##  1 Introduction

Since Becker’s seminal work in the 1960’s, economists and sociologists have
been intensely studying the conflict between family and labor market
obligations (Becker and Becker,, 2009). In more recent years, researchers have
turned their attention to the effectiveness of so-called “family-friendly”
workplaces and legislation in reducing this conflict. Olivetti and Petrongolo,
(2017) suggest that policies making it easier to be a working mother may be
more effective than policies extending parental leave rights in improving
female labor market outcomes. While the United States lags far behind other
developed nations in terms of parental leave and part-time work entitlement
legislation (Blau and Kahn,, 2013; Olivetti and Petrongolo,, 2016), there are
other ways in which current policy has potentially impacted the family-
friendliness of workplaces. One such policy is state legislation requiring a
workplace to provide breastfeeding111In this paper, I use “workplace
breastfeeding” to mean any act of expressing breast milk while at the place of
employment, whether that be physical breastfeeding of the child on the
premises or pumping of milk for storage and later use. amenities. This paper
therefore mainly studies the effect of legislation requiring workplace
breastfeeding amenities on female labor force participation - the requirement
of such amenities represents an exogenous shock which increases the family-
friendliness of a firm.

The acts of working and of breastfeeding are in direct competition for a
mother’s time (Roe et al.,, 1999). For women who report breastfeeding in the
CDC’s Infant Feeding Practices Survey II, the mean number of hours per week
spent breastfeeding is 12.01 (standard deviation 8.42), and 16.45 (8.98) hours
per week in the first 3 months after childbirth. There is therefore an
opportunity cost of breastfeeding, measurable not only in terms of reduced
income from work absences, but in reduction in the rate of acquiring human
capital. The female worker’s decision process also impacts her employer, who
loses productivity when she postpones returning to work in order to extend
breastfeeding durations. The impact of maternal employment on breastfeeding
initiation and duration has been examined at length in the medical literature
(Kurinij et al.,, 1989; Fein and Roe,, 1998; Abdulwadud and Snow,, 2007).
Women who return to full-time employment three months after the birth of a
child breastfeed for a shorter durations (Lubold,, 2016), and returning to
work less than three months after the birth of a child is negatively
associated with both breastfeeding duration and initiation (Chatterji and
Frick,, 2005). The converse of these findings, i.e. the impact of
breastfeeding on labor market outcomes for women, is less frequently studied.

Presumably, prior work has been undertaken with the policy goal of increasing
breastfeeding rates and durations, not in order to understand how recent
increases in breastfeeding rates might be impacting female employment
decisions. This paper therefore attempts to fill this gap in the literature:
as the “motherhood penalty” persists in the labor market (Adda et al.,, 2017),
it is imperative for economists to understand whether workplace breastfeeding
has a positive or negative impact on female labor force participation.
Although modern policies which promote breastfeeding may significantly and
negatively impact female labor supply, it is possible that employer-provided
breastfeeding amenities may lessen the burden on working women, mitigating
these effects. Mandal et al., (2014) suggest that “supportive workplaces” are
related to higher breastfeeding intensity, for example. Del Bono and Pronzato,
(2012) find that availability of workplace breastfeeding facilities in the UK
increases the probability of a mother returning to work by 4 and 6 months
after birth, in addition to increasing the probability of breastfeeding in
highly educated mothers. Hatsor and Shurtz, (2019) exploit a safety recall of
baby formula in Israel as a natural experiment to show that breastfeeding
reduces the number of months in which a new mother works. This paper
contributes to the above literature by examining how workplace breastfeeding
policies impact female labor force participation in the United States.

Using the Panel Survey of Income Dynamics (PSID), the Childhood and Adoption
Supplement to the PSID, and the Infant Feeding Practices Survey II, I estimate
the impact of passage of workplace breastfeeding legislation on labor force
participation. In both a traditional event study and an interaction-weighted
(Sun and Abraham,, 2020) framework, I find a significant and positive impact
of the passing of the legislation on labor force participation of women. In
the two year period after the law goes into effect, labor force participation
for women in the PSID increases by 4.2 percentage points. The effect is even
greater for women who have indicated that they breastfed at some point during
the PSID sample period. Recalling that the treatment, implementation of a
workplace breastfeeding law, is exogenous ex-ante to an individual woman’s
decision to breastfeed (i.e., the state legislation being passed at a
particular time is not driven by the individual’s breastfeeding decision),
these results suggest that the law may causally induce women to return to work
and also to prolong breastfeeding durations. Because the PSID does not collect
information on breastfeeding practices until 2017, I then study the Survey of
Infant Feeding Practices II from the CDC, in which mothers report detailed
information about their work and breastfeeding decisions in the year after
giving birth. This allows me to explore the effects of the law on the group of
women who do breastfeed. In this (non-causal) setting, I find that the
workplace breastfeeding laws may reduce the negative impact of breastfeeding
on returning to work. For women who eventually do return to work, the
breastfeeding decision has no significant impact on the likelihood to work in
any given three month period after giving birth – after the workplace law is
taken into account. As family-friendly workplace policies may carry an
implicit wage cost (Heywood et al.,, 2007), I also examine the effect of the
workplace breastfeeding laws on female wages and labor income - and find no
effect.

Because the decision to work and to breastfeed are endogenous to each other,
most papers analyzing the effect of one on the other make use of simultaneous
equation or bivariate probit models. This paper, however, is examining the
impact of workplace policies (via state legislation), not the impact of the
decision to breastfeed itself. The staggered timing of state-by-state
implementation of the laws should alleviate endogeneity concerns conditional
on controlling for possible confounding variables and other state
policies.222I describe other such state policies and report their timings in
Section 2. Event studies making use of such staggered treatment timing are
commonly estimated using two-way fixed effect (FE) models which add leads and
lags l to the specifications. To address recently discovered shortcomings of
traditional event study models (Goodman-Bacon,, 2018), I implement the
Interaction-Weighted (IW) technique of Sun and Abraham, (2020) alongside two-
way fixed effects analysis for all specifications. In both scenarios, there is
a positive and significant, albeit temporary, effect of the legislation.

Conceptually, workplace breastfeeding legislation reduces the cost of
breastfeeding. Mothers may be induced to breastfeed, thinking they will
continue easily after returning to work - and only learning about the cost of
breastfeeding when they actually do it (see, for example, Kuziemko et al.,
(2018)). This paper shows that the effect on female labor force participation
is indeed positive but transitory. There is possible differential selection
into breastfeeding based on the policy change. Importantly, this does not mean
that the findings of the paper do not demonstrate a combined causal impact of
the policies on both breastfeeding and labor force participation, especially
since the policy appears to impact the entire female labor force. The
temporary effect of the policies may be due to incomplete alleviation of
birth-related career interruptions. The realized personal costs of workplace
breastfeeding may be greater than expected, or outweighed by other birth-
related career impacts. Nonetheless, this paper is one of the first to examine
the effects of breastfeeding legislation on labor market outcomes, and
contributes to the literature on family-friendly policies, the child penalty,
and the gender gap (Bertrand et al.,, 2010; Blau and Kahn,, 2017; Kleven et
al.,, 2019).

##  2 Background

Before moving to the empirical analysis, here I briefly outline the evolution
of breastfeeding practices and workplace policies in the United States. In
section 2.2, I describe the legislation and other maternal workplace policies,
and show that the presence of these other policies is not correlated with
breastfeeding legislation or other state characteristics which may also impact
labor force participation.

###  2.1 Breastfeeding in the United States

Breastfeeding rates have risen steadily in the United States during the past
decades, following recommendation by the WHO that infants be exclusively
breastfed until 6 months of age. Figure 1 shows that the rate of any
breastfeeding at 6 months of age has risen steadily since 2000. In conjunction
with this recommendation, the WHO and UNICEF launched the “Baby-Friendly
Hospital Initiative” in 1991,333This initiative recommends hospitals provide
breastfeeding training and support, and removes availability of free infant
formula to new mothers. which has been found to have a slight positive impact
on maternal and infant health outcomes in the UK (Fallon et al.,, 2019). By
2012, Baby-Friendly hospitals accounted for 7% of births in the US (Pérez-
Escamilla et al.,, 2016). It is unclear in the literature whether state
workplace breastfeeding legislation arose as a result of WHO recommendations
or Baby-Friendly hospital initiatives, and lack of data unfortunately
precludes analysis of any effect of these initiatives on the state
legislatures.444See Murtagh and Moulton, (2011) In the next sections, I
discuss the legislation and other possible confounding effects related to the
timing of its implementation by state.

Figure 1:

Data on female labor force participation taken from the BLS. Data on
breastfeeding rates are from the CDC National Immunization Survey, which
reports breastfeeding by year of infant birth. Any Breastfeeding indicates
whether a child ever was fed breast milk. Breastfeeding at 6 months indicates
whether a child was fed breast milk by the age of 6 months, both exclusively
and in combination with other food sources.

###  2.2 Workplace Breastfeeding Legislation and Other Maternal Policies

Individual states began introducing legal statutes requiring or regulating
various workplace breastfeeding amenities in 1998 (Table 1). As of this
writing, there are 23 states with such statutes.555I provide a list of
statutes used in this paper in the Appendix. The requirements range from
requiring provision of break time for breastfeeding and pumping and requiring
access to facilities and locations for such purposes, to requiring employees
to be allowed to use their break times to express milk, to protecting female
employees from job termination for engaging in lactation activities during
breaks. Qualitative research has found that the majority of breastfeeding
working mothers do not engage in physical breastfeeding during work hours,
instead primarily or always pumping milk at work (Felice et al.,, 2017) \-
however, the legislation often does not distinguish between the two practices.
In some states, break time is explicitly provided only for pumping.

Figure 2 geographically depicts the presence of breastfeeding legislation by
US state. It is clear these laws are not clustered in one area, and they are
present in different political and cultural climates (California and Arkansas
being arguably politically dissimilar, for example).

Figure 2: Breastfeeding Legislation States

There may be some concern that workplace breastfeeding legislation was enacted
in tandem with other policies which could influence a mother’s labor force
participation decisions. However, as shown in Table 1, there are only three
states which provide paid maternity leave, 15 states with job protection for
mothers taking leave after childbirth (whether paid or unpaid), and the
timings of these policy implementations never coincide.666Information on other
policies taken from Gault et al., (2014). Table 2 reports correlations between
the overall presence of these policies, maternal labor force participation,
and average childcare costs in 2013. Importantly, there is no evidence of a
relationship between maternal labor force participation and the presence of
workplace breastfeeding policies. Although there is mild correlation between
the presence of the breastfeeding legislation and job protection following
family leave, Table 1 indicates that the timing of these laws is unrelated.
There is also no discernible pattern: some states enacted job protection
before breastfeeding legislation, and some afterwards. Therefore, from an
identification perspective, the timing of paid leave or job protection laws
should not interfere with any short-term impacts of the breastfeeding
legislation.

Table 1: Maternity Policies by State

State Workplace Breastfeeding Paid Leave Job Protection Maternal LFP Childcare
Costs Alabama No - No - No - 70.1% $5,547 Alaska No - No - No - 68.3% $10,280
Arizona No - No - No - 59.8% $9,166 Arkansas Yes 2009 No - No - 62.9% $5,933
California Yes 2001 Yes 2002 Yes 1993 62.1% $11,628 Colorado Yes 2008 No - No
- 65.1% $13,143 Connecticut Yes 2001 No - Yes 2013 72.2% $13,241 Delaware No -
No - No - 70.8% $9,058 District of Columbia Yes 2007 No - Yes 2013 67.5%
$21,948 Florida No - No - No - 67.7% $8,376 Georgia Yes 1999 No - No - 68.5%
$7,025 Hawaii Yes 2013 No - Yes 1994 65.9% $11,748 Idaho No - No - No - 58.4%
$6,483 Illinois Yes 2001 No - No - 69.8% $12,568 Indiana Weak 2008 No - No -
67.6% $8,281 Iowa No - No - No - 77.0% $9,185 Kansas No - No - No - 69.9%
$10,787 Kentucky No - No - No - 66.1% $6,194 Louisiana Weak 2013 No - No -
68.6% $5,655 Maine Yes 2009 No - Yes 2012 75.3% $9,360 Maryland No - No - No -
72.5% $13,897 Massachusetts No - No - Yes 2009 72.5% $16,549 Michigan No - No
- No - 68.4% $9,724 Minnesota Yes 1998 No - Yes 2014 74.8% $13,993 Mississippi
No - No - No - 73.4% $5,496 Missouri No - No - No - 70.4% $8,736 Montana Yes
2007 No - Yes 1995 69.8% $8,858 Nebraska No - No - No - 72.9% $9,100 Nevada No
- No - No - 65.9% $10,095 New Hampshire No - No - Yes 1992 71.1% $11,901 New
Jersey Yes 2018 Yes 2009 Yes 1989 69.3% $11,534 New Mexico Yes 2007 No - No -
62.2% $7,523 New York Yes 2007 $No^{*}$ - No - 67.5% $14,508 North Carolina No
- No - No - 70.8% $9,107 North Dakota No - No - No - 71.3% $7,871 Ohio No - No
- No - 70.6% $7,771 Oklahoma Yes 2006 No - No - 63.9% $7,741 Oregon Yes 2007
No - Yes 2012 66.0% $11,078 Pennsylvania No - No - No 70.5% $10,470 Rhode
Island Yes 2003 Yes 2013 Yes 1987 71.7% $12,662 South Carolina No - No - No -
68.7% $6,372 South Dakota No - No - No - 80.3% $5,571 Tennessee Yes 1999 No -
Yes 2005 67.7% $5,857 Texas No - No - No - 61.9% $8,619 Utah No - No - No -
52.8% $8,052 Vermont Yes 2008 No - Yes 2013 69.4% $10,103 Virginia Yes 2014 No
- No - 69.2% $10,028 Washington No - No - Yes 2010 63.0% $12,332 West Virginia
No - No - No - 61.3% $7,800 Wisconsin No - No - Yes 2011 76.6% $11,342 Wyoming
No - No - No - 65.3% $9,233 a This table reports implementation of workplace
breastfeeding legislation as well as paid maternal leave policy and leave job
protection. b Paid Leave refers to legislation mandating partial or fully paid
leave following the birth of a child. Job Protection legislation mandates the
ability to return to work after family or parental leave is taken, whether
paid or unpaid. Data on legislation taken from Gault et al (2014). c Maternal
LFP is percentage of mothers in the labor force in 2013. Childcare Costs is
the average cost of childcare in 2013. Both are taken from the IWPR’s analysis
of the American Community Survey Public Use microdata. * New York will enact
paid family leave in 2021.

Table 2: Maternity Policy Correlations in 2013

Breastfeeding Law Paid Leaveb Job Protection Maternal LFP Childcare Costs
Breastfeeding Law 1.00 Paid leave 0.29∗ 1.00 Job Protection 0.43∗∗ 0.37∗∗ 1.00
Maternal LFP -0.05 -0.03 0.18 1.00 Childcare Costs 0.24 0.17 0.51∗∗∗ 0.13 1.00
∗ $p<0.05$, ∗∗ $p<0.01$, ∗∗∗ $p<0.001$ a This table reports correlations
between the presence of different maternity policies and state characteristics
in 2013. b Information on maternal policies, labor force participation, and
childcare costs taken from the IWPR.

##  3 Data

For the main analysis, I use the Panel Survey of Income Dynamics from
1997-2017. The survey has been conducted biennially since 1997. I first wish
to examine the effect of breastfeeding legislation at the household level,
collapsing individual observations into household units with separated
variables by gender. I remove any households who have relocated states or who
have experienced a change in family composition that results in the
predominant female household member (head or spouse) leaving or being
replaced. Removing households who have relocated addresses endogeneity
concerns - if women are relocating states in order to gain access to workplace
breastfeeding amenities after passage of the law, any results may be
uninterpretable. Therefore, removing these women should address this potential
issue. I exclude households without adult women present, as well as households
who have not had a child younger than age 2 present during some point during
the entire sample period. All monetary variables are reported in nominal terms
and therefore I adjust them using the CPI for 1982, obtained from the Bureau
of Labor Statistics. As the variable for labor force participation is coded in
an extremely ambiguous manner,777The same number is used to indicate whether
someone is currently working or whether they have never worked I create a
dummy variable for either gender indicating labor force participation if wages
or labor income are reported to be $>0$ in any two-year wave. After ensuring I
have a panel mostly balanced in calendar time (see section 3), the final
sample contains 1,511 households and 16,413 observations.888NOTE: results are
robust to relaxing the restriction of being balanced in calendar time. With
the resulting larger sample, the effect is still significant and the magnitude
even greater (See Appendix Table A4).

I supplement the main dataset with the Childhood and Adoption History
Supplement from the 2017 PSID (CAH). This cross-sectional supplement
indicates, for each mother and child pair, whether breastfeeding was
initiated. I merge these indicators with the main PSID sample by individual in
order to analyze the effect of the workplace laws only on women who have
indicated that they breastfed a child at some point within the sample period.
This allows for interpretation of effect of the law on an “intention to
breastfeed” sample. Not every individual mother in the entire PSID is included
in the CAH supplement, and the sample size is significantly reduced when
restricting the analysis to this group of households. Nevertheless, results
are robust to this restriction.

In addition to the PSID and CAH, I analyze the effect of workplace
breastfeeding legislation on mothers who participated in the Infant Feeding
Practices Survey II (IFPS), which was conducted by the Food and Drug
Administration (FDA) and Centers for Disease Control and Prevention (CDC) in
2005–2007. The IFPS was a longitudinal study following mother-infant pairs
from the third trimester of pregnancy through the first year of the child’s
life. Questions on the survey include detailed information about duration and
intensity of breastfeeding, work hours and employment status, as well as a
number of questions regarding ease of workplace breastfeeding and pumping. The
CDC clearly notes that the IFPS is not taken from a representative sample of
the population, which I confirm by comparing characteristics of interest for
the IFPS and PSID samples used in this paper. Another drawback of the IFPS
data is the fact that individual labor income is not reported, either in
aggregate or by period, and only categorical information about household-level
income is available.

As in previous literature examining post-birth labor market activity, the IFPS
II sample is restricted to women who worked before giving birth in order to
condition on pre-birth characteristics (Mandal et al.,, 2014). As problems
with breastfeeding are a significant negative determinant of both work
initiation and intensity (Mandal et al.,, 2014), I include this response as a
control variable in all specifications (it should be noted, however, that 88%
of women in the survey report experiencing problems with breastfeeding during
the first two weeks after giving birth). Because maternity leave
characteristics have been found to influence both work and breastfeeding
decisions (Guendelman et al.,, 2009; Baker and Milligan,, 2008), I also
control for the mother’s length of maternity leave and for whether the leave
is fully paid, partially paid, or non-paid.

###  3.1 Descriptive Statistics

Table 1 shows comparison of descriptive statistics for each dataset, using the
PSID wave from 2005 merged with the 2017 CAH supplement to identify households
in which a woman ever breastfed. Women in the IFPS II survey are much less
likely to work than women in the PSID (52.34% versus 78.79%), are younger
(29.14 versus 41.44), wealthier (average household income of $51,475 versus
36,228), more likely to be married (76.63% versus 66.02%), and are
predominantly white (85.45% versus 55.87%). In the PSID sample, women who ever
breastfed have higher rates of labor force participation (80.21%) and work
more hours (28.27) than those who do not (78.52% and 27.08). In contrast,
women who breastfeed in the IFPS II sample work fewer hours (10.25 versus
12.63) and have no differences in their rates of labor force participation.
Women who report ever breastfeeding have fewer years of education than those
who breastfed in the PSID, which contrasts with the fact that breastfeeding
rates are increasing in education (Dubois and Girard,, 2003). This is likely
due to the increased age of women in the PSID sample.

PSID IFPS II Ever Breastfedb Never Breastfed All Ever Breastfed Never
Breastfed All Household Incomec ($) 31,826 37,084 36,228 52,267 46,455 51,475
(30,225) (57,831) (54,337) (27,149) (28,100) (27,346) Labor Incomed ($) 11,074
11,814 11,693 - - - (13,066) (13,528) (13,454) Contribution to HH Income - - -
28.25% 28.33% 28.26% (26.56%) (26.19%) (26.50%) Labor Force Participation
80.21% 78.52% 78.79% 52.34% 52.32% 52.34% (39.88%) (41.07%) (40.88%) (49.96%)
(50.02%) (49.96%) Work Hours, weeklye 28.27 27.08 27.27 10.25 12.63 10.58
(18.57) (18.53) (18.53) (15.15) (16.88) (15.41) Age 41.03 41.52 41.44 29.15
29.11 29.14 (10.48) (11.04) -10.94 (5.43) (5.45) (5.43) Married 63.25% 66.55%
66.02% 77.69% 69.97% 76.63% (48.25%) (47.19%) (47.24%) (41.65%) (45.91%)
(42.32%) White 54.24% 56.19% 55.87% 84.81% 89.47% 85.45% (49.86%) (49.62%)
(49.66%) (35.90%) (30.74%) (35.27%) Black 38.34% 37.84% 37.92% 4.05% 4.95%
4.17% (48.66%) (48.51%) (48.52) (19.72%) (21.73%) (20.01%) Years of Education
12.54 12.98 12.9 - - - (2.48) (2.47) (2.48) Some College 78.17% 59.44% 75.62%
(41.32%) (49.18%) (42.95%) N 566 2,915 3,481 2,048 323 2,371 a This table
reports mean values for summary statistics and standard errors for the main
datasets used in the paper. The 2017 Childhood and Adoption Supplement was
merged with the PSID by person ID and collapsed to household units identified
in both samples containing at least one adult woman. The resulting PSID sample
wave in 2005 is then compared to the first wave of the IFPSII 2005 sample. b
“Ever Breastfed” are those who report breastfeeding in the 2017 CAH supplement
to the PSID. For the IFPSII, they are women who ever breastfed during the
12-month post-partum period (in 2005). c Household income is reported in non-
uniform buckets in the IFPSII. Median values were imputed and used to
calculate the statistics. d Individual labor income is not reported in the
IFPSII. Rather, female income is reported as a percentage of contribution to
total household income. Likewise, number of years of education is not reported
in the IFPSII. e Work hours are reported on an annual basis in the PSID, this
is divided by 52 weeks to obtain a weekly average.

Table 3: Summary Statistics, by Breastfeeding Status

Table A2 (Appendix) reports descriptive statistics by PSID wave. Female labor
force participation drops over the course of the sample period as women age
out of the workforce. Labor income and wages rise, as those who remain
employed are likely to earn more over time. Importantly, there is no major
difference in the treated group before law implementation and all other
control households.

###  3.2 Workplace Legislation

In three states, the statues only apply to public employees, and in two states
only to public school employees. Unlike Singh, (2018) and Hauck et al.,
(2020), I do not include these states in my analysis, since such limited
provisions do not impact the entire labor market. I also exclude states in
which the law only “encourages” firms to provide lactation support.999New
Jersey enacted legislation in 2018, and I exclude this state due to the PSID
sample ending the year prior. By restricting my analysis to states in which
workplace breastfeeding legislation requires provision of amenities or break-
time for all workers, results can be robustly interpreted. For analysis in
this paper I therefore include 18 states which introduced their own
legislation sometime between 1995 and 2013. As the PSID is conducted
biennially, I count a wave as having been treated by the law if the law passed
in the year of the survey or the year prior. Although this results in a mild
reduction of specificity, it helps account for the fact that employers may
take time to implement changes required by the law during the months after it
is passed (Hauck et al.,, 2020).

##  4 Methodology

In this section I describe the various empirical models used in the paper,
including the Interaction-Weighted estimation technique. The main results are
estimated as in 3.1 and 3.2. Section 3.3 describes difference-in-differences
specifications that are not in an event-study framework, which I use for the
IFPS II sample and some restricted PSID samples (due to their small sizes).

In an event study framework, regression coefficients are interpreted as some
average treatment effect for all cohorts that have experienced l periods of
treatment. There is a growing literature examining the validity of these
difference-in-difference estimates (of which the event study framework is a
type) (Goodman-Bacon,, 2018; Athey and Imbens,, 2018; De Chaisemartin and
d’Haultfoeuille,, 2020; Callaway et al.,, 2018; Imai and Kim,, 2020). Sun and
Abraham, (2020), among others, show that these weighted averages are in
actuality not causally interpretable, since they do not necessarily identify
convex averages of cohort specific average treatment effects. This is due to
estimate weights which do not coincide with actual sample
frequencies.101010Borusyak and Jaravel, (2017) further discuss the
consequences of weighting problems in the estimation of average treatment
effects in event studies Long-run treatment effects are potentially
downweighted for cohorts with early treatment onset.

There are many new proposed methods to address this weighting issue Goodman-
Bacon, (2018). In this paper, I use the correction proposed by Sun and
Abraham, (2020) \- importantly, this method is designed specifically with
time-varying treatment effects in mind, for a staggered treatment timing
setting. In contrast, the De Chaisemartin and d’Haultfoeuille, (2020) method
is more generally applied to two-way fixed effects settings without being
specific to staggered treatment timing,111111This method also requires a
2-period stability in treatment and the Callaway et al., (2018) method does
not allow for time-varying controls, which are arguably important in this
setting. My findings (and findings from a traditional two-way FE model)
indicate that effects evolve over time and that the Sun and Abraham, (2020)
approach is most appropriate.

When a control group is present in an event study model, unit fixed effects do
not cause an identification problem (via collinearity) because they “pin down”
the year fixed effects (Borusyak and Jaravel,, 2017).121212Without a control
group, the effects of the passing of relative and absolute time cannot be
disentangled. Therefore, in all specifications, I keep observations in never-
treated states as a control group. As in Sun and Abraham, (2020), I remove the
relative period l = -1 from the specification to avoid multicollinearity among
the relative period indicators $D^{\ell}_{i,t}$. Rather than trim the sample
to contain observations within the estimation window, I instead require the
sample to be balanced in calendar time for at least ten waves. Trimming the
sample would change the composition of the treated cohorts (Sun and Abraham,,
2020), which may be particularly undesirable in this setting as there are a
large number of control observations.

###  4.1 Two-way Fixed Effects Estimation

I first estimate a traditional two-way fixed effects “event study” regression

$Y_{i,t}=\alpha_{i}+\lambda_{t}+\sum_{g\in{G}}\mu_{g}\mathbb{1}\\{t-E_{i}\in
g\\}+\epsilon_{i,t}$ (1)

on a panel of $i=1,...,N$ units for calendar time periods $t=0,1,...T$.
$E_{i}$ is the initial time of treatment for unit $i$, $Y_{i,t}$ is the
outcome of interest for unit $i$ at time $t$, and $\alpha_{i}$ and
$\lambda_{t}$ are the unit and time fixed effects.

###  4.2 Interaction-Weighted Estimation

The interaction-weighted estimation process is described below. For further
details and proofs, I refer the reader to Sun and Abraham, (2020). Step one of
the interaction-weighted estimation process is to use a two-way (time and
individual) fixed effects estimation which interacts cohort indicators
$\mathbb{1}\\{E_{i}=e\\}$ with relative period indicators $D^{\ell}_{i,t}$:

$Y_{i,t}=\alpha_{i}+\lambda_{t}+\sum_{e}\sum_{\ell\neq{-1}}\delta_{e,\ell}(\mathbb{1}\\{E_{i}=e\\}\cdot
D^{\ell}_{i,t})+\epsilon_{i,t}$ (2)

Because there are never-treated cohorts, this estimation is performed on all
individuals. Resulting estimates are the $CATT_{e,\ell}$, cohort average
treatment effects for each cohort in relative time period $\ell$ to treatment.

The second step is to estimate weights by sample shares of each cohort in each
period $\ell$, i.e. estimating $\Pr\\{E_{i}=e\mid E_{i}\in{(\ell,T-\ell})\\}$.

Lastly, the estimates from the first and second steps are used to form the IW
estimator:

$\hat{\nu}_{g}=\frac{1}{|g|}\sum_{\ell\in{g}}\sum_{e}\hat{\delta}_{e,\ell}\hat{\Pr}\\{E_{i}=e\mid
E_{i}\in{(\ell,T-\ell})\\}$ (3)

In other words, the estimator is formed by taking a weighted average of the
$CATT_{e,\ell}$ from step 1, $\hat{\delta}_{e,\ell}$, using the weights
estimated in step two, the shares of each cohort in the relevant period.

###  4.3 Difference-in-Differences Regressions

For analysis of the Infant Feeding Practices Survey II data, I estimate a
difference-in-difference regression as in Mandal et al., (2014). Cross-section
OLS is performed on subsets of the data at months 3 and 6 after birth. Mixed
regression (individual random effects and time fixed effects) of the following
form is estimated:

$\begin{split}Y_{i,t}=&\beta_{1}(WorkplaceLaw*Breastfeeds_{i,t})+\beta_{2}WorkplaceLaw+\\\
&\beta_{3}*Breastfeeds_{i,t}+X_{i}+Z_{i,t}+\lambda_{t}+\epsilon_{i,t}\end{split}$
(4)

Where $WorkplaceLaw$ is a dummy indicating residing in a state with a
workplace breastfeeding requirement law, $Breastfeeds$ is a dummy indicator
for individual $i$ breastfeeding her baby in time $t$, $X_{i}$ is a vector of
time-invariant controls, $Z_{i,t}$ is a vector of time-varying controls, and
$\lambda_{t}$ is time fixed-effects. Time fixed-effects are used in order to
control for feeding requirements changes a baby experiences over time. Unit
fixed effects cannot be used since by definition, the treatment effect remains
constant for an individual over time in this sample.

Importantly, the IFPSII is not a representative sample of the population (see
Section 2), and because of potential endogeneity between the treatments, the
decision to breastfeed and the presence of the law, the estimates from
Equation (4) likely cannot not be interpreted causally. Rather, I include this
analysis to explore the mechanisms behind the event study specifications in
3.1 and 3.2.

##  5 Results

###  5.1 Breastfeeding Legislation and Labor Force Participation

The main results are reported in Table 3, which compares two-way fixed effects
(FE) estimates (Equation 1) to the interaction-weighted (IW) estimates
(Equations 2-3). Column $\hat{\mu}$ contains FE results, column $\hat{\nu}$
contains IW results, and columns $\hat{\delta}_{\textit{i,L}}$ report CATT
results for each cohort $i$. In both specifications, there is a significant
increase in the likelihood of female labor force participation. The robust IW
estimates show that the probability of participation increases by 4.2% and
remains slightly elevated for 5-6 years (until $\ell=2$) compared to before
implementation. The effect is not persistent, however, and participation
decreases in years following the law.

Figure 3: Effect of Legislation on Female Labor Force Participation

(a) Panel A

(b) Panel B

This figure depicts results from Table 4 with 90% confidence intervals. Panel
A shows two-way fixed effects estimates and Panel B the interaction-weighted
event study estimates. Time $\ell=-1$ is normalized to zero to account for
multicollinearity between relative and calendar time.

Table 4: Effect of Legislation on Female Labor Force Participation

FEa IW CATT e,l Estimatesb Estimates Estimates Wave l relative to law
$\hat{\mu}$ $\hat{\nu}$ $\hat{\delta}_{\textit{1,L}}$
$\hat{\delta}_{\textit{2,L}}$ $\hat{\delta}_{\textit{3,L}}$
$\hat{\delta}_{\textit{4,L}}$ $\hat{\delta}_{\textit{5,L}}$ -3 -0.022 -0.008 -
- 0.034 -0.013 -0.001 (0.029) (0.030) (0.014) (0.037) (0.053) -2 -0.0588**c
-0.061** - -0.075 0.016 -0.002 -0.103 (0.026) (0.026) (0.035) (0.012) (0.049)
(0.064) -1c 0 0 0 0 0 0 0 0 0.0422** 0.0415** 0.060 0.019 -0.884 0.089 0.039
(0.021) (0.021) (0.040) (0.030) (0.019) (0.055) (0.060) 1 0.029 0.031 -0.011
0.021 0.113 0.113 0.001 (0.024) (0.024) (0.047) (0.032) (0.019) (0.066)
(0.078) 2 0.005 0.019 -0.066 -0.032 0.105 0.193 0.064 (0.024) (0.024) (0.048)
(0.035) (0.019) (0.065) (0.055) 3 -0.010 -0.004 -0.018 -0.040 0.086 0.101
-0.015 (0.024) (0.024) (0.052) (0.033) (0.020) (0.058) (0.084) a This table
reports the results of the Two-Way Fixed Effects (FE) and Interaction-Weighted
(IW) Event Study models (Equations 1 -3). Data is a balanced sample of the
PSID from 1997-2017 where all individuals are present for at least 9
continuous waves. Treatment is the state workplace breastfeeding law going
into effect. 18 states are treated, the rest are controls. Controls include
age, education, childcare costs, age of the youngest child. SEs are clustered
by household. *** p$<$0.01, ** p$<$0.05, * p$<$0.1. b Cohort Average Treatment
effects are reported for each individual cohort. A cohort is defined as all
states in which the law goes into effect during the same two-year period. c
Wave $\ell=-1$ is normalized to zero due to collinearity.

These results also demonstrate the usefulness of the IW framework: by applying
the technique of Sun and Abraham, (2020), I show a difference in coefficients
between a correctly weighted estimator and the traditional event study (FE)
result. Although the effect of the legislation on labor force participation
only remains significant in the two years directly after implementation, the
IW estimates show a larger and more prolonged positive effect than the FE
estimates. This is a clear example of the long-term FE estimates potentially
being downweighted. Without making use of the correctly weighted procedure,
the effect would appear to be more short-lived.

It should be noted that in wave $\ell=-2$ relative to the law, coefficients
are negative and significantly different from zero. This indicates that the
parallel trends assumption necessary for identification may be violated.
Recent work on the validity of pre-trends testing encourages researchers to
carefully apply economic knowledge when evaluating these assumptions in
addition to statistical tests (Roth,, 2019). Pre-trends may pose an issue if
they indicate “anticipation” of treatment, or that the timing of treatment and
outcomes are related. Negative coefficients for pre-treatment periods in my
setting logically rule out treatment anticipation, since, if there were any
anticipation, we would see a positive coefficient of women beginning to
increase labor force participation in advance of the law implementation.
Perhaps more concerning is the possible endogeneity of the law timing -
however, Section 2 demonstrated that there was no relationship between labor
force participation and the presence of the laws. Further examination reveals
that this “pre-trend” effect is driven solely by two states in which workplace
breastfeeding laws were passed in 2001. There is no economic reason that the
parallel trends assumption is violated if only two states are causing this
drop in coefficients.131313It is possible that, since for these two states
(which include California) period $\ell=-2$ was during a rapid period of
growth of the dot-com bubble, the relative labor force participation as
compared to year $\ell=-1$ was significantly lower. Estimates in $\ell=0$ are
robust to removing these states, and I report the alternate coefficients which
do not exhibit significant pre-trends in the Appendix (Table A3) Removing the
two states with pre-trends from the analysis weakens the IW estimate
magnitudes, although the effect of the law is more prolonged. Taken
altogether, the negative direction, limited presence (to two states) and lack
of correlation with other state outcomes provide ample evidence that the
negative pre-treatment coefficient is not a concern for causal identification.

Repeating the analysis after restricting the sample to women who are non-
white, I find even stronger results (Table 4). In the period in which the law
is passed, female labor force participation increases by over 10%. However,
these results for non-white women are not robust to the exclusion of the
states which exhibit significant pre-trends. This suggests that only minority
women in California and Illinois were positively impacted by the passing of
the laws (if one assumes that the negative pre-trends in these states are not
cause for concern). These findings imply that the workplace breastfeeding
legislation has differential impacts by race, and mainly impacts white women.
Since breastfeeding initiation and duration are well known to differ by race
(Kurinij et al.,, 1989), this is not surprising. White women in the PSID
sample are also more likely to be employed in any given period, and report
higher mean labor incomes. The fact that California is one of the few states
offering paid maternity leave and other “female-friendly” workplace policies
indicates that the breastfeeding legislation may only impact minority female
labor force participation in the presence of other supportive and/or
progressive legislation.

Table 5: Effect of Legislation: Non-white Women

FE IW FE IW Estimates Estimates Estimates Estimates (1)a (2) (3) (4) Wave
relative to law $\mu$ $\nu$ $\mu$ $\nu$ -3 0.010 0.039 0.005 -0.006 (0.052)
(0.056) (0.056) (0.057) -2 -0.0971** -0.070* -0.076 -0.109* (0.033) (0.035)
(0.064) (0.065) -1 0 0 0 0 0 0.127**** 0.111**** 0.040 0.031 (0.027) (0.027)
(0.035) (0.036) 1 0.019 0.024 -0.025 -0.025 (0.029) (0.030) (0.043) (0.043) 2
-0.003 0.013 -0.022 -0.007 (0.032) (0.033) (0.048) (0.047) 3 -0.003 -0.002
-0.029 -0.023 (0.032) (0.036) (0.048) (0.050) a This table reports the results
of the Two-Way Fixed Effects (FE) and Interaction-Weighted (IW) Event Study
models (Equations 2-3). Treatment is the state workplace breastfeeding law
going into effect. Columns 1 and 2 restrict the unbalanced sample to all non-
white women in the main (unbalanced) PSID sample (N=8,258). Columns 3 and 4
further remove observations from California and Illinois. Controls include
age, log childcare costs, marital status, age of the youngest child, and
education. *** p$<$0.01, ** p$<$0.05, * p$<$0.1. b Wave $\ell=-1$ is
normalized to zero due to collinearity.

I now report results for the small sample of women who are known to have ever
breastfed (as reported in the CAH supplement to the PSID) in Table 5. Due to
the small sample size, I examine only the two-year period in which the laws
went into effect. Results are consistent with the main estimates. During the
period in which the laws were passed, women who ever breastfed were 5.6% more
likely to work. The effect is not long lasting, as demonstrated in column (4),
which reports the combined effect of all years after implementation of the law
(see prior section for specifications). These results, taken together with the
main sample results, indicate that there were indeed significant, albeit
temporary, impacts of the workplace breastfeeding legislation on female labor
force participation.

Table 6: Labor Force Participation: Women who ever breastfed

(1) (2) (3) (4) Law Enacteda 0.056** 0.056** 0.064* - (0.028) (0.028) (0.039)
Post-Law Enactedb -0.010 (0.024) Married - -0.0167 - - (0.020) - - Male Labor
Income - -0.029**** - (0.012) - ln(Childcare Expenses) .0223**** 0.0223****
0.027**** 0.023**** (0.002) (0.002) (0.003) (0.002) Constant 0.676 0.689 0.783
0.709 (0.073) ’(0.073) (0.129) (0.069) R-squared 0.105 0.106 0.122 0.102 N
(households) 1,354 1,354 955 1,354 * This table reports the simple pre/post
difference-in-difference effect of the breastfeeding law being passed, in a
reduced sample of women who reported ever breastfeeding in the CAH supplement
to the PSID. The outcome variable is labor force participation. Controls are
as in Tables 1-2. a Law Enacted is a dummy variable indicating the two-year
period in which the breastfeeding law went into effect. Data is from 1997-2017
where all individuals are present for at least 9 continuous waves. b Post-Law
Enacted is a dummy variable indicating all years after a law went into effect.
18 states are treated, the rest are controls. c *** p$<$0.01, ** p$<$0.05, *
p$<$0.1.

####  5.1.1 IFPS II Sample: Labor Force Participation

Because there is no information indicating whether mothers in the PSID sample
were breastfeeding during the years of law implementation, I now extend the
analysis using the IFPS survey data. Results are reported in Table 6. The IFPS
II was conducted from 2005-2006, so I include a state as having been treated
by the law if the law went into effect any year prior to 2005. The outcome
variable $WorkplaceLaw*Breastfeeding_{i,t}$ represents a dummy for women $i$
who reported breastfeeding during a 3-month wave $t$ interacted with the law
treatment dummy. There is no significant effect on labor force participation
in the year after giving birth. However, accounting for the law treatment
(column 2) reduces the magnitude of the effect of breastfeeding on labor force
participation by roughly 8%, and show that it is only in states where the
workplace law is not in effect that breastfeeding has an impact on the
decision to work. Columns 3 and 4 demonstrate an even greater reduction of the
effect of breastfeeding after controlling for the laws in a reduced but
balanced sample, where all women returned their survey responses in 3, 6, and
9 months after giving birth.

The most striking result is shown in columns 5 and 6, where the data are
restricted to women who eventually did return to work postpartum during some
period in the survey year (recall that the initial sample was restricted to
mothers who both worked prior to giving birth and who indicated in the pre-
natal period that they were planning to return to work after giving birth).
For these women who did eventually return to work, the effect of the workplace
breastfeeding law is to completely negate the negative effect of breastfeeding
on labor force participation. Although there is evidence of some feedback
between the decision to breastfeed and the decision to work in prior
literature, this result indicates that workplace breastfeeding policies may
help women separate the two decisions.

Table 7: IFPS II: Labor Force Participation

(1) (2) (3) (4) (5) (6) WorkplaceLaw*Breastfeedingb - -0.041 - -0.043 - -0.023
(0.039) (0.047) (0.023) Law - 0.026 - 0.002 - 0.004 (0.032) (0.034) (0.010)
Breastfeeding -0.039*** -0.031* -0.053*** -0.044** -0.020** -0.015 (0.016)
(0.017) (0.019) (0.213) (0.010) (0.011) Constant 0.530 0.524 0.715 0.706 0.696
0.692 (0.129) (0.129) (0.137) (0.137) (0.053) (0.054) Observations 3,724 3,724
2,464 2,464 3,264 3,264 N (individuals) 931 931 616 616 816 816 a Results
reported for the IFLS II sample as in Equation 4. The outcome variable is
returning to work during a three month period post-birth. Columns (1) and (2)
include the entire sample, columns (3) and (4) are a balanced sample in which
all women completed the final waves of the survey, and columns (5) and (6)
restrict the data to women who returned to work postpartum during the sample
period. b Controls in all specifications include education level, age,
household income, occupation, race/ethnicity indicators, how many house spent
breastfeeding per week, whether the respondent indicated ever having problems
breastfeeding, whether the respondent had paid maternity leave, and whether
the mother had ever breastfed previous children. c *** p$<$0.01, ** p$<$0.05,
* p$<$0.1.

###  5.2 Other Labor and Household Effects

Table 7 reports the effects of the legislation on female labor income, annual
hours of work, and weekly reported hours of housework. For this analysis, I
restrict the sample to women who are employed during the entire sample
period.141414Further estimates (i.e. control coefficients) are reported in
Appendix table A2. Columns (1), (3) and (5) report the two-way fixed effects
estimates and columns (2), (4) and (6) report the interaction-weighted
estimates. As is clear from the table and Figure 4, there is little effect of
the law on any of the outcome variables. There is a slight reduction in work
hours (-48.78) during the period in which the law is passed, according to the
IW estimates (column (4)). This is consistent with women leaving the labor
force for a few days or weeks after giving birth. It may also represent the
fact that women who do return to work after giving birth and choose to pump or
breastfeed at work may work fewer hours while at work. The number of hours is
only slightly more than a full-time work week.

Figure 4: Labor Income and Hours Worked

(a) Panel A

(b) Panel B

This figure depicts results from Table 8 with 90% confidence intervals. Panel
A shows interaction-weighted estimates for labor income and Panel B the
interaction-weighted event study estimates for hours worked. Time $\ell=-1$ is
normalized to zero to account for multicollinearity between relative and
calendar time.

Table 8: Breastfeeding Legislation: Other Outcome Variables

Variables: Labor Income Work Hoursa Housework Hours (1) (2)b (3) (4) (5) (6)
Time -3 -0.025 -0.019 -95.776* -98.433* 0.097 -0.025 (0.064) (0.067) (49.798)
(51.553) (1.166) (1.199) -2 0.012 -0.004 -40.767 -32.148 0.207 0.228 (0.058)
(0.062) (39.197) (41.685) (0.855) (0.924) -1 0 0 0 0 0 0 0 0.024 0.038 -44.768
-48.783* -0.474 -0.429 (0.047) (0.046) (29.462) (29.545) (0.687) (0.698) 1
0.033 0.021 -6.626 -10.574 0.338 0.240 (0.051) (0.052) (38.648) (39.563)
(0.850) (0.884) 2 -0.041 -0.045 22.483 23.526 0.684 0.485 (0.056) (0.056)
(42.005) (41.263) (0.789) (0.786) 3 0.040 0.045 -33.754 -30.441 1.915**
2.023** (0.047) (0.047) (35.337) (36.427) (0.840) (0.848) Labor Income -
374.927*** -0.813*** (25.098) (0.290) Constant 7.914*** -1,644.613***
31.826*** (0.432) (495.367) (9.442) Observations 6,876 6,876 6,876 6,876 6,876
6,876 R-squared 0.748 0.670 0.524 * Estimates using the full PSID household
sample and modelled as in equations 1-3. Columns 1-2 are the labor income
outcome variable, columns 3-4 are work hours, and columns 5-6 are housework
hours. Controls include age, education, ln childcare costs, age of the
youngest child, number of children present in the household, and religious
preference of the household head. a Work hours are reported as annually.
Housework hours reported as weekly. b Columns 1, 3 and 5 report two-way fixed
effect estimates (person and year fixed effects). Columns 2, 4, and 6 report
interaction-weighted estimates as in Abraham and Sun 2020. c *** p$<$0.01, **
p$<$0.05, * p$<$0.1.

###  5.3 Robustness Tests

In addition to repeating the results for a full unbalanced panel and removing
California, I also address concerns about my strict legal classification
procedure by re-estimating the main results including the seven states with
“weak” legal protections for workplace breastfeeding. These states have one of
the following provisions: they either only require workplace amenities for
public or school employees, only “suggest” that firms provide such amenities,
or they have legal provisions for a firm to designate itself as “breastfeeding
friendly” in some formal manner. Because these provisions do not apply to the
entire labor force and in many cases would only apply to a small fraction of
workers, ex ante we would assume that these laws have no impact on the total
labor force participation of women. However, to avoid any confusion about the
classification procedure, I repeat the analysis also including these states as
treated in the event study framework.

Results are reported in Appendix Table A5.151515Note that inclusion of extra
states increases the number of $CATT$ cohorts from 5 to 7. There is no longer
a significant impact of the introduction of breastfeeding legislation on
female labor force participation. This non-result would be expected if the
original classification procedure was correct. There should be no effect in
states where the legislation only applies to a small segment of the workforce,
or is not legally enforceable in some manner.

Lastly, to alleviate concerns that there are confounding characteristics which
may both induce a state’s legislature to pass breastfeeding laws and encourage
mothers to work, I also repeat the analysis controlling for the number of
women elected to a state’s legislature each year. Results are consistent with
this specification, indicating that it is not cultural or gender norms within
a state both influencing passage of the laws and the labor force participation
decision.161616Results available from the author by request.

##  6 Conclusion

This paper demonstrates a significant positive impact of workplace
breastfeeding legislation on female labor force participation. The effect,
however, is not persistent. If optimal amount of breastfeeding is chosen so
that marginal benefits equal the marginal costs (in terms of labor income, for
example), perhaps women who return to work after implementation of the laws
experience realized personal costs of workplace pumping and breastfeeding that
are greater than expected. Therefore, in subsequent years, the legislation has
no effect on the optimization decisions of breastfeeding mothers, as true
marginal costs are not reduced.171717Although prior literature expounds the
health benefits of breastfeeding without much exception, there is emerging
evidence casting significant doubt onto the true magnitude of health effects,
and on the validity and methodology of the most frequently cited studies.
Studies making use of sibling comparisons find no positive cognitive effects
of breastfeeding (Rothstein,, 2013), for example. Oster, (2020) provides a
thorough and practical overview of the current breastfeeding health
literature. If the experienced benefits of workplace breastfeeding are not as
significant as most women are led to believe, or if it inherently difficult to
pump or breastfeed at work regardless of the provision of workplace
breastfeeding amenities, it is understandable that the effects of workplace
breastfeeding legislation would not be persistent ex-post. Indeed, Chen et
al., (2006) find that despite ample provision of workplace breastfeeding
amenities, Taiwanese mothers are not very likely to continue to breastfeed
after returning to work. Felice et al., (2017) show that working mothers
report feeling “isolated and embarassed” for engaging in workplace pumping.

It should be noted, however, that women in the IFPS II sample are much more
likely to report high job satisfaction if they reside in a state with the
workplace breastfeeding policies in place. This demonstrates a potential
spillover effect - even though working mothers may not make significantly more
use of the workplace breastfeeding amenities, their very provision can change
the worker’s perception of her firm, which may increase her likelihood to
return to her job after giving birth.

The main finding, that labor force participation increases during the few
years after implementation of workplace breastfeeding legislation, is
transient. Ex ante, this was not anticipated and is a surprising result. We
would expect that any benefits to returning to work after birth with these
amenities in place would be persistent. This transience could be due to firms
somehow making up for the increased costs of these provisions in ways which
otherwise inconvenience working mothers, or it could be due to a
miscalculation of the benefits of workplace breastfeeding and pumping by new
mothers becoming self-corrected in subsequent years. Determining whether the
supply or demand side of the labor market is responsible for the transient
effect is beyond the scope of this paper without having detailed information
about the rationale behind participation decisions of women. Survey evidence,
however, indicates that work schedule flexibility is related to workplace
breastfeeding continuation (Felice et al.,, 2017), which suggests that the
presence of breastfeeding legislation alone, without other family-friendly
policies in place, may not be enough to support the full-time labor force
participation of mothers. Future research should also examine the impacts of
recent hospital shifts towards Baby-Friendly practices on female labor force
participation (Nelson and Grossniklaus,, 2019).

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##  7 APPENDIX

Table A1: Law Citations By State State | Statute |  |  |
---|---|---|---|---
Arkansas | Ark Code Ann §11–5-116 (2009) |  |  |
California | Cal [Labor] Code §§1030–1033 (2001) |  |  |
Colorado | Colo Rev State §§ 8–13.5–101-104 (2008) |  |  |
Connecticut | Conn Gen Stat §31–40w (2001) |  |  |
District of Columbia | DC Code §2–1401.05 (2007) |  |  |
| DC Code §§2–1402.81–83 (2007) |  |  |
Georgia | Ga Code Ann §34–1-6 (1999) |  |  |
Hawaii | Haw Rev Stat §§378–2, 10 (1999) |  |  |
Illinois | 820 Ill Comp Stat 260/ (2001) |  |  |
Maine | Me Rev Stat Ann tit 26, §604 (2009) |  |  |
Minnesota | Minn Stat §181.939 (1998) |  |  |
Montana | Mont Code Ann §§39–2-215–217 (2007) |  |  |
New Mexico | NM Stat §28–20-2 (2007) |  |  |
New York | NY [Labor] Law §206-c (2007) |  |  |
Oklahoma | Okla Stat tit 40, §435 (2006) |  |  |
Oregon | Or Rev Stat §§653.075, 0.077, 0.079, 0.253 (2007) |  |  |
Rhode Island | RI Gen Laws §23–13.2–1 (2003, amended 2008) |  |  |
Tenessee | Tenn Code Ann §50–1-305 (1999) |  |  |
Vermont | Vt Stat Ann tit 21, §305 (2007) |  |  |
Table A2: Summary Statistics: Untreated Outcomes

All Controls Treated, Before Implementation Labor Force Participation Mean Std
Dev N Mean Std Dev N Wave 1 0.792 0.406 1420 $(E_{i}\geq 1$) 0.698 0.460 301
Wave 2 0.804 0.397 1421 $(E_{i}\geq 1$) 0.740 0.440 334 Wave 3 0.829 0.377
1188 $(E_{i}\geq 2$) 0.837 0.370 160 Wave 4 0.821 0.384 1187 $(E_{i}\geq 3$)
0.830 0.377 159 Wave 5 0.799 0.401 1187 $(E_{i}\geq 4)$ 0.792 0.407 159 Wave 6
0.791 0.407 1087 $(E_{i}\geq 5$) 0.814 0.393 59 Wave 7 0.759 0.428 1028 Wave 8
0.730 0.444 1028 Wave 9 0.711 0.453 1028 Wave 10 0.691 0.462 1028 Female labor
income Wave 1 12,864 12,728 1124 $(E_{i}\geq 1$) 13,761 14,362 210 Wave 2
13,657 11,032 1142 $(E_{i}\geq 1$) 13,880 11,589 247 Wave 3 14,394 11,069 985
$(E_{i}\geq 2$) 15,300 11,209 134 Wave 4 15,068 13,198 974 $(E_{i}\geq 3$)
15,237 10,645 132 Wave 5 15,374 13,065 949 $(E_{i}\geq 4)$ 16,470 11,570 126
Wave 6 14,999 11,548 860 $(E_{i}\geq 5$) 13,396 7,021 48 Wave 7 16,592 13,550
780 Wave 8 16,268 12,683 750 Wave 9 16,313 13,154 731 Wave 10 16,562 13,418
710 Female wages Wave 1 12,862 13,011 1074 $(E_{i}\geq 1$) 13,832 14,705 200
Wave 2 13,593 11,271 1090 $(E_{i}\geq 1$) 13,864 11,794 236 Wave 3 14,385
10,956 938 $(E_{i}\geq 2$) 15,196 11,314 128 Wave 4 15,117 13,435 913
$(E_{i}\geq 3$) 15,358 10,838 126 Wave 5 15,385 12,778 884 $(E_{i}\geq 4)$
16,496 11,730 119 Wave 6 15,210 11,777 807 $(E_{i}\geq 5$) 13,627 6,910 47
Wave 7 16,794 13,837 731 Wave 8 16,368 12,860 716 Wave 9 16,375 13,409 698
Wave 10 16,357 12,687 683 a This table reports summary statistic by PSID wave
for all control households in the main balanced sample, and by treated
households before law implementation. $E_{i}$ refers to the cohort treatment
time group (see Section 3.2). There are five waves in which laws were enacted
in the US (5 $E_{i}$ cohorts beginning in wave 2), and each household in these
states acts as a control unit for treated households prior to implementation.
b Wave 1 corresponds to 1997 with each wave occurring two years apart.

Table A3: Other Outcome Variables: Full Specifications

(1) (2) (3) (4) (5) (6) (7) (8) (9) VARIABLES ln(labor income) Work Hours
Housework l = -3 -0.025 -0.028 0.022 -143.255** -95.776* -142.031** 0.097
0.103 0.215 (0.064) (0.064) (0.084) (61.976) (49.798) (62.915) (1.166) (1.165)
(0.852) l = -2 0.012 -0.015 0.010 -49.621 -40.767 -61.308 0.207 0.198 0.589
(0.058) (0.054) (0.072) (45.335) (39.197) (44.295) (0.855) (0.854) (1.099) l =
0 0.024 0.022 0.020 -48.726 -44.768 -79.193** -0.474 -0.465 -0.358 (0.047)
(0.045) (0.059) (37.386) (29.462) (35.584) (0.687) (0.687) (0.869) l = 1 0.033
0.033 -0.016 7.637 -6.626 14.454 0.338 0.343 0.702 (0.051) (0.049) (0.069)
(47.454) (38.648) (48.455) (0.850) (0.850) (1.091) l = 2 -0.041 -0.050 -0.030
9.686 22.483 -8.922 0.684 0.675 0.571 (0.056) (0.054) (0.070) (49.447)
(42.005) (45.956) (0.789) (0.789) (0.951) l = 3 0.040 0.020 0.017 -25.722
-33.754 -34.628 1.915** 1.907** 0.864 (0.047) (0.046) (0.058) (42.804)
(35.337) (43.067) (0.840) (0.840) (0.995) l = 4 -0.010 -0.006 0.001 -4.272
0.737 35.705 -0.273 -0.273 -0.187 (0.010) (0.006) (0.025) (12.965) (11.317)
(21.896) (0.221) (0.222) (0.447) Education (years) 0.011 -0.001 0.017 11.356
4.299 21.308 0.262 0.254 -0.045 (0.018) (0.018) (0.025) (16.560) (12.835)
(16.588) (0.252) (0.252) (0.310) ln(Childcare expense) 0.022*** 0.019***
0.029*** 18.282*** 5.213* 7.842** -0.102* -0.101* -0.123* (0.004) (0.004)
(0.006) (3.410) (2.941) (3.664) (0.055) (0.055) (0.069) Age of Youngest Child
0.015*** 0.016*** 0.021*** 15.529*** 5.710** 7.171** 0.004 0.005 -0.034
(0.003) (0.003) (0.004) (3.035) (2.467) (3.079) (0.042) (0.042) (0.061) Number
of Children 0.007 0.005 0.002 -40.220*** -32.125*** -37.848*** 0.997***
0.994*** 0.784*** (0.014) (0.013) (0.018) (13.015) (10.820) (14.214) (0.210)
(0.210) (0.273) Black -0.130 -0.147 -0.152 -208.539 -104.521 -190.547 1.560
1.548 -7.324** (0.181) (0.175) (0.286) (171.393) (162.896) (138.508) (2.407)
(2.397) (3.577) Latino/a -0.239*** -0.200*** -0.297*** -4.353 86.311 139.296*
2.451 2.453 -0.413 (0.077) (0.074) (0.095) (70.890) (55.386) (74.649) (1.776)
(1.774) (1.828) Work Hours 0.001*** 0.001*** 0.001*** -0.002*** -0.002***
-0.001*** (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) ln(family income)
0.602*** 40.827 0.192 -0.555 (0.040) (64.075) (0.388) (0.702) Housework Hours
-0.004*** -0.003*** -0.005*** -7.222*** -3.842*** -3.520*** (0.001) (0.001)
(0.002) (1.005) (0.865) (1.101) Catholic -0.168 -0.203 -454.816* -270.834*
0.185 0.168 (0.246) (0.256) (235.947) (154.587) (3.810) (3.821) Jewish -0.079
0.759*** -1,073.106*** -758.387*** -7.022*** -6.757*** (0.163) (0.169)
(144.539) (116.780) (2.246) (2.342) Protestant 0.135 0.379** 0.521 -50.113
-1.542 -1.459 (0.151) (0.152) (139.890) (111.234) (2.179) (2.200) Other Non-
Christian -0.129 0.195 194.775 191.504 -24.495* -24.398* (0.159) (0.169)
(179.901) (145.063) (14.696) (14.734) Greek/Russian Orthodox 0.100 0.252
456.075** 297.235* 27.384*** 27.442*** (0.210) (0.203) (189.377) (164.771)
(2.957) (2.961) ln(male labor income) 0.087* -74.467*** 0.550 (0.051) (23.228)
(0.370) ln(female lab income) 374.927*** 328.198*** -0.813*** -0.868***
-0.959** (25.098) (35.253) (0.290) (0.318) (0.406) Constant 7.914*** 1.817***
6.641*** 1,800.938*** -1,644.613*** -2,533.389*** 31.826*** 30.429*** 36.298**
(0.432) (0.541) (0.953) (519.097) (495.367) (926.240) (9.442) (9.861) (17.142)
Observations 6,876 6,872 4,495 6,876 6,876 4,493 6,876 6,872 4,493 R-squared
0.748 0.789 0.762 0.551 0.670 0.704 0.524 0.524 0.560 Robust standard errors
in parentheses ** p¡0.01, ** p¡0.05, * p¡0.1 a Work hours are reported as
annually. Housework hours reported as weekly. b *** p$<$0.01, ** p$<$0.05, *
p$<$0.1.

Table A4: Main Results: No California or Illinois

FE IW CATTc e,l Estimates Estimates Estimates (1) (2) Wave l relative to law
$\hat{\mu}$ $\hat{\nu}$ $\hat{\delta}_{\textit{1,L}}$
$\hat{\delta}_{\textit{2,L}}$ $\hat{\delta}_{\textit{3,L}}$
$\hat{\delta}_{\textit{4,L}}$ $\hat{\delta}_{\textit{5,L}}$ -3 -0.017 -0.008 -
0.034 -0.013 -0.001 (0.030) (0.030) (0.014) (0.037) (0.053) -2 -0.049 -0.063 -
-0.079 0.013 -0.002 -0.103 (0.037) (0.099) (0.165) (0.012) (0.050) (0.064) -1
0 0 0 0 0 0 0 0 0.056** 0.038* 0.053 0.012 -0.903 0.091 0.046 (0.028) (0.020)
(0.040) (0.047) (0.019) (0.055) (0.060) 1 0.034 0.050* -0.013 0.060 0.097
0.123 -0.006 (0.033) (0.031) (0.047) (0.051) (0.019) (0.066) (0.079) 2 0.028
-0.034 -0.069 -0.139 0.091 0.187 0.060 (0.034) (0.097) (0.048) (0.201) (0.019)
(0.064) (0.056) 3 0.026 0.056 -0.017 0.085 0.077 0.104 -0.015 (0.035) (0.038)
(0.053) (0.069) (0.02) (0.059) (0.084) a N = 13,980. Sample excludes
observations from California and Illinois, two states which exhibit
significant pre-trends in the main analysis. b Wave $\ell=-1$ is normalized to
zero due to collinearity. c Cohort Average Treatment effects are reported for
each individual cohort. A cohort is defined as all states in which the law
goes into effect during the same two-year period.

Table A5: Main Results: Unbalanced Panel

FE IW CATTc e,l Estimates Estimates Estimates (1) (2) Wave l relative to law
$\hat{\mu}$ $\hat{\nu}$ $\hat{\delta}_{\textit{1,L}}$
$\hat{\delta}_{\textit{2,L}}$ $\hat{\delta}_{\textit{3,L}}$
$\hat{\delta}_{\textit{4,L}}$ $\hat{\delta}_{\textit{5,L}}$ -3 0.044 0.049
0.154 0.066 0.017 (0.028) (0.028) (0.010) (0.036) (0.048) -2 -0.046** -0.037*
-0.052 0.126 -0.008 -0.031 (0.022) (0.022) (0.030) (0.010) (0.041) (0.058) -1
0 0 0 0 0 0 0 0 0.072**** 0.071**** 0.048 0.115 -0.765 0.009 0.068 (0.017)
(0.017) (0.033) (0.027) (0.014) (0.043) (0.043) 1 0.033* 0.037* -0.010 0.060
0.228 0.080 -0.049 (0.019) (0.019) (0.038) (0.029) (0.015) (0.048) (0.050) 2
0.008 0.012 -0.071 0.033 0.205 0.058 0.005 (0.020) (0.020) (0.038) (0.030)
(0.014) (0.054) (0.051) 3 -0.027 -0.026 -0.069 -0.013 0.191 -0.014 -0.024
(0.020) (0.020) (0.038) (0.028) (0.015) (0.055) (0.062) a N = 25,380. Sample
is unbalanced in calendar time. Households are present for at least two waves
between 1997-2017. 50% of the sample is present for a consecutive seven wave
period or more. b Wave $\ell=-1$ is normalized to zero due to collinearity. c
Cohort Average Treatment effects are reported for each individual cohort. A
cohort is defined as all states in which the law goes into effect during the
same two-year period.

Table A6: Weak Legal Provitions

FE IW CATTd e,l Estimates Estimates Estimates Wave l relative to law
$\hat{\mu}$ $\hat{\nu}$ $\hat{\delta}_{\textit{1,L}}$
$\hat{\delta}_{\textit{2,L}}$ $\hat{\delta}_{\textit{3,L}}$
$\hat{\delta}_{\textit{4,L}}$ $\hat{\delta}_{\textit{5,L}}$
$\hat{\delta}_{\textit{6,L}}{\textsuperscript{c}}$
$\hat{\delta}_{\textit{7,L}}$ -3 -0.013 -0.016 -0.033 -0.012 0.015 -0.075
(0.022) (0.024) (0.073) (0.030) (0.042) (0.086) -2 -0.021 -0.026 -0.060 0.010
-0.005 -0.034 0.128 (0.019) (0.020) (0.033) (0.058) (0.035) (0.048) (0.075) -1
0 0 0 0 0 0 0 0 0 0 0.022 0.016 0.028 0.064 0.006 -0.053 0.005 0.024 0.055
(0.016) (0.016) (0.044) (0.040) (0.029) (0.043) (0.044) (0.044) (0.098) 1
0.021 0.021 0.052 -0.016 0.004 -0.020 0.049 0.038 0.026 (0.017) (0.018)
(0.044) (0.047) (0.032) (0.066) (0.045) (0.054) (0.110) 2 -0.005 0.028 -0.044
-0.069 -0.036 0.223 0.112 0.048 0.281 (0.019) (0.020) (0.046) (0.049) (0.035)
(0.034) (0.047) (0.051) (0.219) 3 -0.017 -0.004 -0.022 -0.018 -0.048 0.212
0.050 -0.048 (0.019) (0.018) (0.048) (0.053) (0.032) (0.036) (0.043) (0.061) a
N = 8,825. Treated states include those with weak legal provisions for
workplace breastfeeding amenities. States with weak provisions include
Virginia, Louisiana, Texas, Indiana, Mississippi, North Dakota, and
Washington. b Wave $\ell=-1$ is normalized to zero due to collinearity. c Note
that including extra states increases the number of cohorts $\ell$ from 5 to
7. d Cohort Average Treatment effects are reported for each individual cohort.
A cohort is defined as all states in which the law goes into effect during the
same two-year period.