5. Estimation Results
The explanatory variables defined in the previous section have been regressed using time-series
and the results are provided in Table 4.
Table 4 shows the basic regression equations (1) through (6) as described above. Most regressions
have shown the expected signs, a high explanatory power and perform well on specification tests.
Equation (1) confirms a number of known findings regarding the importance of initial levels of
human capital (PE, SE, TE, and HE) as well as growth in human capital (GDPGR). There is negative
impact of unemployment and literacy while there is a positive impact of fertility and higher
education (based on t-values). All of the dummy variables for the various events are insignificant.
More interesting thing in this equation (1) is the finding that both the ratio of graduation from
tertiary school (TE) as well as higher education (HE) has a significant positive impact on female
labor force participation rate while primary (PE) and secondary education (SE) has the opposite
effect. In deed, female labor force improvement is positively associated with education. The
coefficients of the graduated from the different levels of educations are on the expected way. Only
an increase both in the primary and secondary education will decrease the female labor force as
expected. On the other hand, only t-value for graduated females from higher education is
significant (1.99). That means, when the education level of females increase, they start to get more
share in the labor force. Increasing school enrollment has a positive impact on female labor force
participation. By the way, GDP growth rate has a high explanatory power on female labor force
as it is expected. A 1% increase in the ratio of GDP growth rate will raise the female in the labor
force by about 2%. Empirically, female labor force also appears to be related to the health. When
fertility rate is included in the regressions, the direct effects of fertility rate on female labor force
become bigger although it is expected to be negative and the coefficients on fertility rate is in the
wrong direction, but significant. Meanwhile, the coefficient of literacy rate on female labor force
has a positive impact (0.9%) and in the right direction, but it is insignificant. It means that on you
Dependent
Variable
Constant GdpGR PE SE TE HE LITR FERR UNFL D1 D2 D3
R2
Adj. R 2
0,09 0.02 -0,012 -0,03 0,004 0,021 0,014 3,14 -0,02 0,11 0,03 -0,04 0.961
FLFPR
0,858 (2.17) (-0.48) (-0.95) (1.34) (1.99) (0.89) (3.41) (-1.98) (0.52) (0.16) (-0.91) 0.767
0,154 -1,006 -0,03 0,055 0.144 0,07 0.984
FLFPR
2.827 (-0.82) (-0,15) (1.98) (2.12) (2.09) 0.912
32,42 0,06 0.417
FLFPR
(35.12)
(4.06) 0.392
36,84 -0,06 0.084
FLFPR
(0.58)
(-1.39) 0.041
40.94 -1,55 0.492
FLFPR
(39.02) (-4,72) 0.470
36,05 0,06 -0,73 1,14 1,34 0.129
FLFPR
increase the literacy of women, is not enough to raise their position in the labor force. Because
education of women also has to be increased to get a better job or to be in the labor force and
to compete with men.5 However, the relationship between unemployment and female labor force
is negative as expected. When there is 1% increase in the unemployment rate, female labor force
will decrease by 2% and it is also significant.
Equation 2 shows the reduced form estimate of the determinants of education and finds that
higher education are related to higher female labor force growth and higher human capital.
Comparisons between equations (1) and (2) indicate that the effects of education are indeed
sizable as the significance of all coefficients. In addition, reducing gender inequality in labor force
will lead to higher education levels. In particular, female labor force appears to be positively
affected by education. Due to this, education is one of the most important variables for women
for raising their position in the labor force. According to the regression, all the signs of the
coefficients of the equation are on the expected direction and t-values of them are statistically
significant with a very high R2 (98%) except primary and secondary education. That means,
expenditure on human capital is very important and it will return to women as a better job, better
payment and better position in prospect.
Equation 3 also shows that literacy rate has the expected impact so that it can be said that increase
in literacy rate will increase female labor force participation rate. But it can not be concluded that
it has an overwhelming effect on female labor force. Because putting only literacy rate into
regression is meaningless and express nothing although it has a significant impression.
In equation 4, only unemployment is added to determine its effect on female labor force.
Unemployed female share in the sector has a negative and insignificant impact on female labor
force. This result may be expressed with some caution such as the greater access to
unemployment for females, the higher the decrease in the female labor force participation rate.
Equation 5 estimates a model to check the relationship between fertility rate and female labor
force among 1980 and 2004. Every 1% raise in the level of female education reduces the total
fertility rate by 1.55%. It shows that increase in education makes difference to the fertility rate and
birth rates show a decreasing trend, while the ratio of female labor force participation is highly
significant. This clearly demonstrates that increase in the level of education makes reductions in
fertility rate and increase women's share in the labor market.
In equation 6, how GDP growth rate affects female labor force in time is investigated. Dummy
variables which are put into the regression are used to prohibit the particular effects of the defaults
like 1991 war in Iraq, 1994 and 2001 financial crisis and 1999 Marmara earthquake. Every 1% raise
in GDP growth rate decreases the female labor force participation by 6%. The size of the
coefficient is large ( =0.06) but insignificant (I t I=0.88<1.96). In developing countries like Turkey
with low female education, economic growth does not significantly enhance female labor force
5 It is also proved with the variable of higher education. As the education of women increase, they start to find jobs or better location
according to their education level. The variable of literacy is not only enough to raise women`s position in the labor force.
participation rate. Of course, there are other factors like: with a high growth rate in family income,
they do not want to work or it can be said that they work but as an unpaid family workers. As a
result, it can be concluded that rise in GDP growth rate makes improvements in female labor force
participation and increase their share in the labor force. On the other hand, there is a weak
relationship between dummy variables and female labor force participation. The effect of the war
in 1991 on Female labor force is small ( =-0.73) and insignificant (I t I=-0.50<1.96) while other
dummy variables also have small shares in the effect of female labor force ( =1.14, =1.34) and
also insignificant (I t I=1.05<1.96 and I t I=0.95<1.96) effects on female labor force participation
rates.
6. Conclusion
Using time-series regression, this paper empirically concentrates on the effects of the level of
education, GDP growth rate and other human development incidies as well as unemployment on
female labor force participation. Eight indicators are used to run the regression. The results indicate
that the level of education exerts a statistically significant positive effect on women in the society.
There is an increasing trend in the labor force participation of females who are graduated from
higher education. This means that an additional year of female schooling raises the female labor
force participation rate. It is also found that the level of education among the population in Turkey
has an important effect on improvement of gender equality in labor force. On the other hand, still
now, so many women are not permitted to go to the school or carry on their education in the
Eastern and Southeastern parts of Turkey, because they have to work in lands that their families'
use them as unpaid family workers or have to help their families in home. Due to this, for overall,
the female labor force ratio is very low compared to men.
Secondly, the research shows that as the female schooling goes up to higher levels, it directly
lowers fertility rate and raises female activity rates. Therefore, female labor force can be increased
in the society either by reducing fertility and unemployment rates or by increasing their educational
attainment.
In summary, existing evidence indicating that improving the level of education of females will lead
to lower fertility and unemployment rates. In addition to this, the combination of all of these
variables will lead to a higher female labor force participation in the society.
