.

‘

.
NLS

Discussion

Papers

.,,,

*
FINfi =PORT

Department of Labor grant number E-9-J-9-0049

,.,,

The Impact of Private Sector Training on Race and Gender

Wage Differentials and the Career Patterns of

Young Workers

by

Lisa M. Lynch*

M.LT. and NBER

Submitted July, 1991

I would like to thank the participants of seminars at Boston University, Columbia, M.I.T.,

Northwester~ NE, NBER, the Sloan conference on the Lon@tudinaI haIysis of Labor

, Market Interventions at Yale University, and the Department of kbor for helpful

comments on a previous draft. I also benefited from comments by Dan Black, James

Hec_ Peter Kuhn, Edward kear, Jacob Mincer, Michael Pergami~ Fabio

.

Schiantarelfi, Nachum Sicherman, James Walker, and &dy Weiss. Thorough research

support was provided by Adrea Ichino and Pam Loprest. This project was funded by the

U.S. Department of hbor, Bureau of bbor Statistics under Grant Number E-9-J-9-0049.

Opinions stated in this document do not necessarily represent the official position or policy

of the Department of ~bor.

Note: Sections of this report have appeared in Lynch (1991a) and Lynch (1991b).

1
TMLE OF CO~NTS

SE~ON

Recutive Sumq

htroduction

Private Sector Training and Wagex Theoretical

Framework and Data

Private Sector Training and Wag= Empirical Results

Private Sector Training and Wages Conclusion

Private Sector Training and Mobili@ ~eoreticd

Framework and Data

Private Sector Training and Mobili@ Empirical Results

Private Sector Training and Mobili~ Conclusions

Footnotes

References

Tables

Page #

10

19

.28

30.

38

42

45

48

51-65
I. Executive Summa~

Objectives

mile the returns to a college degree or government training p;ograms in the U.S.

have been widely documented, there has been relatively little analysis of the returns to other

;,

forms of human capital investment that non college graduates undertake. This hk been due

-,

primarily to the lack of appropriate data for this type of analysis. However, using the

unique features associated with the National bngitudiml Survey Youth Cohort, NNY, this

study anal~es how personal characteristics including employment histories and local

demand conditions determine the probability of receiving training and their effects ori wages,

~ge wo~h, and employment mobility of workers. More specifically, some of the issues

addressed here include the relative importance of training and tenure for wage

determination and the rate of return to company provided training versus training from forprofit proprietary institutions and regular schooling. me portability of company training

from employer to employer and the existence of differentials in the returns to training by

union status, race and gender are also investigated. Finally, the impact of different types

of training investment on the probability of leaving an employer are examined.

.

Usirig a standard human capital framework this report first estimates the varying

rates of return to trainin~ schooling and tenure by race and gender for young workers. To

control for possible selection issues arising with the non random probability of receiving

training, two stage estimation procedures are applied. In addition a first difference equation

3
is estimated to control for the effect of unobserved time invariant individud fixed effects.

me study then presents estimates on the impact of different ~es of training on the

probability of leaving an employer using a Cox proportional h=ards model with time varying

covariates.

~

~ls report shows that private sector training plays a significant ioie in the

determination of wagesand wage growth of the 70 percent of young workers in the U.S. who

do not graduate from college. Specifically, when private sector training is divided into

different types (on-the-job training, off-the-job training, and apprenticeships) some very

different patterns emerge. For example, the characteristics that appear to influence the

probability of receiving training are primarily race and gender. \Vomen and nonwhites are

much less ~iely to receive training within a firm either through an apprenticeship or other

forms of on-the-job training. ~is differential pattern in the acquisition of training by race

and gender may be a partial explanation of the persistent wage gap between males and

females and whites and nonwhites. Schooling raises the probabili~ of receiving off-the-job

training and apprenticeships but it had a smaller impact on the probability of receiving firm

provided on-the-job training.

Ml types of trai~ng raise wages significantly. In particular, this paper shows that on

average, for ttils sample of non-college graduates, off-the-job training from proprietary

institutions can be useful for increasing wages. ~is ‘k in contrast to a recentstudy by Wlgh

(1989). me prime difference between this study and that of high is that this study allows

for off-the-job training to have an effect not only on current wag~ but on future wages as

4
we~. b addition, it is shown that longer training. spells have a larger effect on wages. .

Wile workers receiving off-the-job training may receive lower wages during their training

spefls they are more likely to use that training to leave their low wage employer and move

to a better pafing job.

Finally, while on-the-job training with the current employer increases wages with the

.,

current employer, this type of training seems to be quite firm specific since on-the-job

training from a previous employer is never significant for current wages. At the same time,

there seems to be some evidence that if general training is being given to any group of

workers on the job it is for those who have not completed high school.

~

mile this project has attempted to shed new light on the skill formation process of

young workers and the consequences of this on their wages and patterns of mobility there

,,

are still many issues that remain unresolved. ~is paper has modeled the determinants of

the duration of the first job after school, not subsequent employment. As the N~Y age

future research should examine for example how some of the gender, race and educational

differences change over time. It would also be interesting to examine the hzard rates by

broad indust~ and occupational categories. Finally, it would be important to see how

.

robust the findings are after additional work is done to address the endogeneity issue for

training.

Nevertheless, there is a story that emerges from the results in this report for young

workers and private sector training.

for young workers in their first job.

Company training in the U.S. is very firm specific, even

Young workers entering the labor market can receive.

5
both ‘good and ‘bad’ draws from the labor market. mere are some workers who get a ‘bad

draw who appear to move to better employment by investing in off-thejob training. nose

in ‘good jobs are more likely to obtain on-the-job training which results in higher wages and

a lower probability of leaving the firm. ~ese effects are pafiicularly strong for women.

me fiiding that on-the-job training is primarily specific is’ consistent with recent

:.

fidings from the Hudson Institute which surveyed 645 firms in the U.S. and found that ordy

8 percent had any sort of general remedial on-the-job training progr-ls. me fact that

Us. firms are more willing to invest in firm specific training than in general training is

understandable given the inability to “capture” the returns on investments in general training.

However, whether or not U.S. firms will be able to remain competitive with this strategy in

the future, given the ,characteristics of the new entrants into the worMorc? and the s~~

demands of new technology, is questionable.
II. Introduction

mile there have been numerous studies devoted to exatining the impact of

governmental traifing programs on workers who have experienced difficulties in the labor

marke~ there has been remarkably little research on the actusd occurrence and

consequences of training provided by the private sector. Since one possible explanation of

,,

the lower productivi~ growth in the U.S. relative to countries such as Germany and Japan

~ tit fires in the U.S. underinvest in their workers, it is crucial to have a better “,

understanding of the human capital strategies of firms and workers and of their

consequences.

Obtairdng an estimate on how much is currently being spent on training by the

private sector in the U.S., however, is extremely difficult to determine. It has been

estimated by Qrnevde (1986) that $150 billion are spent armudly on K-12 educatiow and

~ much as $210 billion are spent annually on formal and informal training by the private

sector. Approximately $25 bfion of the $210 billion are spent on young workers entering

their first jobl. Trainin~ Ma~azine, = in its annual sumey of training by firms with 100 or

more employees, reported that in 1988 over $45 billion were spent on formal training while

Bartel (1989) reported an even larger number of $55 billion for formal training from the

.

private sector in 1987 using firm survey data. Finally, Mincer (1989) calculated that as much

as $148 billion may have been spent on formal training programs by employers in 1987 using

individual data. Therefore, given the $25 billion or more spent by the private sector on

training for young workers, the issue is not that U.S. firms do not invest in their workers,

but rather that the nature and the size of these investments may not be enough for the new

7
entrants in the 199&s.

me difficulty in documenting the actual investment in training in the U.S. is due in

large part to the lack of a comprehensive, representative and longitudinal survey of firms

and their human resource management poficies. As a resul~ we know little about who

receives trainin~ what types of training programs are provided and where, the degree of

h specificity and portability of firm provided training, the impact of training on ‘&e wages

and wage growth of workers, and the effect of training on the probabili~ of remaining with

m employer. Consequently many have had to infer the impact of training on wages from

the shape of wage profiles. Apart from the fact that this is a rather unsatisfactory way to

test human mpital theory, there are several alternative theories which imply rising wage

profiles that have Iittle to do with productivity enhancing training.

One of the primary ways young workers acquire training is through school~g - ~

particular by completing college. me returns to a college degree have been documented

by many, (see =tz and Revenga (1989) and Blackbum, Bloom and Freeman (1989) as

e=mples of recent papers) but we know relatively littie about the skill development process

of the more than 70 percent of young workers who do not finish college. YeL these are the

young workers who are viewed by many as being unready for the new jobs and realities of

the 199Ws. Were do young workers who do not graduate from college acquire training

after school? k it from on-the-job training or from for-profit proprietary business and

vocational institutions? What happens to those young people who do not even fitish high

school? Are they able to obtain the necessary general and specific skills training to become

productive workers?

8
Several studies have attempted to use various memures of private sector training and

tiamine the impact of training on wages directly rather than inferring the effect from the

shape of wage profiles. These studies include Mincer (1983, 1988), Brown (1983, 1989),

Ullard and Tan (1986), Pergarnit and Shack-Marquez (1986), and Barren et. al. (1987).

However, each of these papers is subject to different limitations. Some of the more critical

issues include the lack of complete employment, training and schooling histories on

.

individuals in the various surveys, difficulties in measuring the amount of private sector

training the respondent received, and problems in distinguishing firm-specific from generaf

~es of training.

It is possible, however, to overcome many of these problems and gain new insight

into private sector training in the U.S. using Iongitudind data from the National

hn~tudinaf Sumey youth cohort, N~Y. This data se~ despite its limitations, does allow

one to reconstruct for the first time the entire formal training history for an individual from

the moment they enter the labor market. This event history includes both the occurrence

and duration of each training spell. Given the current debate about the need for more

knowledgeable workers in the 1990’s to deal with the demands of new technologies, firms

may be increasingly required to switch from informal on-the-job training to more structured

.

formaf training programs. h empirical analysis of formal training programs and of their

consequences, therefore, would ‘be useful. Moreover, the NUY data allow the researcher

to distinguish between different’ sources of private sector training -- company provided

traitin~ training from for-profit proprieta~ institutions, and apprenticeships.

mile it is not possible in a single report to investigate all aspects of training

9
investments for young workers, this study analyzes how personal characteristics including

employment. histories and local demand conditions determine the probabfity of receitig

training and their effects on wages, wage growth, and employment mobtity of workers.

More specifically, some of the issues addressed he~e “in~lude the relative importance of

training and tenure for wage determination and the rate of return to company provided

training versus training from for-profit propriet~ institutions and regular schooling. me

portabifi~ of company training from employer to employer and the etitence of differenti~

in the returns to training by union status, race and gender are dso investigated. Finally, the

impact of different types of training investment on the probability of. leaving an employer

are examined.

III. Private Sector Training and Wages Theoretical Framework and Data

Many theories have been advanced to explain individual variation in wages and why

wage profiles slope upwards. According to Beckets (1964) and Mincer’s (1974)

fundamentrd work, wage profiles slope upwards as human capital or skim increase with

experience. Therefore, as workers acquire more training there should be an increase in

their productivi~ and consequently in their earnings. Firm specific training wiIl have some

effect on wages in the form of a premium paid to reduce turnover, but since specific training

is not portable, the size of the premium may not be as large as that paid for general

training. ~erefore, the magnitude of the impact of training on wages will depend in part

on the degree of specificity of the training received and in part on who pays for the traiting.

For example, in a standard human capital model one would expect that individual workers

would pay for general training while firms would pay for and provide fii specific training.

10
Some firms may provide general training but in this case you would expect to obseme wages

negatively related to training ~ tis finance this general trainigg investment by paying

workers a lower wage. At the completion ,of general training, wages should rise

substantiWy. ~gislated or social minimum wages, however, may cause firms to be unable

to reduce wages sufficiently to cover these general training costs and consequently make .!

firms reluctant to provide general training.

.

Wile human capitrd theory provides one explanation about why wag$s are more or

Iess upwardly sloping for workers tiere are alternative explanations of upward sloping wage

profiles that have little to do with training. Specifically, Stigliti (1975) and ken (1981)

discuss how firms offer upward-sloping wage profiles to discourage “shirking” among

workers. An alternative explanation (see Salop and Salop (1976) and Rothschild and Sti~iti

(1976)) might be that firms use upward sloping profiles to discourage “movers” from seeking

employment elsewhere. Recent papers by Abraham and Farber (1987), Mtonji and

Shakotko (1987) and Topel (1987) have examined the importance of, job matching in

explaining upward sloping wage profiles. These studies have examined whether or not (in

the absence of data on training) the inclusion of tenure in a wage equation simply measures

job specific returns (such as training) or captures the fact that workers in long jobs are

.

either better workers, in better jobs, or in better worker-employer matches. If some

measure of job-match quality is not included in the estimation then it is argued that the

coefficient on tenure is biased upwards.

These alternative models of compensation should not be viewed as mutually

exclusive; the most likely case is that compensation is affected by some combination of

11
human capital and other factors. The purpose of some of the recent studies on wages,

however, has been to show that after controlling for job match quafity, the impact of tenure

or seniori~ on wages is small and to infer from this that human capital investments such m

training have a negligible role in the determination of wages. But without detied

information on the type of training undertake% it is diffitilt to sort out the real returns to

:.

human capital investments and whether they reflect general or specific capital, or other

factors e

Since the NHY data on’ training specify starting and ending dates of M training

spells across aU employers it is possible to distinguish between completed and uncompleted

spe~s of on-the-job training, ON-JT, from a current employer and ON-n horn a previous

employer. In human capital theo~ if employer provided training is primarily general then

wages for workers receiving this type of training should be lower during a training spell and

higher afterwards. Specific training, however, will have an ambiguous effect on current

wages since employers and employees will share both the costs and the returns associated

with specific training. Therefore, if ON-JT is primarily general then the coefficient on an

interrupted spell of training with.the current employer should be negative: The impact of

a completed spell of ON-JT with a previous or current employer on wages should be

positive. However, if “better” workers are more likely to receive ON-JT, then simply

inciuding a measure of ON.-JT in a wage equation without controlling for the selection of

these “better” workers into training will result in an upward bi~ on all of the estimated

training coefficients. This means, for example, that a significant and positive coefficient on

training from a previous employer may be due to selection bias or evidence that employer

.

12
provided training is general and portable for young workers. If instead this coefficient is

insignificant then the training is not portable and, therefore, suggests that it is primarily firm

specific.2

Being able to understand the degree of specificity of firm provided training is of

particular importance in judging policies that subsidize employers who hire young workers.

:.

U the subsidy is motivated by a belief that when employers hire young workers ‘the training

.

that is provided is quite general, then it is important to see whether or not this is in fact an

appropriate characterization of firm protided training in the U.S. A test of this assumption

wi~ be important in deciding the level of government support and the degree of monitoring

of employer provided training for young workers. In additio~ given that European and

Japanese employers invest substantially in general training, especially for younger workers,

finding out t;at private sector training in the U.S. is quite firm specific reveals an interesting

difference in the nature of training between the U.S. and its competitors.

& noted above, before examining the impact of training on the wages of young

workers, it is necessary to examine the characteristics of those individuals who actually

receive training. ~is is interesting in its own right and because it helps in tac~ing the issue

of sample selection bias in the wage equation. me selection bias in the wage equation is

,

very similar to the “treatment selection” problem in the evaluation of the effectiveness of

government training programs (see blonde (1986) and Heckman and Hotz (1989). for

acellent surveys on this). If individuals are not randondy assigned to training then the

actual return to training in a wage equation may be biased upwards if this selection is not

controlled for.

13
b order to model the acquisition of private sector training it is important to redtie

that there are two possible agents who may itiuence the probabili~ of a worker receitig

tr+ning – the individual worker and/or the fii. Firms are more likely to invest in those

in~lviduak who they believe will be more attached to the fim ~erefore, tenure on the

job, tot~ work experience, education background and other demographic characteristics

are expected to itiuence the firm’s investment decisiom For example, firms my decide not

to tivest in advanced trtining for their female employees because they believe that women

are more Ekely to leave the firm. If they leave early in their tenure with the firm the firm

would not have sufficient time to recoup its training investment. k addition as kear

(1979) has discussed, the narrowing of the black/white and male/female wage differentials

since the passage of affirmative action legislation may have been accompanied by a different

form of discrimination that resulted in a widening gap in the job-experience’induced rate

of wage growth. h other words, as employers responded to affirmative action legislation

by paying higher wages to women and blacks they may have at the same time reduced the

amount of training provided to these groups. ~erefore, starting wages might be the same

but wage growth would be much slower due to less training investment.

Individuals who do not receive training within the firm due to direct discrimination

or statistical d~crirnination may respond by obtaining “visible off-the-jo~ training to improve

their productivity and opportunities in othef firms. ~is individud investment in training 

could also be used as a signal of their commitment to the workforce. mere is some

evidence of tKls type of behavior in the schooling decisions of blacks (see hng and Ruud

(1986)).

14
Technological requirements of the occupation and industry will dso affect firms’

trting decisions. Those industries or occupations characterized by rapid technological

change are more likely toneed to provide skills training (see Ullard and Tan (1986)). FIrsn

size is ako eWected to itiuence the probabi~ty of receiving company provided training.

hrger firms may have better developed intemd labor markets that rely on intemd training

.,,,

and development of employees in the firm. In addition, the larger size may dso lower the

-

marginaf costs of training workers. Unfortunately information on firm size is not collected

eve~ year in the NNY so this important determinant of training is not included in this

analysis.

Finally, schooling may affect an individua~s probability of receiving training, In

particular, additional years of schooling may signal a certain “stick-to-it-ness” and an interest

and aptitude in learning. On the other hand, workers with poorer initial skills due to fewer

years of schooling may require additional training to get up to speed. In this study of noncollege graduates it will be particularly interesting to obseme the importance of finishing

high school for the probability of acquiring employer provided training.

Previous studies on the role of training in wage determination. have been..lirrtited by

the nature of the data available for analysis. To highlight some of these problems Table 1

*

shows the different questiom contained in a selection of surveys most commordy used. Very

few of these questions actually ask about the training the respondent has acquired on the

current or past jobs. For example, the question used by Brown (198.9) from the Panel Study

of Income Dynamics,” PSID, ,on training is how long it took the “average” person to become

qualified for the job, not how long the respondent actually took to become qualified. In the

15
older NH cohorts analyzed by Mincer (19M, (1988)) and fi~ard and Tan (1986), the data

co~ected relate to training received or used on the current job. One is not abIe to observe

when the training actually took place or whether other types of training had been

undertaken by the respondent. bcomplete information on the total amount of training

received is also a limitation with the ~rrent Population Survey, CPS, data used by Pergamit

‘.

and Shack-Marquez (1986). The CPS questions are urdikely to provide information on the

trairdng eWerience of older workers if this training was acquired from previous employers.

Therefore, cross sectioti anrdysis of the impact of training on wages using the ~S data wifl

have to carefully control for cohort effects. The Employment Opportunity Pilot Project,

EOPP, data used by Barren et. al. (1987, 1989) are interesting since they provide a good

measure of the “representative” length (and costs) of training to an employer. However, the

data collected are r=tricted to information on the most recent hire in the firm. E the most

recent hire k more likely to be in a position of high job turnover then it is possible that the

training investment observed is an underestimate of what more “representative” employees

in the firm receive. In addition, the EOPP firms were predominately low wage firms and

not representative of all firms in the U.S.. Many of these limitations are overcome with the

new NNY.

The NMY is a survey of 12,686 males and females (who were 14 to 21 years of age

at the end of 1978) and contains detailed data on education, jobs, military sefice, training

programs marital status, health and attitudes of young workers. The respondents have been

intemiewed every year since 1979 on all aspects of their labor market qerience. The

response rate in 1985 was over 95 percent of the original cohort. The data on ~es of

16
training (other than governmental traiting or schooling) received are some of the most

comprehensive data available on private sector training. Respondents were asked about

what types of training they had received over the survey year (up to 3 spells not just the

longest) and the dates of training periods by source. Potenti~ sources of training inciuded

bustiess college, nurses programs, apprenticeships, vocational and technical institutes, barber

.

or beauty schools, correspondence courses and company training. Al of the types of

training programs are independent from training received in a formal regular schooling

,. program which is included in the schooling variables. However, the questions ask about

ordy those spells of training that lasted at least 4 weeks (they did not have to be full time).

~i suggests that the NNY measure of training is more likely to capture formal training

spells than informrd on-the-job training. ~erefore, tenure on the job will capture both

returns to seniori~ and returns to training programs lasting less than 4 weeks such as

informal on-the-job training.

For the analysis of the impact of training on wages a subsample of the 12,686

respondents has been selected. I have excluded the milit~ subsample from the analysis

(1280 respondents) and all college graduates. me final sample is composed of individuals

who had completed their schoo~ng by the 1980 interview date (where “completed” is defined *

as not retur~ng to SCIIOOIby the 1983 interview date). me completion of schooling

requirement reduces the s~ple size substantially given the age structure of the NMY (4000

respondents are still 17 or less in 1980). In addition, these individuals had to have wage

observations at both the 1980 and 1983 interview dates.3 ~ls last restrictiori does not imply

that the respondent had to be working at the interview date since the wage data used are

17
wages in current or last job over the survey year. These selection criteria yield a final

sample of 30d4 individuals that will be used in the empiricrd work. Using a constructed

weeMy event history of private sector training, employmen~ and schooling for this subsample

it is possible to examine the patterns and outcomes of training for non-college graduates.4

The training data are separated into three categories - company training (ON-JT),

,:

apprenticeships (mm), and training obtained outside the firm (0~-JT). OW-JT includes

!’

training obttined from business coursfi, barber or beauty school, nurses programs,

vocational and technical institutes and correspondence courses. Each of these three types

of training are allowed to have different types of returns. Since the data are longitudinal

it is possible to distinguish between spells of training in each of these categories received

during employment with a previous employer and spells received during current

employment. In addition, for training received on the current job, it is possible to identi~

both completed and uncompleted spelk of training.

In Table 2 characteristics of this sample are presented. The primary source of formal

training for this sample comes from “off-the-job in terms of the percentage of the sample -

-14.7 percent -- who have experienced this type of training only 4.2 percent of the sample

have had on-the-job trainin~ and 1.8 percent have been apprentices. The number of women

and nonwhites who are in apprenticfihip programs is particularly small and this needs to

be kept in mind when interpreting some of the results in the next section. The number of

individuals in company training may ako seem small compared to numbers that have been

found in other surveys such as the employer EOPP survey. However, when the EOPP data

are restricted to a spell of 4 weeks or more of training, as in the NNY, tie percentages are

18
remarkably simila~. The average length of time spent in these formal training programs

is quite long. The average spell length of an apprenticeship is 63 weeks, of OFF-~ is 41

week and of ON-~ is 31 weeks. Finally, Table 2 shows that there are distinct differences

in the types of training received and the duration of this training by race and gender.

W. Private Sector Training and Wages: Empirical Results

!,”

Table 3 presents estimates of the probabilities of an individual receiving each of the

three ~es of training at some time up to the 1983 intefiew date as a function of their 1983

characteristi~. Differentiating among these various types of training reveals some

interesting patterns. The probability of investing in off-the-job training is lower if the youth

is male or has longer tenure on the job.6 On the other hand, company provided formal onthe-job training is concentrate-d among white married unionized males with greater work

expe~ence7 but tenure in 1983 is not significant. At the same time it is lower for those who

five in high unemployment areas. This suggests that as unemployment rises firms find it

more difficult to provide expensive formal on-the-job training to new young entrants.

Finally, the most important determinants for participating in an apprenticeship include being

white, unionized, and male. Interestingly, living in an high unemployment area means you

are more Ekely to have participated in an apprenticeship program. This may be e~lained *

by the fact that most apprenticeships are in construction and manufacturing which

experienced very high unemployment rates during this period

The role,of schooling in training decisions varies by type of training. For”this sample

of non college graduates when schooling is included as years of completed schooling in each

of the equations it is never significant. However, when the schooling variable is broken into

19
the categories - 1+s than high school, high school graduate and post high school but not

coUege graduate- some different patterns emerge. Staying on fi. school to complete a high

school degree or some post high school experience sigrdficanfly increases the probability of

receiving off-the-job training and (marginally) formal company provided on-thejob trfing. Most apprentices have a high school degree but it is less fikeIy that someone

,.

who has some post high school education will participate in an apprenticeship” program.:

The fourth column in Table 3 examines the probability of individuals in the 1983

smey year to have participated in company provided training in 1983 as a function of their

1983 characteristics. The previous three columns use characteristics in 1983 to predict the

probability of having ever received training by type (even prior to 1983). mile this

increases the number of observations tith training it does not allow for the examination of

how previous spells of trainirig increase the probability of future training and the actual

impact of current tenure on current ON-JT probabilities. In column 2 tenure in 1983 was

not significant in explaining the probability of ever having received training during the 1980-

83 period, whereas experience was significant. In contrast, by speci&ng the timing correctiy,

column four shows that tenure with the current employer increases the probabili~ of

receiting ON-JT and those individuals who have had training with a previous employer are

much more hkely to receive on-the-job trai~ng in the future.

Finally, I have also included broad industry and occupation dummies in the probits

for on-the-job training, off-the-jqb training and apprenticeship. Mthough the detailed resuIts

are not reported here for reasons of space, a. few summary comments are in order.g The

inclusion of industry and occupation dummies did not change very much any of the

20
coefficients for the ON-JT and OFF-JT probits. However, there are some changes in the

apprenticeship probit. The local unemployment rate and being male became insignificant

fatiors when industry and ocwpation were added but being white, a high school graduate

and a union member sti~ raised the probability of participating in an apprenticeship. &

e~ected, apprenticeships are more common in the construction industry and among

.,,

,.

technical workers and craft workers. For the ON-JT probits, those employed & managers,

sales workers, clerical staff or craft workers were more Ekely to have e~erienced a spell of

.

fortnd company provided training while those in the wholesale and retail industry were

sigficantly less likely to have received ON-JT. For OFF-JT, there were two very different

occupations that were more likely to have acquired this type of training - profasional and

technical workers, and sefice workers. None of the industry dummies were significant for

off-the-job training.

Keeping these differential patte~ in the acquisition of training in mind, I now

examine how these three types of training affect the wages of non-college graduates. bg

~ges of young workers are re~essed on a finction of tenure, work experience, schooling,

traifing, and other factors. The training variables are divided into OFF-JT, ON-JT and

aPPrentices~lPs, “~~. ~ese variables are further separated into training received while &

employed with a previous employer and the current employer. F1ndly, I allow completed

and uncompleted spells of MPT and ON-JT from the current employer to have different

IO me additional factors in the wage equation include the 10~1 effects on current wages .

unemployment rate, the number of jobs held since finishing school, whether or not the

respondent lives in an urban area, marital status, race, gender, coverage by a collective

21
agreemen~ and health. Equation 1 in Table 4 presents results from a standard log wage

equation specification excluding the training variables where the dependent variable is the

log of wages in 1983. Equations 2 and 3 in Table 4 include the training variables, with

equation 3 4s0 adding broad industry and occupation categories. Equation 4 contains the

Hechan correction for sample selection. The sample selection issue will be discussed later

;.

and I first focus on the results of equations 1-3 in Table 4.

.

One of the striKng fiidings is the insensitivity of the estimated coefficients on tenure

to the inclusion of the training variables. It appears the training and tenure are basicrdly

uncorrelated since the coefficient on tenure does not alter between equations 1 and 2. The

tenure variable is always significant in the wage equation although there are many factors

it maybe capturing. SpecificaUy, the training variables in the N~Y are good measures of

spells of forrnrd training lasting at least one month but they may not capture au spells of

informal on-the-job training. In this case, the tenure variable is capturing both a pure

“tenure” effect plus the returns to informal training. In additio~ as shown in the jobmatching literature, tenure may represent job match quality so its coefficient is biased

upwards (see Topel for a discussion of the size of this bias). Finally, a positive tenure effect

cotid reflect incentives provided by the fii to reduce shirting and/or to lower turnover.

Equations 2 and 3 in Table 4 show the significant role that training plays in wage

determination. Even after contro~ing for industry and occupation the various training

measures have a significant impact on wages. Periods of off-the-job training and

apprenticeship traiting acquired before the current employer raise wages significantly.

Week of on-the-job training and apprenticeship with the current employer also raise wages.

22
Other variables that significantly raise wages include total work experience, years of schoo~

fiving in an urban area, male, white, married and coverage by a collective agreement. Being

d~abled or living in an area with high Iod unemployment depresses wages significantly.

Uding indust~ and occupation dummies to the estimated wage equation slightly reduces

the she of the effect of training on wages but the training variables that were significant

:..

wifiout industry and occupation dummies remain significant when they are added. Workers

employed in the fining, construction and transportation industries earn more relative to

those in manufacturing while those in wholesale and retail trade, business, repair, personal

and professional related services earn less. Professionrd, managerial and craft workers M

earn a wage premium relative to laborers and farmers.

k order to have a better sense of how the different training variables affect wages

relative to other factors such as tenure and schooling, Table 5 presents calculations of hourly

wages for different characteristics of the sample. This table shows that training, especially

company provided on-the-job training and apprenticeships, raises wages substantially. The

impact of one more year of school or one more year of current tenure (keeping experience

the same) raises wages to almost to the same amount as 6 months of off-the-job training.

me return to additional schooling and tenure is even smaller relative to the return to 6

.

months of on-the-job training from the current employer. The latter raises wages by almost

ten percent while off-the-job training obtained before the current job raises wages by almost

5 percen~ We know from Table 3 that women and nonwhites are much less likely to

receive on-the-job training. However, Table 5 shows that if, for example, a nonwhite male

obtaim 6 months of off-the-job training he can cut the gap in earnings between hi&elf and
a white male with no training in half. Whe and nonwhite female wages rise as well with

off-the-job training but the gap between female and male wages remains quite large. These

findings on the role of training obtained from “for-profit” proprietary institutions is important

for the current debate on whether or not Graduate Student ham and Pen grants should

be continued to be granted to students in these insti~tions. Some cities have expressed

:..

concern about the ability of these institutions (see =RFACE (1989)) to pro~de training

to welfare recipients. However, this paper shows that on average for this sample of nonco~ege graduates that off-the-job training from propnet~ institutions has a sizeabIe impact

on wages.

Some other interesting findings contained in Table 4 concern the variables that are

not sigrdficant. For example, spells of on-the-job training acquired before the current job

have no impact on current wages. ~lis suggests that ON-JT is not portable from empIoyer

to employer for young workers who are not college graduates. This maybe because fo~al

ON-JT for these workers is more firm specific than general. It may abo be bemuse fiose

“trained workers who change employers are not as able as those workers who receive on-the11 However, equation 4 in Table 3 indi=t= job training but do not leave their employer .

that having received training from a previous employer raises the probability of receiving

training in the future which do= not seem consistent with considering trained workers who

change jobs as lower quality workers.

Off-the-job training acquired before current employment has a significant and positive

impact on wages, while off-the-job training during current employment is not significant.

This may be because young workers who are acquiring training from a proprietary institution

24
are planning to use this traiting to move to another employer and career track or the

findings may reflect the sharing of costs of this training with the current employer through

lower wages. In the following section I examine in more detail the link between training

and employer mobifity. Unfortunately, it is difficult to identi~ from the ~Y data who

is paying for the direct costs of training received off-the-job.

Table 6 presents the findings using. the specification of equation 3 in ‘~able 4 but

broken do~ by various subsamples of interest according to gender, race, education and

union status. It should be noted that given the sample composition, as shown in Table 2,

some of the cell sizes (e.g. the number of women in apprenticeships) become extremely

smdL Nevertheless there are some interesting differences amoss these groups. For

example, Johnson and Youmans (1970), Uwis (1986) and Mincer (1983) have discussed the

potential impact of unions on wage profiles and job training. The evidence from many

studies indicates that while unions raise the wages of their members, the wage profiles of

union workers are flatter than that of their nonunion counterparts. The results presented

here co- those findings. The union wage premium for the sample as a whole is around

20 percent yet the equations in Table 6. show that nonunion workers’ wages rise faster

during training spells than union workers’ wages.

bother interesting finding is what happens to the coefficient on current ON-JT when

the sample is divided by education, level. While those who have a high school degree or

some post high school schooling receive a wage premium for ON-JT, those who do not have

a Ylgh school degree actually receive lower wages during an ON-JT spell. This suggests that

firms may be providing more general training for those who do not complete high school

25
and tie costs of th~ training are shared between the workers and the * with workers

receiving a lower wage during the trairdng period. Another finding related to educatiomd

level is that the coefficients on race and gender become much less significant and smder

for those who have some post high school education. ~ls seems to suggest that continuing

on in school reduces the gap in wages between males and females and nonwhites and

whites. ,.

Before reaching any final conclusion on the basis of the restits presented in Tables

4-6 it is necessary to discuss in more detail the possible sources of bias in the training

estimates due to self-selectiom As already mentioned, employers may ordy place empIoyees

in training programs who have some unobservable characteristic “trainabili~~ or individuals

who are more motivated would be more likely to pursue off-the-job traiting. In either case

the estimated coefficient on the various training measure will be biased upwards (i.e. the

treatment selection problem).

A variety of ways to try to address tiis issue are described in Heckrnan (1979) and

Heckrnan and Robb (1986). One method that I used W= a “standard Heckman two-stage

procedure using the probits in Table 3 for ON-~ and OFF-~ with the appropriate inverse

MIUSratios as regressors in the wage equation. The results of this procedure are presented

in equation 4 in Table 4. This is a relatively straightforward procedure if the error terms

in the two probit equations are not correlated. To examine whether or not this was an

appropriate assumption for this sample I estimated a bivariate probit for the probability of

receiving on-the-job training and off-the-job training (results available upon request) and

found the correlation coefficient to be small (-0.12) with a t-statistic equal to -1.67. As

26”
shown in equation 4 in Table 4, none of tbe previous findings are altered with the inclusion

of the inverse Mills ratios (the lambdas) as regressors. Note that the lambdas are not

si~cant in the equation. However, identification using this procedure rests primarily on

functional form or somewhat artificial exclusions of explanato~ variables12. This is a

common problem with this procedure for this type of model since it is difficult to identi& ., $’

a vtiable that you would not include in both the probits and the wage equation.13

.

A second approach to ded with sample selection assumes that self-selection varies

ofly across individu~s and not over time for the individual. An individual’s wage at time

t can be expressed as:

(1) log (wit) = Z’i,d + fi + ei,

where Z is a vector of variables affecting wages that vary for each individud over time, and

fi are all the characteristics which are individual specific but time invariant. The

charactefitics in fi may be correlated with whether workers undergo training. Fitting

equation (1) while omitting fi will lead to bias in estimates of b. By differencing individuals’

wages between 1983 and 1980j dl time invariant effects (both obsetied and unobsewed)

.

drop OULand the coefficients maybe estimated without bias.

The resulfi from this second approach to sample selection are presented in Table 7.

In the fust column of results for the entire sample it is clear that additional weeks of offthe<ob training and apprenticeships significantly raise wage growth. Additional weeks of

ON-JT, however, are never sig@ficant for the entire sample or any sub-group. This suggests

27
that there may be some problem of selection bias for those who have some ON-~. It may

*O be that the ceU sties here are too smrdl for a si~lcant effect to be found. The stie

and si~lcance of the 0~-~ effect remains similar between the cross section and fied

effects modek in Tables 6 and 7. The ody change is that weeks of off-the-job training for

those in a u~on job in 1983 is now a signifi~t factor.14 The specification does not

distinguish between training spells across different employers in the intervaI so there may

be some workers who take a technical course in a propriet~”institution that gets them into

a union job at some later date.

Moving to a job that is covered by a collective agreement has a large and positive

effect on the wage rate. nose employed in a nonunion job in 1980 and a union job in 1983

e~erienced significant wage growth over the period, while those working in a union job in

1980 and a nonunion job in 1983 e~erienced, a large decrease in their wage. Changing

jobss at any time during the 1980-1983 period increases wage growth for the sampIe as a

whole, but again there are differences across the various demographic groups. Ody white

females and all education groups except those with some post high schooI education have

changes in their wage growth if they change employers. Finally, tenure on the job has a

much larger return to nonunion employees than union employees, as e~ected from the

earfier dlscnssion on union wages in Table 6.

W. Private Sector Training and Wages: Conclusions

Wie the returns to a college degree or government training programs in the U.S.

have been tidely documented, there has been relatively little analysis of the returns to other

forms of human capital investment that non college graduates undertake. This paper has

28
,.

shown that private sector tiaining plays a significant role in the determination of wages and

wage growth of the 70 percent of young workers in the U.S. who do not graduate from

college. Specifically, when private sector training is divided into different types (on-the-job

trting, off-the-job training, and apprentic=hips) some very different patterns emerge. For

e-pie, the characteristics that appear to itiuence the probability of receiving training are

primarfiy race and gender. Women and nonwhites are much less Ekely to receive training

witiln a firm either tirough an apprenticeship or other forms of on-the~ob training. ~ls

differential pattern in the acquisition of training by race and gender may be a partial

eWlanation of the persistent wage gap between males and females and. whites and

nonwhites. Schooling raises the probability of receiving off-the-job training and

apprenticeships but it had a smaller impact on the probability of receiving firm provided onthe-job trairring.

Ml~esoftraining raise wages significantly. Inparticular, thikpaper shows that on

average, for this sample of non-college graduates, off-the-job training from proprietary

institutions can beuseful for increasing wages. ~eimpact of these traiting variablesdso

seems to be larger than the impact of tenure on wages. ~is paper does not argue that

there is no role to be piayed by job matching or other e~lanations of rising wage profiles,

.

but rather that when there is appropriate data on training, the impact of training on wages

is quite large relative to other factors for young workers.

Finally, while on-the-job training with the current employer increases wages with the

current employer, this type of training seems to be quite firm specific since on-the-job

traiting from apreviom employer kneversigtificant formrrentwages. “Atthe sometime,

29
there seeti to be some evidence that if general training is being given to any group of

workers on the job it is for those who have not completed high school. me fiidlng that onthe~ob training is primarily specific is consistent with recent findings from the Hudson

Institute which surveyed 645 firms in the U.S. and found that ordy 8 percent had any sort

of general remedial on-the-job training programs’c. The fact that U.S. firms are more

,.”

tilling to invest in firm specific trairdng than in general training is understandable given the

~l~ty to “capture” the returns on investments in general traiting. However, whether or

not U.S. firms will be able to remain competitive with this strategy in the future, given the

characteristics of the new entrants into tie workforce and the ski~ demands of new

technology, is questionable.

Section V. Training and MobiE@ Theoretical Framework and Data

The transition from school to work is ~ically a period in whichmany young workers

e~erience a wide range of.different jobs and experience some of their most rapid wage

growth over their working fife. Hall (1982) has estimated that the first ten years of an

indvidufls working career will include approximately two-thirds of dl life-time job changes.

Topel and Ward (1988) found that over half of young mde new entrants into the labor

market held six or more jobs in their first ten years of work experience. Ordy one young

male worker in twen~ remained with their first employer for ten years in their sample. M

of this suggests that young workers’ early years in the labor market involve severrd

employment transitions.

The purpose of this section of the report is to examine for young workers in their

early ye=s of work experience the determinants of leaving an employer. In particular, this

30
section focuses on the role of different ~es of training on the probability of leaving an

employer. In the previous section of this report I reached the following conclusions. First,

forrnd company provided trainin~ or ON-~, appears to be highly firm specific in the U.S.

~~ fierefOre, is nOt portable from employer to employer. Company provided training

raises wages in the current job but has no effect on the wages earned in subsequent

: ..

employment. Second, formal training received from ‘for-profit’ proprietary im~titutions,or

0~-~, has Iitde effect on the wages earned on the current job but it does raise the

expected wage in subsequent employment. Finally, there are important differences by race,

gender and edu~tion level in the probability of receiving different t~es of formal traiting

and the impact this traiting has on wages and wage growth.

~ese findings have several implications for the impact of training on mobility. One

implication is that if company provided training is primarily firm specific then the probability

of leaving an employer should decline if a young worker has experienced some on-the-job

training k.. additionrd implication is that if workers participate in off-the-job training

progra~ they are more fikely to leave the current employer. In this case, off-the-job

training ~lows a young worker to change career paths and find a ‘better matcti. Using data

from the National hngitudinal Survey Youth cohort, WY, this part of the report

.

examines in detail the factors which itiuence the probability of new entrants leaving their

first job including the differentird effects of company provided training, apprenticeships and

training from ‘for-profit’ proprietary institutions.

mere are a varie~-””of explanations of why young workers change their employment

status so often in the early years of their areers and then seem to ‘settle down’ into more

31
stable employment. k the unionized sector, where seniority rules determine layoff policies,

young workers are more at risk of being laid off in a downturn. Even in the non-utiotied

sector, many firms use seniority as a major determinant of whom to lay off in a period of

fd~mg demand.

mere are other explanatioti of the higher turnover rates of young workers, however,

.,

that have fittle to do with the state of demand. me three main theoretical explanations

include job search, job matching and on-the-job training. Job search theory, as detailed by

Lippman and McCsdl (1976), states that information about where to. find a job and the

nature of that job are difficult to acquire, especially for younger workers. Workers wfil

accept employment and remain in that job as long as the wage paid in that job exceeds the

alternative wage. ~erefore, workers who earn more relative to their dtemative wage are

less Wely to quit.

h dtemative explanation of turnover behavior can be found in Jovanovic (1979a,

1979b, 1984). In the Jovanovic learning model both workers and firms ‘learn’ about the

unobserved characteristics of each other over time. & tenure increases, the quality of the

job match is revealed as firm” obseme workers’ actual productivity and workers discover the

non-pecuniary aspects of their job. In this model there are two countervailing forces for the

rehtionship between tenure and the probability of leaving an employer. On the one hand,

%etter’ workers remain with employers longer leading to negative duration dependence in

the probability of leaving a job. Ori the other hand, as ‘bad matches are revealed the

turnover probability will rise over time.

me process of on-the-job training within the human capital model as described by

32
Mincer (1974) implies that as workers acquire firm-specific training, their productivity arrd,

consequently wages, will rise. ~erefore, the probability of leaving an employer will fall

with training and tenure since the wage will rise relative to the dtemative wage. k

additio~ employers till be less likely to lay off those workers in whom they have invested

in spectilc skills. However, if most of the initird training for young workers is general, there

.,,.

till be either no effect on the quit probabili~ or the quit probability may eve’n rise.

Ml of these theories are not mutually exclusive and clearly some combination of all

of these factors itiuences the probability of a young worker remaining with an employer.

Consequently, it is not the purpose of this repoti to distinguish between these different

theories. Rather, it would be more useful if precise data on employment spells and training

could be found in order to establish the links between different types of training and

turnover behavior.

mere have been relatively few empirical studies which have attempted to examine

the role of training, demand and other factors. in predicting the probability of leaving an

employer. ~is is primarily due to the lack of accurate data on the timing of private sector

training and. the lack of detailed employment histories for workers. Recent exceptions

include Gritz (1988) and Mincer (1988). Gntz uses data tiom the early years of the NNY

and finds that private sector training (not distinguishing between different sources of

training) increases the amount of time in total employment for females but decreases the

amount of time males were employed. Gritz’s study uses data from the very early years of

the NHY when most of the observed training spells occurred before the detailed

employment history begins. Mincer uses data on training and mobility from the Panel Study

33
of Income Dynamics, PSID. me training variable comes from the answer to the foIlowing

question in the 1976 and 1978 interviews “On a job like yours how long does it take the

average new person to become fuUy trained and qudifiedT mile this is potentially a very

broad measure of training it does not measure how much training has actudy occurred for

the specific respondent. In additio~ it captures training information for the current job, not

,:

previous employment.

.

Using data from the N~Y it is possible to examine in more detail than has been 

possible in the past, the role of training, the general state of demand, and other personal 

characteristics in determining tnmover, me probability of leaving employment (for

whatever reason) is also know as the hazard rate or failure rate in renewrd theory. me

h-rd rate or turnover probability can be expressed as follows:

(2) h(t) = g(t)dt/(1 - G(t))

where g(t)dt is the probability of leaving an employer between time t and t+ dt, 1- G(t) is

the probability of being employed at time & and t is the duration of the current spell of

employment. In this paper the following Cox proportioml hazards model is used

(3) h(~z) = hO(t)em

where ho(t) ‘is an arbitrary and unspecified base-line hazard function and z is a vector of

chmacteristics including traiting. me Cox model is convenient for dealing with right

34
censoring and it is nonparametric in the sense that it involves an unspecified base-line

hazard instead of making further distributional assumption such as those required for the

Weibfl or bg-logistic hazard. However, this means that it will not be possible to measure

whether or not there is negative or positive duration dependence in employment, but this

is not a key focus of this paper.

,:

In a model of the role of training in the probability of leating an employer it is

important to be able to ~low training to occur over time with the employer. Mlowing for

covariates such as training to be time dependent implies:

(4) h(~z(t)) = hO(t)ez(’)B

where z(t) is a vector of all fixed and time varying covariates. & discussed in Cox and

Oakes (1984) the components of the vector z(t) can be divided into the following three

categories of variables - treatments that va~ tith time; intrinsic properties of

individurds/jobs that are time invarian~ and exogenous time varying variables.

Obviously the different types of private sector training are the ‘treatment’ variables

of interest. &les of time invariant personal and job characteristics include gender, race,

education, occupation, indust~, union status, location of the job in an urban are% and

whether or not the respondent is disabled. Time varying ‘exogenous’ variables for the

purpose of this study include tie local unemployment rate, marital status and the number

of chddrem

For the analysis presented in this part of the report a different sample” is used to

35
analyze mobifity patterns than was used to examine the determinants of wages. This sample

uses more recent years of the ~Y. & in the wage analysis I have excluded the 1280

respondents in the military subsample from the analysis. However, I have also deleted any

respondent who has completed school before the 1979 interview year. The find sample is

a pooled sample of young workers who have left school and not returned to school for at

least four years ~permanently’ out of school). Therefore, this sample is made up of 5 waves

of sdool leavers -- those who left in 1979, 1980, 1981, 1982 and 1983. In addition, the

respondents had to have obtained a job in the first year after ‘permanently’ exiting school.

The estimated hazard models the determinants of the turnover probability for the first job

after leaving school permanently for this sample. This sample has many more college

graduates in it given the age structure of the NMY compared to the sample used for the

wage study. However, I do not include anyone who completed school before 1979, which

substanttily reduces the sample size. In addition, I do not attempt to model the decision

to leave school over the period (1979-1983). Obviously this was a period in which many

yoqng people may have delayed entry into the labor market given the high unemployment

rate. I include dummy variables for year of entry in the following ansdysis but future work

would benefit from a complete modeling of the schooling/employment/training decisions

taken by young workers.

Characteristic of this sample are presented in Table 8. & can be seen in Table 8,

atiost three quarters of the sample leave their first employer during the first four years

after school. The average duration of employment Qncluding those still employed after four

years) is about a year and a half. Mmost seventeen percent of the sample experienced some

36
form of forrnrd training during their fiist job but the distribution of this job training by

source varied substantially by demographic group. College graduates were much more likely

to have received some form of ON-JT while those with just a high school diploma were

more Ekely to have participated in some form of OFF-JT. Women were more Ekely than

men to have received some form of OFF-JT but there was litfle difference in the probabili~

.,,

of receiting ON-JT by gender (not controlling for other factors). It is important’~o note that

some of the ce~ sizes for training by demographic group are extremely sma~ and this needs

Y to be kept in mind when interpreting some of the following results.

Table 9 presents more detailed information on the relationship between tenure on

the job with the first employer and the various types of training. The first panel shows that

over 80 percent of the sample have left their first employer by the fourth year in the labor

market. Those who left their employer relatively early were much less likely to have had

any formal ON-J-T (ordy 1.3 Yo) than those who stayed with their first employer 3 years of

more (8.170). The pattern is a bit different with participation in OFF-J-T programs. Mmost

a quarter of those who left their first job between 2-3 years received OFF-J,-T. However,

this percentage drops dramatically for those with 3 or more years on the job to ordy 11.7

percenL

The second panel is perhaps even more interesting. This panel shows, condition

on having participated in one of the types of private training, when that training spell begin

during the tenure with the employer. & discussed in the previous sections on training and

wage determination one view of training is that it is a ‘test’ (Weiss and Wang (1990)). In

other words, firms use formal training programs as a way to avail themselves of private

37
information known ofly by the workers. Workers who fail the test leave the firms and those

who pass do not leave. ~is wotid imply that we should obseme ON-J-T occurring early

in a workers’s tenure with the firm. However, in tiis second panel we see that 60 percent

of ON-J-T spelk begin after one year on the job at the fii. This seems to be more

consistent with a job matching story where firsns(workers) make a determination within the

first 6B months on whether or not there is a match, and if yes, the firm then invests in

more costly formal ON-J-T. Since the measure of training used in this paper ody captures

spells that last 4 weeks it maybe possible that shorter formal or informal training spells are

used early in the career with an employer as an indication of. match quality and longer

training spe~s follow later.

Contra~ to the timing of ON-J-T spells almost 60 percent of spells of OFF-J-T begin

within the first year with an employer. This maybe due to employees going outside the firm

to obtain training that they need for their current job, or employees deciding that there is

not a job match and seeking a training program that will allow them to leave their current

employer and get a better job. Finally and not surprisingly, most apprenticeships begin very

early in the tenure with an employer.

W. Private Sector Training and MobIti@ Empirical Results

The results obtained from estimating the Cox proportional hmard with time varying

covariates are presented in Tables 10 and 11. The time va~ing covariates are indicted by

an asterisk The time invariant intrinsic characteristics of the individuals /jobs in Table 10,

equation 1, that seemed to itiuence the probability of leaving an employer included being

disabled, union status, race, and school level. Disabled respondents were more likely to

38
leave their employer while being employed in a job covered by a collective agreement or

being a co~ege graduate significandy lowered the probability of leaving the first employer.

Blacks were more likely to have shorter durations on their first job than whites and

hispanics. There was no significant effect on the length of time with the first employer by

gender. However, there were significant differences in eWected length of employment by

:,.,

school attainment. Those tith a high school degree or less were more likely to leave their

employer, whereas those tith a college degree were less likely to leave.

Of the time v@ng ‘exogenous’ covariates the local unemployment rate was

significant implying that those who lived in high unemployment areas were less likely to

leave their employer. The hurdle for youths in high unemployment areas seems to be

getting a job rather than keeping one. The number of children seemed to have no

significant effect on the expected duration of the first job. Finally, those workers who were

married were more likely to remain with their first employer.

Wth regards to the training variables, those young people who had some formal ONJT were much less Ekely to leave their employer while those who participated in some form

of OFF-JT were more likely to leave. This seems to suggest that ON-JT is more firm

specific while OFF-JT .k more ‘general’. These findings are consistent with the results on

training and wages.

In equation 2 the h=ard is re-estimated including industry and occupation dummies.

The inclusion of industry and occupation does not change the coefficients or significance of

the variables in equation 1 with the exception of college which becomes insignificant. Those

young workers employed in construction, wholesale and retail, and business, repair, personal

39
and professional services were much more likely to leave their employers than those in

manufacturing. The ordy si~lcant occupation was managers with managers more Ekely

to remain with their first employer.

h equation 3 of Table 10 an additionrd variable is added which is the difference

between the log of the current wage (which vanes with time) and a log predicted wage. The

: ,,

predicted wage is obtained by the formula in Table 8 which uses the estimated coefficients

from a log wage equation for the starting wage for this sample. Those individu~ who are

being paid less than their predicted alternative wage are more likely to leave their employer

= shown in both equations 3 and 4 of Table 10. None of the pretious findings from

equations 1 and 2 are altered very much.

k Table 11 the proportional h~ard is re-estimated for various demographic groups

of interest Now the results change dramatically depending upon which sub-group you

examine. Agai~ it is important to remember that some of the cell skes now are very smrdl

so care must be taken in interpreting the results in Table 11. Nevertheless, it is interesting

to see how the results horn the pretious table change when the sample is divided into

demographic categories of interest For example, males, females, and blacks who are high

school dropouts have a shorter expected duration on the first job after they leave school.

However, being a male or black high school graduate has no effect on the duration of

employment, while being a female high school graduate lowers the duration of employment.

Male and black college graduates have longer expected durations of employment, whfie

there is no effect of a college degree on the probability of females remaiting with their first

employer.

40
The differences by race and gender are even starker when one examine time varying

regressors and the effect of training. For wome~ having additionrd children si~lcantly

lowers the eWected duration of their first job relative to those women who do not have

additioti ctidren. At the same time, there is no effect of children on the eapected

duration of male or black employment. Being retied stfil lowers the probability of leaving

an employer for males and women but there is no effect of marital status for ‘blacks.

Finally, ON-JT and OFF-JT are now insignificant determinants. of the duration of

employment for males and blacks. However, ON-JT increases the length of time in

employment in the first job for women while OFF-JT increases their turnover probability.

men the sample is divided by educational attainment. other i.n.teresting results

emerge. For example, those who are high school graduates or had some post high school

education and are covered by a collective agreement are less fikely to leave their employer.

For the sample as a whole there is no difference in the probability of lea~ng an employer

between males and females. However, when the sample is divided by educational level,

males are less fikely to leave their employer than females if they have less than a high

school degree or a college degree, but they are more likely to leave if they have had some

post high school education. In addition, being black raises the probability of leaving an

employer ody if the young worker had a high school degree but was not significant for any

of the other education groups.

The number of children seems to affect the duration of employment with the first

employer ordy for high school graduates, while marital status is significant ordy for college

graduates and high school dropouts. In addition, the unemployment rate is now ody

41
si~lcant for high school graduates. Fmdy, ON-JT appears to lower the turnover

probability if the respondent had a high school degree or less, while OFF-JT seems to raise

this probability for those with a high school degree. Given the smd ce~ sties one must be

cautious in drawing conclusion on variables that are insi~lcrm~ but the different effects

of variables of interest by race and gender are quite striking.

,.

,$

WI. Private Sector Training and Mobifi@ Conclusions

~Is section of the report has focused on the link be~een training and the

probability of leaving an employer. A high percentage of ON-J-T spells begin after young

workers have remained with their employer for at least one year. This seems to be

consistent with a job matching story where firrns(workers) make a determination within the

fist 6-12 months on whether or not there is a match, and if yes, the firm then invm~ in

more costly formal ON-J-T. In contrnst to the pattern associated_with ON-J-T spe~s, almost

60 percent of sp:lk of OFF-J-T begin within the first year with an employer. This maybe

due to employees going outside the firm to obtain training that they need for their current

job, or employees deciding that there is not a job match and seeking a training program that

WI allow them to leave their current employer and get a better job.

mere are significant differences in the patterns of job mobility by race and gender.

Overall there is no difference in the probability of leaving an employer by gender.

However, when the sample is divided by race, gender, and educationrd attainment there are

important differences between males and females. For example, children appear to have

fittle affect on the probability of males leaving an employer. At the same time, they have

a significant and positive effect on the probability of women not remairdng with their

42
employer. bong high school dropouts and college graduates women are more likely than

men to have shorter spells their fiist job, but there is no gender difference among high

school graduates.. In contrast, among those who have had some post high school education

men are more likely to leave their employer.

Evidence presented in the”previous sections of this repofi indicated that on-the-job

,.’

training for young workers in the U.S. appeared to be quite fii specific where~ off-the-job

training appeared more general. The results presented in Tables 10 and 11 seem to

reinforce this conclusion. Those with on-the-job training are more likely to remain longer

with fieir employer which would be consistent with firm specific trainirig. Those who obtain

off-tie-job training are more likely to leave their employer and this would be consistent with

off-the-job training being more general. However, when the sample is divided by

race,gender and educational attainment we see that the training variables are ody significant

in the equation for females.

Oerall it appears that blacks are more likely to leave their employer but this appears

to ordy be true for those blacks who received just a high school diploma. There does not

seem to be any significant difference in the results for hispanics relative to whites. Finally,

there does seem to be some evidence that blacks who receive some on-the-job traiting have

longer e~ected job durations in their first job.

mile this part of the report has attempted to shed new light on the skill formation

process of young workers and the consequences of this on their patterns of mobility there

are still many issues that remain unresolved. This report has modeled the detetinants of

the duration of the first job after school, not subsequent employment. & the NNY age

43
future research should examine how some of the gender, race, and educational differences

change over time. It would dso be interesting to examine the h~ard rates by broad

industry and ocmpationd categories. Finally, it would be important to see how robust tie

fidings are after additiond work is done to address the endogeneity issue for training.

Nevertheless, there is a story that emerges from the results in this report for young

workers and private setior training. Company training in the U.S. is very firm specific, even

for young workers in their first job. Young workers entering the labor market can receive

botb ‘good and ‘bad draws from the labor market. mere are some workers who get a ‘bad

draw who appear to move to better employment by investing in off-the-job training. nose

in ‘goo& jobs are more likely to obtain on-tie-job training which results in higher wages and

a lower probability of leaving the firm. ~ese effeets are particularly strong for women in

spite of the fact that women are less likely to receive on-the-job training.

44
FOOTNOTES

1 Kerns, David, CEO Xerox Corp. in W. MiHer, ‘Employers Wresfle with Dumb

Kids”, IndustV Week July 4, 1988.

2 Even if the training is entirely fiim spectilc you fight sti expect in some cases to

observe a positive effect of past training on wages with a future employer because if the

employer providing the training gives some wage premium for specific training (to lower

turnover) then the worker’s reservation wage should be higher. The size of this effect,

however, becomes an empirical question. ,’

3 Given the age structure of the sample the restriction that the respondent had to

have completed all schooling by the 1983 interview date substantirdly reduces the sample.

The restriction of having wage data further reduced the sample size but this effect was not

as large. In addition, ordy about 120 respondents had completed college by 1980 who dso

had wage data. Therefore, future work with the next waves of the NUY might examine the

role of private sector training for college graduates.

4 The data for the training variables come from the starting and ending dates of spells

of training by source. These dates are given by month and year. In order to match this to

the wee~y emplopent and schooling histories I assume that dl training commences and

ends at the beginning of the month. In the case of a spell which has the same begirming

and ending month I make the ending week the first week of the following month. If many

spells of training were quite short in duration this approximation might be inappropriate.

However, since all training spelk have to be at least 4 weeks and the fact that the average

duration of training for this sample is around six months this should not be too serious a

problem.

s I would like to thank Dan Black for very kindly running the comparable numbers

for the EOPP data. The EOPP data are of hours of training rather than weeks, however,

he found that 3 percent of the EOPP sample had training of over 100.hours (one might

assume 4 weeks of 25 hours per week) and 2 percent had training over 140 hours (4 weeks

of 35 hours). The NNY number for those in firm provided training lasting at least 4 weeks

is 4.2 percent.

6Tenure is specified as totaIweeks on the current job. In an alternativespecification

tenure was represented by a series of dummy variables: less than 6 months; 6 months - 1

yeaq 1-2 yeary and greater than 2 years. This had little impact on the findings presented

here.

7 Experience is total number of weeks of work since finishing school.

g The probits presented in Table 3 assume that the decision about when to finish

school is exogenous with respect to decisions about post-schooling training. However, given

45
that more than 70 percent of U.S. youths do not finish college it is interesting to exhne,

conditioti on completing school, how and who of the non-college graduates acquire training

after school. Future work should examine a more complete model of human capitaf

accutndation from school to training and government training programs.

g The industrial categories used and the percent in each ~n ()) are: agriculture,

forestry and fisheries (3.3); fining (13); construction (5.6); manufacturing (20.0) (omitted

mtegory); transport, commercial and public utifities (5.0); wholesale and retail (27.1);

finance, reaf estate and insurance (5.4); business and repair setices (5.9); personsd services

(6.9); professional and rehted services (15.2); and pubIic administration (4..3). The

occupation categories includti professionrd and technicaf (6.2); managers (3.8); sales (53);

clerical (24.2); craft workers (10.3); operatives (18.2); laborers and farmers (10.7) (omitted

category); service workers including private household (21.1). Detailed results are available

from the author upon request.

10me ~oefficienK on completed and uncompleted OFF-JT were never si@fic~tIY

different in any of the wage equation specifications.

11~ YO” ~ew trai~ng of young workers m a “test” as discussed in Weiss ad Wang

(1990), then this would be consistent with an argument that formal training programs are

a method firms use to avail themselves of private information known by workers. Workers

who “fa~ the test leave the firm and those who “pass” do not leave. Tfds discussion suggests

that it would be important in future work to dso examine the mobility patterns of these

workers and the role of different types of training in the mobility pattern.

12me probits used from Table 3 were column 2 for ON-n and column 1 for OFF-

~. The ofly differences between the explanatory variables included in the ON-~ probit

and the wage equation are that education is entered as a series of dummy variables in the

probit and as years of completed school in the wage equation. Industry and occupation

dummies are ordy included in the wage equation.

13~other S&ate= to de~ ~th selection that is less restrictive k to use imtmment~

variables and include the conditional expectation’of weeks of training in the wage equation.

To do this I first estimated individual probits for each of the types of training (separated

into training from a previous employer and current employer). I then estimated, using ON,

separate equations, conditional on having experienced each of the @es of training (*o

separated into current or previous employer), where the dependent variable was the number

of weeks of training. I then created an expected value of weeks training by type for each

observation using the probits and the OM estimated coefficients and reestimated the wage

equation using I.V. me results (available from the author on request) are not reported for

the sake of brevity but again they suggest that the conclusions reached above are not

altered.

14ln other words, there maY be some young workers who take a tech~c~ course h

46
a proprietary institution that gets them into a union job at some later date. In this case the

coefficient on OFF-~ might become positive and si~lcan~

B me, change job variable is spe~led differently than it was in the cross section

equatiom In the cross section I included the number of jobs since finishing school. In the

tied” effects I included simply a,dummy variable equal to 1 if the respondent changed jobs

at d in the 8083 intemd.

16from the ~~$, “Shortage of Skilled Workers is ~ected, by E.

Fowler, July 31, 1990, p. D16. !’

47
REFERENCES

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Economic Review, June, pp. 278-97.

~tonji, Joseph and Shakotko, Robert (1987). “Do Wages Rise with Job Seniority?”, Review

of Economic Studies July, pp. 437-59.

BarroL J., Black, D. and hewenstein, M. (1987). “Employer Size: The implications for

search, training, capital investment, starting wages, and wage growth”, Jou rnal of

~, Jan., pp. 76-89.

Barro~ J., Black D. and hewenstein, M. (1988). “Job Matching and On-the-Job Training”,

Jou mal of hbor Economic% Jan., pp. 1-19.

Bartel, Ann (1989). “Formal Employee Training Programs and Their Impact on hbor

Productivi~ Evidence from a human resource survey”, NBER working paper no.

3026.

B1ackbu~ M., Bloom, D. and Freeman, R. (1989). “The Declining Economic Position of

bss-SWled American Males”, NBER working paper no. 3186.

Brow J. (1983). “Are Those Paid More Redly No More Productive? Measuring the

relative importance of tenure as on-the-job training in explaining wage growtN,

Princeton Industrial Relations papers.

Brow J. (1989). “Why Do Wages Increase with Tenure?”, American Economic Review,

Dec., pp. 971-991.

Carnevale, A. (1986). “The barning Enterprise: A Report on the Size and Scope of

Training”, Training and Development Journal, pp. 18-26.

b% D.R. and Oakes, D., halysis of Survival Dat% bndon: Chapman and Hall, 1984.

Gritz, R. Mark “The Impact of Training on the Frequency and Duration of Employrnent”,

mimeo, University of Washington, 1988.

Hal~ Rober~ “The ImporWnce of fifetime Jobs in the U.S. Economy”, American Economic

Review, September 1982, 72, 716-24.

Heckman, J. (1979). “Sample Selection Bias m Specification Error”, Econometrics, pp.153-

161.

48
Heckman, J. and Ho@ J. (1989) “Choosing Among Alternative NoneWerimental Methods

for Estimating the Impact of Social Programs The case of manpower Etifing”,

NBER working paper no. 2861.

Heckm~ J. and Robb, R. (.1986). “Nternative Identi&ng Assumptions in Econometric

Modek of Selection Bias”, Advances in Econometrics, vol. 5, JM press, pp. 243-287.

INTERFACE (1989). ~W

~, report, N.Y., N.Y..

!:

Jovanovic, Boyau “Job Matching and the Theory of Turnover”, ~J

Economy, October 1979% 87, 972-90.

Jovanovic, Boyan, “Firm-Specific Capital and Turnover”, J~,

December 1979b, 87, 1246-60.

Jovanotic, Boyan, “Matching Turnover, and Unemployment”, ~,

February 1984, 92, 108-22.

=tz, hwrence and Revenga, Ana (1989). “Changes in the Structure of Wages: The U.S.

versus Japan”, NBER working paper no. 3021.

Lalonde, R. (1986) “Evaluating the Econometric Evaluations of Training Programs with

~erimentd Data”, ~~, pp. 604-620.

Ung, K and Ruud, P. (1986). “Returns to Schooling, Implicit Discount Rates and BlackWhite Wage Differentials”, ~ tatistics, Feb., pp. 41-7.

Lazear, E. (19S1). “Agency, Earnings Profiles, Productivity and Hours Restriction”,

American Economic Review, Sept. pp. 606-620.

Lazear, E. (1979). ‘me Narrowing of Black-White Differentials is nlusio@,American

~conomic Review, Sept. pp. 606-620.

Leigh, D. (19S9) ~, Wh t Hnd of Trainin paper

prepared for GAO.

Llllard, L. and Tan, H. ( 19S6). “Private Sector Training Who gets it and what are its

effects?, Rand monograph R-3331-DOL/RC

Llppman, Steven and McCall, John, “The Econornics of Job Search A Survey, Part ~,

~ , June 1976, 14, 155-S9.

.

Lynch, Lisa M. (1991a) “The Role of Off-the Job vs. On-the-Job Training for the Mobility

49
of Women Workers”, American Economic Review, May 1991,-81, pp. 151-56.

Lynch, Lisa M. (1991b) “Private Sector Training and the Earnings of Young Workers”,

fierican Economic Retiew, forthcoming.

Miller, W. (1988). “Employers Wrestle with Dumb W&, Industw Week, July 4.

Mincer, J. (1989). “Job Training: Costs, returns and Wage Profiles”, NBER working paper

No- 3208, De&, 1989. ,,,“

,’

Mincer, J. (1988). “Job Training, Wage Growth and hbor Turnover”, NBER working

paper no. 2090, August.

Mincer, J. (1983). “Union Effeck Wages, Turnover, and Job Training”, in Research iq

~, pP. 217-252

Mincer, J. (1974). ~, New York Columbia University

Pr~s. ”

Pergarni& M. and Shack-Marquez, J. (1986). “Earnings and Different Types of Training”,

rnimeo, ,BLS and Board of Governors of the Federal Reserve.

Rothschild, M. and Stiglitz, J. (1976). “Equilibrium in Competitive Insurance Markets: A

essay on the economi~ of imperfect information”, ~,

pp. 629-649.

Salop, J. and Sdop, S. (1976). “Self-Selection and Turnover in the Labor Market”, Ouarterly

Journal o~s, pp: 619-627.

Stigfi~ J. (1975). “Incentives, Risk and Information Notes towards a theory of

hierarchy”, Bell Journal of Economics, pp. 552-579.

Topel, R (1987). “Wages Rise with Job Seniority”, mimeo, University of Chicago.

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working paper no. 2649.

Weiss, Andrew and Wang, Ruqu. (1990) “A Sorting Model of Labor Contracts:.

Imphcations for Layoffs and Wage-Tenure Profiles”, mimeo, Boston

UniversiV, June.

50
.

Data: Panel Study of Income Dynamics, 19761980

“On a job like yours, how long would it take the average person””to become fully

qualified?

“&e you leafing s~s on the current job which could lead to a better job or

promotion? .,

,’

National Longitudinal Survey, Young & Older Mens and Young Women

Cohorts

“Do you receive ~ additional training (other than schooling training) on your

job? .

“What was the longest we of training you have had since the last interview?”

Current Population Survey, Janua~ 1983

‘mat trai~ng was needed to get the current or last job and what training is needed

to improve skills on the current jobY

Employment Opportunity Pilot Project Survey, EOPP - Individual Survey

“Describe up to 4 training events occurring between 1/1/79 and the interview data

in 198W (approx. 1 1/2 years)

EOPP - Employer Survey

“Number of hours typically spent by a new employee in the position last filled

watching other people doing the job rather than doing it himself during the first 3

months of employment”

“Number of hours a new employee in the position spends in formal training”

National hngitudinal Survey Youth Cohoti, NLSY

“In addition to your schooling, military and govermnent-sponsored training programs,

did you receive any other types of training for more than one month?”

“Which category best describes where you received this training”

(Both questions asked for up to 3 training spelk per year)

51
TABLE 2 - ~M SMPLE CHARACTERISTI~ (unweighed)

Vari*le

Wage 1980

Wage 1983

% Male

% Nonwhite

School years

Tenure, 83 (wks )

Total Experience, 83

(wks) “

% UnaplO~ent -te

% in SMA

% Healthy

% Married

# with ON-JT

# with OFF-JT

# Apprenticed

Duration of

ON-JT (wks )

Duration of

oFF-JT (WkS )

Duration of

APPT (wks )

smple Size

A21 mite Nsles mite Fmles Nonwhikes

54.27

$5.59

55%

21%

11.97

99.4s

192.63

10.01

71.7

95-s

29.4

128

450

54

31.15

40.90

63.46

3064

“s4.75

$6.29

11.83

100.73

197.58

10.24

70.2

96.4

29.2

77

177

41

34.62

43.47

74.78

1320

S3. SO

S5.05

12.14

{

101.25

195.07

10.12

71.2

95-2

3s.0

37

1s5

9

24.70

39.51

18.78

1090

$4.11

$5.0s

11.94

,,,

94.01 “

178.59

9.37

75.5

95.3

15.0

14

88

4

29.07

38-67

48.0

654

52
TABLE 3 - PROBITS FOR THE PROBABILITY OF RBCEIVING TRAINING BY TYPE

T-statistics in ()

---- .—. —.. —

~;~;tice On-the-Job

in 19B3

VXiable

Constant

Male

Nonwhite

Tenure

Experience

High School grad

Post High School

Union

Unemplovent Rate

&rried

Nufier of Jobs

Previous ON-JT

Previous OFF-JT

Previous Apprentice

Log Likelihood

0s r-tne-JOb On-the-Job

Probit

-1.26

(::.:;)

(-2:91)

-0.04

(-0.59)

-0.001

(-2.82)

0-0004

(:.;:)

(::::)

(2.2B)

-0.01

(-0.09)

0.01

(0.91)

-0.02

(-0.28)

0.006

(0.54)

-

-1254.9

Probit

-2.61

(-9. s3)

0.2s

(3.11)

-0.35

(-2.72)

-0.0008

(-1.21)

0.003

(3.37)

0.19

(;.;:)

(:.::)

(3:63)

-0-03

(-:.::)

(2:27)

0.01

(0.93)

-495.94

-3.07

(-:.::)

(3.51)

-0.51

(-2.52)

-0.0006

(-0.61)

0.0006

(0.29)

-0.01

(-0.71)

-271.60

-2.78

(-S.25)

0.10

(0-96)

-0.33

(-2.14)

0.002

(2.47)

0.002

(1.65)

0.07

(0.51)

0.10

(:.:~)

(3:56)

0.02

(:.:;)

(1:59)

-0-03

(-1.81)

0.02

(3-99)

-0.003

(-0.75)

.-0.10

(-0.50)

-334.77

N@er of observations = 3064

53
TASLE 4 - DETE~INANTS OF ~ WAGES AT 1983 INTERVIEW DATE (N=3064 )

T-Statistics in ()

Varlabl e

Constant

Tenure [wks )

Experience (wk? )

School

Previous ON-JT (wks )

Previous APT (wks )

Previous oFF-JT

(wke)

Current ON-JT (wks )

(uncompleted )

Current ON-JT (wks )

(completed)

Current APT (wks )

(uncompleted)

Current APT (wks )

(completed)

Current OFF-JT

(wks )

Unemplopent Rate

SMSA

Male

Nonwhite

Healthy

Married

Union

Nu&er of jobs

Indust~ and

Occupation Dmies

Eq. 1

0.70

(8.76)

0.0006

(5.66)

0-0018

(11.91)

0.03

(5.36)

-0.007

(-3.63)

0.07

(:.::)

(:;:::)

(-5:18)

0.076

(:.::)

(::;:)

(12:55)

-0.001

(-0.53)

no

no

L-dal (ON-~ probit )

L*da2 (OFF probit )

R-s~ared 0.25

or. Log Likelihood

Eq. 2

0.72

(8.96)

0.00067

(5.97)

0.0017

(11.68)

0.03

(5.03)

0.0006

(0.43)

0.005

(4.28)

0.002

(5.00)

0.003

(2.70)

0.0036

(2.32)

0.0026

(2.49)

0.002

(1.66)

0.0002

(0.27)

-0.00s

(-3.74)

0.07

(:.::)

(10:91)

-0.08

(-4.80)

0.09

(2.58)

0.07

(:.::)

(12:07)

-0.002

(-0.66)

no

no

0.27

Eq. 3

0.82

(10.19)

0.0006

(5.69)

0.001

(10.23)

0.02

(4.43)

0.0002

(0.17)

0.004

(3.29)

0.002

(5.23)

0.0026

(2-25)

0.002

(1.50)

0.0017

(1.70)

0.001

(1.03)

0.0002

(0.26)

-0.004

(-2.05)

0.09

(:.::)

(7:90)

-0.08

(-4.76)

0.086

(:.::)

(3:68)

0.19

(10.64)

-0.002

(-0.73)

yes

yes

0.34

Eq. 4

0.81

(10.15)

0.0006

(5.66)

0.0015

(10.30)

0.02

(4.51)

-0.0001

(-0.06)

0.004

(3.33)

0-002

(3-07)

0.002

(1.94)

0.002

(1.12)

0.002

(1-67)

0.001

(1.05)

-0.0002

(-0.31)

-0.003

(-1.75)

0.09

(:.::)

(7:90)

-0.08

(-:. g:)

(::::)

(::::)

(1; .67)

-0.002

(-0.74)

yes

yes

0.008

(0.38)

0.01

(0.99)

-1170.91

54
TAB~ 5 - PREDICTED HOU=Y WAGES BY SE~CTED C=CTERISTICS

Case 1. ) White male, averaae characteristics:

Case 2.;

.

Case 3.

no training

24 wks previous OFF-JT

24 wks completed current ON-JT

24 wks previous apprenticeship

24 wks completed current apprenticeship

1 additional year of school

1 additional year of tenure

Nonwhite male, average characteristics:

no training

24 wks previous OFF-JT

24 wks completed current ON-~

24 wks previous apprenticeship

24 wks completed current apprenticeship

1 additional year of school

1 additional year of tenure

White female, average ch~acteristics:

no training

24 wks previous OFF-JT

24 wks completed current ON-JT

24 wks previous apprenticeship

24 wks completed current apprenticeship

1 additional year of school

1 additional year of tenure

Case 4. ) Nonwhite female, average characteristics:

no training

24 wks previous OFF-JT

24 wks completed current ON-JT

24 wks previous apprenticeship

24 wks completed current apprenticeship

1 additional year of school

1 additional year of tenure

$5.4?

5..74

5.96

6.17

5.74

5..64

5.65

$5.00 , “

5.24

5.45

5.64

5-25

5-16

5.18

$4:71

4-94

5.14

5.31

4.94

4.s5

4.88

$4.34

4.56

4.74

4.90

4.56

.4.48

4.49

●using the estimated coefficients from e~ation 2 in Table 4. Average

characteristics =e: single, high school graduate, 99 weeks of tenure on the

job, 193 weeks of work experience, local unemplopent rate of 10. 01%, living

in the inner city, healthy, not covered by a collective agreement, and 2 jobs

since finishing school.

.

55
TABLE 6 - DETE~INANTS OF Lw WAGES AT 1983 INTERVIEW DATE BY

DEM@~HIC GROW

Variable

~nst ant

Tenure (wks )

E~erience (wks

School

Previous ON-JT wk )

Previous APT (wks )

Pxevious OFF-JT

Current ON.-JT (wks )

(uncompleted )

Current ON-JT (wks )

(completed)

~rrent -T (wks )

(uncompleted)

Current APT (wks )

(completed)

Current OFF-JT

Unemplopent wte

SMSA

Male

Nonwhite

Healthy

Married

Union

Nutier of jobs

Industry ad

Occupation Dumies

R-s~ared

Smple Size

White

Males

0.96

(7.61)

0.0005

(3.13)

0.002

(8.38)

0.02

(2.79)

0.0003

(0.17)

0.004

(3.12)

0.002

(2.76)

0.003

(1.56)

0.002

(1.11)

0-001

(1.38)

0-0007

(0-58)

-0.003

(-1.99)

-0.005

(-:-::)

(3:13)

0.04

(:.;;)

(3:28)

0.25

(8.44)

-0.008

(-2.00)

yes

yes

0.36

1320

White Nonwhites Union Nonmion

Females

0.s1 1.05 0.93 0.84

(5.99) (5.96) (4.76)

0.0006

(9.52)

0.0008 0.0007 0.0006

(3.49) (3.15) (3.14) (4.72)

0.0013 0.001 0.001

(:.::) (2.94) (:.:)

0.006 .

.o.ooi

(:.%)

(3.34)

-0.001

(-0.34)

-0.002

(-0.26)

0.002

(3.16)

0-003

(1.73)

-0.004

(-0.71)

-0.05

(-1.44)

-0.005

(-0.41)

0.002

(1.81)

-0.007

(-g.;;)

(3:20)

(-1.3s)

yes

yes

0.31

1090

(0.46) (2.67) (3.48) ,

-0.002 -0.0006 0.0006

(-0.70) (-:.:;J (0.34)

0.0007

(0.27) (1:71) (::M

0.003 0.0006 0.002

(2.88) (0.57) (5.05)

-0.003 0.0005 0.004

(-0.45) (0.25) (2.51)

0.017 -0.002 0.004

(1.79) (-0.70) (2.2a)

0.00002 0.002

0.00007

(0.05)

0.0003

(:-::)

(3:93)

0-04

(0.01)

-0.001

(-0.59)

-0.001

(-0.61)

0.00s

(1.86)

0.15

(4.45)

0.20

(1.31)

0.04

(:.::)

(:::;)

(3:47)

0.01

(2.24)

yes

yes

0.35

654

(5.69)

-0.17

(-4.92)

-0.04

(-0.70)

0.03

(0.82)

(1.79)

0.002

(1.23)

0.0005

(0.60)

-0.007

-3.15)

0.07.

(4.19)

0.11

(6.22)

-0.06

-3.06)

0.12

(:.::)

(3:32)

-0.009 ,-0.0005

(-1.35) (-0.19)

yes yes

yes yes

0.43 0.2s

560 2504

56
TABLE 6 CONTINUED

Vari*le

Constant

Tenure (wks

E~erience

School

Previous ON.

wks )

IT (wk)

Previous APT (wks )

Previous OFF-JT

Current ON-JT (wks )

(uncompleted )

Current ON-JT (wks )

(completed)

Current APT (wks )

(uncompleted)

Current APT ( wks )

(completed)

Current OFF-JT

UnemplOpent -te

SXSA

,Male

Nonwhite

Healthy

Xarried

Union

Nutier of jobs

. Industry and

Occupation Dumies

R-s~ared

Smple Size

Less than

High School

0-84

(4.31)

0.0004

(1.64)

0.001

(:.::)

(2.24)

-0.003

(-0.81)

0.005

(1.37)

0.003

(2.84)

-0.007

(-1.57)

0.002

(1.05)

0.0008

(0.32)

-0.002

(-0.95)

-0.002

(-:..::)

(::;;)

(3.51)

-0.06

(-1.93)

-0.03

(-0.50)

0-08

(2.75)

0.20

(5.49)

0.0002

(0.04)

yes

yes

0.31

766

High School

degree only

1.04

(13.12)

0.0008

(5.32)

0.001

(6.25)

0.0006

(0.33)

0.003

(2.02)

0.002

(3.52)

0.003

(1.92)

0.002’

(1.05)

0.001

(0.93)

0-0008

(0.74)

-0.00001

-(0.01)

-0.007

(-2.72)

0.10

(4.62)

0.17

(7.92)

-0.11

(-4.70)

0.17

(3.98)

0.03

(;.::)

(7.98)

-0.003

(-0.90)

yes

yes

0.39

1518

Post High School

but not ~llege Grad

0.46

(1.52)

0.0005

(2.36)

0.002

(:.::)

(2:68)

0.001

(0,.68)

0.004

(1.43)

0.002

(2.75) ,

0.004

(2.14)

0.0005

(0.10)

0.003

(1.41)

0.03

(1.25)

0.001

(0-93)

-0.002

(-0.41)

0.08

(2.14)

0.05

(1.60)

-0.06

(-1.62)

-0.02

(-0.27)

0-08

(2.33)

0.15

(3.67)

-0.001

(-0.27)

yes

yes

0.33

7B0

57
TABLE 7 -“ FI~D EFFE~S ESTI~TES

Dependept Variable: Log Wage (83 ) - Log Wage (80)

All White white Nonwhites Union

wles F-ales

Varifile

Constant

Nonmion

0.02

(0.48)

0.001

(5.24)

0.0007

,,(4.87)

0.0007

(0.56)

0-002

(3-66)

0.001

(1.21)

0.06

(2.73)

-0.17

(-6.92)

0.06

2504

0.002

(0.05)

-0.05

(-0.93)

0.08

(1.65)

-0.03

(-0.41)

-0.01

(-.11)

0.0016

(2.83)

0.0004

(1.53)

-0.002

(-1.20)

0.005

(3.07)

0.002

(1.76)

0.08

(2.08)

0.07

(2.10)

0.06

560

A ExWrience

(wks)

A Tenure

(wks)

A ON-JT (wka )

0.0,007

(2.24)

0.001

(2.00)

0.001

(5.90)

0.002

(5.32)

0.00066

(5.18)

0.0006

(2.99)

0.0007

(3.55)

0.0008

(2.54)

-0.003

(-0-65)

0.005

(4.02)

-0.004

(-0.47)

0.09

(2.07)

0.05

(1.39)

-0.0002

(-0.17)

0.002

(4.38)

0.002

(2.05)

0.07

(3.39)

0.13

(7.86)

-0-001

(-0.69)

0.001

(0.81)

~ OFF-J-T (wks ) 0.001

(1.55)

0.002

(2.39)

A APPT (wks) .0.002

(1.80)

-0.0002

(-0.03)

Job change dumy 0.05

(1.71)

0.07

(2.21)

Union83-Union80 0.20

(7.43)

0.11

(3.84)

R s~ared

S~ple 8ize

O.O6 0.09

1320

0.05

1090

0.05

3064 654

58
TABLE 7 CONTIN~D

Constant

A Experience

(wks )

A Tenure

{wks)

A ON-JT (wks )

a OFF-J-T (wks )

A APPT (wks )

Job change dumy

Union83-Union80

R s~ared

Smple size

Less than Eigh School Post High

Eigh School Graduate School Grad

-0.03 -0.07 0.13

(-0.42) (-1.45) (2.18)

0.001

(3.21)

0.0005

(1.80)

0.001

(0.44)

0.004

(2.83)

0.001

(0.35)

0.09

(1.92)

0.13

(3.59)

0.06

766

0-001

(4.43)

0.001

(5.03)

-0.0005

(-0.36)

0.003

(3.29)

0-002

(1.66)

0.09

(3-50)

0-16

(7-01)

0.08

1518

0.001

(2.85)

0.0004

(1.46)

-0.0006

(-0.28)

0.002

(1.67)

0.002

(1.15)

-0.01

(-0.22)

.0.08

(2.28)

0.03

780

,.

59
Vari*16:

Urban

# of Chil&en*

Dis~led

M=ried*

Union

Black

Hispanic

Hale

72*

.15

4%

22.7%

15.4%

21-2%

16-9%

47.9%

Urate (6-S .9% )●

Urate (9+%)*

ON-~*

OFF-JT*

Apprentice*

Year of entry 1979

. 1980

. 1981

Years of School 12.6

Tenure by year 4 72.5

(in let job (wks) )

Log real waqe. $1.61

Log predicted real wagel $1. s0

% left first employer by 4th year 73.8%

.

notes:

● Denotes time-va~ing covariate

1 This predicted wage is created from the following e~at ion:

36%

37%

3.7%

Q ;8%

1.1%

16%

21%

19%

23%

21*

Log predicted starting wage = .64 + (.14*y1979) + (.07*y1980) + (.04*y1981) +

(.03*y1982) + (.21*0cl) + (.06*oc2) + (.03*0c3) + (.03*0c4) + (.05*0c5) +

(.03*0c6) - (.03*oc8) - (.06*inl) + (.07*in2) + (.05*in4) - (.15*in5) -

(.07*in6) - (.12*in7) - (.33*in8) - (.l*in9) - (.05*in10) + (.13*male) +

(.04*urban) + (.02*#children) - (.03*dis*led) + [.02*marital) + (.15*union) +

- (.06* high

first year of

(.02*hispanic) ‘- (.06*blk) +“ (.Ofi*school) - (~03*;edium urate j

urate ) . All of the explanatory variables ,are evaluated at the

entry.

60
~ 8 (continued)

Industry Oc’cuuat ion

Ag. ~FOrestry, Fisheries,

& Mining

4.0% Professional G

Construction 5.5% Managers

M=ufacturing 17.1% Sales

(otitted category )

Transpo* & Utilities 3.4% Clerical

Wholesale & Retail 31.2%

.

Craft

Finance, Real Estate, 5.6% operatives

& Insurance

Business & Repair 6.0% Laborers & Famers

Services

category )

Personal Services 6.2% Service Workers

PrOfeesiOnal Services 17.2%

Public Abinistration 3-8%

Percent with Training by Demographic Group

ON-JT OFF-JT Apprentice

3.8% 11.3% 1-9%

3-7 12.1 0.3

2.2 11.3 1.3

1.8 4.8 1.1

.2.2 16.2 1.3

4.5 10.1 0-6

8.9 4-7 0.9

Male

Female

Black

Less H-S.

High School

Post H.s.

College +

9.8%

Technical

3.,2%

5.4%

24.0%

8.6%

14.7%

11.4%

(omitted

22.9%

N

=08

1314

535

363 ~

1363

439

.357

61
,~ 9 - *racteristic* of Private Sectir Zratihg

tirnplet~ Tenure bg t with Tratiing by ~pe

~leted Tenure % of s-pie ON-JT oFF-~ -,T

1- 26 weeks 33% 1.3% 10. 6% 0.7%

27 - 52 weeks 20% 1.5% 11.8% 0.9%

1- 2 years 19% 2.6% 15. 6% 2.4% ,!.

2- 3 years 7% 6.6% 23. 6% 0.s%

3- 4 years 21% 8.1% 11.7% 0.8%

tinditional on having training in 1st job - when did it begin?.

Yea

During 1st year

1st - 2nd year

2nd - 3rd year

3rd - 4th year

Male

F=ale

Black

Less E.S.

Eigh School

Post R.s.

College +

ON-JT OFF-JT

39.8% 57.2%

25.6% 14.9%

18.8% 18.1%

15. 8% 9.7%

Percent with Training by

ON-JT OFF-JT

3.8% 11.3%

3.7 12.1

2.2 11.3

1.s 4.8

2.2 16.2

4.s 10.1

8-9 4.7

APT

69.4%

a.3s

8.3%

13.9%

Demographic Group

Apprentice

1.9%

0.3

1.3

1.1

1.3

0.6

0.9

N

120a

1314

535

363

1363

439

357

.

62
Vari*le

Urban

# Children*

Disabled

Married*

Union

Black

Hispanic

Male

Less than H.S.

High School

College

Mediw Urate*

High Urate*

ON-JT*.

OFF-JT*

Apprentice*

Log Wage Diff*

-.06

-1.31)

.09

(1:::)

(1.98)

-.22

-3.44)

-.28

(-4:::)

(2.41)

.05

(0.83)

-.05

(-1:::)

(8:; ~)

(::;:)

(-2.79)

-.17

(-2.95)

-.17

(-2.74)

-.40

(-2:;:)

(1:::)

(0.13)

Industry & no

Occupation dumies no

(-3::;)

(1:::)

(::::)

(-l::;)

(7:;:)

(:::;)

(-1.35)

-.16

(-:::$)

(-::;:)

(-l:;:)

(1::)

(0.40)

yes

yes

Eq. 3

-.04

(-0.95)

.09

(1:::)

(1.96)

-.21

(-~:;;)

(-4.16)

.11

(1:::)

(::::)

(-l::;)

(8.22)

.23

(::::)

(-2.73)

-.1s

(-::;:)

(-2.83)

-.32

(-2:::)

(1.70)

.10

(0.48)

-.64

(-10.04)

no

no

Log Likelihood -14697.7 -14640.7 -14644.4

,.

Notes :

Eq. 4

-.04

(-0-85)

.08

(l:;:)

(::;:)

(-::;:)

(-3.21)

.09

(1:::)

(:::;)

(-;:;;)

(6.95)

.18

(:::;)

(-1.11)

-.17

(-:::)

(-::;:)

(-1.46)

.10

(I:fi)

(::::)

(-10.06)

yes

yes

-14592.0

.,,

● denotes the varying covariates

EWations also include duwy variables for year of entry

63
Varitile

Urban

#, Children*

Dis~led

Xuried*

Union

Black

Hispanic

Male

Less than E.S.

High School

college

Medium Urate*

High Urate*

ON-JT*

OFF-~*

Apprentice*

Log Wage Diff*

Log Likelihood

Nties of Ohs.

Notes:

* denotes ttie

Eqations also

Male=

-.0 6

(-::~)

(-1:::)

(1:::)

(0.35)

.39

(3:48)

.0s

(::::)

(-::;:)

(-2.27)

-.23

(-:::;)

(-1.19)

.03

(::;:)

(-0.13)

-.55

(-6.18)

Females

-.03

(-O::;)

(2::;)

(::::)

(-::::)

(-4:::)

(l:;;)

(0.26)

.93

(7:::)

(3.74)

-.04

(-::%)

(-::%)

(-::::)

(-1.70)

.,19

(2::;)

(::;;)

(-s-44)

Blacks

-.01

(-O:;;)

(0:::)

(::::)

(-::j; )

(-3.67)

.05

(0.44)

.55

(3.14)

.19.

(1.52)

-.54

(-2.40)

-.15

(-::::)

(-::::)

(-1.43)

-08

(:::;)

(-:::;)

(-4.56)

-6337-3 -68?9. 4 -2546.9

1208

varying covziates

include dumy variables

1314 535

for year of entry

,

64
~ 11 - D~INANTS OF ~ PRO--I= OF -VING -MYBR

BY D32400RAPEIC GROW (contfiued )

Vari*le

Urban

# Children*

Dissbled

-xied*

Union

Black

Hispanic

‘ Male

Medim Urat e*

High Urate*

ON-~*

OFF-JT*

Apprentice*

Log Wage Diff*

Log Likelihood

Nutier of Obs.

Notes:

< H.s.

-.26

(-2:::)

(::;:)

(-::::)

(-2. s1)

-.19

(-l::;)

(0:::)

(::::)

(-3.03)

-.15

(-0.97)

-.19

(-1.10)

-1.19

(-::::)

(-0.52)

.22

(::::)

(-2.30)

-1625.1

363

H.S. Post H.s.

-.03

(-0.54)

.12

(1.61)

.40

(2. s0)

-.04

(-~:;:)

(-2.47)

.15

(2::;)

(0.15)

.005

(0.0s)

-.18

(-2.38)

-.19

(-2.34)

-.35

(-1.34)

.11

(1.41)

-.20

(-0.68)

-.76

(-s-30)

-7450.2

1363

-.03

(-0.24)

.11

(::::)

(-::~)

(-::::)

(-2:;:)

(:::)

(-::::)

(-0. s3)

.21

(1.22)

.43

(::~)

(-4. s4)

-1921.5

439

* denotes time varying covariates

E~ations also include d-y variables for year of entry

College

.08

(::::)

(-O:;:)

(1.90)

-.55

(0.37)

-.32

(-2.26)

-.12

(-0.74)

-.08

(-0.48)

-.12

(-0.4s)

.03

(0.08)

.5s

(1.12)

-.67

(-4.00)

-12ss.9

357

65