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How does the 2002 Californian Paid Parental Leave Law (CA-PFL) affect female labor force participation rates within the state?
Constance GouéloWellesley College ‘17
Cassandra AllenWellesley College ‘18
Ellie NeusteinWellesley College ‘18
Danni Ondraskova Wellesley College ‘18
December 22, 20161. Introduction
Today, particularly after the 2016 election, topics on women’s rights have been subjects of
frequent discussion. One of these matters of debate revolved around the federal provision of paid parental
leave. As a forerunner, California was the first state to pass the Paid Family Leave Act in 2002 which
provided mandated salaries for new mothers in order for them to be able to take time off to bond with
their child. According to our preliminary readings in First Impressions: Comparing State Paid Family
Leave Programs in their First Years, which was compiled by the National Partnership for Women and
Families, 1.5 million claims were filed in the first year of the implementation of this law. However,
according to the statute, only mothers in the private sector were eligible for paid leave, with public sector
employees continuing to receive federal unpaid leave. Although not all could enjoy the benefits of this
law, the comparisons between left out groups of mothers provide interesting insights into the efficacy of
the statute. Additionally, paid leave continues to be a popular topic with, as of November 20161, 11 states
considering implementing some form of the paid leave program. As such, we hope to explore the
correlation between an increase in paid parental leave post-childbirth and female labor force participation
rates in California, in order to determine who benefits most from the statute, whether the increased paid
leave improves female participation rates and increases the frequency of leave taken, as well as whether
the law positively impacts women in the short and medium runs.
1 On December 22nd, Washington D.C. passed the paid leave law joining the four other states. It guarantees eight weeks of paid leave to new parents and six weeks of leave for other family caregiving to more than half a million private sector and nonprofit workers, making it one of the nation’s most generous paid family leave laws. The benefits should start paying out in 2020 since the DC City Council passed it with a veto-proof 9-4 majority, but Congress can intervene to stop the law from going into effect.
1
2. Background
In 1987, as women increasingly entered the workforce, the U.S. Supreme Court upheld the
California Fair Employment and Housing Act, which allowed but did not oblige a state to require
employers to provide workers maternity leave and retain their jobs2. For some states providing paid leave
was a heavy financial burden, and few such provisions were implemented. In 1993, policymakers revised
the Fair Employment and Housing Act and introduced the Family and Medical Leave Act (FMLA),
mandating employers to provide 12 weeks of guaranteed unpaid leave to qualifying workers, without the
fear of losing one’s employment. However, many workers found themselves financially unable to take
this leave and forgo salary while taking time off to care for a newborn child or sick family member.
While the US struggled to devise solutions to the lack of paid leave, Germany and Canada, among a host
of other developed nations, enacted federal paid parental leave provisions. Indeed, mothers in Germany
could take a year off from work with 67% of her usual pay, and Canadian mothers obtained 55% of pay
for a year or more.3 In 2002, California became the first state within the union to provide a protected
salary in addition to the unpaid leave of 12 weeks for private sector employees. This particular paid leave
statute, California Paid Family Leave (CA-PFL), granted six weeks of paid family leave with 55% of
usual pay replaced (up to $1,075 per week in 2014). In order to qualify, workers must have worked at
least 1,250 hours (25 hours per week) the year before the leave and their employer must have hired at
least 50 people within a 75 mile radius of their worksite.4 In order to ensure job security and
compensation on leave simultaneously, the law must be taken concurrently with FMLA and CFRA.5
3. Related Literature and Broader Impact
2 California Federal savings & Loans Association v. Guerra, 479 US 272 (1987).3 California’s Paid Family Leave Law: Lessons from the First Decade.4 California Family Leave Laws.5 California’s Paid Family Leave Law: Lessons from the First Decade.
2
In order to understand the impact of this law on women’s labor force participation rates, we
sought to examine the initial effects of unpaid leave before speculating on how paid leave could affect
new mothers. In 1997, researchers Jacob Klerman and Arleen Liebowitz in Unanticipated Effects of
California’s Paid Family Leave Program, used U.S. Census data from 1980 and 1990 to show that:
“Maternity leave statutes increased leave, but had insignificant positive effects on employment and
work.” They observed that initial unpaid leave policy actually increased parental-leave for fathers more so
than mothers. Additionally some companies modified their hiring demographics because they would have
to hire temporary workers during the leave period. As such, they opted to reduce the number of young
women who were capable of becoming pregnant during employment, thus impacting recruitment within
this demographic. This was especially accentuated after the 2002 Paid Family Leave law which further
increased the costs of employing young women and mothers.6 In addition to shifts in the employment of
women within the labor force, trends were also established in terms of who was aware of the PFL and
who was able to actually take the leave. In 2013, only 12% of U.S. workers had access to employer-
provided paid family leave, which was not surprising considering that only four states currently have paid
leave. Furthermore, in a California 2007 survey, only 28.1% adults of the state were aware of the law
itself of which there was a disparity of socioeconomic status apparent between claimants. The highest
proportion of these adults had low-to-moderate incomes from $12,001 to $48,000, followed by the top
bracket, and then the lowest bracket of $12,000 or less.7 Yet since 2005, mostly individuals with higher
incomes filed claims, highlighting underlying factors preventing mothers from applying for paid leave.
Though these laws created shifts within the labor force, California is a model for understanding how paid
leave impacts female participation rates whilst providing some women with more opportunities and
benefits than they had available prior.
4. Goal & Empirical Model
6 Unanticipated Effects of California’s Paid Family Leave Program.7 California’s Paid Family Leave Law: Lessons from the First Decade.
3
In this paper, we seek to determine who reaps the benefits of the policy through examining the
demographic and socioeconomic backgrounds of the women within our study. We want to furthermore
understand whether the paid policy improved female labor force participation as well as whether it
positively impacted women in the labor force two years post implementation. From our results and
observations, we hope to determine how to improve policy making to benefit all women.
To determine the effects of paid parental leave on the Californian female labor force, we
conducted a difference-in-difference regression. We used data from the CPS 2000-2006 censuses to show
the impacts on our 14 variables before paid parental leave was passed and the pre- and post-
implementation periods. We used this classification for our California as well as our aggregate
comparison state models.
Timing Groups I, II, and III (ktiming):● 2000-2002: Pre-Passage of California Paid Family Leave Law (CA-PFL), base group● 2002-2004: Pre-Implementation, captures short-term market reaction● 2005-2006: Post Implementation, captures medium-term market reaction
In order to understand better the effects on the Californian female labor force, we needed
comparison states. We chose the states Washington and Oregon given geographical proximity and the fact
that they had not yet passed paid parental leave laws. As such we created the following binary variable:
Treatment and Comparison Groups (comparisonstate):● Comparisonstate = 0 if California● Comparisonstate = 1 if an aggregate of Washington and Oregon
Furthermore, we needed to have a homogeneous range of ages for the women who could have
given birth. Thus we selected only women from ages 15 to 50 to include within our model and then
selected only those who had given birth within the last year (indicated by the binary variable fertyr).
After cleaning our data, in order to analyze the trends related to women who have given birth in
the past year and who were employed and on leave, we created a composite variable selecting for women
within the private sector (since the public sector was not included in the PFL statute) who were between
ages 15-50 and had just given birth within the last year.
4
We ran a series of initial regressions to determine how demographic and socioeconomic factors
such as race, income, and professional industry, impacted this composite variable.
From there, we ran the following difference-in-difference regression:
Yit= α + ∑k =5
❑
❑γkDitk + δi + ηt + εit
Y was the difference in outcome variable between the treatment and comparison group from 2001
to the different timing groups (k) for total income, on leave employment status (binary), number of
familial generations within household (binary, greater or less than 3 generations), child born within the
last year, highest level of education (binary, greater or less than high school diploma), minority race status
(binary), as well as number of children under age 5 within household, and public sector (binary). α was
the constant and γk represented the change generated in California after the implementation of the policy.
Additionally, Ditk was a binary indicator variable that took the value 1 if the state was equal to California
and the year was after 2004 (policy implementation). Finally, δ i represented state fixed effects, η t showed
the year fixed effects from 2003-2006, and εit was the error term.
5. Data
5
Upon choosing our data, we examined the number of families in a household, whether it was
multigenerational one as well as whether a mother had children in the past year. We additionally looked
at the mother’s education level, race, her employment status, her total income, the number of own
children under five in the household, and whether the parent was in the public industry. We sought to
observe these variables for both the aggregated comparison variable and the state of California, whilst
still taking time tranches into account.We chose to omit some variables, such as mortgage status and
health insurance, as those elements would create much more variability and disparities between our
comparison and treatment states. Though we tried to select a comparison group as similar in economy to
California as possible, we realized that the economies of California, Oregon and Washington were quite
different in terms of industry, tax structure, and trade which we hope to address in future studies.
5.1 Identity of the Women
We separated our data into two different categories: identity of the women and women in the
workforce. For the first topic, we collected data relating to socioeconomic status and familial composition
to answer our research questions (with n = 832,473 and the R-Squared = 0.0029 for the treatment group
and n = 219,754 and R-Squared = 0.0022 for the comparison group).
Table 1 : Change over time of variables related to the Identity of the Women, in CA / Comparison States
California Comparison
6
Group
Variable(name)
Timing 1 to Timing 2
Timing 2 to Timing 3
Overall Change
Timing 1 to Timing 2
Timing 2 to Timing 3
Overall Change
Number of Families in Household(nfams)
-8.52(0.0031)
-0.02(0.0035)
-8.54(0.0023)
-4.31(0.0049)
-1.56(0.0055)
-5.87(0.0038)
Multigenerational Household(multgen)
3.07(0.0024)
-1.89(0.0027)
1.18(0 .0018)
4.20(0.0041)
-1.34(0.0046)
2.86(0.0032)
Child Born within Last Year(fertyr)
0.21(0.0038)
-0.66(0.0023)
-0.45(0.0035)
0.16(0.0068)
-0.42(0.0043)
-0.26(0.0064)
High Overall Level of Education (higheduc)
6.06(0.0018)
-2.91(0.0020)
3.15(0.0013)
5.16(0.0033)
-2.06(0.0038)
3.10(0.0026)
Low Overall Level of Education (loweduc)
-6.06(0.0018)
2.91(0.0020)
-3.15(0.0013)
-5.16(0.0033)
2.06(0.0038)
-3.10(0.0026)
Minority Composition(bin_race = 1 if white, = 0 if minority race)(bin_race)
7.41(0.0018)
-3.64(0.0020)
3.77(0.0013)
3.06(0.0027)
-1.83(0.0030)
1.23(0.0021)
Number of Children Age 5 or Under(nchlt5)
-1.70(0.0021)
-1.31(0.0023)
-3.01(0.0015)
-1.51(0.0039)
-0.20(0.0044)
-1.71(0.0030)
R2= 0.0029 , n = 832,473 R2= 0.0022 , n = 219,754
Key Timing Interval
Variable Change in Mean Between Timing Groups(Standard Error)
Notes: This table presents the changes across time periods for variables related to the identity of the women across both California and our Comparison States. The first column for each state group corresponds to the period prior to the enactment of the legislation. The second column for each correspond to the aggregate mean period during and then post its implementation. Finally, the third column for each group represents the overall change over the three time periods. (n = 832, 473 and the R-Squared = 0.0029 for the treatment group and n=219,754 and R-Squared = 0.0022 for the comparison group).
We first examined women who had given birth within the last year and whom were aged 15 to
50. Of the 15,405 women surveyed in 2000-2002, 91.7% of those who answered had not given birth
within the last year, whereas 8.3% had. In 2002-2004, of the 44,773 women who answered, 92% had not
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whereas 8% had. Finally, of the 92,551 women surveyed and who answered in our last time tranche,
91.6% had not given birth within the last year, while 8.4% had. Overall, we observed a decrease of births
per year of 0.45% (0.0035) between timing one and timing three. In our comparison group on the other
hand, of the 4,843 women surveyed in 2000-2002 and answered, 91.6% had not, while 8.4% had. In
2002-2004, of the 13,937 women we had answers from, 92.1% of the women had not, whereas 7.9% had.
Finally, of the 24,956 women surveyed in our last time tranche who answered, 91.6% had not given birth
within the last year while 8.4% had. Overall, we observed a decrease of births per year of 0.26% (0.0064)
between timing one and timing three within the comparison group. While the negative birth rates held in
both groups, the decline in births per year was higher in California than in the comparison states. As such,
we hypothesized that the decline could be due to increasing female participation within the workplace,
where taking time off to raise a family was generally discouraged, or as a result of societal norms that
encourage a new mother to quit her job and raise a family, thereby influencing women to achieve
professional success before leaving her job to raise a family. The declines follow trend with a decrease in
national birth rates throughout the country8.
We then observed the effect of number of families in a household, which was a potential indicator
of whether a mother took parental leave. We made the assumption that having additional families within
the same household may make it easier for a mother to receive help from these other families’ members
to raise the child and discourage her from taking parental leave. From timings one to three in California,
we saw a decrease in the number of families per household of 8.54% (0.0023). From this observation, we
concluded that the number of families residing within a household have minimal effects on paid leave
taken. In the comparison group, there was a 5.87% (0.0038) decrease in the number of families per
household between timing groups one and three. These numbers reflect the same hypothesis given the
fact that families with more than one families are uncommon.
We also examined race of the mother to determine whether or not it impacted a mother’s ability
to take leave. We hypothesized that this could be important given that race was a factor in employment
8 National Center for Health Statistics.
8
and could thus impact leave allowance through impacting employment options. Over all three time
periods California was 56% “White”, 9.5% “Other Asian or Pacific,” 5.3% “Black,” and 3.3% “Chinese.”
The same parameter for our comparison group kept an identical ranking of proportions. Since the
percentage breakdown of racial composition was similar across time periods in each of the two data sets,
we were able to make a causal conclusion and observe trends of our specific variables such as income.
We do acknowledge possible bias, such as the lesser racial diversity outside of California. Indeed, we
found a 3.77% increase (SE: 0.0013) in respondents who identified as white between timings one and
three for California, and a 1.23% increase (0.0021) in those in our comparison states.
Following this, we examined the highest grade of educational attainment of our female
respondents to see if there was a relationship between education level and taking advantage of the paid
leave. In the sample from California, we saw an increase of 3.15% (0.0013) in respondents with a high
school diploma or above of education between timing one and timing three. In comparison, the
Washington/Oregon group saw an overall increase in higher education (high school diploma and above)
of 3.1% (0.0026) between timing one and timing three. We observed that both groups follow similar
trends in the tranches, indicating that bias was not present in terms of educational attainment between the
two groups even though the comparison group’s educational attainment levels were slightly higher than
California’s with the exception of master's degrees.
Additionally to these variables, we wished to see how many generations lived within the
household and how that would affect mothers taking leave. We hypothesized that with older generations
in a household, a mother may be able to receive aid from her parents or sibling, or those of her partner
and thus would be less likely to take parental leave or take fewer weeks of leave. Overall in California,
we saw an increase of 1.18% (0.0018) in the number of multigenerational households. This statistic was
higher than we expected to see and could potentially have affected the amount of parental leave a mother
decides to take. In our comparison states, we also observed an increase, this time of 2.86% (0.0032).
These were larger percentages than those observed in California and could introduce bias into our
regression.
9
Finally, we looked at the number of own children under age 5 in the household variable. We
believed that the presence of young children could influence a mother to either take more leave to spend
time with her young children. For our rows (number of own children under age 5 in household), we had 0
to 8 children. Because there is a negligible number of families with more than 5 children under age 5, we
summarized the results in three categories: no children under the age of five, 1-4, and 5-8. On average
across all three time periods in California, 80.5% of households had no children under the age of five,
19.4% of households had 1-4 children under the age of five, and .01% of households had 5-8 children
under the age of five. The percentages for households with 0, 1-4, and 5-8 children under the age of five
varied little across different time periods. Overall, we observed a decrease of 3.01% (0.0015) in the mean
number of children under age 5 within a particular household. We were not surprised by these findings
because we expected that with paid parental leave, it would be unlikely that families would suddenly have
twins or other large numbers of children overnight. For our comparison group, our data set only included
up to 6 children under the age of five, but we still used the same categories as before for comparison
purposes. On average across all three time periods, 79.3% of households had no children under the age of
five, 20.6% of households had 1-4 children under the age of five, and .01% of households had 5-8
children under the age of five. The percentages for households with 0, 1-4, and 5-8 children under the age
of five varied little across different time periods, just as in California. Overall, we observed a smaller
decrease of around 1.71% (0.0030) in the comparison group for the mean number of number of children
under the age of 5 within a particular household. As such there was little difference in the average
percentages of households with zero, 1-4, and 5-8 children across the California and comparison state
groups across time periods.
5.2 Women in the Workforce
After examining the identity of the women, we studied their position within the workforce. We
created an analysis of the women who recently had a child and their employment status, using the
variables “Employment Status” and “Child born within the last year.” For our statistics we had a sample
10
size of 832,473 and an R-Squared of 0.0029 for the treatment group and n = 219,754 and R-Squared =
0.0022 for the comparison group.
Table 2 : Change over time of variables related to the Women in the Workforce, in CA / Comparison States
California Comparison Group
Variable(name)
Timing 1 to Timing 2
Timing 2 to Timing 3
Overall Change
Timing 1 to Timing 2
Timing 2 to Timing 3
Overall Change
Total Income(incometotal)
12.84(0.0039)
-0.73(0.0044)
12.12(0.0029)
7.54(0 .0063)
2.69(0.0072)
10.23(0.0050
Employed but On Leave(empstatbin= 1)
1.16(0.0009)
0.21(0.0010)
1.37(0.0007)
0.62(0.0017)
0.34(0.0019)
0.96(0.0013)
Industry Public/Private (public = 1, private = 0)(indpublic)
-0.62(0.0015)
0.98(0.0017)
0.36(0.0011)
-0.51(0.0025)
0.34(0.0028)
-0.17(0.0020)
R2= 0.0029 , n = 832,473 R2= 0.0022 , n = 219,754
Key Timing Interval
Variable Change in Mean Between Timing Groups(Standard Error)
Notes: This table presents the changes across time periods for variables related to the women in the workforce across both California and our Comparison States. The first column for each state group corresponds to the period prior to the enactment of the legislation. The second column for each corresponds to the aggregate mean period during and then post its implementation. Finally, the third column for each group represents the overall change over the three time periods. (n = 832, 473 and the R-Squared = 0.0029 for the treatment group and n=219,754 and R-Squared = 0.0022 for the comparison group).
To grasp changes in the female labor force participation rate, we first examined the employment
status of the women within our sample. In timing group one, 305,584 women are working (56% of the
women in that timing group). In timing group two, 50,388 are working (57%), and in group three,
103,606 women are working (56%). What particularly interested us was that the women who identified as
having a job, but not working (essentially, on leave), received benefits under the PFL. Within the first
11
timing group, 7,619 women (1.4%) identified as such, while it was 1,800 women (2.0%) in the second
timing group, and 3,448 women (1.9%) in the third. As such the rates increased between the Pre-PFL
period passage of the bill, but decreased slightly once the PFL took effect (after timing two). Within our
comparison group, 60.9% of women within the first timing were employed and working, 59.45% in the
second, and 59.25% in the third. Additionally, 1.3% of the first timing group were employed but not
working (presumably on leave), 1.8% of the second group, and 1.8% of the third. Overall, we observed a
more conservative increase of women taking leave (0.96%, SE: 0.0013) within the comparison group
compared with a larger increase in the treatment group. This could indicate some of the initial effects of
the PFL on women’s willingness to take paid leave.
After looking at how many women took the opportunity to take leave, we decided to look at the
type of industry: public or private. We wanted to see if we could confirm this information we learned
from our research in our own census by seeing the proportion of our women in the public versus private
sectors. Within the California sample, we observed a 0.36% (0.0011) increase in public sector jobs
between timings one and three. This increase in public sector employment, where paid leave is not
available, could have impacted the amount of leave taken. On the other hand, in the comparison group
sample, we observed a decrease of 0.17% (0.0020) in public sector jobs overall. The difference in public
sector participation between our treatment and comparison groups could bias our difference-in-difference
regression outcomes.
Finally, we looked at income. Given the wide range of this variable, we decided we wanted to
divide the total income variable into 9 brackets:
Income Distribution (incometotal):● Incometotal = 1 if income falls between $0 - $20,000● Incometotal = 2 if income falls between $20,001 - $40,000● Incometotal = 3 if income falls between $40,001 - $60,000● Incometotal = 4 if income falls between $60,001 - $80,000● Incometotal = 5 if income falls between $80,001 - $100,000● Incometotal = 6 if income falls between $100,001 - $120,000● Incometotal = 7 if income falls between $120,001 - $200,000● Incometotal = 8 if income falls between $200,001 – $500,000● Incometotal = 9 if income falls between $500,001 – $999,999
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We examined the income distribution of women within the three timing groups. For California,
we saw an increase in mean income between timing one and timing two of 12.84% (0.0039). Then,
between timing two and timing three, there was a decrease in mean income of 0.73% (0.0044). There was
an overall increase within the treatment group of 12.12% (0.0029). Within our comparison group, we
observed an income increase of 7.54% (0 .0063) between timings one and two. Between timings two and
three, we observed a 2.69% (0.0072) increase in mean income. We saw an overall mean income increase
in the comparison group of 10.23% (0.0050). We expected this as California’s household income levels
were more volatile than those of Oregon and Washington, given the fact that it possessed such
metropolitan hubs as Los Angeles and San Francisco. We acknowledge that the difference in income
levels between the treatment and comparison groups could bias our difference-in-difference analyses, and
this disparity was something we hope to address in future studies.
6. Key Analysis: Difference-in-Difference
In order to better understand how the CA-PFL law affected family composition and labor market
outcomes in the state of California, we ran a difference-in-difference regression with Washington and
Oregon as the comparison states and 2002 (the year of policy implementation) as the base year. Key
outcomes are summarized below with particular attention paid to the number of women taking leave
(empstatbin) and the number of children born (fertyr).
Table 3: Public Sector Employment Rates
On Leave Employment Status (empstatbin)
Industry Public/Private(public = 1, private = 0)
(indpublic)
Pre-Period (2003) 0.16%(0.0034)
-0.35%(0.0056)
13
Year of Implementation
(2004)
-0.45%(0.0019)
-0.68%(0.0031)
Post Period (2005) 0.12%(0.0025)
-0.19%(0.0040)
R2 0.0006 0.0025
Notes: This diff-in-diff table describes labor force participation rates (in percent terms), across time periods, of women who have given birth in the previous year. The first row corresponds to the period prior to the passing of the
law. The second and third correspond to the period during and then post implementation. This table seeks to analyze the change in leave taken, as well as the change in employment in the public sector (a group excluded from
the paid leave program). Standard error values are displayed in parenthesis. n = 1,041,227.
The most notable aspects of the first column are the stark differences between the time periods (n
= 1,041,227). In the years leading up to to the policy change, the number of employees reportedly having
taken leave increased by 0.16% (0.0034). However in the year of implementation, the number of workers
reporting that they were on leave decreased by 0.45% (0.0019). This indicates that in the year of
implementation, workers were less likely to take leave from employment. Most interesting to note is the
increase in workers reporting on leave in the year directly after policy implementation. In the post-
implementation time period, employees reporting as on-leave rose by 0.12% (0.0025). This is indicative
of positive effects of the paid family leave policy. The relatively small increases in leave-taking align
with evidence from our readings that suggest that many Californians were unaware of the existence of the
PFL program as late as 2008. It would be interesting to measure the change in leave taking in the long run
as more Californians become familiar with and take advantage of the program.
Figure 1: Percent Change in Californian Labor Market Composition in Comparison with Washington and Oregon Before/After PFL Implementation
14
Notes: Using 2002 as a base year, and Washington/Oregon as our base group, this graph intends to demonstrate the percentage change in women on leave as well as women in the public industry in California’s Labor Market
Demographics, across the three time tranches surrounding the 2002 PFL Law. n = 1,041,227.
In addition to examining the amount of leave taken, we also wished to analyze how the change in
PFL laws affected employment in the public sector. As public sector employees were not included as
beneficiaries of the PFL law, we anticipated the participation within the public sector would decrease as
more mothers moved to the private sector where their right to paid leave is protected. In the time period
pre-policy implementation the number of employees participating in the public sector (indpublic = 1)
decreased by 0.35% (0.0056). In the implementation period, a critical window in which deductions were
made from wages to set aside money to finance paid leave (in a way similar to how social security is paid
into), participation within the public sector decreased by 0.68% (0.0031). Finally, in the post-
implementation period, interest in the public sector decreased by 0.19% (0.0040) indicating that our
hypothesis held true and that PFL negatively impacted participation within the public sector. While we
acknowledge that many variables could have impacted participation within the public sector, it is critical
to recognize that introduction of PFL policies could have a lasting impact on interest. These observations
indicate the positive effects paid family leave policy has on the labor market in general, and on the private
sector in particular.
15
Table 4: Family Composition
Number of Famil- ies In Household
(nfams)
Multigenerational Families (multgen)
Child Born in last Year
(fertyr)
Number of Children Under Age 5 (nchlt5)
Pre-Period (2003)
- 0.80%(0.0114)
2.02%(0.0089)
-0.45%(0.0082)
0.49%(0.0080)
Year of Implem- entation (2004)
1.95%(0.0063)
2.44%(0.0050)
0.77%(0.0078)
1.31%(0.0044)
Post-Period (2005)
- 0.84%(0.0083)
1.46%(0.0066)
0.53%(0.0078)
-0.26%(0.0058)
R2 0.0026 0.0061 0.0001 0.0006
Notes: This diff-in-diff table presents variations in family composition over the course of the time periods of interest. The first row corresponds to the period prior to the enactment of the legislation. The second and third rows
correspond to the period during and then post its implementation. Standard error values are displayed in parenthesis. n = 1,041,227.
Table 2 presents variations in family composition over the course of the time periods of interest.
While variations in family composition cannot be causally related to changes in policy, there remains
evident correlation. Columns (1) and (2) describe changes in family composition that we predicted could
influence whether or not a mother takes leave. Between the treatment and comparison groups, we see an
overall decrease in the number of families per household of 0.84% (0.0083) and an overall increase in the
multi-generational composition of the household of 1.46% (0.0066). We hypothesized that having more
families within a household as well as having a multigenerational household would decrease interest in
taking leave because there are more housemates present to care for the child. The fact that number of
families decreased while multigenerational families increased indicates that this relationship is
inconclusive. Column (3) of Table 2 describes the changes in the number of children born within the last
year in California. In the years before the policy implementation the number of children born in the past
year declined 0.45% (0.0082). However, during the time period of policy implementation the number of
children born increased by 0.77% (0.0078), which was a significant increase by any measure. And most
notably, in the time period post policy implementation the number of children born in the past year
experienced another increase, this time by 0.53% (0.0078). The overall increase in birth rates during the
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implementation and post-implementation timing groups indicates that the introduction of PFL had a
significant impact on increasing birth rates among working mothers.
Figure 2: Percent Change in Californian Family Compositions in Comparison with Washington and Oregon Before/After PFL Implementation
Notes: Using 2002 as a base year, and Washington/Oregon as our base group, this graph intends to demonstrate the percentage change in variables related to family composition in California’s Labor Market Demographics, across the three time tranches surrounding the 2002 PFL Law. Those variables include Number of Families, Multigenerational Family, Child Born within the Year, and Number of Children under the Age of 5. n = 1,041,227.
One curious aspect the authors wish to discuss is the decline in children under five reported by
households in the time period after policy implementation. This decline in children under five is
unexpected considering the 0.53% increase in number of children born; however, it is something we
would like to examine further in studies to come as it could indicate that children are being born with
greater time lapses between each child. This could also impact the amount of leave taken by a new mother
given the hypothesis that having more children under age five would influence a mother to take more
leave to care for her young children.
7. Conclusion
From our analyses, there is a significant increase in the amount of leave taken by new mothers
after the passage of CA-PFL. In addition to taking more leave, mothers are also having more children and
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participation within the public sector is decreasing (indicating that mothers may be switching to the
private sector for employment where they will receive PFL benefits). Our results indicate that, while the
increases are small, paid family leave positively impacts mothers by encouraging them to spend more
time bonding with their newborn and increasing the birth rate indicative of the fact that mothers are able
to take adequate leave to give birth without having to sacrifice income. We saw a net decrease in the
number of families per household as well as the number of generations present in a household, both
factors that we believed would negatively impact the desire to take leave. Controlling for these factors in
the future would provide interesting insight into how leave taking is directly influenced by CA-PFL.
Additionally, in future studies we would like to examine the medium and long run effects of the PFL as it
gains recognition throughout the state. Finally, we recognize that the industry differences between
California and our comparison states as well as the presence of large urban centers such as Los Angeles
and San Francisco may render our difference-in-difference regressions biased. In the future, we would
like to compare cities rather than entire states to ensure more homogeneous economies.
As a whole, we believe that Paid Family Leave has the possibility to dramatically improve
female leave taking and labor force participation in the long run, and we advocate to extend the statute
throughout the country.
8. Bibliography
Bartel, A., Baum, C., Rossin-Slater, M., & Waldfogel, J. (2014, June 23). California’s Paid Family Leave Law: Lessons from the First Decade. US Department of Labor. Retrieved December 4, 2016, from https://www.dol.gov/asp/evaluation/reports/PaidLeaveDeliverable.pdf
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Centers for Disease Control and Prevention (2015, July 20). Births and Natality. Centers for Disease Control and Prevention. Retrieved on December 20, 2016, www.cdc.gov/nchs/fastats/births.htm.
Das, T., & Polachek, S. W. (2014, March). Unanticipated Effects of California’s Paid Family Leave Program. Institute for the Study of Labor. Retrieved December 4, 2016, from http://ftp.iza.org/dp8023.pdf
Rossin-Slater, M., Ruhm, C., & Waldfogel, J. (2013). The Effects of California’s Paid Family Leave Program on Mothers’ Leave-Taking and Subsequent Labor Market Outcomes. Journal of Policy Analysis and Management, 32(2), 224–245.
The HR Specialist—McClatchy. (2016, November 7). Many States Consider Paid Parental Leave. CPA Practice Advisor. Retrieved December 04, 2016, from http://www.cpapracticeadvisor.com/news/12276901/many-states-consider-paid-parental-leave
Paid Family Leave California. (n.d.). What is Paid Family Leave? Paid Family Leave California. Retrieved December 04, 2016, from http://paidfamilyleave.org/ask-us/what-is-paid-family-leave
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