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tie unclt.de secunty classification) AN ANALYSIS OF POST-SERVICE CAREER EARNINGS OF FEMALE VETERANS

;rsonal Author(s) Miirk R. Sliepcevic

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; thesis anidyzes the post-service earnings of femiile veterans. A review of the literature on veterans' post-service earnings was
lucted to gjiin some insight on the topic. The literature on womens' labor force participation was also reviewed. An empirical
ysis w:is conducted based on a dataset constructed from the Reserve Comrxjuents Survev of 1986. A log-e^uTiings model was
ified based on human capitid theory. The intent of the model was to measure the effects of mihtary triiining and veteran status
he post-service eiimings of female veterans. These results were compared to a similar model of male veterans to analyze gender
;rences in veteran-nonveteran wage differentials. Overall, no measurable loss of income was incurred by female veterans who
^ferred their military skills to the civilian sector. Nonwhite females realized the greatest return to earnings from mihtary
irience. Also, those female veterans who transfer their miUtiuy-acquired skills may be closing the wage gap between themselves
male nonveterans. The relative gains in wages from military experience may last up to an average of nine years for female
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An Analysis of Post-Service Career Earnings
of Female Veterans

Mark R. Sliepcevic

Lieutenant, United States Navy

B.A., University of Illinois at Chicago, 1982

Submitted in partial fulfillment
of the requirements for the degree of

MASTER OF SCIENCE IN MANAGEMENT

ABSTRACT

This thesis analyzes the post-service earnings of female veterans. A review of the
literature on veterans' post-service earnings was conducted to gain some insight on the
topic. The hterature on womens' labor force participation was also reviewed. An empirical
analysis was conducted based on a dataset constructed from the Reserve Components
Survey of 1986. A log-earnings model was specified based on human capital theory. The
intent of the model was to measure the effects of mihtary training and veteran status on
the post-service earnings of female veterans. These results were compared to a similar
model of male veterans to analyze gender differences in veteran-nonveteran wage
differentials. Overall, no measurable loss of income was incurred by female veterans who
transferred their military skills to the civilian sector. Nonwhite females realized the greatest
return to earnings from military experience. Also, those female veterans who transfer their
military-acquired skills may be closing the wage gap between themselves and male
nonveterans. The relative gains in wages from military experience may last up to an
average of nine years for female veterans.

^^^

TABLE OF CONTENTS

I . INTRODUCTION ........ 1

II. BACKGROUND 5

A. Economic Issues ..... 5

B. Women m the Workforce 7

1. 1900 - 1970 8

2. 1970 to the Present 10

3 . General Observations 12

C. Women in the Military 19

III. REVIEW OF PERTINENT LITERATURE 24

A. The Bridging Effect 26

B. The Military as a Screening Device 27

C. The Transferability of Military Acquired Skills 28

D. Econometric Models of Earnings Potential ... 29

E. Summary 31

IV. DATA SET, METHODOLOGY, AND MODEL DETERMINATION . . 33

A. Data Set 33

B. Methodology 34

1. Survey Questions 34

2. Thesis Questions 35

3 . Restrictions 37

C. Model Determination . 3 8

V. DATA ANALYSIS AND RESULTS ............. 43

A. Descriptive Statistics . 43

1. Comparison of Means by Veteran Status ... 43

2. Comparison of Means for Females by Race . . 45
3 . Summairy 45

B. Multivariate Analyses 52

1. The Effects of Veteran Status for Females . 52

2. The Effects of Veteran Status for Males . . 60

3 . The Results for Race 61

4. Earnings Comparison by Gender and Veteran

Status 66

VI. CONCLUSIONS AND RECOMMENDATIONS 69

APPENDIX 72

LIST OF REFERENCES 74

INITIAL DISTRIBUTION LIST 7 6

I . INTRODUCTION

A plethora of issues surrounds the subject of women's
roles in the military. Among these are:

• females occupying traditionally male military occupations;

• assignment of women in combat-related job categories;

• equity m the distribution of men and women among all
military job categories;

• female officer career patterns influencing selection for
command;

• inequity in sea/shore rotation for navy enlisted.

This thesis will focus exclusively on the impact of military
training and experience on the post-service earnings of female
veterans as an extension of females in traditionally male
occupations .

Much has been written regarding the effect of military
training on the post-service earnings of male veterans. These
studies have found that the transfer of most types of military
training and experience has had a significant impact on
veterans' lifetime earnings.

Women's earnings functions cannot be expected to behave in
the same fashion as men's. Certain gender-related labor force
decisions come into play when determining variables for an
econometric model that deals with women who enter the service.

Female participation in the Armed Forces is purely
voluntary. There may be a bias towards self -selection into

male-oriented occupations, which is not characteristic of the
entire female population. On average female enlistees have
higher mean AFQT scores than male enlistees. Since one factor
m ]ob placement is the AFQT composite score, proportionately
more women on average may qualify for skilled positions.
There may be a propensity for high-ability women who enlist in
the services to seek out occupations that are traditionally
held by men. This self -select ion into male-oriented
occupations may not be characteristic of the entire female
population.

Women have been entering high-tech military jobs that were
traditionally male bastions at an increasing rate. There is
evidence that these types of jobs are economically beneficial
to the veteran's post-service career earnings. If the military
acts as a "bridge" for women to overcome sex-biased obstacles
to male-dominated occupations, female veterans would enjoy
greater job opportunities than do their civilian counterparts.
The higher potential productivity of female veterans and the
savings that private firms would realize in training costs
from hiring women with military backgrounds should translate
into higher wages for the prospective employee and into
general social benefits.

In this thesis, an econometric analysis of the Reserve
Components Survey will be conducted to specify and estimate a
human capital earnings model for female veterans and
nonveterans . By examining the accompanying statistics, the

model's validity will be determined. Care has been used to
ensure that all chosen variables and the functional form of
the equation are relevant to the study. The Reserve Components
Survey contains data on females who have chosen to enlist in
the active force and those who have not. The self -selection
bias that would ordinarily be associated with comparing groups
from the civilian sector with those from the military sector
is reduced. The Reserve Components Survey data controls for
background, taste, and ability factors which are normally the
source of selection bias. Therefore, the cohort under
investigation should be more homogeneous, which will improve
our ability to decompose the effect of the explanatory
variables. Minimizing selectivity bias will mean the economic
return to service in the armed forces can be estimated more
accurately .

The purpose of this study is to:

1. extract information from the Reserve Components Survey
-■ for the year 1986 in order to apply Ordinary Least Squares

methods to measure the effect of military training,
demographic, socioeconomic, and other explanatory variables
on the earnings of female veterans;

2. determine if military service is more valuable to female
veterans than to their male contemporaries, and

3. recommend areas for follow-on research.

The thesis is structured as follows: Chapter II discusses
the history of women in the labor market and in the Armed
Forces. A review of human capital theory is also contained in
this background section.

Chapter III contains the review of pertinent literature.
Topics reviewed include the effects of military training on
men's post-service earnings as well as information regarding
the current military force composition. The role of women in
the armed forces is included in this chapter.

Chapter IV presents a description of the Reserve
Components Survey and the data drawn from this survey. The
specification of the earnings model is contained in this
chapter. The chapter also presents the empirical estimates of
the veteran - nonveteran earnings differential.

Chapter V contains the conclusions and final
recommendations. Implications for current policies and
recommendations for further study are presented in this
section .

The goal of this thesis is to develop a model that will
analyze the effect of military service and training on female
veterans' post-service earnings. The analysis will test the
hypothesis that those women who transfer their military-
acquired skills into the civilian labor force will tend to
improve their economic status relative to their civilian
counterparts, all other factors being equal. With the
downsizing of the military and given the current political
climate, this study should be of significant value to manpower
policy-makers .

II. BACKGROUND

A. Economic Issues

To measure the effects of transferring military acquired
skills to the general labor market, we must examine and
compare the two markets. Transferability of skills requires
some similarity of 30b characteristics. Generally speaking,
the greater the degree of similarity between the military and
civilian occupations, the easier the transition for the
veteran and the lower the cost of retraining to the firm.

The decision to seek training and employment is based on
the utility difference between staying home and the wages that
could be earned. The cost of going to work can be measured by
summing the tasks performed by the individual at home that can
not be done if she works. Also, the value of leisure must be
included in this equation. The individual will work if the
value of acquired earnings is greater than the costs
associated with working.

Investment in human capital is an individual decision
based on the benefits one would receive from the time spent
developing skills. The individual must measure the difference
between the benefits received from the investment (pecuniary
as well as non-wage benefits) and from the costs of training.

A fair amount of time may be necessary to recover all of
the costs associated with skill acquisition. Blau and Ferber
(1986) noted in their work on women m the labor force that
women tend to have disnomted careers. This factor affects
their wages m two ways. Women tend to have a shorter work
life than men, which reduces the return to the individual's
investment. Time spent out of the work force will diminish the
skills that one has developed. This will affect a woman's
wages for the remainder of her working life (Blau and Ferber,
1986) . If a woman can expect to receive less from the
acquisition of skills, she will be less likely to invest in
training .

The labor market participation rates for females will
continue to change as the factors that affect their decision
to seek employment fluctuate over time. One major factor is
the implicit barrier to traditionally male occupations. There
are many factors that have helped build and maintain these
obstructions, although they have eroded recently. These
factors are:

• societal attitudes

• female job seeker's utility

• women as head of household

• women's attitudes towards job training, and

• delays in starting families

Some of these factors are sociological in nature. They are
driven by public attitudes and perceptions. Others are purely

economic m nature. Much of the change in women's roles m the
labor force is due to some combination of these factors.

As more women enter the work force, it is only logical to
assume that they will seek out the higher paying,
traditionally male jobs. Normally, access to these types of
employment is restricted due to the large amount of training
required to perform adequately. However, women are accepting
the challenge to overcome this obstacle to success by
acquiring technological training through formal schooling and
joining the military. As the labor market barriers keeping
women at home erode and the benefits of entering the work
force increase, more women will be willing to invest in their
economic future.

B . Women in the Workforce

The work history of women in the twentieth century
reflects changing participation rates, training levels, and
propensity to increase tenure with a single firm. These are
the key ingredients in an individual's earnings profile. Some
of the changes in participation rates can be explained by
fluctuations in societal attitudes and other demographics. The
blossoming United States economy and its subsequent demand for
laborers contributed to the dramatic female participation rate
increases of the late 1970 's, which are continuing today. The
following sections briefly review the history of female labor

force participation, first for the period 1900-1970, and then
for 1970 to the present.
1. 1900 - 1970

The early years of the twentieth century found mostly
single v;omen in the labor force. Hiring married women was
frowned upon as a matter of personnel policy. Thus, since
marriage was the norm, access to on-the-job training as well
as schooling was limited for many females.

Single females were hired to work in specific job
classifications. Prevailing attitudes of the day set aside
certain job categories as traditionally male. The evolution to
white collar office professionals was the first opportunity
for women to access higher paying jobs.

World War II caused a dramatic shortage of manpower.
The slack in the labor supply was picked up by women eager to
help the war effort. Barriers to certain traditionally male
occupations were dropped as a matter of necessity. The absence
of male workers and the reduction in birth rates allowed women
to enter the labor market during this period. Women had proven
their ability to handle traditionally male jobs. The post
World War II time frame found an increasing birth rate and a
return to the pre-war status quo in the labor market. From
1950 to 1970, relative pay rates (female-male ratio) remained
nearly constant at approximately the 60 percent level.

Table 1 shows that during the period from 1948 to
1964, 10,962,000 new positions (or 685,125 per year) were
created. Women accounted for 6^^ of the growth {7,214,000 new
jobs) . Most of the positions taken by women were low-skill,
entry-level jobs.

Participation rates for women increased by 6 . 0 percent

(Table 2), from a rate of 32.7 m 1948 to a rate of 38.7 in

1964. Male participation rates during this time frame declined

by 5 . 6 percent. Women realized an average gain of .59 percent

per year for the period of 1948 to 1990 (Table 2) .

Labor market experience' levels for women decreased
slightly from 1950 to 1965. For the period beginning in 1965
and lasting until the 1980 's, labor market experience levels
for women grew an average of 8.5 percent for all age
categories (Table 3) .

Education levels for women actually declined from 1950
to 1965. Table 4 shows that growth in education levels for men
outpaced changes in education levels for women by an average
of nearly 50 percent across the 20 to 40 year age range for
the period from 1950 to 1970.

The post World War Two years showed no significant
increase in earnings for women. Hourly wages for women of all
age groups still averaged 61.5 percent of those earned by men

^Labor market experience levels are measured by the number of
years the individual has invested in the workforce. Table 3
expresses this value in fractions of a year.

m 1968 (Table 5). Birth rates increased dramatically after
the war while investment m education decreased. Experience
levels for 2 0 and 2 5 year old women fell 11.4 and 2 9.0
percent , respectively, from 1950 to 1965. Labor market
participation rates increased for some groups of women while
the economy experienced a period of solid growth.
2. 1970 to the Present

Social attitudes towards gender and racial barriers to
traditionally white male occupations changed dramatically
during this period. Women began to invest more in human
capital through schooling. Declining birth rates allowed for
more time to be invested in the labor market. Length of
service (i.e., experience) numbers also increased. These
factors reflected positive changes in skill levels for women
and a rising commitment to the labor force. Increasing human
capital investment and labor force experience led to improved
access to higher paying jobs. Moreover, women showed a greater
propensity to ignore gender boundaries in the labor market.

In 1964, the baby boomers began entering the labor
market. From 1964 to 1990, Table 1 shows that total jobs grew
by 48,609,000 new positions (1,869,000 per year). Growth in
positions occupied by women was 29,648,000. This translates
into 61 percent of the total growth during the period. Female
participation rates grew from 38.7 to 57.5 percent for a total
increase of 18.8 percent from 1964 to 1990 (Table 2). Annual

growth m female participation rates was nearly double that of
the previous period, or .^2 percent per year.

Labor market experience levels for those women 3 0
years of age and younger improved by 14.5 percent between 1965
and 1980. Table 4 shows that for the period from 1970 to 1980,
men 25 and older increased their education level (measured in
years) an average of 14.7 5 percent over women from the same
age groups. However, women who were between 2 0 and 2 5 years of
age increased their education level by 110 percent compared to
men from this group. Table 5 reveals that although females'
wages as a percent of males' were nearly constant from 1964 to
1980, relative wages increased significantly for 20-44 year
old females during the early 1980 's, averaging just over seven
percent. The group of 45-64 year old females' relative wage
improvement was less dramatic (approximately 2.2 percent) .
Table 6 presents data on male income as a percent of female
income for various experience levels and time frames. It shows
that the investment in human capital accomplished by women
during the 1970 's has paid off for all experience and
education levels.

Table 7 presents relative wage data by educational level
for 1976 and 1982. Women have been experiencing a period of
high return on human capital investment during this time-
frame. Increases in educational investment have helped close
the wage gap for women and improved their economic condition
relative to their male counterparts. Females in all categories

experienced an average of 4.3 percent growth in wages relative
to those of men between 1976 and 1982.
3. General Observations

Some gender barriers were lowered during World War II
due to necessity. Women proved themselves to be quite capable
at adapting to traditionally male kinds of work. The post war
period saw the labor market return to the status quo, but only
for a while. The largest increase in female participation
rates was among the least trained. Many of the employed women
were part-time workers.

During the 1970 's, a large influx of women into the
labor market set the stage for wage growth and changing labor
force composition. Increasing education levels and job
experience positioned women to compete more aggressively with
men for the higher paying, traditionally male jobs. Birth
rates also fell, providing women with the opportunity to
pursue human capital investments.

Participation rates among the most educated and well-
trained women increased during this period. Women closed the
wage gap by an average of 13.6 percent for all groups from
1979 to 1987 (Table 6) . Growth in the daycare industry could
be a key indicator of the growing influx of women into the
labor force. The labor market of the 1990 's and the next
century should see dramatic changes in wage differentials.

TABLE 1

CIVILIAN EMPLOYMENT BY GENDER

(IN THOUSANDS)

Year

Male

Female

Total

1948

41,725

16, 617

58,343

1950

41, 578

17,340

58,918

1952

41, 682

18, 568

60,250

1954

41, 619

18,490

60,109

1956

43,379

20,419

63,799

1958

42,423

20, 613

63, 036

1960

43, 904

21,874

65,778

1962

44, 177

22,525

66,702

1964

45,474

23,831

69,305

1966

46, 919

25, 976

72,895

1968

48,114

27, 807

75,920

1970

48, 990

29, 688

78, 678

1972

50,896

31,257

82,153

1974

53, 024

33,769

86,794

1976

53,138

35, 615

88,752

1978

56,479

39,569

96, 048

1980

57, 186

. 42,117

99,303

1982

56,271

43,256

99,526

1984

56,091

45,915

105, 005

1986

60,892

48,706

109,597

1988

63,273

51, 696

114,968

1990

64,435

53,479

117, 914

Source: Department of Labor, Bureau of Labor
Statistics

TABLE 2

CIVILIAN

LABOR FORCE

PARTICIPATION

RATES BY

Year

Male

Female

Total

1948

86.6

32.7

58.8

1950

86.4

33 .9

59.2

1952

86.3

34.7

59.0

1954

85.5

34.6

58.8

1956

85.5

36.9

60.0

1958

84.2

37.1

59.5

1960

83.3

37.7

59.4

1962

82.0 ~"

-- - 3 7.9

58.8

1964

81.0

38.7

58.7

1966

80.4

40.3

59.2

1968

80.1

41.6

59.6

1970

79 .7

43.3

60.4

1972

78.9

43 .9

60.4

1974

78.7

45.7

61.3

1976

77 .5

47.3

61.6

1978

77 .9

50.0

63 .2

1980

77 .4

51.5

63 .8

1982

76.6

52.6

64.0

1984

76.4

53.6

64.4

1986

76.3

55.3

65.3

1988

76.2

56.6

65.9

1990

76.1

57.5

66.4

Source: Department of Labor, Bureau of Labor
Statistics

TABLE 3

YEARS OF LABOR MARKET EXPERIENCE

( FEMALES )

Age

Year 20 25 30 35 40 45

1950 2.81 5.87 7.97 10.57 13.99 16.43

1955 2.74 5.80 8.88 10.72 13.39 16.95

1960 2.70 5.76 8.48 11.83 13.68 16.58

1965 2.49 5.58 8.53 11.29 14.24 16.52

1970 2.63 5.69 8.68 11.21 14.24 17.21

1975 2.81 6.02 8.83 11.39 14.06 17.05

1980 3.00 6.23 9.50 11.70 14.39 16.97

Source: Department of Labor, Bureau of Labor Statistics

TABLE 4
CHANGE IN MALE EDUCATION (IN YEARS) RELATIVE TO

FEMALE EDUCATION

Acfe
Year 20 25 30 35 40

1950-1970 .43 .60 .36 .42 .66
1970-1980 -i.l .16 .11 .14 .18

Source: Kosters, 1991

TABLE 5

HOURLY WAGES OF WOMEN AS A PERCENT OF THOSE OF MEN

IN THE SAME AGE GROUP

Age

Group

Year

20-24

25-34

35-44

45-54

55-64

1964

82.0

62.0

55.2

57 .4

60.7

1968

74.5

62.9

53 .2

55.8

61.2

1972

76.4

64.9

53 .2

55.8

61.2

1976

77 .8

67.5

55.7

53.8

57.4

1980

77.7

68.8

56.2

54.3

56.7

1986

86.2

75.3

62.3

57.0

58.3

Note: Derived from multiple sources

TABLE 6

MALE / FEMALE WAGE RATIOS, YEARS OF EXPERIENCE,

AND YEARS OF EDUCATION

% A(1979-

Yrs Exp

Yrs Ed

1973

1979

1987

1987)

1.44

1.29

1.14

-15.0%

1.29

1.16

-13 .0

1.29

1.24

1.15

-9.0

1.60

1.58

1.39

-19.0

1.55

1.53

1.31

-22.0

1.55

1.51

1.34

-17.0

1.85

1.59

1.46

-13.0

1.66

1.59

1.48

-11.0

2 .04

1.72

1.59

-13.0

1.74

1.63

1.59

-4.0

1.62

1.61

1.47

-14.0

Source: Kosters, 1991

TABLE 7

HOURLY WAGES OF WOMEN AS A FRACTION OF THOSE OF MEN

BY AGE AND EDUCATION LEVEL

Education level Ages 25 - 34 Ages 35-44

1976 1982 1976 1982

Post Graduate 74.4 78.2 61.5 65.1

College Degree 69.9 73.5 54.4 63.3

High School Grad 64.7 69.1 56.7 58.1

Source: Kosters, 1991

C. Women in the Military

Women's participation in the military has been limited by
the types of occupations that they have been able to enter.
Before the early 1970 's, women could not represent more than
two percent of the total force, by law. Their roles were
strictly limited to noncombat and support positions.

Table 8 shows that 88.8 percent of white women, 94 percent
of black women, and 92.6 percent of hispanic women in the
armed services in 1972 were in the occupational skill category
classified as "semiskilled" (Eitelberg, 1988). This category
is comprised of traditionally female occupational fields such
as medical specialist, dental specialist, and administrative

TABLE 8

PERCENTAGE DISTRIBUTION OF ENLISTED PERSONNEL, ALL

SERVICES BY SEX, OCCUPATIONAL SKILL CATEGORY, AND

RACIAL/ETHNIC GROUP, 1972 AND 1984

1972 1984

Skill Category White Black Hisp. White Black Hisp.

Male

Unskilled 28.2 43.8 39.8 31.7 36.3 32.8

Semiskilled 48.0 44.9 45.9 41.4 44.8 47.3

Skilled 23.8 11.3 14.3 26.9 18.9 19.9

Total 100.0 100.0 100.0 100.0 100.0 100.0

Female

Unskilled 1.8 2.8 2.2 14.0 13.2 11.9

Semiskilled 88.8 94.0 92.6 60.6 69.6 69.8

Skilled 9.4 3.2 5.2 25.4 17.2 18.3

Total 100.0 100.0 100.0 100.0 100.0 100.0

Source: Eitelberg, 19i

support. During this period, limited access was granted to
women m the "skilled" positions, primarily
communications and intelligence (9.4, 3.2, and 5.2 percent
respectively). Very few women (1.8, 2.8, and 2.2 percent
respectively) were in occupations classified as "unskilled"
because a majority of these positions were considered to be
directly related to combat and women were excluded from many
of them.

During the late 1970 's and the 1980 's, the distribution of
females in military occupational categories changed
significantly. Moreover, the proportion of women in the
service increased nearly seven-fold, from a low of 1.4 percent
in 1965 to 9.2 percent in 1987 (Table 9). By 1984 female
participation in the "unskilled" category was, at most, less
than half the rate of their male contemporaries (Table 8).
Women comprised nearly the same percentage of "skilled"
occupations as did men in 1984. Still, a majority of women
remained in the "semiskilled" job classification category.

Movement of women from the "semiskilled" category to both
the "unskilled" and the "skilled" categories can be thought of
as "progress towards 'equity of service' or 'equal
opportunity'" (Eitelberg, 1988) . On average, women as a group
more than tripled their participation rate in the "skilled"
categoiry from 1972 to 1984. Since this category requires
advanced occupational training, which may be sought by
civilian employers, movement into the "skilled" occupational

TABLE 9

RESIDENT ARMED FORCES BY SEX, 1950-1987

(IN THOUSANDS)

Year

Males

Females

Total

Percent
Female

1950

1,150

1,169

1.6

1955

2, 033

2, 064

1.5

1960

1,833

1,861

1.5

1965

1, 920

1,946

1.4

1970

2, 081

2, 118

1.7

1975

1, 600

1, 678

4.6

1980

1,479

124

1,604

7.7

1985

1, 556

150

1,706

8.8

1987

1, 577

160

1,737

9.2

Source: Department of Labor, Bureau of Labor Statistics

category should benefit women economically in their post-
service careers. Good jobs are those that develop marketable
skills .

It is interesting to note that as more occupations are
opened to women and greater numbers of women are allowed to
enter the military, the services may be forced to be less

selective of enlisted female applicants. If women's AFQT
scores fall, more women will find themselves in the least
economically desirable catego2ry of "unskilled" .

Labor force participation rates for female veterans are
nearly equivalent to those of nonveterans. When pre-Vietnam
era veterans are eliminated, the participation rate increases
to approximately 75 percent. This represents nearly a 20
percent increase in labor force participation for female
veterans compared to their nonveteran counterparts. Their
unemployment rate was estimated at about five percent in 1986
(Roca, 1986) . Wage differentials (by gender and military
experience) will be estimated for male and female veterans in
the final section of this thesis.

III. REVIEW OF PERTINENT LITERATURE

Long term decisions regarding education, employment,
and training are made by individuals weighing the perceived
costs and economic gams from pursuing each alternative. Women
who join the all-volunteer milita]ry do so expecting to better
themselves by obtaining immediate employment and improving
their work skills.

The military offers a salary that is generally higher than
can be earned m the civilian labor market by a recent high
school graduate. Also, skill training is offered, to those who
qualify, that is often valuable to civilian firms. Much of the
training that is offered by the military could be acquired
through continued education and trade schools at a (direct and
indirect) cost to the individual. Because the military bears
the costs and also pays an enlistee's salary during training,
the military can be a very attractive post-high school
alternative .

Leaving the military is both a social and economic
decision. Economists have attempted to estimate the cost
associated with reenlisting in the military, which is part of
the economic equation that is used to determine the decision
to leave the military. The other portion of this equation is
the potential earnings offered by civilian firms that are
foregone if one reenlists. The explanatory variables often

associated with the potential civilian earnings function of an
individual service member include:

• Length of service m the military

• Skill type or MOS

• Skill transferability to the civilian sector

• Education level

These variables (and a host of others) have been considered by
many authors who have investigated enlistees' civilian
earnings potential.

A preponderance of literature has been written regarding
the transferability of military-acquired skills to civilian
labor markets for male veterans. Other studies have
investigated the role of women in the civilian labor market.
Literature pertaining to female veterans' post-service
earnings is virtually nonexistent. However, econometric models
developed to investigate post-service labor market outcomes
for male veterans should be applicable to female veterans with
only minor modifications.

The following section will investigate other studies that
have estimated models to describe and decompose the
determinants of an individual's wages. Initially, this thesis
will discuss the concept of the military acting as a "bridge"
to facilitate the acquisition of quality skill training by
minority veterans. The concept of "bridging" may also be
applicable to females. The next topic will be the feasibility
of military entrance requirements acting as a screening device

for civilian employers who are seeKing to hire veterans. The
topic of transferability of military-acquired skills has been
noted m previous studies and also will be presented in this
chapter. Finally, a comparison of econometric models that have
been used to determine the variables that affect an
individual's earnings potential will be examined.

A. The Bridging Effect

For certain groups of veterans, positive returns for
military experience are consistent with the military acting as
a "bridge" from school to the civilian work force. Minority
veterans gain access to training and skill development that is
not normally available to their peers who do not enter the
armed forces. The military assists some groups to cross socio-
economic boundaries by improving their work habits, specific
occupational skills, and their productivity.

Martindale and Poston (1979), for example, found that
access to military skill training paid a premium to minority
veterans. Working in the military environment and gaining
experience at functioning within large bureaucratic
organizations influences post-service earnings in a positive
manner. Fredland and Little (1980) associated this gain in
earnings with the acquisition of general skills.

B. The Military as a Screening Device

All servicemembers are required to qualify for entrance
and for specific military occupations. The qualification
process requires high performance on a battery of mental,
physical, and moral examinations. Once employed in the armed
forces, the servicemember gams experience in dealing with
large bureaucracies as well as the value of good order and
discipline. To the extent that a civilian employer is familiar
with the on-the-job training received in the services, time in
the military may be used by employers as a positive screen
when evaluating the applicants.

Defray (1982) focused on the civilian employment screening
process and stated that military status positively influences
employers. Military training is considered an indicator of
high productivity. Schwartz (1986) found that an employer may
be either positively or negatively influenced by military
experience depending on his or her perception of the military
as an institution. Vietnam era veterans were considered to be
negatively impacted by their military experience and were
viewed as less attractive by civilian employers.

Military entrance examinations are purported to sift out
the high quality, readily trainable individual from the
average performer. Berger and Hirsch (1983) felt that
qualifying for the military was a clear indication to an
employer that the individual had characteristics that would be
desirable. Much of the military training received during

enlistment could be classified as general training (Fredland
and Little, 1980). Qualities such as work discipline,
interpersonal communication skills, and others would be highly
sought after by employers. Military qualification and training
could be a good indicator of a successful and readily
trainable employee. Those employers who understand the
military recruiting procedures and training mechanism may
actively seek out veterans to employ.

C. The Transferability of Military Acquired Skills

Transferability of military skills is dependent upon the
servicemember ' s era and skill classification (Miller , 1991 ) .
Magnum and Ball (1989) found that roughly one-half of their
study group felt that military employment helped them find
work while approximately one-third actually transferred their
acquired skills. Most of the general training received in the
military is readily transferable, but whether veterans
actually work at jobs that mirror those they held in the
military depends on numerous, difficult to measure factors.

Many studies (Mehay, 1992; Bryant and Wilhite, 1990;
Daymont and Andrisani, 1986) established that for the first
two to three years after leaving the service, most veterans
experienced an earnings dip. This initial loss of pay is
considered to be consistent with earnings profiles of other
(nonveteran) civilians who change jobs. After the third year,
however, pay for veterans grew beyond that received by their

nonveteran contemporaries. The rate of increase in pay was
continually greater for years after the break even point.
Mehay (1992) and Norrbloom (1976) found that those veterans
who transferred their military skills were even better off
than their veteran contemporaries who did not.

Those with technical military specialties found their
skills more readily transferable to the civilian marketplace.
Since military job categories that are considered to be
technical in nature are growing at a more rapid pace, "it is
likely that the skill transfer between the military and
civilian sectors is more prevalent now than in the past"
(Miller, 1991) .

D. Econometric Models of Earnings Potential

Model specification used to describe and decompose the
determinants of an individual's wages takes on the form of the
standard Mincer log-earnings function (Kosters, 1991). The
generic equation is:

In-earnings = B,, + B^X, -h BoX-j -h e
where the In-earnings represents the natural log of an
individual's earnings; B„ represents the intercept; B^ and B,
are the independent variables' coefficients; X^ and X-j are the
independent variables, and e is the associated error term. The
earnings variable Has been measured in various studies as the
hourly wage rate, or weekly earnings, or annual income.

Most studies reviewed for this thesis have used the log-
earnings functional form m their statistical analyses. This
functional form allows the researcher to investigate the
effect of incremental changes m explanatory variables on the
dependent variable " In-earnmgs " . Each variable ' s contribution
to earnings can be isolated and compared m this
specification.

Choosing variables for the earnings model is often based
as much on limitations of the available data as well as on
fundamental economic theory. Those models that used the
National Longitudinal Survey, Youth Cohort (NLSY) , (Bryant and
Wilhite, 1991; Daymont and Andrisani , 1986 ; Bolin, 1980)
included educational level achieved, civilian and military
training, work experience, race, and length of military
training as explanatory variables in their models. Table 10
lists the explanatory variables that were used in four prior
All Volunteer Force-era studies. The variables that are common
to all of these models in columns are education and
experience. Race and marital status are also common to three
of the models .

These models may be acceptable for generic studies
regarding earnings potential, but the variables representing
military training and experience may have less descriptive
power when analyzing female veterans. Two models used the
Reserve Components Survey (RCS), (Miller, 1991; Mehay, 1992)
to explore the differences between veterans and nonveterans.

The RCS allows the researcher to compare individuals with
similar tastes for military service, thus avoiding any bias
associated with self -selectivity ,

E . Summary

Econometric modeling requires some analysis of explanatory
variables prior to their selection as inputs for the model. By
reviewing all pertinent literature, the researcher can examine
previous models and their associated variables for theoretical
and statistical validity.

Variables that have proven to be statistically significant
in previous studies should be considered for inclusion in the
econometrician' s model. Omission of relevant variables could
lead to bias in the coefficients of the included variables
(Studenmund, 1992) . A complete study of relevant literature is
required before the regression models are estimated.

TABLE 10

EXPLANATORY VARIABLES USED IN PRIOR

EARNINGS STUDIES

Mehay 1992 Bryant and Bolin 1980 Daymont and
Wilhite 1990 Andrisani 1986

EDUCATION

EXPERIENCE

EXPERIENCE2

SELF EMPL.

NONWHITE

MARRIED

CHILDREN

YRSOUT

YRS0UT2

PRIORSERV

TRANSFER

OCCUPATION

EDUCATION

EDUCATI0N2

EXPERIENCE

RACE

MARRIED

AGE

UNEMPLOYMENT

GEO AREA

OCCUPATION

INDUSTRY

LOS MILITARY

SEX

MIL TRAIN

CIV TRAIN

EXPERIENCE2 EDUCATION

RACE

MARRIED

AGE

LOS CIV

HS EDUC
COLLEGE

YRS OUT COL

AFQT

LOS MIL

YRS OUT MIL

Source: Compiled from various sources

IV. DATA SET, METHODOLOGY, AND MODEL DETERMINATION

A. Data Set

This study uses information obtained from the 1986 Reserve
Components Survey to investigate those factors that are
significant determinants of the log-earnings of female
veterans. The Reserve Components Survey was chosen because
sampling includes responses from veterans-^ and nonveterans who
are similar in many respects; therefore, any bias that may
occur due to self -select ion into the active components of the
armed forces and prescreeriing of applicants will be minimized
by using this survey. Although both prior active duty
reservists (veterans) and those with no active duty experience
(nonveterans) receive milita2ry training, the value to civilian
firms of training received while on active duty should create
significant differences in military-acquired skill
proficiencies between the two cohorts. This difference in
skill levels should influence the relationship between active
duty training and future civilian wages.

The 1986 Reserve Components Survey was administered by the
Defense Manpower Data Center in conjunction with the office of

'^Veteran is defined as a reservist with active duty experience
and training. Nonveteran is defined as a reservist who has not been
on active duty and has received reserve training only.

the Deputy Assistant Secretary of Defense for Guard/Reserve
Manpower and Personnel. The survey's purpose was to develop a
data base for all reserve components that would be useful in
investigating the effects of policy decisions regarding
personnel issues. The basic sample included approximately
109,000 officer and enlisted reservists. Respondents were only
considered if they were trained selected reservists. The
response rate for the enlistees was 59.7 percent.

B. Methodology

1. Survey Questions

The Reserve Components Survey asked two questions

regarding the respondents' civilian pay. One question focused

primarily on weekly civilian earnings:

In 1985, what were your USUAL WEEKLY EARNINGS from your
main civilian job or your own business before taxes and
other deductions? Give your best estimate.

A second question was asked regarding annual earnings. This

question asked the respondents to include all income.

During 1985, what was the TOTAL AMOUNT THAT YOU EARNED
FROM ALL CIVILIAN JOBS or your own business before taxes
and other deductions? Include earnings as a Guard/Reserve
technician. Include commissions, tips, and bonuses. Give
your best estimate.

The data set was divided into two basic subsamples: (a)

female veterans and nonveterans, and (b) male veterans and

nonveterans, to capture the value of active duty experience

and direct military acquired skill transfer to the civilian

workforce. Each subsample was used to investigate the natural

log of yearly income as the dependent variable for an ordinary
least squares regression equation.

The distribution of the reserve force by gender is
shown m Tables 11 and 12. Table 11 provides the percentage
makeup for each of the branches of service and includes all
components. The proportion of enlisted reservists who are
female is highest for the Air Force Reserve (19 percent) and
lowest for the Marine Corps (four percent) . Officer and
enlisted gender ratios are similar for the individual branches
of service. Table 12 gives the population size for the
reserve components. Air National Guard and Army National Guard
personnel are combined with their respective reserve forces.
Coast Guard Reserve personnel are excluded from this study.
2. Thesis Questions

Two primary questions are explored in this thesis; (1)
Does active duty military experience of female reservists
(veterans) improve their post-service earnings compared to
nonveteran reservists? and (2) Does the direct transfer of
military-acquired skills lead to higher wages in the civilian
workforce? Question (1) is an attempt to measure the effects
of 'general' training received in the military such as dealing
with large bureaucratic organizations, militairy discipline and
bearing, and the ability to give and take direction. Question
(2) addresses the transfer of 'specific' skills acquired in
the military which are transferred to the civilian job sector.

Bm»wMMai«ni»niiiiitimniii«»niiiii

The respondents were asked if their military occupational
specialty is directly related to their current civilian ] ob .

A secondary issue is the comparison of female
veterans' post-service earnings to those of male veterans.
Future studies may reference these results m order to
determine trends in wages for women.

TABLE 11

GENDER: ENLISTED PERSONNEL AND OFFICERS BY

RESERVE COMPONENT

Ciender

Enlistet;]

Male

Female

(jf fleer

Male
Female

LISAR

UCNR

IJSMCR

83%
17%

82%

18%

88%
12%

91%
9%

96%
4%

98%

UCAFR

81%
19%

7 9%
21%

TOTAL

SELECTED

RESERVE

90%

10%

8 8%

12%

oource: Defense Manpower Data ("enter, rjesc-rir't iijn of nff:
Enliste'fl Personnel in the U.S. Llelectecl Reserve, 1986

^r and

Notes: total DOD numbers include ARNG and ANG personnel
total Selected Reserve numbers include USCCjR personnel

TABLE 12

GENDER: ENLISTED PERSONNEL BY

RESERVE COMPONENT

■/:mB'

i;:;Mi"'R

l:l?^FR

TiiT [.H )[)

Male

172,465 92,555

35,324 12,(d21

ri,122 47,128 768,164

1,297 11,055 85,352

Enlii-r-^^l F'^rrrmnt^l in the \K:2. .'.^l^'-n^c] R^-^-.^^ry^ ^ 193(3
Note: total DiJD numbf-'rs imrluii"-- ARNCi and ANCi [")eL':::onnel

3. Restrictions

Restrictions were imposed on the sample to ensure the
comparability of the observations. First, the dataset included
only full-time civilian employees. Those reservists who
reported part-time employment were deleted. Also, those
respondents who reported their status as 'unemployed' were
deleted from the sample as were full-time students and
homemakers .

The sample was limited to enlisted members who had
successfully completed at least one active duty tour. This
restriction was established by limiting active duty
respondents to the rank of E-3 or higher and by deleting those
respondents with fewer than two years of active service.

Separate regressions were run for males and females.
This allowed for a comparison of female veterans and female
nonveterans by including a veteran status variable to capture

the effects of prior service on civilian wages. The effect of
veteran status was measured separately for both males and
females in order to measure the veteran-nonveteran
differential in wages by gender.

C. Model Determination

A standard Mincer natural log of earnings regression
equation was specified and estimated. Use of the natural log
of wages allows the researcher to investigate the percentage
change in income provided by a one unit change in an
independent variable (Kosters, 1991) .

Independent variables can be categorized as either (1)
personal variables, (2) military variables, or (3)
occupational variables. Table 13 contains a list of the
personal and military variables and their descriptions as
derived from the Reserve Components Survey. The expected signs
of the coefficients for these variables in the OLS earnings
model are also included in Table 13 .

Personal variables attempt to capture the individual
demographic attributes that may affect the earnings of the
survey respondents. The experience variable is included to
capture the effect of on-the-job training. The square of the
experience variable is used to show its declining influence on
wages over time.

Military variables identify the kind of training
transferred to the civilian labor market. The variable XFRVET

TABLE 13
PERSONAL AND MILITARY VARIABLES

Personal

Definition

Expected

Sign

Variables

CHILD

1 if number of

females

dependents is

males

greater than 2

MARRIED

1 if respondent

females

married

males

NONWHITE

1 if respondent

females

not Caucasian

males

EDUCATION

number of years

females

formal education

males

EXPERIENCE

number of years

females

the workforce

males

Military

Variables

XFRVET

if a veteran

females

transferred

males

his/her military

acquired skills

civilian job

VETERAN

1 if respondent

females

changed

males

occupations from

active duty to

civilian

number of years

females

ADJEXP

the respondent has

males

been out of the

service or out o

school

Source : Author

measures the effect of direct skill transfer from the militairy
to the civilian market. The variable VETERAN distinguishes
between those respondents with active service experience and
those without. VETERAN captures the effect of general military
training on a veteran's post-service income. ADJEXP is a
measure of the veteran's time out of the military and the
nonveteran's time out of school.

The expected signs for the personal and military variables
are contained in Table 13 . Signs for military-related
variables should be the same for females as they are for
males. VETERAIJ (pertains to general skills) and XFRVET
(pertains to specific skills) are expected to have positive
coefficients; those respondents with these traits will have
greater earnings than those without them. Some personal
characteristics are expected to have differing signs for women
and men. Women with children can be expected to work fewer
hours and earn a lower annual income (Blau and Ferber, 1986) .
Also, married women are more likely to have disrupted careers,
thus the coefficient for the variable MARRIED should have a
negative sign. All other coefficients' signs are theorized to
be the same for females as they are for males .

Table 14 contains the occupation and industry variables
and their respective definitions. Occupation and industry
variables are coded as dummy variables in order to determine

the returns to earnings for specific 30b categories.

The amount of training required to fill any position will tend

to vary.

The dependent variable, the natural log of annual income,
was derived from the individual's primary civilian job as well
as all other income sources. The deletion of part-time workers
and those who were unemployed during the period will increase
the similarity of the sample population.

TABLE 14
OCCUPATIONAL VARIABLES

Industry
Variables

Definition

SELFEMPLOY
AGRIMIN

FINANCE

MANUFACTURING

ENT/REC

SALES

PRO SERVICE

PUBLIC ADMIN

REPSERV

TRANSPORT

GOVERNMENT
CRAFT

MANAGER
OPMACHINE

OPLABOR
WHOLESALE
RETAIL
PROFESS

SERVICE

j Self -Employed

Agriculture, Forestry,
Construction

Fisheries ,

and

Finance, Insurance, Real Estate, Business

Manufacturing

Entertainment and Recreation

Sales

Professional Services

Public Administration

Repair Services

Transportation, Communication, and other
Public Utilities

State, Local, and Federal Employees

Construction Workers, Mechanics, and
Engineers

Administrative, and Managerial

Precision Production, Machine Operators, and
Assemblers

Other Handlers and Laborers

Wholesale Trade

Retail Trade

Professional, Scientific, Teachers,
Technicians, and Education Administration

Protective Service, Postal, and Food Service

Source: Author; created from Reserve component Survey, lybb

V. DATA ANALYSIS AND RESULTS

A. Descriptive Statistics

1. Comparison of Means by Veteran Status

Tables 15 through 19 present the sample means for the
explanatory variables used below in the earnings models . Each
table has been decomposed by veteran status to measure any
differences in demographic and occupational characteristics.
The means are calculated by gender (Tables 15 and era) . T-
tests' were performed on each variable to determine whether
differences in the means of the characteristics of veterans
and nonveterans are statistically significant. The comparison
of subsample means will give an overall indication of group
homogeneity .

In Table 15 the average reported annual income for the
subsample comprised of all females is $18,238 for veterans and
$18,408 for nonveterans, a difference of only $170, This
difference in annual income is not statistically significant.
In Table 16 differences in annual income between veteran and
nonveteran females who joined the service after 1973 are
dissimilar to the group composed of all females; veterans

^The null hypothesis is that the means are the same

(post-1973) earned on average $2,452 less than nonveterans

(significant at the one percent level) .

In Table 17 mean income for all males was
significantly lower for nonveterans than for veterans ($22,239
to $26,115) . In Table 18 nonveteran males who enlisted m the
reserves during the all-volunteer era also earned
significantly less than their veteran male contemporaries

($17,495 to $19,504) . Both differences were significant at the

one percent level .

Educational levels were different for both groups of

females. For the cohort of all females in Table 15,

nonveterans accrued approximately one-half year less education

on average and are 12 percent more likely to have accumulated

some college experience. In Table 16, for post-1973 females,

the difference drops to about one-fourth of a year, which is

still a significant difference.

The difference in educational attainment is even more

pronounced for both groups of males. In Table 17 nonveterans

from the all male group spent one-half of a year less in

school and were 15 percent less likely to go to college than

their veteran counterparts. In Table 18 nonveterans from the

post-1973 cohort received nearly one-half year less education

and were 11 percent less likely to go to college.

A comparison of the female veteran cohorts in Tables

15 and 16 reveals that 22.8 percent of the group of all female

veterans held civilian jobs similar to their military

occupations (XFRVET) while 24.4 percent of the post -1973
veterans held similar jobs. For both groups of male veterans,
the transfer rates were much lower than they were for females.
For all males only 12.0 percent of veterans held similar :Jobs
(Table 17), about half the percentage for females. For post-
1973 enlistees, 16.3 percent held similar jobs (Table 18).

Marriage rates were not statistically different for
either group or females. The group of all male veterans were
14.5 percent more likely to be married than nonveterans. For
the post-1973 group of males, the difference was 11.2 percent.

2 . Comparison of Means for Females by Race

For this comparison, the cohort comprised of all
females was broken out by race (Table 19) . Annual income for
the two groups was not statistically different, although the
group of nonwhite female veterans earned, on average, $981
more than white female veterans. Both groups had similar means
for COLLEGE and EDUCATION, but white female veterans were 2.2
percent more likely to transfer their military acquired
skills. Also, white females were 12.5 percent more likely to
have achieved veteran status. Overall, the two groups were
reasonably similar.

3 . Summary

Overall, nearly one-half of the variables had
significantly different mean values between veterans and
nonveterans for the sample of all females. Only one-fourth of

the differences in mean values for the post-1973 sample were
statistically significant. Since the T-test examines whether
the means are statistically different, both groups of females
show some statistical resemblance. The opposite can be said
for the two male subsamples. The T-test of means for the
variables for both groups of males are mostly significant . The
implication of the tests is that male veterans and nonveterans
have statistically different means.

TABLE 15

COMPARISON OF MEANS FOR
ALL FEMALES BY VETERAN STATUS

Variable Veterans Nonveterans T-TEST (.01)

INCANN 18,238 18,408 0.2451

COLLEGE 0.6176 0.4991 -4.5727 *

CHILD 0.7380 0.7025 -0.7017

EDUCATION 13.7112 13.2940 -4.4319 *

MARRIED 0.3652 0.3226 -1.6921

NONWHITE 0.2734 0.4082 5.5403 *

ADJEXP 8.0429 10.6842 7.3062 *

XFRVET 0.2275

SELFEMPL 0.0210 0.0227 0.2209

AGRIMIN 0.0669 ----- 0.0664 -0.0370

FINANCE 0.0421 0.0804 3.2198 *

MANUFACTURING 0.1033 0.1189 0.9530

ENT/REC 0.0076 0.0052 -0.5499

SALES 0.0459 0.0769 2.5684 *

PROSERV 0.1721 0.1801 0.3982

PUBADMIN 0.4111 0.3252 -3.3550 *

REPSERV 0.0421 0.0402 -0.1761

TRANSPORTATION 0.0746 0.0586 -1.1914

GOVERNMENT 0.5526 0.4563 -3.6638 *

CRAFT 0.0459 0.0210 -2.5642 *

MANAGER 0.1836 0.1547 -1.4391

OPMACHINE 0.0535 0.0708 -1.3886

OPLABOR 0.0172 0.0262 -1.2185

WHOLESALE 0.0096 0.0149 0.9527

RETAIL 0.0593 0.0944 2.6069 *

PROFESS 0.2084 0.1897 -0.8823

SERVICE 0.1185 0.1198 0.0707

Note: * denotes varialDle is signiticant at tne one percent
level ,

TABLE 16

COMPARISON OF MEANS FOR
POST-197 3 FEMALES BY VETERAN STATUS

Variable Veterans Nonveterans T-TEST (.01)

INCANN 15,687 18,139 3.3829 *

COLLEGE 0.5482 0.4991 -1.5694

CHILD 0.6295 0.6975 1.2544

EDUCATION 13.4940 13.2877 -1.8720 *

MARRIED 0.3645 0.3198 -1.4885

NONWHITE 0.2952 0.4122 4.0074 *

ADJEXP 4.8102 10.1730 19.0767 *

XFRVET 0.2 43 9

SELFEMPLOY 0.0181 0.0213 0.3731

AGRIMIN 0.0693 0.0638 -0.3482

FINANCE 0.0482 0.0804 2.2391 *

MANUFACTURING 0.1355 0.1183 -0.8124

ENT/REC 0.0060 0.0055 -0.0994

SALES 0.0633 0.0795 1.0333

PROSERV 0.1566 0.1848 1.2160

PUBADMIN 0.3614 0.3161 -1.5145

REPSERV 0.0482 0.0416 -0.4985

TRANSPORTATION 0.0693 0.0591 -0.6453

GOVERNMENT 0.5030 0.4529 -1.5982

CRAFT 0.0542 0.0213 -2.4975 *

MANAGER 0.1566 0.1497 -0.3037

OPMACHINE 0.0723 0.0702 -0.1263

OPLABOR 0.0211 0.0259 0.5179

WHOLESALE 0.0151 0.0148 -0.0357

RETAIL 0.0753 0.0989 1.3786

PROFESS 0.1717 0.1848 0.5515

SERVICE 0.1416 0.1238 -0.8196

Note: * denotes variable is significant at the one percent

level

TABLE 17

COMPARISON OF MEANS FOR
ALL MALES BY VETERAN STATUS

Variable

Veterans

Nonveterans T-TEST (.01)

INCANN

COLLEGE

CHILD

EDUCATION

MARRIED

NONWHITE

ADJEXP

XFRVET

SELFEMPL

AGRIMIN

FINANCE

MANUFACTURING

ENT/REC

SALES

PROSERV

PUBADMIN

REPSERV

TRANSPORTATION

GOVERNMENT

CRAFT

MANAGER

OPMACHINE

OPLABOR

WHOLESALE

RETAIL

PROFESS

SERVICE

26,115
0.4889
1.7421
13 .3253
0.7975
0.24138
14.2252
0.1202
0.0592
0.1455
0.0251
0.2054
0.0031
0.0437
0.0745
0.2982
0.0439
0.1246
0.4347
0 .2015
0.1126
0.1326
0.0447
0.0227
0.0518
0.1715
0.1466

Note: * denotes that variable
percent level.

22,239

0.3387

1.3053
12 .7854

0.6530

0.1984
12.6464

0.0687

0.2116

0.0262

0.2178

0.0042

0.0658

0.0617

0.1910

0.0575

0.0837

0.2858

0.2112

0.1007

0.1532

0.0646

0.0360

0.1016

0.1187

0.1210
is signif icarTET

-20.6448 *

-25.0020 *

-25.6071 *

-23.5190 *

■26.3735 *

-8.4174 *

-14.8035 *

3.1285 *

13 .9268

0.5567

2.4624 *

1.5829

7.8026 *

-4.0600 *

•20.4327 *

4.9643 *

10.9228 *

25.4694 *

1.8666 *

-3.1387 *

4.7639 *

7.0266 *

6.3043 *

15.0521 *

12.1999 *

-6.0952 *

~aZ the one

TABLE 18

COMPARISON OF MEANS FOR
POST- 197 3 MALES BY VETERAN STATUS

Variable Veterans Nonveterans T-TEST (.01)

INCANN 19,504 17,495 -6.6202 *

COLLEGE 0.3751 0.2696 -10.3966 *

CHILD 1.1493 0.8715 -10.3959 *

EDUCATION 12.8580 12.4752 -11.6704 *

MARRIED 0.6115 0.4996 -10.5887 *

NONWHITE 0.3354 0.2520 -8.4143 *

ADJEXP 5.0725 6.6047 19.0522 *

XFRVET 0.163 3

SELFEMPLOY 0.0516 0.0572 1.1662

AGRIMIN 0.1855 0.2540 7.9022 *

FINANCE 0.0224 0.0210 -0.4657

MANUFACTURING 0.2101 0.2379 3.1237 *

ENT/REC 0.0054 0.0058 0.2251

SALES 0.0458 0.0654 4.1189 *

PROSERV 0.0683 0.0498 -3.5340 *

PUBADMIN 0.2287 0.1133 -13.7244 *

REPSERV 0.0618 0.0742 2.3249 *

TRANSPORTATION 0.0987 0.0664 -5.2830 *

GOVERNMENT 0.3264 0.1824 -15.0527 *

CRAFT 0.1992 0.2112 1.3895

MANAGER 0.0532 0.0562 1.3895

OPMACHINE 0.1432 0.1771 4.3840 *

OPLABOR 0.0685 0.0871 3.2900 *

WHOLESALE 0.0307 0.0364 1.4797

RETAIL 0.0788 0.1307 8.2719 *

PROFESS 0.1403 0.0840 -8.0043 *

SERVICE 0.1832 0.1358 -5.9037 *

Note: * denotes variable is significant at the one percent

level

TABLE 19

COMPARISON OF MEANS FOR
ALL FEMALE VETERANS BY RACE

Variable

Whites

Nonwhites

T-TEST (.01)

INCANN
COLLEGE
CHILD
EDUCATION
MARRIED
VETERAN
ADJEXP
XFRVET
SELFEMPL
AGRIMIN
FINANCE
MANUFACTURING
ENT/REC
SALES
PROSERV
PUBADMIN
REPSERV

TRANSPORTATION
GOVERNMENT
CRAFT
MANAGER
OPMACHINE
OPLABOR
WHOLESALE
RETAIL
PROFESS
SERVICE
Note: * denotes

17, 996
0.5280
0.5919
13 .4333
0.3472
0.3595
9.7654
0.0795
0.0284
0.0653 ,
0.0587
0.1135
0.0066
0.0662
0.1646
0.3756
0.0417
0.0568
0.4749
0.0407
0.1722
0.0596
0.0293
0.0180
0.0870
0.1996

0.1060
variable is

18, 977
0.5508
0.9246
13 .4105
0.3164
0.2344
9.9951
0.0574
0.0115
0.0688
0.0852
0.1148
0.0049
0.0689
0.2000
0.3115
0.0393
0.0754
0.5066
0.0066
0.1492
0.0754
0.0131
0.0049
0.0770
0.1885
0.1426

-1.3052
1.2910
-6.6041 *
0.2567
1,2910
5.5239 *
-0.6689
1.7572 *
2.5276 *
-0.2798
-1.9800 *
-0.0757
0.4513
-0.2053
-1.7850
2.6756 *
0.2285
-1.4507
-1.2438
4.9432 *
1.2414
-1.221
2.3351 *
2.6425 *
0.7209
0.5531
-2.1511 *

level

significant at the one percent

B. Multivariate Analyses

1. The Effects of Veteran Status for Females

The coefficients of the earnings models for all
females and volunteer-era females are presented in Tables 20
and 21. As expected, both groups of females had positive
returns for transferring milita2ry-acquired skills. However,
the returns for all females (six percent) and post-1973
females (four percent) were both statistically insignificant.
Although insignificant, the positive sign for the coefficients
does reflect some desire on the part of civilian employers to
hire veterans with these skills.

The coefficients for the variable VETERAN in Tables 20
and 21 represent the effect on earnings of changing
occupations. Normally, individuals who change occupations
require training from their new employer. Some of the cost of
this training must be absorbed by the individual in the form
of lower wages. The estimated coefficient for the variable
VETERAN for women is positive but insignificant for both
groups of females. One possibility is that female veterans
realize a slight return from changing occupations to the
civilian sector, and that female veterans are at least not
penalized for their active duty affiliation. Since the primary
reason for the initial drop in income after a job change is
the firm's training costs, the value and transferability of

military-acquired training may at least offset the cost to the
f i rm .

Recall that more than 22 percent of the female
veterans transferred their military acquired skills to
civilian occupations. Tables 20 and 21 indicate that females
who transferred skills that they acquired while on active duty
(XFRVET) gained a larger earnings advantage than veterans who
did not transfer skills. However, once again, the coefficient
for the transfer variable for the two groups of females is not
significant .

The coefficient for the variable ADJEXP"* (Tables 20
and 21) is positive and significant for both groups of
females. The immediate effect is approximately a five percent
relative gain in earnings for the first year of civilian labor
market experience. Tables 22 and 23 were computed by allowing
all of the dichotomous variables to be equal to zero and
computing various possible combinations of the variables
XFRVET, VETERAN, ADJEXP, and ADJEXP2 . The intent is to measure
the partial effects on In-earnings of active duty affiliation
and civilian labor market experience. Table 22 shows the
effects of post-military civilian labor market experience for
female veterans who have civilian jobs similar to those they
held in the military versus females who have chosen different
occupations. The net effect is that annual income increases an

^The variable ADJEXP represents an adjustment in civilian
labor market experience for veterans' time on active duty.

average of nearly four percent per year for a ten-year period,
but IS slightly reduced over time,

Estima-ed coefficients for the active duty-related
variables indicate that female veterans realize no noticeable
earnings penalty upon leaving the service. This observation
holds true for both groups of female veterans. Table 22
indicates that the return for veteran status and skill
transfer increases income by 50 percent after the individual
has been out of the service for ten years .

As presented in Tables 20 and 21, married females with
children earned over five percent less than single females.
This earnings penalty was expected since married women with
children tend to have less time to invest in their own human
capital. However, the coefficient for CHILD was statistically
insignificant .

The greatest return on occupational variables for both
groups of females was for those employed in the finance,
entertainment /recreation, public administration, and
transportation industries. Those who were self-employed or
worked in sales had negative returns.

TABLE 2 0
COEFFICIENTS FOR ALL FEMALES

Variable

Coefficient

T-Statistic

CHILD

-0.0006

-0.033

EDUCATION

0.0391

4.315

-*•-*•*

MARRIED

-0.05507

-1.686

NONWHITE

-0.0200

-0.635

ADJEXP

0.0496

7.036

* * *

ADJEXP2

-0.0008

-3.115

* * *

SELFEMPLOY

-0.2996

-3 .000

* -*• *

AGRIMIN

0.5430

4.259

■*•■*• *

FINANCE

0.7 52 5 ^'-

5.990

* * •*■

MANUFACTURING

0.6327

5.126

■*••*••*•

ENT/REC

0.8909

4.073

* * •*•

SALES

-0.0097

-0.132

PROSERV

0.5366

4.431

* * ■*•

PUBADMIN

0.7204

5.790

* * *

REPSERV

0.4871

3 .714

•*• * *

TRANSPORTATION

0.7723

6.041

* * *

GOVERNMENT

0.0552

1.176

CRAFT

0.3517

3.858

* * *

MANAGER

0.2345

5.261

* • *

OPMACHINE

0.0574

0.813

OPLABOR

0.0172

0.172

WHOLESALE

0.6749

3 .965

* * *

RETAIL

0.3568

2.824

* * *

PROFESS

0.2421

5.334

* * *

SERVICE

0.2024

3.955

■*• * *

TABLE 20 (cont . )
COEFFICIENTS FOR ALL FEMALES

Variable

Coefficient

T-Statistic

VETERAN

XFRVET

INTERCEPT

R- SQUARE

AD J R- SQUARE

F-STATISTIC

Sample Size

0.0243

0.0602

7.9603

.2371

.2245

18.844

0.675
0.965
48.300 ***

1664

* denotes coefficient significant at .10 level
*** denotes coefficient significant at .01 level

TABLE 21
COEFFICIENTS FOR POST-1973 FEMALES

Variable

Coefficient

T-Statistic

CHILD

-0.0091

-0.475

EDUCATION

0.0365

3 .624

■*••*•*

MARRIED

-0 .0578

-1.610

*■

NONWHITE

-0.0078

-0.226

ADJEXP

0.0494

5.588

* * *

ADJEXP2

-0.0006

-1.772

•

SELFEMPLOY

-0.4024

-3.528

* * •

AGRIMIN

0.5689

4.250

* ■*■ •

FINANCE

0.7722

5.917

* * *

MANUFACTURING

0.6220

4.851

*■*•■*■

ENT/REC

0.7907

3.262

* * *

SALES

-0.0077

-0.100

PROSERV

0.5275

4.174

* * *

PUBADMIN

0.7101

5.465

* * •*•

REPSERV

0.4324

3 .160

* * •*•

TRANSPORTATION

0.7519

5.591

* * *

GOVERNMENT

0.0433

0.837

CRAFT

0.3961

3 .996

■*•*•*•

M7VNAGER

0.2327

4.627

■*■ * *

OPMACHINE

0.0368

0.485

OPLABOR

0.0266

0.249

WHOLESALE

0.6369

3.615

* * *

RETAIL

0.3613

2.763

* * *

PROFESS

0.2221

4.330

* * *

SERVICE

0.2126

3.870

* * *

TABLE 21 (cont . )
COEFFICIENTS FOR POST- 197 3 FEMALES

Variable

VETERAN

XFRVET

INTERCEPT

R- SQUARE

AD J R- SQUARE

F-STATISTIC

Sample Size

Coefficient

T-Statistic

0.0228

0.0434

7.9903

.2353

.2204

15.772

0.500
0.562
44.963 ***

1411

* denotes coefficient significant at .10 level
*** denotes coefficient significant at .01 level

TABLE 22
COMPARISON OF CIVILIAN LABOR MARKET EXPERIENCE EFFECTS ON
LN-EARNINGS FOR ALL FEMALE VETERANS

YEAR

WITHOUT XFRVET WITH XFRVET
(A) (B)

CHANGE /YEAR
(from column B)

1
2
3
4
5
6
7
8
9
10

0.0718
0.1194
0.1654
0.2098
0.2526
0.2946
0.3334
0.3714
0.4078
0.4426

0.1324
0.1800
0.2260
0.2704
0.3132
0.3552
0.3940
0.4320
0.4684
0.5032

0.0476
0.0460
0.0444
0.0428
0.0420
0.0388
0.0380
0.0364
0.0348

TABLE 23
COMPARISON OF CIVILIAN LABOR MARKET EXPERIENCE EFFECTS ON
LN-EARNINGS FOR ALL MALE VETERANS

YEAR

WITHOUT XFRVET WITH XFRVET
(A) (B)

CHANGE /YEAR
(from column B)

1
2
3
4
5
6
7
8
9
10

0.1139
0.1576
0.1997
0.2402
0.2791
0.3164
0.3521
0.3862
0.4178
0.4496

0.1832
0.2269
0.2690
0.3095
0.3484
0.3857
0.4214
0.4555
0.4880
0.5189

0.0437
0.0421
0.0405
0.0389
0.0373
0.0357
0.0341
0.0325
0.0309

2. The Effects of Veteran Status for Males

The two groups of males, all and post-1973, showed
positive returns to transferring their military-acquired
skills. The post-1973 group realized a return of only 2.2
percent (Table 25), while the group of all males gained 7.2
percent for transferring skills (Table 24) . The observed
positive coefficients could be a factor of higher demand for
military acquired training. This finding is consistent with
other studies (Mehay, 1992; Mangum and Ball, 1989).

Males received a positive return for leaving the
service and entering the civilian labor market (VETERAN) . The
size of the return was 7.2 percent for all males and 10.0
percent for post-1973 males (Tables 24 and 25) . Since this
positive effect is significant for both groups of veterans,
civilian employers appear to place added value on all training
received in the military, at least for male veterans.

• As was noted for the female veterans, male veterans
see their income grow as the number of years out of service
increases. The coefficients for the variable ADJEXP (Tables 24
and 25) are both positive and significant. The return on years
of civilian labor market experience is approximately five
percent for the group of all males and six percent for the
post-1973 group. These figures closely resemble the observed
values for females' returns on years of experience. Table 23
shows that after the veteran has been out of the service for

ten years, his annual income will have increased by 51.9
percent due to the partial return to wages of the military
variables .

The coefficients for the variables MARRIED and CHILD
(Tables 24 and 25) have the opposite effect on annual income
of males as they do on the annual income of females. This
result IS statistically significant. Males employed in the
manufacturing, public administration, and transportation
industries had the highest returns on annual income. Males
whose occupations were in labor, service, and retail had
negative returns to annual income.
3. The Results for Race -

Table 26 shows the decomposition of the veteran-
related variables by race and gender to measure the partial
effects of veteran status on the four groups after one year of
civilian experience. The Appendix contains a table that
displays the regression results for different racial groups.
Although all of the results were positive, the smallest return
was for white females (6.7 percent) . Nonwhite males and
females had the highest returns to their incomes (23.3 percent
and 27.7 percent, respectively) . This result could be an
indication that the military is an effective "bridge" for
minorities into higher paying occupations.

TABLE 24
COEFFICIENTS FOR ALL MALES

Variable

Coefficient

T-Statistic

CHILD

0.0195

6.628

* + *

EDUCATION

0.0572

26.393

* * *

MARRIED

0.1141

12 .397

* * *

NONWHITE

-0.1146

-13 .153

■*• • *

ADJEXP

0.0459

30.727

* * *

ADJEXP2

-0.0008

-17.968

* * *

SELFEMPLOY

0.0512

3 .439

■*•■*■*■

AGRIMIN

0.1002

2.648

■*■ * *

FINANCE

0.2077

4.987

* * *

MANUFACTURING

0.2302

6.093

■*■ * *

ENT/REC

-0.0251

-0.363

SALES

0.1161

5.909

* ■*• *

PROSERV

0.0115

0.288

PUBADMIN

0.2438

6.235

* * *

REPSERV

0.0070

0.179

TRANSPORTATION

0.3719

9.654

■*•*•*•

GOVERNMENT

0.0358

2.855

* • *

CRAFT

0.0928

7.730

* * *

MANAGER

0.1516

10.400

* • *

OPMACHINE

0.0329

2.375

* * *

OPLABOR

-0.0710

-3 .998

* * •*•

WHOLESALE

0.1351

3.180

* * *

RETAIL

-0.0311

-0.793

PROFESS

0.1642

11.889

* ■*• *

SERVICE

-0.0088

-0.641

TABLE 24 (cont . )
COEFFICIENTS FOR ALL MALES

Variable

VETERAN

XFRVET

INTERCEPT

R- SQUARE

AD J R- SQUARE

F-STATISTIC

Sample Size

Coefficient

T-Statistic

0.0722

0.0718

8.3570

.2677

.2670

355.279

9.565 ***
5 . 002 ***
177.3225 ***

26263

* denotes coefficient significant at .05 level
*** denotes coefficient significant at .01 level

TABLE 25
COEFFICIENTS FOR POST- 197 3 MALES

Variable

Coefficient

T-Statistic

CHILD

0.0107

1.630

■ EDUCATION

0,0594

12.284

-*• * *

MARRIED

0.1075

6.943

■*■ * *

NONWHITE

-0.1154

-7.501

* * •

ADJEXP

0.0591

14.983

* * *

ADJEXP2

-0.0015

-7.887

Tk- ■*• *

SELFEMPLOY

0.1078

3 .631

* * *

AGRIMIN

0.0166

0.279

FINANCE

0.1764

2.511

■*■ * *

MANUFACTURING

0.1196

2.000

* * *

ENT/REC

-0.1149

-1.093

SALES

0.0934

2.704

* * ■*:

PROSERV

-0.1086

-1.660

PUBADMIN

0.1918

2.986

* * *

REPSERV

-0.0684

-1.116

TRANSPORTATION

0.2536

4.042

•*•■*••*•

GOVERNMENT

0.0726

2.856

* * •*■

CRAFT

0.0092

4.524

* * *

MANAGER

0.1868

5.634

■*■ * •

OPMACHINE

0.5544

2.193

* * •*■

OPLABOR

-0.0388

-1.362

WHOLESALE

0.0647

0.950

RETAIL

-0.1060

-1.733

PROFESS

0.2022

7.137

* * *

SERVICE

-0.0183

-0.738

TABLE 2 5 (cont . )
COEFFICIENTS FOR POST-1973 MALES

Variable

VETERAN

XFRVET

INTERCEPT

R-SQUARE

AD J R-SQUARE

F-STATISTIC

Sample Size

Coefficient

T-Statistic

0.0998

0.0216

8.3367

.1389

.1366

60.634

6.157 ***
0.672
95.586 ***

10173

* denotes coefficient significant at .05 level
*** denotes coefficient significant at .01 level

TABLE 26
RETURN TO VETERAN STATUS AND TRANSFER OF SKILLS AFTER
ONE YEAR OUT OF SERVICE

XFI^VET VETERAN ADJEXP ADJEXP2 j Return

White
Females

Nonwhite
Females

White
Males

Nonwhite
Males

+0.011 +0.003 +0.054 -0.0011 +0.067

+0.196 +0.049 +0.033 -0.0001

+0.077 +0.051 +0.047 -0.000!

+0.043 +0.147 +0.043 -0.0007 +0.233

+0.277

+0.173

Note: Appendix includes full regression results for this
table .

4 . Earnings Comparison by Gender and Veteran Status

As presented in Sections One and Two, veterans tend to
have higher earnings than nonveterans upon entering the
civilian labor market. Table 27 presents the results of four
regressions differentiated by gender and veteran status. To
compare the effects of civilian labor market experience on
veterans and nonveterans, all the dummy variables were set to
zero and the variable EDUC was given the value 13 (the
approximate mean for all groups) . The values for INTERCEPT and
EDUC became constants. This left the coefficients for ADJEXP
and ADJEXP2 as the only variables in the In-earnings equation.
The equations for veterans and nonveterans were set equal to
each other to determine at what level of civilian labor market
experience their wages would be equal. Females' annual

earnings merged at 9.3 years of labor market experience. For
males, 10.2 years of labor market experience was the point of
intersection. At this point, nonveteran and veteran males
earned the same annual income.

The derivative of In-earnmgs with respect to ADJEXP
presents the amount of labor market experience at which there
IS no longer any return on one more year of additional
experience. The number of years for both groups of males was
similar, 39.9 years for veterans and 38.8 years for
nonveterans . The difference for females was 25.4 years (26,7
years for veterans and 52.1 years for nonveterans) .

TABLE 27

REGRESSION RESULTS BY GENDER
AND VETERAN STATUS

Femal

Mai

Variable

VETS

NONVETS

VETS

NONVETS

INTERCEPT

8.0271*

7.9206*

8.5434*

8.2696*

CHILD

-0.0195

0.0039

0.0224*

0.0075

EDUC

0.0390*

0.0398*

0.0534*

0.0603*

MARRIED

-0.0404

-0.0764*

0.1148*

0.1037*

NONWHITE

0.0431

-0.0494

-0.0879*

-0.1501*

ADJEXP

0.0444*

0.0544*

0.0384*

0.0564*

ADJEXP2

-0.0010*

-0.0008*

-0.0007*

-0.0010*

SELFEMPL

-0.7523*

-0.1176

0.0385*

0.0578*

AGRIMIN

0.5740*

0.5496*

0.1162*

0.0919*

FINANCE

0.6815*

0.7731*

0.1620*

0.2586*

MANUFACT

0.5982*

0.6455*

0.2559*

0.2055*

ENT/REC

1.1193*

0.7103*

-0.0358

-0.0059

SALES

0.0470

-0.0341

0.1260*

0.1090*

PROSERV

0.5448*

0.5377*

0.0176

0.0191

PUBADMIN

0.8125*

0.6702*

0.2460*

0.2491*

REPSERV

0.5237*

0.4831*

0.0101

0.0097

TRANSPORT

0.7098*

0.8329*

0.4082*

0.3174*

GOVERN

0.0289

0.0719

0.0448*

0.0111

CRAFT

0.4040*

0.2702*

0.0881*

0.0969*

MANAGER

0.2194*

0.2367*

0.1463*

0.1551*

OPMACHINE

0.0978

0.0335

0.0141

0.0532

OPLABOR

0.0632

0.0032

-0.1019*

-0.0396

WHOLESALE

0.5973*

0.7029*

0.1408*

0.1246

RETAIL

0.2541

0.3981*

-0.0342

-0.0247

PROFESS

0.3201*

0.1985*

0.1564*

0.1699*

SERVICE

0.2216*

0.1950*

-0.0209

0.0022

Note: * denotes
percent level.

significance of coefficient at the 10

VI. CONCLUSIONS AND RECOMMENDATIONS

The intent of this thesis was to investigate different
earnings models m order to measure any significant post-
military income differences between veteran and nonveteran
females, and to compare the veteran-nonveteran differentials
for females with that of males. The Reserve Components Survey
allowed for the minimization of selectivity bias by including
respondents who have been screened for military service, thus
ensuring a near homogeneous sample.

The primary question addressed in this thesis was: Do
female veterans have higher earnings after leaving the service
than do their civilian contemporaries? Although the net effect
of the job change (VETERAN) and the transfer of military
skills to their new occupation (XFRVET) may have a positive
effect on the civilian wages of veterans, any differences
evaporate after approximately nine years in the civilian
sector. Therefore, the returns for military service may not be
large, but in the long run active duty females earn somewhat
higher incomes than their civilian contemporaries, all things
being equal .

A secondary question examined in this thesis was: Do
female veterans close the 'gender gap' in relative pay between
males and females. Examination of mean annual incomes reveals

that female veterans earn approximately 80 percent of what
male nonveterans earn, on average. Historically, the gender
gap has been 30 to 40 percent (Blau and Ferber, 1986) . This
thesis shows that white female veterans earn approximately 70
percent of the earnings of male veterans. When female veterans
are compared to male nonveterans, the gap closes to 18
percent '". Comparatively, the average female veteran in this
sample has slightly lower earnings than her nonveteran
counterpart. This differential is due primarily to differences
in years of civilian labor market experience.

Skill training received by individuals in the different
services varies due to operational necessity and specific
mission. The Navy and the Air Force have the greatest number
of technical fields while the Army tends to incorporate more
military specific training that may require lower skills.
Opportunity for the most sought after and financially
beneficial high-tech training is greater in the Navy and Air
Force. The size of the sample necessitated the grouping of all
female veterans. This made interpretation of regression
results by service impossible. Other authors have found
significant differences in returns to military training by
branch of service for males. If this hypothesis holds true for
females, then there may be some impact on wages depending upon
branch of service for this group also. The size of the Army

'Computed from mean annual incomes

cohort could have a significant impact on the coefficient for
grouped veteran status. However, removal of Army veterans from
the sample leaves too few observations to draw any
conclusions. Future studies should examine post-service wage
differentials by branch of service to examine the effects of
each services' training.

The data from this survey are nearly a decade old. If the
trends noted by Eitelberg (1988) hold true, then it is
expected that females will be increasingly interested in the
high-tech occupations in the military. Force composition by
gender may have changed significantly in the last eight years,
and female enlistees should be reaping the benefits of their
military experience. Increasing female participation in the
armed forces should make statistical examination of the
current 1991 Reserve Components Survey more insightful, and
provide greater detail into the investigation of female
veterans' wages.

APPENDIX
REGRESSION COEFFICIENTS BY RACE AND GENDER

Femal

.es

Mal(

Variable

WHITES

NONWHITES

WHITES

NONWHITES

INTERCEPT

7.9712*

7.9286*

8.3449*

8.2701*

CHILD

0.0045

0.0091

0.0278*

-0.0045

EDUC

0.0329*

0.0515*

0.0548*

0.0641*

MARRIED

-0.0820*

-0.0153

0.1019*

0.1479*

ADJEXP

0.0538*

0.0320*

0.0467*

0.0425*

ADJEXP2

-0.0011*

-0.0001

-0.0008*

-0.0007*

SELFEMPL

-0.1455

-0.8984*

0.0600*

-0.0141

AGRIMIN

0.5916*

0.5055*

0.1415*

0.0047

FINANCE

0.8365*

0.6256*

0.2414*

0.1217

MANUFACT

0.6880*

0.5929*

0.2751*

0.1086

ENT/REC

0.8756*

0.7978*

-0.0249

0.0173

SALES

0.0645

-0.2178

0.1246*

0.0733

PROSERV

0.6286*

0.4347*

0.0730

-0.1485*

PUBADMIN

0.7314*

0.6729*

0.2930*

0.1255

REPSERV

0.5258*

0.4614*

0.0510

-0.1035

TRANSPORT

0.8229*

0.6752*

0.3989*

0.3055*

GOVERN

0.1576*

-0.0667

0.0309*

0.0386

CRAFT

0.3652*

0.1514

0.0932*

0.0910*

MANAGER

0.2270*

0.2314*

0.1624*

0.1070*

OPMACHINE

0.0880

-0.0053

0.0241*

0.0668*

OPLABOR

0.0276

0.0260

-0.0697*

-0.0815*

WHOLESALE

0.7702*

0.4395

0.1794*

0.0178

RETAIL

0.3046*

0.5577*

0.0054

-0.1130

PROFESS

0.2444*

0.2374*

0.1716*

0.1414*

SERVICE

0.1514*

0.2697*

-0.0028

-0.0265

Note: '^ denot

es variable

IS signi

ticant at the

10 percent

level

REGRESSION COEFFICIENTS BY RACE AND GENDER

(cont . )

Females

Mai

Variable

WHITES

NONWHITES

WHITES

NONWHITES

VETERAN

0.0026

0.0485

0.0514*

0.1465*

XFRVET

0.0114

0.1960

0.0770*

0.0425

Sample

1055

608

20457

5805

Size

R- SQUARED

.3014

.2013

.2943

.1891

ADJ R-

.2837

.1656

.2934

.1854

SQUARED

F -VALUE

17.071

5.641

327.647

51.825

Note: * denotes coefficient significance at 10 percent

LIST OF REFERENCES

Berger, M. and B. Hersch, "Veteran Status as a Screening
Device During the Vietnam Era." Social Science Quarterly, V.
18, 1983.

Blau, Francine and Marianne Ferber, The Economics of Women,
Men, and Work, Englewood Cliffs, N J . Prentice-Hall, 1986.

Bryant, Richard and Al Wilhite, "Military Experience and
Training Effects on Wages." Applied Economics, V.22, 1990.

Daymont, Thomas and Paul Andrisani, "Job Preferences, College
Major, and the Gender Gap in Earnings." Journal of Human
Resources , Summer 1984.

De Tray, Dennis, Veteran Status and Civilian Earnings, The
Rand Corporation, R-1929-ARPA, March 1980.

Eitelberg, Mark, Manpower for Military Occupations, Office of
the Assistant Secretary of Defense (Force Management and
Personnel) , 1988 .

Fredland, John and Roger Little, "Long Term Returns To
Vocational Training: Evidence from Military Sources." Journal
of Human Resources, V.15, No. 1, 1980.

Rosters, Marvin H., Workers and Their Wages, the AEI Press,
Washington D.C, 1991.

Magnum, Stephen and David Ball, "Military Skill Training: Some
Evidence of Transferability." Armed Forces and Society, V.13,
No. 3, 1987.

Mehay, Stephen, "Post-Service Earnings of Volunteer-Era

Veterans: Evidence from the Reserves." Department of

Administrative Sciences, U.S. Naval Postgraduate School,
Monterey, CA., 1992.

Miller, Caroline J., "Post-Service Earnings of Veterans: A
Survey and Further Research." Masters Thesis, Naval
Postgraduate School, Monterey, CA., March 1991.

Miller, Harman, "Annual and Lifetime Income in Relation to
Education." Armed Forces and Society, V.5, 1979.

Norrbloom, E., An Assessment of the Available Evidence on the
Returns to Military Training, The Rand Corporation, R-1960-
ARPA, July 1977 .

SAS Institute Inc., SAS Procedures Guide, Version 6, Third
Edition, Car-y NC : SAS Institute Inc., 1990.

Schwartz, Saul, "The Relative Earnings of Vietnam and Korean-
Era Veterans." Industrial and Labor Relations Review, V.39,
No . 4 , 19 8 6.

Waite, Linda J. and Sue E. Berryman, Women in the
Nontraditional Occupations, Rand Corporation, R-3106-FF, March
1985.

INITIAL DISTRIBUTION LIST

No. Copies
Defense Technical Information Center 2

Cameron Station
Alexandria VA 22304-6145

Library, Code 052

Naval Postgraduate School

Monterey CA 93943-5002

Stephen L. Mehay, Code AS /MP
Naval Postgraduate School
Monterey CA 93943-5002

Carol Mitchell, Code AS/MI
Naval Postgraduate School
Monterey CA 93943-5002

5. Mark R. Sliepcevic
5819 S. Newcastle
Chicago XL 60638

Y^'^'-cD/6

J^-^^:-i.L^...

, '-'vu.iio^,^-

Thesis

S5712 Sliepcevic

c.l An analysis of post-
service career earnings
of female veterans.

AO-*l<

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