Working Paper Series

Congressional Budget Office

Washington, D.C.

THE EFFECT OF A FIRST CHILD ON FEMALE LABOR SUPPLY:

EVIDENCE FROM WOMEN SEEKING FERTILITY SERVICES

Julian P. Cristia

Congressional Budget Office

Washington, D.C.

E-mail: julian.cristia@cbo.gov

October 2006

2006-11

Working papers in this series are preliminary and are circulated to stimulate discussion

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should not be interpreted as those of the Congressional Budget Office. References in

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Abstract

Estimating the causal effect of a first child on female labor supply is complicated by the

endogeneity of the fertility decision. That is, factors that trigger the decision to have a first

child could also affect baseline labor supply; empirical approaches that do not account for

this difficulty will yield biased estimates. This paper addresses this problem by focusing on a

sample of women from the National Survey of Family Growth (NSFG) who sought help to

get pregnant. After a certain period, only some of these women gave birth to a child. In this

instance, fertility appears to be exogenous to labor supply in that women’s employment

during months prior to seeking help becoming pregnant is uncorrelated with subsequent

fertility. Results using this strategy show that having a first child younger than one year old

reduces female employment by 26.3 percentage points. Unlike previous studies, which found

smaller effects when dealing with the endogeneity problem of fertility, estimates in this paper

are close to ordinary least squares (OLS) estimates obtained using census data and to OLS

and fixed-effects estimates from NSFG data.
1. Introduction1

Estimating the effect of fertility on female labor supply has been a longstanding

problem in economics. Knowing how families optimize their labor supply decisions in

response to the arrival of a child is important for several reasons. First, it is interesting to

know how much of the increase in female labor supply since the World War II can be

explained by delayed childbearing and reduced fertility (Goldin, 1990). Second, some

researchers believe that the interruption of work attributable to childbearing is responsible for

a significant proportion of the female-male wage gap (Goldin and Polachek, 1987; Gronau,

1988; Fuchs, 1989; Korenman and Neumark, 1992), and the size of the impact of

childbearing on female labor supply is an important variable in that calculation. Third, if

declines in labor supply after childbearing correspond to increases in child care time, then

knowing the effect of childbearing on female labor supply will provide information about

time inputs invested in the child (Stafford, 1987; Blau and Grossberg, 1992). Finally, and

above all, economists have been interested in this question from a basic desire to know the

quantitative importance of various determinants of female labor supply.

Hundreds of published studies have examined the relationship between fertility and

female labor supply. However, as Browning (1992) notes in his literature review on this

topic, “Although we have a number of robust correlations, there are very few credible

inferences that can be drawn from them” (p. 1435). The key problem researchers face is that

the fertility decision may be endogenous; therefore, the strong negative correlations found

1The author wishes to thank Julio Caceres Delpiano, Paul Cullinan, Mark Duggan, William Evans, Eugenio

Giolito, Amy Harris, Sandra Hofferth, Arlene Holen, Joseph Kile, BeomSoo Kim, Noah Meyerson, Reed Olsen,

Caroline Polk, John Sabelhaus, Seth Sanders, Jon Schwabish, John Skeen, Michael Simpson, Julie Topoleski,

and Bruce Vavrichek for invaluable comments and suggestions.
between different measures of fertility and female labor supply cannot be interpreted as

evidence of causal effects.

To overcome the type of criticism highlighted by Browning (1992), two strategies

have exploited exogenous changes in family size in order to estimate the effect of fertility on

female labor supply. The first strategy (Rosenzweig and Wolpin, 1980; Bronars and Grogger,

1994; Jacobsen, et al., 1998) used the fact that twins in the first birth represent an exogenous

change in family size in order to estimate the effect of having a second child. The second

strategy (Angrist and Evans, 1998) exploited parental preferences for mixed-sex siblings in

order to estimate the effect of a third or higher order child.

Still, the question of how a first child affects female labor supply has not been

previously addressed with a strategy that directly tackles the problem of the endogeneity of

fertility. It could be argued that the effect of having a first child is the most important one,

given that it applies to a vast majority of women, whereas the effect of having a second or

higher order child only applies to a smaller subset of women.2

This paper examines a situation in which the problem of the endogeneity of fertility is

minimized. In particular, I construct a sample of childless women who sought help with

achieving pregnancy. At the time of seeking help, all of the women wanted to have a child;

after a certain period, some of them gave birth, and others did not. I then compare the

employment rates of women in the “treatment” group (i.e., those who gave birth to a child)

with the controls (i.e., those who did not give birth).3

The contribution of this paper is that while analysis of twins and the preference for

2

In the 1990 census, among women aged 45 to 55, 89.0 percent had at least one child, whereas 78.3 percent

had at least two and 50.4 percent had at least three children.

3

In this paper “treatment” refers to having a child.

2
mixed-sex siblings strategy, under certain conditions, can be used to identify the effect of a

second or higher order child, the estimation strategy pursued here is able to identify the effect

of a first child on female labor supply.

The strategy used in this analysis tackles the problem of fertility being an endogenous

variable because all women wanted to have a child at the time they sought help. Early

success in fertility treatment, however, is not expected to be completely random. Still, I

provide several pieces of evidence suggesting that this strategy consistently estimates the

parameter of interest. First, following Heckman and Hotz (1989), I find that women’s

employment, during months prior to seeking help becoming pregnant, is uncorrelated with

subsequent fertility. Second, estimates of the parameter of interest are very robust to the set

of covariates added to the main regression. Third, observable characteristics of the sample of

childless women that sought help achieving a first pregnancy are quite similar to those of

women who have their first child after age 18.

Using the exogenous assignment of children to women that seek help achieving

pregnancy, I estimate that having a first child younger than one year old reduces female

employment by 26.3 percentage points. These estimates are close to ordinary least squares

(OLS) and fixed-effects estimates obtained from panel data from the National Survey of

Family Growth (NSFG). They are also close to OLS estimates obtained using similarly

defined samples from the 1980 and 1990 censuses. This finding is important because almost

all previous studies that take into account the endogeneity of the fertility decision provide

much smaller estimated effects than studies that assume exogenous fertility. Finally, I

provide evidence of a reduction in the estimated short-term impact of childbearing on female

labor supply of 40 to 50 percent between 1980 and 1990.

3
2. Previous Research

Interest in the effect of fertility on female labor supply is illustrated in the long list of

studies that have focused on the issue. The studies can be classified into four groups,

depending on how they tackle the problem of the endogeneity of the fertility decision. The

first group is illustrated by the studies of Gronau (1973), Heckman (1974), and Heckman and

Willis (1977), who assumed that fertility was exogenous and established a strong negative

correlation between female labor supply and fertility.

A second group of studies (Cain and Dooley, 1976; Schultz, 1978; Fleisher and

Rhodes, 1979) acknowledged the endogeneity of the fertility decision and tried to deal with

the problem by estimating simultaneous equations models. The studies found a much smaller

estimate when treating fertility as an endogenous variable than when treating it as an

exogenous variable. The disadvantage of this approach is that it is difficult to find plausible

exclusion restrictions that could identify the underlying structural parameters.

A third group of studies incorporated actual fertility as a regressor but added the

lagged dependent variable (i.e. labor supply) to control for unobserved heterogeneity across

women. Nakamura and Nakamura (1992) recommended this approach, and it has been used

by a number of authors (Even, 1987; Lehrer, 1992). Although adding the lagged dependent

variable can help control for unobserved heterogeneity, it still does not address the problem

of the endogeneity of the fertility decision.

Finally, a fourth group of studies tackled the endogeneity of the fertility variable by

exploiting exogenous sources of variation in family size. Rosenzweig and Wolpin (1980) first

used this strategy by comparing labor supply of women who had twins at their first birth with

4
that of women who had a single child. Subsequent studies by Bronars and Grogger (1994)

and Jacobsen, et al. (1998) used the same strategy but managed to obtain more precise

estimates by developing an algorithm to detect twin births using census data.

In the same spirit as the twins studies mentioned above, Angrist and Evans (1998)

estimated the effect of a third or higher order child on female labor supply by exploiting the

fact that parents typically prefer mixed-sex siblings. For a sample of couples with at least two

children, they instrumented further childbearing (i.e., having more than two children) with a

dummy variable for whether the sex of the second child matched the sex of the first. Because

sex mix is virtually random, this strategy allows identification of the effect of a third or

higher order child.

My work is most similar to this last group of studies because it uses the fact that the

biology of reproduction is intrinsically stochastic to identify exogenous changes in fertility.

Still, two main differences exist between the earlier studies and this paper. First, I estimate

the effect of a first child on female labor supply, whereas the other studies estimate the effect

of a second or higher order child. Second, whereas the other studies instrument fertility and

then compute two steps least squares (2SLS) estimates, I tackle the endogeneity of fertility

by focusing on a sample of women for whom fertility is plausibly exogenous and then

estimate the impact with just OLS.

This paper is related to other strands of economic literature. First, its results shed

light on a number of studies that have tried to explain the postwar rise in female labor supply

(Mincer, 1962; Goldin, 1990). Second, it is related to a line of research that tries to establish

the effect of childbearing-related withdrawal from the labor market on women’s wages and

earnings (Goldin and Polachek, 1987; Gronau, 1988; Fuchs, 1989; Korenman and Neumark,

5
1992; Miller, 2005).4 Last, it is linked with studies focusing on how maternal work affects

children’s outcomes (Stafford, 1987; Desai, et al., 1989; Blau and Grossberg, 1992).

3. Background: The Reproductive Process and Infertility

Healthy couples having intercourse regularly have only a 20 percent chance of

conceiving during a month. This statistic implies that about 26 percent of healthy couples

will not have conceived after six months of unprotected sex; this number falls to about 7

percent after 12 months. As a result, couples are recommended to start fertility testing and

treatment only after 6 to 12 months of trying to conceive without success. The medical

community typically defines a couple as infertile if they have not conceived after 12 months

of unprotected sex.5 The National Center for Health Statistics (NCHS) estimated that in 1995

there were 2.1 million infertile married couples in the United States and that 6.1 million

women aged 15 to 44 had an impaired ability to have children (Abma, et al., 1997).

Medical researchers have identified a number of factors (besides the conditions

mentioned above) that affect the fertility prognosis of a couple. The woman’s age, education,

smoking status, consumption of recreational drugs, and obesity, as well as sexual frequency,

are important predictors of the probability of conception (Baird and Wilcox, 1985; Dunson,

et al., 2004).

Given the stochastic nature of the reproduction process, physicians usually start

treatment with simple and inexpensive procedures (e.g., advice and testing) and only start

using more invasive and expensive procedures as the simple procedures prove unsuccessful.

4As in this paper, Miller (2005) exploited biological fertility shocks. I aim to estimate the impact of childbearing on female labor supply, whereas Miller was primarily concerned about how changes in the age at first

birth affect long-run earnings and future wages.

5For example, see Mosher (1982).

6
For example, physicians typically recommend in vitro fertilization methods only after all

other options have been exhausted or if they strongly believe that less invasive procedures

will be unsuccessful.

4. Data

This paper uses data from the National Survey of Family Growth (NSFG), a survey

conducted by the National Center of Health Statistics in 6 cycles (1973, 1976, 1982, 1988,

1995, and 2002). Cycles 1 to 5 were conducted at the homes of a nationally representative

sample of women aged 15 to 44. Cycle 6 also sampled men aged 15 to 44. The main purpose

of the surveys was to provide reliable national data on marriage, divorce, contraception,

fertility, and the health of women and infants in the United States.

Data from the NSFG Cycle 5 were chosen for this paper because they provide

retrospective information about births, pregnancies, use of fertility services, demographic

characteristics, and the complete work history for each individual.6In particular, the data

provide the month in which each woman sought help for the first time to achieve pregnancy,

information that is critical for the strategy pursued in this paper. Other important variables

included are age, race, ethnicity, educational attainment, school enrollment, and smoking

history. The survey also reports data on each full-time and part-time employment spell.

The NSFG Cycle 5 used a multistage sampling design that oversampled Hispanic and

black women. It took place between January and October 1995, and the response rate was 79

percent. A total of 10,847 women were interviewed.

6Other cycles included all needed information except monthly employment status for each woman. I cannot

run this analysis without this information because I compare employment 21 months after each woman sought

help becoming pregnant.

7
Data on fertility and employment are collected retrospectively. Although this type of

design has limitations, Teachman, et al. (1998) found the NSFG Cycle 5 data to be of high

quality. They concluded that the employment information matches the Current Population

Survey (CPS) data reasonably well, although the data on employment spells have not been

validated using external records.

5. Empirical Strategy, Parameter of Interest, and Sample Construction

5.1 Empirical Strategy

A hypothetical social experiment aimed at estimating the causal effect of childbearing

on female labor supply would recruit women who wanted to have a child and then assign a

child to women in the treatment group while not assigning a child to a second group (the

control group).7 Given the stochastic nature of conception, this type of experiment can be

approximated. To start, we need a group of women who want to conceive a baby. Second,

some of the women should receive babies in a way that is uncorrelated with baseline

employment. Third, we need to observe female labor supply for both groups of women for a

certain time after they start trying to conceive.

I aim to mimic the ideal social experiment and fulfill the three aforementioned

conditions by focusing on the following situation. I construct a sample of women who sought

help to have a first child (called the HELP sample). Because women in this sample sought

help to achieve pregnancy at different points in time, I normalize time by the month in which

they sought help for the first time (denoted as Month 0). Next, I classify the women

according to whether they had given birth to a child by Month 21. In this way, I obtain two

7To be precise, this experiment estimates the effect of having a child on female labor supply for women who

wanted to have a child, not for all women.

8
groups of women: treatment and control. Finally, I compare employment rates of the two

groups of women in Month 21 to estimate the causal effect of having a first child younger

than one year old on female labor supply.

I compare employment in Month 21 instead of other months for several reasons.

First, at the time of my analysis, 97 percent of babies born are currently younger than one

year old, making the definition of the treatment effect more precise. Second, using a longer

horizon could allow some women in the treatment group to have additional children, which

would complicate the analysis.8 Third, as time since women sought help increases, those

who are unsuccessful at conceiving may adopt a child. Finally, looking at a shorter time span,

it is more plausible that the women received similar types of infertility treatments (e.g., in

vitro fertilization treatments typically are not considered an option in the first 12 months after

seeking help achieving pregnancy).

A potential problem with this empirical strategy arises if women in the control group

adopt a child or start cohabitating with or marry someone with children. In the treatment

evaluation literature, this behavior is denoted as “substitution bias”, and it represents a

situation in which control group members receive close substitutes for the treatment in

question (see Heckman and Smith (1995, pp. 22-24). In the context of this paper, treatment is

defined as having a natural birth, and a close substitute is adopting a child (or acquiring a

stepchild). Even though substitution bias can be a problem in certain social experiments, it is

not in this case.9 Only 2.7 percent of women in the control group adopted or acquired a

8By Month 21 only six women had two children: Five had given birth to twins initially; only one had given

birth twice.

9

In the case of the experimental evaluation of the Job Training Partnership Act (JTPA), Heckman and Smith

(1995) noted that 32 percent of control group members self-reported receiving training from other sources over

the 18 months following random assignment.

9
stepchild in the 21 months after they sought help to become pregnant (and only 0.5 percent in

the treatment group did so).

5.2 Parameter of Interest

In this study, the parameter of interest is the average impact of having a first child

younger than one year old on female labor supply for women who want to have a child. Note

that the study does not provide an estimate of the effect of having a first child for women

whose child is unwanted. All the same, the parameter of interest applies to a fairly large

population. Henshaw (1998), using data from the NSFG Cycle 5, found that 69 percent of

births were planned among women aged 15 to 44 in 1994.

Throughout this study, I focus only on the short-term effects of having a first child

(i.e., the estimated effect of having a child younger than one year old). It is clear that other

treatment effects are worthy of attention; however, for reasons already discussed, the strategy

used in this study is best suited for estimating this treatment effect.

Finally, an estimate of the impact of having a first child younger than one year old is

important for a number of reasons. First, as mentioned above, this effect applies to a much

wider population than estimates that focus on the effect of a second or higher order child.

Second, the consensus is that the short-term effects of childbearing on female labor supply

are substantially larger than the long-term effects (Browning, 1992). Thus, knowing the

short-term effects is useful because it gives an upper bound for the long-term effects. Third,

Shapiro and Mott (1994) provide strong evidence that labor force status following the first

birth is an important predictor of lifetime work experience. This finding implies that changes

in the estimated short-term impact of having a first child on female labor supply could predict

10
a substantial change in total lifetime work experience for women. Finally, using this

empirical strategy I can compare the estimated effects obtained when tackling the

endogeneity problem (i.e., using the HELP sample) with estimates from strategies that do not

tackle this problem (e.g., OLS on census data).

5.3 Sample Construction

The HELP sample includes childless women who sought help with becoming

pregnant when aged 19 to 38. The age restriction in the sample is due to two reasons. First,

the results obtained in the HELP sample are compared to those from census and NSFG

samples, and an age restriction is needed in constructing these samples in order to select

women at risk of having children. Second, work information is only reported for women

aged 18 and older, and I want to know women’s employment status one year before seeking

help with becoming pregnant. Women that sought help in the 21 months preceding the

interview are dropped from the HELP sample, because it is not possible to observe their child

and labor status at this time.

Women seeking help with becoming pregnant are identified as those who answer

affirmatively the question “Have you or your husband ever been to a doctor or other medical

provider to talk about ways to help you become pregnant?” The wording of the question

allows identification of a large group of women who wanted to have children but were

unsuccessful after trying for certain time. The fact that women that just talked to their

medical provider about ways to help with becoming pregnant are included in the HELP

sample explains why, as it will be seen later, women in the HELP sample are fairly

representative of women who have their first child when aged 19 to 38.

11
Table 1 presents the algorithm used in order to construct the HELP sample. The table

shows that only 499 observations are included in the empirical analysis, a fact that may seem

to be an important limitation for this study. As shown in Section 6, however, I precisely

estimate the relevant coefficient of the effect of having a first child on female labor supply.

The basic empirical strategy of this paper is based on comparing women in the HELP

sample who had had a baby by Month 21 with those who did not. To identify the two groups

of women, I defined a variable called AnyChildren21 which equals one if the woman had a

baby by Month 21 and zero if she did not. In this setting, women from the HELP sample for

whom AnyChildren21 equals one are in the treatment group and those for whom

AnyChildren21 equals zero are in the control group.

The plausibility of the proposed empirical strategy rests on the assumption that

treatment is not correlated with baseline labor supply. However, in some scenarios this

assumption will not hold. For example, if women married to high earner men have a higher

probability of success (through access to better fertility treatments) and tend to have a lower

attachment to the labor market (due to an income effect), then the effect of fertility on female

labor supply will be underestimated. To assess the plausibility of the empirical strategy, I

take two steps. First, I compare summary statistics on covariates for the treatment and control

groups in order to check for evidence of selection. Clearly, it is not possible to check for

selection on unobservable factors, but lack of evidence of selection on observable factors

gives assurance that treatment can be taken as exogenous to baseline labor supply.10 Second,

I compare employment rates for the treatment and control groups prior to seeking help (the

10For example, if education levels are similar across the treatment and control groups, then the hypothesis

that high income women have higher probability of success is undermined.

12
results are presented in Section 7).

Descriptive statistics for women in the treatment and control groups are presented in

Table 2. In the NSFG Cycle 5, respondents were asked about all of their employment spells,

and I use those responses to construct three employment variables. The variables

Employed21 and Employed0 are dummy variables that equal one if the respondent was

employed in Months 21 and 0, respectively. Similarly, Employed 12 represents labor status in

Month –12 (i.e., 12 months before the woman sought help for the first time).

Although employment rates in Months 0 and –12 are similar between the treatment

and control groups, employment rates differ by 25.3 percentage points in Month 21.

Moreover, observable characteristics in Month 0 for treatment and control women are quite

similar. As shown in Table 2, differences in means of key covariates between the treatment

and control groups are only statistically significant at the 5 percent significance level for the

dummy variables for Hispanic and smoking.11

A potential caveat for the strategy pursued in this paper is that, as typically is the case

in social and medical experiments, the sample involved in the experiment may not be

representative of the population of interest. To gauge the potential severity of this problem,

Table 3 compares descriptive statistics of women in the HELP sample with those of women

in the NSFG who had at least one child. For women in the HELP sample, time-varying

variables (Age, Year, Employed 12, Education, Married, Smoke) are measured at the time

they first sought help achieving pregnancy, whereas for NSFG women the variables are

measured at the time of first birth. The second column of Table 3 presents statistics for the set

of women in the NSFG who had their first child when aged 19 to 38 because that was the age

11In Section 7, I explore more deeply which variables predict fertility by Month 21.

13
range of women in the HELP sample.

Comparing the second and third column of Table 3, we see that women in the HELP

sample tend to be older and more educated and to have higher employment, marriage, and

smoking rates than women from the NSFG that were aged 19 to 38 when they had their first

child. A lower proportion of HELP women are Hispanic or black than NSFG women. Still,

basic statistics for the HELP sample are not very different from those of their counterparts in

the NSFG. The last column of Table 3 presents basic statistics for the HELP sample when

observations are reweighted to match the distribution by age and year groups for 19- to

38-year-old NSFG women with children. This adjustment makes the proportion of Hispanic

and black women similar across the two samples, and it brings mean education closer.

Figure 1 compares the age distribution of women in the NSFG who gave birth when

aged 19 to 38 to the age distribution of women in the HELP sample. The difference in mean

age across the two groups is driven primarily by the group of women aged 19 to 21. This

difference can be explained by some women in the NSFG group having unplanned children

and by the fact that really young women tend to delay their decision to seek help with

achieving pregnancy.

6. Results

This section presents the main results of the empirical analysis. In essence, I compare

employment rates in Month 21 for treatment and control women in the HELP sample. The

econometric model is represented by this simple OLS equation:12

12Marginal effects results for probit and logit models are similar to those obtained using OLS.

14
Employed21i = α+β AnyChildren21i +γ Xi +ui (1)

where the vector of covariates includes black and Hispanic dummy variables; an indicator for

insurance coverage of infertility treatments; year in which the women sought help for the first

time; and the following variables measured in Month 0: age, smoking status, and years of

education.

To gauge the potential importance of the problem of not having data on certain

variables that may be simultaneously correlated with conception and labor supply, I run a

number of regressions including separate sets of covariates. If the results were sensitive to

the set of covariates added to the regression, they would raise some doubts as to whether the

identification strategy consistently estimates the parameter of interest. Table 4 presents the

regression results.

In the model that includes all covariates (Column 4), I estimate that having a first

child younger than one year old decreases female employment by 26.3 percentage points.

The results indicate that the estimated impact is remarkably robust to the set of covariates

included in the regression. In particular, the estimated effect in a model with no covariates

(Column 1) is –0.253. That is, including the entire set of covariates, the estimated coefficient

changes by just 1 percentage point, or 4 percent of the estimated impact.

Column 5 presents linear probability estimates when observations are reweighted to

match the age-year distribution for the sample of mothers in the NSFG who gave birth to

their first child when aged 19 to 38. The estimated impact is similar to the one obtained from

original NSFG weights (Column 4); this finding is evidence that the obtained estimates could

15
be generalized to the target population. Finally, in Column 6 the model is augmented to

check for varying treatment effects by age of mother and year in which she sought help with

achieving pregnancy. Although the treatment effect does not significantly change with age,

the results suggest that the short-term effects of childbearing have decreased over time (this

issue is examined more deeply in Section 8.2).

Women who have a child decide not only whether to have a job (the extensive

margin) but also how many hours to work (the intensive margin). Unfortunately, the NSFG

does not provide retrospective information on hours worked for women in the sample. Still, it

provides information about whether a woman was working full time or part time and the

availability of maternity leave. Using this information, work status is determined among four

categories (full time, part time, maternity leave, and no job). Table 5 presents multinomial

logit regression results for the impact on work status of having a first child. Having a child

younger than one year old reduces the probability of working full time by 43.1 percentage

points and it raises the probability of being in the other three categories. Interestingly, the

increase in the probability of working part time is quite small (4.8 percentage points).

7. Robustness of the Empirical Strategy

This section explores the robustness of the empirical strategy pursued. First, I try to

identify which covariates can predict treatment and how much of the variation in the fertility

variable is explained by the other variables. Second, I test whether pretreatment differences

exist in the outcome variable (i.e., employment) for the treatment and control groups. Finally,

I check how robust the results are to changes in the specification of the econometric model.

To start, I explore which variables in the data set predict early fertility success in the

16
HELP sample. Table 6 shows that, as documented in the medical literature, women’s age is

one of the most important predictors of fertility. In this linear probability model, an increase

in one year in the age of the woman decreases her expected probability of having a child by

1.6 percentage points. Smoking, also documented in the medical literature as having an effect

on fertility, is a significant negative predictor of fertility success. Finally, Hispanic and highly

educated women are more likely to be successful.

Even though several variables can predict fertility, note that the adjusted R2is only

4.3 percent and that much of the variation in the fertility variable remains unexplained in this

model.

Next, I turn to the issue of whether the significant differences in employment between

treatment and control women in Month 21 can be interpreted as the effect of treatment or as

just heterogeneity in labor market attachment between groups. This test is important for the

empirical strategy pursued in the paper. Before the presentation of the regression results, it is

useful to look at Figure 2, which plots employment rates of the treatment and control groups

for months –12 to 21. Employment rates of both groups are quite similar for months –12 to 0,

but they start diverging around Month 3 and are far apart by Month 21. The continuous

decline in employment rates for the treatment group corresponds to the fact that as time goes

by, additional women give birth until by Month 21 all had given birth.

Table 7 presents the results of regressions of employment status in Month 0

(Employed0) on AnyChildren21. Several specifications are run, in which I control for

different sets of covariates to gauge the robustness of the results. The main conclusion from

this table is that no statistically significant differences in employment rates in Month 0 exist

17
between the treatment and control groups.13

Finally, a number of additional regressions are run to check whether the results are

robust to changes in the specification. First, I again run the regressions whose results are

presented in Table 4, but I add an indicator for pregnancy in Month 21. Second, the main

independent variable AnyChildren21 is replaced with another variable that equals the total

number of children born per woman by Month 21. Third, I replace AnyChildren21 with two

indicators for having one child or two children in Month 21, respectively. Fourth, instead of

running linear probability models of Employed21 on AnyChildren21, I run probit and logit

models using the same set of variables as in Table 4. In all cases, the estimated effects are

similar to those reported in Section 6.

8. Comparison to Estimates from NSFG and Census Data

In his survey of the effect of children in the household, Browning (1992) concludes

that studies that treat fertility as exogenous typically find significantly larger effects of

fertility on female labor supply than those that treat it as endogenous and estimate

simultaneous equations models. Angrist and Evans (1998) provide further evidence about

this argument because they report that their 2SLS estimates of the impact of having more

than two children on female labor supply are statistically significantly smaller than their OLS

estimates. This section compares estimates obtained using the HELP sample with estimates

from similarly defined samples but without restricting them to women who sought help to

become pregnant.

A problem faced in trying to replicate the HELP sample is that this data set includes

13Similar results are obtained when regressing employment at 12 months before women sought help to achieve

pregnancy on AnyChildren21.

18
observations of fertility and labor supply for women who sought help to become pregnant at

different points in time. The implication is that to replicate the results from the HELP

sample, I should construct comparable data sets with observations for women at different

points in time (i.e., panel data or repeated cross-sections). I therefore compare estimates from

the HELP sample to estimates from panel data from the NSFG (in Section 8.1) and to

estimates from census data for 1980 and 1990 (in Section 8.2).

8.1 Comparison to Estimates from NSFG Panel Data

I construct a panel data set from the NSFG Cycle 5 (i.e., NSFG panel data) following

requirements similar to those used to construct the HELP sample. The unit of observation in

this panel data is a woman-month. An observation is included in the NSFG panel data if the

woman was aged 21 to 40 in that month, was childless or had children younger than one year

old, and was cohabitating or married.14

Because the HELP sample corresponds to a cross-section, to use the same source of

variation when estimating both models, I construct a panel data set (i.e., HELP panel data)

including, for each individual in the HELP sample, observations for months –12 to 33

(remember that Month 0 corresponds to when the women first sought help with achieving

pregnancy). Because the goal is to estimate the impact of having a child younger than one

year old, monthly observations for a woman are dropped when her child is older than age 1.

Finally, for women who did not have a baby by Month 21, monthly observations of later

months are dropped if they gave birth to a child.15

14This age restriction is chosen because women in the HELP sample are aged 19 to 38 years old at Month 0

and then in Month 21 (when employment by fertility status is computed), almost all of them are 21 to 40 years

old.

15Defining the sample in this way ensures a balanced distribution of women with respect to their children’s

age in months.

19
Table 8 presents summary statistics for the HELP panel data and the NSFG panel

data. Mean values for key variables are similar and are only statistically significantly

different for proportion employed and with children, calendar year, and children’s age in

months. Still, employment rates are not significantly different across samples once I

condition for fertility status. With respect to differences in the proportion of women who

have a child, this fact should be expected given that everyone in the HELP panel data did not

have children for Month –12 up to (at least) Month 7.

Linear probability estimates of the impact of having at least one child (younger than

one year old) on the probability of having a job are presented in Columns 1 and 3 of Table 9.

In the first column, results are presented for the model estimated using the HELP panel data.

The main independent variable is AnyChildren. The estimated impact (0.260) is similar to the

estimates obtained in Section 6. In the third column, results are presented for the same model

estimated using the NSFG panel data. The key finding from comparing Columns 1 and 3 is

that the estimated impact using the NSFG panel data (0.259) is notably similar to the one

obtained using the HELP panel data.16

To gauge the robustness of the results, I estimate fixed-effects models using both

panel data sets. Results are presented in Columns 2 and 4 of Table 9. For the HELP panel

data, the estimated impact slightly decreases in absolute value to 0.234. In the case of the

NSFG panel data, the estimated impact decreases in absolute value to 0.216. This result

provides some evidence that women who have children tend to have lower employment rates

in the months previous to become pregnant. Still, both estimates are similar, and the t-value

of the test of equality of coefficients is just −0.46.

16The t-value of the test of equality of coefficients is 0.00.

20
Finally, I compare the estimated impact of having a child on work status (working full

time, part time, maternity leave, and no job) for the two panel data sets. As Table 10 shows,

estimates of the marginal effect of having a child on the probability of being in each of the

four work status categories are strikingly similar across the two data sets.

The fact that estimates from the HELP panel data are so similar to those from the

NSFG panel suggests that the endogeneity problem of fertility is not severe with regard to its

effects on biasing estimates of treatment effects. Another explanation is that endogeneity

does create bias on estimates but the samples yield similar results because the differences in

treatment effects across samples compensate for the bias (e.g., there may a positive bias in

estimates on NSFG panel data, but the true treatment effect in the NSFG panel data is larger

than in the HELP panel data). Given that statistics on observable characteristics across the

two samples are so similar, however, the difference in treatment effects across samples

should be based entirely on differences in unobservables, making the lack of endogeneity a

more plausible explanation.

8.2 Comparison to Estimates from 1980 and 1990 Census Data

In the HELP sample, fertility and other covariates are observed between 1972 and

1995. On average, those variables are observed in 1986, and the 10th and 90th percentiles

correspond to years 1978 and 1993, respectively. To construct samples comparable to census

data, women in the HELP sample are assigned to two new samples, the EARLY and LATE

HELP samples, depending on whether they sought help to become pregnant before or after

1985.17

17To construct two samples with roughly the same number of observations, the threshold year is 1985.

21
Using data from the 5-percent 1980 and 1990 Census Public Use Micro Samples

(PUMS) I construct two samples (denoted as the 1980 and 1990 Census samples,

respectively).18 The Census samples include married women aged 21 to 40, who are

childless or have children younger than one year old. To capture women who are “at risk” of

having a child and make the Census samples comparable to the HELP samples, only married

women are kept in the Census and HELP samples.19

Table 11 presents descriptive statistics for these samples EARLY HELP, LATE HELP,

1980 Census and 1990 Census. In the case of the HELP samples, the variable Employed

equals one if the woman had a job in Month 21. For the Census samples, it equals one if the

woman had a job during the week previous to the survey. The variables AnyChildren, Age,

Education, Hispanic and Black are similarly defined in the four samples, and are all

measured in Month 21 (for the HELP samples) or at the time of the survey (for the Census

samples). AnyChildren equals one if the woman had at least one child. Education

corresponds to the number of years of education. Finally, Black and Hispanic are dummy

variables that equal one if the woman is in either group.

The results in Table 11 suggest that the 1980 and 1990 Census samples can be

considered as reasonable comparison data sets for the EARLY and LATE HELP samples,

respectively. Women in the 1980 Census were surveyed in April 1980, where those in the

EARLY HELP sample were observed, on average, in June 1981. Similarly, women in the

1990 Census were surveyed in April 1990, while those in the LATE HELP sample were

observed, on average, in January 1991. Moreover, basic statistics on education and

18For information about the PUMS, see Ruggles, et al., 2004.

19Results obtained by dropping the requirement that women in the HELP and Census samples be married are

similar to those presented in this subsection.

22
proportion black and Hispanic are remarkably close. Conversely, the proportion of women

who have a child is significantly higher in the HELP samples. This finding should be

expected, given that presumably all women in the HELP samples wanted to have children.

Finally, employment rates in the HELP samples, conditional on fertility status, are around 10

percent higher than in the Census samples (perhaps because employment is not defined

exactly the same way in the NSFG as in the census).

Linear probability estimates of the impact of having a child younger than one year old

on employment are presented in Table 12. Comparing Columns 1 and 2, we can see that the

estimated impact is remarkably similar in the EARLY HELP sample and the 1980 Census

sample (0.372 vs. 0.365). Similarly, the estimated impact is also quite close when comparing

the LATE HELP sample and the 1990 Census sample (0.182 vs. 0.228). In both cases, t-tests

of differences in the estimated impact cannot be rejected.

From this set of results two important conclusions can be drawn. First, the estimated

effects for the HELP sample for which I can identify an exogenous change in the fertility

variable are nearly identical to the estimates obtained using OLS on comparable samples

from census data, for which I do not control for the endogeneity of the fertility variable. They

are also close to estimates obtained using panel data from the NSFG, as described in the

previous section. Second, evidence suggests a significant reduction (about 40 to 50 percent)

in the short-term impact of childbearing on female labor supply in the 1980 to 1990 period.

9. Conclusions

This paper explores the issue of the causal effect of childbearing on female labor

supply. This task is complicated by two factors. First, some researchers believe that women

23
who have children at a certain age may have different baseline labor supply from women

with similar observed characteristics who do not have children (Browning, 1992). This

expected unobserved heterogeneity across groups suggests the existence of bias in simple

cross-section comparisons. As noted by Nakamura and Nakamura (1992), we can try to deal

with this problem by adding the lagged values of labor supply to regressions of current labor

supply on number of children.

A second problem, however, complicates the estimation of the effect of childbearing

on female labor supply, and it cannot be solved just by using longitudinal data. The problem

stems from the fact that the fertility decision may be endogenous to the woman and

influenced by potential labor supply. Several studies, starting with Rosenzweig and Wolpin

(1980), use the fact that having twins in the first birth changes (at least temporarily) family

size compared to not having twins. In order to find exogenous variation in the fertility

decision, Angrist and Evans (1998) exploit the fact that parents typically prefer mixed-sex

siblings. Even though these papers have made a major contribution in answering the question

posed, they are only able to estimate the effect of having a second or higher order child.

To deal with the problems of unobserved heterogeneity and endogeneity, I restrict my

attention to a group of women who sought help to achieve pregnancy. In this sample, all the

women wanted to have children, so the problem of endogeneity is minimized. Moreover,

because a major proportion of the fertility variable is random, results likely will not be

contaminated by unobserved heterogeneity across groups. In fact, the attractiveness of the

strategy pursued here is that, by focusing on this sample of women, I mimic a hypothetical

social experiment in which, for a group of women who want to have a child, some women are

assigned children while others are not. Evidence favors the empirical strategy pursued: my

24
results show that women’s employment, during months prior to seeking help becoming

pregnant, is uncorrelated with subsequent fertility.

Using this empirical strategy, having a first child younger than one year old reduces

female labor supply by 26.3 percentage points. Interestingly, evidence strongly suggests that

the estimates obtained using this strategy (which tackles the problem of the endogeneity of

fertility) are similar to estimates derived from approaches that assume the exogeneity of

fertility.

Given that studies that assume the exogeneity of fertility typically find larger effects

of fertility on female labor supply than those that treat it as endogenous, a natural extension

of this paper would be to attempt to understand why my empirical strategy reaches a different

conclusion. One potential explanation is that there is not much selection bias when focusing

on women aged 19 and older wanting a first child. Although Hotz, et al. (2005) found

important differences in observable characteristics when comparing teen mothers to childless

teenagers, for the NSFG and census samples constructed in this paper, observable

characteristics of women with and without children are quite similar.

Another interesting question that this paper leaves unanswered is why fertility and

baseline employment seem to be uncorrelated. Many hypotheses may predict the opposite.

For example, my strategy restricted the sample to women who are homogeneous in that all

wanted to have a child at certain point in time, but clearly they could differ in how much they

wanted to have a child, which in turn could be correlated with baseline labor force

attachment.

A potential explanation for subsequent fertility being uncorrelated with baseline labor

supply could be related to the fact that women in the HELP sample typically wait a number

25
of months before seeking help to achieve pregnancy. This “waiting” pattern could reduce the

heterogeneity of women in the sample with respect to their baseline probability of having a

child. Women with high probability of having a child achieve pregnancy right away and then

do not seek help to become pregnant. Because individuals in the sample have similar

probabilities of having a child, we approach the ideal situation of random assignment, which

is characterized as one in which all individuals have equal probability of being treated. If

evidence is found suggesting that “waiting” is a successful empirical strategy in the sense

that it increases the similarity between the treatment and control groups, then the same

strategy could be applied to other evaluation problems in which dynamic assignment of

individuals to treatment occurs.

26
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29
Figure 1: Age Distribution of Women in the HELP Sample and Childbearing W

the NSFG Sample

02468

101214

19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 

Age

Percent 

Note: HELP=age at which first sought fertility services, women in HELP sample; NSFG=age at first birth, NSFG women having fto 38. NSFG: National Survey of Family Growth.
Figure 2: Employment Rates for Treatment and Control Groups by Month

0.5

0.55

0.6

0.65

0.7

0.75

0.8

0.85

0.9

0.95

-12 -10 -8 -6 -4 -2 0 2 4 6 8 10 12 14 16 18 

Month (0=Month Sought Help for the First Time)

Employment Rate 

Note: TREATMENT= women who had a child by Month 21; CONTROL=women how did not have a child by Month 21.
Table 1: Algorithm for Constructing the HELP Sample 

Step 

Number of remaining 

observations 

1. Start with the whole NSFG sample. 10,847 

2. Drop women who did not seek help to get pregnant. 895 

3. Drop women who sought help for the first time less than 21 months 

prior to the interview. 788 

4. Drop women who were younger than age 19 or older than age 38 

when seeking help for the first time. 745 

5. Drop women who had already a child when seeking help for the first 

time. 553 

6. Drop women who had adopted or step children when seeking help for 

the first time. 536 

7. Drop women who were pregnant at some point of the month in which 

they sought help for the first time.a 500 

8. Drop a woman with missing information in the insurance coverage 

variable. 499 

a

This group could include women who became pregnant right after seeking help for the first time (which 

occurred in the same month), or who were pregnant at the time when they sought help but did not know it. 

In fact, 23 of the 36 women reported as being pregnant in the same month they first sought help became 

pregnant in that month or in the previous one. 
Table 2: Descriptive Statistics 

HELP Sample 

Mean 

(Standard deviation) 

Variable All women Treatment

AnyChildren21=1a

Control

AnyChildren21=0a

Employed21 (=1 if employed in Month 21) 0.798 

(0.402) 

0.624 ** 

(0.484) 

0.877 

(0.329) 

Employed0 (=1 if employed in Month 0) 0.862 

(0.345) 

0.881 

(0.324) 

0.853 

(0.354) 

Employed_12 (=1 if employed in Month –12) 0.855 

(0.352) 

0.841 

(0.366) 

0.862 

(0.345) 

OwnChildren21 (number of own children in 

Month 21) b

0.323 

(0.491) 

1.036 ** 

(0.185) 

0.000 

(0.000) 

AnyOtherChildren21 (=1 if had adopted or 

stepchildren in Month 21) 

0.020 

(0.141) 

0.005 

(0.073) 

0.027 

(0.162) 

Age0 (age in Month 0) 26.3 

(4.3) 

25.9 

(4.7) 

26.5 

(4.1) 

Year0 (year in Month 0 normalized as 

1970=0) 

14.7 

(5.7) 

15.0 

(6.1) 

14.5 

(5.5) 

Education0 (years of education in Month 0) 13.6 

(2.5) 

13.8 

(2.6) 

13.5 

(2.4) 

Hispanic (=1 if Hispanic) 0.069 

(0.254) 

0.113 * 

(0.317) 

0.050 

(0.217) 

Black (=1 if black) 0.087 

(0.281) 

0.078 

(0.267) 

0.091 

(0.287) 

Married0 (=1 if married in Month 0) 0.884 

(0.320) 

0.884 

(0.320) 

0.884 

(0.321) 

Smoke0 (=1 if smoked in Month 0) 0.370 

(0.483) 

0.286 * 

(0.452) 

0.408 

(0.492) 

InsuranceCovered (=1 if insurance covered 

infertility treatments) 

0.789 

(0.408) 

0.792 

(0.406) 

0.787 

(0.409) 

N 499 164 335 

* Significantly different from the control group at the 5% level. 

** Significantly different from the control group at the 1% level. 

a

AnyChildren21=1 if the woman had given birth to at least one child by Month 21. b

Six women had two children. Five had given birth to twins, and one had given birth twice. 
Table 3: Comparison of HELP Sample with Childbearing Women in the NSFG 

Mean 

(Standard deviation) 

Variables a NSFG — All 

Mothers b

NSFG — Mothers 

with first birth when 

aged 19 to 38 c HELP sample 

HELP sample 

reweighted d

Age 22.9 ** 

(4.9) 

24.5 ** 

(4.2) 

26.3 

(4.3) 

24.5 

(4.2) 

Year 14.0 * 

(7.0) 

15.0 

(6.4) 

14.7 

(5.7) 

14.7 

(6.1) 

Employed_12 e N/A 0.787 ** 

(0.409) 

0.855 

(0.352) 

0.835 

(0.371) 

Education 12.3 ** 

(2.6) 

12.8 ** 

(2.5) 

13.6 

(2.5) 

13.1 

(2.4) 

Hispanic 0.125 ** 

(0.331) 

0.112 ** 

(0.316) 

0.069 

(0.254) 

0.098 

(0.298) 

Black 0.150 ** 

(0.357) 

0.110 

(0.312) 

0.087 

(0.281) 

0.112 

(0.315) 

Married 0.702 ** 

(0.457) 

0.782 ** 

(0.413) 

0.884 

(0.320) 

0.857 

(0.350) 

Smoke 0.336 

(0.472) 

0.329 

(0.470) 

0.370 

(0.483) 

0.420 

(0.494) 

N 6,911 5,150 499 499 

*Significantly different from the HELP sample at the 5% level. 

**Significantly different from the HELP sample at the 1% level. 

Note: NSFG: National Survey of Family Growth. 

a

Variables in Columns 2 and 3 are measured at the month in which the women gave birth to their first child 

(except for Employed_12). Variables for women in the HELP sample (Column 4) are measured in the 

month in which they first sought help to get pregnant (except for Employed_12). b

This sample is constructed by selecting from the NSFG sample all women who had at least one child. 

c

Includes all women in the NSFG sample who gave birth their first child when aged 19 to 38. 

d

Observations are reweighted to match the age and year distribution in the sample of NSFG women whose 

first birth occurred when aged 19 to 38. 

e

Employed_12 equals 1 if the woman was employed 12 months before her first birth (third column) or 12 

months before she first sought help to get pregnant (fourth column). In the case of the NSFG — All 

mothers sample (second column) this variable cannot be computed as work status is asked in the survey 

only for months after the woman reaches 18 years old.
Table 4: Linear Probability Estimates. Impact of a First Child on Employment 

HELP sample 

Dependent variable: Employed21 

Coefficient 

(Standard Error) 

Independent variable (1) (2) (3) (4) (5) (6) 

AnyChildren21 –0.253 

(0.045) 

–0.254 

(0.044) 

–0.261 

(0.043) 

–0.263 

(0.043) 

–0.283 

(0.047) 

–0.812 

(0.265) 

AnyChildren21* Age0 — — — — — 0.011 

(0.011) 

AnyChildren21* 

Year0 — — — — — 0.017 

(0.008) 

Age0 — 0.007 

(0.005) 

0.000 

(0.005) 

0.000 

(0.005) 

–0.005 

(0.006) 

–0.004 

(0.006) 

Year0 — 0.010 

(0.004) 

0.010 

(0.004) 

0.011 

(0.004) 

0.014 

(0.004) 

0.004 

(0.004) 

Smoke0 — — –0.045 

(0.042) 

–0.046 

(0.041) 

–0.025 

(0.047) 

–0.044 

(0.040) 

Education0 — — 0.021 

(0.007) 

0.020 

(0.007) 

0.030 

(0.008) 

0.019 

(0.007) 

Hispanic — — –0.131 

(0.071) 

–0.138 

(0.069) 

–0.087 

(0.075) 

–0.150 

(0.070) 

Black — — 0.014 

(0.050) 

–0.016 

(0.051) 

–0.149 

(0.076) 

–0.020 

(0.051) 

Married0 — — — –0.089 

(0.038) 

–0.148 

(0.046) 

–0.104 

(0.037) 

InsuranceCovered — — — 0.109 

(0.049) 

0.177 

(0.055) 

0.099 

(0.048) 

Constant 0.877 

(0.019) 

0.563 

(0.126) 

0.459 

(0.156) 

0.449 

(0.149) 

0.395 

(0.162) 

0.675 

(0.169) 

Adjusted R2 0.0854 0.1190 0.1467 0.1666 0.2151 0.1904 

N 499 499 499 499 499 499 

Note: Observations in regressions 1 through 4 and 6 are weighted using weights from the NSFG. 

Regression 5 observations are reweighted to match the age and year distribution in the sample of NSFG 

women whose first birth occurred when aged 19 to 38. The mean of Employed21 using NSFG weights is 

0.798. For the reweighted sample, the mean of Employed21 is 0.771. NSFG: National Survey of Family 

Growth.
Table 5: Multinomial Logit Estimates. Impact of a First Child on Work Status 

HELP sample 

Marginal effects of changing AnyChildren21 from 0 to 1 

(Standard error) 

HELP sample HELP sample reweighted a

No Job 0.291 

(0.047) 

0.314 

(0.052) 

Maternity leave 0.092 

(0.027) 

0.083 

(0.025) 

Part time 0.048 

(0.027) 

0.054 

(0.036) 

Full time –0.431 

(0.050) 

–0.450 

(0.055) 

N 499 499 

Log pseudo-likelihood value –374.30 –381.25 

Pseudo R2 0.1738 0.2000 

Note: The dependent variable has four categories: no job, maternity leave, part time, and full time. 

Covariates: Age0, Year0, Smoke0, Education0, Hispanic, Black, Married0, InsuranceCovered. NSFG: 

National Survey of Family Growth. 

a

Observations are reweighted to match the age and year distribution in the sample of NSFG women whose 

first birth occurred when aged 19 to 38. 
Table 6: Linear Probability Estimates. Predicting Fertility using Selected Covariates 

HELP sample 

Dependent variable: AnyChildren21 

Coefficient 

(Standard error) 

Independent variable 

Age0 –0.016 

(0.006) 

Year0 0.007 

(0.005) 

Smoke0 –0.102 

(0.046) 

Education0 0.013 

(0.010) 

Hispanic 0.198 

(0.082) 

Black –0.052 

(0.066) 

Married0 –0.012 

(0.051) 

InsuranceCovered 0.022 

(0.054) 

Constant 0.472 

(0.184) 

Adjusted R2 0.0427 

P-value of F-test of joint significance 0.0020 

N 499 

Note: The mean of AnyChildren21 is 0.312. 
Table 7: Linear Probability Estimates. Explaining Employment in Month 0 using 

Fertility Status in Month 21 — HELP Sample

Dependent variable: Employed0 

Coefficient 

(Standard Error) 

Independent variable (1) (2) (3) (4) (5) 

AnyChildren21 0.028 

(0.035) 

0.027 

(0.035) 

0.025 

(0.034) 

0.022 

(0.034) 

0.006 

(0.041) 

Age0 — 0.005 

(0.004) 

0.002 

(0.005) 

0.002 

(0.004) 

–0.003 

(0.005) 

Year0 — 0.007 

(0.004) 

0.007 

(0.004) 

0.008 

(0.004) 

0.011 

(0.004) 

Smoke0 — — –0.019 

(0.041) 

–0.020 

(0.040) 

0.011 

(0.045) 

Education0 — — 0.010 

(0.006) 

0.009 

(0.006) 

0.021 

(0.009) 

Hispanic — — –0.073 

(0.067) 

–0.075 

(0.070) 

–0.013 

(0.067) 

Black — — 0.003 

(0.047) 

–0.024 

(0.048) 

–0.107 

(0.068) 

Married0 — — — –0.071 

(0.037) 

–0.093 

(0.045) 

InsuranceCovered — — — 0.119 

(0.052) 

0.123 

(0.056) 

Constant 0.853 

(0.022) 

0.629 

(0.112) 

0.580 

(0.142) 

0.553 

(0.146) 

0.466 

(0.164) 

Adjusted R2 0.0014 0.0242 0.0333 0.0592 0.0837 

N 499 499 499 499 499 

Note: Observations in regressions 1 through 4 are weighted using weights from the NSFG. Regression 5 

observations are reweighted to match the age and year distribution in the sample of NSFG women whose 

first birth occurred when aged 19 to 38. The mean of Employed0 using NSFG weights is 0.862. For the 

reweighted sample, the mean of Employed0 is 0.847. NSFG: National Survey of Family Growth.
Table 8: Descriptive Statistics 

HELP Panel Data and NSFG Panel Data 

Mean 

(Standard deviation) 

Data NSFG — Cycle 5 (1995) NSFG — Cycle 5 (1995) 

Sample HELP panel data NSFG panel data 

Unit of observation Woman-month Woman-month 

Employed 0.841 * 

(0.365) 

0.808 

(0.394) 

AnyChildren 0.087 ** 

(0.281) 

0.171 

(0.376) 

Age 27.0 

(4.4) 

27.0 

(4.4) 

Education 14.0 

(2.5) 

14.0 

(2.6) 

Married 0.873 

(0.333) 

0.891 

(0.311) 

Smoke 0.361 

(0.480) 

0.327 

(0.469) 

Year (1970=0) 14.8 ** 

(5.5) 

15.6 

(5.8) 

Hispanic 0.059 

(0.236) 

0.066 

(0.248) 

Black 0.076 

(0.264) 

0.056 

(0.230) 

Baby age in months (for 

women with babies) 

5.5 ** 

(3.5) 

6.1 

(3.7) 

Number of observations 19,743 237,751 

Number of women 467 a 4,786 

*Significantly different from the mean of the NSFG panel data at the 5% level. 

**Significantly different from the mean of the NSFG panel data at the 1% level. 

Note: NSFG: National Survey of Family Growth. 

a

Thirty two women included in the HELP sample answered the NSFG less than 33 months after seeking 

help becoming pregnant. These women are not included in the panel data set because it includes monthly 

observations for each woman in the 33 months after seeking help to get pregnant. 
Table 9: Impact of a First Child on Employment 

HELP Panel Data and NSFG Panel Data 

Dependent variable: Employed

Coefficient 

(Standard error) 

Data NSFG — Cycle 5 (1995) NSFG — Cycle 5 (1995) 

Sample HELP panel data NSFG panel data 

Unit of observation Woman-month Woman-month 

Regression model OLS Fixed effects OLS Fixed effects 

AnyChildren –0.260 

(0.036) 

–0.234 

(0.034) 

–0.259 

(0.010) 

–0.216 

(0.010) 

Pregnant –0.092 

(0.020) 

–0.065 

(0.017) 

–0.074 

(0.008) 

–0.050 

(0.007) 

Age 0.003 

(0.004) 

0.004 

(0.007) 

0.000 

(0.002) 

–0.003 

(0.003) 

Education 0.011 

(0.005) 

0.032 

(0.023) 

0.005 

(0.001) 

0.029 

(0.011) 

Married –0.037 

(0.033) 

–0.055 

(0.025) 

0.033 

(0.011) 

–0.020 

(0.010) 

Smoke –0.026 

(0.037) 

0.091 

(0.060) 

0.016 

(0.003) 

0.007 

(0.022) 

Year (1970=0) 0.008 

(0.003) — –0.073 

(0.019) — 

Hispanic –0.103 

(0.066) — 0.024 

(0.018) — 

Black –0.035 

(0.048) — –0.020 

(0.010) — 

Constant 0.568 

(0.143) 

0.321 

(0.287) 

0.576 

(0.045) 

0.616 

(0.158) 

Adjusted R2 0.0813 0.6666 0.0880 0.5761 

N 19,743 19,743 237,751 237,751 

Note: Fixed-effects model for the HELP panel data includes dummy variables for individuals and months 

relative to the first time they sought help to become pregnant. Fixed-effects model for the NSFG panel data 

includes dummy variables for individuals and calendar years. Observations are clustered by individual. 

NSFG: National Survey of Family Growth.
Table 10: Multinomial Logit Estimates. Impact of a First Child on Work Status 

HELP Panel Data and NSFG Panel Data 

Marginal effects of changing AnyChildren from 0 to 1 

(Standard error) 

Data NSFG — Cycle 5 (1995) NSFG — Cycle 5 (1995) 

Sample HELP panel data NSFG panel data 

Unit of observation Woman-month Woman-month 

No Job 0.253 

(0.038) 

0.246 

(0.009) 

Maternity leave 0.115 

(0.015) 

0.116 

(0.004) 

Part time 0.010 

(0.021) 

0.005 

(0.006) 

Full time –0.378 

(0.036) 

–0.368 

(0.009) 

N 19,743 237,751 

Log pseudo-likelihood value –13887.78 –195,128.98 

Pseudo R2 0.0973 0.0904 

Note: The dependent variable has four categories: no job, maternity leave, part time and full time. 

Covariates: Age, Year, Smoke, Education, Hispanic, Black, Married. Observations are clustered by 

individual. NSFG: National Survey of Family Growth. 
Table 11: Descriptive Statistics 

HELP and Census Samples 

Mean 

(Standard deviation) 

Sample EARLY HELP 1980 Census LATE HELP 1990 Census 

Sample description 

Married women 

in HELP sample 

who sought help 

before 1985 

Married women 

aged 21 to 40 

childless or with 

children younger 

than 1 year old 

Married women 

in HELP sample 

who sought help 

on or after 1985 

Married women 

aged 21 to 40 

childless or with 

children younger 

than 1 year old 

Time point 

21 months after 

seeking help for 

the first time 

1980 21 months after 

seeking help for 

the first time 

1990 

(1) (2) (3) (4) 

Observation year 1981.5 ** 

(3.5) 

1980.3 

(0.0) 

1991.0 ** 

(2.6) 

1990.3 

(0.0) 

Employed 0.731 

(0.443) 

0.726 

(0.446) 

0.854 * 

(0.353) 

0.796 

(0.403) 

AnyChildren 0.289 ** 

(0.453) 

0.158 

(0.364) 

0.358 ** 

(0.479) 

0.128 

(0.334) 

Age 26.1 ** 

(3.1) 

27.2 

(4.9) 

30.2 ** 

(4.1) 

29.3 

(5.3) 

Education 13.4 

(2.4) 

13.4 

(2.6) 

14.1 

(2.6) 

13.9 

(2.5) 

Hispanic 0.050 

(0.218) 

0.053 

(0.223) 

0.081 

(0.272) 

0.077 

(0.266) 

Black 0.065 

(0.246) 

0.061 

(0.239) 

0.069 

(0.253) 

0.061 

(0.240) 

N 216 287,292 224 301,371 

*Significantly different from the mean of the Census comparable samples at the 5% level. 

**Significantly different from the mean of the Census comparable samples at the 1% level. 

Note: The EARLY HELP sample is compared to 1980 Census data and LATE HELP to 1990 Census data. 
Table 12: Linear Probability Estimates. Impact of a First Child on Employment 

HELP and Census Samples 

Dependent variable: Employed 

Coefficient 

(Standard error) 

Sample EARLY HELP 1980 Census LATE HELP 1990 Census 

Sample description 

Married women 

in HELP sample 

who sought help 

before 1985 

Married women 

aged 21 to 40 

childless or with 

children younger 

than 1 year old 

Married women 

in HELP sample 

who sought help 

on or after 1985 

Married women 

aged 21 to 40 

childless or with 

children younger 

than 1 year old 

Time point 

21 months after 

seeking help for 

the first time 

1980 21 months after 

seeking help for 

the first time 

1990 

Mean of dependent 

variable 0.731 0.726 0.854 0.796 

Independent variable (1) (2) (3) (4) 

AnyChildren –0.372 

(0.072) 

–0.365 

(0.002) 

–0.182 

(0.055) 

–0.228 

(0.003) 

Age 0.007 

(0.012) 

–0.004 

(0.000) 

–0.011 

(0.007) 

–0.001 

(0.000) 

Education 0.024 

(0.013) 

0.030 

(0.000) 

0.021 

(0.008) 

0.031 

(0.000) 

Hispanic 0.042 

(0.096) 

–0.047 

(0.004) 

–0.259 

(0.099) 

–0.087 

(0.004) 

Black –0.024 

(0.109) 

–0.017 

(0.003) 

0.032 

(0.062) 

–0.033 

(0.004) 

Year0 0.014 

(0.011) — –0.002 (0.008) — 

Constant 0.196 

(0.270) 

0.489 

(0.006) 

1.011 

(0.272) 

0.417 

(0.007) 

Adjusted R2 0.2145 0.1222 0.1253 0.0814 

N 216 287,292 224 301,371