The Impact of US Drone Strikes on Terrorism inPakistan and Afghanistan∗

Patrick B. JohnstonRAND Corporation

Anoop K. SarbahiStanford University

July 14, 2013

Abstract

This study analyzes the effects of US drone strikes on terrorism in Pakistanand Afghanistan. Some theories suggest that drone strikes anger Muslimpopulations, and that consequent blowback incites Islamist terrorism. Othersargue that drone strikes disrupt and degrade terrorist organizations, reducingtheir ability to conduct attacks. We use detailed data on U.S. drone strikesand terrorism in Pakistan and Afghanistan from 2004-2011 to test each theory’simplications. We find that drone strikes are associated with decreases in theincidence and lethality of terrorist attacks, as well as decreases in particularlyintimidating and deadly terrorist tactics, including suicide and improvisedexplosive devices (IED) attacks. These results lend credence to the argumentthat drone strikes, while unpopular, have bolstered U.S. counterterrorism effortsin Pakistan and cast doubt on claims that drone strikes are militarily ineffective.

∗Earlier versions of this article were presented at the 2011 Annual Meetings of the AmericanPolitical Science Association, the Belfer Center for Science and International Affairs at HarvardUniversity’s Kennedy School of Government, and the New America Foundation. For helpful feedbackon earlier versions, we thank Peter Bergen, James Dobbins, C. Christine Fair, Melissa Willard-Foster,Seth G. Jones, Jennifer Keister, Akbar Khan, Peter Krause, Sean Lynn-Jones, Steven E. Miller,Jacob N. Shapiro, Arthur Stein, Katherine Tiedemann and Jeremy Weinstein. Johnston acknowledgefinancial support from AFOSR Award #FA9550-09-1-0314.

1 Introduction

Do drone strikes against terrorists reduce the threat posed by terrorist organizations, or

do they unintentionally increase support for anti-U.S. militants and thus fuel terrorism?

1. Empirical studies of targeted killings and civilian casualties in counterinsurgency

and counterterrorism show that both outcomes are possible.2 Strikes conducted by

remotely piloted aircraft may undermine counterterrorism efforts or enhance them

depending on the nature of the violence, the intentionality attributed to it, or the

precision with which it is applied.3 Existing research has studied the effects of coercive

airpower,4 targeted killings,5 and civilian victimization,6 but social scientists have

1Examples of arguments that drone strikes are ineffective or counterproductive include LivingUnder Drones: Death, Injury, and Trauma to Civilians From US Drone Practices in Pakistan,Stanford Law School and NYU School of Law, September 2012; and Audrey Kurth Cronin, “WhyDrones Fail,” Foreign Affairs, July 1, 2013. Examples of arguments that drone strikes are effectiveinclude C. Christine Fair, “Drone Wars,” Foreign Policy, May 28, 2010; Fair, “For Now, Drones Arethe Best Option,” New York Times, September 26, 2012; and Daniel Byman, “Why Drones Work,”Foreign Affairs, July 1, 2013.

2Benjamin Valentino, Paul Huth, and Dylan Balch-Lindsay, “‘Draining the Sea:’ Mass Killing andGuerrilla Warfare,” International Organization, Vol. 58, No. 2 (Spring 2004): 375407; Alexander B.Downes, “Draining the Sea by Filling the Graves: Investigating the Effectiveness of IndiscriminateViolence as a Counterinsurgency Strategy,” Civil Wars, Vol. 9, No. 4 (December 2007): 420444;Jessica Stanton, Strategies of Restraint in Civil War (New York: Columbia University, 2009); JennaJordan, “When Heads Roll: Assessing the Effectiveness of Leadership Decapitation,” SecurityStudies, Vol. 18, No. 4 (December 2009), pp. 719-755.

3Stathis N. Kalyvas, The Logic of Violence in Civil War (New York: Cambridge UniversityPress, 2006); Downes, “Draining the Sea by Filling the Graves: Investigating the Effectivenessof Indiscriminate Violence as a Counterinsurgency Strategy”; Matthew Adam Kocher, Thomas B.Pepinsky, and Stathis N. Kalyvas, “Aerial Bombing and Counterinsurgency in the Vietnam War,”American Journal of Political Science, Vol. 55, No. 2 (April 2011), pp. 201-218.

4Robert A. Pape, Bombing to Win: Air Power and Coercion in War (Ithaca, NY: CornellUniversity Press, 1996); Michael Horowitz and Dan Reiter, “When Does Aerial Bombing Work?Quantitative Empirical Tests, 1917-1999,” Journal of Conflict Resolution, Vol. 45, No. 2 (April2001), pp. 147173.

5David A. Jaeger, “The Shape of Things to Come? On the Dynamics of Suicide Attacks andTargeted Killings,” Quarterly Journal of Political Science, Vol. 4, No. 4 (December 2009), pp.315342; Jordan, “When Heads Roll;“ Patrick B. Johnston, “Does Decapitation Work? Assessing theEffectiveness of Leadership Targeting in Counterinsurgency Campaigns,” International Security, Vol.36, No. 4 (Spring 2012), pp. 47-79; and Bryan Price, “Targeting Top Terrorists: How LeadershipDecapitation Contributes To Counterterrorism,“ International Security, Vol. 36, No. 4 (Spring 2012),pp. 9-46.

6Kalyvas, The Logic of Violence in Civil War; Jason Lyall, “Does Indiscriminate Violence InciteInsurgent Attacks?: Evidence from Chechnya, Journal of Conflict Resolution, Vol. 53, No. 3(February 2009), pp. 331362; and Luke Condra and Jacob N. Shapiro, “Who Takes the Blame?The Strategic Effects of Collateral Damage,” American Journal of Political Science, Vol. 56, No. 1(January 2012), 167187.

1

conducted little empirical analysis of the effects of drone strikes.7 This lack of attention

is unfortunate: unmanned aerial vehicles, and their lethal targeting capabilities, are

likely to represent a critical aspect of current and future counterterrorism efforts.

The consequences of drone strikes are a critical policy concern. The United States

has frequently been called upon to cease drone strikes in Pakistan in order to protect

noncombatants, but instead it has expanded its use of drones to other countries

in which al Qa’ida-affiliated militants are believed to operate, such as Somalia and

Yemen.8 The laws governing international armed conflict codify and strengthen norms

against targeted killings, yet other interpretations of the laws of war leave civilian

officials and military commanders with substantial latitude to target enemy combatants

believed to be affiliated with terrorist organizations against which the U.S. has declared

war.9 Liberal democratic states face substantial pressures to protect civilians in war,

but at the same time are often confronted with substantial uncertainty as to what

abiding by legal principles such as “discrimination” – the obligation of military forces

to select means of attack that minimize the prospect of civilian casualties – actually

entails.10

Drone strikes are not the only instrument the U.S. can use to fight al Qa’ida terror-

ists; states have used other methods to fight terrorism for centuries. The effectiveness

of drone strikes at countering terrorism lies at the core of U.S. policymakers’ arguments

for their continued use. Yet because of the drone program’s secretive nature and wide

7Exceptions include David A. Jaeger and Zahra Siddique, “Are Drone Strikes Effective inAfghanistan and Pakistan? On the Dynamics of Violence between the United States and theTaliban, IZA Discussion Paper No. 6262, November 2011; and Megan Smith and James Igoe Walsh,“Do Drone Strikes Degrade Al Qaeda? Evidence From Propaganda Output,” Terrorism and PoliticalViolence, Vol. 25, No. 2 (February 2013), pp. 311-327.

8For excellent descriptions of the drone war’s expansion, see Mark Mazzetti, The Way of theKnife: The CIA, a Secret Army, and a War at the Ends of the Earth (New York: Penguin Press,2013); and Jeremy Scahill, Dirty Wars: The World Is a Battlefield (New York: Nation Books, 2013).

9Christine D. Gray, International Law and the Use of Force (New York: Oxford University Press,2000).

10Neta C. Crawford, “Just War Theory and the U.S. Counterterror War,” Perspectives on Politics,Vol. 1, No. 1 (March 2003), pp. 5-25; and Michael Walzer, Just and Unjust Wars: A Moral Argumentwith Historical Illustrations (New York: Basic Books, 2006).

2

disagreement about the effects of drone strikes on terrorist organizations and civilian

populations, U.S. government officials and human rights advocates have both failed to

present compelling, systematic evidence in support of their positions. What is needed

is a rigorous, evidence-based assessment of drone strikes’ impact on terrorism. Such

an assessment should sharpen the debate on drone strikes and help counterterrorism

officials and critics alike to evaluate the tradeoffs associated with drone warfare.

The present study provides such an assessment by using a data-driven approach to

analyze the consequences of drone strikes. Based on detailed data on both drone strikes

and terrorism in Pakistan throughout the course of the U.S. drone campaign there, the

study examines how drone strikes have affected terrorist violence in northwest Pakistan

and bordering areas of Afghanistan. In order to provide the most comprehensive

analysis possible, this study investigates the relationship between drone strikes and

a wide range of militant activities and tactics, including terrorist attack patterns,

terrorist attack lethality, and especially deadly and intimidating tactics such as suicide

and improvised explosive device (IED) attacks.

A systematic analysis of the data reveals that drone strikes have succeeded in

curbing deadly terrorist attacks in Pakistan. Specifically, the key findings of our

study show that drone strikes are associated with substantial reductions in terrorist

violence along four key dimensions. First, drone strikes are generally associated with

a reduction in the rate of terrorist attacks. Second, drone strikes are also associated

with a reduction in the number of people killed as a result of terrorist attacks. Third,

drone strikes tend to be linked to decreases in the use of particularly lethal and

intimidating tactics, including suicide and IED attacks. Fourth, the study finds that

this reduction in terrorism is not the result of militants leaving unsafe areas and

conducting attacks elsewhere in the region; on the contrary, there is some evidence to

suggest that drone strikes have a small violence-reducing effect in areas near those

struck by drones. Taken together, these findings strongly suggest that despite drone

3

strikes’ unpopularity, official claims that drones have aided U.S. counterterrorism

efforts in Pakistan appear to be credible and should not be dismissed out of hand.

The remainder of the article proceeds as follows. In Section 2, we outline the

range of relevant hypotheses on the effects of drone strikes, and briefly discuss the

theoretical logics that undergird them. In Section 3, we describe our dataset and the

methodology used to assess the effects of drone strikes on terrorism. In Section 4, we

discuss the results of our empirical analysis and our interpretation of key findings.

Finally, Section 5 concludes with a discussion of our findings’ implications for policy

and the future of counterterrorism.

2 Hypotheses on Drone Strikes and Terrorism

Two contradictory arguments characterize the debate concerning the effectiveness of

drone strikes. The first focuses on how drone strikes affect the attitudes of the civilian

population, while the second focuses on the impact of drone strikes on insurgent and

terrorist organizations. Below, each argument is discussed in turn.

2.1 Drone Strikes, the Civilian Population, and Militant Mo-

bilization

The first argument is that drone strikes do little to curb terrorism and might increase

it. Critics have suggested, for example, that drone attacks are ineffective or counter-

productive to the U.S.’ strategy of disrupting and dismantling al Qa’ida and other

terrorist networks because they are unpopular among the Pakistani population, largely

because they occasionally inflict civilian casualties.

Consistent with this argument, Smith and Walsh find no evidence that drone strikes

degrade al Qa’ida propaganda efforts.11 Taking this argument a step further, others

11Smith and Walsh, “Do Drone Strikes Degrade Al Qaeda? Evidence From Propaganda Output,”

4

argue drone strikes are the wrong tool to curb militancy – in fact, they may worsen

it – because the tactic itself breeds a counterproductive desire for revenge among

Pakistanis who might otherwise harbor no hostilities toward the United States. In the

words of David Kilcullen and Andrew Exum, “Every one of these dead noncombatants

represents an alienated family, a new desire for revenge, and more recruits for a militant

movement that has grown exponentially even as drone strikes have increased.”12 Given

the expected anticipated anti-U.S. mobilization and desire for revenge among the

civilian population suggested by this logic, we elaborate the following hypothesis:

H1: All else equal, drone strikes increase terrorist violence.

2.2 Disruption, Degradation, and Militant Capabilities

The second argument, which is common among U.S. counterterrorism officials but

rarely buttressed with specific empirical evidence due to the drone program’s secrecy,

contends that drone strikes are effective at reducing the terrorist threat posed by

targeted groups. Two mechanisms are frequently cited: disruption and degradation.

2.2.1 Disruption

The first mechanism counterterrorism officials cite involves “disruption” of militant

operations. The disruption mechanism suggests drone strikes reduce militants’ ability

to operate in a cohesive, effective manner and erode their ability to exercise sovereign

control over local areas. Even if an insurgent or terrorist organization is the only armed

actor on the ground, as they often are in FATA, where state authority is extremely

weak, the greater the threat from above, the more costly it is for the militants to

exercise de facto control in that area.

The standard logic of violence would predict that this innovation should lead

pp. 318-32412David Kilcullen and Andrew McDonald Exum, “Death From Above, Outrage Down Below,”

New York Times, May 17, 2009, p. WK13.

5

us to anticipate an increase in terrorist violence as a result of their efforts to deter

defection.13 In contrast, our argument predicts that, in this scenario, militant violence

should decrease, both in terms of its frequency and its lethality. The reason is that

drone strikes in an area represent a meaningful indication of an increased security

risk to militants operating in that area. The increased risk associated with continuing

to operate in the targeted areas should apply to any type of militant activity that

is vulnerable to drone capabilities, including conducting terror attacks, regardless of

whether militants would otherwise conduct operations at their “average” rate and level

of lethality (i.e., the null hypothesis), or if they would otherwise escalate the frequency

and lethality of their operations to deter potential defectors (i.e., the alternative “logic

of violence” hypothesis). We thus advance the following hypothesis:

H2: All else equal, drone strikes decrease terrorist violence.

2.2.2 Degradation

The second mechanism by which drones can reduce terrorism is through a “degradation

effect.” According to this argument, drone strikes reduce terrorism by taking terrorist

group leaders and other “high-value individuals” (HVIs) off the battlefield, consequently

hindering the terrorists ability to produce violence at a sustained rate. Killing

operational leaders of al Qaida and its affiliated movements is the primary objective

of drone strikes.14

Indeed, drone strikes have resulted in the deaths of many top terrorists. According

to an Obama administration official, the U.S. eliminated at least 20 of al Qaidas

30 top leaders from 2009 to 2012 in Pakistan and Afghanistan.15 In Pakistan alone,

13Kalyvas, The Logic of Violence14“Remarks of President Barack Obama,” speech delivered at National Defense University,

May 23, 2013. Accessed online at http://www.whitehouse.gov/the-press-office/2013/05/23/remarks-president-barack-obama. Last accessed on July 5, 2013.

15“Two-Thirds of Top Qaeda Leaders ‘Removed Since 2009: Obama Aide,” Reuters, December 18,2012. Quoted in International Crisis Group, “Drones: Myths and Reality in Pakistan,” Crisis GroupAsia Report N◦ 247, May 21, 2013, p. 22.

6

according to the New America Foundation, drone strikes killed 51 militant leaders,

including 28 senior al Qai’da operatives, between 2004 and early 2013.16 They have

also killed several high-level Pakistani and Afghan Taliban and al Qa’ida-affiliated

leaders.17

An emerging political science literature investigates the effects of “leadership de-

capitation” – the killing or capture of militant leaders or other HVIs–with a focus

on evaluating the group-level effects of killing or capturing top insurgent or terrorist

leaders, usually on outcomes such as rates of group collapse or group success.18 The

findings of this literature are mixed. On the one hand, using a large-N approach,

Johnston and Price both find evidence that removing the top leaders of insurgent

and terrorist groups helps degrade these organizations, rendering them less lethal,

more vulnerable to defeat, and more likely to end quickly than groups that did not

suffer leadership decapitation.19 On the other hand, Jordan argues that decapitations

of terrorist organizations rarely collapse a group quickly or degrade terrorist group

capabilities to conduct attacks. Jordan suggests decapitation can have counterproduc-

tive effects when performed against larger and older organizations, as well as against

religious and separatist organizations.20

We expect drone strikes that kill terrorist leaders will be associated with reductions

in terrorist attacks. Previous research convincingly demonstrates that conducting

effective terrorist attacks requires skilled individuals, many of whom are well-educated

and come from upper middle-class backgrounds.21 Indeed, captured documents con-

16“The Year of the Drone: Leaders Killed,” New America Foundation. The data reflect figuresuntil January 6, 2013.

17International Crisis Group, “Drones: Myths and Realities,” p. 22.18Scholars disagree about the conceptualization and measurement of these variables. On leadership

decapitation and terrorist group collapse, see Jordan, “When Heads Roll,” pp. 731-733. Ondecapitation and group mortality, see Price, “Targeting Top Terrorists,” pp. 26-33. For a critique ofempirical strategies of leadership decapitation scholarship, see Johnston, “Does Decapitation Work?,”pp. 47-50.

19Johnston, “Does Decapitation Work?;” Price, “Targeting Top Terrorists.”20Jordan, “When Heads Roll.”21Alan B. Krueger, What Makes a Terrorist: Economics and the Roots of Terrorism (Princeton,

NJ: Princeton University Press, 2007); Ethan Bueno de Mesquita, “The Quality of Terror,” American

7

taining detailed biographical data on foreign al Qa’ida militants in Iraq illustrate that

among the foreign terrorists – who are conventionally known to be more sophisticated

than local fighters – their most commonly listed “occupation” prior to arriving in

Iraq was that of “student.” For militants for whom information on “experience” was

available, “computers” was the most commonly listed experience type, just ahead of

“weapons.”22

In the context of northwest Pakistan, where militant freedom of movement is

limited by the threat of drone strikes, we expect that militant groups will be unable

to replace senior leaders killed in drone strikes because recruiting and deploying them,

perhaps from a foreign country with a Salafi jihadist base, will be costly and difficult.

This is not to say that leaders killed in drone strikes are irreplaceable. On the contrary,

other militants are likely to be elevated within their organization to replace them. But

we also anticipate that those elevated to replace killed leaders will be, on average, of

lower quality to the organization than their predecessors. Thus, we predict that the

loss of leaders will be associated with the degradation of terrorists ability to produce

violence. This logic implies Hypothesis 3:

H3: All else equal, drone strikes that kill one or more terrorist leader(s) will lead

to a decrease in terrorist violence.

Based on the contradictory theories and findings in the literature, however, we

cannot dismiss the possibility that killing terrorist leadership might have a counter-

productive effect. We thus elaborate Hypothesis 4:

H4: All else equal, drone strikes that kill one or more terrorist leader(s) will lead

to an increase in terrorist violence.

Journal of Political Science, Vol. 49, No. 3 (July 2005), pp. 515530; Efraim Benmelech, ClaudeBerrebi, and Esteban F. Klor, “Economic Conditions and the Quality of Suicide Terrorism,” TheJournal of Politics, Vol. 74, No. 1 (January 2012), pp. 113128.

22Joseph Felter and Brian Fishman, “The Demographics of Recruitment, Finances, and Suicide,”in Brian Fishman, ed., Bombers, Bank Accounts, and Bleedout: Al-Qa’da’s Road In and Out of Iraq(West Point, NY: Combating Terrorism Center, 2008), pp. 4244.

8

2.3 Diversion

Another possibility is that drone strikes disrupt terrorist activities in their FATA

strongholds by diverting militants to other areas where these activities can be con-

tinued. The terrorists themselves have documented the threat of drones and devised

countermeasures to mitigate the threat. Captured al Qa’ida documents show diversion

as a recommended strategic response to drones. Interestingly, as a counterintelligence

strategy, diversion could push terrorists into rural or urban areas. Each can offer

militants a different type of protection. Rural areas – especially ones with rugged,

mountainous terrain – offer favorable geography for insurgency and, perhaps, a mea-

sure of protection from drones. Urban areas might offer terrorists human camouflage,

enabling them to blend into the population and limiting the U.S.’ ability to conduct

lethal targeting due to concerns about civilian casualties.

This theory implies that drone strikes in FATA might increase militant violence

in rural or urban areas. In documents captured from Osama bin Ladens compound

in Abbottabad, Pakistan – itself an urban area outside of Islamabad, where the al

Qaida leader had been hiding since 2005 – bin Laden advised al Qaida members there

to move to Afghanistans Kunar province for protection from U.S. drones: “Kunar is

more fortified due to its rougher terrain and many mountains, rivers and trees, and

it can accommodate hundreds of the brothers without being spotted by the enemy,”

wrote bin Laden. “This will defend the brothers from the aircraft.”23 Other militants

have taken refuge in urban areas to elude drone targeting.24 Dozens of al Qa’ida and

Afghan Taliban have been arrested in Balochistan since 2009, when the drone war in

FATA escalated.25

23Osama bin Laden, “Letter dated 7 August 2010 from ‘Zamarai’ (Usama bin Ladin) to MukhtarAbu al-Zubayr,“ SOCOM-2012-0000015-HT,” May 2012. Accessed online at http://www.ctc.usma.edu/posts/socom-2012-0000015-english. Last accessed July 2, 2013

24See, for instance, a report in The Times, dated August 8, 2009, which was accessed at http://www.thetimes.co.uk/tto/news/world/asia/article2611093.ece. Last accessed June 11, 2013.

25These statistics came from an assessment by the Institute for Conflict Management, a SouthAsian think tank, based primarily on reporting from Pakistani newspapers. It was accessed online

9

If drone strikes systematically divert militants to other locations, spatial patterns

of observed violence in areas around FATA should increase. This argument implies

the following testable hypothesis:

H5: All else equal, drone strikes increase militant violence in neighboring areas

not targeted by drones.

2.4 Duration

Finally, there is also considerable debate about drone strikes’ short-term versus

long- term utility. Some suggest any effect of drone strikes is tactical and short

term. In this view, a drone strike might affect a militant group’s operations for

several days, but generally speaking these strikes do not significantly curtail militant

activities. Others suggest, however, that drone strikes have longer-lasting operational

or strategic effects. In this view, drone strikes serve to weaken – or strengthen –

militants over time. The former argue that because of drones persistent surveillance

and targeting capabilities, drones are a game changer that have significantly enhanced

counterterrorism capabilities and effectiveness. The latter argue that drone strikes

result in boons in militant mobilization that enhance militant groups overall ability

to conduct violent attacks. These contrasting arguments generate two additional

hypotheses:

H6: Drone strikes have an extended violence-reducing effect. H7: Drone strikes

have an extended violence-increasing effect.26

at http://www.satp.org/satporgtp/countries/pakistan/Balochistan/index.html. Lastaccessed on June 10, 2013.

26For both hypotheses, “extended” is defined as longer than one week.

10

3 Empirical Strategy

In this section, we describe our methodology for evaluating the effects of drones. Our

study spans from January 2007 through September 2011. We analyze how drone

strikes in the FATA region of Pakistan affect militant violence both in FATA and in

other parts of Pakistan and neighboring areas of Afghanistan

We use the agency-week as our unit of analysis. Agencies in FATA are akin to

districts in many other countries. In the present study, they include Bajaur, Khyber,

Kurram, Mohmand, North Waziristan, Orakzai and South Waziristan. Agencies

correspond with the geographic distribution of militant groups in FATA more closely

than does any other administrative unit, making agency-level analysis useful for

tracking secular differences in violence that might arise because of heterogeneity in the

militant groups operating in the region.27 Indeed, as Figure 3 shows, FATA’s seven

agencies suffered varying levels of violence over time.28

Our empirical approach also includes spatial analysis, specifically, tests for a

spillover effect of drone strikes. We examine whether drone strikes effect militant

violence in neighboring areas in both Pakistan and Afghanistan using varying radii

from the center of each agency. We increase the radius of the neighborhood for spatial

27On variation in militant organizations across FATA agencies, see, for example, Shuja Nawaz,“FATA – A Most Dangerous Place: Meeting the Challenge of Militancy and Terror in the FederallyAdministered Tribal Areas of Pakistan,” Center for Strategic and International Studies, January2009; Imtiaz Gul, The Most Dangerous Place: Pakistan’s Lawless Frontier (New York: Viking, 2010);and Brian Fishman, “The Battle for Pakistan: Militancy and Conflict across the FATA and NWFP,”New America Foundation, April 2010.

28Although the first documented drone strike in FATA occurred in June 2004, our analysis focusesprimarily on events between early 2007 through late 2011. Through the end of 2006, only six dronestrikes were reported. The number of strikes in 2007 – five – nearly equaled the number that hadbeen conducted in the entire previous history of the war. This number would increase dramatically inthe following years, peaking in 2010 at 122 and declining to 73 and 48 in 2011 and 2012, respectively.Temporal variation in drone targeting at the local level during the period under study is an importantpart of our identification strategy. Likewise, 2007 is also an ideal starting point because, unlike inprevious years when levels of violence in the region were fairly flat, there was significant variationin militant violence starting in 2007 – both across agencies and in FATA overall – due to conflictescalation largely unrelated to drone strikes. Our data allow us to trace this violence to particularlocations and times, giving us some ability to assess possible endogeneity in the statistical results.

11

analysis from 25 km to 150 km in increments of 25 km.29 This approach enables

us to examine how far any spillover effects of drone strikes appear to extend and

track changes, if any, in the effect of drone strikes on militant activities in response to

increasing distance from the targeted area.

3.1 Identifying Assumptions

Our empirical strategy is motivated by the fact that the week-to-week timing of

drone strikes in FATA’s agencies is subject to a range of quasi-random factors. Many

factors unrelated to militant violence are likely to influence whether a drone is used

in a given week. Drone strikes clearly are not conducted at random, but there is

reason to believe the week-to-week incidence of drone strikes – our temporal unit of

analysis – is only weakly related to levels of terrorist violence. This is because in

practice, the ability to conduct drone strikes depends on a complex range of factors –

meteorological, bureaucratic, and technological, among them – whose unpredictability

from week-to-week means that a drone strike on a terrorist target identified this week

might be conducted this, next week, the following week, or not at all. We describe

seven such complicating factors below.

First, weather patterns play a significant role in drone operators’ ability to identify

and strike targets, for example, introducing a random component into the timing of

drone strikes when they are examined in relatively modest intervals. This random

element in the timing of drone strikes is not only observed by journalists, but also

by al Qa’ida’s leadership in multiple theaters of operation.30 Recently declassified al

Qaida documents show, for example, that Osama bin Laden once advised operatives

29The average radius of a FATA agency is 32 kilometers.30Nelly Lahoud, Stuart Caudill, Liam Collins, Gabriel Koehler-Derrick, Don Rassler, and

Muhammad al Ubaydi, Letters from Abbottabad: Bin Ladin Sidelined? (West Point, NY: Com-bating Terrorism Center, 2012); Associated Press, “The Al-Qaida Papers – Drones,” February21, 2013. Accessed online at http://hosted.ap.org/specials/interactives/_international/

_pdfs/al-qaida-papers-drones.pdf. Last accessed July 5, 2013.

12

not to move from their safe houses on clear days.31 This is consistent with information

from the U.S. sources that “cloudy days” obscure satellites and make it more difficult

to view objects on the ground. Moisture and electrical interference from storms may

also hinder operations.32

Second, drones are a scarce commodity and are in high demand across the theaters

in which the U.S. conducts counterterrorism missions. Thus, the availability of drones

in FATA – whether for intelligence, surveillance, and reconnaissance (ISR) missions

to identify terrorist targets, or for lethal targeting itself – varies with changing ISR

requirements and priorities assigned to other theaters.33

Third, key technological aspects required to conduct drone strikes can, and report-

edly have, varied, at times semi-randomly. A UAV operator relies on imagery from

onboard sensors for target detection, but the quality and timeliness of this imagery

depends on data link and bandwidth limits. Low update rates and long communication

delays, for example, will produce slow and discontinuous imagery to a UAV pilot,

encouraging operators to adopt a “go-and-wait strategy.”34

Fourth, human factors specific to drone operations introduce another possible

element of randomness into the incidence and timing of strikes. A recent study found

that humans ability to operate drones as planned has varied due to the different types

of drones that may be deployed because not all have the same software for mission

planning and some are equipped with differing user interfaces with which a pilot might

be more or less familiar. As with any kind of software, familiarity matters; a pilots

31“Letter dated 7 August 2010 from ‘Zamarai’ (Usama bin Ladin) to Mukhtar Abu al-Zubayr,“SOCOM-2012-0000015-HT,” May 2012, pp. 2-3.

32Robert Tilford, “Al-Qaedas ‘Anti Drone’ Tactics Discussed In Bin Laden Letter,“The Examiner, March 3, 2012. Accessed online at http://www.examiner.com/article/

al-qaeda-s-anti-drone-tactics-discussed-bin-laden-letter; for a detailed analysis of thebin Laden documents, see Lahoud et al., Letters from Abbottabad, pp. 32, 4647.

33Greg Miller, “Military Drones Aid CIAs Mission,” Washington Post, October 3, 2010, p. A1; andAdam Entous, Julian E. Barnes, and Siobhan Gorman, “CIA Escalates in Pakistan: Pentagon DivertsDrones From Afghanistan to Bolster U.S. Campaign Next Door,” Wall Street Journal, October 2,2010.

34Jason S. McCarley and Christopher D. Wickens, Human Factors: Implications of UAVs in theNational Airspace, (Urbana-Champaign, IL: University of Illinois, 2005), p. 7.

13

ability to conduct a given strike may depend on which software suite he or she was

trained.35

Fifth, bureaucratic and logistical factors as mundane as the work schedules of

key lawyers and decision-makers in the United States, who are required to provide

legal counsel and authorization before a strike can occur, might lead a strike to

happen or not for reasons that have little to do with the availability of a strike target.

Key principals are many time zones away from Pakistan in Washington, D.C., and

authorizations apparently can take hours or days to receive–if they are received at

all.36

Sixth, the timing of when a known terrorist presents a clean shot is likely to be

largely random on a week-to-week basis, meaning the treatment could plausibly have

occurred in the preceding or following agency-week as in the current one, making

weekly comparisons of differences in violence across agencies a credible causal estimate.

Seventh, and finally, a key to identification based on any of these factors is to make

the unit-of- analysis relatively small temporally. As the temporal unit of aggregation

increases, the validity of the identifying assumption goes down. The longer the

window, the less factors like the ones described above will matter, consequently

reducing confidence that the relationship identified is causal. As a result, we analyze

the effects of drone strikes at the weekly level instead of a higher level of aggregation,

such as the month or quarter.

35Kevin W. Williams, A Summary of Unmanned Aircraft Accident/Incident Data: Human FactorsImplications, (Washington, DC: United States Department of Transportation Federal AviationAdministration, 2004), p. 12.

36Afsheen John Radsan and Richard W. Murphy, “Due Process and Targeted Killing of Terrorists,”Cardozo Law Review, Vol. 31, No. 2 (May 2009), pp. 412-413.

14

3.2 Estimation

In the analysis presented below, we estimate two-level fixed-effect models with both

agency and temporal (week) fixed effects and a spatial lag of drone strikes (2FESL).37

Fixed-effects regression is a standard econometric approach to panel data analysis.38

Letting i denote the cross sectional index (i.e., the agency) and t the time index (i.e.,

the week), a two-level fixed effect equation is given by:

yit = αi + βxit + ht + εit (1)

where y measures the incidence of terrorism, x is the number of drone strikes, αi are

unobserved agency fixed effects, and ht are time (week) fixed effects.

Agency fixed effects account for all the time-invariant differences between agencies,

such as terrain and elevation, which could otherwise confound cross-sectional analysis.

In practice, the fixed effects are included to control for unobserved factors that might

vary by agency, as well as secular quarterly trends in levels of conflict violence. Week

fixed effects allow us to control for time-specific differences such as heavy snow, flooded

terrain, natural disasters, and religious festivals, which could potentially determine

combatant activity. In addition to the fixed-effects regressions described above, we

also estimate models that include a spatial lag. Phillips and Sul (2003, 2007) have

shown that cross-sectional dependence may cause panel OLS estimates to be biased

and inconsistent. Including a spatial lag enables us to directly model cross-sectional

dependence in the regression.39 A spatial lag model with two-level fixed effects assumes

37The spatial lag in spatial econometrics is equivalent of the temporal lag in time-series analysis. Itis the value of the dependent variable for the unit(s) that constitute(s) the space of the observationunder consideration, which in this article is formed by all agencies or districts in Afghanistan andPakistan falling within a certain distance from the centroid of the agency under consideration.

38See especially Jeffrey M. Wooldridge, Econometric Analysis of Cross Section and Panel Data(Cambridge, Mass.: MIT Press, 2002); and Joshua D. Angrist and Jorn-Steffen Pischke, MostlyHarmless Econometrics: An Empiricist’s Companion (Princeton, NJ: Princeton University Press,2009).

39See, for instance, R.J. Franzese, Jr. and J. C. Hays, “Spatial Econometric Models of Cross-Sectional Interdependence in Political Science Panel and Time-Series Cross-Section Data,” Political

15

the following form:

yit = αi + ρ∑j 6=i

wijyjt + βxit + ht + εit. (2)

where ρ is the spatial autoregressive coefficient, which measures the general strength

of spatial dependence, wij is an element of the spatial weight matrix reflecting the

degree of connection between two units i and j, yjt is the measure of militant violence

for unit j during time period t, xit is the number of drone strikes in unit i at time t, αi

are unobserved agency-specific effects, and ht are quarterly time effects.

3.3 Data and Variables

To examine the effect of drone strikes, we combined detailed data on US drone strikes

in FATA originally collected by researchers at the New America Foundation (NAF)40

with incident-level data on terrorist activities in FATA during the same time period

compiled in the National Counterterrorism Center’s (NCTC) Worldwide Incidents

Tracking System (WITS)41 and incidents of militant violence against tribal elders

compiled by the South Asia Terrorism Portal.42 Incidents from each data source

were georeferenced according to the reported locations of the incidents in the media

accounts used to track and cross-reference each drone strike and militant attack.

The NAF data on drone strikes include information on the incidence, date, and

location of each strike, the high and low estimates of fatalities that have occurred in

each strike, deaths of militant leaders in drone strikes, and the sources of information

Analysis, Vol. 15, No. 2 (2007), pp. 140164. We also performed the Pesaran cross-sectionaldependence (CD) test on the residuals of the estimated models. See M. Hashem Pesaran, “ASimple Panel Unit Root Test in the Presence of Cross-Section Dependence,” Journal of AppliedEconometrics, Vol. 22, No. 2 (March 2007), pp. 265312. The results of the CD test are availableupon request.

40Peter Bergen and Katherine Tiedemann, “New America Foundation Drones Database,” NewAmerica Foundation, 2011.

41“Worldwide Incidents Tracking System,” National Counterterrorism Center, 2012.42The SATP data were accessed online at http://www.satp.org/satporgtp/countries/

pakistan/database/Tribalelders.htm. Last accessed June 15, 2013.

16

that were used to compile each summary. The data were compiled from reports in

reputed international and Pakistani news media sources.

The WITS database uses fairly standard criteria in coding incidents as terrorist

attacks. To be included as a terrorist attack in the WITS database, activities were

required to be “incidents in which sub-national or clandestine groups or individuals

deliberately or recklessly attacked civilians or non-combatants, including military

personnel and assets outside war zones.”43 Moreover, attacks have to be initiated and

executed by non-state militants. Spontaneous violence, hate crimes and genocides are

excluded from the database.

Using data that focuses on terrorist incidents–violence against civilian rather than

military targets–is justifiable for both theoretical and empirical reasons. Theoretically,

Kalyvas (2006) argues that the combatants are likely to target civilians selectively in

their zones of control as a result of real or perceived spying by civilians. A similar

narrative is often used to describe militant responses to drone strikes in FATA: militants

believe drone strikes are the result of informant betrayal, and thus target suspected

informants44

Along these lines, tribal elders – typically associated with a local incumbency –

have been cited as particularly common targets.45 We use data on militant attacks on

tribal elders in Pakistan from 2005 through 2011 compiled by SATP. 46 The inclusion

of this variable is warranted by the suggestion that drone strikes increase attacks on

tribal elders whom militants suspect of collaborating with U.S. or Pakistani military

or intelligence services.

Table 1 summarizes the variables and data sources used in our analysis. We focus

43“Worldwide Incidents Tracking System,” National Counterterrorism Center, 2012.44Dashiell Bennett, “Pakistani Death Squads Target Informants Who Help Drone Attacks,” The

Atlantic Wires, December 29 2011.45Brian Fishman, “The Battle for Pakistan: Militancy and Conflict across the FATA and NWFP,”

Counterterrorism Strategy Initiative Policy Paper. The New America Foundation. Last accessedApril 2010.

46The SATP data were compiled from open-source media reports, primarily from south Asiansources, by the Institute of Conflict Management, New Delhi.

17

on drone strikes and four key measures of terrorist activity. Our data set contains

information on the following variables at the agency-week level:

• UAV: The number of drone strikes in a given agency and week.

• HVT: The number of “senior leaders” killed by drone strikes in a given agency

and week. (Source: New America Foundation)

• Incidents: The number of militant incidents or attacks in a given agency and

week.

• Lethality: The number of dead and wounded in terrorist incidents or attacks

in a given agency and week.

• IED Attacks: The number of IED attacks conducted in a given agency and

week.

• Suicide Attacks: The number of suicide attacks conducted in a agency and

week.

• Attack on Tribal Elder(s): The number of militant attacks against tribal

elders in a given agency and week.

3.4 Descriptive Statistics

For this study, we constructed an agency-week dataset. The time-series spans from

January 1, 2007 through September 30, 2011. Descriptive statistics of key variables

over this time period are shown in Table 1.

Figures 1–3 illustrate the variation in terrorist attacks and drone strikes over space

(Figure 1) and time for all of FATA (Figure 2 ) and for its constituent agencies (Figure

3). Figure 2 shows the monthly time trend of drone strikes and terrorist attacks for all

of FATA from 2007 through September 2011. Militant attacks began trending upward

18

in mid-2007, peaking in early 2009 before declining back to roughly mid-2007 levels by

Fall 2011. Drone strikes (left axis) were relatively rare until Fall 2008 – before August

2008, when four strikes were conducted, there had never been more than one strike in

a month. At the agency level, figure 3 shows that North Waziristan closely mirrors

the macro trend, with trends fluctuating more in South Waziristan and Khyber while

being relatively rare elsewhere in FATA.

4 Empirical Results

A cursory look might suggest the former: as figure 2 shows, violence rose from 2007

until 2009 and was as high in September 2011, when our time-series ends, as in any

year since 2007. Yet figure 2 also shows that the rise of drone strikes appears to

have been a response to a deteriorating environment in which terrorist violence was

increasing dramatically. It is thus plausible that the drone wars escalation occurred as

a result of real and anticipated increases in terrorist violence. Given the upward trend

in terrorist violence prior to the escalation of the drone campaign, and the observed

variation in terrorist attacks across agencies, we use both week- and agency-fixed

effects to mitigate confounding impacts of secular time trends in terrorist violence and

of agency-specific differences, using these within regressions to estimate the average

effect of drone strikes within agencies over time.47

47As a robustness test, we also ran regressions using a series of model specifications includingordinary least square (OLS) and involving temporal lags, spatial lags, and first-differences, bothwith and without fixed effects. We also conducted two panel unit-root tests, the Breitung andPesaran tests, which both allow for cross-sectional dependence. Results of these tests are available onrequest. Jorg Breitung, “The Local Power Of Some Unit Root Tests For Panel Data,” in Advancesin Econometrics, Vol. 15: Nonstationary Panels, Panel Cointegration, and Dynamic Panels (NewYork: JAI Press, 2000), pp. 161-178; and Pesaran, “A Simple Panel Unit Root Test.”

19

4.1 Disruption

Table 2 presents the 2FESL estimates of drone strikes on four measures of militant

violence. The spatial lag included in the 2FESL models measures the value of our

dependent variables in the districts falling within 75 km of the centroid of the agency

in which strikes occurred.

To test Hypotheses 1 and 2, we examine five different measures of militant violence:

the frequency of attacks, the lethality of attacks, the number of IED attacks, the

number of suicide attacks, and the number of attacks on tribal elders. The results do

not support Hypothesis 1 – that drone strikes are associated with increased terrorism.

On the contrary, they support our hypothesis, (Hypothesis 2), that that drone strikes

are associated with decreases in militant violence. We find no evidence in support of

the competing hypothesis (Hypothesis 1) – that drone strikes increase violence. We

discuss these results in more detail below.

The 2FESL estimates in column 2 of table 2 show that drone strikes are associated

with a decrease in militant attacks of approximately 24 percentage points–a result

that is statistically significant at the one percent level. From 2007 through 2011, the

average agency suffered roughly 0.88 militant attacks per week. During weeks in which

a drone strike occurred, agencies suffered an average of about 0.68 attacks.

Given that drone strikes are associated with reductions in insurgent attacks in the

areas where they occur, it makes sense that drone strikes might also be negatively

associated with the lethality, or “quality,” of attacks in those same areas. Consistent

with Hypothesis 2, the estimates presented in column 2 of table 2 suggest that the

lethality of militant attacks declines by more than 36.5 percent as a result of a drone

strike in a given week. On average, 2.77 people were killed or injured in militant

attacks in FATA between 2007 and the end of the third quarter of 2011. This figure

would decline substantially to 1.76 per week as a result of a single drone strike if the

20

number of drone strikes would increase by one per agency-week.48

The disruption hypothesis also implies that drone strikes should reduce militants

ability to conduct complex and coordinated attacks like IED and suicide attacks.

We find support for these propositions in our econometric tests. Drone strikes are

negatively associated with the number of IED attacks in FATA during the period

studied. Based on the estimates in column 3 in table 2, a drone strike is associated

with a 21-percentage point reduction in IED attacks. The marginal effect translates

into an estimated decrease in IED attacks from an average of 0.32 per agency-week to

0.25 per agency-week when there is one drone strike.

Regarding suicide attacks, the coefficient in column 4 of table 2 suggests that drone

strikes are also associated with reductions in these tactics. This result is significant at

the one percent level. Suicide attacks are relatively rare but extremely high-profile

events: the mean number of suicide attacks per agency per week is 0.02, or about

one per agency every year. The point estimate appears small, but the marginal effect

translates into an almost 67 percent decline in the number of suicide attacks in a week

with one drone strike. Thus, the average number of weekly suicide attacks in FATA,

which is 0.14 per week during the period under consideration, would decline to 0.05

per week as a result of one drone strike per agency-week. On balance, the evidence is

clearly consistent with Hypothesis 2 – the “disruption” hypothesis – and not with the

argument that drone strikes trigger increased violence (Hypothesis 1).

4.2 Degradation

Given that killing terrorist leaders or HVIs in terrorist organizations is the purpose of

drone strikes, we evaluate whether patterns of militant attacks differ following strikes

48It is important to note that the estimate of decline in lethality of militant attacks is based on anassumption of a constant linear relationship–an assumption that may or may not be correct. Thepredicted decline is probably an overstatement of the impact drones could realistically have, simplybecause even at the peak of the drone campaign in 2010, when the number of drone strikes was twoand a half times larger than the previous year (119 in 2010, versus 53 in 2009), the number of dronesper campaign-week in 2010 was 0.33, while it was 0.14 in 2009.

21

in which a militant leader was killed. Table 3 provides tests of Hypotheses 3 and 4

against the four metrics of militant violence examined here using the same 2FESL

specifications as in table 2. The results are largely consistent with Hypothesis 3 – that

killing militant leaders is associated with decreased violence. There is little support for

Hypothesis 4, that killing HVIs has counterproductive effects on violence. Controlling

for the number of drone strikes per agency-week, the first column of table 3 shows

that drone strikes that kill a HVI are associated with reductions in the number of

militant incidents that occur. This result is statistically significant at the one-percent

level. There is, however, weaker evidence that HVI removals reduce militant lethality

and IED attacks.49

Overall, the evidence is somewhat consistent with the argument that individuals

matter for a terrorist organizations ability to produce violence at sustained rates.

Along with other evidence from macro-level studies of leadership decapitation, the

present results suggest that critics who argue against the efficacy of removing key

figures may be overemphasizing the extent to which such individuals can be readily

replaced.50

4.3 Diversion

A potential concern with the previous findings is that it is possible that drone strikes

do not actually reduce terrorist violence, but rather displace it. While drone strikes

might cause militant activities to decline in the targeted agencies, they may cause an

escalation in militant violence in proximate areas that are not subject to drone strikes

if militants move their operations in response to UAV targeting in FATA. The concern

49These estimates may be more imprecise than the statistical results suggest, as a result ofheterogeneity in the measurement of the “HVI” variable. Although U.S. government officials considerterrorists targeted by drone strikes target as “senior leaders” or “high-value individuals” (HVI), theU.S. government has not publicly stated the criteria it uses to identify individual terrorists as seniorleaders or HVIs. Available information on individuals identified as leaders killed in drone attackssuggests a degree of heterogeneity

50Johnston, “Does Decapitation Work?;” and Price, “Targeting Top Terrorists.“

22

with spillover effects is not just academic; media reporting points to it as a possible

policy concern.51

To address this issue, we extend the above analysis by estimating the effect of

drone strikes beyond the seven FATA agencies in neighboring areas within various

distances of agencies where strikes have occurred. To do this, we vary the radius of

struck agency’s “neighborhood” from 25 km to 150 km by increments of 25 km. By

testing the effect of drone strikes on militant violence in geographic units that expand

outward to varying distances, we assess how drone strikes affect militancy beyond

FATA.

The results of potential spillover effects are presented in table 4. Each column in

these tables presents estimates of the effect of drone strikes on militant violence in a

neighborhood of a particular radius, beginning with a radius of 25 km in column 1

and ending with a radius of 150 km in column 6. In the first two rows of table 3, we

present the estimates of the effect of drone strikes on the number of militant attacks

in the neighborhood. The sign of the drone strike estimate is negative up to 125 km

and is statistically significant at 25 km and 100 km. The coefficient becomes positive

at a radius of 150 km, but the positive coefficients are statistically insignificant. The

estimates of the effect of drone strikes on the lethality of militant attacks and IED

attacks in the neighborhood display a pattern similar to the estimates of militant

attacks. For suicide attacks, the results deviate slightly from the trend observed for

the other dependent variables. Unlike the other dependent variables, the coefficient

associated with suicide attacks does not change signs from negative to positive–the

results remain negative for each of the radii tested. The evidence in support of a

favorable spillover effect on suicide attacks is somewhat inconclusive, however: only

the coefficient associated with the 25 km radius variable is statistically significant at

conventional levels.

51Alex Rodriguez, “U.S. Concerns Grow as Militants Move Bases Along Pakistan Border,” LATimes, November 7, 2010.

23

5 Duration

If the evidence indicates that drone strikes help disrupt and degrade terrorist group

operations in Pakistan, a final question of considerable importance is whether the

effect is long-lasting. There is some evidence that it extends over a few weeks and is

thus relatively long in duration. Using a model that includes five one-week lags of

drone strikes, there is a significant negative relationship between strikes that occurred

five weeks earlier and both attack lethality and suicide attacks.

Moreover, the sign of the coefficients of UAV at t-5 are negative for both number

of incidents and IED attacks but are not statistically significant at conventional levels.

There is also limited evidence that drone strikes might have a deterrent effect that lasts

between two and five weeks. Indeed, in agencies contiguous to those that were struck,

the lethality of militant attacks has decreased, on average, in the weeks following a

drone strike in a neighboring agency. Several of these results are statistically significant

at the five-percent level. The evidence certainly suggests that drones disruptive effect

is strongest in the week when strikes occur, but the negative results on the lagged

variables both in agencies where drone strikes occurred as well as neighboring agencies

suggests a possible deterrent mechanism at work as well.

Overall, these findings are broadly consistent with Hypothesis 6, and at odds with

Hypothesis 7.

6 Implications

This paper offers a systematic analysis of the relationship between U.S. drone strikes

and militant violence in northwestern Pakistan and eastern Afghanistan. Our analysis

suggests that drone strikes are negatively associated with various measures of militant

violence, both within individual FATA agencies and their immediate neighborhoods.

There is also evidence to suggest that the negative association between drone strikes

24

and three measures of militant violence – incidents, lethality and IED attacks – changes

sign as we increase the neighborhood radius to exceed 125 km. This may or may

not be indicative of drone strikes causing militant activities to move farther away

from FATA. With the current research design and data, we are unable to make any

definitive conclusion regarding the spillover effects. We are also not in a position to

make strong causal claims about the impact of drone strikes on militant violence.

There is evidence of a strong negative contemporaneous correlation between drone

strikes and various measures of militant violence. This may indicate that that drone

strikes have important counterterrorism dividends, but caution should be exercised in

inferring causality due to the selection bias inherent in the data despite the econometric

techniques used to mitigate selection bias in our regression estimates.

Still, our findings appear consistent with the hypothesis that new technologies–

specifically, remote means of surveillance, reconnaissance and targeting–are able, at

least in certain key areas of northwest Pakistan and eastern Afghanistan, to disrupt

and degrade militants in ways that compensate for an incumbent governments lack of

physical presence in and control over these areas, and can consequently limit both the

frequency and the lethality of militant attacks. This suggests that new technologies

that provide information previously available only to actors with a strong physical

presence in a geographic area can alter conventionally accepted “logics of violence” in

civil war.52

The implication of these findings, of course, is that as technology continues to

become increasingly sophisticated, warfare is likely to become increasingly “virtual”

but not bloodless. Adversaries – not only governments, but also non-state actors such

as insurgents, terrorists and criminal organizations – will adapt their organization

and behavior to reduce their vulnerability to adversaries countermeasures, and some

are likely to try leveraging these technologies for their own use against their state

52Kalyvas, The Logic of Violence in Civil War.

25

and non-state enemies. In the near term, for example, insurgents may increasingly

abandon rural areas like FATA in favor of urban areas of the sort that insurgents

have traditionally eschewed, but that may now offer greater protection from drones

and other sophisticated countermeasures. However, the operational constraints on

urban operations might restrain militants’ use of violence, just as they have for state

actors.53

53On urban insurgency, see Brian Michael Jenkins, The Five Stages of Urban Guerrilla Warfare:Challenge Of The 1970s (Santa Monica: Rand Corporation, 1971); Fair, Urban Battle Fields of SouthAsia: Lessons Learned from Sri Lanka, India and Pakistan (Santa Monica, CA: RAND Corporation,2005); and Paul Staniland, “Cities on Fire: Social Mobilization, State Policy, and Urban Insurgency,”Comparative Political Studies, Vol. 43, No. 12 (December 2010), pp. 1623-1649

26

Fig

ure

1:DroneStrikesand

MilitantAttacksin

FATA

&itsNeighborhood

27

Figure 2: Time Trends in Drone Strikes and Terrorist Attacks

28

Fig

ure

3:Tim

eTrendsin

DroneStrikesand

MilitantAttacksbyAgency

29

Tab

le1:

Sum

mar

ySta

tist

ics:

FA

TA

&N

eigh

bor

hood

FATA

Neighborh

ood

Afghanistan

Pakistan

Variable

Mean

S.D

.*M

in.

Max.

Mean

S.D

.*M

in.

Max.

Mean

S.D

.*M

in.

Max.

Mean

S.D

.*M

in.

Max.

UA

V0.

153

0.60

50

8–

––

––

––

––

––

–H

VI

.023

10.

181

03

––

––

––

––

––

––

Inci

den

ts0.

880

1.33

30

130.

183

0.73

20

170.

681

3.04

40

771.

824

5.50

00

91L

eth

alit

y2.

777

14.0

190

285

0.68

96.

759

036

12.

148

21.9

820

1305

7.69

661

.135

022

19IE

DA

ttac

ks

0.31

60.

734

07

0.06

30.

360

011

0.22

91.

592

070

0.65

82.

674

049

Su

icid

eA

ttac

ks

0.02

00.

149

02

0.00

90.

102

04

0.03

80.

559

028

0.08

50.

744

021

Att

acks

onT

rib

alE

lder

s0.

013

0.11

20

1–

––

––

––

––

––

–

Nu

mb

erof

Ob

serv

atio

ns

1729

5082

237

791

1309

1

*Sta

ndar

dD

evia

tion

30

Table 2: Drone Strikes and Terrorist Violence: 2FESL Estimates

Incidents Lethality IED Suicide Attacks on Elders

UAV -0.048*** -0.247*** -0.016*** -0.003*** -0.001**(0.010) (0.090) (0.005) (0.001) (0.001)

Constant 0.023 0.136 0.004 0.000 0.005**(0.020) (0.200) (0.010) (0.002) (0.002)

Observations 1729 1729 1729 1729 1729AIC 473.224 8998.330 -1448.116 -6737.893 -7594.435BIC 620.517 9145.623 -1300.823 -6590.600 -7447.142

Standard errors in parentheses

* p<0.10, ** p<0.05, *** p<0.01

Table 3: Leaders Killed and Militant Violence: 2FESL Estimates

Incidents Lethality IED Suicide

UAV -0.012 -0.136 -0.022*** -0.001(0.010) (0.100) (0.007) (0.001)

HVI -0.092*** -0.057 -0.002 -0.001(0.040) (0.200) (0.01) (0.002)

Constant 0.205*** 0.649*** 0.075*** 0.005***(0.008) (0.08) (0.004) (0.001)

Observations 1729 1729 1729 1729AIC 417.207 8751.883 -1606.601 -6977.664BIC 433.572 8768.249 -1590.235 -6961.298

Standard errors in parentheses

* p<0.10, ** p<0.05, *** p<0.01

31

Table 4: Drone Strikes and Neighborhood Militant Violence

Neighborhood Radius

Dependent Variable 25 km 50 km 75 km 100 km 125 km 150 km

Incidents -0.042*** -0.022 -0.009 -0.007* -0.004 0.002(0.010) (0.010) (0.006) (0.004) (0.004) (0.003)

Lethality -0.252*** -0.152* -0.037 0.081 0.055 0.038(0.090) (0.080) (0.040) (0.050) (0.040) (0.030)

IED Attacks -0.014*** -0.002 -0.002 -0.001 -0.001 0.002(0.005) (0.009) (0.003) (0.002) (0.002) (0.002)

Suicide Attacks -0.003*** -0.003 -0.001 -0.000 -0.000 -0.000(0.001) (0.003) (0.001) (0.0008) (0.0006) (0.0004)

Observations 1722

Standard errors in parentheses. Coefficient estimates for drone strike (UAV) variable. Intercept estimates not presented.

* p<0.10, ** p<0.05, *** p<0.01

32

Table 5: The Duration of the Effect of Drone Strikes

(1) (2) (3) (4)VARIABLES Incidents Lethality IED Suicide

UAV -0.030*** -0.11*** -0.011** -0.0023***(0.0049) (0.0086) (0.0038) (0.000076)

UAVt-1 0.0056 -0.033 0.0054 -0.0010**(0.010) (0.044) (0.0053) (0.00040)

UAVt-2 -0.0011 -0.045 0.0080 0.00047(0.0088) (0.032) (0.0066) (0.0010)

UAVt-3 0.017* -0.061 0.016** -0.0014***(0.0084) (0.035) (0.0064) (0.00019)

UAVt-4 -0.0034 0.088 -0.0090** 0.0019(0.012) (0.14) (0.0029) (0.0015)

UAVt-5 -0.0087 -0.16*** -0.0013 -0.0033***(0.0098) (0.027) (0.0096) (0.00028)

Neigborhood UAVt-1 -0.0045 0.46 0.0047 -0.00019(0.028) (0.56) (0.013) (0.0043)

Neigborhood UAVt-1 -0.0047 -0.34*** 0.011 0.012*(0.019) (0.061) (0.017) (0.0050)

Neigborhood UAVt-2 0.0043 -0.20 -0.016 -0.0058(0.059) (0.32) (0.016) (0.0040)

Neigborhood UAVt-3 -0.015 -0.24 -0.0062 -0.0011(0.059) (0.34) (0.025) (0.0038)

Neigborhood UAVt-4 -0.0047 -0.20** -0.000024 0.0077(0.044) (0.061) (0.025) (0.0059)

Neigborhood UAVt-5 0.077(0.041)

Incidentt-1 0.16***(0.034)

Incidentt-2 0.13***(0.034)

Neighbohood Incidentt-1 0.077(0.041)

Lethalityt-1 0.0053(0.024)

Lethalityt-2 0.00087(0.0098)

Neighbohood Lethalityt-1 -0.043(0.079)

IEDt-1 0.23***(0.043)

IEDt-2 0.049***(0.013)

Neighborhood IEDt-1 0.038(0.039)

Suicidet-1 0.050(0.055)

Suicidet-2 -0.017(0.020)

Neighborhood Suicidet-1 0.014(0.044)

Constant 0.14*** 0.72*** 0.049*** 0.0050***(0.010) (0.057) (0.0057) (0.00053)

Observations 1,694 1,694 1,694 1,694

33

Appendix A: Robustness Tests

Here we evaluate whether the results are sensitive to certain time periods. The dronewar escalated significantly in 2008 relative to previous years; drone strikes increasedagain in both 2009 and 2010, and remained higher in 2011 than in 2008. Giventhat we cannot rule out that unobserved changes in FATA, starting approximatelyin 2008, drive this change, we restrict the sample to 2008 and later to test whetherthe patterns that we observed in the previously discussed results hold during thislater period. Table A-1 shows that the main findings do hold when we estimate the2FESL specification for each of the measures of violence with the sample restricted toobservations after 2007. In Table A-2, we extend our analysis to an additional threeyears by starting from the beginning of 2004, the year of the first-known drone strikein FATA. The results are remarkably similar to the main findings.

Table A-1: Drone Strikes and Terrorist Militant Violence: 2008-2011

Incidents Lethality IED Attacks Suicide Attacks Attacks on Elders

UAV -0.034*** -0.194*** -0.012** -0.001*** -0.001*(0.142) (0.089) (0.005) (0.001) (0.001)

Constant 0.079*** 1.137*** 0.040** 0.004*** 0.005***(0.025) (0.534) (0.015) (0.001) (0.002)

Observations 1456 1456 1456 1456 1456AIC 480.277 7792.078 -1051.775 -5902.082 -6176.432BIC 607.080 7918.881 -924.9727 -5891.515 -6049.629

Standard errors in parentheses

* p<0.10, ** p<0.05, *** p<0.01

Table A-2: Drone Strikes and Militant Violence: 2004-2011

Incidents Lethality IED Attacks Suicide Attacks Attacks on Elders

UAV -0.051*** -0.227*** -0.021*** -0.003*** -0.002***(0.010) (0.076) (0.005) (0.001) (0.001)

Constant 0.120 0.035 0.005 0.0003 0.002**(0.012) (0.086) (0.006) (0.001) (0.001)

Observations 2912 2912 2912 2912 2912AIC -273.484 13654.120 -3737.697 -12867.330 -13228.340BIC -34.42016 13893.180 -3498.633 -12628.270 -12989.270

Standard errors in parentheses

* p<0.10, ** p<0.05, *** p<0.01

34