42  Monthly Labor Review • September 2008

Conference Report

For more than 40 years, the U.S. Department of Labor has undertaken a series of major, national studies 

that track labor force behavior. These studies follow the same men and women, year 

after year, and by doing so reveal much 

about what affects wages and hours of work, 

how new skills influence success in the job 

market, how health and schooling interact 

to influence careers, and how unexpected 

events—from plant closings and bad weather to product innovations and the openings 

of new markets—affect earnings. The National Longitudinal Surveys (NLS) program 

has become one of the Nation’s most respected and influential sources of data about 

the work force since its inception in 1966, 

administered through the Employment and 

Training Administration until 1984 and 

through BLS thereafter. The NLS program 

consists of seven samples of men and women who have been surveyed periodically and 

have reported on many of their behaviors in 

and related to labor markets. These surveys 

have been used in thousands of research 

projects within the Government and in research universities and analytic think tanks. 

The studies constitute a major component of 

what researchers now know about the roles 

of schooling, intellectual ability, health, miDan Black,

Robert Michael,

and Charles Pierret

Dan Black is a professor

of public policy at the

University of Chicago and

Principal Investigator of

the National Longitudi- nal Survey of Youth 1997

Cohort at NORC.

Robert Michael is the

Eliakim Hastings Moore

Distinguished Service

Professor Emeritus at the

University of Chicago

and the Project Director

of the National Longitudinal Surveys Program at

NORC.

Charles Pierret is director

of the National Longitudinal Surveys Program at

the Bureau of Labor Sta- tistics. E-mail:

pierret.charles@bls.gov

The statements in this

article do not necessarily

reflect the views of any

of the aforementioned

institutions.

Knowing younger workers better:

information from the NLSY97

Papers from the 10th anniversary conference 

of the National Longitudinal Survey of Youth, 

1997 cohort, addressed schooling, employment, adolescent 

behaviors, and many other aspects of youths’ lives

gration, community, and family in developing the “human capital” and “social capital” 

that influence the distribution of earnings 

in the United States and the level of our 

Nation’s gross domestic product. 

In May 2008, BLS hosted a conference 

to highlight new research using the most 

recent data from one of these data sources, 

the National Longitudinal Survey of Youth, 

1997 cohort (NLSY97).1 This survey of 

young people born from 1980 to 1984 (age 

12 to 17 in the first year of the survey) has 

now taken place for 10 consecutive years. 

The face-to-face interview of these youths 

asks about their schooling, employment, adolescent behaviors, and many other aspects 

of their lives. In the data that were available 

for study at the time of the conference, these 

nearly 9,000 men and women from across 

the Nation were only in their early- to mid20s, but already their reported experiences 

and behaviors revealed important facts that 

will have an impact on the labor force for 

decades to come. This article offers a brief 

and informal characterization of a few of the 

studies on which presenters reported at the 

conference. The conference presentations 

were based on preliminary research findings 

of these studies that are now undergoing 

peer scrutiny prior to official publication in 
Monthly Labor Review • September 2008  43

scholarly journals and books. (See the box.) 

Employment

Changing characteristics of youth. Employment of the 

NLSY97 youths is perhaps the central behavior of interest. 

One important paper concerning employment presented 

at the conference was written by Joseph Altonji, Prashant 

Bharadwaj, and Fabian Lange from Yale University and 

entitled “Changes in the Characteristics of American 

Youth: Implications for Adult Outcomes.” The paper asks 

what one can predict today about the labor force 20 years 

from now when the NLSY97 cohort will be in its peak 

earning years. The analysis is based on the experiences of 

the National Longitudinal Survey of Youth 1979 Cohort 

(NLSY79)—an earlier NLS cohort, fielded in 1979—with 

respondents born between 1957 and 1964. The authors 

use the relationship between early labor-market-relevant 

characteristics of youths in the NLSY79 and their subsequent mid-career labor market outcomes to predict midcareer labor market outcomes of the NLSY97 cohort on 

the basis of their current characteristics. 

The paper comprises two parts. In the first, the authors 

“create a set of youth characteristics that correlate with 

adult outcomes and are comparable across the NLSY97 

and the NLSY79.” Even though the authors attempt to 

make the two data sets directly comparable, differences 

in sampling, attrition, and questions make this a complicated exercise. For example, the NLSY97 was sampled 

at younger ages (12–17) than the NLSY79 (14–22). Although a greater percent of youths eligible for the sample 

were actually interviewed in the first round of the NLSY97, 

Joseph G. Altonji, Prashant Bharadwaj, and Fabian Lange, 

“Changes in the Characteristics of American Youth: Implications for Adult Outcomes.” 

Joseph G. Altonji, Sarah Cattan, and Iain Ware, “Sibling 

Influences on Teenage Risky Behaviors.”

Alison Aughinbaugh and Rosella M. Gardecki, “Attrition 

in the National Longitudinal Survey of Youth 1997.”

Philippe Belley, Marc Frenette, and Lance Lochner, “PostSecondary Attendance by Parental Income: A Canada-U.S. 

Comparison.” 

Dan A. Black, Kerwin Charles, and Seth Sanders, “The 

Problem with Men.”

Dan A. Black, Robert T. Michael, and Kanru Xia, “The 

Propensity to be an NLSY97 Respondent: Evidence from the 

Screener Data.”

A. Rupa Datta Parvati Krishnamurty, “High School Experience: Comparing Self-Report and Transcript Data from 

the NLSY97.” 

Keith Finlay, “Effect of Employer Access to Criminal History Data on the Labor Market Outcomes of Ex-Offenders 

and Non-Offenders.” 

Tricia Gladden and Charles Pierret, “Employment Before 

Age 16: Does it Make a Difference?”

Jeffrey Grogger, “Speech Patterns and Black-White Wage 

Inequality.”

Carolyn J. Hill, Harry J. Holzer and Henry Chen, “Against 

the Tide: Household Structure, Opportunities, and Outcomes 

among White and Minority Youth,” chapters 3 and 4.

Robert Kaestner and Michael Grossman, “Effects of 

Weight on Adolescent Educational Attainment.”

Jennifer Manlove, Mindy E. Scott, Erum Ikramullah, Kate 

Perper, and Emily Lilja, “Relationship Context and the Transition to a Nonmarital Birth.” 

Kristin Moore, and Kassim Mbwana, “Preventing Risky 

Sex and Adolescent Parenthood: Does the Effectiveness of 

Parenting Practices Differ For Children with Varied Risks?”

Randall J.Olsen, “The Desirability of Partner Traits and 

Two Decades of Change in the Marriage Market: A Oneand-a-Half Sex Model of Marriage.” 

Michael R. Pergamit, “Who Runs Away from Home? An 

Exploratory Analysis.” 

James R. Walker, “Choice, Enrollment and Educational 

Attainment within the NLSY79 and NLSY97.” 

Kenneth I. Wolpin, and Antonio Merlo, “Youth Crime and 

High School Completion.” 

Lawrence Wu and Pamela Kaufman, “Two Decades of 

Change in Premarital First Births: Cohort Comparisons from 

the NLSY79 and NLSY97.” 

Tenth Anniversary Conference Papers, NLSY97, May 29–30, 2008

NOTE: Many of those papers which are available can be found online at: http://harrisschool.uchicago.edu/research/conferences/NLSYConf/
Conference Report

44  Monthly Labor Review • September 2008

subsequent attrition has been higher. Because they were 

younger when they were first interviewed, NLSY97 sample 

members had more years to drop out of the survey before 

age 22, when many of the characteristics that the authors 

study are measured. The authors devote a great deal of effort to ensuring that any differences in measured characteristics are real and not an artifact of survey differences.

The authors’ most substantive finding is important: they 

find that the NLSY97 had more skills at the age of 22 than 

the NLSY79 did. The greatest advantage of the NLSY97 

was in education; along all measured dimensions of educational attainment, the younger cohort was clearly superior to the older cohort. By age 22, the 1997 cohort had 

completed more than one-third of a year more of school, 

was more likely to have a high school diploma—or, failing 

that, to have a GED—and was much more likely to still 

be attending school or to have finished 14 years of school 

than the 1979 cohort. This skills advantage manifested itself in significant gains on the Armed Forces Qualifying 

Test (AFQT), the test the military uses to determine skill 

levels when making admission and job assignments. These 

gains were especially remarkable for minority youth, with 

African Americans’ (or Blacks’) scores improving by 36 

percent and Hispanics’ scores improving by 24 percent 

between the two cohorts (compared with a 5 percent improvement for Whites). Gains in parents’ education were 

also significant, with the average NLSY97 youth having a 

mother with 1 year more of education and a father with 

three-quarters of a year more education than the mother 

and father of the youth’s counterpart in the NLSY79.

Where the 1997 cohort falls short in comparison 

with the 1979 cohort is in the area of family structure. 

A much larger percentage (47 percent versus 25 percent) 

of the 1997 cohort was living in families in which one of 

the parents was not present. So although parents of the 

younger cohort had more skills to impart to their children, 

they had less contact with their children.

The second part of the Altonji, Bharadwaj, and Lange 

paper uses the reported childhood experiences from the 

1979 and 1997 cohorts, along with the experiences from 

adulthood from the 1979 cohort, to predict outcomes for 

the 1997 cohort as adults. Using the characteristics derived in the first part of the paper, the authors estimate 

the impact that changes in skill level will have on the 

wage distribution when the cohort has reached middle 

age. Overall, they expect wages to increase by 6 percent to 

7 percent, though the increase will be greater at the upper 

end of the distribution and lesser at the lower end. This 

means an increase in inequality over the next decades. 

The authors suggest that increases in skills for groups 

that were relatively disadvantaged in the 1979 cohort, 

however, will result in diminishing gaps between the sexes 

and among races. Black and Hispanic males will gain significantly on white males except at the very top of the 

wage distribution. From the bottom of the wage distribution to the 90th percentile, the wage gap should close by 

about 4 percentage points for both black and Hispanic 

males relative to white males. Similarly, wage gains for 

females should exceed those of males, causing the wage 

gap between the sexes to decrease by around 2 percentage 

points. Within-group inequality will grow as skills become 

more unequal within groups, but average skills across sex 

and race groups will become less unequal, resulting in less 

wage inequality across groups. So while the increase in 

inequality that has plagued the economy for the last 30 

years is likely to continue, it will be based less on race and 

sex than it has been in the past. 

The authors remind readers that their conclusions rest, 

necessarily, on the assumptions that the labor market premium or discount for a racial or ethnic group or for one 

sex or the other remains the same over time. Similarly, 

their expectations of the future labor market do not take 

into account broader questions pertaining to how the financial returns of schooling will change as markets and 

products develop or how the continued competitiveness 

of global markets might affect labor market trends. In this 

sense, the analysis undertaken by Altonji, Bharadwaj, and 

Lange offers only a partial answer to the question of how 

the workforce will fare in the years ahead, but their answer, cautiously constructed and conditioned as it is, uses 

these NLSY longitudinal data sets in the best way possible 

and offers a decidedly optimistic assessment of future developments in the labor force.

Employment before age 16. Another paper from the conference that focuses on employment is one that exploits 

the NLSY97’s data on work history and its links across several domains to examine the consequences of employment 

at a very young age among the youths in the cohort. Tricia 

Gladden and Charles Pierret from the Bureau of Labor 

Statistics use the extensive data on very early employment 

in the NLSY97 in their paper “Employment Before Age 

16: Does it Make a Difference?” They point out that collecting information on teen employment was a key reason 

that the survey was started. Standard labor market surveys 

such as the Current Population Survey only report about 

employment starting at age 16. However, a majority of 

youths in the NLSY97 reported doing some work for pay 

before this age. Gladden and Pierret posit that it is unclear whether early employment is ultimately beneficial 
Monthly Labor Review • September 2008  45

to these youths. On the one hand, early employment may 

teach important lessons such as responsibility, perseverance, and self-reliance and allow youths to accumulate 

experience that will prove useful later in their careers. On 

the other hand, early employment may be distracting, 

taking youths away from educational and developmental 

activities that will prove more beneficial than the menial 

jobs that are available to young workers. It may also introduce them to older youths who are engaged in behaviors 

that are not age-appropriate for the young workers. Gladden and Pierret’s paper explores the correlation between 

youth employment and a number of outcomes in the late 

teen years as a first attempt to measure the effects of early 

employment. 

The NLSY97 interviewed youths as young as 12 and 

asked them to report on jobs they held at any time after their 12th birthday. Because these children were not 

legally able to hold a job with an employer, the NLSY97 

concentrated on “freelance jobs” among this group. These 

are informal jobs such as babysitting or yard work where 

the employee works directly for the ultimate consumer of 

the service, usually on an as-needed basis. Respondents 

older than 14 were also asked about traditional “employee 

jobs”—that is, those in which the youth worked for an 

employer who provided goods or services to many consumers. Restaurants and retail establishments provided 

typical employee jobs for teens in the sample.

Gladden and Pierret identify respondents who worked 

in freelance jobs between the ages of 11 and 15 and those 

who worked in employee jobs at 14 or 15. They then follow these youths until the age of 20, examining various 

outcomes along the way. Two findings are notable from 

this research. First, once youths enter the labor force, they 

tend to continue to work throughout their teen years. Between 80 percent and 90 percent of youths who worked at 

a given age worked again at the next age. Thus, those who 

start young will likely continue to work at least part of the 

year until age 20. Second, after controlling for standard 

background variables (race, sex, income, family structure, 

parents’ education, and AFQT score) working at freelance 

jobs at young ages is correlated with a number of negative 

outcomes. Those who worked at freelance jobs before age 

15 achieved less schooling by age 20; smoked, drank alcohol, and used marijuana more often before age 16; and 

were more likely to carry a handgun, assault someone, or 

be arrested by age 18 than youths who waited until age 16 

for their first job. Gladden and Pierret are quick to point 

out that this may be largely an effect of selection—those 

who are likely to work at a young age may also be the type 

to want less schooling and to engage in substance abuse 

and delinquent behavior, in which case the correlation 

does not imply that working per se causes these behaviors. 

But the link between early employment and these outcomes certainly warrants further investigation. 

Access to criminal records. One of the attractive features of 

the NLSY97 data set is that it captures a lot of information 

that is tangentially related to employment. One of these 

pieces of information is the youth’s criminal record—the 

data include information on many illegal actions that 

resulted in arrests, convictions, periods of incarceration, 

and other run-ins with the law. Incarcerations, naturally, 

influence labor market behavior, especially when youths 

are incarcerated long enough to prevent them from participating in the regular labor market. The NLSY97, being 

a longitudinal data set, can be used to assess the impact of 

the incarceration on subsequent employment. 

Keith Finlay from Tulane University, in his paper “Effect of Employer Access to Criminal History Data on 

the Labor Market Outcomes of Ex-Offenders and NonOffenders,” uses the information about incarceration 

and subsequent employment along with one other piece 

of information—the State in which the young man or 

woman resides post incarceration. He points out that over 

the interval of interest for these cohorts of youths—1997 

to 2003—some 16 States, starting with Florida in 1997, 

adopted the practice of releasing on the Internet information from the criminal records of all convicted felons. 

Finlay studies the employment experience of people who 

have and have not been incarcerated, in States with and 

without Internet reporting. An employer may have a notion that a job applicant of a particular type—age, sex, 

race, or ethnic group, for example—is more likely to have 

a criminal record. If this notion causes the employer not 

to hire someone of that type, this is a phenomenon called 

“statistical discrimination.” However, argues Finlay, in a 

State that puts information concerning people’s criminal 

records on the Internet—making it easy for employers to 

determine whether a particular job candidate is a convicted felon—employers have far less reason to “statistically discriminate” against non-felons. In short, this State 

policy is expected to be detrimental to the employment 

prospects of people who have been incarcerated but to be 

helpful to those from high-incarcerated groups who have 

not themselves been jailed. 

Finlay explains that there are 369 NLSY97 respondents 

who have been incarcerated as adults (4.4 percent of his 

whole sample). For men age 19, the cumulative rates of 

adult incarceration were: 3 percent of white males, 8 percent of African-American males, 4 percent of Hispanic 
Conference Report

46  Monthly Labor Review • September 2008

males, and less than 1 percent of each of the three groups 

of females. For men age 24, however, the cumulative rates 

of those same six groups were dramatically higher: 8 

percent of white males, 19 percent of African-American 

males, 12 percent of Hispanic males, and 2 percent to 3 

percent of the respective groups of females. 

Finlay studies the relationship between incarceration 

and employment, wages, and earnings; his findings confirm his expectations: “ex-offenders are less likely to be 

employed, have lower wages, and have lower earnings in 

[S]tates with Internet sites providing information about 

ex-offenders.” And the magnitude of this effect is considerable: in the open-records States, ex-offenders have 

a 5-percentage-point lower likelihood of employment, 9 

percent lower hourly wages and 19 percent lower annual 

earnings. The evidence is less striking, but again affirming, for the effects of open records for non-offenders from 

groups with high rates of incarceration; however, the association is not statistically significant.

Education

Educational attainment. Education is certainly a key factor in the attainment of a successful career. The NLS data 

sets, with their depth of information on the educational 

experiences of cohorts 20 years apart, provide excellent 

data on the change in educational attainment over the 

last 2 decades. James Walker of the University of Wisconsin at Madison, in his paper titled “College Choice, 

Enrollment and Educational Attainment in the NLSY79 

and NLSY97,” provides a detailed comparison of the two 

cohorts and emphasizes some fascinating developments 

in the educational attainment of individuals in the two 

data sets at ages 24 or 25. He reports an increase in mean 

years of schooling of 0.4 year from the 1979 cohort to the 

1997 cohort; median years of schooling increased from 

12 years for the 1979 cohort to 13 years for the cohort 

of 1997. Somewhat surprisingly, the interquartile range 

of schooling increased dramatically, from 1.5 years in the 

NLSY79 to 3.5 years in the NLSY97. 

Walker documents a substantial decline in the percentage of people who did not obtain a high school diploma or 

pass the General Educational Development (GED) tests. 

Among males, for example, this fraction dropped from 

14.8 percent in the 1979 cohort to just 7.6 percent in the 

1997 cohort. For women, the drop was a bit less dramatic, 

from 11.8 percent in the 1979 cohort to 7.8 percent in the 

1997 cohort. One can see the same pattern of improvement in education when considering those without a high 

school degree—either dropouts or those with GEDs. The 

percentage of men without a high school degree declined 

from 23.8 percent in the 1979 cohort to just 16.7 percent 

in the 1997 cohort. For women, the gain is again somewhat muted; in the 1979 cohort, 19.7 percent of women 

did not have a high school degree, but by the 1997 cohort 

the figure had shrunk to 15.1 percent. This decline represents a substantial improvement in human capital across 

these two cohorts.

Results at other levels of education are equally encouraging. About 20.9 percent of men in the 1979 cohort had 

a bachelor’s degree, a figure that increased to 24.2 percent 

in the 1997 cohort. For women, the increase was astonishing; in the 1979 cohort, 18.6 percent had a bachelor’s 

degree, but by the 1997 cohort, 30.4 percent of women 

had a bachelor’s degree. Thus, in the 1979 cohort, there 

were 1.12 men for each woman with a bachelor’s degree, 

but by the 1997 cohort, this had fallen to just 0.80 man 

per woman.

This striking change reflects a difference between the 

sexes in college enrollment rates—while men’s attendance 

at 4-year universities increased from 34.3 percent to 42.3 

percent, women’s attendance at 4-year universities increased from 30.9 percent to 47.8 percent. The graduation 

rate conditional on attending 4-year universities declined 

for men from 60.9 percent in the 1979 cohort to 57.2 

percent in the 1997 cohort. Despite the large increase in 

college attendance among women, their graduation rate 

increased from 60.2 percent to 63.6 percent. Thus, in the 

1997 cohort, women were more likely than men to attend 

university, and those who did were more likely than men 

to graduate. 

African Americans, too, made considerable progress, 

although the gains are much more concentrated in the 

upper end of the distribution for Blacks than for Whites. 

For instance, the percentage of black respondents who 

did not obtain either a GED or a high school diploma 

declined from 16.5 to 13.5 from the 1979 cohort to the 

1997 cohort, whereas the corresponding percentage of 

white respondents declined from 11.3 to 5.8. Thus, despite starting from a smaller percentage of nongraduates, 

Whites experienced a greater decline in the percentage 

who did not obtain either a GED or high school diploma 

than did Blacks. Similarly, the percentage of black respondents without a high school degree was essentially unchanged, increasing from 25.2 in the 1979 cohort to 25.3 

in the 1997 cohort. For Whites, however, that percentage 

dropped from 19.0 in the 1979 cohort to 13.2 in the 1997 

cohort. Progress was even more dramatic for Hispanics. In 

the 1979 cohort, 36.5 percent of respondents did not have 

a high school degree, but this dropped to 19.6 percent in 
Monthly Labor Review • September 2008  47

the 1997 cohort. Thus, in one generation, African Americans replaced Hispanic Americans as the group having the 

highest fraction of youth without a high school degree.

At the other end of the distribution, however, African 

Americans showed a much more substantial improvement 

than did Hispanics. In the 1979 cohort, 8.5 percent of 

the African American population had a bachelor’s degree 

by age 25, but this percentage grew to 15.0 by the 1997 

cohort. In contrast, in the 1979 cohort, 9.4 percent of Hispanics had a bachelor’s degree by age 25, but this grew 

much less rapidly, to 11.7 percent in the 1997 cohort. By 

comparison, the percentage of whites with a bachelor’s 

degree grew from 23.9 in the 1979 cohort to 32.6 in the 

later cohort. 

Thus, there is a very distinctive pattern among the three 

major race/ethnic groups. For Whites, education levels 

have increased across the distribution, with fewer who 

fail to obtain a high school degree and an ever-greater 

proportion obtaining a bachelor’s degree. The 1980s and 

1990s were a period of spectacular increase in the returns 

to investment of schooling, and the change in the behavior of the white Americans in the cohort is generally and 

properly viewed as a response to that increase in returns. 

In contrast, the Hispanic Americans in the cohort exhibited a modest growth in the proportion obtaining a 

bachelor’s degree but a substantial decline in the proportion without a high school degree. Thus, the distribution 

of education levels among Hispanics became much more 

concentrated in younger cohorts. African Americans had 

a substantial expansion in the proportion with a bachelor’s 

degree but virtually no change in the proportion without 

a high school degree. Thus, the distribution of educational 

levels among African Americans became more diffuse in 

the younger cohorts. Understanding the reasons for these 

three distinct changes in the distribution of educational 

levels will be an important goal for future research.

Walker also reports differences in educational attainment by the respondents’ scores on the Armed Forces 

Qualification Test (AFQT). He divides the respondents 

into thirds (“terciles”), and reports the educational attainment of each. Here, again, the news is good: in each ability 

tercile the fraction without a high school degree declined 

and the fraction with a bachelor’s degree increased. Not 

surprisingly, the largest drop in the proportion of people 

without a high school degree was in the lowest tercile of 

AFQT scores. In the 1979 cohort, 39.5 percent of the lowest ability third did not receive a high school degree, but 

this fell to 35.3 percent in the 1997 cohort. Walker also 

documents a large increase in the proportion of people 

getting a GED in this bottom tercile: 11.3 percent did so 

in the 1979 cohort, whereas 14.3 percent did so in the 

1997 cohort. The largest growth in the proportion with a 

bachelor’s degree occurred in the middle tercile of AFQT

scores, a rise from 18.2 percent in the 1979 cohort to 22.1 

percent in the 1997 cohort. 

The effects of parental resources. A similar pattern emerges 

when Walker partitions the sample into terciles by parents’ income, measured in the first round for both cohorts. 

From the 1979 cohort to the 1997 cohort, in each tercile 

the proportion without a high school degree declined, and 

the proportion with a bachelor’s degree increased. There is 

one important difference in the results for parental income 

compared with the results for the AFQT. The greatest gain 

in the proportion obtaining a bachelor’s degree occurred 

in the lowest tercile of the AFQT score distribution but in 

the highest tercile of parental income. Indeed, there is a 

strong monotonic relationship between income and the 

percentage point gain in the proportion with a bachelor’s 

degree: the highest tercile had an 11.4-percentage-point 

increase, the middle tercile had a 7.8-percentage-point 

increase, and the lowest tercile only had a 1.7 percentagepoint increase. Thus, the correlation between the possession of a bachelor’s degree and parental income became 

even stronger in the younger cohort.

This increased correlation of educational attainment 

and parental income suggests a growing importance of 

parental resources in determining who can afford college. 

In a paper they presented at the recent NLSY97 conference, Philippe Belley and Lance Lochner of the University of Western Ontario and Marc Frenette of Statistics 

Canada reported on a preliminary investigation that is 

further exploring this correlation using the NLSY97 and 

a Canadian longitudinal data set.2 They expand upon a 

paper that Belley and Lochner recently published in the 

first issue of the Journal of Human Capital;

3

 in it, Belley 

and Lochner use a structural model and the NLSY79 and 

NLSY97 to estimate the impact of parental resources on 

educational attainment. Consistent with several other 

studies, Belley and Lochner find that parental income and 

resources played virtually no role in the determination of 

enrollment rates for the 1979 cohort. For the 1997 cohort, 

however, parental resources were much more important 

in determining who attended college. The paper explains 

that parental income is important because students are 

constrained from borrowing against their future earnings. 

Thus, though it makes economic sense to attend college, 

many members of the younger cohort were able to do so 

only if their parents could help them financially.

Both the paper published in the Journal of Human Cap-
Conference Report

48  Monthly Labor Review • September 2008

ital and the paper presented at the conference highlight a 

potentially serious problem in American higher education. 

In the years between the two cohorts, the cost of highquality university education has skyrocketed. For instance, 

the Chicago Tribune has reported that the cost of sending 

an in-State student to the University of Illinois at Champaign-Urbana—an elite public institution—now exceeds 

$20,000 a year. Because the growth of college tuition and 

fees has far outstripped the growth in federally funded 

student loans, one might expect that the increased costs 

would limit access to costly, elite schools. Nevertheless, 

Americans face a staggering quantity of choice in higher 

education with wide variation in prices. Community colleges, for example, often represent an attractive option at 

a price that is an order of magnitude lower than the cost 

at an elite school. These drastically lower prices, coupled 

with the possibility of living at home and avoiding additional costs, could lead one to believe that capital market 

constraints would not prevent aspiring college students 

from attending higher education.

However, there exists evidence to the contrary. In a series of papers, Todd R. Stinebrickner of the University of 

Western Ontario and Ralph Stinebrickner of Berea College examine the behavior of Berea College students. Berea 

is an especially useful college to study because it charges 

no tuition and provides students with a modest stipend 

as payment for a campus job. The policy is intended to 

assure that no students are excluded from Berea because 

they cannot afford the tuition bill. Yet, Stinebrickner and 

Stinebrickner find that despite the free tuition and limited 

direct cost of attending Berea, family income is still critically important for graduation.4

 The reason seems to be 

that there are many events and circumstances—a parent’s 

illness or unemployment, for example—that may make 

it difficult for students to complete their college studies. 

Students from wealthier families have a larger number of 

options available to address these difficulties. Understanding the roles of capital markets and family resources in 

accessing and completing college is an important research 

agenda for the future. 

Obesity. A topic that has been a focus of much research 

in health economics is the direction of causality in the 

strong link between health and schooling. Some researchers suggest that schooling affects health, others suggest 

that health affects schooling, and still others suggest that 

there are other factors— third forces—that influence both 

in the same direction, causing the observed positive association. One of the authors of a paper at the recent NLSY97 

conference, Michael Grossman of the City University of 

New York Graduate Center, has been the primary scholar 

in this debate over the past several decades; the paper he 

and his colleague, Robert Kaestner of the University of 

Illinois at Chicago, presented at the conference addresses 

one small piece of this puzzle.5

Kaestner and Grossman note that adolescent obesity has risen dramatically in recent years, and they ask 

whether obesity has an effect on educational attainment 

among adolescents. If it does, that would be one avenue 

through which health status influences the level of education. Kaestner and Grossman point out that a relationship 

between obesity and educational attainment could work 

in several ways logically, and economic theory alone does 

not shed much light on which of several potential routes 

of influence might dominate. Obese adolescents might 

suffer from discrimination from teachers and/or peers 

that could adversely affect their schooling, and they might 

also have related health troubles such as sleeping disorders 

and depression that could adversely affect their cognitive 

functioning or cause them to miss days of school. Conversely, overweight youths might engage less in sports and 

physical activities and even in social activities, and as a 

result they may spend more, not less, time studying and 

thus perform better academically. Kaestner and Grossman 

turn to the NLSY97 data for evidence.

This is a case in which a negative finding is noteworthy. 

After undertaking a quite thorough study, with sophisticated formal theoretical modeling and statistical analyses, 

the researchers conclude that there is very little evidence 

in the NLSY97 data that obesity has any discernible effect on the educational attainment of these young adults, 

either positive or negative. They study boys and girls separately, looking at the extreme tails of the distribution of 

weight and noting the highest grade of school attended, 

the highest grade completed, and whether or not the student dropped out of school. In neither estimates from very 

simple models nor in Kaestner and Grossman’s estimates 

from quite complex and highly controlled models is there 

evidence of an effect of weight on schooling. Obesity, they 

conclude, does not play a direct role in the strong, positive 

association between health and schooling.

Social Behaviors

Although a primary motivation for the NLS program is 

a better understanding of the labor market experiences 

of the workforce, BLS has understood the importance of 

investigating a wide range of other behaviors, both within 

the family and in the community, as forces that affect 

employment, marketable skills, occupation choices and 
Monthly Labor Review • September 2008  49

opportunities, and career trajectories, as well as hours of 

work, wages, and earnings. The NLS data sets have long 

been used for studying many types of youth and adult behaviors, and the recent conference suggests that the most 

recent NLSY97 data have much to contribute to our understanding of family and youth behaviors. 

Marriage and offspring. Robert Michael of the University 

of Chicago, in remarks that opened the conference, pointed 

to both the continuity and change in demographic trends 

between the 1979 and 1997 cohorts. The most dramatic 

trend, he claimed, is found in terms of formal marriage: 

8.7 percent of 18-year-old females in the 1979 cohort had 

married, whereas only 1.6 percent of their counterparts in 

the 1997 cohort had done so. By age 21 the trend was even 

more striking, with 33.4 percent of the females from the 

1979 cohort married but only 12.1 percent from the 1997 

cohort married. Similarly, 15.1 percent of 21-year-old 

men from the 1979 cohort were married, compared with 

5.2 percent from the 1997 cohort. Although these figures 

reflect the well-documented decline in formal marriage in 

the United States, if instead one considers the percentage 

of the 1997 cohort who have formed a dyadic partnership, 

the numbers look much like the 1979 numbers for formal 

marriages: 33.1 percent of the females reported having 

formed a cohabitational partnership, and 19.1 percent of 

the males reported having done so. The big decline is in 

formal marriage, not in forming a dyadic partnership. 

Concerning the percentage of young mothers, there 

was essentially no difference between the 1979 and 1997 

cohorts—7.8 percent of women in the 1979 cohort had a 

child by age 18, compared with 7.6 percent of the 1997 

cohort. The difference between cohorts in the percentage 

of those who were mothers by age 21 is also small; 23.2 

percent of the NLSY79 met the criteria, compared with 

23.8 percent of the NLSY97. For the males, there was a 

slight increase in reported parentage at age 18, with 1.3 

percent of the 1979 cohort having at least one child at 

age 18, compared with 2.3 percent of the 1997 cohort. 

By age 21, 8.6 percent of the males from the 1979 cohort 

reported being a father, compared with 11.2 percent of the 

males in the 1997 cohort.

Adolescent sexual activity. Researchers from Child 

Trends, a Washington, DC, think tank that focuses on 

issues of child development and policy, investigated the 

risky behavior of adolescent sexual activity and the role 

that parents play in affecting this behavior.6 Kristin Moore 

and Kassim Mbwana examined whether the youths who 

were 12–14 at the beginning of the survey began having 

sex before age 17 (53 percent did so), whether they used 

contraceptives or engaged in “unsafe sex” when they did 

have sex (16 percent were judged to have had unsafe sex 

in the 12 months before age 17), whether those who were 

sexually active had multiple partners by the time they 

turned 17 (some 44 percent had two or more partners), 

and whether or not they had become teenage parents before turning 18 (6 percent did so). This study examined 

three aspects of how the teenagers’ parents’ styles of supervision, guidance, and support affected these elements of 

the youths’ sexual behavior. First, the authors investigated 

the influences of different parenting styles on sexual risktaking by adolescents. Second, the researchers examined 

whether the influence of parenting style varied depending 

upon the risks that the adolescent faced. Finally, Moore 

and Mbwana examined whether parental awareness of 

children’s activities prevented the children from engaging 

in sexual activity.

The NLSY97 data have considerable detail regarding 

how parents guide and monitor their children’s social and 

private lives. One set of measures used in this study—measures that are well-explored by developmental psychologists and believed to be influential in the development of 

preschool and elementary school children—characterizes 

parental styles into a four-category typology: some parents are “authoritative” (which means they are rather strict, 

yet highly supportive, of their adolescent children), others 

are “permissive” (which means they are not strict, but are 

quite supportive), others are “uninvolved” (meaning they 

are neither strict with their children nor supportive), while 

still others are “authoritarian” (meaning they are strict, but 

not supportive). Moore and Mbwana’s study borrows this 

typology and uses it to analyze the influence of parenting 

styles on the sexual behaviors of adolescents. In particular, 

the study focuses on the influence of an “authoritative” 

(strict but supportive) style of parenting.

The findings at this stage in the investigation are robust ones: holding constant many of the known factors 

that affect adolescent behaviors, authoritative parenting 

was clearly associated with less sexual risk taking by girls, 

specifically through later initiation of sex, less unsafe sex, 

fewer sex partners, and lower rates of teenage parenting. 

For boys, the effects were not as strong, but where the 

effects were in evidence—in the age of onset of sexual activity—more authoritative parenting was associated with 

a delay in the age at first sex. 

Greater levels of risky sexual activity occurring among 

adolescents’ peers, in their schools, and in their neighborhoods were also associated with a higher probability of 

early sex, unsafe sex, more partners, and teen parenthood; 
Conference Report

50  Monthly Labor Review • September 2008

however, little evidence was found that the importance 

of parenting varies by risk level. These studies concerning parenting styles control for several important factors 

that also influence this behavior. For example, adolescents 

who live with both their biological parents engage in less 

sexual risk taking, those whose mothers were themselves 

teenage parents exhibit more risky sexual behaviors, and 

those who grew up in an impoverished family take more 

sexual risks. 

The last issue that the Moore and Mbwana paper explores is the influence of parental awareness of adolescents’ activities, as measured by how well the parents know 

their child’s close friends, how well they know those close 

friends’ parents, whether they know with whom their 

child spends time when he or she is not at home, and 

how well they know their child’s teachers. The findings 

suggest that parental awareness results in both boys and 

girls delaying sexual activity, engaging in less unsafe sex, 

and being less likely to have multiple sexual partners. The 

study concludes that “[p]arents matter for all adolescents” 

in this important arena of sexual risk taking.

The influence of siblings. Another paper presented at the 

conference also looks within the family at factors that appear to be associated with risky behaviors, but this one 

focuses on the influence of siblings instead of parenting 

styles.7 Joseph Altonji of Yale, Sarah Cattan of the University of Chicago, and Iain Ware of 3iGroup point out 

that several studies have found substantial correlations 

in risky behavior between siblings, raising the possibility 

that adolescents may directly influence the actions of their 

brothers or sisters. The researchers note that there is an 

insightful body of literature in psychology that suggests 

that such sibling effects may exist, particularly for younger 

children who look to their older siblings for cues about 

appropriate teenage behaviors. The authors note, however, 

that much of the published empirical analyses of sibling 

effects are compromised by the difficulty of distinguishing 

direct influences from the impact of shared unobserved 

factors. Multivariate regressions relating the behavior 

of siblings undoubtedly reflect the fact that a variety of 

common influences affect the actions of all siblings in 

a household, so the fact that siblings behave similarly 

does not necessarily imply that one child affects his or 

her brother or sister. Altonji, Cattan, and Ware look at a 

wide range of risky activities from the NLSY97 data set 

and find strong positive sibling correlations. The primary 

contribution of the paper is their assessment of the extent 

to which these correlations are due to causal effects from 

one sibling to another. 

The researchers articulate a sibling model of consumer 

choice that serves as a basis for their econometric identification strategy. It is based on the fact that the behavior of 

a child at a given point in time cannot directly influence 

a sibling’s actions in a prior year. The authors also assume 

that the direction of any influence is from an older sibling 

to a younger sibling. They estimate a joint dynamic model 

of the behavior of older and younger siblings that allows 

for family effects, individual specific heterogeneity, and 

past choices. Their results suggest that smoking, drinking, 

and marijuana use are influenced by the example of older 

siblings, although much of the link between siblings reflects association rather than causation.

Running away from home. One of the more unusual topics explored at the recent conference addressed the issue 

of adolescents running away from home.8 In his paper, 

Michael Pergamit of the Urban Institute explains what 

the published literature reveals about runaways. He states 

that nearly all the available information regarding this 

phenomenon comes from samples of youths in homeless 

shelters, in crisis centers, or living on the street; these data 

sources, unfortunately, do not permit analysts to compare 

youths who have run away with those who have not. For 

example, one cannot investigate the prevalence of running 

away using data of that nature, nor can one track how 

runaways and youths who have never run away differ in 

their developmental pathways prior to or after running 

away. Moreover, the information about the family and 

schooling experiences prior to running away are, in the 

shelter samples, necessarily collected after the running 

away episode and may thereby be tainted or shaded by the 

experience itself. 

The NLSY97 annually asked the youths if they had ever 

run away from home. The survey used the definition supplied by the Department of Justice, that running away 

is “staying away at least one night without parents’ prior 

knowledge or permission.” Each year, as long as the youth 

was residing with parents and was under age 18, he or she 

was asked about incidents of running away occurring since 

the previous interview; consequently, this study captures a 

sample of runaways that reflects the whole set of children 

who ran away, not just those who ended up in shelters or 

crisis centers. In some cases, the data also include key information about the youth from years prior to episodes of running away. The paper exploits these features of the NLSY97 

data, focusing primarily on children who were age 12 or 13 

in the first year of the study.

The prevalence of running away is itself one of the most 

interesting findings in this paper, which estimates that of 
Monthly Labor Review • September 2008  51

the roughly 20 million U.S. youths born between 1980 

and 1984, some 17.8 percent had run away by the age of 

18. The rate is higher for females—19.8 percent—than for 

males—15.8 percent. It is also slightly higher for Hispanic 

youths than for Whites or African Americans: 19.4 percent 

of Hispanics and 17.4 percent of both Whites and African 

Americans had run away by age 18. Of all children who had 

run away, about half had done so only once, but approximately 10 percent had done so seven or more times; of the 

youths who reported incidents of running away, the average number of these incidents was 3.3. About one-third of 

children who ran away had done so before age 14.

In a statistical model that identified which adolescents 

had run away from home while controlling for several attributes, it is interesting that the sex of the adolescents 

was not a factor. As if to illustrate the challenge of summarizing findings from complex studies, however, the 

paper notes that boys who did run away did so less often 

than girls but that boys did so at a younger age than girls. 

African Americans and Hispanics were both less likely to 

run away than were Whites after statistical controls were 

introduced. Similarly, having siblings had no apparent effect on running away. Children with higher scores on the 

AFQT were less likely to run away, while, as one might 

expect, youths who had a poor relationship with parents, 

who scored high on measures of behavioral problems, or 

who had mental health problems were significantly more 

likely to run away. Urban youths were much more likely to 

run away than youths in rural settings. The study also finds 

that “the more things the family does together the lower 

is the probability of running away.” The author notes that 

it will be important to track the effects of running away 

on the life trajectories of these young men and women 

as they age through their 20s and beyond. This is surely 

one of the key benefits of a data set like the NLSY97 that 

identifies behaviors and events early in life and can then 

reveal whether that behavior is associated with later life 

events, and, if so, to what extent. 

THE FINDINGS BRIEFLY SUMMARIZED IN THIS ARTICLE represent about half the research papers delivered at 

the Tenth Anniversary Conference in May 2008. In turn, 

the papers presented at the conference reflect only a small 

portion of the new facts and relationships discovered so far 

by researchers working with the NLSY97 data sets. Assuming the survey respondents continue to be willing to accept 

the request for an hour-long interview each year, as their 

lives unfold over the next decade or so, researchers’ understanding of the U.S. labor market and the behavior of the 

cohort born between 1980 and 1984 will continue to grow. 

The ever-improving understanding of the forces shaping 

labor market experiences should help policymakers, and 

the deeper understanding of the consequences of private 

decisions should be of value to families everywhere. 

Notes

ACKNOWLEDGMENT:  The authors thank Rupa Datta and Donna Rothstein for contributions to this summary paper.

1 The NLSY97 Tenth Anniversary Conference, held in 2008 at the

Bureau of Labor Statistics in Washington, DC, May 29–30, was supported by grants from the Spencer Foundation, the NORC Population

Research Center, and the Harris School’s Center for Human Potential

and Public Policy.

2 Philippe Belley, Marc Frenette, and Lance Lochner, “Post-Secondary Attendance by Parental Income: A Canada-U.S. Comparison.”

Paper presented at the NLSY97 Tenth Anniversary Conference, Washington, DC, May 2008.

3 Phillipe Belley and Lance Lochner, “The Changing Role of Family Income and Ability in Determining Educational Achievement,”

Journal of Human Capital, Winter 2007, pp. 37–90.

4 Ralph Stinebrickner and Todd R. Stinebrickner, “Understanding

Educational Outcomes of Students from Low-Income Families: Evidence from a Liberal Arts College with a Full Tuition Subsidy Program,” Journal of Human Resources, Summer 2003, pp. 591–617.

5 Robert Kaestner and Michael Grossman, “Effects of Weight on

Adolescent Educational Attainment.” Paper presented at the NLSY97

Tenth Anniversary Conference, Washington, DC, May 2008.

6 Kristin Moore and Kassim Mbwana, “Preventing Risky Sex and

Adolescent Parenthood: Does the Effectiveness of Parenting Practices

Differ For Children with Varied Risks?” Paper presented at the NLSY97

Tenth Anniversary Conference, Washington, DC, May 2008.

7 Joseph G. Altonji, Sarah Cattan and Iain Ware, “Sibling Influences on Teenage Risky Behaviors.” Paper presented at the NLSY97 Tenth

Anniversary Conference, Washington, DC, May 2008.

8 Michael R. Pergamit, “Who Runs Away from Home? An Exploratory Analysis.” Paper presented at the NLSY97 Tenth Anniversary

Conference, Washington, DC, May 2008.