Maternity Leave.Baker (1)

Maternity Leave and Fertility Rates
Jessica Baker
University of Alaska Anchorage
Seminar in Economic Research
Professor Lance Howe
December 10, 2015

Abstract
The aim of this paper is to determine the effect of mandated paid maternity leave
on fertility rates in ten OECD countries from 1970 to 2012. The regression analysis using
panel data for the ten countries seemed to indicate that a mandated paid maternity leave
program has no statistically significant effect on fertility rates when controlled for time
fixed effects and country fixed effects.
Introduction
While many American companies have made headlines in the last few years for
offering extensive family leave packages, the United States is one of the only countries in
the Organization for Economic Cooperation and Development (OECD) that does not
require firms to offer paid maternity leave. Though the Family and Medical Leave Act of
1993 requires employers to keep a mother’s job open for 12 weeks while she is on
maternity leave, many families may use their accrued paid leave or dig into their savings
in order to make ends meet. This stress might result in many families postponing children
or having fewer children than they would otherwise as the opportunity cost of having an
additional child may be more than they are willing to incur. However, as other developed
countries increase paid leave for both new mothers and fathers, the fertility rates in most
advanced countries continue to fall. It is the objective of this paper to investigate the
effect of the mandated policy of paid maternity leave on fertility rates in OECD
countries.
My paper will use OECD data from 1970 to 2012 on child-related leave from ten
countries combined with data from the World Bank on fertility rates, GDP per capita,

population, infant mortality rates, percent of those living in urban centers, and labor-force
participation rates. The labor-force participation rates are important to include, as the
amount of women in the labor force may explain dwindling fertility rates or may reflect a
change in maternity leave policy. Many people assume that a falling fertility rate may be
a good thing. For some countries, like Japan with a seriously declining fertility rate, this
may mean that populations could be nearly cut in half by 2100 (Ghosh, 2012). This could
cause severe economic problems especially when looked at from the perspective of the
elderly who will greatly outnumber the working. Social programs that rely heavily on
taxing the workers could be in danger and a production slowdown could be detrimental to
economic growth.
Pro-fertility policies in other OECD countries have been the target of many news
stories in the past decade. More and more countries are choosing to offer “baby bonuses,”
or a cash incentive, for parents having children. As fertility rates plummet around the
world, countries like France, Canada, Australia, Singapore, and Russia offer money to
mothers who have new children. While these policies may vary by size, family income,
and country, the idea is not new. For instance, Australia’s former Prime Minister Andrew
Fisher introduced a baby bonus of £5 per child in 1912 (Day, 2008). Because the “baby
bonus” has been introduced in countries to increase fertility rates, it is only necessary to
include it to tease out any effect on fertility rates that occur because of the bonus and not
because of increased maternity leave. The differences in these bonuses along with the
many stipulations that follow each policy make it difficult to delineate whether or not the
policy affects those targeted. However, in one regression I will introduce a binary
variable for countries that offer any type of “bonus” upon the arrival of a child. This does

not include tax breaks, tax credits, or any other policies that may make having a child
seem more attractive.
I am not concerned about missing values in my data, as the OECD data I am using
only refers to the weeks of paid or protected parental leave and the World Bank data
contains comparative numbers on fertility rates, GDP per capital and population. The
regressions I run on this data will take entity fixed effects into account, as the family and
work culture between the various ten countries may be different, resulting in differing
levels of fertility. I will also take GDP per capita into account because this may also have
an effect on fertility rates.
While other papers have been written addressing this issue, none have used panel
data that covers the amount of consecutive years and with the countries I plan to analyze
in my paper. In one study, researchers found that women in Austria were 4.9 percent
more likely to have an additional child within three years after a policy change that
increased the maximum duration of parental leave from one to two years (R. Lalive & R.
Lalive, 2005). In 2007, Germany expanded its parental benefits to not just cover low-
income families. The new policy allowed middle to upper class mothers to receive
benefits that depended on her pre-birth earnings. Researchers studying this policy found
that the change showed a significant effect on the increase of fertility (A. Raute, 2013).
Another policy analysis conducted by researchers in Australia found that reducing the
private costs associated with having children and increasing maternity leave exclusively
would not only increase fertility rates, but encourage women to return to the workforce
after their paid leave ends (L. Risse, 2006).

However, the paper most similar to what I would like to accomplish in this study,
“Demographic Consequences of Maternal Leave Programs in Industrialized Countries”
by Winegarden and Bracy (1995) looked at 17 OECD countries through four time
periods, 1959, 1969, 1979, and 1989. They found that extending maternity-leave
programs leads to reduced infant mortality rates, increased labor-force participation, and
an increase in birth rates. However, while the paid leave directly increased fertility rates,
maternity leave indirectly reduces fertility rates by increasing the woman’s labor supply,
thus leading to a net decrease in fertility rates. Not just this, but the results they found
related to paid maternity leave and fertility rates were statistically insignificant.
(Winegarden, C.R. & Bracy, P.M., 1995). Another paper by Gauthier and Hatzius (1997)
concluded that variations in maternity leave have no significant impact on fertility rates,
and it would be more useful to test this policy responsiveness between women of various
incomes, education levels, marital status, and labor force status (Gauthier, A.H. &
Hatzius, J, 1997).
The findings in these papers seem to show that as countries increase the weeks of
paid maternity leave for mothers, families will find that the opportunity cost of having a
child diminishes, thus having one or more children and increasing the fertility rates in the
respective countries. Furthermore, increasing paid maternity leave may increase fertility
rates in women that participate in the workforce, encouraging them to return upon
completion of their leave and increasing the female labor force participation. However,
for the women who do not participate in the work force, paid maternity leave will have
no impact on their childbearing decisions. Thus, a paid maternity leave may be better for
working women than a “baby bonus” or stipend, as these types of incentives may not

encourage workforce participation, while the stipend may provide incentives to
nonworking mothers.
Model
There are currently two common types of incentives being offered around the
world to families interested in having children. The first is some kind of paid parental
leave. The second is a family allowance or bonus offered to those who have children.
While this paper will mainly focus on paid maternity leave programs and their effect on
fertility rates, the “baby bonus” will be included in one model to tease out any effects it
may have on fertility rates that are unrelated to maternity leave. It should be noted that
many studies have found that cash benefits may have a more significant short term effect
on fertility rates. However, for the scope of this paper, the focus is on paid maternity
leave.
The first incentive, paid maternity leave, may directly increase fertility rates as
women are able to take leave of their work and still receive a percentage of their income.
However, because these women then return to the workforce, labor participation rates
increase which would in theory lower the fertility rates. As stated earlier, the second
incentive, baby bonuses, have been found by many studies to have a significant short
term effect on fertility rates. This may be due to the present-oriented nature of humans,
who highly discount the future, giving them a high discount rate and being more swayed
by money now rather than a slower, smaller, constant flow of money (e.g. paid maternity
leave and the assurance that a job is waiting for them after family leave).

Since the 1970’s women’s presence in the work force has increased in many
countries due to women’s rights and humanitarian reforms. This revolution has led to the
independence of women from men and challenged the traditional, societal role of women
at home. As more women began to work and depend less on their male counterparts,
divorce rates rose, fertility rates declined, and the work-life balance of having a job,
children, and a family were stretched thin (Quast, L., 2011). Paid maternity leave seems,
on the surface, a policy that would allow mothers and families to have it all. While
countries like the United States offer 12 weeks of protected leave for mothers, meaning
she has a right to take this time off from her job with the assurance of a comparable job
upon her return, paid leave offers some percentage of a woman’s wage while she is
tending to her newborn. This would, in theory, reduce the opportunity cost of having a
child and missing weeks of work all the while keeping women in the labor force.
Another argument that stems from the maternity leave debate is that paid leave
would allow a woman to care for her child longer, thus reducing the mortality rate of
infants. If a woman has more time to nurture her baby, the quality of her care is
improved as she is able to breastfeed for a longer period of time. The amount of income
she receives while taking a break from her job, while not as great as the amount she
would make by staying at work but greater than that she would receive without the
policy, would also allow her to increase her resources and improve the baby’s chance of
survival (Winegarden, C.R. & Bracy, P.M., 1995).
However, once a family with a working woman decides to have a child and the
woman returns to the workforce, what are the chances that she will leave again to have
another child? Without paid maternity leave, women may be more likely to drop out of

the workforce upon having a child. While having another child would result in some
financial obligation, this woman would not have to leave her job in order to do so. This
decision is quite different than the working woman, who may have to elect to put off a
promotion or professional opportunity to have one or more children.
Another problem concerning decreasing fertility rates is that many women with
professional careers choose not to have children or to put off having children all together.
A study published by Yale states that childlessness rates are strongly connected to
women’s educational levels (Chamie, J. & Mirken, B., 2012). For the working woman in
Switzerland, nearly 40% choose to be childless (Quast, L., 2011). Those who decide to
put off having children until they become older may struggle with fertility.
These social changes for women have resulted in substantial macroeconomic
gains. As countries chase gender equality, education rates rise, human capital is
enhanced, and productivity increases (Baptist, S., et al, 2010). While these changes are
good for families and result in better opportunities for the children born to them, the
macroeconomic gains associated with a higher educated female population may subside
as fertility rates plummet and the dying outnumber the born.
Does a higher amount of women in the workforce come at a fertility cost? The
hypothesis going forward is that as paid maternity leave programs increase, the fertility
rate will decrease. Because I suspect that women in the labor force and paid maternity
leave are positively correlated, I expect there to be no positive relationship between paid
maternity leave and increased fertility rates.

Data Description
The data I am using for the analysis of this question comes from multiple sources.
Much of it comes from the World Bank which provides a large amount of data to the
public. The infant mortality rate, GDP per capita, percent of population living in urban
centers relative to the total population, and the dependency rate all come from the World
Bank. This database provided the data for the years 1970 to 2013.
The second database I used was from the Organization for Economic Cooperation
and Development (OECD). From this database I retrieved the maximum length of paid
maternity leave for OECD countries.
The labor force participation rates for females comes from the U.S. Department of
Labor Bureau of Labor Statistics. This dataset contained the labor force participation
rates for ten countries beginning in 1960. Because this was the only data I could find
containing the labor force participation rates for females that covered such a wide length
of time, the decision was made to eliminate the countries which were not included on the
list and to focus instead on the ten countries with the data available. This decision, while
diminishing the amount of countries involved in the study, has enabled me to focus on the
long term effects of a paid maternity leave instead of using data for all OECD countries
beginning in the mid-1990s (when other data sets were available)
Descriptive statistics and a description of the variables used in my dataset are
summarized in Table 1. Using 440 observations from 10 countries over a period of 43
years, the average amount of paid maternity leave given to a woman is 14.7 weeks,
although some countries (the US included) do not offer paid maternity leave and others
(the United Kingdom)give up to a year of paid leave. This table also shows the wide

variation in country’s labor force participation rates, dependency rates, percent of the
population living in urban centers, and the change in infant mortality rates.
A list of the countries used in this analysis are located in Table 2. As previously
stated, these countries were chosen simply based on the available data for each. While
more observed countries would be preferable for this analysis, unfortunately much of this
data was missing.
Table 1. Description of Variables
Variable Description
m_weeks Number of weeks of paid leave available to mothers
f_rate Fertility rate
labfor Percentage of women participating in the labor force
urb Percentage of population living in an urban center
dep Dependency rate. Population age 65+ relative to population aged 15-64
imr Infant mortality rate (deaths per 1000 live births)
gdp_cap Per capita GDP at 2005 prices and exchange rates (US $)
lngdp_cap Natural log of gdp_cap
Table 2. Descriptive Statistics
Variable Obs. Mean Std. Dev. Min. Max.
m_weeks 440 14.7 11.73 0 52
f_rate 440 1.74 0.28 1.19 2.95
labfor 440 49 8.49 25.6 62.7
urb 440 77.05 6.44 61.66 92.49
dep 440 21.27 4.97 10.21 40.37
imr 440 7.97 4.62 2.1 29.7
gdp_cap 440 29544.59 7357.25 15155.83 46036.79
lngdp_cap 440 10.26 0.255 9.63 10.74
*This study uses data from Australia, Canada, France, Germany, Italy, Japan,
Netherlands, Sweden, United Kingdom, and the United States.
Empirical Model
In order to determine whether or not paid maternity leave or the presence of “baby
bonuses” affect fertility rates, I start with a panel regression of “f_rate” on “m_weeks.”

Using fertility rates as the dependent variable, I am able to add other independent
variables to the regression. However, because fertility rate may not be affected by policy
changes occurring in that same year, I conducted further regressions, testing different lag
effects. In total, I have four different methods of testing (lag year = 0, 1, 2, and 3) with
eight regressions for each of them. (See tables 2.1, 2.2, 2.3, 2.4)
I am regressing fertility rate on mandatory maternity leave with a vector of
country specific variables. Each of the equations is slightly different, some taking into
account country fixed effects, time fixed effects, both, or only certain years.
𝑌 = 𝐵0 + 𝐵1 𝑋1 + 𝐵1 𝑍1 + 𝑢 𝑖
As stated previously, to model the data so that fertility rates from a specific year
could be compared to the data from one year, two years, and three years prior, I also
regressed fertility on lagged regressor variables. This can be interpreted as: the fertility
rate of a country is affected by the government’s policy decisions made the previous year.
This similar thought process was used for the lag 2 and lag 3 year tables (Tables 2.3 and
2.4).
The results of the regressions can be seen in Tables 2.1, 2.2, 2.3, and 2.4 in the
Appendix.
These four separate regressions provide interesting details and allow me to
determine whether a change in policy has an immediate effect on fertility rates or whether
the effect does not occur for a couple of years. In all regressions, the final one (for years
1993 – 2013) seemed to have the least amount of omitted variable bias, with a substantial
R² value of at least 0.94. Furthermore, in almost every regression, mandated paid
maternity leave was not shown to have a significant effect on fertility rates. Similarly, we

see that baby bonuses never have a statistically significant effect on fertility rates in any
model.
The coefficient of determination rises as more variables are included into the
model, indicating that omitted variable bias is to blame in regression I, which emitted a
significant maternity leave coefficient. No matter which model is used (0, 1, 2, or 3 year
lag time) we see that the only variables that seem to be significant to the outcome of
fertility rates are the percent of women in the labor force, infant mortality rates, percent
of the population living in urban centers, and the dependency rate.
Discussion
Perhaps the most interesting result of this paper is that for the countries and years
selected, paid maternity leave seemed to have no significant effect on fertility rates. Even
as lag years were taken into account, the paid maternity leave coefficient is never
significant. However, contrary to the findings of Winegarden and Bracy (1995), we find
that in the set of regressions, as women’s participation in the labor force increases, so
does the fertility rate. I have some reservations about concluding that this is a long term
effect. My lag equations only take 1, 2, or 3 years of data into account, and long term
effects may be very different.
Another explanation for this finding is the general increase in women’s labor
force participation which may have occurred regardless of mandated maternity leave, as
it has in the United States. Similarly, an increase in education among women around the
world could have an effect on the labor force participation rate of women and the fertility
rate of women. Unfortunately, this data could not be found for all countries and years

included in this study. I believe that education rates among women may be a piece of
omitted variable bias that might give a biased analysis of this data.
We find that baby bonuses do not have any significant effect on fertility rates.
This may be for a few reasons. Baby bonuses are thought to have a short term effect on
fertility rates (Billari, 2009). Instead of increasing fertility rate, they speed up the births
of children that couples were already planning on having. Secondly, baby bonuses may
not be as attractive for a working woman as paid maternity leave, and thus do not have a
significant effect on labor force participation rates.
Some of the results discovered in these regressions which were not surprising
were that the lag models seemed to produce more significant coefficients than the model
with no lag, as it may take a year or more for the fertility rates of a country to be effected
by policies or changes in demographics. We also find that as the dependency rate
increases, fertility rates decrease. This is not very surprising, as individuals may delay or
forgo childrearing obligations if they are already caring for dependents.
One limitation in my data is the lack of variation in mandated maternity leave and
baby bonus payments over time. Many of the countries in my dataset did not change their
maternity leave policy for many years. Some countries have had baby bonuses in effect
since the 1970’s, while other do not have them at all, and finally even fewer have
implemented baby bonuses in the last couple of years. This makes the policy of paid
maternity leave and baby bonuses a very difficult one to analyze. Similarly, while some
countries may not offer baby bonuses, they may offer tax breaks or other incentives that
make having another child more attractive. For instance, many Japanese companies
pressured by politicians, have begun offering special deals to workers who have children

(Turner, D., 2007). While this may or may not be helpful in increasing fertility rates in
Japan, this could not be included in this macro model. Another difficulty in assessing the
benefit of baby bonuses is that many of these benefits are means-tested and may only
benefit women of a certain socioeconomic level. Other baby bonuses vary based on
whether or not the woman is having her first, second, or third child.
Another thing this study could not account for is the average amount of paid
maternity leave women receive. For instance, while paid maternity leave is not mandated
in the United States, many women receive paid maternity leave from the companies they
work for. This information may have skewed the data, as this was not taken into account.
Also, while some countries may offer a high percentage of a woman’s income to her
during her paid maternity leave, other countries do not. Because I did not have access to
the percentage of a woman’s wage offered to her during her paid maternity leave, I could
not account for these differences.
This data only uses information from ten countries. A shortage of
available data made it impractical to use any more countries. With more data, these
results may have been more significant or shown a larger effect of maternity leave or
baby bonuses on fertility rates. Using only these ten, developed countries may give a
biased analysis of the effect of paid maternity leave on fertility rates.
Conclusion
Unlike my initial hypothesis, it seems that a government mandated paid maternity
leave and “baby bonuses” have little to no significant effect on fertility rates. This could
be for a few different reasons. Firstly, the countries that have a mandated maternity leave

pay different percentages of the average weekly paycheck to new mothers. While one
country may pay new mothers very close to their average income, other countries are not
so generous. Secondly, the mandated maternity leave policies are not binding. So while
some mothers may take only the amount mandated by the government, others may
choose to go back to work earlier (due to the low percentage of paid wages) or stay out of
the workforce longer.
Another interesting piece of information that came up in this study is that while
some countries offer baby bonuses, these bonuses are typically means tested and thus
may only impact certain families with a specific socioeconomic status. In future studies,
it would be interesting to see how baby bonuses affect the fertility rates of families of
different incomes. Also, some companies in countries without baby bonuses offer
bonuses to their employees for new editions to their families. For example, under
pressure from the government, many Japanese companies are beginning to offer cash, cell
phones, and other incentives to new parents (Turner, D., 2007). Tracking these types of
incentives is difficult in a vast study such as the one I have done. Further, it is difficult to
decipher between what can be considered a baby bonus and what is a tax break in certain
situations. Some research shows that the success of baby bonuses increasing fertility rates
may also have to do with how much people trust their governments to follow through
with their promises, and that France has been the only country that has issued a
comprehensive policy that has increased fertility rates. However, this policy includes
mandated maternity leave, paid baby bonuses, and enormous tax breaks (Issues in Debate
Sociology, 2010). This paper did not look at tax breaks, although I believe that further

research may find that tax incentives are more attractive than bonuses and one time
payments.
In one study, researchers in Austria looked at a change in maternity leave policy
which shortened the duration of paid leave by 6 months and found that there was no
change in fertility rates (Lalive & Zweimuller, 2009), which may mean that maternity
leave is relatively inelastic. Even the paper by Winegarden that I modeled my research
after states that their final results showed that paid maternity leave had no statistically
significant effect on fertility rates.
In conclusion, this paper did not find any significant relationship between fertility
rates and paid maternity leave or baby bonuses. Perhaps future research that includes
socioeconomic variables such as income, education, and occupation may find a
correlation. Another interesting idea would be to look at the effect of certain tax policies
on fertility rates and perhaps focusing on one country.

Appendix A
Table 2.1 Regression Analysis of the Effect of Government Mandated Paid Maternity Leave on Fertility Rates
Dependent Variable: fertility rates (average births per woman)
Regressor 1 2 3 4 5 6 7 8
Weeks of Paid Maternity Leave -0.00559
(0.001)*
-0.00689
(0.004)
-0.00026
(0.004)
-0.00259
(0.003)
-0.00019
(0.003)
-0.00252
(0.003)
0.00353
(0.005)
Percent of Women in Labor
Force
0.013654
(0.004)**
0.0088
(0.007)
0.0028
(0.003)
0.01734
(0.008)*
IMR 0.032769
(0.011)**
0.0367
(0.019)*
0.03819
(0.012)**
-0.0469
(0.038)
GDP per Capita 0.000003
(0.000)
0.00002
(0.000)
GDP per Capita (logarithm) -0.30597
(0.266)
0.10636
(.5654)
Population in Urban Areas 0.01073
(0.0040)*
0.0054
(0.004)
-0.00504
(0.005)
Dependency Rate -0.0270
(0.004)**
-0.02694
(0.004)**
-0.00357
(0.0104)
Baby Bonus (binary) 0.02239
(0.043)
-0.008
(0.036)
-0.00753
(0.054)
Years 1970-
2013
1970-
2013
1970-
2013
1970-
2013
1970-2013 1970-
2013
1970-
2013
1993-2013
Country Effects No Yes Yes No Yes Yes Yes Yes
Time Effects No No Yes Yes Yes Yes Yes Yes
R^2 0.0521 0.3902 0.7537 0.5496 0.8084 0.8026 0.8458 0.9435
The individual coefficient is statistically significant at the +10%, *5%, **1%, or ***0.1% significance level.

Appendix B
Regressor 1 2 3 4 5 6 7 8
Weeks of Paid Maternity Leave
*Lagged 1 year
-0.00531
(0.001)**
-0.00457
(0.003)
-0.00054
(0.004)
-0.00241
(0.003)
-0.00112
(0.003)
-0.0008
(0.002)
0.00258
(0.006)
Percent of Women in Labor
Force *Lagged 1 year
0.014477
(0.004)**
0.01131
(0.007)
0.00699
(0.003)*
0.01459
(0.009)*
IMR
*Lagged 1 year
0.031886
(0.011)**
0.0361
(0.019)*
0.03851
(0.0118)**
-0.0634
(0.034)*
GDP per Capita
*Lagged 1 year
0.000003
(0.000)
0.00001
(0.000)
GDP per Capita (logarithm)
*Lagged 1 year
-0.31825
(0.293)
0.39024
(0.628)
Population in Urban Areas
*Lagged 1 year
0.01091
(0.003)**
0.00154
(0.004)
-0.00528
(0.005)
Dependency Rate
*Lagged 1 year
-0.02756
(0.005)**
-0.02433
(0.004)**
-0.00088
(0.0119)
Baby Bonus (binary)
*Lagged 1 year
0.02909
(0.039)
-0.0147
(0.0338)
-0.00982
(0.0501)
Years 1970-
2013
1970-
2013
1970-
2013
1970-
2013
1970-
2013
1970-
2013
1970-2013 1993-2013
R^2 0.0527 0.4345 0.7307 0.5168 0.7995 0.7856 0.8359 0.9443

Appendix C
Regressor 1 2 3 4 5 6 7 8
*Lagged 2 years
-0.00509
(0.001)**
-0.00255
(0.002)
-0.0011
(0.003)
-0.00228
(0.003)
-0.00223
(0.003)
-0.00061
(0.002)
0.0028
(0.006)
Percent of Women in Labor Force
*Lagged 2 years
0.015209
(0.004)**
0.01339
(0.007)*
0.01071
(0.003)**
0.01290
(0.008)+
IMR
*Lagged 2 years
0.03141
(0.011)**
0.0359
(0.019)*
0.03902
(0.012)**
-0.06965
(0.033)*
GDP per Capita
*Lagged 2 years
0.000003
(0.000)
0.00001
(0.000)
*Lagged 2 years
-0.34095
(0.329)
0.64855
(0.722)
*Lagged 2 years
0.01068
(0.003)**
-0.00239
(0.005)
-0.00562
(0.006)
Dependency Rate
*Lagged 2 years
-0.02757
(0.005)**
-0.02183
(0.005)**
-0.00319
(0.0131)
Baby Bonus (binary)
*Lagged 2 years
0.03322
(0.033)
-0.02447
(0.0291)
-0.00896
(0.045)
Years 1970-
2013
1970-
2013
1970-
2013
1970-
2013
1970-
2013
1970-
2013
1970-
2013
1993-2013
R^2 0.0549 0.4931 0.7065 0.4832 0.7941 0.7656 0.8323 0.9462

Appendix D
Regressor 1 2 3 4 5 6 7 8
*Lagged 3 years
-0.00504
(0.001)**
-0.00138
(0.002)
-0.0011
(0.003)
-0.00228
(0.003)
-0.00263
(0.003)
-0.00124
(0.002)
0.00451
(0.005)
Percent of Women in Labor Force
*Lagged 3 years
0.015862
(0.005)**
0.01476
(0.007)*
0.01328
(0.004)**
0.01125
(0.006)+
IMR
*Lagged 3 years
0.031205
(0.011)**
0.0354
(0.018)*
0.03912
(0.011)**
-0.07243
(0.0294)
*
GDP per Capita
*Lagged 3 years
0.000004
(0.000)
0.00001
(0.000)
*Lagged 3 years
-0.3292
(0.372)
0.84025
(0.774)
*Lagged 3 years
0.01064
(0.003)**
-0.00558
(0.006)
-0.00572
(0.006)
Dependency Rate
*Lagged 3 years
-0.02692
(0.006)**
-0.01977
(0.005)**
-0.00751
(0.013)
Baby Bonus (binary)
*Lagged 3 years
0.03901
(0.028)
-0.02902
(0.0282)
-0.00482
(0.041)
Years 1970-
2013
1970-
2013
1970-
2013
1970-2013 1970-
2013
1970-
2013
1970-2013 1993-
2013
R^2 0.0589 0.5474 0.6962 0.4696 0.7987 0.7555 0.8372 0.9488

Bibliography
Chamie, J., and Mirkin, B. (2012, March 2). ”Childless by Choice,” Yale Global Online.
Retrieved from http://www.yaleglobal.yale.edu
Day, D. (2008). “Andrew Fisher: Prime minister of Australia,” HarperCollinsPublishers.
P. 258
Gauthier, A.H., and Hatzius, J. (1997). “Family Benefits and Fertility: An econometric
analysis,” Population Studies, 51, 295-306.
Ghosh, P. (2012). “Japan’s Demographic Doom: Tokyo’s Population will be cut in half
over next century,” International Business Times. Retrieved from
http://www.ibtimes.com/
Issues for Debate in Sociology: Selections from CQ Researcher. Los Angeles:
SAGE/Pine Forge, 2010.
Lalive, R and Lalive, R. (2005). “Does Parental Leave Affect Fertility and Return-to-
Work? Evidence from a ‘True Natural Experiment,’” no. 1614.
Lalive, R and Zweimuller, J. (2009).“How Does Parental Leave Affect Fertility and
Return to Work? Evidence from two natural experiements,” Quarterly Journal of
Economics, 124.3, 1363-1402.
Quast, L. (201, February 14). “Causes And Consequences of the Increasing Numbers of
Women in the Workforce,” Forbes.com. Retrieved from http://www.forbes.com
Raute, A. (2013).“Do Financial Incentives Affect Fertility – Evidence from a reform in
maternity leave benefits,”National Institute of Economic and Social Reform
Risse, L. (2006). “Does Maternity Leave Encourage Higher Birth Rates: An analysis of
the Australian labour force, “Aust. J. Labour Econ., vol. 9, no. 4, pp. 343-370.
Turner, D. (2007). ”Japan offers baby bonus to workers,” The Financial Times. Retrieved
from http://www.ft.com
Ward, J., Lee, B., Baptist, S., and Jackson, H. (2010, September). “Evidence for Action:
Gender Equality and Economic Growth,” Chatham House: Vivid Economics.
Winegarden, C.R. and Bracy, P.M. (1995). “Demographic Consequences of Maternal
Leave Programs in Industrialised Countries: Evidence from fixed-effects models,”
Southern Economic Journal, 61, 1020-1035.

Maternity Leave.Baker (1)

Recommended

Recommended

More Related Content

What's hot

What's hot (20)

Similar to Maternity Leave.Baker (1)

Similar to Maternity Leave.Baker (1) (16)

Maternity Leave.Baker (1)