me National LoMiti&l Smqs ~S) ~ga mppofls my stidm Aimed

to tiwase udmstidmg of tie labor mket md metho& of && co~ection. ~e

Discumion Papers ties timqorates some of the r=-h mtig out of tie pro~a.

Wy of the papm ti tis seriw have b=n deuvmed as fid rqofls cotissimed =

pti of m ewmti ~mt pro~m, wd~ whifi ~ts we aw&d tiough a

com~titive process. me sti~ k tietied h ckctiate the fitigs m titq~ted readers

titi md oukide the Bw=u of Lahr Statistics.

Pasom ti~ested h obtig a wpy of my Di=usion Papa, otha NLS repotis,

or gmad Xomtion atiut the ~S pIo~ md meys shotid contict the Bmmu of

bbor SQtisti~ Ofice of Ecmotic Reseach, Wastigtom DC 202124001, or dl

(202) 606-7405.

Mattid h this publication is k the public domti ad, tith apmopriate ae&L

may be =produced titiout ptisSon.

Qitions md conclusion e~r~sed h MS d-ret =. those of the .uti@s) ad

do not n-stily re~esmt m 05cid psition of tie Bmau of khr Sbtitics or tie

U.S. Dqtient of Labor.
Technological Change and the Still

Acquisition of Young Workers

Ann P. Bartel

Nachum Sicherman

Columbia University

April 1995

The autiors maybe contactd by e-mail CC arrn.bartel@crd~bia.4u md nachum.sichermm@mltiia, edu.

Ttis paper ws funded by the U.S. Department of Labor, BUUU of Labor Statistics ooder smail purchase order,

The tie~vs ~ressed hwe are tiose of tie autiors md do not ntisstiy reflect the tiews of the U.S.

Deptient of Labor.
Technological Change And me Skfll AcquWltion Of Young Workers

Ann P. Bartel and Nachum S1cheman

fiecutive Summa~

~ this paper, we investigate the impact of Echnologicfl change on young workers’

investments in on-thejob training. Human capiti theory dom not provide a clear prediction

on the sign of this relationship. Although higher .rates of obsolescence will d~rease training

investments, technological change may increase the productivity of human =piti, reduce the

cost of training, or incrae the v~ue of time in tinning relative to work. Hence, empind

analysis is needed to determine whether young workers receive more or less on-the-job

training in response ~ technological change, and, in particula, how this relationship depends

on the worker’s edumtion level.

The major problem with earlier work on training and technological change is the

limited information on training that was available to the researchers. We use the Nation4

tingitudind Survey of Youth (~Y) which is unique in terms of the comprehensiveness of

the training information that is reported. Unlike other datasets, it includes detailed.

information on all formal training spells experienced by the individud, including the actu~l

duration of the training. With this dataset, we can conduct a more comprehensive and

reliable study of the training effects of technological change. The ~SY has theadded

advatage of covering the time period 1979 through 1992 enabling us to provide a more

current andytis than previous studies.

The second way in which we improve upon previous research is by utilizing a variety

of measures of technological change. Estimating the rate of technological change fat@ by the

.-
L

worker in his job is very difficult. Since the mmsurement of twhnologicd change outside

the manufacturing sector is very problematic, our analysis is restrictd to workers in

mmufacturing. Eva” witiln this sector, however, no single proxy is likely to be perfect.

We, therefore, link the ~SY with several dtemative datisets that contain proxies for

industries’ rates of technological change. Specifically, our analysis uses the Jorgenson

productivity growth series, the ~ER productivity data, the Census of Manufac~res series

on inv=tment in computers, the R&D/des ratio in the industry, tie industry’s use of

patents, and a measure of the rate of “innovation obtaind from a survey of R&D managers.

Previous studies on training md technological chmge relid solely on the Jorgenson

productivity growth series. Our msdysis enables us to examine the robustness of dtemative

maures of Iechnologicd change, tiereby increasing confidence in the resul~.

~Ird, unlike the wher resmch, we carefully dissect the relationship between

twhnologicd change and training in order to answer the following questions: (1) HOW does

technologid change affect tining investments for workers with different levels of

education? (2) Does twhnologicd change incrae both entry-level training and training of

more experienced workers? (3) Does the pool of trainees incr=se in “res~fise to

technological change, or is it mainly the previously trained workers who tr~rr more

intensively?

Our econometric analysis is restrictd to company training because three-quarters of

private-sector training is providd by the firm. In order to estimate the effect of

technologid change on the likelihood of company training, we adopt a Iogit framework and

include in the regr=sion tie following additionrd variables: mariti shtus, race, yws of
3

education, midence in an SMSA, y-s of experience and its square, tenure and its square,

union membership, whether or not the individud is employd by a large firm, the industry

unemployment rate, union coverage in the industry, and job crwtion and destruction in the

industry. The latter three variables are included be=use, with the exception of the R&D

variable, we use a fixed time period measure of technological change which may act like a

fixti effect for mch industry, capturing other fixed attributes (such as unemployment,

unioni=tion and job creation and destruction) of the industry.

We found that all six proxies for technological change have a positive and

sign~lcant effect on the ticidence of training in the manufacturing sector, indicating that

the negative effect of technologid change due to the incrwxe in the rate of depreciation is _...

outweighed by the positive effects relating to incrmed productivity of human capital,

rtiuctions in the cost of training, smd/or incr~ses “in the value of time in tr+ning relative to ,

work. The impact of technological change on the incidence of training is larger for

production workers than non-production workers.

An important finding is that technologiml change attentuatw the impact of

edu=tion on training. Although more ducatcd workers are more likely to receive

company training, the training gap betwmn tie highly educated and the less educated is

narrower at higher rates of tcchnologicrd change. This may occur because the general skills

of the more edumted enable them to more easily adapt to technologic~ change. This finding

shows that the recent increase in the observed mings gap between the highly educated and

less educated is not due to technological chmge producing a widening gap in the acquisition

of post-schooling human capiti. If anything, tichnologicd change has acted to reduce the
4

gap in the stocks of human capiti accumulate through forrnd company training by different

education groups.

Although the measured effects of the technological change variabla are larger for

individual with I& tkan one year of tenure, au of the technological change proxiw

have positive and signY1-t effects on longer-tenured workers as well. Ongoing

technological change results in training of workers beyond their first ym of tenure.

Technologi=l change increases training at the extensive margin, i.e. increasing

the pool of traineea. A Tobit model of hours of training was estimated which showed that

the positive effect of tmhnolo~d change on hours of compfiy trammg IS due largely to the

. . ..- .

increase in the incidence of training, not the number of hours per training spell. In addition,

by utilizing the panel nature of the ~SY, we analyzed whether higher rates of technologic~

change induce firms to provide training to individuals who have already received training or

to those who did not receive training in the prior period. The results show insignificant

effects of technological chmge for previously trtined workers and significant effects for

individds who did not receive training in the prior year, indicating that the increased

incidence of training attributable to technological change occurs bause different individurds

are now receiving training.

—
I. Introduction

The process of human capital accumulation during the early employment experiences

of young workers has always been an area of active research and concern for policy makers;

the human capital investmerits that occur dui~rrg th&e early years shape an individual’s ~~r

and impact the future productive [y of d~e labor force. In an economy characterized by

increasingly rapid technological change, the sttldy of the process by which young workers

accumulate human capital is especially relevant. Nluch of the current debate on the skills gap

in the workforce rei,olves around the question” of whether general OTspecific knowledge is

more valuable in a rapidly cha]lging eni,ironment. Indeed, increasing wage inequality

between college and high school graduates might be interpreted to suggest that the status of

less educated workers will deteriorate wi[l~ the pace of technological change. T“his prd]ction

ignores the impact of”technolo:icil chanie on the post-schooling investments of-different

education groups; without such a study we tail not explain how technological change will

influence the wage gap between the more and less educated.

In this paper, we investigate the i-mpac[ of technological change on young workers’

investments in on-the-job training, HLIman capital theory does not provide a clear prediction

on the sign of this relationship. Allhough higher ra[Es of obsolescence will decrease training

invmtments, technological chan:j may increase tile-productivity of human capital, reduce the

cost of training, or increase the valt[e o“f[ime in trainin: relative to work. Hence, empirical

analysis “is nmded to .determi:e whether ymlng workers receive more or less on-the-job

training in rtsponse to technological ct)iulge. and, in”partici!lar, how this relationship depends

on the worker’s education Ie\,el.
2

fionorn~sti ha~,e long been interested in the impact of technological change on the

labor market. In the 1950s, the Bureau of Labor ~tatistici began its case studies of the

impact of “automation ‘“”-onemployment, More recently, researchers’ attention has focussd

on the effect”of technological change on the wage structure (Lillard and Tan, 1986; Mincer,

1989; Allen, 1992; Krueger, D93; Rerman, Bound and Griliches, 1994), the demand for

educated workers .(BarteI and Lichtenberg. 1987, 199 1): inter-co~lntry differences in wage

structures (Mincer and Hi:uchi. 1988) and retirement decisions of older workers (Bartel and

Sicherman, 1993).” “BuT,only iw;o”studies, Lillard and Tan (1986) and Mincer (1989) have

considered the impact of technological] chan:e. on young workers and both of these papers

have limitations which otlr pap&r overconi~s. 1

The major problem wilt) earlier \vork on training and technological change is the

limited information on tralnlng” thar \vas av:iilablc to the researchers. We use the National . .

Longitudinal Suwey of YOLIII1(NLSY) \rhich is uni(Iu~ in terms of the comprehensiveness” of

the training information that is repor[e(i. Unlike o[her datasets. it includes detailed

information on all formal training spells experienced by [he individual. inc]uding tile actual

duration of the training. ~.W’ith “this datasct. \ve ciIn conduct a more colnprehensive and

reliable st~ldy of the train in:. effec[s of [cchn~ll~~~ici~lchange, The N“LSY has the added

adirantage of covering the time peri[>d 1979 Ihrc>l!gtl 199?”eniblin: us to provide a mo-re

current analysis than pre”vio~]sst~!dies.

The second way in which we impro~,e upon pre\,ioLls research is by utilizing a variety

of measures of Iechnologcal change. Estimating the rate of technological change faced by the

‘In order to study the training exp<ric”c.t.s of y{,t,ng wt)rkers, Lillard and Tdn ( 1986) used the CPS a“d the

NLS Samples of Youn: hfen ~nd )’<)llng Wonlcj>. \<h]lc h!lncer ( 19S9) ant%lyzed the young workers in the

PSID.

‘Although Lynch ( 1991 x!ld .1992”) ~I<ed tht NLSY d:tta [[>stlldy the deternlinxnts of private sector training,

her work did n[)t analyze the mlt pl:iycd hy [echno[():ic,tl ch:tngt. In ziddition. as we discuss in Sec[ion 111A,

we U* a more at.cll rate &sliz31;4!e<)t’lr;ki”in~ dur:t! i<>”.
3

worker in his job is very difficu] t. S-fnce the meastrrernent of technological. change outside

the manufacturing sector is Very problematic (Griliches, 1994), our” analysis is restricted to

workers in manufacturing. Even w,ithin this seclor, however, no iingieproxy is likely to be

perfect. We, therefore, link the NLSY with several alternative dataiets that contain proxies

for industries’ rates of technological change, Specifically, our analysis uses the Jorgenson

productivity growth series, the NBER produc[fvity ~ata, the Census of Manufactures kefieS

on investment in computers, the R&D/sales ratio in the industry, the industry’s u>e of

patents, and a meas~lre of the rate of innoifa[ion tib”tiined from a sum,ey of R&D managers.

Previous studies on [raining and technological] change relied” solely on the Jorgenson

productivity grow,th seties. O“tir analysis enables us-to examine the robustness of alternative

measures .of technological change, thereby increasing confidence in the results.

Third, unlike the earlier research. u!e caref~[lly dissect the relationship between

technological change a]ld [raining in order [o. anw:er [he following-questions: ( 1) How does

technological change affect training investmeti[s for workers with different levels of

education? (2) Does technological chfin:e increase both entry -le~,el training and training of

more experienced w,orkers? (3) Does the pool of trainees increase in response to

technological change. .or is it ]ll:linly Ihe previo~~sly [rained workers w,I1otrain more

intens”i~,+ly? To our knowledge. this is Ille tirst paper [o address these important qu=tions.

Part II of the paper presen[s [he theoretical framework that guides our empirical

work, In Part III, we discuss the data sources for ot!r study, explain the various m~sures of

training and technological change. and present the basic equations that we estimate.

Regression results are discussed in Part IV. and a summary is given in Par[ V.

—
4

II. Theoretical Frameivork

1. General

In this section we exaniine !he effects .of technological change on job-training,

utilizirig the Ben-Porath (1967) (BP) model of optimal in~restment in human capital. In this

model, individuals allocate their time between work and Qob) training, with work generating

income and trainin~ increasing the s[ock of human capital. Tlle stock of human “capital

increases potential earnings, as well as the ability to generate additional human capiml. The

objective of the individual is to maximize the present value of h~s lifetime earnings, where

retirement time is given; an”d utili[y from leisure is .jgnored. The Be:-Porath model is useful

for providing basic insigh[s in[o human capilal in,,e<tnien[ decisions made by individuals.

Alternatively, one coold model the hl]nlall capital in\,estnle]~t decision from the perspective of

the employer (for example. see Tan ( 1989)). bu[ under standard assumptions (e.g. full

information), the basic predictions u,ill be t~nchanged,

Technological chan:e is likely to affect several parameters in the Ben-Porath model

that determine the level and pltlerns of investment in ht~nlan capi[al. F“irst, at higher rates of

technological change, the rateof obsolescence of b~!m~n capital is likely to be higher and this

will affect the optimal path of inVestnlenl. Secc~nd, technological change may act as a

complement to the stock of human capi[:il Ileld by [he indiiidua] (i. e., with the same stock

the individual is more producdve). [l~ercby increasing the returns to human capital but also

nlaking training more costly becatlse of the increase in opportunity Cost (“fOregOne

earnings”’). Tll.ird, technological change nl:iy reduce the costs of direct inputs in the

production of haman capital (e. g., Iligh {ech le~rning devices)3. Finally, one of the

simplifying assllnrptions in””the BP ]model is that the cost of an hour diverted away from the
5

market is equ~ to the val[le of this time in the production of human ca”pital (the neutrality

hypothesis). But, technological change may incrmse” the value of tim”e in the learning market,

relative to its value in generatirig “income, thereby requiring a relaxation of the neutrality

assumption. Below we examine these. eftkcts.~

2. The Ben-Porath Nlodel

In each time period, the indi\,idual possesses .a stock of human capital, K,, which has

a market rental rate of a“. ~rning capaChy a[ iime t is given by Y = ~oK, . The parameter s,

can be viewed as either the fraction of the available stock of h[lnran capital, or the proponion

of fi”meallocated to the production oflitiinan capital. Therefore. $K, is the proportion of

human capi~l alloca[ed to the prodllc[ion of human capital. The production Fi”nction of

human capital is given by: .:

Q, = 0,,(.T,K,)”’D,” (1)

where ~, ,~z >0 and E, +~1 <1. Investmen[ COSISin training(l =~~rS,K,+ p~, ) ila~,e tWO

components, the opporttlnity costs, and the direct costs.

The objective function of the indi\,idual it time r, is to “iliaxiiiiize the present value of

his disposable earnings, given by:

‘By limiting nur xmilysis !(] [he BP fni!lle\\p(lrk. t>[)[e tl>>!ttvt dc>n<)!consider t~vo extensions that co.ul.d be

importan[ in antityzi n>. i}le ell”ec[s ot” tech,><,l,,>,ic~l cl]angt: ( I) adding leisure and consumption (see Ghez and

Becker ( 1975). Blinder and Weiss [ 1976] find Htiktllan [ t976j), :,nd (2) the role of uncertainty (see U>,hari and

Weiss ( 1974~ a“d Will ian>s i 1979]),
6

subject to (l), 0<s, < I , and K, =Q,-3K, , and where 6 is the rate of depreciation of human

capital, T is the time of?etireineilt, r ihe discount rate, and the expression in brackets is

disposable earnings at titile / (E,). ” BEtl:Porath shows that the solution for Q,, the optimal

production o“f human capital in each period is gi~,en by:

Q, = B,,

~

-1[1 6{F1 ‘-””-p’. @ * .[,_(,.,r.6,[T.,,]-

r+~ 6, P,,

Diffi”feiitia[ing (3)

human capital o\,er time:

.(3j.

u,itll respec[ [o time, gives the optimal change in the production of

[1 $,-;>,

~ = RI-6?,0 ~ - fi,a<,-

,,,.,, ]’

1-B, -B, “ r-b 61P’,

.[, _(,.(r.A,(T.,,]m .(,(r+hl(T.,[_(r+6)1 (4)

We now, examine. the ~,arious ways in which [echnologica] change is likely to afftit

the optimal path of investmen[ in human capital.

3. The Effects of Technological Change 011 investment in Human Capital (Trainissg)

(a) Increase in the mte of obsolesreoct

According to the BP model. higher rates”of depreciation of human capital reduce the

marginal benefit o“f_investment in human capital, thereby decreasing the optimal level of

investment in human capital at any poin[ in titile (see equation 3).

In order to determine the effec[ of obsolescence on the time path (slope) of

investment, we dlfterent]ate (4) witl) respect to 6. The sign of this derivative depends on the

parameters of the production fu(lction (6, -~,), how close th”e”individual is to retirement,

—
7

?

(T-t), and the levels of 6 and r, (r+6). If ~, +B, < IA-, then ~ = ~ <0, imPIYinE a

shaper decline in investment over time. If~, -~,> 1A, it is possible for the investment profiie

to become flatter under certain vall]es of the parameters mentioned above. The change of

sign of the derivative, from” negative to positi~re, is inore likely to occur when the individual

is closer to retirement, and when r and 6 are re!ativ.ely low. III the case of young workers

who are far frolm retirement, an iicre~se in tile rate of obsolescence is, therefore, likely to

result in a more sharply declining investment profile.

This approach assumes that [he impact of technological change on the ~te of

obsolescence is identical for all types of hl!n~an capital. However, certain types of human

capital may be more ilnmune [o the introduction of ne!v v,ork processes. For example, the

rate at which an indi~,idua[’s stock of <ener:~l knowledge and problem-soliring skills

depreciates as a resulr of techilologi.cal changt is likely 10 be less than the rate for specific,

\,ocational skills.

(b) Il]crease in the Rental Price of IIIIill+in C:ipital

M’ithin the BP frarnel~”o”rk, h[lrnon c:lpi[:il is homogeneous; and its rental price is

independent of the le~,el of hilman capilal. H[)ivevei, it is likely that the impact of

technological change on !he ren{at price 01 Il!lmall capital will vary by type of human capital.

For-example, in an envirorrmen[ that changes more rapidly, general knowledge and a

theoretical understanding of processes migh[ become relatively more productive than ad-hoc

knowledge, such as vocational edt!cati~)n or .k]]o\vledge based on experience.

An increase in the ren[al price of hl!man capital lhas two opposite effects: It increases

8MC, the cost of i]ivesfii)ent ( ~ =.s,A,,>0, ?Ild _ > o), but also incr=ses the demand price

d~,, da,,
for human capital ( ~ > o). However, differentiating (3) with r~pect to ao ( ~ > O)

aeo u

I

shows that an increase in the rental price of h~]man capital unambiguously i)lcreases

investment in each period, in spi[e of the increase in tlie cost of investment. To the extent

that the incruse in”the rental price is stronger for g“eneral sklls, this relationship reinforces.

the depreciation effect discussed abo!,e, makin: it t~lore likely that inv_~tment in general

sklls will increase relative to in\,estment in specific skills.

. . .

Differerr[iatin: (4) with respect IO a,,. we find that ~ = “Qr .<0; the investment

dff,, 8[JG,I

profile is steeper when the rental price is higt!er.

(c) Changes in the Val[le of Tin)e i]] lll~esfnlent Relative to \f’ork.

}.s we explained abok,e. the Be[l-Poratll neutrality hypothesis may not hold when

technological chan:e takes place: BP sLIggesIs tha[ 21[more general production function can be

used to accoun[ for such a possit>i]ity: Q=~,J’ h-D” =~,,S>-’(.YA’)’Dti: ““=”“””

If, as a result of technol(~gical cIIII1:c. :7 becomes la[ger. than. or increases more

relative to ~,, (i.e. the value of time in invcslmenl increases relative to its value in work),

the result will be a flattening of the” invcstmcnl profile. or even a stretch of””ti”meover which

investment rises rather than declines, If silch a chan~e occurs more so for certain types of

workers, the increase in training at higher m[cs of technological change will be obsemed

more among those workers. For example, if technological change simplifies the process of

learning new skills, Vz could increase ]more for less educated workers. thereby leading to a

relative (to the more educated) increase in th~ir in~,estment in human capitai.
9

3. Conclusion

We have shown that human capital theory does not provide a clear prediction with

regard to the effect of””technological change on the optimal level of on-the-job tra”ifiing.

Higher rates of obsolescerice will decrease the amo~!llt of investment. .However, if

technological change increases the productivity”of human capital, reduces the cost of training,

and/or increases the value of fiine in training “relati\,e “to work, investment in train”ing will

incrmse. s Our empirical analysis will show, v,hecher the negative effect of a higher

depreciation rate is stronger or weaker than the con>bined positive effects.

We are also interested in analyiing 11.o\vtechnological change affects the relationship —

between education and training. Accordi!lg to [he Ben Porath model, more educated workers

will train more. simply because human czlpilal is an inpl~t in the production of new human

capiml. In the presence of [ech nol ogical change, however, we may see a weaker relationship

between educa[ion ~nd training. .The ciiscussio]l above in sec[ion 2C.SI1OWSthat this could

happen if the process of learning neiv s~ills becomes simpler, thereby increasing the value of

time in investment relati\!ely more for the less eclucated workers. Another reason for.a.

u,eaker relationship between ed~lcation and [mining ti[ higher rates of technological change is

Ihat technological change may” increase tile s(ibstitiltiyity OKeducation and training in the

prodllction of hllnlan capital.’”” The gentrai skills of the more educated may enable them to

adap[ faster to the new, technology>, thereby d:~mpqning the otherwise positive impact of

education on training.
10

III. EmpiriGql Frame\vork

A. Nficrodata

We use the main file and the w,ork history file of the 1987-1992 National Longitudinal

SuReys of Labor Market Exp6fiShce of Youth aged 14-21 in 1979 (NLSY) and restrict our

analysis to males. The main file is the source of”information 00 pey-sonal characteristics such

as main activity during the survey week, edllcation. age, “race, maritaI status, health status,

etc. An individual enters our sample ~,hen he Ilrsl reports that his main act”iyity during the

survey week was “in the, labor force. ” The work history tile contains employment related

spell data, SLICI1as wag-es. tenure. and separations, COIIStrLICtedfrom the main NLSY file. For

each respondent, employment in format~on is reported for a lmaximum of five jobs in each

survey year; The work history tile ellat~les us to dis[in:uish infor~nation for”each job”]

especially the reasol~s for and [iming of job [ransi[ions. One of [hese jobs is designated as a “

“CPS job” and it is [lie most rScent/cl!rren[ job al the lime of the”interview. Typically it is

also the main job. There are a hosl of impurmn[ “ques[ions tha[ are asked for the CPS job

only, s~[ch as indt~s~ry,-occupati orl and firm size. Hence. ollr analysis is restricted to Cm”

jobs.

The NLSY is particular]! WCIIs(li[cil ic~r:1 s{t]clyof employee training because of the

vast amount of information on the s~lbjeci ili:~[is recorded. ~’Data ‘on a-~naximtlm of seven

different training programs taken at ally time since the last interview are” included. Be~innihg

with the 1988 survey, data on “the following i[ems are available for each of the seven training

programs: starting and ending:dates of !he tminin~ program’, the number of weeks that the

individual attended the program, wha[ type of program it was (e.g. apprenticeships, company

‘Like most othtr d:i(~sets. the NLSY l>r,)vldt.s int[ml~:,ti(,n t,,,ly <,” f<~rn>xltraining. l~,no ring informal

training, a ma,jor porti(>,l(If <l]l-thc:i<ih [rt$~”i”;. is a dr:i,, hack (see Sicher]nxn, 1990),

‘Not a~,ailahle ti)r gc)vemtncn[ Pr,,k,r,i],,s.

—
11

training, technical/vocational training Ofi-the job.,. (such as btlsiness s.Ollege, nurses prg.grams,

vocational and technical institutes, barber or bea~lty school, a correspondence course)),

government training.; and how many hours he ,ls\lally devoted per week to this program. In ..=

the NLSY, company training encompasses three types of training: “Tl) training run by the

employer, (2) training run at work, not by employer, and (3) company training .Outtide Of

work.

Prior to 1988, detailed information on type of private sector”training, as well” as the

weeks and hours per week spent in training, were “only recorded. if “the.t.rai.n.ins spell lasted at

Imst follr weeks. In other words, for the 1979 throl[gh 1986 time period, the researcher can

measure incidence of private sector and. govern ment..trainingl b~[t i[ is impossible to

determine if the prii,ate sec[or training was company-provided training, an apprenticeship

prog~m. orobtained in otl]er ways st]ch as a vocationalltechnical institute, business college,

or correspondence co~]rse. In addition, el,en if the trainifig spell lasted at least fo~lr week,

the meast[re of training d~lration pro~,ided in the pre-1988 sL1rveys;S extremely..unreli?ble

because it fs based on the starting and ending dates of the training program. q In 1987, no

training qllestions were asked. Howe\,2r. training information for “1987 can be imputed from

the 1988 data, thereby enabling LISto add one “Inore”of data to oL[r analysis; the regressions

we report co~,er the time period 1987 thr6L!gll 1992.

Table 1 reports the incidence and dL!ration of private sector trairriiig, by education and

size of firm. for the manufact(lring sector for the 1988 lhroLlglllgg~ lime pefiod. Incidence

and dllration are calc~llated on an anntlal basis. The d.a~a show that, .Qn average, 17 percent

of the individuals reported receiving priiate-sector training during the “twelve” month period
12

between consecutive suweys’{’. Nfedian duration of titining-was 40 hours, i.e. about one

week, and the mean duration was 142 hours, or, approximately, three-and-one-half weeks.

The probability of receiving private-sector training increases rilonotoiically with education.

The relationship between trainifig duration and education is not monotonic; as we show

below, this occurs because of the association between type of private sector training and

education level.

The detailed data from the 1988 through 1992 surveys can be used to calculate the

distribution of private sector Iraining across three categories: (1) Company, or in-house,

training: (2) Apprenticeships: and (3) Other training; such a.s training received in a business

college, a nurses. program, a vocational or {echnica] institute, a barber or beauty school, or a

correspondence course. For the entire sample. approximately 76% of private sector training

is provided” by the company. This percen[a:e ranges from a low “of.54 YOfo-r the lowest

education group to a high of 9.5% for the Ilighest education group, Company training has a

median duration of 40 botirs fo~ all ed~ication groups, This is co[isiderably shorter than the

median duration of apprenticeships, and someirha[ shorter than the duration of other private

sector training Thus, although more educiled indi\, idualk are more likely to receive private

sector training, their trainiilg d(tratinii is sh~>rter because their skills are acquired in company

training. programs rather than apprenticeships or other ou[side pro~rami.

mfe d;stinuuislled Iarge”TFonl <mall tirms based on whether the n“ilrnber Of enlplOyWS ----

in the individual’s firm had at least 1000 employe&. The data in Table 1 show that the

incidence of company-provided training in large firms is 2O% compared to only 7.7% in

small firms, corifirlning-”the earlier tinclings of Barren, Black and Loe.wenstein (1987). The

positive effect of firm size on [he incidence of traiiiing holds for all education groups.
13

B. Measures of Technological Change

In order to estimate the model outIi.ned in Part II, we require a measure of the rate of

technological change faced by the individual in his place of work. If we cotlld construct the

ideal dataset, it would be to link the data in the NLSY with dat=on tile firms for which the

individuals work. Unfortunately, the employer name in the NLSY is confidential and

researchers are not allowed access to it. \i’e therefore Iink the NLSY._with several alternative

datasets that contain proxies for the induslry ’s rate of technological changeni’ Below we

describe each of these measures and analyze their strengths and weaknesses. Since no”single

proxy is a perfect measure. wefeel it is important to use several alternative measures in our”

analysis. If similar results are obtained Ivitll dif(eren[ [measures, we””can”have more

confidence””in the reliability of the findings.!?

The six measi!.res of tech nologic~ll cll:~nge .th~l~.\ve use are (1) the total factor

prodllctivity grou,th series calculated by Jorgenson et.al. ( 1987) arid updated through 1989,

(2) the NBER total factor productivity groi([ll-iefiSi. (3) 1982 ~Id 1.987 Census of

Ntanufactures’ da[a on in~,es!men[ in complt[crs. (4) the R&D/sales ratio in the industry as

reported by the NSF, (5) the nrlmbcr of [Jiltents used in the iildustry”and (6) a tnaure of the

industry’s rate of innovation oht~iintcl from Ihc Y;Ile survey of R&D managers. Each of

these measures has advantages-anti disadvan[:l:es as we describe below.

The Jorgenson to~al fac[or produc[ iYity series has been used extensively in previous

research (for example, see Bartel and Sichermml ( 1993), Lillard and Tan (1986), Tan (1989),

Nfincer and Higuchi (1988) an(i Gill ( 1990)). TheiETs substantial evidence from studfes of
14

the manufacturing sector tha[ supports the claim that rates of productivity growth are highly

correlated with technological change. G“riliches and Lichtenberg (1984) showed that for the

time period 1959-1976 there was a significant relationship between an iridustry ’s intensity of

private R&D expenditures and s.ubseq{jent gro\tth in productivity”, Lichtenberg and Siegel

(1991) also found that this relationship existed at the company level in the 1970s and 1980s.

In using the Jorgenson producliyi[y groiv[h series, technological change is measured as the

rate of change in output which “isnot iccouniZd”t-or by th~ grou,th in the quantity and quality

of physical and” human capital. .One problem \viih this a“pproach is that technological change

may not be the only cat!se of productivity :ro\vtfl. ” Other factors, “such as ,fluctuations in

capacity utilization and non-conslan[ retltrns to scale, are also likely to affect productivity

growth. In order to con[ro] for these ef(ec[s.” the empirical analysis fi”ill include controls for

tfle industry unemployment rate and [he m[cs clf en[ry and exit of iirms in the industry. The

Jorgenson series is currently avaitable ior !ht [ime period 1947 through 1989. The main ~”

advantage of the .Jorgenson series is that cll;~n:es in [he quality of the labor input are

carefully! used to correctly measltrc jle~ pr(~(ltlclivity..grolvtf ~... Also. the new Jorgenson series

utilizes. the BEA constan-quali[y price dctliit[~r; [hc earlier series underestimated producti~,ity

growth in high-tech industries (e.g. [he cl~mpultr industry) since quality improvements were

not incorporated into the outpilt price indei. “~hc major disad~,anta:e of the Jorgenson series

is that the data are reported for only 22 broiid I[ldustry categories in the. manufacturing

sector, equivalent to two-digit SIC c~t~gtirics.

The NBER productivity database conmins annual information .on total factor

productivity ~romth for350 njanufacturing ;nd[!stries for the time period 1958 throtigh’ “1989.

The advantage of tile NBER datab~se over th< Jorgenson database is its narrow-industry

categories yieid”ig~ data on approxinixt?ly 100 three-digit industries in “manufacturing. The
15

disadvantage is that the productivity growth nleasllre was not adjusted for changes in labor

quality.

The third measure of technological change that we use is invfitment in computers.

During the 1980s, there was an enormot!s growtil in the amount of comp[lter resources used

in the workplace. Indeed, it has been argued (see Bound and Johnson, 1992) that” the most

concrete example of technological change in the 1980s was the “com~yter revolution” .13

Hence a more direct measure of technological chatlge in the workplace may be the extent to

which firms “invest in ~nformitior “technology. We tneast!re this by t!sing the 1982 and 1987

Censuses of Nfanlifacturei tliat incll]ded a qtlestion on iirms’ investments in computers. We

calculate [he ini,estment in comp~l[ers as a share of total investments in each ymr and use

both the 1982-87 growth in the :Ilare and the 1987 share as alternative indimtors of

]4 Tile ~dva,lta~es of this measure are that (1) unlike technological change in the indus[ry. --

data on R&D expenditures, it measures tisl, Ino[ prodi,ction) of an iri”novation and (2) it is

a~,ailable for sever?l hundred four-digit indt!stries in [Ile nla[itiracftlrifig sector, wh”jch reduces

to approximately 100 three-digit iodils[ries .jor Ihe_NMY sample.

A fourth proxy for techrlological chan:e is the ratio of company R&D funds to net

sales reporled by the Naiiona] Science l:(>~]nd:ition ( 1993) tbr ind~!itries in the manufacturing

sector:” The adtan[age of this vari~~ble i$ th:lt i! is :1 direct measilre” of innovative activity in

the industry, but as indicated al)oi,e. tht inn(~wltive activity refers “only to the illdu.stry “in

which the innovation” originates, not lhe .indt!stry where the ifinovation is actually used.,

The fi”fth measure” of technological change is gbtaiied froln tile dataset cofi=tructed by

Kortum and Lath ( 1995j on the number of patents l[sed in two-digit “manufac[uriri:

—.

L~Kr”ecer(lgg3) “s2d d,lt,, trc~,,,cl,. O..[,,l,.r 19s4 and 1969 Ct~rrent P<)pul;iti<)n Sum$ys to shnw that

w,nrkers who ust ccnnpti[er.< n“ [htir ,it,h e:~rr~ 10 t<) Ij ptr..e”t h~~her twah.es.

I.B_a. B“und z,”d GrilicIltis ( 1994 I SI>C>,V111:111,<>[1>the le\,el and [he ch~ngt in the share Of COmPuter

ink,znls xre ~md proxies for Tech”<)l,>g,c:il c[3:,”;c i“ a“ ind~tscry.
16

industries. Patent da[a are generally collec~ed by technology field but Kortum and hch

(1995) propose a niethod for .con,rerting the number of patents per technology field into the

number of patents used per industry. Their data are a~,ailable for the time period 1957-1983.

Since our analysis begins in 1987, Me need a measure of patents used that is closest to that

year. We could use the number of patents used by the industry during the 1980s, but the

likelihood of an innoiat~on%ting patented has differed historically across technology fields,

and hence, across industries. In order to control for these systematic differences in the

likelihood of patenting across industries. we construct the. following variable for each twodigit manufacturing indi[stry:, the !iumbcr of patents used by the industry during the years

1980 through 1983, divided by the n(tniber of patents ~ised by the industry during the 1970s.

Deflating the 1980s patents by the 1970s i):ltents \rill control tor differences in patenting

probabilities across-technology tields and, l~cnce.. inct~!s[ries,. The main advantage of

proxying technological change by “llse. of pfiten [s” is tha[, like the computer investment

variable discussed earlier, it measures tile direc[ (Isc of inno\, ations. The disad~,antage is that

the data are only reported for [~venty manufaclur in: industries.

Finally, our sixth proxy is obtained from the 198.3-84 Yale Suwey on Industrial

Research and Dei,elopment. .The s~irvcy tv:~s completer by high-le~,el R&D managers who

were knowledgeable aboul the relevant lcchn(~logy. and mar~et conditiofis in their lines of.

business. Six hundred and tifty manfigers fr(~m 130 lines o~ busiriess (4 or 3 “digit

classification) responded 10 the sur~ey.” \\’e t].se Ihe responses to the following question on

the suwey”: “Sirice 1970, 1[ what rats have ne!v of improved prodilction processes been

introduced in this fine of busitiess?” \Vllile this question appears [o be the”ideal description
17

of technological change, the manner in which the responses were coded may limit” the

variable’s usefulness. The managers were asked to respond to the qt!estion by using a scale

that ranged from 1 to 7 witho~lt any guidelines as to the meaning of the nlirnber: on the scale

or any reference points r~ardirrg high or 10U rates of innovation.”- R’esults using this variable

should be treated with caution given the highly subjective nature of the responses.

Table 2 presents industry means of [he various proxies for technological change.

=ch listing is presented in rank order so that tie can observe whether the six proxies

produce similar patterns regarding high and low_technological change industries iri the,

manufacturing sector; We” iind Illat some industries appear at the top or near the top of each

measure’s list. Using the Jorgenson data, noli-electrical machil~e;y has the highest rate of

technological change and electrical machinery is tied ..for second place with petroleum

refining. The comptlter investment data pro\,idei information for more detailed industries; the

three indllstries with the highest comp~)ter share of investment, electronic com”putihg

eq[]ipment. radio. T. V.. and comm~[nicatibn equipment, and oftice and accounting machines,

are members of Ihe broader non-electrical machinery and electrical machinery categories.

For the NBER productiifi”ty “ill~si]re, electronic complltin: equipment has a S“ignffimntly

larger value than ~he other [man~lfact~lring indlislries. The R&D/sales ratio dila show office,

computing and accounting machines as the top-ranking indtlstry. For the patent variable,

office and computing machines and coinmunication and electronics i~”nk at the top. In the

case of the Yale measure, more confidence should be placed in the”indl!stry m=sures that

were obtained from larger nt[mbers of responses per industry. }f Looking at those industries

where at least six observations were obtained. we tind that, as with the other measures,
18

electronic comp~tting equipmen[ a(id radio, T.V. and .communicafion equipment rank at the

top for this measure of technological change.

The fact that the two or three illduStrieS thal we generally think of as “high-tech”

industries rank at the (op for all six measures of technological change is evidence that the six

variables are good indicators of technological change. One might be attempted to generalize

from these cases and conclude that. since all six proxies appear to be measuring the same

thing, perhaps only one proxy should be used for [he analysis. A closer look at the six

listings “indicates, however, that they each contribute unique information about the differences

in the rates of technological change in the tmanufac[uring sector. For example, according to

the computer investment metisure. Iea[her prodllc[s has a relatively high rate of technological

change, but this is ntit captured by the other proxies. By comparison, petroleum refining

ranks high for the Jorgenson and NBER prod~lctivity lmeasures and the patent variable, but

not for the other three proxies. Additional compilrisons of [he six listings also demonstrate

that. in many cases”, the rankings are dissimilar. This indicates the \,alue of using all six

proxies in. our analysis. Technological change is a dif!icul[ concept to quantify in a unique

way: each proxy is Iikelj to c~p:ur~ a dif(eren[ dimension of technological change. If all

proxies produce similar results abo!i[ [he IMIXICIof [echnologicai change on training,

confidence in our conclusions !vill be signi [ic:in[ly enhanced.

C. Nlalching the hf icrodata aIIcl tnd[ist ry \lc,:Isures

Since our NLSY panel co~,ers a short time span ( 1987- 1992) and there is a high

degreeofragdomness in annual changes in the technological change measures that are

available on an ann”iia[ basis. it is impossible to conduct a [rue time-series analysis, Our

analysis therefore relies on cro”ss-sec[ion i,ariations in technolo:ic”al change, All of the

mmsu”res that we use have a common trait. i.e. they are proxies for the iildllstry rate of

techno[ogicai change, ii’e recognize thai an ind~iitry measure “of technolog-ica] change may
19

not have the same impact for all of tile occupations “in that industry. For examp~e, an

innovation in the industry’s production processes may have little or no impact on clerical

employees. By matching an industry measure of technological chan~e to all of the —

individuals in that industry we are less likely to “find a strong effect-of t~ch””n”ological change.

Hence, our ~rn”pirical results are likely to be undet-estimates of the true relationship. We

deal with this issue by conducting separate analyses for production and non-production

workers, since in most cases production workers are more likely to be affected by

technological charrge in the manufacturing sec[or. “Another issue is “~hatthe standard ekrors.

of our estirrlated coefficients may be biasecl do!{i~~vards because industry-level shocks may be.

correlated across “individl!a[s \vitllin a given indllslry.

In order to match the different mtasures of ~echno]ogical change to the industrial

classification used in the NLSY (the Cens[!s of Popiilation classification), tie ~tse industry

employment Ie\,els as weights whenever aggrega[io!) is required. Wllen we utilize the”

Jorgenson and NBER product ij,ity groivlll mcas!)rts. .>ve characterize industry differences in

tile rate of technological chan:e by using [he meatl rate of productivity gro~~th over the most

recent ten-year time period, ie 1977- 19S7. In the case of in(,estment in computers, we use

data from 1982 and 1987 as described earlier. The R&D/sales ratio for each indl.stry ii

calculated as a three-year moving altera~e for Ihe [hree year period prior to the year of

analysis, e.g:averaging data for 1984-1986 for the 1987 NNY, eti”. For the patent data, we

calculate the number of pa[ents (Ised during the time period 1980-83 divided by the number

used during the 1970s. ~iali~. the iilnova[ioll lmeasnre from tfie-Yale survey refers to the

time period 1970-1983. Hence, with the exception of the R&D variable, we use a fixed time

period measure of technological change which may act like a fixed effect for eac~ induslry,

capturing other fixed att[i.bu[es of the industry. We deal with this problem by including

severa[ industry characteristics ‘irl Iht regressions which v,e believe may intluence the
20

relationship bet ween training ‘and our measures of technological change. They are: the

annual industry unemployment rate obtained from Employment and Earnings, annual

measures of percent unionized in the industry compiled from the CPS by””Hirsch and

McPherson (1993), and the annual rates of job creation and job destiilctiori for both start-up

and continuing establishments in tile industry constructed by Davis and Haltiwanger (1992).

C. fionometric nlodels

1. The Llkelihood of Company Trainin:

Ou”r econometric analysis ii restricted to company training because, as was shown in

Table 1, three-quarters of private-sector training is provided by the tirhl, W&’do provide

some evidence of the impac[ of technological change on other forms of””private:sectof training

and contrasi these effects w]th those for company training,

In order to estimate tile effect of tech]iologica] change on the likelihood of company

train”ing, we adopt a simple Logit frrune\+ork. In each period, between two sumeys, an

individilal will TiZcone of the follo\ving two altematii,es described by j: Engage in company”

training Q= 1). or IIOt ~=0).

The choice j “occcrrs when the latent \ariable ~.~>0, where

y; = X,uaj +6j?., - e,v,

where i is the illd”i~,i~lal index, t is [ime, j is the il[emative, x,,, is a vector of individual,

job, and indl!stry characteristics that may vxry over time. The vector-X includes the

following variables: marital status, race, years of education, residerice iri”all S“MSA,-yea& of

experience and its square, tenure and its square, union membership, whether or not the

individual is employed by a large firm, tile industry unemployment rate, union coverage in

the ind!:stry, and job creatio[l and destruction in the industry. ~, is the rate of technological
21

change i.n the jndustry in which the individt]al is working at time -t.. In order to test whether

the effect of” technological change varies by education group, in some of our specifications

we interact the proxies for”technological charrge wifll education grottp.

Assuming that c is logistiWlly distribllted” gives rise “to a. Iogit model in which the

underlying probabilities are

Pj =

e:p(Zflj)

z~=O exp(zb,) ‘

j=O,l.

In order to identify the parameters, the nor[mali~tion ~,, =O”i3 imposed and the

estimated parameters are obtained by inaximum “likelihood.

2. Hours of Company Trailling

In order to estima[e the effects ~[.technologica] change on the amounr of fime spent in

company training, we adopt a standard Tobit model, AS h4cDonald and Moffitt” (1980) show,

the Tobi t coeftiti”enls illeasure the effects Of [he co~,ariates on the dependent variable (hours

of training), reslllting from both the change in [he likelihood of being abo~re the”iimit “Qetting

training). and fro?n the change in the valtle of [Ile dependent variable (hours of ttilnmg) lf it

is already aboi,e the limit. In .Appendix D, !\Feolltline the Tobit model and describe the

decomposition procedure suggested by hlcDonald and Moffitt. The independent variable

used in the Tobi~ models are the same as those used in the Logjt regressions.

IV. Results

A. Incidence of Company Training

A s~lmmary of the estimates fiolil our logit models on the incidence of company

training in the manufact[]ring sector is shown in Table 3. Co!nplete regression results for
22

one model are given in Appelidi x B where ive see the Iypical patterns regarding the effect of

education, firm size, and other characteristics on the incidence of training. ‘g In this section,

we detail the relationship between technological chan:e and the incidence of training; in all

of our specifications, w“econtrol for four additional industry characteristics: the

unemployment rate, percent of workers who tire union nienibers or covered by a union

contract, the annual rate of job creation. and the annt]al rate of job destruction.

Table 3 .*OU,S the effec[s of each of the six technological indicators on.the incidence

of training for all workers in the manufacturing sector (colullln I) and for production and

non-production u,orkers separately (columns ? and 3, respectively). We present the logit

coefficient and the estimated probability tha[ [he coeftlcient is not different” from zero (shown

in parentheses beneath the coefficient), To Ihe right of each coefficient, we show the

derivative (dP/dX) multiplied by [he s[andard deviation of the measitre of technological

chan~e. .This estimate enab[es us [o compfire dle magnilt]des of the effects of the various

technological change measlires. The results in column ( 1) SI1OU!that. all six proxies fol

technological change have a posi[ive and sign ificarr[ eftkcl on” [he incidence of training ii themanufacturing sector, indicating that the tlcg:itive e“ffect of technological change due to the

increase in the rate of deprecii[io[l is ou[\vcighed by tile posi[ive e-ffects Felating to incrased

productivity of human capital, reductions in Ihe COSIof training. and/or increases in the value

of time in “trainifig relative to work. The larges~ impacts ar~observed for the Jorgenson TFP

measure, the R&D/sales ratio and use of pmen[s. Comparing the results in column (2) with

those in colum~ (3) show$s tha!, with the excep[ion of the Yale Survey measure, the impact of

technological change on the incidence “or tmining is larger for production workers than non-
23

IY I” fact, IIle estimated coefficients for non-production production workers, as anticipated.

workers are not statistically signi ticant.

Although three-qtlarters of prii,ate sector training is provided byllie “firm, “young

workers do receive same training otttside the tjrm. In Table 4, we consider whether

technological change” also has a positive itmpact on non-company trairiing. In columns (1)

through (3), the dependent variable is [he likelihood. of any type of private sector training

(company or non-company), and in coltln~ns (4) through (6). we sho~> restdts for the

likelihood of non-company training. Since [he \rasl majority o~pfivate-Sec”tor tra~ning is

company-provided, the results in colt!mns ( I) throllgh (3) are qt!ite similar to those reported

in Table 3. The analysis of non-com~any [raiifin: alone sI1o!{s that, with the exception of

the Jorgenson TFP measure, technolo:ic?l ch:lnge does not ha~,e a. significant effSct.2(’

Hence, the remainder of ollr analysis ii confined to compai)y training.

As we disctlssed in the [ntrodllclion. i.1is impor[ant from a policy perspective to

estimate the etlect of technological change otl [he p~>sl-schoolin: htlman capital investments

of di~feren[” education groups. Ollr [heorc[ical disctlssion prmided two reasons why the

impact of technological change on the incidcnit of training “Inay \,ary by ed~lcatio_n. One

reason is that more educated individ~!a[s m:iy rcqtlire less [raining in response to

technologiul change if [Ilejr general skills enable them to Ielrn the new technology and adapt

to the changed environment, i.e. trainin: atl.d edi!ca[ion are Substitlites in production. This

narrowing of the training_ gap between [he highly edt!ca[ed and the tgss educated Wn also

occur if technological changesimptities the process of ]earning new skills, thereby incrasing
24

the value of. time in investlneiit relati~e to its vallte in work froli~ the-less educated. We test

these hypotheses in Table 5 where the regressions include an” interaction” effect “between

education and the proxy for technological change.

The results in Table 5.show, that for all workers, production and non-production

workers alike, the more edllcated are more likely to receive company training.?] The

interaction effects” show,. howe\,er. that Iect)nological ch~nge attenllates the impact of

edumtion on training. At higher ra[es of technological change, the training gap between

the highly educated and the less edtlcated is narrower. The separate results for the

production and non-prod~lction workers generally stlpport this conclLlsion. “Whenever the

technological change indicator has a posi[ive and significant effect on the incidence of

training. the education-lechno!.ogical Chan:> i~ll+r~c[io!~effect is riegative ai~d usually

significant,

In order to more f:illy understand {he relationship between technological change and

the incidence of training for differen[ edtica!ion grotlps, we estimited the regressions in

Table 4 t~sing a set of d!]mnti~s for edtlcsli(ln gro[ips ( 1-8, 9-11, 12, 13-15, 16, and 17+

years of schooling) in place o~””thecon[inuot~s m?’islire, and interacted the dummy variable ““’”

with the technological change “indic:ttor, The coeificien[s from [hese regressions are shown

in Table 6.. We used t.h~se co.ef!icients to crea[e plots (see Figures” 1-4) that depict the

impact of technological change on the incidence of training for a worker of given

characteristics in each ed~jcation groLlp.~z Figurts I and 2 are based on investment in

comptlters. for production and non prodllctio!l \\,orkers respectively, and figures 3 and 4

‘)See Appendix B for separ,t[e c[)elfic]cnts [)21.dixc:tti<]rl gr[)l!ps. The r.suits show a mnnotonic relationship

betw&n Y-IS of education and [rainin~.

“For these pl(>[s,we xsst,,lledth:it [he ind,vid~,>,lhad the l<>ll<>!vin~charac[eristicx married, liv~ in an

ShfSA, utorks in a large tir!~l. k:s 10 years <]f ,x,:trket experie[>ce. >ind 4 years of te”~,re with his employer. All

o[her wdriahles are. the nlei!n v$tlLies. and the yc:$r IS [992.
25.

utilize the “data on the R&D/sales ra[io. \Vhene\,er a slope is significantly differemt from zero”,

we indicate it with an “S” mark.

There are sei,eral insights from Table 6 and Figures 1-4. .First, at higher rates of

technological change the gap bet\i,een the training incidence of the highly educated and the

less educated narrows. Second, in spite of the narrowi]lg, we still obsewe a positive

correlation between education and training, “Third. the edttca[ion interactions are’”hot

monotonic and significant effects are obsen,ed for only one or two educational groups.23 In

the case of prod tiction worker<, workers \viIll sonle high school and high school graduates

train significantly more at higher rates O( t~chnolo.gical change. Since this group–-repr&ents

three-quarters of oor prodllction uforker Slnlple, this explains the positive relationship

between training and technological change r?ported earlier. For”non-prod~lction workers, we

find that the 13- IS grotlp trains Imore a[ higher rg[e$ of technological change, while those

with more than 16 .ymrs of schooling [rain less aI t]igher, rmes of technological change.z+

These ed~lcation-technology.cal cllangc interaction resldts are consistent with the

hypothesis de!,eloped and les~ed by Bar[et and Lichtenbcrg ( 1987). Bartel and Lichtenberg

argue that highly -educated \yorkcrs tl:ive a comparative advantage u,ith respect to learning

and implementing ne!~, technologies. and hcncc tha~ the demand for these workers relative to

the demand for less-educated workers is ~!declining fi!nc[ion of experience with the

technology. When a new technolog~ is firs! introcl~lced. there” is a great deal of uncertainty

about job tasks an-d high]y edtlcated workers are nesded to help the firm through this difficult

implementation stage. ” The genera] skills O( the highly edl[cated workforce seine “as a ~

substitute for company [rain ing.. As experience i$<fththe lnek, technology is gained, however,

—
26

it is possible to frain the less educated employees to perform the new tasks. Hence, since we

are measuring “permanent” differences across. iridustries in the rate of technological change,

we wollld expect to obsewe a larger ii]lpac[ of technological change on the training incidence

of less educated workers.zs In terms of the policy isslle “discussed in the Introduction

regarding the widenjng earnings gap between the highly edl]cated and the less educated, thae

results show that this gap is “Ilot dtie to a wideniilg gap “in the acquisition of post-schooling

human capiml. If anything, technological change has acted to reduce the gap in the stocks of

human capital accumulated through formal company training by different education groups.

The reasons for the widening tirrnings gap are” more likely d~le.to one or both of the

following: skill-biased tec!lnological change which has increased the market price for the

skills of the hi:hly edtlcated and differences in the rate of accumlllating htmlan capital

through informal, on-the-job learning.

We recognize Lha[ one reaso}l for [he o~sen,ed narrowing of the formal training gap

between education gro[lps cor!ld be selectivity. At higher rates of technological change, firms

are less likely 10 employ or retain the less able employees. within each education group. This

bias. is likely to be more pronolmced for the less educated workers, resulting in an

oirerestimale of tile inlplct of technologic~tl change .on [Ile Jraining of [he less edltcated. We

attempted to correct for this bias by illc]uding a set of ability test scores “(not reported here),

and our results on the impact of Technological change were virtually unchanged. We did find,

however, a positive and sign~ficint correlation between ability (holding schooling constant)

and the likelihood of training, and a smaller coefficient on edllcation.

We ha~,e interpreted oLlr findin:s as indicating that the ob.semed differences in

training are dlle 10 highsr rates pf technological chan~.e. Alternatively. one could argue that

.?.

“lf ,ioh [raining is”tnore” ti~ely to h< it>f<]rlnxl at higher le\,els c)f edtlcation, it could hiss our results. Notice,

however. that UC do iind a m[)n<)t<>nicit>cr~sc of trdining with (ht I<v.1 uf scl>[>[)ling (Appendix B).
27

our results are due to differences iri the natl]re of f~~huolo:y across industries. perhaps

industries that we rank hiiher on the di me!ision ot-[etihnological change are simply industries

that use more” sophisticated technologies. These “techilologies may require more initial

.= training in order for the worker to learn how:.to ~lse them. If this hypothesis is correct, we

would expect to see more training (especially formal” training) when workers join the firm

and virtually no impact of our “technological change” proxies on the training of ‘more

tenured workers.

In order to distinguish these t\\o possible effects, we interact the measures of

technological change with two dummies, one indica!iil: that the worker has tenure of one

year or less with tile employer and the other indicating Ienure of more than one year. Our

assumption is that the eff~ct”of the technological change measures.on longer tenored workers

are more likely to refleci the response [o !ecllnol.ogical charrge.?fi

Table 7 reports the es[imated coeilicients on the t,:chnologica] change variables on the

likelihood of_training, separated. for tenure le~els belou’ and above o!le year. If our earlierresults urere due simply 10 the cross-sec[ioniil differences in the nature of technology, we

would not expect 10 obser\,e significa[lt coefficients for workers beyond their first year of

tenure. The results iii Table 7 .sl]o\v that. although the measured effects of the technological

change variables xre larger for “individ~ia]s with less than one year of tenure, all of the

technological change proxies ha\e positii;e and significailt effects on longer-tenured

production worker.s.z’ Hence these resul~s proi,ide support for our claim that what we are

indeed measuring is the effect of technological ctlange, not the nat~re of technology, and

ongoing technological change resul[s in traillipg. of ~)or.kers beyoud t}leir first..Year of tenure. 
28

B. Hours of Con]pnlly Trniilin:

In Table 8 we report the Tobit estimates of the effects of the various technological

change measures on hours of.company training received since” the last survey. Complete

Tobit regressions (for one specification) are SI1OWI1in Appendix C where it can be observed

that more educated workers ha~,e more Iholirs of [raining. Table 8 reports the partial

derivatives and elasticities onthe technological change measura and then decomposes them

into the change that is due to the increase it? (he incidence of training and that which is due

to the increase in hours of training. :i~,en positive hours. The mai]l finding of the Tobit

analysis is that the change .i!l ~lotlrs of [raining is dlle largely to the incrtise in participation;

the ratio of the derivative dlle to [be change itl. participation dil,ided .by the total derivative is

approximately Sj.

One limitation of [he standard Tohi[ [mtide”lis [ll~t i[ does not allow for different signs

on the effect of technological cha]lge on lhe selec[ion in[o training a.jd its effect on hours of

training. gik,en selection. In order to allo\\ for SIICI1a possibility, we reestimated the models

presenteLil~.Table 8 using a. genera] T[)l>i[ sptciticn[ion, where separate coefficients are

estimated for the effecl of technological] chatlge on selection and its e“ffect on hours. Our

results (not reported here) reject tht lhy]>(~lhcsistha[. Lvhile technological change increases the

incidence of training, it redt[c:es the ntlml>cr ~>fh(~tlrs per spell, We found that, in virtually

all models, the effect of technological ch.ingc 011lho~!rsper spell was positive and

insignificant. “This con fi”fms the fin(lii>gs O( the slandard Tobit model that the effects of

technological change on trainj.ng are inci!lence-, not duration-related_.

C. The Effects of. Prior Traini[):

The results of the Tobil analysis indicate ~hat technological charrge_i:cr~ses training

at the extensive margin, i.e. tile incidence of ttining, not hOLlrSconditional on participation,

increas~. ~~In order to be ]more con fidenl in this conclusion, we exploit the pane] nature Of
29

the ~SY data. We examine Uhether higher rates of technological. change induce firms to

provide training to. individuals who have already received training or to those w“ho did not

receive training in the prior period, If the Iat[er is true, then fecllnolo~ical. change serves an

important function; it acts to increase the proportion of workers ti;ho receive training. We

test this hypothesis in Table 9 by “interacting the various measures of technological change

with two dummy variabl~, one indicatin~ the itidividllal recei~,ed training in the “prior year

(i.e. between t-2 and t-1, since [Ile dependent ~ariable is [raining between t-1 and t), and the

other indicating no training in the-prior year. “In co”ll[mns ( 1) and (2) the sample is restricted

to “individuals who did not chan:e ind~lslries betil;een time periods t-2 and t, and in columns

(3) and (4) we restrict the analysis [o individuals who did no[ change employers between the

two time periods. The results .SI1OWinsignii-lc~nt ef~ecls of technological change” for

previously trained workers and significilnt effects for imos[ of the technological chan~e

indicators t~f-individuals who did [not recci\ e [mining in [he prior year. The increase in

incidence of training due to technological cllaligc occl!rs because different individl[als are

now receiving train ing.

\’. Surnn);it-y and In]plications

The hl)nran capital in~,estmenls thn[ take pl;Ice dt~ring the early years of employment

have important implications tbr futurt carter dcvelopmen[. In tliis paper we have analyzed

the impact of technological change on >oun:” \vorkers’ inifestments in on-the-iob training.

We hai,e shown that human capital theory does not proiride a clear prediction on the sign of

this relationship. While Ihigher rates of obsolesce!~ce ivill decr&se the amount of investment,

on-the-job training “will increase if technological change increases Lhe productivity of-human

capita], reduces the cost of train ing,. or increases dle value of time “in training” relative to

work. The impact of technological change on [he post-school in: investments of different
30

education gro[lps “is also theoretically ambiguous; although more ed~lcated workers train

more, we show that, in the presence” of [ethnological change, a weaker relationship bet weerr

education and trainin”g may exist.

We linked data in the NLSY to six differen[ measures of industry technological

change in order to empirically resol~re the anlbiguotls theoretical predictions. Our findings

can be summarized as follows: ( 1) Con[rolting for a set of worker, job, and industry

characteristics, “workers in industries with higher rates of technological change are more

likely to receive” formal” Coriip;llly training fllafi”those working in “Industries with 10wer”rates

of technological change. ‘(2) This finding Ihb”trtsfor all bu[ one of the six proxies for the rate

of technological change in an. industry. (3) \Vtlile more edl!cated workers are more likely to

receive training. the trainjng gap between the highly educated and the less educated narrows,

on average, as the rate of technological increases. (4) The observed increase in hours of

training due to technological change is due 10 all increase in the likelihood of training, not an

increase in hours of training, gi~!en particip: ilion. Technological] change therefore acts to

increase ihe extensive margin ot-[mining. increasin: the pool of trainees.

Policy ]llakers have been concerile(] fihu~![the likely impact of technological change on

the future careers of young “tiorkers. Our results show that, while edllcation “and training are

complements, at higher rates of technological change. employers compensate for workers’

lower Ie\,els .of education by pr[~viclil}g-t~~(irctmining. The post-school training gap between

the more and less educated actually narro\vs at higher rates..of [ethnological change and the

proportion of individuals recei~ing training increases. Pr.evioujlesarch has shown that

technological change contributes to an “increase in the wage gap between less and more

educated workers. Our fi]idlnjs show [ti~ need for further research [o uncover the actual

mechanisms by which techrrologico] chzmge increases the wage gap.
31

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bbor Economics. J,IIY 1992.

Barren, John hl.. Dan A. Black, and h!tirk ,4, Lc]e\\e”stei”. “Enlph~yer Size: The implications for S&rch,

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1987): 76-S9.

Bartel, Ann P., “Employ.& Tr.inin; Pr<?gctnxs i,] U.S. Btjsincsses, in Dxvid Stem ind Jozef M.M. Ritzen,

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Gill. Indermil, “Techn(>logic;,l Chi,]gc. EJSIC:II]<),I.;,,lJ the Ohscalescencc of Hlllnan C~piral: S(>mti Evidence for

the U. S.,.” nlanll<?rip[. St;tIe Lt”]verh,!y <,iSe., Yt>rk :tt B,!ftitit>.-kl>[y 19”90-

GriIiches, Z\,i. “Prodllcti\ity, R&D. and [11. D:it;t C<>t]>t~ti”t.,’ An]cric.n Ecc>nnnlic Re\ie\v 84 (hlarch 1994).

1.23,

Griliches, Zvi and Frank Lichtenherg, “R&D iind Pr{)dtL;ti\i[y Gr(~\$th ~[ !ht Industry Le\,el: IS There StiJl a

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(Cdmhridge: NBER. 19S4). “- ““-
32

Hirsch, Barry and David A. hfacPher$(>n. “Union Ll<nlhdrship and C[>i,entSe Files from the Current Popuiatio”

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Jorgenson, Dale W., F=tnk M. G(>l[op and Barhfint hl. Frall[l>eni, Prod~tct i\i[v and U, S. Ec(inotic Growth,

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Febrl]ary, 1995.

Kmeger, Alnn B., “Ho\~ Computers Have Chfinged [he Wage StrttctLlre~ E\,idence Yr<>mhlicrodxva, 1984 -1989,”

9Llarterlv JOLIM,I [If Economics, (F$hrL!3ry 1993): 33.-60. “. “..,.”.. 1 ~..

Lichtenberg, Fr~nk and Donald Siegel, “The I!l>p;!ct (>SR&D In\estn]ent on Prodllc[i\,ity: New Evidence Using

Linked R& D-LRD DMva,” Econotllic In<l[lirv 29 (April 1991): 203-228.

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Siegel. Donald, “The []npdct of Tichn<)l<]gical ClvAIlge (,!, Elnplc)yr]>ent and Wages: .~vidence from a Panel of

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33

Tan, “Hong W., “Technical Change find Its C[,nstl],!tnces for Training and Earnings,” n]anuscript. RAND

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Top&l, Robert H. and Michael P. Ward. “Job Mobility and the Careem of Young Me”,,, O,iarterlv J“umal of

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~ 52; (1979h 521:548.

Woipin, Kenneth, ,,The Deternlina”ts of Black-\\lhitc Differe”cis in Early E,nploymeri< Car6erc S~rch,

byoffs. Quits and Endc>~e”oL)<Wa2e Gr<><vth.” J<,~~rr>.1<,f Pc,lit ical Ec<>n<>mv.100 (June 1992): 535-560
34

Tal)le 1

An”t!:tl Incidence ttrld Dtlratii,tl (Mra!!/Mc[li#n) 01 PrivJile-Seclor Training, hy TyI1e of T!-~ining and Scl]o[][ing Lvel

kioIItIlucltIting Indu$lries, Mfiles. 1988-[992

11

rIv, 11,s WTm\ Hrs

,17Q 11?141 ,302

(6(N) (!27) (6K3)

y7i4fl ,2*6

(~lfi) (682)

,111,1 5(K1/.l(x) .015

(3!,(!I

1, ,:,

;[1s4

,,,,

I

111“<b

(21!,11,

IX(! I 7\:4(,

(151>) ,UI)4,

l?fi/40

101J), (317),,

,,

‘,,,: : d,,;{!, ‘ ,141> 111!s? ,11)5

,, (?5$,

1,05/4(1

(?74]

91/40

(?(15)

.(), I

,030 2$j/nt)

i,

,, (+41)

,’,, !.;’,1. ,’

I

,, 1

.040” 142/4(1 .Ojo

(2!!),

,. :, ,,

~,!

“, ,1s0

,’; (?54)

,! l“

,114

,W5 109/40

(~44)

,150

.O1o .013 16U47 100

(1 <4),)

0,0 NONL

(J <4>s,) (6 <>hs,)

,042 99/68 ,052 29j/80

(12s) ,. ~~ (479)

,,. ;

,, ,.

;029 ‘,

,,

,044 240/[92

(4 nha,)

0,0

~!’! ,Ii’

NONE
35

Table 2

Indices for tnd[lstry R:]tes of Technological Change

I. Investment in computers as a share of total investment

CPS Code

189

207

188

239

397

227

338

=8

198..

z?g

209

339

257

197

398

389

?59

187

2Q8

~18

119

357

?48

179

137

299

177

388

158. ”

359

327

319

?37

249 . ...

168

lj7

118

137

378

309

159

238

199

138

279.

148

Indtlstry ~f

investment

.230.

.189

.176

.175

.157

.141

.138

.,138

.135

.132:.

.121

.109

.109

.103

.099

.097

..og~

.090”

.089.

.087

.084...

.083

.079

.077

.076 ‘..

.073

..07? .

.07?.

.067

.06j

.065

..065

.062.

.061

.059

.055:

.053

.051

.0s0

.047

.046

.04~

.041

.038

.038”

.038
169

35s

178

379

269

308

149

278

128

337

387

369

307

297

108

36.8

329

289

367

347

298

167

287

219

31s

34s

139

377

32s

147

2ss

26S I27

j17I09

349

107 1~3456789

10

11

12

13

36

.038”” ““

.037

.037

.037

.037

.036

.034

.033

.031

.030

.028

.027

.027

02G.

.02s

.02s

.024

.024

.0?3

.023

.023

.023

.020

.020

.020

.018

.018

.016

.01s

.014

.014

.014

,011

.ol~

.007

..U02 .-

.000

.]~7

.1122s

.10713

.10209

.07961

.07311

,37:29

.06s1s

.0G443

.0G419

.0638-8

.06336

.0j946
37

14

15

16

17

18

19

20

21

22

23

24

25

26

27

28

29

30

31

32

33

34

35

36

37

38

39

40

41

42

43

44

45

46

47

4s

49

50

51

52

53

54. .

;:

57

58

59

60

61

67

63

64

65

66

67

Miscellaneolls manufacturing ind!!slrits”(39)

Dregs& medicines (2S3)

Tamed, currie~,& finishd leather (3I 1)

Not spwifid m~chinery

Constmction& material handling nrachines (3j3)

Printing, puhlishlng,& allied ind~ls[r]es, exc ne\vs~\ptrs

Metalworking ]rdchinery (354)

Paints, vamish=s,& related prodllcts (28j)

Optivdl& h=lth sen,ica sl,pplies (3S3. 384. 3Sj)

Miscellaneous petroleuln& ccvdlprodltcts (295. 299)

Electrical machine, eql!ipmen[.& supplies, n.e. c.

Not specifid pro fS?sional eq~lipnlen[

Fahrica!ed stmctural n]e?~l prndttcts (344I

Engines& turbines (351)

hfobile dwellings& c.t]lpers (379I)

Miscellaneous ftihric:ile.~ textile pr[)~l>.ss (239)

Pottery& related products (32G)

Grdin-rnill pr(>dllcts (.?04. 0713)

“Cutlery, hxnd tools,& <)thcr h:tnd\v;irc (342)

Flo(>r co~erings, exe. h~rd sl,rtace (227)

Apparel& accessories (23I-23S)

StructilrAl cltiy prodllcts (32j)

hfisellantous fahricxtcd rne[xl pr<)dt[,ts

Watches.. clocks.& clock -w(>rk-[>pcxtled devices (3 S7)

Primary alltnlinllnl indllstrits

Dairy pr<)dttcts (202)

h~iscellan~<)lis n(>nmetxllic ~ll,nex,l& s[<,nc Iprc,JLLct.

Elcctr<>nic con>pLl[in~ .qlripn>cnt (3j73)

Other prim~ry conferrl)Lt< ill~ll~[ries

Hous.hnld applianc~s (3G3)

Fllmirllre& tixtl!rcs (?jj

N(>[ specitied chelllicfils& allied pr[)drL.tb

Canning& preservin> “; frliii., veg.et,+l>les.& hi:, fi,c,ds

Ph<>[<)grdphic eqttiptl]ent& sl,p$]es (386)

Agricultllrxl chtr>~icals (287)

Rt!hher prodklcts (301-303. 306)

S()*Ips& c<>stnetics (2S4)

hflscellane(>l,s pl;tstic pr<>dtlcts (3071

Lfisccll:<nc<>lls h)cId prep:tr:,[t<>”& kbIIJrcd I,,,, Jc*.I.

Cycles& tl]isccll~nec]lls t~,ns[>clrl:t!z,,,l <cItt,ptll..,ll

N[)t specitied l<)(,d ind,tstries

Nc>tspecitied .lcI:,I ind,)stri<s

Petrolel!nl refining (291)

hliscellxneou$ ch.nlic:,ls (28G. 289)

Screw machine prc)dlicts (345)

Farln machinery& <ql!ip!llent (3j?l

Sa\vmills, “plining nlilis.~ nd nlill \v,lrk (242. 243)

Ind!tslrixl chemicals (2S1)

B~\erage indljstries (208)~

Paperboard containers& h[>xcs(?bj 1.

h~otur \,ehicles& ln<)ror vehicle cqt!ipn]cn[ (37I)

Plnstics, Synlherics& resil)s. cxu. !ihcr..

PIIIP, paper.& p,+ptrh<~~rd [l>ills (?61-26j, 26Gj

Miscellaneotts p~per& p~llp pr<)dllcts (264)

.0j81?

.05720

.05714

.05343

.0j12j

,05041

.05032

.04993

.04231

.04! 18.

.03981

.03977

.03909

.0388S

.03ss.3

.03849

.0373I

.03410

.030s5

.030?4

.029Ga

.02961

,ozg23

.o~7~3

.02701

.02j91

.02j07.

.02330

.0232j

.Ozll?

Ozo%

.02023

.o?o IG

.01973

.oiaaG

..01s57.

.01G60.

.ol G4a

.016[3

.01G07

.015ss

.01j60

.01432

..01426

.01390

.o]~~G

.01123

.01116

.01104

.01040

.00790

.06706

.0.06s3

.00G07
68

69

70

71

72

73

74

7s

76

77

78

79

80

81

82

83

III.

1

~

3

4

5

6

7

8

9

10

11

12

13

14

15

16

17

18

19

20

21

Iv.

1

2

3

4

5

hictal stamping (346)

M iscellane<)us [ex[i!e mill prodllcts (229)

Newspaper puhiishing & pritlting (27])

Witting mills (22j)

O{her primary ir[)n & steel indllstries

B~kery prodl!cts (205)

Yam, thrad, & fabric mills (221-224, 228)

Mat products (201)

Con f=iionery & rdlat~d pr08ucts (?07)

“Cement, concrete, gypti-nl, & plaster pr<)dtlcts (324,327)

Ordinance (19)

Miscellaneous wood prodllc~ (244, 249)

Logging (24 1)

Synthetic fibers (28 T3. ?S24J

Dyeing & finishing textiles, exe. w[)(~l & knit g{,{~ds

Blast timflces. SIX1 works, rolling & Ii”ishi”g r]lills

Jorge[lsoll’s TFP

Non-electrical machinery

Petroleum refining

Electrical machinery

Apparel & other textile

Chemicals & allied

Textile nlill prod~lcii

Miscellaneotls Manllfactllring

Rl!bber & plaslic

Other trarisporta,io[i “eq(lipmen[

F1lrllit~lre and iixi~lres

lnstr~lments

Paper & allied prodt~cts

Lulnber and wood prOdllCtS

Fabricated metal

Lest her

Stone, clay and glass

Prilnary metals

Food & kindred prod~tcts

Tobacco man lifac~tlres

hlotor veliicles

Printing & p~lblishing

TFP, NBER Dataset,- hiealls oier 1977-.S7

3s

.00605

.00596

.00S 16

.00336

.00314

.0.0283

-00223

.0018t

.00096

.00M 1

-.000?9

..00077

-.00199

-.00600

.T.01 178

-.01180

.025861.

.020192

.0I9077

.0.16959

.016570

.01j416

.014244

,01~~64

.011727

.010903

.009004

.008890

.008340

.006900

.006687

.0048”65

-002812 .

.002277 “‘

“-.00161 1

.oo~]~” “’ “

-.005576

Electronic comp(iting eq~iipmeil[ .17557

Not specified machinery .04299

Synthetic fibers “ .03719

Ordinance .03564

Miscellaneous textile tilill prod[icts .03456
6 Grain-mill oroducts 7 Radio, T.V-., & co”rnmunicition ~“iiipnlent 8 Petroleum refining 9 Screw machine products

10 Not specified chemicals& allied products

11 .Confectionery& related products

12 Miscellaneous plastic prodticts

13 Knitting mills

14 Optical &“h“ealth services supplies

15 Not specified electrical machinery, equipment,& supplies

16 Floor coverifi~i, exe. hard surface

17 “Agricultural chemicals

18 Rubber products

19 Miscellaneous fabricated textile products

20 Household appliances

21 Beverage industries

22 Industrial chemicals

23 Yam, thread,& fibric mills ““”

24 Sawmills, planing mills,a nd lmill Work

25 Paints, varnisl~es,& related produc[s

26 Pulp, paper,& paperboard mills

27 Apparel& accessories

28 Plastics, synthetics& resins, exe. tibers

2g Structural clay products

30 Logging.

31 Cenient. concrete, gypsuni,”& plasler producls

32 Electrical machine, equipment.& supplies, n.e. c..

33 Misce]]aneous \vo.ad products

34 NliScel]aneOLIS chenlicals

35 Dairy products

36 Bakery produc[s

37 O[her primary CO1lferrOuS industries

38 FurnitL1re& fixtures .—.

39 Fabricated structural metal products

40 Dyeing& finishing textiles, exe. \\OO]

& knit goods

41 Printing, publishing:,& allied industries, excepl newspapers

42 B]ast furnaces, steel works, rolling& t“lnishing lllil]S

43 Not specified professional equipmen[

44 Office& accounting machines_ ..-

45 Not specitied nletal industries

46 Photographic equipment& “supplies

47 Miscellaneous paper& pulp products

48 Other primary iron& steel ind(ls[ries

..-.

49 Miscellaneous fabricated metal prod l~cts.

50 Canning& preserving frui[s, \,egetables,& sea foods

51 Footwear, except rubber

52 Misce]]aneous petro]eum& COal prOdllCtS

.02947

.02815

.02704

.02677

...02449

.02369

.~2338

.02 ioo

.01840.

.01782

.01733

.01731

.01726

.01714

.a1540

.01492

..01460

.01448

.01423 .

.01346

.01342

.01313

.01288

.0.1273

..01255

.01193

.01168

.01124

.01021

.01015

.oa957

.00953

.0a882

.00835

.00792

.00780

.00728

.00710.

.00655

.00630

.00609

.00516

.00489

.00459

.00423

.00415

.003577

39 I

.——
40

53 Mobile dwellings & campers

54 Meat products

55 Pottery & related products

56 Leather products, exe. footwear

57 Glass & glass products

58 Cutlery, hand tools:& other handware

59 Paperboard containers &- boxes

60 Not specified food ;ndu=ries

61 Not specified rnanufactuting industries

62 Miscellaneous manufacturing i:d~s[ries

63 Scientific & controlling .fnitruilents ‘“ ““” “’

64 Watches, clocks, & clock-work-operated de~,ices

65 Miscellaneous food preparation & kiridred prodilcts

66 Miscellaneous nonnietallic mineral & s[one products

67 Drugs & rnedicjnei”.

68 Motor vehicles & motor \ellicle equipment

69 Primary aluminum induitties

70 Cycles & lniscelltifieous transportation eq”uipmerit

71 Metal stamping ,.

72 Aircraft & parts

73 N!achiner?, exe. electrical. n.e, c,

74 Ship & boat building & repairing

75 Soaps & cosmetics

76 Newspaper publishing & prin[ing

77 Metal uorki ng nraclti”nery’

78 Engines & turbines ‘

79 Farm rnachi nery &“”-equipreen t

80 Railroad locomotives “&“equipmen[

81

82

83

\’.

Construction & material liai)dling mgcl)ines

Tann”ed, curried, & tin”is]led leather

Tobacco manr!factures

.,0.035?0

“:00325 1

.om249

-:003090

.003054

.0016S2

.0017”14

.001097

.000785

.00.0784

..000705

.000630

-.000138

.-.000595-.

-.000653

-.0.01119

-.001193...

-,001255

-.001359

-.002037

-.002936

-.”003132

-.003367

-.“004294

-.006743

-.009734

-.017799

-.020352 “,.

-.020607

-:629667.

-.038326

Company and” other (excep( F1,deral) I<&D Iunds as” a percent of net sales in R&Dperforming” nlan[lf:lctllrill: c[}n]p;lnii,~. n]tilns over 1984,1990

Indlistry

Office, computing, and accotinting” machines

Drugs and medicines

Scientific and mechanical measuring instrL[111e,1t5

Electronic components

Instruments

Communication ‘Squip-ment

Industrial chert~icals

Motor vehicles. and nlotor \ehicles equipment

Radio and TV receivili~ equipment.

12.5714

.8.7429

8.5000

8.2143

7.3286..

5.257i

4.2714

3.4143

3.3857
Other chemicals

Other machinery, except electrical

Other transportation equipment

Stone, clay, and glass products

Other electrical equipment

Rubber products

Nonferrous metals and products

Fabricated metal products

Other hfanufacturing Industries

Stone, clay, and glass products

Professional and scientific instruments

Petroleum refining and extraction

Paper and allied products

Lumber, wood products, and furni[ure

Ferrous metals and products

Food, kindred, and tobacco products

Textfles and apparel

41

3..3429”. . .,

28714

2,3143

2.2714

~.2~86

1.7286

1.3143

1..20ao

1.0857

1.0857

1.0857

0.9”286

0.7286- ~

0.6857

0.6000. ~

0.528.6-

0.4429

VI. The Rate of I1ltroduclion of Ne\\ [’rod!lctio[l Processes (Yale Dat:lset).

1

~

3

4

5

6

7

8

9

10

11

12

13

14

15

16

17

18

19

20.

21

22

23

24

25.

Indiist ry

Tobacco manufacturers

Photographic equipment and supplies

Guided [missiles. space \relricles. and ix~r[s

Electronic ccmlpu[ing e~uipment

Cutlery, hand! o.ols...and .olher lhar(ltv~rc

Radio. T. V., and communica[io]l eqtlip!lient

Logging

Aircraft and parts

bleat prod ucts

Not specified” electrical machinery. equipment, & supplies

Sawmills, planing nlills._ancl mill,vork

Pottery and related produc[s

Electrical machinery, “eqt]ip[)lc}l[.al)(l supplies. n.e. c.

Farm- iilachiiery and eqtlipme[){

Metalworking. machinery

Not speci fled machi !nery

Miscellaneous paper and pulp prod!lcts

Glass and glass products

Iron and steel fo(tndries

Not specified professional eqt]ipment

Drugs

Optical and health seri!ices supplies

..Su~ar and confectionery products

Motor vehicles and molor k,ehicle equipment

Paperboard contii]iefs “and boxes

Rate

6.00000

6.00000

5.75000

5.571$3

5. 17j29

4.81008

4.75000

-4.68795.. ~

.4.66667

4.61272

4.55237

4.50000

.4.40495 -

4.40000

“-~.38660

4.33960

4.33333

.4.333”33

4.2”8571

“4.275a.

.4.23529.

4.18992”-

4.17556

4.06938

-4.00000

Observations

1.0000

2.0000

“4mooo,

21.000

1...82.47

16.113

4.0000

11:189

3-00.00

11.716

3.s257.

2.0000

6.”9572

5.0000

.4.8268

9.4429

6..0000.

3.-0000.

7.0000.

10.531 ‘

17.000.

7.5140

1.0000”

12.612

6.0000 _
26

27

28

29

30

31

32

33

34

35

36

37

38

39

40

41

42

43

44

45

46

47

48

49

50.

51

5:

53

54

Sj

56

57

58

59

60

61

62

63

64

65

VII.

Ordnance

Household appliances.

Miscellaneous manufacturing indllstries

“Miscellaneous plastics products

Petroleum refining” “

Constrl[ction and material ha;ldliilg jniachine:

Plastics, synthetics, all:d resins

Mres and inner tubes .“..

Machinery, exe. electrical, n.e. c. =

Scientific and controlling instruments

-.

Other primary metal ihdistries

Not specified-manufacturing ifl~ustries

Industrial and miscellaneolts chemicals

Screw machine prod[lcts

Miscellaneous fabricated metal prodt[cts

Engines and turbines

Soaps and cosmetics :

Not specified metal industries

Toys, am~[sement, and sporting g~ods

F1~rniture and iix [ores

Pulp, paper, and paperboard lmi]ls

Blast furnaces, steelworks, roiling and llnishin~ mills”

Nletal forgings and_staillpings

Railroad locomotives and equipment

hfiscellaneous. nonmetallic mineral and stone products

Not specitled food industries

Agricultural chemicals.

Printing. p~!blishing. allied industries. exe. newspapers

Paints, ~,arnishes, and relatecl prodtlc[s

‘Canned and presemed ‘~rlli!s and vegetables

Fabricated str~lctural me[al produc[s

Grain mill products

Ship and boat building and repairing

hliscel laneo.l!s..food prepara[ ions and ki[ndred products

Dairy products

Primary aluminum industries

Bakery-products

Structural clay prodl[cts

Office and accounting inachines

Cement. concrete, gypst]m. “slid plaster products

-4.00000

4.000.00

4.00000”

3.96429

~..:qmoo

3.85086

3.83760

3.83333

““.3.78388

3.74319

.3.73167

3.69854

3.68717

3.66667

.3.56351

3.54392

3J3891

3.509.47

3“:50000

1.47.86.8

3.46755

3.40000

3.40000

3.3.X333

3.21132

3..15.5.64

3.14821

3.00000

~,Ooggo

2.97157

2.72981

2.67527

2.66667

2.42857

2.41501

2.34286

2.00000.

2.00000

~.00000

1.79645

Pate,lts Used by Izldt,stry (Iot:ll of 1980-83 divided by 1970-79)

42

I .0000

1.0000

1.0000

‘28.000

10.000

S..3.631

13.555 ‘“

6.0000

5.0378

16.156 ..:

1.7146

2.8088

16.962

3.0000

5.0856

3~094

.11.853

4.6228

4.0000

.1.0000

12.866

1.0.000

5.0000

3.0000

2.4397

3.0482

“4.9926

1,0000

8.0000

4.8388

2.2974

3:3025.“

3.0000

7.0000

3.4900

4.4286

2.0000

1.0000

1.0000

2.0859””

Office and computii]g machines .4366

Comtnunication arid electronics .4049

Petroleum refineries & extractions .3962
43

Other electrical equipment

Prof. and scientific ifiitr~ime)lti

Other manufacturing

Drugs

Stone, clay and glass products

Transportation equipment

Industrial chemicals

Fabricated metals products

Other nonelectrical machinery

Primary ””metals product;-

Rubber and plastics prodllcts

Other chemicals

Paper products..

Aircraft ”and”missiles

Food and hndred prOducts

Lumber and furnit(lfe

Textile and apparel

.3779”

.3”s81

.3572

.3528

.3478

.?418

.3418

.3$14

.3386

.3301

.3~gg

3?8~ ..- ...- 3~7j

“.31”99”

.3176

.3166

.2998
44

Table 3

The Effects of Technological Cllange on the Likelihood of Conlpany Training

‘in tlle Mal;tlfactLiring Secto#

I. Jorgenson TFP

II. Share of Investment

in computers

III. Growth of Investment

in comp~lters

IV. NBER TFP

V. Yale Innovation Rate

VI. R&D to Sales ratio

VII. Use of Patenls

A1[

25.26” ..011

(.om)

2.11 .010

(.”09)

3.089 .008

(.’19)

2.36 .006

(.10)

,l~g .011

(.10)

.080.5 ..021

(.001)

6;13 .0[6

Production

32.95 .018

(.004)

3.90 .012

(.058)

4.854:’...008

(.19)

5.99 .01

(.022)

.028 ,oo~

(.81)

162? .026

(.0001)

10.85 .018

(.005)”” (.002s)

Non-Production

9.56.”. .013

(.457)

-.02 -.0002

(.99)

,962 .001

(.76)

.002 .00001

(.999)

:141 .o~

(.20)

.0289 .012

(.378)

1.267 .005 ‘:

(.661)”

‘In parentheses, below the Iogi[ coefficients, are estimated probability that the coefficient is

not different from zero. To the right of each estimated coefficient is Ihe derivative (dP/dX),

multiplied by standard devia[ ion of !nleast[re of techno]igical change. The derivative is

calculated as...fi~( 1- ~), where ~ is the lmr~n incidence of training in the sample.

The valtles for the standard deviations are: .0.086 florjorgenson’s TFP, .03 for Investlnent in

cornpoters, .026 for growth iii”itii,est!llent ill comp~!ters, .027 for. the NBER TFP, .86 for the

Yale meastlre, 2.57 for the R&D to sales ratio, and .027 for llse of patents. ‘The mean rates

of training For the sobsamples in the regressions are .111 for all workers in manufacturing,

.067 for production workers, and .196 for no!l-production workers.

I
45

Table 4

The Effects of Technological Chan:e on the likelihood of All Types of Training & NossCompn ny Training

in the hlanufncttiring SectolJ

The Ukelihood of Any Trainill:

All Pr(bdtlctittn N(,r]-PrgjdtIc<i(bil

1. Jorgenson TFP 24.76 36.43 “-.93 . .

(.003) (.0001) (,94)

II. Share of Investment

in completers

111. Growth of Investment

in computers

IV. NBER TFP

V. Yale Innovation Rate

VI. R&D to Sales ratio

VII. Use of Patents

1.88 3..4”1

(.086) (04)

1.95” .4.16

(.34) (,16)

1.08 1.89

(,41) (,42)

.046 -..01

(.48) (.82)

:033 .072

(.13) (.033)

3.13 4.76.

(.106) (.110)

.2”1

(.89)

-1.67

(.58)

;64 ~7.2) ------

.0.8. .:..

(.42)

.@o

(.51)

.6j7

(.81)

Non-Conlpan~ Training

All Pr<bducti<>,lX[nl-Pr<,d”cti””

25.61 41.62

(.06) (.01)

-.081 .444

(.97) (.87)

1.17 2.31

(.77) (.64)

~3.26 -4.98

(.31) (.33)

0.093 -..103

(.46) (.50)

-.079 <069

(.11) C29)

-3-51 -5.32

(.39) (.33)

-40.85

(.15j

-.~84

(.94)

-6.46

(.41)

300.

(.95)

-.Q62

(.46)

.10.1 ‘“

(.99)

“In parentheses. below tl)e logit coefficients, ire estirna[ed prokabili[ies thai”lhe coeftlcieits are not differe

from zero.
46

Table 5

Interaction Effects of Technological Change and Edncation on the Likelihood of

Conlpany Training in tlle Nl:lntlfacturing Secto#

I. Jo~enson TFP

Y=m of Education

JoI; .* EdUC

II. lnv. in Computem

Years of Educati(>”

COmpu[ers*Educ

III. Grt]\vtll (]F C[)nlputkrs

Years of Educati(>n

C<)n]ptttem*Edltc

IV. NBER TFP

Years of Educa[!<)n

NBER * Ed[[c

V. Y;lle llln[jv~itit!l] R;lte

Ytirs (]I Edltcxtii;i

Y~le*Edt]cit i[~])

\,I. R&D to S;!lcs R~lti,]

Ymrs of Education

R& D* Educati[>n

VII. use of PutenLs

Y=rs of Edl~vation

Paten[s*EJ\!ca[;on

All

58.68

(.10)

.26

(.0001)

-2.54

(.33)

25.76

(.0001)

.347

(.0001)

-1.62

(.0001)

37.91

(.OO.jS)

.304

(.0001)

-2.44?

(.0093)

24.4j

(.003)

,~j

(.0001)

-1 .5?

(,OUG)

.5 2(1

(.2(1)

.321

(.0(1?)

-.030

(.31)

,436

(.0001 )

.?9 [

(.0001)

-.025

(. 0004)

37.56

( .0002)

9s7

(.0001)

-2:197

(.002)

Prod[lction

-3.92

(.9j]

.09

(,26}

3.10

(.56)”

49.61

(:0001) .“

.393

(.0001)”

-3.74

(.0004)

94.2?6

(.0003)

.363j

(.0001)

-7.2s3

(.0006)

10.7s

(.26)

.14

(.U09)

-1.25

(.40s]

~7? .- . .._

[.j4)

..039

[.s08)

.03s

(.54)

.340

(.0s8)

.147

(.03?)

-.0!5

(.341)

.41.6S

(.047)

1.0?9

(.0S6)

-2. j9

(.129)

h’on-Prodnct ion

Izl.s

(.05)

.31

(.0001)

-s.10

(.05)

?4. 76

(.007)

:.332

(.0001)

-1.5s

““(.007s)

29..1 ?5

““(.159) --

.25s

(.0001)

-1.776

(. 1937)

2s:39

‘(. 023)

.24

(.0001)

-1. S6

(.021)

2.247

(.0014)

.761

(.0001)

-.140

(. O02j )

.jOS” –

(.002)

.303

i.0001 )

-.031

(.002)

36.09

( .“022)

1.00

(.007)

-2.2s

(.027 )..
47

,,,

16 yalrs ,,

9-lI yt;lrs

13-15 ywdrs !, ,.

17+ years

!,

,l,,r~l,llx(lll’%

Ip,)

51.7

(.35)

-13.C12

(,47)

~s,ix

(;017)

14,1J { 3?1

‘?(1.35

(051) I5 ((~)

‘, 40)

!)$,46 { 14, 5 0?9 ( ~sol

>i>5

;1(Nyl

3s4

(.17)!

44,9

(,2dj:

.s.1

I

(.9.4]

,,,

.183

(.344

$:

cl.15

(.231

-16,7

(.4q

8.38

(,61)

-35.4

(.097)

I“i ,’

I

(;r(lirlllor($(1,1111.

i,lv<$lkt,rr,{

,,,

31.3j

(.10)1”

(1.67;

(!111

!

.87

.12.7

(80)

-?7.9

(.38!

1.36 ,,! ,,,

(.81~~

1144 ~m). ,:. ,. ,.

,5.1

(.19)

2,63

(.68]

Nlllkts

‘II;!’

.58,9

(~~)

(1.34

(.?0)

5.69

(.04)

4.97

(,078)

.54

( 80)

.1,5(,

(.(.1)

5!5

{.31)

x.9

(!(l(l(>}

5.07,

(.111 ? 94

(.57)

.2.47

(.89)

.ufi4

(.qsl

.240:

(.44]

.11.3

(,70)

3.72,

(,44!

5.451

(.12)

-1.27”

(,58)

.3.06

(.39]

Ynl(.’sGlle Ilr

ilnllilvillitltl

-39

(,46)

,.16,

(.45)

.,07’

(.4?)

,,20

(.2:)

..10

(.44+

,013

(,05)

,12 ( 85)

.,?J

(,,31)

,,07 (.s7)

..0s

(,s4)

.,62

(27)

..57

(,j8]

.117

(,341

,13

(,?8),

-,04,,

(,71)

.27

: (.?7)“

; .,01

(.63)

..0?

(.91)

,!

,26

(.40)

,058

(.47)

,121,

(,0005)

,136

(,0006)

.00113?

(,97](

.005“

(01)

.362

[?4)

,!39

(,00(!)

.170 (.Oooij

.142

(.027)

.,17

(.45)

,051,

(.741 i: i

-4.6:

(.47)

.,45:

(,38)

,.07I

l19j”’!

,135

(.009)’

-.020

(,59)

.,019

(.70)

-3.08,

(.85)

,052

(,99)

.?..7’1

(.oi3j

8,76

(.031)

:s,09!

(.12)1

.323

(.95)

2.15

(.91)

j,,!

(.M):

..131!

‘(.96)“

-.51

(.94)

.31’,76

(.o8)

.5,19

f.84)

.89.9

(.38)

-28,03

,(.31)

,.~.48

(.06) ‘“‘

1?.47

f.o19)

.4.4?

(.~~)

-.338

(.95) I II
48

Table 7

First Year and Beyond: Is the Effect of TecllnoIogicaI Chauge Different in First Year of

Tenure?

Product iotl N’on-Production

1. Jorgenson TFP

Low Tenure ..39,48 .726..

(.027) (.967)

High Tenure 31.69 I 1.572

(.007) (.377)

II. Inv. in Conlputeri

Low Tenure 4:79 -2.38

(. 125) (.330)

High Tenure “’ 3,645 .578 .-

(.092) (.737)

111.Growth of Conlputers

Low Tenure 6:121 -.444

(.334) (.933)

High Tenure 4.55 1.2.8..

(.248) (.696)

11’. NBER TFP

Low Tenure 8.31 -4.74

(.097) (.213)

High Tenure 5.,39. .9“6~

(.060)

~6;7) . ...”’ “

1’. Yale rate of Inuovntiou

Low Tenure

High Ten\lre

1’1. R&D to Sales Rate

Low Tenllre

High Tenure

VII. Use of Patents

Low Tenitre

High Ten ure

.040

(.77)

.026

(.83)

.“165

(.008)

,162

(.0001)

10.5.

(.004)

10.9Y

(.002)

.077

(.59)

:158

(.157)

-.016

(.744)

.038

(.252)

.860 -. (.77)

1.40

(.63)
49

Table 8

The Effects of Technological Chnnge on HolIrs of Coriipany Trairiing

Tobit “Deconlposition” Analysis .—

Using Different h~ea:yres of Technological Chnnge; hlales W’orkers; hlannfacturing

b<eas”re of TK. Ch:inge

& Gm”p Of \Yorkrrs

Jo,z.”so” TFP

All workers

Production

NOn-PrOductioxl

NBER TFP

All workem

PrOductia”

N.”-Prod”ctio”

(standard errors in pfirentlleses)

a~,/axJ

l)vr.

206

(88)

?58

(98)

-74.85

(2?9 )

14.8

(16.8)

38.10

(25.35)

-9 .0?

(33.37)

Share “f In.m!mcn[ i“ c<>,m>\>[cr.

All w,o,ke,, 18.44

(14.08]

Pr.ductio” 14.0:

[1869)

Non-Prod”cli<>t> 17,10

(30,24)

{32.59)

Non-Prod”c[ic>t> 74.82

{54.90)

Yale D:,ra

All WO,kC,S .467

[.974]

Pmductio” -.32

(1.11)

Non-Prod”cLion .918

(2.36)

I:k,st

.234

(.101)

,384

(,146)

.060 .“

[.184)

.0=

(.0?4)

.057

(.038)

-.o]j–

(..o471_

.119

[.092)

,1o-

(.142)

,093

(,165)

.127

(.076)

,078

(.1>1

.170

(.125)-

.156

(.324)

.,,49

(.502)

.206

[.529)

E(Y- )fdF(:)/a~,l

1),,, l<k,st

1~?

(?61

:~~

[S61

.6 I

(189)

12.71

[14,43)

33.37

(22. 18)

-744

(27.55)

15.84

(1:.091

12 2s”

(103?)

1411

,24 .)0,

36 S7

r2? 12)

1s 60

[28 54,

b1 79

,453?,

.400

[ .832,

-,2s7

(,967,

,752

11.93j”

.201

(,086)

.336

(.128)

-.050

[152)

.019

[.021)

.050 —. [.0331

..011.. ‘.

(.0391

.103

[.0?s1

.093

[i?s)

0~7

( 13(,)

,,

,l@?

1 0(,6)

ohs

, 105)

IJI

( 10,

.133

, 27?,

.. 130

(,439]

.169

(.4331

[)t,e t<, izlcreilsd I,c,xtrs

~(z)[a~y - /a~jl

l)er. k;k,st

29” ,033

(123”]””” (.14)

.:31 .037

(12)

...-..1.3.

[40)

2.09

(2.38)

.:4,73

.(3.1 7)

-1.s7

(5,82]

2.60

-(!.991

1.74

(2.32)

2.98

(5.28]

6,04

(3 .63)

2.63

‘[4.04)

13.03

[9.59)

.067

(,140)

-.041

(.139)

..16S

(.425)

(.18)

...-.010

(.072)

.003

-(.0031

..007

(.005)

-.00?

“(,008 )

.017

(.012)

.013

.:(.017]

.016

(.029 I

.018

(.01 ]

..010

(.oist

.029

(.u2j

0!2

(.047)”

-.019

(.0631

.037

(.OTS)

..,
50

Table 8 (cont.)

The Effects of Twhnologi&l Change on Hours of Company Trainirig

Tobit “Decomposition” Analysis

Using Different Measures of TechfioIogical Change; Males Workers; Manufacturing

Measure of T=. Change Totit hlargi”al Eflwt Due 10 Change Participation Due b increasd hours

& Group of Workers

R&D/Sales Ratio

AJ1 workers

W.d..tio”

No”-Pmd.cti.”

use of P.[e”t,

All workers

PrOduclion

NOn-Pr.d”clio.

ay/a~.

Der.

,699”

(.285)

1.034

(,38)

.477

(.602)

47.88

(24.95)

63..43

(33.09)

J 6.90

(52.86)

EO” )Ia~(z)/axjl

Elast. Der. Elast.

.161

(.066)

.259

(.095)

.101

(.12s)

1.64

(.86)

2.92

(1.53.)

.39

(1.23)

.600

(.245)

.-.. .906

(.333)

.394

(.497)

41.11

01.42)

55.5s

PJ.97)

13.94

(43.63)

.J38

(.565)

.~7

(.083)

,083

(.105)

1.41

(.73)

2.56

(1.33)

.32

(1.01)

F(2)~aEy” /a~j

Der. Elast.

:098 L .022

(.041) (,009)

_ .127 .032

(.048) (.012) ““’

.0T7 .083

(.022) (.105)

6.75 .23

(3.54) (.12)

7.85 .36

(4.14) ““(.19)

?.95 .07
51

Table 9

Past Training, Technological Change, and Current Training:

Interacting Technological Change with Past Training Dununies=

Didn’t change industry (2 digit) Didn’t change employer

Product ion Non-Production Production Non-Production

I. Jorgenson TFP

Past Training 2.42

(.94)

No Past Training 31.55

(.08)

11. Inv. in Computers

Past Trainirig - 6:12

(.21)

No Past Training 5.57 ““

(.09)

111. Growth of Computers

Past Training “ 3.13

(.75)

No Past Training 1.05.

(.87)

IV. NBER TFP

Past Training 8.38

(.24)

No Past Trai,ling 9.:60

(.023)

V. Yale rate of Innovation

Past Training -.06

(.85)

No Past Training .21

(.27)

VI. R&D to Sales Rate

Past Training

.15i. .

(.096)

No Past Training .206

(.0006)

VII. Use of Patents

Past Training 11.33

(.23)

No Past Training 14.35

(.019)

-6.61

(.79)

-.53

(.”98)

-3.02

(.37)

.431

(.88)

-8.28

(.29)

1.63

(.76)

-.81

(.83)

-1.78

(.57)

.0Z6

(.91)

.190

(.29)

-.026

(.67)

-.002

(.97)

-2.33

(.67)

4.45

(.36)

-19.2

(.49)

26:5

(.12)

.679

(.87)

4.73

(.138)

5.12

(.55)

3.40

(.57)

-1.72

(.75)

6.54

(.12)

-.28

(.33)

.056

(.75)

..048

(.52)

.179

(.003)

2.17 “““

(.76)

12.26

(.03)

-106-’ 

(.66) ._8;7

(.64)

-2.67

(,42)

3.61

.(.15)

.-8.42

(.30)

8.47

(.068)

-1..40

(.71)

-.58

(.84)

.074

(.75)

“:”182

(.26)

-.024

(.68)

.028

(.54)

-6.47

(.25)

4.4s

(.30)

“ The dummies are: “Past training”= 1 if the person reccitied company training betw~n “t-2 and t-l (the

dependent variable is training between t-1 and t). “No Past Training”= 1”if the person did not train between

I-2 and t-1. In the first two columns the sample is limited to workers who did not change industry since t-2. .

In the last tw columns the sample is limited to workers who did not change employer since t-2.
52

Appendis A

Data

1. General

The data are from 1979-1992 National Longitudinal S(irveys of Labor” MZrket Expetieice of youth ag

14-21 in 1979 (NLSY). Additional data are ob[ained from !he NLSY work histofv file. The NLSY work

hist~ry file contains primarily employment related spell data constructed from the-main NLSY file. Both

files are available in cd-rem format. hfany qlles[ions are asked”’witll regards to the tiine since- the last

survey. For the first survey (1979), the questions, in most cases, are with regards to the time period since

January 1, 1978.

In addition to the NLSY, we Ltse information from \,arie[y of sources. These are industry m=sures o

technological change and other industry Ie\!el variables. They are described in the text.

2. The Sample

The number of men iritemieu,ed in 1979 is 6403. Not all individLials are interviewed each year. The

first observation for an individual (to be includsd in our sanip]e) is the fiisi “silrvev iri which the main

activity reported for the week prior to [he sur\ey is w’orking”( 1), with a job, but ~ot working (2), or

looking for a job (3). Follow, ing- thal. an individual is incltlrted in the sample as long as he is interviewed

(even if leaving the labor market). Other restrictions apply only for specific tinalyses. The panel is

unbalanced, and the number of obsema[ ions per individual varies.

3. CPS Job

For each respondent, employment information on up to a maximum of 5 jobs is “recorded in each

survey year. One of the$e jobs ii desig[iated as i CPS job and ii is tile most recen(/current”job at the time”

of intenriew$. Typically ii is also the [nain job. Each job is identified by a number (-1 to 5) and job #1 in

most cases is also. tbe CPS job. For only [his so called CPS job there are a I1OSIof additional

empIoyer/employee rejated questions [Ila[ are ~]sktd in the NLSY sllrveys. Our analvsis is restricted to CP

h

4. The t$’ork Histor~ File

We use Lhe \vork llis[ory file to constrtjc[ [he tenurt. sepfira!ion and reason for separation variables.

(a) Tracing jobs al~cl Tenllrc with IlnlploJ cr: The Ienure variable is already constructed in the work

history file. The major diftictll!y is {racing CPS jobs over [he”interview years:” A {ariab~e””&lled PRE

allows matching of employers between consecutive inrer~iew years. For each job in a particular sufie

year it gii,es “the job number that “was assigned [{1thal job in [he previous “year (a.ssgming of course th

the current job existed in the previous yenr). Our progr~mming strategy was to pick CPS jobs in whi

the respondents are actual] y emp]uyed <It ttlL> tinlt of inter\ie!v. and to trace these jobs to the next

survey year via the PREV variable in [he succecdi[l: sllrvey year. There are, however, a few cases

where we cannot trace the current CPS jolj in [he s~[cceeding interview year with PREV. The current

tenure value is the total number of weeks \vc~rked up [o the interview date. - A shortcoming of PREV is

that it allows for matching employers bet\veen consecuti\,e interview years only. If, therefore, a

respondent worked for a particular employer say in 1980 bu[ not in 1981 and started working for the

same employer in survey year 1982 then [here i<.no way of knowing the total years of tenure with th

employer since employer numbers are follotved only in contiguous interviews. This ]nay “not be a

problem for tumo~,er analysis sirice r~-~nlploy”nieli( with the same employer after an absence of that

length (i. e., a period longer than tha[ be[\$een t!vo successive interview years) maybe considered a ne

job.

5. ~!’eek bet\veeil SIII.VCYS
53

The number of weeks between sumeys ranges between 26 and 552 weeks. The large numbers are the

results of individuals not being surveyed for several years, In all our analyses we included (when it made

sense) the variable WKSSINCE (weeks since last survey). The variable was excluded if it made no

difference:

6. Training:

A varietY of formal training questions were asked in alI survey years.,. except 1987. Individuals were

askd to report up to two government programs in which ;hey w,ere enrolled since the previous intemiew,

and up to four vocational/technical programs. Un[il 1986 the maximllm was two programs, and in 1988 i

was increased to four.

Up until 1986, only if the’ program lasted moii than 4 weeks, further questions were asked, in

particular the type of program and the dates it started and ended, Starting in. 1988 these questions were

asked about all programs, regardless of length. The fo{ir N,eeks condi,tion””ilp“to ~986 is a majo”r

shortcoming of the data set. Any analysis [hat focus on a specitic type of training (e.g. company training)

has to be limited to post 1986. The fbllowing example illustrates d~e problem: The percentage of workers in

our sample that reported enrollment in company training is 4.7% .mver the period 1976-1990. Limiting the

sample to 1988-1990, the rate i!lcreases to I 19:,

In certain years (80-86. 89-90)..a distinction \vas Imaciebetw”een pro~rams in”ti:hich the individual was

enrolled at the time of the previous inter\, iew, ~Iid progmms that started after the previous interview.zg

When such a distinction is made, up 10 two programs al the time of last interv~ew cari”be reported. A

person was asked abOLlt training that took place at the time of 11s[ “in~eririew, only if the interviewer had a

recmd indicating so. Therefore, far 1980-86, such a record did not exist if training took less than a mont

For all programs the starting an~ ending momll and year ;~re reported. Also reported are the average

number of hours per meek spent ifi [raining.

In o~tr programming we ti;~nlb;f all progrnm> in “Ihe folloi{in: order: the four vocational/technical

programs are numbered 1-4, the IIVOprograms at lime of last inter\,ie\v are numbered 5-6, and the

government programs are numbered 7-8.

Type”of Training: Up to 1986,. the ~ollokving ca!tg[)riesq~grel>or[ed:

1=Business College.

2 = Nurses Program,

3=apprentic~hip,

4 avocational-technical Institution.

5 =Barber Beau[y,

6= Flight Sc.hOOl,

7 =correspondence.

8 =company/n~ilitary,

—.

9=other.

We aggregate them into company ~rainin~ (8). apprenticeship (3), and “other” (1,2,4,5,6,7,9 ). Srarting in

1988, the-breakdown is more detailed:

1-7 -are unchanged.

8= A formal company training run by cmployeror military straining (excluding basic training).

9= Seminars ortrainingprc>rams a[ \vork r{!t~”bysomeone other than employer,

10= Seminars or training programs outside of work,

11= vocational rehabilitation center,

12= other.

We now aggregate 8-10as co!llpany’tmining, and 11-12”as “other”.
54

Below are additional descriptions of some of the variabies. used:

Any Tech/\’oc Training Dunlmy: Whether the worker received any technical or vocational training since

(or at the time o~ last inlerview.

Any Training DILmmy (TANYD): Like the above, .bot also incllldes gov~rnmgnl training .

Company Training D[immy OCONID): If any of the training. programs w“as #8 up to 85, or #8, #9, or

#10 af[er 86. “Nolice that only after 86 the type of program was asked of all workers who reported trainin

Prior to 88, only for those who spent” more than 4 weeks on training the progran) type question was asked

(see above for more discussion of this problem),

hngth of Training: Star[ing in 1988. in addilion to asking when (month and year) did different trtiinirig

program start and end, individuals were” also asked “aliogetl~er, for how many weeks did you attend “this

training?”. The question was not asked of government training. If the answer was O (less than a week), w

re-coded it to half a week.

For each of the e“i~”htprograms. individ[lals were askeci for the average hO1lrS Per week spent training.

Multiplying the hours per week in each program with the weeks in each program, we get the total hours in.

each program.

Imputing trainin: d:~ta for 1987: ]n 1987 no training q~lestions were asked. We lltilize the answers to th

1988 survey to construct training informa~ion ~or the 1987 stlrvey, We do” so by using information on the

starting and ending dates of trailling programs. If reportitlg in 88 that still in trailiing (end month =0 and

endyr=O or Ij \ve set the end date. to [he inlervielv date. For some indi\~id”uals the answer for the beginni

date indica[es “still in train iog”. Th”iiis an error,
Variable

Intercept

If Married

If Non-~ite

1-8 y-n of schooling

9-11

13-15

16

17+

Livm in SMSA

Experience

Experience:

Tenure

Tenure’

Union h4emher

brge Firm

Durabl&s

Industry unemploytnen[

Industry Union C()\,eEtige

Industry jobs Cr*tioh

Industry jobs Destmction

Industry R&D/Sales Rti!io

1988

1989

1990

1991

1992

55

.AppendixB

Thti Likelihood of C[))llpxny Training

Estitlxtted L<)gi[ ResIIlts

h4ale \\’orkers in hfsnllfacluring

All \Vorkem Prtbdtrrti(jtl \\ ’<jrkers

Coefficient Derivlltive Ct)vlficienl Deriv;itive

-4.8S90

(0.0001)

0.2304

(0.0jG4).

-0.2447

(0.0913)

-0.6689

(0.1194)

-0.4227 .“

(0.0335)

0.0s07

(0. G259)

0.7376

(0.0001)

].~l~j

(0.0001)

0.0350

(o.797fj

0.1660

(O. 1436)

.-O.007G?

(O. 1820)

0.0j32

(0. S40G)

-0.00?57

(0. j800)

-0.1 [68

(0.4472)

0.8422

(0.0001)

-0.11s3

(0.447j).

-0.1188

(0.0188)

.0.00164

(0,78j9)

-0:0751

(0.3733)

0.09G5

(0.lj7j)

0.0805

(0.0010)

.1.3174

(0.0001)

1.4009

(0.0001)

1.630?

(0, 0001 )

1.G084

(0.0001)”

1.Gz72

(0.0001)

-0.482

.0.023

-0.024

-0.066

-0.04?

0.00.8

-O:U73

0.120

O.ooj

0.016

:0.001

0:003

-0..000

-0.01?

0.083

~o.u[2

-0.012

0.000

-0.007

:0.010

0.00s

0.130

0.138

0.161

0.159

0.!61

-j.6493

[0.0021)

0.?9SG

(0.1041)

-0.220.1

(0.?617)

-0.2832

(0.jj36)

0.0103

(0.9Gj4)

0,1068

[0. G5j7)

0.731j

(0.0809)

0.s?!3”

(0?0?5)

-U.0037 1

(0.9s43)

0.0513

(0.7477)

-U.00j96

(0.6242)

0.067 I

[0.39s91

-0.00351

([).5s77)

0.2006

(U. ?S92)

O?s[}j

(0.ouol)

-g.:U?.Iu.

[0.7678)

-0. [1(>95”

(u. 3TT2 )

0.00374

(.0. (!451)

-O. lj9S

(0. !SS61..

-0.00s41

(0.930s)

O. 16??

(0.0001)

I .3Sj7

(0. ouls)

I .4792

(0.0008)

1.8657

[0.0001)

[.9472

(0.0001)

I .9j40

(0.000 I)

.0.?291

0.0187

-0.0138

-0.017s

0.0006

0.0068

0.04j9

O.Ojl G

-0.0002

0.00j?.

-0.0002

0.0042.

-0.0002

0.0126

0.0490

-u. gu4j

-0.0044

0.0002

-0.0 I00

-0.000j

0.0102

.0.0s70

0.0928

. ..0. [171

0.1222

0.1226

-5.9714

(0.0001)

0.1440

(o. 3 S42)

.0.24s7

(0.2G74)

-1.3910

(0. 1870)

-1.G773

(0.0020)

-0.394+

(o. 1013)

O.l G9j

(0.4[ 37)

0.G579

(0.009.7)

-0.1 j54

(0.4j79)

0.3109

(0. OjSG)

-0..0133

(o. I02S)

0.0190

(0. SOj?)

-0.00430

(0. j3j3)

-0.427s

(O. 17j7)

0.s311

(0.0001)

-0.033.1

(0. S738)

-O. IG9G

(0.0227)

0.002s1

.(0.7892)

0.0143

(0.9072)

O. 19jG

(0.0.540).

0.02s9

(0.37s2)

I..330s

(0.000”2)

1.39j3

(0.000”1)

I .j4S3

(0.0001)

i.4076

(0.0002)

1.4738

(0.0003)

-0.9406

0.0227

-0.039?

.o.~lgl

.o.~G41

-0.OG? 1

0.0267

0.I03G

-0.0?4j

U;0490

-0.002 I

0.0030

-0.0007 ““

-0.0674

0.1309

-0.go?z

-0.02G7

0.0004

0.0023

0.030s

0.0045

0.Z09G

0.~.198

o.243g

0.?217

o,2j21

..—- 

—

. .

—..

—
56

Limited D. endenr Variab[. Mode”(- CENSORED

Maxi- LiEe( ihOOd Esrimare*

regression

Log- Likelihood.............. .3077.L

Threshold va~ues far the mode[: L..er= ..0000 “pee, =...,,...,

*

N(O,l) used for Sig”ifica”ce Leve(s.

V.r!able Coefficient Std. Error t.rario Prob:t:>x Mea” of

X Std. Oev. ofX

........................................................................ ,.. ...=..

.._. .._- . . . . . . .7... 7... ..,,> L.!IL

MARRD

NOUHIT

SMSAO

NEXP

MEXP2

TENUR

7ENUR2

UNION

LARGFIRM

DuRABLE 1NDUNEMP

uNcOv

?os$o_88

NEG80_88

Y88

Y89

Y90

Y91

Y92

ED18

E09-11

ED13_15

ED16

E017PLS

MEANRD S i gma

,..+. .

32.0S9

-26.970

-:i:g

.-2.4004

-.02498

.42288

-27.413

132.90

-2.4929 - ;:j:;;

-26.443

;;5::;

279.66

272.86

25?.27

247.90

-97.105

-106.73

23.222

133.02

~~i;;

36&.2b

,.,..0.

24.76

W.20

:;:;::

1.797

11.14

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24.90

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1.236

17.67

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52.11”

52.66

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~0.44

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-. ...,.

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-..927

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.?::%

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5.33Z ...07R

.,1 .269

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5.366:

2.681

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57

Appendix D

The Tobit Model and the McDonald &Moffitt Decomposition

Consider the following relationship:

y, =xi6+ul 7YX,8+U, >0

=0 2yxi@+ufso

(1)

where yi is the dependent vtiable, Xi is a vector of independent variables, ~ is a vector of urdorown

coefficients, md Ui is an independently distributed error term assured to-be normal with zero mean and

constant variance ~. Therefore, the assumption is that there is an underlying, stochastic index eqUal to

(XID+l~i) whiChis obse~ed only Wh:nit is positive, andhenCe is~unobsewd,l atentvtiaple.The

expected value of y in the model is

Ey =.xBF(z)+~z),

where ~=x@/a, flz)is the unit normal density, and F(z) is the cumulative normal distribution function. The

expected value ofy for observations above the limit, denoted by};, is X~ plus theexpected value of the

truncated normal error term

Consequently, the basic relationship between the expected value of all observations (Ey), the expected value

conditional upon being above the limit (Ey*), and the. probability of being ab.>ve the limit (F(d), ii:

Ey = F(z)Ey . ,.

(2)

The decomposition suggested by McDontid and Moffitt is obtained by considering the effect of a change in

the j variable of X on Y:

%=”F(Z)[%I+EY[31 (3)

Therefore, the total change in y can be decomposed into two parts: The chmge in y of those above the

limit, weighted by the probability of being abo.ye the limit, and the change in the probability of being above _.

the limit, weighted by the expected value of y “ifabove.

Rch of the above terms can be evaluated at some value of X6. The value of E; cm !e c~culat~ from ~

equation (3). The two partial derivatives that we focus on are

aEy .

axi ‘b+[~]ax [

a —-— afiz) ~ X2)

: F(z)*

= pj 1 -Zm:u

F(z) F(z)z

-. 1..

aF(z)

axj
C2P

0.9

P

: 0.8

b

a b.j b

1

1 0’”6

i

t ‘

v p,5

o

f 0;4

c

~ 0,3

m’

D’

~ 0.2

n,,

v 0.1

T

‘r O.c

a

College

\

\

The likelihood of Company Training

Using Investment in commuters -

Production workers, Interacting with Schooling Dummies

!

,,

\

I I I I I I I I I I I I I I I I I I I I I I I

i

0000 DO OOOOOQ Oooooooo Oooo

n

. . . . . . . . .

. .

,i 00000000 001 ;11 ;1 i ii 12222

n” 01234567 ag012345 67ago 123

9

Investment in CornDuters
C2VP

The likelihood of Company Training

P

r

o

b

a

,b

,,

;

i

,t

IY

o

f

c

o

m

D

a

n

Y

T

r

a

Using Investment in Computers-

—

Non Production workers, Interacting with Schooling Dummies

M“”

,,,

0.5:

I~t

o,4-

0.3-

0,2:

0,1: ,“

,.

q-n

o.o-

,,! I 1 I I I I I I ~ ~~~

‘i 00000000 00000000 00000000

n.

,..

~ i

00000000 00

.’

iil

p

ili “!lli 12222

01234567 89012345 67890123

9’

~, Investment in,, Cofiputers
The likelihood of Company

Us]ng R&D to Sales

Production workers, Using Schooling Oummies

o<9-

P

r

o 0.8-

b

a

b 0,7-

i

1

i 0,6-

t

v

0,5 -

0

f

~ 0.4 -

0 ;

m

~ 0.3 ~

a

n

v 0.2

T

r’o, l .

a -8 Years of Scho

i

n 0.0 -

i [ I I I I I

I 1, I I I I

n

0.4 1.4 2.4 3.4 4.4 5.4 6.4 7.4 8.4 9.4 10.4 11.4 12,4

9

Technological Chan9e
The Ukelihood of Company Training

Using ~~~ to sales

Non Production workers, Interacting with Schooling Oummies

0.6

P

r

o

I

,,

: 0,5- ~

a ha ve !,, ,:

b

I’nt

i’

1

IL

i 0.4-

t,” ~g.

v 1-8 Years of Scho

o

[[,

f o.3- ‘

c

0

m

a

n,, ,: -15 Years of Sc

Y

r

i

n O.:O: ,:

i-%

~, 7 I I I I

8 I I i I I I

, I I

n

0,4 1.4 2.4 3.4 4,4 5.4 6.4 7,4 8.4 9.4 10.4 11.4 i2.4

9

Technological Change

,,

,. 1.::

‘1

—
~. -.–.

01

02

03

04

05

06

07

08

09

10

11

12

13

National Longitudinal Sumeys ~LS)

Discussion Paper Series

Michael R. Pergamit How the Federal Government Uses Data from

the National Longitudtial Suweys

Nofian M. Brtibum A Comparison of Computer-AssistedPersonal

MafiinR. Frmkel Intewiews(CAPI)with Paper-red PencfikterReginaldP. Baker views(PAPI)in the NationalLongitudinalSuwey

MichaelR. Pergamit of Youth

Saul Schwaflz D~amic Models of the Jofit Detemkation of

Robefl Hutchens Labor Supply ad Fmily Sticmre

George J&ubson

A. Colh Cameron The effects of Unemployment Compensation

R. Mark Gritz on the Unemployment of Youth

Thomas MaCurdy

Henw S. Fwber Evalmting Competkg Theories of Worker

Mobility

Frank L. Mott Evaluation of the 1989 Child-~e Supplement

Paula B&er in the National Longitudtial Sumey of Youth

Audrey Light Gender Different= in the Quit Behavior of

Mmuelita Ureta Young Workers

Lisa M. Lynch The Impact of Priiak”Seittii Training on Race

md Gender Wage Differentials and the Cmeer

patterns of Young Workem

Evmgelos M. Falwis

H. Elizabeth Peters

he Hill

June E. OWeill

Arl&n LeibowiU

Jacob Alex Klemm

Linda Waite

“Joseph G. Altonji

Thomas A. Dunn

Responses of Female LAor Supply and Fetiility

to the Demo~aphic Cycle

A Smdy of Intercohoti Change ti WOrn$n’S

Work Patterns and Emtigs

Women’s Employment During Pre@mcy

ad Following Btih

Work Experience, Job Tenure, Job Separation,

md Wage Growth

Family Background and Labor M=ket Outcom=
14 George J. Boti=

Stephen G. Bronars

Stephen J. Trejo

SeK-Selection md Internal Migation in the

United States

15 Jnes J. Hechm

Stephen V. Caeron

Peter Z. Schochet

The Detemtimts ad Consequences of Public

Secmr ad Private Sector Training

16

17

18.

19.

20.

21.

22.

23.

24.

R. Mmk Gritz

Thomm MaCurdy

P~icipation in Low-Wage Labor Mtikets by

Young Men

Retkement h a Family Context A S~cmraI Model

for Husbm& md Wives

Audrey Light Trmsitions from School to Work A Sumey of

Resach Ustig tie National Longimdinal Sumeys

Ctismpber J. Ruhm High School Emplo~ent Consumption or

Invesment

Mak Lowensteti

Jmes Spletier

Infomal Training: A Review of Existing Data ad

Some New Evidence

J=ob Alex Klemm Cbaacteriztig Leave for Matemi~ Model&g tie

NLSY Data

Jacob Alex Klema

Arleen Leibowiti

Employment Conttiuity Among New MoQers

Stephen G. Bronws

CUOI Moore

Incative Pay, Infotiation, md Emti~. Evidence

from the National Longitudinal Smey. of Youth

Donald O. P=sOflS The Evolving SVucti~ of Finale Work Activities

Evidence from the National LongiWdmal Sumeys of

Mamre Women Sumey, 1967-1989

25.

26.

Donald 0, Parsom Povefiy Dynmics hong Mamre Women Evidence

from the National Longitudinal Sumeys, 1967-1989

h P. Btiel

Nachum Sichemm

Technological Chag: ad The Skill Acquisition OF

of Young Workem

* us. GOVmNMEN PRImNe OFFICE 19m.3g6.BI