Abstract
The aim of this paper is to understand the relation
and mutual effect between female labor force participation and total fertility
rates in developing and OECD countries in a cross-country panel dataset. It's
finding a significant effect of the female labor force participation on
fertility rate in both developing countries and OECD countries. First, this
paper empirically discusses and presents a simulation model of the effect of
female labor force participation on fertility rate relating with GDP per
capita, Unemployment rate, and Infant Mortality rate. Second, this paper looks
specifically comparing at both developing and OECD countries and analyzes the
effect of female labor force participation on fertility in same cross-country
panel dataset using the data from the World Bank 2015. Finally, female labor
force participation is negatively related to fertility rate in developing
countries and positively related in OECD countries.
Keywords: total
fertility rate, infant mortality rate, female labor force participation rate,
GDP per capita and both unemployment rate.
1. Introduction
According to the statistic of the
population division of the United Nations (2015), the world’s population is
estimated to reach 7.2 billion in 2014, 6 billion in less developed countries
and 1.2 billion in more developed countries. It has been observed that falling
fertility rates and rising living standards go hand in hand, although the
direction of causality is less certain and the social revolution has been
indicated by a complex mix of economic and social development. There are some
of the core factors have been contributing factors to this trend such as
economic growth, greater access for women to education, income-earning
opportunities, and sexual and reproductive health service (Adsera, 2002). In
the world, we can see the obvious differences between developing countries and
OECD member countries. For instance, the level of economic development,
urbanization, mechanization of agriculture, the level of population growth and
aging are significantly seen among developing countries and OECD
countries. According to the report of
World Bank (2013), In the case of total fertility rate, we had been noticed
that the fertility rate had been decreasing and will continuous to decrease as
the above circumstance happened in the world. In the developed countries,
increasing female labor force participation has been related to the completion
of the fertility transition. However, declining of fertility has been slowed or
delayed in developing countries. Some of the observations indicate that
fertility rates are positively correlated with female labor across OECD
countries during the period 1985-1996. Moreover, the time series observations
show that fertility rates are not likely in all developed countries (Bover and
Arellano, 1994). So, the possible reason for the decline in fertility rates
included that the economic development, the diffusion of birth control
techniques and other factors such as urbanization, labor migration and
education may also play an important role in the world.
In this paper, the objective of the
study is to examine the relationship between fertility rate and female labor
force participation and to observe how to different the relation between developing
and OECD member countries and why.
2. Overview of the Relation between Fertility
Rate and Female Labor Force Participation
In the past decades, some developed
countries have experienced declining fertility rates combined with an increase
in female labor. Among OECD countries, the total fertility rates decreased from
2.6 to 1.6 children per woman during 1970 to 2000 (World Bank, 2016). Due to
the reforming of social environment, female education and participation in the
labor market have been dramatically increasing and these are also strongly
related to family lifestyle including fertility rates and the general standard
of living in the social environment (Gaddis and Klasen, 2014). The scholar Fehr
and Ujhelyiova (2013) discussed that regarding fertility and labor are depend
on a female’s age and geographic location and also many female in developing
countries are less likely to work when they have more children. However, some
are more likely to work possibly because of their personal income and they have
the ability to work from home. However, in the 20 century, the relation between
the fertility rate and the female labor force participation rate shifted from a
negative correlation to a positive among the Organization for Economic
Cooperation and Development (OECD) countries and it can’t say evidently in the
current situation. Thus, social development and economic development are very
closely related to the current situation and these causal relations also more
complicated than the past time. Therefore, we need to clearly identify how much
social sector development is the effect on the current family lifestyle
relating to government public policy. Thus, in the paper, I will examine how
the relation between fertility rate and female labor force rate and also how
other closed factors effect on that causal relation in among the countries.
3. Literature Reviews
There are several empirical results
regarding with the relation between the female labor force participation and
the fertility rate. Mammen and Christina (2000) discussed that the relationship
between female labor rates and per capita income to be U-shaped and it will
conduct with agricultural and industrial economies, and female participation is
high as their family responsibilities. A number of studies, using information
from both developing and developed countries showed that female education is
associated with a decrease in fertility and fertility declines related to
increasing of female employment while studies from various countries (Lam and
Duryea, 1999; Guilkey, 1998; Schultz, 1993). Moreover, one empirical evidence
pointed out that increasing of female income, labor, and education are
associated with a decrease in fertility among the countries in last decades
(Vavrus and Larsen, 2003).On the other hand, increased participation of women
in schooling and the labor market is increase the economic value of their time,
which directly related to raising fertility rate in industrialized countries
(Singh, 1994; BenPorath, 1973).
Furthermore, in the OECD countries, the
correlation of the total fertility rate and female participation rates was the
negative related during 1980 to 2000 and it turned to a positive relationship
in the 20 century (Brewster and Rindfuss, 2000; Higuchi 2004). According to
Engelhardt, Tomas, and Prskawetz (2004) argued that the rising female labor
participation rate in OECD countries have also increased the birth rate in the
current phenomenon. Likewise, the scholar Adsera (2002) argued that the
fertility rates have decreased during the last two decades and it has become
positively related with female labor rates across OECD countries.
Beyond this literature, this paper examines
the cross-country comparison of a relation between fertility rate and female
labor force participation among the developing and OECD countries. Due to the
socio-economic development and increasing of living standard of lifestyles,
these all factors will obviously effect in the direction of the relation
between fertility rate and female labor force participation. Therefore, in the
paper, I will emphasize on how this basic conducted relation between fertility
rate and female labor force participation and also the combined effect of
female labor force participation along with some control variables by using the
secondary data from World Bank (2016).
4. Research Question
What is the correlation between female
labor force participation rate and fertility rate among the developing and OECD
countries?
5.
Significance
of the study
Recent
economic trends highlighted the importance of female labor force participation
especially in developing countries compared to developed countries. According
to the statistic of world employment and social outlook report of the
international labor organization, it’s obviously indicated that the female
labor force participation has been dramatically increasing in both developing
and developed countries. Because, due to the socioeconomic sector developing,
the increasing of job opportunity and the labor migration, the participation
level of the female labor force has been increased and also the individual
income level also has been increased. So, due to the female labor force
participation, how the social sector will continue and how to impact the social
lifestyle. Due to this improved recognition in this field, I would like to
address this issue.
6. Hypothesis
I examined the relationship between
female labor force participation and fertility rate are;
6.1. Hypothesis: Female
labor participation improves the fertility rate in both developing and OECD
countries.
7.
Methodology
The
main objective of the study is to examine the relation between the female labor
force participation rates and total fertility rate, using the panel data
analysis from 2003-2014. I used the secondary data source which is collected by
the World Development Indicators from World Bank (2016).
7.1. Data. In
the paper, I conducted a randomized two groups, developing countries, and OECD
member countries. The data set I use in a twelve-year panel covering the period
from 2003 to 2014 for 10 developing countries and 10 countries which are the
member of OECD as shown in table 1. The dependent variable in our empirical
analysis is total fertility rate (births per woman). The dataset of total
fertility rates is from the World Development Indicators (World Bank 2016).
Moreover, my explanatory variables are the female labor force participation
rate (% of female population ages 15+), GDP per capita (current US$), the both
gender unemployment (% of total labor force) and infant mortality rate (per
1,000 live births). The female labor force participation, GDP per capita, total
unemployment rate, and infant mortality rate are also from the World
Development Indicators (World Bank 2016).
Table 1 list of
developing countries and OECD Countries
Developing Countries
|
OECD Countries
|
|||||||
No.
|
Country
|
No.
|
Country
|
No.
|
Country
|
No.
|
Country
|
|
1
|
Afghanistan
|
6
|
Philippines
|
1
|
Australia
|
6
|
Italy
|
|
2
|
Malaysia
|
7
|
Indonesia
|
2
|
Finland
|
7
|
Japan
|
|
3
|
Thailand
|
8
|
Pakistan
|
3
|
France
|
8
|
Norway
|
|
4
|
Bangladesh
|
9
|
Mongolia
|
4
|
Greece
|
9
|
Ireland
|
|
5
|
Vietnam
|
10
|
Cambodia
|
5
|
Belgium
|
10
|
Turkey
|
|
For
the developing countries, the significance of their above 15 age’s female labor
force participation has been rising from 50 % to 51.9% during 2003 to 2014 as
shown in figure 1. The per capita GDP of
developing countries have also risen since 2003.On the other hand, due to the
statistic of World Development Indicators from Word Bank (2016) indicated that
the fertility rate of developing countries have been decreased in that period
and will continue to decrease. Likewise, due to the same secondary data source
presented that the mortality rate of developing countries also have been
decreased and will continue to decrease. Therefore, it implies that the
fertility rate and female labor force participation have been strongly related
in developing countries.
Figure 1: fertility
rate, female labor force participation, infant mortality rate and GDP per
Capita in developing countries, 2003-2014
Figure 1.1 Fertility rate, total (births per woman) Figure 1.2 Labor force participation rate, female
Figure 1.3
Mortality rate, infant (per 1,000 live births) Figure 1.4 GDP per capita
(current US$)
Source: World Bank (2016)
On
the other hand, during 2003 to 2009, the implication of above 15 age’s female
labor force participation has risen from
46 % to 49% in the OECD countries and
it's still slowly increasing after 2011
as shown in figure 2. The per
capita income of OECD countries is also slightly raised since 2003.On the other
hand, due to the statistic of World Development Indicators from Word Bank
(2016) presented that the fertility rate of developing countries had sharply
increased in 2003 to 2007 but it has been decreased after 2008 and will
continue to decrease. However, due to the same secondary data source showed
that the mortality rate of developing countries also have been slightly
decreased and will continue to decrease. Therefore, this implies that the
relation of fertility rate and female labor force participation will be
correlated with some factors in OECD countries.
Figure 2: fertility rate,
female labor force participation, infant mortality rate and GDP per Capita in OECD countries, 2003-2014
Figure
1.1
Fertility rate, total (births per
woman) Figure 1.2 Labor force participation rate, female
Figure 1.3
Mortality rate, infant (per 1,000 live births) Figure 1.4 GDP per capita
(current US$)
Source: World Bank (2016)
7.2. Method. To
answer the hypothesis, I will use the OLS regression to find the correlation
between dependent and independent variables. To examine whether fertility rate
and female labor force participation I assume
the simple model as following:
7.2.1. Model 1
(Fertility rate) =
β0 + β1 (Female Labor force) +e
This specially
tries to identify the sole impact of female labor force participation.
7.2.2. Model 2
(Fertility rate) =
β0 + β1 (Female Labor force) + β2 (Log_GDP per
capita) +
β3 (Unemployment rate (Both)) + β4
(Infant Mortality rate) +e
This tries to identify the combined
effect of female labor force participation along with some control variables; GDP
per Capita, both employment rate and infant mortality rate.
8. Result and Discussion
Table 2 displays a number of observation, mean
and standard deviation for the five variables of the OECD countries and
developing countries. The average of Female labor, per capita income and the
total unemployment rate of OECD countries are greater than the developing
countries. Nevertheless, the average of infant mortality rate and fertility
rate are less than the developing countries.
Table
2
Average of the variables of developing countries and OECD Countries
OECD countries
|
Developing Countries
|
||||||
Variable
|
N
|
mean
|
Std. Dev.
|
N
|
mean
|
Std. Dev.
|
|
Fertility rate
|
120
|
1.76167
|
0.28177
|
120
|
2.91558
|
1.26695
|
|
Female labor force
|
120
|
48.1167
|
9.95012
|
120
|
50.9275
|
19.1863
|
|
GDP per capita
|
120
|
41716.5
|
20042.2
|
120
|
2320.65
|
2464.67
|
|
Unemployment rate
total
|
120
|
8.06583
|
4.12485
|
120
|
4.8425
|
2.95298
|
|
Mortality rate(infant)
|
120
|
4.9975
|
4.85322
|
120
|
36.3308
|
23.596
|
|
Source:
world Development Indicators (World Bank 2016)
Due
to the empirical analysis for model 1, the dependent variable in the empirical
analysis is fertility rate and the independent variable is female labor force
participation. In the case of model 2, the dependent variable in the empirical
analysis is same but the explanatory variables are the female labor force
participation, GDP per Capita, both unemployment rate and infant mortality
rate. The regression result of model 1
and model 2 are as shown in Table 3a and Table 3b.
Table
3a:
Regression Analysis predicting female labor force participation on fertility
rate in developing countries and OECD Countries.
Total
Fertility rate
|
||
VARIABLES
|
Developing
Countries
|
OECD
Countries
|
Labor force participation
rate, female
|
-0.0499***
|
0.00104
|
(0.00399)
|
(0.00261)
|
|
Constant
|
5.455***
|
1.712***
|
(0.217)
|
(0.128)
|
|
Observations
|
120
|
120
|
R-squared
|
0.570
|
0.001
|
Standard errors in
parentheses
***
p<0.01, ** p<0.05, * p<0.1
Table
3a presents the regression result compares the impact of above 15 age’s female
labor force participation rate on fertility rate for developing and OECD
countries. Results yields that labor force participation rate is significant at
1% for developing countries with a negative coefficient of 0.0499, whereas
female labor force participation rate is not significant for OECD countries. One
reason could be due to lifestyle style change after commencing a job
opportunity. As developing countries are known for slow paced growth, it could
be difficult for them to adapt to new lifestyle change. Therefore, they may
lose family time.
Table 3b: Regression
Analysis predicting related variables on fertility rate in developing countries
and OECD Countries
Developing
|
OECD
|
|
Total Fertility Rate
|
Countries
|
Countries
|
Labor force participation
rate, female
|
-0.0355***
|
0.0169***
|
(0.00708)
|
(0.00309)
|
|
Log_GDP per capita
|
-0.472***
|
0.345***
|
(0.143)
|
(0.0711)
|
|
Unemployment, total (% of
total labor force)
|
0.0117
|
0.0114***
|
(0.0271)
|
(0.00430)
|
|
Mortality rate, Infant (per
1,000 live births)
|
0.0108
|
0.0881***
|
(0.00738)
|
(0.00618)
|
|
Constant
|
7.716***
|
-3.208***
|
(1.678)
|
(0.702)
|
|
Observations
|
120
|
120
|
R-squared
|
0.808
|
0.679
|
Standard errors in
parentheses
***
p<0.01, ** p<0.05, * p<0.1
Table
3b presents the regression result for fertility rate and four variables. In the
table, the Column 1and 2 present how
much female labor force participation rate effect on fertility rate
contribution of GDP per capita, both
unemployment rate and infant mortality rate in both developing
and OECD member countries. Results show that labor force participation
rate is significant at 1% for both developing with a negative coefficient of
0.0355 and OECD countries with a positive coefficient of 0.0169. In the
developing countries, if the about 15 age’s female labor force participation
rate increases by 1 percentage, the fertility rate will decrease by 3.5
percentage. For other variables, the coefficient of GDP per capita is
negatively correlated and it has significant at 1% in developing countries with
a negative coefficient of 0.427. However, the coefficient of infant mortality
rate and both unemployment rate are positively related to fertility rate and
these are not significant in the developing countries.
On
the other hand, the results show that the coefficient of the above 15 age’s
female labor force participation rate is positive and it has 1% significant
level in OECD countries. It implies that if the about 15 age’s female labor
force participation rate increases by 1 percentage, the fertility rate will
increase by 1.7 percentage. For other variables, the coefficient of GDP per
capita is also positively correlated and it has significant at 1% in OECD
countries with a positive coefficient of 0.345. Moreover, the coefficient of
both unemployment is also positively correlated and it has significant at 1%
with a positive coefficient of 0.0881 in OECD countries. Additionally, the
coefficient of infant mortality rate is negatively correlated and it also has
significant at 1% with a negative coefficient of 3.208 in OECD countries.
For
all variables, the coefficient of female labor force and GDP per capita are
significant negatively, and both coefficients of infant mortality rate and
unemployment are not significant positively in developing countries. In the
OECD member countries, the coefficients of female labor force and GDP per
capita are significant positively, and the coefficient of unemployment is
significant positively, but the coefficient of infant mortality rate is
significant negatively in OECD countries.
It
can be well-defined that the female labor force participation is negatively
impacted on the fertility rate in the developing and it also positively
impacting in the OECD countries. It implies that the consequences of the
socioeconomic status, personal income, health, and education status strongly
impact on the relation between female labor and fertility among the developing
and OECD countries. Moreover, these results suggest that the level of economic,
social and health are the statistically significant effect on the countries’
fertility rate in the two decades. Furthermore, in the OECD countries (with
per-capita GDP of at least $20,000), during 2003 to 2014, there was a positive
correlation between the female labor force participation rate and the total
fertility rate. It implies that countries with higher labor force participation
rate also supported to increase the birth rate or fertility rate. However, in
the developing countries, the correlation was negative. Because these countries
with higher labor force participation rate having decreased birthrates. These results
suggest that among the developing and OECD countries, some of the social
environments such as countries policies, gender gap, and job opportunity
systems influence on the both rates and also relating environments have changed
obviously. Therefore, according to the results that were obtained in the table
3a and 3b, it implies that hypothesis is incorrect for the developing
countries. However, the labor force participation rate has a positive effect on
fertility in OECD countries.
Moreover,
the model 2 result can be argued that the advantage of economic development and
labor opportunity eventually lead to the advantage of fertility rate in OECD
countries. However, the advantage of economic development and labor opportunity
eventually leads to the disadvantage of fertility rate in developing countries.
9. Discussion
due
to the empirical analysis for 12-year panel covering the period from 2003 to
2014 for both 10 developing and 10 OECD
countries, these empirical results imply that the effect of female labor force
participation on the fertility rate obviously. This result supports to
quantitatively understand the obvious difference of social environment among
the developing and OECD countries. It mean that cause of the increasing of
female labor force participation and the level of fertility rate in OECD
countries have been significantly related to other factors such as the social
and economic systems, including customary practices such as family
relationships, work and lifestyles, social relationships, and labor market
structure, and public policy and so on.
Additionally,
due to the results of the table 4a for developing countries, the results show
that labor force participation rate is significant at 1% from 2003 to 2009
yearly and it changes to 5% after 2009. And the coefficient of the female labor
force is negative in every year. It implies that the correction between female
labor and fertility rate are significantly related and another factor cannot
too much influence in these mutual relation. Because, due to the results of the
table 4b for developing countries, the results show that the female labor force
participation rate is not significant but still have the negative relation on
fertility. On the other hand, according to the results of the table 5a for OECD
countries, these yearly result of model 2 regression also not significant but
it has the positive relation on the fertility. Besides, the results of the
table 5b, it can obviously see that the most of the significant result and all
the coefficient of female labor are positive. Therefore, the changing of
society, religion, government’s public policy and socio-economic developing are
certainly an influence on the fertility rate of both developing and OECD
countries.
If I
continuously discuss the limitation of the paper, the selecting type for the
sample countries is just randomly selected 10 OECD member countries and
developing countries out of 194 countries and panel data was just focused on
recent decades. Therefore, even there are similar situations in among the OECD
countries, some of the growth levels of developing countries are still
different due to their religion and demographical condition. Thus, in the
paper, to prove the most significant relation between female labor and
fertility rate, some of the closest related factors still need to add in the
empirical analysis.
In order to focus on policy
implication, for developing countries, the government must find and resolve
both adjustment fertility rate controlling and more provide job opportunity to
the female who are staying in their countries. Moreover in order to improve
their public policy relating to female labor force participation and fertility,
the government should consider the Level of economic development, educational
attainment, social dimensions and Institutional setting such as laws,
protection, and benefits.
10.
Conclusion
This paper attempts to find
cross-country comparison of the relation between fertility rate and female
labor force participation among the developing and OECD countries over the
period of 2003-2014. Due to the main purpose of this paper, the result was
clearly pointed out the status of current female labor and investigated the
relationship between female labor and fertility by using secondary data and OLS
regression. The changing nature of female’s participation in the labor force has
been an important aspect of the development process of the countries and the
changing nature of fertility rate also has been serious in all countries.
Eventually, female’s labor is driven by a range of complicated factors,
including their income, fertility rates, social norms, and the opportunity of
their job. And also, the fertility rate is also driven by new lifestyle,
including the development of social environment, the level of education and
health and also labor chance eventually.
11. References
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Bover, O. and
Arellano, M. (1994). Female Labor Force Participation in the 1980s: The Case of
Spain. Working paper 9427. Banco de Española.
Brewster K.,
Rindfuss R. (2000) Fertility and Women’s Employment in Industrialized Nations.
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Prskawetz, A. (2004). "On the changing correlation between fertility and
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Engelhardt, H.,
Tomas K., and Prskawetz, A. (2004). “Fertility and Women’s Employment
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1900-2000.” Population Studies 58: 109-120.
Fehr, H., &
Ujhelyiova, D. (2013). Fertility, female labor supply, and family policy.
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Goldin, C. (1995).
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S. (2014). “Economic development, structural change, and women’s labor force
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Guilkey, D.,
Angeles, G. and Mroz, T. (1998). The Measurement of Indirect Program Impact
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Higuchi, Y.
(2004). Employment for women and measures against a declining birth of Effective measure to Slow Japan’s Declining
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Duryea, S. (1999). “Effects of Schooling
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Appendix
Figure 3: Fertility rate,
total (births per woman) in Developing Countries
Source: World development indicators, The World Bank
(2016)
Figure 4:
Labor force participation rate, female in Developing Countries (% of female
population ages 15+)
Source: World development indicators, The World
Bank (2016)
Figure 5: GDP
per capita (current US$) in Developing Countries
Source: World development indicators, The World
Bank (2016)
Figure 3: Fertility rate, total (births per woman)
in OECD Countries
Source: World development indicators, The World
Bank (2016)
Figure 4: Labor force participation rate, female in
OECD Countries
(%
of female population ages 15+)
Source: World development indicators, The World
Bank (2016)
Figure 5: GDP
per capita (current US$) in OECD Countries
Source: World development indicators, The World
Bank (2016)
Table
4a:
Regression analysis relation between fertility rate and female labor force
participation in developing countries by yearly
Developing
countries
|
||||||||||||
Fertility rate
|
2003
|
2004
|
2005
|
2006
|
2007
|
2008
|
2009
|
2010
|
2011
|
2012
|
2013
|
2014
|
fmlbr
|
-0.0587**
|
-0.0582**
|
-0.0567**
|
-0.0554**
|
-0.0528**
|
-0.0502**
|
-0.0487**
|
-0.0469***
|
-0.0447***
|
-0.0424***
|
-0.0401***
|
-0.0380***
|
(0.0180)
|
(0.0175)
|
(0.0170)
|
(0.0166)
|
(0.0158)
|
(0.0152)
|
(0.0146)
|
(0.0139)
|
(0.0131)
|
(0.0123)
|
(0.0116)
|
(0.0110)
|
|
Constant
|
6.107***
|
6.025***
|
5.916***
|
5.807***
|
5.649***
|
5.481***
|
5.378***
|
5.254***
|
5.106***
|
4.952***
|
4.799***
|
4.649***
|
(0.966)
|
(0.938)
|
(0.916)
|
(0.896)
|
(0.857)
|
(0.826)
|
(0.793)
|
(0.759)
|
(0.716)
|
(0.674)
|
(0.638)
|
(0.606)
|
|
Observation
|
10
|
10
|
10
|
10
|
10
|
10
|
10
|
10
|
10
|
10
|
10
|
10
|
R-squared
|
0.571
|
0.581
|
0.582
|
0.582
|
0.583
|
0.577
|
0.583
|
0.587
|
0.594
|
0.598
|
0.600
|
0.599
|
Standard errors in
parentheses
*** p<0.01, **
p<0.05, * p<0.1
Note: fmlbr is Labor force participation rate, female
(% of female population ages 15)
Table 4b:Regression analysis
predicting related variables on fertility rate in developing country by yearly
Developing Countries
|
||||||||||||
Fertility rate
|
2003
|
2004
|
2005
|
2006
|
2007
|
2008
|
2009
|
2010
|
2011
|
2012
|
2013
|
2014
|
fmlbr
|
-0.0509
|
-0.0563
|
-0.0628*
|
-0.0543
|
-0.0459
|
-0.0373
|
-0.0471
|
-0.0269
|
-0.0175
|
-0.00915
|
-0.00334
|
-0.0135
|
(0.0326)
|
(0.0322)
|
(0.0306)
|
(0.0293)
|
(0.0321)
|
(0.0349)
|
(0.0369)
|
(0.0351)
|
(0.0343)
|
(0.0331)
|
(0.0320)
|
(0.0288)
|
|
Log_Gdperpt
|
-0.838
|
-1.019
|
-1.107
|
-0.963
|
-0.740
|
-0.651
|
-0.875
|
-0.471
|
-0.313
|
-0.157
|
-0.0951
|
-0.274
|
(0.903)
|
(0.844)
|
(0.770)
|
(0.671)
|
(0.729)
|
(0.739)
|
(0.807)
|
(0.712)
|
(0.629)
|
(0.595)
|
(0.582)
|
(0.579)
|
|
unemp
|
-0.00309
|
-0.0147
|
-0.0682
|
-0.0251
|
-0.00979
|
0.0202
|
-0.0246
|
0.0675
|
0.0962
|
0.110
|
0.124
|
0.0953
|
(0.128)
|
(0.109)
|
(0.113)
|
(0.113)
|
(0.122)
|
(0.139)
|
(0.140)
|
(0.121)
|
(0.123)
|
(0.118)
|
(0.109)
|
(0.0975)
|
|
Infantmrt
|
0.000756
|
-0.00726
|
-0.0113
|
-0.00585
|
0.00282
|
0.00604
|
-0.00384
|
0.0121
|
0.0174
|
0.0245
|
0.0268
|
0.0139
|
(0.0365)
|
(0.0361)
|
(0.0338)
|
(0.0309)
|
(0.0348)
|
(0.0370)
|
(0.0392)
|
(0.0377)
|
(0.0349)
|
(0.0345)
|
(0.0343)
|
(0.0335)
|
|
Constant
|
11.27
|
13.20
|
14.66
|
12.88
|
10.56
|
9.279
|
11.95
|
7.030
|
5.113
|
3.213
|
2.271
|
4.692
|
(9.097)
|
(8.745)
|
(8.103)
|
(7.328)
|
(8.183)
|
(8.686)
|
(9.383)
|
(8.522)
|
(7.809)
|
(7.449)
|
(7.313)
|
(7.040)
|
|
Observations
|
10
|
10
|
10
|
10
|
10
|
10
|
10
|
10
|
10
|
10
|
10
|
10
|
R-squared
|
0.792
|
0.818
|
0.839
|
0.852
|
0.836
|
0.846
|
0.857
|
0.859
|
0.864
|
0.860
|
0.871
|
0.870
|
Standard errors in
parentheses
*** p<0.01, **
p<0.05, * p<0.1
Note: fmlbr is Labor force participation rate, female
(% of female population ages 15+), Log_Gdpercpt is Log of GDP per capita
(current US$), unemp is Unemployment, total (% of total labor force) and Infantmrt
is Mortality rate, infant (per 1,000 live births)
Table 4c: Regression
analysis relation between fertility rate and female labor force participation
in developing country by country
Developing
Countries
|
||||||||||
Fertility rate
|
Afghanistan
|
Malaysia
|
Thailand
|
Bangladesh
|
Vietnam
|
Philippines
|
Indonesia
|
Pakistan
|
Mongolia
|
Cambodia
|
fmlbr
|
-0.889***
|
0.370
|
0.0236***
|
-0.259***
|
0.0271
|
-0.0635
|
0.0127*
|
-0.0645***
|
0.107
|
-0.137***
|
(0.0450)
|
(0.428)
|
(0.00713)
|
(0.00840)
|
(0.0249)
|
(0.0641)
|
(0.00674)
|
(0.00586)
|
(0.0802)
|
(0.0268)
|
|
Constant
|
19.23***
|
-14.31
|
0.0163
|
17.08***
|
-0.0424
|
6.440*
|
1.845***
|
5.323***
|
-3.519
|
13.66***
|
(0.668)
|
(18.97)
|
(0.463)
|
(0.474)
|
(1.809)
|
(3.209)
|
(0.343)
|
(0.129)
|
(4.463)
|
(2.085)
|
|
Observations
|
12
|
12
|
12
|
12
|
12
|
12
|
12
|
12
|
12
|
12
|
R-squared
|
0.975
|
0.070
|
0.524
|
0.990
|
0.106
|
0.089
|
0.263
|
0.924
|
0.150
|
0.724
|
Standard
errors in parentheses
***
p<0.01, ** p<0.05, * p<0.1
Note: fmlbr is Labor
force participation rate, female (% of female population ages 15)
Table 4d:Regression analysis
predicting related variables on fertility rate in developing country by country
Developing
Countries
|
||||||||||
Fertility rate
|
Afghanistan
|
Malaysia
|
Thailand
|
Bangladesh
|
Vietnam
|
Philippines
|
Indonesia
|
Pakistan
|
Mongolia
|
Cambodia
|
fmlbr
|
-0.729**
|
0.402*
|
-0.00598
|
-0.0841
|
0.0112
|
0.00972
|
0.0427**
|
0.00203
|
-0.00770
|
0.00158
|
(0.258)
|
(0.174)
|
(0.00332)
|
(0.106)
|
(0.0108)
|
(0.0117)
|
(0.0127)
|
(0.00752)
|
(0.0142)
|
(0.00188)
|
|
Log_GDPerpt
|
0.903*
|
-0.229*
|
0.105**
|
0.355***
|
0.114
|
-0.278
|
0.104**
|
-0.139
|
0.0129
|
-0.0399
|
(0.385)
|
(0.115)
|
(0.0387)
|
(0.0833)
|
(0.0906)
|
(0.153)
|
(0.0339)
|
(0.0954)
|
(0.0866)
|
(0.0216)
|
|
unemp
|
0.0332
|
-0.0308
|
0.0164
|
-0.0260
|
0.00713
|
0.00855
|
0.00741
|
-0.0149
|
-0.00443
|
-0.0185***
|
(0.0985)
|
(0.0524)
|
(0.00996)
|
(0.0224)
|
(0.0129)
|
(0.00965)
|
(0.00418)
|
(0.0134)
|
(0.0203)
|
(0.00337)
|
|
mrtfmlrt
|
0.0823**
|
0.276*
|
0.0296***
|
0.0331**
|
0.0137
|
0.0628*
|
0.0135***
|
0.0326***
|
-0.0309***
|
0.0214***
|
(0.0235)
|
(0.137)
|
(0.00645)
|
(0.0125)
|
(0.0212)
|
(0.0313)
|
(0.00296)
|
(0.00403)
|
(0.00851)
|
(0.000565)
|
|
Constant
|
4.718
|
-15.46
|
0.650
|
3.593
|
0.0132
|
3.181
|
-0.936
|
2.450**
|
3.652**
|
2.235***
|
(4.741)
|
(9.041)
|
(0.392)
|
(6.545)
|
(1.646)
|
(2.230)
|
(0.656)
|
(0.971)
|
(1.353)
|
(0.150)
|
|
Observations
|
12
|
12
|
12
|
12
|
12
|
12
|
12
|
12
|
12
|
12
|
R-squared
|
0.994
|
0.962
|
0.975
|
0.997
|
0.940
|
0.992
|
0.841
|
0.997
|
0.985
|
1.000
|
Standard
errors in parentheses
***
p<0.01, ** p<0.05, * p<0.1
Note: fmlbr is Labor
force participation rate, female (% of female population ages 15+), Log_Gdpercpt
is Log of GDP per capita (current US$), unemp is Unemployment, total (% of
total labor force) and Infantmrt is Mortality rate, infant (per 1,000 live
births)
Table
5a:
Regression analysis relation between fertility rate and female labor force
participation in OECD countries by yearly
OECD Countries
|
||||||||||||
VARIABLES
|
2003
|
2004
|
2005
|
2006
|
2007
|
2008
|
2009
|
2010
|
2011
|
2012
|
2013
|
2014
|
fmlbr
|
-0.00356
|
-0.00463
|
-0.00291
|
-0.000277
|
0.00135
|
0.00263
|
0.00361
|
0.00333
|
0.00406
|
0.00372
|
0.00209
|
0.00246
|
(0.0116)
|
(0.0105)
|
(0.0101)
|
(0.00975)
|
(0.00936)
|
(0.00888)
|
(0.00908)
|
(0.00940)
|
(0.00970)
|
(0.0101)
|
(0.0101)
|
(0.0100)
|
|
Constant
|
1.868***
|
1.935***
|
1.855***
|
1.778***
|
1.716***
|
1.690***
|
1.642***
|
1.652***
|
1.586**
|
1.586**
|
1.627**
|
1.607**
|
(0.554)
|
(0.504)
|
(0.491)
|
(0.475)
|
(0.461)
|
(0.441)
|
(0.450)
|
(0.467)
|
(0.480)
|
(0.499)
|
(0.501)
|
(0.498)
|
|
Observations
|
10
|
10
|
10
|
10
|
10
|
10
|
10
|
10
|
10
|
10
|
10
|
10
|
R-squared
|
0.012
|
0.024
|
0.010
|
0.000
|
0.003
|
0.011
|
0.019
|
0.015
|
0.021
|
0.017
|
0.005
|
0.007
|
Standard
errors in parentheses
***
p<0.01, ** p<0.05, * p<0.1
Note: fmlbr is Labor
force participation rate, female (% of female population ages 15)
Table
5b:
Regression analyses predicting related variables on fertility rate in OECD
countries by yearly
OECD Countries
|
||||||||||||
Fertility rate
|
2003
|
2004
|
2005
|
2006
|
2007
|
2008
|
2009
|
2010
|
2011
|
2012
|
2013
|
2014
|
fmlbr
|
0.0220
|
0.0181
|
0.0209*
|
0.0242**
|
0.0231*
|
0.0267**
|
0.0208*
|
0.0168
|
0.0155
|
0.0233
|
0.0176
|
0.0143
|
(0.0111)
|
(0.0105)
|
(0.0102)
|
(0.00865)
|
(0.00966)
|
(0.00935)
|
(0.00838)
|
(0.0134)
|
(0.0175)
|
(0.0212)
|
(0.0199)
|
(0.0179)
|
|
Log_GDPerpt
|
0.648
|
0.873*
|
0.634*
|
0.562*
|
0.493*
|
0.446*
|
0.516*
|
0.550
|
0.429
|
0.144
|
0.199
|
0.314
|
(0.380)
|
(0.369)
|
(0.300)
|
(0.235)
|
(0.242)
|
(0.219)
|
(0.254)
|
(0.367)
|
(0.457)
|
(0.500)
|
(0.396)
|
(0.349)
|
|
unemp
|
0.0510
|
0.0644
|
0.0701*
|
0.0759**
|
0.0645
|
0.0956**
|
0.0471**
|
0.0422*
|
0.0177
|
0.000645
|
-0.00273
|
-0.00163
|
(0.0487)
|
(0.0362)
|
(0.0330)
|
(0.0278)
|
(0.0321)
|
(0.0325)
|
(0.0176)
|
(0.0209)
|
(0.0206)
|
(0.0190)
|
(0.0139)
|
(0.0128)
|
|
mrtfmlrt
|
0.102**
|
0.118***
|
0.101***
|
0.0991***
|
0.0950***
|
0.0845***
|
0.102***
|
0.108**
|
0.112**
|
0.1000*
|
0.107**
|
0.126**
|
(0.0294)
|
(0.0268)
|
(0.0204)
|
(0.0171)
|
(0.0183)
|
(0.0161)
|
(0.0230)
|
(0.0288)
|
(0.0397)
|
(0.0451)
|
(0.0403)
|
(0.0408)
|
|
Constant
|
-6.935
|
-9.323*
|
-6.932*
|
-6.304**
|
-5.432*
|
-5.255*
|
-5.508*
|
-5.678
|
-4.186
|
-1.320
|
-1.644
|
-2.760
|
(4.220)
|
(3.959)
|
(3.124)
|
(2.431)
|
(2.467)
|
(2.172)
|
(2.522)
|
(3.609)
|
(4.480)
|
(4.798)
|
(3.686)
|
(3.268)
|
|
Observations
|
10
|
10
|
10
|
10
|
10
|
10
|
10
|
10
|
10
|
10
|
10
|
10
|
R-squared
|
0.816
|
0.850
|
0.866
|
0.896
|
0.871
|
0.906
|
0.900
|
0.803
|
0.708
|
0.669
|
0.696
|
0.737
|
Standard errors in parentheses
*** p<0.01, ** p<0.05, *
p<0.1
Note: fmlbr is Labor force participation rate, female
(% of female population ages 15+), Log_Gdpercpt is Log of GDP per capita
(current US$), unemp is Unemployment, total (% of total labor force) and Infantmrt
is Mortality rate, infant (per 1,000 live births)
Table 5c: Regression
analyses relation between fertility rate and female labor force participation
in OECD country by country
OECD Countries
|
||||||||||
VARIABLES
|
Australia
|
Finland
|
France
|
Greece
|
Belgium
|
Italy
|
Japan
|
Norway
|
Ireland
|
Turkey
|
lbrfml
|
0.0547***
|
0.0346**
|
0.0714***
|
0.0190
|
0.0309**
|
0.0246
|
0.0441
|
0.0559**
|
0.0204*
|
-0.0240***
|
(0.0119)
|
(0.0145)
|
(0.0126)
|
(0.0201)
|
(0.0101)
|
(0.0183)
|
(0.0488)
|
(0.0223)
|
(0.0111)
|
(0.00679)
|
|
Constant
|
-1.287*
|
-0.144
|
-1.615**
|
0.551
|
0.353
|
0.455
|
-0.782
|
-1.550
|
0.910
|
2.808***
|
(0.689)
|
(0.818)
|
(0.635)
|
(0.866)
|
(0.467)
|
(0.700)
|
(2.368)
|
(1.365)
|
(0.584)
|
(0.179)
|
|
Observations
|
12
|
12
|
12
|
12
|
12
|
12
|
12
|
12
|
12
|
12
|
R-squared
|
0.680
|
0.364
|
0.762
|
0.082
|
0.485
|
0.153
|
0.075
|
0.386
|
0.253
|
0.556
|
Standard
errors in parentheses
***
p<0.01, ** p<0.05, * p<0.1
Note: fmlbr is Labor
force participation rate, female (% of female population ages 15)
Table 5d:Regression
analyses predicting related variables on fertility rate in OECD country by
country
OECD Countries
|
||||||||||
Fertility rate
|
Australia
|
Finland
|
France
|
Greece
|
Belgium
|
Italy
|
Japan
|
Norway
|
Ireland
|
Turkey
|
fmlbr
|
0.0858***
|
0.0411
|
-0.0628*
|
0.0551*
|
0.0997***
|
-0.0235
|
0.0201
|
0.0616
|
0.0244
|
0.00306***
|
(0.0158)
|
(0.0341)
|
(0.0318)
|
(0.0244)
|
(0.0281)
|
(0.0252)
|
(0.0256)
|
(0.0390)
|
(0.0254)
|
(0.000826)
|
|
Log_GDPerpt
|
-0.0793
|
0.300
|
-0.143
|
0.182
|
-0.372
|
0.319
|
0.0661
|
0.0656
|
0.578
|
-0.0274**
|
(0.122)
|
(0.236)
|
(0.0910)
|
(0.199)
|
(0.279)
|
(0.298)
|
(0.113)
|
(0.266)
|
(0.408)
|
(0.0105)
|
|
unemp
|
-0.0358
|
0.0277
|
0.0104
|
-0.00814
|
-0.0687**
|
-0.00383
|
-0.00555
|
-0.00946
|
0.0302**
|
-0.00135**
|
(0.0337)
|
(0.0313)
|
(0.00644)
|
(0.00549)
|
(0.0248)
|
(0.0177)
|
(0.0241)
|
(0.0575)
|
(0.0113)
|
(0.000569)
|
|
mrtfmlrt
|
0.0351
|
0.0461
|
-0.501***
|
0.00523
|
0.174**
|
-0.121
|
-0.161**
|
0.0961
|
0.230**
|
0.0174***
|
(0.0760)
|
(0.0786)
|
(0.127)
|
(0.0840)
|
(0.0532)
|
(0.149)
|
(0.0526)
|
(0.112)
|
(0.0951)
|
(0.000886)
|
|
Constant
|
-2.199
|
-4.063
|
8.387**
|
-2.745
|
1.011
|
-0.585
|
0.108
|
-2.878
|
-6.727
|
2.032***
|
(1.930)
|
(3.178)
|
(2.578)
|
(2.753)
|
(2.148)
|
(3.754)
|
(2.180)
|
(4.163)
|
(4.097)
|
(0.130)
|
|
Observations
|
12
|
12
|
12
|
12
|
12
|
12
|
12
|
12
|
12
|
12
|
R-squared
|
0.931
|
0.535
|
0.947
|
0.905
|
0.882
|
0.832
|
0.895
|
0.545
|
0.666
|
0.999
|
Standard errors in
parentheses *** p<0.01, ** p<0.05, * p<0.1
Note: fmlbr is Labor force participation rate, female
(% of female population ages 15+), Log_Gdpercpt is Log of GDP per capita
(current US$),
unemp is Unemployment, total (% of total labor force)
and Infantmrt is Mortality rate, infant (per 1,000 live births)
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