ED 210 519

DOCUMENT RESUME

CI C30 793

TITLE Youth Unemployment- -Its Measurement and Meaning.

Research .on Youth Employment and Employability

Development. Youth Knowledge Development Report

2.1.

INSTITUTION. zi):Loyment-and Training Administration (Del.),

Washington,. D.C. Office of Youthyrcgrars..-

PUE DATE .May 80

NOTE 627p.

AVAILABLE FROMQ,Superintendent fifiDocuments,_U.S. Government Printing

Offite, Washington, DC 20402 (Stock Nc.

029-014-00125-2, $1-0.0.0).

BIM PRICE ME03/PC26.Plus Postage.

, DESCRIPTORS Budgeting; Comparative Analysis; Crime; Cultural

Differences; *Data Analysis; Delinquency; Economic

Factors; Education :Work Relationship; Employment

Patterns; Employment Programs: *Employment

Statistics:' Information'Gtilization; LIch Training;

.Labor Force; labor _legislation; Labor Market:

Longitudinal Studies; Males; *Measurktett Techniques;

Military Seivice; National Surveys; Predictor

Variables;,*Research Needs; Research Problems;

Sociocultural Patterns; Unemployment: Work Attitudes;

Work Experience Programs; *Youth Eeplcystent

IDENTIFIERS Current Population Survey; National longitudinal

Surveys; Oakland ,Youth Work Experience Prograr

ABSTRACT.

This collection of reports on.the measurement and

meaning cf unemployment consists of 13 papers devoted to sore of the

- .deficiencies.in youth employment statistics, some of the necessary

considerations in their implications°, and many of'the issues involved

in their application' to assess program impacts. Analy.2ed first are

the youth labor force statistics obtained from the Current Population

SUrvey and the National Longitudinal Surveys. Teenage entry and exit

into the labor market and unemployment flows aregaiscdssed. Also

examined are job search implications of the transition from school to

Work and the role of work attitudes_ and labor .market ,knowledge in

establishing stable and successful employment careers,. Following a

description of economic and sociocultural variables affecting rates

of employment are 'explored. The`effeCts of cffild labor laws on youth

programs are' investigated: Included is a diScussion cf social

,development and group selection, methods of allocating funds to

alleviate teenage unemployment, and the cost of lowering youth

unemployment. (MN)

ic******#*************************.**************************************

.* Reproductions supplied by !DRS are the best that can be made * * from the original document.

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4
YOUTH KN I WL I

./. REPORT

EMI

RESEARCH ON YOUTH EMPLOYMENT

AND EMPLOYABILITY DEVELOPMENT

Youth UnemploymentIts Measurement and Meaning

May 1980

U.S. DEPARTMENT OF EDUCATION NATIONAL INSTITUTE OF EOUCATION

EDUCATIONAL RESOURCES INFORMATION

CENTER IERICI This _document has been reprodvned as

received from the person or organization

onginatog It L , Minor changes have been made to improve

'reproduction quality.

Points of view or opinions stated in this document do not necessarily represent official ME

positron or policy.
44.

U S Department of Labor

Ray Marshall, Secretary

Employment and Training Administration

EmestG Green, Assistant Secretary for

Employment and Training Administration

Office of Youth Programs

Material contained in this publication is

in the public domain and may be

reproduced, fully or partially, without

permission of the Federal Government :

Source credit is requested but not

required Permission is required only

to reproduce any copyrighted material

contained herein

3

4

For sale by the Superintendent of Documents, U.S. Government Printing Mee, Washington, D.C. 20402
5- OVERVIEW,

The statistical parameters of the youth employment problem

are by now familiar litany. Teenagers account for nearly a

fourth oy.th unemployed, and persons under age 25 represent

almost one ha eventhough they constitute only a tenth

and -a fourth- of- e_ labor force respectively. The fate of

unemployment among youth age sixteen to nineteen is two and

a half tomes the overall rate. Two-fifths of black teenagers

in the labor force are without jobs.'

-,, There is general public recognition, based on such statistics,

that youth unemployment is a critical national. issue. Yet

beyond this recognition, there is little agreement about the

actual dimensions of the job deficit, the severity and implications of joblessness, its causes, or the effectiveness of

alternative public policies. Libraries have been written . about youth employment and unemployment, but the literature

raises as many questions as it answers. 4'

One fundamental reason is theinadequacy of our measurement

tools and the uncertainties related to their applicat on.

The first step in addressing any problem in a "rigor us,"

"scientific" manner is to develop a set of measuri rods

which will realistically appraise the characteiistics of the

phenmenon, which will aid in assessing its causes, and

which will provide a baseline for determining the impacts of

interventions. In the employment arena,the statistical system, while extremely sophisticated, was not developed for use

in understanding and measuring youth employment problems. The

concepts and definitions of "employment," "unemployment" ana

"labor force" participation were largely derived,from the

Depression experience, when the primary issue was joblessness

among adult breadwinners. he meaning of these terms becomes. breadwinners.

clouded when applied to ersons with very marginal and fluctuating attachments to the world of work. Interview methods by

which the employment and unemployment data are derived may

also bias results...as they relate to youth, since the head of.

household who is interviewed may not know about the.attitudes

and activities of junior members. There are bo many other

factors involved during the "tempestuous teens" and early

twenties that employment characteristics and developlents cannot be considered alone.

. By the same token, as youth have become an increasing share

of the labor force, their inclusion in the overall employment . and unemployment statitics have generated uncertainties about

the meaning of these numbers. Equal weight is given a sixteenyear-old high school student looking fOr a 5-hour-a-week job

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to pay for clothes ap,is given'the unemployed teacher with

a family to support. The aggregate data mix "apples" and:''

"oranges," and it is increasingly..difficurt to determine the

Implications of any given mixture. Some,hpve argued, for

instabce, that historically high rates of national unemployment

must now be tolerated because of many youth,are counted in the

statistics even though their problems are 'neither serious nor'.'solvakle. v " -

A few illustrations quickly suggest some of the differences

between adult and youth unemployment which cloud the overall

data and make it 'difficult to assess the dateWhich are avail -

able for youth:

o Half of teenagers (sixteen .to nineteen) unemployed,

during the scho year are students. This is a

dramatic change om,the early 1960's when less

than a fourth,of.unem loyed teenagers were in school.

Among twenty to twent -four-year-olds, only an

eighth of the unemployed are students, and the

proportion drops to 7 percent among twenty-five to

thirty- four - year -olds.

o More than a third of sixteen to nineteen-year-olds

are part-time job seekers, compared-to a tenth of (Th

persons twenty-five and over. Teenagers who work

average 28 'hours of employment weekly compared to the

40-hour average of adults.

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1 Youth employment and unemployment is very seasonal.

From December to July, employment among sixteen

to nineteen-year-olds rises by a fourth compared to a

tenth for twentm to twenty-four-year-olds while re- . maining almost donstant for persons twenty-five and

'over. Youth account almost all of the December

to July employment increase.

o Mang persons$ with labor force experience during tHe year,

three-fifths of sixteen'to seventeen-year-olds, twofifths of eighteen to nineteen-year-olds, and a

fifth of twnety to twenty -four- year -olds are in the

LlaboT fcrce less than half the year'compared with

only a tenth of older participants.

o Few g persOns aretpreadwinners. Less than a

tenth of sixteen to nineteen year -oldc in the civilian

labor force are married with a spouse present, compared to two-,fifths of twenty to twenty-four-year-

,olds and seven of every ten twenty-five to thirtyfour-year-olds.

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The 'frequency of entry and exit fro rr the labor force

is a major factor in yoUth uhemploym t.

SeVen of ten "unemployed youth are e trants or reentrants into the labor force-,,compared to. less than, - two-fifths. of.,all unemployed; Simila0.y,half' ., of unempIo ed teenagers ark jobless filA weeks or

leSs compar d with less thah two fifths of the unemployed

age twenty a d addr. . o For eVerS, unemployed youth.age sixteen to nineteen,

there'is another who claims to want a jOb but is

not looking--largely because pf school. attendance.

o Among employed sixteen to nineteen-year-olds, more

than a third earn less than the minimum wage. While

some unemployed south have unrealistic wage expecta=-

ions, two-fifths claim they would takF a job that

paid less than the minimum and another fifth would'

take a job at the minimum. The mean,vage for employed

sixteen to nineteen?year-old males is less than

half that for males,twenty-five and over. For persons under age twenty with work ekperience,mean

income is one third that of workers age twenty to

twenty-four and one fifth that of persons. age twenty-

\ five to twenty-nine.

o Youth with "serious" problems area minority f all

unemployed youth. ,Dropouts represent less th n a

fourth,of unemployed Sixteen.to-twenty-four-y4arolds, and nonwhite school dropouti only one -twentieth.

Only a third.of unemployed youth are from poorAfamilies,

and half from families below the BLS .lower liv,ng

standard. An eighth are from poor, nonwhite f ilies.

Likewise, only one in twenty ,Jobless sixteen t nin

teen -year -olds are lonazterm unemployed (twenEy week

or more) compared to i'Tifth of adult unemploye

o The labor foce data for youth largely reflect

demographic trends. The annual growth rate of the

sixteen to nineteen labor force cohqrt was 3.9'percent

from 1970 to 1975 compared to 2.2 percent for twehtyfive to fifty-four-yearolds. From 1975 to 1980,

the growth rate for youth slowed to ,8 percent ann ally.

From 1980 to 1985, the youth labor force' is expect d

to actually decline by 2:8-percent annually.

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C o. From 1964 to-1970, government employment and training programs and the military accounted for one-fourth'

of the eMployment grow h for sixteen to twenty -four-

;year-olds. From 197*0 1976, the decline in the

military exceeded the xpansion in program enr llments,

so that the government sector activity was acfually'a

negative factor.

These few examp les suggest 7ffe many considerations in interpreting and applying employment and unemployment statistics regarding

youth. The examples are evidence that some data are gathered

onthese important issues. However, the available statistics

- are in many cases sporadic and incomplete--adequate to high-Pt

light issues bUt not to- address them fully. And in many cases

there are Copious data but a great 01,01 of uncertainty about

what they mean.

.To improve understanding of youth employment problems and

programs, it is necessary to begin by improving our understanding of the measurement system, its uses and its abusqs. In

February 1978, a Conference on Employment Statistics and

. _ Youth was held to bring together #e Nation's experts to

assess these issues: The conference was organized bj the

Institute of Intustrial RelationS of -the. University of

California-, Los Angeles, under the joint sponsorship of the

Department,of Labor's Office of the Assistant Sedretdry for

Poli.cy, Evaluation:and Research, and the Office of Youth

Pro4rams, with 'guidance from the. National Commission on Employment and Unemployment Statistics. A Total of twenty-two

papers were commissioned-for-this conference.

The first five papers focus on who is counted and the dynamic

aspects underlying our static measures. In.;"Counting Youth:

A CoMparison of Youth Labor Force Statistics in the Current

Population Survey and the National Longitudinal Surveys,"

Michael Borus, Frank MottandGilbett Nestel document how

differences in survey techniques cah make drastic differences

in measured labor force status. The analysis documents the

very wide mar4in_inplatermining Whether youth are really

inside the labor force, raising fundamental questions about, the

reliance which can be placed on fluctuations in measured

employment and unemployment rates for youth. Ralph'Smith and

Jean,Vanski, in .'The. Volatility of the Teenage Labor Merket:.

Labor Force Entry, Exit, and Unemployment Flows," demonstrate

that for youth even more than adults, the point-in-time

'

measures of employment and -unamploynent mask an enormous flux

as youth move into and out of jobs and the labor force. The

"probabalistic" approach to analysis which cpsideFs these

dynamic facts is complex but frequently is necessary to fully

undey,stand the static labor market statistics.

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The "Measurwent and Interpretation of Teenage Unemployment

in the United States and Other Countries,." by Beatrice Reubens,

assesses our statistics from a comparative international

perspective, indicating that the high rate of teenage unemployment in the United States is due in part to the inclusion of 16- and 17-year-old student job seekers in the tallies,

and in part to the use of household surveys rather than

registration with unemployment agencie's as the source of data

on unemployment. ,The greater proportion of youth in the U.S.

who combine work and schooling is also a source of our Nation's

comparatively high rate of teenage unemployment. Further

analysis cethe school-to-work transition experience in other

countries--its institutional basis and demographic under -

pinnings- -as-well as of employment and unemployment concepts

and data collection systems, are required before the international comparisons have meaning. BeCause other countries

do things differently does not make our approach right or wrong.

Certainly, however, it suggests'the availability and acceptability of options.

Orley Ashenfelter'd analysis of the relationship between

employment and unemployment over time ,in "What Do Teenage,

Unemployment Statistics Measure?" shows that while unemployment and employment move in lockstep for adults--i.e., more

jobs mean fewer unemployed--the pattern does not hold for

youth, particularly 16- and 17-year-olds and female teenagers.

This is another way of stating that there are enormous flows

into and out of the labor force for youth, and.that labor force

participation rates, as well as unemployment rates, must be

considered in any analysis of the youth employment situation.

While the Ashefelter approach yields some interesting

411
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informatiOn--forsinstance, the finding that black teenage

. unemployment is more like that of adults than white teenage

unemployment and, therefore, presumably more "real"--a more , sophisticated approach considering labor force and unemployment

exit and entry probabilities is necessary to.move beyond the

'obvious finding that unemployment means something much different for the average youth than the average adult.

"Youth Participation Rates and the Availability of Jobs," by,

Francine Blau provides a cross-sectional analsis of unemployment and labor force participation rates, considering a number

of other variabled. The.paper TiRds that where unemployment

is high,,all else being equal, 'abbr force participation is low.

On the other hand, the probabilities of labor'force entry or L

. exit over a one -year period do'not vary significantly by o

rate of unemployment. The'author conclude hat net effect of

the ups and downs-of the business cycle on°1 r force participation of youth will not be very great, but t..e 2rolonged

periods of high unemployment can produce a stockpile of dis66traged workers. The conclusion is consistent with other

findings: cross-sectional analyses usually indicate a stronger

relationship between unemployment and labor force participation

than time-series analyses. There are some problemsAwith the

Blau analysis in that the base-year area ,unemployment rather than

' changes in employment are the variable being tested for impact;

the latter would,be more appropriate. Also, given the weak.

,correlation between overall and yotth unemployment rates,

there is not likely to be a strong relationship when overall

rates are used as a proxie. Nevertheless, theanalysis

correctly suggests the comizilexitylof the relationship between

job seeking and.opportunAy, and reiterates the underlying

uncertainties about what is being measured by youth employment rates.

The next nine papers examine labor market, institutional and

societal factors which affect the measured. rates of employment

and unemployment. Stanley Stephenson's analysis, "The

Transition from School to Work"with Job Search Implications"

examines the question whether unemployment during the early

yeas in the labor force ds constructive. One theory is that

jukplessness reflects an exploration process leading to bettgr

job opportunities. ,Another theory is that employment itself *-

teaches job skills and leads to future success. The paper

explores,the evidence in favor of these theories. In "The

Establishment of Stable and Successful Employment Careers:

The Role of Work Attitu

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es and Labor Market Knowledge,,'' Paul

Andrisani examines the ttitudinal and informational

(9
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underpinnings of labor market participation by youths, addressing questions such as whether youttl really want to work and

how they,learn-and how much they know about labor market

opportunities. Daniel Glaser's "Economic and Socioeconomic

Variables Affecting Rates of Youth,Unemploymeht, Delinquency

and Crime" discusses the impact of'unemployment on crime rates

and the extent that illegal activities may provide an

unreported alternative to gainful employment. Paul Osterman's

paper on "Racial Differentials in Male Youth Unemployment"

assesses all of the factors, including discrimination, which-'

result in the Staggeringly high levels of youth unemployment

as well as the consequences of these severe problems. Finally,

"The Effects on Child Labor Laws on Youth Employment" by

Daniel Mitchell and John Clapp analyzes the legal impediments

to youth employment and the way these affect measured rates

of employment and unemployment.

The sidteen to twenty-four school -to'-work transition period is

also the time when most people move from dependency to marriage or self-support. "Family Status and Labor Force Patterns,"

by Martha Hill, suggests that there is'a substantial interrelationship between- family and household structure and labot

L' supply. Youth who are heads of households tend to work, Much

more frequently than those who are dependents or wives. For

males, marriage is related to very substantial increases in,

labor force participation rates and hours worked. For females,

if there are children there will.be substantial declines,

with marriage; for-childless wives, this is not true. There

appears to be a weak relationship between the composition or

change in the parents' household and the labor force participation rate for youth,dalthough nonwhite males from split families

have a lower labor force participation rate than those frordtwoparent families. The analysis provides some important longi- "

tudinal. data on changes in family status and work. However, it

j/is,imp6ssible to determine the direction of cauSality. It is

' uncertain whether increased work opportunities contribute to

the rise.in independent living or whether the trend toward

living alone leads to increased job seeking. It is clear,

'however, that both living arrangements and family status, as 0

well as change, must be considered in determining the meaning'

of employment and unemployment numbers, particularly the prevalence of breadwinningiresponsibilities.

- The question of how. to count students has been raised irik-ft.

other papers. "Education,Thccupation and Work,"'by David O'Shea

looks at the indirect rather than direct impacts of education.

According to the analysis, education is more critical in

determini occupational and social status than income. The

relationsh between earnings and education is apparent mostly
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in high skill, high prestige occupational categories which

account for about one-third of the work force. The payoff

of education apparently increases with age in all occupations. Over time, there has been a convergence of education

levels among workers in all occupations. The findings certainly raise questions whether longer. schooling pays off for

the .average youth who will not go on to professional and

managerial ranks. This is important'relative,to programg tying

employment and training services to school attendance in,orderto encourage school completion. If greater education attain-,

ment does not improve'earnings,.it might be better to foCus

on dropouts. Certainly, these in- school programs .must be , subjected to careful scrutiny., While the analythis ignores a

large part of the literature on human capital theory and

returns to education, and while it suffersthe problems of

most such studies in handling education quality issues, its

fundamental messages are important--that education decisions

cannot be considered from the ecOnomic perspective alone,

that many youth end up over-qualified for-the jobs they

eventually achieve, that there is much we do not know about the

process, and that the indirect, marginal effects of education%on measured rates of employment -and earnings f6r young

people are probably quite limited.'

)

"Alienation and Adjustment to Limited Prospects," by David

GOttlieb., suggests that data on emplbyment or unemployment

provide little useful information'about the real issues facing youth an 4, in particular, the fad-tors at the'key decision

points such as entrance into the military or training prdg,rams,

departure from school, or assumption of family responsibilities.

The paper speculates that youth may not understand survey

'questions or many respond based on their perceptions of what

is or is not wanted rather than reality, but that the attitudes

of disadvantaged yodth probably do not differ much from those

of more advantaged- .the nalysis suffers from a lack of clear

definition of'the term alienation or prdcige information about

how this might affect specific responses, but it does suggest

the need for in-depth personal interviews to supplement

employment and unemployment survey data.

( "Do Youth Really Want to Work," by Patricia Miller and'William

Simon; compares the work attitudes of youth and adults from

a variety of perspectives. Substantial and persistent continuity

is found between the work value's of younger and older men.

Youth emphasize security somewhat less, give a marginally

higher priority to immediate payoffs, and givemore considerar

tion to the, time away from the job, but the differences are

less significant than the similarities; The unemployed have

much he same values as.the emgloyed. Blacks who suffer from

greater job insecurity, lower wages, and lower status'employment,

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ix . f not surprisingly give more eAphasiS. torsecurity, wages, and

,time away frdfr ie job. Most critically, .youth'are much less

likely to rate their current jobs highly by any criterion

than are adults. Given the parallel in attitudes and expectations, this would suggest that, youth leave ,jobs more frequently

simply. because these are not rewarding on the average and

they-can:do.better with age and mobility. "The analysis

is flawed somewhat by the exclusion of adults earning more

than $15,000 annually, butthe fundamental message is that

for the vast majority.of youth, there is no "alienation"

Apparent In work values.

The next four papers discuss the direct and indirect impacts

of government activities. The military is a primary employer

of youth, and 'Richard Cooper's."Youth Labor Markets and the

Military" suggests the substantial'and frequently ignored

' effect of Armed Forces requirements and policies on civilian

employment and' unemployment-Statistics. In "Direct:Effects.

of Employment and-Training Programs on Employment and Unemployment: New Esti,mateS and Implications for Eloyment

Policy," Charles and Mark Killingsworth estimate e impacts

of,:manpower progiam enrollments the official loyment

and unemployment counts. "So ial Development - EMployMent:

An,EVaivation of the Oakland Youth Work Ekperighce Program"

by Delbert Elliott-arid Briah Knowles explores the effects of

a "model," work experience program on subsequent employment And

earnings. While the results are not neceksafily generaliieable:,

they give an indiCation of'some possible considerations . in assessing indirebt impacts of emtilOpment and training pro-.

gram, on future employability. Ernst Stromsdorf.er and, -

Tei-Wei Hu discuss the ways in whichkprogram`impacts on emploxment and earnings can be asselfed in "Control Group Selection.'

The next two papers examine the long-term consequences of

,-, the employment-experiences of youth, Wayne Stevenson in

"The Relationship Between Youth Employment and FuWre Employ:-

ability and alrAingsi" cqncludes that after coittrolling for

related variables, early liabor forte' status has a significant

.impact on future employment and earnings. Those who work'

during the frnasition years,tend to do better than those

do not. The worst off are teenagers who ve out of school

and out of the labor force. Skill training dIllEing the transi

tion period is also,correlated.with higher earnings., These

results were obtained though the analysis of measured labOr

force status in a single week in the base period; if a full

year's- employment. histdry or even a multi -year, history had

been considered, there would probably have been even greater

evidence of impact.

"Employment and Earning Patterns: The Dynamics of Oange,"

by David Farber takes this longer term view. The-analysis of

The earnings patterns of employment and training program

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participants as well as a random sample,Of all wage,earners

J indicates a consistency in the earnings trends of individUals.

Those young per'sons who'experience rising annual earnings in

the early years are likely to continue upward, Those with

mixed earnings patterns are not likely to dO 'a's well.

Because of the high correlations between early patterns and

future earnings, regression analyses should probably include

pattern variables. What is.left#unelearis the extent that-:,'

policy intervention can alter patterns; or whether the patterns

reflect sorting or success building on success. The latter

view would support the finding that work provides

experience and that the labor market is a ladder which must

be climbed; the former explanation would essentially mean

that winners and losers are predetermined by personal

characteristics.

The final two papers focus on the use of employment and unemployment statistics in addressing questions about the scale

of.neededgovernment initiatives and the didtribution of

resources. "Methods of Allocating Funds to Alleviate Teenage

. Unemployment problems," by Joseph Cordes and Robert Goldfarb

examines the formulae by which youth employment and training

resources are distributed and the data bases to which these

formulae are applied._ Paul Flaim anoi?Paul Ryscavage use

available labor market statistics to estimate the job deficit

for youth in "Lbwering-Youth Unemployment° How Much and at

What Cost?"

Overall, then, thi set of papers presents a panoramic review

of the problems of gg thering, ihterpreting and applying employment and unemployment data relating to youth. .The conclusions

axe critically important in seeking to better understand youth

employment problems and programs:

1. 'Survey data responses vary considerably depending.on

whois asked what question. Published Bureau of Labor Statistics

information is frequently considered sacrosanct and used as a

basis for highly sophisticated analysis.. In f°$ct, however, theA

-J-youth.unemployment rate may vary by/ more than half depending ,

on who in a Household is interviewed and how they are approached.

2. The youth labor market is highly dynamic and, static

Conceptualizations are mat always appropriate. For instance,

there is an obvious job deficit for youth, but increasing the

number of jobs may simply bring new entrants into the labor

force, or draw youth away form other openings, and might not

directly reduce unemployment. If the jobs are more stable,

this will reduce'unemplOyment; i.!,-theyare unstable they may

increase shifts' in and out of the labor force and'eAlployment.

To the extent that occupational information and placement ser-

"'vices lead to better job choices they will increase,the stability

of employment; to the extent they prolong the job search, they

will'" raise unemployment:. 13

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t 3. In general, work experience seems to pay off in the

sense that those 'who.hold-jobs during'bsrol tend to have more

stable employment in the future. Howeve , it is unknown . ) whether government created slots which fill the 'job deficit ate

as effective as other employment, or whether the,effects which

prevail on the average also hold *at the margin; i.e., whether

puttir4 another youth to work pays off in the future for

that-youth..

4. Youth in general do not have unrealistic job

expectations, and like most everyone els, they respond in'a

reasonable fashion to labor market realities. Youth who knOw

more about the labor market and who are more positively

oriented to work tend to do better: It is, again, uncertain

whether public intervention can improve labor market knowledge

or motivation, and whether the participants ',rho are motivated

and informed by such programs will do better than those Who

are not.

5. The interaction between crime and unemployment is

apparently a two-way relationship. However, 4b4many.factors

are involved that it is uncertain whether increased employment

opportunities will lead to a direct reduction'in crime. 4k; 6. Black youth employment problems are the result df a

massive array of factors. Unemployment measures only one

Of the symptoms and is not a very accurate reflection of , differentials in opportunity. UnemPloyMent does -not 'have the

same consequences for white youth as it does for blacks.

T 7. Youth underage eighteen are underrepresented in

certain occupations covered by child labor laws'. However,

youth, shift into these occupations as they age, and it isuncertain to what extent the'laws alter overall youth employment or restrict certain groups of youth from getting into more

promising career tracks. Likewise, it.is unclear what risks

are involved in thetcov9red sectors and whether younger persons

are better off dutside.

8. The military is'a major employer of youth. During

the Vietnai era, growth of the Armed.Forces absorbed large

numbers of young men and contributed significantly to declining

unemployment. During the-1970's, cutbacks in military.personnel had the opposite ,effect: Despite the volunteer nature

of military service, employment statistics continue to count . the military sector separately. Inclusion\might be more

appropriate.

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xii

9. Employment and training programs have a direct

effect on youth unemployment. is estimated that expanding

enrollments between 1964 and 1969 accounted for 90 percent of

the decline in unemployment over this period. From 1969 to

.1976, the programs did not expand'any more rapidly thanthe

youth labor force so their contribution to reducing unemployment was little greater in the latter years than in the former.

10. Indirect impacts of programs are extremely difficult

to measure. Random' assignment to experimental and control groups

is really the only effective mechanism, and even this has

such a wide margin of uncertainty that modest increases in

employability frqm short-term youth programs are-riot easily

registered.

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11. Employmerit and training resources-should be distributed

according to needs and the types of problems being addressed.

Because of the absence of unemployment data fqr youth by. prtme

Sponsor, CETA funds must be distributed according to overall

unemployment rates or poverty. These are poor proxies for

youth unemployment and youth needs.

12. Available data cannot indicate how many youth would

workif jobs were .available. However, under,a range of

assumptions, it is possible to esjimate the job deficit and

wage bill necessary to meet it. To reduce teenage unemployment

from 1976 to 1969 levels, assuming the new jobs would pay minimum wage, would cost around $1.7_billion. Economic recovery

and the programs initiated under the Youth EmployMentand

Demonstration Projects Act with a billion dollars of funding

in fisbal 1978 can make a substantival dent in the problem.

These papers do not yield a coherent set of policy recommenda-

-tions. ,Rather, they are suggestive of some the deficiencies

in youth employment statistiqs, some cif the necessary considerations in their'interpreghtion, and. many of the issues involved

in their application to assess program impacts.

The obvious recommendation is for more` and better data concerning youth. Certainly, labor force information,on youth by

labor market area is necessary for the equitable distribution

of resources. Much more heeds to be gathered about the

earnings of youth as well as the types of jobs they fill.'

Family status usually.changes during the teen years and early

twenties, and mo3;e information is required relating marital

and family changes, as well as-treadwining responsibilities,

to labor market changes. Some 'hard" numbers are required on

the income alternatives to work for youth and the activities,

of youth out ofschool and out of the labor force. The dynamic

I
aspects of you h 'labor force participation suggest the need for

:0;re longitudina information with monthly or even weekly, as

posed to annual, interviews in order to better determine the

factors underlying employment status decisions. A determination must be made whether household head or individual

interviews yield the more accurate information.

But the answer is not just more data. Therefore questions on

how these numbers are used. The papers suggest some of the

factors which must be considered in analyzing youth labor

market experience. For instance, A Med Forces levels must be

considered when looking at cyclical and secular trends in

youth employment or 'unemployment. Certainly manpoWer program

enrollments are a'critical factor. Dynamic factors must by

considered along with,changes in the static measures. Effdrts

to assess` employment and training programs must rely increasingly on random ahsignment control group selection "techniques;

Job deficit estimates from aggregate data must be tested by

jo 'guarantee experiments; reservation wage issues must, likeIse, be tested to determine the willingness of youth to work

under alternative conditions. In other words, there are so

many considerations behind the youth employment experience

that analysis gill have to become increasingly sophisticated

to expand the frontiers of knowledge. More data are needed,

but better use can be and needs to be made of the data which

are already available.

Until there -are improvemerits in data and analytical approaches,

it is necessary to fall back on some basic postulates and

common:sense notions:, First, employment programs (including

the military) can reduce the jOb deficit. ThYrelationship

between job creation and unemployment reduction % is uncertain,

but it is tautological that expanding enrollffightwill mean

increasing.emgloYment when participants are counted as employed.

The best guesses about the, universe of need suggest that it

is within the realm af' possibility to drive down youth-unemployment rates to tolerable. levels by steady expansion of job

creation efforts.,

Second, almost'all the measured effects will be the direct

impacts on counted employment and unemployment. Indirect

effects are difficult to determine because of the complexity

of measuring net employment and earnings ohanges over time, as

well as the impossibility of, extensive demonstrations with

randomly selected control groups. Also, 'gains realized in

future years are good for the persons involved but do not

change the status'ofyouth; for instance,. increased pwliOs

at age twenty to. twenty -four will not register for six en`

to nineteen-year-Olds under program directed to teenagers.
xiv--ti

e.

Third; lacking rigorous confirmation of the impacts of government, interventionst there are at least some reasons for

optimism. Work during the .years of transition does seem to

increase success probabilities; better youth jobs yield a

better'adaptation. Youth with more complete occupational

,information tend to have an easier time in the labor market.

Employment provides an alternate to illegal activity even if

the causal effects are difficult to pin down. In other words, the conventional wisdoms underlying youth policy--that more \jobs are needed; that the quality of work and supervision

matters, and that enrichment with occupational information and

counseling makes sense--seem to be headed in the.right directibn. It.remains to be determined whethbp government-created

or accessed jobs have the same impacts as others in thelabor

mar)'et, whether or how much structured, supervised job settings

make'a difference, as well as the impacts of counseling and

occupational information services. 1374r1 the difficulties

of refined assessment, and the shortc ntings of available data,

these effects may never be determined convincingly. It is,

therefpre, necessary to move ahead based on best guesses, and

the bes t guess.is that common-sense notions are realistic.

-Poor. and minorities are worse ff by almost

Fourth,, the problems of disadvantaged youth and disadvantaged

are not simple. The

every dimension; every factowlwhich negatj.liely affects all

youth tends to affect them even'More. On the other hand,

there is evidence from most of the papers that positive actions

and developments also have a magnified-effect. Government

programs and Armed Forces build-ups have their greatest

impact on minori,ty. and low-income youth. The training received

most, benefits those who have no other options. It is disadvantaged youth who,have'the least information about the labor market

and might be expected to benefit most frpm career counseling.

Given the size Of the minority and disadvantaged subgroups of the unemployed youth population, government interventions

on a feasible scale can make a vdry substantial difference.

.Finally, the papers in this volume suggest that w should not

expect too much from "knowledge development". eff rts. One of

the declared purposes of the Youth Employment and Demonstration Projects Act is to determine "what works best_fox Vhim."

Given the uncertainties of the data and their interpretation,

s,it will be difficult to even assess aggregate YEDPA impacts

on measured rates of unemployment much less, to make refined

estimates of incremental impacts of alternative interventions.

The issues most likely to be answered clearly are those h'aving

to do with scale--how much is needed Sand howlmuch can be done

based on the experience of areas with more intensive funding--,

as well as those having to do with direct effects on aspirationd, occupational awareness, motivation and the like, which
XV

dart - be assessed by entrance and termination tests. While:

sophisticated analyses of short- and long-term impacts on

employability must be attempted--and randomly asssigned,,

control groups are needed whenever 'feasible- -the fundamental

uncertainties of data and the complications of youth labor

market activities raise doubts aoubt what can be proven in any

rigorous manner. The "state of the art" in youth labor

market statistics and interpretation of these statistics

does not support overly sophisticated research and assessment (although this should not preclude attempts to Ilseavailable data as rigorously as possible). It must be

accepted that the shOrtcomings in employment statistics and

their interpretation constrain what can be learned about youth

employment problems and their solutions.

This volume is one of the products- of the "knowledge develbpment" effort implemented under the mandate of the Youth Employ-,

ment and Demonstration Projects Act of 1977. The knowledge

development effort consists of hundreds of separate research,

evaluation and demonstration activities. which will result in.

literally thousands of written products. The 'activities have

been structured from the outset so that each is self-standing

but also interrelated with a host of other activities. The

framework is presented in A Knowledge Development Plan for

the Youth Employment and Demonstration Projects Act of 1977,

A Knowledge Development Plan for the Youth Initiatives Fiscal

'1979 and Completing ,the Youth Agenda: .A Plan for Knowledge

Development, Dissemination and Application in Fiscal 1980.

Information is available or will be coming available from the

various knowledge development activities to help resolve an

almost limitless array of issues; 'Art answers to policy

questions will dsually require'integration an& synthesis from

a numberof separate products,. which, in turn, will depend on

knowledge an availability:of these products. A major shortcoming, of.pa t researches evaluation and demonstration activity

has been failure to organize and disseminate the products

-adequately to 6-6guie,,the full exploitation of the findings.

The magnitude and struRture of the youth knowledge development

.effort puts a'premium on organization and dissemination of

findings.

. ).

As part,of its knowledge development mandate, therefore,

the Office of Youth Programs of the Department of Labor will

organize, publish and disseminate the written products of

all major research, evaluation and demonstration activities'

supported directly by, or mounted in conjunction with the

_knowledge development effort. Some of th& .ame products

may also be published and disseminated thrthigh other channels,

but they will be included in the structured series of Youth

Knowledge Development Reports in order to facilitate access

and integration.
4 xvi

The Youth Knowledge Development Reporta, Of Which, this is one, are divided into twelve broad categories : 1. Knowledge Development Framework: The products in

this category are concerned with the structure of knowledge

development activities, the assessment methodologies which

are employed, validation of measurement instruments, the

translation of knowledge into policy, and the strategy for

disseminating findings.

2. Research on Youth Emlo ment and Emlo abilit ..- Development: The products in this category represent analyses

of existing data, presentation of findings from new data

sources, special studies of dimensions on youth labor market

pioblems and policy analyses.

3. Program Evaluations: The products in this category

include impact, process and benefit-cost evaluations of youth

programs including the Summer Youth Employment Program, Job

Corps, the Young Adult Conservation Corps,. Youth Employment

and Training Programs, Youth Community Conservation and

Improvement Projects, and'the Targeted Jobs Tax Credit.

4 Service and Participant Mix: The,evaluations and dmonst - tions summarized in this category concern the matchihg of different types of youth with different service combinations. This involves experiments with work vs., work plus

remediation 1 s. straight remediation as treatment options.

It also incl attempts to mix disadvantaged and more affluent

participants

....'

..% well as youth with older workers.

5. Edu ..,. ti n and Trainin ... A proaches: -The products , in this cate.ory, present the findings of structured experiments to tes the impactand effectiveness of various education

4 and vocational training approaches including specific educe,-

tion methodologies for the disadvantaged, alternative education approaches and advanced career-training.'

6. Pre-Employment and Transition Services: The products in this category present the findings'of structured experiments to test the impact and effectiveness of school -to -work

transition activities, vocatibnal exploration0 job-search

assistance and other efforts to better prepare youth for labor

market success.

7. Youth Work Experience: The products in this category

address the organization of work activities, their output,

productive roles for youth and the impacts of various

employment approaches.

15)
1

'4

xvii

8. Implementation Issues: This category 'includes

cross-cutting analyses of the practical lessons concetning7:

"how-to-do-it." Issues s4h as learning curves, replication

processes and programmatic "batting averages" will be addressed

under this'category, as well as the comparative advantages

of alternative delivery agents.

9. Design and Organizational AIEriatives: The products

in this category represent assessments of demdnstrations of

alternative program and delivery arrangemehts such as consolidation, year-round preparation for summer programming, the use

of incentives and multi-year tracking of individuals.

10. Special peeds Groups:. The products in-this category

- present findings on the special problems of and adapt4tions

needed for significant segments including minorities, young

mothers, troubled youth, Indochinese refugees and the

handicapped.

11. Innovative Approaches: The products in this category

present the findings of those activities designed to explore

new approaches. The subjects covered include tlp Youth

Incentive Entitlement Pilot Projects4_private sector initiatives, the national youth service experiment, and energy initiatives in weatherization, low-head hydroelectric-dam restoration, windpower and the like.

12. Institutional Linkages: The products in this category.

will include studies of institutional arrangements and linkages

as well as assessments of demonstration activities to encourage

such linkages with education, volunteer groups, drug abuse

agencies and the like..

In each.of these knowledge development categories, there

will be a range of discrete demonstration, research and evaluation activities, focused on different policy, program and

.analytical issues. For instance, all experimental demonstration projects have both process and impact - evaluations,

frequently undertaken by different evaluation agents. Findings will be published as they beco available so that there

will usually be a series of reports as evidence accumulates:

To organize 'these products;' each publication is .classified in

tone of the twelve.laroad-knowledge development - categories,

described.in'terms of the more specific'issue, activity or

cluster of activities -to whichit is addressed, with an

identifier of the product and what it represents relative to

other products in the demonstration. Hence, the multiple

products under a knowledge development activity are closely

interrelated and the activities in each broad cluster have

significant interconnections.
A range of theoretical assessments have been comAissioned to

increase understanding of the youth labor market experience.

. Of particular relevance to the present volume are the following: A ReviVWOf Youth Employment Problems, Programs and Policies, Factbook on Youth, The Labor Market Experience of

14-21 Year Olds, Between Two Worlds: youth Transition

From School to Work, The Causes of Youth Unemployment,

and Findings of the National Longitudinal Survey of Young _ Americans.

A

1.11.

ROBERT TAGGART

Administrator

Office of Youth Programs

21
xix

TABLE OF CONTENTS

Page

Overview ,

COUNTING YOUTH: A COMPARISON OF 'YOUTH LIBOR FORCE

1

STATISTICS IN THE CURRENT POPULATION SURVEY AND

THE NATIONAL LONGITUDINAL SURVEYS

Michael E. Borus, Frank L. Mott and Gilbert Nestel,

Center for Human Resource Research, The Ohio State

University e.

A THE VOLATILITY OF THE TEENAGE LABOR MARKET: LABOR

FORCE ENTRY, EXIT, AND UNEMPLOYMENT FLOWS -

. Ralph E. Smith and Jean E. Vanski, The 'Urban Institute

THE TRANSITION FROM SCHOOL TO WORK WITH JOB SEARCH

IMPLICATIONS

§tanley P.'Stephenson, Jr., Department of Economics,

The Pennsylvania State University

THE ESTABLISHMENT OF STABLE AND SUCCESSFUL JEMPLOYMENT

CAREERS: THE ROLE OF WORK 'ATTITUDES AND LABOR MARKET . KNOWLEDGE

x. Paul J. Aidrisani, Industrial Relations Att,Director, Bureau

of Economic and Business ,R4search, Temple University

20

50

71

ECONOMIC AND SOCIOCULTURAL VARIABLES AFFECTING SATES OF, 96

YOUTH UNEMPLOYMENT, DELINQUENCY AND CRIME

Daniel Glaser, Department of Sotiology and Anthropology,

University of SoutherdCaiifornia

%

RACIAL DIFFERENTIALS IN MALE YOUTH UNEMPLOYMENT

Paul Osterman, Department of, Economics, Boston University

127

22
XX

THE EFFECTS OF:CHILD LABOR LAWS ON YOUTH EMPLOYMENT

Daniel J.>B. Mitchell, Brookings Institution and Graduate

School of Management, University of California, Los Angeles

-1 Jo nClapp, Graduate School of Management, University of

California, Los Angeles

. , YOUTH LABOR MARKETS AND THE MILITAOL

Richard B.L. Cooper, The Rana Corporation

DIRECT EFFECTS OF EMPLOYMENT AND TRAINING PROGRAMS ON

EMPLOYMENT AND UNEMPLOYMENT: NEW ESTIMATES AND IMPLICATION

FOR EMPLOYMENT POLICY

1 Charles C. Kollingsworth, University Professor, Michigan State

University A Mark R.-Killingswgrthl Barnard College, Columbia University

SOCIAL. DEVELOPMENT 'AND EMPLOYMENT: AN EVALUATION OF THE 266

OAKLAND 'kOUTH WORK EXPERIENCE PROGRAM

Delbert S. Elliott. and Brian A. Knowles, Behavioral Research

Inetitutef-Boulder, Colorado

162 ,

195.

229

CONTROL GROUP'ULECTION 301

Ernest Si. Stromsdorfer, Indiana University and APT Associates

Teh-wei HU Department of Economics, The.PennsylvanA State

University

METHODS OF ALLOCATING FUNDS TO ALLEVIATE. TEENAGE UNEMPLOYMENT

PROBLEMS

Joseph J. Cordes and Robert S. Goldfarb,, Department of

Econbmica, George Washington University

LOWERING YOUTH UNEMPLOTMENT: .HO.W MUCH AND AT WHAT alp?

Paul O. Flaim and Paul M,'Rrscavage, Bureau of Labor Statistics,

U.S. Department of Labor

r

.23

V

320

345,

r.
COUNTING YOUTH:'

A '60MPARISON OF YOUTH LABOR FORCE STATISTICS

IN THE CURRENT-POPULATION SURVEY AND THE

NATIONAL LONGITUDINAL SURVEYS

By: Michael E. Bcpu§, Frank L. Mott and Gilbert Nestel

ABSTRACT

Thi's study focuses on the accuracy of employment-relatied measures

in the Cbrrent Population Survey (CPS) by comparing the CPS estimates

with those of another- survey- -the National Longitudinal Surveys of

Labor Force Behavior (NLS) which included approximately 5,000 young men

and 5,000 young women between the ages of fourteen to twenty-fout when

the surveys began in 1966 and 1968, respectively. Since this paper is

concerned with youth, employment problems, the analysis is restricted

to respondents between the ages of sixteen and twenty-one.

Differences betweeri the CPS and NLS estimates of survey week employment-related behavior were found. The NLS laborforce participation rates were significantly higher than those of the CPS, particularly among youth attending school. The NLS female unemployment rates

were significantly higher thadthe CPS rates, while for the young men

the NLS rates were slightly lower. The NLS data also showed a larger

number of the unemployed seeking part -time employment than the CPS.

The NLS found higher levels of employment, and among those at 'work the

NLS youth were more likely to work part-time or overtime; tepenoing on

their ages. There was no discernible difference in the CPS and NLS

estimates of mean hours worked by young women but the corresponding

NLS estimates'fpr the young men indicated a slightlxkhigher work.

activity than the CPS,-

The diffaxent estimates of the CPS and NLS could.arise from

diffetences,i/Ourvey procedures. The authors believe that.lack of

self response in the CPS is the most likely, explanation for the

illifferenees observed.

INTRODUCTION

The focus of thispaper is on the accuracy of the information'

dthered on youth by general population surveys such as the Current

IN*

Population Survey (CPS).

1 Data accuracy is particularly important"for

1. This i a revised version of a paper presented to the Conference wo.

on Unemployment Statistics and Youth held at U.C.L.A. on February

11-12,-1978. We wish to thank Jean Haurin for her valuable help

with this project.
2

the CPS since this is the primary source of national employment and

unemployment statistics. This papex will examine the accuracy of em-

. ployment7related variables in the CPS by comparing.the CPS estimates

with those of another survey-..-two cohort.of the National Longitudinal

Surveys of Labor Force Behavior (NLS). The NLS samples included

approximately 5,000 young men, and 5,000 young women between the ages

fourteen to twenty-four when the surveys began in 1966 and.14101-)ret-

. 2 ' pectively. Since this paper is concerned with youth employment

problems we concentrate on the portions of.the cohorts between the ages

bf sixteen and twenty-one. Therefore, the data analyzed are from the

1966, 1967 and 1968 NLS surveys of the young ten, the 1968, 1969 and

1970 NLS surveys of the young women, and from tables published in

Employment and Earnings for the CPS.

3

All data were gathered by the

Census Bureau's CPS interviewers and the current labor force questions

and coding were iantical.

DIFFERENCESIN SURVEY PROCEDURES

4

There were several differences between the two surveys. First,

the NLS interviews the youth directly while the CPS seeks the informaZ. The data are gathered by the U.S. Bureau of the Census and analyzed

, by the Ohio Skate- University under, contract with the Office of Research and Development, ET, U.S. Departmen of - Labor. The views

and opinions in this paper,do not necessarily reflect those of

any of the above agencies. For further information on the surveys

see Herbert S. Parnes, et al., Career Thresholds, Manpower Research

Monograph No. 16, vol. 1 (Washington: Government Printing Office,

197,0) and John R. Shea, et al:, Years for Decision, Manpower Re-.

search Monograph No."24, vol. 1:(Washington: Government Printing

Office, 1971).

3. The, reader should also see Parnes, et al., op. cit., Appendix E

for an earlier comparison of the 19-aTiungFETTsurvey and the

October 1966 CPS.

,r
Pk , AO

Y._

-

1 tion'about the young.person from the,head of the household or some

other responsible adult. the majority of the cases-theli;erson

interviewed by the CPS is a holisewife who would most likell,be the

youth's mother. We have been unable to find1much research,IhNh'ex.7.

plores the effects of nonself-response on labor for>. and employment

status questions. An unpublished memorandum by Charles Jones and

Robert Aquilino of the Census Bureau Indicates that net differences cs

in reports of employment status due to nonself response are not Statis-

,.. ,tically-different from zero at1ie 95 percent confidence level, for 111

males and females, sixteen years f age and older., A sim4arlinding

-ef nonsignificant differences for all adults occurs in the CPS-Census

Match for 197 0. Th CPS-Col4us study, however, ihows that there were

significant differe es for fourteen- to seventeen-year-olds, and the

-report goes onto note that there are noticeable differeqes by age,/,

with the inconsistency dropping substantially as age increases.

5

Another difference between the CPS and NLS surveys isin the de1 signation of the reference week. The CPS data referAto the specific

week which include 'the 12th of the month. The'NLI data are, gathered''

over a period of several months and' refer, to the week prior to the one...,

4. Charles Jones and Robert Aquilino, "Memorandum for Walter M:

Perkins, Subject: Methods Test Phase III:. Second Report on the

Accuracy of Retrospective Interviewing and Effects of Nonself

Response on Labor Force Status," unpublished memorandum, within

the Bureau of the Census, January 2§, 1970.

6

' 5. Bureau of the Census, Accuracy of Data for Selected Population

Charaateristpks as Measured by the1970 CPS -Census,March, PHC(E)-

11..(WaShington: Government Printing Office, 1975), p.11 and Table

33. We'should note that the poorer CPS-Census Match for, the youth :I_

may have been due to factors other, than nonseif response.
tr in which the interview is conducted. . Thus the CPS data are more likely

to be affected by seasonal factors, For our comparisons we have se-'

lected.CPS data for the month. in which the NLS conducted the greatest

number of interviews but we used all of the NLS respondents.

A third, difference relates to the 1966 survey of the young men.

Changes, in the definitions of employment and unemployment were.introduced in the CPS in January, 1967, but were used in the 1966 NLS coding.

Thus CPS-NLS differences for that year may partly reflect these definitional differences.' The definitions were identical for the other

survey years of the young men and all of the surveyQ of the young

women.Differing ages at interview, particularly among the young men,

also could cause variation between-the reports"of the two data sources.

The NLS male sample co/sists of individuals who attained ages sixteen

through twenty-one as of April 1 in the year of interview, whereas,, the

CPS includes individuals who were in that age group as of the survey

month. Since the CPS data refer to Novembdr of each year, the NLS

sample of the young men is approximately seven months older than. the

CPS group,. In the case of the young women the age difference is , considerably smaller. The NLS includes individuals who had attained

the given ages as of January 1 of the interview year, while the CPS

again uses the month ofthe interview. However, since the,CPS data

refer to January or February the age difference is small.

Finally, the longitudinal nature of,the NLS may lead it to

adiffer

from the CPS. The NLS loses some of its sample from year to year.

TheYe is some evidence that there is more attrition among the unemployed%

6

but a multivariate analysis by the authors has shown this

6. See Frederick A. Zeller, et.al., Career Thresholds, Nhnpower Research Monograph No. 16, Vol. 2, (Washington: Government Printing

Office, 1971), Appendix A, and Roger D. Roderick, et al., Years

for Decision, Manpower Research Monograph No. 24, Vol 2 ,(Washing:,

ton: Government'Printing Office, 1973), pp. 15-20.

27
is not substantial. 'Most of the loss is attributable to young men

entering(thm armed forces which removes them from the civiil-ian population. These individuals are excluded from the CPS as well as the NLS.

There also may be conditioning of the xespondent's answers by repeated

questioning. Such changeS apparently occur in the CPS which finds

different'reports of employment status- for different rotation groups.

7

_ . Sibilar conditioning could occur in the later years of the NLS surveys.

RESULTS OF THE CPS-NLS COMPARISON

In this section we compare the levels and rates of labor force

participation, employment, and unemployment; the number of-hours of

work being sought and the duration of unemployment for the unemployed;

and the number of hours worked and occupational distributions for the

employed as measured by the Current Population Survey and the National

Longitudinal Surveysl

Labor force participation. Tables 1 and 2 present the CPS-NLS

cemparisons,of the labor force participation rates for the young men and

women, respectively. As is evident from these tables the labor force

participation rates were significantly higher in the NLS for the total

population and for the two race groups.

8

- The CPS labor orce participa7. Robert *Pearl and Joseph Waksberg, "Effects of Repeated Household

Interviews in the furrent Population Survey," unpublished paper

presented at the 47th National Conference of the American M

, keting Association, Dallas, Texas, June 17, 1964.

dlkil

8. Our use of the word significant means_that-m6.-have rejected the

null hypothesis of equality of proportions in the two surveys.

Each of the statistical tests used a two-tail criterion, the type

1 error was 5 percent and the standard error of the estimator

was increased by 1.4 to reflect the complex design of the two

surveys., The CPS sample isself weighting with each respondent

representing 1,200 other individuals in the universe. The estimated sample sizes were Obtained*by dividing the corresponding

universe totals by 1,200.' The actual number of sample cases

was used for the NLS. We were unable to make tests of significance-of the levels presented in Tables 3-6 since we did not

have the variances for the CPS data. Comparisons of the two

.4

surveys for this information is-descriptive.
TABLE I

CPS AND NLS COMPARISON OF, LABOR FORCE PARTICIPATION RATES AND UNEMPLOYMENT RATES OF

. YOUNG MEN 16 TO 21 YEARS OF AGE, BY RACE AND SCHOOL STATUS, SURVEY WEEKS 1966 TO 1968

. Characteristic

1966a 1967 1948

CPS NLS d

CPS

e NLSf

CPSg NIS

h

LFPR UR LFPR UR LFPR , UR LFPR UR LFPR UR 'LFPR' UR

Total , white

Nonwhite

Major activity

was school

. major activity

not schoOl

.49.8b 10.3c 68.4 12.6

.

55.8

?5:8

55.9

36.5

91.3

11.4

10.0

20.7

13.1

10.2

70.4

69.9

73.4

53.3

93.8

10.5

9.3

17.9

17.1

4.8

54.8

55.1

52.5

37.1

90.7

9.6

8.2

18.9

12.2

14.4

70.4

70.1

72.8

53.3

92.9

9.0

.8.3.

13.8

14.3

4.3

a

b

cd

ef

g

Figures for 1966 only include

December 1966 from Employment

November 1966 from Employment

Survey, conducted October 1966

November 1967 from Employment

Survey conducted October 1967

November 1968 from Employment

Survey conducted,October 1968

men 16 to 19 years

and Earnings, Vol.

and Earnings, Vol.

to February 1967.

and Earnings, Vol.

to January 1968.

and Earnings, Vol.

to January 1969.

23

of age.

13, No. 7, January 1267.

13, No. 6, December 'T966.

14, No. 6, December 1967.

15, No. 6, December 1968.

Alb

0
TABLE 2

CPS AND NLS COMPARISON OF LABOR FORCFPARTICIPATION RATES AND UNEMPLOYMENT RATES OF YOUNG

WOMEN 16 TO 21 YEARS OF AGE, BY RACE ANDSCHOOL STATUS, SURVEY WEEKS 1968 TO 1970

43 1970

Characteristic

19 68

CPS

a NLS CPS

c NLSd

CPS

e NLSt

'UM UR LFPR UR LFPR UR LFPR UR LFPR UR LFPR UR

. Total 43.8 11.7 50.7 15.8 42.8 9.2 53.2 15.1 45.6 11.6 54.5 16.9

White 1,114.9 10:5 50.8 14.6 43.4 7.8 53.6 13.6 47.1 10.1 55.3 .15.5

Nonwhite 37.0 21.3 49.8 24.1 39.2 19.2 50.4 25.8 36.4 23.1 49.3. 27.3

Major activity

was school 27.1 12.2 35.3 19.7 26.0 7.9 39.9 19.2, 29.3 11.5 41.7 28.9

Major activity

k not school 62.3 11.4 65.0 .4 61.7 9.8 64.6 12.7 63.2 11.6 67.0 12.5

a

-b

ce

February 1968, from Employment and Earnings, Vol.

Survey conducted January 1968 to May 1968.

January 1969, from Employment and Earnings, Vol.

Survey conducted December 1968 to March 1969.

February 1970, from Employment and Earnings, Vol.

Survey conducted, January 1970 to March 1970.

14, No. 9, March 1968.

15, No. 8, February 1969.

16, No. 9, March 1970.

9

LJ
tioh rates.for young men, sixteen7to twenty-one-yeaids, were approxi- eic

mately 55%, while the corresponding NLS rates were about fifteen percentage points'higher. Table 3 translates the NLS labbr force participation

rates into estimates of the labor force using asithe base the CPS esti- -, mates of the Civilian honinstitutional population. The result is a labor

force estimate that includes approximately 1.3 to 1--5-m4-1-ien-more-young

men aged sixteen to twenty-one than was found in the-CPS. 9

The major activity of the youth during the, survey week helpsto

explain the large difference between the two surveys. 10

Among the young

men attending school the NLS labor force participation rates were approxi-

, mately sixteen percentage points higher, while there was no significant

difference between surveysin these rates'for those young men not in

school.

. Similar findings occurred among the young women, sixteen to lentyone yews of age. Again the NLS found significantly higher labor force

participation rates than the CPS but the differences (between 7 and 10

percentage points) were smaller than for the young men's cohort. Still,

based on CPS population figures the .NLS found approximately .7 to 1.1

million more labor force participants than the CPS (Table 4). As was

the case for the young men, reporting of labor force partioapation rates

was significantly higher in the NLS among the young women whose major

activity was school during the survey week. The differences between

the rates were three to almost five times larger for the in- school women

thanfor 'those who had some other major activity (Table 2).

Unemployment. Among young men who we sixteen to twenty-one, there

was very

* little differehce in the overall unemployment rate between the

two sources of data. This was in part due to offsetting differences;

e implicitly assume that the attrition from the NLS does not

affect the labor force participation rates.

10. Major activity is defined by the survey respondent in his or her

answer to the question "What was...doing most of last week-- - Working, Keeping house, Going to school, or something else?"

33
4

a

4

'TABLE 3 CPS AND NLS COMPARISON OF THE NUMBER (IN THOUSANIA) OP YOUNG MEN 16 TO 21 YEARS OF AG1

IN THE,LABOR FORCE, BY EMPU)YatENT STATUS AND RACE, SURVEY WEEKS 1967 AND 1968

Characteristic

19671 1966 a CPS lase Cle MSc

Total 'White nonwhite Total White nonwhite Total WhitelNonwhite Total White nonwhite

Civilian noninstitutional

POPulation

In labor force'

,.. k Edployed

Employed-major activity

school

Employed...major activity

not school

Unemploied

Unemployed -mayor activity

school

Unemployed-major activity

not school

Unemployed-seeking full

tine work

Unemployed-seeking part

time work

9,009

5,031

4,458

1.849

, 2,608

574 279

295

289

285

7,821

4,367

3,931

1,706

2,224

437

231

206

199

238

1,188

664

527

143

384

137

48

89

89

48

9,'009 6,342

5,676

2,381

3,296

666

489

166

170

496

7,821

5,167

4,958

2,159

2,799

508

394

11h 118

390

1,188

872

716

9,349

5,12/

4,631

1 8,115'

h,473.

! 4,105

1,234

646

526

157

369

122

69

a 53

51

i isi

9',349

6,582

5,989

2,454

3,536

593 428

165

156 437

8,115,

5,689

5,217

2,195

3,023

472

339 133

117

355

1,234

898- 774 256

513

12491

33h0 . 84

211

498

156

101

5552

104

2,041

2,590

491

284

206

208

283

1,884

2,221

363

215

153

156

212

a Totals may not equal sum of parts due to rounding,

b For data sources see footnotes of Table L .

c The NLS calculations apply NLS rates from Table 1 to CPS totals for the civilian noninstitutional population.

a Q

-4"
TABLE 4

CPS AND NLS COMPARISON OF NUMBER (IN THOUSANDS) OF YOUNG4OMEN 16 TO 21 YEARS OF AGE

IN THE LABOR FORCE, BY EMPLOYMENT STATUS AND RACE, SURVEY WEEKS 1968 TO 1970

Characteristic

1968a 1969a

CPS NIS b CPS , N 2.0 r. Total

e White Nonwhite Total White lorwhite Total White Nonwhite Total Whi*.e ::onwhite Civilian noninstitational

population 10,405 9,041 1,364 10,405 9,041 1,364 10,622 9,201 1,L22 10,622 il,..T01' 1,422

In labor force 4,559 4,055 504 5;275 4,593 679 1:,550 3,994 557 5,651 4,932 717 Employed 4,026 3,629 397 4,442 3,922 516 h,131 3,681 ' 450 4,,798 4,263 532.. EMploied-major activity

school 1,295 4217 78 1,467 1,309 156 '1,342 1,249 93 ,603 1,L56 145 EMployed-major activity

not school _ 2,731 2,412 319 -2,975 2,613 360 2,788 2,432 X57 3,196 2,307 ',V

Unemployed 534 426 108 834 671 16'. 419 313 10' 853 671 . 135 Unemployed-major activity

school 180 149 31 365 316 50 115 85 30 333 327 57 Unemplqyed-major activity

not school 353 27d 77 469. 355 114 305 223 77 ::73 3L4 125

Unemployed-seeki=6 fu11

time woe.c. 331 261 70 410 307 103 27F3 207 71 I 4C2 n'' 1' Unemployed-seeking part

time work 203 .- 165 .38 424 364 61 141 106 36 .

.) 4 :, 373 73 1111 (Table continued on next page.)

`1
TABL 4 continued

Characteristic

19;0 a CPS Nish

Total White Nonwhite

--1

Total - White

Civilian noninstitutional

population 10,755 9,275 1,1480 10,755 9,275 *1,480

In labor force 1 4,905 4,366 539 5,861 5,129 730

Employed 11,338 3,924 414 4,871 4,334 530

Fiployed -major activity

school 1,446 1,358 88 1,732 1,589 139

.Employed-major activity

4 not school 2,891 2,565 326 3,139 2,71414 391 1,

Unemployed 567 .442 v 125 991 795 1, 199

Unemployed-major activity

1 school 188 1149 140 5142 466 77 ! Unemployed -major activfty 4 not iZhool 379 2914 14149 329 122

Unemployed-seeking full

time work 349 268 81 506 382 126

Unemployed-seeking part

time work 218 1714 1414 1)485 413 73

a Totals may not equal sum of parts due to ,rounding.

b For data sources see footnotes of Table 2. c The NLS calculations apply NLS rates from Table 2 to CPS totals for the civilian noninstitutional

population. C

`Q(4.

tit)

4
12

the NLS had higher unemployment rates for those youth who listed their

major activity as school, 11

while the CPS had significantly higher un- employment rates for young men with another major. activity. The CPS

also had significantly hj.gher- unemployment rates than the NLS for nonwhite youth in 1968, 12

The women in the NLS reported significantly higher unemployment

-rates-for-the-entire group and for those attending-school, Tor the three

years"studied, the NLS reported between 300,000 and 434,000 more unemployed young women than did the CPS. These were increases of 56% to 104%

in the number of young women who were classified as unemployed. Due to

the substantially higher unemployment rates in thq.NLS, approximately

40% of the increased labor force participants found among young women

by the NLS survey were unemployed (Table 4).

Approximately 100,000 more young men were classified as unemployed

in the NLS than in the CPS as a result of the higher labor force participation rates in the former survey. Yet, as is seen in the last two rows

of Table 3, the CPS reported substantially more young men seeking fulltime employment. Whereas about half of the CPS sample said they were looking for full-time work, only 25% of the NLS sought a full-time job.

This difference-could be due,to the larger number,of unemployed in the

CPS sample who did not list school as their major activity in the survey

11. The difference was statistically significant in 1967 but was not

in 1968.

12. The NLS sample did not interview persons who were in the armed

forces at the time of the first survey but who returned to

civilian life in d subsequent year. To the extent that these

veterans are more, likelyto participate in the labor force or to

experience unemployment the corresponding NLS rates would be

lower than the CPS rates. Since the NLS labor forcvarticipation

rates exceeded 6'e CPS estimates we feel that this d ference

in survey design is not important for this variable. The lower'

unemployment rate in the NLS for nonwhite youth, however, could

be caused by this difference.

37

=1'
13 -

week. Cite calculated that the CPS Fontained SQ to 130 thousand more

unemployed young man who-said-that somqphing dther than school was their

13

-major actilvityj Almost all of these individuals wanted-full-time

jObs. On the other hand, the CPS had many fewex: unemployed whose major

1? -N

activity was iehoolandwho were primarily interested in part-time jobs.

Those answering that school Was their majorsactivity had much lower labor.

force participation and somewhat lower unemployient rates in the CPS

(Table 1).

Similar to the men, t re were substantially more unemployed young

women seeking part-time Work in the NLS than in the CPS; however, the

NLS indicated more young women seeking fdlyl-time employment as well

(Table 4). the higher labor force participation and_ unemployment rates

in the NLS for women reporting school and women reporting some other

major activity in the sur4y week account for the substantially greater

numbers of unemployed women in the NLS,seekingJ)oth full-time and partA time jobs.

There were no discernible differences in the reports by the young

'men in the CPS and NLS on,durition of unemployment. Approximately the

same-proportions in both surveys reported-being unemployed for lesi

than five weeks and fifteen or more weeks. The young women in the AILS,

on the other hand, reported a considerably shorter durat ±n of unemployment during 1968. and 1970, but had somewhat lower percentages reporting

short periods of unemplo ent during 1969. This difference may have

been due to the use o January data for the CPS in 1969 `and February

data for 1968,and 1 O. Because of the large month-to-month variation

in the CPS reports of duration we hesitate to. draw any conclusion.

13. The CPS and NLS had about the same labor force participation

rates for this-group but the unemployment rate was much higher

in the CPS.

C33
14

JO

Employment, The NLS found significantly higher levels of employment than did the CPSabout 25% . more employed young.men and 10% more- employed young women. For example, the NIBS estimates-of youth employ- S

ment in 1968 exceeded those of the CPS by 'approximate]; 1.8 million,, of

whom-roughly three-quarters were young men. The differences in employment were somewhat more prevalent among the nonwhite segments of both

NLS samples and among those persons listing their major activity as

something other than school:.

'The distribution of hours worked Oring the survey week also was

substantially different in the two surveys. The NLS found moreparttime workers and workers employed overtime (in excess of 40 hours) than

did the CPS (Tables 5 and 61. The number of sixteen to twenty-one year

old youth employed for more than 40 hours was from 50% to 100% greater

in the NLS than in the CPS.

- The differences in the two survey's reports of the numher%of hours

worked by youth were related to the_age of the, respondents. Workers who

were` sixteen and seventeen years of age were more likely to be working

only part-time. Therefore, of the additional workers in these ages

reported by the NLS, approximately 55% of'the young men and 85% of t e

young women were employed less than 35 hours a week. On the other h d

the older youth were more likely to include persons working overtime,

and the NLS found many mote eighteen-,to twenty-one- year -olds working

for over 40 hours per week as comparedyiththe CPS. In some cases the

difference in the number working overtime exceeded the total numerical

differences between the two samples for this age group. Finapy, since

4

the NLS reported more yiputh working overtime and a slightly smaller

percentage working part-time (particularly among the Men), the NLS

found somewhat, higher mean hours of work for:the entire sample and for

workers,on full-time schedules. The dllfaxences were more pronounced

O

39

4
4 O

.4 -_ 15 (`"

among the young men, probably' reflecting, the smaller percentage. working

part-time.

SUMMARY AND CONCLUSIONS

We have found that the NLS when compared to the CPS reports:

11 Significantly higher labor force participation among young men

.and women, particularly among those whose major activity is'

attending school. These differences occurred in both white

and nonwhite groups.

2) Significantly higher unemployment rates for young women 4nd

approximately the same rates for young men. For-both young'

men and women th umber of unemployed is higher.

3) More of the unemployed are seeking part-time employment.

4) Considerably higher levels of employment,' particularly for the

young men.

) The youth are more likely to work eitheXpart-time or overtime

depending on their age, and mean hotWs'worked by the young men

in the survey week are somewhat higher.

'Obviously, we cannot say, conclusively that' the NLS reports are more

accurate than those of the CPS in the light of the differences in the

two surveys mentioned earlier, If, however, the NLS is correct these

findings 'have significant implications. For 1968, the CPSyobth labor

force would have'been understated by almost one-fourth or nearly 2.2

\N

Nos million young men and womiN. 'Employment would have been approximately

1.75 million higher and unemployment would have increased by 400,000

4 (an increase of almost 40'percent over the CPS reported number). This

would mean that there'was a sizable "undercount!' by the CPS.

14. Finally,,the single digit occupational distributions%of the two

samples were Very similar. There iS no evidence that the

additional Workers reported in the NLS were concentrated in any

4

particular occupational groiXp. e. 4 0 4d
16

Although obviously somewhat biased judges, we tend to believe the

NLS estimates. The pattern of reported differences appears consistent

over time, tending to negate the possibility that the longitudinal_ nature

of the studies or the difference in definitions during 1966 leads to the

differences in findings for the two surveys. The fact that the average

NLS respondent was seven months older than his CPS counterpart, while

conceivably causing some of the observed differences, could. not have

accounted for all of the greater labor force participation found in the

NLS. 'As a check, we reran seotions of Table 1 restricting the NLS sample.

to ,young4men fifteen through twenty years of age and compared the findings

with the CPS results for men sixteen thibugh twenty-one years old. Even

though the NLS sample was now yot-niger and the difference between the two

surveys narrowed; we still found higher labor force participation for

the NLS. 15

We also conducted some analyses which restricted the NLS

sample to interviews collectedin the same month'as the CPS data. The

of such a restriction on the NLS data did not appear to ma e

sizeable differences in the estimates of the employment-related va iables.

At the same timethe>nature of the differences we observed between

the two samples is consistent. with what one would expect due to problems

of nonSelf response. When the 4outh are in school one could expect that

their mothers would considqr them out of the labor force. The mother

would tend to disregard or be unaware of part-time employment and might

not even know of sporadic attempts by their children to look for employ-

, ment. It is also quite possible that the mother would not know of overtime work in the survey week and would report the standard kull-time,

schtdule. fcxr those older youths who-are.onlytangentially

attached to the household (e.g., they are away at college or more'in and

out of the. household depending on their financial state and familial

. 15. We were als6Able to use the age attained at survey month, for

the young women in sbme special runs. These too did not noticeably alter the conclusion.

Air

41

A

41
- 17

relationship41, the mother 9akr hare. no idea of their employment status.

16.

While our leanings, are toward the NLS data, there are at least two

? possibilities for testing the accuracy, of tRe data sets. first, the

Census Bureau could expand its Methods Test Panel and seek-a larger

sample.of young self respondents to reinterview 'after another member

of the household has provided labor force data.' The expansion would

have to be subs tial, however, in order to have a large enough sample

in this limite age group.

Second, if the NLS estimates are more accurate and there are more

youth in the labor force seeking employment than the CPS shows,- there

may be some indirect evidence which. we can observe over the next year.

The hew youth programs under the Youth Employment

Projects Act of 1977 (YEDPA) will provide roughly

youth slots.

17 If the CPS is correct the filling

come primarily from among thz-uffemployed-. On the

and Demonstration

200,000 additional

of these slots would'

other hand, if many

of the people the CPS says are out of the labor force are really seeking

work as the NLS implies, the slots will be filled without having much

impact on the CPS measure of youth unemployment. We should be prepared

for the YEDPA programs to "fail" to lower unemployment if in fact we

are presently not counting youth correctly.

.11

16. The fact that we found smaller differences in labor force partici-.

pation rates among the young women is also consistent with the

nonself response hypothesis, since these individuals are more

likely than their male counterparts to be in their own household and, thus, are more likely to be reporting for themselves.

. 17. The estimate is very inexact since it is not clear how the CETA

prime sponsors will divide their funds between in-school and

out-of-schook programs..

4 2
0 4,

-

TABLE 5

CPS AND NLS COMPARISON OF THE NUMBER (IN THOUSANDS)

OF YOUNG MEN AT WORK IN NONAGRICULTURAL INDUSTRIES,

BY HOURS WORKED AND AGE, SURVEY WEEKS 1967 AND 1968a

.47

Characteristic 1967 1968

. cpsb NLSC CPSb NI,Se

Total at work

16-17

18=19

20-21

16-21

On part time schedule

16-17

18-19

20-21 's 16-21

On full time schedule

16-17 4r 18-19

20-21

16-21

Working over 40 hours

16-17

18-19

20-21

16-21

,Mean hours, all at work

16-17

`f20-21

Mean hours, those on

full time schedule

16-17

18-19

20 -21

"16-21 4,

1,064

1,396

1,576.

4,036

883

555

351

1,789

176

841

1,230-

2,247

5o

277

468

795

19

33

40

31,

41

43

44

43

1,618

1.647

1,753

5,018

1,183

600

395

2,178

435

1,047

1,358

2,840

. 146

519

690

1,355

23

35

41

32

42

45

46

44

10.09

1,565.

1,531

4,205

947

665

382

1,994

f162

900

1,150

2,211

39

29o.

389

718

19

31

39

30

40'

41

43

42

1,726

1,956

1,619

"5,301

1,284

681

357

2,322

442

1,275

1,262

2,979

180

687

654

1,521

24

35

41

33

43

-45

45.

45_

, 4, a Totals may not equal sum of parts due to rounding. 4 t For'data sources see footnotesof Tabld 1. c The NLS-Carculations apply NLS rates from Table 1 to CPS totals

for the Civilian noninatitutional populatiOn.s

43

s

oti
C

tl - 19 -

TABLE 6

'CPS AND NLS COMPARISON OF THE. NUMBER (IN THOUSANDS)

OF YOUNG WOMEN AT WORK IN NONAGRICULTURAL INDUSTRIES,

BY HOURS WORKED.AND AGE, SURVEY WEEKS 1968 TO 1970a

,e

. Characteristics

:

. 1 968 1969: 1970

Mb NLSc CFSb NLSc Mb NLSC

_ Total at work . - 16 -17 . ' % 773 1,035 789 1,209 969 1,272

18-19 ' 1,462 1,583 1,419 1,538 1,486 1,510

20-21 . 1,647 1,618 1,773 1,740 1,726 1,877

16-21 3,882 -4,236 3,981- -4487 4,181 4,659

On part time schedule

16-17 t 708

,916

. 730 1,121 859 1,112

18-19 .. 515 579 530' 563 602 598

20-21 374 369 400 433 476 480

16-21

, 1,598 1,864 1,662 2,118 1,936 2,190

__.Thi_fulltimeschedule

16 -17 65 120, 59 88 110 160

( 18-19 . 947 1,004 887 977 ,884 912

20-21 . 1,273 1,248 1,373 1,299 1,250 1,397

16-21 . 2,284 2,372 2,319. 2;365 2,245 2,469

Working over 40 hours

16-17 12 46 4. 11 22 20' 38

18-19 143 230 126 234 110 228

20-21 L - 199 313 226 277 176 344

1641 353 589 362 534 306 610

Mean hours, all at work

16-17 . 14 15 14 14 15 16

18-19 . -\4,*k

31 31 31 30 30 30. 20-21 36 36 36 .35 35 36

16-21 29 29 29 27 28 28

r . . Mean hours, those on

full time schedule

16-17 ' 38 39. 36 39 38 37

18-19 . 39 40 4o 39 39 40

20-21 40 41 . 40 39 39 4o

16-21 . i 40 40 s 40 39 39 4o

a Totals marnot equal sum of parts due to rounding.

b. For datasoureespm,focstnotes of Table 2.

c The NLS calculations'apply'N.LS rates from Table 2 to CPSteitala-for

the civilian noninstitutional population.

,

4
V

- 20 -

THE VOLATILITY OF THE TEENAGE LABOR MARKET:

LABOR FQRCE ENTRY, EXIT., AND UNEMPLOYMENT FLOWS

By: Ralph E. Smith and Jean E. Vanski

ABSTRACT . Gross flow tabulations from the Cdrrent Population Survey provide

estimates of the numbeof people who move between unemployment, emproyment, and non-participation in the labor force from one month to the

next. This paper describes these data and their potential value for

analyzing labor market dynamics and uses the data for teenagers to

analyze recent cyclical, trend, and seasonal patterns, in their unemploythent and labor force participatibn rates.

It is shown that the gross flow series provide important information about the,youth labor market, supplementing the i n obtained from conventional labor force series. Ana s of data

from the 1967-1977 period suggests, for example, that.the chronically

high unemployment,, rate of teenagers is directly linkedflto their

large flows through the labor market. In addition, the deterioration in the relative position of black teenagers during_thisi_period

is a result of both secular and cyclical differences in their behavior: Not only are their rates of labor force entry failing to

keep pace with those of white teenagers, but also their success in

finding Work is decreasing more rapidly, further decreasing their

labor force participation. The widening gap between the unemployment

rates of the two groups directly contributes to the widening gap

in their partiCipation rates.

INTRODUCTION

High unemployment rates among teenagers, especially black

economic,policymakers and'

teenagers, are given considerable attention by

analysts. Debates oft n,focus on the extent to which the rates . really reflect a seri us problem and, if they do, what can be done

to reduce that problem. Discussions of teenage unemployment in-

,ev tably lead to consideration of the high turnover and labor force

mobi ity of youth. Those who minimize the seriousness' of youth um-

' employment point out that,much of it is frictional, resulting from

teenagers moving in and out of the labor force. s ecial job needs

of many teenagIrs, as they combine school and ork, make it more

difficult to design imograms to reduce their unemployment .

4.5

c
)" - .21 -

Tlie dynamic characte of the youth labor market is not reflected

very well in the conventional unemployment tabulations from the Current Population Survey (CPS) published by the Department of Labor.

oft

)These data series provide the net changes from,one month to the next

in unemployment, employment, andnon-participation, but give little

indication of the much larger gross movements that normally occur between each of the three labor market states. For example, during

1976 the published monthly series suggested a fairly static- labor

market in which between 18% and 20% of teenagers who participated

in the labor force 4e7tnemployed. But underlying this static

, picture was a labor force in a constant state of change: each month

about half of all unemployed teenagers either found jobs or left the

labor force, only to be replaced by a roughly equal number of teenagers who previously were outside the labor force'or were employed.

'A little-used source of infoFmation about these labor market

dynamics is the unpublished gross flow series from the CPS,'Maintaineeby the Bureau of Labor _Statistics. Based on the responses of

individuals who were in the CPS sample in consecutive months, these

tabulations provide estimates of the number of people who move be-

. tween unemployment, employmerit, and non-participation in the labor

force each month. -Previously, we have used these gross flow data,

disaggregated by age, race, and sex, to estimate a monthly model of

the U.S. labor Market and to simulate the impact of alternative macroeconomic time paths on the labor market status of each group.

1 For

this conference, we have updated the equations that go into this

model for teenagers to reflect the experience of the recent recession

1. The model simulates the labor force behavior of sixteen demoglPhic groups including white males, white females, nonwhite

males, and nonwhite females, each disaggregated by age groups

(16-19; 20-24; 2S-S9; and 60 and over)., Blacks Comprise'about

90% of the nonwhite categories. For a description,of this model*

see Ralph E. Smith, "A Simulation Model of the Demographic Composition of'Employment, Unemployment, and Labor Force Participation,"

in Ronald G. Ehrenberg, Research in Labor Economics, vol. 1 (1977),

pp.259-303.
- 2 2 -

and the beginning of the recovery.

Our paper'has two pOvOile4 First, we seek to alert analysts

to the potential value (and limitations) of the CPS gross flow data

for understanding the teenage labor market. Second, we wish to report

some of our own. research, which uses these data to analyze recent

cyclical, trend, and seasonal patterns of teenage unemployment and

labor force participation.

Since tI'e gross flow data are not widely known, the first sec- tion'presents a description of the data, its problems, and previous

uses. We have found it neoessary,to adjust the data because of inconsistencies with the net changes in the conventional labor market

series. 'Our adjustment technique and the size of these adjustments

for teenagers are reported. The second section presents new estimates

.6 of the cyclical, secular., and seasonal patterns in'the labor market

activities of teenager;, by race and sex. The final section presents

our conclusions and recommendations.

LABOR FORCE GROSS FLOW DATA

The analysis reported in this paper is based on data describing

gross changes in the labor force. The data are derived from to Current Population Survey (CPS) which provides the Bureau of Labor"

statistics' monthly estimates of labor force status. Their value

ests in their ability to complement the information on monthly

changes in laboforce levels coming from the full CPS with information

on short run market dynamics., This section of the paper will describe

the derivation of the gross flow data and their potential value

for research on labor market behavior; it will discuss reported

errors in the data which have limited their use; ,and, finally, it

will provide a description of a technique which has been developed

to compensate for these errors.

Derivation of Gross Floovi Data

Each month approximately 56,000 households are'interiewed in

the Current Popullation Survey to determine the labor force status

of the civilian ,non-institutional population aged sixteen and over.

47
- 23 -

The CPS is constructed as a panel survey with each household remaining in the Sample for four consecutive months, being removed

for the next eight months, and reentering for another four months.

As the households rotate in and out of the sample, potentially 75%

of them are common'from month-to-month. These households form the

basis for gross flow data.

2 Since,individuals within these, households are in. the sample for

two consecutive months, it is possible to match their current and

previous labor force status. Aggregating data on indiiiduals into

civilimpopulationgroups,P.,the gross change tabulations provide

estimates of the size of flows between thos'e who were employed, E.,

unemplored,.U., and not in the labbr force, N.. Throughout theiremainder of this paper, the gross flow data will be denoted by an

ordered pair of letters representing preceding and current month's

labor force status. For example, UNi, will denote those people-in

.th

the 1 population group who were unemployed last month:but left'

the labor force in the current month.

The gross change data provide information on,the degree of mobility in the labor market as people enter or leave the labor force

or move between employment and unemploymeni,. This is particularly

valuable, for population groups with relatively weak labor force

attachment, such as teenagers. Figure'l illustrates this point

by showing the average 1976 gross flows for sixteen to nineteenyear olds which help generate teenage' levels of employment' unemployment

and nonlabor force status as reported by the CPS.

$

. We are grateful for the4cooperation of Bureau of Labor Statisticg

staff in making these data available. \More-comprehensive descriptions of the data and their potential for.research on labor market dynamics can be found in H. Hilaski, "The Status of Research

on Gross Changes in the -habor-Force;" Employmenty-mi Earnings,

October 1968.
24

Figure 1

Labor Market Stocks and Monthly Flows:- 16-19,Iear Olds

(1976 Monthly Averages)

Employment

(7.3

million)

EN -837,000

2

NE 793,000

EU UN

302,000 487,000

Not-in-Labor

Force

(7.5 million)

411,000 1.7 million)

NU

511,000

The volatility of the youth labor market is obvious from t#ese

statistics. For example, on average ih 1,976, approximately 940,00

teens or 53% of those unemployed one month either found a job or

decided to leave the labor market the next month. The average monthly

figures also show that approximately 1.3 million or 9% of the teenage k

labor force leaires the labor market each month,. w11i1Q another 1.3

million or 18% of those out of t e labor market decide to enter.

For sixteen to ni eteen es, these large inter-labor

market flows'are dominated by transitions' between school and work,

Seasonal patterns in flow data provide a better idea of the vast

numbers of people who-change labor forde states each month to generate

the changes in stocks repOried.by the Current Population Survey. In

June, when most teenagers are out of school and looking for summer

employment, CPS statistics show the size of their labor force increasing

by 1.9 million. The gross flow tabulations indicate that this change

0

.
*

I

is generated by status changes of 3.7 million teenagers moving

between s tes; with 2.8 million either becoming employed or unemployed ployed w en they are not in thellabor force in the preceding month,

and with almost 1 million deciding to drop out of the labor force

, for the summer. The data also indicate that, although the large

increase in unemployment in Juneis primarily due to a dramatic

increase into the,labor force, it is aided by reductions ip flows

out of unemployment, In September, when school. reopehs, the CPS

, ......,. 'labor force declines by 2.1 million teenagers, which i5 ca 'by

. . 3.1 million withdrawing from the labor force and 1 million entering

it,

While some of these observations are intuitive, they do suggest

. the potential the data have for providing insights into trends and

4...cyclical patterns of labor force participation; determinants of un-

. employment among vgrious labor force groups; and information on the

relative impact of4iscOuragemenx and labor force withdrawal on

, different demographic groups. o"'r Errors. in Data 1

4. Despite their potentia for linalYing labor market dynamics,

gross flow data-have gOnelmpublished since 1952 and their use in

research has been sporadic dueto,serious reservations concerning

their reliability. Problems With .'he data are enerally divided _ _ _ into two components: sampling variability response variability.

Of the two possiblessOurces Of'Aror, sampling variability is

the less serious,' and is the result of reducing the full CPS sample

0 to only those individuals who are interviewed for two consecutive

months. By.thetime gross flOw -&ta are'tabulated, sa le size is

reduced to approximately 6596 of .the full cPs survey. Thy excluaion

of nonidenticals, who either fail to respond:. are absent froth home

during the survey week or who have moved' from the household, Make

random error more prominent in gross flow than stockdata. It should

be noted, however, that if nonidenticals differ in characteristics

cl
from identical individuals, this reduction 141,. sample size could

cause the error to be systematic.

3 For example, teenagers who leave

home by the time they are nineteen may be more prone to be in the

labor force than those who remain with their families. Because

they have moved from the interview unit, these youth will be underrepresened in the gross flow sample, and those remaining in the--

sample will tend to understate labor force attachment.

_ Response variability, which is the result of either misclassification of reported labor market status or "rotation group biag,"

is considered more important than sampling variability. Misclassification errors which tend to cancel when determining net chanps

in labor force stocks are a more serious problem-with gross change

data. In the past, the effect of misclassification has been assumed

to be random with an overall effect orinflating the flows depicting

movement between labor force states. It is hard to determine the

magnitude or nature of the misclassification problem for teenagers.

The fact that an adult usually reports labor torce-Statias-for teenagers in a household and that it is often hard to determine the exact

status of a teenager's-labor force attachment could serve either

to exaggerate movements between employment. states or to suggest no 6 / change when, in fact, movements would be indicated if the youth had

been directly surveyed.

, The second cause.of response variability is "rotation group

bias"--a problem which generates systematic errors and which is related

specifically to panel surveys. The hypothesis concerning this source

of error is that the process of reinterview conditions individuals

and patterns their response over time.4 The eight rotation groups

comprising the CPS, which differ only with respect to time in sample,

3. See Susan Palmer, Bureau of the Census;-"On the Character and

Influence of Nonresponse in the Current Population Survey,"

Proceedings of the Social Statistics Section, 1967, American

Statistical Association, pp. 73-80.

4. See Barbara A. Dailar, "'he Effects of Rotation Group Bias on

Estimates from Panel Studies," Journal Of the American Statis=--

tical Association, vol. /70 (March 1975)., pp. 2346. .
4

27

consistently demonstrate different labor force characteristics.

Census Bureau staff have been aware of the problem since the 1950s,

but little is known about why it exists or the extent of its effect Ale

on the data either qualitatively or quantitatively. It is known,

however, that groups newly entering the sample show:higher levels

of unemployment and marginal types of employment' such as part-time

work,.unpaid'family help, and domestics--those categories most

likely to-contain substantial numbers'Nof people with weak labor

force attachment. In subsequent months, a number of the individuals

in these groups, who initially reported being employed or unemployed, report being out of the labor force. This type of change' --

tends to exaggerate the size of the flows leaVing_the_labor force

and, therefore, understates gross change estimates of Participation.

Given the marginal attachment of teenagers to the labor market, it'

is possible that misclas ification and rotation group bias problems

contribute significantly to errors in measuring their changes in

labor force status-.

Data Adjustment

Despite .the sampling and response variability problems, gross

flow data have been used y researchers to analyze labor market behavior. The data have pro uced intuitively acceptable.c.resuAts and'

have supplied a new.dimens on on market dynamics unavailable'in other

data bases. Earlier work ith gross flow data, however, concentrated

on cyclical variability o unemployment and did not concentrate on

participation behavior which would suffer much more from the problems

associated with rotation group bias.

5

5. See W. Lee Hansen. "The,1/4Cyclical Sensitivity of Labor Supply,"

American Economic Review, (June 1941), pp. 299-309; George L. Perry,

"Unemployment Flows in the U.S. Labor Market," Brookings Paper on

economic Activity, vol. 2 (1972), pp. 24578;Claire B. Vickery,

"Why Unemployment Rates Differ by Race and Sex," unpublished Ph.D.

dissertation, University of Maryland, 1973; Thomgs F, Bradshaw,

Bureau of Labor Statistics, "Employment in Perspective: A

Cyclical Analysis of Gross Flows in, the Labor Force," Report 508,

4
- 28 -

The bias problemi in the data Cannot be easily explained and no

one Was yet determined their magnitude or exact effect on the various

transition flows. However, the fact that they existcan'be easily

ascertained by comparing net changes in labor force stocks derived

from the CPS with those derived frOm grois flow data. Mane were to

accept the accuracy of the fUll dPS,6 failure of the gross flow data

4 to replicate these changes is an obvious warning there are

problems measuring the flows. In some instances, even the direction

of change-is inconsistent. While a portion_of the problem is

attributable to random sampling error, the discrepancies do suggest

that systematic errors are present and have to be accounted for when

using the data.

7

Ohe procedure developed to adjust the gross flow data does not

attempt to explain the causes of error; it merely attempts to force

consistency with the data coming from the full, CPS survey.S Using

multivariate regression analysis, monthly changes in,#iployment_ _

status measured by gietS flow data are regressed againti the same

changes measured by the CPS. If earlier conjectures on the nature of

biases in the data are correct, the results of the procedure would be

5. (cont.) 1977; Richard S. Toikka,'"A

Decisions by Workers," The American

(December 1976); pp. 821-34.

Mark6vian Model of Labor Market

Economic Review, vol. 66 . 1 6. The rotation group bias problem affecting gross change data also

affect the estimates of monthly levels of labor force statistics

reported in the full CPS. If it can be assumed that bias patterns

across the eignt survey panels are consistent from one month to

the next, the effects of the- bias on-the 'full samnle_witlicancel

in computing month to month changes. It is the "unbiased" month

changes which are used in the adjustment procedure. Levels of

labor force stocks in the 6111 CPS are affected by rotation group

bias. See Bailar, op. cit.

7. See Stuart H. Altman, "Factors Affecting the Unemployment of

Married Women: A Study of the Dynamics Affecting the Labor Fokce

Behavior of Secondary Family Workers," unpublished Ph.D. dissertation, University of California $t Los,Angeles, 1964. _ _

8: Smith, op. cit.

53
6

a reddction of the flow variables which depict movements between employment, unemployment and non-labor force -status all of which tend

to be exaggerated by misclassification errors. The most pronounced

rechictions should be in ,the two flows out of the labor force, EN and.

UN, which are also inflated by rotation group bias.,

Since the gross flowsubsample does not take account of monthly

increases in population which result from new population. entrants or

net immigration, th,0CPS changes used in the adjustment procedure were

modified to exclude population growth. Adjusted changes in stocks

from the CPS were constructed using the previous month's stock levels

weighted by a ratio of current'topreceding month's population. This

assumes that entrants to the CPS sample demonstrate the same character:.

istics as those already-in the population group. The changes in stocks

measured by gross flows Are computed by calculating the new inflow to

1.0

those stocks. The relevant identities for each group become:

(1)11E:=E.1 -E1..,-1 .(-1

1 )-UE.4.°NE.

1 - EN.

1 P

P. = Ui - U. ., ( ) EU. + NUi - UEi -UNi,

1,-1

P.

1 and,MANc.-Ni-Ni,..1.(1.37----N.-NE. - NI.'

1 1

, 1, ..

1,-1 R

. with the left. side estimated from the full CPS and the right from gross

flow data. N

,The aim of the'procedure is to obtain 'a correction factor on

each flow variable. As can be seen above, each flow appears in two

identity relationships. We therefore pooled observations, and ran

a single regression based on data.from June 1967 through September

1977, simultausgIY estimating changes in employment, unemployment

and,nonparticipation and_producing one adjustment factor for each

flow.

ti
- 30 -

The procedure has gone througha number of modifications. Currently it produce's cyclical correction factors which vary with aggregate labor market conditiqns, represented by the aggregate unemploynlent,rate lagged one period. The resulting relationship between

an adjusted flow, f'., and an observed grUss flow, f, thus becomes: , .

(4) = (B + B . (-) --) f, f If --1,-1

where B f

and B

If are coefficients from a constrained identity

egression. 9

Potentially there is a problem applying the procedure to groups

as small as the four teenage groups being analyzed in this paper. The

smaller the population, the more likely it will be that random error

will dominate the systematic' error we are attempting to eliminate.

As a consequence, among sixteen- to nineteen-year olds, nonwhites

will suffer more than whites Ocause of'sample size. Among the flow

variables, the flows between employment and unemployment are the most

affected since youth behavior is dominated by their labor force

'V withdrawal and reentry.

Table 1 reports average corrections applied to the six gross

flow variables for the labor force groups_analyzed_in_the next

section. The adult white male group is used as a point of comparison.

In idur of the six flows for this group there is a substantial reduction in the reported statistics. The implied overstatement, of

measured data is consistent with the hypothesis that misclassidcation

error exaggerates the number of people changing labor force states.

The severe reduction in EN is consistent with hypotheses on rotation

group bias. We have no explanation for the seeming undermeasurement

of flows between employment and unemployment (EU) and between unemployment and not in the labor force (UN). This is especially. true

a

9. Fof a more complete description of the procedure see Charles

C. Holt, et al., "Labor Markets, Inflation and Manpower Policies,"

Final Report to the U.S. Department of Labor, Appendix C, May

1975.
- 31' -

for UN since it should be influenced by the same rotation group

bias problems affecting the employment to not in the labor force

flow. We'have less confidence in the parameter estimates for the

correction on UN since relatively few white male adults exit the

labor force from unemployment.

The correction technique generally, yields smaller bias adjustments for teenagers than foi white male adults. With the exception

of the smdil nonwhite female cohort, which demonstrates the lowest

1>

measured inter-labor force mobility, most flows are adjusted to

within 15% of their observed value. While this may result from more

accurate data for this group,'it is more likely the result of random

measurement error due to small-sample size of ,offsetting measurement

problems unique to determining the labor force status of teena rs. The suggested reduction in the UN flow is again consistent with,

. rotation group-bias problems; the increase in the EN flow is not.

However, as indicated earlier, no one truly understands the nature of

'biases in the dpta and the technique used for its adjustment only

provides a statistical reconciliation of gross flow and CPS stock

data. does not provide any reasons for-digcrepancies.

TABLE 1

_AVERAGE GROSS ,CHANGE aMRECTION FACTORS

July 1967 to September 1977a . Group/Flow- EN EU UN NU UE NE

16-19 year olds:

White Males . 1.02 1.10 1.05 1.05 .95 1.02

White,Females 1.06 .99 .89 1.06 .98 .98

. ..,- Nonwhite Males -1.00 .91

,.. .97 1.03 .89 .98

Nonwhite Female%_,1.03 .71 .89 1.10 1.20 .87

'25-59 year olds:

White Males.' .56 1,05 1.17 .72 .92 .78

a. Based on unpublished estimates by the authors.

-,
- 32 -

TEENAGE LABOR MARKET PATTERNS: INSIGHTS FROM GROSS-FLOW DATA

The remainder of this er focusses on an examination of the

labor market behavior of teenagers during the past decade. Several

patterns observed with conventional'labor force and unemployment

series are examined more closely with estimates based on the adjusted

grosi flow data:

(1) The labor force participation rates of white teenagers, particularly females, have risen sharply, while those of black male teenagers fell and those of black female teenagers were fairly stable.

(2) Each teenage group's participation rate exhibits some cyclical variation. Between 1974 and 1975, as the aggregate unemployment

rate rose by three percentage points, the secular increases in the

participation rates'of white male and female teens were reversed

and the decline in black male participation Was accelerated. The

participation of black female teens did increase in this recession,

although it fell three points in the milder'1971 recession.

(3) Unemployment rates of every teenage group were much higher

than adult unemployment'rates throughout the decade and were con-

. -siderably worsened by-the recession.

(4) The position of black teenagers relative to white teenagers,

measured by their unemploymient, rates and their participation rates,

.has dramatically deteriorated over the decade. The recession widened

the gap still further.

These arid. other patterns in the regular CPS series are also . found in the, gross flow data'and, to some extent, are explained by

them. Net increases in unemployment occur when the gross flows into

unemployment exceed the flows out. Chronic differences in unemployment rates between tWo groups either reflect differences in

their vulnerability to unemployment (the probability of becoming

unemployed) or in their propensity to leave unempliiyment, yth.

Similar reasoning can be used to account for changes or differences

in participation patterns.

57 .

dr
0

33

vit. The equations reported in the remainder of this section are taken

P from our gross,flow model of the labormarket and depict determinants

of the proportion of agroup's population moving from one labor'market

state toanothk: All transitions affect the group's unemployment or

labor force size directly or indirectly. TheCtie.nsition rates are ,: modeled as group probabilities which are related to aggregate labor

7

market conditions, a secular trend, and a set of'seasonals. The mean

of these rates for the past decade are provided in the Appendix.

When analyzing regression estimates based on gross change data,

it must be remembered that the data suffer much more from sample . variability than the stock data reported in the full CPS. The siz

....,-- of the gross flow subsample is only about 67% of that of the CPS due

to the requirements of matching individual responses for consecutive

, -.. months. The relatively small populations of'the race/sex teenage

cohorts reported in this paper further exacerbates the problem. this

. , problem increases the variance of our parameter estimates. However,

t for purposes of this paper, we are not so much interested in the

i

/ numerical. value of parameters as in the importance of aggregate labor

market conditions, seasonals and time trends in explaining the lilor

force behavior of,sixteen-to nineteen -year' olds over.the past decade.

ANegativ or- positive responses to_explanatory variables_and their

level o 'significance can, in themselves, provide explanations for ;

labor force participation behavior and 'cyclical response notavailable

.,..y from the stock data. 4 Transition Probability Equations

The analysis begins with the estimation of two sets of equations

that depict the movement Of people into the labor force. As illustrated

by Figure 1, the numberl of teenagers who -enter the labor force in a

month is determined- b the number in ihe potential entr3DI_Pool (i.e,

not in the labor for and their probability of entry. Some entrants

are unemployed at the time of the first survey in whichthey are in

the labor force; others have either entered with jobs or-found"them,

between survey periods. Both the decision to enter and the outcome of

5 rd

14
34 -

44

that decision may be affected by cyclical and seasonal factors and

Ivy change over time.

Table 2 and 3 report the equatpkons,used to estimate each group's - 'monthly probability of labor force entry and the proportion of the

entrants that hallre jobs ("successful labor force entry"). In each

of these equations; as well as the ones that follow, the transition

probalOtOty is eXpressed as a multiplicative -function of the aggregate

job vacancy-unemployment' ratio lagged one month, an exponential

time trend, and, a set of seasonal dummies. Estimates are reported

for each of the four race/sex teenage.groups and, for comparison,

for prime-age white males. The estimation period is July 1967

through Septemb5. 1977.

In the labor force entry equation (Table 2), the depeildent

variable.is the log of the proportion of the group's nonparticipants

who entered the diviliandabor force since the preceding month's

survey period. The gross flow data used in the numerator were , first corrected with the procedures described in the preceding section.

iT 4 The aggregate labor market variable is the log of the ratio of the

Conference B6ard's Help- Wanted Index and the CPS aggregate unemployMent series, both seasonally unadjusted and lagged by one month.

The trend term T takes on a value of one in July, 1967 and is incremented by one each successive month. Eleven seasonal dummies,

corresponding to January through November, each take the value of

one or zero, with the coefficient in December constrained to equal

theriegative.pfulestunoftheothercoefficients_is a random

1 . error term with zero mean and constant variance; The numbers in

parenthesis below the parameter estimates are corresponding T -statistics. Each column also reports the regression coefficient of

determination corrected.for-degrees -df-freedomi R2; the-standard

error of estimation, S.E.; and the Durbin-Watson-Statistic, D.W.

Significance afthe .05 level and the .01 level are indicated by.a

single asterjsk. and a double asterisk respectively. The joint influenCe of the seasonals is measured by the F-statistic.

59
- 35 -

TABLE 2

PARAMETERS DETERMINING PROBABILITY OF LABOR FORCE ENTRY

ln(NLi/Ni,J4) u'lnknii + Onliln(V/U).1 + gniiT + Si + . . . + Sli + c

t

t t Ink °

(1) WHITE MALES

16-19

(2) 1.-" 1 WHITE FEMALES

16-19

(3) : NONWHITE MALES

16-19'

(4) NONWHITE FEMALES

16-19

(S) WHITE MALES

25-59

-I.4p8 -2.003 -1.358 -2.222

nli (-14,69)** (-22,05)** (-8.10)** (-11.37)** (-15.21)**

3 0.061 0.006 0.099 -0.037 0.103

nil (1.97)* (0.23) (1.94) (-0.62) (3.01)**

Rnli 0.002

(5.68)**

4 0.002

(7.08)**

-0.001

(-1.11)

-0.000

(-0.58)

-0.003',

(-6.28)*%

Si . -0.294 -0.236. -0.237 0.020 (-8.64)** (-10.35)** (-4.55)** (-3.92)" (0.57) S2 -0.317 -0.211 -0.266 -0.292 -0.008 (-9.81)** (-7.25)** (-5.01)** (-4.72)** (-0.21) S3 -0.274 , -0.249 -0.315 -0.300 -0.042

(-8.58)** (-8.66)** (-6.01)** (-4.91)** (-1.18)

, S4 -0.107 -0.219 -0.245 -0.358 -0.008 (-3.34)** (-7.61)** (-4.66)** (-5.85)** (-0.23) S5 -0.052 -0.099 -0.215 -0.230 0.016

(-1.63) (-3.40)** (-4.06)** (-3.71)** (0.46)

46 0,834 0.702 0.844 0.868 0.085 (25.52)* (23.84)** (15.70)** (13.85)** (2.35)* S7 0.670 0.498 0.826 ' 0.043 -0.008

(21.75) ** (17.94)** (16.33)** (10/91)**

. (-0.24)

S8 -0.185 --',, 0.125 0.440 '0.185 0.057

(6.02) ** (4.52)** (8.71)r*

r (3.14)** (1.67)

'S9 -0:033 0.070 -0.135 0.038 I 0.1/3

(-1.07) . 0.53)* '(-2.66)** (0.64) (3.3I)**

S10 1.115 -0.012 -0.082 0.016. -0.013

(-3.56)*, (-0.41) (-1.56) (0.26) (-0.37)

Sli -0.275 -0.162 -0.259 -0.120 -0.076

(-5.60)** (-4.91)** (-1.96) (-2.13) * (-8,47)**

F(11, 109)

Seasonals 135.42** 109.93** 68.61** 38.18 3.32*4

'112 0,928 0.917 0.864 0.773 0.604

.,..t S.E. 0.106 0.095 0.174 0.203 0.117

D.W. 1.793 2.068_ 1.988 1.758 1.533

'Cu

.0
- 36 -

The first column shows our 2§Iimates of the determinants of the

monthly probability of a white male teenager entering the labor force.

Over the ten-year estimation period, approximately 20% of nonparticipants in this group entered the labox force each month. The estimates -.

in column 1 indicate (1) the probability of entering in any

(-

particular month is strongly associated with the state of the bus-

, .iness cycle; (2) this probability has been increasing over time; a d

(3) it has a strong seasonal component. Jun and July, of course, re the months when a teenager is most likely to enter the,labor'force.

About 92% of the monthly variation in the probability of entering

the labor force for this group is explained by our equation. Most

of this variation >ts explained by the seasonal ies.

White female teenagers show no sign ant cyclical sensitivity

in their entry patterns, but do show a strong, positive entry trend;

nonwhite males have a procyclical entry pattern, but uo trend; and

nonwhite female entry rates have no significant trend or cyclical

patterns. By way of comparison, prime-age white males, whose coefficients are shown in the last column, have a positive cyclical

entry pattern, a negative entry trend, and a seasonal pattern which,

although significant, is much smaller than that of any of the teenage.groups.

Our strongest results concern the probability that a teenager

who enters the labor force will have a job during the month that

he or,she enters (Table 3). 'For the decade as a whole, white teenagers were much more likely than nonwhite teenagers to be successful entrants. AbOut 66% of white males and 63% of white female had

jobs in the entry month Compared with only 47% on nonwhite males /,

/ and 33% of nonwhite females.

As should be expected, for everylrf'oup the probability of a neyi

entrant or re-entrant having a job varied procyclically (i.e., a

positive coefficient on the vacancy-unemployment term). Successful

entry was closely related to the state of the aggregate'labor/market.

A startling finding was the significant negative trends fount for .
,

- 37 -

TABLE 3 - PARAMETERS DETERMINING PROBABILITY OF SUCCESSFUL

LABOR FORCE ENTRY

ln(NEi/NL) lnknei 443neiln(V/U)-1 + gneiT + Si +

(11 (2) (3) WHITE MALES WHITE FEMALES NONWHITE MALES

___ 16-49_, ______. _ _.16+3.9. . _ _ 16 -19 . -

. . . + Sll + c

-nei

(4) NONWHITE FEMALES

16-19

(5) WHITE MALES

25-59 Ink 0.260 -0.147 0.050 0.954 0.327

(4.84)** (-2.56) * (0.27) (2.58)* (4.65)**

8nei

0.162

(9.98)**

0.056

(3.23)**

0.190

(3.38)**

0.473

(4.13)** 0.173

(8.16)**

gnei -0.001 (-4.23)**

-0.001 (-6.66)**

-0.001

(-1.63)

'-0.004 (-3.02)*

-0:001 (-3.61)**

Si -0.000 -0.005 -0.024 -0.090 -0.024

(-0.02) (-0.26) (-0.41) (0.79) (-1.09)

S2 -0.095 -0.006 -0.099 -0.044 , -0.075 (=5.60)*%' (-0.31) (-1.69) (-0.37) (-3.39)**

4 S3 -0.071 -0.015 0.009 -0.394 -0.050 (-4.24)** (-0.82) L- (0.16) (-3.40)** (-2.28)*

0.050 0.012 0.019 0.039 0.021

(2.96)** (0.66) (0.33) (0.34)

ss 0.067 0.006 0.144 -0.111' 0:026

(3.94)** (0.33) (2.46)* (-0.95) (1.16)

S6 0.014 -0.067 -0.158 -0.147 - -0.019

(0.80) (--4-64)** ( .21) (- 0.86) S7 0.051 0.04611/4 -(4-65)** _

0.182 0.278 0.038

(3.20)** (2.67)** r

'(3.25) ** (2.49)* (1.83)

e- S8 0.077 0.001 0.092 0.204 0.008

(4,76)** (0.10) (1.65) (1:83)* (6.40) S9 -0.028 0.004 -0.025 -0.146 0.077

(-1.72) (-0.20) (-0.45) (-1.30)' (3.62)**

S10 0.35 0.024 0.001 0.107 0.022

(2.07)* (1.34) (0.01) (OM) (0.983).

t SII -0.042 -0.04 -0.133 -0.030 -0.029

(-2.49)* (-2.34)* (-2.28)* (-0.25) (-1.33) 1 F(11,109

Seasonals . 11.0S** 2.95** 2.94** 2.39** R 3.33**

0.812 0.626 0.351 0.472 0.727

S.E. 0.056 0.060 0.192 0.383 0.073

. ' 1.802 2.036 2.304 2.064 2.326

I
--V

I -- 38 .

every group. That.;is, adjUsting for cyclical and seasonal fluctuations,

it is becoming increasingly difficult for an entrant or re-entrant to

immediately find a job, with the steepest decline estimated for nonwhite females. This-negative trend, incidentally, was found for

prime-age white males.as.well. F q

Once.an individual is in the Labor force, the probability of

leaving in a given monthmay be related to cyclical, 'trend, and

'seasonal factors. We distinguish between the probability of leaving

the labor force if employed and the-probability of leaving the

labor force if unemployed. The next two tables report our parameter

estimates for each category. No uniform cyclical pattern in exit

\----mtes was found. Looking first at the probability of leaving the

labor force if employed; Table 4 shows a significant reduction for

white females in the probability of leaving as job opportunities

711

expand, bu increase for the-two nonwhite teenage groups and

fo prime-ne males. Negative trends were estimated for both white

teena groups and for nonwhite females. There is an obvious

seasonal pattern, with the probability of a teenager leaving the

labor force extremely high iLSjt.ember.

Table 5 shows that the probability of leaving the labor force

if unemployed is positively related to the state of the aggregate

labor'market for white teenagers and negatively related for nonwhite

female teens. A negative trend in the.exit rate is estimated for

the white teenage groups and a positive trend is estimated for nonwhite females./ The probability of leaving is much lower thanaverage

in the months of June and July.

The mixed findings with respect to cyclical patterns in the probabilities of teenagers leaving the labor force from employment and

unemployment raise the question of the cause of the cyclical labor

force exit patterns, commonly associated with labor force discouragewent. We offer a very 'simple explanation, supported by the gross

change statistics: unemployed people regardless of age, race, sex, or state of the ecomony, are more likely to leh.ve the labor force than

ea
- 39 .-

't TABLE 4

. PARAMETERS DETERMINING PROBABILITY OF LABOR FORCE

'EXIT FROM EMPLOYMENT In(ENini,:0,--lnkera+.111(V/U) + g .T + SI +

Beni + S11 + c . eni -1 eni

(2) WHITE MALES WHITE FEMALES

16-19 16-19

Ink -2.102 -2.117

eni (-17.82)** (-21.75)**

6 0.008

eni (0.23)

Beni -0.002

(-5.18)**

-0.075

(-2.56)**

02.003 :

,(-9.23)*8

SI 0.064 0.163

(1.75) (5.41)**

82 -0.127 -0(151

(-3.41) (-4.90)*

(-5.96)** (.4.76)** S3 -0.22D .4.145'

S4 :0.157 -0.038

(-4.24)** (-1.24) SS -0.018 -0.003

(-0.49) (-0.09) S6 -0.325 -0:065

(-8.60)** (-2.10)*

J S7 -0.473 -0.261 (-13.30)** (-8.89)**

40 S8 0.163 0.092 (4.59)" (3.12)** S9 1.038 0.852

(29.09)** (28.91)** SIO 0.163 0.039

(4.37)** ' (1.26) S11 0.010 -9.164

(0.28) (=5.37)**

F(11,109)

Seasonals 101.98**

.." 94.04**

IT2 0.906 0.902

S.E. 0.122 0.101

D.W. I 1.804 1.898

(3) (4) NONWHITE MALES NONWHITE FEMALES

16-19 16-19' -1.393 -1.122 _

.. (-5.08)** (-4.25)**

(3) WHITE MLLES

25-59

-5,226

(-446.87)** 0.184 0.142 0.156

(2.22)! (1.77) (4.62)**

0.001 .-0.002 0.002

1(1.00) (-2.35)* (3.83)**

0.116 0.046 0.t60

1.37j (0.56) (4.64)**

-0.175 -0.048 -0.186

.4 * (-0.57) (-5.27)**

(:N:'44** (-4.48)** (-2.94)**

-0.370 -0.103<

-0.176 -0.090 -0.001 (-2.05)* (=1.09) N.(-0.04)

-0.134

(00.03r) (-1.61) 44

-0.280 -0.024 =0.-083 .(-3.20)** (-0.29) (:2.3Z)*

-0.355 -0.073 ' 0.118 6 (-4.29)** (-0.92)

. (3.51)**

0.213 .0.299 "" 0.064

(2.58)** (3.77)** (1.88) 1.125 1.001 0.076

(13.58)** (12.54)** (2.24)'

-0.050 '-0.050 -0.039

(-0.57) (-0.61) (-1.12)

0.009 -0.244 0.016

(0.11) (-2.95)** 19.45)

t 20.87** 18.44**f 7-08**

0.655 0 48 0.414

- 0.284 0.273 0.116

1.872' 2.300 2.155
40 -

TABLE 5 PARAMETERS DETERMINING PROBABILITY-OF LABOR FORCE

EXIT FROM UNEMPLOYMENT

In (UNi/Ui,_1) = lnkuni + 8uniln(V/U):1 guniT Si + . : . + 511 c uni

(I) WHITE MALES

16-19

(2) WHITE FEMALES

16-19

(3) NONWHITE MALES

16-19

(4) NONWHITE FEMALES

16-19

(5) WHITE MALES

25-59 //

/ Ink . -0.349 -0.622 -41.169 , -1.600 0.254

(-2.90)** (-5.24)** (-5.43)** (-7.84)** (1.18)

8

L1111

. 0.178

(4.87)**

04084

(2.33)* -0.010

(-0.15) -0.121(-1.96) 0.669

(10.57)**

//

guni

-0.003

(-6.57)**

-0.003

(-7.22)**

0.000

(0.47)

0.002

(2.35)*

-0.000

(-0.13)

Si 0.187. 0.226 ' 0.220 0.031 0.315

(5.07)** (6.18)** (3.31)** (0.50) (4.73)**

-,. S2 -0.050 -0.006 -0.090 0.4 -0.111

(-1.30) (-0s16) (-1.32) (0.82) (-1.63)

. , S3 0.037

(0.99) 0.008(0.22)

0.038

(0.57) -0.040

(-0.62) -0.183

(-2.74)**

S4 0.149 :,.. 0.140 '0.074 0.062 -0.080 (3.94)1 (3.76)** (1.09) (0.97) (-1.19)

<

, S5 0.186 , 0.117 0.182 0.153 -0.098

(4.90)** (3.I4) ** (2.68)** (2.37) *. (-1.44) S6 .4).284 -0.141 -0.153 -0.196 -0.166 (-7.36)** (-3.72)** (-2.23)* (-2.99)** (-2.41)*

S7 -0.289 -0.096 -0.236 -0.181 -0.033

(-7.95)** (-2.69)** (-3.63)** -, (-2.95)** (0.51)- S8 0.057 0.027 -0.112 0.084 0.076

(1.57) ' (0.81) - (-1.72) (1.36) (1.17)

S9 0.150 . -03051 0.322 0.104 -0.069

(4.17).. (-1.41) (4.94)** (1.69) (1.06) SIO -0,083 -0.160 -0.097 -0.046 .063 (-2.19)* (-4:26)** .(-I.42) (-0.72) (1.93) S11 10.027 -0.065 -0.056 -0.027 .096 (-0.72) --,k1.75) (-0.82) 4

.. (-0.42) (1.42)

F(11,109)

Seasonals 17.97** 8.97** 6.04** 2.63** .18**

. --2 R 0.767 0.658 0.312 0.306 0.688

S.E. 0.125 0.123 0.223 0.212 0.223

. D.W. 1.594 , 1.706 1.630 1.800 .557

/, .
- 41' -

employed people in the same demographic group. Hence, an increase

in the unemployment rate generates a higher labor force exit rate

merely by shifting more people into the exit -prone unemployment

stock even if the probability of exit,from that stock does not

thange.

10

Our last two sets, of equations analyze the probabilities of

transition between unemployment and employment. Table 6 provides

our estimates-of theiceTation-strip-b-etweerr-the-probabi-4ty----of-unemployed persons finding a job each month and the same cyclical, trend

and seasonal terms used in the preceding equations. For most teenage groups the probability of finding a job varies4procyclically, as' -N

expected. For nonwhite females, we have an unexpected negative re-

-, lationship. There is also a slight negative trend in the probability of an unemployed nonwhite male teenager finding a job.

Seasonality plays a significant role fozevery group, with success

highest in July and lowest in January.

Table 7 provides estimates of the relationship explaining the

probability of an employed person becoming unemployed each month.

This tdrnover probability is highest for nonwhite male teenagers, and

lowest for white female teens. We expected an inverse cyclical re-

. lationship for every group, but found it only for white female teens.

The probability for most groups is highest in the months ofJanuary,

June, and September and lowest in the spring.

Other Factors

We recognize that *transition rates of teenagers are influenced

by many variables that are not included in our regression equations.

10. For-ar steady. state analysis of this phenomenon, based on previous

transition probability estimates, see Ralph E. Smith, "The Discouraged Worker in a Full Employment Economyg!' Proceedings of

the American Statistical Association, Business and Economics

Section, 1974, pp. 210-25. Subsequent simulations confirmed its'

importance in a dynamic context.
J

1

- 42 -

TABLE 7 PARAMETERS DETERMINING PROBABILITY OF TRANSITION FROM EMPLOYMENT TO UNEMPLOYMENT

ln(Elli/Ei,4) m Inkeui + Beuiln(V/U). I * geuiT + SI +

(1) (2) (3) WHITE MALES' WHITE FEMALES NONWHITE MALES

16-19 16-19 16-19

. . + Sll + ceui

(4) NONWHITE FEMALES

16-19.

(5) WHITE MALES /5-59

Ink -3.235 -4.430 -3.057 -2.613 -6.554 eui (-20.37)** (-20.92)** (-8.38)" (-5.68)** (-46.84)**

eui 0.006

(0.13)

-0.190

(-2.96)"

-0:018

(-0.16)

-0.170

(1.22)

-0.483

(-11.53)**

geui 0.000

(0.59)

0.002

(2.01)*

0.002

(1.40)

-0.002

(-0.88) 0.001

(1.19)

Si 0.252 . 0.152 0.224 0.178 0.288

(5.13)** (2.32)* (1.99)* ,

(1.25) (6.67)**

S2 -0.097 -0.080 -0.271 -0.269 0.144

(-1.93) ( -1.20) (-2.35)* (-1.85) (3.25)**

S3 -0.211 -0.01 -0.291 , ' -0.112 0.063

(-4.24)** (-0.26) (-2.55)* (-0.781 (I.44)'

S4 -0.236 -0.231 -0.186 -0.369 -0.072

(-4.75)** (-3:48)** (-1.63) (-2.56)* (-1.64)

S5 -0.213 -0.169 0.025 -0.387 -0.128

(-4.65)** (.2.53)* . (0.22) (-2,.66)** (-2.91)"

L

S6 0.264 0.414 0.394 0.380 -0.060

1111 (5.19)** (6.10)** (3.37)** (2.57)* (-1.33)

S7 0.088 0.048 0.256 0.062 -0.090.

(1.8,3) . (0.75). (2.33)* (0.44)- (-2.12)*

S8 -0.117 -0.162 -0.147 0.007 0.080

(-2.4erf.. (-2.53)* (-1.34) (0.05) (-1.91)

S9 0.171 ' .0.289 0.236 0.440 -0.088

(3.55)** (4.51)** (2.14)* (3.16)** (-2.08)*

S10 -0.009 0.025 0.124 0.119 -0.041

(-0.10) (0.37) (1.08) (0.82) (-0.93)

sii° 0.201 0.063 -0.077 0.129 0.072

(4.03)** (0.94). ( -0.67) (0.89) (1.65)

F(11, 109)

Seasonals 13.58** 9.15** 3.97** 3.20** 7.36** -2

R 0.530 0.521 0.234 0.212 0.812

S.E. 0.165 0.220 0.378 '0.477 0.145

D.W. 1.853 2.218 "1.763' 1.822 ' 1.410

!pt
.

o

I

rt

- 43

TABLE 6 PARAMETERS DETERMINING PROBABILITY OF TRANSITION FROM

'UNEMPLOYMENT TO EMPLOYMENT

4.

SS, 0.127 0.055 0.291

(3.68)*,* (1.22) (3.16)**

An(UE. in., _i) = Inkue. + 8 ln(V/U)_ Si +

1,-. 4, 1 uei I + gueiT +

, ,., (I) ' (2) (3) y9 WHITE MALES WHITE FEMALES NONWHITE MALES

16-19 ' 16-19 '" 16-19

Inkue

, i -0.333' -1.023 -0.234 (-2.90)** (-6.76)** (-0.77)

B.uel '. 0.244 > 0.085 0.351

(7.01)** (1,84) (i.79)**

. .0.001 0.001 , ' -0.002 guei

(1.32) (1.59) (-1.96) s SI -0.208 -0.388 -0.164 (-5.87)** (-8.29)** (11.73)

:

, S2 -0.113 -0.047 -0.095

(-3.11)** (-0.98) (-.0.99)

. %. S3 -0.126 -0.105 -0.035

(-3.50)** (2.22)* & (-0.37)

) S4 0.051 -0.109 -0.144

If. - (1.43) (-2.30)* (-1.50)

, S5 -0.029 -0.039 -0.170

(-0.78) , (-0.82) (-1.76)

: S6

. 0.152 0.067 0.021 . (4.13)** (1.38) (0.21) S7 0.356 0.279 0.540

(10.28)** (6.10)** (5.86)**

S9 . 0.023

130*) -0.176

(0.67) (0:6* 2 (-1.89)

510 0.065 . 0.149 0.124

(1.80) (2.92)** (1.29) 511 -O.101 el. -0.002 -0.023

(-2.80)** (-0.04) . (-0:24)

F(11, 109)

. . - ... teasonals 21.93** 11.72** 5.45**

. t2

0 0.747 0.535 0.485

S.E. 0.119 0.157 0.317

D.W. 1.909 2.034 1.624

. , . + Sll + CUei e . (4) : (5) 's NONWHITE FEMALES WHITE MALES

16-19 25-59

A

-2.610. -O.6 3

(-7.34)** (-6.83)**

-0.216 0.102. '(-2.01)* (3.48)**

. -0.000 -0.002

, . (-0.27) (-4.29)**

-0:394 -0.203

(-3.58)** (-677)**

I- -0.099 -0.065 0..

( -0.88) (-2.10)* , . 0.10'5 -0.016

(0.945) (-0.54) -0.299 0.075

(-2.69)** - 1 (2.47)**

. , -0.066 0.089 . (-0.59) . (2.90)**

. -0.016 -0.046

(-0.14) (-1.46)

0.552 0.0279.---'

(5.15)** (0.92) 0.191

(1.79) 0.161

(5.50)**

1).101 0.154

.1(0.94) j (5.25)**

a 0.353 0.027

(3.14)** (0.88)

-0.239

(-2.14)*

6.36**

0.332

0.369

2.249

-0.035

(-1.15) 3
44 .0

In particular, decisions to enter or leave the civilian labor market

0 and the outcomes of these decisions are closely related to decisions

regarding schooling, marriage, fertility,'and military service and.

to government activities such as minimum wages and public service

o employment. A comprehensive gross flow model of the teenage labor

o market should take these factors into account.'

During the past decade, probably the most critical influence

teenage males'was the Vietnam War. To explore the impact of the

$ military buildup in the late-sixties, me re-estimated our equations

ith a dummy variable that took the value of one in 1967, 1968 and

1969,,and zero in other years. Since the Vietnam years were also

the period of lowest unemployment and Kere at the beginning of our

estimation period? we

coefficients would-be

00 A superior procedure,

were concerned that our cyclical and trend

quite sensitive to the change in specification.

time and data permitting, would be to model

directly the gross flows between the armed farces and each of the

labor market stocks.

Out results-confirmed the importance of Vietnam to the teenage

labor market,, but suggested that our trend and cycle estimatetwere-9

not particularly sensitive. 'Every group's probability of findi a job when unemployed was increased during the Vietnam period-, significant Vietnam dummies for three of the groups and near-significance for white male teens). .Also, the, probability of a white

male teen leaving the civilian labor force increased, while their

probability of transition from employment t unemployment declined.

Only two other Vietnam dummies were signific nt. Among the or

iginal cyclical and trend terms, only one coefficient (a negative

cyclical term on the white female exit rate from employment) switched

11

11. These were a positive dummy in the equation explaining the

.entry rate of nonwhite females and an unexpected negative

dummy in the equation for the white female entry success

rate.

134
a

IOW

- 45

signs and remained significant; only six of the twenty-five significant trend or cyclical coefficients lost significance; and two new

trends were introduced (positive hire probabilities for white maTes

and white females).

Interpretation of the Labor Force and Unemployment Patterns

What have we learned about the teenagolarticipation and unemployment rates of the past decade by analyzing the underlying gross

flows? First, there are strong trends in both the labor force entry

rates and the exit rates that are underlying the growth in white teenage participation, with steeper trends for the females. Unless these

trends change, the difference in particiaption rates between white

male and white female teens will continue to narrow. These entry

and exit trends were not present for nonwhites. . Second, the cyclical participation patterns of 'teenagers are

generated, in part, by the positive link between their entry decisions

and job availability. In addition; unemployed teenagers are much

more likely than those with jobs to drdp out of the labor force.

This discouraged worker pattern serves to dampen the cyclical amplitude of their reported unemployment rates since only active jobseekers are counted. Since.the recent recession reduced teenage

participation, above-trend growth in.their participatiowates

should be anticipated during the recovery and has begun.

Third, the-ehenically'high unemployment rates of teenagers can

be directly linked to their large flows through the labor market.

12

- 12. An excellent analysis of differences in steady state unemployment

rates between demographic groups is in Stephen T.. Marston, "Employment Instability and High Unemployment Rates," Brookings

Paper on Economic Activity vol. 1 (1976), pp..169-203. Using

our corrected transition probabilities for 1967 through 1973,

Marston finds that the major factor accounting far the difference

between teen and adult unemployment rates is the larger probability.for teens of leaving employment. This is consistent with

our findings.

O

s.,
a

- 46 -

One-third of white teenagers and over half of nonwhite teenagers

experience unemployment during the month that they enter the labor

force. In addition, over 3% of teens with jobs become unemployed

each month, triple the rate for prime-age adults. Once unemployed,

white..-teenagers' probability of finding a job is similar to that of

adults,%, but the likelihood of leaving the labor force is much higher.

Nonwhite teenagers have the additional pioblem of having much lower

probabilities of finding a job in the month that they enter--the

labor force and- much lower monthly probability of 'findifig a job once

unemployed., Also, if employed, their probability of becoming unemployed each month is much higher.

Fourth, the deterioration in the relative position of nonwhite

teenager.s can be traced to bath secular and cyclical differences

1

i their behavior. Na only are their rates. of labor force entry

ailing to keep pace with those for white teenagers, but also their

success in finding work is decreasing more rapidly, which discourages

further participation. The widening gap between nonwhite and white _ teenage unemployment is contributing to the widening gap in their

participation.

CONCLUSIONS

One of the motivations for this paper was to call attention to

a potentially valuable source of data on labor market dynamics of

youth. Clearly:the data ried,to be improved before they can be

considered as valid indicators of month-to-month changes in the state

13 of the economy But thetr.failure to provide monthly signals

that always coincide with those provided by contentional labor

4 market series does not eliminate .their value as an analytic tool.

The trend, cyclical, and seasonal patterns reported in this paper

13. For recommendations regarding the improvement and dissemination

Of gross flow tabulations see our paper, "Gross Change data:

The Neglected Data Base," to be published by the National Commissio# on Employment and UnemploymeAt-Statistics later this

year.

O

.

4
- 47 -

do conform to those of the conventional COS labor force and unemployment series and help'to explain them. Much of the monthly variation

in the transition probabilities is systematic and accounted for by

our equations. Of the twenty-four equations estimated for teen-

. agers,fourteen had cyclical variation.significant at the .05 level;

fourteen had significant trends; and all had significant seasonality;

for white teenagers, between 52% and 92 % .of the variation in the

transition probabilities were explained by our regressions; for nonwhite teenagers, between 23%.and 86% were explained.

In this paper we have not focussed on the policy:implications

7 of our estimates. Qne use of the estimates would e to test the

potential effects on teenage employment and unemployment of alternative__

policy interventions. Typically, a program would have its direct,

impact on one or more transition probabilities Which, °in turnAyould

effect both employment and unemployment. EDr example, expanding the

number of public service employment slots available to teenagers

'would increase their prqbability of being hired and would attract

more teenagers into the labor force. In evaluating.the outcome of

a job-creation'program, our estimates suggest the importance of

measuring the labor force response as well as the impact on unemployment.

Earlier estimates of the transition probability equations have

been used to, assess the impact of macroeconomic policy-on the teenage

labor mbrket.14 It was'found that the 1934-1975 recession had a

severe impact on teenage emproymentwhich(was only partly reflected

in their unemployment rotes. We estimated that macroeconomic policies

alone could alleviate much of the problem, but would leave teenage

unemployment and labor force discouragement rates substantially higher

than those of adults. Structural remedies oriented toward the

special labor market problems an seasonality of the youth labor

14. Ralph E. Sniith, "The Teenage Unemployment Problem--How Much Will

Macro Policies Matter?" in The Teenage Unemployment Problem:

What Are the Options? Report of a Congressional Budget Office

Conference (Washington, D.C. Government Printing Office),

pp. 7-17.
48

,.market are needed.

The increasing difficulty that all teen groups are having in

finding jobs must be diagnosed and cured. Teenagers whose/families

are not plugged into informal job referral networks need alternatives.

This is 'especially the case for minorities. Much of the differele

between white and nonwhite teenagers their unemployment rates

can be traced to the greater difficulty that nonwhites have in obtaining a job once in the market. In addition, nonwhite teenage

job-seekers are more likely to become discouraged and withdraw from

the labor force. Hence, the real difference in the labor market

performance between the two *groups is even greater than the difference

in their unemployment rates. Ways must be found to eliminaN these

disparities. Ourvnalysis of the past decade provides no basis whatsoever for optimism that tile problems are being resolved.,

rt

4
- 49 -

APPENDIX

AVERAGE MONTHLY TRANSITION PROBABILITIESa

(JULY 1967 - JUNE.1977)

White

Male

71

n

r ite Black Black White

Faale Male Female Male

n Teen Teen Age 25-59

Labor Force Entry

(NIP/N ) i i,-1

.22 .16) .19 .13. .10

Successful Entry

(NEi /NLi )

.1. .67 .64, .49 .36 .68

Labor Force Exit From

Employment (ENIT/5,) .11 .14 .15 .18 .003

Labor Force. Exit From UnEmployment (UN'/U4 _1)

, a. .31 .33 .34 .10

Unemployment to Employment.

(UE'/U

i,-1

) ,30 .28 .20 .18 .32

Employment to Unemployment

(EUi/Ei,_1) .04 .03 .06 .04 .01

a. These are the mean proportions of the populations who make the indicated transitions. The flowson which these estimates are based

were corrected, using 'the procedure discussed in the text.

A
- 50 -

THE TRANSITION FROM SCHOOL TO WORK

WITH JOB SEARCH IMPLICATIONS

By: Stanley P. Stephenson, Jr.*

ABSTRACT

This study adapts a human capital model of schooling and earnings to focus on the transition period. The adaptation consists.of two

steps. First, unemployment incidence and duration after last leaving

school, but prior to the first job taken, is included as an intervening,

part of the transition process. A second feature is the consideration

of the extent to which job holding while in school alters subsequent

unemployment and wage rates. , Data used in the.analysis is the NLS, panel of white and black young men who were fourteen- to twenty-four-years-old in 1966. Persons in- cluded in the sample were drawn from the period 1966,to 1971 and had

stopped attending school during this period. White and black men were

studied separately. The complete model comprised a recursive system

of a schooling equation, an unemployient equation, and an hourly wage equation.

The main results concern the unemployment and wage equations.

Holding a job while in school lowers the incidence and duration of

later unemployment and raises the subsequent hourly wage for both

white and black youth. Full-time job effects exceed part-time j'ob

effects in both equations, and all effects are highly significant

statistically. 'Job search theory suggests sothetypes of unemployment

behavior may lead to a higher wage, and a positive but nonsignificant

effect of unemployment on the post - school wage was found for black

youth. For white youth, however, significant and negative impacts of

unemployment on the post-school wage were found.

Policy implications of the analysis suggest that school-work programs may ease transition problems for some youtil-in-terms of raising

post-school wage rates and lowering youth unemployment in the time

immediately after school. An important qualification is that the Present study did not evaluate government-created jobs for youth which may differ from the in-school jobs held by youth'in this study.

*I am grateful for the research assistance._of David Smull. The opinions

are mine alone, however.
tt

- 51 -

INTRODUCTION

This paper examines t dete inants of successful school to work

transition among a nation ly repr sentative sample of black and white

male youth. The analysis involve estimating the parameters in a standard recursive model of'schooling and wage rates and considers two issues

which have not been fully explored in previous empirical analiies.

First, the transition year may involve an intervening period of'unemployment during' which the school leaver searches for a job. This search

behavi?r is included in the recursive Model as an added equation. Some

consider such unemployment as an indicator of an unsuccesfUL.transition. This view is questioned by some results obtained here. Secondly,

the nature of the search' process, as well'as the overall eases the

transition process, may be altered by the youth's,labor force status

during his last year in school. Indeed, Freeman and Coleman in a recent

report urged that federal youth policy'should consider programs which

include work experiences while in school as a.means of easing school to

work transitioin problems.

1 While no special programs are examined here,

. we can examine the impact oflabor status in school. on the subsequent

job.search processand on the wage rate on the job held after last

leaving school. d CONCEPTUAL ISSUES ... .- , Defining ibe Process . .

, HQW one specifies exactly.whqrtheschool to work.transitioA'occurs l, 6

:.... i' 2. is crucial to the ifiterpretation.of the labor market results obtained.

0

1. Richard Freeman and James Coleman, discussion.in,The Teenage Unem

ployment Problem; What are.the Options?, Congressional,Budget.Off e

Report. (Washington: Government Printipg Office, '1976).'

2. Michael D. Ornstein, Entry into the American Labor Force (New York:

Academic Press, 1976).

4

4

*,

.
52

Perhaps the first job held is the point at which the transition process

.,should be measured. Yet such a job, 'if it can be recalled, may have

little relation to a youth's future career or adult labor market exr perience, and, as such, may not indicate a su essful transition. A

next step is to consider the job taken afte leaving school. Yet, a

number of analysts have discussed the iculties arising from ambiguities in responses obtained in surveys which seek to identify the

first full-time job after an individual left school,.

3

Several solutions have been suggested for determining the first such job: (a) "the

first job after you first attended regularischool",4

(b). "the first job

at which the respondent worked for two or more consecutive weeks after

discontinuing regular school",

5 and (c) when, the individual "leaves

full-time schooling and participates in the labor force for a period

of more than sixteen months without returning to full-time education.

- during that time",

6

The choice depends on the goals and purposes of

the analyst.

In this study, we are concerned with the determinants of successful

transition from full-time student status to full-time labor participation, As such, we need to allow for the intermittent nature of the

0 schooling decision by many youth by defining entry point into the labor

3. P. M. Blau and Ir. D. Duncan, The American Occupational Structure

(New York: Wiley ; - 1967); 0. D. Duncak, D. L. Featherman, and' B.

Duncan, Socioeconomic Background and Achievement (New York: Semi- nar

. Press; 1972); Ornstein

' oR. cit.

,4.- Duncan, et al., -op. tit, °' 5. A: Kohen and P. Andrisani, Career Thresholds: A. Longitudinal. /

Study 6f-the Educational and Labor Market E4perience of Male

Youth, vol. 4 CColumbus,-Ohio: The Ohio State University,

Center fob; Human Resource Research, 1973).

6. Ornstein, op. cit. 9

. ,

4!

O 1

( -

A
- 53

k ; / force as the first-jobafter the youth has last left full-time schoTI%

The-:6ataused covers Ox ,years of school. and nonschool Status, 1966-

c:- 1971,. ,Youth not- in full-time school during at least one year in thls%

period; -as well as-youth only in full-time school the entire time, Were

, excluded. As4ndicated; forouth who :ntered and left school: repeat-

- edly, the 4dst schoolsexit was selected. In the empirical section:-

emphasis.is placed on examining responses at'time t, the last year in

school, with responses obtained at t+1, the next year.

7

Model S ecification

-Ahe transition from school to wo is considerecrin this paper as

an'extension of human capital theory. Usually, in such theorettica

models, the individual chooses how far to continue in school, so as to

maximize the discounted value of future earnings.

8 ,In the empiricalversions of such models, individual earnings, Y, are considered a

function of optimal khonlipa years, S, and individtal "ability", A.

where school years are a function of ability and market interest rates,

r. With measures of A and r, such as test scores, family socio-economic

status, family incomeand.so forth, the model is recursive and exactly

identified.

9

7. For some youth, a one year school exit,, even the last such exit,

may not capture the job held after last exit, if school re-entry

.1, occurs. Within the existing data, one could require two successive years of nonschool status, but such,d procedure restricts'

the available samp14. ,

,,,

.:. . 8. Gary Becker, Human Capital and. Personal Distribution of Income,.

1967 Woytinsky Lecture (Ann Arbor, Michigan: University of ,.,7e,''

Michigan, Ipstitute of Public'Administration, 1967). '14 yei,,,

: #-

,,. 9.. Sherwin Rosen, "Human Capital: A Survey of Empirical ReseOW

in R.Ehrelberg, ed., Researchin Labor Economits, vol.I (Gr4tnwi4, 'Conn': JAI P ess, 1977), p .3-39.

r
54

sider a twenty-year old youth with twelve years of education. Ifthe

41) youth did not hold a job while in school, potential experience is two

years, while it is three years if a job was held during the last year

. in school. Because we are interested in measuring successive years in , school and out of school, the problem may be minimal. Yet if schooling

and informal training are substitutes in terms of their effect on future

earnings, we overestimate the returns to schooling by excluding the

general training content of jdbs held while in school12

Ideally, one would like to include an indicator of each year in

which schooling and work were combined. As an approximation, we con- sider the work status in the last year of schooling and distinguish

full-time, part-time (less than thirty hours worked perVieek), unem- ployed, and-not in the labor force. Thus, equations (1) and (2) have

been rewritten as S = G [r,A]

(3)

y

t+1 = F [S,A,LFS

t

]

4 (4)

where LFS

t is individual labor force status at, time t when in school.

,A second modification to the human capital model considers youth

job search behavior.' Following Stiglerliseminal theoretical article

on search theory, 13

several theoretical efforts have been made to

examine 'differential wage levels for homogeneous labor at a given place

and timein terms of the differential length of effort by individuals

12. This statement presumes thatthe job held in school is primarily

Tart-time and that job changing occurs after schooling is com

P1 Accordingto Lazear, "9 ° f students who work change

jo on graduation." ,see Edwa azear, "Schooling as a Wage

Pe ant," Journal of Human Resources, vol. 12, (Spring 1977),

PP. -76.

13. George Stigler, "Information in the Labor Market," Journal of

Political Economy, vol. 70 (October 1962) A

- Ai

S.

ti 73 4 I

A
55

Sr= G fr,A] (1)

.Y = F.[S,A]

(2)

This is the,basic model used in the present analysis.

However, certain modifications are neCessaii, since this does not allow , for sufficient flexibility in an individual's combination of school/work

alternatives. First, the above model implies that the youth undergoeS

a period of full-time schooling followed'by a lifetime of fall -time

earnings. For many youth, such as*tfiose who face imperfect capital

markets, or those who are married and maintain households away from

their parents, e.g., such as a research assistant, full-time schooling

is continued while holding 4 job. In addition, because we have selected the transition definition of the first job after last leaving

school, it, is quite possible that persons in school will Also be active ,

4

in the labor tarket. In contrast, the theoretical model does not allow

for liter wage impacts of jobs held while in school. Granted, thetraining Impact on wages of informal on2the-job.:training. has been established by Mincer,

10 Becker,

11 and others; but there is a problem with

Aincer!s measure of potential work experience as [Age - Education - 6]

in that.W assumes no job was held while in school. For example, con10. -Jacob Mincer, "On-the-Jqb Training: Costs, -Beturns, and Soup

Implications;" Journal of Political Economy, supplement 5, part 21;

vol.'20 (October 1962). -N.- ' Q. . .

, ,

.

. , "The Distribution of Labor Incoffies: A Survey with

. . Special Aference to the Human Capital Approach," Journal of

e eet; 'Economic Literature, Vol. t, 1970.

3. Schooling; Experience and Earnings (NewYork:

Columbia University Press, 1974)..

.4;

. . ,.

), ,11. GarrAecker, !'Investment in Human.Capital:

. A-Theoretical Analysis;" ',.1, JxmmLL'of Political Economy, ,OctOber 1962. . . . .rk . . -. ,-

.3

I

ea

(5

e00

1

I

t "9 kY

a

S.

o

.4
A'. in searching for a job. 14

Search costs, time preferences in consumption,

, wealth,,-and propensity for leisure have also been empirically related to

56

4 youth job arch behavior. 15

According to the theory, the basic dilemma

. fedi F the youth is%whichof many possible offers to accept. OV a low

off is accepted; search length is reduced; but subsequent earnings

are also reduced.' Alternatively, too high a reservation wage leads to.

a prolonged search period and possibly higher post-unemployment earnings,.

If the search for labor market information is viewed as an investment;

primarily time, certain investment optimization rues can be-applied,

much like those applied to school leaving decisions. That'is, some

, youth unemploypent may be beneficial in that it enables the-youth to:

securelii highest wage consistent with his, search:_costs. Yet, some'

outh unemployment may not be ofithis type. A previoUs study'of mine.

and 90% of unemployed youth accepted their first, job offer.16'.To

find any offer may be a main job search goal for many youth. . "-We iRcorfoorate this second modification by including a third and /

intervening equation. ,for.unemployment.4 Schooling and work experience

in school alter the unemplgment

. behaviOr and afl three factors affect

..

,' the wage ohtarded after leaving school. We now rewrite the model as'

5" = g jf,A,Xt:el]

(5)'

= H [r,S,LFSt,Xt,c2] : (6)

0 = F JS,LFSt,UE,Xt.cr,c3] (7)

.

,

.14. Steven Lippinal an otn J. McCall, "The Econotics of Job Search:

A. Survey?" Ebonomid Inquiry, vol;' 14, nos. 2 and 3 (June 1976 and

September 1976):

. . c: ;

. ,

, .*, . u

,- ,1S. Atanley P. Stephenson, Jr., "The Economics of Youth Job Search BeJ\

:havior," The ReView,ef Economics and Statistics, vol. 58 (February.

1976)-, pp .

. . 16.! Stephenson, op. cit.,'

f

U.I.

e,

a

4.

'0
9:1

A. a

rr4

et

,

to the hypothesized decline in reservation wages Over unemployment dura-

- tion.Thismeahsthatnofonlylaill- aYt+1/

DUEe biased, but every

. other term in eqbation (7) that correlated.with UE will be biased.

1,

, r . To correct forthis probltm, we use not the 'actual UE measure, but an

expected UE measure that'considers separately (a) whether or not the

youth becomes unemployed and (b) the durationaof conditional,untmployf

.ment. This prOpedure is derived with.atwin linear probability model.

M

.57

The X

t

and X

t+1

are vectors of relevant'demographic andecOnom'ic variables and the e i are randomly distribUied error terms with:zero means*

that account for left out variables: Unemployment now alters earnings

after thool, yet the sign is ambiguous. If unemployMent is a produillive-

,. job 'search investment, then DY t+1/HJE>0; if, however, is above some

subjectively determined critical point, such*that reservation wage decay Per

has substantially reduced the average acceptance wage, then Mrt.1.1/aUE<0.

Two problems arise in^estimating this model. Unemployment and

reservation wage adjustments are dynamic concepts that we only app ox-,4

imate with.annual survey data on weeks unemployed in the past yea and

the Wage of the job held when interviewed. Many persons''have no un m-

, ployment, which means a clustering of zero values and inefficient 0

estimation of equation (6) parameters yWe, therefore, consider two

measures of UE: (a) whether ornot one becomes unemployed!and (b) the

duration of unemployment. A qtcond issue is-Chat search theory/implies

that e and e are jointly distributed with a negative covariance due.

2 - 3

DATA

..--... The study is based on the National Longitudinal Survey pSnel

4

*

data of 5000 young meninterviewed each year from 1966 td _1971. To

4 -

be included, theyouth had to compleft two 'successive interviews. in 9

.

S.

. . ' . f,

.6 which.he was a full time student in year t and bqt enrolled in year

t+r, 'In catrIlf--sultiplegperiods-in and put ok school, thi last Such

.

,

. . , .17. 'Arthur S-Goldberger, EconometriC.Theory (NeWuyork: John'Willy &'

Sons, 1964), p. 252.

0

0 A.

,

r.

.

0

0

r 0,
O

'

58 -

pair of years was selected. White and black youth were studied separately. Certain key variables were used to additionally screen the

samRle. That is, youth not having a recorded measure of scholastic

aptitude,loca unemployment rate, years of school completed, or family

socio-economic status were excluded. Further exclusions were for . youth With less than eight years of schooling. Sample means and

standard deviations of variables used in the analysis re provided in

Table 1. . The empirical specification of the theoretic odel is a recur-

. sive system of three equations whiCh allow us to examine direct and indirect fnfluences,on postschooling wage rates. The equations are

Schooling= ao+al Scholastic Aptitude + a2 Non-South +

'as Time + a4 Family SES + a5 Health Limitst +

a6 Family Income +,a7 Age (8)

Unemployment = bo+ bl Schooling + b2 Non-South +

b

3

Time + b

4 UE Rate + b

5 Marital Status +

56 Net Family Income b7 Military Exp. +

b

8 UI Payment + b9 LSFT

t +'b

10 LSPT

t

+ b

11 LgUE

t

5 Hourly Waget+1 + ci Schooling + c2 Scholastic

'Aptitude + c3 Non-South c4 Time +

cs UE,Rate + c6 Health Limits

t+1

. c 7Military Exp. + c8 LSFTt + c9 LSPTt +

c10 LtUEt:+ c

11 Unemployment . (10)

where Scholastic Aptitude is a standardized mental ability measure based

on pooling scores from.aNariety of achievement, aptitude, and intelligence tests; Non-$outh is'a.tero-one dummy variable equal to 1 if the

respondent livesin,a'non-scipth census division; Time is a linear trend

measure where Y907=1,.1968=2, etc.;. Family SES is an indexiof the sodioeconomic level of the re'spondent's parental-family based on .(l) father's

I education, (2) father's occupation when respondent Was fourteen, (3)

. mother's education, (4) education of oldest.sibling, and (5) Availability

(9)

11 ^: 83

. . 4 Si. 5,
4 - 59 -

TABLE 1 MEANS AMD STANDARD DEVIATIONS OF VARIABLES USED IN THE ANALYSIS BY RACE

Variable

, White

Mean

Standard

Deviation

Schooling Years

13.62 (2.16)

Scholastic Aptitude 105.34 (13.57)

Non-South Resident :73 ( .44)

Real Family Income/I000

10.36 (6.98)

Real Family Income Less 7.27 (6.86)

Respondent's Incqme/1000

If Health Limits, time t .06 ( .23)

If Health Lilts Work, time t*1 ,09 ( .28)

Family Socioeconomic Status 109.56 (18.18)

F

If Not Married, time t+1 .79 ( .40)

Age When Last in School 19.51 (3.00)

Real Wage Rate per Hour 2.13 (1.56)

If Employed Full-Time, time t .29 ( .45)

If Employed Parr-Time, time t .33 ( .46).

If Unemployed, time t .07 ( .25)

If Ever in Military .07 ( .25)

f LoCM1 Unemployment Rate

4.15 ('2.44) Time (1=1967; 2=1968, etc.) ei

f . 3.32 (1.78)

'Public Unemployment Insurance 90 '(7.46)

..430nefits per Week Unemployed

i A Predicti44onditional Unemployment

A Duratidn of Multiplied by Predicted 1.37 (1'.#6)- Unemployment Incidence

Predicted Unemployment Incidence .29 ( .45) ' Other VilOaliqes ofinterest

Real WeekiY,Earnings, time t+1 93.68 (69.61)

If-had Positive Earnings, time t+l .,90 ( .31)

Average Hrs Worked per Wk,, time t+1 38:08 (16.56)

Unemployment Dur40.42n, u60 8.73 (9:38) ' " .

4 "k

. 84

!Black

Standard

Mean Deviation IL 12.52 (1.64)

87.39 (14.82)

.36 ( .48)

6.69 (4.67) 4.71 (4.11)

.03

.05 93.11

. 90 18.24 1.70

. 20 .26 .14

.03 4.37 2.76

.34 f

3.14

( .16)

( .21)

(18.35)

( .29)

(2.38)'

(1.53)

( .39)

( .44)

( .35)

( .16)

(1.90)

(1.61)

\\.2.5'0)

(3.04) .40 ( (0).*

*,_6114.70 (62.32)

: .90 ( .30)

4 36 ' 55 (14.94)

.#., 8.83 (9.98)

-3
0.

I.

of reading material in the home when respondent was fourteen; Health

Limits is a zero-one dummy variable equal' to 1 if the respondent indicated

the existence of work-limiting health problems; Family Income is the

parental income if the individual is qingle or lives with his parents

4

and is the respondent's family income for married youth not living at

hOme (parental income, initially given in discrete intervals, was made

into a continuous variable by first selecting mid-ranges, then converting

the appropriate measure into constant 1967 dollars via a CPI price de-

_ flator); Age is the respondent's age when laft in school; UE Rate is

the loc'al unemployment rate in the local labor market in yhich the

respondent lived at t+1; Marital Status is a zero-one dummy variable

equal to 1 if the respondent was not married; Net Family Income is

family income less the respondent's annual wage or salary; Military Exo:.

is a-zero-one dummy variable equal to one if the respondent ever served

in the mi.itary as of time t; UI Payment is the annual income from public

unemployment insurance divided by the number of weeks unemployed, or

zero for persons never unemployed; LSFTt, LSPT , and LSUEt are zero-one

dummy variables which refer to the labor force status of the full-time

student and indicate full-time employMent, part-time employment (less

than thirty work hours per week),.and unemployment, respectively. The

dependent variables are Schooling, which is measured as the highest

year of school completed at t+1; Unemployment, which is a zero-one dummy variable equal to one if the respondent was ever unemployed in

the year preceding the interview,ai t+1, or a'continuous variable made

up of the number.of weeks unemployed in the past year; Hourly Wage 'at

t+1, which'is based on the prOcessed response to "How much do yOu

usually earn on this job before deductions?" and is converted into 1967

values. J -; Special attention in this study is placed on Schooling,.unemploy-

. inept, and previous labor status effects on W, hourly wage. We hypo4

thes'ize that schooling and previous work experience will raise W. As. mention44.earDiter, the unemployment variable effect.on W is less clear.

Some attention sis, also given to. the individual equations. In the

85
schooling equation, We expect al, a4, and a7 to be positive as ability,

taste for school, wealth, and age enter schooling decisions. The other

variableS control for time and place differences. In the unemployment

equation, we expect b4, b8, and b11 to be positive. The methoclof computing UI Payment, however, may add some spurious correlation to the

b

8

"estimateand, hence, b

8

shoull be considered more of an "UI eligibility".

effect rather than an exact measure of UI impacts on'unemplOyment incidence

or duration. Schooling influences on unemployment are unclear. Yet, pre;

vious work experience effect4., b9

and b10, are hypothesized to reduce

both unemployment measures. In the wage rate equation, the direct effects

of schooling, previous work experience, and scholastic aptitude are e

pected to be positive.. Job search impacts are less clear. In one sens

successful job search investments might suggest cli 0; yet, reservation

wage decays might lead to c11<0. Of course, we shall also examine the.

indirect effects of selected variables as well. For example, the total

impact on W of being unemployed during one's la'st year in school s - aW /aLSUE which is the sum of the direct effect,ct, and indirect effects,

cll.

{(9P(UE)/aLSUE)<E[UE Duration]/ P(UE)=1)+(aUE Duration/3LSUE)E[P(UE)])

RESULTS

In Table 2 are presented the full regression results and standard

errors. Consider first,the schooling equation results. For white

youth, as expected,' scholastic aptitude, family SES, and age are statistically significant and positivelyfrela:ted to years in school. The

negative and significant effect of family income waspnotexpected.1

Several possible' explanations are (a) nonlinearincome effects (b) was,.

urement problems created by censoring out high income responses (c) the

impacts on measured low income of married college students living apart

from parents. :These alternatives.all require further invtstigkion. . Black youth also had more years of schooling if they hadia high scholastic aptitudo,nfamily SES, and were older. Family fncome effects is

also negative for black youth, but are not significant. Also, NonSouth black youth averaged nearly,.4 of a year less in 5,chool years comthadsouthern blacks.

Qr Q`' OU

4

X
I

- 62

0

TABLE 2 REGRESSION COEFFICIENTS AND STANDARD ERRORS (IN PARENTHESES)

OA Independent Variable 1 Family SES . ../ Scholastic Aptitude 1 Census Division '. (1=Non-South; OsSouth)

Family Income/1000a

0 flealth Limits on Work,

v at t or t+1 (1=Yes;. 0=No4

Age When Left School

in Years

Time in Years

.. Schooling

, Marital Status

Family Income Less

Respondent's Income/1000a

If'Employed Full-Time,

time t (1=Yes; 011No)

0 If Employed Part-Time,

y time t (1=Yes; 0=Nb)

If Unemployed,

. timer (1=Yes; 0=No)

If Ever in Military

(1=Yes; 0-No)

Local Unemploymat Rate,

time t+1/10

White Youth

't Wage Rate

If UE Weeks per Houra

.02***

( .01) .04*** - .04 . R

4 k .01) ( .37).

.09 .04 .45 22.15**

( .09) (.03) , (1.36) (10.62) .02**

, .14 -25.65*

. (,.18) (15.60) .47***

-, ( .02)

.02 .03** .77** 7.23**

( .02) (.01) ( .35) t ( 3.25) -.01 .19 20.06***

(.01) ( .31) ( 2.43)

.15*** 2.53. ° .00 - .03

(.00) ( .08),

-.13*** -3.88** 66.04***

(.04) (1.57) (13.77)

tl -.10** -2.91** 20.89* '

(.04) (1.36) (12.69)

.10* 4.10** 40.64*

(.0y , (1:97) (21.59)

-.06 4.34 90.40*2* (.06) (2.88) (18.80)

.01" - :01 - .31

( .01)

(.04) (2.10)

. --UnemploYment Insurance Payments . Divided by all Weeks Unemployed

. Expected UE Duration

Constant u 1.02'

/?,... R2

. b .64* It3pin Dependent Variable 13.63

(.(1) ( .02) ( .20)

.01** .04 . (.00) (°:04) .

0. 15.43***

(,4.64)

.13 1.99 ..103.32 .10 .10, .21 .29 .8.72 213.00

... le Size 955 ' 955 . 275

. 955 '' 2In .1967 vglUe terns for cents per hour, e.g. the mean Of 213. is $2.13 per hour.

***, *, * significant at it, 5%, and 10% level, respectively,.with a two - tailed t-test---

114
.

- 63 -

TALE 2 (continued)

REGRESSION COEFFICIENTS AND STANDARD ERRORS (IN PARENTHESES)

.. Black Youth

. lndep'endent Variable

.Schooling If UE Weeks UE

Wage Rate

r per Houra

Family SES: .03***

(..01)

Scholastic Aptitude .02** - .26

( .00) ( .73)

Census Division - .39**

0 28.55

. (=Non-South; 0=South)

. Family Income/1000a . HealN4Limits on Work,

at t or t.1 (1=Yes; 0=No)

Age When Left School

in-Years

( .17)

2 °,01

( .02) - .30

( .49) .39..w.

( .04)

P _

'a

' *

(22.60)

. -31.62 N`NN'

(47.74)

' - Time in Years - .05

( .05)

Schooling

Marital Status

.

-.01

(.02) -.01

(.02)

02

.92

( .76)

- :22

( .82) 3.04

5.50

og

,

( 6.93)

S8.04"'

( 6.93)

.

(.13) (5.00)

Family Income Less , .01 - .14

.. Respondent's Income/1000a (.01) ( .30)

If Employed full-Time,

time t (1=Yes; 0=No) 0' -.24**

(.10) -2.61

(4.14)

93.25 ** , (33.80) . IfEmployed Part- Time,. -.11 .45 44.98*

time t (1=Yes; 0=No) (.09) (3.20) (26.18)

If Unemployed r .18* 7.84**

. -10.01 timet (1=Yes; 0=No) (.11)

.. (3.10)

, (58.25)

If Ever in Military .414 1.11 e -164.19**

(1=Yes; 01p) (.22) (5.60) (80,035) Local Unemployment Rate .01 - .05 1.00

. ' time t+1/10 4./1 (.01) ( .06) ( 5.56)`

Unemployment Insurance Payments .02 .17 o Divided by all Weeks Unemployed (.01) ( .31) ° Expected UE Duration

. N 1.38.

( J.96) Constant 1.45 .40 6.75 -358.44

R2 .59 .12 .15 .23

4.. Mean Dependent Variable 1252 .40 8.83 170.81 -SIMple Size 188L-- ). 188 76 :' 188

,,,

aIn 1967 value terms for cents per, hour.

'C}

***0,**, *. significant at 1%, 5%, and10%,level, respectively, witl;Qa two - tailed t-test

"
The second equation examines factors affecting youth unemployment incidence and conditional unemployment duration. Estimating

the unemployment equations by OLS is clearly not as appropriate as

some other estimation techniques; such as Tobit or logit, which

i accounts for the highly skewed unemploftent duration distribution.

That is, because 60% of the black youth and 70% of .the white youth

had no unemployment at all, the response surface elp nonlinear, which

means that the marginal impact of any variable i dependent on where

on the distribution of unemployment it is evaluated. 'Cost constraints

limited the use of the nonlinear estimation techniques. As;an alter:

native, we use the same specification to examine separately the incidence and duration of unemployment. Thislitocedure permits some

discrimination between the marginal impacts of exogenous terms at

li

different points on the distribution of unemplo ent duration.

Youth unemployment changes are probably due, n part, to aggregate

demand swings, population changes, and other supply related changes, and

changes in a thira\set of factors which are unique fo youth and the

labor markets they face. For white youth, we find the incidence and

duration of unemployme7it are positively related to local uneilOyment

rates after controlling for family income and time trend effects. The

size Of the effeot i small, yet tho positive sign suggests, perhaps,

an "additional" worker effect that is puzzling.

For. both race groups, - unemployment insurance is seen to increase

unemployment duration and incidence. BLit only the latter effect is

significant.18 -FUrthermbre, since weeks Unemployed entered the cal-

_ .culation of this variable:the estimate is negatively biased. Being

2 interested in the transitoion from school to work of all young men,

ot

,.

18. Ehrenberg aid Oaxaca find that a UI benefit/earnings replacement

ratio increase of .4 to .5 increases -male youth unemployment . . duration by .2 weeks: see Ronald Ehrenberg and Ronald Oaxaca,

"Unemployment Insurance, Duration of Unemploymentvand Subsequent

Wage Gain," American Economic Review, vol. 66 (December 1976).

6

te.t
. .

. lr

0? 3*

t r . . 441.

$

1

W

56'

11 1

, "AP

we cannot restrAt'the/Sampie'to'personehbleiing a jbb prior to un:-

-employmit 'a2114'calculate the ratio of in benefits to net after tax

, employment earnings. We can,4ohowever consider the cost of a week

of unemployment' as potential earnings less weekly unemployMent in'

a. surance benefits, The latter reduces the cost of unemployment and,

thus, is expected to increase unemployment. Potential earning effects

are approximated by schooling, census divilion, and previous experi-:

ence gained in job holding and military experience. Of these variables, previous job holding stands out in.magnitude and statistical

significance,' especially for white youth.' To have held a job while in

school raises the indivi4ualti potential earnings; which increases

search costs, and, thus, reduces both the incidence and duration of

t unemployment. Furthermore, if full-time jobs pay more than part-time

jobs, then we would expect that unemployment reductions would be

greater for the former group, which is exactly what is found.for whites

and blacks alike. For example, for white youth who held a full-time

job their last year in schOol, there is a 13% redUction in the probability of being unemployed relative to students who neither worked

nor looked for mirk.. The corresponding figure is 10% for holders of

part-time jobs.

t Finally, the persistenceof unemployment is shbwniN the result_

that students who were unemployed when interviewed.at time t cia.2 experienced sharp increases in the,duration and incidence of un-

. - employment in the next twelve months.19 the main point is that if

youth unemployment reduction is aSdlicy goal, then job holding '4

while in school may redfte the unemployment immediately' after school.

Some analysts may point out that youth unemployment in itself

may or may not be wasteful. For example, if theyouth via investimoin unemplOyed job search raises his subsequent net earnings,

19. We' checked on the possibility that some persbns simply stayed

unemployed for the entire yeas and, thus, account for the

positive result. Yet only 1% to 2% of each race group had '4 total annual UE durations of forty weeks, the maximum length.
* a . a

.

. .

64. ). OV7

--ta

- 66 -

then such youth unemployment may be privately, and even socially

beneficial. We examine this possibility in the wage equation.

Full regression model results by race for the,hourly wage equation are presented in Table 2. Results are in terms of cents. For

example, for black youth, an added year of schooling makes a direOt

positive marginal contribution of thirty-eight cents to the average

stage per hour. Our main interest is with unemployment, schoalifig,

and school labor market.activity effects, and each is discussed in

turn.

Sharp racial differences are found in unemployment impacts

on the post-school hourly wage. For white youth expected unemployment duration is associated with significant and negative impact

on W,' whereas fbr black youth, a positive baionsignificant'imPact.

was found. An added week of unemployment duration reduces the

average white wage by 16 cents per added week of unemployment.

Why the racial difference? Can we say unemployed search is productive for black youth? We do not know very much from the N115

data about the details of search behavior of the youth during unemployment. For example, direct marginal search cost differentials

by race could account ,for part-of the observed difference.. A

devious study of mine'estiMaied weekly search costs for October

1971, of white youth at 0.66 vs. $3.90 per week for black youth. 20

If similar direct cost disparities are true for the NLS sample used

in the present study and if search casts increase as time unemployed

continues, then white .youth wage demands may fall more sharply than

At 20. Stephenson, op. cit.

Ar

)

, 1+ .
TABLE 3

MARGINAL CONTRIBUTION OF SELECTED VARIABLES ON

HOURLY WAGE OF FIRST JOB AFTER LAST LEAVING SCHOOL

.4 A (1) (2)

White Indirect Effecta Direct Effect

Schooling (-.16)[(-.61)(8.73);(.19)(.,29)].= -.01 .2Q

' LSFT

t (-.16).[T=M);(8.73) +(-3.88).(.29)2=.36 .66k

LOTt . (-.16)R-.10).(8.73)+(-2.19)(.29)1.f27 4 .21

LSUE

t ck(-.16)C(.10)(8.73)+(4.10)(.29)]= -.32 I

Black

Schooling

LSFT

t LSFTt

LSUEt

(:07)[(-.01)(8.83)+(-.22)(.40)1.- -.01

(.07)[(t..24)(8.83)+(2.61).(.40)]= -.22

(:07)[(-.11)(8.83)+(.45)(.40)]= -.06

(.07) [(.18)(8.83)+(7.84)(.40)]=.33

(3)=(2)4-(1)

Total Effect

. 19 1.02

. 48

.41 .08

?38 .37

.93 .71

,.45

10

.39

.23

alndirectEffectsofLare comfUted as

3W/8E(U6).q(8PNEY3X0(E[UE Duration]/P[UE]-1)+(2UE Duration42Xi)E(PNED]

A

9,3
- ,6&

, . _ . black youth wage demands and the.racial.difference in unemployment

, effects on W will be found. A similar result could be found if whites

continually readjust downward their wage 'demands as new inforinatioi, is

'-.. -

obtained regarding the nature of the wage-offer, distribution they face;, : . '4 yet

' there i'snC.conxincing reason'why white youth shoilld be expected

..---9"- t .

. to be initially further malaligned with the reality of the market than

. . . i blick'youth. 'Clearlk, further analyiis of this result is needed.'.

. - The

.-impact_on the postschool wage of years of schooling and in;

_ * s-4,.. P-

, ... ,.

,$ school labor ,marketiaetivity involve,4iiect and indirect estimate. ., . " shown,

.it Ifi Table 3 are such-Iarenal effects by race. For black youth,

_

. ,_ ,..;". .....:.... as itehtipned, an adde&:-1-yeax 'Of scho-zlo in%sg4 adds thirty-eigh-t- Cents

. .,,_.- . ,,- ;.,.,:\ 4- ,..-, 1 ''t

to the-post§Fnool houkly wage; however, the'indirect effect is such

-.1-_-_. that-tne total' marginal impact of ans., added year of schoq:i,cg is.

...., tgirty;seven cents,: Racial comparisons suggest that the marginal

. value of an addid.ja i. of schooling for white youth is about one-half

...

..,.., ,tharfor blacks: Pareof this difference is probably due to diminish--

--.: ' L,Pi ,,, ' .,,,4 ing margin alretUrns to continued educational investments and the, fact

1 ,4 that tie years of schooling in the sample was 13.5 for whites vs. 12.5

for bl kS.

Having held a .job during one's last year in school both reduces

unemployment and raises the postschool wage level. These effects are

relatively large and statistically significant. For white youth,

.since unemployment is wage depressing., the direct positive wage impact

. of having held a full -time or part-time job is increased by the indirect effect of previols job holding on expected unemployment. For

example,aWAISFTt = .66 from Table 2 in the UE duration equation if

we exprefS the result

/

in dollars,and cents. In addition, part of, the

't effect on W operates through the reduction in expected unemployment duration.'\i The total effect on W.is thus- ( 6 +.30) or'

one dollar and two cents. White yvng men who held a, full-time

job during their last year in school make more than ope dollar an

hour-more than white young men who preViously neither looked for work'

nor held-a part-time job. Further such derivations are presented in

. - Table 3.

94 . . -

m>'
. o .- 69 -

Part-time job-,holding has about one-half the full-time job impact

oft W and merely having looked for work during one's last year in sdhOol

may have some payoff relative to NLF students. Racial patterniare.

similar in terms of the relative marginal ixfluelce of previous

Tull-time and part-time job holding. Howevei:, in school job holding-

,

' -increases the wage of white youth more than for black youth. Another

racial difference arises because black unemployment is wage-augment-'

ing, whereas,' for.white youth unemployment is wage depressing.

The net wage gain to white youth who were unemployed 'in school .s eight cents per hour over NLF yoUth versus the twenty-three cent

o differentlal for black youths,-

SUMARY

We-began the analysis by considering. the' issue of how to define

the transition from school to work. Inga range of definitions we

chose the first job after last aving school-. By selecting this

point, we increased the average observed age and also increased the

possibiAity that the line between'school and work would become increasibgly blurred. This point wasselected, however, not to obfuscate the transition issue, but to choose that point that best.

marks a permanent leaving of full-time student status.' Such a

,point may best serve to need to examine jobs leading to lifetime' . . careers. The analySis was considerably aided by the availability of the

rich,NLS panel data on young men. However, sample attrition, non-

.4 response to key questions, and the transition pro selected all

'41 theme to

I. the final sample studied. Since the data covered

Ithe period from 1966 to 1 71 milAary experience andatime trend

variables were added as control variables. ft The major results of the' analysis were obtained in an expanded

human capital mp,del:. The expansion was (a) to include unemployment

0

A

O

O

-

I.
O

incidence or duration as an intervening equation between the standard

4

% schooling and wage rate equations and .()),to consider the impact on

a unemployment and wage rates of holding a job or looking for a job while

in school. Job holding in school reduces later unemployment for white

and black youth. Furt4exmore, previous job holding, especially fulltime job holding igle a student, sharply increases hourly wage rates

on postschool jobs.

In tents of job search implications during the transition

k

process, racial differences were found. In the sample, 40% of th,e, .

black youth had some unemploAent. For such youth, post- unemployment wage rates were higher than for other black youth. White 4 youth, howe_vrINLglperienced =employment , had lower subsequent

Wages. While further research is needed regarding this important

issue, the preliminary policy implication is that unemployment may

not be either socially or privately wasteful to some blatk,youth .

and that a policy.ofinschool work experience might aid the transition process for bo$rgroups. = 0 1

4

F A

O

'f m

, I

'9G

sOS

.44-.

INN 4
1

- 71 -

THE ESTABLISHMENT'OF STABLE AND SUCCESSFUL EMPLOYMENT CAREERS:

THE ROLE OF WORK'ATTITUDES AND LABOR' MARKET KNOWLEDGE

By: Paul J. Andrisani ' J ABSTRACT

This paper assesses the extent to which table and successful

employment careers are a function of the wo attitudes and labor

market knowledge of youths. The paper examines the work attitudes

and labor market knowledge of youths, considers how they differ from

their older and presumably more mature counterparts, and how they

affect and are affected by successes and failures upon'initial entry

into the work force. The research summarized draws primarily frOm

published works based on theyational Longitudinal (Parnes) Surveys.

The data summarized clearly show the importance of positive

work attitudes and adequate labor market knowledge for the establishment of stable and successful employment careers. Confidence

in these findings is*strengthened by the fact that the observed

relationships were independent of individual differences among the

youths studied in a wide range of skills, abilities,.and demographic

characteristics, and were supported by longitudinal data as well.

The data also document persuasively the inadequacy of all typesof

labor market knowledge as youths cross thecritical threshold from

school to work; -with blacks,. females, and poor white youths possessing the least adequate knowledge. Yet, thedata provide little empiKical justification to consider youths' attitudes towafd work as

inadequate, immature (other than for lack of adequate labor market

. information), anti-work, or the cause of the unique labor market

problems of-youth. the basis of the statistics for-the late

1'960s, youths' attitudes toward work'were hardly dissimilar from

those of older workers; they reflected Considerable ambition in

the setting of career goals, and they were shown to be influenced

in an anti-work direction by unsatisfactory labor market experiences early in work careers.

INTRODUCTION Y

, Referring to out-of-setlool and unemployed teenagers, Charles

Silberman once wrote:

Many of them appear to be unemployable: they are--or seem

to be--uninterested in working, unwilling or unable to adjust to the routine and discipline of a job,-and generally

apathetic, *sPullen or hostile...teenagers also lack the

psychological pressures that make the great majority of

adult men prefer work to idleness.:.holding down a job .

4
6, A

)

.e"

72

is not necessarily a source of status, nor is unemployment

a source of shame. On the contrary, in at least some city

slums, teenage society displays a certain disdain for legitimate

Quite similarlyn the basis of. The ColemanReport's finding

r. that a youth's attitude "was more highly related to achievebent than

any other factor in the Student's ackground school,"

2 James S.

Coleman was prompted to write: . changes,in the Negro, changes in his conc tion\,

of himself in relation to his environment, may have more

effict on Negro achievement than any other single factor. * The determination to overcome relevant obstacles, andhe

belief that he will overcome them...may be the most criti-.

cal element i; achieving equality of opportunity.3

Still further, Etkard Kalachek's analysis of the' youth labor

market has also noted the potential importance of certain work attitudes

for uccess in the world of work:

Reports of operating manpower agencies stress the importance

of attitudinal factors...Clients drawn from these groups

are frequently characterized as alienated, discdUraged, immature,lacking self-esteem, and not conversant with accepted

middle-class work values. Counseling, efforts at building

self-esteem, and emotionally supportive services are Cited

as adtential elements in improving employabi]ity.4

. .

.

Another potentially important but seldom researched factor in

establishing stable and successful work 'careers is the adequacy of

1. C. Silberman, "What Hit The Teenagers?," Fortune, April 1965, as

cited in E. Kalachek, The Youth Labor Market (Washington: National Manpower Policy Task Force Policy Papers in Human Reiources

and IndUstrial Relations No. 12, 1969), p. 74.

2. J.S. Coleman, "Equal Schools or Equal Students?" in D,M. Gordon,

ed., Problems in Political Economy: An Urlfan Perspective (Lexington: D.C. Heath and Co., 1971), p. 193, emphasis in original. . 3. Ibid.

4. Kalachek, op. cit., p. 77.

-03

1.0

%ma
- 73 -

labor market knowledge available to xouths.

5 Inadequate labor market.

knowledge is conceptually linked to work attitudes, since it implies

. a faulty perception of the operation of the labor market. When the

.parents, relatives, friends, teachers, or guidance counselors of

. youths--and of female and disadvantaged ouths in particular--possess

inadequate labor market knOwledge, not on may this develop faulty

perceptions among the,youths, but it may shape important work attitudes

in an anti -4iork di:xection as well. .' 4

It may, for example, preclude youths from adequately preparing

for the caree--1 rs to which they aspire. Or, as has long been the 'case

with young.women, it may cause them to aspire to and prepare for careers

that they may later learn were not what they wancd-or expected, or

, that were not readily available. In each inStance, inadequate labor

market.knowledge may generate unrealistic career aspirations and

expectations on the part of youths that are easily dashed when con-

, fronted with the harsh and oftentimes brutal realities of the world

of work. The.conS quences for the ultimate establishment of stable

----- and successful wor careers quite obviously may_bJe_serious. 'In an

economic system in which youths are presumably free to choose among

.various occupational and employment opportunities, the freedom to

choose is indeed hollow-in'the absence of adequate labor market

knowledge.

The principal purpose of this paper is to assess the extent

to. which stable and successful employment careers are a function of

7 54 H.S. Parnes and A.I. Kohen, "Occdpational Information and Labor

Market StatuirThe Case of Young Men," Journal of Human Resources,

vol. 10, no. 1 (Winter 1975), pp. 44-55. See also: G. Stigler,

"Information in the Labor Market," Journal of Political Economy,

vol. 70, no. 5 (Supplement, October 1962), pp. 94-105.

ad

Ey
A - 74 -

secondary

work attitudes and labor market knowledge of youths. fit A secondary Rurpase is to examine what the work attitudes of youths actually

are, how they differ from-those of their older and presumably more

mature counterparts, and how they are affected by successes and failures upon initial'entry into the work force. To a considerable degree,

the research summarized in this paper draws upon published works from

a unique set of longitudinal data for representative national samples

of 10;000 American youths and 10,000 older workers during the second

half of the 1960s--namely, the National Longitudinal Surveys.?

6. That,is, the extent to which sta6le and successful employment . careers are a function of'work attitudes and labor market knowledge rather than of traditional human capital and demographic

variables most typically considered in labor market studies. These

include such variables as age, race, sex, education, general and

specific training, institutional and on-the-job training, health,

marital status, region of residence, type of local labor market.,

etc. In the human capital model of income distribution developed

by Becket and Mincer, labor market success, particularly in terms

of earnings, is a function of investments in an individual's productive skills and abilities, comprising one's stock of human 7

capital. Variables listed above that do not reflects human caFfital

or supply-side characteristics, are generally presumed to reflect

demand-side or labor market effects. See: G. Becker, Witian Capital (New York: Columbia University Press, 1964)and J. Mincer,

"The Distribution of Labor Incomes: A Survey with Special References4o the Human Capital Approach," Journal of Economic Literal-n-1-P, vol. 8, no. 1 (March 1970), pp. 1-26.

7. Begun in 1966; the NLS is a ten-year longitudinal study of the work

expetience of foug,age-sex,cohorts of the population, each of which

consists of roughly 5,000 respondents 4 men forty-five to,fifty-nine,

women, thirty to forty-four, and young men and women fourteen to

twenty-four years of age'at the date of the initial interviews.

The samples in each case constitute a representative national

probability sample of the noninstitutionalized civilian population

of the particular cohort as of the first survey date. The samples

were drawn and personal interviews conducted by the Bureau of

the Census for The Ohio State University Center for Human Resource

Research under separate contracts with the U.S. Department of Labor.

For an overview of the entire NLS.data base, including a 4emplete

description of the samplint design, interviewing procedures, and

the host of included variablesA see: Ile National Longitudinal

Surveys Handbook (Columbus: The Ohio State University Center for

Human Resource Research, 1975).

k
In' the section

available to youths

be explored. Then

the work attitudes

those of older work

relating to whether

-- their early labor m

tin then mines

early labor mar

concludes with

reported herein.

to follow, the adequacy of labor market knowledge

and the consequences # inadequate knowledge will

in the third sectibnOhe paper will explore what

of youths actually ate, and how they differ from / Ar

ers. In thefOltg section, the empirical evidence

the work attit s of ypu;ths have-any bearing on

arket experienl'Cwill be examined: The fifth sec -

tl, =, the empiricalvidence relating to the effects'of

experience aywork attitOgs, and'the final section

ief discussi4 5A of the implications of .the research

c /

,S,, THE ADEQUACY AND-I RTAKE OP LABOR 'MARKET KNOWLEDGE

According to Parnes, there are four types of labor market knowledge that should be considered 'n discussions of this sort.8 The first

and second are essentially the distinction between "general" and

"specific" human capital brie tments made by Becker.

9 Characteristic-

, ally, general labor market formation is a highly substitutable good

with a market of substanti breadth: It comprises, for example,

41; short run as well as long, n knowledge of alternative occupations,

industries, geographictlbtations, relative income and job security

prospects, avenues of preparation for various jobs, and a host of

other characteristics of'thealternative opportunities available in di

, tbe labor market. In cOnt asi, speeific labor market information in

its most limited form is he opposite side of the coin--ie., at the

limit it is confined to yiingle market and to even a single firm.

.;,

8. H.S. Parnes, "Imppy-ed'Job Information: Its Impact on Long-Run

Labor Market Expince," in Seymour L.' Wolfbein, ed., Labor

Market Inforimati4;for Youths (Philadelphia: Temple University

School of Business'Administration, 1975), pp. 163-83. Much of

the matexial in this section of the paper summarizes a recent

conference bn Imprbving Labor MarketInformation for Youth held

at Temple University on Iktober'21-22, 1974. The proceedings were

published in the'volume cited here.

9. Becker, op. cit.

. ,

4 4
76.

cleal.ly the former is more important than the latter in preparing

youths for the world of work. It may be the latter, however, which,

is ofjelatively greater importance to youths.after adequately pre- - e paring for s4ork--i.e.,during the critical period of settling upon a

specific firm. 10 /

The third type of labor 'market information includes knowledge of

the relative advantages and disadvantagesof various job search strategies and institutions--e.g., public and private employment seYVices,,

trade unions, and various public and private social welfare agencies.

Finally, the fourth type of labor market information of. relevance to

workers, particularly youths, is what.Parnes calls "an understanding

of t he regimentation that is inherent in greater or lesser degree

in,Mbstwork situations." 11 This understanding includes knowledge

of the "importance to success;ef regular attendance, punctuality,

good work habits, concern for the objectives of the employing establishment, and conformity to accepted standards of dress and behavior."

12

Irrespective of whether one endorses all of these or whether all are

essential to satisfactory performance on all jobs, it is Parnes' view

that failure to alert youths to such facts of industrial life would

- indeed be a disservice to them.

Whichever type of labor market knowledge/is considered, there is

persuasive evidence that many youths take their first jobs in a very

haphazard mannfr, and that initial decisions often have a lasting imJ 4

10. There is considerable eviden0, for example, that the differential

in unemployment rates between adults and youths results mainly

from greater turnover, and thus a higher incidence of unemployment.

See M.S. Feldstein, "Lowering the Permanent Rate of Unemployment,"

a study prepared for the use of the Joipt Economic Committee,

Congress of the United States, 93rd Congress, 1st Session, September 1973 (Washington: U.S.Government Printing Office, 1973),

pi. 17-18.

11. Parnes, op. cit., p. 167.

12. Ibid.

102

4

11

111.4

44

4.

'
- 77 -

. pact.

13 Mangum estimates that more than one million youths initially

entering the labor force each year encounter difficulty assimilating

into the world of work, gith blacks considerably overrepresented within this group.14 In the very tight labor market of 1967, for instance,

the average rate of unemployment among nonwhite teenagers was 26.5%,

, - a level seven times the national average of 3.8%.15 Furthermdre, there

is little doubt of the inadequacy of all types df labor market knowledge,among youths--particularly blacks, yourig women, poor whites, and

thOse in rural labor market.

16 Singell's follow-up of high schIbl

graduatds,ip Detroit a year after initial entry into, the labor market

ot. , is typical in its observation that:

Most youths'had,not "chosen" a job in any real sense,

but had either driftedinto one or had taken it because they could find no other....Furthemore, the

youths exhibited extremely vague knowledgelout wages,

13. Ibid., p. 169. Also'see, for example: P.J. Andrisani, An Empiri-

%

cal Mialysjs of the Dual Labor Market Theory (Columbus: The Ohio

State University Center tfor, Human Resource Research, 1973); P.E.

`Davidson and H. Anderson, Occupational Mobility inan American

Community (Stanford: Stanfqrd University Press, 1937);M.D. Ornstein, Entry into the American Labor Force (New York: Adademic.

Press, 1975); J. Piker, Entry into the Labor Force: A Survey of

Literature on the Experiences of Negro and White Youths (Ann Arbor:

Institute of Labor and Industrial.Relaties, University of Michi

'an- -Wayne State University; 1969); Kalachek, op.cit.; and L. Reynolds, The Structure of Labor Markets (Now York: Harper and Broth-'

'erS, 1951).

Nr4

14. G.L. Mangum, "Second Chance'ix the Transition from School to Work",

in P. Arnow et al., eds., The Transition from School to Work,

(Princeton: The Industrial Relations Sedtion, Princeton University,

1968), pp. 231-69. t. 15. '-P. Arnow et al., "The Transition from School to Work", in The Transition from School to Work, op.cit., p.3.

16. Parnes and Kohen, op. cit.; P. Brito and C. Jusenius, "Occupb.tionql

Expectations for Age 35"; in F.L. Mott et al., eds., Years for Dec=

i$ion, IV, (Columbus: The Ohio State University Center for Human

Resource Research, 1977), pp. 113-139.

1

S
4

r

- "78 -

working conditions, steadiness of employment; and chances

of advancement when they accepted their first job.17

Years earlier; Reynolds' classic study of the New Haven labor hiArket produced results that were essentially the same: "Most youngsters

(and their parents) approached the choice of a first job with no clear

conception of wherfNhey were going..." 18 Other studies f rom Davidson

and 'Aliderson in 1937 to a very recent and comprehensive one by Michael

Ornstein are. equally consistent on the point.1 9 When a sample of

sixty-nine vocational educators in twenty-two urban areas were asked

to discuss the labor market problems of high school graduates, for

instance, more than half reported that youths have unrealistic aspirations and expectations, concerning work, and more than 40% felt that

poor attitudses and lack of responsibility, maturity, and self-disipline

were part of the.problem.

20

Furthermore, there is little reason to be any more sanguine about

the adequacy Aof labor market information available to postsecondary

students. Perhaps the only study to be *pressed by the adequacy of

labOr market knowldege of any type is.that of Richard Freeman.

21

As

Parnes has noted, however: . L.D..Singell, Some Private arld.Social Aspects of the Labor Mobility of Young Workers",-The Quarterly Review of Economics and

Business, vol. 6, no. 1 (Spring 1966), p. 23.

18. Reynolds, op. cit.',pp. 213-14.

19. Davidson and Anderson, op. cit.; and Ornstein, op. Cit.

20. A.P.Garbin,,,R..Campbell, D.P. Jackson, and R. Feldman, Problems

in the Transition from High School to Work as Perceived by Vocational Educators (Columbes: The,Ohio State University Center for

Vocational and Technical,Edudation Research Series No. 20, 1967),

pp. 49-50. 0, 21. R. Freeman, The Market for College-Trained Manpower: A Study in

the Economics Of Career Choice -(Cambridge: Harvard University

Press, 1971).

101
- 79 -

1

...even Freeman's data show. that a sixth of the undergraduates and an eighth of the graduate students believed

that the information available to them at the time they

made their career choices was inadequate, while another

fourth regarded it as "barely adequate". Moreover, other

investigators of the process of career choices among college

students have drawn generalizations different from those

of Freeman. A study-of almost two thousand male and female members of the 1972_ graduating classes of five Pennsylvania colleges and universities reported that...twofifths of the students reported that they were "not too.

aware" and one-fifth that they were "not at all aware"

of the job market in the field of their major at the

time they selected it.22,

Of what empirlcal consequence are such differences in labor

market knowledge among yquths? One of the very few studies to

examine the issue is that of Parnes and Kohen.

/^ 23 Using longitudinal 1 data On male youths from the National Longitudinal Surveys, a multivariafe statistical model, and a multi-item measure of occupational

information, Parnes and Koheh found that scores'in 1966 on the NLS r measure of labor market knowledge were significantly correlitedwith

the hourly wage rates and occupational.atiainment of the youths two

years later. Moreover, the resrlts were obtained for young blac

men and their young white.counterparts, witlf-statistical controls

for such human capital and demographic variables as years of schooling,

o IQ, socio-economic status, quality of high school attended, years

of work experience, health status, region of residence, and rural

versus urban residence. As these researchers correctly observed:

Indeed, there is reason to believe that these findings

understate the total caapribution of the kinds of labor

market information measured by the test, since they ignore the indirect effects such knowledge may have on

occupational,status and wages via its effect on educational attainment.21

. 22. Parnes, op. cit., p. 171.

23. Parnes and Kohen, op. cit.

24. Parnes, op. cit., pp. 175 -76.

105

1.

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4

S

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Given the empirical results, hat then'would be the social

consequences of improved labor market knowledge? Would it simply

mean a reshuffling of youths among jobs as the ,gains of those with

superior labor market knowledge are transferred to those youths with

inadequate knowledge? Theoretically, 46e answer to 'the second question

is-no, since impro'ved labor market knowledge implies, movgent of the

labor market from disequilibrium to equilibrium - -with the resultant

reallocation of labor being more nearly optimal as youths.and older

workers become better matched with the jobs-for which they are.best

suited. In theory, an optimal allocation of human resources means

that no one could improve his or her utility or contribution to the

total social product, by making any job change. Thus, the reallocation

shcNld lead to increased productivity and individual welfare. Second,

a more equitable distribOion of labor market knowledge among youths

should reduce

income, since

NLS data both

race, sex, and class di4erences in unemployment and

greater labor market1Rowledge has been shown in the

to be disproportionately possessed by white; male

and hither SES youths and to be significantly linked with a successful transition from school to work.

Third, improved labor market knowledge would enhance the operation of competitive forces in the labor market. Thus, for example,

some employers would feel a stronger need to improve the quality of.

work to keep employee and the labor market would, be better able

to reallocate labor mor fficiently in response to technological

shifts and to shifts in consumer preferences and tastes. Increased

competition should act, to reduce, levels of unemplbyment and to shift

the Phillips curve down to the left. The latter result might be

particularly.qfective for reducing youth unemployment, since it

would reduce youth turnyer and frictional unemployment, which play

so large a role In the differential between youth andIgdult uhem loyment rates.

25

25. Feldstein, op.cit., pp. 17 -18.

106
.'-.

+- 81 -

WHAT ARE THE ATTITUDES OF YOUTHS TOWARD WORK,AND ARE THEY DIFFERENT

FROM THOSE OF THEIR ELDERS?

By way of the data for the fOUr NLS cohorts covering the 1966=

1972 period, it is possible to examine a number of the work attitudes

of-male and female youths and to compare them with those of their older

and presumably more mature counterparts. When respondents who were

in the labor force at the initial surve were-asked whether they would

continue to work if by some chance they here to get enough noney'to

live comfortably without working, 73 -7$% of the *older men, 78-82% of

the younger men, 58-68% of the older women, and 59-70% of the younger

-women replied that they would continue to wOrk.

26 The high degree of

reported commitment to work and the absence of age differences in

responses to this question are entirely consistent with responses,

to eleven other attitudinal items more directly probing commitment

to the Protestant work ethic. For each of the eleven items there

tended to be virtually no age diffeiences in the tendency to ascribe

credence to the)Protestant work ethic belief that hard, work leads to

- success.27

With respect to their occupational goals, young men and women in

1966 aspired to considerably greater occupational' heights than either ' 1

/

they or their older counterparts had attained.

28 Two to three' years

later, as the youths aged and gained more-work experience, occupation04 aspirations in the aggregate changed little for white or black, male

or female youths, Coded in.tierms'of the Duncan index of occupational

status, white young men in 1966 aspired to occupations averaging 55

'points (on a scale from 0 to 100), while blacleyoung men asp4ed

26. This portion of the paper draws heavily from Chapters 5,4, and S

of a research volume on the relationship between wk attitudes

and labor market experience: P.J. Andrisani, with.the assistance

of E. Applebaum, R. Koppel, and R.C. Miljus, Work Attitudes and

'Labor Market Experience: Evidence from the National Longitudinal

Surveys (New York: Praeger Publishersrd1978).

27.,,P,J. Andrisani,-"Internal-External Attitudes, Personal Initiative,

and Labor Market Experiente," in Andrisani et al., op. cit.,

chapter 4.

. . 28. 13..J. Andrisemi, "Work Attitudes and Labor Market Experience: Other , . Findings," in Andrisani et cit., chapter 5.

e
A

82 -

to occupations averaging only 46 points. Among tjhe young women, whiles

in 1968 aspired to jobs averaging 53 points, while blacks aspired to

jobs averaging 48 points. Of the youths who worktd at each survey' date,

an& who were not enrolled in-school, the actual occupational attainments'

of the white and'black young men were 32 and 19 points respectively, in

1966, and 44 and 30 points respectively for whitegand black young Women.

Of those employed in the two older NLS coh9rts at each survey date, the

actual occupational attainments of the white and black menwere 40and 18

points respectively, and 43 and 29 points-respectively for white and black'

women. Yet while aspirations were high, 29% of the young white men and 37%

of the young-black men perceived their chances of attaining their goals as

only "fair" at best. 29 Furthermore, the gap between aspirations and

actual occupational attainment was much greater for blaCk yout s than white

youths. Relative to their actual occupational attainment; or lative to

the occupational attainment of their counterparts in the older cohorts,

black youths aspired to greater heights than white yOuths. Within race

groups, however, young men and Women differed little On the average in

'their occupational goals. Thus, while blacks and fecale youths may be

unrealistic in their occupational goals, they can hardly be accused of

lacking ambition in setting their goals.

When asked whether "wages" or "liking the work" was the more important aspect of a good joip'4 better than half within nearly-every NLS

4 ,30 age-Sex-racelroup responded "liking the work.' Only among black men(

were preference differences between the younger and older NLS cohorts

more than four percentage points, 9nd even in the one instance the sage

differences are hardly suggestive of substantially different work attitudes between youthsand their elders. When asked to report the aspects

of their jobs that they'liked best, white and black, male end female

youths were most inclined to mention "the work j.tself."

31 Other frequently mentioned factors were economic aspects of work and interpersonal relationships. On balance, their responses were not dissimi

29. Ibid.

'30. Ibid.

31, Ibid. /

10

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4.

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to those of older workers. Much the same, when asked to cite the specific aspects of work disliked most, youths reported a wide range of factors,

ultimately constituting patterns quite similar to those reported by their

elders,.

. A 1, How satisfied were youths with their jobs, particularly...in relation

. . to the reported job satisfaction of their older counterparts? Remarkably

few youths expressed a disliking for their jobs at an 'survey date between

1966 and 1972.

32 Fewer than 15% of the employed,resPondents within any of

the NLS age-sex-race groups reported that they disliked their job solfewhat

or disliked it very much. At some points clueing the period, however, age

differences in degree of job satisfaction did tend to be substantial,

particularly between young and middle-aged black men. For example, in_

1966 only 34% Of young black males who were out of ?chool and working re-

, ported themselves to be highly satisfied with their jobs, in contrast to

51% among their older counterparts. For the most part, age.differentials

in levels of job zatisfaction narrowd during the 1966-1972 period.

In sum, with the possible exceptions of their occupational aspirations and degree of job satisfaction, the NLS data provide little evidence

that the work attirUdes of youths are essentially different from those of

their older counterparts. It also is at odds with.the data to suggest

that youths, particularly young blacks and young. women, lack ambition in

setting their employmentegoals. That their goals tend to be so high may

be a cause for concern, especially for the adequacy.of their labor market

'knowledge. It is quite, possible that at least a part of the j6b dissatisfaction, high turnover, and high unemployment rates of youths reflects

their high--and perhaps unrealistic--occUpational aspirations, which In

turn.may reflect the inadequacy otheir labor market information.

32. P.J. Andrisani, "Levels,and Trends in Job Satisfactiop, 1966-1972,"

in Andrisani 'et al. op. cit., Chapter 3.

a

a

_12 0 j

ti
A THE EFFECTS OF WORK ATTITUDES

The NLS data provide clear evidence of the importance o' work

attitudes in conditioning subsequent labOr market success among youths. 33

For example, the relationship between job dissatisfaction and turnover

is unmistakable, suggesting that highly dissatisfied youths were from

, . eighteen to forty-two percentage points more likely than Comparable - , youths who were highly satisfied with their jobs to change employers

subsequently. 34

Moreover, since youths stand less to lose, the data not

suprisingly show a stronger relationship between job dissatisfaction and

turnover among yoUths than older workers. The evidence also suggests that

ow j

job dissatisfaction imposes considerable costs on youths in,terms of ins creased unemployment, 'decreased labor force participation, and belowaverage growth in annual earnings. Furthermore, the data show that the

costs of job dissatisfaction reflect more than the costs of turnover which

were borne disproportionately by dissatisfied youths.- Among comparable

white youths who did not change employers, there is considerable evidence

% that those dissatisfied with their jobs were below-average in occupational

and earnings advancement. Among those who clanged employers, the dissatit'fied were generally above=average in weeks of unemployment as well.

The evidence thus suggests that job dissatisfaction often leads to reduced

productivity and to job, changing that has been less carefully planned than

that which takes place among comparable youths more highly satisfied with

their jobs.

4

33. Andrisani, "Introduction and OvervieW," in Ahdrisani et 1.,

op'. cit., Chapter 1. To assure that workers were comparable e- gressiont.were performed, separately for each of the eight.NL

sex-race groups with control variables for such human capi and . demographic variqles as education, formal training, years of work

experience, seniority with employers, health, marital status, region

of'residence, and 'type of local labor-market:

34. P.J. Andrisani, "Differences Between Satisfied Ad Dissatisfied

Workers in Subsequent Labor Market Experience," in Andrisani et al.,

'op. cit., Chapter 2. 4

a 1.0
85

The NLS data also prbvide,strong and consistent evidence that male

youths with an "internal attitude"--i.e., who perceive payoffs to their

initiative-subsequently experience greater labor market success than

their contempbrar'ies who perceive'less payOffs to their efforts.35 The

ColemanReport found that differ'ences in these attitudes were a more

important factor in explaining achievement among black youth's than all

ofthe differences in school quality and family backgroun combined.

rnternal-external attitudes are, also ofinterest because of a similarity

to the concept of alienation; and bet'ause'some have maintained that racial

differences in labor market experience stem mainly from racial differences

in work ethic attitudes 'closely resemblingthe internal-external distinction.. 1 , While the NLS data suggest that attitudinal change among youths who perceive little payoff to their Personal initiative would result in greatero

initiative and more successful labor market experience, there is little

reason to suspect that. the elimination of age differences in these attitudes would have an appreciable impact upon age differences in work experience. In the main, youths differ only minimally from older workers

in these attitudes.

More specifically, the NLS daa show that white and black young men

with an internal outlook in 1968 were in the better occupations andllad

higher hourly earnings two years later than comparable youths with an

external attitude. 'In the regressions which come closest to identifying

a true causal relatiOship, those examining subsequent growth in earnings

and occupational advancement, internals of both race groups were also more

likely to outdistance comparable externals both in terms of growth in

4 hourly earnings And occupational advancement.

As the magnitude of the relationships, those male youths who were

"slightly internal" in 1968, a score Of eight on the attitudinal measure,

35. Andrisani, 'fInterrial-External Atiitudes,'Pirsonal Initiative, and

Labor Market Experiencel op. cit.

1
- 86 *-

c

J

'were estimated to have enjoyed a 12% differential in hourly earnings

two years f;ii over comparable youths who were "slightly external" in

outlook, a score of twelve. Also, their average hourly earnings were

estimated to have' advanced by $.20 per hour more between 1968 and 1970

than the wage.rates of comparable youths who were "slightly external."

With respect to racial differences, there were only two cases

where the regression coefficients differed between white and black youpig 6'

. . me In the first, it appears-that the'se attitudes are more strongly

relate to annual earnings two years later for whites than blacks. In

. . the second, it appears that the attitudes are more strongly related to

occupational a vancement for blacks than whites. 36 Moreover, the

.. . elimination of ra ial differences in internal-external attitudes among

youths apparently would not appreciably reduce racial differences in

4 their labor market eiPerience, since these are only slight differences,

in these attitudes between white and black youths of either sex.

The.only aspect of.labor market experience examined with the NLS

data that was not significantly related to internal-external attitudes

for either white or black young men is growth'in annual earnings. Although the data do not address the issue directly, this may reflect

greater investments (foregone earnings) that youths with an internal

attitude have made in,order to realize their advan4ge two years later

in hourly earnings and occupational attainment. Greater investments in

job search and mobility, in particular, may expAin why youths with an

internal attitude outdistanced comparableyouths with external outlooks

in growth in hourly earnings and .mow occupational advancement without

exceeding their growth in annual earnings.

Differences among otherwise comparable workers in other ^workrelated attitudes--e.g., career goals,' expectations of achieving goals,

commitment to work, attitudes toward one's work role, attitudes toward

, propriety of working mothers, and spouse's attitude toward one's work

36. Iid.

I

J

441
.

8 7

role--have also.,been shown with the NLS data to bear a relationship to

subsequent labOr market expetience.

37 In particular, the career goals of

male youth, both black' and white, and a belief that one's goals-are

attainable, are linked to a saccessful transition from school to work.

38

The NLS data show that white and black male youths with greater occupa--

tional aspirations in 1966 advanced more in annual earnings over the next

three years than comparable youths who were less,ambitious. Among the

white youths, occupational aspirations were also related to occupational

advancement, to advancement in hourly rates of pay, to the reception of

formal occupational training, and to fewer weeks of unemployment.' Among

the black youths, the more ambitious were also more inclined to receive

d

formal oc tional training, but-there is no evidence that they were

likel tocadvance occupationally, or in wage rates, or that they

were any less likely to encounter unemployment. -One likely explanation why

the aspirations of blck youths are not as clos0, linked to subsequent

labor market success as for whites is of course that employers discriminate

against blacks. Additional or alternative eiplahations include the fact

that the bleak youths are'more deficient in labor market information.

Other regression results with the NLS data for male youths re'inforce

these findings. White and black young men with greater self- confidence,

had considerably

the transitiotal'

reater subsequent success,in the labor market during

eriod from school to work than comparable youths with

less self-confidence. Those black and white young men who perceived their

chances of attaining their career goalsas "excellent" in 1966, for example,

advanced occupationally from seven to eight points more (on the Duncan

'37. Andrisani, "Work Attitudes and Labor Market Experience: Other Findings,"

op. cit.: and E. Appelbaum and R. Koppel, "Tht Impact of Work Attitudes

Formed Prior to Labor Market Entry on the Pro ess of Early Labor Market

4 Attainment," in Andrisani et al., op. cit., Chapter 6.

38. .Andrisani, Ibid.

r
- 88 -

Index) over the next three years than comparable youth who initially rated

their chances as "poor". In addition, their annual earnings advancement

over the next three years exceeded that of those less confident by $825 to

$1150, and the more confident youthsexperienced-three to four fewer weeks

of unemployment as`kll. Among the white youths, the more confident ones

also had considerably greater advancement in hourly rates of pay, fewer

weeks out of the labor force, and were more prone to be geogr'aphically

mobile.

It thus seems clear on the basis of the NLS.data that the work

4 attitudes of youths are important factors in understanding the process of

establishing 'stable and successful employment careers. The NLS results

summarized above show that the effects of the work attitudes of youths are

independent of the effects of a wide range of more traditional labor market

variables--e.g.,human'capial and. demographic variables. They also show

that the attitudes have effects on subsequent labor market experience, thusshowing that cross-sectional relationships between work attitudes and labor

market dkperience do npt reflect exclusively the influence of the latter on

the former.1 Indeed,there is reason to believe t)at these findings may

understate the real effect of work attitudes on the labor market experience

4

of youths, since it has not'been possible to isolate the indirect effect of,

the attitudes on experience via their effect on investments,in education and

other forms of human capital. Nor haS1?64en possible to estimate the

extent to which the effects are understated by using, such crude and simplistie,. and thus inherently less reliable, measures of work attitudes as are

available in the NLS.

Finally, it should also be noted that these findings are quite con-

, sistent with those of other studies of youths from more restricted samples.

According to Sewell. and Hauser's study of Wisconsin youths, for instance:

. . tke inclusiOn of the social psychological

variables has resulted in a.moid-,q22219te expli-'

cation of the attainment process in the educational, occupational, and economic spheres . . The expanded modelwe have discussed in the

present chapter has illuminated the rather complex process by which the effects of socioeconomic

1..

114

fi
N 1 - 89

background on educational, occupational, and economic attainments are mediated by various social

psychological experiences. 39

THE EFFECTS OF LABOR MARKET EXPERIENCE ON,THE WORK ATTITUDES OF YOUTHS

Recent studies with the NLS data for youths havetexamined the extent

to which labor market experiences early in work careers affeCt the occupational aspirations of youths.

40 In one of them, changes in occupational

aspirations among the young men between 1966 and 1969 have been regressed

on their occupational status and hourly earnings between 1966 and 1969.

41

In addition,. o assure that comparable young menyere being considered,

control variables for individual differences at the beginning of the period

were also included in,the regressions--e.g., for years of schooling, completion of formal occupational training, years'of general on-the-jib]) training,

years of service with 1966 employer, health status; marital status, region

of residence, degree of urbanization in the local labor market, and initial

levels off occupational aspirations, work commitment, and preferences for

noneconomic versus-economic rewards.

39. W. H. Sewell and R. M. Hauser, "Social Psychological Factors in

Achievement,"-in W. H. Sewell and R.M. Hauser,, Education, Occupation,

and Earnings: Achievement in the Early Career (New York: Academic

Press, Inc., 1975)', pp. III and 90. See also: A.M.Gasson, A. 0..mis

Haller, and W. H. Sewell, Attitudes and Facilitation in the Attainment

of Status (Washington:,The American Sociological Association Rose Monograph Series, 1972); A. C. Kerckhoff, Ambition and Attainment: A Study

of Four Samples of American Boys (Washington: The American Sociological

Association Rose Monograph Series, 1974); A.O. Haller and A. Fortes,'

"Status Attainment Process,!'' Sociology of Education,.vol. 46 (Winter

1973), pp. 51-91; and'R. C. tdwards, "Indiviotual Traits and Organizational Incentives: What Makes a 'Good' Worker?" 'Journal of Human

(Resources, Vol. 11, No. 1 (Winter 1976), pp.. 51-68:

40.' Brito and jusenius, Andrisaia,et al., op. Chapter 5;

and J. Grasso with A. I. Kohen, "The'Formation andtRevision of Goals

by Young Men," in A. I. Kohen et al:, Career Thresholds, IV;'

(Columbus: Ohio State'University Center for Human Resdurce Research,'

1977), pp. 15-52.

41. Andrisani et al., op. cit., Chapter 5,
I.

- 90 -

Among white and black young men, as previously noted, there were

virtually no changes in occupational aspirations, oh the average, between

1966 and'1969, despitpthe fact that many yquths crossed the critical

threshold from school to work during-the period.

( For young men who were

employed as wage and salary workers and not enrolled in school during the

1966-1969 period; the subset of the total Cohort included in these regressions, aspirations orrthe average changed by only two-thirds of one

, point among the whitd youths and by less than one-twentieth'of one point

among the black youths'. Nonethhel ss, the evidence kiggests that more

fayorable work experience leads to more favorable work attitudes and.that

unsuccessful work experience leads to discouragement and reduced aspirations.

Among both race groups; for example, those in the better occupations

initially, and those who advanced the most occupationdlly during the period,

raised their occupational aspirations between 1966 and.1969, while those

in the lowest status jobs initially and those who'advanced the least or

regressed occupationally reduced their aspirations between 1966 and 1969.

Furthermore, among black youths, those in the better paying jobs initially

and those whose earnings grew the most during the period becle more ambiitiOus in career objectives tetween 1966 and 1969,.while those in the low-

`

---, 1 paying jobs initially and those whose earnings tdvanced the least reduced

their occupatiorial goals during the period. The inhuence of earnings was

% so strong, in fact,'that those black youths whodiffered.by $1.00 in initial

hourly earnings or in growth in hourly earnings, differkd in changq in

their occupational aspirations by S.S and 3.4k points respectively 6 the

Duncan Index.

,-) Other variables also related to changing aspirations included years of ' work experience, health, and Suburban resid nce) , The 16Ager the, youths had

been out'of school in 1666; and hence_the reat4r their experience in tile.

world of work, the less likely they were to revise their aspirations upward

. and the more likely they weie to revise the downward. Among blacks, those

.. with a health problem in 1966,' whose career oals at that pointomy have

been most'seriously-in doubt, raised their aspirations by fourteen points

, ? , mote during the period than comparable youths with nip health limitation in

1966. Among whites, those residing in suburban areas raised their aspirations by three points more than those youths:residing in rural or central

Lli;,

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- 91 - .

city loCations.

Other published data from the NLS have shown that ;lily background,

ethnic origin, schooling, and the extent of a youth's labor market information have a considerable impact on occupational aspirations while youths

are still in schbol.42 Still further, Brito and Jusenius show thataoccupa-

, tional goals of the NLS young women were significlntly related to the

young women's family backgrounds and education, as well as to their actt4al

labor market experience.

43 Quite similarly, Grasso and Kohen report that the

.0- occupational goals Of NLS male youths enrolled in high school in 1966 were

significantly inked to their labor market knowledge and IQ, and--in the

case of the whites only--to their family backgrounds.

44 In their own words:

I

...measures of information (e.g., amount of schoAoling,

availability of reading material in the home) and of

opportunities and constraints (e.g.; encouragement by

parents, school personnel and peers) also contributed

tb'expliining variati.n.s in levels of ,aspiration

While growing' up in a large urban area was associated

with decidedly higher educational and occupational goal's,

the evidence is.that these ioaits are likely 'to be incongruent with each other ancr.unrealistically high...

While work experience gained during high schOol wasnot found to be related to goal level,it is notable

that high school youth with considerable work experience are less likely, all other things equal,

to express educational and occupational goals thaillOr

are incongruent with one another ... More abl -s4Raents

(IQ) not only expressed higher goals, but least

among young whites were also.more likely to profess

congruent and realistic goals and were more optimistic

about being able to aChieve_their goals. 45

- Thus it'appearS that while,wOrk experience shapes work attitudes,

numerdus other forces have a considerable impact as well. As a final

42. H. S. Parnes et-al:, Career Thresholds, I, U. S. Dept.-of Labor Manpower Research Monograph No, 16 (Washington: U. S. Government, Printing . Office, 1970), pp. 178-85.'

43. Brit() and Jusenius, op. cit., pp. 421-32.

44. Grasso and Kohen, op. cit., pp. 28-9.

45. Ibid, pi). 46-7.
92

illustration of the point, while there,arevast age -sex -race differences

in labor market experiefice, differences in internalo.external attitudes.on

the basis of age, sex, and race are virtually nonexistent, 46

CONCLUSIONS

The principal purpose of*thit paper has been to assess the extent

to which stable and successful emplOyment,careers are a function of the

wqrk.attitUdes and labor market knowledge of youths, rather than of

human 'capital and demographic variables most typically considered,

in labor market studies. A secondary purpose has been

work attitudes of youths actually are, how they differ

older'and presumably more mature cpunterparts, and how

to examine what the

0, from those of their

they are affected

by successes and failures upon initial entry into the work force. To a

considerable degree, the research summarized in this paper drawi'upon

published works from a unique set of longitudinal data for representative

national samples of 10,000 American yoUths and 10,000 older workers2-during

the second half of the 1960s--namely, the National Longitudinal,SurveyS.

The empirical findings reported here show clearly the importance of

positive. work attitudes and adequate labor market knowledge for the establishment of stable-and successful employment careers. Confidence in these

findings is strengthened by the fact that the observed relationships were

independent of individual differences among the youths studied in a wide

range of skills, abilities, and demographic characteristics, and were

supported by longitudinal data as well.

The data are also persuasive in documenting the inadequacy of all

types of _labor market knowledge as youths cross, the critical threshold from

school to work--with blacks, females; and poor whites possessing the least

adequate knowledge. Thefact that so milch.6fthe differential in unemproy-

.., ment rates between youths and adults results,trom greater incidences of unemployment among youths, rather than longer average spells of. unemployment,

in itself lends support to the need for more adequate labor market kn6wledge for youths. Moreover, the need to Adequately prepare for careers

long before initial entry into the work force argues for the dissemination

of more adequate labor%market information years in advance of actual labor

markeeentry.

46. Andrisani et al., op. cit.', Chapter 4.
- 93 -

The.role*of positive work attitudes for youths in the establishment of

stable, and successful work careers is less straightforward and'unambiguous.

During the late,1960s, youths who were ambitious, self - confiders com7

mitted to the Protestant work ethic that hard work leads to,,success,

and who liked their jobs were more likely to establish stable andmuccessful employment careers than comparable youths who Alt otherwise. s. Yet there is little empirical'justification to consider youths' attitudes

toward work as inadequate, immature (other than for lack of adequate labor

market information), anti,..work, or the cause of the unique labor market

problems of youths--at least on the basis of the statistics for the late

1960s.' The work -attitudes of youths measured in the NLS data were hardly.

dissimilar from those reported by older workers during the same period,

and there is littlempirical reason to suspect that youths on the

whole'lacked ambition during the late"1960s. Quite the contrary, the

dat-Psuggest that inadequate labor market knowledge may produce a laCk

of reali4n among youths which leads in the aggregate to overambition

rather than to a lack of ambition. It may indeed be inadequate labor . market information which at least in part is responsible for -,the relatively higher levels of job dissatisfactioi, turnover, and frictional

unemployment observed among youths.

The evidence is also, clear and convincing in showing that unsatis-

-)factory labor market

/experiences early in work careers can influence

youths' attitudes in an anti-work direction, thereby reducing the chances

of ever establishing a stable and successful employment career. As

Glen Cain has noted:

X

1 Conventional econdinists have'customarily viewed

'tastes' as exogenous and as one of the (unexplored)/

causal,yariables explaining such labor market

achi4fements as employment, wage earnings and

occupational achievement. The contribution of

the Dual:Radical theories lies not in reiterating

the potential importance of tastes in their research, bde,rather in pointing out how tastes may

be endogenouts and a-Y-e-gbIt-tif-one's.labor market

achievements. Thus, the effects of discrimination,

other systematic factors, or seven random factors

11

we
94

4 thdt can start workers off in the seconday

sector (that is, in 'bad' jobs), Can shape

tastes in an anti-work direction and thus

reinforce the disadvantaged position of lowwage workers.. 47

*1.

Thud, while miork attitudes among youths appear important for

the establishment of,ftable and successful employment careers, they

hardly appear to be the principal cause of the unique labor market piroblems of youths. It seems highly unlikely that secular changes in the

'work attitudes and labor market knowledge of youths since the late 1960s

could have had nearly as much to do with.the worsening youth labor market

situation of this decade as have such factors as changes in the number\of

youths looking for work and Changes in fhe aggregate demand for labor:

While it is unclear how much a 'tart the work attitudes of today's youths

hate to do with the worsening youth labor market\situation, it seems entirely clear why-- effect the loose labor markets of the 1970s have had on

youth' attitudes toward work. Loose labor markets lead to a deterioration

of positive work attitudes among youths which makes it all the$more dif-

, ficult to establish stable and successful employment careers. The loose

labor markets of the 1970s can be expected to have had .such an effect.

The implications for public policy are essentially twofold. First,

increase the availability of all types of labor market information early

in the life cycle, particularly for'young blacks, young women, and pOor

white youths. Second, employment and training programs could seek to

offset the negative, anti-work effects of loose llbor markets and the

pathology of the youth labor market on the work attitudes of youths. Long_

run incentilies to work, and giving good reason for youths to believe that

hard work and hUtan capital investments 'now will eventually pay offIrt\ the y labor market are clearly needed. The anti-work effect' of the loose labor

.1 war

markets in the 1970s on youths' attitudes can be particularly important

for many years to come. After all, the'anti-work effects are being felt

47. G. G. Cain, "The Challenge of Dual and Ridical Theories of the Labor

Market to Orthodox Theory ", paper presented at the ,Americape.Economit

AsSociation Annu etings, San Francisco, December, 1975, pp. 18-9,',

emphasis in origi 1. (mineographed).

, 120
V

95

by/one of the largest cohorts of youths to ever enter the labor market.

In addition, this cohort baby boom youngsters faces what may possibly

be, relative to their aspirations, perhaps the worst JSTf labor market

constraints ever to have faced preceding generations of youths.

a.

Y

$
96 V

ECONOMIC AND SOCIOCULtURAL VARIABLES AFFECTING RATES

OF YOUTH UNEMPLOYMENT, DELINQUENCY AND' CRIME

By: Daniel Glaser

ABSTRACT

Research has.repeatedly found a direct correlation between unemploy-'

ment rates and crime rates, but not so powerful and consistent S.relationship as to exclude other factors in crime causation. This paper 1l.l first survey social-science lerature on economic varkftles affecting

law-violation rates. It will then present briefly'some evidence and

inference that social, cultural and political aspects of the lives of

many of today's youths are major determinants of both their unemployment

and their offenses. . INTRODUCTION 7--

The simple theory that crime is a means'of obtaining wealth or Of

expressingAger when legitimate economic pursuits fail has probably ' been propounded since. ancient times, and has been rested statistically

at least,since thelesily nineteenth century (Vold, 1958: Chaiger 9).

Four distinct methodologieS differentiate these studies, which can be .

-.categorized by the types of data that they employ, as: human ecology,

. social cVass, busihess cycle, and circumstances of the offender. This

research was always handicapped by deficient information, fspecially

on'crime, and probably by failure to investigate adequatdlythe qualifications that the simple theory requires to fit reality. Nevertheless,

eVen a cursory review reveals that findings. from each of('these four

types of investigations, while somewhat inconsistent and controversial,

predominantly shOW that extreme poverty (in,comparison td the rest of

the community) is highly'correlated with crime.

)

ECOLOGICAL STUDIES

A "social survey" movement sprpad from one city to another during the

nineteenth and eafly twentieth centuries. Perhaps the most influential of

its publications in the EnglitshAspeaking world wis Charles`Booth'

Life and Labor of the People of London, issued piecemeal from 43/4.886 to 1903.

a 122

1

V
97

The movement's many similar studies illustrated, for a large number of

metropolises, the concentration of bothorime ana poverty in the same

neighborhoods. Somewhat more objective evidence was provided by

Clifford Shaw and Henry D.'McKay (1929; 1942). Their maps and tables

showed how delinquency rates. varied in Chicago's census tracts. For

cOmparisori they made similar maps and tables for such indices of poverty ' as average rent per residential unit, average number of occupants per

room, and infant mortality rates. Thus these publicatioi1s demonstrated,

but did not measure, the extent 'to which delinquency was concentrated

in areas with peak rates of poverty, disease, di'Yorce and all other

indices of what they called "social disorganization." Their 1942 book

also reported similar studies that their work had inspired in oth's

American cities, all of which had . analogous findings..

For two decades following" World War the analysis of census tract

rates for delinquency and other variables--such'as income, rent, ethnicity,

housing, crowdedness, and every other item that the census tabulates by A these tracts--was made more rigorous In several cities through multivaria

'^

k

ft statistical computations. .Gordon's (1§67) thorough review of this research, however, summarizes:

All of these studies misused such ... procedures as partial

correlation, multiple regression and factor analysis. In

addition, these studies ... have been affected by serious

artifacts stemming from the acceptqd practice.ofxing

indexes with mixed cutting points, some of which are more

sensitive to the tails of their.distributions than others.

When all of these errors are taken into account, it turns

out that the association between delinquency and socioeconomic status is quite unambiguously very strong. Q

He adds an extremely astute message for poldeymakers:

It is the extremely low end of the SES range

40that is most relevant.

This finding ... contains a warning concerning the aonduct

of antipoverty programs. It suggests that, in order to

decrease delinquency, for example, ifis necessary to%reach

the very bottom-most stratum in every census tract. Simply

pumping money into low-income areas may result in helping

needy people, but they may not be the ones chiefly responsib'le 4or the high social pathology indexes from which.

intervention against poverty now derives its main political ,14 justification. To the extent that programs fail to reach

this lowest stratum--however successful they are at assist

ing the more accessible higher- stratum poor--they will fail

1 `)j'

<,

6
O

98 -

lo alleviate the more intractable and socially visible con- sequences of poverty. Certainly there is much to be said,

on humanitarian grounds alone, for directing limited re- sources toward the people best able to take advantage of

them. Nonetheless, there remains the possibility that

the failure of'programs to materially reduce delinquency

and eliminate hard-core poverty will triggerpolitical re- actions that make it impossible to gain support for efforts

that would benefit the very poorest. For these people's

own misery to be used to legitimate help for someone else,

and in a manner that diminishes their own chances of

eventually,receiving help themselves, would be the ultimate

exploitation.

Delinquency, as a legal concept, refers only to offenses committed

by juveniles. While-defined somewhat diversely in our fifty states,'it

usually encompasses all acts by persons below a particular ageUsually

eighteenthat would be prosecutable as crimes if the perpetrators were

older. It also includes acts for which only juveniles can be placed in

state custody. These so-called juveniles status offenses are maihly

truancy from home or school, and incorrigibility (defined as persistent

or flagrant disobedience ofjoarents, teachers or ther-adult authorities).

Adult crime, however, includes many offenses by youths, /since the median

age of arrest for major crimes (the,,FBI's "Index Offenses") was 19.2,

during.1976 (a puzzling increase of about one year over the median in

preceding years), and about one-third of the arrestees were between

eighteen-and-twenty-five-years-odd in every recent year.

' There hai been much less ecological research on the neighborhood

correlates of adult crime rates than on detraggcy. The major study

was Schmid's (1960) factor analysis of a 38 x 38 correlation matrix of

twenty crime indexes and eighteen economic, demographic and social

Variables from the 1950 census for ninety-three census tracts of Seattle.

By far the'highest correlations in the matrix were between the percentage

of males unemployed and the rates of various specific offenses, notably

.82 for drunkenness, .83 for fighting, .85 for other disorderly conduct,

.82 for lewdness, .85 for petty larceny, .80 for theft from automobile,

and :85 for highway and car robbery.' Correlati,ons of median income with

the same offenses ranged from .48 for lewdness to :58 for theft from

e.
99 -

automobile. It should be noted that the location of arrests for drunkenness, disorderly conduct and lewdness often tells as much about neighbor-

,' hood variations in police practices as about variations in citizen behavior.

Schmid's (1960) factor analysis identified eight clusters of inter-'

correlated variables. The first factur, with by far the highest loadings,

he labeled "Low Social Cohesion--Low Family Status." It'revealed strong

correlation of social conditions characteristic of slum rooming house

areas near business districts - -low proportion of homes owner-occupied,

high percentage of labor force unemployed, low education levels, low

percentage of adults married, high proportion of females in the labor

force and low status occupations --with such crimes as automobile theft,

theft from automobiles and shoplifting. The second factor, which was

labeled "Low Social Cohesion--Low Occupational Status," identified by

high factor scores the poorest residential neighborhoods, contiguous

to Skid Row ant niostry occupied then by blacks and'Orientals, but by

low scores it also identified the better residential areas with mainly

white collar occupations or higher blue collar jobs. The' demographic

components of this factor were low status occupations and low education

levels, but a high proportion married, high fertility rates, few of the

females in the labor force and high proportion of housing over thirty

years old; it was linked to the same types of.crime as Factor 1. This

study's extremely high correlations of crime Oith unemployment and its

two strongest factors emphasize the linkage of crime to the poorest of

the poor neighborhoods, much as did the multivariate ,analysis of delinquency rates.

A different ecological research method divided cities into "social

areas" by three composite indicators of census tract characteristics:

Economic- Status, determined by prestigious occupations and high levels

of education; Family Status, shown by high fertility rates, low percentage

of women working, and high percentage of single -family dwellings; Ethnic

Status, 'ascribed to areas with a highpercentage of native-born whites

(Shevky and Bell, 1955). Applying this scheme-to Le'xington, Kentucky in

1960, Quinney (1964) found both crime and delinquency rates inversely
n

100

related to economic and family status but positively related to ethnic

status. Further analysis showed, however, that the ethnic status finding . occurred because offense rates were highest for nonwhites living in areas

occupied predominantly by whites and highest for whites in areas with the

largest proportion of nonwhite residents. Furthermore, high family status

prevented crime only in areas of low economic status.

A quite contrasting type of ecological analysis uses total cities or metropolitan areas as-ibnits, rather than census tracts within these ' 'communities. SchueSsler and Siatin (1964)

0 factor analyzed 1950 and 1960

crime and census variables for cities of over 100,000 population in the

United States.' In both periods the factor most associated with crimes

against the person, which they firk called "Social Frustration" but

relabeled "Minority Relation'," had major loadings from high percentage

nonwhite, high housing-crowdedness, and low percentage of young people

in school. The factor:most associated with property crime, which they

labeled "Anomie," loaded high on only one census variable in 1960, per-

. centage divorced, but in 1950 was also identified by low percentage of

a population engaged in manufacturing, low percentage of population in

families, low median schooling of males over twenty-four-years-old, and

low median monthly rent. Economic variables were clearly far from

dominant in the conditions that this study linked with city crime rates.

Eberts and Schwirian (1968) hypothesized that crime would beevuiced

not by a particular loW level of wealth or income in a city, but by the

re"ative deprivation of some persons there as compared to the rest;

essentially, they ascribed crime more to inequality than to poverty.

Using data on 200 Standard Metropolitan Statistical Areas from the 1960

census and FBI figues qn offenses known to the police, they found crime

rates highest in metropolises with the greatest disparity between the

proportion of the population with annual income over $10,000 and the propOrtion with income under $3,OpO. Thus crime rates were, higher where

there was a large upper class or,a large lower class than where these

extreme strata were nearly equal in size. This finding was clearest in

the biggest metropolitan areas, in' those witIPIA high percentage of nonwhites, and in thesSoutb., outside the South, crime rates were especially

12 '6
- 101 -

r, linked to a high.proportion in the upper class rather than to a more , balanced income distribution or the largest proportion in the poorest

class:

Eberts and Schwirian also c assified mitropolitan areas by the

percentages of their white' and nonwhite populations 'having white-collar

lobs, compared to national percentages for each of these racial groups.

Crime rates were largest in metropOlitan areas where these percentages

were-highest for whites and lowest for nonwhites; it was the disparity

in availability of white collar employment more than the actual_proportion of the white or nonwhite proportion receiving it that was most

related to offense rates. This finding applied to both large and small

metropolises,' and to the non-South as well as the South, although it was

most pronounc0 in areas with a low percentage of the population nunwhite. Thus both of their analyses confirmed that inequality is the`

economic variable most correlated with high crime rates. It would be

interesting,if such analyses were done separately for crimes by youth,

and'for different types of offenses.

A still different type of ecological analysis uses states within

our nation as its units, andits findings have bee of major interest

primarily for homicide. Many criminology textbooks have for decades

suggested that regional cultures are major determinants of crime rates

in the United States, pointing out that most offense rates of states

increased as one moved from the Northeastern to the Far Western states,

but that the Southeastern states combined by far the highest homicide

and assault rates,in the nation with low rates of crimes against property.

*Hackney (1969) and Gastil (1971) extended the analysis gf Southern homicide rates. They emphasized ,(1) the slavery and absence of industry in

Southern history, (2) the traditions of dueling and vigilantism, and

(3) statistics that showed higher correlations between state homicide

rates and whether a state had been in the Confederacy dng the Civil

War than the zero -order or multiple correlations of the homicide rates

with median income, education or a host of-other economicr,and social
t

- 102 -

Loftin and Hill (1974), however, present impressive evidefice that

it is not a state'sSouthernness but its degree of extreme poverty or of poverty-conduci'Ve conditions that is most predictive of its homicide

rate. Thus the' zero-order correlation of state'homicide rates in 1960

was -.43 with median income but .67 with the percentage of a state's

families having annual income under $1,000,..77 with the percentage of

the population illiterate,, .82 with the percentage of the population over

twenty- four - years - old -with less than five years of education, .83 with

the percentage of its inductees failing the Armed Forces Mental Test,

'.85 with its infant mortality rate, .88 with the percentage of-its

children' living with only one parent and .93 with a "Structural Poverty

Index" based on all of the foregoing except median income. The correlation of homicide:rates was .80 with a state's being in the Confederacy - during the Civil War and .87 with Gastil's (1971) index of the, SoUthernness of each state's population (an index for each state, which for nonSouthern states considers how many migrants it received from the South

and other evidence of probable Southern cultural influence). Incidentally,

state homicide rates correlated .85 with percentage of the state popu-,

lation,nonwhite, and the Structural Poverty Index correlated .84 with

Southernness. In two multiple correlations of state homicide rates with

diverse independent variables, one with a dummy variable for Confederacy

and one with the Southernfltss Index, Structural Poverty had by far the

highest Na Weights.

ThusAbether the units of ecological analysis are neighborhoods,

1- ea

cities of states, both zero-order and multiple correlations between rates

of delinquency or crime and indices of extreme poverty are quite high.

Nevertheless, persons who are skeptical that poverty is a primary cause

of crime point to rich people who violate the law and p9or people who

do not. Specifically, critics of ecological studies point to law-abiding

individuals in high offense-rate neighborhoods, and to some peighborhoods

with much poverty but,low offense fates. SUch exceptions are among the

reasons why,statistical correlation coefficients are less than 1.0, but

they mayaiSo be clues to factors that counteract poverty in crime

r.")?r causationt*

123
- 103 -

Quinney's (1964) Lexington study, already cited, was/One of manY...to

find that'cohesive family, bonds 'distinguish the nonoffenders in high

delinquency areas. Similar conclusions were reached by the Gluecks (1950)

in their landmark comparison of delinquents and nondelinquents from the

same Boston neighborhoods, and also by more'recent studies. Hirschi (1969)

I provides persuasive data on family and nondelinquent peer friendship bonds

acting as "controls" against temptations to delinquency to which, he

suggests, most of us would otherwise succumb. Sociologists have also repeatedly demonstrated by illustration--not by adequate statistics--that

when low offense rates characterize very poor neighborhoods or towns, the

residents generally are of the same ethnicity, have cohesive extended - .1

families, and participate actively in local religious or other organizations. Examples in the United States included Chinatowns and JapaneseAmerican neighborhoods that formerly were both extremely poor and relatively free of crime. More recent examples are sections of African citie's

settled by a single tribe as compared with mixed tribal neighborhoods

(Clinard and Abbott, 1973, 1976; Glaser, 1978: 224-25). Yet, interethnic

mixture is inevitable in modern cities; although the weakening of ethnic

institutions and a resulting increase in crime are often its temporary

consequences, itg-long run effects probably are to reduce both inequality

and crime. However, the most influential assertions that economicconditions cause crime focus on conflict among groups with opposing interests

in the means of production, but these assertions have been a source of

di ;cord in the academic community.

SOCIO-ECONOMIC CLASS AND YOUTH CRIME

Karl Marx wrote little on crime, but designated all private property

as'"theft." He was unsympathetic to criminals, whom he called "scum" when

warning his followers against alliances with them (Hirst, 1975). However,

early Marxiit criminologists, notably Holland's Willem Bonger (1905/1969),

ascribed crime to poverty and blamed poverty on capitalism. This tbeme'is

widely asserted in Britain and the United States by selfdlgtyled "radical" or "crit?aar_crininologists. They that we could "rid society of

thievery by abolishing the precondition for theftnamely'private property"

Ire
\) 104

(Taylor et al., 1973:174), ignoring the theft of public property in all

.societies, and that the most serious crimes'everywhere are against

persons, including theft of their legal rights.

The ecological data already cited, and statistics on the income or occupation of arrestees or convicts.and their parents, provide ample and

consistent evidence for those who claim that inequality of socio-economic

status causes crime. Critics of all political persuasions, however,

questiotithe-adequacy of official statistics that link crime to the lower

classes on two major grounds. The first is that crimes of the upper

classes, such as monopolistic practices, misrepresentations in selling,

tax evasion and'Other so-called white-colfar offenses are not greatly

prosecuted, although their dollar costs are always'estimated as totalling

many times that of ordinary property crimes, such as theft, burglary and

'robbery. The second is that ordinary offenses are alsi> committed by the.

middle and upper classes, but police and courts enforce the law disproportionately against low-status offenders. A third, albeit less major,

criticism by some criminologists, is that all violations of human rights

are criminal, and therefore, any act fostering poverty, racism, sexism

or war is a crime (Schwendingers, 1970). There are good methodological

arguments for defining "crime" more narrowly, yet in its broadest legal

sense, as any act for which a court may lawfully impose punishment; but

criminologists,should still be concerned with how economic elites and

other pressure groups tend to determiUe the content of laws that specify

what courts may punish (Glaser, 1978: Ch.2).

Ostensibly compelling evidence that de4nquency rates are not related tosocio-economic class was provided by questionnaires given to

junicliihigh and high school students, who were asked to check which. of

a list of offenses' they had committed. ,Usually these forms were anonymous--the studentS' names were not required, but later research shows

that requesting names makes little difference in the responses. The

"41t students were also asked to indicate the occupations of their parents;

and the surprising finding was that students from every social class

grouping of parental occupations reported delinquency at about the same

rates (Nye etal., 1958; Dentler and Onroe, 19,61; Akers, 1964; Erickson

130

"
- 105 -

and-Empey, 1965; Hirschi, 1969). Diverse checks, including use of a lie

detector, have shown these questionnaire admissions of law-breaking to

quite valid (See Clark and Tifft, 1966).

. , There are two major methodological detects in the above procedures.

In the first-place, all thes studies either were done at only one school.

or pooled the responses froM ifferent schools. It is possible that

, students within any school develop similar standards of conduct regardless

of the occupations of their parents--the school is a subcultural unit=-

but that schools from neighborhoods of very contrasting economic status

differ in the delinquency of their juvehile subcultures. It is also

possible that the classification of parental occupations, )asually by

census categories, is misleading as an index of socio-economic class"

when different types of neighborhoods are pooled, For example, most

business proprietors or salespersons living in a slum may have much less

income than persons with the same occupation who livein a wealthy suburb;

the slum residents may average as little as $10,000 per year and the

suburbanites over $100,000.

. The above hypotheses seemed to be validated by Clark and Wenninger

(1962), who administered questionnaires in a poor rural school district,

an inner city metropolitan ghetto, an extremely wealthy suburb, anca

small industrial city. There was no marked'relationship within any of, . these districts etween father's occupation and admitted delinquency; but

on the whole, of ense rates were lowest in the rural area and highest in

the slum ghetto. The slum juveniles reported more theft, violence, trd7

ancy from school, deliberate disruption of school activities and vandalism than did youngsters elsewhere. The suburban students reported more

drinking,,gmbling and use of pornographic literature, offenses probably

reflecting their affluence, as well as a fair amount of truancy. Hardt

1968) reported similar findings in comparison of lower and middle class

0

*ghborhoods of anOt a metropolitan area. It should be stres ed, however, that the contrasts between neighborhood admitted-delinqu cy rates

reported in these studies were not nearly as %great as those in cated by

police records. Hardt procured arrest records for the areas in Which

he gave questionnaires and he also asked the students whether they had

431
been ticketed or arrested by the police.

are'inuch more inclined to arrest in poor

Several case studies of middle and upper

bliss, 1973) also indicate the ease with

"get away with" crime. .

The results, showed that police

than in middle class areas.

class delinquency'te.g., Chamwhich some affluent youth can

A second source of error in these studies is thatthe most delinquent

students are likely to be truants, hence absent when-the questionnaires

are distriblited. This biases the research'to underreport delinquency.

Such an error was demonstrated' through a study in London, by the Canadian

sociologist LynnMcDonald (1969), who found neighborhood differences in

admitted'delinquency analagouslto_those reported by Clark and Wenninger

and by Hardt. When she traced:and contacted most of the students who

were absent when she administered her questionnaires to the schools, she

found that they had higher Crime rates than those who were present, and

that absenteeism was highest in the low status areas. An additional defect not inherent in the above type of methodology

characterized most o the early.studies. In order to have offenses that

scaled statistically, so that they could ask about many offenses but assign a single delinquency score:to each student, the researchers included inquiries on very petty infractions, such

berately disobeying parents. Sale also failed to

( of offenses. Virtually all studies tha- provide

range of offenses and by frequency, even within

that children of low status p;.1-ents differ from

in reporting that they'more often used violence and stole items of

appreciable value (see also, Gold, 1970).

These admitted offense queptionnaires have been used almost exclusive- , ly with students., In their one major application to adults (Wallerstein

and Wyle, 1947), an amazing volume of serious offenses were admitted, but

the study's Only stateient on the relationship of-crime rates to status

was that: "The mean number of offenses committed in adult life (over age

16) for men, classifidd accordigg to,,sccupation, ranged from 8.2 for

ministers to 20.2 for laborer4,, with a mean of 18 for "all men."

as speeding, and deliask about the frequency

separate data on a wide

school districts, find

4 those of higher status

13 0
10.7

During 1973 the U.S. Bureau of the Census conducted a national

sample survey for the JUsti.ce Department in which persons were asked

if they had suffered anylof a list of major crimes in'the previous year,

in addition to questions on their social and economic characteristics.

Those, who reported the lowest annual incomes most often reported victimization from assaults. and robberies from the person, but the poorest .nd

the wealthiest were highest in household burglaries (Law Enforcement

Assistance Administration, 1975:18, 22). Since all of these crimes except burglary of affluent homes are usually ascribed to youths in the

area and since a neighborhood's residents tend to be of similar economic

status, one can infer from the victim's reports that such crimes are

committed-most often by the poorest youths._ The relationships of

0,4ctimization rates to income were similar for blacks and whites.

In summary, the traditional conclusion that the lowest socioeconomic classes have delinquency and crime rates greater than those of

the upper classes is predominantly supported by questionnaire studies,

o kit less strongly than by arrest rates. Therefore, it is erroneous to

assert that all socio-economic clatses have the same law-violation rates;

most types of ordinary crimes are demonstrably perpetrated more often

by poor than by affluent youths. Yet so-called white-collar offenses

certainly are committed most often by-adults in high-status occupations,

since they have the most opportunity to engage in such crimes.

,CRIME AND THE BUSINESS CYCLE

Efforts to relate fluctuations in the frequency of crime to changes

in various economic indicators, from unemployment rates to the price of

rye, were undertaken during most of the nineteenth century in Europe,

and were extended to the United States during the twentieth century.

The results were highly inconsistent and controversial. After surveying

these undertakings, Vold (1958: 164-81) concludes that: "...assumptions

involving either positive or negative relationships with economic conditions may be supported with some show of statistical significance. The

obvious inference is that the general relations of economic conditions

and. criminality are so indefinite that no clear or definite conclusion

can be drawn."
108

Despite the pessimism that this earlier research evoked, studies

4,

duting the past twenty years have quite consistently related variations'

in crime rates to shifts in the extent of unemployment, especially when

they also considered interacting variables. Glaser and Rice (1959),

controlling only for age, found marked positive correlations between

arrest rates of males twenty- one- year -old or older in Boston, Cincinnati

and Chicago during 1930- 56 and U.S. national Age-Specific Male Labor

Force Unemployment rates negative corr elat ion-sf or-persons-under

twenty-one. The positive correlations of crime with unemployment for

men twenty-one and over We're especially marked and consistent for theft

in all three cities, but only in Boston and Cincinnati for crimes againstpersons and for misdemeanors: Chicago had a variable pattern on these

two typos of offenses. The inverse correlation of arrest rates with

unemployment for males under twenty-one was consistent in all three

.. ,, cities only for crim s against persons and for misdemeanors, but not

for the positive co ations for men twenty-one and over. 4' The Glaser-Rice study focused on these three cities because they

were le only places for,which.we could find age-specific arrestjates

compiled in a uniform manner for a long range of years. The FBI's agespecific national arrest totals had a hi ly variable population base,

for many years came only from cities that Zo4rised under half the

United States population, and reflect drastic changes in methods of

. collecting arrest info tion, especially after 1952; thus we had no

way of knowing for long range of years the population base from

_ which to convert the FBI arrest datar6rates. Nevertheless, we calculated each age pels proportion of total arrests from the FBI figures

for 1432-1950 and correlated these proportions with the percentages o

U.S. Labor Force Unemployed in these years. the 'correlations were consistently positive for ages twenty-one through thirty-four for property

crimes, for crimes against persons, and for misdemeanors; they wereconsistently negative for arrestees eighteen and under for property crimes

and for crimes against persons, but they were also negative for all

$ tfiree categories.of crimes for persons thirty-five and over, and were

inconsistent for other age and offense groups.`' It is possible that the

6

Ai 134
109

negative correlations for thoSe thirty-five and over are artifacts of

using proportions of total arrests instead of rates for each age group,

since this procedure makes figures for the various age categories interdependent. Incidentally, similar results were obtained using total and

age-specific unemployment rates.

In planning this research I /wished o control, for age because I presumed that unemployment would most rongly affect crime rates for

men over twenty -one, most of whom are in the labor force. I hypothesized

that juvenile offenses, conversely, might increase in periods of full

employment, because then more mothers enter the labor force and there

is increased use of commercial recreation that separates parents from

children. The finding that positive correlation with unemployment

for men over twenty-one was strongest and most consistent for property

crimes (since these are illegal substitutes for jobs)4Awhile negative

correlations for ju6niles were more marked and consistent for Crimes

against persons and for misdemeanors (which are mostly either violent

or disorderly acts), seemed to.suppo;t the theory underlying these hypotheses.

Gibbs (1966), however, offers an alternative explanation for our

findings. He interprets our data as support for his theory of status

integration, with which he has analyzed various forms of deviant conduct

and usually has procured positive but also debatable results (see Li,

1971). Essentially, the theory asserts that conduct will be most deviant

under conditions.that are most unusual for persons in a given status, and . unemployment is least unusual in our society for men over twenty-one and

under retirement age. Incidentally, inspection of the raw data indicated

that very high juvenile arrest rates accompanied full employment of adults

only during the World War II and imme fate postwar years, when numerous

adult males were away in the armed forces and many families were relocated

far from kin, leaving adolescents especially on theioln.

More recenteresearch relating crime to unemployment has been done

by economists. They used much more sophisticated statistical methods

than were previously applied to this task. The major study is that of

Belton Fleisher (1966), who limited elf to offenses by persons under

.6
- 110 - *

twenty - four -years ,old. He used multiple- regression analyiis, addi ng

many varkables to the unemployment and arrest data, including: (1') the

number of men In the armed forces, to take into account the effects of

war mobilization on family life; (2) what he calls

/

"taste" variables,

namely, the proportion of women overourteen-yea:old who are separated

or divorced, the proportion of residences that are ouiner-occupied, and

the median years of schooling of the adult population; (3) dummy variables

for region and for years in which there were changes in the FBI's method

of collecting age-specific arrest data; (49 additional variables for

social conditions, notably peicentage nonwhites and g4bgraphib mobility

of the population, He applies these variables to relating unempfoyment

and crime either longitudinally'over time or cross - sectionally attione

time, to 101 U.S. cities and seventy-four Chicago census tracts plus

forty-five.Chicag9 suburbs,, and also presents some British dita. His

time-series analyses show marked-positive correlations, especially for

those over seventeen, but a negative =relation for those under wi,ene

teen in England and Wales. His cross-sectional analysis, which 15 really

an ecological study, finds youth arrests in high arrest'rate areas more

0 a function of mean income than of unemploymext rate. He estimate's that

in these areas "a ten per cent rise in income might well result in' a

twenty per cent decline in delinquency." Although one dare not extrapolate that a fifty percent increase in income would eliminate delinquincy,,there is much to be learned from his data and cogent

commentary. 40 . Phillips, Votey and Ikaxwell (1972a, 1972b) extend Fleisher's Ana-

,. lysis somewhat by (1) considering as separate independent variables re- lated to unemployment (a) the race-specific proportion of an age group 4 in the labor force (which has declined for youth as mote go to college,

but also"as more give 'up searching when long unable to get jobs), (b)

race- specific rates of unemployment.v and (c) the?fra9.tion of eighteen--

and ninkeen-year-pld males of each race in the noninstitutionalized

population (which changes mainly from shifts in the size..of our armed

Ibrces)i,e(2) considering as separate dependent variables the four most

frequent property crimes--larceny, burglary, robbery'and auto theft--

136

-

41

1,

4

.04
111

'41

but only for eighteen-and nineteen-year-olds. Because of collinearity,

the independent-variables predicted crime rates better if racial cateSr were combined and all the population was categorized by either

of two trichotomies: (1) working; nonworking (either unemployed or not

in the labor force) and other; -(2) in the labor force,, not in the labor

force and other. Variations in the values for each of these two trichotomies predicted offense.rates equally well. Age-specific offense

rates were estimated for youth by dividing the proportion of all arrests

in the eighteen-to-nineteen-year-old age category by the ratio of

clearancespby arrest to total repotted offenses; this assumes that there

is the same age distrihution in nonarrested as in arrested offenders.

These procedures permitted remarkably accurate estimation of youth offense

rates from the employment data. They conclude that the increases in

youth crime rates during the 1960s did not indicate that'youths were

becoming more criminal but rather, thdt the economic difficulties con-

. -alltive to their-committing-crimes were incr sin see also, Vote end

Phillips, 1974). Several other multivaribte regressions of crime

unemployment have been published (e.g., Ehrlich, 1973; Greenberg, 19 8),

but none, I believe, that focus on youth crime.

Discussion of crime and'the business cycle should not be-concluded

without considering rather remarkable fluctuations in the-offense

rates of metropolitan youths that occurred in the late 1960s and early

1970s which, as far as I know, have not been analyzed by economists.

Nationally, homicide rates increased from 9.6 to 18.4 per 100,000 in

cities of over a million popufation between 1965=1970, and reached'

. 24.6ain 1975, but declined slightly thereafter. This was mainly a metropoliten phenomenon; the national homicide rate changed from 5.5 in1965

to 8.3in 1970 to 10.2 in 1975, and all city categories of less than a

quarter-million had increases less pronounced than the surge-in national

totals. The rise in robbery rates reported to the FBI was even more

pronounced from 1965 to 1970 in cities of over a million, increasing from

221 per 1000,000 in 1965 to 778 in 1970, then only to 879 in 1975, and

decIihirig thereafter; the-national-robbery rate_went_fron:_72 in 1965 'to

W.IrTIT' 11970 to 218 in 1975.

13 ;.
There are no published national tabulations to indicate the contribution of different, age groups in large cities to such increases in these

two types of, violent crime. Special studies for Chicago, however,

probably indicate developments in most metropolises of the United States

in this period, for the social and cultural krouPings and sentiments

were similar in many cities. Between 1965 and 1975) the rate orhomicides

noted by the Chicigo police more than doubled, but homicide arrests

ofblatkmales fifteen-to twenty-four-years-old almost tripled and homicide victimization'rates for this population category more than tripled.

During this period the percentage of homicides 'that occurred in the course of robberies rose from eight to eighteen in Chicago but the number

robbery killings ascribed to offenders fifteen-to twenty-four-years-old

increased more than sevenfold, with both arrestees and victims in these

offenses disproportionately black. There was also a 791% increase in

the number of homicides ascribed to groups of black males, aged fifteen to

a

twenty-four, as compared with 136% increase in the homicides alleged to

have been done by lone offenders in this population category. Fina

the proportion of homicides ascribed to black males aged fifteen to'twent

four involving the use of firearms increased 444% between 1965 and 1970,

compared to 69% for their homicides by all other means (Block and Zimring,

1973). These trends continued through 1973, but were less pronounced

after 1970 (Block, 1975).

The distinctive feature of the early 1960s in the history of the

United States was the progress of the civil rights' movement. This greatly

increased expectations of increased equality of opportunities among black

youths. The lag in realization of these hopes, especially in the metro-

. politan ghettos, is alleged to have been the main factor in precipitating

a ser ies of riots in these locales, beginning with the Watts riot in Los

Angeles in 1965.42These disturbances intensified,folrowing the assassinations of Martin Luther King and Robert F. Kennedy in 1968, but were largely

terminated in the early 1970s.

From 1965 on, the mass media vividly-portrayed'this violence, bring- 0 ing it pictorially into everyone's living room. This furthered both, black

youth violence and white backlash. - More than ever before, the nation be- .

. 13
.

a

- 113 -

4

came an armed camp, with 'manufacture and importation of handguns rising

from one million in 1965 to over three million in 1969, then declining

to about two million annually by the mid-1970s (Newton and Zimring,

1969:174; U.S. Dip-artment of Commerce, 1976:156).

To claim a full understanding of this upsurge in violent crime

would be presumptuous, but itseems reasonable to infer that it was in

large'paa4t a consequence of a growing sense of relative deprivation

among ghetto yonaS. Their frustration at the inaccessibility of legitimate ways to realize the "American dream".that Dr. King had'portrayed

evoked.an anger that usually was directed at the most cOnvenient-targets,

other blacks. But the wave of ghetto riots were followed by some re:-

duction of both perceived and actual inequality--social, political, and

economic--sufficient to''facilitaa'further progress by nonviolent means.

Although social scientists debate how Much this progress has slowed down

in the 1970s, it does not appear to have stopped; black youth 4noreasin

have seen prospects of improving their lot by educationand have gain

much white collar employment, despite-the fact that an academic degree

may be less of a guarantee of a good job than it was a few years ago

(Hauser and Featherman, 1976; Taussig and Danziger, 1976; Farley, 1977)%

It would be interesting to determine whether there was decreased predictability of black youth crime rates from employment data ImIng the 1970s,

and whether you in the Latina barrios of our metropolitan are re beginningian upsurge similar to that of the black youth a decade earlier,

for someWhatA mparable reasons.

EMPLOYMENT 'STATUS OF OFFENDERS AT THE TIME OF THEIR CRIMES

A fourth method of investigating the economic factor in-law violation

has been to try to determine the rate and qualitrof employment of offenders at the time' they commit their crimes. Such inquiries are most easily

u sued when research'Subject's are under'gome type of government control.

Methads used include (1) interviewing convictsin prisbn about their employMent when they 'committed their crimes, (2) correlating the employment

information routinely collected:on probationers and paYolees_while_they

are undei; sdpei.vision, with their subsequent recidivism rate, aRa (3"in-

. 3)
- -114- -

terviewing them regarding their employment when they make their required

regular visits to the supervision office.

All the above procedures were used in.Glaser's (1964) study of

federal prisoners and.Tarolees. AThe parolee interviews were replicated

and extended by Pownall (1969). Only a quarter of the federal prisoners

were found to have been employed duringt75% or more of their last two

year's in the community, 38% were employed 251 to'74% of this time, 27%

worked less than a quarter of this period and 6 not at all, while 4%

were students throughout these last two years out. Pbstrelease employ.7

ment and earning rates' were inversely related to recidivism rates 9n

parole. Also, in almqst all probation and parole prediction studies

testing this variable, prior employment is inversely related torecidivism.

A 1974 Bureau of the Census survey of inmates in state prisons,

conducted for the Department of Justice, concluded from resiTnses of

the prisoners that almost half had worked for twenty-eight weeks or less

on their last job,42% had annual incomes near or below the $2,492 then

_des _government as the poverty level,--andtheir employment

was disproportionately in unskilled labor, operative and service jobs

(U.S. 14artment of Justice, 1976:25). A Rand Corporation study probed

in depththe official and actual crime and werk careers of forty-nine

Californiprisoners convicted'of robbery who had at least one prior

"t prison term., ,1The study found that only about half had.depended on a

regular job asi their usual source of income, that 10% never were interested ika regular job, and that only about 15% claimed that loss of.a job

had contributed to their 'committing their last offense, Most of their t. jobs Are of poor quality, but those with the best employment records

were the most' intermittent in their offenses (Petersilia et al., 1977).

A Rand survey that contacted about 1400 inmates who offered a fairly

representative sample of California prisoners; found from a factor analysis of their responses to probings on the motivation of their crimes,

that about equal weight was given by them to economic duress (e.g., losing

a job, heavy debts) and "high times" (e.g., togpt money for diugs or,

alcohol, excitement and kicks). Those with the most stable employment

had-the-ieest involvement in:crime-(Stembul 91 Peterson; 1977) . 1 4o
115 -

That destitution evokes crime is suggested also bf experiments in

California and ?Maryland which. demonstrated that recidivism of paroleesis reduced if they are paid modest sums --$60 to $80 per week for up to

12 or 13 weeks - -when unemployed (Reinarman and Miller, 1975; U.S. Department of Labor, 1977). These programs were justified, in part, by the

1 fact that:54igibility for unemployment insurance requires a minimum

amount of insurance-covered employment during the preceding 12 to 18

-months (amount and durat&n depend on state laws), but.a person confined

in this period (e.g., as an inmat

\

e of a prison or a mental hospital)

cannot have such employment. I understand that repetitions of these

experitents in Georgia and Texas did not result in recidivism reduction,

4

but reports on these studies are not yet available. However, California

in-1977 enacted S.B.244 which.provides credit for work in prison toward

payments when unemployed on parole.

. Studies of offenders' circumstances, like the other three types

of inquiry into economics and crime reviewed here, leave the impression

that extreme failure at legitimate employment is highly correlated with

serious law violations. Yet this correlation probably is imperfect not

only because of measurement difficulties, but (1) because of variations

in the way people assess their economic deprivation, and (2) because '

- of other social and cultural influences inhibiting or fostering involvement in crime. Some of\he latter influences may affect not only crime,

but also prospects of employment.

SOCIOCULTURAL AND POLITICAL-FACTORS IN TODAY'S YOUTHTRIME,

Any study of youth unemployment and youth crime should analyze the

history of these problems. A.view of the past yields somewhat different

conclusions for different types of offense (elaborated in Glaser, 1978),

but suggewts that for most youth crimes in the United States today three

developments are especially influential: (1) social separation of youths

from adults, (2) differentiation of students in elementary and secondary , school experien e, and (3) futile criminalization of some types of drug -' use. These tre ds, it can be argued,-not only supplement unemployment

in causing youth crimes but also cause much_youth_unemployability.
a - 116 -_

Segregation of Youth

Fofmerly, the activities of aallits and children were much less

separate than they have now become. The rapid increase in.ownership of

home appliances, automatic central heating, no-iron clothing apd pre-

\processed food has steadily diminished the time required for household

chores and the extent to which they are shared by children and palents.

The replacement-of small family busineises by corpOrate'enterArises has

eliminated what once were intensely collective interests of the parents

and all their offspring old enough to help; family businesses have shrunk

from the major form of employment to a relatively small role in our

---1R:-.01otiy, especially As the proportion of the population engaged in

farming has declined.

The time that youth spend in school has grown continuously. The

median level of education of our population over twenty-four-years-ofd *

rose from 8.6 years in 1940 to 12.1 in 1970. Contrastingly, until the

begirining of the twentieth Century a majority did not even enter high

school anMntil about 1950 ore entrants dropped out than graduated.

The academic year has been le gthened by more than a third during the

past half-century. The amount of time that students spend away from home

per school day and on weekelys h s also grown because of burgeoning extracurricular activities in school d commercial recreational activities

catering primarily to youth. These developments, plus more employment

of both parents away from home, have teadily expanded the proportion of

their waking hours in which youth are in close personal contact only with

persons of about their own age (Gillis, 1974; U.S. President's Science

Advisory Panel on Youth, 1974; Glaser, 1978:Chapter 8).

A basic law of sociology and anthropology is that social separation

produces cultural differentiation Becadse of their increased separation

today's youth probably diverge from their parents in tastes and values

1116, more than did youth of any former era. This contrast between the genera*

tions is readily evident in their music and dancing preferences, and it

was demonstrated statistically tly the 1977 Gallup Poll findings that most

. . Americans eighteen to thirty years old had smoked marijuana, but only 5%

of those. over fifty had tried it. Such generational differences probably

1 12
117

can be reduced only"by increased collaboration'of juveniles with adults.

in common interests and activities. Unless the age segregation in our

society diminishes, not only will the generations disagree on marijuana,

but many youths will be especially deficient in qualifications for

successful employment in today's industrialized and urbanized society.

These handicaps, however, do not divide the generations as much as they

differentiate the yOuth population, and they result from conditions in

our schools.

Differentiation of Students

4

Because the years spent in school and the schooldays per year have

grown while tim spent youths with their parents has diminished, the

school's potential luence on the younger lieneration probably has

(4increased. But those who become seriously retarded in basic learning

skills in the early grades find their classes in.subsequent years

especially frustrating, humiliating and boring. All youths seem to seek

respect from peers, and those who cannot get it by academic work search

for it in alternative types of activities, including delinquency. More

than ever before, research finds that dislike of school,\I)r grades,

and classroom misconduct are predictive of later law violations, both

when offenses are measured by infractions that youths admit on question-

, naires or by their arrest records (Hirschi, 1969; Empey and Lubeck, 1971;

Polk and Schafer, 1972; Frease, 1973). Multivariate analysis indicates

that a poor school record has become more closely related to delinquency

than belonging to a lower socio- economic class or a minority group (Polk

and Halferty, 1966; Jensen, 1976). When juveniles acquire delinquency

records while in school, however, their rate of further offenses diminishes if they drop out, and declines even more if they also marry; get jobs,

or both; thus delinquency becomes less persistent if they leave the isolated social and cultural world of adolescents maladjusted to the student

. , role and acquire legitimate roles in the adultworld and Voss,

1974).

Contrastingly, students who like school and are fairly successful in

-meeting its aglatimdcANL _rements are thereby prepared for legitimate

J

1'3
. - 1 18 -

. , . adult roles. They usually are less separated and,alienated from adults

/ than students doing poorly in school, and they are much less likely. to

Pcqulre a criminal record. Engaging in extracurricular activities is

also inversely correlated mith 'law violation (Hirschi, 1969), and such

activities other than athletics are especially associated with later

mobility in adult occupations (Spady, 1970; Otto, 1976).

It is probable that both classroom and many extracurricular groups

. , prepare youths not just with substantive knowledge needed for adult

occupations, but with a complex of lifestyle and communication habits

needed for successful employment in modern large-scale formal organizeions of business and government. Restriction of their social experience

/,almost exclusively to informal adolescent groups may well be a major

factor in the failure of many delinquent youth to meet the-requirements

. , / of jobs piovided for. them (Loeb, 1973; Glaser, 1978;178-81) . This, of

r cqurse, is in addition to the stigma of a criminal record and, in many

cases, to disabling appetites.

Drugs and Crime

Since ancient times various substances

in ordeito alter their moods. Alcohol has

drug most widely used for this purpose, but

have been consumed by humans

probably always been the

opiates have along history,

as do marijuana and a derivative of the same plant, hashish. Cocaine

and amphetamines also supplemented cqffee and tea as stimulants in many

times and places. Today 10% to 20% of the adult population in many

communities take tranquilizdre and sedatives under very liberal medical

prescriptions or without this legitimation, although most of these

commonly used substances are clearly more disabling than marijuana and

some other leVily prohibited Mood-altering substances. Which one of

these chemicals becomes legitimated and which outlawed has not been a

function of thetiological4

or psychological dangers inherent in their

use, but of the political tactics of various interest status groups

(Goode, 1972; Glaser; 1978:Chapters 2 and 10).

4 The history of all of our drug prohibition legislati.n seems to be

one of ineffectiveness, largeLy because the participants in drug use -do-
Ito

Al> 410 - 119 -

not consider themselves victimized and therefore do not comp ain to the

police. Consequently, only a small percentage of users and sellers are

caught. When the substances are highly addictive, users e a very : inelastic demand for them, making their sale so. profitable hat whenever

law enforcement cuts off one source, others expand and new supply channels

soon open. Also, when scarcities develop, some drugs are .ubstituted

for others. If the substances not only are highly addictive but their

cost becomes more than most users can earn legitimately, hey resort

to crime to pay for drugs. Then police actions that make the supply

. short and the price high increase the amount of property crime and pros-

. titution committed, by addicts(Fuji, 1975: Votey and Phi lips, 1976).

In addition, when prohibited substances are widely sed, even by,,

persons of influential social status, as alcohol was during our national

Prohibition Era and marijuana increasingly has,beenin he past decade,

enforcement of Paws against them becomes glaringly haphlzard, This > development alienates enough of the population to nurt re a gradually

successful movement for regUlation rather than prohibi ion of the

--substance; with responsibility, o 'combatting injuriou use transferred _

from the police to public health and education agencies. 'his has . happened with alcohol, may be on its way with marijuana, and, has been

initiated with opiates through nhadone maintenance programs. All

such. decriminalization reduced ime and other social costs from use of

these substances, and probably diminished employment impairment as well.

Unless this shift from prohibition to regulation accelerates, the high

cost of youthful drug abuse, 'Will undoubtedly continue to be a major

source of both drug and property crimes by youths, as well as an impediment of efforts -to reduce their unemployment rates (these themes are

elaborated and documented in Glaser., 1978:Chaper 10).

CONCLUSIONS

TO combat youth'crime is largely futile unless an effort is al.§.? made to assure legitimate employment for youths. To deal

. effectively

with. both youth crime and youth unemployment in the United States today,

however,majorsocial,culturalandpoliticaddevelopilmtsmustAlm 10

s.

4
120

taken into account. Perhaps the most relevant and least recognized of

these developments are the separation of youth social world$ from those

of adults, the differentiationof youths in school adjustment, and the

futile criminalization of substance abuse.

The practical implication is that youth crime reduction in the

United States today requires not only_full employment, but imaginative

experiments, recognizing that each innovation probably will be effective

for only a segment of the total:offense-pcone'Population.

possibilities include, for example:, subsidizing part -.ti

of youngsters in junior high ana high schools conditional

progress in part- or full -time' schooling, placing such youth individually

or in very small numbers at work in a large variety of adult workplaces,

treating small groups of students Of mixed age and achievement as collaborative teams competing not as individuals but for rewarded increase in

the team's averagescholastic and conduct attainments, involving youth

The many

ployment

on their

n in format managementcommittees,of mixed-age composition for matters that

affect their filailylives (e.g.,4Managing their recreational facilities,

planning thekr.cafeter,la menus), changing youth corrections establishments

from large storage institutions to smajl places,with well-mixed staff and

inmates plus much rewasde4Ard work, mOinfabuse,of psychoactive drugs,

of all types primarily a public health system rather than a criminal justice concern, decentralizing pOlne and courts to get them-closer to their

clientele and oriented more to'irivolving thiytt fighting our younger

generation. All such enileavOrs.regi.Are, analytic rather than the customary

glObal evaluations; we need to learn what works for whom, under what

circumstances and above all whyorowhy not, rather than assessing each

policy only on whether or not it is a panacea (since none is, and all are

. easily rendered ineffective or even counterproductive by poor personnel . in key positions).

st

,

4
-121

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3 0
- 125 -

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15
127 -

RACIAL DIFFERENTIALS IN MALE YOUTH UNEMPLOYMENT

Bye Paul Osterman

ABSTRACT,

This paper seeks to understand the source of racial unemployment

differentials for young men. The approach is to estimate a model of

youth unemployment separately for black and white youth and to compare the results for the two groups. The model consists of an equation for the duration of a completed spell of unemployment and equa-

, tions estimating the probability of quits and layoff-ST-The results

indicate significant racial differences in unemployment duration

which persist even after controlling for various personal characteiictics and labOr market demand. On the other hand, the probability of

,a layoff is not much higher for blacks than whites and blacks are

found more prone to quit into unemployment.

The final section of the paper examines the consequences of unemployment and finds that for-whitei the experience of unemployment

seems to have lew long term effects. However, for blacks high unemployment has long-term adverse consequences and low unemployment

seems to have long-term benefits.

1 INTRODUCTION

The remarkably high unemployment rates experienced by black

. youth are probably the most serious blot on the recent economic

record, and the recent reduction in overall unemployment has not

appreciably eased the plight of"young blacks. 1.. To a certain extent tlie high-unemployment rate experienCed by

' black youth is due to causes common to all young people, the hiring

practices of stable employers and the restlessness of youth, and

will disappear with age.

1 The outstanding question is why the

1. In 1976 the unemployment rate for Black men was 34% for 18-19'

year olds, 20.7% for 20-24 year olds and 11% for 25-34 year old.

The comparable. figures for whites were 15.5%, 10.9%, and 5.6%.

Although these are cross-sectional data the pattern is also

-found in longitudinal samples and_persists after controls for

education and years in the labor force. In part this pattern

results.trom a change in the behavior of youth --quits( decline

with age as do exits from the labor force--and in part from

the reluctance of stable employers to hire teenagers. See

Osterman (1978) and Lester (1954.

153
4

128 -

0 differential it so large between the experience of the two groups.

This diffeiential is the topic of this paper.

There are important mysteries associated with this differential.

It is not enough simply to assert that black youth unemployment

stands in a relatively fixed ratio to white unemployment and that

therefore the high recent rates experienced by black youth are not

unexpected-The-fact-is-that-the-experience of black relative 'to

white teenagers has been worsening. Particularly for males, in the

-mid-sixtiew a shift seems to have occurred and the relative positi

of blacks deteriorated. 2

Why this occurred is unclear. Some hav

suggested that labor supply shifts are the culprit. For example, a

variable-measuring the ratio of youth to adults in the labor force

tends to be significant in time series regressions for black unemploymerit but not for whites. However, in the absence of a convincing zstory about demand it is not clearmhy a surplus.of young blacks

should increase their unemployment relative to whites instead of

doing damage to both. Such a demand -side, story would presumably

emphasize discrimination, but here the mystery deepens. During the

same period in which the unemployment situation of young blacks

seems to have deterioratede their earnings relative to whites improved.

There convincing evidence from a number'ofsources that rates of

O

tions_have been roughly (though_not entirely)

equalized-across races. Even in the absence o' equalization the

trend seems definitely positive. It is difficult to reconcile this

'"new labor market", with respect to earnings with the unemployment

experience.

oIn addition to. supply and demand explanations there are also

some possible structural storips. One of these is the minimum wage,

ro2, Between 1950 and 1959 the ratio of black to white unemployment

for 18-19 year old males was 1.48. Between 1960 and 1965 the

ratio wA.1,75 and from 1966 to 1976 the ratio was 2.16. . 1 4
c

ti

129 "- 4

but I do not find it

. convincing. Even granted a disemployment effect,

recent, evidence does not suggest that the ratio of black to white unemployment has deteriorated because of minimum wage increases.

Another possible explanation,il'suburbanization of employment. More

so than adults, youth tend to work near home and if youth-intensive

jobsHhaye moved out of reach then even in the absence of discriMina744black youth could be saturating the inner city job market.

IPis seems possible, but it must be kept in mind that subdrbanization

is a trend which preaStes-the mid-sixties and that youth---ntensive------------L__

jobs (service and trade employment) have'not suburbanized'as rapidly

as manufacturing. , This paper will address a considerably more limited set of

questions than those posed above. Operating in the context of the

"new labor market" findings and using the viewpoint dnd kind of tools

generally associated with that analysis, we will examine whether

differences in background characteristics and tastes can explain the

cross-sectional differences in the unemployment experiences of black

and white youth.,4 This investigation is important because if back:-

grOundcharacteristics can "explain away" the difference in black

and white unemployment experiences to the same extent that they can

earnings differentials, then this might imply less attention to the

hiring practices of firmss,and more attention both to improving the

endowments of black youth and to structural factors sucl as the

suburbanization of employment opportunities. On the other hand., if

it appears that black youth face different demand curves, then renewed

efforts at equal employment opportunity programs and strengthening

the,placement capabilities of manpower programs and schools may seem

important. To anticipate the answer, the results below show that

after controlling for various factors the unemployment gap narrows

'but that an impo-itant fraction of this gap cannot be "explained

o

a- The next section of this paper will present and estimate a

,cross:sectional model of teenage unemployment, using microdata
d

- 130--

Following that, in the final section,,I will briefly discuss a

topic to which surprisingly little attention has been given, the

long-term consequences of 'teenage unemployment and how this varies

by Rte.

'A MODEL OF YOUTH UNEMPLOYMENT

Lf The idea underlying this'1approach is to estimate a model Of

youth unemployment separately for blacks and whites and then to

compare, the structure-of-that mxlelacross-the two-groups: --In-this

manner we can try to determine to whatceitent the different outcomes are due to different values of the parameters, i.e., differences in treatment of tehavior, and how much they are due to differences,in the values of the variables, i.e., differences in endowments.

Modern theories of unemployment distinguish between spells and

duration. A group may sniffer higher unemployment rates either because it has a disproportionate number of spells, though'each spell

may enequicklyt or because unemployment lasts a very long time when

it does occur. Important differences in interpretation arise, with

long durations implying difficulty .finding work while frequent

spells are thOught to imply either unstable behavioi or'high riskjobs. I will first analyze the determilints of duration and then

4

turn to an analysis of spells.

Duration of Unemployment 0

The model employed here is drawn'-from the implications of

Arious search theories (for example, Mortensen, 1970 and Lippman

and McCall-, 1976). An individual's duration of unemployment is

determined by a two equation system, one equation determining the

reservation or acceptance wage, the other determining the duration

of a spell. This system can be summarized as:

(1) R = R (K,D,C)

(2) D = D (U,R,K) D = Duration of spell

156

C
BR 40

- 131 -

K = Skill Level

R = Reservation Wage

C = Cost of search and time

spent unemployed

BR U = Distribution of job 7 <0

BU

>0 vacancies

3D BD

>cr-- 8K <0,

A straightforwa#:approach to sear hing for racial differentials

in duration is to estimate this system for black's and whites and test

for the differences across the equations. In.particular, it would

be, interesting to know, whether there are differences in the rate at

which the reservation wage falls in response to duration, differences

In the extent to which skill levels reduce duration, andAifferences

in the,imPact of cost reducing factors, such as unemployment insurance, in increasing, the reservation wage.

Estimates of this system will be provided below. However,

there is a.serious difficulty. Although this, systeil provides a

measure of the determinants of reservation wage, the measure of

duration is seriously biased. Rather ph"an being a measure of

completed spells, which is what we want to measure with respect to

our >complete system of unemployment, the simultaneous system above

. provides a-measure-of spells-in-progressPs has been shown'

(Marston, 1975), use of duration in progress provides 'a biased estimate, of the length of completed spells largely because long spells

, have a higher probability than short spells of being sampled at a

,point in time.

Because'of this problem the emphasis in this paper will be on

a reduced form of (1) and (2), namdly

(3) D = D (U,K,t)

where D is now

.1-

_!. completed spells. Unfortunately, this

introduces some am o both the expected signs of the variables and the inte.Ie

r

\, ation of the coefficients. For example, a

high skill leve would reduce duration by making more vacancies
- 132 -

accessible but mighco.lso increase duration by raising the rese

tionwage. Furthermore, a positive coefficient on marriage, f

example, may be due either to married workers being more'eager to

find work (and thus reducing their asking wage) or to them being

mare attractive to employers and hence receiving more or better

offers.

The data employed are those in the National Longitudinal Survey

-

of Young Men (Parnes, 1970). . Information from two survey periods

are employed,-1969-19704collected in_1970) and 1970-1971 (collected

in 1971),. These periods were chosen because they are the first for

which complete-information is available on every job held and on

each spell of unemployment. The sample wlimited to whites and

blacks (other nonwhite being excluded). The analysis is limited to

, . out-of-school youth. 4

In the analysis of the reduced form equation (3) the unit of

observation is each completed spell of unemployment which occurred

between 1969-1971. This procedure insures that we are measuring the

3. The NLS data, for reasons not yet understood, report unemployment

rates below thOse reported by the monthly Census and hence may

seem a poor data source for examining unemployment. However, the

racial ratios are very silkier to those in the Census. For example; the October 1970 Current Population Survey reported racial

ratios-for'out=of=school-male-18-19 year olds of 1.84 and for22-25 year olds 1.65. The NLS ratios for the same period were

1,88 and 1.66 respectively.

4. No observation of a spell is included if it occurred while the

youth was in school. However, because observations are pooled,

as will be explained below, some youth in the sample were. in

school during Some portioncoof the period.

15J
133

theoretically proper dependent variable. 5 In addition, only spells

associated with job changing, entrance, or re-entrance into the labor

market are included. Thus spells associated with temporary recalls

are excluded. Whatever the importance of this class of spells for

adults (Peldstein, 1975) .they are not important for youth 6 and they

are, excluded because of expected differences in the pattern of job

search.

The independent variables are'defined as follows:

Skills and Personal Characteristics

7

AGE: Age in years, measured at the beginning of the year.

KWW: This is the score on a knowledge of the world of work

I- 5. Every spell which occurred-any time between the 1969 and 1971

interviews is included. The only exceptions are spells.in pror

gress at the,time of the 1971 interview. Those are excluded

because information on their length is unavailable. Thus the

measure employed here seems to be the closest, possible approximation of the theoretically appropriate variable. There isostill

some bias since a very long spell, say one which began at the

time of the 1969 interview and was still in progress at the time

of the 1971 interview, would be excluded. However, the fact

that the sample,period is over two years long makes this bias of

little practicaljnoportance since there is plenty of oppoitunity

' to capture long spells.. For further discussion of this issue

see Welch (1977).

6. In the entire sample there were only 68 affirmative responses

in the 1969-1970 period to.the,question "Did you experience a

spell of unemplOytent while holding this job?"

7. In these definitions the term "year" should be understood to

`refer to the interview period, either r969-1970 or 1970-1971.

When a variable is described as measured at the beginning of

the year this means at the time of the 1969 interview if the

spell ocyrred during 1969-1970 and at the time of the 1970

interview if the spell was in the 1970-1971 period.

a.
134 -

test administered by the interviewers. In addition to

the possible direct importance of such a measure in explaining ability to ,find a job, it is also a good proxy

measure of intelligence (Griliches, 1976).

EDUCATION: Years of education, measured at the'beginningof the

year.

DEPEN: Number of dependents, excluding the wifi, measured at the

beginning"of the year.

DRAFT: "1" if eligible for-the draft-,- "0" if not measured at the-----

beginning of the yetr.

MAR: "1" if married at the beginning of the year,. "0" if not.

Search Cost Variables

UI: This is the fraction of wages replaced by unemployment

insurance. The variable is (Total UI dollars received)

/(Hourly wage of most recent job x 35 x weeks unemployed

that year) and it is defined analogously to that employed by Ehrenberg and Oaxaca (1976). It is measured

with error since data on the amount received is available for the entire year but not for each spell. Also,

see footnote 9.

This is nonlabor income (excluding transfer payments)

received during the year. The availability of such

income should permit, And perhaps encourage, more extended search. 10 Demand Variables

U: The local unemployment rate, measured in tenths of a

point.

The local unemployment rate at the end of the year minus

the rate at the beginning of the year.

)'NONWG:

DU:

160
- 135 -

Other Variables

LINE: "1" if the spell began when the respondent

left a previous job and if upon leaving the

,respondent had the next job lined up in advance, "0" otherwise.

LAYOFF: "1" if the spell began with a layoff from

a previous job, 70'4otherwise.

OLF: --"="--i-f----dtrring the-spel 1 of unemployment time-was

also spent out-of-the labor force, in addition to

time unemployed. "0" if not. Time out-of-the

Labor force spent in school or in the armed forces

4s not included in this measure.

An important peculiarity of the youth labdr market is that an

important fraction of unemployment is associated witHLentering and

re-enterinithe,fiabof force.' This pattern is largely due to the

work rhytht impoSed by the school calendar and the process of leaving

school. Spells of unemployment due.to,entrance and re-entrance are

to:differ from spells caused by job leaving or loss and hence

are analyzed separately. The diScussion here will emphasize nonentrance -or-re-entrance spells. 8

8. Twenty' percent of the total spells in the sample were due to /

entrance or re-entrance.' A Chow test was performed to test the

hypothesis that entrants/re-entrants and job leavers share the

same e Vation and the hypothesis was rejected at better than the

.01 leve Within entrance/re-entrance group there were no

significan racial` differences and a Chow test failed to reject

the equality hypothesis. The results for the analysis for entrants and reentrants are available upon request.

1

_I4
136

)

1

The results'of the duration equation are presented in Table 1.

The two racial equations were tested for equality via the Chow test

and the hypothesis of equality was ejected at the .05 level (F =

2.3). ,In addition, race was fully interacted with the variables in

a pooled equation to test for significant differences among specific

coefficients. The coefficients of DRAFT, U, and LINE are significantly different at the .05 level and the coefficients of AGE differ

e at the .10 level.

The interpretation of these coefficients must be tempered by

the realization that the equation is a reduced form. Keeping this

limitation in mind, there are several interesting results. Time

spent out of the labor force, neither working nor looking, reduces

the duration of unemployment. This is plausible since many jobs

are found through word of mouth, and the word can easily be passed

to someone not actively looking. Thus for youth the distinction

between time unemployed and time out -of -the labor force can be tenuous. The impact of unemployment insurance seems marginal, and coefficieils in both equations are insignificant and of opposite sign.

However, as noted above, this variable is measured with potentially

serious error and there are additional possible biases in its use. 9

A change in' the unemployment rate (DU) has an identical impact on

black and white duration, although the level of unemployment has

9. A spurious positive co lation between: duration arid benefits

is caused by the fact at the NLS data do not tell us whether

a worker i$ covered, only that he received benefits. Most

states have waiting periods, and thus a minimum spell length is

required for even a covered worker to receive benefits. See

- Welch (1977).

a 1U,

G
Variable Black White

AGE 0.511 0.006

(0.206) (.174)

-0.024

(0.079) (0.059)

DRAFT 5.141 0.055

(1.474) (0.029)

DEPEN 0.402 -0.092

(0.449) (0.601)

_EDUCATION 0.535 0.223

(0.255) (0.210)

MAR -3.246 -1.577

(1.393) (1.049)

LAYOFF 3.243 0.643

(1.106) (0.841)

LINE 2.314 -2.598

(1.893) (1.353)

U -0.003 - 0.073

(0.028) (0.021)

TABLE 1

DURATION EQUATIONS

(Standard Errors)

41.

Variable Black White

DU 0.055 0.055

(0.031) (0.029)

UI -0.271 0.268

(0.466) (0.585)

NONWG 0.001 0.003

(0.007) (0.001) ( OLF -3.303 -2.385 LAJ A (1,589) (1.059)

CONSTANT -10.1'40 1.600 .` N 247 412

F 3.421(13,233) 2.44(13,398) ' SE 8.395 7.952

.113. .043

163. . 104 .

at;
- 138

.0

oppoSite racial effects. 10 Finally, no n-labor income increases the

duration of spells for both races, although the effect is statisti- 0

cally significant only for whites.

A useful technique for summarizing the results of these equations is to decompose the differential into portions- due to differ-

, ences in the values of the variables of the,two groups and differ-

. ences due to the structure of the equations. Thks-detomposition is

reported in Table 2. 11

Ih this decomposition negative items are

favorable to whites. The results indicate that difference in the

structure of the equations implies that blacks have durations Which

are 1.200 weeks longer than they would be were they treated or beg hayed as whites. Given that their actual duration is 7.915weeks

they suffer durations which are 17% "too long. On the other hand,

blatk characteristics are slightly "favorable". In particular, they

hAve less education and non-wage income than do whites, both of

which increases white duration. The sum of these favorable character-

- istics'reduces their durationi relative to whites by .403 weeks and

this, when subtracted from the aifferential due to equation differ-

. 'ances, leads to an actual differential of .797 weeks.

Because this estimate grows out of a reduced form (Equation 3)

it is unclear how much of the unexplained racial gap is due to be10, In cross-sectional data the level variable may capture long

run.:equilibrium behavior. This is because of thevery high

correlation over time of an area's unemployment rate. Thus

in this equation the level unemployment rate may serve a proxy for structural characteristics of the local economy.

Since high unemployment areas also tend to have high,hourly

wages the welfare interpretation is ambiguous.

11. The formula is:

W B W W ---13 -a w B D = (8 - ao) + E80(,.. - X.) + Ex.o. - .) 1 al

, . where D,is the totaal differential, i indexes the variables, and

the superscripts refer to blacks, and whites. The first and

third terms on the right represent differences in the structure

of the equations while the middle term represents differences

in the values of thevariables.

a

44.1111.-

I6

4
13.9

,

TABLE 2

DECOMPOSITION OF UNEMPLOYMENT DURATION

Difference Difference

Due to Due to

Variable Characteristics Equation Structure

AGE

KWW

U

DU '.116

0

DEPEN .057

-.513

EDUCATION .258 -s.294

UI .014

.079

DRAFT -.002. -1.087

LAYOFF .048 -1.152

NONWG .124 .021

LINE . ,-.015

- .455

MAR -.012

.581

OLF -.114 .012

-.003. -10.988

-.086

.2.97 ..018 3.559

.403

CONSTANT

Iiirt t.)

=12.940

11.740

-1.200

O
4i

- 140 -

havioral differences and how much difference in treatmen t. The most

important likely behavioral difference is the relationship between

duration and reservation wage depicted in Equation 1 above. If young

blacks have an "unreasonably" high reservation wage 12 or if their

reservation wage declines less rapidly than it does for whites in the

face of unemployment 13

then the consequence would be longer duration.

Table 3 presents a two -stage least-squares estimate of the reservation wage equation for youth unemployed at the time of the 1970

or 1971 surveys. 14

"All ofthe variables have-been defined previously,

with the exception of EXXP which is (AGE-EDUCATION-5). This is the

standard experience variable employed-in many earnings functions.

The sample'is'limited to youth out-of-school at the time of the un- employment who were unemployed for reasons other than temporary layoff, waiving for a new job to begin, or a labor dispute.

The results in Table 3-imply that little of the observed difference in dui-ation can be attributed to differences in reservation wage

formation. The coefficient on duration is only s* per week apart fOlr .

12. A too high reservation wage might occur because blacks, perhaps

dile to inadequate information, over-value their potential earnings.

Another possibility is that blacks, perhaps due to changing

attitudes, are refusing to take jobs which-offer them wages

below that which comparable whites would earn. This shift in

the supply curve could also explain the equalization of earnings

found in earnings equatioris since these are actually reduced

forms of supply and demand equations.

13. In fact most studies Of.,youth find they take the first job

offered (Stephenson,. 1976. However, the, zeservation wage

mechanism might 'operate through patterns of search. If youth

have information about the characteristics of firms they may

search only *mong firms whose entry wage is equal to or better

than their reservation wage.

14. The duration eqUation is available upon request.

1 7

v
t - 141 -.

9 TABLE 3

TWO STAGE LEAST SQUARES RESERVATION WAGE'EQUATIONS

(Standard Errors)

DURATION

WHITES BLACKS

2.471

(6.411),

'7.025

(4.384)

MAR 69.296 -26.195

(28.99) 1 (36.011)

LINE -100.162 3.994

(154.009) (82.679)

DRAFT 41.385 -29.418

(24.989) (27.549)

OLF -37.601 , 55.067

(21.958) (27:565)

EXXP 22.149 6.451

(16.245) (10.750)

EXXP2

. -).890 -.163

(1.495) (.805)

UI -9.601 32.963

(8.735) (21.073)

.215 3.538

(1.576) (2.228)

DEPEN 33.625 10.155

(20.259) (13,984)

EDUCATION 21.507 -2.626

(5.121) (7.845)

CONSTANT -95.572 64.831

(70.105) (105.36)

R2 .511 .316

F 71(11,60) 1.88(11,45)

S.E.

74.7739 73.846'

N

72 57

- Note:- . The dependent variable is the hourly reservation wage

in cents.

I k.)L8
- 142 -

the two races.

15

When black mean values were substituted into the

white equation the predicted reservation wage is $2.41 an hour,

slightly higher than the actual value of $2.38. Thus the white'and

black reservation wage structures'are essentially the Same and there

is.no'evidence that black youth unemployment is due to unrealiyically

high reservation wages.

Thus the key conclusion of this section, that an unexplained

racial gap which implies durations of 17% "too long" for blacks,

seems not to be due to behavioral differences, at least those cap-

:, tured in reservation wage behavior.

Spells of Unemployment

In this section we rill 'examine spells of unemployment and seek

to disentangle the effect of differences in endowments on the one

hand and differences in treatment and behavior on the other. The

most important conclusion which emerges from this effort is that,

unlike the case of duration, the racial differences in spells Of

unemployment are largely dueto either diffeYences in background

characteristics or behavior, and apparently not co'differences in

treatment.

The natural approach to this question is to estimate separate

racial equations for the probability of a spell of unemployment and

to compare the equations. ,HoweVer, it is important to distinguish

4

between quits .and layoffs. The same variable, for example the

unemployment rate, has an opposite expected 'impact upoh quits and

layoff and, therefore, we will estimate a separate model for each.

15. The positive, though insignificant, sign on the coefficients is

contrary to that predicted by,theory. Evidently for this sample

duration has no effect on reservation. When the system was es-

. timated using time not working (i.e., duration of unemployment,

plus time out-of-the labor force) the coefficient was 2.8, with

a standard error of 3.7 for black's and'-4.6 with a standard . error of 7.8 for whites.

Co
143

Not all quits and layoffs lead to unemployment. In both instances, though presumably more so'for quits, a'separation can be

followed by immediate acquisition of another job. Furthermore, the

separation can also be followed by movement out-of-the labor force.

The distribution of quits and layoffs into these categories is shown

in Table 4. The figures in the table are the average of the 1969-

1970 and 1970-1971 quit and layoff rates for youth out of school in

those periods and the data reflects all job changes which occurred

in those periods.

As is apparent from lines 4 and 8, both the 'overall layoff and

the quit rates are higher for blacks than whites, the quit rate being

7% higher and layoff rate 20% higher. These results are not surprising; we expect to find blacks laid off more frequently than

whites both because of discrimination and because of lower endow-

. ments. FurtherMore, the poorer jobs held by blacks would lead them

to quit more frequently. What is surprising about this table is

that if one restricts attention to layoffs andquits resulting in

unemployment, then while the differential remains roughly the same

for layoffs (23%) it widens considerably for quits to 62% higher

than the white rate. Evidently while blacks do not quit in general

much more frequently than.do whites, they are considerably more

prone to quit into unemployment and less prone to quit and immediately find another job. It remains, of course, to see if this

pattern persists after controlling for differences in personal

characteristics.

Parenthetically, it.is also interesting to note that this table

supports the commonyiew that most voluntary job changing does not

result in unemployment. However, it is surprising to learn that a

considerable fract

blacks) are also

Our next ste

analysiswhich f

1111 layoffs(46.8% for whites and 44.2% for

ediately followed by another job.

is to estimate quit and layoff models. Inthe

lows attention will be limited to only those quits
TABLE 4

ANNUAL QUIT AND LAYOFF RATES

Layoffs

. Whites

Percent

Blacks

Rate Rate- Percent

(1) Resulting in Unemployient .073 46.2 .096 50.5

(2) Resulting in Labor Force

Withdrawal .011 6.9 , .010 5.2

"(3) Followed by Another Job '.074 46.8 .084 44.2

(4) .158 ' 100 .190 100

Quits

(5) Resulting in Unemployment .072 22:1 .117 34.0

(6) Resulting in Labor Force

Withdrawal .035 10.7 .033 9.5

(7) Followed by Another Jab

.

.218 670 , .194 56.3

(8) .325 100' ..344 100

Note: The rates are averaged_for two periods, 1969L1970 and 1970-1971. Only out-of-school

youth are included. Percentages may not add up to 100 due to rounding. - t . OF

171
- 145 -

and layoffs followed by spells of unemployment. The quit equations

can be motivated by,search theory, human capital theory, or some

amalgam of the two, Accumulati n of specific human capital and high

wages should, holding th other ariables constant, reduce the probability of quitting (P rSons, 1 2). Potential other opportunities,

indexed in this model by the un ployment rate, should increase the

probability of quitting. Personal characteristics, such as marital

status; dependents, and age, haye,an ambiguous effect depending upon

their impact on the individuals' needs and taste for risk.

The variables in the quit vquation'Which are new are TENURE,

which measures years on the job, UNION, a dummy variable which takes

on the value-of "1" if wages are set by collective-bargainin'g and

"0" otherwise, and WAGE, the hourly wage measured in cents. In

addition, a variable introduced ntroduced to test the hypothesis ta; one source of black quitting is discrimination-on the job... -..The variable

WTDIF is constrircted by fitting a wage equation for whites, estimating

what each individual would receive, and taking the difference between

that value and the actual hourly wage.

16 A 'positive value for Blacks

would indidite a wage below that'predkCted by the white equation

. -. and may be correlated with'quitting.

In'the layoff model the expectation is 'that specific huMan

capital and a high.skill,level will reduce the probability of a lay-

- off while increases in the unemployment rate should increase the

probability (0i, 1962). Temporary layoffs are again eXcluded from,

the analysis.

.11111111611 Jr'

16. The-auxiliary:equation was.:

ln(hourly wage)=Bi, +'B EDICATION+B2KWW +-33TENURE + B4UNION+

BS TENURE

2 + B EX1P + B EXXP- + B MAR 2

6 . 7 8

"

See footnote 21 4ot a brief description of Flanagan's (1978) u$e - of a similar variable.,' , 0,

II

4

1'79

I rmt
J

e - 146 -

_ In all the eq ations the dependent variable is dichotomous and

takes on the value of "1" if -Y a quit or layoff followed by unemployment occurred during the year andj"0" otherwise. 17 As was the case

in the duration analysis, the sample is pooled for two years, 1969 - y 1970 and 1970-1971, and only youth out of chool at the beginning and

end of the period are included.

t

,,,

The equation was estimated toffy the logit functional form 18

P = 1/1+e-BX

where P is the dichotomous dependent variable, X is the vector of,

explanatory variable and B are the estimated parameters. A maximum

likelihood estimation procedure was emplickyed. The results of the

logit quit and the layoff equations are;presented in. Table 5. Fredicted probabilities for the mean values of the variables are provided in Table 6. The story told by this equation is somewhat

surprising. 19

The uncontrolled gap for layoffs (Table 4) narrows

but the gap remains. Thus even after controlling for personal

characteristics, demand, experience, and job skills young black men

still face a higher layoff probability, but the gap is not strikingly

large. With respect to quits we find the surprising result that the

gap widens. Blacks seem considerably more prone to quit into un7

employment. ,

17. The incidence%.of multiple quits ortlayoffs dUring one yearlii

. very low. During 1969 -1970 only 1.2% of whites and .7% of blacks

experienced more than One layoff and for quits the figures'are

1.1% and 1.4%.,/,

18.: The logit functional form is convenient because it constrains

'.- predicted values to a (0,1) interval, a characteiistic which

is essential if the equation is to have a. probability. inter -.

. preiation. ?jja-addition,. McFadden (1973) has shown that under

reasonable assumptions the logit form follows from utility max- imizinebehavior.

9 19.. Ordinary least'g4Vares estimates of linear probability models

,produce equivalent results. For example, for the quit equation

-the-biacle value-substituted into the white equation produce a

predicted quit rate of..049.

,

. 173

$

ot.
I

-- 147

TABLE 5

QUIT AND LAYOFF EQUATIONS

(absolute value of t statistics in parentheses)

Black

gal/.

White

CONSTANT -2.178 0.208

(2.074) (0.185)

AGE 0.055 -0.082

(1.033) (1.499)

EDUCATION 0.153' -0.148

(1.735)

TENURE -0.037 -0.619

(0.584) (4.736)

DRAFT 0.556 -0.338

(1.854) (1.108)

DEPENDENTS 0.025 -0.157

(0.265) (1.262)

MAR 0.368 -0.324

(1.044) (0.923)

UNION 0.293 0.045

(0.527) (0.091)

WAGE -0.016 - -0.001

(2.365) (0.248)

IGVW 0.034 0.040

(1.494) (1.937)

U

4 -0.001 0.070

(0.248) (1.943)

DU 0.015 0.010

(1.787) (1.811)

WTDIF 0.014 b.0006

(2.143) (0.100)

. (

- 2 *log likelihood 554.318 807.896',

r

Layoff

Black , - 1.740

(1.918)

- 0.026

(0.679).

-0.033

.579)

-0.243

(2.881)

0.255

(0.839)

- 0.42$

(1.818.)

-0.714

(2.483)

0.002

(2.504)

0.0006

(0.036)

0.002

(0.325)

0.038 we

(4.061)

White

0.461

(6.584)

- 0.003

(0.089)

-0.198

(4.418)

(6.491)

0.087

(0.323)

-0.449

(2.252)

01,28

.215)

0.0001

(0.135)..

-0.003

(0.297)

0.02

(4.986)

0.012

(2.548)

550.046 989.624

1
14 8

'TABLE 6

PREDICTED PROBABILITIES OF QUITS AND

LAYOFFS FOR BLACKS,

White Equation Black Equation-

.% Quits. .034 .120

Layoffs .078 .089

0 175
-7'

In terms of the mechan&s of the equations the key variables are

AGE, EDUCATION, TENURE, AND WAGE. Increases in the values of the

first three variables all decrease the pr6Abliityeilwhite-quits,

but increase the probability of black quits., only offset is

WAGE in which a high wage decreases the probability of black quitting

more so than is the .case for whites.

The variable employed to test,for quittih due to differential

treatment, WTDIF, performs as expected both in its sign and significance. This is interesting because it lends support to the notion

of shifts in supply as well as demand curves (footnote 12); however,

the magnitude of the-effect is small.

The explanation for the quit differential is elusive. The

differential in overall quits,as opposed to q-ui1 into unemployment,

Is considerably smaller and hence (as we will see below) the structure

of'a general quit equation may be more similar across the races than

the unemployment quit equation. Thus blacks may quit not much more

fiequently than whites but they have difficulty lining up the next

job. Possible reasons for this will be mentio ed in the'conclusi6n,

but this ig a good potnt to summarize the res Its of the e ntire sy-

.T stem of equations.

. Table 7 summarizes the results of the'seveial duration and,spell.

equations. and indicates how much additional unemplONment can e attributed t, each of the divergences between the actual black values,

and the value predicted by.each of the white equations. As ts.

apparentoin each instance-- quits; layoffs and duration-- blticks'ex-

. pe4ience more unemploymenf,than they would had they been treated .'

a , (or behaved) like whites. The difference in gait behavior accounts°

for nearrhall of the additional unemployment, follow ed in importance by layoffs and duration. In the period 1969 -1971 the average'-

+-

annual .weeks of unemployment for out -of- school youth was 4.145 for *

. - Blacks and 2.252 for whites, thus the differential was 1.893 weeks.

Column 4 of Table 7 shows the fraction of the total differential

176

I

a
c

'! .4

4

°

/D,

TABLE 7

SUMMARY OF RACIAL DIFFERENTIALS

FOR ,TRE FULL SYSTEM

a`° (1)

QUITS -, .096

Ae

LAYOFFS .117

DURATION 7915

O

Note:

0

er

(1);\ ACtual Values for Blacks.

(2): Predicted Values Using White Equations and Black Characteristics.

('): Extra Annual Weeks of Unemploymen e to Divergence of. (1) and

'(2),lioldirig Remaining Variables Cons nt.

(4): Fractlon oi.Annual Differential in Annu Weeks of Unemployment

Accounted,for byj3)...

a

(2) 3) (4)

.034 .490 25.8%

.078 .308 16.2%

6.715 .255 13.4%

55.4%

I**

4

1 77

r

S
4'

a

15y

accounted for by the difference between actual values and those pre-

- dicted by the white equations. This calculatiOn implies that 5g%

of the total differential is due to differences in.behavior or

Tent while the remaining 45% can be ".explained by differences in , personal characteristics.

As a final point, the reader may wonder why so little has been IV

made of the role of distribution among_industries. It is often

thought that-one reason blacks experience high unemployment is that

they are concentrated in unstabfe industries. This, however,. a-4 not an important issue in these data 'For example, 41 1969-19

blacks out of school at both the beginning and end of the perio

averaged 3.39 weeks of unemployment. Had4rey had .the same indus rial

distribution as whites (but with the black within'induspy unemploy-

. ment'experience) they would have averaged 3.32 weeks or unemployment.

/ The difference fs small. .Furthermore, the inclusion of industry

dummy variables in the quit and layoff equations had no appreciable

erfeCiOnthe-fesUItS-:--1 C

DISCUSSION AND FUTURE TOPICS

-fhe results presented here have shown tSit an important fraction.

of the racial differential remains after the various controls, t

. , the most important source'of this.unexplained differential lies in,'

. quitt behaviar but that .important amportant components are also due to layoffs

S

and duration.

The most puzzling finding is clearly the quit differential:

layoffs and duration differences can plausibly be attributed to discrimination in firing and hiring. We'will offer some additional P.

explanations below, but first it is worthwhIqe to explore the quit

is 'sue=abirthei,(although we willalot be able to resolve it) .

.Itkdoes not seem-to-be the case that blacks quit in general, -17., more frequently than do whites. We sawpin Table 4 that the'over -aLl

quit rate of young black men is not appreciably higher than that of

.

Gb
1

152

whites. We estimated via ordinary least squares a linear probability

model fob all quits (employing the same variables found in Table 5).

. an0 when black mean values were substituted in the white equation the

predicted probability of a quit was .280chigher than the actual back

mean of .264. 20

This stands in contrast with both the logit and he

OLS results for'quitting into:Unemployment.

Thus young black men are not especially quit prone. Rather it

. is that their quits are more likely to lead to unemployment than

41those of whites. One possible explanation Tight be found in the

,

motives fotquitting, but Table 8 seems to dispel this possibility. \.

The're is a clear difference in the reasons for quit g for those

quits followed and not followpd by.unemployment. For example, the

category "found a better job" is more important (for both races)

for quits not followed by unemployment than by those which are.

-However, there odd not seem to be.major racial differences.,- For

example, the nature of the job was cited by 51.9% of the blacks and

50.79% of the whites who quit into unemployment. Blacks do cite

wages more often while whites cite working conditions but the impact

*

20. This. mean is below that in Table 4 because that 'Fable includes

multiple quits by one individual while the dependent variable

in the regressions was only dichotomous. The equ4ions reported

here are availableupon request.

-

1

' to 410

p )1 79

4

a
153

TABLE 8

REASONS FOR QUITTING

Blacks Whites

Followed by Unemployment

Nature of Job

Hours/kind'of work/conditions/

interpersonal/locatioh

Wages

36.8%

15.1%

44.6%

6.1%

Found Better Job

10.4% 9.1%

Health

8.5% 14.4%

Other*

to'

29.2% 25.7%

Not Followed brUnemployment

Nature of Job

Hours/Mind of work/conditions/

interpersonal/location

22.3% 20.5%

Wage

17.6% 1,1.6%

Found Better Job

26,8% 24.0%

Health

6.0%

Other*

26.7% 38.9%

Other includes return to school, military,' prison, family and

personal, and other:

:180
M ,

154

of this is unknown. 21

sit

Another possible explanation of the differential may lie in Y

different access to job contacts. Having decided to leave a job

whites may simply be more able than blacks to line up the next job

without experiencing unemployment. This may also be,an explanation

o-f the difference in the length of durations. Most jobs are found

'through personal contacts (Granoyetter, 1976) and if blacks have

fewer personal contacts than whites they may experience difficulty

locating jobs. Statistical controls for personal characteristics

may fail to capture this important "unobsevable" and since personal

contact networks help people of every education and skill level land

jobs (Granovetter, 1976) it is unlikely that the effect of this

- variable would be fully captured by other measured variables. The °

consequence may be that blacks, even after controlling for personal

characteristics, may still experience a harder time than whites in

finding a job.

21 In a useful paper published after this article was completed

Flanagan (1978) examined racial differences in quit and layoff

experiences. The focus of the article was upon a variable similar

to the WTDIF variable employed here and his results were gendrilly

comparable. As is the case here Flanagan found that the probability

of quitting into unemployment is higher for young blacks than

whites. He argued that this is due to an inverse relationship

between time intensive search methods and wage rates (and skill

levels). Low wage rates,make time intensive search more desirable

than goods intensive seafty.. Because young blacks have lower

average wage levels/Flanaean argued that they more often ch2ose.

to be. unemployed while searching (i.e. use their time to- 'search).

However, this argument is not satisfactory. First, the ILLS data

do not reveal significant racial differences in job finding and

job search methods (see below). Secondly, the quit equations

reported here show a different structure across the races while

Flanagan's arguement would imply a similar structuretwith the

observed differences emerging from the different values of the

variables (wage level and personal characteristic's) which would

eadto different choices of search behavior.

't

a p

, 181 ,

e

4
- 155 -

In the NLS data blacks and whites reported essentially the same

pattern of job findingf. For example, for youth out of school in 1969,

51.3% of the blacks and 44.2% of the whites found their jobs through

personal contacts.

22 However, assuming that the peers and family of

youth are similar to themselves, a smaller fraetion of'these contacts

for blacks will be employed than for whites and thus the network may

be less efficacious. Some evidence of this is found.in interviews we

conducted in Boston. The sample consisted of 150 non-college out of

school males between the ages of sixteen and twenty-six, half from a

white working class community (East Boston) and half from a black

community (Roxbury). Of those who reported finding their current

job via personal contacts 70% of the whites said that the contact

has Working in the firm where the job was found while for blacks the

figure was only 42%. Thus contacts for whites may simply be more

effective.

This argument clearly is.tentativ'e and requires more research,

as-does the general issue of the quit diffel-ential between blacks an d

whites. Thus, though we have come some way toward understanding the,,

nature of the racial differential in-unemployment, there is clearly

more to learn. It should also be recognized thA the results here

are cross-sectional and thus cannot well explain changes in the differential over time:. These shifts are important since in'the mid,-

1960s the ratio of black to white youth unemployment took a sudden

adverse shift for the worse a shift to a level which has remained

with us,..,A satisfactory understanding of this would require, at least

in part,.that we shift our attention away from the personalcharacter- 4

istics and behayior emphasized in this article and focus upon structural

changes in the economy; changes suchss the suburbanization of jobs,

rising labor force participation,of adult womenr-and changes in the

.

. . 22. Direct contact with the employed was used°by 24.9% of the

k IflaCks and 22.8% of the whites. Newspaper ads were employed

by 4.2% of the blacks and 5:7% of . ihe whites.

Et. O

6 a 40
- 156 -

structue of demand for youth labor.

THE CONSEQUENCES OF UNEMPLOYMENT

We know surprisingly little about'the long-term consequences of

teenage unemployment. Are teenagers mto experience considerable unemployment handicapped later in their dareers or is the effect , transitory and,unimportant? Arguments that the impact is considerable down

the road could rely either on the assumed psychological impact of

joblessness early in the working career or upon the missed opportunities

to develop training and skills. On the other-hand, the association of

unemployment with moratorium behavior and the importance of new and'

re-entrants in unemployment would tend to imply that there are few

long-term effects and that the real problem is one of lost output and

income maintainence in the short run.

oa Little work has been done on this question and hence little can . be said with confidence. An initial effort at shedding somelight on

the issue is presented in Table 9.

In, this "table the young men in 1968 are divided into three groups:

those who had no unemployment in 1968, those who. experienced between

one and four weeks, and those who-experienced five or tore weeks. Within'each of these three groups we look at their unemployment experience . . in166 and 1970 and determine the. deviation of their unemployment. in

those years from the average unemployment experienced by the entire

group (or_stbgroup) in that year. Thus4 for example, whites with no Unemployment in 1968 hads26% less unemployment in,1966 than all whiteg,

experienced in 1966 and 22% less unemployment in 19,70 than all whites.

Or, to pick another example; blacks with less than a high school degree

who experienced one to fur weeks of unemployment in,1968 had 12% more

Vynemployment in 1966 than all'blacks with less than a high school,degree

experienced that year and. in

. 1970 had 91% more, unemployment than all

blacks with less than a high school degree had that year.

The advantageofqooking-at 1966 as well as 1970 unemployment is

that the 966 experience was obviously unaffected by the 1968 experienc6

er 183.

r .v

ad'
- 157 -

TABLE 9 . 'DEVIATIONS FROWEXPECTE15 UNEMPLOYMENT IN 1966 AND 1970

BY WEEKS,UNEMPLOYED IN 1968'

0 I

A

0 Weeks, 1968 1-4 Weeks,'1968 5 or More Weeks, -1968

1966 '1970 1966 1970 1966 , 1970 : Whites -26% -22% +77% +35% +266% +126%

-12% y -34% +25% +38% + 361 81%'' 16-141, Years

Whites -47% -48%

Slacks -6% -43%

20-23 Years

g3 Whites - 7% + 6% +37% -2%

Blacks 0% -45% -711% +37%

24-26 Years

Whites -40% -25% +74% +123-%

Blacks =23% -10%

< High School

Degree

Whites ; -29 716%. +44% -17% 419%

Blacks

o High School

9% -42% +12% +91% + 16% + 53%

Degree

P Whites -17% -33% +62% +107% , Blacks -25%

More than HS

Whites -51% -11%

Blacks 0%

.15

Age is as of 1968; ducation as of 1970; Only thcite out-of school in 1967-4

68 included; Data n t reported for cells less than 30'. A' . . 184

O
I.

0

- 158

. . and hence we can control for the unobserved differences in individuals

which might have led them to experience more or less than avetage tan - employment in both 1968 and 1970. Thus, to determine the impact of

1968 unemployment we look atthe difference between the 1966 and 1970

figure.

The lesson froth this table is qui te striking. Considerable unemployment in 1968 does not adversely affect whites in 1970 but it

does damage blacks and, conversely, little unelployment in 1968 does

not-help whites in 1970 tut it does help blacks. This result holds

for virtually all age and education groups.'

This point 'can'be made clearer if we look; for example, at the

rows labled "whites" and "blacks:" Whites who had no unemployment in

1968 had 26% less than average in 1966 and 22% less than'average in

1970. Thus this group is clearly more successful than the average

for whites but the zero unemployment in .1968 did not appreciably

improve the situation in 1970. On the other hand, for blacks, those

with zero,unemployment in 1968 had 12% less than the average in 1966

'and 34% less than the average in 1970. Thus, while the 1966 figure

indicates that the group is somewhat special, the 1970 f' a is an.

improvement beyond ectations.

A similar picture emerges for groups with high 1968 unemployment,

Thus, among all. Willtes with five dc..more Weeks in 1968 the 1966 figure

indicates that the group is in difficulty tb begin with but the 190

figure is animprovement and is a regression to the mean. On the

other hand,.for blacks with five'Or poie-weeks-in-1468-the--1966-figure

also indicates trouble'independent of the 1968 experienceobut the,

1970 figure is a, worsening of ihe.Situation.

These patterns are

p quite consistent throughout the table and deem

, to indicate that whites are relatively unaffected by their early unemployment experience while blacks are very, much affected. I say,

"seem to indicate" because these result's A're only correlational; they

do not prove cause and effect. Nevertheless, the figures seem pow.erAil and persuasive.

185 O W

- 4

6 ; A

It J;

0 , $at

40.
D

0 ,

.... .N I ,, 41'

'ft 'e , .

.

'--

-- . 1:5-9.:: - . .. .,

, ..0 vo, A $ .. : ...4 .. -., ..

. vs Why,. should,we fild(theseaftterns?4-Orei agalit the answer is not.

known,--but tOmeplabsible explanations are ;daiTable. .It,may tie's: for.

.. example, that young blacks become iisome tense mote discouraged than

White:'Qr:/t is:possible that employers hbld a sporadic employment

.. ; . record against blacks put and inclined tp overlook this for whites.'

Both explanations are possible; 6utea. more persuasive explanation relies

on our earlier discussion's of job - finding techniques and how these

differ by race. The data on conseq-uences; just presented seem to imply

that whites in effect get more than one chance to "make it,'- -but that

for a black the chance, if any, comes once, and if available and taken

the young black remains unusuAlly stableand if Missed then a worsening

of employment prospects occurs. A pattern like this would result if

whites relied on informal-contactsand hence could repeatedly receive

help--while blacks were more dependent on formal institutions and thus

both had their access rationed and were likely.to be turned away in

the face of a poor prior record. In this situation; blacks who did

land a stable job,''or a job withsthe potential for stability, would

be much more likely to hold on to it and thus we would observe, as we

do, early stability (or lack of unemployment) being more strongly

correlated with later unemployment for blacks than whites.

This explanation is tentative and speculative and should,be

treated as such. However, the fact seems to remain thai unemployment

has more serious consequence's for black than white teenagers and thus

should add greater urgency to the formulation o 'olicy.

*

O

4.

186

ti

TM.

.

V..
; a

,;"

- 160 -

BIBLIOGRAPHY

Ehrenberg, R.G. and Oaxaca, R.L., "Unemployment Insurance,

Duration of Unemployment, and Subsequent Wage Gain",

American Economic-Review, vol. 66 (December 1976), pp.754-66.

Feldstein, 1.1:6%, "The Importance of Temporary Layoffs: An Empirical

Analysis ", Brookings Papers on Economic Activity (1975). No.3,

'pp'. 735-45.

p

Flanagan, R.J., "Discrimination Theory,.Labor Turnover, and Racial Unemployment Differentials, "Journal of Human Resources, vol. 8

(Spring 1978), pp.'187-207.

Freeman, R., "Changes ih the Labor Market for Black Americans", Brookings

Papers on Economic Activity (1972), No. 1, pp. 67-1120.

Granovetter, M., Getting A Job, (Cambridge:. Harvard University,Press,

4976).

Griliches, Z., "Wages of Very Young Men", Journal of Political Economy,

vol. 84 (August 1976),/pp. S87-S104.

Lester, R., Hiring'Practices and Lalior Competition. (Princeton:

Princeton University, Industrial Relations Section, 1954).

Lippman, S.A. and McCall, J.J., "The Economics of Job Search: A

Survey", Economic Inquiry, vol. 14. (June, Sept. 1976), pp.

155-89, 347-08.

0 Marston, S.T., "The Impact of Unemployment Insurance on Job Search",

Brookings Papers on Economic Activity (1975),,No. 1, pp.13-60.

Mc Fadden, D., "Conditional Logit Analysis of Qualitative Choice

Behavior," in-P..-Zaxembka,_ed-,_Frontiers of Econometrics

(New York: Academic Press, 1973).

, r '''Mortensen, D., "Job Search, The Duration of the Unemployment and the

Phillips Curve", American economic Review; vol. 60 (December

100), pp. 847-62..

0

di, W-T-,=-uliabor As a Quasi-Fixed Factor", Journal of. Political Eeoflomy,

vol. 70 (December 19616) pp. 538-55.

. . Osterman, P:, "Youth, Jobs, 4nd Unemployment", Challenge, May/June (1978).

to 18,7

>

4
0 161. . i3IBLIOGRAPHY (cont.)

t,

Parnes, H.S.,-et al., Career Thresholds: A Longitudinal Study of the

Educational and Labor Market Experience of Male Youth, U.S.

Department of Labor Research Monograph No. 16 (Washington:

Government Printing Office, 1970)'. ' Parsons, D.O., "Specific Human Capital"; An Application To Quit Rates

and Layoff Rates", Journal of Political Economy, vol. SO

'(November /December 1972), pp. 1120-43.,

Welch, F., "What Have We Learned From Empirical Studies of Unemployment

Insurance?", Industrial and Labor Relationl4Review,' vol. 30

.(July 1977), pp..451-61.

, "Back -White Differences in Returns to Schooling",

American Economic Review, vol. 62 (June; 1972), pp.T12-83.

.0 V

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of --* 162 ".

THE EFFECTS OF CHILD LABOR LAWS ON YOUTH , 'EMPLOYMENT

4 By: paniel J. B. Mitchell,& John 'Clapp*..

ABSTRACT

Chill:4,1a4 laws a.re,a relatively neglected aspect of, public policy

towards youth:emiloyAqr.thits.:15. partly due to thedifficulttyoin

ducing:the diverse stafb'afte.federal laws in the.child.rabor.areAimteo..

a quantifiable index. This study attempts to,.surmount '`the qiptification

barrier and analyze the employment effects of child labor,laws: ..Ai,

Despite their naffe, child labor laws really regUlate employment,

teenagers, not children. They typically perMit restricted labor force

activity during ages -fourteen to seventeen and do not apply at older age

levels. Hence, comparisons of the employment behavior of those below age,

eighteen and those eighteen years or older should, reflect the effects of

the laws.

At approximately age eighteen, individuals typically gra duate from

high school. Thus, a general sample of young people is likely to be

dominated by the effects of graduation, particularly in regard to the

availability for work. .To avoid confounding the graduation effect with

the legislative effect; a sample of high school dropouts aged fourteen to

twenty-one was dral'In from the 1970 Census of Population. Comparisons of

those younger than eighteen with those eighteen and above indicate that

employMent-shifts toward sectors restricted by child labor at the eighteenth birthday. This shift suggest that the laws do restrict employment

in selected sectors. A more tenuous analysis suggests that the laws

have the effect of restricting total employment (as opposed to its

: pattern) for dropouts under age eighteen.

Arguments for child labor laws include the proposition that premature

employment leads to "dead-end" jobs. Census' data on occupational mobility

of young people during 1965-1970 do suggest a considerable degree of imMobility. Since dropouts tend to obtain lower status jobs, occupationaT

immobility tends to keep them in such employment. However, the paper

questions whether current public policy is an appropriate response to

this phenomenon.

INTRODUCTION

One_hundred* years ago, the normal activity of a teenager would have

beenrork not ,school. Today, it is assumed that the opposite should be

true. 'Public policies, especially child labor laws and school leaving

laws, reinforce 'this popular conception. Child labor laws restrict_youth

*The authors would like to thank Nasir Karamat, Steven -- Stambaugh, Jose Ortal, and Robert Goldstone who served as research asfistants.

1.89

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'14

1

employment in'cerfinCodcupations and-industries, School leaving laws

limit the availability of young people for work. This paper presents

an empirical analysis of the employment impact of such legislation..

. A 130.41 school leaving laws andchild.labor laW's well the produCts of

40 public reaction to perceived evils in the.period frot the late nineteen-tit

century-to the early twentieth. These laws have been periodically updated.`

_ Butt since the'1930s, they have received little critical attention. Despite

_this neglect, current concerns about youth employment problems must in=

evitably force a re-examination of;t494Mpacts of these programi.

Rethitiing public policies is not an easy task; remaking public

policies is even harderTCarynit:tedistation with regard to

. . --really teenage labor--could pot be changed without creating some adverseeffect . 1 Inevitably, there Would be concern about the impact on the'

AAP

adult labor market of an increase in the supply in teenagers. But the

fact that reform is complicated and raises potentially controversial issuesis not reason for avoiding the needed review.

Indeed, it could be argued that the time is especially right for a

re-appraisal. Historically, child labor laws and female protective labor

laws were seen as part of a package; both women and children were seen as

needing special state protection at1the turn of the century. Although

female protective labor laws seemed sacrosanct, the desexing.of*state

labor codes- -under the impetus of Title 7 of the Civil Rights Act of

2

1964--

was accomplished smoothly. The lessbns from that episode should carry

over into child labor law'reform. Moreover, the recent decline in birth

rates will reduce the&proportion of teenagers in the working age population

during the next two decades. Youths fourteen to seventeen accounted for

1. This study does not concern itself with agriculture, where use of

preteenage children is-a source of controversy. The.youth employment

problems discussed below are basically urban problems.

2. .See Raymond Munts and David G. Rice, "Women'W rkers: Protection or

Equality?" Industrial and Laboi RelatIbns Review, vol. 2.4 (October

1970), pp, 3-13:

1

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bee
4 It4

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-* 10.5% of the population of fourteen and ahove in 1930, but will account

for only 6,8% of"the popUlation by 1990, a reduction of well over one

third. Thus', concerns about the impact of teenage employment On adult

job opportunities should be lessening.

Recent studies on child labor laws have been rare. One unpublished

'study attempted to :relate such laws to juvenile ddllinquency rates. 4

Another relied heavily on employer interviews. 5 The problem has been

that the'laws, which vary from state to state and between the states and

the federal government, have seemed inherently resistant to quantitative

analysis'. -When studying protective legislation, economists have tended

to prefer analysis of the minimum wage and its impact on teenage employ-

..ment, probably because the minimum wage can be expressed as a number.

Ironically, state minimum wage provisions and child labor laws are usually

found in the same labor codes. And, of course, federal chile labor provisions are included in the Fair Labor Standards Act, the same statute

3. The estimate for 199'0 is from the Census Series II projection. Data

from U.S. Bureau of the Census, Statistical Abstract of the United

States, 1976 (Washington: Government ,Printing Office, 1976), p.6.

4. Donald G. Woodworth, The Effects of Laws Governin& Youth Employment

and School Attendance on Youth Offenses and Delinquency, unpublished

report by Stanford Research. Institute prepared for the Office of

Juvenile Delinquency and Youth Development, U.S. Department of Health,

Education and Welfare, December 1965. (Report available from

Educational Resources Information Center.) 40

5. National Committee on Employment of Youth, The Transition from School

to Work: A. Study of Laws, Regulations and Practices Restricting Work

Experien e and Employment Opportunities for Youth, unpublished report_

prepared or the U.S. Office of Eddcation, June 1975. It might be

noted that the committee which sponsored the report--which is critical

of unwarran d legal restrictions on teena ge employment--was formerly

the National 'ld Labor Committee, the major forum for child labor

law advocates in the ear twentieth century. See Walter I. Trattner,

Crusade for the Children: A History of the National Child Labor Committee and Child Labor Reform in America, (Chicago.: Quadrangle Books,

1970).

I ).

4: I
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AO

which establishes the federal minimum wage and overtime prdvisions. Th

a ,researcher interested in teenage minimum wage impacts'. cannot help but

run across thvchild labor provisions in these various statutes.

It would be incorrect to suggest that contemporary observers have

been unconcerned about the possible effects of child labor laws. But

there have been relatively few specific proposals for reform. Most pro- , posals have beenuite general. A typical example is a recent book by

Willard Wirtz ic the former Labor Secretary. Wirtz states that:

Federal and state prohibitions on persons under eighteen

working in "hazardous occupations" are perhaps significantly

'involved (as a barrieWo youth job opportunities). . Although

federal offices report that only about .5 percent of all employment is foreclosed to sixteen and seventeen-year-olds, by

such prohibitions, employers manifest a considerable concern

about their scopep... It adds to this confusion that the

types of employment allowed for. fourteen and fifteen-yearolds are considerably more limited ... A comprehensive 6

effort must be made to eliminate these. misunderstandings.

Wirtz makes two points. First, he suggests that tlie effect ofchild

labor laws should be small--that only 5% of potential employment is

.affected. Thp difficulty with'this'estimate is that it ignores the fact

a, that teenagers are not likely to enter many forms of employment, especially

those requiring professional training and skills.. What matters is the

.) proportion of jobs which could reasonably be sources of employment and

s\.,)

which are in factt restricted by law. For reasons to be discussed below,

it is difficulf to pinpoint precisely which occupations and industries

--, are directly or.indirec ly affected.by "lid labor laws, However, a

\

sample Of high school d pouts was assembled for analysis in this paper.

It was found that rough half the males and a fourth of the females in

A%

the sample worked in occupations and industries whidhappear most,likely

r ' to be affected after they attain age4e8. Since youngsters 18 years of

'age and older are no longer subject to child labor restrictions, the po-. '

tential impact of the laws on younger worker's appears ,larger thaDWirtzls :. 5 percent estimate.

,

.

./e

6. Willard Wirtz, aim, Boundldss Resource: A Prospectus Or an- Education -

Work Policy (WashifigtOn: New Republic Book to.,

192.

0
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L

The second suggestion is that the problem could be alleviated by

supplying more information,to employers. In fact, there is no shortage

- of Labor Department pamphlets on the subject. The difficulty is that

the assertion contained in such pamphlets--that the 1at4,s are basically

simple to understand and obey--is false. 7

Instead, the law's are cm-

-, plicated, confusing, and overlappiing.

DEVELOPMENT OF CURRENT POLICY

Two legal questions surround th'e field of child protective'legislation. First; at what age do individuals become responsible for their own

behavior and chbices? The long-term rise in living standards has gradually permitted this age to rise, and only recently has a countertrend

of "children's rights" begun to develop. .8 Secohd, below the criIical age.

7. As an illustrationicohsider a recent Labor Ilepartment release designed to convince employers "that there is nothlmg complicated about the

laws governing summer jobs." The release goes. on for three pages.describing federal laws, and then notes 1115-employers had better check

up on'state laws as well. ',In fact, a perusal of the release is-sufficient to convince the reader that the laws are complicated and that

the simple way to avoid violating them is to avoid hiring teenagers

in a number of broad industrial,sectors. See "Elisburg Explains the

Laws Applying to Summer Jobs -for Teenagers," U.S. DepartMent of Labor,.

EmployMent Standards Administration.press release, June 12, 1977.

8. Readings on theissue of Children's rights can found in Robertf:WN\

Bremner, ed., The Legal Rights of Children (New York: Arno Press,

1974). Recent litigation has centered on the rights of juvenile

delinquents to due process. procedures, the rights of dependent children in divorce cases; and rights of expression in student newspapers.

The minimum-voting age was lowered to eighteen years by the twentysixth amendment to.the constitution..., Some observers have claimed

that. the age of physics maturity,' i.e., puberty, has been declining.

On'thelast point, See Aohn A. R. Wilson, eeal, Psychological Foundations of Learning and Teaching, second edition (New York: McGraw- , Hill; 1974), p.-302.

cf

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of maturity; who will he the party responsibl e for making decisions for

minors? Basically, the alternatives have been family and state. for

questions of education and work, child labor reformers-gradually pushed

the age'of maturity up to eighteen years. Furthermore, they advocated a

major role for the state - -rather than' the family--in dealing with these

issues.

9

A complicated web of social and economic-forces contributed to the

rise of the child labor law' movement. 'Industrialization brought about a

rise in productivity which made it economically feasible to delaork

until the late teen* years: ,As a consumption good, it might be expecte

that an increased demand for education would arise as living standards

rose. From an investment viewpoint, industrialization appeared to create

a wide disparity in wage diffelsentials between skilled and-unskilled

workers.

10 To the extent that education provided a route into skilled

employment, the demand for it could be expected to increase.

Industrialization also meant that child labor would take place in-the

context of -an employer-employee relaVonship rather than a parent-child

relationship,* at least in urban areag. conversion of child labor into

a market transaction, brought it to greater public attention. In contrast,

reformers tended to romanticize the virtues of child labor on family farms

"--even on subsistence farms--despite some contrary evidence.

11 The fact

9. An excellent compilation of documents relating to child labor; child

labor laws,, and related issues can be found in Robert H. Bremner, ed.,

cnildren and YOUtt in America; A "DocumentarY History (Cambridge, Mass.:

Harvard University Press, 1970-74).

10. See Harry Ober, "Occupational Wage Differentials, 1907= 1947," Monthly

Labor Reiriew, vol. 67 (August 1948), pp. 127-34; and Paul G. Keat;

"Long-Run Changes in Occupational Wage Structure, 1909-1956," Journal

of Political, Economy, vol. 68 (December 1960), pp. 584-600.

'11: For more on this issue, see Daniel J. B. Mitchell, "A Furor Over

Working Children ana7the Bureau of Labor," Monthly Labor Review, vol.

98 (October 1975), pp. 34 -.36.

J94

$
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that,pild labor laws.are generally Iess strict in agriculture is partly

due to tfiis tendency0

Urbinization also weakened the importance.of the family as a selfcontained produption unit. The failure of the family to maintain con-

. trol over children among the urban poor led to the phenomenon of "street

children" who still have counterparts today throughout the less-developed

world and in contemporary American city slums. 13, The.visibility of such

street children.ledinevitably to the view that "the public must in some

measure take the place of those who ought to have been their natural

guardians and protectors."

14

The groundwork was thus laid for state

intervention in matters of employment and education, because the traditional family was viewed as an inadequate control. ('

Opinions on why parental authority was inadequate varied. Some

reformers simply viewed poor parents as greedy exploiters of their own

progeny. _Others assumed that the seeming lack of parents' concern for

their children's educational needs or for the perils of too-early labor

force participation sttmmed Awl the regrettable ignorance of foreign.or

rural immigrants into urban centers. Some also saw required public

education as a,method of immigrant children from their alien

cultures.

- As a resu4 of pressure for child labor laws and schooling laws, most

stateshy the turn of the century required education 'somewhere between ages

12. Stuart M. Blumin, The Urban Threshold (Chicago: University of Chicago

Press, 1976), p. 245.

13. For a view from the early part of this century, see Edward N. Clopper,

Childlabor in City Streets (New York: Macmillan, 1912). A contemporary-description canhe found in Claude Brown The Children of

Ham (New York: Stein and Day, 19.7b1.

14. A quote from officials of the New York House of Refuge, an 'agency

which.began to receive public grants in 1820. See Joseph M. Hawes,

Children in Urban Society: Juvenile Delinquency in Nineteenth Century

Ameriba (New York: Oxford University Press, 1971), p. 46. -- 15. Bremner:, unildren and Youth, vol. 2, p. 605.

195
- 169 -

seven or eight and fourteen to sixteen., In addition, some limitations on

minimum wages and maximum hours of work for minors in industry were often

provided. According to the 1900 Census, children in the age bracket ten

to fifteen generally did not work but a minority of above 26% did, with

agriculture being the most significant employer.

16 Recent evidence suggests that minimum schooling requirements in the nineteenth century tended

to follow actual practice, i.e., those states with high educational attain-

. ment rates tended to raise their minimum school leaving ages. And it does

appear that early state child labor laws restricted employment in certain

sectors.

17

Within the child labor reform movement there developed a rift between

those who wished to'continue pressuring the states for stricter laws, and

those who desired federal legislation. Ultimately, the movement united

behind a push for a federal law. Thtinitial outcome of this effort was

the creation of the federal Children's Bureau of 1912, an agency which

continues today, but no longer has responsibility in the child labor

area.

18 Two attempts to pass federal statutes limiting child labor--one

in 1916 and another in 1918ran afoul of Supreme Court decisions declaring

them unconstitutional. Child labor reformers then sought a constitutional

C - amendment, an effort which failed after a'fierde opposition campaign during

the state ratification process.

The need for a special amendment ended with the change in stance of

the Supreme Court toward such legislation in the late 1930s and early

16. Idem.

17. See William M. Landes and Lewis C. Solmon, "Compulsory Schooling Legislation: An Economic Analysis of Law and Social Change in the Nineteenth Century," Journal of Economic History, vol. 32 (March 1972),

pp. 54-91; and Allen R..Sanderson "Child-Labor Legislation and the

Labor Force Participation of Children" Journal of Economic History)

vol. 34 (March 1974), pp. 297-99.

0. See Robert H. Bremner, ed., TheUnited States Children's Bureau1 1912 -

1972. (New York: Arno, 1974) .

1966
- 170 -

1940s. Congress passed,the Fair Lahor Standards Act (FLSA) in 1938, which

contained child labor provisions along with minimum wages and overtime "

regulations. Since 1938, child labor has regulated at both the state

and federal levels.

CURRENT REGULATIONS

Under the federal FLSA, illegal use of children or teenagers is defined as "oppressive child labor." Enterprises_sensidered to be in inter- -

state commerce under the act, are prohibited from using oppressive child

labor in the production of goods and services. Shipments of such goods

("hot goods") across st to lines are also banned.

Outside agriculture employmentThelow age fourteen is basically proIlibited, except for a few. insignificant exceptions. Employment during ages

fourteen and fifteen is forbidden in manufacturing, mining, and other areas

found,to be hazardous by the Secretary of Labor. At ages sixteen and

seventeen, employment is regulated by imeans of "hazardous orders" issued

. by the Secretary of Labor which declare certain types of jobs to be detrimental to the health and well-being of teenage employees. These orderS,

are most likely_to_affect manufacturing, mining, construction, trportation, and utilities, especially in occupations Where contact with machinery,

chemicals, explosives, and so forth is apossibility.'

Special .regulations issued by the Secretary of Labor limit hours of

work for fourteen and fifteen-year-olds. When school is not in session,

employees in this age bracket m ay not work more than eight hours per day

nor more than forty hours per seek. During the school year, the'limits.

are eighteen hours per week and three hours per school. day., In edition,

other limits are applied on the hours during which work may take place.

Certain exceptions to the general rules apply for vocational programs.

For examplep provisions are made for wort/study arrangements for fourteen

and fifteen-yeart-olds under."lork experience and career exploration program," These WECEP programs are aimed-at dropout-prone students, and

provide work. experience under supervision of local school authorities.

Evidence from these prbgrams suggests that the jobs provided have not in

19i
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fact Interfered with_schbol performance; they may even have improved

. 19

Lt. There are also special exceptionl fox student learners and , apprentices. However, a "Catch-22" applies, since apprenticeship programs often d'or..not accept appliCants below age eighteen, or require a

high school diploma.

20

An important element df ±ederal policy is that the FLSA's section

18(a) permits state' law to\ override the federal regulations, whenever the

state law 's stricter. In short, the provision applicable to an employer

is alws the stricter of the federal and state requirements.. State laws

vary considerably so that the importance of the ,FLSA compared with the

state requirements will vary from state to state. In a given state, le

i 1 FLSA may be applicable to certain youngsters and state law to Otheks.

It is not.possible to review the various state; laws.

22 However, it

is worth pointing out that statilaws may be scattered in various codes,

a feature which makes it more difficult for employers to determine require19. \Dennis Roth and Ernst W.' Strotsdorfer, An Analysis of the Educational

and Economic Impact of the Work Experience and Career Exploration

Program, unpublished working paper of the Office of the Assistant

Secretary for Policy., Evaluation, and Research, U.S. DepArtthent of w.-__ Labor, May 29, 1975. .. 20 Other federal policies have an effect on teenage employment. Ln

particular, although' the FLSA permits some employment of' 14-15 year.

olds, the Walsh-Healey Act - -which applies to :federal contractors%

prohibits employment of youngsters under age sixteen.

21. As an example, in California40b.provision is made for WECEP programs

in the state law, so such programs cannot operate since state law

overrides the federal. However, the FLSA is generally stricter for

youngsters sixteen to seventeen-years-ol than the state law.

22. A somewhat datedsumdary.of state child labor laws can be found in

U.S. Bureau of Labor Standards, State Child Labor Standards, Bulletin

158,revised 1965 [Washington; Government Printing Office.; 1065).

Highlights of revisions in state laws can be found annually in the

Monthly Labor Review:

, 198
172

a

ments. For example, in California it is- necessary to consult the, labor,

education, and insurance codes tolocate the releyantchild.requirements.

in the state. (The insurance code deals partly with, workers' compensatiork

laws, 'which have special features with regard to injury of,illegally employed minorS.) Certain sections of the adminiitrative code are also ap-.

plicableState laws generally follow the FLSA in terms of the types of'cove4-

. , / age. Typical-ly, regulations vary at different age levels,, with younger

.workerssubject to stricter standards. Certain types of employment are

restricted on the grounds of physical or moral hazard. Hours are limited

to doVetail with school requiremens:Nthe main unifying point about both

state and federal laws in this'area is that application basically ceases

at age eighteen. Thus, despite the difficulty in summarizing the details

of the 4aws, comparisons of the.labor market behivior of youngsters below

sage eighteen with that-of those above that,age can provide some indication

of the'impact of.overall child labor policy.

.1./ n SEcTORAL EMPLOYMENT IMPACTS OF CHILD LABOR LAWS

The dearth of prOfessiOnal studies on the impact .of child labor laws

leaves open the question, of whether such laws have any impact_at all.

Perhaps, due' to inadequate enforcement or a "natural" avoidance of modern=

. day teenagers of the types of jobs which are restricted, the laws do not

influence labor market behavior. . In principle, the laws seek to shift

teenage employment from certain sectors which are declared "off limits"

to others1whiefare considered to be more desirable. Hence, it is essen-:

tial to examine available employment data to determine if such an employ-

. mentshifting,effect can be 'detected, If it cannot, there is little point

in considering the reform of laws which=.-for whatever reason--have no

noticeable influence.

ScOol-drOpouts:are more likely than otherteenagers'to be affected'

4 .

. by child labor laws. Dropouts are potentially available for full-time

work while students are not. Hence, if the, impact of child, -labor laws is

"detectable, the logical place to look ii within a sample of high school

dropouts.

199

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The 1970 Census of Population ''public use sample" computer tapes ...

were used to proVide'a data base of individuals rtOt enrolled in school,

with educational, ttainment of less than a high school diplOma. Residents

of rural areas, who are not the focus of this study, were deleted. Analysis was confined to thejVest South Central, Mountaih, and Pacific

regions. 23 The limitation of the study to the western states Was dictated by the availability of computerized data and computer time. It is

believed, however, that the results discussed below have general appli--

.cability depite possible variations of state child labor laws across

regions. This is because the federalregulations'constitute a floor, on

state variation. Only individuals within the age range, fourteen to

twenty-one were included. Thi'S decision permitted observation on bOth

side's of the eighteenth birthday while excluding individuals who were

4 well into adulthood. A total of 7,764 individuals were selected, 48.5%

of whom were male. Since the sample contains only one percent of those

surveyed by the Census, the samplerepresents 776,400 persons in the

western area. Within the sample, 2,724 personswere employed.

Initially,,the goal was to determine whether child labor-regulations

had the anticipated effect of pushing employed dropouts in the sample away

from sectors restricted by these laws. It is difficult to pinpoint indusr tries and jobs which are specifically covered by child labor restrictions.,

But it is possible to designate certain industrial sectors and occupational

classes which seem most prone to regulation.- A reading of the laws suggests

that the industries most likely to be affected are mining, construction,

23. For details on the public use sample, soe U.S. Bureau of the Census,

`Public Use Samples of the-Basic Records from the 1970 Census: Description and Technical Documentation (Washington: Government Printing

-Office, 1972), A comparison of the Western sample with published

Census data for the entire.urban United States suggests that no sub=

stantial demographic distortions were introduced by confining the

sample to the western area However, nonwhites tend to be somewhat

underrepresented in the west compared witlithe entire country (17.4%

,,bf,the dropout population aged fourteen to twenty-one versus 23.9%).

ko, 200
174

1

manufacturing, transportation, and utilities. The occupations mat

24

. likely to he affected are _blue collar-wage-earning positions. (Unpaid family employment is less strictly regulated.) Thus; one approach

used was simply to define employment in the industries and,occupatiOns

enumerated above, as the "covered" sector, and all other employment as

"uncovered. "'

Sance'one goal of child labor legislation is to reduce exposure to

unsafe working conditions, a second method of designating sectors

.likely to be heavily affected by child labor regulation was also'

utilized. As a byproduct.of the Occupational Safety and Health Actor

1970, statistics on industrial work-injury rates are available. Thus,

it is possible to determine accident rates according to detailed industry code and to isolate those industries with above average injuries

per 100 fulltame employee equivalents. Blue collar wage-earning em-

. ployment in such industries was designated as "hazardous" and all

4

.other employment as "nonhazardous."

4 If child laborlaws had been constructed after detailed study of

-hazards, designation based on'work injury rates would presumably capture

25 4

sectors which were not available when,most child labor provisions were

24. Blue collar employment was defined as including the following

Census occupation classes: craftsmen and kindred,workers,

operatives, laborers, and farmers.

, 25. Industries were classified at roughly t1? three-digit S.I.C. level

on the basis of recordable occupational injury and illness rates

in 1973. (In a few cases of,missing 'data, 1972 rates were uged.)

The mean rate for the private sector in 1973 was 11 cases per 100

full-time employee equivalents. Source: U.S. Bureau of Labor

Statistics, Handbook of Labor Statistics 19.75--Refuence Edition

(Washington:' Governmentl'rinting.Office, 1975). The use ofthe

mean rate of injuries to o1assify induptries is obviously arbitrary., It is possible that an alternative cutoff point would have

produced different results, However, the general similarity of

.the results of the two alternative classification schemes descrli)ed

in the text suggests that the general conclusions would not vary

with reasonable definitibns of hazardOus industries..

V
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4 - 175

drawn up. ,So despite the quantitative aspect of the hazardous/nOnhazardous dichotomy, there is no .reason to assume that it Captures effects of

child labor laws better than the simpler covered/uncovered classifications.

In any case, there is considerable overlap between the covered and hazardous sectors.

The first step in the analysis of employment patterns within.the drop-

, out sample was to determine' whether the sectoral compbsition of. employment

shifts toward the covered or hazardous sectors at age eighteen. To make

this determination, the sample was divided into employed individuals age

fourteen to seventeen (ages regulated by child labor laws) and ages eighteeil to twenty-one (ages not regulated by child labor laWs). The sample

was then subdivided into sex, race, and other demographic categories.

Since the sample was telatively small, substantial disaggreilation was not

possible.

Nut Table 1 summarizes these br do s for employed individuals. In all

categories, except for nonwhite females,. a 'higher proportion of those aged

eighteen to twenty-one-worked in the cbvered or hazardous sectors than

those aged fourteen to seventeen. Of course, some of the cells are too

smai to permit much-,to- be said about -significance But in most of

the larger cells, a statistically significant shift in employment patterns

--in the direction child labor law's would lead one td expect--does occur.

' The shift seems to be greater for males than for females, probably reflecting a lower propensity ,of females at All ages.to seek blue collar work in

26. approximately normal distribut on. was used to"test for statisilical

significance. The test statistic was defined as the ratio of the

difference in sample proportions to the standard error of the

rence. See Roberty. Hogg and Allen T. Craig, Introduction to Mathematical 'Statistics, 'third edition (pew York: The MacMillan' Company,

1920) pp. 201-2. For some of the smaller cells of Table 1, this

tes$may be inaccurate. Most of the cells, however, contain at least

_fifty individuals, appoint considered to be the cutoff for reasonable,

accuracy of the test,

- . -

202
1. 176

A

e

the'restricted sectors.

These conclusions continue to hold when the sample is disaggregated

along demographic lines. For example, there is no evidence of any

differential,on'nonwhite females, (Of course, a large sample size for

this grow might be more, revealing.) The resultS for nonwhite males are - not substantially different than those for whites; and are less statistically significant. Young people not living with both parents.and 'Who

are not themselves married with sp-fie present can be expected to have

less adult guidance (or less adult responsibility) than tither youngsters.

But the differences between the sectoral employment compositiomof the -

older and younger individuals not liying ip husband-wife eamflies are,

smaller and less significant than the differences-.for thos'e who do live

in such families. Similarly, since youth employment problems are often

associated with'large urban centers, it' might be expected that those

. living in the larger cities would he more affected by child aabor laws

than those who do not. But the table indicates only-slight differences . in the results fqr larger an smaller urban areas. . Some evidence on the maturity effect'can be found on Table 1. As

was previously noted, comparisons of older and younger wprkers might be

influenced by differences in maturity which alter employability in4various

sectors'. One element of maturity at the age range in' the sample is the

tendency to be married: Only 14% of the males in the younger age group

were arried with spouse present, compared with 41% of the elder males.

,z, The figu -for females werelD% and 41%, respectively. However, even'

when the sample is-broken down into single ana married individuals, the

gaps iri the'Proportion employed in covered or hazardous jobs at the two'

age braekets remain. If marriage is important in determining these sectoral employment ratios=-and it does seem to boost the ratios 2br males- -

it is still not sufficient to \eliminate the marked shiff in employment'

at age eighteen in the dropout sample.

There are, of course, other facets of maturity beSides marriage. But

whatever maturity entails, it seems.unlikely that adulthood-is a quality

'which is`bestowed on an individual as a one-shot event at age eighteen.

Tit seems more reasonable to assume that maturity accrues gradually. If

203 41,

c
,

,

4 - 177

TABLE 1

EMPLOYMENT COMPOSITION OF EMPLOYED

SCH4OL LEAVERS

Males Females

Sector

14-17

-years

18-21 Test 14-17 18-21 Test

years. Statistic years year Statistic

I

4

Sample size

Covered (%)

Hazardous (%)

... Sample size

CoVered.(%)

Hazardous (%)

w , Sample size

Covered (%)

Hazardous .(%)

, . Sample size

Covered(%)

Hazardous (%)

r Sample size

Covered (%)

Hazardous (%)

.

/ l'

399

v. 32.3

42.4

353

32.0

43.1e

-. 46

34.8

, 37.0

' 224 , 39.7

57.1 :

-'24 1 37.5

45.8

_Total Sample

1327 -., 238 760' - 49.1 6.16 13.5 23.7 3.79

52.9 3.73 12.6 19.5 2.65

Whites. ' 1118 1 ' 198 642 , 7 49.3 5.96 12.1 24.8 4.40

53.1 3.32 ..;. 11.6 19. 2.90

Nonwhites

209 40 318 - - 47.8 1.67 -20.0 17.8 -.30

5-1.7 1.86 17.5 18.6 .16,

White Blue Collaorkers (A)'

663 37 190 -. ..57.4 4.81 , 48.6 . 72.1 2.66

62.1 1.35 37.8 - 52.1 ,1.63

.

... Nonwhite Blue Collar Workers (A)

4 . 139 -... 14 27 . s 65.5, 2.62 50.0 74.1. 1:53

70.5 '2.27 42.9 70:4\ .1.73

, e Living in Husband-Wife Families (B)

C SdMple size ' : 291 977 ..159 S35

Covered (96) "33.7 51.4 5.33 . 10)7 23.6 4.20

Hazardous (9-' 44.3 . 56.1 3.55 10.7 4.-18.9 2.75'

sl Not Living in Husband-Wife Families '(B)

c

'4

Sample size 108

Covered (%) 28.9

Hazardous (%) 37.0

Sample size s '342 . Covered OP . 31.0'

Hazardims ,(%)&: . 41.2

350 - - 7, 225

42(.6 2.92 19.0 '24:0

N14.0 1.30 16.5' '2019

Single C . 784. z 192 446 '

45.(2 4.62 14.1 21.7

40.0 -.36 13..0 18.2

204 . . I

7 ' - /.2.42

; 1.69

IV
- .17E1 tTABLE 1

'(Cont' d)

tit

Sector

. Males Females s 14-17 18-21_ Test 14-17 18-21 Test

years years Statistic years years Statistic

Sample size 57

Married:Spouse Pre'sent

r 543 swig 46 , 314

Covered (%) 40.4 54..7 2.10 10.9 26.4 2.98

Hazardous (%) 49.1 59.9 1.54 10.9 21.3 2.04

Living in Smaller Urban Areas (D)

Sample size. 579 108 326

Covered (%) 32.8 45.9 3.22 12.0' 21.5 2.44

Hazardous (%) 42.9 51.1' 1.92- 13.0' 17.8 1.25.

Living in Larger Urban Areas (E)

Sample size

. 222, 748 ' -

ei 130 434

Covered (%) 32.0 51.$ 5.38 14,6 '25.3 2.87

Hazardbus (%) ; 41.9 54.3 .3.28 12.3 '20.7 2.42

(A) See footnote 24 of text for occupations inclUded.

(B) Living with both parents or married,,,spouse present.

(C) Includes never married as well'as divorded., windowed, and separated:

Urbat areas of 500,000 pRulition or fest.

(E) Urban areas of population greater than 500,000.

Source: see text.

.A

205

p
,/.

- 179 -

so, a better understanding of the" maturity effect might be obtained from

a more detailed age .breakdown that was shown on Table 1.

Two problems arise when detailed age groupings are used. First; the

0 sample size-in any cell is reduced, decreasing the predision of the estimateQ. Second, comparisons of fdurteen - &to fifteen-year-olds with sixteen- to seventeen-year-olds are distorted by-the differences in child

labor laws which apply'to these do age brackets, Both these considerations suggest that special attention should be paid to a comparison'of

eighteen- to nineteen-year-olds with twenty- to tw y-one-year-olds.

The sample size. is larger for these older persons t far the younger

group and neither age is.covered by child labor laws, except for possible

"echo" effects for individuals not far beyond their eighteenth birthday.

27

Table 2 shows that for mtles--both white and nonwhite-there is no

:significant shiftdn employment patterns toward restricted work between

ages eighteen to nineteen and twenty to :twenty-one. If maturity was

1 pushing employment toward.the restricted sectors -at these age le'Vels, a

significant shift would be expected. The fact that none is found suggests

that growing maturity itself does not bias employment toward sectors

restricted by child labor laws, at least at Ages not far above the years

when chi]. labor laws ap31y. The gap between, ages sixteen to seventeen

and ages ei hteen to nineteen, in contrast, is always significant and

positive.

- For females, the pattern is less clear. First, nonwhite females- -

when examined on lable 1--sh4 no evidence of a shift toward restricted

4

27. Those who are just beyond their eighteent

sufficient time to change their sector of e

echo effects of'child labor laws will reduce

differences in the sectoral employment ratio

seventeen..year-olds, and eighteen to ,nineteen

described i% strengthened.

206

irthdays may not have had

loyment. Since these

he significant- of

between sixte= to

ear-olds, the na1 sis

-L"
- 180 -

TABLE 2

EMPLOYMENT COMPARISON BY DETAILED AGE, RACE, AND SEX:. ALL WORKERS

14,-15 ' 16-17 18-19 20-21 .L'.

yea years years years

White Males er).

t Covered (%)

. 'Hazardous 04

80

20.0

22,5

-

273

26.7

1.291

4 3

(3. ),

530

42.6

(4.63)

, 48.1

(2.12)

588

45.7'

.(1.04)

47%8

(-.10) . Nonwhite Males (Number)

c Covered (%) ' Hazardous (%) ,

' 15

26.7

- ...)

33:3"

31

16.1

(-.80)

19.4

(-.99)

94

4 4

(2. 2)

42.6

(2.65

115

.1

(.83)

50.4

...: (1.13)

White Females (Number)

- Covered (%).

Hazardous (%)

,

47 ..':-

- 6.4

-14.9

151

13.9

(1.64)

10.6

(-.75)

288

20.1

(1.69)

18.1

(2.22)

,

354

28.5

(2.50)

20:1

(.64)

Nonwhite Females (Number)

Covered (%)

Hazardous (%)

.

10

30.0

. 20.0

is

.

30

. 16.7

(-.83).

F 16.7.

( -.23)-

39

20.5

-,(.40)

23.1

(.67)

79

16.4

( -.53)

16,4

(-.84)

Total Males (Number)

4,

Covered-'(%)

Hazaidous (%)

95 -4 21:1'

- '24.2

- ,

304

25.7

(.94)

38.2

(2:69)

624

42.3

(5.20)

47.3

(2.65)

703

45.8

f1.28)

48.2.

t.33)

Total Females (Number)

\i' Covered

,

.

57

10.5

181

-- 14.4

(.81)

11.6

(-.78)

1

327

20.2

(1.69)

18.7

(2.21)

,.

433f

263

f1.99)

20.1

(.48)

. 'Hazardous (%)

1

. 15.8

-, -

Note: The test statistic in parentheses,compates the figure above

with figure in column to the-left.

Source: See text.

207

41.

e
0

Jr

- 181 -

employmeir-at%xe eighteen. Hence,.a search for amaturity effect amahg-

. femaleS must necessarily be confined to whites. Second, thb da'tgan whit e

females between eighteen to nineteen ages and twenty to twenty-one suggest

the existence of a maturity effect when the covered definition is used,

but not when the hazardous definition is applied. That is, there is a

statistically significant shift toward covered employment by white femItleS

between eighteen to(hineteen and twenty to twenty -ode, but not toward

hazardous employment.

In short, it has been established that a statistically significant

shift towar d employment in sectors restricted by child labor laws occurs

in a sample of western school dropouts between ages fourteen to eighteen

and eighteen to twenty-one. For males, it can reasonably be inferred

that the shift is;due,to the lapsing of these laws at,age eighteen:-.Recall that the sample is composed entirely of high school dropouts. Thus,.

A. the males included inothe sample did'not experience the breaking point

which the rest of.the population,ecounters at a.Tproximately age 18: a high schoorgraduation. Moreover, since there would presumably be,a time

. r lag between a person's eighteenth birthday and a resultant employment

shift, the fact that a statistically significant shift is observed in the

data lends support to the childlabOr 1Iaw inteilretation. Due to over-

,lapping j,urisdittions, theregis no wa

federal impact.

of separating the state versus

Y 4

For females, the evidence is less clear. Among whites, the possibility that some undefined gradual maturity effect is causing the shift

rather than a-cessation of legal restriction cannot be dismissed. How-

. ever,the suggestion that child labor laws affect males more than females

-f is not surprising; the restricted types of employment have tended tobe A

traditionally male. Of course, as women enter "male" occupations in;

greater numbers, this distinction.could fade.-

OVERALL EMPLOYMENT IMPACTS OF CHILD LABOR LAWS

The evidence-presented so far deals only with the mix of employment

between sectors, not with the overall. number employed. In principle, child

labor laws could affect 'themix a d not the magnitude. However, it

, 208
- 182 -

e",

o Possible that the laws domplicate the job search. process for teenag rs, thus'resulting in fewer teenagers actually finding employment.

0 it could Fe'that the laws "crowd" teenagers into certain sectors

,where the resulting depression of'syages either is or becomes limited by

aminimum wage floor. At such a floor,the number of jobs. is inherently

fixed, and teenage ditemployment ould result. Finally, it is possible . that teenagersiii.ew the jobs in th .unrestricted sectors as less desirable

than restricted jobs. If so, labor force partiLpation of teenagers'

might be reduced.'

In exploring the possibility of an overall Oployment.Impact, the

sample of western school dropouts is,again of use, since the individuals

included could be expected to be most sensitive to regulation by, child

labor laws. Even with this sample, it was felt that more could be learned

by using employment rather than unemployment as the dependent variable.

At the younger endof the labor,market, the definition of unNpiloyment

creates well-known problems relating todiscouragement and marginal attachment to the labor force. Other studies have found the employment-toropulation ratio to be useful at this age range since its meaning is less ambiguous. Thus, the calculations described below make use of thisratio4

YoFger school leavers tend to have relatixely low employment7to -popu7

lation ratios when compared with older school leavers. ,he causes of this f diffeience probably include the tendency of younger persons to be perceived N. as having lower productivity, to have less job experience, and to be under

. less, pressure to Seek work dueto lacy of dependents or income from family

sources. .Thus, the difference between:the employment ratios for younger

_individuals below age eighteen anct older-ones should represent an upper:_

limit estimate of the overall child labor law effect. Th estimate can be

soieWhgt refined by confining the older comparison groUP to e hteen- to

nineteen-year-olds, omitting those in he sample aged twe ty to twenty-one. Even so, it should still exaggerate the child labor law impact.

28. See U.S. tureau.of Labor Statistics, Youth Employment and'Minimum

Wages (Washington: Government Printing Office, 1970),11. 43.

209

r

a
4

S

_ - 183 -

1

An alternative procedure is to make an explicit adjustment for

maturity in explaining employment-to-population ratios at ages below

eighteen years. The most obvious adjustment is to take account of the

maturity effect observed between eighteen- to nineteen-year-olds and

twenty- to twenty-one-year-olds, broken doWn by race and sex. Differences

between these older groups can then be projected backwards onto sixteento seventeen-year-olds and fourteen- to fifteen-year-olds on a linear

basis. Such adjusted ratios, when compared with the actual figuies, can

be expected to reprbsent lower-limit estimates of the'effects of child

labor laws, since greater changes in life style are likely to take place

for dropouts between eighteen or nineteen and twenty to twenty-one years

than between fourteen or fifteen and sixteen to seventeen years. In particular, a greater tendency to marry, acquire dependents, or simply be

"on one's own" is likely to be found between the older age brackets than

between the younger ones.

29 However, it must be recognized that a linear

adjustment, is extremely,c'rude and that the resulting estimates are best

viewed as suggestive rather than definitive.

- Table 3 presents the results of the two calculations. Shown is the

-employment "deficit'," the difference between actual employment_and employment that would have been expected in the age range. fourteen to seventeen

years based on employment-to-population ratios of eighteen-'to nineteenyear-olds. The lower - limit' estimates include a. linear adjustment for

ri

. 29. In 197a,'..the year of the census, single men under twenty years had an

overall labor force participation rate of 49.0%, compared with 95.5%

for married men with spouse present at the same age. Married women

under twenty with spouse present, had a rate o 36.0%, not far,

the single rateof 39.5 %. Thus, marriage tends o raise the labor

force activity of males drastically, and lower it or females somewhat. The propensity t%become independent at this ge level is

likely to outweigh the somewhat depressing effect of arriage for

f4males. Data from U.S. President, Employment and Training Report of

the President, 1977 (Washington: Government Printing Office, 1977),

Table B.-2.

e 21U
TABLE 3

EMPLOYMENT "DEFICIT" ESTIMATES FOR 14-17 YEAR OLD DRO1OUTS

Lower-Limit Estimate Upper-Limit Estimate

Estimated

Employment

"Deficit"

"Deficit"

as percent

1/ of nonemployment 2/,

Estimated

Employment

"Deficit" 1/_

"Deficit"

as percent

of nonemployMent 2/

White Males

White Females

Nonwhite Males

Nonwhite Females

Total

-5,200

-5,900

8.3%

6.2%

5.3%

-13,300

-11,900

- 4,600

- 1,000

-30,800

21.3%

12.4%

15.0%

4.8%

14.7%

Each individual in sample represents 100 persons. The "deficit" is the diffeience between

actual employment and emplOyment which would-1e expected based on employmrt ratios of

older persons. See, text for details.

2/Nonemplornt consists of persons unemployed or not in the labor force.

'Source: See text.

\
-Jr

- 185 -

maturity; the upper,limit figures do not,

30 Taken literally, the figures

suggest. that 5-15% of urban school leavers aged fourteen to seventeeen

years in the western area who are not employed Would be employed in the

abSence of legal constraints. These estimates represent a range of 11,100

to 30,800 persons'in the western area. Projected to the national level,

the range would be on_the order Of 38,000 to 106,000. , 41

rt is unfortunate that the range of estimates is so wide. The

authors would lean toward the lower-limit estimates as being more accurate,

despite their exclusion ff nonwhites. That females make up more than half

of the lower-limit estimate and over 40% of the upper-linlit estimate is

suspicious. Nonwhite females did not seem to evidence a sectoral wloyment iniii,act, but they are represented in the upper-limit deficit estimates.

White females are represeited in both upper- and lower-limit deficit estimates. -As was noted in the previous section, evidence on the sectoral

employment effect ,for groupcwas-ambiguous. But it is possible that

, child labor laws could have a discouraging effect on total employment for

females, even in the absence of a sectoral shift effect. The laws impose

restrictions on employment in all sectors with regard to hours, working

paper requirements, workeirs' compensation laws, and soon.

341. For the upper -limit .estimates, the difference between the employment

to-population ratios for eighteen to nineteen-year-olds was compared

with that for fourteen to fifteen and sixteen,to seventeen-year=olds.

The differences were multiplied by populations in the.sample ?t these

two age ranges to estimate the employment, deficit. This calculation

was mate by race and sex. For the lower-limit estimates the,eighteen

to nineteen - year -old ratios were lowered,by a maturity factor to obtain the "expected"_rates for sixteen to seventeen-year,olds. The

derived rate was again lowered by the maturity factor to obtain the,

expected rate for fourteen ifteenqbar-olds. As an example, for

4 white males the employment-ra io for eighteen to nineteen-year-olds

,was 50.0%. This was 6,3'per ntage points below the ratio for twenty

to twenty-one-year-olds. Thus, the maturity factor was set at 6.3.

The expected ratio was set for sixteen to-seventeen-year-olds as 50.0

6,3.= 43.7%. The expected ratio for fourteen to fifteen-year-olds

was set at 43.7 - 6.3 = 37.4%. Actual ratios for these two age

ranges were, respectively, 40e9% and 26.8%. Hence, a"deficit in

employment.is indicated. Forzonwhites, the maturity-adjustment,

technique tended to underestimate the employment ratios, so.rib 416=

ficit is estimated for thet.

. 21 2

ti

4t.
186 - Al

a The analysis of the overall employment effect is certainly tenuous:

But it does suggest that child labor laws do have an eml)loyment-discour-

: aging impact, at least on certain dropouts. Given the magnitudes estimated, relaxing the laws obviously will not solve the entire youth employ;

-ment problem. But it does appear that costs are imposed by current policy.

The next section critically examines some of the benefits that the Jaws

are,said to engender.

4

44.4 0

BENEFITS OF CHILD LABORILAWS

Arguments for child labor legislation have usually centered around

three aspects of youth employment. First, premature work was believed

to endanger a child's health and safety. Second, early work was said to

be a cause of delinquency, while school was held to repress delinquency.

Third, it was argued that premature work would limit a child's education

andtthe benefits derived therefrom. In particular,,it was believed that

premature entry into employment would lead to'a lifetime of "dead-end"

jobs due to insufficient-educationand opportunity fdrtraining. That

is, a dropout's, first job would be typicalof what he or she could expect

over aiilifetime/ These issues are discussed below, and some longitudinal

data from the 1970 Census are .offered on the issue of a lock-in effect

with regard to dead end jobs.

*1-i.) Safety

There is little of an empirical nature that can be said on the safety

issue. Contemporary child labor4Taws really regulate'rthe employment of

teenagers, not children. Thus, the older arguments about stunted g owth

. nollonger apply. ,A key issue is whether teenagers--if they worked in

curtsptly restricted areas - -would have higher accident rates thanadults.

This question cannot be answered from standard sources of labor-market

data. It might tie inferred from automobile-accident records that teenagers

are less careful than,adults. However, unless a case can be made that on=

-the-job 'accident rates Would be higher, it is 4tioCto'see the justification

for special arrangements for teenagersparticularly older onesin the

face of federal job-safety requirements fbr all workers under the Occupa- . f

. 21 3

4

S
- 187 -

I ---, tional Healt and Safefy. Act. Perhaps some., comfort can be taken from

/..the finding ab ve that the hazardous and Covered sectors heavily overlap.

To the extent that a special rationale for teenage safety versus adult

safety can be adduced--e.g., the lifetime cost of a permanent injury is

likely to be higher for a younger person with more years in the laboT

market than an older persoA--it is reassuring to find that child labor,

laws do in fact tend to cover relatively hazardous industries.

(ii.) Work, School, and Delinquency

-/ Although it may seem surprising to contemporary researcher's, in the

early part of this century, employmepf was seen as a cause of delinquency,

not a cure. The so-called "street trades"--shoeshine boys, newsboys, and . messengers--were viewed as little more than excuses to stand around on

,street :corners and get into trouble. J6lajor study'by the old Bureau o

Labor demonstrated that delinquents were more_likely to have been\employed

than nondelinquents, and imputed aqcausal relationship running from work'

to crime._

31 A modern analyst would be- more likely to `assume .that work

and, delinquency ,were both negatively, associated with family'incomeedn

,the Bureau of Labor's sample.

The assumption that school- reduces delinquency has been questioned in

recent literature. The actual effect appears to depend critically on the

type of experience a yoUngstem is having' in school. Failure in school can

be an alienating experience which increases3-t,he propensity for crime:. To

the extent that dropouts can make a reasonable accommodation to adult norms,

while at the .Same time meeting some of their own-aspirations, their delinquent tendencies can be reduced.

32 Improved access to employment'is an

fe

31. U.S.Senate Documents, 61st CongresS,2nd Session, Woman and Child

-Wage-Earners in the United States, volt- 8; "Juvenile DeTinqUency

aria its Relation to Employment" (Washington: Government Printing

Office,"1911).

/,

32. . Delbert S. Elliott, and Harwin L. Voss, Delinquency and Dropouigoronto: Lexington Books, 1974), p. 60.

214
r - 188 -

33 obvious route to both socialization and achiOetent 9f aspiratins.

(ill.) Education and the Lock. 4n Effect

The correlation between income and educatie41 i$ so well known that .'

there is little point in commenting on it. Noi. can the long-standing

debateas to whethes added education truly is the cause of higher income

be discussed here. But one point is wort) noting. g, a gross correlation

between education and income seems to be heavily dep9fid nt on the relationship between education and occupation.' Education appears to be an

entry-way into better occupations--in terms of pay and other features610

Once the occupational effect is removed, the variance in income ekplained

'6y education is reduced. It is easy to find detailed occupational classifications in which dropouts have only minor wage disadvantage--if any--in

171:41-time earnings relatiVe to high school graduates. 34

0

Still, it cannot be denied that.on the average, dropouts fare more

poorly than graduates. Even substitute forms of education do not seem to

close the,gap. For example, dropouts with vocational training enjoy a

33. Some sociologists have emphasized blocked paths to achievement of

aspirations'as a cause of delinquency. isilt simply, illegitimate

paths are Closed, illegitimate paths are more-likely to be chosen.

See Richard A. Clqward and Lloyd E. Ohlin, Delinquency and

Opportunity-(Glencoe, Ill.: Free Press, 1969). Economists have also

pursued this framework/ and find evidence for it. See Belton M.

Fleisher, "The Effect -of Unemployment on Delinquency," Journal of

Political Economy, vol. 71 (December 1963), pp. 543-53, for evidence

that unemployment and delinquency.are positively correlated.

34. On the basis oftmedian earnings, for white males twentylflive years to-

-4'thirty-four years, of age working fifty to fifty-two weeks pe year,

twenty-two occupational classifications can be identified in3which

dropouts have an earnings disadiantage of less than 3%. See U.S. , ' Bureau of the Census, Earnings by Occupation and Edutation, PC(2)-88

, (Washington: Government Printing Office, 1973).,'Table1. Dropouts

are defined as thOse with'one to three years of high school; graduates

are those with no college attainment.. Occupations include police

officers, icashiers, insurance sales workers, bus 'drivers, and bank

officials,

Ur

4o- 215

11
- 189 -

I larger wage( premium relative to untrained dropouts than trained graduates

do relative to untrained graduates, But untrained graduates still enjoy

higher average earnings than trained dropouts.

35, e The original child labor reformers saw great perils

r in premature

employment,, And, xoday,,fears that early entry into "dead-end" jobs

would lock teenagers into such jobs during their entire careers remain

an important source of support for current child labor restrictions. It

is therefore important to examine the available data for evidence of a

,"locked -in" effect. The evidence described below dOes suggest that such

an,effect'may exist. HoWpver, as will be noted at the end of this section,

this finding does not imply that child labor laws are the optimumresponse.

To deal with thelock-in issue; a second sample was drawn from the

public user sample of the 1970 Census of Population for the western area.

Irk the 1970 Census, some respondents were asked about their current employmenand their employment five years before (in 1965). Thus, it is

possible to trace occupational mobility patterns across a five-year

0

period. Since child labor laws, would affect the patterns of anyone, who , -4 was under eighteen years in 1965, the samplk,was confined to individuals

at least eighteen but not more than twenty-fiire years of age in,19.65.

.Again, it Was hoped that the behavior of older individuals would provide

some guide 41,the behavior that younger persons would exhibit in the.

:1. absence of legal constraints. The sample was further confined to urbbn

males with educational attainment of at least nine, but n9t more than

35, A twenty-five to thirty-four-year-old male dropout with vocational

training had median earnings in 1969 144.6 were 17% higher than a

dropout without traiping. A high school- graduate with no coilege

attainment with'training hld only a 7% premium move an untrained,. y'

graduat %. But a trained dropout earned 7% less than an untrained woo.

'graduate. See U.S. Bureau of the Census; Vocational Training,

PC(2) -5C (Washington: Government Printing Office>1973), Table 10.

ti 3
- 190 -

.twelve years:Who had been. employed both in 1965 and 197Q. A total ,of

5,434 individuals were included, representing a western population of

543,400.

For purposes of tfiis study, occupations were ranked by average male

) full-time earnings at all age levels. The all-age average appeared to be

a better guide to lifetime earnings than the earnings of younger workers;

A total of 102 Census'ocoupation's . was used to classify,' employment. A

ove to a,lower=ranked occupation was considered a.deterioration. Since

no two occupati s had exactly the same earnings, no change in status ---

was synonymous w th no change in occupation. . The lock-in effect would be benign if it applied only to high wage

occupations. Thus, concern centers on the possibility of being locked

into low wage positions. Obviously, the cutoffs for defining low and

. high wage jobs are inherently arbitrary. As a matter of convenience, the

.. sample was divided rduihly into thirds on the basis of a ranking of

individuals by their 'occupation's average wages. The bottom third fell

_ in occupations with average earnings below $6,371, whil the.lop third

joiasin to occupations with average above $7,795. These classifications

were then used to categorize the initial type of jeb of each individual.

Each person in the sample experienced one of two basic posslbilities

during 1965-1970. Eitherhe remained in his 1965 occupation or,:he moved,

to one of the,Other 101 occupations. If an individual moved, the shift N is described as an ipprovement on Table 4 if the.,mpr was to a higher

wage occupation. Thus, a move from an occupation with a wage of $5,000

to one with a wage of $5,001 was classified as an improvement although

both are low-wage occupations. Of course, a move from a $5,000 occupation

to an $8;00 occupation--invdlving a jump from a low-wage to a high-wage

occupation- -would also be classified as an improvement. Similarly, a

deterioration is recordedson Table ,;e1 whether the decline was large enough

to drop from high to middle wage, middle to low wage, or simply involved

a dollar dropanti,thin the.high, middle, or low wage classes..

There are few surprises regarding the qualitative aspects of the re- sults. It would be expected that there-would be more upward mobility from

low wage jobs than from high wage jobs.; and vice verse for downward mo- . 21 7 L

......12111e
4

P

a

, TABLE 4

.

,

o do

A

1'

e

W.

. MALE EDUCATION AND OCCUPATIONAL MOBILITY, 1965 - 1970' , ' L

High School

Graduates

., Change'in Status,

1965-1970

High Middle 'Low . Wage .Wage Wage

.., Improvement 9.0% 35.4% 54.1A.--- 36.5%.

No Change 51.9 41.0 37,8 42.3

Detes4oration '39.11t 23.6 8.2 21.2.

- , Total ,

100:0 100.0 100.00, 1+50.0

Number ,432 669 717 1,818

t.

Isi,

High School ., ..--

) Dropouts

. -High

Wage

13.0%

54.5

32.5 :

,100.0

,2,13

Middle Low All

Wage - 'Wage ,-

37.7 % 64.6% 37.7%

'-41.7 29.2

20,6 ! 6.2 20.2

100.0 '100.0 1001-0 .

1,292 1,11`. 3,616..

1

Note: 'See text for source of data and definitions.

e 9 18

1
elk

$

192

4.

a o

6 This is partly because it is hard to go down XrQM the bottom or

up from the top, and partly because of the nature of the tabulation. If

it is assumed that there is a .random error present in occupational placement at any point inpme, at a later date individuals will be found to

move toward their "proper" occupations, where "proper" is defined in terms

of characteristics affecting productivity and employability.

Since absolute,dollar definitions-are'being used for defining low

wage and high wage, and since dropouts are known to_ hame lower avewe

wages than gfaduates, the comparison of mobility across the two groups

also does not produce qualitatively surprising results. It would be expected that with the same definitions, fewer diOpouts than graduates would

show improvement in occupational status from low wage occupations and

more dropouts than graduates wouldshow downwalod mobility from high wage

occupations. This is because graduates will have_a greater tendency to

"belong" in the high wageoccupations than dropouts, while the reverse

will be true,for 1V-wage occupations.

Table 4 Confirms these various expectations. What is surprising is

that both.graduates and dropouts show a high rate of occupational immobili-:

lity=no change in status over the period--despite the relative youthfulness

of the sample.

36 Apparently, the'high degree Ofaobility which is often

associated with yLONI relates more to changes of employer than changes of

occupation. The tale 'shows that 42.3% of the dropouts and 42.1% of the

graduates experienced no change in occupdtion during the five-year period.

36. -According to national data for males Of all ,age groups, the overall

occupational immobility rate was about,6O %a Thus, the immobility

figures on Table 4 for the younger groups are significantly lower,

as expected. See Dixie Sommers and Alan Eck, "Oacupational Mobility

in the American Labor Force," Monthly Labor Review, vol.0100' (January

1977),.p. 9. ,Note that the figUres in the Sommers and Eck paper must

be adjusted to exclude individuals not working or who,diedduring

1965-1970 to obtaill estimates comparable to those discussed in the

text.

219

le

,
193

4'

These rates probably understate'the mobility rates that would apply to

teelagers below age eighteen in the absence of legal constraints, since'

even within the sample; the younger workers are more mobile than the

older.

37 But the fears of child labor reformers of°a lock-in effect

do find some confirmation on the table.

If it is granted that the fears were well-founded, it is still unclear that the policies chosen in response were optimal. These policies

both require full-time attendance at school until some minimum age

(usually fourtetif-to-sixteerrj-arrd-timit-;-tire--j-eb-opPbrtunities-av-ailable

to dropouts.4under age eighteen. ,Both sets of policies provide incentives

to complete high school. Assume for the moment that the laws have the

desired effett of keeping teenagers in school who would 'otherwise drop

out. At best, the laws would prevent the drtpping out of students on

the margin of the dropout decision. This marginal effect would be

achieved by imposing costs on those'who drop out despite the reverse incentives. Only about three-fourths of a high school graduating class

finish their diplomas "on time." So the minority who bear costs is not

an insignificant fraction of the nation's teenagers.

38

IMPLICATIONS

Despite the inherent difficulties in exploring the effects and underlying rationale oftistatutes as diverse as child labor laws, this study has

37. At ages in 1970 of twenty-three to twenty-four, twenty-five to twentysix, twenty-seven to twenty-eight-andenty-nine to thirty years,

the proportions of dropouts experiencing no mobility were, respectively, 33'.5%, 37.5%, 43.9%, and 50.1%. For graduates the figures were

32.3%, 37.6%, 42.9% and 51.2%. The proportions of dropouts experi-:

encing upward mobility were 43,0% 40.6%, 35.3%, and 30.2%. For.. graduates; these proportions weer "44.1 %1:3 %, 38.3%, and 31.0%.

Thus, immobility rises with age, and upward mobility declines.

38. About 74% of the class which entered the fifth grade in 1966 graduated

"on.tinle"-in 1974. See.U.S. Bureau of the Census, Statistical Abstract of the United States 1976 (Washington: Government Printing

011i'c'e";"1:01)7 , 140.

.4

220
194

demonstrated that statistical investigations are possible. However, it

has Aso demonstrated the limitations of suck explorations, when confined

to Census or similAr data s o u r N T he effects.of the laws are detect- )

able in Census data, but it would be difficult to carry out alternative

policy simulations based on the available statistics. . 'Further information on the effects of child labor laws could best

r he aetermined through experimental techniques. It would be interesting

to Alex the laws in particular areas and observe the results. Unfortunately, the current arrangement whereby the applicable law is always the

stricter of the federal or state statutes makes such experimentation

\ difficult. A step toward experimentation and ultimate reform would be

repeal of that pOrtion of Section 18(a) of the FLSA dealing with child

abor.

39

Such repeal would put child labor policy for all employment

under the FLSA exclusively in federal hands. Thereafter, changes in \N

policy would not require the coordination of diverse state laws.

Finally, researchers in the field of youth employment problems

ought not to neklect the impact of child laborand related laws, despite

the difficulty in quantification. It does appear that-the impact is to

push teenage employment--at least among dropouts--into sectors whose

virtues in terms of long-term job prospects are questionable. In

addition, they may well tend to discourage employment of some teenagers

altogether.

39., Section 18(a) permits the states to override the FL&A's child labor

' provisions with stricter regulations.

J ,r
195

YOUTH LABOR iRKETS'AND THE MILITARY

By: Richardj.L. Cooper

A

ABSTRACT

This paper outlineS the effects that the military has.on youth

labor force participation and the youth job market, and concludes

that the military's demand for labor is an important determinant of

both the size and composition of the youth labor force. Changes in

the military's demand for labor can halle significant effects on the

youth labor force, and other variables' affecting American youth. The

military also exerts a major influence on the supply-side behavior

of the youth labor force. Of mosesignificance for the civilian

labor market is the human capital that former service members

bring back when they rejoin the civilian workforce. Thus youth unemployment rates ought to be 4pfined in ,terms of the total labor

force,not just in terms of the civilian labor force. Developing

such appropriate measures of youth unemployment could lead to more

informed policy decisions.

INTRODUCTION

Youth unemploymethi has become an increasingly important problem

in recent years, Youth unemployment rates Mve averaged nearly 200

since 1974, and have run aehigh as 40% in 'some segments of the youth

labor market (e.g., black teenagers). Developing and implementing

solutions to the'youthunemployment problem has therefore become a,

major concern throughopt the policymaking community.

,./ Before the causes of and possible solutions to the youth unemployment problem can be identified, however, it is.both important

and necessary to deverop a better understanding of the youth labor

market -- specifically, -youth labor force participation and the youth

job market. Because the military plays.such an important. role in

the youth labor market, the purpose of this paper is to highlight

some of the effects that the military has on youth labor force

participation and youth job market.° As such, this paper is intended

simply to provide an overview for the; labor economist not too

familiar with the military. That is, it is not intended to break new ground--either theoretically or empirically--but rather to put

, 222
th. - 196 -

into a common framework many of the issues addressed separately by

both labor economists and military manponer analysts.

The next section of this paper examines briefly the effect df

the military's demand for labor on the youth job market. The third

section then turns to,foCus on the role of the military in youth

labor supply and human capital development. The last, section examines

-the implications of the military's role'in the youth labor market for

the development of meaningful labor statistics.

THE MILITARY'S DEMAND FOR LABOR AND THE YOUTH JOB MARKET

'Because the military is such a major claimant of the nation's

resources, and of youth labor in particular, it is useful to begin

by discussing the military's demand for labor. Whether the mil- , itary enters the youth labor market through traditional market

allocating mechanIsms (e.g., wages and other inducements to join) or

through nonmarket allocating mechanisms (e.g., the draft), the military demand for labor can have important effects on the size and

J i composition of the youth labor force. That s, the youth'labor,

force is shaped in significan ways by both the militav's aggregate

demand for labor and the more specific policies that govern the use

of military personnel during their service careers.

The discussion below therefore centers on the demand for military labor. If, as is not the case, the military's demand for labor

made up, say, 0.1 'percent of the labor force, then the subject

might be interesting, but of only academic,interest. If, as is

actually the case, the'military makes up'i significant'shaie of the

Touth labor force--in the neighborhood of 10% to- 20%--then this

question of demand becomes of P articular,policy importance. To the,

extent that labor statistics are not designed to-measure the militaily's effect on either the size or

, composition of the youth laborforce, these statistics are accordingly less useful for policy purposes.

Recogiizing'the importance of the demand side of the equation,

4
- 197 -

the discussion below focuses first on the overall size of the military, and,then turns to the impact of the military on the youth

labor market in particular.

Size'of the Military

. , With"its four to five millio employees, depending o who iscounted, and its approximately $400 billion worth of land and capital

in fiscal 1976, the.Department of Defense is the single largest employer of resources in the nation.'The military's capital stock

consists noteofily_of such obviously military items as tanks, ships,

and aircraft, but also the more mundane items such as forklift trucks,

buildings, desks and file drawers, and so forth.

The Military's labor force includes about 2.1.million active

duty-uniformed personnel, about 1 million reservists (i.e., the socalled "week nd warriors"), 1 million direct -hire civilian personnel

(of which all ut-600,000 are in white collar occupations, while the

remaining 460,000 are in blue collar jobs), about 100,000 indirect--

hire,civi an personnel, 500,000 contract -hire civilian personnel,

and about 250,000 nonappropriated fund employees.

1

re"

qk

1. "Direct-hire" civiliansare those civilian employeep maintained

directly on the defense payroll. "Indirect-hires" are those

foreign and/nationals working on U.S. installations abroad who

are formal]. employed by the host nation, but whose costs are

actually p id by the U,S. military through areimbursement program. " ontract hires" are thosesindividuals who, through actually employed 11y civilian firms, perform contract services for

the, military such as aircraft maintenance, janitorial services,

and kitchen duties, among others. (Contract hires do not, however, include, those civilian workers'engage& in the production

of equipment and construction ultimately. purchased by the military.) "Nonappropriated fund" civilian personnel are individuals

who are paid out of funds not budgeted out of Congressionally

appropriated funds, Theselare largely employees of military

comOsaries and post exchanges who are paid out of the funds

entrated by the sale of goods and services.
L - 198 -

For almost all of the civilian peysonnel, however, there i /little

difference between their ehployer and regular civilian employers. 2

, What is unique about the military, though, are military personnel.. -}

For the most part, the discussion in this paper will focus on active

duty personnel, but it is important to recognize that the nearly l'

million reservists repreSent an, important type of second job holding,

and need to be examined accordingly. 3

Even when we focus only'on military personnelFigure 1 makes it

' cleaethat military perstInnel compiise a significant portion of the

U.S. male labor force. In the immediate post-World War II period,

male military personnel made up about 3.5% of the total male work

force, but jumped to nearly 7.5% during tie Korean.conflict. Between

the Korean and Vietnam wars, male military personnel made up between

5% and 6% of the U,S. male labor force. After jumping up to more

than 6.5% during the Vietnam war, the proportion of the U.S.-male work

force in the military has declined to between 3.5% and 4% during the

past several years. ThuS, although hot a dominant factor in the male

Work force, the military has nonetheless maintained a significant

share Of the labor force in its ranksvdurang the entire post-World

War II period.

The Youth Labor Market

Because the military maintains a "closed" personnel system, the

forepang understates it an important way the impact of the'military

2. There are some "dual-hatted" civilians, primarily maintenance

techniciani, who though employed as civilians, are also members

of the reserve forces. . 3. For a discussion of moonlighting and the reserves, see Bernard

D. Rostker and Robert Shishko, Air Reserve Personnel Study:

Volume II. The Air\Reserve Forces and the Economics of Secondary

Labor Market Partitipation (Santa Monica, Ca.: The, Rand Corporation, ,1973); end Robert Shishko and Bernard D. Rostker,, "The

Economics of Multiple Job Holding," AmericanEconomic RevieW-,

vol. 66 ('June 1976).

2

4
percent

r'`

(2) 18-24 year-olds

, / \

f

, t

....- 30 t

, \

- \

Ils

c.-

20

10

(3) 18-19 year-olds

vow. (

(1) All

/ .

_/'

4

7,

1 I I i I U I (

i I I 1 I r 1 1950 1955 1969 1965' 197D

I

19 75

Fig. 1 -- Number of Male Military Personnel Relative to the Relevant

Ma? Labor Force(percent): (1) All, (2)1.8-24 year-olds

'and (3) 18-19 year-olds. -ON226
- 200 -

o the youth labor market. .Thatjs, the military maintains an "up

ih ough the ranks" personnel system, with littlelateral entry. This

means, \ then, that the military's major influence on laSor markets is

at the\entry point,.typically the crop of recent college graduates

for the officer corps, and the recent crop of high school graduates

for the enlisted ranks (although flout 35% of enlisted recruits

are non-high school graduates). Aia result, about 90% or all en- Ei-steld-pernrffriciin the military the ages of seventeen and

twenty years old. 4

i The implications of the closed military personnel system for the,

youth labor market can be seen in Figu\re 2, which shows that betimen

the 1950s and mid-1960s roughly half Or all young men reaching'military age served in the military. By thid- to late-1970s0 however,

tantially larger youth cohort

e young men would serve in

the military at some time during his lifq

A somewhat different perspectiv on the effect of the military's

demaftd for labor on the youth labor rket ,can be seen in Figure 1

shown earlier. Specifically, Figure also shows the proportions of

1 the eigiqeen- to nineteen-year-old riatt,labor force and eighteento twenty-four-year-old labor force emPloye

\ by the military. According to either of these measures, we see hat between 20% knd 35%

of the youth labor force was employed by theimilitary from the time

of the Korean buildup' through the Vietnam war. Only since the end oit.

declining military force sizes and a sub

meant that only about one out of every fi

4. Because of the "oldest first" draft polic of the 1950s and 1960s,

significant numbers of-personnel then ent red the military in

their mid-twenties.

5. I fait, these demographic trends, more t

responsible for the demise of the draft.

pulition base not only-Ncreated enormous .small few would have to bear the "burden"

while the vast majority could escape sere

possible to attract sufficient numbers o

the threat of a draft.

227

an anythingIelse, were

That is, the growing

inequities (since a

of military service,

ng), but also made if

volunteers without

;(4.

4
2200

2000

1800

1600

1400

1200

D

c). 1000

600

400

200.

Ic

7

19 year old

male age cohort* ,

19 year old eligible

male age cohort *

*3-year .moving overage

Qfficer plus

enlisted

Active-plus reserve

accessions.

Enlisted

accessions,

0

I 1' I 1 1 0 55 60 65 , 70 75 80

Fiscal year

Fig. 2. Military manpowcer procurerpent and ',Population size

228
202

to Vietnam war, with the corresponding reduction in military force

strengths and simultaneous increase in the youth'population,cohort,

has the proportion of_the youth cohort employed by the military

6' dropped significantly--at its current level, to about 10%. Thus,

no matter how we measure it, the military is an important, and in

- some cases' the dominant, player in the youth labor market. Accordingly, policy changes affecting the numbers of yOung men entering

the military -- and /or the nature of their military service--can have

A significant impact on the size and composition of the youth labor

force.

In addition to the quantitative side of demand, there.is an totimportant qualitative aspect to the military's participation in

the youth labor market. Specifically, the military uses a variety of

, criteria to screen potential applicants for enlistment. The individual

must first take a mental aptitude test, the results of which are used

to clasify*the individual into one of five so-called mental categories

(with Mental Category I representing the top 7% of the population and

Category V representing the bottom 10%). ,Those classified into Mental

Category V are legally ineligible to serve. Others ineligible include

those who fail to pass the medical examination, as well as those wno

fail to meet certain other criteria such as a check of police records,

K talks with high school counselors, and so forth. Overall, about 40

out of every 100 applicants forenlistment are rejected outright.

Moreover, of the remainder, the military only allows some Mental

Category IV (i.e., below average) and high school dropouts todoin.

...--- The end result of supply behavior (on the part of the individual)

' and this demand behavior (on the part of the military) can be..seen

in Tables 1 and 2, which compare the mental aptitude and educational,

'6. Note that these-measures of the military's penetration exclude

members of the reserve forces.
- 203 -0

TABLE 1.

DISTRIBUTION OF E LISTED ACCESSIONS AND THE GENERAL

18 TO 21 YEAR-OL E POPULATION BY MENTAL CATEGORY

(percent)

Vt.

-US Population:- Male 18-21 %gar-Olds

Mental

Category Draft AVF

All Non-College

All Non-V All Non-V

I 6 3 7 8 2 2

II ? 31 32 28,, 31 22 25

III 457 34 38 39 45

-41* IV 19 8 21 23 24 28

V

10 13

' 'c TOTAL 100 10Q 100 100 100 100

Tource: Office, ssistani Secretary of Defense (Manpower and

Reserve Af airs)

230
(

TABLE-2._

EDUCATIONAL

Maximum

Educational

Attainment

ATTAINMENT

AND THE U.S:

(percent)

Enlisted

Accessionsa,

OF

MALE P

LISTED ACCESSIONS

ULATION

U.S. Male Populationb

All

18-22

Not in School

18-24 18-21

Blue Collar

Draft AVF 25-44

College Grad. 3 1 8 7 .1

3.

Some College 13. 5 26 13 12 12

High School Grad. 54 59 41 46 49 48

Some High School 26 , 35 19 22 w '27 . 21

Elementary 4 1 6 11 12 16

aSource: Office, Assistant Selretary of Defense (Manpower and

Reserve Affairs)

b

Source: U.S.,Bureau of the Census and U.S. Bureau of

Labor Sta sties.

odes GEDs--i.e., 'thoe who have passed a general'high school

equivAlen test, but who do_notpossess a high school diploma.

7
= 205

)1'

-fteldj attainment, rOispectfirely, of military recruits with their civilian

counterparts.

7

In 'general, these comparisons shr.thaf the military

takes in a reasonably representative sample of ihe nation's youth. In

terms of the nation's policy toward youtfiltd.yoqh uneTployMent

Tables 1 and 2.establish the imifortant.point that the military does

not draw narrowly from any one segment of society;

8 rather, the military plays a significant role in almost all segments of the male youth

labor force.

Finally, the discussion of the military's participaOlin in the

youth labor market would not be complete without giving consideratioAk

to two special issues: the use'of Women in the military and'the

4 racial composition of new recruits. Although participation of women

in the armed forces was limited by law to no more than 2% of military

personnel strengths prior to 1972, these restrictions are now largely

.gone (the only major remaining restilctionibeing that women are not

7. That is, theory tells us that supply behavior should lead to fewer

(than a random sample of) very high mental aptitudevindividuals

joining the enlistgd ranks, since the enlisted ranks correspond

more or less with blue collar occupations and since these very N, I

high mental aptitude youth are more likely to attend college. On

the demand side, the military limits and in some cases excludes

outright) the numbers of below average mental apt'itude and nonhigh school graduates, solthe bottom end of the mental, aptitude

and educational attainment spectra will also tend to be "under -

represented."

Thus,lthe enlisted ranks of the military would

be expected to have prowrtionately fewer members from the very

top and bottom ends of the mental aptitude spectrum or from those

with post - secondary, and no secondary education. (Commtssidled

officers, on the other hand, are drawn exclusively from the

college graduate population.) Note, however, from Tables 1 and

2 that enlisted recruits are quite representative of-the upper

end of the noncollege civilian population- -i.e., those indiviiduals

corresponding most closely with enlisted occupations.

- 8. dther%ork by the author shows that the military draws a reasonably

representative sample of America? youth in other dimensions as

well, such as region of origin and sNio-economic background. See

Richard V.L. Cooper, Military Manpower:and the.AllrVoltinteer Force

(Santa Monica, Ca.: The Rand .Corporation,' 1977).

232

00
- 206' -

s1

to be employed in "combat," though the defination of combat is in the

process of change). As a result, participation of women in the armed,

forces has increased significantly over the past five years, up to

more than of total military personnel today--that is, currently

.there are about 110,000 women in the Military. .Further increases

are planned, such that women in the military should number between

150,000 and 200, 00 by the 1980s. Moreover, women are being used

in a variety o "nontraditional" jobs. That is, whereas women were

. E 'once limited mostly to certain medical Tdakiministrative occupations,

today they are entering a variety of occ11pational areas such as

truck driver, aircraft mechanic and, in some cases; combat support.

Thus, the military, which once only had a minor role in the female

labor market, is now taking a much more active role and will continue

to do so in the future.

For a variety of reasons, mostly economic 'in

. nature., the military'enjoys an even more subitantial participation in the black uth

labor market than it does for the youth labor market as a whol6. Unlike some areas of the civilian labor market, where blacks often face

'inferior economic opportunities, the military does not discriminate

'V

according to race (and this has been perceived by large numbers of

black youth). Asa result, blacks have historically served in:larger

numbers relative to their population than have nonblacks. Indeed,

whereas the military employs about 10% of today's total eighteen- to

twenty=f6ur-year-old male labor force,it employs nearly 20% of the

black male eighteen--to twenty-four-year-old labor force.

9

Increasing

a

The proportion of new 'recruits that are black has in fact increased

significantly Sever the past ten to fifteen years, from about 8%

in 1960 to some6-18% today. The primary reason for this increase

is the increasing proportion of black youth found eligible for

military service. During the 'd-1950s, only, about 12% of black

youth were classified inT.o Me tal Categories I-III (i.e., the upper

70% of the mental aptitude s ectrum)--that is, the so-called "prime"

Ahlanpower pool. .Today, betty en 45% and 50% are so classified. As

a result, black s have incr ased their share of this:prime manpower

group from 2.9% n the 'd-1950s to more than 7% teaay.

233
participation of black college graduates in the officer corps is

perhaps even more impressive. Whereas Slacks made up only 1% of

all new officers in 1960, today they make up about roof the total.

Thus, not only is the military an important factor in the

youth labor. arket in total, it is of increasing importance for

certain segments of that market, especially minorities and women.

.Stated more simply the above discussion has shoWn-that the military is an important factor in the demand side of the youth labor

-force.

Secular, Cyclical, and'Seasonal Variations

in the Military's Demand for Labor

Because the military plays such an important role in the youth

labor market, variations'in the military's demand for labor can have

an important effect on both the size of the civilian youth labor

force and on.the employment and unemployment prospeCts for.these

youths. Three kinds of variations in the military's demand deserve

attention: secular, cyclical] and seasonal.

As can be seen in Figures 1 and 2 shown earlier, there has

been a secular trend toward smaller military forces since the mid1950s (excepting of course for the Vietnam par). Looking ahead,

however, we expect military forces to stay at approximately their

current levels--about 2.1 million members in the.active duty farces.

In other words, barring major unforeseen circumstances, such as

. another war, we should not expect to see major Secular trends 'in

the size of military forces, And hence in the numbers of youth employedloy the .military.

. In the case of cyclical variations, ;the individual military

services seem to exhibit some cyclical recruiting patterns (e.g.,

the Navy seems to.still have a four-year recruiting cycle," which

0

is a result of the Vietnam buildup and drawdown). For the Department

of Defense as a whole, however, there.is not much cyclical variation

234.

6
C

- 208 -

in the demnd for labor, si ly because the cycles of the individual

services tend to be offsetting. Thus, cyclical variations in the

demand for labor on the part of the military would not seem to pose

significant problems for the youth labor force.

Finally, there are significant seasonal variations in the

military's demand for labor, but this seasonal variation is a supplyside not a demand-side phenomenon. That is, the military has adapted

itself to the seasonal variations in recruits' seeking to join the

military. Specifically, the military recruits particularly large

nuTbers in the summer and fall monthsi.e., following June high

school, graduation. Again, seasonal variations in the military's

demand for labor would note appear to pose significant problems for

the youth labor force.

10

In conclusion, there, have been secular and cyclical, fluctuations

historically in the military's demand for labor, but these have been

a Tesult of declining forces sizes and the Korean and Vietnam conflicts. ti

Barring another major buildup in force sizes, we would not expect

much further secular or cyclical variation in the size of military

forces, or in the military's demam-for youth labor: Although- there

is in fact considerable seasonal variation in the military's demand

for labor, this works with, rather than against, youth labor force

participation, since the military is merely responding to The supply

of new recruits enter the job market.

MILITARY. SERVICE AND YOUTH LABOR SUPPLY

Examination of the supply side of youth participation in the

military is impoitant for -two main reasons. First, the continuity

10. During the dtaft,' there was far less sea oval variation in the

demand for labor, since the military cou d simply draft to make

up for. recruiting shortfalls in the "off'! ecruiting months.

This probably caused greater disruption fo the youth labor force'

than the, current seasonal variations which are in response to

the supply of individuals entering the youth job market.

235

s.

tr o

O.
L

209

between military and civilian work experience is much greater than

it sometimes perceived. That is, there are-sufficient similarities

between'military and civilian employment that movement between the

two, sectors is frequently quite easy. Every ear, hundreds of

thdUsands of young men leave the civilian ybuth.labor force to join

the military, just as hundreds of thousands of young men leave the

military to rejoin the .civilian youth labor market.. This means that

modest variations iiithe variables affecting the desirability of

military and civilian empl4ent craii have a significant effect on

the flow between military and civil youth labor markets.

Second, military employment provides the individual with the

opportunity to accumulate sigriificant amounts of human capital.

This human capital, in turn, can frequently be used to obtain subsequent civilian employment. As a result, the military can affect.

not only the size of the civilian youth labor force, but also

qualit tive aspectsof that manpower pool, such-as the skill and

educa ion mix of members of the youth labor force.

The following discussion examines these supply-side aspects

4-

41.

of military employment, focusing first on the decision to'join

the military, then on the military work experience, a d, finally,

on the decision to stay or leave the military.

The Decision to Joid the Military

When an individudl-joins the military, he or she leaves the

cilielian work force. Because of this, it is important to examine

various aspects of the decision to join, as discussed below.

, Factors Affecting the Enlistment Decision

Models of enlistment supply have been the subject of a considerable amount of, research for at least ten years. Among those studying

the enlistment decision are economists, sociqlogisti, and psychologists.

The ecdhomists tend to formulat the enlistment decision as a model

of occupational choice, where the individuirl presumably weighs the

23 6
- 210 -

\\,

various advantages and disadvantages of alternative employment

options and chooses the one that maximizes his or her utility.

Although these are certainly not the only models used to explore

the enlistment decision'process, they are probably better developed

than those of the other disciplines.

11

Genera115, speaking, models of the enlistment decision, irre ective of their disciplinary origin, have highlighted a number of

factors as critical in the individual's decision about whether or not to enlist in the military. Important among. these factorS are

certain economic variables, including the wage rate offered by.the

military, the potential_earnings-from-civilian-employment-f-and-th

chances for obtaining civilian employment (as reflected by the unem- _ployment.rate)_. Economic factors are certainly not the only variables to affect the individual's decision to enlist, as surveys

lop consistently show a number of other factors to be as important,

such as the training and job experience offered by the military,,

the chance to travel and "see the Oorld," patriotism, and a host of

other factors-too-numerous to-mention-.-

The point is simply that. the enlistment decision is shaped

by many different things, so that modest changes in one or.more of

these can significantly'affect both the number.and types of individuals

who join the military. To illustrate, a variety of economic supply

studies conducted over the past ten to twelve years show. that if the

military pays a wage approximating that earned by comparably aged_

and educated civilian wthrkers, the military can attract bdpween

4 11. As various data collection techniques such as surveys have

. improved in recent years, so have the enlistment decision

models of the other academic discipline's, especially°

sociology and psychology.

4."

r.
- 21f.

15$ and 20% of a given youth cohort into military service.

12-

Moreover, most of these studies show that the elasticity of supply

4ith respect to pax is somewhere in the neighborhood of 1.0

actually, estimateih-of;the enlistment supply elasticity range from,

about 0.5 to 2.0, 'With' 1.0 representing probably the best, guess-#..1,

( That Ls, a 10% increase in the military wage rate will yield

approximately a 10% increase in the supply of labor to the military.

Conversely, as.the chances for obtaining civilian employment decrew, the supply oflapor to the military also increases. Again,

most studies done over the past several years show unemployment

elasticities of between 0.1 and 0.4. In other word, Ai 10% increase

in the unemployment rate will lead to a 1% to 4% increase in en-,

listmeni-supply.

r The implication of these results is clear.' Specifically, as

the military makes its employment'offer more (less) attractive

. relative to'civilian employment opportunities, the military can

expect to get more (fewer) individuals to join. In other words,

there is not a well lefined line separating- military- -and civilian

. employment. This means that in studying the factors affecting the

youth labor force and youth unemployment, it is important to recognize

the role the military\plays in trying to attraet young men and women.

-Military Earnings

As indicated' above, previous studies have shown'the importance

of military pay for enlistment supply. In this regard, Figure 3

12. See for example, Dorothy Amey! et al.; Supply Estimation of

lot ,listees to the Military (McLean,. Va.:General Research Corporation,

1976)-; Pre=AVF-Studies with Enlistinents;'L-in-Richard

Cooper, ed., Defense Manpower Policy (Santa Monica, Ca.: The Rand

Corporation0f4thcoming); Stuart H. Altman and Alan E. Fechter,

!'The Supply OfAllitary Personnel in the Absence of a Draft,"

American Economic Review, Vol. 57 (May 1967); and Harry J. Gi man,

"The Supply of Volunteers to the Military Services," in Studi s

Prepared for the president's Commission on an'All-Volunteer A ed

Force (Washington, D.C. -: Government Printing Office, 1970).

< .

lop

238
Civilian - All 18 to 24 year old

5000 full -time. employed males

4000

3000

2000

1000

;- 6

.... / 1

A..0 ... 1 ... ...." Q ..... ..... "" '-Civilian -17 to 20

..........- year old male full-time ,,

.......

...... --"-e--,,employed high-school gradubtes

- ...... .- "'Military - Average RItItC for first two years enlisted

1950 1955 1960

Sourci: See text.

1965

'Fig. 3 Annual Military and Civilian Wages

I

1970 1975
V

4.

- 213 -

wYslows that military pay for new recruits has changed sithificantly

during the course of the past ten years. Specifically, with the

pressure of the draft, there was no "need" to pay military recruits

a market wage. Indeed, as shown in Figure 3, the wages earned by

military ecruits remained virtually unchanged from 1952 through

1965, and from 1965 to 1971 they received cost-of-living pay-

° . increases. Although these wages were icient to attradt some

"true volunteers," the draft or threat,of the draft rovided the.

vast majority of new recruits.

13

Given the demographic trends shown earlier in Figure 2, it is

clear how inequitable the selective service draft had become by the

late-1960s.. That is,.only about One-fifth of the male military,

aged population would have to bear the burden of serving, while the

other four-fifths could find tore lucrative civilian employment.

The President's Commission on an All-Volunteer Armed Force recommended

that this pay discrimination be eliminated. CoKgress concurred old

raised recruit pay to'a level earned by comparably ged and educated

1 civilian workers (basically seventeen- to tmenty= old-high

school graduates), the results of which can be-seen n Figure 3.

The effect of this pay raise was substantial. In fact, by 1975f

the military had doppled the number of true volunteers jo 'ing

relative to ten years earlier.

Again, the more general point to be drawn from this is that as.

the military changes one or more aspects of its employment effer,

there will be a significant impact on the size and composition of

the civilian youth labor force.,

13: During the 1950s and 14.p60s, about one-third of new recruits

mere"true volunteers"about one-third were "draft-motivated"

volunteers (i.e., individuals, who volunteered in order to avoid

being drafted but who wourd not have enlilted in the absence

of a draft); and about one-third were draed outright.
21 4 -

-Pre- Service Employment and the Military

It is-important to recognize that many individuals joining the

military dome not only from the ranks of the unemployed, but directly

from previous civilian employment. A recent survey. by Gay, for ex-

. ample, shows that about 20% of the eiOteen-year-old enlistees'in

1974 were unemployed prior to enlisting, but that about 35o were

employed part time before enlisting and 45% were employed full-time.

14

For those individuals employed prior to joining the military, there

were obviously aspects ofthe military employment offer that they

- fund superior to their then present civilian employment. In other

. words, the military does often compete directly with civilian employ-

. ers for young recruits. This is not altogether surprising, though,

given the kinds of work "that young members of the labor force often

find available. That is, not only is it harder for these young

members of the labor forck e to find work, as reflected by high youth

unemployment rates, but the kinds of jobs they can obtain frequently:

offer less in the way of wages, challenging work, chance to accumulate

human capital, and so forth, than the military can offer. Yet, it

is precisely for these reasonsthat many young men-and women see

military employment as their first or second job after leaving,

school.

' The implication of this is-clear. Although there are many young

men and women who would 'not enter the military under any circumstances"-,

there is a sufficiently-large portion of the'youth labor.force that,

given the proper set of inducemen ;s, would (and in fact does) pin .

the military. Thus, analysis of the youth labor market--both supply

and demand--musvtake the military into 'account.

14. Source unpublished tabulations provided by Robert M. Gay.,

The Rand torporation, 1976.

241

ti

S.
215 7

The Military Work .Experience

Once in the military, the individual is obligated for a period

of service, generally running three to six years. That_is, unlike

civilian employers, the military can obligate for a period of

service .15 The discussion here looks'briefly at the nature of this

military Work experienceincluding the occupations that new recruits afe likely to get, as' well as the accumulationof human capital by military personnel. ° ° Occupational Mix of the Military

,Althoughit is Lost common to view the militaryin terms of the

combat arms component, it is important to recognize'that the mili-

. tary consists of a wide range of011iupations', not at all unlike

what one finds'in the civilian sector. In ha,Tablc 3 shOrs

%

. - s that the combat arms occupations Make up only about 10,% of the en-

. .' ,,,,, tire enlisted'wvk force.' The, other 90% is made'up by .uch diverse

occupations as airCraft mechanicspmedical and "dettal specialists,

f V''

radar repairersfrad io oper/tors, carpenters and plumbers. military

police, intelligence experts, vehicle mephanics, and a host of

other specific occupations. In other wordi, young men and women

6- :A.

joining the military find virtually as many different types and

kinds of jobs in the service as they could in the outside civilian

world.

Moreover, as part of the.iove twthe al olunteer force, it

is common to find young men and women Yoing the military to. work

specifically in a particular job or at a particular military instal-

( lation, or with a particular military uhit.Once frequent stories

1°

about the individual who was an'engineer but forced to be. an Army

cook, or the cook who was forced to become a-4- vehictemoch-anands.

15.. There are ways, however, for the individual to get out of his or

her contractual obligation.

242
216

TABLE 3.

DISTRIBUTION OF THE FORCE BY

OCCUPATIONAL AREA: FY74a

Officer Enlisted

Occupation Percent Occupation Percen

Executives 1.6 Combat Arms 12.3

Tactical:00rations 40.8 Electronics 10.4

Intelligence 3.2 Comm/Intelligence 6.7

Engineer/Maintenance 15.6 Other Specialists 1.9

Scientists/Professionals 6.6 r.Elec/Mechanics 21.6

Medical/Dental 9.4 Medical/Dental 4.6

Administrators 12.8 Admin/Clerks 18.4

(Supply 6.1 Service Supply- 11.0

()thee 3.8 Craftsmen 4.6

Otherb 8.6

)/

aBased on "primary" occupation -designators.

bTraining, Miscellaneous, and Other,

Source: Data were-furnished by the Office, Assistant Secretary

of Defense (Manpower and Reserve Affairs)

2 4 3
- 217 -

so forth, are not only not common now, they are rare. The military

services have made significant strides in better matching intviduals'

tastes and aptitudes with actual job assignments:

16

The foregoing ig not meant as an advertisement for mi-lifary

Service, but rather as an indication.tof the very wide range of

. . occupational specialties that young men and women joining the service

N ,n can and do engage in, and as evidence that many of these young men

and women are in these occupations as a esult of,their own choice.

The implications for the civilian youth 1 bor force are obvious, in

that individuals leaving the military bring with them a set of skills

acquired during their military servi&e. To the extent that young men

and women are engaged in jobs that are found in the civil sector

and than these are the, types of jobs in which these individuals

, would like to continue working, there is the important issue of the

accumulation of human capital, as discussed below.

-4),

...

--,. The Accumulation of Human Capital *

- .

. As indicated Above, military work experience can he characterized

in economic terms, as the accumulated human capital,. Some of this

human capital will tie of a very general variety, such as the maturityA

that goes.along with the individual's early job choices. larts of

this human capital are also very specific--in fact, they are specific

entirely to the military. Examples of this would be the use of

mortars, marching, drill formation, and.so forth. But a substantial

amount of uman capital may be of the general occupational type

thatiS ransferable to similar jobs in the civil sector.

This human capital formation takes place in several ways:

through formal school training, through on-the -job, training (OJT),

and through actual job,experience. The militIffmaintains one of the

largest educational establishments in the nation. All military re16j The major problem here, however, is that young recruits are

frequently unaware of what. they do and do not want.to do. In

fact, for many it is this indecision,that led to their joining

the Military.

244
v 218

cruits attend basic mi litary training--i.e., the so-called "boot

camp." In addition, about 95% of all new 'recruits attend formal

ethnical schools. In these schools, whjch Mast from a-few weeks.

much as two years (and average three ta four months in length),

the individual is taught about his or her new job. The classes

consist of formal lecfurei and training, demonstration, and actual

hinds-on experience.

The on-the-job training actual job experience in the military constitute another form uman capital accumulation. In gct,

given the often theoretical nature of formal technical school tr414-

ing, the OJT and job experience may constitute the more significant

form of human capital accumulation.

In sum, the military work experience is likely to represent a

significant amount of human capital formation by, the individual. And

this fact has not gone unrecogOted by potential recruits, as military training and job experience are two of themost frequently

cited reasons for joining in the, first plate.

. The Decision to Stay or Leave

Upon completing the enlistment tour, the individual must decide

whether to remain in the military (if he is declared "eligible") or

to return to the civilian labor force. The discussion focus on this decision to stay or leave by outlining, first, the facto s

affecting the reqlistmeni decision: second, what individuals do

in their post-service employment; and third, the migratory effects

brought about by military service.

_Factors Affecting_the Reenliatment Decision

As has been the case-with enlistment supply, reenlistment shpply

has been studied extensively during the past ten years or so. Again,

a number of different academic disciplines have been brought to bear

on theiissue, including econoMics, sociology, and psychology, among

2.
2 9/

others. The economic studies tend to show that reenlistment supply

is quite sensitive to military and civilian pay opportunities,

Specifically, the elasticity of supply with respect to pay has been

shown to be in the neighborhood of 1.0 to 4.0, with 2.0 probably

representing the best guess.

17 This means, then, that reenlistment

supply is quite sensitive to military pay. That reenlistment supply

should be more sensitive to pay than enlistment supplysis not surprising, though, since individuals facing the reenlistment point are

thope who have already entered the military. That is, the initial

enlistment Aecision screens out those individuals most opposed to

military service'on "taste" grounds, so that reenlistment supply is

likely to be drawing from a more homogeneous manpower pool.

For a variety of technical reasons, studies have been less

successful in pinpointing the responsiveness of reenlistment to unemployment rates. Nevertheless, the conventional wisdom is that

reenlistment supply is in'fact quite, sensitive to unemployment, and

a casual review pf the evidence bears this point out. Clearly, howeve, more thorough study Of-this it-sue is warran e e ore e in

itive conclusions can be drawn. 4,

Noneconomic factors havalso been shown to play an important

role-in the individual's decision about whether toeenlist. A

'yarietyof studies, mainly using survey techniques, find that certain

attributes of military service will either persuade or dissUade an

'individual from reenlisting. For example, job security is one of

the most frequently cited reasons for individual ciding to reenlist,

while the%1As.of Iersonal freedpm is perhaps the dominant on- economic rea n why individuals choose to leave military se ice , after,the 'end o theiX-first term. Since these are clearly not

17. ,See, for example John H. S eenlistment Bonuses and FirstTerm Retention (S onica, Ca.: The Rand Corporation, 1977);

and*Gary R. Nelson,'"Economic Analysis of First-Term Reenlistments in tim),emy, in Studies Prepared for the President's

Commission, op.cit.

a
- 220 -

only factors that-affect the individual's reenlistment decision, the

larger point is simply that reenlistment is a function of many variables, such that changes in, one or more of these variables can in

the long run significantly affect the numbers and types of individuals

°Teen ing the civilian youth'laborforce.

Pos -Service Employment

As indicated earlier, military service may provide the opportunity

to accumulatthuman capital--human Capital that can be applied to

subsequent civilian employment. The extent to which the military doe!

lead to the accumulation of human capital has in fact been the subject

of considerable study during the past five to ten years.

18

These studies of Veterans' post-service activities'have tended

to focus on ope or more of the following three issues: general employability of military veterans, the extent to which veterans use

some of the knowledge and experience gained during the military

.0 employment for their post-service civilian employment, and.the earn\

ingsof veterans as compared with nonveterans. Although these

.Istudies differ:substantially in their specific'findingsonegenerai

theme does emerge. Specifically, military service in general does

lead to the formation of human capital that can be app to postservice civilian employment. As would be expected, though, the degree to, which human capital accumulated during military service can

18. See, for example, Eva Norrblom, An Assessment of the Available

Evidence on the Returns to Military Training (Santa Monica, Ca.:

The Rand Corporation, 197.7); Eva Norrblom, The Returns to Military and Civilian Training .(Santa Monica, Ca.: The Rand Corp-

. oration, 1976); Robert B. Richardson, "An Examination oft the

Transferability of Certain Military Skills and Experience to

Civilian Occupations," unpublished. -Ph.D. dissertation, Cornell

University, 1966; William Mason, "On the Socio - economic Effects

of Military Service," unpublished Ph-.D. dissertation, University

of Chicago, 1970; Adele P. Mansell and Gary R. Nelson, The,

Estimation of Training Premiums for U.S. Military Personnel

(Santa Monica, Ca.: The Rand Corporation, 1974); and Zvi Griliches

and-William Mason, "Education, Income, and Ability," Journal of

Political Economy, vol. 80 (May/June 1972).

.247
221

be transfered to the civiliansector is obviously greater for individuals serving in military occupations with more direct civilian

counterparts.

In general, these analyses'of veterans', post-service employment

activities show, first, that veterans trained in skill x during the..

Military tend to'be employed in larger n9mbers in skill x in the

civiLsector than would be implied by a Lere random sampling. In

other wordspaveterans tend to gravitate towards the kinds of occupations they, had in the military, Thus, military service may have a

potentially important effect on shaping the kinds of occupations

that members of the youth labor-force will enter in their post-

. service careers.

19 Second, individuals employed in a post-service

occupation similar to their military occupation'tend to show higher

earnings than their veteran counterparts whose civilian occupations

'are not so directly related. In other words, v terans' post-service

earnings in a given ocoupation are higher ifie veteran's military

0 service was in a related occupational area.

Part of the above-cited earnings differential may be due to a

.so-salle4 "certification effect. That.is, for'individuals such as

iiinorities and high school dropouts whose economic. and employment

opportunities are otherwise more limiied, satisfactory completion of

military service may be viewed by,potential employers as an indicator

of greater employability. Casual examination of the evidence bears

19. Some of the correlation.between individuals'-military and post:-

service activities is explained by the fact,that'individuals'

pre-service and Military occupations are also correlated, so

to the extent that individuals' pre- and post-service occupations

correlate, 'so will; their military and post-service occupatiobs.

20. It is important to note that the studies are far from unanimous on

this point. t However, the studies that fail to find much of a

relation have generally been hampei-ed by data problems. In cases

where there:dre sufficient data, they tend to show their positive

earning? effect. See, Eva Norblomm, An Assessment of the Available

Evidence, o cit.

248
- 222 -

this point out, as black and high school dropout veterans 'both tend

to see greater gains from their military service in subsequent civilian employment than do white high school graduate veterans. In gener-

- - al, however, the evidence is sketchy, so further study of the effects

of military service on post-service earnings and employment opportunities clearly deserves more careful attention.

Finally, military service has a significant effect on post-sere employment activities through the educational and training benefits offered by the Veterans Administration. The post World War II

G.. Bill educated literally hundreds of thousands of former servicemen. Although the nature of the G.I. Bill-has recently changed,21

the military will continue to represent otentially impciltant source

of educational benefits for America's outh population. Only recently

have the effects of military service on post- service education and

training begun to be studied,

22

so all effects are far from under-

. stood.

r . The general conclusion to emerge from this review of post-seryice

activities is tAt military service may have important effects not

only on the size.bf the youth labor force, but perhaps more important,

on the composition of that labor force--especially the education and

skiln possessed by former service personnel reentering civilian life.

23

21. The present day GI Bill is contributory. That is, for every dollar

that the service member contributes to his or her post-service

education "fund", the Government contributes two dollars.,

22. ,See, for example, Dave) M. O'Neill, Sue Goetz Ross, and John'T.

Warner, "The Effect of Military Training and GI Bill Training

on Civilian Earnings," iri Richard V.L. Cooper, ed., Defense

Manpower Policy, op. cit.v

23. Note, however, that this does not.imply.that the current milit

civilian mix of education, training, and experience is e lent.

Rather, it merely indicates, that there.may be some important effects.

The relevant policy questions--e.g., what is the "right" amount of

military education and training from a social perspective?--obvi -

ously need to bg,exadined in a broader context.

4,9

JO
223

° .4) At the same time, definitive conclusions about the magnitude of

these effects (olh in some cases, even their direction) must be

tempered by the host of Sample problems that the. analyst encounters

with most data sources. For.example, studies of post-service earnings

encounter a number\of selectivity biases regarding who serves in

the military &lid who does not, and who leaves the military and who

does not.

M6re definitive conclusions regarding these effects must therefore await improved data and analysis.' Indeed,,one of this piper's

principal recommendations is that special labor force data collection

efforts (e.g., the National Longitudinal Survey) specifically focus

on collecting information_aigt all civilian and military work

experience

Migration

Another possible effect of military service is on migratiori

patterns of American's youth. These effects have been relatively

unstudied thus far, but we do know that military retirees, for

example, tend to locate in disproportionately large numbers near

military installations. This is because of the benefits provided ' near military installation's (e.g.,, ,commissaries, free medical care,

and so forth). Military service may have.a similar effect on one -

term vete4ns in terms of geographic migration and location, and

these effects clearly needto be studied further.

THE MILITARY AND YOUTH LABOR STATISTICS

Two important conclusions emerge from the precedidg discussion.

First, the military's demand for labor is an important determinant,

of both the size and, composition of'the youth labor force. This

meanA that changes in the military's demand-for labor can have

significant effects on the youth-labqr market, includinioemploYmot

o 'prospects, the size of the youth labor force, and a host of other

A variables affecting American youth. Second, the military also

4
O

- 224 -

4'

exerts a major influence on the supply-side behavior of the youth

labor force. Specifically, American youth have demonstrated a

considerable degree of mobility between the military and civil

sectors. This includes both the initial decision about joining the

military and later decisions about whether'to remain in or leave the

military. Perhaps the most significant from the point of view of the

civilian labor market'is the human capital that former Service members

back with them when they rejoin the civilian work force.

The implications of these findings for thelmasurement and collec-

,tion of youth labor market information are severaly,

these implications concern (1) the_ measurement of youth unemployment

rates, (2) the collection of. aggregate'labor force statistics, and

( the co4ection of special labor force data depending upon the

particular policy purposes for which this information &4s to be used.

In this regard, laborforce statistics would seem to have at least

two important policy 'uses: (1) as an indicator for monitoring the

"health" of the'ecotiomy targeting particular government programs,

and (2) as a resetrch.tool for investigating the determinants of

labor supply and demand. The'discussi below briefly examines the

appropriateness of including military pe sonnel in labor statistics

from the perspective of each of these policy purposes.

In the first instance, labor foice statistics'--especially unemployment rates--provide useful measures for assessing the economy's

"health." ,The results presented in this paper suggest that, for both

theoretical,and-empirical reasons, military personnel ought, to be

viewed as being part of the labor force. On theoretical grounds,

military service is little different from_many other employers.

Military service is entirely voluntary, 24

so the military competes

as just one among many employers of the. nation's youth. This is

24. On theoretical gtounds, a case can be made (though perhaps

not too persuasively) for excluding the military during periods°

of the draft. That is, since not all military participation is

voluntary during a draft, military may be a more separate and

distinct entity from civilian employment when a draft is present,

251
-'225 -

borne out empirically as well. Not only is military service apparently seen as an attractive employment option by a significant Or-

. tion of the youth labor force -- between 10 and 20 percent of young

men enter the military--but members of the youth labor force have

in fact demonstrated considerable mobility between the military

and labor markets.

25 In other words, military service is an

integral part of theemployment pattern for a sizeable portion of

the Youth labor force.

terms of measuring unemployment rates, the above discussion

suggests that those employed in the military ought to be counted

. ,as part of the labor force. Indeed, there is no more reason for

excluding military persotnel from labor statistics than there ds

for excluding other government employees, or any other employees.

for that matter. Because those serving in the military are*employed,

historical measurements based on the civilian labor forge alone

have therefore overestimated "true" unemployment. rates, and in

some cases substaiitially.

26 For example, .the current procedure

overestimates overall unemployment, rates among black youth by some15

%to 25 percent--as opposed to a 10 percent or so overestimation

.of white yout, mployment rates--simply because such a large

'fraction of the black outh labor force is employed in the military.

27

Although this does not ismiss the problem of black unemployment, it

does Suggest-that the problem is not quite as severe as a casual°

25. If, on the other hand, the military was empirically a distinct

entity from the civilian sector, as in fact has been the case

in some countries,'then a case could be made for excluding the

military from labor force measures on empirical grounds. That

is, ifthere is little flow between the military and civil employment markets, then the inclusion or exclusion of the military from

labor force measures is both less interesting and less important.

26. That is, the current procedure omits those employed in the military from-the denominator of the ratio used to calculqte the unemployment rate.

27. Moreover, because the military tends to select the "cream of the

crop"-from,among black youth, those not serving in the military

would be expected-to be the ones that would have the most difficulty 'securing employment, from either military or,civilian employers.

,25-9
226

review of unemployment rates for black youth in the civilian labor

6? force would seem to imply. More gerally, when the purpose is one

of assessing how well the economy is doing with respect to employing,

the members of its labor force, the foregoing suggests that military

personnel ciaht to be included in labor force statistics.

rat the,same time, the foregoing should not be viewed to _mean

much change in the ways that data are collected or maintained. That

,is, although it seems desirable to define unemployment rates relative

to the total labor force, it is useful to maintain- separate-statistics

on the civilian labor force and onthe total labor force, as is now

the case. This is simply an example of a more general principle.

Specifically, labor force statistics ought to be, maintained for

major segments of the youth labor market--e.g., according to age,

race, geographic location, and so forth. That is, Collecting-and

maintaining labor statistics according to these major, segments of the

youth labor force-helps to pinpoint and spot certain problem areas.

N

' Because the military is in fact an important segment of the youth

labor force, this suggests that the current procedure of maintaining

separate labor force statistics for military and civilian yodth em- ployment is a valid one.

Besides their uses in monitoring aggregate employment an em-

, ploymentactivity, labor statistics' provide the core tool for investi-

, gating the deterMinants of labor force activity: participation, employment;earnings, occupational choice, and so forth;- In this regard, this paper hai argued vigorously that the military represents

a potentially important'source,of human capital for a sizeable

portion of American youth. The limited knowledge currently available

suggests that-military servide can have significant effectg on.

individuals' occupational choices, migration, and earnings, amon

other things.

Since the collection of individual data, both,cross sectional

(e.g., the CPS) and longitudival (e.g., the NLS), represents the primary source fOr investigating the determinants. of labor-force activity,

the Ibregoing argues strongly for incorporating information about-

, A 2 3
227

individuals' military experience into these data collection efforts.

For example, data colle6tion efforts such as the National Longitudinal

Survey, the Current Population Survey, the Census, and so forth, should

place greater emphasis on collecting relevant,information about the

individual's military participation. This would include age of entry

into the military, length of service, a , occupational specialty,

training received (both formal and on- e-job), and so forth. To the

extent that current data sources do not roperly measure this training

and experience, then we are failing to recognize the full effects of

alternative policy optioni. Therefore, greater attention needs to be

-directed toward collecting releVant information about the individual's

military experience, in conjunction with his or her civilian experience.

In general, this calls for greater coordination of data collection

efforts 'between military and civilian authorities. Such 'efforts are

warranted, not only because youth labor markets are affected by both

military and civilian work experience, but also because the controlled

nature of the military environment ,affords the opportunity to examine

certain yotiih productivity and behavioral parameters that can be ob- . served only with great difficulty in the civilian sector. For example,

recent surveys of military personnel have provided valuable information

about the relationships among individuals' attitudes, their earnings

prospects, and their actual oyment decisions.

28 This was made pos-

.

28. A number of recenedata collection efforts illustratethe point.

For example, to estimates the response'to the military's health

professionals scholarship program) a_survey_was_administered to

a stratified random sample of medical students across the country.

1 The- survey essentially asked these students to trace out their

supply curve,'not only with respect to values of the stipend, but

also to such other variables as tour commitment, residency policy,

and so forth. The results have been impressive, as prediction

errors over the past three years have been in the neighborhood of

S percent. In another effort, individuals' productivity on the job

was measured. Combined with the attitudinal information also col-

, lected and the information available from personnel files,valuable

insights have been gained about the determinants of productivity,

attitudes, and supply behavior.'

2 54
; 228 -

sible largely by the.fact that the military was able to first survey

these individuals, then ;rack and compare these individuals' actual

decisions with what they said they had intended. Combined with Alp

vast amounts of information available on the military personnel,

files, these types of surveys can be used to provide valuable information about indiViduals'*attitudeS, supgily behavior, and productivity--not only in,the.military, but in the civilian labor market

as well.

Areas where such cooperation and coordination between military

and civilian data collection efforts could prove to be particularly

4." profitable irelnde certain_angoing_and special civilian collection

efforts, including future National Longitudinal Surveys, the Current

"=Raaulation Survey, and the Census. Alternatively, certain regular

and special data collection efforts in the military could provide

valuable information to civilian researchers and policy makers.

Examples include the annual survey of new recruits at'armed forces

ent nee and examination stations,. exit and post-service surveys

conductc4 by the military, attitudes,of young women toward military

service, and so forth.-

In sum, this paper has argued that the military is a most

t . important factor in youth labor markets. On the one hand; the

military is a major source of jobs for American youth, while on the

'other, it is clear that military experience and training can play , -a significant role in the issues and problems of civilian labor

"market-behavior: It-is-time that labor farce statistics reflected

this important role of the milita y.
-223 -

DIRECT EFFECTS OF EMPLOYMENT AND TRAINING PROGRAMS"

ON EMPLOYMENT AND UNEMPLOYMENT:

NEW ESTIMATES AND IMPLICATIONS FOR EMPLOYMENT POLICY \,

By: Charles C. Killingsworth and Mark R. KillingsworthABSTRACT

4

We first discuss the ways in which participants in employment

and training programs are counted in official figures on the number

of peOons employed, unemployed and not in the labor force. We

then analyze the-consequencks of an expansigNIPfsuchrogpMs, and

present estimates of the purely"itatisti al" effects of expanding

such programs on the number of persons e ployed, unemployed and not

inthe labor force. Thes is appea to be substantial. For

example, our estimates imply tha , in the absence of any empl ent

and training programs, the aggregate unemployment rate in 197 would

have been 0.8 percentage points higher than the actual figure, and

the teenage unemploy'ment rate would have*been 3.9 percentage points

higher than the actual figgre.. We conclude by discussing the

imp tions of our analysis and estimates for employment policy

and empl yment.training programs as an instrument of employment

policy.

INTRODUCTION,

'How do employment and training programs affect prograth particip4lit, and what is the appropriate role for such programs in emplbyment

policy? Most research on the effects of employment and training programs, and most discussions of their merits as an instrument bf employment`policy, focus on what maybe called "ultimate" or "long run"

effects: on the extent to which they affect the human capital .(and

thus the subsequent earnings and employment) of enrollees, and on the

extent to which they change the structure of the labor force, shifting

the economy's Phillips curve and reducing the unemployment rate associated with any given inflation rate. But a few studies (Cohen,,1969;

We are most grateful to Mark Murray for his extremely thorough and

painstaking research assistance; and to Orley Ashenfelter, Burt

Barnow, Paul Flaim and Ralph-Sthith for useful Comments and suggestions.

25 6
- 230 -

Small, 1972) suggest that employment and training programs also have

"short run" or purely "statistical!' effects on employment and the

unemployment rate: depending on the particular program in which

participants are enrolled, enumerators"for the Current 'Population

Survey (CPS) count program participants as employed, unemployed or

not in the labor force. Obviously, then, regardless of the ultimate

_impact of these programs on enrollees' earnings and employment,

_chdhges in-' program enrollments necessarily -have a direct or short run

or statistical impact Oh official CPS figures on employment and on

the unemployment rate.

In this paper we have two objectives. First, relatively little

is,known about the magnitude of the statistical effect of.employment

and training programs; so our "short run" or "statistical" objective

is to present estimates of the magnitude of this effect, with special

reference to youth. We also have a "long run" or "analytical" objective, however: to discuss the role of employment and training programs

as an instrum9a of employment policy. For it turns out that our

analysis Of the "statistical" effects of employment and training

!programs not only provides estimates of these effects but also has a

number of important implications for broader is..ues of employment

THE TREATMENT OF EMPLOYMENT AND TRAINING ENROLLMENT

IN OFFICIAL LABOR FORCE STATISTICS

The substantial grOwth in enrollment in employment and training programs during the 1960s--from 50,000 to nearly 1,000,000 in

----:-- 1971--posed a serious question for Federal statistics-takers: how

should such enrollees be classified in official labor force statistics?

In 1965, a Federal interagency committee, adopted definitions which

have been-followed since that time. As Small (1972, p. 7) puts it:

In,the final analysis; persons (enrolled in such programs)

were to be classified as employed if they were receiving

wages rather than subsistence or other allow nces, or if

they were getting on-the-job training; as unemployed if

they were enrolled in institutional (classr m) training.

25-7
e -231-

Job Corps participants were to be counted as not in the

labor force., The household interviewers (for the CPS)

were given detailed instructions for crassifying those

who volunteered information on participation.

Most of the large manpower programs involved placing

people in jobs rather than in classroom training, and most

participants would therefore be counted among the employed.

This would probably be true whether or not the household

respondent offered any information on the enrollee's

secific'program participation, bedause hOlding a job

and, receiving a wage are fairly clearcut unambiguous activities. On the other hand, the line of-demarcation

betwteen being unemployed and outside the labor force is

much harder to define. Thus, how participants in pro-

'gramS such as the Work Incentive program are actually

. classified in the official labor force survey is uncertain.

Accordingly,'employment and training programs may be classified according to the way in which their enrollees are counted in official employment and labor force statistics. We shall refer to programs whoseenrollees are counted as not in the labor force--e.g., the Job Corps--as

"Type I!' programs; we shall refer to programs whOse enrollees are counted

as unemployed--e.g., institutional training programs--as "Type II"

4 programs;. and, finally, we shall refer to.progrdms whose enrollees are

counted as employed--e.g., Public Service Employment or on-the-job

training programs - -as "Type III" programs.

Table 1 shows how enrollees in each.of the major employment

and training programs undertaken by the Federal goVernment during

1963-76 were classified' in CPS unemployment and labort'force statistics.1

Table 2 summarizes total wage annual enrollments in each type of

program both for the general population and for youth "(persons age

I. For a capsule summary of the history and nature of many of these

_progr4ms, see U.S_. DepartmantJg Labor (197A,m1193-197).

258
TABLE 1.

J

MAJOR FEDERAL EMPLOYMENT AND TRAINING PROGRAMS 1963° -76, ' BY LABOR FORCE CLASSIFICAPON OF ENROLLEES ; 'Program

Type I ppgrams (enrollees counted as not in'the labor force)

Job Corps

Type // progra (enrollees counted as unemployed)

- Manpower Devel pment and Training Act (MDTA) -

institutional training

Work Experience- Program---programs -otter -tben - began 19651 -subsumed under WIN

on-the-job training

Work Incentive Program (WIN) - programs other-than

on-the-job training

Comprehensive Employment and.Training Act'(CETA) -

- institutional training undei Titles I, II, VI_

RnmArks

began 1965(continued under CETA

began 1962: subsumed under CETA

Type III.programs (enrollees counted as employed)

Manpower Development and Training Act (MDTA)

on-the-job training

WorkExperienpe Program - on-the.-job training

Work Incentive Program (WIN)'- on-the-job 'training

Neighborhood Youth-Corps (NYC) - in-school and

summer: out of school

College -work Study

Comprehensive Employment Program (CEP)

Operation Mainstream

Public Service Careers (PSC), including

New Careers and STEP

Job Opportunities in the Business Sector (JOBS) -

Federally- subsidized. - Jobs-Optional (JOP)

Public Employment Program (PEP)

Comprehensive Employment and TrainingAct (CETk)

t 'On-the-job training, public service employment

(PSE), adult work experience, youth employment

programs

began 1969

began 1/74

4

began 1962: subsumed,under JOP

began 1965, subsumed under WINftt

began 1969

began 1965: subsumed under CETA

began 1965

'began 1961; subsumed under CETA

began 1965; subsumed under CETA

began 1970 (New Careers began 1967)1'

subsumed under CETA

began 1968: subsumed under CETA

began 1971: ssbiumed'under CETA

began 1971: subsumed under CETA

began 1974

270
I t

- 233 -

4.

sixteen to nineteen).

2 -Depending on the particular kind of activity,

in Which they were engaged (e.g., on. -the -job training or institutional

)'training), enrollees in. the Work Incentive Program (WIO) were counted

either as.employed or unemployed; hence, WIN is in effect a blend of

Types I and` fren-&-s-e-W.IN enrollments are shown separately in Table).

TheNtables reveal that the great majority of enrollees' were

enrolled in "Type-III" programs--that were counted as employed.

.

. wring the period of their enrollment. In recent years, aggregate

enrollment in all programs combined has bpen high both in absoluteterms (e.T., in excess of-1,000,900) and relative to the'level prevailingin the 1960s.

. 2. 146,th-some exceptions, the entries in, Table 2 represent actuar

annual averages of current enrollment in each kind of prograM,

A

These were calculated by aggregating annual average enrollment . figures, taken from a variety of sources (e,g., issues of the

Employment and'Training Reporttof the President), for each of

the individual programs listed in Table 1. Two major exceptions

are'as,follows. First, available data for enrollments, under

CETA during 1974-1976 provide information one new (ratherthan 4'

total current) enrollments by quarter; for this reason, the

entries in Table 2 for 1974-1976 include annual averages of

new (rather than total current) CETA enrollMents. To the extent that enrollees in CETA Programs remained enrolled for more

than a quarter, the 1974'-1976 figures shown in Table 2 of course

understate the actual total current annual; enrollments. Second,

available data for enrollments under the College Work Study ; program- during_19651926_refer_to awards (in effect, "slots") a'

available during a given fiscal year rather thariltg actual

.. recipients (enrollees on work-study payrolls) during a given

. calendar year. In arriving at the figures shown in Table 2, ! 0 we treated,fiscal and calendar year figures as,equivalent,'and

multiplied the number of awards by 0.8 to obtain an estimate of

the annual average number of actuar recipients. Further, between

fiscal ,(or, under our assumptibns, calendar) years 19.75 and 1976,

the number of awards increased sharply,, from 570,000 to 973,000--

°'

la ,so sharplyothat we suspect that colleges and universities did 0 -.-IM not adjust in time to fill as much as 80 per cent of the maximum

number of slots available in 1976. In arriving.at the figpres t shown in T ble 2, we therefore assumed that the "effective"

number of w

A.! rds available in 15'76 was midway between-the 1976

figure and he figure for 1977, 895,000, and'multiRdied this

"effective" number by 0.8 to get an estimated annuil average i number of actual recipients during 1976. ix.

,,t ,

ti

t 1 ,

8 260 0
1

TAIILE 2.

.o TOTAL ENROLLMENT AND YOUTH ENROLLMENT IN MAJOR

FEDERAL EMPLOYMENT AND TRAINING PORGRAJ4S 1963-96, BY TYPE OF PROGRAM

(Annual Averages, In Thousands Of Persons)

WIN/Work - Experience Type I Type II Type III Total

.age age age age , Year Total' 16-19 Total 16-19 Total 16-19 . Total 16-19

gage Total 16-19

1963 0 0 0 0 22 3 '2 24 '3 -

: 1964 40 0 0 0 44 ill3 10

. 1 53 10

1. . 0 .

1965 .4,, *, 10 9 63 17- .170 243 152

1966 28 26 62 14 395 297 485 3.37 1967

, 42 38 48

.12 542 401 632 450

,1 1968 3 33 30 53 13 543 399 632 442

1969 59' . 6, 24 21* 48 10 624 421 755 458

1970, * 91. i 10

t 20 15 51 9 674 462 836 497

1971 . 109 12 21 16 58 13 756 503 943 543

1972

. .115 fl 6 14 22' 17 53

, 10 1001 600 1191 641

1973 163 18 20 15 38 8 893 529 1114 570

I 60 s 1974 21-2' 21 19 15 86 24 884 494 1202 555

. a . 1975 215 *24 20 16 , 1.33 49. , 1264 655 1'632 743

/976 265 29, 20 16 159 46 1420 748

"

1864 839

Notes: t negligihle.

to "Total finur,e

Due to rounding, ,column

,howl ) la,t rolunn.

entries do not always sum

s or oule, so, toxt.

along rows

0°

261
a

235

But whilethe size of these enrollments is substantial, the

existence of substantial employment programs sponsored by the

federal government is not, of course, unprecedented: enrollments . in federal work-relief programs during the 1930s were still larger.

There is, however, an interesting difference: in contrast with

the procedures adopted in 1965, under which Type III enrollMents

of the 1960s and 1970s are'counted as employed,' participants in

these work-relief programs during the 1930s were--and, in the

historical statistics, still are--counted-das unemployed. (For

elaboration on this point seesDarby, 1976, pp. 2-8, and the references therein.) Table 3 illustrates the cOnsequences'of counting

1930s work-relief program participants as employed (as they would

be counted under current procedures) rather than as unemployed (as

they were, and still are, counted in official statistics).

3

Presumably, the growth in employment and training program enrollments

during the 1960s and 1970s may, also have had an important effect

So .

3 Darby gets "recalculated" figures (or, in his'terminology, "correc-

-ted" figures) which differ somewhat from ours. (Compare, for example, the fourth column in Table 3 in Darby, 1976, p. 8, with the

third column in our Table 3.) These differences seem to he the

result of using different data on work-relief enrollment: Darby

uses data on total Federal, state and local work-relief enrollment

appearing in a 1966 Department of Commerce publication (see notes.

to Tdble 2 in Darby, 1976, p. 7, especially in reference to column

7 of that table); while we use data.on Federal work-relief enrollment only appearing in the 1943 and 1946 editions of the Statistical

Abstract of the United States (see notes to our Table 3). Despite

these differences, our "recalculated" unemployment rate figures for

1933-1941 are similar to Darby's "corrected" figures for the same

years; and, like us, Darby concludes that classifying work-relief

enrollees as employed rather than unemployed would have a substantial

effect On measured unemployment rates for those years.

262
a

TABLE 3. PARTICIPANTS IN FEDERAL WORK-RELIEF PROGRAMS AND

REPORTEQ AND RECALCULATED UNEMPLOYMENT RAXES, 1933-42

(Mtidir-AVerages)

Year ' 1933

1934

1935

1936

1937

1938

1939

1940

1941

1942

. . Work-Relief Program

Participants*

(thousands)

Unemployment Rites ' Fqrcentage reduction in

Reported** Recalculated*** unemployment rate****

4151 24.9 18.3 26.5

661 21.7 20.7 4.6

3817 20.1 13.8 31.3

3.666 16.9 10.8 34.9

4.2553 14.3 10.0 30.1

4210 19.0 12.3 35.3 ,,

3246. 17.2 12.0 30.2

2869 , 14;6 10.0 31.5

1767 9.9 7.0 29.3

386 4.7 4.1 12.8

Notes: * = includes Civil Works Administration, Civilian Conservation Corps, National/-'

Youth Administration, Works Progress Administration and other Federal agencyN

projects financed frqm emergency funds. Data do not include administrative

personnel. (Source: Statistical Abstract of the United States, 1943 edition,

Table 204; and1946 edition, Table 259.)

** . Source: 1947 Handhoot of Labor Statistics, 'fable Al2.

*** = Calculated by switching all..participahts in Federal'Work-relief programs

from "dnempioypd" to "employed" category.'

**** = (column12 - column 3)/column 2. 2"
I

I

- 237 -

on reported employment and on the reported unemployment rate.

4

But

. how may one measure the purely " statistical" effects of these pro-

%71k grams, and just how large has thiseffect actually been?

.MEASURING THE STATISTICAL EFFECTS OF EMPLOYMENT

AND TRAINING PROGRAMS

For purposes of measuring the purely statistical effects of an

expansion of employmentsnd training programs, it is'useful to divide

the total effect of Mich expansion into two components: °immediate"

and "induced." First consider the immediate effect of each kind, of

program. When a person enters a Type I program, hii post-enrollment

status is always "not in the labor force," so that the immediate

effect of an expansion of a Type I program is to increase the number

of persons counted as absent from the labor forte by an amount equal

to the number of enrollees who were in the labor force (that'is", were

employed or unemployed) prior,to enrollment; and to reduce the numbers .

'. 4. Note that the third column in our Table 3 does not show what unemployment rates would have been during 1933-1941 in the absence of

goveKnment work-relief programs. It simply shows what official unemploytent'rate statistics for these years would look like if the

official definition of "unemployment" used for those years were the

same (141.:p respect to enrollees in work-relief and similar govern-.

ment programs) as the one in current, use. Hence a comparison of

the second and third columns of Table 3 reveals only "the impact

of this purely definitional change"; in and of. itself, it does not

have any particular analytical importance, since it does not show

the difference, under either definition, between reported unemployment rates and the unemployment rates which would have prevailed in

the absence of work-relief programs. Thodgh he never says so in so

many words, Darby seems to believe that the'"corrected" (in his

terms") or "recalculated" (in o terms) unemployment rate would

have prevailed either in the prPgnce or the absence of work-relief

programs. . In particular, he appears to believe that such programs

"crowded out" other employment on a one-for-one basis or, more br

less equivalently, that the statistical effect of such programs on

recorded aggregate employment (under either the 1930s or the current

definition) was zero. (See Darby, 1976, pp. 5-6, especially the

second complete sentence on p.,6.) Our analysis in Section II and

in Appendix kimplicitly calls this notion into question. For

additional criticism of Darby's "crowding-out" assumption, criticism

which is addressed,explicitly to Darby's paper; see Kesselman nd

Savin (1977). 264
'238

of persons counted as employed or unemployed by amounts equal to the

numbers of enrollees who were employed or unemployed, respectively,

prior.to enrollment. (Of course, persons not in the labor force prior

to enrollment are still counted ,as not in the labor force after they

enter a Type I program.) Similarly, on net the immediate effect of

expanding a Type II program is to increase the number of persons counted

as unemployed by an amount equal to the number of enrollees who were

not unemployed (that is, were either employed or not in the labor

force) prior to enrollment; and to reduce the numbers of persons

counted as employed or not in the labor force by eitiounts equal to the

numbers of enrollees who were emploYed'or not in the labor' force,

respectively, prior to enrollment. (Oft course:persons who were

unemployed prior to enrollment are still counted, -as unemply0vafter

they enter a Type II program), Finally, on net the immediate effect

of expanding a Type III' program is to increase the number of persOns

,counfed as employed by an amount equal to the number of persons who .

were not employed (that is, were either unemployed or not in the labor

forge) before they entered the program;.and to reduce the numbers of

persons counted as unemployed or not in the labor force by amounts

equal to, respectively; the numbers of enrollees who were unemployed

or not in the labor force prior to enrollment. (Of course, persons

who were employer prior tb enrollment are still counted as employed

after entering a Type III program.)

Hence it is quite simple, and straightforward to work. out the

" immediate" effect on reported labor force statistics of expansion

of any of the employment and training programs just mentioned. bescribing the "induced" effects on reported labor force statistics is

more difficult. This is because these "induced" effects refer (i) to

Ole reaction of the rest.of the economy to enrollment increases or

decieases in the "employment and training sector" and (ii) to the re- . action of the rest of the economy to any changes in the economic,

environment which accompany and are the consequence of,such.enrollment changes. It is usually possjle to say what the direction

465
739-

of these "induced" effects on the rest of the economy will be. (For

example, it seems reasonable to suppose that employers will fill at

least some of the jobs which are vacated by employed persons who quit

in order to enter employment and training program's.) However, the

magnitude of such effects is difficult to determine a'priori without

certain Kinds of quantitative information(e.g., on elAsticities of ,

demand, supply and substitution); and in general this information

is neither readily avglablenor, even if available, very reliable.

For this reason, previous work on the:question of the statis-i

tical effects of employment and training plograms (see Cohen, 1969,

and Small, 1972) has been based on an ad hoc assumption of convenience 7-- "

about aggregate labor market behavior: that measures of what we have

here called the "immediate" effects of the expansion of such programs

on reported aggregate labor force statistics provide a satisfactory

approximation to the total effects of such programs on aggregate

labor force statistics; or, more or less equivalently, that "induced"

effects of such programs on these statistics are of secondary importance

and may be neglected in arriving at an approximate measure of the

total statistical effects of employment and training programs.

Hence, for example, this amounts to an assumption that increasing

Type I program enrollment reduces reported employment by an amount

equal to the number of new enrollees who were in the labor force

prior to enrollment. The assumption may also be applied in reverse:

here it amounts to.tn assumption that abolishing a Type I program

would increase reported employment by an amount equal to the number

of enrollees who were employed prior to enrollment, and would

increase the reported labOnOrce by an amount equal'to the number

of enrollees who were in the labor force prior to enrollment.

Similarly, in the case of Type II programs, the ad 'hoc assumption

of convenience amounts to an assumption that abblishing a Type II

program would increase reported employment by an amount equal to

the number of enrollees who were employed prior to enrollment, and

would reduce the reported labor force by an amount equal to the

A
240

number of enrollees who were not in the labor force prior to enrollment.

case of Type III programs, this amounts to an assumptiOn

that abolishing a Type III program would reduce reported employment by

an amount equal to the number of,enrollees who were nvt employed prior

to enrollmenl, and would reduce the reported labor force by an amount

equal to the number of enrollees who were not in the labor force prior

to enrollment.

, It is important to note that this ad hoc assumption of convenien refers only to aggregate totals for persons employed, unemployed

and not in the labor force, and does not entail any assumptions about

the, effect of abolishing (or increasing enrollment in) any program

on the labor force status of any particular individual, enrolled or

not. For example,, the assumption does say that, if all of the enrollees in a Type I program were in the labor force prior to enrollment,

then abolishing, that prograwould increase the reported labor force

by an amount equal,to the number of enrollees. But the assumption

does not imply anything about the effect of abolishing the program

on the employment status of any particular individual whether

previously enrolled in the program or not. In fact,.if one is interested only in measuring.the effects of expanding or'contracting

employment and training prograyls on aggregatt'labor force statistics,

then,, in general, assumptions or results about the effectg of such

changes on the labor force status of given individuals are neither

necessary nor particularly relevant. All that is retired is the

ad hoc assumption of convenience which we have stated above.

In fact;'neither Small nor Cohen states theassumption in the form

given here. 'Rather, they both say that, in deriving estimates of

the total statistical effects of employment and training programs,

they assume that " enrollees would have continued at their previous

(i.e., pre-enrollment) employment status during their participation

insthe federal manpower program if there had been no federal manpower program" (Cohen,. 1969, p. 498), or,'equivalentljr, that "en-

./ rollees would have continued their pre-enrollment status in the

absence of the programs" (Small, 1972, p., 11). This seems to have

given rise to a fair amount of unnecessary confusion and doubt as

267

.
a - 241 -

Since it refers to aggregates rather than to particular individuals, the ad hoc assumption of convenience used in previous work'

on the statistical effeCts of employment and training programs is

not unlike the assumptions used to c nstruct and interpret supply and

demand schedules. For exampjIe, the oint'of intersection of supply

and demand pr labor schedules gives the total number of persons

who will be employed in equilibrium at a given point in time, but

it does not show which persons will- be employed. To say anything

about the latter will require aditionil assumptions (e.g., about

labor turnover); but such assumptions are unnecessary if the objective

simply to dis.cuss the aggregate level of employment. Indeed,

. . t is similarity between the ad hoc assumption of convenience about

the effects of employment and training programs on aggregate labor

force statistics and the assumptions used to. construct and inteeret

supply and demand schedules is an advantage. As will become evident

below and in Appendix A, this similarity makes it possible to analyze

the effects of such programs on aggregate labor force statistics

withln'the context of a .simple model of supply and dethand in the

labor market.

Xt

.

In sum, the ad hoc assumptIon of convenience discussed above

Constitutes an Id hoc estimation method--or AHEM, for shOrt-,cwhich

may be used to estihate the effects of employment and training pro-

, . grams on aggregate statistics on persons employed, unemployed and

not in the labor force. AHEM estimates refex.to whatme have called

S. ,(cont.) to the validity of'their estimates,-since its 'version,.

of the assumption is extremely stni4envand, as Smith (1971;

1973) has noted, is probably quite unrealistic. However, it is ' . important to note that Cohen and Small discuss-only.the effects

of employment ,and training programs on aggregate labor force

statistics, so that none of theif work actually requires this

extremely strong assumption about effects or. particular individuals. The only assumption required for any oftheir estimates

is the much more general ad'hoc assumption of convenience,

stated in the text, about aggregate behiirior.

II.

4

14

'A

4
. - . .

- 242 -

"immediate" program effects only, and ignore all "induced" effects

of the expansion of these programs on the rest of the economy.

Inition therefore suggests that AHEM estimates.of the total

._._ statistical effect of such programs are probably conservative. In v particular, it can be shown that, because of the nature of.the

,... "induced!;effects neglected by the AHEM procedure, the AHEM tech-

- nique is i.ways downward-biased: AHEM estimates will consistently

overstate any decreases in employment, labor force or the unemployment

rate which are attributable to increases in program enrollment and

will consistently understate any increases in these variables which'

are the resuftof'increases ih enrollnts.

In Appendix A we present a technical discussion of the reasons

why this is so. Appendix A analyzes the statistical effects of

employment and training programs in the context of a formal (though

relatively simple) model of the labor market, and as such-may be of

some interest to the technically-oriented leader (e.g., the profess01:

ional economist).*To summarize the results in Appendix A for the

general reader, We.think it sufficient, for Our'present purposes, to

note that AppendiX A suggests that there are tw basic reasons why

the AHEM procedure is "Conservative" in tfie sense iven above. 'first,

the'AHEM procedure ignores the fact thatsome the vacancies

o

../created in tht rest of the. economy-when employed workers,depart to

enroll in employint and training programs will be filled with new

emPleyeesso that, even when the labor market nears, employment

in the rest of-the economy is not reduced on netby as mach'as the

number of new enrollees who were employed in the rest 'of the economy

prior tO-- enrollment. Second, to the extent that.marlets do not

. cl,ear--bi, Iociasly speaking, to the extent that the economy is

beloW "fulg.41oyment"--and the government decides to expand

aggregate demand py expanding employment and training program enroll- . ment, the AHEM pro'C'edilre also ignores the multiplier effects on

employment rn.t e rest of the economy which occur as the result of

this expansion in aggregate demand.

. .

tv

"

1

,

269

$

tie

r
243`

JWith this as prologue, it is not hard to discern the general

nature of the statistical effects of employment and training programs

in the United States in the 1960s and 1970s. In essence, Type III

programs. take persons out of the "unemployed" and "not in the labor

force" categories and put them in o the "employed" category; and

these "immediate" effects of,Typ III programs are reinforced by

additional ."induced" effects: employers fill some of the jobs

vacated-by the employed persons who'enroll, and the additional

aggregate demand generated by the expansion of the programs stimulates aggregate employment still further. Moreover, Type III program enrollments have generally been much.larger than enrollments

in other programs (see Table 2), so, loosely speaking, "As Type III

programs go, so. -goes the economy." This means that, on the whole,

the expansion of enrollment in employment and training programs

which occ red during the 1960s and 1970s increased bdth recorded

employnnt and the recorded labor force to levels above (and reduced

the recorded unemploymjit rate to a level below) what would }lave

prevailed in the absence of these programs. But what is the magnitude of these effects, and what implications do they have for'

employment policy? We turn to these questions in the next two parts

of the paper.

ESTIMATES OF THE STATISTICAL EFFECTS OF

.EMPLOYMENT AND TRAINING PROGRAMS

In principle, one could estiAle_the direct effects of employment and training programs using either of two procedures. First,

one could specify a compltote model of labor markets, representing

both the supply of and demand for enrollments in employment and

.training programs explicitly in that model.; estimate the parameters

. of the' model"; and then measure the direct effects of employment and

training programs by (for example) simulating the behavior of the

model with the supply of enrollments set at zero and then comparing

these results with obseTilBd.behavior. ..Unfortunately, specifying

and then-estimating such a model for use in simulations of this
IP

!it kind is no simpfe task; doingthe job properly may not require

building a complete macromodel, but it certainly calls for more

than few.ordinary least squares regressions--and it probably

calls for more data than are actually available. Developing esti-

- 244 -

mates based on a complete model of labor markets is an important

goal for future research; for the present, we shall content ourselves

with presenting estimates of the airect effects of employment and

training programs derived from the second procedure, AHEM. In

view of.the discussion abg.14,,e, it is worth stressing once again

that estimates based on AHEM are conservative: if anything, the

"true" statistical effects are probably gre4ter than the estimates

which we shall presentbelow.

With-this in mind, consider Tables 4-7, in which we present

official CPS figures on aggregate unemployment, employment, etc.,

and AHEM estimates of what these labor market variables would have

-been in the absence of any employment, and training programs. (For

a detailed discussion of the procedures used in deriving the AHEM

estimates set out in.Tables 4:7, see Appendix B.) Table 4 contrasts

CPS figures on actual levels of aggregate employment, unemployment

and labor force (that-is, thp actual values of these labor market'

Variables, obterved in the presence of employment and training

programs) with AHEMfigures (that is, our estimates of what these

variables would have been in the absence of the programs) for the

total population. 114nce the columns headed "diff" in Table 4 gives

the purely statistical effect of employment and training programs

on the variables considered there. Table S presenti similar aompar-

. isohs for the aggregate employment-population ratio, the ,unemployMA

t rate and the labor force participation rate for the population

whole. FinallyTables 6 and 7 present analogous results for

eenage population (persons age sixteen to nineteen).

Both fdr the population as a whole and for the teenage population, our estimates imply thatthe statistical effects of expansion

of.emp16.ymentand training programs (esNhially employment programs)

ti 27.E,

4ow
O

TABLE 4. 10'

? AHEM AND CPS MEASURES OF EMPLOYMENT, UNEMPLOYMENT

AND LABOR FORCE FOR TOTAL POPULATION, 1963-76

(Annual Averages, In Thousands Of Persons)

Year' Employment

not

Unemployment i Labor Force

CPS AHEM Jiff CPS AHEM Jiff ' CPS

=

AHSI4 diff

1963

1964

ot I, 1965

i 1966

1967

1968

1-9-61 1970

-197.1 `---'l922

1973

67762

69305

'71088

12885

74372

75920

077902

78627

79120

81702

$4409

85936

8(753

87485

67763

69302

70957

72609

73982

75522

77424

"78111

08138

80909

. 83A6

8524'7 83776

86388

4.

*3 132

286

390

397

476

516

. 582

793

684

688

1006

1097

.f

4070

3786

3366

2875

2975

2817

2832

4088

4993

4840

4304

5076

7830

7288

t

sum to

4070

3789

3749 ,

* 3169°

3296

3130

3705

4473

*5415

544,1

479)

5520

8501

7992

total due

-3

-113

-234

-321

-314

-373

-386

-422

-602

-87

-44?

-671

-705

to

71833

'''' 7.3091

''..74455

75770

. 77347

' 78737

80733

82715

-3 84113

8,542

88714

91011

92613

94773

rounding':. *

71832 * 73090 1 o 74436 19

75718 52

77277 70

78653 84

80629 104

82584 130

83951 160

86351 191

8Q517 197

90768 243

92278 335

94381 392

negligible,

1974

1975

1976

Note: Figures within rows may

(SI° Sti

.

3
If

si

aid

TABLE 5.

4., V AHEM AND CPS MEASURES OF EMPLOYMENT, LINEMPUOYMENT

AND LAIIORFORCE RATIOS FOR TOTAL POPULATION, 1963-76

(Annual Averages, In Per Cent)

Yearn

1963

1964

1965

1960

1967

1941; 6 1 69

4 1970

1-71. 1972- .1973 .'

1974

1 1975

U

: 4976

tote:

Employillent- Unem4loyment

po:,ulatIo;t ratio . sate

, CPS AHEM dIf f CPS 'HEM di ff

A 55.4,... 55.4 * 5.7 5.7

55.7 55.7 . -. At.2 5.2 * 56.2 56. r6 0.1 4.5 4.7 -0.2

5.9 56.7 0.2 3.8 4.1 -0.3

, 57.3 57.0 0.3 3.8 4.3 -0.4

. 57.5 57.2 0..3.'' '3.6 4.0 -0.4

. .

. a , .'58,0 57.6 :0.4 3.5 4.0 2.0.5

. 57.i: 57.0 0.4 4.9 5.4 -0.5

56.6 56.2t 0,4 5.9 6.4 -0.5

57.0 56.5 0.6 ' 5.6 -6.:3 -0.7

. to

57.8 5'7:4 0.5 4.9 5.4 -0.6

.

'51. tr 57.4e 0:5

. 5.6 6.1 -0.5

. +.0 56.0 55.4 0.7 , 8.5 9.2 -0.8

11% ,

56.84" 56.1 0.7 . 7.7 8.5 -0.8

:, , 4 Figures within rows may rrot sum to total due to rounding.

1-

Labor force

participation rate

CPS AHEM di f f

, 50.7 58.7 * 58.7 58.7 * 58.9 '58.8

59.2 59.1 * 59.6 59.6 0.1

59.6 59.6 0.1' 60.1 60.0 0.1

60.4 60.3 -o.,

60.2 60.1 0.1

60.4 60.2 0.1

60*.8 60.7 0.1

61.2 61.1 0.2

I61.2 61.0 0.'2

iT 61.6 61.4 0.3

.

* = negligible.
4

AHEM AND CPS MEASURES OF EMPLOYMENT, UEMPLOYMENT AND LABOR FORCE

FOR POPULATION AGE 16-.19, 1963-7

(Annual Averages& In Thousands of Persons)

Epoloyment Unemployment LAIlozr force

, Yeare ,CPg AHEM d4ff CPS AHEM dip CRS THIM dIff P %

0

1 I 1963 4255 4255 * 883. 883 .4 .. ' 5138 5138 * N

.

0 ,.. 1964 4516 4516 . * 872 '872 . 5390 5'390 * * .. , .

. 1965 1 5036 4933 103 874 964 -90 '5550 5924 14

,,,, 19,66 5721 5494` 227 836 1024 -188 6557 6595 3.8

. ' 19t) 5682 5379 303 838' 1090 , -252 6519, 6570

0

s 4968 5780 5481 299 839 1096 X237 6618 868p

.

.

.. 1969 6117 t803 314 8'53 1091 -238 6970 7046 76

.1970 6141 5795 346 1105 1358 -?Ple 7246 7340 94

.4. 1971 " 6195

. . 5816 379 1257 1532 -275 7453 75,7 104'

1 1972 ' 6722 6266 456 1302 1633 -331 8024 814 9 125

a

e 6

i A 1973 7236 6859 377 1225 1484 -259 . 8461 13579 ' 118

v. .../-

, . 1974 7403 7058 345 1410 1628 -218 . '8813 8940 17,-.

. 1975 7046 6589 457 7.2' 2039 -2.87 879'9 8969 170

Sc. 1976 '" 7269 6767 507 170 004 1 -301 8970 q 68 ,h98

.... 1 ,N Q 1,r774c.A.,

Not ": riqures oilt hin rows may noi'' sum to total dur o rounding.

,

?74

S
t

TABLE 7

. . AIIEM.AND CPS MEASURES OF EMPLOYMENT, UNEMPLOYMENT AND LABOR FORCE RATIOSFOR POPULATION AGE 16-19, 1963-76

(Annual Averages, In Per Eeni)

' Year

Employmentpopulation ratio

CPS -AHEM diff

1963 37.4 -37.4

1964 37.3 37.3

1965 38.9 38.1' 0.8

1966 42.1 40.4 1.7

1967, .4 42.1 39.9 2.2

1968 42.2 40.0 2%2

1969 t 43.4 41.2 2.2

1970 42.3 39.9 2.4

1971 41.3 38.8 2.5

'1972 4-3.5 40.6 -3.0 1973 ..46.0 43:6 2'.4 1974 46.1 44.0 2.1

1975 43.3 40.5 2.8

- 1976 44%3 41.2 3.1

Notes Figures within rows may not sum

. Unemployment

rate

CPS AHEM diff

17.2 17.2

16.2 16.2

14.8 16.3 -1.6

12.7 15.7. -3:0

12.8 16.8 -4.0

12.7 16.4 -3.7

124 15.8 -3.6

15.2 19.0 -3.7

16.9 20%9 -4.0

16.2 20.7 -4.4

'14.5 17.8 -3.3

16.0' 18.7. -2.3.

19.9 23.6 -3.7 . 19.0 22.8 -3.9

to total

Labor force

participation rate

CPS AHEM diff

_ 45.2 45.2 * .--3

4.5 44t5 *

t 45.7 45.6 0.1

48.2 48.0 0.2

"8.4 .48:0 0.4.- 48.3 47.9 4'0.5

49.4 48.9 .0.5

49.9 49.3 Q.6

49.7 49.0 0.7

52.0 51.1 0.8

53.7 53.0 0.8

54.9 54.1 0.8

54.1 53.1 1.0

9.6. .53.4 1.2

due 10,14ounding. * = negligible.

1J

1 A *
- 249 -

since 1963 have grown steadily importance. The effect of employment and training programs on repated levels and rates of unemployment has increased steadily over time; the effect on reported levels

of employment and labor force, and on reported employment-population

and labor force participation figures, has likewise increased steadily

over time.

In view of our discussion in Part II above, this is hardly

astonishing; but the magnitudes.of these effects

0 thing of a surprise. For example, the estimates

the absence of,employment and training programs,

may come as somesuggest that, in

the national unemployment rate would have,been about eight-tenths of a percentage

point higher in 1976 than the actual figure; and that the teenage

unemployment rate would have been about 3.9 percentage points higher

than the officia11976 figure. .Similarly, the estimates imply that,

in the-absence of the programs, the aggregate employment-population

ratio would have been about seven-tenths of q percentage point

lower in 1976 than the actual figure; and that the teenage employ-,

ment-population ratio would have been about'3.1 percentage. points

lower than the actual 1976 figure!)

Finally, the estimates also shed some interesting light on

the experience of the seconehalf of the 4960s. Both overall and

for teenagers, unemployment rates hit their lowest level for the

entire decadesin 1969, and employment-population ratios reached

6. Of course, these are estimates, whichas indicated in n. 2 above,

are subject to, some measurement error. Accordingly, they should

be interpieted with some caution. However, to the extent that

measurement errors induce systematic biases, we suspect that

they work in a downward or .conservative direction. Both because

of the nature of the AHEM approach and because 1974-1976 CETA

enrollment data used in our calculations almost certainly under -

'state actual average total current enrollment (see Tr,. 2 above),

it seems probable that the differences between the official 1976

employment, labor force,etc., figures, and those which would obtain'in the absence of employment and training programs are larger

than the ones given in the text.

2 7G

o
.4

-250

their highest level for the entire decade in 1969. Tables 5 and 7 . suggest that the effect of employment and training programs on

these patterns was considerable. , According to -We estimates presented in Tables 5 and 7, almost 30% of the total decline in the

overall national unemployment rate between 1964 and 1969. was a con- sequence of the statistical effects of employment-and training programs; and tht these statistical effects account for nearly all (90%)

. - of the decrease in the teenage unemployment rate between 1964 and

1969.

Of course, the line between'unemployment and absence from the

laboriforce is' not Always easy to draw, and so some observers--notably

.,Geoffrey Moore--have argued that for many purposes the-employmentpopulation ratio is a better measure of.labor market behavior than the

unemployment rate. In the present case, however, comparison of the

employment- population ratio With and without employment and training

programs over 1964-1969 tells essentially the same story as the comparison of unemployment rates during the same period. Tables 5 and 7

imply that over 15% of the increase in the aggregate employmentpopulation ratio during 1964-1969 was a consequence of the expansion

of employment and training programs, and that over 35% of the increase

in the teenage employment-population ratio during 1964-1969 was likewise the result of this expansion. 7

7. That is, the increase between 1964 and 1969 in the reported aggregate employment-population ratio was 2.3 percentage points; ..but.the

-repotted emplbyment-pdpulation ratio in 1969 would have been 0.4

percentage pointS lower than.it,actually was had there been no

employment and,training,programs, according to our estimates. (See. Table S.) Hence the program's-purely statistical effetts. '!account"

for0.4/2.3 = 17.4%-of the total increase in the aggregate employ-

. ment-population ratio during/1964-1969. Similarly, the programs .

. 'account for 2.2/6.1 = 36.1% of the increase.in the teenage employment-population ratio between 1964'and 1969;. for 0.5/1.7 = 29.4% of

,othe ddcrease in the aggregate uhemploYinent rate during these years;

and for'34/4..0'.= 900 'of the 1964-1969 decrease in the teenage,unemploymenk'rate.

277
C. 251

POLICY IMPLICATIONS

The estimates presented. here have an obvious empirical significance; moreover, taken in conjunction with the analysis above (which

is presented in more detail in Appendix A), the estimates he a number of, implications for employment.policy and for employment and

training programs as instruments of emplo ent.policy.

That there is'still debate on these is ues might seem somewhat

surprising, since, as is 111 known; the de ate was supposed to have

been over with more than ten years ago. To be sure, even in the

good old days of the 1960s, some advocat employment and, training

programs as means of effecting structural changes in the labor forte

and reducing the iriflition rate associated with any given unemployment

rate. But asecond sdhool sought what might be called the lift of a.

driving demand: they argued for monetary and fiscal measures to expane aggregate demand and move the:economy aflong a negative-sioped

o Phillips curve, incurring an acceptably small increase in.inflation

in exchange for a thuch desired reduction in the unemployment rate.

1 In the end, policy gave much more emphasis to aggregate demand measures

than.to structural measures,' and, as shOwn in Tables 5 and 7; unem,.

ployment rates as reflected in CPS'Oata fell sharply. The proponents

of what has now become the new old-time religion of demand eXpansion

maintained that this experience renders a :'clear-cut verdict" (Heller,

1966, p. 64) on the merits of aggregate demand measdres, making "...it

...as cfear'today as it can possible,be" (Ackley, 1966) that the

strategy "worked amazinglypilr' (Oken,,1976,.y. 70): the view chat

( "...the inadequate'demand camp vial, right and the structuralists were

wrong" (Ackley, 1966) ".'..was gloriously Confirmed by the ease with

which new lobs were created and unemployment diminiShed in the subsequent expansion of Aggregate demand" (Tobin, 1974, p. 16)--thus_

.jemonstrating "-..-.that macroeconomics cat, black yoiths aside, achieve

full emkoyment" (Sariluelson, 1969)' and th t "the American economy can

3, -- reach unemployment rates of close to 3% though,the use of.simple

fiscal and monetary policies" (Thurow, 1973, p. 84).

J '0
5

1

t 252 -

If thellgebate was suppbsed to be Over, whyhas it continued?

Primarily, it seems, because economists rediscovered supply;, pd

realized that the scissors which the aggregate demand school wielded

had only one blade.' First came the realization that demand expansion not only reduces unemployment but alSo.raises prices, acid

that eventually monerdwages will adjust to catch up with prices-- thereby cancelling at least some of the previous gains in employment: the demand-expansion road up the Phillips curve seems, in

the long run, to be appreciably steeper and more dangerous than

initially thought. 'More recently has come the realization that

government supply-side programs have contributed in no 'small way

tal-ecent gains in a variety of economic indicators--gains which

were once thought to be attributable entirely to the expansion of

aggregate dethand during the 1960s,

Our analysis and estimate gear on hiS debate in several

respects. First, just as oth r au hors avp found that Various

supply-side programs--as opp sed to "simple fiscal and monetary . policies" as such--made an important contribution t 'the attainwent of various°policy goals during the 1960s, 8

we, too, conclude

0. that supply-side (i.e., employment-and training) pro:,..is account

for a not inconsiderable part of the reduction'-in nemploym

ate§, (and the increase in employment-population ratios) during the

1960S., The "clitar=cut%verdict" which emerges,from a consideration of _ . Tables S and 7 is that the purely statistical effects.of employment

and training - programs had an important effect on key indicators of

8. For example, Plotnitk and Skidmore (197) find that much of the

decrease in the incidenA 'of recorded poverty during-the 1960s

1L4..? as the result of the substantial expansion of goirernment transer programs (rather than expansion ofaggregate demand per se) -° during that period., LikeWise,.Butier and Heckman (1977) conclude_

, that "...the supply side effects .of recent policy [such as - government welfare and, transfer programs] play ah important role

in explaining the reCent measured increase 4n the ratio of wages

and incomes of blacks to the wages and incomes of whites" (p. PS).

a 427.)

4.

4
.

J

- 253 -

employment and unemployment; and the most'ipterksting hypothesis f

about aggregate demand which is "gloriously confirmed!' by these

o

-tables is the propostfion that members of the aggregate demand

school -are no more immune frtim the post.hoc fallacy than are other

economists-.

Second,our analysis also suggests that greater reliance on

employment and training programs as a means of reducing unemploymelt by shifting.the economy's Phillips c rve may be preferable

tit

to what now appears to be the.rather perilous policy of reducing

unempNyment by relying solely on deman

along a given--and potentially quite s

Indeee-, our discussion of the statist

(Type III) programs under the conditi n

or, loosely speaking, "full employme t"

implies that expansion Of such prog

unemployment rate and increase repo

conditions of general market clear

effect of a once-and-for-all demand expansion will be an increase

in the price level, and when monetary and fiscal policies to drive

the unemployment rate still lower will generate only an accelerating rate of inflation.

9 Since teenage.unemployment,eespei cially among blacks, is high even at "full employment," this is

of course particularly,,important for policies to reduce teenage

expansion and moiling

p-±Phillips curve.

1 effects ofiiemployment

of general market clegring

(see especially Appendix°A)

ams can reduce the reported

tied employment even under

ng--w en lon g-run

unemployment.

. Finally, our discussion in'Appendix A of the statistical

°

effects of employment and training programs under conditions of non- market clearing (or what is usually, somewhat imprecisely, called

"below full-employment") suggests that expans-ion of such programs

in conditions of not-market-clearing can be a particularly potent

J.

.7 .

. 9. of the ."natural rate of unemployment" hypothesis may

thus find it of some interest that an expansion of employment ,

programs is a means of reducing the unemployment rate vithout;,

e. at the same time, incurring an accelerating rate of inflation. f .-- t
a

ti

ot

- 254,7.

IL form of demand expansion- -one which, moreover, can actually be

more powerful than conventional monetaryrand fiscal policies'A a.

means of reducing "deficient-demand" unemployment. The analysi of

^Pakt II above leads directly-to the conclusion that expansion of

, t employment 'programs will reduce unemployment and increase employment

under conditions of non-market-clearing by more than, say, a tax

cut which raises effectiye aggregate demand y the same amount."

,, Indeed, if the government expands employment programs under condi- i' tions. of non-market-clearing entirely by enrolling pieviolisly unemployed persons, it will create at least as many jobs in

sector as a tax cut involving-the same dollar outlays 11

- course will create additional jobs in the public sector

the private

-and of

as well.

10. This is evident froMiFigure 2 of Appendix A. An increase in

aggregate #mand effected 10._:,,f4ay, a tax cut will' shift firms'

demand from the D' schedule igure 2 to the -D schedule,land

\

. would raise total employment from e' to e:. In contrast, an

expansion of employment programs -which had the same effect on

aggregate demand would raise employment in the,reSt-of-theeconomy sector from e' to ea, and would raise training sector

employment"by an additional amount dx 1=' dx , with the total'

being larger than-ihateffected by the e cut. I':

,.., . 11. Of course,.this is simply a straightforward extension of the r1 familiar balanced-budget multiplier theorem. (See; for example;

Kirlingsworth and King, 1977, esp. pf) 26-28.) To the extent

that so-called "fiscal,substitution"effectg' operate, they

. simply.reduce the difference between the effectiveness of em- .ployment programs and-thatAof a tax cut. (See, again, Killingsworth and King, 1977, esp.. pp.2,3-25.)"

4.:

. ; 4 A ,^

g ^ . er4
,

. ,

4 :_,.2,55 -N'

f ::, . e + ' . 0

. 1, a %. ...t ° IT,

0.

0 ..k.:410' .% ... 4 7, ii .., , ., -

-y ql.BLIOGRApgY. 4; ....i IS

. .

'.1" - . 4-; .4 *040' 14

. .

.

- ,

rif

e

"it

Acklel, Gardner, adaess at.Southern Illinois OVersity, October 26,

- 19256, mimewgraphed);

4r-

. 4

Barro, Robert, and Grossman, Hershel, Money, Employment and Inflation

(New York: CAmbridge University,Press, 1975 .. Butler, Richard, and Heckman, James J., "The Government's Impact on

the Labor Market Status of Black Americans: A Critical Review,"

) Leonard J. Hausman et al., eds., Equal Rights and Industrial RelatiOns (Madison, Wisc.: Industrial Relations Research Association., 1977), pp. 235-81. r Cohen, Malcolm S., "The Direct Effbcts of Federal

in Reducing Unemployment," Journal, of Human

No. 4 (Fall 1959), pp., 491 -507.

Darby, Michael R.', "Three and a Half Million Employees Have Been Mislaid: Or, an Explanation of Unemployment; 1934-1941," Journal

of Political-Economy, vol. 84, No. 1 (February 1976), pp. 1-16.

Manpower Programs

Resources, vol. 4,

)

Hansen, Bent, "Excess Demand, Unemployment,-Vacancies and Wages,"

Quarterly Journalof Economicsvol. 84, No. 1 (February 1970),

. pp. 1-23.

Walter W., New Dimensions of Political Economy (Cambridge,

Mass.: Harvard University Press, 1966).

Kesselman, Jonathan, and Savin, N.E., 'Three-and-a-Half Million Workers

Never.Were Lost," Discussion Paper No. 77-26, Depa;tment of

Economics, University of British.Columbia, September *977.

Killingsworth, Charles d.; and King, Christopher T., 'Tax Cuts-ancb,

Employment Policy," in Robert Taggart,.ed., Job Creationt,

What Works? (Salt rake city: Olympils POlishing Co,, 1977),.

pp. 1-33/.

-%

ti

tpkun, Arthur M., "Conflicting National. Goals," in Eli Ginzberg,'ed.,

Jobs for Americans_.(Englewood Cliffs,.N.J.: Prentice-Hall, Inc.,

1976),

Plotnick, Robert, and Skidmore, Felicity, Progress Against'Poverty

(New York:°Academic Press., 1975):

'$amuelson, Paul A., column in Newsweek, July.14, 1969, p. 79.

Smith, Ralph E. , "The Oppdrtunity Cost. of Participating in a Training

. Program," Journal of HumaPResources, vol. 6, No., 4 (Fa11,1971),'

pp. 510-19. .-

. ; 232

'

1.0
/

<4

256

, "Manpower Programs and Unemployment Statistics',"

Monthly Labor Review, vol. 96, No. 4 (April 1973), pp. 63-6.

Thurow, Lester, "Redistributional Aspects of Manpower Training Programs,"

in Lloyd Ulman, ed., Manpower Programs in the Policy Mix (Baltimore, Md.: Johns Hopkins University Press, 1973).,

Tobin, James, The New Economics One Decade Older (Princeton, N.J.:

Princeton University Press, 1974).

U.S. Department of Labor, Manpower Report of the President1970 (yashington, D.C.: U.S. Government Printing Office, 1970).

)

'or 283
4

r a- 257(

APPENDIX A

A SIMPLE MODEL OF ,THE STATISTICAL EFFECTS

OF EMPLOYMENT AND TRAINING PROGRAMS

to see clearlyalbeit in a somewhat intuitive fashion which

suppresses various detailsthe nature of the statistical effects

of employment and training programs, and :to see the nature of AHEM

estimates of these effects, it is useful to consider the case of an

expansion in enrollment. of a Type III program in the context of a

simple,mRdel of the labor market. (As is evident from Table 1 and

2, enrolllent in Type III programs is by far the largftt of that in

any program,'so the results presented here may be taken as a fairly

hP itffect of all such enrollments: the effects

of Type III prOgr4s will swamp the effects-of Type I and II programs.)

Two "sectors" of the labor market are t considered: the-"empluymen

and training sectul," aid the "rest -of -the economy" sector (or ROE,

for short). In terms of our discussion in the text, the "immediate"

effects of employment and training programs pertain to the "employment and training sector," while what we have called "induced" effects

refer to effects on 'the ROE. Since the "immediate".effects have been

discussed in some detail in the text, we focus here'on the "induced"

effects of employment and training programs and hence on the ROE

sector. .

. As usualv* the supply of.labor to the ROE, S, is positively

. related to the real wage, w, while the demand for labor in the ROE,

16,is negatively related to the real wage. F'igu're 1 presents, these

two schedules. Now rsuppo§epurelX for-the sake of argudentthat

all;markets clear and that the prevailing real wage rate is w*, at

which supply and demand are equal afd equilibrium employment in the

ROE is e*

R

1 Next suppose'that the governffient expands enrollment

in a,Type III program by an amount dx.',This enrollment increase

.comes from three sources: from per$onstprvtbusly employed in the

rest of the economy, dxe;-from persons Deviously unemployed and

seeking work in the rest of .the economy, dxu; and from persons pre -

vio, ly not in the labor force, dxn. So

dx = dxe. dxu ,,dxn /

1. Of course, labor turnover and j Asearc

tive unemplognt rate even at his po

clearing. See, for example, Hansen .(19

. . Presumably, this increase comes about ei

ment offers bettet stipends or 'training

existing stipends and training-were sufii

create an excess of applidants over availab

government now decides to satisfy more fully,

of these two things.

1 generate some posigeneral market

4 t. ,

'se the govern-.

4es, or because

ttractive to

; the

or ; 'combination
..258 4

, Now, in terms of the model of the, labor market of the ROE

... depicted in Figure 1, expansion of the Type III progfam shifts the

ROE-labor supply schedule to the left by an,amount.equal to dxe`- - that is, dxe fewer peciple are available to the ROE at anyfreal wagb

in the vicinity of thg current'level,-w*. Hence dxe vacancies open

up in the;ROE'; labor is now somewhat scarcer there, and so, 'competing

with each other to,get more,-.firms raise wages by'an amount dw to

a riewievel,7)0;*, ,8ereby attracting (3S/Dw)dw new workers. That

is, having 13....trie; original equiribrium, to paint

2 as the.resn,t of enrollmNitr,incTease's the, program,, theWE

labor market obw adjusts to fil1'some of thiN-vacincies-'crated bjrthat;enrollment increase; in doingiso, it moves from point.2 to

poilt 3. 'Thus the net change in employment 'in the rest of theeconomy-is only deR, where

deR-=----dxt-+-faSI-aw}dw

,

while the net change in total reported-employment--that is, inrthe employment-and-training sector plus the ROE-- is 'deT, where

deT = deR + dx = -dxe +1(a5/3w)dw + dx

= dxty+ dxn + (3S/Dw)dw

Under the AHEM procedure, however,-the total change in reported

employment is assumed to be equal to the number of new enrollees

who were not employed (i. e., who were either unemployed or not

in the labor force) prior to enrollment.' In other-words, under

the AHEM prOcedure, the estimate of the total change in reported

Ai employment is only deT, where

deT = dxu

+ dxn

an0i, where, of course, del, is strictly'less than deT: the AHEM

prodedure yields a conservative (or, in technical terms, downwardbiased) estimate of the actual effect on total employment of an expansion of a Type JII program.

All this assumes that the ROE labor market does in fact clear.

If this is not the case, then, in general, the downward bias in

'the AHEM procedure is even greater, since this procedure also

ignores the multiplier-effects which are associated with the program-expansion-induced increase in effective aggregate demand and

the resulting return to market-clearing conditions. To see this in

an intuitive way, recall that the D curve of Figure 1 is constructed on the conventional textbook assumption that markets do in fact

clear anoWthus'that firms are able to sell all they want to at prevailingPrices. Obviously, this curve cannot be used to analyze

behavior under non-Market-clearing conditions. For a very.simple

model3 of the labor market under non-market-cleAing conditions,

3. See, for example, Barro and Grossman (1975). The model presented

here suppresses most of the details to be found in.heir work, but

is entirelyradequate for our purposes and.contains may of the

' essential 'ideas to be found in their much more elaborate treatment.

285.
w

y**

w*

IC 4. . 4. 34 0

4

O

- 259- -

v

I

dx - j. v I r - j

e*-dx e** e*

R e R . R

.4 Figure 1. Effect litr Type III Program

Under General Market Clearing

. ,

D, Ss

0 el e** e* Figure 2. Effect of Type III Progra

Under C9nditions of Non-Market-Clear ng-'
1

Alk 260 -

consider Figure 2, The curves labelled D and S are the same as

those of Figure 1--that is, these two schedules represent the

demand and supply for labor schedules which would prevail in the

ROE if and wheh firms are satisfied that the labor and commodity

markets clear. In this case, the equilibrium real wage would 'be W*

and the equilibrium level of employment would be as before.

However, when'markets do not clear, e, g., because the aggregate

effective demand for commodities falls short of the output of

commodities, firms discover that they cannot sell more than some

particular amount of output y' which is less than the marketclearing amount y*. Until conditions improve, they therefore will

hire at most only'e' workers, less than the market-clearing amount.

e*, because only < e*) workers are*needed to produce y' ( < y*),

h- II II II II

t

Hence, because the commodity market does.not clear and firms

are output-constrained, the ROE-labor demand curve takes the form

of the broken line age' shown in Figure 2, implying that no more

than e' workers will be hired at any positive wage. It follows that,

even if the real wage is less than the "right" level, wf, firms will 4

not hire more than e' workers, simply because whenAmarkets do not

clear firms will be unable to sell the additional output which any

additional workers would produce. By the same token, "failure" of

the real wage to fall is ,not the reason why the economy does not

return.to its original position, e*.4 Of course, competition . between workers may drive the real wage down to 'some-extent, though,

sirke.thig will have no effect on the effective demand for labor,

e', nothing will happen to employment in consequence. Rather, the

workers must compete for a fixed number of jobs, e'.

In any event, suppose that at present, the real' wage is some

. -amount w' as shown in Figure 2; that the government decided to foster

a return togeneral market-clearing conditions by expanding aggregate

,effective demand; and that it decides to expand dem'and byexpanding

a Type III program `The increase in program enrollment shifts the.

ROE labor supply schedule S to the left by an amount cbc + dx (i.e.,-

the program enrolls some persons who were employed in the ROE prior

to enrollment, and also enrolls some persons who were "involuntarily

unemployed" in the ROE labor force prior to enrollment). At the same

' time, the expansion in aggregate effective demand associated with

expansion of the Type III program induces firms td switch from the

i

,non-market-clearing deman curve D' back to the market.Lclearing ' schedule D. Thus, then() labor market moves from"r6iht 1, the initial

employment position, to p int 2 duepto the enrollment expansion per se,

and then moves from point 2 to point,3 due to the aggregate demand . expansion associated with the enrollment expansion., . jh this case, af course, the net changein employment in the rest

of the economy, de is positiire, from e' tole**. (Note that, by

construction, et,*.- e' is equal to 'OS/9w)dw, where d4= w** -w*

4. See, for example, Barro and Grossman (1975, especially pp. 61 62).

r
V a - 261 - 4

40

'as in Figure 1. This puts the Conclusion in the sharpest possible

I way, but, of course, the validity of the conclusion in no way

,depends on it.) Further, the net change in total reported,;employment; deT--that is, the change in the employment-and-training

sector. plus the change in the rest of 'the economy--induced by,

expansion of the program is even greater, i. e.,

deT';)dett.+ dx = (e4* - e;) + dxe + dxu + dxn

= dxu + dx.n--->": 6S/ 3widw + dxe . But -''the AHEM estima41 of the empiOyment'effects of this program ex.:

,..;;.- pension is still only .., deT, = dxu tdxn

t So the downward bias of the AHEM estimate, deT der, is even

larger in this case--one of adipstment from non - market clearing

conditions--thari it was in the previous case, which assumed general

market clearing.

Finally; suppose that employment in the ROE is'subject to a

minimum wages (The effectpof.the minimum wake on aggregate employment may not'be very large,-Mit its effect on teenage labor markets

may be more important.) ,Precise conclusions as to the magnitude of

the downward bias of the AHEM procedure in the presence' of a mini-

-mum wage must rest on precise assumptions about various otber magni-

- tudes, including (i) the extent to which the minimum wage exceeds

the equilibrium or-market-clearing,ltvel, (ii) the extent to which

Coverage under the minimum wage law fn the ROE is less than complete

/ and (iii) the e)Olit to which expansion of employment and training

. programs attracts enrollees previously employed in the "covered

vbsector" of the ROE. However, the direction of theobias of AHEM

estimates is in no way altered'by a minimum wage. Indeed, the downward bias of the AHEM procedure will be at an absolute inaximum if

'ali employed persons who enter Type III programs come from the

"covered subiector" of the ROE. This is because'the effect of the

minimum wage is to makethe supply 9f labor to the covered subsector

infinitely elastic, so that employers in that subsector will fill

- all of the vacancies which will arise when some of their employees

depart in order to enroll a Type, III program.

Thistitainduced" effect sociated with minimum wages will be

somewhat smaller if some of the-employed persons entering the Type,

III,program tome - instead from:the ROE subsectof that is not covered

by the minimum wage law (the "uncovered sector," for short);'but.

'even in this case there will still'be an "induced" effect of same magnitude, In fact, Figures 1.and 2.and the above discussion of

,their implications may, be taken to Pefer to the, limiting case in

which 41 employed persons who enter Type III programs are 'recruitta.d".

from the uncovered subsector of the ROE, the covered subsector is . left undisturbed and ,the bias in AHEM estimates.is*therefoie at an

absolute minimum.
- 262 -

Of course, all these,resulqixo"glnPlY intuitive partial - equilibrium examples rather than rigorous general-equilibrium

comparative-statics proofs, and they do not attempt to explore

either statistical, effects on variables other than employment

(e. g., the unemployment rate) or to examine differential statistical effects on different labor markets (0. g., for,youth vs.

other per sons). 'Nonetheless, these results are certainly suggestive;

and it is possible to derive thes and other results on th9 'statistical effects of employment and training progroms--both for

"disadvantaged" workers (i; e., the "target" or eligible population

group) and for the population as a whole--using a.formal model of

labor markets anj/tiMple comparative-statics techeiques.5 For

purposes of this Appendix, we content ourselves with simply stating

some of these results, in particular the following general

proposition:

5. Two impor.tant assumptions underlying those derivations which

pertain to unemployment seem worth mentioning here. First, we

assume that an increase in the general level of real wages

increases the labor force by less than it increases the "supply

of employment" S (i. e., the number of persons-who are willing

to accept lob offers rather than continue to search), and hence

that the number of people who wish to continue to search for

work rather than acwpt work falls as the genqral level of

real wages rises. .(Quits vary procyclically,'but the duratiqn of

unemployment falls as the cycle reaches its peak; and it can be

argued that these variables reflect search behavior per se. To

theextent that this is so, we assume that the former effect is

dominated by the latter, generating a negative relatioV between

the real wage level and the amount of "search unemployment.")

SecOnd, we assume that enrollment opportunities if employment

and.traini4 programs are rationed, Fe- that the number actually

enrolled is an exogenous goliermept-detewined policy variable.

To the extent that admission-into suc kograms 'depends on

labor force status--e. g., on being or having been unemployed- -

individuals may deliberately change their labor force status in

order to, improve their chances of admission; however, to

simplify the analysis, we assume that the extent of such behavior

is negligible. In. fact, the assumption 'that enrollment opportunities are /rationed more or less implies' this. Individuals who

attempt to improv their cbances of admission by becoming . ,unemployed trade ff a certain loss of earnings against a highly'

uncertain and--when enrollment opportunities are rationed--pre-

, sumably rather small increment in the - probability of admission.

Some may make-this trade-off, but, at least as a first approximations, we assumettlat the aggregate importance of-this,phenomenon

is-trivial.

2 Q
- 263

AHEM estimates of the statistical effects of an

expansion of any employment and training program- -

whether Type I, II or III--are downward-biased,

both for "disadvantaged" workers and for the population as a whole. s. It is also possible to derive other results which are concerned

specifically with the effects of each particular type of program.

The results whichtefer to the; statistical effects of Type II3"

programs--which-, given the size of these programs, are probably

of greatest interest--may b.e summarized as follows:

If the government expands' enrollment in a Type III

program, then:

1. Both for disadvantaged workers. and in 'the aggregate,

reported employment and labor force levels will both

rise and the reported unemployment rate will fall.

2. Both for disadvantaged workers and in the aggregate,

the actual'increase in reported employment (the

reported labor force) will exceed the number of

-new enrollees who were not employed (not in the

labor force) prior to enrollment. Hence, both for

disadvantaged workers and in the aggregate, AHEM

estimates understate both the employment and labor

force. increases attributable to expansion of the

program.

3. Both for disadvantaged workers and in the aggregate,

the difference between the actual increase in

.reported employment and- the number of new enrollees

.who were not employed prior to enrollment will

_exceed the difference between the actual increase

in the reported labor force and the number of new

enrollees who were not in the labor force prior to

enrollment. Hence; both for disadvantaged worker'

and in the aggregate, AHEM estimates understate the

reduction in the unemployment rate attributable to

expansion of the program.

-

290

A
to.

- 1264

APPENDIX B

DERIVATION OF AHEM ESTIMATES

t

In Tables 4 -7 of the te,it we present AHEM estimates of the levels

of employment, unemployment, btc., which would have prevailed during

1963-1976 in the absence of employmerit and training programs. These

estimates are derived in the following manntr.,-----

Hirst, E, the level of employment whic would prevail in.theabsence of the programs, is assumed to be even by

. (1) E = E - + D X

WIN wIN q Iq + i X__ri e r IIr

a

where E is the,level of aggregate employment recorded in official

statistics:temm is the fraction of enrollees in WIN who were . ' 'employed prior fo enrollment, e is the fraction of enrolleesin

the zth Type -p program who werepzemployed prior to enrollment, Xwm.

is total WIN enrollment and X

Type-p program.' Likewise, tRi

in the aggregate labor force in

(2) L = L - X.

WIN(1.-ZWIN)

is total enrollment in the zth

number of persons wilt would be

the absence of the programs is

qEXZ

-EX (1-Z ) Iq Iq r IIr IIr

i XIIIr(1-ZIIIr)

where L ikp the number of persons in the labor force as recorded

in official statistics,Zwm is the fraction of enrollees in WIN

who were in the labor force prior to enrollment and 2, is the

fraction Ofrenrollees in the zth Type-P program who wEre in the

labor force prior to enrolltent. Together with official data 2n

Land E, data on overall values of X, e and St for each program

lead directly to a measure pf the aggregate unemployment rate

which would'prevail in the absence of the programs, u, where

(3) u = (L-E)/L

1. Notepthat (1) assumes that 20% of all WIN enrollees ate irtonthe-job traiAllig and similar activities which would cause them

to be classified as " employed" by CPS interviewers. Here we

follow Small (1972),'who, in the absence of reliable data on

the composition of WIN enrollees by type of activity, adopted

thesame-assumption.' (The limited data available suggest

that the 20% assumption is not unreasonable, though it is nOt

possible to be sure whether it is equally Appropriate for

each-year of the WIN and Work Experience programs.)

2.. In most cases we have been able to use actual data on X for each

201 1

4
- 265 -

Recall yet again that ii, 1, and E are all,conservative estimates

of the "true" effects of-abandoning employment and training programs: that is, u is a lower bound on the true unemployment rate

which would prevail in the absence of the programs, while both L

and E are upper bounds. This is because, as noted in Part I and

Appendix A, the AHEM procedure ignores induced effects (on employment in the rest-ofLthe-economy sector and via the multiplier)

arising from abandonment of the programs. Finally, given data on

the civilian noninstitutional population age sixteen and over, P, one

may compute estimates of the emplo t-population ratio and labor

'force participation rate which would obtain in the absence of the

programs, e.,

. (4) n t/P

(5) A = L/P

respectively.

Given data on the employment, E , labor force, L , and population, p, of persons age 16-19, one &)uld easily use gxpressions

analogogs to (1) - (5) above to compute similar estimates for the

.youth labor force, if data on program enrollments for youths and

the composition of young enrollees by previous labor force status- -

that is, X, e and t--were available. Unfortunately, data on 0 and 2.

for persons age sixteen to nineteen (or for any 'other particular age

group) are almost never available.. Instead, therefore, wd.are

forced to construct estimates of the direct effects of emplo ent

and training programs on youth by assuming that 'the compositi of

young enrollees by pre-enrollment labor force status^was^the^ tame as

thatof_all enrollees. Hence, we 'computeestimates E, L , u

and A .for youth, analogous to E, L, u, n and 5, .respgctiely),' foY

the Jerall population, by inserting E and L in place,of E and L

in (1) and (2), respectively, and thenYmultiking each total program

enrollment figure X by y the fraction of enrollees in the zth

Type-p program who Lre beween sixteen and nineteen years of age.

program (but see n. 2 in the text above). Data on e, t and

(see below) y for each program are also usually available, but

in some cases only for a few selected years. Here, too, ;44'4'

have had to make a variety''-of assumptions and estimates when

the requisite information was unavailable. For purposes of the

present paper, suffice it to say that in this case, as in

most other empirical work, the results are subject to an

unknown, and probably unknowable, degree of measurement error.

292
2'66 -

k

SOCIAL DEVELOPMENT AND EMPLOYMENT: AN

EVALUATION OF THE OAKLAND YOUTH WORK EXPERIENCE PROGRAM

By: Delbert S. Elliott and Brian A. Knowles

A ABSTRACT

This report on the evaluation of the Oakland Youth Work Ixperience

Program presents data froM at experimental study incorporating a pretest

and two follow -up interviews occurring at six-month intervals after the

) pretest. For most participants, these data rfpresent the attitudes and.

perceptions of youth after six months and One year in the program but,

pior to entry into regular jobs or work careers. The results,:therefore, are 1pited to this short-term impact of the program and,mey re-

, -veal little' about its impact following termination'from.the program or " I' 1 du4ng a subsequent work career.

In general, here was no eMpirical evidence of favorable program

impact on pariicipa is (experimentals) during the first six-month

evaluation period, .e.,,there were no significant differences favoring

experimentals on any Of the twelve impact measures at the first followup. There was limifedrevidence that participation for a full year had

some beneficial effects for experimental respondents. In particular,

Powerlessness was found to decline between the first and second followups and.Self-Esteem appeared to increase-between the pretest and-the

second follow -up.

In general' control respondents showed little change during the

first six-month evaluation perinand appeared to show some evidence

of unf notable change by the time of the second follow-up, particularly

an incr ase in negative Parent/Child Relationships.

A argeprOportion 'of controls reported some work experience during

both evaluation periods. About 75% worked during the first evaluation

perlod and about 50% did so during the second period. Program participants did especially well when compared ply with controls who did not

work, primarily because the latter showed consistent negative change.

Controls who did work did at least as well as experimental respondents.

For both program participants and controls, favorable change on the impact scales was systematicafly related to theif perceived satisfaction

with their jobs.

Tests of the theoretical model upon which the evaluation was based

revealed substantial support. Changes on the impact variables were predictive of changes in Self-Reported Delinquency.

The results from the general nalysis were somewhat disappointing

from the perspective of the progr . Theie was no evidence that participation in-the,YWEP resulted in ositive change relative to no participation. In the restricted compa isons involving only experimentals with

matched controls who did pt work, the program (i.e., work) appeared to

have some positive effects, although this wad, primarily the result of

declining levels on the various measures for controls rather than gains

for experimentals.. There was thus some support for the positive impact

of work per se as suggested in the ,theotetical model. It must also be

anticipated that some of the benefits of,,program particination may yet

293
Iro

4

267

be realized.' As of the time of the final follow-ups, most of the youth'

had been unable to make practical use of their training and experience.

f r INTRODUCTION ' A review of published research in the area of youth employment re-

)1Tals numerous studies examining the effects of personality and social

characteristics of individuals upon their work choices, career patterns,

jab satisfaction, absenteeism, morale, and so forth. Likewise, much

research has examined the effect of participation in Manpower programs

on subsequent earnings (see Rawlins, 1972 for a review).' It is interesting to note; however,that few studies'have examined the impact

of employment upon the social development of youth. Shore (1972), in

his review of studies evaluating this tatter relationship, cites only

five'empirical studies and cond<des.that little is known about how

particular work experiences impact upon the deyelopmental procesS.

While several additional studies could be included in a current review

(Hackier, 1966; Robins, 1969 and 1974; Walther. and Magnusson, 1969;

Ahlstrom and Harighurst, 1971; Jeffery and,Jeffery, 197Q), we still

know relatively lithe about,the impact of employment on the social

develOpment of youth.

The few studies focusing.upon the relationship between work and

social development have failed to provide any compelling evidence for

the postulated positive effects of work on the attitudes, perceptions,

'goals or values of youth. It is the case that all studies have.

rei)orted negative findings (see, for example, Gartner, et al., 1971;

Massimo and Shore, 1963; Walther and Magnusson, 1969; Engel, et al.,

1967; Kohen and Parnes, 1971), but rather thpt the theoretical and

methodological adequacy of the research casts doubt upon,,the validity . or generality of these findings whether positive or negative with

respect to the poStulated impact of work on social development.

Methodologically sample sizes were often small, few studies_ employed

4

it

294

4
-268 -

control groups, and none involved an experimental design with random

assignment.

1 .Given these limitations and the'inconsistency in findings,

little can be concluded regarding the impact of work on youth development.

Of equal importance in,the evaluation ofpast research is the al- . most totalabsence of any explicit theoretical rationale which ought ,

to guide the design and implementation of the work' program and Link

specific program components to evaluation outcomes or objectives. !In

the absence of specific postulated outcomes based,upcn some theoretical

model or perspective, it is difficult to interpret the emiAricaltind-

- ings of evaluation studies._ In most instances, a very broad range of 0

"possible," "hoped for" outtome§,are identified or implied by program

personnel with a forced post hoc rationale, or a general search for

fk, "positive" outcomes of any kind is. initiated.

In the recent past, the rationale dr justification for work programs has focused-increasingl y-upon a reduction in crime or delinquency

as the general program objective, but the theoretical connection bet-

. 'ween involvement in a.-work progrpm and delinquent behavior is seldom

I explicit, or involl'es ve'y crude notions such as the assertion that

1. The Robin's- (1969, 1974).study is presented as involving an experimental design with random assignment, but.does not achieve this'objective. The random assignment involvePyouth op a waiting list

who were eligible for paiticipatilon in the In- School NYC Program

(Experimental Group 1). These persons were randomly assigned to a

Summer Only Program (ExpettMental Group 2) and the Control

No matching prior to assignment was involved, and the Control Group

constitutes an adequate comparison group only for the Summer Only

.Experimentals. Yet, the major comparisons `and conclusions involve

the In-School Experimentals. It is also the case that the Initial

(pretreatment) measures for the In- School Group occurred sometime

after their entry into the.program, not before. There was also a

serious loss of cases across time, with no documentation on the

possible bias this loss mighk have on the results presented.

Nevertheless, this study is one of the better studies available

on the impact of work on attitudes, perceptions, and behavior

(including police contacts for delinquent behavior).

295
- 269

wOrk keeps youth occupied, reducing the time that is available for engaging in delinquent behavior. Apart from the obvious possibility

that the work setting may afford new opportunities for crime, studies

utilizing this theoretical rationale seldom make any deliberate attempt

in the structure of the program to maximize time involvement, tide

critical intervening variable in the connection between work and delinquenCi-reduction; and given the very liited time involvement typically,

required of participating youth, there is no reasonable justification

for assuming that the program should be effective in reducing-delinquent

behavior. In.mOst instances, these intervening variablps involve such

things as attitudes toward the norms or authority, goals or aspirations,

perceived opportunities, and values, i.e., social development varia es. Finally, evaluation studies utilizing reduction in delinquency as

the program objective have uniformly failed to measure delinquency in

4 a way which is consistent with their theoretical rationale (even though

crude). With few exceptions, work experience is postulated to impact

upon delinquent-behavior, yet evaluation studies consistently,utilize- '

police contacts/arrests as the measure of delinquency. A reduction of

Police arrests may or may not- reflect an. actual reduction in delinquent

activity; likewise, an increase in arrests does not necessarily reflect

an actual increase in delinquent behavior.

Given the theoretical and methodological difficulties noted above,

the present state of knowledge regarding the impact of employment upon

the social development of youth continues to be very limited. The

study described,here involved a deliberate attempt to deal with these

deficiencies In both the'structure and rationale for work experience

programs dnd the methodological adequacy of the evaluation. too fi THE OAKLAND YOUTH WORK EXPERIENCE PROGRAM2

The Oakland Youth Work Experience Program (YWEP) was based con2. The National Office for Social Responsibility (NOSR) developed this

program as a model youth work experience program based on thd OYD

strategy for youth development (Gemignani, 1971) uncle); a contract

with.the U.S. DdpartMent of Labor, Offender Rehabilitation Division

(No. 99-4-0009-013).

2'9 6
- e

270

ii ceptually upon a theoretical model developed by the Office of Youth

Development (OYD), DREW and included selected, elementt of both the Inand Out-of-School Neighborhood Youth Corps (NYC)'program and the Vocational Exploration in the Private Sector (VEPS) program. It was developed as a model program for the U.S. Department of Labor and the initial

contract included funds for a comprehensive, long range evaluation of

its effectiveness in realizing its stated youth development goals. Only

a brief description of the conceptual model and the actual program will

be presented'here.

3

The basic premise of the OYD strategy for youth development is that

a satisfactory pattern of physical, social and psychological develOPment

represents the most effective deterrent to delinquent behavior. An

analysis of the developmental processes which result in-normative, constructive, prosocial behavior patterns for most youth suggested three

major patterns: (1) law-abiding youth are involved in meaningful,

legitimate, and satisfying social roles and peiceive that they have

access to similar roles as adults) (2) these young people believe that

their parents, friends, teachers and employers view them positively and

they view themselves positively; and (3) they are accepted and integrated into their families and communities, and perceive a high degree

of control over both their prebent and future lives. The development

model thus incorporates these three elements as basic to a satisfactory

process of youth development and postulates that they insulate youth'

from involvement in serious'or repetitive patterns of delinquent -behavior. The youth development(model is.presented s.cDematically in

Figure 1.

1

3. For a more detailed description of the theoretical model, see

'Elliott, et al., 1976; Brennan and Huizinga, 1976; CAR, 1971;

Polk and Kobrin, 1972; demignani; 1971. For a detailed description

of the Program, see NOSR, 1976.

297

<1,4...

j
4

Problem

Behavior and

Deviant Roles

N.,

FIGURE'l

4

Access to

Desirable

Social Roles

I

Alienation

From the

Social Order

Commitment and

Attachment to the

Social Order

+ = 6igh access, positive labeling

- = low access, negative labeling

.298

Conformiw

Behavior' and

Conventional

Social Roles
- 272 -

Desirable Social Roles--A Stake in, Conformity

One of the reasons that many youth do not engage in deviant behavior

is that they have little togain and much,to lose by doing so. Youth

who are loved and respected at Mime, successful at school, comfortable

among their friends and look forward to a worthwhile work career put all

A f of these in jeopardy if they become involved,in,delinquent activities.

Participation in meaningful social roles, therefore, serves to insulate

youth from'involveMent in delinquent behavior by providing them pith a stake in conventional roles and behaviot. What is notable about\these

positive social roles is that they are provided by a handful pf

tutions--primarilythe family, school, and work. In large measure then,

we look to these institutions to ensure that youth are provided the

opportunity to achieve desirable social roles,

In practice, most youth do experienCe meaningful'involvement and a

favorable course of development Within these 'institutional' settings.

Yet for some youth, these same social contexts systematically limit or

deny them access to desirable roles: they are ,not loved and respected

at home, are not successfu4-,4 school and do not hold much hope for

rewarding work careers. Consequently, they may feel that they have no

stake in conformity, and that they place little or nothing inl jpopardy

if they experiment with illegal forms of behavior. In fact, certain

types of criminal activity May offer the only hope they see for financial

and material rewards.

, Negative Labeling

Once an individual is tagged or defined' as a "troublemaker," "a

,

. truant" or "a delinquent" others tend to view him as such andto treat

him according to this label. When parents, teachers and friends and em-

, ployers begin to use such.labels as a basis for their interaction with a

person, the individual is under great pressure to define himself/herself

in a similar way and.to behave in a manner which is consistent with this

definition and 'the social role it implies. At this point, the individual

has become what he has been labeled, thereby-confirming the original

definition. The fact that this was' a self-fulfilling prophecy, and that

; 299.

A
273

those participating in the labeling process contributed to'the youth's

becoming a troublemaker, truant or delinquent, often gots unnoticed.

Of particular concern are the.negativedabeiing,processes which

'occIr in the school, at work and in the, juvenile justice system.' Because the negative labels employed in these institutional settings are ' more visible and formal thant.hqie generated else1here, they tend to

have a greater effect on an indiyidual's life. Being defined as a ' ,troublemaker at school, for example, has an imiact not only On the

courses one takes (vocational as opposed toollege bound) but also on

L

participation in extracurricular activities, assignment to particular

teachers, and even seating locations within the classroom--all of which

impinge-ffilam an individual's future educational and occupational

opportunities.

The application of the label "juvenile ,delinquent" to yo h processed in the juvenile jt\stice system has similar negaAve.effects.

Often these youth find themsely,...* cut off from contact with conventional,

law=abiding youth pnd thrust into association with youth Who are committedrto delinq nt roles; their future education& and occupational

opportunities a diminished by their haVIng an official record; and

friends., paren s, teachers and. significant others in-theirli*es begin'

to view them s being "different" and to respond to theleselectively in,

terms of the elinquent label.4 The labeling protess becomes complete

when, as a consequence_of this new definition and the resultant expectations-of others, the youth domes to'view himself as a delinquent

tepon.s.

The problem with such institutional labeling is that it is often

discriminatory and'inappropriate, dictated by stereotypic attributes or

system requirements 'rather than by a careful evaluation of an individUals

abilities, values and commitment to particular kinds of behavior. The'

danger of labeling, then, lies in the very real possibility that while

the youth has not make any commitment to the specific behavior whi

generated'the label, the labeling process itself may 'serve to reinforce

the very behavior which was viewed as,being objectionable.

3O 0

E.
- 274

Alieriation

,Limited access to meaningful, responsible and satisfying Social

roles and negative labeling result primarily, from child-rearing practices

at home and institutional processing procedures at school, work and in

the juvenile justice system. Alienation repiesents a type of individual

'response to such experienoes'within these"institutional settings.

Difficulties at home, failure at school and little hope for a rewarding

work career result` in many youth feeling defeated and rejected. They

feel that they have no stake in these institutions and, consequently,

that they have no reason to be committed to the appropriate rules-of

conduct within these settings. In its broadest sense, alienation denotes a destructiOn of one's affective ties to the social order: a weakening of oneos feelings that he or she belongs and is a part of the

family, the school or the community; that he or 'she is morallrobligated

to obey the rules; or that there are any positive rewards for striving

to do what is right. In essence, then, alienation is a rejection of

one's rejectors--a psychological disengagement frQin institutions and

the general social order which they support.

The significance of this type of response to negative labeling and

limited access to desirable social roles at home, school and work is

that it constitutes a psychological form of "permisionu., to ignore or

rules. If one feels that he or she doesn't belong, has no

of realizing any rewards from continued involvement and,

has no moral obligation to those in authority in these

violate the

pdssibility

ultimately,

institutions, then he or she is, free to engage in any forms "f behavior

which are personally gratifying. There is no investment and, henoe,

nothing ,to lose.

The model depicted in Figure)-is a dynamic model in which feedback

relations reinforce the basic processes. On the deviant side of the

model, negative labeling and limitIO opportunities lead to alienation

which-in turn accentuates an:A/Mounds the negative experiences. Ultimately, thiS leads to deviant roles and,a dependence upon iVegitimate'

opportunities or means for attaining social rewards. The objectivi of

youth development programs such as the Oakland Youth Work Experience:

O
- 275 -

Program is is to intervene in and disrupt the process by increasing access,

to desirable social roles, byrreducing negative labeling and alienation,

and ultimately by reinforcing conforming or prosocial lehavior patterns:

4

THE YWEP PROGRAM

The goals of the project are based uppn the youth development strategy 'discussed above, The'intended program-outcomei are thus: (1) to

increase youth's access to socially acceptable and meaningful roles both

at school.and in relation to present and.future work roles; (2) to

reduce negative.labeling of youth at home, school and work; (3) to reduce

feelings of alienation and rejecti2n and, as a consequence, (4) to reduce

involvement in delinquent behavior.

The target population for tie Oakland YWEP involved delinquent and

pre-delinquent youth aged sixteen to eighteen. Delinquent youth were

those who had been adjudicated as suth by official cpurt action. Pre-

. delinquent youth were defined as those whose behavior might ultimately

lead to court action if unchecked. In addition, jtwas,necessary that

program participants meet poverty level guidelines as

'\ specified by heOffice of Management and Budget. c Three methods were used'to obtain applicants for the program. First,

the county probation department was asked for referrals of delinquent

youth. Second, a number of community agencies such as schools, recreation

departments, churches, health facilities, and others were asked, for referrals of predelinciuent youth. And third, all of the available public

0 0

. , 4. This model is.derived conceptually from several longstanding theoretical perspectives on delinquency, particularly opportunity theory

, (Cloward and Ohlin, 1960; Elliott and Voss, 1974), control theory

(Hirschi, 1969) and labeling theory (Lemezt, 195IA Becker, 1963;

Schur, 1971 and 1975).

5. In fact, the proportion of

( adjudicated delinquents among participants.

'and controls was small (less than 10%).

302
YM

276 -

media were used to attract youtMespecially dropouts). not known to

existing agencies. Since there were many more applicants than positions

)0. available, a selection procedure (described in a latersection) was developed to determine which youth wire to be admitted tp the program.

At the start of the program, special teams were formed consisting

of twenty-five youth participants, a member of the project staff and

assigned volunteers. The intent of'the teams was to develop a' greater

intimacy for the program to provide'more special attention to the specific needs of each participant and provide a positive social setting and

group support for each participant. The.team worked together during

orientation to emphasize continuing self-assessment and individucl and

,team responsibility far decision making.

In additiontto providihg work

also attempted to meet other basic nee

ence, the Oakland,YWEP program

o i participants including

the provisionfor improvement of'basic educational skills and requirements necessary40 in the world of work. Participants' in the Oakland pro-

__ ject were given classroom training designed to fill gaps in their formal

straining. Following an assessment, of each participant's mathematics,'

English, and reading abilitY, an individualized educationalplan was

developed. The plans included participation in accredited programs . offered by a igh schools,.adultoeducation programs, public schools,

a street academy, d a local community college. The purposes of educational plans were to,gilFb.youth the oppoitunity to participate in 'a curric culum designed to bertlevant to career possibilies, to increase their,

perception Of career opportunities, and to improve th r chances of work

success,4while obtfining educational credits toward their program participation.

.44 'The work stations that were made available to youth , participants

represented a wide variety of occupations offering career opportunities

and skill develOpment. In geheral, they were intended to increase per- _ ception of the realities of the work world and to provide meaningful,

satisfying workexperiences. More specifically, th objectives of mirk

experience were (1) to provide experience in oceupat sns in short supply

/ and which, if possible, relate to the occupational goals and capabilities

of the participants; (2) to provide a range of work experiences from

303.
'N\ c

V

4

277

general, exploratory work to beginning'and intermediate skill levels to

advanced (3) to provide opportunities for-maximum skill acquisi=

tion commensurate with participant ability and taking account of time

limitations; and (4) to provide an opportunity to experience new kinds

of relationships with Others in a work role,aided by advice of counselors, site supervisors, and participant teams.

The final phase of-the program involved intensive preparation re-

. lated to a participant's placement goals. Youth. ere given assistance

and training in the preparation of resumes, applications, and entrance

tests. If the youth desired job placement, a wide range of-fulr-time

employment opportundties Were explored and every effort was made ta

place the youth in -a permanent job. Others were motivated toward careers

requiring further, specific skill acquisition, and contracts with the

appropriate institutions were established for the youth. For those who

developed an interest in completing formal educational (goals, assistance

was given in terms of curriculum development and sour 'es of financial

assistance. Finally, information and orientation weree-proVided for

those who became interested in enlistment in one of the military services.

In short, participating youth were prepared to compete more effec-

,

. tively for work roles and provided some first hand experience in positive work roles through a , comprehensive programNInvolving need assess-

"ment, educational training, w&rk experience, and finally career placement. In the process,.youth completed approximately 675 hours of paid

participation about equally divided between classroOm training and work

experience.

PROGRAM EVALUATION

. The evaluation-. was intended to provide an assessment of t e- effects

4

of the YWEP experiu-On participating youth. As the Oak1an.el program

4 had been.organized around a broad strategy foi the development of prosocial' behavior in-disadvantaged delinquent, and predelinquent youth,

the focus of the evaluation was derived primarily from that conceptual

model. Thus, the Variables of the conceptual model described above and

the, relatiohships among them were examined in detail.

3 0 4

$
278 -

Research_ Design

The research design ,employed for the Oakland YWEP evaluation is depicted in Figure '2. Pt is a classic experimental design with matched

experimential and control groups ands both pre- and post-tests on_pe

.dependent variables. The experimental treatment in the present, research

is participation in the work experience program.

As can f)e seen in. Figure 2, one post -test was planned at six months,

after entry and a second post-test was planned at twelve months after

entry. FolloWing six months in the work experience program, .experimenial

4 4

. participants were Selected for one of three alternate paths. That is,

they were: (1) continued in the work expe rience program for an additional six months, (2) admitted to an Action Program involving public sector

jobs, or (3) terminated from the work program.

In the notation of Campbell and Stanley (1966) the design would

appear ,as:

tl - t2

- .0 X 02

0

4

07

0

10

X.- .0

, S X 08

0

11

A 06

09

0

12

4

where'X is the work experience program described in detail above,kA is

theActynworkprogram,andthe0.are the various administrations of

the Impact Questionnaire; t1 is May-June, 1975; t2 is December-January,

. 1976; and t

3

is June-July, 1976. 4 P.articipant Selection'and Assignment

ttOt As described earlier, applicants for the program were solicited from

several sources including referrals from the probation department, from.

Iother public and private .agencies, and through advertisements in the

media. If a youth met eligibility requirements rage, family income,

residence) he was asked to complete an application. Youth submitting an

aAplication were told that there would likely be many more applicants

305

R
FIGURE 2

OAKLAND YOUTH-WORK EXPERIENCE PROGRAM

RESEARCH DESIGN

I

,e Application' Matching Random Work

Assignment Program

Impact

Scales

Interview

By 0-Type

Age, Sex,

Ethnicity . & Referral

Source

* t2 - t1 = 6 months

t3 - t2 = 6 months

Experimentals

N = 200

Gontrols

N = 200

Post-tests

t *

r - - - -I 2

`," ;";_

,

. 306

Q.

Impact

Scales

t 3

400 Impact

Scales

[Impact

Scales
280

than positions in the program. Those acceptedvwould, therefore, be

randomly selected by computer and the chances of being selected were

about one in three. About 600 applications were received.

Each youth was given application and parental permission forms.

When these were returned, the Impact Questionnaire (evaluation instrument) was administered. This questionnaire includes a number of items

concerning social and demographic background in.addition to twelve . social psychological scales.

The Impact Questionnaire was self-adminlered in a room with as

many as ten youths'at one time. Applicants were specifically assured

that their responses to the evaluation instrument would in no way affect

their chances of selection for the program. Staff members were present

throughout to answer questions,.

6

Using a typological analysis procedurq, youth were matched in pairs.

The members of each pair were highly. similar on all twelve scale scores

and were identical in terms of age, sex, ethnicity, and referral source

(probation referral vs: oth r source). By this procedure, 202 matched

pairs were obtained. Each I' was then split randomly one member being

assigned to the experimental gro ald one to,thi control group. The

experimental group was then admitted to the work program.

Impact Scales

r Before proceding to the analysis, d brief description of each okf the'

3111 twelve impact scales is presented.

7

1. Access to desirable social roles. Three scales were used to

1.

assess this component: '`

a. Perceived access to desirable educational roles is a five6. When necessary, a 'staff member was present who was fluent in both

English and the respondent's native language.- 'In about twenty cases,

the entire questionnaire was'redd to a respondent.

7. The actual scale items and the documentation of .psychometric properties of each scale are available and may be obtained by writing to

the authors.
1

- 281 -

item scale intended to measure-perCeived likelihood of achieving

educational gals. A respondent is first asked how far he would

like to go in school and then is asked th five questions con-

- cern4ng the probability "of attaining this similar goals. The

higher a person's score on this scale, the higher is his perceived

chance of obtaining the desired educational goal.

b. Perceived access to desirable occupational roles is a sixitem scale intended to measure perceived likelihood of achieving

occupational goals. Again, a respondent is first asked what kind

of job he would like as an adult and then is ask d the six questions

relating to the probability that this or similar goals will be

reached. A higher score eon this scale reflects a higher degree of

1P perceived access to occupational goals.

c. Parental/child roles is a five-item scale measuring a

youth's belief that his parents are interested in him and would

help him if he needed it. The scale represents a measure of

access to desirable social roles for youth in the home: The

lower a respondent's score on this scale,, the better is his

perceived social rolej.n his-family.

2. Negative labeling: Four scales were used to assess different

ettimensions of perceived negative labeling:

a.-c. The content of these three perceived negative labeling

scales is identiCal, but the reference point varies (parent, teacher,

and _peers). In each case, the scale is a six-item measure of perceived negative or antisocial categorization by signific t others.

The scale consists of six semantic differential dimensions wi a seven-point continuum.. The respondent is to indicate where oneach

dimension he feels he is seen by his parents (or teachers or peers).

A higher score on these scales implies a higher degree of perceived

negative labeling.

d. Self-esteem is measured with a ten-item scale similar to

Rosenberg's (1965) scale. It assesses the extent to which a youth

values, accepts, and respects himself. The focus is on self -

acceptance.- A Nigher score on,this.scale reflects'a higher levelof self-esteem." (

41%

3os
- 282

3. Alienation. This component of the conceptual model was assessed ' with three scales, each measuring a different dimension of alienation: - a. Normlessness is measured with a six-item scare designed to

assess the extent to which an individual believes that socially

unapproved behaviors are required to achieve given-goals. A higher

score indicates a greater level of normlessness.

b. Powerlessness, a twenty-one-item scale, is a version of a

scale developed by Nowicki and Strickland (1973). This scale measures a youth's sense of control over'evenis in his life, his

feelings of power over activities and circumstances which affeCt

him. A higher.score implies a higher degree of powerlessness.

c. Societal'estrangement is assessed with a sixteen-item

scale measuring the extent to which a youth feels estranged or

alienated from the larger society. The present scale is a modified

version of a scale of anomie developed by,McClosky and Schaar (1963).

The goal of the modifications was to make the scale more suitable

to youth. A high score reflects a high degre of societal estrange-

. ment.

4. ,Normative pressure from peers. Thisis an eight-item scale in-:

tended 'to measure the extent of pregslle towards conforming or deviant

-At* behavior felt by a youth from his friendsh4 group. It reflects the

) type of peer group environment in which the individuariparticipates and

indirectly refleCtS his social role in the larger adolescent/peer culture.

A higher score on this sc*le reflects a higher perceived pressure from

peers to be deviant.

5, Self-reported delingtency. One scale was used to measure

participation in,delinquent roles. 'This is a 19 -item scale of selrf-.

reported delinquency. The scale is an adaptation of.the Nye-Shott (1957)

Delinquency Checklist. Te higher one's score on this scale, the higher

the amount of delinquendy-reporn-tr.,

309_
- 283 -

k

ANALYSIS -- IMPACT ON YOUTH

1. Initial Comparisons ,

The primary analysis utilized in the evaluation of the Oakland YWEP

involved simple t-tests comparing the.experimental and control groups.

Statistical tests comparing experimental and control scale scores at the

first administration of e Jhpact Questionnaire revealed no significant

differences. This was to b expected since these groups were matched at

that time;-nevertheless, the tests verified the initial assignment prop, cess. Since there were no differences at time 1, experimental-control

comparisons at subsequent times were made directly on m the raw scores.

That is, it was unnecessary to.compute gain scores or rely on analysis

of covariance.

2. First Follow-up Comparisons

Six months after the initial administration,-the first follow-up

A 4-SImpact questionnaire was administered. The timing of this post measure

,coincided with the completion of the participants' initial six months

work experience program and the administration of the interview schedule

Nt' was included as part of a general exit interview for all experimentals.

For controls, personal interviews were arranged, in which .controls were

asked to come to the!project office and complete the questionnaire.

They were offer9d a $2.50,payment as an incentive to complete this follow-

)). up schedule., For experimental youth, the questionnaire was,identieal to

thatadiministered,atthe,beginning. For the control group, a number of

items were added

to posale work

project so as to

influence on experimental-control comparisons. Overall, 189 of the 202

experimentals (94%) completed the flist follo1W-up interview schedule compared to 152 of the 202 contrci1%. (75%). .

for the first follow -up, These additional, items related

experiences occurring since the youth''applied to the

, *N. allow for the control of:this potentially contaminating

The first analysis undertaken involved a test for selective loss of

either experimental or control participants. Statistical tests comparing

the initial scores-of the thirteen expeiimenlal cases who were lost at
- 284 -

time 2 with the corresponding scores of the 189 respondents who were

retained revealed no significant differences (t-tests). On only one

scale (Access to Educational Roles) did the difference even approach

conventional levels of significance..

The analysis comparing the initial scores of the 50 control cases

who were lost at time 2 with the corresponding scores of the 152 cases.,

that were retained revealed several Significantedifferences. Respondents who were lost at time 2 were lower on both Access to Education

Roles and Access to Occupational Roles and higher on Negative Labeling.

by Peers.

Overall, there were relatively few differences found here indicating

selective mortality. Nevertheless, the three differences found among

the control respondents all tended to favor those who were retained at

time 2. That is, those who were ,retained had more desirable scores at

time 1. The possibility of.a slightly biased control sample must be

accepted. For t-tests comparing experimentalsand controls, the effect

of this bias is in the direction of making the tests more conservative.

The next set of analyses was intended to test for chaUges from time%

.11

1 to time 2 in either the experimental Or corol groups. For experimental cases who were interviewed at both times, four significant

differences were Obta ned over this six month pre-post period:. 10n Normlessness, Access to Oc pational Rbles, Negative Labeling by Peer's and

Self-Reported Delinquency, scores increased from tlme1 to time 2. Only

one of these, the increase in Access to Occupational Roles, can be regarded as a favorable change. For the control cases who ?ere interviewed

at both times, only one scale (Negative Labeling by Peers) wed significant change (an increase) from time 1 to time 2. In general, both,

groups evidenced some negative change on perceived labeling by peers

across time. While the eXperimental.,,group reported an increase in perceived access to occupational goals, they also'reported more normlessnesg

and' delinquency.

Next, the scores of experimental and control repondents were compared

at time 2. -For this analysis, only matched pairs at title 2were included,

There were 144 pairs.' The results of this comparison are present4-in*

Table 1. Only two significant differences were found, both favorinvthe

311.

10,
3.

- 285 -

TABLE 1

COMPARISON OF TIME 2 SCALE MEANS

FOR EXPERIMENTAL AND CONTROL CASES MATCHED AT TIME 2

op

Scale

Normlessness

Societal

Estrangement

'Powerlessness

Access to

Educational Roles

Group

Direction of

-2 Change from

t1 t-score

142 Experimental' 12.24

Control 12.11

1 143 Experimental 38,19

Control 36.60

143 Experimental 28.59

Control 27.98

143 Experimental 11.95

Control 12.02

Access to 143 Experimental 13.54

.Occupational Roles Control 13.16 0

Negative Labeling/ 130

Parents

Negative Labeling/ 130

Peers

Negative Labeling/ 126

Teachers

Self- Reported

Delinquency

Self-Esteem

%

Experimental 16,98

Control 16.95

Experimental

Control

Experimental

Control

17:42

17.95

16.62

16.2

143/ Experimental 23.46

Control 22.45

,r

143 Experimental 31.57

Control 31.68

Normative Ptessure 142 Experimental

Control

13.68

13.44

000,

.40 , NS

2.46 <.05

1.44 NS

-.31 NS

1.37 NS

.04 NS

-.64\ NS

.34 NS

k

1.57 NS

-,26 NS

.67 NS

Parental/Child 140 Experimental 8.28 2.47 <.05

Roles COntroi 7.64

1 lb
P.

"248 6

. 7 control group- Experimental respondents scored higher on both Societal

. . Estrangement and Parental/child role. In both cases, the change from

t

1 to t

2 was in opposite directkons for experimentals and controls,

with experimettals reporting negative gains over time while controls

reported positive gains. This explains why significant diffelences

were found between experimentals and controls at t2 on these variables

while neither group alone evidenced significant changes across time on

these variables.

,. In general; the results described above are disappointing. There

were only two significant changes for either control or experimental

cases,

1

ases, and the .ty of nonsignificant changes that occurred also _ favored the control roup. In order to investigate the program impact

more closely, several /urther analyses were performed.

One potential source of change resulting from work experience was

A, the youth's aspiration levels. To test this, all of the foregoing

analyses were repeated using the respondents' reported levels'of

Occupational and Educational Aspiration. 8 There was no significant

difference between experimental and control cases at time 2-on either

aspiration score. 1 > Further, analysis of thg control data revealed that many of the control cases, while not participating in the YWEP,Aid obtain jobs on their

own in the period between interviews. With),respect to the evaluation of

the YWEP progr, the fact that controls were also working'poses no

w ,

i 8. Cases lost at time '2 were compared with cases retained at time 2

with respect to these two variables. For neither experimental cases

nor control cases was there'any difference between cases loSt or

retained. Further, the experimental cases showed no ange on

either Educational or Occupational Aspiration from t 1 to time 2.

However, the control cases revealed a significant inc ase (t=3.18;

df=108, p<.005) in Educational Aspiration from time 1 t time 2. , There was no change in Occupational Aspiration for the control. cases.
- 287 -

special p.roblem,<SVIce the intended comparison is between youth in,the

program and youth n ing benefit of the program and it is assumed

,

. that many youth would have obtained work in the absence of the YWEP.

To the ext-ent,tht the stu dy was also concerned with measuring the, impact

. -. of work per se, the fact that 75 percent of the control cases

found work on their own contaminates the general analysis.. The theoreticaq model suggests, that to the extent-work involves participation in a

desiiable social role, it shVUld have ome direct effast upon perceived

L

egativ labeling and feelings of aliena ion. To evaluate this plistu1 mpact of work, further analyses we perfl?med on the "pure"

cases,. those matched experimental and cont ollcases in which the control case did not work at all in the

to, t Eval. There were thirty-five

such pairs with complete data.

The experimental cases and control cases were examined separately

for changes from time 1 to time 2. The experimental cases showed no

significant changes from time 1 to time 2 while he control cases indicated changeonTwo scales over this period. Th ere was a' *(ignificant

decrease on both Normlessness (L=2.38, k.05) and Access td-\--

Occupational Roles (L72.67, 4=34, p<.05).

The experimental and control cases were also compared on their,

scores at time 2. Here, three significant differences were found. The

experimental cases scored significantly higher at time 2. on Access to

Occupational Roles (L=2.28, J±=34, p<.05), Educational Aspiration

(L=2.90, df=20, p<.01) and Occupational Aspiration g=20,

2<.05). The difference on Access to Occupational Roles resulted from

a modest but ntnsignificant (p<.10) increase-for the'experimental cases

combined with a signilicant4decrease for the control cases (see above).-

.,, The differences on Educational and OccupationaliAspiration were both

the result of nonsignificant differences favoring the experimental group'

at time 1 combined with a nonsignificant increase, by the experimental

group and a nonsignificant decrease by the control"-group. These results

suggest that there was some favorable impact associated with work which was

masked by the general compirison between experimentals and controls.

. 4

Q)

*s
- 288 -

For controls and experimentals working, theie continu d to be no significant differqnces,S

r.i., there were no observed progrm effects.

3. Second Follow-up Comparisons

The second follow-up questionnaire was administered approximately , one year after the initial administration, or about six months a er,

the first follow-up. This administration coincided with the mpletionof the Extension program or the Action program for t ticipants

and was.included as part of'the exit procedure. Ctrols and expe

mental respondents who were terminated after one six -month cycle of

YWEP were contacted in their homes and elsewhere and were p id $2. 0

to complete the questionnaire. Although a few new scales we adde , all of the basic impact scales were*repea edNfrom the earlier adminis

trations. In all, 182 (90%) of the 202 riginal experimental participants

completed the second .follow-up interview :s did 173 (86%) of the 202

control participants. 9

The first analysis undertaken involved omparisons from t2 to t3

for the three experimental groups to dete ne if there was a differential,impact associated with these three o tions, i.e., an eXtension of

the basic YWEP for a second six months, w

\rk wits Action for a second six

% 9. As with, he firstiollow-up, a test was-made for selective loss of

participants from either the experimental or the control groups.

That is, t-tests were computed comparing the initial scale scores.

(ti) of the 20 experimental respondents who were lost at the second,

follow -up (t7) with the initial scores of the 182 respondeftits who

were retained at t3. No differences were found at the .05 level of

significance. Similarly, the initial icoi4s of the 29 control:orespondents who were lost at the second follow-tp were compared.with

the corresponding scores of the 173 respondents who wereointerViewed

at that time. Again no significant differences were found. From

these data, it was concluded that selective mortality was not likely

a problem in this research. In fact, the retention of 88% of the

total population after a full year is

315

considered quit satisfactory.
- 289 -

months, or exit from the work program after the first six months. While

some statistical differences were observed at t3, tne analysis revealed

that they were the result of selection factors, i.e., the differe ces

existed at t

2 and t

1 and could not be attributed t6 differences in the

type of work option assigned. Of equal-importance, the failure to find

differences betWeen the twelve month work options and the six month

option suggests that a more extensive involvement with a work program,

produced no significant gains.

The next analysis compared the time 3 sc scores.of all experimental cases and their matched controls. Again t e comparisons were made

for Extension, Action, and Terminated experimental cases. The results

are summarized in Table 2. These results are striking priMarily for the

fact that not a single comparison yielded a statistically significant

difference. That is, for none of the three groups was there any difference between the mean scale scores of the experimental cases and their

matched control cases after twelve months. It is worth noting that for

the Action group, the experimental cases scored more favorably than the

control cases on twelve of the fourteen measures although the differences

were consistently small. While the through time analys0. (t1-t2:q) for

experimentals and controls in each of these treatment groups did show

some change, it was slight and resulted in no overall differences between

experimentals and conrolsTat t3 which were significant.

As was the case in examining changes from time 1 to time 2, the

aboire sets of analyses are generally disappointing, yielding few significant, results. Again it was found that m y of the control cases had

found work on their own during this peri and special analyses were

undertaken to isolate the effect of work

. For the first set of analyses,,"pure cases" were again selected.

These were matched pairs of respondents in which the experimental resp\mdent participated either in the ExtensiOn or Action program from

time 2 to time 3 and the control respondent did not work. There were

thirty-eight such pairs. For these pairs at time 2 there were no

signfficant differences on any scales and the,differences.that existed

were uniformly small. The analysis of change from time 2 to 3 for these

cases revealed that experimentals (Extension and Action) showed signifi316
TABLE 2 COMPARISON OF TIME 3 SCALE SCORE MEANS FOR EXPERIMENTAL AND CONTROL

Scale

Normlessness

Societal

Estrangement _

Powerlessness

Access to i 'Educational Roles

Access to

Occupational Roles

Negative Labeling/

Parents Negative Labeling/

Peers

Negative Labeling/

Teachers

Self-Reported

Delinquency

Self-Esteem . . Normative

Pressure

Parental/Child

Roles

%Educational

Aspiration

Occupational

Aipiration

PAIRS IN EACH EXPERIMENTAL OPTION

Group

Terminated

N=70 X t

ir Action Option

N=30

X .t Extended Option

N=53

X Experimental 12.09 -1.60 10.93 -1.64 12.54 .46 Control 12.81 11.97 12.32 Eiperimental 36.43' -1.12 35.51 - .63 36.85 .76 Control 37.53 3&.48 35.85

t Experimental 28.81 .65 26.51 . 27.25 .54 Control . 28.47 27.20 27.67 Experimental .73', 12.06 - .64 , 11.62 L- .08 Control

__11.73 11.55 12.33 11.64 Experimental 13.47 1.43 13.51 .49 13.30 .61 Control 12.97 13.20 13.00 Experimental 17.01 -.30 13.82 -1.17 15.88 - .79

Control 16.65 15.82 17.14 Experimental 17.46 .07 15.31 - .06 17.43 - .71

Control 17.37 15.42 18.49 Experimental 15.21 - .90 13.37 - .33 14.68 -1.09

Control 16.23 13.93 16.57 -- Experimental 24.20 .17 21.83 -1.38 N 25.63 1.07 Control 24.00 23.30 23.99 Experimental 31.14 1.24 33.07 - .35 32.55 1.34 Control 30.28 33.34 31.29 6 Experimental 13.89 - .81 13.85 - .63 14.12 .54 Control 14.22 13.90 13.81 Experimental 8.54 -1.39 7.99 - .22 7.69 ,-1.57

Control 9.16 8.14 8.48 Experimental 2.11 -1.03 ,..- .53 2.30 - .16

Control 2.32 2.23 2.35,

Experimental 3.65 1.77 2.59 -_.91 2.88 - .56

Control 2.7 31.06 3.15 3 17
A

4

291

cant changes on both Powerlessness (a decrease) And Access to Educational

Roles (also a decrease).-. Control cases, whO-dia not work from time 2 to

time 3 yiNed a number of significant changes across that time span.

Inall there were seven significant differences, all unfavorable. That

is, Pdwerlessness, Negative Labeli4 byPeers and Teachers, Normative

Pressurel, and Parental /child -roles all increased and Access to Educational

sand Occupational Roles th decreased. In addition, the remaining seven

differences, while not -' ignificant, were all in an undesirable direction.

The simple comparison of time 3 scores resulted in two significant

differences (on egative Labeling by Tigers and by Teachers), both of

which favored t e experimental group as did all of the remaining nonsignificant dif erences.

One other set of analyses was used to examine the importance of

working versus not working for the controls. For these analyses, all

4 control cases who worried between time 2 and time 3 were compared,with

all control cases who:did not work, regardless of their associated

experimental group. At time 2, onl1 a single significant difference was

e found (on Normative Pressure, which was higher among those who subsequently did not work). I.--The remaining se-ales showed no consistent pattern

in favorkg one group or the other. At time 3, every difference favored

the'controls who'worked, and six'differences were statistically significant.. The measures yielding significant differences were Societal Es-

. jangement,

Powerlessness, Access to Educational Roles, Negative Labeling

by Teachers, Normative Pressure, and Parental/child roles.

One final analysis was undertaken to complete the above picture,

That is, control re ondents who did work,between'time,2 and time 3' were

again compared with their Match.* experimental cases (Extension or Action

groups). At time 3, no significant differences existed between these

groups.

. )

AL. Job Satisfaction and Impact

ti As part of the time 3 questionnaire, youth in both control and ex-

, perimental groups were asked to rate" their satisfaction with the job(s)

they had between time 2 and time 3. Satisfaction was considered in terns

fl 318
- 292 -

of the work itself (fascinating, boring, creative, challenging, etc.),

the, supervision (impolite, annoying, taotful, etc.), Ae pay (adequate,.

insecure, -ress than.deseived, etc.), propbtions (opportunity, frequency;

etc.) and coworkers (stimulating, intelligent, lazyTetc.). "Scbres were

obtained on each of these five satisfaction dimensions and a total of all

five was also computed. These six scores were then correlated with the

raw gain from time 2 t6 time 3 for each of the fourteen impact measures.

Correlations were obtained separately for control respondents al0 for

experimental respondents.

Most of the correlations were quite-low, but ,a number did attain

statistical significance. Furthermore, virtually all of the significant . correlations were in a direction which reflects the fact that a desirable

change on the impact dimensions was related to greater job satisfaction. , Among the-controls, for example, change on Normlessness correlated -.IS'

with Satisfaction with the work itself. That is, the greater the satisfaction with intrinsic aspects of one's job, the greater the relative

decline in Normlessness from time 2 to time 3.

For controls, satisfaction with the, Kork itself was the satisfaction

dimension most often related to change on the impact scales, consistently

- being associated with favorable change on those scales s, The satisfaction

dimension least often,related to change on the impact scales was satisfaction with promotions. This is reasonable since it would be muc' less

salient in the six month period of interest than any of the other dimensions, all of which would be manifest virtually every day.

Several departures from the general pattern should also be noted,

First, increased Satisfaction with pay was associated with a relative

increase in Normlessness (r = 718). Similarly, Satisfaction with promotions was associated with increased Negative Labeling by Peers (r =

.23). And third, Satisfaction with pay was associated with a relative

decrease inEducational,Aspiratioh. This latter result may reflect the

fact that satisfactibn with pay could mitigate feeling& of the need for

further education..

The correlations for experimental respondents indicate 'somewhat

weaker 'relationships with job satisfaction, perhaps because the work

313
se

-1

- 293 -

experience programs involved a variety of activities only.a part of

Lv which was the job itself. For experimentals, however, all significant

correlations were consistent with, the general statement that satisfaction with a-job was related to favorable change on the impact scales.

5. Test of the Theoretical Model

Regardless of the impact of the YWEP on the youth development

variables, it was desirable to determine whether the conceptual model

was valid: That is, was change on Self-Reported Delinquency related

to change in the other variables? To test this, a Stepwise multiple

regression analysis was, performed with raw gain scores on Self-Reported

Delinquency as the dependent-variable and the initial scores and raw

gain scores on the other scaleas predictors. Grobps (experimental

or control) and initial and rawigain scores on aspiration level were

also included as predict,ors.10 These analyses are summarised in

Table 3. k

15L, While the pApOition f variance explained is not as high as reported in previous research ith this model (Brennan and Huizinga,

1975), there is substantial confirmation in thesedata. It appears

that predictions for males are slightly better than prediction for

females, although, over the twelve months pre-post lag, the level of

prediction is more simirkr.

The introdUction Of the experimental-control variable in the step-

.wise analyses produced no significant increase in total explained vari-,

mice, indicating tha4.....this variable made no unique independent contribution to the prediction of changes idelinquent behavior. This simply

confirms the previous analysis. It is,important to note; however, that

gains in occupational aspirations and access'to occupational roles.were

bath predictive of a decrease in delinquent behavior, and both of these

variables were associated with work experience in the pure case analysis.

10. This analysis was performed with both raw gain scores And residual

gain scores with only slightly different results. Only the raw

gain analysts is reported here.

320
0

294

TABLE 3.

MULTIPLE CORRELATIONS PREDICTING. GAINS IN SELF-REPORTED DELINQUENCY

WITH THEORETICAL VARIABLES

PERIOD

Experimental

6 Month Follow-up 12 Month follow-Up

R

2 R

2

s, Males .30 .31

Females '.24 .19

Controls

Males .48 - .46

Females .09 .35

321

.1
DISCUSS ION

st,

- 295

A

At the global level, omparing all experimentals and controls, the

results of the follow-up analyses revealed no favorable outcomes with

respect to the youth development variables which could be associated

with participation in the Oakland Youth Work Experience Program. -The

two statistically significant differences observed in the, first foll6Wup, while substantively small, nevertheless favored controls rather than

participants. At the second follow-up, there were no significant

differences. In general, experimental respondents did,not change relative

to the control respondents over the twelve month study period.

Special analyses with a restricted focus upon-the impact of work

involved thirty-five,(time 1-2) and thirty-eight (time 2-3) pairs of

experimentals and controls, where controls had no work experience in the

particular six month pre-post test.period. While the small N's render

our findings somewhat tentative, the first follow-up analysis of these

"pure" cases did produce the hypothesized favorable outcomes for experimentals (i.e., those in work roles) in three areas--increasededucational

and occupational aspirations and a perceived increase in oortunities for

achieving occupational goals. It isiprecisely in these areas that we

would have predicted the work experience should have had its most immediate effect--on aspirations and perceived opportunities for jobs.

The second kollow-up analysis also found significant differences

favoring experimentals (less negative labeling by parents and teachers)

and every other difference, although nonsiRificant, favored the experimental group. This occurred, it was found, not because the experimental

casgt improved from time 2 to time 3 (in general they did not) but because the control cases who had not worked hid shown unfavorable change

on every scale, statistically significant change on seven of them.

Furthermore; when control respondents who had worked were compared with

those who had not worked, every difference favored those who worked,.

with six differences being statistically significant. Again this was not

becausethe controls who worked had improved. Rather, they had simply

maintained previous levels while the controls who did not work reported

.D increasingly negative changes across time. A final pure case analysis

322
V

- 296 -

\\ comparing experimentals and control pairs who were both working, indicated

no significant differences on any of the youth'development variables for

either follow-up. - .. Taken together, the special work analyses support the view that

wo\k experience per se does have'some positive impact, particularly upon

job aspirations, Perceived future work opportunities and upon a youth's

perception of how teachers and-Parents respond to hilt,. It should be noted,- however, that the relative gains associated with work,were not dramatic, . and that in absolute terms those with work experience did not generally

report gains but maintained initial levels on youth development variables.

It also appears'to make little difference whether the work experience is _ . obtained through a work experience program with its special attention to

skill development and job

/ counseling or through work secured by more

'conventional processes available to all youth: And finally,the more

.sfying the work experibnce, the greater the impact upon youth

ds2lopment variables. Interestingly,_the degree of satisfaction with

work was. unrelated to whether it was obtained in connection with YWEP or

through one's own efforts.

The test A the theoretical modelwas generally supportive and

suggests that programs which are-effective in increasing perceived access

to constructive, meaningful social- roles, and generating more positive

labels for youth, will impact upon.delinquentspehavior. Work, whether

secured through normal process-or through special work experience,programs, appears to constitute such a role for youth.

The above findings appear to question the value of the counseling,

tutoring and skill development_incorporated into most work experience

prdgrams. We are hesitant to draw,this conclusion. Even twelve months

after entry into the YWEP,few participants had made any serious attempt

to enter the labor force in-a full-time capacity. We are concerned that

the length of the follow-up ds too short reasonably to expect the full

Pmpaciof the work experience program to be manifest. This argumen

appears particularly relevant for the skill development/educationa

, component of the program, for YWEP youth simply have had no opportunity`'

to utilize these skills in an effort to secure, maintain oreimprove their

303

1'
(

occupation 1 statuses or develop their work careers. We thus agree

with Robin s (1974) assessmentthat a valid confirmation of work program

effectiveness requires a long range follow-up of actual occupational

performance in the "real world and in the honest-to-goodness labor

market," to determine if the program resulted in lower unemployment

rates, more, efficient work patterns, higher morale and work satisfaction

more advancements and upward occupational mobility as Nell as improvement,

on-the youth development variables suggested here. Such an analysis

sis clearly beyond the scope of,the short range evalultion studies done

to 'aate.

11

11. Shore and Massimo (19697-report on a five year follow-up, but the

.sample size is so small (N=10) that no generalization can be made.

° 8
2?8 -

BIBLIOGRAPHY I

Ahlstrom, Winton M., arld.Havighurst, Robert J., 400 Losers (San

Francisco: Jossey-Bas, 1971). s. Becker, Howard S., Outsiders (New York: The Free Press, 1963).

Brennan,'Tim, and Huizinga, David, "Theory Validation and Aggregate

National Data: Integration Report of OYD Research FY 1975

(Boulder, Colorado: Behavior Research Institute, September, 1975):

CAR (Center for Action Research), Naticnal Strategy for Youth

Development and Delinquency Prevention and Conceptual

Issues, $ureau of Sociological Research, University of

Colorado, Boulder, Colorado Document No. 34, 1971.

Cloward, Richard, and Ohlin, Lloyd E., Delinquency and Opportunity:

A Theory of Delinquent Gangs (Glencoe, Ill.: Free Press,

1960). -' Ageton, S.S., Hunter, M., and Knowles, B., Research

Handbook for Community Planning and Feedback Instruments

. (Boulder, Colorado: Behavioral Research Institute, 1976).

, and Voss, Harwin L., Delinquency and Dropout

(Lexington, Mass.: D.C. Heath and Company, 1974)

Engel, M., Morsden, G., and Woodman, S.,

Concept of Lifestyle," Psychiatry,

1 Gartner, A.,Kohler, M., and Riessman, F

(New York: Harper and Row, 1971).

Gemignani, Robert, "National Strategy for Youth Development and

Delinquency Prevention," as cited in Center for Action

Research, University of Colorado, Bureau of Sociological

Research, Document No. 34, October, 1971.

"Children Who Work and the

vol. 30 (1967), pp.392-404.

., Children-Teach Children

Hackler, James C., "Boys, Blisters`, and Behavior," Journal of

Research in Crime and Delinquency, vol. 3 (1966), pp.155 -64.

411' Hirschi, Travis, Cases of Delinquency (Berfeley: University bf

California Press, 1969).

325,
- 299 -

4 BIBLIOGRAPHY (cont.)

4

Jeffery, C. Ray, and Jeffery, Ina A., "Delinquents and Drdpouts: An

Experimental Program in Behavior Change," Canadian Journal of

Corrections,,vol. 12 0970), pp. 47-58.

Kohen, A.I., and Parnes,

for Human Resource

University, 1971).

Lemert, Edwin, Social Pat

H,S_Career_Thresholds: Vol. 3, Center

Research (Columbus, Ohio: 'Ohio State

C y (New York: McGraw4Hill, 1951).

McClosky, H., and Schaar, J.H., "Anomie in Psychological Dimensions,

of Anomie," American Sociological Review, vol. 30 (1963),

pp. 14-40.

Massimo, Joseph L., and Shore, Milton. F., "A Comprehensive, Vocation- -'-- ally Oriented Psychotherapeutic Program for Delinquent Boys,"

American Journal of Orthopsychiatry, vol. 33(1963), pp.634-42.

NOSR,Diational Office for Social Responsibility), Youth Work Experence Process Wel Program Development, Final Report,

Arlington, Val/11976.

. AL

'Ndicki, S., and Strickland, B.R., "A Locus of Control Scale for

Children," Journal of Consulting and Clinical Psycholpgy,

vol. 40 (1973), pp. 148-54.

Nye, Ivan F., and Short, Jr., James F., "Scaling Delinquent Behavior," American Sociological Review, vol. 22, pp. 326-31.

/// 'Polk, Kenneth, and Kobrin, Sol, Delinquency Prevention -Through Youth

`4Development (Wash. gton, D.C.: U.S. Department of Health,

Education and W fare, SRS 73-26013, 1972).

Rawlins, V. Line, "Manpower Programs for Disadvantaged Youths,"

Industrial Relations,vol. 2 (1972), 184-97.

Robin, Gerald R., "Anti-Poverty Programs and Delinquency," The

Journal of Criminal Law, Criminology and Police Science,

vol. 60, no. 3 (1969), pp. 323-31.

, "The In-School:Neighborhood Youth Corps Program,"

iA Evaluation, vol. kno. 1 (1974), pp. 53-57.

s berg, Morris, Society and the Aholescent Self-Image-/Princeton:

Princeton University Press, 1965).

.4v 326

4
fle

- 300 -

A BIBLIOGRAPHY (cont.)

Schur, Edwin M., Radical Non-Intervention: Rethinking the Delinquency

Problem (Englewood Cliffs: Prentice-Hall, 1973).

, tabWIing-D-6VtaTt-B-enVIUr:--Tts Sociological Implication (New York: Harper and Row, 1971).

Shore, Milton F., "Youth and Jobs: Educational, Vocational, and

Mental Health Aspects, "...Journal of Youth and.Adolescence,

vol. 1, no. 4 (1972), pp. 315-23.

, and Massimo, Joseph L., "Five Years Later; A Followup Study of Comprehensive Vocationally Oriented Psychotherapyl"

American Journal of Orthbpsychiatry, vol. 39, no. 5 (1969),

pp. 769-73.

Walther, R.E., and Magnusson, N.B., "A Retrospecti*e Study of the

Effectiveness of Out -of- School Neighborhood Youth Corps Programs in Four Urban Sites," as cited j Gerald R. Robin,

"Anti-Poverty Programs and Delinquency," The Journal of

Criminal Law, Criminology and Police Science, vol. 60, no. 3

(1969) .

Il

O

7

,

es'
4

- 301. -

CONTROL GROUP SELECTION

By: Ernst W._StromsdOrfer and Teh-wei Hu,

ABSTRACT

0. This paper diScusses several aspects pf preferred research

design. It asserts that the classical'randomized experimental

design offers the best chance of estimating the unbiased effects

of a social program, such as youth training or employment programs.

Coincidentally, compared to the costs of non-Tandomized program

*evaluations at a given level of statistical signifiCance,the

randomized experimental design is less expensive per marnal unit

. of information gained by non-random methods. Thus, this review sit. paper makes a general plea for morc extensive use of the classical

randomized experimenta rittel.gn and provides some observations on

the relative validity of se ral criticisms concerning the feasibility,

.4 usefuaness, and the ethics of random assignment in a social

experiment or social'program. .

In the case of non-:random assignment, this paper provides T

straight-forWard discussion of a method to overcome the problem of

. selection bias by means of regression techniques. It illustrates

solutions to problems of non-random sampling schem46 in terms of

the problems of errors in model,specification and errors in variables.

However, due to the lack of data and lack of knowledge pn model

specification, evaluation results obtained from On-random selection

data usually remain inconclusive. Since randomization avoids this4

problem, a strong case for randomized experimental design remains.

INTRODUCTION

*so Ar

This paper reviews the current.state-of-the-artwith respect to

the use of randomized assigaent in the evaluation of social prpgrams

such as the Job Corps or similar employment and training programs. 'As

such, it is not original. We assert at the outset that the classical

randomized' experimental design offers the best chance of discovering,

for instance, why the youth unemployment problem i, as it is and what

. , to do abot&it. Compared to other techniques of analyzing social

problems and devising social programs to deal with them, the classical

randomized experimental deSign:, 4

I

, 3?

t.
J

- 302 - ) 41

a) allows one to unambiguously assert cause and effect;

b) allows one to measure the net effect of a social program

without statisti al bias; and,

c) is less expensi e &per marginal unitof information, that is,

a given level of statistical precision can be achieved at a

(lower cost.

) N

All three of these benefits are crucial for society as it attempts to

solve its social problems, but this paper focuses only on the'second of

the three benefits.

-THE NATURE OF THE° PROBLEM

The plight of youth in the market place has been severe for

approximately a decade. Research interest has characterized the

discussion as one of "transition from school to work." Various

programs have been devised or rediscovered as means to aid unemployed

d

11,

youth, such as the Job Corps, The Neighborhood Youth Corps, Ca eer \

Education, and the programs contained in the Youth Employment nd

Demonstration Act of 1977. However, while there has been a considerable amount of data generated through research on the "youth program,"

an equal amount of information has not been concomitantly_ created: ..------

We know very little about the absolute or relative efficacy of most

, of the ptograms designed to aid youth having problems in school or

, the labor market. And, in.fact, through the use of longitudinal data,

.

. it is just recently that the procesi of transition from school to

. work has been adequately dq.cribed, much less understood.",This state

'of ignorance is all the more distressing when one recognizes that tens

of millions of dollars have been spent to dispel this ignorance.

Essentially, we know a great deal about the characteristics of youth

who are suffering these disabilities but little about behavioral

processes or how programs ;to aidyouths are likely to work.

i- A. Programs to aid youth have been predicated upon reasonable but

, essentially untested hypotheses about links among the characteristics

.

. and behavior of y uth, training processes and labor market processes.

329
- 30 3 0..%\il c,

, Nae,

The Neighborhood Youth Corps, for instance, never had a clearly

articulated policy statement as to how it was expected'to return

youth to school or improve their employment' prospects. The basic

idea underlying Career Education is one of a structural relationship

between education in its broadest sense and work in a broad sense

which continues throughout most of one's produbtiveyfe. But the

relationship has never been rigorously spelled. out. In short, our

understanding of the "youth problem" is in relative disarray and

beset by.more ignorance, ambiguity and uncertainty than is conscionable given the time and resources expended on the problem to date.

Part, but not all, of the reason for this can be traced to'the use

of faulty research design and analysis. An additional share can be

traced to a failure to carefully articulate how programs designed to

deal with the problems were designed to work. These two phenomena

clearly interact.

THE CLASSICAL RANDOM EXPERIMENTAL DESIGN

The "youth problem" has two components. First, there is the

problem of analyzing why youth, and especially black youth, have had

such highland persistent unemployment in the present and recent past.

Second, there is the problem of devising a set of programs to correct

the problem.

Suppose a youth employment or training program is to be evaluated

by comparing the experiences of an experimental group of youths who

were part of the program to the experiences of.a control.group 'of youths

who were not. The classical advice of statisticians would be to assign

subjects at the outset -to the experimental and'control groups at random,

such that any given subject has the same,probabilify of experimental

group assignment as-Any other subject. Such randomization assures

that,differences between experimental and control group experiences,

beyond those differences attributable purely to.chance, are legiti- - mately attributable to the,program. In the absence of such randomization; analysts may: end-up-with experiineirt-ai-:and-contred---group-experi-ene

which systematically diffel for reaidhs other than the program. Given

4

330
4.

- 304 -

the appropriate data, there are statistical techniquei for adjusting * away such nuisance differences. But, in,practice, given imperfect

information, there is virtually never concensus among the experts

that any given adjustments are adequate. Without adequate adjustMent,

estimates of program effectiveness suffer to an unknown degree from

selection bias. This selection bias problem has thoroughly bedeviled

P past program evaluations. Indeed, it has been the main problem of past . evaluations. Since randomization avoids the problem, there is a strong

case fOr randomization. The case has been made before; but it deserves

repeating.

RESPONSE TO CRITICISMS OF,CLASSICAL RANDOM DESIGN-4

Resistance to use of the classical. randomized assignment to a

program treatment has remained curiously persistant and severe. It

is'instructive to list the arguments tendered against the use of

random assignment and _to evaluate the relevance of_their_criticism.

Following Boruch, these criticisms can be classed according to the

following:

I. Feasibility and Usefulness

Under this general heading classical randomized experiments ar

asserted to be:

(1.) virtually impossible to implement in real world settings

or impossible to perform for some programs;

(2.)' relatively more expensive andtime consuming than other

equally effective, unbiased methods of analysis; ,

1 These arguments and their discussion are largely drawn f4m Robert

F. Boruch, "On Common,Contentions abOut Randomized Field Experiments," in Gene.lt.- Glass) editor, Evaluation Studies, Review

Annual, 1976. Throughout this discussidh, we mean "experimental

design" to imply the test of a program by means of random assignment of prdgram,participants to an experimental and a control group

for a given social program. This rand assignmint may also involve

or require random assignment to dif rent mixes of treatments within

a progrApDSM imen t __Th. . II ch_do not involve

random assignmentme choose to label as demonstration projects,

quasi-experiments or natural experiments331

41%

`.

O
0

11)

0 305

0%

(3.) unnecessa y since other quasi=experimental designs or

st al procedures to estimate effects are just as

effective; 2

(4.) ioa narrow in scope and inherently-neglectful of important

process or ins'itutional factors and target groups,' and

theiefore unable to yield results which can be gendalized.

4,(1.) Inability to realistically impletment. As Boruch points tut

there has been a wide variety of social experiment . Experiments

generally must remain relatively simile in order o be manageable

conceptually and facilitate clear interpretation. Experiments

can nevertheless"be made to reflect a policy maker's definition of,

reality, even though this implies greater complexity. However,

complex treatment on a diverse set of tatget groups cause a multiplicative increase in de sample size. This increases the absolute

cost of a study considerably. The sample size requirements alone

often cause one to argue that Vie, necessary test is not possible.

Obviously, When the policy maker is-confronted with these costs,

he is then forced to make choices among the policy questions he seeks

answers to, the program'treatments he would to test and the target

populations he would like to test them.on. This is a painful process

since our experience hai own that the practical policy maker, other

things equal, would rather .ot have to make such choices, at least not

explicitly. Likewise, th policy maker often does not understand the

interrelationships bean sample size, ex ected treatment impacts and

the statistical precision of the desired es mates he seeks. Thus, he

is bound to be2Sme disenchanted with the 'tesearch process, regardleSs

of whether it is a classical experimental design. He turns,natura Ly,

a- then, to such methods ai the case study design which promises a ri hness of detail for which he has a greater taste'and familiarity. But,

what he often qils to recognize, or even asserts not to be the case,

is that he has opted foVmuch less reliable information on a greater

number of facets of,his problem rattier than more reliable information

oh .a smaller.setof more'critical facets.

2. The later part of this paper will discuss and evaluate this assertion

in detail. , 3 332
- 306 -

We find this situation frustrating since there re certain str\tegic problems concerning youth unemployment which are readily addressed'

by an experimental design. First, as indicated above, we would like to

know the process and causes of youth unemployment. How might certain

1 social programs affeCt youth labor market, experience, for instance?

Economic theory supplies hypotheses concerning the 'effect of minimum

wages and income transfe4s on unemployment which can be readily tested

in an experimental framework. An experimental design to test the disN. employment effect of minimum wages is not an institutional or research

impossibility except for the fact that the Comprehensive Employment and

Training Act essentially outlaws such an effort. Differential subsidies

connected with unemployment insurance payments (UI) could be used to

test the degree to which UI leads to greater job search, higher resulti".

ing wages and other reallocations of one's time among work, leisure, jab

search and home production. But changes in existing law Auld be needed

to perform such a study. 3

(2.) Relative Cost. If legal constraints' were cleared away, the

cost.of discovering the effect of different policies on youth unemployment would be relatively small. The policy options to test are relatively few: 1) wage subsidies (to interact with the minimum wage), 2) retraining or education in its various forms and, 3) public service employment (PSE). Likewise, the target groups of greatest interest are well

identified and relatively few: the two sexes, several ethnic groups,

and two to three different age groups within the youth category. This

implies, say, 36 separate treatment, sex, ethnic and age groups. If, on

the average, between 500 and .1,000 observations per cell are -needed to

make acceptably precise statements as to treatment effects, we are discussing a sample size in the neighborhood of 18,000 to 36000 observations. r

3. To its credit, policy makers in the Department of Labor's Employment

and Traini Administration attempted to get legal permission to

conduct ep. .s withitrithertETA-program-the Unemployment Insurance and Employment Service programs in the fall of 1976. However,

. the effort was abondoned in the face of broad-based resistance in

the Department as a whole along with the unwillingness to use up

political capital on the:Struggle required to press the issue.

333
.00 307

4 It would be possible to design an experiment that would have no

more thin three or for interviews required per sample unit. Thus, at

about $150 per interview, such a study might cost as much as $21,600,000

for data alone. Analysis costs would add two million or so to the cost.

\Thus, with a parsimonious design that restricted itself in a disciplined

0 way to major target groups and treatments one would likely be able to

ascertain, after three to .four years elapsed time, whether traPing, PSE

or wage subsidy was more efficacious in reducing youth unemployment.

Many subtle nuances could not be handled in such an analysis, but since

we do not know even the broad answers to the abo4 questions, the loss of

information on nuancds hardly seems critical at this juncture.

What would be the net social value of such an effort? As Borudh\

points out, there have been few fordal analyses of the costs and benefits

to society of doing no evaluation compared to performing equivocal eValuations,or rigorous tests of social programs. IefaCt, in the employment

nV_iraining nrr there have heen_no such eitimatesl Suchenanalysis

would be useful for it would, help clarify the difference beteen 1) cost

per unit of information acquired versus 2) the expected cost of information to acquire a given increment of social benefit or improvement In

social policy. For the first condition, it is always the case that it

will be less expensive to acquire a given datum at a specified level of

statistical significance by using a classical random design than by some

other more impressionistic method. This is so because a random assignment

assures that there is no intercorrelation.between program treatment and

other independent, variables of interest. Thus, fewer observations are

needed to achieve a given level of statistical precision. This implies

'a lower cost (Pitcher, 1978).

An assessment of the second case abdve depends in part on the weight

given too information in the political decision-making process and the

reasons why information of a given kind is use(k. Some policy maker's

clearly only go through the motions of appea ling to-rational information

o. in making a given decision,,' For them, objective information has a

weight of -zero in theactuel-decision mak-i -ng process-: Insuch a-basey-the

method used to acquire the information does not really matter. In other

situations, if one wantssto establish ye general directior\of an effect,

.334
- 308 -

then non-experimental data is usable. A case in point was the simple

twoLw#y cross-clasSification which showed to Congress that program enrollees in the Public Employment Program (PEP) had higher average

economic qualifications than the labor force as a whole. Congress did

not know the ultimate extent of substitution of higher for lower employable persons under PEP. It did not need to in order to revise the CETA

law in the fall of 1975 to attempt to stop the practice of state and local

governments of hiring other than the disadvantaged into Public Service

Employment Jobs. The'point is that one could have used a claSsical

experimental design to attempt to measure the exact degree of this occupational substitution under different constraints but such informationsimply was not needed for the immediate purpose. All that was required

was simple evidence,on the very existence. of the phenomenon. The cost

benefit ratio in this case was very high. However, when more precise

thavioral knowledge _is neededan experimental design with random

assignment is the only technique which will provide unambiguous infor -'

mation rather than factoids. A case in point is the attempt to measure

the impact of an income maintenance program on work incentives through

various data sets such as the Current Population Survey or `t.tie Survey of

Economic Opportunity. A dozen or more quality studies.by highly skilled

economists were conducted but with-very dtVergent results (Cain and

Watfs 1973, .Chapter 9). It has only been the several negative income

tax experiments which have shown any consistency in their estimates

(Watts and Rees, 1977). In the former case, among the studies, measures

of income and substitution elasticites not only differed in magnitude

but in sign, whilei except for problems caused by small samples, the

negative irlcome tax experiments report generally consistent estimates

across studies forigiven.target groups. Use of the former data is next

to worthless for policy purposes'sjnce there is no objective criterion

for choosing among the contending results. Nor.does the accumulated

evidence describe a narrow enough range of effects. Thus, while peda-

,, gogically the various labor supply studies, were of considerable value,

their value in aiding specific welfare reform.pdlicy was next to useless ti -for they gave no guidanceon the possible.cost of lost output in setting

i'gixen level of income guarantee or tax rate on eaYnings. While it is
.01

- 309 -

. , possible that such studies, through improved data and statistical' techniques, could have reduced the range in the,estimates of decreased work /

effort, the NIT experiments actually achieved the result more quickly

for a few millions of dollars of economic costs (as distinct 'from the

transfer payment costs used to conduct the experiments).

(3.) Quasi-Experimental Designs and Statistical Adjustments. A

major theme of this paper is that it should be possible. to model

structurally the behavior which leads.to self-selection into a program.

This will then remove a major source of bias in nonlexperimental studies.

Though, even then, we should point out, in the absence of a classical

experimental design, we cannot logically assert cause and effect between

treatment and outcome, but only a correlation.

In the act of operating a program, however, systematic selection

biases abound. There.is selection bias on the part of program operators.

Then there is self-selection bias on the part of the individual

program participant. If both these processes could be modeled, teen

such bias could be eliminated. To model these processes one must gain

acce ss to them when a program begins. As a prd'c'tical matter, this has

proven difficult.to,do. These arguments are carried further in the

Regression Model Section below.

(4.) Narrowness of Scope and Lack of Institutional RelevancS.

Except in the case where it is statutorily prohibited, it should be

possible to model the most salient components of a program experimentally

and randomly assign individuals and a control group to it. Indeed, some

existing programs suchsas the" unemployment insu rance or the minimum wage

program, inasmuch as:they.offer almost universal coverage, iiay be-

, impossible to test using a non-experimental design becauSe it is not

possible to establish,a comparable group who has not been affected by

the program treatment. However, it is possible to use random assignment

to test various behavioral effects of, say, the UI program, by randomly

varying the amount of une4lOythent insurance benefits among eligible

recipients. There is, of course, no zero treatment group. Everyone

receives some level of benefit: However, one can discover the differential effects of UI payments over the range of payment amounts that are

politically acceptable. The case of the impact of the minimum wage on

336
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youth displacement is similar. 'Given a specific legislated minimum

wage, vouchers which amount to differing wage subsidies could be

randomly awarded to all Unemployed youth, including vouchers of zero

value. Such a scheme, if the-traditiondl.vested interests did not find

it too threatening, would measure the existence and extent of disemployment effects of the federal or any state minimum wage. As noted

above, however, studies of wage subsidies per se are currently illegal

under CETA.

With respect to training programs we should note that a body of

experimental evidence could have largely avoided the curious phenomenon whereby training was considered a panacea for employment problems

for a decade before opinion in Washington, D.C. largely reversed

itself about 1974 and asserted that such progfams did not work. Of

course, such reversals of opinion are exactly that, reversals of opinion.

The existing body of non-experiniental evidence can be interpreted either

for or against training. The fact is that the conceptual links between

training and its addition to human capital on the one hand'and the

increase in human capital and its effect on earnings and employment on

the other simply are not well understood. A non-experimental model of

analysis; however, puts more demand on one's understandingiof this conceptual linkage than an experimental design does since there is a greater .6'6

need for more precisesspedifica ion of the causal model at hand to overcome,potential biases in the es kimated results.

Of course,, as Boruch and others have noted,. this modelling process

should not be left to the analyst alone. Rather, the program operator

or policy maker must participate

4 ate in-this specification. This state-

..., went holds for the development of policy questions asked, for the hypotheses to be tested and therefore for the model to test them with the

target groups and treatments under consideration.

Thus, when properly conducted, the Charge that an experiment is

too narrow in scope and-can't.be generalized, or does not deal with the

correct target groups or treatments or yields irrelevant results is

. 4. As a practical matter, this, too, is very difficult to achieve,

given the incentives'to which policy makers and program managers

respond.

337

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- 311

simply incorrect. Persons are most often incorrectly generalizing from

observed behavior wherein program operators, policy makers and program

analysts fail to interact properly.

THE ETHICS OF CLASSICAL RANDOM EXPERIMENTS

In the analysis of the effects of employment and trainingprograms.,

mind and body are seldom placed it jeopardy, at least not directly.

Also, a given treatment will not always yield net benefits nor does the

denialof a treatment necessarily; imply a net loss. Either the experi-

,o mental or the control group may gailmr.lose, depending on elputcome

of the experiment. Therefore, to the exent,that economic tities

are the major matters of concern, provisions can be made beforehand to

compensate the losers in an exp"Mental test of a program treatment.

Another alternative would be to provide the treatment, should it prove

beneficial, to the, control grobp at a later date. Nwever, some

economic compensatiqn is still required since the control group will

have a shorter period over-which:togain the benefits from the program.

Finally, -We should pOint out that it may'beeqUally unethical to

crea'tesocialSrogriiiiS whose objectives cannot be realized due to

inappropriate design. This amounts; after ,all, to subjecting people to

social "experiments" indirectly and thi-dashed hopes or unfulfilled

expectations are no lest of g'eost to those who experience them for all

that the programs were put forth with.honorable and humane intentions.

In either case, people have been'maiiipulated by the system and have

.?. incurred costs. This is a common chargemadato the "War on Poverty."

Thus, in general, tht argument ti.kt classical experimental designs

are unethical in education and labor market analysis is incorrect. In

a study with proper safeguards, the

0 losers'and gainers are known, the

extent of loss and gain can beknown, and'society's resources are

sufficient to compensate the losers if it chooses to do so..

. PROBLEMS OF SELECTION BIAS IN THE REGRESSION MODEL

A randomized experiment'is free of selection bias. Thus, comparisons between experimental and control groups can reveal the true effects

ofa social program. However almost all past program evaluations such

as'those of Headstart.or the sefieral orms of the'Neighborhood Youth'

_
$

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Corps have not been based on randomized data. (However, see Robin, 1969,

for a study of the Neighborhood Youth Corps which uses a random assignment design). Though analysis of non-randomized data can be valuable

when carried out properly (Barnow, 1972; Goldberhr 1972a, 1972b), the

results are. seldom conclusive due to the doubts about whether selection

bias has truly been netted out. The inconclusive Headstart evaluation

provides a good example, (Campbell and Erlebacher 1970) in which the

control group appeared more able-than the experimental gro because the

usual procedures of selection and analysis produced sy ematic biases in

the direction of underestimating the effects of the experimental program.

In a statistical framework, these biases can be considered errors in

specification and errors in variables (Barnow 1972; Goldberger 1972a,

1972b; Cain 1975; and Heckman 1976), either of which may.bias regression

00 coefficients. In the case of errors in specification, as long as

AV the

omitted variables are introduced into the equation, the model w/11 be

propeTly specified and the expected value of the error term will be zero,

i.e., the regression coefficient which measures program effect will be

unbiased. In the case of errors in variables, as long as the regressor

and tip error terms are not correlated, `tberegressia will be,unbiased.

In fact, errors in specification and errors in variables may be interdependent in the case o f non-random sample selection, as will be shown

in this section.

Errors In Specification.

Using a youth training program such as Job Corps as an example, the

sources of selection bias and suggestions-for obtaining unbiased estimates are presented in the framework of regression analysis. Regression

is not the only statistical technique.for program evaluation, but it-is

an especially convenient tool. In mathematical derivation, regression

analysis is equivalent to analysis of'covariance'(Goldberger 1964).

. A simple linear model for evaluating the effect of a social program

is to let

(1) '"Y=ce 0 +ccZ+e

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- 313 -

where is post program achievement, Z is a dummy variable fox the social

progr., (defined as 1 if a youth received program services and defined as

0 if a youth did not receive program services). The error term captures

the -rrors in measuring Y and other omitted variables. the sample

se -ction of experimental and controlygroups is randomized, then Z and e

ar- no Correlated. Therefore, the estimated value of al is the unbaised

to of the effect of training in Y.N

the control group or the experimental group is not'randomly

selected, .ut is selected based on a set of -sociodemographic factors such

as a set of tree ability measurements, then the Z variable and error term

e in Equation (1) ill b rrelated. In this case a more accurate model

of specification fo the so al program evaluation must include these

variables. For the sa , f simplicity, only the true ability variable

will be included in the model:

(2) Y = a

0

+ al Z + a2 X* + V

4 ik

where X* is. the true ability of a youth and V is a new error term equal

to (c - a

2X*). Assume that once X* is introduced into the equation,

there is no correlation between Z and V.

A blaSed estimate of the program effect could result if the true

ability variable is omitted from the estimation of Equation (1) when

control groups or experimental groups are not randomly selected. The

source of bias is thepossible correlation between the training variable

Z and error term e. That is, e contains the ability variable X*, which

may correlat with training (Z), such that

(...--e\-- 3) = yZ + e

where y measures the degree of association between training and ability,

and e is the error term.

Substituting (3) into (2), yields

. , (4) Y = ao + alZ + a2( yZ + e) +V

=a0+ (al + a2Y ) + a2e)

Let a

1 = al + a

2y . The difference between a

1 and

.a1 in Equation (1) is

due'to y . The relation between treatment and true ability may be

4 34
- 314 -

analyzed as follows:

i) If y= 0, (that is, when program treatment is assigned

lr ndomly with respect to true ability),...then

5) al = (al + a2 0) = af.

Therefore, no bias exists. The results of a

1 in Equation (4) will be

the same as a

1 in Equation (1). This is the case of a randomized

experiment in which regression technique provides an unbiased estimate

of the program ffects.

ii) If y< , that is, when the treatment is assigned to youths

0 of lower ability than those in the control group, then

(6) a, = (al - a2y)< al.

iii) If y> 0, that is when program treatment is assigned to

: n-those-in-the control group, then

(7) al (all+ a2Y)>al.

In this case, regression analysis can lead to a bias in favor of

program treatment effects, reflecting a type of 'creaming' or selfselection process such as may occur in various employment and training

programs.

The results of the previous three cases imply that thdt are two

approaches to obtaining unbiased estimates of the effect of.youth

employment and training programs. The first approach is random assignment of experimental and control groups before the program begins to

ensure zero correlation between the treatment variable Z and-the-error

term cin Equation (1). The second approach, in the case of nonrandom

assignment, is to formulate a fully specified regression mddel, including the true ability variable and other sociodemographic variables on

which selection into the program was based to ensure zero correlation

between Z and V in Equation (2). To know what kinds of variables are

omitted in Equation_(1) requires. familiarity with program selection

procedures. Once the relevant omitted variables are included in the

regression model, then errors of specification can be avoided'and one

source of correlation between the training variable'and the error term
10,

cambe eliminated. 'However, the problem in practice is that the variable

or variables on which selection takes place (the ability variable X* in

our simple example) are either not known or-are known but have not been

measured. The analyst is then forced into procedures analogous to

regressing Y and Z as if Equation (1) were the true model when Equation.

(2) is in fact the true model. The result is a biased estimate-of the

program's effect. Therefore, under nonrandom selection it is virtually

never possible ,to argue persua ;ively that an estimated program effect

has truly accounted for all variables on which the nonrandom selection

was based. The only remaining foolproof option is randomization. That

_is, randomization negates the bias caused by omitted variables.

Errors in Variables , r

When random selection is not possible, three other bases for

selection may be used. One is based on true ability, X*; the second

is based on achieveMent,'X; and the third is unknown selection.

Suppose that true ability )i* is normally distributed, with an

expected mean of zero and variance a

2

:

(8) X*1, N(0, a2*) . Furthei-more;-issume that pretraining achievement X and post-training

, achievement Y are erroneous measurements of true ability, such that

Arr (9) X= X* + U

(10) Y = X* + V

Where U and V are normally distributed with zero mean and common

variance, N(0, a2). Let Z' be a dummy variable for training as/defined

before. And, let U be independent of V, X*

1

, and Z,,such thdt

(11) p(U, Z) = p(U,V) = p(U,V, = (U,X1 ) = p(V, X:)/= p(V, Z) = 0

when selection is based on true ability and X* is unavailable for the

evaluation--only,a retraining acheivement measure is available. Therefore the regr on model becomes

(12) Y = a

0

+ a 1Z- + a 2,X + V

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316 -

0

where V

4 = V - a

1 U.- The use of a pretraining achievement measure X for

X' is subject.to the problem of errors in variables. In regression

analysis, errorsin variables cause biased estimates, such that al al

and a2 a2 (as shown in Barnow (1972)).

1 Wh n selection is based on pretraining achievement, then tis-determined y X. Thus Z depends-on both X* and U, but remains independent of

V. Pretraining achievement reflects only a part of true ability in socalled incidental selection (Lord and Novick 1968), since the control

group does not conie entirely from the high ability Segment of the

population; the control group als43 includes low-ability individuals who

'happened to achieve unusually well on ameasurof labor market skill.

When selection is explicitly based on pretraining achievement (not on

true ability) and pretraining achievement is used as a'regressor in the

regression model,

(13) Y =

0

+ 01Z + X + V

where X is not correlated with V, then Seven though the control group is

moreqAble,,there is no bias in the estimate of the effects of training.

CAlpbell (1969) refers to this type of evaluation as a "regression

N discontinuity design" and recommends its use when nonrandom selections

are made. Goldberger (1972a) has shown this advantage by_ means of mathematical derivation.

When the selection process is not fully known, there are two ways

to eliminate correlation between Z and V in Order to obtain unbiased

estimates of the effect of 'training. One way is to rely on labor market

behavior and economic theory to provide relevant control variables which

may correlate with the training variable as well as the post /raining

achievement variable, as suggested by Cain (1975). This approach may

reduce the size of the error term and eliminate correlation between Z and

V. The other approach is to consider the training variable Z itself as

an endogenous variable which correlates with V. Therefore, Z'can be

estimated as a function of a set of relevant selection factors which

are not correlated with V. Once Z, the estimated probability of participation in the program, is obtained and used as a regressor, the regression

estimates of the training effect 'should be consistent (Heckman 1977).

343 1

4
- 317 -

, This is an instrumental variable approach. It is known that although

this approach provides consistent estimates, the empirical results of

this approach are difficult to interpret.

In summary, the most desirable approach to evaluation of a social

P

'program is the randomized experiment. When random assignment is not

posiible, proper specification of the evaluation model is necessary in

order to Ascertain the unbiased effects of a program. However, to repeat,

due to the lack of data and lack of knoWledge of the selection process,

evaluation lesnl Ls -obtained flom nonrancknn-samp-I-e-s-eIect-ion-date----are

-At inconclusive. Therefore, it is important to encourage evaluators and

fl econometricians to pay more attention to the problem of sample selection

`bias, so that the results of program evaluation can be useful and mea ing-

, ful to policy makers.

SUMMARY

This paper discusses,severL aspects of'proper research desig

namely, the proper procedure to conduct a Social program evaluat on.

It makes a general plea for more extensive use of the classical andom

research design, Presents the standard criticisms of such a design, and

provides some observations on the relative validity of thes= criticisms.

Within this context, this paper has discussed the merits past studies

on the evaluation of manpower training programs, with special reference

a

to the nonrandom sample designs.

This Paper provides a simple version of dealing with problems of

selection bias in a regression model framework. It illustrate's problems

of the nonrandom sampling scheme in terms of the problems of errors in

model specifications and errors in variables. It.is'hoped that future

program evaluation will rely upon randomized experimental design so tliat

evaluation bias can be.minimized. This will provide more positrve know-

, ledge about ultimate program implcts and do so at lower social cost than

has been the*cas

7 S
- 318 -

/7)

BIBLIOGRAPHY

Barnow, Burt S., ."Comparisons for the Presen or Absence of

, a Bias in

Treatment Effect: Some Statistical Models or Head Start

---Evatuation;' Institute- for-ReseaTch-bn-P0Vttry;-university of

Wisconsin Madison Wisconsin, Report No. 122-72, 1972.

ri

'sk

'BorUch, Robe F., "On Common Contentions about Randomized Field

Experiments," in Gene V. Glass, Evaluation Studies, Review

.Aknual, Vol...11.(Beverly Hills, CA.: Sage Publications, Inc.,d976).

Cain, Glen G., :"Regression and Selection Models to Improve Nonexperi-

.. mental Comparisons," In Carl Bennett and'Arthur Lunsdaine, eds.,

Evaluation and' Experiment: Some Critical Issues in Assessing

Social Programs (New York: Academic Press, 1975J

Cain, Glen G., and Hollister, Robinson, "The Methodology ofEvaluating

Social Action Program," in Arnold_Webero, Frank H. Cassell and

-Woodrow Ginsburg, eds., Public-Private Manpower Policies

(Madison, WI. Industrial Relations Research Association, 1969.)

anA Harold W. Watti, eds., Income Maintenance and Labor Supply,

(New York: Academic Press, 1973).

Campbell, D.T.., and Erlebacher. A., "Hbw Regression Artifacts in Quasi-

. Experimental Evaluations Can Mistakenly Make Compensatory Education

Look Harmful," in J. Hellmuth, ed., Compensatory Education: A

N--3.orl-Ereba., Vol. III of The Disadvantaged Child ((ew YoTX:

\ . Brunner Mazel-Co., 1970).

9

Goldberger, Arthur S.,IEconometric Theory (New YOrk: John Wiley, 1964).

"Selection Bias in Evaluating Treatment Effects: Some Formal'

Illustration," Institute for Research on Poverty, Univer.sity.of

. Wisconsin, Madison, Wisconsin, Report No. 123-72,1972.

. /

, "Selection Bias inTreatment EfiCt: The Case, of Interaction," .Institute for Research on Poverty, University of _ Wisconsin, Madison, Wisconsin, Report No. 129-72, 1972.

Hardin, Einar, "On the Choice of Appropriate Control Groups," in Mich el

Borus, ed., Evaluating the Impact of Manpower Programs (Lexing

MA.: D.C. Heath, & So., 1972).

Heckman, James J., "Sample Selection Bias as a Specification Error.,"

Unpublished paper at the University of Chicago, 1976.

4, ;345
319

, -"Dummy Endogenous Variables'in a Simultaneous Equation

System," Center Report No. 7726, Center for Mathematical Studies

in Busintseand Economics, University of Chicago, 1977.

Lord, F.M., and Novick, M.R. Statistical Tteories'of Mental Test Scores

(Reading, MA.: Addison - Wesley, 1968).

Perxy; Charles R., et al., The Impact of Government Manpower Programs,

(Philadelphia,- PA: Industrial_Reaearch_Unit, The Wharton School,

University of Pennsylvania,. 1974). 0 Pit-cher, Hugh M., "A Sensi vity Analysis to Determine Sample Sizes f

Performing Impact E aluation of the CETA Programs,", in Farrell

Bloch, ed., Evaluatineakanpower Training Programs. (Greenwich,

Connecticut: Johnson Assotiates, Inc., 1978 forthcoming),.

Robin, raid D., An Assessment of the In-Public School Neighborhood

Youth Corps Projects in Cincinnati and Detroit, with Special

Reference to Summer-Only and Year Round Enrollees (Philadelphia:

National Analysts Final Report, February 1969). m

Y , Watts, Harold W., and Rees, Albert, The New Jersey Income Maintenance

8xperiment, Labbr Supply Responses, Volume,,II (New York Academic

Press, 4977).

A

.

.01.,

c,

316

kr

7

)A

o
320

44

METHODS OF,ALLOCATING

FUNDS TO KLLEVIATE TEENAGE

)0; UNE?1PLOYMENT PROBLEMS

By Joseph J. Cordes & Robert S. Goldfarb

ABSTRACT

This paper examines methods for _allocating funds currently available for combatting youth labor market problems. Since available

studies disagree about the nature of the teenage unemployment problem,

we propose allocation methods consistent with various alternate.views:

(1) that teenage unemployment has,only short-run effects on earnings; ' (2) that it affects future (lifetime) earnings and achievement. The

implications of a cohort-specific versus a continuing teenage unemployment problem are-also considered.

Our analysis suggests that decisions about program mix and eligibility requii-ements should be made-separately from decisions'aboui

allocation of funds to areas. Both program mix and fund allocation

decisions are likely to vary depending on whether teenage unemployment

is seen as having port-term or long-term effects. Specific allocation

formulas are proposed that take these distinctions into account.

We then compare recommended program mix and fund allocation procedures with those found in the Youth Employment and Demonstration,

Projects Act. -Several discrepancies emerge. First, the Act does not

ensure uniform program mix across areas, even though uniform program_

mix may be desirable under certain views of the teenage unemployment.

program. Second, the statistical indices by which funds are allocated

in the Act frequently differ from those recommended. However,conceptually different indices may produce similar empirical results. We

therefore compare the distribution of funds by state under the Act with - the distribution of funds under various recommended allocation schemes.

Regional distributions are shown to vary considerably under the actual

and recommended distributions/

INTRODUCTION

Continuing concern over youth unemployment has led to Congressional

funding of programs tb alleviate youth labor Market problems. The

YOuth Emplbyment and Demonstration Projects Act of 1977 will provide

$1.5 billion for this purpose, and future Congresses may provide additional funding. How ishould these funds be allocated to obtain the

gre4test benefit per'dollar spent? 'It is tempting to approach this

3 4,-.
ert

321

important policy'question by turning immediately.to available statistics.

There are any number of statistical sources--yOuth unemployment arid em-

, ployment rates, youth labor force and population statistics, poverty

data, and so forth--wliich.Aan to provide relevant information. This

temptation should be avoided, however, since statistical series viewed

in isolation, 'without an analytical framework, will not generally pro-'

vide the best possible swers. There,are at least two reasons why the statistics alone are in-

, sufficient. First, different statistical series may suggest alternative

allocations of funds. Fdr example, if states are ranked by youth unem-

, ployment rates and poverty rates, the Spearman rank correlationcoefficient betwehe wo rankings is quite low.' This indicates that

the geographical ibution of youth employment and training funds

would be rather dif ent if such funds were allocated by 'th unemployment rates ratl* than poverty rates.

SeCond, and mo e:fndamentally, the underlying problem must be

carefully analyzed if policy goals areto be specified concretely.

For example, suppose the goal is to ameliorate the "bad effects" of

teenage' unemployment. ,If teenage unemployment implies higher adult

(

unemployment when the cohort of teenagers ages, the goal of attacking

youth unemployment may actually be to prevent adult un,Mployment,in the

future. If teenage unemployment does not lead to higher adult unem1/4 ployment,sthen the gOa.1 may be to prevent julienne delinquency, provide

'income earning opportunities to members of low income families, and so

forth.

1. s correlation was calculated from 1970 Census Data and data on

youth unemployment rates for twenty-five states presented in U.S.,

Congress, Congressional Budget Office, Policy Options for the

Teenage Unemployment Problem, Background Paper No. 13. (Washington:(

Government Printing.Office, 1976), p. 92.

4.17,1

343)

a
322

Section I of the paper explores the nature of.the youth employment

problem and evaluates its public policy implications. This analysis is

used in Sections II, III, and IV to derive and evaluate allocation

schemes based-en various statistical indicators.

I. DIAGNOSIS OF THE PROBLEM AND POLICY IMPLICATIONS

Certain basic features of the teenage unemployment situation are

illustrated by Table 1. Table 1 presents teenage and adult unemployment

rates for 1950-75. Since the 1950s the teena&e unemployment rate has

been between 2.5 and 4.7 times the adult rate. Moreover, the ratio, of

teenage to adult unemployment in the late1960s was higher than i pre-

.ceeding and following periods,. Furthermore, nonwhite youth rates have

consistently been higher than rates for white youths. SinCe the labor

force participation rate in the 1970s was considerably lower for non-

_ white male youths than for whites; the measured racial unemployment

.differential probably understates the true differential.

EXPLANATIONS OF TEENAGE UNEMPLOYMENT

.These basic findings have been subActed to different interpreta-.

tions. Three.recent analyses represent a range of informed opinion

about teenage unemployment. We briefly review these analyses and then

discuss their relevance to allocation decisions.

The first analysis is Martin Feldstein's. 2 In his view, chronic

high youth unemployment is due to two main causes: (1) slow absorption

of liew entrants into the labor market,and (2) low job attachment among

young workers. A relatively high percentage of youthful job seekers

are new entrants who must spend `some time in job search before obtaining

their first employment. In 1975, roughly 36% of unemployed teenagers

2. Martin Feldstein, "The Economics of the New Unemployment,' The

Public Interest, vol. 33 (Fall 1973), pp. 3-42.

349
4

- .323 -

TABLE 1

UNEMPLOYMENT RATES FOR TEENAGERS

COMPARED WITH NON-TEENAGE UNEMPLOYMENT RATES

IN THE UNITED STATES, 1950-1975

11

Year

(1)

Age 16-19

1950 12.2 , 1951 8.2

1952 8:5

1953 76

1954 12.6

195 11.0

1956 11.1

1957 11.61

1958 15.9

1959 14.6

1960 14.7

1961 p 16.8

1962 14.7

1963 17.2

1964 / .16.2

1965 14.8

1966 12.8

1967 12.9

1968 .12.7

1969 12.2

1970 15.2

1971' 16.9

1972 16.2`;:.

1973 14.5

1974 16.0

'1975 19.9

- (2)

Age 20+

(1) - (2)

Difference -

(1)+6?

Ratio

4.8 7.4 2.5

3.0 '5.2 , 2.7

.2.7 5.8 3.1

2.6 5.0 2.9

5:1 7.5 2.5

.'.....9 7.1 2.8

3.7 7.4 i 3.0

3.,8 7.8 3.1

,6.2 9.7,

4.8 9.8 3.0

'4.8 9.9 - 3.1

5.9 10.9 2.8

4.9 e 9.8 3.0

4.8 12.4 3.6.

4.3 11.9 3.8

3.6 11.2- 4.1

2.9 9-.9 4.4

v3.0 9.9 4.3

12.7 10.0 4.7

2.7 9.5 4.5

4.0 11.2 3.81/4 4.9 12.0 3.4

4:5 -11.7 3.6

3.8 10.7 3.8

1.5 - 11.5 3.6

7.3 12.6 2.7

SOURCE: Columns (1) and (2), Bureau of Labor Statistics,,

from the data base of Data Resources, Inc. (DRI).
- 324 -

were new entrants to the labor force.

Low job attachment among young workers offers an explanation of

youth unemployment based on the unattractiveness of jobs rather than

their unavailability. Entry level jobs forteenagers often offer

neither valuable training, nor oppo4tunites for advancement., As a

result, employers are less reluctant to lay off young workers when

demand falls because no-- investment has been made in these workers.

Moreover, young workers, are more16.kelx to leave jobs perceived as

dead-end, particularly if comparable jobs are readily available.

Feldstein also argues that the "true" social and economic problem

of teenage unemployment is overstated by existing data. First, much

, of the movement of .youths both between jobs and into and out of the

labor force merely reflects the fact that attending school is the major

.----- activity of roughly 401 of-the labor force aged sixteen to twenty-one

years..

3 Students typically look for full-time employment in the summer

omi sion of part-time workers would substantially an lower the unemployment

part-time jobs during the school year. Feldstein'argues that

,*

ment rate calculated for sixteen to twenty-one-year-olds.

4

The problem of teenQe unemAument may also be overstated as a

result of differences in attitude and motivation between youths and

adults. Most young workers do not have the same family responsibiltties

as older workers; and they may desire more leisure than is consistent

with full-time employment and permanent attachment'to a firm. Hence,

teenagers are more likely,than adults to alternate between working and

other activities. This tendency may have been heightened by rising

, wage rates which "permit a comfortable standard of living with less - work, or less, responsible work than was possible twenty years ago."5

r

3. Ibid., p. 13.

4. Ibid.

S.

.351

91
32.

_

V

In contrast to Feldsteih's'analysis, Edward Kalachek points to

labor supply factors as causes for the recent increase in teenage un6

employment. In 1955, the ratio of the teenage to the adult unemploy-

' ineit----rete was 2.8 while in 1969"this ratio had risen to 4.5. Kalachek

argues that this deterioration in the relative labor market position

of teenagers is due to.a bulge in the teenage population.

Between 1960 and 1971, the teenage population rose by approximately

50%. In the absence of instantaneous labor market adjustments, this

increase in the supply of y9ung workers would result in higher teenage

unemployment. Moreover, certain aspects of the yo th labor market

would slow the process of adjustment.

that the problem of increased supply

Specifically, Kalachek argues

aggravated by rising, school enrollments. Because students seek part-year or part-time wok, the

standard job is hot appropriate to their needs. This need for

"different" jobs constrains the ability of labor markets to adjust

rapidly to teenage population growth.

HoWever, with time, employers would adjust their production processes and hiring procedures to take advantage o the ready availability

of young workers at reduced wages. Kalachek argues that this process

should soon be completed if,only because teenage population will stop

,increasing. Teenage population will fall from 10.7% of the population

in 1975 to 10.1% by_1980. HoWever, he cautions that teenage' unemployment will remain higher than adult rates in the new "Nst-population

bulge" equilibrium. This is largely because the involvement of teenagers in school activities results in higher rates of labor force en-

. trance and reentrance than among adult's".

6. Edward Kalachek, Labor Markets and Unemployment (Belmont, Ca.:

Wadsworth, 1973).

o 3 5

o
326

A third analysis of the teenage,unemployment problem stresses the

impact of the minimum wage on the employment opportunities of teeffliers.

Redent econometric studies provide consistent evidence that teenage

employment has been decreased by the minimum wage, but studies focusing .1

on unemployment give ambiguous results. Two explanations may be offered

for these results. First, if the minimum wage discourages labor force

participation, it is possible for employment to decrease while'unemploymerit remains unaffected. Second, Goldfarb has argued that the ambiguity

is due to the difficulty. of controlling for population shifts (such as

Kalachek.*14ply bulge).7 Indeed,in a number of statistical studies,

population shifts,seem to have an independent effect on unemployment,

but this may-in-part be due to a (hidden) minimum wage effect.

8' In addition to direct effects of minimum wages on teenage employ-,

ment, there are some indirect effects. Feldstein, for example, argues

that minimum wages prevent employers from hiring teenagets for jobs

with significant on -the -job training potential because they must be

paid the minimg; wage immediately even though they have not acquired

necessary on-the-job training.

9 This restricts the nuMb9r of jobs

with ')career potential" available to teenagers, and hence contributes

to the job attachment problem mentioned by Feldstein as a cause'of

higher teenage unemployment rates.

'7. Robert S; Goldfarb, "The Policy Content of Quantitative Minimum

Wage -Research," in Induttrial,Relations Research Association,

Proceedings of the Twenty-Seventh Annual Pinter Meeting,

December 1974, pp. 261%-268.

8. The statistical studies indicate that unemployMent increases are

correlated with-increases in teenage population. Suppose these

teenage population increases are,treated as outward shifts in the.

supply curve of teenage labor. Such outward shifts would nbt

necessarily lead to rises in unemployment in the absence of a

minimum wage. Without a minimum wage, wages" might adjust downward to clear the market, so no-extra unemployment would result.

9. Feldstein, 22.. cit., p. 15.

35:3
I

- 327 -

None of the above analyses focus on the distinction'hetween ite

and nonwhite-teenage unemployment problems. Indeed, Iden has pointed

out that reasons for somewhat different levels and cyclical patterns

of white and black teenage unemployment are not well understood.

10

LocationaI)differences, for example, do not seem to explain as much of

the black-white unemployment differential as one would expect.,

11

Moreover, the falling participation rate of black youths has not been well

explained.

IMPLICATIONS FOR ALLOCATION CRITERIA

These analyses suggest two distinctions relevant for allOcation

criteria. The first distinction is between immediate ansi lifetime

effects of teenage,unemployment., If a teenager experiences unemployment

today, that probably lowers family earnings today. Howeverunemploy-

,ment may also increase the teenager's probability of unemployment (or'

more generally lower chances for career achievement) in later life.

The "cocoon" view of teenage unemployment stresses the immediate

rather than lifetime impact of teenage unemployment. Under thi view,

teenage unemployment is high because a-high proportion of teenag s are in school and looking for part-time work unrelated to future career

plans. Even, those not in school may not have established independent

households or acquired fbmilY responsibilities. For ;hose w ers, a

serious commitment to work and career may not yet have be developed.

However, just as the caterpillar turns butterfuly, these teen-'

agers will become serioulfrkers once they leave school; get married,

and otherwise settle down.,

10. George.Iden, "Business Condition §, Demography, and the Teenage

Unemployment Problem," unpublished paper presented at the Annual

Meeting of the Southern Economic Association, Nei Orleans,

November 3, 1977.

11. Ibid., p. 20.

1.
s: - 328 -

In contrast the ',hangover" view of teenage unemployment stresses

lifetime impacts. \Under this view, teenage unemployment results in

missed opportunities to learn good job habits and to obtain valuable

training. Apart from any immediate impact on family income, teenage

unemployment results in, diminished future opportunities for unemployed'

youths.

-N

The analyses summarized above may be interpreted in terms of this

distinction, Feldstein's analysis is fundamentally artbiguous, containing elements consistent with both views. On the one hand, teenagers

are not able to obtain enough jobs with career potential, implying that

teenage unemployment has lifetime impacts. On the other hand, teenage

unemployment is attributed to "special" characteristics of lYoung workers,

such as school attendance and a greater desire for leistire, which presumably will change as young workers mature into adult workers. Under

this view, the effects of teenage uhemployment are more immediate than

lifetime. Kalachek's "supply bulge" explanation does not really address

the "cocoon /hangover" distinction apart from suggesting that impacts,

whether immediate or lifetime, are likely to be concentrated on the

group of teenagers that comprise the population bulge. Finally, the

minimum wage explanation is consistent with both views. To the extent

that a higher minimum wage limits employer provision of training, there

is a definite hangover. On the other hand, if the minimum wage primarily eliminates part-time jobs with little training component, the

bad effects of-teenage unemployment are.more likely to have an immediate, but not a lifetime impact.

A second important issue concerns the existence of specific cohost

effects. IS high teenage unemPloymerit (with or without -hangovers), ex-

, pected to attack a particular cohort especially severely? While Feld-

, stein's analysis does-not really address this issue, the other two

analyses certainly do. Kalachek's supply bulge analysis clearly iffiplies

that "excessive" teenage unemployment is cohort-specific; once the

population bulge passes, unemployment rates will return to a lower

"ecillilibrium" level. The minimum wage analysis contains a similar

s,

4
. 329 -

' implication. We argued above that whatever the independent effects'of

population increases, they are likely also to intensify minimum wage

effects on employment. Thus, if there is an unusually rarge cohort of

teenagers, the minimum, wage effects are likel9igto be unusually large. 9

II. ALLOCATION-SCHEIES CONSISTENT WITH DIFFERENT VIEWS OF TEENAGE

-4-UNEMPI-OWENT e r

0 )

In this section we suggest allocation schemes that take acc t of the hangover-cocoon distinction and the cohort-specific versus nonspecific distinction. The desired scheme varies with one's view of the

nature of the teenage unemployment problem. Once these various "ideal"

allocation criteria have been described,, we Compare them to the-alio- A

cation rules of the Youth Employment and Demonstration Projects Act

(YEDPA).

Suppose that the "cocoon" diagnosis of teenage unemployment is

deemed'to be the appropriate one; teenage unemployment has current

effects on the teenager's income and that of his family, but has no

impact on his future earnings or career pattern. Since unemployment asa teenager has short run rather than'lifetime effects, allocation of

, 4 funds should be aimed at ameliorating these short run effects. A

t

central feature of the short run effect is loss of income, but 'a

;-.- plausible argument is that, from society's point of view',..this income

. loss is ore serious the lower the income of the teenlger's.family.

This sug ests that funds be allocated primarily to lid lower income

.

. familiei.

In distributing funds, the decision maker is faced with two cons

.. . ceptually'distinct allocation problems.. The first involves allocating ' 1p funds to different prograts so as to achieve a desirable program mix, -11%3' .

.,

l"------' The second involves allocating funds to different geographic area; once

, 1-

.

. ,. program mix has been determined.

C .

. Consider first the problem of determiningprogram mix. Presumably

there exists'a menu of available programs, each of which constrains ,

, . a

participation in various ways through eligibility raquirements: Since

4
S

33q, -

f

the.fdistribution of program benefits by income class may differ for

programs with different eligibility rules, it is appropriate to choose

amix of prOgrams that achieves a desired distribution of benefits by

income level. Once this desired program mix is,chosen, each region

shoulebe required to use programs in that specified mix. Given

silgtilax regions, this protedure.would help ,assure that the desired

income distribution of clients was achieved iA each region.

An example may be useful. Cohsiaer the case where all competing

programs are equably effective at increasing teenage incomes; that is,

$1 of program cost raises teenage income $X for each program. eThen

if Wm estimate of the income distribution isavailable'for each pro-

, gram, determining the income distribution of benefits produced by

various program mixes is siraightforwird. 12 Once alternative distribuffons are calculated, the one "closest" to the'desired distributioh

pan be picked.

Wheredifferent programs are differentially effective at raising

, 4 teenage incomes, choice of mix is more complex. If the desired distribution Vattern can be obtained for a program mix using only the most

transfer-effective programs (those that transfer the mos

teenagers per dollar of expenditure), then this is the preferred solution. If, on the other hand, the desired mix cannot 'be obtained using

onl the most transfer-effective programs, then alternative mixes using

4

12. For example, consider two programs, each of which%,Taises teenage

income $X Per $1 spent. Program 1 gives a% of this $X to teenagers

with faril-15 incomes between $0 and $4000, b% to teenagers With

family i comes of $4000 to $8000, and c% to-teenagers with family

rater than $8000, where a;b+c=100%. Progrem2 transfers

0, and i% to the same groups. Then if, for each $1 spent on

gram 1, 0 are spent on program 2, the distribution to the

ree income classes per dollar spent is a+Rg to the lowest*iricome

crass, b+ to the middle income class, 1+R and 01i to the highest 1+R income Class. 1+R

357
- 331 -

less efficient programs must be considered. If any of these produces

a-mord desirable distribution of benefits than the best mix of efficient

programs, the decision maker must choose between "efficiency" (more.

income transferred to teenagers) versus "equity" (a more desirable dis,

13

tribution of the dollars transferred).

Once desired program mix is decided uponr.the second allocation

problem must be faced; the allocation of funds to areas must be deterimined. Since the aim is to allocate funds disproportionately to unemployed teenagers in low income families, a useful procedure

is to allocate funds to areas in proportion to the number, of unemployed , teenagers in poor families. This has the attraction of allocating to- -' ward the index--low income,.-which the programs are trying to bolster.

Suppose teenage unemployment is viewed as having long run rather

than just short run effects. In this case, how isthe 'allocation decision to be made? Once again it is uibful to distinguiSh between program mix decisions and funds allocatiOn decisions given program mix.

In designing youth employment programs, one is faced with a tradeoff between the goals of immediate job creation and future employability. Emphasis on future employability as opposed to job creation

would seem to favor programs that devoted resources to structured work,

,training, and supervision for participants. Since the costs per job

13. One specific aspect of choosing efficient programs needs to be

mentioned. Since the problem being attacked is teenage unemployment, programs which admit teenagers who are not unemployed are

,'unlikely to be in the efficient set. Thus, efficient programs

are likely to require that only unemployed teenagers be eligible.

More generally, choice of program mix and choice of eligibility

requirements are part of the same decision.
a

332

provided would tend to be higher in programs emphasizing future em-

. ployability than in those aimed at job creation, the decision maker

would face a trade-off between number of jobs and training slots.

- The distinction between mediate vs: lifetime impacts of teenage

unemployment is certainl elevant to the evaluation of this trade-off.

Suppose reduction in future employability was the major undesirable_

impact'of teenage unemployment; then a program mix would be chosen

which emphasized future empldyability and training. However, this

decision would not completely solve the allocation pi-Oblem. Given

this program mix, decisions would still have to be made concerning the

allocation of funds to different geographical areas. In our previous analysis, when teenage unemployment was viewed

solely as a short run problem, the choice of,geographical allocation

guidelines seemed fairly straightforward. This is so because lack of

employment by itself reduces the family income of unemployed teenagers.

Thus, the'only policy issue is weighting income losses experienced by

different income classes. Once the appropria

has been specified, funds can be allocated by

social welfare funCtion

relative number of

"deserving" families in different regions. In contrast, it is less

certain that teenage unemployment by itself causes future unemployabi.n-: Thus one must consider how allocation guidelines would Ira with different hypotheses about the link between teenage unemployment

and future adult employability.

Suppose one-accepted the proposition that teenage unemployment per

se reduced employability as an adult. Then the teenage unemployment

i ,problem wou41 be severest in regions lith the, most unemployed . teenagers.

Youth employment and training funs could therefore be allocated solely

On 'the basis of number of unemployed teenagers.

An altern'a--r

e hypothesis would be that diminished future employability is not caused solely by teenage unemployment, but rather by the

interaction-of teenage unemployment with other factors such as race and"'

it low income family status. In this'case, exclusive reliance on unemployment data in making allocation decisions would not be appropriate: For

35J

4
- 333 -

0

. example, suppose that teenage unemployment implied higher adult unemployment:only. for teenagers in low income families. Then two regions

could have equal numbers of -unemployed teenagers yet face unequarlongterm teenage unemployment problems. This would suggest that youth employment funds be allocated according to the number of unemployed youths0,

in low income families. Similarly, if future employability problems

were rel ".fly more severe fbr unemployed nonwhite youths,' allocation

of fu ds.on the basis of youth unemployment and race would be appropria e.

Finally, we briefly consider the issues raised by the distinction

c . bet cohort-specific versus nonspecific effects. If high levels of

I

t f.enage unemployment are confined to a particular age cohort, the benets per dollar spent On information gathering would be less *th'am if

. & f. teenage.tnemployment were expected to effect cohorts of youths in the

future. However, if relatively high teenage unemployment was not confined to a specific age cohort, funds should be allocated in part with

reference to the potential,for obtaining policy-relevant information..

That is, regardless of program mix, the absence of cohort-specific

,. effects would imply that relatively more funds be devoted to obtaining

feedback on each pTogram. Moreover, in each program mix, more funds

should be allocated to programs that are policy experiments.

In summary, the differences, between the short-term and the long-.

term effects of ieenage',unemproyment imply some differences in alloca-'

-. tion criteria._ In both ,.the short- and long-run case, it is useful to

separate conceptually allocation decisions pertaining to desired program hi from decisions about the allocation of funds to different

regions s ven program mix. Desired program mix cannot be decided on.

the basis of simple statistical isOtators. When dealing with the

short-term effects of teenage unemployment, specification of program

mix is dependen4s on explidit or implicit specification of a social

welfare function. Similarly, evidence on the long -term effects of

teenage unemployment s needed to choose the desired mix of programs

-emphasizing job creation. and programs emphasizingfuture employability.

360 1
f

I

- 334 -

di0

Once the desired program mix has beefs chosen, scarce program funds must

be allocated to different regions. The choice of the appropriate allocation statistic depends on one's views about the nature of the Unemployment problem. Finally, one's views about' the existence of cohortspecific effects determine the portion of funds allocated to information

gathering.

III. ALLOCATION GUIDELINES IN THE YOUTH EMPLOYMENT DEMONSTRATION

PROJECTS ACT

The Act'specif4 a.number of different programs, elig.bility requirements for-each gram, and .fund allocation limitations ,on each

program. How consistent is,this complex set of regulations with the

f , preferred allocation rules derived above? Our preferred solutions contained separate program mix and fund allocation'rules. Thrfteimaintains this distinction; it establishes a centrally determined program

mix, at least for major program divisiops. However, the similarity

'betwen,our allocation rules and those of the Act breaks down for less

aggregate program mix choices. When teenage unemployment is viewed

as a short-run phenotenon, we suggested identical program mixes in each

region. However, the Act does not ensure identical program tix by

region, since within sevezal broad program categories it allows each

area to initiate and design its own .programs, subject to some central

review. This departs from the recommended solution, thereby implying

that marginal benefits per dollar need not.be identical across regions.

There are, however, a number of considerations which might justify

the Aot's procedure. First, suppose that teenage unemployment has longrun effects. In this case, regions might differ in crucial respects,

making an identical policy mix across regions less attractive. For

e ample, suppose that long-run effects are most severe for particular ,

demographic groups. If p

across areas, and differ

others, different mixes in

icular'groups are not uniformly represented

t pr rams' work better for some"groups-than

di ferent areas are desirable. It is not.

ti 361

J

41
VS.

335

clear, however, that the program selection features of the Act are designed to ensure assignment of the most appropriate programs to each

area. A second argument for program diversity across areas involves

Organizational ability., If areas differ with respect to organiiational

and administrative skills, local program initiation and design may be

more likely to produce'programs tailored to local capabilities. Third,

if if is, not known Which programs are the most effective, local initiation may provide a, forum for experimentation and development of new

program ideas. 'Finally, it can be-argue4 that CETA (the act which

YEDPA,amends) considers local autonomy for the prime sponsor as a basic value in itself.

14

Turning from program mix to funds allocations, we can again ask

how close these funds allocations are to our recommdhdations. If teenage unemployment is viewed solely as a short run phenomenon our solution suggeSted that funds be allocated according to the number of unemployed teenagers in poor families. If, on the other hand; teenage unemployment is viewed as having longer run effects, allocatiOn rules

would vary with different hypotheses about the link between teenage

unemplbyment and long-term employment problems.

Consider first the Youth Community Conservation Improvement Projects (YCCIP). Seventy -five percent of the available funds go to the

states according to the relative number of unemployed persons,withip

each state, subject to minimum percentage requirements for each state

and areas such as Guam. The remainder is for discretionary'use by the

14. We owe this point-tea-referree.

argues, consistent with sour third

knowledge through experimentation

- specifying piobgram mix in advance

Indeed, thelsame referree also . point, that the development of

is a major season for nOt

'6
. :

336

Secretary of Labor. The entire amount is subject to the requirements

that at least 2% be used for native American youth and 2% for youth

in migrant and seasonal farmworker families. At least with respect to

the 75% of YCCIP funds allocated directly to states; the funding rules

do not seem consistent with our guidelines for allocating funds to

deal with short run effects of teenage unemployment. The Act allocates

funds primarily by number of unemployed per ns, whereas the preferred

allocation would be by numbers of unemploye teenagers in-low income

families.

One might ask whether the YCCIP funding rules would be appropriate

if teenage unemployment isvviewed as having long run impacts. However,

the Federal Register discussion of the'Act seems to portray YCCIP as a

program aimed at short run problems, placing predominant emphasis on the development and provision of jobs. 15

Any training must be directly

related to the development of specific skills needed for the job.

Consider next the Youth Employment and Training Program (YETP)

component of the Act. The basic allocation guidelines are as follows:

(1) 75% °if the funds are made available directly to local prime sponsors

and 5% to governors. Of this 80,,37.5% is allocated both across and

within states.by the number of unemployed persons, 37.5% is allocated

by the number of unemployed persons 'residing in areas of subst,antial

unemployment, and 25% by the number of persons in low income families;

(2) 22% of the 75% given to prime sporisors must be used for programs

serving in-school youth; (3) at least 2% of the overall funds must be ' used Eor native American youth and 2% for youth asonal and migrant

farmworker families. The remaining funds can be disperse the

Secretary of Labor's discretion.

If unemployment is perceived to be a short run phenomenon, we

argued that funds should be allocated according to the number of poor

. . . ,15: Federal Register, Vol. '42, No. 180, September 16, 1977, p. 46730. ..

363
- 337

unemployed teenagers. The Act's rules.seem to differ from"this benchmark. ,Seventy-five percent of the funds are allocated according to

the number of unemployed persons, n t the number of unempdyyed teenagers. In addition, while 9% of th funds are allocated on a basis'

of poverty population, poor persons Cher than 'poor teenager ar used as the index.

The Federal Register'states that YETP's pukpose is:

not ... to provide make-work activities but rather

to proVide youth, especially` economically disadvantaged youth with opportunities to learn and

earn which will lead to meaningfulue'mployment

opportunities after they have completed the prograin.n

This seems to imply the YETP is designe to attack presuded on run

. . impacts of teenage unemployment: If so, divergences from recomporded .

allocation rules arise that are similar to those discussed i4 the short

run case. An additional complication is that demographic characteris-

.. tics other than low income status may need to be 'keh into account.

.Finally, we consider the proposed.allocatibj of funds under the

'Young Adult Conservations (YACC) and the Youth Incentive Entitle--

.Sk ment Pilot Projects (YIEPP). The Labor bepartment Planning Charter

states'that YACC will select participants by a "basicalb7 random f cess" with prefe'rence given to applicants from areas of "substantial

unemployment'" (those with a rate of 6.596'or highei).17- since YACC

relies on total rather than teenage unemployment data to allocatp

funds, itis subject to the same diff iculties discussed ove'in

0

connection with Yip' and YETP. -The, final program, YIEP , cOmbines

features of an experimehtal and demdnefrationlproject. Since'neither,

16. Ibid., p. 46734.

17. U.S. Department of Labor'; Planning Charter fox the Youth Employ -

ment and Demonstration Projects Act (Washingtbn: 'Government , Printing Office, 1977). c-----4r"

R . -........::.I . .

4

,AE

, 4

4s t

4
t

- .338 -

I

4'

. experiments nor demonstratiOn projgcts are designed to distribute

I services so,as to achieve a desired distribution of benefits; evalti wition,of the YIEPP guidelines are outside the scope of this paper.

rv. COMPARISON OF ALTERNATIVE FUNDS ALLOCATIONS

We have argued that several of the allocation guidelines in YEDPA

differ from the allocation schemes proposed above. It is possible,

however, that differences in concept do not result in significant

differences in actual funds allotmentS. To investigate the empirical' 4,

: impact of using differeht allodation rules, we compare the distribution

of both.YCCIP and YETP funds under provisions of the Act with allocation obtained fraM formulas suggested by our analysis. Table 2 pre-

, sent the correlatikon coefficient (r) between the Act's distribution

of YCCIP funds and a distribution based on estimated state shares ips

the number of unemplOyed poor youth, the recommended allocation if . teenage unemployment has nly short run effects. 18. We also present the

correlation

and

between .an approximation of the Act'i distribution of YETP e , 1.

fdnds d dls ibutions implied by two of the formulas consistent with

1 -, 18.. The number of unemployed poor youths by state was estimated as

fol ws. .D4a from the 1970 census was used because fi-malloWed

mor extensive disaggYegation. First, the number of-poorsyouths

by s ate were by cOmbinir ! numbers of youth aged "fourteen

to t enty-one in poor families with number of poor unrelated'individuals aged fourteen to twenty-four. The,slightly different

age categories were'vsed,betause exactly compatible pUblished data

were unavailable. The state poverty data were then multiplied by

tate unemployment ratios (number..of unemployed Oivided by.zopulafoil) for fourteen to twenty-:one- ear-olds, also obtaified-from

ublished 19 census data. Whit unemployment ratios actually

MeaSured.fat oor youths would:be esirable, these were not available fr:om published data. The aut rs believe that the magnitude,,

of error is unlikely,to le significant in estimating he relative

impact of different allocation schemes See U.S. C sus, Detailed 4 Population Oharacristics, Tables 166 and 207.

A

,41' 4
339

different beliefs about the existence and cause of long run effects.

19

One formula allocates funds on the basis of relative number of un7

employed poor youths, the other on the basis of relat/ve numbers of

unemployed youths. The correlation coefficient varies between .88

and .97. The significantly higher correlation between the distri

bution of funds implied by the YETP formula and the formula based on

the number of unemployed youths is due to the disproportionate weight

given to unemployment in the YETP formula.

Though the ,correlation falls when the Act's formulas are compared

with alternatives that give more weight to polierty, the correlations ob-.

tained are still relatively high. However, *this does not necessarily

mean that the allocations of funds would be similar under alternative

formulas. This is readily seen in Table 3, which pAsents the percentage allocation of YCCIP and YETP funds to regions under 'alternative

19. In order to approximate the distribption of funds under YETP, the

following procedure was used. For the 37.5% of relevant YETP funds . allocated according to the total number of unemployed in each state,

census data on unemployment of individuals sixteen and over was

used. For the 37.5% of YETP funds allocated to states according

to the number of individuals in areas of substantial unemplo t,

the actual 1973 measures used in making CETA allocations for Fiscal

1974 were employed. These wereobtained in unpublished form from

the Employment and Training Administration, U.S. Department of ., Labor. The 1973 data were used since the actual CETA procedure

for specifying areas of substantial unemployment is complex and

nonreproducable , so that determining what such areas would have

been in 1970 if CETA had existed is impossible. The year 1973 was

used because that wIt the first year after 1970 that such figdres

were calculated. For thd 25% allocated according to the number of

persons in families below a particular low income

, line, we used

census data on the number bf family,members below 125% of tile

poverty lirib: While. thiS is conceptually appqaling, and consistent.

with the language in the Act, it does differ from the actual administrative proce&r, which fails to allow for,different income

cutoffs by faMily size! 'We believe that using the administrative

proCedure would have resulted i larger empirical divergences between theericommend-el,alLocation rules and those in the Act thanthose-reported in the text. Census data used' in the YETP calculaion'are fl'oM U.S. Census, Detailed Population Characteristics,

Tables 168 and 207.

. r

a

.4
s.

_at 340 - '

allocation schemes. For example, relative to the distribution 'of funds

implied by ille\Act, formulas based on poverty-as well as unemployment

would distribute significantly less funds to the Northeast and North

Central states JO significantly more funds to Southern states. These

results may, of course, be subject to qualification due to regional

price level differences, existence of state and local minimum wage

laws, and ot her difficulties.?°-

20, A referree suggested these qualifications. Ifathere are price disparities across regions, dollar allocations may need to be adjusted

for purchasing power differences. Such an adjustment would probably result in a decline in the percentage allocation going to , the. South; and a rise in the percentage allocation going to the

Northeast. Unfortunately, the art of measuring .such regional

differentials is still quite primitive, and there is reason to

believe that frequently used measures overstate differences (see,

for example, Mark Sherwood "Family Budgets and Geographical

Differences in Price Levels," Monthly Labor Review, vol.98, April

1975, pp. 8-15.and Timothy Smeeding, "Measuring the Economic Welfare of Low Income Households, and the Antipoverty Effectiveness. --- 1 of Cash and Non-Cash TransferPrograms," unpublished Ph.D. dis-

. sertatipn University of Wisconsin, 1975,). Moreover, there are a 7

variety of existing federal transfer programs which do not make

such corrections, and there exists considerable controversy concerning the appropriateness of such corrections. State and local

-minimum wage laws raise two issues. First,suppose that a particular state has a minimum wage law that raises teenage unemployment (either because the state minimum wage is higher than that

federal, or, because. it covers same jobs the federal minimum wage

does not cover). Thai state is "rewarded" for itsohigh minimum

, wage by getting more federal funds under our proposed allocation 4, . formula. Even if one wanted to "correct" for this result, it. is

note obvious how one would do so. Second, if the state minimum

wage law requires higher pay or eat teenage job - creation programs,

"the same amount of dollar fun kill not create as many job:slots.

that state.- This problem b ars a resemblance to the regional

e variation problEm. A fi al difficulty concerns teenage unoyment among illegal aliens and other "undoCumented workers."

he extent that the measured nemployment rate fails to include

these individuals, areas with he vy concentrations of such individuals may be getting smaller,allo ations than,their "true" unemploymenxsituatiom warrants. Of ourse, one's, views on. this issue

depend .on one's beliefs about whet ei= golynment programs should

be concerned with unemployment of llegal liens along with "native"

unemployment.

, 367

4. a

1
ob.

a

-341-

P,

+Yr

Our calculations indicate that different series may produce'

different funds alaocations by 'state. Our analysis implied that funds

be allocated on the basis t detailed characteristics o I unemployed

teenagers readily available only in the decennial census. Since funding

..,

, . decisions must be made yearly, however, ways of,uting'annual data to

generate allocation rules would hay to be developed. We now consider

how census information could be used"to improve the quality of these

rules; .. There are many seriesvarious unemployment rates, poverty rates,

aft so forth - -which haCie some relevance for allocation decisions. How

can these series be combined to prod? information,asclose as possible4 to.cenJuS data? Suppose t eenageunemployment is-viewed as a short

run pheriomenon, so that he desired allocation , statistic is the number'

of unempl6Yed poor teenagers. Unfortunately, this statistic is not

available annually. Suppose, however, that separate 'series on unemploymtnt and poverty by state were available. Thesetwo statistical series

could be combined to form a proxy for the desired statistic.throughregression analysis. Cross- section data on the number of unemployed poor

teenagers obtainable from the 1970 Census could be-redressed on 1470

observations of the annually available series on unemployment and poverty!

The estimated regression could then be used in subsequentyears'to estimate the numbe44of unemployed Poor teenager e t the anntally atailable. °4

data.

1 o .. 6

. . Two comments on this procedure,are rele First, the\ actual 4

"k) ,choi4 of unemployment or povtrty series would be based on stati4ticah ,

. . . .

. criteria, such as, goodness of fit. econd, the attractiveness of this "

r . procedure' depends' on the stability ftheestimated relationship through

time. Thud, fot y, choice- of se, ies this procedurt'hasthe attraction

that those series would be combi d on the bails Of their rplationthip

to the*desired allocation stati tic, rather than through ad' hoc weights.

a
342 -

oa

C. ;

TABLE 2

COEFFICIENT OF CORRELAXION Cr) BETWEEN THE DISTRIBUTION

OF YqCIP.ANDYETP FUNDS UNDER ALTERNATIVE ALLOCATION RULES

Between the.Aces..distribution of

and 4i.stribution of funds according

shares_ of unemployed poor,orouths.

Between the Act's distribution of

and disti-ibution of funds according

shaTes of unemployed poor youths.

Betweell the Act's distribhtion of

and distribution' of 4unds according

shares of unemOroyed'youths.

YCCIP funds

to state .90

(.83-.94)*

YETP funds

to state

YETP funds

to state

r.88

(.80-.93)

(.95-.99)

X *Numbers in parentheses are the 95 percent wftfidence intervals for the

correlation coefficipts.

O

t.,1 . 1

4-

361;)

4

se

c?,

f

S
TABLE 3

EtRCENTAGE ALLOCATION OF YCCIP AND YETP,FU&DS TO GEOGRAPHICAL

REGIONS UNDER ALTERNATIVE ALLOCATION FORMULAS

Percent Allocation

Under Act's YCCIP

Region Formula:

Percent Allocation

Under Act's YETP

SOrmula

Northeast

r 22.2 24.8

North 'Central '27.1 24.6

South 26.2 27.2

I

West 24.6 23.4

'L

ef 11 4

Percent Allocation Based ' Percent Allocation Based

on State Shares of on State Shares of

Unemployed Poor Youths Unemployed Youths '

eat

13.1 18.2

J22.4 30.0

42.4 28,5

22.1 23.3

S

370 3:71

O

a

_34

A
34'4

V. SUMMARY AND IMPLICATIONS

Several results have emerged from our analysis: First, it is

useful to separate conceptually program mix decisions from funds alio-

. cation decisions given program mix. Second, the appropriate allocation

scheme depends on whether the bad effects of'teenage unemployment are

believed to be temporary or permanent. Xhird, different allocation

statistics can result in divetgent distribution of funds. FOurth,

even though annual data on desired allocatiOn statistics may not be

available, it may be possible to construct reasonably'good proxies

frpm available data. Specifically; we have suggested a potentially

useful method for combining series based on regression techniques:

The Act already specifies the way in which a majority of funds

must be allocated. However, there reiains a sizable block of funds

for which specific allocation rules have not been mandated. Two alternative approaches for allocating these,funds may be fidentified. ,One.

procedure would be to decide de novo on a desired program mix and then

, allocate funds given program mix using the general guidelines set forth

above. An alternative approach based on "second best" considerations

would be to: (1) calculate a desired overall distribution, (2) determinerthe divergence between the, desired distribution and.that resulting

from allocation formulas specified in the Act, and (3) distribute discretionary funds so as to.' ffset this divergence.

. 4 4

4

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LOWERING YOUTH UNEMPLOYMENT:

HOW MUCH AND AT WHAT COST?

By: Paul 0. Flaim

and

Paul M. kyscavage

ABSTRACT*.

This paper examines the issue of how low.the.unemployment rate for

youths might be brought/ and points out that for the entire sixteen to

twenty-four group the rate has never averaged less than 8% for any year

since the Korean War. In addition, we found that in some recent "fullemployment" estimates the youth jobless rates have been. pegged even

higher.

With regard to the wages that would have to be paid to youths

plated in job programs, we presented several distributions, all of

which showa large dispersion. Many youths, particularly in the'Sixteen to nineteen group, are apparently willing to work for less thari

the federal mini,mum. At the same time, many others, particularly,.

those twenty to twenty-four, are'generallyearning much morel-and ex-.

pect to earn much more--than the minimum.

The averages from these earnings'diStributions wert used along

with the Federal minimum

? .to compute some illustrative estimates of

' what it might have cost in 1976 to place enough Unemployed youths in

job so as to lower their incidence of unemplbyment to the levels of

1949 and, alternatively, to those of 1973. The lowest cost estimates

were obtained by using the federal Minimum and the highest by applying

the "prevailing earnings" of empllyed yoUths.

INTROdUCTION

The purpose of this paper Kas to make some estimates of what it

might cost to put the unemployed youth to work given various assumptions A. as to how low uthei,r unemployment rate could be brought and as to how

much they would have to be paid in order to get them totake jobs.

In making these estimates, we,had to examine various questions, such

as:

1 .11 The\fstimates and opinions which are presented in this papeii not

necessarily reflect the view's. or Ave the endorsement of the Bur au

of Labor Statistics. .

'

-t

_if
4.

(1.) Would it be possible -or even desirable--to And or create

jobs for all of the unemployed youth?

(20 If not, how many could be realistically placed into job

programs?

' (3.) 'What type of jobs--,full time, part time, minimum wage,

or otherwise--would these have tb be?

(4.) Might the 'availability of such jobi brinegd4tional ; youth into the labor 'market and thus vitiate the efforts

to lower the group's unemployment'?

/ (S.) If so, could it still be determined objectively whether

the job programs have been a success, a partial success

or 'a failure?.

In examining these difficult questions, we had adcess to some

interesting new data on unemployed youths as well as to the

. traditional

historical series. And while the new data, which will be discussed

o below, do not go very far towar& answering the questions posed above,

they should shed some useful light on these issues.

HOW LOW CAN YOUTH UNEMPLOYMENT BE BROUGHT?

Most people would readily agree.l.that uth unemployment cannot be

")... .eliminated in,its entirety; that there is some -minimal, "natural," or "frictional" rate of unemployment for every population group, and that

for youth--the group most lacking in 'skills and experience--this rate

is higher than for any other group. The problem s that such a rate

is not readily available'. Even if sought through mpirical research

and a.most detailed look at the'data, such a rate would prove to be

. very elusive. At best, research might yield a range within which such

a rae might be locgted.

O

4

ff

374 44
- 347 =

While the search for such a rate is outside the scope of this

paper, history does prOvide some guidelines as to the lowest levels 0

,-- att§oined by the unemployment rate for youth under various economic

conditions:,'As shown in Table 1; for example, the rate for youth

sixteen to nineteen has never been below fO% since the end of the

Korean War.' And this period includes some years--such as 1955 and

1956, and then again 1969--when the economy.is generally regarded as

'having operated at or.possibly.even above its full-employment potential.

As for the rate for youths twenty to twenty-four years of age, , only dipped below 6% in four years over he 1954-1977 period. This was

during the Vietnam'war when mrh Of th potential Labor force in this

age group was being syphoned off into the Armed Forces (and when quite

4,

a-few of these youths prolonged their schooling or skipped the country'

in order to avoid the drafts.

If we look at the jobless rate-for the entire sixteen to twentyfour group, we seeLthat it has never been below 8% since the Mid...1950's,

Over this period theire:were seven years in which it dropped to the 8-9%

range, but somehoWit seemed unable to move below this range. And here

we might note that if youths spent one-twelfth of their labor force

time in the job search process--that is, one ponth ayeqx if they are

in the labor' force all year2-unemployment among their group would"'

average 8,25%, very much in line with the "lows" attained by their

jobless rate over the pasttwo .decades.

° And there are those who would'peg the unemployment goals tor youths--

even higher for the current years. .They would do so generally on the

basis that the youth's proportion of the lOor force has increased

considerably since the mid- 19.60s and that the resulting "crowding

effect" has gradually raised what one might call the "natural" unemployment rate for youths. Thus, Michael L. Wachter,,i computing a "noninflationary unemployment rate" for the nation, which he estimated to

be 5.5% for 1975,' pegged the youth rates for the same year al °25.77., for

males sixteen to nineteen, 8.2% for males twenty to twenty-four, 16.9%

v-Ifor females sixteen to nineteen and 9% for 'females twenty to twenty - & Ai

.
I.

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A TABLE 1.

. UNEMPLOYMENT RATES FOR YOUTHS 16 TO 24 YEARS OF AGE: AVNUAL AVERAGES, 1954-1977 ,

4 Y.

Year Total, 16 to

24 years

,16 to 19 years

20to 24

Total, 16 to years

19 years . 16 and 17 18 and 19

1954 10.6 12.6 13.5 12.0 ' 9.2

1955 8.7 11.0 12.3 10.0 .0

1956 8.5 11.1 12.3 6.6

1957 9.0 11.6 12.5 10.9 7.1

1958 13.1 15.9 16.4 15.5 11.2

1959 11.0' 14.6 15.3 14.0 8.5

1960 11.2 14.7 15.5 )14.1 8.7

1 13.0 '16.8 18.3 15.8 .10.4

1962 11.3 14.E 16.2 13.6 9.0

1963; 12.2 17.2 19.3 15.6 8.8

1964 11.5 16.2 17.8 14.9 6

1965 - 10.1 14.8 16.5 13.5 6.7

1966 8.6 14.8 11.3 5.3

1967 8.7 12.9 34.7 11.6 5.7

1968 8.7 12.7 14.7 11.2 '5.8

1969 8.4 12.2 14.5 10.5 5.7

1970 11.0 15.3 17.1 . 13.8- 8.2

. 1971 12:7 15.9 18.7 15.5 9.9

1972 -12.1 16.2 18.5 14.6 9.3

1973 10.5 14.5 17.3 12.4 7.8,

.1974 11.8 e 16.0 18.4 14.2

1975, 16.1 19.9 21.4 18.9 13.6

1976 14.7 19.0 2111 17.4 12.0

1977 13.6 17.7 19.9

376

16:2 10.9
- s , 3 9 -

S . a tr. I

s

A ft d. % 4, :1; .2

*.

s 4

at

- 4'

,

four. And Petei.,:alik,iin arriving at a hendh,

matkiunemp4oyment rate" for 19.77, pegged the .yOuth rates at the follok-

.. . 148% foX.Iliales sixteen to

to twenty-four,'1;.'7% for t/omen'siXieen

As Shown below, while Wachter's and Clark's unemployment "goals"

for youths,in'1975 and '19774-ae substantialbi ower than the actual

rates experienced by youths in recent years, they are still much higher

than the rates which had been achieved by youths in 1969. Wachter'S

estimates, in particular, are even somewhat higher than the rates ex-'

perienced by youths in 1973, when the overall unemployment rate for the

nation (4.9%) was at about the halfway point between the 1969 rate

(3.5%) and the current rate (6.4%.as of December 1977). Only as the

youth ,groups diminish in size relative to the other population groups--

a development that is now in lips initial stages and which will continue

through. the 1980s, .given the sustained decline ilithe birth rate during

the 1960s and early 170s--would the jobless rates for youths, as estimated by Wachter and Clark, be expected to decline much below their

.1973 levels.

Nevertheless, while cognizant of the Wachter and Clark estimates

shown above, we still decided to.use the actual number for1196and

1973 as-the benchmarks for our calculations of. the size and costiOf

the youth job deficit. In essence, whin we 'did was to calCulatP4a,

twenty to twenty-four.

2

nineteen, 7.1% for males twenty

;.) to nineteen, and.9.I%.for women

1.

V.

I4

Michael L. Wachter, "The Debogrhic Impact on Unemployment:11* Past

Experience and the Outlook for the Future," in Demographig/Wends

and. Full Employment, Special /Wort Number 12 of the National ' Commission for Manpower Poliq)-Washington, D.C., 1976, pp. 27-99.

2. Peter K. .Clark "Potential GNP in the United States: 1948-1980"

in..U.S.'Productive Capacity: Estimating the Utilization Gap,".

Center for the Study of American Business, Washington University,

St. Louis, Mo., December 1977, p. 37.

is.
e S

sof - 350 -

it would cost to lower the unemployment rates for youths- from the 1976

levels to the 1969 levels and, alternatively, what itxould cost to span

the much narrower differences betwen the 1976 rates for youihSand

those for 1973. In so doing, we are endorsing neithe) the 1969 nor the

1973 rates as the most desirable goals. We make no suchjudgments,

although we are aware that many will, regard the 1969 rates as too low

and highly inflationary, while many others might regard the 1973 rates . as still,far too high. Yet others might look at the data for specific

groups of youth, such as black teenagers, and'argue that eVen the 1969

rate for this group (24%) was excessively high.

It is simply that, as we shall see'below, the method we chose for

making our alternative sets of cost estimates dictated the use of data

on the distribution of unemployment for given years. And the juxtar

position of data for 1969 and 1973 with those for 1976 yields estimates

which, while dif fering sufficiently from each other, are wii.n the

bounds of historical reality.

WHAT JOBS AND WHAT WAGES FOR YOUTHS? 4

Even after we had chosen a set of unemployment rates, a ual or

hypothetical, we still had but one of the three basic factors needed

for our calculations. Two other important factors are also needed:

the n ber of hours, and weeks of work to be provide,1 to the youths w o

woul have to be placed in job -in order to reach the desired rates o

une loyment and the wages that would have to be paid to them in oral.

. to attract them to these jobs,

O ;While the decisions-with Tegard to these issuet would, in the

final analysis, be made by0p91-icy makers, they must be made in light

of the, desires and aspirations of the youths they would effect. Although theseaspiraiiOns cannot be pinpointed with.precis n, new data

'collected in recent years, combined with data which have ong been

available,.shed considerable light both onthe expects ons of unemployed youths as well as on the work patterns and e ngs pf the employed-ones.

378

f

O

4
4

1

- 351 -

\.;

46,

In terms/ of the number hours of work that might be possibly provided to unemplOyed youths, one can look at..thp.ery detailed data that

have,tbeen available for many years on the hours actually worked by employed youth. Also available are data on4the unemployed in terms of"

whether they want a full -time or A part-time job, C-whether their last

job was full-time or part-time nature.

In terms of'the hOurly wages that would be requed to entice

youths to take jobs, one could, of course, assume that the minimum wage

could be applied cross the board. However, while we have used the

Federal minimum to construct some of our alternative estimates, we have

found IlTat there is a great diversity among youths both in terms of the

act wages earnesikor those- who are employed as well as in terms of the

irationS of se who are looking for jobs: This diversify is

amply "illustrated in Tables 2, 3, and 4, Tables 2 and 3,--wbtich are

based on special data collected from a sample of unemployed persons in

t May 1976, show, respectively, the. hourly rates of pay for the last job

previously held by unemployed youths and the so-called "reservationwages" in terms of the jobs they were seeking at the time. Table 4,

based on data collected each year, shows what employed youths were

earning in May 1976.

As shown, while some of he overall averages for these tables

(particularly the medians) d of differ widely from the federal minimum

wage--which was $2.30 an hour in ay.41976--the actual distributions

show a consderable dispersion aro d the averages. For example, the

ta in Table 2 show that many of the unemployed youths with previous

, wo xperlence had last worked for wages far below the minimum while-

. i , . many others had apparently earned much more t the minimum. And

even when it came. to so- called' reservat age ",or the lowest

'wages that youths were willing-to settle for, jobs

(Table 3), we again see a large dispersion ar verages.

4 Apparently, maw youths were willing to accept , derably lower

than the &lam= (which is still legal in -many' ,while many

y

(I others had much higher expeCtations. And the earnings distribution for

lir
T

I

,

ti

a

TABLE 2.

REPORTED HOURLY EARNINqS FOR LAST JOBS HELDBY YOUTHS 16 TO 24 YEARS DLO WHO WERE UNEMPLOYED'IN MAY 1376 ..4.bet 40.00'''

.

. Age, sex, and race .,

. .._

"7 'YR ,; (thousands) $ AO";

..

,. cr---.., ,szt29

.$2'.30

- - $2.31-:

'to *$2.99

$3.00' to s9.

$4:06 t

' tp

P.. $4,99- , $5.00 '..k ,...5rid *ovcr

..`"i Mean

". IP Median

°

. Total, 16 to 24 years'

16 to 24

20 to 24 years years Males, 16 to 24 years

----16 to 19 years

20 to 24 years

Females, 16 to 24 years - - - -

16 to 19 years - - - -

20 to 24 years - - - -

*lite, 16 to 24 years . N Black, 16 to 24 years

1,001

481

529

542

' 269

273

459

212

247 817

177

%

_

._97 623432

__23865

39

26

76

'20

.

_'".219

356

217 139

137 105

32 112

107 285

71

87 ) 49

38

51

30

21

36

1917

7710

198

95

104

110 _ 60

49

89'

34

54

156

39

127,

3295

94

2767

33

528

109

17

".. 721754

601446

12

39

60

12

64

9

'55599

, 410.75

Ir2.37

3.10

3.11

2.54

3.61--

2.32

2:16

2.46

2.77 2.60

$2.30

2.20

2.50 2.50

2.30

3.25 2.20

2.10

2.25

2.30

2.25

' 50

5' --

5 54

8

3S0

v.(
f

TABLE 3. LOWEST ACCEPTABLE HOURLY, EARNINGS OF YOUTHS 16 TO 24 YEARS OLD WHO WERE UNEMPLOYED IN MAY 1976

1'

. 401 , Age, sex, and' race

. , Taal

(thousands)

Under

$2.00

.

$?'..12° to $2.29

$2.30

$2.31

$2.99

to $3.00

to $3.99

$4.00

to

$4.99

$5.60

and

over

, . , Median

Total, 16 to 24 fears

.-, 16 to 19 years'

20 to 24 years

Males, 16 to 24 years

. 16 to 19 yefrs--L;-

20e,to 24. years

Females, 16 to 24e;Ars

16 to 19 years

20 to 24 years -- -

White, 16 to 24 years

4 Black, 16 to 24 years

: 1,494

816

678-

784 423

362

. 68 - 46

2230

25

5

: 387-

279

107

158, , 130

27,

266

173

93

-. .-104 73

. 32 A 4

1.61", . 100

61'

' 218

' 4S

. 4

334

184

151 161 94

67 17391

83 245

87

;79

96 183

192

66 126

88

31

57 220

53

__

-. 722349

5920

,39

.1331066

.6

'

;

,

88

157380

1566

- 8'

--

7 69

19

,

'

_. -

.

.

$2.75

2.47

3.10

3.05

! 261

3.57

$2.35

2.30

2.50

2.50

2.30

3.00

710

-394

316 1,175

302

,

38' , 2r .---t.,149

18, 57

11

......._ v 229

- 8Q

30/.

79

. 2.42

2.32

' 2.56

.. -2.76

2.74

2.30

2.30

2.30

..2.35

2.50

4

381
0

/ $

TABLE 4.

4 ACTUAL HOURLY EARNINGS OF EMPLOYED YOUTHS 16 TO 24 YEARS OLD WHO WERE PAID BY THE HOUR, MAY 1976

Age, sex, and pace

Total

(rtehpoannds) Under

$2.00

$2.00'

$2to24

$2.25

$2to4 9 $2.50

$2to9 9

$3.100

$3to99 $4.00

$4to9 9 $5.00

$5to9 9 $6.00

$7to9 9 $8.00

oavnedr Mean in

,

TOtal16 to 19 years' 4,770 ' 583 1,016 1,332 898 678 145 70 41 8 $2.52 $2.40

20 to 24 years 6,357 193 371 800 1,070 1,866 1,013 566 403 75 3.60 3.33

4 Hales, 16 to 19 years 2,534 174 531 622 508 467 131 61 36 5 2.70 2.48

20 to 24 years 3,671 . 42 123 284 520 1,086 736 453 358 68 3.96 3.78

I :. Females, 16 to 19 years 2,237 -409 486 709; 390 211 14 10 5 3 .2.32 2.33

. 20 to 24 years 2,685 . 151 247 515 550 , 781 278 ,2113 45 7 3.11 2.89

te, 16 to 19'years 4,450 561 919 1,240 854 625 139 65 40 8 ' 2.53 2.40

20 to 24 years 5,570 170 312 645 939 1,667 897 518 367 56 3.37 3.60

dc, 16 to 19+years , 321 21 98 92 44 53 5 6 1 --

--., 2.50 2.36

20 to 24 years -767 22 59 155 132 199 117 48 36 19 3.65 3.09

'.1

3 C" )0
-, 355 -

O

youths who were actually working in May (Table 4.) also shows a wide

dispersion. The earnings averages for these employed youpo'Were

also'generally higher than those reported by unemployed4ouths either

in terms of Previous -or future jobs. 0 What all of these tables also show, and not unexpectedly, is

that youths twentAltotwenty-four earn much more--and expect to earn

much more--thanithose sixteen to nineteen years'of age. For the older group, the earnings averages,both in terms of actual earnings on

past or current jobs as well as in terms of the reservation wages,

are considerably higher than'lhe $2.30 minimum wage which prevailed

when these data were obtained. Only about one-third of these youths,

compared with a 'little offer one-half of those, sixteen to nineteen,

appear to have been willing to take jobs paying only the Federal

minimum.

As shown, the wage aspirations of unemployed black youths did

not differ that much from those of unemployed white youths. The

medians for blacks were either the saw or slightly lower than those

for whites. In terms of the means, the pattern was not as consisT

tent, but it must be remembered that here we are dealing with relatively small distributiowhei-e the\Aeans can be affected by only

a few unusually high values." (Thus, the fact that the mean reser-'

vation wages for black teenagers turned out to be slightly higher

than that for whkte teenagers should not be given too much weight.)

The arnings data fo youthi who are actually employed are

perhaps the most relevant rr determining what it would cost to put

their unemployed counterparesto work. The problem which arises in

using these data is that unemployed workers as a group do not have

the same characteristics in terms of skills and education as do those

whg are employed and could not, thAefore, be expected to command

the same wages. It is only after account'is taken of the major

'differences in personal characteristics,between employed and unemployed youth that the-earnings data for the former can be used

0
3.56

to figure out what it would cost to employ the latter.

Earnings averages Which do take into accoupt such differences,

both for the unemployed youth as well as for older unemployed persons, were recently develop0 by applying regresi*On analysis to

t e data on usual weekly.edrnings that are collected each May.

Th se regression-derived estimates show what the weekly earnings

of the unemployed would have been, assuming that they would have

received the same rites of pay as did employed persons who had the

same characteristics in terms of 'age, sex, race, education, occupation, geographic residence, and full- or part-time status.3

While the distributions of-such hypothetical earnings were not

tabulated, averages were computed for various youth.groups as well

as for older workers.-

By using this "peer group" Methodology, it was estimated that

the average (mean) amount of earnings lost because of unemployment,

in May 1978 was about $110 a week for males sixteen to nineteen,

$85 a week for females sixteen to nineteen,-$158 a week for men

twenty to twenty-four, and $135 a week for women twenty to twentyfour. 'These "peer group" estimates, which might be regarded as

representing the "potential" prevailing earnings of unemployed

youths--provided, of course that they had.access to the same mixture of jobs as were then being held by employed` youths and that the

wage structure would remain the same--have been used to make one of.,

the alternative sets of calculations of the total cost of i2wering

youth uneMploYMent: Different calculations were made.on the basis

of the reservation wages d on'ihe basis of the federal minimum.

ESTIMATING THE YOUTH WAGE ILL

After settling on two sets of unemployment benchmarks for youths,

3. Paul MI Ryscayage and Curtis L. Gilroy, "Earnings Foregone by

, the Unemployed," in Proceeding of the Business and Ecbnomics,

Statistics Section, American S tistical Association, 19//;

pp. 65419.

384
A

- 357 -

that is, the incidence ofunemoloyment encountered among them in 1969

and in1973, the general procedure used in estimating the cost of

reaching these goals was as follows: First we estimated, on the basis

of the three wage assumptions, what it would4have actually cost in terms

of total 'wages to put all of the unemployed youth to work in 1976--that

is, to bring youth,unempIoyment down to zero. We then estimated what

it would have cost if the incidence of youth unemployint and its distribution in 1976 were consistent with the situation in h969 and 1973. -

We then computed the differences between the actual total wage cost in

1976 and the two hypothetical_ costs. These differences represent our

estimates of the wage costs associated with job programs designed to

lower the incidence of youth unemployment,rom its levels of 1976-to

those of 1969 and 1973, when.the.economy was operating much closer to

(or perhaps even slightly above) its full potential..

The:job deficit. The statistics that were used to determine how

many yoUths would, have to have been placed in jobs in'.1976--and for how'

Ibhg--it-order to achieve these goals were those fr m the annual "work

experience" survey. These data show the total num er of youths who

encounter one or more spells of unemployment during the year, as well

as the, total length of their unemployment. 4 For-1969, a year when the

4. The tptarnumber of different persons who encounter some unemployment during any given year is obtained through special questions which

are asked each March. and which relate to employment and the number and

duration of tiossibleirspells of. unemployment encounters during the year.

In a loose way, these "work experience" numbers may be egarded'as an

aggregation of the "flows" into and out of unemployment uring a given

year, whereas both the monthly and the annual average' numbers might be

regarded as reflecting the "stock" or the average of various measure- . ments of the stock taken during the year. In theory, these two sets of

numbers should be consistent with each other in terms of yielding the

same number of person-weeks and person-hours of unemployment for a given

year, In practice, however, these two sets of data have been found to

yield somewhat different aggregates.. For youths, in particular, the

work experience data have been found to underetimate'unemployment relative to the estimates obtained from the/Monthly'surveK. For a detailed discussion.of the different estimates of unemployMnt that emerge

from the "stock" and from the "flow" measurements, see R.D. Morgenstern

and N.S. Barret, "The Retrospective Bias in Unemplo nt,Reporting by

Sex, Race, and Age," Journal df the Ametican Statist cal Association,

June 1974, pp. 335-57.

ti
unemployment rate for the nation averaged only 3.5% and the rate fox.

muths sixteen to twenty-four averaged 8.4%, these "work. experience"

numbels-show that a total of 4.8 million youths encountered one.or more

spellkof unemployment during the year (See,Table 5). These 4.8 million

.youths accountecrfor 16.4% of their civiliqn noninstitutional population.

During 1976, the number of youths who encountered at, least one

spell of unemployment was 8.5 million, or 24.1% of the youth's civilian

noninstitutional population: In order to quantify properly the differ-

, ence between the number of youths who would have been unemployed in

1976 had the 1969 condition prevailed, but still allow for the growth

-7- in population between these two years, the proportion of youths who encountered unemploym

The differen6e beta en the a ones based on 1969 proportion

would have to be placed in jobs o

9 was applied to the 1976 youth population.

tual numbers for 19 andthetypothetical

represented th- umber of youths that

duratitn in 1976 in order to

bring their rates of unemployment to 1969 levels (Table 5). The same

procedure was then followed to calculate the number of youth that would

have requir.ed jobs in 1976 in order to lower their incidence of unemployment tdthe levels encountered in 1973. According to these procedures,

/2.8 million youths would have needed jobs of various duration in 1976

in order to bring their group's joblessness to 1969 levels. About 1.9

million would,1?ave needed jobs of various duration order to reduce

their group's jobless rates to 1969 levels.

The mext step in the estimating procedure was to calculate for how

long each of these youths would have to be employed in terms of weeks

and whether the-jobs to be provided them would have to be of a full- or

part-time nature. ,5

In order to do this, thepercent distributions of

5. It was assumed that youths whose longest job during the year was of

full-time nature would be looking for full-time jobs, while those whose

longest job was of part...time nature would have' wanted part-time jobs.

The small group who had looked for work but did not have a job during

the yeaf was divided into two equ'L parts, with hAf assumed to have

been looking for full:time work and half assumed to have wanted parttime jobs.

3 g P
4.

TABLE 5. , UNEMPLOYMENT RATES AND THE EXTENT OF UNEMPLOYMENT AMONG, YOUTH, 1969, 1973, AND 1976

Selected CharactexistiCs by age

Actual data for:

1969 1973 .1976

Hypothetical data for 1976 if

unemployment were distributed as in:

1969 1973

Annual average unemjelymept rate,all

persons 16 years and over

Youth unemployment rate, 16 to 24 (percent)-

Teenage unemployment rate, 16 to 19

(percent)

Young waiters' ummployment rate,

20 to 24 (percent)

Civilian norlinstitutional population 1/

16 tb 24 years of age (000)--- -

Percent of youth. population with some

\unemployment during year

Persons 16 to 24 years of age with some

unemployment during year (000)-

3.5 8.4

12.2

5.7

29,382

16.4

4,827

4.9

10.5

14.5

7.8

33,531

18.8

6,306

7.7 14.7

19.0

12.0

35,405

24.1

.8,524

8:4 - ,12.2

5.7

35,405

16.4

5,806

10.5

14.5

1/ These are the noninstitutional population counts for the month of March following the year

337

f reference.

7.8

35,405

18.8

/ 6,658
36.0

youth unemployment for 1969'.and 1973 (in terms of age, sex, full or

partLtime status /^, and length of unemployment) were applied to the hypothetical levels of unemployment for 1976. 'The resulting distributions

of unemployment were then costed out according to several different

assumptions about the wages to be paid.

4 rt should be noted that an Important assumption that is implicit

in this procedure is tha4 short-duration unemployment can be dealt with

rArough job creation as effectively as unemplo4ent of longer duration.

Yet it is doubtful that much could be realiseCally done through job

programs to deal with unemployment of short duration --say4 that lasting

our weeks or less. And, as can be seen from Table 6; a very-large . proportion of the yo hs who enco tered some unemployme- nt in 1976 were.

jotpss for only ve short period,.

Another critical' assumption tha ,eras made is that, if placed in

job programs, these youths would hav) remained there only for as long

as. they would have been unemployed if they had not taken these placements.' In other words, no allowance was made in making these estimates

for any possible increase in the labor supply of youths in response to

:these programs. This is, admittedly, another very questionable assumption.

Youth's wages and'earnings. In order to produce a range of estimates associated with the-youth job deficit we-Used the minimum wage,

the lowest acceptable wages and the prevailing earnings of youth in our

calculations.

In so)doine, we first assumed that the minimum wage of $2.30 would

ve applied acr9ss the board to all unemployed youth, regardless of

age, sex, or type of job last held. On the other hand, the calculation

based on the lowest acceptable wages and the peer group earnings (or

!'prevailing earings") made use of different averages (means) for

specific age-sex iroups. Specifically, four different lhwest acceptable

wage levels (by age and sex) were applied to thelost hours of unemployment accumulated by youths, while eight different peer-group earnings

levels (by age, sex, full-time and p art-time status) were applied to

338

Pt
TABLE 6. DISTRIBUTION OF YOUTHS WITH SOME UNEMPLOYMENT IN 476 BY NUMBER OF WEEKS UNEMPLOYED

Age, sex, race,

and work status during year

Total

with sane

unemployment

(thousands)

Percent distribution by total weeks of unemployment ' Percent with:

- Total 1 to 4

weeks

5 to 10

weeks

11 to 14

weeks

J.5 to 26

\leeks

27 weeks

and over

2 spells of

unemployment WORKED AT LEAST PART OF YEAR

'Total: 16 and 17 years------ - 889 100.0 39.6 19.5 10.4 16.8 13.7 17.3 16.0

18 and 19 years 1,819 "lob.o 31.9 21.2 11.4 17.2 18.3 21.2 15.3

21a 0;424 years-- - ---- - '4,540 look() . 28.1 '20.2 12.0 22.6 17.1 18.2 15.9

Male : 16 and 17 years 463 100.0 34.0 19.2 10.2 18.0 18.3 20.7 ' 17.4

18 and 19 years 966' 100.0 ' 28.4 19.9 12.3 17.3 22.1 23.2 17.2

20 to 24 years 2,707 100.0 23.7 20:6 12.4 24.3 19.0 20.3 17.7

White: 16 to 19 years 265 100.0 31.5 19.6 12.1 17.6 19.2 22.1 17.1

20 to 24 years 137 100.0 24.9 21.6 12.4 23.8 17.3 20.2 16:8

BLack: 16 to.19 years 168 100.0 21.7 18.4 7.7 17.5 34.7 24.8 19.3

20 to 24 years, 336 100.0 16.8 13.8 12.0 27.8 29.6 22.6 22.5 LOOKED FOR JOB BUT DID NOT WORK DURING YEAR

Total: 16 and 17 y . 414 100.0 41.3 20.3 12.1 13.8 12.6 N.A. N.A. 18 to 24 y 862 100.0 32.7 17.2 8.9 11.8 29.4 N.A. N.A. Male : 16 and 17(y 229 100.0 37.3 21.8 -11.4 14.2 15.2 N.A. N.A. 18 to 24 years 328 100.0 20.6 13.4 11.6 10.6. 43.8 N.As N.A. Female: 16 and 17 years 185 100.0 46.5 18.4 13.0 13.0 9.2 N.A. N.A. 18 to 24,ye4s 534 100.0 40.1 19.5 7.3 - 12.6 20.5 N.A. N.A. N.A.=NOT AVAILABLE

449

3g9
- 362

Ili

the weeks afiemployment accumulated during he year by the various

groups of youths. In the last two cases, we a sumed that the wage and

. earnings leVels which were obtained in May were representative of the

average for the,entire year. (The data relating to the earnings onthe

last'jobs held by the unemployed were not used as the basis for any

. calculations both because they are, by definition, the most dated, as

well as because the averages from these series did not differ that much

from those based on the reservation wages. For a fuller description

of.the sources of the data used and of the assumptions made, see the

Technical.Note beginning on pate 394.)

The results. Table 7 shows what it would have cost in 1976 to put

youth back to work at the minimum wage, the lowest acceptable wage, and

the prevailing earnings of youth, both by using data for 1969 and 1973

as the benchmarks. As sh own, paying the 2.7 million unemployed youths

qi> the same earnings as their employed counterpgrts would have been the

most expensive way to lower the youth jobless rate. Using this wage

scenario-And all-the other assumptions that had to be made--we estimated that it would have cost $8.9 billion to reach the 1969-based

goals and $6.0 billion to reach the 1973-based goals. On the other

hand, under the minimum wage scenario it would have cost-S5.7 billion

to lowerthe. 1976 incidence of youth joblessness to 1969 levels and

$3.8 billion to lower it to the 1973 levels. ` A closer examination of the youth wage bill under each different

wage - earnings scenario can be made from Table 8, where the costs based,

. on the various assumptions are brokdn down by age-sex group. It is not

too surprising to see that the greatest co t would be incurred in providing jobs to workers age twenty to twen -fourjespecially

Such workers are mostaikely to be receiving higher-wages than teenagers

and to be:sepking full-time jobs. For all workers a4d twenty to

twenty-four*the wage bill would have amounted to $7.0 billion under pre-

, vailing wages and $4.0 billion if the minimum wage were used as the

standard. In contrast, even' under the prevailing earnings assumption,

the cost of putting teenagers sixteen to nineteen to work would-have

306

A
a TABLE 7, , . ..

. ; ESTIMATED COSTS OF PUTTING UNEMPLOYED YOUTHS TO WQRK IN 1976 ACCORDING `

TO VARIOUS ASSUMPTIONS ABOUT THE INCIDENCE OF YOUTH UNEMPLOYMENT AND THE WAGE RATES FOR YOUTHS

. Number of unemployed .and

wage raq assumptions

Actual data

for 1976

Hypothetical

data for 1976

based on 1969

incidence of

unemployment

Job deficit

and cost

(1) - (2)

Hypothetical

data for 1976

based on 1973

incidence of

unemployment

. Job deficit,

and cost

(1) - (4)

. 4

Number of persons 16 to 24'

with some Unemployment . during year (in thousinds)---

Costs of wage bill (in

millions of dollars) at:

Prevailing earnings

Lowest acceptable wage

Minmimat wage

(1)

8,524

$14,776.9

12,104.4

9,391.9

'

(2)

4

5,806

.$5,830%5

4,641.1

3,715.9

.

--/

,

(3)

2,71

$8,946.4

7,463.3

'5,676.1

.(4) .

. 6,658

$8,830.4

7,130.2

5,617.9

(5)

1,866

$5,973.5

.4,974.2

3,774.1

J

391

k 392

4

rn
1

1

TABLE 8.

REDUCTION IN YOUTH UNEMPLOYMENT AND ITS COSTS IN 1976 UNDER VARIOUS EARNINGS AND WAGE ASSUMPTIONS

- 1

_

. Groups

1 Iv Reduction in

unewllyment

(000)

Prevailing earnings

(000,000)

LoWest

acceptable wage

(000,000)

Mininual wage

(000,poo)

Assuming the 1969 incidence of

youth unemployment

,

I.

.

2,718

709

. .

:

.

,

$8,946.4

,

$7 463.3

1:1127

t

:---

$5,606.1 Total youth

Teenagers, 16 to 19

Males , Females , Young workers, 20 to 24

Malei

Females '

.

. Assuming the 1973 incidence of

youth-unemployment

1,914.6 1,695.8

301 . 408

2 010

1 tal

742

1,866

577

.1,115.5

799.1

7,031.8

-,- 1,117.2

715.6

5,630.5

,9t6.4

709.4

3,980.2

5,102.7

1,849.0

-.

, 5,973.5

. 4,242.9

1,387.6

, -

4,974.2

2,7 3.5

1,246.7.

3,774.1 Total youth

. `Teenagers, 16 to 19 "-- Males

Females

Young workers, 20 to 24

Males

Females

1282.9 1,2411 1,142.3

104

273

1,293

758.2

524.7

4,690.6

799.6

441.5

3,733.1

704.6

4377.7

2,631.8

1,830.5

801.2

851

442

3,475.4

1,215.3

2,841.3

891.8-

3.9

I

8 9

116 1
365 -

r

amounted to only $1,9 billion, a figure not much higher than that obtained through minimum wage assumptions.

However, as already noted, the minimumwage may'not be sufficiently

high to attract many of the unemployed youths into jobs, especially

those aged twenty to twenty-four. Undoubtedly, many would refuse to

tyke minimum wage jobs, as their lowest acceptable, wage is considerably

above(the minimum wage. a,

.-.,

r 4 LIMITATIONS AND QUESTIONS

The estimates we have presented should be regarded as merely , illustrative as they are subject to many, limitations. First of all,

AW

they relate to the employment situation fof youths during 197 . More

current estimates, and even better estimates derived on the is of

projections for future years, would, needless to say, be much ore useful for policy guidance. However, 1976 was the last year for

which "work experience data" were available and the Only tecent year

in which reservation wages were collected. In ordeK to 'make such

estimates for future years one must not only project the size and dis-

. tribution of youth unemployment but also make some assumptions about'

the future course of wages, and this is simply too difficult a task.

(For thoNwho would like to attempt such projections, we might indicate

4 two'elements whose future course is sufficiently well known: the

minimum wage, which has been legislated to rise by specific amounts On

specific dates over'several years, and the size of the youth population,

which is now increasing-at a much slower pace than has been the casecf

the past decade and,whichwill begin to decline rapidly during the

early 1980s.)

As already noted, these cost estimates were based on the assomptioft

that short - ,duration. unemployment could be alleViated through rob pro-

.grams as effectively as longer - duration unemployment. Thli admittedly, a very Questionable assumption, which we,made only with the

knowledge that the estimates could ',be easily reconstructed to exclude\

-

I

395

yr

r

4
- 366 -

'the short uration unemployed 1Xom the calculations. 6

Another obvious limitation of these cost estimates has to do with

the quality of the earnings data used as one'of the principal ingredients in the estimating procedure. Except for the minimum wage figure,

the earningsta we have used have been obtained through the Current

Population Survey, and they are subject to response biaras well as to

the normal variance associated with such sample surveys. Recent studies

have indicated that these 46ousehold data tend generally to underestimate

the true earnings of the wo\ers in question, with the overall averages

ranging from 3% to 5% lower than they would be if based, actual pay- '' roll records.

7 However, no findings have yet been.derived from these

studies with regafd, to theAualityof the ,earnings data for youths,

.s, . Furthermore, since the "prevailing earnings" statistics for the unemployed were developed through regression analysis,,, they may be subject to

further statistical error and potential bias, associated with this estimating device. (And here it might be noted that the even lower reli- -.

....) ! '. 6. We experimented with such a procedure and calCulated that if the

goal in 1976 had been to place all unemployed youths in job programs

after'they had accumulated 4 weeki of unemployment., the wage costs

would have been $7.2 billion on the basis of the Federal minimum,

$9.3 billion on the basis of the lowest acceptable wages, and $11.4

',billion on the basis ofthe prevailing earnings. Of course, these

calculations are based on the, assumption that the youths would have

remained in these job rogrdMs for only as long as they would have

remained unemployed if they hadmcit taken these jobs,,and.this is

another questionable assumption.

7. ,These are the findings from a "validation" study conducted in,Janu-

-ary 1977, when the data'obtain0 om the Current Population Survey

' on earnings of about 4,000 worker ere subsequently checked 'against

the records of their employers. ile the detailed report on this

study has not yet been published, a discussion of the quality of

the earnings data derived from the household survey had appeared in

a previous BLS report. See Special Labor Force Report NO- 195",

"Weekly and,ftourly; Earnings Data from the Current Population:Survey",

U.S. Department of Labor, Bureau of"Labor Statistics, 1977. '-

396
A

t

3-67

r , r

ability of the subnational clata..on earningsycobbiiied with the paucity

of information on the distribution. unemployment'attlie,local aleai

-level, discouraged us from even attent'ng to make any geotiaphically

oriented cost estimates.)

..._ It is also important to note, with regardto-the cost estimates ,.

. i. discussed above, that they-are limited purely to the.wages that would

. haNth to be paid and do not make any alrlserices for fringe benefits or

. administrative expen es. Yet we know that such costs would be substantiar. In 1974, or example, fringe benefits ofVarious kinds reised'

. .s. the total costs of labor to employers by one third

8-, over-the pure cost t 4.

of straight time wages. And while it would be reasonabi to assume

that the employment of youths wou not have.to)).e,accomp ied by the

payment of fringe benefits as costly as those that world generally go

to older workers with high seniority, it must be noted hat payments for

, 1 $xialtSecurity, workers compensation, and so forth-Must be made by

employers even in the case of newly hired youths--and,these payments are

, n 4.-,-,.,...;.:..., notnecessarily negligible. As for the administratiVe,costs of runni4g

7.VC yoqth job programs, this is an additional cost that,vgt'be taken into

I . , `,4,4_,._- . account but which can probably he better estimated by?aaministrators

-c, - than by economists, unfamiliar with administrative machine .

, Of course, offsetting all these Costs, there we 3-elsO be some

i obvi s.benefits that would floW from the employment of large numbers: .

7

of youths. Presumably, production would increase; disposable income

would rue; some youths might' be diverted from less desirable endeavors,

4

and-so on. But we lutow of no way even to,begin to put a dollar estimate

on this' side of the ledger.

Finally,-*one might ask whether thecreation of a large number of

jobs, for youths,'particularly if at wage levels that most youth would

find quite ecceptable, could wind upl'in attracting many additional youths

,

8. BLS Measures of Compensation, U.S. Department of Labor, Bureau of

Labor Statistics, 1977, Bulletin 1941, .

4.7
- 368 -

V into the labor market and thus'vitiate the efforts to lower their unemployment rates.' Given the relatively low labor force participation,

rates of some groups wi hin the 'youth populationblack

(youthS._for

example- -such a

some

evidence tha

ility could kt be ruled out. Theris, in fadt, e

is would be the case.

9

Should such a situation arise, one\objective way to tssess the imt of job pr ograms wouldbe to focus o the employment-population

tios of the affected group. Af if the employment-population

6 ratig.for black youths were to remain muc lower than that for white

youths, as is now the-case, something would sti ll be seriousljr ami§s

even if their unemploymentrate drops sharply. Conversely, an increase

in their employment - population, ratio should be regarded as a favorable

development even if their unemployment rate fails to recede significant 1y. 1- / SUMMARY AND CONCLUSIONS

/ In this er we have examined the issue of how low the unemployment rate r youths might be brought,'and pointed out that for the

entire sillcteen to twenty-four group the rate has never averaged less

than 8% for any year since the Korean War. In addition, we indicate

that in some recent "full- employments' estimates the youth jobless rates

have been pegged ever higher.

With regard to'the wages that would have to be paid to youth

placed in job programs, we presented several distributions, all of which

show a large dispersion. Many youths, particularly in the sixteen to

nifitteen group, are apparently willing to work for less than the federal

miniinum. At the same time, many others,, particularly those twenty to

.twenty - four, are generally earning much more--and expect to earn much

more--than the minimum.'

We, for example, Stanley L. Friedlander, Unemployment in the Urban

/Core: An Analysis of Thirty Cities with Policy Recommendations.

TRNYon: Praegar: 1,9Z2). Reference to the' increase in the

supply of labor resulting from the creation of a given number of

jobs can be found on page 133.

393
369 c

Finally, we used the averages from these distritiutions, along with

the federal minimum, to compute some estimate of what it might have cost

in 1976 to place enough unemployed youths in jobs so as to lower their

incidence of unemployment to the levels of 1969 and, alternatively, to

those of 1973. Although these estimates are based on many assumptions

and are subject to many limitations, they illustrate the potential of

the demographically-:oriented earnings data which have become available

in recent years.

a

4

--L

390

i!
SOURCES OF DATA

379,.

TECHNICAL NOTE

P

.4-

The cost estimates presented in this paper were developed from data

obtained from the Current Population Survey (CPS) conducted by the

Bureau of the Census. Specifically, the statistics on youth unemployment during 1969, 1973, and 1976 were collected in the March 1970,

March 1974, and March 1977 surveys. These data, along with the employment and unemployment data for other workers, appeared in various issues

of the Bureau of Labor Statistics' Special Labor Force Reports (Report

#127, #171, and #201). However, some unpublished data from these surveys were also used in the calculations., With respect to the wages and

earnings used in the calculatiOns, the lowest acceptable wages of youth

were collected in a special CPS survey of the unemployed taken in May

1976 (these data have never before been published); the prevailing

earnings of the unemployed were developed through regression analysis

that used-the usual weekly earnings of employed workers also collected

in the May 1976 CPS (see footnotes 3 and 6 of text for specific references).'.

METHODOLOGY

The general principle involved in estimating the costs of youth

mployment consisted of multiplying the number.of person weeks, or

person-hours, of youth unemployment by first, the minimum Wage ($2.30

in 1976), second, the lowest acceptable hourly wages of youth, and,

third, the prevailing earnings of unemployed youth. r All of the estimates were first developed for eight groups of

youth (men, sixteen to nineteen, looking for full-time jobs; men sixteen

to nineteen, looking for part-time jobs; women sixteen to ineteen,

looking for full-time jobs.; women sixteen to nineteen, lob ing for parrtime jobs; and the four corresponding groUps of workers ag twenty to

twenty-four). These estimates were then summed to yield the total cost

estimates. The specific steps followed in making these estimates were

as follows: ,

41

Step 1. Estimating the total costs of youth unemplo ent in 1976.

For each of the eight groups of youth mentioned above, estimates

were made of the number of persilk-hours and person-weeks lost

through unemployment in 1976. The number of person- ours lost

-----Ws---use-dtoprepare*-the costestimates using the min um wage

and the lowest acceptable wage; the number of person eeks lost

was used in making the estimates based on the potent al weekly

earnings of the, unemployed.

The extent. of youth unemployment in 1976 was ta ulated in

weekly intervals (1 to 4 weeks, S to 10-,--11 to 14, 1 to 26, 27

to 40, and 40 or more weeks). It was assumed that youths classi400
-371-

lied in each interval experienced the number of weeks of unemployment represented by the mid-point of each interval- (ie. the midpoint of the 1 to 4 weeks interval was 2.5 weeks).' Since these

calculations yielded estimates of the number of person-weeks of

unemployment for each of the eight groups of youth, it was now

possible to multiply them by the potential weekly earnings of

the unemployed to arrive at one of the three total:cost estimates.

To produce the cost estimates based on the minimum wage and

lowest acceptable hourly wage, it was necessary to convert the

person-weeks of youth unemployment into person-hours. Usint forty

hours for those seeking full-time work and the actual annual

average number of part-time hours worked by youth; person-hours

of unemployment for youth in 1976 were estimated. , The minimum

wage and the lowest acceptable hourly wages were then multiplied

by these person-hours to yield the other two total cost estimates

of youth unemployment. #

Step 2. Estimating the job deficit. To estimate the number of

jobs that would have been retired to reduce youth joblessness

in 1976 to 'the rates experienCes in 1973 and 1969, the following

procedure was Used: The'proportions of the youth population

(civilian noninstitutional population). in 1969 and 1973 who experienced some unemployment during these years were applied to

the, youth population in 1976.-1'he products of these multiplications were then subtracted from the actual 1976 level of youth

Unemployment to arrive at the size of the job deficits.

Obviously, we have assumed that the proportions of youths

experiencing' unemployment during 1969 and 1973 are consistent.

with the rate, or incidence, ,of ,youth unemployment in,the.se years.

These hypothetical levels of unemployment for 1.976, based on

the 1969 and 1973 experiences, were then used to calculate two

alternative total costs of youth unemployment.

Step 3. .Estimating total costs of youth unemployment in 1976 under

two-different assumptions about the level and distribution of

youth unemployment. To prepare total cost estimates of youth unemployment under two different assumptions about its.level and

distribution, it was necessary to return to the actual-;,data for

thqse years. The actual percent distributions of youth unemployment were calculated (the eight youth groups mentioned above,

,. cross-classified by weeks of unemployment, comprised the distribution) for both 1969 and 1973: (The same procedure as in Step (1),

wa used in devel4ping the distribution of person-hours of unemNw

plo ent.) These two percent distributions,were then applied to

the o hypothetical level's of unemployment estimated in Step (2).

Once the actual numbers of person-weekSand person-hours-had been

calculated, they were multiplied by the minimum wage, lowest

acceptable wages, and prevailing earnings of youths to arrive at

,the total costs estimates.

'V!
372

0 Subtracting these two sets of e6tal costs from the actual

total costs of youth unemployment in 1976, yielded the costs involved in bringing down youth unemployment to rates experienced

in 1969 and.1976.

L

4 0 ;
4,

- 373 -

TABLE OF CONTENTS

THE MEASUREMENT AND INTERPRETATION OF TEENAGE UNEMPLOYMENT

IN THE UNITED STATES AND OTHER COUNTRIES

Beatrice G. Reubens, Conservation of Human ResourceS, Columbia

University 5 IP

Page

375

WHAT DO TEENAGE UNEMPLOYMENT STATISTICS MEASURE? 39.9

Orley Ashenfelter, Princeton University

YOUTH PARTICIPATION RATES AND THE AVAILABILITY OF JOBS

1417

Francine,D. Blau, Associate Professor of Economics and Labor and

Industrial Relations, University of Illinois at Urbana-Champaign

FAMILY STATUS AND LABOR FORCE PATTRNS

438

Marth S. Hill, Institute of Social Research, the University of

Michigan

EDUCATION, OCCUPATION, AND EARNINGS

473

David O'Shea, School of*Education, ersity of California,

Los Angeles

ALIENATION

David

AND-ADJUSTMENT TO LIMITED1PROSPECTS. 500,

Gottlieb, College of Social Sciences, the University of

Houston

DO YOUTH REALLY WANT TO WORK: A COMPARISON OF THE WORK VALUES

521

AND JOB PERCEPTIONS-0EADUNGER AND OLDER MEN

Patricia Y. Miller and William Simon, Institute for Urban Studies,

The University of Houston 4O3
- 374--

THE RELATIONSHIP BETWEEN YOUTH EMPLOYMENT AND FURTURE EMPLCYABILTTY 548

AND EARNINGS

Wayne Stevenson, Department of Econtimics, The University of Utah

EMPLOYMENT AND EARNING PATTERNS: THE DYNAMICS OF CHANGE 564

. David J. Farber, Labor Economist, ETA, U.S. Department of Labor

Vi

404 if
THE MEASUREMENT AND INTERPRETAT6rOBJEENAGE UNEMPLOYMENT

IN THE UNITED STATES AND OTHER COUNTRIES

ByWBeatrice G. Reubens

ABSTRACT

In comparison with the United.States, other advanced-countries

are less concerned about all-inclusive measurement of unemployment

and more concerned hbout providing programs that aid the vast majority of unemployed teenagers. Because of their recent experience with

full employment, these countries are more resistant than we to accepting high rates of unemployment as normal and less inclined to focus

heavily on disadvantaged groups, although the latter. ire not neglected.

The-age of leaving compulsory school,in other countries usually coincides with finishing a recognized and credentialled stage of education,

avoiding our ambiguous labor market, position of school dropouts who

have completed compulsory education,' European countries also tend to

ignore most unemployment among in-school teenagers during the school

year.,

It is desirable to separate all Statistics for American teenagers

into sixteen to sevenieen and eighteen to nineteen to list in-school

and out-of-school separately within the age gtoups: Pethaps we-should

reconsider the labor force activities of sixteen to seventeen in-school

youth. In any case, unemployed in-school youth and other part-time workersshould be translated into full-time equivalents. The sixteen to

,seventeen out-ofrschool population is in need of a variety of services,

whether or not they are unemployed. Teenagers who are neither in the

labor force nor in school, especially the males nonwhites, require,

more investigation and attention.

Although the labor force survey is a preferr method of collecting

unemployment statistics, countries with fairly complete registration Of

unemployment and active local employment service organizations may be

in as good or better position to keep track of monthly changes in local

unemployment and to recommend and administer youth programs. Whether

such registration statistics could be similarL used in the U.S. is '

questionable, since the facilitating circumsta s are lacking.

INTRODUCTION

This paper considers how the United StRtes measures and reports on

teenage unemployment in relation to the need of policymakers. Policymakers have to decide what amount, types and location of youth unempIoy-

---tefit-warrant public policy action, The question is whether sufficient

information on youth unemployment is easily available so that policymakers can devise-appropriate programs and establish the criteria for

admission to those-programs, given the'prevalling social value s and

4Q)
- 376 -

goals. In order to broaden the perspective, the paper will discuss

the American situation in comparison with the concepts, methods, data

and policies of other countries.

b It is beyond the scope of this pape

data about youth and their families -that

their need for explanations and theories

these are clearly relevant.'

i-to consider the other, rel ted

pylicymakers may'desire or

of youth unemployment, although

An unemployment rate for the, whole labor force serves several functions in national and international life and is an emotionally charged

statistic. :Ile overall rmemployment rate is used as a measure of the

economic and social hardship of individuals; It also provides a gauge

of the underutilizalion of human, resources, of foregone output, and of

the full employment gap. It offers a guide to cyclical and secular

trends in the economy. It is regarded as an indicator of a society's

stability and an economy's soundness. It is cited in comparative assess

ments of different social systems .. It is used as a guide to the establishment of programs and the allocation ofpublit funds amoung

ties.

In t

ment'ra

of t

I United States doubts have arisen t t the overall unemploye or any single measure, such as une lqment, can serve.allJ

ese diversooperprieraiOnce. There is even doubt that, the unemployment rate adequately measures the extent of hardship or the job

needs of individuals.) Moreover, widespread

'reporting ratio

ndi un

prevails that a

measure reporting the incidence, frequency, and dun of spells of

. unemployment would be superior to the crows-sectioemployment rate,

but it has not yet become an official U.S. statistic''. Canadian studies

1 Julius Shishkin, "Employment and Unemployment: the. Doughnut/or the

Hole?" Monthly Labor ReView, vol. 99, (February 1976), pp. 3 -10;

Willard Wirtz and Harold Goldstein, A Criticaltabk at the Measuring

of Work (Washington National Manpower Institute,1.975); Henry WalIla,WWhat ia to be Done?" Challenge, (November/December 1977),

p. 36; Carolyn Shaw Bell, "Biii.FliaTand Economic Policy," Challenge, (November/December 1977), pp. 46-47; Curtis Gilrolc, "Supplemental Measures of Labor ForcefUtilizalion," Monthly LaborReview,

vol. 98 (May 1975), pp. 13-23; Julius Shishkin and jobert L4. Stein,

"Problems in Measuring Unemployment," Monthly Labor Review (August

1975), 37.10; Julius Shishkin, "A New Role for,Economic Indicators,"

Monthly Labor Review, vol. 100 (November 1977), pp. 3-5.

406

a
- 377 -

studies have computed these components of-unemployment by age groups.

One of the tasks of the U.S. Commission on Employment and Unempl,oyment

will be to review measurement methods and to consider whether the don-

- dept of unemployment should be enriched by additional information abOut , the employment/pop ulation ratio, gross arid net flows into.and out of unemployment, type of emplOyment,i'earnings, hours of-work (including involuntary and voluntary part- time), and family si3tus.

2

Some of these issues have also been discussed in other countries and in the international organizations. 3

Teenage unemployment rates serve more restricted purposes. They

are used chiefly to indicate individual hardship, but the emphasis is

somewhat diffekent in the United. States than it is European countries.

American analyAa tend to accept a large part of.teenage unemployment

as a transitory experience and part of the maturation and adjustment

process, offering varied explanations of the fact that American teenage

/ *

2 John E. Bregger, "Establishment of a New Employment Statistics Re-'

view Commission," Monthly Labor Review, vol.100 (March 1977),

pp. 14-20; "Jobless Rate: Elusive Statistic," New York Times, January 13, 1978; M. McIllieen and H. Sims, The Flow Components of Unemployment in Canada, paper presented at Regional Science Association meeting, May 30, 1977; F.T. Denton, C.H. Feaver and A.L. Robb,

"Thetihort Run Dynamics of the Canadian Labor Market" (Ottawa: Economic Council of Canada, 1975).

3. Adrian:Sinfield,. "INgoyed Statistics'on 13nemployment: a First

Step in Preventing PrOlongedablessness and Poverty," OECD Observer,ADecember 1968T, pp. 8-10; Maurice Peston, "Unemployment:

Why We Need a New Measurement," Lloyds Bank Review ;% (April 1972),

pp. 1-7; James J. Hughes, "How Should We Measure Unemployment?"

British Journal of Industrial Relations, vol; 13 (July 1975),

13]? S17-33; John_ BWood,_How MuchAnemployme4Lann4on: The Institute of Economic Affairs, Research Monograph 78; April 1972); Jim

Bourlet and Adrian Bell, Unemployment and Inflation: The Need For

A Trustworthy 10employment Indicator (London: Economic Resear6k

Councils October 973); OECD, Entry of Young People into Working

Life, General Rep rt (Paris: OECD; 1977), pp. 59-69; International

Labor Office, Measurement of Underemployment: Concepts and Methods

(Geneva: I.L.O:, 1966).

407
-378-

unemployment rates have greatly exceeded those'of most European cowl-

* triA in most years of the postwar period. 4

The possibility should be

faced that prolonged exposure to a difficult situation breeds tolerance

and fosters attempts to establish it as normal. It also seems to lead

iff-selective concern. Thud, at a conference on teenage unemployment

ducted by the Congressional Budget Office at the end of 19A, sever- al part pants were saidito believe "that the unemployment problem

among white teenagers is relatively limited, but that there is a very

serious proble' with ,regard to the inner city, particularly minority

teenagers."5 This view i fairly prevalent toy among high policymakers in the

executive branch and occasionally results in declarations that mAly teen-

. age blacks do not find jobs until they reach their twenties. There is/

no hard evidence for this belief. But'there are data suggesting frequent turnover in low-level jobs and intervening periods of unemployment for hjack youth., Yo(7th unemployment data which record the incidence, duration and number of spells of unemployment in a yearor-over

a longer time period are available and should be given equal attention

with unemployment rates. It makes a major difference to policy planning

whether a substantial.group of'minori'ty or other youth remain unemployed

for years at a time or whethqr the prevailing patteln is one of shortlived jobs with considerable turnover in the ranks of the unemployed.

. Most European countries have enjoyed long periods when nei entrants

-had a choice ofgany jobs and few young people experienced unemployment

on changing jobs, even in thelyears when the baby-boom generation entered work in large,numbers. Therefore, interruptions of that pattern

are regarded as abnormal and unacceptable. Several Scandinavian,coun-

,. tries have announced a public goal. to provide each teenager with a job,

4. Beatrice G. Reubens, "Foreign and American Experience with the Youth,

. Transition" in National Commission for Manpower Policy, From School

to Work: -ImOroving the Transition (Washington: Government Printing

Office; 1976), pp. 274-79; U.S. Bureau of Labor Statistics, Interna-,

tionarComparisons of Unemployment (Washington: Government Printing

__--Office, 1978).

5. , ssional Budget Office, The. Teenage Unemployment Problem:, What

tions? (Washington: Government Printing Office, 1977),

p. 69.

4

408
- 379 --- training, or education, leaving none to be unemployed. When a ttention is

given to disadvaztased groups 'among unemployed youth in European coun- . tries, it is almost always within a framework of policies.that cater to

the entire,group. In contrast to our racial-ethnic divisdons in,Ae-siatistics, other countries-tend.to stress socio- economic status as the primary liffere iatdr, considel=ing ethnic variations as amadditipnal dimension. It c n be argued that we have lost something by our emphasis. In A` Europe unemployment isviewed primarily as affecting individuals, not, as

in the United States, as a gilup phenomenon where a high proportYbn of

ack une Toyed is more significant 'than a larger absolute number,of

.wh tes, hen'identical criteria of hardship or need are used.

ropean countries believe that special,ee6orts should be made to , minimize the unemployment of all new entrants to the labor market and

that.young people should have unemployment' rates close to the natipnal

average. There are three strands in the European attitude,towa4d youth

unemployment. First, a period'of unemployment at the outset of one's

working career is viewed as damgging to long-run attitudes, ambitions

and behavior. S onger positions are taken in E ope on the ev1. )S of

.entrance unemplo ent than in the U.S:

6 Secondly, in some countries

the theme of indi;laual hardship and alienation is strongly overlaid

with politicaljears about the reactions of the radical segment of-stu-

, dents who graduate from universities into unemployment.

There is relatively little talk in Europe about youth as Secondary

workers who live with their families and do not need the income. There

is not much excuse-making for youth unemployment in Europe on th e' grounds that job-search tiMeis valuable or that high. ofjob-changing by youth inevitably produce periods of unemployment. However, as

in the U.S., private and public employment policies tend to favor estab-

. lished workers,espeCtilly male heads of households. . Thirdly, the adverse reaction to youth unemployment ih European

countriesalso springs Yaom a general belief that it weakens the economy. Viewing the new entrants as the source of renewal and advance for

the economy and the labor force, European youth unemployment programs

6. E.g., British Youth Council, Youth Unemployment: Causes and Cures,

Report` of a Working Party, London, March 1977, p.l.

a 40.9 /
n

t.4

380

4.

have attempted to compensate for the recession deficits in private sector training Of young people., It is of more t n passing interest, al-

, though beyond the scope of this paper, that t United States is vir-.

tually the only advanced industrial nation without an organized national

approach toward skill training of new entrants, apart from remedial programs.' °Our remedial training, programs for unemployed young people are

concerned with helping the'unemployed to improve their own. position and

livelihood. Because. there is relatively little.pressure for trainin

due to existing or'anticipated skill shortages, American youth unemployL

ment prqgrams can cater heavily to the disadvantaged and attempt to in-

. still skills that suit the capacities of the,trainees. European programs assume that there willbe a skill shortage dnd they try to match

the trainees to those requirements.

With these differences in attitudes and policy initiatives as an

introduction, the discussion calf turn to the'chief differences in the

way countries measure teenage unemployment.

AGE LIMITS AND AGE GROUPS

The most common arrangement is to'count young people as unemployed

from the age when compulsory education ends and the law permits full -

time work to begin. In' most countries these are simultaneous dates, usually fifteen or sixteen, but in Italy the school leaving age hag been

,fourteen and the legal age to start work has been fifteen, leaving a

difficult gap. Most American states provide that compulsory education '

ends and the legal working age begins at sixteen. A cut-off date at

the lowest legal age both for leaving school and beginning wank means

Mk

than those under the legal age who are in the labor market alp omitted'

JToeunemploymentstatistics. This causes aproblem only in countries

111, where observance of the law is lax, as in Italy, or where the laws vary.

in the subdivisions of a federal country, as in the U.S. o; West Germany.

Unlike the United'States, almost all countries coordinate the end

of compulsory education with,the completion of a recognized phase of

the educational sequence. When they raise the age of compulsory education, they reorganize the educational sequences so that credentials can

.w be given to those who leave school at the compulsory age. The American'

arrangements make dropouts of those who leave sdhool at the legal age of

410

iy

,

0.

ro
a

a

a - 381

sixteen, since no recognition is given by society to the completion of

../- junior high school. Other countries award socially acceptable credentials to those who comVete a similar sequence. An'ambiguous and unfavorable labor market situation confronts American out-of-school youth

who are not high school graduates.

A related issue arises having to do with the-differenee between-th

legal school-leaving age and the actual age at which the majority of

youpg people move from school to the full-time labor market. Countries

divide into those like Great Britain; Austria, Switzerland, Australia,

and Germany where most young people leave education at the earliest

legal moment or soon theieifter, and others like Japan, France, the Scandinavian countries, Canada and the United States where a small minority

of the age group leaves education at the legal age: Countries should

present data on the youth labor force and on employment and unemployment using the appropriate divisions to suit their individual,educational circumstances. Many countries_do this but, hecause of the desire

to standardize unemployment data, the international agencies have urged

countries to produce two main youth unemployment rates--for teenagers

and young adults. However, these rates can conceal important diffetences within the age group.

American analysis would benefit by making sharp. distinctions in

all youth/labor markeildata'between sixteen to Seventeen and eighteen

to nineteen among the teenagers, and between twenty to twenty-two and , twenty-three to twenty-four among the young adults. Data grouping sixteen to nineteen year-olds should be avoid d. Distinctions would reflect the fact that the sixteen to sevent en year-Otis usually are not:

high school graduates and that the age g up is overwhelmingly in -

school, in sharp contrast to the eighteen to nineteen-year group.

(Tables l and 2). In the same way,,,_ two-fold division among the

young adults wOuld'cautbre most of those still in college.

In addition to using age breaks that relate to the actual situation

of various teenage subdivisions, other countries pay much more attention

thanwe do to studying separately the annual cohort of new entrants to

the full-time labdr market, dividing them according to the accepted national educational levels. ,Using the entire cohort as the universe, the

411
TABLE 2

PERSONS 18 AND 19'YFARS' OF AG& CIVILIAN NONINSTITUTIONAL POPULATION, PERCENT ENROLLED

IN SCHOOL, LABOR FORCE PARTICIPATION, MD UNEMPLOYNENT, UNITED STATES, OCTOBER OF 1955-

L975.

YePersons 18 and 19-Years of Age

Civilian Noninstitulional

Population

Civilian Labor rorce Labor Force Participation

Rate

Unemployed

Total Percent Total Percent Enrolled Not Enrolled Total Percent

'Aber Enrolled Number Enrolled in in Number Enrolled

(000) in (000) in School School (000) in

School School School

1955 3905 31.5 2455 18.9 37.7 74.4 175 25.1

1956 3978 35.4 2332 20.6 34.2 72.0 137 17.5

'1957 4041 34.9 21106 19.4 33.1 73.7 193 15.5

1958 4158 37.6 2393 21.7 33.2 72.2 298. 13.8

1959' 4353 36.8 2496 21.1 32.9 71.6 321 13.1. 1960 4733 38(4 2716 21.4 -32.0 73.2 367_ 14.2

1961 5119 38.0 2905 21.2 31.6 71.8 398 14.6

1962 .5129 41.8 2821 22.2 29.2 73.5 340 18.5

1963 50143 40.9 2880 23.8 33.3 73.6 396 17.7

1964 5276 41.6 2922 23.5 31.3 72.6 384 16.7

1965 6329c 46.3 3500' 27.7 33.1 74.4 415 26'.3 1966 6724 47.2 3714 30.6 35.8 72.6 374 26.5

1967 6359 47.6 3518 30.6 36.0 72.9 455 27.3

1968 6588 50.3 3633 34:8 38.1 72.4 392 31.4

1969 6679 50.2 3840 35.4 40.5 74.6 402 38.1

1970 6958, 47.8 3982 33.0 39.6 73.4 600 32.2

1971 7231 49.2 4111 34.9 40.3 72.9 599 30.2

1972 7462 46.3 4490 32.0 111.5 76.3 595 _30.8

1973 7648 42.9 4738 29.2 42.1 76.8 547 27.6

1974 7822 43.1 4919 28.9 42.1 -Z8.7 741 24.0

1975 8024 46,.9 4138: 32.0 41.5 7'8.1 857 27.2

1976 8148 46.2 th 5130 32.6 44.4 78.9 854 27.6

Source: U.S. Dept. of Labor, Employment and Trainin Report of the President 1977( Washington, LC.,

U.S. Government Printing Office, ,19 , Table 13 -6, pp. 196, 197, 198, Table B-7, p. 200. U.S

Dept. oft. ja)or, Bureau of Labor Statistics, Students, Graduates and Dropouts in the Labor

Market, October 1976, Special*Labor Force Report 200. 4 1 1 . ,:..,
4 TABLE 3. PERSONS 16 AND 17 YEARS OF AGE:. CIVILIAN NONINSTITUTIONAL POPULATION, PERCENT ENROLLED

IN SCHOOL, LABOR FORCE PARTICIPATION AND UNENPLOYKENT, UNITED STATES, OCTOBER OF 1955-

1975.

tck

Persons 16 and 17 Years of Acre

Civilian Noninstitutional

Population

Civilian Labor Force Labor Force Participation

Rate

Unemployed

Total

Number

Percent

Entolled

in ' School

Total

Number

(000)

Percent

'Enrolled

in

School

Enrolled

in . NotEnrolled

Total

Number

(000)

Percent

Enrolled

-En

School

(000) School in

School

1955 4460 77.4 1677 " 61.2 29.7 64.5 155 38.1

1956 4500 78.4 1751 63.4 31.5 65.9 157 52.2

1957 4647 80.5 1735 68.0 31.5 61.2 164 49.4

1958 5001 80.6 1795 66.5 a 29.6 61.9 247 40.9

1959 5448 82.9 '-.1859 69.6 28..7 60.6 243 50.2

1960 5573 82.6 1940 67.6 28.5 64.7 254 53.9

1961 5437 83.5 1760 68.0 26.3 63.0 266 55.6

1962 5622 84.1 1814 72.4 27.7 56.9 219 61.6

1963 6549 87.1 2138 77.1 _28.9 58.0 329 60.2

1964 7050 87.7 2195 78.2 27.8 55.1 300 68.3

1965 6925 87.4 2451 78.1 31,6 61.5 308 66.9

1966 6922 88.5 2469 81.6 32.9 57.1 286 63.9

1967 7051 88.8 2610 83.0 34.6 56.3 373 74.7

1968 7266 90.2 2609 85.6 34.1 52.7 338 76.6

1969 7481 89.7 2953 84.7 37.3 58.8 435 78.6

1970 7099 90.0 2944 85.2 36.2 56.3 532 TO.5

1971 7871 90.2 2983 85.4 35.8 56.8 562 78.8

1972 8065 88.9 3179 83.2 36.9 59.6 551 76.6

1973 8195 88.3 3571 83.4 A1.2 61.8 573 79.1

1974 8298 87.9 3670 112.1 41.3 65.1 659 76.8

1975 8313 89.0 3529 84.6 40.3 59.5 744 73.3

1976 8303 89.1. 3490 85.0 .40.1 58.0 713 77.8,

Source: Report of the President 1977( Washington,

Tablell- 6, pp. 196, 197, 198, Table 8-7,

Labor Statistics, Students, Graduates and

Special Labor Force Report 200.

U.S. Dept. of Labor,'Eployment and

D.C., U.S. Government Printing Office, 1977

p. 200; U.S. Department of Labor, Bureau o

Dropouts in the Labor Markel, October 1976,

413
384 -

statistics present information on the length of time it t

, . to find first jobs and identifies the sizable number w1r

entrance unemployment because their jobgibave been prearr

concept of new entrants is recognized e statistics o

Japan, France, and Italy, among others. is treatment s

kes each group

xperience no

nged.

7

The

Great Britain,

ould,be distinguished from the U.S. approach to entrance and reentr ce unemployment. Our universe is the totality of the unemployed an entry and reentry are of interest as reasons for being unemployed. oreover, the

usual presentation of this information does not separate young people

in the labor force who are stall at school from others, = difficulty in

out overall treatment of teenage unemployment which is discussed below.

It would improve American analysis of the teenage abor market and

of unemployment specifically if both our cross-sectiona data and longitudinal studies introduced the category of "new out-of school entrants

to the labor market" as a prime object.for data collect on and interpretation. We would use the following educational divisio s: less than

high school graduate, high school graduate, junior college graduate,

less than four-year college and four-year college. The last category

would be very small if the analysis is confined to teenagers. If it ex- ,

tends to all under twenty-five years of age, a further category could

be added of "post college." Allowance might also be made for other

types of training and education, such as a post-high school secretarial

or technical course that precedes entrance to the full-time labor market.

IN SCHOOL AND IN THE LABOR FORCE

Probably the most confusing information, about the total amount of

teenage unemployment in the United States, compared with other countries,

arises from statistics that do not distinguish4between teenagers, expecially the sixteen to seventeen year-olds, who are still in school and

seek part-time jobs during the school year, and those who are out of

school. Whatever merit there is in counting students"as unemployed if

they fail to obtain jobs over the long summer vacation, there is,a serious question whether their school-time unemployment should receive the

7. Beatrice G. Reubens,-Bridges to Work: International Comparisons

of Transition Services (Montclair, N.J.: Allenheld, Osmun 4 Co.,

1977), pp. 163-68..

414
-\385 -

same weight as is given to young unemployed people in the full -time labor market. Our practide distorts the actual size and nature of our

teenage unemployment problem, leading to an overstatement'of labor mar-

, ket entrances and exits and often to confused policy initiatives. It

also exaggerates our problem in comparison with that of other countries

where'a lower proportion of the age group is in school in most cases,

fewer of those"who are in schcibl-seek work during the school year-or in

short vacao.Oni, and those who are in school and seek part-time jobs unsuccessfully often do-not appear in the_unemplOyment statistics.

it consideration of the U.S. in- and out-of-school teenage labor.

fo ,rce, divided into the sixteen to seventeen and eighteen to nineteen

year-olds, is in or494*. As Table 1 indicates for October, a time of

full school schedules, s far back as 1955 over three-fourths of the

sixteen to seventeen-year opulation was enrolled in school. Over the

years, the'increased size o

was matched .by rising propo

r_ around SY-90% since 1968.

the sixteen to seventeen year populations

rt'on enrolled in school, settling in-at

Gro ing slightly faster than population, the

total,civilian-labor force of Sixteen to seventeen-year olds as far back

as -1955 consisted largely of th'bse enrolled in school and since 1967

that proportion has risen almost without ipterruption from around 60%

to 83-85%. The labor force participation rates of the enrolled have

I risen from around 3.0% to over 40% while the nonenrolled minority has

shown a small decline which. is i3articuiarly conspicuous among the males.

Enrolled females have moved up [from a 21.4% participation rate in 1955

to 38 -39.% in recent years, whille nonenrolled-have s bwn no distinct

trend. : I k The total number of unemployed sixteen to seventee year-olds has

risen over the years at a more rapid rate than their tota labor force

or population. The division of the unemployment.between th enrolled

and nonenrolled has alte ed in the direction of increasing th share-of

the enrolled from 38.1% in 1955 to 77.8% in 1976, but the enrolled share

of unemployment has-been con ii%ntly.loWer thantheir share of floe sixteen to seventeen year-old lab r force. The trend for-males and females

is much the same, but enrolled males have had a point or two larger

ce. share of'the total unemployient than enrolled males'. Viewed in terms
- 386

of unemployment rates, both the enrolled and the nonenrolledzhave had , marked increases. Enrolld,litale unemployment rates have risen from

6.2% in (1955 to a historical high of 19.6% in 1976, while nonenrolled

males inCreased from 18.4% in 1955 to the all-time high of 35.7% in -. 1976. For enrolled females, the advance from 4.8% in 1955 to 19.2% in

1976 and for nonenrolled females the climb was from 8.5% in 1955 to

agaia historical high points. These data also show the

persistent disadvantage of out-of-school youth.

8

Turning to the eighteen to nineteen year-olds, one finds, as

might be expected, that their total population is much more firmly out

of school than the sixteen to, seventeen year-olds (Table 2). Although

a general trend toward a rising proportion of the:age group in school

_.. can be observed from 1955 through 1976, the peak of just over 50% was

reached in 1968 and 1969, largely due to male enrollments because of

Vietnam draft threats. Thereafter the in-school proportion dropped

through 1973 and then rose_somewhat to around 46-47% in 1975 and 1976.

Male school entollment rates run considerably higher than female in this

age group, unlike the younger cohort where they are very nearly the same.

Among the eighteen-to nineteen year-old males, the1955 rate was 42.5%

.while the female rate was 22.5%; but by 1975 the female rate of 44.2%

was only 5% behind the male 1975 enrollment rate.

The eighteen to nineteen'year-old labor force is a highe roportion of.the total age group and more heavily out-of-school than is the

sixteen to seventeen year labor force. Although the trend has-been for

an increasing share of the eighteen to nineteen labor force to be inschool, the highest percentage reached was only 35.4% in 1969 and it was

as low as 18.9% in 1955. Labor force participation rates for the ghteen to nineteen age group show that out-of-school youth were almost

twice as-likely to be in the labor force as those in-school. The upward

trend in participation rates for the enrolled was somewhat more marked

8. Employment and Training Report of the President (Washington: Government Printing Office, 19771-, Table B-7; U.S. Bureau of Labor Statistics, Students, Graduates and Dropouts in the Labor Market-i-October

1970, Special Labor Force Report 200, Table A; U.S. Bureau of Labor

Statistics, Work Experience of the Population in 1976, Special Labor

Force Report 201, Table 813..

: 4 6
-.387 -

than for the nonenrol,larktut neither showed the striking rise that the

sixteen to seventeen enrolled did. In contrast to the level or declihing trends among eighteen to nineteen year-old males, females of the same

age, in and out of school, have exhiVited the same soaring partiCipation . rates aS have older age cohorts of women in recent years.

Likethe sixteen to seventeen year-olds, the enrolled eighteen yearolds had a smaller share of the unemployment of their age group thairof

the labor force in almost all years. From one-fourth to 30% of unpin-

. ployment was accounted for by the enrolled from 1965 on. For enrolled

males there were seven years when their share of unemployment etceeded

. . their share of the labor force. The eighteen to nineteen unemployment

rates for enrolled and nonenrolled are much closer to one another than

are the rates for the sixteen to seventeen year-olds.

It is clear that the eighteen to nineteen year-old group differs,

substantially from the sixteen to seventeen in the importance of school

as the major activity. To treat the age groups together or to ignore

enrollment status, especially in computing employment/population ratios

and similar indicators, is to miss important variations which are as

marked for black as for white youth. All of the foregoincalculations

are based on the Current Population Survey and its special questions in.

the October interview which, according to BLS officials, provide a mores

reliable guide to enrollment status than data for other months. The

paper at this conference by Borus, Mott and Nestel compares NLS data

with CPS data for other months than October and uses different breakdowns of the.data. Their conclusions therefre- may not be fully applicable tb the data presented here-.It_krourd be useful if NLS data could

be4tompared with CPS October data along the age and enrollment breaks

used in this paper..

There are relatively few studies in other countries of students

in the labor force during the school years and even less on their unem-,

ployment as distinguished from that of other young people. 'Most information does not separate teenagers from young adplts. An OECD study of

part-time employment noted-that-in-European-countries students seeking

part-time yobs during the school year were the exception rather than the

rule, 'Noting the prevalence of this kind of work among American Stu417
388 -

dents, the study observed that students_elsewhere took up. casual jobs

since part-time work was generally not adapted to their requirements. 9

anada probably comes closest to the United States in the proportion

of tudents who seek jobs during the school year. In its revised labor.

.fore survey which has produced monthly data for 1975 onward, Canada now

has a more accurate and comparable record of the labor force and unemploymeDt rates of students. However, Canadian teenagers cannot be separated out from the age group fifteen years and over. As Table 3 shows'

for October 1976 when schOols were in session, American students are

more prone to be in the labor force than Canadian and also have a higher

unemployment rate. Information also is available from the same sources as Table 3 9n the proportions of students who were employed.full-time

and part-time in October 1976:

Males

Full-time

Canada 49.0

41.0

Employed Students

Females

Part-time Part-time

Percent

51.0 -38.0 62.0

59:0 29.2 70.8

Canadian students seem slightly more likely to work full-time than

American, but differences in the age and educational composition of ale,

groups in the two countries may be chiefly responsible. Canadian analysts of the flow components of unemployment found that out-of-school

teenagers on average became unemployed more frequently and-had a longer.

duration of unemployment, but had about the same number of spells of idleness as the.total teenage labor, force. For the twenty to twenty-four

year group, enrollment status had little effect on the components of un-.

e lo ent . 10

SOme information is available for Japan from the triennial Em oy- ,menttatus Survey which regularly presents information on th9se wh are at work but`whose main activity is sChool. Only about 50,000 to 6 ,000

9. Jean Hallaire, Part-time Employment: Its Extent and Its Problems

(Paris: OECD, 1968), p. 34.

10. McIlveen and Sims, op. cit.,

4 1 8

4,
- 389 -

. TABLE 3

STUDENTS IN THE LABOR FORCE, CANADA AND THE UNITED STATES.

OCTOBER, 1976

Students Labor Force 4 .Total In ,Participation Unemployment

Student labor --Rate of Rate of

Pppulation Force Students Students

tooa) Percent;'

Canada. (15

years & over) 2,335 892 38.2 6.5

4 United States

(16-34) 18,130 9,033 49.8. 12.2

Source: Statistics Canada, Labour Force Survey Division, Labour Force

Activities and Characteristics of Students, Research Paper

no. 14, Ottawa, July 1977, Table 1; U.S. Bureau of Labor

statistics, Students, Graduates and Dropouts in the Labor Market,

October 1976, Special Labor Force Report 200, Waghington, 1977,

Table A.

. s
- 390 -

are in this category, constitutin less than 1% of employed persons aged

fifteen to twenty-four. 11

S e of the reasons why few students in other

countries seek jobs when classes are in session are suggestive 'for American education and eMploymetnt policy. The burden of studies is so heavy,

even in upper secondary school, that relatively few young people have

much time for p4id employment. In countries where government grants or

loans"are paid to students, with or without a family income means test,

paid work may be,7forbidden during the school period. With rising costs

of living due to inflation and the fdilure of grants and loans to keep

pace, more students are finding it necessary to supplement theif other

sources of income. But the pressure to work is reduced by the government study allowances as. well as the policy of continuing childrens''

allowances and offering tax deductions to fdMilies in which young people

remain in school beyond the compulsory stage. Work is more common during the summers and over long vacations and some countries capture this

unemployment.

Some countries say that they monitor'student unemployment during

n

the schoollear in their labor force surveys, but they publish . no separate information on the category and it is likely that many students

are not counted. 12

Survey questions must be worded very carefully in

order to capture the full school component. Until Canada introduced

its new }survey questions, it had been undercounting students in the

labor force. A different situation obtains in countries that rely en-

. tirely or largely on registrations at the employment service offices for

their unemployment statistics. Students are not accepted as registrants

because they are not available for full-time work; in many cases the offices do not register those wishing only part-time work. However, a

number of students may get into the unemployment count ig,they are required to register in order to qualify for benefits other than unemploy11. Japan, Office of the Prime Minister, Bureau of Statistics, Employment Status Survey, Tokyo, triennial; U.S. Bureau of Labor Statistics, Students in the Labor Force: An International Comparison for . Major Countries, unpublished, p. 3.

12. _U-SBureau of Labor_Statistics, Internat onal Comparisons of Unemployment, loc. cit., pp. 13-17, 21-22; U. Bureau of Labor Statistics, Students in the Labor Force: An Int rnatienal .Cbmparison for

Major Countries; Reubens, "Foreign and Ame ican Experience with the

Youth Transition," loc4Qty, pp. 277-80.
- 391 -

went insurance. In Great Britain this situation developed in relation

to claims for supplementary benefit, 2 kind of welfare payment, which

students applied for over short vacation periods when they did not find

jobs. At the- beginning of 1976 over 120,000 students above the age of

eighteen were registered as unemployed and the authorities decided that

they confused and distorted the statistics and would henceforth be excluded from the.unemplo nt count becausethey were not available for

permanent jobs. The re 'ons in regard to study grants also were

changed, covering vacatio periods so that applications for supplementary

benefit during the school year would not be made, although' those unemployed during thesummers could apply.

13

Some countries have auxiliary categories of unemployed which can

include those who would like to work but whose requirem.e.ats as to hours,

place and type of work may not be consonant with existing job vacancies.

This is not quite the same as our "discouraged unemployed." The Swedish

concept of "latent likployment" can include students who say they would

wok while studying, if all conditions met their requirements. But they

have not necessarily been searching for work and are not discouraged unemployed. In French labor force surveys there is a marginal category of

jobseekers which can include students during the school year.

Our knowledge about thel.interrelationships between attending school

and working at the same time is quite incomplete and requires further

study among American to agers.

14 Two surveys conducted by educational

researchers have som hat different findings than those of labor market

researchers, including papers presented at this conference. The educational researchers find lower propo'rtions at'work than do the CPS or NLS

surveys and those with jobs seem to work fewer hours, according to the

educational surveys. The latter alsb indicate that work is undertake

for the earnings and with little thought to the type of job or its relation to future careers. Some studies find little benefit from working

during the school years, either in regard to work-attitudps and know-

, 'lege or the jobs later obtained. Analysis of attrition from college

13. House of Commons, Hansard, February 23, 1976.'

14. Beatrice G. Reubens, Preparation for Work: A,Cross-Country Anal -

ysis (Montclair, N.J.: Alleneld & Osmun, forthcoming).

421
- 392 -

after the first year shows that those in full -time employment were twice

as prone to-withdraw from education as those working part-time ol.rnot at

all.- The effect on college grades of working more thah fifteeehours a , week has bile found to be adverse. In a survey of high school-students,

those working over fifteen hours a week had below average grades and

doubts about finishing high school. The most common positive finding

"ha's been that those who have worked while at school find their first outof-school job more quickly than others.

The following conclusions may be drawn from a consideration of the

American teenage unemployed who are enrolled in school and seek jobs during the school year: 15

(1) All unemployment data should clearly separate in-school from

out-of-school unemployed, preferably by narrower age bands than are

now commonly Used.

(2) If the in-school unemployed are to be included in an overall

15. Anita-M. Mitchell, Career Development Needs of Seventeen Year Olds:

How to Improve Career Development Programs (Washington: National

Advisory Council for Career Education, September 1977), Table 9;

Measurement and Research Center, Purdue University, Vocational

Plans and Preferences of Adolescents, Report .of Poll No. 94 of the

Purdue Opinion Panel (Lafayette, Ind., May 1972), p. 3a; Measurement and Research Center, Purdue' University, Sources of Information for Career Decisions, Report of Poll No. 98 of the Purdue

Opinion Panel (Lafayette, Ind. June 1973), pp. lla, 15a; National

Center for Edyatiop Statistics; National Longitudinal Study of

the High School Class of 1972,, Attiition from College: The Class

of 1972 Two and One-Half Years After High School Graduation (Washington: U.S. Department of Health, Education and Welfare, 1977);

p. 6; Stephen_J.,Carroll, Part-time .Experience and the Transition

from School to Work (Santa Monica: Rand Corporation, 1970); J.E.

Hay, Keith Evans, and C.A. Lindsay, "Student Part-Time Jobs: Relevant or Non-relevant," Vocational Guidance Quarterly, (Detember

1973); Jerome Johnston and Jerald Bachman, The Transition from High

School to Work:, The Work Attituddan&Earry Occupational Experiences of toung Men (Ann Arbor, University of Michigan Institute

for Social Research, 1973), pp. 81-85; U.S. Bureau of Labor Statistics, Out of School Youth, February 1963, Part II, Special.Labor Force Report No. 47 (Washington, 1965), pp. 1418-19; A.P. Garbin, J.J. Salomone, D.P. Jackson, J.A..Ballweg, Worker Adjustment:

Problems of Youth in Transition from High School to Work (Columbus:

Ohio State University, Center o Vocational and Technical EducatiamT-11M),-pp. 47-5 , . . . . Meyer,,Career Thresholds (Columbus. Ohio State University Center

for Human Resource Research, Oct. 1970), vol.. 2, p. 79.

4 22

ti
I

-w -.393 , 7

4 Unemployment rate, the total numbers should be reduced to full-time equio valents.

(3) special survey shoul be undertaken through an October CPS

which asks detailed questions about the number of hours worked in the

reference week, the precise type of job held, the actual earnings, the

family income, the amount and methods-ef job search, the uses of earn-:

ings, and the relation between working and academic performancexand ambitions. The.scattered information available on these subjects suggests

that this group might be better served by related education, training

and income programs, freeing them from the. eed to work and transferring some of their jobs to older teenagers.

(4) Because of deficiencies in basic and occupational skill. and

low educational attainment, altost all of the sixteen to seventeen ° year-old, out-of-school population presents a series of labor marktt,

social, educational' and personal problems, not just an unemployment problem. Nuibering over 800,000 andMth more than half a millioir in the

labor force, disproportionately black but mostly white, they require a

total approach, ju.i.t as older members of the lab*. force who entered at

Sixteen to seventeen or younger are'the most likely candidates for remedial programs.

NOT IN SCHOOL AND NOT IN THE LABOR FORCE

Teenagers who are not enrolled in _full-time school and are not employed or unemployed have lonk been a concern in the United Stated.

Data on this group, presented in Table 4,'Nust be treated carefully. On

the*ne hand, there isno allowance for young people whose legitimate

activities, such as keeping house, illness, or attendance at various

kinds of training courses, account for their failure to appear in, one of

o the two major categories. Moreover, some overstatement of the total resalts from inclusion of the summer months during which.those on vacation

from school and not working are reco"ed as neither in school nor in the

labor force. On the other hand, surveys such as7the Current Population

Survey may undercount the young people who are neither in school nor in

the labor force. Low income youth, especially minority,Vuth in central

'cities,-are particularly likely to be missed. Among them are some who

O

42:i

- V

cg'
41

TABLE 4

to TEENAGERS NOT IN SCHOOL AND NOT IN T 80R FORCE AS A

PERCENTAGeOF THEIR CIVILIAN NONINSTUT DAL POPULATION,

UNITED STATES, 1962-1976. .41 Sex

Age

Race

Year

: Males Females

'16-17 18-19 16-17

White Nonwhite White Nonwhite White Nonwhite . Percent '

18-19

White Nonwhite

1962 10.0 12.1 5.8 6.5 20.4 26.8 y 25.0 35.2

1963 10.1 11.6 5.4 7.0. ... 19.2 25.4 25.8 34.0

1964 10.1 12.1 5.8 7.9 18.8 23.5 25.7 33.7 ..^77 1965 9.0

''''-"--, 10.7 5.2' 7.1 18.1 21.4 23.8 33.5

1966 9.0 9.9 5.3 7.3 17.3 21.2 , 22.3- 29.7

1967 8:11 -8.9 5.3 7.6 16.9 22.0 21,0 26.8

1968 8.31 11.0 6.0 8.9 016.4 20.2 20.7 29.1

1969 8.5 9.7 5.7 9.6 16.6 19.2' 19.8 29.6

1970 8.7 12.4 6.5 11.12 1'6.1 18.6 21.1 28.8

1971 7.9 12.3' 6.5 10.4 -15.7 19.1 20.2 28.3

1972 9.1 9.8 6.0 9.8- 15.4 18.6 19.9 27.9

1973 8.2 12.3 5.8 10.3 15.8 9.3 19.9 28.2

1974 8.8 12.2 6.0 10.2 15:0 18..8 19.5 29.6

1975 8.6 13.4 5.7 11.1 14.9 19.9 18.7 26.9

1976 8 12.8 6.0 13.0 14.5 18.0 17.6 26.1

P

Nib Source: Annual.averages, tabblated froM Current Population Survey data

by the U.S, Burdau of Labor Statistics.- 424 O

a
395

Noo

participate in the "subterranean" economy or engage in illicit activi-

, ties. If they a counted as unemployed, that figure may be inflated.

Yet it is equa ly mi leading to view them as having no occupation or income. a Table 4 shows that females in both age groups and of all races have

had a marked decline in the proportion of the age group th'at is recor6ed

as not in school or in the labor force. Furthermore, the spread between

the age groups and races has narrowed over time. These trends are part

of the phenomenal rise in female labor force, participation rates. Males

reveal little trend, except that eighteen to nineteen year-old nonwhites

.have had a distinct rise over the per'od 1962-1976. Certain stabilities

are apparent. Nonwhites show higher rcentages than whites and.female

proportions exceed male, as might be e pected because of the sex division of housekeeping and childrearing functions. Among males the older

age group has generally had the lower percentages, but for females it

has been the reverse.

In other countries this category of teenagers hastaroused less at-

, tention, although France and Italy have referred to such young people

as problem groups, Census data inmost countries produces a statistical

category that appears to.be neither in the labor force nor in school,'

but it seems a smaller. proportion in most countries than the American

percentage. An experimental program in Sweden to contact all such teenagers through joint operations by the employment service, schoaas,vand

social agencies revealed that the numbers were not as large as had been

feared and that most young people were responsive to the outreach efforts.

16 The issue may be not so much to count this category as to de-

, cide what can be done for them and who should do it.

. , . SOURCES OF INFORMATION ON TEENAGE UNEMPLOYMENT

Countries obtain their basic unemployment statistics either from a

labor; force surve> at intervals,,ranging from monthly to annually, or

from the monthly registration;of the unemployed in'connection'with

16. Sweden, National Labour Market Board, Yocational...Guidance Division,

Unemployment among Young People in Sweden --Measures and Experience

(Stockholm, 1977), App. 5. .

O
396 -

ingfor financial benefits, establishing eligibility for.programs, or seeking assistance in finding jobs. However good the cross- sectional

unemployment data of a country are from a fabor force-survey or registration statistics, it is desirable to supplement these with longitudinal

studies and analyses of flowkinto and out of unemployment. The labor

forte survey generally is Considered superior to registration data in

generating compreh siveAmemployment data for the whole labor force.

Seeking greater u iformity in the methods of individual countries,-the

international agen ies have sponsored the labor force survey as a supplement to or replacement for registration statistics. But at present

only a few countries conduct a monthly labor force survey and use it ex- clusively to obtain official unemployment. data. Sweden has the best of

both worlds, having a monthly labor force _survey and monthly registration statistics of good quality and wide coverage.

Whatever merit the labor force survey may have for overall "unemployment data and international comparisons, a persuasive case can be

made that, under favoitle circumstances, countries using registration

statistics can gti a good understanding of month to month changes in

teenage unemployment and can recommend and carry out programs at the

local level. They often can do this better than countries that rely

on frequent, comprehensive labor force surveys. The United States, h

ever, is not in this position, nor is it likely to be in the near future.

To give satisidctory service, registration statistics must contain L.

a reasonable proportion of'the teenage unemployed, especially of the new

entrants who have never worked before. 17

Financial incentives make this

group register in a number of countries. In northern European countries

where*the employment service-is strong and there- is-a-- separate -youth

division or special officers, or, as in Great Britain, which has 'a specialized Careers Service for young people below the university level,

. The Unregistered Unemployed in Great Britain," Department of Employment Gazette; December 1976, pp. 1331-36; Guy Standing, "The Distributionof Concealed Unemployment in Great Britain," British Journal

of Industrial Relations,-vol--1XL(.Iuly_1972), pp-291-99; Christian

Brinkmann and Karen Schober- Gottwald, "On the occupational Reintegration of the Unemployed during the 1974/75 Recession," Mitteilungen

and der Arbeitsmarkt--und Berufsforschung,110. 2,1'1976, pp. 95e97.

.6. 4.26
a

there is an intimate, grass roots acquaintance with the local dimensions

lbof youth unemployment and employment that compensates for some underregistration.

These local offices also have a'good notion about the young people

who are registered as unethpIbyed for various reasons but are not sink.. cerely,seeking,work. They are able to mount special outreach programs

% or concentrate activities on particular groups, based on the close obskvation of the flow through their officeg. Close contact even emboldens some of them to!declare, as a Swedish report does, that substantial proportionsaof unemployed young people suffer from such work disabilities as alcohol or drug addiction, criminal records or detention

_histories, and other social problems which are particularly prominent

in metropolitan areas. Facing the actual circumstances of the registrants, the agencies place as much emphasis onechanging the characteristics of these young people as on finding jobs for them.

18 The sociey

is not held entirely totblame, as tends to be the case when American

national data are interpreted and prograths are established for youth.

ympa y or unwi in yers and an expectation that the-young-people will be rehabilitated or reformed is far more common when programs

arise from the direct experience of employment service personnel with

youth.

Registration statistics seem to serve some countries quite well as

a barometer of changes in unemployment to which policymakel.s should pay

attention. Changes in the numbers of registrants, the duration of their

unemployment and the composition of 'the registrants are observed by local officials.-`f the employment service is so organized that it has

the chief responsibility for youth unemployment programs, its proximity

to the dae-ta collection_is_an_advantageAs_a result, Britain has decided not to conduct a' frequent household survey°.

In contrast to other countries, the United States has lacked adequate geographic unemployment data. These are now urgently needed be18. Sweden, ,National Labour Market Board, op. city

V

eV-
cause program allocations hinge on local unemployment-rates.19 We do

not produce the equivalent of the cumulation of loca office youth unemployment statistics into disttiet and regional totals that is achieved

by the Careers Service in each British Local Education Authority, the

Swedish County Labor Boards, the German Arbeitsamt or the Japanese PESO.

But more important than the co lection of statistics from the

groundup is the ability of the sam agencies to participate in the con-

. struction of programs and their execution. Even with the best will in

the world and adequate resources, policymakers will face difficult decisions about the time pe'rspectile of their policies,' the division of

programs and funds between general unemployment programs and special pro-

' grams for teenagers, the amount of attention and program dollars to con -

centxate on disadvantaged teenagers out of the total allocated to teen-

.. agers, and the part of teenage unemployment that should be left untouchdd.

by public policy. The answers must come out of the experience and val-.

A ues of each country, based on adequate information. But we'should alWays be aware that good information is a necessary condition of good

policymaking, although it definitely is not a sufficient condition.

19. Janet L. Nortood, "Reshaping a Statistical Program to Meet Legislative Prio ities," Monthly LaborReview, vol. 100 (November 1977)

pip-6,11; Martin_Ziegler, uFffnrts to Improve Estimates of State

and Local Unemployment," Monthly Labor Reviewvol. 100 (November

977), pp. 12-18.

42.8
- 399 -

WHAT DO TEENAGE UNEMPLOYMENT STATISTICS MEASURE?

By: Orley Ashenfelter

ABSTRACT

In the theory of labor markets, employment and labor supply are ' the unambiguous concepts thatprovide the focus for analysis and unemployment is merely the difference between them. In our labor force

statistics, on the other hand, employment and unemployment are the -focus

for measurement and labor supply, is merely the sum of them. Ash result of this measurement scheme the question of whether measured unemployment is really a genuine part of the offer to sell labor is never

raised because the set-up of the statistics has inlicitly answered it.

Meanwhile, the measured extent of teenage unemploymbnt has continued

, to increase throughout the postwar period and thii has led to a flurry

of resea7chactivity geareeto find an explanation for this phenomenon.

At the same time there have begun to be doubts about what the teenage

-unemployment statistics actually measure and serious questions about

whether they measure a phenomenon comparable to that for adult workers.

In this paper I set out a simple consistency check on the extent

,to which measured teen e unemployment behaves as if.it were part of

the offer to sell labor. I. also apply this consistency test to the

une*loyment' data for adul workers where it passes with flying colors.

As it turns out, the measur unemployment data for some teenage groups

does not seem to pass the same est. Tbis-neeessarily raises some diffictilt questions about the actual economic content of these statistics,

and suggests an important agenda for future research.

The basic idea behind these consistency tests is that if labor

- supply were a known quantity and if unemployment were measured without

error there would be a one-to-one negative relationship between employment and unemployment for any particular group. Of course, the labor

supply of any group is unobservable, but if two groups offered the

same quantity of labor to the market then there would still be an observable one -to -one negative relationship between the differences in

employment and unemployment for the two groups. I implement this

scheme empirically for male and female fourteen to fifteen, sixteen

to seventeen, eighteen to nineteen, twenty to twenty-four and thirty- ' five, to forty-four year-olds by assuming that the unobserired labor supply of blacks and whites is identical apart from a constant and'steady

trend. Although there are a number of econometric-difficulties associated with this. procedure, the res4ts suggest that for males eighteen

and over, and for females twenty,and'over there is a clear negatiye relationshipsbetween employment and unemployment differences, but that

for younger groups there is not. This suggests that considerable re =,

search and experimentation with the measurement of teenage unemployment should be undertaken before these statistics are taken.at face

value.

'

)

420
- 400 -

INTRODUCTION

In the theory of labor markets employment and labor supply are the

unambiguous concepts that provide the focus for analysis and unemployment is merely the difference between them. In our labor force statistics, on the other hand, employment and unemployment are the focus for

measurement and labor supply is merely the sum of them. As a result

of this measurement scheme the question of whether measured unemployment is really a genuine part of the offer to sell labor is never

raised because the set-up of the statistics has implicity answered it.

Meanwhile, the measured extent of teenage unemployment has continued

tot...increase throughout the postwar period and this has led to a flurry

of research tivity geared toifind an explanation for this phenomenon.

At the same time-there have 3b ,gun to be doubts about what the teenage

unemployment statistics actually measure and serious questions about

whether they measure a phenomenon comparable to that for adult workers.

In this paper I set out a simple consistency check on the extent

to which measured teenage unemployment behaves as if it were a part of

the offer to sell labor.* I also apply this consistency test to the

unemployment data for adult workers where it passes with flying colors.

As it turns out, the measured unemployment data for some teenage

groups does not seem to pass the same test. This necessarily raises

some difficult questions about the actual economic content ok these

statistics and suggests an important agenda for future research.

It is obviously important to get this issue clarified because

quite a lot will be at stake in this area-in future years. First,

several'new programs designed to affect thelabcm market status of

teenagers are being developed and if these are to be evaluated in

terms of their-impact on teenage-unemployment-statist-its-we must-know \

just what to expect. .Second, the continuing process of analyzing the

*Following the 'usual:dia.= that a graduate student who writes your

paper receives no acknowledgement, while a graduate student who does

most of the work on it receives a footnote reference, I am tempted to

ignore the substantial assistance I have received from David Bloom in

the preparation of this paper.

# , 430
a - 401

usefulness of the overall unemployment rate as economic and social

indicator necessarily requires the integration of discussions of the

usefulness of the teenage unemployment statistics Finally, decisions

about the size and allocation of resources devote to labor market programs for youth must inevitably be guided to some xtent by the state

of the labor market for young people and it is imp rtant to know

whether the current unemployment Measurement device for these-groups

is sufficient for this task.

The plan of the paper is as follows: The first section sets out

some well known facts about the change, in the employment of both black

enty-years. The second section

contains the conceptual discussion of a simple test for the consistency, of the observed movements in employment and unemployment 'of

,/

teenagers, while the third and fourth sections report on some initial

empirical tests and modifications of these tests.to cope with various

econometric problems. Concluding remarks and issues for further research are contained in a final section.

e/

SOME FACTS

Most of the basic facts about teenage employment are contained in

Figure 1. As a benchmark, it may be seen that apart from a 'small

drift upward, adult employment has remained at around 60% of adult pop-

. ulation throughout most o t ast two decades. Though more erratic

and at a lower level; the emp t/population ratio of white male

teenagers (fourteen-to nineteen-years old) has.followed a similar pat-

, - 2

tern. Employment/populatio-ratios for white females, on thp other

hand, have drifted continuously upward in'a qualitative_ pattern much

the same as that for white female adults. For.black youngsters, how- -

'ever, both the employment/population-ratios of males and males have

been trending steeply doWn for the last two decades. It i -this latter, largely unexplained phenomenon, that has suggested a cause-for-`

alarm.
co

70.0

60.0

a.z

FIGURE 1

EMPLOY/ENT/POPULATION RATIOS FOR ADULTS'AND SELECTED

CATEGIORItS-15F TEENAGERS, ANNUAL AVERAGES, 1954-1977

40N0

30,0

20.0

10.0

WHITE MALES

... .

... . r '

.k. .

. .. . \ **

. \

. \ ? .. \ -.. ,./ .....e...... ....

./..e.. N... - .7 . ?--C......................". WHITE FEMALES"

.... .. ''' / ." ' .... ,, . .. 0..0 MM.

%. ....

\

r

KEY

E/P-RATIO-FOR- ALL INDIVIDUALS

OVER 40 YEARS

OLD

E/P -RATIO -FOR

14-19 YEAR OLD

WHITE MALES E/P RATIO FOR

14-19 YEAR OLD

BLACK MALES

BLACK MALES -

AirBLACK FEMALES

E/P RATIO'fOR

14-19 YEAR'OLD WHITE FEMALES

E/P RATIO FOR

14 -19 YEAR OLD BLACK FEMALES 0.0

54 SS 56 57 58 59 60 61 62 63 64 gis 66 e7 4 69 70 71 74 75 76 77 YEAR (19__)

4 32 t4
Table 1 contains a more systematic elaboration of these patterns.

The coefficients_ in this table give the annual average trend in the

'employment/population ratio_of the indicated group while holding con-

. stant the employment/population ratio of,all adults aged thirty-five

to forty-four. Thus, these coefficients may be thougheof as the trends

in teenage.employment/population ratios relative to those of adults i

their prime working years. Thisetable indicates the same basic facts

as Figure 1 and establishes an additional o irgt; there

no discernible trend in the relative e ofmene/population atios of

. v. whites/aged fourtewthrough s enteen in the last two aec al-

. though there has been some se ular decline for white male eighteenandnineteen-year-olds. Sec nd, there has been a substantial decline

in the.relative employment/pepUiCon ratio of all black teenagers

and'this decline has' beencespeciilli sharp for black males. Finally,

Table 1 also indicates that the decline of the relative employment/pop-

, Illation ratio for black males extends to the twenty- to twenty-four-

. year-old group ,as well.

In the-fag * n

"g;. , ratio of black-teenagerstit Would hardly be surprising to find that

the unemployment of this group had increased considerably relative,to

its population size. -Indeed,;:,thishas be precisely what has hap-

. pened as black teenage unemployment/popula ion rates have increased

from three to ten percentage pointS.odepending on.the specific age-sex

group. What is more puzzling, however, is that4hite teenage unemployment has also increased siognificantlyjelative to its population size.

It is this last Nenomenon that leads to the±question_of_just what

economic interpretation it is sensibletto-give to the measured teenage

unemploment statistics. Afteroall, is it.not surprising that the in-

. creasing employmeht of white teenagers is associated with simultaneously increasing unemployment for this group while,jUst the

is the case for black teenagers? , A natural initial, response is that simultaneously increasing employment /population and unemployment/population ratios for white teenagers indicates that their labor supply has increased secularly as well.

433
'. 404 -

. TABLE 1

AVERAGE ANNUAL CHANGES IN THE EMPLOYMENT/POPULATION RATIO\

OF TEENAGERS RELATIVE TO ALL 35-44

YEAR-OLD ADULTS, 1954-1977

Age Group Black Males White Males

-.0031 14-15 Year-Olds -.0100

(.0030) (.0023)

16-17 Year-Olds -.0170 -.0046

(.0044) (.0031)

18-19 Year-Olds -.0196 -.0077

(.0056) (.0025)

20-24 Year-Olds -.0168 -.0081

(.0052) (.0026)

Blac

Femal

White

Females

-.o048-- .0023-

(.0030) (.0016)

-.0051 7.41p05

(.0037) (.0o24)

-.0052 -.0012

(.0065) (.002811

-.0042 410058

(x046) (.0021)

4

Source: Regressions of the first-differences of the annual average

empl yment/nopulation ratio of the indicated group on the

ar first-differences of the employment/population

ra io of 35-44 year-old adults. The coefficients in the

table are the'constant term estimates (and estimated

standard errors) from these regressions. The data are

based on the Current Population Survey estimates and are

taken from various issues of the Monthly Labor Review.
- 405 -

This merely restates the known facts, however, because the°conventional

statistics define the labor supply as the sum of employment and unemployment. The question therefore naturally arises as to whethqs,the actual

increase in measured teenage unemployment is a genuine part of the

true but generally unobserved labor supply.

A CONS TENCY TEST

the absence of forced work, the labor supply necessarily includes 1 employed workers and may also include other workers that

are not emp r. d. In stylized Keynesian models of the labor market,

unemployment rep sents h rs that workers are unable to sell on the

market at the curr t (or pr vailing) wage rate for one reason or

another.

1 This dise 1 ium component of the labor supply is pre-

. sumably related at least loosely to measured unemployment statistics.

In order to investigate this relationship in concrete terms suppose

.th that fora the 1 group of workers true unemployment U* is related to

100 I

measured unemployment U as

{1) U?

t -I it,

where al andEi are, parameters and vit is an error of measurement with

mean of zero. The true labor supply is Lit =

it

E + Uit so that from

-1,- (1) it follows that

..c. is

+ a.U. + Si + vit = Lit.

it i

The implicit,assumption in the conventional labor force statistics is

thata.I =Oanda.=1.0 for all groups and the variance of v results

only from sampling errors. In. principle it should be possible to test

one, or more of these assumptions.

It is worth observing that ai need not be smaller than unity because the true labor supply may be larger than the mere sum of measured

employment and unemployment. In this case the quantity Si + (ai - 1) vi

1. For a rather more formal discussion see Orley Ashenfelter, "Unemployment as Disequilibrium.in a Model of Aggregate Labor Supply,"

Working Paper No.-104,"Industrial Relations Section, Princeton

University, Princeton; N.J., November 1977.

43u
- 406 -

would measure discouraged workers who did not report themselves as unemployed for one reason or another. It is also worth obseriring that the

case a = 0 does not imply there is no "true" -unemployment. because a may

be very,karge. The case a = 0 does imply, however, that movements in

measured unemplOyment are poor estimates of movements in true unemployment.

'''tquation(2)suggeststhattheparametersa.and ai might be estimatable by regression after rewriting it as

(3) E. = L

it

+

1 i it

aU + v

it it

Of course, Lit is not observed and so 4urther/hypotheses are clearly an required to implement this approach. To proc d suppose that the labar-supply of two groups is known to be the sam rt from some estim-

.th

atable function. In particular, assume that for the 1-- age-group the

labor supply of black and white workers of the same sex is related as

(4) Lby = Lwt + yo yit + e

t , whe4 e

t

is a disturbance term. The logic of equation (4) is that the

same sorts of variables, such at the wage tate and family income, determine the labor supply Of black and white workers and these variaLIA-6-s ought to move smoothly relative to each other over time.

Finally, writing (3) for black-and white workers separate4r sub-

. tracting the latter from'the Former, and substituting (4) leads to

(SI Ebt Ewt YO (Eh Ew) awUwt abUbt 4-ei

Although there are a number of econometric difficulties that I-shall

take up below, equation (5) suggests, that a simple regression of the

difference in the employment/population rates between blacks and whites

on the unemployment/populat:icin rates of blacks and whites can, in principle, identify the paramat era ab and'aw . Note, however, that the

parameters and d yo cannot be identified so that whether measured

,unemployment is an upward or downward biased estimate of true unemployment cannot be determined eitheir. Still, estiraatos of the a

1 parameters are suggestive of the extent to which movements in measured

vment over

. fill time have an unambiguous_ Keynesian-style interpretation.

436

4
- 407.-/

Finally, in the case where the parameters ab and aw are equal it

is possible to write (5) as

(5a) Ebt Ewt YO (ab 8w) lit a (uwt ubi) q

The form of the estimating equation brigs out clearly the simple logic

of the empirical exercise I shall report below.- In effect, the-assump4'

, tion of a close relationship between the labor supply,of blacks and

whites implies that with clean measuremerof unemployment the slope

of the relationship between differences in the employment/populatj' jon

ratios of blacks, and whites and differences in the unemployment/population ratios of blacks and whitos should be minus unity. It is to

my preliminary efforts to estimate thi\ relationship that I shall turn

r

next. A . r/-.

INITIAL ESTIMATES

The estibates of equation (5a) in Tables 2 and 3 fOr the age

groups indicated are based on annual average employment and unemploya. ment per capita over- the-period-19541-to-197-7-, which---is-the_pe_riod for

which consistent data on teenagers are available4Oehave used annual

averages so as to avoid the adjustments in the seasonal pattern of

these data that would be necessary using quarterly or monthly data.

Thys strong seasonal pattern also may imply more complicated labor de-

, cisions than would be captured by so :simple a specification as equa-.

tion (4) and so it seemed best to avoid these difficulties at this

point. I have also fitted these equations in first-differences throughout because initial experimentation with a standard procedure, that allowbd h edisturbances to follow a first-order autoregressionlproduced

_fittedse_rial_sarrelation_coefficients that were near uniy for all

groups.

The results for males in Table 2 clearly suggest that for the

older gioups a is,near unity. The results for thirty-filie to fortyfour - year -old Table 2 are included to see how the consistency test

implied by equation (5a) works for a prime-aged group where unemployment presumably has the least ambiguous interpretation. As can be seen

a

4 0
eel

- 408 -

TABLE 2

LEAST SQUARES ESTIMATES OF EQUATION (5a)

FOR'MALES, 1954-1977

I,

Age Group

15

16 -

0 18 - 19

20.- 24 .

'4 35 - h4

a Estimated standard errors i parentheses. The data are

from the same source as note in Table 1.

Estimate

a

off': Constant a R2

-.0075 -.922 .147

(.0033) (.484)

fr

Durbin-Watson

Statistic

1.71

-.0103 .535 .300 2.73

(.0031) (.178)

-.0113 , 1.06 .596 2:09

(.0038) (.190)

-.0051 .998 .574 1.89

(.0026) (.188)

-.0014 "1.02 .808 1.98

(.0011) (.109)

A

f

C
409

TABLE 3

LEAST SQUARES ESTIMATES OF EQUATION (Sa)

FOR FEMALES,' 1954-1977

Age Group

i.

Constant

Estimate' of:

it> a'

2____DurbinWatson

R . Statistic

. 14 15 -.0082 -.702 .098 2.49

(.0030) (.464)

16 - 17 -.0081 -.204 .01'86 2.86

(.0033) (.323)

18-19 -.0038 .047 .000 2.10

(:0059) (.343)

20' - 214 -.0055 1.30 .306 1.39.

(.0043) (.438)

.. 35 '44 -.0059 . .381 .082 1.41

(.0022) (.277)

4

a Estimated standard errors in parentheses. The data are

from the same source as noted in Table 1.

.44

as. I

439

I
- 410 -

from the table, a = 1.02 for this group and this estimate is quite precise in the statistical sense, so that these data clearly pass the test.

,Though somewhat less precise, the estimates for twenty- to twenty-four

year-olds and eighteen-year-olds are also very close to unity. The estimate for sixteen- to seventeen-year-olds, n the other hand, is near

one-half and is precisely enough estimated that Q--'))oth the hypotheses , that a = 1.0 and a = 0.0 maybe firmly rejected. Finally, the estimate

of a for fourteen- to fifteen- year -olds has a perverse sign and is estimated precisely enough so that even though the hypothesis a = 0 cannot

be rejected, the data are not consistent with a value much larger than

zero either. In sum, the measured unemployment data for male-fourteenand fifteen-year-olds, and to a lesser extent for sixteen- and seventeenyear-olds, does not behave as would be expected if they were free of

measurement error. By comparison, the data for older groups does behave in the expected fashion.:

, As can be seen from Tabfe 3, the estimates of a for females are

imprecisely estimated compared to those for males. Although'this may

be a result of greater measurement error variance I suspect it is more

a result bf a larger variance iu the,relationship between black and

c:White labor supply measures in equation (4) and is a reflection of the

known greater volatility in labor supply for women. In either case,

'however, the estimates of a for all 'three ofthe age groups from-fourteen through nineteen are not significantly different from zero. Moreover, they are precisely enough estimated that the hypothesis a = 1.0

can be firmly rejected. For the twenty- to twenty-four:year-old group

a F 1.3 and the hypothesis & = 0 can certainly be ruled out. For the

. thirty-five to forty- four - year -old group, thee, other' hand, the estimate of a is cldser to zero than unity, but the latter hypothesis can

only barely be rejected by the usual tests. Taken together, these re-

.1, sults raise serious questions About the interpretation of the measured

unemployment statistics for all teenage females.

Table-4 contains the results of relaxing the assumptionob =,av V

,., and fitting equation (5) to the data for the various age groups. 'As can be seen from the table, apart from the increase in estimated stand- ''
-41-

TABLE 4

ESTIMATES ,OF 51 AND am FROM EQUATION Ma'

Age Group

l4 - 15

16 - 17

18. -19

20 - 24

,35 - 44

ro

Estimates for Males Estimates for F es ah

a 'ir a

b

clic -.883 =1.36 -.657 .770

(.513) (1.64) (.474) (2.14)

41

.531 .326

J.166) (.453)

-.129 .624

(.329) (.821)

1.08 .942 -.224

(.197) .268) (.364) (.717)

, .924 .630

(.172) (.229)

.976 .903

(.171) (.346)

a Estimated standard errors in parentheses.

4 1.1

A

1.22 .396

(.425) (.811)

.520 1.12

(.304) (.732)

"ftelition

els

a.
of t

a

- 412

44

and errors the results for males are changed very little compared to

those in Table 2. Although none of the differences between the estimates of ab and aw for males in the table are statistically significant, . "there is a clear indication that the a estimates for blacks are larger

than for whites. This especially the case for twenty- to twenty-four-

, year41ds, but it is also the case for all of the,other groups as well.

Taker% face value theSe results suggest that the dramatic increases

. . in millusbred black unemployment rates among the eighteen and over cat -

egories are indeed serious constraints on the labor. parket choices of

these workers.

The point estimates for females in Table 4 are also.generally unchanged from those in Table 3,' but they are even more poorly determined

in a statistical sense. As a result it seems pointless to try to draw

further conclusions regarding female behavior from these results.

stead, I turn next,to a number of econometric problems with these re-

. sults, including especially an attempt to reduce the imprecision in.

the estimates of the af

that is motivated by the imprecision of the

estimates for females.

SOME COMPLICATIONS

In the absence of additional data the only way to increase the

precision of the estimates of the al parameters in Tables 2 and 3 is

by the introduction of some plausible prior information inp3.the estimation process. Since I expected that, if anything, these coefficients

would be likely to increase with age this seemed like a plausible place

to start. In particular, suppose we assume ab = aw and array these

coefficients by age so that i is now an index of age. Suppose further that (3. = a(i) is a polynomial function in age. For a particular

sex group, and ignoring thirty -five.to forty-four-year-olds, this gives

four points along the polynomial so that nothing is gained by taking a

polynomial greater tlian degree three because this requires the estima-

, tion,of four parameters.. Infact, I started by assuming that-the-ai-

.fell along a first-degree polynomial, then estimated the resulting

equations by the familiar "seemingly unrelated regressions" method,

4.12
- 413 -

and finally tested the implied restrictions.

2 Following this procedure

sequentially led to a first-degree polynomial for the female coefficients and a second-degree polynomial.for the male coefficients.

3

The

estimates of the ai'falowing this procedure are tontained in Table S.

As can be seen from the table, the coefficient estimates for males

change very little 'from the unrestricted estimates in Table 2 and there

is very little gain in statistical precision either. On the other hand,

the coefficient estimates for females in Table S are also little changed

from tip unrestricted estimates in Table 3, but estimated standard errors decline by almost one-tliird. The resuit---1s--that-previous-conclusions about females become stronger, with the implication that the in-

. terpretation of measured female teenage unemployment rates is, even

more suspect.

A final serious shortcoming with the least squares estimates of

equations (S) and (Sa) i$ that the error terms in those equations may

he-correlated with the unemployment/population,ratios used as righthand variables'in them. For example, a positive fillip to the error -"-

term in equation (4)*1mplies that true black labor supply increases

relative to true w1041abor supply. But if measured unemployment

really is a componeirit oethkoffer to sell labor so that a # 0, then

both'meatured black employmerit and measured black unemployment may increase. The result is that, he composite. error term in equation (Sa)

2. See Arnold Zellne , "An Efficient Method of Estimating Seemingly

Unrelated Regiessions and Tests for Aggregation Bias," Journal of

the American Statistical Association, vol. 57(June 1962), pp.348-68.

3. The calculated F-ratio for testing the first-degree polynomial restriction against the unrestricted estimates for females was .05,

with two and eight-four degrees of ffeedom For male's this calculated F-ratio was 6.24, which implies clear rejection of the restrictions. With the second degree polynomial the comparable

F-ratio was .08.for males. As a matter of interest, the fitted

polynomial for females was ai = .99 + .2Si, where i = 1, 3, S, and

8.5, and these correspond.to the scaled midpoints of the fourteen

to fifteen,. sixteen to seventeen, eighteen to nineteen, and twenty (

to twenty-four age ranges. For males the fitted polyndmial was

a. = 1.9 + 1.1i . The coefficients in Table 5 are estimated values of 13i based on these equations.

Y."
- 414 -

.---- TABLE 5

SEEMINGLY UNRELATED REGRESSION

ESTIMATErOF a er ab se aw CONSTRAINED TO

POLL& A POLYNOMIAL IN AGE

Age Group c.

.

.1. 0

Estimates

a

for

14 - 15

16 - 17

Males Females

.896

(.06)

.491

.753

e. (r308)

-.26I.

(.321) (.206),

18 - 19 1.03 .231

(.148) (.182)

20 - 24 .878 1.b9

(.175) (348)

- '.:,I

/ a Es-timated-standard-err-ors in

,

parenihesesi

114

,-
- 415 -

will be correlated with the unemployment/population ratio used as an

independent variable and the least squares estimator will be biased.

41k There are two points that may be /made about this issue. -First,

this form of "simultaneity bias" need not invalidate an appropriate

test of the hypothesis a=0 based on the least squares estimates.

After-all, under this null hypothesis the error term and the unemployment ratio will be uncorrelated so that the probability of a type 1

error will be properly controlled even though the power of this test

may be exceedingly low. Second, it is in principle possible to remedy this deficiency-by- adopting -an appropriate-instrumental variable

estimator.

In practiCe I, have experimented with a simple instrumental variable estimator for equation (5a) with only mixed success. I have

used the employment/ population ratios for black and white thirty-five

to forty-four-year-old adults as instruments for the difference between the unemployment/population ratios of the various younger groups.

As could be predicted from the low explained variances for females in

Table 3 this procedure led to estimated standard errors for the a coefficients for the female groups that were so large,as to render the

estimates themselves useless. The results for the male groups were

somewhat better determined and they are contained in Table 6.

As can be seen from the table these estimates of,the ai coeffi-:

cients for males are generally Aarger than the least squares estimates

in Table 2. Still, the basic conclusions remain generally unchanged-.--

.. For the fourteen- to fifteen-year-old group the estimate of a isessentially zero as before. For the Sixteen- to seventeen-year-old

group -the estimate of a is now greater than unity, but its associated

standard 'error is so large as to render this conclusion very weak.

Clearly, better estimates along these lines would be useful and this

may be a good topic for future research.

1
I 41.6

CONCLUSIONS

In this paper I have set out'a simple consistency check on the

extent to which measured teenage unemployment behaves as if it were a

part of the offer to sell labor. The basic idea behind this consistency check is that if labor supply were a sknown quantity and if unemployment were measured without error there would be a onelto-one negative relationship between'employment-and unemployment. Of course,

the labor supply of any group is unobserved, but if two groups offered

the same quantity of labor td the market then there would still be an- . observable one-to-one-negative relatioh-Shig-between the differences in

employment and unemployment for the two groups. I implement this

scheme empirically for male and female fourteen to fifteen, sixteeh

to seventeen; eighteen to nineteen, twenty to twenty -four, and thirty-

. five to forty-four year-olds by assuming that the unobserved labor

A supply of blacks and whites is identical apart from a constant and . steady trend. Although there are a number of econometric difficulties

associated with this procedure the results suggest that for'males

eighteen and over and for females twenty and over there is a clear neg-

. ative relationship between employment and unemployment differences,

but that for younger groups there is not. This suggests that considerable research and experimentation with the measurement of teenage

unemployment should be undertaken before these statistics are taken

at face value.
417 -

..4 YOUTH PARTICIPATION RATES AND THE

AVAILABILITY OF JOBS

By: Francine D. Blau

ABSTRACT

This paper uses data from the National Longitudinal Surveys to examine the relationship between local area unemployment rates and the

labor supply behavior of youth aged eighteen to twenty-four in 1970.

The net effect of the unemployment rate on the probability of labor

force participation in 1970 is found to be negative. Net discouragement appears to be greater among yoUfig women than among-young men, and

to belarger among blacks than among whites. Since local labor market

unemploymtnt rates tend to be correlated over.tiMe, the coefficient on

the unemployment rate variable in the participation 'regressions was held

to approximate a long-term supplyjrespons to persiAent intercity differences. The net impact of the uneMplo ent rate on labor supply adjustments over a one-year periodyis also xamined. No signific nt effect of the unemployment rate on the probability of labor forc entry

or exit between 1970 and 1971 is obtained.

These findings suggest that net effect of the ups and downs of the

business cycle on the labor force participation of youth would not be

very great. However, prolonged periods of high unemployment could produce'net discouragement for this age group. At the level'of the local

labor market our results suggest the efficaOy and importance of policies

designed to stimulate,aggregate demand in depressed ,labor markets.

INTRODUCTION

The level of economic activity may have both direct and indirect

effects on young adults.* Economic conditions directly affect the

probability of becoming unemployed and current earnings. In addition,

the-unemployment-rate-May-indirectly affect youth by influencing their

labor force participation decisions. Thisvpaper investigates the rea

spon'siveness of the labor supply of young men and women to the level

of unemployment.

Economists view the response of labor supply to the unemployment

rate as being the net result of two opposing effects. The additional

worker effect predicts that, during times of high unemployment, if

the primary earner(s) becomes (become) unemployed, other family members may enter or postpone exit from the labor forCe in order to maintain family income. Such individuals may leave the labor force when

*I am indebted to Lawrence Kahn for helpful comments and suggestions.

I would like to thank Ronald Seeber for research assistance. Any

remaining errors or omissions are the responsibility of the au hor.

4417 A *
< ^418 - a economic conditions improve and the primary earner(s) is (are) again

employed on a regular basis. Alternatively, the discouraged worker

effect holds that, during times of high unemployment, when individuals

become unemployed.,.thek may become discouraged and drop out of the labor force after a fruitless period of job search. Others may postpone

.labor -force entry until economic conditions improve: Theoretically

both these effects may operate simultpeously on labor force entries

and exits to produce a net effect on labor force participation. The

direction of this-net-effect-must-be-determine0Lempitically, although

0

the,cyclical sensitivity of labor supply behavior is expected to be

greater for groups which have traditionally-had-a-weaker-attachment

to the labor force, like married women and young adults.

Knowledge of the direction and magnitude of the net effect of

the unemployment rate on the labor force participation of youth has

extremely important policy implications. If the discouraged worker

effect prevails, unemployment statistics will tend to underestimate

the job creation task ahead for this group. This may result in the

adoption of monetary and fiscal policies which are not sufficient to

reach desired goals. In addition, to the extent that time out of

the labor force is spent unproductively, young people will suffer negative effects on their subsequent earningi due to delayed labor force

entry and/or breaks in their work histories.

If they additional worker effect is dominant, unemployment statistics_wili tend to overestimate the future need for jobs).possibly_

resulting i over-stimulatory macro-policies and exacerbated inflation

problems.- praddition, the need to supplement family income may cause

young peopid to interrupt or curtail their schooling with negative

consequences on their future earning power. In addition, to the extent that young people are concentrated in particular occupations or

industrie, accelerated entry during recessions would increase'Competition for these jobs and worsen employment conditions in these sectors.

The empirical evidence on the direction and magnitude of the discouraged-additional worker effect is mixed. Recent time'series studies

413
419

suggejt that in most cases the discouraged worker effect predominates,

but that the negative effect of the unemployment` rate on labor force

1 participation is not very large. Using gross flow data from the current population survey, Smith presents, evidence suggesting that the

observed cyclical sensitivity of the labor force is due to the increase

in the number of unemployed 'workers during a recession coupled with

the strong likelihood of leaving the labor force when unemployed. According to Smith, postponementof labor force entry during times'of

--high unemployment does not play an important role.

2 Such time series

studies suffer from a lack of detailed data on the personal characteristics of workers. This means that the time series results do not unambiguously measure the impact of changes in the unimployment rate on

the labor supply decisions of otherwise iBentical individuals, since

the estimated relationship is affected by compositional factors as

well.

Cross-sectional studies may be regarded -measluring the longrun impact of unemployment on participatioh rags. es. They generally.indi-.

cate that the impact of the local labor market unemployment rate on

the area labor force participation rate is negative, significant, and

,.-- larger thanthe results obtained for time series stu.dies.o

3

However,

such findings have been questioned by Fleisher and Rhodes because of

the aggregation problems entailed in the use of average data for the

local labor market. After correcting for these problems, Fleisher and

1. See, for example, Michael Wachter, "A Labor Supply Moldel for Secondary Workers,: The iew of Economcs and Statistics, vol. 54

May 1972) and Peter th, "Unemployment and Labor Force Participation," Southern Economic Journal, vol. 34 (January 1968). For

an excellent review of the early literature on this su.bject. see

Jacob Mincer, "Labor Force Participation and Unemployment: A Review of Recent Evidence" in Margaret Gordon and Robert Gordon, eds.,

Prosperity and Unemployment (New York: John Wiley and ,Sons, 1966).

2. Ralph Smith, "The Discouraged Workerin a Fulls Employment Economy,"

Proceedings of the American Statistical Association, Bus iness and

Economics Section, 1974.

3. See, for example, William Bowen pnd T.' Aldrich Finegan, The Economics of Labor Force Participation (Princeton, N.J.: Princeton

University Press, 1969).
- 420 -

and Rhodes find no evidence that the unemployment rate hasp. significaht

negative effect on the labor force participation rate.

4 However, using

microdata for a sample of mature married women, Blau found evidence of

sigicificant net eithcouragement among whites and a significant additionet worker effect among blacks.5

This paper uses data from the National Longitudinal Surveys of

Young Men and Women to examine the impact of the unemployment rate on

labor 'force parti cipation.6 The sample is restricted to youth aged

eighteen to twent:y-four in 1970, the initial year of the-analysis. A

-cross-sectional approach is used in which the impact of the local labor

market unemploymient rate is ascertained while the effects of other variables which in fluence labor supply behavior are held constant. Since

data on individuals are used, aggregation problems are not% an issue.

The net e ffect of the level of unemployment is investigated by

'introducing th.e local labor market unemployment rate into'regression

equations estimating: (1) the probability that a respondent will participate in t he labor force during the 1970 survey week, (2) the condi4. Belton Fleisher and GeorgeRhodes, "Unemployment and the Labor

Force Participation of Married Men and Women: A Simultandbus

Model," The Review of Economics and Statistics, vol. 58 (November 197677 See also Belton Fleisher and George Rhodes, "Individual Labor Force Decisions and Unemployment in Local Labor

Markets: A Foundation for Policy Planning," unpublished working

paper, undated. The authors claim that OLS estimates using aggre-2

gate i:ross-sectional data are biased, since unemployment rates in

local labor markets are in fact IdeterMiried- simultaneously withlabo7: force, participation rates..

5. -Francine Blau, "The Impact of the Unemployment Rate on Labor Force

' Entries and Exits," unpublished paper presented at the Secretary

of 'Labor's Invitational Conference on the National Longitudinal

Surveys of Mature Women, Washington, D.., January 26,,1978.

6. ?wr a descil.ption of the National Longitudinal Surveys data for

these cohorts see Paul Andrisani and Andrew Kohen, Career Threshol.ds: A Lo1rtgitudinal Study of the Educational and Labor Marltet

rq5=Iceof Male Youth, vol. 5 (Columbus, Ohio: Center for Human

resource Researc , hio State University, 1975) and Frank Mott.

o 1'7 t al., Years for vision: A Longitudinal Study of the Educational, Labor Market and Family Experiences of Young Women, 1968 to

1973, vol. 4 (Columbus, Ohio: Center for n Resource Research,

tIETE State University, 1977).
- 421 -

tional probability that a respondent will enter the labor forc e by the

1971 survey week (given s/he is out of the labor force at the time of

the 1970 survey), and (3) the conditional probability that a respondent.

will exit the labor force by the 1971 survey week (given s/he is in the

labor force at the time of the 1970 survey). Local unemploymeht rates

tend tg'be correlated over time. Thus, the measured iMpactof the'unemployment rate on the participation probability (specification one)

would reflect long-term adjustments to persistent-intercity differences.

Where the focus isupon,labor force entry and exit, as i n stcl!ications *

two and three, a one-year period-has uaen-allowed-for-the change-in-la-

\ bor supply behavior to take place. If, for-example, there are considerable transaction.c9sts involved in moving into and out of the labor

force,

7 the impact of the unemployment rate on supply behavior flay be A

leis in the case of entry and'exit probabilities than in the case of

the participation probabilities.

7

Moreover, both types of cross -sectional estimates may exceed the cyclical sensitivity of labor supply

to short-term chapges in the unemployment rate.8

In the participation regression, the dominance of the discouraged

worker effect would be indicated by a negative coefficient on the unemployment rate, the dominance of the additional worker effect,',by a

7. James Heckmap and Robert Willis, "A Beta-logistic Model for the

Analysis of Sequential Labor Force Participation-by Married Women," Journal of Political Economy, vol. 85 (February 1977). ; On the other han4T-the-samille-includes_indivivivals who changed I. their residences between -1970 and 1971. Sucb an opportdnity to alter the labor market conditions une, fdees throu6 intercity migration has no counterpart when the national economic situation is the

'reletrant context. ,This consideration would reduce the magnitude of

the upward bias discussed in-the text (of the cross-sectional as

compared to the cyclical responses), but most probably would not

eliminate it. It was decided to include individuals who changed

to their residences over the period so as to eliminate any selectiv;

Ity bias that would result from restricting the sample to those te"'

who did not attetpt to alter their situation. See Edwargalacheko

Donald Larson and Fredrick Raines, "An Investigation of athic Labor Supply Adjustment," unpublished working paper prepared for the

Employment and Training Administration, U.S. Depaxtment of Labor,

March 1977, for an analysis which considers the 4bestion of the

speed with which labor supply\adflistments in wont hours art made.

45 A

- 2
- 422 - ,0

positive coefficient. It the entry and exit iegressions, an increase

in tbe unemployment rate would, other things equal, work to decreases

theprobability of entering the labor force and/or to increase the

,probability of exiting the labdt force, if the discouraged workereffect

predominates. the additional worker effect is dominant, a rise-in

the unemployment rate would, all else equal, be associated with an in- . crease in the probability of entering the labor force and / car a decrease

in the probability of exiting the 140or torce.:

The labor force status of the young men and women in the\sample is

summarized in Table 1. In thigage group, ma le labor force pticipa- . ° tion rates were one quarter to a third higher than female rates in 1970.

9

Womeilweie somewhat more likely than men to experience unemployment,

while the incidence of unemployment among blacks, was considerably higher

than among whites. Young women were four times as likely as young men

to exit from the labor force between 1970 and 1971, and one half to

three. fifths as likely to enter the labor force between thoseotwo years.

90 THE MODEL',

The labor-force participation decision-of an individual may be con-

. ceptualiZedas one in which the individual and his or her family determine the allocation of each member's time between work in the market and

various nonmarket activities such as school attendinC'exitinvestment in fu-

. ture market productivity), home work and leisure. 9 This decision process involves comparing the relative advantages of market and nonmarket

activities at each point in time. An individual participates in the labor force at time t if the value of his' or her time in-the market, wt,

exceeds the value of his or her time in nonmarket activities, wt*.

9 9. See, for example, Gary Becker, "A Theory of the Allocation of

Time," The Economic Journal, vol. 75 (September 1965); Jacob

Mincer, "Labor Force Participation of Married Women: A Study'of

Labor Supply" in H. Gregg Lewis, ed., Aspects of Labor Economics,

A Conference of the Universities--National Bureau Committee of Economic Research (Princeton, N.J.: Princeton University Press, (1962);

Reuben Gronau,."The Intrafamily Allocation of Time: The Value of

the Housewives' Times," American Economic Review, vol. 63 (September 1973); and James Heckman; "Shadow Prices, Market Wages and

Labor Supply," Econometrica, vol. 42 (July 1974).

417:-() tJ 4.,

4
- 423 -

TABLE 1

LABOR FdOtE STATUS, 1970-71

(percent)

a.

o.

Males

Whites

(n = 1840)

Blacks

(n = 681)

Labor Force Status, 1970

Employed 75.1 74.6.

Unemployed 6.3 12.0

Out of Labor Force 18.6 13.4

Change in Status, 1970-71

Entry ratea 55.7 60.4

Exit rateb 4.8 5.6

Females (n = 2163) (n = 851)

Labor Force Status, 1970

.Employed 54.6 46.2

a Unemployed 6.7 13.0

Out of Labor Force 38.7 0 40.8

Change in Status, 1870-7 1 Entry ratea 0 29.2 37.5

Exi e rate

b 20.0 23.4

a- Proportion of those respondents wh were'out of the labor force

during the 1970'survey week whq had entered by the 1971 survey

week.

b Proportion of those respondents who were in the labor foce during

the 1970 survey week who had exited by the 1971 survey week.
- 424 - A

(w

t* is the shadow price of time in nonmarket activities when zero work

hours are supplied to the market.) S/he does not participate if w

t is

legs than wt *. The tendency of many families to practice sex role spe-

. cializationin the allocation of market and nonmarket tasks means that

the same variables will not necessarily influence the'value of w* in

the same way for men and women. For example, other things'equar, marriage may raise w* for women, but lower it for men. It is also important to point out that labor market discrimination on the basis of

race or sex may influence labor supply decisions by lowering w. In

the context of the labor supply model,, individuals aAte viewed as purr suing optimizing behavior. However, outcomes are not necessarily op-

, timal from a social point of view, since they may reflect a disadvantaged.rlabor market status,for some individuals.

10

An individual may change his or her labor force status between

time t and tq,l if the value of market and/or home time changes. The

effect of a given change in the value of market or home time depends;

however,lon the initial situation, that is, the relative magnitudes of

wt and For For example, suppose over the course of a year a woman

has an additional 'child. This is expected to increase the value of

her nonmarket time. If she was previously in the labor force, will'''

shenow exit? Not necessarily. If the value of her market time'initially greatly exceeded the value of her nonmarket time, then it is

possible that she will.remain in the labor force. On the other 11;.nd,

were the gap between w

t and w

t

* smaller; she might exit. Thus, the

probability of labor force exit or entry depends on both the initial

values of hRme and market time and on the changes in those values that

have occux:rpd Over the period. P, 4

While the notions of the valneof market time and nonmarket time

4

are useful theoretical concepts, it is generally not possible to observe directly or measure w

t

and w

t

*. This is obviously. the case with

10. Marianne Ferber and Bonnie Birnbaum ("The 'New Home Economics': /

Retrospects and Prospects," Journal'of Consumer Research, vol. 4

(hme.1977) have criticized household decision-making models for

their assumption of rationality and underelphasis of the role of

tradition in determining the division of tasks within the faMily.

4
41.

- 425 -

respect to the value of nonmarket time. The value of market time, in

the form of the market wage rate, can be observed for individuals who

are currently employed. However, even in this case, it is not clear

that the observed wage rate fully represents all aspects of the value

of market time. The value of an individual's time.spent in the market

depends on the nonpecuniary as well as the pecuniary aspects of his or

her work. It depends not simply on the current wage, but on the pros- e

pects for wage growth in the future and the wage penalty associated.

with the labor force withdrawal in each type of work. In addition, if

an individual becomes unemployed, the alternative opportunities open

to him or'her must also be considered,11 and market conditions must be

taken into account.

Thus it may be both necessary and desirable to represent 'both wi

and w

t * by the-set of exogenous variables that determine them. rather

than by their actual values. In this study, labor supply behavior is

explained by a vector of personal characteristics, including race, education, potential experience, health, marital status, number of dependents,and net family assets, 'and labor market characteristics, including the labor market unemployment rate, the size of the local labor

market and southern residence (table 2).

In the exit regressions, job characteristics such as union coverage and occupation are included to represent such factors as the nonpecuniary aspects of work, opportunities for wage growth through employment and the wage penalties for discontinuous participation. However,,

the ability to locate a job with specific characteristics may depend

on economic conditions in the locality, In addition, job characteristics may represent self-selection as well as environmental factors.

Thus results are presented both with and without these variables included. 014,

In both,the entry and exit regressions, the impact of changes

over the 1970-71 period are considered. Changes in some of the .eXplanatory variables were not considered to be solely causes of changes in

11. That is, the whole wage distribution must be considered.

4 5

\
- 426 -

TABLE 2 7 VARIABLE DEFINITIONS

Dependent Variables

ILF Dummy variable equaling one if respondent was in the labor force

during the 1970 survey week, and'zero otherwise.

NL Dummy variable equaling one if respondent was in the labor force

during the 1971 survey week (given respondent was out of-the labor

force at the time of the 1970 survey), and zero otherwise.

LN Dummy variable equaling one if respondent was out of the labor force

during the 1971 survey week (given respondent was, in the labor force

at the time of the 1970 survey), and zero otherwise.

Explanatory Variables

RACE Dummy variable equaling one if respondent is black, and zero otherwise.

'ED Highest grade attained (years).

. POTEXP Potential labor market experience = Age - Education - Six. Experience

is constrained not to begin before age 14.

HEALTH Dummy variable equaling one if health limits the kind or amount of

work the respondent can do, and zero otherwise.

MSP Dummy variable equaling one if respondent is married'spouse present,,

and zero otherwise.

, DEPS Number of dependents, eXcluding spouse.

ASSET Net family assets ($1,000s).

UNION Dummy variable equaling one if respondent's wages are set by collective

bargaining, and zero otherwile.

PTM Dumm-yariable equaling one if respondent is a professional, technical

ormanagerial worker, and zero otherwise:

SERV. Dummy variable equaling one if respondent is a service worker, and

zero otherwise.

UE cal labor market unemployment rate (annual average).

SOUTH Dummy variable equaling one if respondent resides in .the south, and

zero otherwise.

b. . 'SIZE Size of local labor market (100,000S)0

4". f` k ..) kJ
V CHDEP

CHMSP

CHWDS

CHIH

CHDH

n

. r- 4,27

TABLE 2

VARIABLE DEFINITIONS (con'd.)

Change in number of dependents.

Dummy variable equaling4one

married spouse present, and

Dummy variable equaling one

from married spobse present

zero otherwisli

Dummy variable equaling one

zero otherwise.

Dummy variable equaling one

zero otherwise.

UERACE UE X RACE.

4

if respondent' changes marital status to

zero otherwise.

if respondent changes marital status

to widowed, divorced or separated, and

if respondent's health improves, and

if respondent's health deteriorates, and

. 457

a
L - 428 -

the individual's. labor force behavior. For example, the magnitude of

assets might Change as other family members responStito a change:in the

individual's behavior. In addition, changes in assets over the period

may be'due to economic conditionssrand thus should be.omitted so that

the full effect of economic conditions is captured by4the unemployment

rate variable. The.impact of changes in educational"ittainment -Over

the period.yould also be ambiguous' as to causation. The focus is upon

changes in health, marital status and number of dependents. It may be

argued that changes in labor force status, marital. status and number

of dependents between 1970 and 1971 ate all determined simultaneously.

The assumption made here ishat changes in marital status and number

of dependents are determined prior to the change in labor force behav=,

ior. This means that these variables will be uncorrelated with the

error term so that unbiased parameter estimates may be obtained with

ordinary least squares.' However, in this case also, the regression

results are presented with and without these variables included.

EMPIRICAL RESULTS . The regression results obtairlitt using ordinary feast squares are

shown in Table 3 for young men and'Table 4 for young women.12 The rel

-ativelylow adjusted R2 's for the regressions are characteristic of

Cross-sectional studies, particularly in the case of binary dependent

variables. In addition, small sample size is a problem in the case

of the male entry regression. v. The strongest results for the unemployment rate are found in the

labor force participation regressions (equation 1). The coefficient

on the unemployment variable is negative and significant in the female

regression and negative and larger than its.standard error in the male

regression.

suggests that

(although the

The inclusion of a racial interaction term in equation 2

net discouragement ds greater among blacks than whites

racial difference is not significant in the case of

males). Other things equal, a one percentage point rise in the labor

market unemployment rate would result.in.a.decrease of 1.06 percentage

. 12. Considerations of time precluded experimentation with the more

\

appropriate logit or probit.specifications,

4E3
4. TABLE 3

REGRESSION RESULTS: YOUNG MEN

(Standard Errors)

Explanatory Variables

Personal

CWaracteristics (1970)

Participation (ILF) )/. Entr (n)

(1) (2) (3) (4) Exit (LN)

(5) (6) (7)

RACE ' 0.03946

b 0.05564 -0.0152r -0.022 4 0.02142c 0.025292 6

' 0.0248313 (0.01807) (0.04351) (0.070531 ....(0.07?8) (0.01262) (0.01272) (0.01279) ED 0.00212 0.00211 0.01271 0.01'62 -0.00519 -0.00437 -0.00403

(0.00459), (0.00459) (0.01974) (0.02 07) . (0.00315) (0.00333) (0.00334) POTEXP 0.03416a 0.033880 -0.03977 6 0.04 926 -0.01285a -0.01271a -0.01226a

(0.00447) (0.00443) (Q.01956) (001 68) (0.00298) (0.06300) (0.00304) HEALTH 1 -0.03878 -0.03891 0.04885 0.20 17 -0.00100 0.00011 0.00328'

. (0.02614) (0.02615) (0.10153) (0.1 33) (0.01945)" _(0.01944) (0.03198)

IMP 0.13631a 0.136240 0.24570

c 0.2 661c -0.01885 -0.01824 -0.02392c

(0.019(,7) (0.01967) (0.14469) (0.1 755) (0.01270) (0.01273e10.01410)

DEfS 0.00919 0.00926 .70.00583 -0.0 444 0.00184 .0.04206 -0.00062

(0.01052) (0.01052) (0.12046) (0.1 138) (0.00667) (0.00667) (0.00751) ASSET 0.00125 0.00126,, -0.01227 -0.01481 -0.00020 -0.00020 -0.00022

(0.00095) 4.00095) (0.01179) (0.0482) (0.00058) (0.00058) (6.00058).

Job' Characteristics

(19170)

. UNION -0.01872 -0.01638

(0.01196) (0.01201) Sit PTM

. -0.00841 -0.00902

(0.01579) (0.01579)

SERI' -0.03349

b 0.03426b

' (0.01844) (0.01843)

4

. 45 ;)
`TABLE 3-

_)

REGRESSION RESULTS: YOUNG MEN

.(continued) J.

Explanatory Variables

Labor Market

Characteristics

LESOUTH

SIZE Changes (197041)

CHDEP

CHYSP

PartiCipation (ILF) Entry (NL) Exit (LN)

(1) (10)(2) (3)

-

(4) (5) (6)

J -0.00545 -0.00445 0.00298 0.00157 -0.00212 -0.00183

(0.00357) (0.00433) (0.01240) (0.01247) (0.00249) (0.00250)

-0.04592a -0.04558a 0.10441.. 0.09188 -0.02519

b -0.02816b (0,01763) (0.01766) (0.06355) (0.06490) (0.01223) (0.01241)

0.00015 0.00015 0.00113 0,00113 0.00028 0.09029

(0.00070) (0.00070) (0.00250) (0.00251) (0.00049) (0.00049)

0.06159

. (0.04716)

0.05641

(0.11789) CH DS f (n.a.)

'CHIN -0.24049 ' (0.20266) CHDH -0.03670

(0,14002)

Intraction

UERACE -0.00310

(0.00758)

,

1p

V

(7)

-0.00180

(0.00249)

- 0.02674b°

(0.01242)

0.00028

(0.00049) .

- 0.00678 (0.00811)

- 0.04286b (0.01708)

0.01697

(0.03744)

- 0.00220 (0.03920)

0.01539

(0.02612)
I TABLE 3.

REGRESSION RESULTS: YOUNG MEN

(continued)

Explanatory Variables Participation (ILF) Entry (NL) Eicit (LN)

(1) , (2) (3) (4) (5) (6) (7) Constant Term 0.71350 0.70821 0.31490 0.29625 0.16322 0.16021 0.16179

Adjusted R2 0.107, 0.107 0.018 0.016 0.021 0.023 0.025

Fstatistic 29.864 27.154 1.649 1.41C 4.745 4.077 3.413

Observ'ations 2398 2398 353 353 1717 1717 1717

n.a. not applicable.

a Significant at the 1i level on a two-tailed test,

b Significant at the S. level on a two-tailed test.

c Significant at the 10% level on a two-tailed test.

4n*
4

4\_,,TABLE 4. REGRESSION RESULTS: YOUNG WomgN

(Standard Errors)

Explanatory Variables

Personal

Characteristics (1970)

Participation (ILF)

(1) 1 (2)

'RACE 0.03064 0.16132a

(0 02284) (0.05738) ED 005863a 0.05848a

(0.00593) -(0.00592) POTEXP. 0.03955

a 0.03986

a

. .(0.00573) 6.00572)

HEALTH -0.08587b -0.08572

b (0.04299) (0.04295)

HSP -0:11871a -0.11820-

(0.02202) (0.02200) DEPS -008592a -0.08687a

(0.01202) (0.01202)

7kSSE.T -0.00044 -0.00049

(0.00085) (0.00085) ;

Entry (NL) Exit (LN)

(3)

0.09267a

(0.03535)

0:017371)

(0.00880)

-0.01873

b (0.00938)

.0.00403

(0.05908)

- -0.04093

(0.03620)

-0.01135

(0.01771)

-0.00161

(0.00121)

Job Characteristics

(1970)

' UNION

PTM

SZN7 4C2

(4) (5) (6) (7)

0.07999

b 0.01748 0.02683 0.01933

(0.035_83) (0.02502) (0.02508) (0.02507)

0.01687c -0.03738a -0.03760al -6.03309a

(0.00878) (0.00702) (0.00776) (0.00771)

-0.01840c -0.02337a -0.022390a -0.02429a

(0.00941) (0.00614) (0.00615) (0.0061D)

-0.08598 0.08238 0.07335 0.02752

(0.09863) (0.05179) (0.05160) (0.07836)

-0.05466 0.06078a "---0.061464a 0.07437a

(0.03511) (0.02330) (0.02319) (0.02477)

-0.01214 0.03505b 0.03245

b 0.05217a (0.01846) (0.01421) (0.01416) (0.01445)

-0.00140 0.00145 0.00142 0.00108

-(0.00120) (0.00099) -6,00099) (0-311008)

-0.08794a -0.09249a

(0.02697) (0.02655)

0.04882 0.03445

(0.03489) (0.03443)

0.06670 0.06266b (0.02818) (0.02788)

S

k
A

0

o

TABLE 4. REGRESSION RESULTS: YOUNG WOMEN

(continued)

Explmiatoiy Variables Participation (ILF) Entry (NL) Exit (LN)

(1) (2) (3) (4) (5) (6) (7) Labor Market

Characteristics (1970)

wUE -0.01994a -0.01061 0.00149 -0.Q0041 -0.00051 0.00050 0.00061

(0.00554) (0.00669) (0.00812) (0.00808) (0.00635) (0.000634) (0.00625) SOUTH -0.01840 -0.01540 0.02019 0.02998 -0.00277 -0.01379 -0.01395

(0.02199) (0.02200) (0.03462) (0.03459) (0.02384) (0.02401) (0.02363) SIZE -0.00030 -0.00035 -0.00108 -0.00110 0.00031 0.00046 0.000.75 (0.00090) (0.00090) (0.00146) (0.00146) (0.00094) (0.00094) (0.00094)`

Changes (1970-71)

0 CHDEP . -0.01345 4

- . 4 . (0.02708) CHMSP . 0.04273 " (0.05953)

I CHUM 0 0.39492

a (0.99321) CHIH 0.14553

(0.11880) CHDH A 0.01511'

(0.07181)

Interaction'

UERACE

0

.4;

-0.02866 1 (0.01155)

4n

7 0.09690

a (0.01791)

014225a. (0.03205)

-0.0860

(0.06392)

0.09977

(0.10148)

0.05814

(0.05317)

e

A

's.
D

4 , TABLE 4: REGRESSION RESULTS: YOUNG WOMEN

(continued)

Explanatory Variables Participacipn (Iu)

(1) .. .(2)

Entry (NL)

(3) , (4)

. (5) Exit (LN)

(6) 40

(7)

Constant-Term 7-- 0.00595 0-0.03855 0.16146 0.17693 0.66660 0.66235 0.57968

2

#1-', . o Adjusted

. R . 0.085 0.087 0.044 0.060 0.027 0.037 0.068

F-Statistic 24.390

,..., 22.778 5.485 5.103 5.321 ' 5.576 7.225 Observations 2506 2506 7 971 Aof . 971 1531 1531 1531,

ft "'Significant it the _1 Krcent level on a two-tailed test.

b'Significant at the 5 percent level on:a two-tailed test.

cSignificant at the 10 percent level on h. two-tailed test.

ca

ie

,

(t.

4° 4

1 ..1, P 5, ",
points in the participatiort probabi ity of white young women and 3.92

percentage points in the participation prObability of black yourig wo=

men. Among male's, the effects.aTe smaller. An increase ofbne percentage point in the unemployment rate would reduce the participation probability by .45 percehtage points for whit nd .76 percentrevoints (11

f f

for blac4s.

No evidence of k significant,ndt effect of the unemployment rate

on entry or exit probabilities`is obtained. (Thesefindings were not

altered by the inclusion of Aa. racial interaction term.) Unfortunately,

since tne'regression coefficient measures the net effect of the une

plOyment rate, it is not potsible to itifer.a ng regarding the m

tudes of the additional and discouraged worker effects,taien'separat _

. HOwever, it seems reasonable to suppose that hese findings support

4.

expectation of a larger impact of the uneIIoyment rate on the longterm labor supply adjustments estimated in the partivipatioA regresly.

sions than on the shorter -term adjustments estimated in the entry and ,

exit regressions.

0 The regression results shed light on a ndmber of other factors influencing

.

. young people's labor supply decisions, At the mean unemploy-

.g. er prhaBility ofparticipating in thela-

. erpart,,Howeverb while black females

force than their white counterparts,

ity of exiting thari whites. This '

ment rates blacks have a h

. bor force than their white co

are more likely to enter the lab

bfacX males have a higher probabi

suggests that the racial differential'in participation probabilities . among males would be narrowed or even Teversed as the cohort ages,

\\-while the participation gap among females would-be widened. (Tis ac-

,

A'

1.-

cords with cross-sectional Aata on labor force participation by race.)

13

The regression rpsults'bear outer expectation that, given the

current division of labor by sex in many'families,'demographic

factors .

' affect male female participation afferently.

Marriage is strongly positively correlated With male particidation. .Additional dependwits tend aLso to be pos itively related to participation. (The lack .0

. . ' 13.

, Employment and Training Report of the President

* (Washington, ngton, DEC.:

po

.- .

Goyernmen-p.Printing Office, 1976)., pp. 217:118i

,, . 4

a,

47
- 436 -

of statistical significance of DEPS and CHDEP, as well as the occasional inconsistent signs of the former are probably due to their

colinearity with-marital status.) Among females, marriage and additional dependents are strohgly negatively related to partidipation.

Marital,dissolution over the period is a strong predictor of labor

force entry (equation 4).

Such demographic variabls appear to be an important factor in the

large sex difference in exit rates for this age group that was noted

earlier. "Among females, other things equal, remaining or becoming

r married (spouse Present) during the periodis positively associated with

the probability of exiting the labor force. S.mitarlfr the pTesence

ofdependents and increases in the number of depepdents over the pef-

. iod contributed to an increase in:the exit rate, all else equal. How-

' ever, after controlling for the impact of such factors, variables corre- lated with market opportunities such as education, potential experience,

union coverage and occupation are also important. This suggests that

narrowing sex differences in market opportunities might rechice sex differences in laboi supply behavior. '

1

CONCLUSION

This paper used data from the National Longitudinal Surveys to ex- . amine the relationship. between local area unemployment rates and the

labor supply behavior of youth aged eightetn to twenty-four in 1970.

The net effect of the unemployment rate on the probability of labor

force participation in 1970 was found to bft negative. Net discouragement appeared to be greater among young women than among young men, and

to be larger among blacks than among whites. Since local labor market

unemployant rates tend to be correlated over time, the coefficient on

the unemployment rate-variable in the partidipation regressions wag

held to approximate a long-ter; supply response to persistent inter-

) ..,- city differences. The net, impact of the unemployment rate on labor

supply adjustments over

. a one-year period was also/examined. No significant effect,OT the unemployment rate on the probability

1 of labor force A. entry or exit

- between.l970 and.. 1071 was obtained. 4' f

t

, 1

t

P ot , l /

4 EC

Y"
- 437 -

The possibility that these findings are an artifact of the particular time period investigated cannot bedismissed.

14 In particular, it

would be interesting to confirm these results for a period when the national unemployment rate was above 4.9%, 1 the average for 1970. However, we may tetltatively conclude that t,hese findings are onsistent

with the notion that'there exist considerable tranAction costs of

moving into and out of the labor force and that the impact of the unemployment rate on the long-term determination of labor supply exceeds

its impact on short-term adjustments over a one-year period. This in

turn leads us to expect that the net effect of the ups and downs of the

business cycle on the labor force participation of youth would not be

very great. However, prolonged periods of high unemployment could produce net discouragement for this age group. At the level of the local

labor market -our results suggest the efficacy and'importanof policies designed to stimulate aggregate demand in depressed labor markets.

S

14. Stanldy Stephenson, Jr. ("The Transition from School to Work of

Young men," unpublished working paper, December 1977) found no

real impact of last year's labor market unemployment rate on

young men's school enrollment and labor force participation probabilities for the years 1967 and 1969.

IS. Employment and Training Report of the President, p. 211.
4

' --438

FAMILY STATUS AND LABOR FORCE PATTERNS

py: Martha S. Hill

ABSTRACT

0 Shifts in both par= tal and own living arrangements among y6uth

. and young adults are a alysed in terms of their,effects on the labor

supply of these groups. A general theoretical model of household

utility maximization ba ed on the youth's evaluation of household

member preferences is a.plied to a sample of noninstitutional individuals aged sixteen t. twenty-four in 1975 who were classified as

a child of the househo d head of a Panel Study of Income Dynamics

(PSID) family in 1968 sing Multiple Classification Analysis (MCA). - The results indicate th t there is a strong correlation between living arrangements and labor supply as measured by employment incidence

and Mean hours worked with young people living alone supplying significantly more labor than those still living in parental households.

Males living away from parents but not alone have greater labor supply than their female counterparts. Black males are the only survey,

group whose-labor supply is at all affected by parental living arrangements and they are affected negatively in terms of incidence and hours

of work. However, a more reliable survey instrument on living arrangements and labor supply to better be able to discern the interrelation-,

ships and the direction of causality is clearly needed.

INTRODUCTION

In recent years, there have been substantial shifts in the family

status of American youth, both in terms of their own living arrangements and the living arrangements of their parent's. Since household

structure is fundamentally, associated with life s*le, these shifts,

no doubt, have implications for many facets of the lives of American

youth. This paper will explore one such facet--labor supply, It will

concentrate on the major types of changes in household structure affecting youth and the associated changes in their labor force behavior.

It will'also discuss implications of'this analysis and the needs for

S further research.,

Trends in Living Arrangements

Over the last decade and a half, theesize of.American households

. has been steadily declining. The average number of persons per household has decreased from 3:33 in 1960,,to 3.14 in 1970, 2.94 in 1975,

.

°

e.

eze

4
4 te"..

- 439 -

I. and 2.86 in 1977.

1 The major factors contributing to this decline have -. been shifts toward one-person households and one-parent households combined with falling fertility rates. Given the subject- matter'of this

paper, aspects of these factors which are most relevant to youth will

be examined here.

Recent years have seen an increasing tendency toward independent

living by young adults who are postponing marriage. the percent of

oo

young adults aged eighteen to twenty-four who were yet to marry has risen steadily since 1960.2 And as the proportion ofyoung adults remaining

single has increased, so tpo has the tendency of these persons to establish their own households. This trend has contributed to the 40% increase from 1970 to 1976 in the proportion of all households consisting

of or person.

3

Another factor in the changing family status of young,adu4s has

been declining fertility. The numbr of children ever born per 1,000

women in the age`i.anges fifteen to nineteen and twenty to twenty-iOur

in 1975 were half of what they were in 1960. These changes are not entirely the restilt of the trend toward remaining single. Among ever-mar-

, .ried)women in these age ranges; the percent who were childless rose

.substantially between 1960 and 1975, with the percent of ever-married

0 women aged twenty to twenty-four wholvere childless increasing from

t24.2 to 42.8.

4 ° kt the same time that these changes have been taking place, there

has been a growing trend toward one-parent households. From 1960 to

1976, the proportion of children under age eighteen living with both

parents fell,from 87.50 'to 80.0%,with,most of this change due to a rise

1. U.S. Bureau of the Census, Current Population Reports,.

No. 313, September 1977.

2. U.S. Bureau of,the Cqsus, Current Population'Reports,

Series P-20,

Series P-30,

No. 292, March 1976, Table 7.

. Paul C. Glick and Arthur J. Norton; Population Bulletin:. Marrying,

Divorcing, and Living Together in the W.S. Today (Washington, D.C.:

Population Reference Bureau, Inc., 1977),p. 31.

4. U.S. Bureau of the Census, Current Population Reports; Serie? P-20,

No. 292, March 1976, Table 4.

469

P

4
O

- 440 -

in the prdOlortion of childreh living with their mothers only.S. Black

children have been particularly affeCted by the growth of one-parent : households, with the proportion of black children under age eighteen

living with both parents falling from,71% in 196S 6 to SO% in p76. 7. Greater marital disruption has'been the major contribu'or to the

growth in one-parentehouseholds. 8

Divorce rates have increased sub-

"' stantially since 1960, and desertions have been on the rise in recent

years. But rising illegitimacy rates have also been a primary contrib- -A

utor. In the past twenty-five years the illegitimacy.rate has more

than doubled, from 4 ,per 100 live births to 10 per 100 1iy0-brs,

with about 80% of illegitimate births now being attributable to women

under age twenty-five. 9

These trends have resulted in greater proportions of young adults coming from "broken homes" and living unmarried

with children of their own. 0 a In sum, recent years have seen young adults shifting away from 4D marriage with children toward more independent.. living, either alone or

with children, and an increase in their probability of having lived

with one pirent only at sometime, with this probability likely to increase even more in the fture.-10

Related Literature

Studies of the -effects of family status and living arrangements

Of youth on.their labor force behavior are fewand far between. Bowen

and Finegan, A their epic 1969 volume on labor force participation,

5. Glick and,Worton, op. cit., p. 28.

6. Brofenbrenner, Urie, "The Changing American Family,' The American

° 'Federalist, February 19774 p: 9.

7. Glick and Norton, op: cit., p. 28.

8- Brofenbrenner, op. cit., p. a:

9. BrofeAbrenner, op. cit., p. 8. . 10. Brofenbrenner (p. 8) 'states that the trend toward one-parent house- v holds haseen:most rapid for children under six. And Glick and

Norton (1977) predict that as many as 45% of all'children born in

1977, will "live for a period of at least several months as members

. , of a one-parent family" (p. 29):

a
-441-

touch on this subject.

it They analyze the cross-sectional effects of

the presence of both parents on labor supity of.never-married children

, . aged fourteen to seventeen and the cross-sectional effects of Carriage

on tie labor force behavior.of eighteen to twenty-four year-old males.

They find a positive effect'of own marriage on the labor forge partici-,

pition rates of eighteen to twenty-four year-old males. whether or not . they were enrolled in school, and a generally negative effect of the

presence of both parehts on the labor force participation of the four-

.1. teen $o seventeen year-qlds.

12 These effects emerged.even with con- . trols for economic circumstances"and other demographic characteristics. e Thus, their work suggests that family status and living -arrangements

of youth do exert independenteffetts n their labor force behavior.

0

There are, however, many problems with this analysis. To,begin

with, the effects of coming from a broken home arp examined 6nly, for

very young adults (aged fourteen to seventeen),living in the parental

home. These effects may not be generalizable to all young adults.

Effectsof own living Arrangements receive only cursory treatment

6

through the analysis of the effects of.marital status. And a major

subgroup of young adults is not included in this analysis--eighteen

to twenty-four year-old females. Additionally, labor force behavior

is primarily'measured in-terms of labor force pahiglipation.rates;

hours worked are, not analyzed in detail. One of their findings spggests that this could, indeed, be a major omission. When they do

look at bothahours worked and labor force. participation rates, they

find that among males aged eighteen to twenty-four enrolled in,sgEool,!

thos0e.who are 'married registered higher labor force participation.rates

but'fewer hours worked than did those who were single and living at

home.

Consequently, this analysis leaves many questions concerning ef-

-7-

11, William G. Bowen and AldricA T. Finegan, ThecEconomics of-Libor

Force Participation4(Princeton: Prin eton University Press, 1969).

12. Effects of presence bf both parents v ied by sex and fchool. enrollment. Of the fourteen to seventeen ear -olds in school, the ef-

,fect was more strongly negative for females than males'. Of the

fourteen to seventeen year-olds not iii school, the effect was quite

large did negative for males but nonexistent for females.

4 7.1
442

-

fectirof_family status and living arrangements on labor force behavidi

unanswered. To boot, it is a cross-sectional analysis, looking at

ferences across ihdividUals. If characteristics associated with the 1

, likelihood of an individual falling into a particular category of family status are not adequatelycontrolledu_then the findings o such anaslysis can' be misleading.

Duncan, Featherman, and Duncan '(1972) touch on of ects of parental

'Irk .family stability with respect to something closely related to labbr

force behavior--socioeconomic status of the current occupation.

Their work suggests that coming fr.bm a parental family with both Parents present versus from a female-headed parental family has a small"

positive effect oh the occupational status of twenty4ive to sixty -four

a .

I

year-old males, independentof other socioeconomic background factors.

Although the age-group analyzed has already_passed the young adult

stage, this effect could, at least in part, be the result of differential labOT force behavior,during youth.

Work with the Parnes' panels of youngjten and Women suggests that

changes in marital status affect the labor supply decisions4f.young

males and females in different ways. Kohen and Parnes (1971) find

that over a two- yeaperiod,there vas no substantial effect of a change

in marital status on labor force participatiOn rates -of yoking men.

14

Roderick and Kohen (1973) find subS'tantial effects of changes in marital status over'a two-year Perriod;,on the labor force participation:of

young women;1over the observation period young women whomarried were

much more likely than those who .dienot

. marry to decrease their labor v. . force participation, and white womenwho divorced were more likely tlim

O

10 13. 0.D. Duncan, D.L. Fedtherman, and . Duncan. Socioecopomic Back,;.

ground Ind Achievements (New York: Seminar Press, 1972).

14. Andrew T.-Kohen and Herbert S.' P es.' Career Thresholds:, A Long-

. itudinal Study of the Education and Labor Market Experience of

Male Youth, Vol. 3 (Columbu§ompAio: Center for Human Resource Re-

. .search,.The Ohio State Univ4iii4, 101), pp. 38-41. r

lit.

,v4e SM
. Sc -

s

.,.. . .

** ' . -> 443 =v -. , > ... ,

.., .. '.. , a.,. . "...x- .., a t .. - -.., . ..

.

. .. .4

othefs to increase theirparticipatj.on,

1S (Thee analkees,.however,

..., ,

. conCentrated:Innly on4,.martial status rather than.phe wider range,: ofik,

% N.. 4 , r .

. 7.

possible living arrangements, and they omit mCty-pOtentially relevant':

.s. cnrit-rols.,7such.es number of own chilaren and socioeconomic background.

'The above lifera:turt on fmily status and

youth treats thdrelationihip as gne-way, with

tus affecting labor supply but not the reverse

labor force patterns of.

changes in .family sta.

. With some types of

changes in family status, such a$ parental divorce, this seems a plausible approach;_, However, it is not entirely satiyactory for many oth:-

4.

er types of.changes'in family status. Changes, in the young adult's

own living arrangements dre probably interre (ted with labor ;supply in

a more cplex fashion, affecting the labor supply decision but also

being affectedly it. A sufficiently high amount A earnings may he

required for some_to_ifford living alone or with-a spouse. This argues

_ . for analyz g the relationship between own living arrangement and la-

. bor supply a simultaneous framework. Given the constraints of present data sources this,turrently can be accomplished only with.the use

of statistical techniques such as two-stage least squares.

- This method wat'used by Hill (1977) in studying the interrelation

( f)etween youth's decisions to split off from their parents, marry, attend school, and work full time.

16 In thiS study Hill found strong,

positive correlations between working full time and both splitting-off

. and marriage for males and weak correlations between workIng full time

and these other actions forfemales. _The two-stage feast squares arm,

alysis of the interrelatiOns, however-, pr+d unsatisfactory. This

work failed to yield clear implications, dill part because of the problems associated with the use of this technique with'behavioral models

for using microdata. 'Two-stage least squares, in effect, employs.in-

,

- strumental variables to alleviate the problem of correlation between

regressors and the error term. As pointed out by Wonnacott and Wonna-.

(

15."Rodger D. Roderick and Andrew J. Kohen. Years for Decision': A Longitudinal Study of the Educational and Labor Market Experience of

Young Women, (Columbts, Ohio: Center' for Human Resource Research,

The Ohio Vate University, 1973), pp. 24-27. . e*

4

-16. Martha S. Hill, "The Decision by Young-Adults to Split-off from . Their Parents' Households," PhD dissertation, University of Michigan-1977.
C . - 44 4 - JP

cott (1970), an effective instrumental variable must be both highly correlated with the regressor causing the problem and not direct an,explanation of the dependent variable (and thus not correlated with its

error term).

17 The task of finding such instrumental variables in behavioral models using microdata is amesomeN It is difficult to find a

measured variable which satisfies one of the two requirements, much less

both. At the individual levef, variables which affect one decision in

. - ' an interrelated decision matrix may,p19ibly affect the other decisions

as well. Also, microdata do not yield the high correlations between variables which are characteristic of aggregate data,

This'argues for.collecting data which specifically aslyndividuals

about the interrelation between such decisions as labor supply and own

living arrangement. Such data are not currently available. Thos, al-

... though 'recognizing the inadequacies of a one-way analysis of the'relaL tionship, this paper will be confined to that approach.

THEORETICAL FRAMEWORK

Curreqtly, there is no well-developed theory on the labor supply responses of young adults to changes in family status. However, recent developments in the theory of the labor supply decisions of married women

address some effects of changing househdld structure on labor supply.

4 Therefore, this sectio will attempt to draw upon aspects of these de- . velopments which are applicable to youth.

Recent motels of the labor supply decisions of' married women, such

as those of Becker,18 Gron au,2° and Gramm21 are based on the

notion that a wife's labor supply is determined in cdnjunction with other household-related decisions in the process of-maximi;ing household

. t 17. R. Wonnacott and T.' Wonnacott, Econoietrics,(Ner York: J. Wiley,

1970), pp. 150-60. . , 18. Gary S. Becker, "A The4ry of the Allocation of Time," Economic

. Journal (September-1965), pp:1493-517. f 19. Robert J, Willis, "A New Approach to the EconomiC-Aeory of Fertility Behavior," Journal of Political Economy, (March/April 1973),

pp. 514-S64.

20. ReubenGronau, "The Effectk of ChildrelOon the HoUsewife'S Value

of Time,",Journal of Political Economy (March/April 1973,

pp. S168-S199e

21. Wendy Lee GrMm, "Household Utility Maximization and the Working

.Wife, " ,American Economic Review (MarCh 1975), pp. 90-100.

. 74
4

41.utility. Household utiIity,is a function of commodities produced byk

the household by 'tombini g goods purchased in the market with the home

time iivauts of the hous hold members. The nature ofwthe utility

tioi) is determined by file preferences of all household members as evaluated y the husband and wife, who make the household decisions. The

natu e of the household production function is deterbined by the state

the houie4oild's technology, just as a../firm's production function is

determined by the state of available production technology. In maximizing the household utility function, household members are constrained

by househdld income and their time, which can be allAb.tedlbetween mar-

, ket uses and home uses. In applying this model to married women, economists have standdly treated the income of household members other

than the-wife-as-exogenously determined and assumed that the home time

of members other. than the wifeis unproductive. From these assumptions,

economists have been able .to'draw implications concerning the reservation wage of a married woman and consequently her rabor supply. -the

reservation wage is defined as the minimum wage a person must be able

to obtain in phe market whith makes'it worthwhile to take a job. Mathematically this can be expressed as the ratio of the marginal utility

I product of home time to the marginal utility product of income in terms

of market gdods, evaluated at zero'hours of market work. When the

wife's reservation wage is less than or equal to her market wage, then

household utility is maximized by her working in the labor market.

Ceteris paribus, factors which increase the marginal utility product of

her home time decrease-her dhood-of- working in the-labor market,

whereas factors which'-increase t marginal utility product of market

goods increse the. likelihgod of h r working in the labor market.

Some insight into the labor pply decisions of young adults may

lie gained by adapting this general framework. In this model, the young

adult will be assumed to maximize a utility function as he or she per-

. ceives it. Unlike the model for married women,.thiS utility function

is assumed to be baSed on-the individual's evaluation of the preferences

of the household members rather than on the parents' 'evaluation. Since

parents and their children who are in transition to adulthood are likely

to have different sets'ofpreferences, the young adult'l evaluation of

I

(
to.

* * 446 4 :

. \

parents' preferences wouldno doubt differ frbm heir actua preferences.

Thus, the utility furiction being maximized by a young adult. iii his/her

parents' household'is not necessarily identical to ther.parents' household

utility function. - The young adult's utilityfunction is assumed to depend on the househeld proliWiti4of commiidlties, which in turn. area function of tine

and market, inputs of t ,hOuseho is members, 'Both the pung adult's. age.

a, and livirig arrangement, 4will be aisuMa to affect theahouseholid,

production oduCtion technolo living arrangement will also be assumed-t

affect the amount of tim and goods inputs of the other household members, which the young a ult takes as given but aria subject to exogenous

change. In maximizing his or her utility function, the young adultis

constrained by both own income and time, with time to be allOcated between market and home uses.

.This problem can be stated as:

(1) Kiximize U(Z)

subject to: Z = g(Xi, Hi, Xo, Ho, a; LA)

+ M - pX. 0

1 1 1

. i

T.-H.

41101(ne Xi is market goods purchased by the young adult,

Hi ,is home time input of the youpg adult,

Xo is market goods purchased by other household members,

Ho is home time input of e other household members,

coi is the young adult' arket wage,

Ti is total timeof tie young adult,

M is nonlabor income of the young adult,

Pi is the vector of prices.

The living arr ngement of a young adult can undergo many changes.

Here we will cohcen,rate on five major ypes of changes; (1) the -movement out of the parental household to a one-person household; (2) the

movement, instead, to a married household; (3) the movement to a married

household with children; (4) the movement to an unmarried household with

children, and (5) the loss of one of two parents. Each of these changes

g

involves the lost and in some cases,the gain of household members;-so

a

-

V 14,
a

each affec:cts the amountof X0 and Ho in the household and the household's

productidtechnology. ' In the case of movement out Of the,parentai househola to a one-per- i

sonhoutehold, the young adult tends toundergo a loss of both the mar-

, ket goods and time of the other members of the parental household, pre-

, 4)dominant ly of the parents. And, notdoubt, the household's technology

changes. Movement out of a family to live alone places,greater reliance

of the,young,adult on, his or her market work to provide market goods

and greater reliance on own home time to produce househOld characteristits. However, since certain'market goods, such atfoo4, clothing, a* and sherter,'ird necessary for survival, it seemslikely-that the young

adul.t would reach a ntw_ltailibrium ih tbf one- person household that_en:

tailed, ate zero hours of market4:k, a higher marginal utility prOduct

of market goods relative to the marginal utility prodifct of home time

1 'than in the 'parental household. If slc this would mean a lower reservation-wage-and-consequently a greater likelihood of working.

at In the-case-of-movement_outparental household to live with

a spouse, the situation is further complicated by the addition of another adult. If the spouse decides to specialize relatively more in mar-

/

ket production than in home production, most typically the case with

husbands, then it seems likely that the young adult would reach a new

equilibrium that entailed, at zero hours of market work, a lower mar-

'ginal utility product of market goods relative to the marginal utility

of home time than in the case of the one-person household. If the

spouse decides to specialize .relatively.more in home production than

in market production, the stereotypical case with wives, then it seems

t

,. likely that the young adult would reach a ng quilibrium that entailed,

at zero hours of market work,

( a higher marginal utility productof market goods relative to the marginal utility of home time than in the,

case of the one-person household. If this is. so, then young miles who

marry would *en to have lower reservation wages than oung males, who

form one-periOn households and, consequently, would b 'even more likely

to work. Yo4tg females who marry would tend to iia.ve higher reservation

, . wages than ungfemales who form one - person,households, and consequent-

_ . 4 , ' ly. -would.

b1e -16ss likely to work.

.. .

i , 4 7

t
0.

O b- 448 -

4 is not inilakely that having children would tend to accentuate

the degree of market /home specialization of both spouses. If so,

--young males living with14.spouse and.children would tend to work more

than their childless counterparts,. whereas young women living. with a

spouse and children would tend to work less than their childless counter4

ir parts.

/In the case of young adults living with children and no spouse,

there is no other adult present to contribute market goods and home

time to household production. There is a greater need for market goods

than in one-person households, which would,tend to increase labor supply, but care for the young children tends to be quitetime-intensive,

thus tending to decrease labor supply. If there are available sources

of non-labor income with-which to purcAse a minimal amount of market

goods, as with ADC DC for example, then it is likely that the labor supply

of ybung adults with this living arrangement would fall somewhere in

between that of,one-person households and households conaWin.i\a

spouse as well as children.

A loss of a parent from the young adult's household of origin is

particularly difficult to ascertain from this framework. The loss of

a parent can mean the loss of income, and/or 'the loss of market goods

and home time of that household members, depending on the given-liking arrangement of the young adult. Additionally, these losses can

spur subsequent changes in the market/home time allocation of the re- _ maining parent as well as changes in household technolqgy. Such a complex of contingencies precludes clear implications for the young

adult's labor supply at this time. However, the framework suggests

that all changes in living arrangements partially affects the labor

supply decision of youth

I

by altering household money income relative

Am. needs, but that addition effects result from subsequent changes in

home tithe inputs available to the household and changes in household

production technology.

,

...J. 5

. 478

a
- 449 - 4

Kr".'

THEE EMPIRICAL ANALYSIS

The 'empirical analysis will-concentrate on differences in family status across.individuals at a point in time, 1975, using Multiple Classification'Analysis (MCA), a form of dummy variablregression.

22

Changes over

time for the same individuals will also be investigated.

The data used for this work come from the Panel Study of Income Dynamics (PSID), a longitudinal study of a national sample of over 5,000

American families.

23 The PSID is well-suited to an analysis of effects

of changes in famil,status on the labor supply of youth'since it-fol-

. 4 lows all membe4of households interviewed at the start of the study,

1968, even if they leave the households. Thus, both changes in the parIND

ental household and the formation of new households have been recorded,

as has the number ofhours worked by each household member.

24

The sample used for the analysis consists of noninstitutional individuals aged sixteen to twenty -our in 1975 who were classified A a

child of the householp head of a PSID family in 1968, iben the study

. began:

25 The sample is restricted to individuals who 'were initially

children-in order to ascertain parentalAftamily status and socio-eeonomisc

background.

26 Young males and young_females are analyzed separately

since both the l,terature and prelimintry investigation of the data indicated differential labor force behavior by sex.- Small cell sizes in

a variable of primary interest., own living arrangement, prevent further

subdivisions of the sample.

22. The longitudinal analyses that, will be referred to in the text used

this same technique to observe the effects of changes in family status on changes in lab r supply--1975 relative to 1968.

23., The PSID oversamples or families but weights the data to correct for this oversaw ing,and for differential nonresponse in

order to arrive at a nationally representative sample.

24. It s ould be noted, however, that the PSID data are based on inforon provided by the household head, so information concerning

other household members is not gathered directly from those individuals. #

25. Individuals in institutions are excluded from the analysis since

their hours worked are not ascertained in the PSID.

/6. The longitudinal analysis that will be referred to in 'the text further restricts the sample toIindividuals not in institutions in - 1968 for whom hours worked was ascertained.

47j

Ao.

A
6'

450

For the purposes of this paper famiy--fatus is identified on the

basis of two variables-own living arrangement and parents' living arrangement., The variable representing own living arrangement consists

of six categories: living with spouse and children, living with spouse

and no children; living'with children and no spoup, living alone, living with other adults, and in parental household. 27'

A young adult ,falls

into .one.of the first five categories only if he or she is 2household

head or Wife as of 1975. / 1- With respect to parental family status, this paper will be primarily

concerned with whether or n the youth is from a broken home, i.e.,-

whether or not he.or she Co es from a stable two-parent family. This

distinction will be based on the presence of the youth's parents in

the parental household between the beginping of the PSID, 1968, and the.

time the youth becaMe a househO head or wife or 1975, whichever came

first. Since black yo h are more likely than white youth to come from

_...,. V

broken homes, there may be race differential effects of parental status.

In order to investigate this possibility, parental family status will

..- be combined with race to form a pattern variable. The parental family

status /,race variable will consist of four categories: same two parents

present until left home/white; other/white; same two parents present un- same tildeft home/non-white; other/non-whit

-e

. 28

.

. Labor supply will be ident'fied both on the basis of employment incidence (whether working) and olume'of hours worked. An individual

will be-,classified as working if 197 annua ours of work equal or exceed 250. hours, and the volumes of houts Worked will be measured as the

number of hqurs worked in 197.4.29 \,

Analysis of a dichotomous dependent variable, such a whethei Work-

. 27.' The individual is classifi d. as living with a spouse if the PSID , indicates'that the young a ult is a head or wife and a wife is

present in his or her ho ehold, as indicated by age of wifebeing

non-zero. This does not in all cages mean that the young adult is

legally married.

28. Here, "left home" means becoming'a household head or wife or 1975,

whichever came. first.

29. In the PSID, annual hours worked 4certainea in a given year apply

to the prededing calendar year, e.g., work hours ascertainedin

1975 are annual hours worked in 1974.

6.4 0

t
-. 453. .

, A

ing,,i optimally done using some maximum 'likelihood procedure such as

logit analysis. But with proportions not too close to l or 0, substantial

sample sizes, and categorical predictors which can handle nonlinearities

more flekibly than any arbitrary transformation of the. dependent vari:2

able, the remaining problems of using MCA instead are thinor.

30

Control variables included in the analysis will consitt of demographic,,

economic, and socio- economic backgiound,factors: age, educatOn, other

family income/fami needs,' and the county unemployment rate all as ascertained in 1975; d family income/needs, parehtal head's education, if

parental head is white collai- worker, and if mother is working all as

ascertained in 1968.3

1

As shown in Table 1, loss than half of 'thelample of youth had formed

their own households by 1975.

32 -Household formation was more common

among females than males,.witlrabout 45% of the females bei4 a household head or wife in 1975 as'opposed to about 35% of the males. Most of

the youthwho had-left the parental household were living with7a spouse.

Howevet, a substantial percentage, particularly of males, had formed

one-person households, and a not trivial percentage of females had

formed one)-parent hotisehOlds. Those living with a'Spoute were almost

as likely to be.childless as to'have children present, in the household.

)

. With respect to parents' living arrangement, although about 85% of

the sample,begah the panel period in a two-parent household, by 1978(,

only 73% could -be classified as coming from a stable two-parent family.

As Table 2 indilateso; sex differences in parents' living arrangement

were very minor; however, race differences were substantial. Nonwhites

were much less lirely than whites to come from stable two-parent families; only abd'ut 50% of the nonwhites were from komes where the same

30. The remaining problems are the possible prediction of probabilities

outside the 0 to 1 range and the existence of heterogeneous variance. 31: Additional controls in the longitudinal analysis are change in the.

county unemployment rate and change in other.familY.incgMeineeds.

born measured as the difference between the measure ascertained in

1975 and that ascertained in 1968. Like hours worked,these me'sures ascertained in a given year apply to the preceding calendgr

year.

32. See Appendix Table A.2 for sample distributions with respect to

change in parents' living arrangement.

4 Si
p - 452 -

%. TABLE 1 2

J OWN LIVING ARRANGEMENTIN 1975 BY. SEX

(Noninstitutional Individuals Aged 16-24 in 1975

Who Were in Their Parents' Households. in 1968)

Own Living Arrangement

in.1975, .

Males Females

Number of

Observations

Weighted

*Percent

NuMber of

Observations

Weighted

Percent . .. 4

, With Spouse and Children 146 12.9 . 22p 19.0

With/Spouse and no

. Children 102 9.9 142 13.5

With Childien and no

Spouse 4 0.3 124 /1' 4.9

Living Alone' 175 13.0 108 9.2

With Other Adults 15 1.2 8 0.6

In Parental Household 908 62.6 879 52.7

TOTAL 1,350 100.0 1,481 100:0

KA

4"

Yw
.1

)

453 -

TABLE 2

PARENTS' LIVING ARRANGEMENT, TY RACE AND SEX

(Noninstitutional Individuals Aged 16-24 in 1975

.Who Were in Their Parents' Households in 1968)

Males Females

Parents',Living Number of

Arrangement/Race Observations

Weighted

Percent

Number of

Observations

Weighted

Percent

se 'Same Two Parents Present

Until Left Home/White 529 66.5, 522 62.0

Other/Wlite 145 17.8 162 19.0

Same Two Parents Present

Until Left Home/Non-white 307 7.7 367 9.9

Other/Non-white 369 8.0 '430

TOTAL 1,350 100.0 1,481 100.0

Iv
' 454 - two parents were present during the observation period.

Of the individuals not from 'stable two-parent families, there were substantial differences by race as to the type of parental living ar- rangement. Of the whites, about 50% lost one parent during the observation period and 30% were from stable one-parent families. Of the nonwhites, about 30% lost one parent during the observation period and 60%

were from stable one-parent families. 33

Labor Supply and Own Living Arrangement

2 The MCA results indicate that there is a strAg relationship between

the livinf arrangements of'young adults and their lab6r supply. Living arrangements are significant,and sizably correlated witp both employment incidence and mean hours worked, even with controls for basic

demographic, economic and socio -ecc omic background factors.

34

The

nature of these relati ns varies by hex as shown in Tables 3 and 4.

Males who have left-the parental household to establish either a

married household or a one-person household have much higher incidence

of employment and mean hours worked than do those who are still in the

parental household. Labor supply differences among young males who have

formed their own households are relatively small. There is virtually

33. See Appendix Table A.2 for details concerning sample distributions

with respect to change in parents' living arrangement.

34. There are two potentially important measurement problems which * could be associated with this finding. The first is that the

respondent's relation to the youth varies with the youth's living

arrangement (the respondent'is the household head), and reports

of youth's work hours may be lower if his/her parent is the re-_,

spondent than if he/she is the actual respondent: Since mean work

hours in 1974 correspond closely to change in work hours regardless of change in living arrangement, this, probably is not a major problem. The other potential problem is the timing of the

measurement of work hours and living arrangement. Living arrangement is measured at a point in time whereas work hours are measured over the time span of a year. The present analysis uses living arrangement in the spring of l975 and 1974 annual work hours.

Living arrangeMent in spring 1974 could have been used instead,

.but that measure would not have been satisfactory either. Identical MCA's to those Presented in the paper were run with living

arrangement in 1974 substituted for the 1975 measure to identify

the extent of this problem. The results indicated weaker but still

highly significant differences in labor supply with respect to,own

living arrangement, and the pattern of the relationships were similar but less pronounced.

434

-st
- 455 -

TABLE 3'

. - UNADJUSTED AND ADJUSTED* PROPORTIONS OF YOUNG ADULTS WORKING IN 1974,

BY OWN LIVING ARRANGEMENT IN 1975 AND SEX

(Noninstitutional Individuals Aged 16-24 i,r1 1975

WhO Were in Their)Rarents' Householdsin 1968).

00n. Living Arrangemd t

c

in 1975

With Spouse .alef-4- A Children

With Spouse and

no Children.

With Children and

no Spouse

Living 'Alone

With Other Adults

In Parantal Household

Overall Mean

Standard Deviation

Eta

2

Beta

Males Females

Unadjusted

Proportion

.975

.989

1.000a

.919

.716a

.542

Adjusted

Proportion+

16)."

.877

.702a

.841

.642a

.603

.695.

.461

.190**,

n71**

4

Unadjusted Adjusted

Proportion Proportion+,

_451 .395

.807 .692

.649

.949

1.000a

.671

.844

1.015a

.446 .512

f .556

.497 - .133**

.073**

+Adjusted for the effects of: age in 1975, education in 1975, other family

income/family needs in 1974, 1974 county unemployment rate, family income/

family needs in 1967, 1968 parental head's education,, whether 1968 parental

head was white collar worker, whether mother 1)as working in 1968, and

parents' living arrangement/race.

aFigure based on fewer than 20 observations.

**Significant at :01 level.
456 -

TABLE 4

UNAD.TUSTED AND ADJUSTED* MEAN HOURS WORKED IN 1974, . BY OWN LIVING ARRANGEMENT, IN 1975 AND SEX

(Nonin'stitutional Individuals Aged 16-24'in 1975,

Who'Were in Their Parents' Households in 1968)

Own Living Arrangement

'in 1975

Males Females

Unadjusted.

Mean

Adjusted.

Mean+

Unadjusted

Mean

Adjusted

. Mean+

With Spouse and Children 1,950 1;448 577 11380 . . With Spouse and

noChildren 1,846 1,600 1,149 960

With Children and

'no Spouse 2,373a 1,852a 802 724

Livipg Alone 1,536 1,310 1,491 1,287

WthOther Adults 1,452a 1,291a 1,312a 1,263a

In Parehtal Household 584 748 452" 614

.

. Overall Mean r 1,026 688

Standard 'Deviation 903 760

ei)---

0. Eta. .419** .14 .213**

. ' Beta

2 '

*.168" .115** /.

A

. - +Adjusted for the effects of: age in 1975,-education in 1975, otbuefamili,'

income/family needs in 1974, 1974 county unemployment rate, family income/

family needs in 1967, 1968 parental head's education, whether 1968 par &ntal

head was, white collar worker, whether mother was working in 1968, anti

parent's' livinarrangement/race.

aFigure based on fewer than 20 observations.

, **Significant at .01 level.

4 2 CI
4, - 457 -

ti

o.

.,, no Itfference in this respect between married males-with children and

married males without children. Howelfer, young males in one-person

households tend to work fewer hours than do young males in married households. ''Control variables for other demographic,factors, socio-economic

background, other family ncome/fathily needs, and the unemployment rate

reduce the strength but do not alter the p4ttern of these -relationships

, between labor supply and own living arrangement. Of these variables,

age is,the primary adjustment factor.

35

Among young women, the relationships between own living arrangement

-- and labor supply are more complex. As with males, both. the employment

incidence and mean hours worked are lower for individuals still'in,

. - their parental-household than he average for those who have formed,

their own households. However, fethale labor supply varies widely with

the type of ,household formed, and the control factors play a more important role for females than for males. Both with and without controls,

though, one result is clear--of the young -females, those who form one-

," person households are most likely to be working and work the most hodr4

annually. Interestingly,,the labor supply of femai.who forAvne-per-

, son households' is quite -similar to that of male's who form off- person

households. Females with other living.arrangements tend to work less.

. than males with the same living arrangement, particularly if they are

married.

With respect to these other living arrangements, simple unadjusted

. means indicate that: (1) females living with a spouse and children work

as much as females still in the parental household,. and (2) females

living with a spouse and no children work more than females living with

children and no spouse. However, females living with a spouse and chil- . dren tend to be older than those still .the parental home, and age hat

a strong positive-but declining effect on labor supply. Thus, when the

ft females are statistically placed in otherwise similar circumstances, particularly with respect to age, these relative effects .of living arrange-

.0"

35. MCAt,s were run without-age as a control variable, and the results

indicated relatively minonadjustments on the part of the remain--,

ing control variables; . 4,"

4 oi .14

ti
ao

41

4

\. -- 458 -

o 3e ments ch ange. Females living with a spouse and children are less like

ly to work and work feWer hours than their' counterparts sitlrin the

.parental household. So, with controls for other factors, priMarily age,

e - we find that young females living with a spouse and children have the

lowest labor market supply.

The control variables also play a role in labor supply differences

of young females living with a spouse but without children vs. those

living with children and no spouse. .(Much of this alteration is, un--,

doubtedlY,-dueto the intervening effects of race. Being nonwhite as

opposed to white tends to have A negative effect on labor supply (which

is.somewhat stronger for employment incidencle than for hours workid). A

proportionately larger percentage of females living with children and F.

%spzjse are no white (40% opposed to 7%"of those living with a spouse

with'nO children). Thus, when females in these two groups are statis-

#

"%> tically. placed in otherwise similar circumstances, we find that females

living with childrdn and no spouse-are almost, as likely to be working as t

. females living with a spouse and to children, although they work fewer

hours. 4 - The relationships between change in own living arrangement and- "

change in labor supply were also.-inve-Stigated.fising MCA analysis. 'Since

these results were so similar to those just discussed, the tables presentingthe longitudinal relation/ships are relegated the appendix. 37

o Labor Stpply and Parents 'Living Arrangement

As indicated by Tables 5 and 6, effects of parents' living:arrange-I

ment on the labor supply of Most youth are relatively 11. ,The-labor

supply of white males and of females is essentially un rfected by wheth-

, I er or not the individual is from a stable two-parent family, both in

36, MCA's were run without age as a control variable, and the results

indicated only minor adjustments of,the remaining control variables

on the labor supply of these groups of females.

37. Changes were measured as the difference between initial, as ascer--

tained in 1968,.conditions and end, as ascertained in 1975, cow

Lions. 'Since the entire sample started out in the parental house- / hold and over 90% of the youthArere initially nonworkers, it is

not surprising that the cross2sectionallifferences in both own living arrangement and labor supply closely match the longitudinal

changes in these conditions.

4
4

- 4 59 -

TABLE 5

UNADJUSTED AND ADJUSTED' PROPORTIONS OP YOUNG ADULTS WORKING IN 1974.

BY PARENTS' LIVING ARRANGEMENT, RACE, AND SEX

(Noninstit.utional Individuals Aged 16-24 in 1975

Who Were in Their Parents' Households in 19!8)

Males

Parents' Living . "Unadjusted

Arrangement/Race Proportion

Adjusted

Proportion+

Same Two Parents Present

from 148 Until Left

Home/White .724 .712

4

Other/White .719 ':725.

Same Two Parents Present

froth 1968 Until Left

Home/Nonwhite , .6K5 , .646

Other /Nonwhite .476 .535

Overall Mean i .695

, A

Standardileviation . .461

Eta2 .., .024**

% 1 Beei

2 .012**

1

Females

Unadjusted t Adjusted

Proportion Proportion+

.590 .593

.607 .593

.398 .392

.381 .403 , , .556 I

.

. .497. . .026**

.024**

4. -

v -value for Pattern Variable

as opposed to additive , 4.61* D.12 ------1 model

"Be +Adjusted for the effects of: age in 1975, education in 1975, other family

income/family needs in ]74, 1974 county unemployment rate, family income/

family-needs in 1967, 108 parental head's education, whether 1968 parental

head was white collar worker, whether Mother was working in 1481.lown

44...living arrangement in 1975.

*Significant at .05 level.

**Significant at .01 level.

C.

. le

1

*

' .11.,

I .

,or4
/

\- o - 460 - 5,

terms

f of employment incidence and annual hours worked. The labor supply

of black males, however; is affected by parental living arrangement. . Black males not from stable two-parent families are less likely to

work and working somewhat fewer hours than those from stable two-parent

families. This finding is not without importance since half of the

young black males do not come from stable two- parent families. Inter1 estingly, these effects of parental living arrangements are essentially

unaffected by'the control variables; the adjusted means are Izirtually

equivalent -tothe unadjusted means. ,

Changes in Parental Living Arrangements'and Labor Supply

Tables 7, 8, and 9 address the issue of whether the loss of a .parellt

changes the labor force behaviorof young adults. Consequently, the

variable measuring chahge in parental living arrangement is defined in

such a manner as to distinguish young adults from stable two-parent,

homes, those from stable one-parent homes, and those from where one of

the two parents recently left. The remaining category on the variable

is a catch-all category, for which,the small 'sample size did not allow

further distinction. .

. The results'indicate that the lo'Ss of one parent ovethe panel period and prior to leaving home does not alter substantially the labor.

supply of young adults.

38 White youth, both males and femaleSi were'-

.,'somewhat more likely -to enter,the york force if they lost a parent,

whereas nonwhite females were somewhat less likely to enter the work

force under the same circumstances., Howaver, in'terms of change in

hours worked differences between youth from stable,two-parenthotes-

, d those.who lost one parent during the observation period were rela-

. t velk small particularly when one controlled for other factors.

Other effects of change in parental Aving arrangetents were relative more important. Membership in the catch-all "other" category

of c ange in parental status constrained the increase in labor supply

. of yo th, particularly nonwhite,youth. These results, however, are

not s .ject to clear,interpetation since small cell size precluded

38,. In viduals are classified as losing a parent if two parents, were

pres-it in 1968; but only one parent was later present while the

youth still apart of the household. ,

4-90
A

I

- 461 r.

TABLE 6

UNADJUSTED AND ADJUSTED+ MEAN HOURS WORKED IN 1974

BY PARENTS! LIVING ARRANGEMENT, RACE, AND SEX

(NoninstitutionalIndividuals Aged 16-24 in 1975

'Who Were in Their Parents' Households in(1968)

Parents4_ Living

Arrangement/Race

Males Fem #es

Unadjusted *Adjusted

Mean Mean+

Unadjusted .Adjusted

Mean :. Mean+)

-Same Two Parents Present

from 1968 Until LeYt

Home/White 1,068 1,067 728 725

1 Other/White

AP. 1,090 .. 1,048 754 .' '714 o Same Two Parents Preseit

from 1968 Until Left

. Home/Nonwhite 850 897 42.3 528

Other/Nonwhite 705 754 A92 556

x \

. Overall Mean 1,026 . 688 . Standard Deviatiaon 903 :, 760 ' r 1

Eta

2 .015** .016**

. . : Beta

2

' 4 4.010 * *' .009* ,-t--

e, F-value for Pa$ ttern Variable

as opposed to additive

model.' 1.08* 0.40 . +Adjusted for the effects (4'.: age in 1975, educatiomin 1975, other family

Income/family needs in 1974, 1974 cqunty unemployfnent rate, family income/ , family needs in 1967, 1968 parental head'i education, whether 1968 parehtai

head was white collar worker, whether mother was working in 1968, and own

living arrangement in 1975.

*Significant at .05 level.

**Significant at :01 level. V

I

003
TABLE 7

UNADJUSTED AND ADJUSTED 'P PORTIONStF YOUNG HAW CHANGING WORK STATUS,

BY CHANGE IN ARENT'S' LIVING ARRANGORERT AND RACE

(Noninstitutional Males Agee,16-24 in 197S Vho Were in Their Parents'

Households in 1968 and For Whom Hours Worked in 1967 Was Ascertained)

Entered Left

Change'in Work Force Work Force

Parents' Living Unadj. Adj. Unadj. Adj.

Arrangement/Race - Prop. Prop.+ Prop. Prop.+

Same Two Paredts 1968

'Until Left/White .604 .601 .003 .004 Same One Parent 1968

Until Left/White '.724 .708 .000 -.004 Lost One Parent Between -

1968 and Left/White .630 .666 :000 -.002 . Other/White ...530 .524 000 .003.

VSame Two Parents 1968

III

Until Left/Nonwhite .537 .525 .005 .005 Same One Parent 1968 0 Until Left/Nohiihite '.407 .428 Lost One Parent Between \

, and Left/Nonwhite .590 .526 .000 .001 Other/Nonwhite .243 .339 .000 .004 Overall Mean .590 '.001 Standard Deviation .492 . .050 Eta

2 .020** .001

,..11, Bete2 .017 .002

I

F-value for Pattern

Variable as opposed to

additive model 1.99 ° 0.02

Remained in Remained out.

Wm? FOce of Work Force

Unadf. Adj. Unadj. Adj.

Prop. Prop.+ . Prop. Prop.+

.091 .089 .301 .308 .090 .043 .185 .252 .035 .032 .335 305

:180 .166 .289 .302 i

_..7 .076 .097 ' .362 .372

I .090 .533 .482

-----. .027 .116 .383 .356 :000 :.......,49 .757 .650 84 .324

. .277 .468 .010 .029*

'.009 .015

3.04*

+Adjusted for the effects of: age in 1975, education in 197', other family income/

family .needs in 1974, 1974 county ungmployment rate, family income/family needs in

1967, 1968 parental d's education, whether 1968 parental head was white dollar

worker, whether mothe was working in'1968 o family income/family needs 1974-1967,

county unemployment r to 1974-1967, and than own living arrangement. , *Significant. at. .05 level.

**Signifisant at .01 level.
o

16.

ttc - 463 -

I

'TABLE 8

UNADJUSTED AND'ADJUSTEe PROPORTIONS OF YOUNG FEMALES C)IANGING WORK STATUS,

BY CHANGE IN PARENTS' LIVING ARRANGEMENT AND RACE

(Noninstitutional Females Aged 16-24 in 197S Who Were in Theft. Parents'

Households in 1968 and For Whom Hours Wogd,in 1967 Was Ascertained)

Entered Left

'Work Force Work Force Change

Remained,in

Work Force

Rained out

of Work. Force in Parente' hiving .Unadj. Adj. Unadj. Adj. 'Unndj.

/Arraniement/Race Prop. Prop+ Prop. Prop.+ Prop.

Adj.

Prop+

Unadj. Adj.

Prop. Prop.+

Same Two Parents 1968

Until Left/White .565 .570 ' .018 .017 .025 .025 .391 .388 Same One Parent 1968

Until Left/White .676 .593 .000 .003 .019 .015 .305 .388 Lost One Parent Between

I

. 1968 and Left/White .552 .613 .023 .021 .015 .009 .410 .356

ID Other/White .562 .506 .000 -.002 .045 .078 .393 .418 Same Two Parents 1968

Until Left/Nonwhite .389 .380 .020 .024 .012 .002 .580 .594 Same One Parent 1968

Unt r Left/Nonwhite '.396 .402 .000 .001 .035 .044 .569 .553. Lost 0 e Parent Between

1968 d Left/Ronwhite ,304 .327 .000 .0124)4,qo .001 .696 .660' Other/Nonwhite .262..193.000.015.

.?"'si .000 .032 .738 .761 Overall Mean .535 '.015,. .023 Standard Deviation .499 .122 .150

2 4' rta .029** .003 .0

..028**- .0030

F-value for- Pattern 3- Variable as opposed to

additive model 0.61 1.47 0.62

.427

.495 .033**

.030** 0.20

4Adjuste&for the effects of: aip in 1975, education in 1975, other family income/

family needs in,1974, 1974 county unemployment rate, family income/family needs in

1967, 1968 parental head's education, whether 1968 parental head was white collar

'worker, whether mother was working in 1968, Other family income/family needs 1974-1967,

county unemployment rate 1974-1967, and change in own living arrangement.

**Significant at .01 level.

,t

1, 493

I
- 464 -

TABLE 9

UNADJUSTED AND ADJUSTED MEAN CHANGE IN HOURS WORKED,

BY CHANGE IN PARENTS' LIVING ARRANGEHENT, RACE AND SEX.

* (Noninstitutional Males Aged 16-24 in 1975

Who Were in Their Parents' Households in 1968

and For Whom Hours Worked in 19G7 was Ascertained )

o Change in Males . Females

t Parents' Living Unadjusted

Arrangement /Race Mean

. Adjusted

Mean

Unadjusted Adjusted

Mean Mean Same Two Parents 1968 ,

. Until Left/White 937 953 -689 692 Same One Parent 1968

Until Left/White 1,119 903 914 717 Lost One Parent Between

j368 and Left/White 1,01,8 1,011 619 717

Y ' Other/White 856 853 43 543 Ao

Same Two Parents 1968

Until left/Nonwhite 804 811 485 503

Same One Parent 1968

Until Left/Nonwhite 647 700 539 562. --'--'hmst One Parent Between

1968 and Left/Nonwhite 841 831 340 477 Other/Nonwhite 424 , 355 426 390

r

...,. Overall Mean 918 657

N Standard Deviation,

f 852 751 Eta

2

. .615 i £022** Beta

2 .013 , .011

4 F-value for Pattern Variable

as opposed to additive

model 0.86 0.17

Alt

a

+Adjusted for the effects of: age in 1975, education in 1975, other fanny. income/

family needs in 1974, 1974 county unemployment rate, family income/family needs

in 1967,, 1968 parental head's educatidn, whether 1968 parental head was white

collar worker, whether motherfwas working,in 1968, other family income/family needs'.

1974-1967, county unemployment rate 1974-1967, and,change in own living arrangement.

441. **Significant at .01 level.

494

0
4

-465-

3

subdivision-pf this category into distinct types of change. Effects

../

of coming frdni-a stable one- parent family are not as large but are more

interpretable. The effects are sizable only for males and'then strongest with respect tochange in work status. Nonwhite males from stable

one-parent fathilies are.less likely to enter the work frce than are

theit couiterp+s from stable two-parent families. The reverse holds

for thite males. The change in work hours, however, does not differ

much with respect to whether the youth is from astable one-parent or

stable two-parent fatilyi particularly if the youth is white.

IMPLICATIONS. AND NEEDS FOR FURTHERS RESEARCH

-Major trends in living arrangements affecting"youth-ihclude:

(1)-a decrease in the proportion coming from two-parent families, (2) a

shift away from their parents' household and marriage toward one - person`

or one-parent 491.1s'eholds,:and (3)'a shift away from marriage with

\Jrdren toward marriage without children-.

ISID data indicate that whether or not the youth is froth a stable

t*o-pafent.family tends tothaye relatively inconsequential implications

, , for labor supply except for nonwhite males.

39 Nonwhite males frdm

broken homes, predominantly those prom stable onerparentJaniilies,

tend :to work less than-their counterparts from stable two-parent fam-

. .

. ilies. This is partidularly important for young nwhite males since

about one-third of them come from stable one-parent families. Apparently ihe atmosphere of a one-parent home tends to deter either the deIsire or the ability of nonwhite mule youth to work. Better data on

job opportunities and,atttitudes of the youth are needed toAlncover the

, source of this effect. Since several studies have found-little evidence

39. This does not correspond to the findings of Bowen and Finegan

but their analysif of this effect was restricted to a

, A younger age group (fopfteen to seventeen year-olds), did not

differentiate the effect by and used less current data.

495

ti
4

of strong effects of attitudes on subsequent economic attainment,

40

the

heed is probably greatest in the area of better data concerning the access to work. The.network.for obtaining jobs, both in .erms of search,

influence, and supply, mayrary extensively with the youth's,background.

With respect to Own living atrangement, the PSID dataCindicates a

strong relationship with labor supply. Males who left home Ito foi'm mar- ried or one-person households increased their labor supply much more

than their counterparts'who remained in their parents' lousehold, with

those forming one-person households working somewhat fewer hours than

,those forming married households. Females who left home to form oneperson, one-parent, or childless married households increased heir la-

/

bor supply considerably more than their counterparts who either re-

_ mained in their parents' households brIformed married-households with

children. In fact, females who formed one7person households wprked

about as much'as males in the same situation.

Implications of these findings are not clear without better understanding of the cause and,effect relationship between own living arrangements and labor supply. If changes in own living arrangement ca

changes in labor supply, but not the reverse, then these findings indicate that recant shifts in living arrangements may have contributed substantially to increase labor supply of youth. However, more plausibly,

causality runs in both directions, with. -labor supply affecting and being

.affected by own living arrangement. This analysis did not 'attempt to

disentangle any interrelation between these, two decision areas.

40. See for example: Greg Duncan and Daniel Hill, "Attitudes, Behavior,

and Economic Outcomes; A Structural Equations Approach," in Greg.

,J. Duncan and James N. Morgah,-Five Thousand Families '- - Patterns

of Economic Progress III, (Ann Arbor: Institute for Social Research,' 1975); James.N. Morgan, "ISeven-Year Check on the Pbssible Effects of Attitudes, MotOrs and Behavior Patterns on

Change in Economic Status," in G.J.'buncan and J.N. Morgan, Five

Thousand American Families-n,Patterns of Economic Progress IV,

(Ann Arbor: Institute for Social Research, 1976); Paul- J. Andresani, "Internal-External Attitudes, Personal Initiative, and the

Labor Market Experience of Black and White Men," Journal of Human

Resources, Vol. 12 (Summer 1977); and G. J. Duncan and J.N. Morgan,-

- "Sense of Efficacy and Changes in Economic Status--A Comment on

Andresani," currently submitted to Journal of Hui4an Resources.

Of
4C

- 467 -

Present data are not well-suited to'analysis of the causal relation:

, . ship between these two decisions.' Various statistical methods could be

used with data such as the PSID to analyze tile interrelation, but these

methods tend to be quite sensitive to model specification. 'Timing

patterns with respect to the decisions couldoalso be misleadihg since

decisions are not necessarily executed in the same order in which they

are reached.. A more reliable instrument for understanding the causal

relationship between living arrangement and labor supply would be a

survey which directly 'asked youth about the interrelation.. In effect, it

would be a survey concerned with the economic socialization process,

the process by which a young adult chosses a living arrangement and

work situation.41

41. A proposal for a survey along these lines has been submitted to the

4 National Science Foundation for review--"A Proposal For A Retro- I, spective study of Economic Socialization" bx J. N. Morgail, M. Hill,

and A. Thornton: Institute for Social Research, 1977.

# r 4 9 7 .1
0

- 468 -

APPENDIX

TABLE A.1

Jr

CHANGE IN OWN LIVING ARRANGEMENT BETWEEN 1968 and 1975 SE .

(Noninstitutional Individuals Aged 16-24 in, 1975

Who Were in Their Parents' Households in 1968

and For Whom Hours Worked in 1967 Was Ascertained)

Males Females Change in Own * Living Arrangement Number of Weighted Number of Weighted

Between 1968 and 1975 Observations Percent Observations Percent

Left Home, Acquired

Spouse, Had Childred

r 130

Left Home,

Acquired Spbuse 92

1 Left Hope,

Had Children 3

Left Home to

41, Live Alone 157

Left Home to Live

With Other Adults \ 15

. lid Not Leave Home

, 886

TOTAL 1,283

4,98

11.5 203 17.8

.. 9.5 135 13.1

0.2, 114 4.5

,4

11.9 104 9.3

1.4 8 0:6

. , 65.5 869 54.5

140 1,433 100.0

t
469

TABLE A.2

CHANGE IN PARENTS' LIVING'ARRANGEMENT BETWEEN 1968 AND PRIOR

TO THE YOUTH LEA1/INGIOT, BY RACE ,AND SEX

(Noninstitutional Individuals Aged 16-24 in 1975

Who Were in Their Parents' Households in 1968

and or Whom Hours Worked in 1967 Was Ascertained)

Males Females

Change in Parents' Number of Weighted Number of Weighted

Living Arrangement/Race Observations Percent Observations, Percent

, Same Two Parents

Until Left/White

Same phe Parent

Until Left/White'

1 Lost One Pareni Between

1968 and Left/White

° Other/Whfte

1 Same Two Parents

Until Left/Nonwhite -'-- Same One Parent _ Until Left/Nonwhite

Lost One Parent Between

mi 1968 and Left/Nonwhite

Othey/Nonwhite

N

TOTAL

S. I

484

41 s

64.9

5.4

498

61

71' 9.6 66

28 3.6 28

298 8.2 356

241 C9 268

p 79 // 2.1 109

t 41 1.4 47

1,283' 100.0 1,433

.4

4

61.9

7.2

8.4

3.4-

9.7

5.9

2.9

100.0

4
-- 470

TABLE A.3

UNADJUSTED AND ADJUSTED PROPORTIONS OF YOUNG MALES CHAefit-WORK STATUS

, BY CHANGE iN OWN LIVING ARRANGEMENT

(Nonlnstitutional Males Aged 16-24 in 1975 Who Were in Their

Parents' Households in 1968

snd For Whom Hours Worked in 1967 Was Ascertained)

Change in Own

Living. Arrangement

Left Home, Acquired

Spouse, Had Children .778 .789 .000 -.001 .192 .087

Left Home,

Acquired Spouse .791 .001 -.002 .197 Left Home,

Had Children . 1.000a .977a .0002 .006a .000a -.225a

Left Home to

Live Alone .767, ;775 .018 .013 .159' .086

Entered Left Remained le- Remained out

Work Force Work Force Work Force of Work Force

Unadj. Adj. Unadj. Adj. Unadj. Adj.

Prop. Prop.+ Prop.' Prop.+ Prop, Prop.+

Left ,Home to Live

With Other Adults .507a .419a., .000a- -.003a .241a .209a

A Did Not Leave Home ,4 .497 .000 .002 .031 .080

Unadj.

Prop.

Adj. Pro .+

.030 .126

:011 .129 .000a .242a

V

.055 .133 .252a .375a

- .472 .420 ,overall Mean ) Standard De4iation

452

Eta

Beta?,

1590

.492

.073**

$.075**

.

.00i

050

.013*

.007

r

, .083

.277

/ .071**

..005

.324

'".4¢1

443**

.085**

+Adjusted for the effects of: age in 1975, education in 1975, other family income/family

nee.in 1974, 1974 county.ulpmployment rate, family income/family needs in 1967,,..1968

parental head's education, whether 1968 parental head vas white collar worker, whether

mother was working in 1968; other family income/family needs 1974-1967, county unemployment.rate 1974 - 1963, and change in parents' living-arrangement/race.

aFigure based on fewer than 20 observations.

-* Significant at .01 level.

**Significant at .05 level.

er

....i1
h

S

- 471 q

0.

TABLE A.4

UNADJUSTED AND ADJUSTED. PROPORTIONS OF'YOUNG FEMALES CHANGING WORK STATUS

BY CHANGE IN OWN LIVING ARRANGEMENT

(Noninstitutional Females Aged

i 16 -24 in 1975 Who Were in TheirParents' Households; in 1968

ane For WhomHours Worked in 1967°Was Ascertained)

Change in Own

Living Arrangement

Left Home. Acquired

Spouse, Had Children

Left 'Home. AcquiredSPOuse

'Left H Had Children

( LefHome to

Live AlohaLeft Hometo Live

With Other Adults

%Did Not Leave Home

o- Overall Mean

Standard Deyiation

Eta

2

) Beta

2

'

tnterea JAIL xemain n lema.neirmut

Work Force Work Force Work Force of Work Force

Unadj. Adj. finadj. Adj. Dnadj. kdj. Unadj. Adj.

Peop. Frop.+ 112p4 PI I:45/-, tag!. Prop.* Prop. Proir.+

o k

.416 ..374 .058 .051 .031 .010 .495 .565

.773 .665 .008 .000 .053 .048 .165 .287

.666" .721 .000 ' .002 .007 .. -.013 - .327 \ .285

.693 .793 .:000 -.011 .052 .031 .055 \..187 .790a .7678 .000a -.0088 .2108 .2238 .6308 .018a

.442 s494 .007 .013 .007 .030 .544 .473

.535 .615 423 .427

,, .499 .122 " .150 .495 .112** . 02i** ;.,.. .026** .130**

.064** .023** .020**: '.059**

IL

a

/ 4%

t I 4Adjusted for the effects'of: age in 1975, education in 1975, other family income/family . needs in 1974, 1974 county unemployment rate, family income /family needs in 1967, 1968

A parental head's education, whether 19684,arental head was white collar worker, whether

mother was working in 1968, other family income/family needs 1974-1967, county pnemployAnent rate 1974-1967, and change in parents' livingerrangement/tace. l aFigure based on fewer than 20

_ **Significant at .01 level.

ikervations.

or

S

a 501.

;0

s.
- 472 -

TABLE A.5

A

UNADJUSTED' AND ADJUSTED' MEAN CHANGE IN HOURS WORKED

BY CHANGE IN OWN LIVING ARRANGEMENT.AND SEX r (NoninstitutLeRal Individuals Aged 16-24 in 1975

Who Were in Their Parents' Households in 1968

and For Whom Hours Worked in 1967Were Ascertained)

Males Females

Change in Own

Living Arrangement

Unadjusted

Mean

Adjusted

Mean+

Unadjusted

Mean

Adlbsted'

Mean+

Left Home, Acquired

Spouse;.Had Children 1,804 1,544 534 360

Left Home,

, k Acquired Spouse 1,761 1,574 1,115 937

Left Home,

Had Children , 2,368a 1,961a 785 766

-.Left Home to

Live Alone

7 rillF

1,405 1,212 1,442 1,216

Left Home- to Live

Vith Other Adults 1,420a 1,127a 4' 1,.167a ?,087a

Did Not LeaveL Home 537 651 436 576

Overall Mean 918 657 N

`Standard Deviation. 852 751

Eta

2

.1,6** L207**

Beta2

P .200** 4. .107**

+Adjusted for the effects of: age in 1975, education in 1975, other family

income/family needs in 1974, 1974 county unemployment rate, family income/

family need in 1967, 1968 parental head's education, whether 1968 parental

head was white collar worker, whether mother was working in 1968, other

f ily income/family,needs.1974-1967, county unemployment rate 1974 -1967,

an change in parents' living arrangement/race. I t aFig e Cased on fester th 20 obserVitions.

**Significant at'.01 level. ... e,

. . t.
- 473 -

0

EDUCATION, OCCUPATION, AND EARNINGS

By:-David O'Shea

A ABSTRACTThe probability that pertons of equal educational attainment

will have equal incomes i* very low, as Jencks and his colleagues

point, out. However, disfggregation of data dealing wit4t.he education-income relationghip shows that, while obviously alamble,

rewards for educational attainment actually are substantial among

persOns who succeed in acquWng access to professional, managerial,

and technical type occupations. As these latter account for about

40% of all jobs, an economic incentive model remains viable as a

basis for interpreting the motivation of Students to compete for,

and invest in, educational attainment.

In addition, disaggiegated data show.that two further perspectives, ,./es, or models, illuminate ot14r. aspec5g4of the motivations linkisig the educational and occupational systems. A social determinist,

or sociological, perspective revea1ls that as the level of_education

in the population increases, employers raise entry level educational

requirements, resplting in,education actually. creating its demand, pressuring young men and women to stay on in school for a dont tinually increasing, length of time. Further; a social-psychological

perspective on the education-income relationship shows that, mediating between years of schooling and occupational attainment, there

is,the tendency for a correspondence to exist between individual -

talent and psychological characteristics on the one hand, and the

'characteristics of given occupations-on the other. Data supportive

of all three models - the economic incentive, sociological,, and

social-psychological - are presented here, and some conclusions

proposed,

46,

INTRODUCTION . In 1972 Jencks.eti\. created quite a furor by pointing out

t that, despite the conventional wisdothe probability of persons

of equal educational attainment achieving eqUal incomes was re-

'markablelow. For example, for ,white adult males, census data

show that the correlation between years of schooling and annual

at

503
- 474 -

earnings is 0.35. 1 This tells us that years of schooling accounts

* for only 12% of the variance in earned income. However, controlling

for )4t le effects of family status and academic ability would reduce

...y this 6 approximately 7%.-iThe remaining 93% of unexplained variance

Jencks et al. (1973) attribute to "luck.".

On the.face of it, these. findings suggestuthat the pursuit of

educattea, while-no doubt of value in itself, may prove econoically

unrewarding. It must be emphasized, however, that the fihdings are

based upon highly aggregated data and are reported in terms of Average relationships. A more realistic picture emerges when the re1-

4evant data are disaggregated, either on the basis of specific occur

. pational categories, or in relation to the positioP that persons 4

occupy in the social structure of the economy as employers, managers, or workers. Both types of disaggregated data are presented

here. They demonstrate that for about one t }ird of he positions

in the work forc the returns actually are very su antial. The

N

fact that the are such irsiti6ns,'and that higher education typically is condition of access, makes investment in education at

least as .f anal as investment in the stock.market, Each contains

. . an element risk, but the potential rewards.for thcise who are

"lucky" serve as a major incentive.

r

Infpractic , of course, 'the fact that there are

.. onomic

. returns to educa on within specific occupatio is well,kn mn to

the public, creaming competition between social grou around the

it

issue of equal access to educational facilities, as w 11 as generating competition betty en individuals. Group competition finds

rnntemnnrai'v /axpressior in movements for the rights of minoritie

and of women, leading to school desegr gation and affirmativt

.

.4)

. Christopher Jencks; Marshall Smithf Henry cland, Mary Jo sane,

David Cohen, Herbert Gintils, Barbara Heyns, and"Stephen Michel-4 lc

son,.Inequality, A Reassessment of the Effec of Family and

Schooling in America (New York:, Harper and Row, 1973).

4

5 .1

00'
- 475 -

action program$.: Oliiletitioh between individuals within schools has,

helped bring about an alignment of the separate status structures

. of the occupational and edudational systems. As Rehb erg ford frOmr--

_, .\

studying the'progress over time of students in six New York State

,

b. high schools,

1

. . . at least as early as the ninth grade it is possible

to distinguish systematically between groups of students

on a wide range of variables which relate, in the short ,,v . run,-to their occupationailkattainmeht.2 , 0-- Relevant variables include family status, intelligence,' aCadem=

is aciievemen't, educatidnal ambition, and self-image. Ninth graders

)

-....._., who registered high across these variables'Werelthose students most

\likely to enter four-year colleges, and then proceed to professional

.4,' and managerial occupat.lons. Those ninth graders measuring low 4mic

--- the same variables were most likely to terminate schooling with the

. twelfth grade, 94then take on blue collar or lower level white

collar positions in the economy. Finally, the h graders who

fell into the mid-positicin were most likely to en r community -IX

colleges, and then move on to employment as-technicians or as aides '''

',to professionals in a variety of fi9lds'.3f

, 2. Richard A. Rehberg, The Two-Year College Entrant: Comparisons.

with thel4gh School Graduate and with the'Four-Year College -

.. -

Entrant (Binghamton, New York: Centers for,Social Analysis,

State University-of New York at%Binghamton, 11976), p. 63.

tr, . 3. Samuel Bowles and Herbert antis, Schooling in Capitalist , erica (New York: Basic Books, Inc., 1976). For Bowles aacl

intis the coincidende bf occupational and educational struc-

.,tures represents the outcome-of what theycall thecorrespon-

, dence principle. However, they over-interpret the meaning of

this correspondence,7 arguing that it reflects ,no simply student, and parental, response toTccupational incentives, bilt

also.ihe intent of dominant groups in society. The latter are

elleged to influence educational policy so that the "educational system tailOrs thb.self-concepts,. aspirations, and social

class identifications of individuals to the requirements of the

socialodArision of labor." (P..129) Actually, schools are'

notably ineffective institutions for influencing the personalities of their students. Both student attitudes ana.early.

cognitive development are, largely products of pre-school ex-

, .. periences in the home, which even relatively intensive privrams

of compensatory education fail to change.

. A

a

4

3.
2

O 6

1 476 - Clearly, a good deal of competition and self-selection is involved

as students seek their place within the academic and social structures

of their schools, aria balance their backgrounds, abilities, personalities, and academic performance against parental expectations and their

own career interests.

This in-school competition is biased in favor of students from

higher status backgrounds, as Bowles and Gintis emphasize.4 However,'

legitimating the process is the fact that lower status students with

academic ability alsb succeed: For example, using longitudinal data'

on. Wisconsin high School students, Sewell demonstrates that parental

status of students accounts for 18% of the variance in postsecondary

schooling attained.5 When academic ability, ofetudents is added to

parental status in the prediction model, the proportion of explained

variance in schooling attained inpreases to 30%. In effect, student

ability, net of parental status, uniquely explains 12% of the vari- ' ance in years of schooling.

As the competition for educational attainment probably finds

its strongest incentive in the potenilal economic rewards within

the occupational structure, for the first section of this paper an

economic incentive model, provides the,organizing perspectivi'. Such

a model does not, of course, identify all relevant factors involved

in the decisions that individuals make regarding-thelength of time

they commit to formal schooling, as becgmes apparent when one looks,

at student motivatiorfrom other perspectives.

THEORETICAL PERSPECTIVES

Apart from the pull of monetary rewards associated wiql high

status occupations, also operative, as ap incentive for schooling is

the.push generated by the fact that, as Coll2ns argues,

6 educati°A)

4. Ibid. . R

5. William H. Sewell,'"Inequality

cation," American Socidlogical

pp. 793-809.

of Opportunity for Higher EduReview, vol. 36 (Ottober.1971),

6. Randall.Collins, "Functional

al StratifiCation,"

(December 1971), pp. 1002-19.

d Conflict Theories of Educationvol. 36 ,w

,e)
r I

4 - 477 -

creates its own demand. ,The higher the educatiOiTal level/in the

population, the higher employers set their entry requirements.

These requirements then react back upon the educational supply,

pressuring' young people to stay in school longer, thus raising the

level of education in successive age cohorts in the population.

This occurs.despite the fact that, as Folger and Nam point out,

7

__there is little evidence of the need for increased'entry level

/knowledge or skills in most occupations. Not surprisingly, there-,'

fore, once job entry is attained, educational background receives

little additional economic reward in about two - thirds of the occu-.

,Rational categories Used by the Bureau of the:Census, as we shall

see. Social pressure to stay in school, generated by escalating

job entry requirements, falls within the purview of a social des terhinistic, or sociological, model of-educational attainment.

Hoviever, apart from the pressures accounted for by the econalc

incentive and sociological models, yet another source of influeece

upon individual educational decision making is identified by a

social-psychological model. This isjhe infludnce,of'personal talent and interests, factors that constrain individual ambitions and .

career plans. As Gottiredsbn points out,8 sstudiei of the contribution of education to earnings typically concentrate upon a single

ditension at both the educationand income sides of the relationship. On the education side, ability is ta en to be intellectual

7. John K. Folger and Charles B. Nam, PT ends in Education in Relation.to the Occupational Structure," Sociology of- Education,

vol. 38 (1964) pp. 19-33. For a more extended discussion of

the problem of oredentialling see'Ronald Dore, The Diploma

Disease: Education, QualifiCation'and Development '(Berkeley,

Cal,: University of California Press, 1976).

8. Linda S.' Gottfredson, A Multiple-Labor Market Model of Occu-

, gitional Achievement (Baltimore,Md.: Center for Social Organization of. Schools, Johns Hopkins University, Report No.

225, 1977.)
- 478 -

only, -the diversity of human talents being ignored. On the earnings side, work is treated as a, homogeneous activity, perhaps varying between levels of occupational status. To go beyond this

-- limited approach, Gottfredson has analyzed education-income relationships in terms of occupations classified according to six categories developed by Holland on the basis of research in vocational

psychology. These six types elate to activities preferred and

competencies required in giv occupations.

While the sociological model overlaps the economic one, it

being difficult to separate out the relative contribution of push

and pull factors to educational attainment, the social psychological model complements the economic, helping to interpret the relationship between years of schooling and sulsequent4earnings.

Data relative to all three models are pre.sented here, beginning

with the economic model, as this has the greatest utility in ac- counting,for educational attainment within the population.

THE ECONOMIC INCENTIVE MODEL

As longitudinal data have become available, these have made

possible a series of analyses of the causal determinants learned

income. Some of the more productive studies have utilized data

that were initiated with a survey of high school seniors in Wisconsin in 1957.. These data show that a large part of the effectiveness of education in facilitating highei earnings result from

the fact that education enables people to enter higher status occupations. For example, Griffin, 10 using the Wisconsin data, re9. For details of the Wisconsin survey see William H. Seweh,

Archibald O. Haller and:George W. Ohlendorf, "The Educational..,.

and Early Occupational Status Attainment Process: Replication

and Revision," American Sociological Review; vol. 35 (December

1970), pp. 1014-27.

10. Larry J. Griffin, "Specification Biases in Estimates of Socioeconomic Returns to Schooling," Sociology of Education, vol.

49 (April 1976).
4

- 479

.

grtssed ealnings on a number of predictor variables, including

parental education, occupation, and income. To these were added

.characte4i*ics of the_respondents, including mental'ability, years

of education, and occupational status. The coefficients for each

variable indicate itslnpact upon earnings-, net of the,influence

of all other variables?in the regression. Re Its show that each

$1,000 of parental income is worth $112 of son's ,,arnings. Each

year of education is worth, on average, $97. 'Occupational status

generates $15 per annum for each point on the status scale., which

.runs from 1 through 98.. Of special interest, though, is the fact

that when occupational status is removed from the regression, the

earnings generated by each yeai of,education jump from $97 to $206,

an increase of 112%. Evidently a large nart of the effect of eau- , -

cation is mediated by occupational status, as becomes evident when

the economic returns t40 education are disaggregated by types of

occupation.

Disaggregating the Data

As occupational status plays a substantial mediatin role

between years of education,attained:and earned income, th occu- pational structure appears to be potentially the most fruitful

wee basis in terns of which to explore the education-income relationship in more detail. There are, in fact, two approaches to the

contribution of education to earnings that have promise for our

pb4Poses. Stolzenberg, 11 has analyzed economic returns to education separately for .each of the 11 census categories of occupational'status. Wright and Perrone have looked at education-income relations within ;economic, clsses,12 the latter, being defiptd,

11. Ross M. Stolzenberg, "Education, Occupation, and Wage Differences between White and Black Men," American Journal pf Soci--

ology, vol. 81 (September 1975), pp. 200-323. . 12. Erik Olin Wright and Dica Perrone, "Marxist Class Categories

and Income Inequality,'" Amer-can Sociological Review, vol. 42

(February 1977),,p. 32-55.
- 480 -

by the role people play within the structure of social relationships.

Turning first to Stolzenberg's approach, 13 he separated earnings

and educ tion on the basis of data from the 1960 U.S. Census:14

Using th data for white males, he regressed earnings on education

4 separately for each of four age groups and ten categories of occu- pational status. This approach generates forty separate regression

coefficients providing estimates of the eff7ct of education upon

earnings. Stolzenberg's interest in this procedure was to demonstrate marked non - linearities in the education-earnings relationship,_

and, interaction between age and education within this relationship.

The non-linearities are evident when the distribution of regression

coefficients is diagrammed in Figure 1, but the most striking find-

, ing is the very large difference in economic returns to education

between the highest and lowest status occupation categories.

As shown in Figure 1, returns to education between occupations

came closest at the early stages of persons' careers for the earnings data gathered by the 1960 census. Even here though, for the

I __ twenty-five to thirty-four years of age group, the gap was $264 between the return of $108 for one year of schooling for clerical

workers and the return of $374 for managers, officials, and proprietors. By,age thirty-five to forty-four the gap had greatly widened.

The return for clerical workers increased to $215, but that for man- agers, officials, and proprietors rose much faster, reaching $961.

In this same age group the largest gap was between laborers, at $146,

and professional, technical, and kindred workers, who experienced a return of $1,042.

Overall, two occupational categories that employed 27% of white

13. Stolzenberg, op. cit. 4. 14. U.S. Bureau of the Census, U.S. Census of Population: 1960.

Subject Reports. Occupation by Earnings and Education, 'Final

Report PC(2) -7B (Washington, D.C.: Government Printing Office,

1963). , 46

510

1
4.

481

KM: Professional &

... technical

Managers, officials,

N\

1- &proprietors

(ftsmen, i foremen

Glee workers - &kindred ---. . % OM 01

. icr ...... .. 6.0 Clerical workers ----J

.., "' ... 0

Service workeri - -

% . Operatives - - - 1 $.. 4

l 9- m Laborers (Overlaps Opers)

. °

. .

,-I All 'workers

,-I c

11o ) . /

:: 81

.' /

0

0

...4

. /

cs

a /

= 7 /:, .' /

. 00 1 / , =

. / H

... / *t 6- ° o . I

,....

ow ems. 0

. . / ...'

r4

4)

43 .. A, o /

a5. .0 .1/ , ._

. a' / .1

;.

/

/

4.4 . A

0 4' / / 4, / z g /

O1 4i

0

0 . moms sow.* 2

.

.... Ole ;74

0,

25-34 35-44 45..64

(14. Age (Years)

ti

*Moak

04,

a OM,

"':A.L.'0 al.,.

...a. a

4 *A.,.

- , Figure I. --Coefficients generated by regressing white males' earnings on

years of school completed in 1960, by major occupation group and

age.-Based-on-dats-in-Table-2,--p.5104-RoWtolunhert._

"Edueatio, Occupation, and Wage Differences between White and

Black Men. American lou al of Sociology, 81 (2):299-323, 1975.

5.S64

r -

.
482

males in 1970 - professional and technical workert, manag /rs, officials and propsietort enjoyed extraordinarily high returns relative to all the others. Sales workers came a:close third, a cate-

. gory employing 7.4% of males in the 1970 workforce. The balance

66%, fell into the remaining occupational categories, among which

returns were relatively low, and-declined after early middle-age..

These data help interpret the lack of academic motivation character10.4. c ittic of many students in high school who see their future as most

likely falling into one of the lower'status occupational categories.

Actually, of course, the magnitude of returns to schoolin

'shown here aie probably overestimated by 35"-40%, according to Grif:

fin. 15 This is the proportion of the education-income relationship

that is-due to the influence"Upon both variabies_of parental socioeconomic status and the individual's own mental ability. Controlling for these factors would reduce the dollar amounts and narrow

the gaps somewhat, but the overall pattern would remain the same.

Stolzenberg's approach is expanded upon from a slightly different direction by Wright and Perrone.16 These authors argue that

in predicting income on the basis of education, occupational status,

and age, one can add to the predictive power of the statistical

model by adding data on the social class of members of the workforce.

Retuins to Education by Sop41 Class

In exploring the'coaribution bf social class,lenet of occuApatior nal status, to income returns for years of schooling, Wright

and Perrone take the view that occupational status and social class

represent quite different, though overlapping, sets of relationships.

"Occupation" designate,s positions within the technical

15. Griffin, op. cit p. 135.

16. Wright and Perrone, op. cit.

- 512

.r
0.

". - 483 :

division of labor, i.e., an occupation represents a set of

activities fulfilling ceKtain technically defined functions.

Class, on the other hand, designates positions within the,

social relations of production,

' it designates the

social. relations between actors."

The class typology used by,Wright and Perrone is based pbn

three questions asked of respondents in the two surveys from which

their data are derived.

18

1. "Most of thetime on the job do you work for yourself or

someone else?"

. 2. "If you are Self-employed, are there any people who work

for you and are paid by you?"

3. "Do you supervise anybody as part of your job?"19

i. Using data generated by about 1,500 respondents to a national.

sample of adults interviewed in 1969-for a surly of working con-/

ditions, the above three questiolp generated five categories of,

social clars,-as illustrated in Table 1.

e survey responses also provided information on leve of(

educati n and annual income, a slightly different variable fr m

. amnual eatnings, but closely correlated. Given these, data Wright

and Perrone separated respondents into the class categories of employers, managers, and workers. They omitted the petty bourgeoisie

and ambiguous categories, numbers being too few. Within each class

they regressed annual income on education, with the results diagrammed in Figure.2. Education is coded by revel rather than by

yeari (0 = elementary school or less; 1 = completed elementary

17. Ibid.,.p. 35.

18. Ibid., 1i. 36.

19. Ibid.

4

4.

4*.
4§4 -

Educational Level (See text)

Figure 1. -- Regression of income on education, by categories or social clads, for nonfarm, full-time participants in the labor force. Data from a 1969

national sample of 1,533 adults,,-16 years of age and over. Based upon Figure 2,

p. 45; Erik Olin Wright and Luca Perrone, "Marxist Class Cdtegories and Income

Inellaality." American Sociological Review. 42 (1): 32-45, 1977.-

-/i

0 514

a
-'485

Table 1

Mb

CRITERIA FOR SOCIAL CLASSa

CLASS

4

Employed

Have . Employees'

Have Subordinates

. Employed N %

on the 'fob . AL Employers Yes Yes Yes -No '110 7.4

-"Managers No No .\'es Yes 561 37.4

Wor rs No No No Yes 739.49.2

Pett

Bourgeeoiiie Yes No No No ,_65. 4.3

Ambiguous Yes No Yes No 24 1.8

.,,

40

aSource: Table 3, p. 37. Wright and Perrone. See text.

school; 2 = some high school; 3+=, high schooli4 = some college;

5 = college degree; 6 = postcollege). The actual iegressiofi coefficients for college within each of the class categories are:

Employers $4,091

Managets 1,797

Workers 678

These Coefficients represent the increment.in income for each

additional stage of education. .When occupatiohal status, age, and

number of years in present job are added to education in the regression the education coefficients reduce but remain high, testify-

, ing to the independent_contribiltion of social clasi, net of the

other three variables including occupational status. The revised

coefficients are:

Employers `'k. $3,170

Managers , 1,477

Workers 607
4

a

- 486 -

4 In terms of vari explained, education, age and occupational

status were found to explain 19:1% of the.variance in income. When

, social class was adde&this increased the proportion of variance

explained by 7:6%, for a total of 26.7%, again indicating the substantive importance of the class variance..

The very large differences in returns to education within the

three Social classes as demonstrated by Wright and Perrone par lel

e

Stolzenberg's findings presented earlier. The differences in returns to education evidently are an interactive phenomenon, result-,

ing from Characteristics of occupational settings. Economists assumethat, in general, earnings are an index of the contribution of

each individual to the overall productivity of the economy. Apparently, therefore, education does enhance individual productivity among

employers and managers, and among persons in professional, technical,

and sales occupations. In these latter occupational categories,

earnings increased substantially with level of education. This is

rather what one would anticipate. Not anticipated is the relatively

weak impict of education upon productivity (earnings) in the remaining occupational categories, and these categories account for 60%. or

more of the labor fOrce. \4

However, noted earlier, even among occupations where education has relatively little impact upon earnings, entry is increasingly dependent upon the ppssession of educational credentials. The

consequences of this "push" factor upon the supply of education in

the labor force are reflected in changes over time in the pattern

of relationship between given occupational categories and the level

of education of those employed in these categories.-- THE SOCIOLOGICAL MODEL

Despite low returns to education for roughly 60% of employed

% 7 persons, most of whom are,found in blue collar, service, and farm

occupations, the level of education among this 60% has been steadily

rising. It is plausible to propose, therefore, chat a large, if

516 1
1

487

0

indeterminate,.proportion of this rise reflect's pressute generated

by increases in jOb entry educational requirements which, in turn,

have'escalated in responSeto the increased availability of persons with high school and post-secondary certificates. In effect,

a typical vicious, or perhapin this case beneficent, circle has

been at wOrkyan'interpret tion that finds some support from data

regarding change over tim n median levels of education within

the lab2r force.

Between 1959 and 1972 the median years of education in the

total employed labor force rose from 12 years to 12.4 years. However, within specific pccupational,categories, and within the black

population, changes were much more dramatic. Looking first at the

data for black men and women, shown in Table 2, from 1959 to 1972

black men gained by years and black women by 2.8 years. Whites,

/'

by contrast, changed y little, and what change did occur represeilksa convergence of both ten an4 women to the same level of 12.5

years.' In fact, the overall pattern,of change suggests a conver- "

gence within all sectors of the population, rather than a tendency

toward continued upward moveme1it over time.

ABLE 2

MEDIAN YE OF EDI ATION COMPLETED AMONG

CIVILIAN BOR FORCE, BY S X AND COLOR, FOR

1959 d 1972 (ALL OCCUPATION GROUPS)a

Color Sex 1959

White

Black

Male

Female

Male

Female

1972

Change

1959-1972

12.5

12.5

11.6

12.2

0.3

3.4'

2.8

aSource:'Nable B-42, p. 181. Manpower Report of the President,

1973. U.S. Dept. of Labor.

4
488

This trend toward a stable upper limit appears also when one

looks at change within specific occupational categories.' These

changes, as experienced by white males, are diagrammed in Figure 3.

Occupati 1 categories within which median years of education were

lowest in 1959 experienced the largest gains by 1972. Farmers,

farm ]borers, and nonfarm laborers moved up 2.5 years. Service

workers 'gain by years. These latter four occupational categories

, together accounted for 17.5% of the forty-threq million

white males employed in 1970. Overall, 40.5% of employed males

fall into the blue collar occupations, among whichlhe median years

of education rose 1.2 years for craftsmen, and for operatives 1.8

years. The remaining occupational categories, all in white collar

j sectors, experienced little change over the thirteen year period, ' having begun at or above e 12.5 years mark in 1959.

%A. These data for white males suggest three trends. First, occupations in the highest status category, professional and technical

workers, arq distinctive in their very high median years of education

(--

which hai remained at over 16 years since 1959. Second, the median

years of education in managerial, administrativeand sales occupations has been moving slowly toward 13 years. Thirdly, all'other

occupational categories are converging over time with the median

for clerical work, which s 12.6 years in 1972, almost unchanged

et from 12.5 years in 1959. gs these latter categories account for

two-thirds of all employed males, their educational levels strongly

influence the aggregate median, and as Ile sam'earlier, 'there is

) little economic incentive for persons to go beyond high s)hool if

. _.._

. they_enter clerical or blue collar jobs, income returns to education

.$ in these jobs being minimal. By contivt,..,ireturns to education in

;. the highsr level professional and manageriC positions are substantial.

. Though not presented here, the pattern. of change in median

years of education for white women is very similbr to that for inn,

51

Lei

r-
519

17-1

16-

KEY: Professimhal and

technical workers 41

--Managers -and administrators

Sales workers

Clerical workers

Craftsmen and kindred

- om ....

'''k _Oat, a am*

.

. --

I $

Operatives

Service workers,

Farmers and farm managers

Farm laborers aid fopemen

Nonfarm laborers

ALL WORKERS

aw. woo am Va. .- amme.

'''"

a

am ow.

a

I1 aft I

ioefe e I

a a a a a I

- a. 'S

.

..".

I

Figure

a

0

164 166

YEAR

I

167 '68 '645 'f '7i 172

3 = . Changes from 1959 to 1972 in median years of school completed by males in the empl6yed

civilian labor force, by census categories of occupations. Data from Table B-12, p. 181,

Manpower Report of the President, 1973.U.S. Dept. of Labor.

ti 6
- 490 -

while their respective distributions between occupations, differ.

Women'are concentrated in clerical rather than blue collar work.

Among blacks, though overall there was a large increase in education, this Vat not true of those in occupational categories where

the black median already,was close to the white level in 1959.

Specifically, black prpfessional and technical workers had a median

of 16 years of education in 1959 and in 1972, and black clerical

and sales workers were close to 12.5 years' at both times. The large

changes in levels of education between 1,959 and 1972 along blacks,

as among whites, occurred not in the higher status occupatiOns but

in the blue collar, service, and farm_cattgorie_s

In interpreting these data on change over time, referring back

to Figures 1 and 2, one can argue that.ifincreased educational ,- levels within many occupational categories results from actual job

needs for higher levels of pre-training, this fact would be reflected in a substantially stronger relationship between education and

earnings than presently exists. The more parsimonious interpre-

------tation-is-thatrLdemand-for-educational-requirements-goes up as-the.

supply of educated manpower increases.'

Further insight into factors associated with differences in

returns t-oNducation is provided by Gottfredson' s work.2° In this

she explores theepossibility that psychological factors orient in-

- dividtial job choices, thus mediating between level of education and

occupational statuseand therefore influencing the education-earnings

relationship. Gottfredson's approach calls for a more complex view

of.the nature of the labor market. The latter is conceived of as

being divided into multiple sectors, each sector attracting persons

with distinctive talents and interests.

o THE SOCIAL - PSYCHOLOGICAL MODEL

Gottfredson argues that education is rewarded differently be-

. Or20. Gottfredson, op. cit.
-491-

tween occupations in part because occupations vary considerably in

the extent to which academic preparation has actual utility, even

o though required4for entry; Some occupations

require skills primarily for working with people,

whereasoothers require skill. for worki,pg with data

orqhings. We might. expect that schools do not foster

all the types of talent that are important in the occupational world. Ip turn, we would not expect education to be as valuable in the lines of work-that

require non-academic talent." 21

t.,

To explore these possibilities, Gottfredson classifies occupations by means of a typology developed by John Holland on the basis

of research in vocational psychology. Holland, allocates occupations

into six types according the activities performed and the competencies required. These types are labelled realistic, investigative, artistic, social, enterprising, and conventional. By way of

examples, sales and management jobs would-be in, the enterprising

category; science and medicine in the investigative; clerioal work

and - accounting in the conventional; blue collar jobs and engineer-

.ing in the realistic, and educatiob and social-SWVid-6-in-the--

social.

Holland assumes that education contributes-differently to

worker productivity within each of his six types, an assumption

that can be tested by adding the types to the usual statistical

model for predicting income on the basis of education'and occupational statul.Gottfredion has done this, using a 1/1000 sample

developeg_by the 1970 census of white males in the full time

21. Linda S. Gottfredson, "Differential Educational Payoff Models

and Theories of the Diversity of Human Talents," in James M.

MCPartland and Edward L. McDill, organizers, Alternative Research Perspective; on the Effects of,School Organization

and Social Contexts (Baltimore, Md.; Center for Social Organization of Schools, Johns Hopkins University, Report No. 234,

1977), pp. 1-2.

I.

91,
- 492 -

/N.

4

civilian, nonfarm, labor force.22 The sample sire was 27,067.

The data from the sample were used to predict income on the basis

of years of education, weeks worked -in 1969, hours worked during

the survey week, and occupational prestige. These variables explained 25.4% of.the variance in income. Adding dummy variables

for. the Holland types increases the variance explained to 31.1%,

a substantial proportionate increase.

Insight into the actual pattern of differences between Holland's

types in terms of income returns to education is provided by diagramming these, as in Figure 4. Gottfredson generated these data

simply by regressing income on education. The lines shown in Figure 4 join the computed regression coefficients for education generated by running the regression equation separately for each of foul

_ _ _ age groups, and the six types of occupations. Among the types, en-.

terprising occupations obviously incorpoPate the jobs having the

'highest returns-to education. This category overlaps the census

classifications of sales, managers, officials, and proprietors, that

register high in Stolzenbers analysis, and the manager and employex categories used by Wright and Perrone. As shown in Table 3, about

one quarter of all employed men, aged thirty-six to forty-five, fall

within the enterprising type. Reflecting the technological' character of our society, the realistic type incorporates the jobs held by

more than half of all employed men.

With the' realist/c and enterprising occupations accounting together for 80% of men in the sample, the remaining 20% are distributed between the investigative, social, conventJonal, and ar- tistic'types. /,-

Educational characteristics' of men within the six occupational

types are suggestive of the pattern of linkages to the educational

system. Looking at the third column of Table 3, one can see the

r

22. Gottfre4on, A Multiple-Labor Market Model, op. cit.

k
1'

N

KEY: Occupational Type

Fhte rpr lng

Investiga ve

11 Conventio 1

10

</.

- 49 3 -

Social

Realistic

All

a 0.54m Oam.

. .

;.4

-

' .

.

\ ,

OM

".

P6-35

t

46-55

Age (In .years)

56-65

Figure i...Coefficients for education from regressing income on education, for men infull-time, civilian, nonfarm occupations, by age. Derived

from Table,7, p. 43: Linda Gottfredson, A Multiple.Labor Market Model of

Occupational Achievement. Baltimore, Md., Center for Social Organization of

Schools, Johns Hopkins University. Report No. 225, 1977.

524
6

DISTRIBUTION OF WHITE

IN 1970, AGED 36-65,

OF ALL EMPLOYED

EDUCATION

V

- 494

TABLE'3

MALES IN CIVILIAN, NONFARM, LABOR FORCE

BY TYPE OF OCCUPATION, AND BY PERCENT

MALES HAVING 16 OR MORE YEARS OF

BEtWEEN,AND WITHIN TYPES OF

OCCUPATIONa

Type of Employed

Occupatio males

Percent men with

16 or more years f

education between

types"

Percent men with

16 or more years

education withir5

types

Realistic 53.8 10.2 3.1

NA. Enterprising 25.6 38.6 24.7

Investigative- 7.4 20.8 45.1

.Social 5.8 19.2 54.2

Conventional .5.8 7.2 20.3

Artistic 1.6 4.0 55.8

Total 100.0 100.0.

N _19,286 3,i64

aSource: Adapted from data provided in Table 8, p. 44, Linda S.

Gottfredson. A Multiple-Labor Market Model of Occupational

Achievement. (See text).

proportion of men within each type that possess sixteen or more

years of education; in effect, the

Three types collie close to 50 %: the

tic. The investigative and social

Lists,_ university faculty members,

proportion of college graduates.

investigative, social and artishave large proportions of scl.enteachers and social workers, all

A of which are jobs that require a higher education. However, among
- 495 -

1.

rf -.

ir

all persOns in the sample who have college degrees, the largest

single concentration is within the enterprising type occupations,

as shown in column-2 of Table 3, the type also that provides the

highest economic returns to education. presumably the relationship is not coincidental.

Realistic occupations, with the lowest returns to education,

.attract 10.2% of all college graduates in the sample, though of all

'men in realistic occupations only 3.1% are college graduates, as

shown in column 3 of Table 3. Most of these probably are engineers.

The educational disparity between the enterprising and realistic

occupations is further emphasized if one looks at the proportions

of men in each who terminated schooling at the twelfth grade, or

below. Among all me 'n -the sample the proportion is.72%. For the

enterprising type o upatioh the proportion falls to 56%, but rises

to 90% for those in he realistic' category. Associated with these

differences in educ tional levels, and returns to educaion, are

parallel differences i\mean income. In 1969, for men aged thirty-

_ six to forty-five, 'those in realistic occupationi hid-ATfnean income

of $8,992 compared to. $14,346 for those in the enterprising category.

From. the point of view of the educational 'system, the low returns to education in realistic occupations is a serious issue,

- , given the number of peo.ple involved. In 1970 the realistic category.

accounted for close to S4% of all-white employed men, and 80% among

blacks.. Students who find them'selves poorly fitted, or unmotivated,

v to pursue postsecondary education, and who perceive their future as

-.)

being in a realistic type occupation, are not being unreasonable

when they argue that education, at least beyond the.twelfth grade,

-it-not particularly relevant-for-them. To-transform-the-motivation-

--al 'climate among such students would require a transformation of 6. 4

the structure of rewards for'education in realistic occupation's,

though how this could be done for-jobs'in which academic_ traihing

adds little to individual productivity is difficult to fathom.
4

- 496 -

The typical alternative of providing vocational training is 9f

doubtful value in the context of lifetime careers, though it may be

useful as a means of keeping some students' occupied while they are in school. A compromise might, be to a cept the fact that half the

jobs in society are unlikely to reward a training beyond that 4

required to meet minimum levels of educational rtification: then,

proceed to explore opportunities for students to achieve status, if

not always income, outside their regular occupations. In effect,

one would aim to legitimate the notion that education should pie-

: pare for leisure as much.as for work. More emphasii Gould be placed

upon music, art, and crafts, expanding greatly the existing extracurriculum, within which athletics provides an excellent model.

--Of-ColitS6;-111-the-caselalblack men" and women, and of white

0, 4 women, there ;,s need for substantial changes within the existing ,oc-

--/ __,upational structure. The problem here is one of redistributing

people between occupations. Within categories of occupations,_ as'

both Stolzenberg and Gottfredson report,23 returns to education for

blacks are less than for whites. Gottfredson,24 an also Wright and

Perrone,23 found the'same to be true for'women. Further analysis,

by Stolzenberg forblacks,2 and by Gottfredson for 'both blacks and

women,27 indicates that th source of the differential returns, as

Well as lower incomes, is n t discrimination in salary policies so

much as differential patterns oerecruitment. Within a given occupational status level, or social class level, blacks and women receive positions in the lower paying jobs within that' category. IA

,r-

, 23. Gottfredson, Ibid., Stolzenberg, op. cit.

24. Gottfredson, Ibid.

.;\

25. Wright and Perrone, op. cit.

26. Stolzenberg, op. cit.

27, Gottfredson,'A Multiple-Labor Market Model, op. cit.

fl 5 -r 4, I
terms of occupational types, Gottfredson found black males concentrated in the. realistic category, and women in the social and

conventional.categories. Over time, the combination of affirmative

action programs and changed socialization patterns, especially regardingtsex role stereotypes, should generate more balance in the

distributional patterns of blacks,ogomen, and minorities generally,

across occupations.

CONCLUSIONS

In summary, while the overall contribution of educatio v

earnings is low relative to the contributiondt makes tO'th1,ttainment of occupational positions, disaggregation of the data in terms

, of categories of occupations revpacthat for about onethird of all

--jobs-thf-economic-returns-actually__are rather high. This finding

reinforces the traditional viewpoint of economists that the potential of financiaqreturns to education is a major incentive for

students, and'that this incentivedsone A. the more important pro-

-',:,-- cesses linking education and work28 Given the likely returns, both

' COMspetition,for occupational status and 'earnings.

This, then, is the situation in general. Clearly, there are

particular instances of aistortiotis in thecompetitilreprocess, and

these are most visible in the case of minorities and of women.

- 28.. For summary discussion of'the economics f education see Mary

Jean Bowman, "The Hum&n,InvestdedeRevolution in Economic

Thought," Sociology-of Education, vol. 39 (Spring 1966),

pp. 111-37. For a more detailed §pproach to the disaggre on of education-income relationships'than'presented in this par

see Richard S. Eckhaus, Estifilfting the Returns to Education.

A-Disagiregated Approach-(Berkeley, Cal.: The Carnegie Cbmssion on Higher tducationi, 4973). Among other things, Eckfinds that if one uses hourly earnings rather than nnual

earnings as the dependent variable,'returns-to education for

professions come much closertto the retUrns for nonprofessionals, the latter typically putting in less hoUrs Per week in

their occupations. A

A 5' 598
I - 498 -

-"ow

Resolution of these distortions, which inhibit persons entering

occupations commensurate with their level'of education, requires

continuation of affiimative action progr . 4Ultimately, equaliZing access to the occupational structure s likely to be_the most

effective way of equalizing educational a tainment'between groups

in society, at least to the extent that attainment really is a response to occupationally based incentives.

.The most troubling finding, of course, is that while education

pays off in a sufficiently high proportion of jobs to create powerful incentives among students to compete for these positions, thus

railing the general level of education in the population as a sort

of spillover effect, a majority of students eventually arrive in

jobs for which returns to education are minimal. Given thii,*it

would seem worthwhile, to explore ways in which students mightalso

be trained in unacademic skills which would be serviceable for

their personal development, if not their economic progreis.

A related issue, of course, is the question of student motiI

vation to acquire an education. Economic incentives and the ambition to develop pe4onal talents no. doubt are dominant, and complementary, influences. le major problem lies with those students

who-respond to neither of these influences, but are carried along

within the educational system by social pressures that demand the

acquisitiodlof educational credentials. As the source of this

problem is within the occupational rather than the educational

system, more research is required regarding the conditions under

which formal education really does, or does.noi, tontribute toward

job performance.

FinalL, to completr"the picture of education-work relations

we not only need to pursue in more detail the analysis of the occupational structure and its linkages to various levels and types of

ed1ation. Also necessary are data on the reverse of the occupation-__

al structure-- the structure of individual careers within and

529
- 499 -

across occupations.. As Spilerman notes,29 education "provides

status and earnings rewards through facilitating access to valued

Amy ..-. career lines, through differentiation among entrants with respect

to rate of advancement, or through a combination of both.Career

analysis can help reveal the actual process s through which education en aces individ

t

al productivity and earnings. Such analysis

also h s potential fo a more detailed understanding of unemployment, h lping to specify the.conditions under which people with

0. different b ck und characteristics, and in different occupations,

enter and e it

cation, takes its aning fr the structure of,emp ent, and

ultimately can only e dealt ith in terms of its conse Oros for

the labot.market as a w

e occupat'onal structure. Unemployment, like edu29.

SIt

eymour Spilerman, "Careers, Labor Market Structure, and Socioec \

omic Achievement," American Journal of Sociology, vol. an 83

(Not tuber 1977), p. 585. '

,

5.30

sr

,*

r
- 500 -

ALIENATION AND ADJUSTMENT TO LIMITED PROSPECTS

By: David Gottlieb

ABSTRACT

This paper seeks to provide information with regard t matters of

alienation and adjustment to limited prospects as experienced by disadvantaged youth:` A second task is to identify the ways in which important

attitudes, experiences, and behaviors of poor youth may not be reflected

in -current employment surveys.

An analysis of the available literature and data leads to the conclusion that very little is, in fact, known about how the young, particularly those who are poor, perceive or respond to limited opportunities.

Serious question is raised as to the validity and usefulness of employ-

.ment data currently being collected. Clearly, knowing the percentage of

youth in the labor force or the percentage employed or not employed does

not tell us very much about how youth respond to their work and job status,

Further, the available data and literature frequently treat the

young as a monolith, failing to differentiate among youth of different

ages, socio-economic backgrounds, race, ethnicity, sex, or place of residende. ,pluitivariate analysis is not a characteristic of current meth -

odologies

Moreover,.most surveys do not takelto consideration that employ-

/. ment is a two-sided coin involving the potential employee and the potential employer. The overriding tendency is to place the burden of proof

upon the potential employee by focusing upon the perceived shortcomings

of the individual as opposed to the barriers established by potential

lifloloyers. The data do more than suggest that failure to gain employment may be more the result of work, force barriers than a lack of skill

or desire on the part of the 5roung.,:.

Reference is also made.to the existence of a dual labor mat:

one which consists of jobs whidh are considered appropriate for youth

and a "regular job market" which is reserved for adults. The majority

of jobs in the youth market, while perhaps functional for youth in school,

summer or holiday employment, or part -time work, neither require much

in the way of skills nor do they provide an opportunity for an adequate

transition into the regular, adult job market.

Finally,,effOrts are made to identify a number of investigative

areas and strategies which, if pursued, could provide systematic and

empirical4nswers to the critical question of youth reactions and responses to,perceived and real barriers.

}

4
- 5017'

My assignment here is twofold: first, to deal ith the issue of

tienation and adjustment to limited prospects as exper enced by disadvantaged youth; and second, to identify the ways in whi h/adjustment

styles might or might not influence how these youth respond to ethployMent-,

surveys. Within the.framework of.our concern fdr youth and matters of

employment, my task can be restated in the following way: "What are-157

the behavioral and attitudinal responses of poor youth ;hen confronted

with barriers to employment?"

Unfortunately, precise answers to itese questions are difficult

to find. The combination of irrelevant data, inappropriate methodologies, and a lack of consensus as to the meaning 'of such terms as alien-

.. ation and adjustment allow for little more than speculation, heady inference, and an occasional reference to scattered rempirical evidence.

The problenris made all the more complex since even at this late

date we have not been able to differentiate among the various youth

groupings which might fall into the category of disadvantaged. I think

we can all agree that poor youth are hardly a monolith. Income level

alone may be an effective method for determining eligibility for entrance imp training programs, but it is not an effe give mechanism for

identifying critical population differences. Furt er, despite a variety IrfrIr,

of methodological innovations and national surveys no one seems really

to believe that we have a fairly precise fix on just how many youth are

employed or unemployed; the factors which lead to acceptance or rejection of employment opportunities; how much of the unemployment variation

among yodth can be explained by their rejection of jobs as opposed to

job availability or employer'preference for older applicants. Although

other conference participants will deal with specific problems: of data

collection and data validity I do want to cite several examplet which '

help illustrate my point. , /

The bulk of youth-related employment data consists primarily of

, ctatistics noting the number of people in or out of the labor market,

the number emplo-full time, part time, or unemployed. These same,

data are displayed in a manner which allows for comparison between white

'and nonwhite youth, youth of different age grouping, males and females,

4t,

e;

r.
502

and those who reside in urban as opposed to rural and suburban locations. With few exceptions there is a paucity of hard data dealing

with the day-to-day bus4ness of how the young negotiate relationships

between themselves and their neighborhoods, communities, and society.

Nor, is there very much information available as to relationships between unfulfilled job aspirations or expectations and adjustment response. Obviousry all youth do not hold opimilar aspirations and expectations and all youth will not react in the same manner to perceived or

real barrieri.

Yet even when data are available, there is serious question as

to accuracy or meaning. For example, when a coMparison is made of

youth unemployment statistics, for a similar period of time, as presented by the Current Population'Survey (CPS, upon which unemployment

rates are based) and a National Longitudinal Survey (NLS) significant

4

differences are apparent. Unemployment for in-school males, ages

teen to seventeen" was repOted as 17.4% by NLS and 9.2% by CPS, a difference of some nine percent. For out of school males of the same age,

there is a reversal with CPS citing a figure which is nine percent

greater than that reported by NLS.

1 There are other data deficiencies which. contribute tothe prOblem. Each year 400,000 people ages eighteen to twenty-four enter the

armed forces. The military absorbs roughly a third of all non-college

bbund_youth. ADst_are from lower income backgrounds and a disproportionate number are nonwhite. Information as to why these youth enroll

' 4 in the military is lacking. Similarly, the extent to which the mili- - tary experience enhances civilian employment opportunities is not known.

Nor can we say very much about the reasons why large numbers of these

youth (the estimates range from 30% to 40% for the Army and Navy) leave

prior.to completion of the first.term of service. Given that the mile itary is frequently viewed as a magnet for those who cannot or choose

not to remain in the civilian realm, it would make sense to find out

1. Policy Options for the Teenage Unemployment Problem, Background

Paper No. 13 (Washington: Congressional Budget Office, 1976

5

O
- 503 -

what happens to them when they do leave the military. Assuilling that

there i4 some positive relationship between policy and data, we

would have to conclude that the military has determined that the major

barrier to completion of the first term of seyice is age and a high

school diploma. Current enrollment policies specify that an applicant

must be at least eighteen years of age or if seventeen he or she must

possess a high school diploma or its equivalency.

2

At the same time,

it is-interesting to pointItut that thergare few officials in the mill-

. tary,who

.would argue that a high school diploma is an assurance of academic competency or skill. For the most part, that particular creden-

,

. tial is viewed as evidence of "stick -tQ -it -ness" (or as expressed by

one official, "if a kid, especially, a black kid, can stick it out

. - through high school, we figure he can take most anything").

Data deficiences are also apparent when we seek to determine answers to even the most basic questions pertaining to school enrollment

mmi school attrition. Although the emphasis is placed upon high school

completion, we know there are numerous students who do not even get as

far as secondary school. A number df investigators have pointed out

that.school'enrollmentand attrition rates are frequently inflated

since in m y.states formula funding is, based upon body counts. More

important perhaps, than enrollments, few studies have focused in upon

the actual role of the school in contributineto student attrition and

failure. 3 For the most part, high school dropout studies have sought.

to explain the problem by focusing upon the characteristics of the student, his family, and his community. Little differentiation is made

. between those who have chosen to leave and those who have been forced

to leave: Nor is there,much in the way of empirical consensus to

'just what role the school does play in enhancing occupational mobility;

responding tothe,..-neehof students who have experienced occupational

2. First Term Enlisted Attrition. Vol. I: Papers (Washington: Navy

Manpower R and D Program of the Office of Naval Research,-1977)-.----

3.' James MaPartland, Edward L. McDill, et al., Student Participation

in High School Decisions, Report No. 9E. (Baltimore: Johns Hopkins

Center for Social Organizatioja.of Schools, 101),.p.46. .

4 5
- 504 -

4

.barriers; nor what there is about ducation other than credentials which

will influence how an employer w4. 1 respond to a particular youthful applicant.

One style of adjustment to limited oppo tunities practiced by

many disadvantaged youth has-been enrollment in ny one of a number of

federally funded employment programs, Yet even in this case, conjecture and inference takes the upper hand over empirical data. No .one

a.ny-yei;ih-have-beeh

enrolled in the variety of programs, much less the number who successfully completed, their training. We really do not know whether. or not

those who enter are representative of the most estranged of poor youth;

whether they are like the majority of poor youth, or whether or not

they are a very select slice of the most highly motivaFirof poo' youth.

I do know that the deck can be stacked and that in the early days of

Job Corps we talked about selecting only the "cream of the crop" at

least until we had achieved some acceptable level of prograM credibility. Asidekfrom the selectivity question, there are serious data gaps

with regard to why some youth enroll and others do not; why some remain

while others depart; which program components are most likely to en- hance job entry, stability, and mobility; what happens to those who

leave the program; whether the critical variable is program treatment

or maturity; whether those who participate in such programs do fare

better than similar youth who-were not program enrollees.

Youth in prison represent yet another adjustment response.

Whether or not there is a positive relationship between unemployment and

crime is a topic to be addressed by some other conference participant. T only seek here to make the point that youth in prison are yet one more

important population for which we have, little data. Whether or not

the prison experience affects different yoyth in different ways is

not a question which can be answered at this time. Nor is it possible

to know the.ways in which criminal and prison experiences influence emf,

ployment-related4behavior or problems of adjustment.
- 505 -

p

Joining wit other critics, for whatever value it might be, it is

appropriate once again to make, the point that current methodologies

utilized for the measurement .of youth unemployment are not without.serious limitations.\\To be included in'the official definition of "unemployed" a person must be at least sixteen years ofage, without a jOb,

and available for and looking for work. An individual who works, however briefly, for wages during the survey week is considered "employed."

Thus, the definition of unemployment is arbitrary and it'does not

o

C

allow'for-the collecting of much information-about each individual -' -s

work status or work situation. For example, a youth looking for full- _ . .._ time employment would not beited as being unemployed if he or she

held a part-time job'of only one tour per week. Further, youth who

are interested in work, 'IAA have stopped the search, for whatever the

reason, are not included in the unemployment category since they are

not viewed as labor force participants. The current system does not

allow for a differentiation between thOse seeking full-time work and

those seeking pait-time employment. Moreover, as noted earlier, the

discrepancies between NLS and CPS reports would indicate that we have

yet to"determine reliable sources for employment-related information'.

Having been c#tical about the data .gaps and methodological flaws,/

I feel compelled to say that f am not indifferent to the many problems.

involved i ,seeking out, information from the young,. particularly youth

who are not readily accessible. The problem is further compounded by

----.--the-fact-that-poor youth-imore so than middle claSs pinta, have difficulties in handling standard paper and pencil questionnaires and

_probably somewhat mote reluctant to share. persOnal information.

The Job Corps experience can beillustrative of what can happen

when the best of research-evaluation plans are tested out in the real

world. Prior to the arrival of the first JOb Corps enrollee an elaborate research and evaluation system had been designed andA.transmdtted

to appropriate Job Corps Center staff. Briefly, the plan called for

the collection of entry data (why they came, how they heard about the

program at they expected to gain, and so forth) as well is a series

of tests4iich would allow for some measurement of reading and math

536

1
o

- 506 -

44

skills. The plan called, also, for a mid-training interview and retest,

and a final exit Interview with a third educational achievement test.

In order to be able to make comparisons between enrollees and nonenrollees, we contracted for an outside firm to give similar interviews

and tests to a control population. Finlly, in order to show the Congress that the payofftwas well worth the investment, arrangements were ; mlOmWor a six and twelve month follow-up of both enrollees and control

group members.

I believe it is accurate to say that within fifteen minutes of the

arr±va -1 of-the-first gioup of enrollees, bur-research7evAuation sys- '084 tern collapsed. First, we discovered that our commitment to science

was necessarily shared by front line staff. As articulated by one

center' 1 ctor, "I have more to do than fuck around with your stupid-

_ assed tests." Apparently this sentiment was shared by the majority of

those who had responsibility for the impleMentation of our research format. Enrollees werei;equally unenthusiastic. Many complained that such

tests were just school revisited and did not have anything to do with

why they,bame to Job Cbrps. Still others resented the -kinds, of questions which were being asked and many were more than a little apprehensive as to just how the information would be, utilized. I should

also point out that more than a few of the enrollees had already had

wide experience with such data-collecting techniques and tests and

hence were quick to note that such data were, i fact, biased.' ,Yet'

not er barrier to successful implementation w t efi-a-t that some

enrollees arrived, took one quick look at theme home to be,*and promptly demanded to be returned whqnce thecame. Neealess to say, the4situation seemed hardly appropriate for the pursuit of respondent based

data. In any t, the mix of enrollee resistance, staff indifference,

and the real' ies of. he setting was sufficient to bring our research

efforts to

better in a

. of respo

virtual halt. Our outside contractor did not fare much

empts to assemble and stu the control groups. Problems

ent resistance mobility as well as a fear on the part of in-

. ewers to enter certain neighborhoods all contributed to a moratorium on such ambitious research undertakings.

. I
- 507 -

I recognize also that research is a costly business; that there is

only so much time which can be devoted to an individual interview and

hence, not all good questions can be included; that there is a

reluctance on the part of people.to participate .in survey as well as

other studies; and-that the processes of research entry are becoming

all the more difficult because of emerging freedom of information and

privacy protection regulations. At the same time, I do believe that

the barriers are not insurmountable and-that we can do a Vetter job in

-the-selection of questions; the-pretesting and validat-ion-bf-research--

instruments; greater eclecticism in research methodologies (a better

mix between in-depth interviews and surveys); moreeffective use of

youth as data collectors; being more sensitive to the apprehensions

left_ felt by respondents and doing more to show respondents as well as policy makers the necessity,' relevance, and functional value of our research;

0 More, in the way of collaborative efforts among the various social science disciplines in both the design and analysis stages of the research;

and finall, a more humble posture as we attempt to bridge tYrgarcbetween our'data and'the problems of the real world.

I rioted earlier that an important part of my assignement here is

to say-something about alienation as a characteristic of poor youth or

as a youthful repsonse to perceived or real barriers' arriers to opportunity.

Alienation is a term frequently utilized by be avioral scientists, particularly sociologists. It is a term that imies feelings of powerless- "

ness, anonymity, estrangement, or a lack of some acceptable relationship between the individual and the instittitiops of the society. It

is also a Urm which seems to be utililed in a most cavalier manner..

Alienation is used as both an independen)Fand dependent variable. 'Sbie

investigators take the position that alienation contributes. to failure

or underachievement in academic studies, interpersonal relationships,

employment, and a host of other behavioral areas. Conversely, other social scientists view alientation as being the result of failures in academic achievement, interpersonal skills, and employment goal attainment. Most sociologists and psychologists agree that there is a significantrelationship between self-esteem and expressed sentiments of

alienation. Disagreemeht arises over the question of which of the two

is the contributing variable.

5 `)
r - 508

The term alienation has been-,used to explain the behavior of stu-

'e'' dent activists, the women's-liberation movement, past and most recent

-black disruptions-in utban areai, juvenile and adult delinquency, and

I the shift to alternative"life styles. It is a very popular variable ,

or concept. A systematic review of the utilization of the term alienation would suggest that it is probably more a product of the sociological imagination than an accurate reflection of why some people behave

the-way-they-do--Whether-or-not-alienation, no-matter-the-criteria

utilized, flows from limited prospects is not a question which can be

answered fully byoavailable data. It is true that sociologists do

find a positive/relationship between socio-economic status and admissions of personal owerlessness. At the same time, the research at hand

would indicate t at numerous other variables can and do interypeto_

distort this relationship. For example, Rosenberg notes that factors

4 such as age, sex, race; and the racial composition of a school will *ft

influence how students of varying socio-economic backgrounds view them-,

selves and their abilities to control their destinies.4

I do not mean to suggest that feelings of alienation are unimportant or that they are not unrelated to bloclised opportunities. Rather,

I am taking the position that at this point at least, we cannot say

very much about the interplay between alienation and response to limited

prospects. Further, there is little reason to believe that alienation

0 is_more_9pharacteristic_of_youth_than_of other age and_so_cial_groups or

cthat it is more+prevalent among spoor youth than among more affluent

youth. Some would argue that limited prospects would, in fact generate

greater adjustment problems for the middle class rather than lower class

young simply because ore affluent youth hold higher expectations and

are under greater ocial pressure to achieve. What we can say is that

there is a real eed to learn more about the various coping methods utilized by youth rich and poor, if our intent is better to understand both

the role an consequences of limited opportunities:

4. Morris Rosenberg, "The Dissonant Context and the Adolescent SelfConcept" in Sigmund E. Dragastin and Glen H. Elder, Jr.; eds., Adolescence in-the Life Cycle (Washington, D.C..: Hemisphere, 1975),

pp. 911116. 5j
ti

- 509 -

The war on poverty,, while calling national ttention to the condition, of the poor, also-gifted as a stimulus for e publication of num- c

erous reports, monographs, texts, and public commentaries all seeking

to provide a realistic portrait of poor youth. In reviewing these materials, one can only be struck by the paucit of data. Without the ben- ' efit of compaiative samples,, samples, of suffi ient size, control for

such critical variables as age, sex, race, and community,precise definitions of poverty or disadvantaged, or any data reflecting respond4

ent attitudes, values, experiences, or needs sweeping generalizatio

1 were made and poor youth became a monolith. "You see one_poor ki and

you've seen them all." Rather than viewing-poor youth within the usual

framework of the adolescent-youth development process, poor youth were

portrayed as products of some separate and distinct culture.

Two incidents from the early days of Job Corps might help illustrate.my point. Early in 1965, Job Corps administrators thought it

would be wise politically to bring a small groufof Job Corps enrollees

to Washington, the idea being to give Congressional representativesean

opportunity to observefirst hand the results of their congressional

actions: The plan was to assign these enrollees to various federal

agencies and congressional offices. Needless.to say, great care was

given to the selection of this group of Job Corps members. Upon their

arrival in Washington,. each of ty enrollees was briefed and provided,

with the Job'Corps dress uniform: gray slacks, blue blazer (with Job

Corps-insignia), blue-,- button-down-shirtr-pin strip-tie, and black

'loafers.. Beneath the uniform they might have been poor, but clothes

do make the man., The reaction should have been predictable. Congressman Flood of Pennsylvania took one look and accused us of not fulfilling

our mandate. More than a few 0E0 employees would not accept the fact

that these were Job Corps enrollees and not ringers. The director

Job Corps held a similar view: these kids did not fit the tragic i age

we had painted for the Congress and, hence, their middle class appear-

"

ance would be dysfunctional to the cause.

and the.Job Corps program. Having been one of the principals in the

My second example held more serious implications fbr both enrollees,

Sop
510

design and implementation of the Job Corps, I am compelled to admit

that we did fail to differentiate between our public rhetoric and the

realities of tie situation. In a desire to explain away high attrition rates, center disturbances, failure to achieve enrollee quotas,

and to persuade Congress of the difficulties of our task, we did much

to contribute to the image of enrollees as alienated, hostile, emotion-

, ally distressed, and deficient in even the most basic of tognitiVe and

interpersonal skills. Certainly, the Job Corps enrollees were, in comparison to middle class youth, less accomplished inintellectual skills,

more likely not to have completed high school, more likely to be from

disorganized families, more likely to have been involved in acts of

delinquency; and more likely to have experienced prolonged periods of

unemployment. We could not have expected otherwise since the attributes noted were supposed to be characteristic of those admitted to the

Job Corps. Relative. differences were not really taken into considera,

tion. The strengths and motivations of enrollees were underplayed.

Rather, the prevalent theme in many centers was to stress the emotional and attitudinal dimension while sacrificing relevant job skill tAining and the building of bridges between the enrollee and specific emploympnt and job opportunities. The emphasis upon the minds, heads,

and internal dynamics of enrollees did little to enhance job skills

or post-training job placement. Further, I would think that this eclectic approach did help to stimulate 'attrition and discontent since many

enrollees failed to see any relevant relationship between their expec-

. expectations for job training and job placement and the day-to-day activities within Job Corps centers.

As Taggart points out, it was only when Job Corps.began to take

its legislated mission seriously that significant changes did occur:

Second, overall Job Corps performance improved

rather than deteriorated when many of the

frills were slashed. Training and education

were narrowed to specifit job requirements.

The key seemed to be the ability to gain access
to better jobs rather than the efforts to

alter the attitudes and values of enrollees.

There is yet another lesson which can be learned from the Job

Corps experience, and one that too many in policy, research, and program positions are reluctant to confront. In matters of youth employment training and formal Schooling, much too much of the burden of

proof has been placed upon the clieht, student, or trainee and,too lit -

a%ntion has been given to those institutions responsible for the

design of work settings; those responsible for increasing work opportunities;those responsible for the absorption and integration of ne0-

comers into the work force. Obviously, in the business of education

and work, it takes two to tango:4 people who are prepared to handle

work responsibilities and institutions de icated to maximizing work and

career fulfillment. My own'assessment of the education-work pcture

leads me to conclude that current youth employment training policies

and programs reflect either naivete or a deliberate avoidance of databased reality: naive in behaving as if proper skill training and an

assertive work attitude alone will lead,to productive and satisfying

, employment: unfairin implying that the major.problem is with people,

young or older, who are either unwilling or unable to take on reason-

, able work or non-dead end employment. Indifference to the hard facts

of shrinking job opportunities; increased competition in job entry; , a reluctance on the part of employers to hire the young, particularly

those who have not completed high school and those who are black, and

indifference to the fact that most of the jobs available to poor youth,

particularly those who have not completed high school and those who are

not white, do not require much in the way of skill training and do not

represent an opportunity for career stability and even less in the way

of job satisfaction.

Without Unduly belaboring the obvious, or going too far afield of

my aseignmentI want to make the point again that if our concern is

with minimizing youthful alienation and problems of adjustment to lim5. Robert Taggart, "Employment and Training Programs for Youth" in

National Commission for Manpower Policy, From School to Work: . proving the Transition (Washington: Government Printing Office,

1976), p.121.

54

4,
512 -

ited opportunities, we would be wise,

,II

believe to consider the commen-

,.....-

. Excepting a minority o

V

f youths who need special n assistance to find and retain jobs, it would

, seem that allhe help,most teenagers needed to

function effectively in the work force was enough

jobs to go around. In labor markets with large

job deficits, it's only to be expected that the

inexperienced will be shoved to the end Of the

line and,some will give up completely. My pre-

------scription for- the --dad is that the-best-way-to

reduce unemploymeng-,for youth as well as adults--

is to create jobs.

. - While I would concur with.Levitan, I would add that in the case of

the young, particularly poor youth, it is necessary to be somewhat more

precise as to the kinds of jobs we have in mind.

Barton and his associates have conducted a variety of analysis which

woul confirm the existence of two labor markets--one for the young and

(

, 0

tary of Sar Levitan:

one for adults.

Areenage e loyment is different, even for those

working full time, and even when they have been

certifi d with a high school diploma. To make

the poi t a distinction will be made between

"youth obs" and "adult jobs" or "regular jobs."._

It is n t a precise one, and all jobs cannot be

neatly placed into one category or the other.

But roughly speaking the distinction7exists,

and it is important to recognize it.

In pursuitilof evidence to' support the proposition that ,age is a

A;

Critical factor and perhaps,,especially among adolescents, of greater

significance than a high school diploma or certified'Aill training,

Barton reveals reveals a number of provocative findings.

Utilizing spuial data from the Bure of LdbOr Statistics, he is

. 6. Sar A. Levitan, "Coping With Teenage ein ftent" in National Commission for Manpower Poli:cy, The Teenais,:_employment Problem: What

Are the Options? (Washington: Government printing Office, 1976),

p.64.

7. Paul E. Barton, "Youth Employment and Career Entry" in Seymour LT

Wolfbein, ed., Labor Market Information for Youths (Philadelphia:

Temple University 1975), p.85.

543
- 513 -

',.. able to show that when comparisons are made of male high school grad-

. uates employed in 1969, of those ages eighteen and nineteen, 58% were

operatives or non-farm laborers. For the age range of twenty-five to

forty-four of the same educational level, only 27% were in these occupations. Further, a comparison of a wider range of occupational groupings

shows that among males of the same age, eighteen and nineteen, no gig -

nificant differences are-nund in occupat nal dis ibution when comparisons are made between high school and non-high 'schoo graduates.

Reports published by the University of Michigan Survey Center re=

flect similar findings. The research, starting 1966, involved a national panel of 2,000 boys at that time in the tenth grade. The findings

summarized below were based upon the 1970 survey.

8

,-. (A) Mean weekly earning far graduates was $112.00 compared

to $119.00 for dropouts. , (B) Graduates were found to have a slim eAge in occupational

status, although not statistically stinificant.

(C) In response to the question, "What I have learned in

high school helps me to do a better job," 13% of the

dropouts answered very, true, compared with 16% of the

1

- graduates.

Other data, including a Bureau of Labor statistics survey of the

hiring prietices of firms in ten communities proVides some revealing

findtpgs. For nonoffice occupations, the percent of firms not hiring

. people under age twenty ranged from a low'of 48% to a high of 76%. For

office occupations the youth barriers were less severe refl ting the

:... fact that young women with high school diplomas can move fairly quickly

into clerical work.9

The findings present above, - available follow-up data from employment training programs, and the rather extensive investigation conducted

by Kalachek suggest that the failure of youth to be employed is not necessarily the result of either holding or not holding a high school diploma; secondly, failure of youth to be employed may be more the result

of restrictive- and - arbitrary hiring.-policies than the result of a lack

8. Jerald Bachman, et al., Youth inCi=iiion, vol. 3 (Ann Arbor: Survey Research Center, University of Michigan, 1971).

Barton, op. cit.;.p.91.

544
514 --

of necessary motivation oz' skills;1° further, that one consequence of

this restricted and distinct youth job market combined with the increase

in Ten ins the labor force and the extension of the retirement age will

be more and more youth competing for fewer and fewer lobs.. More than

one analyst has noted that without expansion of the job market and a

critical examination of the current criteria utilized in the decision

to hire or reject youth we will continue to experience significant

youth unemployment; increased competition among youth.for fewer and

fewer "regular jobs"; and a hardening of those barriers which currently

operate to separate the young, particularly those with the fewest bar-

, gaining resources, from entry into even those occupations which are

considered as being an appropriate career siarting point for the young.

Knowing the unemployment rate for youth does not tell us very much

about how the young feel or deal, with unemployment. Again, the available national or large scale inquiries do not provide answers as to the

behaviors, problems, attitudes, or consequences of youth employment or

unemliloyment.

,Much of the attitudinal research fodusing upon youth is most often

restricted to the presentation of marginals. There is limited multivariate analysis, a failure to differentiate between age, SES, sex,

and racial groupings., We know much more about thosf in college than

`those who are not students, more about males than finales, more about

the affluent than the poor, more about whites than nonwhites.

Still, there are a mixture of investigations which allow for comparisons between/youth of varying SES, educational, and racial back-

. grounds. There are also several field studies which seek to describe

the milieu and life style of the disadvantaged. Finally,..there are

studies whidh dosTrilvide some insights as to the factors which appear

to be associated with variations in employment and educational achievement among poor youth.

Whatever the source of data, it is clear that contrasts in the

achi7ement of youth of different socio-economic backgrounds

Edward Kalachek, The Youth. Labor Market (The University of Michigan and the National Manpower Task Force, 1969).

t
- 515 --

is more the result of s cietal and resource constraints than signif -,

cant differences in att* udes, values, or.aspirations.

An American Council of Education research report entitled "Cow In-

-come Students: Do They Differ from 'Typical' Undergraduates?" concludes

with the following statement:,

,The implicationsef the present study are

clear. AOlde from the expected dissimilar;

ities in their demographic and background

characteristics e.g. father's occupation),

the low income dergraduate does not differ

dramatiqlfy fr m;his are affluent classmates. He sh es with them the same life

goals, degree aspirations, activities, and

interests.1Hie may be more likely to drop

out- -but only temporarily; he may shy away t. from student deomonstrations; fie may get

slightly lower grades; 'but overall, the likelihood of his attaining his degree in four

years is reasonably cliie to that of his 'more

privileged clasi*tes.

,Similarily, a national survey conducted by Yankelo"vich in 1969

shows that with few exceptions, college and noncollege youth are not

very, different in expressed'attitudes, values, and beliefs.

12. If anything, noncollege youth tend to be more traditional in responses, placing greater stress on the importance of law and order, a hawkish approach

to View Nam, and respect for their elders. Noncollege youth, more so

than those'in college, emphasize the importance of "having the love and

respect of your family': and "living the good Christian life." Finally,

with regard to work-career related attitud9s, noncolliege respondents

are more likely to believe thgt "hard work will always pay off'if you

have faith in yourself and stick to it"; "hard work keeps people from

loafing and getting into trouble"; and "the individual who plan's ahead

. (Ian look forward to success and achievement of persen' oals."

* My own study of the work aspirations and expectations fgraduating

11. Enign I. Holmstrom, "Low Income'Students: Do They Differ from

"Typical" Undergraduates?" research report .(Washidsgton, D,C.:

American Council°of Education; 1973), pp.19-20.a 12. Daniel Yankelovich, "What They Believe," FortunditMagazine,'(JunS4

1969) pp.70-71.

f a.

"ter

f

c-
.ft

516

4 college seniors reflects minimal variations between white and nonwhite

stucrents. Major contrasts are foundAlcpostcollege degree plans and

field of study. As might be expected, nonwhite graduates are confined

to fewer fields of study gnd are more likely not tq plan on enrolling in

graduate or professional school immediately following college gradua- tion. 1 A 1966 study of some 1,300 male Job Corps enrollees provides both

attitudinal and enrollee badkground trate:114 The,typital enrollee had,

completed nine years of formal education at the time of entrance into

the program. Reading sources indicate a 6.7 grade level; 63% had no

previous record of any type of delinquent behavior, 27% had committed

minot,acts of antisocial behavior and 10% had been convicted of more

t serious offenses. Less than a fourth had contact with a doctor or dentist during-a four year period prior to Job Corps enrollment. Only

10T had been employed in full-time jobs during thecone year period

preceding `enrollment and

cents per'hour.

Comparisons of white

this working group earned less than eighty

and black enrollees indicate both differences

and similarities. When askbd to rank the importance of certain job

and work characteristics, the great majority (90%) of both racial

groups agreed that "if you work hard you can get ahead." Generally,

black enrollees were More likely to have come from single parent homes,

with parents having completed fewer years of formal schooling and with

parents who have experienced longer periods of nonemployment. Significant differences were found in matters of preferred work settings and

work styles. Blacks much more so than whites showed a preference for

indoor jobs,,jobs where you have an opportunity to "use,your own ideas."

However, the majority of enrollees, white and black, agreed that education and hard work were the critical factors in allowing a person to

13. David Gottlieb. Youth and the Meaning of Work (Washington:

Government Printing Office, 1974)."

14. David Gottlieb, "Poor Youth Do Want to Be Middle Class, But It's

Not Easy," Personnel and Guidance Journal, vol. 15 (October 1967),

pp.116-22.

51,7

a
- 51 7 -

U

"get ahead." Further, White and 1105nwhite. enrollees were in agreement

that for the most part, parents and teachers were supportive and desirous of enhancing enrollee success. At the.same time, less than a fifth

of all enrollees felt that there was a high degree of consensus between

their cover aspirations and the activities or requirements of the

schools in which they were enrolled.

Utilizing employment status as an indicator of successful transition from school to work would give the impression that for most youth

time alone will heal all problems associated with unemployment. That

is,,if the young can just hang in there long enough and avoid the detrimental consequences of prolonged detachment from school and employment,

then eventually the majority will somehow be absorbed and integrated

into the socially acceptable workings of the system. Again, we can

say little about the emotional, social or economic damage caused by

prolonged periods of isolation from school, work, or training program.

Nor, as noted earlier, can we assume that involvement with school, work

or training implies that all is well with the young.

From the available data, it-seems safe to conclude that the vast

majority (97% in 1971) of fourteen- to sixteen-year-old males are enrolled in school. About a fourth of those in school are Wrking,

mostly part time. The major shift to work occurs around the age of

seventeen and is largely completed by age twenty-four or twenty-five,

with more than 85$ of the youth at that 'age out of school and more

than 90% of those out of school working or serving in the armed forces.

Young women follow a similar pattern except in that they show a

tendency to terminate schooling at a somewhat faster rate after age

sixteen. There has, however, been a leveling off and the gap between

males and females is rapidly declining with "ncreases in female college and postcollege enrollment and marked incr in labor force

participation.

Disadvantaged' youth are among those most likely to experience bar-

. riers in the normal or usual transitionoprocess. In general, hard-toh71)

hard -toemploy youth are defined as members of a minority group, of regular

members of the labor force, urban dwellers, with less(t an a secondary

school education, and of very low socio-economic backgrounds.

, \

548
I

- 518

An analysis of historical trends in youth unemployment Makes clear

that it is the black population which is least likely to become .connected With employment. As Regis H. Walther concluded. in his extensive

review of youth employment studies:

The labor market problem's of black youth are

matters of particular concern because of the

size of the black population (about 13% of the

youth population), the long history of overt

discrimination, and the severity of their

labor market problems. Black youth do worse than

white youth in every age, sex, and education . achievement c tegory, except perhaps for col-

/lege gradua . A look at t long term trends indicates that

the situati n has been'steadily deteriorating

for a numbe of years. Twenty years ago, non=

white and white males within the same age brackj

et of 16-1 had roughly comparable participation And employment rates. Non -white females

had a significantly higher unemployment rate

than white females in 1954, but the gap has

been steadily widening in the intervening

years. In 1975 the unemployment rate for

out -of- school 16-17 year old non-whites

exceeded 60%. This exceptionally high rate

was partly a result of the economic down-turn

during 1975, but in the previouS year when cigditions were not as bid, it had reached 50%.

Walther also identifies a number of factors which others have proposed as being of critical importance in differentiating between "hard

4 to employ,youth" who either succeed or fail in employment training programs.

16

Birthplace:

Marital',"

Status:

Higher retention for those from the South as compared

to those from the urban North.

Married youth perform better than unmarried youth.

Obviously an important intervening variable with

those between the ages of sixteen to eighteen showing

less program commitment than is the case for older

youth.

15.. Regis H. Walther, "Analysis and Synthesis of DOL Experience in

Youth Transition to Work Programs," 1976 report available from.

National Technical-Information Service, VA 22151, p.47.

16. Ibid, pp.53-56.

5j

I

V

4
519

Test Scores: Scores on IQ and reading achievement tests are positively associated with good performance.

Family

Background:

Family

Atmosphere:

School

Activities:

Contact with

Police:

Previlous

Work Record:

Role Models:

Optimism:

Socialized

Values:

Trust:

Personal

Competence:

Self Evaluation:

Staff Evaluation:.

Planning

Ahead:

Youth from two-parent families not on welfare do

better than those who are products of familial disorganization and those who come from mother-headed

welfare families.

Parental punitiveness and lack of support are highly

associated with program failure.

The more successful enrollees are those who did well

in school, completed more years of schooling, and were

not classified as disciplinary problems.

Poor perfoMers were more likely to have delinquency

records.

Good performers have better work records and are less

likely to have experienced prolonged perjods of unemployment.

Identification with positive role models is more

characteristic of those who perform well than of . those who leave the program prior to completion.

Good performers express a more positive attitude and

a greater self-confidence.

Belief in education and hard work are consideed important ingredients for successful performance.

Good performers are more trusting of others.

Past successful achievement in school or work is

more characteristic "of good performers.

The more positive the self evaluation the better

the performancei014

41 Those ranked most highly at time of program entrance

were later judged to be better performers,

Better performers have better crystalized personal

goals and plans.

3
520

Peers: Performance is associated with involvement with and

dependency upon peers and the nature of the peer

group. Good performers are less dependent upon

peers and less-likely to be involved with peers

engaged in antisocial-behavior.

With regard to the Walther listing, several observations are in

order. First, whether the population be poor.oryich, male or female,

black or white and whether the setting be school, work, or training

program, these same variables typically emerge as being of critical importance. Second, measurements of success leave a great deal to be desired. For the most part the dependent variable is program completion

and attrition. Those who complete the program are labeled as good per-

, formers, those who leave are classified as poor performers. Third, it

is difficult, if not impossible, to determine whicji are the independent

and which are the deperident variables. Is good performance a product

of high esteem or doellpigh'self-esteem generate good performance? No

doubt there is some mutual variable interaction, but these studies fail

to define just what factors, in just what settings, with just what

kinds of youth, will predict or explain good or poor performande..

Throughout this paper I have attempted to speak to the question

of alienation and adjustment to limited pr9spects as experienced by 'o

disadvantaged youth. As indicated throughout the body of this paper

there is, in fact, little in the way of reliable or empirical data

which would shed' much light on the subject. .I have also sought to

identify areas of inquiry which should be pursued if there is serious

interest in knowing the ways in which youth, particuldrly poor youth,

respond and adjuit to perceived barriers. Further there is a,need to

study the ways in which current hiring policies act to block youth

4

''L

from Taployment.

It is my hope that through the combination of in-depth personal

interviews, ethnographic field research, and validated survey instruments, we will, in the future, be able to provide answers to the many

*portant questions being raised in this conference.

. 551
1

DO YOUTH REALLY WANT TO WORK:'

A COMPARISON OF THE WORK VALUES AND JOB

PERCEPTIONS OF YOUNGER AND OLDER MEN

By Patricia Y. Miller and William Simon

ABSTRACT

Recognizing the dearth of available research on the work values

of youth, the paper eKamines these using data drawnfrom a random sample of 1992 men betwan the ages of eighteen and forty-nine. A varimax factor analysis of twenty-seven items concerning work values proAL.

duces six factors: Intrinsic Work Rewards, Economic Rewards, Security

Rewards, Social Rewards, Interpersonal ReArds, and the Antiwork Ethic,

Considering only men whose annual incomes were less than $15,000, small

but significant differences are found between younger and older men with

reference to the value attached to Economic Rewards, Security Rewards and

the Antiwork Ethic. Examination of a comparable set of items concern- , ing worker perceptions of access to these rewards indicates that older

men consistently and; sometimes, substintially report greater satisfaction in their present jobs in terms of these. Unemployment is not

associated with work values. Ethnic minorities appear to undervalue

Intrinsic Work Rewards and overvalue conomic Rewards, Security Rewards and the Antiwork Ethic. The alysis concludes that substantial

continuity characterizes the work v lugs of younger and older men.

INTRODUCTION

All too often, the major source of specific policy problems is to

: be found in the unanticipated consequences of otherwise effective improvements in the system of social arrangements that obtain in one or

another sector of the society. Thus, improved law enforcement fre-

. 0

quently appears to exacerbate the crime "problem," improved health care

delivery systems create an elderly citizen "problem" and industrial

innovations compound the unemployment "problem." Characteristically,

'those in the "helping professiong," particularly in advanced western so-

. cieties, experience enormous frustration in their efforts to improve

the lot of such groups. These frustrations 'ye rise to the, impulse

: an to blame the victim, to stress hypothetical plationl,A6 locate

the source of'the "problem" in the real or imagined characteristics of

the client. A predictable feature in the evolution of virtually every

5
- 522 -

social problem is the evolution of at least one explanation emphasizing.

the complicity of the individual in his or her own difficulties.)

With reference to unemployment, Kalachek has observedrithat rising

unemployment is accompanied by increased cynicism toward Nfunem4,1f

ployed, as critics mobilize 40cepticism about their sincerityvin seeking

jobs or their willingness actually to work.

2

The most recent expressions of such cynicism are profitably considered against the backdrop

of the late 1960s.

During these years, the conjunction of industrial expansion, a

wartime economy and federal programs directed toward the hard-core unemployed provided an abundance of entry level employment opportunities

for the young, particularly those With college training. As the intellectual founders of the social sciences would have predicted, the expectations of many young people, assured of their "right" to work, es-

. callated to encompass other, in this case moral, concerns. They said

they wanted jobs that were intrinsically interesting and socially responsible. 'A-good_wage and the promise of career advancement opportunities were no longer sufficient in themselves to attract an adequate

number of youth to certain kinds of jobs or specific industries..

'These rising expectations asserted by the young caused specula-

. tion that a radical disjuncture had occured in the,work values of

some Americans corresponding to or in cqpsequence of their political

410, opposition to American foreign and domestic policy in.general and the

unpopular war in Viet Nam in particular.'More judicious observers

suggested that,a long-term transformation in the ethics of work_

accounted for the emergence of social responsibility as a pivotal value.

3

* 1. In circumstances where blaming the victim is impractical due to

the intensity of popular sentiment, responsibility for the probleM

may be directed toward a surrogate chosen from among those in proximity to the actual victim, e.g., kin.

Edward Kalachek-, The Youth Labor'Market-(Univeysity of Michigan

aid -the -National Manpower Task Force, 1969). pp.,72-73.

3. .Jacob W. Getzels, "On the Transformation of Values: A Decade After-

, Port Hpron," School Review, vol. 80 (August 1972); pp. 505-19,

.t 40"

553
523 -

Some argued that the assimilation of this generation and its emphasis

on social' responsibility into the labor force would profoundly alter

the character of work as well as the moral context in which economic

activity occurs. (How remote/all at nce appears the warning of a . "youth revolution," how distant the p int of a demographic accident

that gave us."hali the population under twenty-five," as if that marked

some cultural watershed.) Less optimistically, others suggested.that

the rising expectations of youth with reference to work presaged a

diminished-commitment to work itself in the maturing cohort, that

their posture tdwards work promised, u nwholesome consequences, for both

industry and the economy.

A subsequent scarcity of employment opportunities in-the intervening years fostered, we are told, a growing preoccupation amoloothe

young with the availability and security of employment. 'With scarcity, the mechanisms of relative deprivation generating rising expectations moved into reverse and the chorus of concern for intrinsic re- r"/-

f

wards- and social responsibili in the job setting was substantially

muted. The legacy of the 1960s, however, is a lingering suspicion

that the present difficulties youth experience in acquiring and maintaininiemployment are somehow conditioned by flaws in timeirwork

values 'which render them poor candidates for full participation in

the labor force. It iecertainly recognfzed ihai 'contemfmrary youth

are disadvaqaged by an economy that can provide them with fewer jobs

than they .neand a dual labor market that decidedly'fa rs4he educated, trained and.experienced workers in the high caste r'15rikary

sector. Nev,rtheess, tnderlyingthes* objective obstacles to yoUth'I

adjustments to work is the suggestign that the.young are further disadvantaged with regard to,emplvment by unrealistic expectations concerning the content and rewards of participation in the labor force.

Curiously, thepeen ous swings allegedd in tiie'wo'rk values of

. youth leftjew empirical sidues. FroldtheTerspective Of' the theory

testing that organizea"normal-sAende, "-lhe paucity of research on

4- the work values of youth is not i Sarticularly surriVisAng; While such

4

1.0
- 524 -

1

.values are potentially important !Tom a practical perspedtive,they

fall into the interstices of the theoretical concerns that dominate

research on this population. Particularly marked is the neglect of

thole whose entry into the labor force (a) occurs earliest, and

(b) without benefit of the services of some protective institution.

We can only speculate about the factors that account for the disinterest in these youth apparent among those responsible for policy.

The handful of extant studies provides very. limited data from

which to evaluate the stability of work values across cohorts of youth.

Somewhat more research is available torl'eollege students. Davis'

classic study stresses the salience of intrinsic work rewards for a

sample Of college students in the 1960s.

4 Compared to such factors

as money, career advancement and opportunities for the exercise of

leadership, these students cited opportunities to be helpful to others

and useful to society, or to be creative and original, substantially

more often. By theigate 1960s Yinkelovich hadistingu shed "fore- ' runners" from "practical minded" college students. a former disproportionately stressed the altruistic and instrinsic a ects of work;

the "practical minded" were somewhat more likely to value ob security,

income, and prestige.5 Studying college seniors in the Gottlieb

similarly fount} tOt they_ emphasized the altruistic or social responsibility component_of work as well as its intrinsic rewards. Moreove

he.observed that they place.less importance on money; prestige and

status compared to the students interviewed by Davis about ten years

'earner: Interestingly, the ?slues of these seniors varied markedly e

, aCcordinutdAheir fields of study. Thus, for'example, men,in bus-

.

. iness_adAinistration and engineering wer more likely to,view work pre-

.

.. 1 ..' Companyv 1964)---,

..

.' 5, hEnier-Yan6lbvich, "What They Believe,!,' Fortune, vol. 79 (January

. .1969);pp. 70-71;.179-181. - 9f'F". 0

e d .,. . ,

4., James A. Davisi,Great,Aspir,tions clgo: Aldine Publishing

.

4 -

. . 4

sirdt( Al A

t

'" 555
A.

525

dominately as a means td make money while men in agriculture dispraAlortionately believed that work provides a means for becoming a better

person.

6

In a follow-up study one year later, these college seniors reported on their early experiences in the world of work. Among those

working, the "most important reasons" cited for taking'their current

jobs were "interest in job" (33% of the males, 40% of the females) and.

"needed money to live" (26% of the'males, 27% of the females). Moreover, half reported that they were earning less than they had expected

to before 'they entered the labor force. While a substantial number

appear to have experienced the economic realities of work as somewhat

harsh, the majority found intrinsic and altruistic work rewards accessible to them.

7 Our review of the limited research available on the work values

of college students in recent years suggests that their values have

remained fairly stable. Nevertheless, the work of both Gottlieb and

Yankelovich indicates that there is considerable heter:ogeneity in

values among"students which, corresponds to their'orientations toward

the interface between education and career. Further research.on the

development and maintenapce of work values and the,ways in which these

are translated into educational endeavors and career plans would appear to be indicated.

'Research related to the work expeiience of nonstudents is par-

., ticularly sketchy. Studies are available to confirm common -sense

assumptions regarding the impact of education, work experience, race,

gender and levels of occupational information on objective indicators

of work adjustment such as earnings, unemployment experience and occdTational'status.

8 Additional researql documents the particular prob6. David Gottlie10,Youth and the Meaning of Work (Washington: U.S.

Department of Labor, 1p74).

,

. David Gottlieb, Youth fiiot the Meaning of

' Work, Part "II (Housfon:

The University of Houston, 1974).

. Herbert S. Parnews and-4pdrew I. Ko en, "Labor Market Experience

of NoncollegeYouth: A Longitudinal Analysis,",in From School to

Work: Improving the Transition (Washington: ,government_Printing

Office, 1976), pp. '57-88.

r

;". 4t

i4,r

556
- 526 -

) -lems experienced by low-income ethnic minorities with respect to employ-

. ment.

9

VeryQttle is iiibwRabout the social-Tsycholo cal dimension

of "work for noncolleie-youth::

Not surprisingly, the handful of available, studies suggest that

the work values tf noncollege youth dif er substantially from those

of young men and women attending cope. Based on the YankeloviCh

data, nondollege youth are less likely to emphasize altruistic and in

trinsic work valuei. Moreover, compared to the._" Torerunners" among

college-students, ,substantially greater numbers of noncollege youth

stress the,importance of money and prestige. In fact, the value at-

) tached to money and prestigeby noncollege students is comparab

to'that of "practical minded" college students." Similarly, practical

concerns,"such as job security, ages and opportunities appear prominently in the job concerns of high school students. 11 A British

study-of early entrants into the labor force suggests that intrinsic

secondarily, practical concerns impact on job satisfacGottlieb concludes that the work values held by Job CorpS

are essentially middle class. 13

factors and,

tion.

12

And

.participants

, It appears, then, that the work values of noncollege youth are not

radically dissimilar to those of college youth. The meaningfulness

0 ,

. _ of work is a high priority for both groups, but less so for those not

attending college. ,Practical concerns for job security, salary and

-opportunities for adyancikentte secondary to a concern for meaningfl;f,:satisfying work. activity for'both groups, but such pTactical con- .., 4.- 9. Paul Bullock, Aspiration Vs. Opportunity: "Careers" in the Inner

City:(Ann Arbor: Institute of Labor and'Industriallelations, 1973).

10. 'Ye elovich, op. cit.

11. A. rlick and A.R. Starry, Vocational Plans and Preferences of

A cents, Poll 94 (West Lafayette, Ind.: Purdue University, 1972).

12. Joa aizels,Adolescent Needs and the Transition from School to

Work (London: The Athlone Press, 1970), p. 256.

13. David Gottlieb, "Poor Youth Do Want to be Middle Class, But It's

Not Easy," Personnel and Guidance JournalAl. 15 (October 1967),

pp. 116-22.,

9. e

.1

o

O

if
14,

527 .

----;r° cerns are mere prominent among noncollege youth.

As we indicated earlier, the mechanisms of relative deprivation

naturally function to escalate expectations where basic needs are predictably secure. Thus, the work values of dottlieb's college seniors- - who believed that their salary requirements would be ea ily satisfied- -

emphasized intrinsic and altruistic work factors. Noncollege youth,

we can be sure, are less secure in the belief that their basic needs

or practical requirements will be satisfied and the contrast between

intrinsic and economic values is less striking for them compared to

college students.

The critical question of continuity in work values remains

While some young,people undoubtedly have difficulty adjusting t

ployment because their expectations are unrealistics,it is not

4 whether#the difficulties of contemporary youth with reference

into the labor force can be traced ,to the values of this coho

answer thig question requires the comparison of Values held

in the labotNforce with those of older men who claim essent

occupational niches. As the classic study, The America

clear

o entry

t. To

y youth

lly simSoldier

demonstrates, it is the "veterans" who create the context

neophytes experience and'evaluatt their own responsibilit

wisdom.is reinforced with refeience to work orientations

rgues against the ghettoization of youth, citing)th

tablis Oilers in the occupational socialization of

We must also avid the impulse to ghettoize youth in ou

forts; it is to the established worker that we must lo

aid by which to judge theneophyte. For this reason,

sciously chosen to focus exciu.sively on noncollege. yo th

older counterparts.in the analySis of work values we

14. S.A. Stouffer, A.A. Lumsdaine, R.M. Williams, J

I.L. Janis, S.A. 'Star, and\L.S.

Soldier: Combat and Its Afrermath (Princeton:

versify Press, 1949.

15. James S. Coleman, et.a1.-, Y, , Transition t

cago: University of Chicago Press, 197 , pp.

55,

ithin which

es.

14 This

y Coleman,

role of es -,

e young

,.15

research.efC.f for the stand-

. have self -con -. 4

and their 3 are undertaken.

A.B. Smith,

e American'

Princeton 'UhlAdulthoo (Chi32-33., 4. A

, I

.1/

*
528 C./

fa,

The woikjalues of college youth are certainly important and should be

pursuedor this data set, but time-cost-space considerations dictate

1 a narrower focusWhere. More. critically, we believe our decision enables us to contribute a perspective that is presently unavailable in

the literatur.e..

In passing, wg, Quid like tefobserve that values are not fixed

attributes. They aredynamic in response to the characteristics of

specifio situations. Rich rewards would undoubtedly flow from the rev search effort.tO irate the itifts in values that occur with entry into

the lal;oirfprce and themtoncrete work experiences that

; fallow. We

would expect the expressed values of workers to reflect a compromise

betweeh their ideal values and their perceptions' of their own access to specific work rewards as well as the climates of value that describe

their work worlds at any given time. Moreover, shifts in.work values' i Occur in response to changes in other, nonwork-related sectors of. tkt

individual's life, Contemporary changes in family values and family

formation behavior,'whiie beyond the scope of this analysis, are un-

- doubtedly_ feverberating into the world of work in as yet unknown ways.

: Our evaluation of the work values of young men is based on the

x ..sqcondary analysis

: of interview data collected during 1976.:77 from t

'national. random sample pf 1992 men. The sample was restricted to those

between'the. es of eighteen and forty-nine but included men liv ing '' both in'ousqollds*and on ,college campuses. Compared to the 1970 Census, the demographic characteristics of the sample, with respect to age,'

race, occupational itatug, income and education -are within the norma4,;,,V.:''.0!,

range of sampling error.

II'

"..

4,,

Since our particular concern is with the work values and job pereptions of youth actually inthe full-time labor force, the 318 stIV't ° dents who fell into the sample have been.excluded from this analysis.

1,11 addit*oh tro,the students, fifteen men who were serving in the military were also excluded here as well as fifteen additional cases where'

e the respondent was handicapped or otherwise disqualified.

Ce;tain common sense predictions can be made with considerable as-

, surance about the objective and subjectivVpresent and future experi4 5

Pa,

I
O

.1) ** --. 529 -

Af

ences of contemporary young m n.with limited education. .They enter

the,labor.force in marginal, unskilled or minimally skilled occupations. i They earn modest wages, somewhat higher than the legal minimal wage. ' Initially, they experience higher unemployment and interfirm

L than, virtually any other group of men. time, their labir force,

participation stabilizes as they moveinto the skilled or semiskilled

trades or the lower - middle levels of white collar technical did

ical occupations. While the high earning potential`of some,of the

blue-collar trades in recent years has become legend, the majorityof

a

.

such men will continue to earn

educated counterparts. Although cle many, values with -

theirfellow Americans, their lifestyles and val es will remaih-som

ubstantiall s than,their colle "4

what distinctive.

The recognition that such men possess a distinctive cons ation

f political and economie values and, additionally, occupy spepific

structural location'in American society persuades us that gross com7,

T'arison of younger with older workers would be confoun e by cohort

effects that favor the,older worker with minimal schooling. We are',

of course, interested in omparing.recent entrants Into the,labor,,.

forcelaith established worke . For the 'comparison to be meaningful,

howevef, ,some assurance.is requi ed thy.obsefved differences are due to age and its correlates rather than to extraneous uncontrolled . variables.

Education is one such variable that is particularly troublesome.

The number.of men completing secondary and post-secondary education

has increased steadily over the years since World War II when the

oldest men in this study enured the labor force. AcCOMPanying the ov

proliferation of higher education hasibeangporadic.changes in. the

meaning of specific educational aohievemeAs for occupational mobility: For this-reason, the'analysis will be.restricted to mtn whose

. dA. annual iqconies during l9,',75 were'less than $15,000, on

that income ItatherAhan education, occupation or'some

these provides the'best available marker of restricted

achieveme4.- This resulted In dropping a,tofal,of 40

0 /-) I

the assumption,

Combination of

I

occupational

.

. . cases; the

,7

rt
- 530

preponderance of these were men over thirty who accounted for 345 of

. the excluded respondents. There remain substantial differences in annual income .that correspond with age--the zero-order correlation (r)

between ageand income is .286. The median income for men between

eighteen and twenty-two is $4;953; for men between twenty-three and

twenty-nine the median increases to $7,337 and finally reaches $10,187

for men aged ,thirty to forty-nine.

Nevertheless, the exclusion of these cases d' result in greater

4

homogeneity across age with respect to the dependent variables. The , reader should be advised, however, that the substa e conclusions_

of the present analysis were sustained inta parallel analysis of data

for the entire sample exclusive of students.

Respondents were asked to answer more than 400 questions concerns

. ing their family, political, economic and leisure activities and values.

Included were twenty-seven items designed to, evaluate work values.

The items concerned various aspects of thework context--salaries and

benefits, co-workers, the physical environment, the meaningfulness of

work, and so forth. Respondents were asked to assess the importance

to themselves of each work attribute. The available response categories were then weighted as.follows:

. 4=Very.Important/3=5omewhat Imoottailt/2=OnlySliekly, Important

I=Not Important %All

Responses were entered into a varimax factor rotation and six

r factors were extracted (Table 1). It was possible to assign twenty-five

of the tritpty-seven iteis to one or another factor. In a couple of in7

at stances, face validity was used to determine to which of two competing

V

factors an item :should be assigned. This occurred where an item ob-

'tained approximatelyeqpal loadings on each* of two factors. While we

. . .

. 0. obviously anticipated that work values would factor--Gottlieb's finding ing thavval,perespond to course of study among college students

implies as much -- earlier research has neglected the po4tial of this-

& -0.

'

. . technicille4 - ,....... .

1 , !

I1 o6i
TABLE 1

VARBRX ROTATED FACTOR LOADINGS

/Factor 1 Factor 2 Factor 3 Factor 4 Factor 5 Factor 6

Intrinsic

Inter-

. Work Security Antiwork Social personal Economic

Rewards Rewards Ethic Rewards Rewards Rewards

Doing meaningful things .682 .043 .035 .085 .169 -.092

A chance to use your- mind

and abilities .677 -.015 -.026 -.052 .184 .020

New challenges .660 -.053

Intellectual stimulation . .598 .002 :2(1

129 .113 .073'

.353

. -.053 .038

A chance for personal growth .552 .065 -.025 .266 , .202 .111

Freedom to decide how to do

the job .468 0..5 .095 .145 .006 .066

Working fOr a company you

, respect .432 .227 -.028 .214 .295 .095

Appreciation for a job

well done' .403 .087 .113, -.040 .351 .078

A good pension plan :032 .818 .140 .049 .088 .045

Job security -.024 .591 .095 .022 .219 .185

Fringe benefits .109 . .588 .241 '.138 .105_1 .204

Being able to retire early

enough with good money .048 .538 .341

Not having to work too hard -.068 .235 .682-

A comfortable routine that

is easy to handle -.D68 * .213 .677

Getting away from problems

at hOme -.049 .033 .467

Paving enough free time to

enjoy other things .13 .136 .436

Opportunity for travel .1 5 =.014 .316

Meeting interesting people .4 3 -.009 .212

Contributing to company's.

progress r 345 .199 .009

Contribution, to society .425 .150 .059

562 ,'

.007 - .1047

.065 -.028

'.678 .203

.217 .160

.' -.013 -.027

.555 . .035

.533, .112

.476 .136

.428 * - .029

.162

.151

.081

.117

.034

.157

.087

.082

,164

. 5,63
TABLE 1 (cont.)

VARIMAX ROTATED FACTOR LOADINGS

e

,

Factor 1

.

Factor Factor 3 Factor 4

Intrinsic,

Work

Rewards

Security

Rewards

Antiwork

Ethic

Social

Rewards

Having

.the respect of the

.

.. people you work with .40 .196', ., .099 .097

Friendly people to work /

with ..307 .087 .228 .076

An opportunity to make a

great deal of money .064 .211 , .291 .174

A good salary .004 .310 .174 -.030

A chance for advancement .285 .207 .035 .174

Having sebewhere to go,

sonething to do everyday .145 :038 .2g2 ,

..° - .196

Pleasant physical

surroundings .229 so _ -220 .335, ..

-. .226 \

Eigenvilues /-

(before rotation) 6.460 .312 1.1.)52 1.293 -

V

4

4

-Factor S Factor 6

Interpersonal Economic

Rewards Rewards

.527 .052

.. :418 -.031

-.005 .643 1 .033 .522 .in

.p4, (..., .400 NJ.

' I .067

.. .210

,..302 4/.097

. -I

. 1.120 1.068

1 1$ p.

R
- 533

4,4 The analysis is organized in terms qftheffactors, which we call

Intrinsic Work Rewards, Security Rewards,-the Antiwork Ethic,,Social

: Rewards, Interpersonal'Rewards,and Economic Rewards. Six unweighted

sum scores were compUted fqr each respondent based on his answers to

the items which define each factor. Five ofthese

16 and the responses

to the associated items themselves provide the basis for much of. the

analysis. Examining these in terms of age will enable us to evaluate

the extent to which the work values or expectations'of young workers

4.

. differ from those..of olde estab4.ished workers. . ,. lcIn a parallel set o items, men whowere employed at' thecime

of the interview were asked to evelua their own jobs in terms of the

samerCharacteristics of tie work conte t, i.e., salaries, benefits, co-

, workers and the rest. The vailable response categories wpre,then

'weighted as follows:

4'= Excellent/3 = Pretty-Good/2 = Only Fair/1 = Poor

A set of sum scores was computed for each respondent corresponding

to those created above with reference to work values. Examining these

for men in different age groups

17 will enable us to evaluate differences in men's perceptions of their access to various kinds of work

rewardt as they move through the life cycle.

INTRINSIC WORK REWARDS AND THE4ANTIWORK ETHIC

Men p4e a high value on intrinsic work rewards, the rewards of

work activity itself. A majority of the men in this study said that . challenge, growth, autonomy and the sense that they were makilig.'Mean-**

ingfui contributions were "very important" to them (Table 2). Iri fact,

with, one exception--intellectual stimulation--a majority of the respondents indicated that each component of the Intrinsic Work Reward Index

was extremely important in their assessment of a job. It is not par-

,

144 16:- The analysis of the Social .Rewards factor was dropped due to

space considerations,.

ts ,17. For ease of presentatiOn, the data concerning men between ages

-4,!: . 'twenty-three and twenty-nine are not reported in the tables. k,

4.s.

.r.,. I. "{' ;,ZAA These mgn are included in the correla 'ons (r's) reported throughout

ro.:lg the,text. 4 A' , _ 1

'4,

. 5-6C

4
o .

- 53'4

° TABLE 2

INTRINSIC WORK VALUES AND JOB PERCEPTIONS BY AGE, INCOME

AND LABOR FORCE STATUS (MEN EARNING LESS ,THAN $15,000

A YEAR WHO ARE NOT IN SCHOOL OR IN THE MILITARY)

4.

lnffinsic Work Rewards

Percent Responding

"Very ImportAht:1

To Each Intrim;ic

Work Reward

v.

Percej Rating Own

J "Excellent"

With Rwect To . Each Work Reward

Age

l'8;-.22 30-49

Age

18-22 30-49

Doing meaningful things, 63.5 65.2 16.5 27.7

A chance to use your mind

and abilities '71.9 72.7 19.7 30.2

New challenges 6 55.] 18.7 26.6

Intellectual stimulation 41.9 40.9 7.3 15.2

A chance for personal growth 63:6 58..8 18.11. 17,1

Freedom to decide how to

do the job 54.2 57.7 18.3 32.0

Worklbg for a company your

respect 6'9.3 20.3 27.1

. Appreciation for a job

well done 59.E 60.7 15.3 20.3

Intrinsic Work Rewards Indexes X 27.74 27.68 X '20.29 23.0t

SD 4.59 4.33 SD 6.25 5.43

0 N- 256 454 N 169. 361

t 0.18 t 4.83***

4

. ***p...001

a

O
I

's

.

' .

, V . v. *

1. 4 44 %. .. -- 535- y.

'NI -'- s.------. . , 4.

. I ..:: , l a... 0. y A 4 . .1Y ,.0- . . . .,

: 4 `, . ticularly.surprising that .intelledttal:s,$injul4t/on iStevartied by these

men sincethar-Tath4i,14mited educations ji.elie 'ail;: serious iniereNS

4 ,- . - .1, . sin intelleqtual pursuits. 4,-. . . . pa.iTprestivepercelfage.of men stress the importance of intrinsic

.0. work rewards; hence, minimal emphagis on those values sutsumed under ,

. *, what we call the antiwork ethic is to beexpected. The antiwork

, ,. ethic,

.. which refers'to opportunities to avoid 'challenge in work and to maxi-'

mize extra work goals, is conspiduous becauge overall it represents

. . those values With the leas,t salience for our respondent's (Table 3).

:.--- Less than a third cite thiee of the four item -not having to work

too hard, a comfortable routine and getting a ay from problem at home

--as "very important." 'Roughly half of the men stress "having enough

4 . free time to enjoy other things." A slightly greater number of young

men emphasize the impOrtance of free time and this difference is the

major factor providing a statistically significant difference for the

index. While the Antiwork Ethic loom's larger among yoUng men, an

examination of the actual mean scores indicates an impressive continuity of values between young.and older men. Extrawork and antiwork

gpals are not characteristic of men at, any age. Interestingly, the

partial" correlation. (r) of the index with employment status

18 control-

. ling for age (.09) indicates a slightly greater tendency for men who ' are unemployed to hold antiwork ethidvalues. Similarly. ethnic minority men19 are more likely to value the antiwork ethic((r = .21).

cift .t1,5 The value attached to,intrinsicpwork rewards is not conditioned

by age. The Intrinsic Work -Values I x i2,correlated with ethnicity

(-.11)-and the size of this correlation is

,.where age is controlled. -Thus, compared to mitori

anged. in the partial'

, white men are

somewhat more likely to value intrinsic work rewards. More critically,

the small, insignificant negative correlation between Intrinsic Work

"\\ Values and emplbyment status (-.04) indicates that both employed and

unemployed men value intrinsic workltewards equally.

* 1&. Dummy variable: 0=emplom1/1=unemployed.

i9. Dummy variable: 0=white/1=black, Puerto Rican, Oriental, American

Indian,-Mexican American.

- 56u

A ' C

s4

*

r
536 -=

).TABLE 3

ANTIWORK ETHIC VALUES AND JOB PERCEPTIONS BY AGE, INCOME

AND LABOR FORCE STATUS (MEN EARNING ,LESS THAN $15,000

. A YEAR WHO ARE NOT IN SCHOOL OR IN THE MILITARY)

Antiwork Ethic Rewards

NOvhaving to work too hard

A comfortable routine that

01- is easy to handle,

Getting away from problems

at hone

ving enough free time

to enjoy other things

4

0 4tAstiwork Ethic Indexes

4

fl

Percent Responding

"Very Important"

To Each Amtiwork

Ethic Job Reward

Percent Rating Own

Jobs "Excellent"

With Respect, o

Each Mork Reward

18-22

Age , ,30-49Age

18-22 30-49

17.8 20.7 8.3 7.9

29.0 f 30.4 11.8 14.4

'19.2 15.7' 9.0 16.6

53.8 47.8 15.5 15.8 k

7 10.75 10.06 10.06 10.79

SD 3;37 3.58

,7

SD 2.52 2.56

N 2S8n..r 457 N 160 323

t 3.02 **

**p < .01

0 (1)0
4

74. 4 - 537

While the value men attach to intrinsic work rewards is not influenced by age, older men do perceive greater ac ess to these zewaArs.

Escept for one item--a chance for personal gr wth--more older men,

tho'se between thirty and forty-nine, indica ed that the rewards pro-

,/arvidld,by their own jobs were "excellent." Moreover, the magnitudes of

the differences between the two groups.* workers is substantial in

most cases, a fact that is Clearly reflected in the signicant differs ence found in the Intrinsic Work Rewaids Index.

In sum, younger and older men equally value work 'for the rewards

inherent'in the activity., Quite consistently, though, fewer young

men believe thaiNheir jobs are effectively providing them with these

rewards. But even among older men, the per(-entages rating their jobs

as°"excellent".with respect to each item is, in virtually every case,

less than half of the percentage stating that a given work value i$

"very important" to them.

20 - Modern industrial societies rely on the

internalization of certain values, values that advance worker involvement in the,kask. .Based'on our data, the American system is conspic4

uously successful in fostering these values but markedly less successful in providing for their satisfiction.

This disparity between values and satisfaction clearly raises a

significant issue, as the character of work in industrial andpostindustrial societies continues to *change, to become progressively more

,abstract and routine. Aec panying these changes is a decline in access

to creative and meaningful j activity. This would appear to be particularly true for the kinds o entry-level positions available to

younger mefi with:limitedleducati , jobs requiring minimal skills and

virtually no experience. As these young men gain experience in the

labor foice, the content of their work roles will presumably,be upgraded, providing somewhat greater access to intrinsic work rewards

20. Through t the analysis, the percentages ratink their jobs as_.

"excell nt" with respect.to-a given characterittic senerally

increas s (but the disparity by age is.sustained) when il,Ot Ooge

mei describing.it.as "very important" are considerek, us, men' who consider a trait imi)ortant appear to be more IikOr.thanothers to find it in,their jobs.,

70

%AL

y. t

.44

'S,.r554

lr
e'

A

MA,

4N4 -o'538, -

for 'some. For dthers, alternative

study of4automobile workrs. 21

To

ations still applicable? DO other

,'tivity, fake on added significance -

-york? The development and mainten , . t ,

. remaininT Ai dd.'.

...,.. . .

, Y . . . .r. SECURITY AND ECONOMIC WORK REWARDS

.;

11

MO outcomes are suggested,by Chinoy's

what degree.is displaceientof aspirsectors of life, such as leisure acfor men deprived of meaning in their

ance of values across tfie,life cycle

lag

a

, - Understandably, most men are highly concerned with the security P

. . of their jobs. In fact, more men cite job security as "very important"

to thewthan Any other work value considered in this analysis (Table 4).

The very_modests insignificant tendency for more'older men, those between thirty and forty-nine,tb attach considertle importance to job

security is perhaps Su ising in view of the well recognized' fact

thatthe risks of unemplyment are borne disproportionately by the

young: About-ten percent of the men in -this study who are twenty-three

or older describe themselves as unemployed. Among nonstudents between

eighteen and twenty-two this rises to 24%. Young men are less likely

to have dependents and, mo ;e critically, experience fewer concerns for

protecting their pension interests. While job insecurity and unemployment are undesirable for the overwhelming majority of all men (indeed, only 1% of the men in this study said that job security was "not

important at all" to them), it creates fewer personal,dislocations for

young men. For the older worker, it is the magnitude of dislocation

4 rather than the actual risk of unemployment that provokes such marked

concern for job security.

Monetary concerns rival concern for job security among these men

(Table 5). This recognition implies that the trade-off of income forsecurity that may once have shaped occupational values is no longer vi-

. able. Anon younger- respondents, the,percentage saying "a good salary"

is "very important" (72%) is roughly equivalent to the number stressing ° "job security." While somewhat fewer older men emphasize salary, the

21. Ely Chinoy, Automobile. Workers and the American Dream (Garden City:

Doubleday, 1955).

571

. "144.:4,i*

,
X

-4)

4

t,

539

TABLE 4

SECURITY WORK VALUES AND JOB PERCEPTIONS BY AGE, INCOME

AND LABOR FORCE STATUS -(MEN EARNING LESS THAN $15,000

A YEAR WHO ARE NOT IN SCHOOL OR IN THE MILITARY)

t

Securiiy'Rewards

A goad pension plan

Job security

Fringe benefits ,

Being able to. retire early

enough with good money

Security Work Rewards Indexes

Percent Responding

.'lery important" --

To Each Seturity

Work Reward ,

Percent Rating Own

-Jobs VExce4enti!

With Respect To

Each Mirk Reward

. Age

18-22 30-49

*Age.

18-22 30!4.9.

.0/ 49.0 66.8 9.6 12.5

72.8 75.9 12.0 24.0

51.2 59.0 10.2 19.9

39.7 48.8 2.4 9.5

Y 13.30 13.83 7 8.54 10.20

SD 2.67 2.62 SD 3.14 2.98

N 260 460 N 1$6 371

t 2.56* t 5.56***

*p < .05

***P < .001
'A

- 540 -

TABLE 5

ECONOMIC WORK VALUES AND JOB PERCEPTIONS BY AGE, INCOME

AND LABOR FORCE STATUS (MEN EARNING LESS-THAN $15,b00

A YEAR WHO ARE NOT IN SCHOOL OR IN THE MILITARY)

Economic Work Rewards _N

4

Percent Responding

"Very Important"

To Each Vonomic

Work Reward

Age

18-22 ' 30-49

Percent Rating Own

Jobs "Excellent"

With Respect To

Each Work Reward

Age

An opportunity to make a'

great dear of money

A good salary

A chance fo advancement

Economic Work\11ewards Indexes

44.6

72.0 -

66.2

10.55

SD 1.55

N 259

35.4

68.1

57.4

9.97

2:01

459

t 4.34***

18-22 30-49

9.3 8.9

9.2 11.0

15.1 14.2

X 6.95 7.49

SD 2.34 2.10

,N 176 ' 380

t '2.58**

**p < .01

***p < .001

0 g73

4
I

v..

"

. - - 541 -

O

percentage is still substantial (68%). The conclusion that practical

concerns are disproportionately sane* for working-class men is rein-

% forced if we compare these findings with data forcollege-seniors.

,. Based on the studies reported by Davis

22 and Gottlieb

23 -which inclut.

college women, who presumably devalue such practical concerns, the percentage of young men in this study who say that "an opportunity to

make a great deal of money" is "very important" (45%) is substantially

greater. Thirty-o4 percent of the,co men in the present sample

share thisyiewApsuggesting that even in the context of an unfavorable

economic climate, the disparity persists.

. The differences between younger and older men in the value attached

4$

to both economic rewards and security rewards are once again

very small but, nonetheless, statistically significant. Older men are

.somewhat more likely to stress Security Rewards, tost,rtably as'these

relate to Pension considerations. Older men were exped to emphasize

; f3fr

security; it was surprising, however, to find.a roughly comparable level of concern for this kind of security'among young men. 'Conversely,

young men disproportionately value economic rewards in terms of both

income and promo,ion opportunities. Dt should be remembered that young

. men report substantially lower earnings; moreover, the major economic

investments of their lives lie, in an uncertain future, with marriage,

children and home ownership. Again, however, the explanation for

age dispar es appears more coy pelling than the observed disparities

msegiv s, whi- a mi or order. The inescapable conclusion is

that ressive continuit ists with refe ence to economic and security va ues, or the practi 1 goals of work.

Men who are unemploye are -no more likely to stress security,

.

- (r = .-.01) and are virtually indistinguishable from employed men in

thei emphasis on economic rewards (r =4.08), suggesting that unrealisti salary expectations are probably not implicated in their unemploy-

-. ment. Ethnic 'norities are slightly more disposed to emphasize seAuri .., 22. Davis, op. cit. ... 4

23. David Gottlieb, Youth and t4 Meaning of Work (Washington: U,S.

Department of Labor, 1974). . h.

0

If

.4

574
.,542 -

ity (r = .10) and economic rewards (r = .14).

There is a measurable gap between the importanye men attach to security and economic values and their perceptions of their own access to

these rewards. The gap is more evident 'for young workers, particularly

with reference to security rewards. Disenchantment with access to the

practical goals-of work is not widespread, however. Fdr both indexes--

Economic ewards and SeAlrity Rewards--the hypothetical "average" re-. an spondent says that these are "very important" values; he rates his own

job as merely "good" with respect to,these same rewards. And in both

, . cases - -in terms ors4urity and economic relfards--young workers are

somewhat less satisfied that their goals are being realized in their

present jobs. .

i. INTERPERSONAL WORK REWARDS

The impact of,coworkers, or the geneial interperson41 climate describing the workplace, on work performance and satisfaction is one of

the major themes of modern industrial sociology: The individual's social and psychological requirements often effectively compete with economic interests; as the Western Electric studies demonstrated over a gen!.

eration ago, to maintain the esteem or good fellow workers

individual is often prepared to .forego,'in some meas 'ure, his own immediate economic self-interest.

24

Consistent with this well documented aspect of indISFial social

psychology, a substantial majority. of American workers rate hhaving

the respect of fellow workers" and "working around people you like" as

being "very important" for job satisfaction (Table 6). The disparity

between older Nei younger'men 4in the importance they assign-to the

quality of the interpersonal'climate of work place is not statis-

. ticallrsignificant. At all age'levels, majorities of men endorsetbe

' importance pf social rel tionships .for satisfaction.

While, the importance of a positive interpersonal climate at the

work place achieves high and generally equal levels of support, there

24. F. J. Roethlisberger and Wiliam J. Dickson, Management and the

Worker (Cambridge, Mass.: Harvard University Press, 1939). _
- 543 -

TABLE 6

' ' INTERPERSONAL WORK VALUES AND JOB PERCtPTLONS AGE, /NCOME

AND LABOR FORCE STATUS (MEN.EARN*G LEgS'THAN $15,000

A YEAR WHO ARE NOT IN SCHOOL OR IN'THE MILITARY)'

4

Interpersonal Work.Rewards

Percent Responding

"Very Ipportant"

To EacftInterpersonal Work'Reward

''Age

18-22 30-49

Having the respect of the

people you work with

Friendly people to work

With

Interpersonal Work

Rewards indexes

56.9 166.4 . 68.2

Percent Rating Own

Jobs "Excellent"

With Regpect To

Each Work Reward

Age - 18 -22 30-49

18.1 30.9

.29.7 34.9

X 7.12 7.17 X 5.89 6.34

SD 1.11 1.21 SD 1.37, 1.26

N 260 461 N 181 . 380

t 0.63 t .86***

***p < .001

1

*.
A

1- 544, -

s

.not an equivalent generality'in the realization-of this kind of in7

i

terpersonal work climate. At all age levels, .only a portion of the men

Who see this aspect of work as important also describe their present

jobs as Tullyvproviding it. Once again,it is among the younger work-

. ers that this dispatity between .commitnent to,the value and its reali-

...

. --- zatio in their present jobs is the greatest.

or older workers, the dispaiity is smaller than we have observed

la

. in terms of other mafd/. values', such as meaningful activity, income

4 4 .and job security. However,,given that a supportive interpersonal climate should be, at least theoretically, a "free.resource," not dependent on the specific eharacter of work--something that is, as it were,

the 'gift of fellow workers -e fact of any disparity raises some inter-

, esting,Oestions. Clearly, `we still have much to leapn about the factorg determining the interpersonal climates that develop in specific

wOrkenvironments.

The greater disparity fox' younger workers between the level at

.which the interpersonal is viewed as important and what they find in

their'present jobs raises some additional questions. We noted earlier

Coleman's argument that certain advantages follow the early integration

of youth into adult society, particularly where the context ofserious

work is concerned. Ont such advantage, of considerable significance,

was the presumed minimization of tendencies to develop an alienated

self-image--a self-image drawn in, isolation from, and ultimately hostile

\`

towards, the adult world. The age-linked disparities we have ob-

' served would suggest-that involvement in the world of adult work (or

- with older adults within the world of work) does not necessarily eliminate this risk. Indeed, even for those who have learned to value the

approval and respect of other; presumably older, workers, the experi-,

ence of,approvalland/or respect does not automatically follow. Entry

into the world of work, with the implication of at least a partial

withdrawal from adolescent or yovh ghettos, may actually lessen the

risks of alienative responses to the,adult world but it far from eliminates such risks. Further research might well focus on the kinds of

entry strategies that maximize opportunities for winning respect

. .

4.

r.
- 545 -

andapproval as against those which do not. Consistent with the other

kinds of work values\ve have examined, acceptance of conventional val- .

ues does not appear to be lacking among young workers; it, is their realization that is Uncertain:-

SUMMARY

The dominant theme in our analysis has been that substantial and

persistent continuity characterizes the present work values of younger

and older men. We have noted very small but statistically significant

4 differences:,in their values with reference to three of the five value

indexes we considered -- security values, economic values and the anti-

. workethic. These differences provide a basis, albeit weak, for cone cluding that more youths compared to older workers, have abandoned

traditional work values and that immediate self-interest is more likely'

to characte rize their concerns. We believe that such conclusions would

be irresponsible in view of the magnitudes of the differences we find.

Despite differences, a majority of men at all ages share similar valu t

regarding whit is desirable in ork..

An obvious difficUlty 'confronts us with reference to ie distinction between cohort and generational effects. Since these young men

A 0 have matured during an historical period distinct from that conditioning the initial entry into the labor force of the older men, the modest disparities in values we observe may, in fact, mark an ongoing evolution in the work values of American men. Alternatively, with growing experience in the world of work, these young men may become fully

indistinguishable from their older counterparts. Values are not formed

in a vacuum. They are learned, tested and modified in the experiences

of a lifetime. Continuity in experience carries the promise of continuity in values.

Throughout the analysis, we have noted the influence of two,facIF tors besides age on work values--ethnicity and employment stat us:- Where.

the first-order correlations (controlling for age) implied that either

of these factors conceivably accounted forlt least I% of the variance

in values, we have noted that a causal relationship may exist. In no ' instance did employment status appear to substantially influence val-

. 4

5 7 8
546

ues (or vice versa). .Thus, on the whole,' unemployment experience among,

these men appears to be independent of their values.

Minority status does seem to influence values; it appears to dim-

, inish intrinsic work values, to increase the emphasis on economic and

security rewards and to be associated with greater endorsement of the

antiwork ethic. It should be recognized that the occupational niches

of these minority men undoubtedly continue to deny them equal access

to meaningful work, job security and equal pay., Moreover, the coefficients are very small, suggesting that'among the yeomanry of the Amer-

'ican labor force there is a single pre6Minant climate of values and

these have remained fairly traditional. If the'subqultural

experience is different, it-is not markedly different with reference

to work values. Subculturaj differences might best be explored on

the level of iff ences iAi labor force opportunity and experience.

The emphasis of minority men on the antiwork ethic is more substantial. The first-Order partial correlation implies that'minority

status may account for as much as 4% of the variance in the antiwork

ethic. The critical question--one that requires more detailed data

than we have--is whether the content of work intervenes in the relationship between minority status and the antiwork ethic. Men locked

into jobs that notably deprive them of.access to meaningful job activ-.

ity may come particularly to value antiwork rewards, what Becker has

called "side bets."

25 Since minorities ,are more likely to hold such

jobs, it may be the job rather than ethnic background that accounts

for the correlation.

A second dominant theme emerged that was significantly and consistently related"to age. There was a recurring pattern where relatiVely

few men report full realization in their present jobs of aspects of

work that they highly value. While not suggestive of massive distress

or' discontent,' we are not provided with a picture of uniformly high

levels.of satisfaction. The discrepancy between ideal values and the

realities of work experience j.§ Agost extreme for the younger workers.

25. Howard S. Becker, "Notes on the Concept ofCommitment," American

Journal of Sociology, vol. 66 (July 1960), pp. 32-40.

a 57)
- 547 -

Y C4q,

This discrepancy may flow from several factors. It may reflect the impatience of youth who ha:4 not yet made their: best'bargain with the,

. world of work. Alternatively, it may reflect differences in the in-1:

,.., tensiities 4.4th which otherweSe similar maliies are 41e14.

Whether the potential for'diss 'sfaction will deepen or erode

with time cannot be 'assessed using these data. The data unambiguously

i

indicate, however. that there has been overwhelting continuity in he

i . values of, contemporary' American men. This, in turn, `suggests

that to the degree that patterns oflabor forCe participatioby younge

workers has taken on aspects'of the problematic,.these can only be understood by a comprehensive exploration of the concrete labor force experiences of such young workeriand the broader context of their 'values

andlife style commitments.

Tice question asked at the outset, "Do youth. really want; to work?"

can now be at' least partially answered. They appear to value work an&

t'o value it in .ways not.unlike their fathers and older brothers. Whether this translads into effective work careers obviously depends, on the . kinds of work and work rewards that are madeVvailable to diem.. the

oItcome may also depend on changes in values, opportunities and experiences in other sectors of life, secars of life which we monitor even

less well than we monitor the current work experience, which is not . very well at all.

5o

e
548

THE RELATIONSHIP BETWEENYOUTWEMPLOyMENT

AND FUTURE EMPLOYABILITY AND EARNINGS

B WaYne Stevenson

.1 ABSTRACT0. In makinvthe transition from school to work most teenagers and

young adulti experience some periods of unemployment. The sporadic

nature of youth labor force: participation in conjunction with,high

turnover and the patt-time nature of youth jobs no doubt contributes

to the higA1n,cidence.of unemployment'observed among this-age.group.

The probability of expeTiencing unemployment, however, declines

rapidly withage, suggesting that it represents" a fairly shorV.term

transitional problem. In thA paper it is shown that-after controlling

for related variables, early labor.force status has a signiiicant.0

impact on subsequent employability,and earnings. Jobless periods,

particularly for out-of-snool youth, constitute-a loss which r ults

in real disadvantage for years to come.

,The ,1970s.have seen unemployment among teenagers (sixteen to nine-

. teenjetrs of age) and young adults (twenty to twenty-four of age) reach

record'proportions, with nearly one-haleof all unemployed Americans ! from these.age categorie.* Roughly twenty percent Of all teenage labor

, force participants were unemployed in 107, with the rate among blacks

reaching thirty-seven peicent. In urban areas the'rate among black teentigers is well over forty percent.

1 Fulthermore, while nn average of

3.4 million sixteen to twenty-four-year-olds were-unemployed at any given

time in 1946, the number experiencing some -unte-Mployment durni the year

is estimated to be three times as great.

2 The magnitude and persistence

of the problem has paced youth unemployment in the forefront of labor'

4 For comments on an earlier draft I am indebted to Atiil V. Adams,

GartI Mangum, and Stephen Seninger. Mary Patterson/and Stephen:Rich

provided invaluable research assistande. Any errors, omissions, and

opinions are the solertsponsibility of the author.

1. Garth L. Mangum and Stephen F. Seninger, Coming of Age in the Ghetto:

The Dilemma of Ghetto Youth Unemployment, a report submitted to-the

Ford Foundation,Tecember 1977.

2. Arvil V. AdaMs et al., The Lingering Crisis, of Youth.Unemployment, a

report submitted to the E.W.Upjohn Institute, for EMpleyment Research,

1978.

r 1
549

\

market policy and has created considerable interest in the question of

causes and consequences joblessness among the nation's youth. ' The high AnCidenCe of yOuth unemployment can no doubt be attri5uted

in-]arge part to the process of transition from school to work. Folk

3

and Kaladhek, 4 for example; argue that teenage entry and reentry into

the labor force (which accounted for 70% of teenage unemployment in 1976)

. plus the part -time status 'of youth jobs combine to create highly unstable

eMiloYment patterns. 'Teenagers account for a disproportionate share, of

job seekers and, according to Freedman,

5 change jobsand move in and but

of Oft labor.force with greater. fre9uency than any other age group. ,s intermittent'entrante andreentrance into the labor-forceresults in

less than one-fourth4"of all unemployed teens being job losers while over

fait.), percent of older unemplOyed workers left their last j.ob involuntarily.. ' Another important characteristic of youth unemployment is that the

situation improves with aging. As workers reach the age of twenty-five,

labor force participation rises, workbecomes predominantly ful-time,

and most significantly,. unemployment'rates fall ('ee Table 1). Thus,

the high incidence of unemploytent among youth is viewed by Kalachek,

6

Johnston and Backman,

7 and others as playing a functional role in

3. Hugh Folk, "The Problem of,youth Unemployment," in The Transition

From School.to Work: Processdings of the Princeton Manpower

Symposium., May 9 -10, 1968 (Princeton: Princeton University Press,

r1 1968).

A 4. Edward Kalachek, The Youth LaborMarket, Policy Papers'in Human

Resources and Industrial Relations, No. 12-(Ann Arbor, Michigan:

4 Institute of Labor and Industrial Relations, University of Michigan-

° Wayne

-. e State, 1969). .

. 5. Marcia Freedman, "The Youth Labor Market" in Nationil Commission for

Manpower Policy, From School to Work: Improving the-Transition

(Washington: Government Printing Office, 1976),

6. Kalachek, op. cit.

'7:' Johnston and Backman; The Transition from High School to Work: The

Work Attitude; an4 Early Occupational Experience.of Young Men (Ann

Arbor; Michigan: The University of Michigan, 1973).

582

4
.

S

TABLE 1,

UNEMPLOYMENT RATE BY AGE X AND RACE 1976

14-15 16-17 18-19 20 -24 25-34 .5-44 45-54 55-64

Total 14.8 21.1 17.4 12.0 7.1 4.9 4.5 4.5 5.1

Males 16.2 21.4 17.6' 12.0 6.2 4.1 4.0 4.2 5.2

4ite 13.7- 19.7 15.5 10.9 5.6 3.7 3.7 4.0 4.8

- o

In - Nonwh. 41.3 37.7 34.0 20.7 11.0 7.3 7.2 6.2 9.3.

I -Females 13.1 20.7 17.3 11.9 8.5. 6.1 5.2 4.9 5.0

* white 10.3 18.1 15.1 10.4 7.6 5.8 5.0 4.8 5:3

Nonwh: 45.5 46.4 35.0 21.7 13.0 8.1 6.1 5.5 2.6

. .

. ,_ 4 , - .Source: 'U.S. Dept. of Labor, Employment and Tra,ning Rep4 of the President.

Employment and Training Administration, U.S. Go rt. Printing Office,4

Washington, D.C., 1977, Table A-19%

S3
.4r

1

551 7

providing Useful labormarket experience.. Frequent jobless_ periods and

turnover aid new labor force entrants in learning effective methods of

job search and in adjusting. expectations to the ,realities of the labor

market. It may even be the case that high turnover and the'associated

joblessness experienced by teenagers are consistent with the temporary,

I- part-time employment 'desired by youth workers. 8 These observations, in

combination with the apparent improvement over time, frequently`lead tothe conclusion that youth unemployment is a transitory problem experienced

by most and not 'greatly hindering successful assimilation into he labor

force. Theory'and intuition suggest, however, that to be f quently

unemployed during the early years deprives an individual valuable

wdrk experience, information, skills and contacts and likely to hive

injurious effects latex on.

It is the. purpose of this paper to document the long-run consequences,

of the youth labor market experience. This is done'by.following longit udinally bne group of young men and women from their teenage years into

early adulthood. -this analysis.demonstrates that early labor market

experiences are related to subsequent measures of labor market success.

4

Not only does youth joblessness identify a target group of individuals°

who are likely to have - trouble later on,' but after controlling for a , number of personal characteristics, youth labor fQrce status is,seen to

xert an impact of its own on subsequent experiences. These findings

suggest that youth unemployment is ,more than a.short-term problem and its

consequences may include,long-term problems of employability and earning'

capacity.

DATA AND METHODOLOGY

The conclUsions drawn-in this paper are based on analysis of the

National Longitudinal Surveys of Young Men and Young Women.

9 The NLS

data were designed totallow for measurement of sources of vari tion in

the labor mar)et experiences of each cohort. The data theigfKEInclude

8. Paul Ostermah, "The Structure of the Labor Market fOr Young Men,"

working paper, Boston University, 1977.

9. Fora description of this data see Herbert Paines, The National Long-

. ifudinal Surveys (Columbus, Ohio: Center for'Human Resources

Research, 1974).

pt.
'552

, , a broad sampling of economic, social, and attitudinal characteristics of

.. the youth population, Labor force concepts and definitions are consistent with those employed in the'Current Population'Surveys.

The analysis described here is based on data for males and females

who were sixteen to nineteen years of agesin the. initial survey year.

Data were collected ovgr a period of seven years be k"nning in 1966 for

the men and in 1968 for the women. In the initial survey -years over

"two thirds of'these respondents were enroilecrin school., By the final

su'ey. year; when those examined ranged.in age from twenty-three to

twenty-six years of age, less than 15% were still attending school.

Furthermore, 90% of the males and 70% of the females were labor force

\ . participants, about

e5% of whom were.0 employed. So aside from redreation and family responsibilities, the major activity of most teenagers is

. k school. On the other hand, by the time the cohortsrange in age from

twenty-three to twenty-six the dominant activity .is work-. Thug, the

' important years of the school to work transition arecovered by the

analysis. I YOUTH LABOR FORCE STATUS, ND'LATER EXPERIENCES

\.. N Many aspects of youth's labor market experience during the school to

work transition have been well documented. Temporary, part-timework.

' begins for most before school has been completed. Early4w7A'experience

a tends tolbe in occupations and industries associated with high turnover,

low pay and a'high incidence Of part-time work. In contrast, employment

patterns of those in their mid-twenties reelect fairly consistently those 4b

obgerved' among' adult workers.

, Parnes and Kohen have shown,. i1 a longitudinal analysis of 2,10Q

noncollege males, that movement up the occupational ladder correlates

highly with education, work experience, and increased labor makei

xperience10 The role of family background and other s9cio-economic

Herbert Parnes and Andrew Kohen, "Labor Market Experience of NonCollege YOuthf: A Longitudinal Analysis" in National Commission for

Manpower Policy, From School to Work: 'Improving the Transition

(Washington: Government Printing Office, 1976).

5t-it) C)....-

o.
4.

- 553 7 r

status measureshave been examined/by Adams et al,

11

Hall and Kasten,

12

and Corcoran.

13 Race, sex, education, marital status, and region of

residence seem to have the greatest most consistent effect on early

labor market success. The importance of entry level jobs is devtrated

by Ornstein: 14 What is not well documented, however, is the effect

early labor market experience has on liter employability and eatning

capacity. .

4 There is little surprise in the finding that labor force participation increases, unemployment declines, and earnings grow4asindividuils

move from their teens into adulthood. While a large majority find

employment, howeer,"there is considerable variation in the stability,

security, and rewards associated wit4 the work. What is ofcinterestis

theei'cZent to, which unsatisfactory -experences at an early age are

related to later labor market activity and'sucCess. Eco is theory

suggests that eduCitiOn and labor market experience are i ortant

determinants of i..0.--fOrceStat-lis and earningS.is -St-for those making

the transition from school to wprk, time spent in,school or working

repretsents-fhe acquistion of skills, exPerience\or contactswhich can

be expected to be beneficial later on, Being Unemployed or out of the

labor force while in school will have little effect as basic education

is a good substitute for on-the-job,training and work experience. Job- 1

lessness experienced while out of school, however, represents a serious

4 11. Adams et al., op cit.

. 12. Robert E. Hall and Richard A. 'caster', "Occupational Mobility and

the Distribution of Occupational Success Among Young-Men," American

Economic Review, vol. 66, No. 2 (May 1976), pp. 309-15. c. 13% Mary Corcoran, Christopher Jencks, and Michael OlneCk, "The Ef

of Family Background on Earnings," American'conomic-Review, vol.

No. 7 (May 1970', pp: 430-31. 4

14. Michael D. Ornstein, Entry IntO The American Labor Force (Toronto:

York University Press, 1976).

1S. See, for example, G.S. Becker, Human Ca ital (New York: National -

Bureau of Economic Research, 1964); a G. Hanoch, "An Economic

Analysis of Earnings and Schooling," Journal of Human Resources,

4

vol. 2 (Summer 1967), pp, 310-29.

1

c

4

I

.4
11. I

loss which. can be expected to re ult in relative disadvantagater on.

The National Longitudinal rveys of Young Men and YOung'Women

llow for a comparison of yo labor market activity and success at a

040

554

416 I

later stake. pen with the comprehensive data available it is,.not

A possiblet6 recreate the work history of each individual. It is possible,

however, to relate urvey week labor market and school enrollment status

with subsequent exp rience. ,While this does not isolate those with 1

chronic or persistent labor market problems, itdOei fairly accurately 1.'

, reflect the activities of the youth population 9tany`particular point

in time and is consistent with Bureau of Labor Statistics 'methods of

... classification. Comparing later labor market activity and success

proiiides considerable insight into the meaning and Impc$1fance of the

high teenage unemployment rate reporterin,recent years.

* Table 2 shows the relationship between labor force status in the

final survey year and that experienced during earlier periods. While

92.4% of the young men 'and 60% of the young women were in the labor force,

the proportions change considerable depending on earlier experiences.

a ThOse out of school nd out of work as teenagers' are far less likely to

be currenf labor force participants. Fpr those currently in the labor

force, the probability of being unemployed is-much higher if the teenage

period was spent both out of work and out of school. Current labor force

participation is the highest for those who were employed as teenagets.'

As expected, being out of work but ii schoolhas a much smaller effect

on subsequent employability than being out of work and out of school.

Only for the women is teenage unemployment whil# in school. related to

later participation rates. Being out of the labor force while in school,

.however, has a negligible effect, ,:Young adult labor force status is

o more closely correlated r3 th later experiences. Labor force participats. *The following tables and portions of the corresponding discussions have

been extracted with modification from Wayne Stevenson, "The Relationship

between Early Work Experience and Future Employability," in Arvj.1 V.

Adams, et al., The Lingers g Crisis of Teenage UnemployMent, a report . 4 submitted to theW.E. Upjoh Institute for Employment Research, 1978.

1
1

- 555 -

TABLE 2

LABOR FORCE STATICS IN FINAL SURVEY YEAR BY EARLIER SCHOOL ENROLLMENT AND LABOR FORCE,STATUS

Status and ,Age Per

'Survey Year

Final:Survey) Year (23 to 26 Years of Age)

Men ;Oen:

IEmployed Unemployed

taborof tabor

Force Employed

Out of

Unemployed Labor

Force First Survey Year 1 (16 .to 19 Years of Age):

In schoolEmployed 92.2

Unemployed . 92.0

Out of labor force 61.4

Out of school--

Employed 95.2

Unemployed 89.7

Out of labor force 78.9

Fifth Survey Year

(20 to 23 Years of Age):

In school,-

Employed 95.4

Unemployed b S 100.0

Out of Labor forcd 94.3'

Out of school.-

Employed 95.2

Unemployek 4 . 80.0

Out of lab4 ;%torce 62.2

TOTAL 92.4P

)

5.1 2.7 j 67.8 8.4 23.8

8.0 0.0 51.4 6.8 41.9

3.7 4.9 67.4 7.2 25.4

1.7 3.1 6u.4 6.1 33.6

6.9 3.4 ° 42.3 8.5 49.3

7.9 13.2 37.7 7.7 54:5

a 3.3 1.3 91.7 1.7 6.6

0.0 '0.0 57.1 14.3 28.6

3.4 '2.3 76.9 9.6 13.5

2.9 1.9 72.2 ' 6.7 21.1

9.3 10.7 49.5 13.8 36.7

13.3 24.4 32.8 7.5 59.7

4.0 3.5 60.0 7.3 32.7

Source: National' Longitudinal Surveys..

w

0

588

4,0
556

tion is considerably lower anethe probability of being unemployed in

the final survey year is higher for both Young men and young women who

-were out of'school and out of work as young adults twenty to twen0-

thrkyears of age.

Early school enrollment and labor force status are clearly correlated with later labor market activity. Table 3 shows that this carries

over to differences iri wage and salary income. Once again, /youths out

of .pool and out of work as teenagers'or young adultS%darry this disadvantage with them into the early adult years., The most serious disadvantage is found among those whO spent an earlier period out of school

and out of the labor force. Current earnings for these individuars are

, about half the average for their race-sex cohort.

There appears to be little question that, on the average, those

having difficult labor market experiences as youthi are the same individuals who.have difficulties later on. While many unemployed youths

successfully move into well paying, permanent pcitions,manY will not

do so by the time they are in theil mid-twenties and, as a result, face

a real disadvantage as adult workers. Youths who are unemployed and out

of the labor force and are also out of school define an important target

population that can be expected,to find,labor force assimilation difficult. This is especially true ifcthey6are blacks or women.

THE NET EFFECT ON SUBSEQUENT EXPERIENCES

That youth labor force and,.school enrollment statps is correlated

with later labor market success does not necessarily suggest-a causal .-

, w relationship. ITIlisame factors contributing. tearly experiences may be

-.. . . operating later on as/well. Whether or not the pattern remains the same

:`after controlling for differences in bacyground, education, and other'

factors is the issue addressed inthis section. 1/4e Table 4 shows the results of multiple regression analysis designed

to isolate those factors which explain differgnces in earnings of young

adults who are out of school. The groups analyzed consist of young men

and young women who.were sixteen to nineteen years of age in the initial

survey year and out of'school in the final survey. year. Earnings are

observed. seven years later when the group ranges in age from twenty-three

53

de%

f
- 5'5 7 - J

Es TABLE 3 MEAN EARNINGS BY PRIOR LABOR FORCE AND SCWOL ENROLLMENT STATUS

FOR AGING COHORTS OF YOUNG MEN ANt\YOUNG WOMEN

WHO WERE OUT OF SCHOOL IN FINAL SURVEY YEAR

Status and Age Per -

SurVey Year

Final Survey Year (23 to 26 Years of Age)

Men Women Av White Bieck. White Black

Grand Hian 7553.59 5648.50 3646.48 3469.14-

First survey year

(16 to 19 years of age):

In school-- v

. . Emplpyed 7784 5747 4188 - 4295

Unemployed , .6858 5644 3465 .3224

Out of labor force 6939 6 5564 4292 3803

( Out of school--

Employed 7423 . 5937 3308 3/91

UniMployed' 6816 4077 2226 3120,

Out of labor force 4676 3921 1912 1879

F=3.25*** F=1.60 F=10.81  F=5.21** **

of Fifth survey year

(20 to 23 years of age):

IhNschool--

,Eiploye4 ,7988 6036 5695 5985

Unemployed 8036 4550 5344 4118

Out oc.labOr force 6309 3785 471q 4806

Out"Of school- - 1 Employed ' 7918 6065 4744 4757

Unemployed 6289 3124 2753 2670

Out of labor force 3959 2084 1222 1530

74.55  5.14** ** P=51.09*:** 727.61****

Significance Levels: =102 (*) 52 (*1:1) .12 (***) 0.12 ( )

Source: National Longitudinal Surveys

t

590

J

4-

SO

1
I.

a 558

TABLE 4

COkRELATES OF WAGE AND SALARY INCOME', IN FINAL SURVEY AR

' FOR OUT-OF-SCHOOL YOUNG MEN AND YUNG WohlENa Explanatory

Variables White

Males

Black

Females

nick

296.00** 

(2.29)

404.69****

(5.64)

Age

Years of School Completed

Socioeconomic Status

Married 7t>

Living in SMSA

Labor Market Knowledge

Training?

338.80 *** (2.78)

235.46*

(1.41)

227.67**** 170.24**

(3.41) (1.85)

4.17 18.12*

(-0.54) (1.60)

1909.44**** 1987.58 

(6.86) (5.13)

959.97** ** 678.1u*

(3.59) (1.59)

44.90** 10.82

(2.08) (0.38)

1933.76 

(4.12)

1546.50** **

(5.33) ,

- 95.99. (0.94)

357.62**4*

(6.A7)

-1.33 9.36

(0.20) (1.20)

-1846.89**** -431.93*

(7.37) (1.49) na as

250.15****

(3.48)

1569.69 

(6.44)

207.82** *

(3.05)

2339.04****

(6.72) R

2 (adjusted)

- Degrees of Freedom

0.13 0.19

1008 299

0.18 0.29

980 ,384

*Wage and salary, income reported'in final survey year for those ;abo were sixteen

to nineteen in initial survey year.

bDichotomous variable assuming value 1 for those who received training and reported

using it on the job and zereotherwise.

Significance Levels: 10% (*) 5% (**) 1% (***) 0412 (*

Source: National LongitudimalSurIO

t. C444

.
- 559 -

or

to twenty-six. Each cohort. is partitioned on the basis of race, an

element whose effect on earnings is well known.

Ay Stratified by race and sex, the regression equations explain between

13% and 29% of the variation in annual wage and salary income. Age,

education, and marital status have significant effects upon earnings.

Earnings tend to rise with age, even' within the small range represented

here, and also increase with each year of school completed. The is true

for blacks as well,as for whites.

Socio - economic status does not appear to have its expected effect

on earnings after one controls for education and labor market knowledge

(which is highly correlated with background, education, an j earnings).

So, while their separate effects are difficult to isolate, education,

background, and labor market information (or general. intelligence)

combine to influence. earnings significantly.

Other factors demonstrating a predictable effect are marital status.,

and region of residence. For men, being married is associated with

A +I nearly $2,000 per year A additional earnings. Women, on the other hand,

-earn considerably less if they' are married%'' It is also of interest that . men living in .a city or its suburbs can expect to earn more than com0Lr-

. ible rural residents (these statistics are not available for women).

. One additional variable is included: in each survey yeat respondents

were, skedyhether any training was received in addition to formal

t . school&g, and whether the)training was'ever, used on the job.. With this

in-formation it is, possible to isolate those indiiiiduals who received and

used training during the school to work transition. One out of three °, respondents indicated that he,or she had received such training., .Blacks

were lsss likely than whites to participate in training, and young men

.less"likely than young women.

The differences in earnings associated with training,used on the job

are substantial, ranging from about $1,500 per year for whites to more

than $2,300 for black women. Along with the data on years of School,

completed, this finding for out-of-school ybuth is highly Significant in "-

policy terms for those concerned with the low earnings'of youth and the

earning differences-by race and sex. Although thes results,d6 not

' ' 597

a

;
,

s '560

4. ,1istinguish among specific training programs, they do. indicate that when

training is tailored to the needs of the labor market as well as the

'Individual, reflected in use on th4e job, the gains are generally

/

sub -

stantialtand lasting. More importantly, these gains are'present,for

yOnng.men and young women, blaCk and white.

Controlling4for-theseZcorrelates of wage and sallary earnings,,one

can also determine how the early labor market experiences of youth affect

subsequent earnings. At issue is whether:individuals with roughly' equivalent:bickgrounds but different early labor market experiences. can expect

subsequent earnings to differ in ?predictable manner. Table 5 allows

such comparisons after adjusting for differences age, education,'

socioeconomic status, marital status, and place of residence. The obser-

-trtionS are stratified by race and sex to account for these differences;

,Theresults are based Multiple Classificaiion Analysis, etechnique which

L

linear regression estimates and analysis of variance, to test for

significant differences in mean values of a dependent variable after _

controlling for the tcffects of other variables included in.the mode

The differentes in the ddjusted means are not as pronounced a the

differences for unadjusted mews reported p..1 Table 3', but'fhesame

M 1 apneral pattern's emerge: Again, being without a job while, out of school

has a significant adverse effect, on subsequent ehrnipgs. Tht:impact is

more pronounced when,it occurs betwken the ages of-twenty,and twentyfour, but teenage labor,forcestatus;is also r'lated to subsequent *eV

and Salary earnings. v .1 ' ' 4°. \... '>..' *

.

,,e. , , Whether for young met young women, black or white, time spe4 out

C.-,--

16 sx.,.. of School and Out of"the,labor force represents 1 Toss'ofexperience.that

8 . is associated with a clear earningS disadvantage later .on.\ While the

effect is not so great, being unemployed during the school to work tran-

-sition-also-has-an'adverse effect. This is particularly true if the

O

spell of unemployme t comes, while the person is out of school.- Those who

experience unemployment while in school can expect to earn less on an

average tpan those successfully employed while in school or out of the

labor gorce and devoting full time to school activities.:

The differencesby:race'aridsex are evenigiehter. Among'young men,

I

593 I

.0, 4. s
4.

- 561 -

-J

TABLE S

I

ADJUSTED MEAN EARNINGS°BY PRIOR LABOR FORCE AND SCHOOL ENI(OLLMENT STATUS FOR AGING COHORTS OF YOUNG MEN AND YOUNG WOMEN

WHO WERE OUT OF SCHOOL IN FINAL SURVEY YEAR

Status and Age

per Survey Year

Final Survey Year (23 to 26Years of Age)

'MID ce' Wo'nen

tihite Black White Black

,

. 4ran4 Mean .0

7621.90 5713.46 3694.66 3518.39

Firit Survey Year 16 to 19 Years

In7School . Employed

Unemployed \ - Out of labor force.

Out of School

Employed

'Unemployed

Out of labor 'force

..

i

. 79484 5989

7445. 5852

7546 6096

#

.g. 7481 5596

7287 4857

'5834 s 3940

3936

3246

3944

3793

2871 "

,2871

4004

. 2532 3691

3790

2988

2855

,

, F2.36** F2.0** P.11.045  F5.18** *

, Fifth Survey Saar 20 to 23 Tsars

In School

Employed . Unemployed . .. Outof labor force

*4 . . Out of School

Employed . '''' ,, Unemployed

'Out of labor force "

.,.

h 7565

.(

4334

6647 . ,\ 3051

'6649. 3407

. . 7972, - ..6244 474 4928

, 48 3787

.

4720

4530' 3715

, . - 4792

2684

.1821

4728

3789

3808

. 443

2905

1860

. . ,/;

1 \

. F07:50*Alli't .01*** F52.26   !.29.25** *

Adjusted to account for differences in education, socioadonoric status, sarital,statas,

age, and living in an SMSA.

Significance Levels: 10* (*) 5 **) 1Z (***) 0.12 (****)

Scarce: National Longitudinal S

. 594

4 I.

4.

I

4
- 562 -

out_of school blaCk youths are more seriously

labor market experiences. Among young women,

by race are apparent. Young women, blackand

affected by adverse early

howeveri no differe,'es

white, are more seriously

'affected by adverse early labor market experiences than young men These

4.°#sti.ndings sharply contradict the thesiS that youth unemployment is a phase

c

;through which every youth passes, with no long-term adverse consequences.

CONCLUSIONS .

The relationship demonstrated here between early'labor market ,status

and subsequent employment experience provides useful information for

future policy 'making. Yout7joblessness (particularly for those out of

school) represents not only an immediate lbss but one which persists

throdghout the'period of school to work transition and into the early

adult years. Thus, job creation through expansionary policy as,well as

targeted youth -programs has more than a Short-term impact. Programs

should, however,.provide incentives for school completion. Job creation

may be counterproductive ifopportunities_are attractive enough to

encourage discontinuation of school activities.

While simple job creation_can be expected to ease the school to work

transiticn, the results aleo.suggest that the impact can be magnified

" throdgh training. The maintenance of youth jobs which also involve the

acquistion of useful skills can be expected to improve opportunities

greatly for 'ears to come.

. In short, any policies which have anlimpact on the,labor market

opportunities-of youth (such as a lowering of the minimum wage for teen-

- agers) should be analyzed with long run consequences ion mind. Any

.reduction'in youth unemployment (particularly among out-of-school youth)

can be expected to ease the transition to the labor force and to generate

benefits now and for.the future.

.

11 0

.4/
/

1

I

EMPLOYMENT AND EARNING PATTERNS:' THE DYNAMICS OF CHANGE

By: David J. Fgrber

ABSTRACT

10'

o

, This paperk-attempts to documentuthe thesis that the change

in earning capacit9 is governed by certain principles, is determinative in nature, dhd can be -projected on the basis of earning patiews in a five-year base\perio4.* These earning patterns are)

hostic in- nature, and can be quantified by tht rate of earning 4

cumulation in the base, period.

The c ange in manyeal.s of elployment and in earningi frolp 1958

to 196S-6 or each of eight age group's -- whether of the members

of the So 'al Secur4y'AdAlinistration's Continuous.Work History

Sample, allof the 1.964 MDTA institutional trainees correlates

significantly_ with the rdte of. earning cumulation in 1958-62. This'

relationship holds, true Asir Men sand women of eachrace,whesi_age or

education areihqld constant, and when the CWHS and trainee eamples

are coalesced_ilito a single population.

The analysis'is based on actual earning histories^as disclosed

, by Social Security records'for the 49S8-69 peMod, and represents

one of the few instances T: perhapsthe only instance -- in which

changes in employment'and earnings of the identical individuals . have been systematically traced for an'extended period of years.

I

* The opinions expressed,in this,paper are the author't, and do not

necessarily, repiesent the views "of --the agency in, which he is

employed.

0
. 1.

1

I

- 564

EMPLOYMENT AND EARNING PATTERNS: THE DYNAMICS OF CHANGE

/

Do federally-sponsored training programs enhance the earning

capacity of program participants? As one considers the analytical

apparatus which so many economists bring to bear on this issue, its

intellectual foundations, in my,view, require increasing scrutiny.

Consider, for' example, three typical statements culled from the

wr tinge of several of the more prolific economists who have been

e, volved in'the evalUation of training programs. Randomly-selected

control groups are prerequisites for scientific evaluations of train-

, ing'programs. Childless women have longer-working lives than mothers.

Lifetime earnings of collegebgraduates exceed those, of individuals

with lesser schooling. , These statements share a common, char cteristic; they lack any

empirital foundation. Not a single training program of any significant size has been evaluated onjthis basis, and hence this allegation must be relegated to the realm of untested opinion.

1 Tables of working life, like their counterpart tables of lifetime

earnings of lifetime income, are essentially extrapolations of

,lai5o;.forceir.status or earning levels ina.given base year, from a

1. A panel of the National Academy of Sciences found "only one evaluation study,,a case study of black girls in, an NYC program in

Cincinnati\, Ohio, which used a true experimental design with randpm assignment of a studY'sample to an, experimental and control

group." Even this small study was flawed by "nonresponse bias

through,attrition.of both controls and experimentals." Final

Report of the Panel on Manpower Training Evaluation, National

Academy of Sciences, Washington, D.C., January 1974, pp. 12-13.

a". 597

r
a

565

given age to retirement. They do not represent the actual latior

market experience of men and women, but are constructs which assume

merely the absence of -any change in.the course of a lifetime.2

-The Itatic concepts and measures used in constructing these

table's are nonetheless precisely the 'same as those used by most econ--

.11 °mists to estimate the changt in earnings attributable to participation in training programs. Their means of measurement, I suggest,

cdntradict their purpose:

Purpose and Theme . T ;.,*- To d9monstrate that changes in employment and earnings do occur

.

during the aging process,.that they are determinativein nature,

. and

are governedby certain. principles is the overarching purpose .of my

, paper. Based.on the empirical evidence disclosed by several hundred

'thousand actual employment and earning histor , it will be seen

that such changes represent a continuous, co istent and predictable

process which,proceeds through time. Over an-extended period of.

years, I shall demonstrate changes in employment and earnings are

2. The 1970 tables of working life indicated that "most working life

tables... are based upon part or current rates of mortality and

/, labor force participation .in thiS case, 1970, but are often

used to infer the length.of future,working life... In the Case

4' of women, work life expectancies are those anticipated if their

martial statui,dpes not change during the rest of theirYlifetime.",

Special Labor Force Report 187, "Length of Working Life for Men

and WOmen, 1970." Bdreau of Labor Statistics, U.S. Department of

abor, February 1976, pp. A-2 and A-3. These static assumptions

are equally characteristic of tables of lifetime earnings or income. Typical is the table of lifetime income-for men, published

by the Bureau of the Census. "Lifetime,, income estimates," the , table notes, "are acmeasure of the incomes that could be expected

.bymembers of specific education groups in a lifetime...4f the

mean income estimate* by age and education, and life expectancy

crates did not change from those existing in the refgrence year;

e.g., 1972." Consumer Income, Series P-60, No. 92, March 1974,

Bureau of the CenSus, U.S. Department of Commerce.

3. It is interesting that a recent 'volume which \summarizes more than

200 evaluations of training programs faithfdlly reflects the fact

(continued on next page)

598

4
.

- 566- -

outgrowths (40, and are correlated and commensurate with, four basic

patterned rates of earning cumulation which characterize the initial

segment'of that period.

. Thii thesis is historical) and develoifiental in character, and

loigitudinal in nature. An individual firstOlterini the labor market, it posits, .offers to an employer the attributes, aptitudes and

ski1ls he has developed from infancy through AdolescenOdto adulthood - -the. product of'his history. His labor market experiences.' thereafter, represent the outgrow of his history--his past-- as it interacts with the demand for his ser ces in the labor market crthe present-. From the convergence and resolution of these two forces, there

evolve a multitude of earning patterns which increasingly differOntiate

the labor force--whether men or women, blacks or whites, the older or

the younger, the more or the lesser schooled, the disadvantaged or the

more fortunate. And because these earning patterns reflect the interactional effects of all the forces--known and unknown-- responsible'

for the change in earning capacity, they are'continuous in character,

and are cumulatively and increasingly determinative in their effects.

Four Earning Patterns

Although earning patterns may be virtually numberless, they may

all be reduced to four basic types. Assuming that individuals seek

to maximise their earnings within a.given time we play initially

distinguish between workers It two extremes of a continuum of earning

patterns. At each( extreme are those whose annual earnings increase

or decrease during

. a'five year base period. The first, we character-

-ize as RISERS-44, the second as DEREASERS (D). Intermediate

(continued from previous page)

that in none of these evaluations is the' measurement of the

change of earnings considered a problem., Indeed, the index to

the entire colpme contains no item which refers to "ch ge," or

to its measurement. (Perry, Anderson, Rowan_and' Northrup, The

IH.act of Government Man ower P o aims, The Wharton Scho

University o Pennsylvania, 197..)
- 567 - Et,

/ between these jio are the MIXERS (M)--those whose annhal earnings

A both increaseand decreaie during the five year base period. -he '

remaining patteifr group-'-the CONSTANTS (C)--includes,those Teg-and

women whose annual e'arnings neitherincrease\per decreased'during

the five year. base perid0

Each of these purely normative descriptors is quantified or indexed by..._a characteristic Cumulative Earning, Rate--the CER. This

measure is calculate4d by dividing the cumulative earnings ofxan in-,

. .diiridual for the last four

14

years, bithe-first year, of the five year

base period. The resulting ratio, of course, can be converted'readily

to a compound annual rate of earning cumu}ation. For present purposes,

however, such a conversion:is not necessary, but for the sake of con-'

venience we shall refer to.the CER as the Cumulative Earning Rate.

Since'the histOries discussed here are taken'from Sod.....1b.1 Security ..

records of the identical individuals, periods of nonemployment are

'necessarily reflected in the eatning patterns. Thus, earningpatterns,-

and their index, the CER, are sensitive not erely to,the_171 of

earnings, but to changes in earnings resulti g from thi effectrs,..of

. _ .,.: nonempioyment,or underemployment., ,,,This means, that the pattern concept

reflects the. organic relationship between employment and earnings . which, as we shall see; permits a realistic evaluation of the change

in earning capacity.

CHANGE AS A CONTINUOUS PROCESS

Concepts and Measures;

For mfny years, perceptive econobists have recognized' that measurement of,change involves concepts.and methods which differ radieally

from the mensuration of statics. Alfred Marshall, for examp le, reco4-

nized this distinction, and it has been rediscovered and reiterated

4. For classification purposes increase or decreases in annual

earnings were considered significant when they exceeded, $100..

b00

4,
- '588 -

by contemporary economists since his,time--by Selma Goldsmith, 'Joan

Robinson, Wladimir Woytinsky, and others as well.5 Recurringly,

o economists have been reminded that

"Following Joan Robinson's stricture that it is most

.important not to apply theories obtained from the

analysis of differences to situations of change

(or at least to be aware of the act of faith involved

in doing this) modern writers have usually been ost

careful to stress that their analysis is'essent. ally

Alfred Marshall clearly distinguished between static and, dynamic

measures and concepts. When, he wrote, "we take a man as he is,

without allowing time for any change.... ithe marginal utility of

a thing to him dimini es steadily with every increase in his

supply." .This princ le, he warns, is only contingently valid.

"There is an implil t condition in this Jaw which should'be made

clear. It is that 'We do mot suppose time to be allowed for any

alteration in the character4or tastes of the man himself. It is

therefore no exception'to theflaw that the more good music a man

hears, the. stronger is his taste for it likely to become... (In

this latter instance) our observations, range over some.period of

time; and the tan is not the.same at the beginning as at the end

of it." (Alfred Marshall, ?ripciples of Economics, eighth Edition,

MacMillan and Co., Lim&ted, London, 1946, p. 94). Mrs. Goldsmith

noted that longitudinal analysis--in which the histgries of the

identical individuals are traced for a succession of .years--is the

appropriate means of measuring change. "When we compare income

shares of a given quintile... in two periods," she wrote, "we are

not comparing what has happened to an identical group of faRilies,

because the families comprisingathe quintile may be quite different in the-two periods..." In interpreting the change in the income share of the top quintile (for example),'... over, say a fiv9E'

to-ten-year time span, it would be extremely' helpful to know the

extent to which the families comprising the top sector differed

in the terminal periods." (Selma Goldsmith, "Changes in the Size

Distribution of Income," American Economic Review, May 1957, p.'

511). Woytinsky was aware that longitudinal analysis_ of earnings

would disclose changes which would differ substantially from findings based on cross-sectional wage data. Writinin,1943, he

suggested that if "it were possible to follow the record of real

earnings of an average man throughout his whole lifetime... his

earnings history would show ups and downs very different from 0 those of the recorded trend in prevailing' level of: 'wages..."

W.S. Woytinsky," "Income Cycle in the Life ofFamilies and Individuals," Social Security Bulletin, June 1943 p. 8,

q

601

to

e
569 -

the compariso of different.. situations oneyith

another and t they are'not analyzing processes."6.

For the measurement of the change in earningcapacity, the conceptual

and methodological consequences of this central distinction are immense. They affect our understanding of the process of change, and

the validity and interpretation of all assessments of such changes.

This is true primarily because the longitudinal measures needed for

the analysis-of-the change- in earn-I-rip-require-that we view earning .

levels at any given moment of tide within the context of a continuous

series of changes which have occurred in the past, and which will proceed into the future.

Earning Patterns and Earning Levels (Table 1)

The insights into the process of change which such a time perspective

permits--and the longitudinal measures required to understand that

process--may be demonstrated by an analysis of the actual earning

histories of more than 5,200 men and women in the 1958-69 period.

Initially, let us consider the earnings of more than 1,900 of them.

All were twenty to twenty-four years of age in 1964, all participated

in the 1964 MDTA Institutional raining program, and all were either

RISERSor MIXERS during the 1958-62 base period. (See Chart 1.)

0

For both pat tern groups, average annual earnings as disclosed by , :their Social Security records were well belwo the maximum taxable

earnings limit in each year during the 958-62 base. period. These

specially selected histories also rev 1 one additional characteristic.

In each of the years of the base per od, average annual earnings of

/file RISERS were very much lower than those of MIXERS. In 1965-69,

howevei, (and in each year of the period), there was a drastic

2

rever-

. sal

. of positions. The RISERS earned from 1.7 to 6.7 times as much

6. G.C. H court, "Some Cambridge Controversies on The Theory of

Capit " Journal of Economic Literature, June 1969, p. 387.

.4

6O 4:
Pfteern

1958-62

R

R/M

R , M

RIM

R

M

R/M

. R

M

R/M

CHART 1.

PATTERNS OF THE RISERS AND THE MIXERS', 1958-62

1965-

1958 1959 1960 1961 1962 69

MALE WHITE

$ 89 $ 269 $ 578 $1011 $1740 $29,453

562

_ 1262 1945 2377 2579 17,121

.158. .213 .297 .425 .676 1,720

.-,---

. MALE NEGRO

$ 49 $ 174 $ 460' : $ 813 $1585 $26,120

481 1462 2150 2404 2056 10,774

.102 .119 .214 . .338 ,771 2.424

FEMALE WHITE u

$ 14 $ 47 $ 180 $ 556 $1443 $17,753

286 940 1832. 1973 2010 2,656

.049 .050 .098 .281 .718 6.684

- .(... FEMALE NEGRO

, $ 7 $ 38 $ 125 $ 335. $1194 $17,668

643 883 ., 1007- 1672 1683 3,498

.013 .043 .124 .200 .709 5.051

603
ri - 571 7

as the MIXERS. . The earning histories of these 1,900 youthful RISER and MIXER

trais are summarized in Table 1, as are those of 3,300 additional

trainees who werertwenty-five to .4 fifty-nineyears of age in 1964.

IA all these in ces, average ann al earnings of the RISE

7

were lower thanthosraf'the-MIXERS in--e h year of the 1958-6 21 period. - And in each year of the 1965-69 perio , RISER earnings exceeded those

of the MIXERS.

Conventional cross-sectional analysis cannot aitcount for this

0 drastic reversal of earnings of the RISERS and MIXERS. Primarily,

this is true because it isolates the/earning level at a given moment

. 4

of time from the Continuing process of change of which it is a product. Viewed longitudinally, however, the turnaround is neither exotic nor surprising, and is readily understandable.

Initially, it is useful to think"of the earning histories of

these RISERS and MIXERS in terms of earning differentials. For each

Of the age groups, the R/M line indicates that despite their lower

earnings in 1958-62, RfSERS were increasing their earnings at a

faster rate than theMIXERS. Among-the twenty to twenty-four-year- , old female blacks, for example; RISERS earned 1% as much as MIXERS

in 19584% as much in 1959, 12% as much in 1960, 20% as much in

1961, and'71% as much in 1962. The identical erosion of the RISERMIXER differential also occurred among the trainee'in each of the

seven older age groups. And in each of these instances, too, in

1965-69, RISER earnings exceeded those of the MIXERS by substantial

amounts.

M

Continuity of Change: The CER (Table 2)

The earning. patterns of these men and women may,be conveniently

indexed by their respective rates of earning cuMulation during 1958-

62. For each age group, the Cumulative Earning Rate fai the period-

-the CER--was veil much higher for RISERS than MIXERS. This was

60,4
SELECTED 1964 MDTAIINSTITUTIONAL

THE 1958-62 CER AND THE CHANGE IN EARNINGS FROM 1958 TO 1965-69

TABLE 1 0

NEES WHO WERE RISERS OR MIXERS IN 1958-62:

AGE -211--13

1964 Pan62

CER 1965-69 CER 1965 -69 CER I. 1 1 6 -6 1963-69

-1938 1958- 1965- L

62 62 69 1958

1958-

62

1958 1958- 1965-

62 69 v17

1958 1958-1 1958 1958- 965-

62 62 \69

r. 1958- 1958 62 1958 19551- 1965-

62 69 1958.

ti Total MALE WHITE MALE NEGRO FEMALE WHITE FEMALE NEGRO °,

20-24 IM

0.427

4 5241 89 $ 3687 $29.453 330.932

562 8725 17.121 30.466 61.877

16.781 494 3081 $26,120

481 8553 10.774

33.061 22.399

159.000111 14 $ 2240 $17,753

23 618 286 7041 2,656

1268.071

9 282 241.714

8.157 7'$ 1699 $17,668

643 5888 3.498 2524.000

5.440 RiM I 1 .158 .423 1:7201 10.118

6.031

1 .102 .360 2.4241

484 5381 26,178

1429 100 4 12 693

54.087 8 88218.859 5 296

5 .049 .318 6.684

191 3793 17,472

1418 8928 50 91.476

3 881 20.9876.2 6

.011 .288 5.050

163 3584 18.101

l' 6 111.049 6 256

25-29 IFE 8 984

.549

707 7059 29.7421 42.068

1609 1 539 15 710 9.761

11/M .439 .670 1.8931 1 .339 .535 2.062 .135 .425 3.175

t .137 .415 2.425

30-34 R 1.1.616 M 4.637

146 3302 22,1601151.1801

1841 10378 12.533 6.807

35.180

4.826

50 1809 22.734

1782 10047 10.019 54.680

5_784 ;1.619

5 024

120 2723 19.723

1615 9730 6,572

164.358 4 069

12.893

5 455

234 3251 19,103

1405 9067 5.411

81.637

3.851 RIM I 1 .,079 .318 1.

I .029 .179 2.269

1 .074 .280 3.001 .166 .359 3.530 35%39 1.0 853

4 075

68 2846 21.832

2054 10426 10.424

321.0591 5.08

I 442 3860 17.704 40.054 37.682 4.35063 2437 20,041

1748 9352 6.492 318.111

3 714 1203.500

4.334 2 -2409 19.0411807 9639 4,368

9520.000

2.417 1 0

I .215 .387 1.915

__. .voi .250 4.359

40-44 288 4339 21,583

3072 15553 12,21

I 9.455 4 177

167 1746 22.047

1760 9112 7 786

132.018 6 67120.031 4,504i

128 269i 18.983

1648 9075 5,732

148.304 3.478 32.130

4.971

92 1048 16.560

1810 6.280

180.000

3.470

.094 .279 1.766

I 68.207 13.063 14697 4.19

.094 .192 2.8321

111 1561 17.2341 1944 10110 8.358

55.261

4.79

59.767 4 778

.078 .297 3.312 43 2613 18.261

1769 10222 6.353

424.674

3 591 8.094

3 537

10807

.051 .282 2.637

603 5484 19.286

2376 10781 7,284

31.983

3.066 45-49 M

1781 3.246343 3491 13.395

2124 9826 7.852 RiM 1 .161 .355 2.979! I 66.972 9.110

3.401 1.657

.057 .154 2.0621

299 3023 13,695 2260 10528 4-5.304

45.803

7.147

1 16.113 4 437

.024 .;56 2.874 202- 3457 16.811

1800 9786 6,540

83.223

3.1§13 8.2773.932

.254 .509 2.648

418 3878 12,191

2228 10990 3,991 29.165

1.191 50-54 R 113 M

773

3.6961

187 420 19.221

2180 10238 7.414 RIM 1 1 .152 .414 13 9

C

.132. .287 2.5821 .112 .353 2.570 .188 .353 3.055 55-59 110.8691 111111

4.006

382 4534 18.098 3131_15423 9.489 47.377 11.232 3.070 4.737

392 4795 11.6341 2654 15366 9,336

29.6781 17.487

3.517 4.022

230 4252 20,773

2114 10616 5.780 ---

----

___ - .122 .289 1.907 II 1 148 .114 .109 4093,524_____

0

60'3
AGE

IN

1964

.

TABLE 2

SELECTED 1964 MDTA INSTITUTIONAL TAINEES WHO WERE RISERS og MIXERS IN 1958-62:

THE 1958-62CUMULATIVE EARNING RATE AND THE CHANGE IN EARNING.5,41958 T0;4965-69

PATTE CER 1965469 CER 1965-69 CER 1965769 CER 1965-69

1958- 1958-: 1958- 1958- i'A 1958- i

62 : 62 .1958 62 1958 62 1958 62 1958

20-24 R-N

25-29 R 30-34 R 35-39

1

RH

40-44 R 45-49 R

50-54 R

55-59 R

MALE WHITE

40.427

14.524

J30.932

30.466

8.984 42.068

5.549 9.763

21.616 151.780

4.637 6.807

40.853 321.059

4.075 5.080

14.066 74.941

4.063 3.978

9.178 68.207

3.626 3.697

13.773 66.972

3.696 3.401

10.869 47.377

4.006 - 3.030 re .987 Sig 12

MALE NEGRO

61.877

16:781

533.061

22.399

10.118 54.087

6.031 8.882

25.160 120.635

4.826 5.784

7.733 40.054

3.859'. 4.456

0 9.455 132.018

4.117 4.423

13.063 155.261

4.198 4.299

9.110 45.803

3.657 2.347

11.232 29.678

4.737 3.517

r* ,947 Sig 1%

FEMALE WHITE

159.000

23.618,

1268.071

9.287

. 18.859 91.476

5.296 3.881

21.619 164.358

5.024 4.069

37.682 318.111

4.350 3.714

20.031 148.304

0.506 3.478

59.767 424.674

4.778 9.591

4

16.113 83.223

4.437 3.633

17.487 90.317

4.022 2.734

rot 091 Sig 1%

*Sig at 12 unless otheiwise inquated

606

I.

FEMALE NEGRO

241.714

8.157

2524.000

5.440

20.987

6.236

111.049' 6.256

Al2.893 (81.637

-5.455 3.851

I,233.500 9520.000

4.334 2.417

32.130 180.000

4.971 3.470

8.094 31.983

3.537 3.066 8.277 29.165

3.932 1.791

x4 :997 Sig 12

4

.982

.954

.999 .885

.979 .993

.973
, .

-.) . - 5'14 -

. " , . V

N. true despite the`i-owerannUal.earning levels of the RISERS in each

year of theperiod.

If, as'our thesis suggests, the change in earnings] s a continuous and consistent proCesst, then the 4458-62 CEshould be measurably related to.the change in earnings from 1958 to 1965-69. And

indeed, the correlation-roefficients--do--reveal±a-statisticalfY significant relationship between these two variables for each sex-ra?

group.as a whole (r varies from .94 to .99). At least 88% of die'

changOn earnings from 1958 to 1965-69 may be explained by the

rate of. earning cumulation in the 1958-62 base period.

While this holds true for each sex-race group (two paired observations for the eight age cohorts) the relationship is equally

firm,within each age group, when the eight paired observations are coalesced and the §ex'and race designations are ignored. For the . twenty to twenty-four fearolds, for example, more than 98% of the

change in earnings from 1958'to 1965-69 may be explained bykthe CER

in 1958-62. And that relationship is also significAnt for each if

the successively older age groups, as shown in Taiple 2.'

4.' V )1 Earning Patterns and Employment Changes

(

'ANL

t Traditionally, economic data on employment,and earnings are

. published on a dichotomous basis. The Bureau of LabOr Statistics

and the Bureau of the Census, for example, publish one set of data.

*

.1.- on employment, and another on earnings.

. 441sistent with this convention, I shall consider first the

change in ;in years of employment, and the principles which appear

to govern such changes.

Throughout the discussion, primary reliance will be placed qn

an analysis of the labor market experiences of a 0.1% le of

49,245,000 members of the Social Security Administrate. tise r's Contintpus Work History, Sample (CWHS)--a random sample of individuals with

earnings of $1 or more in 1964 from j obs covered by the Social

607
- 575 -

Security system. To further test the validity of my hypothesis, I

shall also refer to CWHS data for ether time peri4ds, and to the his-

. tories of participants in-anumber"of training programs for the same

4.

time periods as well.

The employment)changes of these individuals may b

conveniently in the form of an Employment Ratio (ER).

described)

This measure

reflects the ratio of man years of gainful ethployment to the numb

fi - of calendar years in a given span of-years. During a period of five--

calendar years, for example, individuals with earnings in two year

have an ER of .4000 (2/5); in three years, an ER of .6000'(3/5); in

four years, an ER of .8000 (4/3);'and so on. The ER measure, there-

_ fore, is consistent with the pattern concept, since if is affected

adversely or positively by te.contintlity of man years of gainful,

employment during-any given span of years.

CWHS: ER Changes (Table 3) .

.1" Ty-emploYment history summaries of the 49,000 CWHS members are ,of

. interest for several reasons. While restrictedto employment cover- . ed by Social Security, they are remarkably consistent with respect

to the experiences of bah men and women, whether white or black.'

Unlike Tables of Working Life whiCh assume tlie absence o change

froXentry into the labor force to':tetirement, the exl5erience of

all the4WHS Members indicates that chanfes in the level of employment ment do occur duripg the aging process. . When classifie by earning patterns in 1958-62--RISERS, MIXERS,

and DECAEASERS--t

Among those twent

there is a marked

of RISERS; and MI

RISER men and-wome

than the MIXERS.

se changes are also marked by a singular symmetry.

to twenty-four years df age in 1964, for example,

difference between the 1958 and 19N-62 ERclevel

. Throughout the 1958-62.base period; the

of each race_had fewer man years of employment

n 1965-69, however, one witnessei.a reversal

in position. The ER of the RIEERS.exceeds that of the MIXERS, a

60&

. V

gm.

O
V.

TABLE 3

I

TABLE 3 - CtBS MEMBERS WHO WERE RISERS, MIXERS, OR DECRWERS DURING 1958-62: THE 1958-62 CUMULATIVE EARNING RATE AND

WEE CHANGE IN MAN YEARS OF EMPLOYMENT (ER) FROM 1958,70 1965-69, BY SEX, RACE AND AGE IN 1964

Of

18 1958-

2944 62

4 Num- 195 1958 1958- 1965- per 62 62 69 965-69

i 1958 Nu.

bar

CER

1958-

62 1958 1958- 1965-

62 69 965-6 i1958 Nunbar

CER

1958-

62 965-69

1958 1958- 1965- 1. 62 69 1958 Nu 1958- CE2 ber 62 1958 1958-1965-

62 69 II 945-49

i1958

Ih MALE WHITE N. MALE NEGRO N FEMALE WHITE H- ( FEMALE NEGRO

. Total 29 849 3388 14084 . 1924 14-24 R 261710-196-.71450 .6242 .9604 .920 326 '58.840 .1411 .5392 .9429 .612 1529 2.891 .1464 .5424 .7213 .926 148 55.382 .0743 .4040 .8148 10.996 M 1863 ,3.625 .5373 .8030 .9480 .764 206 3.118 .4660 .7524 .9252 .985 986 3.931 .4604 .7146 .6987 .517 103 24.614 .3106 .6155 .7825 2.519 D 24 1.603 1 0000 .5916 .8416 .842 7 1.638 1.0000 .5142 ,8571 .857 23 f, 0.596 .9130 4956 .5965 .607 3 1 069 10000 5333 .7333 .733

4 05_20 0 1655 7.238 .8785 .9440 .9789 .114 , 179 7.748 .7430 .8871 .9664 .300 562 8.076 .5978 .7701 .77931;.303 77 13.105 .3506 .6753 .9090 2.592 H 2401 5.559 .9250 .9430 .9486 .025 ' 330 5.866 .8787 .9115 ,9206 .047 981 4.896 .8287 .844Q .7388 .891 196 5.587 .7448 .8061 .8 1.156 D 49 1.590 1.0000 .6755 .832 .833 10 0.757 11.0000 4800 9200 .920 98 1.010 0000 .4775 .6959 .696 17 2.504 A.0000 6941 .6 .642

313-34 6 2122 5.381 .9344 .9705 .9768 .047 175 5.990 .8628 .9611 .113 504 6.888 15376 .7313 .8488 .578 94 10.291 .4042 .6617 .8510 2.105

18 1636 4.:23 .9407 .9434 .9427 .002 312 4.845

.9314

.9102 .9326 .024 825 4.554 .7866 .8363 .8070''.025 148 4.774 .8040 .8243 .8094 1.006 0 45' 1 892 1 0000 .6888 .8488 ,849 15 2 344.9205 1 0000 8133 87733 773 71 0 862 1 0000 ,4676 .6788 ,679 8 6.313 1 0000 5500 .5000 .500

'35-39 I 2430 4 919 .9621 .9808 .9819 1.021 160 5.402 .9700 691 6.440 /084 .7470 .8862 .615 96 7.281 .5416 .7416 .9166 1.692 M 1496 4.393 .9438 .9494 .9352 .991 297 4.734 .8562 .9137 .132 942 4.832 .8956 .070 172 4.664 .8081 8581 .8662 1.071 0 52 1.920 1 0000 .7192 8153 815 10 I 815 .9427 .9441 .9131 ).0000 6600 8600

.968 860 71 0.980

.7993 .8562

1 0000 5261 .7111 721 11 1 821 A .0000 6363 .7454 .745 2482 4.791 .9621 .9827 .9753 1.014 154 5.000 .8896 1.072 863 6.376/ .5990 .7828 .9068 ..513 95 6.431 .5894 .7915 .8673 1.471 .. 14.6 4.299 .9412 .9445 .9208 .978 270 4.654 .9246 .9545

.9333 992 4.707 .8417 .8868 .8546 .4115 149 4.846 .8389 8979 .8456 1.007 D 45 2.116 1.0000 7777 8088 .809 20 2 682).9511).9111 0000 7300 8700

.976 870 63 1.494 1,0000 ,6380 .7:69 727 24 1.982 8.0000 6333 .7750 .775

i 6 2190 4.745 .9610 .9806 .9675 1.005 137 5.099 .9270 .9604 .9343 1.007 '49 5,989 .6725 .8247 .9062!'.347 119 5.873: .5461 7075 .8773 1.606

18 1404 4.221 .9458 .9450 .9118 .964 226 4.189 .9159 .9415 .8973 .979 904 4.569 .8539 9033 .84021 .983 112 4.551 .8839 .8964 .8482 .959 D 39 2.02101 0000 8051 .7741 .774 19 1.622 9473 6526 7368 778 55 2.094 9818 .6109 .7200: .733 15 2.527 1 0000 .6933 .7466A_.,71L.

1 1 1 1860 4.764 .9580 .9798 .9549 .997 114 4,928 .8859 .9421 .9508 1' .072 796 5.535 .7211 .8469 .90301..252 85 5.805 .6470 .8254 .8894 1.374 H 1272 4.218 .9488 .9482 .8949 083 188 4.376 .8404 .8529 .8345 .992 8321 4.441 .8744 .9096 .8473! .965 99 4.167 .876h- 8787 .0060 .917 0 48 2 101 1 0000 7500 7708 ,771 21 1,225 2 0000 7523 6380 .638 551 1 936 1.0000 7090 6800, .680 21 2.194 1 0000 7904 .7142 .714 5-59 1 1472 4.759 .9544 .9763 .9263 '.971 71 4.920 .8591 .9380 .8647 1.006 649 5.231 .7704 .8105.8644 '.122 58 5.665 .5689 737 .8241 1.448 H 1101 4.144 .9391 .9404 .8653 ..921 131 4.239 :8854 .9236 .8687 .981 705 4.441. .8907 .9171 .8286' .930 67 3.615 .9104 922 .7970 .875 D 48 2 189 1 0000 8083 7291 729 10 1 '80 1 0000 8000 7200 720 44 2 336, 0000 7772 6818, 682 7 1.88511 0000 628 7428 .742 4

6'3-0
.

'tu round we have.discussed in another connection earner in this

page . And while the number of twenty to twenty-four-year-old

DEC SERS is quite small, it is noteworthy that while their ER

exeed that of the RISERS and MIXERS in 194, for the 1962 pe iod

is a whole, and in 1965-69'as well, it'is substantially lower than

that of the two other pattern groups.10And while some recovery does

.occur:in DECREASERS do not regain their initial advan

-tage in employmen . er

The most significant characteristics of thesehistdries is the

congrugnce between the earning"pattern during 1958-62 and -the change

in ER from 1958 to 1965,-69... In each of the eight age groups',the

RISER CUMulative Earning Rate exceeds that of the MIXERS, which in

turn exceeds that of-the DECREASERS. Interestingly, the rank order

s-of change in ER from 1958. to 1965-69 is compritely consistent with

this array. The change in employment is most favorable for RISERS

lest favorable for MIXERS, and least favorable for DECREASERS.

It is true, of course; that the CER is generally lower for each

successively older age groyp, but so'toois the change in employment

What is particularly striking is that this consistency marks both

the positive and negative employment ohanges. Along older workers,

for example, MIXER employment declines to a lesser degree than that

of DECREASERS, as for example, in the forty and.olikr age groups.

Institutional Trainees: ER Changes (Table 4)

This sumMary'of one of the principle characteristics of the

employment changes of the CWHS members reveals the existence of'differential or structured rates of change which, characterize various

age groups in the labor force, and of different segments within each

age group. That CWHS RISERS may increase their ER, whils the employment of MIXES and DECREASENbchanges negatively, suggests that

the aging process is not monolithic, but' affects employability with

a certain consistency of effect.

b.

1

6
TABLE 4

TABLE 4: CWHS MEMBERS AND 1964 HUTA INSTITUTIONAL COMPETERS AND NONCOMPLETERS

>WHO WERE RISERS0 MVRS, OR DECRf4ERS IN 1958-62:

THE tORRELATION BETWEEN THE 1958-§2 CER AND THE CHANGE IN MAN YEARS OF EMPLOYMENT (ER) FROM 1958 TO 1965-69

6,

[awl nu

on- 61 "err-tiss

Ion. , 4/ 1M

4141112I8S rdi - nu. ; al 194

1111104111 ea7,6r76.

9111- i

..62......)9511 _1=1 .01111,41141 14/4446141101 tInis 4 NI NA 011 Ili P. --rrip r %ft IN/

,antleS . ,...-..---

,

,- All/

Ala IV" a Ln 1141 a

11S4- i *SI- ',. al 19111 a/ 94

1 II 5

to4a. ; 62 a 1. no ion- i

a 47 191i cril 9 1154- 1 a/ I4

on 116109

1464 I a/ 914 PIA 1114.1 144. I al 19111 4.4 194 - 1111

"KU/ 411117

'-ll"- MAU 154,400

Is.nl 4.174

9.94 1017 o 44 009

871411 011117 00.111 9.411

'1, 041 1 44

0 421 all 47.054 11.741

19.41 2.612 0.)71 .111

III 10 P, J4 414 1.419

1,061 711 mils Main

to* 410 4 051 4.142 1.005 0 /14 41

171 MO fa 061

10 174 1 I// 0 I10 1 044 4 111 7 No

II 491 1 74

1 601 442 4) 74 0 44

14.74 I WA

0 4/4 1 04

09 54 1 04

4.6111 1 9711

i IN 1 014

90.140 caw

11 NO I 141

1 416 IS/ n31. 7 554 14 041 1 441 IS) 1 COO Si 41 7 40

70.744 2.011 1.414 I 040

P,

/ 24 I 110 , 101 1.021

I 1411. an

0 2/2 1.121 1 5001,,, $ /41 1141 0 215 1 222

, 574

1 124

/ NI I 164 o 242 410 --

5 010 1 111

4 041 1 474

2 144 HI

5 40/ I III 4 /4 140 1 015 44

50 121 1 8/2 5 1/91.40

1 451 011

.

/ 11/9 1 7117 4 44 1 012

1 4/9 /01 4 in I 4/ 4 201 1..,gr

1 140 NM

11 44 1.94%

1/11) 4/4

1 111 41

1 4111 I 160 1.111 1 0/1

7 111 /IA

7 44 1 )40 4 III, 411

1 01/1 414

10/4 I 41 4 44 AIN

3 plo ,44

10 94 2 111 SON 1.06/ o 416 ./44

11 114 1:944 4 NA .081

5 .2,1

11 105 1.112 5.4/ 1.114

7 444 641 1/.710 2405

1.150 I-14' 1.014 MO

21.742 1 Irnn 5461 '2.4

0 444 241 v. In

11O

0

i 41 1 04/

4 in 1 0{17 1 47 49

4 704 I 141 4 011 1 10, 44

4s6 1 911 1 711 ^.64 047

41 7 4 4 1.174

4,544 1.011 4/ art

, 111 1.41

4.214 1 III LAO /50

71 7 1.41

4.911 1.04 LAN 540 10.711 2 101 4.7/4 1.000 1 III 100 11.141 2 469 4.9/4 1.114 I III IN

4 714 1.241 6 114 148 0 449 111 r. 44

4O 4 III I 011

4 141 NI 1 920 III 1 101 1 Ill 4.14 3 04

1.016 III

III 4_44 44

1/34 462

jr a.440 1 all 4.04 I 070

41

II /11 7.410 4 //I 1 )44 1 /II /4

8 ISI 1 UP

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0.
579

The analytical role of pattern, the CWHS histories suggeit, is

to permit the classification of labor market histories into meaningful categories di change. Thus, although the CWFS members were all

employed in 1964, the 39,792 men and women participants in the'1964

MDTA Institutional training program were largely unemployed in the

period preceding their entry intothe program. While more precise

statement concerning their employment status cannot be made, most

analyst's agree(tbat CWHS histories as a whole are upwardly biased, while those of t Institutional trainees are skewed negatively.

In addition, some analysts have suggested that the men and women

CWHS members are better educated and higher paid'than the disadvantaged and unemployed trainees, and that the lastories-of the two

groups are simply not comparable.

When djsaggregated and classified by erring patterns in 1958-

62, however, the process underlying the employment changes of both

the trainees Alpe CWHS members is seen to be identical. The

trainee histories are shown separatelr for those who did and did

not complete their training. In each of eight age groups of these

men or women of either race, the symmetry between the 1958-62 CER

and the change in employment is apparent. The larger the rate of

a

earning cumulation in the base period, the more favorable is the

change in man years of employment.

The Continuity of Change (Table 4)

The ubiquity of this symmetry suggests an underlying relationship between the 1958-62 CER of the various pattern groups, and the

change in employment from 1958 to 1965-69. And indeed, these two

variables are closely and significantly correlated. Three aspec

of that relationship are particularly noteworthy. The CER-ER

change relationship,is significantly correlated not only for the

CWHS members, but Tor the Institutional Compreters aid Non-Completers as well. As the correlation coefficients at the foot of Table

612;
- 580 -%

4 indicate, this holds true not only for the CWHS, Completer,

-tb and Noncompleter samples considered independently, but alio when

the Completer or Noncompleter paired observations are coalesced

with those of the CWHS members. The correlation coefficients re-

, main significant and unchanged:

The third, and g bably the most important aspect of that

relationship, is revealed by the correlation coefficients for each

age group, when the CWHS, Completer and Noncompleter. paired observations are coalesced without regard to sex or race. For each of

the eight age groups, the coefficients reveala persistent and pervasive relationship between the rate of earning cumulation in 1958'--

62 and the chilafge in man years of employment from 1958 to 1965-69.

From 75% to 93% of the change-in,employment of the age groups may

be explained by the rate of earning cumulation-in 1958-62.

The case of the twenty to twenty-four-year-olds in 1964 is

particularly striking. Because their employment histories are

traced from 1958 to 1969, the.1958-62 CER reflects their labor market experience six years earlier, when they were fourteen to eighteen years old, and ends five years later, when they were twenty-five

to twenty-nine years old. As might be expected, the rates of earning cumulation reflect their_ rapid absorption into the labor market.

And despite the very great disparity between the white and black CER

of each sex, and between the CER of the mit and trainee samples,

the relationship between the CERsand'the ER change is clearly evident. More_ than 93% of the change can be attributed to%the rate

of earning cumulation in the base period.

Earning Patterns and Earning Ch.gesfr

a As might be expected, the earning histories of thmoCWHS members and the trainees parallel their changes.in man years of employment, and reveal the same congruence. between the rate of earning

cumulation in 1958-62 and the change in earnings from 1958pto 1965-

69.
- 581 -

7

CWHS Earning Changes (Table.5)

r". The earning histOries of the CWHS members reveal the same symme;ry.

between the CER in 1958-62 and then change in earnings that characterized

their employmentWithin each sex-race group as a whole, and in each

age group as well, RISERS, MIXERS, and DECREASERS cumulate earnings at

successively lower-rates;-a-rank ,order which-is casistently accompanied

-by respectively lower changes in earnings f 1958 to 1965-69.. And, as

in the case of their employment changes, th nge in earnings of the

CWHS members is age-related. The older the g oup, the lower is the

414 1958 -62 CER, and the lower is the change in earnings from 19581 to 1965-69.

4 Trainer Earning Changes (Table 6)

P A comparison of the dynamics of the change in earnings reveals the

presence of the same underlying fo es in the earning histbries of the

1964 Institutional Completers -and Nrcompleters. And as indicated at the

fOot of Table 6, the correlation between the *ate of earning cumulation

during 1958-62, and the change in earnings from 1958 to 1965-69, is as

strong and as significant for the Institutional trainees as far the CWHS*.

members. Whether one examines the earning changes of the CWHSimembers,

the Institutional Completers 6r Non-Completers, regardless of sex or race,

a

I A at least 90% of the change in earnings m 1958 to 1965-69 may beattributed to the rate of earning cumulationduring 1958#62.

And as in thexase.of the change inER, the paired observations for

the coalesced CWHS and\trainee groups as a whole reveal a virtually'

unchanged set of correlation coefficients. This is alsodtruefor,each

age group, when we test the. relationship between the CER during i95842..

and the change in earnings. The correlation coefficients in each instance

reveal a firrnhd stabel relationship between these two variables.

This section of he paper has considerecpthe changes in employment

and loarnings of some 89,000 individuals--more th'in 49,000 CWHS members 0 and some 40,000 Institutional Trainee Completers and Non-Completers--

during the 1958-69 period. And although their respective employment and 4 earning levels differed, we 'discovered a prOcess,of change which was

n to them all.'

2

a

4

,VIMO

614
O

014 TABLE 5

TABLE 5 - CWHS MEMBERS WHO WERE RISERS,, MIXERS OR DECREASERS DURING 1958-62:

THE 1958-62CUMULATIVE EARNING RATE AND THE C ANGE''IN EARNINGS FROM 1958 TO 1965-69, BY SEX, RACE AND AGE IN 1964

8 ALE

In !RN 1964 19586e. CER 444958-

62 965-6e

1 1958 19511. 1965 i

1 62 69 1958,,ber goo-1958-

EER

62

1958 1958- 1965-

62 69 96 -6

i 1958 um-

.. CER

1958-

62 1958 1958- 1965- 62 69

'965 -69

;1958

116.

6,.. CER

1958 62

'1958 1958- 1965-

62 69 965-69

It 1958 62 N. ,

T0761 291149' HALF WHITE

'N.3388 HAIF mrc80

N.14894 FEMALE ti4HIT

H.924 FEMALE NEGRO

-- 25-29

30-14

111 _alp.

Min

-. IM

10_..._.:441

FitD

18631

24!

1655 2401

. 3.625

1 602 7 218 5 559

1.590

w 1 1 354 5180 22758

567 _

1477 14686

_,.....___ 1884 1551e 28873

2712 11230 25140

1944 5036 16708

154,23' 34,71 -_

15.32 14.685

18,595

206

7 179 330

10

.8 1.118 1.638 7.748 5.866

0.757

50 '. ) /62 3709 16336 675 1781 8916

1157 10:0/2 23218

1096 75;8 17865 1018 179(1 16659

62 35

13.20 20.06

16.30 16.36

986

21

502° 98198

13 931 0.546

8.076

4.896

1.010

257 3842 10618 525 818 43(18 1141 0161 15722 1353 7978 11404

1491 2998 9048

. 41.115 8.206

13.340

8.424

6.068

: 1035'4.614

il

77 196

17

1. .352 1.069 7.10

5.587 2.504

$ 27 $1845 $10797

86 2211 10543

461 955 5697 199.188 1280593

12.35 -._ -- ---

521 5245 1 04

764 5038 1 0 909 3187 5176 _38.555

15.301

5.694

2122 1638

45 5.181 4.723

1.892

3142 20052 30257

24511 14027 23996

1993 5767 16412 9.63' 9.794

8.234 5.990

4.865

2.344

----1.-- 5.402

4.734

1.815

2074 14505 21'805 1706 9971 18358

1628 5444 12440

11.42

10.76, 7.64

504

825

71 6.888

4.554 0.862

1280 10100 16580 1501 8353 11081

1646 3066 7515

12.953 8.703 4.11694

148

8 0.291 4.774

1.313 506 5721 1s855

'1056 6097 10878

1032 2387 2956 24.871

10.301 2.864

175

312

1.5 35-39 R

im

4 '0 2630

1496'1

52 1 4 819 3707 21574 30783 62393 2808 15146 21470

1.920 2158 6102 13090

8.304

8.358

6.066

2527 16181 24912[

2031 11649 19334

1897 5340 13677 9.85

9.51'

7.211 6.440

4.832 0.980

1471 10947 18612 4494 8716 14112

1557 3085 8476 r

12.652 9.4461172

5.444

927 7680 15840

1207 6836 12078 1133 3198 8382

17.087 10.006 7.398

160 '97

10 691

942

71 863

992id

849

904

55 -- 1-5-995

832

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96

II 7.281 4.664

1.821 40-44 8 2482 :4_791 f

18 1446 14.244:

'C.

. 65 12.1161

12_

'3777 21876 30461 2823 14962' 21977

75)1 7888 16268

8.065

7.785 6.427

154 270

10

5.000

4.654 2.682 2768 16609 . 24308 1654 8665,1 15153

2051 75529 11497

8.78 9.16

6.58 -i.27

8.19

5.72 8.3

7.86

/.88(

7.25. 6.59

1.19

6.376

4.707

1.494 5.999 4.560

2.094

..___. 4.441

1.916

5.211 4.491 2.336

1516 11186 18804 1666 9513 14510

1452 3625 7968

_____

12.403

8.709 5.469

.95

149 24 6.431 4.846

1.982

1028 7646 12201 1228 7181 10601 1719 1696 6746

11.868 8.640

5.445 6-49 8 12190 ',4 745 3840 22060 10214 H 11404'4 2211 2825 14754 21178- D 1 19 't 2.0Z3, 2466 .7459 10601 - , _4_-__ 4_ __.-4,..-

7.873

7.497 4.101

137 726

19 5.099

4.389

1.622 2862 17459 23682

2097 11305. 17190

1882 4936 11)768 _ 7:17--

2619 15886 22/52 2132 T62 16714 2073 . 612 82811

2529 14967' 18351. 2)01 72068 15/84A

2117 6498 , 147,21

1253 12275 0956

1817 10105 4060

1528 4729 0011

10.813

7.738 6.552

119

112

15 5.873

4.551

2.527 1081- 7430 12193 1309 ;271 9999780 2752 5183 11.279 7.638

6.645 50-5.4-871860446 3785 21823 2910

M 11,- 1272 .5..214 2829 14763 19922

10 1 48 12.101462203 6833 loam

I. 55 -5918 1472 I 4 75911741 1741 21541 27(105 1 M ,1101 1441/711 13950 17741

in J.. l'i 9. 7i411 '7776 9604 4

-....- 7.706

7 042 4.903

7 219 6.544

4.194

-

114

18821 )0.225

71 131

In

....-- 4.928

4.376

-, 4.920

..239

1.780

-12987- \ 1147 7808 12624

14/1 7395 9367

1130 3613 4145 11.006 6.545

3.668

10.816 5444

3.495

1887- 1885 10261 13691

2101 6169 7889

2133 13915 18560

1901 10440 12769

1792 5980 7114

..

7.263

).755

8.311 6.716 4.093t_

. 99

2158 67

7

.405 4.167

2.194

5.665

3.635

1.885 1046 6977 11335

1251 5799 7312

1164 1936 4767

a

0

613

A

.4

r
TABLE 6

2

No0

6: MS MEMBERS AND 1964 MDTA INSTITUTIONAL COMPLETERS AND NONCOMPLETERS WHO WE RISERS, MIXERS OR DECREASERS IN

1958-62 THE CORRELATION BETWEEN THE 1958-62 CER AND THE CHANGE IN EARNINGS FROM 1958 TO 1965-69

As [ 4> 4

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V 616
584

Analysis of the labor market experience refi*ted-in 113,500 earning,

histories of CWHS RISERS, MIXERS and DECREASERS for two other time p iods,

1962-66 to 1969 and 1963 -67 to 1969, serves merely to /eplicat findings for the longer 1958-62 to 1965-69 period. The CER in the base

periods is invariably correlated significantly with the changes in ER and . . earnings from the initial bate year to the terminal year. The changes in

.employment and earnings Of more than 105,000 trainees in the 968 MDTA

Institutional program, the Mainstream program, and the JOBS .t.ontract and - Non- Contract programs reveal precisely the same relationships, whether

considered independently, or in conjunction with the paired observations

of the CWHS'groups.

In all, we have analyzed,about 220;000 employment and earning

histories. Change does characterize those histories, and the process is

marked by a singular uniformity and consistency which reveal a continuing , set of patterns of change which characterize the labor market,exPerience

du'ring the aging procets.

THE-HOLISTIC NATURE OF THE CHANGE IN EARNINGS

In an earlier reference to the earning pattern concept, I have

described it as reflecting the interactional effects of the totality of

forces responsible for the change in earning capacity. It is this

tic characteristic of "pattern,; and of the longitudinal system of

analysis of which it is a component, which permits isolation and demon-

.. stration of-the thread of continuity which links the past and future,

labor market experiences of men and women as they age.

Edlication As A Variable: RR and MM Trainees and CWHS Me bers

Nowhere is this holistic quality better illustrated Nn in the

olisearning histOries of he Institutional trainees when classified by years

of schooling. As we shall see, the education variable is not monolithic

in its effects, and by itself cannot-explain why, for example, some high

chool dropouts have more favorable changes in earnings thanhigh school

graduated. In short, these earning histories demonstrate that education

6J7° 4
- 585 -

is not an independent variable, and that it is not causitively related to

. earnings as implied.by the careless writings of some economists. Indeed,

these histories suggest that the,relative significance of the education2 earnings relationship,changes gas individuals a , and that the influence

of education on earnings diminishes with incre sing agd'I

Sdhooliu, and the RR and MM Trainees (Table 75

The implicit assumption in Tables of Lifetime Earnings that the

education- earnings .relationship remains unchanged throughout a lifetime

is not supported by the earning histories of 9,221 Institutional trainees.

Table 7 highlights the changes of two groups of these trainees;

those who were RISERS in 1958-62 and-iv 1965 -69 (RR), and those who were

MIXERS in both instances (MM). The earning pattoerns of each group, therefore, remained constant from 1958 to 1969. Also held constant are the

number of years of '`schooling. The RR group includes only high school

dropouts; while the' MM trainees had all graduated from high school, and

same-had gore- on-tor-cierrege . The-mare-favore-d-RR-parrern groups were

thus educationallr-disadvantaged. The less favored MM trainees, however,

. were more favored educationally.

A comparison of the 1958-62 CER of each group discloses that for

each sex and race, the RR high school dropouts--despite their relative

disadvantage in years of schodling--were cumulating earnings at a .faster.

rate than those MM trainees who were high school graduates. And the

correlation coefficients indicate that the changes in earnings form 1958 -

to 1965-69'Nwere commensurate with the rate of earning cumulation in the

1958 -62 base period.

2'-Of even greater interest are the correlation coefficients fox%,-h

age group as a whole, when race and sex characteristics are disregarded.

The differences between the CER of the blacks and the whites of each sex

are clearly reflected in their respective CER. And change in earnings is

4

always commensurate with the 1958-62 CER, as revealed by the correlation

coefficients. The date in Table 7, therefore, reflect 'lot only sex, race

and age differences, but differences betweenthe years of schooling of ;

61 8
TABLE 7

TABLE 7: RR AND MM 1964 MDTA INSTITUTIONAL TRAINEES WITH 9-11 OR 12 YEARS OF SLUNG RESPECTIVELY -- THE CORRELATION

BETWEEN THE 1958-62 CER AND THE CHANGE IN EARNINGS FROM 19,58.70 1965-69, BY EARNING PATTERN, 1958-62/1965-69 ' PITRN Al' Four

ACE 1958- School- CER 1965169 CER 1965-69 CER 1965-69 CER 1965-69 Sex - In 62 6 Ing 0.1958- .1 1958- i. 1958- i 1958- 1 Race

1964 1965-6% '- 62 1958' 1 62 1258 . 662_ .1958 62 19a____ Groups

' Nw4206 Nw1268 N=2451 Nw1296 r*

MALE WHITE MALE NEGRO FEMALE WHITE FEMALE NEGRO

20-24 RR 9-11 44.828 342.636 53.936 565.342, 91.954 648.863 81.781,A01157... .890 MM 12+ 13.791 63.862 16.087 87.623 20.454 76.284 26.6648r 30.754

25 -29w RR 9-11' 7.469 19.459 8.126 27.552 11.834 46.707 37.220 221.837 .998 MM 12+ 5.550 14.410 5.631 16.485 5.048 11.303 5.620 17.645

30-34 RR 9-11 5.874 6.853 ispi 10.121 43.944 9.203 26.900 .940 MN 12+ 4.685 9'.345 4.963 10.523 4.768 10.891 4.986 ULM_

3.

35-19 RR 9-11 4.922 8.801 5.031 11.437 7.020' 21.279 17.028 57.700 .996 NM 12+ 4.260 7.820 4.118 8.014 4.768 9.929 4.306_ 10.499

40-44 RR, 9-11 5.034 8.407 4.974 9.075 15.567 77.042 55.716' 285.017 .999. MM 12+ 3.924 6.578 4.028 7.573 _4 621 9.613 4.524 9.614

45-49 RR 9-11 4.781 8.168 5.368 13.895 6.876 22.719 .990 NM 12+ 4.134 6.246 4.387 6.510 4.691 8,630 4.740 9.899

50-54 RR 9-11 4.857 8.498 4.889 19.992 9.439 33.292 4.883 8.339 .902 MM 2+ 3.996 5.876 5.283 11.34 4.966 7.412 4.596

55-59 RR 9-11 5.025 7.501 13.943 34.442 6.190 10.548 .924 MM 02+ 3.835 5.267 ',4.061 7.502 4-082 6,160 2.575 12.260

rw.997 rw.985 'rw.989 *Sig. at 1% unless otherwise indicated.

619

rw.899

4

S.
- 587 -

the RR and MM trainees as well. It is on the basis of this and other

evidence shown bel6w, that ones may reasonable infer the holistic character

of the change in.earnings.

RR and MM CWHS Ilembers..and Trainees (Table 8)

most striking eviden f the holistic nature of the Change in

earnin is found when we compare the histories of ,the CWHS; member - -for

whom data On'schooling are not available--with those of the Institutional

trainees, wiemetlassified as high school dropouts or graduates. For the

RR an'4MM graduates and dropouts alike, the correlation coefficients'

revoal'tla clearly delineated relationship between the 1958-62 CER and the

ch ge in earnings from 1958 to 1965-69.. This is equally true of the

berg,. And although we are somethimes assured that the CWHS

,iiiembers are better schooled, higher paid and more advantage than'the

i'ldisadliantaged trainees, the process of change is precisely t same for

Tall three cohorts. Whether one coalesceg the,CWHS observations with those

of the,dropouts or the graduates, ofeach sex, race, and age, the hypo -

y"thesis holds true. Cumulative Earrfing Rate. ust reflect the differential

J

effects of schooling levels on the change in earnings.

II

c.

CWHS Members and Trainees (Tables 9 and 10)

Precisely the same holistic characteristics mark the change in.earn-

, ings of the high school dropouts and graduates among the Institutional

trainees,_and the CWHS members when the 1965-69 earning pattern is held

constant. Whether the RR andMR earning,changes are compared (Table 9),

or the RM and MM changes are compared, the findings are the same.

The' CER in 1958-62 .presages the change ik_earnihgs, and is come enwith the earning changes from 1958 to 1965-69... This is equally

-tri or the high school graduates, ih dropouts, and the CWHS members,

ana when their histories are coalesced in o,a single synthetic cohort.

IMPLICATIONS FOR PUBLIC' POLICY

Thrpughout this paper, I have emphasized the empirical evidence that.

AP.A 620

c.
4

TABLES

TABLE 8:1 RR AND MM CWHS MEMBERS AND,1964 MDTA INSTITUTIONAL TRAINEES WITH 9-11 OR 12 YEARS OF ScHOOLING

THE CORRELATION BETWEEN THE 1958-62 CER AND

'THE CHANGE IN EARNINGS FROM 1958 TO L965 -69, BY EARNING PATTERN, 1958-62/196r4A

U.S I.;VW". 1-10 IL 1 4,1 "Lig let- :1 II ; I 1916- ; 19111- i

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TABLE 9

TABLE 9: RR AND MR CWHS MEMBERS AND 1964 MDTA INSTITUTIONAL TRAINEES WITH 9-11 OR 12 YEARS OF SCHOOLING -THE CORRELATION BETWEEN THE 1958-62 CER AND THE CHANGE IILEARHIINGS FROA 1958 TO 1965-69,

BY EARNING PATTERN, 1958-62/1965-69

1.1* cr. 1.41 4' 1 11441 111i 41 41 It ViriFf."' :so 114 211 NI 17 4711 84 4./

....21421

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NI 01

rim

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,

- 591 -

4

changes in earning capacity-ere governed by principles which can be

isolated and quantified within a longitudinal analytical system. The

implications of my findings are many and varied, but space limitations

permit comment only on one of the most significant.

The Change In Pattern (Tables 11 and 12)

The evidence concerning the determinative nature of change in the

main has been discussed in terms of the change from 1958 to 1965-69 and

its relationship to;earning,patterns in 1958-62. For public policy, this.

0 0 relationship can be expressdd.in another, and more pr a tic manner.' In

Tables 11 and 12, we seek to answer two questipns0 w was the

. .hood that individuals with a given earning pattern in 1958-62 experienced

an improvement or a deterioration in earning pattern'in 1965-69? Awl was

the process of change dif erent for the CWHS members and the 1964 Institutional trainees? Answers to these questions are based on the changes in

earning patterns of 114,775,men and women CWHS members and Institutional

trainees.

58-62 'RISERS and'iMIXERS

A comparison of the 108-62 RISER and MUER pattern changes reveals

a clear relationship bet441 the 1958-62 pattern and the change in pattern.

For those under twenty in 1964)*the percentage of MIXERS who become RISERS

, in 1965-69 exceeds the perce9iage of 195$62 RISERS who remain RISERS

in 1965-69. This is readilyekpliceb if one posits'that MIXERS in this

age group tend to leave school earlier than the RISERS. In each successively older age group, however,ethe percentage of 1958-62 MIXERS who

become RISERS in 1965-69 declines. The percentage OT.RISERS who become

MIXERS; however, also tends-to decline in each successive13, older age

group. In short, the older''the age gtoun, the greatekis the likelihood

that a favorable pattern in 1958-62 will be replicated in,1965-69.

Similarly, an unfavorable pattern in 1958-62 increase the likelihood of

a less favorable pattern 'in 1065-69.

In each instance, however, in 1965-69 sizeable.percentages of RISERS

do become MIXERS, and MIXERS do become RISERS.

624
S

,

- 592''t

TABLE 11: MALES -- THE CHANGE IN EARNING PATTERNS FMR0 1958-62 to 1965-69 --

CORRELATICN BETWEEN THE CHANGES OF 1964 OKHS1( MEMBERS AND MDTA

INSTITUTIONAL TRAINEES, BY EARNING PATTERN IN 1958-62

Pattern

1958-62

1964 CWHS

Total % With Pattern in 1965-69

Mmber % R D M C

RM

C'

R

iMC

'D

RMC

D

RM

C

D

R

M

C

Age in 1964:

836 100.0 31.0 1.1 67.5 0.5 128 100.0 40.6' 2.3 55.5 1.6 2,708 100.0 29.0 1.9 68.4 0.7

3 CORRELATION, CMS and TRAINEE PERCENTS : 4

Age in 1964:

3,003 100,0 45.7 2:3 51.5 0.5 ' 2,069 100;0 43.9 2.6 52.7 0.8 521 100.0 34.9 4.2 59.3 2.1

31 100.0 38.7 9.7 48.4 3.2 CORRELATION, CWHS and TRAINEE PERCENTS:

L' 4-'1864 nenryrIONAL TRAINEES

tal % With Pattern in 1965-69

Nanber % Under 20

1,123 100.0

154 100.0

3,091 100.0

5 20-24

5,415

3,803

1,563

33

100.0

,100.0 100.0

100.0 Age in 1964: 25-29

1,834 100.0 67.3 1.4 30.8 0.4

2,731 100.0 47.5 2.8 48.4 1.3 99' 111e 38.4 6.1 41.4 14.1

59 10 35.6 5.1 54.20 5.1 CORRELATION, CVHS and TRAINEE PERCENTS:

1,058 100.0

4,098 100.0 124-100.-0

120 100.0 Age in 1964: 30-34

2,297 100.0 74.9 0.9 23.8 0.4 1,950 100.0 42.7 3.4 52.9 1.0

90 100.0 28.9 7.8 45.5 17.8 '60 100.0 30.0 15.0 53.3 1.7 CORRELATION, ChES.and TRUCE PERCENTS:

2,590 100.0

1,793 100.0 "'NU 100.0

586 2,587

129 139

100.0 100.0

100.0 100.0 Age in 1964: 35-39

75.5 1.1 23.3 0,2 508 100.0

38.0 3.1 57.0 1.9 1,985 100.0 29.5 17.0 44.3 9.1 169 100.0

17.7 11.3 66.2 4.8' 119 10 CORESTION, CWHS and TRAINEE PERCENTS:.

Age in 1964:- 40-44

R 2,636 100.0 73.4 1.4 24.5 0.6 M 1,716 100.0 34.6 4.0 59.4 2.0 C 105 100.0 32.4 15.2 40.0 12.4 D 65 100.0 27.7 13.8 52.3 6.2 GORRELNTDON, ONES and,TRAINEE PERCENTS:

\\

Ft 2,327 100.0 M 1,630 100.0. C 97 100.0 D 58 100.0 6.9 8.6 74.2 10.3 CORRELATION, CAHSand TRAINEE PERCENTS:

Age in 1964:

73.0 2.2 23.9 . 0.9 33.1 4.2 59.6 2.6 26.8 13.4 44.3 15.5

Age in 1964: R 1,974 100.0 57.3 2.2 .29.3 1.2 M 1,460 100.0 29.1 ,6.2 62.1 .5 C 97 .100.0 27.8 12.4 44.3 13.5

69 100.0 18.8 14.5 59.5 7.2 CORRELATION, CWHS ,and TRAINEE PERCENTS:

'\\,A9e,in 1964: 55-59

461 100.0

1,717 100.0

177 100.0

121 100.0 45-49

323 1,127

115

79

O

27.5

30.5

24.8

2.7 1.9

2.0

69.2 66.9

70.8

0.6

0.7

2.4 r = .985 Sig 1%

33.1 . 2.8 63.3 0.8

30.0 2.9 65.6 1.4 25.5 3.1 68.3 3.1

21.2 3.0 75.8 0 r = .919 Sig 1%

40.6 3. 55.4 1.7

32.0 3.1 63.5 1.3

-24.2 4.0 58.9 12.9

29.2 7.5 59.2 4.1 r = .853 Sig 1%

41.5 3.6 52.5 2.4

29.2 4.8 63.2 2.7

18.6 4309 59.7 417.8 33.1 8.6 49.7 8.E

r = .827 Sig 1%

46.1 4.3' 46.2 3.3 28.4 4.5 63.3 3.8

23.7 5.5 48.5 22.5

22.7 6.7 64.7 5.9 r = .884 Sig 1%

39.5 6.1 48.2 6.3

26.7 6.5 61.8 5,0

20.9 9.6 48.5 20.9

24.8 5.0 57.0 13.2 r = .833 Sig 1%

416 100.0

100.0

100.0 100.0

42.7 7.4 44.0 5.9 25.5 7.4. 62.0 5.1 27.0 7.0 41.7 24.3

21.5- 8.9 55.7 13.9 r = .888 Sig 1%

50-54

211 100.0 33.2 7.6 55.4

I 738_ _100.0 23.3 8.0 60.4

100 160.0 19.0 10.-0 43.0 ---78 100.0. 24.4 11.5 48.7

r = .821 Sig 1%

3.8 8.3 28.0

15.4

R 1,543 100.0 56.4 5.5 6.4 1.7 112

t .'14232 100.0. 22.7 10.6 ' 4. 2.7 367 C ' 71 100.0 31.0 14.1 42. 12.7 52 D 58 100.0 19.0 20.7 ,4 5 43.8 34 1/ Continua:3 Mark astory Samgr1211*

CORRELATION, CWHS and TRAINEE

6 91'.. A.,

.

100.0 31.2 7.1 54.5

100.0 22.3 4.9 61.8 100.0 21.2 9.6 36.5 100.0 14.7 17.6 50.1 r 84,2 Sig 1%

7.1

10.9 28.0

17.6
- 593 -

TABLE 32: FEMALES -- THE CHANGE IN EARNING PATTERNS FROM 1958-62 TO 1965-69 --

CORREIATICN BETWEEN THE CHANGES OF 1964 CWHS- MEMBERS AND MDMA

INSITIUTIONAL TRAINEES, BY EARNING PATTERN IN 1958-62

Pattern

1958-62

1964 CWHS

'Dotal % W± Pattern in 1965-69

.Nteber % R D M C

1964 INSTITUTICNAL TRAINEES

Total !_With Pattern in 1965-69

NUmber % R D M C

Age in 1964: Under 20

401 100.0 16.9 9.0 69.6 4.56, 513 100. 15.8 9.6

47 100.0 19.1 17.0 59.6 , . 4.3 97 100. 12.4 10.3

1,908 100.0 19.8 9.0 66.0 5.3 3,036 100. 16.0 14.2

4 - 4 CORRELATION, OHS and TRAINEE PERCENTS:

in 1964: 20-24

2,182

1,783

2.646

85

R 1,671 M 1,089 1 C 633 100 D , 26 100

15.9 57.6 7.8 13.1 61.3 8.1

.1 57.8 14.4

.5 61.6 11.5 07RRELATION, PERCENTS:

100.0

100.0

100.0! Age in 1964: 25-29 R . 639 100.0 29.4 12.2 52.1 6.3 451 100.0 M 1,177 100.0 21.7 12.1 58.5 7.6 1,665 100.0 C 234 100.0 20.1 11.1 52.1 16.7 605 100.0 2 D 115 100.0 24.3 9.6 55.7 10.4 179 100.01 2 OORRELATION*CWHS and TRAINEE PERCENTS: --r =

r = .981 Sig 1%

69.2 , 5.4

75.3 2.1

65.6 8.2

19.1 11.2 64.4 5.4

14.8 11.1 66.6 7.6

17.3 10.0 62.0 10.7 10.6 4.7 71.8 12.9 r = .994 Sig 1%

22.8 10.6 59.4

19. 9.2 64.0

10.4 52.1

10.0 59.9

'orl% Age in 1964: R 598 "100.01 32.1 8.5 54.0 5.4 M k 973 100.0 24.4 9.8 59.6 6.3

328 100.0 23.8- 10.4 51.5 14.3

79 100.0 19.0 13.9 48..1 19.0 CORRELATION, CWHS and TRAINEE PERCENTS: R * 787 i00.01 M 1,114 100.01

382

39 100.01

100.0

Age in 1964:

43.3 6.4 46.2 4.1

26.2 6.6 62.6 4.6 28.9 13.9 43.4 13.9

'25.6 20.7 42.7 11.0 CORRELATION, OHS and TRAINEE PERCENTS:

R 958 , J00.0 M 1,141' '100.0 C 362 100.0

El 87 100.0

Age in 1964:

42.9 7.0 47.1 3.0 27.0 8.7 59.2 5.1

. 28.7 11.0 48.6 11.6 18.4 12.6 56.4 12.6 CORRELATION, CWHS and TRAINEE PERCENTS:

Age in 1964: R 968 100.0 46.8 7.6 41.8 3.7 M . 1:016 100.0 26.7 -9.5 57.3 6.5 C 273 100.0 34.4 10,2 37.7 17.6 D 70 100.0 25.7 14.3 48.6 11.4 °CREMATION, CWHS and TRAINEE PERCENTS: R

M

881 100.0

931 100.0

228 100.0

46.9 26.6

33.8 R

M

C

D

111 .0 :

Age in 1964:

6.6 43.1 3.4

10.4 56.0 7.0 ' 14.9 33.8 17.5

.4 COMMATICN, CMS and

707 100.0

772 100.0

170 100.0

51 100.0

TRAINEE PERZENIS: 39.6

19.6

18.2

19:6-

Age in 1964:

11.5 44.1 4.8

14.0 60.0 6.5

13.5 45.3 22.9' 15.7 49.0 15,7.

CORREIATICN, CMS and

Contionaxis Work

TRAINEE PERCENTS:

History Sarri4e

I

30-34

421 1,240

678

183

100.0

100.0

100.0

100.0

35-39

438 1,189

777

154

27.8 8.1 58.2 23.1 8.2 61.4 28.9 8.4 49.4 21.3 8.2 58.5 r = .981 Sig 1%

7.1

7.4 15.3

8.9

5.9

7.3 13.3

12.0

100.0

100.0

100.0

100.0

40-44

' 491

1,134

728

122

100.0

100.0

100.0

100.0

45-49

330

964 547

108 50-54

212 537

308

100.0

180.0

100.0

100.0

33.6 5.0 56.4

''`22.3 8.5 61.6

27.4 7.3 51.2

22.7 10.4 57.8

r = .949 Sig 1%.

5.0

7.6 14.0

9.1

32.0 7.5 51.9 8.6% 27.0 8.6 57.2 7. 31.3 9.3 42.3 17.1:210

7-3o73-- 12.3 43.5 13.9 r= .953, Sig 1%

32.7 9.7 46.7 10.9 29.0 8.9 54.7 7.4 32.4 6.8 38.0 22.8 28.7 6.5 58.3 6.5 r = .944 Sig 1%

100.0 100.0

100.0

554-596t 213

138, 25 626

32.5 9.0 50.0 8.5 30.7 8.8 51.2 9.3 29.5 7.5 31.5 31.5 42.6- 15.0

r = .850 Sig 1%

100.0

100.0

100.0

100.0

33.3 7.6 47.0 23.9 8.4 55.4 18.1 8.7 38.4 28.0 8.0 48.0 r = .931 Sig 1%

12.1

12.2 34.8

16.0
594

1958-62 DECREASERS

While the number of 1958-62 DECREASERS is small, this group,*in the

main, shifts to the MIXER category in 1965-69. While, this is the predominant change, there is a significant, albeit relatively small shift to

the RISER pattern in 1965-69. Relatively few individuals changeto a

CONSTANT pattern in - 1969 -69.,

1958-62 CONSTANTS

, By definition, the CONSTANTS experienced neither increases nor

decreases in annual earnings during 1958-62 and hence were excluded from

Y the discussion in earlier sections. In fact, their total earAings' in

1958-62 were,very clOse to zero, as might be expected. Nonetheless,

I

although they tend to be predominan in the under twepty age groups,

there is a distinct tendency for ma y, indeed for/most CONSTANTS tomove

into the MIXED pattern group in 1965-69.

CER and Trainee Pattern Changes

Perhaps the most interesting confirmation of my thesis is to be found

in the correlation between the respective changes in pattern of the .CWHS

members and the 1964 MDTA Institutional trainees. For both men and women,

the pattern changes of the CWHS members and the trainees reveal the same

process of dhange, and the determinative influence of the 7158-62 pattern

on the change from 1958-62 to 1965-69. For each age-group, whether of

men or women, the CWHS andtrainee pattern changes are significantly

correlated, as shown in Table 11 and 12.

The correlations suggest that from entry into gainful employment to

retirement, changes in earning capacity become increasingly patterned arid

deterTinative, and that effOrts to permanently improve the earnings of

disadvantaged individuals require prOgrams and means commensurate with the

deeply-rooted and holistic factors reflected by the earning patterns of

individuals.

60-1

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