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ISSN: 2321-7782 (Online) e-ISJN: A4372-3114 Impact Factor: 7.327

Volume 5, Issue 11, November 2017

International Journal of Advance Research in Computer Science and Management Studies

Research Article / Survey Paper / Case Study Available online at: www.ijarcsms.com

A panel analysis for the total fertility rate Xhuljeta Meçaj

Ph.D. Candidate Faculty of Natural Sciences,

University of Tirana

Tirana – Albania

Abstract: This paper deals with the total fertility rate with respect to the factors that cause it’s change. It is divided into two

main parts: respectively, in the first one is a review of literature, citing different authors about their theories in this field.

From this review are highlighted the main factors affecting the total fertility rate such as: the rate of female labor force

participation in relation to total, net migration rate, the increase of female and male income in a household, economic

growth, social factors etc. In the second part, the econometric analysis with panel data is done. Four countries have been

studied: Albania, Macedonia, Croatia and Bulgaria for data on three variables: the female employment rate in relation to

total, net migration rate, and he growth of the gross domestic product for capital. Data has been collected for the years 2000-

2013 from various sources.

Keywords: Total fertility rate, panel data, female labor force, gross domestic product, net migration rate.

I. INTRODUCTION

The total fertility rate (TFR) is a measure of the average number of live births a woman would have, assuming she survives

by the end of her reproductive life (15 to 49 years) and was born according to a specific norm age of fertility fertility (SIS, 2000)

and this is important in the population changes. (Sibley & Hone, 2002) claim that fertility is one of the main indicators of the

population change, which is crucial to the future projections. (Pick, Jones, Butler, & Nag, 1990) further emphasizes the

importance of predicting fertility for the future population. The factors affecting fertility are different in different places and also

at different times. Some of these factors may be difficult to measure because they go into subjectivism and may differ between

cultures. This makes it even more difficult to find variables that can be used to make predictions about this rule.In this paper,

various social behaviors influencing fertility rates will be analyzed. The characteristics that cause changes in this rate are related

both to the economic situation and to the social factors. Some of the social factors that can affect the fertility rate are: the race,the

level of education, religious belief, abortion, migration, children as workforce (mainly in rural families), children as a support for

older couples, child rearing, government programs to promote or curb the birth rate, age of first birth, and divorce rate.

II. REVIEW OF THE LITERATURE

The theories that try to explain the changes in the fertility rate in different economic conditions are numerous. Below we

present some of the main theories:

Richard Easterlin (1987) has formulated the theory of changing fertility rates as a result of the economic environment,

according to him, individuals born and belonging to a low fertility rate group would have a smaller competition in the labor

market . This would bring opportunities for higher salaries and better promotion opportunities.In recent decades, with

demographic changes, there has been a steady increase in the female participation in the workforce, and changing the role of

women has brought a lot of changes into the family structure. Rindfuss and Brewster (1996) stated that increasing the female

participation in the workforce, the total fertility rate decreases. Increasing their role in the workforce and, consequently,

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increasing female independence, have led to changes in fertility rates based also on changes in the number of marriages,

increased divorce rates, higher family planning, abortions and absenteeism willingness of fertility. All of these factors have

reduced the fertility rate. This has been reflected (Macunovich, 1996) based on her theory based on Easterlin's previous study

(1987) but with the increase of the active women's salary factor unlike the Easterlin theory. This model focuses on the

relationship between the relative income and the women's pay. She believes that women have married aspirations, so when they

start projecting involvement in the workforce, they begin to develop at higher levels of education by postponing marriage time

until the late age and consequently lowers the fertility rates. According to Macunovich, an increase in the level of male incomes

would account for a rise in fertility, while an increase in the level of female wages would have an adverse effect on fertility.

(Lappegård, 2000) and (Kravdal, Education and fertility in Sub-Saharan Africa: Individual and Community Effects, 2002)

have studied the effects of educating women on their fertility. Both concluded that women who graduate high school or a college

four years’ experience a delay in the birth of their first child and thus fewer cases of two or three or more children being born

due to age of them. Neoclassical theory explains how investment in human capital growth affects many women participating in

the labor market, fertility behavior of families has been forced to change in favor of less children (Singh, 1994). Empirical

evidence from developed and developing countries reveals that the female education is associated with a decline in fertility

(H.A., 2005); (Lam & Duryea, 1999); (Ainsworth, Beegle & Nyamete, 1996); (Vavrus & U. Larsen, 2003); Singh, 1994; (Ben-

Porath, 1973); (Gardner, 1973); (Schultz T., 1973)). Increasing the female participation in the education and the labor market

raises the economic value of their time, which increases the opportunity cost of child growth (Guilkey, Angeles, & Mroz, 1998);

(Singh, 1994); (Ben-Porath , 1973) (Gardner, 1973); (Schultz T., 1973). Studies on women's education and fertility lead to the

conclusion that women's education leads to a decline in the fertility ie with higher levels of education, the number of children

born per woman decreases.(Guilkey, Angeles, & Mroz, 1998); (Ben-Porath, 1973); (Gardner, 1973), (Schultz T., 1993), 1993,

1974, 1973). (Schultz T., 1993) confirms that female education is associated with the smallest desired size of families throughout

the world. This negative relationship between women and the education, on the other hand, fertility with the desired size of the

family, is explained by a number of factors that have been explored by both economists and sociologists. First, with higher levels

of education, income expectations per a woman in the future are very high, increasing the opportunity cost of giving birth to and

raising children. Second, with the attitude of a woman at school, the chances of many children births are reduced. Regards, to

this is the fact that more the education and exposure women gain the more information about themselves and are more in able to

use this information in their advantage (Vavrus & U. Larsen, 2003); (Singh, 1994)). The growing influence of women's

upbringing on their autonomy leads to later marriages, increases the use of the protective methods and decreases the fertility,

which is discussed by (Mason, 1986).In fact, the link between women's autonomy and fertility is much more stronger than that of

a man. Most importantly,the women with high levels of education are associated with the lowest rates of child mortality, so that

about 5-10% for each additional year of maternal education (Schultz T., 1993); (Mensch, Lentzner & Preston, 1985); (Cochrane,

Leslie, & O "Hara, 1980)). This is because women with high levels of education improve child care, nutrition, and basic health

and achieve better child results - health and school achievement (Strauss & D. Thomas, 1995).

EButz and Ward (1977) explain the changes in fertility rates by taking into consideration three main factors affecting the

fertility rate: the female participation in the labor force, the female incomes, and the male incomes. The approach taken in this

theory is about increasing male incomes that will lead to an increase in births, while women's income (represented by wages)

will lead to lowering these births. Increasing women's pay leads to lower fertility. Butz and Ward conclude there is a negative

link between the female employment and fertility.The fertility rates are positively linked to family income and negatively

related to female employment and salaries. The correlation between women's pay and fertility increases with increasing female

employment.

Based on the theory of John Ermisch (1983), the main factor influencing the fertility rate is the increase in the demand for

women's work. Factors affecting this demand may be: the increase of husband's income, its growing potential for profit and the




change in preferences in regard to female work at home. Usually, female participation in labor force is interrupted at the moment

of childbirth. During the periods of the economic growth, with the growth of employed women will result in a decrease of the

fertility rate. The author explains in his theory that more and more women who are involved in the labor force prolong the time

of the first birth, and the interval between births decreases in this way.

The women who have important positions in their work tend to prolong the period of marriage and the birth of the first

child. The birth of the child up to a late age is another cause cited for reducing fertility rates; this is directly related to women in

the labor force. (Bongaarts, 2002); (Kravdal, High Fertility of College-educated Women in Norway: An Artifact of Separate

Modeling of Each Parity Transition, 2001); (Lesthaeghe & Moors, 2000). This is more likely for stabilized women in the labor

market before they have children. Waiting for children, women have fewer years of fertility in which they can have children, and

the risk increases with births over the age of 35.

According to an article published by the well-known economic magazine "The Economist" in August 2002, a factor

affecting the fertility rate is migration. In 1994, in 1994, TFR rates for immigrants were higher than the fertility rates for the

local American population. For other countries, mainly Europe, the influence of this variable on the fertility rate is increasing

with the rate of the domestic migration growth rate.

Another factor affecting the fertility rate is the economic situation and the economic cycle in which the country is located.

During a recess period, the total fertility rate drops. This is ascertained by the "World population data sheet" for studies

conducted mainly in the countries such as Italy, Spain, Sweden and other European countries.

III. THE ECONOMETRIC MODEL

The data

In this study, the data for various variables were obtained from different sources namely: The total birth rate for data from

2000 to 2012 was obtained from Indexmund, while for 2013 were taken from the institute "NationMaster". The data on Gross

Domestic Product (GDP) and the female employment rates are received by the World Bank, while Indexmundi and the CIA

LIBRARY database is used for the migration rate.

Building the model

The general shape of the model. The econometric model we have chosen for studying variables and achieving the above-

mentioned objectives is that of panel analysis with the balanced data. We have considered four countries, respectively; Albania,

Macedonia, Bulgaria and Croatia for a period of time from 2000 to 2013, analyzing the three main variables.

General form:

(TFR)=α + β1 log(GDPi,t) + β2 LFFi,t + β3 NMRi,t + ui,t

These variables are:

TFR shows the average number of children born per woman if all the women were to live by the end of the fertility

period and the children would be born at a certain fertility rate at any age.

GDP / GDP for capital - shows GDP based on purchasing power parity divided by population on 1 July of the same

year.

Feminine workforce (in% over total) - Labor force is the current number of people ready for work. The workforce of a

country includes both; employed and unemployed. This variable contains the labor force of women in percentage on the

total workforce.

Net migration rate - This statistic shows the difference between the number of people entering and leaving a country

during the year per 1000 people (based on the mid-year population).




The initial model being treated is:

TFR=α + β1 GDPi,t + β2 LFFi,t + β3 NMRi,t + ui,t

This model had significant heteroskedicity problems and to eliminate this problem, the mixed model with two logarithmed

variables was tested because it reduces the variables' stairs and thus their variances. Initially, the graphical side of the variables

was studied and it was apparent that these variables had trends. To eliminate this and starting from the fact that GDP is taken for

equity, then the log function can be used because this variable has no negative values. We do the same with the TFR dependent

variable, while LFF and NMR did not show the need to logarithm.

For model variables to emerge, the best model's shape would be:

log(TFR)=α - β1 log(GDPi,t) - β2 LFFi,t + β3 NMRi,t + ui,t

The Hausman Test was applied to determine the effects of the panel in the time and spatial dimension. The hypotheses of

this test are Ho: The case with random effects is more convenient and Ha: Fixed-effect model is more convenient.From the

implementation of the Hausman Case with random effects, cross-section probability has a zero value which is less than 0.05 and

χ2 = 38.88 greater than χ

2kr = 3, where the zero hypothesis falls down. The model with fixed or no effects will be more

convenient.From the implementation of the Hausman Test with random effects according to the probability period comes 0.65

and χ2 = 1.644, consequently the zero hypothesis lies. The random-case model will be more appropriate according to period.

This test did not offer us the best model, because from the various tests, it turned out that the most fitting model was the

random cross-sectional effect with 34.84% explanation where all the variables are significant, so with a probability less than

0.05. The latter is the model that we will study as it is the best model resulting from different tests

LOG(TFR) = 2.29 - 0.088 LOG(GDP) - 0.0249 LFF + 0.0124 NMR + [CX=R]

This model tells us that there are no effects in the time data, but random cross-section effects occur, so the differences

between countries affect in the dependent variable.

CROSSID Effect

1 Albania 1.37E-10

2 Macedonia -9.86E-11

3 Bulgaria -7.92E-11

4 Croatia 4.10E-11

Referring to the above table, the countries surveyed have differences between them, where the country with the largest

effect of the variables is Albania followed by Croatia. While the country with the lowest effect on variables is Macedonia.

Testing stationarity

Coorelogran Theory:

The model is non-stationary. To turn it into stationary, we make it with the first difference, where as we see in the second

graphic that the autocorrelation values fall within the boundaries.




So we can say that the TFR series is not stationary initially, but with a difference it returns into stationary and it is called

integral of the first order or stationary of the first order.

Unitary root test:

Since the panel data includes also time series, it was thought to test stationarity through unit roots. The hypotheses of this

test are Ho: There is unitary root, so it is non-stationary and Ha: There is no unitary root which indicates that it is stationary.The

probability of Levin Lin & Chu and Breitung probability comes out approximately zero, so hypothesis H0 falls down, thus the

model has no common unit root for all model variables. This is reinforced even more with the first margin.According to Im

Pesaran and Shin and ADF there is an individual unitary root, but this is rejected by PPFisher with the first margin.

Johansen's co-integration test based on Engle-Granger states that a linear combination of two or more non-stationary series

may be stationary and this is considered cointegration. The series is cointegrated if the respective residues are stationary. In our

study all the probabilities are greater than zero, consequently the zero hypothesis lies, so there is no cointegration.

Granger's test of probability tests Ho's hypothesis: The first variable does not cause the second

Ha: The first variable does not cause the second. All variables are probed and their probabilities are all greater than 0.05

with lag = 1, so no variables cause the other in the short term. We can emphasize here that the case of zero-lag NMR causes TFR

and GDP causes NMR.

According to the histogram of the waste distribution, it turns out that S = -0.25 and K = 2.76, which should be respectively

close to S = 0 and K = 3, consequently the wastes have normal distribution.

The waste histogram tests Ho: The residues have normal distribution and Ha: The residues do not have normal distribution.

According to JB, the waste, have normal distribution since

JB <JBkr. The probability according to this test was p = 68.4% which is greater than α = 5%, consequently Ho stands and

shows that the residues have normal distribution.

0

2

4

6

8

10

12

-0.3 -0.2 -0.1 0.0 0.1 0.2 0.3 0.4

S e r i e s : S t a n d a r d i z e d R e s i d u a l s S a m p l e 2 0 0 0 2 0 1 3 O b s e r v a t i o n s 5 6

Mean 6.35e-16 Median -0.008577 Maximum 0.364542 Minimum -0.314145 Std. Dev. 0.150053 Skewness 0.258276 Kurtosis 2.758626

Jarque-Bera 0.758535 Probability 0.684362




IV. CONCLUSION

According to Rindfuss and Brewster (1996), with the increasing female participation in the workforce, the total fertility rate

decreases and this is confirmed in the model studied by us.

From the model it is tested that the net migration rate affects the total fertility rate, which is also treated by various authors

mentioned in the literature review, where the same thing cannot be said about the GDP variable.

Our model has an explanation rate of 34.84%, which is relatively high compared to other panel studies, because for the panel

data reaching a high explanation is very difficult because of the panel itself.

The countries surveyed have differences between them according to the random-case model, where the country with the

largest effect of the variables is Albania followed by Croatia, while the lowest effected country in the variables is Macedonia.

The model did not study the rate of the female education as a variable affecting the total fertility rate since no data were

available for Albania. If this variable is included in the model, then it is expected to obtain a greater explanation of the model.

References

1. Ainsworth, M., Beegle, K., & Nyamete, A. (1996). The impact of women’s schooling on fertility and contraceptive use: a study of fourteen Sub-Saharan

African countries. The World Bank Economic Review, 85-122.

2. Ben-Porath, Y. (1973). Economic analysis of fertility in Israel: point and counterpoint. Journal of Political Economy,, 202-233.

3. Bongaarts, J. (2002). The End of the Fertility Transition in the Developed World. Population and Development Review, pp. 419-443.

4. Cochrane, S., Leslie, J., & O‟Hara, D. (1980). The Effects of Education on Health. Washington D.C: World Bank.

5. Gardner, B. (1973). Economics of size of North Carolina families. . Journal of Political Economy, 99-112.

6. Guilkey, D., Angeles, G., & Mroz, T. (1998). The measurement of indirect program impact through the effect of female education on fertility and

mortality. . Carolina Population Centre, University of North Carolina.

7. H.A., S. (2005). Female labour force participation in Ghana: the effects of education. African Economic Research Consortium.

8. Kravdal, O. (2001). High Fertility of College Educated Women in Norway: An artifact of separate Modeling of Each Parity Transition. Demographic

Research, 187-216.

9. Kravdal, O. (2002). Education and fertility in sub-Saharan Africa: individual and community effects. . Demography. , 233-250.

10. Lam, D., & Duryea., S. (1999). Effects of schooling on fertility, labour supply and investments in children, with evidence from Brazil. Journal of Human

Resources, 160-192.

11. Lappegård, T. (2000). New fertility trends in Norway. Demographic Research.

12. Lesthaeghe, R., & Moors, G. (2000). Recent Trends in Fertility and Household fomation in the Developed World. Review of Population and Social policy,

121-170.

13. Macunovich, D. J. (1996). Marriage and Divorce in the U.S. : Testing for Cohort Size Effects. Department of Economics, 176.

14. Mason, K. (1986). The Status of Women: Conceptual and Methodologicaal Issues in Demographic Studies. Sociological Forum, 284-300.

15. Mensch, B., Lentzner, H., & Preston, S. (1985). Child Mortality Differential in Developing Countries. New York: United Nations.

16. Pick, J., Jones, G., Butler, E., & Nag, S. (1990). Socioeconomic Influences on Fertility in the Mexican Borderlands Region. Mexican Studies, 11-42.

17. Schultz, T. (1973). Explanation of birth rate changes of space and time. . Journal of Political Economy., 238-274.

18. Schultz, T. (1993). Mortality Decline in the Low-income World: Causes and Consequences.

19. American Economic Review, 337-42.

20. Sibley, R. M., & Hone, J. (2002). Population growth rate and its determinants: an overview.

21. Philosophical Transactions of the Royal Society. London.

22. Singh, R. (1994). “Fertility-Mortality Variation Across LDCs: Women‟s Education, Labour Force Participation and Contraceptive. 209-229.

23. SIS. (2000). 2000 Census of Population: Social and Economic Characteristics of Population. State Institute of Statistics . Ankara: State Institute of

Statistics Printing Division.

24. Strauss, J., & D. Thomas. (1995). Human resources: Empirical modeling of household and family decisions. Handbook of Development Economics, 1183-

23.

25. Vavrus, F., & U. Larsen. (2003). “Girls‟ Education and Fertility Transition: An Analysis of Recent Trends in Tanzania and Uganda. Economic

Development and Cultural Change, 945-76.

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