Correlation between WGI indicator of terrorism and number of terroristincidents in Colombia. Right: Correlation between WGI indicator of terrorism andnumber of attacks from Colombia towards Ireland. Source: ITERATE]

30

Varaibles Description

Terrorism Variables

Attacks (host-sender)

Total Intern. Attacks (host)Total number of international terrorist incidents originated from FDI host country.

Source: ITERATE

Total Domestic Attacks (host) Total number of domestic terrorist incidents occurred in the FDI host country. Source: GTD

Economic Variables

FDI Foreign Direct Investment from sending to receiving country. Source: UNCTAD

FDI stock Total Foreign Direct Investment in the FDI receiving country. Source: UNCTAD

Log GDP (host) Log of GDP of the FDI receiving country

Log GDP per capita (host) Log of GDP per capita of the FDI receiving country

Log GDP (sender) Log of GDP of the FDI sending country

Tertiary

Gross enrollment ratio is the ratio of total enrollment to the population that officially corresponds to the level

of tertiary level of education.

Log Population Population of FDI host country in millions

5 year regional growth ratesFuture 5 year average GDP growth rates for five regions: East Asia and Pacific; Europe and Central Asia; Latin

America and Carribbean; Middle East and North Africa; Sub-Saharan Africa, and Arab world.

Source: World Bank Development Indicators

I H S Global Insight

Security RiskHow widespread political unrest is, and how great a threat it poses to investors. If country suffers from terrorist

threat.

Political RiskHow mature and well established the political system is. How well the population and organised interests can

make their voices heard in the political system.

Economic Risk Economic stance of the government and economy. How continuous is economic stance in the country.

Legal Risk Assessment if necessary business laws are in place, and whether there are any outstanding gaps.

Tax Risk Lack of clarity, logic and transparency of taxation system.

Operational Risk Assessment of the government's stance on foreign investment. If foreign investors stand to benefit or suffer

from quality of infrastructure in the country.

Indexes are available from 1999 to 2010. Estimates of indexes range from 1 to 4. 4 indicating worse conditions.

Source: IHS Global Insight

World Governance Indicators

Political Stability and Absence

of Terrorism

Reflects perceptions of the likelihood that the government will be destabilized or overthrown by

unconstitutional or violent means, including politically-motivated violence and terrorism.

Rule of Law

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.

Government Effectiveness

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.

Control of Corruption 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.

Regulatory QualityReflects perceptions of the ability of the government to formulate and implement sound policies and

regulations that permit and promote private sector development.

Voice and AccountabilityReflects 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.

Indexes are available from 1996-2010. Estimate of governance (ranges from approximately -2.5 (weak) to 2.5

(strong) governance performance). Source: World Development Indicators

Table A1. Descriptions of Variables

Number of terrorist incidents originated from FDI receiving country towards entities of FDI sending country in

the year of observation. Source: ITERATE

Note: Each of the HIS Global Insight country risks are described in three to five criteria, for details check http://www.ihs.com/products/global-

insight/country-analysis/global-risk.aspx. For detailed description of the World Bank Development Indicators check:

http://info.worldbank.org/governance/wgi/sc_country.asp. In the Tables variable Pol. Stability and Absence of Terrorism/Violence is labeled as

Pol. Stability

FDI receiving

country

Terrorist

Incidents FDI receiving country

Terrorist

Incidents FDI receiving country

Terrorist

Incidents

1 Philippines 76 20 Turkey 7 39 Romania 1

2 Pakistan 72 21 Brazil 5 40 Armenia 0

3 Algeria 70 22 Ethiopia 5 41 Bulgaria 0

4 Egypt 53 23 Russian Fed. 5 42 Costa Rica 0

5 Peru 50 24 Singapore 5 43 Croatia 0

6 Saudi Arabia 33 25 Honduras 4 44 Fiji 0

7 Nigeria 30 26 Tunisia 4 45 Kazakhstan 0

8 Indonesia 29 27 Bangladesh 3 46 Kyrgyzstan 0

9 Cambodia 28 28 China 3 47 Moldova, Rep. 0

10 Chile 24 29 Ecuador 3 48 Myanmar 0

11 Colombia 21 30 Azerbaijan 2 49 Oman 0

12 Yemen 21 31 Mauritius 2 50 Paraguay 0

13 India 16 32 Mexico 2 51 Qatar 0

14 Morocco 14 33 Papua New Guinea 2 52 Syrian Arab Rep. 0

15 Bosnia-Herzeg. 13 34 Trin. and Tobago 2 53 Thailand 0

16 Panama 10 35 Venezuela 2 54 UAE 0

17 El Salvador 9 36 Dominican Rep. 1 55 Tanzania 0

18 Argentina 7 37 Georgia 1 56 Vanuatu 0

19 Bolivia 7 38 Malaysia 1 Total 643

FDI sending country

Terrorist

Incidents FDI sending country

Terrorist

Incidents FDI sending country

Terrorist

Incidents

1 United States 353 9 Belgium 13 17 Ireland 2

2 France 76 10 Netherlands 11 18 New Zealand 2

3 Germany 45 11 Switzerland 9 19 Cyprus 1

4 Japan 34 12 Austria 4 20 Greece 1

5 Italy 25 13 Portugal 4 21 Finland 0

6 Canada 20 14 Denmark 3 22 Luxembourg 0

7 Spain 18 15 Norway 3 23 United Kingdom 0

8 Australia 16 16 Sweden 3 Total 643

Appendix 2a. FDI receiving countries and total number of terrorist incidents against 23 FDI sending

countries (1990-2010)

Appendix 2b. FDI sender countries and total number of terrorist incidents perpetrated by 56 FDI receiving

countries (1990-2010)

Variable Obs. Mean Std. Dev. Min Max

Terrorism:

Attacks (host-sender) 17811 0.04 0.39 0 22

Total International Attacks (host) 17811 0.63 2.19 0 26

Total Domestic Attacks (host) 17811 24.74 73.68 0 655

Economic Variables :

FDI 17811 90.75 453.81 -1667.355 9592.86

FDI stock 17191 35366.76 71251.23 0 578818

Log GDP (host) 17288 180434.80 450685.60 173.11 5739359.00

Log GDP per capita (host) 17288 4178.39 6397.74 112.52 82567.93

Log GDP (sender) 17811 1486514.00 2500556.00 5777.09 14500000.00

Log Population (millions) 17811 84.78 229.91 0.15 1338.30

Tertiary 12790 25.58 15.91 0.33253 78.36485

5 year regional GDP growth rates 17549 3.06 2.04 -4.30 8.55

IHS Global Insight:

Security Risk 9168 2.88 0.74 1 4.5

Political Risk 9168 2.89 0.59 1 4.25

Economic Risk 9168 2.90 0.65 1.25 4.25

Legal Risk 9168 2.81 0.63 1 4.25

Tax Risk 9168 2.61 0.63 1 4

Operational Risk 9168 3.02 0.70 1 4.5

World Bank Indicators:

Political Stability 12717 -0.41 0.76 -2.70 1.42

Rule of Law 12709 -0.32 0.64 -1.65 1.76

Government Effectiveness 12709 -0.32 0.64 -1.65 1.76

Control of Corruption 12709 -0.34 0.69 -1.73 2.39

Regulatory Quality 12709 -0.05 0.63 -2.35 2.23

Voice and Accountability 12717 -0.30 0.64 -2.22 1.22

Table 1. Summary Statistics

Note: Summary statistics shows 10-to-90 percentile range of FDI flow between countries. See Table 1. for complete definitions and

sources of variables. Variables GDP (host); GDP (sender); GDP per capita (host); FDI stock are in millions of USD dollars. FDI stocks

are total stocks in the host country (including other investors than 23 in this study.)

Pol. Stability Rule of LawGov.Effectiven

ess

Control of

Corruption

Regulatory

Quality

Voice and

Accountability

Attacks (host-

sender)

Pol. Stability 1

Rule of Law 0.6849*** 1

Gov. Effectiveness 0.0028 -0.0042 1

Control of

Corruption0.6393*** 0.8891*** 0.0016 1

Regulatory Quality 0.5612*** 0.8077*** -0.0107 0.8383*** 1

Voice and

Accountability0.3723*** 0.3765*** 0.0355*** 0.4068*** 0.4698*** 1

Attacks (host-sender) -0.0935*** -0.0316*** -0.0039 -0.0323*** -0.0424*** -0.0578*** 1

Security Risk Political Risk Economic Risk Legal Risk Tax RiskOperational

Risk

Attacks (host-

sender)

Security Risk 1

Political Risk 0.6867*** 1

Economic Risk 0.5388*** 0.7942 1

Legal Risk 0.5059*** 0.7141*** 0.6328*** 1

Tax Risk 0.5597*** 0.7016*** 0.6776*** 0.7517*** 1

Operational Risk 0.7208*** 0.7835*** 0.714*** 0.7906*** 0.7656*** 1

Attacks (host-sender) 0.079*** 0.0605*** 0.0329** 0.0747*** 0.055*** 0.0486*** 1

Note: *, **, and *** denote significance at the 10%, 5%, and 1% level, respectively.

Table 2a. Correlation between World Governance Indicators and Terrorist Attacks, 1996-2010

Table 2b. Correlation between I H S Global Insight Risks and Terrorist Attacks, 1999-2009

Note: *, **, and *** denote significance at the 10%, 5%, and 1% level, respectively.

1 2 3 4 5 6 7 8 9 10 11

VARIABLES FDI FDI FDI FDI FDI FDI FDI FDI FDI FDI FDI

FDI stock 0.00281*** 0.00295*** 0.00140*** 0.0025*** 0.0026*** 0.0013*** 0.0013*** 0.0013*** 0.0013*** 0.0014*** 0.0013***

(0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000)

LogGDP (host) 186.4 159.3 165.6 213.1* 196.1 159.3* 165.9* 169.0* 126.2 179.3** 170.6*

(182.900) (186.100) (131.200) (122.000) (123.700) (88.830) (88.880) (89.010) (89.030) (88.990) (88.990)

LogGDPpc(host) -41.04 -17.63 -123.9 -127.8 -121 -127.5 -139.6* -125 -103.8 -173.7** -128.7

(175.400) (178.800) (125.900) (115.700) (117.400) (84.210) (84.580) (83.980) (83.920) (85.480) (84.030)

LogGDP (sender) -118.9 -106.9 -183.9*** -53.44 -41.59 -107.9*** -109.6*** -108.5*** -109.6*** -110.9*** -107.8***

(75.740) (79.640) (54.470) (56.290) (58.770) (41.040) (41.040) (41.040) (40.990) (41.020) (41.030)

Tertiary -1.591 -0.313 1.081 -0.663 0.58 0.97 1.416 0.959 1.277 1.621* 1.062

(1.623) (1.711) (1.165) (1.198) (1.249) (0.872) (0.905) (0.872) (0.874) (0.894) (0.873)

5 year GDP growth

rate1.849 2.702 3.758 -8.373* -8.764* -4.279 -3.939 -4.453 -3.251 -3.977 -3.766

(5.821) (6.143) (4.185) (4.503) (4.707) (3.283) (3.289) (3.284) (3.288) (3.283) (3.294)

Security Risk 83.11*** 74.02** 57.10*** Pol. Stability 30.23 27.93 16.03

(29.390) (30.310) (21.090) (20.760) (21.920) (15.110)

Political risk -9.351 21.57 -5.776 Rule of Law 50.04*

(34.430) (36.470) (24.720) (27.260)

Economic risk-39.32 -40.24 -6.279

Gov.

Effectiveness -0.0696*

(30.720) (31.840) (22.080) (0.042)

Legal Risk-72.15** -84.23** -45.62*

Control of

Corruption94.39***

(36.160) (38.130) (25.960) (21.020)

Tax Risk-74.86 -99.95** -129.3***

Regulatory

Quality72.59***

(45.990) (47.950) (33.030) (22.340)

Operat. Risk59.08 58.32 43.1

Voice &

Accountability44.97**

(36.850) (38.770) (26.450) (22.480)

Constant -100.6 29.73 1,831* -508.3 -838.6 718.7 746.3 586.8 929.4 859.4 599.5

(1321.000) (1376.000) (949.800) (901.800) (993.300) (657.200) (658.000) (656.800) (658.100) (658.700) (656.200)

Observations 7,296 6,723 7,279 9,113 8,495 9,096 9,096 9,096 9,096 9,096 9,096

R-squared 0.056 0.065 0.05 0.052 0.062 0.047 0.047 0.047 0.049 0.048 0.047

Number of pair_id 812 807 812 826 826 826 826 826 826 826 826

Table 3. Fixed Effects Estimation of Market Indicators and FDI, 1990-2010.

I H S Global Risks World Governance Indicators

Note: The dependent variable is the FDI outflow from sender to host country per year in millions of current US dollars. All specifications are with country-pair and year fixed effects. Robust standard errors are reported in

parentheses: *, **, and *** denote significance at the 10%, 5%, and 1% level.

Table 4. Fixed Effects Estimation of Terrorism, and FDI (1990-2010)

1 2 3 4 5

FDI=0 for FDI<0 FDI >=0 w/o outliers

VARIABLES FDI FDI FDI FDI FDI

FDI stock 0.00266*** 0.00269*** 0.00279*** 0.00171***

(0.000) (0.000) (0.000) (0.000)

LogGDP (host) 45.18 45.16 36.25 28.6

(85.970) (84.240) (87.470) (64.020)

LogGDPpc(host) 1.037 0.692 2.062 -13.06

(80.300) (78.690) (81.680) (59.800)

LogGDP (sender) -59.86 -58.28 -58.33 -97.03***

(39.340) (38.550) (40.900) (29.320)

Tertiary -0.35 0.22 0.617 0.64

(0.951) (0.932) (0.986) (0.708)

5 year GDP growth rate -7.035* -6.915* -7.440* -4.206

(3.730) (3.655) (3.861) (2.781)

Attacks (host-sender) -20.99** -27.24* -28.04* -30.27* -27.19**

(9.974) (15.400) (15.090) (16.010) (11.470)

Attacks (host-sender) t-1 1.907 0.802 0.83 1.223 1.593

(9.188) (13.720) (13.450) (14.180) (10.220)

Attacks (host-sender) t-2 1.508 -3.01 -2.804 -3.222 -2.177

(9.179) (13.660) (13.380) (14.780) (10.170)

Attacks (host-sender) t-3 1.904 2.181 1.916 1.452 2.856

(9.159) (13.810) (13.530) (13.970) (10.280)

Total Int. Attacks (host) t-1 2.224 2.182 2.044 1.374

(3.125) (3.062) (3.202) (2.327)

Total Domestic Attacks (host) t-1 0.0332 0.0232 0.0399 0.0752

(0.093) (0.091) (0.097) (0.069)

Constant -3.753 130.9 100.8 182.8 986.8*

(17.950) (685.500) (671.700) (707.900) (510.700)

Observations 17,617 11,665 11,665 10,987 11,648

R-squared 0.024 0.069 0.075 0.08 0.072

Number of pair_id 849 828 828 828 828

Note: The dependent variable is the FDI outflow from sender to host country per year in millions of current US dollars.All specifications are with country-pair and

year fixed effects. Robust standard errors are reported in parentheses: *, **, and *** denote significance at the 10%, 5%, and 1% level.

Table 5. Fixed Effects Estimation of Terrorism, Global Insight Risks, and FDI (1996-2010)

1 2 3 4 5

FDI=0 for FDI<0 FDI >=0 FDI=0 for FDI<0

VARIABLES FDI FDI FDI FDI FDI

FDI stock 0.00282*** 0.00283*** 0.00140*** 0.00146*** 0.00143***

(0.000) (0.000) (0.000) (0.000) (0.000)

logGDP (host) 155.4 165.7 156.2 152.4 169.3

(184.100) (179.000) (132.100) (133.700) (128.900)

logGDPpc(host) -10.65 -15.6 -111.8 -122.5 -127.8

(176.600) (171.700) (126.700) (128.500) (123.700)

logGDP (sender) -115.8 -109.7 -180.2*** -183.1*** -178.6***

(75.810) (73.730) (54.510) (57.060) (53.230)

Tertiary -1.657 -0.918 1.122 2.672** 1.841

(1.626) (1.581) (1.167) (1.224) (1.139)

5 year GDP growth rate 1.834 2.619 4.061 3.568 3.284

(5.830) (5.670) (4.191) (4.395) (4.092)

Attacks (host-sender) -44.72* -46.75** -38.59** -43.01** -39.91**

(24.510) (23.840) (17.590) (17.620) (17.180)

Attacks (host-sender) t-1 -5.8 -6.334 -4.382 -4.717 -4.751

(21.200) (20.610) (15.200) (15.230) (14.840)

Total Int. Attacks (host) t-1 -1.188 -0.96 -2.521 -2.288 -2.421

(5.469) (5.319) (3.923) (4.062) (3.831)

Total Domestic Attacks (host) t-1 -0.294 -0.286 0.0661 0.163 0.0603

(0.282) (0.274) (0.202) (0.214) (0.198)

I H S Global Insight Risks

Security Risk 90.72*** 81.12*** 58.29*** 48.61** 52.17**

(29.910) (29.090) (21.470) (22.010) (20.960)

Political risk -5.045 7.273 -6.846 17.5 1.603

(34.680) (33.730) (24.900) (26.230) (24.310)

Economic risk -43.4 -43.22 -5.66 -5.424 -7.316

(30.950) (30.100) (22.240) (22.910) (21.710)

Legal Risk -71.88** -70.88** -40.46 -41.14 -37.62

(36.650) (35.640) (26.310) (27.600) (25.690)

Tax Risk -77.82* -91.62** -131.8*** -135.6*** -128.3***

(46.050) (44.780) (33.070) (34.290) (32.290)

Operational Risk 62.87* 66.68* 43.16 28.64 37.15

(36.970) (35.960) (26.540) (27.780) (25.920)

Constant 106.1 -58.44 1,859* 2,006** 1,801*

(1322.000) (1286.000) (950.500) (987.400) (928.100)

Observations 7,296 7,296 7,279 6,711 7,279

R-squared 0.057 0.061 0.051 0.059 0.055

Number of pair_id 812 812 812 807 812

w/o outliers

Note: The dependent variable is the FDI outflow from sender to host country per year in millions of current US dollars.All specifications are with country-pair

and year fixed effects. Robust standard errors are reported in parentheses: *, **, and *** denote significance at the 10%, 5%, and 1% level.

Table 6. Fixed Effects Estimation of Terrorism, WGI and FDI (1999-2009)

1 2 3 4 5 6 7 8 9 10

FDI=0 for FDI<0 FDI=0 for FDI<0 FDI >=0

VARIABLES FDI FDI FDI FDI FDI FDI FDI FDI FDI FDI

FDI stock 0.00251*** 0.00254*** 0.00134*** 0.00140*** 0.00140*** 0.00133*** 0.00133*** 0.00136*** 0.00137*** 0.00135***

(0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000)logGDP (host) 210.5* 206.7* 159.2* 152.1* 152.1* 168.9* 166.5* 124.9 177.6** 174.7*

(122.500) (119.500) (89.120) (89.880) (89.880) (89.250) (89.290) (89.270) (89.290) (89.410)logGDPpc(host) -127.3 -122.7 -130.7 -139.8 -139.8 -146.3* -122.2 -103 -171.8** -132

(116.500) (113.600) (84.750) (85.490) (85.490) (85.220) (84.340) (84.250) (85.890) (84.500)logGDP (sender) -49.91 -47.18 -104.3** -103.7** -103.7** -106.4*** -105.2** -106.2*** -107.6*** -104.3**

(56.330) (54.950) (41.050) (42.630) (42.630) (41.050) (41.050) (41.000) (41.040) (41.040)

Tertiary -0.673 0.0177 0.942 2.137** 2.137** 1.513* 0.947 1.263 1.607* 1.049

(1.199) (1.169) (0.872) (0.905) (0.905) (0.905) (0.872) (0.874) (0.894) (0.873)

5 year GDP growth rate -8.519* -8.064* -4.543 -5.223 -5.223 -4.163 -4.618 -3.491 -4.168 -3.985

(4.518) (4.407) (3.293) (3.418) (3.418) (3.295) (3.294) (3.296) (3.292) (3.299)

Attacks (host-sender) -40.44* -41.94* -38.15** -42.79*** -42.79*** -38.24** -37.88** -38.55** -38.31** -37.65**

(22.040) (21.510) (16.050) (16.050) (16.050) (16.050) (16.050) (16.030) (16.040) (16.040)

Attacks (host-sender) t-1 -2.148 -2.267 -1.47 -1.194 -1.194 -1.56 -1.258 -1.608 -1.517 -1.061

(18.910) (18.440) (13.750) (13.750) (13.750) (13.750) (13.750) (13.740) (13.750) (13.750)

Total Int. Attacks (host) t-1 -1.351 -1.268 -1.917 -1.921 -1.921 -1.813 -2.02 -1.777 -1.621 -1.619

(4.845) (4.726) (3.525) (3.630) (3.630) (3.525) (3.524) (3.520) (3.524) (3.528)

0.132 0.119 0.200* 0.206* 0.206* 0.223* 0.148 0.198* 0.153 0.221*

(0.163) (0.159) (0.118) (0.122) (0.122) (0.118) (0.113) (0.113) (0.113) (0.117)

Pol. Stability 35.42 35.65* 23.84 21.23 21.23

(21.770) (21.230) (15.840) (16.540) (16.540)

Rule of Law 64.68**

(28.430)

Gov. Effectiveness -0.0698*

(0.042)

Control of Corruption 97.97***

(21.110)

Regulatory Quality 72.66***

(22.350)

54.94**

(23.390)

Constant -529.4 -573.4 697.5 713.2 713.2 726.4 551.5 895.4 822.2 539.2

(902.200) (880.100) (657.200) (720.100) (720.100) (658.000) (657.300) (658.300) (659.100) (656.900)

Observations 9,113 9,113 9,096 8,483 8,483 9,096 9,096 9,096 9,096 9,096

R-squared 0.053 0.058 0.048 0.057 0.057 0.048 0.048 0.05 0.049 0.048

Number of pair_id 826 826 826 826 826 826 826 826 826 826

w/o outliers

Note: The dependent variable is the FDI outflow from sender to host country per year in millions of current US dollars.All specifications are with country-pair and year fixed effects. Robust standard errors are reported in parentheses:

*, **, and *** denote significance at the 10%, 5%, and 1% level.

Total Domestic Attacks

(host) t-1

World Governance Indicators

Voice and Accountability