armed forces & society-2016-singh-0095327x16667085

Article

Soldiers’ Recruitment inSouth Asia: An EmpiricalCase Study of thePropensity of IndianGujarati Youth to Enlist

Mainpal Singh1

AbstractThis study uses a survey to examine the propensity of Indian Gujarati youth to enlist inthe Army.The predictors were organized in three categoriesof demographic, individualcharacteristics of personality, routine and behavior, and socioeconomic and culturalaspects to measure their impact on the intention to enlist. The relationship betweenson’s intent to enlist and the father’s intent to allow the son’s enlistment was tested bylogistic regression. The results of the study showed that non-Gujarati domiciles ofGujarat and the higher number of people working in the industrial plants had positiveeffect on enlistment propensity, whereas location of a factory near their residence hadnegative effect on the intention to enlist. Members of National Cadet Corps and thosewho did not have a family role model showed a positive intention to enlist.

Keywordsyouth, propensity to enlist, Army recruitment, enlistment model, logistic regression

In South Asia, Indian and Pakistani military recruits from various states are selected

as per their share (recruitable male population [RMP]) in the national population

(Government of India, 2005). However, the experience after independence has

1 Bhavnagar University, Bhavnagar, Gujarat, India

Corresponding Author:

Mainpal Singh, A 303 Devsangam Apartments, Koteshwar Motera Road, PO Sabarmati, District

Ahmadabad, Gujarat 380005, India

Email: [email protected]

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shown skewed representation from some states in both countries (Fair, Grammich,

Vanzo, & Nichiporuk, 2005; Gautam, 2008). During the period 1968–1971, the

Indian states of Punjab and Himachal Pradesh were overrepresented at 15.3% and

4.68%, respectively, in the Indian Army compared to the state quota of 2.6% and

0.6%, respectively (Sudarshan, 1989). On the other hand, extra efforts are required to

recruit soldiers from the states of Sindh and Baluchistan (Pakistan Army, 2012) in

Pakistan. Both India and Pakistan are interested in a military that mirrors the demo-

graphics of the country. They look for an army of citizen soldiers who represent a

cross section of the society they serve. Disproportionate enlistment across internal

regions is a problem for both India and Pakistan. When states do not meet their share

of RMP, the disproportionate representation by the youth of other states is the net

outcome. Gujarat is one such Indian state. It is difficult to measure the direct

enlistment behavior of the Gujarati youth, so the author like many other scholars

studying enlistment has used the enlistment intention as the source of inference.

Motivation for the Study

Most Indian recruitment literature has focused on the overrepresentation of com-

munities like Punjabis and Gurkhas in the Army. While it is true that the commu-

nities of Sikhs and Gurkhas in India and Punjabi Muslims in Pakistan provide more

recruits than their quota, there has been no study to investigate the underrepresenta-

tion of recruits in the Indian Army from various geographic regions, say, from the

states of Gujarat or Bengal.

Gujarat is one of the most economically developed states in India. It has unique

demographic and migratory patterns, which can have a potential impact on the

collective belief system of the local population. Migration has historically resulted

in the significant outmigration of its younger population to international regions.

The Gujaratis at home are applauded for their successful business acumen. This may

lead to a negative perception toward government service and the armed forces in

general. As a result, the propensity of the youth to enlist or the fathers allowing their

sons to enlist in this social environment is likely to suffer.

The present deficit in the army recruitment quotas of Gujarat (Government of

Gujarat, 2005) and the historical evidence of lack of interest in military participa-

tion motivated the author to study factors which could have influenced this low

propensity. Both economic and noneconomic incentives (social prestige) encour-

age men to join the army (Kolff, 1990).The focus of this study is the noneconomic

or sociocultural aspects that influence youth’s intention to enlist in a specific

geographic region.

Review of Literature–Military Recruitment in South Asia

Qualitative studies based on secondary data on the Imperial Army of British India by

authors such as General Mac Munn (1952) and historian Stephen P. Cohen (1990)

2 Armed Forces & Society



after independence portray military recruitment based on the martial race theory.

Later, Dirk Kolff (Kolff, 1990) studied the 19th-century peasant soldiers from the

Avadh region of India. Young (2002) and Alavi (1995) studied the community-

based composition of the armed forces in colonial India. Kundu (1994) and Khalidi

(2001) highlighted the behavior/disparity in representation of some communities in

the Indian Army. There has been no study of underrepresented states based on

primary data so far, presumably as the national recruitment targets are met. Fair

& Nawaz (2011) studied officer recruitment in the Pakistan army by analyzing the

district-wise data of officer trainees in the military academy. Voluntary military

service in India and Pakistan disallows any coercion in military recruitment, and

therefore some ethnic groups/states are traditionally over-/underrepresented. The

present study explores the factors that influence recruitment of soldiers in the Army

from a geographical area by collecting primary data on the intentions to enlist.

The theoretical framework of the enlistment decision of youth depends on the

combined effect of many factors such as individual propensity and economic condition,

incentives offered in the labor market, recruiters’ efforts, and the image of the organi-

zation (Asch & Bruce, 1994). A higher salary than civilian jobs and noneconomic

incentives such as relaxation in the entry standards for those who have low educational

or physical standards may attract greater enlistment. The recruitment outcome is also

affected by the number of recruiter contacts established with potential recruits. The

scope of this study was limited to the individual propensity as an influence on enlistment

decision, and other influences of enlistment decision like incentives offered, remunera-

tion, image of the organization, and recruiters’ effort were dropped.

The area of military recruitment propensity has been widely researched in the

European and American contexts due to the declining interest of youth in military

service. The research area of military propensity has not attracted South Asian

military scholars presumably because the shortfalls in annual recruitment targets

are insignificant at the national level (Gautam, 2008). However, there are states/

provinces in India and Pakistan which are inadequately represented as a percentage

of their national population. The specific cases of Gujarat in India and Sindh and

Baluchistan in Pakistan show aggregate lower propensity to join the army. A case

study of Gujarati youths’ low enlistment propensity will facilitate our understanding

of some of the causes of poor enlistment in this region.

The role of parents in the enlistment decision of the youth has been reported by

Buchman, Segal, Freedman-Doan, and O’Malley (2000), and Buchman, Freedman-

Doan, and O’Malley (2000). Individual enlistment outcomes have been studied from

different perspectives such as the interplay between demand and supply in a labor

market (Asch & Bruce, 1994); the effect of economic incentives; and as a result of

the demographic, personality, and socioeconomic–cultural characteristics and atti-

tudes of the youth (Asch & Bruce, 1994, Buchman, Freedman-Doan, Segal, &

O’Malley, 1997). Janowitz (1960), the renowned military sociologist, has stated,

‘‘The selection of the military career, like the selection of any career, represents the

interplay of opportunity plus a complex of social and personality factors’’ (p. 85).

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Out of the various perspectives, the author chose to examine the social factors that

impact the enlistment decision.

Demographic characteristics (Asch & Bruce, 1994) like race/caste/region/rural–

urban status influence the soldiers’ recruitment. For instance, in Indian history, only

those from the ascribed warrior caste (Rajputs) were preferred as soldiers (Edmunds,

1988). Youth from southern American states were overrepresented among all new

recruits in 2002 (Sacket & Mavor, 2003). In a state in the United States, the per-

centage of African Americans who enlisted was greater than their proportion in the

total population of that state (Buchman et al., 1997). In India, during 1968–1971,

15.6% Punjabis enlisted against a state quota of 2.6% (Sudarshan, 1989). Hence,

demographic parameters formed the first group of characteristics to study enlistment

propensity.

Past studies have established that individual characteristics of personality, aca-

demic performance (Buchman et al., 1997; Goldman & Segal, 1976), lifestyle

(Edmunds, 1988), timings of choice (Lowe & Krahn, 1993), and daily routine impact

occupational choices. Military service enthusiasts consider the military offering

them personal security (Edmunds, 1988; Leal, 2005). The attributes of personal

choice of a job, timing of decision-making and attempts to get a job, personal

reasons, and appeal to enlist constituted the major perceptions leading to a recruit-

ment decision.

Military life was considered as an opportunity for social advancement for the

socially disadvantaged, especially people with rural backgrounds (Tocqueville,

2000). The socioeconomic status and cultural life of the family (Thiessen, 2001),

the availability of social/educational/industrial infrastructure in the community (Fur-

stenberg & Hughes, 1995), military role models in the family and the community

(Leal, 2005), employment avenues, and socioeconomic attributes (Segal & Segal,

2004) were the broad community effects which influenced career choice. Socio-

economic security as a reason for volunteering was hence a selected response item.

Roberts (1997) observed that socioeconomic factors such as the type of education

and information received, values held, and observable role models act as filters of

information on career choices. Leal (2005) stated that ‘‘young people’s career

opportunities depended heavily, although not exclusively, on their social class ori-

gins’’ (p. 124).

Role of Intention for Enlistment

The first of the enlistment decision models based on demographic and economic

factors for military recruitment was evaluated by economist Bruce Orvis in 1982.

Later, another study established that the probability of enlistment depended on

personal characteristics and family background (Hosek, Peterson, & Eden, 1985).

Nord, Schmitz, and Wieland (1986) subsequently examined the determinants of

propensity and the relationship between propensity and enlistment. They found that




youth who state a strong positive intention to enlist are substantially more likely to

enlist than others.

Intention information has been used as a means of distinguishing individuals who

are more likely to enlist from those who are less likely to do so (Orvis, Gahart, &

Ludwig, 1992). Second, intention information has been used at the aggregate level,

for example, by geographical area or at the national level, to provide a barometer of

enlistment rates. Regional intention analyses have assisted recruiting efforts.

The Research Problem

The historical records of the 19th and 20th centuries indicate a lack of military

participation of indigenous Gujaratis in the princely states’ armies in the Gujarat

region and the colonial army of the Bombay Presidency (Copland, 1982). The annual

army enlistment shortfall from the state during 2004–2009 (Government of Gujarat,

2005) coupled with the historical evidence provided reasons to study the low pro-

pensity of Gujarati youth to enlist in the Army. The aim of this article is to explain

the Gujarati youth’s low propensity to enlist by examining the effects of demo-

graphic factors, individual personality, and performance factors and socioeconomic

attributes.

The Research Objectives and Questions

The study was designed to investigate the effects of the demographic, personal

choices and actions, and social environment of the stakeholders on the intent of the

sons/fathers allowing their sons to enlist in Gujarat. This article attempts to address

the following research questions.

Research Question 1: Which demographic, personality, and socioeconomic

characteristics significantly influence the propensity of Gujarati youth to enlist

in the Indian Army?

Research Question 2: What is the magnitude and direction of the effect of

significant attributes on the propensity when viewed as three distinct charac-

teristic subsets and the combined model with all three subsets?

Model of Enlistment Decision

In the absence of a prior military recruitment model incorporating the determinants

of propensity in South Asia, the author referred to the international recruitment

literature. Taking cues from literature, three broad categories of characteristics were

selected, namely, (i) demographic, (ii) personal choices and actions, and (iii) socio-

economic and cultural characteristics that might impact the propensity for enlistment

(Asch & Bruce, 1994). A basic study model was visualized as given in Figure 1.

Singh 5



Predictors of Enlistment Intention

The attributes in three categories were not assumed to influence the propensity

equally and hence variations were to be captured in the four hypotheses.

Hypothesis 1: The demographic attributes were assumed to affect the pro-

pensity of enlistment as under:

Non-Gujarati domiciles of Gujarat were more likely to enlist than Gujarati youth.

The propensity of a father to allow his son to enlist was the same as the son’s

propensity to enlist. The propensity of respondents would increase with an increase

in age. Higher the education received by the sons, lesser the likelihood of enlistment

or of the father permitting the son to enlist. Higher the caste of the respondent, lower

was the intent of enlistment. The respondents living in large families were likely to

enlist more than those who were from small families. The intent of the backward

castes (Dalits) to enlist was likely to be more than that of the upper castes.

Hypothesis 2: The individual attributes of boys were assumed to affect the

propensity of enlistment as under:

Better academic performance (1, 2, 3 division; 1 ¼ higher to 3 ¼ lower grades)

was likely to reduce the propensity. Late starters in the job search (high school to

Demographic Characteris�cs

Personal Choices and Behavioural Characteris�cs

Socioeconomic andCultural

Characteris�cs

Propensity EnlistmentDecision

Figure 1. Enlistment decision model.




under graduation) were likely to have lesser propensity. Youth who made more

attempts (1, 2, 3) at enlistment were likely to have greater propensity. Youth who

were members of the National Cadet Corps (NCC) were likely to have greater

propensity than those who were not members. Greater the number of months when

enlistment intent was felt by the youth (not yet to 3 months to 3 years ago with a

maximum value for 3 years ago), greater was the positive propensity to enlist.

Hypothesis 3: The socioeconomic and cultural attributes of respondents were

assumed to affect the propensity of enlistment as under:

Respondents living in families that have a high number of earning members were

likely to have lesser propensity. The respondents who had better dwelling units (built

up) were likely to have a lower propensity. The availability of an industrial plant/

factory near the residence of the respondents was likely to reduce the propensity of

the youth. Greater the distance of the residence from the industrial area, greater was

the propensity of the youth. Greater the number of people working in the industrial

plant, lesser was the propensity (local job considered better than enlistment). Higher

the course taught in school/college near the residence, lower was the propensity from

such areas. Higher the class of the caste category, lesser was the propensity (back-

ward castes to general or unreserved castes). Higher the status of the parent’s

occupation, lesser was the propensity. Greater the distance of the main road from

the residence, greater was the propensity of respondents. Respondents with relatives

in the armed forces were likely to have greater propensity.

Hypothesis 4: The combined effect of the three categories of attributes on the

propensity of enlistment would be greater than the individual categories.

Data Description and Method

Dependent Variable

The intent to enlist as an army soldier in future or to send the son to the army

(fathers’ response) was chosen as the dependent variable measured on a scale of

1 ¼ yes and 0 ¼ no (1 ¼ positive intention and 0 ¼ negative intention).

Organization of Variables

The explanatory variables were categorized into three groups, that is, demographic,

personal choices and actions, and socioeconomic and cultural characteristics and

treated as three submodels. Table 1 lists the variables with values assigned.

Sampling Design

The literature indicated a significant influence of parents on the occupational

choices of the school going male youth of 16–18 years (Vandergrift & Hunt

Singh 7



Table 1. Variable Measurements.

Variable Questionnaire Item Code Value

Dependent variableDependent—Intentionto enlist by the son andthe father’s will to allow

Do you want to enlist? Willyou allow your son toenlist in the army?

0 ¼ did not intend to enlist1 ¼ intended to enlist

Independent variablesDomicile Domicile of Gujarat or not 0 ¼ not a domicile of Gujarat

1 ¼ domicile of GujaratCandidate or father Response by the candidate

or the father0 ¼ candidate, 1 ¼ father

Age What is your age in years? NumberEducation Education in years of

respondentsNumber

Caste Caste of the respondent inthe social order (6 > 5 >4 > 3 > 2 > 1)

1 ¼ Dalit, 2 ¼ Adivasi, 3 ¼ Muslimand Vankar, 4 ¼ Other castes,Vaisya, non-Gujarati castes, 5 ¼Patel and Durbar, 6 ¼ Brahminand Baniya

Size Size of the family 1¼members up to 4, 2¼ 5 persons,3¼ 6 persons, 4¼ 7 persons, 5¼greater than 7

Dalit Belongs to Dalit caste 0 ¼ other than Dalit, 1 ¼ DalitAcademic Academic performance in

10th class (4 > 3 > 2)1 ¼ still studying, 2 ¼ third division,

3¼ seconddivision, 4¼ first divisionSearch Started searching for a job 1 ¼ after 10th, 2 ¼ after 12th, 3 ¼

while in BA, 4 ¼ after BA, 5 ¼ notyet

Attempts Attempts made to enlist 1 ¼ one, 2 ¼ two, 3 ¼ threePart of NCC Part of NCC or not 0 ¼ not part of NCC, 1 ¼ part of

NCCDecision when How long ago did you think

of trying to enlist?1¼ not taken, don’t want to enlist,

2¼ 3 months, 3 ¼ 6 months, 4¼1 year, 5 ¼ 2 years, 6 ¼ 3 years

Earning Earning members in thefamily

Number

House Type of house 1 ¼ thatched, 2 ¼ built upFamily income Family income in rupees NumberIndustry Do you have an industrial

plant/factory near yourresidence?

0 ¼ no and 1 ¼ yes

Distance Distance of the plant inkilometers from yourvillage

1 ¼ 1–5,2 ¼ 6–10, 3 ¼ 11–20,4 ¼ greater than 20

(continued)




1997). Accordingly, male students and fathers who had a son of recruitable age

(father, if alive, or else the mother) were selected as the two respondent

categories.

Sample

The sample was planned purposely from eight selected districts representing four

from the North, three from the Central, and one from the South Gujarat comprising

200 school going boys and 200 fathers from rural areas. The purposive selection of

the districts matched the historical annual enlistment rates from North, Central, and

South Gujarat in descending order. Fifty questionnaires (for 25 sons and 25 fathers)

were prepared for each of the eight districts, making an overall total of 400

questionnaires.

The respondents with non-Gujarati names and domiciled in Gujarat were

considered as nonethnic domiciles of Gujarat. These were selected at random

without predefining their ethnicity in terms of their geographical area of

origin/known proactive intention of enlistment. Their parents had migrated

to Gujarat from different states of India. The overrepresentation from one

specific region, caste, or community for nonethnic Gujaratis was disallowed

by purposive selection. Their overall number as a part of total N was

restricted to avoid design bias.

Table 1. (continued)

Variable Questionnaire Item Code Value

People Number of people workingin the plant/factory

1 ¼ 1–25, 2 ¼ 26–50, 3 ¼ 51–75,4 ¼ 76–100, 5 ¼ greater than100

Educational institute Is there a school or collegein your village?

1 ¼ school no, 2 ¼ school yes,3 ¼ college yes

Category What is your officialcategory of caste?

1 ¼ scheduled tribe, 2 ¼ scheduledcaste, 3 ¼ SEBC, 4 ¼ general

Occupation father What is the occupation ofyour father?

1 ¼ Labor casual/farm; 2 ¼ serviceclass, pensioner, small land holder;3 ¼ professional, self-employed,business (3 > 2 > 1)

Highway What is the distance of thehighway in kilometersfrom your residence?

1 ¼ 1–5, 2 ¼ 6–10, 3 ¼ 11–20,4 ¼ greater than 20

Relative Do you have a relative inthe military?

0 ¼ no, 1 ¼ yes

Note. NCC ¼ National Cadet Corps; SEBC ¼ socially and educationally backward classes.

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Questionnaire

The questionnaire was decided on the basis of the literature cited (Woodruff & Ryan,

2006). The developed questionnaire had 54 items organized in several content areas to

draw the descriptive and inferential analysis. Responses to items were both closed ended

and open ended. Nominal, ordinal, and some interval measures were used as required.

Administration of Questionnaire

The questionnaires were answered during the period November 2006 to July 2007 by

the school going boys of standards 9th to 12th and the fathers in the rural areas of

eight Gujarat districts. The variable category of the respondents stratified the data for

cross-sectional analysis into son and the father. The questionnaire was prepared in

the Gujarati script for easy comprehension.

Statistical Analysis

The statistical tool of principal component analysis (PCA) was used to reduce the

number of predictor variables in the model from 42 to 23.

Results

Demographic Profile of the Sample

A total of 348 respondents responded to the field survey. The response rate of sons

was 89.5% with 179 questionnaires filled and that of fathers was 84.5% with 169

filled questionnaires. Of this, 320 were ethnic Gujaratis (91.6%) and 28 were non-

Gujaratis (8.4%).

Age profile of the boys. Of the 179 boys, 167 (93.3%) were between 13 and 21 years of

age and 11 boys (6.1%) were between 22 and 24 years of age and were eligible to get

recruited as clerks and tradesmen.

Age profile of fathers. Most of the fathers were between 41 and 50 years old (71%).

About 21% of the fathers were between 34 and 40 years of age and a minority of

8.3% was 51 years or older.

Educational profile of boys and fathers. Boys spent between 6 and 17 years at school; the

mean number of years spent at school was 11 (SD¼ 1.44). The fathers spent between 0

and 18 years at school; the mean number of years at school was 9 (SD ¼ 3.73).

Measurement of Propensity

The result of likely nonoptees and optees for enlisting was 217 and 131, respectively.

The study thereafter analyzed the reasons for this disinterest.




Logistic regression was performed to measure the effect of the explanatory vari-

ables on the likely propensity of enlistment. Firstly, the variables in the three sub-

models were regressed logistically to test the hypotheses in three different

categories. The Cox and Snell R2 values in each model indicated the relative strength

in explaining the variability. Secondly, the combined model consisting of all the 23

variables was again regressed logistically. The results of the combined model pro-

vided the most significant predictors determining the propensity to enlist.

Demographic Model

The logistic regression of seven demographic variables and the intent to enlist/allow

the son to enlist as regressand gave the results shown in Table 2.

Model 1 summary (logistic regression). The findings in Table 3 show that the demo-

graphic model explained 16.7% of the variance in the intention to enlist. Compared

to Gujarati respondents, the odds of non-Gujarati respondents enlisting increased

by 8.13 (p ¼ .000; highly significant). At the 10% level of significance, compared

to fathers, the sons showed lower propensity to enlist, that is, Exp (B) ¼ .304

(p¼ .095). The higher age (in this case, the fathers’ age) was found to be statistically

insignificant. Dalits (scheduled and tribal castes) showed insignificant positive pro-

pensity to enlist. Insignificant negative effect on the propensity was seen in the case

of respondents with higher education, higher caste hierarchy, and large families.

Individual Characteristics Model

The effects of individual characteristics on the propensity to enlist are shown in the

logistic regression below (see Table 4).

Model 2 summary (logistic regression). The results showed that at a significance level of

p¼ .06, the increase in the grade in 10th class resulted in the propensity declining by

.854. At p < .05 significance level, it was observed that as the number of attempts

increased, the propensity to enlist increased by 1.37 times. This category of variables

contributed to 6% variability in the model (see Table 5).

Socioeconomic and Cultural Model

The logit analysis was done with 11 socioeconomic variables. The results showed

that only the type of house (built up vs. thatched), the availability of an industrial

plant near the village, and the number of people working in the industrial unit were

significant predictors (5% significance level). The respondents who owned built-up

dwellings were more inclined to join than the ones who owned thatched dwellings.

The respondents who resided close to an industrial area showed a negative propen-

sity to enlist. With the increase in the number of people working in the local

Singh 11



industrial unit, the propensity to enlist increased by 1.27 times. Other socioeconomic

attributes showed statistically insignificant effects on propensity in either direction

as given in Table 6.

Model 3 summary (logistic regression). The model summary indicated that socioeco-

nomic attributes contributed 16.1% variation in the propensity to enlist (approxi-

mately the same as in the case of the demographic model; R2 ¼ 16.7; see Table 7).

Table 2. Effect of Demographic Variables on the Intention to Enlist in the Army.

Demographic Variables B SE Wald df Sig. Exp(B)

Domicile 2.096 0.514 16.646 1 .000 8.133Candidate or father �1.92 0.714 2.788 1 .095 0.304Age 0.014 0.023 0.372 1 .542 1.014Education �0.028 0.029 0.974 1 .324 0.972Caste �0.043 0.109 0.156 1 .693 0.958Size of family �0.078 0.100 0.603 1 .438 0.925Dalit 0.344 0.398 0.748 1 .387 1.411Constant �0.323 1.125 0.082 1 .774 0.724

Table 3. Goodness of Fit for Model 1.

Model �2 Log Likelihood Cox and Snell R2 Nagelkerke R2

Model 1 398.998 .167 .228

Table 4. Effect of Individual Variables on the Intention to Enlist in the Army.

Individual Variables B SE Wald df Sig. Exp(B)

Academic �.157 .085 3.450 1 .063 0.854Search �.104 .119 0.769 1 .380 0.901Attempts .316 .134 5.610 1 .018 1.372Part of NCC .156 .283 0.305 1 .581 1.169Decision .107 .081 1.731 1 .188 1.113Constant �.606 .230 6.950 1 .008 0.546

Note. NCC ¼ National Cadet Corps.



Model 2 437.544 .061 .082




Combined Model

The logistic regression of 23 combined attributes (i.e., 7D þ 5I þ 11S) gave the

results as shown in Table 8 and Table 9. The variables found significant up to p < .10

significance levels are given in Table 8. The combined model accounted for a 29.6%effect of the explanatory variables on the propensity of respondents to enlist/allow

the son to enlist.

Cross-Sectional Analysis of Sons and Fathers

There were 23 independent variables for analysis including two nominal demogra-

phical variables of ‘‘candidate–father’’ and ‘‘residence.’’ These two variables were

excluded in the analysis of effects of independent variables on the outcome of two

sections of sons and fathers. Out of the remaining 21 variables, seven independent

variables pertaining to socioeconomic aspects were answered only by fathers and

three independent variables related to individual aspects/actions only by sons. Ele-

ven were responded to by both the fathers and the sons.

The logistic regression tests were done to see the variations in the effect of 11

independent variables answered by both sons and fathers separately. Please refer

Table 6. Effect of Socioeconomic Variables on the Intention to Enlist in the Army.

Socioeconomic Variables B SE Wald df Sig. Exp(B)

Earnings 0.055 0.228 0.058 1 .809 1.057House(1) �1.578 0.397 15.782 1 .000 0.206House(2) 0.275 0.456 0.364 1 .546 1.317Family Income �0.066 0.050 1.778 1 .182 0.936Industry �0.830 0.370 5.015 1 .025 0.436Distance 0.096 0.192 0.248 1 .618 1.101People 0.240 0.122 3.856 1 .050 1.271Educational Institute 0.219 0.212 1.067 1 .302 1.245Category 0.015 0.148 0.010 1 .921 1.015Father’s Occupation �0.072 0.178 0.164 1 .685 0.931Highway 0.023 0.128 0.031 1 .860 1.023Relative(0) 1.331 1.086 1.503 1 .220 3.785Relative(1) �0.155 0.500 0.096 1 .756 0.856Constant 0.857 0.827 1.074 1 .300 2.356



Model 3 401.470 .161 .220

Singh 13



to Table 10 for results. The results showed that the effect of nine independent

variables on the outcome variable in case of category of sons and fathers was

similar. For example, the statistical significance of the variable ‘‘Domicile’’ was

observed to be significant in both categories and the other eight variables were

found to be insignificant in both categories. Differences were noted only in the

effect of two variables—caste category and distance of highway which were

significant in case of fathers. When the caste category was higher, the propensity

was found to be more among fathers; and greater the distance of the highway

from the residence, lesser was the propensity in case of fathers as opposed to sons

who had lesser propensity than the fathers if they were from the higher caste

category and higher propensity if they were from distant rural areas although at

insignificant level.

Discussion

The historical evidence and Gujarat’s inability to meet the army recruitment quotas

led to the investigation of the propensity of Gujarati youth to enlist. The study

developed a model of intention to enlist among Gujarati youth based on commonly

perceived characteristics which could likely influence the enlistment propensity.

Predictive Power of the Models

The influence of factors on enlistment intention with the magnitude and direction of

their effect was analyzed. The results did not prove all the hypothesized strengths of

relationships and their direction of impact as true.

Table 8. Summary of Significant Variables.

Type of Variable(s) Attribute Name b Value Significance Exp(B)

Demographic 1 Non-Gujarati domiciles 3.026 .000 20.616Individual 1 Member of NCC 0.701 .062 2.016Social 1 Closeness to industrial area �1.399 .002 0.247Social 2 People working in the local industrial

plant0.296 .027 1.344

Social 3 No relative in the armed forces 2.718 .071 15.148


Table 9. Goodness of Fit for Model 4 (Mixed Model).


Model 4 338.001 .296 .402




The model fits showed that at the submodel level, the values of R2 were .167, .16, and

.061 for socioeconomic, demographic, and individual variables, respectively. This

indicates that the personal choice(s) effects were the least predictive of the propensity

to enlist/fathers agreeing to send the son to the army compared to the demographic or

socioeconomic attributes which approximately accounted for a 16% effect (each) on the

propensity. This result is inconsistent with the common impression that an individual’s

propensity for any job would be most related to one’s personal choice/preference(s)/

performance than attributes outside his personality. The demographic and socioeco-

nomic variables affected the propensity more than individual personality/performance.

The combined model incorporating (7Dþ 5Iþ 11S¼ 23 variables) gave a higher value

of R2 as .30 showing enhanced predictability on expected lines. Under the testing of

hypotheses, the following assumptions were proved or disproved:

Hypothesis 1

The analysis confirmed low propensity among sons as compared to fathers, lower age of

the respondents (sons as compared to fathers), ethnic Gujaratis, higher order castes, and

more educated respondents. The fact that lower status castes (Dalits) had a high propensity

to enlist was confirmed as assumed. Assumption of high propensity among large families

was disproved unexpectedly. Large families did not show positive propensity to enlist.

Hypothesis 2

The analysis confirmed significant low propensity among higher academic achievers

in the 10th class. High achievers showed a lesser propensity than lower graders. A

Table 10. Cross-Sectional Effects.

Variablep Value of Variable Effect on theOutcome Variable in Case of Son

p Value of Variable Effect onthe Outcome Variable in Case

of Father

Domicile .042 .009Age .219 .414Education .270 .354Caste .890 .751Caste category .112 .049Relative .495 .198Occupation .647 .323Highway .327 .023Education institute .467 .478Decision taken when .345 .480Part of NCC .199 .784


Singh 15



late job searcher was less likely to enlist (as assumed), although this was statistically

insignificant. Similarly being part of NCC and a decision made well in advance were

not likely to increase propensity significantly. The propensity increased significantly

by 1.372 times with an increasing number of attempts.

Hypothesis 3

The analysis confirmed significant low propensity in case of availability of an

industrial plant in the neighborhood. Low propensity assumed to be related to more

people working in the industrial plant was disproved. With higher number of people

working in the industry, higher propensity to enlist was observed at the 5% signifi-

cance level which could be due to the unavailability of jobs to the young or the fact

that the enlistment was considered better than being jobless. The respondents at

greater distances from the industrial area showed insignificant positive propensity.

People having built-up dwelling units were 1.3 times more inclined to join than those

who lived in thatched houses. Those who did not have a role model showed insig-

nificant greater propensity to enlist than those who had relatives in the armed forces.

This result was contrary to our assumption. The remaining socioeconomic attributes

did not contribute significantly.

Hypothesis 4

The combined model of respondents’ attributes confirmed low propensity in the case

of availability of an industrial plant in the neighborhood. The propensity increased

1.3 times with 1 unit increase in the number of people working in the industry. The

non Gujarati domiciles were 20.6 times more willing to enlist compared to ethnic

Gujaratis, and the attribute was found to be highly significant. Those who did not

have a role model in the family were 15.1 times more willing to enlist at p < .10

significance level, whereas those who had role models in the family were just 1.3

times more willing to enlist but not at a significant level. Those who were members

of NCC were 2 times more willing to enlist than nonmembers of NCC at p < .10 level

of significance.

Implications of the Study

Most of the sample responses, 217 of 348, stated a negative propensity to enlist in the

army. Sons and fathers both combined were disinterested in army enlistment. The

finding of lack of propensity proved the contemporary deficits in the enlistment. The

main implication therefore is to create a basic military market in Gujarat.

The remedial actions to overcome the lack of enlistment propensity in Gujarat are

visualized as follows:

(i) Contact young boys in high schools for enlistment as with increasing

education and age their propensity drops.




(ii) Motivational efforts should be targeted at Gujarati boys, as the non-

Gujaratis and their parents were found to be positively motivated to enlist.

(iii) The high graders in Class 10 were negatively inclined; hence, the effort

should be to motivate the low performers in 10th class. They could be

encouraged to get qualifying marks to face the enlistment process and

ensure an early and secure job.

(iv) The boys who made more attempts showed a higher propensity to enlist.

Hence, the failed candidates should be contacted as a likely pool of

potential enlistees and persuaded to retry.

(v) Candidates who live close to industrial areas need extra efforts to moti-

vate them to enlist since they have a high likelihood of absorption in the

local industry. Unemployed youth from such areas could be potential

enlistees.

(vi) Socioeconomically poor candidates and their parents should be contacted

more as those who had better socioeconomic assets (e.g., a built-up house)

tended to enlist on their own. This appears to be an institutional orienta-

tion of well–to-do rural family candidates to offer their services to the

nation, presumably considered a noble cause.

(vii) Contact potential candidates from remote areas (away from road heads) as

they could be unaware or ill informed of the prospects as a soldier.

(viii) The presence of a role model in the community did not make a marked

difference to the intent to enlist. Boys who had relatives and who did not

have relatives in the armed forces both showed positive propensity. Those

without a role model in the family were more enthusiastic about enlist-

ment than those who had role models.

(ix) Lower castes, such as Dalits and backward, need to be encouraged to

enlist as they showed positive propensity. The upper castes in society,

such as the Patels and Durbars, who are rich tend to enlist on their

own.

Global implications. The implications at serial (i), (iii), (iv), (v), and (viii) for Gujarat

apply universally as seen in literature. The implications at (ii) and (ix) will vary

from country to country. Implications at (vi) and (vii) are less likely to affect

people in developed countries with better socioeconomic parity. Different ethni-

cities such as non-Hispanic Whites, Hispanics, and American Blacks in the United

States enlist in the armed forces in different proportions. The Indian armed forces

also draw soldiers from different castes in different geographical regions. The

castes in South Asia are like the ethnic groups in the United States. Role models

in the family or community in American enlistment studies have been observed to

affect the propensity positively, but in the present Indian Gujarat context, this is not

a significant predictor, as respondents with or without role models were both

positively inclined to enlist.

Singh 17



There are cultural differences with respect to (1) enlistment of women—the

developed societies allow women enlistment in combat units of armed forces due

to shortage of males, which is not the case in South Asian countries and (2) prestige

of the armed forces among subpopulations of different regions (such as Punjabis in

Pakistan, Sikhs, Jats, Rajputs, and people from the north Indian hills) perceive

service in the armed forces as very prestigious. The social prestige of military

service in the developed societies of Europe is lower than in the certain regions of

South Asia (Manigart, 2005).

The findings established that regional disparities exist in the supply side of

recruitment even in a recruit demand constrained country. Gujarati youths/

parents have a lower propensity than non-Gujarati domiciles to enlist. There

are likely to be populations in certain regions, all over the world, which do not

meet their military recruitment targets due to low propensity. The identifica-

tion of such regions can help the recruiters in those countries to encourage

youth for enlistment and increase their participation in the national military

manpower.

Student cadets show positive propensity to enlist at the 10% level of significance.

The increased cadetship of students in the regions which show lower propensity for

enlistment could overcome the recruitment deficits from such areas. This model is

adoptable globally.

The propensity of rural youth/parents is higher than the urban-based youth/par-

ents. The respondents residing close to industrial areas are less likely to enlist/send

the son to enlist than those residing in remote areas.

Limitations of the Study

First, the study design was limited to the recruitment of Personnel below Officers’

Rank in the Indian Army. The findings cannot be generalized to officers’ ranks in the

Indian Army/Air Force/Navy. Second, the study has considered respondents from

only 8, namely, Banaskantha, Gandhinagar, Ahmedabad, Himatnagar, Panchmahal,

Kheda, Baroda, and Surat, of the 17 districts which form part of the Gujarat main-

land. Hence, the results do not reflect the choices of the population living in the other

nine districts of the Gujarat region. The findings are not applicable to the regions of

Saurashtra and Kutch.

The filled sample sizes from Godhra (40), Gandhinagar (38), and Banaskantha

(32) were less than the designed average of 46 from each district. Hence, the sample

from Ahmedabad was purposely raised to 60.

The sample consisted of cross-sectional responses of sons and fathers who shared

most of the demographic and socioeconomic and cultural characteristics but differed

substantially in their individual strata of age and education.

Some measures of socioeconomic status and culture received limited responses.

This could imply that they were left unanswered intentionally due to lack of pro-

pensity or incomprehension. However, there was no clear pattern observed.




Assumption of ‘‘not answered response’’ as indicative of a certain intention could

form part of another study.

Future Research

One area for future research can be to measure the impact of socioeconomic status

on the propensity by conducting tightly focused group interviews. Another future

research area could be to examine the attributes of actually enlisted youth before

their dispatch to the recruit training centers. A study could be considered to measure

the impact of recruiter contact with the potential enlistees and conversion of contact

into enlistment. A follow-up study could analyze the reasons for the response of the

respondents who said yes to enlistment intent.

Conclusion

India does not feel the pinch of Gujarat’s poor propensity, as other contributing

states make up for the shortfall. The ideology of inclusiveness demands greater

representation in terms of ethnic and religious backgrounds and in terms of geo-

graphy. This case study of the recruitment problem of Indian Gujarati youth has

broader implications for the armies of other countries also. Countries with smaller

populations and better employment avenues may have a manpower crisis if the

recruitment is not broad based. Countries employ different strategies such as

higher materialistic and educational incentives, make conscription mandatory for

some years, enlist soldiers from neighboring countries as is done in India in the

case of Gurkhas from Nepal (although not as a compulsion), or increase the

number of women soldiers.

The universal nature of the study variables with minor contextual variations makes

it equally interesting for the Western world. However, history, cultural, and regional

contexts affect various military manpower systems differently. Women soldiers in

American and European armies get enlisted in other ranks cadres, whereas cultural

reasons have discouraged South Asian armies from inducting women into other ranks.

The prestige of the military in Western developed countries (Manigart, 2005) is

overall lower than other jobs, but such is not the case in South Asia where subpopula-

tions of Punjab in Pakistan, Sikhs from Indian Punjab, Jats in Haryana and Rajasthan,

and hilly people from Himachal Pradesh and Uttarakhand regard army jobs as quite

prestigious. Since military service in India is voluntary, there is no place for institu-

tional coercion to enlist youth from specific areas; however, there is a case for initiat-

ing measures to get more representation from underrepresented states.

Author’s Note

The views and evaluations expressed in this article are personal views of the author and do not

necessarily represent those of the Government of India or the Indian Army. The author alone

is responsible for any errors of fact or interpretation.

Singh 19



Acknowledgments

The author would like to thank Dr. Patricia Shields for regular suggestions and two unknown

reviewers who gave very helpful ideas to improve the article. Thanks to Dr. Urmil Verma of

Haryana Agricultural University, Hisar, Haryana, for the statistical help.

Declaration of Conflicting Interests

The author declared no potential conflicts of interest with respect to the research, authorship,

and/or publication of this article.

Funding

The author received no financial support for the research, authorship, and/or publication of

this article.

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Author Biography

Mainpal Singh is an Indian Army veteran settled at Ahmadabad, Gujarat, after

retirement. He is an alumnus of National Defence Academy, Khadakwasla, Defence

Services Staff College, Wellington, and Indian Institute of Management,

Ahmadabad.




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