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