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Maternal Employment and Adolescent Risky Behavior

Alison Aughinbaugh, U.S. Bureau of Labor Statistics

Maury Gittleman, U.S. Bureau of Labor Statistics

Working Paper 366

February 2003

The views expressed are those of the authors and do not necessarily reflect the policies of the U.S. Bureau of 

Labor Statistics or the views of other staff members.
Maternal Employment and Adolescent Risky Behavior

Alison Aughinbaugh

U.S. Bureau of Labor Statistics

Maury Gittleman

U.S. Bureau of Labor Statistics

First draft: April 2002

Current version: December 2002

Abstract: This paper examines the impact of maternal employment during a child’s first three years and 

during adolescence on his or her decisions to engage in a range of risky behaviors: smoking cigarettes, 

drinking alcohol, using marijuana and other drugs, engaging in sex and committing crimes. Using data 

from the NLSY79 and its young adult supplement, we find little evidence that mother’s employment early 

in the child’s life has lasting consequences on participation in risky behaviors. Similarly, with the 

possible exception of drinking alcohol—our results do not indicate that maternal employment during 

adolescence is correlated with increased involvement in risky activities.

An earlier version of this paper was presented at the Annual Meeting of the Population Association of America,

Atlanta, GA, May 9-11, 2002 and the Annual Congress of the European Society of Population Economics, Bilbao, 

Spain, June 13-15, 2002. We are grateful to Suzanne Bianchi, our discussant at the PAA meetings, and to 

participants in both conferences for their helpful comments. We also wish to thank Phil Levine for sharing data with 

us. This work does not reflect the opinions of the U.S. Bureau of Labor Statistics or any of its other staff members. 

Correspondence may be sent to aughinbaugh_a@bls.gov, Bureau of Labor Statistics, 2 Massachusetts Ave. NE, 

Room 4945, Washington, DC 20212, 202-691-7520 phone, or 202-691-7425 fax.
1

Adolescence is a period of rapid physical, intellectual and emotional change. Many teens 

experiment with smoking, alcohol, ma rijuana and other drugs, become sexually active, and, to a 

lesser extent, participate in other illicit activities. While recent years have seen divergent trends 

in teen participation in such activities, the levels of such behavior are still cause for concern 

(Gruber, 2000). For many teens, there are few long-term consequences from engaging in this 

kind of behavior. But for those who become addicted to cigarettes, alcohol or drugs, who 

unintentionally become pregnant, or who find themselves with a criminal record, the costs to 

themselves, their families and society as a whole can be tremendous (Figlio and Ludwig, 2000; 

Pergamit, Huang, and Lane, 2001).

Owing to the rapid rise in labor force participation by women with children in recent 

decades and the resulting changes in arrangements for child care and parent-child relationships 

more generally, a large literature crossing disciplinary lines has examined the impact of maternal 

employment on children. Interestingly, recent work challenges the conventional wisdom that 

children are now spending less time with their parents than they had in the past, a subject to 

which we will return (Bianchi, 2000; Sandberg and Hofferth, 2001). Researchers have focused, 

for the most part, on the cognitive outcomes of children who are quite young, paying less 

attention to possible effects on children above elementary school age.1 In this study, we aim to 

fill part of this gap by examining the relationship between maternal employment and risky 

behavior by adolescents, that is, participation by teens in activities that have potentially harmful 

long-term consequences. 

In this effort, we use the National Longitudinal Survey of Youth, 1979 (NLSY79), and, in 

particular, its young adult supplement to analyze the decisions of children aged 15 and above 

 

1

 Harvey (1999) and Ruhm (2001) provide surveys of this part of the literature.
2

with respect to a range of risky activities: smoking cigarettes, drinking alcohol, using marijuana 

and other drugs, engaging in sex and committing crimes.2 Although there are many factors that 

influence such behaviors—including the actions and attitudes of one’s peers, neighborhood 

conditions, and genetic pre-dispositions—there is a consensus among developmental 

psychologists on the primacy of the mother-child relationship. Though this bond can be affected 

by a mother’s decision to work at any time during a child’s life, we focus on two time spans: the 

first three years of the child’s life and the period of adolescence itself.3 The recent shift in 

welfare policy in the U.S. to encourage or require mothers with young children to work

underscores the need to understand the effects of maternal employment on children and 

adolescents. 

I. Background

“If we want to have a real significant impact, not only on children’s success in school and 

later on in life, healthy relationships, but also an impact on reduction in crime, teen 

pregnancy, drug abuse, child abuse, welfare, homelessness and a variety of other social 

ills, we are going to have to address the first three years of life. There is no getting 

around it. All roads lead to Rome.” Rob Reiner, as cited in Bruer (1999, p. 8).

“If we wait until adolescence to help our children develop the sense of self that is needed 

to resist the draw of smoking we will be sorry....The research is conclusive. Early 

investments have a lifelong impact.” T. Berry Brazleton, as cited in Bruer (1999, p. 63)

The view that the first three years of life are crucial for subsequent development and 

gaining a sense of self, has gained support in recent years, owing in part to the influence of two 

policy documents, the Carnegie Task Force on Meeting the Needs of Young Children report 

Starting Points (1994) and Rethinking the Brain (Shore 1997), which was released in conjunction 

with the April 1997 White House Conference entitled “Early Childhood Development and

 

2

 Antecol and Bedard (2002) is another study using this data. They, however, focus on the influence of single 

parenthood on the likelihood of engaging in risky behavior. 

3

 Our analysis is limited to the impact of the number of hours worked. It is beyond the scope of this paper to 

consider, as Parcel and Menaghan (1994) did, whether the effects differ by the kind of work done by the mother.
3

Learning: What New Research on the Brain Tells Us About Our Youngest Children” (Bruer, 

1999). Though there is controversy over the extent to which neuroscience supports the 

deterministic view -- expressed in the quotations above by the actor Rob Reiner in his capacity as 

co-founder of the “I Am Your Child” campaign and foundation and by the prominent 

pediatrician T. Berry Brazleton -- that after the age of three it is too late to prevent a child from 

going down the wrong path, it is indisputable that the brain grows rapidly during this period, 

making it essential that children have the right kind of stimulation in order for the brain to 

develop normally. 

From a different perspective, developmental psychologists stress that advances in the 

early years lay the groundwork for future development. Thus, if in the first three years, in 

addition to the brain development just mentioned, the child develops important emotional 

relationships, attains a basic sense of self and others, and has a variety of learning experiences, it 

will be more likely that the child will continue on a positive path (Chase-Lansdale, 1998). On 

the other hand, children whose environments are not conducive to cognitive, social and 

emotional development will be less ready for school than their peers, thus making it more likely 

that they will fall further behind and find themselves on a path filled with not only academic but 

emotional setbacks. 

These and related views imply that circumstances that hinder early child development, 

whether linked to maternal employment or other factors, will have lasting effects. Of course, 

maternal employment also may yield advantages for children. A perspective referred to as the 

“human capital” or “financial resources” model emphasizes that the income maternal 

employment brings in can be invested in the development of the child, whether it is used to 

improve the physical environment for learning, to ensure the child remains in good health, or to 
4

purchase goods and services that will aid in cognitive stimulation. An alternative perspective 

highlights the emotional benefits of additional income for parent-child interactions, for example, 

through reduced levels of stress or a lower likelihood of parental depression (Guo and Harris 

2000; Yeung, Linver, and Brooks-Gunn 2001). While more than a decade elapses between 

toddlerhood and adolescence, recent research suggests that early intervention programs such as 

Head Start can have long-term effects on outcomes such as being charged or convicted of a 

crime (Garces, Thomas, and Currie 2002; Currie 2001).

To the potential advantages and disadvantages of maternal employment attendant when a 

child is very young, a number of others can be added in the case of adolescents. Mothers who 

work while their children are teens may have less time to provide emotional support, to monitor 

their children’s behavior, and to foster the adolescent’s involvement in activities in the school or 

community (Chase-Lansdale, 1998). Results from the recent synthesis of research on the impact 

of various welfare experiments by the Manpower Demonstration Research Corporation (Morris 

et al. 2001) underscore the vulnerability of adolescents to changes in their environment. 

Maternal employment may also hinder the accumulation of social capital of the kind discussed 

by Coleman (1988). The reduced availability of parents during the day may weaken the social 

capital inhering in the parent-child relationship, making the children less likely to identify with 

parental goals and values (Parcel and Menaghan, 1994). Maternal employment may also lead to 

a loss of social capital in the neighborhood, as parents are less likely to get together and watch 

each other’s children. As a result, the influence of the community on children may diminish, 

possibly making it more likely that children become involved in antisocial activities (Bianchi 

2000). On the positive side, a mother who is working may serve as a better role model for her 

children, or may find it easier to grant a teenager needed independence. 
5

Of course, whether the impact of maternal employment will be positive or negative will 

depend in many instances on the family context. Movement into the labor force by a mother will 

generally affect the parents’ relationship with each other, and have spillover effects on the child. 

For adolescents, not having a stay-at-home parent implies greater responsibility, with some teens 

benefiting and others not (Lerner and Noh, 2000). The importance of context no doubt explains,

in part, the fact that the results of research on the impact of maternal employment on various 

outcomes for adolescents is mixed (Trzcinski and Brandell, 2002).

II. Empirical Approach

In the empirical work that follows, we estimate models of the form:

RBijt AVGHRS j 1 j 2 j 3 ijt = 1 α + AVGHRS2 X α + α + ε (1)

where the dependent variable RBijt corresponds to a measure of the ith behavior of the jth

adolescent for time period t, AVGHRS1 is the average annual number of hours worked by the 

child mother’s in the first three years of life, AVGHRS 2 is the corresponding variable for the 

three calendar years preceding time t, X is a vector of regressors, the α ’s are parameters to be 

estimated, and e is the disturbance term. In most cases, our dependent variables indicate 

whether or not an adolescent has engaged in a particular behavior, though we also consider 

whether or not s/he has surpassed some threshold level of the behavior. If e is drawn from the 

logistic distribution, equation (1) implies a standard binary logit model. For some of the 

behaviors, we know not only whether a teen has ever participated in a given risky activity, but 

also the extent to which the s/he engaged in the activity, usually measured in terms of the number 

of days over a period that a given behavior has occurred. For these intensity variables, equation 

(1), with the addition of appropriate cut-off points, implies an ordered logit model. 
6

As mothers who work differ from those who do not, it is clear that estimating equation 

(1) will not, in general, lead to an estimate of the causal impact of hours worked by the mother 

on the measures of the incidence or intensity of risky behavior. First, mothers who work differ 

from those who do not on the basis of both observable and unobservable characteristics. Second, 

the decision to work or stay at home is endogenous; it is apt to be closely tied to complex 

decisions such as those affecting marital status or spousal labor force participation and may also 

be influenced by how a child is behaving, rather than or in addition to the reverse (Dunifon and 

Taylor 2002).

We use three approaches to address the statistical problems that arise. While each 

approach has limitations, consistency across the methods will enhance confidence in our 

findings. The first method relies on the estimationof a series of specifications, with each 

specification including additional variables. While it is not possible to control for all sources of 

heterogeneity, the pattern of the coefficients and its changes as other covariates are added should 

be informative.4 Given the richness of the NLSY79, it is possible to include a wide range of 

controls for the characteristics of the mother. 

Second, we utilize fixed-effects models, taking advantage of the fact that there are both 

siblings and cousins among the adolescents in the sample. In “mother” fixed-effect models, 

differences across siblings in outcomes can be related to variations among siblings in hours 

worked by their mother in their first three years of life and in adolescence. Provided any 

correlation between maternal employment and the error term is attributable to a mother-specific 

 

4

 Ruhm (2001) employs a similar approach in assessing the impact of parental employment on child cognitive 

development. He states that the results will underestimate the negative impact of parental employment on outcomes 

for the child, asserting that parents who work come from more privileged backgrounds and have characteristics that 

foster cognitive development. Because children from more advantaged backgrounds may have some characteristics 

that may make them more likely to engage in risky behavior and for which there are not adequate controls, such as 

degree of risk aversion or financial means, Ruhm’s argument is not tenable here. For instance, Gruber and Zinman 
7

fixed effect, fixed-effect estimates of the coefficients on the hours worked variables will be 

consistent. A second type of fixed-effects models takes advantage of the fact that some of the 

mothers in our sample are sisters. If the correlation between the regressors and error term are 

attributable to a fixed effect common to the mother and her sisters, these “grandparent” fixedeffect models will provide consistent estimates of the impact of maternal employment.

While these models remove the impact of unobserved family heterogeneity that is 

constant over time, one might reasonably wonder why there are differences across siblings and 

cousins in maternal employment and whether these differences are related to the dependent 

variables. The estimates will be biased if, for instance, mothers vary their hours worked in 

response to a perceived need to provide greater supervision to certain children.

Our third approach relies on instrumental variables. It will provide consistent estimates 

of the parameters of interest, provided the instruments are correlated with the variables for 

maternal employment in the first stage but are not related to the dependent variables in the 

second stage, except via their relationship with maternal employment. We use variation among

states and over time in child care regulations, the average wages of child care workers and of all 

workers, welfare benefit levels, and the status of welfare reform in the state.5 These variables are 

likely to influence the mother’s decision to work, but seem unlikely to have a direct effect on the 

behavior of youth, thus meeting the requirement for consistent IV estimation. Yet if the 

relationship between the instruments and maternal employment is weak, the coefficients on the 

hours variables will not be estimated precisely or, worse, will be biased and inconsistent (Bound, 

Jaeger and Baker, 1995).

 

(2000) find that, increasingly over time, smoking is rising among white suburban youth who have good grades and 

college-educated parents.

5We are grateful to Phil Levine for providing many of these variables, which were used as instruments for maternal 

employment in Anderson, Butcher and Levine (2002). We updated the wage variables in their dataset by using data 
8

III. Data

A. Dependent Variables

As noted, we use the National Longitudinal Survey of Youth, 1979 (NLSY79), and, in 

particular, its young adult supplement (NLSY79-YA) to assess the relationship between maternal 

employment and risky behavior by adolescents. In the NLSY79, the mothers of these young 

adults have been interviewed since 1979, when they were between 14 and 22 years of age. 

Beginning in 1986, the children of mothers were assessed and information about the child’s 

health, behavior, and material well-being was collected biennially. A big advantage of these data 

over those used in many studies of adolescent behavior is that data are available from when the 

children are quite young. 

Starting in 1994, children aged 15 or over were interviewed every other year, and asked 

about topics including drug, alcohol and cigarette use, sexual activity and crime. Information is 

collected not only on whether an adolescent engages in a certain kind of behavior, but also on its 

frequency and the year it began. Our analysis makes use of data on these behaviors for all the 

years for which it is currently available, 1994, 1996, 1998 and 2000. It should be noted that the 

young adults in our sample were disproportionately born to young mothers. For instance, for a 

female respondent of the NLSY79 to have had a child 15 years of age in 1994, she would have 

had to give birth when she was between the age s of 14 and 22. Even by 2000, a mother of a 15-

year old must have given birth when she was between the ages of 20 and 28. The Appendix 

provides some additional information about the representativeness of the NLSY79-YA and 

compares its reports of risky behavior with those from national data.

 

from the outgoing rotation groups of the CPS and updated the child care regulations using the child care center and 

family child care licensing studies published by the Children’s Foundation.
9

Table 1 provides descriptive statistics on engagement in these behaviors. Panel A of this 

table presents the incidence of involvement in risky behaviors both for all person-year 

observations in the dataset and all person-year observations in estimation. 6 Focusing on the 

former, we see that, on average over the 1994-2000 period, about three-fifths of these teens have 

ever drunk alcohol, and about half have smoked cigarettes or engaged in sexual intercourse. 

Roughly a third of the sample has smoked marijuana, while about 11 percent have used some 

other illicit drug or been convicted of a crime.7 Among those sexually active, about three in ten 

used no form birth control the most recent time they had sex.

When variables are defined such that use must have passed a certain threshold— drinking 

alcohol at least several times a month, smoking cigarettes every day, and smoking marijuana at 

least 1 or 2 days per week—the incidence rates fall markedly. Turning to the intensity variables, 

while about half of the sample has had a drink in the last year, the vast majority has not smoked 

cigarettes (76 percent) or marijuana (90 percent) over the last 30 days. 

B. Hours Variables

While some studies have found that effects of employment in the first year differ from 

those in the second and third year, statistical tests indicated that it was almost never the case that 

one could reject the hypothesis that the coefficients for the first three years were the same.8 As a 

 

6

 The reason why, for the incidence rates, person-year observations used in estimation are a subset of all person-year 

observations is as follows: In the multivariate analysis, we are interested in the factors that influence whether a 

person who has not yet engaged in a particular behavior will engage in that behavior. Thus, in a way analogous to 

analysis using duration models, observations for each individual are included in the estimation sample until the 

youth does engage in the behavior (or moves beyond a given threshold). The incidence variable that measures 

whether the youth used birth control at most recent sex is an exception to this. For birth control use to be measured, 

the youth must, of course, have had sex. Further, because of the time reference, each observation can be included in 

the estimation. The same is true for estimation using intensity variables, as behavior may differ fro m one year to the 

next. We adjust the standard errors to take into account of the presence of multiple observations per child.

7

 Respondents are asked about the use of substances that are sniffed, cocaine, crack, hallucinogens and sedatives, 

and these comprise “other drugs”. 

8

 The studies that have found differential effects for maternal employment have assessed its impact on preadolescents. As a result, the focus has been on cognitive development and behavioral problems, not on engagement 
10

result, we took a simple average of hours worked by the mother in the first three years of the 

child’s life. For employment during adolescence, we used the average of the mother’s 

employment during the three calendar years preceding the interview. Here, too, statistical tests 

were consistent with combining hours variables across different ages during adolescence. We 

also found that the results differed little between entering each hours variable individually and 

using them together in a regression.

The first panel of Table 2 presents descriptive statistics on the two hours worked 

variables and their subcomponents for the 9,395 person-years (accounted for by 2,542 

individuals) for which mother’s hours of work were available for the first three years of the 

child’s life and for the three calendar years preceding the interview. Not surprisingly, the table 

shows that mothers worked more when their children were adolescents than when they were 

infants and toddlers. In the first year of a child’s life, more than half the women did not work at 

all. The average hours worked for a child’s first year is 540 hours, or a little more than quarter 

time. Hours are higher in the second and third years, but even by the third year, both the average 

and median number of hours worked are still below the number of hours equivalent to half time.

The picture is different when the children are adolescents. Mean hours over the three 

most recent years are 1,360 hours, or a little under 70 percent time. Median hours are some what 

higher, exceeding three quarters time.9

As noted, women who work differ in some ways from those who do not. As Table 2b 

shows, those who are not employed have lower levels of characteristics thought by some to be 

associated with better parenting – AFQT scores and education level (Ruhm 2001). On average, 

 

in risky behavior. Examples include Blau and Grossberg (1992), Ruhm (2001), and Waldfogel, Han and BrooksGunn (2002). 

9

 The gap between hours worked when the children are young and when they are in adolescence may be smaller for 

later cohorts. Olivetti (2001) finds a secular increase in the amount of hours worked by mothers with young 
11

women who are not employed have about one fewer years of schooling and an AFQT score that 

is 12 points lower than those employed, relationships that hold both for early maternal 

employment and employment during adolescence. Despite these differences, the standard 

deviations are high enough that one cannot reject the hypothesis that these variables are equal 

across the two groups. 

IV. Multivariate Analysis

A. Logit Estimates

1. Main Results

Table 3 summarizes the results from our first approach to estimating the impact of 

maternal employment on the incidence of risky behavior. Three different specifications are used. 

In the first, only the hours variables are included as regressors. In the second, we add controls 

for the age and sex of the child, and also include variables that are outside of the mother’s 

control and thus can be considered exogenous. These variables include measures of the location 

of the mother’s birth, whether she lived in an SMSA, the household structure of her family when 

she was age 14, the highest grade attended by her parents, whether she had newspapers or 

magazines in her home or held a library card at age 14, her religious affiliation and attendance in 

1979, and whether her mother worked at age 14. These variables may be correlated with factors 

that will affect the adolescent’s susceptibility to engaging in the risky behaviors, including child 

endowments (passed on either genetically or environmentally), attitudes towards the behaviors of 

interest, and parenting style, among many others. 

In this second specification, we are careful not to include variables that may be 

endogenous. Doing so would mean that the equation would no longer be a reduced form, leading 

 

children, one that she attributes to its becoming more costly for mothers with young children to cut back on their 

hours, given that the return to experience for women has risen relative to that for men. 
12

to simultaneity bias. In addition, one runs the risk of holding constant variables that may be 

determined simultaneously with or in response to hours worked, making it difficult to interpret 

the coefficients. For instance, marital status and number of children, not to mention the hours 

worked by a spouse are examples of variables that may be chosen jointly with the hours worked 

by the mother (Killingsworth 1983; van der Klaauw, 1996; Francesconi 2002).10 Because many 

previous studies of the effects of maternal employment on children include controls for mother’s 

human capital, household structure and composition, and family resources, we employ a third 

specification that includes such controls, allowing us to assess whether the additional variables 

influence the results. For instance, if the impacts of employment are coming through the 

additional financial resources flowing in, the inclusion of the income variable should absorb 

these effects. The additional controls include the mother’s age at the birth of the child, her 

highest grade completed, marital status, score on the Armed Forces Qualifying Test, the number 

of female and male adults in the household, the number and ages of the children in the 

household, birth order, and family income. 

Table 3 summarizes the results from logit regressions for the incidence measures, both 

those measuring whether the respondent has eve r engaged in a particular behavior, and those 

measuring whether a threshold level of activity has been surpassed. Instead of coefficient 

estimates, we report the marginal effect of increasing hours worked by 1000 on the probability 

that those who have not already engaged in a particular behavior (or surpassed a threshold level) 

will do so. An increase of 1,000 in hours worked corresponds roughly to the change in hours 

that results from moving from not working at all to being half time through the year, or moving 

from half time to full time.

 

10 See Blau (1999) for a similar approach.
13

The evidence in Table 3 seems to imply that higher hours of maternal employment in the 

early years of a child’s life do not make it more likely that s/he will engage in risky behaviors. 

In more than half the cases, the sign of the coefficient for hours worked in the first three years is 

negative, consistent with reduced participation in risky behaviors. It is, however, rare for these 

negative coefficients to be statistically significant, though it occurs in two out of three 

specifications for ever having had sexual intercourse. Out of the 30 regressions summarized in 

Table 3, there is only one instance where there is a positive and statistically significant 

association between maternal employment in the first three years and participation in risky 

behaviors, the third specification for ever having been convicted of a crime. 

As with maternal employment in the first years, there is little evidence to suggest that 

greater hours of maternal employment during adolescence increases the riskiness of adolescent 

behavior. In fact, there are no cases where there is a significant positive association between 

risky behaviors and maternal employment during adolescence. For the first two specifications, 

women who work more are less likely to have children who have committed a crime or engaged 

in unsafe sex, but these results are not robust to the inclusion of additional variables in the third 

specification.

We now turn to an analysis of the relationship between maternal employment and the 

intensity of the risky behaviors, making use of ordered logits. In the results that are summarized 

in Table 4, we report both the coefficients for the hours variables, as well as the estimates’ 

implied impact on the dependent variable. As with the incidence results, there is little evidence 

of an association between maternal employment during the first three years and risky behavior. 

Out of a total of nine regressions summarized in Table 4, there is only one case where this

relationship is significant, with the coefficient suggesting reduced alcohol use. For hours worked 
14

during adolescence, we see for the first time a significant relationship for all three specifications, 

suggesting that maternal employment is associated with a greater number of days alcohol is used; 

a 1,000 hours change in the time worked implies an increase of 1-2 days of alcohol use over the 

past year. We see no evidence that hours worked – both when the child is young and during 

adolescence—are related to the intensity of cigarette or marijuana use.

2. Sensitivity Analysis

In this subsection, we examine whether our results differ by sex, race/ethnicity and 

marital status. It is clear that the relationship between risky behaviors and maternal employment, 

both early on and in adolescence, may differ by the sex of the child.11 If, for example, boys are 

more vulnerable than girls to the absence of their mothers, they are more likely to be harmed 

when their mothers work (Han, Waldfogel, and Brooks-Gunn 2001). More generally, given that 

for both genetic and environmental reasons boys will respond differently than girls to their 

mother’s behavior and vice versa, the impact of a mother’s decision to work may differ by sex. 

To see if this is the case, we ran the regressions of Tables 3 and 4 separately by the sex of child, 

making use of the second specification. The results, presented in Tables 5 and 6, do not differ 

substantially by sex, with a few exceptions. Perhaps of greatest interest are the results for the 

intensity of alcohol use, as this dependent variable was the only one where a statistically 

significant relationship was robust to changes in specification. Both males and females whose 

mothers worked more than average during adolescence drank alcohol more frequently in the last 

12 months than their counterparts, though the coefficient is only significant for females. On the 

other hand, more hours of early maternal employment is associated with a reduction in the 

likelihood of drinking alcohol several times a month for males but not females. Increased work 

 

11 For examples of studies where the impact of maternal employment differed by the sex of the child, see Desai, 

Chase-Lansdale and Michael (1989) and Bogenschneider and Steinberg (1994) and the references therein. 
15

effort in a child’s early years is associated with a reduction in the likelihood of having had sexual 

intercourse among males, but not among females. It is also apparent that the association of 

employment during adolescence with a reduction in the likelihood of being convicted of a crime 

is driven by results among male teens, perhaps not surprisingly given that 18% of the males 

versus 7% of the females in this sample have been convicted of a crime.12 Males (but not 

females) whose mothers worked while they were young had a higher likelihood of not using 

birth control, while employment during adolescence is associated with a greater likelihood of 

using birth control only for females. 

In general, there are apt to be differences in the relationships under study by race and 

ethnicity. 13 These may arise because of racial or ethnic differences in the quality of child care 

arrangements that are available to working mothers, if only because of the variation in income 

across racial and ethnic groups (NICHD Child Care Network, 1999). In later years, the quality 

of after-school activities that are available to young adults whose mothers work may also vary by 

race and ethnicity. Table 7 and 8 summarize the results for regressions run separately for 

African Americans, Hispanics and whites. As with the regressions by sex, the results by 

race/ethnicity14 show no evidence of a relationship in the vast majority of cases, though it is of 

interest to see which groups are driving the results. The association between alcohol use and 

maternal employment adolescence seems to be concentrated among whites; for this race, an 

increase in hours worked by 1,000 is associated with a 4 percent higher chance of trying alcohol 

 

12 In this sample, the conviction rate by gender is more similar than statistics on juvenile crime would indicate. For 

instance, in 1998 76% of youths tried in juvenile court were male and in 1997 and in 1999 about 86% of the youths 

in custody were male (Stahl, 2001; Sickmund and Wan, 2001).

13 For example, Han, Waldfogel and Brooks-Gunn (2001) and Waldfogel, Han, and Brooks-Gunn (2002) find 

differences by race/ethnicity in the effects of early maternal employment on child cognitive development, while 

Anderson, Butcher and Levine (2002) find differences by race in the impact of maternal employment on a child’s 

being overweight.

14 The sample was divided into three groups: Hispanics, African Americans, and those neither Hispanic nor African 

American.
16

and an increase of 1.7 days in the days used alcohol in the last year. The negative association

seen in Table 3 between early maternal employment and ever having had sexual intercourse 

seems to be driven mainly by the relationship among African Americans. Somewhat perversely, 

among African Americans higher work effort during adolescence is associated with an increased 

likelihood of using birth control, while for Hispanics the opposite is true. Among African 

Americans, there is a reduced likelihood of smoking in the last 30 days that is associated with 

greater work effort during adolescence. 

As a final sensitivity test, we examine the results of the second specification by marital 

status.15 Though marital status may be endogenous, consideration separately by marital status is 

clearly of interest. When single parents work, there is no other parent to shoulder the burden of 

child care and a lack of resources may constrain the choice of child care arrangements. The 

improvements in economic well-being and reduced stress levels, however, may be greater from a 

single mother’s employment than from a married mother’s employment. Thus, a priori, it is 

difficult to say whether or not hours worked by a single mother are likely to be more harmful for 

a child than if a married mother worked. With one exception, the relationship between hours 

worked by single parents in a child’s first three years and risky behavior was not significant. 

Conducting the analysis separately by marital status uncovered some cases where hours worked 

during adolescence by a married parent was associated with a greater extent of risky behavior: 

ever having had a drink of alcohol, ever having had sexual intercourse and the number of days of 

alcohol use. In contrast, hours worked by a single mother during adolescence are associated with 

a decrease in some risky behaviors: ever having been convicted of a crime and using marijuana 

 

15 Given that a woman may be married in one period but not the other, it is not possible to split the sample by marital 

status. Instead for both early and adolescent maternal employment, marital status was interacted with the hours 

variables. For each of the two time periods, two marital status variables are defined. The first equals 1 if the 

youth’s mother was married at the three relevant interviews and no marital changes occurred between interviews and 
17

at least one or two days per week. Maternal hours worked during adolescence is associated with 

increased probability of using birth control as last sex regardless of mother’s marital status—as 

was the case when this relationship was examined jointly across mother’s marital status.

B. Fixed-Effect Estimates

As noted above, we estimate two types of fixed-effect models, both using the conditional

logit approach of Chamberlain (1980). In the first, all siblings observed during adolescence are 

assumed to have the same fixed effect, so differences across siblings in outcomes are regressed 

against differences across siblings in hours their mother worked and other variables that vary by 

sibling. The other model type, “grandmother” fixed effects, assumes a constant factor across 

anyone with a maternal grandparent in common. Thus, the differences across cousins in 

outcomes will be related to variation across their mothers in the amount of time worked when 

their children are under the age of three and in adolescence. For both types of fixed-effects 

models, we use the second specification of Tables 316, which adds to the hours variables 

characteristics that are almost certainly exogenous.

17 We used Hausman tests to compare the 

estimates from the conditional logit fixed effects models to those from the standard logit models. 

The hypothesis that there are no systematic differences in the coefficient estimates can be 

rejected for six of the ten binary dependent variables for the “mother” fixed effects and in eight 

of ten instances for the “grandmother” fixed effects, while in two instances the asymptotic 

conditions required for a valid Hausman test are not met.

 

the second equals one if she was not married at any of these interviews or experienced a marital transition in the 

interim. 

16 We do not re-estimate the models of Table 4 using fixed-effects, because of the difficulty of doing fixed effects 

with ordered logits. We did, however, experiment with changing the threshold and running binary logit fixed-effects 

models; the results were consistent with those reported in Table 11.

17 In the “mother” fixed effect models, the child’s age and gender, and the year indicators are the only regressors 

besides mother’s hours worked that differ across siblings. In the “grandparent” fixed effect models, all control 

variables except those for race and ethnicity can, in theory, differ across cousins.
18

The first eight columns of Table 11 summarize the results of the fixed effect models for 

the incidence variables. In order to be included in the likelihood function for conditional fixedeffect models, it must be the case that there is variation across the individuals with a factor in 

common. For the “mother” fixed effects, therefore, one sibling must have engaged in a behavior 

or surpassed a threshold at one point in time, while the other(s) did not, or vice versa. For the 

“grandmother” effects, similarly, there must be variation across the cousins in the dependent 

variable. This requirement means that the sample sizes are smaller for the conditional logit fixed 

effects than they are for their standard binary-logit counterparts.18 

The fixed-effects results in Table 11 are consistent with those in Table 3 in the sense that 

it is rare for the coefficients on the hours worked variables to be statistically significant. There 

are no cases where employment during adolescence has a significant relationship with the 

incidence variables. For employment in the first three years of the child’s life, the only 

statistically significant relationship that is consistent between the two types of fixed effects is 

that for whether birth control has been used the last time the individual had sex; early childhood 

employment is associated with an increase of about eleven percentage points in the likelihood of 

using birth control. For the “mother” fixed effects, more hours of maternal employment in the 

first three years of the adolescent’s life is associated with a greater likelihood of ever having had 

an alcoholic drink. 

C. Instrumental Variables Estimates

For our instrumental variable estimation, as with the fixed effects models, we again make 

use of the second specification. We focus on the incidence and threshold dependent variables, 

and, for ease of implementation, we use linear probability models in the IV estimation. 

 

18 We compared the results of the conditional fixed-effect logit models to those from linear probability models with 

fixed effects and found little difference.
19

We first test to see if it is necessary to use instrumental variables -- that is, whether OLS 

estimates are, indeed, inconsistent -- employing a test suggested by Davidson and MacKinnon 

(1993). Somewhat surprisingly, there are a number of cases where we cannot reject the 

consistency of OLS, four out of the 10 dependent variables for maternal employment in the first 

three years of the child’s life and six of ten in the case of maternal employment in the three most 

recent years. But, as the power of exogeneity tests have been called in question, we present IV 

estimates for all dependent variables.

The instruments we use can be divided into three groups. The first are regulations that 

apply to child care centers: the maximum ratios of children to staff for ages 0-5 and whether 

liability insurance is required. The second is the same set of variables, but applying to family 

child care. Both sets of child care variables are likely to influence whether the mother can find 

child care that is at a low enough cost and high enough quality; a failure to find child care that 

meets these standards makes it less probable that a mother will work (Hotz and Kilburn, 1996; 

Baum 2002). The third set of instruments includes welfare benefit levels, the status of welfare 

reform in the state, and the mean hourly wage for all workers and for child care workers. These 

variables influence the financial cost and benefits of the decision to work.19 

Recent research has highlighted the problems that may arise—inconsistency and finitesample bias—if there is not a strong relationship between the instruments and the hours variables 

that we are treating as endogenous in the IV estimation (Bound, Jaeger and Baker 1995). These

problems can be exacerbated by having a large number of instruments, which in our case number 

around 40. Diagnostics suggested to examine if weak instruments may be a problem are: 1) 

calculating the R-squared of a regression of the endogenous regressors--the two hours of work 
20

variables—on the instruments, after the exogenous variables that appear in both the first and 

second-stage equations have been partialed out and 2) calculating the F-statistics of the excluded 

instruments in the first stage equations, for each of our endogenous regressors. Though there is 

some variation across dependent variables because of differences in sample, the partia l Rsquared for the first-stage equation for the variable measuring hours worked early in the child’s 

life is generally in the neighborhood of 0.03-0.4, while the F-statistic is usually between 3.0 and

4.0. For the variable measuring hours worked during adolescence, the partial R-squareds are 

also around 0.03-0.04, and the F-statistics are generally between 2.0 and 3.0.

The partial R-squared and F-statistics are low enough to suggest that IV estimation is 

potentially problematic. According to a table in Bound, Baker and Jaeger (1995), the F-statistic 

combined with the number of instruments suggest that the bias of IV estimates relative to OLS 

estimates is about 20-30 percent. One approach to addressing this bias would be to calculate

standard errors that take this bias into account, such as in Staiger and Stock (1997). We did not 

follow this approach, because, as will be evident below, it is rare for the coefficients on the 

endogenous regressors to be significant; it is likely that calculating standard errors taking 

account of the finite sample bias would only make this even more rare.

Our IV models are overidentified so it is possible to test whether the overidentifying 

restrictions – the exclusion of the instruments from the second stage equation – can be rejected, 

which would cast doubt on the validity of the instruments. We conducted tests both on all 

instruments as well as on groups of instruments (Hayashi 2000). For the test on all instruments, 

we used Sargan’s statistic and found-- somewhat surprisingly, given the weak relationship 

between the instruments and the endogenous regressors – that the validity of the instruments was

 

19 To make use of these instruments, we match to the respondent the instruments for the appropriate state in the six 

years for which the maternal employment hours are taken. To correspond to how we measure hours of maternal 
21

rejected in half the cases.20 We then calculated a C statistic for each of our six groups of 

instruments (three sets x two time periods) for each dependent variable, to indicate whether the

group of instruments can be considered exogenous. We then excluded any group of instruments 

for which exogeneity is rejected from the instruments. Doing so greatly reduced the number of 

cases for which the validity of all instruments used was rejected. We, however, found virtually 

no difference in the results when these groups of instruments were excluded, so we report results 

from regressions where all the instruments were used.

These results are presented in the last two columns of Table 11. There are no cases where 

there is a significant relationship between hours worked during adolescence and the likelihood of 

engaging in or surpassing a threshold for any of the risky behaviors. For the maternal 

employment hours variable for first three years, there is one significant relationship, which 

suggests that early maternal employment reduces the likelihood of ever having smoked by about 

20 percent. 

V. Conclusions

Taking our three approaches as a whole, we find very little evidence consistent with the 

view that what happens in the first three years of a child’s life can have lasting effects on the 

child’s development, at least in terms of whether maternal employment affects the likelihood of 

engaging in the risky behaviors. Reasons for this (non) finding can be placed in two categories. 

The first is that there is enough time between the age of three and adolescence to undo any harm 

that may have come about from maternal employment in the child’s first years. Future research 

might usefully explore whether behavioral problems at a young age are a good predictor of 

problems as an adolescent.

 

employment, we then calculate the averages for each instrument, one for each three-year period.
22

The second category includes reasons why the assumption that maternal employment 

greatly reduces the time that parents spend with their children may not have a firm empirical 

basis. Consistent with this view is the finding by Sandberg and Hofferth (2001) that children 

spent no less time with their parents in 1997 than in 1981. Bianchi (2000) offers several reasons 

why the increase in labor force participation by mothers has not led to large changes in the 

amount of time children spend with their parents. First, the time that non-working mothers can 

devote to children is diminished by the amount of time spent on other household activities such 

as cleaning and cooking. Working mothers may be able reduce time spent on these activities by 

hiring a cleaning service, or relying more on take-out or frozen dinners. Second, working 

mothers, particularly those with flexible work schedules, may be able to ensure that their paid 

work does not substantially reduce the time with their children. Third, both the fact that families 

are smaller and that preschool age children now spend more time in school-like settings reduces 

the time demand on mothers, independent of their work status. Finally, in many cases, the 

involvement of men in child rearing may have increased to offset reduced time by women.

With the possible exception of drinking alcohol, our results do not indicate that maternal 

employment during adolescence is correlated with increased involvement in risky activities. 

Many of the reasons that could explain why parental time with children may not be reduced 

substantially when mothers work apply here as well. In addition, the positive effects of working 

– such as serving as a positive role model or allowing teens needed independence -- may serve to 

offset the harmful effects. 

It is clear, moreover, that there are many other influences on the behavior of young 

adults. Within the family, the amount of time a mother works is just one factor affecting her 

 

20 Our instruments vary only by state and year. Hoxby and Paserman (1997) show that when this kind of grouped 

data is used that overidentifying restrictions are rejected too frequently.
23

relationship with her children. Other aspects of family background – for example, the level of 

income and its components, the adolescent’s relationship with the other parent, birth order and 

the presence on more siblings in general -- are also likely to play a role. Outside the family, 

peers, the neighborhood and community in which the adolescent lives, and the school s/he 

attends all are likely to have important impacts (Peters and Mullis, 1997; Brooks-Gunn, Duncan 

and Aber 1997; Duncan, Boisjoly and Harris 2001; Kooreman and Soetevent 2002; Mocan, 

Scafidi and Tekin 2002; Argys, et. al., 2002). 
24

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28

Table 1: Youth Involvement in Risky Behaviors

Table 1a: Incidence of Involvement in Risky Behaviors 

All Observations Observations Used in Estimation

Person-Year 

Observations

% Participated 

in Behavior

Person-Year 

Observations

% Participated 

in Behavior

Ever:

 Drank Alcohol 4553 62.29 3141 50.11

 Smoked Cigarette 4386 48.97 3254 33.31

 Used Marijuana 4576 34.16 3828 22.99

 Used Other Drugs 4510 10.75 4302 7.44

 Had Sexual Intercourse 4603 52.12 3460 37.37

 Convicted of Crime 4678 11.44 4436 6.61

Use Above Threshold:

 Drank Alcohol at least Several Times a Month 4517 13.62 4052 11.38

 Smoked Cigarettes Everyday 4610 14.23 4240 10.38

 Used Marijuana at least 1 to 2 Days per Week 4444 6.14 4256 5.08

Used No Form of Birth Control at Most Recent Sex 2399 29.80 2399 29.80

Table 1b: Intensity of Involvement in Risky Behaviors 

Person-Year 

Observations

% Participated 

in Behavior

Intensity of Use:

 Alcohol Use during Last 12 Months (n=4370)

 None 2195 50.23

 1 to 2 days in Past 12 Months 562 12.86

 3 to 5 days in Past 12 Months 297 6.80

 Every Other Month or So 285 6.52

 1 to 2 Times per Month 415 9.50

 Several Times a Month 236 5.40

 1 to 2 Days per Week 276 6.32

 Almost Daily, 3-6 Days per Week 66 1.51

 Daily 38 0.87

 Cigarette Use in Last 30 Days (n=4546)

 None 3459 76.09

 Less than once per week 200 4.40

 1 or 2 days per week 110 2.42

 3 or 4 days per week 74 1.63

 5 or 6 days per week 47 1.03

 Every day 656 14.43

 Marijuana Use in Last 30 Days (n=4384)

 None 3942 89.92

 Less than once per week 169 3.85

 1 or 2 days per week 98 2.24

 3 or 4 days per week 56 1.28

 5 or 6 days per week 35 0.80

 Every day 84 1.92
29

Table 2

Maternal Annual Hours of Employment and Characteristics by Employment Status

Table 2a: Statistics on Maternal Annual Hours of Employment

Year Mean Median 10th percentile 90th percentile

1

st year of Child’s Life 539.65 0 0 1812

2

nd year of Child’s Life 823.88 560 0 2080

3

rd year of Child’s Life 977.37 900 0 2080

Average over 1st 3 Years 780.30 622.22 0 1920

3 Years Ago 1291.03 1728 0 2340

2 Years Ago 1394.03 1720 0 2420

1 Year Ago 1393.65 1520 0 2405

Average over Last 3 Years 1359.82 1560 8 2337.33

Note: Sample size is 2542 individuals with 9395 person-years.

Table 2b: Means of Characteristics by Employment Status

Child’s First Three Years Last Three Recent Years

Nonemployed Employed Nonemployed Employed

Highest Grade Attended 11.53 12.49 11.25 12.37

(2.32) (1.91) (2.43) (1.98)

AFQT score 21.24 33.65 19.72 31.72

(22.08) (24.34) (31.72) (24.31)

Note: Standard deviation in parentheses
30

Table 3

Marginal Effect of Hours Worked by Mother on Incidence of Risky Behaviors, Logit Estimates

Specification Maternal Employment only

Maternal Employment and Core 

Regressors

Maternal Employment, Core, and 

Additional Regressors

Ave. annual hours, 

First 3 yrs.

Ave. annual hours, 

Last 3 yrs.

Ave. annual hours, 

First 3 yrs.

Ave. annual hours, 

Last 3 yrs.

Ave. annual hours, 

First 3 yrs.

Ave. annual hours,

Last 3 yrs.

Ever:

 Drank Alcohol (n=3141) -0.016 0.020 -0.010 0.022 -0.013 0.021

(0.013) (0.011) (0.014) (0.012) (0.015) (0.012)

 Smoked Cigarettes (n=3254) -0.017 0.002 -0.024 0.009 -0.016 0.008

(0.013) (0.010) (0.013) (0.010) (0.014) (0.011)

 Used Marijuana (n=3828) -0.018 0.000 -0.017 -0.001 -0.011 0.004

(0.010) (0.008) (0.010) (0.008) (0.011) (0.009)

 Used Other drugs (n=4302) 0.004 0.001 -0.010 0.000 -0.001 -0.013

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

 Had Sexual Intercourse -0.054** 0.010 -0.041** 0.003 -0.026 0.011

 (n=3460) (0.011) (0.009) (0.013) (0.011) (0.014) (0.011)

 Convicted of crime (n=4436) -0.001 -0.013** 0.001 -0.013** 0.009* -0.006

(0.005) (0.004) (0.005) (0.004) (0.004) (0.004)

Use Above Threshold:

 Drink Alcohol at least -0.015** 0.006 -0.010 0.001 0.011 0.001

 Once a month (n=4052) (0.007) (0.006) (0.007) (0.005) (0.007) (0.005)

 Smoked Cigarettes Everyday -0.004 -0.003 -0.004 -0.005 0.003 -0.001

 (n=4240) (0.007) (0.005) (0.007) (0.005) (0.007) (0.005)

 Used marijuana at least 1 to 2 -0.006 -0.005 -0.005 -0.004 -0.002 -0.003

 Days per week (n=4256) (0.005) (0.004) (0.004) (0.003) (0.004) (0.003)

Used No form of birth control -0.023 -0.036** -0.022 -0.033** -0.003 -0.018

 at most recent Sex (n=2399) (0.015) (0.011) (0.015) (0.011) (0.016) (0.012)

Note: Sample sizes are based on person-year observations. Standard errors are presented in parentheses. * indicates coefficient is significant at .05-level, ** at .01-

level. Hours variables are measured in units of 1,000.
31

Table 4

Coefficient Estimates of Hours Worked by Mother on Intensity of Risky Behaviors, Ordered Logit Estimates

Specification Maternal Employment only

Maternal Employment and Core 

Regressors

Maternal Employment, Core, and 

Additional Regressors

Ave. annual hours, 

First 3 yrs.

Ave. annual hours, 

Last 3 yrs.

Ave. annual hours, 

First 3 yrs.

Ave. annual hours, 

Last 3 yrs.

Ave. annual hours, 

First 3 yrs.

Ave. annual hours, 

Last 3 yrs.

Alcohol Use in Last 12 -0.074* 0.160** -0.039 0.107** -0.041 0.092*

 Months (n=4370) (0.050) (0.040) (0.053) (0.042) (0.055) (0.044)

 Change in Mean # of Days -1.011 2.169 -0.501 1.344 -0.517 1.118

Cigarette Use in Last 30 Days -0.032 -0.025 -0.027 -0.083 0.021 -0.072

 (n=4546) (0.066) (0.051) (0.073) (0.053) (0.076) (0.056)

 Change in Mean # of Days -0.132 -0.106 -0.106 -0.324 0.080 -0.279

Marijuana Use in the Last 30 -0.120 -0.079 -0.107 -0.100 -0.069 -0.083

 Days (n=4384) (0.086) (0.070) (0.093) (0.070) (0.097) (0.073)

 Change in Mean # of Days -0.131 -0.088 -0.103 -0.098 -0.069 -0.085

Note: Sample sizes are based on person-year observations. Standard errors are presented in parentheses. Changes in mean number of days on which the activity is 

undertaken are presented in square brackets. * indicates coefficient is significant at .05-level, ** at .01-level. Hours variables are measured in units of 1,000.
32

Table 5

 Effect of Average Annual Hours Worked by Mother on Incidence of Risky Behaviors

By Sex

Ave. annual hours, First 3 yrs. Ave. annual hours, Last 3 yrs.

Male Female Male Female

Ever:

 Drank Alcohol -0.007 -0.013 0.028 0.022

 (0.021) (0.020) (0.017) (0.016)

[1544] [1597]

 Smoked Cigarettes -0.033 -0.015 0.009 0.013

 (0.019) (0.019) (0.015) (0.015)

[1591] [1663]

 Used Marijuana -0.015 -0.020 0.0046 -0.004

 (0.015) (0.014) (0.0121) (0.011)

[1869] [1959]

 Used Other drugs 0.008 -0.007 -0.003 0.002

 (0.006) (0.007) (0.005) (0.005)

[2106] [2196]

 Had Sexual Intercourse -0.048** -0.034 0.012 -0.003

 (0.018) (0.019) (0.016) (0.015)

[1710] [1750]

 Convicted of crime -0.000 0.003 -0.023** -0.005

 (0.009) (0.005) (0.007) (0.004)

[2111] [2325]

Use Above Threshold:

 Drink Alcohol at least -0.024* 0.003 0.003 -0.000

 Several Times a month (0.010) (0.009) (0.008) (0.007)

 [1968] [2084]

 Smoked cigarettes -0.004 -0.004 -0.003 -0.006

 Everyday (0.010) (0.008) (0.007) (0.006)

[2063] [2177]

 Used marijuana at least -0.001 -0.008 -0.002 -0.0060

 1 to 2 Days per Week (0.007) (0.005) (0.005) (0.0038)

 [2071] [2185]

Used No Birth control at 0.049* 0.005 0.000 -0.066**

 last sex (0.022) (0.022) (0.016) (0.015)

[1158] [1241]

Note: Standard errors are presented in parentheses. Sample sizes are based on person-year 

observations and are in square brackets. * indicates coefficient is significant at .05-level, ** at 

.01-level. Hours variables are measured in units of 1,000.
33

Table 6

 Effect of Average Annual Hours Worked by Mother on Intensity of Risky Behaviors

By Sex

Ave. annual hours, First 3 yrs. Ave. annual hours, Last 3 yrs.

Male Female Male Female

Alcohol Use in Last 12 -0.083 0.022 0.108 0.119*

 Months (0.076) (0.073) (0.060) (0.058)

 Change in Mean # of Days -1.209 0.220 1.605 1.189

 [2143] [2227]

Cigarette Use in Last 30 Days -0.003 -0.067 -0.071 -0.073

 (0.102) (0.106) (0.075) (0.075)

 Change in Mean # of Days -0.009 -0.237 -0.294 -0.258

[2220] [2326]

Marijuana Use in the Last 30 0.044 -0.316* -0.073 -0.110

 Days (0.122) (0.137) (0.099) (0.099)

 Change in Mean # of Days 0.055 -0.202 -0.095 -0.069

[2149] [2235]

Note: Standard errors are presented in parentheses. Sample sizes are based on person-year observations and 

are in square brackets. * indicates coefficient is significant at .05-level, ** at .01-level. Hours 

variables are measured in units of 1,000.
34

Table 7

 Effect of Average Annual Hours Worked by Mother on Incidence of Risky Behaviors

By Race/Ethnicity

Ave. annual hours, First 3 yrs. Ave. annual hours, Last 3 yrs.

African 

American Hispanic White

African 

American Hispanic White

Ever:

 Drank Alcohol -0.030 -0.009 -0.0101 0.021 -0.006 0.0442*

 (0.023) (0.033) (0.022) (0.018) (0.024) (0.0181)

[1203] [665] [1273]

 Smoked Cigarettes -0.031 -0.061 -0.015 0.005 -0.001 0.020

 (0.019) (0.032) (0.023) (0.014) (0.025) (0.019)

[1319] [695] [1240]

 Used Marijuana -0.018 -0.024 -0.023 -0.007 0.006 0.005

 (0.016) (0.026) (0.016) (0.011) (0.020) (0.013)

[1455] [792] [1581]

 Used Other drugs 0.003 -0.005 -0.006 0.001 0.002 0.000

 (0.004) (0.015) (0.010) (0.002) (0.013) (0.008)

[1616] [928] [1758]

 Had Sexual Intercourse -0.074** -0.047 -0.007 -0.012 0.010 0.0197

 (0.024) (0.028) (0.019) (0.019) (0.024) (0.0153)

[1211] [766] [1483]

 Convicted of crime -0.005 0.006 0.001 -0.010 -0.015 -0.0111

 (0.008) (0.010) (0.007) (0.006) (0.008) (0.0060)

[1649] [963] [1824]

Use Above Threshold:

 Drink Alcohol at least -0.008 -0.0031 -0.012 0.005 -0.014 0.006

 Several Times a month (0.010) (0.0165) (0.010) (0.007) (0.012) (0.008)

 [1479] [890] [1683]

 Smoked cigarettes -0.006 -0.021 0.003 -0.004 -0.002 -0.000

 Everyday (0.007) (0.013) (0.013) (0.004) (0.009) (0.010)

[1622] [933] [1685]

 Used marijuana at least -0.000 -0.014 -0.004 -0.008 0.005 -0.006

 1 to 2 Days per Week (0.007) (0.011) (0.005) (0.004) (0.008) (0.005)

 [1567] [904] [1785]

Used No Birth control at -0.029 -0.031 -0.030 -0.052** 0.083** 0.002

 last sex (0.025) (0.040) (0.021) (0.0168) (0.030) (0.017)

[1016] [504] [879]

Note: Standard errors are presented in parentheses. Sample sizes are based on person-year 

observations and are in square brackets. * indicates coefficient is significant at .05-level, ** at 

.01-level. Hours variables are measured in units of 1,000.
35

Table 8

 Effect of Average Annual Hours Worked by Mother on Intensity of Risky Behaviors

By Race/Ethnicity

Ave. annual hours, First 3 yrs. Ave. annual hours, Last 3 yrs.

African 

American

Hispanic White African 

American

Hispanic White

Alcohol Use in Last 12 -0.021 -0.010 -0.081 0.110 0.059 0.137*

 Months (0.102) (0.115) (0.077) (0.070) (0.090) (0.066)

 Change in Mean # of Days -0.289 -0.180 -1.006 1.538 .947 1.669

[1570] [972] [1820]

Cigarette Use in Last 30 Days -0.083 -0.247 0.022 -0.190* 0.020 -0.0318

 (0.152) (0.152) (0.099) (0.0961) (0.112) (0.0767)

 Change in Mean # of Days -0.237 -1.239 0.127 -0.536 0.103 -0.184

[1671] [989] [1886]

Marijuana Use in the Last 30 -0.115 -0.240 -0.090 -0.223 0.039 -0.083

 Days (0.195) (0.202) (0.128) (0.118) (0.145) (0.119)

 Change in Mean # of Days -0.102 -0.411 -0.071 -0.204 0.067 -0.067

[1615] [936] [1833]

Note: Standard errors are presented in parentheses. Sample sizes are based on person-year observations and are in 

square brackets. * indicates coefficient is significant at .05-level, ** at .01-level. Hours variables are 

measured in units of 1,000.
36

Table 9

 Effect of Average Annual Hours Worked by Mother on Incidence of Risky Behaviors

By Marital Status

Ave. annual hours, First 3 yrs. Ave. annual hours, Last 3 yrs.

If Married If Not Married If Married If Not Married

Ever:

 Drank Alcohol -0.018 -0.006 0.060** -0.004

 (n=2930) (0.021) (0.021) (0.016) (0.017)

 Smoked Cigarettes -0.016 -0.032 0.024 0.005

 (n=2997) (0.019) (0.020) (0.015) (0.015)

 Used Marijuana -0.005 -0.026 0.013 -0.009

 (n=3520) (0.015) (0.015) (0.012) (0.011)

 Used Other drugs -0.006 0.008 -0.002 -0.002

 (n=3919) (0.006) (0.007) (0.006) (0.005)

 Had Sexual Intercourse -0.030 -0.056** 0.040* -0.013

 (n=3215) (0.019) (0.019) (0.016) (0.015)

 Convicted of crime -0.001 0.005 -0.005 -0.016**

 (n=4040) (0.007) (0.006) (0.007) (0.005)

Use Above Threshold:

 Drink Alcohol at least -0.019 -0.009 0.009 -0.004

 Several Times a month (0.010) (0.009) (0.008) (0.006)

 (n=3729)

 Smoked cigarettes 0.008 -0.009 -0.006 -0.005

 Everyday (n=3868) (0.010) (0.009) (0.007) (0.007)

 Used marijuana at least -0.007 -0.003 0.006 -0.009*

 1 to 2 Days per Week (0.007) (0.006) (0.006) (0.004)

 (n=3881)

Used No Birth control at -0.047 -0.022 -0.042* -0.030*

 last sex (n=2106) (0.026) (0.021) (0.019) (0.015)

Note: Standard errors are presented in parentheses. Sample sizes are based on person-year 

observations and are in square brackets. * indicates coefficient is significant at .05-level, ** at 

.01-level. Hours variables are measured in units of 1,000.
37

Table 10

 Effect of Average Annual Hours Worked by Mother on Intensity of Risky Behaviors

By Marital Status

Ave. annual hours, First 3 yrs. Ave. annual hours, Last 3 yrs.

If Married If Not Married If Married If Not Married

Alcohol Use in Last 12 -0.086 -0.024 0.185** 0.071

 Months (n=3978) (0.075) (0.078) (0.060) (0.061)

 Change in Mean # of Days -0.795 -0.227 1.727 0.676

Cigarette Use in Last 30 Days 0.138 -0.178 -0.058 -0.101

 (n=4131) (0.105) (0.105) (0.078) (0.075)

 Change in Mean # of Days 0.516 -0.669 -0.218 -0.380

Marijuana Use in the Last 30 -0.061 -0.152 -0.018 -0.136

 Days (n=3992) (0.133) (0.143) (0.108) (0.096)

 Change in Mean # of Days -0.055 -0.140 -0.017 -0.128

Note: Standard errors are presented in parentheses. Sample sizes are based on person-year observations 

and are in square brackets. * indicates coefficient is significant at .05-level, ** at .01-level. Hours 

variables are measured in units of 1,000.
38

Table 11

 Effect of Average Annual Hours Worked by Mother on Incidence of Risky Behaviors

Fixed Effects, Conditional Logit Models and Instrumental Variables, Linear Models

“Mother” Fixed Effects “Grandparent” Fixed Effects Instrumental Variables

Ave. annual hours, 

First 3 yrs.

Ave. annual hours, 

Last 3 yrs.

Ave. annual hours, 

First 3 yrs.

Ave. annual hours, 

Last 3 yrs.

Ave. annual 

hrs, First 3 yrs.

Ave. annual 

hrs, Last 3 yrs.

Coefficient 

Estimate

Marginal 

Effect

Coefficient 

Estimate

Marginal 

Effect

Coefficient 

Estimate

Marginal 

Effect

Coefficient 

Estimate

Marginal 

Effect

Coefficient 

Estimate

Coefficient 

Estimate

Ever:

 Drank Alcohol 0.633** 0.086 0.170 0.023 0.131 0.019 -0.086 -0.013 -0.033 0.047

(0.246) (0.174) (0.177) (0.122) (0.081) (0.066)

[1414] [1597] [3141]

 Smoked Cigarettes 0.177 0.029 0.324 0.054 -0.136 -0.023 0.191 0.032 -0.211** 0.038

(0.269) (0.174) (0.191) (0.132) (0.077) (0.064)

[1143] [1325] [3254]

 Used Marijuana 0.322 0.050 -0.082 -0.013 0.150 0.023 -0.045 -0.007 0.002 -0.016

(0.256) (0.162) (0.196) (0.127) (0.060) (0.054)

[1398] [1576] [3828]

 Used Other drugs 0.214 0.035 0.001 0.000 0.222 0.034 0.043 0.007 -0.005 -0.040

(0.375) (0.250) (0.312) (0.208) (0.037) (0.036)

[585] [679] [4302]

 Had Sexual Intercourse 0.075 0.008 0.101 0.010 -0.266 -0.030 0.043 0.005 -0.082 0.057

(0.242) (0.173) (0.190) (0.125) (0.072) (0.060)

[1861] [1990] [3460]

 Convicted of crime 0.140 0.021 -0.070 -0.011 -0.276 -0.039 -0.088 -0.012 0.065 -0.035

(0.355) (0.212) (0.297) (0.171) (0.033) (0.030)

[708] [822] [4436]

Use Above Threshold:

 Drink Alcohol at least 0.055 0.007 -0.291 -0.038 0.006 0.001 -0.102 -0.013 0.004 -0.006

 Several Times a month (0.325) (0.201) (0.267) (0.165) (0.040) (0.033)

[1112] [1227] [4052]

 Smoked cigarettes 0.435 0.059 0.145 0.020 0.176 0.023 -0.089 -0.012 -0.081 -0.017

 Everyday (0.315) (0.227) (0.265) (0.177) (0.042) (0.038)

[913] [1019] [4240]

 Used marijuana at least -0.436 -0.069 0.207 0.033 -0.557 -0.084 0.005 0.001 0.011 -0.017

 1 to 2 Times per Week (0.480) (0.311) (0.371) (0.250) (0.027) (0.024)

[462] [541] [4256]

Used No Birth control at -0.615* -0.118 -0.072 -0.014 -0.596* -0.110 -0.155 -0.029 0.105 -0.097

 last sex (0.308) (0.152) (0.246) (0.133) (0.063) (0.061)

[1143] [1232] [2399]

Note: Standard errors are presented in parentheses. Sample sizes are based on person-year observations and are in square brackets. * indicates coefficient is significant at .05-

level, ** at .01-level. Hours variables are measured in units of 1,000. For the conditional logits, marginal effects are calculated as Pi(1-Pi)a. (Greene 1997)
39

Appendix: Discussion of NLSY79-YA Sample

The NLSY79-YA is administered biennially and data from 1994, 1996, 1998, and 2000 

are currently available. Although the NLSY79 is nationally representative, as explained in the 

text, the sample of young adults who were eligible for the Young Adult supplement by 2000 is 

composed only of these women’s children who were born in 1985 or earlier. Hence, the 

NLSY79-YA overrepresents youth born to young mothers, as roughly 40 to 45% of all children 

that will eventually be born to the NLSY79 women had been born by 1985. In addition, the 

NLSY79 oversamples African Americans and Hispanics, which means that African American 

and Hispanic youth are overrepresented in the NLSY79-YA as well. It is inappropriate to use 

weights to make the sample more representative because multiple years of data are used and the 

sample varies by year. Despite these shortcomings, the NLSY79-YA has a big advantage over 

other datasets, in that data on both the young adults and their mothers are available from the time 

the children were very young.

Table A1 compares the incidence rates of risky behaviors for high school youths from the 

NLSY79-YA with those from two nationally representative data sets, Monitoring the Future 

(MTF) and the Youth Risk Behavior Survey (YRBS). For all years considered, engagement in 

cigarette, alcohol, and drug use is substantially lower in the NLSY79-YA. Part of this difference 

is due to the overrepresentation of African Americans in the NLSY79-YA, as the incidence rates 

of whites are higher than those of African Americans for cigarette use, frequent cigarette use, 

alcohol use and drug use. When the rates of risky behavior reported in the NLSY79-YA and in 

the national surveys are compared by race, they are much closer for all years except 2000—with 

the exception of drug use where rates from the NLSY79-YA continue to be lower. In fact, when 
40

comparisons are done by race, the differences are rarely significant; admittedly this is in part 

driven by small sample sizes for the NLSY79-YA. 

Although the data on sexual activity and condom use in 1994 from the NLSY79-YA 

appear consistent with the YRBS in 1994, the rates diverge in the later years with the incidence 

of sex being lower and condom use being higher in the NLSY79-YA. 
41

Table A1: Comparison of Rates of Risky Behaviors, NLSY79-YA vs. National Surveys

1994 1996 1998 2000

NLSY79-

YA

National 

Survey

NLSY79-

YA

National 

Survey

NLSY79-

YA

National 

Survey

NLSY79-

YA

National 

Survey

Cigarette Use

Smoked Daily in Last 30 daysa

 10th graders 5.6 16.3 (1995) 11.6 18.3 7.1 15.8 6.5 14.0

 12th graders 4.5 21.6 8.3 22.2 12.6 22.7 15.9 20.9

Currently Smokesb

 9th through 12th graders 19.2 31 (1993) 22.9 35 (1995) 19.8 36 (1997) 14.9 35 (1999)

Frequently Smokes 

 9th through 12th gradersb8.0 14 (1993) 10.6 16 (1995) 11.4 17 (1997) 8.8 17 (1999)

Alcohol Use

Drank in Last 30 daysa

 10th graders 27.3 -- 28.8 -- 24.3 38.8 43.9 52.0

 12th graders 29.5 -- 35.5 -- 30.1 41.0 55.8 50.0

Drug Use

 Marijuana in Last 30 daysa

 10th graders 9.5 19.1 (1995) 14.4 20.4 8.1 18.7 4.3 19.7

 12th graders 9.1 21.1 (1995) 9.2 21.9 11.5 22.8 5.6 21.6

Inhalant in Last 30 daysa

 10th graders 2.3 3.5 (1995) 0.9 3.3 1.3 2.9 0 2.6

 12th graders 3.4 3.2 (1995) 0.9 2.5 0 2.3 0.04 2.2

Cocaine in Last 30 daysa

 10th graders 0 1.7 (1995) 0.3 1.7 0.3 2.1 0 1.8

 12th graders 1.1 1.8 (1995) 0.9 2.0 0 2.4 0 2.1

Sexual Activity

Ever Sexual Intercourseb

 9th to 12th graders 49.0 43.5 53 (1995) 39.2 48 (1997) 29.5 50 (1999)

Had Sexb

 In last 30 days, 9th-12th graders 29.4 24.3 22.5 12.3

 In last 3 mo., 9th-12th graders 38 (1993) 38 (1995) 35 (1997) 36 (1999)

 In last 6 mo., 9th-12th graders 41.2 35.0 32.0 23.8

Condom used at last sexb

 Had sex in last 30 days, 9th-12th graders 51.4 62.7 62.0 42.3

 Had sex in last 3 mo., 9th-12th graders 53 (1993) 54 (1995) 57 (1997) 58 (1999)

 Had sex in last 6 mo., 9th-12th graders 59.1 64.6 65.1 42.5

Notes: a indicates that the data from the national survey are from Monitoring the Future (MTF) and b indicates that the data from the national survey are from the Youth Risk 

Behavior Survey (YRBS). The source for MTF and YRBS data is U.S. Department of Health and Human Services (2001).