EFFECTS OF PAID MATERNITY LEAVE ON BIRTH WEIGHT
A Thesis
submitted to the Faculty of the
Graduate School of Arts and Sciences
of Georgetown University
in partial fulfillment of the requirements for the
degree of
Master of Public Policy
in Public Policy
By
Katherine Morrison, B.A.
Washington, DC
April 11, 2016
ii
Copyright 2016 by Katherine Morrison
All Rights Reserved
iii
EFFECTS OF PAID MATERNITY LEAVE ON BIRTH WEIGHT
Katherine Morrison, B.A.
Thesis Advisor: William Encinosa, Ph.D.
ABSTRACT
This paper evaluates the effects of paid maternity leave on infant birth weight in the United
States using data from the National Survey of Family Growth. Given the health costs and long
term health implications for low birth weight infants, I test whether taking paid maternity leave
reduces the likelihood of giving birth to a low birth weight infant compared to employed women
who do not take paid maternity leave. I use two methods to estimate the relationship between
taking paid maternity leave and the likelihood of delivering a low birth weight infant – logistic
regression and propensity score matching. The logistic regression results suggest that paid
maternity leave may substantially reduce the likelihood of delivering a low birth weight infant
among women with lower levels of income and educational attainment. Analyses using propensity
score matching reinforce this relationship between paid maternity leave and reduced likelihood of
low birth weight, however the results are not statistically significant at conventional levels (p =
.14). While these results cannot be interpreted causally, they support the hypothesis that wage
replacement during periods of maternity leave may reduce the likelihood of giving birth to a low
birth weight infant for employed women.
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TABLE OF CONTENTS
Introduction ................................................................................................................... 1
Justification for Analysis ...............................................................................................2
Literature Review and Institutional Background ...........................................................3
Conceptual Model ..........................................................................................................7
Empirical Model and Estimation Strategy .....................................................................9
Description of Data ......................................................................................................12
Results ..........................................................................................................................16
Policy Implications and Limitations ............................................................................21
Conclusion ...................................................................................................................25
Appendix ......................................................................................................................26
References ................................................................................................................... 36
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LIST OF EXHIBITS
Exhibit 1. Conceptual Model ...................................................................................... 26
Exhibit 2. Descriptive Statistics and Difference of Means Test for Independent
Variables ..................................................................................................................... 27
Exhibit 3. Percentage of Low Birth Wiight Deliveries by Poverty Level and
Educational Attainment ...............................................................................................28
Exhibit 4. Effect of Paid Leave on Low Birth Weight (Logit Regression) .................29
Exhibit 5. Effect of Paid Leave on Low Birth Weight by Poverty Level and
Educational Attainment (Logit Regression) ................................................................31
Exhibit 6. Distribution of Propensity Scores in Treatment and Control (Test of
Common Support) ........................................................................................................33
Exhibit 7. Descriptive Statistics (After Propensity Score Matching)– Test of
Balance) .......................................................................................................................34
Exhibit 8. Average Treatment Effect on the Treated (ATT) (Propensity Score
Matching) .....................................................................................................................35
1
INTRODUCTION
Policy makers, employers, and researchers are increasingly debating paid family leave in
the United States. Three states (California, New Jersey and Rhode Island) have implemented laws
mandating paid family leave and 2016 presidential candidates Hillary Clinton and Bernie Sanders
have proposed federal paid leave policies as cornerstones of their campaigns.
Studies about paid leave abroad have found that paid maternity leave improves birth
outcomes and childhood health outcomes (Ruhm, 2000; Tanaka, 2005) but to date, these results
have not been replicated in the United States. Most domestic research on maternity leave has
focused on unpaid leave and the implications of the Family Medical Leave Act of 1993 (FMLA).
Studies have found that the FMLA has increased leave-taking among new mothers (Rossin-Slater,
Ruhm & Waldfogel, 2013) and has decreased infant mortality rates (Rossin, 2011) in the overall
population. However, the effects of unpaid maternity leave under FMLA disproportionately have
affected women in higher income and education brackets who are less likely to rely on income
from work during periods of maternity leave (Rossin, 2011).
In this study, I evaluate the effect of taking paid maternity leave on rates of low birth
weight in the United States compared to women taking unpaid maternity leave and/or no maternity
leave. In addition, I will assess whether the effect differs across income and education levels.
2
JUSTIFICATION FOR ANALYSIS
Poor birth outcomes such as low-birth weight have profound effects on long term health
outcomes and health costs. Low birth weight has been linked to long term health complications
such as learning and behavioral problems, cerebral palsy, lung problems, and vision and hearing
loss (MCHB, 2014). Low weight births were estimated to account for half of infant hospitalization
costs and one quarter of all pediatric costs (Russel et al., 2007).
Although low birth weight deliveries have profound long-term health and financial effects,
they are largely preventable. The Center for Medicaid and CHIP Services (CMCS) requires states
to report annual rates of low birth weight under the child health Core Set quality measures (CMCS,
2014). The inclusion of this measure in the Core Set is predicated on the assumption that low birth
weight is frequently preventable through delivery of appropriate prenatal care and early attention
to medical and behavioral risk factors.
Despite the preventable nature of low birth weight deliveries, there are significant racial
and ethnic disparities between rates of low birth weight among white infants and non-white infants.
In 2013, non-Hispanic black infants were twice as likely as non-Hispanic white infants to be low
birth weight (Martin et al, 2015). Reducing disparities in health, including rates of low birth
weight, is one of the Healthy People 2020 priorities.
The proposed analysis will examine the effect of the paid maternity leave on rates of low
birth weight across income and education levels. As policy makers and private employers
increasingly consider providing paid maternity leave for working women, it is important to explore
the effects of paid leave on birth outcomes, especially as a means of reducing disparities and
cutting health care costs.
3
LITERATURE REVIEW AND INSTITUTIONAL BACKGROUND
Relevant literature on the effects of maternity leave can be grouped into three categories:
studies of paid leave on birth outcomes in Europe and other members of the Organization for
Economic Cooperation and Development (OECD); studies of unpaid leave on birth outcomes in
the United States; and studies of paid leave on other outcomes in the United States. To date, there
have not been any peer-reviewed studies analyzing the effects of paid maternity leave on birth
outcomes in America.
Studies of Paid Leave from Europe and Other OECD Countries
Although research on paid leave in the United States is limited, there have been a number
of studies documenting the effects of paid leave on maternal and child health outcomes in
European and Asian countries. For example, Ruhm (2000) found that extending government-paid
leave by 10 weeks may reduce infant mortality rates by 1.7 to 2.5 percent based on analyses using
cross-country data from European countries between 1969 and 1994. Tanaka (2005) also found a
significant negative association between weeks of paid maternity leave and neonatal (less than 28
days), post-neonatal (28 days to 1 year), and infant (less than one year) mortality rates using data
from 18 OECD countries from 1969 through 2000. In addition to finding a 2.3% decrease in
neonatal mortality rates, a 4.1% decrease in post-neonatal morality rates, and a 2.6% decrease in
infant morality rates for every additional 10 weeks of paid maternity leave, Tanaka identified a
1.7% decrease in rates of low birth weight (defined as birth weights of 2,500 grams or less).
Other studies drawing on recent expansions in paid leave policy abroad suggest mixed
findings. A study examining the expansion of paid maternity leave in Canada from six months to
a year did not identify statistically significant effects on early childhood development and
4
outcomes (Baker & Milligan, 2010). This is not surprising given the duration of leave at baseline
and may suggest that the effects of additional weeks of paid leave are more significant for women
with lower levels of maternity leave at baseline.
Studies on the Family Medical Leave Act of 1993 and unpaid maternity leave
The federal Family Medical Leave Act (FMLA) of 1993 affected employed women in the
United States by mandating that eligible women working at companies with 50 or more employees
receive up to 12 weeks of unpaid, job-protected maternity leave. Eligibility requirements include
working for an employer for at least twelve months, having worked at least 1,250 hours during the
past 12 months, and working at a location where the company employs 50 or more employees
within 75 miles (U.S. Department of Labor, 2015). FMLA was passed in 1993 and led to
significant increases in the number of women covered by maternity leave policies (Waldfogel,
1999) and the number of new mothers taking maternity leave (Rossin-Slater, Ruhm, & Waldfogel
2013). However, rates of leave-taking only increased at statistically significant levels among
higher-income mothers whereas the rate of leave taken by lower-income mothers was not affected
by FMLA (Han, Ruhm & Waldfogel, 2009).
One study examined the effects of FMLA on birth outcomes using Vital Statistics data and
found that infant mortality rates decreased by .05% and rates of low birth weight decreased by .2%
across the United States population as a result of the legislation (Rossin, 2011). A sub-group
analysis by educational attainment found that the coefficients on these variables have greater
magnitude and significance for women with at least a college education whereas the coefficients
on the birth outcome and mortality variables were insignificant for women with lower levels of
educational attainment. This suggests that women with lower levels of educational attainment may
5
not be able to take advantage of FMLA compared to women with higher education levels because
their jobs are less likely to meet the eligibility criteria and the women are more likely to rely on
income from their job during pregnancy and after giving birth.
State paid leave policies
While FMLA provides context to study the effects of unpaid maternity leave, the effects
of paid maternity leave in the United States have primarily been explored through state-level paid
leave policies. To date, four states have passed paid leave laws and three have implemented them.
California instituted the law in 2004, followed by New Jersey in 2009 and Rhode Island in 2014.
Washington passed legislation in 2008 but delayed the implementation indefinitely due to
budgetary constraints.
California’s Paid Family Leave (PFL) law stipulates that all eligible individuals, including
women and men, receive six weeks of partially paid leave to bond with a newborn or adopted child
or to care for a sick relative. Almost all private sector employees are eligible regardless of the size
of employer. Beneficiaries receive 55% wage replacement up to a cap based on the state’s average
wage. The program is financed through a payroll tax (California Employment Development
Department, 2015). Employees may not be eligible for PFL if they are receiving other disability
benefits, workers compensation or other benefits through cash sickness programs.
New Jersey’s law also provides up to six weeks of leave with wage replacement, is funded
through payroll tax, and is not offered to individuals receiving other disability or workers
compensation benefits. To be eligible, employees must have earned $7,300 during the 52-weeks
prior to taking leave or earned $145 or more per week for at least 20 weeks during the prior year.
Unlike California’s PFL law, wage replacement is set equal to two-thirds of an employee’s average
6
weekly wage up to $595 and continues for 6 weeks or until the benefit amount exceeds a third of
the employee’s earnings during the 52 weeks prior to taking leave (State of New Jersey Department
of Labor and Workforce Development, 2015).
Rhode Island expanded its temporary caregiver insurance (TCI) program financed through
employee payroll deductions to include provisions for paid leave to bond with a new child. In
Rhode Island, all employees who provide 30-days notice (unless unforeseeable circumstances
arise) of taking paid leave during a birth event can receive a weekly benefit equal to 4.62% of
wages earned during the most lucrative quarter of the previous 52-week period or “base period”.
This payment may be collected for four weeks during a calendar year (Rhode Island Department
of Labor and Training, 2015).
Employers are free to offer paid leave to employees in cities and states where paid family
leave is not mandated, but few offer these benefits. According to a June 2014 White House report
entitled The Economics of Paid and Unpaid Leave, only 11% of workers in 2014 were covered by
formal paid family leave policies that would enable them to collect some income during periods
of leave after giving birth (White House).
Early research indicates that the paid family leave program in California has caused leave-
taking to double in the state and has improved mothers’ labor market outcomes nine to twelve
months after childbirth (Baum & Ruhm, 2013; Rossin-Slater, Ruhm, & Waldfogel 2013).
Although it seems likely that the increase in leave-taking would lead to improved birth outcomes
for a larger sub-set of women based on the results from Rossin’s (2011) study of FMLA, increased
leave-taking among women in states with paid leave legislation has not yet been linked to changes
in maternal or infant health in peer-reviewed literature.
7
CONCEPTUAL MODEL
Researchers propose that maternal stress during pregnancy acts as the mechanism through
which paid family leave affects birth outcomes (Rossin, 2011; Ruhm, 2000; Tanaka, 2005).
Medical literature shows that maternal stress during pregnancy may have negative effects on the
fetus through neuroendocrine changes, changes in immune function, and/or behavioral pathways
that impact a woman’s actions, which ultimately result in poor birth outcomes (Dunkel-Schetter,
2011). In a meta-analysis, Beydoun & Saftlas (2008) report that 9 of 11 empirical studies between
2000 and 2006 found significant effects of maternal stress on preterm birth rates. Other studies
have found similar effects of maternal stress on low birth weight (Rondó et al., 2003) and abnormal
birth conditions such as meconium aspiration syndrome and use of ventilator for greater than 30
minutes (Currie & Rossin-Slater, 2012).
Numerous individual, family, and community factors contribute to maternal stress during
pregnancy and therefore impact rates of low birth weight (see Exhibit 1). Among these factors is
the availability of paid maternity leave for employed women. I hypothesize that individuals that
receive paid maternity leave benefits experience less prenatal stress and are less likely to
experience poor birth outcomes. In particular, I predict that paid leave decreases prenatal stress
levels for women who might have difficulty taking time off from work around child birth in the
absence of paid maternity leave, such as women in lower income and education brackets and
women who are more reliant on their paycheck. For these women, the availability of paid leave
alleviates concerns during pregnancy about their future financial situation and their ability to take
time off from work to recover from child birth and care for their newborns. A conceptual model is
included in Exhibit 1. The model suggests that the magnitude of the effect would be greatest for
8
women who are less likely to have the financial resources to take unpaid leave in the absence of
paid leave, because they would experience the highest levels of prenatal stress about this issue.
9
EMPIRICAL MODEL AND ESTIMATION STRATEGY
To estimate the relationship between paid maternity leave and infant birth weight, I first
use a basic logistic regression framework. The binary dependent variable, low birth weight, is
regressed on indicators for maternity leave and controls for individual and family characteristics,
as noted in the conceptual framework. My initial model takes the following form:
Y = 𝜷𝟎 + 𝜷𝟏PAIDLEAVE𝒊 + 𝜷2UNPAIDLEAVE𝒊 + 𝜷3INDIVIDUAL
CHARACTERISTICS + 𝜷4FAMILY CHARACTERISTICS +
𝜷5COMMUNITY CHARACTERISTICS + 𝒆
In this model, Y represents the probability that an infant is less than 5.5 pounds, with the unit of
analysis being an individual pregnancy. The coefficient on paid leave, 𝜷𝟏, allows me to address
my research question regarding the effects of paid leave on probability of giving birth to an infant
less than 5.5 pounds.
To assess whether there are differences in probability of low birth weight across different
levels of income and educational attainment, I will conduct sub-analyses stratified by federal
poverty level and years of educational attainment. I hypothesize that the magnitude of 𝜷𝟏 for
pregnancies completed by women in lower income and lower educational attainment groups will
be larger in the negative direction; women in these groups are less likely to be financially able to
take unpaid maternity leave, if needed, so the reduction in probability of a low-weight birth will
be greater.
One downside of using logistic regression to estimate the effects of paid maternity leave
on birth outcomes is that the model requires that all variables correlated with low birth weight and
paid maternity leave status be included in the model, or else the results will be biased. Given the
10
restrictive nature of the National Survey of Family Growth (NSFG) data set (described in greater
detail in the Description of Data section), there are numerous unobserved variables that are
correlated with both the independent and dependent variables. One example is occupation during
pregnancy. Women in certain occupations are more likely to have the option of paid leave than
women in other occupations. Similarly, occupation may be correlated with birth outcomes. On one
hand, women in highly stressful occupations may be more likely to deliver low birth weight
infants. On the other hand, women in certain low-skill occupations, which are also tied to race and
income, may be more likely to deliver low birth weight infants due to work conditions. As such, it
is difficult to predict the sign of the bias on the coefficient. In attempt to address bias caused by
endogenous variables, I conduct a second analysis using propensity score estimation to eliminate
some of the bias associated with omitted variables.
Propensity score matching is useful when there are systematic differences between the
treatment (i.e., pregnancies completed by a woman taking paid leave) and control (i.e., pregnancies
completed by a woman taking unpaid leave or no leave) groups. For example, women who take
paid leave are more likely to have additional years of education and earn higher incomes than
women who do not take paid maternity leave. Other examples of systematic differences between
these two groups are discussed in the Description of Data section. By matching pregnancies in the
treatment group to those in the control group using observable variables that affect selection into
the treatment group, I will reduce selection bias. The empirical model used to estimate the average
treatment effect on the treated (ATT) using nearest neighbor propensity score matching is defined
below:
Equation 1: LBW = 𝜷𝟎 + 𝜷𝟏PAIDLEAVE𝒊 + 𝜷2Xi+ 𝒆 (same as above)
Equation 2: PAIDLEAVEi = ηXi + ui
11
Equation 3: ATTi = E[ßi | Ti = 1]- E[ßi | Ti = 0]
Equation 1 is identical to the one used for the logistic regression model where 𝜷𝟏 is the effect of
paid leave on low birth weight and Xi represents all other covariates in the model. Equation 2 is
used to obtain the propensity scores. In this analysis, the treatment group is comprised of
pregnancies completed by women taking paid maternity leave. Therefore, a probit model with paid
maternity leave status as the dependent variable regressed on covariates that influence selection
into the treatment group provides the likelihood of taking paid maternity leave given the pre-
treatment variables (mainly demographic variables and individual health variables). This value
serves as the propensity score for which the observations in treatment are matched. Equation 3
represents the calculation of the average treatment effect of paid leave on the treated (ATT), which
is the difference in the effect of paid maternity leave on rates of low birth weight (𝜷𝟏) between
the matched treatment and control groups. By comparing observations in the treatment group to
similar observations in the control group based on characteristics that influence the likelihood that
a woman takes paid maternity leave, some of the bias inherent in the initial unmatched logistic
regression model will be reduced.
12
DESCRIPTION OF DATA
This study uses the National Survey of Family Growth (NSFG) 2006- 2010 Pregnancy
dataset for analysis. The dataset contains cross-sectional self-reported data on 20,492 pregnancies.
I limit my analysis to 3,086 pregnancies that: 1) ended in a live birth and 2) were completed by
women who were employed at a job for pay during their pregnancy. It is important to note that
observations do not necessarily represent unique women but rather, represent unique pregnancies
(i.e., a female respondent may report data on multiple pregnancies and therefore, multiple
observations in the file may be associated with a single woman). I address this in the results section.
Below is a description of the key independent variable, key dependent variables, and covariates
(categorized into three groups - leave and employment variables, health and behavior variables,
and demographic variables). Descriptive statistics for these variables are included in Exhibit 2.
Dependent Variable
The main dependent variable is an indicator for low birth-weight. The variable equals 1 for
pregnancies in the dataset where the infant is less than 5.5 pounds (or 2,500 grams) at birth. This
variable is self-reported by women responding to the survey. Respondents who were unable to
respond with their infant’s weight at birth were asked a series of follow-up questions to help
identify whether the infants were above or below the 5.5 pound threshold for low birth weight.
Key Independent Variables
The primary independent variable of interest is a set of indicators (NOLEAVE,
UNPAIDLEAVE and PAIDLEAVE) representing whether a woman took no maternity leave, all
13
unpaid maternity leave, or paid maternity leave, of which at least one week was paid. Women
taking less than one week of total leave (including paid and unpaid leave) are categorized as taking
no leave. Although data is available for more recent waves of the NSFG, I utilized the NSFG 2006
– 2010 dataset because the survey items corresponding to the leave variables of interest were not
administered during more recent waves of the survey. The NSFG dataset does not include data on
types of maternity leave (paid, unpaid, no leave) offered to sample members during their pregnancy
– only on the types of leave taken.
In addition to leave status, I used two additional variables for stratified sub-analyses – a
continuous variable representing a participant’s income compared to the federal poverty level (a
value of 100 indicates that the observation belongs to a woman whose annual household income
was at the federal poverty level in 2010, which equals $10,830 with an additional $3,740 allowance
for each additional household member), and a continuous variable representing years of
educational attainment at time of interview. The sample mean values for these variables (with
complex survey weights applied) are 228% and 13.7 years, respectively. For both variables, the
mean values for women taking paid leave are significantly higher (at the p<.01 level) when
conducting a difference of means test.
Exhibit 3 shows the distribution of births less than 5.5 pounds by leave status, income level,
and education level. Approximately 6.5% of sample members taking paid leave reported low birth
weight, whereas 7.0% of the unpaid leave sample and 9.6% of the no-leave sample reported births
under 5.5 poudns. As expected, the differences in outcomes between women taking paid leave
compared to unpaid leave and no leave are larger for women below 100% of the federal poverty
level and with less than high school educations.
14
Leave and Employment Variables
In addition to the key dependent and indendent variables, the model includes a number of
covariates. Leave and employment variables include an indicator for part-time employment at time
of interview, an indicator for full-time employment at time of interview, and a continuous variable
indicating the percent of leave that was paid. As displayed in Exhibit 2, the difference in rates of
full time employment between women taking paid leave and women taking unpaid leave or no
maternity leave is roughly 25%. This is not surprising, given that employers may require that
workers be employed full-time to qualify for paid leave benefits.
Health and Behaviors
I include several variables in the model related to health status, access to care, and health
behaviors, which are likely to affect infant birth weight. These variables include: a continuous
variable for body mass index (BMI) at time of interview, an indicator for whether the infant was
delivered by C-section, an indicator for whether a woman smoked cigarettes at any point during
her pregnancy, a continuous variable for number of weeks pregnant when first receiving prenatal
care, an indicator for infant sex, a continuous variable indicating how many times a woman has
been pregnant at the time of interview, a continuous variable representing birth order (whether the
infant was the woman’s first, second, third, etc. live birth), an indicator for whether the delivery
was paid by a government program (such as Medicaid or Medicare), an indicator equaling 1 if the
woman gave birth in a hospital, a continuous variable for infant birth year, and an indicator
equaling 1 if the respondent said she was happy to find out she was pregnant. This data is reported
for each observation in the file.
15
Exhibit 2 shows that there are statistically significant differences in the means of all of
these variables, except BMI, infant sex, infant birth year, and whether the birth took place in a
hospital. Based on the descriptive statistics, sample members taking paid leave were more likely
to receive prenatal care earlier in the pregnancy and were less to smoke during pregnancy or have
a delivery paid for by a government program than sample members who did not take paid leave.
Other Demographics
I include several demographic variables in the model to control for differences between
women who take paid leave and those who do not. The complete list of covariates is displayed in
Exhibit 2 and includes continuous variables for age at conception and indicators for marital status,
race, ethnicity, receipt of public assistance within 1 year of the interview, religion (1 if the woman
reported not practicing religion at the time of the pregnancy, 0 if the woman reported that she did
practice religion at time of interview), and residence in an urban area at time of interview (1 if the
woman reported living in an urban area, 0 if the woman reported living in a rural or suburban area).
Exhibit 2 displays the descriptive statistics for all covariates in the conceptual model with
the complex survey weights applied. In addition, I conducted difference of means tests to compare
the unweighted values for the paid maternity leave group (treatment) to all other women in the
sample (control). The difference of means test confirmed that there are systematic differences
between observations in the treatment and control groups. Observations in the treatment group are
associated with women who are more likely to be white, tend to have higher income levels, and
are less likely to receive public assistance than observations in the control group.
16
RESULTS
Logistic Regression
To assess the effects of paid leave on birth weight, I ran a logistic regression of the binary
dependent variable, low birth weight, on paid leave, controlling for the covariates discussed in the
Description of Data section. I weighted the observations using the complex survey weights. I
addressed the issue of survey respondents providing multiple birth records by specifying that the
standard errors allow for intragroup correlation across unique women (identified by the CASEID
variable) using the vce(cluster) command in stata.
Results from the hierarchical regression on the full sample of pregnancies completed by
women who were employed during pregnancy are displayed in Exhibit 4. Model 1 shows the logit
results from regressing low birth weight on leave status excluding all other covariates. The results
are statistically significant at the p<.05 level and indicate a negative relationship between paid
leave and rates of low birth weight, as hypothesized. However, once other covariates are added to
the model, the coefficient on paid leave is no longer statistically significant at conventional levels
for the full sample. The results indicate that for the overall population, paid maternity leave is no
better than random chance at predicting whether an infant is low birth weight.
To assess whether the effects of paid maternity leave vary across income levels and
education levels as suggested by Rosin (2011), I conducted stratified sub-group analyses based on
the federal poverty level of the woman giving birth and her education level at the time of interview.
To obtain the estimates, I use the same logistic regression model specified in Exhibit 4, Model 3
stratifying by federal poverty level and education. I used the specifications from Model 3 instead
of Model 4 to maximize the number of observations included in the regression (the BMI variable
included in Model 4 contains missing data). As with the regression model for the full sample, I
17
used the complex survey weights and accounted for intragroup correlation using the vce(cluster)
command. Results are included in Exhibit 5.
The sub-group analysis suggests that paid leave improve rates of low birth weight for
pregnancies completed by women below 100% of the federal poverty level and women with less
than a high school degree at the p<.05 and p<.01 confidence levels, respectively. Results were not
statistically significant for women at or above 100% of the federal poverty level or with greater
than a high school degree.
The logit model for women below 100% of the federal poverty level utilized weighted data
on 917 pregnancies completed by women who reported an annual income below the federal
poverty level at time of their interview. The coefficient on paid leave for those below the poverty
line (β = -.74) suggests that pregnancies completed by women who take paid leave (compared to
women taking unpaid leave or no leave) are 47% less likely to result in low birth weight infants
below 5.5 pounds. There is only 4% chance that we would find this level of difference in rates of
low birth weight infants between paid leave births and unpaid leave births at random. The logit
regression of paid leave on low birth weight for women above the federal poverty line did not yield
statistically significant results.
The analysis by education level shows the effects of paid maternity leave on likelihood of
delivering low birth weight infant among pregnancies completed by women at three different
education levels: less than high school degree, high school degree but no bachelor’s degree, and
bachelor’s degree and above. I ran three logit regressions specifying the education level for
inclusion in each model. Two of the models (high school graduates and college graduates) did not
yield statistically significant results. However, the model for pregnancies completed by women
with less than high school degrees was highly statistically significant. There is less than a .01%
18
chance that the differences in rates of low birth weight between women taking paid leave and not
taking paid leave among those with less than a high school degree could be caused by random
chance. If unbiased, the coefficient on paid leave (β = -3.53) suggests that taking paid maternity
leave causes a 92% decrease in low birth weight infants among women with less than a high school
degree. This is a much higher magnitude than anticipated for this group of women. Rossin (2011)
and Tanaka (2005) found that paid maternity leave affected birth outcomes by tenths of a percent.
While their results measured the average treatment effect (rather than effect of those treated, as I
do in my study), it seems likely that omitted variables may be biasing the coefficient in my model
the downward direction, consequently making paid maternity leave seem more influential than it
is in reality. I discuss this in greater detail in the Policy Implications and Limitations section.
Propensity Score Matching
In attempt to reduce bias associated with unobserved variables, I conduct a secondary
analysis comparing the difference in rates of low birth rate among women taking paid leave
compared to those who do not take paid maternity leave using propensity score nearest neighbor
matching with replacement for two nearest neighbors. The nearest neighbor method selects a
defined number of comparison units (in my case, I use 2 units) from the control group with
propensity scores that are most similar to that of the observation in the treatment group. When the
characteristics of the control and treatment groups are systematically different, matching with
replacement can improve the quality of the match because an observation in the control group can
be used multiple times if it is the closest match to multiple treatment observations. Based on the
conceptual model and the findings from the logit regression, I would expect the average effect of
19
treatment on the treated (ATT) to be negative (that is, the rate would be lower for pregnancies in
the paid leave group compared to pregnancies in the control group).
As noted in the Description of Data section and displayed in Exhibit 2, there are
statistically significant differences between women taking paid maternity leave (treatment group)
compared to those who do not take paid maternity leave (control group) on variables such as:
employment status (full time versus part time), delivery method (c-section versus vaginal birth),
smoking status during pregnancy, number of weeks pregnant at first prenatal care visit, insurance
status during pregnancy, self-reported level of happiness upon finding out about pregnancy,
marital status, age at conception, years of education, race, receipt of public assistance, poverty
level, and religion. Propensity score matching reduces the systematic differences between these
groups. Overall, propensity score estimation reduced the median bias of the covariates by 69%
(using the pstest command in stata)
The propensity score for each observation, estimated using the psmatch2 command in stata,
predicts the likelihood of each observation to represent a pregnancy in the treatment group (i.e., a
pregnancy completed by a woman taking paid maternity leave) based on a set of pre-treatment
variables. A value of 1 indicates the pregnancy has a 100% chance of representing a pregnancy
completed by a woman taking paid maternity leave, whereas a value of 0 indicates the pregnancy
has a 0% likelihood of representing a birth delivered by a woman taking paid maternity leave. Pre-
treatment variables used for calculating the propensity scores are noted in Exhibit 7 and primarily
include demographic variables (race, ethnicity, age) and employment status variables (full time
employment, part time employment).
After calculating the propensity scores, I checked to see whether the matching technique
yielded common support and balance. Exhibit 6 shows the distribution of propensity scores in the
20
treatment and control samples. The exhibit indicates that there is considerable overlap (or common
support) between the treatment and control groups. Propensity scores for the treatment group span
from .05 to .91 whereas the propensity scores for the control group range from .03 to .89. Six
observations within the treatment group fall above the range of common support and sixteen
observations in the untreated group fall below the range of common support. Exhibit 7, which
displays the difference of means of covariates post-matching, shows the balance attained through
propensity score matching. By matching on pre-treatment variables, I eliminated significant
differences between the treatment and control groups at conventional levels of significance (p<.05)
for all but 1 covariate (infant sex at birth). This variable was not included in the matching function
because it is not a determinant of treatment.
Exhibit 8 shows the results of propensity score matching using nearest neighbor matching
on 2 comparison units with replacement. The average effect of treatment on the treated (ATT) is
not significant at conventional levels of significance (p = 0.136) so one needs to be cautious when
further interpreting the results. However, the coefficient on paid leave (β1 = -0.23) indicates that
among women in the treatment group, paid leave reduces likelihood of low birth weight compared
to women not taking paid leave by roughly 20% (Odds Ratio = .79). As expected, the coefficient
on paid leave has a smaller magnitude than the coefficient when weighted by the complex survey
weights, suggesting that the propensity score model likely reduced some bias from the initial
model.
21
POLICY IMPLICATIONS AND LIMITATIONS
The results from this study have a few implications. First, for the general population, it is
unclear whether paid maternity leave reduces a woman’s likelihood of giving birth to an infant
that is less than 5.5 pounds compared to women taking unpaid leave and/or no leave. Results from
analyses using the full sample of pregnancies (including the full-sample logistic regression
displayed in Exhibit 4 and the propensity score matching model displayed in Exhibit 8) indicate a
negative association between paid leave and low birth weight (as hypothesized), but the estimates
are slightly outside the range of conventional statistical significance. This may indicate that unpaid
maternity leave may be a viable option for women at middle and upper levels of education and
income (which make up more than 50% of the sample), so the marginal effect of going from unpaid
leave to paid leave is not discernable in the data. Alternatively, these findings could be the result
of limitations in the data, discussed in greater detail later in this section. Additional research needs
to be undertaken to better assess the effects on the population at large.
Second, although the impact of paid maternity leave on birth weight was not statistically
significant for the full sample of women, I found statistically significant reductions in low birth
weight deliveries for women below the federal poverty line and with less than a high school degree.
These groups have historically experienced disparities in maternal and infant health outcomes so
it is especially important that policies be enacted to reduce these disparities. Furthermore, these
women are more likely to work in low-wage, low-skill jobs, for which employers are less likely to
offer paid leave benefits in the absence of legislation. In other words, the women who are most
likely to experience improved birth outcomes from paid maternity leave are the least likely to be
offered paid leave benefits at present. Federal legislation mandating paid leave across all
employers in the United States would help ensure that women in these groups who are most likely
22
to experience improved birth outcomes from paid leave benefits have access to paid maternity
leave.
There are a number of caveats and limitations to these results. First, the public use NSFG
dataset contains a limited set of variables. Consequently, unobserved factors contained in the error
term are likely correlated with the outcome variable and covariates in my model, so the estimates
are likely to be biased. For example, the data set does not contain any data on medical conditions
and comorbidities (such as gestational diabetes), detailed information on industry and occupation,
or geographic indicators (such as zip code, county, or state). Based on the large magnitude of the
PAID LEAVE coefficient in the logistic regression sub-analyses stratified income and educational
attainment, it seems likely the model is biased downwards – that is, the logit model is
overestimating the effect of paid maternity leave on birth outcomes.
Furthermore, the limitations in available data restricts the type of identification methods
that I was able to utilize to explore causal effects. If I had access to data on state of residence, I
could have used a difference-in-difference model to analyze effects on birth outcomes among
sample members in one of the three states that have implemented paid family leave laws before
and after the legislation took effect. Without access to this information, I was limited to use logistic
regression and propensity score techniques, which are more likely to be biased by unobserved
factors.
The propensity score matching model attempted to correct for some of the bias due to
omitted variables but was not without its own limitations. Propensity score estimation requires that
variables that influence selection into treatment be included in the equation to calculate propensity
scores. However, the NSFG dataset did not contain industry/ occupation data, geographic data, or
data indicating whether or not a woman was offered paid maternity leave. These variables are most
23
likely closely associated with an employed woman’s probability of taking paid maternity leave –
one would assume that women in white collar jobs and women in states that have passed paid
family leave laws are more likely to take paid maternity leave. By excluding these variables from
the model, the propensity scores do not accurately reflect selection into treatment.
Another limitation to both methods was the way some variables were reported during
survey data collection. Several variables, such as employment status (full time versus part time),
poverty level, and educational attainment were measured at the time of interview, which does not
directly correspond to the measurement during pregnancy. For over 80% of observations in the
data set, the interview took place less than three years after the pregnancy ended, however we were
required to make the assumption that these variables were stable between infant birth and the
interview. This is particularly problematic for the logistic regression sub-group analyses, which
utilize the educational attainment and poverty variables. It is possible that an observation was
associated with 13 years of education at the time of interview but had only attained 11 years of
education at the time of delivery, which confounds the results.
This issue is also problematic for the propensity score technique. Variables used to
calculate propensity scores are intended to be measured pre-treatment. Most of the variables
utilized to calculate the propensity scores were stable over time (race, ethnicity, birth year);
however, in the absence of poverty, education, and employment status variables corresponding to
the time of pregnancy, I used the variables measured at time of interview (post-treatment) as a
proxy. Although we expect movement between these groups to be relatively limited given the short
time period from child birth to interview, the measurement of these variables further confounds
the results.
24
Lastly, the dataset contains self-reported information on birth outcomes as a proxy for
actual birth outcome data. This is subject to recall error. Because self-reported infant birthweight
was collapsed into a binary variable, errors in reporting will only be problematic for a small sub-
set of individuals who incorrectly reported these variables across the threshold of 5.5 pounds.
Furthermore, a study by Walton et al. (2000) compares maternal reports of birth weight with linked
objective data and found that 85% of parents were able to recall correctly. This finding suggests
that self-reports are a suitable proxy for measurements reported in vital statistics or administrative
data.
25
CONCLUSION
The results of this paper add to the discussion on the effects of paid maternity leave on
birth weight in the United States. Although the logistic regression model did not yield significant
results for the full sample, the stratified sub-analyses indicated that taking paid maternity leave
reduces the likelihood of delivering low birth weight infants among women in lower income and
education brackets. Results from the propensity score matching model reinforce these findings.
While the ATT estimated through propensity score matching was not conventionally significant
(p = .14), it indicated that the odds of low birth weight delivery among women taking paid leave
were 21% lower than women not taking paid leave.
Although there are significant limitations to my study, additional research should be
undertaken on the subject. First, I suggest undertaking an analysis using administrative records
such as birth records to eliminate bias associated with self-reported data and to obtain a larger
sample size. Second, I suggest focusing future research on a state that has passed paid maternity
leave. This would enable researchers to better control for endogeneity by conducting a difference-
in-difference analysis of rates of low birth weight before and after enforcement of paid maternity
leave legislation. With more complete data, it would not be surprising if future research identifies
a causal effect of paid maternity leave on birth outcomes like low birth weight, preterm birth, and
infant mortality in the United States, similar to findings from research abroad.
26
APPENDIX
Exhibit 1. Conceptual Model
27
Exhibit 2. Descriptive Statistics and Difference of Means Test for Independent Variables
Variable Description
Mean
Overall
N =
3,086
Paid
N= 1,206
Unpaid
N = 765
No Leave
N =
1,115
Leave and Employment
Employment at time of interview
Full time .449 .586** .432 .285
Part time .203 .165** .254 .212
Percent of leave taken that was paid .483 .766** .000 .000
Health and Behaviors
Body Mass Index (BMI) at time of interview 28.1 29.1 28.9 28.2
C-section .301 .335** .291 .263
Smoked during pregnancy .108 .054** .128 .165
Weeks pregnant when first receiving prenatal care 8.94 7.97** 9.61 9.70
Infant sex (male) .511 .529 .502 .494
Number of times pregnant (at time of interview) 2.21 2.08** 2.44 2.19
Number live birth (at time of pregnancy) 1.91 1.82** 1.99 1.97
Insured by Medicaid, Medicare or other government
program during pregnancy
.382 .171** .514 .555
Gave birth in hospital .980 .985 .971 .979
Infant birth year 2005 2005 2005 2005
Happy about pregnancy1 .508 .546** .493 .469
Other Demographics
Married or cohabitating at time of infant birth .842 .895** .813 .797
Age at conception 24.2 29.4** 26.4 25.5
Years of education 13.7 14.7** 13.1 13.0
Race
White .727 .748* .729 .698
Black .173 .157** .155 .208
Other .099 .095 .115 .094
Hispanic .164 .133* .185 .188
Received public assistance .454 .277** .588 .581
Federal poverty level2 228 286** 189 183
Does not practice religion .179 .149* .202 .201
Metropolitan residence .301 .260 .305 .352 Source: NSFG 2006- 2010. Exhibit 2 shows descriptive statistics for 3,086 pregnancies that resulted in live birth and
were completed by women who worked during pregnancy. Statistics are weighted. T-tests were conducted to compare
the means for sample members taking paid leave to all other sample members (including women taking unpaid leave
and women taking no leave). **p<0.01, * p<0.05 1Respondents were asked “On a scale of 1 to 10, how happy were you about your most recent pregnancy?”. Those
who responded 6 or above were categorized as being happy about their pregnancy. 2 Federal poverty level is based on the 2010 limit (i.e., $10,830 per year with an additional $3,740 annual allowance
for each additional household member).
28
Exhibit 3. Percentage of Low Birth Weight Deliveries by Poverty Level and Educational
Attainment
Type of Maternity Leave Taken
N
Paid leave
N = 1,206
Unpaid leave only
N = 765
No leave taken
N = 1,115
Overall
287 6.5% 7.0% 9.6%
By Poverty Status:
Below 100% FPL1 113 6.0% 9.2% 13.8%
At or above 100% FPL1
174 6.6% 6.1% 7.9%
By Educational Attainment:
High school or less 152 5.6% 7.7% 11.8%
More than high school
135 6.9% 6.2% 7.3%
Source: NSFG 2006 – 2010. The sample includes 3,086 pregnancies that resulted in live birth and were completed
by women who worked during pregnancy. Low birth weight is defined as less than 5.5 pounds. 1 FPL is defined as the federal poverty level in 2010 (i.e., $10,830 per year with an additional $3,740 annual
allowance for each additional household member).
29
Exhibit 4. Effect of Paid Leave on Low Birth Weight (Logit Regression)
Coefficients
Model 1 Model 2 Model 3 Model 4
Leave and Employment
Paid Leave -.43
(.21)**
-.32
(.23)
-.29
(.25)
-.09
(.27)
Unpaid Leave -.35
(.26)
-.32
(.26)
-.28
(.26)
.01
(.28)
Employed part-time at time of interview -.07
(.26)
Employed full-time at time of interview -.17
(.22)
Health and Behaviors
Body Mass Index at time of interview -.01
(.01)
Delivered by C-section .86
(.19)
.88
(.21)***
Smoked during pregnancy -.03
(.28)
.13
(.30)
Weeks pregnant when first receiving prenatal
care
.01
(.01)*
.01
(.01)*
Infant sex (male) -.17
(.18)
-.28
(.19)
Number live birth (parity) -.63
(.21)***
-.61
(.21)***
Delivery paid by Medicaid, Medicare or other
government program
.12
(.26)
-.08
(.27)
Gave birth in hospital .71
(.68)
.92
(.84)
Infant birth year1 ˗ ˗
Happy about pregnancy .13
(.19)
.03
(.21)
Other Demographics
Married or cohabitating at time of infant birth .01
(.26)
.13
(.20)
.31
(.24)
Age at conception .01
(.02)
-.01
(.02)**
.004
(.02)
Years of education .01
(.05)
.03
(.04)
-.01
(.05)
Race2
Black .86
(.27)***
.71
(.30)**
.96
(.29)***
Other .43
(.30)
.43
(.30)
.72
(.30)***
30
Source: NSFG 2006- 2010. The sample includes 3,086 pregnancies that resulted in live birth and were completed by
women who worked during pregnancy. Statistics are weighted. Coefficients from logit regression are displayed in the
table. Robust standard errors included in parenthesis. ***p<0.01, ** p<0.05, *p<.10. 1 A set of indicators for birth year were included in Models 3 and 4 but are excluded from the exhibit due to space.
None of the year indicators are statistically significant at the p<0.10 level. 2 The models use white as the baseline for the race indicators.
Exhibit 4 (cont.)
Coefficients
Model 1 Model 2 Model 3 Model 4
Hispanic .06
(.30)
-.12
(.31)
-.18
(.29)
Received public assistance within 1 year of
interview
-.10
(.28)
.05
(.28)
.06
(.31)
Federal Poverty Level (%) -.0009
(.001)
-.001
(.001)*
-.001
(.001)*
Does not practice religion -.13
(.25)
-.10
(.24)
-.02
(.26)
Metropolitan residence -.10
(.22)
-.10
(.22)
-.12
(.22).
Constant -2.24
(.16)***
-2.57
(.74)***
-3.78
(1.30)***
-3.58
(1.42)***
Pseudo R2 .005 .03 .07 .09
Number of observations 3,086 3,086 3,083 2,815
31
Exhibit 5. Effect of Paid Leave on Low Birth Weight by Poverty Level and Educational Attainment (Logit
Regression)
Coefficients
Poverty Level Educational Attainment
Below
FPL
At or
above
FPL
Less than
HS
HS
Graduate
but no
college
College
or above
Leave and Employment
Paid Leave -.74
(.37)**
-.13
(.35)
-3.53
(.89)***
.04
(.27)
-.17
(.58)
Unpaid Leave -.26
(.37)
-.12
(.38)
-.26
(.55)
-.30
(.30)
-.29
(.65)
Employed part-time at time of interview -.34
(.38)
.03
(.32)
-.34
(.79)
-.34
(.37)
.52
(.45)
Health and Behaviors
C-section .48
(.34)
1.03
(.25)***
1.03
(.57)*
.91
(.25)***
1.08
(.42)***
Smoked during pregnancy .65
(.40)
-.85
(.46)*
.63
(.54)
-.25
(.38)
N/A1
Weeks pregnant when first receiving prenatal
care
.01
(.01)**
.01
(.02)
.02
(.01)***
-.01
(.02)
-.20
(.07)***
Infant sex (male) -.37
(.28)
-.05
(.23)
-1.26
(.52)**
-.10
(.23)
.28
(.42)
Number live birth (parity) -.26
(.12)**
-.55
(.15)***
-.65
(.17)***
-.27
(.13)**
-.81
(.27)***
Delivery paid by Medicaid, Medicare or other
government program
-.18
(.31)
.39
(.34)
-.99
(.65)
.39
(.30)
.71
(.75)
Gave birth in hospital .19
(.93)
1.3
(.1.08)
-1.13
(1.39)
.87
(1.13)
N/A1
Infant birth year2
˗ ˗ ˗ ˗ ˗
Happy about pregnancy -.35
(.28)
.11
(.25)
-1.08
(.47)**
.04
(.27)
1.0
(.51)*
Other Demographics
Married or cohabitating at time of infant birth .48
(.30)
-.15
(.37)
-.43
(.47)
.54
(.29)*
.42
(.77)
Age at conception .001
(.03)
.04
(.03)
.18
(.04)***
.00
(.03)
.01
(.05)
Years of education -.06
(.10)
.03
(.06)
.48
(.30)
.14
(.13)
.37
(.15)**
Race
Black3 .90
(.35)**
.56
(.39)
1.08
(.57)*
.82
(.32)**
.05
(.58)
32
Exhibit 5 (cont.)
Coefficients
Poverty Level Educational Attainment
Below
FPL
At or
above
FPL Less than
HS
HS
Graduate
but no
college College
or above Other .44
(.61)
.56
(.36)
.46
(.74)
.80
(.39)**
-.08
(.86)
Hispanic .23
(.52)
-.40
(.35)
-.65
(.64)
-.04
(.38)
-.83
(.96)
Received public assistance .56
(.46)
-.36
(.34)
-1.37
(.73)*
.02
(.30)
-.54
(.93)
Federal Poverty Level (%) -.010
(.010)*
-.002
(.001)
-.012
(.004)***
-.002
(.001)
-.003
(.677)
Does not practice religion -.71
(.42)**
-.08
(.31)
-.20
(.51)
.07
(.33)
-1.02
(.68)
Lives in urban area at time of interview -.18
(.32)
-.0005
(.28)
.54
(.49)
-.05
(.28)
-.41
(.46)
Constant -2.41
(2.12)
-4.62
(1.85)**
-6.90
(4.17)*
-5.27
(2.42)**
-7.72
(3.81)**
Pseudo R2 .11 .10 .42 .09 .25
Number of observations 917 2,166 553 1,771 705 Source: NSFG 2006- 2010. The sample includes 3,086 pregnancies that resulted in live birth and were completed by women who
worked during pregnancy. Statistics are weighted. Coefficients from logit regression are displayed in the table. Robust standard
errors included in parenthesis. ***p<0.01, ** p<0.05, *p<.10. 1 Variables were automatically dropped from the model due to perfect collinearity. 2 A set of indicators for birth year were included in Models 3 and 4 but are excluded from the exhibit due to space. None of the year
indicators are statistically significant at the p<0.10 level. 3 White is used as the baseline for the race indicators.
33
Exhibit 6. Distribution of Propensity Scores - Test of Common Support
05
01
00
150
200
Num
ber
of P
regn
ancie
s
0 .2 .4 .6 .8Propensity Score
Distribution of Propensity Scores for Control (No Paid Leave)0
20
40
60
80
100
Num
ber
of P
regn
ancie
s
0 .2 .4 .6 .8 1Propensity Score
Distribution of Propensity Scores for Treament (Paid Leave)
34
Table 7. Descriptive Statistics (After Propensity Score Matching) – Test of Balance
Variable Description
Mean
Treatment
(Paid Leave)
Control
(Unpaid/ No Leave)
Leave and Employment
Employment at time of interview
Full timeᵟ .592 .595
Part timeᵟ .161 .159
Health and Behaviors
Body Mass Index at time of interviewᵟ 29.6 29.8
C-section .336 .335
Smoked during pregnancyᵟ .061 .067
Weeks pregnant when first receiving
prenatal care
8.41 8.28
Infant sex (male) .531 .567***
Number pregnancyᵟ 1.84 1.90
Delivery paid by Medicaid, Medicare or
other government program
.236 .234
Gave birth in hospital .980 .986
Infant birth yearᵟ - -
Happy about pregnancyᵟ .510 .500
Other Demographics
Married or cohabitating at time of infant
birth
.842 .845
Age at conceptionᵟ 28.7 28.9
Years of educationᵟ 14.2 14.2
Raceᵟ
White .662 .678
Black .223 .202
Other .115 .120
Hispanicᵟ .202 .180*
Received public assistance ᵟ .382 .379
Federal Poverty Levelᵟ 253 251
Does not practice religionᵟ .168 .148*
Metropolitan residenceᵟ .364 .335** Source: NSFG 2006- 2010. The sample includes 3,086 pregnancies that resulted in live birth and were completed by
women who worked during pregnancy. Exhibit shows the sample after propensity score matching with 2 nearest
neighbors using replacement on employment status (full time, part time), demographic variables, and other pre-
treatment variables including BMI, smoking status, infant birth year, and whether the respondent reported being
happy about the pregnancy. T-test were conducted to compare the means for the treatment group (i.e., women taking
paid leave) to the control group (i.e., all other women in the sample).
****p<.05, ***p<0.10,** p<0.15, *p<0.20 ᵟVariable was used to calculate propensity scores.
35
Exhibit 8. Average Treatment Effect on the Treated (ATT) (Propensity Score Matching)
Coefficient
Standard
Error
Odds
Ratio
Paid Leave (ATT)
-.23 .15
(p = .13)
0.79
Employment
Employed Part- time -.17 .26
Employed Full-Time .01 .19
Health and Behaviors
BMI -.01 .01
C-section .1.01*** .16
Smoked during pregnancy -.40 .34
Weeks pregnant when first receiving prenatal care .002 .01
Infant sex (male) -.29 .15
Number of live births total at time of interview -.41* .34
Birth order -.12 .29
Delivery paid by Medicaid, Medicare or other
government program
.30 .21
Gave birth in hospital .73 .85
Infant birth year1 - -
Feelings about pregnancy ..26
.18
Other Demographics
Married or cohabitating at time of infant birth -.19 .22
Age at conception -.003 .02
Years of education .04 .04
Race
Black2 .57*** .21
Other .15 .26
Hispanic -.33 .23
Received public assistance .29 .21
Federal Poverty Level (%) -.001* .001
Does not practice religion -.30 .24
Lives in urban area at time of interview -.45** .17
Constant -2.87* 1.62
Source: NSFG 2006- 2010. Model uses propensity score matching with 2 nearest neighbors using replacement.
Variables used to calculate propensity scores are displayed in Exhibit 7. Results represent the effect of taking paid
leave on the treated (ATT). ATT is significant at p = .13.
***p<0.01, ** p<0.05, *p<.10 1 A set of indicators for birth year were included in the model but are excluded from the exhibit due to space. None
of the year indicators are statistically significant at the p<0.10 level.
2The model uses white as the baseline for the race indicators.
36
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