Relationship between Education Levels and Fertility Rate

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.

Countries Total fertility rate Primary completion rate Lower secondary completion rate Adult literacy rate
Female Male female Male female Male female
% 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
2017 2017 2017 2017 2017 2008-17 2008-17
Afghanistan 4.6 .. .. 68 39 45 18
Albania 1.6 108 105 94 88 98 96
Algeria 3 106 106 71 88 83 68
Argentina 2.3 102 103 85 92 99 99
Armenia 1.8 91 92 88 91 100 100
Azerbaijan 1.9 107 108 89 88 100 100
Bahrain 2 101 101 97 98 96 93
Bangladesh 2.1 114 123 69 84 76 70
Bhutan 2 91 99 75 85 66 48
Cambodia 2.5 87 92 52 57 87 75
Central African Republic 4.8 51 34 12 8 51 24
Chad 5.8 50 34 22 9 31 14
Macao SAR, China 1.2 104 106 104 101 98 95
Colombia 1.8 105 105 72 81 94 95
Comoros 4.3 76 77 47 50 56 43
Congo, Dem. Rep. 6 70 70 65 36 89 66
Costa Rica 1.8 96 97 54 60 97 98
Cote d’Ivoire 4.7 80 66 55 39 51 37
Croatia 1.4 97 98 92 92 100 99
Cuba 1.6 91 93 93 99 100 100
Cyprus 1.3 96 98 98 98 99 98
Dominican Republic 2.4 96 94 82 88 94 94
Ecuador 2.5 104 105 102 106 95 93
Egypt, Arab Rep. 3.4 95 95 78 84 76 66
El Salvador 2.1 91 91 77 79 91 87
Equatorial Guinea 4.6 40 42 25 23 97 92
Eritrea 4.1 48 43 31 31 75 55
Estonia 1.6 95 98 109 107 100 100
Eswatini 3 86 86 55 55 84 82
Ethiopia 4.4 55 53 30 29 .. ..
Gambia, The 5.3 68 73 63 63 51 34
Georgia 2.1 117 118 106 109 100 100
Germany 1.6 99 99 58 57 .. ..
Ghana 3.9 95 95 76 73 78 65
Greece 1.4 95 93 91 89 98 97
India 2.2 93 97 83 88 79 59
Indonesia 2.3 100 98 88 93 97 94
Iran, Islamic Rep. 2.1 100 99 93 94 90 81
Israel 3.1 104 105 101 103 .. ..
Italy 1.3 99 98 100 100 99 99
Jamaica 2 .. .. 84 87 83 93
Japan 1.4 100 101 102 103 .. ..
Jordan 2.8 .. .. 60 61 98 97
Kazakhstan 2.7 108 109 109 111 100 100
Kenya 3.6 102 102 81 81 84 74
Kiribati 3.6 96 103 87 100 .. ..
Kuwait 2.1 96 98 87 95 97 95
Kyrgyz Republic 3 104 103 96 96 100 99
Lao PDR 2.7 103 102 72 68 90 79
Latvia 1.7 100 99 98 99 100 100
Lebanon 2.1 75 73 50 55 94 88
Lesotho 3.2 71 87 36 51 68 85
Lithuania 1.7 102 102 102 101 100 100
Luxembourg 1.4 78 80 97 101 .. ..
Madagascar 4.1 65 70 31 42 75 68
Malaysia 2 98 100 83 87 96 91
Maldives 1.9 98 91 104 104 99 99
Mali 6 52 47 32 27 45 22
Malta 1.4 101 103 103 96 92 95
Mauritania 4.6 65 71 35 35 .. ..
Mexico 2.2 101 102 91 96 96 94
Montenegro 1.7 91 90 101 98 99 98
Morocco 2.5 92 93 62 68 80 59
Mozambique 4.9 49 44 22 23 71 43
Myanmar 2.2 96 97 57 64 80 72
Namibia 3.4 74 81 66 74 89 88
Nepal 2 108 118 85 94 72 49
North Macedonia 1.5 91 91 88 88 99 97
Oman 2.9 110 105 100 99 97 93
Pakistan 3.6 78 65 57 49 69 44
Palau 2.2 97 95 103 107 97 96
Peru 2.3 95 95 86 88 97 91
Philippines 2.6 102 106 81 91 96 97
Poland 1.4 99 101 96 95 99 98
Puerto Rico 1.1 73 79 75 83 92 92
Qatar 1.9 97 95 78 90 93 94
Romania 1.6 92 92 86 87 99 98
Russian Federation 1.8 97 98 97 101 100 100
Rwanda 4.1 66 86 35 39 76 66
Samoa 3.9 99 103 98 103 99 99
Sao Tome and Principe 4.4 84 89 67 81 95 85
Saudi Arabia 2.4 121 108 107 103 97 91
Senegal 4.7 55 66 35 39 65 40
Serbia 1.5 99 99 95 95 99 98
Seychelles 3.6 120 109 124 118 93 94
Sierra Leone 4.4 69 67 50 49 41 25
Singapore 1.2 100 99 110 105 99 95
Slovenia 1.6 96 99 95 97 100 100
Solomon Islands 4.4 85 89 70 72 .. ..
Spain 1.3 97 98 89 95 99 98
Sri Lanka 2.2 103 101 97 96 93 91
Sudan 4.5 63 61 59 57 60 47
Suriname 2.4 84 95 33 64 95 91
Tajikistan 3.6 93 92 96 92 100 100
Tanzania 5 55 61 27 31 83 73
Thailand 1.5 93 94 75 82 95 91
Timor-Leste 4.1 93 98 76 85 64 53
Togo 4.4 95 87 54 39 77 51
Tunisia 2.2 103 104 64 78 86 72
Turkey 2.1 93 92 97 93 99 94
Uganda 5.1 50 52 27 25 79 62
Ukraine 1.4 103 104 95 95 100 100
United Arab Emirates 1.4 103 107 82 82 .. ..
World 2.4 91 90 76 77 90 83
East Asia & Pacific 1.8 97 97 88 91 97 94
Europe & Central Asia 1.8 97 98 91 92 99 99
Latin America & Caribbean 2 98 99 76 82 94 93
Middle East & North Africa 2.8 93 89 74 75 86 73
North America 1.7 .. .. .. .. .. ..
South Asia 2.4 95 96 79 83 79 62
Sub-Saharan Africa 4.8 71 67 46 41 72 57
Low income 4.6 68 64 44 38 69 53
Lower middle income 2.7 93 93 75 78 83 70
Upper middle income 1.9 96 95 86 88 97 93
High income 1.6 98 98 92 92 .. ..

 

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 rate Fertility 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 male 1.234
(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 consumption 0.0108 -0.0136**
(0.00586) (0.00569)
Infant mortality rate 0.828*** 0.647***
(0.231) (0.204)
GDP PPP 1.299 1.399
(0.990) (0.955)
Contraceptive prevalence 0.0545 0.964
(0.349) (1.336)
Constant -4.219** -3.574**
(1.872) (1.822)
Observations 114 114
R-squared 0.4254  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.

But male labor force participation rate and GDP PPP has no significant effect on fertility rates.

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