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Full text of "An analysis of post-service career earnings of female veterans."

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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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jppiementary Notation The views expressed in this thesis iu-e those of the author and do not reflect the official policy or position of 
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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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Approved for public release; distribution is unlimited. 

An Analysis of Post-Service Career Earnings 
of Female Veterans 



by 



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. 



m 



^^^ 



CI 



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 

iv 









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 



V 



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 . 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 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 



10 



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 
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 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 

11 



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. 



12 



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 



13 





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 



14 



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 



15 



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 



16 



TABLE 6 

MALE / FEMALE WAGE RATIOS, YEARS OF EXPERIENCE, 

AND YEARS OF EDUCATION 













% A(1979- 


Yrs Exp 


Yrs Ed 


1973 


1979 


1987 


1987) 


5 


8 


1.44 


1.29 


1.14 


-15.0% 




12 


1.29 


1.29 


1.16 


-13 .0 




16 


1.29 


1.24 


1.15 


-9.0 


15 


8 


1.60 


1.58 


1.39 


-19.0 




12 


1.55 


1.53 


1.31 


-22.0 




16 


1.55 


1.51 


1.34 


-17.0 


25 


8 


1.85 


1.59 


1.46 


-13.0 




12 


1.66 


1.59 


1.48 


-11.0 


-" 


16 


2 .04 


1.72 


1.59 


-13.0 


35 


8 


1.74 


1.63 


1.59 


-4.0 



12 



1.62 



1.61 



1.47 



-14.0 



Source: Kosters, 1991 



17 



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 



19 



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 

20 



TABLE 9 

RESIDENT ARMED FORCES BY SEX, 1950-1987 

(IN THOUSANDS) 



Year 


Males 


Females 


Total 


Percent 
Female 


1950 


1,150 


19 


1,169 


1.6 


1955 


2, 033 


31 


2, 064 


1.5 


1960 


1,833 


28 


1,861 


1.5 


1965 


1, 920 


27 


1,946 


1.4 


1970 


2, 081 


37 


2, 118 


1.7 


1975 


1, 600 


78 


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 

21 



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. 



22 



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 



23 



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 

24 



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. 



25 



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 

26 



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 



27 



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. 



28 



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. 

29 



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. 



30 



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 



IQ 



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 



31 



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. 



32 



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 



33 



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. 



34 



B m » wMMai«ni»nii ii itimniii«»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% 

2% 



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 



35 



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 



36 



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 



37 



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 


is 


females 






married 




males 


+ 


NONWHITE 


1 if respondent 


is 


females 


7 




not Caucasian 




males 


7 


EDUCATION 


number of years 


of 


females 


+ 




formal education 




males 


+ 


EXPERIENCE 


number of years 


in 


females 


+ 




the workforce 




males 


+ 


Military 










Variables 










XFRVET 


if a veteran 




females 


+ 




transferred 




males 


+ 




his/her military 


- 








acquired skills 


to 








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 


f 








school 









Source : Author 



38 



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 



39 



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. 



40 



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 



41 



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 



42 



(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 

i 

43 



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 



44 



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. 



45 



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 , 



46 



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 



47 



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 
.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 



48 



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 



49 



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 



50 



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 



51 



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. 



52 



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. 



53 



TABLE 2 
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 


■*• * * 



54 



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 



55 



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 


* * * 



56 



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 



57 



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 



58 



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 



59 



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. 



60 



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 





61 



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 



62 



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 





63 



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 



64 



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 



65 



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) . 



66 



TABLE 27 



REGRESSION RESULTS BY GENDER 
AND VETERAN STATUS 





Femal 


es 


Mai 


es 


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 



67 



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 



68 



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 

69 



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. 



70 



APPENDIX 
REGRESSION COEFFICIENTS BY RACE AND GENDER 





Femal 


.es 


Mal( 


ss 


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 











71 



REGRESSION COEFFICIENTS BY RACE AND GENDER 

(cont . ) 





Females 


Mai 


es 


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 



72 



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. 



73 



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. 



74 



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75 



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<