Monthly Labor Review August 2001 51

Racial Differences in Youth Employment

Racial differences

in youth employment

Rosella M. Gardecki

Rosella M. Gardecki is

a senior research associate, Center for

Human Resource Research at The Ohio

State University.

E-mail:

gardecki@postoffice.

chrr.ohio-state.edu

Work experience at an early age positively impacts

labor force attachment of different racial groups;

however, racial gaps in employment that are present

in the early teen years seem to continue into adulthood

Since the late 1960s, researchers have noted

large differences in employment and unemployment rates among black workers,

Hispanic workers, and white workers. These differences have generally been the greatest for

younger workers. For example, Robert Flanagan

documents that white workers have historically

held jobs at a higher rate than black workers; for

young workers, this gap widened in the 1960s

and the 1970s when the employment rate of black

teens decreased further.1 Recent studies show

that this early joblessness has an impact on later

employment probabilities and wage outcomes.2

However, few studies have examined the impact

of jobholding on later employment probabilities

among the youngest workers.

Data from the National Longitudinal Survey

of Youth 1997 cohort (NLSY97) indicate that the

youngest teens follow the same employment

trends. Slightly more than half of the NLSY97 14-

year-olds report some type of work activity;

nearly 24 percent of them are working at an employee-type job (that is, working for an employer), while about 43 percent report employment at a freelance job (for example, babysitting,

snow shoveling, pet care).3 Jobholding among

14- and 15-year-old nonblack/non-Hispanic

youths is markedly higher than among their black

and Hispanic counterparts.

Working at a freelance job differs from working at an employee-type job in a number of aspects that may make freelance jobs a more viable

option for many teens. Periods of actual work at

freelance jobs typically are more sporadic and

generally have low hours requirements. In addition, freelance jobs are not subject to the Fair

Labor Standards Act—that is, they have neither

maximum hours constraints nor the need for parental permission—and can be held at any age.

As a result, many 12- and 13-year-olds, who are

not eligible for most employee-type jobs, hold

freelance jobs. Freelance jobholding in the

NLSY97 does not stop at these ages, but continues to be a regular source of employment and

income throughout the teenage years.4

This article examines the factors that affect

different types of jobholding among teens in order to better understand employment decisions

the youngest workers must confront, and how

these decisions may differ by racial group. It focuses on the individual, family, neighborhood,

and spatial characteristics that affect jobholding

among teens living in a parental household. The

pattern of an employee-type jobholder is examined separately from that of a freelance jobholder

in an attempt to measure differences between the

propensity to hold either type of job. This article

presents a brief review of the existing literature

on teen employment; explains the data used and

the selection criteria for the NLSY97 sample; lists

the factors that affect employment at any job for

young workers—those aged 12 through 18 are
52 Monthly Labor Review August 2001

Racial Differences in Youth Employment

considered—as a whole and by racial group (black, Hispanic,

nonblack/non-Hispanic); examines the types of jobs held—

that is, employee-type jobs and freelance jobs, and discusses

the effect of holding a job, during the year that the youth was

14, on the probability of employment among teens ages 16

and older.

What previous research shows

According to recent figures, racial differentials in employment have continued into the present decade, improved economic conditions notwithstanding.5 Despite the sizable gap

in employment for teens, only a limited number of studies

focus on this group; most researchers consider differences

in labor force status among older workers who have finished

their formal education.

In one of the few studies to examine jobholding among

younger teens, Robert Michael and Nancy Brandon Tuma

used data from the NLSY79 to consider differential employment effects for 14- and 15-year-old workers.6 Nearly 25 percent of 14-year-olds reported being employed at the time of

the survey, and this percentage increased for each age group.

Even among the youngest workers, large differences in employment patterns were present between racial groups. White

teens were more likely to be employed than their black or

Hispanic counterparts at any age. Further, 16- and 17-yearold youths who reported employment prior to the age of 16

were more likely to be employed and were working more hours

than those without prior experience.

Most of the remaining research focuses on racial differences in employment among older teens—those who are at

least aged 16 years.7 Although debate continues regarding

which factors cause the differential, a number of characteristics have been identified. These factors can be grouped into

four areas: individual characteristics, family determinants,

neighborhood and geographic factors, and spatial mismatch

measures.

Individual characteristics. Aside from the typical demographic characteristics (for example, age, race, gender, schooling),

other individual characteristics may impact on the probability of a youth working. In particular, a number of studies

focus on the relationship between employment and criminal

activity. Not surprisingly, most of these studies support the

hypothesis that crime and employment are competing forces

for a youth’s time.8 As a result, participation in criminal activity decreases the probability that a teen is employed. Richard Freeman used self-reports of criminal activity as a measure of the tradeoff between crime and employment. He found

that youths who reported committing a crime in the previous

month were less likely to be employed than those who did

not report criminal activity. Least likely to be employed were

youths who reported high income from criminal activities or

who had been jailed in the previous year.9

John Bound and Richard Freeman, and Jeff Grogger found

that a proportion of the employment differential between black

and white youths can be attributed to whether a youth has a

criminal record.10 These figures do not necessarily result entirely from a time tradeoff between criminal activity and employment, because an arrest may signal to employers that the

youth will not be a dedicated worker. On the other hand,

using the National Longitudinal Survey of Young Men, Freeman found that church attendance is a strong indication that

a youth will “escape” a background of poverty to become

employed.11

Family characteristics. Characteristics of the youth’s family

may also affect his or her probability of working. These factors may either indicate unobserved family characteristics

that promote labor market attachment or point to household

characteristics that may ease the youth’s transition into the

labor market (for example, established job networks or employment opportunities that arise through another household member’s work). Factors most often considered include

the employment behavior of the respondent’s parents and

siblings, single parent households, and the poverty status of

the household.

Mary Corcoran, Richard Gordon, Deborah Laren, and Gary

Solon used a sample of men from the Panel Study of Income

Dynamics (PSID) to examine the relationship between family,

community, and employment.12 Their strongest result indicates that family or community welfare receipt negatively affects the men’s probability of working; not surprisingly, those

from families who have spent more time in poverty are also

less likely to work. Albert Rees and Wayne Gray found that

parental characteristics have no effect on employment, while

siblings who work positively affect the respondent’s work

behavior.13

Neighborhood and geographic factors. A number of studies have found that the characteristics of the youth’s geographic area—and especially the immediate neighborhood—

affect whether a youth begins working at an early age. The

types of jobs prevalent in the region may determine the availability of teen jobs; for example, areas dominated by heavy

industry may present teens with fewer opportunities due to

safety regulations. In addition, neighborhood factors may

impact on the probability of working in two ways, according

to Bruce Weinberg, Patricia Reagan, and Jeffrey Yankow.14

First, these factors may influence the decision to work by

changing the stigma attached to unemployment; for example,

a neighborhood with a high unemployment rate may attach
Monthly Labor Review August 2001 53

less of a stigma to not working than may a low-unemployment neighborhood. Second, certain neighborhood characteristics may determine how effective the job network is. High

unemployment may degrade the job network because fewer

residents are in the labor market and able to pass along information about employers or job openings.

Bound and Freeman showed that geographic location accounts for part of the racial difference in employment.15 Finding that the black/white employment and earnings gaps in

the 1970s and 1980s were larger in the Midwest than elsewhere, they attribute part of this outcome to regional changes

in industrial composition and dominant occupations. In a

separate study, Freeman determined that economic activity

in a geographic area impacts on the probability of youth employment. 16 He found that the important indicators of economic activity are the area’s unemployment rate, poverty status, growth in personal income, industrial composition, and

proportion of older to younger workers.17

On a neighborhood level, Katherine O’Regan and John

Quigley used 1990 Census data to consider the effects of

living in a predominantly nonblack/non-Hispanic census tract

and of living in a census tract with higher poverty, which

they refer to as social isolation.18 Focusing on black and

Hispanic youths who live at home, their results indicate that

the employment probabilities of a minority youth living in a

predominately white or lower poverty census tract are higher

than minorities residing elsewhere. Finally, Weinberg, Reagan,

and Yankow examined the block-groups in which male NLSY79

respondents reside, and determined that neighborhood characteristics do affect the probability of employment. However, they also find that ordinary least square overstates the

impact of neighborhoods, and that including individual fixed

effects reduces the residence effect by two-thirds or more

when compared to ordinary least square regressions.19

Spatial mismatch. According to the spatial mismatch theory

first advanced by John Kain, housing segregation affects

black employment opportunities because jobs are located

outside of the urban areas with high black populations.20

Also applied to Hispanics in subsequent studies, this theory

is particularly appealing when considering the employment

of teens because this group is generally tied to their residential area by their parents. Neighborhood factors differ from

spatial mismatch in that the first takes into account the composition of the residence along a number of dimensions while

the second considers the proximity of jobs to the residents of

an area. Richard Arnott states that spatial mismatch may either involve problems encountered in job search or reflect

job access or difficulties with transportation.21

First, the job search aspect of spatial mismatch theory

suggests that urban teens cannot search effectively for a job

due to poor connections to an area rich in jobs. Using a

sample of nonenrolled civilian men in the 1981 and 1982 NLSY79

surveys, Harry Holzer found that informal job search, which

involves job networks (for example, checked with friends or

relatives and direct application without a reference), results

in the most job offers for both black (60 percent) and white

teens (70 percent).22 However, white youths have a higher

probability of a job offer using any search method. Holzer

concluded that this difference accounts for the racial gap in

employment, with job offers resulting from informal job search

leading to 87 to 90 percent of the differential.

The second problem relates to access to jobs and lack of

transportation for youths ages 14 and 15—a problem that is

aggravated by their inability to drive. Research suggests the

impact of this factor is not uniform across metropolitan areas.

A number of researchers used microdata and discovered that

proximity to a job-rich area, as measured by commute times,

affects the probability that a youth is employed.23 Keith

Ihlanfeldt and David Sjoquist found that the “nearness” of

jobs impacts on both black and white youth employment in

Philadelphia, but only affects black youths in Los Angeles

and Chicago. Using an index of commute times in four metropolitan areas in New Jersey, O’Regan and Quigley determined

that longer commutes have a negative impact on minority

teen employment, but the magnitude and significance of their

measure varies across metropolitan areas.

Combining the two problems, Holzer, Ihlanfeldt, and

Sjoquist considered the effect of longer travel times on both

work and search behavior.24 Although they focus on slightly

older workers—NLSY79 respondents aged 16 to 24—this

group is similar to younger workers in that a number lived in

the parental home. Their findings indicate that greater travel

for work or search activity was positively related to wage

gains. Due to residence constraints and nonownership of an

auto, black respondents faced higher travel costs per mile,

resulting in a smaller search area.

Constructing the study

Aside from extensive employment data, the round 1 NLSY97

provides current and retrospective data on all youth respondents, limited data on the current status of other household

members, and, in cases in which the parent interview was

completed, extensive data on the responding parent. The

round 1 NLSY97 sample consists of 8,984 respondents who

were aged 12 to 18 at the time they were interviewed.25 In

round 2 NLSY 97, 8,386 of the youth respondents completed

an interview; in addition to answering questions about themselves, they also provided all updates and changes to household composition and parental information, excluding family

income.
54 Monthly Labor Review August 2001

Racial Differences in Youth Employment

What factors influence youth employment? This question

is addressed using round 1 NLSY97 data. How does early

work experience affect the probability that older teens work?

To address this question requires restricting the sample to

youths age 16 or older. In round 1, a large majority of youths

interviewed were aged 15 or younger, so data from rounds 1

and 2 of the NLSY97 were merged.26

Variable definitions. The employment variables discussed

in this article equal 1 if the respondent worked at any job, at

a freelance job, or at an employee-type job within 4 weeks of

the interview date and zero otherwise. To measure the probability of working at an employee-type or a freelance job at

age 14, a dummy variable is formed based on the starting and

ending dates of the respective job types and birth dates reported by youths in the NLSY97.

Both age and highest grade completed are continuous variables taken directly from the survey instrument and measured

as of the interview date. The dummy variables for black and

Hispanic are created from the expanded race and ethnicity

codes collected in the screener part of the round 1 instrument. Any youth who is reported as Hispanic in either the

race or the ethnicity question is considered Hispanic in this

study; black respondents who are also Hispanic are coded as

Hispanic.27 Enrollment is defined as continuous enrollment;

thus, youths on summer vacation are still considered enrolled.

The variable “summer” equals 1 if the respondent was interviewed during the summer months (June, July, and August)

and zero otherwise. Because teens tend to hold jobs with

greater frequency during the summer, this variable is included

to capture differences in jobholding based solely on the time

of year the youth was interviewed. Youths with any number

of children are coded as a 1 in the “has a child” variable; all

other youths are coded as zero.

Because the respondents are at the age where peer effects

may influence their present and future behaviors, the NLSY97

presents a unique opportunity to look at the groups that

impact on youths by asking the respondent a number of questions about his or her peers. The survey includes a number of

questions about the respondent’s perception of peer involvement in various activities and of peer educational plans; from

these questions, three variables were formed to capture teen

activities.28 The first looks at the percent of peers who plan

to attend college. The effect that this variable might have on

teen work is uncertain. If more motivated youths tended to

go to college, this variable would have a positive effect on

employment; conversely, if teens who were focused on college spent more time studying, they would work less. The

second variable focuses on “positive activities” for youths

as measured by church-going activity and volunteerism. It is

unclear whether these types of activities serve as a signal

that a youth is a “good kid” or whether knowing large numbers of people who participate in these activities actually

changes the behavior of teens.29 Finally, NLSY97 considers

whether knowing a number of peers who participate in “negative activities”—such as taking part in illegal activities or

belonging to a gang—affects employment behavior. Like the

positive activities, it is unclear whether associating with peers

involved in these activities provides a signal for future employers or indicates something about the pre-existing work

ethic of respondents.

For the purposes of this article, “crime” is defined as stealing something worth $50 or more, selling drugs, or other property crimes, such as fencing stolen property, possessing or

receiving stolen property, or deliberately selling something

for more than it was worth. Youths who have been institutionalized for any crime are coded as 1 in the criminal institution variable.

The family variables are derived from the household roster, created during the screener portion of the NLSY97 interview. Siblings equals 1 if siblings of any type (biological,

adoptive, step, or foster) were recorded in the household; the

employment of siblings is a count of the number of these

siblings age 16 or older who were employed as of the survey

date. The employment of parental figures and living in a female-headed household are determined for the same relationships (for example, biological, adoptive, step, and foster).

Residence variables were originally created by NLSY97 staff

based on respondent addresses. For this article, four dummy

variables indicate the respondent’s region of residence.30

The urban/rural variable on the NLSY97 Main CD was similarly

changed into a dummy variable, with residence in an urban

area equal to 1.31 Taken from the NLSY97 Geographic CD, the

unemployment rate in the respondent’s metropolitan area is a

continuous exact number based on the March CPS. The poverty rate variable indicates the percent of households in the

respondent’s county of residence with incomes below the

poverty level.

Finally, the spatial access variable used in this article is

the mean travel time to work at the county level; it is derived

from the 1994 County-City Data Book.

32 Although this variable is constructed from data for older workers (those aged

16 and older), it serves as a proxy for the distance between

youths and jobs.

Sample construction. Sample one uses the employment behavior of at-home teens, regardless of age, as reported in the

round 1 survey. This sample includes at-home respondents

who have valid data for all variables as defined above. These

youths were aged 12 to 18 during round 1 of the survey. In

addition, sample one includes only respondents with an identifiable parent or a parent figure. After these restrictions are
Monthly Labor Review August 2001 55

applied, the sample includes 8,511 respondents, of whom 5,743

were at least 14 years old and eligible to report employeetype jobs. Details on the number of respondents excluded

due to each restriction are provided in the appendix.

Sample two is used to address the issue of whether work

experience at age 14 impacts on the employment probabilities

of older teens. Answering this question requires information

on the youth’s residence both at age 14 and at the interview

date because the spatial mismatch literature indicates that

this may impact on employment probability. Because most

respondents had not yet turned 16 at the date of the first

interview, data from both round 1 and round 2 of the NLSY97

survey are examined to increase the size of sample two. At

the time this research was conducted, round 2 addresses were

not geocoded, and only teens who met certain residence restrictions (described in the appendix) were included in the

sample. In short, these restrictions required teens to remain

at the same residence for a period of time. After imposing the

residence restrictions, 2,512 respondents remained in sample

two.

Note that rather than measuring the effect of jobholding in

the 14th year on later employment, this article addresses

whether, among at-home teens who remain in the same labor

market from the age of 14, those who hold a job do better than

those who do not work. Although the difference may seem

subtle, the issue of sample selection bias must be considered. Nonmoving youths may have more stable job networks

(for example, family, friends, former employers) and may be

more likely to be employed at older ages as a result. In addition, movers may have a lack of familiarity with the area and

with available employers that hinders their ability to find work.

Finally, since most teen positions are awarded on the basis of

recommendations rather than work experience, movers may

have more difficulty obtaining a known reference in a new

area.

Due to these factors, the returns to holding a previous job

may be biased upward for this sample. The following tabulation shows the weighted effect of residence restrictions on

probability of employment at age 14:

Variable In-scope Out-of-scope

Any job at age 14 ............................. 0.61 0.58

Employee-type job at age 14 ........ .27 .24

Freelance-type job at age 14 ......... .47 .44

Employee type job at age 16 ........ .46 .39

A larger percentage of the nonmovers held any type of job

(61 percent versus 58 percent for movers) at the age of 14; the

same was true of employee-type jobs (27 percent versus 24

percent) and freelance jobs (47 percent versus 44 percent).

Further, a larger percentage of nonmovers than movers in this

sample held a job within 1 month of the latest survey date (46

percent versus 39 percent). In addition, this type of sample

selection bias may affect the family variables because older

family members who lived in the same location for a period of

time may have better connections to jobs for the respondent

through friends and coworkers. This may cause returns to

employment of the respondent’s parent(s) or siblings to be

overstated. However, determining how this sample selection

may bias other measures is beyond the scope of this article.

Racial disparities

In the last several decades, jobholding among teens—especially those in the youngest age group—has been increasing. Looking at the first year of the NLSY79, Michael and Tuma

found that approximately 25 percent of 14-year-olds held

jobs, with the rate increasing to slightly more than 50 percent

by age 17.33 The following tabulation shows the weighted

employment rates by age group:

 Survey Age Age Age Age

14 15 16 17

NLSY79 ...................................... 25.1 26.9 38.0 50.9

NLSY97 any job ......................... 41.7 44.4 54.3 61.2

NLSY97 employee-type job ... 41.7 44.4 54.3 61.2

NLSY97 freelance job .............. 41.7 44.4 54.3 61.2

NLSY97 both freelance

 and employee-type job ...... 41.7 44.4 54.3 61.2

Also presented in the above tabulation is the percentage

of NLSY97 teens eligible for this article, as described above,

who hold jobs.34 The percentage of eligible youths holding

jobs in the NLSY97 is higher than in the NLSY79 when all jobs

are considered. Although this may reflect that more teens are

working in the late 1990s than in the early 1980s, when economic conditions would have been worse, it may also reflect

differences in the fielding periods that contribute to the NLSY97

collecting higher numbers of jobs.35

The increase in jobholding as the cohort ages follows the

same pattern as in the NLSY79. A lower percentage of younger

respondents in both surveys work and that percentage increases over time.) As expected, employee-type jobholding,

increases with age as respondents in the NLSY97 both become more mobile and age out of the Fair Labor Standards act

restrictions to become eligible to work at more jobs. Freelance

jobholding increases and then falls as more youths begin

holding employee-type jobs; the percentage of those holding freelance jobs is higher for 14-year-olds than for any other

age group.

Across all respondents, who are between the ages of 12 and

18 in round 1, jobholding by youths in the NLSY97 is the norm
56 Monthly Labor Review August 2001

Racial Differences in Youth Employment

rather than the exception. About 66 percent of the round 1

respondents in this sample reported having ever held either

an employee-type job or a freelance job. Jobholding was

higher among nonblack/non-Hispanic youths (71.1 percent)

than either black (52.2 percent) or Hispanic youths (51.6 percent). (See table 3.) Looking at type of job reveals that a larger

percentage of nonblack/non-Hispanic youths (44.6 percent

and 55.9 percent, respectively) hold either employee-type or

freelance jobs than their black or Hispanic counterparts. More

Hispanic than black teens reported holding employee-type

jobs, although more black youths reported participating in

freelance activity.

Given the racial disparities with respect to the prevalence

and type of youth employment, it is important to consider

whether holding any type of job during the early teenage

years has an impact on later employment probabilities. In

short, it does. The percentage (67) of youths who reported

holding any type of job during their 14th year worked at an

employee-type job after the age of 16, compared with only 53

percent of those who were not early jobholders. The following tabulation shows the weighted probabilities of employment at employee-type jobs for older teens by employment

status at age 14:

 Variable All Black Hispanic Nonblack/

youths youths youths nonHispanic

youths

No job held ................. 0.53 0.40 0.39 0.59

Held any job ............... .67 .58 .66 .69

Employee-type

 job ......................... .82 .73 .74 .84

Freelance job ........... .59 .51 .62 .60

Both employee-type

 and freelance jobs . .73 .56 .69 .75

This is true regardless of racial group. It is interesting to

note that the type of job also seems to matter. Those who

held an early employee-type job (82 percent) were working

after their 16th birthday, while only 59 percent of those reporting a freelance job during their 14th year were working

after the age of 16. This holds for all racial groups, although

the probability of attaining an employee-type job after the

age of 15 is about the same for nonblack/non-Hispanic teens

who held a freelance jobs during their 14th year as it is for

those who did not work.

Other differences exist among the groups included in the

survey, with potential effects on early employment. (See table

1.) Across racial groups, there are only minor differences in

age and years of schooling. The majority of respondents (89.4

percent) report at least one parent who works; black teens are

the least likely to have a parent working, and nonblack/nonHispanic youths are the most. Conversely, nearly 51 percent

of black teens live in female-headed households, while only

20 percent of nonblack/non-Hispanic youths do so.

Turning to peer effects, about 57 percent of the respondents reported that a large percentage of their peers planned

to attend college, while only 21 percent reported that most of

their peers were involved in negative activities. Interestingly,

more black youths reported that their peers were involved in

both negative (28.4 percent) and positive activities (34.4 percent) than their nonblack/non-Hispanic (19.0 percent and

30.6 percent, respectively) or Hispanic (23.8 percent and 28.1

percent, respectively) counterparts. Despite their peers’ behavior, only about 16 percent of each group reported committing a serious crime themselves and about 2 percent reported

having been institutionalized for any crime.

Geographically, more Hispanic respondents live in the

West, in urban areas, and in areas of relatively high unemployment, while the majority of black youths are located in

the South, in urban areas, and in counties with a high poverty

rate. Finally, nonblack/non-Hispanic respondents live in

counties with lower average travel times to work than either

black or Hispanic teens.

Probability of employment

What is the impact of individual, family, geographic, and access measures on the probability that a youth is working at

any type of job?36 Being black or Hispanic significantly reduces the probability of employment. (See table 2.) Conversely, being female is positively associated with working.

Completing more years of formal education has a significant

positive relationship to working, although being enrolled has

no effect.

Youth behavior also impacts on the probability of working. Believing that at least 75 percent of one’s peers intend to

attend college increases the probability of employment; other

peer behaviors have no significant effect. Contrary to previous studies, those who report committing a serious crime are

nearly 4 percentage points more likely to be employed. This

counterintuitive finding may indicate that working teens have

more opportunities to commit a crime (such as theft) than do

nonworking teens. Further, the base amount for stealing may

be too low to be considered a serious crime since most youths

caught stealing this small amount would most likely not face

serious consequences, such as institutionalization. As expected, being institutionalized for any crime—regardless of

the crime’s severity—has a negative impact on the probability that a teen is employed.

The work behavior of a parent has a significant positive
Monthly Labor Review August 2001 57

Ever held any job ....................................................... 0.659 0.522 0.515 0.711

................................................................................... (.474) (.500) (.500) (.453)

Ever held employee job ............................................. .410 .306 .329 .446

................................................................................... (.492) (.461) (.470) (.497)

Ever held freelance job ............................................ .513 .402 .373 .559

................................................................................... (.500) (.491) (.484) (.497)

Age ............................................................................. 14.4 14.4 14.4 14.3

................................................................................... (1.51) (1.52) (1.49) (1.51)

Female ....................................................................... .486 .490 .464 .489

................................................................................... (.500) (.500) (.499) (.500)

Enrolled in school ...................................................... .978 .983 .967 .979

................................................................................... (.147) (.128) (.179) (.145)

Highest grade completed ........................................... 7.74 7.66 7.70 7.76

................................................................................... (1.59) (1.61) (1.61) (1.58)

Interviewed in summer ............................................... .226 .240 .239 .221

................................................................................... (.418) (.427) (.427) (.415)

Has a child ................................................................. .005 .014 .003 .003

................................................................................... (.069) (.119) (.056) (.055)

Percent of peers who expect

 to attend college .................................................... .574 .470 .493 .609

................................................................................... (.494) (.500) (.500) (.488)

Percent of peers in positive activities ...................... .308 .344 .281 .306

................................................................................... (.462) (.475) (.450) (.461)

Percent of peers in negative activities ..................... .210 .284 .238 .190

................................................................................... (.407) (.451) (.426) (.393)

Ever committed a crime ............................................. .164 .163 .167 .163

................................................................................... (.370) (.370) (.373) (.370)

Ever been institutionalized for a crime ..................... .021 .020 .026 .021

................................................................................... (.144) (.139) (.160) (.142)

Has a sibling .............................................................. .844 .826 .884 .840

................................................................................... (.363) (.380) (.320) (.366)

Siblings employed ...................................................... .308 .306 .399 .292

................................................................................... (.581) (.625) (.662) (.555)

Female head of household ........................................ .253 .506 .294 .195

................................................................................... (.435) (.500) (.456) (.396)

Parents employed ...................................................... .894 .794 .849 .921

................................................................................... (.308) (.405) (.358) (.269)

Northeast ................................................................... .187 .148 .151 .201

................................................................................... (.390) (.355) (.358) (.401)

North-central .............................................................. .266 .169 .122 .311

................................................................................... (.442) (.375) (.327) (.463)

South .......................................................................... .325 .574 .277 .283

................................................................................... (.468) (.495) (.448) (.451)

West ........................................................................... .222 .110 .450 .205

................................................................................... (.416) (.313) (.498) (.404)

Urban .......................................................................... .537 .645 .725 .482

................................................................................... (.499) (.479) (.446) (.500)

Unemployment rate .................................................... 5.17 4.80 6.74 4.96

................................................................................... (2.60) (2.12) (3.82) (2.31)

Percent in poverty (county) ...................................... 9.88 13.1 10.8 9.07

................................................................................... (5.51) (7.20) (5.63) (4.79)

Mean travel time to work (county) ............................ 21.9 23.2 23.6 21.3

................................................................................... (4.79) (4.96) (4.93) (4.61)

Number in sample ...................................................... 8,511 2,105 1,808 4,598

1 Youths aged 12 and 13 do not specifically report employee jobs. Therefore, the number of cases for this variable differs from all other variables. The

numbers of cases are as follows: total: 5,743; black: 1,454; Hispanic: 1,201; and nonblack/non-Hispanic: 3,088.

NOTE: Standard errors are in parentheses.

Table 1. Weighted sample means by racial group

Hispanic Nonblack/

non-Hispanic Variable Total Black
58 Monthly Labor Review August 2001

Racial Differences in Youth Employment

effect on the employment probability of the youth. (See table

2.) As pointed out earlier, this may indicate either the presence of better connections to the labor market or unobserved

family characteristics. However, the number of siblings, the

siblings’ employment status, and living in a female-headed

household do not significantly affect the employment probability of the youth.

Youths living in the Northeast are about 6 percentage

points less likely to be employed than those in the west;

however, the employment probability of youths who live in

the north central or the southern region is not significantly

different from those in the West. Not surprisingly, living in

areas with high unemployment rates or high poverty rates

decreases the probability of employment, although living in

an urban area does not significantly impact on the probability of work.

Finally, table 2 presents a measure of average travel time

to work, reflecting job access and spatial mismatch. Living in

areas in which the average travel time to work is higher has a

significant negative impact on the probability that a teen is

employed.

Employee-type jobs. The probability that a youth works at

an employee-type job is included in table 3. As in the previous set of regressions, being black or Hispanic has a significant negative relationship to holding an employee-type job.

Regardless of race, being male or completing more years of

education increases the probability of holding an employeetype job. These effects are strongest for nonblack/non-Hispanic youths.

In general, the behavior of the teen’s peers does not affect

the probability of attaining an employee-type job; for

nonblack/non-Hispanic youths, however, associating with

peers engaged in positive activities significantly decreases

the probability of working. This seems counterintuitive; however, this may reflect nonblack/non-Hispanic youths trading time

that would have been spent at a job for time spent at church

or volunteering. In addition, having an employed sibling

significantly increases the probability that nonblack/non-Hispanic youths work. Regardless of racial group, the employment

of a parent is positively associated with the teen working at

an employee-type job, supporting the theory that unobserved family characteristics may influence the youth’s employment decision or that an established job network may

help the respondent find a job.

Finally, geographic factors influence the probability that

teens are working at employee-type jobs. Neighborhood factors have a significant negative impact on the probability

that a minority youth works at an employee-type job. Black

teens living in areas of high poverty, in counties with longer

commute times, or in areas of high unemployment are less

likely to work. Only the unemployment rate is negatively associated with the probability of working for Hispanic teens.

Nonblack/non-Hispanic youths are not significantly affected

by these neighborhood characteristics; however, living in

the north central or southern regions increases the probability of employment for these teens.

Freelance jobs. The factors affecting freelance jobholding

are in table 4. Being black or Hispanic is negatively related to

holding a freelance job, as it was with employee-type jobs.

However, being younger or being female is positively associated with holding freelance jobs, regardless of race. Completing

more years of education does not impact on the probability

that teens hold a freelance job; however, being enrolled in

school increases the probability that a Hispanic youth works

at this type of job.

Peer behavior affects freelance jobholding. Knowing a

larger number of peers who plan to attend college increases

the probability that Hispanic and nonblack/non-Hispanic

youths work at a freelance job. Rather than college plans, it is

the percent of the teen’s peers involved in “good activities”

that positively impacts on the probability that a black teen

works; this supports Freeman’s finding that church attendance positively affects jobholding among black teens.37 As

expected, hanging out with a “bad crowd” or committing a

crime has a significant negative impact, but only for nonblack/

non-Hispanic youths. Conversely, committing a crime is positively associated with the probability that black and Hispanic

teens work for themselves. This counterintuitive result may

reflect the fact that working at a freelance job provides an

opportunity for committing a crime (for example, stealing from

customers) or it may reflect the reporting of illegal activity as

a freelance job (for example, lookout for a drug seller). As

expected, the effect of serving time in a criminal institution on

freelance jobholding is negative, but only for Hispanic

youths; serving time does not seem to affect the other groups.

Turning to family factors, having at least one parent in the

labor force or the presence of a sibling is positively related to

the probability that nonblack/non-Hispanic youths work.

This result does not extend to black or Hispanic teens. Unlike

employee-type jobholding, the employment of the

respondent’s sibling(s) does not matter.

Finally, geographic location affects the probability that

teens will work for themselves. Living in the West is positively related to holding a freelance job for black teens, while

living in the South or in urban areas negatively impacts on

the probability that Hispanic youths work for themselves.

Living in areas of high unemployment increases the probability that nonblack/non-Hispanic teens work at a freelance job,

possibly because fewer employee-type jobs are available. Regardless of racial group, living in counties with high poverty
Monthly Labor Review August 2001 59

NOTE: Standard errors are in parentheses. Partial derivatives of probability of outcome associated with dependent variables with respect to

independent variables.

Table 2. Probability of employment for all youths

Geographic

effects

Spatial

effects Variable Individual effects Family effects

Age ............................................................................. 0.012 0.016 0.017 0.014

................................................................................... (.008) (.008) (.008) (.008)

Black .......................................................................... –.204 –.189 –.167 –.150

................................................................................... (.012) (.012) (.014) (.014)

Hispanic ..................................................................... –.212 –.206 –.179 –.165

................................................................................... (.012) (.012) (.014) (.014)

Female ....................................................................... .042 .044 .043 .042

................................................................................... (.011) (.011) (.011) (.011)

Enrolled in school ...................................................... .006 –.010 –.009 –.008

................................................................................... (.037) (.038) (.038) (.038)

Highest grade completed ........................................... .045 .041 .041 .045

................................................................................... (.007) (.008) (.008) (.008)

Interviewed in summer ............................................... .014 .015 .016 .017

................................................................................... (.014) (.014) (.014) (.014)

Has a child ................................................................. –.082 –.082 –.087 –.090

................................................................................... (.061) (.061) (.061) (.061)

Percent of peers who expect

 to attend college .................................................... .042 .038 .037 .039

................................................................................... (.011) (.011) (.011) (.012)

Percent of peers in positive activities ...................... –.0002 –.001 .0003 .002

................................................................................... (.012) (.012) (.012) (.012)

Percent of peers in negative activities ..................... .007 .010 .008 .008

................................................................................... (.014) (.014) (.014) (.014)

Ever commmitted a crime .......................................... .036 .037 .028 .027

................................................................................... (.016) (.016) (.016) (.016)

Ever been institutionalized for a crime ..................... –.100 –.085 –.091 –.096

................................................................................... (.034) (.035) (.035) (.035)

Has a sibling .............................................................. – .020 .023 0.025

................................................................................... – (.016) (.016) (.016)

Siblings employed ...................................................... – –.004 –.003 –.002

................................................................................... – (.009) (.009) (.009)

Female head of household ........................................ – –.012 –.009 –.011

................................................................................... – (.013) (.013) (.013)

Parents employed ...................................................... – .101 .090 .085

................................................................................... – (.016) (.017) (.017)

Northeast ................................................................... – – –.053 –.034

................................................................................... – – (.017) (.018)

North-central .............................................................. – – –.008 –.008

................................................................................... – – (.017) (.017)

South .......................................................................... – – –.021 –.015

................................................................................... – – (.016) (.016)

Urban .......................................................................... – – –.015 –.013

................................................................................... – – (.012) (.012)

Unemployment rate (Metropolitan Statistical Area) .. – – –.0008 –.0006

................................................................................... – – (.0002) (.0002)

Percent in poverty (county) ...................................... – – –.006 –.007

................................................................................... – – (.001) (.001)

Mean travel time to work (county) ............................ – – – –.008

................................................................................... – – – (.001)

P-value, family variables (LR test) ............................ – .000 – –

P-value, neighborhood variables (LR test) ............... – – .000 –

P-value, access variable (LR test) ........................... – – – .000

Number in sample ...................................................... 8,511 8,511 8,511 8,511

Pseudo R2 .................................................................. .066 .070 .076 .080
60 Monthly Labor Review August 2001

Racial Differences in Youth Employment

Table 3. Probability of working at an employee-type job by racial group

Hispanic

youths

Nonblack/

non-Hispanic

 youths

All youths Black

youths

Variable

Age .............................................................................. 0.047 0.010 0.038 0.074

.................................................................................... (.008) (.012) (.015) (.014)

Black ........................................................................... –.091 – – –

.................................................................................... (.013) – – –

Hispanic ...................................................................... –.056 – – –

.................................................................................... (.014) – – –

Female ........................................................................ –.065 –.042 –.049 –.088

.................................................................................... (.011) (.017) (.021) (.017)

Enrolled in school ....................................................... –.044 –.026 –.068 –.039

.................................................................................... (.035) (.060) (.062) (.054)

Highest grade completed ............................................ .061 .054 .040 .067

.................................................................................... (.007) (.011) (.013) (.012)

Interviewed in summer ................................................ .035 .018 –0.021 .077

.................................................................................... (.013) (.020) (.023) (.021)

Has a child .................................................................. –.016 –.004 –.050 –.009

.................................................................................... (.051) (.049) (.098) (.123)

Percent of peers who expect

 to attend college ..................................................... .005 –.004 .018 –.0003

.................................................................................... (.011) (.017) (.021) (.017)

Percent of peers in positive activities ....................... –.019 .006 –.011 –.035

.................................................................................... (.012) (.019) (.024) (.019)

Percent of peers in negative activities ...................... .017 –.001 .012 .030

.................................................................................... (.013) (.017) (.023) (.020)

Ever committed a crime .............................................. .022 .008 .030 .024

.................................................................................... (.015) (.023) (.028) (.022)

Ever been institutionalized for a crime ...................... –.067 –.070* –.056 –.071

.................................................................................... (.025) (.030) (.039) (.043)

Has a sibling ............................................................... –.019 –.012 .003 –.030

.................................................................................... (.015) (.022) (.033) (.023)

Siblings employed ....................................................... .018 –.014 .002 .052

.................................................................................... (.009) (.014) (.015) (.014)

Female head of household ......................................... –.024 .001 –.034 –.028

.................................................................................... (.013) (.017) (.023) (.021)

Parents employed ....................................................... .082 .048 .071 .102

.................................................................................... (.015) (.019) (.024) (.027)

Northeast .................................................................... –.001 .014 .003 .017

.................................................................................... (.018) (.040) (.032) (.028)

North-central ............................................................... .024 .020 –.014 .059

.................................................................................... (.017) (.034) (.035) (.026)

South ........................................................................... .023 .029 –.032 .046

.................................................................................... (.016) (.028) (.025) (.028)

Urban ........................................................................... .004 .004 –.024 .015

.................................................................................... (.011) (.019) (.023) (.017)

Unemployment rate (Metropolitan Statistical Area) ... –.0007 –.0008 –.001 –.0002

.................................................................................... (.0002) (.0005) (.0003) (.0004)

Percent in poverty (county) ....................................... –.005 –.005 –.003 –.002

.................................................................................... (.001) (.001) (.002) (.002)

Mean travel time to work (county) ............................. –.003 –.005 –.004 –.001

.................................................................................... (.001) (.002) (.002) (.002)

P-value, family variables (LR test) ............................. .000 .112 .015 .000

P-value, neighborhood variables (LR test) ................ .000 .001 .000 .134

P-value, access variable (LR test) ............................ .004 .011 .104 .497

Number in sample ....................................................... 5,743 1,454 1,201 3,088

Pseudo R2 ................................................................... .132 .123 .125 .121

NOTE: Standard errors are in parentheses. Partial derivatives of probability of outcome associated with dependent variables with respect to

independent variables.
Monthly Labor Review August 2001 61

Table 4. Probability of working at a freelance job by racial group

Variable All youths Black

youths

Hispanic

youths

Nonblack/

non-Hispanic

 youths

Age ................................................................................ –0.029 –0.026 –0.015 –0.036

...................................................................................... (.007) (.011) (.012) (.011)

Black ............................................................................. –.098 – – –

...................................................................................... (.012) – – –

Hispanic ........................................................................ –.138 – – –

...................................................................................... (.012) – – –

Female .......................................................................... .084 .036 .040 .124

...................................................................................... (.010) (.018) (.018) (.014)

Enrolled in school ......................................................... .062 –.030 .126 .048

...................................................................................... (.032) (.071) (.028) (.052)

Highest grade completed .............................................. .010 .012 .010 .009

...................................................................................... (.007) (.011) (.011) (.011)

Interviewed in summer .................................................. –.006 –.014 –.023 .008

...................................................................................... (.012) (.021) (.020) (.019)

Has a child .................................................................... –.081 –.049 –.057 –.217

...................................................................................... (.055) (.057) (.117) (.108)

Percent of peers who expect

 to attend college ....................................................... .029 –.018 .038 .041

...................................................................................... (.010) (.018) (.018) (.015)

Percent of peers in positive activities ......................... .008 .034 .005 –.003

...................................................................................... (.011) (.020) (.020) (.016)

Percent of peers in negative activities ........................ –.008 .007 .027 –.039

...................................................................................... (.012) (.020) (.022) (.019)

Ever committed a crime ................................................ .010 .053 .073 –.034

...................................................................................... (.014) (.027) (.028) (.021)

Ever been institutionalized for a crime ........................ –.039 –.048 –.101 .008

...................................................................................... (.034) (.053) (.035) (.054)

Has a sibling ................................................................. .033 .028 .022 .036

...................................................................................... (.013) (.023) (.028) (.020)

Siblings employed ......................................................... –.008 –.001 –.008 –.015

...................................................................................... (.008) (.014) (.013) (.013)

Female head of household ........................................... .003 .008 .029 –.004

...................................................................................... (.012) (.018) (.021) (.019)

Parents employed ......................................................... .043 .015 .029 .074

...................................................................................... (.015) (.022) (.024) (.027)

Northeast ...................................................................... –.034 –.097 –.038 –.003

...................................................................................... (.015) (.028) (.026) (.023)

North-central ................................................................. –.024 –.079 .058 –.002

...................................................................................... (.015) (.027) (.037) (.022)

South ............................................................................. –.028 –.098 –.045 .015

...................................................................................... (.014) (.030) (.022) (.023)

Urban ............................................................................. –.020 –.036 –.033 –.001

...................................................................................... (.010) (.020) (.020) (.015)

Unemployment rate (Metropolitan Statistical Area) ..... .00002 –.00007 –.0004 .001

...................................................................................... (.0002) (.0005) (.0003) (.0003)

Percent in poverty (county) ......................................... –.004 –.002 –.003 –.006

...................................................................................... (.001) (.001) (.002) (.002)

Mean travel time to work (county) ............................... –.007 –.005 –.010 –.006

...................................................................................... (.001) (.002) (.002) (.002)

P-value, family variables (LR test) ............................... .008 .735 .524 .017

P-value, neighborhood variables (LR test) .................. .000 .004 .000 .003

P-value, access variable (LR test) .............................. .000 .026 .000 .000

Number in sample ......................................................... 8,511 2,105 1,808 4,598

Pseudo R2 ..................................................................... .054 .025 .061 .034

NOTE: Standard errors are in parentheses. Partial derivatives of probability of outcome associated with dependent variables with respect to

independent variables.
62 Monthly Labor Review August 2001

Racial Differences in Youth Employment

rates or with long commute times negatively impacts on the

probability of freelance jobholding.

Jobholding of older youths. Does holding a job as a 14-yearold impact on the probability that youths age 16 and older

work at an employee-type job? It should again be noted that

the composition of this sample differs greatly from the composition of the prior sample, due to the residence restriction.

These results, while informative, cannot be generalized to the

teen population but rather address only nonmoving teens.

(See table 5.) Column 1 of the table presents the base specification without the prior jobholding information included; in

column 2, a dummy variable indicates whether the teen held

any type of job at the age of 14; column 3 separates the jobs

held at 14 into freelance and employee-type jobs. The results

indicate that holding any type of job at age 14 increases the

probability that older teens work for an employer. Further,

teens who held an employee-type job in their 14th year are

more likely to hold an employee-type job later compared with

freelance jobholders; however, both types have a positive

significant association with jobholding at older ages.

Holding an employee-type job as a teen increases the probability of employment regardless of racial group. (See table

6.) Hispanic youths gain the most from early work at an employee-type job, although black and nonblack/non-Hispanic

teens also benefit. Holding freelance jobs at age 14 is positively associated with the employment probabilities of black

and Hispanic youths, but has no effect on the nonblack/nonHispanic group.

Although these results provide an indication of the effect

of early employment, they may not capture the extent to which

unobserved characteristics affect early jobholding. For example, older nonworking teens who did not hold a job may

have chosen to devote their time to schooling or volunteer

activities rather than investing in work experience. Conversely, those who took a job between the ages of 14 and 15

may possess an unobserved family trait that makes them more

likely to work than other teens at later ages. Thus, the results

may bias the coefficient on holding a job of either type at

aged 14 and older.38

Groundwork for further study

Overall, this article has attempted to shed some light on

whether historical racial differences in both employee-type

and freelance jobholding were present for today’s teens and

to determine whether early differences affected later employment. Due to the ages of the respondents and the number of

rounds completed, these later outcomes were limited to teens

aged 16–19 with a stable geographic residence. Regardless,

the information presented here is important because racial

gaps that are present for young workers seem to continue

into adulthood. Even more valuable would be a follow-up

study that considered similar questions after more rounds of

data are complete (for example, does early work experience

impact on labor market attachment when the respondent’s

formal education is finished?).

Keeping the strict sample selection criteria in mind, this

article has found that having previous work experience positively impacted on labor force attachment for different racial

groups for the NLSY97 cohort, as it did for the NLSY79 cohort.39 That is, nonmoving teens who held an employee-type

job at age 14 were more likely to work at an employee-type job

at age 16 and older than were their counterparts, regardless

of race or ethnicity. The effect of holding a freelance job was

not as clear. Black and Hispanic youths who worked for

themselves at an early age were more likely to work at an

employee-type job than were those who did not; however,

freelance jobholding had no effect for nonblack/non-Hispanic

youths.

It should be reiterated that these results may overstate the

effect of holding an early job because nonmovers have advantages in an area that movers may not have. For example,

nonmovers may be more familiar with the employers in the

area and have established job networks. More research is

needed on the effect of early jobholding for youths who move

between their 14th and 16th birthdays to determine the effects of early work experience when a youth must adjust to an

area.

Throughout this article, the employment of older teens

was considered to be a positive outcome, and to this end, the

results can be interpreted to offer some support for programs

that encourage early jobholding, preferably employee-type

jobs. Although additional analysis may be needed, these

findings suggest that a successful program would address

issues such as teen job opportunities and job search networks. These would include programs that provided information about labor market opportunities or that established job

networks to assist in job search. Some of these programs may

fit into the school-to-work transition, with internships or cooperative education providing experience—although more

research should be done on this topic before a positive recommendation is made. Other programs may be communitybased to target areas in which neighborhood characteristics

indicate a problem. Given the finding that parent’s work behavior is positively associated with the probability that the

respondent holds an employee-type job, these programs may

be extended to adult workers in certain communities.

Full support for these policy provisions would require

more analysis. In particular, using block-level neighborhood

effects rather than county-level effects would provide better

recommendations as teens are usually constrained geographi-
Monthly Labor Review August 2001 63

Table 5. Probability of working at an employee-type job for nonmovers

Variable

Base

specifications

Hold

any job

Freelance/

employee-type

job holding

Held any job at age 14 ............................................... – 0.115 –

.................................................................................... – (.020) –

Held an employee job at age 14 ................................. – – .186

.................................................................................... – – (.025)

Held a freelance job at age 14 ................................... – – .025

.................................................................................... – – (.021)

Age .............................................................................. .105 .101 .113

.................................................................................... (.014) (.014) (.014)

Black ........................................................................... –.130 –.117 –.124

.................................................................................... (.025) (.026) (.026)

Hispanic ...................................................................... –.077 –.056 –.061

.................................................................................... (.028) (.029) (.029)

Female ........................................................................ –.024 –.029 –.013

.................................................................................... (.020) (.021) (.021)

Enrolled in school ....................................................... .024 .021 .022

.................................................................................... (.012) (.011) (.012)

Highest grade completed ............................................ .053 .050 .047

.................................................................................... (.012) (.012) (.012)

Interviewed in summer ................................................ .014 –.003 .026

.................................................................................... (.113) (.111) (.114)

Has a child .................................................................. .012 .025 .025

.................................................................................... (.070) (.071) (.071)

Ever committed a crime .............................................. .063 .058 .055

.................................................................................... (.028) (.028) (.028)

Ever been institutionalized for a crime ...................... –.016 –.022 –.022

.................................................................................... (.052) (.052) (.052)

Has a sibling ............................................................... –.004 –.009 –.006

.................................................................................... (.028) (.028) (.028)

Siblings employed ....................................................... .041 .041 .038

.................................................................................... (.018) (.018) (.018)

Female head of household ......................................... .012 .013 .011

.................................................................................... (.026) (.026) (.026)

Parents employed ....................................................... .053 .053 .054

.................................................................................... (.039) (.039) (.039)

Northeast .................................................................... .006 .008 .003

.................................................................................... (.033) (.033) (.033)

North-central ............................................................... .010 .102 .097

.................................................................................... (.033) (.032) (.033)

South ........................................................................... .093 .094 .010

.................................................................................... (.032) (.032) (.032)

Urban ........................................................................... .018 .019 .018

.................................................................................... (.021) (.021) (.021)

Unemployment rate (Metropolitan Statistical Area) ... –.001 –.001 –.001

.................................................................................... (.0004) (.0004) (.0004)

Percent in poverty (county) ....................................... –.008 –.008 –.008

.................................................................................... (.002) (.002) (.002)

Mean travel time to work (county) ............................. –.007 –.006 –.006

.................................................................................... (.002) (.002) (.002)

P-value, early employment (LR test) .......................... – – .000

Number in sample ....................................................... 2,512 2,512 2,512

Pseudo R2 ................................................................... .115 .125 .133

NOTE: Standard errors are in parentheses. Partial derivatives of probability of outcome associated with dependent variables with respect to

independent variables.
64 Monthly Labor Review August 2001

Racial Differences in Youth Employment

Table 6. Probability of working at an employee-type job for nonmovers by racial group

Variable Black

youths

Hispanic

youths

Nonblack/

non-Hispanic

youths

Held an employee job at age 14 ................................. 0.169 0.244 0.171

.................................................................................... (.054) (.067) (.031)

Held a freelance job at age 14 ................................... .099 .107 –.018

.................................................................................... (.043) (.053) (.028)

Age .............................................................................. .063 .113 .136

.................................................................................... (.023) (.031) (.020)

Female ........................................................................ –.007 –.022 .0001

.................................................................................... (.038) (.044) (.028)

Enrolled in school ....................................................... .045 –.010 .030

.................................................................................... (.031) (.023) (.015)

Highest grade completed ............................................ .054 .050 0.028

.................................................................................... (.020) (.026) (.017)

Interviewed in summer ................................................ .104 – .138

.................................................................................... (.316) – (.146)

Has a child .................................................................. .008 –.008 .148

.................................................................................... (.084) (.141) (.132)

Ever committed a crime .............................................. .004 .166 .087

.................................................................................... (.052) (.056) (.038)

Ever been institutionalized for a crime ...................... –0.100 .053 –.035

.................................................................................... (.069) (.106) (.075)

Has a sibling ............................................................... .021 –.014 –.016

.................................................................................... (.045) (.074) (.038)

Siblings employed ....................................................... .046 .049 .015

.................................................................................... (.030) (.034) (.026)

Female head of household ......................................... .031 .016 –.018

.................................................................................... (.038) (.054) (.038)

Parents employed ....................................................... .061 .075 –.022

.................................................................................... (.049) (.066) (.072)

Northeast .................................................................... –.115 –.071 .044

.................................................................................... (.062) (.065) (.046)

North-central ............................................................... .078 –.017 .133

.................................................................................... (.085) (.074) (.043)

South ........................................................................... .115 .074 .112

.................................................................................... (.062) (.062) (.046)

Urban ........................................................................... .094 .011 .015

.................................................................................... (.039) (.047) (.028)

Unemployment rate (Metropolitan Statistical Area) ... –.001 –.001 .0002

.................................................................................... (.001) (.001) (.0007)

Percent in poverty (county) ....................................... –.005 –.006 –.010

.................................................................................... (.003) (.004) (.003)

Mean travel time to work (county) ............................. .005 –.001 –.008

.................................................................................... (.004) (.005) (.003)

P-value, early employment (LR test).......................... .000 .000 .000

Number in sample ....................................................... 580 478 1,451

Pseudo R2 ................................................................... .150 .198 .101

NOTE: Standard errors are in parentheses. Partial derivatives of probability of outcome associated with dependent variables with respect to

independent variables.
Monthly Labor Review August 2001 65

cally. This is especially true for younger workers who may

have trouble finding transportation outside their immediate

neighborhood. Additionally, further investigation of movers

may suggest that this group of teens would benefit more

than nonmovers from job networking programs geared toward the immediate neighborhood.

Beyond the scope of this article is the question of whether

Notes

This article is based on a report prepared for the NLSY97 Early

Results Conference at the Bureau of Labor Statistics and funded by the

Department of Labor. Points of view or opinions stated in this document are the author’s and do not necessarily represent the official

position or policy of the Department of Labor.

1

 Robert J. Flanagan, “On the Stability of the Racial Unemployment Differential,” American Economic Review, May 1976, 302-08.

2

 See David T. Ellwood, “Teenage Unemployment: Permanent Scars

or Temporary Blemishes?” in Richard B. Freeman and David A. Wise,

eds., The Youth Labor Market Problem: Its Nature, Causes, and Consequences (Chicago, University of Chicago Press, 1982), 349–85;

Christopher J. Ruhn, “Is High School Employment Consumption or

Investment?” Journal of Labor Economics, October 1997, 735–76;

Robert H. Meyer and David A Wise, “High School Preparation and

Early Labor Force Experience,” in Richard B. Freeman and David A.

Wise, eds., The Youth Labor Market Problem: Its Nature, Causes, and

Consequences (Chicago, University of Chicago Press, 1982), 277-

339; and Brian E. Becker and Stephen Hills, “The Long-Run-Effects

of Job Changes and Unemployment among Male Teenagers,” Journal

of Human Resources, Summer 1983, 197–212.

3 BLS press release announcing the NLSY97 Round 1 data, "Employment experience and other characteristics of youths: results from a

new longitudinal survey, USDL 99-110, Apr. 30, 1999.

4

 Using the NLSY97 sample as defined in section two, 15 percent of

male 17-year-olds and 23 percent of female 17-year-olds report holding a freelance job in the month prior to the survey.

5

 According to figures from the October 1999 CPS, the employment-to-population rate for white teens aged 16 to 19 was 47.7; for

black teens ages 16 to 19, the corresponding rate was 25.4.

6

 Robert T. Michael and Nancy Brandon Tuma, “Youth Employment: Does Life Begin at 16?” Journal of Labor Economics, April

1984, 464–76. According to current Child Labor Laws, teens aged 14

and 15 may work outside school hours in various nonmanufacturing,

nonmining, and nonhazardous jobs. The job may include no more

than 18 hours per week during the school year and 40 hours per week

during vacation periods. An exception to this rule is when these teens

are enrolled in an approved Work Experience and Career Exploration

Program (WECEP); in this case, they may be employed for up to 23

hours during a school week.

7

 For an overview of this literature, see Albert Rees, “An Essay on

Youth Joblessness,” Journal of Economic Literature, June 1986, 613–29.

8 See Gary Becker, “Crime and Punishment: An Economic Approach,” Journal of Political Economy March-April 1968, 169-217.

9

. Richard B. Freeman, “The Relation of Criminal Activity to

Black Youth Employment,” Review of Black Political Economy, Summer-Fall 1987, 99–108.

10 John Bound and Richard B. Freeman, “What Went Wrong? The

Erosion of Relative Earnings and Employment Among Young Black

Men in the 1980s,” The Quarterly Journal of Economics, February

1992, 201-32; Jeff Grogger, “Arrests, Persistent Youth Joblessness,

and Black/White Employment Differentials,” Review of Economics

and Statistics, February 1992, 100–06.

11 Richard B. Freeman, “Who Escapes? The Relation of ChurchGoing and Other Background Factors to the Socio-Economic Performance of Black Male Youths from Inner-City Poverty Tracts,” NBER

Working Paper No. 1656, June 1985.

12 Mary Corcoran, Richard Gordon, Deborah Laren and Gary Solon, “The Association between Men’s Economic Status and Their

Family and Community Origins,” Journal of Human Resources, Fall

1992, 575–601.

13 Albert Rees and Wayne Gray, “Family Effects in Youth Employment,” in Richard B. Freeman and David A. Wise, eds., The Youth

Labor Market Problem: Its Nature, Causes, and Consequences (Chicago, University of Chicago Press, 1982), 453–64.

14 Bruce A. Weinberg, Patricia B. Reagan, and Jeffrey J. Yankow,

“Do Neighborhoods Matter? Evidence from the NLSY79.” Unpublished

paper, 1999.

15 Bound and Freeman, “What Went Wrong...,” 201–32.

16 Richard B. Freeman, “Economic Determinants of Geographic

and Individual Variation,” in Richard B. Freeman and David A. Wise,

eds., The Youth Labor Market Problem: Its Nature, Causes, and Consequences (Chicago, University of Chicago Press, 1982), 115–48.

17 Freeman points out that these factors should be used to determine the employment rate of young workers—and not to distinguish

between the states of unemployed and out of the labor force—since

his findings indicate that factors that affect employment also affect

labor force participation.

18 Katherine M. O’Regan and John M. Quigley, “Teenage Employment and the Spatial Isolation of Minority and Poverty Households,”

Journal of Human Resources, Summer 1996, 692-702.

19 Weinberg, et. al., “Do Neighborhoods Matter?...,” 1999.

20 John F. Kain, “Housing Segregation, Negro Employment, and

Metropolitan Decentralization.” The Quarterly Journal of Economics, May 1968, 175–97. Some disagreement exists over the extent to

which the data support the spatial mismatch theory. For a review of

this literature, see Harry J. Holzer, “The Spatial Mismatch Hypothesis: What Has the Evidence Shown?” Urban Studies, February 1991,

105–22 and John F. Kain, “The Spatial Mismatch Hypothesis: Three

Decades Later,” Housing Policy Debate, volume 3, issue 2, 1992,

371-462.

21 Richard Arnott, “Economic Theory and the Spatial Mismatch

Hypothesis.” Unpublished paper, 1997.

jobholding among early teens provides valuable work experience that encourages later employment or whether it signals

characteristics that make the teen more likely to engage in

labor market activity in future years. It is also unclear whether

encouraging very early labor market attachment leads to more

successful adult outcomes as measured along other dimensions, such as wages or the provision of employee benefits.
66 Monthly Labor Review August 2001

Racial Differences in Youth Employment

22 Harry Holzer, “Informal Job Search and Black Youth Unemployment.” American Economic Review, June 1987, 446–52.

23 See Keith R. Ihlanfeldt and David L. Sjoquist, “Job Accessibility

and Racial Differences in Youth Employment Rates,” American Economic Review, March 1990, 267–76; and Katherine O’Regan,

Katherine and John M. Quigley, “Spatial Effects upon Employment

Outcomes: The Case of New Jersey Teenagers.” Unpublished paper,

1998.

24 Harry J. Holzer, Keith R. Ihlanfeldt, and David L. Sjoquist, “Work,

Search, and Travel among White and Black Youth,” Journal of Urban

Economics May 1994, 110–30.

25 The NLSY97 sampling frame focused on youths who were 12 to 16

years old on December 31, 1996; round 1 interviews were conducted

from January to October 1997 and from March to May 1998. For

more information, see the NLSY97 User’s Guide (Columbus, OH, Center for Human Resource Research, Ohio State University, 2001).

26 The round 1 data are from an internal version of the NLSY97

Public Use & Event History Data (Release 1.1); the round 2 variables

are from an internal version of the round 2 data from September

1999. Survey staff may edit these data as additional information becomes available.

27 Hispanic was not a choice in the race question; some screener

respondents reported that the household members did not fit into

standard racial categories and specified “Hispanic” in answer to the

race question.

28 For each NLSY97 question, a value of 1 was assigned if the youth

reported 75 percent or more of his or her peers engaged in that

activity; otherwise, the variable was coded as a zero. When more than

1 activity is combined to form a dummy variable, the youth must have

answered 75 percent to at least 1 of the NLSY97 questions for the value

to equal one; otherwise the variable was coded as a zero.

29 Freeman, “Who Escapes?...,” NBER Working Paper 1656, June 1985.

30 Respondents living in the northeast are coded as 1 if they reside

in Connecticut, Maine, Massachusetts, New Hampshire, New Jersey,

New York, Pennsylvania, Rhode Island, or Vermont and zero otherwise. Respondents coded as living in the north central region included

those who lived in Illinois, Indiana, Iowa, Kansas, Michigan, Minnesota, Missouri, Nebraska, North Dakota, Ohio, South Dakota, and

Wisconsin. The states that comprised the southern region included

Alabama, Arkansas, Delaware, District of Columbia, Florida, Georgia,

Kentucky, Louisiana, Maryland, Mississippi, North Carolina, Oklahoma, South Carolina, Tennessee, Texas, Virginia, and West Virginia.

Residents of other states were coded as living in the West.

31 This variable defines urban as a place with a population of 2,500

residents or more. Youths in urbanized areas with less than 2,500

youths are defined as “rural.”

32 County and City Data Book: 1994 (U.S. Department of Commerce, Bureau of the Census, 1995).

33 Using monthly totals from the CPS and aggregating to an annual

figure, Diane Wescott (in “The youngest workers: 14- and 15-yearolds,” Monthly Labor Review, February 1981, 65-69) found lower

employment rates than did Michael and Tuma. She reports that nearly

21 percent of 14- and 15-year-olds worked in the late 1970s.

34 It should be noted that Michael and Tuma used the CPS section

from the NLSY79 to calculate their figures; this section asks about labor

force activity in the week prior to the survey, regardless of month.

The NLSY97 numbers come from the YEMP section, in which all employee-type jobs and all freelance jobs are collected in a roster. The

percentages stated in this article refer to employee-type jobs that the

respondent reported working in the 4 weeks prior to the survey or to

freelance jobs that were current at the time of the survey. It should be

noted that respondents ages 12 and 13 are only able to report jobs in

the freelance section, regardless of the job type (for example,,

freelance or employee-type). Undercounting of employee-type jobs

at these ages is most likely minimal, as these youths are not legally

allowed to hold most employee-type jobs.

35 The 1979 field period for the NLSY79 was January 1979 to August

1979, with the majority of respondents interviewed during the school

year when jobholding would have been lower. The round 1 NLSY97 field

period began in January 1997 to October 1997 and March 1998 to

May 1998. Due the screen-and-go method used in the NLSY97, more

respondents were interviewed during the summer months than in late

winter and spring. In addition, the NLSY79 did not distinguish between

freelance jobs and employee-type jobs, whereas the NLSY97 prompted

respondents to report each type of job separately. As a result, it is not

possible to determine the extent to which the NLSY97 may collect

information on more jobs due to survey design rather than actual

increases in jobholding.

36 Tables 5–9 contain probit estimates. The partial derivatives of

probability of outcome associated with dependent variable with respect to independent variables, and standard errors of these estimated

derivatives, are reported.

37 Freeman, “Who Escapes?...,” NBER Working Paper 1656, June 1985.

38 A fixed effects model using NLSY97 sibling data may remove the

family-specific portion of the unobserved heterogeneity; however,

this cohort is not yet old enough to yield enough sibling pairs to do

this type of estimation.

39 Michael and Tuma, “Youth Employment...,” 464–76.

Any member without a valid age, race, and ethnicity is deleted, leaving 8,960 respondents. Next, 359 youths without at

least one biological parent, adoptive parent, stepparent, or

foster parent listed on the household roster are deleted from

the sample. Other relatives listed on the household roster

may serve as a legal guardian (for example, grandparent, aunt,

uncle); however, it is unclear in the literature whether the

employment behavior of a legal guardian has a different impact on the youth than the employment behavior of a parent.

As a result, respondents living in this situation are dropped

Appendix: Sample construction

This appendix explains the selection of respondents for consideration in this research. Due to the types of information

required for this study, some NLSY97 respondents had to be

excluded if given variables were unavailable. The criteria for

the two parts of the study are somewhat different; the restrictions for the examination of job holding at age 14 are described first and the restrictions for the examination of early

employment as an indicator of employment at age 16 are then

discussed.

The selection criteria for the sample one are as follows.
Monthly Labor Review August 2001 67

from the sample. Nine respondents who have missing or incomplete freelance or employee-type job data are dropped

from the sample, leaving 8,592 eligible sample members. Finally, 81 observations with other missing values are dropped

from the sample. The final sample size from the round 1 data is

8,511 respondents; of these, 5,743 were age 14 or over at the

time of the survey and were eligible to report employee-type

jobs in the employment section (all respondents answered

questions about freelance jobs).

The article examines whether work experience at the age of

14 has an impact on employment probabilities for youth aged

16 and older. Answering this question requires information

on the youth’s residence both at age 14 and at the most recent interview date because the spatial mismatch literature

indicates that this may affect employment probability. Because most respondents had not reached age 16 at the time of

the round 1 interview, the study also uses data from round 2

to increase the sample size. (Residence data in the round 1

survey are fairly limited; parents report the number of residences in which the youth respondent has lived since age 12

but do not provide any information about the location of

these residences. Thus, youths who had moved cannot be

included in the sample. Including the round 2 data increases

the full sample size to 2,512; of these 581 respondents are

black, 478 are Hispanic, and 1,451 are nonblack/non-Hispanic.)

The restrictions imposed for sample two are presented in

table A-1. Construction of the sample for this section begins

with the 8,511 respondents eligible in sample one. There are

several additional requirements. First, all respondents must

be age 16 or older by their most recent interview date. This

decreases the sample to 4,930 respondents. Youths who lived

outside of the parental home in round 2 were dropped from

the sample, leaving a sample size of 4,628. As in the first

sample, those with missing data for jobs and other variables

were also deleted from sample two; 4,591 remained eligible.

Sample two further requires that the respondent must not

have moved during the period under consideration to ensure

that residence information is accurate. This requirement is

needed because the locations of residences prior to the initial

interview are not recorded and the round 2 geocode data

were not available when this research was conducted. Respondents remaining in the sample had to fulfill one of the

following restrictions.

1. The respondent was age 16 or older during round 1,

had not moved since the age of 12, and was not interviewed in round 2. All information is from the round 1

interview.

2. The respondent was age 15 or older during round 1,

had not moved since the age of 12, and did not move

between round 1 and round 2. All information, except

the since age 12 residence question, is from the round

2 interview. Round 2 location information is taken from

the interviewer locator questions at the end of the

survey, when respondents are asked to report

changes to their address. However, including only

those who report no changes understates the number of nonmovers since some respondents updated

incorrect round 1 address information but had not

moved. Regardless, this restriction is necessary (until the round 2 geocode data are available) to ensure

that all county-level information is correct.

3. The respondent was age 14 or younger during round

1 and did not move between the round 1 and round 2

survey dates. All information is from the round 2 interview. As in the second restriction above, the location information is taken from the locator questions

at the end of the survey and may understate the number of nonmovers.

This information about the construction of the sample is summarized in table A-1.

Sample one

Full round 1 sample .......................................................... 8,984

Delete observations with age, race,

 or ethnicity missing ....................................................... 8,960

Delete observations without parent listed

 on the household roster ................................................ 8,601

Delete observations with employment data missing ......... 8,592

Delete observations with other data missing .................... 8,511

Sample two

Delete observations less than 16 years of age

 by latest interview date ................................................. 4,930

Delete observations without parent listed

 on household roster ...................................................... 4,628

Delete observations with employment data missing ......... 4,591

Delete observations with other data missing .................... 4,569

Delete observations with missing residence data ............ 2,512

Table A-1.

Sample

modification

Total sample

remaining

NOTE: Residence data in the round 1 survey are fairly limited;

parents report the number of residences in which the youth has lived

since age 12, but do not provide any information about the location of

these residences. Thus, youths who had moved cannot be included in

the sample. Including the round 2 data increases the full sample size

to 2,512; of these, 581 respondents are black, 478 are Hispanic, and

1,451 are nonblack/non-Hispanic.

NLSY97 sample construction