RELATIONSHIP BETWEEN EDUCATION LEVELS AND FERTILITY RATE

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INTRODUCTION

Now a days rising population with limited available resources is getting a serious issue especially in underdeveloped or developing countries. One possible reason that usually considered is lack of awareness among parents about their own health along with their childrens’ health. And the most important source of getting awareness is education. Educated females are also considered as backbone for the development of any nation because education provides a source of income along with awareness to women that enhances the power and autonomy of women at home. Thus gauging the relationship between fertility rate and education is an important theme to do research.

Different trends are expected among different cultures and different demographic behaviors for fertility rates. The countries on different development stages might also have different fertility rates. I compared the data of many developed, developing and under developed countries to see the relationship between fertility and education.

There are several ways education can effect fertility. First of all when women get education her preferences gets change and then she might changes her preference about number of children she bear. And there is a possibility that after getting education a female might desire to have less number of children because now she will be aware of the importance of her own health and for the health of her children, also now she will be more familiar with cost of bearing and rearing children with their limited resources.

Second way the education can effect fertility rate is the awareness about contraception. Because education is most common resource of awareness so when female gets education they are more likely to use contraceptives when they do not want to bear any more children.

Also when females got education their participation in labor force increases and working women tends to bear less children due to their work pressure and absence from home. Another reason for education to reduce fertility is the time a female spends getting education and late marriages due to higher education.

Along these factors, strong son preferences by females or their husbands or even by their families can also change the number of total children a female bear. Because if a female give birth to 3 girls and she was preferring only three children but she belongs to a family who prefers sons over daughters then she might be forced to bear more children.

Infant mortality is another possible reason which could affect the desired fertility rates because if a loving mother lost her first or even two infants then she might decide to bear more children.

And lastly, alcohol consumption by mothers can also effect fertility levels because consumption of alcohol by pregnant females is dangerous for the health of fetus thus we can expect the alcohol consumption to cause the reduction of number of children a female have.

Considering all these possible ways I collected data for different countries and for different variables available on world-bank website.

My Paper proceeds as following; Section 2 discusses previous literature, Section 3 elaborates theory part. In section 4 I describes methodology and data that I used to answer my research question and in Section 5, I expresses results in detail.

LITERATURE RIVIEW

A vast literature discusses the relationship between fertility rates and education in different ways. There is a common finding of many studies that confirms a negative relation between higher education and number of children a female bore. i.e; Martin, T. C., & Juarez, F. (1995), Weinberger, M. B. (1987), Drèze, J., & Murthi, M. (2001) and Isen, A., & Stevenson, B. (2010). While on the other hand (Shirahase, S. 2000) states that it is the age at marriage and not the education that reduces the birthrate in Japan.

Jejeebhoy, S. J. (1995) studies the relationship between women’s education and reproductive behavior among several developing countries and he states that education empowers women, providing them with increased autonomy and resulting in almost every context in fewer children but traditional kinship of women complicates this relationship between fertility and education.

In another study (Martin, T. C; 1995) uses data from the Demographic and Health Surveys for 26 countries and examine the relationship between fertility and women’s education and he finds that higher education is consistently associated with lower fertility. However, a considerable diversity was found in the magnitude of the gap between upper and lower educational strata and in the strength of the association. Catering this fact in this paper I also do separate \analysis for primary education of women and higher level of completed education of women.

Along with the factors discussed above; Akmam, W. (2002) fond another factor named modernization that causes lowering the desired number of children. He states that with passing time, increasing education trends and growing modernization people spends their income and money on different consumables rather than on growing children because of rising economic and time cost of growing children.

THEORY

A traditional economic theory (Becker, 1981) and a demographic transition theory (Lesthaeghe, 1995) both theories answering the relationship between education level and fertility rate and shows a negative correlation among these. First theory states that the opportunity cost of childbearing and childrearing are heavier for highly educated than for less educated women. Thus women with higher education levels who got into marriage relatively late bear lesser children.

According to second theory, “a modernized society, open to social and cultural changes, allows couples and individuals to develop personal lifestyles, so that having children becomes only one of many possible options”, therefore, the more educated people are, the less they want to have children. Thus one can say that both these theories expresses that women with higher levels of education have delayed childbearing and less number of children as compared to the women with low levels of education.

DATA AND METHODOLOGY

I use aggregate data at country level for the year 2017 and I collected this data from World Bank website for 114 countries on number of variables including women’s fertility rate as dependent variable and primary completion rate separately for males and females, lower secondary completion rate separately for males and females, literacy rate separately for females and males as main independent variables. To escape any bias in my results I put in maximum available control variables like; female labor force participation rate, male labor force participation rate, total alcohol consumption by females, contraceptive prevalence, under five death rate and gross domestic product by purchasing power parity.

The Word Bank definitions of these variables are as follows; Labor force participation rate for females, (% of female population ages 15+) represents the proportion of the population ages 15 and older that is economically active or in other words all people who supply labor for the production of goods and services in a specific period of time, according to the world bank definition.

Primary completion rate, (% of relevant age group) ; or gross intake ratio to the last grade of primary education, is the number of new entrants (enrollments minus repeaters) in the last grade of primary education, regardless of age, divided by the population at the entrance age for the last grade of primary education. Data limitations preclude adjusting for students who drop out during the final year of primary education.

Lower secondary completion rate, (% of relevant age group) is measured as the gross intake ratio to the last grade of lower secondary education (general and pre-vocational). It is calculated as the number of new entrants in the last grade of lower secondary education, regardless of age, divided by the population at the entrance age for the last grade of lower secondary education.

Literacy rate, adult (% of males ages 15 and above) is the percentage of people ages 15 and above who can both read and write with understanding a short simple statement about their everyday life.

Fertility rate, total (births per woman); represents the number of children that would be born to a woman if she were to live to the end of her childbearing years and bear children in accordance with age-specific fertility rates of the specified year.

Contraceptive prevalence rate, modern methods (% of women ages 15-49) is the percentage of women who are practicing, or whose sexual partners are practicing, at least one modern method of contraception. It is usually measured for women ages 15-49 who are married or in union. Modern methods of contraception include female and male sterilization, oral hormonal pills, the intra-uterine device (IUD), the male condom, injectables, the implant (including Norplant), vaginal barrier methods, the female condom and emergency contraception.

The following table shows all countries that are included in this analysis and their data for our dependent variable fertility rate and independent variables of education. I choose these 114 countries out of all available countries on the basis of maximum data availability.

CountriesTotal fertility ratePrimary completion rateLower secondary completion rateAdult literacy rate
FemaleMalefemaleMalefemaleMalefemale
% of children per women% of relevant age group% of relevant age group% of relevant age group% of relevant age group% ages 15 and older% ages 15 and older
201720172017201720172008-172008-17
Afghanistan4.6....68394518
Albania1.610810594889896
Algeria310610671888368
Argentina2.310210385929999
Armenia1.891928891100100
Azerbaijan1.91071088988100100
Bahrain210110197989693
Bangladesh2.111412369847670
Bhutan2919975856648
Cambodia2.5879252578775
Central African Republic4.851341285124
Chad5.850342293114
Macao SAR, China1.21041061041019895
Colombia1.810510572819495
Comoros4.3767747505643
Congo, Dem. Rep.6707065368966
Costa Rica1.8969754609798
Cote d’Ivoire4.7806655395137
Croatia1.49798929210099
Cuba1.691939399100100
Cyprus1.3969898989998
Dominican Republic2.4969482889494
Ecuador2.51041051021069593
Egypt, Arab Rep.3.4959578847666
El Salvador2.1919177799187
Equatorial Guinea4.6404225239792
Eritrea4.1484331317555
Estonia1.69598109107100100
Eswatini3868655558482
Ethiopia4.455533029....
Gambia, The5.3687363635134
Georgia2.1117118106109100100
Germany1.699995857....
Ghana3.9959576737865
Greece1.4959391899897
India2.2939783887959
Indonesia2.31009888939794
Iran, Islamic Rep.2.11009993949081
Israel3.1104105101103....
Italy1.399981001009999
Jamaica2....84878393
Japan1.4100101102103....
Jordan2.8....60619897
Kazakhstan2.7108109109111100100
Kenya3.610210281818474
Kiribati3.69610387100....
Kuwait2.1969887959795
Kyrgyz Republic3104103969610099
Lao PDR2.710310272689079
Latvia1.7100999899100100
Lebanon2.1757350559488
Lesotho3.2718736516885
Lithuania1.7102102102101100100
Luxembourg1.4788097101....
Madagascar4.1657031427568
Malaysia29810083879691
Maldives1.998911041049999
Mali6524732274522
Malta1.4101103103969295
Mauritania4.665713535....
Mexico2.210110291969694
Montenegro1.79190101989998
Morocco2.5929362688059
Mozambique4.9494422237143
Myanmar2.2969757648072
Namibia3.4748166748988
Nepal210811885947249
North Macedonia1.5919188889997
Oman2.9110105100999793
Pakistan3.6786557496944
Palau2.297951031079796
Peru2.3959586889791
Philippines2.610210681919697
Poland1.49910196959998
Puerto Rico1.1737975839292
Qatar1.9979578909394
Romania1.6929286879998
Russian Federation1.8979897101100100
Rwanda4.1668635397666
Samoa3.999103981039999
Sao Tome and Principe4.4848967819585
Saudi Arabia2.41211081071039791
Senegal4.7556635396540
Serbia1.5999995959998
Seychelles3.61201091241189394
Sierra Leone4.4696750494125
Singapore1.2100991101059995
Slovenia1.696999597100100
Solomon Islands4.485897072....
Spain1.3979889959998
Sri Lanka2.210310197969391
Sudan4.5636159576047
Suriname2.4849533649591
Tajikistan3.693929692100100
Tanzania5556127318373
Thailand1.5939475829591
Timor-Leste4.1939876856453
Togo4.4958754397751
Tunisia2.210310464788672
Turkey2.1939297939994
Uganda5.1505227257962
Ukraine1.41031049595100100
United Arab Emirates1.41031078282....
World2.4919076779083
East Asia & Pacific1.8979788919794
Europe & Central Asia1.8979891929999
Latin America & Caribbean2989976829493
Middle East & North Africa2.8938974758673
North America1.7............
South Asia2.4959679837962
Sub-Saharan Africa4.8716746417257
Low income4.6686444386953
Lower middle income2.7939375788370
Upper middle income1.9969586889793
High income1.698989292....

The dots (..) in the table shows that no data was available for these values. I tabulated all the data for rest of the variables in the same manner but because of the length of tables these are not shown here.

Because my dependent variable is a simple continuous variable I uses Ordinary Least Square regression for my analysis.

My regression equation will be as follows;

Fertility rate= α + β (primary education rate) + γ (secondary education rate) + δ (GDP PPP) + λ (labor force participation rate) + σ (alcohol consumption by female) + ƞ (contraceptive prevalence) + Ɵ (under five death rate) + ε

Where α represents constant, ε represents the error term and all other symbols represents the coefficient terms of their corresponding variables.

RESULTS

I run OLS regression on STATA and got the following results.

VARIABLES      Fertility rateFertility rate
Lower secondary completion rate female  -0.263***        (0.0891)
Lower secondary completion rate male-0.551***(0.205)
Primary completion rate female-0.567***
(0.216)
Primary completion rate male1.234
(0.410)
Adult literacy rate female-0.0845**        (0.0328)
Adult literacy rate male-0.551***         (0.205)
Female labor force participation rate-0.964**           (0.455)0.000236(0.000187)
Male labor force participation rate-4.29e-09-6.02e-09
(8.83e-09)(8.29e-09)
Female alcohol consumption0.0108-0.0136**
(0.00586)(0.00569)
Infant mortality rate0.828***0.647***
(0.231)(0.204)
GDP PPP1.2991.399
(0.990)(0.955)
Contraceptive prevalence0.05450.964
(0.349)(1.336)
Constant-4.219**-3.574**
(1.872)(1.822)
Observations114114
R-squared0.4254 0.5306
 Standard errors in parentheses
*** p<0.01, ** p<0.05, * p<0.1

Primary and secondary education completion rates have correlation with literacy rate, to escape this correlation I run separate regressions for literacy rate and primary and secondary education completion rates. Blank space in front of any variable indicates absence of that variable in that regression equation.

The above table shows that primary completion rate and lower secondary completion rate of females is highly significantly negatively effecting the fertility rate as all these three variables have negative coefficients which are significant at even 1 percent level of significance. Primary completion rate of male is not effecting the fertility rate. Likewise, in second regression adult literacy rates for both male and female are negative and highly significant. So we can say that generally education is negatively effecting the fertility rate and an increase in education will lead to lowering the birth rate.

Infant mortality rate or under five death rate is also significantly and positively effecting the fertility rate which means that if younger children or siblings die in their earlier lives then parents are more likely to have more children.

Likewise, female labor force participation rate and total consumption of alcohol by females also negatively effecting fertility rate. Which means if women are working then they are less likely to produce children and if women are consuming alcohol then also they are less likely to give birth to more children.

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