Technical Report e66

A Microdata Model of Delayed

Entry Program (DEP) Behavior

Chester E. Phillips and Edward J. Schmitz

Manpower and Personnel Policy Research Group

Manpower and Personnel Research Laboratory

SDTIC S ELECTE SEP 2 4 1988

B

U. S. Army .

Research Institute for the Behavioral and Social Sciences

February 1985

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A. TITLE (and Subtlitle) S. TYPE OF REPORT A PERIOD COVERED

A MICRODATA MODEL OF DELAYED ENTRY PROGRAM (DEP) June 1984-January 1985

BEHAVIOR______________

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7. AUTHOR(&) Go CONR ACT OR GRANT NUMBFSR(d)

Chester E. Phillips,

Edward .. Schmitz

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18. SUPPNLEMENTARY NOTES

19. KEY WORDS (Continuo onl revroo. .1,1. it nec...ary and identify by block number)

DEP Personnel allocation

Delayed Entry Program Manpower policy

DEP loss

Logit model

*~20. ABS3TRAcrr l it'nt~ue wsroeree @tote it n.~ea*wV oid identitty by block number)

A growing problem associated with the Army's Delayed Entry Program is that

of DEP loss. This research examines DEP loss as a function of sociodemograph-ic

* and policy variables at the midrodata level. A logistic regression (logit)

model is specified, e-stimating loss probabilities on an individual basis.

categories ofl high and low loss risk individuals are. identified. This multi.-

variate analys is represents a major step toward unders taniding factors associated with DEP loss and provides valuable information for the development of

an improved policy of DEP management.

DO 1473 EITION OF NoV t6. IS OitSOLF T? NLASFE

i ,I C)11ryrj UNJFCLASIOOF ED TIS F(h

'4WiiX~e~ iAVtN0VV~I PA, V.,I~
Technical Report 666

A Microdata Model of Delayed

Entry Program (DEP) Behavior

Chester E. Phillips and Edward J. Schmitz

Submitted by

Curtis GIlroy, Chief

Manpower and Personnel Policy Research Group

Approved as technically adequate

and submitted for publication by

Joyce L. Shields, Director

Manpower and Personnel

Research Laboratory

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

5001 Eisenhower Avenue, Alexandria, Virginia 22333

Office, Deputy Chief of Staff for Personnel

Department of the Army

February 1985

Army Projeot Number Manppwer end Personnel

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iv
FOREWORD

The Manpower and Personnel Policy Research Group of the Army Research

Institute for the Behavioral and Social Sciences (ARI) is concerned with developing more effective methods for utilizing Army manpower resources. This

research represents a step toward gaining a better understanding of the effects of the Delayed Entry Program. Endeavors such as this may lead to more

effective methods for managing scarce manpower rer ources.

EDGAR M. JOHNSON

Technical Director

v
A MICRODATA MODEL OF DELAYED ENTRY PROGRAM (DEP) BEHAVIOR

EXECUTIVE SUMMARY

Requirement:

The Manpower and Personnel Policy Research Group (MPPRG) of the U.S.

Army Research Institute examines personnel issues of particular importance

to the Army. Personnel losses from the Delayed Entry Program (DEP) is one

such issue. In this paper a model is developed to predict DEP loss. The

model will provide an increased understanding of the DEP loss problem along with valuable information concerning identification of individuals most

likely to become losses. )

Proced'":..----•/

'TTwo DEP loss models are created: one including high school graduates

and nongraduates and a separate model for high school seniors. Maximum likelihood logistic regression (logit) estimates are made from individual data

for the first half of FY82 and FY83.

Both individual characteristics and policy variables are used in the

analysis. These include age, gender, race, AFQT score, education, contracted

DEP length, training MOS, region of the country, and enlistment and incentive

options. Scenarios are staged to measure the effect of different combinations

of relevant variables. C__

Findings:

Several variables were found to have con Lderable influence in the prediction of DEP loss. Longer DEP lengths produced consistently higher loss

rates. Education and gender were found to be significant, with high school

seniors having lower predicted DEP loss probabilities than high school graduates or nongraduates having similar personal characteriattcs. Females were

also shown to have higher predicted loss rates than males. Army College

Fund (ACF) participation also consistently reduced an individual's loss

probability.

The model presents a significant improvement over previous research

because it permits measurement of the effects of changing several parameters

simultaneously, ultimately arriving at a DEP loss probability for an individual. This allows for the identification of low and high risk categories.

These categories ranged from male high school seniors (lowest risk) to female

high school graduates (highest risk).

vii
Utilization of Findings:

The results of this analysis can he best used to identify those individuals already within the system most likely to become DEP losses. With

this understanding, it would become easier to prevent its occurrence by more

efficiently allocating recruiting resources. Results can also be used in conjunction with one of the currently used forecasting models, obtaining a more

accurate estimate of accessions.

viii
A MICRODATA MODEL OF DELAYED ENTRY PROGRAM (DEP) BEHAVIOR

CONTENTS

Page

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

II. THE DELAYED ENTRY PROGRAM ...... ............. .............. 1

TII. THE DEP LOSS MODEL . . ............. .. ..... ....................... 6

IV. RESULTS ............... ......................... . 11

"VV. POLICY IMPLICATIONS . . . . . . . . . . . . . . 15

VI. SUMMARY AND CONCLUSIONS . . . . . . . . . . . . . 19

REFERENCES . . . . . . . . . . . . . . . . . . . .. . . . . . . . . 25

APPENDIX A. DEP CONTROL MESSAGE .................... 27

B. DISTRIBUTION BY MONTHS IN DEP, FY82 AND PY83 . . . . . . . 29

C. LOGISTIC REGRESSION RESULTS ............... ................ 31

D. PREDICTED DEP LOSS PROBABILITIES USING ENLISTMENT

INCENTIVES, FY82 AND FY83 . . . . . . . . . . . . . . 33

LIST OF TABLES

Table 1. Reasons for DEP loss .......... ....................... 4

2. Data set characteristics . ................ . 10

3. Model elasticities . ................. ................... 13

4. Predicted DEP loss probabilities ........ ............... ... 17

LIST OF FIGURES

Figure 1. High school graduate DEP loss by contracted DEP period . ... 8

2. Non-high school graduate DEP loss by contracted DEP period . 8

3. High school senior DEP loss by contracted DEP periodd ..... 8

4. Predicted DVP loss, Male high school graduate, AFQT 85 ... 16

ix
CONTENTS (Continued)

Page

Figure 5. Predicted DEP loss range, AFQT category I-IlIIA

male high school graduates, FY83 . . .. . . .. .. 18

6. Effect of enlistment options at four months DEP . ..... 20

7. DEP loss risk categories, FY82 ....................... . 21

S. DEP loss risk categories, FY83 . . .......... ............... 21

x
I. INTRODUCTION

The Delayed Entry Program (DEP) has become an important management tool

to aid recruiting and assure a smooth flow of accessions. It allows a person

to delay the beginning of active duty up to 12 months after signing an

enlistment contract,

Recently, there has been a rising trend in the number of persons dropping

out of the DEP prior to accession. A growing concern of the Army, this

problem (DEP los) affects recruiting productivity and the filling of future

training slots.

This paper examines the DEP loss problem. A miorodata-level model is

developed to prediot its occurrence. The model is then used to identify

certain "high risk" categories of individuals. The influence of Army

policies upon DEP loss is also examined.

Section I1 examines the DEP loss problem in general. Current resoarch on

the subject is reviewed. Loss trends are reported. The third section

explains model formulation, Including data and methodology. Results of the

model are used in several scenarios examining the effects of individual

characteristics and alternative policy options.

I. THE DELAYED ENTRY PROGRAM

The Delayed Entry Program is a major organizational innovation assisting

both recruiting and training. This section disoussess the main features of

the DEP, Including some of its positive and negative aspects.

The DEP serves two direct purposes. It manages the flow of accessions

and aids in attracting qualified Individuals to the Army. Upon signing a

contract, a person can either enter the DEP or become a "direct ship", and

imediately access. In FY81 over 98 percent of all Army enlistees

participated in the DEP (Schmitz and Nelson 1984). The program allows an
eligible individual up to 12 months before accessing. While in the program,

an individual is considered a reservist, collecting no pay but accruing time

in service tor longevity raises.

DEP length varies by individual and current Army policy. For example,

while a male non-prior service (NPS) AFQT oategory I-XI-A (above average)

high school senior may be allowed to remain in the DEP for 12 months, a high

school graduate with similar characteristic may only be permitted 3 months.

Maximum permitted DEP length has also varied over time, depending upon

accession goals of the Army. When immediate accession goals must be met,

maximum DEP lengths will be shorter than when recruiting is not as

constrained.

As previously noted, the DEP also produces several indirect impacts.

Morey (1983) pointed out how the DEP aids recruiting by returning enlistees

to their neighborhoods. The recruits are then able to positively influence

their peers concerning an Army enlistment. The program serves as a

management tool, allowing a smooth flow of accessions by spreading out the

-- ks and valleys of recruiting success. In addition, while an individual

t be able to obtain a desired Military Occupational Specialty (NO0)

immeouately, it may be available at a later date through the DEP. This could

increase the contract signing likelihood for that individual. The DEP also

allows the Army to tap the lucrative market of high school seniors, allowing

completion of high school before accessing.

As previously noted, periodic adjustments are made in the time

individuals are allowed to remain in the DEP. These policies are transmitted

to the Military Entrance Processing Stations (MEPS) in the form of DEP

control messages and are input to the REQUEST system. (The REQUEST system is

a reservation system used by the Army guidance counselor at the MEPS, listing

OS and training slots for which an applicant is qualified.) In addition to

limitations on DEP lengths for particular supply groups, closed NOS are

specified. An example of a DEP control message is included as Appendix A.

During the first six months of FY82, persons were not permitted to remain in

the DEP beyond the end of the fiscal year (with exceptions made for

infrequently scheduled training Classes). Only high school seniors in test

2
categories I-IIIB were allowed the maximum DEP length. Other categories were

not permitted to DEP beyond four months. While these were the general DEP

policies for the first half of FY82, exceptions were made for those with

special skills or enlisting in specific HOS.

Several disadvantages can be associated with the DEP. There are costs

associated with running the program. The time that an Individual remains In

the DEP counts as time in the Army when base pay is calculated. This

tranblates into more rapid advancement in pay grade for an individual, and

therefore higher cost to the Army. It also counts as time in service when

calculating retirement benefits. (This will be eliminated as of January 1,

1985, however.) Horey (1983) points out the inability the system would have

to adapt if accession requirements were suddenly decreased, making the syatem

relatively inflexible. Recruiter time is also spent keeping track of those

in the DEP. (It is the duty of the recruiter to keep track of the individual

in the DEP. If the person becomes a DEP loss, it is the recruiter's

responsibility to find a replacement.) While the time devoted to managing

persons in the DEP has not been estimated, it reduces time a recruiter spends

attracting new recruits.

However, a limited amount of DEP loss may actually be desirable.

Participation in the DEP has been shown to reduce later attrition. A Rand

study (Buddin 1981) found lower attrition rates among DEP participants,

particularly those remaining In the DEP over three months. Baldwin et al.

(1982) also found lower attrition rates among DEP participants. Some who

become DEP losses may have attrited at a later date. Since the cost of

keeping a person in the DEP is likely to be lower than the cost incurred

during and after training, it would be more cost effective to lose the

Individual early in the process, before too sizable an investment is made.

With widespread use of the DEP, the problem of DEP loss becomes extremely

important. (A person who drops out of the DEP at any time prior to accessing

will be defined a "DEP loss".) By the end of FY83, over 7 percent of all NPS

AFQT category I-IIUA males were being lost in the DEP (USAREC 1984). A loss

rate of over 11 percent for all participants was experienced early in 1984

(Maze 1984a), intensifying the situation.

3
Table 1 examines reasons for DEP loss for a sample of FY82 contracts.

Medical disqualifications composed almost 25 percent of total losses. (For

the purposes of this analysis, it will be assumed thi3 those who become

losses for medical reasons are spread evenly throughout the population.) An

analysis of DEP loss for FY83 and the beginning of FY84 (USAREC briefing

1984) Is In accordance with these results. That analysia found the four most

common reasons for DEP loss to be apathy (refusal to enlist, failure to

report, personal reasons), medical disqualification, moral disqualification,

and the continuance of one's education.

TABLE I

REASONS FOR DEP LOSS

(Sample-First six months of FY82)

N PERCENT

Medical Disq. 512 24.76

Apathy/Personal 330 17.39

Moral Disq. 276 14.54

Did not Graduate H.S. 205 10.80

Pursuit of Education 108 5.69

Pregnancy 86 4.53

Refused to Enlist 78 4.11

Did not Appear 72 3.79

Concealed Prior Service 43 2.27

Dependency Disq. 24 1.26

* Hardship D1sq. 22 1.16

No Longer Qualified 22 1.16

Temp. Disq./Denles Alt. 13 .69

Other 149 7.85

TOTAL 100.00

A recent analysis (Celeste 1984) examined characteristics of individuals

associated with DEP loss. Using cohorts for FY81, FY82, and the first six

months of FY83, the analysis examined loss rates by age, gender, AFQT

category, month of contract, length of DEP, and MO. Their findings include:

1. Lower AFQT oategory individuals were more likely to become DEP

losses.

2. The ages associated with highest DEP loss rates were 18-19 and over

30.

4
3. Females had higher loss rates than males.

4. Higher loss rates were found where longer DEP lengths were

oontraoted.

5. There was high variability In DEP loss within MOS and CNF.

To attract high quality individuals ("high quality" will refer to AFQT

category I-IZIA HSGs and HSSRs) to specifio MO0 or enlistment terms, the Army

currently employs a set of enlistment and incentive options. The enlistment

options most often considered include:

o Airborne - enlistment in an Airborne MOS.

o Station of choice - this allows for selection of first duty station

after training.

o Training of choice - permits the enlistee to choose a specific skill.

o Unit of choice - enables the enlistee to select a unit after basic

training.

o Two year enlistment - only open to AFQT category I-IlIA HSG, it

guarantees a two year term with training in a selected MOS.

The Veteran's Educational Assistance Program (VEAP), implemented after

the termination of the G! Bill in 1976, enables an individual to save for

post-servioe education while still in the Army. A soldier contributes from

$25 to $100 per month while on active duty (for at least one year). At the

same time, the Army matches the personal contribution at a ratio of 201.

Currently, tihe maximum total is $7,200 for a two year term and $8,100 for

three to four year terms (This includes individual and government

contributions).

VEAP kickers (bonus money added for education) are used as an added

incentive, attracting high quality individuals to particular MOB or

enlistment terms. Also known as the Army College Fund (ACF), VEAP kickers

contain funds earmarked apecifioally for post-service education or training

at an approved facility and the government paid portion may not be used for

any other purpose. (The total ACF package can amount to over $20,000 for a

three or four year enlistment in a specific OS).

5
Cash bonuses are another enlistment incentive. They are designed to

attract qualified Individuals to specific combat and technical MOS. Bonuses

currently range from $1,500 to $8,000, and are restricted to high school

graduates with above average test (AFQT) scores enlisting for four year

terms.

III. THE DEP LOSS MODEL

DEP loss has been shown to be an Important "cost" in the recruiting

process. This section develops a formal model estimating DEP loss

probabilities as a function of various factors. The following section

provides estimates of the model's parametars and discusses their

significance.

This analysis examines DEP loss as a function of sociodemographic and

Army policy variables. Examination of these variables siuultaneously makes

this project unique among current DEP loss research. Sociodemographio

variables are specific to an individual and unchangeable by Army policy.

Included are gender, age, marital status, education, AFQT score, prior

military service, and region of the country.

Past research has largely ignored policy variables. It Is here however,

that the Army may have the greatest impact in reducing DEP lose.

Counterproductive policies could be revised or eliminated. Conversely,

po.icies leading to lower loss rates could be encouraged. Policy variables

examined include contracted length of DEP, training MOS, enlistment term,

enlistment bonuses, Army College Fund participation, enlistment options, and

month of contract signing. By examining these variables suiulto'neously with

sociodemographic variables, the total DEP loss picture may be more clearly

understood.

-.One of the hypotheses tested is that the longer a person remains in the

DEP, the greater the loss risk. The effect of time In DEP was examined using

the contract data aggregated into three educational groupings at time of

contract: high school graduates (HSG), non-high school graduates (NHS), and

high school seniors (HSSR). Figure I shows FY82 and FY83 loss rates (first

-6--1' ' 0 . .1 . . . -. -M
six months of the fiscal year) for HSGs by length of DEP. An upward trend in

loss rate as DEP length increases is clearly evident. Also, while loss rates

remained similar at short DEP periods, longer DEP resulted in higher loss

rates in FY83. By nine months contracted DEP, the loss rate had exceeded 25

percent, Similar, although more severe results are apparent among NHS

contracts (Figure 2). While the loss rate for a nine month DEP was

approximately 20 percent in FY82, the rate rose to over 35 percent in Fy83.

High school seniors face different DEP constraints than either HSG or NHS

contracts. They are permitted longer DEPs (up to one year) and are not fully

exposed to the job mrket while completing school. Therefore, DEP loss

patterns are likely to be different from the other two educational groups.

This is verified in Figure 3. While loss rates Increased with longer

contracted DEP, the rise was more gradual, not accelerating as quickly as for

HBG and NHS contracts. During the two periods, the loss rate peaked at a

little over 8 percent (at ton months contracted DEP in FY82). In contrast

with the other two groups, loss rates dropped slightly in FY83.

Two equations were specified for each year: one including both HSG and

NHS contracts and another for HSSR contracts. It has already been noted that

seniors are under different DEP constraints than HBO or UBS contracts. This

was evident when examining the distribution by month in DEP. DIP loss is

uncommon for seniors at short periods because of the likelihood that they are

still in school and not pursuing other options, Seniors may also be less

influenced by current economic conditions, not having been fully exposed to

the job market. The effect of DEP policy on HSSRs appears to be longer

contracted DEP periods and lower loss rates (when controlling for months in

DEP). To use this information fully the separate model was necessary.

Preliminary examination of thq date was accomplished using ordinary least

squares (OLS) regression. The final combinations of variables were then used

in tHe speCification of a maximum likelihood logistic regression (logit)

model. Recommended by Amemiya (1981), this two step procedure was followed

for several reasons. OLS requires substantially less Computing time than the

loSit. While the algorithm used by the Statistical Analysis System (SAS)

completes an OLS run in a single step, several iterations are required for

7 i~~~~ ~ ~ ~ oi 4N01 - I.A Ad .........
.30 HIGH SCH4OOL GRADUATE DIP LOSS Dy CONTRACTED DEP PERIMO

.20.

.10

.10

.080

DIP Lenah In Moe~he

VWAII. 2

.3 6 M~ON-HIGH SCHOOL GRADUATE ; y**

DIP LOSS BY CONTRACTED DEP PERIOD

.30

I. ~~~1 40*Pal

I do

DIP Length in Monthe

FlAUS 3

.20 HIGH SCHOOL SENIOR DEP LOS !S BY CONTRACTED DIP PERIOD

FY82

I 2. 3 . .4 G.' 0 7 a 9

DIP Lengfh in Montho
the logit. Using OLS for initial estimation permitted a greater number of

specification tests, and therefore a more accurate model fit. (Because of

biased estimates and the fact that OL does not constrain values of the

dependent variable to between 0 and 1, it could not be used for final

parameter specifications.) Due to the greater CPU time requirements for the

logit (a single logit run with 15,000 observations and 16 variables required

over 111 seconds of CPU time on an IBM 3081) 30-50 percent samples of the

original data sets were used for final parameter estimation.

Logit models are particularly well suited where dichotomous variables are

used. Based on the cumulative logistic probability function, the maximum

likelihood logit restricts values of the dependent variable to between 0 and

1. In this case the dependent variable is the probability of becoming a DEP

loss. The logistic distribution function can be expressed as:

P(i)MON -((ABx(i))

1 +e

where:

P(i)u Probability of individual becoming a DEP loss

A a Intercept

B a Beta coefficient of independent variable

x(i)u Characteristics of the contract

This model also has other advantages. It enables the use of individual

observations rather than grouped data for estimating the probability of

success or failure (In this case DEP loss al). Continuous variables may be

used and parameter estimates are consistent and efficient. A more detailed

discussion of the logit model can be found in Pindyck and Rubinfeld (1981),

Bickel and Doksum (1977), or Amemlys (1981).

Contracts signed during the first six months of FY82 and FY83 were

examined using USAREC Minimaster contract files (updated through the end of

the fiscal year). While some of these cases remained open at the end of each

fiscal year, the number was relatively small (approximately five percent).

By using two fiscal year's data, consistency of the effects of variables

could be examined. Records with missing or invalid information for the

9
variables examined were eltminated. The data set included only those persons

who partioipated in the DEP for at least one day. Approximately 95 percent

of the total number of cases took one of five primary options (training of

choice, unit of choice, station of choice, airborne, and the two year

option). In order to liimt the analysis to these options, the remaining five

percent were eliminated. Approximately 67,000 oases remained for analysis in

FY82 and 81,600 oases In FY83. Characteristics of the data sets can be seen

on Table 2.

TABLE 2

DATA SET CHARACTERISTICS

Nu67,047 N.81602

VAR VALUE FY82 FM83

Gender Male 85.1 86.8

Female 14.9 13.2

DEP Loss 4.4 5.0

Age 17 20.5 19.6

18 23.3 24.4

19 15.8 16.5

20-22 22.8 23.3

23-25 9.8 9.3

Over 25 7.8 6.9

Term 2 5.7 6.3

3 56.5 58.9

4-6 37.8 34.8

Original Education Senior 44.5 32.7

HSG 46.8 53.6

NHS 8.7 13.7

Race White 72.6 74.9

Non White 27.4 25.1

VEAP Participant 24.9 33.9

Bonus 21.4 18.0

DEP Time Mean 88.1 Days 111.5 Days

Std. Dev 84.9 76.9

AFQT Mean 54.0 56.6

Std. Dev 21.6 20.3

The distributions of educational groupings by months in DEP were examined

for comparison (see appendix B). During the first half of FY82 all three

categories had the greatest number of cases contracting DEP periods of one

10
month or less. There are similar patterns for HSG and NHS contracts, with a

general decline in contracts as DEP time increases. Approximately 79 percent

of HSG and 80 percent of NHS contracted for periods of four months or less.

Seniors, however, experienced a second peak at 6-8 months of DEP. Only 60

percent had DEP periods of four months or leas, substantially lower than in

the previouis two cases.

In general, DEP periods lengthened in FY83. All three educational groups

had their greatart number contracting for three months. This extended the

average period frow 88 days in FY82 to 111 days in FY83. Again, while

percentagea tailed off for H1G and NHS contracts as DEP time increased, HSSRs

reached a second peak at 6-9 months. (Note again that these distributions

are associated only with oontraotz signed the first six months of each fiscal

year. Patterns may differ slightly for the entire year.)

IV. RESULTS

As previously noted a total of four models were speoifiedi

o HSO/HHS for FY82

o HSSR for FY82

o HSO/NHS for FY83

o HSSR for FY83

Using both years enabled a comparison of the consistency of results during

different time periods. Alternative representations of variables were

considered. For example, AFQT and Time in DEP both best fit the model as

continuous rather than categorical variables. Several configurations were

also examined for age, enlistment option, bonuses, and ACF participation.

Variables examined for the analysis are:

Individual o Education at Contract Signing

o Age

o Gender

o Prior Service

o Race

o AFQT Score

o Region of Country

11
Policy o Training MOS

o Term of Enlistment

o VEAP Participation

o VEAP Kicker

o Length of DEP

o Month of Contract

a Enlistment Option

Variables Included In the final logit models are race (whitenon-white),

age (17 versus 18 and 19 year olds In the H33R model and under 20 versus over

20 in the H3G/NNS model), enlistment term (years), enlistment bonus (Y,N),

Army College Fund (Y,N), gender, AFQT (11-99), end DEP period In days. An

interaction term was used for non-white females. All variables were in 001

form except AFQT and Days In DEP which were continuous variables. Parameter

estimattes for the four models are included as Appandix C.

Days in DEP was found to have a large Impot upon DIP loses. The longer

the person remained In the DiP, the greater the risk of loss. While this has

been found in other analyses, it has not been Investigated in detail as part

of a sultivariate DEP loss model. Elasticities for this variable ranged from

.592 and ,626 for the HSOtNHO and M33R models in FY82, to 1,012 and ,991 for

the two models, respectively, in FY83. An elasticity Is the percentage

change in the dependent variable caused by a one percent change in an

independent variable, all other variables held constant. (Notes Under

normal circumstances elasticities are only reported if statistically

significant. In this case all elasticities are reported to better interpret

results, since the beta coefficients estimted in the logit are not directly

interpretable across equations.) The difference in elastloities indicates

that DEP loss became more sensitive to time In DEP during the one year

period. A one percent rise in average DEP time using the FY83 models would

result in over a one percent rise In DEP lose in both models. Elasticities

for all variables are included in Table 3.

The DiP loss models identified females as high risk individuals. This

variable was found to yield consistently significant results, all with

positive signs. Due to historically lower loss rates for non-white females,

they were included In a separate term. This variable was found to be negative

and significant in the HSO/NHS models but provided inconsistent results for

12
TABUE 3

NODIL ELASTMCI135

Ea HS/NH$ MODEL SENZOR MODEL -8 E13"2 J18_

Non-hitse -o001 .015 -W0824 M.0604

(0.00) (0.38) (5.61) (3.62)

Age 17 SOW m. .0970 -. 1320

(10.61) (8.82)

Under 20 -. 102# -. 1676 -- --

(5.51) (23.141)

ferm 2 .011 1008 .0014 ,000

(1.37) (0.72) (0.12) (0.00)

Term 4 -,017 -M0576 .0884 -W.0441

(0.21) (3.60) (5.10) (1.014)

Bonus M.0711 .011 -. 1560 005

(5.38) (0.21) (20.47) (0.03)

ACF -.0530 -.0147 -. 018 -. 0140

(3.68) (2.412) (0.31) (0.88)

AFQT -,.178 (2170 -. n,1814 .087

(2.29) 7(1.73) (2.09) (0.28)

DIP .5921 1.012' .6260 .991'

(362.38) (622.38) (125.57) (104.15)

Female .1260 .1120 .1630 .1370

(60.25) (65.16) (135.04) (115.A82)

Non-White Female -M0170 -. 0300 .001 -. 007

(2.77) (10.54) (0.00) (0.88)

Non-HIgh Sohool .021 .1115 --

(1.69) (91.68)

S ilgnltloant .10

Chi-Square In parenthesee

13
HSSRs. Elasticities for females dropped slightly in both models for FY83,

moving from .126 to .112 in the HSG!NHS model and from .163 to .137 in the

HS3R model.

It was estimated in the HSG/NHG models that those under 20 years of age

were les likely to become losses than their older counterparts. Similar

results were found In the HS3R models, with 17 year olds being less likely to

be lost then 18 or 19 year olds.

Several variables had weak but uniform effects. Non-whites had a lower

predicted loss probability in three of four cases but it was only significant

for HSSRs. The AFQT variable provided unexpectedly weak results. It was

found to be statistically significant in only one of four oases (HSO/NHS

model In FY83). While it is negative in both HSG/NHS speoifiaotions, it is

positive (but not significant) in the case of HSSRs in FY83. Term of service

generally was not statistically significant. This indicates that enlistment

term presents little information for the prediction of DEP loss.

Enlistment options and incentives provided inconclusive results. In

early specification runs, training of choice was the only enlistment option

found to produce significantly different DEP loss rates. When included in

the logit models, however, it provided poor results. It was therefore dropped

from the final model specification. Those who did not sign for the ACF were

more likely to be lost than those who did. This was true in all four oases

(one significant). Results for enlistment bonuses also proved inconclusive,

producing significantly lower DEP loss in FY82, but positive and not

significant results in FY83.

Several of the variables chosen for analysis were not found to be

statistically significant and were therefore dropped. It is possible that

other interpretations of these variables could lead to significant DEP loss

relationships. Region of the country (Recruiting region) is one such case.

Although not found to be statistically significant during specification,

particular locales may produce statistically difforent loss rates. The same

holds true for training MOB and month of contract. Due to the large number

of possible MOB and CPU time limitations of the logit, a sample of large

14
representative NOS was taken. This may not have identified all NOS

differences. An especially attractive high-tech MOS may have a signifioantly

lower loss rate, for example. However, it is likely that such differenoes

would only marginally affect aggregate projections. Since only contracts for

the months of October-March in each year were used, monthly patterns for the

total year could not be examined. Prior military service was not found to be

significant. Marital status was not included due to a high percentage of

missing cases.

V. POLICY IMPLICATION3

For the remainder of the analysis, oontracts were broken into fourteen

supply groups. These groupings are consistent with those being used for tbe

Enlisted Personnel Allocation System (EPAS) being developed at ARI (HeWhite

et al. 1984) They ares

1. Male upper quartile (AFQT 750) HSG.

2. Male second quartile (AFQT 50-74) HSG.

3. Male third quartile (AFQT 31-49) H15.

4. Hale fourth quartile (AFQT 11-30) HSG.

5. Male upper quartile HSSR.

6. Male second quartile HSSR.

7. Male third quartile HSSR.

8. Male fourth quartile HSSR.

9. Male upper half (AFQT 50+) NHS.

10. Male lower half (AFQT <50) NHS.

11. Female upper half HS0.

12. Female lower half HSO.

13. Female upper half HSSR.

14. Female lower half HSSR.

To examine the effect of particular policies and characteristics upon DEP

losas several scenarios were modeled, These include:

o Time in DEP.

o AFQT differences.

..o. Enlistment and incentive options.

15
In the first scenario, the effect of time in DEP is examined. Particular

supply groups are studied, with variables other than time in DEP held

constant. Figure 4 shows graphically the loss probability by contracted

months in DEP for a typical upper quartile male HSG (white, AFQT 85, three

year enlistment, no options taken). As Was evident from observed data, the

model predicts much higher loss probabilities for long contracted DEP periods

In FY83. At six months contracted DEP, the FY83 loss probability exceeds 8

percent. Few HSG contracts remain in the DEP this long, however.

PIRI a

PREDICTED DEP LOSS PROBABILITY

MALE HIGH SCHOOL GRADUATE, AFOT 85

.30

ja

1115 ,4 s

.10 o -

1 2 3 4 5 0 7 8

DEP Length In Months

A contrast can be seen when the previous results are compared to an HSSR

with Identical characteristics. Table 4 shows that the predicted loss rate

actually fell slightly In FY83, with a lose probability of only a little over

3 peroent at slx months DEP. This Is less than one half the loss probability

for a HSO.

16

S. .. . .. . .. .., . ! ! . .. ! ' ... -.. 
TABLE 11

PREDICTED DIP LOSS PROBABILZTICS

MALE FEMALE

Months Zn

DEP WS HSSRI VHS#* S USSR#

FY82 1 .019 .013 .023 .046 .050

2 .024 .018 .030 .059 .059

3 .031 .019 .039 .075 .070

4 .040 .022 .050 .095 .082

5 .052 027 .064 ,120 .097

6 .066 .032 .082 .150 .108

7 .085 .036 .103 .187 .133

8 .107 .045 .130 .230 .155

9 .135 .053 .162 .280 .180

FY83 1 .013 .013 .031 .033 .044

2 .019 .015 .045 .048 .053

3 .028 .018 .065 .069 .062

A .040 .021 .092 .098 .074

5 .058 .026 .129 .137 .087

6 .082 .031 .178 .189 .103

7 .116 .037 .241 .254 .121

8 .161 .044 .318 .333 .1141

9 .219 .052 .40.5 .422 .164

White, Three Year Enlistmnt, No Options

* Evaluated at AFQT m 85

Of Evaluated at AFQT a 60

17 ,.__ j._. -- • • : •:•-.. •_ ___-_•~i•-'-• x-- -- •I,• V•_ • .• -• -.-•----T----•--.-"',
These are both low risk categories of individuals, however. Non-high

school graduates and females have much higher predicted loss probabilities.

At six months DEP (very few N$S contracts are permitted to DEP longer), the

loss probability for a male NHS Graduate (AFQT 60) Is close to .18 in FY83,

over twice the predicted FY82 loss rate. The contrast between years is even

more distinct in the case of females. The predicted loss probability at six

months for a female HSO (AFQT 60) Is approximately .19 in FY83. Female HSS~s

experience lower predicted loss rates and a representative of this group had

a predicted loss probability of about .10 at six months contracted DEP in

FY83. With the exception of HBSRs, all groups experience higher loss

probabilities at long DEP periods In FY83 models.

AFQT plays a minor part In the DEP loss mdels. Coefficients were

generally small and not significant. Figure 5 examines predicted loss

probability range for AFQT category I-XilA (AFQT 50-99) mle HSO contracts

(white, three year term, no options). At four months contracted DEP (a

typical DEP period), the difference Is less than one percent in the loss

probability, and only four percent at nine months.

uzmi s

PREDICTED DEP LOSS RANGE

APOT CATEGORY I-lIA MALE HIGH SCHOOL GRADUATES FY63

APQT 80

.20 AFOT 90

.16

.10

.06

2 3 4 5 e 7 g

DEP Length in Months

18
The effects of enlistment and Incentive options were examined. In each

case, a white male with an AFQT score of 85 and four year enlistment term

(most likely term for having taken options) was chosen. Only time in DEP was

varied. In this manner, the relative effect of having taken an enlistment

bonus, the Army College Fund, both options, or neither could be examined for

HSGs and HSSRs at different DEP periods. In FY82 a contract who had taken

the ACF, bonus, or both would have had a lower loss probability than having

taken no Incentive. The lowest projected loss probability was for a person

enlisting with a bonus and ACF (Results can be seen In appendix D). Figure 6

.points out graphically the contrast in loss rates at a DEP period of four

months. A high school graduate with an enlistment bonus and ACF in FY82

would have had a projected DEP loss probability of two percent, about half

the loss probability for the same person taking no options. The same

relationship holds trut for an HSSR.

As previously noted, the effect of all options diminished in FY83. In

this case the results were not statistically significant and therefore

inconclusive.

VI. SUIOIARL AND CONCLUSIONS

The analysis presented indicates that predictable DEP loss patterns do

occur. Several of the findings concur with those found by others:

o Females exhibit higher loss rates than males.

o Less educated persons have greater projected loss rates.

o The risk of DEP loss Increases with increased time in the DEP.

While these findings are not new, examining these variables simultaneously

produced some interesting results. In FY83 female HSSRs had lower loss

probabilities then all except male HSSRs (when all other variables are

oontSolled for). Earlier analyses could not have predicted this. Also found

was a great contrast between HSSRs and HSG/NHS contracts.

19
FIGURE 6

EFFECT OF ZNLISTIENT OPTIIONS AT FOUR MONTHS DIP

(White Male. AFQT 85, 4 Year Enlistment)

.. onus÷ACF

" .... 3. Bonus

-.. ... J ACF

High School Graduates

ACF

- , _ n- Jlonus+ACF

FY83 ... l V.... on*

,3 ZousACF _

u]inus,

FY82 " ..... "'1 ACF

- _.... . . .... • - None

High Sichool Seniorse

' iACF

S.... • ]lou@+4ACF

FT83

mjg,.mI1 Nonue

.01 .02 .03 .04 .05

Lose Probability

20
FIGURE 7

DEP LOSS RISK CATEGORIES- F782

(Evaluated at 4 Month DR?, 3 Year Term, No Options, Age 18)

SMale USSR (Age 17)

Male NBG

_Female eSSR (Age 17)

Male MiS

______________________ Female HSG

-. 2 .04 .06 .68 .1o .12

Predicted Lose Rate W AFQT Range 11-99

FIGURE 8

DEP LOSS RISK CATEGORIES- FY83

(Evaluated at 4 Months DEP, 3 Year Term, No Options, Age 18)

= , le HSSR (Age 17)

Male NSC

_ Female nSSR (As* 17)

_______________________________________Male VHS

Female NSG

.02 .04 .06 .08 .10 i12 .1.4

Predicted Loss Rate

21
From the models, high and low risk supply categories can be identified.

Figures 7 and 8 provide a ranking of these groups, evaluated at 4 months DEP.

(Results were estimated for a 3 year term and taking no options. In order to

provide accurate comparisons, HSGs and NHS graduates were evaluated at age 18

while HSSRs were estimated at age 17.). AFQT ranges are also shown. As

expected, male HSSRs present the lowest risk as a group, with a small AFQT

range (less then one perceit..). This indicates that all male HSSRs should be

treated similarly with respect to DEP loss. Male HSGs present the second

lowest risk. In this ease the AFQT differences are broader (about 1.5 to 2

percent). Female HSSRs were next, followed by male NHS graduates. Female

HSGs were the highest risk category. As shown in the figures, there is some

overlapping of predicted loss rates. For example, in Figure 11 predicted

loss probabilities for male NHS graduates range from 7.2 to 9.6 percent and

range from 8.8 to 11.5 percent for female HSGs.

Some of the other findings presented here have not previously been

reported:

o Prior military service was not found to be significant in predicting

DEP loss.

o There was no measurable difference in predicted rates from different

regions of the country.

o Non-white females exhibited lower loss rates than white females.

o Training OS was not found to be significant.

If the sole objective of recruiting was to sign individuals who assured

minimum DEP loss, generalizations could be made based upon this analysis.

Contracted DEP lengths should be kept as short as possible. Continue

recruiting as many male AFQT category I-IIlA HSGs and HSSRs as possible.

They are in the lowest DEP loss risk categories. Get as many 17 and 18 year

olds as possible to sign contracts. Limit the number of NHS graduates

recruited. Encourage the use of some of the enlistment and incentive

options associated with lower DEP loss.

22
It is not reasonable to assume, however, that recruiting strictly to

achieve minimum DEP loss is attainable or even desirable. There are

tradeoffs to be made with other Army policies (such as longer DEP periods

being assooiated with lower attrition rates). The factors associated with

DEP loss need to be systematically compared to behavior after enlisting.

The results of this analysis can be best used to identify those

individuals already within the system most likely to become DEP losses. These

persons could then be monitored. For example, while an HSO male with an

AFQT score of 90 enlisting for two years Is generally a low risk individual,

he would become a high risk at a DEP length of eight months.

The DEP loss model presented here could also be used in conjunction with

one of the models currentiy used to forecast contracts in order to ultimately

forecast accessions. The number of losses could be projected by the

characteristics of the people in the DEP. This would provide a better

indication of the number and type of accessions than simply deflating the

number of contracts in a blanket fashion.

The models have been very successful in explaining DEP behavior.

Systematic knowledge has been developed concerning which factors do and do

not relate to DEP loss. This analysis has also identified three areas where

additional DEP research would be beneflcial:

o DEP loss trends.

o The impact of the DEP on recruiting productivity.

o The relationship between enlistment policies and post-enlistment

behavior.

This model of DEP behavior was developed from cross-seotional data. This

tends to hold constant many significant factors, such as the economy,

relative military to civilian pay, and social attitudes toward the military.

(Whiae differences over time were examined somewhat by using data from two

different years, further research is necessary.) It would be important to

explore whether these kinds of factors significantly affect people in the

DEP. Knowledge of these relationships would enhance the ability to forecast

enlistments from contracts.

23
The DEP affects recruiter productivity. USAREC would benefit by having

an Improved model of the Implicit Costs associated with DEP management. Such

information could lead to increased recruiter output. For example, an

improved system for allocating recruiting resources between contracting and

DEP management could be helpful in increasing recruiter output.

Finally, the DEP needs to be examined from the standpoint of effective

personnel management. For example, while time in DEP may lead to higher pay

and greater use of recruiting resources, It also has been shown to lower

attrition. Other enlistment policies (e.g. ACF) may reduce both DEP loss and

attrition. A thorough analysis on the impact of these policies should be

done to develop program that achieve total Army goals.

24
REFERENCES

Amemiya, Takeshi. "Qualitative Response Models: A Survey." Journal of

Economic Literature. Vol. XIX (December, 1981), pp. 14837-1536.

Baldwin, Robert H., Daula, Thomas V., and Pagan, Thomas W,. "As Our Policy

Maker's Questions Become More Sophisticated, Our Research Methods Must

Match That Sophistication-, Some Microdata Evidence of Enlistment,

Attrition, and Reenlistment." Army Center for Economic and Manpowler

Research, Department of Social 3cignces, West Point. November 1982.

Bickel, Peter J. and Dokaum, Kiell A.* Mathematical Statistics: Oastic Ideas

and Selected Topcis,. San Francisco: Holden-Day, Inc., 1977.

Buddin, Richard L.. The Role of Service Experience In PQJt-Trsininh

Attrition In the Army and Air Force. The Rand Corporation# November

1981.

Celeste, Joanna, "Delayed Entry Program Contracting Cohort Loss Analysis$ A

Replication Study: Part I and Part 11," WESTAT, May 1984. CAR!

Working Paper)

Fernandez, Richard L.. .En.1stment Effects and Policy ImRlicatigna of the

Educational Assistance Tost Progtam. The Rand Corporation, September

1 982.

Maze, Rick. "Better Economy Sends Recruiters Scrambling#" Army Timest August

13, 1984.

Maze, Rick. "Delayed Entry Recruits To Lose Pay Credit For Inactive Time."

Army Times, October 15, 1984.

McWhite, Peter. Enlisted Personnel Allocation SZstem: Draft Annual Report

(3rd Year), October 1983 - September 1984. General Research

Corporation, 29 October 1984.

Morey, Richard C.. "Management of the Armed Services' Delayed Entry Pools: A

Comparison of Recruiting Philosophies and Issues.* Center of Applied

Business Research, Duke University, January 1983.

Pindyck, Robert S. and Rubinfeld, Daniel L.. Econometric Models and,

Economic Forecasts. Now York: McGraw-Hill# Ino.,T191

Schmitz, Edward and Nelson, Abraham. Modeling the Enlistment Process for

Personnel Planning. AR! Research Report 1416, In Process.

U.S. Army Recruiting Command. 1984 U.S. Army Pocket Recruiting Guide, 1984.

USAREC Briefing Slides, unpublishoed June 1984.

25
APPENDIX A

DEP CONTROL MES3AGE

USAREC MESSAGE

*98*SHeP9099OeeOe999UO69eoo*o~*9$*.ebtgo#9eee9gpou,..9ep,.*e9.,...

101200Z DEC 82 ROUTINE UNCLAS USAREC eSG#82-192 PART I

FROM: CDRUSAREC FT SHERIDAN IL.

TO: RRC/DRC COMMANDERS AND GUIDANCE COUNSELORS

SUBJECT: USAR ACCESSION CONTROL MEASUlES

I 4EFFE1 EFFEJIIE 28 DECEMBER 92. THE FOLLOWING USAR ACCESSION CONTROLS ARE

NPS MALES AFOY GROUPING RESTRICTED TO

GM/SM 31-99 270 DAYS

GM/SM 16-30 90 DAYS CIHS 60-99 180 DAYS

CIHS 16-49 CLOSED

NM/GED 31-99 90 DAYS *

NM/GED 16-30 CLOSED

NPS FEMALE AFOT GROUPING RESTRICTED TO

GF/SF 31"99 270 DAYS

rFSF 16-30 CLOSED

50-99 0HS 180 DAYS

CIHS 16-49 CLOSED

NF/GED 16-99 CLOSED *4

PRIOR SERVICE NA NA

ASTJRISKED ITEMS (04) INDICATE CHANGES TO ACCESSION CONTROL MEASURES

CURRENTLY INF RCE.

2. EFFECTIVE 28 DECEMBER 62. ALL USAR HALE GED ACCESSIONS WILL BE

ACCESSED AS NON HIGH SCHOOL GRADS.

3. NO GED FEMALES WILL BE ACCESSED EFFECTIVE 28 DECEMBER 82.

4. ALL FEMALE MORAL COMMITMENTS WILL BE FULLFILLED BY 27 DECEMBER 82.

NO EXCEPTION WILL BE GRANTED.

S. POC THIS HEADOUARTERS: USARCRO-O THROUGH RrGION IS CPT ROGERS/trC(P) DELBARTO. AV 459-2325/2747. CHL (312) 926-2325/2?47.

ROBERT A. LINGO. COL. GS. DEPUTY DIRECTOR RECRUITING OPERATIONS

IOISOOZ DEC 82 ROUTINE UNCLAS USAREC MSG#82-192 PART II

FROM: CDRUSAREC FT SHERIDAN IL

TO: RRC/DRC COMMANDERS AND GUIDANCE COUNSELORS

SUBJECT: PUSH MOS MISSIONS

.USS REC REOUEST SG#82-187. DTG 29100OZ NOV 82. SUBJ: PUSH MOS IS.INS TRD QUAR ER. FY 83

1. OS 3SC HAS BEEN CLOSED TO RECRUITING FOR FY 03. AND IS DELETED

FROM REF A.

. IFFECTIVE E IMMEDIATELY. MOS 43E IS ADDED TO PUSH MOS MISSIONS

EGIMNINI WITH RSM FEB THRU JUN 83. RRC ARE MISSIONED FOR MOS 43E

AS FOLLOWS:

Pil HE SE SW MW W TOTAL

17 17 11 20 11 76

MAR 19 19 11 21 11 79

APR 23 23 15 24 15 100

MAY 30 30 19 32 19 130

JUN 12 12 9 13 9 53

3. POC THIS HO. USARCRO-O. MAJ KILLAM/MSG SEABROOk. AV 459-3320

CL 312-926-3320.

NOE1. 0. GREGG. COL. GS. DIRECTOR. RECRUITING OPERATIONS

27
APPENDIX B

DISTRIBUTION BY MONTHS IN DEP - FY 82

MONTHLY PERCENTAGE DISTRIBUTION OF DEP LENGTHS

PY82

Months High School High School Non-Graduates

in DEP Graduates Seniors

1 38.11 27.07 38.90

2 26.64 18.88 26.19

3 10.27 8.31 10.44

4 3.98 5.31 4.34

5 3.75 5.39 2.79

6 4.31 7.86 2.82

7 3.03 8.12 8.00

8 3.97 8.58 2.77

9 3.47 6.26 2.22

10 1.61 2.45 1.09

11 0.43 1.17 0.18

12 0.39 0.60 0,25

TOTAL 100.0 100.0 100.0

29
APPENDIX B - con't

DISTRIBUTION BY MONTHS IN DEP - FY 83

MONTHLY PERCENTAGE DISTRIBUTION OF DEP LENGTHS

FY83

Months High School High School Non-Graduate@

in DEP Graduate& Senior@

1 7.90 4.57 7.42

2 22.57 8.81 15,19

3 41.99 15.12 49.04

4 12,80 5.18 13.61

5 5.06 6.02 5.71

6 3.71 10.24 4,09

7 2.47 13.53 2.21

8 1.54 12.40 1.31

9 0.85 10.44 0.59

10 0.58 7.54 0.33

11 0.33 3.55 0.30

12 0.30 2.61 0.19

TOTAL 100.0 100.0 100.0

30
APPENDIX C

LOGISTIC REGRESSION RESULTS

HS/NHS MODEL

MODEL CHI-SQUARE 681.78 (11) 962.40 (11 )

MODEL R o304 .317

4. LOG LIKELIHOOD 5638627 7037.18

N 111989 16603

INTERCEPT -3.6636 too -3.9474•ff .e

NON-WHITE - 00051 .0610

UNDER 20 - .2131 so - .3590 6e

TERM 2 .2357 01502

TERM 11-6 - .01165 - .1834

ENLISTMENT BONUS M 03466 40 .0656

ACF - '2297 * - .1617

AFQT - 00034 - .0046 It

DEP .0087 "o .0127 6#0

FEMALE .8273 *" .83941 40

NON-WHITE FEMALE .3158 * - .6396 #*"

NHS .1365 .7692 "

0 SIG .1

#0 SIG .05

go# 0 .01

S. ..... .. .. .. . .... .. •=• . .. ... -__-lmln• Idll~ial lklll- . ..... • imc
APPENDIX C

LOGISTIC REGRESSION RESULTS- continued

SENIOR MODEL

MODEL CHI-SQUARE 161.96 (10) 268.09 (10)

MODEL A .274 .219

.2 LOG LIKELIHOOD 4928.65 4324,13

N 14986 13107

INTERCEPT -3.8548 *to -4.3596 00

NON-WHITE - .3117 0 - .22741 *

AGE 17 - .3371 00# - 03063

TERM 2 .0704 - .0055

TERM 4-6 .2362 00 - .1176

ENLISTMENT BONUS - ,7151 6e .0244

ACF - .0717 .f 1113

AFQT - .0036 .0016

DEP .0060 Go# .0061 *"

FEMALE 1.2760 v** 1.3091 6Ou

NON-WHITE FEMALE .0054 - .2565

.510 .1

3IG .05

Off 310 .01

32
APPENDIX D

PREDICTED DIP LOSS PROIABIILITIZS USING

ENLISTMENT INCENTIVE - 1762

(Male$ 4 Year Term, AlQT 85)

DIP IN ACY

DAYS NONE AC? BONUS BONUS

<1 .015 .012 .010 .008

1 .019 .015 .u13 .011

2 4024 6019 .017 .014

3 .031 .015 .022 .018

150 4 .040 .032 .029 .023

5 .052 o041 .037 .030

6 .066 .054 .046 .038

7 .085 .068 .061 .049

8 .107 .087 .078 .063

9 .135 1110 .099 .080

<1 .019 .018 .1009 .009

1 .023 .022 .011 11010

2 .027 .026 .013 .012

3 *032 .031 .016 .015

USSR 4 .039 .036 .01.2 .018

5 .046 .043 .022 .021

6 .055 .051 .027 .025

7 .065 .061 .032 .030

8 .077 .072 .038 .035

9 .060 .085 .045 .042
PREDICTED D&P LOSS PROB5AILITIIS USING

ENLISTMINT I.N•NTIVE - Fl33

(M4a.o, 4 Year Toris, AMT 85)

MN0TH1S AC?+

IN4 DIP NONE AC? O ~ u

<1 .007 .006 .008 .007

1 .011 .009 .012 o010

2 .016 .014 .017 ,015

3 023 .020 .025 .021

SG 4 .034 ,029 ,036 ,031

3. .048 .042 .052 .044

6 .069 ,060 .074 ,064

7 .099 l0s5 .105 .090

a .138 .119 .146 .127

9 .190 .166 .200 .176

<1 e004 .003 ,004 ,003

1 .004 .004 .004 o004

1005 .005 .005 .005

3 1006 ,006 .006 ,006

USSR 4 .007 .008 .007 .007

5 .008 ,009 .009 ,008

6 .01i 011 .011 ,010

7 ,013 .013 .013 .012

8 .015 .014 .015 ,014

9 .018 .016 .018 .017

860919D