me National Longitidtid Smeys ~S) Powm wpp~s mmy ~di~ desi~ed

to fir-e ud~stitig of the labor _ket md methods of dati mllwtion. me

Discussion Papers sti~ rnco~oraks -e of tie rexach m- o“t of the pmga.

Mmy of the wpeIs ti b series have b~n deliv~d m N IWO- cotissiond x

@ of m -mwd gat proym, mder wtich ~mts ~e amded tiougb a

mmpetitive process. me series is titm&d to ckculate tie fmtigs to titereswd I~h

titi md ouki& the Bin-u of Labm Sm&tiGs.

P-ns ti~=td h obtig a copy of my Discwim Papr, oth= NLs rq~,

or gmd ti-ation about tie ~ pro~m md smeys shotid conhct the Bu~u of

bbor S&tistics, Ofice of Econotic R-h, Wastigtin, DC 20212~001, or d

(202) 606-7405.

Wtirial k MS publication k h the public domti md, titi” appropriate crdt,

my& repmdwed titbout ptissiom

~lnions md mnclmiom eqressd h tis do.ment ae tiose of the autbo,(s) md

do not necesstiy rep~esmt m offIcM position of tie Bu~u of bhr StitiAw ox tie

U.S. Dqtimt of Labm.
Trairdng, Wages, and the Human Capital Model

Jonathan R. Veum

U.S. Bureau of Labor Statistics

2 Massachusetts Ave., ~, Suite 4945

Washington, DC 20212

Phone: (202) 606-7387

Fax: (202) 606-4602

E-mtil: veumj @bls.gov

November 1995

The views expressed in this paper are those of the author and do not reflect the policies or

views of the Bureau of Labor Statistics. The author thanh Alexander Eldekan for

excellent research assistance.
E=CUT~ SU~ARY

~Ie there area number of tieories as to why wages increase over an

individual’s work life, a commonly accepted interpretation is that upw~d sloping wage

profiles reflect investments in human capital, particularly investments in job training.

The tradition human capital model predicts that training lowers the starting wage and

incremes wage growth, This study uses recent data from the National Longitndind

Survey of Youth (~Y) to examine the predictions of the human capital model

concerting the relationship between training and wages. k sum, the results, particularly

the findings regarding training and the starting wage, do not support the conventional

version of the human capital model and suggest that dtematives to the traditional model

should be considered.

The results from estimating starting wage regressions indicate that there is not a

negative relationship between starting wages and current company training. If anything,

starting wages and company training appear to be positively related. Also, the data

indicate that off-site company paid training is portable across employers, or is general.

Taken together, these results suggest that firms, rather than workers, pay for generrd

training, which is inconsistent with the standard human capital model.

The estimates from the wage growth regressions are more consistent with the

human capital model. Training that is company financed has a positive impact on wage

growth independent of tenure at the current job. Company training that takes place

outside the work place is ptiicularly effective in enhancing wages. This restit is

interesting ~ven that this form of training appears to be the most general. Hence, while

companies appear to finance training that provides stills which are useful both within and

across firms, this training may differ from what is comrnody considered as “on-the-jo~

training.
1

I. ~RODUCTION

There are zmrmber of tieories as to why wages increase over an indlvidutis

work life. Acomonly accepted inte~retation of WsrelationsMp istitiupwwdsloptig

wage profiles reflect investments in human capital, particularly investments in job

training (Becker 1962; Mincer 1962). Aindividuds decision toinvest inhuman capiti

is based upon an examination of the net present value of the costs and benefits of such m

investment. hdividuds reassumed totivest ti&afilng dutingan initial period and

receive returns totheinvestment in subsequent periods. Workers pay fortrainingby

receiving a wage which is lower than what could be received elsewhere while being

trained. Since training isthought tomakeworkers moreproductive, workers collect the

returns from their investment in later periods through higher marginal products and

higher wages.

Human capital models usu~ly decompose training into specific training, which

increases productivity inonly one firm, andgeneraf training, which increases productivity

inmorethan one firm. Purely generdtraining is financed by workers, and the workers

receive dlofthe returns to this training. hcontrast, employees andemployers will share

inthecosts andreturns ofspecific training. Despite these differences between general

and specific training, the model predicts that both forms of training lower the swing

wage and increase wage growth.

k tie absence of direct empirical evidence on training to test the human capital

model, a number of alternative theories which minimize the role of training have emerged

toexplain upward-sloping wageprofiles. Forinstance, itmaybe true that wages rise with

tenure because irrformation about the quality of the match between a worker and a firm

reveafs itself overtime. Wages adjust toreflect tkequdity of thematch and well

matched workers remain on the job while those who are poorly matched are most likely

to leave (Jovanovic 1979). Other models imply that wages increme with tenure in order

to reduce supervision costs, to reduce turnover costs, or to do both (fierlof and Katz
1989; Luear 1981; Sdop and Sdop 197Q. Since each of these theories have stiar

predictions, they are not necessarily mutually exclusive md it is difficult to test these

dtemative theories: Also, since factors such as job training, match quality, and

supervisory inputs are difficult to observe, assessing the relative importance of each

theory in the generation of wage-tenure profiles is problematic.

Recent improvements in the available data on training have produced a growing

body of literature which analyzes the different aspects of the human capital model and

documents the consequences of haining. h particular, most studies fmd that tiaining

received from the current employer is associated with increased wage growth (Mtonji and

Spletzer 1991; Barren, Black, and Loewenstein 1989, 1993; J. Brown 1989; Duncan and

Hoffman 1979; Mincer 1988). However, there have been only limited tests of other

aspects of the human capital model. For instance, Barren, Black, and Loewenstein (1989)

and Parsons (1989) both find no statistically significant relationship between training and

the sttiing wage. Also, although Barren, Berger, and Black (1993) find that training has

a negative effect on the starting wage, this effect is small relative to the impact of training

on productivity.

h addition, there is only limited evidence as to whether training is specific or

general. Lynch (1992), using data from the early years ot the National Longitudind

Survey of Youth, concludes that compmy training is pnmtily firm-specific. Booth

(1993 ),”using data from a group of British graduates, finds some evidence that training is

portable across employers, but is much more so for men than for women.

h this paper, recent data from the National Longitu~nd Survey of Youth are used

to examine the impact of training on starting wages and wage growth. The analysis dso

provides evidence as to whether training is gener~ or specific. It.is found that “while

trtinin-g is associated with increased wage ~owth, the other predictions of tie tradition

human capital model are not cotimed by the data. h particular, there is evidence that
3

company training does not lower the starting wage and that training is general. This

implies that fitis bear the cost of training which is portable across employers.

The paper proceeds as fo~ows. h the next section, a description of the data used

here is provided. Section ~ presents results from estimating the impact of training on

st~ing wages, while Section W provides estimates from wage growth equations.

Section V offers some concluding remarks.

D. T~ DATA

In this anrdysis, data from the National Longitudind Survey of Youth ~SY) are

used to examine the impact of prior and current training on starting wages and wage

growtk Past research using the NLSY, such as that by Lynch (1992) md Parsons (1989)

has used information from the 1979-86 surveys, where time spent in private “sector

training is only available for programs that last over a month. In subsequent years, the

training questions in the survey were changed so that respondents were asked about dl

types of training (up to four programs) since the last interview, regtidless of duratio”n.1

Consequently, past research using the NLSY captures the effects of participation in

relatively formaf training programs. Lynch (1992). reports a company training incidence

of 4.2 percent, while the more recent NLSY data indicates that the incidence is about 20

percent (Bureau of Labor Statistics 1993), suggesting that early NLSY data misses the

majority of training events.

The NLSY is a sample of approximately 10,000 young men and women who were

between the ages of 14 and 22 in 1979 and who have been interviewed anmrdly since that

year.2 It is possible to create a measure of hours spent in training programs taken after the

1986 interview date by taking the product of answers to separate questions about the

number of weeks of training and hours per weeks of training. The training progr~

exclude any training received through formal schoohng. Mso, while the measures of
4

training are more comprehensive than those avtilable from the 1979-86 surveys, they do

not capture the extent of inforrnd training.

A key feature of the NLSY is that it garners infotiation in an event history

format, in which dates are collected for tie beginning and ending of important life events.

In particular, the starting dates and ending dates of dl jobs are recorded, as we~ as are the

timing of training programs. Based upon the timing of these events it is possible to create

measures of training received on the current job along with measures of training received

prior to the current job.

mile the earlier yems of the ~Y data primarily provide information on where

the training took. place, the more recent data include information both on training location

and on who pays the direct costs of this trainifig.- Incorporating data on the payer of the

direct costs of trairilng is particularly important when estimating the effects of titifing on

the sttiing wage. Presumably, even though some employers pay for the explicit costs of

training, employees indirectly pay for “company paid” trtiting through a lower starting

wage. .

The issue of who pays for the training is dso important since many company

training programs take place “off-the-job.” For instance, classes wtich offer training in

the latest developments in the field, snch as chmges in accounting laws, advancements in

computer technology, or new me~cal teckiques may not take place at the work site, but

be directiy financed by the employer. Yet there may dso exist some forms of training

that take place “on-the-job,” but are financed by the employee. k particular, seminars or

classes which provide more general skills, such as those,in management, leadership,

public speaking, or a foreign language m-ay occur at the work site but be paid for by the

worker.

Consequently in this analysis, training is separated into categories based upon

location and payer.3 Since the focus here is primtily on the effect of company or “ontie-joW training on wages, location is divided tito categories of “on-site” and “off-site,”
5

and payer is broken into “company paid’ and “other paid,” where “other paid” includes

training paid for by fie intividud, family, government, or other exte.md sources.4 The

resulting four categories ue: on-site, company paid (ON-CP); on-site, other paid (ONOP); off-site, company paid (0~-CP);”and off-site, other paid (0~-OP). When

estimating the impact of training on starting wages and wage growth, these categories are

dso broken into training received prior to the current job and training received while at

the current job.

The prima~ sample used here is restricted to those who were working for pay and

not enrolled in school in 1992, who s~ed the 1992 job after the 1986 interview date,

and with nonmissing information on other variables used in the analysis. The

employment restriction does not imply that the respondent was working at the 1992

interview date, but had to be working at some time over the interview year. The resulting

sample is a group of 4,309 young men and women who were age 27-3.5 in 1992, and it is

important to note that the results are specific to this age cohort. However, the SU

acquisition of this age group is of particular interest given that past reports indicate that

most forrnd employer-based training is provided to workers between the ages of 25 and

34 (Carnevde and Gainer 1986).

Since the sample is limited to those who began the 1992 job after he 1986

interview date, complete data on training received while working with the current

employer are available for afl sample members. While information on training received

prior to the 1992 job is incomplete, the impact of previous training may dso be p“tidly

captured by examining the effect of prior work experience “onwages. In addition,

although only those with six or fewer years of current job tenure are analyzed, the impact

of this restriction on the rmdonmess of the sample is minimized given that young

workers are extremely mobile (Bnreau of Labor Statistics 1992, Topel and Ward 1992).5

Table 1 provides information on the receipt of the different forms of training and

time spent in training by sample members. Approximately 25 percent of the sample
6

received on-site, company paid training, while over 14 percent received company paid

traintig which took place outside the work place. About 16 percent participated in offsite training which was self paid or not employer financed, while slightiy over three

percent received training at the work site which was not directly financed by the fum.

The percentage of individuals receiving company training is slighfly higher than that

suggested by previous research, which indicates that between five and twenty percent of

workers ~eceive company training (C. Brown 1989), although the samples, time frame,

and measures of training vary substanti~y across studies.c h particular, most prior

smdies examine training received from the current employer, whereas in this study

training received from multiple employers over m extended time frame is analyzed.

Recipients of off-site, other paid training spent on average over 450 hours in these

programs, wtich is more than any other source.. This category includes training received

from vocationtitechnicd schools, business schools, and correspondence courses, and

these programs are probably more formrd than some of the on-the-job training programs.

The standard deviations for each of the forms of training are relatively large, and the

medians of the training durations indicate that these measures are skewed to the right.7

The medians also indicate that dl forms of training other than off-site, other paid trtining

are relatively short in duration. For instance, one-half of the recipients of on-site,

company paid training spent 50 hours, or on average about one full working day per year

in ths form of training over the six year time span.

H. T~G ~D START~G WAGES

The fipact of traifing on stardng wages is estimated by specifying the following

wage equation:

(1) lnws=~Tp+~Tc+~X+E

where in WS is the log starting wage rate of the job held in 1992, Tp is training received

prior to the current job, Tc is training received at the current job, X is a vector of worker
7

and fifi characteristics, and &is a standmd error terrn.s The X vector ticludes variables

such as priorwork experience, sex, race/ethnicity, education, firm size measures, urban

residence, locrd unemployment rate, herdth status, union status, mital status, as well as

occupation and industry dummy variables. b addition, an individud’s score on the

Armed Forces” Qualifying Test (AFQT) is included and taken to be a measure of ability.g

As mentioned, except for the studies by Lynch (1992) and Booth (1993), the

impact of prior training o.n wages has been ignored, since creating a measure of past

training (Tp) requires the use of Iongitudind or quality retrospective data. The human

capital model predicts that trtining received at the current job is negatively related to the

sttiing wage (~ < O). Prior training has a positive impact on the wage if trtining is

general (~> O), but has no impact if training is firm-specific (~ = 0)

Mthough the data provide for an extremely rich set of variables which control for

individud heterogeneity, including a measure of ability, there remains tie possibility that

individuals are nonrandotiy selected into training based on unmeasured factors, which

biases the estimates of the effects of “kaining on wages. For insttice, if individuals who

participate in trtining are exceptionally motivated, the estimates of the effect of the

training ‘measures may”be biased upwards.

There are two methods typically used to deal with this problem. The fwst is to use

a “treatment effects” two-step procedure sitia to that described by Heckman (1979),

where the “treatment” is participation in training. A training receipt probit is estimated in

which the dependent variable takes the value one when any form of training is received,

and the independent variables are similar to that used in the wage regressions. Yet

successful implementation of this model requires the inclusion of an instrument in the

...—....& training participation equation which is uncorrelated with wages. ~ this analysis,

identification of this model is achieved by merging variables on occupation~ schools to

the NLSY. These variables are available from the 1992-93 kstitutiond Characteristics

Survey sponsored by the National Center for Education Statistics. A variable which
representstie number of occupational schools in an individua~slocrdity which offer

programs of two ye~~or less, m well as a variable which reflects tie ske of the largest

occupational school in the locality, based on total enrolhent, ae included in the training

10 ~resumably these instimtiond receipt probit, but are excluded from the wage equation.

measuresreflect an indlvidud’s potential acc~s to training,and are reasonable candidates

as determinantsof trairiing,but not of wages.

The second method used to correct for unobserved heterogeneityinvolves

estimatinga first-differenced fixed-effects model such as:

(2) Alnw~=~ATp +~ATc+fi AX+A&

in which tie dependent variable is the change in log startingwages between two time

periods, and the independentvariables are changes in prior training,currenttraining,and

other individual characteristicsbetween periods. k order to generatemeasures of

changes in wages as well as changes in thekey independentvariables,particularly

changes in prior training,it is necessary to examine people who changedjobs atleast

twice between 1986 and 1992 (the change in prior trainingis ~ro for those who held only

one job after 1986). Consequently in the ~xed-effects specifications, the subsample

includes those individuals for which the 1992 job representsat leastthe second job

change from 1986-92. There is an additional selection issue since this subsample is more

likely to consist of “movers” andmaybe selected nonrandody. If the propensity to

change jobs is considered to vary across individuals and not over time for a.given

individurd,it is eliminated by the first-dlfferencing procedure. Otherwise, it is necessary

to include a selection term in the wage change regressionswhich accounts for the

normmdomness of the subsample. In thewage chmge estimations, separate

specifications which include and (:xclude a selection termgenerated from ajob mobility

probit equation are reported.’1

Table 2 presentsestimatesfrom log startingwage equations. For ease of

presentation,only the estimatesof the training,experience, and abifity coefficients are
presented. Also, due to the skewness of the trahring measures tid so that the estimates

can be interpreted m elasticities, these independent variables are in logarithmic forrn.’z

The estimates in colum ( 1) provide no evidence that current tiairring is negatively related

to Stmtig wages, as_~l of the variables which reflect trtining received by the current

employer are unrelated or are actually positively related to tie starting wage.

Interestingly, both on-site and off-site company paid traiting are positively related to the

stardng wage. WMe there is a negative association between being trained at the

interview date and st~ing wages, it is not statistically significant.’3

Previous 0~-CP is positively related to the starting wage, indicating that this

form of company paid training is gener~ since it is portable across jobs. Conversely,

previous 0~-OP is negatively related to st~ing wages, suggestig that those who

receive this form of training, which includes training received from vocational schools

and business schools and is usually seff paid, do not see a return to this form of training

through an incremed starting wage. The difference in the impact of OW-CP and 0~-

OP on wag= implies that firms are particularly effective in finmcing sM1 enhancements

which are useful to otier employers.

The results indicate that education, ability, and prior experience =e dl positively

related to starting wages, as might be expected. Each of these variabl~ has a much larger

impact on wages than previous company” paid training, although the experience variables

mY also ptiidly capture the effect of prior training. A ten percent increase in previous

0~-CP increases s=~ing wages by about .2 percent, while simil~ increases in

education, ability, and experience improve starting wages by approximately 4.6 percent,

1.2 percent, and .7 percent, respectively.

Results from utilizing the two-step treatment effects model are presented in

specification (2).’4 The estimates for the training variables and the other variables are

fairly similar those in specification (1) and the selection term is not significant,

suggesting that there is little selection bias in the reduced form wage equation. *5 Hence,
10

if this method adequately controls for self-selection, the results imply that off-site

company paid training is portable across.jobs, and that workers. do not pay for this

training through a lower starting wage, in contrast to the predictions of the human capital

model.

These findings are somewhat different than that found by Barren, Berger, Wd

Black (1993) who found a small negative effect of current training on the starting wage,

md that of Lynch (1992) who found that company training is specific and not portable

across employers. These differences “maybe due to the different measure of trtining used

here as opposed to those used in the other studies.. For instance; Barren, Berger, and

Black use training intensity, or cumulative hours per week of training, because they argue

that this measure is less Ekely to be comelated with unobserved ability than totaf training

duration. Also Lynch, due to restrictions imposed by the data, uses only training

durations for programs which lasted over four weeks.

Specification (3) presents estimates when training intensity is included as tie

training measure ”rather than training duration. “me results are siruilm to the o~gr

specifications except that the positive impact of both current md past training on wages is

somewhat larger and more ,signific~t, suggesting that using the training intensify

measure rather hart training duration does not necessarily lessen any effect of unobserved

ability on the estimates. Specification (4) presents results which include only training

durations of fo.u~weeks or more. Simifar to Lynch, the estimates. indicate that these

training programs are “notportable across employem. Hence, some of the longer forms of

company training may be more fro-specific thsnr the shorter programs. Yet unlike

Lynch, who found that off-the-job training is primarily generaf, the results imply that offsite training which is not company paid is negatively related tq wages. These differences

indicate that proprietq institutions play a less impo~t role in enhancing wages at later

ages as opposed to when workers me just out of school.
Specifications (5) and (6) present estimates when the sample is stratified by

gender. The estimates suggest that men are likely to experience a positive association

between current ON-CP and starting wages, while women are not. Yet women who

undertake on-site training at their own expense or acquire funding outside the firm

receive a higher starting wage than other women. Also; previous 0~-CP appears to be

particularly portable among women compared to men.

Mthough the two-stage method is an attempt to control for unobsemed factors

which may bias the estimated training coefficients, a first-dfferenced fixed-effects log

starting wage specification Mght be considered “cleaner” since it does not rely on the

nature of the training receipt probit and the instruments used in that equation. Table 3

presents results from estimating first-dtiferenced log starting_wage equations for those

employed in 1992 and who changed jobs at least twice horn 1986 to 1992, which is about

hdf of the sample (2,237 out of 4,309). ]6 Similar to the results for the wage level

estimates, current ON-CP is strongly positively related to starting wages, although current

0~-CP is unrelated to starting wages in the change specification. The other forms of

current tining are u~elated to the change in the starting wage. Hence, the estimates

provide no evidence that training lowers the starting wage.

Similar to the wage level results, previous O~~CP is positively’ related to the

change in starting wages, suggesting that company financed off-site trairilng is portable.

The chmge specification also indicates that prior on-site traihing which was paid by the

individud or other non-company sources has little benefit at future jobs and is actually

negatively related to a change in the starting wage. Somewhat su~risingly, change in

experience has no significant impact on the change in starting wages. Thus previous offsite company paid training appears to be more valuable across j obs than other forms of

training as well as past work experience.

Since the f~st-differenced estimates me based upon a subsample of people who

change jobs multiple times, or are more Wely to be “movers,” the results in specification
12

(2) include a selection term which accounts for the possible nonrandomrress of the

subsample. The selection term is positive and significant, indicating that the firstdifferenced specification does not completely remove the effects associated with

movements across jobs, and that “movers” gain, which is consistent with findings by

Topel and Ward ( 1992).” Despite the significmce of the selection term, the estimata, on

the training variables md the other variables are otiy slightly altered when using the twostage approach.

Estimates .fFom specifications stratified by gender are presented in columns (3)

and (4).17 Similar to the starting wage level results, the estimates indicate mat there is a

pOShiVe association between current ON-CP &d starting wages “form~es, but not for

females. Changes in previous 0~-CP are positively related and chmges in previous

ON-OP ae negatively related to starting wages only for mafes. kcreases in work

experience, which may partiafly reflect the extent of previous training, have a positive

impact on wage change for women, but not for men. However ud.ike the wage level

results, no form of training” is significantly related to starting wages for fedes .in the

change specification. TMs gender difference in the effects of prior training is similar to

findings by Booth (1993) for British graduates, who finds that men receive training which

is more portable across jobs thm women. The difference between the wage level and the

wage change results suggests that heterogeneity may play a larger role when examining

the relationship between wages md training for women th”m for men.

Hence, both the starting wage level and the wage change estimates indicate that

there is not a negative relationship between stiing wages and current company trai~ng.

If anything, starting wages “andcompany training appem to be positively related. Mso,

the data indicate that off-site company paid training is portable across employers, or is

general. Taken together, ~ese results suggest that firms, rather than workers, pay for

general training: which is inconsistent with the standard human capital model.

—
W. TRA~G ~ WAGE GROWTH

The impact of training on wage”growth is estimat@ using the specification:

(3) in (WC/W5)= ypTp + ycTc + e Y + v

where Wc is the current wage at the 1992 job, TP Tc, are defined as in the previous

section, Y is similar to the previously defined X vector but rdso includes tenure at the

current job, and v is the error term. The human capital model predicts that current

training is positively related to wage growth (yc > O), while previous training should have

no impact on wage growth (YP= O). Similar to the starting wage equation, the estimated

impact of training may be affected by unobserved factors such as ability and motivation.

In order to address tis issue, specifications which include the selection term generated

from the training receipt probit discussed in the previous section are rdso estimated.

h addition, the following fwst-differenced fried effects model is estimated:

(4) Aln(wc/ws)= ypATp+yc ATc+OAY+Av

where the dependent variable is the change in log wage growth between the 1992 job and

the job held irrrrne&lately prior to the 1992 job, and the independent variables also

represent changes between jobs. Again, since change in prior training only occurs for

those who change jobs multiple times, this equation is only estimated for the subsarnple

of “movers” and the results from a specification which includes a selection term based

upon job mobility is presented.

The results from estimating equation (3) are presented in Table 4. The estimates

indicate that current company paid training, both on-site and off-site, is positively related

to wage ~owth, which is similar to the findings from most prior studies (Altonji and

Spletzer 1991; Barren, Black, and hewenstein 1989, 1993; Brown 1989; Duncan and

Hoffman 1979; Mincer 1988). Also, tenure on the curreflt job, which may reflect the

extent of iuformrd training, is positively related to wage growth. The implied elasticities

indicate that a ten percent increase in tenure increases wage growth by .3 percent, while

0~-CP and ON-CP do so by slighfly less than.2 percent and. 1 percent, respectively. k
addition, as predicted by the human capital model, prior training is unrelated to wage

growth, as are education, prior experience, and ability.

These results, along with the findings on starting wages, suggest that fums use

education, experience, and ability primarily to offer competitive starting wages. After the

worker spends some time with the firm and the employee is trained, wage increases

within the firm are largely a function of tenure and trtilng. Hence, while education,

experience; md ability may serve to gain access to jobs and to receive training

oPPofiunities, fiey do not necesstily improve wage growth.

Estimates from a specification wtich includes the selection term generated from

the previous described training receipt probit are presented in column (2). Stilar to the

stating wage regressions, the training receipt selection term is insignificant, Wd the

results are only slighfly altered when the .terrn is included in the wage WOwti equation.

Regressions stratified by gender, presented in specifications (3) Md (4), indicate that

current ON-CP. is particularly effective in increasing the wages of females, while 0~-CP

has a significant impact on wage improvement for both men and women. For males,

previous ON-CP is negatively related to wage growth, which likely OCCUrSbecause fis

formof.tmining primarily affects starting wages rather than current wages.

Table 5 presents results from estimating the first-differenced wage growth

equations .18 Similar to tie previous wage growth estimates, specifimtion ( 1) indicates

that changes in current OFF-CP are positively related to wage growti. However, unlike

the previous results, the change specification suggests that changes in current ON-CP are

unrelated to wage growth. Mthough changes in tenure are positively related to the

change in wage growth, the magnitude of the impact of tenure is actually less than that of

current OFF-CP. A ten percent increase in previo”us OFF-C.P increases wage change by

over.3 percent, while a similar increase in tenure increases wage change by about .2

percent.
15

Changes in current OFF-OP are negatively related to wage groti, and given the

results from Table 2 which indicate tiat previous OFF-OP is negatively related to the

starting wage, there is Iitfle evidence of any type of return to this form of tmining. k

particular, individuals who receive trtilng from business schools or vocational schools

and who self-pay or receive finmcing Wough other sources appear not to receive any

improvements in starting wages or in wage growth from the training.

Specification (2) includes a selection term to account for the fact that the f~stdifferenced estimates are restricted to those who have changed jobs multiple times from

1986 to 1992. Similar to the starting wage change results, the estimates on the training

variables and tie otier variables are ody slightiy altered when using the two-stage

approach. Yet unlike the starting wage estimates, the selection term is insignificant,

suggesting that the first-differenced approach eliminates unobserved differences -

associated with job mobility.

Spaifications (3) and (4) present separate estimates by gender. For both men and

women, ch~ges in cuqent .O~-CP are positively related to changes h wage growth.

Females who experience increases ti ON-CP dso undergo increased wage growth,

although the magnitude of the impact of this form of training is relatively small, as it is

less than hdf that of OFF-CP. Among women, the impact of both these forms of tmining

ae greater than that of tenure, which is unrelated .to changes h wage growth. Also for

females, there is”a negative relationship between changes h previous ON-C”P and changes

in wage ~owth.

Thus, the wage growth estimates are for the most part consistent with the human

capital model. Training which is company firrmced has a positive impact on wage

growth independent of tenure at the current job. Aso as predicted by the model,

previously accumulated human capital is either unrelated or negatively related to wage

growth. The most curious result is that the type of company training which is particularly

effective in enhancing wages takes place outside the work place. Ttis result is
16

particulwly interesting given that this form of training appears to be the most general.

Hence, wtie companies appear to fiance training that provides skills which are use~

both within and across firms, this training may differ from what is cornmordy considered

as “on-the-job” training.

V. CONCLUSION

This” study uses recent data from the National Longitndind Survey of Youth to

extine predictions of the human capital model concerning the relationship between

training and wages. The restits do not support the conventional version of this model.

While current company training is positively related to wage growth, the data indcate

that workers do not pay for company training through a lower starting wage. Mso, offsite company paid training is portable across employers, or is general.

Why does it appear that employers pay for general training? What do ~ese resdts

mean for the tradition human capital model? There are a number of possible

implications. First, the conventional human capital model might still hold we if there

remains unobserved factors which affect the estimates. h particular, if the highly able

and tie most motivated are the ones who are trained, receive higher starting wages, and

experience greater wage growth than others, these unobservable could be tiving the

results. Yet this explanation is doubtful given the multiple metiods used here to control

for heterogeneity, which includes the use of a particularly rich set of explanato~ variables

and the estimation of a two-stage treatment effects model as well as a fixed-effects

specification.

Second, it maybe the case that workers pay for training through reduced nonwage compensation rather than through a lower starting wage. For instance, health

benefits, vacation days, sick days, or other non-wage components maybe altered by fiis

in order to finance the provision of training. This hypothesis is difficult to test using the

NLSY given that it provides no information on the receipt or value of fringe benefitk.]g
17

Evidence from Barren, Berger, and Black (1993), however, indicates that there is no

relationship between training receipt and the provision of fringe benefits.

Third, the tradition human capital model might be altered sfightiy by introducing

factors such as uncertainties about the usefulness of skills, the transactions costs of

moving, or some form of implicit contracts. Fm will provide trtining if they how that

they can somehow recoup the costs of this training, regardless if the training is specific or

general. Since most. company training programs are relatively short, the costs of these

pro~ams maybe relatively low. So for example, if employers can increase job tenure by

a small amount by offefig workers a post-training wage which is less than the v~ue of

marginal product but greater than the aftemative wage get of me transactions costs of

moving, firms could potentially recoup training costs without lowering the starting wage.

Fourth, the traditionrd human capital model could be considered in conjunction

with other models, such as the matching model. For instance, the finding that traiting is

portable across employers may suggest that training partially serves to improve future job

matches. Potentird employers may view the skius of trained workers as less uncerttin

than that of untrained workers, and training may serve to improve workers’ knowledge

about the types of jobs for which their s~s are best suited.

Fifth, the results might suggest that the standard human capital model should be

completely abandoned in favor of other models which provide other explanations of

upward sloping wage profiles. In particular, Nerlof and Katz (1989) present a second

best model in which workers do not pay up-front employment bonds, but employers

utilize deferred payment mechanisms and must pay efficiency wages to minimize

shirting. This implies the existence of a dud labor market or of “good and ‘rba~ jobs,

and the good jobs are associated with higher starting wages, greater wage growth, and

more training. Data sources on training which provide detailed information on employer

practices as well as on the characteristics of workers will go a long way towards sorting

out the alternatives to the tradition human capital model.
Notes

1Mthough no training questions were included in the 1987 survey, the training

questions in the 1988 survey refer to afl training progrms dating back to the 1986

interview. Respondents were asked about training arrnuafly after 1988.

2In 1992, the Youth survey included oversarnples of blacks and Mspanics.

3 The ody type of training in which categorization is somewhat mbiguous is

apprenticeships. Apprenticeships often involve both on-the-job training along witi

course work which may tie place off-site. k this rmdysis, apprenticeships are included

in the “on-site” category, afthough the results for the most part are unaffected if

apprenticeships are considered “off-site” (95 sample members participated in

apprenticeships).

4 Another reason that the components of the “other paid” catego~ are grouped

together is because cell sizes within each of these components are relatively smafl,

particularly when subdivided into previous and current training. The primary component

of “other paid titiing is self or ftily (63.2 percent), followed by govemmen7.(14.7

percent).

51,217 individuals were dropped due to eliminating those with six or more yews

of tenure. Mean tenure arnongsample members is 118.M weeks.

6 Since ifidividurds can participate in more than one form of training, the overafl

percentages for the location or payer categories are stightly less than the sum of the

percentages in the table. For instice, 27.3 “percent received “on-site” traifing, while 33.2

experienced “compmy paid” training.

7 In creating the sample, observations that are clemly oufliers were omitted (abont

2 percent of the reported durations).

s Respondents can report earnings over any time frame (hour, day, month, etc.).

For those who do not report an hourly wage, one is constructed using usuaf hours worked
19

over the time frame. The ~1-U-Xl is used to convert d wages to 1992 dollars. The

average starting wage is $9.17.

g The WQT was administered to al respondents in 1980. Since individuals were

of different ages when they took the test, the score used in the regressions is divided by

the mean score for the respondent’s age.

10The ~stimtion~ information was merged by matching the first three digits of

zip codes of the occupational schools with that of ~SY respondents. The fnstitutiond

Chmacteristics Survey was sent to dl postsecondary institutions in the United States and

ouflying areas. Of the 10,760 institutions which were identified, 9,981 responded to the

survey.

11~hlle no single insmment~ vmiable is used to identify the job mobifity

selection term, identiflcatiori is actieved through the different nature of me speciflcatiorss..

The job mobility. probit is estimated using 1992 chmacteristics as independent variables,

while in the freed-effects wage regressions, the independent variables are in change form.

‘2 Since the duration of training variables can equal mro, the training vtiables

used in the regressions were generated by taking the natud log of one plus the training

duration.

133.2 percent of the sample were in training at the 1992 intewiew date. men the

“currendy in training” variable is divided into location and payer categories, tie estiated

coefficients on these variables are N insignificant.

14Estimates from the trtining receipt probit as well as from the job mobility

probit are provided in the Appendix.

15The use of a single selection term implicitly assumes that tie same unmeasured

characteristics affect participation in the different forms of training. Specifications which

include separate selection terms for “compmy paid” and “other paid,” as well as

specifications which include selection terms for “on-site” and “off-site” yield qrrtitatively

similar to those reported here, and the selection terms are insigntilcant.
20

16 ~ fie fwSt-diffefenCed e~”fi~ns, r~ults from parsimonious specificafi”ons

which include change in experience as the only control variable are reported. The

estimates on the reported coefficients are virtually unchanged when other variables, such

as the change in the local unemployment rate or change in mariti status are dso

included.

17 EStiates from specifications Wfich include job mobility selection te~s for

males and females are sitiar to the reported results and the selection terms are

irrsignific~t for both men and women.

18~itim to fie ~hmge in sting wage regressions, a Hmited set of con~ols

(change in experience and change in tenure) are included in the first-differenced wage

growth quations. Estimates from specifications which include additiond controls are

sitilar to those reported.

19The NLSY does provide information as”to whetier an employer makes

available certain fringe benefits to workers. Yet there is no information as to whether

workers actually receive any of the benefits or the value of the benefits.
References

21

Akerlof, George”A., and Lawrence F. Katz. “Workers’ Trust Funds and the Logic of Wage

Profiles,” Quarterly Journal of Economics, Vol. 104, No. 3, August 1989,525-36.

Altouji, Joseph G., and James R. Spletzer. “Worker Characteristics, Job Characteristics,

and the Receipt of On-the-Job Training, ” Industrial and hbor Relations Review,

Vol. 45, No. 1, October 1991,58-79.

Barren, John M., Mark C. Berger, and Dan A. Black. “Do Workers Pay For On-the-Job

Training,” Working Paper, University of KenWcky, 1993.

Barren, John M., Dan A. Black, and Mark A. Loewenstein. “Job Matching and On-theJob Training,” Jouwl of hbor Economics, Vol. 7, No. 1, January 1989, 1-19.

Barren, John M., Dan A. Black, and Mark A. Lowenstein. “Gender Differences in

Training “Capitrd and Wages,” Joirrral of Human Resources, Vol. 28, No. 2,

Spring 1993,343-64.

Becker, Gary S. “hvestment in Human Capital: A Theoretical Analysis,” Journal of

Political Economy, Vol. 70, No. 5, Part 2, October 1962,9-49. “”

Booth, Alison L. “Private-Sector Training tid Graduate Earnings,” Review of Ecoflomic~

and Statistics, Vol. 75, No. 1, February 1993, 164-70.

Brown, Charles. “Empirical Evidence on Private Sector Training,” in Investing in People,

Vol. I, WasMngton DC: Commission on Workforce Quality and ~or Market

Efficiency, U.S. Deptiment of Labor, 1989, 301-30.

Brown, James N. “Why Do Wages Increase with Tenure? On-the-Job Training and LlfeCycle Wage Growth Observed Within Firms,” American Economic Review, Vol.

79, No. 5, December 1989,971-91.

Bureau of Labor Statistics. “Work and Family: Jobs Held and Weeks Worked by Young

Adults,” Report No. 827, August 1992.

Bureau of Labor Statistics. “Work and Family Employer-Provided Training Among

Young Adults,” Report No. 838, Febru~ 1993.

Carnevde, Anthony P., and Leila J. Gainer. me Learning Enterprise, Washington DC,

Employment and Training Administration, 1986.

Duncan, Greg J., and Saul Hoffman. “On-the-Job Training and E@ngs Differences by

hce and Sex,” Review of Economics and Statistics, Vol. 61, No. 4, November

1979,594-603.
22

Heckman, James J. “Sample Selection Bias as Specification Error,” Econometrics, Vol.

47, No. 1, January 1979, 153-61.

Jovanovic, Boyan. “Job Matching and the Theory of Turnover,” Journal of Political

Economy, Vol. 87, No. 5, October 1979,972-90.

Linear, Edward P. “Agency, Earnings Profiles, Productivity tid Hours Restriction,”

American Economic Review, Vol. 71, No. 4, September 1981,606-20.

Lynch, Lisa M. “Private-Sector Training and the Earnings of Young Workers,” American

Economic Review, Vol. 82, No. 1, March 1992,299-312.

Mincer, Jacob. “On-the-Job Training Costs, Returns, and Some hplications,” Journal

of Political Economy, Vol. 70, No. 5, Part 2, October 19~2, 50-79.

Mincer, Jacob. “Job Training, Wage Growth, and Labor TumoverY NBER Worting

Paper No. 2690, August 1988.

Parsons, Donald O. “On-the-Job Le-ing and Wage Growth; Wor&ng Paper, Ohio

State University, 1989.

Salop, Joanne, and Steven Sdop. “Self Selection and Turnover fi the Labor Market;

Quarterly Journal of Economics, Vol. 90, No. 4, N.ov.ernber 1979,619-27.

Topel Robert H., and Michael P. Ward. “Job Mobility and the Careers of Young Men,”

Quarterly Journal of Economics, Vol. 107, No. 2, May 1992,439-79.
Table 1. The Incidence and Duration of Training from 1986 to 1992,

Type of kaining

Percent Wo

Reaivd Trtining

On-sik, Cornpmy ptid (ON-CP) 25.41

On-sit., other paid (ON-OP) 3.41

Off-site, company paid (0~-CP) 14.60

Off-kite, otier ptid (0~-OPj 16.20

Note The number of obsmvations is 4309.

Dmtion in hours amongrecipien~

Stantid

w Deviation ..=

162.46 347.29 50.00

151.95 347.46 32.00

130.39 “396.79” ..40.00

450.73 682.95 160.00
Table 2. Log Stting Wage Recessions.

Vmiable

Previous:

ON-CP

ON-OP

om-cP

om-oP

Cmenr

ON-CP

ON-OP

OW-CP

om-oP

Cuently in kaiting

Retious expedience

Education

MQT

k (training probit)

COnsmt

(1)

M

.009

(1.55)

.005

(.3s)

.02?**

(2.44)

-.013$*

(3.02)

.012**

(2.42)

.020

(1.19)

.015**

(2.14)

.001

(.06)

-.020

(.4s)

.074s$

(6.S7)

.464**

(9.01)

.117**

(4.59)

.174

(.34)

(2)

u

.009

(1 .47)

.005

(.39)

.023**

(2s1)

-.012**

(2.44)

.013**

(2.18)

.020

(1.20)

.016**

(2.07)

:001

(.15)

-.019

(.44)

.074**

(6.91)

.464**

(9.06)

.117**

(4.55)

-.002

(. 14)

.050

(.32)

Specification

(3)

~

.014**

(2.00)

.014

(:s5)

.043**

(4,40)

-.021””

(2.84)

.017**

(2.63)

.020

(.99)

,02S**

(3.22)

:008

(.65)

-.019

(.46)

.073**

(6.79)

.455**

(8.84)

.114**

(4.49)

.163

(:32)

(4)

a

.W6

(.39)

-.026.

(,79)

-.019.

(1.00)

-.02S**

(3.10)

.001

(.0s)

.092*

(1.68)

-.005

(.30)

-.004

(.2s)

-.00s

(,19).

.076**

(7.0s)

.473”*

(9.16)

.130**

(5.10)

.173

(.34)

(s)

m

.012

(1.601

.021

(1.17)

.016

(1.57)

-.016**

(2.48)

.020**

(2.71)

.001

(.05)..

.013

(1.39)

-.m4

(.3s)

-:060

(.99)

.087**

(4.s9)

.371””

(4.s7)

.119**

(3.37).

.469

(.s3)-

(6)

.003

(.40)

-.01.7

(.99)

.035”$ . . x..

(3.10)

-.013**

(2.35j

.003

(.48)

.0S4”*

(1.96)

.015

(1.37)

.002

(.1s)

.030

(.62) _.—

.0S7**

(4.32)

.S67””

(8.11)

.127**

@.40)

.044

(.19)
Table 2 (Cont.)

(1) (2) (3) (4) (5) (6)

u ~ u u w ~

gz .31 .31 ,31 .30 .26 .33

Number of observations 4309 4309 4309 4309 2221 2088

Notes N~bers in parentheses ae t-statistics, Adtitiond covtiat~ i“cl”de sex, race/etb”icitY, union,

SMSA, fim size (3 vtiables), mmid, healthy and indust~ md occupation dummia (see Appendix for

mems). All continuous vtiablm me in logtithmic form. In specifications (1), (2), (5), and (6), the

training v~iables refer tO log hours. In specifimti.on (3), the mtining vwiables refer to log kaining in~nsity

(cumulative hours per week). In spwification (4), tie training vtiables to log weeks only for tiose

durations of four week or mor%

* Statistically significmt at the 10-percent level.

‘* Statistically signifiwnt at the 5-percent level.
Table3. Ftied-Effect Chmgein Log St@kg Wage Regressions.

Vtiable

A Previous:

ON-CP

ON-OP

om-cP

om-oP

A Cument

ON-CP

ON-OP

om-cP

om-oP

C~ently in tiaining

A Experience

k (mohlity probit)

COnstmt

~z

Number of obsmvations

(1)

M

.011

(1.46)

-.040**

(2.14)

.025*

(1.91)

-.016

(1.21)

.023**

(3.32)

.003

(.11)

.009

(.86)

.011

(1.01)

.W5

(.69)

.0002

(.82)

.079”*

(3.68)

,01

2237

Specification

(2)

.010

(1.22)

-.039**

(2.13)

.025**

(1.98)

-.017

(1.31)

.022”*

(3.04)

.002

(.00

.00.8

(.77)

.011

(.95)

.039.

(.60)

.0Q02

(1.24)

.231.”

(2,76)

-.101

(1.47)

.01

2237

(3)

w

.015

(1.45)

-.044”

(1.76)

.032*

(1.86)

-.022””

(1.27)

.030**

(3.2oj

-.005.

(.17)

.011

(.77)

.014

(.88)

.056

(.71)

-.0001

(.40)

.092””

(3.12)

.01

1206

(4)

.006

(.52)

-.032

(1.11)

.017

(.87)

-.010

(.52)

.015

(1.45)

..o16

(.42)

.007

(.42)

.009

(.58)

.031

(.27)

,0005*

(1.68)

.063**

(2.02)

.01

1031

Note: Absolute value oft-smtistics meinpaentieses. Mlvtiabla except cumentiy innainingtidtbe

selwtivity tem me in logtitbmic form.

*Statistically significant at tie 10-pement level.

** Statistidly significtit at tie 5-percent level.
Table 4. Wage Growti Re~ssions.

Previouc

ON-CP

ON-OP

om-cP

om-oP

Cumenti

ON-CP

ON-OP

OW-CP

OW-OP

Cmendy in @aining

Tenure

Previous experience

Education

~QT

L (haining probit)

Constmt

(1)

u

-.005

(1.34)

-.mz

(.27)

-.001

(.11)

.001

(.38)

.009”*

(2.49)

.008

(.70)

.017**

(3.50)

-.002

(.43)

.007

(.25)

“.030**

(6.76)

-.007

(.92)

.031

(.90)

.014

(.82)

-.413

(1.X)

Specification

(2)

~

-.W5

(1.28)

-.002

(.29)

-.001

(.15)

.001

(.24)

.008*$

(2.10)

.008

(.69)

.016**

(3.26)

-.m3

(.54)

.006

(.22)

.030””

(6.79)

-.007

(.92)

.030

(.89)

.015

(.86)

.011

(1.10)

.129

(1.24)

(3)

w

-.OIO**

(1.99)

.001

(m)

-,002

(.37)

-.002

(.49)

.005

(1.02)

.017

(1.23)

.015**

(2.47)

.003.

(.47)

.022

(.57)

.032**

(5.31)

.003

(:24)

.061.

(1.27)

.010

(.43)

-565,

(1.58)

(4)

.002

(.32)

-.W6

(.51)

-.001

(.09)

.004

(.93)

.012**

(2.32)

-.m9

(.45)

.o19**

(2.47)

-.004

(.62)

-.W9

(.22)

.027**

(4.10)

-.014

(1.44)

.010

(.20)

.026

(:98)

.002

(.01)
Table 4 (Cont.)

(1) (2) (3) (4)

& M u ~

~2 .04 .04 .04 .05.

Number of obsemations 4309 4309 2221 2088

Note: AbsOluk value Oft-statistics aeinparenti~=. Additional covtiatesme thesmemin TabIe2.

Allvtitiles except cwently inkaining andtieselectivity temaeinlogaitiic form. =

*Statistitidly significmt at tie iO-percent level.

**Statistically significant at the 5-percent level.
Table 5. Fixed-Effect Wage Growth Re~essions.

Variable

A Previous:

ON-CP

ON-OP

om-cP

om-oP

A Current

ON-CP

ON-OP

om-cP

om-oP

Cunently in Training

A Tenure

A Experience

Z (mobility probit)

COnstmt

~z

(1)

&

-.009

(1.28)

.019

(1.14)

.018

(1.50)

-.015

(1.24)

.005

(.80)

.013

(.59)

.034*”

(3.35)

-.017”

(1.68)

-.017

(.29)

.023**

(3.22)

-.0003

(1.60)

.0S2**

(3.s9)

.02

Number of observations 2237

Specification

(2)

a

.,~og

(1.31)

.019

(1.15)

.01s

(1.52)

-.015

(1.25)

.005

(.78)

.013

(.59)

.034**

(3.35)

-.017*

(1.68)

-.018

(.30)

.022””

(3.08)

-.0003

(1.58)

.033

(.44)

.057

(.93)

.02

2237

(3)

w.

.001

(.04)

-.007

(.30

.015

(1.11)

-.016

(1.17)

-.008

(:98)

.156

(.61)

.028**

(2.47)

-.019

(1.55)

“.001”

(.01)

.031**

(3.79).

-.0001

(.51)

.065**

(2.57)

.02

1206

(4)

-.021*

(1.70)

..047

(1.59)

...020

(.98)

-.014

(.68)

.oi9*

.(1.78)

.Om.

(.53)

.042**

(2.34)

-.017

(1.02)

.060

(.5@

.013

(1.05)

,.0M5

(1.57)

.097**

(2.78)

.02

1031 ...

Note Absolute value oft-statistics tie in pwentheses. All vtiables &x&pt cumntly in training and the

selectivity term =e in Iogzitimic form.

*Statistitilly significant at the 10-permnt level.

**Statistically significant at tie 5-percent Ievel.
Appentix

~

Previois experience (h weekij

Education

Score on &mcd Forces Quahfying Test

(AFQT)

Estiblishent size

Multiple site firm

Over 1000 employees at other lo~tion

Loc& unemployment rate

Male

Black

Hispanic

Union mem~r

Reside in SMSA

Mmied

Healthy

Professional ad Techticd

Managers

=

391.03

13,00

64.83

477.95

.61

.34

7.90

.52

.29

.19

.15

.77

.53

.94

.17

.10

Trtining Receipt

m

.0001

(.08)

.024**

(2.00)

.687**

(7.89)

.ml

(.90)

.145**

(2.83)

.034

(.65)

-.W1

(.06)

.008

(.17)

.167**

(2.97)

.03 I

(.52)

.059

(1,00)

.047

(.86)

.096**

(2.31)

-.124

(1.49)

.306**

(2.86)

.511**

(4.68)

JobMobility

w.

.Mi**

(8.41)

.030*.*

(261)

-.114

(1.35)

-ml

(1.2ij”

.117**

(2.33)

-.075

(1.45)

-.W

(.51)

.068

(1.50)

.073

(1.35)

.003

(.06)

-.117**

(2,05)

-.128

(.26)

-.175**

(4.37)

-.098

(1.22)

-.034

(.34)

-.118

(1.13)
AWendx (Cont.)

~

Sdw

Clencd

Operative

Ctis Workem

Service ad Wvate Household

Agdculture and Mining

COnsuuctiOn

Tmspo~tion

Wholesale and Remil Trade

finance

Busin~

Pemonal Sctices and Ente-inment

Rofessiond Services

Public Adminis@ation

Nuber of occupational schools in

10dity

&

.05

.18

.14

.11

.17

.03

.07

.06

.18

.06

.08

.06

.22

.06

20.22

Enrollment of Imgest occupational school 9299,50

in loctiI~

Ttining Receipt

w

“.501**

(3.83)

.239”*

(2.34)

.108

(1.09)

.331**

(3.27)

.356**

(3.49)

-.239”

(1.82)

-.416**

(4.16)

.209**

(2.28)

-.212**

(2.93)

.317**

(3.07)

.107

(1.23)

-.189*

(1.85)

.012

(.16)

.437*”

(4.21)

-.ms

(.50)

.0001”*

(2.26)

Job ~mge

m

-.114

(.91)

.049

(,51)

.042

(.45)

-,065 .. ... ....

(.69)

.077

(.80)

-.191

(1.58)

.113

(1.21)

.017

(.20)

-.007

(.10)

-.043

(,44)

-.058

(.68)

-.137

(1.38)

..089

(1.23)

-.086

(.86)
Appendix (Cont.)

Trtinirig Receipt Job Moblfity

~ h w m

Constant . I;5z’* -.423 **

0.51) (2.20)

Log-likelihood -2727.0 .. -2983.6

Number of observations 4309 4309

Notw: Absoluk value of t-statistics we in puentb~es. me otitted occupationd”catego~ is laborers md

f-em and tie omitted industiid categOW is maufactting.

*StafisticdIy significant at the 10-percent level.

**Stifisticdly significant at the 5-percent level.
-

92-01

92-02

9243

9244

9245

9246

9247

9248

9249

92-1o

92-11

92-12

92-13

National Lon@tidinal Sumeys (NLS)

Discussion Paper Series

Nom M. Bmdbm

Hn R. Frdel

Re~ndd P. B&er

Mckel R Pergtit

Sard SchwRobert Hutchem

George J~bson

A. COli Cmeron

R Mak Griti

Them MaCudy

Heq S. Faber

Frti L. Mott

Patia B&er

Au&ey Light

Mmliw Urem

Lisa M. L~ch

Evagelos M, Fdaris

H. Eliabeti Peters

be fill

Jwe E. O’Ne~

Aleen Xlbotiti

Jacob Nex ~ema

Lti& Wtite

k A. Lillmd

Joseph G. Mtonji

Thom~ A. hm

~

HOWthe Fedad Govement Uses Data from

the National Longimdiml Smeys

A Comptision of Compukr-Assisted PusonaJ

Intetiews (CAPI) wkh Paper-~d Penal Interviews @@I) in the NationaJ Longimdkrd SumW

of Youth

D@c Models of the Joint Detetimtion of

Lsbor Supply and Fatily Stitie

The effds of Unempl~ent Compensation

on the Unemployment of Youth

Evaluating Competing Theories of Worker

Mobility

Evaluation of the 1989 Child-e Supplement

in tie Natioml Longitidind Sumq of Youth

@rider Differences h the Qtit BeMtior of

Yowg Workers

The hpact of Private Sector Trtifing on Wce

md Gender Wage DifSerentids md the Caeer

Pakm of Young Workers

Respomes of Female hbor Supply ad Fetili$

to the Demo~aphic Cycle

A S~dy of Intercohofi Chmge in Women’s

Work Patterns ad Etings

Women’s Empl~ent Dting Pre~q

md Folloting Bifi

Work E~erience, Job Tenme, Job Sepmatio&

md Wage Groti

Ftily Backgroud and Labor Mmket Outmmes
92-14

92-15

93-16

94-17

94-18

94-19

95-20

95-21

95-22

95-23

95.-24

95-25

95-26

95-27

95-28

96-29

Gmrge J. Bmjaa

Stqhen G, Bro~

StWhen J. Trejo

Sti-Seletion md Intefi Migmtion h the

Urdted Swt=

Jaes J. H=-

Stephm V. tieron

Peter Z. Schochet

Au&q Light

Ctistopher J. Rti

Mmk Lowetieti

Jaes Spletier

Jacob Alex ~em

Jamb Mex Hem

klem Leihtiti

Stqhen G. Bromti

hoI Moore

Dodd 0, Wrsons

h BMel

Nachum Sichea

Auhy Light

bwa Brmden

R Wk Griti

Michel R Pergtit

ThomasDw

Dou@s Holti-Etin

The Detetimts ud Co~uenws of ~bEc

Swtor md Private Se&or Trtitig

Phtipation in Low-Wage hbor Mketa by

.Yomg Men

Retkemmt in a Ftily Cmrte~ A Stmtid Mdel

for Hmbmr& md Wives

Tmsitions from School to Work A Swq of

Resewch Using the National Longitoditi SU~

figh School EmpIo~ent Con~ption or

lnves~ent

ItiomaJ Traifing A Rtieti of Etiting Dab and

Some Na Etidence

Chamderiting Leave for Mateti&. Modeling the

NLSY Dam

Empl~mt Continuity hong New Mothem

hcentive Pay, Momtion, md Eti~ Etidence

fmm the Natioml Lmr@mdinaI SWq of Youth

The Evolting Stitmes of Fetie Work Aditities:

Etidenm fmm the Natiod Longimdtid Sueya of

Wtwe Women SWq, 1967- 1989..

Pmeq mm “~ong Matie Womex Etidence

from the Natimmf Lon~mbal S-s, 1967-1989

Tectiologicd Cbge mrd the S~ Aqtisitimr of

Young Workas

Wgh School Empl~ent

The Effect of Intetiew Lmrgth on Atition in the

NatimmJ Longitidlnd Suwey of Youth

&pitd Mwket Cmrmaints, P~enM WmkJr and tie

Tramition to seK-Empl~ent mong Men ad

Women
96-30

96-31

Jeff Gogger

Nick WH

JOMtim Vew

The Intergenerationti Effects of Fatierlmsness on

Educatioti Atiment and Ent~-Ievel Wag~

Trtitig, Wages, ad the Humm Capi@l Model