Asif 1.FINAL 498 Nov 29 2016

DOES COMPLETION OF HIGHER LEVEL OF EDUCATION REDUCE THE LEVEL OF FERTILITY, AMONG WOMEN IN RURAL BANGLADESH?

This paper investigates the effect of

education level on the fertility rate of

women in rural Bangladesh

Does completion of higher level of education reduce the level of fertility, among women in rural Bangladesh?

Asif Siddique Tarafdar ID:12205023


ASIF SIDDIQUE TARAFDAR 1

Contents Literature Review: ........................................................................................................................................ 2

Hypothesis: ................................................................................................................................................... 4

Methodology ................................................................................................................................................. 4

Regression ..................................................................................................................................................... 6

Bibliography .................................................................................................. Error! Bookmark not defined.



Abstract:

Female education and reduction of population growth rate are the two demographics that are

echoed continuously in developing nations. In Bangladesh, over the years there has been

government, non-government and international efforts to promote female education. One of

the many reasons of this promotion was to control population growth rate. This paper aims to

investigate how the level of education has affected the population growth rate in rural

Bangladesh. The findings of the study,if the hypothesis is accepted, could be a big step

forward in empowering women to make better decisions. The paper considers the number of

children born to women as the indicator of fertility rate and education level is considered by

completion of education categorized by systems i.e. Pre-primary, primary. Ordinary multiple

regression is used to analyze the net effect of education on fertility. Control variables, such as

age, income and husband’s age etc., are also used to understand the effects on population

growth rate.

Literature Review:

There are innumerable studies that have been carried out to measure the level of association

between the level of education and fertility. A landmark study conducted by (Lam & Duryea,

1999)concluded that there is a negative relation between female education and fertility. Their

study included data from both developed and developing countries.

Lam and Duryea (1999), vividly portrays the scenario of the developing countries by conducting

their research on Brazil, a country which experienced a steep decline in fertility. The most

intriguing fact the authors highlighted was that Brazil faced a steep decline in fertility despite the

presence of any major family planning efforts. The authors estimate that slow but steady

improvements in the distribution of schooling, especially at low levels, combined with a strong

negative effect of schooling on fertility, can only explain the mammoth 70 percent decline in

fertility from 1935 to 1939. The authors identified several key players responsible for fertility

rate, including age at first marriage, husband’s education, income etc. In addition to the

importance of women’s education, higher levels of education of people in the community have a

strong negative impact on fertility.

This conclusion is supported by (Becker,1960) and Willis (1973), where the authors suggested

that higher education lead to more earnings. The high earnings of the women raise the

opportunity cost of leaving the labor market to rear children. This substitution effect tilts

women’s optimal fertility choices towards fewer children. On the other hand, higher earnings



should be positively related to fertility because families can afford more children (Becker et al.,

1960). This income effect may be however weaker if parents with higher income prefer children

of higher quality (Becker and Lewis, 1973). The more parents invest in each child, the fewer

children they can afford.

Although an extensive empirical literature documents a negative association between female

education and fertility, a causal relationship is difficult to establish because of potential reverse

causality and selection on unobservable factors. Some recent studies approach these problems by

using exogenous variation from school entry policies (McCrary and Royer, 2011) or changes in

compulsory schooling laws (see, e.g., Black et al., 2008; Monstad et al., 2008; Silles, 2011) as

instruments for education.

In broad terms, education may affect a woman’s fertility and child-investment choices through

either income or learning (Michael 1973). Education increases a woman’s income stream

through the labor market.

In light of the strong correlation between education and other characteristics influencing

fertility, it is not surprising that the empirical evidence on the education-fertility relationship is

inconclusive. Black, Devereaux, and Salvanes (2008) uncover large teen fertility effects

exploiting compulsory schooling reforms in the US and Norway. In contrast, McCrary and Royer

(2011)) find no effects when they utilize variation in educational attainment due to school

starting rules. Currie and Moretti (2003) estimate significant fertility responses to college

openings but have crude measures of fertility. However, this literature suffers from several

weaknesses. Much of the literature (Black, Devereaux, & Salvanes, 2008) and (McCrary &

Royer, 2011) relies on difference-in-difference analyses, which are subject to endogeneity

concerns if trends are not properly controlled for A more recent literature attempts to address

these earlier empirical challenges by employing regression discontinuity designs. For example,

McCrary and Royer (2011) exploit a discontinuity in exact date of birth, though they are unable

to examine the impact of completed education and are limited to measuring fertility over a

narrow age range. The authors try to develop a concrete link between education and fertility

using data primarily collected from the rural areas of Bangladesh where fertility has been a

looming problem for more than a decade.

In recent times, the relationship between education and fertility has generally shown a negative

relation. However, it should be noted that studying the effect of education on fertility is

extremely difficult (Chowdhury, 1977). Education has lot of socio economic implications. The

relation between education and fertility behavior is complex. Education promotes a lot of socio-

economic changes which in turn affect the overall fertility rate. For example- a higher level of

education delays the age of pregnancy, which is helpful in reducing the number of children a

woman is likely to give birth to. Female education also promotes personal advancements and

social awareness which will influence women to have fewer children. A more educated woman,

who earns, is more likely to have a say in family matters. Also, education increases the chances



of a woman to participate in the labor force. The participation of women creates a new situation

within an economy, “it increases the opportunity cost of child bearing and creates tradeoff

between having some child and earnings for the family” (Jain, 1981). Therefore, it is very

important that our empirical model truly reflects the overall effect of education on fertility.

Hypothesis:

Ho:µ1 = -αµ2

H1 : µ1 ≠ -αµ2

My research hypothesis is “Femaleeducation has a negative impact on overall fertility rate”.

We have assumed that fertility is negatively related to education. Majority of the studies that we

have come across in this topic conclude thateducation has a negative impact on fertility rate.1

Another strong influence of our assumption came from learning the fertility trend in developed

countries. In many developed countries fertility is decreasing by the day, one of the reasons

behind this decline in fertility can be traced to the impact of education on involvement in the

workforce – “glass ceiling effect”. Women and men in developed countries are usually equally

educated. The education rate of women has surged over time across the developed nations,

especially sincethe end of the eighteenth century. This surge also affected the fertility rate of

countries like Germany and Japan that have been experiencing a negative population growth

over the last few decades. Countries like Singapore are experiencing a slow birth rate as their

level of Education across both genders is increasing. As there are obvious signs and evidence

that rise in education rate promotes a negative fertility rate, I feel very comfortable in moving

forward with our hypothesis.

Regression Outline

The paper aims to collect data from the rural areas of Bangladesh to postulate the relation

between education and fertility. The reason behind this is we believe it will generate a more

accurate picture of the actual effect. In urban areas, women are more exposed to the

opportunities of education and there are less socio-economic constraints compared to rural areas.

1 As per the list presented in the bibliography



Also, women in urban areas have more access to information. It is also more expensive to raise a

child in urban area which can bring biasness in our result. We are interested in women living

“Union Porishads (UP)” and number of children they bear. There are more than 4500 UP’s in

Bangladesh which will give us a very rich data.

Methodology:

Endogenous Variable: (Dependent Variable)

In this model, total number of children ever born and use of contraceptive are the two dependent

variables. This information will be gathered by asking the respondent a series of questions. We

will only include women who are married or had been married. Considering the socio-economic

condition of Bangladesh, the number of unmarried women with children is very low. We can

expect an accurate result even if we exclude few rare cases. Therefore, respondent will be a

married woman who will be asked, the total number of children she has had. Also, we are using

the use of contraception as a dependent variable because it is big factor in determining fertility

rate.

Exogenous Variable:

Our independent variable will be years of education attained by the mother. It is determined by

the highest grade obtained or the degree passed by the respondent. The non-formal education

attained by female is also taken in consideration, as information about the use of contraception is

also learned in workplace. Therefore, participation in the labor force is taken into account as

non-formal education.

Control Variables:

In a regression model, we sometimes have to make arrangements to offset the influences of

variables which can affect both endogenous and exogenous variable at the same time.

To verify the effect of female education on fertility, a few variables are brought under statistical

control. They include:

1. Age.

2. Duration of marriage.

3. Husbands income.



Age is calculated by counting the year of birth at the time of the survey. In the same way

duration of marriage is calculated, duration from the year of marriage and year of survey.

Husband’s income is his monthly income before expenditure.

Similarly, the effect of education on use of contraception is evaluated by using control variables

which are:

1. Age

2. Husband’s income

3. Exposure to mass media communication

Exposure to mass media is determined by asking how frequently the respondent watches

television or reads newspaper.

Regression

The effect of education and use of contraceptive is determined by using Ordinary Multiple

Regression. We would run separate regressions, of different sub-sets of age and education and

compare them to find out how much education is having an effect on fertility, we also need to

consider whether we may reject or fail to reject our null hypothesis.

Data Analysis Method

When studying levels of fertility, we have learned that establishing a correlation between

education and fertility is not always simple. There is no direct causal relationship between the

two variables. There are so many socio-economic factors at work, so many variables that need to

be reviewed. Each case appears to be unique. However, if fertility of each age group is evaluated

categorized by different levels of education, it can provide a clearer picture. Age group is an

important factor, when studying levels of fertility. Fertility levels also vary within different age

groups. Moreover, number of children is a continuous variable. As the number of children is a

continuous variable, great care is required in defining what the fertility rate is. This paper

considers fertility rate as the total number of children born to a woman. A woman can give birth

at the age of sixteen until she is forty. Therefore, a woman who has two children at the age of

twenty can end up with having five by the time she turns forty. So, this paper will divide women

into age groups and then evaluate the effect of education level so that biasness is removed to

some extent. Fertility rate of a specific age group may change over time.

According to our design, sample is classified into three age groups:

1. A=16-24



2. B=25-34

3. C=35-40

Our level of education will be divided into four categories:

1.Pre-primary (Playgroup-Standard 7)

2.Primary (Completed Standard 8)

3.Secondary (Completed Secondary School)

4.Higher (Completed Secondary and Above)

A specific age group is chosen for evaluation, then compare the average number of children born

to that age group across different levels of education. In this paper, fertility level is defined as the

total number of children born to a woman. Within a single age group, average number of

children born to women with different levels of education will be evaluated.

For example, we are considering age group “A”.

Age group “A”:16-24

Level of Education Average Number of children born

Pre-primary

Primary

Secondary

Higher



Similarly, we will also look at the effect of spouse’s education level on rate of fertility. Although

not in our primary hypothesis, this is an important piece of information. Knowledge or the desire

to have a better life standard is heavily influenced by spousal relationship. The influence of

spouse in general is very important in determining the number of children in a marriage.

Therefore, education level of the husband is also an important indicator in this study.

Age group “A”:16-24

Level of Education (Husband) Average Number of children

born(Woman)

Pre-primary

Primary

Secondary

Higher

The role of informal education in limiting fertility rate is also something that needs to be

reviewed. Expectations are higher for promoting oriented programs that is run by different NGOs

and international organizations such as UN and USAID. For quantitative study the average

number of child born to women across different age groups will be compared against their

affiliation to NGOs. Similar to our previous example, we will use age as a control variable.

Two different groups are:

Affiliated-responded to the programs and used services provided by NGOs.

Not Affiliated- did not respond to the programs and the services provided by NGOs.

Age Group: A, B or C

Relation to NGOs Average number of children.

Affiliated

Not Affiliated



In this paper, we have also considered the use of contraception as a measure for fertility. Where

more use of contraception is the desired practice in reducing fertility. We will consider use of

contraception as a binary variable, where there will be two variables:

1.Use contraceptive

2.Do not use contraceptive.

We will compare the percentage of women who use contraception with different levels of

education, across different age groups.

Education level Percentage of contraception use.

Pre-primary

Primary

Secondary

Higher



Rate of contraception use will also be evaluated against informal education. As we have also

considered that informal education has an effect on rate of fertility, we will evaluate our data in

similar manner as our previous examples.

Level of Affiliation Percentage of Contraception use

Not Affiliated

Irregularly Affiliated

Affiliated

In order to evaluate the data collected quantitatively, Ordinary Multiple Regression is to be run.

The net effect of education on fertility is found out by ordinary multiple regression using dummy

variables. There will separate groups of sample depending on factors such as level of education

and affiliation to NGOs, dummy variable will be assigned, for example If a person belongs to

one group he will be valued one if not then zero. Each factor has to be converted into number of

sub-factors, for example a sub factor for different level of education, different sets of regressor

minus one. We omit one category from each of the variable group, it is used as a standard of

reference and other co-efficient are compared with it.



The relation between fertility and other independent variables are determined by using OLS

(Ordinary Least Squares) method by the following equation:

Y=XoBo+X1B1+X2B2+X3B3……U

Here, X1 and X2 are the different groups of variables already defined in this paper. Where

Y denotes the total number of children born to women i.e. the fertility rate.

Household

expenditure

levels

Age Group Education

Level

Affiliation

to NGOs

Family

Background

Serial

number of

first male

child.

Number of

children

Household expenditure Level:

Expenditure level is an indicator of financial capability of a household. It is a good indicator of

the income level of that household. The desire to well-being also reflects on its expenditure.

Expenditure on items like Television and fridge indicates a more standard living. More

willingness to spend money in order to make life easier and comfortable. Therefore, there is

bound to be a difference in preference regarding number of children.

Serial number of the first male child:

We live in man’s world. Even though we have come far along time, the statement is still relevant

today. Male child preference is dominant from posh western countries to small tribal groups in

remote areas around the world. It is highly noticeable in rural Bangladesh. In our society, male



child is a symbol of power, a certainty that the family name will continue. There is immense

pressure on a woman to bear a male child for the family from her husband. Sometimes, the

pressure is directed towards her from husband’s family and also her own. The existing law on

inheritance in Bangladesh is also a major reason for preference of male child. If a father does not

have a male child, his daughters are not entitled to his wealth otherwise stated so. Therefore,

sometimes a couple is desperate to have a male child to keep the wealth within the family. This

factor is bound to affect the number of children in the family. As some couple, would keep

having babies until a male child is born.

Family Background:

Family background is important in the context of this paper, to find out who is more dominant in

the spousal relationship or more relevant to rural Bangladesh, how dominant is the husband. A

wife from a powerful and or wealthy family has more influence in her husband’s family. A

woman from a poorer family is more likely to be dominated by her husband and his family.

These things play a role in determining the number of children in the family.

Conclusion:

Throughout the length of this paper, whole hearted effort has been made to quantify education-

fertility relationship. Various papers have been reviewed that look at this relationship. Different

authors have proposed different methods of looking at it. It has been noticed that there is vast

amount of difference regarding preference of having children in different countries. Fertility

level and practices is not uniform across developing countries. Fertility trend in Brazil is entirely

different compared to that of Pakistan, even if both are considered as developing countries.

People from different countries react differently to changing situations. Therefore, it is absolutely

crucial that research regarding such demography is optimally designed to every individual

country. Keeping this particular factor in mind, effort has been made to accommodate most

socio-economic factors that influence fertility decisions. The social, economic, religious and

even historical factors were considered while designing the research. The control variables and

other variables such as age, family background and serial number of first male child were

specifically chosen for rural Bangladesh. It is not logical to comment on the expected outcome

but certain aspects of the present situation paint a picture. Educated women are expected to have

less children as “it increases the opportunity cost of child bearing and creates tradeoff between

having some child and earnings for the family (Jain, 1981). The authors stated that because

educated women tend to join the labor force. In rural Bangladesh, there are very few number of

jobs that require special kind of academic training. Educated women doing jobs in the formal

sectors (other than schools) are non-existent in rural Bangladesh. Women are restricted by their

family obligations and social construct to engage only in doing household work. Some women

are involved in farming or small businesses that have very little or nothing to do with academic

qualification. In light of this, one cannot help but be a little skeptic about the effect of education



on fertility and how the socio-economic environment affects the outcome. Finally, this paper

considers the level of fertility as the total number of children born to a woman. Different

demography has been considered and linked to the number of children born. There is lot of

opportunity to carry out lot of simultaneous study regarding reproductive health of rural women

in Bangladesh.



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