Christine Fair New Paper

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The Supply Side Drivers of Recruitment in the Pakistan Army

C. Christine FairAnirban Ghosh

March 11, 2012

Words: 11,039

1. Introduction to the PuzzlePakistan attained independence following the breakup of the British Raj in 1947. Since then,

Pakistans army has governed Pakistan, indirectly or directly, for most of the states existence.1

In recent years, Pakistan has been the source of extensive nuclear proliferation, both horizontal

to other non-nuclear statesand verticalan increase in the quality and quantity of its own

arsenal.2

In addition, Pakistan has for decades been connected to international, regional, and

domestic Islamist militancy.3Since the 1980s, Pakistans army, under its first covert and later

overt nuclear umbrella and acting principally through the intelligence agency it controls (the

1Shuja Nawaz, Crossed Swords: Pakistan, Its Army and the Wars Within (New York: Oxford

University Press, 2008); Ayesha Siddiqa, Military Inc.: Inside Pakistans Military Economy

(London: Pluto Press, 2007); Hussain Haqqani, Pakistan: Between Mosque and Military

(Washington, D.C.: Carnegie Endowment for International Peace, 2005).2

Paul K. Kerr and Mary Beth Nikitin, Pakistans Nuclear Weapons: Proliferation and Security

Issues (Washington D.C.: Congressional Research Service, 2011).3

Arif Jamal,A History of Islamist Militancy in Pakistani Punjab (Washington D.C: The Jamestown

Foundation, 2011); C. Christine Fair, The Militant Challenge in Pakistan,Asia Policy11

(January 2011): 105-37; Praveen Swami, India, Pakistan, and the Secret Jihad: The Covert War

in Kashmir, 19472004 (New York: Routledge, 2007); Haqqani, Pakistan: Between Mosque and

Military; Rizwan Hussain, Pakistan and the Emergence of Islamic Militancy in Afghanistan

(Burlington: Ashgate, 2005); Barnett R. Rubin, The Fragmentation of Afghanistan (New Haven:

Yale University Press, 2002).


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Inter-Services Intelligence Directorate, or ISI), has become increasingly brazen in its support for

Islamist militants operating in India, particularly Indian-administered Kashmir.4

In light of these discomfiting facts, international and Pakistani analysts fear that the

Pakistan army is Islamizing and that elements of the force are becoming increasingly

sympathetic to or infiltrated by Islamist militants.5

Under these conditions, some analysts and

commentators speculate that the integrity of the army itself may be compromised, with the

result that militants may acquire the know-how necessary to build a nuclear weapon, if not a

device itself.6

These fears are further galvanized by the longstanding role that the army and the

ISI have played in raising, training, financing, and directing a raft of Islamist militant

organizations that menace the region and, increasingly, the world beyond South Asia.7

Thus the

mention of the Pakistan armyconjures up in the minds of many the frightening vision of

4S. Paul Kapur, Dangerous Deterrent: Nuclear Weapons Proliferation and Conflict in South Asia

(Stanford: Stanford University Press, 2007); Ashley J. Tellis, C. Christine Fair and Jamison Jo

Medby, Limited Conflicts Under the Nuclear UmbrellaIndian and Pakistani Lessons from the

Kargil Crisis (Santa Monica: RAND, 2001).5George Friedman, Stratfor Geopolitical Intelligence Report - Pakistan and its Army, 6

November 2007. Available athttp://www.airforce.forces.gc.ca/CFAWC/Contemporary_

Studies/2007/2007-Nov/2007-11-06_Pakistan_and_its_Army.pdf(accessed 9 March 2012);

Jonathan Paris, Prospects for Pakistan, (London: The Legatum Institute, 2010); Frdric Grare,

Pakistan: The Myth of an Islamist Peril(Washington D.C.: Carnegie Endowment for International

Peace, 2006).6Karin Brulliard, Pakistans top military officials are worried about militant collaborators in

their ranks, The Washington Post, 17 May 2011; Raza Rumi, The Spectre of Islamist

Infiltration, The Friday Times, 27 May 2011; Syed Saleem Shahzad, Al-Qaeda had warned of

Pakistan strike, The Asia Times, 27 May 2011; Voice of America, Two Soldiers Convicted in

Musharraf Assassination Attempts, VOA News, 24 December 2011; David Leigh, WikiLeaks

cables expose Pakistan nuclear fears: US and UK diplomats warn of terrorists getting hold of

fissile material and of Pakistan-India nuclear exchange, The Guardian, 30 November 2010;

Muqaddam Khan and Azaz Syed, Ex-soldier, brothers held on Tarbela attack suspicion, TheDaily Times, 15 September 2007; Jane Parlez, Pakistan Retakes Army Headquarters; Hostages

Freed,The New York Times, 10 October 2009.7

Haqqani, Pakistan: Between Mosque and Military; Hussain, Pakistan and the Emergence of

Islamic Militancy in Afghanistan; Rubin, The Fragmentation of Afghanistan; Paris, Prospects for

Pakistan; C. Christine Fair et al., Pakistan: Can the United States Secure an Insecure State?

(Santa Monica: RAND, 2010); Julian Schofield and Michael Zekulin,Appraising the Threat of

Islamist Take-Over in Pakistan, Research Note 34 (Montreal: Concordia University, 2007).
http://www.airforce.forces.gc.ca/CFAWC/Contemporary_%20Studies/2007/2007-Nov/2007-11-06_Pakistan_and_its_Army.pdfhttp://www.airforce.forces.gc.ca/CFAWC/Contemporary_%20Studies/2007/2007-Nov/2007-11-06_Pakistan_and_its_Army.pdfhttp://www.airforce.forces.gc.ca/CFAWC/Contemporary_%20Studies/2007/2007-Nov/2007-11-06_Pakistan_and_its_Army.pdfhttp://www.airforce.forces.gc.ca/CFAWC/Contemporary_%20Studies/2007/2007-Nov/2007-11-06_Pakistan_and_its_Army.pdfhttp://www.airforce.forces.gc.ca/CFAWC/Contemporary_%20Studies/2007/2007-Nov/2007-11-06_Pakistan_and_its_Army.pdfhttp://www.airforce.forces.gc.ca/CFAWC/Contemporary_%20Studies/2007/2007-Nov/2007-11-06_Pakistan_and_its_Army.pdf


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international Islamist terrorists acquiring nuclear weapons and using them to menace the

international community.

Despite these pressing concerns, very little of an empirical nature is known about the

Pakistan army. Given the importance of the institution to regional and international security,

general scholarship on the Pakistan Army is surprisingly thin or dated. Stephen P. Cohens The

Pakistan Army8

remains the most empirically-based analysis of the organization. However, that

volume was published in 1984, and much has transpired since Cohens research was done, most

significantly the Islamizing efforts of General and President Zia ul Haq, the overt nuclearization

of the subcontinent in 1998, the Kargil War with India in 1999, the 9/11 attacks in the United

States, and several high-profile Islamist militant assaults in India which nearly brought the two

countries to the brink of war. Ayesha Jalals The State of Martial Rule: The Origins of Pakistans

Political Economy of Defence9

remains one of the best and most authoritative accounts of the

rise of authoritarianism in Pakistan. However, her work is now some two decades old. Hassan

Askari Rizvis Military, State and Society in Pakistan and The Military and Politics in Pakistan:

1947199710

are the most insightful treatments ofPakistans civil-military relations. Other

recent works by Ayesha Siddiqa and Shuja Nawaz address the growing role of the army in the

management ofthe state and Pakistans fraught civil-military relations.11

Still, Siddiqa, Nawaz,

and others can only speculate about the composition of the Pakistan army officers corps andthe determinants that influence recruitment outcomes.

This essay is an effort to address this scholarly and empirical lacuna. It does so by

employing rare district-level data on Pakistan armys officer recruitment from 1970 to 2002 and

building upon recently-published descriptive analysis of the same data.12

Unfortunately, the

8Stephen P Cohen, The Pakistan Army(Berkeley: University of California Press, 1984).

9Ayesha Jalal, The State of Martial Rule: The Origins of Pakistans Political Economy of Defence

(Cambridge: Cambridge University Press, 1990).10

Hassan Askari Rizvi, Military, State and Society in Pakistan (London: Palgrave, 2000); Hassan

Askari Rizvi, The Military and Politics in Pakistan: 19471997(Lahore, Pakistan: Sang-e-Meel

Publications, 2000).11

Siddiqua, Military Inc.: Inside Pakistans Military Economy; Nawaz, Crossed Swords: Pakistan,

Its Army and the Wars Within.12

C. Christine Fair and Shuja Nawaz, The Changing Pakistan Army Officer Corps, Journal of

Strategic Studies 34, No. 1 (February 2011): 63-94; C. Christine Fair, Increasing Social


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dataset employed here only contains data on officers. No comparable dataset on the non-

commissioned ranks is publically available. Where appropriate we discuss the limitations posed

by these data. Because individual attributes of recruited officers, i.e. officer-level data, are not

publicly available, we perform the next best analysis: an ecological study which examines how

the attributes of districts explain changes in officer recruitment across districts and across time.

To preview our argument, three principle findings are robust in our analysis. First, socio-

economic status negatively correlates with military recruitment. Second, while a fundamental

measure of human capital (the ability to do simple sums) is positively associated with

recruitment, once we control for a basic level of numeracy, the effect of additional male

educational attainment on army recruits is negative. Third, when we compare across all districts

by excluding year and district fixed effects, we find that Pakistan army officers come from

districts that both are more urban and characterized by higher female education levels. In

Pakistan, higher levels of female education correlate with increased social liberalism.13

These findings suggest that in recent decades the Pakistan army has recruited its officer

corps from those districts in which men meet baseline educational requirements but who are

increasingly poorly educated. Because we have only district level data, we can say for certain

that the actual individuals recruited have these characteristics. (We cannot overcome the

ecological fallacy problem of our data and analysis.) However, if these district characteristics doreflect those of the officer corps, one must query whether the diminishing educational

attainment of the officer corps could have significant impact on the ability of the Pakistan army

to introduce more sophisticated weapon systems and strategically assess the impact of tactical

and operational decisions, among other challenges in a global environment of technological and

other revolutions in military affairs. However even though the officers are coming from less

financially prosperous areas, they continue to hail from more socially liberal communities, as

indicated by the positive effect of female educationeven if we cannot say that the officersthemselves have such characteristics.

Conservatism in the Pakistan Army: What the Data Say,Armed Forces and Society

(forthcoming 2012).13

Samina Malik and Kathy Courtney, Higher education and womens empowerment in

Pakistan,Gender and Education23, No. 1, (January 2010): 2945.
http://www.tandfonline.com/doi/abs/10.1080/09540251003674071http://www.tandfonline.com/doi/abs/10.1080/09540251003674071http://www.tandfonline.com/doi/abs/10.1080/09540251003674071http://www.tandfonline.com/doi/abs/10.1080/09540251003674071http://www.tandfonline.com/toc/cgee20/23/1http://www.tandfonline.com/toc/cgee20/23/1http://www.tandfonline.com/toc/cgee20/23/1http://www.tandfonline.com/toc/cgee20/23/1http://www.tandfonline.com/doi/abs/10.1080/09540251003674071http://www.tandfonline.com/doi/abs/10.1080/09540251003674071


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The remainder of this paper is organized as follows. After a brief description of how the

Pakistan army recruits, we describe the analytical methods and empirical specification that we

employ to test these hypotheses. In this section we also present a conceptual model of army

recruitment generally and of the Pakistan army in particular. We also suggest several

hypothesized relationships between recruitment outcomes and the posited independent

variables that follow our conceptual model. In the fourth section we present our results,

followed by an exposition of the several robustness tests we conducted. We conclude with

some reflections upon the implications of our findings.

2. Pakistan Army Recruitment14

Pakistans army is an all-volunteer army. However, both officers and enlisted personnel join the

Pakistan army with the expectation that their period of service will fill much of their productive

lives. This expectation marks a point of difference with the recruitment of both officers and

enlisted ranks in the U.S. army, where officers and enlisted alike join the military with relatively

short initial service obligations (ranging from two to six years for enlisted personnel and at least

five years in active service and an additional three years in the reserves for officers

commissioned from a service academy). Recruits join the U.S. armed forces for many reasons,

and few officers or recruits will make a career out of service.15

Some persons join the army,

either as officers or enlisted, to obtain better education while in uniform through active-duty

educational opportunities. Others (mostly enlisted personnel) serve in the military to obtain

educational benefits, such as the Montgomery G.I. Bill, which help soldiers who honorably

complete their terms of service pay for higher education. Still others will join the army in part to

receive loan repayment assistance for education already completed. For many, service in the

14For a more detailed account of army officer recruitment, see C. Christine Fair and Shuja

Nawaz, The Changing Pakistan Army Officer Corps,Journal of Strategic Studies 34, No. 1

(February 2011): 63-94; Schofield, Insidethe Pakistan Army: A Woman's Experience on the

Frontline of the War on Terror(Westport: Dialogue, 2011).15

For a breakdown of retention rates by seniority, see Beth J. Asch et al., Cash Incentives and

Military Enlistment, Attrition, and Reenlistment(Santa Monica: RAND, 2010).


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U.S. army will comprise an important phase in the persons career but will not be his or her only

vocation.16

In this sense, the Pakistan army differs substantially from the U.S. army. Whereas the

U.S. army, at times, has struggled to retain high-quality enlisted and even officers when their

contracts are complete; when individuals join the Pakistan army, they do so with an expectation

of serving in that institution for the duration of their careers. Enlisted personnel typically retire

after eighteen years of service, although they can serve up to thirty-four years.17

Officers are

subject to an up or out rule and typically retire between the ages of 52 and 60, depending

upon the maximum rank they obtain before they are passed up for promotion and thus retire.

The principal institution involved in training Pakistani army officers is the Pakistan

Military Academy (PMA) at Kakul. Each year, the PMA commences two long courses, with one

cohort inducted in the spring and another in the fall. After graduating from the two-year

program called the long course, cadets are commissioned with the rank of second lieutenant.18

The selection process is extremely competitive, with far more applicants than billets: each year

there are roughly 3,000 applicants for about 320 cadet places in each regular long course.19

Candidates must satisfy a number of eligibility criteria: they must be single, hold at least an

intermediate degree (i.e., 12 years of schooling), and be between 17 and 22 years of age .

Recruits must obtain a score of at least 50 percent in their matriculation (10th grade) or FA(12th grade) exams.

20

Applicants undergo initial testing and screening at eight regional selection and

recruitment centers across the country: Rawalpindi, Lahore, and Multan (in the province of the

Punjab); Hyderabad and Karachi (in the province of Sindh); Quetta (in the province of

16Asch et al., Cash Incentives and Military Enlistment, Attrition, and Reenlistment; Beth J. Asch,

Can Du, andMatthias Schonlau, Policy Options for Military Recruiting in the College Market:

Results from a National Survey(Santa Monica: RAND, 2004); Beth J. Asch,M. Rebecca Kilburn,andJacob Alex Klerman,Attracting College-Bound Youth into the Military: Toward the

Development of New Recruiting Policy Options (Santa Monica: RAND, 1999).17

Official Pakistan Army Recruitment Website, FAQs, n.d. Available at:

http://www.joinpakarmy.gov.pk/.18

Fair and Nawaz, The Changing Pakistan Army Officer Corps.19

Fair and Nawaz, The Changing Pakistan Army Officer Corps.20

Official Pakistan Army Recruitment Website, Induction: PMA Long Course.
http://www.rand.org/pubs/authors/d/du_can.htmlhttp://www.rand.org/pubs/authors/d/du_can.htmlhttp://www.rand.org/pubs/authors/s/schonlau_matthias.htmlhttp://www.rand.org/pubs/authors/s/schonlau_matthias.htmlhttp://www.rand.org/pubs/authors/s/schonlau_matthias.htmlhttp://www.rand.org/pubs/authors/k/kilburn_m_rebecca.htmlhttp://www.rand.org/pubs/authors/k/kilburn_m_rebecca.htmlhttp://www.rand.org/pubs/authors/k/kilburn_m_rebecca.htmlhttp://www.rand.org/pubs/authors/k/klerman_jacob_alex.htmlhttp://www.rand.org/pubs/authors/k/klerman_jacob_alex.htmlhttp://www.rand.org/pubs/authors/k/klerman_jacob_alex.htmlhttp://www.joinpakarmy.gov.pk/http://www.joinpakarmy.gov.pk/http://www.joinpakarmy.gov.pk/http://www.rand.org/pubs/authors/k/klerman_jacob_alex.htmlhttp://www.rand.org/pubs/authors/k/kilburn_m_rebecca.htmlhttp://www.rand.org/pubs/authors/s/schonlau_matthias.htmlhttp://www.rand.org/pubs/authors/d/du_can.html


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Baluchistan); Peshawar (in the province of Khyber-Pakhtunkhwa (KPK, formerly NWFP); and

Gilgit (in the administrative area of Gilgit-Baltistan, previously known as the Northern

Areas).21

Selected candidates then proceed to the Inter-Services/General Headquarters

Selection and Review Board in Kohat or to satellite centers in Gujranwala (Punjab), Malir

(Sindh), or Quetta (Balochistan) for further screening. Successful candidates are then

recommended for the PMA. Each year, the Army General Headquarters determines the precise

number of slots for PMA cadets based upon regimental reports of shortfalls. As the previous

discussion indicates, officer selection is generally based upon on merit, with the exception of

episodic efforts to increase recruitment from underrepresented provinces such as Sindh and

Balochistan.22

3. Data and MethodsWhile changes in the individual attitudes and attributes of new officers in the Pakistan Army are

extremely important, officer level for the Pakistan army data do not exist. (Nor do such data

exist for enlisted personnel.) The cooperation of the Pakistani government and of the army in

particular would be absolutely necessary for the collection of officer-level data, and such

cooperation is exceedingly unlikely to be forthcoming within any relevant time horizon.

Fortunately, recent district-leveldata on aggregate officer accessions and retirement

between 1970 and 2005 offer some insights into the ways in which variation in the

characteristics of officer-producing districts explains variation in district-level recruitment

trends.23

The recruitment data we use are the annual numbers of candidates accepted into the

Pakistan Military Academy at Kakul, aggregated by the officers home district. (The district is the

level of governance below the provincial level, which in turn is below the federal government.)

Areas of origin also include the Federally Administered Tribal Areas (FATA), Kashmir, and Gilgit-

Baltistanareas that are not typically included in Pakistans federal surveys. The dataset also

includes district-level aggregates of the numbers of officers who retired from those districts.

21Official Pakistan Army Recruitment Website, Contact Us.

22Fair and Nawaz, The Changing Pakistan Army Officer Corps.

23Fair and Nawaz, The Changing Pakistan Army Officer Corps.


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The optimal empirical approach to explaining the determinants of officer recruitments

would require officer-level data, currently publicly unavailable, on the social, economic, and

attitudinal characteristics of individual officers. Thus this study perforce takes a second best

approach that models the relationship between changes in officers home districts across time

and officer recruitment across time and district. This approach provides some insights into

changes in the larger social and economic environment of those districts that produce officers.

This analysis cannot overcome a fundamental ecological fallacy in that we cannot

assume that the characteristics of any given district are similar to characteristics of any given

officer. That said, it would be difficultthough not impossibleto argue that a significant

difference between the characteristics of officer-producing districts and of the officers coming

from those districts could sustain itself across time and space. Despite the sub-optimal nature

of the data, they do offer limited insights into the determinants of officer recruitment

outcomes in Pakistan.

Data: A Brief Overview24

This study employs two kinds of data. First, it uses annual army data which provide district-level

aggregated recruits and retirements between 1970 and 2005. (Figure 1 shows the annual intake

of officers into the PMA, aggregated annually across all districts.)

24For a more detailed exposition of methods, see Fair and Nawaz, The Changing Pakistan Army

Officer Corps.


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Figure 1. Annual Intake of Officers for All Provinces (1970-2005)

0

200

400

600

800

1000

1200

1400

1600

1 97 0 1 972 1 97 4 1 976 1 97 8 19 80 1 98 2 19 84 1 98 6 19 88 1 99 0 19 92 1 99 4 1 99 6 1 99 8 2 000 2 00 2 2 004

Year

TotalOfficers

Source: In-house manipulations of army officer recruitment data.

Second, it utilizes district-level estimates of economic, demographic, and social

characteristics derived from the 1991, 1995, 1998, and 2001 waves of household surveys

conducted by Pakistans Federal Bureau of Statistics (FBS). (In 2012 the Federal Bureau of

Statistics became part of a restructured organization called the Pakistan Bureau of Statistics.)

These survey data are derived from household surveys as well district-level assessments of

facilities and government services for the four provinces of the Punjab, Sindh, Baluchistan, and

KPK. The data are weighted appropriately to ensure that they are representative at the district

level for our analysis.

The analytical dataset was constructed using only those districts and years for which

three conditions hold. First, recruitment data are used only for years for which household

survey data are available across all waves of the FBS data. Second, recruitment data are used

only for those years for which one-year lag variables (e.g. years in which data for the previous


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year were also available) could be created. (Socio-economic and other household variables may

not affect recruitment outcomes in the same year.) Third, models use only those districts and

years for which the full set of individual and community characteristics were collected by the

FBS across all years in each district. The resultant dataset included district-level recruitment

data from 1992, 1996, 1999, and 2002 and district-matched FBS data from 1991, 1995, 1998,

and 2001. These cumulative restrictions yielded a data set of 343 observations.25

Trends in Pakistan Army Recruitment

As Figure 1 shows, the Pakistan Officer Corps underwent a period of expansion in the early

1980s, with recruitment stabilizing at around 1,000 new recruits per year from 1990 onwards.

While the number of officers recruited per year has remained relatively steady, there have

been significant changes in the geographical composition of the Corps. In Figure 2, we attempt

to demonstrate the shifts in district-level market share. (Market share for any given year is

defined by the number of recruits from a given district divided by the total number of officer

recruits that year.) Doing so allows us to graphically demonstrate that since the 1970s, the

number of districts producing zero officers each year has decreased steadily. This trend was

accompanied by a steady rise in districts that produce a small number of officers (1 to 5). In

other words, over time, Pakistans officer corps is drawing from an increasingly diverse set of

districts.

25There are a maximum of 392 districts (4 years for each of 98 districts in the army data). After

restrictions (e.g. districts for which there are no recruitment data in specific years), we have

343 observations.


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Figure 2. Share of districts that produce 1-5 officers per year and share of districts that produce zero

officers per year.


This observation is consistent with a trend of recruiting officers from smaller and more

remote districts. Figure 3 further illustrates this trend for the timeframe used in this analysis

(1992-2002). Figure 3 demonstrates that those districts which were the highest producers of

officers in 1992 (Rawalpindi, Lahore, and Karachi) had all lost market share by 2002. The other

districts in the top ten in 1992 (Sargodha, Faisalabad, Peshawar, Sialkot, Gujrat, Islamabad, and

Jhelum), which in that year accounted for 28% of all new recruits, also steadily lost market

share over this period, producing less than 21 percent of new recruits in 2002. In contrast, the

other districts under study experienced a more than ten percent increase in market share

between 1992 and 2002.

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

1970

1973

1976

1979

1982

1985

1988

1991

1994

1997

2000

2003

Share of Districts withZero Recruits

Share of Districts with 1to 5 Recruits


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Figure 3. The share of officers recruited from Rawalpindi, Lahore, Karachi, the other top 10

districts, and all other districts.


The increasing geographical diversity of the officer corps is also displayed in a graphical

depiction of officer recruitment figures by district. Figure 4 presents the share of officer recruits

by district for 1992, 1996, 1999, and 2002. Darker colors correspond to districts with higher

market share. Districts that did not produce any officers are in grey. This alternative

representation confirms our earlier observation that the Pakistani army is becoming more

geographically diverse. Finally, Figure 5 demonstrates the change in market share for districts

between 1992 and 2002. Those districts depicted in shades of blue are net losers while those

colored either orange and red are net gainers. As Figure 5 illustrates, the losing districts tend

to be in the Punjab while the gainers are in Balochistan, Sindh, and Khyber Pakhtunkhwa.

Share 1992 Share 1996 Share 1999 Share 2002

RAWALPINDI 17.8% 19.9% 18.5% 15.3%

LAHORE 6.4% 11.2% 10.0% 6.6%

KARACHI 5.5% 6.9% 5.8% 4.1%

Districts 4th to 10th in 1992 27.8% 24.7% 23.0% 20.6%

All Other Districts 42.5% 37.2% 42.7% 53.4%

17.8%19.9% 18.5%

15.3%

6.4%11.2% 10.0%

6.6%

5.5%

6.9% 5.8%4.1%

27.8%24.7%

23.0%20.6%

42.5%

37.2%

42.7%

53.4%

0.0%

10.0%

20.0%

30.0%

40.0%

50.0%

60.0%

ShareofOfficerRecruits


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Figure 4. Share of Officer Recruits by District: 1992, 1996, 1999 and 2002.

LegendPercentage share of Officer Recruits from a given district


Figure 5. District-level Shifts in Market Share of Officer Recruits between 1992 and 2002

Officer

Shares 1992

Officer

Shares 1996

OfficerShares 2002

Officer Shares1999


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Despite the persistence ofclaims that the Pakistan army is a Punjab-dominated

organization, it is clear from the foregoing empirical analysis and discussion that the Pakistan

army officer corps is more geographically diverse than at any time in the past. (N.B: We cannot

assume that geographical diversity is isomorphic to ethnic diversity. It is entirely possible that


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recruits from Balochistan or elsewhere are ethnically Punjabi. However, it would be difficult to

contend that Punjabis from Balochistan would share the views of their co-ethnics in the Punjab.

Thus, even though we cannot instrument ethnic change, it is still a reasonable assumption that

the changing geographic base of the army has brought with it a diversification of views).

While increasing geographic diversity can be clearly established, it is less clear whether

this development has any socio-economic correlates. Does the change in officer recruitment

outcomes mean that officers are recruited from less-urban or less-educated districts, or is the

recruitment strategy employment-driven? To answer these questions, we investigate officer

production through an econometric framework whereby we relate district-level socio-economic

variables to district-level officer recruitment.

4. Conceptual Model of Pakistan Army Recruitment

The scholarly work on military manpower focuses heavily on enlistment of soldiers rather than

accession of officers. Research on officer accessions tends to be the purview of military

institutions, government agencies, or policy institutes.26

We present a conceptual framework

for understanding accessions to the Pakistani officer corps that is based on a similar model

developed by scholars of the U.S. army.27

Since this is a simple model of labor supply, there is

26Casey Wardynski, David S. Lyle, , Michael J. Colarusso,Accessing Talent: The Foundation of a

US Army Officer Corps Strategy(Carlisle Barracks PA: Amy War College, Strategic Studies

Institute, 2010); Kevin D. Stringer, The War on Terror and the War for Officer Talent: Linked

Challenges for the U.S. Army, Land Warfare Paper No. 67 (Arlington, VA: The Institute of Land

Warfare, 2008); Charles A. Henning,Army Officer Shortages: Background and Issues for

Congress (Washington D.C.: Congressional Research Service, 2006).27

John T. Warner, Curtis J. Simon, and Deborah M. Payne, The Military Recruiting ProductivitySlowdown: The Roles Of Resources, Opportunity Cost And The Tastes Of Youth, Defence and

Peace Economics 14, No. 5 (October 2003): 329342; Bruce R. Orvis andBeth J. Asch, Military

Recruiting: Trends, Outlook, and Implications (Santa Monica: RAND, 2001); Bruce R. Orvis,

Narayan Sastry, and Laurie L. McDonald, Military Recruiting Outlook: Recent Trends in

Enlistment Propensity and Conversion of Potential Enlisted Supply(Santa Monica: RAND, 1996);

Beth J. Asch and Bruce Orvis, Recent Recruiting Trends and Their Implications (Santa Monica:

RAND, 1994).
http://www.rand.org/pubs/authors/a/asch_beth_j.htmlhttp://www.rand.org/pubs/authors/a/asch_beth_j.htmlhttp://www.rand.org/pubs/authors/a/asch_beth_j.html


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no obvious reason why recruitment in Pakistan army, also an all-volunteer force, should not be

driven by similar considerations.28

This literature suggests several factors that likely affect an individuals likelihood of

joining the Pakistan army officer corps. A first consideration is the influencer market. In studies

of U.S. military recruitment, analysts consistently find that areas whose inhabitants are

positively disposed towards service in the armed forces tend to produce persons with a higher

propensity (or taste) for military service.29

A second cluster of drivers is the fundamental

human capital endowments of the person and other facets of their socio-economic standing

(SES) in their communities. Volunteer armies, like other hirers of labor, have quality and

aptitude standards which must be met as a precondition for selection into the force. A third

cluster of factors pertains to features of the labor market: the competing opportunities

available to an individual (dependent on his human capital endowments education, experience,

aptitude, etc.) and thus the opportunity cost of enlistment. Presumably, persons have more

economic opportunities during periods of economic growth and fewer during economic

retrenchment.30

As discussed above, recruitment in the Pakistan army officer corps is highly similar to

U.S. army recruitment. First, the recruitment process imposes baseline educational and literacy

standards. At the same time, young men whose education level exceeds the minimum requiredmay choose more profitable career paths. This is true despite the fact that the Pakistan army

offers numerous perquisites that are increasingly lucrative for those who achieve higher ranks.

For lower-ranking officers, the armys compensation is unlikely to compete with the private

28For the use of a similar approach to examine re-enlistment decisions in the Turkish armed

forces, see Jlide Yildirim and Blen Erdin, The Re-Enlistment Decision In Turkey: A Military

Personnel Supply Model,Defence and Peace Economics18, No. 4 (August 2007): 377-389 andJlide Yildirim, Nebile Korucu, and Semsettin Karasu, Further Education Or ReEnlistment

Decision In Turkish Armed Forces: A Seemingly Unrelated Probit Analysis,Defence and Peace

Economics21, No. 1 (2010): 89-103.29

Warner et al., The Military Recruiting Productivity Slowdown; Orvis et al. Military

Recruiting Outlook;John H. Faris, The All-Volunteer Force: Recruitment from Military

Families,Armed Forces and Society7, No. 4 (1981): 545-559.30

Warner et al., The Military Recruiting Productivity Slowdown.
http://www.ingentaconnect.com/content/routledg/gdpe;jsessionid=6gukvcr67tkq2.alexandrahttp://www.ingentaconnect.com/content/routledg/gdpe;jsessionid=6gukvcr67tkq2.alexandrahttp://www.ingentaconnect.com/content/routledg/gdpe;jsessionid=6gukvcr67tkq2.alexandrahttp://www.tandfonline.com/toc/gdpe20/21/1http://www.tandfonline.com/toc/gdpe20/21/1http://www.tandfonline.com/toc/gdpe20/21/1http://www.tandfonline.com/toc/gdpe20/21/1http://www.tandfonline.com/toc/gdpe20/21/1http://www.tandfonline.com/toc/gdpe20/21/1http://www.ingentaconnect.com/content/routledg/gdpe;jsessionid=6gukvcr67tkq2.alexandra


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sector, especially given the hardships of frequent permanent changes of station (as opposed

to temporary duty postings) and time spent away from family.31

A fourth set of considerations includes the various transaction costs associated with

seeking military employment (e.g. distance to a recruitment center, cost incurred in traveling to

such a center, etc.) and the resources available to military recruiters and related bureaucracies.

In the United States, these transaction costs can be substantially lowered with recruitment

effort and utilization of recruiting resources. Recruiters are based in high schools, will come to a

recruits home to meet with parents and to complete all paperwork, and will even ensure that a

student has transportation to the various appointments and evaluations required. In order to

facilitate the youths decision to enlist, the U.S. army deliberately deploys its own resources to

decrease the costs incurred by a recruit. (To a lesser extent, this is true even for officer

accessions.) Unsurprisingly, U.S. recruiter effort and skill have been found to be important

factors in the armys ability to meet its accession targets in a supply-limited market.32

In

Pakistan, however, the situation is starkly different. Because officer recruitment is demand-

constrained with a full order of magnitude more recruits that slots at the PMA, the Pakistan

army has no incentive to lower the barrier to pursue the army as a career. In fact, the reverse is

true: the ability of the potential officer to navigate the logistical and other aspects of the

recruitment process may be self-selecting for quality, motivation, and resourcefulness.In effort to model district-level recruitment outcomes, we employ the theoretical

approach employed by Dale and Gilroy33

and by Warner and Asch.34

Dale and Gilroy model the

determinants of military enlistment rates in the United States as a function of: the business

cycle (as a measure of competition for recruits); the level of military pay compared to

compensation offered by other employment opportunities; the various benefits of enlistment,

31Nawaz, Crossed Swords: Pakistan, Its Army and the Wars Within.

32

James N. Dertouzos and Steven Garber, Human Resource Management and ArmyRecruiting: Analysis of Policy Options (Santa Monica, Calif.: RAND Corp., 2006); James N.

Dertouzos and Steven Garber, Performance Evaluation and Army Recruiting (Santa Monica:

RAND Corp., 2008).33

Charles Dale and Curtis Gilroy, The Economic Determinants of Military Enlistment Rates

(Arlington, V.A.: U.S. Army Research Institute for the Behavioral and Social Sciences, 1983).34

John Warner and Beth Asch, The Economics of Military Manpower, inHandbook of Defense

Economics: Volume I, eds. Keith Hartley and Todd Sandler (Amsterdam: Elsevier 1995), 347-398.


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such as recruitment bonuses and educational benefits for the recruit and family members;

expenditure on recruiting and advertising (as a measure of recruitment resources expended to

convert persons with propensities to join the military into actual recruits); and time-specific

indicators, such as a dummy for GI Bill expiration and seasonal indicators.

Warner and Asch employ a model which takes the equilibrium number of enlistees in

the US military as the dependent variable. Their independent variables include military pay,

baseline eligibility requirements, and recruiter effort. An important contribution of second-

generation research on military recruitment, such as that of Warner and Asch, is the

recognition that recruiter effort plays an important role in both the quantity and the quality of

the recruited personnel. This is because, in the United States, recruitment is generally supply

constrained, with the US military struggling to make its recruitment targets. This is an

important departure from the Pakistani context, which is extremely demandconstrained. Thus

recruiter efforteven if it were available to uslikely would be irrelevant to this case.35

Both Dale and Gilroy36

and Warner and Asch37

use familiar empirical specifications of a

reduced form equation, where the dependent variable, the number of military personnel

recruited or enlisted, is modeled on a number of independent variables which they contend

affect military recruitment outcomes. We are forced to modify their empirical specifications

somewhat for this study because there are facets of the recruitment process that we cannotobserve without individual level data. However, we can develop hypotheses about district-level

observables and how they may relate to district-level outcomes, both across districts and across

time. We also modify our approach to account for the unique attributes of the Pakistan army.

In particular, as discussed above, we know that every year there are more people in Pakistan

who want to be part of the officer corps than there are positions offered, with the result that

the number of recruits is demand-constrained. Thus our paper focuses on the supply-side

drivers of army recruitment in Pakistan.

35Admission to the PMA is very competitive. Each year there are roughly 3,000 applicants and,

on average, about 320 cadet places in each regular long course in the PMA. See Fair and Nawaz,

The Changing Pakistan Army Officer Corps and Schofield, Insidethe Pakistan Army.36

Dale and Gilroy, The Economic Determinants of Military Enlistment Rates.37

Warner and Asch, The Economics of Military Manpower.


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First, we hypothesize that a basic level of human capital is necessary to satisfy the

Pakistan armys recruitment demands. However, individuals with more than this level of

education will likely pursue private sector jobs.38

Our proxies for human capital include the

ability to do simple sums and the average years of schooling for males in the district. (The

literacy measure was too unreliable to use.) We expect the former to be positively correlated to

recruitment as this variable should not have diminishing margins of return. In contrast, we

expect that male educational attainment will be non-linear: most Pakistani males do not meet

the educational requirement of 12 years and those who have pursued education beyond 12

years will likely seek employment in the private sector. According to the most recent (1998)

Pakistani census, 87 percent of Pakistanis do not have more than a tenth grade education. The

remainder of the population is divided roughly equally between those with twelve years of

education (less than 7 percent) and those with more than twelve years of education (also less

than 7 percent).39

Thus, 12 years is the optimal education level for officer production:

educational attainment levels higher or lower than 12 years result in a lower probability of

becoming an officer.

Similarly, we expect higher socio-economic status of a district to be negatively

correlated with recruitment outcomes. Available information on the Pakistan army suggests

that its officers to a growing extent no longer come from elite families but rather from themiddle class and upper lower class. In fact, sons of enlisted noncommissioned officers are

increasingly joining the ranks of the army corps.40

We use the number of private high schools in

a district to proxy the socio-economic characteristics of a given district in a given year. While at

first blush one would expect little variation in this figure, in fact variation is considerable, likely

owing to the fact that private schools in Pakistanas elsewhereaggregate local demand for

elite education and are typically more expensive than public schools. Thus private schools open

and close subject to market conditions. Therefore, we expect the coefficient on private highschools to be negative, since the parents of children going to comparatively more expensive

38Author field work in Pakistan between 2002 and 2010.

39Pakistan Population Census Organization, Educated Population by Level of Education, 1998.

Available at: http://www.census.gov.pk/LevelofEducation.htm.40

Schofield, Inside the Pakistan Army; Cohen, The Pakistan Army.


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private high school are more likely to consider tuition an investment leading to further

education and well-compensated private sector opportunities rather than military service.

We also use district-level Gini coefficients as a measure of income disparity in the

district. The sign on this variable is difficult to predict a priori. Given that the army officer corps

increasingly draws from non-elite families, one could argue that the sign should be positive.

However, in the Pakistan context, middle and upper-lower class families may be less likely to

produce sons who meet the basic educational and aptitude requirements or even the basic

physical fitness requirements.

Another important district characteristic that we use in our model is the share of

households in the district that live in an urban area (percent urban). We expect urban districts

to produce more officers, if for no other reason than most of Pakistans recruitment centers are

in cities (e.g. Peshawar (Khyber Pakhtunkhwa); Rawalpindi, Lahore, Multan (Punjab);

Hyderabad, Karachi (Sindh); Quetta (Baluchistan), and Gilgit (Gilgit Baltistan)).41

Cities in

Pakistan tend to be better served by public transportation and thus travel within them would

not require overnight stays in hotels. In contrast, persons in rural areas are less served by

recruitment facilities and would have to undertake costly journeys to apply. Therefore, we

would expect that urban districts, all else being equal, will produce more officers.

To proxy for labor market conditions, we use average male wages in the district, laborforce participation rates, and the share of the population made up of males between 20 and 29.

(This age bracket is the closest available proxy for the target age group of the officer corps.)

We expect districts with better labor market opportunities to produce fewer officers, all else

being equal. We also expect the number of recruits to be negatively correlated with both male

wages and labor force participation, and positively correlated with the share of eligible men in

the population.

While we cannot account for individual tastes without individual level data, we can saysomething about the relative progressive or conservative orientation of the districts which

produce officers. As a proxy for individual taste and preferences, we include variables for

female educational attainment and womens average age at marriage. If the army is recruiting

41Pakistan Army Official Recruitment Website, Home.


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from districts that are more socially conservative, then we expect the coefficient on these

variables to be negative, and vice versa if the army is recruiting from more progressive districts.

These variables also offer some insight on the trends in piety and conservatism within the office

corps.

As a proxy for the influencer market, we include the districts past history of producing

officers. Given the recent information that sons of junior non-commissioned officers are

increasingly entering the officer corps,42

enlistment figures are presumably also an important

component of this influencer or network effect. However, we are not in a position to control for

this. Relying only upon officer recruitment data, we expect that districts with greater officer

production in the past will be more likely to supply officers in the present. The reasons for this

expectation are two-fold. First, the community of officers (retired and serving) in the district is

likely to foster a more favorable view of military service.43

Second, the presence of retired and

currently serving officers also likely produces strong network effects and information

asymmetries between relatively recruitment-heavy districts and recruitment-sparse districts.

For example, potential recruits in recruitment-rich districts can more easily seek counsel from

other officers on how best to prepare for the selection process (including physical and

intellectual tests and several interviews) than those in districts with little or no history of army

service. While these network effects are important, over the time period used here (1992-2002), we expect them to be relatively constant. We anticipate that the district level fixed

effects proxy both any unexplained district shocks or other features outside of our model, but

also any network effects. These variables and the hypothesized relationships are summarized

in Table 1.

42Schofield, Inside the Pakistan Army.

43Officers in the Pakistan army receive numerous perquisites at various points in their careers,

including lucrative real estate in exclusive areas of Pakistans cities. Officers can sell theseproperties at market rates and turn a considerable profit. Army personnel (both retired and

serving officers and enlisted men) have access to their own hospitals and schools, which are

better than those available to the general population. Retiring officers receive lucrative

appointments, either at the Armys vast foundations or at private sector companies with

connections to the army. One of the biggest Army foundations is the Fauji Foundation (lit. Army

Foundation), which manufactures everything from cornflakes to concrete. For a detailed

discussion, see Siddiqa, Military Inc.: Inside Pakistans Military Economy.


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Table 1. Hypothesis Table with Proxy Variables and Expected Effects

Variables Hypothesized Relationshipto Recruitment Outcomes

Reason

District-average maleeducation attainment

Negative Average educational attainment for a districtwill exhibit decreasing marginal returns inrecruit production.

District-averagenumeracy

Positive A minimum level of ability necessary to bean army recruit.

The number of privatehigh schools in a district

Negative Same reason as male educational attainment

District Gini Positive Greater inequality, accompanied by fewerjob opportunities outside of the army, willpositively affect district recruitments.

Share of population livingin urban areas of a

district

Positive Urban districts, which proxy transactioncosts, will produce more officers, all else

being equalDistrict male wages Negative District-level labor market indicators will

negatively affect district recruitment.Labor force participationfor the district

Negative District-level labor market indicators willnegatively affect district recruitment.

Share of population in adistrict made up of malesbetween 20 and 29

Positive Size of market for recruits

District-average femaleeducational attainment

A priori ambiguous If army is recruiting from more conservativedistricts, the sign is negative, and vice-versa.

Average age of marriagefor women in the district

A priori ambiguous If army is recruiting from more conservativedistricts, the sign is negative, and vice-versa.

Analytical Methods and Empirical Specification

The variable of interest for this study is the number of officers that are recruited from a given

district, i, for a given year, t. Thus, we use the empirical specification:

Rit = Xi,t-1+ i+ t + it (equation 1)

where Rit is the number of officers recruited from district i at time t. X is a vector of

independent variables which we believe influence army officer recruitment in Pakistan, as

described above. We lag the independent variables by one year to model a more intuitive


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chronology according to which there is a time lag between the decision to join the army and the

actual act of joining the officer corps.44

Moreover, the economic situation in a given year could

not conceivably influence propensity to join the military in the same year, but are more likely to

do so in the following year. We model the error term to have a year specific element, t, a

district specific element, i, and an independent element. Thus, when we use year and district

fixed effects, the resulting model can be estimated using ordinary least squares regression, and

the s are the coefficients of interest.45

Similar specifications of army recruitment have been used by other analysts of military

manpower to model army recruitment in the United States.46

Benmelech, Barrebi and Klor47

use a similar specification when modeling the quality and production of suicide bombers in

Palestine.

In addition to modeling the number of army officer recruits in a given year for each

district, we also model a second dependent variable, net recruits, which is the number of army

officer recruits less the number of officers who retire from the army into this district. This

measure of net recruits gives us a measure of the change in net supply of army officers from a

district in a given year.

Table 2 shows the summary statistics of the two dependent variables and the set of

independent variables used in this study. Note that for each district, we have 4 years of data.For the dependent variables, the years are 1992, 1996, 1999, and 2002. For the independent

variables, the years of observation are 1991, 1995, 1998, and 2001. (This is because these

variables are lagged relative to the dependent variable.) On average, each year less than 10

army officers are recruited from any given district; however, there is large variance across

districts. Unsurprisingly for Pakistan, the mean number of years of education for males, at 7.1

years, is higher than for females, at 5.9 years. Numeracy is defined as the share of the

44The lag structure may also alleviate any reverse causality, at least to some extent.

45We also cluster the errors at the district level in all our specifications.

46e.g. Dale and Gilroy, The Economic Determinants of Military Enlistment Rates; Warner and

Asch, The Economics of Military Manpower.47

Efraim Benmelech, Claude Berrebi, and Esteban F. Klor, Economic Conditions and the

Quality of Suicide Terrorism,The Journal of Politics 74, No. 1 (Jan. 2012): 113-128.


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population that is able to do a simple math problem. The remaining variables are self-

explanatory and discussed above.

Table 2. Summary Statistics of Dependent and Independent Variables

Observations NumberofDistricts

Mean S.D. Min Max

Recruits 343 98 9.53 20.23 0 194

Net recruits 343 98 8.95 18.81 -1 170

Male Wages in 000s 343 98 27.15 18.99 1.75 197.11

Private high schools 343 98 1.06 3.93 0 66

District Gini 343 98 0.5 0.14 0.18 0.82

Percentage urban 343 98 0.31 0.4 0 1

Share of population, Males 20 to29 343 98 0.08 0.01 0.04 0.12

Male years of education 343 98 7.11 1.54 2.92 10.96

Female years of education 343 98 5.9 1.96 1 13

age at marriage females 343 98 19.18 2.42 15.29 26.23

Labor force participation 343 98 0.22 0.12 0 0.5

Numeracy 343 98 0.67 0.21 0.2 1

5. ResultsIn Table 3, we present the results of the main regressions in our study, using recruits and net

recruits as our independent variables. In Model 1, we model district recruitments (our

dependent variable) as a function of several independent variables without any year or district

fixed effects. In Model 2, we augment Model 1 by including year and district fixed effects. This

is important because we suspect that district fixed effects may also instrument for network

effects that facilitate officer recruitment from districts with a history of officer production. Our

suspicions about network effects are borne out in the data: for example Rawalpindi is the only

district that recruited in excess of a hundred officers in each of the years in our data.

(Rawalpindi, home to the Armys General Headquarters, remains an important destination for

officer retirements due its amenities there and proximity to the nations capital, Islamabad.)

Statistically, the average variance of the number of recruits across districts, at 18.3, is 3 times

higher than the average variance within a district, 6.24. This statistic shows that high-producing


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and low-producing districts retain this attribute across time an argument for the need for

district level fixed effects.

The results of Model 1 provide evidence that the Pakistan army recruits from more

urban districts. The districts of Lahore or Quetta, which are more urban than Haripur or

Khushab, produce greater number of officers. Similarly, Model 1 results indicate that the army

recruits from districts with higher female attainment. However, as shown in Model 2, when we

augment Model 1 with district and year fixed effects, urbanicity per se ceases to be significant,

as does female attainment.

More interestingly, when fixed effects are included, the number of private high schools

in a district and the level of male educational attainment are both negative and statistically

significant, at least at the 5% level. (Without district and year fixed effects, the number of

private high schools is not significant.) Numeracy is positive and significant at the 5% level in

Models 1 and 2. However, labor force participation is slightly significant only in the model with

both district and year fixed effects. Taken together, these findings provide evidence for our

hypothesis that once we control for a basic level of numeracy, the effect of additional

educational attainment on army recruits is negative. These findings persist whether we

measure education directly, through average years of schooling, or indirectly, through the

number of private high schools (which can be seen as a signal of intention to go on to college).Next, we further investigate the relationship between educational attainment and

officer recruitment by allowing education to have non-linear effects on recruitment outcomes.

However, we do not find support for our hypotheses that wages (average male wages),

inequality (as measured by district Gini coefficients), and size of the recruit pool (share of male

population of the district that is between 20 and 29 years of age) affect officer recruitment.

In Model 3 and Model 4, we employ net recruits (defined as the difference between

new officer recruits and retirees in a district at time t) as our dependent variable, modeling it asa function of the same set of independent variables, with and without district and year fixed

effects.

Our a-priori expectation is that the same variables that affect recruitment will also affect

net recruits, and in the same direction. This assumption is largely borne out by comparing the


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results of Model 2 (full models of recruits) and Model 4 (full model of net recruits). Private high

schools and male education are negative and significant in both models, and numeracy is

positive and significant, although in the model with net recruits the significance is only at 10%.

Other variables that are significant at the 10% level in the net recruits model but not in the

recruitment model are female years of education and age at first marriage. Age at first

marriage proxies social liberalism, with more socially liberal families choosing to marry their

daughters at an older age. (In Pakistan most marriages are arranged by the family rather than

according to the preferences of the young men and women in question.)

The principle insight drawn from these results is that the Pakistan army is increasingly

recruiting from districts with lower male education levels and lower numbers of private high

schools (a proxy for investment in higher quality education). Nonetheless, while the army is

recruiting from increasingly lower-educated districts, prospective officers still meet certain

minimum requirements. This is shown by the positive and significant coefficient on the

numeracy variable, which measures a general literacy level. Taken together, this suggests that

the Pakistan army no longer receives the cream of the elite families.48

Other variables which we hypothesized would be significant predictors of army

recruitment appear to have no impact once we account for district-level and time fixed effects.

These variables include percentage of the population that is urban and mean years of femaleeducation. District level inequality, male wages, and share of the male population between the

ages of 20 and 29 are never significant. This is probably a consequence of the supply-

unconstrained nature of the recruiting process. Should there be a time when the supply of men

willing to join the army becomes a binding constraint, we would expect the district economic

indicators to become significant.

48Nawaz, Crossed Swords: Pakistan, Its Army and the Wars Within; Cohen, The Pakistan Army;

Schofield, Inside the Pakistan Army.


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Table 3Drivers of Pakistan Officer Recruitment

(1) (2) (3) (4)

Variables recruits recruits Net recruits Net recruits

Male Wages in 000s 0.0873 -0.0049 0.0918 0.0033(0.0694) (0.0259) (0.0690) (0.0281)

Private high schools 0.3154 -0.1523*** 0.2721 -0.1594***(0.2096) (0.0384) (0.1886) (0.0349)

District Gini -8.3601 -0.2044 -8.0667 0.5774(12.3083) (4.2542) (11.4676) (3.7938)

Percentage urban 16.0343** -2.5168 14.8673** -1.2640(6.4016) (1.9390) (5.9161) (1.5482)

Males 20 to 29s Share of population -26.6408 -32.4076 -30.4165 -27.5729(60.1386) (40.7425) (56.9457) (30.4288)

Male years of education -2.2890** -0.9504** -2.0488** -0.8487**

(0.9674) (0.4733) (0.8805) (0.4056)Female years of education 1.9933** 0.3807 1.9445** 0.3834*

(0.8011) (0.2473) (0.7498) (0.2061)age at marriage females 0.8259 -0.5477 0.8462 -0.4981*

(0.6322) (0.3696) (0.6016) (0.2995)Labor force participation -11.1733 8.0097* -9.7608 6.0198

(7.7576) (4.5799) (7.3061) (3.8071)Numeracy 7.2160** 3.6517** 6.2011** 2.9399*

(3.3643) (1.7550) (3.0857) (1.5108)yr1996 -4.5842 -3.0214

(3.0063) (2.2550)yr1999 -3.1282* -1.2671

(1.7911) (1.4309)yr2002 -6.4842** -4.2419*

(2.6535) (2.1511)Constant -5.7047 27.7557*** -7.2853 23.5360***

(9.2742) (8.1002) (9.0484) (6.7649)

District Fixed Effects No Yes No Yes

Observations 343 343 343 343R-squared 0.1856 0.2023 0.1876 0.1795Number of districts 98 98 98 98

Notes:OLS coefficients with errors clustered at the district level. The omitted year is 1992. Standard errors in parentheses. ***p


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Non-Linear Effects of Education on Recruitment

The results presented in table 3 indicate that the average level of male education in a district is

negatively correlated with officer recruitment from that district. This result is robust and

persists across all four models, both with and without fixed effects, and whether we measure

gross or net recruitment. Because of the specification used in Model 1 through Model 4, we

force the marginal effect of an additional year of education to be the same for every level of

education. However, we suspect that the returns on male education are non-linear, as

described above, with district average educational levels below or above 12 years negatively

predicting recruitment outcomes.

Empirically testing this supposition is difficult with the current data: the only measure of

education available is averaged at the district level, and thus does not offer the granularity

required to instrument for the optimal (individual) level. When measuring district-level

educational attainment, we need to factor in the low levels of attainment of the general

population. Figure 5 presents the distribution of district-level average male attainment in our

sample. It is similar to that of Pakistans census data, with fewer than 12 percent of districts

reporting average male attainment equal to or in excess of nine years. (According to the

Pakistan Population Census Organization,49 about 13 percent of all persons have attained more

than ten years of education.)

In the previous section (Models 1 through Model 4), our specification of either recruits

or net recruits restricted the male education coefficient to have the same marginal effect for

each additional year of education, regardless of the level of education. (That is, our

specification forced the relationship between male education and recruitment outcomes to be

linear.) For example, per the results from Model 2 (in Table 3), if the district-average male

education attainment level increases by 1 year, the expected number of officers being recruited

from this district diminishes by 0.95 officers. However, if there is indeed an optimal level of

education (hovering at or slightly above 12 years of education), there should be varying

49Pakistan Population Census Organization, Educated Population By Level Of Education.


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marginal effects of education upon recruitment outcomes, with education at the optimal level

having the greatest positive effect on recruitment, and negative effects observed below or

above this optimum. In our data, the district-average male educational variable ranges between

2.9 to 10.9 years (see Figure 5).

To measure these different marginal effects, we estimate the unrestrictedresponse of

district education levels to officer recruitment. To do so, we create a series of indicator

variables which take the value of 1, if the district falls in a given category described above, and

0 if it does not. We model these dummy educational attainment variables on the dependent

variable, officer recruits. In Table 5 we report the results this analyses. The excluded variable in

these regressions is the 9-and-over category, so all the other dummy variables are compared to

that category. While 9 years is still below that required to qualify for the officer corps, this

figure is the district average in which males will have less but also more than nine years of

education. (This is also the highest category of educational attainment we have in these data).

To examine the possible non-linear relationship between average male education and

officer recruitment outcomes in a district, we test specifications with and without the two other

variables related to education, numeracy, and number of private schools excluding outliers,

and finally the full specification of table 3 model 2, with the male education variable

unrestricted. All specifications have both year and district fixed effects, with errors clustered at

the district level.

Figure 5the distribution of the average years of education by district in Pakistan, 1992-2002.

10.9%

17.4% 17.6%

26.3%

16.2%

11.5%

0.0%

5.0%

10.0%

15.0%

20.0%

25.0%

30.0%


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Source: In-house data analysis.

Results for model 1 and 2 in Table 5 suggest that there is an optimal level of

education, with the highest recruitment coming from districts with average male education

between 5 and 6 years. When we control for the number of private high schools and numeracy,

we see further that those districts with years of education between 5 and 6, 6 and 7, and 7 and

8 are high officer-producing districts. We also consistently observe that districts with average

education levels greater than 8 years, or less than 5, are not significant producers of officer

recruits. In addition, we estimate (in models 3 and 4) the specification without outliers;50

the

conclusions remain the same. Both in terms of magnitude and significance, the districts with

education level between 5 and 6 years produce the greatest number of recruits. Moreover, the

coefficient decreases for higher years of education, becoming statistically insignificant when the

average years of education exceed 8.

In model 5, we reproduce the full model from table 3, model 2; however, in this

specification male education is captured by a series of dummy variables. Once we control for all

the other variables, as we move towards progressively less education levels, we increase the

expected number of officer recruits from these districts. This is consistent with the previous

finding of a negative coefficient on mean years of education. Moreover, with the added

flexibility in this specification, we can see from the magnitude of each coefficient that there is

an optimal level of education. A district average of between 5 and 6 years of male education

produces the greatest number of officer recruits (followed by the 6 to 7 years bracket).

This analysis provides further support for our original conclusion that Pakistan Army is

likely to recruit officers from districts with lower levels of education. Additionally, we provide

evidence that the greatest officer-producing districts are those with average education levels

between 5 and 6 years. Consistent with our expectation, inhabitants of districts with higher

levels of male education, i.e. 8 years or more on average, usually indicate a preference for

opportunities outside the officer corps of the Pakistan Army.

50Districts with over 100 recruits are excluded. In the next section, we run additional

robustness checks.


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Table 4Drivers of Pakistan Officer Recruitment with an unrestricted male education specification

(1) (2) (3) (4) (5)Variables Recruits recruits recruits recruits recruits

Male Wages in 000s 0.0073

(0.0302)Private High Schools -0.1633*** -0.1493*** -0.1564***

(0.0402) (0.0407) (0.0392)District Gini -0.3524

(4.2040)Percentage urban -1.8089

(1.7626)Males 20 to 29s Share of Popl -28.5886

(41.0309)Male Education less than 5 yrs 2.4779 2.4594 1.5644 1.5528 4.2836**

(1.9941) (2.0629) (1.7095) (1.7906) (2.0806)

Male Education 5 to less than 6 5.2388** 5.4168** 3.0048** 3.1824** 6.0404**(2.5200) (2.5509) (1.2557) (1.3062) (2.3846)

Male Education 6 to less than 7 3.8000* 4.1308** 2.0628** 2.3807** 4.9029**(1.9996) (2.0462) (1.0127) (1.0902) (2.1031)

Male Education 7 to less than 8 3.6621* 3.8526** 2.0293** 2.2249** 4.4431**(1.8589) (1.8910) (0.9836) (1.0482) (1.9906)

Male Education 8 to less than 9 1.5491 1.6788 0.1996 0.3585 2.0734(1.8463) (1.8327) (1.3582) (1.3670) (2.1567)

Female years of education 0.4500*(0.2604)

Age at marriage females -0.4354(0.3744)

Labor force participation 6.7714(4.8486)

Numeracy 2.3647 2.2720 3.2506*(1.5692) (1.4642) (1.7776)

yr1996 -6.3650*** -6.6996*** -5.3549*** -5.6646*** -5.9049(1.4185) (1.4556) (1.0008) (1.0272) (3.5793)

yr1999 -1.5926 -1.8985* -1.3804 -1.6642* -4.1613*(0.9725) (0.9831) (0.8989) (0.9045) (2.4398)

yr2002 -3.8045*** -4.3601*** -3.0135** -3.5477*** -7.3047**(1.4313) (1.4991) (1.1903) (1.2617) (3.2851)

Constant 9.0841*** 7.8237*** 8.4460*** 7.2148*** 15.1565**

(1.5743) (1.7637) (1.1967) (1.4071) (7.5446)

District FE Yes Yes Yes Yes Yes

Observations 357 357 353 353 343R-squared 0.1848 0.1950 0.1996 0.2146 0.2203Number of alt_district_int 98 98 97 97 98


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Notes:OLS coefficients with errors clustered at the district level. The omitted year is 1992. Omitted male education variable isdistricts with more than 9 years of average male education. Standard errors in parentheses. *** p


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Lastly, we want to ensure that we are not observing a spurious correlation between

recruitment outcomes and one or more of our independent variables, suggesting a relationship

between recruitment outcomes and general development trends in Pakistan. To do so, we

estimate a first difference model based upon Model 2 in Table 3. In this first difference model,

we estimate the between-year change in gross recruits as a function of between-year changes

in each of the independent variables employed. (Note that we exclude fixed effects in this first

difference model because they are invariant dummy variables.) These results are presented in

the fourth column (D) of Table 5. We find that two results endure: namely, private high school

inventory and male educational attainment are significantly and negatively correlated with

recruitment outcomes.


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Table 5 - Robustness Checks of Models 2 and 4 (Table 3)

(A) (B) (C) (D)

Variables Recruits Recruits Net

recruits

Recruits-All

variables are in first

difference

Evaluates

Model 2,

Table 3

Evaluates

Model 2,

Table 2

Evaluates

Model 4,

Table 2

Male Wages in 000s -0.0026 0.0607 -0.0136 0.0603

(0.0244) (0.0616) (0.0176) (0.0623)

Private high schools -0.1470*** -0.1422*** -0.1785*** -0.1615***

(0.0410) (0.0475) (0.0343) (0.0420)

District Gini 0.2382 7.0963 -0.0911 -3.2237

(4.0929) (6.7071) (3.6171) (5.5930)

Percentage urban -2.0557 -3.3997 -0.8644 -1.3478

(1.8208) (2.4093) (1.4783) (2.3346)

Males 20 to 29s Share of population -1.0737 -72.7130 -9.3401 -42.6505

(25.4270) (70.0515) (21.5943) (44.6380)

Male years of education -0.6335* -1.7712* -0.5468* -2.0132***

(0.3445) (0.9268) (0.2991) (0.7496)

Female years of education 0.2772 0.7103 0.2769 0.3863*

(0.2182) (0.4679) (0.1788) (0.2310)

age at marriage females -0.6141* -0.5048 -0.5073* -0.3310

(0.3324) (0.7514) (0.2781) (0.4991)Labor force participation 4.5480 7.3051 3.2231 10.4266

(2.9080) (6.8572) (2.6297) (6.6001)

Numeracy 3.5578** 6.4830* 2.6002* 0.6999

(1.6513) (3.5368) (1.4177) (2.2420)

yr1996 -3.0432 -7.5953 -2.2390

(2.3781) (5.0338) (1.9839)

yr1999 -2.3495 -3.5913 -1.1518

(1.4913) (3.1210) (1.2851)

yr2002 -4.9443** -9.2196** -2.9889*

(2.0836) (4.3855) (1.7789)

Constant 22.7122*** 33.3029** 19.9696*** -0.0738(7.2037) (13.0638) (5.8755) (3.5715)

Observations 339 228 338 236

R-squared 0.2304 0.2884 0.2166 0.2514

Number of districts 97 75 97Notes: OLS coefficients with errors clustered at the district level. The omitted year is 1992. Standard errors in parentheses. ***p


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7. Discussion and ImplicationsThis paper provides clear evidence that over time the geographical base of Pakistani officer

corps recruitment has extended beyond the traditional stronghold of the Punjab. Furthermore,

we have obtained three principle robust findings from our efforts to employ regression analysis

to determine the drivers of district-wise recruitment outcomes. First, within a given district and

year, both the numbers of private schools in a district and the average level of male educational

attainment correlate negatively with officer production. Second, numeracy (e.g. the ability to

do simple math) does not exhibit diminishing margins of return and remains positively

correlated with officer production. Third, when we compare across districts by excluding year

and district fixed effects, we find that army recruits in Pakistan come from districts that are

more urban and boast higher female education levels. However, this finding is no longer

significant once we take district and year fixed effects into account. Thus a district which is

more urban (such as Karachi, the megacity in Sindh) will have more recruits than a district less

urban (such as Loralai, in sparsely populated Balochistan). But this does not mean that, had

Karachi been less urban in a particular year, it would have seen a decrease in recruitment in

that year.

These conclusions have several possible implications. First, the negative correlation

between education and recruitment is worrisome, as is that between recruitment and the

presence of private schools. Whereas in the past, the Pakistan army drew from Pakistans elites,

this is evidently no longer the case; better-educated men are pursuing other careers. As

modern warfare continues to evolve and as Pakistan seeks to introduce ever more complex

weapons systems, the receding human capital pool may render the army less able to optimize

these new systems, if not preclude their comprehensive induction in the first instance. Given

the widening gap in conventional military capabilities between Pakistan and its principle

nemesis, India, this may be cause for alarm for those responsible for Pakistans defense.

While this is worrisome from the point of view of military effectiveness, it need not have

implications for the orientation of the army with respect to social conservatism. Despite the

common belief that poorer Pakistanis are more inclined to support militancy, this is not borne

out by robust studies of Pakistani opinion. A 6,000 person survey fielded among a nationally


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representative sample of Pakistanis using a unique method (endorsement experiment) found

that while in general Pakistanis exhibit a weak support for militancy, poor Pakistanis dislike

militant groups more than their middle-class counterparts.51

The survey determined that this

effect is strongest for the urban poor, who tend to be more exposed to the negative

externalities of militant violence. On average, the study found that poor people in urban areas

exhibit an aversion to Islamist militant groups that is nearly three times stronger than for poor

Pakistanis overall and sixtimes stronger than for Pakistanis as a whole. These findings become

stronger as the definition of poverty is restricted. Without individual-level data, inclusive of

socio-economic factors, it is very difficult to discern whether officers are coming from the

militant-averse urban poor or the less-averse middle class populations.52

The present study uses an ecological approach to offer limited insights into the drivers

of officer recruitment. Clearly there is room for more detailed work in this area. While no data

sources for a more granular study currently exist, it would not be complicated to collect such

data. Such an effort would be resource-intense, however. It would require a multi-stage sample

which would first establish the distribution of military households across Pakistan and then

draw a sample that would allow researchers to obtain insights into the characteristics of

military households relative to other households. But even such an ambitious effort would

likely not allow analysts to look below the household level. Given the pressing concerns aboutideological trends in the Pakistani army, the present effort suggests a clarion call for more, and

more in-depth, research.

51Graeme Blair et al., Poverty and Support for Militant Politics: Evidence from Pakistan, (2

May 2011). Available at SSRN: http://ssrn.com/abstract=1829264.

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