18 Monthly Labor Review August 2001

Job Initiation Job Initiation

Youth initiation

into the labor market

Lynn Huang,

Michael Pergamit,

and

Jamie Shkolnik

About half of 12- and 13-year-olds surveyed

engage in some sort of work;

such work is more likely among youths

from higher socio-economic backgrounds

or who have engaged in ‘delinquent’ behaviors

Lynn Huang is

research scientist,

and Michael Pergamit

is research vice

president at the

National Opinion

Research Center,

Washington, DC;

Jamie Shkolnik is senior

research scientist at

American Institutes for

Research,

Palo Alto, CA.

e-mail:

pergamit@

norcmail.uchicago.edu

Young people acquire substantial work experience before age 16, the age at whichofficial statistics begin counting employment. Using the National Longitudinal Survey of

Youth—1979 cohort (NLSY79), R.T. Michael and

N.B. Tuma examined the amount of work performed by 14- and 15-year-olds using definitions

from the Current Population Survey (CPS).

 1 Importantly, they found significant differences between black and white youths, and also found

that youths who worked at ages 14 and 15 were

more likely to be working 2 years later. They

concluded that social scientists should include

such early work experience in their models. Their

findings were influential in the design considerations for the National Longitudinal Survey of

Youth—1997 cohort (NLSY97).

Other surveys that capture information about

youths as young as 12 do not typically include

information on their work activities,and data that

focus on work have not sampled those below

age 14.2 The Fair Labor Standards Act prohibits

employment of those younger than age 14, and

restricts the hours and jobs allowed for those

younger than 16. However, many youths have

“jobs” before these ages. These jobs, while not

always like those of adults, frequently involve

learning work behavior (for example, showing up

at a particular time every week), personal responsibility (for example, caring for someone’s child),

remuneration, and other characteristics that teach

young adolescents the basic nature of working

for someone else.

The NLSY97 provides a unique opportunity

to study the very early work experiences of

youths and relate these experiences to future labor market behavior. For 12- and 13-year-olds,

information was collected about jobs they had

held since age 12.

This article examines exclusively 12- and 13-

year-olds, focusing on who holds jobs and the

nature of those jobs. Is early initiation into the

labor market (age at obtaining first job) associated with youths from upper income, more educated families, or does it occur among those who

most likely will not pursue advanced schooling?

Does work serve to supplement household income in lower-income, single-parent families?

These and related questions are examined in relation to race/ethnicity; parental income, education,

and marital status; and the presence of siblings.

Schooling achievement—as measured by the

Peabody Individual Achievement Test (PIAT)

Mathematics score—and time use are compared

for youths who have jobs with those who do

not. Measuring time use can determine if homework, outside classes, and so forth are substitutes for, or complements to, work. We also observe whether youths in early-age jobs also engaged in or had early initiation into risky behaviors (such as drug and alcohol use, or other delinquency). Finally, we can observe how youths
Monthly Labor Review August 2001 19

Chart 1.

found jobs (that is, did they have help from their parents,

other help, or no help at all).

Study methodology

Data for this article are from the first wave of the National

Longitudinal Survey of Youth, 1997 Cohort, sponsored by the

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

survey’s main goal is to document the transition from school

to work for the U.S. population born during the 1980–84 period. The first wave of the survey includes 9,022 youths aged

12–18 when interviewed.3

Work experience. The NLSY97 has a unique set of questions

on employment that permits investigation of youth initiation

into the labor market. The 12- and 13-year-old respondents

were asked about any job experiences since their 12th birthday. This experience could include working for a particular

employer (for example, delivering newspapers) or doing tasks

for several people—freelance jobs (for example, baby-sitting

or mowing lawns). Most jobs reported at these ages were

freelance jobs. Respondents aged 14 and older were also

asked about their experience in freelance jobs since their 14th

birthday. However, information on initial entrance into the

labor market is incomplete for those respondents, as freelance

jobs that ended before age 14 are not included. For this

reason, respondents aged 14 and older were excluded from

this study.

Youths were asked first to list the kinds of jobs they have

had since their 12th birthday. Then they were asked whether

they got help in finding this kind of work; who helped them;

when they started doing this kind of work; whether they are

currently doing this kind of work, and if not, when was the

last time they did. In addition, for the beginning and end of

each kind of job, youths were asked the usual number of

hours worked per week, the usual amount of money earned

per week, and the number of days and hours worked on weekdays and weekends.

A main purpose of this study was to identify youths who

work and the number of hours they worked. Therefore, the

analysis focuses on two measures of youth employment: A

discrete variable measures whether the respondent reported

any jobs; a count variable measures the number of hours the

respondent worked per week. The information on the number of work hours per week was collected for each kind of

work performed—when it was initiated and when it was last

(or is currently) performed. However, we do not know the

sequential order of jobs performed or whether jobs were (are)

performed at the same time. Given this, we use the hours

when jobs were last (or are currently) performed, and among

those, we use the hours per week on the job with most hours.4

Associated factors. Which youths are more likely to enter

the labor market in the early adolescent years? Which youths

work more hours than others? It is of particular interest to

examine labor market initiation with other factors that affect

Percent

Age started first job (years)

Cumulative age initiation for 12- and 13-year-olds who have worked by the time

they were interviewed, NLSY97

Percent

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14

0

20

40

60

80

100

0

20

40

60

80

100
20 Monthly Labor Review August 2001

Job Initiation

[In percent unless noted otherwise]

 Weighted mean

Total Male Female

 Job characteristic

Percent who reported jobs ......... 52.5 51.2 53.9

Conditional on having a job:

 Number of jobs ......................... 1.6 1.7 1.5

 Age started job (years) .......... 11.6 11.5 11.7

Percent who got help in finding

jobs ........................................ 61.8 64.2 59.4

Hours per week on job with most

hours ...................................... 7.1 6.6 7.5

Hours per week for those

finding a job with help of:

Parents .............................. 7.9 7.2 8.7

Someone other than

parents ............................ 7.4 7.5 7.3

No one ............................... 5.9 5.4 6.4

Earnings per week on job with

highest earnings .................... $22.93 $26.87 $18.94

Top 10 job types from the first

 job listed:

Baby-sitting .............................. 49.3 17.0 81.8

Mowing / other yard work ......... 22.8 42.7 3.5

Paper route ............................... 3.5 4.7 2.3

Snow shoveling ........................ 2.9 5.7 .2

Chores, odd jobs ...................... 2.1 2.5 1.7

Farm work ................................. 2.0 3.5 .5

House cleaning ......................... 1.8 2.0 1.7

Pet care .................................... 1.5 1.0 2.1

Selling ...................................... 1.1 1.7 0.6

Carpentry, building, painting,

construction .......................... 1.1 2.1 0

Demographic characteristics

Sex .......................................... 100.0 51.5 48.5

White, non-Hispanic .................. 62.6 66.5 65.8

Black, non-Hispanic .................. 15.6 15.4 15.8

Hispanic .................................... 12.5 12.9 12.1

Other race/ethnicity .................. 5.7 5.2 6.4

Northeast .................................. 17.8 17.6 18.1

North Central ............................. 25.1 26.1 24.1

West .......................................... 33.7 33.6 33.8

South ......................................... 23.3 22.7 24.0

Metropolitan Statistical Area ..... 80.0 81.7 78.2

Lived with two parents/

guardians ............................... 69.3 70.7 67.8

Lived with siblings ..................... 84.5 84.5 84.4

Parents’ earnings:

$0–9,999 ................................ 17.9 17.4 18.4

10,000–19,999 ....................... 10.3 9.9 10.8

20,000–39,999 ....................... 20.7 21.6 19.7

40,000–79,999 ....................... 29.7 29.9 29.5

80,000 and more .................... 9.9 10.5 9.2

Missing ................................... 11.5 10.7 12.5

Characteristics of 12- and 13-year-old youths, NLSY97

Total Male Female

 Variable

Mother’s educational attainment:

High school dropout .................. 17.0 16.9 17.1

High school graduate ................ 32.6 32.4 32.7

Some college ............................ 23.9 22.7 25.3

College and up .......................... 20.3 21.5 19.1

Missing ...................................... 6.2 6.5 5.8

Father’s educational attainment:

High school dropout .................. 14.5 13.9 15.1

High school graduate ................ 30.4 30.7 30.1

Some college ............................ 17.0 17.0 17.1

College and up .......................... 20.8 21.9 19.8

Missing ...................................... 17.2 16.5 18.0

School performance

and time use

Ever suspended from school ...... 18.4 25.5 10.9

PIAT-Math percentile score ........... 54.8 55.5 54.5

Spent time doing homework ........ 91.2 90.8 91.7

Hours on homework per week ... 6.4 5.6 7.2

Spent time on extra classes ........ 30.4 26.4 34.6

Hours on extra classes

per week ................................... 3.7 3.4 3.9

Spent time watching TV ............... 96.6 97.1 96.2

Hours watching TV per week ..... 19.8 20.8 18.8

Spent time reading for pleasure .. 65.6 60.7 70.9

Hours reading for pleasure

per week .................................. 3.4 5.0 13.5

 Substance use

and delinquency

Ever smoked ............................... 25.3 25.4 25.3

Ever drank alcohol ...................... 24.3 27.0 21.5

Ever used marijuana ................... 6.9 7.9 5.9

Ever ran away ............................. 6.0 6.5 5.4

Ever carried a handgun ............... 7.7 13.5 1.5

Ever purposely

destroyed property .................... 25.3 32.1 18.2

Ever attacked another person ..... 14.6 19.3 9.7

Ever been arrested for illegal or

delinquency offenses .............. 3.2 4.5 2.0

Weighted mean

Variable

Table 1.
Monthly Labor Review August 2001 21

Percent of 12- to 13-year-old youths who report

jobs and hours per week on job with most hours,

by demographic characteristics, NLSY97

Demographic Percentage who Hours/week on job

categories report jobs with most hours

Gender:

Boy .......................................... 51.2 6.6

Girl ........................................... 53.9 7.5

Race/ethnicity:

White, non-Hispanic ................. 58.9 6.7

Black, non-Hispanic ................. 40.6 7.5

Hispanic ................................... 39.3 9.1

Others ...................................... 38.5 6.8

Region:

Northeast ................................. 55.8 6.4

North Central ............................ 58.8 7.1

South ....................................... 46.9 7.2

West ........................................ 51.4 7.4

Metropolitan Statistical Areas:

Not in MSA .............................................. 57.5 8.1

In MSA ...................................................... 51.3 6.8

Number of parents/guardians:

Living with two parents ............ 53.3 6.6

Other........................................ 50.7 8.1

Siblings in the household:

Yes ........................................... 53.1 7.1

No ............................................ 48.6 6.9

Parents’ earnings:

$0–$9,999 ................................ 49.1 8.0

10,000–19,999 ......................... 49.8 7.2

20,000–39,999 ......................... 51.9 8.5

40,000–79,999 ......................... 57.2 8.6

80,000 and more ..................... 59.3 5.0

Missing .................................... 43.2 7.1

Mother’s educational attainment:

High school dropout ................. 43.8 8.4

High school graduate ............... 53.8 7.1

Some college ........................... 56.4 7.3

College and up ......................... 57.9 5.2

Missing .................................... 36.6 10.2

Father’s educational attainment:

High school dropout ................. 46.7 7.2

High school graduate ............... 53.3 7.6

Some college ........................... 57.2 7.1

College and up ......................... 57.8 5.0

Missing .................................... 45.1 8.8

Table 2.

the overall well-being of youths’ lives and their future adult

lives. This issue is examined from different aspects: demographic background, family background, school performance, time use, and risky behaviors.

The two job measures, “ever held a job” and “hours per

week on the job with the most hours,” are examined first by

demographic and family backgrounds—age, race/ethnicity,

region, and metropolitan/nonmetropolitan area residence; the

number of parents/guardians and siblings in the household;

mother’s highest grade completed; father’s highest grade

completed; and parents’ earnings.

Then we examined the two job measures with school performance and time use. There are two measures for school

performance: ever being suspended from school and Peabody

Individual Achievement Test-Math percentile score. The

PIAT-Math score is categorized into quartiles: 0–25, 26–50,

51–75, and 76–100. For time use, respondents were asked

about four typical youth activities: doing homework, taking

extra classes, watching TV, and reading for pleasure. They

were asked if they ever spent time on each activity, then the

hours per day spent on each activity on weekdays and weekends. We created discrete variables measuring whether respondents ever spent time in each activity, and variables measuring hours per week spent on each activity.

The next issue examined was whether youths who work

were more likely to be associated with risky behaviors. Discrete variables for substance use include cigarette smoking,

drinking alcohol, and marijuana use. Discrete variables for

delinquent behaviors include running away from home, handgun possession, purposely destroying property, attacking

another person, and being arrested last year.

Results

Job characteristics. Of the almost 3,000 12- and 13-year-olds

surveyed, 52.5 percent had held a job. (See table 1, page 5).

Of those who had worked, the average number of jobs held

was 1.6, and the average age of initiation was 11.6 years (the

median age of initiation was 12.0). Chart 1 on page 4 shows

the cumulative distribution of age initiation for those who

have worked by the time they were interviewed. It is easy to

see a major upturn in initiation around age 12. While recalling that nearly half the youths had not yet been initiated into

the labor force, it is clear that age 12 is a time when many

adolescents first get exposure to the working world.

These respondents are too young to work legally at restaurants, stores, and similar businesses. The types of jobs

held by 12- and 13-year-olds include primarily “freelance”

jobs, mainly baby-sitting, lawn mowing, and other yard work.

These three jobs make up nearly 75 percent of the jobs listed.

On average, these youths spend approximately 7 hours per

week on the job with the most hours (although the median is

only 4 hours per week),5

 and their earnings average about $23

a week. Of the youths sampled, 62 percent had received

some kind of help in finding jobs.

Demographic characteristics. Girls seem somewhat more

likely than boys to hold jobs and spend more hours on the

job. (See table 2.) In fact, 54 percent of girls held a job,

compared with 51 percent of boys; 59 percent of white youths

held jobs, compared with about 40 percent of blacks, Hispanics, and others. Similarly, the higher the parents’ earnings, the

higher the likelihood that their child will have a job. This is

not surprising, as most of these jobs will be performed for

neighbors and friends who probably have similar income lev-
22 Monthly Labor Review August 2001

Job Initiation

Percent of 12- to 13-year-old youths who report

jobs and hours per week on job with most

hours, by school performance, PIAT-Math score,

and time use, NLSY97

Percentage who Hours/week on job

report jobs with most hours

School performance:

Ever suspended

from school .......................... 53.4 8.6

Never suspended

from school .......................... 52.4 6.7

PIAT-Math percentile score:

0–25 ........................................ 45.3 7.0

26–50 ...................................... 53.9 7.6

51–75 ...................................... 56.6 7.8

76–100 .................................... 58.1 6.3

Time use:

Spent time on homework ........ 53.5 6.9

Did not spend time

on homework ........................ 44.6 9.4

Spent time on extra classes ... 57.4 6.5

Did not spend time

on extra classes .................. 50.5 7.3

Spent time watching TV .......... 52.9 7.1

Did not spend time

watching TV ......................... 44.8 5.9

Spent time reading

for pleasure .......................... 53.2 7.2

Did not spend time reading

for pleasure .......................... 51.4 6.8

Table 3.

Categories

Percent of 12- to 13-year-old youths who report

jobs and hours per week on job with most

hours, by substance use or delinquent

behavior, NLSY97

Percentage who Hours/wk. on job

report jobs with most hours

Substance use

Ever smoked cigarettes ................. 63.1 8.6

Never smoked cigarettes ............... 49.0 6.4

Ever drank alcohol ......................... 59.2 7.8

Never drank alcohol ....................... 50.4 6.8

Ever used marijuana ....................... 63.0 9.0

Never used marijuana ..................... 51.8 6.9

Delinquency

Ever run away from home .............. 59.1 8.2

Never run away from home ............. 52.1 7.0

Ever carried a hand gun ................. 53.4 8.4

Never carried a hand gun ............... 52.5 6.9

Ever purposely destroyed

property ...................................... 58.3 7.0

Never purposely destroyed

property ...................................... 50.6 7.1

Ever attacked another person ........ 56.0 7.9

Never attacked another person ...... 52.0 6.9

Arrested past year ......................... 63.8 8.2

Not arrested past year ................... 52.2 7.0

Categories

Table 4.

els; lower income neighbors may be less likely to pay someone to do these jobs. Comparable results are found for

mother’s and father’s education level—the higher the parents’

educational attainment, the more likely the child is to have a

job. Note, however, that working youths who are minority,

poorer, and have less-educated parents seem to work longer

hours.

School performance and time use. A similar pattern emerges

when examining the percentile score of the PIAT-Math

achievement test. The higher the youth’s score, the more

likely he or she is to have a job. (See table 3.) This is consistent with the previous relationships because minorities and

lower socio-economic status youths, on average, are likely to

have lower test scores. Number of hours worked is lowest for

the top quartile of students, but students in the quartile with

the second highest scores work the most hours per week.

Those who have at some time been suspended from school

are as likely to work as those never suspended; but when they

work, they work more hours.

One argument that has been raised against permitting 12-

and 13-year-old children to work is that it takes time away

from other important activities, such as schoolwork and reading. Conversely, working could have positive effects through

the responsibility it teaches and by reducing time spent in

unproductive activities. Respondents were asked whether

they spend time on the following activities: homework, extra

classes, watching television, and reading for pleasure. Surprisingly, in each case, those who responded yes were more

likely to hold a job. The most dramatic difference is in those

who report spending time on homework; of them, 53.5 percent reported holding a job. Of those who reported not doing

any homework (9 percent of the sample), only 44.6 percent

held jobs. Student workers who spend no time on homework

work an average of 9.4 hours per week, while those who do

spend time on homework spend only 6.9 hours per week on

the job. Similarly, student workers who do not take extra

classes work more hours than do student workers who take

extra classes (7.3 hours compared with 6.5).

It is possible that work is complementary with these four

activities. However, it is impossible to determine, as there is

no complete description of the youths’ time use. A bigger

caveat is that the time-use measure is essentially contemporaneous with the interview date, whereas the work hours measure could be from any time period. And given the probable

over-representation of summer jobs, it is doubtful that these

data represent either time substitution or complementarity.

Substance use and delinquency. Youths who exhibit risky or

dangerous behaviors are consistently more likely to have jobs

and tend to work longer hours than their more cautious counterparts. Respondents were asked questions about smoking,

drinking, smoking marijuana, running away from home, carrying handguns, destroying property, fighting, and getting arrested. In every category, youths who answered yes to the

dangerous behavior are more likely to hold jobs. (See table 4.)
Monthly Labor Review August 2001 23

Intercept ..................................................... –1.11 (0.001)

Age ............................................................. .10 (0.0001)

Male ............................................................ .03 (0.0003)

Black, non-Hispanic .................................... –.12 (0.0002)

Hispanic ...................................................... –.14 (0.0002)

Other race/ethnicity .................................... –.20 (0.0002)

Northeast .................................................... .02 (0.0002)

North Central .............................................. .03 (0.0002)

West ........................................................... –.05 (0.0002)

Metropolitan Statistical Area ....................... –.03 (0.0001)

Number of parents/guardians .................... –.05 (0.0001)

Number of siblings ..................................... .004(0.00005)

Parents’ earnings, with greater than or

equal to $80,000 as the reference group:

$0–$9,999 ............................................... –.04 (0.0003)

10,000–19,999 ........................................ –.04 (0.0003)

20,000–39,999 ........................................ –.03 (0.0002)

40,000–79,999 ........................................ –.02 (0.0002)

Missing .................................................... –.12 (0.0003)

Mother’s education, with college graduate

as the reference group:

High school dropout ................................ –.02 (0.0002)

High school graduate .............................. .01 (0.0002)

Some college ........................................... .01 (0.0002)

Missing .................................................... –.15 (0.0003)

Father’s education, with college graduate

as the reference group:

High school dropout ................................ –.05 (0.0002)

High school graduate .............................. –.02 (0.0002)

Some college ........................................... .01 (0.0002)

Missing .................................................... –.08 (0.0002)

Boys:

Ever suspended from school .................. .04 (0.0002)

PIAT-Math percentile score ....................... .0002 (0.000003)

Hours/week on homework ....................... .004 (0.00001)

Hours/week on extra classes .................. –.001 (0.00002)

Hours/week watching TV ......................... –.002 (0.00001)

Hours/week reading for pleasure ............. –.001 (0.00001)

Ever smoked cigarettes .......................... .09 (0.0002)

Ever drank alcohol .................................. –.002 (0.0002)

Ever used marijuana ............................... .07 (0.0003)

Ever run away from home ....................... .10 (0.0003)

Ever carried a hand gun .......................... –.07 (0.0002)

Ever purposely destroyed property ......... –.01 (0.0002)

Ever attacked another person ................ .09 (0.0002)

Arrested past year .................................. .16 (0.0004)

Girls:

Ever suspended from school .................. .09 (0.0003)

PIAT-Math percentile score ....................... .001 (0.000003)

Hours/week on homework ....................... –.0002 (0.00001)

Hours/week on extra classes .................. 006 (0.00002)

Hours/week watching TV ......................... –.002 (0.00001)

Hours/week reading for pleasure ............. –.001 (0.00001)

Ever smoked cigarettes .......................... .15 (0.0002)

Ever drank alcohol .................................. .03 (0.0002)

Ever used marijuana ............................... –.07 (0.0004)

Ever run away from home ....................... –.01 (0.0004)

Ever carried a hand gun .......................... –.11 (0.0006)

Ever purposely destroyed property ......... .09 (0.0002)

Ever attacked another person ................ –.09 (0.0003)

Arrested past year .................................. .02 (0.0005)

LR test (Chi-Square) ................................ 737025153

P-value .................................................... .0000

Table 5. Estimated marginal effects from probit

regression modeling the probability of

12- to 13-year-old youths having a job, NLSY97

Variable Estimated marginal effect

Also, they work at least as many hours as those who answered that they had not participated in the drug use or delinquency activities. There could be several explanations.

For example, these youths may engage in work to support

the cost of the dangerous or risky activities. However, the

causality could be in the other direction. With more disposable income, the youths have more choices about the types

of activities in which they can participate. Of course, it could

be an unobserved factor that influences both early initiation

into work and risky behaviors. Regardless of the explanation, this is interesting in light of the earlier results indicating

that youths who were white, had higher socio-economic status, and were higher-achieving students were more likely to

hold jobs.

Regression analysis. The previous analysis measures the

differences in the likelihood of employment (and hours

worked) for various characteristics of youths. However, the

analysis considers each variable separately, not all at once.

A regression model is used to examine the impact of all of

these variables on whether or not a youth has ever been

employed.6 Specifically, a probit regression model is used to

measure the impact of each variable on the probability of

having ever been employed.7

Table 5 shows the results of a probit regression of the

probability of having a job as related to demographic, school

performance and time use, and substance use and delinquency variables. The impact of the non-demographic variables can vary by sex. The model confirms earlier results that

being white and of higher socio-economic status (higher parental earnings and education levels) increases the probability that an individual will hold a job. However, the “number

of parents” variable indicates that children in two-parent families are less likely to hold a job than those in one-parent

families. Also, unlike the simpler tabulations, boys are more

likely to work than girls.

All six of the schooling and time-use variables have statistically significant effects for both sexes; however, not all

are numerically significant. Youths with higher PIAT-Math

test scores are more likely to hold jobs, though the effect is

not large. Most time-use variables have quite small effects,

although females who take extra classes are more likely to

hold jobs. Interestingly, the one strong effect is that students who had been suspended from school are more likely

to hold jobs.

As shown earlier, having smoked or been arrested in the

past year have positive associations with the likelihood of

having a job for both sexes. Males who had smoked marijuana, run away from home, or fought also had positive associations, as did females who drank alcohol or purposely destroyed property. Some rates are small, but having smoked

cigarettes is strongly associated with having worked. Inter-
24 Monthly Labor Review August 2001

Job Initiation

estingly, having carried a handgun is negatively associated

with having worked for both sexes, but having fought and

smoked marijuana are negative for females only.

To summarize, youths from families of higher socio-economic status, with better school performance (higher PIATMath scores), and who engage in positive time-use activities

such as reading and homework are more likely to be employed.

At the same time, youths who engage in risky behaviors or

have been suspended from school also have increased likelihood of early employment. It is difficult to speculate why

these various relationships exist. It could be that some

youths are “go-getters” who initiate early into many activities, good and bad. Or there could be two different types of

youth that engage in early work activity. As additional data

are released, it will be possible to estimate longitudinal models that are better suited to control for unobserved heterogeneity. In the future, the relationship between various adolescent behaviors, good and bad, with employment might be

better understood.

More research needed

This article used a rich new data source, the NLSY97, to look

at youths’ initiation into the labor market. Unlike any previous data set, the NLSY97 collects information on employment

from adolescents as young as 12 years. The NLSY97 provides detailed information about the responding youths, their

parents, and other family members, allowing a study of the

antecedents to labor market participation and labor supply.

The data also allow a better understanding of factors that

influence entry into the labor market.

Much concern exists over the impact of youths working

while in school, and the evidence is unclear.8 Models that

can capture earlier experiences will be richer in controlling for

the types of unobserved heterogeneity that confound the

work-school relationship. This article seeks to lay the groundwork for later research. We find that youths who are white

and come from higher socio-economic families are more likely

to initiate work earlier. These results are consistent with studies of older adolescents. However, a number of contrasting

results suggest there is more to the story. For example, youths

who have been suspended from school at some time and

youths who smoke (as well as engage in other deviant activities) are also more likely to work at early ages.

The long-term effects of early initiation into the labor market will become apparent as more years of data are collected

and as these 12- and 13-year-olds complete their schooling

and fully enter the labor market. The cumulative years of data

will allow a much better job of modeling work development

and the pathways to successful adult outcomes.

Notes

ACKNOWLEDGMENT: An earlier version of this article was presented

at the Conference of Early Results for the National Longitudinal

Survey of Youth, 1997 Cohort, November 18–19, 1999, Washington, DC. This project was funded by the U.S. Department of Labor.

However, views or opinions stated in this article are the authors and

do not necessarily represent the official position or policy of the

Department of Labor.

1

R.T. Michael and N.B. Tuma, “Youth Employment: Does Life

Begin at 16?,” Journal of Labor Economics, 1984, vol. 2, no. 4, pp.

465–476.

2

See for example, the National Educational Longitudinal Survey–

1988 or the Youth Risk Behavior Survey.

3 Analysis of the interview data revealed the final sample size to be

8,984.

4 The measure we’ve chosen will most likely over-represent summer

jobs that have a greater number of hours. This over-representation

may affect the relationships we estimate with some of the variables.

5

The distribution has a very long right tail. We considered truncating the distribution to eliminate some very high values, but could not

establish clearly that these were errors.

6

Because youths aged 12–13 are not officially in the labor force and

no information is available on whether or not a respondent is looking

for employment, we do not distinguish between the concept of being in

the labor force and the concept of being employed.

7

A probit model is indicated because the outcome variable, whether

a respondent ever worked, is discrete. See D.R. Cox, Analysis of Binary

Data (London, Cambridge University Press, 1970).

8 See for example C.J. Ruhm, “Is High School Employment Consumption or Investment?,” Journal of Labor Economics, 1997, vol.

15, no. 4, pp. 735–776.