Attrition in the National Longitudinal Survey of Youth 1997 

Alison Aughinbaugh* 

Bureau of Labor Statistics 

Rosella M. Gardecki 

Center for Human Resource Research, The Ohio State University 

First Draft: July 2007 

Current Draft: September 2007 

*The views expressed are those of the authors and do not reflect the policies of the Bureau of Labor Statistics or the views of other BLS 

staff members. We thank Chuck Pierret, Donna Rothstein, and Mike Pergamit for helpful comments. All errors are our own. 

Corresponding author: Alison Aughinbaugh, 2 Massachusetts Ave NE, Room 4945, Washington, DC 20212, aughinbaugh.alison@bls.gov, 

202-691-7520. Keywords: attrition panel data 
 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

The National Longitudinal Survey of Youth 1997 (NLSY97) is a national sample of about 9,000 youth who were 

ages twelve to sixteen on December 31, 1996, and living in the US at that time. Starting in 1997, interviews have 

been conducted annually. Currently, data through Round 8 are available (respondents are ages 20 to 25) and data 

through Round 10 have been collected (respondents are ages 22 to 27). 

Though the focus of the NLSY97 is employment, the data set covers a broad array of topics including schooling, 

training, marriage, fertility, and income, thus permitting one to examine how different areas of life are related to 

labor market outcomes. The survey takes approximately one hour to complete. Incentive payments for respondents 

ranged from $10 to $35 over the period considered in this paper.1 

Respondents are currently completing their schooling and entering both their careers and a demographically dense 

period of their lives in which many marriages and births occur. Consequently, the NLSY97 is becoming a valuable 

source for examining how early events and decisions—such as teenage pregnancy, employment during high school, 

and dropping out of school—are related to later outcomes and decisions—such as educational attainment, earnings, 

and career choice. As with any panel data set, the ability of the NLSY97 to provide estimates of the effect of early 

events on later outcomes depends on whether the NLSY97 remains representative of the population of interest. 

Although unit non-response has always been a concern for longitudinal surveys, over the past fifteen years the levels 

of attrition have increased. Atrostic, et al. (2001) study attrition in six U.S. government household surveys and find 

that over the 1990’s the rate of unit non-response increased in all six. A similar pattern emerges in the National 

Longitudinal Surveys. Where the response rate in the NLSY97 was 89.9 in Round 4, but in its predecessor survey, 

the NLSY79, the response rate did not fall below 90 percent until Round 16. (NLSY79 User’s Guide) While larger 

sample loss reduces the precision of estimation, it does not necessarily result in attrition bias. Bias results when 

attrition is non-random. 

Recent examinations of two longstanding U.S. panel data sets show that attritors differ significantly from nonattritors, but the effects of this non-random attrition do not impact the pictures of labor market outcomes presented 

by the data. (Fitzgeral, Gottschalk, and Moffitt 1998, MaCurdy, Mroz, and Gritz 1998, Zabel 1998) In the current 

study, we examine the effects of attrition in the NLSY97—a newer and younger data set—using methods employed 

in previous studies of attrition in the NLSY79 and the Panel Study of Income Dynamics (PSID). 

This paper measures the level, the patterns, and the implications of attrition in the NLSY97. Much of the survey 

methodology literature considers participation in surveys as a multi-step process, where step 1 is establishing contact 

and step 2 involves gaining cooperation (Watson and Woods 2006). Because few NLSY97 sample members are 

unlocatable, however, we study attrition as a simple one-step process.2 

The first section of this paper describes the patterns of wave non-response, first attrition, and return in the NLSY97. 

The second section estimates (1) the probability of first attrition, and (2) among attritors, the probability of return in 

a subsequent round as functions of employment, schooling, and demographic events at the most recent interview 

thus we can assess whether certain groups of individuals (e.g. the unemployed, students, the married) are more likely 

to leave and return to the NLSY97. In the third section, we estimate quantile regressions in an attempt to examine 

whether attritors and returnees differ from those who remain in the survey with respect to the distribution of wage 

rates and total earnings. Lastly, we conclude by summarizing what the estimates presented here tell us about the 

nature and implications of attrition in the NLSY97. 

I. Attrition Patterns in the NLSY97 

1

 In the first three rounds, respondents, as a token of thanks, were paid $10 for participating. Over Rounds 4 and 5, 

the incentive amount was raised to $20, which remained the payment level through Round 8. As an experiment, the 

incentive was raised to $15 in Round 4 and then to $20 in Round 5 for half the sample, whereas for the other half the 

full increase to $20 occurred in Round 4. A second incentive experiment was conducted in Rounds 7 and 8 in which 

respondents who had missed the previous interview were offered $5 additional per round missed since the last 

interview for up to 3 missed rounds to complete the current interview to compensate for the longer interview. 

2

 Of any round, the greatest number of sample members, 278 or 3.1 percent, were unlocatable in Round 5. 
 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Using unweighted data, Table 1 summarizes the pattern of attrition in each of the first eight rounds of the NLSY97 

data by round for all 8984 sample members in the NLSY97. It also shows attrition in separate columns for the 2236 

individuals who make up the oversample of Black and Hispanics youth3

 and the 6748 sample members who 

comprise the cross-sectional sample. For each round of data, the table presents statistics on the rates of wave nonresponse, first attrition, never returning to the NLSY97 after missing a round, and return after having missed one or 

more interviews. 

Because the denominators vary across these statistics, we provide definitions for the statistics that we present in 

Table 1. Wave non-response is simply missing an interview, and the rate of wave non-response is the number that 

misses an interview out of all sample members. The rate of first attrition is the number of sample members who 

misses an interview for the first time out of those who have responded in all previous waves. “Never return” is 

equal to 1 for those sample members who attrite for the first time in a given round and who do not participate in any 

subsequent round. The rate of “never return” is defined as the number of respondents who “never return” out of 

those who attrite for the first time in a given round. The fraction of returnees is the portion that has missed at least 

one previous interview out of those who participate in the current survey round. 

The rate of wave non-response increases with round. In the first few rounds of data collection, the rates of wave 

non-response range from 7 to 10 percent for the entire sample. By Round 8, the most recent round for which data 

are available, over 16 percent of the sample did not participate. First attrition never exceeds the 7 percent that

occurs in Round 2. In all other rounds the rate of first attrition lies between three and five percent. The patterns of 

wave non-response are comparable for the whole sample, the oversamples, and the cross-sectional sample. 

Most of the NLSY97 sample members who miss an interview return to the survey. 4 Of those who do not participate 

in the Round 2 interview, only 20 percent have never returned to the NLSY97 by Round 8. Obviously, those who 

initially leave the survey in an earlier round have had more chances to return to the survey, which helps explain why 

the rate of never returning rises across rounds to 41 percent in Round 7. The rate of returnees (those who have 

previously missed at least one interview out of all previous rounds) interviewed climbs across rounds and ranges 

from 4 percent in Round 3 to 13 percent in Round 8. 

Table 2 examines the same patterns of attrition and return, but for subsamples that are defined on demographic 

characteristics. When comparing the subsamples, three patterns emerge. First, as has been found in other 

longitudinal surveys, wave non-response is higher for men than for women. (Burkam and Lee 1998, Fitzgerald, 

Gottschalk and Moffitt 1998, MaCurdy, Mroz, Gritz 1998) Second, across the various sub-samples, differences in 

survey participation begin to appear in recent rounds. Non-response rates are comparable in rounds two through 

five for Blacks, Hispanics, and whites, but are higher for whites than for Black or Hispanics in Round 6 and later.5 

In contrast, Blacks—as compared to non-Black, non-Hispanics—have higher rates of never returning to the 

NLSY97. Because of the racial and ethnic composition of the oversample versus the cross-section, a similar pattern 

is seen for the oversample versus the cross-section. Third, the NLSY97 wave non-response rates are decreasing in 

birth year. That is, sample members born earlier are less likely to participate in each round. 

Compared to its predecessor survey, the NLSY79, the levels of attrition are higher. In Round 8 of the NLSY79, 

only 9.2 percent of male sample members and 7.0 percent of female sample members did not participate (MaCurdy, 

Mroz, and Gritz 1998). The comparable numbers are twice as large in the NLSY97, 18.8 and 14.0 percent. Because 

the levels of unit non-response are so much lower, it is not surprising that the percentage of participants who have 

missed at least one previous interview is also much lower in the NLSY79. In Round 8 of the NLSY79, 8.5 percent 

of male respondents and 6.2 percent of female respondents were returnees versus 14.7 and 11.5 in Round 8 of the 

3

 The oversample provides sufficient sample size to permit analysts to estimate results separately for these minority 

groups. 

4

 Because of the event history format of much of the NLSY97, missing an interview does not mean that data are 

never collected on a given year’s activities (Olsen 2006). However, a longer recall period that comes with skipping 

an interview and returning may result in lower data quality (Pierret 1998). 

5

 The group referred to as “Whites” in this paper is actually composed of non-Black, non-Hispanics. We refer to 

them as White for simplicity. 
 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

NLSY97. In the time between these two surveys, attrition levels have risen substantially. In addition, there are 

other differences. For example, about 90 percent of the youth eligible to be sample members agreed to participate in 

Round 1 of the NLSY79, while a higher percentage, 91.6 percent, located for the NLSY97 agreed to participate in 

Round 1 (Moore et al. 2000). Also, the NLSY79 sample began when the sample members were ages 14 to 21 while 

the NLSLY97 began at younger ages of 12 to 16. 

The statistics in the previous two tables show the extent to which sample members leave and return to the NLSY97 

and how the patterns vary by basic demographic characteristics. However, these tables do not provide information 

about whether attrition causes the sample to be nonrepresentative. Table 3 provides some information on whether 

the mean characteristic measured at Round 1 are related to attrition behavior by comparing these characteristics 

across the samples of those who never missed an interview (the continuous sample), those who missed at least one 

interview and returned to the NLSY97 (intermittent sample), and those who miss an interview and never return. 

The variation in demographic characteristics based on patterns of participation in the NLSY97 provides some 

corroboration for the patterns seen in the previous table. First, women are over-represented among those who 

participate in all rounds, while men are under-represented. Second, as compared to all NLSY97 sample members, 

the continuous sample is made up of a greater percentage of those born in later years. Among those who participate 

in all rounds, 16 percent were born in 1980 and 22 percent were born in 1984, while the whole sample is closer to 

having roughly 20 percent come from each birth year. Third, the racial composition of the continuous sample 

closely matches that for the whole sample, although the sample of those who never return to the NLSY97 contains a 

higher percentage of Blacks and a lower percentage of Whits than the whole sample. In contrast, the intermittent 

sample is composed of a lower percentage of Blacks and a higher percentage of Whites than the whole sample. All 

of these characteristics are accounted for in the sampling weights of the NLSY97, which are adjusted each survey 

round to take account of selective attrition based on these exogenous demographic characteristics of age, 

race/ethnicity, and sex. 

Those sample members for whom a parent interview was conducted are over-represented in the continuous sample, 

which may imply that sample members who come from more cooperative families are themselves more cooperative 

(Laurie, Smith, and Scott 1999). Sample members from more advantaged families—measured by parental 

educational attainment, whether they live with their biological parents at the Round 1 interview, and family income 

in Round 1—are more apt to participate in the survey in all rounds. Among those who participate in all rounds of 

the survey, the mean for father’s highest grade completed is 12.75 and for mother’s highest grade completed is 12.63 

compared to 12.42 and 12.22, respectively, in the sample who leave and never returned. A greater proportion of the 

continuous sample lived with their biological mother and biological father (0.89 and 0.58) at the time of Round 1 

than that of the intermittent sample (0.85 and 0.55) or of those who miss a round and do not return (0.85 and 0.52). 

On average, family income for those who continuously participate and for the intermittent sample is greater than 

family income for those who attrite and do not come back to the NLSY97. For the most part, the variation in 

proxies for socio-economic status is comparable across the three samples. The one exception is that the standard 

deviation in father’s highest grade completed is about one grade greater for those sample members who leave and 

never come back to the survey than that for the other two subsamples. This is not the case for mother’s highest 

grade completed where the standard deviation for sample members who attrite is 2.7 grades versus a standard 

deviation of 3.5 grades for the whole sample. 

Two youth outcome measures that are reported in Round 1 (having ever repeated a grade and having ever smoked) 

are presented in the bottom rows of Table 3. For both of these measures, stronger participation in the survey is 

correlated with better outcomes. Those in the continuous sample have the lowest incidence of both measures, with 

16 percent having repeated a grade and 38 percent having smoked. Those who attrite and do not return to the 

NLSY97 are most likely to have repeated a grade (22 percent) and to have smoked (44 percent). Of course, the 

pattern that sample members from earlier birth years are more apt to leave the survey is confounded with the pattern 

on cigarette smoking. Those who are older in Round 1 are more like to attrite and more likely to have smoked.6 

6

 This may also be true for grade retention, though to a lesser extent. Initiation into cigarette smoking most often 

occurs in the teenage years, while the bulk of grade retention occurs in kindergarten and 1st grade. 
 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Using a different tack, Table 4 also considers the question of whether characteristics of attritors differ from the 

characteristics of sample members who are interviewed. Table 4 attempts to address this question by examining 

each birth cohort that makes up the NLSY97 in the round that corresponds to the birth cohort being age 16 to 17. 

Thus, the characteristics available in Round 1 for sample members born in 1980 are compared to the characteristics 

reported in Round 5 for sample members born in 1984. The bottom row shows that 92 to 93 percent of the 1981 

through 1984 sample members are interviewed in the round in which they are 16 to 17 years old. Because the 1980 

birth cohort is age 16 to 17 in Round 1, by definition, 100 percent of the 1980 birth cohort is interviewed. 

Presumably, in Round 1, prior to any attrition, the 1980 birth cohort is representative of the population. One caveat 

to this analysis is that any trend in teenage behavior may be attributed erroneously as an effect of attrition. 

However, given the short timeframe over which we are looking, we ignore this possibility. 

Two sets of descriptive statistics are presented—the first unweighted and the second weighted by the sampling 

weight of the round from which the data are drawn. In the unweighted statistics, sex composition does not vary 

across the birth cohorts as observed in the various rounds. The percentage of black respondents is greater among the 

subsequent birth years than among those born in 1980. Grades in 8th grade increase slightly across the birth years; 

the sum of the percentage of sample members in the “half A’s and half B’s” and “mostly A” categories rises with 

each birth year from 34 percent among the 1980 birth cohort to 40 percent among the 1984 birth cohort, while the 

percentage of sample members with 8th grade grades of “half B’s and half C’s” or lower generally declines. In 

addition, the proportion of the 1980 birth year sample that report having had sex by Round 1 is substantially lower 

than that reported by the later birth cohorts, while the percentage who report having repeated a grade is higher. 

Any difference by birth year in racial/ethnic composition is virtually eliminated in the weighted statistics. The other 

differences, however, remain after weighting the data. The percentage who earn “half A’s and half B’s” or “mostly 

A’s” rises by 6.2 percentage points from the 1980 birth cohort to the 1984 birth cohort in the weighted calculations 

versus a 6.3 percentage point increase in the unweighted numbers—though the levels of those with high 8th grade 

grades is about 3 percentage point higher when the data are weighted. Similarly, for the teen outcomes of having 

had sex and having repeated a grade, though the levels of incidence again differ in the unweighted versus weighed 

data, the differences across the birth years are similar in the weighed and unweighted statistics. In the unweighted 

data the percentage of respondents who reports having had sex rises by 9.5 percentage points from birth year 1980 to 

birth year 1984 and by 10.2 percentage points in the unweighted data. For having repeated a grade, the comparable 

numbers are an 8.5 percentage point decline when the data are unweighted versus an 8.9 percentage point decline 

when the data are weighted. 

One additional difference emerges in the weighted statistics. Income levels are greater in the weighted data. We 

compare household income as reported in Round 1 when the sample members are ages 12 to 17. Average income is 

greatest for the initial birth year and lower for all subsequent birth years, perhaps hinting that that those who do not 

participate in the survey at ages 16 to 17 come from families with higher incomes in 1996. The differences in 

average income by birth year are larger in the weighted versus the unweighted data. 

Taken together, Tables 3 and 4 may imply non-random attrition with respect to teen outcomes and decisions as well 

as the SES of the youths’ parents. In addition, the comparison of weighed versus unweighted statistics in Table 4 

suggest that the weights provided by the NLSY97 account for non-random attrition by sex and race, as they are 

designed to, but that they do not remedy the impact of attrition that is non-random along other dimensions.7 

II. Estimates of Attrition and Return 

IIA. Probability of Initial Attrition 

The previous tables examined attrition patterns using bivariate statistics. This section further examines how attritors 

are different from nonattritors by estimating attrition and return as a function of the respondent’s life activities at 

interview t and how those activities are related to attrition at time t+1. Equation (1) is a logit that estimates the 

7

 Though not shown here, the weighted statistics for Table 3 were also produced. Using the weighted data, the 

patterns across columns are generally consistent with those presented in Table 3. Again, income levels are higher 

using weighted data. In addition, parents’ educational attainment and youth’s cigarette smoking is higher, and the 

incidence of grade repetition is lower. 
 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

probability that an individual attrites at the next round of data collection, conditional on the respondent having 

participated in all previous rounds. 

(1) P(A + ==β 1) X A ,..., A X + β +ε = 0 t 1 i 1 it 2 it 1 t 

where At+1 indicates whether the individual attrites at Round t+1, X is the general notation for the covariates 

included in this analysis, some of which are permanent characteristics and others are measured in the previous round 

(Round t), and β are the parameters to be estimated. The estimates of β gauge the extent to which attritors come 

from certain segments of the population. The life activities for which the equations control are whether the sample 

member had become a parent to a biological child, was married or cohabiting, whether she was in school, and 

whether she was employed—all measured over the six months prior to the round t interview. If ε is drawn from it 

the logistic distribution, Equation 1 implies a binary logit model. 

Equation 1 is estimated for the full sample as well as estimated separately for the cross-section and oversample of 

the NLSY97, by sex, and by race/ethnicity. All estimates control for the birth year of the sample member, round of 

data collection, and incentive amount paid in Round t. All estimates are unweighted. 

Table 5 presents the estimated marginal effects for Equation 1. For the NLSY97 as a whole, initial attrition appears

to be non-random with respect to the decisions that the NLSY97 sample members are making about school, work, 

and fertility, though attrition appears to be random with respect to union formation. The results from the full sample 

indicate that those who have given birth are 15 percentage points more likely (or over 300 percent more likely than 

for the average observation where the chance of not participating next round is approximately 5 percent) to be nonrespondents in the next round. Those sample members who are in school and those who are employed are less likely 

by 1.7 and 0.9 percentage points to attrite from the survey in the next round. 

Some of the patterns that are evident in the descriptive statistics also emerge in the regressions. Looking at the 

results for the whole sample (Column 1) shows that compared to the omitted birth year of 1980, those born in later 

years are less likely to attrite with the magnitude of the effect increasing in birth year. Depending on the sample 

considered, those born in 1982, 1983, and 1984 are 0.8, 1.0, and 1.4 percentage points less likely to leave in the next 

round. In Rounds 3 and 4, sample members are less likely to attrite than they were in Round 2 (the omitted round). 

Additionally, in the last round, the sample members are more likely to attrite. 

The estimates from the cross-section closely match those from the whole sample. Likewise, for the oversamples, the 

estimated marginal effects of life events are quite similar to those estimated in the full sample and in the crosssection. Because of its smaller sample size, the standard errors in the oversample estimates, however, are up to two 

times larger, causing fewer of the regressors to be statistically significant. 

Separate estimates by sex show that the effects of becoming a parent, of being in school, and of being employed are 

comparable for men and women. The process of attrition for women and men differs in that for women the 

probability of leaving the survey varies less by birth year. Compared to women, men born in higher birth years are 

less likely to leave the survey relative to their counterparts born in 1980. Women are less likely to leave the survey 

after Rounds 3 through 6 than they were in Round 2. For men, two of the round indicators suggest a different 

probability of attrition as compared to that in Round 2—(1) in Round 3 where the chance of leaving the survey was 

half a percentage point lower compared to following Round 2 and (2) in Round 8 where the chance of leaving is 

about 4 percentage points higher or 90 percent greater than in Round 2. 

When attrition is estimated separately by race/ethnicity, parenthood and school enrollment have effects similar to 

those discussed above. Some differences do emerge in the estimates by race/ethnic group. First, employment 

appears unrelated to prospective attrition for both the Black and Hispanic subsamples. Compared to the estimated 

effect of employment in the White subsample, the estimates for the Black and Hispanic sub-samples are about 35 

percent smaller. In addition, the standard errors on the estimates in the Black and Hispanic subsamples are larger, 

rendering the employment effects not statistically significant. Second, the effects of birth year are especially strong 

for Blacks, for whom being born in 1982, 1983, or 1984 decreases the chance of attrition by at about 1.5 percentage 

points or by over 26 percent. 
 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

In sum, prospective attritors are more likely to come from those sample members who become a parent relatively 

early in their lives and less likely to come from those employed and in school. Through Round 8, attrition appears 

independent of current marriage and cohabitation status. 

Differences in magnitude and patterns of significance for the coefficient estimates on the round and birth year 

indicators imply that the attrition process varies by race and ethnicity. In a number of the samples considered, the 

chance of attrition in the next wave increases with round; however, this is not the case for the oversample, Blacks, 

Hispanics, or women. In every sample examined, a gradient on birth year emerges showing those sample members 

born in later years are less likely to miss their next wave’s interview. The incentive amounts, which vary by round, 

appear unrelated to initial attrition from the NLSY97. 

MaCurdy, Mroz, and Gritz (1998) estimate a similar equation to examine the nature of attrition in the NLSY79. 

They also find that men are less likely to attrite if employed. However, among women they find that attritors are 

more likely to come from the non-employed. Their specification differs from our Equation 1 in a number of key 

ways. First, they permit the impact of life events to vary by age group and find that, for men, the impact of 

employment increases with age. In their analysis of the NLSY79, the sample members range in age from 14 to 34 

whereas we have fewer years of data and a smaller age range of 12 to 24. Second, in MaCurdy, Mroz, and Gritz, 

schooling and employment are defined as mutually exclusive activities at ages 20 and younger where schooling 

takes precedence over employment. Third, we control for demographic events—births, marriages, and 

cohabitation—while they focus on the labor market and control only for schooling and work. 

To check the robustness of our results, we estimate the initial attrition equation on two further subsamples, and three 

alternative specifications.8

 The additional subsamples are defined based on the reason for non-interview provided in 

the round of the survey that the sample member initially missed. The first group examined separately is composed 

of sample member who refuse, hostilely refuse, or very hostilely refuse to participate in a survey round. When 

Equation 1 is estimated only on the refusers, some key differences emerge. The effect of a recent birth reduces the 

likelihood of leaving the survey by between 6 and 12 percentage points depending on the sample. The estimated 

effects of recently becoming a parent is smaller and of opposite sign compared to the results reported in Table 5. 

Similarly, the impact of employment changes signs when the sample is restricted to the refusers. Working becomes 

positively related to leaving the survey and the effect is statistically significant for refusers from the whole sample, 

the cross-section, Hispanics, and women. Among refusers, the gradient on birth year is no longer significant. 

However, the effect of round becomes statistically significant with the probability of not participating in the survey 

increasing with each round. 

Those sample members who miss their first interview because they are unlocatable are the second group that is 

examined separately. In this instance, the effect of having a recent birth matches the estimates reported in Table 5. 

However, neither being enrolled in school nor being employed is significantly related to first attrition among those 

who are unlocatable. The loss of significance is likely due to the decreased sample size; the number of annual 

observations from the sample of those who become unlocatable is over 25 times smaller than the number of annual 

observations in Table 5. 

We also estimate some alternative specifications, each of which adds controls to the basic specification reported in 

Table 5. One of these adds a control for when during the field period the sample member was interviewed in the 

previous round, which may proxy for the sample member’s level of cooperativeness. The field period is divided 

into three sections; the first consists of the first three months of the field period, the third of the last three months of 

the field period, and the second of any intervening months (usually 3 or 4 months long). The estimated marginal 

effects for the variables indicating when the respondent was interviewed are sizable and statistically significant for 

every sample. Those interviewed in the second and third sections of the field period are 1.6 and 4.1 percentage 

points more likely to leave in the next round. After controlling for when the sample member was interviewed, the 

effect of having a recent birth drops by about 20 percent (compared to the estimates in Table 5) to about 12 

percentage point increase in the odds of leaving the survey in the next round. In contrast, the estimates of being 

enrolled in school are unaffected and those on being employed rise by about 30 to 50 percent, and are now 

8

 The additional results are not shown here; they are available upon request. 
 

 

 

 

 

 

 

 

 

 

 

 

 

statistically significant in all subsamples. The estimated effects of birth year are dampened slightly, but the pattern 

that respondents born in later birth years are less likely to attrite remains evident. 

In contrast, in separate specifications when the county-level unemployment rate (as a proxy for local economic 

conditions) or the number of respondents from the original household are added, the estimates of the marginal 

effects match those presented in Table 5. Though the unemployment rate is statistically significant and negative for 

all samples with the exception of the cross-section, the estimated marginal effects are small ranging from a low of -

0.0007 for both the male sample and the white sample to a high of -0.003 for a one-point increase in the 

unemployment rate. 

IIB. Probability of Return Following Initial Attrition 

Equation (2) parallels Equation (1), but estimates the probability of returning to the survey after having missed an 

interview for the first time. The annual observations included are those that occur after first attrition and up to the 

round in which the respondent first returns to the NLSY97. 

(2) P(R 1) X + α + X α + ε ==α X A ,..., A + = 0 and A ,...A =1 t+1 i 1 iLI 2 it +1 3 it 1 1 LI LI 1 + t+1 

where Rt+1 indicates whether the individual attrites at the next wave, X is the general notation for the covariates 

included in this analysis some of which are permanent characteristics and others are measured in the last round in 

which the respondent was interviewed (Round LI), round and incentive amount are included as controls and are 

measured at t+1, and α are the parameters to be estimated. 

Looking at the marginal effects of various life events on the probability of return among those in their first spell of 

attrition, Table 6 shows that having had a birth has a consistently significant and positive impact on the probability 

of returning to the survey after missing at least one interview. For all samples considered, becoming a parent 

increases the likelihood of return by almost 60 percentage points or by 115 to 120 percent (the average chance of 

return is in the neighborhood of 0.5). This is good news as this same group of sample members is more likely to 

leave the NLSY97, and these results indicate that the parents are more likely to return to the survey after initially 

missing an interview. In half of the samples (whole sample, oversample, females, and whites), being employed 

decrease the chance that the individual returns to the survey. Other life events such as marriage, cohabitation, and 

being enrolled in school at the date of the last interview are unrelated to the probability of returning to the NLSY97 

after initially attriting. 

Birth year is generally unrelated to return behavior—though the estimated effect for birth year 1983 is significant 

and negative in five of eight cases. Incentive amounts increase the likelihood of returning to the NLSY97 by 20 to 

30 percentage points for every increase of $10. Because incentives increase with round, it may be difficult to 

interpret the incentive effect independently from the effects on the round indicators. Later rounds are negatively 

associated with returning to the NLSY97. Overtime, the NLSY97 appears to have become less successful at 

bringing back sample members after they have missed at least one interview.9

 The sign, magnitude, and 

significance of most of the estimates of Equation (2) are consistent for the various subsamples examined and provide 

no indication that the process of return varies based on key demographic characteristics. Alternative specifications 

similar to those discussed above for the outcome of the first attrition are also estimated for the outcome of returning 

following the first survey missed. For return, the estimates are stable for additional subsamples and also when 

additional controls are included in the regressions. With the exception of the indicators for when during the fielding 

period the sample member was interviewed at last interview, none of the added controls is consistently significantly 

related to the probability of returning to the NLSY97 after initially missing an interview. 

III. Earnings and Wages of Attritors and Returnees 

We estimate quantile regressions to assess how the earnings and wage distributions differ for attritors versus those 

who remain in the survey and for returnees versus those who remain out of the survey. The basic specification is 

(3) ln y = A γ + BY γ + Round γ + ε it it 1 i t 3 it + 1 2 

9 While this statement may be true for Rounds 1 through 8, Round 10 was quite successful with 7559 or 84.1 percent 

of the sample interviewed. 
 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

where yit is the annual earnings of respondent i in round t, BYi

 is a vector of indicators of the sample member’s birth 

year, Roundt

 is a vector of indicators for the round of data collection, the γ ’s are the coefficients, and εit is the error 

term. We also estimate the logarithm of average wage rates as a function of the same regressors in Equation (3). 

Equation (3) is estimated at the median, 10th, and 90th percentiles for earnings and for wage rates to examine where 

attritors fall in the earnings and wage distributions relative to non-attritors. In addition to estimating the quantile 

regressions that examine the effects of attritors on wage and earnings distributions, we estimate an equation that 

takes the same form and looks at how returnees influence labor market outcomes among those who have left the 

survey. 

Table 7 presents coefficient estimates from the quantile regressions on annual earnings for attritors in Columns 1 

and 2 and for returnees in Columns 3 and 4. The results for the median regression are in Rows 1 through 4, for the 

10th percentile regression in Rows 5 through 8, and for the 90th percentile regressions in Rows 9 through 12. The 

estimates are presented for four samples: whole sample, cross-sectional sample, males, and females. 

Throughout the distribution, the level of annual earnings is increased by including attritors. At the median and 10th 

percentiles, including the attritors increases the percentiles by roughly 20 percent in all four samples. At the 90th 

percentile of earnings, attritors increase the earnings level by 14 to 15 percent in the whole sample and in the crosssection. For the females, the quantile regressions show that attritors raise the 90th percentile of earning by almost 24 

percent, though for men there is no effect. Thus, the results imply that among female earners, attritors are among 

the highest earners. Among male earners, attritors have earning above the median. 

The effects on the distribution of wage rates of including attritors are smaller and only significant for the median 

where the estimates show that including attritors increase the median wage by 3 to 4 percent, indicating that attritors 

have wages above the median, but below the 90th percentile of the wage distribution. Among workers, attritors 

appear to earn more and have higher wages than their counterparts who participate in all rounds of the NLSY97. 

Among attritors, including returnees increases estimates of median earnings and median wages, but does not impact 

the tails of the distribution. In both cases, the estimates imply that, among workers, attritors have earnings and 

wages greater than the median, but less than the 90th percentile. 

For the full sample, including returnees increases median earnings and median wages by roughly 15 percent and 3 to 

4 percent, respectively. With respect to earnings, when the samples of males and females are examined separately 

the effect of including returnees is not statistically significant for males and statistically significant only the 0.10-

level for females, though the size of the estimate is comparable to that for the whole sample. For wages, the effect 

of including attritors is not significant when estimated by sex. Without exception, including returnees does not 

affect the 10th or 90th percentiles of either the earnings or the wage distributions. Thus, it appears that the inclusion 

of returnees in the sample of attritors raise median earnings and wages but does not affect the tails. Because 

returnees earn more than the average attritor and attritors earn more than the average non-attitor, we can infer that 

returnees are above average in earnings and wages when compared to the sample who participates in all rounds of 

the NLSY97. 

IV. Concluding Comments 

Attrition in the NLSY97 appears to be non-random with respect to socioeconomic status and outcomes in early 

adulthood. The logistic regressions of first attrition and of first return show that respondents with children born in 

the 6 months before the most recent completed survey are more apt to leave the NLSY97, but are also more likely to 

return to the survey (when the child is older). Sample members who are enrolled in school are less likely to attrite 

from the survey. Moreover, among workers attritors appear to have higher earnings and wage rates than those 

sample members who remain in the survey. 

Though the evidence implies that attrition from the NLSY97 is non-random, because of the ages of the sample 

members it is difficult to assess the impact of attrition. Especially for the sample members who are still in or who 

have recently left school, earnings and wages are unlikely to accurately reflect lifetime income. Future work should 

explore how the estimates vary when employment is defined more stringently. 
 

 

 

 

Returnees appear better off then the average attritor. With respect to family income in Round 1 and the propensity 

to have repeated a grade, those who attrite and return look more like the sample who participates in all rounds than 

those who leave and do not come back to the NLSY97. When compared to all attritors, those who return are more 

likely to have been employed at their last interview before leaving the survey. The quantile regressions imply that, 

among workers, those who miss an interview and come back earn more than the average attritor does. It is 

important to note that the level of incentive offered for participation in the NLSY97 is positively associated with 

returning to the survey after having missed an interview. 

Past work on attrition in longitudinal studies tends to find that attrition is non-random, but that the effect of attrition 

on results from multivariate analysis is negligible. The regressions, however, tend to control for the same 

characteristics along which attritors differ from non-attritors, which may explain why the presence of attritors does 

not alter regression results. Future work should consider how, within a group (for instance, black women), sample 

members who attrite, return, and never leave the survey differ and how the relationships between key variable differ 

within the group. 
 

 

 

 

 

 

 

Works Cited 

Burkam, David T., and Valerie E. Lee. 1998. "Effects of Monotone and Nonmonotone Attrition on Parameter 

Estimates in Regression Models With Educational Data: Demographic Effects on Achievement, 

Aspirations and Attitudes." Journal of Human Resources 33(2):555-574. 

Fitzgerald, John, Gottschalk, Peter, and Robert Moffit. 1998. "An Analysis of Sample Attrition in Panel Data: The 

Michigan Panel Study of Income Dynamics." Journal of Human Resources 33(2):251-299. 

Laurie, Heather, Rachel Smith, and Lynne Scott. 1999. “Strategies for Reducing Nonresponse in a Longitudinal 

Panel Survey.” Journal of Official Statistics 15(2): 369-282. 

MaCurdy, Thomas, Mroz, Thomas, and R. Mark Gritz. 1998. "An Evaluation of the National Longitudinal Survey 

of Youth." Journal of Human Resources 33(2):345-436. 

Moore, Whitney, Steven Pedlow, Parvati Krishnamurty, and Kirk Wolter. 2000. National Longitudinal Survey of 

Youth 1997: Technical Sampling Report. National Opinion Research Center: Chicago, IL. 

NLSY79 Users Guide. 2006. Center for Human Resources Research, The Ohio State University: Columbus, OH. 

Olsen, Randall J. 2005. “The Problem of Respondent Attrition: Survey Methodology is Key.” Monthly Labor 

Review 128(2): 63-70. 

Pierret, Charles R.. 2001. "Event History Data Survey Recall: An Analysis of the National Longitudinal Survey of 

Youth 1979 Recall Experiment." Journal of Human Resources 36(3):439-466. 

Watson, Nicole and Mark Wooden. (2006) “Modelling Longitudinal Survey Response: The Experience of the 

HILDA Survey.” HILDA Project Discussion Paper Series, No2/06. 

Zabel, Jeffrey E. 1998. "An Analysis of Attrition in the Panel Study of Income Dynamics and the Survey of Income 

and Program Participation with an Application to a Model of Labor Market Behavior." Journal of Human 

Resources 33(2):479-506. 
 

 

 

 

 

 

Table 1: Attrition Rates and the Fraction of Participants who are Returnees, by Sample 

Round All Oversample Cross-section 

Sample Size 8984 2236 6748 

Wave Non-response 

Round 2 0.067 0.058 0.070 

Round 3 0.086 0.089 0.085 

Round 4 0.101 0.094 0.103 

Round 5 0.123 0.122 0.123 

Round 6 0.121 0.106 0.126 

Round 7 0.137 0.118 0.143 

Round 8 0.165 0.149 0.170 

First Attrition 

Round 2 0.067 0.058 0.070 

Round 3 0.051 0.058 0.049 

Round 4 0.046 0.042 0.048 

Round 5 0.051 0.064 0.046 

Round 6 0.036 0.035 0.036 

Round 7 0.039 0.033 0.041 

Round 8 0.052 0.056 0.051 

Never Return 

Round 2 0.204 0.225 0.198 

Round 3 0.231 0.252 0.223 

Round 4 0.270 0.337 0.251 

Round 5 0.369 0.446 0.333 

Round 6 0.344 0.365 0.337 

Round 7 0.405 0.491 0.382 

Return 

Round 3 0.041 0.036 0.042 

Round 4 0.062 0.059 0.063 

Round 5 0.083 0.071 0.087 

Round 6 0.095 0.081 0.100 

Round 7 0.110 0.095 0.115 

Round 8 0.131 0.112 0.137 

Note: The first attrition proportions are defined out of those who have never missed an 

interview. The "never return" proportions defined out of those who attrite for the first time in 

that round. The return proportions are defined out of those who are interviewed that round. 
 

 

 

 

 

Table 2: Attrition Rates and the Fraction of Participants who are Returnees, by Selected Characteristics 

Sub-sample 

By Sex By Race/Ethnicity By Year of Birth 

Male Female Black Hispanic White 1980 1981 1982 1983 1984 

Wave Non-response 

Round 2 0.069 0.064 0.056 0.068 0.071 0.088 0.077 0.061 0.063 0.044 

Round 3 0.093 0.079 0.087 0.090 0.085 0.122 0.110 0.080 0.069 0.051 

Round 4 0.105 0.096 0.090 0.106 0.104 0.148 0.129 0.095 0.070 0.062 

Round 5 0.133 0.112 0.130 0.119 0.121 0.167 0.156 0.122 0.093 0.076 

Round 6 0.131 0.111 0.106 0.116 0.131 0.163 0.152 0.120 0.097 0.075 

Round 7 0.146 0.127 0.118 0.136 0.147 0.177 0.162 0.132 0.108 0.106 

Round 8 0.188 0.140 0.146 0.156 0.179 0.199 0.196 0.148 0.147 0.134 

First Attrition 

Round 2 0.069 0.064 0.056 0.068 0.071 0.088 0.077 0.061 0.063 0.044 

Round 3 0.056 0.046 0.055 0.056 0.047 0.079 0.073 0.043 0.037 0.026 

Round 4 0.051 0.042 0.046 0.049 0.046 0.079 0.062 0.039 0.031 0.027 

Round 5 0.059 0.043 0.064 0.050 0.044 0.070 0.063 0.053 0.039 0.034 

Round 6 0.042 0.030 0.034 0.039 0.035 0.041 0.042 0.038 0.035 0.024 

Round 7 0.042 0.035 0.031 0.048 0.039 0.057 0.028 0.037 0.035 0.040 

Round 8 0.068 0.037 0.055 0.048 0.053 0.060 0.069 0.042 0.046 0.049 

Never Return 

Round 2 0.199 0.209 0.191 0.240 0.190 0.216 0.179 0.142 0.272 0.218 

Round 3 0.205 0.265 0.303 0.192 0.206 0.238 0.238 0.216 0.242 0.205 

Round 4 0.267 0.274 0.271 0.305 0.254 0.268 0.242 0.266 0.300 0.311 

Round 5 0.369 0.369 0.449 0.413 0.291 0.418 0.383 0.329 0.344 0.352 

Round 6 0.347 0.340 0.375 0.356 0.323 0.380 0.407 0.368 0.283 0.243 

Round 7 0.381 0.434 0.500 0.414 0.366 0.424 0.421 0.481 0.451 0.267 

Return 

Round 3 0.044 0.037 0.036 0.041 0.043 0.054 0.046 0.041 0.036 0.027 

Round 4 0.063 0.061 0.051 0.066 0.066 0.088 0.080 0.062 0.044 0.037 

Round 5 0.088 0.078 0.080 0.080 0.087 0.119 0.111 0.079 0.061 0.047 

Round 6 0.101 0.089 0.081 0.088 0.106 0.137 0.124 0.091 0.070 0.055 

Round 7 0.118 0.102 0.096 0.103 0.120 0.143 0.145 0.106 0.083 0.074 

Round 8 0.147 0.115 0.110 0.125 0.144 0.167 0.156 0.120 0.115 0.098 

Note: Sample sizes are 8984 for the entire sample, 4599 for the male sample, 4385 for the sample of females, 2335 for the black sample, 1901 for the hispanic sample, 83 

for the mixed sample, 4665 for the non-black, non-hispanic (white) sample, the sample sizes are 1691, 1874, 1841, 1807, 1771 for birth years 1980, 1981, 1982, 1983, and 

1984. The first attrition proportions are defined out of those who have never missed an interview. The "never return" proportions defined out of those who attrite for the 

first time in that round. The return proportions are defined out of those interviewed that round. 
 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Table 3: Characteristics by Attrition Status 

Variable Whole Sample In All Rounds Attrite and Attrite and 

Return Never Return 

Demographic Characteristics 

Male 0.512 0.489 0.562 0.576 

Female 0.488 0.511 0.438 0.424 

Black 0.260 0.260 0.242 0.290 

Hispanic 0.212 0.208 0.219 0.222 

White 0.519 0.523 0.531 0.479 

Birth Year 1980 0.188 0.164 0.251 0.240 

Birth Year 1981 0.209 0.193 0.246 0.244 

Birth Year 1982 0.205 0.211 0.196 0.180 

Birth Year 1983 0.201 0.213 0.165 0.183 

Birth Year 1984 0.197 0.218 0.142 0.153 

Parent interview conducted 0.884 0.906 0.817 0.855 

Family Background at Round 1 

Highest Grade Completed--Bio Father 12.657 12.745 12.448 12.420

(4.234) (3.944) (4.613) (5.281) 

Highest Grade Completed--Bio Mother 12.498 12.627 12.160 12.215

(3.662) (3.961) (2.778) (2.762) 

HGC Bio Father--Missing 0.207 0.198 0.224 0.244 

HGC Bio Mother--Missing 0.077 0.070 0.094 0.097 

Lives with Bio Mother 0.880 0.891 0.854 0.854 

Lives with Bio Father 0.569 0.581 0.554 0.516 

Household Income 46361.70 47098.19 46874.83 40449.34

(42143.50) (42225.22) (43522.56) (38827.88) 

Region at Round 1 

Northeast 0.176 0.172 0.194 0.176 

North Central 0.228 0.229 0.233 0.215 

South 0.374 0.378 0.348 0.389 

West 0.222 0.221 0.224 0.220 

Youth Outcomes at Round 1 

Ever Repeat a Grade 0.172 0.163 0.181 0.221 

Ever Smoke a Cigarette 0.393 0.378 0.422 0.437 

Sample Size 8984 6328 1646 1010 

Note: Data are unweighted. Standard errors are in parentheses for continuous variables. 
 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Table 4: Comparison of Selected Characteristics by Birth Year and Round in which Sample Members are Age 16 to 17 

Year of Birth and Round Observed 

1980 in Round 1 1981 in Round 2 1982 in Round 3 1983 in Round 4 1984 in Round 5 

Unweighted Weighted Unweighted Weighted Unweighted Weighted Unweighted Weighted Unweighted Weighted 

Basic Demographics 

Male 0.505 0.518 0.505 0.510 0.516 0.512 0.513 0.504 0.517 0.518 

Female 0.495 0.482 0.495 0.490 0.484 0.488 0.487 0.496 0.483 0.482 

Black 0.281 0.151 0.256 0.156 0.268 0.155 0.260 0.160 0.247 0.151 

Hispanic 0.205 0.128 0.212 0.129 0.213 0.130 0.202 0.129 0.218 0.124 

Non-Black, Non-Hispanic 0.504 0.709 0.523 0.704 0.509 0.701 0.530 0.700 0.524 0.710 

Grades in 8th Grade 

Less than mostly C's 0.105 0.101 0.118 0.108 0.114 0.109 0.125 0.115 0.120 0.113 

Mostly C's 0.135 0.126 0.118 0.116 0.142 0.134 0.128 0.116 0.122 0.108 

Half B's and Half C's 0.262 0.240 0.243 0.233 0.222 0.200 0.230 0.216 0.203 0.186 

Mostly B's 0.147 0.145 0.138 0.139 0.147 0.140 0.127 0.129 0.129 0.129 

Half A's and Half B's 0.202 0.211 0.216 0.218 0.197 0.210 0.233 0.244 0.239 0.249 

Mostly A's 0.135 0.164 0.152 0.169 0.158 0.187 0.140 0.164 0.161 0.188 

Other 0.014 0.013 0.016 0.016 0.019 0.019 0.017 0.016 0.028 0.027 

Have a parent interview 0.876 0.882 0.898 0.908 0.878 0.888 0.901 0.904 0.900 0.908 

Household Income, Round 1 47704.14 54414.00 47331.24 52534.46 45862.91 52134.88 46647.59 52429.25 44694.12 50433.14 

Ever had sex 0.458 0.434 0.531 0.509 0.566 0.528 0.528 0.507 0.553 0.536 

Ever smoked a Cigarette 0.548 0.582 0.565 0.601 0.595 0.622 0.546 0.569 0.546 0.566 

Ever Repeated a Grade 0.244 0.220 0.203 0.167 0.187 0.163 0.165 0.145 0.158 0.131 

Sample Size 1691 1729 1694 1680 1636 

% of cohort interviewed 100% 92.30% 92.00% 93.00% 92.40% 

Note: When weighted, the data are weighted by sampling weight from the round corresponding to the respondents being ages 16 to 17. 
 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Table 5: Estimated Marginal Effects of Probability of Initial Attrition 

Sample 

All Cross Oversamples Males Females Black Hispanic White 

Section 

Incentive -0.002 -0.003 -0.001 -0.010 0.006 -0.001 -0.002 -0.003 

(0.004) (0.005) (0.009) (0.006) (0.005) (0.008) (0.010) (0.005) 

Event/Status 

Birth 0.154*** 0.158*** 0.142*** 0.156*** 0.151*** 0.151*** 0.153*** 0.155*** 

(0.007) (0.008) (0.012) (0.009) (0.010) (0.012) (0.014) (0.010) 

Married 0.002 0.002 0.004 0.007 0.001 0.002 0.004 0.003 

(0.003) (0.003) (0.007) (0.005) (0.004) (0.008) (0.007) (0.004) 

Cohabiting 0.003 0.003 0.003 0.005 0.003 0.005 0.001 0.003 

(0.003) (0.003) (0.005) (0.004) (0.003) (0.005) (0.006) (0.003) 

In School -0.017*** -0.016*** -0.018*** -0.019*** -0.015*** -0.017*** -0.023*** -0.014*** 

(0.002) (0.002) (0.004) (0.003) (0.002) (0.004) (0.004) (0.002) 

Employed -0.009*** -0.009*** -0.009* -0.010** -0.009*** -0.007 -0.007 -0.011*** 

(0.002) (0.002) (0.004) (0.003) (0.003) (0.004) (0.005) (0.003) 

Birth Year 

1981 -0.004* -0.004* -0.003 -0.006* -0.002 -0.006 -0.004 -0.002 

(0.002) (0.002) (0.004) (0.002) (0.002) (0.003) (0.004) (0.002) 

1982 -0.008*** -0.007*** -0.010** -0.011*** -0.004 -0.014*** -0.008 -0.005* 

(0.002) (0.002) (0.003) (0.002) (0.002) (0.003) (0.004) (0.002) 

1983 -0.010*** -0.010*** -0.011** -0.013*** -0.008*** -0.013*** -0.011* -0.009*** 

(0.002) (0.002) (0.004) (0.002) (0.002) (0.003) (0.004) (0.002) 

1984 -0.014*** -0.015*** -0.013** -0.018*** -0.011*** -0.017*** -0.014** -0.013*** 

(0.002) (0.002) (0.004) (0.002) (0.002) (0.003) (0.004) (0.002) 

Round 

Round 3 -0.006*** -0.009*** 0.002 -0.005* -0.007*** 0.002 -0.006 -0.009 

(0.002) (0.002) (0.005) (0.003) (0.002) (0.005) (0.004) (0.002) 

Round 4 -0.007*** -0.007** -0.007 -0.006 -0.008** -0.002 -0.008 -0.008** 

(0.002) (0.002) (0.004) (0.003) (0.002) (0.005) (0.005) (0.002) 

Round 5 0.000 -0.004 0.011 0.008 -0.008* 0.014* -0.003 -0.004 

(0.003) (0.003) (0.008) (0.005) (0.003) (0.008) (0.007) (0.003) 

Round 6 -0.008 -0.007 -0.008 0.003 -0.015** -0.006 -0.009 -0.007 

(0.004) (0.004) (0.009) (0.008) (0.004) (0.008) (0.010) (0.005) 

Round 7 -0.003 -0.001 -0.008 0.007 (0.010) -0.007 0.001 -0.001 

(0.004) (0.001) (0.009) (0.008) (0.005) (0.008) (0.012) (0.006) 

Round 8 0.013* 0.012* 0.015 0.039*** -0.006 0.018 0.005 0.015* 

(0.006) (0.007) (0.013) (0.013) (0.005) (0.013) (0.013) (0.008) 

Psuedo-R2 0.171 0.183 0.141 0.169 0.175 0.142 0.144 0.203 

Sample Size 53744 40359 13385 27156 26588 14000 11282 27960 

Note: Robust standard errors are in parentheses. *indicates significance at the 0.10-level, ** at the 0.05-level, and *** at the 0.01-level. 

Annual observations come from 8984 individuals for the entire sample, 2236 for the over-sample, and 6748 for the cross-sectional sample, 

4599 for the male sample, 4385 for the sample of females, 2335 for the black sample, 1901 for the Hispanic sample, 4665 for the nonblack, non-hispanic (white) sample. Data are unweighted. 
 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Table 6: Estimated Marginal Effects of Probability of Initial Return Among Attritors 

Sample 

All Cross Oversamples Males Females Black Hispanic White 

Section 

Incentive 0.265*** 0.277*** 0.214*** 0.244*** 0.291*** 0.222*** 0.221*** 0.296*** 

(0.016) (0.018) (0.029) (0.021) (0.024) (0.030) (0.031) (0.022) 

Event/Status 

Birth 0.566*** 0.585*** 0.513*** 0.566*** 0.572*** 0.556*** 0.513*** 0.592*** 

(0.013) (0.015) (0.027) (0.018) (0.020) (0.026) (0.030) (0.019) 

Married 0.009 0.014 -0.017 0.075 -0.051 -0.079 0.02 0.035 

(0.030) (0.034) (0.058) (0.039) (0.044) (0.073) (0.050) (0.042) 

Cohabiting 0.012 0.023 -0.022 -0.006 0.026 -0.028 0.009 0.05 

(0.025) (0.030) (0.046) (0.038) (0.035) (0.045) (0.049) (0.038) 

In School -0.002 0.015 -0.053 0.013 -0.007 -0.006 -0.073 0.051 

(0.025) (0.029) (0.049) (0.035) (0.037) (0.048) (0.055) (0.035) 

Employed -0.048* -0.03 -0.091* -0.022 -0.082* -0.01 -0.069 -0.075* 

(0.021) (0.026) (0.037) (0.029) (0.032) (0.037) (0.040) (0.033) 

Birth Year 

1981 -0.007 0.008 -0.054 -0.008 -0.013 -0.006 -0.032 0.006 

(0.019) (0.022) (0.036) (0.025) (0.029) (0.035) (0.039) (0.027) 

1982 0.022 0.008 0.06 0.060* -0.035 -0.025 0.041 0.045 

(0.021) (0.024) (0.039) (0.026) (0.033) (0.039) (0.042) (0.029) 

1983 -0.069*** -0.050* -0.121** -0.051 -0.100** -0.132*** -0.036 -0.043 

(0.021) (0.024) (0.037) (0.027) (0.032) (0.035) (0.043) (0.030) 

1984 0.003 -0.003 0.014 -0.031 0.032 -0.054 0.019 0.026 

(0.023) (0.028) (0.042) (0.031) (0.035) (0.040) (0.045) (0.034) 

Round 

Round 4 -0.210*** -0.205*** -0.216*** -0.249*** -0.153** -0.278*** -0.179* -0.168** 

(0.034) (0.040) (0.054) (0.041) (0.056) (0.048) (0.064) (0.052) 

Round 5 -0.323*** -0.303*** -0.349*** -0.325*** -0.319*** -0.296*** -0.333*** -0.325*** 

(0.029) (0.036) (0.040) (0.037) (0.045) (0.047) (0.049) (0.046) 

Round 6 -0.370*** -0.345*** -0.407*** -0.373*** -0.365*** -0.402*** -0.381*** -0.326*** 

(0.027) (0.034) (0.038) (0.034) (0.043) (0.040) (0.046) (0.045) 

Round 7 -0.417*** -0.403*** -0.432*** -0.417*** -0.418*** -0.426*** -0.414*** -0.398*** 

(0.026) (0.032) (0.039) (0.034) (0.040) (0.040) (0.046) (0.043) 

Round 8 -0.466*** -0.458*** -0.465*** -0.438*** -0.503*** -0.467*** -0.459*** -0.452*** 

(0.025) (0.031) (0.041) (0.035) (0.037) (0.041) (0.046) (0.041) 

Psuedo-R2 0.306 0.316 0.279 0.289 0.336 0.278 0.264 0.337 

Sample Size 9134 6869 2265 5033 4101 2340 2024 4691 

Note: Standard errors are in parentheses. * indicates significance at the 0.10-level, ** at the 0.05-level, and *** at the 0.01-level. The 

samples consist of those respondents who have missed at least one interview in the survey rounds subsequent to missing their first 

interview. The samples are composed of 2306 respondents for the entire sample, 1730 for the cross-sectional sample, 576 for the 

oversample, 1283 for males, 1023 for females, 596 for blacks, 591 for Hispanics, and 1172 for whites (non-black, non-Hispanic 

respondents). Data are unweighted. 
 

 

 

 

 

 

 

 

 

Table 7: Effect of Attrition and Return in Quantile Regressions

 Attrite Return

 Ln(Annual Ln(Hourly Rate Ln(Annual Ln(Hourly Rate 

Earnings) of Pay) Earnings) of Pay) 

Median 

All 0.207*** 0.039*** 0.149** 0.033** 

(0.000) (0.000) (0.054) (0.013) 

[27555] [37238] [1726] [2720] 

Cross-Section 0.223*** 0.033*** 0.150* 0.043** 

(0.000) (0.001) (0.074) (0.017) 

[22234] [28683] [1319] [2021] 

Women 0.193*** 0.034*** 0.192* 0.012 

(0.000) (0.001) (0.124) (0.018) 

 [13182] [18304] [725] [1234] 

Men 0.216*** 0.039*** 0.182 0.049 

(0.000) (0.001) (0.089) (0.026) 

10th Percentile 

[14373] [18934] [1001] [1486] 

All 0.118*** 0.005 0.000 0.000 

(0.008) (0.004) (0.153) (0.015) 

[27555] [37238] [1726] [2720] 

Cross-Section 0.182*** 0.010*** 0.000 0.018 

(0.031) (0.002) (0.192) (0.030) 

[22234] [28683] [1319] [2021] 

Women 0.182*** 0.010 -0.260 0.010 

(0.030) (0.007) (0.246) (0.040) 

 [13182] [18304] [725] [1234] 

Men 0.124 0.008 0.182 -0.016 

(0.037) (0.011) (0.193) (0.035) 

90th Percentile 

[14373] [18934] [1001] [1486] 

All 0.141*** 0.034* 0.041 0.028 

(0.017) (0.016) (0.061) (0.045) 

[27555] [37238] [1726] [2720] 

Cross-Section 0.146*** 0.021 0.030 -0.028 

(0.033) (0.019) (0.074) (0.061) 

[22234] [28683] [1319] [2021] 

Women 0.236*** 0.017 0.206 -0.002 

(0.030) (0.020) (0.122) (0.068) 

 [13182] [18304] [725] [1234] 

Men 0.063 0.020 0.000 0.000 

(0.034) (0.020) (0.071) (0.063) 

[14373] [18934] [1001] [1486] 

Note: Standard errors are in parentheses. The number of annual observations used in the estimation are in 

brackets. * indicates significance at the 0.10-level, ** at the 0.05-level, and *** at the 0.01-level. Round of 

survey also included in all regressions. Annual observations come from 8984 individuals for the entire sample, 

2236 for the over-sample, and 6748 for the cross-sectional sample, 4599 for the male sample, 4385 for the sample 

of females, 2335 for the black sample, 1901 for the Hispanic sample, 4665 for the non-black, non-Hispanic 

(white) sample. Data are unweighted.