New wine in old wineskins? Growth, terrorism and the resource curse in sub-Saharan Africa

New wine in old wineskins? Growth, terrorism and the resource curse insub-Saharan Africa

S. Brock Blomberg a,⁎, Nzinga H. Broussard a, Gregory D. Hess a,b

a Robert Day School of Economics and Finance, Claremont McKenna College, 500 E. Ninth St., Claremont, CA 91711, United Statesb CESifo, Germany

a r t i c l e i n f o a b s t r a c t

Article history:Received 1 November 2010Received in revised form 10 June 2011Accepted 12 June 2011Available online 28 June 2011

Since 1995, growth in sub-Saharan Africa has averaged more than 5% per year reversing a two-decade decline of real income per capita. In this paper, we explore the extent to which thenascent growth is sustainable or not due to higher incidences of terrorism and commodity pricedeclines. Our analysis is based on a rich unbalanced panel data set with annual observations on46 countries from 1968 to 2004. We explore these data with cross-sectional and panel growthregression analysis and quantile regressions. We estimate the economic and statistical effect ofterrorism on growth in sub-Saharan Africa, controlling for a variety of other factors. We theninvestigate the extent to which there appears to be a structural break in the estimatedrelationships. We find that the terrorist-oriented fragility of sub-Sahara has increased in themost recent period. We find that most of the fragility can be explained by the growth incountries that are primary fuel exporters. Indeed, our evidence points to the fact that resource-rich countries have not done an adequate job of investing in counter-terrorist policies.

© 2011 Elsevier B.V. All rights reserved.

JEL classification:E6D74O11

Keywords:GrowthConflictTerrorism

1. Introduction

The 1990s brought about a number of changes for many African countries: since 1995, growth in sub-Saharan Africa averagedmore than 5% per year, democratization reemerged with citizens enjoying more political and civil rights, and countries becamemore open, with many countries playing a crucial role in the global economy as they exported important commodities (WorldBank, 2007). While many economists believe these are all crucial requirements for economic development, it may also play a rolein howwell developing countries can protect themselves or recover from adverse shocks such as civil conflict, terrorist attacks andcommodity price declines. Whether these advancements in development are sustainable or not depends partly on policycharacteristics which can either hinder or promote development.

In an earlier paper, Blomberg et al. (2004a, 2004b, 2004c) investigate the macroeconomic consequences of terrorism amongdifferent sets of countries. This paper expands on their earlier work and focuses on sub-Sahara Africa to measure the economiclosses associated with terrorism. Developing countries constitute a special case because they may be less likely to absorb adverseshocks and may be ill equipped to prevent or combat new forms of shocks such as terrorist attacks. These new challenges mayrequire different attention and resources than the civil conflict and natural disasters that many African nations have historicallyexperienced. As economies grow, governments must decide how to allocate additional resources. One concern is that manyAfrican governments' security and counterterrorism efforts are not keeping pace with the spread of more sophisticated terroristattacks and the increased presence of terrorist groups in many African countries. There may be a number of reasons why this may

European Journal of Political Economy 27 (2011) S50–S63

⁎ Corresponding author. Tel: +1 909 607 2654; fax: +1 909 621 8249.E-mail address: [email protected] (S.B. Blomberg).

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be the case, ranging from too few resources to devote to counterterrorism measures to a perception that terrorism is not a majorconcern that African governments face.1 While it is true that, at least on paper, most African countries are committed to theprevention of terrorism (e.g. the African Union established an African Centre for the Study and Research on Terrorism in Algiers,and Algeria to increase the capacity of the Union members to prevent and combat terrorism), it is not clear whether these effortsare sufficient.

Institutional shortcomings have adversely affected Africa's economic development, and counter terrorismmeasures should notbe overlooked as important government responsibilities. Gaibulloev and Sandler (2009) investigate the effects of terrorism in Asia,highlighting a key observation that development matters for how well countries can absorb the shocks associated with terrorism.Blomberg et al. (2004a, 2004b, 2004c) show that while terrorist attacks are less common in non-democratic countries, the impactof terrorist attacks on growth are much larger than in democratic countries. Collier et al. (2006) suggest that governments canmitigate adverse shocks by adopting policies prior to the shocks. Much of the growth occurring in Africa can be attributed to asingle primary commodity: oil in Nigeria and Angola, diamonds in Zaire and Sierra Leon, coffee beans in Ethiopia. Collier andHoeffler (1998) suggests that a country's dependence upon primary commodity exports increases the risk of civil wars, similarreasoning would suggest that this dependence on primary commodity exports increases the risk of terrorism.

Conflict in general has both economic and non-economic causes (Keynes, 1919; Pigou, 1940; Meade, 1940; Robbins, 1942;Crenshaw, 1981). This paper investigates the economic consequences of terrorism among the African economies during a time periodwhen growth-enhancing opportunities appear to be readily available to these countries as they have seen increases in trade openness,democracy, and improved institutions. Although the economic consequences of terrorismhas been explored, the possibility that theseconsequences are dependentupon the level of democracy, the level of tradeopenness, orwhether the country is anoil exporter hasnotbeen explored. Each of these factors can either enhance or undermine the economic consequences of terrorism. There are severalreasonswhy terrorist attacksmight affect developing countries differently than developed countries: developing countries haveweakpolitical and economic institutions that can easily be shaken by terrorist attacks; terrorism provides uncertainty about investmentopportunities,make itmore costly todobusinesswithin the country, and affect industries like tourism,whichare important sectors forgrowth; and developing countries tend to depend on a small number of sectors for economic growth, making these sectors moresusceptible to attacks which in turn increases the economic impact of attacks.

Recently, economic researchers have examined theoretical aspects of terrorism and as data on terrorist attacks are becomingavailablemore empirical research is being published (Lapan and Sandler, 1988, 1993; Garfinkel, 2004; Enders et al., 1990; Enders andSandler, 1993;O'Brien, 1996; Enders et al., 1992; Blomberg et al., 2004a, b). Identifying the economic impacts of terrorism and conflictin developing countries is important for understandingwhether growth is sustainable. Developing countries are less able to protect itscitizens and assets from terrorist attacks and this can have severe consequences for the development process. In Blomberg et al.(2009), the authors show that terrorist groups in Africa have become a larger presence since 1995: terrorist groups in Nigeria, theUNITA in Angola and the Revolutionary United Front in Sierra Leon all appeared among the top ten most active terrorist groups.

In this paper we contribute to the literature by investigating the economic consequences that terrorism has had on the Africaneconomies and if the impact has worsened post-1990. We investigate whether the recent trends in globalization and democracyhave made the African economies more or less resilient to terrorist attacks and whether regimes which are more susceptible toresource-curse driven corruption (oil exporting countries) are even less resilient.

Our analysis is based on a panel data set with annual observations on 54 countries from 1968 to 2003. The dataset bringstogether information from the Penn World Table, the ITERATE dataset for terrorist events, and data on external and internalconflict. We explore these data with cross-sectional and panel growth regression analysis. We estimate the economic andstatistical effect of terrorism on growth, controlling for a variety of other factors. We then investigate the extent to which thereappears to be a structural break in the estimated relationships. We find that the fragility of Africa due to terrorism has increased inthe most recent period. Results show that the African economies are more susceptible to terrorist shocks post 1994 while itssusceptibility to internal and external conflicts has remained unchanged post 1994. We find that most of the fragility can beexplained by the growth in countries that rely most heavily on oil. We interpret this result to suggest that countries that haverelied on fuel-based growth have not done an adequate job of counter-terrorist prevention. These results are consistent with theobservation that oil exporting countries tend to have less diversified economies and have poorer institutional quality, which leadsto a larger impact of terrorist attacks on growth.

The outline of the paper is as follows. Section 2 describes the data and presents some basic empirical regularities of the data.Section 3 provides the econometric estimates and results, and presents a case studywith implications for Nigeria. Section 4 concludes.

2. The data and empirical regularities

In this section,wedescribe our data sources and examine somebasic empirical regularities of the data around theworld and inAfrica.Economic data comes from the Penn World Table, based on the work by Summers and Heston (1991). Our macroeconomic variablesconsist of real GDPper capita at PPP prices (Y), investment as a percent of GDP (I/Y), and economic openness ((exports+imports)/GDP).

Conflict data is obtained from three different sources. We include data on terrorist attacks and various forms of conflict. Weinclude: measures of internal conflict such as genocide, ethnic war, revolutions and irregular regime changes; and measures ofexternal conflict that allow for both home and away wars. Addition of these variables allow us to examine the relationship

1 Hereafter, we refer to sub-Saharan Africa as Africa.

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between terrorism and the economy while controlling for different types of conflict which may impact the economy and may becorrelated with the incidences of terrorist attacks.

To measure terrorist activities, we employ the latest update of the “International Terrorism: Attributes of Terrorist Events”(ITERATE) data set from Mickolus et al. (2002). The internal conflict data are obtained from Gurr et al. (2003) and the externalconflict data are obtained from the most recent update to Brecher et al. (1988). In all, the resulting data set covers 177 countriesover 35 years providing an unbalanced panel data set of over 4000 observations.

The ITERATE data set defines an international terrorist event as:

“the 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 theattitudes and behavior of a target group wider than the immediate victims and when, through the nationality or foreignties of its perpetrators, its location, the nature of its institutional or human victims, or the mechanics of its resolution, itsramifications transcend national boundaries.” Mickolus et al., page 2.

ITERATE provides a rich micro-level data set of over 14,000 incidents of terrorism across 179 countries from 1968 to 2003. Theraw data is grouped into four broad categories that denote incident characteristics, terrorist characteristics, victim characteristicsand life and property losses. Since we cannot control for the significance of individual events or identify some of the underlyinginformation that may be missing, we define a dummy variable TERRORwhich takes the value 1 if a terrorist event is recorded for agiven country year and 0 otherwise. This measure also has the advantage of defining the incidence of terrorism in a mannercomparable to the incidence of other forms of conflict in the data set.

Ourmeasure of external conflict comes from Brecher et al. (1988). External conflict (EXT) is defined by Brecher et al. (1988) as:

“a specific act, event or situational change which leads decision-makers to perceive a threat to basic values, time pressurefor response and heightened probability of involvement in military hostilities. A trigger may be initiated by: an adversarystate; a non-state actor; or a group of states (military alliance). It may be an environmental change; or it may be internallygenerated.” (page 3)

We define a dummy variable EXTwhich takes the value 1 if a given country was engaged in an interstate conflict in a given yearand 0 otherwise.

Our measure of internal conflict is obtained from Gurr et al. (2003). Gurr et al. (2003) define four broad categories of internalconflict. First, ethnic conflict (ETHNIC) is defined as conflict between the government and national ethnic, religious, or othercommunalminorities seeking changes in their status. In order to be considered awar,more than 1000 individuals had to bemobilizedand 100 fatalities must have occurred. Second, genocide (GENOCIDE) is defined to include the execution, and/or consent of sustainedpolicies by governing elites or their agents that result in the deaths of a substantial portion of a communal group (genocide) or apoliticized non-communal group (politicide). In contrast to ethnic conflict, victims counted are non-combatants and thepercentage ofthose killed in each group is given more weight than the number of dead. Third, revolutionary conflict (REVOL) is defined as conflictbetween the government and politically organized groups seeking to overthrow those in power. Groups include political parties, labororganizations, or parts of the regime itself. Once again, in order to be considered a conflict, more than 1000 individuals had to bemobilized and 100 fatalities must have occurred. Finally, regime change (REGIME) includes state collapse and shifts from democraticand authoritarian rule as defined by a shift of at least 3 points on the Freedom House polity scale.2 This measure does not includenonviolent transitions. Our measure of internal conflict (INTERNAL) is the union of these four measures. The main qualitative resultsare not sensitive to this transformation.

2.1. The dynamics of growth, globalization and democratization in Africa

Africa has experienced periods of good and bad outcomes over the past several decades. In this sub-section, we highlight howthe various engines of socio-, politico-, and economic change have evolved during this period. In doing so, we highlight how suchmovements may have left this region more or less susceptible to the consequence of terrorist attacks.

During the period between 1970 and the early 1990s, economic conditions in Africa became more difficult. Fig. 1 plots theaverage growth rate per capita in Africa and growth in countries that do not primarily export fuel. Fig. 1 demonstrates that growthin the late 1960s slowly declined over time until it systematically dropped to negative 4% by 1991. The rest of the world,particularly in Western Europe, North America, and East Asia, enjoyed positive growth during the same time period. This is whythe growth literature consistently has found that a dummy variable for Africa was significantly negative even after controlling formany other policy, geographical and institutional factors during the time period in question. However, starting in the mid 1990s,there was a change in the growth dynamics for Africa. Fig. 1 shows that growth from 1995 to 2004 was consistently positive andmany countries in the region out-performed the average growth for countries across the world.

The change from low- to high-growth since the mid 1990s has been associated with other positive dynamics. Fig. 2 shows thatOpenness (OPEN), as measured by exports plus imports as a percentage of GDP, has risen drastically during the period of high growth.

2 Freedom House measures the extent of democratization of a regime using two measures—civil liberties and political rights. Both measures are scaled 1 to 7with 1 being the most democratic.

S52 S.B. Blomberg et al. / European Journal of Political Economy 27 (2011) S50–S63

Thismovement has been seen in both fuel oriented and non-fuel oriented economies, though there has been a slightly higher increase inopenness among fuel based economies during the recent commodity-price boom. This shift to more economically-integrated societieshas been championed by the international financial community, and many even credit this openness to the recent rise in growth.

Fig. 3 shows that the openness to trade since themid 1990s has also been associatedwith greater political freedoms. Fig. 3 plotsthe fraction of countries that were democratic over time.3 While the percentage of countries is still lower than in most regions,there has been a shift toward democratic rule in Africa. Some have credited this change in institutional structure as an importantdeterminant in the recent boom.4

Even though there has been such positive economic and political change in Africa, there continues to be the danger that conflictwill undo all of the recent improvements. One of the central contributions of our paper is to investigate the extent to whichcountries in Africa have invested in anti- or counter-insurgent technologies during the recent period. If they have, then if there isan increase in terrorism, countries will be better equipped to respond, and bemore resilient to such attacks. Fig. 4 plots the averagenumber of attacks from 1970 to 2004. Notice that the trend is somewhat similar to what has been shown in the previous threefigures. During the 1970s and 1980s, as the economy in Africa stagnated and as governments continued to be non-democratic andnot-globalized, the number of terrorist attacks steadily increased (though at lower levels than in most countries). Since the mid1990s, terrorism has fallen, though there have been a few notable exceptions in 1995 and 1999–2000. Our paper examines therelationship between all of these factors to see the extent towhich any increases in terrorism (as in 1995 and 1999–2000) have leftcountries more vulnerable.

Fig. 1. Average growth in sub-Sahara from 1970 to 2004.

3 A country is defined to be a democracy if the Polity IV score is above 5.4 See Cinyabuguma and Putterman (2006).

Fig. 2. Average number of attacks in sub-Sahara from 1970 to 2004.


2.2. The incidence of terrorism and other forms of conflict in sub-Sahara and across the world

In this subsection, we parse the data in a variety of ways to examine the extent to which terrorism and other forms of conflictare more or less likely to occur in Africa. In doing so, we provide some preliminary evidence on the fragility of Africa relative to therest of the world.

We begin this sub-section by exploring how individual nation-states performed during our time of analysis, 1968–2004.Table 1A provides a comparison of a country's average GDP per capita growth rate (Δy) versus the time spent in various types ofconflict, be it terrorism (T), internal conflict (I) or external conflict (W). The first regularity that Table 1A demonstrates is thatwhile there is substantial variation in country performance, most nation-states performed well below the average growth rate of0.5% per annum during the time examined. In fact, only 8 of 46 countries grew as fast as the average world growth rate. This isparticularly troubling as all of the countries in the sample are in the lower income category suggesting that there has been littleeconomicmobility for those in Africa. There are a few notable exceptions. Botswana, in particular, has experienced growth rates ofabove 6 percent which is similar to the recent growth experienced in China and India.

The second empirical regularity demonstrated in Table 1A is that terrorism is about as likely to occur as internal conflict. This isat odds with what is experienced across the globe as terrorism is ten times more likely to occur than internal conflict. Thisempirical regularity can be due to the fact that terrorism is less likely to occur or internal conflict is more likely to occur in Africa. Itturns out that both are true. In fact, the global incidence for terrorism in Africa is about twice the world average. The globalincidence of internal conflict in Africa is about one fourth the world average. For some countries such as Angola, Somalia, Sudan,and Uganda, the vast plurality of time has been subject to terrorist attacks and internal war. However, for most countries terrorismoccurs relatively infrequently.

Fig. 3. Average openness in sub-Sahara from 1970 to 2004.

Fig. 4. Average percent of democracies in sub-Sahara from 1970 to 2004.


Table 1B demonstrates other empirical regularities. Table 1B provides the analogous information from Table 1A, but it parsesthe data by decade, region, governance, income, and export orientation. The third empirical regularity is that terrorism has fallenrecently, just as economic growth has experienced a resurgence during the 2000s. This “peace dividend” may provide some hopefor Africa which will be investigated more completely in the next section.

The fourth empirical regularity is that terrorism ismore likely to occur in richer andmore democratic regions.West Europe, NorthAmerica, High IncomeOECD countries and Democracies have experienced among the highest rates of economic growth but have alsobeen subject to the greatest incidence of terrorism. Other factors such as export orientation provide less systematic variation.

Taken together, Tables 1A and B show that Africa is a region that is seemingly less subject to terrorism, as it is less democratic,poorer and more isolated from other centers of terror such as the West and the Middle East. Moreover, it appears that internalconflict may be a greater challenge to future progress. However, the empirical regularities discussed here do not entirely addressthe question posed in our paper. Even if Africa is less subject to terrorism, is it any more resilient to attacks? In other words, even

Table 1AFraction (PCT) of time spent by conflict type 1968–2004.

Region Type of conflicts

Subsahara Δy T I W NOBS

Angola . 0.65 0.76 0.05 0Benin 0.70 0.00 0.03 0.00 36Botswana 6.02 0.16 0.00 0.00 34Burkina Faso 1.23 0.05 0.03 0.00 37Burundi 0.27 0.27 0.30 0.00 36Cameroon 1.15 0.08 0.00 0.00 36Cape Verde 2.65 0.00 0.00 0.00 36Central African −0.75 0.08 0.03 0.00 33Chad −0.33 0.19 0.16 0.05 36Comoros −1.06 0.00 0.11 0.00 36Congo, Dem. Rep. −3.54 0.00 0.41 0.14 34Congo, Rep. 0.78 0.16 0.03 0.00 36Cote d'Ivoire 0.51 0.16 0.08 0.00 36Djibouti −0.40 0.16 0.00 0.00 24Equatorial Guine 6.39 0.05 0.30 0.00 36Eritrea 1.12 0.11 0.00 0.08 12Ethiopia 1.17 0.68 0.24 0.14 36Gabon −0.77 0.08 0.00 0.00 37Gambia, The 0.21 0.00 0.03 0.00 36Ghana 2.07 0.11 0.05 0.00 36Guinea 0.18 0.05 0.00 0.00 37Guinea-Bissau −0.27 0.00 0.08 0.00 36Kenya −0.29 0.30 0.03 0.00 36Lesotho 2.74 0.11 0.08 0.00 36Liberia −5.35 0.16 0.19 0.00 33Madagascar −1.39 0.03 0.00 0.00 37Malawi 1.03 0.03 0.00 0.00 37Mali 1.84 0.03 0.00 0.00 37Mauritania 0.32 0.03 0.00 0.00 33Mauritius 3.56 0.00 0.00 0.00 37Mozambique 1.10 0.46 0.00 0.00 36Namibia 0.17 0.11 0.00 0.00 33Niger −1.13 0.05 0.03 0.00 37Nigeria 1.15 0.22 0.11 0.00 37Rwanda 0.97 0.08 0.03 0.00 36Sao Tome and Pri 0.80 0.00 0.00 0.00 34Senegal −0.28 0.11 0.00 0.00 36Seychelles . 0.03 0.00 0.00 0Sierra Leone −1.88 0.22 0.19 0.00 33Somalia −2.09 0.51 0.49 0.03 34South Africa 1.13 0.51 0.00 0.00 37Sudan 0.50 0.65 0.73 0.00 33Swaziland 3.52 0.32 0.03 0.00 34Tanzania 1.23 0.19 0.00 0.05 36Togo −1.28 0.16 0.00 0.00 37Uganda 0.25 0.54 0.49 0.05 36Zambia −0.81 0.38 0.16 0.00 36Zimbabwe 0.24 0.43 0.03 0.00 36Total 0.52 0.18 0.11 0.01 33.40

Notes: First column denotes country. Column 2 denote annual per capita growth rate of real PPP-adjusted GDP. Column 3 denotes the percent of years withterrorism (T), column 4 denotes the percent of years with internal conflict (I) and column 5 denotes the percent of years with external war (W). The final columndenotes the number of observations with economic variables.


though attacks are less frequent, are they more damaging in terms of reducing economic growth? That is the central questionposed in our paper and is addressed in the following section.

3. Econometric evidence

The purpose of this section is to provide evidence on the fragility of Africa due to terrorist attacks. In particular, we investigatewhether or not terrorism is more or less damaging as the economies of Africa have grown since 1995. The section begins by firstexamining the effect of terrorism on growth by running cross section regressions and investigates the extent to which it isdifferent for African countries. We estimate the average impact of terrorism on growth from panel growth regressions using OLSand quantile regression analysis to see the extent to which these results are consistent across various specification. We then runregressions for only the subset of African countries over the entire 1968–2004 time period and during themost recent time period,1995–2004, to investigate its relative resilience. Finally, we condition the regression to see which, if any, channels have played afactor in its relative resilience.

3.1. Cross country growth regressions

Following the literature on growth, we first run cross country growth regressions. The baseline model includes investment as ashare of GDP (I/Y) and the log of initial GDP (lny0i) to control for transitional dynamics. We include two additional controlvariables found to significantly explain growth; we include a dummy for Africa (SSAFR) to control for geography and we include adummy variable for non-oil commodity exporters (COM) (Easterly and Kraay, 2000). We also interact the Africa dummy withterrorism to examine the extent to which Africa is more or less resilient to terrorist attacks. Our model is given as:

Δyi = β0 + β1COMi + β2SSAFRi + β3lny0i + β4I = Yi + β5Ti + εi: ð1Þ

where Δyi and (I/Y)i are country i's average per-capita growth rate and investment rate over the full sample. We follow De LaCroix and Doepke (2003) and control for the possible endogeneity bias of investment by employing initial values of investment((I/Y)1968) as instruments.

Table 2 reports the results from the purely cross-sectional regressions. Column 1 is the base case following the growth literature.Column 2 includes terrorism (T). Columns 3 and 4 replicates columns 1 and 2 using IV estimation. The final columns, 5 and 6, includeterrorism interacted with the African dummy (T*SSAFR) using OLS (column 5) and IV estimation (column 6).

Column 1 shows that themodel conforms to what has been found in the literature—investment has a positive impact and initialincome, Africa and commodity exporters have a negative impact. Note that the regression results are similar to the results found in

Table 1BFraction (PCT) of time spent by conflict type 1968–2004.

Grouping Type of conflicts

Δy T I W NOBS

1970s 2.72 0.27 0.07 0.02 30.991980s 0.80 0.36 0.05 0.00 30.991990s 0.98 0.34 0.06 0.01 30.992000s 2.03 0.16 0.04 0.01 31.01West Europe 2.46 0.62 0.00 0.00 36.65N. Amer 2.10 0.76 0.00 0.00 37.00S. Amer 2.46 0.39 0.11 0.01 34.63East Asia 2.60 0.21 0.06 0.01 33.64East Europe 1.61 0.13 0.02 0.01 15.22Lat Amer 1.53 0.35 0.03 0.00 34.08Mid East N.Afr 1.51 0.50 0.06 0.04 31.19Sub-Sahara 0.52 0.18 0.11 0.01 33.40Democracies 1.94 0.49 0.02 0.00 34.16Nondemocracies 1.43 0.23 0.07 0.01 29.89High-income, OECD 2.34 0.61 0.00 0.00 36.87High-income, non-OECD 2.17 0.22 0.01 0.02 32.79Upper-middle-inc 2.24 0.30 0.03 0.00 29.52Lower-middle-inc 1.81 0.29 0.04 0.01 28.70Low-income 0.61 0.22 0.12 0.02 31.24Fuel exports 0.61 0.31 0.10 0.02 29.18Manufacturing exports 3.21 0.31 0.03 0.00 26.00Primary mtl export 0.66 0.24 0.12 0.01 33.66Service export 1.75 0.23 0.01 0.01 32.73Diverse 1.83 0.45 0.04 0.00 33.79Total 1.56 0.37 0.03 0.00 34.24

Notes: See Table 1A.


Blomberg et al. (2004a, 2004b, 2004c). The differences are attributed to the larger sample of countries used in this study. The signsand significance are the same. Column 2 provides our first direct estimate on the impact of terrorism. The impact is negative andstatistically significant, implying that if a country were to experience a terrorist event in each year in the sample, per capita growthwould drop by about .75%. The remaining columns suggest that these results are not sensitive to endogeneity issues (columns3,4,6) and the impact of terrorist attacks is not statistically different in Africa (columns 5,6).

3.2. Global panel growth regressions

The cross-sectional regression results presented in Table 2 suggested that Africa is no more or less susceptible to terroristattacks. The results from Table 2 could be due to omitted variables or it could be that the results are capturing only long run effectsof terrorism and the long run impact of terrorism is no different in Africa than in the rest of the world. It could be the case thatterrorist attacks have a larger short run impact on Africa. For example, if sectors like tourism are main drivers of growth, terroristattacks may have a significant impact on these sectors, and growth in the short run will be disproportionately worst in countrieswho rely on tourism for growth. In Table 3, we re-examine the evidence presented in Table 2 using panel data regressions. Wecontrol for time and country fixed effects in the regressions, which capture aggregate and time-invariant country specific factorswhich may be correlated with growth and terrorism, and we allow for other conflict variables to affect growth such as internalconflict (I) and external war (W). We also allow for lagged impacts of terrorism (Tlag) and estimate the model over variousquantiles using quantile regression techniques. The model to be estimated is:

Δyit = γ0 + γ3lnyit−1 + γ4I = Yit−1 + γ5Tit + γ6Iit + γ7Wit + ϕZ + εit ð2Þ

where Z is a set of time and country fixed effects and ϕ is a vector of nuisance coefficients.5

Column 1 once again shows that I/Y and lagged GDP per capita are statistically significant and have the theoretically predictedsign. We find that the coefficient on terrorism continues to be negative and statistically significant. Column 2 also shows that lagsof terrorism have no significantly relevant information in addition to the contemporaneous impact. Column 3 shows that internaland external conflicts are also significant and have much larger effects than terrorism. Column 3 implies that a terrorist attackreduces growth by approximately .45% in a given year.

Columns 4–6 provide results using quantile regression analysis. Quantile estimation is based on estimating various quantiles(such as the median) of a population. Clearly, the impact of terrorism on growth may differ across high growth and low growthcountries. This may be particularly true of Africa wheremany, though not all, countries experience low growth. Another advantageof using quantile regressions is that it is more robust in response to large outliers.

Column 4 estimates the model using quantile regression techniques estimated for low growers or Q=.25. Columns 5–6, thenconsider other points along the distribution of growth, from Q=.75 to Q=.9. The regressions also include regional dummies. Theimpact of terrorism on growth is negative and statistically significant at points on the higher portion of the distribution but isinsignificant for countries on the lower portion of the growth distribution. One interpretation is that terrorismmay have less of an

5 Estimating the model using a random effects estimator instead of a fixed effects estimator does not provide any qualitative change to what is reported inTables 4 and 5.

Table 2Estimated effect of conflict on growth in cross section 1968–2004.

(1) (2) (3) (4) (5) (6)

Base and T IV:Base IV:and T and T IV:Base

com −0.846*** −0.847*** −0.855*** −0.854*** −0.868*** −0.875***[0.302] [0.300] [0.301] [0.299] [0.302] [0.301]

ssafr −0.807* −0.893** −0.833** −0.912** −1.113** −1.121**[0.410] [0.414] [0.417] [0.419] [0.499] [0.501]

lny0 −0.398*** −0.397*** −0.381** −0.382** −0.396*** −0.380**[0.149] [0.146] [0.150] [0.148] [0.146] [0.148]

I/Y 0.111*** 0.117*** 0.103*** 0.110*** 0.119*** 0.111***[0.019] [0.020] [0.021] [0.022] [0.020] [0.022]

T −0.757* −0.728* −0.890** −0.849*[0.406] [0.404] [0.435] [0.436]

Tssafr 1.255 1.182[1.286] [1.297]

Observations 184 184 184 184 184 184R-squared 0.27 0.28 0.27 0.28 0.28 0.28

Notes: Robust standard errors are presented in square brackets. ⁎, ⁎⁎ and ⁎⁎⁎ represent statistical significance at the .10, .05 and .01 levels, respectively. Models (1)through (6) are different specifications of cross country growth regressions. Models (1) (2), (5) are the basic OLS model adding separately the different forms ofconflict, i.e. terrorism (T), and terrorism interacted with dummy variable for Africa (Tssafr). Models (3,4,6) repeat the exercises but estimate the model as IV/GMMwith initial investment as a percent of GDP (I/Y) as the instrument. The first-stage t-statistic on initial investment is 24.05, 23.46, and 23.53 respectively. Includedin each regression is a dummy for non-oil exporting commodity countries (com), Africa (ssafr), initial GDP per capita (lny0) and average investment as a percent ofGDP (I/Y).


effect for many countries in Africa. Another interpretation is that terrorismmay havemore of an impact in Africa during the periodof high growth (1995–2004). This is partly seen by the fact that the dummy for Africa is negative and statistically significant forgrowth in the lower portion of the distribution and becomes less significant at the higher portion of the distribution. Part of thisissue could be resolved by including individual country fixed effects. However, econometrically it is not possible to include fixedeffects for countries as they control for averages not for differences in the quantiles of the distribution.

3.3. Sub-Sahara panel growth regressions

In this subsection, we reconsider themodel in the previous subsection and only consider African countries. Table 4 provides theresults from this exercise. In this case we estimate six models. Column 1 provides the results for the baseline Solow model withterrorism. Column 2 provides the results when we include other forms of conflict such as internal conflict (I) and external war(W). Column 3 includes controls for globalization (ln(open)), column 4 includes controls for democracy (dem) and column 5includes all covariates. There may also be a concern that terrorist attacks are endogenous. This may be the case if slow-growingcountries are more prone to terrorism than fast-growing countries. To address this concern we run a Arellano and Bond (1991)first step GMM estimator. Column 6 provides the results from the Arellano and Bond (1991) regression.

Table 4 shows that that the impact of terrorism is roughly two times as large in Africa than in the rest of the world. This meansthat even though terrorism is less likely to occur, Africa is less resilient when it does. Other types of conflict, especially war, alsoharm growth (column 2). Openness also seems to be associated with increased growth (column 3), though democracy appears tohave no significant impact (column 4).

The impact of terrorism is surprisingly robust across all the specifications. In this case, we estimate the impact of a terroristattack to decrease growth by approximately 1 percentage point, ceteris paribus. Since, the average growth rate in this region is0.50%. This result suggests that terrorism, all things equal, maymove an African economy into a growth recessionary environment.

There may be endogeneity concerns if terrorist attacks are endogenous to the error term, if this is the case, then the fixed effectsestimate on terrorist attackswill be biased. To overcome this problem of endogeneity, we employ Arellano and Bond (1991) first stepGMM regression. This method takes first differences of the above regression and then uses lagged levels of the endogenous variables

Table 3Estimated effect of conflict on growth: panel of 183 countries.

(1) (2) (3) (4) (5) (6)

OLS OLS OLS Q=.25 Q=.75 Q=.9

T −0.518* −0.478* −0.451* 0.132 −0.700*** −1.389***[0.274] [0.277] [0.270] [0.172] [0.179] [0.320]

lnyit−1 −5.114*** −5.134*** −5.559*** −0.919*** −1.049*** −1.759***[0.384] [0.385] [0.382] [0.146] [0.162] [0.321]

I/Yit−1 0.216*** 0.216*** 0.211*** 0.113*** 0.102*** 0.106***[0.021] [0.021] [0.021] [0.011] [0.011] [0.022]

Tit−1 −0.256[0.277]

I −3.477***[0.486]

W −7.804***[0.935]

ssafr −1.054*** −0.810** −0.304[0.314] [0.349] [0.653]

latca −0.504* −0.787*** −0.725[0.280] [0.288] [0.515]

easia −0.268 0.523* 0.279[0.278] [0.292] [0.523]

menaf −1.640*** 0.245 1.745***[0.293] [0.309] [0.542]

lowin −2.269*** −1.850*** −2.653***[0.281] [0.312] [0.607]

highin 1.057*** −0.931*** −1.326**[0.294] [0.328] [0.598]

namer 0.717 0.132 −0.357[0.689] [0.714] [0.995]

ocean 1.115** −0.115 −0.815[0.434] [0.465] [0.840]

Observations 5827 5827 5827 5827 5827 5827R-squared 0.07 0.07 0.1

Notes: Robust standarderrors are presented in square brackets.⁎, ⁎⁎ and ⁎⁎⁎ represent statistical significance at the .10, .05 and .01 levels, respectively. All specificationsinclude time and individualfixed effects.Models (1) through (6) are different specifications of panel growth regressions.Models (1) through (3) are thebasicOLSmodeladding separately the different forms of conflict, i.e. terrorism (T), internal conflict (I), and externalwars (W).Models (3) through (6) repeat the exercises but estimatethe model using quantile regression analysis, centered on quantiles .25, .75., and .9. Included in quantile regressions is a dummy for Africa (ssafr), Latin American andCaribbean (latca), East Asia (easia), North America (namer),Middle East andNorth Africa (mena), oceania (ocean), and high- and low-income countries (highin,lowin).Included in each regression is lagged GDP per capita (lnyit−1) and average investment as a percent of GDP (I/Y)it−1. R-squared is calculated without fixed effects.


(ln(yit−1) and T) as instruments. Column6 reports the results fromtheArellano andBond (1991)first stepGMMregression. The pointestimates on terrorism and all the other variables except lagged per capita GDP growth are virtually the same.

Table 5 continues the exercise with one additional wrinkle; namely, we consider if the effect of terrorism is greater over the lastdecade of high growth, 1995–2004. Each column is analogous to the previous table. The results are qualitatively similar. Theexpected sign and significance of lagged income is negative (though the magnitude is considerably larger) and lagged investmentis positive.War continues to have a negative and statistically relevant impact. Openness is still positively related to growth, thoughit is no longer statistically valid. The biggest surprise might be the impact of terrorism during this time sample. In this case, weestimate the impact of terrorism on growth to be three times the average impact. So, as Africa has grown more rapidly, so too hasits growth susceptibility to terrorism. An interesting observation that comes from Table 5 is that while the African economies aremore susceptible to terrorist shocks post 1994 they do not appear to be more susceptible to internal and external conflicts post1994 (primarily wars). This could be due to the more sophisticated techniques employed by terrorist groups.

This leads us to question why has Africa become less resilient to terrorism? There are several possible scenarios we consider,each related to the dynamics we alluded to during our preliminary data analysis. During the period of high growth, Africa also sawhigh rates of globalization, democratization and commodity price inflation. Are any or all of these factors to blame for this loss inresilience?

To see how this may be true, we provide some theoretical conjectures. Globalization has led to less economic resilience toterrorism. Suppose that openness to trade also means that borders aremore open in general. Then outside agitators of terror wouldhave more access to valued infrastructure and are more able to create larger disruptions as countries globalize. To take this intoaccount, we will interact our terrorism variable with measures of openness to see the extent to which we can explain the loss inresilience through increased globalization.

Democratization has led to less economic resilience to terrorism. Suppose that greater political freedom also means that a countryis less able to suppress dissent through intimidation. Then outside agitators of terror would have more access to valuedinfrastructure and are more able to create larger disruptions once a country has become democratized. To take this into account,wewill interact our terrorism variable withmeasures of democracy to see the extent to which we can explain the loss in resiliencethrough increased democratization.

Oil price/corruption has led to less economic resilience to terrorism. Suppose that openness to trade, particularly openness to oilexporting, also means that borders are more open in general. Further suppose that this is accomplished in a regime moresusceptible to resource-curse driven corruption. Then outside agitators of terror would have more access to valued infrastructureas trade increases when fuel prices rise. To take this into account, we will interact our terrorism variable with measures ofopenness and a dummy for oil exporting countries to see the extent to which we can explain the loss in resilience throughincreased Oil Price/Corruption.

Table 6 provides the results from this exercise using the entire time period. Column 1 is the baseline case in which terrorism isincluded in Solow growth specification. Column 2 interacts terrorism with democracy (Democratization has led to less economicresilience), Column 3 interacts terrorism with a dummy for oil exporting countries (Oil Price/Corruption has led to less economic

Table 4Estimated effect of conflict on growth: panel of 46 sub-Saharan countries.

(1) (2) (3) (4) (5) (6)

OLS OLS OLS OLS OLS A–B

T −1.099* −1.094* −0.993* −1.088* −0.986* −1.116*[0.618] [0.617] [0.617] [0.619] [0.617] [0.6187]

lnyt−1 −4.570*** −4.740*** −5.281*** −4.589*** −5.424*** −13.365***[0.810] [0.810] [0.838] [0.812] [0.838] [1.032]

I/Yt−1 0.397*** 0.396*** 0.370*** 0.397*** 0.371*** 0.493***[0.040] [0.040] [0.040] [0.040] [0.041] [0.047]

I 0.069 0.119 0.026[1.241] [1.239] [1.631]

W −6.343*** −6.060*** −6.088***[1.976] [1.974] [1.981]

ln(open) 1.953*** 1.859*** 2.346***[0.612] [0.611] [0.650]

dem 0.323 0.262 −0.591[0.887] [0.883] [0.991]

Δyt−1 0.079***[0.025]

Observations 1603 1603 1603 1603 1603 1511R-squared 0.12 0.13 0.13 0.12 0.13

Notes: Robust standarderrors are presented in square brackets.⁎, ⁎⁎ and ⁎⁎⁎ represent statistical significance at the .10, .05 and .01 levels, respectively. All specificationsinclude time and individualfixed effects.Models (1) through (6) are different specifications of panel growth regressions.Models (1) through (5) are thebasicOLSmodeladding separately the different forms of conflict, i.e. terrorism (T), internal conflict (I), and external wars (W). Model (6) estimatesModel (5) using Arellano and Bondestimator allowing for T to be endogenous. Included ineach regression is the lag of ln(exports+imports/gdp), (ln(op)), dummyvariable for democracy (dem) [polity IVscore N7], lagged GDP per capita (lnyit−1) and average investment as a percent of GDP (I/Y)it−1. R-squared is calculated without fixed effects.


resilience), Column 4 interacts terrorismwith openness (Globalization has led to less economic resilience) and Column 5 and 6 considervariants of each of the scenarios.

The empirical results found in Column 1 again show the standard result that a terrorist attack in a given year drops GDP growthby 1 percentage point. Column 2 shows that there appears to be no statistically significant effect from interacting democracy withterrorism suggesting that greater political freedom is not to blame. Column 3 shows that interacting fuel with terrorism does causegrowth to fall. However, both the statistical significance of terrorism itself, and terrorism interacted with the fuel price, are belowconventional levels. Column 4 has a similar impact in that openness to trade appears to be a channel that may have reducedgrowth through its impact on terrorism, though there is no statistically significant result. Hence, in columns 3 and 4, we willconsider alternative specifications due to the possible multicolinearity in our specifications. Column 5 interacts the oil exportingdummy with openness and terrorism. In this case, we find a strong statistically significant and negative result. Terrorism in oil-exporting countries that have opened their borders are less resilient. Column 6 repeats the exercise including other measures ofconflict and shows that this result is robust. The scenario associated with this result is that Oil Price/Corruption has led to lessresilience.

As a final robustness check, we reproduce this same exercise for the period 1995–2004. Table 7 provides the results. In this case,we find similar results as in Table 6, though the impacts are more pronounced. Democratization does not seem to be the channel(column 2). Fuel interacted with terrorism (column 3) or interacted with terrorism and openness (columns 5,6) appear to makeAfrica less resilient. In short, the scenario associated with this result is that Oil Price/Corruption has led to less resilience and theimpact in the most recent period is approximately 4 to 5 times greater. We interpret this result to suggest that countries that haverelied on fuel-based growth have not done an adequate job of counter-terrorist prevention. These results are consistent with theobservation that oil exporting countries tend to have less diversified economies and have poorer institutional quality, which leadsto a larger impact of terrorist attacks on growth. Outside agitators of terror have more access to valued infrastructure when tradeincreases as fuel prices rise.

3.4. Case study: Nigeria

This section provides implications for one of the fastest growing economies in Africa, Nigeria. The main finding of this paper isthat a country's dependence upon primary commodity exports increases the economic impact of terrorist attacks as the countrybecomesmore open. This assertion appears to be true for oil exporting countries such as Nigeria, Congo and Liberia. One argumentfor this is that as countries becomemore open, this may give terrorists more access to important industries and infrastructure thatwere previously more insulated. In this case, shocks to these commodities will have a tremendous impact on growth. Matchedwith corruption and inefficient institutions, these countries are also more susceptible to adverse shocks.

Nigeria joined the Organization of the Petroleum Exporting Countries (OPEC) in 1971 and is the 12th largest producer ofpetroleum in the world and the 8th largest exporter. Nigeria's economy depends heavily on its oil exports, accounting for over 80%of government revenue. Growth in Nigeria tends to fluctuate with oil prices. For example, from the period 1977–1990, GDP growth

Table 5Estimated effect of conflict on growth: panel of 46 sub-Saharan countries post 95.

(1) (2) (3) (4) (5) (6)

OLS OLS OLS OLS OLS A–B

T −3.445*** −3.529*** −3.360** −3.443*** −3.454*** −2.402**[1.320] [1.312] [1.319] [1.322] [1.316] [1.196]

lnyt−1 −24.615*** −25.022*** −25.174*** −24.600*** −25.421*** −17.689***[2.825] [2.817] [2.848] [2.830] [2.849] [2.464]

I/Yt−1 0.364** 0.337** 0.319** 0.364** 0.303** 0.574***[0.141] [0.142] [0.145] [0.141] [0.145] [0.124]

I −2.142 −2.11 −2.266[2.065] [2.082] [1.910]

W −7.092** −6.524** −6.424**[3.151] [3.201] [3.3037]

ln(open) 3.273 2.488 2.740*[2.317] [2.354] [1.577]

dem −0.409 −0.298 −1.212[2.315] [2.330] [2.121]

Δyt−1 0.031[0.044]

Observations 385 385 385 385 385 385R-squared 0.3 0.32 0.31 0.3 0.33

Notes: Robust standard errors are presented in square brackets. ⁎, ⁎⁎ and ⁎⁎⁎ represent statistical significance at the .10, .05 and .01 levels, respectively.All specifications include time and individual fixed effects. Models (1) through (6) are the basic models adding separately the different forms of conflict, i.e.terrorism (T), internal conflict (I), and external wars (W). Model (6) estimates Model (5) using Arellano and Bond estimator allowing for T to be endogenous.Included in each regression is the lag of ln(exports+imports/gdp), (ln(op)), dummy variable for democracy (dem) [polity IV score N7], lagged GDP per capita(lnyit−1) and average investment as a percent of GDP (I/Y)it−1. R-squared is calculated without fixed effects.


was only 1.8% a year, partly due to the fall in oil prices in the 1980s, while from 1990 to 1997, GDP growth averaged 3.8% a year,following the 1990 oil price shock.

Nigeria has struggled with corruption, conflict, and weak political and economic institutions. All which make the countrysusceptible to terrorist attacks. The 1990s and the 2000s have witnessed an increase in more advanced and sophisticated terroristattacks. The 1990s has also shown an increase in terrorist activities in Nigeria. Between 1968 and 2004, Nigeria spent 22% of thetime involved in some form of transnational terrorist activity (Table 1A). Since the early 1990s, the Niger Delta region, Nigeria'sprimary oil producing region, has witnessed an increase in terrorist activities, primarily targeted at foreign oil corporations.Nigerian terrorist groups have moved to the top ten of most active (in terms of number of terrorist attacks) terrorist groups.

The interaction between Nigeria's openness and its dependence on oil production is what makes Nigeria an interesting case tostudy the dynamics of terrorism and growth. Nigeria is one of the most corrupt countries in the world, and many, if not most ofNigeria's citizenshave failed to reap thebenefits of the country's oil revenues. Terrorist attacks havedisrupted oil productionandmadeit costly to do business in Nigeria. Recent accounts have demonstrated how dependent the Nigerian economy is on oil revenues. Thefindings in this paper suggest that terrorist activities can have a tremendous impact on GDP growth. One policy recommendation thatshould be evident from this study is that countries like Nigeria should seriously consider improved counter-terrorism measures.

As mentioned earlier, a number of different reasons can explain why African countries, and in particular oil exporting Africancountries, are more susceptible to terrorist attacks: weak institutions, the inability to absorb shocks, or the lack of economicdiversity. While Nigeria heavily depends on oil exports (oil and gas exports makes up over 90% of total exports earnings), they dohave a relatively diverse economy. The share of GDP coming from agriculture, industry, and services in 2005was 32%, 42%, and 26%respectively. Africa as a whole had 17%, 31%, and 51% share of GDP in agriculture, industry, and services respectively in 2005(World Bank, WDI).6 Nigeria's manufacturing sector only accounts for 3% of GDP while the average for Africa as a whole was 13%.These numbers suggests that Nigeria may be benefit from a more diversified economy but they do not suggest that economicdiversity is a distinguishing characteristic of Nigeria. Sala-i-Martin and Subramanian (2003) show that natural resources(primarily fuel and minerals) are negatively correlated with institutional quality. The authors provide a case study of Nigeria,suggesting that poor oil revenue management and corruption are the primary causes for the country's poor economicperformance. The same rational can explain why Nigeria may be more susceptible to terrorist attacks. The story of Nigeria mimicsmany other African countries. These economies are weak and are not well diversified to handle shocks. Add to this weakinstitutional quality, corruption, and natural disasters, any adverse shock can have a tremendous impact on growth.

6 Nigeria is slightly less diversified than Africa as a whole but more diversified than a country like Saudia Arabia, with the share of GDP coming fromagriculture, industry, and services in 2005 equaling 3%, 63%, and 34% respectively.

Table 6Estimated effect of conflict on growth: through which channel?

(1) (2) (3) (4) (5) (6)

OLS OLS OLS OLS OLS OLS

T −1.099* −1.326** −0.82 1.773 −0.808 −0.653[0.618] [0.654] [0.641] [3.189] [0.640] [0.639]

lnyt−1 −4.570*** −4.568*** −4.531*** −5.284*** −4.512*** −5.395***[0.810] [0.812] [0.810] [0.838] [0.809] [0.837]

I/Yt−1 0.397*** 0.399*** 0.394*** 0.367*** 0.393*** 0.366***[0.040] [0.040] [0.040] [0.040] [0.040] [0.041]

lnop 2.218*** 1.959**[0.681] [0.612]

dem 0.008 0.226[0.931] [0.883]

demT 1.872[1.681]

fuelT −3.764 26.615 28.708[2.295] [19.075] [18.998]

lnopfuelT −6.947* −7.539*[4.330] [4.312]

lnopT −0.704[0.796]

I 0.101[1.238]

W −6.122***[1.971]


Notes: Robust standarderrors are presented in square brackets.⁎, ⁎⁎ and ⁎⁎⁎ represent statistical significance at the .10, .05 and .01 levels, respectively. All specificationsinclude time and individual fixed effects. Models (1) through (5) are the basic OLS model adding separately the different forms of conflict, i.e. terrorism (T), internalconflict (I), and external wars (W); and terrorism's interaction with primary fuel exporters (fuel, Tfuel), openness (ln(op), ln(op)T), democracy (dem, demT) andopenness of primary fuel exporters (lnopfuelT). Included in each regression is the lag of ln(exports+imports/gdp), (ln(op)), dummy variable for democracy (dem)[polity IV score N7], lagged GDP per capita (lnyit−1) and average investment as a percent of GDP (I/Y)it−1. R-squared is calculated without fixed effects.


4. Conclusions

This paper provided some important insights into the consequences of terrorism for sub-Sahara Africa. Using a dataset onterrorist events from 1968 to 2007 we found that terrorist attacks are less common in sub-Sahara Africa relative to the rest of theworld, but the impact of terrorist attacks on growth is larger. These results suggest that the African economies are lesseconomically resilient to terrorist attacks. We provide strong evidence that these findings are in large part driven by oil dependenteconomies. We interpret these results to suggest that countries that have relied on fuel-based growth have not done an adequatejob of investing in counter-terrorist policies.

These finding have important policy implications for sub-Sahara Africa. The continent has experienced a decrease in internalconflict, increases in democracy and openness, and more efficient safety nets which reach the poor in times of crises, all of whichhas helped lead to the growth many countries have experienced since the nineties. One must wonder is this growth sustainable.Our results suggest that much of the growth experienced can be easily reversed through terrorist activities, providing an argumentfor providing for adequate counterterrorism measures in these fragile states.

Acknowledgements

Part of this researchwas supported by the United States Department of Homeland Security through the National Center for Riskand Economic Analysis of Terrorism Events (CREATE) under grant number 2007-ST-061-000001. Any opinions, findings, andconclusions or recommendations in this document are those of the authors and do not necessarily reflect the views of the UnitedStates Department of Homeland Security.

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Table 7Estimated effect of conflict on growth post 95: through which channel?

(1) (2) (3) (4) (5) (6)

OLS OLS OLS OLS OLS OLS

T −3.445*** −3.541** −2.089 7.41 −2.088 −2.153[1.320] [1.420] [1.370] [5.898] [1.346] [1.339]

lnyt−1 −24.615*** −24.582*** −24.701*** −25.081*** −24.537*** −25.100***[2.825] [2.836] [2.787] [2.838] [2.739] [2.747]

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lnop 3.743 2.561[2.321] [2.289]

dem −0.461 −0.452[2.334] [2.289]

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lnopT −2.765*[1.476]

I 0[0.000]

W −6.938**[3.095]


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New wine in old wineskins? Growth, terrorism and the resource curse in sub-Saharan Africa