Technical Report 723

Propensity and the Enlistment Decision

00

Roy D. Nord, Edward J. Schmitz, and Thomas A. Weiland

Manpower and Pers(nnel Policy Research Group,•1

Manpower and Personnel Research Laboratory

U. S. Army

Research Institute for the Behavioral and Social Sciences

August 1986

Approved foe public releaso; distributi I milod.

87

NA
U. S. ARMY RESEARCH INSTITUTE

FOR THE BEHAVIORAL AND SOCIAL SCIENCES

A Field Operating Agency under the Jurisdiction of the

Deputy Chief of Staff for Personnel

WM. DARRYL HENDERSON

EDGAR M. JOHNSON COt,, IN

Technical Director Commnanding

Technical review by '

Rebecca Pliske

Naomi Verdugo b

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Roy D. Nord, Edward J. Schmitz, and

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IW SUPPLEMENTARY NOTES

I$. KEY WOnDS (continue on reersre side it flecoa Cal) r nd Identify by block number) -

'Propensity) Intent ions

EnlisGtment Probability of enlistment

Nat ional Longitudinal Survey.

Logistic regressionj, g-*_____________________

a0, A NS" ACT (Cadm ameovetee std If ngeaedWeo sd Idoellfy by block numbot)

J* This research examined the relationship between actual military enlistments

and enlistment intentions, educational expectations, and other factors. A

,number of factors, 'including enlistment intentions, were found to predict

enlistment behavior. Projected enlistment rateis, given changes in significant

factors, are rer- LLA.Z.

DD I A'7M 1473 EDfT1oMOF INOV 055IS OBSOLETE UCASFE

SECURITY CLASSIFICATION OF THIS PAGE (Wlteni Dtel Entered)

i%
Technical Report 723

Propensity and the Enlistment Decision

Roy D. Nord, Edward J. Schmitz, and Thomas A. Welland

Manpower and Personnel Policy Research Group

Curtis L. Gilroy, Chief

Manpower and Personnel Research Laboratory

Newell K. Eaton, Director

U.S. ARMY RESEARCH INSTITUTE FOR THE BEHAVIORAL AND SOCIAL SCIENCES

5001 Eisenhower Avenue, Alexandria, Virginia 22333-5600

Office, Deputy Chief of Staff for Personnel

Department of the Army

August 1986

Army Project Number Manpower and Personnel

20263731A792

Approved for public release: distribution unlimited.

iii ;

".4.. 'A
ARI Research Reports and Technical Reports are intended for sponsors of

R&D tasks and for other research and military agencies. Any findings ready

for implementation at the time of publication are presented in the last part

of the Brief. Upon completion of a major phase of the task, formal recommendations for official action normally are conveyed to appropriate military

agencies by briefing or Disposition Form,

Ii. ,5,

! 'v .1
FOREWORD

The Manpower and Personnel Policy Research Group of the U.S. Army Research

Institute for the Behavioral and Social Sciences is concerned with understanding the effect of social, demographic, and policy factors on Army enlistments.

This research examines how various behavioral factors can explain enlistment

behavior. The results will enable the Army to better assess the effectiveness

of recruiting policies and improve the allocation of resources.

EDGAR M. JOHNSON

Technical Director

V

Pj.P~~ ~ .-
ACKNOWLEDGMENTS

We gratefully acknowledge the efforts of other rsearchers in the .,!'tnower

and Personnel Policy Research Group of the Army Research Institute for al.. ag

in the preparation of this report, The assistance of Cavan Capps, Donal. Cox,

and Sidney Sachs was particularly helpful to our effort.

V[

* - ~ *. r j~r
PROPENSITY AND THE ENLISTMENT DECISION

EXECUTIVE SUMMARY

Requirement:

To explain the extent to which individuals' stated propensity to enlist

can improve understanding of the decision to enlist.

Procedure:

Enlistment intentions, educational expectations, and socio-demographic

factors were examined in the context of their pairwise relationship to the enlistment decision. A model was then developed relating enlistments to stated

intentions and other variables. This model was then statistically estimated

from a sample from the National Longitudinal Survey. Logistic regression was

used to predict enlistments from informiation on intentions and backgrounds.

Findings:

"* Individuals enlisting in the military experienced an increase in educational expectations that was not reflected in the general population.

"* There is a considerable shift among enlistees with respect to original

service enlistment intentions. Most Navy enlistees initially planned

to join the Air Force, while most Army recruits intended to join the

Navy 3 years prior to enlisting.

"* Enlistment intentions or propensity was the strongest explanatory factor in predicting enlistment.

"* Many other factors, such as educational intentions, race, gender, and

test scores, also contributed substantially (and independently of propensity) to explaining enlistment behavior.

"* A positive change in propensity over time has a positive effect on

enlistment probability above and beyond the effect of propensity

measured at a single point in time.

"* The effect of AFqr scores on the probability of enlistment is positive,

but the size of this effect diminishes as AFQT increases.

"* A desire for training beyond high school but outside of college is a

good predictor of enlistment probability.

vi t.
Utiliztion of Findings:

The information in this report can be used to aid efforts in recruiting,

advertising, and general marketing of the military.

S1

v~lS.
PROPENSITY AND THE ENLISTMENT DECISION

CONTENTS

Page

INTRODUCTION .1. . .. . .. .. . . . . . . . . . . . . . 1

BACKGROUND . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2

Related Research . . . . . . . . . . . .e .* .* . .. . 2. 2

Data . . . . . e . . . . .. . .. . . . . . . . . . . .* . . .

Bivariate Analysis a. . ............... 7

METHODOLOGY AND RESULTS . . . . ... . . . 25

Models of the Enlistment Decision . . . . . . . . . 25. 

Multivariate Analysis . ... . ... .. .. . .. . 28

Enlistment Probability Projections ....... . . .... 33

DISCUSSION . . * . . e e • . o . * 9 * * . * . . ... ..* * *4 . 5 

RE E S . , .. . . . , . . . . . . . . . , . 56

APPENDIX A--NLS VARIABLES ..... ...... . . ... g . . 57

IjST OF TABLES

Table 1. Variables Hypothesizod to be Related

to the Enlistment Decision .... a o . I . I a .1 a 6

2. /FQI' Adjustments . . e. .1 . . . ... . . . .. . . . . 7

I. Descriptive Statistics .. . . . . . . e. .. . . . . .. . . . 31

4. Logistic Regression Results . .. . . . . . . 32

LIvvT or FIGURES

t 'igure 1 . Military 8'ampl., Ennlistmen t Year . . .. g. ......... 9

2. Miilt.try Sampl•, Ago ait En].ttiitunt . . ... . . 10

3. All Groups, Desired Education Over

Time:, At Lerist 2 Ynars; Collegq .a. , . g . . ............ ... 11

4. Al1. Groups, Expo( toad Educationi Over

Tijmr, At .ei•it 2 Ymi.rr CCo.l Inqv ..... ...... . . . 12

'. . . . , , ,a, a.- a. I I I I I
CONTENTS (ContLnued)

Page

LIST OF FIGURES (Continued)

Figure 5. All Groups, Expected Education Over

Time, At Least 4-Yr Degree . . e . a . . . . . . . .. .. 13

6. Non-EnlipLees, Expectation to

Enter Military Over Time ..* * # s a * @ * * * * .. . .. . 14

7. Military Enlistees, Expectation to

Enter Military Over Time . . e.. .... . . . . e . , a # 15

8. Army Enlistees, Expectation to

Enter Military Over Time ................. . 16

9. Navy Enlistees, Expectation to

Enter Military Over Time a e * a * e a * .o. . . . . . . . . 17

10. Air Force Enlistees, Expectation to

Enter Military Over Time a i . .a s * .# v a # .* * * .o o 18

11. Military Sample Actual Enlistments .... . .... . .... 19

12. Non-Enlistees, Expected Branch

To Join, Over Time . . . . . . . . . . .. . .. . . 20

13. Military Enlistees, Expected Branch

To Join, Over Time . . . . . . . . . . . . . . . . . . . . 21

14. Army Enlistees, Expected Branch

To Join, Over Time . . . . . a . . . . . . , . . . . . . . . . 22

15. Navy Enlistees, Expected Branch

To Join, Over Time . . . . . . . . . . . .... . . ................ 23

16. Air Force Enlistees, Expected Branch

To Join, Over Time .. . . * # . . . . . . 24

17. Models of the Enlistment Decision . .. . ... .. . . 26

18. The LoYistic Regression Curve . . . ....... ............... . 29

19. Projected EnlI.stment Rates by AFQT and

.Entent, White 17-Yr-Old HSI)G Males . . ........... 34

20. Projected Enlist~ment- Rates by hF'QT and

IntentL, Hlack 17-Yr-Old IISI)G Mates . ......... . . . . 35

21. Prnj Jcted Enl 1. x tlm.nt Riatn-j by APQT o nd

l.. tm...t., ... .. Wh. tr. 1. -Yr-Ut d l.It i .............. ..
CONTENTS (Continued)

Page

LIST OF FIGURES (Continued)

Figure 22. Projected Enlistment Rates by AFQT and

Intent, Black 17-Yr-Old HSDG Females ............. 37

23. Change in Enlistment Rate by

Change in Enlistment Intentions,

White 17-Yr-Old HSDG Males ........ .......... 38

24. Change in Enlistment Rate by

Change in Enlistment Intentiols,

Black 17-Yr-Old HSDG Males . e . . . . . . . . . . . . . ... 39

25. Change in Enlistment Rate by

Change in Enlistment Intentions,

White 17-Yr-Old HSDG Females ........ ......... 40

26. Change in Enlistment Rate by

Change in Enlistment Intentions,

Black 17-Yr-Old HSDG Females • * . , ....... * . . . . 41

27. Projected Enlistment Rate by

Desire for Additional Training,

White 17-Yr-Old HSDG Males. ................. 42

28. Projected Enlistment Rate by

Desire for Additional Training,

Black 17-Yr-Old HSDG Males . . . . . .. . . . . . . . . . 43

29. Projected Enlistment Rato by

Desire for Additional Training,

White 17-Yr-Old HSDG Females ....... . ................ ... 44

30. Projected Enlistment Rate by

Desire for Additional Training,

Blac]. 17-Yr-Old HSDG Females . . . . . . . .. . . . . 45

31. Projected Enlistment Rate by College

Expectations, White 17-Yr-Old HSDG Males . . . . . . . . .. 46

32. Projected Enlistment Rate by College

IUxpectations, Black 17-Yr-Old HSDG Males ........... . . . . 47

33. Projected Enlistment Rate by College

Expectatioris, White 17-Yr-Old HISDG Females.. ..... .......... 46

34. Projected EnlLstment Rate by College

1-,xpctattions, Black 17-Yr-Old IISI)(l Fema] ...... .......... 49

Xi
CONTENTS (Continued)

Page

LIST OF FIGURES (Continued)

Figure 35. Projected Enlistment Rate by

Change in Educational Expectation,

White 17-Yr-Old HSDG Males . . * . . . ..4. . ... .* * a . 50

36. Projected Enlistment Rate by

Change in Educational ExpectAtion,

Black 17-Yr-Old HSDG Malese .......... ....... 51

37. Projected Enlistment Rate by

Change in Educational Expectation,

White 17-Yr-Old HSDG Femal.es ................. 52

38. Projected Enlistment Rate by

Change in Educational Expectation,

Black 17-Yr-Old HSDG Females .. .. . ... .... 53

xii
INTRODUCTION

Survey int~ention information it widely usedl to develop, plan, and evaluate

niarketing xtrrre~gies ini die private sector. The military uses similnr inforination

from the Youth Attitude tracking Survey (VATS) and Like Notional Longitudinial Survey (NLS) to assets the enlistment intentions of time recruiting market.

Thmese Purveys are used to indicate recruiting difficulties, assets time impact of

advertising and marketing programs, and allocate resources geographmically and

among thme services.

One Important policy Issue Is how well does propensity actually relate t~o enlistment. One would like to know how accurately propensity predicts

en listinent, whIethier other factors independently contribute to explaining en.

listinezts, and whether there is a dynamic relationship between propensity and

en listmnemit.

This paper uses data from the National Longitudinal Survey (NLS) to analyseo time links between! stated propensity, other explanatory variables, and Like

enlistmenit decition.. In the ntext section, related research, data sources for investigating propensity, and an initial bivariate analyses are described, Thme

Methodology and R~esults section presents both thme niodel we time to investigate

the enlistmewm. decision and its parameter estimates, The fiinal section providts

coinclurions and discussion baAed upon this research.

1
BACK GROUND

Related Res earehi

The enlistment decisioni has been hypothesized to be related to aitnumber of

alternative factors. Aggregat~e economic miodels such as Brown's pooled time

rneries/cross-sectiozial data (1985) have showni that rising unemployment rates

and higher relative military wages are associated wit' ',icreased en list ments.

Cox (1086) has shown t hat, at. thle individual level, race . Idesire for additional

job training (a type of intention data) are associated with higher enlistment

probabilities.

The use of initen~tioni data is a common practice throughout marketing, It

provides nmarket. researchers with quick and inexpensive Information onl the desirability or acceptance of a particular product. Consequently, such data are used

to evaluate flew products, market segmentation, and test advertising. However,

anl important research issue in mnarket~ing science is how well one Canl predict. actual purchasing decisionis front such hypothetical data. Morrison (1079) points

out, that, there has beeni little follow-up of individuals to find out if those surveyed

actually behaved as they intended, Kalwanii and Silk (1982) surveyed several

studies where there was, follow-up data collected and found that. durable goods

exhibit a linear relationship between Intentions and purcha'ses, while branded

packaged goods display n thre-thold effect..

Thle military measures intentions through similar questions front two surveysthle YATS (Youth Attitude Tracking Survey) and the NLS (National Longit~udinial Survey). The question asks how likely the youth iF to enlist. Thle fourpossible responses are "very likely", 'somewhat likely", "som~ewhmat unlikely",

and "very unlikely". Thus, a youth is defined to have a positive propensit~y

to enlist if his response is in one of the first. two categories. lIn addition to this

question, the YATS has anl open-ended "unaided memntion" question, about what,

the youth intends to be doing in the next few years. If thle youth answers that.

hie or shte intends to enlist hie or shte is considered to exhibit unmaided mnention

of enlistment.

Comprehensive recent studies of propensity wo enlist and its links to tile enlist mient decision have beeni carried out by researchers at tile Rand Corporation~.

Orvis (1982) and Orvis and (Gahart (1985) explore(] these link,- using data fromt

Armied F'orces Vocationial Aptitude Battery (ASVA B), Milit ary Enllistmlent. Proceesilig Conmanaid (MEP-COM) records, along withI survey dat~a from thle YATS

a11d tlr,. N LS. The niaiii t hrost. of this work concerned the relatijonship of inteil.

2

A '-
tion to enlist (often called propensity to enlist.) aid actual eitlist~mcnt. behavior.

Tile)- also explored dthy question of how the effect of Ptated iienteitolns onl subsequlo~l hellaviou hi lges witl (i te passage of tinnit.

j4 ~In addit ion to quest~ions Oin propensity, Orvis and Oa1,art include(] demo.

graphiic, at~titudlinal, and econoinic variables in his analysis. Allonig the results

are:

*The starndard measure of enlistment propensity, stated intentions, is a

st~at~ist~ically significant. predictor of nctual enlistment. Those who Are "very

likely" to enlist. are indeed most likely to enlist, This trend also holds

true for the "somewhat likely", "somewhat unlikely", and "very unlikely"

groups.

* High-quality, positive propensity applicants are more likely to enlist thanl

lower-quality, positive propensity applicants~, This is at. least, partially due

to policies that di-courage enlistments aniong low-qluality applicant.4,

*Those who exhibit. unaided mention of enlistment (a separate question

onl YATS but not onl NLS), in addition to a positive propensity oil thle

categorical scale, are more likely to enlist than those who only show a

positive propensity onl the categorical scale.

* One half of all accessions come from individuals with negative propenftity.

* Wages are niegatively correlated withi enlistment,

* There are lagged effects of propensity on enlistment probability. Thle effect

of a stated intention to enlist Onl enlfStmlenit behavior tende to increase

rapidly immediately after the statement is, mtade and then level off after

122mioiit us,

* Those whio exhibit unaided inent ion and also intend to joini the Armly have

a greater chance of actually enlisting in the Army,

These studies have beeii very valuable in relating personal ch~aracterist ics and

intentions to actual en list n~eits. However, this researchi suffers front several lim~.

itations. The. k analyse's relied upon cont ingency table analysis of the pairw 'isc

relationships between enlistmneti decisions, propjensit~y, andt various, cli aracterimtics. While this approach is sufficienit it) proiduce roughly accurate estimates of

tht' significanice of thet vuriabkes examniied, it is less reliable as a m~eans of comnparing thle magnitudes of effects. Altermint ives incluide lugit or probit mnodels

(Amiemiyalt )

3I

& 4N k, bk Z Zýý%":
One important issue addressed only partially in Orvif' work is the analysis

of the offerts of the explanatory variables over time. Thie is difficult to do with

YATIS dat a, whicho uses only cross-sectional sam~ples, T16i1 limits the abilit y

toI explore the effects of time-dependent cliaracteriotticit, suCh aIs lactjnl

expectat ions, on eit-her propensity or enlhistment.. ~I the analysis of NLS data,

Orvis examined the cumulative effect. aver time of propenusity, but did not look

at the effect of changes in either propensity or other explanatory variables,

lit an earlier paper, Nord and Welland (1985) examined the determinants

of propensity as well aE. the relationship between propensity and emdistuient.

Using logit regression on three years of NLS data (1079-1081), they found the

following resulis:

a Non-h1igh-school graduates have higher enlistment. propensities than grad.

u ates.

e Youths scoring below average on the Armed Forces Qualification Test,

(AFQT) have higher enlismnent propensities than those scoring higher,

a Males have higher enlistment propensities than femiales.

e Blacks and Hispanics have higher enllistmient, propensities than Whites,

* Of those who have positive propensities to enlist hin the m~ilitary, only

a smnall percentage, When compared to the proportion of actual service

en list ments, have a positive propensity to join the Army as a first. choice

of services,

a Youths who state a strong positive intention to enlist are substantially

more likely to enlist thani others.

* Youthsý who state a weak positive intention to enlist are more likely to

enlist. than those who state a negative intention,

e Youths who have recruiter contact while still in high school are more likely

to enlist than those who do not,

This analysis can he ext-ended in two wayq: first, by examining the effects oif

changes in propensity anid other variables on subsequent enfistneie~t decisions,

and seconid, by exploring whether or not the effect of AFQT is constant. over

its entire range. Also thet development of alternat ive specifications of the links

among individual charact~eristics, economic rondit 10115, stated intentions, and

the eii listmenitt deccision, need to be explored.

4
Data

T it dat a for thnie a ia lysis w as t akeni fromi t he N a Iluna I Long itud innalI Survey

of Labor Force Experience Profile of Amnerican Youth sub-samiple. Tile NLS

follows a sample of 12,686O youths, aged 14-22 in 2970, over time. The data used

iii this analysis hiclude observations for thle years 10750 to 1983.

We examined :nonprior-service (NPS) enlistments for the years 19801983.

There were 655 NPS accessions (about. 5% of the NLS) over the four years of

NLS data. Attitudinal, behavioral, socio-deniographic, familial, educational,

and economic variables were used. (See Appendix A).

The lhnks aniong stated intentions, tile enhsmtnent decision, and a wide array of potwntial explanatory variables were explored using both bivariate and

multivariate models, Table 1 provides a list, of tile variables examined.

The AFQT test. for the NLS sample was administered in 1080, when thle

sample population wast aged 15-23. There are significant variations in age.

specific inean AFQT scores withink this ranige. Thus, to avoid confounding thle

effects of age with those of AFQT in our analyses, we "renormed5 AFQT scores

so that. thle distribution of score within each annual cohort was tile samte (60th

percentile was used as thle standard). Table 2 presents these AFQT scores. Raw

imean score refers to the meant observed in each age group, while weighted mean

utilime sample weights, and adjusted meant both adjusts for sample weights and

normialiteg gcores, with a ineant of 50.

Age 1.7 was used ast thte reference year for desired and expected education, and

propensity. With respect to educational att~ainnment, individuals were classified

as high s~chool diploma graduates (uSD0G) if they received a diplonia any time

prior to 1983,

To examine the effects of the explanatory variables over time, we constructed

a set. of relative-time-specific variables, This was dome by defining a G, for

each respondent and then redefining tinne-specifir variables in termls of that.

point.. For enlistees, t,, was the year or enlistment., For non-emilistees, thet ref.

erentce year was the year tile respondent turned 19. Because thne reference year

is tnot conitt~ait within the N LS, (lhe amnple sizeF decrease somnewhat it. thne

relative-year increases or decreases, The mean age for entlistees at ti, is approximat ely eighteen-and-one-half. Since we are interested in, pliedici ing bhelav ior,

the relative-year of interest ig t. 1. Thus, all of thne variablv.ý studied comncernm a

17 year-old.
Table 1

Val-inblell hypotheixied to he Rehlaed t~o tlwe Eiflist menut Decision

Variable Descriptioni

Expetatin toenter Stated intention by year

Branch of the military expected to First choice, by year, for respondents

enter expressing positive intention

Actual enary into the military Entry by year

Branch of'the military entered Branch by year

Sex Glender

Age Age hin years

Parental education Highest year of educatiomi completed

Parental occupation Dict-ionary of Occupational Titles

(DOT) code

Ethnicity Self reported prima~ry elthnicity

H ace Self reported race

Dependency st~atus Dependency on parents

Mnrital Status Latest nmarital status

Type of last school attended General, Vocational, Technical,

Academic

Pay and payperiod Dollars per period and period unit.

Desired and expected future Schooling in years

schooling

Incentives for enlistment (bonus) Dollars received, enlistees only

Satisfaction With present. income 4-point scale

Savings (binary variable) I if tavings > 0

Talked to nmilit-ary/army recruiter By year

Family income Dollars per year

Relatives in military Number of family miembers

with military experience

Expectation of whether niilit~ary or binary

civilian sector providev more income

AFQT percentile Normed on 19)80 population

Desire to acquire additional training Training outside of college

Local unemployment rates Rates in percents

11J M Ili

F 

ý ý
Table 2

AFQT Adjust-ments I.R AW -W El(01TEI ) ADJIs j IPf

AC~E FREQ MEAN MEAN M EA N

050 34 .4 30050.5

16 15633 3. 205,

17 1562 37.2 43,2 .50.5

~*is 1484 30. 34.30.

10 1504 30.5 47.3 50.s

20 1382 40,15 47.7 50.6

21 1283C 43.7 00 -OP IF~

22 1240 43.8 51.0 s0'5

Blvariate Analysis

To begin study of the daut, we analyzed simple relationships between propensity, enlistment., and ai numbIer of other variables esimat~ed two at a timle, These

results are graphically depicted in Figures 1 10, Most. of these graphics show

treads over time, as opposed to simple differences at one point in time,

Figure 1 shows the percent of military enlistees who enlisted in each year of

the NLS survey, Nearly S0 percent of thle enlistments occurred its 1981,

Figure 2 shows the age at, which individuals in the m~iiary sample enlist-ed.

Tile majority of enlistees were either 18 or 10 years old.

Figure 3 illustrat~es educational deosires of various groups of the data set over

time. This desire represents thle amount. of total lifet~ime education that each

individual (possibly unrealistically) wants. This graph, showing thle percent

of each group desiring at least. 2 years of college, begins at T.. ) (three years

before t~he enlistmnent-relative year), and end8 at T'43 (two years after). Tile

en list ment-relatfive year is shown in darker color, All groups enlisting inl tile

milit ary experience a subst-antial rise in expectations during thle enmlimtnment year,

while non-enlistees' expectations remain constant,

Figure 4 shows educational expectations over timie, Thie graph is sinmilar

to time previous graph, hui represents each individual's realistic expectation of

towl~ lifethnme educ ation, All milit ary enlistment groups except thle Navy Show

risin g expert at ions during the enlistmnent year, w hile' thet niio-enlistee doS 1 not.

Figure 5~ parallel!, t(lie a hove graphi, but. shows the~ percent of each group

expecting at leaist 4 yearF of e'ducaution.

7
Figure 6 indicates the expectation of en!tst, ment (propensity to enlist.) of

iion-enlistees over time, This represents expect atIons from age 14 to 17,

Figure 7 paraliel.; the ahove graph, hbut indical es the propensit~iee of military

enlistees for the four years prior to actual enlistment, Positive propensities

increase in each year prior to enlistment. However, over one third of enlistees

exhibit, negative propensities prior to enlisting.

Figure 8 breaks out Army enlistees fromn the military sample for enlistment.

propensities. Patterns are generally similar to those in Figure 7.

Figure 1 breaks out Navy enlistees from the military !ample for enlistment

propensitiet, Navy enlistees do not generally exhibit rising propensities. In fact,

the "very unlikely to enlist" category increases each year prior to enlistment,

Figure 10 breaks out, Air Force enliotees from the military sample for enlipt.

ment propensities, No particular patt-erits are apparent.

Figure 11 indicates which branch of the military that the military enlistiees

actually joined, (The Other group hicludes Marines, all branches of the Guard,

and all branches of the Reserves-these samples were combined due to the small

size of each group.)

Figure 12 shows which branch of the military that the non-enlistees expected

to join (for those who actually expected to join the military). This representr

their expectations from age 14 to age 17. The greatest proportion expected to

join the Air Force in each year,

Figure 13 parallels the previous graph, but indicates the military branch

which the actual military enlistees expected to join for the four years prior to

hilisting. Those expecting to join the Army increased oach year, while those

expecting to enlist in the Navy declined.

Figure 14 breaks out Army enlistees from the military sample for expected

branch to join. Interestingly, four years prior to enlisting, nearly half of Army

recruits expected to join the Navy.

Figure 15 breaks out Navy enlistees from the militlary Pamnplh for expect.ed

branch to join. Many Navy eidistees originally plamined t-o join the Air Force.

Figure 16f• breakp out Air Force etmisleeIs from the, military snmple for ex-

;pect.ed bronch to join. U ulike, the, Army anid Navy Air Force recruitik ntever

exhibited a strong propeli=ity for anly service ot hm'r thlian the Ah Force,

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24
METHODOLOGY AND RESULTS

}Resun ll'ci 1i111 sllowil propensity t• hie corull•t•ed witlh ln• i llsAtet, How•ver, I 1

mimlber of ahteriialtIve modelh could he propos•ed to explain the proiet , In thifs

sectiloln we discuss sote of thle models tChat could be proposed, specify u particular, model, estima'te its coetticionts, and Ilse this model to ,smake projectioII of

how altering various frctors would cliaige eullsti memt rates.

Models of the Enlittment Decision

Thiere are several different approtiches tIhat would expinill the relat 1o0116il6 of

propeiisity to enlistment, Figure 17, model I Illustrates one such mechian•sit., In

thnis model it Is hypothesised that proponsiity and the evldistment decision are mi.

aiuhtarismouily Influenced by maniy of Clio same facorst educational expectationýs

demographics, and economic conditions, However, propensity Ilt also affected

by observable factors that are not directly related to the enlistment decisioll,

Model 2 provides another hypothetical relationship between propensity and

*nlistment, Here the various independent factors are related to propensity anld

the enllstieno, decision In a recursive manner, Advertlsing, soclo-economlc status, economic conditions, and demographics all influenace propensity, which in

turn det, erbies the outcome of the enlistment decision, If this model is correct.,

then the enlistment decision canm be itmodeled withlmt knowing propensity, shlice

propenstty is determined by other observable measures, Note, however, Lthat if

thips is the correct model, knowledge nf propensity will Improve the accuracy of

prediction, because propensity provides Information about "taste for military

service" thav is not contained in other variables,

A second issue in the imtodeling of the enlistment decision is the dynamic Ikature of the proces., Eveni if propensity measures an incdepeindenit characteristic,

it. i6 Important to know whether such a factor is fixed or changes over time, If

a variable changes, and if such changes are associated withI altered decisionls,

it matty be feksible to change decisions by changhig variables that predict the

decision, through advertisinig and market lig,

ejI,
MODELS OF THE ENLISTMENT DECISION

FIGURE j7

-- - - . . .M ODEL 1

"i "TASTE" FOR i

MILITARY i"

ADVEIHTISING

8 sO.IO-ECONOMIC .

STATUS 

PROPENSITY

DEMO(sHAPHIOC . S. ............ . . . • • o z s~

SEDrwCATIONALb .. •

EXPECTATIONS . E........ ... . ..... ... "-; ~ 1. * ff,.

IWONOMIC .NLISTMENT 0,'oN ) 1 T 10N'S DECISION

21I
MODEL 2

Li ERTISIik,

SOCIO-ECON

ENLITMN

FDFMo.,'- PROPENSITY("TMO

L DE ISINT

EIDI I'ATl'o NAL'

EXPEC'ATON.S

Ek',ONOMI(,'

MODEL 3

LPROPENSITY

CHiIANGE IN

PROPENSITY

EDUCATIONAL ENLISTMENT

EXPFCTATIONSý L F.CSU

EXPECTATION2. i

N
This kind of dymamic model, shown it) Model 3, was developed for estimation

purposes. fti principal features hiriude:

1, A test. of whether propensity produces an enlistnent eflct itdepettdeli,

of socio-demnographic characteristics, anid

2. How c.lmuges ill variables over time affect the enlistment decision.

Thir ýecontd aspect of the model is particularly important atnd is all extension

over previous research. For example, those individuals who have always planned

to attend college may be hypothesised to have a low enlistment probability,

However, tie effect for those who change their educationtal expectatio'lm is less

clear. Rising expectations could produce decreasing enlistment rates, or they

could be associated with increasing enlistments if the militpry is viewed as a

way to finance college,

MultiUariate Analysis

To determtine the relationshils, among ewlisitment, propensity to enlist, and

barkground variables, a logistic regression (logit) model was used. Thir appro•ach models the probability of enlistment as a function of enlistment propensity and other inlividual characteristics, The model takes the following form:

1

Prob(C, =) 1) = 

Where:

"* Prob(C', = 1) denotes the probability that individual s will choose to enlisi

"* c is the natural anti-log

"* ,i ;preseits the logirtic regression intercept term

"* X,, denotes the value of the j'i explantatory variable for i(lindividual

"* k 1,* the t, oltil nnilwr of exiplnaJory' varial)Ils

0 (t repreents the effect of the .1"' -xlflauatory variable oii the probability

of ellist illeitt

28

ii
This form of the equation constrains the dependent variable, here the prob.

ability of emlistmient, to take oil values between 0 and 1. This 1.. illustrated in

Figure 18:

Probability

of

Ellistiitmet.

-00 0 +00

Figure 18. The Logistic Regre.sicn C"urve

The vertical axis, representing the probability of enlimt.inent, is constrained

by the valtue 0 amd 1. The regression line approches 0 and I aysymptotic ally

a., the value nf the explanatory %'ariable (on the X axis) approaches negative or

pot, itive illfl~llity,

Mising values for variableF used in the muhivariate analyses were replaced

by means of the non-missinp values, In addition, since we were primarily intereugted in the behnvior of mniltary-eligible high school diplonil graduates, individi.a. who did not receive a diploma by thet( end of the sample iperiod and those

w9th "adjusted" AFQT sCores beklo the' tenth percentile were also dropped,

Tbhis left a fnial sample of 0239 i6tdcividualr., 373 of whomi enlisted in the military

Wit tilm tOl Kailiple p)eri(od.

Ilk develvpilig the Imlodel, a nutm1belr of Varniables hypothtesii.'d t1o have predic-

;-I
tive validity were examined. However, niany of there variables were correlated

with bui not as strong n predklor of enlisment,F n,• the finial variables. The hy.

poi lheiied full aodel ilncluded local uuiemlpluymnol roves (based oil State-level,

mI~ale, prihte-aged, innllUfacturing unelploynienm rates) family income or poclo.

economic status, relatives having served ill the military, contact. with a military

recruiter while still il high school, AFQT, intentions to •nlist, race/ethnic group,

gender, and educational/training aspirations. (Por the miodel, prior service personnel, those with APQT scoren less thana the 11 th percentile, and non-high

school degree graduates are eliminated),

The reduced multivariate model includes the following variables:

o AFQT refer# to "renormed" AFQT score at' age 17, and takes values from

11 to9),

* ANT SQUARED is a variable which determines the shape of the AFQT

curve.

* POSITIVE INTENT is n binary, with I referring to a stated intention of

enlistment, of either "omnewhat likely" or "very likely",

* NEGATIVE INTENT is a binary, with I referring to a stated intention

of enlistment of "very unlikely", Thus, if the positive litent. and negative

intent variables both have values of 0, the stated itention of enlistment

io "somewhat uldikely",

* BLACK is a binary, with I referring to Blacks, 0 to all others,

* FEMALE is a binary, aith I referriag to females,

* ADDITIONAL TRAINING 1 a biinary, with I referring to people who

desire additional tr~iaming outside of school.

* EXPECT 4-YEAR DEGREE is a binary, with I referring to people who

expect to receive at. least a 4-year college degree.

* CHANGE IN EDUCATIONAL EXPECTATION refers to the difference

ill number of years of expected education, between approximately 1(6-17

and 17-18 years of age. It, ranges from -0 to 10 years, although is usually

either 0, 2, or 4 years. A reasonable range expectation would be -4 to 6

years, reflecting changing expect.ationý of college and post-college work,

9 CHANGE IN ENLISTMENT EXPECTATIONS refers to tIhe difference

il expectation (inteitions) 1t, enlist blwewen 16I-17 and 17-18 ears of age,

30

:&74U

* . . ' . U b M ~ ~ W ~ 4 . . * ~ b ~ .' 5 b b ~ . b "
aud ranges fromi A Value Of -3 to 4 3. Positive vahies hidical e higher expectat ions to ettlit, This variable reflects the difference between two years

of Ilie hItent varilbl), which line tIilt foluowihig I'vepollses to the quest ioll

"How likely nre you to elikt, ilh lie Military?":

1, Very likely

2, Somewhat likely

3, Somewhat. unlikely

4, Very unlikely

Trhus, a value of +3 indicates that the individual changed his expectatdom to enlifist froim "very unlikely" to "very likely", while .3 inldicates the

opposite.

Table 3 presents sumnmary statistics for these variables, Table 4 provides

coeffimcet estimates and standard error* for the multivariate model,

Table 8. Descriptive Statistics

VARIABLE RANGE "MEAN STD

"Enlist 0 or 1 0.0508 0,2371

Adjusted AFQT 11 to 90 52.7359 25.7027

IPositive Intent 0 or 1 0.2369 0.4240

Negative _ntent 0 or I 0.3880 0.4874

Black 0 or 1 0.1941 0.3955

-emale o1 or I 0,5501 0,497

Additional Trainiing 0 or 1 0,6657 0.4718

Expect 4-Yr, Degree 0 or I 0,4593 0,4984

Change in Educational Expectation -4 to +.6 0,1037 1,6787

Change in Einlists';ent Expectation .3 to +3 0.0259 0.84191

;~P

Us
Table 4. Logistic Itegresslon Results

VARIABLE o P

Adiji,,eA .QT . . 0.... .00 M0•10 0.0056

-AFQTI• S~i'ed- ... ,Nl,' 0.0002 0.00"•

Positive In.tent. 1.2700 016088 0.0000

Negative Intent -07121 0.2397 0.0030

Black 0,4238 0.1402 0.0025

Female -1.1312 0.1214 0.0000

Addit-ional T-aining 0.4078 0.1518 0.0072

Expect 4-Yr. Degree "0.0375 0, 0000

CilatgeinEdcaioalExpectat.ion 0.182~ 0.0808 0,0424

"-Change ii Enlistment Expectation 0.2-000.'95i 0

Total Observation* 6239

Enlistments = 373

.2 Log Likelihood = 2405.0-

Fraction of C(oncordaint Pairs 0,729

Rank Correlation 0.512

Here, P refers to each variable's coefficient. value, where the intercept -

The standard error for each # is a, and P is the probability that each variable

is ot. zero. The -e Log Likelihood, Fraction of Concordant Pairs, and Rank

C(orrelation statistics are measures of how well the models predict enlistment

decision1s, Amionig the finldings of this logit analysis were:

"" Over itP lowest ranges, AFQT increases have a positive effect omi enlistment

probability (holding other factors constant). This effect declines as AFQT

becomf 'arger, and becomes negative for AFQT scores larger thanI 55.

" Positiv. i.teht to join the milt•ary is strongly related to actual enlistments.

Furthermore, a rise in the expectation of enlisting has a significant effect,

over and above the level of intent itself,

" A !tatement of strong negative intent to join the military significantly

reduces the probability of enlistment,, as compared to a mildly negative

stateielmt,

"* Blacks are more likely to enlist than are other groups.

"* Femnales are less likely to en list thant males.

"• Those desiritig additional t.rininig out.side of school are more likely to

elklist t0hal1 are people who Jo h1ot desire this t-raiiing.

3A
*Expect at ion of a 4-year college education at age 17 Is a tiegative indicator

of cnlist itent, However, risbing expecit tions of vducationi inl Lte two years

IprDio to enilist itliel 6 is usocillt d witlli h1giger. wni Istilient probu hihit.y

Eiilimtjneit Probability Projections

The Informtation from the logistic regression Call be used to project, enlisuinenil

rates foi, various types of people. To make these projections, hypothetical values

were used foi- tht! explanatory variable, representing a fairly typical recruit. This

"t-ypicar" recruit represents a white, 17-year-old, High School Diploma Graduate

mnale with anl AFQT score over 10, Each of the other model variables were set, to

their respective mueanis (with AFQT = 0 ), and one variable changed at a Limle.

This allows oue t-o see the impact of changes in the variable of interest, While

holding other explaniatory factors cons41tant To assess Impacts due to gender

and race, thec "typical" recruit. was changed to reflect. male and female, whlite

and black.

Figures 10.22 show Cthe effect of propensity and AFQT oin wilistmeaat rate.

The mnaxinmuma enlistment probability is for anl Individual with a test score

slightly ab~ove 50. At that score a white muale with a positive intent (very

or somewhat, likely) would enlist at, a rate of about 22 percent, compared to 7

percent for one( who was neut~ral, or 3 percent. for those Wilih nefgative intentionls.

Figures 23-26i show Lthe effect of changing intentione (positive values hidiCate

increases inl propensity). A white imale whose intentions had imoved one unit

more positive had an enlist mment rate almost 2 percent greater thtan predicted

front his intention level alone. Thus, dynamiic information about int-entionl behavior adds suibstintial explanat-ory power to st~atic Information onl imteitions

at anly point, inl Lillie.

The next figures shlow how education and training attitudes affect enlistument

behavior. Figures 27-30 illustrate that. those desiring training other than college

enlipt, at a substtantially higher rate. Figures 31-34 and 35.38 sihow the differenices

between statir and dynamic educational expectations. Those intenlding to go

to college have Pubaitatiiilly lower enlistment rates. However, Individuals who(

experience anl increase inl educational expectatioits have substantially higher

enlistment ra~tei4. For example, Whiite males with anl increase inl ed micat ion~al

expect ations of four years had niearly twice tile mean enlistmentt rate.

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DISCUSSION

The analysis of propensity aud enlistment. behavior produced iteveral new

findings., Military enlistees experienced a substantial Increase jinedocational ex.

pectations, Increased expectations may lead to enlistments, because the military

is viewed as a mnechanisin to. achieve education, or they may simply be conrelated Witlh t0e discussion of educational benefits that occurs with enlistment. iH

enlistment in the military raises educational expectations aid achievement over

what. they Would have been otherwise, Military enlistmenit could lead to indirect

social benefits.

There was also an interesting relationship between early enlistment, inten-.

tions and the service actually joinied, While most Air Force enlistees always

intended to join the Air Force, most Navy recruits also intended to join thle Air

Force, and m~ost Arnny accessions planned to serve in the Navy originally. This

may reflect an enlistment hierarchy, where the Air Force is viewed as the most.

desirable service to join, In any case, there is substantial fluctuation amiong

hight school students as to their specific service 'intention*,

The inultivariate model produced several new resultsi

Is AFQT displayed a curvilinear relationship with enlistmient rates,

v Positive miovement in propensity was related to increased enlistment, rates.

Is Increasing educational expectations were associated with an increaMe likelihood of enlistment,

Previous research by Orvis and Gahuart (1985) had found only a linear positive relationship between AFQT and enlistmient rates for high school graduates.

Our research indicates that this effect is curvilinear, with enlittment rates declining beyond an AFQT percentile of 56, tven after controlling for propensity.

Lower enlistm~ent probabilities for those with AFQT scoresq between I1I and 30

may be biased stomewhat low due to the limited requirements of the services

for Individuals in~ that range during late FY 82 through FY 83, However, this

finding of curvilinearity )s consittent with the hypothesis that labor market atid

educ ational opportunities, apart fromt simiply propensity, affect eitlistnients.

The findings regarding the dynamic nature of propensity and educational

expectations are perhaps the most significant results from this research. The

fact that substantial movements in propenstity occur and are associated with

54
similar changes in enlistment rates indicates that propensity is dynamic and

could be Influenced. Additional research is needed to determine the degree to

which this finding reflects more accurate infornintion in a(ddition to simply shifts

in propensity, however.

The positive relationship between educational expectations and enlistment

may indicate the military is viewed a a positive mechanism for entering higher

education. It would be Interesting to investigate what sort of results would

be obtained from a similar analysis, since most of the enlistees II our sample

occurred prior to the introduction of tits Army College Fund and the New G.I.

Bill.

The results of this research provide some strong indications of where future enlistment, modeling should proceed. Clearly, given the economic research,

one should expect labor market conditions and family %tatus and wealth to be

significant factors. Certainly measurement speci6ieftion problems need to be explored, In any case, this research would tend to, s•uort exploration alomg the

liies of a simultaneous system model (model 1) ,Vri a reurnive model (model

2), Other facaors besides simply propensity appear to contribute substantially

to the power of the model. Furthernmore, such a model should include dynaplic

explanatory factors, since such variables have been found to contribute to the

model's reliability,

55

' 'A, ' A A. k IA aji I 4 I'' AI '. I V 1I)6 Iý I. I.' Ikt
REFERENCES

Ameiy~am, T. (iii.Qualitative' Rvspjonse Models. A Susrvey. Journ~al of eo

ntomic Literaiture, 19 (Dec.), 1483-1636.

Brown,, 0. (1086). Military Enlistments: What Oaw We Learn From Geographic

Veiriaiionf' Amterican Economic Review, 75 (1), 228&234.

Center for IHuman Resource Rese~arch (1985). The Ohio State University, Codebook, National Longitusdino4 Survey of Youth 102U, 1979-198S.

C'enter for Human Hesburco- Research (1983). The Ohio State Univbrxity, Th4,

?P'ational Lon~itudinal Surveys Hand book 198.9-1984.

Cox, D.F. (19801. Jnsitd O)ccupational Choice: Military Service, Civilian Emplo yment, o, C.ollege, Mlanpower asid Personnel Policy Research Group, U.S.

Army Research Institute, Forthcoming Technical Report, 1086

Kalwaiii, KU, and Silk., A... (1082). Oft the Reliability and Validity of Purchase

Intention Mediumes. Marketing Science, 1 (Suminer), 243-286.

Morrison, D.C. (1979). Purchase Intentions and Purchahe Behavior, Journial of

Marketing, 43 (Spring), 65-74.

Nord, H.D., and( Weiland, T.A. (1985). Projected Youth Populations and Propentattics to E~nlist, )Y.PPRIG Working Paper.

Orv'is, BzA. (1982). Forecas~ting Eniiutment Actions From Intention Information:

I ,liditv and Improvement, Rasid Corporation, N- 1954.MRAL.

Orvis- B.R. a,,d Cahiart, M.T. (1985'). Relationship, of Enhatment Intention and

Market Su4rvey Information to Enlistment in Active Duty Military Service, Rand

Curporution, N-2292-MIL.
AP'PENDIX A

NLS V'ARIAB3LES

The followi'ng variables were analysed for possible inclusion hi the propensity

to enlist miodels (NLS variablo reference niumbers listed lost):

"* Expectation to eister the military/bratich of the military

431, 2357, 4238, 8711, 0128, 432, 2358, 4239, 8712, 9129

"* Actual entry into tlhe anilit-ary/brancli of the milhitary

291, 2412, 4203,'6770, 9187

"* Sex

8102, 10401

"* Age

6, 2202, 4105, 0501, 9001

"* Parentail education

15, 79

"* Parental occupation

69,83

"* Ethnicity (primary, second~ary, etc,)

90, 97, 98, 99, 100

". Race

1727, 3203

"* Dependency an parentse

1503, 1551, 3117, 4822, 7817

"* Marital Status

116, 2260, 4136, 6561, 9012

"* Type of last school atteivded

IM{, 2295, 4177. (304$, 9002

SCurrently attendinbg HS

180, 229)4, 4176, 6647, 9061

* Pay and paypercod

917, 018, 338Q), 3390, 5466, 5467, 14,10, 8411, 10082, 10883

57

L N A

,. dm -.
Desired and expected future kchooling

234, 235, 4190, 4107, W7, 088

SIntcentives fur eilistine,,t (honuum)

329, 2495, 4370, 0877, 9207

"a Satisfaction with presant income

494, 2064, 4478, 7039

e Savings (binary variable)

1688, 3277, 4082, 7083

* Talked to military/army recruiter

412, 413, 2328, 2329, 4206, 4Z07, 8882, 8083, 9098, 0000

e Family income (Note: this particular variable, though promising, is unusahie hi sits pretent form; perhaps the NLS containi some similar but better

variables)

4040, 8130, 8305, 10778

* Military history of the family

0080, 9087, 9088, 9089, 0090

* Expectation of whether military or civilian sector provides amore income

2510, 2526, 4391, 4407, 6892, 6908, 9313, 9329

e AFQT percentile

items 0147 through 6177

* Desire to acquire additional training

Front Cox dataset (see Cox, 1980)

* Local unemployment rates

From Cox dataset (see Cox, 1986)

58