special comment terrorism has a long -lasting negative ... · countries: iraq (with 24% of all...

SPECIAL COMMENT

Table of Contents:

EXECUTIVE SUMMARY 2INTRODUCTION 31 EMPIRICAL REGULARITIES OF TERRORIST INCIDENTS 42 CONSTRUCTING AN ANNUAL TERRORISM INDEX 73 METHODOLOGY 94 RESULTS 105 ROBUSTNESS CHECKS 136 CONCLUSION 17APPENDIX 1 18APPENDIX 2 21MOODY’S RELATED RESEARCH 25OTHER REFERENCES 25

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Sovereigns and Terrorism

Terrorism Has a Long-lasting Negative Impact on Economic Activity and Government Borrowing Costs

Terrorist attacks have long-lasting negative effects on economic growth. They strain public finances and lead to an increase in sovereign borrowing costs. In this Special Comment we quantify the impact of terrorism on economic growth, government expenditure, investment and the cost of borrowing. Using data from the Global Terrorism Database (GTD) covering 156 countries for 1994-2013 for our analysis, we find the following:

» The type and frequency of terrorist attacks seen in the 10 most terrorist-inflicted countries in 2013 immediately weaken GDP growth between 0.51 and 0.80 percentage points (pps); growth further deteriorates between 0.37pps and 0.59pps after one year, and 0.05pps and 0.07pps after three years.

» Such terrorist events also reduce investment growth (and therefore impair potential growth), by between 1.3pps and 2.1pps in the year of the terrorist attacks.

» These events increase governments’ cost of borrowing which jumps between 41 basis points (bps) and 65bps within one year and a further 51bps-81bps one year after the events.

Terrorist incidents are defined as “the threatened or actual use of illegal force and violence by a non-state actor to attain a political, economic, religious, or social goal through fear, coercion, or intimidation.” That is, the incident has to be intentional, it must entail some level of violence or threat of violence, and the perpetrators must be sub-national actors. Given the diversity of terrorist events, to measure the impact of terrorism on the economy we construct a terrorism index as a weighted average of the number of terrorist incidents; the number of fatalities caused by terrorism; the number of injuries from terrorist attacks; and a measure of the total property damage from terrorism.1

1 “National Consortium for the Study of Terrorism and Responses to Terrorism (START)” (2014). Global Terrorism Database (GTD) December 2013.

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Executive Summary

The number of terrorist incidents has increased rapidly, more than doubling to 11,823 by the end of 2013 from 5,000 incidents in 2011. In per capita terms, the number of incidents tripled to 2.4 incidents per million people in 2013 from 0.7 incidents per million people in 2011. Terrorism negatively affects economic growth by directly destroying production inputs, along with physical and human capital. This Special Comment quantifies the impact of terrorism on economic growth, investment, government expenditure and the government cost of borrowing.

Terrorist attacks are diverse in terms of the personal and property damage inflicted. They are also unevenly distributed across countries. More than 60% of all incidents in 2013 were concentrated in just four countries: Iraq (with 24% of all terrorist incidents), Pakistan (19%), Afghanistan (12%) and India (5.8%). To properly measure the impact of terrorism on the economy, we construct an index of terrorism as a weighted average of the total number of terrorist incidents, the number of fatalities, the number of injuries and the property damage inflicted by the terrorist event. The index also accounts for the size of the country and the concentration of terrorism in a few countries: an additional terrorist attack in a country with high incidence of terrorism would likely have a lower impact than if that same attack were on a country with zero terrorist events.

Our sample covers 156 countries for the period between 1994 and 2013.

The type and frequency of terrorist events in just 2013, in the top 10 countries ranked by the value of our index of terrorism, weaken GDP growth immediately by 0.51 to 0.80pps. GDP growth further deteriorates between 0.37pps and 0.59pps after one year of the attack, and by 0.05pps and 0.07pps after three years. This means that in the absence of terrorist events, a country’s GDP could be 1.1% to 1.7% higher. These single-year events also affect investment and the government cost of borrowing. Investment growth shrinks between 1.3pps and 2.1pps in the year of the attack and leaves the level of investment between 1.8% and 2.8% lower. The cost of borrowing jumps between 41bps and 65bps within one year and a further 51bps to 81bp one year after the attack.

In reality acts of terrorism in a country are not rare or isolated so that their individual impact can be measured. In countries with high incidence of terrorism, there are generally multiple attacks year after year. It is therefore useful to consider the absence of terrorist events against the totality of terrorist events in country when measuring the impact of terrorism (as opposed to a terrorist event). A simple exercise that compares how GDP would have performed had there not been a single terrorist event between 2008 and 2013 to what actually occurred reveals that, for example, GDP in Iraq could have been by the end of 2013 8.2% higher, and in Pakistan, 5.1% higher.

Similarly, absent terrorist events, the level of investment in Pakistan could have been 9.3% higher, and in Iraq, 15.1% higher between 2008 and 2013. During that timeframe, the cost of borrowing could have been 100bps lower in Pakistan and 150bps lower in Iraq, on average had no terrorist activity taken place in these two countries...

2 OCTOBER 5, 2015 SPECIAL COMMENT: SOVEREIGNS AND TERRORISM: TERRORISM HAS A LONG-LASTING NEGATIVE IMPACT ON ECONOMIC ACTIVITY AND GOVERNMENT BORROWING COSTS

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Introduction

Terrorist events directly destroy infrastructure, physical and human capital reducing production and economic growth. Such events also increase uncertainty, disrupt household and business spending and investment plans, and divert foreign direct investment. Terrorist events also lead to a reallocation of resources from growth-enhancing investment to spending designed to increase national security or to capture terrorists and their assets. In this study we aim to measure the impact of terrorist events on economic growth, government expenditure and investment, and government cost of borrowing.

The data on terrorist incidents are from the Global Terrorism Database (GTD), version 0814, produced by the National Consortium for the Study of Terrorism and Responses to Terrorism (START), a United States Department of Homeland Security Center of Excellence at the University of Maryland. The GTD is the most comprehensive dataset of terrorist activity and has codified over 125,000 terrorist incidents. The GTD was produced in several phases, always relying on publicly available source materials such as media articles, electronic news archives, other existing datasets and secondary source materials (books, journals and legal documents). START does not add information to the GTD unless and until it has determined that the sources are credible. If new documentation about an event becomes available, START may modify and entry, as necessary and appropriate. For each GTD incident, information is available on the date and location of the incident, the weapons used and nature of the target, the number of casualties, and, when identifiable, the group or individuals responsible.

For the purposes of this study, terrorist incidents are defined (as in the GTD) as “the threatened or actual use of illegal force and violence by a non-state actor to attain a political, economic, religious, or social goal through fear, coercion, or intimidation.”2 That is, (1) the incident has to be intentional – the result of a conscious calculation on the part of the perpetrator, (2) it must entail some level of violence or threat of violence against people and (or) property., and (3) the perpetrators must be sub-national actors – we do not consider acts of state terrorism in this study.

This paper adds to the existing research on the macroeconomic effects of terrorism. For example, Blomberg, Hess and Orphanides (2004) finds that the incidence of terrorism may have an economically significant negative effect on growth and that terrorism is associated with a redirection of economic activity away from investment spending and towards government spending.3 The study is limited to only the number of terrorist incidents reported and does not take into account any information on the number of casualties and property damage.

Similarly, Gaibulloev and Sandler (2008) in a study for 42 Asian countries between 1970 and 2004 finds that an additional terrorist incident per million people reduces GDP per capita growth by 1.5%. While this impact seems extremely large, it must be kept in perspective. For example. a populous country with 100 million people would have to experience 100 more transnational terrorist events to have this kind of impact.

Gaibulloev and Sandler (2010) investigates the determinants of income per capita growth for 51 African countries between 1970 and 2007, controlling for the effect of the number of terrorist attacks normalized by population size (as well as internal conflicts and wars). The authors find a modest impact of transnational terrorism on income per capita growth. However, they do not document any impact of domestic terrorist events on economic growth.

2 “National Consortium for the Study of Terrorism and Responses to Terrorism (START)” (2014). Global Terrorism Database (GTD) December 2013. 3 Authors use cross-sectional and panel growth regressions and a structural four variable VAR (including, in order, the log-level of real GDP per capita, and dummy

variables for internal conflict, external conflict and terrorism),

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These and similar studies generally focus on number of events and document a relatively modest impact on economic growth from terrorist attacks. However, the richness and availability of databases such as the GTD in terms of the characteristics of the registered events (such as casualties and property damage) offers us a unique opportunity to explore the impact of various “attributes” of terrorism incidents (in addition to the sheer count of incidents) on economic growth of a country. Additionally, the fact that terrorist attacks are not evenly distributed across countries and the existence of diminishing returns in terms of the psychological and economic impacts need to be accounted for in any such analysis. An additional attack in a country with high incidence of terrorist events would likely have a lower impact compared with an attack on a country with no prior terrorist events. In this study we account for both. In our study we fully leverage the dataset that is available to us. Additionally, ours is a global study, with emphasis on no particular region or group of countries.

The rest of the study is organized as follows. Section 1 describes the empirical regularities of terrorism incidents. In Section 2 we explain the construction of an index of terrorism. We describe the methodology in Section 3. The main results are presented in Section 4, with some robustness checks in Section 5. Section 6 concludes.

1 Empirical Regularities of Terrorist Incidents

Terrorist activity has increased rapidly since 2011 (Exhibit 1). The number of terrorist incidents more than doubled by the end of 2013 to 11,823 and more than tripled in per capita terms to 2.4 incidents per million people from about 5,000 incidents, or 0.7 incidents per million people in 2011.

EXHIBIT 1

Terrorist Incidents, 1994-2013

Source: GTD, Haver Analytics and Moody’s calculations

More than 60% of all incidents in 2013 were concentrated in just four countries (Exhibit 2): Iraq (with 24% of all terrorist incidents), Pakistan (19%), Afghanistan (12%) and India (5.8%). Even normalized by the size of the country, Iraq and Afghanistan are at the top of the list with 82 and 47 incidents per million people, respectively, in 2013. This compares with a global average of 2.4 incidents per million people in 2013. Libya (with 48 incidents per million people), Bahrain (44), Somalia (32) and Lebanon (27) are next by the number of terrorist incidents per million people in 2013. In the mid-1990s, when the number of incidents per million people were close to the figures seen in the last couple of years, the countries with most incidents per capita where Bahrain and Lebanon again, but also, Colombia, Macau, Cyprus,4 Israel, Algeria, Burundi and Sri Lanka.5

4 Cyprus only had a handful of terrorist attacks in the mid-1990s, although high by its own historical standards still low relative to other countries, but given its small population it ranks high once the number of incidents are normalized by the county’s size.

5 See Appendix 1 for a complete count of terrorist events by country and year for our study sample.

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Today, terrorist acts are concentrated in the Middle East and Africa (MENA) and South Asia, while in the mid-1990s, even though the MENA region was at the top of the list with an average of 3.5 incidents per million people, terrorism was a bit more diffuse across regions. There were 1.3 -1.4 incidents per million people in South America, Central America and the Caribbean, Western Europe and Russia and the Newly Independent States (NIS) regions.

EXHIBIT 2

Number of Terrorist Incidents per Year

Source: GTD and Moody’s calculations

The number of people wounded or killed per year from terrorist incidents is increasing rapidly (Exhibit 3). Since its most recent peaks in 2007, the number of people injured and killed in 2013 rose by 73% to 36,708 people and by 60% to 18,337 people, respectively. Similarly, 21 countries had more than 50 deaths from terrorist events in 2013 compared to an average of 15 countries over the previous 15 years (Exhibit 4).

EXHIBIT 3

Number of Casualties due to Terrorist Incidents


EXHIBIT 4

Number of Countries with More than 50 Deaths in a Given Year from Terrorist Activity

Not surprisingly, the countries with the largest number of people wounded or killed are also those with the higher number of terrorist incidents: Iraq, Pakistan, Afghanistan, Nigeria, Syria and Yemen (Exhibit 5). Over 60% of all deaths in terrorist attacks in 2013 occurred in only three countries: Iraq, Pakistan and Afghanistan. However, Mexico, Syria, the Central African Republic, Niger, Nigeria and the Democratic Republic of Congo had some of the highest fatalities per terrorist incident in 2013 (Exhibit 6). For example,

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Mexico had an average of 5.9 fatalities per terrorist incident in 2013, compared with 1.7 and 2.3 fatalities per incident in Afghanistan and Iraq, respectively. Additionally, there were no deaths from terrorist attacks in more than 60% of countries.

EXHIBIT 5

Total Killed in Terrorist Attacks, 2013


EXHIBIT 6

Killed or Wounded per Terrorist Incident, 2013

Most of the deaths from terrorist attacks in 2013, about 66%, were claimed by just four terrorist organizations: the Islamic State of Iraq and the Levant (ISIL), Boko Haram, the Taliban and Al-Qaeda.

The deadliest terrorist events since the mid-1990s were the 11 September 2001 attacks in the US by Al-Qaeda and the 1994 incident in Rwanda. A total of 2,996 people (not including the 10 hijackers) died in the World Trade Center attacks, 184 people (not including the five hijackers) were killed in the Pentagon attack and 40 more (not including four hijackers) died in the attack initially directed towards the Capitol Building in Washington D.C. The terrorist attack in Gikoro, Rwanda in 1994 by the Hutus tribe killed 1,180 people from the Tutsi tribe who were seeking refuge inside a Catholic church.

The maximum number of wounded people from a single terrorist event since 1994 occurred in the city of Tokyo in Japan in 1995. The religious movement Aum Shinrikyo carried out a chemical attack using sarin gas in the Tokyo subway system injuring 5,500 and killing 13 people. The second biggest event by the number of injured people took place in Kenya in 1998: suicide attackers detonated a vehicle bomb outside the United States Embassy in Nairobi, killing 224 people and injuring 4,000. The attack was perpetrated by Al-Qaeda.

In 2013, the deadliest attack took place in Nigeria where gunmen in military fatigues set up illegal checkpoints and attacked civilians in Beni Shiek village, Borno state. At least 142 people were killed and an unknown number of people were injured. Boko Haram claimed responsibility for the incident and stated that the group would not allow democracy to thrive. In 2013, more than 100 people were killed in two separate events. In Syria, assailants attacked soldiers and civilians in the town of Khan al-Assal, in the Aleppo governorate. The attack, which lasted two days, resulted in 123 deaths and an unknown number of injuries. Al-Nusrah Front claimed responsibility for this attack. In South Sudan, heavily armed perpetrators attacked villagers migrating with their cattle in Walgak, Akobo county. More than 100 people died and 14 individuals were injured. Authorities suspected that the ethnic Murle tribe, particularly the anti-government group, David Yau Yau Militia, was responsible for the incident.

Iraq36%

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Afghanistan14%

Nigeria8%

Syria8%

Yemen3%

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The majority of the terrorist incidents resulted in “no property damage” or are listed as “the damage was not quantified” (Exhibit 7). While there has been an increase in the proportion of incidents that result in property damage since 2009, average damage has been limited to less than $1 million.6

EXHIBIT 7

Extent of Property Damage as a Result of Terrorist Attacks


2 Constructing an Annual Terrorism Index

Given the diversity of terrorist attacks and their uneven distribution across countries, we construct an annual index of terrorism, similar to the Global Terrorism Index (GTI) built by the Institute of Economics & Peace. As in the GTI, the four factors in each country index are (a) the total number of terrorist incidents in a given year (with a weight of one); (b) the total number of fatalities caused by terrorist incidents in a given year (weight of three); (c) the total number of injuries from terrorist attacks in a given year (weight of 0.5); and (d) a measure of the total property damage from terrorism in a given year (weight of two). The weights (and factors) were determined by the Global Peace Index (GPI) Expert Panel, within the Peace and Conflict Studies Unit at the Institute of World Economy & International Relations.

The property damage measure is the sum for each year and country of an indicator that takes the value of zero if the damage is unknown or nil, the value of one if the damage is less than USD1 million, the value of two if the damage is between USD1 million and USD1 billion, or the value of three if the damage is greater than USD 1Bn. About 75% of the time this measure takes a value of zero (meaning that there was no property damage or this was unknown for all terrorist events in a given year and country), and for 8% of the time it takes a value of one. That is, for most of the time there is not a major property loss from terrorist events.

To account for the size of the country, we normalize the index by the country’s population based on the belief that terrorism incidents or resulting casualties in the single digits from such attacks would have less of an impact on a country with 40 million than on one with five million people.

Terrorist events are concentrated in a few countries, and as others have indicated, there are “diminishing returns to terrorism in terms of its psychological, economic, political and cultural impact.”7 That is, an additional terrorist attack in a country with a high incidence of terrorism, would likely have a lower impact

6 For more information on the types of terrorist incidents, their country distribution and case studies, please see the report “Global Terrorism Index, 2014” published by the Institute of Economics & Peace.

7 See “Global Terrorism Index, 2014” published by the Institute of Economics & Peace.

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than if that same attack were on a country with zero terrorist events. Hence, we take the natural logarithm of our reconstructed index. Exhibit 8 compares the raw terrorism index and the natural log (ln) of the raw terrorism index (that we called Terrorism Index), with the latter having a more even distribution than the raw index. As an example take Iraq and Pakistan with 82 and 12 terrorist events per million people, respectively, in 2013: that is, Iraq had 6.8 times more events per million people in 2013 than Pakistan. However, the value of the terrorism index for Iraq in 2013 was 6.83 and for Pakistan 4.35, that is, only 1.6 times higher. It is this terrorism index that we use to evaluate the impact of terrorism on the economy.

Exhibit 9 shows how the Terrorism Index compares with the number of terrorist attacks year-on-year, both normalized by population. Exhibits 10 and 11 plot the top 17 countries by the value of the terrorism index, for 2013 and 2000, respectively. Iraq and Afghanistan ranked highest for terrorism incidents in 2013, measured by a more comprehensive indicator. But Lebanon, Syria and Libya, which ranked lower by the simple count of terror incidents per year, rank very high once their size and number of people killed or injured and property damage are taken into consideration. For example, Lebanon and Libya had a considerably lower number of terrorist incidents in absolute terms compared to Pakistan in 2013, but in per capita terms they had 2.2 and 4 more incidents than Pakistan. The incidents in Lebanon and Pakistan were more taxing in terms of the relative number of people wounded, and in Libya also in terms of the property damage inflicted. Syria, where there were a similar number of terrorist attacks per million people had 1.4 more deaths and 3.2 more injuries per million people than Pakistan.

EXHIBIT 8

Terrorism Index


EXHIBIT 9

Terrorism Index Compared with Number of Terrorist Attacks

For comparison, Exhibit 11 shows which countries had the highest values on the terrorism index in 2000. For example, St. Lucia only had one incident in the year 2000 when two people were killed and 12 wounded. However, as it is a small country, the index of terrorism takes a high value once normalized by population. Sri Lanka, by contrast, had 68 terrorist attacks, most of them perpetrated by the Liberation Tigers of Tamil Eelam (LTTE), with more than half of the attacks causing more than 20 deaths.

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EXHIBIT 10

Countries with the Highest Value of Terrorism Index, 2013


EXHIBIT 11

Countries with the Highest Value of Terrorism Index, 2000

3 Methodology

As stated earlier, geopolitical risk, and in particular terrorist attacks, disrupt economic growth and strain public finances, thus likely pressuring the sovereign’s creditworthiness. In this section, we briefly describe our method of estimating the impact of such events on economic growth, through their effect on investment, government expenditure and the cost of borrowing.

We use a reduced-form vector autoregression (VAR) approach applied to an unbalanced panel of 156 countries between 1994 and 2013. By using panel data, we allow for country-specific unobserved heterogeneity (i.e. fixed effects) while investigating the effects of terrorism on economic growth.8 And by using a reduced-form approach, we do not have to rely on strong assumptions of how the different variables interact together. The only assumptions that we need to make are which of our variables are more exogenous than others.

In a VAR specification, each endogenous variable is explained by its own lags and by lagged values of the other variables. We introduce the terrorism index as an exogenous variable in the VAR specification. Specifically we have:

𝑌𝑌𝑖𝑖𝑖𝑖 = 𝐴𝐴0 + 𝐴𝐴1𝑌𝑌𝑖𝑖𝑖𝑖−1 + 𝐵𝐵0𝑥𝑥𝑖𝑖𝑖𝑖 + 𝐵𝐵1𝑥𝑥𝑖𝑖𝑖𝑖−1 + 𝛼𝛼𝑖𝑖 + 𝑢𝑢𝑖𝑖𝑖𝑖 (1)

Where 𝑌𝑌 is a vector of covariates (log change in government consumption, treasury interest rates, log change in investment and log change in GDP),9 A and B are matrices of parameters to be estimated, 𝛼𝛼𝑖𝑖 are the country specific effects and 𝑢𝑢𝑖𝑖𝑖𝑖 is the residual vector of innovations to each of the covariates.

As we treat terrorism events as exogenous to our system of macroeconomic variables, our focus is on the dynamic multiplier functions. The dynamic multiplier functions measure the impact of a unit increase in the

8 The fixed effects are correlated with the regressors due to lags of the dependent variables. Forward mean-differencing (Helmert procedure) removes the mean of all the future observations available for each country-year (see Arellano and Bover, 1995). This transformation preserves the orthogonality between transformed variables and lagged regressors, so we can use lagged regressors as instruments and estimate the coefficients by system GMM. Specifically, we estimate the VAR using the STATA pvar and helm programs by Inessa Love. The programs are available at http://go.worldbank.org/E96NEWM7L0.

9 All macro variables are yearly and downloaded from Haver Analitics. GDP based on PPP (purchasing power parity) valuation of country GDP.

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exogenous variable (i.e. the terrorism index) on each of the endogenous variables (our macroeconomic variables).10 The dynamic multipliers can be obtained from:

𝑌𝑌𝑖𝑖𝑖𝑖 = ∑ 𝐷𝐷𝑗𝑗𝑋𝑋𝑖𝑖,𝑖𝑖−𝑗𝑗∞𝑗𝑗=0 + ∑ 𝛷𝛷𝑗𝑗∞

𝑗𝑗=0 𝑢𝑢𝑖𝑖,𝑖𝑖−𝑗𝑗 (2)

Where 𝐷𝐷𝑗𝑗 are the dynamic multipliers, which can be calculated directly from the estimates of the matrices of coefficients A and B in equation (1).

The orthogonalized impulse-response functions (IRFs), by contrast, show the response of one endogenous variable (e.g. GDP) to innovations on another endogenous variable (e.g. treasury rates) while holding all other shocks equal to zero. To obtain the orthogonalized IRFs, we have to decompose the residuals in a way that makes them orthogonal. To do so, we apply the causal ordering VAR identification procedure, that is, the Choleski decomposition of variance-covariance matrix of residuals.11 The identifying assumption is that the variables that appear earlier in the system (i.e. government consumption) are more exogenous, and those that appear later (i.e. investment share and GDP growth) more endogenous. This is equivalent to saying that the variables that appear earlier in the system affect variables that follow, contemporaneously and with lags, whereas the variables that appear later in the system only affect the previous variables with a lag. Our ordering is as follows: government consumption, interest rates, investment and GDP.

4 Results

Exhibit 12 shows the impulse responses of GDP growth to one standard deviation increase in the terrorism index, together with the 90% and 95% confidence intervals generated by Monte Carlo simulation. As hypothesized, GDP growth responds negatively to terrorist attacks.

EXHIBIT 12

Dynamic Multiplier Function and Error Bands GDP growth response to one standard deviation increase in the terrorism index

Notes: (a) we randomly generate a draw of coefficients using the estimated coefficients and their variance–covariance matrix and re-calculate the dynamic responses. We repeat this procedure 500 times .We generate 5th (10th) and 95th (90th) percentiles of this distribution which we use as a the 95th (90th) confidence interval for the impulse-responses. (b) The horizontal axis are years. Source: GTD and Moody’s calculations

10 To compare with the most commonly known impulse-response functions, we plot the responses of the endogenous variables to one standard deviations increases in the terrorism index instead of a unit increase.

11 For a formal discussion of VAR and identification procedure, please see Hamilton (1994).

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One standard deviation increase in the terrorism index leads to a drop of 0.32pps in GDP growth within one year, a further deterioration of 0.24pps after one year and of 0.03pps after two years. There are statistically significant effects on GDP growth all the way until year ten. This means that one standard deviation increase in the terrorism index leaves the level of GDP about 0.7% lower than otherwise. These effects do not seem, at first glance, economically significant. But a one standard deviation in the terrorism index, which is a value of 2.76 for the terrorism index, should be put into context. For example, in 2013 the terrorism index was between 4.31 (Central African Republic) and 6.83 (Iraq) for the top ten countries ranked by their values in the terrorism index. A shock of this magnitude would lead to a deterioration of between 0.51pps and 0.80pps in GDP growth within a year, a further slowdown of between 0.37pps and 0.59pps after one year, and 0.05pps and 0.07pps after three years. This means that the level of GDP would be between 1.1% and 1.7% lower than that in the absence of these terrorist events.

Exhibit 13 shows GDP with and without terrorism in Iraq and Pakistan. Both countries that had the highest number of terrorist events over our sample period and in 2013. Specifically, the charts in Exhibit 16 report actual GDP (in purchasing power parity (PPP) valuation terms) in levels and year-on-year percentage change (green lines) and what GDP could have been in the absence of any terrorist event between 2008 and 2013 (light blue lines).

EXHIBIT 13

Iraq GDP Based on PPP Valuation

Pakistan GDP Based on PPP Valuation

Source: Haver Analytics and Moody’s calculations

0

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Between 2008 and 2013 annual GDP growth in Pakistan would on average have been 0.8pps stronger and in Iraq 1.3pps stronger without any terrorist events. In other words, by 2013 the level of GDP was lower by around 5.1% for Pakistan and 8.2% for Iraq than it would have been in the absence of terrorism.

The effects of one standard deviation increase in the terrorism index on investment growth are shown in Exhibit 14. The effects are negative and statistically significant in the year of the event. On the year of the terrorist event, investment growth falls by more than GDP growth, by about 0.84pps.

Terrorist events of the frequency and type seen in 2013 for the ten most affected countries would reduce investment growth by between 1.31pps and 2.07pps in the year of the event.

EXHIBIT 14

Dynamic Multiplier Function and Error Bands Investment growth response to one standard deviation increase in the terrorism index

Notes: (a) we randomly generate a draw of coefficients using the estimated coefficients and their variance–covariance matrix and re-calculate the dynamic responses. We repeat this procedure 500 times .We generate 5th (10th) and 95th (90th) percentiles of this distribution which we use as a the 95th (90th) confidence interval for the impulse-responses. (b) The horizontal axis are years. Source: GTD and Moody’s calculations

Exhibit 15 shows the response of government consumption to terrorist events. The effects of terrorism on government consumption is not statistically significant.

EXHIBIT 15

Dynamic Multiplier Function and Error Bands Government consumption response to one standard deviation increase in the terrorism index

Notes: (a) we randomly generate a draw of coefficients using the estimated coefficients and their variance–covariance matrix and re-calculate the dynamic responses. We repeat this procedure 500 times. We generate 5th (10th) and 95th (90th) percentiles of this distribution which we use as a the 95th (90th) confidence interval for the impulse-responses. (b) The horizontal axis are years. Source: GTD and Moody’s calculations

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Exhibit 16 shows the effects of one standard deviation increase in the terrorism index to the government’s cost of borrowing (as measured by treasury rates).12 The effects are statistically significant. One standard deviation increase in the terrorism index raises the cost of borrowing by about 26bps in the year of the event and by a further 34bp the following year. Even after five years, rates are 1.5bp higher.

EXHIBIT 16

Dynamic Multiplier Function and Error Bands Interest rate response to one standard deviation increase in the terrorism index

Notes: (a) we randomly generate a draw of coefficients using the estimated coefficients and their variance–covariance matrix and re-calculate the dynamic responses. We repeat this procedure 500 times. We generate 5th (10th) and 95th (90th) percentiles of this distribution which we use as a the 95th (90th) confidence interval for the impulse-responses. (b) The horizontal axis are years. Source: GTD and Moody’s calculations

If instead of a one standard deviation increase in the terrorism index we evaluate the effects on rates at the value that the terrorism index takes for the most affected countries, the cost of borrowing rises between 41bp to 65bp within one year and by 51bp to 81bp after one year.

5 Robustness Checks

The results presented above are robust to different definitions of the terrorism index. Exhibit 17 plots the dynamic responses of each of the endogenous macroeconomic variables to one standard deviation increase in alternative definitions of the terrorism index. The green line depicts the responses we have seen earlier when the terrorism index was defined in relative terms to the population of the country and for the given set of weights explained in the empirical regularities of terrorism events section. The light blue line uses those same weights but instead of deflating the index by the country’s population it does it by its GDP. The dark blue line gives equal weights to the number of terrorist events, number of fatalities, number of wounded people and property damage as a result of the event, and deflates the index by population. The orange line uses the same definition of the terrorism index as our benchmark or preferred model (in green) but recodes observations above the 90th percentile of the index to that 90th percentile value to test if our results were driven by extreme events.

12 If 3-month treasury rates were not available for a given country we use the closest possible interest rate when available.

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EXHIBIT 17

Alternative Definitions of Terrorism Index

Source: Moody’s calculations

We have also run the model separately for advanced economies (36 countries in our sample) and emerging markets (120 countries in our sample) to check if the results were driven by a particular set of countries. The effects of terrorism on GDP and investment growth are not qualitatively different between those for advanced economies and for emerging markets. However, it seems the cost of borrowing does not change significantly following a terrorism event in the advanced economies, but governments in the advanced economies do cut some expenses on the year following the attack.

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EXHIBIT 18

Advanced Economies vs Emerging Markets


We also run an alternative model where government consumption and investment were not expressed in growth terms but as shares of GDP. The top panels in Exhibit 19 shows that the responses of GDP growth and treasury rates are the same whether we include government consumption and investment in growth terms or as shares of GDP. The bottom panel shows the responses of government consumption and investment shares to one-standard deviation increase in the terrorism index. Again, the response of government consumption is not statistically significant but the response of investment is and, in line with the results from our benchmark model, the investment share falls after a terrorist attack.

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EXHIBIT 19

Responses to one-standard deviation increase in the terrorism index


One could also potentially think of terrorism as an endogenous autoregressive variable in our VAR specification as any of the macroeconomic variables, so that the terrorism index is also explained by its own lags and the lags of the macroeconomic variables. Exhibit 20 plots the impulse-response functions of the macroeconomic variables to one-standard deviation shock to the terrorism index and compares them with the responses when we treated the terrorism index as an exogenous, non-autoregressive, variable.

Qualitatively the effects of terrorism on the economy are the same whether we treat terrorism as endogenous or exogenous, except maybe for more lagged effects if we believe terrorism is autoregressive. A Granger causality test,13 though, reveals that the macroeconomic variables do not Granger cause the terrorism index, so our preferred model remains the one with terrorism index treated as exogenous.

13 Panel VAR Granger causality Wald test.

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EXHIBIT 20

Terrorism Index Treated as Endogenous


6 Conclusion

We define terrorist incidents as “the threatened or actual use of illegal force and violence by a non-state actor to attain a political, economic, religious, or social goals through fear, coercion, or intimidation.” That is, for an incident to be classified as a terrorist attack (1) it has to be intentional, (2) it must entail some level of violence or threat of violence, and (3) the perpetrators must be sub-national actors. We find that:

» Terrorist attacks have long-lasting and significantly negative effects on economic growth.

» Terrorist events of the type and frequency seen in the top ten most terrorism-inflicted countries just in 2013 immediately weaken GDP growth between 0.51pps and 0.80pps; they further deteriorate growth between 0.37pps and 0.59pps after one year, and by 0.05pps and 0.07pps after three years.

» Terrorist events reduce investment growth (and hence impair potential growth) on the year of the terrorism event, by between 1.31pps and 2.07pps, for the top ten most affected countries.

» Terrorist events lift the government cost of borrowing. In the most terrorist-inflicted countries, the cost of borrowing jumps between 41 and 65bps within one year and by 51-81bps after one year of the event.

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Appendix 1

EXHIBIT A1.1

Number of Terrorist Events 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 All Years

All Countries 3290 3009 3009 3141 871 1322 1707 1741 1238 1201 1123 1963 2707 3145 4706 4710 4806 5044 8406 11823 68962 Iraq 18 17 12 21 6 12 10 3 6 87 319 618 837 1042 1104 1137 1179 1307 1438 2852 12025 Pakistan 154 666 180 206 37 39 49 53 45 29 67 78 164 259 565 666 711 1011 1651 2212 8842 India 107 179 211 193 62 112 179 234 183 196 108 145 166 154 517 673 662 642 611 690 6024 Afghanistan 9 6 4 1 1 9 14 13 38 100 88 155 282 340 416 502 540 422 1467 1443 5850 Colombia 201 124 409 598 94 116 137 207 150 98 37 42 43 30 133 140 136 94 115 145 3049 Philippines 72 63 61 57 18 31 132 50 48 107 32 25 58 65 275 230 205 149 247 652 2577 Thailand 24 12 19 20 4 4 4 15 12 6 44 155 202 292 200 298 253 181 286 477 2508 Algeria 227 185 130 344 151 106 138 113 132 75 67 104 152 124 107 108 100 15 41 21 2440

Russia 47 37 66 77 26 54 138 135 89 76 43 63 56 51 170 152 251 188 151 141 2011 Somalia 57 13 16 19 1 4 9 5 3 1 2 6 10 156 172 122 129 184 318 331 1558 Nigeria 8 1 11 20 2 18 7 5 6 9 6 9 37 62 76 42 63 175 616 341 1514 Sri Lanka 35 126 176 64 35 46 68 36 3 9 33 133 217 132 101 38 3 0 4 14 1273

Turkey 300 133 54 44 24 109 35 19 5 19 27 41 40 29 32 13 20 51 189 42 1226 Yemen 19 8 8 19 7 17 10 7 7 7 0 7 5 7 22 23 112 118 312 424 1139 UK 256 22 37 78 63 76 61 94 21 23 5 25 6 21 39 22 57 47 49 134 1136

France 97 71 270 130 12 46 28 21 32 34 11 33 34 16 13 9 3 8 65 8 941 Nepal 3 0 5 9 0 8 23 33 58 15 62 70 83 90 121 39 38 46 46 102 851

Israel 54 12 17 13 14 8 15 79 75 37 18 43 78 53 130 36 14 51 66 37 850 Spain 55 44 63 86 17 47 112 79 41 21 31 24 23 11 37 21 3 0 1 5 721 Egypt 143 102 51 22 3 1 0 0 0 0 2 2 5 0 1 2 2 18 49 315 718 Bangladesh 68 74 161 17 2 11 22 15 5 4 11 22 24 9 19 28 22 14 18 134 680

Indonesia 5 24 65 28 3 61 101 105 43 18 17 15 10 2 13 19 4 21 38 32 624 Greece 42 8 20 21 28 35 28 14 11 12 4 6 23 15 53 115 49 11 22 53 570 Lebanon 67 38 41 46 23 42 12 5 8 5 2 17 7 18 58 14 5 10 15 121 554 United States 56 62 36 39 26 53 33 39 34 32 9 23 6 9 19 11 17 9 13 15 541

Syria 0 0 2 1 1 0 0 0 0 0 1 0 1 0 2 0 0 49 176 272 505

Burundi 16 83 35 79 13 13 17 37 18 18 1 5 5 3 7 0 29 8 4 2 393 Libya 0 1 4 0 0 0 0 0 0 0 0 0 0 1 1 0 0 2 58 293 360 Germany 79 147 50 12 5 12 7 4 2 2 1 1 2 2 3 4 1 8 4 0 346 South Africa 174 32 47 17 6 17 21 0 8 0 0 0 0 2 1 0 0 0 4 13 342 Mexico 42 29 75 95 2 1 4 7 0 1 1 1 7 10 8 1 5 2 16 8 315 Peru 91 44 42 58 5 5 3 3 2 1 3 2 1 4 1 4 0 0 6 10 285

Kenya 5 4 3 17 2 0 4 7 2 2 0 2 2 12 10 1 12 41 79 79 284

Uganda 13 7 29 24 18 19 20 24 24 30 10 11 0 1 8 2 6 0 0 0 246

Guatemala 84 82 26 26 0 1 1 2 0 3 2 0 4 0 0 0 0 0 1 6 238

Sudan 5 4 10 3 0 5 7 9 13 0 5 6 24 23 32 26 28 38 238 Dem. Rep. of Congo

7 6 13 6 4 3 3 4 7 6 5 4 5 11 20 54 18 16 21 19 232

Iran 43 8 3 2 5 8 15 5 0 2 0 5 14 9 8 15 14 13 6 11 186 Myanmar 8 12 13 35 0 4 0 3 4 9 2 8 2 3 20 14 11 2 17 17 184 Cambodia 39 56 33 18 7 8 4 7 3 1 0 0 0 0 2 0 0 0 0 2 180 China 13 7 62 18 2 2 4 13 2 3 2 1 1 0 20 7 1 4 4 12 178

Georgia 18 17 3 6 12 7 7 4 2 6 3 6 3 0 33 25 7 3 3 2 167 Tajikistan 31 34 22 40 11 4 6 6 0 0 0 1 0 1 0 1 1 0 5 0 163 Angola 9 10 4 7 20 34 22 40 6 0 0 0 0 0 0 1 2 0 0 0 155 Italy 18 1 8 8 6 7 8 11 7 15 3 6 4 0 2 4 10 3 10 7 138 Haiti 27 34 27 16 1 5 9 5 2 5 2 1 1 2 0 0 0 0 0 0 137 Rwanda 33 17 15 33 9 0 1 3 0 0 0 1 0 0 1 1 8 2 6 4 134 Bosnia-Herzegovina

9 6 33 42 9 5 6 2 3 1 0 1 1 2 4 1 1 1 0 1 128

Bahrain 1 8 16 11 0 0 0 0 0 1 0 0 1 0 1 0 2 1 26 52 120 Brazil 19 23 18 39 2 1 2 3 1 1 0 0 3 0 0 0 0 0 1 3 116 Mali 13 1 0 3 0 0 0 0 0 0 0 1 0 5 9 6 1 4 14 58 115

Macedonia 1 1 0 2 5 3 3 67 8 5 0 0 3 1 9 1 0 0 1 0 110 Venezuela 12 11 12 41 1 5 0 5 5 8 0 0 0 2 1 3 0 0 1 0 107 Ireland 10 3 1 4 3 3 0 2 0 1 0 0 1 1 5 0 4 4 29 26 97 Chile 15 17 8 6 1 3 2 3 1 2 0 0 1 8 4 8 5 6 2 4 96

Sierra Leone 22 14 5 3 7 9 24 0 0 0 0 0 0 0 0 0 0 0 0 0 84

Senegal 2 8 0 26 1 1 4 8 3 0 0 1 0 1 2 3 1 4 12 3 80 Argentina 14 16 19 11 0 1 0 2 0 1 0 3 0 0 0 1 5 1 2 2 78 Ethiopia 4 5 7 6 0 2 1 0 3 1 2 5 11 5 8 2 2 3 3 5 75 Honduras 6 12 19 22 0 0 0 0 1 0 1 0 0 0 0 8 0 1 0 2 72

Nicaragua 19 18 26 7 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 72 Japan 9 20 8 5 1 2 13 3 2 2 0 0 0 0 3 1 0 0 0 0 69 Albania 2 0 6 41 7 3 2 1 0 1 0 0 0 0 0 1 0 0 0 1 65


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EXHIBIT A1.1


Saudi Arabia 2 3 1 0 0 0 5 3 2 8 17 1 3 1 0 3 0 2 6 6 63 Ivory Coast 2 7 2 1 0 0 2 1 1 0 1 1 3 1 0 0 7 3 17 4 53 Central 1 0 1 0 0 0 1 1 0 0 0 0 1 1 2 5 13 3 4 18 51 Ukraine 10 2 3 2 0 6 1 0 3 1 0 0 0 1 1 2 4 3 8 4 51 Ecuador 6 6 5 4 3 2 4 5 2 4 2 0 0 0 2 1 0 1 1 2 50 Niger 8 2 1 3 0 2 0 0 0 0 0 0 0 6 9 4 3 2 1 4 45 Canada 4 5 1 2 4 2 5 0 0 0 2 0 3 0 5 4 2 0 2 3 44 Chad 1 3 1 1 1 1 0 0 4 0 0 0 11 7 8 3 1 0 0 0 42 Tunisia 1 1 0 0 0 0 1 1 2 0 0 0 0 1 2 0 0 3 1 29 42 Paraguay 2 12 0 6 0 1 2 0 0 0 0 0 0 0 0 0 2 4 2 10 41 Australia 9 5 5 4 6 0 1 2 0 0 0 0 2 0 3 2 1 0 0 0 40 El Salvador 21 9 7 2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 39 Hungary 2 0 13 17 2 0 0 1 0 0 0 0 0 0 2 1 0 0 0 0 38 Austria 5 12 4 1 1 0 0 0 0 0 0 0 1 1 7 3 0 1 0 1 37 Cyprus 6 3 10 0 1 1 2 1 0 0 1 0 0 1 0 0 0 0 1 9 36 Bulgaria 2 2 12 3 2 1 2 1 1 0 0 0 0 0 2 0 0 2 2 3 35 Netherlands 3 7 6 4 0 1 1 1 3 3 1 0 0 0 1 1 1 2 0 0 35 Jordan 6 3 2 3 1 2 2 2 2 0 0 4 1 0 0 0 2 0 2 2 34 Macau 0 1 10 16 6 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 33 Mozambique 3 3 0 0 0 1 3 0 0 0 0 0 0 1 0 0 0 0 1 17 29 Namibia 1 0 0 0 0 1 21 6 0 0 0 0 0 0 0 0 0 0 0 0 29 Zimbabwe 1 2 0 0 0 0 1 3 8 1 0 0 1 1 5 0 1 1 0 4 29 Azerbaijan 11 2 2 1 0 0 1 4 1 0 0 0 0 1 2 2 0 0 0 0 27 Belgium 5 1 0 2 3 2 0 0 3 8 0 0 0 1 0 1 0 0 1 0 27 Croatia 4 1 3 8 0 0 2 3 0 0 0 1 0 0 2 1 0 0 0 2 27 Papua New Guinea

3 10 10 2 0 0 0 0 1 0 0 0 0 1 0 0 0 0 0 0 27

Switzerland 0 5 4 1 1 2 4 2 0 1 0 0 0 2 0 0 0 3 0 2 27 Cuba 9 0 1 14 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 25 Bolivia 8 2 1 4 0 0 0 0 0 1 0 1 0 3 0 3 0 0 1 0 24 Kyrgyzstan 0 0 5 0 0 3 4 1 2 1 0 2 3 1 0 0 1 1 0 0 24 Dominican Republic

7 0 3 12 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 23

Malaysia 0 0 0 0 0 0 3 1 0 0 0 0 0 0 2 1 0 0 2 13 22 Sweden 1 1 1 4 0 2 1 0 0 0 0 3 1 0 1 1 3 1 2 0 22 Taiwan 5 4 5 1 0 0 0 2 0 0 2 0 0 0 1 0 0 0 0 2 22 Tanzania 1 1 0 1 1 0 2 4 2 0 1 0 0 0 1 0 1 0 0 7 22 Cameroon 6 2 0 0 1 0 0 0 0 0 0 0 0 1 1 1 3 3 0 3 21 Zambia 0 1 4 0 0 7 8 0 1 0 0 0 0 0 0 0 0 0 0 0 21 Congo-Brazzaville

8 0 1 2 0 3 0 1 3 1 0 0 0 0 0 0 0 0 0 1 20

Panama 3 6 2 6 0 1 1 0 0 0 0 0 0 0 0 1 0 0 0 0 20 Poland 4 4 6 3 2 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 20 Kazakhstan 1 0 0 2 0 0 1 1 0 0 0 0 0 0 1 0 0 5 4 4 19 Czech Republic 1 3 3 4 0 0 0 1 0 1 0 0 0 0 2 1 0 1 0 1 18 Guinea 2 0 1 0 0 0 12 0 0 0 0 1 0 0 0 0 0 0 0 2 18 Uzbekistan 0 1 0 0 0 8 0 0 0 0 5 2 0 0 0 2 0 0 0 0 18 Armenia 4 1 1 0 2 2 2 2 0 0 0 0 0 1 0 1 0 0 0 1 17 Liberia 3 0 1 0 0 2 1 3 3 2 0 0 0 0 1 0 0 0 1 0 17 Madagascar 0 3 2 1 0 0 0 0 5 0 0 0 1 0 0 1 0 1 1 2 17 Morocco 2 1 2 0 0 0 0 0 0 5 0 0 0 6 0 0 0 1 0 0 17 Slovak Republic

1 3 5 4 2 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 17

Togo 13 1 1 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 16 Laos 1 0 1 0 0 0 8 1 0 2 0 0 0 0 0 0 0 0 1 0 14 Latvia 2 1 0 2 4 1 3 0 0 0 0 0 0 1 0 0 0 0 0 0 14 Mauritania 0 0 1 0 0 0 0 0 0 0 1 1 0 0 2 5 1 3 0 0 14 Trinidad and Tobago

1 4 3 0 0 0 0 0 0 0 0 2 0 0 0 0 1 0 0 3 14

Belarus 1 0 0 2 0 1 0 1 0 0 0 0 1 0 1 0 2 1 3 0 13 Hong Kong 4 1 3 0 0 0 2 1 0 0 0 1 0 0 1 0 0 0 0 0 13 Jamaica 2 1 1 8 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 13 Kuwait 2 0 0 2 0 0 0 2 1 3 0 2 0 0 0 0 0 1 0 0 13 New Zealand 1 0 2 1 0 0 0 1 0 3 0 0 0 0 5 0 0 0 0 0 13 Estonia 6 2 1 0 0 1 0 1 0 0 0 0 0 0 0 0 0 1 0 0 12 Guyana 0 0 0 1 2 0 0 1 1 1 0 0 2 0 3 0 0 0 0 0 11 Denmark 0 6 0 0 0 1 0 0 0 0 0 0 0 0 1 0 1 0 0 1 10 Eritrea 0 2 1 0 0 0 0 0 0 0 0 0 0 2 2 1 1 1 0 0 10 Norway 2 0 1 0 0 0 0 1 0 1 0 0 1 0 0 0 1 3 0 0 10 South Korea 2 4 1 0 0 0 0 1 0 1 0 0 1 0 0 0 0 0 0 0 10 Guinea-Bissau 0 0 0 0 0 0 0 1 0 1 0 4 1 0 0 1 0 0 1 0 9 Swaziland 0 1 0 1 1 1 0 0 0 1 0 0 1 0 3 0 0 0 0 0 9


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EXHIBIT A1.1


Uruguay 4 1 1 2 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 9 Vietnam 1 1 1 2 0 0 0 4 0 0 0 0 0 0 0 0 0 0 0 0 9 Fiji 0 0 0 0 0 3 4 1 0 0 0 0 0 0 0 0 0 0 0 0 8 Ghana 4 1 2 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 8 Moldova 1 0 2 1 0 0 1 0 0 0 0 0 1 0 0 1 0 0 1 0 8 Benin 3 1 2 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 7 Djibouti 2 2 0 1 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 6 Lithuania 0 2 3 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 6 Maldives 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 0 0 0 0 3 6 Portugal 1 3 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 2 0 0 6 Slovenia 1 0 3 1 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 6 Timor 0 2 0 1 0 2 1 0 0 0 0 0 6 Bhutan 0 0 0 0 0 0 0 0 0 0 0 0 1 0 3 0 0 1 0 0 5 Lesotho 3 0 0 0 0 0 0 0 0 0 0 0 1 0 0 1 0 0 0 0 5 UAE 0 2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 2 0 0 1 5 Comoros 1 0 0 3 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 4 Costa Rica 1 0 1 1 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 4 Finland 1 1 0 0 0 0 0 0 0 0 0 0 0 1 1 0 0 0 0 0 4 Qatar 0 0 1 0 0 0 1 1 0 0 0 1 0 0 0 0 0 0 0 0 4 Solomon Islands

0 0 0 0 0 1 1 0 1 1 0 0 0 0 0 0 0 0 0 0 4

Bahamas 0 0 0 2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 3 Gabon 1 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 3 Gambia 1 1 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 3 Luxembourg 3 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 3 Malta 0 2 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 3 Suriname 1 0 0 2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 3 Belize 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 1 2 Romania 0 1 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 2 St. Kitts and Nevis

0 2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 2

Vanuatu 0 0 2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 2 Antigua and Barbuda

0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1

Botswana 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 Burkina Faso 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 Dominica 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 Equatorial Guinea

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 1

Iceland 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 1 Malawi 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 1 North Korea 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 Singapore 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 St. Lucia 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 1 Turkmenistan 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 1 Barbados 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Brunei 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

Grenada 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Seychelles 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0


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Appendix 2

For completeness, this appendix collects the impulse-response functions associated with our benchmark model.

EXHIBIT A2.1

Impulse-Response Function to One-Standard Deviation Shock in Government Expenditure Growth


-5

0

5

10

15

20

0 1 2 3 4 5 6 7 8 9 10

% change in G : % change in G95% confidence band90% confidence band

Percentage points

-30

-20

-10

0

10

20

30

0 1 2 3 4 5 6 7 8 9 10

% change in G : Treasury Rates95% confidence band90% confidence band

Basis points

-2

-1

0

1

2

3

4

5

0 1 2 3 4 5 6 7 8 9 10

% change in G : % change in I95% confidence band90% confidence band

Percentage points

-0.1

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0 1 2 3 4 5 6 7 8 9 10

% change in G : % change in GDP95% confidence band90% confidence band

Percentage points


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EXHIBIT A2.2

Impulse-Response Function to One-Standard Deviation Shock in Treasure Rates


-1

-0.8

-0.6

-0.4

-0.2

0

0 1 2 3 4 5 6 7 8 9 10

Treasury Rates : % change in G95% confidence band90% confidence band

Percentage points

0

100

200

300

400

500

0 1 2 3 4 5 6 7 8 9 10

Treasury Rates : Treasury Rates95% confidence band90% confidence band

Basis points

-4

-3

-2

-1

0

1

2

0 1 2 3 4 5 6 7 8 9 10

Treasury Rates : % change in I95% confidence band90% confidence band

Percentage points

-0.6

-0.5

-0.4

-0.3

-0.2

-0.1

0

0 1 2 3 4 5 6 7 8 9 10

Treasury Rates : % change in GDP95% confidence band90% confidence band

Percentage points


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EXHIBIT A2.3

Impulse-Response Function to One-Standard Deviation Shock in Investment Growth


-0.5

0

0.5

1

1.5

2

0 1 2 3 4 5 6 7 8 9 10

% change in I :% change in G95% confidence band90% confidence band

Percentage points

-60

-40

-20

0

20

40

60

0 1 2 3 4 5 6 7 8 9 10

% change in I : Treasury Rates95% confidence band90% confidence band

Basis points

-5

0

5

10

15

20

25

0 1 2 3 4 5 6 7 8 9 10

% change in I : % change in I95% confidence band90% confidence band

Percentage points

-0.5

0

0.5

1

1.5

2

0 1 2 3 4 5 6 7 8 9 10

% change in I : % change in GDP95% confidence band90% confidence band

Percentage points


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EXHIBIT A2.4

Impulse-Response Function to One-Standard Deviation Shock in GDP Growth


-0.5

0

0.5

1

1.5

2

2.5

0 1 2 3 4 5 6 7 8 9 10

% change in GDP : % change in G95% confidence band90% confidence band

Percentage points

0

5

10

15

20

25

30

35

0 1 2 3 4 5 6 7 8 9 10

% change in GDP : Treasury Rates95% confidence band90% confidence band

Basis points

-0.5

0

0.5

1

1.5

2

2.5

3

3.5

0 1 2 3 4 5 6 7 8 9 10

% change in GDP : % change in I95% confidence band90% confidence band

Percentage points

-0.5

0

0.5

1

1.5

2

2.5

3

3.5

4

0 1 2 3 4 5 6 7 8 9 10

%change in GDP : % change in GDP95% confidence band90% confidence band

Percentage points


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Moody’s Related Research

» Moody’s Default Definition and its Application to Sovereign Debt, October 2014 (1000468)

» Guide to Moody’s Default Research: July 2013 Update, July 2013 (156875)

» Sovereign Bond Ratings, Rating Methodology, September 2013 (157547)

» Sovereign Default and Recovery Rates, 1983-2013, April 2014 (166650)

» Ratings Symbols and Definitions, August 2014 (79004)

» How Geopolitical Risks Affect Creditworthiness, December 2014 (1000834)

To access any of these reports, click on the entry above. Note that these references are current as of the date of publication of this report and that more recent reports may be available. All research may not be available to all clients.

Other References

» Arellano, M., and O Bover (1995): “Another look at the instrumental variable estimation of error component models”. Journal of Econometrics, 68, 29–51.

» Blomberg SB, GD Hess and A Orphanides (2004): “The Macroeconomic Consequences of Terrorism”, CESIFP Working Paper No. 1151.

» Gaibulloev, K and T Sandler (2008): “The Impact of Terrorism and Conflicts on Growth in Asia, 1970-2004”, ADB Institute Discussion Paper No. 113.

» Gaibulloev, K and T Sandler (2010): “The Adverse Effect of Transnational and Domestic Terrorism on Growth in Africa”, Published Articles & Papers. Paper 152.

» Gleditsch, NP, P Wallensteen, M Eriksson, M Sollenberg and H Strand (2002): “Armed Conflict 1946-2001: A New Dataset”, Journal of Peace Research 39(5), pp 615-37.

» “Global Terrorism Index 2014 – Measuring and Understanding the Impact of Terrorism”, Institute for Economics & Peace.

» Hamilton, J (1994): “Time Series Analysis” (Volume 10). Cambridge: Cambridge University Press.

» Love, I and L Zicchino (2006): “Financial Development and Dynamic Investment Behaviour: evidence from Panel VAR”,The Quarterly Review of Economics and Finance, 46, pp 190-210.

» National Consortium for the Study of Terrorism and Responses to Terrorism (START) (2014). Global Terrorism Database (GTD) December 2013.

» Themnér, L and P Wallensteen (2014): “Armed Conflict, 1946-2013”, Journal of Peace Research 51(4).


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Report Number: 184486

Author Merxe Tudela

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