Employer Learning and the Signaling VaIue of Education

Joseph G. Altonji

Center for Urb& M&s and Pohcy Research

and Department of Economics, Northwestern University

and

NBER

Chades R Rerret

Bureau of Labor Statistics, U.S. Department of Labor

November 1997

~is project was funded by tie U.S. Department of Labor, Bureau of Labor Statistics

under small purchme contract 41USC252C3 (Order No. B9J3463 3). me authors

gratefully acknowledge the tremendous assistance provided by Nachmn Sicherman titi

the NLSY data. ~ey dso titi participants at the Biw&o conference for helpti

comments. Opinions stated in tis document do not necesstily represent offlcid

position or poficy of tie U.S. Department of Labor.
Employer Learning and the Signaling Value of Education

Abstract

If profit mtimiting tis have firnited information about the general productivhy of new

workers, they may choose to use easfly observable characteristics such ‘asyears of education to

“statisticdy discriminate” among workers. The pure credentid value of education W depend on

how quicMy firms learn. To obtain Mormation on employer Ie-g, we work with a wage

equation that contains both the interaction between experience and a hard to observe variable that

is positively related to productivity and the interaction between experience and a variable that

tis can easfiy observe, such as years of education. The time path of the coefficient on the

unobservable productivity variable provides information about tbe rate at which employers l=

about worker productivity, Using data from the NLSY we obtain pre~tiary estimates of the rate

at which employers learn about worker qudhy and use these, along with some strong au~ary

assumptions, to explore the empirical relevance of the education screening hypothesis. We

show that even if employers learn relatively slowly about the productivity of new workers, the

portion of the return to education that could reflect signrding of abfity is ~ited.

EL Classification D83, J31

Joseph G. Mtonji Charles Piemet

Department of Economics Bureau of Labor Statistics

Northwestern Univerai~ 2 ~ssachusetts Ave. ~ Suite 4945

Evanston, K 60208 Waabingto~ D.C. 20212

(847) 492-8218 (202) 606-7519

dtonji@nw.edu pierret_c@bIs.gov

and NBER
1

1. Introduction

Mtis have ~ited tirmation about productivity or the personal attributes that

determine productivity (such as knowledge, aptitude, and motivation), they til have an incentive

to “statistically discritiate” among young workers on the basis of easfiy observable variables that

are correlated with productivity, such as education.* By the same tokeu the sigrrrding value of

education is ~ikelyto be an important part of the return to education ody to the extent that tis

lack good inforsnation.about the productivity of new workers and learn slowly overtime. k this

paper, we provide some pretinrinq evidence on how much tis know about new workers and

how quic~y they learn over time and then use this tiormation to address the issue of how much

of the return to education could be “due to signrdirrg rather tb to the direct effeet of edumtion on

productivity.

The key difficulty for our investigation of sidg is how to measure how quic~y

employers learn. Our analysis uses an approach developed more My in Mtonji and Plerret (1995,

hertier W). It is based on a model in which firms have ordy titd information about the qudty

of workers in the early stages of their careers. They statisticdy dlscrimirrate among workers on

the basis of easfiy observable variables that are corrdated with productivi~ such as years of

education or degree, the qurdi~ of the school the person attende~ race, and gender. They weigh

this information with other information about outside activities, work expdence to date, and the

information contained in references, the job interview, and perhaps foti testing by the fi.

Each period, the firm observes noisy indicators of the worker’s performance. Over time, these

1We use the term “statisdd discrimination” to mean that in the absence of W irrformatio~

firms disdnguish between individrrrds with different characteristics based on statisti~ re~tities.

That is, ~ form rational expectations ~ven the tiorrnatimr they have. Many papers thti use the

term statisticrd discrisnkmdon rmdyze race or gender differentials that arise because ti have trouble

processing the information @ey receive about the pefiormance of minority “~oup members. See

~gner and Cti (1977), Lundberg and S- (1983), Lang (198~, arrd Coate and Loury (1993) and

Oetiger(1996j.
make the information observed at the stti redundant. Wages become more closely tied to actual

produdivity and less strongly dependent upon the information that was r~dily atiable at the

beginning of a worker’s career... We dmwifieren%s about how quicuy firms learn by observing

the rate at which the weight in a wage equation s~s from variables that the h can easfly

observe (such as schoo~ig) to a variable tht wodd be hard to observe.

Our investigation of employer learning bufids on some previous work particularly Farber

and Gbbons (1994).2 Farber and @bbons investigate three irnpfications of employer ltig.

tigine a variables (say schootig) which firms cm observe dwdy and a second variable, z (say

~QT test scores) which tis aot observe directly. They show fist that employer learning

~ fiply that the coefficient ons ti a wage regression W change with experience. This is

because fiture observations, on average, simply vddate the rektionship between expected

productivity ands for new entrants. This point has been made previously as a criticism of

attempts to test screcnin~si~ng models of the return to edumtion based on changes in the

education coefficient over time. Second, they show that the part of z that is orthogorrrd to

information avtiable to employers at the beginning of a workeds careers d have an increasin#y

large association tith wages as t~e passes. Third, they note that wage growth@ be a

Hngde process, at least in the case in which productivity of the worker is constant.

h this paper and ~ we make use of a dfierent but related proposition. Specifidy, the

proposition conce~” how controfig for the experience protie of the effect of z on wages alters

the interaction between experience ands. We show that not ordy should the coefficient on z rise

‘ Other relevmt referenc& are Oibbons md =tz (1991) which we &cuss below”and Pasons

(1993). Foster and Rosenzweig (1993) use data on piece rate and time-rate workers to investigate

several implications of imperfect information on tie pti of employers that are ~erent from tie one

studied here. Theti resdts tipIy tiat tie ticompleteness of employer informadorr is m tipomt issue.

Parsons (198Q, Weiss (1995) ad Carmichael (1989) provide rrsefi discussions of some of the

theoretid issues on the li betw&n wages and employer perceptions about productivity.

Montgomery (1991) is part of a large literature on labor market networks. Mbrecht (1982) conducts a

test of screening models of eduation based on the idea that eduwtion W have less impact on the

probabihty a worker will be hired if tie worker was referred to the h by.sarotier worker bemuse

some of the information con~ed in education til be trwrnitted through the referral.
3

with time in the labor market, but the coefficient ons should fd. We dso show that the time path

of the coefficients provides information about employer learning h the current paper we present

our basic framework and an initial set of empirical results.3 We use our results to assess the

signalfirrg model of edumtion.

The paper proceeds as follows. b Section 2 we use two simple examples to show that

evidence on how much firms know about workers and how quicMy they learn is hi@y relevant to

assessing the potentird importance of si@]ng in the return to education. k Section 3 we present

our basic theoreticrd framework and our approach to assessing whether employers learn and the

rate at wtich they learn. In Section 4 we discuss the ~SY data used in the study. h Section 5

we present estimates of the wage model and provide a prex assessment of the evidence that

employers statisticdy discriminate among workers and 1= over time. k. Section 6 we use our

estimates of the experience profle of the effect of MQT scores on wages along with some

autilhary assumptions about employer learning to provide a range of estimates of what the intemd

rate of return to education would be if education has no direct effect on productivity and if a year

of education raises the log-of productivity by .05. We show that the “sign~ng component” of the

return to education is probably ody a sd pti of the percentage dfierence in wages associated

with education. Thus, while we find evidence that information is imperfect and tis do

statistically discriminate among young workers on the basis of educatio~ our estimates suggest

that they learn quicHy enough to ~it the return to a costly signal such as education.

In Section 7, we consider a potenti role for other less cosdy si@s of productivity in the

labor market. Specifidly, we demonstrate that interpreting our estimates of the time pro~e of

the effect of ~QT on wages as the result of employer learning imphes that high abtity workers

would have a substarrtid fmancid incentive to take the MQT to dflerentiate themselves &om

those who are less able in this dimension. The fact that we do not obseme ttis raises an issue for

3 Our research on the theoretical and econometric issues surrounding employer I-g and

statisficrd diicritition md “ourempirical analysis using National Longitudind Survey OfYouth

~SY) is ongotig and till be presented in Ml detti ti a revised version of N.
4

fiture resemch. We close the paper with a research agenda in section 8

2. The&teat which Employers Learn and the Signaling Value of Education

k this section we use two ~ples to estabfish the connection between the rate at which

firms learn about worker qudlty and the quantitative signifimce of si~ig models of the return

to education. The eKarrrples make the point that if education does not rtise SM and employers

can observe SW tier a short period of time, then the r-to going to school wordd have to be

s~l. The e-pies set the stage for our analysis in section 6, where we compute the inted rate

of return to education condition on assumptions about the duect effd of education on

productivity and information about the rate at which firms l-.

Let Y denote the productivity of a particdar worker.. Ignore training considerations and

assume that Y is time invariant and the same for M employers. Suppose wages W are

W=Y,

where V is the employer’s estimate of Y for a particdar worker. There are two types of workers,

Oand 1, who have productivity YOand Y1. Suppose a person has T (T>2) years to divide between

work and school, the interest rate is O, and persons can choose to go to school either Oymrs or 1

year. School involves a nonpecuni~ cost that is Ofor type YI workers and large for type YO

workers. The wage differential associated with the year of school is such that ody Y, workers

attend school. Then in sidg equfibnum (I+K) = Y, / YOis the ratio of produtivi~ of

workers who choose school to workers who do not. &some that the ordy ~ormation * have

about a new worker is his schoobg decisioq and they learn nothing for 2 years. Asume that

tier 2 year, firms know whether productivity is Y, or YOindependent of the schoohg choice.

Then the present value of earnings for a person of type Y1 who chooses 1 year of school is

(T-1) (I+K)YO

because the first year is spent in school. The present value for a type Yl who does not attend

school is

2%+ (T-2)( I+K)Y0
5

bemuse tier 2 years in the labor market productivity is know regardless of whether the person

went to school or not.

The above imphes that a type YI will choose school if

(T-1) (l+K)YO > 2%+ (T-2)( I+K)Y0.

The parameter K must be greater than or equal to 1 for the type YI person to choose school, That

is, for school to make sense for a type YI, employers must pay double for someone who they

beheve is type Y1. Since a more refllstic estimate of the percent incrwse in earnings associated

with a year of school is .08 or. 1, it is CIW that if employers can observe productivity within a

couple of years and the information is efficiently pubfic to force tis to adjust wages in

response, then the si-g value of education cannot be the main reason why education is valued

in the market

A second eKarnple may help estrrhfish the inverse relationship between the rate at which

employers learn about the skill of workers and the potential importmce of si~fllng in the return

to education. Let the average productivity of workers with ski~ level s’ be Y(s’), and assume that

education has no direct effect on labor market productivity. Suppose that the ordy information

tis have about workers when they enter the labor market is their education levels. Suppose

that the relationship between SW s’ of a worker and the mst of acquiring education and between

s and wages is such that in equ~lbrium a worker with sM1 levels’ choosess years of schoohg. k

this case the average productivi~ of a worker withs years of education is Y, = Y(s’). Let the

parameter (I+K) be equal to Y&~I , wtich is dso equal to Y(s’)H(s’-I). Rrrns acquire

information about the productivity of workers by observing their performance in the labor market.

Suppose that in the absence of information on schoofin~ their estimate of the productivity of a

type s’worker who has chosen s-1 years of schoofing rather than the usual value of S=S’for this

type is

Ye =yg-*l y(~~[l e-*f]- -Y$-l (I+K)[] - ‘- *’]

s-l,s{,t

—
6

The above equation says that in the absence of information on productive@ the fi uses the work

force mean Y~I as an estimate of Y for a worker withs- 1 years of education. & t goes to

tity, the estimate converges to Y(s’) = Y,-, (1 +K)(l “ ‘e-’1 The larger the value of $, the

more rapidly the ti learns. W* is .05 and I+K = 1.1, then &er the first year Y~~~,, ~ /Y(s ~ is

>,

1.005. When t is 10 the vakre is 1.038, and when t is 20 the value is 1.062. Thus, in this case

ti learn relatively slowly about worker productivity in the absence of information about

schookg. One may ask “What is the vdge of the learning parameter * such that the present

value of a year of education is o assuming a borrowing rate p and a career of 40 years?” tip is

.05, the solution for $ is the value that solves

with (1+K) = 1.1 and T set to 40, The solution is. 0424, which irnphes a slow Ie-g rate kr that

tier 15years Y,~l ,,, /Y(s ~ is stiH ody 1.045. h this case, the inted rate of return to

,,

education is otiy .05, which is just hdf of tie assumed percentage increase in earnings associated

tith a year of education. A faster rate of learning would imply an even lower rate of return to

education.

We would We to be able to. solve for p given empirical information about the rate at which

information about skiHs is reflected in wages. In the ne~ sectioq we examine the irnpfications of

employer learning for wage e~utions, and in section 5 we provide estimates of the parameters of

the rate at which employers learn. These estimates enable us to estimate the time path of

employer Iefig up to sale. In Section 6 we will use these estimates with some atiary

assumptions to petiorm some calculations of what the inted rate of return to schoo~mg would

be if school has no direct effect on productivity.

— 
7

3. kpfications of Statistid Discrimination and Employer Learning for Wage Growth.

In this setiiorr we outhe a model of employer learning and wages. We use the model to

show how coefficients in a wage equation on characteristics that employers can observe directly

and on characteristics they cannot observe directly ~ change as employers become better

informed about worker produtiivity.4

The basic setup of the model is s~ar to Farber and Gibbons (1994). Let yi be the log of

labor market productivi~ of worker i with ti years of experience. yi is determbred by

(l)yit=rsi +H(ti)+alqi+<+qi

where Siis yews of schoohg, q is a correlate of produaivi~ that is not observed directiy by

employers but is avtiable to the econometrician, and H(ti) is the experience pro~e of

productivity. The variable qi consists of other determinants of productivity and is not direcfly

observed by the employers or the econometrician. The variable q might be a test score, the

income of arr older sibhg, or father’s education. To simp~ the notation but without loss of

generality we scale z and qi so that they have unit coefficients in the productivity equation. In

addition to Si, the employer observes a variety of other tigs about the worker that are relevant to

productivity, which we denote by the vector ~. For now we assume that the experience protie of

productivity does not depend on q, ~, qi, or qi, but we wi~ briefly consider the consequences of

relaxing ttisassumption below. k most of the analysis we suppress the i subscript. Ml variables

are expressed as deviations from population means. In this paper we use years of schoo~mg as our

example ofs, but the basic argument apphes to any variable that employers can easily observe.

For e~ple, in W we consider race as well.

Firms do not observe yit and so must form an estimate of it. We assume that the

condhiond expectation of z Qvens and q, E(z]s,q) and E(q\s,@, are Enear in q ands, so

4 See W for a more complete development of the model.
8

(2) z= E(z\s,@ +v=Ylq+yzs+v

(3) q =E(q\s,@+ e=azs+e,

where v and e have mean Oand are uncorrelated tith q mds by definition of an expectations

Note that the W between z and q ands maybe partidy due to a causal effect of s.6 The SUM

v+e is uncorrected tith q but in addition we assume that v +e is independent of q ands. The sum

v + e is the error in the employer’s befief about the log productivity of the worker at the time the

worker enters the labor market.

Each period that a worker is in the labor market, firms observe a noisy si~ of the

productivity of the worker,

(4) E,=y+e*

where y and et reflects transitory variation in the performance of worker i and the effects of

variation in the firm environment that are bard for the firm to control for in evaluating the worker.

we continue to suppress the i subscripts.) The term et is independent of the other vtiables h the

model.

Since the employers know q ands, observing ~ is quivderrt to observing

(s) ~= v + e + E* = ~*-E&]s,~

The v-or D,={dl,d=.. .,~} srsrrunarizea the workeds performance history. Let pi be the

dmerence between tie and E(tie\DJ. By definition pt is uncorrelated with De q ands but in

addkion we assume Pt is distributed independently of Dt, q ands.

.

5The exclusion of q from the conditiomd mean of q is irmocuous, since we are stiply defirrirrg

q ad the coefficient vector a, on q in (1) so that the mem ofq does not depend on q.

s For example, below we use the Wed Forces Qualification Test (~QT as z md yem of

education ass, and N4 rmd Johnson (1995) present evidence that y=s of education have a sizeable

positive effect on WQT.
9

We dso assume that q, s, and Di are known to d] employers, as in Farber and Oibbons. &

a result of competition among firms, the worker receives a wage Wt equal to the expected value of

productivity Yt (Yt = exp(y~) times the mdtipficative error component exp(~~ that reflects

measurement error and firm spedc factors outside the model.

(6) W~=E(YJs,q,Dt)e c’

Using (1) (2), and (3) ~d (6) the wage equation is

Taking logs and collecting terms leads to

(8) w,= (r+ y2 + a2)s + H*(;) + (Y1 + al)q + E(v+e lDt) + q,

where WI= log(WJ and H*(ti) = H(ti) + log@(e ‘))

Mthough some autiors have purported to test screetig models by testing whether the

coefficient ons decbes with experience (e.g., Layard and Psacharopoulos (1 974)), Mey (1979)

and others have noted that udess the relationship between schootig and acmd productivity

chrmges, the coefficient ons d not ckge. This is true regwdless of ~s is related to

productivity. Farber and Gbbons make this point by showing in a more general version of the

model above that the expected value of the coefficient of an OLS regression of wt ons does not

depend on t. They estimate an equation of the form

(7a) w,= b,~ + ~(t) + (al+ y,)q + E(v + e]D)
,10

with q treated as an error component. They fid that b~ does not depend mu&on t.7

Farber and @bbons dso make a second point, wtich is that if one adds tie component z’

of (v + e) that is uncorrelated with the employefs initial informations and q to the wage equation

and estimates

(8b) w,= b~s + b~” + ~(t) + (al + yl)q + E(v + e\DJ,

the aefficient ons does not depend on t. They provide evidence from ~SY that b~ is relatively

constit and bzl is increasing in t,

h M we estabbsh and make use of a third resuk, wtich is closely related to the second.

Let the regression equation relating w, to s, z and H(t) be

(9) Wt = b~ + b~ + H“(t)+ (al + yl)q + E(v + e(DJ ,

where we have added the component z to (7) rather than fo~owed Farber md ~bbons in adding

ody the part of z that is orthogonal to the firm’s information set and where (al + y ~)q is part of

the error term. men the individud starts work (t is O)this equation is

(9’) W,= b,os + baZ + H(0) + (al + yl)q

&ume that the sarnp[e has been drawn so thats and t are uncorreiated. Mso assume that z and t

are uncorrelated, which is reasomble at least when t is potential qerience ands md t are

uncorrelated in the sample.

(12a) b== b,. + e,@s

(12b) bz = bzo + 6t@Z

Then it is easy to show (see W) that

——

7 Farber md Gbbons fofitiate their model h terms of levels of productivity and wsses rather

than logs.
11

where @s and @z are the coefficients of the regression of v+e ons end z and

et= cov(E(v+e]DJ, z)/cov(v+e, z) =cov@(v+elD~,v)/cov(v+e, v)

is a parameter that is specfic to experience level thats ummarizes how much the h bOWS at

time t.

To detetie the behavior of Bt@s and Ot@z over time, note first that OS <0 and @z >0 if

Cov (v, v+e) >0 and COV(S,Z)>0. The latter condition is true whens is schootig and z is ~QT.

The condkion cov(v,v+e) >0 simply states that the unobsemed productivity subcomponent v and

composite unobserved productivity term v+e have a positive covariance. This seems plausible to

us.

The time paths of b~ and ba are determined by Or This parameter is bounded between O

and 1. It is Oin period O, because in this period employers know nothing about v + e, so

E(v+elDO)=O. The coefficient is 1 if E(v+elD) is We, since in this case the employer has I-cd

what v+e is and thus knows productivity y. It is intuitive that et is nondecreasing in t because the

addltiond tiormation that arrives as the workefis career progesses permits a tighter estimate of

v+e. k AP we discuss condkions on the 61process that are sufficient for et to converge to 1 as t

becomes large.

There are two conclusions, which we summarize in Proposition 1 end 2

Propotifion L: Utier the asstarrptionsof the above model, the regression coeficienf b=tis

nondecreasing in f. me regression coeficienf b~tis nonincreasing in t.s

PrO.Dosifion 2: Iffirms have complete information about thepraductivip of nw wortirs, then

&.{& = &z/d= o.

These results underfie our empiricrd analysis in AP (which also considers gener~itions to

8 me coefficients on an unfavorable z characteristic, such as critid tivolvement or alcohol

use, will become more negative to tie atent fiat these reflect permment trahs. Ass-gs is

negatively correlated with the unfavorable L b,t till rise tith t,
12

vectors ofs and z) and the present paper. We are dso able to estimate the time profile of 6t up to

swle. Under the strong assumption that employers learn about v and e at the same rate, this

enables us to estimate the time protie of employer Ieting about productivity up to scde.g In

section 6 below we e- e the implications of our estimates for pure signrdiig models of the

return to education. 10

The model rdso impfies a third result, which we state k proposition 3. 

P~: U&r the asmmptiorrsof the above m&l, &3{d = -&zi~ Cov(z,s)flm(s).

Siice COV(Z;S)WW(S)is stiply the regression coefficient of z ons and can be estimated, the

coefficient restriction io Proposition 3 may”provide leverage in differentiating ~etweeq the

learnin~statiiticd discritilon model rmddternative e~l-tirms for the behavior of b~ and b=

3.1 Modifications to the Modek

Proposition 1, 2,&d 3 are not robust to allowing the effect of z antiors in the

productivity equation (1) to depend on t. For e-pie, ifs andor z are complementary with

learning by doing or enhance the productivi~ of trtig investments, then the productivity

gWe elaborate on this assumption in Setimr 6. The assumption is natnrd if ti ordy see v+e

plus noise, as in the model. However, it seems more rdlstic to assume that firms observe a vector of

indicators of productivity. Suppose that the firm observes an indlmtor dlt of v and an indicator dz of e.

Suppose fiat v rmd e are independent md the firm knows tis. men if d,, is a less noisy ~dicator than

da, the ti W lem about v faster than e. This m- that the drne path of 0, will depend upon the

choice of z.

10~~tion~ t~~ ~e possible if the econometriciarr @ a set of vtiables B that are observed

direcdy by employers, are negatively related to the dir~ costs and nonpecrmiary costs of school and

are unrelated to productivity. Both human ~itrd and screening models imply tit schoolings @ rise

with B. h the screening me, ti”~ot &ectly observe y. Consider the e-gs equation

Wt= Bn+rs+q

h the screening case, rr wiu be negative. The factor B condates the relatiombip betweens ad

mrobsewed productivhy y. FIm, by ttig account of factors that affect schookg choices but not

productivity, “c”tifofi a better prediction ofy than ifti”~ rely onus done. Unfortunately, we have not

been able to identi~ a set of variables that have the properdes of B, and so we have not pursued this

~ie of research. There is a large theoretid Eteratie on screerrirr~si~~g models, but the empirical

literature is far from conclusive. (See Weiss (1995) for a rrseti discussion of some of the evidence.) h

future work it tight be interesting to see if the “diploma effect” deckes with t wtie the coefficients on

hard to observe productivity characteristics that correlate with getig a diploma rise. See Frtis

(1993) for a recent arrdysis of whether tiere is a diploma effect.
13

equation (net of traiting cosfi) might take the form

(13)y, =rs+r1st+r2z t+ H(t)+ (al+y1)q+z+q, rlz O, r2>0.

The interactions between t ands and z in the productivity equation would tiuence the

interactions between t and s and z in the wage equatio~ although the precise effects depend on the

rate at which the h 1-s about z. Consequendy, our estimates of the time path b* will be

biased as estimates (up to the scale parameter @z) of the rate at which employers 1- about v +

e.

h M we are investigating how this modtication tiects our analysis of the hypothesis that

employers learn about productivity over the career. Most discussions of human ~itrd and most

of the empiricrd evidence on employer provided training suggest that education snakes workers

more trainable and that educated workers receive more training. (See, for emple, Mtonji and

Spletzer (1992), Bartel rmd Sicherrnan (1992), Lynch (1992) and Mncer (1993)). k ~ we fid

that both highest grade completed (ours variable) and ~QT (the z variable in this paper) have

strong positive effects in a probit model of whether a worker remives company training during tie

year. b tfis case rl till be grwter than O. Below and in M we fid that the edumtion slope of

wages has a strong negative relationship with t, wtich is o~y consistent ~th a ~fig.

interpretation if education reduces lag by doing, the productivi~ of training investments,

andor the quantity oftraiting investments. The presence of r2z t in the productivity equation

seems urdikely to lead a negative bias in ~~dt when ~ is excluded from the model. 11 If both rl

md rz are positive the introduction of ~ to the wage model that contains ~ could lead the

coefficient on ~ to fall, but W not lmd it to become negative urdess r, is negative. This seems

ufllkely. However, our use of bz in section (5) to draw inferences about the rate at wtich

employers learn implicitly assumes that the duect effect of z on sM1 accumulation is O.

1I Fmber ~d Gibbons noti that a trtig explanation of their fmtig that ~bz*Jdt >0 is ~d to

reconcfle witi tieir tidtig that ~b,~dt is close to Oor negative.
i4

The issue of whether information about productiti~ ‘ii’p6bXc”;&rpd$at2 ““deieti~i” ‘“’7:’“’;””;

discussion. The equation that relates wages to expected productivi~ rests on the assumption that

the information available to the employer leaks out to other ernploy%s.” However, Ettle”is bofi’

about how much of an employer’s, a supervisor’s, or a co-workers’ knowledge of the general

productivi~ of a Worker becomes tio~ to-other prospective employers.’ Theoretic~ pap;r”s by

tieenw~d (198~, Wddman (1984), L=ear (198@,-tid Gbbons and Kati (1991 ))”discuss

whether information about productivi~ will be reflected h promotion paths and wage” incteas~s

fit~ ~, They ~so tilSCUSS the strate~cs *S fight’ use to t~’to fide’~o~tioi”abbtit

good workers. In AP we tivestigate some imphcations of these models for turnover and the

relationship between quits, layoffs, and wage gains but our results to date are fti fiom”eoriclusive.

h section 6 W&”brieflyconsider how the possibfity that some of the Mormation obtained by

employers is private and not reflected in wages wi~ eff~ our”anrdysis of sign~g.

4. Data

The empirid arrdysis is based on the 1992 release of the NSY The ~SY is a panel

study of men and women who were aged 14-22 in 1978. Sample members were surveyed

annually since 1979. We restrict the analysis to men who have completed 8 or more years of

education and hve vdld data on d variables used in the analysis. ~spanics are =cluded from

the ansdysis. We exclude labor market observations prior to the fist t-me that a person lmves

school and accumulate experience from that poirrt.=ach panel member contributes at most one

obsaation for a ptiitilar year. Khe ii working at two OFmore jobs, we cotiider ordy the wage

for the CPS job.

Actual experience is the number of weeks in which the person worked more than 30 hours

divided by 50. Potential experience is defined as age minus years of ithoohng mkms 6. The

WQT score is noti&ed to have a standard deviation of 1 in the popdatiou but has a sightly
15

larger standard deviation in our sample. 12 The means, standard deviations, -m ~d

maximums of the variables used in analysis are protided in Table 1, rdong with the variable

definitions. The mean of actual experience is 4.9. The mean of potential experience is 7.3, and tie

mean of education is 12.7. ml statistics in the paper are unweighed. Blacks are oversarnpled in

the ~SY and contribute 29.1 percent of our obsewations.

5. Estimatea of the Wage Equation

k Table 24 we report estimates of various specifications of the wage model. In table 2

we use potential experience as the experience measure and use OLS to esdmate the model. The

equations dso control for a cubic in experience, a quadratic time trend, and residence in an urban

arm. These variables are not reported in the tables,

In column 3 we present an equation that includess, Black and s*t. This corresponds to

(7a) with b< restricted to b== b~ + b,l*t. The coefficient on s*t is -.00075 (.00040), suggesting

tht the effect of education on wages deches sfightly with experience. k column 4 we add

~QT. As has been we~ documented, MQT has a powefil association with earnings even after

controlhng for education. As~ in AFQT from 1 standard deviation below the mean to I

standard deviation above is associatd with an increase in the log wage of. 164, The inefficient .on

education decfines to ,0808, but note that b,l fds ody shghtly, to -.00102.

In column 5 we add hear interactions between t and our z variable, MQT. The resulting

equation corresponds to (9) with the restriction that b~ = bw + b~l*t and ba = ba + b=,*t. The

estimates imply that the effect of ~QT on the wage increases gready with experience t. bMQ~l,

which is the coefficient on AFQT*t, is .0090 (.0008). bM~Tti which is dtiJMQT, rises from

I

12 me we of he SapIe members a the time the AFQT was administered varies somewhat ~

the NLSY sample. This hduces some variation in schooltig levels at the time the AFQT is taken. The

stidardimtion procedure uses the entire NLSY sample weighted so as to be nationally representative,

Follotig Ned Wd Johnson (1995), each bti cohort year is standardized to a mean of Oand a

s@dard deviation of 1. (The lowest stmdard deviation is for the 1963 cohort (35.54) and the highest is

for the 1960 cohort (3682), so standard deviation adjustment makes little difference.) The fact that

the merm of this va.able is not Oin our sample is due to the over sample of d~advmtaged youths.
16

cndy .0164 when experience is Oto .1067 when experience is 10. The results imply that when

~enence is 10 and education is held constant, persons with a value of ~QT that is 1 standard

detiation above the mean have a log wage that is .211 larger than persons 1 standard deviation

bdow the mew while the dflerence is ordy .033 when expedience is O.

Our results for ~QT psrdel Farber and Gbbon’s resdts in which they use the

components of &QT and an indicator for whether the Hy had a hbrary card when the person

is 14 that are orthogonal to the wage on the fist job md education. The key new result is that the

coefficient on s*t decbes sharply (to -.00348 (.00046)) when MQT*t is added. The knpfied

effect of an extra year of education for a person with 10 years of experience is ody .0586.

Strikin@y, the coefficient ons rises to .0987 which is ahnost exdy what we obtin when we

exclude W terms involving ~QT from the model (columns 1 and 3).

These results are consistent with the hypothesis that employers have tited itiornmtion

about the productivity of labor force entrants. Early wages are based on expected productiti~

conditional on easily obsewable variables such as education. & experience accumulates, wages

become more strongly related to vtiables that are &ely to be correlated with productivity but

hwd for the employer to obseme directiy. We one might argue W the positive coefficient on

NQT*t is due to an association between ~QT and trfig intensity, it is hard to recorrcfie this

view with the negative coefficient on s*t. me measurement error in schoobg may partidy

explain the dec~me ins between columns 1 and 2, it does not provide a simple explanation for the

behavior of the interaction terms with experience in columns 3 and 4.

b Table 3 we present OLS results using actual experience ti place ofpotenti~ experience

as the experience measure t, The main dflerence betwmn this table and table 2 is that the return to

education is lower and the s*t interaction is positive and faidy Imge in the quations that exclude

NQT*t. However, the coefficient on s*t decfines from .0021 in cohnnn 5 to -.0004 when the

interaction terms are added in column 6 of Table 3. Tkis dec~ie is stiar to the decbe that we

obtain in Table 2.

The results in Table 3 are d-~cult to interpret, because the intensity of work experience
17

may be conveying information to employers about worker quality. It is an outcome mwsure itself

Conditioning on a~al work experience raises some of the issues that would arise if we

conditioned on wages in t- 1. On the other hand, the results based on potential experience are ~iely

to be biased by the fact that potential experience nrismeasures actual. For this rason, in Table 4

we report the results of re-estirnating the models by instrument variables (~, treating d terms

involving actual experience as endogenous tith corresponding terms involving potentird

experience as the instrumenfi. The results in columns 4 and 5 of Table 4 are basicrdly consistent

with those in Table 2. The coefficient on ~QT is .0155 (.0062) and the coefficient on WQT*t is

.0126 (.001 1). These estimates imply that condition on years of schootig, ~QT has ordy a

small effect on inhird wages, but when t is 10, a shift in NQT from 1 stidard deviation below to

one standard deviation above the mean rtises the log wage by .284. The coefficient on s*t

declines from -.0018 when the interactions are excluded in column 4 to -.0055 in column 5.

It is interesting to ask whether the experience profles of the education and ~QT

coefficients satis~ the restrictions in Proposition 3. Conaidw the spwification with the tinesr

interactions in column 5 of Table 2. Proposition 3 says that product of the value of

-cov(s,z)/var(s) and the coefficient on the interaction between t and ~QT, which is equal to

-.00253, shodd be close to the coefficient -.00348 on the interaction betweens and t, The

estimates differ but are in same neighborhood. The corresponding estimates for Table 4 are -

.00357 and -.00552.13

Nofinear Spe&ns of the ~e Prom of the E&s of~OT mds on Was=

The above analysis assumes that the effects of ~QT ands area finear finction of

experience. In this section we presents r=ults for sphe fictions and fourth order polynomial

specifications, in part to examine the sensitivity of estimates of the time path of the effects of

WQT to functional form. Specificdy, we estimate models of the form

13 13me nmbem we c~c~ated m -,00253= .00896 *-.2828 ~d -.00357=.01 264*-

.2828,
18

Wt= qz>t;b) + h(s,t;b~ ) + H(t)+ eit

where bz and b~are now vectors of parameters. Column 2 of table 5 is based on a model in which

~z,t;b~ is a finear sp~e with break points at t=2, t+, t=7 and t=l O. h that column the &nction

h(s,~b,) is simply the b,os + b, Is*t. N of the models in the tables conti the other control

variables discussed above.

Rows d to h report ~w~d~QT ti various experience levels. The inefficient on ~QT in

row c is the derivative when =0. The table shows that the derivative increases steaddy from

.0196 when t is 1 to .1193 when t is 12. For purposes of compariso~ in mlumrr 1 we dso report

the corresponding derivatives when ~Z~b~ = baz + bfl z t as in Table 24. The pattern is similar,

suggesting that to a fist approxirnatio~ the effect of ~QT rises hearly with experience. A

noted earker, employer l-g imphes that ~w~dWQT is nondecreasing in t ( i.e.,

d2wJd&QT~Jt 20), with a strict @equAty Wely if some new information arrives each period on

y. If the noise in observations Ofyt are fid, then the rate of increase is decfining, as shown in the

expression et above. k this case, ~3w/J~QT,~2t <0. The rate of increase must decfine

eventually bemuse the amount of addhiond information h additionrd observations of labor market

performance is dectig. (et is bounded at 1.) However, it is possible that the fist two or three

observations on a worker are particdarly noisy bemuse of factors that we have lefi out of the

model. For ei~ple job spectic or occupation specfic match qutity maybe more variable for

new workers than more experienced ones.

b rows i to m of the table we report #wJ~mQT,~ for various experience levels. We

ofly go out to t= 12 because sample information becomes thin at higher values. h the kear mse

in column 1, the values of %~~~QT,Jt is constant. In column .2, the.v~ue rises 80m -.0003

(.01 54) when t is 1 to .0135 when t is 3, increases sfigbtly to .0154 when t is 5.5 and then decfines

to .0065 when t is 8.5 and to .0037 when t is 12. These results are reasonably mnsistent with a

dechne in the amount of new intorrnation with experience fier the fist few years in the labor
19

market.14

Column 5 reports results when flz,t;b> and h(s,~b$) are both fourth order polynotids in t,

with coefficierrb so that J2w~~~QT Jt = Oand ~zw~~s at = Owhen t is 25. This is”the

specifimtiori that underfies our dculations of the intemd rate of return to education k the nefi

section. The restrictions captures that idea that firms learn whatever they are going to 1- about

the information about productivity that is contained in ~QT by the time t is 25. It is important to

point out that the 99th percentile value for potential e~enence is 16 and the 95th percentile value

is 14, so there is little information in the sample beyond t= 15 or so. The results based on the

restricted quartic polynomial are sitiar to those based on the spke finctions. The effect of

~QT increases monotonically,.. The rate of increase rises at fist but then decfines between P8. 5

and t= 12, k column 7 we report results for unrestricted quartic polynomials. These estimates are

very similar to those for the restricted polynofisds but the standard errors are bigger. 15

6. The kte at which Employers Learn and the Signafing Value of Education

We are now ready to etine the irnphcations of employer learning for the quantitative

si~cance of signdlng models of the remm to education. The intemd rate of return p to an

additional yar of schoohng is imphcitly defied as the solution to the equation

14 ~ ~ol- 3 we es~ate a model tifi a spline for the educatiotiexperience ~~raction

h(s,~b,c). The resdts in rows a and n-r show an increase in ~w~~s in the isritid years fo~owed by a

decline. Gven the stidard errors we wordd not want to tie too much of tie titid increase. For

the same spectilcation the effect of ~QT declmw slightly dtig the first 2 years k the labor market

before incre%fig. The nntid decline is not consistent with the pattern implied by theory. However,

.-. ..—

given the standad errors on ~QT terms we are not sure how much to mke of this.

15~ Table 6 we replwe potential experience with ~ti experience, ad ~e~ acm~

experience as endogenous. The 99th percentie value for this variable is ordy 13.33, so there not much

smnple information “ont beyond this point. Focussing on the model whh unrestricted quardc

specifications for boti f( ) and h( ) (column 7) we fid tiat the effect of~QT increases monotoficrdly

whh experience. The rate “ofticrease rises at first from. 01042 when t = 1 to .0144 when @5.5, but

deckes to .0059 when t = 12. However, the standard errors on these derivatives are””qnitelarge.

These resdts are loosely consistent tith the proposition that tie rate at which new information about

tih-d productivity arrives declkes with experience, but the estimates are not sticiendy precise to say

much about tis. & the NLSY sample ages, it till be interesting to revisit tie issue. The return to

education decbes slighdy in the first year or two but more rapidly &er that.
20

(15) O = P*, *l,..., VT,r, KW.-l, g = JOre ‘p’W,-le “K$<dt-j Te”P’W3_1e’’Kd*

1

h the above equation W,.l is the earnings of the avwage worbr Mth s-l years of educ?tiow T is

number ofye~s untd retirement, which we set to 40, r is the duect effect of education on

productivity and we redefie K so that K=~ + y2 is the relationship betweens md components of

productivity tie that the ~ cannot observe wheg new workers enter the ~or market. The

total dflerence in productivity associated with an e~a year of school is r + K+ T, where T is the

slope of the relationship betweens and the productivi~ component al q that is observable to the

h when the individud enters the labor market. h our cdculatiom we assume that r + K is. 10,

which is a bit above the estimate of the relationship between education and the log wage in our

sample. (See Table 2, column 1) TMs estimate will overstate r + K by T. The parameter Vt is the

coefficient relating the firm’s expectation of tie @ven the informations@ Dt to tie, with

$1= Cov@(v+elDt),v+e)Nar(v+e)

If the h is fi~y informed by period t, $i is 1.

We wish to solve for p given empirical information about the rate at wtich information

about skills is reflected in wages. The models underling Tables 5 ~d 6.~low us to ~mPu!e the

time profile of the eff- of ~QT out”to about 15 yews. &smne, perhaps heroidly given that

MQT is positively related to receipt of company trti~ that none of the ticrase in the effect of

~QT reflects training. 16 ~sme fires lem about ~ ~mponents of the productivity of the

worker at the same rate. This assumption ties out the possibfity that the firm rni~t learn about

16 we befieve fiat MQT is positively related to receipt Ofgened. ~-g ~d % ‘pecfic.

training, but it it worth poindng out that if the ~SYv@g measure captures training that is ticed

by the firm @erhaps because it is hi~y firm specfic), then the association betweeri *g and”~QT

~dt nOt ~X ow ~fiis. HOwever, tie .~K!ductiOn.~f match sp%ific ~Pi~_ ~t~ tie ~wis (ei~?r

through heterogenei~ in match qtity or firm specific titig) complicates our simple competitive

model of wage determination because it 1- to a gap in *“e vsdue of the worker to the current

employer and outside employers. The effect this wodd have on ow amdysis is unclear.
21

competence on the job more rapidy t~rur it learns about absenteeism and the propensity to quit.

Specticdly, we need to assume that 6t = $,, or, using the defitions ofthese p~et~

Cov(E(tielDJ,v)/Cov(We,v) = Cov@(@e\Dt),tie)War(v*)

~s assumption is consistent with the assumption in our model that b receive tiormation

about v + e rather than about separate components of productivity. Hit is corre~ then bn-bm, the

dfierence between the derivative of the wage with respect to z at time t and time O, W be an

estimate of V~@z We pin down the sde parameter @zby making specfic assumptions about how

much the firm knows when t is 15. For example, suppose that by the dme t is 15 the tiormation

avdable to employers about workers explains 750/0of the variation io tie, which is the

component of productivity that the firms do not know when the worker enters the market. Then

tis would imply that $15= 015 =.75.. ti estimate of $t is @a-b@z15- bzo)$l~ when t is Iess

than 15. We will report results setting this weight $15 to various vakres. Some discip~me on the

appropriate value for $lS is provided by the fact that @,15-bzO)/~15is an estimate of the scale

parameter @= which is the regression coefficient relating v+e to z. For example, assuming that

$15 is ody.5 impfies that controUmg fors, persons with 15 YWS experien~ who are 1 standard

deviation above the mean in the ~QT are 29.2 percent more productive but are ordy ptid 14.6

percent more. F1rrdy, we assume that between @l 5 and T#O the weight on ~QT rises Unearly

to the level $40, so that $t=$ls + (t-15)(~40-$15)/(40-1~ when t is between 15 and 40. We vary

the assumption about $40.

In table 7 we report vakres of the inteti rate of return p to education for various values

of $1 ~ and $40. The estimates of ba - bfi are based on the wage equation underlying Table 5,

column 5, which uses interactions between ~C and the first four powers of potential

experience. We set r to Oin the left panel, which corresponds to the pure signdfing rose. If r is O

and employers are fiUy informed tier 15 years, with *,5=1, then p is negative. E ~15 is .75 the

internal rate of return p is ordy .032 even if tis never learn rmytting else about y tier t=l 5
22

($40=.75). The value of p is .017 Fthey are filly informal by the time t40. Even if the weight

on K is as small as .5 when t=l 5 and fis do not learn aything else, p is a maximum of .062.

When we use vakres of $, based on the IV estimates using actual experience as the experience

measure (Table 6, column 5), we obtairr even lower estimates of the intemd rate of return that

could be due to sigrrdhng.

In the right panel we consider a human capittisimg model by setting r to .05. The

results for the mixed model show that if the “human capit~ component of the return to education

is .05 then the totrd return is about. 08 if the weight on v+e is. 5 when experience is 15 and fis

never learn anything else. The sigrudfing component of the return is ody. 03, which is ody 600/0

of what one obtains by subtracting .05 from the total education ~erentid (r + K) of. 1

We conclude from this that even a relatively slow rate of learning wodd e~ate much of

the economic return to using education to si@ productivi~. We have emphasked that our use

the time path of bz to itier the path of $t requires a strong assumption about the flow of

information to fis which impfies that tiey are learn about d component of productivity at the

same rate. If this is not the case, then the time profile of learning might be sensitive to our use of

~QT as the z variables. We rdso stress that our estimates of b~ ~@t.he ~ected by Othw

factors. This would affect our rate of return cdctiations.

In additio~ there are two other important caveats that deserve discussion. FKs& it is

hkely that the type of job an employee is in fiuences the type of information the employer

receives. For example, employers probably do not I- much about the managefid abtity,

techrricd traifing, or cormnurrications SMIS from obsewations on the performance ofjanitors.

Education may itiuence the initial assignment, rmd infomtion flOWSmay be tited fiOm some

jobs. ~le our estimates of the pro~es of the ~QT and s vtiables are not very sensitive to

addkrg controls for 1 digit occu”patio~ more analysis is needed. @esdts not reported.) Secon& to

the efient that itiormation is private, workers may need to resort to education because it is a

public signal of abfity. While the two caveats may hdt employer learnin~ the empirical rmdts

suggest that learning does take. place.
23

7. The Potential for Testing Services to Certify SW

~le education maybe too expensive to seine as a means for able workers to certifi

themselves to employers, perhaps other mechanisms could perform this finctio~ at I=st for some

determinants of productivity Here we point out that interpreting our estimates of the time profile

of the effect of MQT on wages w the result of employer learning isnpfies that tigb abihy workers

would have a substantial finaneid incentive to take the ~QT to differentiate themselves from

those who are less able in this dimension.

Suppose that a third party were to administer the MQT and certify the resdts to outside

employers, in much the same way that the Educatiod Testing Service administers the SAT

exams. Using our estirnatw of the learning pro~e and the same range of assumptions about the

fiaetion of information contained in ~QT that is known to fis by the time experience is 15 that

we used in Table 7, we have computed how much a person who befieves that he is 1 standard

deviation above the meao for the MQT would pay to take the test at the time he enters the

wor~orce. 17 The OLS intimates using potential experience (Table 5, COhunn 5) fiPIY that if

firms become fifly informed about productivity by the time experience is 15 and the interest rate is

.1, then the person would be wiUlng to pay .559 of the fist year’s salary for the test.’8 The

corresponding value when we use the IV estimates in Table 6, cokunn 5 is .330.

These c~culations raise the issue of why such a testing service has not emerged if

information is initially imperfect. One answer is that firms are not aware that the ~QT captures

characteristics that have a strong associationtith produtiivity. It is ody rwentIy, with the

availabihty of the ~SY, that labor economists have become aware of this. Another is that it

would be difficult for a testing firm to become estabhshed at a mtioti level. A third is that, given

race differences in distribution of ~QT scores, firms who make use of ~QT information in

17~ a worker &d not how tis abdi~, he cordd take a practice test Onfis o~. pms~ably>

this wodd not rtise the toti cost of the test very much.

18Here we me ~s~g tit Ody I worker takes the test md i~ofig tie fmt hat tie

composition” of the pool of workers who choose to take the test ti equilibrium wotid tiuence return

for a particular me of worker.
24

hiring for a specfic job would have the burden of establishing that they are relevant to productivity

in that job or run the risk of violating discrimination laws. This would be true even if inditiduds

provided firms with the test results. However, we do not fid these answers to be fi~y

satisfacto~. 19~yses based on v~&Ies such as the wage rates of tibtigs or father’s education

may be less vuhrerable to this objection. We report qutitatively stilar findings for these variables

in M.

8. Conclusion

This paper presents some prehmin~ evidence on the rate at which employers learn about

productivity and uses that evidence to guide an exploration of the extent to which the return to

education could be due to signfllng. Our basic idea is that if employers learn quic~y, then the

signfllng component of the return to education must be sd. To get evidence on how quic~y

employers learn, we work with a model that is based on the premise tbt firms use the tiormation

they have available to them to form judgments or befiefs about the productivity of the workers

and then revise these behefs as additiond information becomes available. Building upon some

previous work particularly Farber and Oibbons (1994), we show that as fis acquire more

information about a worker, pay may become more dependent on productivity and less dependent

on easfiy observable characteristics or credentids. Our result that the effect of ~QT on the wage

rises with mperience and the effect of education decfines is consistent with the model

Taken at face value, our estimates iden~ the rate at which employer knowledge of

worker quality rises with experience up to a scale parameter. We use tiese estimates along with

some strong auxifiary assumptions to provide a range of estimates of what the intemd rate of

return to education would be if education has no duect effect o.n productivity. Our dcrdations

suggest that the “sign&g component” of the return to eduation is probably ordy a small part of

19 Note ~so hat ~ fie absence of ~ institution such as the Educarionrd Tes~g se~ice> a fi

might provide tie test. Some firms perform their own testig.. However, if the resdts were avaitable

to the employees or otier h how that a particrdar firm tests its employees, then the firm wodd not

be able to capture tie ml return to tesdng.
z

the percentage diffmence in wages associated ~th edutien. fiw wtile we fid etidenw fiat

information is “impetiect and - do statistically disctite among wung workers on the basis

of educatio% our estimates suggest that they learn quictiy enough TO“tit the.retum to a costiy

sigrrrd such as education. This does not m-, of coorse, tkt none of the ram of drrcation is a

return to signtilng.

We wish to stress that we are st~ in a relatively early stage in our tiysis of employer

learning and statistical discrimination on the basis of education and other characteristics, and so

the estimates underlying our analysis of the signaling return to education are somewhat

pr~lminary. bung the issues we are pursuing is the possl~lhty that dfierences among workers

in training rather than statisticrd discrimination with Ieting explains our restits. We are dso

extending the analysis to other variables that are in the ~SY but wodd be hard for employers to

observe, such as characteristics of the father, mother and sibtigs. In prefi- work we have

experimented whh both the wages of sibfings with 5 t.o.8 years of experience and with fatheds

education. It will be interesting to repeat our calculations of the sigruding vrdu.e of education with

the learning profiles suggested by other m=sures. Fin&y, we are investigating the assumption

that irrforrnation about labor market performance is pubfic.
26

References

Aigrrer, D, and C% G., “Statistid Theories of Dlscrirnination in tie Labor Market,” ~

md Labor Relations Revi~ 30 (J~U~ 197?, 175-87.

Mbrecht, J., “A Procedure for Tesdng the Signrding Hypothesis,.” _ ~,

(1981); 123-32.

~tonji, J. G. and Plerret, C. .R., “Employer Learning and Statistid Dlscrititio~” (September

1995), revision in progress.

Mtonji, J. G. and Spletzer, J, “Worker Charaderistics, Job Chartieristics, and the Receipt of Onthe-Job Trainitig’’,~~

~,. .

w 45(1) (October 1991): 58-79.

Bartel, A. P. md Slcherrnq N,, “Twhrrologicd Change and On-The-Job Trfig of Young

Worker%” urrpubfished paper, Columbia University (July 1993).

Borjas, G., “Ethnic Capital and ktergenerationd Mobifity,” ~ Jouti of Econorni~, 117

(1) @ebruary 1992), 123-150.

Carmichael, H. L.. “Self-Enforchg Contrads, Sbirkin~ and Life Cycle Inwntives,” The Joumd of

Economr ‘c Perspe~ k, 3 @M, 1989), 65-84.

Coate, S. and Loury, G. C., (1993 )-r’Wfl ~rrnative Action Pofities E~ite Negative

Stereo~es?” ~ Eco nornr“cRe= ‘ew, 83 (5) Qemmber 1993): 1220-1240

DeVine, T.J. and Kiefer, N.M., ~ Labor Eco nondcs, New York OMord UrriversiV Press,

1991.

Farber, H., and Gibbons, R. , “Lefing and Wage D-es,” Industrid Relations Se@ion

Working Paper, Princeton University (1994).

Foster, A. D. and Rose-eig, M. R., “~ormatio% Learning and Wage Rates in Low Income

Rural Areas; Joumd of _ Res ources@dl1993).

Frti, H., ‘rSelection Bias and the Degree Effect,” Joumd of ~ 28 (3): (Summer

1993), 538-554.

Gibbons, R., and Kati, L., “Layoffs and Lemons,” Journal of Labor Econotics. 9 (January, 1991),

351-80.

Greenwdd, B., “Adverse Sele~ion in the Labor Market,” Review of Ec~ Stufie

. .

s, 53

(1986), 325-47.
Hoker, H., “Se~c,h”Method.s .Us.@_b~unern~royedYoutfi%. ,Jobmd of La ‘r.? ~; ~ i ;,;:;:’ l’’””}{ ‘o”no”&c$,

(1988), 1-20. .,. ~

.:rj

Jovanotic, B,, “Job Matching.rmd the Theory oflTumov6rfl ~- of Po~:87 ‘~”:‘“

(1979), 972-90. .. . .. .

;,.:. !:”.:i :;J :::, , li:i”3]9 .?? ;i,:. ,r:,:?a:i

Lmg K.,”A kguage Theory of Disctinatiom~ ~uarte rlYJournal of Eco-, 101 @ay

1986): 363-82.

L=errr, E., “Raids md Offer Matching” ~earc h in Labor ECono ti-~ 8@art A):(19S6) 141-165.

Layard, R., and G. Psacharopoulos, “The Screening Hypothesis and the Returns to Educatioq”

Joufi of Pofiticrd Economy 82 (September 1974), 985-99S.

Lundberg S., and Stti, R, “Private Discrimination and Social htervention in Competitive

Labor Markets,” - ~w, 73 (June 1983), 340-347.

Lync~ L., “Private Sector Training and the Earnings of Young Workers,” ~C

Ra ‘ew, 82 ~ch 1992), 299-312,

Medo~ J. and Abr- K., “Experience, Performance and Earnings,” Ouarterlv Joud of

Eco nornics, @ecember 1980), 703-736.

Mncer, J., ~ Hu man CaDital, E. Elgar, 1993..

Montgomery, J,, “SociaJ Networks and bbor Market Outcomes Toward and Economic

Analysis,” ~ticrnr Econornic Reviewx 81 (1991), 1408-18.

Ned D. A., and Johnso~ W. R.;The Role of Pre-~ket Factors in BIack-~te Wage

Differences,” NBER working paper no. 5124, way 1995).

Oettiriger, G. S., “Statistical Dls crinrination and the Early Career Evolution of the Black-White

Wage Gap,” forthcoming JoumsJ of Labor Economics , (September 1993).

Parsons, D. O., “The Emplo~ent Relationship: Job Atichment, Worker Effow and the Nature

of Contracts,” in Asherrfelter, 0. and byard, R., eds. mook of Labor Ec~,

Amsterdam: North-Holland Pubfistig Co,, 1986,

Parsons, D. O., “Reputation Bonding of Job Performance The Wage Consequences of Being

Fired,” unpublished paper. (July 1993).

Rees, A,’’Momation Networks in Labor Markets,” ~norm ‘c Review, 56 (1966)

559-66.

Riley, J. G., “Testing the Education Screening Hypothesis,’r

(October 1979), S227-52.

Joumd of Politicrd Ec onomy, 87
28

Wddmw M., “Job Assignment, Sign&g, and Efficient,” ~. 15

(1984), 255-67.

Weiss, A., “Human Ca@td vs. Si~&g_E~lanatiom of Wages,” ~C mi

~ ‘v ,9 @d] 1995), 133-54.
Table 1: Descriptive Statistics

Variable Mean Standard mum ~

Detiation

Red Hourly Wage 8.366 4,763 2.01 96.46

bg of Real Hourly 2.005

0,475 ......

0.70 4.57

Wage (w)

Potential EWerience (t) 7.325- 3..657 0.00 “’ 21.00 ~

Acti E~erience [t) 4.914 3.413 0.00 18.26

Education (s) 12.715 2.140 8,00 18.00

Black dununy @lack) 0.291 0.454 0 1

Standardized ~QT Score -0.138 1.042 -2.780 1.922

(MQT)

Durruny for Urban Dweller 0,780 0.414 0 1’

Year 86.643 3.548 79 92-

Sample size = 26,651 observations.
0, c
Table 3: The Effects of Standardized NQT and Schoofirrg on Wages

Dependent Vtiable: Log Wage. Experience Measure: Actual Experience.

OLS estimates (standard errors)

Model: (1) (2) (3) (4) (5)

(a) Education 0.0810 0,0609 0.0713 0,0510 0.0629

(0.0012) (0.0015) (0.0022) (0,0023) (0.0027)

(b) Black -0.1396 -0.0658 -0.1399 -0.0661 -0.0661

(0,0056) (0.0065) (0.0056) (0.0065) (0,0065)

(c) Standardized 0.0762 0,0762 0.036~

WQT (0.0034) (040034) (0.0053)

(d) Education * 0,0021 0,0021 -0.0004

Experience (0.0004) (0.0004) (0.0’005)

(e) ~QT * 0.0081

Experience (0,0008)

R-squar4 ,2695 .2829 .2703 .2838 .2862

Note Ml equations controlfor a quadratic time trend, a cubic experienw profile, and urban residerree.The sample size is 26,651.
Table 4: IV Estimates of the Effects of Standardized MQT and Schooling on Wages

Dependent Variable: Log Wage. Experience Measure: Actual Experience tith Potential Experience as Instmments

IV estimates (standard errors)

Model: (1) (2) (3) (4) (5)

(a) Education 0,0817 0.0616 0.0889 0.0702 0,0884

(0.0013) (0,0016) (0.0031) (0.0032) (0.0035)

(b) Black -0.1390 -0.0636 -0.1389 -0.0634 -0,0632

(0.0058) (0,0’066) (0.0058) (0,0066) (0,0066)

(c) Standardwed 0.0770 0.0771 0.0155

NQT (0.0034) (0.0035) (0.0062)

(d) Education * -0.00153 -0.00183 -0,00552

Expetierrw (0,00056) (0.00056) (0.00065)

(e) NQT * 0.01264

Experience (0,00106)

R-squared .2376 .2530 ,2364 .2515 .2554

Note Al equations controlfor a quadratictime trend, a cubic experienceprofle, and rrrberrresidence. The irrstrrrrrrerrtivariables are the

eerresporrdingterms involtig potential experienceand tie otker variables h tie model. me sample size is 26,651,

,,!,

1,

I

1

!

,,,

,,,
TAle S: The Effects of Standard~edWQT and Schoo~ig on Wages

Nodinear bteractions of ~QT and Education titi E~erienti

Dependent VariAle: Log Wage. E~erienw Measure: Potential EWerience.

OLS esttites (standard errors)

Ltiear Splbes Quartic Polynoti Q@ic Polyno~

h-on -~ed Derivative

NQT & M MQT Otiy MQT & Ed MQT OdY MQT& Ed ~QT Ody MQT & Ed

(1) (2) (3) (5) (6) (7)

(a) Edu@tion 0.0987 0.0992 0.0815 0.0993 0.0798 0.0993 0,0791

(0.0037) (o.oo3~ (0.0066) (o.oo3~ (0.0074) (0.0037) (0.0083)

@) Black 4.0993 4.0993 4.0992 4.0994 4.0992 4.0994 4.0993

(0.0066) (0.006~ (0.0066) (0.0066) (0.0066) (0,0066) (0.0066)

(c) ~QT 0,0164 0,0199 0.0480 0.0018 0.0226 4.0030 0.0211

Permntile (0.0069) (0.0277) (0.0306) (0.0175) (0.020Q (0.0207) (0.0223)

Ierivati+e”of Wages With R- to MQT, by e~rience Iwel @s)

(d) 1 0.0253 0.0196 0.0361 0.0098 0.0246 0.0094 0.0242

(0.0070) (0.0317) (0.0349) (0.0198) (0.0233) (0.0244) (0.0260)

(e) 3 0.0433. . 0.0327 0.0345 0,0358 0.0378 0.0359 0.0380

(0,0071) (o.035~ (0.0393) (0.0239) (0.0284) (0.0311) (0.0329)

(0 5.5 0.0657 0.0693 0.0660. 0.0685 0.0641 0.0686 0.0640

(0.0072) (0,0364) (0.0400) (0.0286) (0.0342) (0.0389) (0.0408)

(g) S.5 0.0925 0.1021 0,0992 0.1013 0,0979 0.1012 0.0977

(0.0073) (0.0368) (0.0404) (0.0341) (0.0408) (0.0478) (0.0499)

[hj) 12 0.1239 0.1193 0.1220 0.1235. 0.1266 0.1236 0.1271

(0.0075) (0.0372) (o.040~ (0.0401) (0.0482) (0.0S80) (0.0603)

ilopeof ~QT Effec~by -en= Iwel m.)

(i) 1 0.0090 4.0003 4.0119 0.0123 0.0038 0.0128 0.0045

(0.0008) (0.0154) (0.0168.) (0.0092) (0.0112) (0.0132) (0.0137)

0) 3 0.0090 0.0135 0.0102 0.0134 0.0089 0.0134 0.0089

(0.0008) (0.0060) (0.0062) (0.0097) (0.0117) (0.0142) (0.0147)

K) 5.5 0.0090 0.0154 0.0142 0.0125 0.0115 0.0124 0.0113

(0.0008) (0.0033) (0.0033) (0.0103) (0.0124) (0.0153) (0.0159)

(1) 8.5 0,0090 0.0065 0.0079 0.0091 0.0104 0.0091 0.0105

(0.0008) (0.0031) (0.0031) (0.0110) (0.0133) (0.0167) (0.0173)

(m) 12 0.0090 0.0037 0.0055 0.0035 0.0055 0.0036 0.0057

(0.0008) (0.0025) (0.0026) (0.olln (0.0142) (0.0183) (0.0188)
Table 5 (cont.)

)erimtive-of Wages Witi R~ea to Edumtiou by e~rience Iwel ~.)

(n) I 0.0952 0.0956 0.0841 0.0957 0.0834 0.0957 0.0831

(0.0037) (0.0037) (0,0069) (0.W37) (0.0080) (o.oo3~ (o.oo91j

(0) 3 0.0883 0.0884 0.0865 0.0885 0.0864 0.0885 0.0865

(0,0038) (0.0038) (0.Q073) (0.0038) (0.0092) (0.0038) (0.0107)

@) 5,5 0.0796 0,0795 3.0824 0.0796 0.Q834 0,0796 0.0836

(0.0039) (0.0039) (0.0075) (0.0039) (0.0105) (0.0039) (0.0124)

(q 8.S 0.0692 0.0688- 0.0719 0,0688 0.0723 0.0688 0.0722

(0.0039) (0.0039) (0.0075) (0.0039) (0,0121) (0.0039) (0.0145)

(r) 12 0.0s70 0,0563 0.0s31 0.0562 0.QS29 0,0S63 0.0527

(0.0040) (0.0040) (0.0077) (0.0040) (0.0139) (o.oo40j (0.0167)

;1OF of Eduwtion Effe~ by e+rimce 1-1 @

(s) 1 4,003s +.0036

(0.000s) (0.0005)

(t) 3 4.003s 4.0036

(0.0005) (0.0005)

(u) S.s 4.0035 ~.0036

(0,000s) (0,000s)

(v) 8.S 4.003s 0.0036

(0.000s) (0.0005)

(w) 12 4.003s 4.0036

(0.000s) (0.000s)

.)

0.0026 0.0036 0.0029 ~.0036 0.0032

(0.0021) (0.000s) (0.0031) (0.000s) (0.0038)

4.0003 4.0036 0.0002 4.0.036 0.0003

(0,0013) (0.000s) (0.0032) (0.0.00s) (0.0040)

4.002s 4.0036 4.002s 4.0036 4.002s

(0.0006) (0.000s) (0.0034) (0.0005) (0.0041)

4,004s 4.0036 4.0047 0.0036 4.0048

(0.0006) (0.000s) (0.003s) (0.0005) (0.0043)

4.0060 4.0036 4.0061 4.0036 4.0060

(0.0009) (0.000s) (0.0037) (0.000s) (0.0046)

NI eqmtions con=ol for a qmhtic time tiena mbit ~rience profile, ad &m &den=. The aplinefiction in

colm 2 consim of intemtiom be~een ~QT ad a tible eqti to the Mmm of e~rienw md L the pr~uct

of a dmy vtiable eqti to 1 when ~rience is g=ter h 2 and tie ~mti of e~rience fiw 2 snd 2, tie

prduct of aday vtiable eqti to 1 when e~rienm is ~ter than 4 and the Mmm of -mm tinw 4

md 3, tie product of a dmy vtiable eqti to 1 when e~rience is gmter than 7 and the timm of ~rimm

finus 7 and 3, md the produa of a dmy tiable eqti m 1 when e~rienm is ~tw M 10 md e~rience.

Colm 3 conti S* intemctions boltings md ~rienw. COIU 4-7 COnti We inte~@on.&~=n

MQT md a foti order Plynotid in t. Cohonn S md 7 contin sitilw inkmctions kw~s and afoti order

polynotid in t. In COIW 4 and S the =fficien= of tie polynotiaf m codd so that ~‘ w / ~~QT, 5 t

is O“when t is 25.
Table 6: The Effects of Standartied WQT and Schooling on Wages

Nodinear Interactions of ~QT md Education tith Experience

Dependent Variable: Log Wage.

Experience Measure: A@d Experience” bstrumented tith Potential Experience.

IV estimates (standard mors)

Linw Spikes Querdc polynoti Quardc PoIwotid

interaction Mricted Derivative

MQT & Ed MQT OrdY NQT & Ed ~QT Ody ~QT & Ed MQT OdY MQT & Ed

(1) (2) (4) (0 ~

[a) Education 0,0884 0.0885 0.0766 0,0886 0.0827 0.0887 0.0884

(0.0035) (0.0036) (0.0103) (0.0035) (0.0079) (0.0035) (0.0091)

~) Black -0.0632 4.0633 4.0641 4.0633 4.0630 ~.0632 4.0633

(0.0066) (0.0067) (0.0069) (0.0066) (0.006@ (0.0066) (0.0066)

(c) MQT 0.0155 0.0474 0.0945 0.0207 0.0284 0.0285

Percentile

0.0180

(0.0062) (0.0302) (0.0422) (0.0144) (0.0168) (0.0181) (0.0191)

derivative of Wage s Wlti w@ m ~QT, by experienm Imel m.)

(d) 1 0.0281 0.0274 0.0131 0.0283 0.0277 0.0278 0.0274

(0,0063) (0.0449) (0.0643) (0.0197) (0.0232) (0.0284) (0.0290)

(e) 3 0.0534 0.0419 0.0566 0.0504 0.0462 0.0471 0.0512

(0,0065) (0.0704) (o. 1029) (0.0282) (0.0337) (0.0446) (0.0448)

(0 5.5 0.0850 0.0989 0.0905 0.0849 0.0875 0.0872 0.0861

(0,0067) (0.092S) (0.1380) (0.0376) (0.04s0) (0.0625) (0.062S)

@) 8.S 0.1229 0.1188 0.1298 0.1269 0.1340 0.1294 0.1277

(0.0069) (0.1116) (0.1741) (0.0481) (0.057T (0.0832) (0.0828)

(h) 12 0.1672 0.1626 0.1404 0.1650 0.1s23 0.1S82 0.1619

(0.0072) (0.127S) (0.2141) (0.0s99) (0.0719) (0.1073) (O.1064)

;lwe of MQT Effect, by experienm Iwel ~.)

(i) 1 0.0126 4.0200 0.0814 0.0089 0.0033 0.0036 0.0104

(0.0011) (0.0333) (0.048s) (0.0137) (0.0165) (0.0227) (0.0226)

0) 3 0.0126 0.034s 0.1248 0.0127 0,0139 0.0141 0.0131

(0.0011) (0,0427) (0.0641) (0.0149) (0.0180) (0.02S8) (0.02S7)

~) 5.5 0.0126 0.01s0 4.0606 0.0144 0.0176 0.0164 0.0144

(0.0011) (0,034s) (0.0s40) (0.0164) (0.0198) (0.0296) (0.0293)

(1) 8.5 0.0126 4.0017 0.0868 0.0130 0.0120. 0.0111 0.0127

(0.0011) (0.0374) (0.0677) (0.0181) (0.0218) (0.0338) (0.0334)

(m) 12 0.0126 0.0232 0.0598 0.0084 4.0023 0.0070 0.00s9

(0.0011) (0.0293) (0.06s8) (0.0200) (0,0241) (0.038S) (0.0380)
Tdle 6 (cont.)

lerivativi ofWage With tiqea to Wumtio% by e~enm Iwel ~.)

(n) 1 o.og29 0.0830 0.0837 0.0830 0..0832 0.0831

(0,0036)

0.0839

(0.0036) (0,0131) (0.0036) (0.0101) (0.0036) (0.0121)

(0) 3 0.0718 0.0718 0.0766 0.0719 0.0748 0.0719 0.0712

(0.0037) (0,0037) (0.0160) (0.0037) (0.0133 (o.oo3~ (0.0169)

@) 5.5 0.0580 0.0579 0..0563 0.0580 0.0562 0,0579 0.0558

(0.0039) (0.0039) (0.0167) (0.0039) (0.0177) (0.0039) (0.0221)

(0 8.5 0.0415 0,0412 0.0322 0.0413 0.0366 0.0411 0.0428

(0.0040) (0.0040) (0.0182) (0.0040) (0.0222) (0.0040) (0.0279)

(r) 12 0,0222 0.0218 0.0459 0.0218 0.0330 0.0216 0.0239

(0,0042) (0.0042) (0.0295) (0.0042) (0.0272) (0.0042) (0.0343)

310F of Edumtion Effec~ by e~r

(s) I -0.0055.

(0.0006)

(t) 3 4.0055

(0.0006)

(u) 5.5 -0.0055

(0,0006)

(v) 8.5 4.0055

(0,0006)

(w) 12 4.0055

(0.0006)

+

4.0056 0.0071

(0.000q (0.0080)

~.0056 4,0141

(0.000q (0.0044)

4.0056 4.0041

(0.0006) (0.0018) +

~.0056

(0.0006)

0.0056

(0.0006)

4,0056

(0.0006) *

4.0014 4.0056 4.0054

(0.0063) (0.0006) (0.0081)

~.W63 ~.0056 4.0067

(0.0068) (0.0006) (0.0087)

~.0078 0.0056 4.0053

(0.0074) (0.0000 (0.0094)

4.0056 4.0120 4.0056 4.0046 ~.~56 4.0037

(0.0006) (0.0057) (0.0006) (0.0081) (0.0006) (0.0102)

4,0056 0.01S8 4.00S6 0.0029 4.0056 4.0091

(0.000q (0.0156) (0.0006) (0.0088) (0.0006) (0.0111)

Ml eqmtiom contil for a qwtitic time tien~ tim residen=, md dmy vtiables to contiol forwhetherFathefs

educationis fi~tig md whether MQT is ti.tig, ad intentions bemeen thesedmy vtisbl= ad e~rien~

when ioteratiom &mmn ~QT md F_EDad e~mm m ticluded. me ~ en~ *sbles ~e the

compounding tem hvrdting Potefid e~rien= md the other vtiables h the model. The spfine fition in

COIU 2 comists of intdom kmmn NQT ad a +*le qti to the titiw of @en= ad 2, tie prtiud

of a d- vtisble eqti to 1 when e~rienm is ~=ter than 2 md the tirnn of ~rienm bus 2 md 2, the

produ~ of a -y vtisble qti to 1 when e~rienm is grater ti 4 md the nritiw of e~rienm tim 4

md 3, the pmdua of a day vtiable qti to 1 when e~rienw is mm ~ 7 ad the fitiw of e~rienm

tinw 7 md 3, ad tie produti of a day vtiable eqti to 1 when e~rimce is ~ater -10 md e~rience.

COIW 3 mntins sitim intentions involtigs md ~rienw. G1- 4-7 contin the inte~on &Ween

MQT snd a foti ordm pol~ondsf in t. Colm 5 md 7 mntin sitiw intemtiom kwmns ad a fad order

pol~otid in t. In COI- 4 ad S the mfficients of the PIwotid m m-d w bt 82 w / 5MQT, d t

is Owhen t is 2S. .-
m: The btemd Rate of Return to Education in a Pure Si@ling Model and a Mixd

Model under Various Assumptions about the Rate tht Employers Learn about Productivity.

Calculations based on OLS Estimate of Wage Equation Using Potential Experience a

Weight on Pure Si@g Model: ti.O wed Model: rO.05

Productivity

when t+O, Weight on Productivity when *I5 Wei@t on Productivity when t=15

*4O Q15 *15

0,1 0.25 0.5 0.75 1 0.1 0.25 0.5 0.75 1

0.1 .092 .095

0.25 .091 .082 .094 .091

0.5 .088 .079 .062 .094 .089 .082

0.75 .086 .076 .057 .032 .093 .088 .081 .073

1 .083 ,072 .050 .017 .Oob .091 ,087 .079 .071 .062

a) When ts 15, the weight $(t) of actual productivity in employer expectations of productivity is

wls=@,lt +btitz + bfit3 + b~t4)/@.1 15 +b~152 + bti(153) + b,4(154))>~d b.1,..., bz4~e the

coefficients on the interactions between ~C sod the first four powers of experience t in the

regression model underlying column 5 in Table 5 in the case of Tab!e 7 and column 5 in Table 6 in

the case of TAle 8. $15 is set to the values in the column heading. When t is ~eater ban 15,

$(t) is $,5+ (*40-~ 15)(t-tl)/(40-tl), where 40 is the length of the career. The intemrd rate of

return should be compared to the log(Yfl,.l) = .1.

b) The intemd rate of return to a year of school is negative.
M The Internal Rate of Return to Education in a Pure SiWting Model and a tied

Model under tious assumptions about the Rate that Employers L- about Produ~itity.

Coefficients from IV Estimate of Wage Equation Uskg Actual Expenenm’

Weight on Pure Signdhng Model: FO.O Mxed Model: H.05

Productiti&

when t=40, Weight on Productivity when t=15 Weight on Produ@itity when t=l 5

*4O

$15 *15

0.1 0..25 0.5 0.75 1 0.1 0.25 0.5 0.75 1

0.1 .091 .094

0.25 .089 .078 .094 ,0.89

0.5 .087 .075 ,053 .093 ,087 .078

0.75 .084 .071 .046 .015 .092 .086 .077 .066

1 .081 ,067 .038 .Oob ,Oob .091 .0.84 .075 .064 .053

a, b) See Table 7.
a

92-01

92-02

92-03

92-04

92-05

92-06

92-07.

92-08

National Longitidinai Suweys ~S)

Discussion Paper Series

-

Michael R. Pergmit

Nomm M. Bradbw

Mtih R. Fr*eI

Reghdd P. BAer

Michael R. Pergmit

Saul Schwmz

RobefiHutihens

GeorgeJ&bson

A. CO~i tieron

R. Mxk Gnti

ThomasMaCwdy

.H~ S. Fwber

Fr~ L. Mott

PaulaB&er

~lSa M. LWch

92-09 Evmgelos M. Fdtis

H. E~nbeth Petem

92-10 he HIU

Jme E. ONeill

92-11 Arleen Leibowim

Jacob Alex Klemm

Lii& Waite

92-12 Lee A. Lfllwd

92-13 JosephG. Altonji

~omw A. mm

HOW the Federal Govemmt Uses Dab horn

tbe Natioml Longitidmal Sumeys

A Comptision of Computer-Asskted Pemonal

Intemiews (CAFI) with Paper-fid Pencil hkrtiews (P~I) ti the National Longimtid Smey

of YOuti

D~mic Models of tie Jotit De@mhatiOn of

Labor Supply md Fmily Stictie

The effec~ of UnemplO~at Compensation

on the UnemplO~at of Youth

EvduAhg.Compettig ~eories of Workw

MobiEw

Evaluation of the 1989 Child-cae Supplement

h the National Long]tidmal Smey of Youth

Gender Differences ‘h the Quit Behavior of

Youg Workers

The Impwt of Wvate Swtor Trtihg on fice

ad Gender Wage D“fierentials md the Cmeer

Patterns of Yowg Workers

Responses of Fade Labor Supply md Fefifi@

to the Demogapbic Cycle

A Stidy of Intercohofi Chmge h Women’s

Work Pmems Wd Emtiga

Women’s Employmat Dutig %e~mq

md Followtig Bti

WOA Experience Job Tenwe, Job SePmatkm,

md Wage GrOti

Fmily Backgoud ad Labor Mmket Outcomes
Self-SeIetiion md Mkmal Mi~tiOn “fi the

United Shtes

92-14 George J. Bofias

Stephen G. Bronms

Stephen J. Trejo

92-15 Jm= J. Hecbm

Stephen V. Cmeron

Peter Z. Schochet

Tke Detmtimts md Consequmces of Wbfic

Sector md Mvate Sector Tmtig

Ptiicipation h Low-Wage Labor tikew by

Yomg Men

93-16

94-17

94-18

94-19

95-20

95-21

95-22

95.23

95-24

R M~k Griti

Thomas MaCutdy

Alm L. Gu*m

Thomas L. Stetieier

httiemcnt h a Fmily ConteW. A Stic~l Model

for Husbm& md Wives

Au&y Light Trmsitiom from School to Work A Swey of

Rese=h Ustig the National Longimtid Sweys

High School Employment COm~PfiOn 0?

Eies@ent

hfomal Tmtiig A Review of ExiStig Data md

Some New Evidace

M%k Lowensteti

Jaes Spletier

Jacob Alex KIemm Chaactetig Leave for Mateti~ Modelhg tie

NLSY Data

“Jacob Alex Memm

tileen Leibowiti

bplo~ent Contiui& bong NewMothers

hmntive Pay, hfomation, md Etifigx Evidence

from the National Longitidmal Smey of Youti . .

Stephen G. Bron=s

CmOl Moore

The Evolvtig S~ctia of Female Work Ativitiex

.—

Evidence horn the National Longitidfial Sumeys of

Matie Women Smey, 1967-1989

Donald O. Pmsons

POve~ D~wics tiong Matie Women Evidence

Corn the National Longitidmal Smeys, 1967-1989

95-25

95-26

95-27

95-28

Donald O. Pmsons

Technological Chage md the Skill Acquisition of

Youg Workers

‘“Au&ey Light High School Emplofient

The Eff=t of htwiew Length on A&ition h the

Nationd Longimdmal Swey of Youti

La~a Brmden

R Mmk Grim

Michml R. PergWit

Capital Mmket ConsuatiG, PmentiI Wealth ad the

Trmition to self-Emplo~ent mong Men md

Womm

96-29”
96-30 Jeff Grogger

Nick Rena

96-31 Jonathm Vem

96-32 : ‘“’M]chaeI R. Pergmit

96-33 Eric P. Slade

97-34 Bemmd Dugoni

Lisa Lee

Roger Tomgeau

97-35 Joseph G. Al@nj i

Chmles K Plemet

~e Intirgenerationd EffecB of. Fatherlessness On

Edutiional Attatiment ad En@-level Wages

Tratiig Wages, md the Hmm Capitil Model

Asewtig School ~ Work Trmsitions h the United

Sates

An halysis of the Comequences of Employer

Lticked Health hmace Co~erage ti U.S.

Repoti on the NLSY Roud 16 Redl Expetient

Employer Leming md the Signaling Value of

Edumtion